diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000..211a8b2
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,42 @@
+
+
+Book
+----
+
+This book is licensed under a Creative Commons Attribution
+Non‐Commercial Share‐Alike 4.0 International License, which permits
+use, sharing, adaptation, distribution and reproduction in any medium
+or format, as long as you give appropriate credit to the original
+author(s) and the source, provide a link to the Creative Commons
+license, and indicate if changes were made. You may not use the
+material for commercial purposes. If you remix, transform, or build
+upon the material, you must distribute your contributions under the
+same license as the original. To learn more, visit creativecommons.org
+
+
+Code
+----
+
+Copyright 2021 Nicolas P. Rougier
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
+ notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the
+ distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..bfdf05e
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,182 @@
+#
+# The build requires docutils 0.17 since 0.18 breaks the build process.
+#
+TEX_DIR = tex
+PDF_DIR = pdf
+RST_DIR = rst
+RST_FILES := $(wildcard $(RST_DIR)/*.rst)
+
+DEPS := $(TEX_DIR)/book.tex \
+ $(TEX_DIR)/main.tex \
+ $(TEX_DIR)/00-dedication.tex \
+ $(TEX_DIR)/00-preface.tex \
+ $(TEX_DIR)/00-introduction.tex \
+ $(TEX_DIR)/00-acknowledgments.tex \
+ $(RST_FILES)
+
+all: directories pdf
+
+.PHONY: help
+help:
+ @echo "Targets"
+ @echo "-------"
+ @echo ""
+ @echo " pdf - Build PDF (body + cover)"
+ @echo ""
+ @echo " hardcover - Build PDF (body + cover) for a hardcover printed book"
+ @echo " hardcover-cmyk - Convert hardcover PDF to CMYK colors"
+ @echo " hardcover-count - Enumerate color pages in the hardcover PDF (body)"
+ @echo ""
+ @echo " softcover - Build PDF (body + cover) for a softcover printed book"
+ @echo " softcover-cmyk - Convert softcover PDF to CMYK colors"
+ @echo " softcover-count - Enumerate color pages in the softcover PDF (body)"
+ @echo ""
+
+
+.PHONY: directories
+directories:
+ mkdir -p pdf
+
+hardcover: hardcover-body hardcover-cover
+
+softcover: softcover-body softcover-cover
+
+hardcover-cmyk: hardcover-body-cmyk hardcover-cover-cmyk
+
+softcover-cmyk: softcover-body-cmyk softcover-cover-cmyk
+
+pdf: $(DEPS) $(TEX_DIR)/front-cover.pdf $(TEX_DIR)/back-cover.pdf
+ @latexmk -pdfxe -cd -bibtex -shell-escape -silent -jobname="book" -pretex="" -use-make -usepretex $(TEX_DIR)/book.tex
+ @cp $(TEX_DIR)/book.pdf $(PDF_DIR)
+
+# Build front cover from the hardcover
+front-cover: softcover-cover
+ @latexmk -cd -silent -pdf -use-make $(TEX_DIR)/front-cover.tex
+
+# Build back cover from the hardcover
+back-cover: softcover-cover
+ @latexmk -cd -silent -pdf -use-make $(TEX_DIR)/back-cover.tex
+
+
+
+# Build softcover book (body)
+softcover-body: $(DEPS)
+ @latexmk -pdfxe -cd -silent -jobname="softcover-body" -pretex="\def\softcover{1}" -use-make -usepretex $(TEX_DIR)/book.tex
+ @cp $(TEX_DIR)/softcover-body.pdf $(PDF_DIR)
+
+# Build softcover book (cover)
+softcover-cover: $(TEX_DIR)/full-cover.tex
+ @latexmk -pdfxe -cd -silent -jobname="softcover-cover" -pretex="\def\softcover{1}" -use-make -usepretex $(TEX_DIR)/full-cover.tex
+ @cp $(TEX_DIR)/softcover-cover.pdf $(PDF_DIR)
+
+# Convert softcover book (body) to CMYK
+softcover-body-cmyk: $(TEX_DIR)/softcover-body.pdf
+ @gs -dSAFER -dNOPAUSE -dBATCH \
+ -dAutoRotatePages=/None \
+ -sColorConversionStrategy=CMYK -sProcessColorModel=DeviceCMYK \
+ -dOverrideICC=true -dRenderIntent=3 -dDeviceGrayToK=true \
+ -sDEVICE=pdfwrite -sOutputFile=$(PDF_DIR)/softcover-body-cmyk.pdf \
+ $(TEX_DIR)/softcover-body.pdf
+
+# Convert softcover book (body) to CMYK
+softcover-cover-cmyk: $(TEX_DIR)/softcover-cover.pdf
+ @gs -dSAFER -dNOPAUSE -dBATCH \
+ -dAutoRotatePages=/None \
+ -sColorConversionStrategy=CMYK -sProcessColorModel=DeviceCMYK \
+ -dOverrideICC=true -dRenderIntent=3 -dDeviceGrayToK=true \
+ -sDEVICE=pdfwrite -sOutputFile=$(PDF_DIR)/softcover-cover-cmyk.pdf \
+ $(TEX_DIR)/softcover-cover.pdf
+
+softcover-count: $(TEX_DIR)/softcover-body.pdf
+ @gs -o - -sDEVICE=inkcov $(TEX_DIR)/softcover-body.pdf \
+ | tail -n +6 \
+ | sed '/^Page*/N;s/\n//' \
+ | sed -E '/Page [0-9]+ 0.00000 0.00000 0.00000 / d' \
+ | cut -f 2 -d ' ' \
+ | xargs -I {} echo -n {},; echo
+
+
+
+# Build hardcover book (body)
+hardcover-body: $(DEPS)
+ @latexmk -pdfxe -cd -silent -jobname="hardcover-body" -pretex="\def\hardcover{1}" -use-make -usepretex $(TEX_DIR)/book.tex
+ @cp $(TEX_DIR)/hardcover-body.pdf $(PDF_DIR)
+
+# Build hardcover book (cover)
+hardcover-cover: $(TEX_DIR)/full-cover.tex
+ @latexmk -pdfxe -cd -silent -jobname="hardcover-cover" -pretex="\def\hardcover{1}" -use-make -usepretex $(TEX_DIR)/full-cover.tex
+ @cp $(TEX_DIR)/hardcover-cover.pdf $(PDF_DIR)
+
+# Convert hardcover book (body) to CMYK
+hardcover-body-cmyk: $(TEX_DIR)/hardcover-body.pdf
+ @gs -dSAFER -dNOPAUSE -dBATCH \
+ -dAutoRotatePages=/None \
+ -sColorConversionStrategy=CMYK -sProcessColorModel=DeviceCMYK \
+ -sDEVICE=pdfwrite -sOutputFile=$(PDF_DIR)/hardcover-body-cmyk.pdf \
+ $(TEX_DIR)/hardcover-body.pdf
+
+# Convert hardcover book (body) to CMYK
+hardcover-cover-cmyk: $(TEX_DIR)/hardcover-cover.pdf
+ @gs -dSAFER -dNOPAUSE -dBATCH \
+ -dAutoRotatePages=/None \
+ -sColorConversionStrategy=CMYK -sProcessColorModel=DeviceCMYK \
+ -sDEVICE=pdfwrite -sOutputFile=$(PDF_DIR)/hardcover-cover-cmyk.pdf \
+ $(TEX_DIR)/hardcover-cover.pdf
+
+hardcover-count: $(TEX_DIR)/hardcover-body.pdf
+ @gs -o - -sDEVICE=inkcov $(TEX_DIR)/hardcover-body.pdf \
+ | tail -n +6 \
+ | sed '/^Page*/N;s/\n//' \
+ | sed -E '/Page [0-9]+ 0.00000 0.00000 0.00000 / d' \
+ | cut -f 2 -d ' ' \
+ | xargs -I {} echo -n {},; echo
+
+
+
+$(TEX_DIR)/00-%.tex: $(RST_DIR)/00-%.rst
+ @echo "Building $@"
+ @./rst2latex.py --documentclass=book \
+ --no-doc-title \
+ --table-style=booktabs \
+ --trim-footnote-reference-space \
+ --use-latex-citations \
+ --figure-citations \
+ --reference-label=ref* \
+ --strip-comments \
+ --template=$(RST_DIR)/chapter.tex \
+ $< > $@
+
+$(TEX_DIR)/%.tex: $(RST_DIR)/%.rst $(RST_FILES)
+ @echo "Building $@"
+ @./rst2latex.py --documentclass=book \
+ --use-part-section \
+ --no-doc-title \
+ --table-style=booktabs \
+ --use-latex-citations \
+ --figure-citations \
+ --reference-label=ref* \
+ --strip-comments \
+ --template=$(RST_DIR)/chapter.tex \
+ $< > $@
+
+.PHONY: clean
+clean:
+ @rm -f $(TEX_DIR)/*.aux
+ @rm -f $(TEX_DIR)/*.log
+ @rm -f $(TEX_DIR)/*.blg
+ @rm -f $(TEX_DIR)/*.fls
+ @rm -f $(TEX_DIR)/*.run.xml
+ @rm -f $(TEX_DIR)/*.bcf
+ @rm -f $(TEX_DIR)/*.xdv
+ @rm -f $(TEX_DIR)/*.toc
+ @rm -f $(TEX_DIR)/*.out
+ @rm -f $(TEX_DIR)/*.fdb_latexmk
+ @rm -f $(TEX_DIR)/book.pdf
+ @rm -f $(TEX_DIR)/hardcover-body.pdf
+ @rm -f $(TEX_DIR)/softcover-body.pdf
+ @rm -f $(TEX_DIR)/main.tex
+ @rm -f $(TEX_DIR)/00-preface.tex
+ @rm -f $(TEX_DIR)/00-introduction.tex
+ @rm -f $(TEX_DIR)/00-dedication.tex
+ @rm -f $(TEX_DIR)/00-acknowledgments.tex
+ @echo "Cleanup complete!"
diff --git a/README.md b/README.md
index a8090e4..4889da8 100644
--- a/README.md
+++ b/README.md
@@ -1,126 +1,53 @@
-
+ Produced by OmniGraffle 7.18.5\n2021-08-11 14:14:05 +0000
+
+ Canvas 1
+
+ Layer 1
+
+ Bézier
+
+
+
+
diff --git a/code/beyond/dyson-hatching.py b/code/beyond/dyson-hatching.py
new file mode 100644
index 0000000..a64ef51
--- /dev/null
+++ b/code/beyond/dyson-hatching.py
@@ -0,0 +1,85 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import bluenoise
+import numpy as np
+import scipy.spatial
+import shapely.geometry
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+
+
+# Hatch pattern with given orientation (or random if None given)
+def hatch(n=4, theta=None):
+ theta = theta or np.random.uniform(0, np.pi)
+ P = np.zeros((n, 2, 2))
+ X = np.linspace(-0.5, +0.5, n, endpoint=True)
+ P[:, 0, 1] = -0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 1] = +0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 0] = X + np.random.normal(0, 0.025, n)
+ P[:, 0, 0] = X + np.random.normal(0, 0.025, n)
+ c, s = np.cos(theta), np.sin(theta)
+ Z = np.array([[c, s], [-s, c]])
+ return P @ Z.T
+
+
+# Points (blue noise distribution)
+P = bluenoise.generate((10, 10), radius=0.5)
+
+# Voronoi cells
+V = scipy.spatial.Voronoi(P)
+
+# Sorted distances between pojnts
+D = scipy.spatial.distance.cdist(P, P)
+D.sort(axis=1)
+
+
+# Actual drawing
+fig = plt.figure(figsize=(9, 3.25))
+
+
+# Point + Voronoi cells
+ax = plt.subplot(1, 3, 1, aspect=1, xlim=[0, 10], xticks=[], ylim=[0, 10], yticks=[])
+for i in range(len(V.point_region)):
+ region = V.regions[V.point_region[i]]
+ if not -1 in region:
+ verts = np.array([V.vertices[i] for i in region])
+ cell = plt.Polygon(verts, edgecolor=".25", facecolor="None")
+ ax.add_artist(cell)
+ax.scatter(P[:, 0], P[:, 1], s=5, color="black", zorder=10)
+ax.set_title("Random points (blue noise)")
+
+
+# Hatches (unclipped)
+ax = plt.subplot(1, 3, 2, aspect=1, xlim=[0, 10], xticks=[], ylim=[0, 10], yticks=[])
+S = []
+for i in range(len(V.point_region)):
+ region = V.regions[V.point_region[i]]
+ if not -1 in region:
+ verts = np.array([V.vertices[i] for i in region])
+ S.extend((1.25 * D[i, 1] * hatch(5) + P[i]))
+ax.add_collection(LineCollection(S, color="black"))
+ax.set_title("Unclipped hatches")
+
+
+# Hatches (clipped)
+ax = plt.subplot(1, 3, 3, aspect=1, xlim=[0, 10], xticks=[], ylim=[0, 10], yticks=[])
+S = []
+for i in range(len(V.point_region)):
+ region = V.regions[V.point_region[i]]
+ if not -1 in region:
+ verts = np.array([V.vertices[i] for i in region])
+ poly = shapely.geometry.Polygon(verts)
+ for l in 1.25 * D[i, 1] * hatch(5) + P[i]:
+ line = shapely.geometry.LineString(l)
+ intersect = poly.intersection(line)
+ if intersect:
+ S.append(intersect.coords)
+ax.add_collection(LineCollection(S, color="black"))
+ax.set_title("Clipped hatches")
+
+plt.tight_layout()
+plt.savefig("../../figures/beyond/dyson-hatching.pdf")
+plt.show()
diff --git a/code/beyond/interactive-loupe.py b/code/beyond/interactive-loupe.py
new file mode 100644
index 0000000..ede8ddd
--- /dev/null
+++ b/code/beyond/interactive-loupe.py
@@ -0,0 +1,107 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+from matplotlib.patches import Circle
+from matplotlib.figure import Figure
+from matplotlib.backends.backend_agg import FigureCanvas
+
+
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+P = np.random.uniform(0, 5, (500, 2))
+C = np.random.uniform(5, 25, 500)
+
+
+def plot(ax):
+ ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
+ ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ ax.yaxis.set_major_locator(ticker.MultipleLocator(1.0))
+ ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ ax.yaxis.set_minor_formatter(ticker.FormatStrFormatter("%.2f"))
+ for i, label in enumerate(ax.get_yticklabels(which="minor")):
+ label.set_size(7)
+ ax.xaxis.set_minor_formatter(ticker.FormatStrFormatter("%.2f"))
+ for i, label in enumerate(ax.get_xticklabels(which="minor")):
+ label.set_size(7)
+ ax.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ ax.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+
+ ax.scatter(P[:, 0], P[:, 1], C, color="black", zorder=10)
+
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_subplot(xlim=(0.0, 5), ylim=(0.0, 5), aspect=1)
+plot(ax)
+
+ifig = Figure(figsize=(6, 6), dpi=100, frameon=False)
+canvas = FigureCanvas(ifig)
+iax = ifig.add_subplot(xlim=(0.0, 5), ylim=(0.0, 5), aspect=1)
+plot(iax)
+plt.tight_layout()
+canvas.draw()
+Z = np.array(canvas.renderer.buffer_rgba())
+del ifig
+
+
+background = fig.canvas.copy_from_bbox(ax.bbox)
+
+
+def on_motion(event):
+ x, y = event.xdata, event.ydata
+ if x is None or y is None:
+ return
+ fig.canvas.restore_region(background)
+
+ circle_bg.set_center((x, y))
+ ax.draw_artist(circle_bg)
+
+ circle_fg.set_center((x, y))
+ ax.draw_artist(circle_fg)
+
+ image.set_extent([0 - x, 10 - x, 0 - y, 10 - y])
+ image.set_clip_path(circle_bg)
+ ax.draw_artist(image)
+
+ fig.canvas.blit(ax.bbox)
+
+
+cid = fig.canvas.mpl_connect("motion_notify_event", on_motion)
+
+circle_bg = Circle(
+ (1, 1),
+ 1,
+ transform=ax.transData,
+ zorder=20,
+ clip_on=False,
+ edgecolor="none",
+ facecolor="white",
+)
+ax.add_artist(circle_bg)
+
+circle_fg = Circle(
+ (1, 1),
+ 1,
+ transform=ax.transData,
+ zorder=30,
+ clip_on=False,
+ linewidth=2,
+ edgecolor="black",
+ facecolor="None",
+)
+ax.add_artist(circle_fg)
+
+image = ax.imshow(Z, extent=[0, 5, 0, 5], zorder=25, clip_on=True)
+image.set_extent([0, 10, 0, 10])
+image.set_clip_path(circle_bg)
+
+plt.tight_layout()
+plt.show()
diff --git a/code/beyond/polygon-clipping.py b/code/beyond/polygon-clipping.py
new file mode 100644
index 0000000..6526d6a
--- /dev/null
+++ b/code/beyond/polygon-clipping.py
@@ -0,0 +1,194 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate polygon and clipping
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.path as mpath
+import matplotlib.patches as mpatches
+
+
+def circle(center, radius):
+ """ Regular circle path """
+
+ T = np.arange(0, np.pi * 2.0, 0.01)
+ T = T.reshape(-1, 1)
+ X = center[0] + radius * np.cos(T)
+ Y = center[1] + radius * np.sin(T)
+ vertices = np.hstack((X, Y))
+ codes = np.ones(len(vertices), dtype=mpath.Path.code_type) * mpath.Path.LINETO
+ codes[0] = mpath.Path.MOVETO
+ return vertices, codes
+
+
+def rectangle(center, size):
+ """ Regular rectangle path """
+
+ (x, y), (w, h) = center, size
+ vertices = np.array([(x, y), (x + w, y), (x + w, y + h), (x, y + h), (x, y)])
+ codes = np.array(
+ [
+ mpath.Path.MOVETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ ]
+ )
+ return vertices, codes
+
+
+def patch(
+ ax, path, facecolor="0.9", edgecolor="black", linewidth=1, linestyle="-", clip=None
+):
+ """ Build a patch with potential clipping path """
+ patch = mpatches.PathPatch(
+ path,
+ linewidth=linewidth,
+ linestyle=linestyle,
+ facecolor=facecolor,
+ edgecolor=edgecolor,
+ )
+ if clip:
+ # If the path is not drawn, clipping doesn't work
+ clip_patch = mpatches.PathPatch(
+ clip, linewidth=0, facecolor="None", edgecolor="None"
+ )
+ ax.add_patch(clip_patch)
+ patch.set_clip_path(clip_patch)
+ ax.add_patch(patch)
+
+
+def subplot(cols, rows, index, title):
+ """ Shortcut to subplot to factorize options"""
+ ax = plt.subplot(cols, rows, index, aspect=1)
+ ax.text(-1.25, 0.75, "A", size="large", ha="center", va="center")
+ ax.text(+1.25, 0.75, "B", size="large", ha="center", va="center")
+ ax.set_title(title, weight="bold")
+ ax.set_xlim(-1.5, +1.5), ax.set_xticks([])
+ ax.set_ylim(-1, +1), ax.set_yticks([])
+ return ax
+
+
+V1, C1 = rectangle((-1.5, -1), size=(3, 2))
+V2, C2 = circle((-0.5, 0), radius=0.75)
+# For A, we use clock-wise circle vertices
+A = mpath.Path(V2, C2)
+# For ¬A, we use counter clock-wise circle vertices
+A_ = mpath.Path(np.concatenate([V2[::-1], V1]), np.concatenate([C2, C1]))
+
+V2, C2 = circle((+0.5, 0), radius=0.75)
+# For B, we use clock-wise circle vertices
+B = mpath.Path(V2, C2)
+# For ¬B, we use counter clock-wise circle vertices
+B_ = mpath.Path(np.concatenate([V2[::-1], V1]), np.concatenate([C2, C1]))
+
+
+fig = plt.figure(figsize=(9, 5))
+rows, cols = 3, 4
+
+# A
+ax = subplot(rows, cols, 1, "A")
+patch(ax, A, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="-", linewidth=1.5)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+
+# B
+ax = subplot(rows, cols, 2, "B")
+patch(ax, B, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="-", linewidth=1.4)
+
+# ¬A
+ax = subplot(rows, cols, 3, "¬A")
+patch(ax, A_, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="-", linewidth=1.5)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# ¬B
+ax = subplot(rows, cols, 4, "¬B")
+patch(ax, B_, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="-", linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# A ∪ B
+ax = subplot(rows, cols, 5, "A ∪ B")
+patch(ax, A, edgecolor="None")
+patch(ax, B, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A_, linewidth=1.5)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B_, linewidth=1.5)
+
+# A ∪ ¬B
+ax = subplot(rows, cols, 6, "A ∪ ¬B")
+patch(ax, A, edgecolor="None")
+patch(ax, B_, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A_, linewidth=1.5)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B, linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# ¬A ∪ B
+ax = subplot(rows, cols, 7, "¬A ∪ B")
+patch(ax, A_, edgecolor="None")
+patch(ax, B, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B_, linewidth=1.5)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A, linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# ¬A ∪ ¬B
+ax = subplot(rows, cols, 8, "¬A ∪ ¬B")
+patch(ax, A_, edgecolor="None")
+patch(ax, B_, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B, linewidth=1.5)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A, linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# A ∩ B
+ax = subplot(rows, cols, 9, "A ∩ B")
+patch(ax, A, edgecolor="None", clip=B)
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B, linewidth=1.5)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A, linewidth=1.5)
+
+# A ∩ ¬B
+ax = subplot(rows, cols, 10, "A ∩ ¬B")
+patch(ax, A, edgecolor="None", clip=B_)
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B_, linewidth=1.5)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A, linewidth=1.5)
+
+
+# ¬A ∩ B
+ax = subplot(rows, cols, 11, "¬A ∩ B")
+patch(ax, B, edgecolor="None", clip=A_)
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A_, linewidth=1.5)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B, linewidth=1.5)
+
+# ¬A ∩ ¬B
+ax = subplot(rows, cols, 12, "¬A ∩ ¬B")
+patch(ax, A_, edgecolor="None", clip=B_)
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A_, linewidth=1.5)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B_, linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+plt.tight_layout()
+plt.savefig("../../figures/beyond/polygon-clipping.pdf")
+plt.show()
diff --git a/code/beyond/radial-maze-path.npy b/code/beyond/radial-maze-path.npy
new file mode 100644
index 0000000..3d631af
Binary files /dev/null and b/code/beyond/radial-maze-path.npy differ
diff --git a/code/beyond/radial-maze.py b/code/beyond/radial-maze.py
new file mode 100644
index 0000000..5eaa019
--- /dev/null
+++ b/code/beyond/radial-maze.py
@@ -0,0 +1,165 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import matplotlib.path as path
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+# Maze parameters
+n_arms = 8
+n_squares = 5
+square_width = 1.5
+square_height = 1.5
+inner_radius = 3
+outer_radius = inner_radius + n_squares * square_height
+delta = 0.1 # cosmetic to widen a bit maze arms
+
+W, C = [], []
+for i in range(n_arms):
+ theta = i / n_arms * 2 * np.pi
+
+ # Build external walls
+ N = (square_width / 2 + delta) * np.array([np.sin(theta), -np.cos(theta)])
+ V_in = inner_radius * np.array([np.cos(theta), np.sin(theta)])
+ V_out = (outer_radius + delta) * np.array([np.cos(theta), np.sin(theta)])
+ W.extend([V_in + N, V_out + N, V_out - N, V_in - N])
+
+ # Build arm start (pie)
+ t0 = theta - 0.5 / n_arms * 2 * np.pi
+ t1 = theta + 0.5 / n_arms * 2 * np.pi
+ V = []
+ for t in np.linspace(t0, t1, 25):
+ V.append(1.00 * inner_radius * np.array([np.cos(t), np.sin(t)]))
+ for t in np.linspace(t1, t0, 25):
+ V.append(0.25 * inner_radius * np.array([np.cos(t), np.sin(t)]))
+ V.append(V[-1])
+ C.append(V)
+
+ # Build arms squares
+ N = square_width / 2 * np.array([np.sin(theta), -np.cos(theta)])
+ T = square_height / 2 * np.array([np.cos(theta), np.sin(theta)])
+ for j in range(n_squares):
+ r = inner_radius + (j + 0.5) / n_squares * (outer_radius - inner_radius)
+ V = r * np.array([np.cos(theta), np.sin(theta)])
+ C.append([V - T + N, V + T + N, V + T - N, V - T - N])
+
+W.append(W[0])
+W = np.array(W)
+
+
+class PathTracer:
+ """ Path tracer on a figure
+
+ You can trace a path using your mouse:
+ 1. Click to start
+ 2. Move th mouse
+ 3. Click to stop and save (if save=True)
+ """
+
+ def __init__(self, line=None, load=True, save=True, filename="path.npy"):
+ self.line = line
+ self.save = save
+ self.filename = filename
+ self.xs = []
+ self.ys = []
+ self.active = False
+ if load and os.path.exists(self.filename):
+ P = np.load(self.filename)
+ self.xs = P[:, 0].tolist()
+ self.ys = P[:, 1].tolist()
+ self.line.set_data(self.xs, self.ys)
+ self.line.figure.canvas.draw()
+ line.figure.canvas.mpl_connect("button_press_event", self.on_press)
+ line.figure.canvas.mpl_connect("motion_notify_event", self.on_motion)
+
+ def on_press(self, event):
+ if event.inaxes != self.line.axes:
+ return
+ if not self.active:
+ self.active = True
+ self.xs = [event.xdata]
+ self.ys = [event.ydata]
+ else:
+ self.active = False
+ if self.save:
+ P = np.c_[self.xs, self.ys]
+ np.save(self.filename, P)
+ self.line.set_data(self.xs, self.ys)
+ self.line.figure.canvas.draw()
+
+ def on_motion(self, event):
+ if event.inaxes != self.line.axes or not self.active:
+ return
+ x, y = event.xdata, event.ydata
+ d = np.sqrt((x - self.xs[-1]) ** 2 + (y - self.ys[-1]) ** 2)
+ if d < 0.1:
+ return
+ self.xs.append(event.xdata)
+ self.ys.append(event.ydata)
+ self.line.set_data(self.xs, self.ys)
+ self.line.figure.canvas.draw()
+
+
+# The maze-path as been drawn by hand using the path tracer above
+# Here we count the number of path points contained in each part
+# of the same such as to set the color usng a colormap
+P = np.load("radial-maze-path.npy")
+N = np.zeros(len(C))
+for i, vertices in enumerate(C):
+ codes = [path.Path.MOVETO] + [path.Path.LINETO,] * (len(vertices) - 1)
+ p = path.Path(vertices, codes)
+ N[i] = p.contains_points(P).sum()
+N = 1 - (N - N.min()) / (N.max() - N.min())
+cmap = plt.cm.get_cmap("viridis")
+colors = cmap(N)
+
+# Alternatively, you can also set colors on each arm
+# colors = [] # this will be a list of n_arms * n_squares colors
+# for arm in range(n_arms):
+# for square in range(n_squares+1):
+# if arm < 6:
+# color = cmap(square/n_squares)
+# else:
+# color = 0.00, 0.0, 0.00, 0.05
+# colors.append(color)
+
+# -------------------------------------------------------
+fig = plt.figure(figsize=(8, 8))
+ax = plt.subplot(1, 1, 1, aspect=1, frameon=False, xticks=[], yticks=[])
+
+# Borders
+plt.plot(W[:, 0], W[:, 1], color="black", linewidth=2, zorder=10)
+
+# Squares
+collection = PolyCollection(
+ C, closed=True, linewidth=2, facecolors=colors, edgecolors="white"
+)
+ax.add_collection(collection)
+
+# Letters
+for i in range(n_arms):
+ theta = i / n_arms * 2 * np.pi
+ x = (outer_radius + 1) * np.cos(theta)
+ y = (outer_radius + 1) * np.sin(theta)
+ ax.text(
+ x,
+ y,
+ chr(ord("A") + i),
+ weight="bold",
+ rotation=i / n_arms * 360 - 90,
+ va="center",
+ ha="center",
+ size="xx-large",
+ )
+
+# Path tracker click to start and end (clear previous path)
+(line,) = ax.plot([], [], linestyle="-", color="black", linewidth=1.0, alpha=0.75)
+tracer = PathTracer(line, save=False, filename="radial-maze-path.npy")
+
+plt.tight_layout()
+plt.savefig("../../figures/beyond/radial-maze.pdf")
+plt.show()
diff --git a/code/beyond/stamp.py b/code/beyond/stamp.py
new file mode 100644
index 0000000..f48f5bd
--- /dev/null
+++ b/code/beyond/stamp.py
@@ -0,0 +1,53 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import FancyBboxPatch
+
+I = imageio.imread("../data/mona-lisa.png")
+height, width = np.array(I.shape)
+
+cmap = plt.get_cmap("cividis")
+fig = plt.figure(figsize=(width / 150, height / 150))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ aspect=1,
+ frameon=False,
+ xlim=[0, width],
+ xticks=[],
+ ylim=[0, height],
+ yticks=[],
+)
+
+box = FancyBboxPatch(
+ (0, 0),
+ width,
+ height,
+ zorder=40,
+ boxstyle="roundtooth,tooth_size=24",
+ lw=8,
+ ec="white",
+ fc="None",
+)
+ax.add_patch(box)
+box = FancyBboxPatch(
+ (0, 0),
+ width,
+ height,
+ zorder=50,
+ boxstyle="roundtooth,tooth_size=24",
+ lw=3,
+ ec="black",
+ fc="None",
+)
+ax.add_patch(box)
+
+im = ax.imshow(I, extent=[0, width, 0, height], zorder=30, cmap=cmap)
+im.set_clip_path(box)
+
+plt.savefig("../../figures/beyond/stamp.png", dpi=300)
+plt.show()
diff --git a/code/beyond/tikz-dashes.py b/code/beyond/tikz-dashes.py
new file mode 100644
index 0000000..88fe404
--- /dev/null
+++ b/code/beyond/tikz-dashes.py
@@ -0,0 +1,136 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def style(index, e=0.001):
+ patterns = [
+ ("densely dotted", (-0.5, (e, 2 - e))),
+ ("dotted", (-0.5, (e, 3 - e))),
+ ("loosely dotted", (-0.5, (e, 5 - e))),
+ ("densely dashed", (-0.5, (2, 3))),
+ ("dashed", (-0.5, (2, 4))),
+ ("loosely dashed", (-0.5, (2, 7))),
+ ("densely dashdotted", (-0.5, (2, 2, e, 2 - e))),
+ ("dashdotted", (-0.5, (2, 3, e, 2 - e))),
+ ("loosely dashdotted", (-0.5, (2, 5, e, 5 - e))),
+ ("densely dashdotdotted", (-0.5, (2, 2, e, 2 - e, e, 2 - e))),
+ ("dashdotdotted", (-0.5, (2, 3, e, 3 - e, e, 3 - e))),
+ ("loosely dashdotdotted", (-0.5, (2, 5, e, 5 - e, e, 5 - e))),
+ ]
+ return patterns[index]
+
+
+fig = plt.figure(figsize=(4.25, 1.5))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ xlim=[-1, 23],
+ ylim=[-1, 7],
+ frameon=False,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+)
+
+for i in range(4):
+ for j in range(3):
+ name, pattern = style(i * 3 + j)
+ X = np.array([j * 8, j * 8 + 6])
+ Y = np.array([i * 2, i * 2])
+ plt.plot(
+ X,
+ Y + 0.25,
+ color="0.4",
+ linewidth=2.0,
+ dash_capstyle="projecting",
+ linestyle=pattern,
+ )
+ plt.plot(
+ X, Y, color="0.3", linewidth=2.0, dash_capstyle="round", linestyle=pattern
+ )
+ plt.text(j * 8 + 3, i * 2 - 0.5, name, ha="center", va="top", size="x-small")
+
+ # Because of butt capstlye, we have to modify patterns a bit
+ name, pattern = style(i * 3 + j, e=0.25)
+ plt.plot(
+ X,
+ Y + 0.5,
+ color="0.5",
+ linewidth=2.0,
+ dash_capstyle="butt",
+ linestyle=pattern,
+ )
+
+plt.savefig("tikz-dashes.pdf")
+plt.show()
+
+
+# # Line width
+# # -----------------------------------------------------------------------------
+# y = 18
+# for i,linewidth in enumerate([1,2,3,4,5]):
+# X,Y = [.5+4.25*i, .5+4.25*i+3], [y,y]
+# plt.plot(X, Y, color="black", linewidth=linewidth, solid_capstyle="round")
+# plt.text((X[0]+X[1])/2, y-.75, "%.1f" % linewidth,
+# size="x-small", ha="center", va="top")
+# plt.text(0.5, y+0.75, "Line width", size="small", ha="left", va="bottom")
+
+# # Solid cap style
+# # -----------------------------------------------------------------------------
+# y = 14
+# for i,capstyle in enumerate(["butt", "round", "projecting"]):
+# X,Y = [.75+7*i, .75+7*i+5.5], [y,y]
+# plt.plot(X, Y, color=".85", linewidth=8,
+# solid_capstyle="projecting")
+# plt.plot(X, Y, color="black", linewidth=8,
+# solid_capstyle=capstyle)
+# plt.plot(X, Y, color="white", linewidth=1, marker="|", markersize=5.0)
+# plt.text((X[0]+X[1])/2, y-.75, capstyle,
+# size="x-small", ha="center", va="top")
+# plt.text(0.5, y+0.75, "Solid cap style", size="small", ha="left", va="bottom")
+
+# # Dash cap style
+# # -----------------------------------------------------------------------------
+# y = 10
+# for i,capstyle in enumerate(["butt", "round", "projecting"]):
+# X,Y = [.75+7*i, .75+7*i+5.5], [y,y]
+# plt.plot(X, Y, color=".85", linewidth=8, linestyle="--",
+# solid_capstyle=capstyle, dash_capstyle="projecting")
+# plt.plot(X, Y, color="black", linewidth=8, linestyle="--",
+# solid_capstyle=capstyle, dash_capstyle=capstyle)
+# plt.text((X[0]+X[1])/2, y-.75, capstyle,
+# size="x-small", ha="center", va="top")
+# plt.text(0.5, y+0.75, "Dash cap style", size="small", ha="left", va="bottom")
+
+
+# # Dash style
+# # -----------------------------------------------------------------------------
+# y = 6
+# for i,linestyle in enumerate(["-","--",":","-.", (0,(0.01,2))]):
+# X,Y = [.5+4.25*i, .5+4.25*i+3], [y,y]
+# plt.plot(X, Y, color="black", linewidth=2.0, linestyle=linestyle,
+# solid_capstyle="round", dash_capstyle="round")
+# if isinstance(linestyle,str):
+# plt.text((X[0]+X[1])/2, y-.75, '"%s"' % str(linestyle),
+# size="x-small", ha="center", va="top", family="monospace")
+# else:
+# plt.text((X[0]+X[1])/2, y-.75, "(0,(0.01,2))",
+# size="x-small", ha="center", va="top")
+# plt.text(0.5, y+0.75, "Classic dash style", size="small", ha="left", va="bottom")
+
+
+# # Color cycle
+# # -----------------------------------------------------------------------------
+# y = 2
+# X = np.linspace(1,20,10)
+# Y = np.ones(len(X))*y
+# C = ["C%d" % i for i in range(10)]
+# plt.scatter(X, Y, s=200, color=C)
+# for x,c in zip(X,C):
+# plt.text(x, y-0.75, '"%s"' % c,
+# size="x-small", ha="center", va="top", family="monospace")
+# plt.text(0.5, y+0.75, "Color cycle", size="small", ha="left", va="bottom")
+
+
+# plt.tight_layout()
+# plt.savefig("reference-lines.pdf", dpi=600)
+# plt.show()
diff --git a/code/beyond/tinybot.py b/code/beyond/tinybot.py
new file mode 100644
index 0000000..e912f82
--- /dev/null
+++ b/code/beyond/tinybot.py
@@ -0,0 +1,285 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.path import Path
+from matplotlib.patches import PathPatch
+from matplotlib.ticker import MultipleLocator
+from matplotlib.patches import Circle, Rectangle
+from matplotlib.collections import LineCollection
+
+
+def line_intersect(p1, p2, P3, P4):
+
+ p1 = np.atleast_2d(p1)
+ p2 = np.atleast_2d(p2)
+ P3 = np.atleast_2d(P3)
+ P4 = np.atleast_2d(P4)
+
+ x1, y1 = p1[:, 0], p1[:, 1]
+ x2, y2 = p2[:, 0], p2[:, 1]
+ X3, Y3 = P3[:, 0], P3[:, 1]
+ X4, Y4 = P4[:, 0], P4[:, 1]
+
+ D = (Y4 - Y3) * (x2 - x1) - (X4 - X3) * (y2 - y1)
+
+ # Colinearity test
+ C = D != 0
+ UA = (X4 - X3) * (y1 - Y3) - (Y4 - Y3) * (x1 - X3)
+ UA = np.divide(UA, D, where=C)
+ UB = (x2 - x1) * (y1 - Y3) - (y2 - y1) * (x1 - X3)
+ UB = np.divide(UB, D, where=C)
+
+ # Test if intersections are inside each segment
+ C = C * (UA > 0) * (UA < 1) * (UB > 0) * (UB < 1)
+
+ X = np.where(C, x1 + UA * (x2 - x1), np.inf)
+ Y = np.where(C, y1 + UA * (y2 - y1), np.inf)
+ return np.stack([X, Y], axis=1)
+
+
+class Maze:
+ """
+ A simple 8-maze made of straight walls (line segments)
+ """
+
+ def __init__(self):
+ self.walls = np.array(
+ [
+ # Surrounding walls
+ [(0, 0), (0, 500)],
+ [(0, 500), (300, 500)],
+ [(300, 500), (300, 0)],
+ [(300, 0), (0, 0)],
+ # Bottom hole
+ [(100, 100), (200, 100)],
+ [(200, 100), (200, 200)],
+ [(200, 200), (100, 200)],
+ [(100, 200), (100, 100)],
+ # Top hole
+ [(100, 300), (200, 300)],
+ [(200, 300), (200, 400)],
+ [(200, 400), (100, 400)],
+ [(100, 400), (100, 300)],
+ # Moving walls (invisibles) to constraing bot path
+ [(0, 250), (100, 200)],
+ [(200, 300), (300, 250)],
+ ]
+ )
+
+ def draw(self, ax, grid=True, margin=5):
+ """
+ Render the maze
+ """
+
+ # Buidling a filled patch from walls
+ V, C, S = [], [], self.walls
+ V.extend(S[0 + i, 0] for i in [0, 1, 2, 3, 0])
+ V.extend(S[4 + i, 0] for i in [0, 1, 2, 3, 0])
+ V.extend(S[8 + i, 0] for i in [0, 1, 2, 3, 0])
+ C = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY] * 3
+ path = Path(V, C)
+ patch = PathPatch(
+ path, clip_on=False, linewidth=1.5, edgecolor="black", facecolor="white"
+ )
+
+ # Set figure limits, grid and ticks
+ ax.set_axisbelow(True)
+ ax.add_artist(patch)
+ ax.set_xlim(0 - margin, 300 + margin)
+ ax.set_ylim(0 - margin, 500 + margin)
+ if grid:
+ ax.xaxis.set_major_locator(MultipleLocator(100))
+ ax.xaxis.set_minor_locator(MultipleLocator(10))
+ ax.yaxis.set_major_locator(MultipleLocator(100))
+ ax.yaxis.set_minor_locator(MultipleLocator(10))
+ ax.grid(True, "major", color="0.75", linewidth=1.00, clip_on=False)
+ ax.grid(True, "minor", color="0.75", linewidth=0.50, clip_on=False)
+ ax.set_xticklabels([])
+ ax.set_yticklabels([])
+ ax.tick_params(axis="both", which="major", size=0)
+ ax.tick_params(axis="both", which="minor", size=0)
+
+
+class Bot:
+ def __init__(self):
+ self.size = 10
+ self.position = 150, 250
+ self.orientation = 0
+ self.n_sensors = 8
+ A = np.linspace(-np.pi / 2, +np.pi / 2, self.n_sensors + 2, endpoint=True)[1:-1]
+ self.sensors = {
+ "angle": A,
+ "range": 75 * np.ones((self.n_sensors, 1)),
+ "value": np.ones((self.n_sensors, 1)),
+ }
+
+ def draw(self, ax):
+
+ # Sensors
+ n = 2 * len(self.sensors["angle"])
+ sensors = LineCollection(
+ np.zeros((n, 2, 2)),
+ colors=["0.75", "0.00"] * n,
+ linewidths=[0.75, 1.00] * n,
+ linestyles=["--", "-"] * n,
+ )
+ # Body
+ body = Circle(
+ self.position,
+ self.size,
+ zorder=20,
+ edgecolor="black",
+ facecolor=(1, 1, 1, 0.75),
+ )
+
+ # Head
+ P = np.zeros((1, 2, 2))
+ P[0, 0] = self.position
+ P[0, 1] = P[0, 1] + self.size * np.array(
+ [np.cos(self.orientation), np.sin(self.orientation)]
+ )
+ head = LineCollection(P, colors="black", zorder=30)
+
+ # List of artists to be rendered (sensors, body, head)
+ self.artists = [sensors, body, head]
+ ax.add_collection(sensors)
+ ax.add_artist(body)
+ ax.add_artist(head)
+
+ def update(self, maze):
+
+ sensors, body, head = self.artists
+
+ # Sensors
+ verts = sensors.get_segments()
+ linewidths = sensors.get_linewidth()
+ A = self.sensors["angle"] + self.orientation
+ T = np.stack([np.cos(A), np.sin(A)], axis=1)
+ P1 = self.position + self.size * T
+ P2 = P1 + self.sensors["range"] * T
+ P3, P4 = maze.walls[:, 0], maze.walls[:, 1]
+ for i, (p1, p2) in enumerate(zip(P1, P2)):
+ verts[2 * i] = verts[2 * i + 1] = p1, p2
+ linewidths[2 * i + 1] = 1
+ C = line_intersect(p1, p2, P3, P4)
+ index = np.argmin(((C - p1) ** 2).sum(axis=1))
+ p = C[index]
+ if p[0] < np.inf:
+ verts[2 * i + 1] = p1, p
+ self.sensors["value"][i] = np.sqrt(((p1 - p) ** 2).sum())
+ self.sensors["value"][i] /= self.sensors["range"][i]
+ else:
+ self.sensors["value"][i] = 1
+ sensors.set_verts(verts)
+ sensors.set_linewidths(linewidths)
+
+ # Body
+ body.set_center(self.position)
+
+ # Head
+ P = np.zeros((1, 2, 2))
+ P[0, 0] = self.position
+ P[0, 1] = P[0, 0] + self.size * np.array(
+ [np.cos(self.orientation), np.sin(self.orientation)]
+ )
+ head.set_verts(P)
+
+
+# ------------------------------------------------------------------------------
+if __name__ == "__main__":
+ from matplotlib.gridspec import GridSpec
+ import matplotlib.animation as animation
+
+ maze = Maze()
+ bot = Bot()
+ bot.position = 150, 250
+ bot.orientation = 0
+ bot.sensors["range"][3:5] *= 1.25
+
+ fig = plt.figure(figsize=(10, 5), frameon=False)
+ G = GridSpec(8, 2, width_ratios=(1, 2))
+ ax = plt.subplot(G[:, 0], aspect=1, frameon=False)
+
+ # Maze + bot
+ maze.draw(ax, grid=True, margin=15)
+ bot.draw(ax)
+
+ # Sensor plots
+ plots = []
+ P = np.zeros((500, 2))
+ (trace,) = ax.plot([], [], color="0.5", zorder=10, linewidth=1, linestyle=":")
+ for i in range(bot.n_sensors):
+ ax = plt.subplot(G[i, 1])
+ ax.set_xticks([]), ax.set_yticks([])
+ ax.set_ylabel("Sensor %d" % (i + 1), fontsize="x-small")
+ (plot,) = ax.plot([], [], linewidth=0.75)
+ ax.set_xlim(0, 500)
+ ax.set_ylim(0, 1.1)
+ plots.append(plot)
+ X = np.arange(500)
+ Y = np.zeros((bot.n_sensors, 500))
+
+ def update(frame):
+
+ # Update bot position according to sensors
+ dv = (bot.sensors["value"].ravel() * [-4.0, -3, -2, -1, 1, 2, 3, 4]).sum()
+ if abs(dv) > 0.5:
+ bot.orientation += 0.01 * dv
+ bot.position += 2 * np.array([np.cos(bot.orientation), np.sin(bot.orientation)])
+ bot.update(maze)
+
+ # Alternate wall moving to bias bot behavior
+ if bot.position[1] < 100:
+ maze.walls[12:] = [[(0, 250), (100, 300)], [(200, 200), (300, 250)]]
+ elif bot.position[1] > 400:
+ maze.walls[12:] = [[(0, 250), (100, 200)], [(200, 300), (300, 250)]]
+
+ # Sensor plots
+ n = len(bot.sensors["angle"])
+ if frame < 500:
+ P[frame] = bot.position
+ trace.set_xdata(P[:frame, 0])
+ trace.set_ydata(P[:frame, 1])
+ for i in range(n):
+ Y[i, frame] = bot.sensors["value"][i]
+ plots[i].set_ydata(Y[i, :frame])
+ plots[i].set_xdata(X[:frame])
+ else:
+ P[:-1] = P[1:]
+ P[-1] = bot.position
+ trace.set_xdata(P[:, 0])
+ trace.set_ydata(P[:, 1])
+ Y[:, 0:-1] = Y[:, 1:]
+ for i in range(n):
+ Y[i, -1] = bot.sensors["value"][i]
+ plots[i].set_ydata(Y[i])
+ plots[i].set_xdata(X)
+
+ return bot.artists, trace, plots
+
+ anim = animation.FuncAnimation(fig, update, frames=100_000, interval=10)
+ plt.tight_layout()
+
+ # Animation saving
+ if 0:
+ fig.set_frameon(True)
+ anim = animation.FuncAnimation(fig, update, frames=1000, interval=10)
+ import tqdm
+
+ writer = animation.FFMpegWriter(fps=30)
+ print("Encoding animation (maze.mp4")
+ bar = tqdm.tqdm(total=1000)
+ anim.save(
+ "maze.mp4",
+ writer=writer,
+ dpi=100,
+ progress_callback=lambda i, n: bar.update(1),
+ )
+ bar.close()
+ fig.set_frameon(False)
+
+ plt.show()
diff --git a/code/colors/alpha-scatter.py b/code/colors/alpha-scatter.py
new file mode 100644
index 0000000..66dd778
--- /dev/null
+++ b/code/colors/alpha-scatter.py
@@ -0,0 +1,64 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from matplotlib.backends.backend_agg import FigureCanvas
+from scipy.ndimage import gaussian_filter
+
+# Initialize random generator
+np.random.seed(123)
+
+# Create a figure that pyplot does not know about.
+fig = Figure(figsize=(10, 10))
+# attach a non-interactive Agg canvas to the figure
+# (as a side-effect of the ``__init__``)
+canvas = FigureCanvas(fig)
+ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+
+n = 50_000
+X = np.random.normal(0, 1, n)
+Y = np.random.normal(0, 1, n)
+ax.scatter(X, Y, s=75, facecolors="C0", alpha=0.10, linewidth=0)
+ax.set_xlim(-2, 2), ax.set_ylim(-2, 2)
+
+# ax.plot([1, 2, 3])
+canvas.draw()
+I = np.array(canvas.renderer.buffer_rgba())[..., 0]
+I = 1.0 - I / I.max()
+
+# now display the array X as an Axes in a new figure
+fig = plt.figure(figsize=(12, 4.5))
+
+ax = plt.subplot(1, 3, 1, aspect=1)
+ax.scatter(X, Y, s=10, facecolors="black", alpha=0.10, linewidth=0, rasterized=True)
+ax.set_xlim(-2, 2), ax.set_ylim(-2, 2)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_title("Scatter plot (n=50,000)")
+
+ax = plt.subplot(1, 3, 2)
+ax.imshow(I, cmap="gray_r", extent=[-2, 2, -2, 2])
+ax.set_xlim(-2, 2), ax.set_ylim(-2, 2)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_title("Scatter/Image plot (n=50,000)")
+
+ax = plt.subplot(1, 3, 3)
+image = ax.imshow(gaussian_filter(I, 25), cmap="gray_r", extent=[-2, 2, -2, 2])
+ax.contour(
+ gaussian_filter(I, 25),
+ extent=[-2, 2, -2, 2],
+ linestyles="--",
+ linewidths=1.0,
+ colors="white",
+ alpha=0.5,
+)
+ax.set_xlim(-2, 2), ax.set_ylim(-2, 2)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_title("Density plot")
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/alpha-scatter.pdf", dpi=600)
+plt.show()
diff --git a/code/colors/alpha-vs-color.py b/code/colors/alpha-vs-color.py
new file mode 100644
index 0000000..d85f495
--- /dev/null
+++ b/code/colors/alpha-vs-color.py
@@ -0,0 +1,219 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Open-color colors from https://yeun.github.io/open-color/
+colors = {
+ "gray": {
+ 0: "#f8f9fa",
+ 1: "#f1f3f5",
+ 2: "#e9ecef",
+ 3: "#dee2e6",
+ 4: "#ced4da",
+ 5: "#adb5bd",
+ 6: "#868e96",
+ 7: "#495057",
+ 8: "#343a40",
+ 9: "#212529",
+ },
+ "red": {
+ 0: "#fff5f5",
+ 1: "#ffe3e3",
+ 2: "#ffc9c9",
+ 3: "#ffa8a8",
+ 4: "#ff8787",
+ 5: "#ff6b6b",
+ 6: "#fa5252",
+ 7: "#f03e3e",
+ 8: "#e03131",
+ 9: "#c92a2a",
+ },
+ "pink": {
+ 0: "#fff0f6",
+ 1: "#ffdeeb",
+ 2: "#fcc2d7",
+ 3: "#faa2c1",
+ 4: "#f783ac",
+ 5: "#f06595",
+ 6: "#e64980",
+ 7: "#d6336c",
+ 8: "#c2255c",
+ 9: "#a61e4d",
+ },
+ "grape": {
+ 0: "#f8f0fc",
+ 1: "#f3d9fa",
+ 2: "#eebefa",
+ 3: "#e599f7",
+ 4: "#da77f2",
+ 5: "#cc5de8",
+ 6: "#be4bdb",
+ 7: "#ae3ec9",
+ 8: "#9c36b5",
+ 9: "#862e9c",
+ },
+ "violet": {
+ 0: "#f3f0ff",
+ 1: "#e5dbff",
+ 2: "#d0bfff",
+ 3: "#b197fc",
+ 4: "#9775fa",
+ 5: "#845ef7",
+ 6: "#7950f2",
+ 7: "#7048e8",
+ 8: "#6741d9",
+ 9: "#5f3dc4",
+ },
+ "indigo": {
+ 0: "#edf2ff",
+ 1: "#dbe4ff",
+ 2: "#bac8ff",
+ 3: "#91a7ff",
+ 4: "#748ffc",
+ 5: "#5c7cfa",
+ 6: "#4c6ef5",
+ 7: "#4263eb",
+ 8: "#3b5bdb",
+ 9: "#364fc7",
+ },
+ "blue": {
+ 0: "#e7f5ff",
+ 1: "#d0ebff",
+ 2: "#a5d8ff",
+ 3: "#74c0fc",
+ 4: "#4dabf7",
+ 5: "#339af0",
+ 6: "#228be6",
+ 7: "#1c7ed6",
+ 8: "#1971c2",
+ 9: "#1864ab",
+ },
+ "cyan": {
+ 0: "#e3fafc",
+ 1: "#c5f6fa",
+ 2: "#99e9f2",
+ 3: "#66d9e8",
+ 4: "#3bc9db",
+ 5: "#22b8cf",
+ 6: "#15aabf",
+ 7: "#1098ad",
+ 8: "#0c8599",
+ 9: "#0b7285",
+ },
+ "teal": {
+ 0: "#e6fcf5",
+ 1: "#c3fae8",
+ 2: "#96f2d7",
+ 3: "#63e6be",
+ 4: "#38d9a9",
+ 5: "#20c997",
+ 6: "#12b886",
+ 7: "#0ca678",
+ 8: "#099268",
+ 9: "#087f5b",
+ },
+ "green": {
+ 0: "#ebfbee",
+ 1: "#d3f9d8",
+ 2: "#b2f2bb",
+ 3: "#8ce99a",
+ 4: "#69db7c",
+ 5: "#51cf66",
+ 6: "#40c057",
+ 7: "#37b24d",
+ 8: "#2f9e44",
+ 9: "#2b8a3e",
+ },
+ "lime": {
+ 0: "#f4fce3",
+ 1: "#e9fac8",
+ 2: "#d8f5a2",
+ 3: "#c0eb75",
+ 4: "#a9e34b",
+ 5: "#94d82d",
+ 6: "#82c91e",
+ 7: "#74b816",
+ 8: "#66a80f",
+ 9: "#5c940d",
+ },
+ "yellow": {
+ 0: "#fff9db",
+ 1: "#fff3bf",
+ 2: "#ffec99",
+ 3: "#ffe066",
+ 4: "#ffd43b",
+ 5: "#fcc419",
+ 6: "#fab005",
+ 7: "#f59f00",
+ 8: "#f08c00",
+ 9: "#e67700",
+ },
+ "orange": {
+ 0: "#fff4e6",
+ 1: "#ffe8cc",
+ 2: "#ffd8a8",
+ 3: "#ffc078",
+ 4: "#ffa94d",
+ 5: "#ff922b",
+ 6: "#fd7e14",
+ 7: "#f76707",
+ 8: "#e8590c",
+ 9: "#d9480f",
+ },
+}
+
+
+# Setup
+fig = plt.figure(figsize=(6, 2))
+
+X = np.linspace(0, 2 * np.pi, 256)
+Y1 = np.zeros((50, len(X)))
+Y2 = np.zeros((len(Y1), len(X)))
+noise = 0.5
+for i in range(len(Y1)):
+ Y1[i] = np.cos(X) + noise * np.random.uniform(-1, 1, len(X))
+ Y2[i] = np.sin(X) + noise * np.random.uniform(-1, 1, len(X))
+
+
+# Usage of transparency
+ax = plt.subplot(1, 2, 1)
+
+Y, SD = Y1.mean(axis=0), Y1.std(axis=0)
+ax.fill_between(X, Y + SD, Y - SD, facecolor="C0", alpha=0.25, zorder=-40)
+ax.plot(X, Y, color="C0", zorder=-30)
+
+Y, SD = Y2.mean(axis=0), Y2.std(axis=0)
+ax.fill_between(X, Y + SD, Y - SD, facecolor="C1", alpha=0.25, zorder=-20)
+ax.plot(X, Y, color="C1", zorder=-10)
+
+ax.set_xticks([]), ax.set_yticks([])
+
+
+# Usage of explicite colors
+ax = plt.subplot(1, 2, 2)
+
+Y, SD = Y1.mean(axis=0), Y1.std(axis=0)
+ax.fill_between(X, Y + SD, Y - SD, facecolor=colors["blue"][1], zorder=-40)
+ax.plot(X, Y, color=colors["blue"][7], zorder=-30)
+
+Y, SD = Y2.mean(axis=0), Y2.std(axis=0)
+ax.fill_between(
+ X,
+ Y + SD,
+ Y - SD,
+ facecolor=colors["orange"][1],
+ edgecolor="white",
+ linewidth=0.75,
+ zorder=-20,
+)
+ax.plot(X, Y, color=colors["orange"][7], zorder=-10)
+
+ax.set_xticks([]), ax.set_yticks([])
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/alpha-vs-color.pdf")
+plt.show()
diff --git a/code/colors/color-gradients.py b/code/colors/color-gradients.py
new file mode 100644
index 0000000..56ff512
--- /dev/null
+++ b/code/colors/color-gradients.py
@@ -0,0 +1,129 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate the different colorspaces (sRGB, RGB and Lab)
+# ----------------------------------------------------------------------------
+import math
+import numpy as np
+from skimage.color import rgb2lab, lab2rgb, rgb2xyz, xyz2rgb
+import matplotlib.pyplot as plt
+
+plt.rc("font", family="Roboto")
+
+
+def sRGB_to_Lab(color):
+ color = np.asarray(color, dtype=float)
+ shape = color.shape
+ if len(shape) == 1:
+ color = color.reshape(1, 1, shape[0])
+ elif len(shape) == 2:
+ color = color.reshape(1, shape[0], shape[1])
+ return rgb2lab(color)
+
+
+def Lab_to_sRGB(color):
+ color = np.asarray(color, dtype=float)
+ shape = color.shape
+ if len(shape) == 1:
+ color = color.reshape(1, 1, shape[0])
+ elif len(shape) == 2:
+ color = color.reshape(1, shape[0], shape[1])
+ return lab2rgb(color)
+
+
+def sRGB_to_RGB(color):
+ color = np.asarray(color, dtype=float).reshape(-1, 3)
+ R, G, B = color[..., 0], color[..., 1], color[..., 2]
+ R = np.where(R > 0.04045, np.power((R + 0.055) / 1.055, 2.4), R / 12.92)
+ G = np.where(G > 0.04045, np.power((G + 0.055) / 1.055, 2.4), G / 12.92)
+ B = np.where(B > 0.04045, np.power((B + 0.055) / 1.055, 2.4), B / 12.92)
+ return np.c_[R, G, B]
+
+
+def RGB_to_sRGB(color):
+ color = np.asarray(color, dtype=float).reshape(-1, 3)
+ R, G, B = color[..., 0], color[..., 1], color[..., 2]
+ R = np.where(R > 0.0031308, 1.055 * np.power(R, 1 / 2.4) - 0.055, R * 12.92)
+ G = np.where(G > 0.0031308, 1.055 * np.power(G, 1 / 2.4) - 0.055, G * 12.92)
+ B = np.where(B > 0.0031308, 1.055 * np.power(B, 1 / 2.4) - 0.055, B * 12.92)
+ return np.c_[R, G, B]
+
+
+def gradient(color0, color1, mode="sRGB", n=256):
+ T = np.linspace(0, 1, n).reshape(n, 1)
+ if mode == "Lab":
+ C = (1 - T) * sRGB_to_Lab(color0) + T * sRGB_to_Lab(color1)
+ return Lab_to_sRGB(C)
+ elif mode == "RGB":
+ C = (1 - T) * sRGB_to_RGB(color0) + T * sRGB_to_RGB(color1)
+ return RGB_to_sRGB(C)
+ else:
+ return (1 - T) * color0 + T * color1
+
+
+def hex(color):
+ color = (np.asarray(color) * 255).astype(int)
+ r, g, b = color
+ return ("#%02x%02x%02x" % (r, g, b)).upper()
+
+
+def plot(ax, color0, color1, yticks=True):
+ rows, cols = 16, 256
+ Z = np.zeros((3, rows, cols, 3))
+ Z[0] = gradient(color0, color1, "sRGB")
+ Z[2] = gradient(color0, color1, "RGB")
+ Z[1] = gradient(color0, color1, "Lab")
+
+ ax.tick_params(axis="both", length=0, labelsize="xx-small")
+ ax.imshow(Z.reshape(3 * rows, cols, 3), extent=[0, cols, 0, 3 * rows])
+
+ if yticks:
+ ax.set_yticks([rows // 2, rows // 2 + rows, rows // 2 + 2 * rows])
+ ax.set_yticklabels(["Lab", "RGB", "sRGB"])
+ else:
+ ax.set_yticks([])
+ ax.set_xticks([])
+ plt.text(0, -2, hex(color0), ha="left", va="top", fontsize="xx-small")
+ plt.text(cols, -2, hex(color1), ha="right", va="top", fontsize="xx-small")
+
+
+fig = plt.figure(figsize=(4.25, 3.5))
+plt.rcParams["axes.linewidth"] = 0.5
+
+rows, cols = 6, 2
+
+ax = plt.subplot(rows, cols, 1)
+plot(ax, (1, 1, 1), (1, 0, 0))
+ax = plt.subplot(rows, cols, 2)
+plot(ax, (1, 0, 0), (0, 0, 0), False)
+
+ax = plt.subplot(rows, cols, 3)
+plot(ax, (1, 1, 1), (0, 1, 0))
+ax = plt.subplot(rows, cols, 4)
+plot(ax, (0, 1, 0), (0, 0, 0), False)
+
+ax = plt.subplot(rows, cols, 5)
+plot(ax, (1, 1, 1), (0, 0, 1))
+ax = plt.subplot(rows, cols, 6)
+plot(ax, (0, 0, 1), (0, 0, 0), False)
+
+ax = plt.subplot(rows, cols, 7)
+plot(ax, (1, 0, 0), (0, 1, 0))
+ax = plt.subplot(rows, cols, 8)
+plot(ax, (0, 1, 0), (0, 0, 1), False)
+
+ax = plt.subplot(rows, cols, 9)
+plot(ax, (1, 0, 1), (1, 1, 0))
+ax = plt.subplot(rows, cols, 10)
+plot(ax, (1, 1, 0), (0, 1, 1), False)
+
+ax = plt.subplot(rows, cols, 11)
+plot(ax, (1, 1, 1), (0, 0, 0))
+ax = plt.subplot(rows, cols, 12)
+plot(ax, (0, 0, 0), (1, 1, 1), False)
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/color-gradients.pdf", dpi=600)
+plt.show()
diff --git a/code/colors/color-wheel.py b/code/colors/color-wheel.py
new file mode 100644
index 0000000..48c6195
--- /dev/null
+++ b/code/colors/color-wheel.py
@@ -0,0 +1,222 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Color wheel in HSV
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.collections import QuadMesh
+from matplotlib.font_manager import FontProperties
+
+# Curved label (polar coordinates)
+def polar_text(text, angle, radius=1, scale=0.005, family="sans"):
+ prop = FontProperties(family=family, weight="regular")
+ path = TextPath((0, 0), text, size=1, prop=prop)
+ V = path.vertices
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ V[:, 0] = angle - (V[:, 0] - (xmin + xmax) / 2) * scale
+ V[:, 1] = radius + V[:, 1] * scale
+ patch = PathPatch(path, facecolor="black", linewidth=0, clip_on=False)
+ ax.add_artist(patch)
+
+
+# Polar imshow using quadmesh
+def polar_imshow(
+ ax, Z, extents=[0, 1, 0, 2 * np.pi], vmin=None, vmax=None, cmap="viridis"
+):
+
+ Z = np.atleast_3d(Z)
+ nr, nt, d = Z.shape
+ rmin, rmax, tmin, tmax = extents
+
+ if d == 1:
+ cmap = plt.get_cmap(cmap)
+ vmin = vmin or Z.min()
+ vmax = vmax or Z.max()
+ norm = colors.Normalize(vmin=vmin, vmax=vmax)
+ facecolors = cmap(norm(Z))
+ else:
+ facecolors = Z.reshape(nr, nt, 3).reshape(-1, 3)
+
+ R = np.linspace(rmin, rmax, nr + 1)
+ T = np.linspace(tmin, tmax, nt + 1)
+ T, R = np.meshgrid(T, R)
+ nr, nt = R.shape
+ R, T = R.ravel(), T.ravel()
+ coords = np.column_stack((T, R))
+ collection = QuadMesh(
+ nt - 1,
+ nr - 1,
+ coords,
+ rasterized=True,
+ facecolors=facecolors,
+ edgecolors="None",
+ linewidth=0,
+ antialiased=False,
+ )
+ ax.add_collection(collection)
+ return collection
+
+
+radius = 1.05
+scale = 0.085
+family = "Yanone Kaffeesatz"
+
+fig = plt.figure(figsize=(8, 4))
+
+n = 50
+R = np.linspace(0, 1, 2 * n)
+T = np.linspace(0, 1, 10 * n)
+T, R = np.meshgrid(T, R)
+H, S, V = T, R, np.ones_like(T)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(2, 4, 1, polar=True, frameon=True)
+Z = colors.hsv_to_rgb(np.dstack([H, S, V]))
+ax.set_xticks([]), ax.set_yticks([])
+polar_text(
+ "<––– hue –––", family=family, angle=np.pi / 4, radius=radius, scale=2 * scale
+)
+ax.text(
+ 3 * np.pi / 4,
+ 1.5,
+ "value = 1.00",
+ family=family,
+ size=10,
+ bbox={
+ "pad": 1.5,
+ "linewidth": 0.5,
+ "boxstyle": "round,pad=.2",
+ "edgecolor": "black",
+ "facecolor": "white",
+ },
+)
+
+ax.text(np.pi, 0.0, "––– saturation –––> ", size=8, family=family)
+polar_imshow(ax, Z)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(2, 4, 2, polar=True, frameon=True)
+Z = colors.hsv_to_rgb(np.dstack([H, S, 0.75 * V]))
+ax.set_xticks([]), ax.set_yticks([])
+polar_text(
+ "<––– hue –––", family=family, angle=np.pi / 4, radius=radius, scale=2 * scale
+)
+ax.text(
+ 3 * np.pi / 4,
+ 1.5,
+ "value = 0.75",
+ family=family,
+ size=10,
+ bbox={
+ "pad": 1.5,
+ "linewidth": 0.5,
+ "boxstyle": "round,pad=.2",
+ "edgecolor": "None",
+ "facecolor": "0.75",
+ },
+)
+ax.text(np.pi, 0.0, "––– saturation –––> ", size=8, family=family)
+polar_imshow(ax, Z)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(2, 4, 5, polar=True, frameon=True)
+Z = colors.hsv_to_rgb(np.dstack([H, S, 0.5 * V]))
+ax.set_xticks([]), ax.set_yticks([])
+polar_text(
+ "<––– hue –––", family=family, angle=np.pi / 4, radius=radius, scale=2 * scale
+)
+ax.text(
+ 3 * np.pi / 4,
+ 1.5,
+ "value = 0.50",
+ family=family,
+ size=10,
+ color="white",
+ bbox={
+ "pad": 1.5,
+ "linewidth": 0.5,
+ "boxstyle": "round,pad=.2",
+ "edgecolor": "None",
+ "facecolor": "0.5",
+ },
+)
+ax.text(np.pi, 0.0, "––– saturation –––> ", size=8, family=family, color="1.0")
+polar_imshow(ax, Z)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(2, 4, 6, polar=True, frameon=True)
+Z = colors.hsv_to_rgb(np.dstack([H, S, 0.25 * V]))
+ax.set_xticks([]), ax.set_yticks([])
+polar_text(
+ "<––– hue –––", family=family, angle=np.pi / 4, radius=radius, scale=2 * scale
+)
+ax.text(
+ 3 * np.pi / 4,
+ 1.5,
+ "value = 0.25",
+ family=family,
+ size=10,
+ color="white",
+ bbox={
+ "pad": 1.5,
+ "linewidth": 0.5,
+ "boxstyle": "round,pad=.2",
+ "edgecolor": "None",
+ "facecolor": "0.25",
+ },
+)
+ax.text(np.pi, 0.0, "-–– saturation –––> ", size=8, family=family, color="1.0")
+polar_imshow(ax, Z)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(1, 2, 2, polar=True, frameon=False)
+n = 100
+R = np.linspace(0, 1, n)
+R -= R % (1 / 5.99)
+T = np.linspace(0, 1, 10 * n)
+T -= T % (1 / 11.99)
+T, R = np.meshgrid(T, R)
+H, S, V = T, R, np.ones_like(T)
+Z = colors.hsv_to_rgb(np.dstack([H, S, V]))
+polar_imshow(ax, Z)
+ax.set_xticks(np.linspace(0, 2 * np.pi, 13))
+ax.set_yticks(np.linspace(0, 1, 7))
+ax.set_yticklabels([])
+ax.set_xticklabels([])
+ax.grid(linewidth=1, color="white")
+labels = [
+ "red",
+ "orange",
+ "yellow",
+ "lime",
+ "green",
+ "turquoise",
+ "cyan",
+ "skyblue",
+ "blue",
+ "violet",
+ "purple",
+ "magenta",
+]
+for label, x in zip(labels, np.linspace(0.5, 12.5, 12)):
+ label = label.upper()
+ angle = x / 13 * 2 * np.pi
+ polar_text(
+ label.upper(), family=family, angle=angle, radius=radius, scale=1.25 * scale
+ )
+ax.set_theta_offset(-np.pi / 12)
+
+R = np.ones(100)
+T = np.linspace(0, 2 * np.pi, 100)
+ax.plot(T, R, color="white")
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/color-wheel.png", dpi=600)
+plt.savefig("../../figures/colors/color-wheel.pdf", dpi=600)
+plt.show()
diff --git a/code/colors/colored-hist.py b/code/colors/colored-hist.py
new file mode 100644
index 0000000..cf26f44
--- /dev/null
+++ b/code/colors/colored-hist.py
@@ -0,0 +1,296 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+material = {
+ "red": {
+ 0: "#ffebee",
+ 1: "#ffcdd2",
+ 2: "#ef9a9a",
+ 3: "#e57373",
+ 4: "#ef5350",
+ 5: "#f44336",
+ 6: "#e53935",
+ 7: "#d32f2f",
+ 8: "#c62828",
+ 9: "#b71c1c",
+ },
+ "pink": {
+ 0: "#fce4ec",
+ 1: "#f8bbd0",
+ 2: "#f48fb1",
+ 3: "#f06292",
+ 4: "#ec407a",
+ 5: "#e91e63",
+ 6: "#d81b60",
+ 7: "#c2185b",
+ 8: "#ad1457",
+ 9: "#880e4f",
+ },
+ "purple": {
+ 0: "#f3e5f5",
+ 1: "#e1bee7",
+ 2: "#ce93d8",
+ 3: "#ba68c8",
+ 4: "#ab47bc",
+ 5: "#9c27b0",
+ 6: "#8e24aa",
+ 7: "#7b1fa2",
+ 8: "#6a1b9a",
+ 9: "#4a148c",
+ },
+ "deep purple": {
+ 0: "#ede7f6",
+ 1: "#d1c4e9",
+ 2: "#b39ddb",
+ 3: "#9575cd",
+ 4: "#7e57c2",
+ 5: "#673ab7",
+ 6: "#5e35b1",
+ 7: "#512da8",
+ 8: "#4527a0",
+ 9: "#311b92",
+ },
+ "indigo": {
+ 0: "#e8eaf6",
+ 1: "#c5cae9",
+ 2: "#9fa8da",
+ 3: "#7986cb",
+ 4: "#5c6bc0",
+ 5: "#3f51b5",
+ 6: "#3949ab",
+ 7: "#303f9f",
+ 8: "#283593",
+ 9: "#1a237e",
+ },
+ "blue": {
+ 0: "#e3f2fd",
+ 1: "#bbdefb",
+ 2: "#90caf9",
+ 3: "#64b5f6",
+ 4: "#42a5f5",
+ 5: "#2196f3",
+ 6: "#1e88e5",
+ 7: "#1976d2",
+ 8: "#1565c0",
+ 9: "#0d47a1",
+ },
+ "light blue": {
+ 0: "#e1f5fe",
+ 1: "#b3e5fc",
+ 2: "#81d4fa",
+ 3: "#4fc3f7",
+ 4: "#29b6f6",
+ 5: "#03a9f4",
+ 6: "#039be5",
+ 7: "#0288d1",
+ 8: "#0277bd",
+ 9: "#01579b",
+ },
+ "cyan": {
+ 0: "#e0f7fa",
+ 1: "#b2ebf2",
+ 2: "#80deea",
+ 3: "#4dd0e1",
+ 4: "#26c6da",
+ 5: "#00bcd4",
+ 6: "#00acc1",
+ 7: "#0097a7",
+ 8: "#00838f",
+ 9: "#006064",
+ },
+ "teal": {
+ 0: "#e0f2f1",
+ 1: "#b2dfdb",
+ 2: "#80cbc4",
+ 3: "#4db6ac",
+ 4: "#26a69a",
+ 5: "#009688",
+ 6: "#00897b",
+ 7: "#00796b",
+ 8: "#00695c",
+ 9: "#004d40",
+ },
+ "green": {
+ 0: "#e8f5e9",
+ 1: "#c8e6c9",
+ 2: "#a5d6a7",
+ 3: "#81c784",
+ 4: "#66bb6a",
+ 5: "#4caf50",
+ 6: "#43a047",
+ 7: "#388e3c",
+ 8: "#2e7d32",
+ 9: "#1b5e20",
+ },
+ "light green": {
+ 0: "#f1f8e9",
+ 1: "#dcedc8",
+ 2: "#c5e1a5",
+ 3: "#aed581",
+ 4: "#9ccc65",
+ 5: "#8bc34a",
+ 6: "#7cb342",
+ 7: "#689f38",
+ 8: "#558b2f",
+ 9: "#33691e",
+ },
+ "lime": {
+ 0: "#f9fbe7",
+ 1: "#f0f4c3",
+ 2: "#e6ee9c",
+ 3: "#dce775",
+ 4: "#d4e157",
+ 5: "#cddc39",
+ 6: "#c0ca33",
+ 7: "#afb42b",
+ 8: "#9e9d24",
+ 9: "#827717",
+ },
+ "yellow": {
+ 0: "#fffde7",
+ 1: "#fff9c4",
+ 2: "#fff59d",
+ 3: "#fff176",
+ 4: "#ffee58",
+ 5: "#ffeb3b",
+ 6: "#fdd835",
+ 7: "#fbc02d",
+ 8: "#f9a825",
+ 9: "#f57f17",
+ },
+ "amber": {
+ 0: "#fff8e1",
+ 1: "#ffecb3",
+ 2: "#ffe082",
+ 3: "#ffd54f",
+ 4: "#ffca28",
+ 5: "#ffc107",
+ 6: "#ffb300",
+ 7: "#ffa000",
+ 8: "#ff8f00",
+ 9: "#ff6f00",
+ },
+ "orange": {
+ 0: "#fff3e0",
+ 1: "#ffe0b2",
+ 2: "#ffcc80",
+ 3: "#ffb74d",
+ 4: "#ffa726",
+ 5: "#ff9800",
+ 6: "#fb8c00",
+ 7: "#f57c00",
+ 8: "#ef6c00",
+ 9: "#e65100",
+ },
+ "deep orange": {
+ 0: "#fbe9e7",
+ 1: "#ffccbc",
+ 2: "#ffab91",
+ 3: "#ff8a65",
+ 4: "#ff7043",
+ 5: "#ff5722",
+ 6: "#f4511e",
+ 7: "#e64a19",
+ 8: "#d84315",
+ 9: "#bf360c",
+ },
+ "brown": {
+ 0: "#efebe9",
+ 1: "#d7ccc8",
+ 2: "#bcaaa4",
+ 3: "#a1887f",
+ 4: "#8d6e63",
+ 5: "#795548",
+ 6: "#6d4c41",
+ 7: "#5d4037",
+ 8: "#4e342e",
+ 9: "#3e2723",
+ },
+ "grey": {
+ 0: "#fafafa",
+ 1: "#f5f5f5",
+ 2: "#eeeeee",
+ 3: "#e0e0e0",
+ 4: "#bdbdbd",
+ 5: "#9e9e9e",
+ 6: "#757575",
+ 7: "#616161",
+ 8: "#424242",
+ 9: "#212121",
+ },
+ "blue grey": {
+ 0: "#eceff1",
+ 1: "#cfd8dc",
+ 2: "#b0bec5",
+ 3: "#90a4ae",
+ 4: "#78909c",
+ 5: "#607d8b",
+ 6: "#546e7a",
+ 7: "#455a64",
+ 8: "#37474f",
+ 9: "#263238",
+ },
+}
+
+
+np.random.seed(123)
+plt.figure(figsize=(8, 4))
+ax = plt.subplot(1, 1, 1, frameon=False)
+
+
+def bars(origin, color, n, index):
+ X = origin + np.arange(n)
+ H = 20 * np.random.uniform(1.0, 5.0, n)
+ H.sort()
+
+ ax.bar(
+ X,
+ H,
+ width=1.0,
+ align="edge",
+ color=[material[color][1 + 2 * i] for i in range(n)],
+ )
+ ax.plot([origin - 0.5, origin + n + 0.5], [0, 0], color="black", lw=2.5)
+ ax.text(origin + n / 2, -1, "Group " + index, va="top", ha="center")
+
+
+ax.axhline(50, 0.05, 1, color="0.5", linewidth=0.5, linestyle="--", zorder=-10)
+ax.axhline(100, 0.05, 1, color="0.5", linewidth=0.5, linestyle="--", zorder=-10)
+
+n = 3
+bars(0 * (n + 2), "red", n, "A")
+bars(1 * (n + 2), "indigo", n, "B")
+bars(2 * (n + 2), "orange", n, "C")
+bars(3 * (n + 2), "teal", n, "D")
+bars(4 * (n + 2), "pink", n, "E")
+bars(5 * (n + 2), "cyan", n, "F")
+
+ax.set_xlim(-2, 6 * (n + 2) - 1.5)
+ax.set_xticks([])
+ax.set_ylim(0, 111)
+ax.set_yticks([0, 50, 100])
+ax.set_yticklabels(["0%", "50%", "100%"])
+
+
+plt.legend(
+ # bbox_to_anchor=(0.0, 1.0, 1.0, 0.1),
+ loc="upper right",
+ borderaxespad=0.0,
+ ncol=3,
+ handles=[
+ mpatches.Patch(color=material["blue grey"][1], label="2018"),
+ mpatches.Patch(color=material["blue grey"][3], label="2019"),
+ mpatches.Patch(color=material["blue grey"][5], label="2020"),
+ ],
+)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/colored-hist.pdf")
+plt.show()
diff --git a/code/colors/colored-plot.py b/code/colors/colored-plot.py
new file mode 100644
index 0000000..55f3099
--- /dev/null
+++ b/code/colors/colored-plot.py
@@ -0,0 +1,33 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate colored plots
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+
+
+def plot(ax, X, Y, cmap, alpha):
+ P = np.array([X, Y]).T.reshape(-1, 1, 2)
+ S = np.concatenate([P[:-1], P[1:]], axis=1)
+ C = cmap(np.linspace(0, 1, len(S)))
+ L = LineCollection(S, color=C, alpha=alpha, linewidth=1.25)
+ ax.add_collection(L)
+
+
+fig = plt.figure(figsize=(12, 3))
+fig.patch.set_facecolor("black")
+ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+X = np.linspace(-5 * np.pi, +5 * np.pi, 2500)
+for d in np.linspace(0, 1, 15):
+ dy = d / 2 + (1 - np.abs(X) / X.max()) ** 2
+ dx = 1 + d / 3
+ Y = dy * np.sin(dx * X) + 0.1 * np.cos(3 + 5 * X)
+ plot(ax, X, Y, plt.get_cmap("rainbow"), d)
+ax.set_xlim(X.min(), X.max())
+ax.set_ylim(-2.0, 2.0)
+plt.savefig("../../figures/colors/colored-plot.pdf")
+plt.show()
diff --git a/code/colors/flower-polar.py b/code/colors/flower-polar.py
new file mode 100644
index 0000000..3ecd57d
--- /dev/null
+++ b/code/colors/flower-polar.py
@@ -0,0 +1,124 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Flower polar / flower power (poly collection and polar projection)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+from matplotlib.collections import PolyCollection
+
+
+def flower(ax, n_branches=24, n_sections=4, lw=1):
+ R = np.linspace(0.1, 1.0, 25)
+ paths = []
+ facecolors = []
+ n_sections += 1
+
+ for i in range(n_branches):
+ for j in range(n_sections - 1):
+ R_, T_ = [], []
+ T = np.linspace(
+ i * 2 * np.pi / n_branches,
+ (i + n_sections / 2) * 2 * np.pi / n_branches,
+ len(R),
+ )
+ t = np.interp(
+ np.linspace(j + 1, j + 2, 20), np.linspace(0, n_sections, len(T)), T
+ )
+ r = np.interp(
+ np.linspace(j + 1, j + 2, 20), np.linspace(0, n_sections, len(R)), R
+ )
+ R_.extend(r[::1].tolist())
+ T_.extend(t[::1].tolist())
+
+ T = np.linspace(
+ (i + 1 + j + 1) * 2 * np.pi / n_branches,
+ (i + 1 + j + 1 - n_sections / 2) * 2 * np.pi / n_branches,
+ len(R),
+ )
+ t = np.interp(
+ np.linspace(j + 1, j + 2, 20), np.linspace(0, n_sections, len(T)), T
+ )
+ r = np.interp(
+ np.linspace(j + 1, j + 2, 20), np.linspace(0, n_sections, len(R)), R
+ )
+ R_.extend(r[::-1].tolist())
+ T_.extend(t[::-1].tolist())
+
+ T = np.linspace(
+ (i + 1) * 2 * np.pi / n_branches,
+ (i + 1 + n_sections / 2) * 2 * np.pi / n_branches,
+ len(R),
+ )
+ t = np.interp(
+ np.linspace(j, j + 1, 20), np.linspace(0, n_sections, len(T)), T
+ )
+ r = np.interp(
+ np.linspace(j, j + 1, 20), np.linspace(0, n_sections, len(R)), R
+ )
+ R_.extend(r[::-1].tolist())
+ T_.extend(t[::-1].tolist())
+
+ T = np.linspace(
+ (i + 1 + j) * 2 * np.pi / n_branches,
+ (i + 1 + j - n_sections / 2) * 2 * np.pi / n_branches,
+ len(R),
+ )
+ t = np.interp(
+ np.linspace(j, j + 1, 20), np.linspace(0, n_sections, len(T)), T
+ )
+ r = np.interp(
+ np.linspace(j, j + 1, 20), np.linspace(0, n_sections, len(R)), R
+ )
+ R_.extend(r[::1].tolist())
+ T_.extend(t[::1].tolist())
+
+ P = np.dstack([T_, R_]).squeeze()
+ paths.append(P)
+ h = i / n_branches
+ s = 0.5 + 0.5 * j / (n_sections - 1)
+ v = 1.00
+ facecolors.append(colors.hsv_to_rgb([h, s, v]))
+
+ collection = PolyCollection(
+ paths, linewidths=5.5 * lw, facecolors="None", edgecolors="black"
+ )
+ ax.add_collection(collection)
+
+ collection = PolyCollection(
+ paths, linewidths=4 * lw, facecolors="None", edgecolors="white"
+ )
+ ax.add_collection(collection)
+
+ ax.fill_between(np.linspace(0, 2 * np.pi, 100), 0.0, 0.5, facecolor="white")
+
+ collection = PolyCollection(
+ paths, linewidths=lw, facecolors=facecolors, edgecolors="white"
+ )
+ ax.add_collection(collection)
+
+
+fig = plt.figure(figsize=(12, 4))
+
+ax = fig.add_subplot(1, 3, 1, polar=True, frameon=False)
+flower(ax, 6, 3, lw=2.0)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_rlim(0, 1.1)
+
+ax = fig.add_subplot(1, 3, 2, polar=True, frameon=False)
+flower(ax, 12, 4, lw=1.5)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_rlim(0, 1.1)
+
+ax = fig.add_subplot(1, 3, 3, polar=True, frameon=False)
+flower(ax, 24, 5, lw=1.0)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_rlim(0, 1.1)
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/flower-polar.pdf", dpi=600)
+# plt.savefig("../../figures/colors/flower-polar.png", dpi=300)
+plt.show()
diff --git a/code/colors/material-colors.py b/code/colors/material-colors.py
new file mode 100644
index 0000000..3306276
--- /dev/null
+++ b/code/colors/material-colors.py
@@ -0,0 +1,294 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatch
+from matplotlib.patches import FancyBboxPatch
+
+colors = {
+ "red": {
+ 0: "#ffebee",
+ 1: "#ffcdd2",
+ 2: "#ef9a9a",
+ 3: "#e57373",
+ 4: "#ef5350",
+ 5: "#f44336",
+ 6: "#e53935",
+ 7: "#d32f2f",
+ 8: "#c62828",
+ 9: "#b71c1c",
+ },
+ "pink": {
+ 0: "#fce4ec",
+ 1: "#f8bbd0",
+ 2: "#f48fb1",
+ 3: "#f06292",
+ 4: "#ec407a",
+ 5: "#e91e63",
+ 6: "#d81b60",
+ 7: "#c2185b",
+ 8: "#ad1457",
+ 9: "#880e4f",
+ },
+ "purple": {
+ 0: "#f3e5f5",
+ 1: "#e1bee7",
+ 2: "#ce93d8",
+ 3: "#ba68c8",
+ 4: "#ab47bc",
+ 5: "#9c27b0",
+ 6: "#8e24aa",
+ 7: "#7b1fa2",
+ 8: "#6a1b9a",
+ 9: "#4a148c",
+ },
+ "d.purple": {
+ 0: "#ede7f6",
+ 1: "#d1c4e9",
+ 2: "#b39ddb",
+ 3: "#9575cd",
+ 4: "#7e57c2",
+ 5: "#673ab7",
+ 6: "#5e35b1",
+ 7: "#512da8",
+ 8: "#4527a0",
+ 9: "#311b92",
+ },
+ "indigo": {
+ 0: "#e8eaf6",
+ 1: "#c5cae9",
+ 2: "#9fa8da",
+ 3: "#7986cb",
+ 4: "#5c6bc0",
+ 5: "#3f51b5",
+ 6: "#3949ab",
+ 7: "#303f9f",
+ 8: "#283593",
+ 9: "#1a237e",
+ },
+ "blue": {
+ 0: "#e3f2fd",
+ 1: "#bbdefb",
+ 2: "#90caf9",
+ 3: "#64b5f6",
+ 4: "#42a5f5",
+ 5: "#2196f3",
+ 6: "#1e88e5",
+ 7: "#1976d2",
+ 8: "#1565c0",
+ 9: "#0d47a1",
+ },
+ "l.blue": {
+ 0: "#e1f5fe",
+ 1: "#b3e5fc",
+ 2: "#81d4fa",
+ 3: "#4fc3f7",
+ 4: "#29b6f6",
+ 5: "#03a9f4",
+ 6: "#039be5",
+ 7: "#0288d1",
+ 8: "#0277bd",
+ 9: "#01579b",
+ },
+ "cyan": {
+ 0: "#e0f7fa",
+ 1: "#b2ebf2",
+ 2: "#80deea",
+ 3: "#4dd0e1",
+ 4: "#26c6da",
+ 5: "#00bcd4",
+ 6: "#00acc1",
+ 7: "#0097a7",
+ 8: "#00838f",
+ 9: "#006064",
+ },
+ "teal": {
+ 0: "#e0f2f1",
+ 1: "#b2dfdb",
+ 2: "#80cbc4",
+ 3: "#4db6ac",
+ 4: "#26a69a",
+ 5: "#009688",
+ 6: "#00897b",
+ 7: "#00796b",
+ 8: "#00695c",
+ 9: "#004d40",
+ },
+ "green": {
+ 0: "#e8f5e9",
+ 1: "#c8e6c9",
+ 2: "#a5d6a7",
+ 3: "#81c784",
+ 4: "#66bb6a",
+ 5: "#4caf50",
+ 6: "#43a047",
+ 7: "#388e3c",
+ 8: "#2e7d32",
+ 9: "#1b5e20",
+ },
+ "l.green": {
+ 0: "#f1f8e9",
+ 1: "#dcedc8",
+ 2: "#c5e1a5",
+ 3: "#aed581",
+ 4: "#9ccc65",
+ 5: "#8bc34a",
+ 6: "#7cb342",
+ 7: "#689f38",
+ 8: "#558b2f",
+ 9: "#33691e",
+ },
+ "lime": {
+ 0: "#f9fbe7",
+ 1: "#f0f4c3",
+ 2: "#e6ee9c",
+ 3: "#dce775",
+ 4: "#d4e157",
+ 5: "#cddc39",
+ 6: "#c0ca33",
+ 7: "#afb42b",
+ 8: "#9e9d24",
+ 9: "#827717",
+ },
+ "yellow": {
+ 0: "#fffde7",
+ 1: "#fff9c4",
+ 2: "#fff59d",
+ 3: "#fff176",
+ 4: "#ffee58",
+ 5: "#ffeb3b",
+ 6: "#fdd835",
+ 7: "#fbc02d",
+ 8: "#f9a825",
+ 9: "#f57f17",
+ },
+ "amber": {
+ 0: "#fff8e1",
+ 1: "#ffecb3",
+ 2: "#ffe082",
+ 3: "#ffd54f",
+ 4: "#ffca28",
+ 5: "#ffc107",
+ 6: "#ffb300",
+ 7: "#ffa000",
+ 8: "#ff8f00",
+ 9: "#ff6f00",
+ },
+ "orange": {
+ 0: "#fff3e0",
+ 1: "#ffe0b2",
+ 2: "#ffcc80",
+ 3: "#ffb74d",
+ 4: "#ffa726",
+ 5: "#ff9800",
+ 6: "#fb8c00",
+ 7: "#f57c00",
+ 8: "#ef6c00",
+ 9: "#e65100",
+ },
+ "d.orange": {
+ 0: "#fbe9e7",
+ 1: "#ffccbc",
+ 2: "#ffab91",
+ 3: "#ff8a65",
+ 4: "#ff7043",
+ 5: "#ff5722",
+ 6: "#f4511e",
+ 7: "#e64a19",
+ 8: "#d84315",
+ 9: "#bf360c",
+ },
+ "brown": {
+ 0: "#efebe9",
+ 1: "#d7ccc8",
+ 2: "#bcaaa4",
+ 3: "#a1887f",
+ 4: "#8d6e63",
+ 5: "#795548",
+ 6: "#6d4c41",
+ 7: "#5d4037",
+ 8: "#4e342e",
+ 9: "#3e2723",
+ },
+ "grey": {
+ 0: "#fafafa",
+ 1: "#f5f5f5",
+ 2: "#eeeeee",
+ 3: "#e0e0e0",
+ 4: "#bdbdbd",
+ 5: "#9e9e9e",
+ 6: "#757575",
+ 7: "#616161",
+ 8: "#424242",
+ 9: "#212121",
+ },
+ "blue grey": {
+ 0: "#eceff1",
+ 1: "#cfd8dc",
+ 2: "#b0bec5",
+ 3: "#90a4ae",
+ 4: "#78909c",
+ 5: "#607d8b",
+ 6: "#546e7a",
+ 7: "#455a64",
+ 8: "#37474f",
+ 9: "#263238",
+ },
+}
+
+nx, dx = 10, 5
+ny, dy = 19, 5
+
+fig = plt.figure(figsize=(8, 10), dpi=100)
+ax = plt.subplot(
+ 1,
+ 1,
+ 1,
+ frameon=False,
+ xlim=(-1, nx * dx),
+ ylim=(-1, (ny - 1) * dy + 2),
+ xticks=[],
+ yticks=[],
+)
+
+
+for palette, y in zip(colors.keys(), range(0, ny * dy, dy)):
+ for level, x in zip(range(10), range(0, 10 * dx, dx)):
+ fancy = FancyBboxPatch(
+ (x, y),
+ dx - 1,
+ 0.3 * dy,
+ facecolor=colors[palette][level],
+ edgecolor="black",
+ linewidth=0,
+ )
+ ax.add_patch(fancy)
+
+ name = "%s %s" % (palette.upper(), level)
+ ax.text(
+ x,
+ y - 0.15 * dy,
+ name,
+ size=8,
+ family="Roboto",
+ weight="regular",
+ ha="left",
+ va="top",
+ )
+
+ value = colors[palette][level].upper()
+ ax.text(
+ x,
+ y - 0.375 * dy,
+ value,
+ size=7.5,
+ color="0.5",
+ family="Roboto",
+ weight="regular",
+ ha="left",
+ va="top",
+ )
+
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/material-colors.pdf")
+plt.savefig("../../figures/colors/material-colors.png", dpi=300)
+plt.show()
diff --git a/code/colors/mona-lisa.py b/code/colors/mona-lisa.py
new file mode 100644
index 0000000..6b8ef75
--- /dev/null
+++ b/code/colors/mona-lisa.py
@@ -0,0 +1,57 @@
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+
+I = imageio.imread("../data/mona-lisa.png")
+
+
+def plot(ax, cmap, name=None):
+ ax.imshow(I, cmap=plt.get_cmap(cmap), rasterized=True)
+ ax.text(
+ 0.5,
+ 0.025,
+ name or cmap,
+ transform=ax.transAxes,
+ color="white",
+ ha="center",
+ va="bottom",
+ size="large",
+ family="Roboto Slab",
+ )
+ ax.set_xticks([]), ax.set_yticks([])
+
+
+rows, cols = 3, 4
+plt.figure(figsize=(9, 10.25))
+
+ax = plt.subplot(rows, cols, 1, frameon=False)
+plot(ax, "cividis", "Cividis")
+ax = plt.subplot(rows, cols, 2, frameon=False)
+plot(ax, "viridis", "Viridis")
+ax = plt.subplot(rows, cols, 3, frameon=False)
+plot(ax, "magma", "Magma")
+ax = plt.subplot(rows, cols, 4, frameon=False)
+plot(ax, "inferno", "Inferno")
+
+ax = plt.subplot(rows, cols, 5, frameon=False)
+plot(ax, "bone")
+ax = plt.subplot(rows, cols, 6, frameon=False)
+plot(ax, "YlGn_r", "YlGn\n(Yellow Green)")
+ax = plt.subplot(rows, cols, 7, frameon=False)
+plot(ax, "RdPu_r", "RdPu\n(Red Purple)")
+ax = plt.subplot(rows, cols, 8, frameon=False)
+plot(ax, "OrRd_r", "OrRd\n(Orange Red)")
+
+
+ax = plt.subplot(rows, cols, 9, frameon=False)
+plot(ax, "Greys_r", "Greys")
+ax = plt.subplot(rows, cols, 10, frameon=False)
+plot(ax, "GnBu_r", "GnBu\n(Green Blue)")
+ax = plt.subplot(rows, cols, 11, frameon=False)
+plot(ax, "BuPu_r", "BuPu\n(Blue Purple)")
+ax = plt.subplot(rows, cols, 12, frameon=False)
+plot(ax, "YlOrBr_r", "YlOrBr\n(Yellow Orange Brown)")
+
+plt.subplots_adjust(left=0, bottom=0, right=1, top=1, hspace=0.00, wspace=0.00)
+plt.savefig("../../figures/colors/mona-lisa.pdf", dpi=300)
+plt.show()
diff --git a/code/colors/open-colors.py b/code/colors/open-colors.py
new file mode 100644
index 0000000..c6e2398
--- /dev/null
+++ b/code/colors/open-colors.py
@@ -0,0 +1,224 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatch
+from matplotlib.patches import FancyBboxPatch
+
+# Open-color colors from https://yeun.github.io/open-color/
+colors = {
+ "gray": {
+ 0: "#f8f9fa",
+ 1: "#f1f3f5",
+ 2: "#e9ecef",
+ 3: "#dee2e6",
+ 4: "#ced4da",
+ 5: "#adb5bd",
+ 6: "#868e96",
+ 7: "#495057",
+ 8: "#343a40",
+ 9: "#212529",
+ },
+ "red": {
+ 0: "#fff5f5",
+ 1: "#ffe3e3",
+ 2: "#ffc9c9",
+ 3: "#ffa8a8",
+ 4: "#ff8787",
+ 5: "#ff6b6b",
+ 6: "#fa5252",
+ 7: "#f03e3e",
+ 8: "#e03131",
+ 9: "#c92a2a",
+ },
+ "pink": {
+ 0: "#fff0f6",
+ 1: "#ffdeeb",
+ 2: "#fcc2d7",
+ 3: "#faa2c1",
+ 4: "#f783ac",
+ 5: "#f06595",
+ 6: "#e64980",
+ 7: "#d6336c",
+ 8: "#c2255c",
+ 9: "#a61e4d",
+ },
+ "grape": {
+ 0: "#f8f0fc",
+ 1: "#f3d9fa",
+ 2: "#eebefa",
+ 3: "#e599f7",
+ 4: "#da77f2",
+ 5: "#cc5de8",
+ 6: "#be4bdb",
+ 7: "#ae3ec9",
+ 8: "#9c36b5",
+ 9: "#862e9c",
+ },
+ "violet": {
+ 0: "#f3f0ff",
+ 1: "#e5dbff",
+ 2: "#d0bfff",
+ 3: "#b197fc",
+ 4: "#9775fa",
+ 5: "#845ef7",
+ 6: "#7950f2",
+ 7: "#7048e8",
+ 8: "#6741d9",
+ 9: "#5f3dc4",
+ },
+ "indigo": {
+ 0: "#edf2ff",
+ 1: "#dbe4ff",
+ 2: "#bac8ff",
+ 3: "#91a7ff",
+ 4: "#748ffc",
+ 5: "#5c7cfa",
+ 6: "#4c6ef5",
+ 7: "#4263eb",
+ 8: "#3b5bdb",
+ 9: "#364fc7",
+ },
+ "blue": {
+ 0: "#e7f5ff",
+ 1: "#d0ebff",
+ 2: "#a5d8ff",
+ 3: "#74c0fc",
+ 4: "#4dabf7",
+ 5: "#339af0",
+ 6: "#228be6",
+ 7: "#1c7ed6",
+ 8: "#1971c2",
+ 9: "#1864ab",
+ },
+ "cyan": {
+ 0: "#e3fafc",
+ 1: "#c5f6fa",
+ 2: "#99e9f2",
+ 3: "#66d9e8",
+ 4: "#3bc9db",
+ 5: "#22b8cf",
+ 6: "#15aabf",
+ 7: "#1098ad",
+ 8: "#0c8599",
+ 9: "#0b7285",
+ },
+ "teal": {
+ 0: "#e6fcf5",
+ 1: "#c3fae8",
+ 2: "#96f2d7",
+ 3: "#63e6be",
+ 4: "#38d9a9",
+ 5: "#20c997",
+ 6: "#12b886",
+ 7: "#0ca678",
+ 8: "#099268",
+ 9: "#087f5b",
+ },
+ "green": {
+ 0: "#ebfbee",
+ 1: "#d3f9d8",
+ 2: "#b2f2bb",
+ 3: "#8ce99a",
+ 4: "#69db7c",
+ 5: "#51cf66",
+ 6: "#40c057",
+ 7: "#37b24d",
+ 8: "#2f9e44",
+ 9: "#2b8a3e",
+ },
+ "lime": {
+ 0: "#f4fce3",
+ 1: "#e9fac8",
+ 2: "#d8f5a2",
+ 3: "#c0eb75",
+ 4: "#a9e34b",
+ 5: "#94d82d",
+ 6: "#82c91e",
+ 7: "#74b816",
+ 8: "#66a80f",
+ 9: "#5c940d",
+ },
+ "yellow": {
+ 0: "#fff9db",
+ 1: "#fff3bf",
+ 2: "#ffec99",
+ 3: "#ffe066",
+ 4: "#ffd43b",
+ 5: "#fcc419",
+ 6: "#fab005",
+ 7: "#f59f00",
+ 8: "#f08c00",
+ 9: "#e67700",
+ },
+ "orange": {
+ 0: "#fff4e6",
+ 1: "#ffe8cc",
+ 2: "#ffd8a8",
+ 3: "#ffc078",
+ 4: "#ffa94d",
+ 5: "#ff922b",
+ 6: "#fd7e14",
+ 7: "#f76707",
+ 8: "#e8590c",
+ 9: "#d9480f",
+ },
+}
+
+
+nx, dx = 10, 5
+ny, dy = 13, 5
+
+fig = plt.figure(figsize=(8, 8), dpi=100)
+ax = plt.subplot(
+ 1,
+ 1,
+ 1,
+ frameon=False,
+ xlim=(-1, nx * dx),
+ ylim=(-1, (ny - 1) * dy + 2),
+ xticks=[],
+ yticks=[],
+)
+
+
+for palette, y in zip(colors.keys(), range(0, ny * dy, dy)):
+ for level, x in zip(range(10), range(0, nx * dx, dx)):
+ fancy = FancyBboxPatch(
+ (x, y),
+ dx - 1,
+ 0.3 * dy,
+ facecolor=colors[palette][level],
+ edgecolor="black",
+ linewidth=0,
+ )
+ ax.add_patch(fancy)
+
+ name = "%s %d" % (palette.upper(), level)
+ ax.text(
+ x,
+ y - 0.15 * dy,
+ name,
+ size=8,
+ family="Roboto",
+ weight="regular",
+ ha="left",
+ va="top",
+ )
+
+ value = colors[palette][level].upper()
+ ax.text(
+ x,
+ y - 0.35 * dy,
+ value,
+ size=8,
+ color="0.5",
+ family="Roboto",
+ weight="regular",
+ ha="left",
+ va="top",
+ )
+
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/open-colors.pdf")
+plt.savefig("../../figures/colors/open-colors.png", dpi=300)
+plt.show()
diff --git a/code/colors/stacked-plots.py b/code/colors/stacked-plots.py
new file mode 100644
index 0000000..7513a5a
--- /dev/null
+++ b/code/colors/stacked-plots.py
@@ -0,0 +1,277 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.ndimage import gaussian_filter1d
+
+material = {
+ "red": {
+ 0: "#ffebee",
+ 1: "#ffcdd2",
+ 2: "#ef9a9a",
+ 3: "#e57373",
+ 4: "#ef5350",
+ 5: "#f44336",
+ 6: "#e53935",
+ 7: "#d32f2f",
+ 8: "#c62828",
+ 9: "#b71c1c",
+ },
+ "pink": {
+ 0: "#fce4ec",
+ 1: "#f8bbd0",
+ 2: "#f48fb1",
+ 3: "#f06292",
+ 4: "#ec407a",
+ 5: "#e91e63",
+ 6: "#d81b60",
+ 7: "#c2185b",
+ 8: "#ad1457",
+ 9: "#880e4f",
+ },
+ "purple": {
+ 0: "#f3e5f5",
+ 1: "#e1bee7",
+ 2: "#ce93d8",
+ 3: "#ba68c8",
+ 4: "#ab47bc",
+ 5: "#9c27b0",
+ 6: "#8e24aa",
+ 7: "#7b1fa2",
+ 8: "#6a1b9a",
+ 9: "#4a148c",
+ },
+ "deep purple": {
+ 0: "#ede7f6",
+ 1: "#d1c4e9",
+ 2: "#b39ddb",
+ 3: "#9575cd",
+ 4: "#7e57c2",
+ 5: "#673ab7",
+ 6: "#5e35b1",
+ 7: "#512da8",
+ 8: "#4527a0",
+ 9: "#311b92",
+ },
+ "indigo": {
+ 0: "#e8eaf6",
+ 1: "#c5cae9",
+ 2: "#9fa8da",
+ 3: "#7986cb",
+ 4: "#5c6bc0",
+ 5: "#3f51b5",
+ 6: "#3949ab",
+ 7: "#303f9f",
+ 8: "#283593",
+ 9: "#1a237e",
+ },
+ "blue": {
+ 0: "#e3f2fd",
+ 1: "#bbdefb",
+ 2: "#90caf9",
+ 3: "#64b5f6",
+ 4: "#42a5f5",
+ 5: "#2196f3",
+ 6: "#1e88e5",
+ 7: "#1976d2",
+ 8: "#1565c0",
+ 9: "#0d47a1",
+ },
+ "light blue": {
+ 0: "#e1f5fe",
+ 1: "#b3e5fc",
+ 2: "#81d4fa",
+ 3: "#4fc3f7",
+ 4: "#29b6f6",
+ 5: "#03a9f4",
+ 6: "#039be5",
+ 7: "#0288d1",
+ 8: "#0277bd",
+ 9: "#01579b",
+ },
+ "cyan": {
+ 0: "#e0f7fa",
+ 1: "#b2ebf2",
+ 2: "#80deea",
+ 3: "#4dd0e1",
+ 4: "#26c6da",
+ 5: "#00bcd4",
+ 6: "#00acc1",
+ 7: "#0097a7",
+ 8: "#00838f",
+ 9: "#006064",
+ },
+ "teal": {
+ 0: "#e0f2f1",
+ 1: "#b2dfdb",
+ 2: "#80cbc4",
+ 3: "#4db6ac",
+ 4: "#26a69a",
+ 5: "#009688",
+ 6: "#00897b",
+ 7: "#00796b",
+ 8: "#00695c",
+ 9: "#004d40",
+ },
+ "green": {
+ 0: "#e8f5e9",
+ 1: "#c8e6c9",
+ 2: "#a5d6a7",
+ 3: "#81c784",
+ 4: "#66bb6a",
+ 5: "#4caf50",
+ 6: "#43a047",
+ 7: "#388e3c",
+ 8: "#2e7d32",
+ 9: "#1b5e20",
+ },
+ "light green": {
+ 0: "#f1f8e9",
+ 1: "#dcedc8",
+ 2: "#c5e1a5",
+ 3: "#aed581",
+ 4: "#9ccc65",
+ 5: "#8bc34a",
+ 6: "#7cb342",
+ 7: "#689f38",
+ 8: "#558b2f",
+ 9: "#33691e",
+ },
+ "lime": {
+ 0: "#f9fbe7",
+ 1: "#f0f4c3",
+ 2: "#e6ee9c",
+ 3: "#dce775",
+ 4: "#d4e157",
+ 5: "#cddc39",
+ 6: "#c0ca33",
+ 7: "#afb42b",
+ 8: "#9e9d24",
+ 9: "#827717",
+ },
+ "yellow": {
+ 0: "#fffde7",
+ 1: "#fff9c4",
+ 2: "#fff59d",
+ 3: "#fff176",
+ 4: "#ffee58",
+ 5: "#ffeb3b",
+ 6: "#fdd835",
+ 7: "#fbc02d",
+ 8: "#f9a825",
+ 9: "#f57f17",
+ },
+ "amber": {
+ 0: "#fff8e1",
+ 1: "#ffecb3",
+ 2: "#ffe082",
+ 3: "#ffd54f",
+ 4: "#ffca28",
+ 5: "#ffc107",
+ 6: "#ffb300",
+ 7: "#ffa000",
+ 8: "#ff8f00",
+ 9: "#ff6f00",
+ },
+ "orange": {
+ 0: "#fff3e0",
+ 1: "#ffe0b2",
+ 2: "#ffcc80",
+ 3: "#ffb74d",
+ 4: "#ffa726",
+ 5: "#ff9800",
+ 6: "#fb8c00",
+ 7: "#f57c00",
+ 8: "#ef6c00",
+ 9: "#e65100",
+ },
+ "deep orange": {
+ 0: "#fbe9e7",
+ 1: "#ffccbc",
+ 2: "#ffab91",
+ 3: "#ff8a65",
+ 4: "#ff7043",
+ 5: "#ff5722",
+ 6: "#f4511e",
+ 7: "#e64a19",
+ 8: "#d84315",
+ 9: "#bf360c",
+ },
+ "brown": {
+ 0: "#efebe9",
+ 1: "#d7ccc8",
+ 2: "#bcaaa4",
+ 3: "#a1887f",
+ 4: "#8d6e63",
+ 5: "#795548",
+ 6: "#6d4c41",
+ 7: "#5d4037",
+ 8: "#4e342e",
+ 9: "#3e2723",
+ },
+ "grey": {
+ 0: "#fafafa",
+ 1: "#f5f5f5",
+ 2: "#eeeeee",
+ 3: "#e0e0e0",
+ 4: "#bdbdbd",
+ 5: "#9e9e9e",
+ 6: "#757575",
+ 7: "#616161",
+ 8: "#424242",
+ 9: "#212121",
+ },
+ "blue grey": {
+ 0: "#eceff1",
+ 1: "#cfd8dc",
+ 2: "#b0bec5",
+ 3: "#90a4ae",
+ 4: "#78909c",
+ 5: "#607d8b",
+ 6: "#546e7a",
+ 7: "#455a64",
+ 8: "#37474f",
+ 9: "#263238",
+ },
+}
+
+
+np.random.seed(1)
+plt.figure(figsize=(8, 4))
+ax = plt.subplot(1, 1, 1)
+
+X = np.linspace(0, 1, 500)
+
+Y0 = np.ones(len(X))
+for i in range(10):
+ Y = Y0 + np.random.uniform(0, 1 / (i + 1), len(X))
+ Y = gaussian_filter1d(Y, 3)
+ ax.fill_between(
+ X,
+ Y,
+ Y0,
+ edgecolor="white",
+ linewidth=0.25,
+ facecolor=material["blue grey"][9 - i],
+ )
+ ax.fill_between(
+ X[325:425], Y[325:425], Y0[325:425], facecolor=material["yellow"][9 - i]
+ )
+ ax.plot(X[325:425], Y[325:425], color="black", linewidth=0.25)
+ Y0 = Y
+
+ax.axvline(X[325], color="black", linestyle="--")
+ax.axvline(X[424], color="black", linestyle="--")
+
+ax.set_xlim(0, 1)
+ax.set_xticks([])
+
+ax.set_ylim(1, 3)
+ax.set_yticks([])
+
+plt.tight_layout()
+plt.savefig("../figures/stacked-plots.pdf")
+plt.show()
diff --git a/code/coordinates/collage.py b/code/coordinates/collage.py
new file mode 100644
index 0000000..b60ad64
--- /dev/null
+++ b/code/coordinates/collage.py
@@ -0,0 +1,52 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.transforms as transforms
+
+
+def imshow(ax, I, position=(0, 0), scale=1, angle=0, zorder=10):
+ height, width = I.shape
+ extent = scale * np.array([-width / 2, width / 2, -height / 2, height / 2])
+ im = ax.imshow(I, extent=extent, zorder=zorder, cmap="cividis")
+ transform = transforms.Affine2D().rotate_deg(angle).translate(*position)
+ trans_data = transform + ax.transData
+ im.set_transform(trans_data)
+ x1, x2, y1, y2 = im.get_extent()
+ ax.plot(
+ [x1, x2, x2, x1, x1],
+ [y1, y1, y2, y2, y1],
+ "white",
+ linewidth=25 * scale,
+ transform=trans_data,
+ zorder=zorder - 0.1,
+ )
+ ax.plot(
+ [x1, x2, x2, x1, x1],
+ [y1, y1, y2, y2, y1],
+ "black",
+ alpha=0.25,
+ linewidth=40 * scale,
+ transform=trans_data,
+ zorder=zorder - 0.2,
+ )
+
+
+fig = plt.figure(figsize=(5, 5))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, frameon=False, xlim=[0, 1000], ylim=[0, 1000])
+
+
+np.random.seed(123)
+I = imageio.imread("../data/mona-lisa.png")
+for i in range(200):
+ position = np.random.uniform(-100, 1100, 2)
+ scale = np.random.uniform(0.20, 0.25)
+ angle = np.random.uniform(-75, +75)
+ imshow(ax, I, position, scale, angle, zorder=10 + i)
+
+plt.savefig("../../figures/coordinates/collage.png", dpi=300)
+plt.show()
diff --git a/code/coordinates/transforms-blend.py b/code/coordinates/transforms-blend.py
new file mode 100644
index 0000000..2c00c8a
--- /dev/null
+++ b/code/coordinates/transforms-blend.py
@@ -0,0 +1,29 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.transforms import blended_transform_factory, ScaledTranslation
+
+fig = plt.figure(figsize=(6, 4))
+
+ax = fig.add_subplot(1, 1, 1, aspect=1)
+ax.set_xlim(0, 10)
+ax.set_xticks(range(11))
+ax.set_ylim(0, 5)
+ax.set_xticks(range(11))
+
+point = 1 / 72
+fontsize = 12
+dx, dy = 0, -1.5 * fontsize * point
+offset = ScaledTranslation(dx, dy, fig.dpi_scale_trans)
+transform = blended_transform_factory(ax.transData, ax.transAxes + offset)
+
+for x in range(11):
+ plt.text(x, 0, "↑", transform=transform, ha="center", va="top", fontsize=fontsize)
+
+plt.tight_layout()
+plt.savefig("../../figures/coordinates/transforms-blend.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-exercise-1.py b/code/coordinates/transforms-exercise-1.py
new file mode 100644
index 0000000..1910dac
--- /dev/null
+++ b/code/coordinates/transforms-exercise-1.py
@@ -0,0 +1,30 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(8, 2))
+
+ymin, ymax = [0, 2]
+xmin, xmax = [0, 8]
+ax = plt.subplot(1, 1, 1, aspect=1, xlim=[xmin, xmax], ylim=[ymin, ymax])
+point = fig.dpi / 72
+X = 0.5 + np.arange(8)
+Y = np.ones(len(X))
+
+# Marker size is expressed in point^2 and a point is defined as fig.dpi/72
+# This means that a size of 10 really means (10*point)^2
+# Problem is thus to convert points into data coordinates:
+# PT_to_DC = lambda x: x * ax.get_window_extent().width / (xmax-xmin)
+# Note that the DC_to_PR is validonly for a given window size
+
+DC_to_PT = lambda x: x * ax.get_window_extent().width / (xmax - xmin) / point
+S = DC_to_PT(1) ** 2
+plt.scatter(X, Y, s=S, facecolor="none", edgecolor="black", linewidth=1)
+
+plt.savefig("../../figures/coordinates/transforms-exercise-1.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-floating-axis.py b/code/coordinates/transforms-floating-axis.py
new file mode 100644
index 0000000..4596191
--- /dev/null
+++ b/code/coordinates/transforms-floating-axis.py
@@ -0,0 +1,76 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate rotated & translated axis (using axisartists toolkit)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.transforms import Affine2D
+from matplotlib.ticker import MultipleLocator
+import mpl_toolkits.axisartist.floating_axes as floating_axes
+
+
+fig = plt.figure(figsize=(8, 8))
+
+# Main axis
+ax1 = plt.subplot(1, 1, 1, aspect=1, xlim=[0, 10], ylim=[0, 10])
+ax1.xaxis.set_major_locator(MultipleLocator(1.00))
+ax1.xaxis.set_minor_locator(MultipleLocator(0.50))
+ax1.yaxis.set_major_locator(MultipleLocator(1.00))
+ax1.yaxis.set_minor_locator(MultipleLocator(0.50))
+ax1.grid(linewidth=0.75, linestyle="--")
+
+# Floating axis
+center = np.array([5, 5]) # data coordinates
+size = np.array([5, 3]) # data coordinates
+orientation = -30 # degrees
+T = size / 2 * [(-1, -1), (+1, -1), (+1, +1), (-1, +1), (-1, -1)]
+rotation = Affine2D().rotate_deg(orientation)
+P = center + rotation.transform(T)
+
+# Floating axis bounding box visualization
+# T = rotation.transform(T)
+# xmin, xmax = T[:,0].min(), T[:,0].max()
+# ymin, ymax = T[:,1].min(), T[:,1].max()
+# T = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)]
+# P = center + T
+# plt.plot(P[:,0], P[:,1], color="black", linewidth=0.75)
+
+# Actual floating axis
+DC_to_FC = ax1.transData.transform
+FC_to_NFC = fig.transFigure.inverted().transform
+DC_to_NFC = lambda x: FC_to_NFC(DC_to_FC(x))
+xmin, ymin = DC_to_NFC((P[:, 0].min(), P[:, 1].min()))
+xmax, ymax = DC_to_NFC((P[:, 0].max(), P[:, 1].max()))
+transform = Affine2D().scale(1, 1).rotate_deg(orientation)
+helper = floating_axes.GridHelperCurveLinear(transform, (0, size[0], 0, size[1]))
+ax2 = floating_axes.FloatingSubplot(fig, 111, grid_helper=helper, zorder=0)
+ax2.set_position((xmin, ymin, xmax - xmin, ymax - xmin))
+fig.add_subplot(ax2)
+
+
+# Cosmetic changes
+ax2.axis["bottom"].major_ticks.set_tick_out(True)
+ax2.axis["bottom"].major_ticklabels.set_visible(False)
+ax2.axis["top"].major_ticks.set_visible(False)
+ax2.axis["left"].major_ticks.set_tick_out(True)
+ax2.axis["left"].major_ticklabels.set_visible(False)
+ax2.axis["right"].major_ticks.set_visible(False)
+aux = ax2.get_aux_axes(transform)
+aux.text(
+ 0.1,
+ 0.1,
+ "Floating & rotated axis",
+ ha="left",
+ va="bottom",
+ size=10,
+ rotation=orientation,
+ rotation_mode="anchor",
+)
+
+aux.set_xticks([0, 1])
+
+plt.savefig("../../figures/coordinates/transform-example.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-hist.py b/code/coordinates/transforms-hist.py
new file mode 100644
index 0000000..e5b2889
--- /dev/null
+++ b/code/coordinates/transforms-hist.py
@@ -0,0 +1,163 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate rotated & translated axis (using axisartists toolkit)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+from matplotlib.transforms import Affine2D
+import mpl_toolkits.axisartist.floating_axes as floating_axes
+
+
+# Reroducibility seed
+np.random.seed(123)
+
+# Generate some data
+Z = np.random.normal(0, (1.25, 0.75), (150, 2))
+Z = Affine2D().rotate_deg(35).transform(Z)
+Zm = Z.mean(axis=0)
+
+
+# Principal components analysis
+# Note that for some seeds, the PC1 and PC2 needs to be inverted
+# It could be fixed by looking at the orientation but I'm lazy
+W, V = np.linalg.eig(np.cov(Z.T))
+PC1, PC2 = V[np.argsort(abs(W))]
+rotation = 180 * np.arctan2(*PC1) / np.pi
+T = np.array([PC1[1], PC1[0]]) # tangent to PCA1
+O = np.array([T[1], -T[0]]) # orthogonal to PCA1
+
+
+# Draw
+fig = plt.figure(figsize=(5, 5))
+ax1 = fig.add_axes([0.05, 0.05, 0.9, 0.9], aspect=1)
+
+# Main scatter plot
+ax1.scatter(Z[:, 0], Z[:, 1], s=50, fc="C0", ec="white", lw=0.75)
+ax1.set_xlim([-3, 6])
+ax1.set_xticks([-3, 6])
+ax1.set_xticklabels([])
+ax1.set_ylim([-3, 6])
+ax1.set_yticks([-3, 6])
+ax1.set_yticklabels([])
+ax1.spines["top"].set_visible(False)
+ax1.spines["right"].set_visible(False)
+
+
+# Draw main PCA axis
+P = np.vstack([Zm - T * 10, Zm + T * 10])
+ax1.plot(P[:, 0], P[:, 1], color="black", linestyle="--", linewidth=0.75, zorder=10)
+
+# Compute the width of the distribution along orthogonal direction to the PCA
+# main axis. This is made by rotating points and taking max on the Y axis.
+transform = Affine2D().rotate_deg(-rotation)
+P = transform.transform(Z - Z.mean(axis=0))
+d = np.abs(P[:, 1]).max()
+
+# Draw a rectangle surrounding the distribution & oriented along PCA main axis
+P = np.vstack(
+ [
+ Zm - 10 * T - d * O,
+ Zm + (6 - d) * T - d * O,
+ Zm + (6 - d) * T + d * O,
+ Zm - 10 * T + d * O,
+ ]
+)
+ax1.add_patch(
+ Polygon(
+ P,
+ closed=True,
+ fill=True,
+ edgecolor="None",
+ facecolor="C0",
+ alpha=0.1,
+ zorder=-50,
+ )
+)
+P = np.vstack([Zm - 10 * T, Zm + (6 - d) * T]) - d * O
+plt.plot(P[:, 0], P[:, 1], color="C0", linestyle="-", linewidth=0.75, alpha=0.25)
+P = np.vstack([Zm - 10 * T, Zm + (6 - d) * T]) + d * O
+plt.plot(P[:, 0], P[:, 1], color="C0", linestyle="-", linewidth=0.75, alpha=0.25)
+
+# Some markers on the axis to show the mean (we could compute exactly the delta
+# for placing the marker but it is not the point of this example)
+ax1.scatter(Zm[0], -2.85, s=50, color="black", marker="v", clip_on=False)
+ax1.scatter(-2.85, Zm[1], s=50, color="black", marker="<", clip_on=False)
+
+
+# Now the complicated stuff to orientate and translate the secondary axis
+
+# 1. Compute the center of the histogram
+C = Zm + 6 * T
+
+# 2. Compute the coordinate and the size in normalized figure coordinates
+x, y = fig.transFigure.inverted().transform(ax1.transData.transform(C))
+xo, yo = fig.transFigure.inverted().transform(ax1.transData.transform(C + 2 * d * O))
+h = w = np.sqrt((xo - x) ** 2 + (yo - y) ** 2)
+
+# 3. Create the secondary axis
+# Warning: it must be squared, ie. xmax-xmin = ymax-ymin
+# It is possible to have non squared axis, but it would complicate things.
+xmin, xmax = -16, 16
+ymin, ymax = 0, xmax - xmin
+transform = Affine2D().rotate_deg(rotation - 90)
+helper = floating_axes.GridHelperCurveLinear(transform, (xmin, xmax, ymin, ymax))
+ax2 = floating_axes.FloatingSubplot(fig, 111, grid_helper=helper, zorder=0)
+
+# This auxiliary axis is necessary to draw stuff (no real idea why)
+ax2_aux = ax2.get_aux_axes(transform)
+
+# 4. We know the size of the axis we want but it is rotated. When we specify
+# the size and position, it related to the non-rotate axis and we thus need
+# to compute the bounding box. To do that, we rotate the four coordinates
+# from which we deduce the bounding box coordinates.
+transform = Affine2D().rotate_deg(rotation - 90)
+R = transform.transform(
+ [
+ (x - w / 2, y - h / 2),
+ (x + w / 2, y - h / 2),
+ (x - w / 2, y + h / 2),
+ (x + w / 2, y + h / 2),
+ ]
+)
+w = R[:, 0].max() - R[:, 0].min()
+h = R[:, 1].max() - R[:, 1].min()
+ax2.set_position((x - w / 2, y - h / 2, w, h))
+fig.add_subplot(ax2)
+
+# 5. Some decoration the secondary axis
+ax2.axis["left"].major_ticklabels.set_visible(False)
+ax2.axis["bottom"].major_ticklabels.set_visible(False)
+ax2.axis["bottom"].major_ticks.set_tick_out(True)
+ax2.axis["left"].set_visible(False)
+ax2.axis["right"].set_visible(False)
+ax2.axis["top"].set_visible(False)
+ax2.set_xticks([0, 1])
+ax2.patch.set_visible(False)
+
+# 6. Display the histogram, taking care of the extents of the X axis
+counts, bins = np.histogram(-Z @ PC1, bins=12)
+X = (bins - bins[0]) / (bins[-1] - bins[0])
+X = xmin + (xmax - xmin) * X
+Y = np.array(counts)
+ax2_aux.hist(X[:-1], X, weights=Y, facecolor="C0", edgecolor="white", linewidth=0.25)
+
+# 7. Adding some labels
+dx, dy = (X[1] - X[0]) / 2, 0.75
+for x, y in zip(X, Y):
+ ax2_aux.text(
+ x + dx,
+ y + dy,
+ "%d" % y,
+ ha="center",
+ va="center",
+ size=8,
+ rotation=rotation - 90,
+ )
+
+# Save
+plt.savefig("../../figures/coordinates/transforms-hist.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-letter.py b/code/coordinates/transforms-letter.py
new file mode 100644
index 0000000..96c4f0f
--- /dev/null
+++ b/code/coordinates/transforms-letter.py
@@ -0,0 +1,41 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.transforms import ScaledTranslation
+
+fig = plt.figure(figsize=(6, 4))
+
+ax = fig.add_subplot(2, 1, 1)
+plt.text(
+ 0.1,
+ 0.1,
+ "A",
+ fontsize="x-large",
+ weight="bold",
+ ha="left",
+ va="baseline",
+ transform=ax.transAxes,
+)
+
+ax = fig.add_subplot(2, 1, 2)
+dx, dy = 10 / fig.dpi, 10 / fig.dpi
+offset = ScaledTranslation(dx, dy, fig.dpi_scale_trans)
+
+plt.text(
+ 0,
+ 0,
+ "B",
+ fontsize="x-large",
+ weight="bold",
+ ha="left",
+ va="baseline",
+ transform=ax.transAxes + offset,
+)
+
+plt.tight_layout()
+plt.savefig("../../figures/coordinates/transforms-letter.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-polar.py b/code/coordinates/transforms-polar.py
new file mode 100644
index 0000000..a469060
--- /dev/null
+++ b/code/coordinates/transforms-polar.py
@@ -0,0 +1,32 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.transforms as transforms
+
+fig = plt.figure(figsize=(5, 5), dpi=100)
+ax = fig.add_subplot(1, 1, 1, projection="polar")
+ax.set_ylim(-1, 1), ax.set_yticks([-1, -0.5, 0, 0.5, 1])
+
+FC_to_DC = ax.transData.inverted().transform
+NDC_to_FC = ax.transAxes.transform
+NDC_to_DC = lambda x: FC_to_DC(NDC_to_FC(x))
+
+P = NDC_to_DC([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]])
+plt.plot(
+ P[:, 0],
+ P[:, 1],
+ clip_on=False,
+ color="k",
+ linewidth=1.0,
+ linestyle="--",
+ zorder=-10,
+)
+plt.scatter(P[:-1, 0], P[:-1, 1], clip_on=False, facecolor="w", edgecolor="k")
+
+plt.tight_layout()
+plt.savefig("../../figures/coordinates/transforms-polar.pdf")
+plt.show()
diff --git a/code/coordinates/transforms.py b/code/coordinates/transforms.py
new file mode 100644
index 0000000..f043eac
--- /dev/null
+++ b/code/coordinates/transforms.py
@@ -0,0 +1,34 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.transforms as transforms
+
+fig = plt.figure(figsize=(6, 5), dpi=100)
+ax = fig.add_subplot(1, 1, 1)
+
+ax.set_xlim(0, 360)
+ax.set_ylim(-1, 1)
+
+DC_to_FC = ax.transData.transform
+FC_to_DC = ax.transData.inverted().transform
+
+NDC_to_FC = ax.transAxes.transform
+FC_to_NDC = ax.transAxes.inverted().transform
+
+NFC_to_FC = fig.transFigure.transform
+FC_to_NFC = fig.transFigure.inverted().transform
+
+
+print(NFC_to_FC([1, 1])) # (600,500)
+print(NDC_to_FC([1, 1])) # (540,440)
+print(DC_to_FC([360, 1])) # (540,440)
+
+DC_to_NDC = lambda x: FC_to_NDC(DC_to_FC(x))
+
+print(DC_to_NDC([0, -1])) # (0.0, 0.0)
+print(DC_to_NDC([180, 0])) # (0.5, 0.5)
+print(DC_to_NDC([360, 1])) # (1.0, 1.0)
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diff --git a/code/defaults/defaults-exercice-1.py b/code/defaults/defaults-exercice-1.py
new file mode 100644
index 0000000..aea2e79
--- /dev/null
+++ b/code/defaults/defaults-exercice-1.py
@@ -0,0 +1,66 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Custom defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+p = plt.rcParams
+
+p["figure.figsize"] = 6, 2.5
+p["figure.facecolor"] = "#fff"
+
+p["axes.axisbelow"] = True
+p["axes.linewidth"] = 1
+p["axes.facecolor"] = "#f9f9f9"
+p["axes.ymargin"] = 0.0
+
+p["axes.grid"] = True
+p["axes.grid.axis"] = "x"
+p["grid.color"] = "#999999"
+p["grid.linestyle"] = "--"
+
+p["axes.spines.bottom"] = True
+p["axes.spines.left"] = True
+p["axes.spines.right"] = False
+p["axes.spines.top"] = False
+p["font.sans-serif"] = ["Fira Sans Condensed"]
+
+p["xtick.bottom"] = True
+p["xtick.top"] = False
+p["xtick.direction"] = "out"
+p["xtick.major.size"] = 0
+p["xtick.major.width"] = 1
+p["xtick.major.pad"] = 65
+
+p["ytick.left"] = True
+p["ytick.right"] = False
+p["ytick.direction"] = "out"
+p["ytick.major.size"] = 5
+p["ytick.major.width"] = 1
+
+p["lines.linewidth"] = 2
+p["lines.marker"] = "o"
+p["lines.markeredgewidth"] = 1.5
+p["lines.markeredgecolor"] = "auto"
+p["lines.markerfacecolor"] = "white"
+p["lines.markersize"] = 6
+
+
+fig = plt.figure()
+ax = plt.subplot(1, 1, 1, aspect=1)
+ax.plot(X, C, markevery=(0, 64), clip_on=False, zorder=10)
+ax.plot(X, S, markevery=(0, 64), clip_on=False, zorder=10)
+ax.set_yticks([-1, 0, 1])
+ax.set_xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
+ax.set_xticklabels(["-π", "-π/2", "0", "+π/2", "+π"])
+ax.spines["bottom"].set_position(("data", 0))
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-exercice-1.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-1.py b/code/defaults/defaults-step-1.py
new file mode 100644
index 0000000..b9f0bd4
--- /dev/null
+++ b/code/defaults/defaults-step-1.py
@@ -0,0 +1,18 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Implicit defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+plt.plot(X, C)
+plt.plot(X, S)
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-1.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-2.py b/code/defaults/defaults-step-2.py
new file mode 100644
index 0000000..267dc13
--- /dev/null
+++ b/code/defaults/defaults-step-2.py
@@ -0,0 +1,59 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / explicit defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+p = plt.rcParams
+
+fig = plt.figure(
+ figsize=p["figure.figsize"],
+ dpi=p["figure.dpi"],
+ facecolor=p["figure.facecolor"],
+ edgecolor=p["figure.edgecolor"],
+ frameon=p["figure.frameon"],
+)
+
+ax = plt.subplot(1, 1, 1)
+
+ax.plot(
+ X, C, color="C0", linewidth=p["lines.linewidth"], linestyle=p["lines.linestyle"]
+)
+
+ax.plot(
+ X, S, color="C1", linewidth=p["lines.linewidth"], linestyle=p["lines.linestyle"]
+)
+
+xmin, xmax = X.min(), X.max()
+xmargin = p["axes.xmargin"] * (xmax - xmin)
+ax.set_xlim(xmin - xmargin, xmax + xmargin)
+
+ymin, ymax = min(C.min(), S.min()), max(C.max(), S.max())
+ymargin = p["axes.ymargin"] * (ymax - ymin)
+ax.set_ylim(ymin - ymargin, ymax + ymargin)
+
+ax.tick_params(
+ axis="x",
+ which="major",
+ direction=p["xtick.direction"],
+ length=p["xtick.major.size"],
+ width=p["xtick.major.width"],
+)
+
+ax.tick_params(
+ axis="y",
+ which="major",
+ direction=p["ytick.direction"],
+ length=p["ytick.major.size"],
+ width=p["ytick.major.width"],
+)
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-2.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-3.py b/code/defaults/defaults-step-3.py
new file mode 100644
index 0000000..dd6e8fd
--- /dev/null
+++ b/code/defaults/defaults-step-3.py
@@ -0,0 +1,65 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Custom defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+p = plt.rcParams
+
+p["figure.figsize"] = 6, 2.5
+p["figure.edgecolor"] = "black"
+p["figure.facecolor"] = "#f9f9f9"
+
+p["axes.linewidth"] = 1
+p["axes.facecolor"] = "#f9f9f9"
+p["axes.ymargin"] = 0.1
+p["axes.spines.bottom"] = True
+p["axes.spines.left"] = True
+p["axes.spines.right"] = False
+p["axes.spines.top"] = False
+p["font.sans-serif"] = ["Fira Sans Condensed"]
+
+p["axes.grid"] = False
+p["grid.color"] = "black"
+p["grid.linewidth"] = 0.1
+
+p["xtick.bottom"] = True
+p["xtick.top"] = False
+p["xtick.direction"] = "out"
+p["xtick.major.size"] = 5
+p["xtick.major.width"] = 1
+p["xtick.minor.size"] = 3
+p["xtick.minor.width"] = 0.5
+p["xtick.minor.visible"] = True
+
+p["ytick.left"] = True
+p["ytick.right"] = False
+p["ytick.direction"] = "out"
+p["ytick.major.size"] = 5
+p["ytick.major.width"] = 1
+p["ytick.minor.size"] = 3
+p["ytick.minor.width"] = 0.5
+p["ytick.minor.visible"] = True
+
+p["lines.linewidth"] = 2
+p["lines.marker"] = "o"
+p["lines.markeredgewidth"] = 1.5
+p["lines.markeredgecolor"] = "auto"
+p["lines.markerfacecolor"] = "white"
+p["lines.markersize"] = 6
+
+fig = plt.figure(linewidth=1)
+ax = plt.subplot(1, 1, 1, aspect=1)
+ax.plot(X, C, markevery=(0, 32))
+ax.plot(X, S, markevery=(0, 32))
+ax.set_yticks([-1, 0, 1])
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-3.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-4.py b/code/defaults/defaults-step-4.py
new file mode 100644
index 0000000..f864192
--- /dev/null
+++ b/code/defaults/defaults-step-4.py
@@ -0,0 +1,24 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Custom defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.style.use("./mystyle.txt")
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure(linewidth=1)
+ax = plt.subplot(1, 1, 1, aspect=1)
+ax.plot(X, C, markevery=(0, 32))
+ax.plot(X, S, markevery=(0, 32))
+ax.set_yticks([-1, 0, 1])
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-4.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-5.py b/code/defaults/defaults-step-5.py
new file mode 100644
index 0000000..20d394c
--- /dev/null
+++ b/code/defaults/defaults-step-5.py
@@ -0,0 +1,34 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Custom defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.style.use("./mystyle.txt")
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure()
+ax = plt.subplot(1, 1, 1, aspect=1)
+ax.plot(X, C)
+ax.plot(X, S)
+
+ax.set_yticks([-1, 1])
+ax.set_yticklabels(["-1", "+1"])
+ax.set_xticks([-np.pi, -np.pi / 2, np.pi / 2, np.pi])
+ax.set_xticklabels(["-π", "-π/2", "+π/2", "+π"])
+
+ax.spines["bottom"].set_position(("data", 0))
+ax.spines["left"].set_position(("data", 0))
+
+ax.plot(1, 0, ">k", transform=ax.get_yaxis_transform(), clip_on=False)
+ax.plot(0, 1, "^k", transform=ax.get_xaxis_transform(), clip_on=False)
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-5.pdf")
+plt.show()
diff --git a/code/defaults/mystyle.txt b/code/defaults/mystyle.txt
new file mode 100644
index 0000000..1581872
--- /dev/null
+++ b/code/defaults/mystyle.txt
@@ -0,0 +1,36 @@
+
+figure.figsize: 6,2.5
+
+axes.linewidth: 1
+axes.axisbelow: True
+axes.ymargin: 0.1
+axes.spines.bottom: True
+axes.spines.left: True
+axes.spines.right: False
+axes.spines.top: False
+font.sans-serif: Fira Sans Condensed
+
+axes.grid: False
+grid.color: black
+grid.linewidth: .1
+
+xtick.bottom: True
+xtick.top: False
+xtick.direction: out
+xtick.major.size: 5
+xtick.major.width: 1
+xtick.minor.size: 3
+xtick.minor.width: .5
+xtick.minor.visible: True
+
+ytick.left: True
+ytick.right: False
+ytick.direction: out
+ytick.major.size: 5
+ytick.major.width: 1
+ytick.minor.size: 3
+ytick.minor.width: .5
+ytick.minor.visible: True
+
+lines.linewidth: 2
+lines.markersize: 5
diff --git a/code/introduction/matplotlib-timeline.py b/code/introduction/matplotlib-timeline.py
new file mode 100644
index 0000000..84d4a22
--- /dev/null
+++ b/code/introduction/matplotlib-timeline.py
@@ -0,0 +1,86 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def annotate(ax, x, y, text, fc="#ff7777", y0=0):
+ y = y - 0.5
+ ax.annotate(
+ " " + text + " ",
+ xy=(x, y),
+ xycoords="data",
+ xytext=(0, 12),
+ textcoords="offset points",
+ color="white",
+ size="x-small",
+ va="center",
+ ha="center",
+ weight="bold",
+ bbox=dict(boxstyle="round", fc=fc, ec="none"),
+ arrowprops=dict(
+ arrowstyle="wedge,tail_width=1.", fc=fc, ec="none", patchA=None
+ ),
+ )
+ plt.plot([x, x], [y, y0], color="black", linestyle=":", linewidth=0.75)
+
+
+fig = plt.figure(figsize=(5, 2))
+ax = fig.add_subplot(111, xlim=(2002.5, 2021.5), ylim=(0, 6.5), yticks=([]))
+ax.tick_params("x", labelsize="x-small", which="major")
+plt.plot([2002.5, 2021.5], [0, 0], color="black", linewidth=1.0, clip_on=False)
+X = np.arange(2003, 2022)
+Y = np.zeros(len(X))
+plt.scatter(
+ X,
+ Y,
+ s=50,
+ linewidth=1.0,
+ zorder=10,
+ clip_on=False,
+ edgecolor="black",
+ facecolor="white",
+)
+
+annotate(ax, 2021, 4, "3.4")
+annotate(ax, 2020, 3, "3.3")
+annotate(ax, 2019, 4, "3.2")
+annotate(ax, 2019, 2, "3.1")
+annotate(ax, 2018, 3, "3.0", y0=1.5)
+annotate(ax, 2018, 1, "2.2", fc="#777777")
+annotate(ax, 2017, 4, "2.1", y0=2.5)
+annotate(ax, 2017, 2, "2.0")
+annotate(ax, 2015, 2, "1.5")
+annotate(ax, 2014, 1, "1.4")
+annotate(ax, 2013, 2, "1.3")
+annotate(ax, 2012, 1, "1.2")
+annotate(ax, 2011, 3, "1.1", y0=2.5)
+annotate(ax, 2011, 2, "1.0")
+annotate(ax, 2009, 1, "0.99")
+annotate(ax, 2003, 1, "0.10")
+
+x0, x1 = 2002.5, 2011.9
+ax.plot([x0, x1], [5, 5], color="black", linewidth=1, marker="|", clip_on=False)
+ax.text((x0 + x1) / 2, 5.1, "J.D. Hunter", ha="center", va="bottom", size="x-small")
+
+x0, x1 = 2012.1, 2017.9
+ax.plot([x0, x1], [5, 5], color="black", linewidth=1, marker="|", clip_on=False)
+ax.text((x0 + x1) / 2, 5.1, "M. Droettboom", ha="center", va="bottom", size="x-small")
+
+x0, x1 = 2014.1, 2021.5
+ax.plot([x0, x1 + 1], [6, 6], color="black", linewidth=1, marker="|")
+ax.text((x0 + x1) / 2, 6.1, "T. Caswell", ha="center", va="bottom", size="x-small")
+
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["bottom"].set_visible(False)
+ax.set_xticks(np.arange(2003, 2022, 2))
+
+plt.tight_layout()
+plt.savefig("../../figures/introduction/matplotlib-timeline.pdf")
+plt.savefig("../../figures/introduction/matplotlib-timeline.png", dpi=300)
+plt.show()
diff --git a/code/introduction/visualization-landscape.graffle b/code/introduction/visualization-landscape.graffle
new file mode 100644
index 0000000..876fd1c
Binary files /dev/null and b/code/introduction/visualization-landscape.graffle differ
diff --git a/code/layout/complex-layout-bare.py b/code/layout/complex-layout-bare.py
new file mode 100644
index 0000000..ae4770d
--- /dev/null
+++ b/code/layout/complex-layout-bare.py
@@ -0,0 +1,58 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+
+fig = plt.figure(figsize=(6.5, 6))
+gspec = gridspec.GridSpec(
+ ncols=7, nrows=7, figure=fig, height_ratios=[1, 0.25, 1, 1, 1, 1, 1]
+)
+
+plt.rc("axes", edgecolor=".75")
+ax = plt.subplot(
+ gspec[1, :2], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+ax = plt.subplot(
+ gspec[0, 2:4], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+ax = plt.subplot(
+ gspec[0, 5:7], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+plt.rc("axes", edgecolor="black")
+
+for col in range(2, 7):
+ ax = plt.subplot(
+ gspec[1, col], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+ )
+
+for row in range(2, 7):
+ ax = plt.subplot(
+ gspec[row, :2], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+ )
+ for col in range(2, 7):
+ ax = plt.subplot(
+ gspec[row, col],
+ aspect=1,
+ frameon=True,
+ xlim=[0, 1],
+ xticks=[],
+ ylim=[0, 1],
+ yticks=[],
+ )
+
+
+ax = plt.subplot(
+ gspec[0, 4], frameon=True, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+
+ax = plt.subplot(
+ gspec[0, :2], frameon=True, xlim=[0, 10], xticks=[], ylim=[0, 4], yticks=[]
+)
+
+plt.savefig("../../figures/layout/complex-layout-bare.pdf")
+plt.show()
diff --git a/code/layout/complex-layout.py b/code/layout/complex-layout.py
new file mode 100644
index 0000000..fccd95c
--- /dev/null
+++ b/code/layout/complex-layout.py
@@ -0,0 +1,185 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+
+fig = plt.figure(figsize=(6.5, 6))
+gspec = gridspec.GridSpec(
+ ncols=7, nrows=7, figure=fig, height_ratios=[1, 0.25, 1, 1, 1, 1, 1]
+)
+cmap = plt.get_cmap("Blues")
+
+
+for row in range(2, 7):
+ ax = plt.subplot(
+ gspec[row, :2], frameon=False, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+ )
+ ax.text(
+ 1,
+ 1,
+ "Very long text using\n newlines and small font",
+ family="Roboto Condensed",
+ horizontalalignment="right",
+ verticalalignment="top",
+ )
+
+ for col in range(2, 7):
+ ax = plt.subplot(
+ gspec[row, col],
+ aspect=1,
+ frameon=False,
+ xlim=[0, 1],
+ xticks=[],
+ ylim=[0, 1],
+ yticks=[],
+ )
+ ax.axhline(0.5, color="white", lw=1)
+ ax.axvline(0.5, color="white", lw=1)
+
+ Z = np.random.uniform(0.25, 0.75, (2, 2))
+ ax.imshow(Z, extent=[0, 1, 0, 1], cmap=cmap, vmin=0, vmax=1)
+ if row == 2:
+ ax.text(
+ 0.5,
+ 1.1,
+ "Subject %d" % (col - 1),
+ ha="center",
+ va="bottom",
+ size="small",
+ weight="bold",
+ )
+
+
+ax = plt.subplot(
+ gspec[0, 4], frameon=True, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+ax.axhline(0.5, color="black", lw=0.75)
+ax.axvline(0.5, color="black", lw=0.75)
+
+
+# First quadrant
+ax.text(
+ -0.5, 0.75, "First quadrant", ha="right", va="center", size="small", weight="bold"
+)
+ax.text(
+ -0.5,
+ 0.55,
+ "Explanation of first quadrant",
+ ha="right",
+ va="center",
+ size="x-small",
+ family="Roboto Condensed",
+)
+ax.plot(
+ [-0.25, 0.25],
+ [0.75, 0.75],
+ color="black",
+ marker="o",
+ markevery=[-1],
+ clip_on=False,
+ linewidth=0.75,
+ markersize=2,
+)
+
+# Second quadrant
+ax.text(
+ -0.5, 0.25, "Second quadrant", ha="right", va="center", size="small", weight="bold"
+)
+ax.text(
+ -0.5,
+ 0.05,
+ "Explanation of second quadrant",
+ ha="right",
+ va="center",
+ size="x-small",
+ family="Roboto Condensed",
+)
+ax.plot(
+ [-0.25, 0.25],
+ [0.25, 0.25],
+ color="black",
+ marker="o",
+ markevery=[-1],
+ clip_on=False,
+ linewidth=0.75,
+ markersize=2,
+)
+
+# Third quadrant
+ax.text(
+ 1.5, 0.75, "Third quadrant", ha="left", va="center", size="small", weight="bold"
+)
+ax.text(
+ 1.5,
+ 0.55,
+ "Explanation of third quadrant",
+ ha="left",
+ va="center",
+ size="x-small",
+ family="Roboto Condensed",
+)
+ax.plot(
+ [1.25, 0.75],
+ [0.75, 0.75],
+ color="black",
+ marker="o",
+ markevery=[-1],
+ clip_on=False,
+ linewidth=0.75,
+ markersize=2,
+)
+
+# Fourth quadrant
+ax.text(
+ 1.5, 0.25, "Fourth quadrant", ha="left", va="center", size="small", weight="bold"
+)
+ax.text(
+ 1.5,
+ 0.05,
+ "Explanation of fourth quadrant",
+ ha="left",
+ va="center",
+ size="x-small",
+ family="Roboto Condensed",
+)
+ax.plot(
+ [1.25, 0.75],
+ [0.25, 0.25],
+ color="black",
+ marker="o",
+ markevery=[-1],
+ clip_on=False,
+ linewidth=0.75,
+ markersize=2,
+)
+
+
+# Legend
+ax = plt.subplot(
+ gspec[0, :2], frameon=False, xlim=[0, 10], xticks=[], ylim=[0, 4], yticks=[]
+)
+ax.scatter([1, 1, 1], [3, 2, 1], color=[cmap(0.25), cmap(0.50), cmap(0.75)])
+ax.text(
+ 2, 1, "Large", ha="left", va="center", size="small", weight="bold", family="Roboto"
+)
+ax.text(
+ 2, 2, "Medium", ha="left", va="center", size="small", weight="bold", family="Roboto"
+)
+ax.text(
+ 2,
+ 3,
+ "Limited",
+ ha="left",
+ va="center",
+ size="small",
+ weight="bold",
+ family="Roboto",
+)
+
+plt.savefig("../../figures/layout/complex-layout.pdf")
+plt.show()
diff --git a/code/layout/layout-aspect.py b/code/layout/layout-aspect.py
new file mode 100644
index 0000000..3dcce82
--- /dev/null
+++ b/code/layout/layout-aspect.py
@@ -0,0 +1,64 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["figure.dpi"] = 100
+p["figure.facecolor"] = "#ffffff"
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+p["axes.grid"] = True
+p["grid.color"] = "0.5"
+p["grid.linewidth"] = 0.5
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols = 2, 2
+gspec = gridspec.GridSpec(
+ ncols=ncols, nrows=nrows, figure=fig, width_ratios=[1, 2], height_ratios=[1, 2]
+)
+
+
+def plot(ax, xmax=1, ymax=1):
+ ax.set_xlim(0, xmax)
+ ax.set_xticks(np.linspace(0, xmax, 4 * xmax + 1))
+ ax.set_xlabel("X Label")
+ ax.set_ylim(0, ymax)
+ ax.set_yticks(np.linspace(0, ymax, 4 * ymax + 1))
+ ax.set_ylabel("Y Label")
+ ax.set_title("Title", family="Roboto", weight=500)
+
+
+if 0: # No constraints
+ plot(plt.subplot(gspec[0, 0]))
+ plot(plt.subplot(gspec[1, 0]))
+ plot(plt.subplot(gspec[0, 1]))
+ plot(plt.subplot(gspec[1, 1]))
+
+if 0: # Aspect is constrained
+ plot(plt.subplot(gspec[0, 0]))
+ plot(plt.subplot(gspec[1, 0], aspect=1))
+ plot(plt.subplot(gspec[0, 1], aspect=1))
+ plot(plt.subplot(gspec[1, 1], aspect=1))
+
+if 1: # Aspect is constrained but limits fit
+ plot(plt.subplot(gspec[0, 0]))
+ plot(plt.subplot(gspec[1, 0], aspect=1), ymax=2)
+ plot(plt.subplot(gspec[0, 1], aspect=1), xmax=2)
+ plot(plt.subplot(gspec[1, 1], aspect=1), xmax=2, ymax=2)
+
+plt.savefig("../../figures/layout/layout-aspect-3.pdf")
+plt.show()
diff --git a/code/layout/layout-classical.py b/code/layout/layout-classical.py
new file mode 100644
index 0000000..f79edad
--- /dev/null
+++ b/code/layout/layout-classical.py
@@ -0,0 +1,49 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+p["axes.grid"] = True
+p["grid.color"] = "0.5"
+p["grid.linewidth"] = 0.5
+
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure()
+nrows, ncols = 3, 3
+
+
+def plot(ax, text):
+ ax.set_xlim(0, 1)
+ ax.set_xticks(np.linspace(0, 1, 5))
+ ax.set_xlabel("X Label")
+ ax.set_ylim(0, 1)
+ ax.set_yticks(np.linspace(0, 1, 5))
+ ax.set_ylabel("Y Label")
+ ax.text(
+ 0.5, 0.5, text, alpha=0.75, ha="center", va="center", weight="bold", size=12
+ )
+ ax.set_title("Title", family="Roboto", weight=500)
+
+
+for i in range(1, nrows * ncols + 1):
+ plot(
+ plt.subplot(nrows, ncols, i, aspect=1), "subplot(%d,%d,%d)" % (nrows, ncols, i)
+ )
+
+plt.tight_layout()
+plt.savefig("../../figures/layout/layout-classical.pdf")
+plt.show()
diff --git a/code/layout/layout-gridspec.py b/code/layout/layout-gridspec.py
new file mode 100644
index 0000000..ec5057d
--- /dev/null
+++ b/code/layout/layout-gridspec.py
@@ -0,0 +1,51 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+p["axes.grid"] = True
+p["grid.color"] = "0.5"
+p["grid.linewidth"] = 0.5
+
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols = 3, 3
+gspec = gridspec.GridSpec(ncols=ncols, nrows=nrows, figure=fig)
+
+
+def plot(ax, text):
+ ax.set_xlim(0, 1)
+ ax.set_xticks(np.linspace(0, 1, 5))
+ ax.set_xlabel("X Label")
+ ax.set_ylim(0, 1)
+ ax.set_yticks(np.linspace(0, 1, 5))
+ ax.set_ylabel("Y Label")
+ ax.text(
+ 0.5, 0.5, text, alpha=0.75, ha="center", va="center", weight="bold", size=12
+ )
+ ax.set_title("Title", family="Roboto", weight=500)
+
+
+for i in range(ncols):
+ plot(plt.subplot(gspec[0, i]), "subplot(gspec[0,%d])" % i)
+for i in range(1, nrows):
+ plot(plt.subplot(gspec[i, 0]), "subplot(gspec[%d,0])" % i)
+plot(plt.subplot(gspec[1:, 1:]), "subplot(gspec[1:,1:])")
+
+plt.savefig("../../figures/layout/layout-gridspec.pdf")
+plt.show()
diff --git a/code/layout/standard-layout-1.py b/code/layout/standard-layout-1.py
new file mode 100644
index 0000000..2b5edb8
--- /dev/null
+++ b/code/layout/standard-layout-1.py
@@ -0,0 +1,56 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+
+# Some data
+n = 64
+X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+)
+Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+
+def f(x, y):
+ return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-(x ** 2) - y ** 2)
+
+
+x = np.linspace(-3, 3, 100)
+y = np.linspace(-3, 3, 100)
+X, Y = np.meshgrid(x, y)
+Z = 0.5 * f(X, Y)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols = 1, 2
+w1, w2 = 20, 1
+gspec = gridspec.GridSpec(ncols=ncols, nrows=nrows, figure=fig, width_ratios=[w1, w2])
+
+ax = plt.subplot(gspec[0, 0], aspect=1)
+ax.set_xlim(0, 1)
+ax.set_xticks(np.linspace(0, 1, 4 + 1))
+ax.set_xlabel("X Label")
+ax.set_ylim(0, 1)
+ax.set_yticks(np.linspace(0, 1, 4 + 1))
+ax.set_ylabel("Y Label")
+ax.set_title("Title", family="Roboto", weight=500)
+I = ax.imshow(Z, extent=[0, 1, 0, 1])
+
+ax = plt.subplot(gspec[0, 1], aspect=w1)
+plt.colorbar(I, cax=ax)
+
+plt.savefig("../../figures/layout/standard-layout-1.pdf")
+plt.show()
diff --git a/code/layout/standard-layout-2.py b/code/layout/standard-layout-2.py
new file mode 100644
index 0000000..55236b6
--- /dev/null
+++ b/code/layout/standard-layout-2.py
@@ -0,0 +1,63 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 6, 6
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+
+X = np.random.normal(0.5, 0.15, 5000)
+Y = np.random.normal(0.5, 0.15, 5000)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols, ratio = 2, 2, 5
+gspec = gridspec.GridSpec(
+ ncols=ncols,
+ nrows=nrows,
+ figure=fig,
+ height_ratios=[1, ratio],
+ width_ratios=[ratio, 1],
+)
+
+ax = plt.subplot(gspec[1, 0])
+ax.scatter(X, Y, s=15, facecolor="black", linewidth=0, alpha=0.25)
+ax.set_xlim(0, 1), ax.set_xticks(np.linspace(0, 1, 5))
+ax.set_ylim(0, 1), ax.set_yticks(np.linspace(0, 1, 5))
+
+ax = plt.subplot(gspec[0, 0])
+ax.set_xlim(0, 1), ax.set_xticks(np.linspace(0, 1, 5))
+ax.set_xticklabels([]), ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.hist(
+ X, bins=np.linspace(0, 1, 21), facecolor="0.75", edgecolor="white", linewidth=0.5
+)
+
+ax = plt.subplot(gspec[1, 1])
+ax.set_ylim(0, 1), ax.set_yticks(np.linspace(0, 1, 5))
+ax.set_yticklabels([]), ax.set_xticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["bottom"].set_visible(False)
+ax.spines["top"].set_visible(False)
+H = ax.hist(
+ Y,
+ bins=np.linspace(0, 1, 21),
+ facecolor="0.75",
+ edgecolor="white",
+ orientation="horizontal",
+ linewidth=0.5,
+)
+
+plt.savefig("../../figures/layout/standard-layout-2.pdf")
+plt.show()
diff --git a/code/optimization/line-benchmark.py b/code/optimization/line-benchmark.py
new file mode 100644
index 0000000..03d73ae
--- /dev/null
+++ b/code/optimization/line-benchmark.py
@@ -0,0 +1,49 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from timeit import default_timer as timer
+
+n_lines, n_points = 1_000, 2
+X = [np.random.uniform(0, 1, n_points) for i in range(n_lines)]
+Y = [np.random.uniform(0, 1, n_points) for i in range(n_lines)]
+
+fig = plt.figure(figsize=(9, 3.5))
+
+# -----------------------------
+ax = fig.add_subplot(1, 3, 1, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[])
+start = timer()
+for x, y in zip(X, Y):
+ ax.plot(x, y, color="blue", alpha=0.1, linewidth=0.5)
+end = timer()
+ax.set_title("Individual plots: %.4fs" % (end - start))
+
+
+# -----------------------------
+ax = fig.add_subplot(1, 3, 2, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[])
+start = timer()
+X_, Y_ = [], []
+for x, y in zip(X, Y):
+ X_.extend(x), X_.extend([None])
+ Y_.extend(y), Y_.extend([None])
+ax.plot(X_, Y_, color="blue", alpha=0.1, linewidth=0.5)
+end = timer()
+ax.set_title("Unified plot: %.4fs" % (end - start))
+
+# -----------------------------
+from matplotlib.collections import LineCollection
+
+ax = fig.add_subplot(1, 3, 3, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[])
+start = timer()
+V = [np.stack([x, y], axis=1) for x, y in zip(X, Y)]
+lines = LineCollection(V, color="blue", alpha=0.1, linewidth=0.5)
+ax.add_collection(lines)
+end = timer()
+ax.set_title("Line collection: %.4fs" % (end - start))
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/line-benchmark.png", dpi=600)
+plt.show()
diff --git a/code/optimization/multisample.py b/code/optimization/multisample.py
new file mode 100644
index 0000000..fe82591
--- /dev/null
+++ b/code/optimization/multisample.py
@@ -0,0 +1,133 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate multisample on data or screen space (using imshow)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+
+plt.rc("font", family="Roboto")
+
+fig = plt.figure(figsize=(2 * 4.25, 2 * 2.5 * 4.25 / 4), dpi=100)
+
+xmin, xmax = 0 * np.pi, 5 * np.pi
+ymin, ymax = -1.1, +1.1
+
+
+def f(x):
+ return np.sin(np.power(x, 3)) * np.sin(x)
+
+
+# --- X linear space ----------------------------------------------------------
+ax = plt.subplot(
+ 311,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+ frameon=False,
+ xlim=[xmin, xmax],
+ ylim=[ymin, ymax],
+)
+
+X = np.linspace(xmin, xmax, 10000)
+Y = f(X)
+plt.plot(X, Y, linewidth=0.5, alpha=0.25, color="black")
+ax.set_title("Line plot", ha="left", loc="left")
+
+# --- X linear space ----------------------------------------------------------
+ax = plt.subplot(
+ 312,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+ frameon=False,
+ xlim=[xmin, xmax],
+ ylim=[ymin, ymax],
+)
+
+X = np.linspace(xmin, xmax, 10000)
+Y = f(X)
+
+segments = np.zeros((len(X) - 1, 2, 2))
+segments[:, 0, 0], segments[:, 0, 1] = X[:-1], Y[:-1]
+segments[:, 1, 0], segments[:, 1, 1] = X[1:], Y[1:]
+
+ax.add_collection(LineCollection(segments, linewidths=0.5, alpha=0.25, colors="black"))
+ax.set_title("Line collection", ha="left", loc="left")
+
+
+# --- Multisample (screen space) ----------------------------------------------
+n_samples = 8
+ax = plt.subplot(
+ 313,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+ frameon=False,
+ xlim=[xmin, xmax],
+ ylim=[ymin, ymax],
+)
+x0, y0 = ax.transAxes.transform((0, 0)).astype(int)
+x1, y1 = ax.transAxes.transform((1, 1)).astype(int)
+rows, cols = y1 - y0, x1 - x0
+shape = n_samples * rows, n_samples * cols
+Z = np.zeros(shape)
+
+I, J = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]))
+# N = np.random.uniform(-0.5,0.5,I.shape)
+N = np.random.normal(0, 0.5, I.shape)
+X = xmin + ((I + N) / (shape[1] - 1)) * (xmax - xmin)
+Y = np.floor(((f(X) - ymin) / (ymax - ymin)) * shape[0]).astype(int)
+Z[Y, I] = np.minimum(np.abs(Y - J), 1)
+ax.imshow(
+ Z,
+ extent=[xmin, xmax, ymin, ymax],
+ origin="lower",
+ cmap="gray_r",
+ interpolation="lanczos",
+ vmin=0,
+ vmax=1.5,
+)
+ax.set_title("Multisample (imshow)", ha="left", loc="left")
+
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/multisample.png", dpi=600)
+plt.show()
+
+
+# # --- Multisample (data space) ------------------------------------------------
+# ax = plt.subplot(412, xticks=[], yticks=[], aspect=1,
+# xlim=[xmin, xmax], ylim=[ymin, ymax])
+
+# x0, y0 = ax.transAxes.transform((0,0)).astype(int)
+# x1, y1 = ax.transAxes.transform((1,1)).astype(int)
+# rows, cols = y1-y0, x1-x0
+
+# X = np.linspace(0, cols-1, n_samples*n_samples*cols) #1000)
+# #X += np.random.normal(0,.5)
+# X = xmin + (X/(cols-1))*(xmax-xmin)
+# Y = f(X)
+
+
+# #dx = (xmax-xmin)/((x1-x0) * n_samples * n_samples)
+# #for i in range(n_samples*n_samples):
+# # X = np.linspace(xmin,xmax,1000)
+# # Y = f(X+dx*i/n_samples)
+# plt.plot(X,Y, linewidth=0.1, alpha=1.0, color="black")
+# ax.set_title("Multisample (data space)")
+
+
+# # --- X log space -------------------------------------------------------------
+# ax = plt.subplot(413, xticks=[], yticks=[], aspect=1,
+# xlim=[xmin, xmax], ylim=[ymin, ymax])
+
+# X = 1 - np.logspace(0,3,10000,endpoint=True)/1000
+# X = xmin + X*(xmax-xmin)
+# Y = f(X)
+# plt.plot(X,Y, linewidth=0.25, alpha=0.5, color="black")
+# plt.plot(X,Y, linewidth=0.25, alpha=0.5, color="black")
+# ax.set_title("X log space")
diff --git a/code/optimization/multithread.py b/code/optimization/multithread.py
new file mode 100644
index 0000000..6a59215
--- /dev/null
+++ b/code/optimization/multithread.py
@@ -0,0 +1,57 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from matplotlib.backends.backend_agg import FigureCanvas
+
+X = np.random.normal(4.5, 2, 5_000_000)
+Y = np.random.normal(4.5, 2, 5_000_000)
+
+
+def plot(extent):
+ """ Offline rendering """
+
+ xmin, xmax, ymin, ymax = extent
+ fig = Figure(figsize=(2, 2))
+ canvas = FigureCanvas(fig)
+ ax = fig.add_axes(
+ [0, 0, 1, 1],
+ frameon=False,
+ xlim=[xmin, xmax],
+ xticks=[],
+ ylim=[ymin, ymax],
+ yticks=[],
+ )
+ epsilon = 0.1
+ I = np.argwhere(
+ (X >= (xmin - epsilon))
+ & (X <= (xmax + epsilon))
+ & (Y >= (ymin - epsilon))
+ & (Y <= (ymax + epsilon))
+ )
+ ax.scatter(
+ X[I], Y[I], 3, clip_on=False, color="black", edgecolor="None", alpha=0.0025
+ )
+ canvas.draw()
+ return np.array(canvas.renderer.buffer_rgba())
+
+
+if __name__ == "__main__":
+ from multiprocessing import Pool
+
+ extents = [[x, x + 3, y, y + 3] for x in range(0, 9, 3) for y in range(0, 9, 3)]
+ pool = Pool()
+ images = pool.map(plot, extents)
+ pool.close()
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = plt.subplot(xlim=[0, 9], ylim=[0, 9])
+ for img, extent in zip(images, extents):
+ ax.imshow(img, extent=extent, interpolation="None")
+
+ plt.savefig("../../figures/optimization/multithread.png", dpi=600)
+ plt.show()
diff --git a/code/optimization/scatter-benchmark.py b/code/optimization/scatter-benchmark.py
new file mode 100644
index 0000000..4097c73
--- /dev/null
+++ b/code/optimization/scatter-benchmark.py
@@ -0,0 +1,46 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from timeit import default_timer as timer
+
+n_points = 100_000
+np.random.seed(1)
+X = np.random.normal(0, 1, n_points)
+Y = np.random.normal(0, 1, n_points)
+
+fig = plt.figure(figsize=(9, 3.5))
+
+# -----------------------------
+ax = fig.add_subplot(
+ 1, 3, 2, aspect=1, xlim=[-5, 5], xticks=[], ylim=[-5, 5], yticks=[]
+)
+start = timer()
+ax.scatter(X, Y, s=4, color="black", alpha=0.008, linewidth=0)
+end = timer()
+ax.set_title("Scatter: %.4fs" % (end - start))
+
+# -----------------------------
+ax = fig.add_subplot(
+ 1, 3, 3, aspect=1, xlim=[-5, 5], xticks=[], ylim=[-5, 5], yticks=[]
+)
+start = timer()
+ax.plot(X, Y, "o", markersize=2, color="black", alpha=0.008, markeredgewidth=0)
+end = timer()
+ax.set_title("Plot: %.4fs" % (end - start))
+
+# -----------------------------
+ax = fig.add_subplot(
+ 1, 3, 1, aspect=1, xlim=[-5, 5], xticks=[], ylim=[-5, 5], yticks=[]
+)
+start = timer()
+ax.hist2d(X, Y, 128, cmap="gray_r")
+end = timer()
+ax.set_title("Hist2d: %.4fs" % (end - start))
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/scatter-benchmark.png", dpi=600)
+plt.show()
diff --git a/code/optimization/scatters.py b/code/optimization/scatters.py
new file mode 100644
index 0000000..b5da809
--- /dev/null
+++ b/code/optimization/scatters.py
@@ -0,0 +1,65 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+n = [10_000, 100_000, 1_000_000]
+alpha = [0.2, 0.02, 0.002]
+fig = plt.figure(figsize=(9, 9))
+
+index = 1
+for i in range(3):
+
+ X = np.random.normal(0, 2, n[i])
+ Y = np.random.normal(0, 2, n[i])
+
+ ax = plt.subplot(3, 3, index, aspect=1)
+ ax.scatter(
+ X, Y, 5, facecolor="black", edgecolor="None", alpha=alpha[i], antialiased=True
+ )
+ ax.set_xlim(-5, 5), ax.set_xticks([])
+ ax.set_ylim(-5, 5), ax.set_yticks([])
+ ax.text(
+ -5,
+ 4.75,
+ " scatter, n={:,}, alpha={:.3f}".format(n[i], alpha[i]),
+ ha="left",
+ va="top",
+ size="small",
+ )
+ index += 1
+
+ ax = plt.subplot(3, 3, index, aspect=1)
+ ax.hist2d(X, Y, 128, cmap="gray_r")
+ ax.set_xlim(-5, 5), ax.set_xticks([])
+ ax.set_ylim(-5, 5), ax.set_yticks([])
+ ax.text(
+ -5,
+ 4.75,
+ " hist2d (128x128 bins), n={:,}".format(n[i]),
+ ha="left",
+ va="top",
+ size="small",
+ )
+ index += 1
+
+ ax = plt.subplot(3, 3, index, aspect=1)
+ ax.hexbin(X, Y, gridsize=64, cmap="gray_r", linewidth=0, antialiased=0)
+ ax.set_xlim(-5, 5), ax.set_xticks([])
+ ax.set_ylim(-5, 5), ax.set_yticks([])
+ ax.text(
+ -5,
+ 4.75,
+ " hexbin (64x64 bins), n={:,}".format(n[i]),
+ ha="left",
+ va="top",
+ size="small",
+ )
+ index += 1
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/scatters.png", dpi=600)
+plt.show()
diff --git a/code/optimization/self-cover.py b/code/optimization/self-cover.py
new file mode 100644
index 0000000..6cb73dd
--- /dev/null
+++ b/code/optimization/self-cover.py
@@ -0,0 +1,56 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.path import Path
+import matplotlib.patches as patches
+
+fig = plt.figure(figsize=(8, 1.5))
+
+n = 3
+X = np.append(np.linspace(0, n * 2 * np.pi, 500), [0])
+Y = np.sin(X)
+
+ax = plt.subplot(
+ 1,
+ 2,
+ 1,
+ frameon=False,
+ xlim=(0, n * 2 * np.pi + 1),
+ xticks=[],
+ ylim=(-1, 1.1),
+ yticks=[],
+)
+ax.plot(X, Y, "C0", linewidth=8, alpha=0.5, solid_capstyle="round", clip_on=False)
+ax.set_title("No self covering")
+
+ax = plt.subplot(
+ 1,
+ 2,
+ 2,
+ frameon=False,
+ xlim=(0, n * 2 * np.pi),
+ xticks=[],
+ ylim=(-1, 1.1),
+ yticks=[],
+)
+ax.plot(
+ X[:-1], Y[:-1], "C0", linewidth=8, alpha=0.5, solid_capstyle="round", clip_on=False
+)
+ax.plot(
+ [X[-2], X[-1]],
+ [Y[-2], Y[-1]],
+ "C0",
+ linewidth=8,
+ alpha=0.5,
+ solid_capstyle="round",
+ clip_on=False,
+)
+ax.set_title("Simulated self covering")
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/self-cover.pdf")
+plt.show()
diff --git a/code/optimization/transparency.py b/code/optimization/transparency.py
new file mode 100644
index 0000000..b3801b2
--- /dev/null
+++ b/code/optimization/transparency.py
@@ -0,0 +1,60 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(7, 1.25))
+ax = plt.subplot(frameon=False)
+ax.set_xlim(0.5, 7.5), ax.set_xticks([])
+ax.set_ylim(0, 1.5), ax.set_yticks([])
+
+
+X, Y = [1], [1]
+alpha = 1
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point\nalpha={:.2f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [2], [1]
+alpha = 0.1
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [3] * 10, [1] * 10
+alpha = 0.1
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [4] * 25, [1] * 25
+alpha = 0.1
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [5] * 10, [1] * 10
+alpha = 0.002
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [6] * 64, [1] * 64
+alpha = 0.002
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [7] * 512, [1] * 512
+alpha = 0.002
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/transparency.pdf")
+plt.show()
diff --git a/code/ornaments/annotate-regular.py b/code/ornaments/annotate-regular.py
new file mode 100644
index 0000000..7ead5ae
--- /dev/null
+++ b/code/ornaments/annotate-regular.py
@@ -0,0 +1,65 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(6, 4))
+ax = plt.subplot(
+ 1,
+ 1,
+ 1,
+ xlim=[-1, +1],
+ xticks=[-1, 0, 1],
+ ylim=[-1, +1],
+ yticks=[-1, 0, 1],
+ aspect=1,
+)
+
+np.random.seed(123)
+X = np.random.normal(0, 0.35, 1000)
+Y = np.random.normal(0, 0.35, 1000)
+
+ax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+
+
+I = np.random.choice(len(X), size=8, replace=False)
+Px, Py = X[I], Y[I]
+I = np.argsort(Y[I])[::-1]
+Px, Py = Px[I], Py[I]
+
+ax.scatter(Px, Py, edgecolor="black", facecolor="white", zorder=20)
+ax.scatter(Px, Py, edgecolor="None", facecolor="C1", alpha=0.5, zorder=30)
+
+y, dy = 1.0, 0.125
+style = "arc,angleA=-0,angleB=0,armA=-100,armB=0,rad=0"
+
+for i in range(len(I)):
+ ax.annotate(
+ "Point " + chr(ord("A") + i),
+ xy=(Px[i], Py[i]),
+ xycoords="data",
+ xytext=(1.25, y - i * dy),
+ textcoords="data",
+ arrowprops=dict(
+ arrowstyle="->",
+ color="black",
+ linewidth=0.75,
+ shrinkA=25,
+ shrinkB=5,
+ patchA=None,
+ patchB=None,
+ connectionstyle=style,
+ ),
+ )
+
+ax.spines["right"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["left"].set_position(("data", -1.1))
+ax.spines["bottom"].set_position(("data", -1.1))
+
+# plt.tight_layout()
+plt.savefig("../../figures/ornaments/legend-regular.pdf")
+plt.show()
diff --git a/code/ornaments/annotation-direct.py b/code/ornaments/annotation-direct.py
new file mode 100644
index 0000000..f0a4917
--- /dev/null
+++ b/code/ornaments/annotation-direct.py
@@ -0,0 +1,52 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as path_effects
+
+fig = plt.figure(figsize=(4, 4))
+ax = plt.subplot(1, 1, 1, xlim=[-1, +1], xticks=[], ylim=[-1, +1], yticks=[], aspect=1)
+
+np.random.seed(123)
+X = np.random.normal(0, 0.35, 1000)
+Y = np.random.normal(0, 0.35, 1000)
+
+ax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+
+I = np.random.choice(len(X), size=5, replace=False)
+Px, Py = X[I], Y[I]
+I = np.argsort(Y[I])[::-1]
+Px, Py = Px[I], Py[I]
+
+ax.scatter(Px, Py, edgecolor="black", facecolor="white", zorder=20)
+ax.scatter(Px, Py, edgecolor="None", facecolor="C1", alpha=0.5, zorder=30)
+
+y, dy = 1.0, 0.125
+style = "arc,angleA=-0,angleB=0,armA=-100,armB=0,rad=0"
+
+for i in range(len(I)):
+ text = ax.annotate(
+ "Point " + chr(ord("A") + i),
+ xy=(Px[i], Py[i]),
+ xycoords="data",
+ xytext=(0, 18),
+ textcoords="offset points",
+ ha="center",
+ size="small",
+ arrowprops=dict(
+ arrowstyle="->", shrinkA=0, shrinkB=5, color="black", linewidth=0.75
+ ),
+ )
+ text.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+ )
+ text.arrow_patch.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+ )
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/annotation-direct.pdf")
+plt.show()
diff --git a/code/ornaments/annotation-side.py b/code/ornaments/annotation-side.py
new file mode 100644
index 0000000..a034759
--- /dev/null
+++ b/code/ornaments/annotation-side.py
@@ -0,0 +1,55 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as path_effects
+
+
+fig = plt.figure(figsize=(5.5, 4))
+ax = plt.subplot(1, 1, 1, xlim=[-1, +1], xticks=[], ylim=[-1, +1], yticks=[], aspect=1)
+
+np.random.seed(123)
+X = np.random.normal(0, 0.35, 1000)
+Y = np.random.normal(0, 0.35, 1000)
+
+ax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+
+I = np.random.choice(len(X), size=5, replace=False)
+Px, Py = X[I], Y[I]
+I = np.argsort(Y[I])[::-1]
+Px, Py = Px[I], Py[I]
+
+ax.scatter(Px, Py, edgecolor="black", facecolor="white", zorder=20)
+ax.scatter(Px, Py, edgecolor="None", facecolor="C1", alpha=0.5, zorder=30)
+
+y, dy = 0.25, 0.125
+style = "arc,angleA=-0,angleB=0,armA=-100,armB=0,rad=0"
+for i in range(len(I)):
+ text = ax.annotate(
+ "Point " + chr(ord("A") + i),
+ xy=(Px[i], Py[i]),
+ xycoords="data",
+ xytext=(1.25, y - i * dy),
+ textcoords="data",
+ arrowprops=dict(
+ arrowstyle="->",
+ color="black",
+ linewidth=0.75,
+ shrinkA=20,
+ shrinkB=5,
+ patchA=None,
+ patchB=None,
+ connectionstyle=style,
+ ),
+ )
+ text.arrow_patch.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+ )
+
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/annotation-side.pdf")
+plt.show()
diff --git a/code/ornaments/annotation-zoom.py b/code/ornaments/annotation-zoom.py
new file mode 100644
index 0000000..75d5c23
--- /dev/null
+++ b/code/ornaments/annotation-zoom.py
@@ -0,0 +1,84 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+from matplotlib.patches import Rectangle
+from matplotlib.patches import ConnectionPatch
+
+
+fig = plt.figure(figsize=(5, 4))
+
+n = 5
+gs = GridSpec(n, n + 1)
+
+ax = plt.subplot(
+ gs[:n, :n], xlim=[-1, +1], xticks=[], ylim=[-1, +1], yticks=[], aspect=1
+)
+
+np.random.seed(123)
+X = np.random.normal(0, 0.35, 1000)
+Y = np.random.normal(0, 0.35, 1000)
+ax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+
+I = np.random.choice(len(X), size=n, replace=False)
+Px, Py = X[I], Y[I]
+I = np.argsort(Y[I])[::-1]
+Px, Py = Px[I], Py[I]
+
+ax.scatter(Px, Py, edgecolor="black", facecolor="None", linewidth=0.75)
+
+dx, dy = 0.075, 0.075
+for i, (x, y) in enumerate(zip(Px, Py)):
+ sax = plt.subplot(
+ gs[i, n],
+ xlim=[x - dx, x + dx],
+ xticks=[],
+ ylim=[y - dy, y + dy],
+ yticks=[],
+ aspect=1,
+ )
+ sax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+ sax.scatter(Px, Py, edgecolor="black", facecolor="None", linewidth=0.75)
+
+ sax.text(
+ 1.1,
+ 0.5,
+ "Point " + chr(ord("A") + i),
+ rotation=90,
+ size=8,
+ ha="left",
+ va="center",
+ transform=sax.transAxes,
+ )
+
+ rect = Rectangle(
+ (x - dx, y - dy),
+ 2 * dx,
+ 2 * dy,
+ edgecolor="black",
+ facecolor="None",
+ linestyle="--",
+ linewidth=0.75,
+ )
+ ax.add_patch(rect)
+
+ con = ConnectionPatch(
+ xyA=(x, y),
+ coordsA=ax.transData,
+ xyB=(0, 0.5),
+ coordsB=sax.transAxes,
+ linestyle="--",
+ linewidth=0.75,
+ patchA=rect,
+ arrowstyle="->",
+ )
+ fig.add_artist(con)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/annotation-zoom.pdf")
+plt.show()
diff --git a/code/ornaments/bessel-functions.py b/code/ornaments/bessel-functions.py
new file mode 100644
index 0000000..3616e09
--- /dev/null
+++ b/code/ornaments/bessel-functions.py
@@ -0,0 +1,110 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+from scipy.special import jn, jn_zeros
+import matplotlib.pyplot as plt
+
+plt.rc("font", family="Roboto Condensed")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+
+fig = plt.figure(figsize=(6, 3), dpi=100)
+ax = plt.subplot(xlim=[0, 20], ylim=[-0.5, 1])
+
+X = np.linspace(0, 20, 1000, endpoint=True)
+n = 6
+for i in range(n):
+ Ji = jn(i, X)
+ linewidth = 1.5 if i == 0 else 1
+ linestyle = "-" if i == 0 else "-"
+ color = "C1" if i == 0 else "%.2f" % (i / n)
+ label = r"$J_%d$" % i
+
+ ax.plot(X, Ji, color="white", clip_on=False, zorder=10 - i, linewidth=2.5)
+
+ ax.plot(
+ X,
+ Ji,
+ color=color,
+ clip_on=False,
+ zorder=10 - i,
+ linewidth=linewidth,
+ linestyle=linestyle,
+ label=label,
+ )
+
+ k = np.argmax(Ji)
+ ax.text(
+ X[k],
+ Ji[k] + 0.05,
+ label,
+ color=color,
+ usetex=True,
+ ha="center",
+ va="bottom",
+ size="small",
+ )
+ # k = np.argmin(Ji)
+ # ax.text(X[k], Ji[k]-0.05, label, color=color,
+ # ha="center", va="top", size="small")
+
+ Zx = [x for x in jn_zeros(i, 6) if x < 20]
+ Zy = np.zeros(len(Zx))
+ ax.scatter(Zx, Zy, s=15, zorder=20, edgecolor=color, facecolor="white", linewidth=1)
+
+ if i == 0:
+ ax.annotate(
+ "Root",
+ (Zx[0], Zy[0]),
+ size="small",
+ xytext=(-30, -30),
+ textcoords="offset points",
+ arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=-0.3"),
+ )
+
+ Zy = -0.6 * np.ones(len(Zx))
+ ax.scatter(
+ Zx,
+ Zy,
+ s=30,
+ zorder=20,
+ clip_on=False,
+ marker="|",
+ facecolor="black",
+ linewidth=0.5,
+ )
+
+ax.set_title(
+ "Bessel functions",
+ x=1,
+ weight="light",
+ family="Roboto Condensed",
+ transform=ax.transAxes,
+ ha="right",
+)
+ax.text(
+ 1,
+ 0.98,
+ r"$J_n(x) = \frac{1}{2\pi} \int_{-\pi}^\pi e^{i(x \sin \tau -n \tau)} \,d\tau$",
+ va="top",
+ transform=ax.transAxes,
+ size=12,
+ ha="right",
+ usetex=True,
+)
+
+ax.set_yticks([-0.5, 0, 0.5, 1])
+ax.set_xticks([0, 10, 20])
+ax.axhline(0, color="0.5", linewidth=0.5)
+
+ax.spines["right"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["left"].set_position(("data", -1))
+ax.spines["bottom"].set_position(("data", -0.6))
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/bessel-functions.pdf")
+plt.show()
diff --git a/code/ornaments/elegant-scatter.py b/code/ornaments/elegant-scatter.py
new file mode 100644
index 0000000..297c209
--- /dev/null
+++ b/code/ornaments/elegant-scatter.py
@@ -0,0 +1,87 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.size"] = 10.0
+plt.rcParams["font.serif"] = ["Source Serif Pro"]
+plt.rcParams["font.sans-serif"] = ["Source Sans Pro"]
+plt.rcParams["font.monospace"] = ["Source Code Pro"]
+
+
+fig = plt.figure(figsize=(5, 5))
+ax = plt.axes()
+
+ax.set_xlabel("Height (m)", weight="medium")
+ax.set_ylabel("Weight (kg)", weight="medium")
+ax.set_title(
+ """Distribution of height & weight\n""" """according to sex & age (fake data)""",
+ family="serif",
+)
+
+n = 250
+np.random.seed(1)
+X, Y = np.zeros(2 * n), np.zeros(2 * n)
+S, C = np.zeros(2 * n), np.zeros(2 * n)
+
+X[:n] = np.random.normal(1.60, 0.1, n)
+Y[:n] = np.random.normal(50, 10, n)
+S[:n] = np.random.uniform(25, 50, n)
+C[:n] = 0
+
+X[n:] = np.random.normal(1.75, 0.1, n)
+Y[n:] = np.random.normal(75, 10, n)
+S[n:] = np.random.uniform(25, 50, n)
+C[n:] = 1
+
+cmap = plt.get_cmap("RdYlBu")
+scatter = plt.scatter(X, Y, s=S, edgecolor="black", linewidth=0.75, zorder=-20)
+scatter = plt.scatter(X, Y, s=S, edgecolor="None", facecolor="white", zorder=-10)
+scatter = plt.scatter(X, Y, c=C, s=S, cmap=cmap, edgecolor="None", alpha=0.25)
+
+handles, labels = scatter.legend_elements(
+ num=3,
+ prop="sizes",
+ alpha=1,
+ markeredgewidth=0.5,
+ markeredgecolor="black",
+ markerfacecolor="None",
+)
+legend = plt.legend(
+ handles,
+ labels,
+ title=" Age",
+ loc=(0.6, 0.05),
+ handletextpad=0.1,
+ labelspacing=0.25,
+ facecolor="None",
+ edgecolor="None",
+)
+legend.get_children()[0].align = "left"
+legend.get_title().set_fontweight("medium")
+ax.add_artist(legend)
+
+handles, labels = scatter.legend_elements(markeredgewidth=0.0, markeredgecolor="black")
+labels = ["Female", "Male"]
+legend = plt.legend(
+ handles,
+ labels,
+ title=" Sex",
+ loc=(0.75, 0.05),
+ handletextpad=0.1,
+ labelspacing=0.25,
+ facecolor="None",
+ edgecolor="None",
+)
+legend.get_children()[0].align = "left"
+legend.get_title().set_fontweight("medium")
+
+
+plt.scatter(X, [19] * len(X), marker="|", color=cmap(C), linewidth=0.5, alpha=0.25)
+plt.scatter([1.3] * len(X), Y, marker="_", color=cmap(C), linewidth=0.5, alpha=0.25)
+
+plt.savefig("../../figures/ornaments/elegant-scatter.pdf")
+plt.show()
diff --git a/code/ornaments/label-alternatives.py b/code/ornaments/label-alternatives.py
new file mode 100644
index 0000000..b0552ca
--- /dev/null
+++ b/code/ornaments/label-alternatives.py
@@ -0,0 +1,70 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(6, 6))
+
+n = 5
+for i in range(n):
+ index = i + 1
+ ax = plt.subplot(
+ n,
+ 1,
+ index,
+ facecolor="None",
+ xlim=[0, 10.5],
+ xticks=[],
+ ylim=[0, 10],
+ yticks=[],
+ )
+
+ for j in range(5):
+ ax.plot(
+ [0, 10],
+ [1 + j * 1.5, 1 + j * 1.5],
+ clip_on=False,
+ linewidth=5,
+ color=".9",
+ solid_capstyle="round",
+ )
+
+ x = np.random.uniform(1, 9)
+ ax.plot(
+ [0, x],
+ [1 + j * 1.5, 1 + j * 1.5],
+ clip_on=False,
+ linewidth=5,
+ color="C1",
+ solid_capstyle="round",
+ )
+
+ ax.text(
+ 10.5,
+ 0,
+ " %d " % (2000 + index),
+ font="Roboto Condensed",
+ weight="medium",
+ color="black",
+ ha="center",
+ va="center",
+ size="small",
+ bbox=dict(
+ facecolor="white",
+ edgecolor="black",
+ linewidth=0.75,
+ boxstyle="round,pad=0.25",
+ ),
+ )
+
+ ax.spines["right"].set_visible(False)
+ ax.spines["top"].set_visible(False)
+ ax.spines["left"].set_visible(False)
+ # ax.spines['bottom'].set_position(('data', -1.25))
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/legend-regular.pdf")
+plt.show()
diff --git a/code/ornaments/latex-text-box.py b/code/ornaments/latex-text-box.py
new file mode 100644
index 0000000..d05a38d
--- /dev/null
+++ b/code/ornaments/latex-text-box.py
@@ -0,0 +1,134 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import matplotlib.patches as patches
+import matplotlib.patheffects as path_effects
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+
+rows, cols = 2, 3
+fig = plt.figure(figsize=(cols * 2, rows * 2.45))
+
+for row in range(rows):
+ for col in range(cols):
+ index = row * cols + col + 1
+
+ if index == cols * rows:
+ ax = plt.subplot(rows, cols, index, aspect=1, frameon=False)
+ ax.set_xticks([]), ax.set_yticks([])
+
+ plt.rcParams.update(
+ {"font.family": "sans-serif", "font.sans-serif": ["Helvetica"]}
+ )
+
+ ax.text(
+ 0,
+ 1.06,
+ r"\begin{minipage}{4.3cm}\small "
+ r"\textbf{Figure caption} "
+ r"can be directly inserted from within matplotlib using the full \LaTeX~engine. To fit the axes, you can use a minipage and adjust its width to the axes. Of course, the width can be also computed automatically but from time to time it is ok to do it by trials and errors (I did). Fun part is that we could also include some tikz rendering inside the figure. "
+ r"\end{minipage}\vspace{-5mm}",
+ va="top",
+ transform=ax.transAxes,
+ clip_on=False,
+ usetex=True,
+ )
+ continue
+
+ ax = plt.subplot(rows, cols, index, aspect=1, frameon=True)
+ ax.tick_params(which="both", direction="in")
+ ax.tick_params(which="both", right=True)
+ ax.set_axisbelow(True)
+
+ ax.set_xlim(0, 10)
+ ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ ax.set_xticklabels([])
+
+ ax.set_ylim(0, 10)
+ ax.yaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ ax.set_yticklabels([])
+
+ ax.grid(color=".9", linestyle="--")
+
+ n = 50
+ x0, y0 = np.random.uniform(2, 8, 2)
+ s = np.random.uniform(0.5, 2.0)
+ X = np.random.normal(x0, s, n)
+ Y = np.random.normal(y0, s, n)
+ S = np.random.uniform(100, 200, n)
+ ax.scatter(X, Y, alpha=0.5, facecolor="C0", edgecolor="None")
+
+ Xm, Ym = X.mean(), Y.mean()
+ ax.axvline(Xm, linewidth=0.5, color="black")
+ text = ax.text(
+ Xm,
+ 9.5,
+ "%.1f" % Xm,
+ transform=ax.transData,
+ size=7,
+ rotation=90,
+ ha="center",
+ va="top",
+ )
+ text.set_path_effects(
+ [
+ path_effects.Stroke(linewidth=2, foreground="white"),
+ path_effects.Normal(),
+ ]
+ )
+
+ ax.axhline(Ym, linewidth=0.5, color="black")
+ text = ax.text(
+ 9.5,
+ Ym,
+ "%.1f" % Ym,
+ transform=ax.transData,
+ size=7,
+ rotation=0,
+ ha="right",
+ va="center",
+ )
+ text.set_path_effects(
+ [
+ path_effects.Stroke(linewidth=2, foreground="white"),
+ path_effects.Normal(),
+ ]
+ )
+
+ ax.scatter([Xm], [Ym], s=10, facecolor="black", edgecolor="None")
+
+ divider = make_axes_locatable(ax)
+ cax = divider.append_axes("top", size="15%", pad=0)
+ cax.get_xaxis().set_visible(False)
+ cax.get_yaxis().set_visible(False)
+ cax.set_facecolor("black")
+ cax.text(
+ 0.05,
+ 0.45,
+ "SESSION %2d" % index,
+ size=10,
+ color="white",
+ ha="left",
+ va="center",
+ )
+ cax.text(
+ 0.95,
+ 0.45,
+ "σ=%.1f" % s,
+ size=10,
+ color="white",
+ ha="right",
+ va="center",
+ weight="bold",
+ )
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/latex-text-box.png", dpi=600)
+plt.show()
diff --git a/code/ornaments/legend-alternatives.py b/code/ornaments/legend-alternatives.py
new file mode 100644
index 0000000..5b63b63
--- /dev/null
+++ b/code/ornaments/legend-alternatives.py
@@ -0,0 +1,161 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def plot(ax):
+ ax.set_xlim([-np.pi, np.pi])
+ ax.set_xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
+ ax.set_xticklabels(["-π", "-π/2", "0", "+π/2", "+π"])
+ ax.set_ylim([-1, 1])
+ ax.set_yticks([-1, 0, 1])
+ ax.set_yticklabels(["-1", "0", "+1"])
+
+ ax.spines["right"].set_visible(False)
+ ax.spines["top"].set_visible(False)
+ ax.spines["left"].set_position(("data", -3.25))
+ ax.spines["bottom"].set_position(("data", -1.25))
+
+ (plot1,) = ax.plot(X, C, label="cosine", clip_on=False)
+ (plot2,) = ax.plot(X, S, label="sine", clip_on=False)
+
+ return plot1, plot2
+
+
+n = 4
+fig = plt.figure(figsize=(6, n * 1.9))
+
+X = np.linspace(-np.pi, np.pi, 400, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+ax = plt.subplot(n, 1, 1)
+plot1, plot2 = plot(ax)
+ax.text(
+ X[-1],
+ C[-1],
+ " — " + plot1.get_label(),
+ size="small",
+ color=plot1.get_color(),
+ ha="left",
+ va="center",
+)
+ax.text(
+ X[-1],
+ S[-1],
+ " — " + plot2.get_label(),
+ size="small",
+ color=plot2.get_color(),
+ ha="left",
+ va="center",
+)
+
+ax = plt.subplot(n, 1, 2)
+plot1, plot2 = plot(ax)
+ax.text(
+ X[100],
+ C[100],
+ " " + plot1.get_label(),
+ family="Roboto Condensed",
+ size="small",
+ bbox=dict(facecolor="white", edgecolor="None", alpha=0.85),
+ color=plot1.get_color(),
+ ha="center",
+ va="center",
+ rotation=42.5,
+)
+ax.text(
+ X[200],
+ S[200],
+ " " + plot2.get_label(),
+ rotation=42.5,
+ family="Roboto Condensed",
+ size="small",
+ bbox=dict(facecolor="white", edgecolor="None", alpha=0.85),
+ color=plot2.get_color(),
+ ha="center",
+ va="center",
+)
+
+ax = plt.subplot(n, 1, 3)
+plot1, plot2 = plot(ax)
+
+ax.annotate(
+ "cosine",
+ (X[100], C[100]),
+ size="small",
+ color=plot1.get_color(),
+ xytext=(-50, +10),
+ textcoords="offset points",
+ arrowprops=dict(
+ arrowstyle="->", color=plot1.get_color(), connectionstyle="arc3,rad=-0.3"
+ ),
+)
+ax.annotate(
+ "sine",
+ (X[200], S[200]),
+ size="small",
+ color=plot2.get_color(),
+ xytext=(-50, +10),
+ textcoords="offset points",
+ arrowprops=dict(
+ arrowstyle="->", color=plot2.get_color(), connectionstyle="arc3,rad=-0.3"
+ ),
+)
+
+ax = plt.subplot(n, 1, 4)
+plot1, plot2 = plot(ax)
+index = 10
+ax.scatter(
+ [X[index]],
+ [C[index]],
+ s=100,
+ marker="o",
+ zorder=10,
+ clip_on=False,
+ linewidth=1,
+ edgecolor=plot1.get_color(),
+ facecolor="white",
+)
+ax.text(
+ X[index],
+ 1.01 * C[index],
+ "A",
+ zorder=20,
+ color=plot1.get_color(),
+ ha="center",
+ va="center",
+ size="x-small",
+ clip_on=False,
+)
+
+ax.scatter(
+ [X[index]],
+ [S[index]],
+ s=100,
+ marker="o",
+ zorder=10,
+ clip_on=False,
+ linewidth=1,
+ edgecolor=plot2.get_color(),
+ facecolor="white",
+)
+ax.text(
+ X[index],
+ 1.05 * S[index],
+ "B",
+ zorder=20,
+ color=plot2.get_color(),
+ ha="center",
+ va="center",
+ size="x-small",
+ clip_on=False,
+)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/legend-alternatives.pdf")
+plt.show()
diff --git a/code/ornaments/legend-regular.py b/code/ornaments/legend-regular.py
new file mode 100644
index 0000000..ff9b022
--- /dev/null
+++ b/code/ornaments/legend-regular.py
@@ -0,0 +1,33 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(6, 2.5))
+ax = plt.subplot(
+ xlim=[-np.pi, np.pi],
+ xticks=[-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],
+ xticklabels=["-π", "-π/2", "0", "+π/2", "+π"],
+ ylim=[-1, 1],
+ yticks=[-1, 0, 1],
+ yticklabels=["-1", "0", "+1"],
+)
+
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+ax.plot(X, C, label="cosine", clip_on=False)
+ax.plot(X, S, label="sine", clip_on=False)
+
+ax.spines["right"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["left"].set_position(("data", -3.25))
+ax.spines["bottom"].set_position(("data", -1.25))
+ax.legend(edgecolor="None")
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/legend-regular.pdf")
+plt.show()
diff --git a/code/ornaments/title-regular.py b/code/ornaments/title-regular.py
new file mode 100644
index 0000000..ecad9d8
--- /dev/null
+++ b/code/ornaments/title-regular.py
@@ -0,0 +1,47 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(6, 3))
+ax = plt.subplot(
+ xlim=[-np.pi, np.pi],
+ xticks=[-np.pi, -np.pi / 2, np.pi / 2, np.pi],
+ xticklabels=["-π", "-π/2", "+π/2", "+π"],
+ ylim=[-1, 1],
+ yticks=[-1, 1],
+ yticklabels=["-1", "+1"],
+)
+
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+ax.plot(X, C, label="cosine", clip_on=False)
+ax.plot(X, S, label="sine", clip_on=False)
+
+ax.spines["right"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["left"].set_position(("data", -3.25))
+ax.spines["bottom"].set_position(("data", -1.25))
+ax.legend(
+ edgecolor="None",
+ ncol=2,
+ loc="upper right",
+ bbox_to_anchor=(1.01, 1.225),
+ borderaxespad=0,
+)
+ax.set_title("Trigonometric functions", x=1, y=1.2, ha="right")
+
+ax.set_xlabel("Angle", va="center", weight="bold")
+ax.xaxis.set_label_coords(0.5, -0.25)
+
+ax.set_ylabel("Value", ha="center", weight="bold")
+ax.yaxis.set_label_coords(-0.025, 0.5)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/title-regular.pdf")
+plt.show()
diff --git a/code/reference/axes-adjustment.py b/code/reference/axes-adjustment.py
new file mode 100644
index 0000000..4128844
--- /dev/null
+++ b/code/reference/axes-adjustment.py
@@ -0,0 +1,213 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from matplotlib.collections import PatchCollection
+
+
+fig = plt.figure(figsize=(4.25, 4.25 * 95 / 115))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ frameon=False,
+ aspect=1,
+ xlim=(0 - 5, 100 + 10),
+ ylim=(-10, 80 + 5),
+ xticks=[],
+ yticks=[],
+)
+
+
+box = mpatches.FancyBboxPatch(
+ (0, 0),
+ 100,
+ 83,
+ mpatches.BoxStyle("Round", pad=0, rounding_size=2),
+ linewidth=1.0,
+ facecolor="0.9",
+ edgecolor="black",
+)
+ax.add_artist(box)
+
+box = mpatches.FancyBboxPatch(
+ (0, 0),
+ 100,
+ 75,
+ mpatches.BoxStyle("Round", pad=0, rounding_size=0),
+ linewidth=1.0,
+ facecolor="white",
+ edgecolor="black",
+)
+ax.add_artist(box)
+
+
+box = mpatches.Rectangle(
+ (5, 5), 45, 30, zorder=10, linewidth=1.0, facecolor="white", edgecolor="black"
+)
+ax.add_artist(box)
+
+box = mpatches.Rectangle(
+ (5, 40), 45, 30, zorder=10, linewidth=1.0, facecolor="white", edgecolor="black"
+)
+ax.add_artist(box)
+
+box = mpatches.Rectangle(
+ (55, 5), 40, 65, zorder=10, linewidth=1.0, facecolor="white", edgecolor="black"
+)
+ax.add_artist(box)
+
+# Window button
+X, Y = [5, 10, 15], [79, 79, 79]
+plt.scatter(X, Y, s=75, zorder=10, edgecolor="black", facecolor="white", linewidth=1)
+
+
+# Window size extension
+X, Y = [0, 0], [0, -8]
+plt.plot(X, Y, color="black", linestyle=":", linewidth=1, clip_on=False)
+
+X, Y = [100, 100], [0, -8]
+plt.plot(X, Y, color="black", linestyle=":", linewidth=1, clip_on=False)
+
+X, Y = [100, 108], [0, 0]
+plt.plot(X, Y, color="black", linestyle=":", linewidth=1, clip_on=False)
+
+X, Y = [100, 108], [75, 75]
+plt.plot(X, Y, color="black", linestyle=":", linewidth=1, clip_on=False)
+
+
+def ext_arrow(p0, p1, p2, p3):
+ p0, p1 = np.asarray(p0), np.asarray(p1)
+ p2, p3 = np.asarray(p2), np.asarray(p3)
+ ax.arrow(
+ *p0,
+ *(p1 - p0),
+ zorder=20,
+ linewidth=0,
+ length_includes_head=True,
+ width=0.4,
+ head_width=2,
+ head_length=2,
+ color="black"
+ )
+ ax.arrow(
+ *p3,
+ *(p2 - p3),
+ zorder=20,
+ linewidth=0,
+ length_includes_head=True,
+ width=0.4,
+ head_width=2,
+ head_length=2,
+ color="black"
+ )
+ plt.plot([p1[0], p2[0]], [p1[1], p2[1]], linewidth=0.9, color="black")
+
+
+def int_arrow(p0, p1):
+ p0, p1 = np.asarray(p0), np.asarray(p1)
+ ax.arrow(
+ *((p0 + p1) / 2),
+ *((p1 - p0) / 2),
+ zorder=20,
+ linewidth=0,
+ length_includes_head=True,
+ width=0.4,
+ head_width=2,
+ head_length=2,
+ color="black"
+ )
+ ax.arrow(
+ *((p0 + p1) / 2),
+ *(-(p1 - p0) / 2),
+ zorder=20,
+ linewidth=0,
+ length_includes_head=True,
+ width=0.4,
+ head_width=2,
+ head_length=2,
+ color="black"
+ )
+
+
+x = 0
+y = 10
+ext_arrow((x - 4, y), (x, y), (x + 5, y), (x + 9, y))
+ax.text(x + 9.5, y, "left", ha="left", va="center", size="x-small", zorder=20)
+
+x += 50
+ext_arrow((x - 4, y), (x, y), (x + 5, y), (x + 9, y))
+ax.text(x - 4.5, y, "wspace", ha="right", va="center", size="x-small", zorder=20)
+
+x += 45
+ext_arrow((x - 4, y), (x, y), (x + 5, y), (x + 9, y))
+ax.text(x - 4.5, y, "right", ha="right", va="center", size="x-small", zorder=20)
+
+y = 0
+x = 25
+ext_arrow((x, y - 4), (x, y), (x, y + 5), (x, y + 9))
+ax.text(x, y + 9.5, "bottom", ha="center", va="bottom", size="x-small", zorder=20)
+
+y += 35
+ext_arrow((x, y - 4), (x, y), (x, y + 5), (x, y + 9))
+ax.text(x, y - 4.5, "hspace", ha="center", va="top", size="x-small", zorder=20)
+
+y += 35
+ext_arrow((x, y - 4), (x, y), (x, y + 5), (x, y + 9))
+ax.text(x, y - 4.5, "top", ha="center", va="top", size="x-small", zorder=20)
+
+int_arrow((0, -5), (100, -5))
+ax.text(
+ 50,
+ -5,
+ "figure width",
+ backgroundcolor="white",
+ zorder=30,
+ ha="center",
+ va="center",
+ size="x-small",
+)
+
+int_arrow((105, 0), (105, 75))
+ax.text(
+ 105,
+ 75 / 2,
+ "figure height",
+ backgroundcolor="white",
+ zorder=30,
+ rotation="vertical",
+ ha="center",
+ va="center",
+ size="x-small",
+)
+
+int_arrow((55, 62.5), (95, 62.5))
+ax.text(
+ 75,
+ 62.5,
+ "axes width",
+ backgroundcolor="white",
+ zorder=30,
+ ha="center",
+ va="center",
+ size="x-small",
+)
+
+int_arrow((62.5, 5), (62.5, 70))
+ax.text(
+ 62.5,
+ 35,
+ "axes height",
+ backgroundcolor="white",
+ zorder=30,
+ rotation="vertical",
+ ha="center",
+ va="center",
+ size="x-small",
+)
+
+
+plt.savefig("reference-axes-adjustment.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/collection.py b/code/reference/collection.py
new file mode 100644
index 0000000..df5016c
--- /dev/null
+++ b/code/reference/collection.py
@@ -0,0 +1,337 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.path as mpath
+from matplotlib.collections import LineCollection
+from matplotlib.collections import PathCollection
+from matplotlib.collections import CircleCollection
+from matplotlib.collections import PolyCollection
+from matplotlib.collections import EllipseCollection
+from matplotlib.collections import RegularPolyCollection
+from matplotlib.collections import StarPolygonCollection
+from matplotlib.collections import AsteriskPolygonCollection
+
+
+fig = plt.figure(figsize=(4.25, 8 * 0.4))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ xlim=[0, 11],
+ ylim=[0.5, 8.5],
+ frameon=False,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+)
+y = 8
+
+
+# Line collection
+# ----------------------------------------------------------------------------
+n = 50
+segments = np.zeros((n, 2, 2))
+segments[:, 0, 0] = np.linspace(1, 10.25, n) - 0.2
+segments[:, 0, 1] = y - 0.2
+segments[:, 1, 0] = segments[:, 0, 0] + 0.2
+segments[:, 1, 1] = y + 0.15
+linewidths = np.linspace(0.5, 2.5, n)
+
+collection = LineCollection(segments, linewidths=linewidths, edgecolor="black")
+ax.add_collection(collection)
+
+ax.text(1 - 0.25, y + 0.25, "Line collection", size="small", ha="left", va="baseline")
+ax.text(
+ 10 + 0.25,
+ y + 0.25,
+ "LineCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Circle collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+sizes = np.linspace(25, 100, n)
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+
+collection = CircleCollection(
+ sizes,
+ # linewidths = linewidths,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+
+ax.text(
+ X[0] - 0.25, y + 0.35, "Circle collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "CircleCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Ellipse collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+angles = np.linspace(0, 45, n)
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = EllipseCollection(
+ widths,
+ heights,
+ angles,
+ # linewidths = linewidths,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ X[0] - 0.25, y + 0.35, "Ellipse collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "EllipseCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Polygon collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n) - 0.2, y + 0.1
+X, Y = offsets[:, 0], offsets[:, 1]
+verts = np.zeros((n, 4, 2))
+verts[:] = [0, 0], [1, 0], [1, 1], [0, 1]
+sizes = np.linspace(0.25, 0.50, n)
+verts *= sizes.reshape(n, 1, 1)
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+numsides = 5
+rotation = np.pi / 4
+
+offsets[:, 1] -= sizes / 2 - 0.25
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = PolyCollection(
+ verts,
+ # linewidths = linewidths,
+ sizes=None,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ 1 - 0.25, y + 0.35, "Polygon collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ 10 + 0.25,
+ y + 0.35,
+ "PolyCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Path collection
+# ----------------------------------------------------------------------------
+n = 10
+paths = []
+for i in range(n):
+ angle1 = np.random.randint(0, 180)
+ angle2 = angle1 + np.random.randint(180, 270)
+ path = mpath.Path.wedge(angle1, angle2)
+ verts, codes = path.vertices * 0.25, path.codes
+ path = mpath.Path(verts, codes)
+ paths.append(path)
+
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n) + 0.2, y
+X, Y = offsets[:, 0], offsets[:, 1]
+sizes = np.ones(n) / 100
+offsets[:, 1] -= sizes / 2 - 0.125
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = PathCollection(
+ paths,
+ sizes=None,
+ linewidths=1.0,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets - (0.2, -0.25),
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(1 - 0.25, y + 0.35, "Path collection", size="small", ha="left", va="baseline")
+ax.text(
+ 10 + 0.25,
+ y + 0.35,
+ "PathCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Regular collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+numsides = 5
+rotation = np.pi / 4
+sizes = np.linspace(100, 200, n)
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = RegularPolyCollection(
+ numsides,
+ rotation,
+ # linewidths = linewidths,
+ sizes=sizes,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ X[0] - 0.25,
+ y + 0.35,
+ "Regular polygon collection",
+ size="small",
+ ha="left",
+ va="baseline",
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "RegularPolyCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Star collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+numsides = 5
+rotation = np.pi / 4
+sizes = np.linspace(100, 200, n)
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = StarPolygonCollection(
+ numsides,
+ rotation,
+ # linewidths = linewidths,
+ sizes=sizes,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ X[0] - 0.25, y + 0.35, "Star collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "StarPolygonCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Asterisk collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+numsides = 8
+rotation = np.pi / 4
+sizes = np.linspace(50, 150, n)
+linewidths = np.linspace(0.5, 2.5, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.35, 0.65, n)]
+collection = AsteriskPolygonCollection(
+ numsides,
+ rotation,
+ linewidths=linewidths,
+ sizes=sizes,
+ edgecolor=facecolors,
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ X[0] - 0.25, y + 0.35, "Asterisk collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "AsteriskPolygonCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+plt.savefig("reference-collection.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-diverging.py b/code/reference/colormap-diverging.py
new file mode 100644
index 0000000..2b13115
--- /dev/null
+++ b/code/reference/colormap-diverging.py
@@ -0,0 +1,45 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+cmaps = (
+ "PRGn",
+ "PiYG",
+ "RdYlGn",
+ "BrBG",
+ "RdGy",
+ "PuOr",
+ "RdBu",
+ "RdYlBu",
+ "Spectral",
+ "coolwarm_r",
+ "bwr_r",
+ "seismic_r",
+)
+
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.22), dpi=100)
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.18, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps[::-1]:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-diverging.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-qualitative.py b/code/reference/colormap-qualitative.py
new file mode 100644
index 0000000..eab2f34
--- /dev/null
+++ b/code/reference/colormap-qualitative.py
@@ -0,0 +1,45 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+cmaps = (
+ "tab10",
+ "tab20",
+ "tab20b",
+ "tab20c",
+ "Pastel1",
+ "Pastel2",
+ "Paired",
+ "Set1",
+ "Set2",
+ "Set3",
+ "Accent",
+ "Dark2",
+)
+
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.22))
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.18, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps[::-1]:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-qualitative.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-sequential-1.py b/code/reference/colormap-sequential-1.py
new file mode 100644
index 0000000..a7460c6
--- /dev/null
+++ b/code/reference/colormap-sequential-1.py
@@ -0,0 +1,51 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+cmaps = (
+ "Greys",
+ "Reds",
+ "Oranges",
+ "YlOrBr",
+ "YlOrRd",
+ "OrRd",
+ "PuRd",
+ "RdPu",
+ "BuPu",
+ "Purples",
+ "YlGnBu",
+ "Blues",
+ "PuBu",
+ "GnBu",
+ "PuBuGn",
+ "BuGn",
+ "Greens",
+ "YlGn",
+)
+
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.22))
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.15, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps[::-1]:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-sequential-1.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-sequential-2.py b/code/reference/colormap-sequential-2.py
new file mode 100644
index 0000000..5c679ae
--- /dev/null
+++ b/code/reference/colormap-sequential-2.py
@@ -0,0 +1,46 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+cmaps = (
+ "bone",
+ "gray",
+ "pink",
+ "afmhot",
+ "hot",
+ "gist_heat",
+ "copper",
+ "Wistia",
+ "autumn_r",
+ "summer_r",
+ "spring_r",
+ "cool",
+ "winter_r",
+)
+
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.22))
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.15, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps[::-1]:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-sequential-2.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-uniform.py b/code/reference/colormap-uniform.py
new file mode 100644
index 0000000..4dd3fd8
--- /dev/null
+++ b/code/reference/colormap-uniform.py
@@ -0,0 +1,32 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+cmaps = "viridis", "plasma", "inferno", "magma"
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.23))
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.15, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-uniform.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colorspec.py b/code/reference/colorspec.py
new file mode 100644
index 0000000..de1bf9e
--- /dev/null
+++ b/code/reference/colorspec.py
@@ -0,0 +1,56 @@
+# -----------------------------------------------------------------------------
+# Matplotlib cheat sheet
+# Released under the BSD License
+# -----------------------------------------------------------------------------
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(5.5, 1.5))
+ax = fig.add_axes([0, 0, 1, 1], frameon=False) # , aspect=1)
+# ymin, ymax= 0, 7
+# xmin, xmax = 0, figsize[0]/figsize[1]
+# ax.set_xlim(xmin, xmax), ax.set_xticks([])
+# ax.set_ylim(ymin, ymax), ax.set_yticks([])
+# ax = plt.subplot()
+
+palettes = {
+ "raw": ["b", "g", "r", "c", "m", "y", "k", "w"],
+ "rgba": [(1, 0, 0), (1, 0, 0, 0.75), (1, 0, 0, 0.50), (1, 0, 0, 0.25)],
+ "HexRGBA": ["#FF0000", "#FF0000BB", "#FF000088", "#FF000044"],
+ "cycle": ["C%d" % i for i in range(10)],
+ "grey": ["%1.1f" % (i / 10) for i in range(11)],
+ "name": ["DarkRed", "Firebrick", "Crimson", "IndianRed", "Salmon"],
+}
+
+
+ymin, ymax = 0, len(palettes)
+xmin, xmax = 0, 22
+ax.set_xlim(xmin, xmax), ax.set_xticks([])
+ax.set_ylim(ymin, ymax), ax.set_yticks([])
+
+
+for y, (name, colors) in enumerate(palettes.items()):
+ C = mpl.colors.to_rgba_array(colors).reshape((1, len(colors), 4))
+ ax.imshow(C, extent=[xmin, xmax, y + 0.025, y + 0.95])
+ dx = (xmax - xmin) / len(colors)
+ for i in range(len(colors)):
+ color = "white"
+ if colors[i] in ["1.0", "w"]:
+ color = "black"
+ text = str(colors[i]).replace(" ", "")
+ ax.text(
+ (i + 0.5) * dx,
+ y + 0.5,
+ text,
+ color=color,
+ zorder=10,
+ family="Source Code Pro",
+ size=9,
+ ha="center",
+ va="center",
+ )
+
+plt.savefig("../../figures/reference/colorspec.pdf")
+plt.show()
diff --git a/code/reference/font.py b/code/reference/font.py
new file mode 100644
index 0000000..b074993
--- /dev/null
+++ b/code/reference/font.py
@@ -0,0 +1,171 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import matplotlib.pyplot as plt
+from matplotlib.font_manager import FontProperties
+
+fig = plt.figure(figsize=(4.25, 3.8))
+ax = fig.add_axes(
+ [0, 0, 1, 1], frameon=False, xticks=[], yticks=[], xlim=[0, 40], ylim=[0, 38]
+)
+
+y = 1
+
+# -----------------------------------------------------------------------------
+variants = {
+ "normal": "/Users/rougier/Library/Fonts/Delicious-Roman.otf",
+ "small-caps": "/Users/rougier/Library/Fonts/Delicious-SmallCaps.otf",
+}
+
+text = "The quick brown fox jumps over the lazy dog"
+for i, variant in enumerate(variants.keys()):
+ ax.text(
+ 1,
+ y,
+ text,
+ size=9,
+ va="center",
+ fontproperties=FontProperties(fname=variants[variant]),
+ )
+
+ ax.text(
+ 39,
+ y,
+ variant,
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65
+y += 1
+
+# -----------------------------------------------------------------------------
+styles = ["normal", "italic"]
+
+text = "The quick brown fox jumps over the lazy dog"
+for i, style in enumerate(styles):
+ ax.text(1, y, text, size=9, va="center", style=style, family="Source Sans Pro")
+
+ ax.text(
+ 39,
+ y,
+ style,
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65
+y += 1
+
+
+# -----------------------------------------------------------------------------
+families = {
+ "Pacifico": "cursive",
+ "Source Sans Pro": "sans",
+ "Source Serif Pro": "serif",
+ "Source Code Pro": "monospace",
+}
+
+text = "The quick brown fox jumps over the lazy dog"
+for i, family in enumerate(families):
+ ax.text(1, y, text, va="center", size=9, family=family, weight="regular")
+
+ ax.text(
+ 39,
+ y,
+ "%s" % (families[family]),
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65
+y += 1
+
+
+# -----------------------------------------------------------------------------
+weights = {
+ "ultralight": 100,
+ "light": 200,
+ "normal": 400,
+ "regular": 400,
+ "book": 400,
+ "medium": 500,
+ "roman": 500,
+ "semibold": 600,
+ "demibold": 600,
+ "demi": 600,
+ "bold": 700,
+ "heavy": 800,
+ "extra bold": 800,
+ "black": 900,
+}
+
+text = "The quick brown fox jumps over the lazy dog"
+for i, weight in enumerate(["ultralight", "normal", "semibold", "bold", "black"]):
+ ax.text(1, y, text, size=9, va="center", family="Source Sans Pro", weight=weight)
+
+ ax.text(
+ 39,
+ y,
+ "%s (%d)" % (weight, weights[weight]),
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65
+y += 1
+
+# -----------------------------------------------------------------------------
+sizes = {
+ "xx-small": 0.579,
+ "x-small": 0.694,
+ "small": 0.833,
+ "medium": 1.0,
+ "large": 1.200,
+ "x-large": 1.440,
+ "xx-large": 1.728,
+}
+
+text = "The quick brown fox"
+for i, size in enumerate(sizes.keys()):
+ ax.text(
+ 1,
+ y,
+ text,
+ size=size,
+ ha="left",
+ va="center",
+ family="Source Sans Pro",
+ weight="light",
+ )
+
+ ax.text(
+ 39,
+ y,
+ "%s (%.2f)" % (size, sizes[size]),
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65 * max(sizes[size], sizes["small"])
+
+
+plt.savefig("reference-font.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/hatch.py b/code/reference/hatch.py
new file mode 100644
index 0000000..81f5927
--- /dev/null
+++ b/code/reference/hatch.py
@@ -0,0 +1,132 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+
+fig = plt.figure(figsize=(4.25, 4.5 * 0.55))
+ax = fig.add_axes(
+ [0, 0, 1, 1], xlim=[0, 11], ylim=[0.5, 4.5], frameon=False, xticks=[], yticks=[]
+) # , aspect=1)
+y = 3.75
+
+
+# Hatch pattern
+# ----------------------------------------------------------------------------
+patterns = "/", "\\", "|", "-", "+", "x", "o", "O", ".", "*"
+w, h = 0.75 * 10 / len(patterns), 0.5
+X = np.linspace(1, 10 - w, len(patterns))
+
+for x, pattern in zip(X, patterns):
+ rect = Rectangle(
+ (x, y),
+ w,
+ h,
+ hatch=pattern * 2,
+ facecolor="0.85",
+ edgecolor="0.00",
+ linewidth=1.0,
+ )
+ ax.add_patch(rect)
+ plt.text(
+ x + w / 2,
+ y - 0.125,
+ '"%s"' % pattern,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+
+plt.text(X[0] - 0.25, y + 0.65, "Hatch pattern", size="small", ha="left", va="baseline")
+plt.text(
+ X[-1] + w + 0.25,
+ y + 0.65,
+ "hatch",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1.5
+
+
+# Hatch density
+# ----------------------------------------------------------------------------
+patterns = "/", "//", "///", "////"
+w, h = 0.75 * 10 / len(patterns), 0.5
+X = np.linspace(1, 10 - w, len(patterns))
+
+for x, pattern in zip(X, patterns):
+ rect = Rectangle(
+ (x, y), w, h, hatch=pattern, facecolor="0.85", edgecolor="0.00", linewidth=1.0
+ )
+ ax.add_patch(rect)
+ plt.text(
+ x + w / 2,
+ y - 0.125,
+ '"%s"' % pattern,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+
+plt.text(X[0] - 0.25, y + 0.65, "Hatch density", size="small", ha="left", va="baseline")
+plt.text(
+ X[-1] + w + 0.25,
+ y + 0.65,
+ "hatch",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1.5
+
+
+# Hatch linewidth (hack)
+# ----------------------------------------------------------------------------
+# We cannot have different hatch linewidth in a single figure and the solution
+# was to generate images and to show them here. Images were generated from the
+# make-hatch-linewidth.py script.
+widths = 1, 2, 3, 4, 5, 6
+w, h = 0.75 * 10 / len(widths), 0.5
+X = np.linspace(1, 10 - w, len(widths))
+for (x, width) in zip(X, widths):
+ image = imageio.imread("hatch-%d.png" % width)
+ ax.imshow(image, extent=[x, x + w, y, y + h])
+ rect = Rectangle((x, y), w, h, facecolor="none", edgecolor="black", linewidth=1.0)
+ ax.add_patch(rect)
+ plt.text(
+ x + w / 2,
+ y - 0.125,
+ "%d" % width,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.65, "Hatch linewidth", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + w + 0.25,
+ y + 0.65,
+ "rcParams['hatch.linewidth']",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1.5
+
+plt.savefig("reference-hatch.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/line.py b/code/reference/line.py
new file mode 100644
index 0000000..a2d2dd5
--- /dev/null
+++ b/code/reference/line.py
@@ -0,0 +1,224 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(4.25, 6 * 0.55))
+ax = fig.add_axes(
+ [0, 0, 1, 1], xlim=[0, 11], ylim=[0.5, 6.5], frameon=False, xticks=[], yticks=[]
+)
+y = 6
+
+
+def split(n_segment):
+ width = 9
+ segment_width = 0.75 * (width / n_segment)
+ segment_pad = (width - n_segment * segment_width) / (n_segment - 1)
+ X0 = 1 + np.arange(n_segment) * (segment_width + segment_pad)
+ X1 = X0 + segment_width
+ return X0, X1
+
+
+# Color
+# ----------------------------------------------------------------------------
+X0, X1 = split(10)
+for i, (x0, x1) in enumerate(zip(X0, X1)):
+ ax.plot([x0, x1], [y, y], color="C%d" % i, linewidth=10, solid_capstyle="butt")
+ plt.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ '"C%d"' % i,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+ax.text(X0[0] - 0.25, y + 0.2, "Line color", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "color / c",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+
+y -= 1
+
+# Line width
+# ----------------------------------------------------------------------------
+X0, X1 = split(5)
+LW = np.arange(1, 6)
+for x0, x1, lw in zip(X0, X1, LW):
+ ax.plot([x0, x1], [y, y], color="black", linewidth=lw, solid_capstyle="round")
+ plt.text((x0 + x1) / 2, y - 0.2, "%d" % lw, size="x-small", ha="center", va="top")
+ax.text(X0[0] - 0.25, y + 0.2, "Line width", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "linewidth / lw",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Solid capstyle
+# ----------------------------------------------------------------------------
+X0, X1 = split(3)
+styles = "butt", "round", "projecting"
+for x0, x1, style in zip(X0, X1, styles):
+ ax.plot([x0, x1], [y, y], color=".85", solid_capstyle="projecting", linewidth=7)
+ ax.plot([x0, x1], [y, y], color="black", solid_capstyle=style, linewidth=7)
+ ax.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ '"%s"' % style,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+
+ax.text(X0[0] - 0.25, y + 0.2, "Cap style", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "solid_capstyle",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Dash capstyle
+# ----------------------------------------------------------------------------
+X0, X1 = split(3)
+styles = "butt", "round", "projecting"
+for x0, x1, style in zip(X0, X1, styles):
+ ax.plot(
+ [x0, x1],
+ [y, y],
+ color=".85",
+ dash_capstyle="projecting",
+ linewidth=7,
+ linestyle="--",
+ )
+ ax.plot(
+ [x0, x1],
+ [y, y],
+ color="black",
+ linewidth=7,
+ linestyle="--",
+ dash_capstyle=style,
+ )
+ ax.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ '"%s"' % style,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+ax.text(X0[0] - 0.25, y + 0.2, "Dash cap style", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "dash_capstyle",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Line style
+# ----------------------------------------------------------------------------
+X0, X1 = split(5)
+styles = "-", ":", "--", "-.", (0, (0.01, 2))
+
+for x0, x1, style in zip(X0, X1, styles):
+ ax.plot(
+ [x0, x1],
+ [y, y],
+ color="black",
+ linestyle=style,
+ solid_capstyle="round",
+ dash_capstyle="round",
+ linewidth=3,
+ )
+ if isinstance(style, str):
+ text = '"%s"' % style
+ else:
+ text = "%s" % str(style)
+
+ ax.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ text,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+ax.text(X0[0] - 0.25, y + 0.2, "Line style", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "linestyle / ls",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Color
+# ----------------------------------------------------------------------------
+X0, X1 = split(2)
+for i, (x0, x1) in enumerate(zip(X0, X1)):
+
+ X = np.linspace(x0, x1, 500)
+ Y = y + 0.1 * np.cos(5 * np.pi * (X - x0) / (x1 - x0))
+
+ ax.plot(X, Y, color="black", linewidth=0.75, antialiased=i, rasterized=1 - i)
+ plt.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ "%s" % bool(i),
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+
+ax.text(X0[0] - 0.25, y + 0.2, "Antialias", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "antialiased",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+
+y -= 1
+
+plt.savefig("reference-line.pdf", dpi=200)
+plt.show()
diff --git a/code/reference/marker.py b/code/reference/marker.py
new file mode 100644
index 0000000..682dff8
--- /dev/null
+++ b/code/reference/marker.py
@@ -0,0 +1,288 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(4.25, 8 * 0.55))
+ax = fig.add_axes(
+ [0, 0, 1, 1], xlim=[0, 11], ylim=[0.5, 8.5], frameon=False, xticks=[], yticks=[]
+) # , aspect=1)
+
+X = np.linspace(1, 10, 10)
+Y = np.zeros(len(X))
+y = 8
+
+# Marker edge color
+# ----------------------------------------------------------------------------
+C = ["C%d" % i for i in range(10)]
+plt.scatter(X, y + Y, s=200, facecolor="white", edgecolor=C, linewidth=1.5)
+for x, c in zip(X, C):
+ plt.text(
+ x,
+ y - 0.25,
+ '"%s"' % c,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Marker edge color", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "mec / ec",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Marker face color
+# ----------------------------------------------------------------------------
+C = ["C%d" % i for i in range(10)]
+plt.scatter(X, y + Y, s=200, facecolor=C, edgecolor="None")
+for x, c in zip(X, C):
+ plt.text(
+ x,
+ y - 0.25,
+ '"%s"' % c,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Marker face color", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "mfc / fc",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Marker edge width
+# ----------------------------------------------------------------------------
+LW = 1 + np.arange(10) / 2
+plt.scatter(X, y + Y, s=100, facecolor="white", edgecolor="black", linewidth=LW)
+for x, lw in zip(X, LW):
+ plt.text(
+ x,
+ y - 0.25,
+ "%.1f" % lw,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Marker edge width", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "mew / lw",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Marker edge width
+# ----------------------------------------------------------------------------
+S = (1 + np.arange(10)) * 25
+plt.scatter(X, y + Y, s=S, facecolor="black", edgecolor="None")
+for x, s in zip(X, S):
+ plt.text(
+ x, y - 0.25, "%d" % s, size="x-small", ha="center", va="top", family="monospace"
+ )
+plt.text(X[0] - 0.25, y + 0.25, "Marker size", size="small", ha="left", va="baseline")
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "ms / s",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+X = np.linspace(1, 10, 12)
+
+# Filled markers
+# -----------------------------------------------------------------------------
+M = [".", "o", "s", "P", "X", "*", "p", "D", "<", ">", "^", "v"]
+for x, marker in zip(X, M):
+ plt.scatter(x, y, s=256, color="black", marker="s", fc=".9", ec="none")
+ plt.scatter(
+ x,
+ y,
+ s=100,
+ color="black",
+ marker=marker,
+ fc="white",
+ ec="black",
+ linewidth=0.75,
+ )
+ plt.text(
+ x,
+ y - 0.25,
+ '"%s"' % marker,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Filled markers", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "marker",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Unfilled markers
+# -----------------------------------------------------------------------------
+M = ["1", "2", "3", "4", "+", "x", "|", "_", 4, 5, 6, 7]
+for x, marker in zip(X, M):
+ if isinstance(marker, str):
+ text = '"%s"' % marker
+ else:
+ text = "%s" % marker
+
+ plt.scatter(x, y, s=256, color="black", marker="s", fc=".9", ec="none")
+ plt.scatter(
+ x, y, s=100, color="black", marker=marker, fc="none", ec="black", linewidth=0.75
+ )
+ plt.text(
+ x, y - 0.25, text, size="x-small", ha="center", va="top", family="monospace"
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Unfilled markers", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "marker",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Unicode markers
+# -----------------------------------------------------------------------------
+M = ["♠", "♣", "♥", "♦", "→", "←", "↑", "↓", "◐", "◑", "◒", "◓"]
+for x, marker in zip(X, M):
+ ax.scatter(x, y, s=256, color="black", marker="s", fc=".9", ec="none")
+ ax.scatter(
+ x,
+ y,
+ s=100,
+ color="black",
+ marker="$" + marker + "$",
+ fc="black",
+ ec="none",
+ linewidth=0.5,
+ )
+ ax.text(
+ x,
+ y - 0.25,
+ '"\$%s\$"' % marker,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+ax.text(
+ X[0] - 0.25, y + 0.25, "Unicode markers", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "marker",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Spacing
+# -----------------------------------------------------------------------------
+n_segment = 4
+width = 9
+segment_width = 0.75 * (width / n_segment)
+segment_pad = (width - n_segment * segment_width) / (n_segment - 1)
+X0 = 1 + np.arange(n_segment) * (segment_width + segment_pad)
+marks = [10, [0, -1], (25, 5), [0, 25, -1]]
+
+for x0, mark in zip(X0, marks):
+ X = np.linspace(x0, x0 + segment_width, 50)
+ Y = y * np.ones(len(X))
+ ax.plot(
+ X,
+ Y,
+ linewidth=1,
+ color="black",
+ marker=".",
+ mfc="white",
+ mec="black",
+ mew="1",
+ markevery=mark,
+ )
+
+ ax.text(
+ (X[0] + X[-1]) / 2,
+ y - 0.1,
+ "%s" % str(mark),
+ size="x-small",
+ ha="center",
+ va="top",
+ )
+
+
+ax.text(1 - 0.25, y + 0.25, "Marker spacing", size="small", ha="left", va="baseline")
+ax.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "markevery",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+
+
+plt.savefig("reference-marker.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/scale.py b/code/reference/scale.py
new file mode 100644
index 0000000..dc12ad5
--- /dev/null
+++ b/code/reference/scale.py
@@ -0,0 +1,192 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter, SymmetricalLogLocator
+
+
+X = np.linspace(-2, 2, 1001)
+Y = np.cos(X * 5 * 2 * np.pi)
+
+
+figure = plt.figure(figsize=(6, 6))
+
+# Style
+# -----------------------------------------------------------------------------
+# plt.rc('font', family="Roboto Condensed")
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+# Linear scale
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 1, ylim=(-3, 2))
+plt.plot(X, Y, color="C1", linewidth=1.5)
+ax.set_yticks([])
+ax.text(
+ 0.01,
+ 0.95,
+ "Linear scale",
+ transform=ax.transAxes,
+ weight=600,
+ horizontalalignment="left",
+ verticalalignment="top",
+ size="medium",
+)
+
+# Respective domains for scales
+plt.plot([-2, 2], [-1.5, -1.5], color="black", linewidth=1, marker="|")
+ax.text(
+ 0,
+ -1.5,
+ "SymLog scale domain",
+ transform=ax.transData,
+ size="x-small",
+ bbox=dict(facecolor="white", edgecolor="None", pad=1.0),
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+
+plt.plot([0, 2], [-2.0, -2.0], color="black", linewidth=1, marker="|")
+ax.text(
+ 1,
+ -2,
+ "Log scale domain",
+ transform=ax.transData,
+ size="x-small",
+ bbox=dict(facecolor="white", edgecolor="None", pad=1.0),
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+
+plt.plot([0, 1], [-2.5, -2.5], color="black", linewidth=1, marker="|")
+ax.text(
+ 0.5,
+ -2.5,
+ "Logit scale domain",
+ transform=ax.transData,
+ size="x-small",
+ bbox=dict(facecolor="white", edgecolor="None", pad=1.0),
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+
+
+# Log scale (X > 0)
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 2, ylim=(-2, 2))
+ax.set_xscale("log", basex=10)
+plt.plot(X, Y, color="C1", linewidth=1.5)
+ax.set_yticks([])
+ax.text(
+ 0.01,
+ 0.95,
+ "Log scale (x > 0)",
+ transform=ax.transAxes,
+ weight=600,
+ horizontalalignment="left",
+ verticalalignment="top",
+ size="medium",
+)
+
+
+# SymLog scale
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 3, ylim=(-2, 2))
+t = 0.05
+ax.set_xscale("symlog", basex=10, linthreshx=t, linscalex=1)
+plt.plot(X, Y, color="C1", linewidth=1.5)
+ax.set_yticks([])
+ax.xaxis.set_minor_locator(
+ SymmetricalLogLocator(base=10, linthresh=t, subs=np.arange(2, 10))
+)
+
+ax.text(
+ 0.01,
+ 0.95,
+ "Symlog scale",
+ transform=ax.transAxes,
+ weight=600,
+ horizontalalignment="left",
+ verticalalignment="top",
+ size="medium",
+)
+ax.fill_betweenx([-2, 2], [-t, -t], [+t, +t], facecolor="black", alpha=0.1)
+ax.text(
+ 0.50,
+ 0.05,
+ "Linear",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.text(
+ 0.15,
+ 0.05,
+ "Logarithmic",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.text(
+ 0.85,
+ 0.05,
+ "Logarithmic",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+
+
+# Logit (0 < X < 1)
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 4, ylim=(-2, 2))
+ax.set_xscale("logit")
+plt.plot(X, Y, color="C1", linewidth=1.5)
+ax.set_yticks([])
+ax.xaxis.set_minor_formatter(NullFormatter())
+ax.text(
+ 0.01,
+ 0.95,
+ "Logit scale (0 < x < 1)",
+ transform=ax.transAxes,
+ weight=600,
+ horizontalalignment="left",
+ verticalalignment="top",
+ size="medium",
+)
+ax.fill_betweenx([-2, 2], [0.2, 0.2], [0.8, 0.8], facecolor="black", alpha=0.1)
+ax.text(
+ 0.50,
+ 0.05,
+ "Quasi linear",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.text(
+ 0.2,
+ 0.05,
+ "Logarithmic",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.text(
+ 0.8,
+ 0.05,
+ "Logarithmic",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+
+# Show
+plt.tight_layout()
+plt.savefig("reference-scale.pdf")
+plt.show()
diff --git a/code/reference/text-alignment.py b/code/reference/text-alignment.py
new file mode 100644
index 0000000..1d19c08
--- /dev/null
+++ b/code/reference/text-alignment.py
@@ -0,0 +1,91 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+dpi = 100
+fig = plt.figure(figsize=(4.25, 1.5), dpi=dpi)
+ax = fig.add_axes(
+ [0, 0, 1, 1], frameon=False, xlim=(0, 4.25), ylim=(0, 1.5), xticks=[], yticks=[]
+)
+
+fontsize = 48
+renderer = fig.canvas.get_renderer()
+horizontalalignment = "left"
+verticalalignment = "center"
+position = (0.25, 1.5 / 2)
+color = "0.25"
+
+# Compute vertical and horizontal alignment offsets
+text = ax.text(0, 0, "Matplotlib", fontsize=fontsize)
+yoffset = {}
+for alignment in ["top", "center", "baseline", "bottom"]:
+ text.set_verticalalignment(alignment)
+ y = text.get_window_extent(renderer).y0 / dpi
+ yoffset[alignment] = y
+
+xoffset = {}
+for alignment in ["left", "center", "right"]:
+ text.set_horizontalalignment(alignment)
+ x = text.get_window_extent(renderer).x0 / dpi
+ xoffset[alignment] = x
+
+# Actual positioning of the text
+text.set_horizontalalignment(horizontalalignment)
+text.set_verticalalignment(verticalalignment)
+text.set_position(position)
+
+
+for name, y in yoffset.items():
+ y = position[1] - y + yoffset[verticalalignment]
+ plt.plot([0.1, 3.75], [y, y], linewidth=0.5, color=color)
+ plt.text(3.75, y, " " + name, color=color, ha="left", va="center", size="x-small")
+
+for name, x in xoffset.items():
+ x = position[0] - x + xoffset[horizontalalignment]
+ plt.plot([x, x], [0.25, 1.25], linewidth=0.5, color=color)
+ plt.text(x, 0.24, name, color=color, ha="center", va="top", size="x-small")
+
+P = []
+for x in xoffset.values():
+ x = position[0] - x + xoffset[horizontalalignment]
+ for y in yoffset.values():
+ y = position[1] - y + yoffset[verticalalignment]
+ P.append((x, y))
+P = np.array(P)
+
+ax.scatter(
+ P[:, 0],
+ P[:, 1],
+ s=10,
+ zorder=10,
+ facecolor="white",
+ edgecolor=color,
+ linewidth=0.75,
+)
+
+epsilon = 0.05
+plt.text(
+ P[3, 0] + epsilon,
+ P[3, 1] - epsilon,
+ "(0,0)",
+ color=color,
+ ha="left",
+ va="top",
+ size="x-small",
+)
+plt.text(
+ P[8, 0] - epsilon,
+ P[8, 1] + epsilon,
+ "(1,1)",
+ color=color,
+ ha="right",
+ va="bottom",
+ size="x-small",
+)
+
+plt.savefig("reference-text-alignment.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/tick-formatter.py b/code/reference/tick-formatter.py
new file mode 100644
index 0000000..3afe31b
--- /dev/null
+++ b/code/reference/tick-formatter.py
@@ -0,0 +1,109 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+# Setup a plot such that only the bottom spine is shown
+def setup(ax):
+ ax.spines["right"].set_color("none")
+ ax.spines["left"].set_color("none")
+ ax.yaxis.set_major_locator(ticker.NullLocator())
+ ax.spines["top"].set_color("none")
+ ax.xaxis.set_ticks_position("bottom")
+ ax.tick_params(which="major", width=1.00, length=5)
+ ax.tick_params(which="minor", width=0.75, length=2.5, labelsize=10)
+ ax.set_xlim(0, 5)
+ ax.set_ylim(0, 1)
+ ax.patch.set_alpha(0.0)
+
+
+fig = plt.figure(figsize=(8, 6))
+n = 7
+
+# Null formatter
+ax = fig.add_subplot(n, 1, 1)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(ticker.NullFormatter())
+ax.xaxis.set_minor_formatter(ticker.NullFormatter())
+ax.text(0.0, 0.1, "NullFormatter()", fontsize=16, transform=ax.transAxes)
+
+# Fixed formatter
+ax = fig.add_subplot(n, 1, 2)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+majors = ["", "0", "1", "2", "3", "4", "5"]
+ax.xaxis.set_major_formatter(ticker.FixedFormatter(majors))
+minors = [""] + [
+ "%.2f" % (x - int(x)) if (x - int(x)) else "" for x in np.arange(0, 5, 0.25)
+]
+ax.xaxis.set_minor_formatter(ticker.FixedFormatter(minors))
+ax.text(
+ 0.0, 0.1, "FixedFormatter(['', '0', '1', ...])", fontsize=15, transform=ax.transAxes
+)
+
+
+# FuncFormatter can be used as a decorator
+@ticker.FuncFormatter
+def major_formatter(x, pos):
+ return "[%.2f]" % x
+
+
+ax = fig.add_subplot(n, 1, 3)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(major_formatter)
+ax.text(
+ 0.0,
+ 0.1,
+ 'FuncFormatter(lambda x, pos: "[%.2f]" % x)',
+ fontsize=15,
+ transform=ax.transAxes,
+)
+
+
+# FormatStr formatter
+ax = fig.add_subplot(n, 1, 4)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter(">%d<"))
+ax.text(0.0, 0.1, "FormatStrFormatter('>%d<')", fontsize=15, transform=ax.transAxes)
+
+# Scalar formatter
+ax = fig.add_subplot(n, 1, 5)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.AutoLocator())
+ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ax.xaxis.set_major_formatter(ticker.ScalarFormatter(useMathText=True))
+ax.text(0.0, 0.1, "ScalarFormatter()", fontsize=15, transform=ax.transAxes)
+
+# StrMethod formatter
+ax = fig.add_subplot(n, 1, 6)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(ticker.StrMethodFormatter("{x}"))
+ax.text(0.0, 0.1, "StrMethodFormatter('{x}')", fontsize=15, transform=ax.transAxes)
+
+# Percent formatter
+ax = fig.add_subplot(n, 1, 7)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(ticker.PercentFormatter(xmax=5))
+ax.text(0.0, 0.1, "PercentFormatter(xmax=5)", fontsize=15, transform=ax.transAxes)
+
+# Push the top of the top axes outside the figure because we only show the
+# bottom spine.
+fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=1.05)
+
+plt.savefig("reference-tick-formatter.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/tick-locator.py b/code/reference/tick-locator.py
new file mode 100644
index 0000000..44fff8f
--- /dev/null
+++ b/code/reference/tick-locator.py
@@ -0,0 +1,97 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+# Setup a plot such that only the bottom spine is shown
+def setup(ax):
+ ax.spines["right"].set_color("none")
+ ax.spines["left"].set_color("none")
+ ax.yaxis.set_major_locator(ticker.NullLocator())
+ ax.spines["top"].set_color("none")
+ ax.xaxis.set_ticks_position("bottom")
+ ax.tick_params(which="major", width=1.00)
+ ax.tick_params(which="major", length=5)
+ ax.tick_params(which="minor", width=0.75)
+ ax.tick_params(which="minor", length=2.5)
+ ax.set_xlim(0, 5)
+ ax.set_ylim(0, 1)
+ ax.patch.set_alpha(0.0)
+
+
+plt.figure(figsize=(8, 6))
+n = 8
+
+# Null Locator
+ax = plt.subplot(n, 1, 1)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.NullLocator())
+ax.xaxis.set_minor_locator(ticker.NullLocator())
+ax.text(0.0, 0.1, "NullLocator()", fontsize=14, transform=ax.transAxes)
+
+# Multiple Locator
+ax = plt.subplot(n, 1, 2)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.5))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.1))
+ax.text(0.0, 0.1, "MultipleLocator(0.5)", fontsize=14, transform=ax.transAxes)
+
+# Fixed Locator
+ax = plt.subplot(n, 1, 3)
+setup(ax)
+majors = [0, 1, 5]
+ax.xaxis.set_major_locator(ticker.FixedLocator(majors))
+minors = np.linspace(0, 1, 11)[1:-1]
+ax.xaxis.set_minor_locator(ticker.FixedLocator(minors))
+ax.text(0.0, 0.1, "FixedLocator([0, 1, 5])", fontsize=14, transform=ax.transAxes)
+
+# Linear Locator
+ax = plt.subplot(n, 1, 4)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.LinearLocator(3))
+ax.xaxis.set_minor_locator(ticker.LinearLocator(31))
+ax.text(0.0, 0.1, "LinearLocator(numticks=3)", fontsize=14, transform=ax.transAxes)
+
+# Index Locator
+ax = plt.subplot(n, 1, 5)
+setup(ax)
+ax.plot(range(0, 5), [0] * 5, color="white")
+ax.xaxis.set_major_locator(ticker.IndexLocator(base=0.5, offset=0.25))
+ax.text(
+ 0.0, 0.1, "IndexLocator(base=0.5, offset=0.25)", fontsize=14, transform=ax.transAxes
+)
+
+# Auto Locator
+ax = plt.subplot(n, 1, 6)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.AutoLocator())
+ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ax.text(0.0, 0.1, "AutoLocator()", fontsize=14, transform=ax.transAxes)
+
+# MaxN Locator
+ax = plt.subplot(n, 1, 7)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MaxNLocator(4))
+ax.xaxis.set_minor_locator(ticker.MaxNLocator(40))
+ax.text(0.0, 0.1, "MaxNLocator(n=4)", fontsize=14, transform=ax.transAxes)
+
+# Log Locator
+ax = plt.subplot(n, 1, 8)
+setup(ax)
+ax.set_xlim(10 ** 3, 10 ** 10)
+ax.set_xscale("log")
+ax.xaxis.set_major_locator(ticker.LogLocator(base=10.0, numticks=15))
+ax.text(
+ 0.0, 0.1, "LogLocator(base=10, numticks=15)", fontsize=15, transform=ax.transAxes
+)
+
+# Push the top of the top axes outside the figure because we only show the
+# bottom spine.
+plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=1.05)
+
+plt.savefig("reference-tick-locator.pdf", dpi=600)
+plt.show()
diff --git a/code/rules/graphics.py b/code/rules/graphics.py
new file mode 100644
index 0000000..1cc732d
--- /dev/null
+++ b/code/rules/graphics.py
@@ -0,0 +1,229 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import ImageGrid
+from mpl_toolkits.axes_grid1.inset_locator import mark_inset
+from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
+
+from projections import *
+
+# -----------------------------------------------------------------------------
+def polar_frame(ax, title=None, legend=False, zoom=False, labels=True):
+ """ Draw a polar frame """
+
+ for rho in [0, 2, 5, 10, 20, 40, 60, 80, 90]:
+ lw, color, alpha = 1, "0.00", 0.25
+ if rho == 90 and not zoom:
+ color, lw, alpha = "0.00", 2, 1
+
+ n = 500
+ R = np.ones(n) * rho / 90.0
+ T = np.linspace(-np.pi / 2, np.pi / 2, n)
+ X, Y = polar_to_cartesian(R, T)
+ ax.plot(X, Y, color=color, lw=lw, alpha=alpha)
+
+ if not zoom and rho in [0, 10, 20, 40, 80] and labels:
+ ax.text(
+ X[-1] * 1.0 - 0.075,
+ Y[-1],
+ u"%d°" % rho,
+ color="k", # size=15,
+ horizontalalignment="center",
+ verticalalignment="center",
+ )
+
+ for theta in [-90, -60, -30, 0, +30, +60, +90]:
+ lw, color, alpha = 1, "0.00", 0.25
+ if theta in [-90, +90] and not zoom:
+ color, lw, alpha = "0.00", 2, 1
+ angle = theta / 90.0 * np.pi / 2
+
+ n = 500
+ R = np.linspace(0, 1, n)
+ T = np.ones(n) * angle
+ X, Y = polar_to_cartesian(R, T)
+ ax.plot(X, Y, color=color, lw=lw, alpha=alpha)
+
+ if not zoom and theta in [-90, -60, -30, +30, +60, +90] and labels:
+ ax.text(
+ X[-1] * 1.05,
+ Y[-1] * 1.05,
+ u"%d°" % theta,
+ color="k", # size=15,
+ horizontalalignment="left",
+ verticalalignment="center",
+ )
+ d = 0.01
+ ax.set_xlim(0.0 - d, 1.0 + d)
+ ax.set_ylim(-1.0 - d, 1.0 + d)
+ ax.set_xticks([])
+ ax.set_yticks([])
+
+ if legend:
+ ax.set_frame_on(True)
+ ax.spines["left"].set_color("none")
+ ax.spines["right"].set_color("none")
+ ax.spines["top"].set_color("none")
+ ax.xaxis.set_ticks_position("bottom")
+ ax.spines["bottom"].set_position(("data", -1.2))
+ ax.set_xticks([])
+ ax.text(
+ 0.0,
+ -1.1,
+ "$\longleftarrow$ Foveal",
+ verticalalignment="top",
+ horizontalalignment="left",
+ size=12,
+ )
+ ax.text(
+ 1.0,
+ -1.1,
+ "Peripheral $\longrightarrow$",
+ verticalalignment="top",
+ horizontalalignment="right",
+ size=12,
+ )
+ else:
+ ax.set_frame_on(False)
+ if title:
+ ax.title(title)
+
+
+# -----------------------------------------------------------------------------
+def logpolar_frame(ax, title=None, legend=False, labels=True):
+ """ Draw a log polar frame """
+
+ for rho in [2, 5, 10, 20, 40, 60, 80, 90]:
+ lw, color, alpha = 1, "0.00", 0.25
+ if rho == 90:
+ color, lw, alpha = "0.00", 2, 1
+
+ n = 500
+ R = np.ones(n) * rho / 90.0
+ T = np.linspace(-np.pi / 2, np.pi / 2, n)
+ X, Y = polar_to_logpolar(R, T)
+ X, Y = X * 2, 2 * Y - 1
+ ax.plot(X, Y, color=color, lw=lw, alpha=alpha)
+ if labels and rho in [2, 5, 10, 20, 40, 80]:
+ ax.text(
+ X[-1],
+ Y[-1] + 0.05,
+ u"%d°" % rho,
+ color="k", # size=15,
+ horizontalalignment="right",
+ verticalalignment="bottom",
+ )
+
+ for theta in [-90, -60, -30, 0, +30, +60, +90]:
+ lw, color, alpha = 1, "0.00", 0.25
+ if theta in [-90, +90]:
+ color, lw, alpha = "0.00", 2, 1
+ angle = theta / 90.0 * np.pi / 2
+
+ n = 500
+ R = np.linspace(0, 1, n)
+ T = np.ones(n) * angle
+ X, Y = polar_to_logpolar(R, T)
+ X, Y = X * 2, 2 * Y - 1
+ ax.plot(X, Y, color=color, lw=lw, alpha=alpha)
+ if labels:
+ ax.text(
+ X[-1] * 1.0 + 0.05,
+ Y[-1] * 1.0,
+ u"%d°" % theta,
+ color="k", # size=15,
+ horizontalalignment="left",
+ verticalalignment="center",
+ )
+
+ d = 0.01
+ ax.set_xlim(0.0 - d, 2.0 + d)
+ ax.set_ylim(-1.0 - d, 1.0 + d)
+ ax.set_xticks([])
+ ax.set_yticks([])
+ if legend:
+ ax.set_frame_on(True)
+ ax.spines["left"].set_color("none")
+ ax.spines["right"].set_color("none")
+ ax.spines["top"].set_color("none")
+ ax.xaxis.set_ticks_position("bottom")
+ ax.spines["bottom"].set_position(("data", -1.2))
+ ax.set_xticks([0, 2])
+ ax.set_xticklabels(["0", "4.8 (mm)"])
+ ax.text(
+ 0.0,
+ -1.1,
+ "$\longleftarrow$ Rostral",
+ verticalalignment="top",
+ horizontalalignment="left",
+ size=12,
+ )
+ ax.text(
+ 2,
+ -1.1,
+ "Caudal $\longrightarrow$",
+ verticalalignment="top",
+ horizontalalignment="right",
+ size=12,
+ )
+ else:
+ ax.set_frame_on(False)
+ if title:
+ ax.title(title)
+
+
+# -----------------------------------------------------------------------------
+def polar_imshow(axis, Z, *args, **kwargs):
+ kwargs["interpolation"] = kwargs.get("interpolation", "nearest")
+ kwargs["cmap"] = kwargs.get("cmap", plt.cm.gray_r)
+ # kwargs['vmin'] = kwargs.get('vmin', Z.min())
+ # kwargs['vmax'] = kwargs.get('vmax', Z.max())
+ kwargs["vmin"] = kwargs.get("vmin", 0)
+ kwargs["vmax"] = kwargs.get("vmax", 1)
+ kwargs["origin"] = kwargs.get("origin", "lower")
+ axis.imshow(Z, extent=[0, 1, -1, 1], *args, **kwargs)
+
+
+# -----------------------------------------------------------------------------
+def logpolar_imshow(axis, Z, *args, **kwargs):
+ kwargs["interpolation"] = kwargs.get("interpolation", "nearest")
+ kwargs["cmap"] = kwargs.get("cmap", plt.cm.gray_r)
+ # kwargs['vmin'] = kwargs.get('vmin', Z.min())
+ # kwargs['vmax'] = kwargs.get('vmax', Z.max())
+ kwargs["vmin"] = kwargs.get("vmin", 0)
+ kwargs["vmax"] = kwargs.get("vmax", 1)
+ kwargs["origin"] = kwargs.get("origin", "lower")
+ im = axis.imshow(Z, extent=[0, 2, -1, 1], *args, **kwargs)
+ # axins = inset_axes(axis, width='25%', height='5%', loc=3)
+ # vmin, vmax = Z.min(), Z.max()
+ # plt.colorbar(im, cax=axins, orientation='horizontal', ticks=[vmin,vmax], format = '%.2f')
+ # axins.xaxis.set_ticks_position('bottom')
diff --git a/code/rules/helper.py b/code/rules/helper.py
new file mode 100644
index 0000000..777a294
--- /dev/null
+++ b/code/rules/helper.py
@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import os
+import numpy as np
+
+from parameters import *
+
+
+def disc(shape=(1024, 1024), center=(512, 512), radius=512):
+ """ Generate a numpy array containing a disc. """
+
+ def distance(x, y):
+ return (x - center[0]) ** 2 + (y - center[1]) ** 2
+
+ D = np.fromfunction(distance, shape)
+ return np.where(D < radius * radius, 1.0, 0.0)
+
+
+def gaussian(shape=(25, 25), width=0.5, center=0.0):
+ """ Generate a gaussian of the form g(x) = height*exp(-(x-center)**2/width**2). """
+ if type(shape) in [float, int]:
+ shape = (shape,)
+ if type(width) in [float, int]:
+ width = (width,) * len(shape)
+ if type(center) in [float, int]:
+ center = (center,) * len(shape)
+ grid = []
+ for size in shape:
+ grid.append(slice(0, size))
+ C = np.mgrid[tuple(grid)]
+ R = np.zeros(shape)
+ for i, size in enumerate(shape):
+ if shape[i] > 1:
+ R += (((C[i] / float(size - 1)) * 2 - 1 - center[i]) / width[i]) ** 2
+ return np.exp(-R / 2)
+
+
+def stimulus(position, size, intensity):
+ """
+ Parameters
+ ----------
+
+ position : (rho,theta) (degrees)
+ size : float (degrees)
+ intensity: float
+ """
+
+ x, y = cartesian(position[0] / 90.0, np.pi * position[1] / 180.0)
+ Y, X = np.mgrid[0 : shape[0], 0 : shape[1]]
+ X = X / float(shape[1])
+ Y = 2 * Y / float(shape[0]) - 1
+ R = (X - x) ** 2 + (Y - y) ** 2
+ return np.exp(-0.5 * R / (size / 90.0))
+
+
+def best_fft_shape(shape):
+ """
+ This function returns the best shape for computing a fft
+
+ From fftw.org:
+ FFTW is best at handling sizes of the form 2^a*3^b*5^c*7^d*11^e*13^f,
+ where e+f is either 0 or 1,
+
+ From http://www.netlib.org/fftpack/doc
+ "the method is most efficient when n is a product of small primes."
+ -> What is small ?
+ """
+
+ # fftpack (not sure of the base)
+ base = [13, 11, 7, 5, 3, 2]
+ # fftw
+ # base = [13,11,7,5,3,2]
+
+ def factorize(n):
+ if n == 0:
+ raise (RuntimeError, "Length n must be positive integer")
+ elif n == 1:
+ return [
+ 1,
+ ]
+ factors = []
+ for b in base:
+ while n % b == 0:
+ n /= b
+ factors.append(b)
+ if n == 1:
+ return factors
+ return []
+
+ def is_optimal(n):
+ factors = factorize(n)
+ # fftpack
+ return len(factors) > 0
+ # fftw
+ # return len(factors) > 0 and factors[:2] not in [[13,13],[13,11],[11,11]]
+
+ shape = np.atleast_1d(np.array(shape))
+ for i in range(shape.size):
+ while not is_optimal(shape[i]):
+ shape[i] += 1
+ return shape.astype(int)
diff --git a/code/rules/parameters.py b/code/rules/parameters.py
new file mode 100644
index 0000000..f64346f
--- /dev/null
+++ b/code/rules/parameters.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import numpy as np
+
+second = 1.0
+millisecond = 0.001
+dt = 5 * millisecond
+duration = 10 * second
+noise = 0.01
+
+retina_shape = np.array([4096, 2048]).astype(int)
+projection_shape = np.array([512, 512]).astype(int)
+n = 128
+colliculus_shape = np.array([n, n]).astype(int)
+
+# Default stimulus
+stimulus_size = 1.5 # in degrees
+stimulus_intensity = 1.5
+
+# DNF parameters (linear)
+sigma_e = 0.10
+A_e = 1.30
+sigma_i = 1.00
+A_i = 0.65
+alpha = 12.5
+tau = 10 * millisecond
+scale = 40.0 * 40.0 / (n * n)
diff --git a/code/rules/projections.py b/code/rules/projections.py
new file mode 100644
index 0000000..6ae510e
--- /dev/null
+++ b/code/rules/projections.py
@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import os
+import numpy as np
+from parameters import *
+
+
+def cartesian_to_polar(x, y):
+ """ Cartesian to polar coordinates. """
+
+ rho = np.sqrt(x ** 2 + y ** 2)
+ theta = np.arctan2(y, x)
+ return rho, theta
+
+
+def polar_to_cartesian(rho, theta):
+ """ Polar to cartesian coordinates. """
+
+ x = rho * np.cos(theta)
+ y = rho * np.sin(theta)
+ return x, y
+
+
+def polar_to_logpolar(rho, theta):
+ """ Polar to logpolar coordinates. """
+
+ # Shift in the SC mapping function in deg
+ A = 3.0
+ # Collicular magnification along u axe in mm/rad
+ Bx = 1.4
+ # Collicular magnification along v axe in mm/rad
+ By = 1.8
+ xmin, xmax = 0.0, 4.80743279742
+ ymin, ymax = -2.76745559565, 2.76745559565
+ rho = rho * 90.0
+ x = Bx * np.log(np.sqrt(rho * rho + 2 * A * rho * np.cos(theta) + A * A) / A)
+ y = By * np.arctan(rho * np.sin(theta) / (rho * np.cos(theta) + A))
+ x = (x - xmin) / (xmax - xmin)
+ y = (y - ymin) / (ymax - ymin)
+ return x, y
+
+
+def retina_projection(Rs=retina_shape, Ps=projection_shape):
+ """
+ Compute the projection indices from retina to colliculus
+
+ Parameters
+ ----------
+
+ Rs : (int,int)
+ Half-retina shape
+
+ Ps : (int,int)
+ Retina projection shape (might be different from colliculus)
+ """
+
+ filename = "retina (%d,%d) - colliculus (%d,%d).npy" % (Rs[0], Rs[1], Ps[0], Ps[1])
+ if os.path.exists(filename):
+ return np.load(filename)
+
+ s = 4
+ rho = (np.logspace(start=0, stop=1, num=s * Rs[1], base=10) - 1) / 9.0
+ theta = np.linspace(start=-np.pi / 2, stop=np.pi / 2, num=s * Rs[0])
+
+ rho = rho.reshape((s * Rs[1], 1))
+ rho = np.repeat(rho, s * Rs[0], axis=1)
+
+ theta = theta.reshape((1, s * Rs[0]))
+ theta = np.repeat(theta, s * Rs[1], axis=0)
+
+ y, x = polar_to_cartesian(rho, theta)
+
+ xmin, xmax = x.min(), x.max()
+ x = (x - xmin) / (xmax - xmin)
+
+ ymin, ymax = y.min(), y.max()
+ y = (y - ymin) / (ymax - ymin)
+
+ P = np.zeros((Ps[0], Ps[1], 2), dtype=int)
+ xi = np.rint(x * (Rs[0] - 1)).astype(int)
+ yi = np.rint((0.0 + 1.0 * y) * (Rs[1] - 1)).astype(int)
+
+ yc, xc = polar_to_logpolar(rho, theta)
+ xmin, xmax = xc.min(), xc.max()
+ xc = (xc - xmin) / (xmax - xmin)
+ ymin, ymax = yc.min(), yc.max()
+ yc = (yc - ymin) / (ymax - ymin)
+ xc = np.rint(xc * (Ps[0] - 1)).astype(int)
+ yc = np.rint((0.0 + yc * 1.0) * (Ps[1] - 1)).astype(int)
+
+ P[xc, yc, 0] = xi
+ P[xc, yc, 1] = yi
+ np.save(filename, P)
+ return P
diff --git a/code/rules/rule-1.py b/code/rules/rule-1.py
new file mode 100644
index 0000000..04f2171
--- /dev/null
+++ b/code/rules/rule-1.py
@@ -0,0 +1,300 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright (c) 2014, Matplotlib Development Team. All Rights Reserved.
+# Distributed under the (new) BSD License. See LICENSE.txt for more info.
+#
+# Author: Nicolas P. Rougier
+# Source: New York Times graphics, 2007
+# -> http://www.nytimes.com/imagepages/2007/07/29/health/29cancer.graph.web.html
+# -----------------------------------------------------------------------------
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+# ----------
+# Data to be represented
+diseases = [
+ "Kidney Cancer",
+ "Bladder Cancer",
+ "Esophageal Cancer",
+ "Ovarian Cancer",
+ "Liver Cancer",
+ "Non-Hodgkin's\nlymphoma",
+ "Leukemia",
+ "Prostate Cancer",
+ "Pancreatic Cancer",
+ "Breast Cancer",
+ "Colorectal Cancer",
+ "Lung Cancer",
+]
+men_deaths = [
+ 10000,
+ 12000,
+ 13000,
+ 0,
+ 14000,
+ 12000,
+ 16000,
+ 25000,
+ 20000,
+ 500,
+ 25000,
+ 80000,
+]
+men_cases = [
+ 30000,
+ 50000,
+ 13000,
+ 0,
+ 16000,
+ 30000,
+ 25000,
+ 220000,
+ 22000,
+ 600,
+ 55000,
+ 115000,
+]
+women_deaths = [
+ 6000,
+ 5500,
+ 5000,
+ 20000,
+ 9000,
+ 12000,
+ 13000,
+ 0,
+ 19000,
+ 40000,
+ 30000,
+ 70000,
+]
+women_cases = [
+ 20000,
+ 18000,
+ 5000,
+ 25000,
+ 9000,
+ 29000,
+ 24000,
+ 0,
+ 21000,
+ 160000,
+ 55000,
+ 97000,
+]
+
+# ----------
+# Choose some nice colors
+matplotlib.rc("axes", facecolor="white")
+matplotlib.rc("figure.subplot", wspace=0.65)
+matplotlib.rc("grid", color="white")
+matplotlib.rc("grid", linewidth=1)
+
+# Make figure background the same colors as axes
+fig = plt.figure(figsize=(12, 7), facecolor="white")
+
+
+# ---WOMEN data ---
+axes_left = plt.subplot(121)
+
+# Keep only top and right spines
+axes_left.spines["left"].set_color("none")
+axes_left.spines["right"].set_zorder(10)
+axes_left.spines["bottom"].set_color("none")
+axes_left.xaxis.set_ticks_position("top")
+axes_left.yaxis.set_ticks_position("right")
+axes_left.spines["top"].set_position(("data", len(diseases) + 0.25))
+axes_left.spines["top"].set_color("w")
+
+# Set axes limits
+plt.xlim(200000, 0)
+plt.ylim(0, len(diseases))
+
+# Set ticks labels
+plt.xticks([150000, 100000, 50000, 0], ["150,000", "100,000", "50,000", "WOMEN"])
+axes_left.get_xticklabels()[-1].set_weight("bold")
+axes_left.get_xticklines()[-1].set_markeredgewidth(0)
+for label in axes_left.get_xticklabels():
+ label.set_fontsize(10)
+plt.yticks([])
+
+
+# Plot data
+for i in range(len(women_deaths)):
+ H, h = 0.8, 0.55
+ # Death
+ value = women_cases[i]
+ p = patches.Rectangle(
+ (0, i + (1 - H) / 2.0),
+ value,
+ H,
+ fill=True,
+ transform=axes_left.transData,
+ lw=0,
+ facecolor="red",
+ alpha=0.1,
+ )
+ axes_left.add_patch(p)
+ # New cases
+ value = women_deaths[i]
+ p = patches.Rectangle(
+ (0, i + (1 - h) / 2.0),
+ value,
+ h,
+ fill=True,
+ transform=axes_left.transData,
+ lw=0,
+ facecolor="red",
+ alpha=0.5,
+ )
+ axes_left.add_patch(p)
+
+# Add a grid
+axes_left.grid()
+
+plt.text(165000, 8.2, "Leading Causes\nOf Cancer Deaths", fontsize=18, va="top")
+plt.text(
+ 165000,
+ 7,
+ """In 2007, there were more\n"""
+ """than 1.4 million new cases\n"""
+ """of cancer in the United States.""",
+ va="top",
+ fontsize=10,
+)
+
+# --- MEN data ---
+axes_right = plt.subplot(122, sharey=axes_left)
+
+# Keep only top and left spines
+axes_right.spines["right"].set_color("none")
+axes_right.spines["left"].set_zorder(10)
+axes_right.spines["bottom"].set_color("none")
+axes_right.xaxis.set_ticks_position("top")
+axes_right.yaxis.set_ticks_position("left")
+axes_right.spines["top"].set_position(("data", len(diseases) + 0.25))
+axes_right.spines["top"].set_color("w")
+
+
+# Set axes limits
+plt.xlim(0, 200000)
+plt.ylim(0, len(diseases))
+
+# Set ticks labels
+plt.xticks(
+ [0, 50000, 100000, 150000, 200000],
+ ["MEN", "50,000", "100,000", "150,000", "200,000"],
+)
+axes_right.get_xticklabels()[0].set_weight("bold")
+for label in axes_right.get_xticklabels():
+ label.set_fontsize(10)
+axes_right.get_xticklines()[1].set_markeredgewidth(0)
+plt.yticks([])
+
+# Plot data
+for i in range(len(men_deaths)):
+ H, h = 0.8, 0.55
+ # Death
+ value = men_cases[i]
+ p = patches.Rectangle(
+ (0, i + (1 - H) / 2.0),
+ value,
+ H,
+ fill=True,
+ transform=axes_right.transData,
+ lw=0,
+ facecolor="blue",
+ alpha=0.1,
+ )
+ axes_right.add_patch(p)
+ # New cases
+ value = men_deaths[i]
+ p = patches.Rectangle(
+ (0, i + (1 - h) / 2.0),
+ value,
+ h,
+ fill=True,
+ transform=axes_right.transData,
+ lw=0,
+ facecolor="blue",
+ alpha=0.5,
+ )
+ axes_right.add_patch(p)
+
+# Add a grid
+axes_right.grid()
+
+# Y axis labels
+# We want them to be exactly in the middle of the two y spines
+# and it requires some computations
+for i in range(len(diseases)):
+ x1, y1 = axes_left.transData.transform_point((0, i + 0.5))
+ x2, y2 = axes_right.transData.transform_point((0, i + 0.5))
+ x, y = fig.transFigure.inverted().transform_point(((x1 + x2) / 2, y1))
+ plt.text(
+ x,
+ y,
+ diseases[i],
+ transform=fig.transFigure,
+ fontsize=10,
+ horizontalalignment="center",
+ verticalalignment="center",
+ )
+
+
+# Devil hides in the details...
+arrowprops = dict(arrowstyle="-", connectionstyle="angle,angleA=0,angleB=90,rad=0")
+x = women_cases[-1]
+axes_left.annotate(
+ "NEW CASES",
+ xy=(0.9 * x, 11.5),
+ xycoords="data",
+ horizontalalignment="right",
+ fontsize=10,
+ xytext=(-40, -3),
+ textcoords="offset points",
+ arrowprops=arrowprops,
+)
+
+x = women_deaths[-1]
+axes_left.annotate(
+ "DEATHS",
+ xy=(0.85 * x, 11.5),
+ xycoords="data",
+ horizontalalignment="right",
+ fontsize=10,
+ xytext=(-50, -25),
+ textcoords="offset points",
+ arrowprops=arrowprops,
+)
+
+x = men_cases[-1]
+axes_right.annotate(
+ "NEW CASES",
+ xy=(0.9 * x, 11.5),
+ xycoords="data",
+ horizontalalignment="left",
+ fontsize=10,
+ xytext=(+40, -3),
+ textcoords="offset points",
+ arrowprops=arrowprops,
+)
+
+x = men_deaths[-1]
+axes_right.annotate(
+ "DEATHS",
+ xy=(0.9 * x, 11.5),
+ xycoords="data",
+ horizontalalignment="left",
+ fontsize=10,
+ xytext=(+50, -25),
+ textcoords="offset points",
+ arrowprops=arrowprops,
+)
+
+
+# Done
+plt.savefig("../../figures/rules/rule-1.pdf")
+plt.show()
diff --git a/code/rules/rule-2.py b/code/rules/rule-2.py
new file mode 100644
index 0000000..68c79e6
--- /dev/null
+++ b/code/rules/rule-2.py
@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import os
+import numpy as np
+
+from helper import *
+from graphics import *
+from projections import *
+
+
+if __name__ == "__main__":
+ import matplotlib.pyplot as plt
+ from matplotlib.patches import Polygon
+ from mpl_toolkits.axes_grid1 import ImageGrid
+ from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
+ from mpl_toolkits.axes_grid1.inset_locator import mark_inset
+
+ P = retina_projection()
+
+ # Checkerboard pattern for retina
+ grid = 2 * 32
+ even = int(grid / 2) * [0, 1]
+ odd = int(grid / 2) * [1, 0]
+ R = np.row_stack(int(grid / 2) * (even, odd))
+ R = R.repeat(grid, axis=0).repeat(grid, axis=1)
+
+ # Mask with a disc
+ R = R * disc(
+ (retina_shape[0], retina_shape[0]),
+ (retina_shape[0] // 2, retina_shape[0] // 2),
+ retina_shape[0] // 2,
+ )
+
+ # Take half-retina
+ R = R[:, retina_shape[1] :]
+
+ # Project to colliculus
+ SC = R[P[..., 0], P[..., 1]]
+
+ fig = plt.figure(figsize=(10, 8), facecolor="w")
+ ax1, ax2 = ImageGrid(fig, 111, nrows_ncols=(1, 2), axes_pad=0.5)
+ polar_frame(ax1, legend=True)
+ polar_imshow(ax1, R, vmin=0, vmax=5)
+ logpolar_frame(ax2, legend=True)
+ logpolar_imshow(ax2, SC, vmin=0, vmax=5)
+
+ plt.savefig("../../figures/rules/rule-2.pdf")
+ plt.show()
diff --git a/code/rules/rule-3.py b/code/rules/rule-3.py
new file mode 100644
index 0000000..3b2baad
--- /dev/null
+++ b/code/rules/rule-3.py
@@ -0,0 +1,111 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
+from mpl_toolkits.axes_grid1.inset_locator import mark_inset
+
+
+def simulate():
+ d = 0.005
+ x = np.random.uniform(0, d)
+ y = d - x
+ x, y = np.random.uniform(0, d, 2)
+
+ dt = 0.05
+ t = 35.0
+ alpha = 0.25
+ n = int(t / dt)
+ X = np.zeros(n)
+ Y = np.zeros(n)
+ C = np.random.randint(0, 2, n)
+
+ for i in range(n):
+ # Asynchronous
+ if 0:
+ if C[i]:
+ x += (alpha + (x - y)) * (1 - x) * dt
+ x = max(x, 0.0)
+ y += (alpha + (y - x)) * (1 - y) * dt
+ y = max(y, 0.0)
+ else:
+ y += (alpha + (y - x)) * (1 - y) * dt
+ y = max(y, 0.0)
+ x += (alpha + (x - y)) * (1 - x) * dt
+ x = max(x, 0.0)
+ # Synchronous
+ else:
+ dx = (alpha + (x - y)) * (1 - x) * dt
+ dy = (alpha + (y - x)) * (1 - y) * dt
+ x = max(x + dx, 0.0)
+ y = max(y + dy, 0.0)
+ X[i] = x
+ Y[i] = y
+ return X, Y
+
+
+np.random.seed(11)
+S = []
+n = 250
+for i in range(n):
+ S.append(simulate())
+
+
+plt.figure(figsize=(20, 10))
+ax = plt.subplot(121, aspect=1)
+axins = zoomed_inset_axes(ax, 25, loc=3)
+for i in range(n):
+ X, Y = S[i]
+ if X[-1] > 0.9 and Y[-1] > 0.9:
+ c = "r"
+ lw = 1.0
+ axins.scatter(X[0], Y[0], c="r", edgecolor="w", zorder=10)
+ else:
+ c = "b"
+ lw = 1.0
+ ax.plot(X, Y, c=c, alpha=0.25, lw=lw)
+ axins.plot(X, Y, c=c, alpha=0.25, lw=lw)
+
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_xlabel("x position")
+ax.set_ylabel("y position")
+ax.set_title("%d trajectories of a dual particle system (x,y)" % n)
+axins.set_xlim(0.01, 0.02)
+axins.set_xticks([])
+axins.set_ylim(0.01, 0.02)
+axins.set_yticks([])
+
+ax = plt.subplot(122, aspect=1)
+axins = zoomed_inset_axes(ax, 50, loc=3)
+axins.set_facecolor((1, 1, 0.9))
+n = 9
+for i in range(n):
+ X, Y = S[i]
+ ls = "-"
+ if i == 2:
+ ls = "--"
+ if X[-1] > 0.9 and Y[-1] > 0.9:
+ c = "r"
+ lw = 2.0
+ axins.scatter(X[0], Y[0], s=150, c="r", edgecolor="w", zorder=10, lw=2)
+ else:
+ c = "b"
+ lw = 2.0
+ ax.plot(X, Y, c=c, alpha=0.75, lw=lw, ls=ls)
+ axins.plot(X, Y, c=c, alpha=0.75, lw=lw, ls=ls)
+
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_xticks([0, 1])
+ax.set_yticks([0, 1])
+ax.set_xticklabels(["0", "1"], fontsize=16)
+ax.set_yticklabels(["0", "1"], fontsize=16)
+ax.set_xlabel("x position", fontsize=20)
+ax.set_ylabel("y position", fontsize=20)
+# ax.set_title('%d trajectories of a dual particle system (x,y)' % n)
+axins.set_xlim(0.01, 0.02)
+axins.set_xticks([])
+axins.set_ylim(0.01, 0.02)
+axins.set_yticks([])
+
+plt.savefig("../../figures/rules/rule-3.pdf")
+plt.show()
diff --git a/code/rules/rule-5-left.py b/code/rules/rule-5-left.py
new file mode 100644
index 0000000..63e1624
--- /dev/null
+++ b/code/rules/rule-5-left.py
@@ -0,0 +1,11 @@
+from pylab import *
+
+fig = plt.figure(figsize=(8, 4))
+
+n = 256
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+plot(X, C), plot(X, S)
+
+savefig("../../figures/rules/rule-5-left.pdf")
+show()
diff --git a/code/rules/rule-5-right.py b/code/rules/rule-5-right.py
new file mode 100644
index 0000000..12299e3
--- /dev/null
+++ b/code/rules/rule-5-right.py
@@ -0,0 +1,63 @@
+from pylab import *
+
+figure(figsize=(8, 5), dpi=80)
+subplot(111)
+
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
+plot(X, S, color="red", linewidth=2.5, linestyle="-", label="sine")
+
+ax = gca()
+ax.spines["right"].set_color("none")
+ax.spines["top"].set_color("none")
+ax.xaxis.set_ticks_position("bottom")
+ax.spines["bottom"].set_position(("data", 0))
+ax.yaxis.set_ticks_position("left")
+ax.spines["left"].set_position(("data", 0))
+
+xlim(X.min() * 1.1, X.max() * 1.1)
+xticks(
+ [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],
+ [r"$-\pi$", r"$-\pi/2$", r"$0$", r"$+\pi/2$", r"$+\pi$"],
+)
+
+ylim(C.min() * 1.1, C.max() * 1.1)
+yticks([-1, +1], [r"$-1$", r"$+1$"])
+
+
+legend(loc="upper left")
+
+t = 2 * np.pi / 3
+plot([t, t], [0, np.cos(t)], color="blue", linewidth=1.5, linestyle="--")
+scatter([t,], [np.cos(t),], 50, color="blue")
+annotate(
+ r"$sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$",
+ xy=(t, np.sin(t)),
+ xycoords="data",
+ xytext=(+10, +30),
+ textcoords="offset points",
+ fontsize=16,
+ arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"),
+)
+
+plot([t, t], [0, np.sin(t)], color="red", linewidth=1.5, linestyle="--")
+scatter([t,], [np.sin(t),], 50, color="red")
+annotate(
+ r"$cos(\frac{2\pi}{3})=-\frac{1}{2}$",
+ xy=(t, np.cos(t)),
+ xycoords="data",
+ xytext=(-90, -50),
+ textcoords="offset points",
+ fontsize=16,
+ arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"),
+)
+
+for label in ax.get_xticklabels() + ax.get_yticklabels():
+ label.set_fontsize(16)
+ label.set_bbox(dict(facecolor="white", edgecolor="None", alpha=0.65))
+
+
+savefig("../../figures/rules/rule-5-right.pdf")
+show()
diff --git a/code/rules/rule-5.tex b/code/rules/rule-5.tex
new file mode 100644
index 0000000..8a73908
--- /dev/null
+++ b/code/rules/rule-5.tex
@@ -0,0 +1,6 @@
+\documentclass[varwidth]{standalone}
+\usepackage{graphicx}
+\begin{document}
+\includegraphics[height=3cm]{../../figures/rules/rule-5-left.pdf}
+\includegraphics[height=3cm]{../../figures/rules/rule-5-right.pdf}
+\end{document}
diff --git a/code/rules/rule-6.py b/code/rules/rule-6.py
new file mode 100644
index 0000000..5e0786b
--- /dev/null
+++ b/code/rules/rule-6.py
@@ -0,0 +1,71 @@
+import numpy as np
+import matplotlib
+
+import matplotlib.pylab as plt
+import matplotlib.patheffects as PathEffects
+from matplotlib.ticker import MultipleLocator
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+import matplotlib.gridspec as gridspec
+
+
+def make(ax1, ax2, cmap, title, y, color="k"):
+ # -----------------
+ ax1.set_xlim(0, 1)
+ ax1.set_ylim(0, 1)
+ ax1.set_xticks([])
+ ax1.set_yticks([0, 0.5, 1])
+ ax1.get_yaxis().tick_left()
+
+ ax1.axhline(y, lw=1, c=color, xmin=0, xmax=1)
+ ax1.text(0.025, y + 0.015, "Slice y=%.2f" % y, fontsize=10, color=color)
+ ax1.imshow(Z, cmap=cmap, origin="upper", extent=[0, 1, 0, 1])
+ ax1.set_xticks([]), ax1.set_yticks([])
+ ax1.set_title(title)
+
+ ax2.set_xlim(0, 1)
+ ax2.set_ylim(-0.1, +1.1)
+ ax2.set_xticks([0, 0.5, 1])
+ ax2.get_xaxis().tick_bottom()
+ ax2.set_yticks([0, 1])
+ ax2.get_yaxis().tick_left()
+ ax2.plot(T / np.pi, Z[int(1024 * (1 - y))], c="k", lw=0.5)
+ ax2.axis("off")
+ ax2.text(0.025, 1.25, "Slice detail")
+
+
+if __name__ == "__main__":
+ fg = 0.0, 0.0, 0.0
+ bg = 1.0, 1.0, 1.0
+ matplotlib.rcParams["xtick.direction"] = "out"
+ matplotlib.rcParams["ytick.direction"] = "out"
+ matplotlib.rcParams["font.size"] = 12.0
+ matplotlib.rc("axes", facecolor=bg)
+ matplotlib.rc("axes", edgecolor=fg)
+ matplotlib.rc("xtick", color=fg)
+ matplotlib.rc("ytick", color=fg)
+ matplotlib.rc("figure", facecolor=bg)
+ matplotlib.rc("savefig", facecolor=bg)
+
+ plt.figure(figsize=(18, 6))
+
+ G = gridspec.GridSpec(2, 3, width_ratios=[1, 1, 1], height_ratios=[15, 1])
+
+ T = np.linspace(0, np.pi, 2 * 512)
+ X, Y = np.meshgrid(T, T)
+ Z = np.power(Y / 2, 5) * np.sin(np.exp(np.pi / 2 * X))
+ Z = (Z - Z.min()) / (Z.max() - Z.min())
+
+ ax1 = plt.subplot(G[0, 0], aspect=1)
+ ax2 = plt.subplot(G[1, 0])
+ make(ax1, ax2, plt.cm.rainbow, "Rainbow colormap (qualitative)", y=0.3)
+
+ ax1 = plt.subplot(G[0, 1], aspect=1)
+ ax2 = plt.subplot(G[1, 1])
+ make(ax1, ax2, plt.cm.seismic, "Seismic colormap (diverging)", y=0.2)
+
+ ax1 = plt.subplot(G[0, 2], aspect=1)
+ ax2 = plt.subplot(G[1, 2])
+ make(ax1, ax2, plt.cm.Purples, "Purples colormap (sequential)", 0.1, "white")
+
+ plt.savefig("../../figures/rules/rule-6.pdf")
+ plt.show()
diff --git a/code/rules/rule-7.py b/code/rules/rule-7.py
new file mode 100644
index 0000000..d6a4cce
--- /dev/null
+++ b/code/rules/rule-7.py
@@ -0,0 +1,71 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+n = 20
+Z = np.linspace(0, 1, n * n).reshape(n, n)
+Z = (Z - Z.min()) / (Z.max() - Z.min())
+Z += np.random.uniform(0, 1, (n, n))
+
+plt.figure(figsize=(20, 8))
+
+ax = plt.subplot(1, 2, 1, aspect=1)
+values = [30.0, 20.0, 15.0, 10.0]
+x, y = 0.0, 0.5
+for value in values:
+ r1 = 0.5 * (value / values[0])
+ r2 = 0.5 * ((np.sqrt(value / np.pi)) / (np.sqrt(values[0] / np.pi)))
+ ax.add_artist(plt.Circle((x + r2, y), r1, color="r"))
+ ax.add_artist(plt.Circle((x + r2, 1.5 + y), r2, color="k"))
+ fontsize = 2 * value
+ # plt.text(x+r2, y, "%d" % value, color="white", family = "Times New Roman",
+ # fontsize=fontsize, ha="center", va="center")
+ # plt.text(x+r2, 1.5+y, "%d" % value, color="white", family = "Times New Roman",
+ # fontsize=fontsize, ha="center", va="center")
+ x += 2 * r2 + 0.05
+plt.axhline(1.25, c="k")
+plt.text(
+ 0.0,
+ 1.25 + 0.05,
+ "Relative size using disc area",
+ ha="left",
+ va="bottom",
+ color=".25",
+)
+plt.text(
+ 0.0,
+ 1.25 - 0.05,
+ "Relative size using disc radius",
+ ha="left",
+ va="top",
+ color=".25",
+)
+plt.xlim(-0.05, 3.5)
+plt.ylim(-0.05, 2.6)
+plt.axis("off")
+
+
+ax = plt.subplot(1, 2, 2, aspect=1)
+
+plt.axhline(5, c="k")
+plt.text(
+ 0.0, 5 + 0.15, "Relative size using full range", ha="left", va="bottom", color=".25"
+)
+plt.text(
+ 0.0, 5 - 0.15, "Relative size using partial range", ha="left", va="top", color=".25"
+)
+
+
+n = 10
+np.random.seed(123)
+X = 0.25 + np.arange(n)
+Y = 2 + np.random.uniform(0.75, 0.85, n)
+
+plt.bar(X, 5 * (Y - 2.2), 0.9, color="k", ec="None", bottom=6)
+plt.bar(X, 20 * (Y - 2.75), 0.9, color="r", ec="None", bottom=1)
+
+plt.xlim(-0.05, 10.5)
+plt.ylim(-0.05, 10.5)
+plt.axis("off")
+
+plt.savefig("../../figures/rules/rule-7.pdf")
+plt.show()
diff --git a/code/rules/rule-8.py b/code/rules/rule-8.py
new file mode 100644
index 0000000..b4d8720
--- /dev/null
+++ b/code/rules/rule-8.py
@@ -0,0 +1,55 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Data
+# -----------------------------------------------------------------------------
+p, n = 7, 32
+X = np.linspace(0, 2, n)
+Y = np.random.uniform(-0.75, 0.5, (p, n))
+
+# -----------------------------------------------------------------------------
+fig = plt.figure(figsize=(20, 8))
+ax = plt.subplot(1, 2, 1, aspect=1)
+ax.patch.set_facecolor((1, 1, 0.75))
+for i in range(p):
+ plt.plot(X, Y[i], label="Series %d " % (1 + i), lw=2)
+plt.xlim(0, 2)
+plt.ylim(-1, 1)
+plt.yticks(np.linspace(-1, 1, 18))
+plt.xticks(np.linspace(0, 2, 18))
+plt.legend()
+plt.grid()
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(1, 2, 2, aspect=1)
+Yy = p - (np.arange(p) + 0.5)
+Xx = [p,] * p
+rects = plt.barh(
+ Yy, Xx, align="center", height=0.75, color=".95", ec="None", zorder=-20
+)
+plt.xlim(0, p), plt.ylim(0, p)
+
+for i in range(p):
+ label = "Series %d" % (1 + i)
+ plt.text(-0.1, Yy[i], label, ha="right", fontsize=16)
+ plt.axvline(0, (Yy[i] - 0.4) / p, (Yy[i] + 0.4) / p, c="k", lw=3)
+ plt.axvline(
+ 0.25 * p, (Yy[i] - 0.375) / p, (Yy[i] + 0.375) / p, c=".5", lw=0.5, zorder=-15
+ )
+ plt.axvline(
+ 0.50 * p, (Yy[i] - 0.375) / p, (Yy[i] + 0.375) / p, c=".5", lw=0.5, zorder=-15
+ )
+ plt.axvline(
+ 0.75 * p, (Yy[i] - 0.375) / p, (Yy[i] + 0.375) / p, c=".5", lw=0.5, zorder=-15
+ )
+ plt.plot(X * p / 2, i + 0.5 + 2 * Y[i] / p, c="k", lw=2)
+ for j in range(p):
+ if i != j:
+ plt.plot(X * p / 2, i + 0.5 + 2 * Y[j] / p, c=".5", lw=0.5, zorder=-10)
+plt.text(0.25 * p, 0, "0.5", va="top", ha="center", fontsize=10)
+plt.text(0.50 * p, 0, "1.0", va="top", ha="center", fontsize=10)
+plt.text(0.75 * p, 0, "1.5", va="top", ha="center", fontsize=10)
+plt.axis("off")
+
+plt.savefig("../../figures/rules/rule-8.pdf")
+plt.show()
diff --git a/code/rules/rule-9.py b/code/rules/rule-9.py
new file mode 100644
index 0000000..0bbfc62
--- /dev/null
+++ b/code/rules/rule-9.py
@@ -0,0 +1,60 @@
+import numpy as np
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+plt.xkcd()
+
+X = np.linspace(0, 2.32, 100)
+Y = X * X - 5 * np.exp(-5 * (X - 2) * (X - 2))
+
+fig = plt.figure(figsize=(12, 5), dpi=72, facecolor="white")
+axes = plt.subplot(111)
+
+plt.plot(X, Y, color="k", linewidth=2, linestyle="-", zorder=+10)
+
+axes.set_xlim(X.min(), X.max())
+axes.set_ylim(1.01 * Y.min(), 1.01 * Y.max())
+
+axes.spines["right"].set_color("none")
+axes.spines["top"].set_color("none")
+axes.xaxis.set_ticks_position("bottom")
+axes.spines["bottom"].set_position(("data", 0))
+axes.yaxis.set_ticks_position("left")
+axes.spines["left"].set_position(("data", X.min()))
+
+axes.set_xticks([])
+axes.set_yticks([])
+axes.set_xlim(1.05 * X.min(), 1.10 * X.max())
+axes.set_ylim(1.15 * Y.min(), 1.05 * Y.max())
+
+t = [10, 40, 82, 88, 93, 99]
+plt.scatter(X[t], Y[t], s=50, zorder=+12, c="k")
+
+plt.text(X[t[0]] - 0.1, Y[t[0]] + 0.1, "Industrial\nRobot", ha="left", va="bottom")
+plt.text(X[t[1]] - 0.15, Y[t[1]] + 0.1, "Humanoid\nRobot", ha="left", va="bottom")
+plt.text(X[t[2]] - 0.25, Y[t[2]], "Zombie", ha="left", va="center")
+plt.text(X[t[3]] + 0.05, Y[t[3]], "Prosthetic\nHand", ha="left", va="center")
+plt.text(X[t[4]] + 0.05, Y[t[4]], "Bunraku\nPuppet", ha="left", va="center")
+plt.text(X[t[5]] + 0.05, Y[t[5]], "Human", ha="left", va="center")
+plt.text(X[t[2]] - 0.05, 1.5, "Uncanny\nValley", ha="center", va="center", fontsize=24)
+
+plt.ylabel("- Comfort Level +", y=0.5, fontsize=20)
+plt.text(0.05, -0.1, "Human Likeness ->", ha="left", va="top", color="r", fontsize=20)
+
+X = np.linspace(0, 1.1 * 2.32, 100)
+axes.fill_between(X, 0, -10, color="0.85", zorder=-1)
+axes.fill_between(X, 0, +10, color=(1.0, 1.0, 0.9), zorder=-1)
+
+# X = np.linspace(1.652,2.135,100)
+X = np.linspace(1.5, 2.25, 100)
+Y = X * X - 5 * np.exp(-5 * (X - 2) * (X - 2))
+axes.fill_between(X, Y, +10, color=(1, 1, 1), zorder=-1)
+
+axes.axvline(x=1.5, ymin=0, ymax=1, color=".5", ls="--")
+axes.axvline(x=2.25, ymin=0, ymax=1, color=".5", ls="--")
+
+plt.savefig("../../figures/rules/rule-9.pdf")
+plt.show()
diff --git a/code/scales-projections/geo-projections.py b/code/scales-projections/geo-projections.py
new file mode 100644
index 0000000..50a88ef
--- /dev/null
+++ b/code/scales-projections/geo-projections.py
@@ -0,0 +1,51 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import cartopy.crs
+
+mpl.rcParams["font.serif"] = "Roboto Slab"
+mpl.rcParams["font.size"] = 10
+mpl.rcParams["font.weight"] = 400
+mpl.rcParams["font.family"] = "serif"
+
+dpi = 100
+inch = 2.54
+fig_width = 2 * 10.85 / inch
+fig_height = fig_width * 1.25
+
+fig = plt.figure(figsize=(fig_width, fig_height), dpi=dpi)
+
+projections = [
+ (cartopy.crs.Mercator(), "Mercator",),
+ (cartopy.crs.PlateCarree(), "Plate Carree"),
+ (cartopy.crs.LambertCylindrical(), "Lambert Cylindrical"),
+ (cartopy.crs.Orthographic(), "Orthographic"),
+ (cartopy.crs.Mollweide(), "Mollweide"),
+ (cartopy.crs.EqualEarth(), "Equal Earth"),
+ (cartopy.crs.LambertConformal(), "Lambert Conformal"),
+ (cartopy.crs.EquidistantConic(), "Equidistant Conic"),
+ (cartopy.crs.AlbersEqualArea(), "Albers Equal Area"),
+ (
+ cartopy.crs.NearsidePerspective(
+ central_latitude=50.72, central_longitude=-3.53, satellite_height=10000000.0
+ ),
+ "Nearside Perspective",
+ ),
+ (cartopy.crs.NorthPolarStereo(), "North Polar Stereo"),
+ (cartopy.crs.SouthPolarStereo(), "South Polar Stereo"),
+]
+
+
+for i, (projection, name) in enumerate(projections):
+ ax = plt.subplot(4, 3, i + 1, projection=projection, frameon="False")
+ ax.coastlines(resolution="110m", linewidth=0.5)
+ ax.stock_img()
+ ax.set_title(name, weight=400)
+
+plt.savefig("../../figures/scales-projections/geo-projections.png", dpi=300)
+plt.show()
diff --git a/code/scales-projections/polar-patterns.py b/code/scales-projections/polar-patterns.py
new file mode 100644
index 0000000..5741d80
--- /dev/null
+++ b/code/scales-projections/polar-patterns.py
@@ -0,0 +1,131 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def shotgun_pattern(T, a, b, c=1, d=1, e=10000):
+ """
+ Equations taken from [1] or [2] + some modifications by Matthieu LEROY
+ in order to squish the pattern and make a log-based pattern.
+
+ [1] https://math.stackexchange.com/questions/1808380/non-symmetrical-lemniscate-curve-parameterization
+ [2] https://www.jneurosci.org/content/22/18/8201
+
+ Parameters
+ ----------
+ T: array-like of floats
+ Angle between -pi and pi.
+ a: float
+ Amplitude.
+ b: float
+ Asymetric parameter.
+ c: float
+ Squish parameter??
+ d: float
+ Not sure what it does.
+ e: float
+ Not sure what it does.
+
+ Returns
+ -------
+ Shotgun pattern.
+ """
+ x = a * (np.cos(T) + b) * np.cos(T) / (c + np.sin(T) ** 2)
+ y = d * x * np.sin(T)
+ res = np.sqrt(x ** 2 + y ** 2) # to polar coordinates
+ return np.exp(res) / e # to get log-based plots after
+
+
+def plot(ax, title, alpha=1, shotgun=False):
+ T = np.linspace(-np.pi, np.pi, 5000)
+ if shotgun:
+ R = shotgun_pattern(T, 4, 0.2, 0.1, 4) + shotgun_pattern(
+ T - np.pi / 2, 3.5, 0, c=0.15, d=1
+ )
+ else:
+ R = alpha + (1 - alpha) * np.cos(T)
+ R = np.log(1 + np.abs(50 * R)) / np.log(10)
+ R = 1000 * (R / R.max())
+
+ ax.set_theta_offset(np.pi / 2)
+ ax.set_thetalim(0, 2 * np.pi)
+ ax.set_rorigin(0)
+ ax.set_rlabel_position(np.pi / 2)
+
+ ax.fill(T, R, zorder=20, color="C1", clip_on=True, alpha=0.25)
+ ax.plot(
+ T,
+ R,
+ zorder=30,
+ alpha=0.75,
+ color="C1",
+ linewidth=1.0,
+ linestyle=":",
+ clip_on=False,
+ )
+ ax.plot(T, R, zorder=40, color="C1", linewidth=1.5, clip_on=True)
+ ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2])
+ ax.xaxis.set_tick_params("major", pad=-2.5)
+ ax.set_xticklabels(
+ ["0°", "", "180°", ""],
+ family="Roboto",
+ size="small",
+ horizontalalignment="center",
+ verticalalignment="center",
+ )
+ ax.set_yticks([200, 400, 600, 800, 1010])
+
+ for y, label in zip([390, 590, 790], ["-20 dB", "-15 dB", "-10 dB"]):
+ ax.text(
+ 0,
+ y,
+ label,
+ zorder=10,
+ family="Roboto Condensed",
+ size="small",
+ horizontalalignment="center",
+ verticalalignment="center",
+ bbox=dict(facecolor="white", edgecolor="None", pad=1.0),
+ )
+ ax.set_yticklabels([])
+
+ ax.set_ylim(200, 1010)
+ ax.set_title(title, family="Roboto", weight="bold", size="large", y=-0.2)
+
+
+fig = plt.figure(figsize=(8, 7))
+fig.suptitle(
+ "Microphone polar patterns", family="Roboto", weight="bold", size="xx-large"
+)
+
+# Omnidirectional
+ax = plt.subplot(2, 3, 1, projection="polar")
+plot(ax, "Omnidirectional", 1.00)
+
+# Subcardioid
+ax = plt.subplot(2, 3, 2, projection="polar")
+plot(ax, "Subcardioid", 0.75)
+
+# Cardioid
+ax = plt.subplot(2, 3, 3, projection="polar")
+plot(ax, "Cardioid", 0.50)
+
+# Supercardioid
+ax = plt.subplot(2, 3, 4, projection="polar")
+plot(ax, "Supercardioid", 0.25)
+
+# Bidirectional
+ax = plt.subplot(2, 3, 5, projection="polar")
+plot(ax, "Bidirectional", 0.00)
+
+# Shotgun (not quite right yet)
+ax = plt.subplot(2, 3, 6, projection="polar")
+plot(ax, "Shotgun", shotgun=True)
+
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/polar-patterns.pdf")
+plt.show()
diff --git a/code/scales-projections/projection-3d-frame.py b/code/scales-projections/projection-3d-frame.py
new file mode 100644
index 0000000..b8e6770
--- /dev/null
+++ b/code/scales-projections/projection-3d-frame.py
@@ -0,0 +1,145 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from mpl_toolkits.mplot3d import Axes3D, proj3d, art3d
+import matplotlib.patheffects as path_effects
+
+
+# Style
+# -----------------------------------------------------------------------------
+# plt.rc('font', family="Roboto Condensed")
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, projection="3d")
+ax.set_axis_off()
+ax.set_xlim(0, 10)
+ax.set_ylim(10, 0)
+ax.set_zlim(0, 10)
+
+for d in np.arange(0, 11):
+ color, linewidth, zorder = "0.75", 0.5, -100
+ if d in [0, 5, 10]:
+ color, linewidth, zorder = "0.5", 0.75, -50
+ ax.plot([0, 0], [d, d], [0, 10], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([0, 0], [0, 10], [d, d], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([0, 10], [0, 0], [d, d], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([d, d], [0, 10], [0, 0], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([0, 10], [d, d], [0, 0], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([d, d], [0, 0], [0, 10], linewidth=linewidth, color=color, zorder=zorder)
+
+size, color = "small", "0.75"
+for d in np.arange(1, 11):
+ ax.text(d, 10.75, 0, "%d" % d, color=color, size=size, ha="center", va="center")
+ ax.text(10.5, d, 0, "%d" % d, color=color, size=size, ha="center", va="center")
+ ax.text(0, 10.75, d, "%d" % d, color=color, size=size, ha="center", va="center")
+ ax.text(10.5, 0, d, "%d" % d, color=color, size=size, ha="center", va="center")
+ ax.text(d, 0, 10.5, "%d" % d, color=color, size=size, ha="center", va="center")
+
+ ax.text(0, d, 10.5, "%d" % d, color=color, size=size, ha="center", va="center")
+
+
+plt.plot([0, 10], [0, 0], [0, 0], linewidth=1.5, color="red")
+ax.text(10.5, 0, 0, "X", color="black", ha="center", va="center")
+
+plt.plot([0, 0], [0, 10], [0, 0], linewidth=1.5, color="green")
+ax.text(0, 10.5, 0, "Y", color="black", ha="center", va="center")
+
+plt.plot([0, 0], [0, 0], [0, 10], linewidth=1.5, color="blue")
+ax.text(0, 0, 10.5, "Z", color="black", ha="center", va="center")
+
+
+text = ax.text(0, 0, 0, "O", color="black", size="large", ha="center", va="center")
+text.set_path_effects(
+ [path_effects.Stroke(linewidth=3, foreground="white"), path_effects.Normal()]
+)
+
+
+rect = mpatches.Rectangle((0, 0), 7, 5, facecolor="k", alpha=0.03, zorder=50)
+ax.add_patch(rect)
+art3d.pathpatch_2d_to_3d(rect, z=6, zdir="z")
+
+rect = mpatches.Rectangle((0, 0), 7, 6, facecolor="k", alpha=0.06, zorder=50)
+ax.add_patch(rect)
+art3d.pathpatch_2d_to_3d(rect, z=5, zdir="y")
+
+rect = mpatches.Rectangle((0, 0), 5, 6, facecolor="k", alpha=0.09, zorder=50)
+ax.add_patch(rect)
+art3d.pathpatch_2d_to_3d(rect, z=7, zdir="x")
+
+
+class Arrow3D(mpatches.FancyArrowPatch):
+ def __init__(self, xs, ys, zs, *args, **kwargs):
+ mpatches.FancyArrowPatch.__init__(self, (0, 0), (0, 0), *args, **kwargs)
+ self._verts3d = xs, ys, zs
+
+ def draw(self, renderer):
+ xs3d, ys3d, zs3d = self._verts3d
+ xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)
+ self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))
+ mpatches.FancyArrowPatch.draw(self, renderer)
+
+
+ax.add_artist(
+ Arrow3D(
+ [7, 0],
+ [5, 5],
+ [6, 6],
+ mutation_scale=10,
+ lw=1.5,
+ arrowstyle="-|>",
+ color="black",
+ )
+)
+ax.add_artist(
+ Arrow3D(
+ [7, 7],
+ [5, 0],
+ [6, 6],
+ mutation_scale=10,
+ lw=1.5,
+ arrowstyle="-|>",
+ color="black",
+ )
+)
+ax.add_artist(
+ Arrow3D(
+ [7, 7],
+ [5, 5],
+ [6, 0],
+ mutation_scale=10,
+ lw=1.5,
+ arrowstyle="-|>",
+ color="black",
+ )
+)
+
+ax.scatter([7], [5], [6], s=50, facecolor="black", edgecolor="black", linewidth=1.5)
+
+ax.text(
+ 7.5, 5, 6, "(7.0, 5.0, 6.0)", color="black", size="small", ha="left", va="center"
+)
+
+ax.plot([7, 0], [5, 0], [6, 6], color="blue", linewidth=1.5, linestyle="--", marker="o")
+ax.text(0.5, 0.5, 6.5, "6", color="black", size="small", ha="left", va="bottom")
+
+ax.plot([7, 7], [5, 0], [6, 0], color="red", linewidth=1.5, linestyle="--", marker="o")
+ax.text(0, 5.5, 0, "7", color="black", size="small", ha="right", va="bottom")
+
+ax.plot(
+ [7, 0], [5, 5], [6, 0], color="green", linewidth=1.5, linestyle="--", marker="o"
+)
+ax.text(7.5, 0, 0, "5", color="black", size="small", ha="left", va="bottom")
+
+
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/projection-3d-frame.pdf")
+plt.show()
diff --git a/code/scales-projections/projection-polar-config.py b/code/scales-projections/projection-polar-config.py
new file mode 100644
index 0000000..46853fc
--- /dev/null
+++ b/code/scales-projections/projection-polar-config.py
@@ -0,0 +1,44 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def polar(ax, r0, rmin, rmax, rticks, tmin, tmax, tticks):
+ ax.set_yticks(np.linspace(rmin, rmax, rticks))
+ ax.set_yticklabels([])
+ ax.set_rorigin(r0)
+ ax.set_rmin(rmin)
+ ax.set_rmax(rmax)
+
+ ax.set_xticks(np.linspace(np.pi * tmin / 180, np.pi * tmax / 180, tticks))
+ ax.set_xticklabels([])
+ ax.set_thetamin(tmin)
+ ax.set_thetamax(tmax)
+
+ text = r"""$r_{0}=%.2f,r_{min}=%.2f,r_{max}=%.2f$""" % (r0, rmin, rmax)
+ text += "\n"
+ text += r"""$t_{min}=%.2f,t_{max}=%.2f$""" % (tmin, tmax)
+
+ plt.text(
+ 0.5, -0.15, text, size="small", ha="center", va="bottom", transform=ax.transAxes
+ )
+
+
+fig = plt.figure(figsize=(8, 4))
+
+ax = fig.add_subplot(1, 3, 1, aspect=1, projection="polar")
+polar(ax, 0.00, 0.00, 1.00, 4, 0, 360, 16)
+
+ax = fig.add_subplot(1, 3, 2, aspect=1, projection="polar")
+polar(ax, 0.00, 0.25, 1.00, 8, 0, 360, 32)
+
+ax = fig.add_subplot(1, 3, 3, aspect=1, projection="polar")
+polar(ax, 0.00, 0.50, 1.00, 10, 0, 90, 20)
+
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/projection-polar-config.pdf", dpi=600)
+plt.show()
diff --git a/code/scales-projections/projection-polar-histogram.py b/code/scales-projections/projection-polar-histogram.py
new file mode 100644
index 0000000..baa395d
--- /dev/null
+++ b/code/scales-projections/projection-polar-histogram.py
@@ -0,0 +1,137 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as path_effects
+
+# Setup
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, projection="polar", frameon=True)
+ax.set_thetalim(0, 2 * np.pi)
+ax.set_rlim(0, 1000)
+ax.set_xticks([])
+ax.set_xticklabels([])
+ax.set_yticks(np.linspace(100, 1000, 10))
+ax.set_yticklabels([])
+ax.tick_params("both", grid_alpha=0.50, grid_zorder=-10, grid_linewidth=0.5)
+
+
+# Theta ticks
+radius = ax.get_rmax()
+length = 0.025 * radius
+for i in range(360):
+ angle = np.pi * i / 180
+ plt.plot(
+ [angle, angle],
+ [radius, radius - length],
+ linewidth=0.50,
+ color="0.75",
+ clip_on=False,
+ )
+for i in range(0, 360, 5):
+ angle = np.pi * i / 180
+ plt.plot(
+ [angle, angle],
+ [radius, radius - 2 * length],
+ linewidth=0.75,
+ color="0.75",
+ clip_on=False,
+ )
+for i in range(0, 360, 15):
+ angle = np.pi * i / 180
+ plt.plot([angle, angle], [radius, 100], linewidth=0.5, color="0.75")
+ plt.plot(
+ [angle, angle],
+ [radius + length, radius],
+ zorder=500,
+ linewidth=1.0,
+ color="0.00",
+ clip_on=False,
+ )
+ plt.text(
+ angle,
+ radius + 4 * length,
+ "%d°" % i,
+ zorder=500,
+ rotation=i - 90,
+ rotation_mode="anchor",
+ va="top",
+ ha="center",
+ size="small",
+ family="Roboto",
+ color="black",
+ )
+for i in range(0, 360, 90):
+ angle = np.pi * i / 180
+ plt.plot([angle, angle], [radius, 0], zorder=500, linewidth=1.00, color="0.0")
+
+
+# Radius ticks
+def polar_to_cartesian(theta, radius):
+ x = radius * np.cos(theta)
+ y = radius * np.sin(theta)
+ return np.array([x, y])
+
+
+def cartesian_to_polar(x, y):
+ radius = np.sqrt(x ** 2 + y ** 2)
+ theta = np.arctan2(y, x)
+ return np.array([theta, radius])
+
+
+for i in range(0, 1000, 10):
+ P0 = 0, i
+ P1 = cartesian_to_polar(*(polar_to_cartesian(*P0) + [0, 0.75 * length]))
+ plt.plot([P0[0], P1[0]], [P0[1], P1[1]], linewidth=0.50, color="0.75")
+
+for i in range(100, 1000, 100):
+ P0 = 0, i
+ P1 = cartesian_to_polar(*(polar_to_cartesian(*P0) + [0, +1.0 * length]))
+ plt.plot([P0[0], P1[0]], [P0[1], P1[1]], zorder=500, linewidth=0.75, color="0.0")
+ P1 = cartesian_to_polar(*(polar_to_cartesian(*P0) + [0, -1.0 * length]))
+ text = ax.text(
+ P1[0],
+ P1[1],
+ "%d" % i,
+ zorder=500,
+ va="top",
+ ha="center",
+ size="x-small",
+ family="Roboto",
+ color="black",
+ )
+ text.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+ )
+
+# Circular bands
+n = 1000
+T = np.linspace(0, 2 * np.pi, n)
+color = "0.95"
+ax.fill_between(T, 0, 100, color=color, zorder=-50)
+ax.fill_between(T, 200, 300, color=color, zorder=-50)
+ax.fill_between(T, 400, 500, color=color, zorder=-50)
+ax.fill_between(T, 600, 700, color=color, zorder=-50)
+ax.fill_between(T, 800, 900, color=color, zorder=-50)
+plt.scatter([0], [0], 20, facecolor="white", edgecolor="black", zorder=1000)
+
+
+# Actual plot
+np.random.seed(123)
+n = 145
+T = 2 * np.pi / n + np.linspace(0, 2 * np.pi, n)
+T[1::2] = T[0:-1:2]
+R = np.random.uniform(400, 800, n)
+R[-1] = R[0]
+R[1:-1:2] = R[2::2]
+ax.fill(T, R, color="C1", zorder=150, alpha=0.1)
+ax.plot(T, R, color="white", zorder=200, linewidth=3.5)
+ax.plot(T, R, color="C1", zorder=250, linewidth=1.5)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/projection-polar-histogram.pdf")
+plt.show()
diff --git a/code/scales-projections/scales-check.py b/code/scales-projections/scales-check.py
new file mode 100644
index 0000000..ae67b72
--- /dev/null
+++ b/code/scales-projections/scales-check.py
@@ -0,0 +1,54 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter, SymmetricalLogLocator
+
+
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, aspect=1, xlim=[0, 100], ylim=[0, 100])
+P0, P1, P2, P3 = (0.1, 0.1), (1, 1), (10, 10), (100, 100)
+transform = ax.transData.transform
+print("Linear scale")
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P0[0], P1[0], abs((transform(P1) - transform(P0))[0])
+ )
+)
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P1[0], P2[0], abs((transform(P2) - transform(P1))[0])
+ )
+)
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P2[0], P3[0], abs((transform(P3) - transform(P2))[0])
+ )
+)
+print()
+
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, aspect=1, xlim=[0.1, 100], ylim=[0.1, 100])
+ax.set_xscale("log")
+transform = ax.transData.transform
+
+P0, P1, P2, P3 = (0.1, 0.1), (1, 1), (10, 10), (100, 100)
+print("Log scale")
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P0[0], P1[0], abs((transform(P1) - transform(P0))[0])
+ )
+)
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P1[0], P2[0], abs((transform(P2) - transform(P1))[0])
+ )
+)
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P2[0], P3[0], abs((transform(P3) - transform(P2))[0])
+ )
+)
diff --git a/code/scales-projections/scales-comparison.py b/code/scales-projections/scales-comparison.py
new file mode 100644
index 0000000..cb1e66e
--- /dev/null
+++ b/code/scales-projections/scales-comparison.py
@@ -0,0 +1,92 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+
+# Style
+# -----------------------------------------------------------------------------
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+fig = plt.figure(figsize=(6, 2))
+
+# Linear axis
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 1, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "linear scale (default)",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+ax.tick_params(axis="both", which="major")
+
+
+# Log axis
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 2, xlim=[0.1, 1.0], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "log scale ('log')",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_xscale("log")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+# Logit axis
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 3, xlim=[0.1, 0.9], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "logit scale ('logit')",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_xscale("logit")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+# Show
+# -----------------------------------------------------------------------------
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/scales-comparison.pdf")
+plt.show()
diff --git a/code/scales-projections/scales-custom.py b/code/scales-projections/scales-custom.py
new file mode 100644
index 0000000..eba2ded
--- /dev/null
+++ b/code/scales-projections/scales-custom.py
@@ -0,0 +1,100 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+
+# Style
+# -----------------------------------------------------------------------------
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+fig = plt.figure(figsize=(6, 2))
+
+# Linear axis
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 1, xlim=[0.0, 1.0], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "linear scale",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+ax.tick_params(axis="both", which="major")
+
+
+def forward(x):
+ return x ** 2
+
+
+def inverse(x):
+ return x ** (1 / 2)
+
+
+# x**2 scale
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 2, xlim=[0.1, 1.0], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "$x^2$ scale",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_xscale("function", functions=(forward, inverse))
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+# sqrt(x)
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 3, xlim=[0.1, 1.0], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "$\sqrt{x}$ scale",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_xscale("function", functions=(inverse, forward))
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+# Show
+# -----------------------------------------------------------------------------
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/scales-custom.pdf")
+plt.show()
diff --git a/code/scales-projections/scales-log-log.py b/code/scales-projections/scales-log-log.py
new file mode 100644
index 0000000..1097bf7
--- /dev/null
+++ b/code/scales-projections/scales-log-log.py
@@ -0,0 +1,88 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter, MultipleLocator
+
+X = np.linspace(0.001, 90, 5000)
+figure = plt.figure(figsize=(6, 6))
+
+# Style
+# -----------------------------------------------------------------------------
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+# X-linear Y-linear
+# -----------------------------------------------------------------------------
+ax1 = plt.subplot(2, 2, 1, xlim=(0.0, 10), ylim=(0.0, 10))
+ax1.plot(X, 10 ** X, color="C0")
+ax1.plot(X, X, color="C1")
+ax1.plot(X, np.log10(X), color="C2")
+ax1.set_ylabel("Linear")
+ax1.xaxis.set_major_locator(MultipleLocator(2.0))
+ax1.xaxis.set_minor_locator(MultipleLocator(0.4))
+ax1.yaxis.set_major_locator(MultipleLocator(2.0))
+ax1.yaxis.set_minor_locator(MultipleLocator(0.4))
+ax1.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ax1.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+ax1.tick_params(which="both", labelbottom=False, bottom=False)
+
+ax1.text(1.25, 8.50, "$f(x) = 10^x$", color="C0")
+ax1.text(5.75, 5.00, "$f(x) = x$", color="C1")
+ax1.text(5.50, 1.50, "$f(x) = log_{10}(x)$", color="C2")
+ax1.set_title("X linear - Y linear")
+
+
+# X-log Y-linear
+# -----------------------------------------------------------------------------
+ax2 = plt.subplot(2, 2, 2, xlim=(0.001, 100), ylim=(0.0, 10), sharey=ax1)
+ax2.set_xscale("log")
+ax2.tick_params(which="both", labelbottom=False, bottom=False)
+ax2.tick_params(which="both", labelleft=False, left=False)
+ax2.plot(X, 10 ** X, color="C0")
+ax2.plot(X, X, color="C1")
+ax2.plot(X, np.log10(X), color="C2")
+ax2.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ax2.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+ax2.set_title("X logarithmic - Y linear")
+
+
+# X-linear Y-log
+# -----------------------------------------------------------------------------
+ax3 = plt.subplot(2, 2, 3, xlim=(0.0, 10), ylim=(0.001, 100), sharex=ax1)
+ax3.set_yscale("log")
+ax3.plot(X, 10 ** X, color="C0")
+ax3.plot(X, X, color="C1")
+ax3.plot(X, np.log10(X), color="C2")
+ax3.set_ylabel("Logarithmic")
+ax3.set_xlabel("Linear")
+ax3.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ax3.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+ax3.set_title("X linear - Y logarithmic")
+
+# X-log Y-log
+# -----------------------------------------------------------------------------
+ax4 = plt.subplot(2, 2, 4, xlim=(0.001, 100), ylim=(0.001, 100), sharex=ax2, sharey=ax3)
+ax4.set_xscale("log")
+ax4.set_yscale("log")
+ax4.tick_params(which="both", labelleft=False, left=False)
+ax4.plot(X, 10 ** X, color="C0")
+ax4.plot(X, X, color="C1")
+ax4.plot(X, np.log10(X), color="C2")
+ax4.set_xlabel("Logarithmic")
+ax4.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ax4.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+ax4.set_title("X logarithmic - Y logarithmic")
+
+
+# Show
+# -----------------------------------------------------------------------------
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/scales-log-log.pdf")
+plt.show()
diff --git a/code/scales-projections/text-polar.py b/code/scales-projections/text-polar.py
new file mode 100644
index 0000000..13610a3
--- /dev/null
+++ b/code/scales-projections/text-polar.py
@@ -0,0 +1,111 @@
+# -----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# -----------------------------------------------------------------------------
+# Illustrate the use of polar projection and rotated text (with transformation)
+# -----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.patches import Ellipse
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1)
+
+
+size = 0.1
+vals = np.ones(12)
+np.random.seed(123)
+
+# A nice set of colors for seasons
+cmap20c = plt.get_cmap("tab20c")
+cmap20b = plt.get_cmap("tab20b")
+colors = [
+ cmap20c(0),
+ cmap20c(1),
+ cmap20c(2), # Winter
+ cmap20c(10),
+ cmap20c(9),
+ cmap20c(8), # Spring
+ cmap20c(4),
+ cmap20c(5),
+ cmap20c(6), # Summer
+ cmap20b(15),
+ cmap20b(14),
+ cmap20b(13),
+] # Autumn
+
+# Simple pie
+ax.pie(np.ones(12), radius=1, colors=colors, wedgeprops=dict(width=size, edgecolor="w"))
+
+
+# Rotated and transformed label
+def label(text, angle, radius=1, scale=0.005):
+ path = TextPath((0, 0), text, size=10)
+ V = path.vertices
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ ymin, ymax = V[:, 1].min(), V[:, 1].max()
+ V -= (xmin + xmax) / 2, (ymin + ymax) / 2
+ V *= scale
+ for i in range(len(V)):
+ a = angle - V[i, 0]
+ V[i, 0] = (radius + V[i, 1]) * np.cos(a)
+ V[i, 1] = (radius + V[i, 1]) * np.sin(a)
+ patch = PathPatch(path, facecolor="k", linewidth=0)
+ ax.add_artist(patch)
+
+
+# This could be made through a list but it is easier to red this way
+label("JANUARY", 0.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("FEBRUARY", 1.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("MARCH", 2.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("WINTER", 1.5 * 2 * np.pi / 12, 1 + size, 0.0125)
+
+label("APRIL", 3.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("MAY", 4.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("JUNE", 5.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("SPRING", 4.5 * 2 * np.pi / 12, 1 + size, 0.0125)
+
+label("JULY", 6.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("AUGUST", 7.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("SEPTEMBER", 8.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("SUMMER", 7.5 * 2 * np.pi / 12, 1 + size, 0.0125)
+
+label("OCTOBER", 9.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("NOVEMBER", 10.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("DECEMBER", 11.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("AUTUMN", 10.5 * 2 * np.pi / 12, 1 + size, 0.0125)
+
+
+# Add a polar projection on top of the previous one
+ax = fig.add_axes([0.15, 0.15, 0.7, 0.7], projection="polar")
+
+# Some ellipses that will enforce polar projection
+for i in range(250):
+ p = np.random.uniform(0, 2 * np.pi), np.random.uniform(0.05, 0.95)
+ w = h = 0.01 + 0.05 * np.random.uniform(1, 2)
+ color = colors[int(np.floor((p[0] / (2 * np.pi)) * 12))]
+ ellipse = Ellipse(
+ p,
+ width=2 * w,
+ height=h,
+ zorder=10,
+ facecolor=color,
+ edgecolor="none",
+ alpha=0.5,
+ )
+ ax.add_artist(ellipse)
+ax.set_xlim(0, 2 * np.pi)
+ax.set_xticks(np.linspace(0, 2 * np.pi, 12, endpoint=False))
+ax.set_xticklabels([])
+ax.set_ylim(0, 1)
+ax.set_yticks(np.linspace(0, 1, 6))
+ax.set_yticklabels([])
+ax.set_rorigin(-0.25)
+
+
+# Save results and show it
+plt.savefig("../../figures/scales-projections/text-polar.pdf")
+plt.show()
diff --git a/code/showcases/contour-dropshadow.py b/code/showcases/contour-dropshadow.py
new file mode 100644
index 0000000..00f22f0
--- /dev/null
+++ b/code/showcases/contour-dropshadow.py
@@ -0,0 +1,96 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Contour with drop shadows
+# ----------------------------------------------------------------------------
+import scipy
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from matplotlib.backends.backend_agg import FigureCanvas
+from matplotlib.patheffects import Stroke, Normal
+from scipy.ndimage import gaussian_filter
+
+# Some data
+n = 64
+X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+)
+Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+
+def f(x, y):
+ return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-(x ** 2) - y ** 2)
+
+
+x = np.linspace(-3, 3, 100)
+y = np.linspace(-3, 3, 100)
+X, Y = np.meshgrid(x, y)
+Z = 0.5 * f(X, Y)
+
+n = 11 # Number of levels
+dz = (Z.max() - Z.min()) / n
+levels = np.linspace(Z.min(), Z.max(), n, endpoint=True)
+cmap = plt.get_cmap("viridis")
+
+
+def drop_shadow(Z, l0, l1, sigma=5, alpha=0.5):
+ """Compute the drop shadow for a contour between l0 and l1.
+
+ This works by first:
+ 1. render the contour in black using an offline image
+ 2. blue the contour using a Guassian filter
+ 3. set the alpha channel accordingly
+ """
+ fig = Figure(figsize=(5, 5))
+ canvas = FigureCanvas(fig)
+ ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+ ax.contourf(
+ Z,
+ vmin=Z.min(),
+ vmax=Z.max(),
+ levels=[l0, l1],
+ origin="lower",
+ colors="black",
+ extent=[-1, 1, -1, 1],
+ )
+ ax.set_xlim(-1, 1), ax.set_ylim(-1, 1)
+ canvas.draw()
+ A = np.array(canvas.renderer.buffer_rgba())[:, :, 0]
+ del fig
+ A = gaussian_filter(A, sigma)
+ A = (A - A.min()) / (A.max() - A.min())
+ I = np.zeros((A.shape[0], A.shape[1], 4))
+ I[:, :, 3] = (1 - A) * alpha
+ return I
+
+
+fig = plt.figure(figsize=(8, 8), dpi=100)
+ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+
+zorder = -100
+for i in range(len(levels) - 1):
+ l0, l1 = levels[i], levels[i + 1]
+ I = drop_shadow(Z, l0, l1)
+ ax.imshow(I, extent=[-3, 3, -3, 3], origin="upper", zorder=zorder)
+ zorder += 1
+ ax.contourf(
+ Z,
+ vmin=Z.min(),
+ vmax=Z.max(),
+ levels=[l0, l1],
+ origin="lower",
+ cmap=cmap,
+ extent=[-3, 3, -3, 3],
+ zorder=zorder,
+ )
+ zorder += 1
+ax.set_xlim(-2.9, 2.9)
+ax.set_ylim(-2.9, 2.9)
+
+plt.savefig("../../figures/showcases/contour-dropshadow.png", dpi=600)
+# plt.savefig("../figures/contour-dropshadow.pdf", dpi=300)
+plt.show()
diff --git a/code/showcases/domain-coloring.py b/code/showcases/domain-coloring.py
new file mode 100644
index 0000000..782f87a
--- /dev/null
+++ b/code/showcases/domain-coloring.py
@@ -0,0 +1,43 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patheffects import Stroke, Normal
+
+
+def normalize(Z):
+ zmin, zmax = Z.min(), Z.max()
+ return (Z - zmin) / (zmax - zmin)
+
+
+T = np.linspace(-2.5, 2.5, 2048)
+X, Y = np.meshgrid(T, T)
+Z = X + 1j * Y
+Z = Z + 1 / Z
+A = normalize(np.angle(Z))
+N = normalize(np.abs(Z)) * 2 * np.pi * 200
+
+
+fig = plt.figure(figsize=(8, 8))
+e = 0.001
+ax = fig.add_axes([e, e, 1 - 2 * e, 1 - 2 * e], frameon=True, facecolor="black")
+
+ax.imshow(
+ A,
+ interpolation="bicubic",
+ cmap="Spectral",
+ rasterized=True,
+ alpha=1 - (N < 1.5 * np.pi) * 0.25 * abs(np.cos(N % (np.pi / 2))) ** 2,
+)
+ax.contour(np.abs(Z.real - np.round(Z.real)), 1, colors="black", linewidths=0.25)
+ax.contour(np.abs(Z.imag - np.round(Z.imag)), 1, colors="black", linewidths=0.25)
+
+
+ax.set_xticks([])
+ax.set_yticks([])
+plt.savefig("../../figures/showcases/domain-coloring.png", dpi=600)
+plt.savefig("../../figures/showcases/domain-coloring.pdf")
+plt.show()
diff --git a/code/showcases/escher-movie.py b/code/showcases/escher-movie.py
new file mode 100644
index 0000000..c33f3a6
--- /dev/null
+++ b/code/showcases/escher-movie.py
@@ -0,0 +1,72 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+
+
+def line(A, B, thickness=0.005, n=100):
+ T = (B - A) / np.linalg.norm(B - A)
+ O = np.array([-T[1], T[0]])
+ P0 = A + thickness * O
+ P1 = B + thickness * O
+ P2 = B - thickness * O
+ P3 = A - thickness * O
+ P = np.concatenate(
+ [
+ np.linspace(P0, P1, n),
+ np.linspace(P1, P2, n // 10),
+ np.linspace(P2, P3, n),
+ np.linspace(P3, P0, n // 10),
+ ]
+ )
+ X, Y = P[:, 0], P[:, 1]
+ return np.dstack([np.exp(X) * np.cos(Y), np.exp(X) * np.sin(Y)]).squeeze()
+
+
+n = 250
+X = np.exp(-5 * np.linspace(-1, 1, n, endpoint=True) ** 2)
+A = X * 7 + (1 - X) * 13
+B = X * 3 + (1 - X) * 11
+
+for k, (a, b) in enumerate(zip(A, B)):
+ y = 2 * np.pi / (a + b * b / a)
+ x = b * y / a
+ v1, v2 = np.array([x, y]), np.array([-y, x])
+
+ fig = plt.figure(figsize=(8, 8))
+ ax = plt.subplot(1, 1, 1, aspect=1)
+
+ for i, z in enumerate(np.linspace(0, 1, 3 * 10, endpoint=False)):
+ zorder = 0
+ color = "0.5"
+ thickness = 0.001
+ if i % 10 == 0:
+ thickness = 0.0025
+ color = "0.0"
+ zorder = 10
+ P0, P1 = (2 + z) * b * v2, (2 + z) * b * v2 + 4 * a * v1
+ L = line(P0, P1, thickness=thickness, n=500)
+ ax.add_patch(Polygon(L, facecolor=color, zorder=zorder))
+
+ for i, z in enumerate(np.linspace(0, 1, 7 * 10, endpoint=False)):
+ zorder = 0
+ color = "0.5"
+ thickness = 0.001
+ if i % 10 == 0:
+ thickness = 0.0025
+ color = "0.0"
+ zorder = 10
+
+ P0, P1 = (1 + z) * a * v1, (1 + z) * a * v1 + 4 * b * v2
+ L = line(P0, P1, thickness=thickness, n=500)
+ ax.add_patch(Polygon(L, facecolor=color, zorder=zorder))
+
+ ax.set_xlim(-np.pi, np.pi), ax.set_xticks([])
+ ax.set_ylim(-np.pi, np.pi), ax.set_yticks([])
+ plt.tight_layout()
+ # plt.show()
+ plt.savefig("escher-frame-%03d.png" % k, dpi=100)
+ print("Frame escher-frame-%03d.png" % k)
+ del fig
+
+# Encode with (slow):
+# ffmpeg -i escher-frame-%03d.png -framerate 30 -vcodec libvpx-vp9 -b:v 1M output.webm
diff --git a/code/showcases/escher.py b/code/showcases/escher.py
new file mode 100644
index 0000000..f4966e6
--- /dev/null
+++ b/code/showcases/escher.py
@@ -0,0 +1,68 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+
+
+def line(A, B, thickness=0.005, n=100):
+ T = (B - A) / np.linalg.norm(B - A)
+ O = np.array([-T[1], T[0]])
+ P0 = A + thickness * O
+ P1 = B + thickness * O
+ P2 = B - thickness * O
+ P3 = A - thickness * O
+ P = np.concatenate(
+ [
+ np.linspace(P0, P1, n),
+ np.linspace(P1, P2, n // 10),
+ np.linspace(P2, P3, n),
+ np.linspace(P3, P0, n // 10),
+ ]
+ )
+ X, Y = P[:, 0], P[:, 1]
+ return np.dstack([np.exp(X) * np.cos(Y), np.exp(X) * np.sin(Y)]).squeeze()
+
+
+a, b = 11, 9
+y = 2 * np.pi / (a + b * b / a)
+x = b * y / a
+v1, v2 = np.array([x, y]), np.array([-y, x])
+
+
+fig = plt.figure(figsize=(8, 8))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, frameon=False)
+
+for i, z in enumerate(np.linspace(0, 1, b * 10, endpoint=False)):
+ zorder = 0
+ color = "0.5"
+ thickness = 0.001
+ if i % 10 == 0:
+ thickness = 0.0025
+ color = "0.0"
+ zorder = 10
+ P0, P1 = (2 + z) * b * v2, (2 + z) * b * v2 + 4 * a * v1
+ L = line(P0, P1, thickness=thickness, n=500)
+ ax.add_patch(Polygon(L, facecolor=color, zorder=zorder))
+
+for i, z in enumerate(np.linspace(0, 1, a * 10, endpoint=False)):
+ zorder = 0
+ color = "0.5"
+ thickness = 0.001
+ if i % 10 == 0:
+ thickness = 0.0025
+ color = "0.0"
+ zorder = 10
+
+ P0, P1 = (1 + z) * a * v1, (1 + z) * a * v1 + 4 * b * v2
+ L = line(P0, P1, thickness=thickness, n=500)
+ ax.add_patch(Polygon(L, facecolor=color, zorder=zorder))
+
+
+ax.set_xlim(-np.pi, np.pi), ax.set_xticks([])
+ax.set_ylim(-np.pi, np.pi), ax.set_yticks([])
+plt.savefig("../../figures/showcases/escher.pdf")
+plt.show()
diff --git a/code/showcases/mandelbrot.py b/code/showcases/mandelbrot.py
new file mode 100644
index 0000000..d24d4a9
--- /dev/null
+++ b/code/showcases/mandelbrot.py
@@ -0,0 +1,52 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+from matplotlib import colors
+import matplotlib.pyplot as plt
+
+
+def mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon=2.0):
+ X = np.linspace(xmin, xmax, xn, dtype=np.float32)
+ Y = np.linspace(ymin, ymax, yn, dtype=np.float32)
+ C = X + Y[:, None] * 1j
+ N = np.zeros(C.shape, dtype=int)
+ Z = np.zeros(C.shape, np.complex64)
+ for n in range(maxiter):
+ I = np.less(abs(Z), horizon)
+ N[I] = n
+ Z[I] = Z[I] ** 2 + C[I]
+ N[N == maxiter - 1] = 0
+ return Z, N
+
+
+xmin, xmax, xn = -2.75, +1.25, 4000
+ymin, ymax, yn = -1.5, +1.5, 3000
+maxiter = 200
+horizon = 2.0 ** 40
+log_horizon = np.log(np.log(horizon)) / np.log(2)
+Z, N = mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon)
+
+# Normalized recount as explained in:
+# https://linas.org/art-gallery/escape/smooth.html
+# https://www.ibm.com/developerworks/community/blogs/jfp/entry/My_Christmas_Gift
+with np.errstate(invalid="ignore"):
+ M = np.nan_to_num(N + 1 - np.log(np.log(abs(Z))) / np.log(2) + log_horizon)
+
+width = 10
+height = 10 * yn / xn
+fig = plt.figure(figsize=(width, height), dpi=100)
+ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect=1)
+
+# Shaded rendering
+light = colors.LightSource(azdeg=315, altdeg=10)
+M = light.shade(
+ M, cmap=plt.cm.hot, vert_exag=1.5, norm=colors.PowerNorm(0.3), blend_mode="hsv"
+)
+plt.imshow(M, extent=[xmin, xmax, ymin, ymax], interpolation="bicubic")
+ax.set_xticks([])
+ax.set_yticks([])
+plt.savefig("mandelbrot.png", dpi=600)
+plt.show()
diff --git a/code/showcases/mosaic.py b/code/showcases/mosaic.py
new file mode 100644
index 0000000..d1149cc
--- /dev/null
+++ b/code/showcases/mosaic.py
@@ -0,0 +1,127 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.spatial import Voronoi
+from math import sqrt, ceil, floor, pi, cos, sin
+from matplotlib.collections import PolyCollection
+
+
+def blue_noise(shape, radius, k=30, seed=None):
+ """
+ Generate blue noise over a two-dimensional rectangle of size (width,height)
+
+ Parameters
+ ----------
+
+ shape : tuple
+ Two-dimensional domain (width x height)
+ radius : float
+ Minimum distance between samples
+ k : int, optional
+ Limit of samples to choose before rejection (typically k = 30)
+ seed : int, optional
+ If provided, this will set the random seed before generating noise,
+ for valid pseudo-random comparisons.
+
+ References
+ ----------
+
+ .. [1] Fast Poisson Disk Sampling in Arbitrary Dimensions, Robert Bridson,
+ Siggraph, 2007. :DOI:`10.1145/1278780.1278807`
+ """
+
+ def sqdist(a, b):
+ """ Squared Euclidean distance """
+ dx, dy = a[0] - b[0], a[1] - b[1]
+ return dx * dx + dy * dy
+
+ def grid_coords(p):
+ """ Return index of cell grid corresponding to p """
+ return int(floor(p[0] / cellsize)), int(floor(p[1] / cellsize))
+
+ def fits(p, radius):
+ """ Check whether p can be added to the queue """
+
+ radius2 = radius * radius
+ gx, gy = grid_coords(p)
+ for x in range(max(gx - 2, 0), min(gx + 3, grid_width)):
+ for y in range(max(gy - 2, 0), min(gy + 3, grid_height)):
+ g = grid[x + y * grid_width]
+ if g is None:
+ continue
+ if sqdist(p, g) <= radius2:
+ return False
+ return True
+
+ # When given a seed, we use a private random generator in order to not
+ # disturb the default global random generator
+ if seed is not None:
+ from numpy.random.mtrand import RandomState
+
+ rng = RandomState(seed=seed)
+ else:
+ rng = np.random
+
+ width, height = shape
+ cellsize = radius / sqrt(2)
+ grid_width = int(ceil(width / cellsize))
+ grid_height = int(ceil(height / cellsize))
+ grid = [None] * (grid_width * grid_height)
+
+ p = rng.uniform(0, shape, 2)
+ queue = [p]
+ grid_x, grid_y = grid_coords(p)
+ grid[grid_x + grid_y * grid_width] = p
+
+ while queue:
+ qi = rng.randint(len(queue))
+ qx, qy = queue[qi]
+ queue[qi] = queue[-1]
+ queue.pop()
+ for _ in range(k):
+ theta = rng.uniform(0, 2 * pi)
+ r = radius * np.sqrt(rng.uniform(1, 4))
+ p = qx + r * cos(theta), qy + r * sin(theta)
+ if not (0 <= p[0] < width and 0 <= p[1] < height) or not fits(p, radius):
+ continue
+ queue.append(p)
+ gx, gy = grid_coords(p)
+ grid[gx + gy * grid_width] = p
+
+ return np.array([p for p in grid if p is not None])
+
+
+I = plt.imread("../data/poppy.png")
+P = blue_noise((1, 1), radius=0.005)
+P = np.append(P, [[+999, +999], [-999, +999], [+999, -999], [-999, -999]], axis=0)
+voronoi = Voronoi(P)
+
+
+polys, colors = [], []
+for i, region in enumerate(voronoi.regions):
+ if -1 in region:
+ continue
+ poly = [list(voronoi.vertices[i]) for i in region]
+ if len(poly) <= 0:
+ continue
+ polys.append(poly)
+ index = np.where(voronoi.point_region == i)[0][0]
+ row, col = int((1 - P[index, 1]) * I.shape[1]), int(P[index, 0] * I.shape[0])
+ colors.append(I[row, col])
+colors = np.array(colors)
+collection = PolyCollection(
+ polys, linewidth=0.5, facecolor=colors, edgecolors=colors * 0.5
+)
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[0, 1], ylim=[0, 1], aspect=1)
+ax.axis("off")
+
+ax.add_collection(collection)
+
+plt.savefig("../../figures/showcases/mosaic.pdf")
+plt.show()
diff --git a/code/showcases/recursive-voronoi.py b/code/showcases/recursive-voronoi.py
new file mode 100644
index 0000000..786c064
--- /dev/null
+++ b/code/showcases/recursive-voronoi.py
@@ -0,0 +1,284 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import scipy.spatial
+import matplotlib.pyplot as plt
+import matplotlib.path as mpath
+from shapely.geometry import Polygon
+from matplotlib.collections import PolyCollection
+from math import sqrt, ceil, floor, pi, cos, sin
+
+
+def blue_noise(shape, radius, k=30, seed=None):
+ """
+ Generate blue noise over a two-dimensional rectangle of size (width,height)
+
+ Parameters
+ ----------
+
+ shape : tuple
+ Two-dimensional domain (width x height)
+ radius : float
+ Minimum distance between samples
+ k : int, optional
+ Limit of samples to choose before rejection (typically k = 30)
+ seed : int, optional
+ If provided, this will set the random seed before generating noise,
+ for valid pseudo-random comparisons.
+
+ References
+ ----------
+
+ .. [1] Fast Poisson Disk Sampling in Arbitrary Dimensions, Robert Bridson,
+ Siggraph, 2007. :DOI:`10.1145/1278780.1278807`
+ """
+
+ def sqdist(a, b):
+ """ Squared Euclidean distance """
+ dx, dy = a[0] - b[0], a[1] - b[1]
+ return dx * dx + dy * dy
+
+ def grid_coords(p):
+ """ Return index of cell grid corresponding to p """
+ return int(floor(p[0] / cellsize)), int(floor(p[1] / cellsize))
+
+ def fits(p, radius):
+ """ Check whether p can be added to the queue """
+
+ radius2 = radius * radius
+ gx, gy = grid_coords(p)
+ for x in range(max(gx - 2, 0), min(gx + 3, grid_width)):
+ for y in range(max(gy - 2, 0), min(gy + 3, grid_height)):
+ g = grid[x + y * grid_width]
+ if g is None:
+ continue
+ if sqdist(p, g) <= radius2:
+ return False
+ return True
+
+ # When given a seed, we use a private random generator in order to not
+ # disturb the default global random generator
+ if seed is not None:
+ from numpy.random.mtrand import RandomState
+
+ rng = RandomState(seed=seed)
+ else:
+ rng = np.random
+
+ width, height = shape
+ cellsize = radius / sqrt(2)
+ grid_width = int(ceil(width / cellsize))
+ grid_height = int(ceil(height / cellsize))
+ grid = [None] * (grid_width * grid_height)
+
+ p = rng.uniform(0, shape, 2)
+ queue = [p]
+ grid_x, grid_y = grid_coords(p)
+ grid[grid_x + grid_y * grid_width] = p
+
+ while queue:
+ qi = rng.randint(len(queue))
+ qx, qy = queue[qi]
+ queue[qi] = queue[-1]
+ queue.pop()
+ for _ in range(k):
+ theta = rng.uniform(0, 2 * pi)
+ r = radius * np.sqrt(rng.uniform(1, 4))
+ p = qx + r * cos(theta), qy + r * sin(theta)
+ if not (0 <= p[0] < width and 0 <= p[1] < height) or not fits(p, radius):
+ continue
+ queue.append(p)
+ gx, gy = grid_coords(p)
+ grid[gx + gy * grid_width] = p
+
+ return np.array([p for p in grid if p is not None])
+
+
+def bounded_voronoi(points):
+ """
+ Reconstruct infinite voronoi regions in a 2D diagram to finite regions.
+
+ Parameters
+ ----------
+ vor : Voronoi
+ Input diagram
+
+ Returns
+ -------
+ regions : list of tuples
+ Indices of vertices in each revised Voronoi regions.
+ vertices : list of tuples
+ Coordinates for revised Voronoi vertices. Same as coordinates
+ of input vertices, with 'points at infinity' appended to the
+ end.
+ """
+
+ vor = scipy.spatial.Voronoi(points)
+ new_regions = []
+ new_vertices = vor.vertices.tolist()
+ center = vor.points.mean(axis=0)
+ radius = vor.points.ptp().max() * 2
+
+ # Construct a map containing all ridges for a given point
+ all_ridges = {}
+ for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):
+ all_ridges.setdefault(p1, []).append((p2, v1, v2))
+ all_ridges.setdefault(p2, []).append((p1, v1, v2))
+
+ # Reconstruct infinite regions
+ for p1, region in enumerate(vor.point_region):
+ vertices = vor.regions[region]
+
+ if all(v >= 0 for v in vertices):
+ # finite region
+ new_regions.append(vertices)
+ continue
+
+ # reconstruct a non-finite region
+ ridges = all_ridges[p1]
+ new_region = [v for v in vertices if v >= 0]
+
+ for p2, v1, v2 in ridges:
+ if v2 < 0:
+ v1, v2 = v2, v1
+ if v1 >= 0:
+ # finite ridge: already in the region
+ continue
+
+ # Compute the missing endpoint of an infinite ridge
+ t = vor.points[p2] - vor.points[p1] # tangent
+ t /= np.linalg.norm(t)
+ n = np.array([-t[1], t[0]]) # normal
+
+ midpoint = vor.points[[p1, p2]].mean(axis=0)
+ direction = np.sign(np.dot(midpoint - center, n)) * n
+ far_point = vor.vertices[v2] + direction * radius
+
+ new_region.append(len(new_vertices))
+ new_vertices.append(far_point.tolist())
+
+ # sort region counterclockwise
+ vs = np.asarray([new_vertices[v] for v in new_region])
+ c = vs.mean(axis=0)
+ angles = np.arctan2(vs[:, 1] - c[1], vs[:, 0] - c[0])
+ new_region = np.array(new_region)[np.argsort(angles)]
+
+ # finish
+ new_regions.append(new_region.tolist())
+ return new_regions, np.asarray(new_vertices)
+
+
+def poly_random_points_safe(V, n=10):
+ def random_point_inside_triangle(A, B, C):
+ r1 = np.sqrt(np.random.uniform(0, 1))
+ r2 = np.random.uniform(0, 1)
+ return (1 - r1) * A + r1 * (1 - r2) * B + r1 * r2 * C
+
+ def triangle_area(A, B, C):
+ return 0.5 * np.abs(
+ (B[0] - A[0]) * (C[1] - A[1]) - (C[0] - A[0]) * (B[1] - A[1])
+ )
+
+ C = V.mean(axis=0)
+ T = [(C, V[i], V[i + 1]) for i in range(len(V) - 1)]
+ A = np.array([triangle_area(*t) for t in T])
+ A /= A.sum()
+
+ points = [C]
+ for i in np.random.choice(len(A), size=n - 1, p=A):
+ points.append(random_point_inside_triangle(*T[i]))
+ return points
+
+
+def poly_random_points(V, n=10):
+ path = mpath.Path(V)
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ ymin, ymax = V[:, 1].min(), V[:, 1].max()
+ xscale, yscale = xmax - xmin, ymax - ymin
+ if xscale > yscale:
+ xscale, yscale = 1, yscale / xscale
+ else:
+ xscale, yscale = xscale / yscale, 1
+ radius = 0.85 * np.sqrt(2 * xscale * yscale / (n * np.pi))
+ points = blue_noise((xscale, yscale), radius)
+ points = [xmin, ymin] + points * [xmax - xmin, ymax - ymin]
+ inside = path.contains_points(points)
+
+ P = points[inside]
+ if len(P) < 5:
+ return poly_random_points_safe(V, n)
+ np.random.shuffle(P)
+ return P[:n]
+
+
+def voronoi(V, npoints, level, maxlevel, color=None):
+ if level == maxlevel:
+ return []
+
+ linewidths = [1.50, 1.00, 0.75, 0.50, 0.25, 0.10]
+ edgecolors = [
+ (0, 0, 0, 1.00),
+ (0, 0, 0, 0.50),
+ (0, 0, 0, 0.25),
+ (0, 0, 0, 0.10),
+ (0, 0, 0, 0.10),
+ (0, 0, 0, 0.10),
+ ]
+
+ if level == 1:
+ color = np.random.uniform(0, 1, 4)
+ color[3] = 0.5
+
+ points = poly_random_points(V, npoints - level)
+ regions, vertices = bounded_voronoi(points)
+ clip = Polygon(V)
+ cells = []
+ for region in regions:
+ polygon = Polygon(vertices[region]).intersection(clip)
+ polygon = np.array([point for point in polygon.exterior.coords])
+ linewidth = linewidths[level]
+ edgecolor = edgecolors[level]
+ facecolor = "none"
+ if level > 1:
+ alpha = color[3] + (1 / (level + 1)) * 0.25 * np.random.uniform(-1, 0.5)
+ color = color[0], color[1], color[2], min(max(alpha, 0.1), 1)
+ if level == maxlevel - 1:
+ facecolor = color
+ zorder = -level
+ cells.append((polygon, linewidth, edgecolor, facecolor, zorder))
+ cells.extend(voronoi(polygon, npoints, level + 1, maxlevel, color))
+ return cells
+
+
+np.random.seed(12345)
+T = np.linspace(0, 2 * np.pi, 100, endpoint=False)
+R = 100
+X, Y = R * np.cos(T), R * np.sin(T)
+V = np.c_[X, Y]
+
+V = 100 * np.array([[-1, -1], [-1, 1], [1, 1], [1, -1]])
+
+fig = plt.figure(figsize=(8, 8))
+d = R - 1
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, xlim=[-d, d], ylim=[-d, d])
+ax.axis("off")
+
+cells = voronoi(V, 11, level=0, maxlevel=5)
+zorder = [cell[-1] for cell in cells]
+cells = [cells[i] for i in np.argsort(zorder)]
+polygons = [cell[0] for cell in cells]
+linewidths = [cell[1] for cell in cells]
+edgecolors = [cell[2] for cell in cells]
+facecolors = [cell[3] for cell in cells]
+
+collection = PolyCollection(
+ polygons, linewidth=linewidths, edgecolor=edgecolors, facecolor=facecolors
+)
+ax.add_collection(collection)
+
+plt.savefig("../../figures/showcases/recursive-voronoi.pdf")
+plt.savefig("../../figures/showcases/recursive-voronoi.png", dpi=600)
+plt.show()
diff --git a/code/showcases/text-shadow.py b/code/showcases/text-shadow.py
new file mode 100644
index 0000000..cf316fe
--- /dev/null
+++ b/code/showcases/text-shadow.py
@@ -0,0 +1,87 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.collections import PolyCollection
+from matplotlib.font_manager import FontProperties
+
+red = np.array([233, 77, 85, 255]) / 255
+darkred = np.array([130, 60, 71, 255]) / 255
+prop = FontProperties(family="Source Sans Pro", weight=100)
+
+fig = plt.figure(figsize=(14.8 / 2.54, 21 / 2.54))
+
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, xlim=[-10, 10], ylim=[-14.2, 14.2])
+ax.axis("off")
+
+# Text path
+path = TextPath((0, 0), "MATPLOTLIB", size=2.5, prop=prop)
+
+# Text centering
+V = path.vertices
+xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ymin, ymax = V[:, 1].min(), V[:, 1].max()
+V -= (xmin + xmax) / 2 + 1, (ymin + ymax) / 2
+
+
+# Compute shadow by iterating over text path segments
+polys = []
+for (point, code) in path.iter_segments(curves=False):
+ if code == path.MOVETO:
+ points = [point]
+ elif code == path.LINETO:
+ points.append(point)
+ elif code == path.CLOSEPOLY:
+ points.append(points[0])
+ points = np.array(points)
+ for i in range(len(points) - 1):
+ p0, p1 = points[i], points[i + 1]
+ polys.append([p0, p1, p1 + (+20, -20), p0 + (+20, -20)])
+
+# Display shadow
+collection = PolyCollection(
+ polys, closed=True, linewidth=0.0, facecolor=darkred, zorder=-10
+)
+ax.add_collection(collection)
+
+# Display text
+patch = PathPatch(path, facecolor="white", edgecolor="none", zorder=10)
+ax.add_artist(patch)
+
+# Transparent gradient to fade out shadow
+I = np.zeros((200, 1, 4)) + red
+ax.imshow(I, extent=[-11, 11, -15, 15], zorder=-20, clip_on=False)
+I[:, 0, 3] = np.linspace(0, 1, len(I))
+ax.imshow(I, extent=[-11, 11, -15, 15], zorder=0, clip_on=False)
+
+
+ax.text(
+ 6.5,
+ -1.75,
+ "a versatile scientific visualization library ",
+ color="white",
+ ha="right",
+ va="baseline",
+ size=10,
+ family="Pacifico",
+ zorder=30,
+)
+
+# Save and show result
+plt.savefig("../../figures/showcases/text-shadow.pdf")
+plt.savefig("../../figures/showcases/text-shadow.png", dpi=600)
+plt.show()
+
+
+# Bad alternative (slow and memory hungry)
+# ----------------------------------------
+# ax.text(0, 0, "MATPLOTLIB", color="white", ha="center", va="center",
+# size=48, family="Source Sans Pro", weight=100, zorder=10)
+# for x in np.linspace(0,7,500):
+# ax.text(x, -x, "MATPLOTLIB", color="darkred", ha="center", va="center",
+# size=48, family="Source Sans Pro", weight=100, zorder=-10)
diff --git a/code/showcases/text-spiral.py b/code/showcases/text-spiral.py
new file mode 100644
index 0000000..ed30b00
--- /dev/null
+++ b/code/showcases/text-spiral.py
@@ -0,0 +1,60 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import PathPatch
+from matplotlib.textpath import TextPath
+from matplotlib.collections import PolyCollection
+from matplotlib.font_manager import FontProperties
+
+n = 100
+A = np.linspace(np.pi, n * 2 * np.pi, 10_000)
+R = 5 + np.linspace(np.pi, n * 2 * np.pi, 10_000)
+T = np.stack([R * np.cos(A), R * np.sin(A)], axis=1)
+dx = np.cos(A) - R * np.sin(A)
+dy = np.sin(A) + R * np.cos(A)
+O = np.stack([-dy, dx], axis=1)
+O = O / (np.linalg.norm(O, axis=1)).reshape(len(O), 1)
+
+L = np.zeros(len(T))
+np.cumsum(np.sqrt(((T[1:] - T[:-1]) ** 2).sum(axis=1)), out=L[1:])
+
+import mpmath
+
+mpmath.mp.dps = 15000
+text = str(mpmath.pi)
+
+path = TextPath((0, 0), text, size=6, prop=FontProperties(family="Source Serif Pro"))
+Vx, Vy = path.vertices[:, 0], path.vertices[:, 1]
+X = np.interp(Vx, L, T[:, 0]) + Vy * np.interp(Vx, L, O[:, 0])
+Y = np.interp(Vx, L, T[:, 1]) + Vy * np.interp(Vx, L, O[:, 1])
+Vx[...], Vy[...] = X, Y
+
+fig = plt.figure(figsize=(8, 8))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1)
+patch = PathPatch(path, facecolor="k", linewidth=0)
+ax.add_artist(patch)
+
+# from matplotlib.patheffects import Stroke, Normal
+# text = fig.text(.05, .060, "Scientific Visualization — Python & Matplotlib",
+# va="bottom", size="16", color="white",
+# transform=ax.transAxes,
+# weight="bold", family="Roboto Condensed")
+# text.set_path_effects([Stroke(linewidth=2, foreground="black"), Normal()])
+# text = fig.text(.05, .055, "github.com/rougier/scientific-visualization-book",
+# size = 10.2, transform=ax.transAxes,
+# va="top", color="blue", family="Roboto Mono", weight="bold")
+# text.set_path_effects([Stroke(linewidth=5, foreground="white"), Normal()])
+
+plt.rcParams["text.usetex"] = True
+ax.text(-3, 0, "$\pi$", ha="center", va="center", size=500, color="white", alpha=0.6)
+
+ax.set_xlim(-200, 200), ax.set_xticks([])
+ax.set_ylim(-200, 200), ax.set_yticks([])
+
+# plt.savefig("/Users/rougier/Desktop/spiral-pi.png", dpi=150)
+plt.savefig("../../figures/showcases/text-spiral.pdf")
+plt.show()
diff --git a/code/showcases/waterfall-3d.py b/code/showcases/waterfall-3d.py
new file mode 100644
index 0000000..cf9a4d6
--- /dev/null
+++ b/code/showcases/waterfall-3d.py
@@ -0,0 +1,153 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from shapely.geometry import box, Polygon
+from scipy.ndimage import gaussian_filter1d
+from matplotlib.collections import PolyCollection
+from matplotlib.transforms import Affine2D
+from matplotlib.text import TextPath
+from matplotlib.patches import PathPatch
+from mpl_toolkits.mplot3d import Axes3D, art3d
+from matplotlib.font_manager import FontProperties
+
+
+def text3d(ax, xyz, s, zdir="z", size=0.1, angle=0, **kwargs):
+ x, y, z = xyz
+ if zdir == "y":
+ xy, z = (x, z), y
+ elif zdir == "x":
+ xy, z = (y, z), x
+ else:
+ xy, z = (x, y), z
+ path = TextPath((0, 0), s, size=size, prop=FontProperties(family="Roboto"))
+ V = path.vertices
+ V[:, 0] -= (V[:, 0].max() - V[:, 0].min()) / 2
+ trans = Affine2D().rotate(angle).translate(xy[0], xy[1])
+ path = PathPatch(trans.transform_path(path), **kwargs)
+ ax.add_patch(path)
+ art3d.pathpatch_2d_to_3d(path, z=z, zdir=zdir)
+
+
+# Some nice but random curves
+def random_curve(n=100):
+ Y = np.random.uniform(0, 1, n)
+ Y = gaussian_filter1d(Y, 1)
+ X = np.linspace(-1, 1, len(Y))
+ Y *= np.exp(-2 * (X * X))
+ return Y
+
+
+def cmap_plot(Y, ymin=0, ymax=1, n=50, cmap="magma", y0=0):
+ X = np.linspace(0.3, 0.7, len(Y))
+ Y = gaussian_filter1d(Y, 2)
+
+ verts = []
+ colors = []
+ P = Polygon([(X[0], 0), *zip(X, Y), (X[-1], 0)])
+
+ dy = (ymax - ymin) / n
+ cmap = plt.cm.get_cmap(cmap)
+ cnorm = matplotlib.colors.Normalize(vmin=ymin, vmax=ymax)
+
+ for y in np.arange(Y.min(), Y.max(), dy):
+ B = box(0, y, 10, y + dy)
+ I = P.intersection(B)
+ if hasattr(I, "geoms"):
+ for p in I.geoms:
+ V = np.array(p.exterior.coords)
+ V[:, 1] += y0
+ verts.append(V)
+ colors.append(cmap(cnorm(y)))
+ else:
+ if I.exterior.coords:
+ V = np.array(I.exterior.coords)
+ V[:, 1] += y0
+ verts.append(V)
+ colors.append(cmap(cnorm(y)))
+
+ return verts, colors
+
+
+fig = plt.figure(figsize=(10, 10))
+fig.patch.set_facecolor("black")
+ax = fig.gca(projection="3d", proj_type="ortho")
+ax.patch.set_facecolor("black")
+
+# Make panes transparent
+ax.xaxis.pane.fill = False # Left pane
+ax.yaxis.pane.fill = False # Right pane
+ax.zaxis.pane.fill = False # Right pane
+
+# Remove grid lines
+ax.grid(False)
+
+# Remove tick labels
+ax.set_xticklabels([])
+ax.set_yticklabels([])
+ax.set_zticklabels([])
+
+# Transparent spines
+ax.w_xaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
+ax.w_yaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
+ax.w_zaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
+
+# Transparent panes
+ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
+ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
+ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
+
+# No ticks
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_zticks([])
+
+
+np.random.seed(1)
+
+# text3d(ax, (1.05, 0.5, 0), "Series A", zdir="x",
+# size=0.05, facecolor="white", edgecolor="None", alpha=.25)
+
+# text3d(ax, (1.05, 0.5, 0.6), "Series B", zdir="x",
+# size=0.05, facecolor="white", edgecolor="None", alpha=.25)
+
+# text3d(ax, (1.05, 0.5, 1.2), "Series C", zdir="x",
+# size=0.05, facecolor="white", edgecolor="None", alpha=.25)
+
+
+for zs in np.linspace(0, 1, 50):
+ Y = 0.1 * random_curve()
+ verts, colors = cmap_plot(Y, ymin=0, ymax=0.075, n=50, cmap="magma", y0=-0.2)
+ collection = PolyCollection(
+ verts, antialiased=False, edgecolors="None", facecolor=colors
+ )
+ ax.add_collection3d(collection, zdir="x", zs=zs)
+
+ Y = 0.1 * random_curve()
+ verts, colors = cmap_plot(Y, ymin=0, ymax=0.075, n=50, cmap="magma", y0=0.4)
+ collection = PolyCollection(
+ verts, antialiased=False, edgecolors="None", facecolor=colors
+ )
+ ax.add_collection3d(collection, zdir="x", zs=zs)
+
+ Y = 0.1 * random_curve()
+ verts, colors = cmap_plot(Y, ymin=0, ymax=0.075, n=50, cmap="magma", y0=1.0)
+ collection = PolyCollection(
+ verts, antialiased=False, edgecolors="None", facecolor=colors
+ )
+ ax.add_collection3d(collection, zdir="x", zs=zs)
+
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_zlim(0, 1)
+
+ax.view_init(elev=40, azim=-40)
+
+plt.tight_layout()
+plt.savefig("../../figures/showcases/waterfall-3d.pdf")
+# plt.savefig("./waterfall-3d.png", dpi=300)
+plt.show()
diff --git a/code/showcases/windmap.py b/code/showcases/windmap.py
new file mode 100644
index 0000000..649a301
--- /dev/null
+++ b/code/showcases/windmap.py
@@ -0,0 +1,242 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation, writers
+from matplotlib.collections import LineCollection
+
+
+class Streamlines(object):
+ """
+ Copyright (c) 2011 Raymond Speth.
+
+ Permission is hereby granted, free of charge, to any person obtaining a
+ copy of this software and associated documentation files (the "Software"),
+ to deal in the Software without restriction, including without limitation
+ the rights to use, copy, modify, merge, publish, distribute, sublicense,
+ and/or sell copies of the Software, and to permit persons to whom the
+ Software is furnished to do so, subject to the following conditions:
+
+ The above copyright notice and this permission notice shall be included in
+ all copies or substantial portions of the Software.
+
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+ DEALINGS IN THE SOFTWARE.
+
+ See: http://web.mit.edu/speth/Public/streamlines.py
+ """
+
+ def __init__(
+ self, X, Y, U, V, res=0.125, spacing=2, maxLen=2500, detectLoops=False
+ ):
+ """
+ Compute a set of streamlines covering the given velocity field.
+
+ X and Y - 1D or 2D (e.g. generated by np.meshgrid) arrays of the
+ grid points. The mesh spacing is assumed to be uniform
+ in each dimension.
+ U and V - 2D arrays of the velocity field.
+ res - Sets the distance between successive points in each
+ streamline (same units as X and Y)
+ spacing - Sets the minimum density of streamlines, in grid points.
+ maxLen - The maximum length of an individual streamline segment.
+ detectLoops - Determines whether an attempt is made to stop extending
+ a given streamline before reaching maxLen points if
+ it forms a closed loop or reaches a velocity node.
+
+ Plots are generated with the 'plot' or 'plotArrows' methods.
+ """
+
+ self.spacing = spacing
+ self.detectLoops = detectLoops
+ self.maxLen = maxLen
+ self.res = res
+
+ xa = np.asanyarray(X)
+ ya = np.asanyarray(Y)
+ self.x = xa if xa.ndim == 1 else xa[0]
+ self.y = ya if ya.ndim == 1 else ya[:, 0]
+ self.u = U
+ self.v = V
+ self.dx = (self.x[-1] - self.x[0]) / (self.x.size - 1) # assume a regular grid
+ self.dy = (self.y[-1] - self.y[0]) / (self.y.size - 1) # assume a regular grid
+ self.dr = self.res * np.sqrt(self.dx * self.dy)
+
+ # marker for which regions have contours
+ self.used = np.zeros(self.u.shape, dtype=bool)
+ self.used[0] = True
+ self.used[-1] = True
+ self.used[:, 0] = True
+ self.used[:, -1] = True
+
+ # Don't try to compute streamlines in regions where there is no velocity data
+ for i in range(self.x.size):
+ for j in range(self.y.size):
+ if self.u[j, i] == 0.0 and self.v[j, i] == 0.0:
+ self.used[j, i] = True
+
+ # Make the streamlines
+ self.streamlines = []
+ while not self.used.all():
+ nz = np.transpose(np.logical_not(self.used).nonzero())
+ # Make a streamline starting at the first unrepresented grid point
+ self.streamlines.append(
+ self._makeStreamline(self.x[nz[0][1]], self.y[nz[0][0]])
+ )
+
+ def _interp(self, x, y):
+ """ Compute the velocity at point (x,y) """
+ i = (x - self.x[0]) / self.dx
+ ai = i % 1
+
+ j = (y - self.y[0]) / self.dy
+ aj = j % 1
+
+ i, j = int(i), int(j)
+
+ # Bilinear interpolation
+ u = (
+ self.u[j, i] * (1 - ai) * (1 - aj)
+ + self.u[j, i + 1] * ai * (1 - aj)
+ + self.u[j + 1, i] * (1 - ai) * aj
+ + self.u[j + 1, i + 1] * ai * aj
+ )
+
+ v = (
+ self.v[j, i] * (1 - ai) * (1 - aj)
+ + self.v[j, i + 1] * ai * (1 - aj)
+ + self.v[j + 1, i] * (1 - ai) * aj
+ + self.v[j + 1, i + 1] * ai * aj
+ )
+
+ self.used[j : j + self.spacing, i : i + self.spacing] = True
+
+ return u, v
+
+ def _makeStreamline(self, x0, y0):
+ """
+ Compute a streamline extending in both directions from the given point.
+ """
+
+ sx, sy = self._makeHalfStreamline(x0, y0, 1) # forwards
+ rx, ry = self._makeHalfStreamline(x0, y0, -1) # backwards
+
+ rx.reverse()
+ ry.reverse()
+
+ return rx + [x0] + sx, ry + [y0] + sy
+
+ def _makeHalfStreamline(self, x0, y0, sign):
+ """
+ Compute a streamline extending in one direction from the given point.
+ """
+
+ xmin = self.x[0]
+ xmax = self.x[-1]
+ ymin = self.y[0]
+ ymax = self.y[-1]
+
+ sx = []
+ sy = []
+
+ x = x0
+ y = y0
+ i = 0
+ while xmin < x < xmax and ymin < y < ymax:
+ u, v = self._interp(x, y)
+ theta = np.arctan2(v, u)
+
+ x += sign * self.dr * np.cos(theta)
+ y += sign * self.dr * np.sin(theta)
+ sx.append(x)
+ sy.append(y)
+
+ i += 1
+
+ if self.detectLoops and i % 10 == 0 and self._detectLoop(sx, sy):
+ break
+
+ if i > self.maxLen / 2:
+ break
+
+ return sx, sy
+
+ def _detectLoop(self, xVals, yVals):
+ """ Detect closed loops and nodes in a streamline. """
+ x = xVals[-1]
+ y = yVals[-1]
+ D = np.array(
+ [np.hypot(x - xj, y - yj) for xj, yj in zip(xVals[:-1], yVals[:-1])]
+ )
+ return (D < 0.9 * self.dr).any()
+
+
+# See https://stackoverflow.com/questions/11578760/
+# matplotlib-control-capstyle-of-line-collection-large-number-of-lines
+# -----------------------------------------------------------------------------
+import types
+from matplotlib.backend_bases import GraphicsContextBase, RendererBase
+
+
+class GC(GraphicsContextBase):
+ def __init__(self):
+ super().__init__()
+ self._capstyle = "round"
+
+
+def custom_new_gc(self):
+ return GC()
+
+
+RendererBase.new_gc = types.MethodType(custom_new_gc, RendererBase)
+
+
+# -----------------------------------------------------------------------------
+Y, X = np.mgrid[-3:3:100j, -3:3:100j]
+U, V = -1 - X ** 2 + Y, 1 + X - X * Y ** 2
+speed = np.sqrt(U * U + V * V)
+
+fig = plt.figure(figsize=(8, 8))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, frameon=False)
+
+lengths = []
+colors = []
+lines = []
+
+cmap = plt.get_cmap("Blues_r")
+s = Streamlines(X, Y, U, V)
+for streamline in s.streamlines:
+ x, y = streamline
+ points = np.array([x, y]).T.reshape(-1, 1, 2)
+ segments = np.concatenate([points[:-1], points[1:]], axis=1)
+ n = len(segments)
+
+ D = np.sqrt(((points[1:] - points[:-1]) ** 2).sum(axis=-1))
+ L = D.cumsum().reshape(n, 1) + np.random.uniform(0, 1)
+ C = np.zeros((n, 3))
+ C[:] = (L * 1.5) % 1
+
+ C = cmap(((L * 1.5) % 1).ravel())
+
+ linewidths = np.zeros(n)
+ linewidths[:] = 2 - ((L.reshape(n) * 1.5) % 1)
+ line = LineCollection(segments, color=C, linewidth=linewidths)
+
+ lengths.append(L)
+ colors.append(C)
+ lines.append(line)
+
+ ax.add_collection(line)
+
+ax.set_xlim(-3, +3), ax.set_xticks([])
+ax.set_ylim(-3, +3), ax.set_yticks([])
+plt.savefig("../../figures/showcases/windmap.png", dpi=600)
+plt.show()
diff --git a/code/threed/bunnies.py b/code/threed/bunnies.py
new file mode 100644
index 0000000..c0a0a6d
--- /dev/null
+++ b/code/threed/bunnies.py
@@ -0,0 +1,134 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def ortho(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=float)
+ M[0, 0] = +2.0 / (right - left)
+ M[1, 1] = +2.0 / (top - bottom)
+ M[2, 2] = -2.0 / (zfar - znear)
+ M[3, 3] = 1.0
+ M[0, 2] = -(right + left) / float(right - left)
+ M[1, 3] = -(top + bottom) / float(top - bottom)
+ M[2, 3] = -(zfar + znear) / float(zfar - znear)
+ return M
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+
+def mesh(MVP, V, F, cmap=None, clip=True):
+ V = np.c_[V, np.ones(len(V))] @ MVP.T
+ V /= V[:, 3].reshape(-1, 1)
+ V = V[F]
+ T = V[:, :, :2]
+ Z = -V[:, :, 2].mean(axis=1)
+ zmin, zmax = Z.min(), Z.max()
+ Z = (Z - zmin) / (zmax - zmin)
+ I = np.argsort(Z)
+ T = T[I, :]
+ if cmap is not None:
+ C = plt.get_cmap(cmap)(Z)
+ C = C[I, :]
+ else:
+ C = 1.0, 1.0, 1.0, 0.5
+ return PolyCollection(
+ T, closed=True, linewidth=0.1, clip_on=clip, facecolor=C, edgecolor="black"
+ )
+
+
+fig = plt.figure(figsize=(6, 6))
+
+ax = plt.subplot(
+ 2, 2, 1, xlim=[-1, +1], ylim=[-1, +1], xticks=[], yticks=[], aspect=1, frameon=False
+)
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -2.5)
+proj = perspective(25, 1, 1, 100)
+ax.add_collection(mesh(proj @ view @ model, V, F, "magma", False))
+
+ax = plt.subplot(
+ 2, 2, 2, xlim=[-1, +1], ylim=[-1, +1], xticks=[], yticks=[], aspect=1, frameon=False
+)
+model = xrotate(90)
+view = np.eye(4)
+proj = ortho(-1, 1, -1, 1, 0, 100)
+ax.add_collection(mesh(proj @ view @ model, 2 * V, F))
+
+ax = plt.subplot(
+ 2, 2, 3, xlim=[-1, +1], ylim=[-1, +1], xticks=[], yticks=[], aspect=1, frameon=False
+)
+model = yrotate(90)
+view = np.eye(4)
+proj = ortho(-1, 1, -1, 1, 0, 100)
+ax.add_collection(mesh(proj @ view @ model, 2 * V, F))
+
+ax = plt.subplot(
+ 2, 2, 4, xlim=[-1, +1], ylim=[-1, +1], xticks=[], yticks=[], aspect=1, frameon=False
+)
+model = view = np.eye(4)
+proj = ortho(-1, 1, -1, 1, 0, 100)
+ax.add_collection(mesh(proj @ view @ model, 2 * V, F))
+
+plt.tight_layout()
+plt.savefig("../../figures/threed/bunnies.pdf")
+plt.show()
diff --git a/code/threed/bunny-1.py b/code/threed/bunny-1.py
new file mode 100644
index 0000000..ef25d3e
--- /dev/null
+++ b/code/threed/bunny-1.py
@@ -0,0 +1,35 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-1.pdf")
+plt.show()
diff --git a/code/threed/bunny-2.py b/code/threed/bunny-2.py
new file mode 100644
index 0000000..5e0a82b
--- /dev/null
+++ b/code/threed/bunny-2.py
@@ -0,0 +1,57 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+V = np.c_[V, np.ones(len(V))] @ perspective(25, 1, 1, 100).T
+V /= V[:, 3].reshape(-1, 1)
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-2.pdf")
+plt.show()
diff --git a/code/threed/bunny-3.py b/code/threed/bunny-3.py
new file mode 100644
index 0000000..bb867c7
--- /dev/null
+++ b/code/threed/bunny-3.py
@@ -0,0 +1,58 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+V += (0, 0, -3.5)
+V = np.c_[V, np.ones(len(V))] @ perspective(25, 1, 1, 100).T
+V /= V[:, 3].reshape(-1, 1)
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-3.pdf")
+plt.show()
diff --git a/code/threed/bunny-4.py b/code/threed/bunny-4.py
new file mode 100644
index 0000000..5d25aa0
--- /dev/null
+++ b/code/threed/bunny-4.py
@@ -0,0 +1,85 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V = np.c_[V, np.ones(len(V))] @ MVP.T
+V /= V[:, 3].reshape(-1, 1)
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-4.pdf")
+plt.show()
diff --git a/code/threed/bunny-5.py b/code/threed/bunny-5.py
new file mode 100644
index 0000000..faac679
--- /dev/null
+++ b/code/threed/bunny-5.py
@@ -0,0 +1,95 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V_ = np.c_[V, np.ones(len(V))] @ MVP.T
+V_ /= V_[:, 3].reshape(-1, 1)
+T = V_[F][..., :2]
+
+fig = plt.figure(figsize=(8, 8))
+for i, (fovy, z) in enumerate([(25, -3), (40, -2.0), (65, -1.25), (80, -1.0)]):
+ view = translate(0, 0, z)
+ proj = perspective(fovy, 1, 1, 100)
+ MVP = proj @ view @ model
+ V_ = np.c_[V, np.ones(len(V))] @ MVP.T
+ V_ /= V_[:, 3].reshape(-1, 1)
+ T = V_[F][..., :2]
+ ax = plt.subplot(2, 2, i + 1, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+ ax.axis("off")
+ collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+ )
+ ax.add_collection(collection)
+ ax.set_title("Aperture: %d" % fovy)
+
+plt.tight_layout()
+plt.savefig("../../figures/threed/bunny-5.pdf")
+plt.show()
diff --git a/code/threed/bunny-6.py b/code/threed/bunny-6.py
new file mode 100644
index 0000000..1555256
--- /dev/null
+++ b/code/threed/bunny-6.py
@@ -0,0 +1,85 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V = np.c_[V, np.ones(len(V))] @ MVP.T
+V /= V[:, 3].reshape(-1, 1)
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="0.9", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-6.pdf")
+plt.show()
diff --git a/code/threed/bunny-7.py b/code/threed/bunny-7.py
new file mode 100644
index 0000000..9b14610
--- /dev/null
+++ b/code/threed/bunny-7.py
@@ -0,0 +1,91 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V = np.c_[V, np.ones(len(V))] @ MVP.T
+V /= V[:, 3].reshape(-1, 1)
+V = V[F]
+
+T = V[:, :, :2]
+Z = -V[:, :, 2].mean(axis=1)
+I = np.argsort(Z)
+T = T[I, :]
+
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="0.9", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-7.pdf")
+plt.show()
diff --git a/code/threed/bunny-8.py b/code/threed/bunny-8.py
new file mode 100644
index 0000000..13f0bf3
--- /dev/null
+++ b/code/threed/bunny-8.py
@@ -0,0 +1,93 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V = np.c_[V, np.ones(len(V))] @ MVP.T
+V /= V[:, 3].reshape(-1, 1)
+V = V[F]
+
+T = V[:, :, :2]
+Z = -V[:, :, 2].mean(axis=1)
+zmin, zmax = Z.min(), Z.max()
+Z = (Z - zmin) / (zmax - zmin)
+C = plt.get_cmap("magma")(Z)
+I = np.argsort(Z)
+T, C = T[I, :], C[I, :]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor=C, edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-8.pdf")
+plt.show()
diff --git a/code/threed/bunny.obj b/code/threed/bunny.obj
new file mode 100644
index 0000000..18abc08
--- /dev/null
+++ b/code/threed/bunny.obj
@@ -0,0 +1,7474 @@
+# OBJ file format with ext .obj
+# vertex count = 2503
+# face count = 4968
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diff --git a/code/threed/bunny.py b/code/threed/bunny.py
new file mode 100644
index 0000000..1632660
--- /dev/null
+++ b/code/threed/bunny.py
@@ -0,0 +1,135 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4))
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[2, 0] = (right + left) / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[3, 2] = -2.0 * znear * zfar / (zfar - znear)
+ M[2, 3] = -1.0
+ return M.T
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(fovy / 360.0 * np.pi) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def scale(x, y, z):
+ return np.array(
+ [[x, 0, 0, 0], [0, y, 0, 0], [0, 0, z, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def zoom(z):
+ return scale(z, z, z)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def obj_load(filename):
+ V, Vi = [], []
+ with open(filename) as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ Vi.append([int(x) for x in values[1:4]])
+ return np.array(V), np.array(Vi) - 1
+
+
+# -----------------------------------------------------------------------------
+
+# Loading and centering
+V, Vi = obj_load("bunny.obj")
+V = (V - (V.max(axis=0) + V.min(axis=0)) / 2) / max(V.max(axis=0) - V.min(axis=0))
+
+# Computing model-view-projection matrix
+model = zoom(1.5) @ xrotate(20) @ yrotate(45)
+view = translate(0, 0, -4.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+# Applying MVP
+VH = np.c_[V, np.ones(len(V))] # Homogenous coordinates
+VT = VH @ MVP.T # Transformed coordinates
+VN = VT / VT[:, 3].reshape(-1, 1) # Normalization
+VS = VN[:, :3] # Normalized device coordinates
+
+# Actual faces
+V = VS[Vi]
+
+# Backface culling
+CW = (
+ (V[:, 1, 0] - V[:, 0, 0]) * (V[:, 1, 1] + V[:, 0, 1])
+ + (V[:, 2, 0] - V[:, 1, 0]) * (V[:, 2, 1] + V[:, 1, 1])
+ + (V[:, 0, 0] - V[:, 2, 0]) * (V[:, 0, 1] + V[:, 2, 1])
+)
+V = V[CW < 0]
+
+# Rendering as a collection of polygons (triangles)
+segments = V[:, :, :2]
+zbuffer = -V[:, :, 2].mean(axis=1)
+
+# Color according to depth
+zmin, zmax = zbuffer.min(), zbuffer.max()
+zbuffer = (zbuffer - zmin) / (zmax - zmin)
+colors = plt.get_cmap("magma")(zbuffer)
+
+# Sort triangles according to z buffer
+I = np.argsort(zbuffer)
+segments, colors = segments[I, :], colors[I, :]
+
+# Actual rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+ax.axis("off")
+
+for fc, ec, lw in [
+ ("None", "black", 6.0),
+ ("None", "white", 3.0),
+ (colors, "black", 0.25),
+]:
+ collection = PolyCollection(
+ segments, closed=True, linewidth=lw, facecolor=fc, edgecolor=ec
+ )
+ ax.add_collection(collection)
+
+plt.savefig("../../figures/threed/bunny.pdf", transparent=True)
+plt.show()
diff --git a/code/typography/projection-3d-gaussian.py b/code/typography/projection-3d-gaussian.py
new file mode 100644
index 0000000..b660b32
--- /dev/null
+++ b/code/typography/projection-3d-gaussian.py
@@ -0,0 +1,331 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.text import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.colors import LightSource
+from matplotlib.transforms import Affine2D
+from mpl_toolkits.mplot3d import Axes3D, art3d
+from matplotlib.font_manager import FontProperties
+
+
+def text3d(ax, xyz, s, zdir="z", size=0.1, angle=0, **kwargs):
+ x, y, z = xyz
+ if zdir == "y":
+ xy, z = (x, z), y
+ elif zdir == "x":
+ xy, z = (y, z), x
+ else:
+ xy, z = (x, y), z
+ path = TextPath((0, 0), s, size=size, prop=FontProperties(family="Roboto"))
+ V = path.vertices
+ V[:, 0] -= (V[:, 0].max() - V[:, 0].min()) / 2
+ trans = Affine2D().rotate(angle).translate(xy[0], xy[1])
+ path = PathPatch(trans.transform_path(path), **kwargs)
+ ax.add_patch(path)
+ art3d.pathpatch_2d_to_3d(path, z=z, zdir=zdir)
+
+
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, projection="3d")
+ax.view_init(elev=30, azim=-55)
+ax.set_axis_off()
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_zlim(0, 1)
+
+xrange = 0, 5
+yrange = 0, 5
+zrange = 0, 2
+xticks = (1.0, 0.2)
+yticks = (1.0, 0.2)
+zticks = (1.0, 0.2)
+
+maxrange = max(xrange[1] - xrange[0], yrange[1] - yrange[0], zrange[1] - zrange[0])
+xtransform = lambda x: (x - xrange[0]) / maxrange
+ytransform = lambda y: (y - yrange[0]) / maxrange
+ztransform = lambda z: (z - zrange[0]) / maxrange
+xmin, xmax = xtransform(xrange[0]), xtransform(xrange[1])
+ymin, ymax = ytransform(yrange[0]), ytransform(yrange[1])
+zmin, zmax = ztransform(zrange[0]), ztransform(zrange[1])
+
+
+# Minor gridlines
+linewidth, color = 0.25, "0.75"
+for x in np.arange(xrange[0], xrange[1] + xticks[1], xticks[1]):
+ x = xtransform(x)
+ ax.plot(
+ [x, x, x],
+ [ymin, ymax, ymax],
+ [zmin, zmin, zmax],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+for y in np.arange(yrange[0], yrange[1] + yticks[1], yticks[1]):
+ y = ytransform(y)
+ ax.plot(
+ [xmax, xmin, xmin],
+ [y, y, y],
+ [zmin, zmin, zmax],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+for z in np.arange(zrange[0], zrange[1] + zticks[1], zticks[1]):
+ z = ztransform(z)
+ ax.plot(
+ [xmax, xmin, xmin],
+ [ymax, ymax, ymin],
+ [z, z, z],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+
+# Major gridlines
+linewidth, color = 0.50, "0.50"
+for x in np.arange(xrange[0], xrange[1] + xticks[0], xticks[0]):
+ x = xtransform(x)
+ ax.plot(
+ [x, x, x],
+ [ymin, ymax, ymax],
+ [zmin, zmin, zmax],
+ linewidth=linewidth,
+ color=color,
+ zorder=-25,
+ )
+for y in np.arange(yrange[0], yrange[1] + yticks[0], yticks[0]):
+ y = ytransform(y)
+ ax.plot(
+ [xmax, xmin, xmin],
+ [y, y, y],
+ [zmin, zmin, zmax],
+ linewidth=linewidth,
+ color=color,
+ zorder=-25,
+ )
+for z in np.arange(zrange[0], zrange[1] + zticks[0], zticks[0]):
+ z = ztransform(z)
+ ax.plot(
+ [xmax, xmin, xmin],
+ [ymax, ymax, ymin],
+ [z, z, z],
+ linewidth=linewidth,
+ color=color,
+ zorder=-25,
+ )
+
+# Frame
+linewidth, color = 1.00, "0.00"
+ax.plot(
+ [xmin, xmin, xmax, xmax, xmin],
+ [ymin, ymax, ymax, ymin, ymin],
+ [zmin, zmin, zmin, zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-10,
+)
+ax.plot(
+ [xmin, xmin, xmax, xmax, xmin],
+ [ymax, ymax, ymax, ymax, ymax],
+ [zmin, zmax, zmax, zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-10,
+)
+ax.plot(
+ [xmin, xmin, xmin, xmin, xmin],
+ [ymin, ymin, ymax, ymax, ymin],
+ [zmin, zmax, zmax, zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-10,
+)
+
+# Minor ticks
+linewidth, length, color = 0.25, 0.02, "0.5"
+for x in np.arange(xrange[0], xrange[1] + xticks[1], xticks[1]):
+ x = xtransform(x)
+ ax.plot(
+ [x, x],
+ [ymin, ymin - length],
+ [zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+for y in np.arange(yrange[0], yrange[1] + yticks[1], yticks[1]):
+ y = ytransform(y)
+ ax.plot(
+ [xmax, xmax + length],
+ [y, y],
+ [zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+for z in np.arange(zrange[0], zrange[1] + zticks[1], zticks[1]):
+ z = ztransform(z)
+ ax.plot(
+ [xmin, xmin - length],
+ [ymin, ymin],
+ [z, z],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+
+# Major ticks & labels
+linewidth, length, color = 0.75, 0.04, "0.0"
+for x in np.arange(xrange[0], xrange[1] + xticks[0], xticks[0]):
+ x_ = xtransform(x)
+ ax.plot(
+ [x_, x_],
+ [ymin, ymin - length],
+ [zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+ text3d(
+ ax,
+ (x_, ymin - 2 * length, zmin),
+ "%.1f" % x,
+ size=0.04,
+ facecolor="black",
+ edgecolor="None",
+ )
+for y in np.arange(yrange[0], yrange[1] + yticks[0], yticks[0]):
+ y_ = ytransform(y)
+ ax.plot(
+ [xmax, xmax + length],
+ [y_, y_],
+ [zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+ text3d(
+ ax,
+ (xmax + 2 * length, y_, zmin),
+ "%.1f" % y,
+ angle=np.pi / 2,
+ size=0.04,
+ facecolor="black",
+ edgecolor="None",
+ )
+for z in np.arange(zrange[0], zrange[1] + zticks[1], zticks[0]):
+ z_ = ztransform(z)
+ ax.plot(
+ [xmin, xmin - length],
+ [ymin, ymin],
+ [z_, z_],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+ text3d(
+ ax,
+ (xmin - 2 * length, ymin, z_),
+ "%.1f" % z,
+ zdir="y",
+ size=0.04,
+ facecolor="black",
+ edgecolor="None",
+ )
+
+
+# Data
+n = 101
+X = Y = np.linspace(-1, 1, n)
+X, Y = np.meshgrid(X, Y)
+R = np.sqrt(5 * (X ** 2 + Y ** 2))
+Z = 0.5 * np.exp(-0.5 * R * R)
+X, Y = (1 + X) / 2, (1 + Y) / 2
+
+# Plot
+C = np.ones((n, n, 3))
+I1 = LightSource(azdeg=0, altdeg=25).hillshade(Z).reshape(n, n, 1)
+I2 = LightSource(azdeg=120, altdeg=25).hillshade(Z).reshape(n, n, 1)
+I3 = LightSource(azdeg=240, altdeg=25).hillshade(Z).reshape(n, n, 1)
+C = 0.5 * C + (I1 * (1, 0, 0) + I2 * (0, 1, 0) + I3 * (0, 0, 1))
+C = np.minimum(C, (1, 1, 1))
+C[::2, ::2] = C[1::2, 1::2] = 0.0, 0.0, 0.0
+
+
+# ax.plot_wireframe(X, Y, Z, rstride=n-1, cstride=n-1, color="black",
+# linewidth=3, antialiased=True, zorder=25)
+ax.plot_wireframe(
+ X,
+ Y,
+ Z,
+ rstride=1,
+ cstride=1,
+ color="black",
+ linewidth=5,
+ antialiased=True,
+ zorder=10,
+)
+ax.plot_wireframe(
+ X,
+ Y,
+ Z,
+ rstride=1,
+ cstride=1,
+ color="white",
+ linewidth=2,
+ antialiased=True,
+ zorder=20,
+)
+ax.plot_surface(
+ X,
+ Y,
+ Z,
+ rstride=5,
+ cstride=5,
+ facecolors=C,
+ shade=False,
+ edgecolor="None",
+ linewidth=0,
+ antialiased=True,
+ zorder=30,
+)
+
+text3d(
+ ax,
+ ((xmin + xmax) / 2, ymax, 1.05 * zmax),
+ "X axis",
+ zdir="y",
+ size=0.05,
+ facecolor="black",
+ edgecolor="None",
+)
+text3d(
+ ax,
+ (xmin, (ymin + ymax) / 2, 1.05 * zmax),
+ "Y axis",
+ zdir="x",
+ size=0.05,
+ facecolor="black",
+ edgecolor="None",
+)
+text3d(
+ ax,
+ (1.05 * xmax, ymax, (zmin + zmax) / 2),
+ "Z axis",
+ angle=np.pi / 2,
+ zdir="y",
+ size=0.05,
+ facecolor="black",
+ edgecolor="None",
+)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/projection-3d-gaussian.pdf")
+plt.show()
diff --git a/code/typography/text-outline.py b/code/typography/text-outline.py
new file mode 100644
index 0000000..7fc8f21
--- /dev/null
+++ b/code/typography/text-outline.py
@@ -0,0 +1,58 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patheffects import Stroke, Normal
+
+fig = plt.figure(figsize=(8, 3))
+ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+family = "Pacifico"
+size = 100
+cmap = plt.cm.magma
+text = "Matplotlib"
+
+for x in np.linspace(0, 1, 20):
+ lw, color = x * 225, cmap(1 - x)
+ t = ax.text(
+ 0.5,
+ 0.45,
+ text,
+ size=size,
+ color="none",
+ weight="bold",
+ va="center",
+ ha="center",
+ family=family,
+ zorder=-lw,
+ )
+ t.set_path_effects([Stroke(linewidth=lw + 1, foreground="black")])
+ t = ax.text(
+ 0.5,
+ 0.45,
+ text,
+ size=size,
+ color="black",
+ weight="bold",
+ va="center",
+ ha="center",
+ family=family,
+ zorder=-lw + 1,
+ )
+ t.set_path_effects([Stroke(linewidth=lw, foreground=color)])
+t = ax.text(
+ 1.0,
+ 0.01,
+ "https://matplotlib.org ",
+ va="bottom",
+ ha="right",
+ size=10,
+ color="white",
+ family="Roboto",
+ alpha=0.50,
+)
+
+plt.savefig("../../figures/typography/text-outline.pdf")
+plt.show()
diff --git a/code/typography/text-starwars.py b/code/typography/text-starwars.py
new file mode 100644
index 0000000..f40ba0d
--- /dev/null
+++ b/code/typography/text-starwars.py
@@ -0,0 +1,57 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate the use of TextPath and access to raw vertices
+# ----------------------------------------------------------------------------
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+
+fig = plt.figure(figsize=(4.25, 2))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, xlim=[-40, 40], ylim=[-1, 25])
+ax.axis("off")
+
+text = [
+ "Beautiful is better than ugly.",
+ "Explicit is better than implicit.",
+ "Simple is better than complex.",
+ "Complex is better than complicated.",
+ "Flat is better than nested.",
+ "Sparse is better than dense.",
+ "Readability counts.",
+]
+
+y = 0
+size = 6
+xfactor = 1 / 50
+yfactor = 1 / 120
+
+for i, line in enumerate(text[::-1]):
+ path = TextPath((0, 0), line, size=size)
+ V = path.vertices
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ ymin, ymax = V[:, 1].min(), V[:, 1].max()
+
+ # X centering
+ V[:, 0] -= (xmax + xmin) / 2
+
+ # Moving whole text at y coordinates
+ V[:, 1] += y
+
+ # Rescaling along y
+ V[:, 1] *= 1 - (V[:, 1] * yfactor)
+
+ # Rescaling along x
+ V[:, 0] *= 1 - (V[:, 1] * xfactor)
+
+ # Update interlines
+ y += size * (1 - ymin * yfactor)
+
+ # Display
+ patch = PathPatch(path, facecolor="%.2f" % (i / 10), linewidth=0, clip_on=False)
+ ax.add_artist(patch)
+
+plt.savefig("../../figures/typography/text-starwars.pdf")
+plt.show()
diff --git a/code/typography/tick-labels-variation.py b/code/typography/tick-labels-variation.py
new file mode 100644
index 0000000..672d154
--- /dev/null
+++ b/code/typography/tick-labels-variation.py
@@ -0,0 +1,105 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+
+# Style
+# -----------------------------------------------------------------------------
+plt.rc("font", family="Roboto Condensed")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+fig = plt.figure(figsize=(8, 2))
+
+#
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 1, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.05))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.2f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.01))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+ax.tick_params(axis="both", which="major")
+labels = ax.get_xticklabels()
+for i, label in enumerate(labels):
+ if i in [1, 6, 11, 16, 21]:
+ label.set_font("Roboto")
+ label.set_size(8.5)
+
+#
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 2, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.05))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.2f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.01))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+labels = ax.get_xticklabels()
+for i, label in enumerate(labels):
+ if i not in [1, 6, 11, 16, 21]:
+ label.set_size(7)
+
+#
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 3, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.05))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.2f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.01))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+labels = ax.get_xticklabels()
+for i, label in enumerate(labels):
+ if i not in [1, 6, 11, 16, 21]:
+ label.set_weight(200)
+
+
+#
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 4, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.05))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.2f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.01))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+labels = ax.get_xticklabels()
+for i, label in enumerate(labels):
+ if i in [1, 6, 11, 16, 21]:
+ label.set_color("white")
+ label.set_weight(600)
+ label.set_bbox(
+ dict(
+ boxstyle="round,pad=.25",
+ linewidth=0.5,
+ facecolor="black",
+ edgecolor="black",
+ )
+ )
+
+# Show
+# -----------------------------------------------------------------------------
+plt.tight_layout()
+plt.savefig("../../figures/typography/tick-labels-variation.pdf")
+plt.show()
diff --git a/code/typography/typography-font-stacks.py b/code/typography/typography-font-stacks.py
new file mode 100644
index 0000000..5d76c3d
--- /dev/null
+++ b/code/typography/typography-font-stacks.py
@@ -0,0 +1,70 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.font_manager import findfont, FontProperties
+
+
+def font_text(x, y, text, family, dy=0):
+
+ ax.text(x, y + dy, text, family=family, size=size, va="bottom")
+ font = findfont(FontProperties(family=family))
+ filename = os.path.basename(font)
+ ax.text(
+ x,
+ y,
+ filename,
+ va="top",
+ family="Roboto Condensed",
+ weight=400,
+ size=10,
+ color="0.5",
+ )
+
+
+plt.figure(figsize=(8, 2), dpi=100)
+size = 20
+dx = 15
+xmin, xmax = -1, 4 * dx - 5
+
+ax = plt.subplot(
+ 3, 1, 1, frameon=False, xlim=(xmin, xmax), ylim=(-1, 1), xticks=[], yticks=[]
+)
+font_text(0 * dx, 0, "Serif", "Serif")
+font_text(1 * dx, 0, "Sans", "Sans")
+font_text(2 * dx, 0, "Monospace", "Monospace")
+font_text(3 * dx, 0, "Cursive", "Cursive", -0.25)
+
+
+ax = plt.subplot(
+ 3, 1, 2, frameon=False, xlim=(xmin, xmax), ylim=(-1, 1), xticks=[], yticks=[]
+)
+font_text(0 * dx, 0, "Serif", "Roboto Slab")
+font_text(1 * dx, 0, "Sans", "Roboto Condensed")
+font_text(2 * dx, 0, "Monospace", "Roboto Mono")
+font_text(3 * dx, 0, "Cursive", "Merienda", -0.25)
+
+
+ax = plt.subplot(
+ 3, 1, 3, frameon=False, xlim=(xmin, xmax), ylim=(-1, 1), xticks=[], yticks=[]
+)
+font_text(0 * dx, 0, "Serif", "Source Serif Pro")
+font_text(1 * dx, 0, "Sans", "Source Sans Pro")
+font_text(2 * dx, 0, "Monospace", "Source Code Pro")
+font_text(3 * dx, 0, "Cursive", "ITC Zapf Chancery")
+
+# ax.text(0*dx, 0, "Serif", family="Roboto Slab", size=size)
+# ax.text(0*dx, -0.5, find("Roboto Slab"),
+# family="Roboto Slab", weight=300, size=8, color="0.25")
+
+# ax.text(1*dx, 0, "Sans-serif", family="Roboto Condensed", size=size)
+# ax.text(2*dx, 0, "Monospace", family="Roboto Mono", size=size)
+# ax.text(3*dx, 0, "Cursive", family="Pacifico", size=size)
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/typography-font-stacks.pdf", dpi=100)
+plt.show()
diff --git a/code/typography/typography-legibility.py b/code/typography/typography-legibility.py
new file mode 100644
index 0000000..ea01adc
--- /dev/null
+++ b/code/typography/typography-legibility.py
@@ -0,0 +1,74 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patheffects import Stroke, Normal
+
+dpi = 200
+figsize = (4.25, 3.0)
+
+shape = 20, 80
+I = np.random.uniform(0, 1, shape)
+cmap = plt.cm.get_cmap("gray")
+x, y = 0.5, 2
+
+fig = plt.figure(figsize=figsize, dpi=dpi)
+
+# Regular
+ax = fig.add_subplot(4, 2, 1, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(x, y, "Read me", color="black", transform=ax.transData)
+
+ax = fig.add_subplot(4, 2, 2, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(x, y, "Read me", color="white", transform=ax.transData)
+
+# Bold
+ax = fig.add_subplot(4, 2, 3, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(x, y, "Read me", color="black", weight="bold", transform=ax.transData)
+
+ax = fig.add_subplot(4, 2, 4, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(x, y, "Read me", color="white", weight="bold", transform=ax.transData)
+
+# Boxes
+ax = fig.add_subplot(4, 2, 5, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(
+ x,
+ y,
+ "Read me",
+ color="black",
+ transform=ax.transData,
+ bbox={"facecolor": "white", "edgecolor": "None", "pad": 1, "alpha": 0.75},
+)
+
+ax = fig.add_subplot(4, 2, 6, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(
+ x,
+ y,
+ "Read me",
+ color="white",
+ transform=ax.transData,
+ bbox={"facecolor": "black", "edgecolor": "None", "pad": 1, "alpha": 0.75},
+)
+
+ax = fig.add_subplot(4, 2, 7, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+text = ax.text(x, y, "Read me", color="black", transform=ax.transData)
+text.set_path_effects([Stroke(linewidth=1.5, foreground="white"), Normal()])
+
+ax = fig.add_subplot(4, 2, 8, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+text = ax.text(x, y, "Read me", color="white", transform=ax.transData)
+text.set_path_effects([Stroke(linewidth=1.5, foreground="black"), Normal()])
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/typography-legibility.pdf", dpi=600)
+plt.show()
diff --git a/code/typography/typography-math-stacks.py b/code/typography/typography-math-stacks.py
new file mode 100644
index 0000000..c2e47a9
--- /dev/null
+++ b/code/typography/typography-math-stacks.py
@@ -0,0 +1,51 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.rcParams["text.usetex"] = False
+
+
+def plot(family):
+ plt.rcParams["mathtext.fontset"] = family
+ fig = plt.figure(figsize=(3, 1.75), dpi=100)
+ ax = plt.subplot(
+ 1, 1, 1, frameon=False, xlim=(-1, 1), ylim=(-0.5, 1.5), xticks=[], yticks=[]
+ )
+ ax.text(
+ 0,
+ 0.0,
+ r"$\frac{\pi}{4} = \sum_{k=0}^\infty\frac{(-1)^k}{2k+1}$",
+ size=32,
+ ha="center",
+ va="bottom",
+ )
+ ax.text(
+ 0,
+ -0.1,
+ 'mathtext.fontset = "%s"' % family,
+ size=14,
+ ha="center",
+ va="top",
+ family="Roboto Condensed",
+ color="0.5",
+ )
+
+ # plt.tight_layout()
+ plt.savefig("../../figures/typography/typography-math-%s.pdf" % family, dpi=600)
+ plt.show()
+
+
+plot("cm")
+plot("stix")
+plot("stixsans")
+plot("dejavusans")
+plot("dejavuserif")
+plot("custom")
+
+# Process result with
+# pdfjam --nup 3x2 ../figures/typography-math-cm.pdf ../figures/typography-math-custom.pdf ../figures/typography-math-dejavusans.pdf ../figures/typography-math-dejavuserif.pdf ../figures/typography-math-stix.pdf ../figures/typography-math-stixsans.pdf --outfile ../figures/typography-math-stacks.pdf; pdfcrop ../figures/typography-math-stacks.pdf ../figures/typography-math-stacks.pdf
diff --git a/code/typography/typography-matters.py b/code/typography/typography-matters.py
new file mode 100644
index 0000000..76ab14a
--- /dev/null
+++ b/code/typography/typography-matters.py
@@ -0,0 +1,95 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib
+from matplotlib import ticker
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as path_effects
+
+plt.figure(figsize=(8, 5), dpi=100)
+
+ax = plt.subplot(2, 1, 1, aspect=0.25)
+ax.text(
+ 0.5,
+ 0.5,
+ "Typography matters",
+ family="Times New Roman",
+ size=60,
+ color=".85",
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+ax.set_title(
+ "Typography is an important dimension of a scientific figure",
+ x=0,
+ y=0.965,
+ horizontalalignment="left",
+ verticalalignment="baseline",
+)
+ax.set_xticks(np.linspace(0, 1, 21))
+
+
+ax = plt.subplot(2, 1, 2, aspect=0.25)
+font = matplotlib.font_manager.FontProperties(
+ family="Roboto Condensed", weight="regular", size=9
+)
+ax.set_xticks(np.linspace(0, 1, 21))
+ax.set_yticks(np.linspace(0, 1, 5))
+
+for label in ax.get_xticklabels():
+ label.set_fontproperties(font)
+for label in ax.get_yticklabels():
+ label.set_fontproperties(font)
+
+for label in ax.get_xticklabels()[1:-1:2]:
+ label.set_size(7.5)
+ label.set_weight("light")
+
+for label in ax.get_yticklabels()[1:-1:]:
+ label.set_size(7.5)
+ label.set_weight("light")
+
+ax.text(
+ 0.5,
+ 0.5,
+ "Typography",
+ family="Times New Roman",
+ size=80,
+ color=".85",
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+ax.text(
+ 0.8375,
+ 0.725,
+ "matters",
+ family="Times New Roman",
+ size=30,
+ color="0.25",
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+
+font = matplotlib.font_manager.FontProperties(
+ family="Roboto Slab", weight="regular", size=15
+)
+text = ax.set_title(
+ "Typography is an important dimension of a scientific figure",
+ fontproperties=font,
+ x=0,
+ y=0.975,
+ horizontalalignment="left",
+ verticalalignment="baseline",
+)
+text.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/typography-matters.pdf", dpi=100)
+plt.savefig("../../figures/typography/typography-matters.png", dpi=300)
+plt.show()
diff --git a/code/typography/typography-text-path.py b/code/typography/typography-text-path.py
new file mode 100644
index 0000000..01300b5
--- /dev/null
+++ b/code/typography/typography-text-path.py
@@ -0,0 +1,113 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import scipy
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.font_manager import FontProperties
+
+
+def interpolate(X, Y, T):
+ dR = (np.diff(X) ** 2 + np.diff(Y) ** 2) ** 0.5
+ R = np.zeros_like(X)
+ R[1:] = np.cumsum(dR)
+ return np.interp(T, R, X), np.interp(T, R, Y), R[-1]
+
+
+def contour(X, Y, text, offset=0):
+
+ # Interpolate text along curve
+ # X0,Y0 for position + X1,Y1 for normal vectors
+ path = TextPath(
+ (0, -0.75), text, prop=FontProperties(size=2, family="Roboto", weight="bold")
+ )
+ V = path.vertices
+ X0, Y0, D = interpolate(X, Y, offset + V[:, 0])
+ X1, Y1, _ = interpolate(X, Y, offset + V[:, 0] + 0.1)
+
+ # Here we interpolate the original path to get the "remainder"
+ # (path minus text)
+ X, Y, _ = interpolate(X, Y, np.linspace(V[:, 0].max() + 1, D - 1, 200))
+ plt.plot(
+ X, Y, color="black", linewidth=0.5, markersize=1, marker="o", markevery=[0, -1]
+ )
+
+ # Transform text vertices
+ dX, dY = X1 - X0, Y1 - Y0
+ norm = np.sqrt(dX ** 2 + dY ** 2)
+ dX, dY = dX / norm, dY / norm
+ X0 += -V[:, 1] * dY
+ Y0 += +V[:, 1] * dX
+ V[:, 0], V[:, 1] = X0, Y0
+
+ # Faint outline
+ patch = PathPatch(
+ path,
+ facecolor="white",
+ zorder=10,
+ alpha=0.25,
+ edgecolor="white",
+ linewidth=1.25,
+ )
+ ax.add_artist(patch)
+
+ # Actual text
+ patch = PathPatch(
+ path, facecolor="black", zorder=30, edgecolor="black", linewidth=0.0
+ )
+ ax.add_artist(patch)
+
+
+# Some data
+n = 64
+X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+)
+Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+
+def f(x, y):
+ return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-(x ** 2) - y ** 2)
+
+
+n = 100
+x = np.linspace(-3, 3, n)
+y = np.linspace(-3, 3, n)
+X, Y = np.meshgrid(x, y)
+Z = 0.5 * f(X, Y)
+
+
+fig = plt.figure(figsize=(10, 5), dpi=100)
+levels = 10
+
+# Regular contour with straight labels
+ax = fig.add_subplot(1, 2, 1, aspect=1, xticks=[], yticks=[])
+CF = plt.contourf(Z, origin="lower", levels=levels)
+CS = plt.contour(Z, origin="lower", levels=levels, colors="black", linewidths=0.5)
+ax.clabel(CS, CS.levels)
+
+# Regular contour with curved labels
+# ! aspect=1 is critical here, else text path would be deformed
+ax = fig.add_subplot(1, 2, 2, aspect=1, xticks=[], yticks=[])
+CF = plt.contourf(Z, origin="lower", levels=levels)
+CS = plt.contour(
+ Z, origin="lower", levels=levels, alpha=0, colors="black", linewidths=0.5
+)
+
+for level, collection in zip(CS.levels[:], CS.collections[:]):
+ for path in collection.get_paths():
+ V = np.array(path.vertices)
+ text = "%.3f" % level
+ if level == 0.0:
+ text = " DO NOT CROSS •••" * 8
+ contour(V[:, 0], V[:, 1], text)
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/typography-text-path.png", dpi=600)
+plt.savefig("../../figures/typography/typography-text-path.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/3d/README.md b/code/unsorted/3d/README.md
new file mode 100644
index 0000000..01e73ff
--- /dev/null
+++ b/code/unsorted/3d/README.md
@@ -0,0 +1,2 @@
+
+Temporary non commented / documented /cleaned code !!!
diff --git a/code/unsorted/3d/__pycache__/glm.cpython-37.pyc b/code/unsorted/3d/__pycache__/glm.cpython-37.pyc
new file mode 100644
index 0000000..6f7bf00
Binary files /dev/null and b/code/unsorted/3d/__pycache__/glm.cpython-37.pyc differ
diff --git a/code/unsorted/3d/__pycache__/plot.cpython-37.pyc b/code/unsorted/3d/__pycache__/plot.cpython-37.pyc
new file mode 100644
index 0000000..bbc1570
Binary files /dev/null and b/code/unsorted/3d/__pycache__/plot.cpython-37.pyc differ
diff --git a/code/unsorted/3d/axis.py b/code/unsorted/3d/axis.py
new file mode 100644
index 0000000..16d6d69
--- /dev/null
+++ b/code/unsorted/3d/axis.py
@@ -0,0 +1,13 @@
+import glm
+import plot
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+ plt.show()
diff --git a/code/unsorted/3d/bar.py b/code/unsorted/3d/bar.py
new file mode 100644
index 0000000..4d5cf23
--- /dev/null
+++ b/code/unsorted/3d/bar.py
@@ -0,0 +1,29 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+
+ n = 24
+ X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ )
+ Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+ cmap = plt.get_cmap("magma")
+ norm = mpl.colors.Normalize(vmin=-0.25, vmax=0.75)
+ facecolors = cmap(norm(Y))[..., :3]
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+ plot.bar(ax, camera, Y, facecolors=facecolors, edgecolors=facecolors * 0.25)
+
+ plt.show()
diff --git a/code/unsorted/3d/bunnies.py b/code/unsorted/3d/bunnies.py
new file mode 100644
index 0000000..950b598
--- /dev/null
+++ b/code/unsorted/3d/bunnies.py
@@ -0,0 +1,60 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+# Wavefront loader (only vertices and faces)
+def obj_load(filename):
+ V, Vi = [], []
+ with open(filename) as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ Vi.append([int(x) for x in values[1:4]])
+ return np.array(V), np.array(Vi) - 1
+
+
+# Model loading
+V, F = obj_load("bunny.obj")
+V = glm.fit_unit_cube(V)
+
+
+fig = plt.figure(figsize=(8, 8))
+
+ax = plt.subplot(221)
+camera = glm.camera(20, 45, 1.25, "perspective")
+plot.axis(ax, camera, ticks=False)
+plot.mesh(ax, camera, V, F, facecolor="black", edgecolor="black", linewidth=2.5)
+plot.mesh(ax, camera, V, F, cmap=plt.get_cmap("magma"), edgecolor="none")
+ax.text(0.99, 0.99, "Perpective", transform=ax.transAxes, ha="right", va="top")
+
+white = (1.0, 1.0, 1.0, 0.75)
+black = (0.0, 0.0, 0.0, 1.00)
+
+ax = plt.subplot(222)
+camera = glm.camera(90, 0, 2, "ortho")
+plot.axis(ax, camera, ticks=False)
+plot.mesh(ax, camera, V, F, linewidth=0.25, facecolor=white, edgecolor=black)
+ax.text(0.99, 0.99, "Orthographic (XZ)", transform=ax.transAxes, ha="right", va="top")
+
+ax = plt.subplot(223)
+camera = glm.camera(0, 90, 2, "ortho")
+plot.axis(ax, camera, ticks=False)
+plot.mesh(ax, camera, V, F, linewidth=0.25, facecolor=white, edgecolor=black)
+ax.text(0.99, 0.99, "Orthographic (XY)", transform=ax.transAxes, ha="right", va="top")
+
+ax = plt.subplot(224)
+camera = glm.camera(0, 0, 2, "ortho")
+plot.axis(ax, camera, ticks=False)
+plot.mesh(ax, camera, V, F, linewidth=0.25, facecolor=white, edgecolor=black)
+ax.text(0.99, 0.99, "Orthographic (ZY)", transform=ax.transAxes, ha="right", va="top")
+
+plt.tight_layout()
+plt.show()
diff --git a/code/unsorted/3d/bunny.obj b/code/unsorted/3d/bunny.obj
new file mode 100644
index 0000000..18abc08
--- /dev/null
+++ b/code/unsorted/3d/bunny.obj
@@ -0,0 +1,7474 @@
+# OBJ file format with ext .obj
+# vertex count = 2503
+# face count = 4968
+v -3.4101800e-003 1.3031957e-001 2.1754370e-002
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diff --git a/code/unsorted/3d/bunny.py b/code/unsorted/3d/bunny.py
new file mode 100644
index 0000000..3f5989c
--- /dev/null
+++ b/code/unsorted/3d/bunny.py
@@ -0,0 +1,43 @@
+import glm
+import plot
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# Wavefront loader (only vertices and faces)
+def obj_load(filename):
+ V, Vi = [], []
+ with open(filename) as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ Vi.append([int(x) for x in values[1:4]])
+ return np.array(V), np.array(Vi) - 1
+
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+ax.axis("off")
+
+camera = glm.camera(20, 45, 1.15, "perspective")
+plot.axis(ax, camera)
+
+V, F = obj_load("bunny.obj")
+V = glm.fit_unit_cube(V)
+plot.mesh(
+ ax,
+ camera,
+ V,
+ F,
+ cmap=plt.get_cmap("magma"),
+ linewidth=0.5,
+ edgecolor=(0, 0, 0, 0.5),
+)
+
+plt.show()
diff --git a/code/unsorted/3d/contour.py b/code/unsorted/3d/contour.py
new file mode 100644
index 0000000..5ff6cf0
--- /dev/null
+++ b/code/unsorted/3d/contour.py
@@ -0,0 +1,34 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+
+ n = 32
+ X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ )
+ Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+ def f(x, y):
+ return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-(x ** 2) - y ** 2)
+
+ n = 100
+ x = np.linspace(-3, 3, n)
+ y = np.linspace(-3, 3, n)
+ X, Y = np.meshgrid(x, y)
+ Z = 0.5 * f(X, Y)
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+ plot.contour(ax, camera, Z)
+
+plt.show()
diff --git a/code/unsorted/3d/glm.py b/code/unsorted/3d/glm.py
new file mode 100644
index 0000000..0156edf
--- /dev/null
+++ b/code/unsorted/3d/glm.py
@@ -0,0 +1,138 @@
+import numpy as np
+
+
+def normalize(X):
+ return X / (1e-16 + np.sqrt((np.array(X) ** 2).sum(axis=-1)))[..., np.newaxis]
+
+
+def rescale(V, vmin=0, vmax=1):
+ Vmin, Vmax = V.min(), V.max()
+ return vmin + (vmax - vmin) * (V - Vmin) / (Vmax - Vmin)
+
+
+def center(V):
+ return V - (V.min(axis=-1) + V.max(axis=-1)) / 2
+
+
+def clip(V, vmin=0, vmax=1):
+ return np.minimum(np.maximum(V, vmin), vmax)
+
+
+def degrees(angle):
+ return 180 * angle / np.pi
+
+
+def radians(angle):
+ return np.pi * angle / 180
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def ortho(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 / (right - left)
+ M[1, 1] = +2.0 / (top - bottom)
+ M[2, 2] = -2.0 / (zfar - znear)
+ M[3, 3] = 1.0
+ M[0, 2] = -(right + left) / float(right - left)
+ M[1, 3] = -(top + bottom) / float(top - bottom)
+ M[2, 3] = -(zfar + znear) / float(zfar - znear)
+ return M
+
+
+def scale(x=1, y=1, z=1):
+ return np.array(
+ [[x, 0, 0, 0], [0, y, 0, 0], [0, 0, z, 0], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def translate(x=0, y=0, z=0):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def xrotate(theta=0):
+ t = radians(theta)
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def yrotate(theta=0):
+ t = radians(theta)
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def zrotate(theta=0):
+ t = radians(theta)
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, -s, 0, 0], [s, c, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def fit_unit_cube(V):
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ ymin, ymax = V[:, 1].min(), V[:, 1].max()
+ zmin, zmax = V[:, 2].min(), V[:, 2].max()
+ scale = max([xmax - xmin, ymax - ymin, zmax - zmin])
+ V /= scale
+ V[:, 0] -= (xmax + xmin) / 2 / scale
+ V[:, 1] -= (ymax + ymin) / 2 / scale
+ V[:, 2] -= (zmax + zmin) / 2 / scale
+ return V
+
+
+def transform(V, mvp):
+ V = np.asarray(V)
+ shape = V.shape
+ V = V.reshape(-1, 3)
+ ones = np.ones(len(V), dtype=np.float32)
+ V = np.c_[V.astype(np.float32), ones] # Homogenous coordinates
+ V = V @ mvp.T # Transformed coordinates
+ V = V / V[:, 3].reshape(-1, 1) # Normalization
+ V = V[:, :3] # Normalized device coordinates
+ return V.reshape(shape)
+
+
+def frontback(T):
+ Z = (
+ (T[:, 1, 0] - T[:, 0, 0]) * (T[:, 1, 1] + T[:, 0, 1])
+ + (T[:, 2, 0] - T[:, 1, 0]) * (T[:, 2, 1] + T[:, 1, 1])
+ + (T[:, 0, 0] - T[:, 2, 0]) * (T[:, 0, 1] + T[:, 2, 1])
+ )
+ return Z < 0, Z >= 0
+
+
+def camera(xrotation=25, yrotation=45, zoom=1, mode="perspective"):
+ xrotation = min(max(xrotation, 0), 90)
+ yrotation = min(max(yrotation, 0), 90)
+ zoom = max(0.1, zoom)
+ model = scale(zoom, zoom, zoom) @ xrotate(xrotation) @ yrotate(yrotation)
+ view = translate(0, 0, -4.5)
+ if mode == "ortho":
+ proj = ortho(-1, +1, -1, +1, 1, 100)
+ else:
+ proj = perspective(25, 1, 1, 100)
+ return proj @ view @ model
diff --git a/code/unsorted/3d/platonic-solids.pdf b/code/unsorted/3d/platonic-solids.pdf
new file mode 100644
index 0000000..7f2bd13
Binary files /dev/null and b/code/unsorted/3d/platonic-solids.pdf differ
diff --git a/code/unsorted/3d/platonic-solids.png b/code/unsorted/3d/platonic-solids.png
new file mode 100644
index 0000000..620af2e
Binary files /dev/null and b/code/unsorted/3d/platonic-solids.png differ
diff --git a/code/unsorted/3d/platonic-solids.py b/code/unsorted/3d/platonic-solids.py
new file mode 100644
index 0000000..dc6c94c
--- /dev/null
+++ b/code/unsorted/3d/platonic-solids.py
@@ -0,0 +1,334 @@
+import glm
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def tetrahedron():
+ """ Tetrahedron with 4 faces, 6 edges and 4 vertices """
+ a = 2 * np.pi / 3
+ vertices = [
+ (0, 0.5, 0),
+ (0.5 * np.cos(0 * a), -0.25, 0.5 * np.sin(0 * a)),
+ (0.5 * np.cos(1 * a), -0.25, 0.5 * np.sin(1 * a)),
+ (0.5 * np.cos(2 * a), -0.25, 0.5 * np.sin(2 * a)),
+ ]
+ faces = [(1, 2, 3), (1, 2, 0), (2, 3, 0), (3, 1, 0)]
+ return np.array(vertices), np.array(faces)
+
+
+def octahedron():
+ """ Octahedron with 8 faces, 12 edges and 6 vertices """
+
+ r = 0.5 * 1 / np.sqrt(2)
+ vertices = [
+ (0, 0.5, 0),
+ (0, -0.5, 0),
+ (-r, 0, -r),
+ (r, 0, -r),
+ (r, 0, r),
+ (-r, 0, r),
+ ]
+ faces = [
+ (2, 3, 0),
+ (3, 4, 0),
+ (4, 5, 0),
+ (5, 2, 0),
+ (3, 2, 1),
+ (4, 3, 1),
+ (5, 4, 1),
+ (2, 5, 1),
+ ]
+ return np.array(vertices), np.array(faces)
+
+
+def dodecahedron():
+ """ Regular dodecahedron with 12 faces, 30 edges and 20 vertices """
+
+ r = (1 + np.sqrt(5)) / 2
+ vertices = [
+ (-1, -1, +1),
+ (r, 1 / r, 0),
+ (r, -1 / r, 0),
+ (-r, 1 / r, 0),
+ (-r, -1 / r, 0),
+ (0, r, 1 / r),
+ (0, r, -1 / r),
+ (1 / r, 0, -r),
+ (-1 / r, 0, -r),
+ (0, -r, -1 / r),
+ (0, -r, 1 / r),
+ (1 / r, 0, r),
+ (-1 / r, 0, r),
+ (+1, +1, -1),
+ (+1, +1, +1),
+ (-1, +1, -1),
+ (-1, +1, +1),
+ (+1, -1, -1),
+ (+1, -1, +1),
+ (-1, -1, -1),
+ ]
+ # faces = [ (19, 3, 2), (12, 19, 2), (15, 12, 2),
+ # (8, 14, 2), (18, 8, 2), (3, 18, 2),
+ # (20, 5, 4), (9, 20, 4), (16, 9, 4),
+ # (13, 17, 4), (1, 13, 4), (5, 1, 4),
+ # (7, 16, 4), (6, 7, 4), (17, 6, 4),
+ # (6, 15, 2), (7, 6, 2), (14, 7, 2),
+ # (10, 18, 3), (11, 10, 3), (19, 11, 3),
+ # (11, 1, 5), (10, 11, 5), (20, 10, 5),
+ # (20, 9, 8), (10, 20, 8), (18, 10, 8),
+ # (9, 16, 7), (8, 9, 7), (14, 8, 7),
+ # (12, 15, 6), (13, 12, 6), (17, 13, 6),
+ # (13, 1, 11), (12, 13, 11), (19, 12, 11) ]
+ faces = [
+ (19, 3, 2, 15, 12),
+ (8, 14, 2, 3, 18),
+ (20, 5, 4, 16, 9),
+ (13, 17, 4, 5, 1),
+ (7, 16, 4, 17, 6),
+ (6, 15, 2, 14, 7),
+ (10, 18, 3, 19, 11),
+ (11, 1, 5, 20, 10),
+ (20, 9, 8, 18, 10),
+ (9, 16, 7, 14, 8),
+ (12, 15, 6, 17, 13),
+ (13, 1, 11, 19, 12),
+ ]
+ vertices = np.array(vertices) / np.sqrt(3) / 2
+ faces = np.array(faces) - 1
+ return vertices, faces
+
+
+def icosahedron():
+ """ Regular icosahedron with 20 faces, 30 edges and 12 vertices """
+
+ a = (1 + np.sqrt(5)) / 2
+ vertices = [
+ (-1, a, 0),
+ (1, a, 0),
+ (-1, -a, 0),
+ (1, -a, 0),
+ (0, -1, a),
+ (0, 1, a),
+ (0, -1, -a),
+ (0, 1, -a),
+ (a, 0, -1),
+ (a, 0, 1),
+ (-a, 0, -1),
+ (-a, 0, 1),
+ ]
+ faces = [
+ [0, 11, 5],
+ [0, 5, 1],
+ [0, 1, 7],
+ [0, 7, 10],
+ [0, 10, 11],
+ [1, 5, 9],
+ [5, 11, 4],
+ [11, 10, 2],
+ [10, 7, 6],
+ [7, 1, 8],
+ [3, 9, 4],
+ [3, 4, 2],
+ [3, 2, 6],
+ [3, 6, 8],
+ [3, 8, 9],
+ [4, 9, 5],
+ [2, 4, 11],
+ [6, 2, 10],
+ [8, 6, 7],
+ [9, 8, 1],
+ ]
+ vertices = np.array(vertices) / np.sqrt(a + 2) / 2
+ faces = np.array(faces)
+ return vertices, faces
+
+
+def cube():
+ vertices = [
+ (0, 0, 0),
+ (1, 0, 0),
+ (1, 0, 1),
+ (0, 0, 1),
+ (0, 1, 0),
+ (1, 1, 0),
+ (1, 1, 1),
+ (0, 1, 1),
+ ]
+ faces = [
+ [0, 1, 2, 3],
+ [4, 5, 6, 7],
+ [0, 1, 5, 4],
+ [1, 2, 6, 5],
+ [2, 3, 7, 6],
+ [3, 0, 4, 7],
+ ]
+ return (np.array(vertices) - 0.5) / np.sqrt(2), np.array(faces)
+
+
+def plot(ax, camera, V, F):
+ ax.axis("off")
+
+ T = glm.transform(V[F], camera)
+ V, Z = T[..., :2], T[..., 2].mean(axis=-1)
+ V = V[np.argsort(-Z)]
+ collection = PolyCollection(
+ V,
+ antialiased=True,
+ linewidth=1.0,
+ facecolor=(0.9, 0.9, 1, 0.75),
+ edgecolor=(0, 0, 0.75, 0.25),
+ )
+ ax.add_collection(collection)
+
+
+fig = plt.figure(figsize=(8, 5.5))
+camera = glm.camera(25, 35, 1.5, "perspective")
+
+ax = plt.subplot(2, 3, 1, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+ax.axis("off")
+ax.text(
+ 0.5,
+ 0.6,
+ "PLATONIC",
+ transform=ax.transAxes,
+ weight="black",
+ size=24,
+ va="bottom",
+ ha="center",
+ family="Source Sans Pro",
+)
+ax.text(
+ 0.5,
+ 0.6,
+ "S O L I D S",
+ transform=ax.transAxes,
+ weight="light",
+ size=22,
+ va="top",
+ ha="center",
+ family="Source Sans Pro",
+)
+ax.text(
+ 0.5,
+ 0.475,
+ "matplotlib.org",
+ transform=ax.transAxes,
+ weight="light",
+ size=13,
+ va="top",
+ ha="center",
+ family="Source Code Pro",
+)
+
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 2, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *tetrahedron())
+ax.text(
+ 0.5,
+ 1.0,
+ "Tetrahedron",
+ transform=ax.transAxes,
+ weight="bold",
+ va="bottom",
+ ha="center",
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "4 faces, 4 vertices, 6 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 3, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *cube())
+ax.text(
+ 0.5, 1.0, "Cube", transform=ax.transAxes, weight="bold", va="bottom", ha="center"
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "6 faces, 8 vertices, 12 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 4, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *octahedron())
+ax.text(
+ 0.5,
+ 1.0,
+ "Octahedron",
+ transform=ax.transAxes,
+ weight="bold",
+ va="bottom",
+ ha="center",
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "8 faces, 6 vertices, 12 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 5, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *dodecahedron())
+ax.text(
+ 0.5,
+ 1.0,
+ "Dodecahedron",
+ transform=ax.transAxes,
+ weight="bold",
+ va="bottom",
+ ha="center",
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "12 faces, 20 vertices, 30 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 6, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *icosahedron())
+ax.text(
+ 0.5,
+ 1.0,
+ "Icosahedron",
+ transform=ax.transAxes,
+ weight="bold",
+ va="bottom",
+ ha="center",
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "20 faces, 12 vertices, 30 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+
+plt.tight_layout()
+plt.savefig("platonic-solids.png", dpi=300)
+plt.savefig("platonic-solids.pdf")
+plt.show()
diff --git a/code/unsorted/3d/plot.py b/code/unsorted/3d/plot.py
new file mode 100644
index 0000000..01b08f8
--- /dev/null
+++ b/code/unsorted/3d/plot.py
@@ -0,0 +1,595 @@
+import glm
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.collections import PolyCollection
+
+
+# -----------------------------------------------------------------------------
+def axis(ax, camera, ticks=True):
+ """
+ Draw three dimension axis with ticks and tick labels.
+
+ Parameters:
+ -----------
+
+ ax : matplotlib.axes instance
+ The regulmat axes where to draw
+
+ camera : 4x4 numpy array
+ A transformation matrix in homogenous coordinates (4x4)
+
+ ticks : bool
+ Whether to draw tick and tick labels
+
+ Note:
+ -----
+
+ The axis is drawn inside the unit cube centered on the origin:
+
+ - XZ lies on the plane Y = -0.5
+ - XY lies on the plane Z = -0.5
+ - YZ lies on the plane X = +0.5
+ - xticks goes from (-0.5, -0.5, +0.5) to (+0.5, -0.5, +0.5)
+ - zticks goes from (-0.5, -0.5, -0.5) to (-0.5, -0.5, +0.5)
+ - yticks goes from (-0.5, -0.5, -0.5) to (-0.5, +0.5, -0.5)
+ """
+
+ # Mandatory settings for matplotlib axes
+ # --------------------------------------
+ ax.set_xlim(-1, 1)
+ ax.set_ylim(-1, 1)
+ ax.set_aspect(1)
+ ax.axis("off")
+
+ # Parameters
+ # ---------------------------------
+ axis_linewidth = 1.00
+ grid_linewidth = 0.50
+ ticks_linewidth = 0.75
+
+ grid_color = "0.5"
+ axis_color = "0.0"
+ ticks_color = "0.0"
+ ticks_length = 0.025
+ ticks_pad = 0.05
+
+ # ticks and ticklabels
+ nx = 11
+ xticks = np.linspace(-0.5, +0.5, nx, endpoint=True)
+ xticklabels = ["%.1f" % x for x in xticks]
+
+ ny = 11
+ yticks = np.linspace(-0.5, +0.5, ny, endpoint=True)
+ yticklabels = ["%.1f" % y for y in yticks]
+
+ nz = 11
+ zticks = np.linspace(-0.5, +0.5, nz, endpoint=True)
+ zticklabels = ["%.1f" % z for z in zticks]
+
+ colors = []
+ segments = []
+ linewidths = []
+
+ # XZ axis
+ # ---------------------------------
+ X0 = np.linspace([0, 0, 0], [1, 0, 0], nx) - 0.5
+ X1 = X0 + [0, 0, 1]
+ X2 = X1 + [0, 0, ticks_length]
+ X3 = X2 + [0, 0, ticks_pad]
+ X0 = glm.transform(X0, camera)[..., :2]
+ X1 = glm.transform(X1, camera)[..., :2]
+ X2 = glm.transform(X2, camera)[..., :2]
+ X3 = glm.transform(X3, camera)[..., :2]
+
+ Z0 = np.linspace([0, 0, 0], [0, 0, 1], nz) - 0.5
+ Z1 = Z0 + [1, 0, 0]
+ Z2 = Z0 - [ticks_length, 0, 0]
+ Z3 = Z2 - [ticks_pad, 0, 0]
+ Z0 = glm.transform(Z0, camera)[..., :2]
+ Z1 = glm.transform(Z1, camera)[..., :2]
+ Z2 = glm.transform(Z2, camera)[..., :2]
+ Z3 = glm.transform(Z3, camera)[..., :2]
+
+ for p0, p1, p2, p3, label in zip(X0, X1, X2, X3, xticklabels):
+ # grid line
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ if not ticks:
+ continue
+
+ # tick
+ segments.append([p1, p2])
+ linewidths.append(ticks_linewidth)
+ colors.append(ticks_color)
+
+ # label
+ ax.text(p3[0], p3[1], label, ha="center", va="center", size="x-small")
+
+ for p0, p1, p2, p3, label in zip(Z0, Z1, Z2, Z3, zticklabels):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ if not ticks:
+ continue
+
+ # ticks
+ segments.append([p0, p2])
+ linewidths.append(ticks_linewidth)
+ colors.append(ticks_color)
+
+ # label
+ ax.text(p3[0], p3[1], label, ha="center", va="center", size="x-small")
+
+ # axis border
+ segments.append([X0[0], X0[-1], X1[-1], X1[0], X0[0]])
+ linewidths.append(axis_linewidth)
+ colors.append(axis_color)
+
+ # XY axis
+ # ---------------------------------
+ X0 = np.linspace([0, 0, 0], [1, 0, 0], nx) - 0.5
+ X1 = X0 + [0, 1, 0]
+ X0 = glm.transform(X0, camera)[..., :2]
+ X1 = glm.transform(X1, camera)[..., :2]
+ Y0 = np.linspace([0, 0, 0], [0, 1, 0], ny) - 0.5
+ Y1 = Y0 + [1, 0, 0]
+ Y2 = Y0 - [ticks_length, 0, 0]
+ Y3 = Y2 - [ticks_pad, 0, 0]
+ Y0 = glm.transform(Y0, camera)[..., :2]
+ Y1 = glm.transform(Y1, camera)[..., :2]
+ Y2 = glm.transform(Y2, camera)[..., :2]
+ Y3 = glm.transform(Y3, camera)[..., :2]
+
+ for p0, p1 in zip(X0, X1):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ for p0, p1, p2, p3, label in zip(Y0, Y1, Y2, Y3, yticklabels):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ if not ticks:
+ continue
+ # ticks
+ segments.append([p0, p2])
+ linewidths.append(ticks_linewidth)
+ colors.append(ticks_color)
+
+ # label
+ ax.text(p3[0], p3[1], label, ha="center", va="center", size="x-small")
+
+ # axis border
+ segments.append([X0[0], X0[-1], X1[-1], X1[0], X0[0]])
+ linewidths.append(axis_linewidth)
+ colors.append(axis_color)
+
+ # ZY axis
+ # ---------------------------------
+ Z0 = np.linspace([1, 0, 0], [1, 0, 1], nx) - 0.5
+ Z1 = Z0 + [0, 1, 0]
+ Z0 = glm.transform(Z0, camera)[..., :2]
+ Z1 = glm.transform(Z1, camera)[..., :2]
+
+ Y0 = np.linspace([1, 0, 0], [1, 1, 0], ny) - 0.5
+ Y1 = Y0 + [0, 0, 1]
+ Y0 = glm.transform(Y0, camera)[..., :2]
+ Y1 = glm.transform(Y1, camera)[..., :2]
+
+ for p0, p1 in zip(Z0, Z1):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ for p0, p1 in zip(Y0, Y1):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ # axis border
+ segments.append([Y0[0], Y0[-1], Y1[-1], Y1[0], Y0[0]])
+ linewidths.append(axis_linewidth)
+ colors.append(axis_color)
+
+ # Actual rendering
+ collection = PolyCollection(
+ segments,
+ closed=False,
+ clip_on=False,
+ linewidths=linewidths,
+ facecolors="None",
+ edgecolor=colors,
+ )
+ ax.add_collection(collection)
+
+
+# -----------------------------------------------------------------------------
+def mesh(
+ ax,
+ camera,
+ vertices,
+ faces,
+ cmap=None,
+ facecolor="white",
+ edgecolor="none",
+ linewidth=1.0,
+ mode="all",
+):
+
+ # Mandatory settings for matplotlib axes
+ # --------------------------------------
+ ax.set_xlim(-1, 1)
+ ax.set_ylim(-1, 1)
+ ax.set_aspect(1)
+ ax.axis("off")
+
+ facecolor = mpl.colors.to_rgba_array(facecolor)
+ edgecolor = mpl.colors.to_rgba_array(edgecolor)
+
+ T = glm.transform(vertices, camera)[faces]
+ Z = -T[:, :, 2].mean(axis=1)
+
+ # Facecolor using depth buffer
+ if cmap is not None:
+ cmap = plt.get_cmap("magma")
+ norm = mpl.colors.Normalize(vmin=Z.min(), vmax=Z.max())
+ facecolor = cmap(norm(Z))
+
+ # Back face culling
+ if mode == "front":
+ front, back = glm.frontback(T)
+ T, Z = T[front], Z[front]
+ if len(facecolor) == len(faces):
+ facecolor = facecolor[front]
+ if len(edgecolor) == len(faces):
+ facecolor = edgecolor[front]
+ # Front face culling
+ elif mode == "back":
+ front, back = glm.frontback(T)
+ T, Z = T[back], Z[back]
+ if len(facecolor) == len(faces):
+ facecolor = facecolor[back]
+ if len(edgecolor) == len(faces):
+ facecolor = edgecolor[back]
+
+ # Separate 2d triangles from zbuffer
+ triangles = T[:, :, :2]
+ antialiased = True
+ if linewidth == 0.0:
+ antialiased = False
+
+ # Sort triangles according to z buffer
+ I = np.argsort(Z)
+ triangles = triangles[I, :]
+ if len(facecolor) == len(I):
+ facecolor = facecolor[I, :]
+ if len(edgecolor) == len(I):
+ edgecolor = edgecolor[I, :]
+
+ collection = PolyCollection(
+ triangles,
+ linewidth=linewidth,
+ antialiased=antialiased,
+ facecolor=facecolor,
+ edgecolor=edgecolor,
+ )
+ ax.add_collection(collection)
+
+
+# -----------------------------------------------------------------------------
+def surf(
+ ax,
+ camera,
+ Y,
+ facecolor="white",
+ edgecolor="black",
+ facecolors=None,
+ edgecolors=None,
+ linewidth=0.5,
+ shading=(1.00, 1.00, 1.00, 0.75, 0.50, 1.00),
+):
+
+ # Mandatory settings for matplotlib axes
+ ax.set_xlim(-1, 1)
+ ax.set_ylim(-1, 1)
+ ax.set_aspect(1)
+ ax.axis("off")
+
+ # Facecolor
+ if facecolors is None:
+ facecolors = np.zeros((Y.shape[0], Y.shape[1], 3))
+ facecolors[...] = mpl.colors.to_rgb(facecolor)
+ facecolors[...] = facecolors[..., :3]
+
+ # Facecolor
+ if edgecolors is None:
+ edgecolors = np.zeros((Y.shape[0], Y.shape[1], 3))
+ edgecolors[...] = mpl.colors.to_rgb(edgecolor)
+ edgecolors[...] = edgecolors[..., :3]
+
+ # Surface
+ n = Y.shape[0]
+ T = np.linspace(-0.5, +0.5, n)
+ X, Z = np.meshgrid(T, T)
+ V = np.c_[X.ravel(), Y.ravel() - 0.5, Z.ravel()]
+ F = (np.arange((n - 1) * (n)).reshape(n - 1, n))[:, :-1].T
+ F = np.repeat(F.ravel(), 6).reshape(n - 1, n - 1, 6)
+ F[:, :] += np.array([0, n + 1, 1, 0, n, n + 1])
+ F = F.reshape(-1, 3)
+ V = glm.transform(V, camera)
+ T = V[F]
+
+ # Create list from array such that we can add lines
+ polys = T[..., :2].tolist()
+ zbuffer = (-T[..., 2].mean(axis=1)).tolist()
+ edgecolors = ["none",] * len(polys)
+ antialiased = [False,] * len(polys)
+ linewidths = [1.0,] * len(polys)
+ facecolors = ((facecolors.reshape(-1, 3))[F].mean(axis=1)).tolist()
+
+ # Helper function for creating a line segment between two points
+ def segment(p0, p1, linewidth=1.5, epsilon=0.01):
+ polys.append([p0[:2], p1[:2]])
+ facecolors.append("none")
+ edgecolors.append("black")
+ antialiased.append(True)
+ linewidths.append(linewidth)
+ zbuffer.append(-(p0[2] + p1[2]) / 2 + epsilon)
+
+ # Border + grid over the surface
+ V = V.reshape(n, n, -1)
+
+ # Border
+ for i in range(0, n - 1):
+ segment(V[i, 0], V[i + 1, 0])
+ segment(V[i, -1], V[i + 1, -1])
+ segment(V[0, i], V[0, i + 1])
+ segment(V[-1, i], V[-1, i + 1])
+
+ for i in range(0, n - 1):
+ for j in range(0, n - 1):
+ segment(V[i, j], V[i + 1, j], 0.5)
+ segment(V[j, i], V[j, i + 1], 0.5)
+
+ # Sort everything
+ I = np.argsort(zbuffer)
+ polys = [polys[i] for i in I]
+ facecolors = [facecolors[i] for i in I]
+ edgecolors = [edgecolors[i] for i in I]
+ linewidths = [linewidths[i] for i in I]
+ antialiased = [antialiased[i] for i in I]
+
+ # Display
+ collection = PolyCollection(
+ polys,
+ linewidth=linewidths,
+ antialiased=antialiased,
+ facecolors=facecolors,
+ edgecolors=edgecolors,
+ )
+ ax.add_collection(collection)
+
+
+# -----------------------------------------------------------------------------
+def bar(
+ ax,
+ camera,
+ Y,
+ facecolor="white",
+ edgecolor="black",
+ facecolors=None,
+ edgecolors=None,
+ linewidth=0.5,
+ shading=(1.00, 1.00, 1.00, 0.75, 0.50, 1.00),
+):
+
+ # Mandatory settings for matplotlib axes
+ ax.set_xlim(-1, 1)
+ ax.set_ylim(-1, 1)
+ ax.set_aspect(1)
+ ax.axis("off")
+
+ # Facecolor
+ if facecolors is None:
+ facecolors = np.zeros((Y.shape[0], Y.shape[1], 3))
+ facecolors[...] = mpl.colors.to_rgb(facecolor)
+ facecolors[...] = facecolors[..., :3]
+
+ # Facecolor
+ if edgecolors is None:
+ edgecolors = np.zeros((Y.shape[0], Y.shape[1], 3))
+ edgecolors[...] = mpl.colors.to_rgb(edgecolor)
+ edgecolors[...] = edgecolors[..., :3]
+
+ # Here we compute the eight 3D vertices necessary to render a bar
+ shape = Y.shape
+ dx, dz = 0.5 / shape[0], 0.5 / shape[1]
+ X, Z = np.meshgrid(
+ np.linspace(-0.5 + dx, 0.5 - dx, shape[0]),
+ np.linspace(-0.5 + dz, 0.5 - dz, shape[1]),
+ )
+ P = np.c_[X.ravel(), np.zeros(X.size), Z.ravel()]
+ P = P.reshape(shape[0], shape[1], 3)
+ dx = np.array([dx, 0, 0])
+ dz = np.array([0, 0, dz])
+ dy = np.zeros((shape[0], shape[1], 3))
+ V = np.zeros((8, shape[0], shape[1], 3))
+
+ dy[..., 1] = -0.5
+ V[0] = P + dx - dz + dy
+ V[1] = P - dx - dz + dy
+ V[2] = P + dx + dz + dy
+ V[3] = P - dx + dz + dy
+
+ dy[..., 1] = -0.5 + Y
+ V[4] = P + dx - dz + dy
+ V[5] = P - dx - dz + dy
+ V[6] = P + dx + dz + dy
+ V[7] = P - dx + dz + dy
+
+ # Transformation of the vertices in 2D + z
+ T = glm.transform(V, camera)
+ V = T[:, :2].reshape(8, shape[0], shape[1], 2)
+ Z = -T[:, 2].reshape(8 * shape[0] * shape[1])
+
+ # Normalization of the z value such that we can maipulate zbar / zface
+ # Drawback is that it cannot be sorted anymore with other 3d objects
+ Z = glm.rescale(Z, 0, 1)
+
+ # Building of individual bars (without bottom face)
+ # and a new z buffer that is a combination of the bar and face mean z
+ faces, colors, zbuffer = [], [], []
+ indices = (
+ [4, 5, 7, 6], # +Y
+ [0, 1, 3, 2], # -Y
+ [0, 1, 5, 4], # +X
+ [1, 3, 7, 5], # -X
+ [2, 3, 7, 6], # -Z
+ [0, 2, 6, 4],
+ ) # +Z
+ FC = np.zeros((shape[0], shape[1], len(indices), 3))
+ EC = np.zeros((shape[0], shape[1], len(indices), 3))
+ F = np.zeros((shape[0], shape[1], len(indices), 4, 2))
+ Z_ = np.zeros((shape[0], shape[1], len(indices)))
+
+ # This could probably be vectorized but it might be tricky
+ for i in range(shape[0]):
+ for j in range(shape[1]):
+ index = np.arange(8) * shape[0] * shape[1] + i * shape[1] + j
+ zbar = 1 * Z[index].mean()
+ for k, ((v0, v1, v2, v3), shade) in enumerate(zip(indices, shading)):
+ zface = (Z[index[v0]] + Z[index[v1]] + Z[index[v2]] + Z[index[v3]]) / 4
+ F[i, j, k] = V[v0, i, j], V[v1, i, j], V[v2, i, j], V[v3, i, j]
+ Z_[i, j, k] = 10 * zbar + zface
+ FC[i, j, k] = facecolors[i, j] * shade
+ EC[i, j, k] = edgecolors[i, j]
+
+ # Final reshape for sorting quads
+ Z = Z_.reshape(shape[0] * shape[1] * len(indices))
+ F = F.reshape(shape[0] * shape[1] * len(indices), 4, 2)
+ FC = FC.reshape(shape[0] * shape[1] * len(indices), 3)
+ EC = EC.reshape(shape[0] * shape[1] * len(indices), 3)
+ I = np.argsort(Z)
+
+ collection = PolyCollection(
+ F[I], linewidth=0.25, facecolors=FC[I], edgecolors=EC[I]
+ )
+ ax.add_collection(collection)
+
+
+# -----------------------------------------------------------------------------
+def contour(ax, camera, Y, n_levels=32):
+
+ n = Y.shape[0]
+ T = np.linspace(-0.5, +0.5, n)
+ X, Z = np.meshgrid(T, T)
+ C = ax.contour(X, Z, Y, n_levels)
+
+ cmap = plt.get_cmap("magma")
+ ymin, ymax = Y.min(), Y.max()
+ norm = mpl.colors.Normalize(vmin=2 * ymin, vmax=ymax)
+
+ dy = 0.99 * (ymax - ymin) / n_levels
+
+ segments = []
+ facecolors = []
+ edgecolors = []
+ closed = []
+ antialiased = []
+ epsilon = 0.0025
+
+ for level, collection in zip(C.levels, C.collections):
+
+ local_segments = []
+ local_zbuffer = []
+ local_antialiased = []
+ local_edgecolors = []
+ local_facecolors = []
+ local_closed = []
+
+ # Collect and transform all paths
+ paths = []
+ for path in collection.get_paths():
+ V = np.array(path.vertices)
+ V = np.c_[V[:, 0], level * np.ones(len(path)), V[:, 1]]
+ V0 = V
+ V1 = V0 - [0, dy, 0]
+ T0 = glm.transform(V0, camera)
+ T1 = glm.transform(V1, camera)
+ V0, Z0 = T0[:, :2], T0[:, 2]
+ V1, Z1 = T1[:, :2], T1[:, 2]
+ paths.append([V, V0, Z0, V1, Z1])
+
+ for (V, V0, Z0, V1, Z1) in paths:
+
+ for i in range(len(V0) - 1):
+ local_segments.append([V0[i], V0[i + 1], V1[i + 1], V1[i]])
+ local_zbuffer.append(-(Z0[i] + Z0[i + 1] + Z1[i + 1] + Z1[i]) / 4)
+ local_antialiased.append(False)
+ local_edgecolors.append("none")
+ facecolor = np.array(cmap(norm(level))[:3])
+ local_facecolors.append(0.75 * facecolor)
+ local_closed.append(True)
+
+ local_segments.append([V1[i + 1], V1[i]])
+ local_zbuffer.append(-(Z1[i + 1] + Z1[i]) / 2 + epsilon)
+ local_antialiased.append(True)
+ facecolor = np.array(cmap(norm(level))[:3])
+ local_edgecolors.append(facecolor * 0.25)
+ local_facecolors.append("none")
+ local_closed.append(False)
+
+ if ((V[0] - V[-1]) ** 2).sum() > 0.00001:
+ local_segments.append([V0[i + 1], V0[i]])
+ local_zbuffer.append(-(Z0[i + 1] + Z0[i]) / 2 + epsilon)
+ local_antialiased.append(True)
+ facecolor = np.array(cmap(norm(level))[:3])
+ local_edgecolors.append(facecolor * 0.25)
+ local_facecolors.append("none")
+ local_closed.append(False)
+
+ I = np.argsort(local_zbuffer)
+ local_segments = [local_segments[i] for i in I]
+ local_facecolors = [local_facecolors[i] for i in I]
+ local_edgecolors = [local_edgecolors[i] for i in I]
+ local_closed = [local_closed[i] for i in I]
+ local_antialiased = [local_antialiased[i] for i in I]
+
+ segments.extend(local_segments)
+ facecolors.extend(local_facecolors)
+ edgecolors.extend(local_edgecolors)
+ closed.extend(local_closed)
+ antialiased.extend(local_antialiased)
+
+ for (V, V0, Z0, V1, Z1) in paths:
+ if ((V[0] - V[-1]) ** 2).sum() < 0.00001:
+ segments.append(V0)
+ antialiased.append(True)
+ facecolor = cmap(norm(level))[:3]
+ facecolors.append(facecolor)
+ edgecolors.append("black")
+ closed.append(True)
+
+ collection.remove()
+
+ collection = PolyCollection(
+ segments,
+ linewidth=0.5,
+ antialiased=antialiased,
+ closed=closed,
+ facecolors=facecolors,
+ edgecolors=edgecolors,
+ )
+ ax.add_collection(collection)
diff --git a/code/unsorted/3d/scatter.py b/code/unsorted/3d/scatter.py
new file mode 100644
index 0000000..872afb0
--- /dev/null
+++ b/code/unsorted/3d/scatter.py
@@ -0,0 +1,76 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+ from matplotlib.patches import Ellipse
+ from matplotlib.collections import PolyCollection
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+
+ np.random.seed(1)
+ n = 1024
+ P = 0.2 * np.random.normal(0, 1, (n, 3))
+
+ # Bottom shadow
+ V = glm.transform(P * [1, 0, 1] - [0, 0.5, 0], camera)
+ T = np.linspace(0, 2 * np.pi, 12)
+ radius = 0.015
+ X, Y, Z = radius * np.cos(T), np.zeros(len(T)), radius * np.sin(T)
+ C = np.c_[X, Y, Z]
+ polys = []
+ for i in range(n):
+ V = glm.transform(C + [P[i, 0], -0.5, P[i, 2]], camera)[:, :2]
+ polys.append(V)
+
+ collection = PolyCollection(
+ polys, linewidths=0, alpha=0.5, zorder=+10, facecolors="0.5", edgecolor="none"
+ )
+ ax.add_collection(collection)
+
+ # Actual scatter
+ V = glm.transform(P, camera)
+ X, Y, Z = V[:, 0], V[:, 1], V[:, 2]
+ I = np.argsort(Z)
+ X, Y = X[I], Y[I]
+ facecolor = [
+ (0, 0, 0, 0),
+ (0, 0, 0, 0),
+ (0, 0, 0, 0),
+ mpl.colors.to_rgba("C4"),
+ (1, 1, 1, 0.25),
+ (1, 1, 1, 1),
+ ] * len(X)
+ edgecolor = [
+ (0, 0, 0, 0.05),
+ (0, 0, 0, 0.10),
+ (0, 0, 0, 0.15),
+ (0, 0, 0, 1.00),
+ (0, 0, 0, 0.00),
+ (0, 0, 0, 0.00),
+ ] * len(X)
+ linewidth = [6, 4, 2, 0.5, 0.0, 0.0] * len(X)
+ dX = (0, 0, 0, 0, -0.0035, -0.0035) * len(X)
+ dY = (0, 0, 0, 0, +0.0025, +0.0025) * len(Y)
+ size = np.array((1, 1, 1, 1, 0.25, 0.05) * len(X)) * 50
+ X, Y = np.repeat(X, 6), np.repeat(Y, 6)
+ ax.scatter(
+ X + dX,
+ Y + dY,
+ s=size,
+ linewidth=linewidth,
+ zorder=10,
+ facecolor=facecolor,
+ edgecolor=edgecolor,
+ )
+
+ plt.savefig("scatter.png", dpi=300)
+ plt.savefig("scatter.pdf")
+ plt.show()
diff --git a/code/unsorted/3d/sphere.py b/code/unsorted/3d/sphere.py
new file mode 100644
index 0000000..bb1db4a
--- /dev/null
+++ b/code/unsorted/3d/sphere.py
@@ -0,0 +1,72 @@
+import glm
+import plot
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def sphere(radius=1.0, slices=32, stacks=32):
+ slices += 1
+ stacks += 1
+ n = slices * stacks
+ vertices = np.zeros((n, 3))
+ theta1 = np.repeat(np.linspace(0, np.pi, stacks, endpoint=True), slices)
+ theta2 = np.tile(np.linspace(0, 2 * np.pi, slices, endpoint=True), stacks)
+
+ vertices[:, 1] = np.sin(theta1) * np.cos(theta2) * radius
+ vertices[:, 2] = np.cos(theta1) * radius
+ vertices[:, 0] = np.sin(theta1) * np.sin(theta2) * radius
+
+ indices = []
+ for i in range(stacks - 1):
+ for j in range(slices - 1):
+ indices.append(i * (slices) + j)
+ indices.append(i * (slices) + j + 1)
+ indices.append(i * (slices) + j + slices + 1)
+
+ indices.append(i * (slices) + j + slices + 1)
+ indices.append(i * (slices) + j + slices)
+ indices.append(i * (slices) + j)
+
+ indices = np.array(indices)
+ indices = indices.reshape(len(indices) // 3, 3)
+ return vertices, indices
+
+
+def lighting(F, direction=(1, 1, 1), color=(1, 0, 0), specular=False):
+
+ # Faces center
+ C = F.mean(axis=1)
+ # Faces normal
+ N = glm.normalize(np.cross(F[:, 2] - F[:, 0], F[:, 1] - F[:, 0]))
+ # Relative light direction
+ D = glm.normalize(C - direction)
+ # Diffuse term
+ diffuse = glm.clip((N * D).sum(-1).reshape(-1, 1))
+
+ # Specular term
+ if specular:
+ specular = np.power(diffuse, 24)
+ return np.maximum(diffuse * color, specular)
+
+ return diffuse * color
+
+
+V, F = sphere(0.5, 2 * 32, 2 * 32)
+facecolor = lighting(V[F], (-1, 1, 1), (1, 0, 0), True)
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+
+camera = glm.camera(20, 45, 1.15, "perspective")
+plot.axis(ax, camera)
+plot.mesh(
+ ax,
+ camera,
+ V,
+ F,
+ mode="front",
+ linewidth=0,
+ facecolor=facecolor,
+ edgecolor=(0, 0, 0, 0),
+)
+plt.show()
diff --git a/code/unsorted/3d/surf.py b/code/unsorted/3d/surf.py
new file mode 100644
index 0000000..4ad366b
--- /dev/null
+++ b/code/unsorted/3d/surf.py
@@ -0,0 +1,29 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+
+ n = 24
+ X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ )
+ Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+ cmap = plt.get_cmap("magma")
+ norm = mpl.colors.Normalize(vmin=-0.25, vmax=0.75)
+ facecolors = cmap(norm(Y))[..., :3]
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+ plot.surf(ax, camera, Y, facecolors=facecolors)
+
+plt.show()
diff --git a/code/unsorted/advanced-linestyles.pdf b/code/unsorted/advanced-linestyles.pdf
new file mode 100644
index 0000000..34bbf35
Binary files /dev/null and b/code/unsorted/advanced-linestyles.pdf differ
diff --git a/code/unsorted/advanced-linestyles.py b/code/unsorted/advanced-linestyles.py
new file mode 100644
index 0000000..81315a9
--- /dev/null
+++ b/code/unsorted/advanced-linestyles.py
@@ -0,0 +1,204 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(4.25, 6))
+ax = plt.subplot(111, aspect=1, frameon=False, xticks=[], yticks=[])
+
+X = np.linspace(0, 2 * np.pi, 100)
+Y = 0.5 * np.sin(X)
+yticks, ylabels = [], []
+
+
+# 1
+# -----------------------------------------------------------------------------
+y = 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(X, y + Y, "black", linewidth=1)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=10,
+ solid_capstyle="butt",
+ dash_capstyle="butt",
+ linestyle=(0.0, (0.1, 1.0)),
+)
+
+# 2
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X,
+ y + Y,
+ ".25",
+ linewidth=5,
+ solid_capstyle="butt",
+ dash_capstyle="butt",
+ linestyle=(0.0, (0.1, 0.45)),
+)
+ax.plot(X, y + Y, "black", linewidth=1)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=10,
+ solid_capstyle="butt",
+ dash_capstyle="butt",
+ linestyle=(0.0, (0.1, 1.0)),
+)
+
+# 3
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(X, y + Y, "black", linewidth=8, solid_capstyle="round")
+ax.plot(X, y + Y, "white", linewidth=6, solid_capstyle="round")
+ax.plot(X, y + Y, "black", linewidth=1, solid_capstyle="round")
+
+# 4
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=8,
+ solid_capstyle="round",
+ dash_capstyle="round",
+ linestyle=(0.0, (0.01, 1.5)),
+)
+
+# 5
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X, y + Y, "black", linewidth=1.5, marker="o", markevery=10, mec="black", mfc="white"
+)
+
+# 6
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X, y + Y, "black", linewidth=1.5, marker="o", markevery=10, mec="white", mfc="black"
+)
+
+# 7
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=1.5,
+ markersize=10,
+ mew=2.5,
+ mec="white",
+ mfc="white",
+ marker="$↑$",
+ markevery=10,
+)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=0.0,
+ markersize=10,
+ mew=0.5,
+ mec="black",
+ mfc="black",
+ marker="$↑$",
+ markevery=10,
+)
+
+# 8
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+for i in range(9):
+ ax.plot(
+ X,
+ y + Y,
+ "%.2f" % (i / 10),
+ linewidth=4,
+ solid_capstyle="round",
+ dash_capstyle="round",
+ linestyle=(i + 1, (0.01, 9.0)),
+ )
+
+# 9
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+Y = Y.mean() + 0 * Y
+ax.plot(
+ X,
+ y + Y,
+ marker=(3, 0, -90),
+ mew=2.5,
+ mec="black",
+ mfc="black",
+ markersize=7,
+ linestyle="None",
+ markevery=2,
+)
+ax.plot(
+ X,
+ y + Y,
+ marker=(3, 0, -90),
+ mew=1,
+ mec="white",
+ mfc="white",
+ markersize=7,
+ linestyle="None",
+ markevery=2,
+)
+
+# 10
+# -----------------------------------------------------------------------------
+y += 0.5
+yticks.append(y), ylabels.append("%d" % (y + 0.5))
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=6,
+ solid_capstyle="butt",
+ ms=10,
+ mew=1.5,
+ mec="white",
+ mfc="None",
+ marker=5,
+ markevery=(5, 10),
+)
+
+# 11
+# -----------------------------------------------------------------------------
+y += 0.5
+yticks.append(y), ylabels.append("%d" % (y + 1))
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=1,
+ linestyle="--",
+ marker="$✁$",
+ markevery=(10, 1000),
+ ms=20,
+ mew=0.75,
+ mec="black",
+ mfc="white",
+)
+
+ax.set_yticks(yticks)
+ax.set_yticklabels(ylabels)
+ax.tick_params(axis="both", which="both", length=0)
+
+plt.tight_layout()
+plt.savefig("advanced-linestyles.pdf")
+plt.show()
diff --git a/code/unsorted/alpha-compositing.py b/code/unsorted/alpha-compositing.py
new file mode 100644
index 0000000..bb4cd95
--- /dev/null
+++ b/code/unsorted/alpha-compositing.py
@@ -0,0 +1,193 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate alpha compositing (simulated)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.path as mpath
+import matplotlib.patches as mpatches
+
+
+def circle(center, radius):
+ """ Regular circle path """
+
+ T = np.arange(0, np.pi * 2.0, 0.01)
+ T = T.reshape(-1, 1)
+ X = center[0] + radius * np.cos(T)
+ Y = center[1] + radius * np.sin(T)
+ vertices = np.hstack((X, Y))
+ codes = np.ones(len(vertices), dtype=mpath.Path.code_type) * mpath.Path.LINETO
+ codes[0] = mpath.Path.MOVETO
+ return vertices, codes
+
+
+def rectangle(center, size):
+ """ Regular rectangle path """
+
+ (x, y), (w, h) = center, size
+ vertices = np.array([(x, y), (x + w, y), (x + w, y + h), (x, y + h), (x, y)])
+ codes = np.array(
+ [
+ mpath.Path.MOVETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ ]
+ )
+ return vertices, codes
+
+
+def patch(
+ ax,
+ path,
+ facecolor="0.9",
+ edgecolor="black",
+ linewidth=0,
+ linestyle="-",
+ antialiased=True,
+ clip=None,
+):
+ """ Build a patch with potential clipping path """
+ patch = mpatches.PathPatch(
+ path,
+ antialiased=antialiased,
+ linewidth=linewidth,
+ linestyle=linestyle,
+ facecolor=facecolor,
+ edgecolor=edgecolor,
+ )
+ if clip:
+ # If the path is not drawn, clipping doesn't work
+ clip_patch = mpatches.PathPatch(
+ clip, linewidth=0, facecolor="None", edgecolor="None"
+ )
+ ax.add_patch(clip_patch)
+ patch.set_clip_path(clip_patch)
+ ax.add_patch(patch)
+
+
+def subplot(cols, rows, index, title):
+ """ Shortcut to subplot to factorize options"""
+ ax = plt.subplot(cols, rows, index, aspect=1)
+ # ax.text(-1.25, 0.75, "A", size="large", ha="center", va="center")
+ # ax.text(+1.25, 0.75, "B", size="large", ha="center", va="center")
+ ax.set_title(title, weight="bold")
+ ax.set_xlim(-1.5, +1.5), ax.set_xticks([])
+ ax.set_ylim(-1, +1), ax.set_yticks([])
+ return ax
+
+
+def blend(A, B, f):
+ xA, xB = np.array(A), np.array(B)
+ aA, aB = xA[3], xB[3]
+ aR = aA + aB * (1 - aA)
+ xaA, xaB = aA * xA, aB * xB
+ xR = 1.0 / aR * ((1 - aB) * xaA + (1 - aA) * xaB + aA * aB * f(xA, xB))
+ return xR[:3]
+
+
+def blend_multiply(A, B):
+ return blend(A, B, lambda x, y: x * y)
+
+
+def blend_screen(A, B):
+ return blend(A, B, lambda x, y: x + y - x * y)
+
+
+def blend_darken(A, B):
+ return blend(A, B, lambda x, y: np.minimum(x, y))
+
+
+def blend_lighten(A, B):
+ return blend(A, B, lambda x, y: np.minimum(x, y))
+
+
+def blend_color_dodge(A, B):
+ return blend(A, B, lambda x, y: np.where(A < 1, np.minimum(1, B / (1 - A)), 1))
+
+
+def blend_color_burn(A, B):
+ return blend(A, B, lambda x, y: np.where(A > 0, 1 - np.minimum(1, (1 - B) / A), 0))
+
+
+V1, C1 = rectangle((-1.5, -1), size=(3, 2))
+V2, C2 = circle((-0.5, 0), radius=0.75)
+A = mpath.Path(V2, C2)
+V2, C2 = circle((+0.5, 0), radius=0.75)
+B = mpath.Path(V2, C2)
+
+
+fig = plt.figure(figsize=(9, 5))
+rows, cols = 3, 4
+
+cA = np.array([0.7, 0.0, 0.0, 0.8])
+cB = np.array([0.0, 0.0, 0.9, 0.4])
+
+# No Blend
+ax = subplot(rows, cols, 1, "No blend")
+patch(ax, B, facecolor=cB)
+patch(ax, B, clip=A, facecolor="white", antialiased=False)
+patch(ax, A, facecolor=cA)
+
+# Default blend
+ax = subplot(rows, cols, 2, "Default blend")
+patch(ax, A, facecolor=cA)
+patch(ax, B, facecolor=cB)
+
+# Multiply
+ax = subplot(rows, cols, 3, "Multiply")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_multiply(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Screen
+ax = subplot(rows, cols, 4, "Screen")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_screen(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Darken
+ax = subplot(rows, cols, 5, "Darken")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_darken(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Lighten
+ax = subplot(rows, cols, 6, "Lighten")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_lighten(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Color dodge
+ax = subplot(rows, cols, 7, "Color dodge")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_color_dodge(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Color burn
+ax = subplot(rows, cols, 8, "Color burn")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_color_burn(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# A ∩ B
+ax = subplot(rows, cols, 9, "")
+
+# A ∩ ¬B
+ax = subplot(rows, cols, 10, "")
+
+# ¬A ∩ B
+ax = subplot(rows, cols, 11, "")
+
+# ¬A ∩ ¬B
+ax = subplot(rows, cols, 12, "")
+
+
+plt.tight_layout()
+# plt.savefig("polygon-clipping.png", dpi=600)
+# plt.savefig("polygon-clipping.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/alpha-gradient.py b/code/unsorted/alpha-gradient.py
new file mode 100644
index 0000000..01649a2
--- /dev/null
+++ b/code/unsorted/alpha-gradient.py
@@ -0,0 +1,35 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Setup
+fig = plt.figure(figsize=(6, 3))
+
+X = np.linspace(0, 4 * np.pi, 256)
+Y1 = np.zeros((50, len(X)))
+Y2 = np.zeros((len(Y1), len(X)))
+for i in range(len(Y1)):
+ Y1[i] = np.cos(X) + np.random.uniform(-1, 1)
+ Y2[i] = np.sin(X) + np.random.uniform(-1, 1)
+
+
+# Usage of transparency
+ax = plt.subplot(1, 1, 1)
+
+Y, SD, VAR = Y1.mean(axis=0), Y1.std(axis=0), Y1.var(axis=0)
+
+ax.fill_between(X, Y + VAR, Y - VAR, facecolor="C0", alpha=0.25, zorder=-40)
+ax.fill_between(X, Y + SD, Y - SD, facecolor="C0", alpha=0.25, zorder=-30)
+ax.plot(X, Y + VAR, color="C0", linestyle="--", linewidth=1)
+ax.plot(X, Y - VAR, color="C0", linestyle="--", linewidth=1)
+ax.plot(X, Y, color="C0", zorder=-20)
+
+ax.set_xticks([]), ax.set_yticks([])
+
+plt.tight_layout()
+# plt.savefig("../figures/alpha-gradient.pdf")
+plt.show()
diff --git a/code/unsorted/dyson-hatching.py b/code/unsorted/dyson-hatching.py
new file mode 100644
index 0000000..4aad409
--- /dev/null
+++ b/code/unsorted/dyson-hatching.py
@@ -0,0 +1,248 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Requirements:
+# - noise library at https://pypi.org/project/noise/
+# - shapely library at https://pypi.org/project/Shapely/
+# - "Celtic Garamond" font at https://www.dafont.com/celtic-garamond-2nd.font
+# - "Morris Roman" font at https://www.dafont.com/morris-roman.font
+# - Nessy image from Irina Miroshnichenko (Sudowoodo)
+# (not included in the book because of rights)
+#
+# Note: After having installed font, you might need to erase your font cache
+# such that newly installed font can be found. Have a look at
+# https://matplotlib.org/faq/troubleshooting_faq.html
+# ----------------------------------------------------------------------------
+
+import noise
+import numpy as np
+import scipy.spatial
+import shapely.geometry
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+from matplotlib.collections import PolyCollection
+from math import cos, sin, floor, sqrt, pi, ceil
+
+
+# This is important because "cities" have been manually positionned
+np.random.seed(1)
+
+
+# Blue noise sampling
+# -------------------
+def blue_noise(shape, radius, k=32, seed=None):
+ """
+ Generate blue noise over a two-dimensional rectangle of size (width,height)
+
+ Parameters
+ ----------
+
+ shape : tuple
+ Two-dimensional domain (width x height)
+ radius : float
+ Minimum distance between samples
+ k : int, optional
+ Limit of samples to choose before rejection (typically k = 30)
+ seed : int, optional
+ If provided, this will set the random seed before generating noise,
+ for valid pseudo-random comparisons.
+
+ References
+ ----------
+
+ .. [1] Fast Poisson Disk Sampling in Arbitrary Dimensions, Robert Bridson,
+ Siggraph, 2007. :DOI:`10.1145/1278780.1278807`
+ """
+
+ def sqdist(a, b):
+ """ Squared Euclidean distance """
+ dx, dy = a[0] - b[0], a[1] - b[1]
+ return dx * dx + dy * dy
+
+ def grid_coords(p):
+ """ Return index of cell grid corresponding to p """
+ return int(floor(p[0] / cellsize)), int(floor(p[1] / cellsize))
+
+ def fits(p, radius):
+ """ Check whether p can be added to the queue """
+
+ radius2 = radius * radius
+ gx, gy = grid_coords(p)
+ for x in range(max(gx - 2, 0), min(gx + 3, grid_width)):
+ for y in range(max(gy - 2, 0), min(gy + 3, grid_height)):
+ g = grid[x + y * grid_width]
+ if g is None:
+ continue
+ if sqdist(p, g) <= radius2:
+ return False
+ return True
+
+ # When given a seed, we use a private random generator in order to not
+ # disturb the default global random generator
+ if seed is not None:
+ from numpy.random.mtrand import RandomState
+
+ rng = RandomState(seed=seed)
+ else:
+ rng = np.random
+
+ width, height = shape
+ cellsize = radius / sqrt(2)
+ grid_width = int(ceil(width / cellsize))
+ grid_height = int(ceil(height / cellsize))
+ grid = [None] * (grid_width * grid_height)
+
+ p = rng.uniform(0, shape, 2)
+ queue = [p]
+ grid_x, grid_y = grid_coords(p)
+ grid[grid_x + grid_y * grid_width] = p
+
+ while queue:
+ qi = rng.randint(len(queue))
+ qx, qy = queue[qi]
+ queue[qi] = queue[-1]
+ queue.pop()
+ for _ in range(k):
+ theta = rng.uniform(0, 2 * pi)
+ r = radius * np.sqrt(rng.uniform(1, 4))
+ p = qx + r * cos(theta), qy + r * sin(theta)
+ if not (0 <= p[0] < width and 0 <= p[1] < height) or not fits(p, radius):
+ continue
+ queue.append(p)
+ gx, gy = grid_coords(p)
+ grid[gx + gy * grid_width] = p
+
+ return np.array([p for p in grid if p is not None])
+
+
+# Hatch pattern with given orientation (or random if None given)
+def hatch(n=4, theta=None):
+ theta = theta or np.random.uniform(0, np.pi)
+ P = np.zeros((n, 2, 2))
+ X = np.linspace(-0.5, +0.5, n, endpoint=True)
+ P[:, 0, 1] = -0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 1] = +0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 0] = X + np.random.normal(0, 0.025, n)
+ P[:, 0, 0] = X + np.random.normal(0, 0.025, n)
+ c, s = np.cos(theta), np.sin(theta)
+ Z = np.array([[c, s], [-s, c]])
+ return P @ Z.T
+
+
+# Actual drawing
+fig = plt.figure(figsize=(6, 6))
+fig.patch.set_facecolor("#ffffff")
+ax = plt.subplot(1, 1, 1, aspect=1, frameon=False)
+
+
+# Figure border using the hatch pattern. They are first spread according to
+# a blue noise distribution, scaled according to the distance to the nearest
+# neighbour and then lines composing the hatch are clipped against the
+# corresponding Voronoi cell.
+h = 4 # Number of line segments composing a hatch
+radius = 0.2 # Minimum radius between points
+# (the smaller, the longer to compute)
+
+P = blue_noise((11, 11), radius=radius) - (0.5, 0.5)
+D = scipy.spatial.distance.cdist(P, P)
+D.sort(axis=1)
+S = []
+vor = scipy.spatial.Voronoi(P)
+for i in range(len(vor.point_region)):
+ region = vor.regions[vor.point_region[i]]
+ if not -1 in region:
+ verts = np.array([vor.vertices[i] for i in region])
+ poly = shapely.geometry.Polygon(verts)
+ H = 1.25 * D[i, 1] * hatch(h) + P[i]
+ for i in range(len(H)):
+ line = shapely.geometry.LineString(H[i])
+ intersect = poly.intersection(line)
+ if intersect:
+ S.append(intersect.coords)
+
+# Grey background using thick lines
+hatches = LineCollection(S, color="#eeeeee", linewidth=7, capstyle="round", zorder=-20)
+ax.add_collection(hatches)
+
+# Actual hatches
+hatches = LineCollection(S, color="black", linewidth=1.5, capstyle="round", zorder=-10)
+ax.add_collection(hatches)
+
+# Plain rectangle
+rectangle = plt.Rectangle((0, 0), 10, 10, fc="none", ec="white", lw=3.5)
+ax.add_patch(rectangle)
+rectangle = plt.Rectangle((0, 0), 10, 10, fc="white", ec="black", lw=2.5)
+ax.add_patch(rectangle)
+
+
+# A cheap map using Perlin noise and contour/contourf
+shape = 256, 256
+scale = 150
+octaves = 5
+persistence = 0.5
+lacunarity = 2.5
+Z = np.zeros(shape)
+for i in range(shape[0]):
+ for j in range(shape[1]):
+ Z[i][j] = noise.pnoise2(
+ i / scale,
+ j / scale,
+ octaves=octaves,
+ persistence=persistence,
+ lacunarity=lacunarity,
+ repeatx=1024,
+ repeaty=1024,
+ base=0,
+ )
+X = np.linspace(0, 10, 256)
+Y = np.linspace(0, 10, 256)
+plt.contourf(X, Y, Z, 2, colors=["#eeeeee", "#ffffff"], zorder=10)
+plt.contour(X, Y, Z, 2, colors="black", linestyles="-", zorder=10)
+
+
+# Text (could be factorized)
+plt.text(1.0, 5.25, "MATPLOTLIB", family="Celtic Garamond the 2nd", size=16, zorder=20)
+
+plt.scatter([6.5], [6.15], s=25, color="black", zorder=20)
+plt.text(6.65, 6.20, "Beautiful", family="Morris Roman", size=12, zorder=20)
+
+plt.scatter([1], [7.5], s=25, color="black", zorder=20)
+plt.text(1, 7.75, "Versatile", ha="center", family="Morris Roman", size=12, zorder=20)
+
+plt.scatter([1.7], [1.7], s=25, color="black", zorder=20)
+plt.text(1.7, 1.35, "Powerful", ha="center", family="Morris Roman", size=12, zorder=20)
+
+plt.scatter([6.2], [3.2], s=25, color="black", zorder=20)
+plt.text(6.2, 2.8, "Scalable", ha="center", family="Morris Roman", size=12, zorder=20)
+
+
+# Wind rose at the bottom right
+V = np.zeros((8, 2, 3, 2))
+for i in range(4):
+ theta = np.pi / 4 + i * np.pi / 2
+ c, s = np.cos(theta), np.sin(theta)
+ Z = np.array([[c, s], [-s, c]])
+ V[i, 0] = [(0, 0), (-1, 1), (0, 5)] @ Z.T
+ V[i, 1] = [(0, 0), (+1, 1), (0, 5)] @ Z.T
+ theta -= np.pi / 4
+ c, s = np.cos(theta), np.sin(theta)
+ Z = np.array([[c, s], [-s, c]])
+ V[4 + i, 0] = [(0, 0), (-1, 1), (0, 5)] @ Z.T
+ V[4 + i, 1] = [(0, 0), (+1, 1), (0, 5)] @ Z.T
+V = V.reshape(16, 3, 2)
+V = 0.9 * V / 5 + (9, 1)
+FC = np.zeros((16, 4))
+FC[0::2] = 0, 0, 0, 1
+FC[1::2] = 1, 1, 1, 1
+collection = PolyCollection(V, edgecolors="black", facecolors=FC, lw=0.75, zorder=20)
+ax.add_collection(collection)
+
+
+# Done
+ax.set_xlim(-1, 11), ax.set_xticks([])
+ax.set_ylim(-1, 11), ax.set_yticks([])
+plt.tight_layout()
+# plt.savefig("dyson-hatching.pdf")
+plt.show()
diff --git a/code/unsorted/earthquakes.py b/code/unsorted/earthquakes.py
new file mode 100644
index 0000000..c56ce29
--- /dev/null
+++ b/code/unsorted/earthquakes.py
@@ -0,0 +1,108 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import urllib
+import numpy as np
+import cartopy
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+
+# -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
+feed = "http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"
+
+# Significant earthquakes in the past 30 days
+# url = urllib.request.urlopen(feed + "significant_month.csv")
+
+# Earthquakes of magnitude > 4.5 in the past 30 days
+url = urllib.request.urlopen(feed + "4.5_month.csv")
+
+# Earthquakes of magnitude > 2.5 in the past 30 days
+# url = urllib.request.urlopen(feed + "2.5_month.csv")
+
+# Earthquakes of magnitude > 1.0 in the past 30 days
+# url = urllib.request.urlopen(feed + "1.0_month.csv")
+
+# Read data
+data = (url.read().decode()).split("\n")
+E = np.genfromtxt(
+ data, delimiter=",", names=True, usecols=("latitude", "longitude", "mag")
+)
+X, Y, M = E["longitude"], E["latitude"], E["mag"]
+M = 25 * (M - 4) ** 3
+
+# Plot data
+fig = plt.figure(figsize=(8, 6))
+ax = plt.subplot(1, 1, 1, projection=cartopy.crs.EqualEarth())
+ax.set_global()
+
+ax.add_feature(cartopy.feature.OCEAN, zorder=0, facecolor="blue", alpha=0.1)
+ax.add_feature(
+ cartopy.feature.LAND, zorder=0, facecolor="white", edgecolor="0.25", linewidth=0.5
+)
+
+# Visual effect for a more salient information
+ax.scatter(
+ X, Y, M, lw=1, ec="black", alpha=1, zorder=10, transform=cartopy.crs.PlateCarree()
+)
+ax.scatter(
+ X,
+ Y,
+ M - 1,
+ ec="none",
+ fc="white",
+ alpha=1,
+ zorder=20,
+ transform=cartopy.crs.PlateCarree(),
+)
+ax.scatter(
+ X,
+ Y,
+ M - 1,
+ ec="none",
+ fc="red",
+ alpha=0.25,
+ zorder=20,
+ transform=cartopy.crs.PlateCarree(),
+)
+ax.scatter(
+ X,
+ Y,
+ 2,
+ ec="none",
+ fc="black",
+ alpha=1,
+ zorder=30,
+ transform=cartopy.crs.PlateCarree(),
+)
+
+# Title & subtitles
+ax.text(
+ 0.5,
+ 1.01,
+ "Earthquakes with magnitude > 4.5 in the past 30 days",
+ va="bottom",
+ ha="center",
+ transform=ax.transAxes,
+ family="Source Serif Pro",
+ size=12,
+ weight=600,
+)
+ax.text(
+ 0.5,
+ -0.01,
+ datetime.now().strftime("%m/%d/%Y, %H:%M:%S")
+ + " - data from http://earthquake.usgs.gov",
+ va="top",
+ ha="center",
+ transform=ax.transAxes,
+ family="Source Serif Pro",
+ size=12,
+ weight=400,
+)
+
+plt.tight_layout()
+plt.savefig("earthquakes.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/git-commits.py b/code/unsorted/git-commits.py
new file mode 100644
index 0000000..f88b822
--- /dev/null
+++ b/code/unsorted/git-commits.py
@@ -0,0 +1,125 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import git
+import numpy as np
+import matplotlib
+
+matplotlib.use("module://imgcat")
+import matplotlib.pyplot as plt
+
+from matplotlib.patches import Polygon
+from datetime import date, datetime
+from dateutil.relativedelta import relativedelta
+
+
+def calmap(ax, year, data, origin="upper", weekstart="sun"):
+ ax.tick_params("x", length=0, labelsize="medium", which="major")
+ ax.tick_params("y", length=0, labelsize="x-small", which="major")
+
+ # Month borders
+ xticks, labels = [], []
+
+ start = datetime(year, 1, 1).weekday()
+
+ _data = np.zeros(7 * 53) * np.nan
+ _data[start : start + len(data)] = data
+ data = _data.reshape(53, 7).T
+
+ for month in range(1, 13):
+ first = datetime(year, month, 1)
+ last = first + relativedelta(months=1, days=-1)
+ if origin == "lower":
+ y0 = first.weekday()
+ y1 = last.weekday()
+ x0 = (int(first.strftime("%j")) + start - 1) // 7
+ x1 = (int(last.strftime("%j")) + start - 1) // 7
+ P = [
+ (x0, y0),
+ (x0, 7),
+ (x1, 7),
+ (x1, y1 + 1),
+ (x1 + 1, y1 + 1),
+ (x1 + 1, 0),
+ (x0 + 1, 0),
+ (x0 + 1, y0),
+ ]
+ else:
+ y0 = 6 - first.weekday()
+ y1 = 6 - last.weekday()
+ x0 = (int(first.strftime("%j")) + start - 1) // 7
+ x1 = (int(last.strftime("%j")) + start - 1) // 7
+ P = [
+ (x0, y0 + 1),
+ (x0, 0),
+ (x1, 0),
+ (x1, y1),
+ (x1 + 1, y1),
+ (x1 + 1, 7),
+ (x0 + 1, 7),
+ (x0 + 1, y0 + 1),
+ ]
+
+ xticks.append(x0 + (x1 - x0 + 1) / 2)
+ labels.append(first.strftime("%b"))
+ poly = Polygon(
+ P,
+ edgecolor="black",
+ facecolor="None",
+ linewidth=1,
+ zorder=20,
+ clip_on=False,
+ )
+ ax.add_artist(poly)
+
+ ax.set_xticks(xticks)
+ ax.set_xticklabels(labels)
+ ax.set_yticks(0.5 + np.arange(7))
+
+ labels = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
+ if origin == "upper":
+ labels = labels[::-1]
+ ax.set_yticklabels(labels)
+
+ ax.text(
+ 1.01,
+ 0.5,
+ "{}".format(year),
+ rotation=90,
+ ha="left",
+ va="center",
+ transform=ax.transAxes,
+ size="28",
+ weight="bold",
+ alpha=0.5,
+ )
+
+ # Showing data
+ cmap = plt.cm.get_cmap("Purples")
+ im = ax.imshow(
+ data, extent=[0, 53, 0, 7], zorder=10, vmin=0, vmax=8, cmap=cmap, origin=origin
+ )
+
+
+def get_commits(year=2021, path="../.."):
+ "Collect commit dates for a given year"
+
+ n = 1 + (date(year, 12, 31) - date(year, 1, 1)).days
+ C = np.zeros(n, dtype=int)
+ repo = git.Repo(path)
+ for commit in repo.iter_commits("master"):
+ timestamp = datetime.fromtimestamp(commit.committed_date)
+ if timestamp.year == year:
+ day = timestamp.timetuple().tm_yday
+ C[day - 1] += 1
+ return C
+
+
+for year in [2019, 2020, 2021]:
+ fig = plt.figure(figsize=(10, 2), dpi=250, frameon=False)
+ ax = plt.subplot(xticks=[], yticks=[], frameon=False)
+ calmap(ax, year, get_commits(year), origin="upper", weekstart="sun")
+ plt.tight_layout()
+ fig.show()
diff --git a/code/unsorted/github-activity.py b/code/unsorted/github-activity.py
new file mode 100644
index 0000000..3a420c9
--- /dev/null
+++ b/code/unsorted/github-activity.py
@@ -0,0 +1,139 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import html.parser
+import urllib.request
+import dateutil.parser
+from datetime import date, datetime
+from dateutil.relativedelta import relativedelta
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+
+
+def github_contrib(user, year):
+ """ Get GitHub user daily contribution """
+
+ # Check for a cached version (file)
+ filename = "github-{0}-{1}.html".format(user, year)
+ if os.path.exists(filename):
+ with open(filename) as file:
+ contents = file.read()
+ # Else get file from GitHub
+ else:
+ url = "https://github.com/users/{0}/contributions?to={1}-12-31"
+ url = url.format(user, year)
+ contents = str(urllib.request.urlopen(url).read())
+ with open(filename, "w") as file:
+ file.write(contents)
+
+ # Parse result (html)
+ n = 1 + (date(year, 12, 31) - date(year, 1, 1)).days
+ C = -np.ones(n, dtype=int)
+
+ class HTMLParser(html.parser.HTMLParser):
+ def handle_starttag(self, tag, attrs):
+ if tag == "rect":
+ data = {key: value for (key, value) in attrs}
+ date = dateutil.parser.parse(data["data-date"])
+ count = int(data["data-count"])
+ day = date.timetuple().tm_yday - 1
+ if count > 0:
+ C[day] = count
+
+ parser = HTMLParser()
+ parser.feed(contents)
+ return C
+
+
+def calmap(ax, year, data, origin="upper", weekstart="sun"):
+ ax.tick_params("x", length=0, labelsize="medium", which="major")
+ ax.tick_params("y", length=0, labelsize="x-small", which="major")
+
+ # Month borders
+ xticks, labels = [], []
+
+ start = datetime(year, 1, 1).weekday()
+
+ _data = np.zeros(7 * 53) * np.nan
+ _data[start : start + len(data)] = data
+ data = _data.reshape(53, 7).T
+
+ for month in range(1, 13):
+ first = datetime(year, month, 1)
+ last = first + relativedelta(months=1, days=-1)
+ if origin == "lower":
+ y0 = first.weekday()
+ y1 = last.weekday()
+ x0 = (int(first.strftime("%j")) + start - 1) // 7
+ x1 = (int(last.strftime("%j")) + start - 1) // 7
+ P = [
+ (x0, y0),
+ (x0, 7),
+ (x1, 7),
+ (x1, y1 + 1),
+ (x1 + 1, y1 + 1),
+ (x1 + 1, 0),
+ (x0 + 1, 0),
+ (x0 + 1, y0),
+ ]
+ else:
+ y0 = 6 - first.weekday()
+ y1 = 6 - last.weekday()
+ x0 = (int(first.strftime("%j")) + start - 1) // 7
+ x1 = (int(last.strftime("%j")) + start - 1) // 7
+ P = [
+ (x0, y0 + 1),
+ (x0, 0),
+ (x1, 0),
+ (x1, y1),
+ (x1 + 1, y1),
+ (x1 + 1, 7),
+ (x0 + 1, 7),
+ (x0 + 1, y0 + 1),
+ ]
+
+ xticks.append(x0 + (x1 - x0 + 1) / 2)
+ labels.append(first.strftime("%b"))
+ poly = Polygon(
+ P,
+ edgecolor="black",
+ facecolor="None",
+ linewidth=1,
+ zorder=20,
+ clip_on=False,
+ )
+ ax.add_artist(poly)
+
+ ax.set_xticks(xticks)
+ ax.set_xticklabels(labels)
+ ax.set_yticks(0.5 + np.arange(7))
+
+ labels = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
+ if origin == "upper":
+ labels = labels[::-1]
+ ax.set_yticklabels(labels)
+ ax.set_title("{}".format(year), size="medium", weight="bold")
+
+ # Showing data
+ cmap = plt.cm.get_cmap("Purples")
+ ax.imshow(
+ data, extent=[0, 53, 0, 7], zorder=10, vmin=0, vmax=10, cmap=cmap, origin=origin
+ )
+
+
+fig = plt.figure(figsize=(8, 7.5), dpi=100)
+year = 2014
+n = 5
+for i in range(n):
+ ax = plt.subplot(n, 1, i + 1, xlim=[0, 53], ylim=[0, 7], frameon=False, aspect=1)
+ calmap(ax, year + i, github_contrib("rougier", year + i), origin="upper")
+
+plt.tight_layout()
+
+# plt.savefig("github-activity.png", dpi=300)
+# plt.savefig("github-activity.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/hatched-bars.py b/code/unsorted/hatched-bars.py
new file mode 100644
index 0000000..02e2c08
--- /dev/null
+++ b/code/unsorted/hatched-bars.py
@@ -0,0 +1,73 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+
+
+def lighten_color(color, amount=0.66):
+ import matplotlib.colors as mc
+ import colorsys
+
+ try:
+ c = mc.cnames[color]
+ except:
+ c = color
+ c = np.array(colorsys.rgb_to_hls(*mc.to_rgb(c)))
+ return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
+
+
+cmap = plt.get_cmap("tab10")
+color = cmap(0)
+plt.rcParams["hatch.color"] = lighten_color(color)
+plt.rcParams["hatch.linewidth"] = 8
+
+
+fig = plt.figure(figsize=(4.25, 2))
+ax = plt.subplot(1, 1, 1)
+np.random.seed(123)
+
+x1, y1 = 3 * np.arange(4), np.random.randint(25, 50, 4)
+x2, y2 = x1 + 1, np.random.randint(25, 75, 4)
+
+ax.bar(x1, y1, color=color)
+for i in range(len(x1)):
+ plt.annotate(
+ "%d%%" % y1[i],
+ (x1[i], y1[i]),
+ xytext=(0, 1),
+ fontsize="x-small",
+ color=color,
+ textcoords="offset points",
+ va="bottom",
+ ha="center",
+ )
+
+ax.bar(x2, y2, color=color, hatch="/")
+for i in range(len(x2)):
+ plt.annotate(
+ "%d%%" % y2[i],
+ (x2[i], y2[i]),
+ xytext=(0, 1),
+ fontsize="x-small",
+ color=color,
+ textcoords="offset points",
+ va="bottom",
+ ha="center",
+ )
+
+ax.set_yticks([])
+ax.set_xticks(0.5 + np.arange(0, 12, 3))
+ax.set_xticklabels(["2016", "2017", "2018", "2019"])
+ax.tick_params("x", length=0, labelsize="small", which="major")
+
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+
+plt.tight_layout()
+# plt.savefig("hatched-bars.pdf")
+plt.show()
diff --git a/code/unsorted/layout-weird.py b/code/unsorted/layout-weird.py
new file mode 100644
index 0000000..e1143e3
--- /dev/null
+++ b/code/unsorted/layout-weird.py
@@ -0,0 +1,81 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Weird axes layout (just for fun)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+p["axes.grid"] = True
+p["grid.color"] = "0.5"
+p["grid.linewidth"] = 0.5
+
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols = 2, 2
+gspec = gridspec.GridSpec(ncols=ncols, nrows=nrows, figure=fig)
+
+
+ax = plt.subplot(1, 1, 1)
+ax.set_xlim(0, 1)
+ax.set_xticks(np.linspace(0, 1, 5))
+ax.set_xlabel("X Label")
+ax.set_ylim(0, 1)
+ax.set_yticks(np.linspace(0, 1, 5))
+ax.set_ylabel("Y Label")
+ax.set_title("Close-up", x=0.25, family="Roboto", weight=500)
+
+# Manual edit for the axes limits
+ax2 = fig.add_axes([0.53, 0.515, 0.4485, 0.4505])
+ax2.spines["top"].set_visible(False)
+ax2.spines["right"].set_visible(False)
+ax2.grid(False)
+ax2.set_xticks([])
+ax2.set_yticks([])
+
+n = 10000
+X = np.random.normal(0.25, 1, n)
+Y = np.random.normal(0.25, 1, n)
+
+# Manual edit for the axes limits
+ax3 = fig.add_axes([0.575, 0.56, 0.4, 0.4])
+ax3.set_title("Distribution", family="Roboto", weight=500)
+ax3.set_xlim(-3, 3)
+ax3.set_ylim(-3, 3)
+S = ax3.scatter(X, Y, s=0.5, linewidths=0, color=".5")
+S = ax3.scatter(X, Y, s=0.5, linewidths=0, color="black")
+from matplotlib.patches import Polygon
+
+p = Polygon(
+ [(0, 0), (0, 1), (0.5, 1), (0.5, 0.5), (1, 0.5), (1, 0), (0, 0)],
+ transform=ax3.transData,
+ closed=True,
+ facecolor="None",
+ edgecolor="black",
+ linewidth=0.75,
+ zorder=50,
+)
+S.set_clip_path(p)
+ax3.add_artist(p)
+
+
+S = ax.scatter(X, Y, s=100, linewidths=1, color="black")
+S = ax.scatter(X, Y, s=100, linewidths=0, color="white")
+for s in np.linspace(5, 100, 10):
+ S = ax.scatter(X, Y, s=s, linewidths=0, alpha=0.05, color="red")
+S = ax.scatter(X, Y, s=3, linewidths=0, alpha=0.75, color="black")
+
+# plt.savefig("../figures/layout-weird.pdf")
+plt.show()
diff --git a/code/unsorted/make-hatch-linewidth.py b/code/unsorted/make-hatch-linewidth.py
new file mode 100644
index 0000000..7bb52f5
--- /dev/null
+++ b/code/unsorted/make-hatch-linewidth.py
@@ -0,0 +1,35 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+from matplotlib.backends.backend_agg import FigureCanvasAgg
+
+figsize = 4.25, 5 * 0.55
+xlim = 0.0, 11.0
+ylim = 0.5, 5.0
+dx = figsize[0] / (xlim[1] - xlim[0])
+dy = figsize[1] / (ylim[1] - ylim[0])
+widths = 1, 2, 3, 4, 5, 6
+w, h = 0.75 * 10 / len(widths), 0.5
+figsize = w * dx, h * dy
+dpi = 600
+
+for width in widths:
+ plt.rcParams["hatch.linewidth"] = width
+ fig = plt.figure(figsize=figsize, dpi=dpi)
+ ax = fig.add_axes([0, 0, 1, 1], xlim=[0, 1], ylim=[0, 1])
+ ax.axis("off")
+ canvas = FigureCanvasAgg(fig)
+ rect = Rectangle(
+ (0, 0), 1, 1, hatch="/", facecolor="0.85", edgecolor="0.00", linewidth=0.0
+ )
+ ax.add_patch(rect)
+ canvas.draw()
+ image = np.frombuffer(canvas.tostring_rgb(), dtype="uint8")
+ image = image.reshape(int(figsize[1] * dpi), int(figsize[0] * dpi), 3)
+ imageio.imwrite("hatch-%d.png" % width, image)
diff --git a/code/unsorted/metropolis.py b/code/unsorted/metropolis.py
new file mode 100644
index 0000000..bfeed00
--- /dev/null
+++ b/code/unsorted/metropolis.py
@@ -0,0 +1,110 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import tqdm
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from scipy.ndimage import gaussian_filter
+from matplotlib.collections import LineCollection
+from matplotlib.backends.backend_agg import FigureCanvas
+
+
+# Nothing complicated here.
+# Directly translated from https://github.com/marcusvolz/metropolis
+# See also https://github.com/marcusvolz
+#
+# Don't use n > 11000 or you'll be stuck forever...
+def metropolis(width=10000, height=10000, n=11000, step=75, branching=0.1, noise=2.0):
+ delta = noise * np.pi / 180
+ points = np.zeros(
+ n, dtype=[("x", float), ("y", float), ("dir", float), ("level", int)]
+ )
+ edges = np.zeros(
+ n,
+ dtype=[
+ ("x0", float),
+ ("y0", float),
+ ("x1", float),
+ ("y1", float),
+ ("level", int),
+ ],
+ )
+ points[0] = width / 2, height / 2, np.random.uniform(-2 * np.pi, 2 * np.pi), 1
+
+ for i in tqdm.trange(1, n):
+ while True:
+ point = np.random.choice(points[:i])
+ branch = 1 if np.random.uniform(0, 1) <= branching else 0
+ alpha = point["dir"] + delta * np.random.uniform(-1, +1)
+ alpha += branch * np.random.choice([-np.pi / 2, +np.pi / 2])
+ v = np.array((np.cos(alpha), np.sin(alpha)))
+ v = v * step * (1 + 1 / (point["level"] + branch))
+ x = point["x"] + v[0]
+ y = point["y"] + v[1]
+ level = point["level"] + branch
+ if x < 0 or x > width or y < 0 or y > height:
+ continue
+ dist = np.sqrt((points["x"] - x) ** 2 + (points["y"] - y) ** 2)
+ if dist.min() >= step:
+ points[i] = x, y, alpha, level
+ edges[i] = x, y, point["x"], point["y"], level
+ break
+ return edges[edges["level"] > 0]
+
+
+np.random.seed(12345)
+width, height, border = 10000, 10000, 500
+if 1: # Set to 0 after computation
+ edges = metropolis(width, height)
+ np.save("egdes.npy", edges)
+else:
+ edges = np.load("egdes.npy")
+
+
+segments = []
+for edge in edges:
+ x0, y0, x1, y1, level = edge
+ segments.append([(x0, y0), (x1, y1)])
+
+# Drop shadow pre-processing
+# We render into an array and we apply Gaussian blur
+fig = Figure(figsize=(6, 6))
+canvas = FigureCanvas(fig)
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ aspect=1,
+ frameon=False,
+ xticks=[],
+ yticks=[],
+ xlim=[0, width],
+ ylim=[0, height],
+)
+sigma = 2.0
+collection = LineCollection(segments, linewidths=1.5, colors="black", capstyle="round")
+ax.add_collection(collection)
+canvas.draw()
+I = np.array(canvas.renderer.buffer_rgba())[..., :3]
+I[:, :, 0] = gaussian_filter(I[:, :, 0], sigma=sigma)
+I[:, :, 1] = gaussian_filter(I[:, :, 1], sigma=sigma)
+I[:, :, 2] = gaussian_filter(I[:, :, 2], sigma=sigma)
+
+
+# Actual rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ aspect=1,
+ frameon=False,
+ xticks=[],
+ xlim=[border, width - border],
+ yticks=[],
+ ylim=[border, height - border],
+)
+ax.imshow(I, extent=[0, width, 0, height], alpha=0.5)
+collection = LineCollection(segments, linewidths=1.0, colors="black", capstyle="round")
+ax.add_collection(collection)
+plt.savefig("metropolis.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/poster-layout.py b/code/unsorted/poster-layout.py
new file mode 100644
index 0000000..91627f1
--- /dev/null
+++ b/code/unsorted/poster-layout.py
@@ -0,0 +1,177 @@
+# -----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# -----------------------------------------------------------------------------
+# Complex layout and text effects
+# -----------------------------------------------------------------------------
+import numpy as np
+import imageio
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+from matplotlib.text import TextPath
+import matplotlib.patches as mpatches
+import matplotlib.patheffects as path_effects
+from matplotlib.font_manager import FontProperties
+
+
+def box(ax, index, x, y, width, height):
+ rectangle = mpatches.Rectangle(
+ (x, y), width, height, zorder=10, facecolor="none", edgecolor="black"
+ )
+ ax.add_artist(rectangle)
+
+ ax.text(
+ x + (width - 1) / 2,
+ y + (height - 1) / 2,
+ "%d" % index,
+ weight="bold",
+ zorder=50,
+ size="32",
+ va="center",
+ ha="center",
+ color="k",
+ alpha=0.25,
+ family="GlassJaw BB",
+ )
+
+
+fig = plt.figure(figsize=(6.5, 9.3))
+
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ aspect=1,
+ frameon=False,
+ xlim=(0, 65),
+ ylim=(0, 93),
+ xticks=[],
+ yticks=[],
+)
+
+ax.xaxis.set_major_locator(ticker.MultipleLocator(10))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(1))
+ax.yaxis.set_major_locator(ticker.MultipleLocator(10))
+ax.yaxis.set_minor_locator(ticker.MultipleLocator(1))
+# ax.grid(which="both", linewidth=0.5, color="0.85", zorder=-10)
+
+boxes = [
+ (1, 1, 15, 21),
+ (17, 1, 15, 10),
+ (17, 12, 15, 10),
+ (33, 1, 15, 15),
+ (49, 1, 15, 15),
+ (1, 23, 31, 8),
+ (1, 32, 8, 14),
+ (10, 32, 22, 14),
+ (1, 47, 15, 13),
+ (17, 47, 15, 13),
+ (33, 45, 15, 15),
+ (33, 32, 15, 12),
+ (49, 32, 15, 18),
+ (49, 51, 15, 9),
+ (1, 61, 15, 15),
+ (17, 61, 36, 15),
+ (54, 61, 10, 15),
+ (49, 77, 15, 15),
+]
+
+for index, (x, y, width, height) in enumerate(boxes):
+ box(ax, index, x, y, width, height)
+
+# fig.add_axes([33/65, 17/93, 31/65, 14/93], xticks=[], yticks=[])
+
+ax.text(
+ 33,
+ 17.25,
+ """Matplotlib can also be used to layout\n"""
+ """a poster where each box can be filled\n"""
+ """with different subplot.\n"""
+ """\n"""
+ """To compute the boxes, just draw them\n"""
+ """on a piece of graph of paper and use\n"""
+ """the measure to get the bounds.\n"""
+ """\n"""
+ """The cartoon fonts used in this example\n"""
+ """are "Lint McCree" and "GlassJaw"\n"""
+ """""",
+ size=6,
+ color="k",
+ family="Lint McCree Intl BB",
+)
+
+
+# Title
+# -----------------------------------------------------------------------------
+textpath = TextPath(
+ (8, 80), "MATPLOTLIB", size=8, prop=FontProperties(family="GlassJaw BB")
+)
+patch = mpatches.PathPatch(
+ textpath, facecolor="none", edgecolor="none", zorder=5, joinstyle="round"
+)
+patch.set_path_effects(
+ [
+ path_effects.Stroke(linewidth=8, foreground="black"),
+ path_effects.Stroke(linewidth=6, foreground="yellow"),
+ path_effects.Stroke(linewidth=3, foreground="black"),
+ ]
+)
+
+transform = ax.transData + mpl.transforms.Affine2D().rotate_deg(2.5)
+patch.set_transform(transform)
+
+ax.add_artist(patch)
+Z = np.linspace(1, 0, 100).reshape(100, 1)
+im = ax.imshow(Z, cmap="autumn", extent=[1, 100, 79, 87], zorder=15)
+im.set_transform(transform)
+im.set_clip_path(patch._path, patch.get_transform())
+
+ax.text(
+ 47,
+ 79,
+ "Scientific visualization made simple & beautiful",
+ color="black",
+ zorder=20,
+ family="Lint McCree Intl BB",
+ weight="bold",
+ size="xx-small",
+ ha="right",
+ va="baseline",
+)
+
+ax.text(
+ 8,
+ 87,
+ " BSD Licensed ",
+ color="white",
+ zorder=30,
+ rotation=-1.5,
+ family="Lint McCree Intl BB",
+ ha="center",
+ va="center",
+ size=7,
+ bbox=dict(boxstyle="roundtooth", fc="k", ec="w", lw=1, pad=0.75),
+)
+
+
+# Box 18
+# -----------------------------------------------------------------------------
+I = imageio.imread("../data/John-Hunter-comic.png")
+ax.imshow(I, extent=[49, 49 + 15, 77, 77 + 15], zorder=0, interpolation="bicubic")
+
+ax.text(
+ 49.7,
+ 77.5,
+ "John D. Hunter III",
+ color="black",
+ zorder=30,
+ family="Lint McCree Intl BB",
+ weight="bold",
+ ha="left",
+ va="bottom",
+ size=5,
+ bbox=dict(boxstyle="square", fc="w", ec="k", lw=1, pad=0.5),
+)
+
+# plt.savefig("poster-layout.png", dpi=300)
+plt.show()
diff --git a/code/unsorted/scale-logit.py b/code/unsorted/scale-logit.py
new file mode 100644
index 0000000..cb935c6
--- /dev/null
+++ b/code/unsorted/scale-logit.py
@@ -0,0 +1,43 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+from matplotlib.ticker import NullFormatter
+
+X = np.random.uniform(0, 1, 1000)
+Y = np.random.uniform(0, 1, 1000)
+S = np.sqrt((X - 0.5) ** 2 + (Y - 0.5) ** 2)
+C = np.ones((1000, 4)) * colors.to_rgba("C0")
+C[S > 0.5] = colors.to_rgba("C1")
+
+figure = plt.figure(figsize=(8, 4))
+
+# Linear scale
+ax = plt.subplot(1, 2, 1, xlim=(0, 1), ylim=(0, 1))
+ax.scatter(X, Y, s=25, facecolor=C, edgecolor="none", alpha=0.5)
+T = np.linspace(0, 2 * np.pi, 100)
+X2 = 0.5 + 0.5 * np.cos(T)
+Y2 = 0.5 + 0.5 * np.sin(T)
+ax.plot(X2, Y2, color="black", linestyle="--", linewidth=0.75)
+
+#
+ax = plt.subplot(1, 2, 2, xlim=(0.01, 0.99), ylim=(0.01, 0.99))
+ax.set_xscale("logit")
+ax.set_yscale("logit")
+ax.scatter(X, Y, s=25, facecolor=C, edgecolor="none", alpha=0.5)
+ax.yaxis.set_minor_formatter(NullFormatter())
+ax.xaxis.set_minor_formatter(NullFormatter())
+T = np.linspace(0, 2 * np.pi, 256)
+X2 = 0.5 + 0.5 * np.cos(T)
+Y2 = 0.5 + 0.5 * np.sin(T)
+ax.plot(X2, Y2, color="black", linestyle="--", linewidth=0.75)
+
+
+# Show
+plt.tight_layout()
+# plt.savefig("scale-logit.pdf")
+plt.show()
diff --git a/code/unsorted/stacked-bars.py b/code/unsorted/stacked-bars.py
new file mode 100644
index 0000000..66f207f
--- /dev/null
+++ b/code/unsorted/stacked-bars.py
@@ -0,0 +1,71 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+
+
+def lighten_color(color, amount=0.66):
+ import colorsys
+
+ try:
+ c = mcolors.cnames[color]
+ except:
+ c = color
+ c = np.array(colorsys.rgb_to_hls(*mcolors.to_rgb(c)))
+ return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
+
+
+# cmap = plt.get_cmap("Blues")
+cmap = plt.get_cmap("tab10")
+
+
+V = np.array(
+ [
+ [50, 23, 20, 7],
+ [45, 33, 12, 10],
+ [35, 43, 10, 12],
+ [54, 23, 15, 8],
+ [65, 23, 7, 5],
+ ]
+)
+
+
+fig = plt.figure(figsize=(6, 2), dpi=100)
+ax = plt.subplot(111, xticks=[], ylim=[-0.5, len(V) - 0.5])
+
+ratings = ["Excellent", "Good", "Average", "Awful"]
+Y = np.arange(len(V))
+L = np.zeros(len(V))
+
+for i in range(4):
+ color = lighten_color(cmap(3), 1.00 - i / 4)
+ ax.barh(Y, V[:, i], left=L, color=color, label=ratings[i])
+ for j in range(len(V)):
+ ax.text(
+ L[j] + V[j, i] / 2,
+ Y[j],
+ "%d%%" % V[j, i],
+ zorder=10,
+ ha="center",
+ va="center",
+ color="white",
+ size="x-small",
+ )
+ L += V[:, i]
+
+
+ax.legend(frameon=False, bbox_to_anchor=(1.0, 1), loc=2, borderaxespad=0)
+ax.set_yticks(Y)
+ax.set_yticklabels(["Rating %d" % (i + 1) for i in Y])
+
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+ax.spines["bottom"].set_visible(False)
+
+plt.tight_layout()
+plt.savefig("stacked-bars.pdf")
+plt.show()
diff --git a/cover/back-cover.aux b/cover/back-cover.aux
new file mode 100644
index 0000000..b640121
--- /dev/null
+++ b/cover/back-cover.aux
@@ -0,0 +1,2 @@
+\relax
+\gdef \@abspage@last{1}
diff --git a/cover/back-cover.log b/cover/back-cover.log
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index 0000000..75b25e1
--- /dev/null
+++ b/cover/back-cover.log
@@ -0,0 +1,159 @@
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+ restricted \write18 enabled.
+ %&-line parsing enabled.
+**back-cover.tex
+(./back-cover.tex
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+Document Class: standalone 2018/03/26 v1.3a Class to compile TeX sub-files stan
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+)
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+)
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diff --git a/cover/back-cover.pdf b/cover/back-cover.pdf
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diff --git a/cover/back-cover.tex b/cover/back-cover.tex
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+++ b/cover/back-cover.tex
@@ -0,0 +1,38 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\makeatletter
+\newcommand{\definetrim}[2]{%
+ \define@key{Gin}{#1}[]{\setkeys{Gin}{trim=#2,clip}}%
+}
+\makeatother
+
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+
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+
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+\setlength{\coverwidth}{148mm}
+\setlength{\bleedwidth}{19mm}
+\setlength{\spinewidth}{23mm}
+\setlength{\marklength}{3mm}
+
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+\setlength{\trimbottom}{\dimexpr \bleedwidth \relax }
+\setlength{\trimtop}{\dimexpr \bleedwidth \relax }
+\definetrim{front}{\trimleft{} \trimbottom{} \trimright{} \trimtop{}}
+
+\begin{document}
+\includegraphics[front,height=216mm]{print-cover.pdf}
+\end{document}
+
+% Largeur: 371.7 - 210.7 - 22
+% Hauteur: 260 mm
diff --git a/cover/cover-pattern.pdf b/cover/cover-pattern.pdf
new file mode 100644
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diff --git a/cover/cover-pattern.py b/cover/cover-pattern.py
new file mode 100644
index 0000000..7b07887
--- /dev/null
+++ b/cover/cover-pattern.py
@@ -0,0 +1,27 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PatchCollection
+
+inch = 25.4
+width = 371.7
+height = 260
+
+fig = plt.figure(figsize=(width/inch,height/inch))
+ax = fig.add_axes([0,0,1,1], aspect=1, frameon=False)
+
+radius = 25
+patches = []
+for y in range(11,-1,-1):
+ for x in range(8,-1,-1):
+ center = 2*x*radius + y%2*radius, y*radius
+ for i,r in enumerate(np.linspace(25, 2.5, 10)):
+ patches.append(plt.Circle(center, r))
+
+collection = PatchCollection(patches, edgecolors="0.1", facecolors='black')
+ax.add_collection(collection)
+
+ax.set_xlim(0, width)
+ax.set_ylim(0, height)
+
+plt.savefig("cover-pattern.pdf")
+plt.show()
diff --git a/cover/front-cover.aux b/cover/front-cover.aux
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+(fontspec) <->"Avenir/I/OT:script=latn;language=dflt;letterspace=25
+.0;mapping=tex-text;"
+(fontspec) - 'italic small caps' (m/scit) with NFSS spec.:
+(fontspec) - 'bold italic' (b/it) with NFSS spec.:
+(fontspec) <->"Avenir/BI/OT:script=latn;language=dflt;letterspace=2
+5.0;mapping=tex-text;"
+(fontspec) - 'bold italic small caps' (b/scit) with NFSS spec.:
+
+[1
+
+] (./print-cover.aux) )
+Here is how much of TeX's memory you used:
+ 16010 strings out of 476477
+ 375415 string characters out of 5814314
+ 738946 words of memory out of 5000000
+ 36197 multiletter control sequences out of 15000+600000
+ 403622 words of font info for 51 fonts, out of 8000000 for 9000
+ 1348 hyphenation exceptions out of 8191
+ 99i,11n,103p,440b,648s stack positions out of 5000i,500n,10000p,200000b,80000s
+
+Output written on print-cover.pdf (1 page).
diff --git a/cover/print-cover.pdf b/cover/print-cover.pdf
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diff --git a/cover/print-cover.tex b/cover/print-cover.tex
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index 0000000..d170047
--- /dev/null
+++ b/cover/print-cover.tex
@@ -0,0 +1,102 @@
+\documentclass[
+ coverheight=248mm, %% 210mm + 2x19mm
+ coverwidth=167mm, %% 148mm + 19mm
+ %% bleedwidth=19mm, % -> margin
+ bleedwidth=3mm, %
+ spinewidth=23mm, % (13mm + 10mm)
+ marklength=0mm, %
+ markcolor=white
+]{bookcover}
+
+\letnamebookcoverpart{back typing area}{back}[19mm,19mm,0mm,19mm]
+\letnamebookcoverpart{front typing area}{front}[0mm,19mm,19mm,19mm]
+\letnamebookcoverpart{spine typing area}{spine}[5mm,19mm,5mm,19mm]
+
+\usepackage{setspace}
+\usepackage{xcolor}
+\usepackage{fontspec}
+\setmainfont{Avenir}
+\renewcommand{\baselinestretch}{1.5}
+
+\begin{document}
+
+ \begin{bookcover}
+
+ \bookcovercomponent{picture}{whole page}{cover-pattern.pdf}
+
+ %% DEBUG
+ %% \bookcovercomponent{color}{bg whole}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{back}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{back typing area}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{front}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{front typing area}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{spine}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{spine typing area}{white,opacity=0.05}
+
+ %% \begin{bookcoverelement}{tikz}{bg whole}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{front}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{front typing area}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{back}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{back typing area}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{spine}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{spine typing area}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% END DEBUG
+
+
+ \bookcovercomponent{center}{spine}{
+ \vspace{-31mm}
+ \addfontfeature{LetterSpace=45.0}
+ \rotatebox[origin=c]{-90}{\textcolor{yellow!80!white}{\Large
+ SCIENTIFIC~~PYTHON~~VOLUME~~II}}}
+
+ \bookcovercomponent{normal}{spine typing area}[,,,181mm]{
+ \centering
+ \includegraphics[width=13mm]{./gravatar.png}
+ }
+
+ \bookcovercomponent{normal}{front typing area}[,,,20mm]{
+ \centering
+ \addfontfeature{LetterSpace=52.0}
+ \textcolor{yellow!80!white}{\small SCIENTIFIC~~PYTHON~~VOLUME~~II}
+
+ \addfontfeature{LetterSpace=22.0}
+ \textcolor{white}{\Large \bfseries SCIENTIFIC VISUALIZATION}
+
+ \vspace{-1pt}
+ \addfontfeature{LetterSpace=65.0}
+ \textcolor{white}{\large PYTHON \& MATPLOTLIB}
+ }
+
+ \bookcovercomponent{normal}{front typing area}[,,,187mm]{
+ \centering
+ \addfontfeature{LetterSpace=25.0}
+ \textcolor{white}{NICOLAS~~P.~~ROUGIER}
+ }
+
+ \bookcovercomponent{normal}{back typing area}[,,,180mm]{
+ \setstretch{1.0}
+ \centering
+ \textcolor{white}{In memory of\\
+ \textbf{John D. Hunter}
+ \&
+ \textbf{Maxim Shemanarev}\\
+ Two brilliant minds that are dearly missed.}
+ }
+
+
+ \end{bookcover}
+\end{document}
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--- /dev/null
+++ b/figures/cheatsheets/cheatsheets-1.tex
@@ -0,0 +1,7 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\begin{document}
+\includegraphics[trim=0cm 0cm 17.82cm 0cm, clip,height=210mm]{cheatsheets.pdf}
+\end{document}
+
diff --git a/figures/cheatsheets/cheatsheets-2.pdf b/figures/cheatsheets/cheatsheets-2.pdf
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+++ b/figures/cheatsheets/cheatsheets-2.tex
@@ -0,0 +1,7 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\begin{document}
+\includegraphics[trim=11.88cm 0cm 5.94cm 0cm, clip,height=210mm]{cheatsheets.pdf}
+\end{document}
+
diff --git a/figures/cheatsheets/cheatsheets-3.pdf b/figures/cheatsheets/cheatsheets-3.pdf
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+++ b/figures/cheatsheets/cheatsheets-3.tex
@@ -0,0 +1,8 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\begin{document}
+\includegraphics[trim=23.76cm 0cm 0cm 0cm, clip, page=1, height=210mm]{cheatsheets.pdf}
+\includegraphics[trim=0cm 0cm 23.76cm 0cm, clip, page=2, height=210mm]{cheatsheets.pdf}
+\end{document}
+
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--- /dev/null
+++ b/figures/cheatsheets/cheatsheets-4.tex
@@ -0,0 +1,7 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\begin{document}
+\includegraphics[trim=5.94cm 0cm 11.88cm 0cm, clip, page=2, height=210mm]{cheatsheets.pdf}
+\end{document}
+
diff --git a/figures/cheatsheets/cheatsheets-5.pdf b/figures/cheatsheets/cheatsheets-5.pdf
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--- /dev/null
+++ b/figures/cheatsheets/cheatsheets-5.tex
@@ -0,0 +1,7 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\begin{document}
+\includegraphics[trim=17.82cm 0cm 0cm 0cm, clip, page=2, height=210mm]{cheatsheets.pdf}
+\end{document}
+
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+++ b/figures/cheatsheets/handout-beginner.tex
@@ -0,0 +1,7 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\begin{document}
+\includegraphics[angle=90]{handout-beginner-landscape.pdf}
+\end{document}
+
diff --git a/figures/cheatsheets/handout-intermediate-landscape.pdf b/figures/cheatsheets/handout-intermediate-landscape.pdf
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--- /dev/null
+++ b/figures/cheatsheets/handout-intermediate.tex
@@ -0,0 +1,7 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\begin{document}
+\includegraphics[angle=-90]{handout-intermediate-landscape.pdf}
+\end{document}
+
diff --git a/figures/cheatsheets/handout-tips-landscape.pdf b/figures/cheatsheets/handout-tips-landscape.pdf
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--- /dev/null
+++ b/figures/cheatsheets/handout-tips.tex
@@ -0,0 +1,7 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\begin{document}
+\includegraphics[angle=90]{handout-tips-landscape.pdf}
+\end{document}
+
diff --git a/figures/colors/alpha-scatter.pdf b/figures/colors/alpha-scatter.pdf
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+ Far clip plane Near clip plane Camera Perspective projection (P)
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+
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diff --git a/fonts/roboto/Apache License.txt b/fonts/roboto/Apache License.txt
new file mode 100644
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--- /dev/null
+++ b/fonts/roboto/Apache License.txt
@@ -0,0 +1,203 @@
+Font data copyright Google 2012
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
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\ No newline at end of file
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diff --git a/fonts/source-code-pro/SIL Open Font License.txt b/fonts/source-code-pro/SIL Open Font License.txt
new file mode 100644
index 0000000..295975a
--- /dev/null
+++ b/fonts/source-code-pro/SIL Open Font License.txt
@@ -0,0 +1,43 @@
+Copyright 2010, 2012 Adobe Systems Incorporated (http://www.adobe.com/), with Reserved Font Name 'Source'. All Rights Reserved. Source is a trademark of Adobe Systems Incorporated in the United States and/or other countries.
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at: http://scripts.sil.org/OFL
+
+-----------------------------------------------------------
+SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
+-----------------------------------------------------------
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide development of collaborative font projects, to support the font creation efforts of academic and linguistic communities, and to provide a free and open framework in which fonts may be shared and improved in partnership with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and redistributed freely as long as they are not sold by themselves. The fonts, including any derivative works, can be bundled, embedded, redistributed and/or sold with any software provided that any reserved names are not used by derivative works. The fonts and derivatives, however, cannot be released under any other type of license. The requirement for fonts to remain under this license does not apply to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright Holder(s) under this license and clearly marked as such. This may include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting, or substituting -- in part or in whole -- any of the components of the Original Version, by changing formats or by porting the Font Software to a new environment.
+
+"Author" refers to any designer, engineer, programmer, technical writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining a copy of the Font Software, to use, study, copy, merge, embed, modify, redistribute, and sell modified and unmodified copies of the Font Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components, in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled, redistributed and/or sold with any software, provided that each copy contains the above copyright notice and this license. These can be included either as stand-alone text files, human-readable headers or in the appropriate machine-readable metadata fields within text or binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font Name(s) unless explicit written permission is granted by the corresponding Copyright Holder. This restriction only applies to the primary font name as presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font Software shall not be used to promote, endorse or advertise any Modified Version, except to acknowledge the contribution(s) of the Copyright Holder(s) and the Author(s) or with their explicit written permission.
+
+5) The Font Software, modified or unmodified, in part or in whole, must be distributed entirely under this license, and must not be distributed under any other license. The requirement for fonts to remain under this license does not apply to any document created using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT SOFTWARE.
\ No newline at end of file
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diff --git a/fonts/source-sans-pro/SIL Open Font License.txt b/fonts/source-sans-pro/SIL Open Font License.txt
new file mode 100644
index 0000000..295975a
--- /dev/null
+++ b/fonts/source-sans-pro/SIL Open Font License.txt
@@ -0,0 +1,43 @@
+Copyright 2010, 2012 Adobe Systems Incorporated (http://www.adobe.com/), with Reserved Font Name 'Source'. All Rights Reserved. Source is a trademark of Adobe Systems Incorporated in the United States and/or other countries.
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at: http://scripts.sil.org/OFL
+
+-----------------------------------------------------------
+SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
+-----------------------------------------------------------
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide development of collaborative font projects, to support the font creation efforts of academic and linguistic communities, and to provide a free and open framework in which fonts may be shared and improved in partnership with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and redistributed freely as long as they are not sold by themselves. The fonts, including any derivative works, can be bundled, embedded, redistributed and/or sold with any software provided that any reserved names are not used by derivative works. The fonts and derivatives, however, cannot be released under any other type of license. The requirement for fonts to remain under this license does not apply to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright Holder(s) under this license and clearly marked as such. This may include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting, or substituting -- in part or in whole -- any of the components of the Original Version, by changing formats or by porting the Font Software to a new environment.
+
+"Author" refers to any designer, engineer, programmer, technical writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining a copy of the Font Software, to use, study, copy, merge, embed, modify, redistribute, and sell modified and unmodified copies of the Font Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components, in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled, redistributed and/or sold with any software, provided that each copy contains the above copyright notice and this license. These can be included either as stand-alone text files, human-readable headers or in the appropriate machine-readable metadata fields within text or binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font Name(s) unless explicit written permission is granted by the corresponding Copyright Holder. This restriction only applies to the primary font name as presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font Software shall not be used to promote, endorse or advertise any Modified Version, except to acknowledge the contribution(s) of the Copyright Holder(s) and the Author(s) or with their explicit written permission.
+
+5) The Font Software, modified or unmodified, in part or in whole, must be distributed entirely under this license, and must not be distributed under any other license. The requirement for fonts to remain under this license does not apply to any document created using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT SOFTWARE.
\ No newline at end of file
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diff --git a/fonts/source-serif-pro/SIL Open Font License.txt b/fonts/source-serif-pro/SIL Open Font License.txt
new file mode 100644
index 0000000..a53e4fb
--- /dev/null
+++ b/fonts/source-serif-pro/SIL Open Font License.txt
@@ -0,0 +1,43 @@
+Copyright 2014 Adobe Systems Incorporated (http://www.adobe.com/), with Reserved Font Name 'Source'. All Rights Reserved. Source is a trademark of Adobe Systems Incorporated in the United States and/or other countries.
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at: http://scripts.sil.org/OFL
+
+-----------------------------------------------------------
+SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
+-----------------------------------------------------------
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide development of collaborative font projects, to support the font creation efforts of academic and linguistic communities, and to provide a free and open framework in which fonts may be shared and improved in partnership with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and redistributed freely as long as they are not sold by themselves. The fonts, including any derivative works, can be bundled, embedded, redistributed and/or sold with any software provided that any reserved names are not used by derivative works. The fonts and derivatives, however, cannot be released under any other type of license. The requirement for fonts to remain under this license does not apply to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright Holder(s) under this license and clearly marked as such. This may include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting, or substituting -- in part or in whole -- any of the components of the Original Version, by changing formats or by porting the Font Software to a new environment.
+
+"Author" refers to any designer, engineer, programmer, technical writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining a copy of the Font Software, to use, study, copy, merge, embed, modify, redistribute, and sell modified and unmodified copies of the Font Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components, in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled, redistributed and/or sold with any software, provided that each copy contains the above copyright notice and this license. These can be included either as stand-alone text files, human-readable headers or in the appropriate machine-readable metadata fields within text or binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font Name(s) unless explicit written permission is granted by the corresponding Copyright Holder. This restriction only applies to the primary font name as presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font Software shall not be used to promote, endorse or advertise any Modified Version, except to acknowledge the contribution(s) of the Copyright Holder(s) and the Author(s) or with their explicit written permission.
+
+5) The Font Software, modified or unmodified, in part or in whole, must be distributed entirely under this license, and must not be distributed under any other license. The requirement for fonts to remain under this license does not apply to any document created using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT SOFTWARE.
\ No newline at end of file
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diff --git a/rst/00-acknowledgments.rst b/rst/00-acknowledgments.rst
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--- /dev/null
+++ b/rst/00-acknowledgments.rst
@@ -0,0 +1,108 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. include:: common.rst
+.. _chap-acknowledgments:
+
+
+Acknowledgments
+===============
+
+I would like to thank all my sponsors & supporters for their great
+contributions, comments and questions that help to improve the book
+and the associated code. It's really great to have these people on
+your side you when you're writing a book.
+
+Sponsors (in no specific order)
+-------------------------------
+
+* Scott Lasley (`@selasley `_)
+* Steven Armour (`@GProtoZeroW `_)
+* Daniel Gomez (`@dangom `_)
+* Nathan Howell (`@neh `_)
+* Aymen-lng (`@Aymen-lng `_)
+* Paniterka (`@paniterka `_)
+* t. m. k. (`@teehemkay `_)
+* Alberto Mario Ceballos-Arroyo (`@alceballosa `_)
+* Panagiotis Simakis (`@sp1thas `_)
+* Anton Emelyanov (`@emertonyc `_)
+* Florent Langenfeld (`@FloLangenfeld `_)
+* Andrei Berceanu (`@berceanu `_)
+* Robin Mattheussen (`@romatthe `_)
+* Robert Crim (`@ottbot `_)
+* Jonas Bernoulli (`@tarsius `_)
+
+
+Supporters (in no specific order)
+---------------------------------
+
+* Andrew (`@ahuang11 `_)
+* Alister Burt (`@alisterburt `_)
+* `@argapost `_
+* `@artinus `_
+* Avihay Bar (`@avihaybar `_)
+* Georgios Bakirtzis (`@bakirtzisg `_)
+* `@BCMarquez `_
+* Behrooz Bashokooh (`@BehroozBashokooh `_)
+* Bill Little (`@bjlittle `_)
+* Benjamin Morgan (`@bjmorgan `_)
+* Sébastien Boisgérault (`@boisgera `_)
+* Brandon Rohrer (`@brohrer `_)
+* Brian Hamilton (`@bsxfun `_)
+* Christopher Anderson (`@chrisLanderson `_)
+* Chris Short (`@ChristopherShort `_)
+* `@ciscostud `_
+* Chris Morgan (`@cmorgan `_)
+* Onuralp (`@cx0 `_)
+* Jochen Schröder (`@cycomanic `_)
+* David Ignacio Cortes (`@davidcortesortuno `_)
+* Danylo Malyuta (`@dmalyuta `_)
+* `@earlev4 `_
+* Eitan Lees (`@eitanlees `_)
+* Javier González Monge (`@Enterprixe `_)
+* Folgert Karsdorp (`@fbkarsdorp `_)
+* Federico Vaggi (`@FedericoV `_)
+* Francisco (`@fpozon `_)
+* Giovanni d'Ario (`@gdario `_)
+* Georg Wille (`@georgwille `_)
+* Luciano Gerber (`@gerberl `_)
+* Jeff Borisch (`@horshacktest `_)
+* `@imsalte `_
+* `@jafvert `_
+* Jonathan Whitmore (`@jbwhit `_)
+* Julien Hillairet (`@jhillairet `_)
+* Joseph Szymborski (`@jszym `_)
+* `@ltosti `_
+* Matthieu Leroy (`@m-leroy `_)
+* Markus Degen (`@MarkusDegen `_)
+* Michael Dick (`@midick `_)
+* Niru Maheswaranathan (`@nirum `_)
+* `@ofrighil `_
+* `@oszaar `_
+* `@paulgoulain `_
+* Paul Nakroshis (`@paulnakroshis `_)
+* `@qsandi `_
+* Rik Huygen (`@rhuygen `_)
+* Rich Teague (`@richteague `_)
+* Rocco Meli (`@RMeli `_)
+* `@s7oneghos7 `_
+* Sébastien Le Maguer (`@seblemaguer `_)
+* Andrew Slabko (`@slabko `_)
+* wonjun (`@sleepyeye `_)
+* Serge Toropov (`@sombr `_)
+* `@samdani-1729 `_
+* Shen Zhou (`@szsdk `_)
+* Thomas Lentali (`@tlentali `_)
+* VO-PY (`@VO-PY `_)
+* Xavier Olive (`@xoolive `_)
+* Izaak "Zaak" Beekman (`@zbeekman `_)
+* zguo (`@zguoch `_)
+* Zhang Zhou (`@zznature `_)
+
+
+A special thanks to **Eitan Lees** who reviewed several chapters to
+correct my poor English. All remaining typos are my own. I also would
+like to thank **Sébastien Boisgérault** who helped me a lot with the
+automated build process (well, actually, he setup everything).
diff --git a/rst/00-dedication.rst b/rst/00-dedication.rst
new file mode 100644
index 0000000..eee531f
--- /dev/null
+++ b/rst/00-dedication.rst
@@ -0,0 +1,11 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+
+.. epigraph::
+
+ In memory of **John D. Hunter** (1968 — 2012), creator of the Matplotlib
+ library & **Maxim Shemanarev** (1966 — 2013), creator of the antigrain
+ geometry library. Two brilliant minds that are dearly missed.
diff --git a/rst/00-introduction.rst b/rst/00-introduction.rst
new file mode 100644
index 0000000..b63ad6d
--- /dev/null
+++ b/rst/00-introduction.rst
@@ -0,0 +1,171 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. include:: common.rst
+.. _chap-introduction:
+
+Introduction
+============
+
+The Python scientific visualisation landscape is huge (see figure
+:ref:`fig-landscape`). It is composed of a myriad of tools, ranging from the
+most versatile and widely used down to the more specialised and
+confidential. Some of these tools are community based while others are developed
+by companies. Some are made specifically for the web, others are for the desktop only,
+some deal with 3D and large data, while others target flawless 2D rendering.
+
+.. figure:: introduction/visualization-landscape.pdf
+ :width: 100%
+
+ Python scientific visualisation landscape in 2018 (not exhaustive). Adapted
+ from the original idea of `Jake Vanderplas `__
+ :label:`fig-landscape`
+
+Facing such a large choice, it may be thus difficult to find the package that
+best suit your needs, simply because you may not even be aware that this or that
+package exists. To help you in your choice, you can start by asking yourself a
+few questions:
+
+* Do you target desktop or web rendering?
+* Do you need complex 3D rendering?
+* Do you need publication quality?
+* Do you have very large data?
+* Is there an active community?
+* Are there documentation and tutorials?
+
+.. figure:: introduction/matplotlib-timeline.pdf
+ :width: 100%
+
+ Matplotlib has been originally written by John D. Hunter and the first
+ public version was released in 2003. Michael Droettboom was nominated as
+ matplotlib's lead developer shortly before John Hunter's death in August
+ 2012, joined in 2014 by Thomas Caswell who is now (2021) the
+ lead-developer. The latest version is 3.4 (at the time of writing), and is
+ Python 3 only while the 2.2 version is a long term support version
+ compatible with Python 2 and Python 3. **Sources:**
+ :source:`introduction/matplotlib-timeline.py` :label:`fig-matplotlib-timeline`.
+
+Depending on your answers, you may be able to decide which package to use and
+to invest some time learning it. For example, if you need interactive
+visualization in the browser with seamless integration with jupyter, bokeh_
+might be an answer. If you have very large data and needs 3D on the desktop,
+vispy_ or mayavi_ might be an option. If you're interested in a very intuitive
+tool to rapidly build beautiful figures, then seaborn_ and altair_ are your friends.
+However, if you're working in geosciences, then you cannot overlook cartopy_, etc. I cannot list
+them all and I'm sure that between the writing of this chapter and the actual
+publication of the book, new visualization libraries will have been created. A
+good source of information is the pyviz_ website (Python tools for data
+visualization) that offers a lot of pointers and has an up-to-date list of
+active packages (as opposed to dormant).
+
+.. figure:: introduction/black-hole.jpg
+ :width: 100%
+
+ The supermassive black hole at the core of supergiant elliptical galaxy
+ Messier 87, with a mass ~7 billion times the Sun's, as depicted in the
+ first image released by the Event Horizon Telescope (10 April 2019).
+ **Source:** `Wikipedia `__
+ :label:`fig-black-hole`
+
+In this landscape, Matplotlib has a very special place. It was originally
+created by `John D. Hunter `_ in
+2003 in order to visualize electrocorticography data. Here is the `official
+announcement `__ posted on the Python mailing list on May 23, 2003 [#]_.
+
+.. [#] Many thanks to Anthony Lee for pointing me to this archive.
+
+::
+
+
+ Matplotlib
+
+ Matplotlib is a pure python plotting package for python and
+ pygtk. My goal is to make high quality, publication quality
+ plotting easy in python, with a syntax familiar to matlab
+ users. matplotlib is young, and several things need to be
+ done for this goal is achieved. But it works well enough to
+ make nice, simple plots.
+
+ Requirements
+
+ python 2.2, GTK2, pygtk-1.99.x, and Numeric.
+
+ Download
+
+ See the homepage - nitace.bsd.uchicago.edu:8080/matplotlib
+
+ Here are some of the things that matplotlib tries to do well
+
+ * Allow easy navigation of large data sets. Right click on
+ figure window to bring up navigation tool bar for pan and
+ zoom of x and y axes. This requires a wheel mouse. Place
+ the wheel mouse over the navigation buttons and scroll away.
+ * Handle very large data sets efficiently by making use of
+ Numeric clipping. I have used matplotlib in an EEG
+ plotting application with 128 channels and several
+ minutes of data sampled at 400Hz, eg, plotting matrices
+ with dimensions 120,000 x 128.
+ * Choose tick marks and labels intelligently
+ * make easy things easy (subplots, linestyles, colors)
+ * make hard things possible (OO interface for full control)
+
+ Matplotlib is a class library that can be used to make plots
+ in pygtk applications. But there is a matlab functional
+ compatibility interface that you can get with, eg::
+
+ from matplotlib.matlab import plot, subplot, show, gca
+
+ Example scripts and screenshots available at
+ http://nitace.bsd.uchicago.edu:8080/matplotlib
+
+ John Hunter
+
+
+The initial goal was to replace the popular Matlab graphics engine and to
+support different platforms, to have high quality raster and vector output, to
+provide support for mathematical expressions and to work interactively from the
+shell. The first official release was made in 2003 (see figure
+:ref:`fig-matplotlib-timeline`) and more than 15 years later, the initial goals
+remains the same even though they have been further developed and
+polished. Today, the Matplotlib library is a *de facto* standard for Python
+scientific visualization. It has, for example, been used to display the first
+ever photography of a black hole (see figure :ref:`fig-black-hole`) and to
+illustrate the existence of `gravitational waves
+`__. Matplotlib is
+both a versatile and powerful library that allows you to design very high
+quality figures, suitable for scientific publishing. It offers both a simple and
+intuitive interface (`pyplot`) as well as an object oriented architecture that
+allows you to tweak anything within a figure. Note that, it can also be used as
+a regular graphic library in order to design non-scientific figures, as we'll
+see throughout this book. For example, the Matplotlib timeline figure (see
+figure :ref:`fig-matplotlib-timeline`) is simply made of a line with markers and
+some styled annotations.
+
+This book is organized into 4 parts. The first part considers the fundamental
+principles of the Matplotlib library. This includes reviewing the different
+parts that constitute a figure, the different coordinate systems, the available
+scales and projections, and we'll also introduce a few concepts related to
+typography and colors. The second part is dedicated to the actual design of a
+figure. After introducing some simple rules for generating better figures, we'll
+then go on to explain the Matplotlib defaults and styling system before diving
+on into figure layout organization. We'll then explore the different types of
+plot available and see how a figure can be ornamented with different
+elements. The third part is dedicated to more advanced concepts, namely 3D
+figures, optimization, animation and toolkits. Lastly, the fourth and final
+part is a collection of showcases and their analysis.
+
+
+.. --- Links ------------------------------------------------------------------
+.. _bokeh: https://bokeh.pydata.org
+.. _altair: https://altair-viz.github.io/
+.. _seaborn: http://seaborn.pydata.org/
+.. _vispy: http://vispy.org/
+.. _mayavi: https://docs.enthought.com/mayavi/mayavi/
+.. _cartopy: https://scitools.org.uk/cartopy/docs/latest/
+.. _pyviz: https://pyviz.org/
+.. ----------------------------------------------------------------------------
+
diff --git a/rst/00-preface.rst b/rst/00-preface.rst
new file mode 100644
index 0000000..7e86f59
--- /dev/null
+++ b/rst/00-preface.rst
@@ -0,0 +1,112 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. include:: common.rst
+.. _chap-preface:
+
+Preface
+========
+
+About the author
+----------------
+
+Nicolas P. Rougier is a full-time researcher in computational cognitive
+neuroscience, located in Bordeaux, France. He's doing his research at Inria
+(the French institute for computer science) and the Institute of
+Neurodegenerative Diseases where he investigates decision making, learning and
+cognition using computational models of the brain and distributed, numerical
+and adaptive computing, a.k.a. artificial neural networks and machine
+learning. His research aims to irrigate the fields of philosophy with regard to
+the mind-body problem, medicine to account for the normal and pathological
+functioning of the brain and the digital sciences to offer alternative
+computing paradigms. Beside neuroscience and philosophy, he's also interested
+in open and reproducible science (he has co-founded ReScience C with Konrad
+Hinsen and ReScience X with Etienne Roesch), scientific visualization (he
+created glumpy, co-created VisPy), Science outreach (e.g. The Conversation) and
+computer graphics (especially digital typography).
+
+Nicolas P. Rougier has been using Python for more than 20 years and Matplotlib
+for more than 15 years for modeling in neuroscience, machine learning and for
+advanced visualization. Nicolas P. Rougier is the author of several online
+resources and tutorials and he's teaching Python, NumPy and scientific
+visualisation at the University of Bordeaux as well as at various conferences
+and schools worldwide.
+
+
+About this book
+---------------
+
+This open access book has been written in reStructuredText_ converted to LaTeX
+using docutils and exported to Portable Document Format using XeLaTeX. Sources
+are available at `github.com/rougier/python-scientific-visualisation
+`_
+
+
+How to contribute
+-----------------
+
+If you want to contribute to this book, you can:
+
+* Review chapters & suggest improvements
+* Report issues & correct my English
+* Star the project on GitHub & buy the printed book
+
+
+Prerequisites
+-------------
+
+This book is not a Python beginner guide and you should have an intermediate
+level in Python and ideally a beginner level in NumPy. If this is not the case,
+have a look at the bibliography for a curated list of resources.
+
+Conventions
+-----------
+
+We will use usual naming conventions. If not stated explicitly, each script
+should import NumPy, SciPy and Matplotlib as:
+
+.. code:: Python
+
+ import scipy
+ import numpy as np
+ import matplotlib.pyplot as plt
+
+
+We'll use up-to-date versions (at the date of writing, June 2019) of the
+different packages:
+
+.. code:: Python
+
+ >>> import sys; print(sys.version)
+ 3.7.4 (default, Jul 9 2019, 18:13:23)
+ [Clang 10.0.1 (clang-1001.0.46.4)]
+ >>> import numpy; print(numpy.__version__)
+ 1.16.4
+ >>> import scipy; print(scipy.__version__)
+ 1.3.0
+ >>> import matplotlib; print(matplotlib.__version__)
+ 3.1.0
+
+
+License
+-------
+
+This volume is is licensed under a Creative Commons Attribution Non Commercial
+Share Alike 4.0 International License, which permits use, sharing, adaptation,
+distribution and reproduction in any medium or format, as long as you give
+appropriate credit to the original author(s) and the source, provide a link to
+the Creative Commons license, and indicate if changes were made. You may not
+use the material for commercial purposes. If you remix, transform, or build
+upon the material, you must distribute your contributions under the same
+license as the original. To learn more, visit `creativecommons.org`_.
+
+Unless stated otherwise, all the figures are licensed under a Creative Commons
+Attribution 4.0 International License and all the code is licensed under a BSD
+2-clause license.
+
+.. --- Links ------------------------------------------------------------------
+.. _reStructuredText: http://docutils.sourceforge.net/rst.html
+.. _creativecommons.org: https://creativecommons.org/
+.. ----------------------------------------------------------------------------
diff --git a/rst/anatomy.rst b/rst/anatomy.rst
new file mode 100644
index 0000000..2fe63d2
--- /dev/null
+++ b/rst/anatomy.rst
@@ -0,0 +1,483 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-anatomy:
+
+Anatomy of a figure
+===================
+
+A matplotlib figure is composed of a hierarchy of elements that, when put
+together, forms the actual figure as shown on figure :ref:`fig-anatomy`. Most
+of the time, those elements are not created explicitly by the user but derived
+from the processing of the various plot commands. Let us consider for example
+the most simple matplotlib script we can write:
+
+.. code:: python
+
+ plt.plot(range(10))
+ plt.show()
+
+In order to display the result, matplotlib needs to create most of the elements
+shown on figure :ref:`fig-anatomy`. The exact list depends on your default
+settings (see chapter chap-defaults_), but the bare minimum is the creation of
+a Figure_ that is the top level container for all the plot elements, an Axes_
+that contains most of the figure elements and of course your actual plot, a
+line in this case. The possibility to not specify everything might be
+convenient but in the meantime, it limits your choices because missing elements
+are created automatically, using default values. For example, in the previous
+example, you have no control of the initial figure size since is has been
+chosen implicitly during creation. If you want to change the figure size or
+the axes aspect, you need to be more explicit:
+
+.. code:: python
+
+ fig = plt.figure(figsize=(6,6))
+ ax = plt.subplot(aspect=1)
+ ax.plot(range(10))
+ plt.show()
+
+.. figure:: anatomy/anatomy.pdf
+ :width: 100%
+
+ A matplotlib figure is composed of a hierarchy of several elements that,
+ when put together, forms the actual figure (sources: :source:`anatomy/anatomy.py`).
+ :label:`fig-anatomy`
+
+In many cases, this can be further compacted using the subplots_ method.
+
+.. code:: python
+
+ fig, ax = plt.subplots(figsize=(6,6),
+ subplot_kw={"aspect"=1})
+ ax.plot(range(10))
+ plt.show()
+
+
+Elements
+--------
+
+You may have noticed in the previous example that the plot_ command is attached
+to `ax` instead of `plt`. The use of `plt.plot` is actually a way to tell
+matplotlib that we want to plot on the current axes, that is, the last axes
+that has been created, implicitly or explicitly. No need to remind that
+*explicit is better than implicit* as explained in the The Zen of Python, by
+Tim Peters (`import this`). When you have choice, it is thus preferable to
+specify exactly what you want to do. Consequently, it is important to know what
+are the different elements of a figure.
+
+Figure_:
+ The most important element of a figure is the figure itself. It is created
+ when you call the `figure` method and we've already seen you can specify its
+ size but you can also specify a background color (`facecolor`) as well as a
+ title (`suptitle`). It is important to know that the background color won't
+ be used when you save the figure because the savefig_ function has also a
+ `facecolor` argument (that is white by default) that will override your
+ figure background color. If you don't want any background you can specify
+ `transparent=True` when you save the figure.
+
+Axes_:
+ This is the second most important element that corresponds to the actual area
+ where your data will be rendered. It is also called a subplot. You can have
+ have one to many axes per figure and each is usually surrounded by four edges
+ (left, top, right and bottom) that are called **spines**. Each of these
+ spines can be decorated with major and minor **ticks** (that can point inward
+ or outward), **tick labels** and a **label**. By default, matplotlib
+ decorates only the left and bottom spines.
+
+Axis_:
+ The decorated spines are called axis. The horizontal one is the **xaxis** and
+ the vertical one is the **yaxis**. Each of them are made of a spine, major and
+ minor ticks, major and minor ticks labels and an axis label.
+
+Spines_:
+ Spines are the lines connecting the **axis** tick marks and noting the
+ boundaries of the data area. They can be placed at arbitrary positions and
+ may be visible or invisible.
+
+Artist_:
+ Everything on the figure, including Figure, Axes, and Axis objects, is an
+ artist. This includes Text objects, Line2D objects, collection objects, Patch
+ objects. When the figure is rendered, all of the artists are drawn to the
+ canvas. A given artist can only be in one Axes.
+
+
+Graphic primitives
+------------------
+
+A plot, independently of its nature, is made of patches, lines and
+texts. Patches can be very small (e.g. markers) or very large (e.g. bars) and
+have a range of shapes (circles, rectangles, polygons, etc.). Lines can be very
+small and thin (e.g. ticks) or very thick (e.g. hatches). Text can use any font
+available on your system and can also use a latex engine to render maths.
+
+.. figure:: anatomy/bold-ticklabel.pdf
+ :width: 100%
+
+ All the graphic primitives (i.e. artists) can be accessed and modified. In
+ the figure above, we modified the boldness of the X axis tick labels
+ (sources: :source:`anatomy/bold-ticklabel.py`).
+
+Each of these graphic primitives have also a lot of other properties such as
+color (facecolor and edgecolor), transparency (from 0 to 1), patterns
+(e.g. dashes), styles (e.g. cap styles), special effects (e.g. shadows or
+outline), antialiased (True or False), etc. Most of the time, you do not
+manipulate these primitives directly. Instead, you call methods that build a
+rendering using a collection of such primitives. For example, when you add a
+new `Axes` to a figure, matplotlib will build a collection of line segments for
+the spines and the ticks and will also add a collection of labels for the tick
+labels and the axis labels. Even though this is totally transparent for you,
+you can still access those elements individually if necessary. For example, to
+make the X axis tick to be bold, we would write:
+
+.. code:: Python
+
+ fig, ax = plt.subplots(figsize=(5,2))
+ for label in ax.get_xaxis().get_ticklabels():
+ label.set_fontweight("bold")
+ plt.show()
+
+One important property of any primitive is the `zorder` property that indicates
+the virtual depth of the primitives as shown on figure :ref:`fig-zorder`. This
+zorder value is used to sort the primitives from the lowest to highest before
+rendering them. This allows to control what is behind what. Most artists
+possess a default zorder value such that things are rendered properly. For
+example, the spines, the ticks and the tick label are generally *behind*
+your actual plot.
+
+.. figure:: anatomy/zorder.pdf
+ :width: 50%
+
+ Default rendering order of different elements and graphic primitives. The
+ rendering order is from bottom to top. Note that some methods will override
+ these default to position themselves properly (sources: :source:`anatomy/zorder.py`).
+ :label:`fig-zorder`
+
+
+Backends
+--------
+
+A backend is the combination of a renderer that is responsible for the actual
+drawing and an optional user interface that allows to interact with a
+figure. Until now, we've been using the default renderer and interface
+resulting in a window being shown when the `plt.show()` method was called. To
+know what is your default backend, you can type:
+
+.. code:: Python
+
+ import matplotlib
+ print(matplotlib.get_backend())
+
+In my case, the default backend is `MacOSX` but yours may be different. If you
+want to test for an alternative backend, you can type:
+
+.. code:: Python
+
+ import matplotlib
+ matplotlib.use("xxx")
+
+If you replace `xxx` with a renderer from table :ref:`table-renderers` below,
+you'll end up with a non-interactive figure, i.e. a figure that cannot be shown
+on screen but only saved on disk.
+
+.. table:: Available matplotlib renderers.
+ :label:`table-renderers`
+ :align: left
+
+ ========= ================== =============================================
+ Renderer Type Filetype
+ ========= ================== =============================================
+ Agg raster Portable Network Graphic (PNG)
+ PS vector Postscript (PS)
+ PDF vector Portable Document Format (PDF)
+ SVG vector Scalable Vector Graphics (SVG)
+ Cairo raster / vector PNG / PDF / SVG
+ ========= ================== =============================================
+
+.. table:: Available matplotlib interfaces.
+ :label:`table-interfaces`
+ :align: left
+
+ ========== =============== ===============================================
+ Interface Renderer Dependencies
+ ========== =============== ===============================================
+ GTK3 Agg or Cairo PyGObject_ & Pycairo_
+ Qt4 Agg PyQt4_
+ Qt5 Agg PyQt5_
+ Tk Agg TkInter_
+ Wx Agg wxPython_
+ MacOSX — OSX (obviously)
+ Web Agg Browser
+ ========== =============== ===============================================
+
+The canonical renderer is Agg which uses the `Anti-Grain Geometry C++ library
+`__ to make a raster image of the figure (see figure
+:ref:`fig-raster-vector` to see the different between raster and vector). Note
+that even if you choose a raster renderer, you can still save the figure in a
+vector format and vice-versa.
+
+.. figure:: anatomy/raster-vector.pdf
+ :width: 75%
+
+ Zooming effect for raster graphics and vector graphics (sources:
+ :source:`anatomy/raster-vector.py`). :label:`fig-raster-vector`
+
+If you want to have some interaction with your figure, you have to combine one
+of the available interface (see table :ref:`table-interfaces`) with a
+renderer. For example `GTK3Cairo` or `WebAgg`.
+
+
+
+For example, to have a rendering in a browser, you can write:
+
+.. code:: Python
+
+ import matplotlib
+ matplotlib.use('webagg')
+ import matplotlib.pyplot as plt
+ plt.show()
+
+.. warning::
+ **Warning.** The `use` function must be called before importing `pyplot`.
+
+Once you've chosen an interactive backend, you can decide to produce a figure
+in interactive mode (figure is updated after each matplotlib command):
+
+.. code:: Python
+
+ plt.ion() # Interactive mode on
+ plt.plot([1,2,3]) # Plot is shown
+ plt.xlabel("X Axis") # Label is updated
+ plt.ioff() # Interactive mode off
+
+If you want to know more on backends, you can have a look at the `introductory
+tutorial `__
+on the matplotlib website.
+
+
+An interesting backend under OSX and `iterm2 `__
+backend that allows to render a figure directly inside the terminal,
+emulating a kind of jupyter notebook as shown on figure
+:ref:`figure-imgcat`
+
+.. figure:: anatomy/imgcat.png
+ :width: 100%
+
+ Matplotlib imgcat backend
+ :label:`figure-imgcat`
+ (sources: :source:`anatomy/imgcat.py`).
+
+.. code:: python
+
+ import numpy as np
+ import matplotlib
+ matplotlib.use("module://imgcat")
+ import matplotlib.pyplot as plt
+
+ fig = plt.figure(figsize=(8,4), frameon=False)
+ ax = plt.subplot(2,1,1)
+ X = np.linspace(0, 4*2*np.pi, 500)
+ line, = ax.plot(X, np.cos(X))
+ ax = plt.subplot(2,1,2)
+ X = np.linspace(0, 4*2*np.pi, 500)
+ line, = ax.plot(X, np.sin(X))
+ plt.tight_layout()
+ plt.show()
+
+For other terminals, you might need to use the `sixel `__ backend that may work with xterm (not tested).
+
+
+Dimensions & resolution
+-----------------------
+
+In the first example of this chapter, we specified a figure size of `(6,6)`
+that corresponds to a size of 6 inches (width) by 6 inches (height) using a
+default dpi (dots per inch) of 100. When displayed on a screen, dots
+corresponds to pixels and we can immediately deduce that the figure size
+(i.e. window size without the toolbar) will be exactly 600×600 pixels. Same is
+true if you save the figure in a bitmap format such as png (Portable Network
+Graphics):
+
+.. code:: python
+
+ fig = plt.figure(figsize=(6,6))
+ plt.savefig("output.png")
+
+If we use the `identify` command from the ImageMagick_ graphical suite to
+enquiry about the produced image, we get:
+
+.. code:: bash
+
+ $ identify output.png
+ Image: output.png
+ Format: PNG (Portable Network Graphics)
+ Mime type: image/png
+ Class: DirectClass
+ Geometry: 600x600+0+0
+ Resolution: 39.37x39.37
+ Print size: 15.24x15.24
+ Units: PixelsPerCentimeter
+ Colorspace: sRGB
+ ...
+
+This confirms that the image geometry is 600×600 while the resolution is 39.37
+ppc (pixels per centimeter) which corresponds to 39.37*2.54 ≈ 100 dpi (dots per
+inch). If you were to include this image inside a document while keeping the
+same dpi, you would need to set the size of the image to 15.24cm by 15.24cm. If
+you reduce the size of the image in your document, let's say by a factor of 3,
+this will mechanically increase the figure dpi to 300 in this specific
+case. For a scientific article, publishers will generally request figures dpi
+to be between 300 and 600. To get things right, it is thus good to know what
+will be the physical dimension of your figure once inserted into your document.
+
+.. figure:: anatomy/figure-dpi.png
+ :width: 100%
+
+ A text rendered in matplotlib and saved using different dpi (50,100,300 &
+ 600) (sources: :source:`anatomy/figure-dpi.py`). :label:`figure-dpi`
+
+For a more concrete example, let us consider this book whose format is A5
+(148×210 millimeters). Right and left margins are 20 millimeters each and
+images are usually displayed using the full text width. This means the physical
+width of an image is exactly 108 millimeters, or approximately 4.25 inches. If
+we were to use the recommended 600 dpi, we would end up with a width of 2550
+pixels which might be beyond screen resolution and thus not very convenient.
+Instead, we can use the default matplotlib dpi (100) when we display the figure
+on the screen and only when we save it, we use a different and higher dpi:
+
+.. code:: Python
+
+ def figure(dpi):
+ fig = plt.figure(figsize=(4.25,.2))
+ ax = plt.subplot(1,1,1)
+ text = "Text rendered at 10pt using %d dpi" % dpi
+ ax.text(0.5, 0.5, text, ha="center", va="center",
+ fontname="Source Serif Pro",
+ fontsize=10, fontweight="light")
+ plt.savefig("figure-dpi-%03d.png" % dpi, dpi=dpi)
+
+ figure(50), figure(100), figure(300), figure(600)
+
+Figure :ref:`figure-dpi` shows the output for the different dpi. Only the 600
+dpi output is acceptable. Note that when it is possible, it is preferable to
+save the result in PDF (Portable Document Format) because it is a vector format
+that will adapt flawlessly to any resolution. However, even if you save your
+figure in a vector format, you still need to indicate a dpi for figure elements
+that cannot be vectorized (e.g .images).
+
+Finally, you may have noticed that the font size on figure :ref:`figure-dpi`
+appears to be the same as the font size of the text you're currently
+reading. This is not by accident since this Latex document uses a font size of
+10 points and the matplotlib figure also uses a font size of 10 points. But
+what is a point exactly? In Latex, a point (pt) corresponds to 1/72.27 inches
+while in matplotlib it corresponds to 1/72 inches.
+
+To help you visualize the exact dimension of your figure, it is
+possible to add a ruler to a figure such that it displays current
+figure dimension as shown on figure :ref:`figure-ruler`. If you
+manually resize the figure, you'll see that the actual dimension of
+the figure changes while if you only change the dpi, the size will
+remain the same. Usage is really simple:
+
+.. code:: python
+
+ import ruler
+ import numpy as np
+ import matplotlb.pyplot as plt
+
+ fig,ax = plt.subplots()
+ ruler = ruler.Ruler(fig)
+ plt.show()
+
+
+
+.. figure:: anatomy/ruler.pdf
+ :width: 100%
+
+ Interactive ruler :label:`figure-ruler`
+ (:source:`anatomy/ruler.py`).
+
+
+Exercise
+--------
+
+It's now time to try to make some simple exercises gathering all the concepts
+we've seen so far (including finding the relevant documentation).
+
+**Exercise 1** Try to produce a figure with a given (and exact) pixel size
+(e.g. 512x512 pixels). How would you specify the size and save the figure?
+
+.. figure:: anatomy/pixel-font.png
+ :width: 100%
+
+ Pixel font text using exact image size :label:`figure-pixel-font`
+ (:source:`anatomy/pixel-font.py`).
+
+
+**Exercise 2**
+The goal is to make the figure :ref:`figure-inch-cm` that shows a dual axis, one
+in inches and one in centimeters. The difficulty is that we want the
+centimeters and inched to be physically correct when printed. This requires
+some simple computations for finding the right size and some trials and errors
+to make the actual figure. Don't pay too much attention to all the details, the
+essential part is to get the size right.
+
+.. figure:: anatomy/inch-cm.pdf
+ :width: 100%
+
+ Inches/centimeter conversion :label:`figure-inch-cm`
+ (**solution**: :source:`anatomy/inch-cm.py`).
+
+
+**Exercise 3**
+
+Here we'll try to reproduce the figure :ref:`figure-zorder-plots`. If you look
+at the figure, you'll realize that each curve is partially covering other
+curves and it is thus important to set a proper zorder for each curve such that
+the rendering will be independent of drawing order. For the actual curves, you can start from the following code:
+
+.. code:: Python
+
+ def curve():
+ n = np.random.randint(1,5)
+ centers = np.random.normal(0.0,1.0,n)
+ widths = np.random.uniform(5.0,50.0,n)
+ widths = 10*widths/widths.sum()
+ scales = np.random.uniform(0.1,1.0,n)
+ scales /= scales.sum()
+ X = np.zeros(500)
+ x = np.linspace(-3,3,len(X))
+ for center, width, scale in zip(centers, widths, scales):
+ X = X + scale*np.exp(- (x-center)*(x-center)*width)
+ return X
+
+.. figure:: anatomy/zorder-plots.pdf
+ :width: 100%
+
+ Multiple plots partially covering each other :label:`figure-zorder-plots`
+ (**solution**: :source:`anatomy/zorder-plots.py`).
+
+
+.. --- Links ------------------------------------------------------------------
+.. _Figure: https://matplotlib.org/api/figure_api.html
+.. _Axes: https://matplotlib.org/api/axes_api.html
+.. _plot: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html
+.. _ImageMagick: https://imagemagick.org
+.. _savefig: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html
+.. _subplots: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html
+.. _Axis: https://matplotlib.org/api/axis_api.html
+.. _Axes: https://matplotlib.org/api/axes_api.html
+.. _Ticks: https://matplotlib.org/api/ticker_api.html
+.. _Spines: https://matplotlib.org/api/spines_api.html
+.. _Artist: https://matplotlib.org/tutorials/intermediate/artists.html
+.. _pygobject: https://pygobject.readthedocs.io/en/latest/
+.. _pycairo: https://pycairo.readthedocs.io/en/latest/
+.. _pyqt4: https://www.riverbankcomputing.com/software/pyqt/intro
+.. _pyqt5: https://www.riverbankcomputing.com/software/pyqt/intro
+.. _tkinter: https://wiki.python.org/moin/TkInter
+.. _wxpython: https://www.wxpython.org/
+.. ----------------------------------------------------------------------------
+
+
diff --git a/rst/animation.rst b/rst/animation.rst
new file mode 100644
index 0000000..ceeb79f
--- /dev/null
+++ b/rst/animation.rst
@@ -0,0 +1,403 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-animation:
+
+Animation
+=========
+
+Animation with matplotlib can be created very easily using the
+`animation framework
+`_. Let's start
+with a very simple animation. We want to make an animation where the
+sine and cosine functions are plotted progressively on the screen. To
+do that, we need first to tell matplotlib we want to make an animation
+and then, we have to specify what we want to draw at each frame. One
+common mistake is to re-draw everything at each frame that makes the
+whole process very slow. Instead, we can only update what is necessary
+because we know that (in our case) a lot of things won't change from
+one frame to the other. For a line plot, we'll use the `set_data`
+method to update the drawing and matplotlib will take care of of the
+rest.
+
+.. code:: python
+
+ import numpy as np
+ import matplotlib.pyplot as plt
+ import matplotlib.animation as animation
+
+ fig = plt.figure(figsize=(7,2), dpi=100)
+ ax = plt.subplot()
+
+ X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+ C, S = np.cos(X), np.sin(X)
+ line1, = ax.plot(X, C, marker="o", markevery=[-1],
+ markeredgecolor="white")
+ line2, = ax.plot(X, S, marker="o", markevery=[-1],
+ markeredgecolor="white")
+
+ def update(frame):
+ line1.set_data(X[:frame], C[:frame])
+ line2.set_data(X[:frame], S[:frame])
+
+ ani = animation.FuncAnimation(fig, update, interval=10)
+ plt.show()
+
+Notice the end point marker that move with the animation. The reason
+is that we specify a single marker at the end (`markevery=[-1]`) such
+that each time we set new data, marker is automatically updated and
+moves with the animation. See figure
+:ref:`figure-animation-sine-cosine`.
+
+.. figure:: animation/sine-cosine.pdf
+ :width: 100%
+
+ Snapshots of the sine cosine animation
+ :label:`figure-animation-sine-cosine`
+ (sources: :source:`chapter-12/sine-cosine.py`).
+
+
+If we now want to save this animation, matplotlib can create a movie
+file but options are rather scarce. A better solution is to use an
+external library such as `FFMpeg `__ as shown below:
+
+.. code:: python
+
+ writer = animation.FFMpegWriter(fps=30)
+ anim = animation.FuncAnimation(fig, update, interval=10, frames=len(X))
+ anim.save("sine-cosine.mp4", writer=writer, dpi=100)
+
+You may have noticed that when we save the movie, the animation does
+not start immediately because there is actually a delay that
+corresponds to the movie creation. For sine and cosine, the delay is
+rather short and it is not really a problem. However, for long and
+complex animations, this delay can become quite significant and it
+becomes necessary to track its progress. So let's add some information
+using the `tqdm `__ library.
+
+.. code:: python
+
+ from tqdm.autonotebook import tqdm
+ bar = tqdm(total=len(X))
+ anim.save("../data/sine-cosine.mp4", writer=writer, dpi=300,
+ progress_callback = lambda i, n: bar.update(1))
+ bar.close()
+
+Creation time remains the same, but at least now, we can check how
+slow or fast it is. Here is some output of the animation:
+
+.. image:: animation/sine-cosine-frame-032.pdf
+ :width: 100%
+.. image:: animation/sine-cosine-frame-128.pdf
+ :width: 100%
+.. figure:: animation/sine-cosine-frame-255.pdf
+ :width: 100%
+
+ Still from the sine/cosine animation
+ (sources :source:`animation/sine-cosine.py`).
+
+Make it rain
+------------
+
+A very simple rain effect can be obtained by having small growing
+rings randomly positioned over a figure. Of course, they won't grow
+forever since ripples are supposed to damp with time. To simulate this
+phenomenon, we can use an increasingly transparent color as the ring
+is growing, up to the point where it is no more visible. At this
+point, we remove the ring and create a new one. First step is to
+create a blank figure.
+
+.. code:: python
+
+ fig = plt.figure(figsize=(6,6), facecolor='white', dpi=50)
+ ax = fig.add_axes([0,0,1,1], frameon=False, aspect=1)
+ ax.set_xlim(0,1), ax.set_xticks([])
+ ax.set_ylim(0,1), ax.set_yticks([])
+
+Then we create an empty scatter plot but we take care of settings
+linewidth (0.5) and facecolors ("None") that will apply to any new
+data.
+
+.. code:: python
+
+ scatter = ax.scatter([], [], s=[], lw=0.5,
+ edgecolors=[], facecolors="None")
+
+Next, we need to create several rings. For this, we can use the
+scatter plot object that is generally used to visualize points cloud,
+but we can also use it to draw rings by specifying we don't have a
+facecolor. We also have to take care of initial size and color for
+each ring such that we have all sizes between a minimum and a maximum
+size. In addition, we need to make sure the largest ring is almost
+transparent.
+
+.. code:: python
+
+ n = 50
+ R = np.zeros(n, dtype=[ ("position", float, (2,)),
+ ("size", float, (1,)),
+ ("color", float, (4,)) ])
+ R["position"] = np.random.uniform(0, 1, (n,2))
+ R["size"] = np.linspace(0, 1, n).reshape(n,1)
+ R["color"][:,3] = np.linspace(0, 1, n)
+
+Now, we need to write the update function for our animation. We know
+that at each time step each ring should grow and become more
+transparent while the largest ring should be totally transparent and
+thus removed. Of course, we won't actually remove the largest ring but
+re-use it to set a new ring at a new random position, with nominal
+size and color. Hence, we keep the number of rings constant.
+
+.. code:: python
+
+ def rain_update(frame):
+ global R, scatter
+
+ # Transparency of each ring is increased
+ R["color"][:,3] = np.maximum(0, R["color"][:,3] - 1/len(R))
+
+ # Size of each rings is increased
+ R["size"] += 1/len(R)
+
+ # Reset last ring
+ i = frame % len(R)
+ R["position"][i] = np.random.uniform(0, 1, 2)
+ R["size"][i] = 0
+ R["color"][i,3] = 1
+
+ # Update scatter object accordingly
+ scatter.set_edgecolors(R["color"])
+ scatter.set_sizes(1000*R["size"].ravel())
+ scatter.set_offsets(R["position"])
+
+Last step is to tell matplotlib to use this function as an update
+function for the animation and display the result (or save it as a
+movie):
+
+.. code:: python
+
+ animation = animation.FuncAnimation(fig, rain_update,
+ interval=10, frames=200)
+
+
+.. figure:: animation/rain.pdf
+ :width: 100%
+
+ Still from the rain animation (sources :source:`animation/rain.py`).
+
+
+Visualizing earthquakes on Earth
+--------------------------------
+
+We'll now use the rain animation to visualize earthquakes on the
+planet from the last 30 days. The USGS Earthquake Hazards Program is
+part of the National Earthquake Hazards Reduction Program (NEHRP) and
+provides several data on their website. Those data are sorted
+according to earthquakes magnitude, ranging from significant only down
+to all earthquakes, major or minor. You would be surprised by the
+number of minor earthquakes happening every hour on the planet. Since
+this would represent too much data for us, we'll stick to earthquakes
+with magnitude > 4.5. At the time of writing, this already represent
+more than 300 earthquakes in the last 30 days.
+
+First step is to read and convert data. We'll use the urllib library
+that allows us to open and read remote data. Data on the website use
+the CSV format whose content is given by the first line::
+
+ time,latitude,longitude,depth,mag,magType,nst,gap,dmin,rms,...
+ 2015-08-17T13:49:17.320Z,37.8365,-122.2321667,4.82,4.01,mw,...
+ 2015-08-15T07:47:06.640Z,-10.9045,163.8766,6.35,6.6,mwp,...
+
+
+We are only interested in latitude, longitude and magnitude and
+consequently, we won't parse the time of event.
+
+.. code:: python
+
+ import urllib
+ import numpy as np
+
+ # -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
+ feed = "http://earthquake.usgs.gov/" \
+ + "earthquakes/feed/v1.0/summary/"
+
+ # Magnitude > 4.5
+ url = urllib.request.urlopen(feed + "4.5_month.csv")
+
+ # Magnitude > 2.5
+ # url = urllib.request.urlopen(feed + "2.5_month.csv")
+
+ # Magnitude > 1.0
+ # url = urllib.request.urlopen(feed + "1.0_month.csv")
+
+ # Reading and storage of data
+ data = url.read().split(b'\n')[+1:-1]
+ E = np.zeros(len(data), dtype=[('position', float, (2,)),
+ ('magnitude', float, (1,))])
+
+ for i in range(len(data)):
+ row = data[i].split(b',')
+ E['position'][i] = float(row[2]),float(row[1])
+ E['magnitude'][i] = float(row[4])
+
+
+We need to draw the earth to show precisely where the earthquake
+center is and to translate latitude/longitude in some coordinates
+matplotlib can handle. Fortunately, there is the `cartopy
+`_ which
+is rather standard for non-specialists like me.
+
+.. code:: python
+
+ import cartopy.crs as ccrs
+ import matplotlib.pyplot as plt
+
+ fig = plt.figure(figsize=(10,5))
+ ax = plt.axes(projection=ccrs.PlateCarree())
+ ax.coastlines()
+
+ plt.show()
+
+.. figure:: animation/platecarree.pdf
+ :width: 100%
+
+ Equirectangular projection
+ :label:`figure-animation-equirectangular`
+ (sources: :source:`animation/platecarree.py`).
+
+We can now adapt the rain animation to display eartquakes. To do that,
+we just need to add a `transform` to the scatter plot such that
+coordinates will be automatically transformed (by cartopy).
+
+.. code:: python
+
+ scatter = ax.scatter([], [], transform=ccrs.PlateCarree())
+
+
+
+.. figure:: animation/earthquakes-frame-50.pdf
+ :width: 100%
+
+ Earthquakes still (July 23, 2021 at 11am CET)
+ :label:`figure-animation-earthquakes
+ (sources: :source:`animation/earthquakes.py`).
+
+
+Scenarized animation
+--------------------
+
+We've seen the basic principles of animation. It is now time to define
+a more elaborated scenario for our animation. To do that, we'll play
+with fluid simulation because it's fun. In
+:source:`animation/fluid.py`, you'll find an implementation of stable
+fluid simulation written by `Gregory Johnson
+`__ based on the paper of
+`Joe Stam
+`__.
+
+I've modified the original script and written an `inflow` method that
+define a source at a given position (angle). At each frame, we want to
+define active sources such that the overall animation displays a
+sequence of emitting sources.
+
+In the scenario below, I define arbitrarily a rotating sequence of
+sources to maximize blending in the center but you could also imagine
+synchronizing this animation with some music for example.
+
+.. code:: python
+
+ import numpy as np
+ from fluid import Fluid, inflow
+ from scipy.special import erf
+ import matplotlib.pyplot as plt
+ import matplotlib.animation as animation
+
+ shape = 256, 256
+ duration = 500
+ fluid = Fluid(shape, 'dye')
+ inflows = [inflow(fluid, x)
+ for x in np.linspace(-np.pi, np.pi, 8, endpoint=False)]
+
+ # Animation setup
+ fig = plt.figure(figsize=(5, 5), dpi=100)
+ ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+ ax.set_xlim(0, 1); ax.set_xticks([]);
+ ax.set_ylim(0, 1); ax.set_yticks([]);
+ im = ax.imshow( np.zeros(shape), extent=[0, 1, 0, 1],
+ vmin=0, vmax=1, origin="lower",
+ interpolation='bicubic', cmap=plt.cm.RdYlBu)
+
+ # Animation scenario
+ scenario = []
+ for i in range(8):
+ scenario.extend( [[i]]*20 )
+ scenario.extend([[0,2,4,6]]*30)
+ scenario.extend([[1,3,5,7]]*30)
+
+ # Animation update
+ def update(frame):
+ frame = frame % len(scenario)
+ for i in scenario[frame]:
+ inflow_velocity, inflow_dye = inflows[i]
+ fluid.velocity += inflow_velocity
+ fluid.dye += inflow_dye
+ divergence, curl, pressure = fluid.step()
+ Z = curl
+ Z = (erf(Z * 2) + 1) / 4
+
+ im.set_data(Z)
+ im.set_clim(vmin=Z.min(), vmax=Z.max())
+
+ anim = animation.FuncAnimation(fig, update, interval=10, frames=duration)
+ plt.show()
+
+
+.. figure:: animation/fluid-animation.png
+ :width: 100%
+
+ Fluid simulation
+ :label:`figure-fluid-animation`
+ (sources: :source:`animation/fluid-animation.py`).
+
+Note that in the update function, I took care of updating the limits
+of the colormap. This is necessary because the displayed image is
+dynamic and the minimum and maximum values may vary from one frame ot
+the other. If you don't do that, you might have some flickering.
+
+You can also have much more elaborated scenario such as in the
+following example which is a `remake
+`__ of an animation
+originally designed by dark horse analytics.
+
+
+.. figure:: animation/less-is-more.png
+ :width: 100%
+
+ Less is more :label:`figure-less-is-more`
+ (sources: :source:`animation/less-is-more.py`).
+
+
+Exercise
+--------
+
+The goal of this exercise is to create an animation showing how
+`Lissajous curves `__
+are generated. Figure :ref:`figure-lissajous` shows a still from the
+animation. Make sure to try to copy the exact style.
+
+.. figure:: animation/lissajous.pdf
+ :width: 100%
+
+ Lissajous curves :label:`figure-lissajous`
+ (sources: :source:`animation/lissajous.py`).
+
+
diff --git a/rst/beyond.rst b/rst/beyond.rst
new file mode 100644
index 0000000..eb447ff
--- /dev/null
+++ b/rst/beyond.rst
@@ -0,0 +1,325 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-beyond:
+
+
+Graphic library
+===============
+
+Beyond its usage for scientific visualization, matplotlib is also *a
+comprehensive library for creating static, animated, and interactive
+visualizations in Python* as written on the `website
+`__ that provides methods to
+compute intersection with generic polygons. You can see the result on
+the right part of figure :ref:`figure-dyson-hatching` and it looks
+much nicer (in my honest opinion).
+
+.. figure:: beyond/dyson-hatching.pdf
+ :width: 100%
+
+ Dyson hatching
+ :label:`figure-dyson-hatching`
+ (sources: :source:`beyond/dyson-hatching.py`).
+
+We are not done yet. Next part is to generate a dungeon. If you search
+for dungeon generator on the internet, you'll find many generators,
+from the most basic ones to the much more complex. In my case, I
+simply designed the dungeon using inkscape and I extracted the
+coordinates of the walls from the svg file:
+
+.. code:: python
+
+ Walls = np.array([
+ [1,1],[5,1],[5,3],[8,3],[8,2],[11,2],[11,5],[10,5],
+ [10,6],[12,6],[12,8],[13,8],[13,10],[11,10],[11,12],
+ [2,12],[2,10],[1,10],[1,7],[4,7],[4,10],[3,10],
+ [3,11],[10,11],[10,10],[9,10],[9,8],[11,8],[11,7],
+ [9,7],[9,5],[8,5],[8,4],[5,4],[5,6],[1,6], [1,1]])
+ walls = Polygon(Walls, closed=True, zorder=10,
+ facecolor="white", edgecolor="None",
+ lw=3, joinstyle="round")
+ ax.add_patch(walls)
+
+The next step is to restrict the hatching to the vicinity of the
+walls. Since hatches corresponds to our initial point distribution, it
+is only a matter of filtering hatches whose centers are sufficiently
+close to any wall. It thus only requires to compute the distance of a
+point to a line segment. At this point, we do not care it the the
+hatch is inside or outside the dungeon since the internal hatches are
+hidden by the interior of the dungeon (see zorder above). I proceeded
+by adding dotted squares inside corridors using a collection of
+vertical and horizontal lines as well as some random "rocks" which are
+actually collection of small ellipses. Last, I added a nice title
+using an old looking font. I used `Morris Roman
+`__ font by Dieter
+Steffmann.
+
+The result looks nice but it can be further improved. For example, we
+could introduce some noise in walls to suggest manual drawing, we
+could improve rocks by adding noise, etc. Matplotlib provides
+everything that is needed and the only limit is your imagination. If
+you're curious on chat could be achieved, make sure to have a look at
+`one page dungeon `__ by
+Oleg Dolya or the `Fantasy map generator
+`_ that introduces a graphical
+method originating from the computer graphics domain that is used for
+the arbitrary placement of cells over a two-dimensional manifold. The
+figure represents a schematic slice of the basal ganglia (striatum and
+globus pallidus) that has been split in four different subfigures:
+
+* **Subfigure A** is made of a bitmap image showing an arbitrary
+ density of neurons. I used a bitmap image because it is not yet
+ possible to render such arbitrary gradient using
+ matplotlib. However, I also read the corresponding SVG image to
+ extract the paths delimiting each structure and plot them on the
+ figure.
+
+* **Subfigure B** is represents the actual method for positioning an
+ arbitrary number of neurons enforcing the density represented by the
+ color gradient. To represent them, I used a simple scatter plot and
+ colored some neurons according to their input/output status.
+
+* **Subfigure C** represents an interpolation of the activity of the
+ neurons and has been made using a 2D histogram made. To do that, I
+ simply built a big array representing the whole image and I set the
+ activity around the neuron using a disc of constant radius. This is
+ only a matter of translating the 2d coordinates of the neuron to a
+ 2D index inside the image array. I then used an `imshow` to show the
+ result and I drew over the frontiers of each structure. This kind of
+ rendering helps to see the overall activity inside the structure.
+
+* **Subfigure D** is probably the most complex because it involved the
+ computation of Voronoi cells and their intersection with the border
+ of the structure. Once again, the shapely library is incredibly
+ useful to achieve such result. Once the cell have been computed, it
+ is only a matter of painting them with a colormap according to their
+ activity. For efficiency, this is made using a poly collection.
+
+This is actually quite a complex example, but once you've written the
+code, it can be adapted to any input (the SVG file in this case) such
+that your final result is fully automated. Of course, the amount of
+work this represents should be balanced with your actual needs. If you
+need the figure only once, it is probably not worth the effort if you
+can do it manually.
+
+
+.. figure:: beyond/basal-ganglia.pdf
+ :width: 100%
+
+ A schematic view of a slice of the basal
+ ganglia. Sources availables from the `spatial-computation
+ `_ repository on
+ GitHub. :label:`figure-basal-ganglia`
+
+
+
+Exercises
+---------
+
+**Stamp like effect** `Fancy boxes `_ offer several style that can be used to achieve different effect as shown on figure :ref:`figure-mona-lisa-stamp`. The goal is to achieve the same effect.
+
+.. figure:: beyond/stamp.png
+ :width: 75%
+
+ Mona Lisa stamp
+ :label:`figure-mona-lisa-stamp`
+ (sources: :source:`beyond/stamp.py`).
+
+
+**Radial Maze** Try to redo the figure :ref:`figure-radial-maze` which
+displays a radial maze (that is used quite often in neuroscience to
+study mouse or rat behavior) and a simulated path representing a rat
+exploring the maze (this has been generated by recording the
+(computer) mouse movements). The color of each block represents the
+occupancy rate, that is, the number of recorded point inside the
+block.
+
+.. figure:: beyond/radial-maze.pdf
+ :width: 100%
+
+ Radial maze
+ :label:`figure-radial-maze`
+ (sources: :source:`beyond/radial-maze.py`).
+
diff --git a/rst/chapter.tex b/rst/chapter.tex
new file mode 100644
index 0000000..f03c6f3
--- /dev/null
+++ b/rst/chapter.tex
@@ -0,0 +1 @@
+$body
diff --git a/rst/cheatsheets.rst b/rst/cheatsheets.rst
new file mode 100644
index 0000000..5356783
--- /dev/null
+++ b/rst/cheatsheets.rst
@@ -0,0 +1,23 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-cheatsheets:
+
+Quick References
+================
+
+Here are cheatsheets and handouts I've designed for Matplotlib. They
+are also available in A4 format at `github.com/matplotlib/cheatsheets
+`__.
+
+
+:raw-latex:`\pdfinclude{190mm}{../figures/cheatsheets/cheatsheets-1.pdf}`
+:raw-latex:`\pdfinclude{190mm}{../figures/cheatsheets/cheatsheets-2.pdf}`
+:raw-latex:`\pdfinclude{190mm}{../figures/cheatsheets/cheatsheets-3.pdf}`
+:raw-latex:`\pdfinclude{190mm}{../figures/cheatsheets/cheatsheets-4.pdf}`
+:raw-latex:`\pdfinclude{190mm}{../figures/cheatsheets/cheatsheets-5.pdf}`
+:raw-latex:`\pdfinclude{200mm}{../figures/cheatsheets/handout-beginner.pdf}`
+:raw-latex:`\pdfinclude{200mm}{../figures/cheatsheets/handout-intermediate.pdf}`
+:raw-latex:`\pdfinclude{200mm}{../figures/cheatsheets/handout-tips.pdf}`
diff --git a/rst/colors.rst b/rst/colors.rst
new file mode 100644
index 0000000..69838ba
--- /dev/null
+++ b/rst/colors.rst
@@ -0,0 +1,245 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-colors:
+
+A primer on colors
+==================
+
+Color is a `highly complex topic
+`_ and a whole book would
+probably not be enough to explain each and every aspect. This is the
+reason why I won't try to explain everything here, the other reason
+being that I'm simply not knowledgeable enough on the topic. There are
+nonetheless a few things that are good to know, for example
+how are colors represented on a computer. To represent a color on a
+computer, we use (most of the time) the notion of a `color model
+`_ (how do we represent a
+color) and a `color space
+`_ (what colors can be
+represented). There exists several color models (RGB, HSV, HLS, CMYK,
+CIEXYZ, CIELAB, etc.) and several color spaces (Adobe RGB, sRGB,
+Colormatch RGB, etc.) such that you can access the same color space
+using different color models. The standard for computers (since 1996)
+is the `sRGB color space `_ where
+the `s` stands for standard. This color space uses an additive color
+model based on the RGB model. This means that to obtain a given color,
+you need to mix different amounts of red, green, and blue light. When
+these amounts are all zero, you obtain black and when these amounts
+are all at full intensity, you obtain white (D65 white point, see `CIE
+1931 xy chromaticity space
+`_).
+
+Consequently, when you specify a color in matplotlib
+(e.g. `"#123456"`), you need to realize that this color is implicitly
+encoded in the sRGB color model and space. This draws immediate
+consequences. For example, if you try to produce a gradient between
+two colors using a naive approach, you'll get wrong perceptual results
+because the sRGB model is not linear. This is illustrated on figure
+:ref:`figure-color-gradients` where I plotted gradients using the sRGB
+naive approach (first line on each gradient). You can observe that the
+result is far from being satisfactory. A better way to build a
+gradient is to first convert colors to the linear RGB space, apply
+the gradient, and then convert it back to the sRGB color model. This is
+illustrated on the second line of each gradient that are now
+perceptually smoother. A third (and better) solution is to use the
+`CIE Lab `_ model
+that has been tailored to the human perception and provides a
+perceptually uniform space. It is a bit more complicated to manipulate
+and you'll need external packages such as `scikit-image
+`_ to make the
+conversion between the different models and spaces, but results are
+worth the effort.
+
+.. figure:: colors/color-gradients.pdf
+ :width: 100%
+
+ Linear color gradients using different color models
+ :label:`figure-color-gradients` (sources: :source:`colors/color-gradients.py`).
+
+Another popular model is the `HSV
+`_ model that stands for
+Hue, Saturation and Value (see figure :ref:`figure-color-wheel`). It
+provides an alternate color model to access the same color space as
+the sRGB system. Matplotlib provides methods to convert to and
+from the HSV model (see the `colors `_ module).
+
+.. figure:: colors/color-wheel.pdf
+ :width: 100%
+
+ Color wheel (HSV)
+ :label:`figure-color-wheel` (sources: :source:`colors/color-wheel.py`).
+
+
+Choosing colors
+---------------
+
+Maybe at this point the only question you have in mind is "Ok,
+interesting, but how do I choose a color then? Do I even have to
+choose anyway?" For this second question, you can actually let
+Matplotlib choose for you. When you draw several plots at once, you
+may have noticed that the plots use several different colors. These
+colors are picked from what is called a color cycle:
+
+.. code:: python
+
+ >>> import matplotlib.pyplot as plt
+ >>> print(plt.rcParams['axes.prop_cycle'].by_key()['color'])
+ ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
+ '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
+
+These colors come from the `tab10` colormap which itself comes from
+the `Tableau `_ from the matplotlib website.
+
+.. figure:: colors/colormap-tree.pdf
+ :width: 100%
+
+ How to choose a colormap?
+ :label:`colormap-tree`
+
+Problem is, for each type, there exist several colormaps. But if you pick the right type, the choice is yours and depends mostly on you aesthetic taste. As long as you choose the right type, you cannot be wrong. Figure :ref:`figure-mona-lisa` a few choices associated with sequential colormaps and they all look good. In this case, one selection criterion could be the fact that the image represents a human being and we may prefer a colormap close to skin tones.
+
+.. figure:: colors/mona-lisa.pdf
+ :width: 100%
+
+ Variations on Mona Lisa (Leonardo da Vinci, 1503).
+ :label:`figure-mona-lisa` (sources: :source:`colors/mona-lisa.py`).
+
+Diverging colormaps needs special care because they are really composed of two gradients with a special central value. By default, this central value is mapped to 0.5 in the normalized linear mapping and this works pretty well as long as the absolute minimum and maximum value of your data are the same. Now, consider the situation illustrated on figure :ref:`figure-colormap-transform`. Here we have a small domain with negative values and a larger domain with positive values. Ideally, we would like the negative values to be mapped with blueish colors and positive values with yellowish colors. If we use a diverging colormap without any precaution, there's no guarantee that we'll obtain the result we want. To fix the problem, we thus need to tell matplotlib what is the central value and to do this, we need to use a `Two Slope norm `_ instead of a `Linear norm `_.
+
+.. figure:: colors/colormap-transform.pdf
+ :width: 100%
+
+ Colormap with linear norm vs two slopes norm.
+ :label:`figure-colormap-transform`
+
+
+.. code:: python
+
+ >>> import matplotlib.pyplot as plt
+ >>> import matplotlib.colors as colors
+ >>> cmap = plt.get_cmap("Spectral")
+
+ >>> norm = mpl.colors.Normalize(vmin=-3, vmax=10)
+ >>> Print(norm(0))
+ 0.23076923076923078
+ >>> print(cmap(norm(0)))
+ (0.968, 0.507, 0.300, 1.0)
+
+ >>> norm = mpl.colors.TwoSlopeNorm(vmin=-3, vcenter=0, vmax=10)
+ >>> print(norm(0))
+ 0.5
+ >>> cmap = plt.get_cmap("Spectral")
+ >>> print(cmap(norm(0)))
+ (0.998, 0.999, 0.746, 1.0)
+
+
+Exercises
+---------
+
+**Exercise 1** The goal is to reproduce the figure :ref:`figure-colored-plot`. The trick is to split each line is small segments such that they can each have their own colors since it is not possible to do that with a regular plot. However, for performance reasons, you'll need to use `LineCollection `_. You can start from the following code:
+
+.. code:: python
+
+ X = np.linspace(-5*np.pi, +5*np.pi, 2500)
+ for d in np.linspace(0,1,15):
+ dx, dy = 1 + d/3, d/2 + (1-np.abs(X)/X.max())**2
+ Y = dy * np.sin(dx*X) + 0.1*np.cos(3+5*X)
+
+.. figure:: colors/colored-plot.pdf
+ :width: 100%
+
+ (Too much) colored line plots (sources :source:`colors/colored-plot.py`)
+ :label:`figure-colored-plot`
+
+
+**Exercise 2** This exercise is a bit tricky and requires the usage of `PolyCollection `_. The tricky part is to define, in a generic way, each polygon depending on the number of branches and sections. It is mostly trigonometry. I advise to start by drawing only the main lines and then create the small patches. The color part should then be easy because it depends only on the angle and you can thus use HSV encoding.
+
+.. figure:: colors/flower-polar.pdf
+ :width: 100%
+
+ Flower polar (sources :source:`colors/flower-polar.py`)
+ :label:`figure-flower-polar`
diff --git a/rst/common.rst b/rst/common.rst
new file mode 100644
index 0000000..28f7f5f
--- /dev/null
+++ b/rst/common.rst
@@ -0,0 +1,31 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. Date: June 2019
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+
+.. role:: ref
+.. role:: nameref
+.. role:: label
+.. role:: cite
+.. role:: code
+.. role:: source
+.. default-role:: code
+
+.. role:: raw-latex(raw)
+ :format: latex
+
+
+.. |newline| raw:: latex
+
+ \newline
+
+.. |newpage| raw:: latex
+
+ \clearpage
+
+
+.. |br| raw:: html
+
+ `__
+function to apply translation, scaling and rotation and I call the
+function 200 times with random values.
+
+.. code:: python
+
+ def imshow(ax, I, position=(0,0), scale=1, angle=0):
+ height, width = I.shape
+ extent = scale * np.array([-width/2, width/2,
+ -height/2, height/2])
+ im = ax.imshow(I, extent=extent, zorder=zorder)
+ t = transforms.Affine2D().rotate_deg(angle).translate(*position)
+ im.set_transform(t + ax.transData)
+
+
+.. figure:: coordinates/collage.png
+ :width: 100%
+
+ Collage
+ :label:`figure-collage`
+ (sources: :source:`coordinates/collage.py`).
+
+
+Transformations are quite powerful tools even though you won't manipulate them
+too often in your daily life. But there are some few cases where you'll be
+happy to know about them. You can read further on transforms and coordinates
+with the `Transformation tutorial`_ on the matplotlib website.
+
+
+Real case usage
+---------------
+
+Let's now study a real case of transforms as shown on figure
+:ref:`fig-transforms-hist`. This is a simple scatter plot showing some Gaussian
+data, with two principal axis. I added a histogram that is orthogonal to the
+first principal component axis to show the distribution on the main axis.
+This figure might appear simple (a scatter plot and an oriented histogram) but
+the reality is quite different and rendering such a figure is far from
+obvious. The main difficulty is to have the histogram at the right position,
+size and orientation knowing that position must be set in data coordinates,
+size must be given in figure normalized coordinates and orientation in
+degrees. To complicate things, we want to express the elevation of the text
+above the histogram bars in term of data points. |newline|
+
+.. figure:: coordinates/transforms-hist.pdf
+ :width: 100%
+
+ Rotated histogram aligned with second main PCA axis
+ (:source:`coordinates/transforms-hist.py`). :label:`fig-transforms-hist`
+
+You can have a look at the sources for the complete story but let's concentrate
+on the main difficulty, that is adding a rotated floating axis. Let us start with
+a simple figure:
+
+.. code:: Python
+
+ import numpy as np
+ import matplotlib.pyplot as plt
+ from matplotlib.transforms import Affine2D
+ import mpl_toolkits.axisartist.floating_axes as floating
+
+ fig = plt.figure(figsize=(8,8))
+ ax1 = plt.subplot(1,1,1, aspect=1,
+ xlim=[0,10], ylim=[0,10])
+
+Let's imagine we want to have a floating axis whose center is (5,5) in data
+coordinates, size is (5,3) in data coordinates and orientation is -30 degrees:
+
+.. code:: Python
+
+ center = np.array([5,5])
+ size = np.array([5,3])
+ orientation = -30
+ T = size/2*[(-1,-1), (+1,-1), (+1,+1), (-1,+1)]
+ rotation = Affine2D().rotate_deg(orientation)
+ P = center + rotation.transform(T)
+
+In the code above, we defined the four points delimiting the extent of our new
+axis and we took advantage of matplotlib affine transforms to do the actual
+rotation. At this point, we have thus four points describing the border of the
+axis in data coordinates and we need to transform them in figure normalized
+coordinates because the floating axis requires normalized figure coordinates.
+
+.. code:: Python
+
+ DC_to_FC = ax1.transData.transform
+ FC_to_NFC = fig.transFigure.inverted().transform
+ DC_to_NFC = lambda x: FC_to_NFC(DC_to_FC(x))
+
+We have one supplementary difficulty because the position of a floating axis
+needs to be defined in term of non-rotated bounding box:
+
+.. code:: Python
+
+ xmin, ymin = DC_to_NFC((P[:,0].min(), P[:,1].min()))
+ xmax, ymax = DC_to_NFC((P[:,0].max(), P[:,1].max()))
+
+We now have all the information to add our new axis:
+
+.. code:: Python
+
+ transform = Affine2D().rotate_deg(orientation)
+ helper = floating.GridHelperCurveLinear(
+ transform, (0, size[0], 0, size[1]))
+ ax2 = floating.FloatingSubplot(
+ fig, 111, grid_helper=helper, zorder=0)
+ ax2.set_position((xmin, ymin, xmax-xmin, ymax-xmin))
+ fig.add_subplot(ax2)
+
+The result is shown on figure :ref:`fig-transforms-floating-axis`.
+
+
+Exercise
+--------
+
+**Exercise 1** When you specify the size of markers in a scatter plot, this
+size is expressed in points. Try to make a scatter plot whose size is expressed
+in data points such as to obtain figure :ref:`fig-transforms-exercise-1`.
+
+.. figure:: coordinates/transforms-exercise-1.pdf
+ :width: 100%
+
+ A scatter plot whose marker size is expressed in data coordinates instead of points
+ (:source:`coordinates/transforms-exercise-1.py`).
+ :label:`fig-transforms-exercise-1`
+
+
+.. figure:: coordinates/transforms-floating-axis.pdf
+ :width: 100%
+
+ A floating and rotated floating axis with controlled position size and
+ rotation (:source:`coordinates/transforms-floating-axis.py`).
+ :label:`fig-transforms-floating-axis`
+
+
+.. --- Links ------------------------------------------------------------------
+.. _text: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.text.html
+.. _Transformation tutorial: https://matplotlib.org/tutorials/advanced/transforms_tutorial.html
+.. ----------------------------------------------------------------------------
+
+
diff --git a/rst/defaults.rst b/rst/defaults.rst
new file mode 100644
index 0000000..d59d8be
--- /dev/null
+++ b/rst/defaults.rst
@@ -0,0 +1,271 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-defaults:
+
+Mastering the defaults
+======================
+
+We've just explained (see rule 5 in chapter chap-rules_) that any visualization library or software comes with a set of default settings that identifies it. For example, figure :ref:`figure-sine-cosine-variants` show the sine and cosine functions as rendered by Google calculator, Julia, Gnuplot and Matlab. Even for such simple functions, these displays are quite characteristic.
+
+.. figure:: defaults/sine-cosine-variants.png
+ :width: 100%
+
+ Sine and cosine functions as displayed by (A) Google calculator (B) Julia,
+ (C) Gnuplot (D) Matlab. :label:`figure-sine-cosine-variants`
+
+Let's draw sine and cosine functions using Matplotlib defaults.
+
+.. code:: python
+
+ import numpy as np
+ import matplotlib.pyplot as plt
+
+ X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+ C, S = np.cos(X), np.sin(X)
+ plt.plot(X, C)
+ plt.plot(X, S)
+ plt.show()
+
+
+Figure :ref:`figure-defaults-step-2` shows the result that is quite characteristic of Matplotlib.
+
+.. figure:: defaults/defaults-step-1.pdf
+ :width: 100%
+
+ Sine and cosine functions with implicit defaults
+ (sources :source:`defaults/defaults-step-1.py`)
+ :label:`figure-defaults-step-1`
+
+Explicit settings
+-----------------
+
+Let's now redo the figure but with the specification of all the different settings. This includes, figure size, line colors, widths and styles, ticks positions and labels, axes limits, etc.
+
+.. code:: python
+
+ fig = plt.figure(figsize = p['figure.figsize'],
+ dpi = p['figure.dpi'],
+ facecolor = p['figure.facecolor'],
+ edgecolor = p['figure.edgecolor'],
+ frameon = p['figure.frameon'])
+ ax = plt.subplot(1,1,1)
+
+ ax.plot(X, C, color="C0",
+ linewidth = p['lines.linewidth'],
+ linestyle = p['lines.linestyle'])
+ ax.plot(X, S, color="C1",
+ linewidth = p['lines.linewidth'],
+ linestyle = p['lines.linestyle'])
+
+ xmin, xmax = X.min(), X.max()
+ xmargin = p['axes.xmargin']*(xmax - xmin)
+ ax.set_xlim(xmin - xmargin, xmax + xmargin)
+
+ ymin, ymax = min(C.min(), S.min()), max(C.max(), S.max())
+ ymargin = p['axes.ymargin']*(ymax - ymin)
+ ax.set_ylim(ymin - ymargin, ymax + ymargin)
+
+ ax.tick_params(axis = "x", which="major",
+ direction = p['xtick.direction'],
+ length = p['xtick.major.size'],
+ width = p['xtick.major.width'])
+ ax.tick_params(axis = "y", which="major",
+ direction = p['ytick.direction'],
+ length = p['ytick.major.size'],
+ width = p['ytick.major.width'])
+ plt.show()
+
+.. figure:: defaults/defaults-step-2.pdf
+ :width: 100%
+
+ Sine and cosine functions using matplotlib explicit defaults
+ (sources :source:`defaults/defaults-step-2.py`)
+ :label:`figure-defaults-step-2`
+
+The resulting figure :ref:`figure-defaults-step-2` is an exact copy of :ref:`figure-defaults-step-1`. This comes as no surprise because I took care of reading the default values that are used implicitly by Matplotlib and set them explicitly. In fact, there are many more default choices that I did not materialize in this short example. For instance, the font family, slant, weight and size of tick labels can be configured in the defaults.
+
+User settings
+-------------
+
+Note that we can also do the opposite and change the defaults before creating the figure. This way, matplotlib will use our custom defaults instead of standard ones. The result is shown on figure :ref:`figure-defaults-step-3` where I changed a number of settings. Unfortunately, not every settings can be modified this way. For example, the position of markers (`markevery`) cannot yet be set.
+
+.. code:: python
+
+ p["figure.figsize"] = 6,2.5
+ p["figure.edgecolor"] = "black"
+ p["figure.facecolor"] = "#f9f9f9"
+
+ p["axes.linewidth"] = 1
+ p["axes.facecolor"] = "#f9f9f9"
+ p["axes.ymargin"] = 0.1
+ p["axes.spines.bottom"] = True
+ p["axes.spines.left"] = True
+ p["axes.spines.right"] = False
+ p["axes.spines.top"] = False
+ p["font.sans-serif"] = ["Fira Sans Condensed"]
+
+ p["axes.grid"] = False
+ p["grid.color"] = "black"
+ p["grid.linewidth"] = .1
+
+ p["xtick.bottom"] = True
+ p["xtick.top"] = False
+ p["xtick.direction"] = "out"
+ p["xtick.major.size"] = 5
+ p["xtick.major.width"] = 1
+ p["xtick.minor.size"] = 3
+ p["xtick.minor.width"] = .5
+ p["xtick.minor.visible"] = True
+
+ p["ytick.left"] = True
+ p["ytick.right"] = False
+ p["ytick.direction"] = "out"
+ p["ytick.major.size"] = 5
+ p["ytick.major.width"] = 1
+ p["ytick.minor.size"] = 3
+ p["ytick.minor.width"] = .5
+ p["ytick.minor.visible"] = True
+
+ p["lines.linewidth"] = 2
+ p["lines.markersize"] = 5
+
+ fig = plt.figure(linewidth=1)
+ ax = plt.subplot(1,1,1,aspect=1)
+ ax.plot(X, C)
+ ax.plot(X, S)
+
+ plt.show()
+
+
+.. figure:: defaults/defaults-step-3.pdf
+ :width: 100%
+
+ Sine and cosine functions using custom defaults
+ (sources :source:`defaults/defaults-step-3.py`)
+ :label:`figure-defaults-step-3`
+
+
+Stylesheets
+-----------
+
+Changing default settings is thus an easy way to customize the style of your figure. But writing such style inside the figure script as we did until now is not very convenient and this is where `style `_ comes into play. Styles are small text files describing (some) settings in the same way as they are defined in the main resource file `matplotlibrc `_:
+
+.. code:: text
+
+ figure.figsize: 6,2.5
+ figure.edgecolor: black
+ figure.facecolor: ffffff
+
+ axes.linewidth: 1
+ axes.facecolor: ffffff
+ axes.ymargin: 0.1
+ axes.spines.bottom: True
+ axes.spines.left: True
+ axes.spines.right: False
+ axes.spines.top: False
+ font.sans-serif: Fira Sans Condensed
+
+ axes.grid: False
+ grid.color: black
+ grid.linewidth: .1
+
+ xtick.bottom: True
+ xtick.top: False
+ xtick.direction: out
+ xtick.major.size: 5
+ xtick.major.width: 1
+ xtick.minor.size: 3
+ xtick.minor.width: .5
+ xtick.minor.visible: True
+
+ ytick.left: True
+ ytick.right: False
+ ytick.direction: out
+ ytick.major.size: 5
+ ytick.major.width: 1
+ ytick.minor.size: 3
+ ytick.minor.width: .5
+ ytick.minor.visible: True
+
+ lines.linewidth: 2
+ lines.markersize: 5
+
+And we can now write:
+
+.. code:: python
+
+ plt.style.use("./mystyle.txt")
+
+ fig = plt.figure(linewidth=1)
+ ax = plt.subplot(1,1,1,aspect=1)
+ ax.plot(X, C)
+ ax.plot(X, S)
+ ax.set_yticks([-1,0,1])
+
+
+Beyond stylesheets
+------------------
+
+If stylesheet allows to set a fair number of parameters, there is still plenty of other things that can be changed to improve the style of a figure even though we cannot use the stylesheet to do so. One of the reason is that these settings are specific to a given figure and it wouldn't make sense to set them in the stylesheet. In the sine and cosine case, we can for example specify explicitly the location and labels of xticks, taking advantage of the fact that we know that we're dealing with trigonometry functions:
+
+.. code:: python
+
+ ax.set_yticks([-1,1])
+ ax.set_xticklabels(["-1", "+1"])
+
+ ax.set_xticks([-np.pi, -np.pi/2, np.pi/2, np.pi])
+ ax.set_xticklabels(["-π", "-π/2", "+π/2", "+π"])
+
+We can also move the spines such as to center them:
+
+.. code:: python
+
+ ax.spines['bottom'].set_position(('data',0))
+ ax.spines['left'].set_position(('data',0))
+
+And add some arrows at axis ends:
+
+.. code:: python
+
+ ax.plot(1, 0, ">k",
+ transform=ax.get_yaxis_transform(), clip_on=False)
+ ax.plot(0, 1, "^k",
+ transform=ax.get_xaxis_transform(), clip_on=False)
+
+
+You can see the result on figure :ref:`figure-defaults-step-5`. From this, you can start refining further the figure. But remember that if it's ok to tweak parameters a bit, you can also lose a lot of time doing that (trust me).
+
+.. figure:: defaults/defaults-step-5.pdf
+ :width: 100%
+
+ Sine and cosine functions using custom defaults and fine tuning.
+ (sources :source:`defaults/defaults-step-5.py`)
+ :label:`figure-defaults-step-5`
+
+Exercise
+--------
+
+Starting from the code below try to reproduce figure :ref:`figure-defaults-exercise-1`
+by modifying only rc settings.
+
+.. code:: python
+
+ fig = plt.figure()
+ ax = plt.subplot(1,1,1,aspect=1)
+ ax.plot(X, C, markevery=(0, 64), clip_on=False, zorder=10)
+ ax.plot(X, S, markevery=(0, 64), clip_on=False, zorder=10)
+ ax.set_yticks([-1,0,1])
+ ax.set_xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
+ ax.set_xticklabels(["-π", "-π/2", "0", "+π/2", "+π"])
+ ax.spines['bottom'].set_position(('data',0))
+
+
+.. figure:: defaults/defaults-exercice-1.pdf
+ :width: 100%
+
+ Alternative rendering of sine and cosine
+ (solution :source:`defaults/defaults-exercice-1.py`)
+ :label:`figure-defaults-exercise-1`
diff --git a/rst/layout.rst b/rst/layout.rst
new file mode 100644
index 0000000..a998974
--- /dev/null
+++ b/rst/layout.rst
@@ -0,0 +1,237 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-layout:
+
+Size, aspect & layout
+=====================
+
+The layout of figures and sub-figures is certainly one of the most
+frustrating aspect of matplotlib and for new user, it is generally
+difficult to obtain the desired layout without a lot of trials and
+errors.. But this is not specific to matplotlib, it is actually
+equally difficult with any software (and even worse for some). To
+understand why it is difficult, it is necessary to gain a better
+understanding of the underlying machinery.
+
+Figure and axes aspect
+----------------------
+
+When you create a new figure, this figure comes with a specific size, either implicitly using defaults (as explained in the previous chapter) or explicitly through the `figsize` keyword. If we take the height divided by the width of the figure we obtain the figure aspect ratio. When you create an axes, you can also specify an aspect that will be enforced by matplotlib. And here, we hit the first difficulty. You have a container with a given aspect ratio and you want to put inside an item with a possibly different aspect ratio and matplotlib has to solve these constrains. This is illustrated on figure :ref:`figure-aspects` with different cases:
+
+**A**: figure aspect is 1, axes aspect is 1, x and y range are equal
+
+ .. code:: python
+
+ fig = plt.figure(figsize=(6,6))
+ ax = plt.subplot(1,1,1, aspect=1)
+ ax.set_xlim(0,1), ax.set_ylim(0,1)
+
+
+**B**: figure aspect is 1/2, axes aspect is 1, x and y range are equal
+
+ .. code:: python
+
+ fig = plt.figure(figsize=(6,3))
+ ax = plt.subplot(1,1,1, aspect=1)
+ ax.set_xlim(0,1), ax.set_ylim(0,1)
+
+**C**: figure aspect is 1/2, axes aspect is 1, x and y range are different
+
+ .. code:: python
+
+ fig = plt.figure(figsize=(6,3))
+ ax = plt.subplot(1,1,1, aspect=1)
+ ax.set_xlim(0,2), ax.set_ylim(0,1)
+
+
+**D**: figure aspect is 2, axes aspect is 1, x and y range are equal
+
+ .. code:: python
+
+ fig = plt.figure(figsize=(3,6))
+ ax = plt.subplot(1,1,1, aspect=1)
+ ax.set_xlim(0,1), ax.set_ylim(0,1)
+
+
+**E**: figure aspect is 1, axes aspect is 1, x and y range are different
+
+ .. code:: python
+
+ fig = plt.figure(figsize=(6,6))
+ ax = plt.subplot(1,1,1, aspect=1)
+ ax.set_xlim(0,2), ax.set_ylim(0,1)
+
+
+**F**: figure aspect is 1, axes aspect is 0.5, x and y range are equal
+
+ .. code:: python
+
+ fig = plt.figure(figsize=(6,6))
+ ax = plt.subplot(1,1,1, aspect='auto')
+ ax.set_xlim(0,1), ax.set_ylim(0,1)
+
+**G**: figure aspect is 1/2, axes aspect is 1, x and y range are different
+
+ .. code:: python
+
+ fig = plt.figure(figsize=(3,6))
+ ax = plt.subplot(1,1,1, aspect=1)
+ ax.set_xlim(0,1), ax.set_ylim(0,2)
+
+**H**: figure aspect is 1, axes aspect is 1, x and y range are different
+
+ .. code:: python
+
+ fig = plt.figure(figsize=(6,6))
+ ax = plt.subplot(1,1,1, aspect='auto')
+ ax.set_xlim(0,1), ax.set_ylim(0,2)
+
+**I**: figure aspect is 1, axes aspect is 2, x and y range are equal
+
+ .. code:: python
+
+ fig = plt.figure(figsize=(6,6))
+ ax = plt.subplot(1,1,1, aspect='auto')
+ ax.set_xlim(0,1), ax.set_ylim(0,1)
+
+
+.. figure:: layout/aspects.pdf
+ :width: 100%
+
+ Combination of figure and axes aspect ratio
+ :label:`figure-aspects`
+
+
+The final layout of a figure results from a set of constraints that makes it difficult to predict the end result. This is actually even more acute when you combine several axes on the same figure as shown on figures
+:ref:`figure-layout-aspect-1`, :ref:`figure-layout-aspect-2` & :ref:`figure-layout-aspect-3`. Depending on what is important in your figure (aspect, range or size), you'll privilege one of these layout. In any case, you should now have realized that if you over-constrained your layout, it might be unsolvable and matplotlib will try to find the best compromise.
+
+.. figure:: layout/layout-aspect-1.pdf
+ :width: 100%
+
+ Same size, same range, different aspect
+ (sources :source:`layout/layout-aspect.py`)
+ :label:`figure-layout-aspect-1`
+
+
+.. figure:: layout/layout-aspect-2.pdf
+ :width: 100%
+
+ Same range, same aspect, different size
+ (sources :source:`layout/layout-aspect.py`)
+ :label:`figure-layout-aspect-2`
+
+
+.. figure:: layout/layout-aspect-3.pdf
+ :width: 100%
+
+ Same size, same aspect, different range
+ (sources :source:`layout/layout-aspect.py`)
+ :label:`figure-layout-aspect-3`
+
+
+Axes layout
+-----------
+
+Now that we know how figure and axes aspect may interact, it's time to organize our figure into subfigures. We've already seen one example in the previous section, but let's now look at the details. There exist indeed several `different methods `_ to create subfigures and each have their pros and cons. Let's take a very simple example where we want to have two axes side by side. To do so, we can use `add_subplot `_, `add_axes `_, `GridSpec `_ and `subplot_mosaic `_:
+
+.. code:: python
+
+ fig = plt.figure(figsize=(6,2))
+
+ # Using subplots
+ ax1 = fig.add_subplot(1,2,1)
+ ax2 = fig.add_subplot(1,2,2)
+
+ # Using gridspecs
+ G = GridSpec(1,2)
+ ax1 = fig.add_subplot(G[0,0])
+ ax2 = fig.add_subplot(G[0,1])
+
+ # Using axes
+ ax1 = fig.add_axes([0.1, 0.1, 0.35, 0.8])
+ ax2 = fig.add_axes([0.6, 0.1, 0.35, 0.8])
+
+ # Using mosaic
+ ax1, ax2 = fig.add_mosaic("AB")
+
+
+As a general advice, I would encourage users to use the gridspec
+approach because if offers a lot of flexibility compared to the
+classical approach. For example, figure :ref:`figure-layout-classical`
+shows a nice and simple 3x3 layout but does not offer much control
+over the relative aspect of each axes whereas in figure
+:ref:`figure-layout-gridspec`, we can very easily specify different
+sizes for each axes.
+
+.. figure:: layout/layout-classical.pdf
+ :width: 100%
+
+ Subplots using classical layout.
+ (source :source:`layout/layout-classical.py`)
+ :label:`figure-layout-classical`
+
+.. figure:: layout/layout-gridspec.pdf
+ :width: 100%
+
+ Subplots using gridspec layout
+ (source :source:`layout/layout-gridspec.py`)
+ :label:`figure-layout-gridspec`
+
+The biggest difficulty with gridspec is to get axes right, that is, at
+the right position with the right size and this depends on the initial
+partition of the grid spec, taking into account the different height &
+width ratios. Let's consider for example figure
+:ref:`figure-complex-layout` that display a moderately complex
+layout. The question is: how do we partition it? Do we need a single
+axis for the small image of individual axes ? Are the text on the left
+part of axes or do they use their own axes. There are indeed several
+solutions and figure :ref:`figure-complex-layout-bare` shows the
+solution I chose to design this figure. There are individual axes for
+subplots and left label. There is also a small line of axes for titles
+in order to ensure that subplots have all the same size. If I had used
+a title on the first row of subplots, this would have modified their
+relative size compared to others. The legend on the top is using two
+axes, one axes for the color legend and another for detailed
+explanation. In this case, I use the central axes and write the text
+outside the axes, specifying this does not need to be clipped.
+
+.. figure:: layout/complex-layout.pdf
+ :width: 100%
+
+ Complex layout
+ (source :source:`layout/complex-layout.py`)
+ :label:`figure-complex-layout`
+
+.. figure:: layout/complex-layout-bare.pdf
+ :width: 100%
+
+ Complex layout structure
+ (source :source:`layout/complex-layout-bare.py`)
+ :label:`figure-complex-layout-bare`
+
+
+Exercices
+---------
+
+**Standard layout 1** Using gridspec, the goal is to reproduce figure
+:ref:`figure-standard-layout-1` where the colorbar is the same size as the main axes and its width is one tenth of main axis width.
+
+.. figure:: layout/standard-layout-1.pdf
+ :width: 100%
+
+ Image and colorbar
+ (sources :source:`layout/standard-layout-1.py`)
+ :label:`figure-standard-layout-1`
+
+**Standard layout 2** Using gridspec, the goal is to reproduce figure
+:ref:`figure-standard-layout-2` with top and right histograms aligned with the main axes.
+
+.. figure:: layout/standard-layout-2.pdf
+ :width: 100%
+
+ Scatter plot and histograms
+ (sources :source:`layout/standard-layout-2.py`)
+ :label:`figure-standard-layout-2`
diff --git a/rst/main.rst b/rst/main.rst
new file mode 100644
index 0000000..49b0178
--- /dev/null
+++ b/rst/main.rst
@@ -0,0 +1,97 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. include:: common.rst
+
+============
+Fundamentals
+============
+
+.. include:: anatomy.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: coordinates.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: projections.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: typography.rst
+.. include:: colors.rst
+
+:raw-latex:`\clearchapter`
+
+=============
+Figure design
+=============
+.. include:: rules.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: defaults.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: layout.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: ornaments.rst
+
+:raw-latex:`\clearchapter`
+
+=================
+Advanced concepts
+=================
+
+.. include:: animation.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: threed.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: optimization.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: beyond.rst
+
+:raw-latex:`\clearchapter`
+
+
+=================
+Showcase
+=================
+
+.. include:: showcase.rst
+
+
+:raw-latex:`\clearchapter`
+
+==========
+Conclusion
+==========
+
+.. include:: conclusion.rst
+
+:raw-latex:`\clearchapter`
+
+========
+Appendix
+========
+
+:raw-latex:`\appendix`
+
+.. include:: resources.rst
+
+:raw-latex:`\clearchapter`
+
+.. include:: cheatsheets.rst
+
diff --git a/rst/optimization.rst b/rst/optimization.rst
new file mode 100644
index 0000000..b70aba7
--- /dev/null
+++ b/rst/optimization.rst
@@ -0,0 +1,314 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-optimization:
+
+Architecture & optimization
+===========================
+
+Even though a deep understanding of the matplotlib architecture is not
+necessary to use it, it is nonetheless useful to know a bit of its
+architecture to optimize either speed, memory and even
+rendering.
+
+Transparency levels
+-------------------
+
+We've already seen how to use transparency in a scatter plot to have
+a perception of data density. This works reasonably well if you don't
+have too much data. But what is too much exactly? It would be hard to
+give a definitive limit because it depends on a number of parameters
+such as the size of your figure, the shape and size of your markers
+and the alpha level (i.e. transparency). For this latter, there is
+actually a limit in how much transparent a color can be and it is
+exactly 0.002 (= 1/500). This means that if you plot 500 black points
+with a transparency of 0.002, you obtain get a quasi black marker as
+shown on figure :ref:`figure-transparency`.
+
+.. figure:: optimization/transparency.pdf
+ :width: 100%
+
+ Transparency levels
+ :label:`figure-transparency`
+ (sources: :source:`optimization/transparency.py`).
+
+It is not exactly black for 500 points because it also depends on how
+alpha compositing is computed internally but it provides nonetheless a
+useful approximation. Knowing this limit exists, it explains why you
+get a solid color in dense areas when you have a lot of data. This is
+illustrated on figure :ref:`figure-scatters` where the number of data
+is respectively 10,000, 100,000 and 1,000,000. For 10,000 and 100,000
+points we can adapt the transparency level to show where are the dense
+areas. In this case, this is simple normal distribution and we can
+observe the central are is darker. For one million points, we reached
+the limit of the transparency trick (alpha=0.002) and we now have a
+central dark spot that hide information.
+
+.. figure:: optimization/scatters.png
+ :width: 100%
+
+ Scatter, hist2d and hexbin
+ :label:`figure-scatters`
+ (sources: :source:`optimization/scatters.py`).
+
+This means we need a new strategy to display the data. Fortunately,
+matplotlib provides `hist2d
+`__
+and `hexbin
+`__
+that will both aggregate points into bins (with square or hex
+shape) that are eventually colored according to the number in the
+bins. This allows to visualize density for any number of data points
+and do not require to manipulate size and/or transparency of
+markers. You're now ready to reproduce Todd W. Schneider's astonishing
+visualization of NYC Taxi trips (`Analyzing 1.1 Billion NYC Taxi and
+Uber Trips, with a Vengeance
+`_
+and save the result in an image:
+
+.. code:: python
+
+ def plot(extent):
+
+ xmin, xmax, ymin, ymax = extent
+ fig = Figure(figsize=(2,2))
+ canvas = FigureCanvas(fig)
+ ax = fig.add_axes([0,0,1,1], frameon=False,
+ xlim = [xmin,xmax], xticks=[],
+ ylim = [ymin,ymax], yticks=[])
+ epsilon = 0.1
+ I = np.argwhere((X >= (xmin-epsilon)) &
+ (X <= (xmax+epsilon)) &
+ (Y >= (ymin-epsilon)) &
+ (Y <= (ymax+epsilon)))
+ ax.scatter(X[I], Y[I], 3, clip_on=False,
+ color="black", edgecolor="None", alpha=.0025)
+ canvas.draw()
+ return np.array(canvas.renderer.buffer_rgba())
+
+Note that I took care of selecting X and Y that are inside the
+provided extent (modulo epsilon). This is quite important because we
+do not want to plot all the data in each subparts. Else, this would
+slow down things.
+
+We can now put back every parts together using several imshow:
+
+.. code:: python
+
+ from multiprocessing import Pool
+
+ extents = [[x,x+3,y,y+3] for x in range(0,9,3)
+ for y in range(0,9,3)]
+ pool = Pool()
+ images = pool.map(plot, extents)
+ pool.close()
+
+ fig = plt.figure(figsize=(6,6))
+ ax = plt.subplot(xlim=[0,9], ylim=[0,9])
+ for img, extent in zip(images, extents):
+ ax.imshow(img, extent=extent, interpolation="None")
+
+ plt.show()
+
+
+If you look at the result on figure
+:ref:`figure-multithread-rendering`, you can observe a flawless
+montage of the different pieces. If you set the epsilon value to zero,
+you'll observe white spaces appearing between the different parts. The
+reason is that if you enforce very strict clipping, a marker whose
+center is outside extent will not be drawn while it may overlap because
+of its size.
+
+.. figure:: optimization/multithread.png
+ :width: 100%
+
+ Multithread rendering
+ :label:`figure-multithread-rendering`
+ (sources: :source:`optimization/multithread.py`).
+
+Such multithread rendering is not totally straightforward to implement
+because it depends on the possibility to split your in segregated
+elements. However, if you have a very complex plots that take several
+minutes to render, this is an option worth to be explored.
diff --git a/rst/ornaments.rst b/rst/ornaments.rst
new file mode 100644
index 0000000..71ff794
--- /dev/null
+++ b/rst/ornaments.rst
@@ -0,0 +1,231 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-ornaments:
+
+Ornaments
+=========
+
+Ornaments designate all the extra elements you can add to a figure to
+beautify it or to make it clearer. Ornaments include standard elements
+such as legend, annotation, colorbars, text but you can also design
+your own element specifically for your figure. For example, figure
+:ref:`figure-bessel-functions` displays a mix of standard elements
+(annotation and text) as well as specific elements (roots reported on
+the x axis using vertical markers).
+
+.. figure:: ornaments/bessel-functions.pdf
+ :width: 100%
+
+ Bessel functions
+ :label:`figure-bessel-functions`
+ (sources: :source:`ornaments/bessel-functions.py`).
+
+There is no theoretical limit in the number of ornaments you can add
+to a figure. However, you have to take care that your figure is still
+easily readable and not too overloaded.
+
+
+Legend
+------
+
+`Legends
+`__
+are quite easy to use and only require for the user to name
+plots. Matplotlib will take care of placing the legend automatically
+(which is really tricky and consequently it can fail) and display the
+necessary information (see figure
+:ref:`figure-legend-regular`). Legend comes with several options that
+allows you to control every aspect of the legend even if, most of the
+time, a simple `legend()` call is sufficient as shown below:
+
+.. code:: python
+
+ fig = plt.figure(figsize=(6,2.5))
+ ax = plt.subplot()
+
+ X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+ ax.plot(X, np.cos(X), label="cosine")
+ ax.plot(X, np.sin(X), label="sine")
+ ax.legend()
+
+ plt.show()
+
+.. figure:: ornaments/legend-regular.pdf
+ :width: 100%
+
+ Regular legend
+ :label:`figure-legend-regular`
+ (sources: :source:`ornaments/legend-regular.py`).
+
+.. figure:: ornaments/legend-alternatives.pdf
+ :width: 100%
+
+ Alternative legends
+ :label:`figure-legend-alternatives`
+ (sources: :source:`ornaments/legend-alternatives.py`).
+
+However, there are some cases where legend might not be the most
+appropriate way to add information. For example, when you have several
+plots, it may become tedious for the reader to go back and forth
+between plots and legend. An alternative way is to add information
+directly on the plots as shown on figure
+:ref:`figure-legend-alternatives`.
+
+This figure introduces four different ways to directly label plots
+even though there is no best alternative because it really depends on
+the data. For the displayed sine / cosine example (which is quite
+simple) the four solutions are appropriate, but when used with real
+data, it may happen that none of these alternative suits the data. In
+such case, you might need an alternate way of labelling data or you
+may need to split your figure into several plots.
+
+Title & labels
+--------------
+
+We've already manipulated title and labels using `set_title`,
+`set_xlabel` and `set_ylabel` methods. When used without any extra
+argument, they do a fairly good job and their placement is usually
+good for most figures. Nonetheless, it is possible to play with the
+various parameters such as to beautify the figure as shown on figure
+:ref:`figure-title-regular`. In this example, I simply displaced the
+labels in order to be closer to the axis and I took care of removing
+the central tick that would have else collided with the label. For the
+title, I simply moved it to the right and I moved the legend box
+(using two columns) accordingly, that is, under the title. Nothing
+complicated here but I think the result is visually more pleasant.
+
+.. figure:: ornaments/title-regular.pdf
+ :width: 100%
+
+ Regular title
+ :label:`figure-title-regular`
+ (sources: :source:`ornaments/title-regular.py`).
+
+In some cases (e.g. conference poster), you may need to have titles to
+be a little more eye catching like shown on figure
+:ref:`figure-latex-text-box`. This is made possible by dividing
+each axis with the `make_axes_locatable` method and to reserve 15% of
+height for the actual title. In this figure, I also inserted a fully
+justified text using Latex that may be considered as another form or
+(advanced) ornament.
+
+.. figure:: ornaments/latex-text-box.png
+ :width: 100%
+
+ Advanced text box
+ :label:`figure-latex-text-box`
+ (sources: :source:`ornaments/latex-text-box.py`).
+
+
+Annotations
+-----------
+
+Annotation is probably the most difficult object to handle inside
+matplotlib. The reason is that it involves a number of different
+concepts that results in a potential high number of parameters.
+Furthermore, there is a supplementary difficulty because annotation
+involve text whose size is expressed in points. In the end, you may
+have to mix absolute or relative coordinates in pixels, points,
+fractions or data units. If you add the fact that you can annotate any
+axis having any kind of projection, you may now realize why the annotate
+method offer so many parameters.
+
+The simplest way to annotate a figure is to add labels very close to
+the points you want to annotate as shown on figure
+:ref:`figure-annotation-direct`. In this figure, I took care of adding
+a white outline to the labels such that they remain readable,
+independently of the data distribution. However, if you have too many
+points, all the different labels may end up cluttering your figure and
+hide potentially important information.
+
+.. figure:: ornaments/annotation-direct.pdf
+ :width: 65%
+
+ Direct annotations
+ :label:`figure-annotation-direct`
+ (sources: :source:`ornaments/annotation-direct.py`).
+
+An alternative is to push labels on the side of the figure and to use
+broken lines to establish the link between the point and the label as
+shown on figure :ref:`figure-annotation-side`. But this is far from
+being automatic and to design this figure, I had to compute pretty
+much everything. First, to have lines to not cross each other, I order
+the point I want to label:
+
+.. code:: python
+
+ X = np.random.normal(0, .35, 1000)
+ Y = np.random.normal(0, .35, 1000)
+ ax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+
+ I = np.random.choice(len(X), size=5, replace=False)
+ Px, Py = X[I], Y[I]
+ I = np.argsort(Y[I])[::-1]
+ Px, Py = Px[I], Py[I]
+
+
+From these points, I've been able to annotate them using a quite
+complex connection style:
+
+.. code:: python
+
+ y, dy = .25, 0.125
+ style = "arc,angleA=-0,angleB=0,armA=-100,armB=0,rad=0"
+ for i in range(len(I)):
+ ax.annotate("Point " + chr(ord("A")+i),
+ xy = (Px[i], Py[i]), xycoords='data',
+ xytext = (1.25, y-i*dy), textcoords='data',
+ arrowprops=dict(arrowstyle="->", color="black",
+ linewidth=.75,
+ shrinkA=20, shrinkB=5,
+ patchA=None, patchB=None,
+ connectionstyle=style))
+
+.. figure:: ornaments/annotation-side.pdf
+ :width: 75%
+
+ Side annotations
+ :label:`figure-annotation-side`
+ (sources: :source:`ornaments/annotation-side.py`).
+
+
+It is also possible to annotate objects outside the axes using a
+`connection patch
+`__
+as shown on figure :ref:`figure-annotation-zoom`. In this example, I
+created several rectangles showing areas of interest around some
+points and I created a connection to the corresponding zoomed
+axes. Note how the connection starts on the outside of the rectangle,
+which is one of the nice feature offered by annotation: you can
+specify the nature of the object you want to annotate (by providing a
+patch) and matplotlib will take care of having the origin of the
+connection to the border of the patch.
+
+
+.. figure:: ornaments/annotation-zoom.pdf
+ :width: 75%
+
+ Zoomed annotations
+ :label:`figure-annotation-zoom`
+ (sources: :source:`ornaments/annotation-zoom.py`).
+
+
+Exercise
+--------
+
+It is now your turn to experiment with ornaments by trying to
+reproduce the figure :ref:`figure-elegant-scatter` which displays
+several ornaments, including a custom one on the left and bottom
+side. This ornament provides a quick way to show respective
+distributions along weight and height and can be rendered with a
+scatter plot using large vertical and horizontal markers.
+
+.. figure:: ornaments/elegant-scatter.pdf
+ :width: 100%
+
+ Elegant scatter plot
+ :label:`figure-elegant-scatter`
+ (sources: :source:`ornaments/elegant-scatter.py`).
diff --git a/rst/projections.rst b/rst/projections.rst
new file mode 100644
index 0000000..6d7b612
--- /dev/null
+++ b/rst/projections.rst
@@ -0,0 +1,328 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: BSD
+.. ----------------------------------------------------------------------------
+.. _chap-projection:
+
+Scales & projections
+====================
+
+Beyond affine transforms, matplotlib also offers advanced transformations that
+allows to drastically change the representation of your data without ever
+modifying it. Those transformations correspond to a data preprocessing stage
+that allows you to adapt the rendering to the nature of your data. As explained
+in the matplotlib documentation, there are two main families of transforms:
+separable transformations, working on a single dimension, are called
+:nameref:`scales`, and non-separable transformations, that handle data in two
+or more dimensions at once are called :nameref:`projections`.
+
+
+Scales
+------
+
+Scales provide a mapping mechanism between the data and their representation
+in the figure along a given dimension. Matplotlib offers four different scales
+(`linear`, `log`, `symlog` and `logit`) and takes care, for each of them, of
+modifying the figure such as to adapt the ticks positions and labels (see
+figure :ref:`figure-scales`). Note that a scale can be applied to x axis only
+(`set_xscale`), y axis only (`set_yscale`) or both.
+
+.. figure:: scales-projections/scales-comparison.pdf
+ :width: 100%
+
+ Comparison of the linear_, log_ and logit_ scales.
+ :label:`figure-scales` (sources: :source:`scales-projections/scales-comparison.py`).
+
+The default (and implicit) scale is linear_ and it is thus generally not
+necessary to specify anything. You can check if a scale is linear by comparing
+the distance between three points in the figure coordinates (actually we should
+compare every points but you get the idea) and check whether their difference
+in data space is the same as in figure space modulo a given factor (see
+:source:`scales-projections/scales-check.py`):
+
+.. code:: python
+
+ >>> fig = plt.figure(figsize=(6,6))
+ >>> ax = plt.subplot(1, 1, 1,
+ aspect=1, xlim=[0,100], ylim=[0,100])
+ >>> P0, P1, P2, P3 = (0.1, 0.1), (1,1), (10,10), (100,100)
+ >>> transform = ax.transData.transform
+ >>> print( (transform(P1)-transform(P0))[0] )
+ 4.185
+ >>> print( (transform(P2)-transform(P1))[0] )
+ 41.85
+ >>> print( (transform(P1)-transform(P0))[0] )
+ 418.5
+
+Logarithmic scale (log_) is a nonlinear scale where, instead of increasing in equal
+increments, each interval is increased by a factor of the base of the logarithm
+(hence the name). Log scales are used for values that are strictly positive since
+the logarithm is undefined for negative and null values. If we apply the previous
+script to check the difference in data and figure space, you can now see the
+distances are the same:
+
+.. code:: python
+
+ >>> fig = plt.figure(figsize=(6,6))
+ >>> ax = plt.subplot(1, 1, 1,
+ aspect=1, xlim=[0.1,100], ylim=[0.1,100])
+ >>> ax.set_xscale("log")
+ >>> P0, P1, P2, P3 = (0.1, 0.1), (1,1), (10,10), (100,100)
+ >>> transform = ax.transData.transform
+ >>> print( (transform(P1)-transform(P0))[0] )
+ 155.0
+ >>> print( (transform(P2)-transform(P1))[0] )
+ 155.0
+ >>> print( (transform(P1)-transform(P0))[0] )
+ 155.0
+
+If your data has negative values, you have to use a symmetric log scale (symlog_)
+that is a composition of both a linear and a logarithmic scale. More precisely,
+values around 0 use a linear scale and values outside the vicinity of zero uses
+a logarithmic scale. You can of course specify the extent of the linear zone
+when you set the scale. The logit_ scale is used for values in the range ]0,1[
+and uses a logarithmic scale on the "border" and a quasi-linear scale in the
+middle (around 0.5). If none of these scales suit your needs, you still have
+the option to define your own custom scale:
+
+.. code:: python
+
+ def forward(x):
+ return x**(1/2)
+ def inverse(x):
+ return x**2
+
+ ax.set_xscale('function', functions=(forward, inverse))
+
+In such case, you have to provide both the forward and inverse function that
+allows to transform your data. The inverse function is used when displaying
+coordinates under the mouse pointer.
+
+.. figure:: scales-projections/scales-custom.pdf
+ :width: 100%
+
+ Custom (user defined) scales.
+ :label:`figure-scales-custom` (sources: :source:`scales-projections/scales-custom.py`).
+
+Finally, if you need a custom scale with complex transforms, you may need to
+write a proper scale object as it is explained on the `matplotlib documentation
+`_.
+
+Projections
+-----------
+
+Projections are a bit more complex than scales but in the meantime much more
+powerful. Projections allows you to apply arbitrary transformation to your data
+before rendering them in a figure. There is no real limit on the kind of
+transformation you can apply as long as you know how to transform your data into
+something that will be 2 dimensional (the figure space) and reciprocally. In
+other words, you need to define a forward and an inverse
+transformation. Matplotlib comes with only a few standard projections but offers
+all the machinery to create new domain-dependent projection such as for example
+cartographic projection. You might wonder why there are so few native
+projections. The answer is that it would be too time-consuming and too difficult
+for the developers to implement and maintain each and every projections that
+are domain specific. They chose instead to restrict projection to the most
+generic ones, namely polar_ and 3d_.
+
+We've already seen the polar projection in the previous chapter. The most simple
+and straightforward way to use is to specify the projection when you create an
+axis:
+
+.. code:: Python
+
+ ax = plt.subplot(1, 1, 1, projection='polar')
+
+This axis is now equipped with a polar projection. This means that any plotting
+command you apply is pre-processed such as to apply (automatically) the forward
+transformation on the data. In the case of a polar projection, the forward
+transformation must specify how to go from polar coordinates :math:`(\rho,
+\theta)` to Cartesian coordinates :math:`(x,y) = (\rho cos(\theta), \rho
+sin(\theta))`. When you declare a polar axis, you can specify limits of the axis
+as we've done previously but we have also some dedicated settings such as
+`set_thetamin`, `set_thetamax`, `set_rmin`, `set_rmax` and more specifically
+`set_rorigin`. This allows you to have fine control over what is actually shown as illustrated on the figure :ref:`figure-projection-polar-config`.
+
+.. figure:: scales-projections/projection-polar-config.pdf
+ :width: 100%
+
+ Polar projection
+ :label:`figure-projection-polar-config`
+ (sources: :source:`scales-projections/projection-polar-config.py`).
+
+
+If you now try to do some plots (e.g. plot, scatter, bar), you'll see that
+everything is transformed but a few elements. More precisely, the shape of
+markers is not transformed (a disc marker will remains a disc visually), the
+text is not transformed (such that it remains readable) and the width of lines
+is kept constant. Let's have a look at a more elaborate figure to see what it
+means more precisely. On figure :ref:`figure-projection-polar-histogram`, I
+plotted a simple signal using mostly `fill_between
+`_
+command. The concentric grey/white colored rings are made using the
+`fill_between` command between two different :math:`\rho` values while the
+histogram is made with various :math:`\rho` values. If you now look more closely
+at the :math:`\rho` axis with ticks ranging from 100 to 900, you can observe
+that the ticks have the same vertical size. It is indeed an *anomaly* I
+introduced deliberately for purely aesthetic reasons. If I had specified these
+ticks using a plot command, the length of each tick would correspond to a
+difference of angle (for the vertical size) and they would become taller and
+taller as we move away from the center. To have regulars ticks, we thus have to
+do some computations using the inverse transform (remember, a projection is a
+forward and an inverse transform). I won't give all the details here but you can
+read the code (:source:`projection-polar-histogram.py`) to see how it is
+made. Note that the actual role of the inverse transformation is to link mouse
+coordinates (in Cartesian 2D coordinates) back to your data.
+
+.. figure:: scales-projections/projection-polar-histogram.pdf
+ :width: 95%
+
+ Polar projection with better defaults.
+ :label:`figure-projection-polar-histogram`
+ (sources: :source:`scales-projections/projection-polar-histogram.py`).
+
+Conversely, there are some situations were we might be interested in having
+the text and the markers to be transformed as illustrated on figure
+:ref:`figure-text-polar`.
+
+.. figure:: scales-projections/text-polar.pdf
+ :width: 90%
+
+ Polar projection with transformation of text and markers.
+ :label:`figure-text-polar`
+ (sources: :source:`scales-projections/text-polar.py`).
+
+On this example, both the markers and the text have been transformed
+manually. For the markers, the trick is to use `Ellipses
+`_ that are
+approximated as a sequence of small line segments, each of them being
+transformed. In the corresponding code, I only specify the center, and the size
+of the pseudo-marker and the pre-processing stage takes care of applying the
+polar projection to each individual parts composing the marker (ellipse),
+resulting in a slightly curved ellipse. For the text, the process is the same
+but it is a bit more complicated since we need first to convert the text into a
+path that can be transformed (we'll see that in more detail in the next
+chapter).
+
+
+The second projection that matplotlib offers is the 3d projection, that is the
+projection from a 3D Cartesian space to a 2 Cartesian space. To start using 3D
+projection, you'll need to use the `Axis3D
+`_ toolkit that is generally
+shipped with matplotlib:
+
+.. code:: python
+
+ from mpl_toolkits.mplot3d import Axes3D
+ ax = plt.subplot(1, 1, 1, projection='3d')
+
+With this 3D axis, you can use regular plotting commands with a big difference
+though: you need now to provide 3 coordinates (x,y,z) where you previously
+provided only two (x,y) as illustrated on figure
+:ref:`figure-projection-3d-frame`. Note that this figure is quite different from
+the default 3D axis you may get from matplotlib. Here, I tweaked every settings
+I can think of to try to improve the default look and to show how things can be
+changed. Have a look at the corresponding code and try to modify some settings to
+see the actual effect. The `3D Axis API
+`_ is fairly well
+documented on the matplotlib website and I won't explain each and every command.
+
+.. note:: **Note** The 3D axis projection is limited by the absence of a proper
+ `depth-buffer `_. This is not a
+ bug (nor a feature) and this results in some glitches between the elements
+ composing a figure.
+
+.. figure:: scales-projections/projection-3d-frame.pdf
+ :width: 80%
+
+ Three dimensional projection
+ :label:`figure-projection-3d-frame`
+ (sources: :source:`scales-projections/projection-3d-frame.py`).
+
+For other type of projections, you'll need to install third-party packages
+depending on the type of projection you intend to use:
+
+`Cartopy `_
+ is a plotting library for use with matplotlib to make ternary plots plots in
+ the two dimensional simplex projected onto a two dimensional plane. The
+ library provides functions for plotting projected lines, curves
+ (trajectories), scatter plots, and heatmaps. There are several examples and a
+ short tutorial below.
+
+`pySmithPlot `_
+ is a matplotlib extension providing a projection class for creating high
+ quality Smith Charts with Python. The generated plots blend seamlessly into
+ matplotlib's style and support almost the full range of customization options.
+
+`Matplotlib-3D `_
+ is an experimental project that attempts to provide a better and more
+ versatile 3d axis for Matplotlib.
+
+ ..
+ .. figure:: scales-projections/geo-projections.pdf
+ :width: 100%
+
+ Some of the standard and not so standard geographic projections
+ :label:`figure-geo-projections` (sources: :source:`scales-projections/geo-projections.py`).
+
+If you're still not satisfied with existing projections, your last option is to
+create your own projection but this is quite an advanced operation even though
+the matplotlib documentation provides some `examples
+`_
+
+
+Exercises
+---------
+
+**Exercise 1** Considering functions :math:`f(x) = 10^x`, :math:`f(x) = x` and
+:math:`f(x) = log_{10}(x)`, try to reproduce figure :ref:`figure-scales-log-log`.
+
+.. figure:: scales-projections/scales-log-log.pdf
+ :width: 100%
+
+ Combining linear and logarithmic scales.
+ :label:`figure-scales-log-log` (sources: :source:`scales-projections/scales-log-log.py`).
+
+
+**Exercise 2** The goal is to produce a figure showing `microphone
+polar patterns
+`__
+(omnidirectional, subcardioid, cardioid, supercardioid, bidirectional
+and shotgun). The first five patterns are simple functions where
+radius evolvse with angle while the last pattern may require some
+works.
+
+.. figure:: scales-projections/polar-patterns.pdf
+ :width: 100%
+
+ Microphone polar patterns
+ :label:`figure-polar-patterns`
+ (sources: :source:`scales-projections/polar-patterns.py`).
+
+
+
+
+.. --- Links ------------------------------------------------------------------
+.. _linear: https://matplotlib.org/api/scale_api.html?#matplotlib.scale.LinearScale
+.. _log: https://matplotlib.org/api/scale_api.html?#matplotlib.scale.LogScale
+.. _symlog: https://matplotlib.org/api/scale_api.html?#matplotlib.scale.SymmetricalLogScale
+.. _logit: https://matplotlib.org/api/scale_api.html?#matplotlib.scale.LogitScale
+.. _polar: https://matplotlib.org/api/projections_api.html#module-matplotlib.projections.polar
+.. _3d: https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html
+.. ----------------------------------------------------------------------------
+
diff --git a/rst/resources.rst b/rst/resources.rst
new file mode 100644
index 0000000..ac9323b
--- /dev/null
+++ b/rst/resources.rst
@@ -0,0 +1,60 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-resources:
+
+External resources
+==================
+
+One of the strength of matplotlib is the extensive documentation available from
+the website. But there is also a large set of external resources that are
+incredibly useful when you're looking to design a specific figure:
+
+* The matplotlib documentation is the primary source of information whenever
+ you need to know the parameters of this or that function. It has also a very
+ well written set of tutorials that are worth to be read:
+
+ - The `starter guide `_ helps you to get started with Matplotlib
+ - The `pyplot tutorial `_ is an introduction to the `pyplot` interface
+ - The `image tutorial `_ explains the basic of image display
+ - The `catalogue `_ shows some neat and standard visualizations
+ - The `frequently asked questions `__ are worth a read
+
+
+* Online help is available from two mailing lists (users and
+ developers) and from stack overflow which has a dedicated matplotlib
+ tag and a lot of questions have been asked and answered:
+
+ - The `matplotlib users mailing list `_
+ - The `matplotlib developers mailing lists `_
+ - `Stack overflow `__ (more than 40,000 questions related to matplotlib)
+
+
+* There exist various galleries online among which the matplotlib gallery that
+ is probably the best gallery to start with. The other galleries are worth a
+ look because they explain the rationale for each kind of plot:
+
+ - The `Matplotlib Gallery`_ is probably the best resource if you're looking
+ for how to do a specific figure. Just search for the closest example and
+ get the code.
+ - The `Data Visualization Catalogue`_ is a project developed by Severino
+ Ribecca to create a library of different information visualisation types.
+ - The `Python Graph gallery`_ displays hundreds of charts and aims to
+ showcase the awesome dataviz possibilities of Python.
+ - The `R Graph Gallery`_ is a collection of charts made displayed in several
+ sections, always with their (R) reproducible code available.
+
+* Finally, there exists many books about matplotlib and data visualization. I
+ selected only a few of them that are well written and open access. You can
+ find them in the :nameref:`chap-bibliography` appendix.
+
+
+
+.. --- Links ------------------------------------------------------------------
+.. _Matplotlib Gallery: https://matplotlib.org/gallery.html
+.. _Data Visualization Catalogue: https://datavizcatalogue.com/
+.. _Python Graph Gallery: https://python-graph-gallery.com/
+.. _R Graph Gallery: https://www.r-graph-gallery.com/
+.. ----------------------------------------------------------------------------
diff --git a/rst/rules.rst b/rst/rules.rst
new file mode 100644
index 0000000..b11bcb6
--- /dev/null
+++ b/rst/rules.rst
@@ -0,0 +1,441 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-rules:
+
+Ten simple rules
+================
+
+.. note::
+
+ **Note.** This chapter is an article I co-authored with Michael Droettboom
+ and Philip E. Bourne. It has been published in `PLOS Computational Biology
+ `_ under a Creative Commons
+ CC0 public domain dedication in September 2014. It is five years old but
+ still relevant and very popular.
+
+Scientific visualization is classically defined as the process of graphically
+displaying scientific data. However, this process is far from direct or
+automatic. There are so many different ways to represent the same data: scatter
+plots, linear plots, bar plots, and pie charts, to name just a
+few. Furthermore, the same data, using the same type of plot, may be perceived
+very differently depending on who is looking at the figure. A more accurate
+definition for scientific visualization would be a graphical interface between
+people and data. In this short article, we do not pretend to explain everything
+about this interface. Instead we aim to provide a basic set of rules to improve
+figure design and to explain some of the common pitfalls.
+
+Rule 1: Know Your Audience
+--------------------------
+
+Given the definition above, problems arise when how a visual is perceived
+differs significantly from the intent of the conveyer. Consequently, it is
+important to identify, as early as possible in the design process, the audience
+and the message the visual is to convey. The graphical design of the visual
+should be informed by this intent. If you are making a figure for yourself and
+your direct collaborators, you can possibly skip a number of steps in the
+design process, because each of you knows what the figure is about. However, if
+you intend to publish a figure in a scientific journal, you should make sure
+your figure is correct and conveys all the relevant information to a broader
+audience. Student audiences require special care since the goal for that
+situation is to explain a concept. In that case, you may have to add extra
+information to make sure the concept is fully understood. Finally, the general
+public may be the most difficult audience of all since you need to design a
+simple, possibly approximated, figure that reveals only the most salient part
+of your research (Figure :ref:`fig-rule-1`). This has proven to be a difficult
+exercise.
+
+.. figure:: rules/rule-1.pdf
+ :width: 100%
+
+ **Know your audience.** This is a remake of a figure that was originally
+ published in the New York Times (NYT) in 2007. This new figure was made with
+ matplotlib using approximated data. The data is made of four series (men
+ deaths/cases, women deaths/cases) that could have been displayed using
+ classical double column (deaths/cases) bar plots. However, the layout used
+ here is better for the intended audience. It exploits the fact that the
+ number of new cases is always greater than the corresponding number of
+ deaths to mix the two values. It also takes advantage of the reading
+ direction (English [left-to-right] for NYT) in order to ease comparison
+ between men and women while the central labels give an immediate access to
+ the main message of the figure (cancer). This is a self-contained figure
+ that delivers a clear message on cancer deaths. However, it is not
+ precise. The chosen layout makes it actually difficult to estimate the
+ number of kidney cancer deaths because of its bottom position and the
+ location of the labelled ticks at the top. While this is acceptable for a
+ general-audience publication, it would not be acceptable in a scientific
+ publication if actual numerical values were not given elsewhere in the
+ article. (:source:`rules/rule-1.py`). :label:`fig-rule-1`
+
+
+Rule 2: Identify Your Message
+-----------------------------
+
+A figure is meant to express an idea or introduce some facts or a result that
+would be too long (or nearly impossible) to explain only with words, be it for
+an article or during a time-limited oral presentation. In this context, it is
+important to clearly identify the role of the figure, i.e., what is the
+underlying message and how can a figure best express this message? Once clearly
+identified, this message will be a strong guide for the design of the figure,
+as shown in Figure :ref:`fig-rule-2`. Only after identifying the message will
+it be worth the time to develop your figure, just as you would take the time to
+craft your words and sentences when writing an article only after deciding on
+the main points of the text. If your figure is able to convey a striking
+message at first glance, chances are increased that your article will draw more
+attention from the community.
+
+.. figure:: rules/rule-2.pdf
+ :width: 100%
+
+ **Identify your message.** The superior colliculus (SC) is a brainstem
+ structure at the crossroads of multiple functional pathways. Several
+ neurophysiological studies suggest that the population of active neurons in
+ the SC encodes the location of a visual target that induces saccadic eye
+ movement. The projection from the retina surface (on the left) to the
+ collicular surface (on the right) is based on a standard and quantitative
+ model in which a logarithmic mapping function ensures the projection from
+ retinal coordinates to collicular coordinates. This logarithmic mapping
+ plays a major role in saccade decision. To better illustrate this role, an
+ artificial checkerboard pattern has been used, even though such a pattern is
+ not used during experiments. This checkerboard pattern clearly demonstrates
+ the extreme magnification of the foveal region, which is the main message of
+ the figure. (:source:`rules/rule-2.py`) :label:`fig-rule-2`
+
+
+Rule 3: Adapt the Figure to the Support Medium
+----------------------------------------------
+
+A figure can be displayed on a variety of media, such as a poster, a computer
+monitor, a projection screen (as in an oral presentation), or a simple sheet of
+paper (as in a printed article). Each of these media represents different
+physical sizes for the figure, but more importantly, each of them also implies
+different ways of viewing and interacting with the figure. For example, during
+an oral presentation, a figure will be displayed for a limited time. Thus, the
+viewer must quickly understand what is displayed and what it represents while
+still listening to your explanation. In such a situation, the figure must be
+kept simple and the message must be visually salient in order to grab
+attention, as shown in Figure :ref:`fig-rule-3`. It is also important to keep
+in mind that during oral presentations, figures will be video-projected and
+will be seen from a distance, and figure elements must consequently be made
+thicker (lines) or bigger (points, text), colors should have strong contrast,
+and vertical text should be avoided, etc. For a journal article, the situation
+is totally different, because the reader is able to view the figure as long as
+necessary. This means a lot of details can be added, along with complementary
+explanations in the caption. If we take into account the fact that more and
+more people now read articles on computer screens, they also have the
+possibility to zoom and drag the figure. Ideally, each type of support medium
+requires a different figure, and you should abandon the practice of extracting
+a figure from your article to be put, as is, in your oral presentation.
+
+.. figure:: rules/rule-3.pdf
+ :width: 100%
+
+ **Adapt the figure to the support medium** These two figures represent the
+ same simulation of the trajectories of a dual-particle system
+ (:math:`\dot{x} = (1/4 + (x-y))(1-x)`, :math:`x \ge 0`, :math:`\dot{y} =
+ (1/4 + (y-x))(1-y)`, :math:`y \ge 0`) where each particle interacts with the
+ other. Depending on the initial conditions, the system may end up in three
+ different states. The left figure has been prepared for a journal article
+ where the reader is free to look at every detail. The red color has been
+ used consistently to indicate both initial conditions (red dots in the
+ zoomed panel) and trajectories (red lines). Line transparency has been
+ increased in order to highlight regions where trajectories overlap (high
+ color density). The right figure has been prepared for an oral
+ presentation. Many details have been removed (reduced number of
+ trajectories, no overlapping trajectories, reduced number of ticks, bigger
+ axis and tick labels, no title, thicker lines) because the time-limited
+ display of this figure would not allow for the audience to scrutinize every
+ detail. Furthermore, since the figure will be described during the oral
+ presentation, some parts have been modified to make them easier to reference
+ (e.g., the yellow box, the red dashed line). (:source:`rules/rule-3.py`)
+ :label:`fig-rule-3`
+
+
+Rule 4: Captions Are Not Optional
+---------------------------------
+
+Whether describing an experimental setup, introducing a new model, or
+presenting new results, you cannot explain everything within the figure
+itself—a figure should be accompanied by a caption. The caption explains how to
+read the figure and provides additional precision for what cannot be
+graphically represented. This can be thought of as the explanation you would
+give during an oral presentation, or in front of a poster, but with the
+difference that you must think in advance about the questions people would
+ask. For example, if you have a bar plot, do not expect the reader to guess the
+value of the different bars by just looking and measuring relative heights on
+the figure. If the numeric values are important, they must be provided
+elsewhere in your article or be written very clearly on the figure. Similarly,
+if there is a point of interest in the figure (critical domain, specific point,
+etc.), make sure it is visually distinct but do not hesitate to point it out
+again in the caption.
+
+
+Rule 5: Do Not Trust the Defaults
+---------------------------------
+
+Any plotting library or software comes with a set of default settings. When the
+end-user does not specify anything, these default settings are used to specify
+size, font, colors, styles, ticks, markers, etc. (Figure :ref:`fig-rule-5`).
+Virtually any setting can be specified, and you can usually recognize the
+specific style of each software package (Matlab, Excel, Keynote, etc.) or
+library (LaTeX, matplotlib, gnuplot, etc.) thanks to the choice of these
+default settings. Since these settings are to be used for virtually any type of
+plot, they are not fine-tuned for a specific type of plot. In other words, they
+are good enough for any plot but they are best for none. All plots require at
+least some manual tuning of the different settings to better express the
+message, be it for making a precise plot more salient to a broad audience, or
+to choose the best colormap for the nature of the data. For example, see
+chapter chap-defaults_ for how to go from the default settings to a nicer
+visual in the case of the matplotlib library.
+
+.. figure:: rules/rule-5.pdf
+ :width: 100%
+
+ **Do not trust the defaults.** The left panel shows the sine and cosine
+ functions as rendered by matplotlib using default settings. While this
+ figure is clear enough, it can be visually improved by tweaking the various
+ available settings, as shown on the right
+ panel. (:source:`rules/rule-5-left.py` and :source:`rules/rule-5-right.py`)
+ :label:`fig-rule-5`
+
+
+Rule 6: Use Color Effectively
+-----------------------------
+
+Color is an important dimension in human vision and is consequently equally
+important in the design of a scientific figure. However, as explained by Edward
+Tufte, color can be either your greatest ally or your worst enemy if not used
+properly. If you decide to use color, you should consider which colors to use
+and where to use them. For example, to highlight some element of a figure, you
+can use color for this element while keeping other elements gray or black. This
+provides an enhancing effect. However, if you have no such need, you need to
+ask yourself, “Is there any reason this plot is blue and not black?” If you
+don't know the answer, just keep it black. The same holds true for
+colormaps. Do not use the default colormap (e.g., jet or rainbow) unless there
+is an explicit reason to do so (see Figure :ref:`fig-rule-6`). Colormaps are
+traditionally classified into three main categories:
+
+* **Sequential**: one variation of a unique color, used for quantitative data
+ varying from low to high.
+* **Diverging**: variation from one color to another, used to highlight deviation
+ from a median value.
+* **Qualitative**: rapid variation of colors, used mainly for discrete or
+ categorical data.
+
+Use the colormap that is the most relevant to your data. Lastly, avoid using
+too many similar colors since color blindness may make it difficult to discern
+some color differences.
+
+.. figure:: rules/rule-6.pdf
+ :width: 100%
+
+ **Use color effectively.** This figure represents the same signal, whose
+ frequency increases to the right and intensity increases towards the bottom,
+ using three different colormaps. The rainbow colormap (qualitative) and the
+ seismic colormap (diverging) are equally bad for such a signal because they
+ tend to hide details in the high frequency domain (bottom-right part). Using
+ a sequential colormap such as the purple one, it is easier to see details in
+ the high frequency domain. (:source:`rules/rule-6.py`) :label:`fig-rule-6`
+
+
+Rule 7: Do Not Mislead the Reader
+---------------------------------
+
+What distinguishes a scientific figure from other graphical artwork is the
+presence of data that needs to be shown as objectively as possible. A
+scientific figure is, by definition, tied to the data (be it an experimental
+setup, a model, or some results) and if you loosen this tie, you may
+unintentionally project a different message than intended. However,
+representing results objectively is not always straightforward. For example, a
+number of implicit choices made by the library or software you're using that
+are meant to be accurate in most situations may also mislead the viewer under
+certain circumstances. If your software automatically re-scales values, you
+might obtain an objective representation of the data (because title, labels,
+and ticks indicate clearly what is actually displayed) that is nonetheless
+visually misleading (see bar plot in Figure :ref:`fig-rule-7`); you have
+inadvertently misled your readers into visually believing something that does
+not exist in your data. You can also make explicit choices that are wrong by
+design, such as using pie charts or 3-D charts to compare quantities. These two
+kinds of plots are known to induce an incorrect perception of quantities and it
+requires some expertise to use them properly. As a rule of thumb, make sure to
+always use the simplest type of plots that can convey your message and make
+sure to use labels, ticks, title, and the full range of values when
+relevant. Lastly, do not hesitate to ask colleagues about their interpretation
+of your figures.
+
+.. figure:: rules/rule-7.pdf
+ :width: 100%
+
+ **Do not mislead the reader.** On the left part of the figure, we
+ represented a series of four values: 30, 20, 15, 10. On the upper left part,
+ we used the disc area to represent the value, while in the bottom part we
+ used the disc radius. Results are visually very different. In the latter
+ case (red circles), the last value (10) appears very small compared to the
+ first one (30), while the ratio between the two values is only 3∶1. This
+ situation is actually very frequent in the literature because the command
+ (or interface) used to produce circles or scatter plots (with varying point
+ sizes) offers to use the radius as default to specify the disc size. It thus
+ appears logical to use the value for the radius, but this is misleading. On
+ the right part of the figure, we display a series of ten values using the
+ full range for values on the top part (y axis goes from 0 to 100) or a
+ partial range in the bottom part (y axis goes from 80 to 100), and we
+ explicitly did not label the y-axis to enhance the confusion. The visual
+ perception of the two series is totally different. In the top part (black
+ series), we tend to interpret the values as very similar, while in the
+ bottom part, we tend to believe there are significant differences. Even if
+ we had used labels to indicate the actual range, the effect would persist
+ because the bars are the most salient information on these
+ figures. (:source:`rules/rule-7.py`) :label:`fig-rule-7`
+
+
+Rule 8: Avoid "Chartjunk"
+-------------------------
+
+Chartjunk refers to all the unnecessary or confusing visual elements found in a
+figure that do not improve the message (in the best case) or add confusion (in
+the worst case). For example, chartjunk may include the use of too many colors,
+too many labels, gratuitously colored backgrounds, useless grid lines,
+etc. (see left part of Figure :ref:`fig-rule-8`). The term was first coined by
+Edward Tutfe, in which he argues that any decorations that do not tell the
+viewer something new must be banned: "Regardless of the cause, it is all
+non-data-ink or redundant data-ink, and it is often chartjunk." Thus, in order
+to avoid chartjunk, try to save ink, or electrons in the computing era. Stephen
+Few reminds us that graphs should ideally "represent all the data that is
+needed to see and understand what's meaningful." However, an element that could
+be considered chartjunk in one figure can be justified in another. For example,
+the use of a background color in a regular plot is generally a bad idea because
+it does not bring useful information. However, in the right part of Figure 7,
+we use a gray background box to indicate the range [−1,+1] as described in the
+caption. If you're in doubt, do not hesitate to consult the excellent blog of
+Kaiser Fung, which explains quite clearly the concept of chartjunk through the
+study of many examples.
+
+
+.. figure:: rules/rule-8.pdf
+ :width: 100%
+
+ **Avoid chartjunk.** We have seven series of samples that are equally
+ important, and we would like to show them all in order to visually compare
+ them (exact signal values are supposed to be given elsewhere). The left
+ figure demonstrates what is certainly one of the worst possible designs. All
+ the curves cover each other and the different colors (that have been badly
+ and automatically chosen by the software) do not help to distinguish
+ them. The legend box overlaps part of the graphic, making it impossible to
+ check if there is any interesting information in this area. There are far
+ too many ticks: x labels overlap each other, making them unreadable, and the
+ three-digit precision does not seem to carry any significant
+ information. Finally, the grid does not help because (among other
+ criticisms) it is not aligned with the signal, which can be considered
+ discrete given the small number of sample points. The right figure adopts a
+ radically different layout while using the same area on the sheet of
+ paper. Series have been split into seven plots, each of them showing one
+ series, while other series are drawn very lightly behind the main
+ one. Series labels have been put on the left of each plot, avoiding the use
+ of colors and a legend box. The number of x ticks has been reduced to three,
+ and a thin line indicates these three values for all plots. Finally, y ticks
+ have been completely removed and the height of the gray background boxes
+ indicate the [−1,+1] range (this should also be indicated in the figure
+ caption if it were to be used in an article). (:source:`rules/rule-8.py`)
+ :label:`fig-rule-8`
+
+
+Rule 9: Message Trumps Beauty
+-----------------------------
+
+Figures have been used in scientific literature since antiquity. Over the
+years, a lot of progress has been made, and each scientific domain has
+developed its own set of best practices. It is important to know these
+standards, because they facilitate a more direct comparison between models,
+studies, or experiments. More importantly, they can help you to spot obvious
+errors in your results. However, most of the time, you may need to design a
+brand-new figure, because there is no standard way of describing your
+research. In such a case, browsing the scientific literature is a good starting
+point. If some article displays a stunning figure to introduce results similar
+to yours, you might want to try to adapt the figure for your own needs (note
+that we did not say copy; be careful with image copyright). If you turn to the
+web, you have to be very careful, because the frontiers between data
+visualization, infographics, design, and art are becoming thinner and thinner
+[9]. There exists a myriad of online graphics in which aesthetic is the first
+criterion and content comes in second place. Even if a lot of those graphics
+might be considered beautiful, most of them do not fit the scientific
+framework. Remember, in science, message and readability of the figure is the
+most important aspect while beauty is only an option, as dramatically shown in
+Figure :ref:`fig-rule-9`.
+
+.. figure:: rules/rule-9.pdf
+ :width: 100%
+
+ **Message trumps beauty.** This figure is an extreme case where the message
+ is particularly clear even if the aesthetic of the figure is
+ questionable. The uncanny valley is a well-known hypothesis in the field of
+ robotics that correlates our comfort level with the human-likeness of a
+ robot. To express this hypothetical nature, hypothetical data were used
+ (:math:`y=x^2 - 5e^{-5(x-2)^2}`) and the figure was given a sketched look
+ (xkcd filter on matplotlib) associated with a cartoonish font that enhances
+ the overall effect. Tick labels were also removed since only the overall
+ shape of the curve matters. Using a sketch style conveys to the viewer that
+ the data is approximate, and that it is the higher-level concepts rather
+ than low-level details that are important. (:source:`rules/rule-9.py`)
+ :label:`fig-rule-9`
+
+
+Rule 10: Get the Right Tool
+---------------------------
+
+There exist many tools that can make your life easier when creating figures,
+and knowing a few of them can save you a lot of time. Depending on the type of
+visual you're trying to create, there is generally a dedicated tool that will
+do what you're trying to achieve. It is important to understand at this point
+that the software or library you're using to make a visualization can be
+different from the software or library you're using to conduct your research
+and/or analyze your data. You can always export data in order to use it in
+another tool. Whether drawing a graph, designing a schema of your experiment,
+or plotting some data, there are open-source tools for you. They're just
+waiting to be found and used. Below is a small list of open-source tools.
+
+* **Matplotlib** is a python plotting library, primarily for 2-D plotting, but
+ with some 3-D support, which produces publication-quality figures in a
+ variety of hardcopy formats and interactive environments across platforms. It
+ comes with a huge `gallery `__ of
+ examples that cover virtually all scientific domains.
+
+* **R** is a language and environment for statistical computing and graphics. R
+ provides a wide variety of statistical (linear and nonlinear modeling,
+ classical statistical tests, time-series analysis, classification,
+ clustering, etc.) and graphical techniques, and is highly extensible.
+
+* **Inkscape** is a professional vector graphics editor. It allows you to
+ design complex figures and can be used, for example, to improve a
+ script-generated figure or to read a PDF file in order to extract figures and
+ transform them any way you like.
+
+* **TikZ and PGF** are TeX packages for creating graphics
+ programmatically. TikZ is built on top of PGF and allows you to create
+ sophisticated graphics in a rather intuitive and easy manner, as shown by the
+ `Tikz gallery `__ by Fred Weinhaus will
+ provide virtually any effect you might want to achieve.
+
+* **D3.js** (or just D3 for Data-Driven Documents) is a JavaScript library that
+ offers an easy way to create and control interactive data-based graphical
+ forms which run in web browsers, as shown in the `gallery
+ `__.
+
+* **Cytoscape** is a software platform for visualizing complex networks and
+ integrating these with any type of attribute data. If your data or results
+ are very complex, cytoscape may help you alleviate this complexity.
+
+* **Circos** was originally designed for visualizing genomic data but can
+ create figures from data in any field. Circos is useful if you have data that
+ describes relationships or multilayered annotations of one or more scales.
diff --git a/rst/showcase.rst b/rst/showcase.rst
new file mode 100644
index 0000000..8b8e64f
--- /dev/null
+++ b/rst/showcase.rst
@@ -0,0 +1,222 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-showcase:
+
+Showtime.
+
+:raw-latex:`\fullpagefigure{showcases/contour-dropshadow.png}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Filled contours with dropshadows}`
+
+**Filled contours with dropshadows** is a nice effet that allows you
+to use a sequential colormap (here Viridis) while distinguishing
+positive and negative values. If you look closely at the figure, you
+can see that the drop shadow is external for positive values and
+internal for negative values. To achieve this result, the contours
+need to be rendered individually twice. In the first pass, I render a
+contour offscreen and read the resulting image into an array. I then
+use a Gaussian filter to blur it a bit and transform the image to make
+it full black but the alpha channel. I can then display the image
+using imshow and it will gracefully blend with elements already
+presents in the figure. I then add the same contour using the colormap
+and I iterate the process. The trick is to start from bottom to top
+such that dropshadows remain visible.
+
+**Sources:** :source:`showcases/contour-dropshadow.py`
+
+
+:raw-latex:`\fullpagefigure{showcases/domain-coloring.pdf}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Domain coloring}`
+
+**Domain coloring** is a "technique for visualizing complex functions
+by assigning a color to each point of the complex plane. By assigning
+points on the complex plane to different colors and brightness, domain
+coloring allows for a four dimensional complex function to be easily
+represented and understood. This provides insight to the fluidity of
+complex functions and shows natural geometric extensions of real
+functions" `[Wikipedia]
+`__. On the figure on
+the left, I represented the imaginary function :math:`z +
+\nicefrac{1}{z}` in the domain :math:`[-2.5, 2.5]^2`. I used the angle
+of the (complex) result for setting the color and the absolute cosine
+the norm for modulating it periodically. I could have used a cyclic
+colormap such as `twilight` but I think the `Spectral` is visually
+more pleasant, even though it induces some discontinuities. To draw
+the grid, I used a contour plot using integer values in the real and
+imaginary domain.
+
+**Sources:** :source:`showcases/domain-coloring.py`
+
+:raw-latex:`\fullpagefigure{showcases/escher.pdf}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Escher like projections}`
+
+**Escher like projections** can be obtained using the complex exponential
+function and some specific periods that can be computed quite
+easily. To do the figure on the left, I mostly followed explanations
+given by Craig S. Kaplan, professor at the University of Waterloo on
+his blog post `Escher-like Spiral Tilings (2019)
+`_. I
+developed with Georgios Detorakis a `randomized self-organizing map
+`_ based on blue noise
+distribution of neurons. The figure on the left shows the
+self-organisation of the map when fed with random RGB colors. The
+figure itself is made of a polygon collection where each polygon is
+painted with the color of the neuron. No real difficuly but I had to
+take care fo disabling antialias, else, thin lines appear
+between polygons.
+
+**Sources:** `github.com/rougier/VSOM `__
+
+
+:raw-latex:`\fullpagefigure{showcases/waterfall-3d.pdf}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Waterfall plots}`
+
+**Waterfall plot** "is a three-dimensional plot in which multiple
+curves of data, typically spectra, are displayed
+simultaneously. Typically the curves are staggered both across the
+screen and vertically, with 'nearer' curves masking the ones
+behind. The result is a series of "mountain" shapes that appear to be
+side by side. The waterfall plot is often used to show how
+two-dimensional information changes over time or some other variable"
+`[Wikipedia] `__ To do
+the figure, I used a 3D axis and polygons (i.e. not filled plot). The
+reason to use polygon is to obtain the color gradient effect on each
+curve. The only way to do that (to the best of my knowledge), is to
+slice horizontally each curve in several stripes and to render the
+slice using a specific color. The difficulty is to compute those
+irregular slices and this is the reason I use the `Shapely library
+`_ that allows, among many other
+things, to compute the intersection between polygons.
+
+**Sources:** :source:`showcases/waterfall-3d.py`
+
+
+:raw-latex:`\fullpagefigure{showcases/windmap.png}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Streamlines}`
+
+**Streamlines** are a "family of curves that are instantaneously
+tangent to the velocity vector of the flow. These show the direction
+in which a massless fluid element will travel at any point in time"
+`[Wikipedia]
+`__. The
+figure on the left shows such stream lines and is actually a still
+from an animation. Each streamline has been split into line segments
+and gathered in a line collection such that each segment has its own
+color. From there, it is easy to suggest stream direction using
+gradients. Note that I could have used a single line collection for
+all streamlines. Strangely enough, the only difficulty in this figure
+are the line round caps. For the reason explained `here
+`_, I had to create a
+specific graphic context such as to have round caps.
+
+**Sources:** :source:`showcases/windmap.py`
+
+
+:raw-latex:`\fullpagefigure{showcases/mandelbrot.png}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Mandelbrot set}`
+
+The **Mandelbrot set** "is the set of complex numbers :math:`c`for
+which the function :math:`f_{c}(z) = z^{2} + c` does not diverge when
+iterated from :math:`z = 0`, i.e., for which the sequence
+:math:`f_{c}(0)`, :math:`f_{c}(f_{c}(0))`, etc., remains bounded in
+absolute value `[Wikipedia]
+`__. To plot the figure
+on the left, I used a regular imshow with shading and normalized
+recounts that is explained on this post `Smooth Shading for the
+Mandelbrot Exterior
+`__. The script is
+also present in the matplotlib gallery which I contributed some years
+ago.
+
+**Sources:** :source:`showcases/mandelbrot.py`
+
+
+:raw-latex:`\fullpagefigure{showcases/recursive-voronoi.pdf}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Recursive Voronoi}`
+
+This **recursive Voronoi set** has been quite painful to design
+because it requires some quite precise settings to obtain what I think
+is a beautiful result. These settings are the placement of random
+points with good visual properties and for that, I use the `Fast
+Poisson Disk Sampling
+`__
+by Robert Bridson which is simple and fast. I also use quite
+extensively the shapely library to clip he different polygons and I
+discovered in the meantime how to draw random points inside a
+polygon. Finally, I played with lines thickness, polygons color and
+transparency to achieve this result, involving 5 levels of
+recursion. On my computer, it takes around 1 minute to compute.
+
+**Sources:** :source:`showcases/recursive-voronoi.py`
+
+:raw-latex:`\fullpagefigure{showcases/elevation.png}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{3D Heightmap}`
+
+A **3D heightmap** of Mount St Helens after it exploded. This has been
+made with my `experimental 3D axis
+`__. Nothing really
+complicated here, just a bit slow because it needs to sort a bunch of
+triangles.
+
+
+:raw-latex:`\fullpagefigure{showcases/mosaic.pdf}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Voronoi mosaic}`
+
+This **Voronoi mosaic** is based on blue noise distribution where each
+Voronoi cell has been painted according to the color of the center of
+the Voronoi cell in the original image. This results in a cheap
+stained glass window effect.
+
+**Sources:** :source:`showcases/mosaic.py`
+
+
+:raw-latex:`\fullpagefigure{showcases/text-shadow.png}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Text shadow}`
+
+This **shadowed text** is harder to design than it seems. I started
+from a text path object and iterated over the segments composing the
+path in order to create sheared rectangles that constitute the shadow. To
+make the shadow disappear in the background, I created an image with a
+vertical gradient using semi-transparent color (fully transparent on
+top and fully opaque on the bottom). This results in a nice fading
+shadow effect.
+
+**Sources:** :source:`showcases/text-shadow.py`
+
+
+:raw-latex:`\fullpagefigure{showcases/text-spiral.pdf}`
+:raw-latex:`\phantomsection \addcontentsline{toc}{chapter}{Text spiral}`
+
+
+This **spiral text** has been made using an `Archimedean spiral
+`__ (:math:`r =
+a + b\theta`) that guarantees a constant speed along a line that
+rotates with constant angular velocity. Said differently, successive
+turnings of the spiral have a constant separation distance. Starting
+from a very long text path representing some of the decimals of pi
+(using the `mpmath `__
+library), it's then only a matter of transforming the vertices to
+follow the spiral.
+
+**Sources:** :source:`showcases/text-spiral.py`
diff --git a/rst/threed.rst b/rst/threed.rst
new file mode 100644
index 0000000..a2e2793
--- /dev/null
+++ b/rst/threed.rst
@@ -0,0 +1,364 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-3D:
+
+Going 3D
+========
+
+Matplotlib has a really nice `3D interface
+`_ with
+many capabilities (and some limitations) that is quite popular among
+users. Yet, 3D is still considered to be some kind of black magic for
+some users (or maybe for the majority of users). I would thus like to
+explain in this chapter that 3D rendering is really easy once you've
+understood a few concepts. To demonstrate that, we'll render the bunny
+on figure above with 60 lines of Python and one matplotlib call. That
+is, without using the 3D axis.
+
+
+.. figure:: threed/bunny.pdf
+ :width: 100%
+
+ Stanford bunny.
+ (sources: :source:`threed/bunny.py`).
+ :label:`figure-bunny`
+
+
+Loading the bunny
+-----------------
+
+First things first, we need to load our model. We'll use a simplified
+version of the `Stanford bunny
+`_. The file uses the
+`wavefront format
+`_ which is one of
+the simplest format, so let's make a very simple (but error-prone)
+loader that will just do the job for this specific model. Else, you
+can use the `meshio library `__
+object that allow to render efficiently a collection of non-regular
+polygons. Since, we want to render a bunch of triangles, this is a
+perfect match. So let's first extract the triangles and get rid of the
+`z` coordinate:
+
+.. code:: python
+
+ T = V[F][...,:2]
+
+We can now render it:
+
+.. code:: python
+
+ fig = plt.figure(figsize=(6,6))
+ ax = fig.add_axes([0,0,1,1], xlim=[-1,+1], ylim=[-1,+1],
+ aspect=1, frameon=False)
+ collection = PolyCollection(T, closed=True, linewidth=0.1,
+ facecolor="None", edgecolor="black")
+ ax.add_collection(collection)
+ plt.show()
+
+Result is shown on figure :ref:`figure-bunny-1`.
+
+.. figure:: threed/bunny-1.pdf
+ :width: 50%
+
+ Stanford bunny without any transformation.
+ (sources: :source:`threed/bunny-1.py`).
+ :label:`figure-bunny-1`
+
+
+Perspective Projection
+----------------------
+
+The rendering we've just made is actually an `orthographic projection
+`_ while the
+previous bunny uses a `perspective projection
+`_:
+
+.. figure:: threed/projection.pdf
+ :width: 100%
+
+ Orthographic and perspective projections.
+ :label:`figure-projections`
+
+
+In both cases, the proper way of defining a projection is first to
+define a viewing volume, that is, the volume in the 3d space we want
+to render on the scree. To do that, we need to consider 6 clipping
+planes (left, right, top, bottom, far, near) that enclose the viewing
+volume (frustum) relatively to the camera. If we define a camera
+position and a viewing direction, each plane can be described by a
+single scalar. Once we have this viewing volume, we can project onto
+the screen using either the orthographic or the perspective
+projection.
+
+Fortunately for us, these projections are quite well known and can be
+expressed using 4x4 matrices:
+
+.. code:: python
+
+ def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+ def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5*radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+For the perspective projection, we also need to specify the aperture
+angle that (more or less) sets the size of the near plane relatively
+to the far plane. Consequently, for high apertures, you'll get a lot
+of "deformations".
+
+However, if you look at the two functions above, you'll realize they
+return 4x4 matrices while our coordinates are 3d. How to use these
+matrices then ? The answer is `homogeneous coordinates
+`_. To make a
+long story short, homogeneous coordinates are best to deal with
+transformation and projections in 3D. In our case, because we're
+dealing with vertices (and not vectors), we only need to add 1 as the
+fourth coordinates (w) to all our vertices. Then we can apply the
+perspective transformation using the dot product.
+
+.. code:: python
+
+ V = np.c_[V, np.ones(len(V))] @ perspective(25,1,1,100).T
+
+
+Last step, we need to re-normalize the homogeneous coordinates. This
+means we divide each transformed vertices with the last component (w)
+such as to always have w=1 for each vertices.
+
+.. code:: python
+
+ V /= V[:,3].reshape(-1,1)
+
+
+Now we can check the result on figure :ref:`figure-bunny-2` that looks totally wrong.
+
+.. figure:: threed/bunny-2.pdf
+ :width: 50%
+
+ Wrong rendering when camera is inside.
+ (sources: :source:`threed/bunny-2.py`).
+ :label:`figure-bunny-2`
+
+
+It looks wrong because the camera is actually inside the bunny. To
+have a proper rendering, we need to move the bunny away from the
+camera or to move the camera away from the bunny. Let's do the
+later. The camera is currently positioned at (0,0,0) and looking up in
+the z direction (because of the frustum transformation). We thus need
+to move the camera away a little bit in the z negative direction and
+before the perspective transformation.
+
+.. code:: python
+
+ V = V - (0,0,3.5)
+ V = np.c_[V, np.ones(len(V))] @ perspective(25,1,1,100).T
+ V /= V[:,3].reshape(-1,1)
+
+
+The corrected output is shown on figure :ref:`figure-bunny-3`.
+
+.. figure:: threed/bunny-3.pdf
+ :width: 50%
+
+ Corrected rendering with camera away.
+ (sources: :source:`threed/bunny-3.py`).
+ :label:`figure-bunny-3`
+
+
+Model, view, projection (MVP)
+-----------------------------
+
+It might be not obvious, but the last rendering is actually a perspective
+transformation. To make it more obvious, we'll rotate the bunny around. To do
+that, we need some rotation matrices (4x4) and we can as well define the
+translation matrix in the meantime:
+
+.. code:: python
+
+ def translate(x, y, z):
+ return np.array([[1, 0, 0, x],
+ [0, 1, 0, y],
+ [0, 0, 1, z],
+ [0, 0, 0, 1]], dtype=float)
+
+ def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array([[1, 0, 0, 0],
+ [0, c, -s, 0],
+ [0, s, c, 0],
+ [0, 0, 0, 1]], dtype=float)
+
+ def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array([[ c, 0, s, 0],
+ [ 0, 1, 0, 0],
+ [-s, 0, c, 0],
+ [ 0, 0, 0, 1]], dtype=float)
+
+We'll now decompose the transformations we want to apply in term of model
+(local transformations), view (global transformations) and projection such that
+we can compute a global MVP matrix that will do everything at once:
+
+.. code:: python
+
+ model = xrotate(20) @ yrotate(45)
+ view = translate(0,0,-3.5)
+ proj = perspective(25, 1, 1, 100)
+ MVP = proj @ view @ model
+
+and we now write:
+
+.. code:: python
+
+ V = np.c_[V, np.ones(len(V))] @ MVP.T
+ V /= V[:,3].reshape(-1,1)
+
+You should obtain results shown on figure :ref:`figure-bunny-4`.
+
+.. figure:: threed/bunny-4.pdf
+ :width: 50%
+
+ Rotated and translated bunny.
+ (sources: :source:`threed/bunny-4.py`).
+ :label:`figure-bunny-4`
+
+Let's now play a bit with the aperture such that you can see the
+difference (see figure :ref:`figure-bunny-5`). Note that we also need
+to adapt the distance to the camera in order for the bunnies to have
+the same apparent size
+
+.. figure:: threed/bunny-5.pdf
+ :width: 100%
+
+ Different apertures
+ (sources: :source:`threed/bunny-5.py`).
+ :label:`figure-bunny-5`
+
+
+Depth sorting
+-------------
+
+Let's try now to fill the triangles and see what happens (figure
+:ref:`figure-bunny-6`).
+
+.. figure:: threed/bunny-6.pdf
+ :width: 50%
+
+ Rendering without depth sorting.
+ (sources: :source:`threed/bunny-6.py`).
+ :label:`figure-bunny-6`
+
+As you can see, the result is totally wrong. The problem is that the
+PolyCollection draws the triangles in the order they are given
+while we would like to have them from back to front. This means we
+need to sort them according to their depth. The good news is that we
+already computed this information when we applied the MVP
+transformation. It is stored in the new z coordinates. However, these
+z values are vertices based while we need to sort the triangles. We'll
+thus take the mean z value as being representative of the depth of a
+triangle. If triangles are relatively small and do not intersect, this
+works beautifully:
+
+.. code:: python
+
+ T = V[:,:,:2]
+ Z = -V[:,:,2].mean(axis=1)
+ I = np.argsort(Z)
+ T = T[I,:]
+
+
+And now everything is rendered correctly as shown on figure
+:ref:`figure-bunny-7`.
+
+.. figure:: threed/bunny-7.pdf
+ :width: 50%
+
+ Rendering with depth sorting.
+ (sources: :source:`threed/bunny-7.py`).
+ :label:`figure-bunny-7`
+
+
+Let's add some colors using the depth buffer. We'll color each triangle
+according to it depth. The beauty of the PolyCollection object is that you can
+specify the color of each of the triangle using a numpy array, so let's just do
+that:
+
+.. code:: python
+
+ zmin, zmax = Z.min(), Z.max()
+ Z = (Z-zmin)/(zmax-zmin)
+ C = plt.get_cmap("magma")(Z)
+ I = np.argsort(Z)
+ T, C = T[I,:], C[I,:]
+
+And our final display is show on figure :ref:`figure-bunny-8`.
+
+.. figure:: threed/bunny-8.pdf
+ :width: 100%
+
+ Rendering with depth colors.
+ (sources: :source:`threed/bunny-8.py`).
+ :label:`figure-bunny-8`
+
+The final script (:source:`threed/bunny-8.py`) is 57 lines but
+hardly readable. However, it shows that 3D rendering can be done quite
+easily with matplotlib and a few transformations.
+
+Exercises
+---------
+
+Using the definition of the orthographic projection, try to reproduce
+the figure :ref:`figure-bunnies` that mix perspective (top left) and
+orthographic projections. Each bunny is contained in one axes.
+
+.. figure:: threed/bunnies.pdf
+ :width: 100%
+
+ Stanford bunnies
+ (sources: :source:`threed/bunnies.py`).
+ :label:`figure-bunnies`
diff --git a/rst/typography.rst b/rst/typography.rst
new file mode 100644
index 0000000..341ccce
--- /dev/null
+++ b/rst/typography.rst
@@ -0,0 +1,282 @@
+.. ----------------------------------------------------------------------------
+.. Title: Scientific Visualisation - Python & Matplotlib
+.. Author: Nicolas P. Rougier
+.. License: Creative Commons BY-NC-SA International 4.0
+.. ----------------------------------------------------------------------------
+.. _chap-typography:
+
+Elements of typography
+======================
+
+Typography is *the art of arranging type to make written language legible,
+readable, and appealing when displayed* (Wikipedia). However, for the neophyte,
+typography is mostly apprehended as the juxtaposition of characters displayed on
+the screen while for the expert, typography means typeface, scripts, unicode,
+glyphs, ascender, descender, tracking, hinting, kerning, shaping, weight, slant,
+etc. Typography is actually much more than the mere rendering of glyphs and
+involves many different concepts. If glyph rendering is an important part of the
+rendering pipeline as it will be explained below, it is nonetheless important
+to have a basic understanding of typography. Unfortunately, I cannot write here
+a full course on typography and I advise the interested reader to read
+`Practical Typography `_, `serif
+`_, `monospace
+`_ and `cursive
+`_. The default font stack is
+based on the `DejaVu `_ fonts that
+are based on the `Bitstream Vera
+`_ fonts. DejaVu fonts offer good
+unicode coverage but they come with only two weights (regular and bold) which
+might be a bit limiting and the project seems to have been abandoned
+since 2016. The default cursive font is `Apple Chancery
+`_. Note
+however that these are only the primary default choices and Matplotlib can fall
+back to other typefaces if the defaults are not installed. To check which font is actually used, you can type:
+
+.. code:: python
+
+ from matplotlib.font_manager import findfont, FontProperties
+ for family in ["serif", "sans", "monospace", "cursive"]:
+ font = findfont(FontProperties(family=family))
+ print(family, ":" , os.path.basename(font))
+
+You can also design your own own font stack by choosing a set of alternative
+font families. Figure :ref:`figure-typography-font-stacks` shows some
+alternative font stacks based on the Roboto and Source Pro Family which both
+have serif, sans and monospace typefaces and comes with several weights.
+
+.. figure:: typography/typography-font-stacks.pdf
+ :width: 100%
+
+ Font stack alternatives
+ :label:`figure-typography-font-stacks`
+ (sources: :source:`typography/typography-font-stacks.py`).
+
+This font stack can be used as the default by modifying either the `rc
+`_ file or the
+stylesheet (we'll see that in the section :nameref:`chap-defaults`) but you can also use a specific font face for any textual object such as tick
+labels, legend, figure title, etc. However, for consistency, it's better to
+use the same family of fonts (serif, sans and mono) for the whole figure.
+
+
+Rendering mathematics
+---------------------
+
+The case of mathematical text is slightly more complicated because it requires
+several different fonts possessing all the necessary mathematical symbols and
+there are not so many such fonts. Matplotlib offers five different families,
+namely `DejaVu `_ (sans and serif),
+`Styx `_ (sans and serif) and
+`computer modern `_:
+
+.. figure:: typography/typography-math-stacks.pdf
+ :width: 100%
+
+ Mathematics font stacks.
+ :label:`figure-typography-math-stacks`
+ (sources: :source:`typography/typography-math-stacks.py`).
+
+Matplotlib possesses its own `TeX parser and layout engine
+`_ which is quite capable
+even though it suffers from some imperfections. For comparison, here is the
+same mathematical expression as rendered by LaTeX:
+
+.. math::
+
+ \frac{\pi}{4} = \sum_{k=0}^\infty\frac{(-1)^k}{2k+1}
+
+We can notice some obvious differences (alignment, weights, line widths). If
+this is unacceptable for your case, you still have the option to use the real
+TeX engine by setting the usetex variable:
+
+.. code:: python
+
+ import matplotlib as mpl
+ plt.rcParams.update({"text.usetex": True})
+
+
+A note about size
+-----------------
+
+When you manipulate textual objects you need to specify a size (either
+explicitly or through the defaults) that is expressed in point (pt). In
+matplotlib, a point corresponds to 1/72 inches (0.35mm) (while for LaTeX, a
+point corresponds to 1/72.27 inches). The question is then what does this size
+measure exactly? It corresponds to 1 *em* which is a typographic unit and more or
+less corresponds to a bounding box that can contain any glyphs. No need to say
+more at this point because the important information is that font sizes are
+specified in inches and the apparent size is thus directly linked to the
+resolution of your figure (not the dimension) through the dots per inch (dpi)
+parameter. You can thus define either a very large or tiny figure, and a font with
+size 10 will have the same visual aspects on your screen.
+
+**Exercise** Using different fonts, weights and size, try to reproduce the figure :ref:`figure-tick-labels-variation`.
+
+.. figure:: typography/tick-labels-variation.pdf
+ :width: 100%
+
+ Tick label variations
+ :label:`figure-tick-labels-variation`
+ (sources: :source:`typography/tick-labels-variation.py`).
+
+Legibility
+----------
+
+For a traditional document, text is usually rendered in black against a white
+background that maximizes legibility. The case of scientific visualization is a
+bit different because there are some situations where you cannot control the
+background color since it is part of your results.
+
+
+.. figure:: typography/typography-legibility.pdf
+ :width: 100%
+
+ Typograpy legibility variations.
+ :label:`figure-typography-legibility`
+ (sources: :source:`typography/typography-legibility.py`).
+
+This is especially true if you add text over an image such as shown on figure
+:ref:`figure-typography-legibility`. The first line shows what happens if you
+add white or black text over a random grey image. The result is nearly
+impossible to read unless you zoom in. The second line is a bit better thanks to
+the weight of the font that has been made heavier but the text remains difficult
+to read. On the third line, I added a semi-transparent background to enhance
+contrast. This dramatically improves legibility but the result is not really
+aesthetic and hides a lot of data in the meantime. The best option is shown on
+the last line where I outlined the font with a thin border. Here the text is
+legible, aesthetic and does not hide too much data.
+
+**Exercise** Try to reproduce exactly the figure :ref:`figure-text-outline`
+which uses the `Pacifico `_ font
+family. Colors come from the magma colormap. Make sure to use different outline
+widths to get the thin black line between each color.
+
+.. figure:: typography/text-outline.pdf
+ :width: 100%
+
+ Text with far too many outlines.
+ :label:`figure-text-outline`
+ (sources: :source:`typography/text-outline.py`).
+
+
+At this point, it is important to understand that Matplotlib offers two types of
+textual object. The first and most commonly used is the regular `Text
+`__ that is used
+for labels, titles or annotations. It cannot be heavily transformed and most of
+the time, the text is rendered following a single direction (e.g.horizontal or
+vertical) even though it can be freely rotated. There exists however another
+type of textual object which is the `TextPath
+`__. Usage is very simple:
+
+.. code:: python
+
+ from matplotlib.textpath import TextPath
+ from matplotlib.patches import PathPatch
+ path = TextPath((0,0), "ABC", size=12)
+
+The result is a path object that can be inserted in a figure
+
+.. code:: python
+
+ patch = PathPatch(path)
+ ax.add_artist(patch)
+
+What is really interesting with such path objects is that it can now be
+transformed at the level of individual vertices composing a glyph as shown on
+figure :ref:`figure-typography-text-path`.
+
+.. figure:: typography/typography-text-path.pdf
+ :width: 100%
+
+ Better contour labels using text path.
+ :label:`figure-typography-text-path`
+ (sources: :source:`typography/typography-text-path.py`).
+
+In this example, I replaced the regular contour labels with text path objects
+that follow the path. It requires some computations but not that much
+actually. The results is aesthetically better to me but it must be used
+wisely. If your contour lines are too small or possesses sharp turns, it will
+make the text unreadable.
+
+.. figure:: typography/projection-3D-gaussian.pdf
+ :width: 70%
+
+ Example of 3D text paths.
+ :label:`figure-projection-3d-gaussian`
+ (sources: :source:`typography/projection-3d-gaussian.py`).
+
+Another interesting usage of text path is the case of 3D projection as
+illustrated on figure :ref:`figure-projection-3d-gaussian`. On this figure, I
+took advantage of the `3D text API
+`_ to orient and project
+tick labels and axes titles. Note that such projection is fine a long as the
+figure is properly oriented. If you rotate, text might be difficult to read and
+this the reason why the default for 3d projection is to have text that always
+face the camera, ensuring legibility.
+
+
+**Exercise** Try to reproduce figures :ref:`figure-text-starwars`. A simple
+*compression* on X vertices depending on the Y level should work. Vertices
+of a path can be accessed with `path.vertices`.
+
+.. figure:: typography/text-starwars.pdf
+ :width: 100%
+
+ In a far distant galaxy.
+ :label:`figure-text-starwars` (sources: :source:`typography/text-starwars.py`).
+
+
+..
+ .. figure:: typography/text-spiral.pdf
+ :width: 50%
+
+ Text shaped along a spiral
+ :label:`figure-text-spiral` (sources: :source:`typography/text-spiral.py`).
+
+
+
diff --git a/tex/.DS_Store b/tex/.DS_Store
new file mode 100644
index 0000000..5008ddf
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diff --git a/tex/back-cover.pdf b/tex/back-cover.pdf
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diff --git a/tex/back-cover.tex b/tex/back-cover.tex
new file mode 100644
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--- /dev/null
+++ b/tex/back-cover.tex
@@ -0,0 +1,36 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\makeatletter
+\newcommand{\definetrim}[2]{%
+ \define@key{Gin}{#1}[]{\setkeys{Gin}{trim=#2,clip}}%
+}
+\makeatother
+
+\newlength{\coverheight}
+\newlength{\coverwidth}
+\newlength{\bleedwidth}
+\newlength{\spinewidth}
+
+\newlength{\trimleft}
+\newlength{\trimright}
+\newlength{\trimbottom}
+\newlength{\trimtop}
+
+\setlength{\coverheight}{210mm}
+\setlength{\coverwidth}{148mm}
+\setlength{\bleedwidth}{3mm}
+\setlength{\spinewidth}{10.5mm}
+
+\setlength{\trimleft}{\dimexpr \bleedwidth \relax }
+\setlength{\trimright}{\dimexpr \bleedwidth + \coverwidth + \spinewidth \relax }
+\setlength{\trimbottom}{\dimexpr \bleedwidth \relax }
+\setlength{\trimtop}{\dimexpr \bleedwidth \relax }
+\definetrim{front}{\trimleft{} \trimbottom{} \trimright{} \trimtop{}}
+
+\begin{document}
+\includegraphics[front,height=216mm]{softcover-cover.pdf}
+\end{document}
+
+% Largeur: 371.7 - 210.7 - 22
+% Hauteur: 260 mm
diff --git a/tex/book.bbl b/tex/book.bbl
new file mode 100644
index 0000000..a6c8824
--- /dev/null
+++ b/tex/book.bbl
@@ -0,0 +1,290 @@
+% $ biblatex auxiliary file $
+% $ biblatex bbl format version 3.1 $
+% Do not modify the above lines!
+%
+% This is an auxiliary file used by the 'biblatex' package.
+% This file may safely be deleted. It will be recreated by
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+%
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+ {\@latex@error
+ {Missing 'biblatex' package}
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+ \aftergroup\endinput}
+ {}
+\endgroup
+
+
+\refsection{0}
+ \datalist[entry]{nyt/global//global/global}
+ \entry{Butterick:2013}{book}{}
+ \name{author}{1}{}{%
+ {{un=0,uniquepart=base,hash=15906aa9ea8f28e899c6d015206420ca}{%
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+ \field{labeltitlesource}{title}
+ \field{abstract}{$\bullet$$\bullet$$\bullet$ This open access book will make you a better typographer. I’m not here to tell you that typography is more important than the substance of your writing. It’s not. But typography can enhance your writing. Typography can create a better first impression. Typography can reinforce your key points. Typography can extend reader attention.}
+ \field{title}{Practical Typography}
+ \field{year}{2013}
+ \true{nocite}
+ \verb{urlraw}
+ \verb https://practicaltypography.com/
+ \endverb
+ \verb{url}
+ \verb https://practicaltypography.com/
+ \endverb
+ \endentry
+ \entry{Hunter:2007}{article}{}
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+ \field{journaltitle}{Computing in Science {\&} Engineering}
+ \field{number}{3}
+ \field{title}{Matplotlib: A 2D Graphics Environment}
+ \field{volume}{9}
+ \field{year}{2007}
+ \true{nocite}
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+ \verb{doi}
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+ \field{abstract}{$\bullet$$\bullet$ There are a lot of techniques that you don't find in books and such techniques are mostly learned through experience. The goal of this open access book is to explain some of these techniques and to provide an opportunity for making this experience in the process.}
+ \field{title}{{From Python to Numpy}}
+ \field{year}{2017}
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+ \field{volume}{12}
+ \field{year}{2018}
+ \true{nocite}
+ \verb{doi}
+ \verb 10.3389/fninf.2018.00012
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+ \verb{url}
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+ \entry{Rougier:2014}{article}{}
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+ giveni={N\bibinitperiod\bibinitdelim P\bibinitperiod},
+ givenun=0}}%
+ {{un=0,uniquepart=base,hash=4778156ee049a7c63b5dfde9beea6d36}{%
+ family={Droettboom},
+ familyi={D\bibinitperiod},
+ given={Michael},
+ giveni={M\bibinitperiod},
+ givenun=0}}%
+ {{un=0,uniquepart=base,hash=e8f011b361a44c99e0b3cd105b7553dc}{%
+ family={Bourne},
+ familyi={B\bibinitperiod},
+ given={Philip\bibnamedelima E.},
+ giveni={P\bibinitperiod\bibinitdelim E\bibinitperiod},
+ givenun=0}}%
+ }
+ \list{publisher}{1}{%
+ {Public Library of Science ({PLoS})}%
+ }
+ \strng{namehash}{7a77fd6d2261e11e7d0213e48ac6d76b}
+ \strng{fullhash}{7a77fd6d2261e11e7d0213e48ac6d76b}
+ \strng{bibnamehash}{7a77fd6d2261e11e7d0213e48ac6d76b}
+ \strng{authorbibnamehash}{7a77fd6d2261e11e7d0213e48ac6d76b}
+ \strng{authornamehash}{7a77fd6d2261e11e7d0213e48ac6d76b}
+ \strng{authorfullhash}{7a77fd6d2261e11e7d0213e48ac6d76b}
+ \field{sortinit}{R}
+ \field{sortinithash}{5e1c39a9d46ffb6bebd8f801023a9486}
+ \field{extradatescope}{labelyear}
+ \field{labeldatesource}{year}
+ \field{labelnamesource}{author}
+ \field{labeltitlesource}{title}
+ \field{abstract}{$\bullet$$\bullet$$\bullet$ This classic and popular article provides a basic set of rules to improve figure design and explain some of the common pitfalls.}
+ \field{journaltitle}{{PLoS} Computational Biology}
+ \field{number}{9}
+ \field{title}{{Ten Simple Rules for Better Figures}}
+ \field{volume}{10}
+ \field{year}{2014}
+ \true{nocite}
+ \verb{doi}
+ \verb 10.1371/journal.pcbi.1003833
+ \endverb
+ \endentry
+ \entry{Vanderplas:2016}{book}{}
+ \name{author}{1}{}{%
+ {{un=0,uniquepart=base,hash=0fd9a0e34f1b2adda41357c948d14986}{%
+ family={VanderPlas},
+ familyi={V\bibinitperiod},
+ given={Jake},
+ giveni={J\bibinitperiod},
+ givenun=0}}%
+ }
+ \list{publisher}{1}{%
+ {O'Reilly}%
+ }
+ \strng{namehash}{0fd9a0e34f1b2adda41357c948d14986}
+ \strng{fullhash}{0fd9a0e34f1b2adda41357c948d14986}
+ \strng{bibnamehash}{0fd9a0e34f1b2adda41357c948d14986}
+ \strng{authorbibnamehash}{0fd9a0e34f1b2adda41357c948d14986}
+ \strng{authornamehash}{0fd9a0e34f1b2adda41357c948d14986}
+ \strng{authorfullhash}{0fd9a0e34f1b2adda41357c948d14986}
+ \field{sortinit}{V}
+ \field{sortinithash}{afb52128e5b4dc4b843768c0113d673b}
+ \field{extradatescope}{labelyear}
+ \field{labeldatesource}{year}
+ \field{labelnamesource}{author}
+ \field{labeltitlesource}{title}
+ \field{abstract}{$\bullet$$\bullet$ This book is meant to help Python users learn to use Python's data science stack–libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools–to effectively store, manipulate, and gain insight from data.}
+ \field{title}{{Python Data Science Handbook}}
+ \field{year}{2016}
+ \true{nocite}
+ \verb{urlraw}
+ \verb https://jakevdp.github.io/PythonDataScienceHandbook/index.html
+ \endverb
+ \verb{url}
+ \verb https://jakevdp.github.io/PythonDataScienceHandbook/index.html
+ \endverb
+ \endentry
+ \entry{Wilke:2019}{book}{}
+ \name{author}{1}{}{%
+ {{un=0,uniquepart=base,hash=8e86aa1915156f8016600d977acef303}{%
+ family={Wilke},
+ familyi={W\bibinitperiod},
+ given={Claus\bibnamedelima O.},
+ giveni={C\bibinitperiod\bibinitdelim O\bibinitperiod},
+ givenun=0}}%
+ }
+ \list{publisher}{1}{%
+ {O'Reilly}%
+ }
+ \strng{namehash}{8e86aa1915156f8016600d977acef303}
+ \strng{fullhash}{8e86aa1915156f8016600d977acef303}
+ \strng{bibnamehash}{8e86aa1915156f8016600d977acef303}
+ \strng{authorbibnamehash}{8e86aa1915156f8016600d977acef303}
+ \strng{authornamehash}{8e86aa1915156f8016600d977acef303}
+ \strng{authorfullhash}{8e86aa1915156f8016600d977acef303}
+ \field{sortinit}{W}
+ \field{sortinithash}{4315d78024d0cea9b57a0c6f0e35ed0d}
+ \field{extradatescope}{labelyear}
+ \field{labeldatesource}{year}
+ \field{labelnamesource}{author}
+ \field{labeltitlesource}{title}
+ \field{abstract}{$\bullet$$\bullet$ This open access book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures.}
+ \field{title}{Fundamentals of Data Visualization}
+ \field{year}{2019}
+ \true{nocite}
+ \verb{urlraw}
+ \verb https://serialmentor.com/dataviz/
+ \endverb
+ \verb{url}
+ \verb https://serialmentor.com/dataviz/
+ \endverb
+ \endentry
+ \enddatalist
+\endrefsection
+\endinput
+
diff --git a/tex/book.bib b/tex/book.bib
new file mode 100644
index 0000000..192f7df
--- /dev/null
+++ b/tex/book.bib
@@ -0,0 +1,93 @@
+
+@Book{Butterick:2013,
+ author = {Butterick, Matthew},
+ title = {Practical Typography},
+ publisher = {Online},
+ year = {2013},
+ url = {https://practicaltypography.com/},
+ abstract = {$\bullet$$\bullet$$\bullet$ This open access book will make
+ you a better typographer. I’m not here to tell you that
+ typography is more important than the substance of your
+ writing. It’s not. But typography can enhance your writing.
+ Typography can create a better first impression. Typography
+ can reinforce your key points. Typography can extend reader
+ attention.},
+}
+
+@Book{Wilke:2019,
+ author = {Wilke, Claus O.},
+ title = {Fundamentals of Data Visualization},
+ publisher = {O'Reilly},
+ year = {2019},
+ url = {https://serialmentor.com/dataviz/},
+ abstract = {$\bullet$$\bullet$ This open access book takes you through
+ many commonly encountered visualization problems, and it
+ provides guidelines on how to turn large datasets into clear
+ and compelling figures.},
+}
+
+@article{Rougier:2014,
+ doi = {10.1371/journal.pcbi.1003833},
+ year = 2014,
+ publisher = {Public Library of Science ({PLoS})},
+ volume = 10,
+ number = 9,
+ author = {Nicolas P. Rougier and Michael Droettboom and Philip
+ E. Bourne},
+ title = {{Ten Simple Rules for Better Figures}},
+ journal = {{PLoS} Computational Biology},
+ abstract = {$\bullet$$\bullet$$\bullet$ This classic and popular article
+ provides a basic set of rules to improve figure design and
+ explain some of the common pitfalls.}
+}
+
+
+@Book{Rougier:2017,
+ author = {Nicolas P. Rougier},
+ title = {{From Python to Numpy}},
+ publisher = {Zenodo},
+ year = {2017},
+ url = {https://www.labri.fr/perso/nrougier/from-python-to-numpy/},
+ abstract = {$\bullet$$\bullet$ There are a lot of techniques that you
+ don't find in books and such techniques are mostly learned
+ through experience. The goal of this open access book is to
+ explain some of these techniques and to provide an
+ opportunity for making this experience in the process.}
+}
+
+@Book{Vanderplas:2016,
+ author = {Jake VanderPlas},
+ title = {{Python Data Science Handbook}},
+ publisher = {O'Reilly},
+ year = {2016},
+ url = {https://jakevdp.github.io/PythonDataScienceHandbook/index.html},
+ abstract = {$\bullet$$\bullet$ This book is meant to help Python users
+ learn to use Python's data science stack–libraries such as
+ IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related
+ tools–to effectively store, manipulate, and gain insight
+ from data.}
+}
+
+@article{Hunter:2007,
+ author = {John D. Hunter},
+ title = {Matplotlib: A 2D Graphics Environment},
+ journal = {Computing in Science {\&} Engineering},
+ year = {2007},
+ publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
+ volume = {9},
+ number = {3},
+ pages = {90--95},
+ doi = {10.1109/mcse.2007.55},
+}
+
+
+@article{Rougier:2018,
+ doi = {10.3389/fninf.2018.00012},
+ url = {https://doi.org/10.3389/fninf.2018.00012},
+ year = {2018},
+ month = mar,
+ publisher = {Frontiers Media {SA}},
+ volume = {12},
+ author = {Nicolas P. Rougier},
+ title = {A Density-Driven Method for the Placement of Biological Cells Over Two-Dimensional Manifolds}
+}
\ No newline at end of file
diff --git a/tex/book.tex b/tex/book.tex
new file mode 100644
index 0000000..361389b
--- /dev/null
+++ b/tex/book.tex
@@ -0,0 +1,671 @@
+% \documentclass[10pt,a5paper]{book}
+\documentclass[10pt]{book}
+
+
+% --- Page layout -------------------------------------------------------------
+\usepackage[a5paper,includeheadfoot]{geometry}
+% \usepackage[pass,paperwidth=148mm,paperheight=210mm,includeheadfoot]{geometry}
+\geometry{right=15mm, left=20mm, top=15mm, bottom=15mm}
+
+\usepackage{xcolor}
+
+% --- ISBN --------------------------------------------------------------------
+\usepackage[ISBN=978-2-9579901-0-8,SC2]{ean13isbn}
+
+
+%% --- Hardcover book ---------------------------------------------------------
+\ifdefined\hardcover
+\usepackage[height=21.634truecm, width=15.434truecm, axes, pdftex, center]{crop}
+\definecolor{codecolor}{cmyk}{0, 0, 0, 1.0}
+\definecolor{stringcolor}{cmyk}{0, 0, 0, 0.8}
+\definecolor{commentcolor}{cmyk}{0, 0, 0, 0.5}
+\definecolor{admcolor}{cmyk}{0, 0, 0, 0.1}
+\definecolor{citecolor}{cmyk}{0, 0, 0, 1.0}
+\definecolor{linkcolor}{cmyk}{0, 0, 0, 1.0}
+\definecolor{urlcolor}{cmyk}{0, 0, 0, 0.8}
+
+\else
+%% --- Softcover book ---------------------------------------------------------
+\ifdefined\softcover
+\usepackage[height=21.6truecm, width=15.4truecm, axes, pdftex, center]{crop}
+\definecolor{codecolor}{cmyk}{0, 0, 0, 1.0}
+\definecolor{stringcolor}{cmyk}{0, 0, 0, 0.8}
+\definecolor{commentcolor}{cmyk}{0, 0, 0, 0.5}
+\definecolor{admcolor}{cmyk}{0, 0, 0, 0.1}
+\definecolor{citecolor}{cmyk}{0, 0, 0, 1.0}
+\definecolor{linkcolor}{cmyk}{0, 0, 0, 1.0}
+\definecolor{urlcolor}{cmyk}{0, 0, 0, 0.8}
+
+\else
+%% --- PDF book ---------------------------------------------------------
+\definecolor{codecolor}{HTML}{311b92} %% Material Deep purple 900
+\definecolor{commentcolor}{HTML}{607d8b} %% Material Blue grey 500
+\definecolor{stringcolor}{HTML}{e65100} %% Material Orange 900
+\definecolor{admcolor}{HTML}{fffde7} %% Material Yellow 50
+\definecolor{citecolor}{HTML}{37474f} %% Material Blue grey 800
+\definecolor{linkcolor}{HTML}{37474f} %% Material Blue grey 800
+\definecolor{urlcolor}{HTML}{37474f} %% Material Blue grey 800
+\fi
+\fi
+
+%% \DeclareGraphicsExtensions{.png,.pdf}
+
+
+
+% --- Specialized packages ----------------------------------------------------
+% \usepackage{ulem} %% Strike-through font (\sout and \xout)
+\usepackage{enumitem} %% Fine control over itemize/enumerate/description
+\usepackage{multicol} %% Local multi-columns
+\usepackage{pdfpages} %% For inclusion of PDF (\includepdf)
+\usepackage{hologo} %% Latex logos
+\usepackage{float} %% Fine control of floats
+\usepackage{tikz} %% Nice inline graphics
+\usepackage{alltt} %% tt mode
+\usepackage{parskip} %% tt mode
+\usepackage{amsmath} %% math (equation)
+\usepackage{hologo} %% Latex logos
+\usepackage{nicefrac} %% Nice (simple) fractions in math mode
+
+
+
+% --- English stuff -----------------------------------------------------------
+\usepackage[english]{babel}
+\usepackage{xspace}
+% \usepackage{csquotes}
+
+% --- Graphics ----------------------------------------------------------------
+\usepackage{graphicx}
+\graphicspath{{../figures/}{../cover/}}
+\floatplacement{figure}{htb!} % (h)ere (t)op (b)ottom (p)age
+
+% Reduce Space Around Floats (Algorithm, Figures, etc) in Latex
+\setlength\floatsep{1.\baselineskip plus 3pt minus 2pt}
+\setlength\textfloatsep{1.\baselineskip plus 3pt minus 2pt}
+\setlength\intextsep{1.\baselineskip plus 3pt minus 2 pt}
+% The First Line is for length between two adjacent floats
+% The Second Line - for floats on top and bottom of text only
+% For floats at top - length between float and text below it
+% For floats at bottom - length between float and text above it
+% The Third Line- for floats in the middle of text only -
+% length between text above it, and text below it.
+
+
+% --- Header & footer ---------------------------------------------------------
+\usepackage{fancyhdr}
+\fancyhf{}
+\pagestyle{fancy}
+\renewcommand{\chaptermark}[1]{\markboth{#1}{}}
+\fancyhead[LE,RO]{\footnotesize\bfseries\sffamily \thepage}
+\fancyhead[RE]{\footnotesize\bfseries\sffamily \chaptername~\thechapter}
+\fancyhead[LO]{\footnotesize\bfseries\sffamily \leftmark}
+\fancyfoot{}
+\renewcommand{\headrulewidth}{0pt}
+
+\fancypagestyle{frontmatter}{
+ \fancyhead[LE,RO]{\footnotesize\bfseries\sffamily \thepage}
+ \fancyhead[RE]{\footnotesize\bfseries\sffamily \leftmark}
+ \fancyhead[LO]{\footnotesize\bfseries\sffamily \leftmark}
+ \fancyfoot{}
+ \renewcommand{\headrulewidth}{0pt}
+}
+
+\fancypagestyle{backmatter}{
+ \fancyhead[LE,RO]{\footnotesize\bfseries\sffamily \thepage}
+ \fancyhead[RE]{\footnotesize\bfseries\sffamily \leftmark}
+ \fancyhead[LO]{\footnotesize\bfseries\sffamily \leftmark}
+ \fancyfoot{}
+ \renewcommand{\headrulewidth}{0pt}
+}
+
+\fancypagestyle{mainmatter}{
+ \fancyhead[LE,RO]{\footnotesize\bfseries\sffamily \thepage}
+ \fancyhead[RE]{\footnotesize\bfseries\sffamily \chaptername~\thechapter}
+ \fancyhead[LO]{\footnotesize\bfseries\sffamily \leftmark}
+ \fancyfoot{}
+ \renewcommand{\headrulewidth}{0pt}
+}
+
+\fancypagestyle{plain}{
+ \renewcommand{\headrulewidth}{0pt}
+ \fancyhf{}
+}
+\usepackage{emptypage} % No header & footer on empty pages
+
+
+% --- Fonts -------------------------------------------------------------------
+\usepackage{fontspec}
+\usepackage[babel=true]{microtype}
+%\defaultfontfeatures{Ligatures=TeX}
+\setmainfont{Source Serif Pro}[
+ Path = ../fonts/source-serif-pro/SourceSerifPro-, Extension = .otf,
+ Ligatures=TeX,
+ UprightFont= Light,
+ ItalicFont = LightIt,
+ BoldFont = Regular,
+ BoldItalicFont = It ]
+\setsansfont{Roboto}[
+ Ligatures=TeX,
+ Path = ../fonts/roboto/Roboto-, Extension = .ttf,
+ UprightFont = Light,
+ ItalicFont = LightItalic,
+ BoldFont = Regular ]
+\setmonofont{Source Code Pro}[
+ Path = ../fonts/source-code-pro/SourceCodePro-, Extension = .otf,
+ UprightFont = Light,
+ BoldFont = Regular ]
+\newfontfamily\RobotoCon{Roboto Condensed}[
+ Path = ../fonts/roboto/RobotoCondensed-, Extension = .ttf,
+ Ligatures=TeX,
+ UprightFont = Regular,
+ ItalicFont = Italic,
+ BoldFont = Bold ]
+\newfontfamily\Roboto{Roboto}[
+ Path = ../fonts/roboto/Roboto-, Extension = .ttf,
+ Ligatures=TeX,
+ UprightFont = Regular,
+ ItalicFont = Italic,
+ BoldFont = Black ]
+
+
+% --- URL, href and colors ----------------------------------------------------
+% \usepackage{xcolor}
+%% \definecolor{darkred}{HTML}{CF232B}
+%% \definecolor{darkblue}{HTML}{000099}
+
+%% See https://tex.stackexchange.com/questions/75451/xcolor-black-isnt-black-enough
+% \definecolor{darkgray}{cmyk}{0,0,0,.8} %%{HTML}{555555}
+
+%% \definecolor{lightgray}{HTML}{cccccc}
+%\colorlet{citecolor}{black}
+%\colorlet{linkcolor}{black}
+% \colorlet{urlcolor}{darkblue}
+%\colorlet{urlcolor}{darkgray}
+
+\usepackage[
+ %% pdfusetitle,
+ pdfauthor={Nicolas P. Rougier},
+ pdftitle={Scientific Visualization: Python & Matplotlib},
+ pdfsubject={Scientific Visualization},
+ pdfkeywords={Python, Matplotlib, Open Science, 42},
+ %%
+ bookmarks=true,
+ breaklinks=true,
+ pdfborder={0 0 0},
+ citecolor=citecolor,
+ linkcolor=linkcolor,
+ urlcolor=urlcolor,
+ colorlinks=true,
+ linktocpage=false,
+ hyperindex=true,
+ colorlinks=true,
+ linktocpage=false,
+ linkbordercolor=white]{hyperref}
+% \urlstyle{sf}
+\usepackage{bookmark}
+
+\newsavebox{\ExternalLinkIcon}
+
+\begin{lrbox}{\ExternalLinkIcon}
+ \begin{tikzpicture}[x=1.2ex, y=1.2ex, baseline=-0.5ex, scale=0.75]
+ \begin{scope}[x=1ex, y=1ex]
+ \clip (-0.1,-0.1)
+ --++ (-0, 1.2)
+ --++ (0.6, 0)
+ --++ (0, -0.6)
+ --++ (0.6, 0)
+ --++ (0, -1);
+ \path[draw=urlcolor, line width = 0.5,
+ rounded corners=0.5] (0,0) rectangle (1,1);
+ \end{scope}
+ \path[draw=urlcolor, line width = 0.5] (0.5, 0.5) -- (1, 1);
+ \path[draw=urlcolor, line width = 0.5] (0.6, 1) -- (1, 1) -- (1, 0.6);
+ \end{tikzpicture}% <--- important!
+\end{lrbox}
+
+
+\makeatletter
+\def\cleartoevenpage{\clearpage\if@twoside \ifodd\c@page
+ \hbox{}\newpage\if@twocolumn\hbox{}\newpage\fi\fi\fi}
+\makeatother
+
+\newcommand{\fullpagefigure}[1]{
+ \cleartoevenpage
+ %% \begin{tikzpicture}[remember picture,overlay, shift={(current page.north west)}]
+ %% \node[anchor=north west, xshift=-3mm, yshift=-23mm,inner sep=0pt]{
+ %% \makebox[154mm][c]{\includegraphics[height=230mm]{#1}}};
+ %% \end{tikzpicture}
+\begin{tikzpicture}[remember picture,overlay, shift={(current page.center)}]
+ \node[anchor=center, xshift=0mm, yshift=0mm,inner sep=0pt]{
+ \includegraphics[height=220mm]{#1}};
+\end{tikzpicture}
+% \begin{figure}[p]
+% \vspace*{-4cm}
+% \makebox[\linewidth]{
+% \includegraphics[height=230mm]{#1}}
+% \end{figure}
+ \clearpage
+ \vspace*{\fill}
+}
+
+\newcommand{\pdfinclude}[2]{
+ \clearpage
+ \thispagestyle{empty}
+ \begin{tikzpicture}[remember picture,overlay, shift={(current page.center)}]
+ \node[anchor=center, xshift=0mm, yshift=0mm,inner sep=0pt]{
+ \includegraphics[height=#1]{#2}};
+ \end{tikzpicture}
+}
+
+% --- Pagination --------------------------------------------------------------
+\def\cleared{\empty}
+\def\cleartorecto{\clearpage\if at twoside \ifodd\c at page\else
+ \hbox{}\thispagestyle{cleared}%
+ \newpage\if at twocolumn\hbox{}\newpage\fi\fi\fi}
+\def\cleartoverso{\clearpage\if at twoside
+ \ifodd\c at page\hbox{}\thispagestyle{cleared}%
+ \newpage\if at twocolumn\hbox{}\newpage\fi\fi\fi}
+\def\clearchapter{\clearpage~\thispagestyle{empty}\mbox{}\cleartorecto}
+
+% --- Listings ----------------------------------------------------------------
+\usepackage{listings}
+
+
+\lstset{%
+ language=Python,
+ basicstyle=\ttfamily\footnotesize\color{codecolor},
+ keywordstyle=\color{codecolor}\bfseries,
+ commentstyle=\color{commentcolor},
+ identifierstyle=\color{codecolor},
+ stringstyle=\color{stringcolor},
+ backgroundcolor=\color{white},
+ numbers=none,
+ upquote=true,
+ numberstyle=\ttfamily,
+ stepnumber=2,
+ showspaces=false,
+ showstringspaces=false,
+ showtabs=false,
+ frame=none,
+ framerule=0.5pt,
+ tabsize=2,
+ rulesep=5em,
+ captionpos=b,
+ breaklines=true,
+ breakatwhitespace=false,
+ framexleftmargin=0em,
+ xleftmargin=0em,
+ framexrightmargin=0em,
+ xrightmargin=0em,
+ aboveskip=10pt,
+ belowskip=0pt,
+}
+
+% --- Bibliography ------------------------------------------------------------
+\usepackage[
+ backend=biber,
+ % style = numeric,
+ style=authoryear,
+ % refsection=chapter,
+ % backref=true,
+ % backrefstyle=three,
+ sorting = nyt,
+ giveninits = true,
+ maxcitenames=3,
+ mincitenames=1,
+ maxbibnames=10,
+ isbn = false,
+ url = false,
+ doi = false,
+ natbib = true]{biblatex}
+\usepackage{bibentry}
+
+% Some space between items
+\setlength\bibitemsep{.5\baselineskip}
+
+% Small font for bib entries
+\renewcommand*{\bibfont}{\small \sffamily}
+\addbibresource{book.bib}
+
+% Make sure rightmark and leftmark are right
+\defbibheading{bibliography}[\bibname]{%
+ \chapter{#1}%
+ \markboth{References}{References}}
+
+% Make bib entry title clickable
+\newcommand{\doiorurl}{%
+ \iffieldundef{doi}
+ {\iffieldundef{url}
+ {}
+ {\strfield{url}}}
+ {http://dx.doi.org/\strfield{doi}}%
+}
+\newcommand{\myhref}[1]{%
+ \ifboolexpr{%
+ test {\ifhyperref}
+ and not test {\iftoggle{bbx:url}}
+ and not test {\iftoggle{bbx:doi}}
+ }
+ {\href{\doiorurl}{#1}}
+ {#1}%
+}
+\DeclareFieldFormat{title}{\myhref{\mkbibemph{#1}}}
+\DeclareFieldFormat
+ [article,inbook,incollection,inproceedings,patent,thesis,unpublished]
+ {title}{\myhref{\mkbibquote{#1\isdot}}}
+
+% Add abstract when available
+\DeclareFieldFormat{abstract}{\par\footnotesize#1}
+\renewbibmacro*{finentry}{\printfield{abstract}\finentry}
+
+
+% --- Captions ----------------------------------------------------------------
+\usepackage[labelsep=newline,singlelinecheck=false]{caption}
+\renewcommand{\captionfont}{\small}
+\renewcommand{\captionlabelfont}{\small\sffamily\bfseries}
+
+
+% --- Style --------------------------------------------------------------------
+%% Default itemize aspect
+\setlist[itemize]{leftmargin=*, noitemsep, topsep=5pt}
+\setlist[description]{font=\bfseries\sffamily,labelindent=0pt, leftmargin=15pt}
+
+%% No indent on paragraph
+\setlength\parindent{0pt}
+
+%% A more versatile quoting environment
+\usepackage[font={small},vskip=5pt,leftmargin=0pt,rightmargin=0pt]{quoting}
+
+
+
+% --- Part, chapter and sections ----------------------------------------------
+\usepackage{titlesec}
+\titleformat{\part}
+ {\large\bfseries\sffamily}{\thepart}{1em}{}
+% this alters "before" spacing (the second length argument) to 0
+\titlespacing*{\part}{0pt}{-15pt}{100pt}
+
+\titleformat{\chapter}
+ {\large\bfseries\sffamily}{\thechapter}{1em}{}
+% this alters "before" spacing (the second length argument) to 0
+\titlespacing*{\chapter}{0pt}{-15pt}{100pt}
+
+\titleformat{name=\chapter,numberless}
+ {\large\bfseries\sffamily}{}{1em}{}
+% this alters "before" spacing (the second length argument) to 0
+\titlespacing*{name=\chapter, numberless}{0pt}{-15pt}{100pt}
+
+\titleformat{\section}
+ {\normalfont\bfseries\sffamily}{}{0em}{}
+\titlespacing*{\section}{0pt}{10pt}{10pt}
+
+
+% --- Table of content --------------------------------------------------------
+% ToC font
+\usepackage{tocloft}
+\renewcommand{\cftpartfont}{\normalfont\sffamily\bfseries}
+\renewcommand{\cftchapfont}{\normalfont\small\sffamily}
+\renewcommand{\cftsecfont}{\normalfont\sffamily}
+\renewcommand{\cftsubsecfont}{\normalfont\sffamily}
+\renewcommand{\cftsubsubsecfont}{\normalfont\sffamily}
+\renewcommand{\cfttoctitlefont}{\large\sffamily\bfseries}
+\cftpagenumbersoff{part}
+\renewcommand\cftpartafterpnum{\vskip 0pt}
+\renewcommand\cftchapafterpnum{\vskip -10pt}
+
+
+% --- Macros ------------------------------------------------------------------
+\newcommand*\cleartoleftpage{%
+ \clearpage
+ \ifodd\value{page}\hbox{}\newpage\fi
+}
+
+
+% --- Docutils ----------------------------------------------------------------
+\usepackage{booktabs}
+\usepackage{longtable,ltcaption,array}
+\newlength{\DUtablewidth} % internal use in tables
+\providecommand*{\DUrole}[2]{{\csname #1\endcsname {#2}}}
+\providecommand*{\DUlegend}[2]{#2}
+\newcommand{\code}[1]{\ttfamily\small\color{darkgray} {#1}}
+\newcommand{\source}[1]{\href{https://github.com/rougier/scientific-visualization-book/blob/master/code/#1}{#1}}
+
+
+
+% class handling for environments (block-level elements)
+% \begin{DUclass}{spam} tries \DUCLASSspam and
+% \end{DUclass}{spam} tries \endDUCLASSspam
+\ifx\DUclass\undefined % poor man's "provideenvironment"
+ \newenvironment{DUclass}[1]%
+ {\def\DocutilsClassFunctionName{#1}% arg cannot be used in end-part of environment.
+ \csname \DocutilsClassFunctionName \endcsname}%
+ {\csname end\DocutilsClassFunctionName \endcsname}%
+\fi
+
+
+%%% Fallback definitions for Docutils-specific commands
+% numeric or symbol footnotes with hyperlinks
+\providecommand*{\DUfootnotemark}[3]{%
+ \raisebox{1em}{\hypertarget{#1}{}}%
+ \hyperlink{#2}{\textsuperscript{#3}}%
+}
+\providecommand{\DUfootnotetext}[4]{%
+ \begingroup%
+ \renewcommand{\thefootnote}{%
+ \protect\raisebox{1em}{\protect\hypertarget{#1}{}}%
+ \protect\hyperlink{#2}{#3}}%
+ \footnotetext{#4}%
+ \endgroup%
+}
+
+\renewenvironment{quote}{}{}
+
+\newenvironment{epigraph}{\thispagestyle{empty}
+ \vspace*{\stretch{1}} % some space at the top
+ \itshape % the text is in italics
+% \raggedleft
+ \small}{\vspace{\stretch{3}}
+ \cleardoublepage}
+
+\usepackage[framemethod=TikZ]{mdframed}
+\mdfdefinestyle{Highlights}{%
+ fontcolor=black,
+ linecolor=white,
+ outerlinewidth=0.0pt,
+ roundcorner=3pt,
+ innertopmargin=.5\baselineskip,
+ innerbottommargin=.5\baselineskip,
+ innerrightmargin=5pt,
+ innerleftmargin=5pt,
+ backgroundcolor=admcolor,
+ skipabove=10pt,
+}
+
+\newenvironment{highlights}{\begin{mdframed}[style=Highlights]\small }{\end{mdframed}}
+% \newenvironment{warning}{\begin{mdframed}[style=Highlights]\small }{\end{mdframed}}
+
+% admonition (specially marked topic)
+\providecommand{\DUadmonition}[2][class-arg]{%
+ % try \DUadmonition#1{#2}:
+ \ifcsname DUadmonition#1\endcsname%
+ \csname DUadmonition#1\endcsname{#2}%
+ \else
+% \begin{center}
+ % \fbox{\parbox{1.0\linewidth}{\small #2}}
+ \begin{mdframed}[style=Highlights]\small #2 \end{mdframed}
+% \end{center}
+ \fi
+}
+
+% title for topics, admonitions, unsupported section levels, and sidebar
+\providecommand*{\DUtitle}[2][class-arg]{%
+ % call \DUtitle#1{#2} if it exists:
+ \ifcsname DUtitle#1\endcsname%
+ \csname DUtitle#1\endcsname{#2}%
+ \else
+ % \smallskip\noindent\textbf{#2}\smallskip%
+ % \textbf{#2}
+ {}
+ \fi
+}
+
+
+\let\originalltt\alltt
+\let\originendalltt\endalltt
+
+\renewenvironment{alltt}{\originalltt \footnotesize}{\originendalltt}
+\let\orighref\href
+
+\ifdefined\hardcover
+\else
+\ifdefined\softdcover
+\else
+\renewcommand*{\href}[2]{\orighref{#1}{#2\,\usebox{\ExternalLinkIcon}}}
+\fi
+\fi
+
+% -----------------------------------------------------------------------------
+\title{ {\sffamily \bfseries Scientific Visualisation}\\
+ {\Large \sffamily Python \& Matplotlib} }
+\author{\normalsize \scshape Nicolas P. Rougier}
+\date{~\vfill \small \scshape Scientific Python -- Volume II}
+
+%\raggedbottom
+\flushbottom
+\begin{document}
+
+% -----------------------------------------------------------------------------
+\frontmatter \pagestyle{frontmatter}
+% -----------------------------------------------------------------------------
+
+%% --- Softcover book ---------------------------------------------------------
+\ifdefined\hardcover
+ \thispagestyle{empty} \mbox{}
+ \else
+
+%% --- Softcover book ---------------------------------------------------------
+\ifdefined\softcover
+\thispagestyle{empty} \mbox{}
+
+
+%% --- PDF book ---------------------------------------------------------
+\else
+ \pdfinclude{216mm}{front-cover.pdf}
+
+%% \thispagestyle{empty}
+%% \mbox{}
+%% \newpage
+
+%% \thispagestyle{empty}
+%% \mbox{}
+%% \newpage
+
+ \fi
+\fi
+
+\newpage
+
+%% \thispagestyle{empty}
+%% \mbox{}
+%% \newpage
+
+%% \thispagestyle{empty}
+%% \mbox{}
+%% \newpage
+
+\input{copyright}
+\maketitle
+
+\newpage
+\ % The empty page
+\newpage
+\input{00-dedication}
+
+\clearpage
+\vspace*{-60pt}
+\setcounter{tocdepth}{0}
+\tableofcontents
+\addtocontents{toc}{\vspace{50pt}}
+
+\newpage
+\ % The empty page
+\newpage
+
+\input{00-preface}
+
+\newpage
+\ % The empty page
+\newpage
+
+\input{00-acknowledgments}
+
+\newpage
+\ % The empty page
+\newpage
+\input{00-introduction}
+
+% -----------------------------------------------------------------------------
+\mainmatter \pagestyle{mainmatter}
+% -----------------------------------------------------------------------------
+
+\input{main}
+
+% -----------------------------------------------------------------------------
+\backmatter \pagestyle{backmatter}
+% -----------------------------------------------------------------------------
+
+\chapter{Bibliography \label{chap-bibliography}}
+\nocite{*}
+% \printbibliography[title=Bibliography,heading=bibliography]
+\printbibliography[heading=none]
+\newpage
+
+% Empty page
+\thispagestyle{empty}
+\mbox{}
+\newpage
+
+\vspace*{\fill}
+
+%% \begin{footnotesize}
+%% \noindent Achevé d’imprimer en Novembre 2021 en France par Pumbo pour le compte
+%% de Nicolas P. Rougier, Bordeaux.\\
+%% Dépôt légal : Novembre 2021.
+%% \end{footnotesize}
+
+%% % Empty page
+%% \thispagestyle{empty}
+%% \mbox{}
+%% \newpage
+
+
+% Empty page
+%% \thispagestyle{empty}
+%% \mbox{}
+%% \newpage
+
+% Empty page
+%% \thispagestyle{empty}
+%% \mbox{}
+%% \cleartoleftpage
+
+
+
+%% --- Softcover book ---------------------------------------------------------
+\ifdefined\hardcover
+ \thispagestyle{empty} \mbox{}
+ \else
+
+%% --- Softcover book ---------------------------------------------------------
+\ifdefined\softcover
+\thispagestyle{empty} \mbox{}
+
+
+%% --- PDF book ---------------------------------------------------------
+\else
+ \pdfinclude{216mm}{back-cover.pdf}
+\fi
+\fi
+
+
+\end{document}
+
diff --git a/tex/copyright.tex b/tex/copyright.tex
new file mode 100644
index 0000000..3da72bf
--- /dev/null
+++ b/tex/copyright.tex
@@ -0,0 +1,27 @@
+~\vfill
+{\footnotesize \linespread{1.0}
+
+ \noindent {\scshape Scientific Visualisation, Python \& Matplotlib}\par
+ \noindent Copyright © 2021 Nicolas P. Rougier. Some rights reserved.\\
+
+ \noindent This volume is licensed under a Creative Commons Attribution
+ Non-Commercial Share-Alike 4.0 International License, which permits use,
+ sharing, adaptation, distribution and reproduction in any medium or format,
+ as long as you give appropriate credit to the original author(s) and the
+ source, provide a link to the Creative Commons license, and indicate if
+ changes were made. You may not use the material for commercial purposes. If
+ you remix, transform, or build upon the material, you must distribute your
+ contributions under the same license as the original. To learn more, visit
+ \href{https://creativecommons.org}{creativecommons.org}.\par
+ ~\\
+ \noindent First Edition: November 2021.\\
+ \ISBN\\
+ ~\\
+ \noindent This book is typeset in
+ {\em Roboto}, {\em Source Serif Pro} \& {\em Source Code pro}. It has been written in reStructuredText, converted to \hologo{LaTeX} using docutils and exported to Portable Document Format using \hologo{XeLaTeX}.\par
+~\\
+\noindent Printing:
+~\\
+\noindent 1 2 3 4 5 6 7 8 9 10\\
+}
+\cleardoublepage
diff --git a/tex/cover-pattern.pdf b/tex/cover-pattern.pdf
new file mode 100644
index 0000000..93459ce
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diff --git a/tex/cover-pattern.png b/tex/cover-pattern.png
new file mode 100644
index 0000000..b184ac3
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diff --git a/tex/cover-pattern.py b/tex/cover-pattern.py
new file mode 100644
index 0000000..7841095
--- /dev/null
+++ b/tex/cover-pattern.py
@@ -0,0 +1,28 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PatchCollection
+
+inch = 25.4
+width = 2*148 + 10 +2*3
+height = 210 + 2*3
+
+fig = plt.figure(figsize=(width/inch,height/inch))
+ax = fig.add_axes([0,0,1,1], aspect=1, frameon=False)
+
+radius = 25
+patches = []
+for y in range(11,-1,-1):
+ for x in range(8,-1,-1):
+ center = 2*x*radius + y%2*radius, y*radius
+ for i,r in enumerate(np.linspace(25, 2.5, 10)):
+ patches.append(plt.Circle(center, r))
+
+collection = PatchCollection(patches, edgecolors="0.1", facecolors='black')
+ax.add_collection(collection)
+
+ax.set_xlim(0, width)
+ax.set_ylim(0, height)
+
+plt.savefig("cover-pattern.pdf")
+plt.savefig("cover-pattern.png", dpi=600)
+plt.show()
diff --git a/tex/front-cover.pdf b/tex/front-cover.pdf
new file mode 100644
index 0000000..26a68ea
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diff --git a/tex/front-cover.tex b/tex/front-cover.tex
new file mode 100644
index 0000000..e51023a
--- /dev/null
+++ b/tex/front-cover.tex
@@ -0,0 +1,36 @@
+\documentclass{standalone}
+\usepackage{graphicx}
+
+\makeatletter
+\newcommand{\definetrim}[2]{%
+ \define@key{Gin}{#1}[]{\setkeys{Gin}{trim=#2,clip}}%
+}
+\makeatother
+
+\newlength{\coverheight}
+\newlength{\coverwidth}
+\newlength{\bleedwidth}
+\newlength{\spinewidth}
+
+\newlength{\trimleft}
+\newlength{\trimright}
+\newlength{\trimbottom}
+\newlength{\trimtop}
+
+\setlength{\coverheight}{210mm}
+\setlength{\coverwidth}{148mm}
+\setlength{\bleedwidth}{3mm}
+\setlength{\spinewidth}{10.5mm}
+
+\setlength{\trimleft}{\dimexpr \bleedwidth + \coverwidth + \spinewidth \relax }
+\setlength{\trimright}{\dimexpr \bleedwidth \relax }
+\setlength{\trimbottom}{\dimexpr \bleedwidth \relax }
+\setlength{\trimtop}{\dimexpr \bleedwidth \relax }
+\definetrim{front}{\trimleft{} \trimbottom{} \trimright{} \trimtop{}}
+
+\begin{document}
+\includegraphics[front,height=210mm]{softcover-cover.pdf}
+\end{document}
+
+% Largeur: 371.7 - 210.7 - 22
+% Hauteur: 260 mm
diff --git a/tex/full-cover.tex b/tex/full-cover.tex
new file mode 100644
index 0000000..e11aedb
--- /dev/null
+++ b/tex/full-cover.tex
@@ -0,0 +1,136 @@
+
+
+%% --- Hard cover -------------------------------------------------------------
+\ifdefined\hardcover
+\documentclass[
+ coverheight=248mm, %% 210mm + 2x19mm
+ coverwidth=167mm, %% 148mm + 19mm
+ bleedwidth=3mm, %
+ spinewidth=23mm, % (13mm + 10mm)
+ marklength=0mm, %
+ markcolor=white
+]{bookcover}
+\usepackage[ISBN=978-2-9579901-0-8,SC2]{ean13isbn}
+\letnamebookcoverpart{back typing area}{back}[19mm,19mm,0mm,19mm]
+\letnamebookcoverpart{front typing area}{front}[0mm,19mm,19mm,19mm]
+\letnamebookcoverpart{spine typing area}{spine}[5mm,19mm,5mm,19mm]
+\else
+
+%% --- Soft cover -------------------------------------------------------------
+\ifdefined\softcover
+\documentclass[
+ coverheight=210mm, % 210mm
+ coverwidth=148mm, % 148mm
+ bleedwidth=3.17mm, %
+ spinewidth=15.14mm, % 12.7mm (254 pages, paper 115g)
+ marklength=0mm, %
+ markcolor=white
+]{bookcover}
+\usepackage[ISBN=978-2-9579901-0-8,SC2]{ean13isbn}
+\letnamebookcoverpart{back typing area}{back}[5mm,5mm,5mm,5mm]
+\letnamebookcoverpart{front typing area}{front}[5mm,5mm,5mm,5mm]
+\letnamebookcoverpart{spine typing area}{spine}[0mm,5mm,0mm,5mm]
+\fi
+\fi
+
+\usepackage{setspace}
+\usepackage{xcolor}
+\usepackage{fontspec}
+\setmainfont{Avenir}
+\renewcommand{\baselinestretch}{1.5}
+
+\begin{document}
+
+ \begin{bookcover}
+
+ \ifdefined\softcover
+ \bookcovercomponent{picture}{whole page}{cover-pattern.pdf}
+ \else
+ \ifdefined\hardcover
+ \bookcovercomponent{color}{whole page}{black}
+ \fi
+ \fi
+
+
+ %% DEBUG
+ %% \bookcovercomponent{color}{bg whole}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{back}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{back typing area}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{front}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{front typing area}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{spine}{white,opacity=0.05}
+ %% \bookcovercomponent{color}{spine typing area}{white,opacity=0.05}
+
+ %% \begin{bookcoverelement}{tikz}{bg whole}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{front}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{front typing area}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{back}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{back typing area}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{spine}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% \begin{bookcoverelement}{tikz}{spine typing area}
+ %% \draw [fill=none, white, opacity=0.5] (0,0) rectangle (\partwidth,\partheight);
+ %% \end{bookcoverelement}
+ %% END DEBUG
+
+
+ \bookcovercomponent{center}{spine}{
+ \vspace{-31mm}
+ \addfontfeature{LetterSpace=45.0}
+ \rotatebox[origin=c]{-90}{\textcolor{yellow!80!white}{\Large
+ SCIENTIFIC~~PYTHON~~VOLUME~~II}}}
+
+ \bookcovercomponent{normal}{spine typing area}[,,,184mm]{
+ \centering
+ \includegraphics[width=0.8\linewidth]{./gravatar.png}
+ }
+
+ \bookcovercomponent{normal}{front typing area}[,,,20mm]{
+ \centering
+ \addfontfeature{LetterSpace=52.0}
+ \textcolor{yellow!80!white}{\small SCIENTIFIC~~PYTHON~~VOLUME~~II}
+
+ \addfontfeature{LetterSpace=22.0}
+ \textcolor{white}{\Large \bfseries SCIENTIFIC VISUALIZATION}
+
+ \vspace{-1pt}
+ \addfontfeature{LetterSpace=65.0}
+ \textcolor{white}{\large PYTHON \& MATPLOTLIB}
+ }
+
+ \bookcovercomponent{normal}{front typing area}[,,,187mm]{
+ \centering
+ \addfontfeature{LetterSpace=25.0}
+ \textcolor{white}{NICOLAS~~P.~~ROUGIER}
+ }
+
+ \bookcovercomponent{normal}{back typing area}[,,,20mm]{
+%% \bookcovercomponent{normal}{back typing area}[,,,180mm]{
+ \setstretch{1.0}
+ \centering
+ \textcolor{white}{In memory of\\
+ \textbf{John D. Hunter}
+ \&
+ \textbf{Maxim Shemanarev}\\
+ Two brilliant minds that are dearly missed.}
+ }
+
+ \bookcovercomponent{normal}{back typing area}[,,,170mm]{
+%% \bookcovercomponent{normal}{back typing area}[,,,180mm]{
+ \centering \fcolorbox{white}{white}{\EANisbn}\\
+%% \centering \textcolor{white}{39 euros}\\
+ }
+
+ \end{bookcover}
+\end{document}
diff --git a/tex/gravatar-2.png b/tex/gravatar-2.png
new file mode 100644
index 0000000..bd1eb84
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diff --git a/tex/gravatar.png b/tex/gravatar.png
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diff --git a/tex/softcover-body.bbl b/tex/softcover-body.bbl
new file mode 100644
index 0000000..a6c8824
--- /dev/null
+++ b/tex/softcover-body.bbl
@@ -0,0 +1,290 @@
+% $ biblatex auxiliary file $
+% $ biblatex bbl format version 3.1 $
+% Do not modify the above lines!
+%
+% This is an auxiliary file used by the 'biblatex' package.
+% This file may safely be deleted. It will be recreated by
+% biber as required.
+%
+\begingroup
+\makeatletter
+\@ifundefined{ver@biblatex.sty}
+ {\@latex@error
+ {Missing 'biblatex' package}
+ {The bibliography requires the 'biblatex' package.}
+ \aftergroup\endinput}
+ {}
+\endgroup
+
+
+\refsection{0}
+ \datalist[entry]{nyt/global//global/global}
+ \entry{Butterick:2013}{book}{}
+ \name{author}{1}{}{%
+ {{un=0,uniquepart=base,hash=15906aa9ea8f28e899c6d015206420ca}{%
+ family={Butterick},
+ familyi={B\bibinitperiod},
+ given={Matthew},
+ giveni={M\bibinitperiod},
+ givenun=0}}%
+ }
+ \list{publisher}{1}{%
+ {Online}%
+ }
+ \strng{namehash}{15906aa9ea8f28e899c6d015206420ca}
+ \strng{fullhash}{15906aa9ea8f28e899c6d015206420ca}
+ \strng{bibnamehash}{15906aa9ea8f28e899c6d015206420ca}
+ \strng{authorbibnamehash}{15906aa9ea8f28e899c6d015206420ca}
+ \strng{authornamehash}{15906aa9ea8f28e899c6d015206420ca}
+ \strng{authorfullhash}{15906aa9ea8f28e899c6d015206420ca}
+ \field{sortinit}{B}
+ \field{sortinithash}{d7095fff47cda75ca2589920aae98399}
+ \field{extradatescope}{labelyear}
+ \field{labeldatesource}{year}
+ \field{labelnamesource}{author}
+ \field{labeltitlesource}{title}
+ \field{abstract}{$\bullet$$\bullet$$\bullet$ This open access book will make you a better typographer. I’m not here to tell you that typography is more important than the substance of your writing. It’s not. But typography can enhance your writing. Typography can create a better first impression. Typography can reinforce your key points. Typography can extend reader attention.}
+ \field{title}{Practical Typography}
+ \field{year}{2013}
+ \true{nocite}
+ \verb{urlraw}
+ \verb https://practicaltypography.com/
+ \endverb
+ \verb{url}
+ \verb https://practicaltypography.com/
+ \endverb
+ \endentry
+ \entry{Hunter:2007}{article}{}
+ \name{author}{1}{}{%
+ {{un=0,uniquepart=base,hash=a2f53dcc6bb05a06b8ad94176f80fe26}{%
+ family={Hunter},
+ familyi={H\bibinitperiod},
+ given={John\bibnamedelima D.},
+ giveni={J\bibinitperiod\bibinitdelim D\bibinitperiod},
+ givenun=0}}%
+ }
+ \list{publisher}{2}{%
+ {Institute of Electrical}%
+ {Electronics Engineers ({IEEE})}%
+ }
+ \strng{namehash}{a2f53dcc6bb05a06b8ad94176f80fe26}
+ \strng{fullhash}{a2f53dcc6bb05a06b8ad94176f80fe26}
+ \strng{bibnamehash}{a2f53dcc6bb05a06b8ad94176f80fe26}
+ \strng{authorbibnamehash}{a2f53dcc6bb05a06b8ad94176f80fe26}
+ \strng{authornamehash}{a2f53dcc6bb05a06b8ad94176f80fe26}
+ \strng{authorfullhash}{a2f53dcc6bb05a06b8ad94176f80fe26}
+ \field{sortinit}{H}
+ \field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
+ \field{extradatescope}{labelyear}
+ \field{labeldatesource}{year}
+ \field{labelnamesource}{author}
+ \field{labeltitlesource}{title}
+ \field{journaltitle}{Computing in Science {\&} Engineering}
+ \field{number}{3}
+ \field{title}{Matplotlib: A 2D Graphics Environment}
+ \field{volume}{9}
+ \field{year}{2007}
+ \true{nocite}
+ \field{pages}{90\bibrangedash 95}
+ \range{pages}{6}
+ \verb{doi}
+ \verb 10.1109/mcse.2007.55
+ \endverb
+ \endentry
+ \entry{Rougier:2017}{book}{}
+ \name{author}{1}{}{%
+ {{un=0,uniquepart=base,hash=75c00f0c05731e74db1167b4a8fe5936}{%
+ family={Rougier},
+ familyi={R\bibinitperiod},
+ given={Nicolas\bibnamedelima P.},
+ giveni={N\bibinitperiod\bibinitdelim P\bibinitperiod},
+ givenun=0}}%
+ }
+ \list{publisher}{1}{%
+ {Zenodo}%
+ }
+ \strng{namehash}{75c00f0c05731e74db1167b4a8fe5936}
+ \strng{fullhash}{75c00f0c05731e74db1167b4a8fe5936}
+ \strng{bibnamehash}{75c00f0c05731e74db1167b4a8fe5936}
+ \strng{authorbibnamehash}{75c00f0c05731e74db1167b4a8fe5936}
+ \strng{authornamehash}{75c00f0c05731e74db1167b4a8fe5936}
+ \strng{authorfullhash}{75c00f0c05731e74db1167b4a8fe5936}
+ \field{extraname}{1}
+ \field{sortinit}{R}
+ \field{sortinithash}{5e1c39a9d46ffb6bebd8f801023a9486}
+ \field{extradatescope}{labelyear}
+ \field{labeldatesource}{year}
+ \field{labelnamesource}{author}
+ \field{labeltitlesource}{title}
+ \field{abstract}{$\bullet$$\bullet$ There are a lot of techniques that you don't find in books and such techniques are mostly learned through experience. The goal of this open access book is to explain some of these techniques and to provide an opportunity for making this experience in the process.}
+ \field{title}{{From Python to Numpy}}
+ \field{year}{2017}
+ \true{nocite}
+ \verb{urlraw}
+ \verb https://www.labri.fr/perso/nrougier/from-python-to-numpy/
+ \endverb
+ \verb{url}
+ \verb https://www.labri.fr/perso/nrougier/from-python-to-numpy/
+ \endverb
+ \endentry
+ \entry{Rougier:2018}{article}{}
+ \name{author}{1}{}{%
+ {{un=0,uniquepart=base,hash=75c00f0c05731e74db1167b4a8fe5936}{%
+ family={Rougier},
+ familyi={R\bibinitperiod},
+ given={Nicolas\bibnamedelima P.},
+ giveni={N\bibinitperiod\bibinitdelim P\bibinitperiod},
+ givenun=0}}%
+ }
+ \list{publisher}{1}{%
+ {Frontiers Media {SA}}%
+ }
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+ \field{volume}{12}
+ \field{year}{2018}
+ \true{nocite}
+ \verb{doi}
+ \verb 10.3389/fninf.2018.00012
+ \endverb
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+ \verb https://doi.org/10.3389/fninf.2018.00012
+ \endverb
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+ \field{abstract}{$\bullet$$\bullet$$\bullet$ This classic and popular article provides a basic set of rules to improve figure design and explain some of the common pitfalls.}
+ \field{journaltitle}{{PLoS} Computational Biology}
+ \field{number}{9}
+ \field{title}{{Ten Simple Rules for Better Figures}}
+ \field{volume}{10}
+ \field{year}{2014}
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+ \verb 10.1371/journal.pcbi.1003833
+ \endverb
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+ \field{abstract}{$\bullet$$\bullet$ This book is meant to help Python users learn to use Python's data science stack–libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools–to effectively store, manipulate, and gain insight from data.}
+ \field{title}{{Python Data Science Handbook}}
+ \field{year}{2016}
+ \true{nocite}
+ \verb{urlraw}
+ \verb https://jakevdp.github.io/PythonDataScienceHandbook/index.html
+ \endverb
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+ \verb https://jakevdp.github.io/PythonDataScienceHandbook/index.html
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+ \field{abstract}{$\bullet$$\bullet$ This open access book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures.}
+ \field{title}{Fundamentals of Data Visualization}
+ \field{year}{2019}
+ \true{nocite}
+ \verb{urlraw}
+ \verb https://serialmentor.com/dataviz/
+ \endverb
+ \verb{url}
+ \verb https://serialmentor.com/dataviz/
+ \endverb
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+ \enddatalist
+\endrefsection
+\endinput
+
Scientific Python — Volume II
-**Scientific Visualization – Python & Matplotlib**, Nicolas P. Rougier
+## Scientific Visualization: Python + Matplotlib
+**Nicolas P. Rougier, Bordeaux, November 2021.**
-
+
-An open access book on scientific visualization using python and matplotlib to
-be released during Spring 2021 (hopefully). Sources will be available in this
-repository, the PDF book will be open-access and the printed book will cost 50$.
+The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies. Some are made specifically for the web, others are for the desktop only, some deal with 3D and large data, while others target flawless 2D rendering. In this landscape, Matplotlib has a very special place. It is a versatile and powerful library that allows you to design very high quality figures, suitable for scientific publishing. It also offers a simple and intuitive interface as well as an object oriented architecture that allows you to tweak anything within a figure. Finally, it can be used as a regular graphic library in order to design non‐scientific figures. This book is organized into four parts. The first part considers the fundamental principles of the Matplotlib library. This includes reviewing the different parts that constitute a figure, the different coordinate systems, the available scales and projections, and we’ll also introduce a few concepts related to typography and colors. The second part is dedicated to the actual design of a figure. After introducing some simple rules for generating better figures, we’ll then go on to explain the Matplotlib defaults and styling system before diving on into figure layout organization. We’ll then explore the different types of plot available and see how a figure can be ornamented with different elements. The third part is dedicated to more advanced concepts, namely 3D figures, optimization & animation. The fourth and final part is a collection of showcases.
-If you want to support the book, you can:
+### Read the book
- * Star the project
- * [Tip a few euros (10 €)](https://www.paypal.me/NicolasPRougier/10)
- * [Access the private repository (25 €)](https://www.paypal.me/NicolasPRougier/25) during the writing
- (and let me know about your github handle)
- * [Sponsor me](https://github.com/sponsors/rougier) through GitHub sponsorship program
- * **[Nominate me](https://stars.github.com/nominate/)** for the GitHub stars program (it's free)
-
-Note that in any case, the repository will be made public at the end of the writing and the PDF will be
-available for free.
+You can read the book **[PDF](https://hal.inria.fr/hal-03427242/document)** (95Mo) that is open access and hosted on
+[HAL](https://hal.archives-ouvertes.fr/) which is a French open
+archive for academics.
+Sources for the book (including code examples)
+are available at
+[github.com/rougier/scientific-visualization-book](https://github.com/rougier/scientific-visualization-book).
-In the meantime and if you're impatient, you can read:
+### Buy the book
-* [Python & OpenGL for Scientific Visualization](https://www.labri.fr/perso/nrougier/python-opengl/)
-* [From Python to Numpy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)
-* [100 Numpy exercices](https://github.com/rougier/numpy-100)
-* [Matplotlib cheat sheet](https://github.com/rougier/matplotlib-cheatsheet)
-
-
-### Latest news
-
-
-You can read them on them [news issue](https://github.com/rougier/scientific-visualization-book/issues/1) and subscribe to this specific issue to get notified about new posts.
-
-
**Fancy axes (locatable axes + Latex)**
-
-
-
**Spiral Pi (text path)**
-
-
-
**Dropshadowed contours (contourf + imshow)**
-
-
-
**Domain coloring (imshow and contours)**
-
-
-
**Waterfall 3d (poly collection)**
-
-
-
**Flower Polar (poly collection)**
-
-
-
**Textual contours (text path)**
-
+If you want to buy the book, you can order a **printed edition** at
+[amazon.com](https://www.amazon.com/dp/2957990105) for 49$. If you want to support or sponsor my
+future work on Python (and
+[Emacs](https://github.com/rougier/nano-emacs)), you can use
+[paypal](https://www.paypal.com/paypalme/NicolasPRougier/10),
+[github](https://github.com/sponsors/rougier) or
+[liberapay](https://en.liberapay.com/rougier/).
-
**Typography (text, ticks)**
-
+
+ 
+ 
+
+
-
**Complex axes layout (axes, text, imshow)**
-
-
**Signal multisampling (plot, collection, imshow)**
-
+### See also
-
**Polygons & testing**
-
-
-
**Polygons & clipping**
-
-
-
**Post-processing**
-
-
-
**3D projection**
-
-
-
**Polar projection**
-
-
-
**Scales** (plot + scales)
-
-
-
**Matplotlib map** (Contour + lines collection)
-
-
-
**Escher style** (Polygons)
-
-
-
**Many plots** (plot & fill_between)
-
-
-
**Oriented histogram** (axisartist toolkit)
-
-
-
**Seasonal plot** (polar plot & patches)
-
-
-
**Hatched bars** (bar)
-
-
-
**Platonic solids** (PolyCollection)
-
-
-
[**Calendar heatmap (github activity)**](https://github.com/rougier/calendar-heatmap) (Imshow, PolyCollection)
-
-
-
[**Recursive Voronoi**](https://github.com/rougier/recursive-voronoi) (PolyCollection)
-
+* [Python & OpenGL for Scientific Visualization](https://www.labri.fr/perso/nrougier/python-opengl/)
+* [From Python to Numpy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) (Scientific Python Volume I)
+* [100 Numpy exercices](https://github.com/rougier/numpy-100)
+* [Matplotlib cheat sheets](https://github.com/matplotlib/cheatsheets)
-
**Scatter 3D** (PolyCollection, scatter(s), Ellipses and custom 3D projection)
-
-
**Text shadow** (TextPath, PolyCollection and imshow)
-
+### Screenshots
-
**Boots** (scatter plot)
-
+
+
+
+
+
+
+
+
diff --git a/code/.DS_Store b/code/.DS_Store
new file mode 100644
index 0000000..e30addc
Binary files /dev/null and b/code/.DS_Store differ
diff --git a/code/anatomy/anatomy.py b/code/anatomy/anatomy.py
new file mode 100644
index 0000000..2b9f8e8
--- /dev/null
+++ b/code/anatomy/anatomy.py
@@ -0,0 +1,161 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import AutoMinorLocator, MultipleLocator, FuncFormatter
+
+np.random.seed(123)
+
+X = np.linspace(0.5, 3.5, 100)
+Y1 = 3 + np.cos(X)
+Y2 = 1 + np.cos(1 + X / 0.75) / 2
+Y3 = np.random.uniform(Y1, Y2, len(X))
+
+fig = plt.figure(figsize=(8, 8))
+ax = fig.add_subplot(1, 1, 1, aspect=1)
+
+
+def minor_tick(x, pos):
+ if not x % 1.0:
+ return ""
+ return "%.2f" % x
+
+
+ax.xaxis.set_major_locator(MultipleLocator(1.000))
+ax.xaxis.set_minor_locator(AutoMinorLocator(4))
+ax.yaxis.set_major_locator(MultipleLocator(1.000))
+ax.yaxis.set_minor_locator(AutoMinorLocator(4))
+ax.xaxis.set_minor_formatter(FuncFormatter(minor_tick))
+
+ax.set_xlim(0, 4)
+ax.set_ylim(0, 4)
+
+ax.tick_params(which="major", width=1.0)
+ax.tick_params(which="major", length=10)
+ax.tick_params(which="minor", width=1.0, labelsize=10)
+ax.tick_params(which="minor", length=5, labelsize=10, labelcolor="0.25")
+
+ax.grid(linestyle="--", linewidth=0.5, color=".25", zorder=-10)
+
+ax.plot(X, Y1, c=(0.25, 0.25, 1.00), lw=2, label="Blue signal", zorder=10)
+ax.plot(X, Y2, c=(1.00, 0.25, 0.25), lw=2, label="Red signal")
+ax.plot(X, Y3, linewidth=0, marker="o", markerfacecolor="w", markeredgecolor="k")
+
+ax.set_title("Anatomy of a figure", fontsize=20, verticalalignment="bottom")
+ax.set_xlabel("X axis label")
+ax.set_ylabel("Y axis label")
+
+ax.legend()
+
+
+def circle(x, y, radius=0.15):
+ from matplotlib.patches import Circle
+ from matplotlib.patheffects import withStroke
+
+ circle = Circle(
+ (x, y),
+ radius,
+ clip_on=False,
+ zorder=10,
+ linewidth=1,
+ edgecolor="black",
+ facecolor=(0, 0, 0, 0.0125),
+ path_effects=[withStroke(linewidth=5, foreground="w")],
+ )
+ ax.add_artist(circle)
+
+
+def text(x, y, text):
+ ax.text(
+ x,
+ y,
+ text,
+ backgroundcolor="white",
+ # fontname="Yanone Kaffeesatz", fontsize="large",
+ ha="center",
+ va="top",
+ weight="regular",
+ color="#000099",
+ )
+
+
+# Minor tick
+circle(0.50, -0.10)
+text(0.50, -0.32, "Minor tick label")
+
+# Major tick
+circle(-0.03, 4.00)
+text(0.03, 3.80, "Major tick")
+
+# Minor tick
+circle(0.00, 3.50)
+text(0.00, 3.30, "Minor tick")
+
+# Major tick label
+circle(-0.15, 3.00)
+text(-0.15, 2.80, "Major tick label")
+
+# X Label
+circle(1.80, -0.27)
+text(1.80, -0.45, "X axis label")
+
+# Y Label
+circle(-0.27, 1.80)
+text(-0.27, 1.6, "Y axis label")
+
+# Title
+circle(1.60, 4.13)
+text(1.60, 3.93, "Title")
+
+# Blue plot
+circle(1.75, 2.80)
+text(1.75, 2.60, "Line\n(line plot)")
+
+# Red plot
+circle(1.20, 0.60)
+text(1.20, 0.40, "Line\n(line plot)")
+
+# Scatter plot
+circle(3.20, 1.75)
+text(3.20, 1.55, "Markers\n(scatter plot)")
+
+# Grid
+circle(3.00, 3.00)
+text(3.00, 2.80, "Grid")
+
+# Legend
+circle(3.70, 3.80)
+text(3.70, 3.60, "Legend")
+
+# Axes
+circle(0.5, 0.5)
+text(0.5, 0.3, "Axes")
+
+# Figure
+circle(-0.3, 0.65)
+text(-0.3, 0.45, "Figure")
+
+color = "#000099"
+ax.annotate(
+ "Spines",
+ xy=(4.0, 0.35),
+ xytext=(3.3, 0.5),
+ color=color,
+ weight="regular", # fontsize="large", fontname="Yanone Kaffeesatz",
+ arrowprops=dict(arrowstyle="->", connectionstyle="arc3", color=color),
+)
+
+ax.annotate(
+ "",
+ xy=(3.15, 0.0),
+ xytext=(3.45, 0.45),
+ color=color,
+ weight="regular", # fontsize="large", fontname="Yanone Kaffeesatz",
+ arrowprops=dict(arrowstyle="->", connectionstyle="arc3", color=color),
+)
+
+plt.savefig("../../figures/anatomy/anatomy.pdf")
+plt.show()
diff --git a/code/anatomy/bold-ticklabel.py b/code/anatomy/bold-ticklabel.py
new file mode 100644
index 0000000..09917da
--- /dev/null
+++ b/code/anatomy/bold-ticklabel.py
@@ -0,0 +1,15 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(5, 2))
+for label in ax.get_xaxis().get_ticklabels():
+ label.set_fontweight("bold")
+
+plt.tight_layout()
+plt.savefig("../../figures/anatomy/bold-ticklabel.pdf")
+plt.show()
diff --git a/code/anatomy/figure-dpi.py b/code/anatomy/figure-dpi.py
new file mode 100644
index 0000000..8c9760a
--- /dev/null
+++ b/code/anatomy/figure-dpi.py
@@ -0,0 +1,33 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import matplotlib.pyplot as plt
+
+
+def figure(dpi):
+ fig = plt.figure(figsize=(4.25, 0.2))
+ ax = plt.subplot(1, 1, 1, frameon=False)
+ plt.xticks([]), plt.yticks([])
+ text = "A text rendered at 10pt size using {0} dpi".format(dpi)
+ ax.text(
+ 0.5,
+ 0.5,
+ text,
+ fontname="Source Serif Pro",
+ ha="center",
+ va="center",
+ fontsize=10,
+ fontweight="light",
+ )
+ plt.savefig("../../figures/anatomy/figure-dpi-{0:03d}.png".format(dpi), dpi=dpi)
+
+
+figure(50)
+figure(100)
+figure(300)
+figure(600)
+
+# Using ImageMagick
+# convert -resize 2550x -append figure-dpi-*.png figure-dpi.png
diff --git a/code/anatomy/inch-cm.py b/code/anatomy/inch-cm.py
new file mode 100644
index 0000000..b12fcaa
--- /dev/null
+++ b/code/anatomy/inch-cm.py
@@ -0,0 +1,70 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+# The goal is to display a metric axis whose physical size (once printed) is
+# correct. The figure will be printed on A5 papersize (210x148mm) with 20mm
+# margin on each side
+#
+# Figure width (mm) is thus 148 - 2x 20 = 108mm = 10.8cm
+# Figure width (inch) (10.8/2.54) ~ 4.25 inches
+#
+# However, we need to have margins in our figure (or tick labels will be cut)
+# so we'll have 0.25 inches margin on each side of the axis such that the axis
+# will be 4 inches.
+# ----------------------------------------------------------------------------
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from matplotlib.ticker import MultipleLocator
+
+mpl.rcParams["font.serif"] = "Source Serif Pro"
+mpl.rcParams["font.size"] = 10
+mpl.rcParams["font.weight"] = 400
+mpl.rcParams["font.family"] = "serif"
+mpl.rcParams["axes.labelweight"] = 400
+
+inch = 2.54
+fig_width = 10.8 / inch # ~4.252 inches
+fig_height = 1.25 # 1.250 inches
+margin = 0.125 # 0.125 inches
+
+fig = plt.figure(figsize=(fig_width, fig_height))
+plt.subplots_adjust(
+ left=0.5 * margin / fig_width,
+ right=1 - 0.5 * margin / fig_width,
+ bottom=0.5 * margin / fig_height,
+ top=1 - 0.5 * margin / fig_height,
+)
+
+ax1 = plt.subplot(1, 1, 1)
+xmin, xmax = 0, 4
+ymin, ymax = 0, 1
+
+# Inches graduation
+ax1 = plt.subplot(1, 1, 1, yticks=[])
+ax1.spines["right"].set_visible(False)
+ax1.spines["left"].set_visible(False)
+ax1.spines["top"].set_visible(False)
+ax1.set_xlim(xmin, xmax)
+ax1.set_ylim(ymin, ymax)
+ax1.spines["bottom"].set_position(("axes", 0.45))
+ax1.xaxis.set_major_locator(MultipleLocator(1.00))
+ax1.xaxis.set_minor_locator(MultipleLocator(0.25))
+ax1.tick_params(axis="both", which="major")
+ax1.set_xlabel("inch")
+
+# Centimeter graduation
+ax2 = ax1.twiny()
+ax2.spines["right"].set_visible(False)
+ax2.spines["left"].set_visible(False)
+ax2.spines["bottom"].set_visible(False)
+ax2.set_xlim(xmin * inch, xmax * inch)
+ax2.spines["top"].set_position(("axes", 0.55))
+ax2.xaxis.set_major_locator(MultipleLocator(1.00))
+ax2.xaxis.set_minor_locator(MultipleLocator(0.10))
+ax2.tick_params(axis="both", which="major", labelsize=10)
+ax2.set_xlabel("cm")
+
+plt.savefig("../../figures/anatomy/inch-cm.pdf", dpi=600)
+plt.show()
diff --git a/code/anatomy/pixel-font.py b/code/anatomy/pixel-font.py
new file mode 100644
index 0000000..04893c6
--- /dev/null
+++ b/code/anatomy/pixel-font.py
@@ -0,0 +1,131 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Translated from
+# http://www.piclist.com/tecHREF/datafile/charset/extractor/charset_extractor.htm
+font = np.array(
+ [
+ (0x00, 0x00, 0x00, 0x00, 0x00, 0x00),
+ (0x10, 0xE3, 0x84, 0x10, 0x01, 0x00),
+ (0x6D, 0xB4, 0x80, 0x00, 0x00, 0x00),
+ (0x00, 0xA7, 0xCA, 0x29, 0xF2, 0x80),
+ (0x20, 0xE4, 0x0C, 0x09, 0xC1, 0x00),
+ (0x65, 0x90, 0x84, 0x21, 0x34, 0xC0),
+ (0x21, 0x45, 0x08, 0x55, 0x23, 0x40),
+ (0x30, 0xC2, 0x00, 0x00, 0x00, 0x00),
+ (0x10, 0x82, 0x08, 0x20, 0x81, 0x00),
+ (0x20, 0x41, 0x04, 0x10, 0x42, 0x00),
+ (0x00, 0xA3, 0x9F, 0x38, 0xA0, 0x00),
+ (0x00, 0x41, 0x1F, 0x10, 0x40, 0x00),
+ (0x00, 0x00, 0x00, 0x00, 0xC3, 0x08),
+ (0x00, 0x00, 0x1F, 0x00, 0x00, 0x00),
+ (0x00, 0x00, 0x00, 0x00, 0xC3, 0x00),
+ (0x00, 0x10, 0x84, 0x21, 0x00, 0x00),
+ (0x39, 0x14, 0xD5, 0x65, 0x13, 0x80),
+ (0x10, 0xC1, 0x04, 0x10, 0x43, 0x80),
+ (0x39, 0x10, 0x46, 0x21, 0x07, 0xC0),
+ (0x39, 0x10, 0x4E, 0x05, 0x13, 0x80),
+ (0x08, 0x62, 0x92, 0x7C, 0x20, 0x80),
+ (0x7D, 0x04, 0x1E, 0x05, 0x13, 0x80),
+ (0x18, 0x84, 0x1E, 0x45, 0x13, 0x80),
+ (0x7C, 0x10, 0x84, 0x20, 0x82, 0x00),
+ (0x39, 0x14, 0x4E, 0x45, 0x13, 0x80),
+ (0x39, 0x14, 0x4F, 0x04, 0x23, 0x00),
+ (0x00, 0x03, 0x0C, 0x00, 0xC3, 0x00),
+ (0x00, 0x03, 0x0C, 0x00, 0xC3, 0x08),
+ (0x08, 0x42, 0x10, 0x20, 0x40, 0x80),
+ (0x00, 0x07, 0xC0, 0x01, 0xF0, 0x00),
+ (0x20, 0x40, 0x81, 0x08, 0x42, 0x00),
+ (0x39, 0x10, 0x46, 0x10, 0x01, 0x00),
+ (0x39, 0x15, 0xD5, 0x5D, 0x03, 0x80),
+ (0x39, 0x14, 0x51, 0x7D, 0x14, 0x40),
+ (0x79, 0x14, 0x5E, 0x45, 0x17, 0x80),
+ (0x39, 0x14, 0x10, 0x41, 0x13, 0x80),
+ (0x79, 0x14, 0x51, 0x45, 0x17, 0x80),
+ (0x7D, 0x04, 0x1E, 0x41, 0x07, 0xC0),
+ (0x7D, 0x04, 0x1E, 0x41, 0x04, 0x00),
+ (0x39, 0x14, 0x17, 0x45, 0x13, 0xC0),
+ (0x45, 0x14, 0x5F, 0x45, 0x14, 0x40),
+ (0x38, 0x41, 0x04, 0x10, 0x43, 0x80),
+ (0x04, 0x10, 0x41, 0x45, 0x13, 0x80),
+ (0x45, 0x25, 0x18, 0x51, 0x24, 0x40),
+ (0x41, 0x04, 0x10, 0x41, 0x07, 0xC0),
+ (0x45, 0xB5, 0x51, 0x45, 0x14, 0x40),
+ (0x45, 0x95, 0x53, 0x45, 0x14, 0x40),
+ (0x39, 0x14, 0x51, 0x45, 0x13, 0x80),
+ (0x79, 0x14, 0x5E, 0x41, 0x04, 0x00),
+ (0x39, 0x14, 0x51, 0x55, 0x23, 0x40),
+ (0x79, 0x14, 0x5E, 0x49, 0x14, 0x40),
+ (0x39, 0x14, 0x0E, 0x05, 0x13, 0x80),
+ (0x7C, 0x41, 0x04, 0x10, 0x41, 0x00),
+ (0x45, 0x14, 0x51, 0x45, 0x13, 0x80),
+ (0x45, 0x14, 0x51, 0x44, 0xA1, 0x00),
+ (0x45, 0x15, 0x55, 0x55, 0x52, 0x80),
+ (0x45, 0x12, 0x84, 0x29, 0x14, 0x40),
+ (0x45, 0x14, 0x4A, 0x10, 0x41, 0x00),
+ (0x78, 0x21, 0x08, 0x41, 0x07, 0x80),
+ (0x38, 0x82, 0x08, 0x20, 0x83, 0x80),
+ (0x01, 0x02, 0x04, 0x08, 0x10, 0x00),
+ (0x38, 0x20, 0x82, 0x08, 0x23, 0x80),
+ (0x10, 0xA4, 0x40, 0x00, 0x00, 0x00),
+ (0x00, 0x00, 0x00, 0x00, 0x00, 0x3F),
+ (0x30, 0xC1, 0x00, 0x00, 0x00, 0x00),
+ (0x00, 0x03, 0x81, 0x3D, 0x13, 0xC0),
+ (0x41, 0x07, 0x91, 0x45, 0x17, 0x80),
+ (0x00, 0x03, 0x91, 0x41, 0x13, 0x80),
+ (0x04, 0x13, 0xD1, 0x45, 0x13, 0xC0),
+ (0x00, 0x03, 0x91, 0x79, 0x03, 0x80),
+ (0x18, 0x82, 0x1E, 0x20, 0x82, 0x00),
+ (0x00, 0x03, 0xD1, 0x44, 0xF0, 0x4E),
+ (0x41, 0x07, 0x12, 0x49, 0x24, 0x80),
+ (0x10, 0x01, 0x04, 0x10, 0x41, 0x80),
+ (0x08, 0x01, 0x82, 0x08, 0x24, 0x8C),
+ (0x41, 0x04, 0x94, 0x61, 0x44, 0x80),
+ (0x10, 0x41, 0x04, 0x10, 0x41, 0x80),
+ (0x00, 0x06, 0x95, 0x55, 0x14, 0x40),
+ (0x00, 0x07, 0x12, 0x49, 0x24, 0x80),
+ (0x00, 0x03, 0x91, 0x45, 0x13, 0x80),
+ (0x00, 0x07, 0x91, 0x45, 0x17, 0x90),
+ (0x00, 0x03, 0xD1, 0x45, 0x13, 0xC1),
+ (0x00, 0x05, 0x89, 0x20, 0x87, 0x00),
+ (0x00, 0x03, 0x90, 0x38, 0x13, 0x80),
+ (0x00, 0x87, 0x88, 0x20, 0xA1, 0x00),
+ (0x00, 0x04, 0x92, 0x49, 0x62, 0x80),
+ (0x00, 0x04, 0x51, 0x44, 0xA1, 0x00),
+ (0x00, 0x04, 0x51, 0x55, 0xF2, 0x80),
+ (0x00, 0x04, 0x92, 0x31, 0x24, 0x80),
+ (0x00, 0x04, 0x92, 0x48, 0xE1, 0x18),
+ (0x00, 0x07, 0x82, 0x31, 0x07, 0x80),
+ (0x18, 0x82, 0x18, 0x20, 0x81, 0x80),
+ (0x10, 0x41, 0x00, 0x10, 0x41, 0x00),
+ (0x30, 0x20, 0x83, 0x08, 0x23, 0x00),
+ (0x29, 0x40, 0x00, 0x00, 0x00, 0x00),
+ (0x10, 0xE6, 0xD1, 0x45, 0xF0, 0x00),
+ ],
+ dtype=np.uint8,
+)
+
+text = "The quick brown fox jumps over the lazy dog!"
+Z = np.zeros((8, len(text), 6, 4))
+for i in range(len(text)):
+ Z[:, i, :, -1] = np.unpackbits(font[ord(text[i]) - ord(" ")]).reshape(8, 6)
+Z = Z.reshape(8, len(text) * 6, 4)
+
+height, width, _ = Z.shape
+dpi = 100
+fig_width = width / dpi
+fig_height = height / dpi
+
+fig = plt.figure(figsize=(fig_width, fig_height), dpi=dpi)
+ax = fig.add_axes([0, 0, 1, 1], frameon=False, xticks=[], yticks=[])
+ax.imshow(Z, interpolation="nearest")
+
+plt.savefig(
+ "../../figures/anatomy/pixel-size.png", dpi=4 * 1200, transparent=True
+) # dpi/10)
+# plt.show()
diff --git a/code/anatomy/raster-vector.py b/code/anatomy/raster-vector.py
new file mode 100644
index 0000000..9397588
--- /dev/null
+++ b/code/anatomy/raster-vector.py
@@ -0,0 +1,101 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from matplotlib.backends.backend_agg import FigureCanvasAgg
+from matplotlib.patches import Rectangle
+
+
+def pixelated_text(dpi=100):
+ fig = Figure(figsize=(1, 1), dpi=dpi)
+ canvas, ax = FigureCanvasAgg(fig), fig.gca()
+ ax.text(0.5, 0.5, "a", fontsize=75, ha="center", va="center")
+ ax.axis("off")
+ canvas.draw()
+ image = np.frombuffer(canvas.tostring_argb(), dtype="uint8")
+ image = image.reshape(dpi, dpi, 4)
+ image = np.roll(image, 3, axis=2)
+ return image
+
+
+def square(position, size, edgecolor, facecolor, zorder):
+ rect = Rectangle(
+ position,
+ size,
+ size,
+ transform=ax.transAxes,
+ clip_on=False,
+ zorder=zorder,
+ linewidth=0.5,
+ edgecolor=edgecolor,
+ facecolor=facecolor,
+ )
+ ax.add_artist(rect)
+
+
+image = pixelated_text(75)
+fig = plt.figure(figsize=(4.25, 2), dpi=100)
+
+# Left (raster)
+ax = plt.subplot(
+ 1, 2, 1, frameon=False, aspect=1, xticks=[], yticks=[], xlim=[0, 1], ylim=[0, 1]
+)
+
+ax.imshow(image, extent=[0.1, 1.0, 0.1, 1.0], zorder=10, interpolation="nearest")
+square((0.1, 0.1), 0.9, "black", "None", 20)
+
+square((0.0, 0.0), 0.2, "black", "white", 20)
+ax.imshow(image, extent=[0.0, 0.2, 0.0, 0.2], zorder=30, interpolation="nearest")
+square((0.0, 0.0), 0.2, "black", "None", 40)
+
+ax.text(0.55, 1.025, "Raster rendering", fontsize="small", ha="center", va="bottom")
+ax.text(
+ 0.6, 0.1 - 0.025, ".PNG / .JPG / .TIFF", fontsize="x-small", ha="center", va="top"
+)
+
+# Right (vector)
+ax = plt.subplot(
+ 1, 2, 2, frameon=False, aspect=1, xticks=[], yticks=[], xlim=[0, 1], ylim=[0, 1]
+)
+ax.text(0.55, 0.55, "a", fontsize=100, ha="center", va="center", color="#000099")
+square((0.1, 0.1), 0.9, "#000099", "None", 20)
+square((0.0, 0.0), 0.2, "#000099", "white", 20)
+ax.text(
+ 0.1,
+ 0.1,
+ "a",
+ fontsize=22,
+ ha="center",
+ va="center",
+ clip_on=False,
+ zorder=30,
+ color="#000099",
+)
+square((0.0, 0.0), 0.2, "#000099", "None", 40)
+
+ax.text(
+ 0.55,
+ 1.025,
+ "Vector rendering",
+ fontsize="small",
+ ha="center",
+ va="bottom",
+ color="#000099",
+)
+ax.text(
+ 0.6,
+ 0.1 - 0.025,
+ ".PDF / .SVG / .PS",
+ fontsize="x-small",
+ ha="center",
+ va="top",
+ color="#000099",
+)
+
+
+plt.savefig("../../figures/anatomy/raster-vector.pdf", dpi=600)
+plt.show()
diff --git a/code/anatomy/ruler.py b/code/anatomy/ruler.py
new file mode 100644
index 0000000..e6d3b71
--- /dev/null
+++ b/code/anatomy/ruler.py
@@ -0,0 +1,87 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+
+class Ruler:
+ """ Ruler add a whole figure axis whose ticks indicate figure
+ dimensions and adapt itself to figure resize event.
+ """
+
+ def __init__(self, fig=None):
+ self.fig = fig or plt.gcf()
+ self.ax = None
+ self.show()
+
+ def show(self):
+
+ if self.ax is None:
+ ax = fig.add_axes([0, 0, 1, 1], zorder=-10, facecolor="None")
+ ax.spines["right"].set_visible(False)
+ ax.spines["bottom"].set_visible(False)
+
+ ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.1))
+ ax.tick_params(
+ axis="x", which="both", labelsize="x-small", direction="in", pad=-15
+ )
+ ax.xaxis.tick_top()
+
+ ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.1))
+ ax.yaxis.tick_left()
+ ax.tick_params(
+ axis="y", which="both", labelsize="x-small", direction="in", pad=-8
+ )
+ ax.yaxis.tick_left()
+ for label in ax.yaxis.get_ticklabels():
+ label.set_horizontalalignment("left")
+
+ self.text = ax.text(
+ 0.5, 0.4, "cm", ha="center", va="center", size="x-small"
+ )
+ ax.grid(linestyle="--", linewidth=0.5)
+
+ self.ax = ax
+
+ self.update()
+ plt.connect("resize_event", self.update)
+
+ def update(self, *args):
+
+ inch = 2.54
+ width_cm = self.fig.get_figwidth() * inch
+ height_cm = self.fig.get_figheight() * inch
+
+ n = int(width_cm) + 1
+ self.ax.set_xlim(0, width_cm)
+ self.ax.set_xticks(np.arange(n))
+ self.ax.set_xticklabels([""] + ["%d" % x for x in np.arange(1, n)])
+
+ markersize = self.ax.xaxis.get_ticklines(True)[0].get_markersize()
+ for line in self.ax.xaxis.get_ticklines(True)[2::9]:
+ line.set_markersize(1.5 * markersize)
+
+ n = int(height_cm) + 1
+ self.ax.set_ylim(height_cm, 0)
+ self.ax.set_yticks(np.arange(n))
+ self.ax.set_yticklabels([""] + ["%d" % y for y in np.arange(1, n)])
+
+ markersize = self.ax.yaxis.get_ticklines(True)[0].get_markersize()
+ for line in self.ax.yaxis.get_ticklines(True)[1::9]:
+ line.set_markersize(1.5 * markersize)
+
+
+# width = page width - left margin - right margin
+width = (14.8 - 1.5 - 2.0) / 2.54
+height = width / 2
+
+fig = plt.figure(figsize=(width, height), dpi=100)
+ax = plt.subplot()
+ruler = Ruler()
+
+plt.savefig("../../figures/anatomy/ruler.pdf")
+plt.show()
diff --git a/code/anatomy/zorder-plots.py b/code/anatomy/zorder-plots.py
new file mode 100644
index 0000000..1af280e
--- /dev/null
+++ b/code/anatomy/zorder-plots.py
@@ -0,0 +1,87 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+# Some nice but random curves
+def curve():
+ n = np.random.randint(1, 5)
+ centers = np.random.normal(0.0, 1.0, n)
+ widths = np.random.uniform(5.0, 50.0, n)
+ widths = 10 * widths / widths.sum()
+ scales = np.random.uniform(0.1, 1.0, n)
+ scales /= scales.sum()
+ X = np.zeros(500)
+ x = np.linspace(-3, 3, len(X))
+ for center, width, scale in zip(centers, widths, scales):
+ X = X + scale * np.exp(-(x - center) * (x - center) * width)
+ return X
+
+
+np.random.seed(123)
+cmap = mpl.cm.get_cmap("Spectral")
+fig = plt.figure(figsize=(8, 8))
+
+
+ax = None
+for n in range(3):
+ ax = plt.subplot(1, 3, n + 1, frameon=False, sharex=ax)
+ for i in range(50):
+ Y = curve()
+ X = np.linspace(-3, 3, len(Y))
+ ax.plot(X, 3 * Y + i, color="k", linewidth=0.75, zorder=100 - i)
+ color = cmap(i / 50)
+ ax.fill_between(X, 3 * Y + i, i, color=color, zorder=100 - i)
+
+ # Some random text on the right of the curve
+ v = np.random.uniform(0, 1)
+ if v < 0.4:
+ text = "*"
+ if v < 0.05:
+ text = "***"
+ elif v < 0.2:
+ text = "**"
+ ax.text(
+ 3.0,
+ i,
+ text,
+ ha="right",
+ va="baseline",
+ size=8,
+ transform=ax.transData,
+ zorder=300,
+ )
+
+ ax.yaxis.set_tick_params(tick1On=False)
+ ax.set_xlim(-3, 3)
+ ax.set_ylim(-1, 53)
+ ax.axvline(0.0, ls="--", lw=0.75, color="black", zorder=250)
+ ax.text(
+ 0.0,
+ 1.0,
+ "Value %d" % (n + 1),
+ ha="left",
+ va="top",
+ weight="bold",
+ transform=ax.transAxes,
+ )
+
+ if n == 0:
+ ax.yaxis.set_tick_params(labelleft=True)
+ ax.set_yticks(np.arange(50))
+ ax.set_yticklabels(["Serie %d" % i for i in range(1, 51)])
+ for tick in ax.yaxis.get_major_ticks():
+ tick.label.set_fontsize(6)
+ tick.label.set_verticalalignment("bottom")
+ else:
+ ax.yaxis.set_tick_params(labelleft=False)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/anatomy/zorder-plots.png", dpi=600)
+plt.savefig("../../figures/anatomy/zorder-plots.pdf")
+plt.show()
diff --git a/code/anatomy/zorder.py b/code/anatomy/zorder.py
new file mode 100644
index 0000000..2cf98cf
--- /dev/null
+++ b/code/anatomy/zorder.py
@@ -0,0 +1,64 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.text import TextPath
+from matplotlib.transforms import Affine2D
+import mpl_toolkits.mplot3d.art3d as art3d
+from matplotlib.patches import Rectangle, PathPatch
+
+
+def text3d(ax, xyz, s, zdir="z", size=None, angle=0, **kwargs):
+ x, y, z = xyz
+ if zdir == "y":
+ x, y, z = x, z, y
+ elif zdir == "x":
+ x, y, z = y, z, x
+ else:
+ x, y, z = x, y, z
+ text_path = TextPath((0, 0), s, size=size)
+ trans = Affine2D().rotate(angle).translate(x, y)
+ p = PathPatch(trans.transform_path(text_path), **kwargs)
+ ax.add_patch(p)
+ art3d.pathpatch_2d_to_3d(p, z=z, zdir=zdir)
+
+
+fig = plt.figure(figsize=(6, 8))
+ax = fig.add_subplot(111, projection="3d", xticks=[], yticks=[], zticks=[])
+ax.set_axis_off()
+ax.set_xlim(0, 10), ax.set_ylim(0, 10), ax.set_zlim(0, 10)
+for i, text in enumerate(
+ [
+ "Figure (background)",
+ "Axes (spines, ticks & labels)",
+ "Patches (zorder=1)",
+ "Lines (zorder=2)",
+ "Text (zorder=3)",
+ "Inset axes & legend (zorder=5)",
+ ]
+):
+
+ p = Rectangle((0, 0), 10, 10, edgecolor="None", facecolor="white", alpha=0.5)
+ ax.add_patch(p)
+ art3d.pathpatch_2d_to_3d(p, z=i, zdir="z")
+
+ p = Rectangle((0, 0), 10, 10, edgecolor="black", facecolor="None")
+ ax.add_patch(p)
+ art3d.pathpatch_2d_to_3d(p, z=i, zdir="z")
+
+ text3d(
+ ax,
+ (-0.25, 0.25, i),
+ text,
+ zdir="z",
+ size=0.5,
+ angle=np.pi / 2,
+ ec="none",
+ fc="k",
+ )
+
+plt.savefig("../../figures/anatomy/zorder.pdf")
+plt.show()
diff --git a/code/animation/__pycache__/fluid.cpython-38.pyc b/code/animation/__pycache__/fluid.cpython-38.pyc
new file mode 100644
index 0000000..8fdc894
Binary files /dev/null and b/code/animation/__pycache__/fluid.cpython-38.pyc differ
diff --git a/code/animation/earthquakes.py b/code/animation/earthquakes.py
new file mode 100644
index 0000000..b7d95fa
--- /dev/null
+++ b/code/animation/earthquakes.py
@@ -0,0 +1,82 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import urllib
+import numpy as np
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+
+
+def rain_update(frame):
+ global E, R, scatter
+
+ current = frame % len(E)
+ i = frame % len(R)
+
+ R["color"][:, 3] = np.maximum(0, R["color"][:, 3] - 1 / len(R))
+ R["size"] += R["growth"]
+
+ i = frame % len(R)
+ R["position"][i] = E["position"][current]
+ R["size"][i] = 5
+ R["growth"][i] = 0.1 * np.exp(E["magnitude"][current])
+ R["color"][i, 3] = 1
+
+ scatter.set_edgecolors(R["color"])
+ scatter.set_sizes(R["size"].ravel())
+ scatter.set_offsets(R["position"])
+
+ if frame == 50:
+ plt.savefig("../../figures/chapter-13/earthquakes-frame-50.pdf")
+
+ return (scatter,)
+
+
+# -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
+feed = "http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"
+
+# Magnitude > 4.5
+url = urllib.request.urlopen(feed + "4.5_month.csv")
+
+# Reading and storage of data
+data = url.read().split(b"\n")[+1:-1]
+E = np.zeros(len(data), dtype=[("position", float, (2,)), ("magnitude", float, (1,))])
+
+for i in range(len(data)):
+ row = data[i].split(b",")
+ E["position"][i] = float(row[2]), float(row[1])
+ E["magnitude"][i] = float(row[4])
+
+fig = plt.figure(figsize=(10, 5), dpi=75)
+ax = plt.axes(projection=ccrs.PlateCarree())
+ax.coastlines()
+scatter = ax.scatter(
+ [],
+ [],
+ s=[],
+ transform=ccrs.PlateCarree(),
+ linewidth=0.5,
+ edgecolors=[],
+ facecolors="None",
+)
+
+n = 50
+R = np.zeros(
+ n,
+ dtype=[
+ ("position", float, (2,)),
+ ("size", float, (1,)),
+ ("growth", float, (1,)),
+ ("color", float, (4,)),
+ ],
+)
+R["position"] = np.random.uniform(0, 1, (n, 2))
+R["size"] = np.linspace(0, 1, n).reshape(n, 1)
+R["color"][:, 3] = np.linspace(0, 1, n)
+
+animation = animation.FuncAnimation(fig, rain_update, interval=10, frames=200)
+plt.tight_layout()
+plt.show()
diff --git a/code/animation/fluid-animation.py b/code/animation/fluid-animation.py
new file mode 100644
index 0000000..9a6cf3a
--- /dev/null
+++ b/code/animation/fluid-animation.py
@@ -0,0 +1,63 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+from fluid import Fluid, inflow
+from scipy.special import erf
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+
+shape = 256, 256
+duration = 500
+fluid = Fluid(shape, "dye")
+inflows = [inflow(fluid, x) for x in np.linspace(-np.pi, np.pi, 8, endpoint=False)]
+
+# Animation setup
+fig = plt.figure(figsize=(5, 5), dpi=100)
+ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+ax.set_xlim(0, 1)
+ax.set_xticks([])
+ax.set_ylim(0, 1)
+ax.set_yticks([])
+im = ax.imshow(
+ np.zeros(shape),
+ extent=[0, 1, 0, 1],
+ vmin=0,
+ vmax=1,
+ origin="lower",
+ interpolation="bicubic",
+ cmap=plt.cm.RdYlBu,
+)
+
+# Animation scenario
+scenario = []
+for i in range(8):
+ scenario.extend([[i]] * 20)
+scenario.extend([[0, 2, 4, 6]] * 30)
+scenario.extend([[1, 3, 5, 7]] * 30)
+
+# Animation update
+def update(frame):
+ for i in scenario[frame % len(scenario)]:
+ inflow_velocity, inflow_dye = inflows[i]
+ fluid.velocity += inflow_velocity
+ fluid.dye += inflow_dye
+ divergence, curl, pressure = fluid.step()
+ Z = curl
+ Z = (erf(Z * 2) + 1) / 4
+
+ im.set_data(Z)
+ im.set_clim(vmin=Z.min(), vmax=Z.max())
+
+ text.set_text("Frame %d" % frame)
+ if frame in [30, 60, 90, 120, 150, 180, 210, 240]:
+ plt.savefig("../../figures/animation/fluid-animation-%03d.png" % frame, dpi=300)
+
+ return im, text
+
+
+text = ax.text(0.01, 0.99, "Test", ha="left", va="top", transform=ax.transAxes)
+anim = animation.FuncAnimation(fig, update, interval=10, frames=duration)
+plt.show()
diff --git a/code/animation/fluid.py b/code/animation/fluid.py
new file mode 100644
index 0000000..df22cf2
--- /dev/null
+++ b/code/animation/fluid.py
@@ -0,0 +1,114 @@
+# Code by Gregory Johnson at https://github.com/GregTJ/stable-fluids
+# This is free and unencumbered software released into the public domain.
+
+import numpy as np
+import scipy.sparse as sp
+from math import factorial
+from itertools import cycle
+from functools import reduce
+from scipy.sparse.linalg import factorized
+from scipy.ndimage import map_coordinates, spline_filter
+
+
+def difference(derivative, accuracy=1):
+ # Central differences implemented based on the article here:
+ # http://web.media.mit.edu/~crtaylor/calculator.html
+ derivative += 1
+ radius = accuracy + derivative // 2 - 1
+ points = range(-radius, radius + 1)
+ coefficients = np.linalg.inv(np.vander(points))
+ return coefficients[-derivative] * factorial(derivative - 1), points
+
+
+def operator(shape, *differences):
+ # Credit to Philip Zucker for figuring out
+ # that kronsum's argument order is reversed.
+ # Without that bit of wisdom I'd have lost it.
+ differences = zip(shape, cycle(differences))
+ factors = (sp.diags(*diff, shape=(dim,) * 2) for dim, diff in differences)
+ return reduce(lambda a, f: sp.kronsum(f, a, format="csc"), factors)
+
+
+class Fluid:
+ def __init__(self, shape, *quantities, pressure_order=1, advect_order=3):
+ self.shape = shape
+ self.dimensions = len(shape)
+
+ # Prototyping is simplified by dynamically
+ # creating advected quantities as needed.
+ self.quantities = quantities
+ for q in quantities:
+ setattr(self, q, np.zeros(shape))
+
+ self.indices = np.indices(shape)
+ self.velocity = np.zeros((self.dimensions, *shape))
+
+ laplacian = operator(shape, difference(2, pressure_order))
+ self.pressure_solver = factorized(laplacian)
+
+ self.advect_order = advect_order
+
+ def step(self):
+ # Advection is computed backwards in time as described in Stable Fluids.
+ advection_map = self.indices - self.velocity
+
+ # SciPy's spline filter introduces checkerboard divergence.
+ # A linear blend of the filtered and unfiltered fields based
+ # on some value epsilon eliminates this error.
+ def advect(field, filter_epsilon=10e-2, mode="constant"):
+ filtered = spline_filter(field, order=self.advect_order, mode=mode)
+ field = filtered * (1 - filter_epsilon) + field * filter_epsilon
+ return map_coordinates(
+ field,
+ advection_map,
+ prefilter=False,
+ order=self.advect_order,
+ mode=mode,
+ )
+
+ # Apply advection to each axis of the
+ # velocity field and each user-defined quantity.
+ for d in range(self.dimensions):
+ self.velocity[d] = advect(self.velocity[d])
+
+ for q in self.quantities:
+ setattr(self, q, advect(getattr(self, q)))
+
+ # Compute the jacobian at each point in the
+ # velocity field to extract curl and divergence.
+ jacobian_shape = (self.dimensions,) * 2
+ partials = tuple(np.gradient(d) for d in self.velocity)
+ jacobian = np.stack(partials).reshape(*jacobian_shape, *self.shape)
+
+ divergence = jacobian.trace()
+
+ # If this curl calculation is extended to 3D, the y-axis value must be negated.
+ # This corresponds to the coefficients of the levi-civita symbol in that dimension.
+ # Higher dimensions do not have a vector -> scalar, or vector -> vector,
+ # correspondence between velocity and curl due to differing isomorphisms
+ # between exterior powers in dimensions != 2 or 3 respectively.
+ curl_mask = np.triu(np.ones(jacobian_shape, dtype=bool), k=1)
+ curl = (jacobian[curl_mask] - jacobian[curl_mask.T]).squeeze()
+
+ # Apply the pressure correction to the fluid's velocity field.
+ pressure = self.pressure_solver(divergence.flatten()).reshape(self.shape)
+ self.velocity -= np.gradient(pressure)
+ return divergence, curl, pressure
+
+
+def inflow(fluid, angle=0, padding=25, radius=7, velocity=1.5):
+ """ Source defnition """
+
+ center = np.floor_divide(fluid.shape, 2)
+ points = np.array([angle])
+ points = tuple(np.array((np.cos(p), np.sin(p))) for p in points)
+ normals = tuple(-p for p in points)
+ r = np.min(center) - padding
+ points = tuple(r * p + center for p in points)
+ inflow_velocity = np.zeros_like(fluid.velocity)
+ inflow_dye = np.zeros(fluid.shape)
+ for p, n in zip(points, normals):
+ mask = np.linalg.norm(fluid.indices - p[:, None, None], axis=0) <= radius
+ inflow_velocity[:, mask] += n[:, None] * velocity
+ inflow_dye[mask] = 1
+ return inflow_velocity, inflow_dye
diff --git a/code/animation/imgcat.py b/code/animation/imgcat.py
new file mode 100644
index 0000000..adcd376
--- /dev/null
+++ b/code/animation/imgcat.py
@@ -0,0 +1,24 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib
+
+# Needs OSX and iterm2
+matplotlib.use("module://imgcat")
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(8, 4), frameon=False)
+
+ax = plt.subplot(2, 1, 1)
+X = np.linspace(0, 4 * 2 * np.pi, 500)
+(line,) = ax.plot(X, np.cos(X))
+
+ax = plt.subplot(2, 1, 2)
+X = np.linspace(0, 4 * 2 * np.pi, 500)
+(line,) = ax.plot(X, np.sin(X))
+
+plt.tight_layout()
+plt.show()
diff --git a/code/animation/less-is-more.py b/code/animation/less-is-more.py
new file mode 100644
index 0000000..91eb884
--- /dev/null
+++ b/code/animation/less-is-more.py
@@ -0,0 +1,370 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Usage:
+# $ for i in `seq 1 35`; do echo $i; python less-is-more.py $i; done
+# $ cp frame-35.png frame-36.png
+# $ cp frame-35.png frame-37.png
+# $ cp frame-35.png frame-38.png
+# $ cp frame-35.png frame-39.png
+# $ cp frame-35.png frame-40.png
+# $ convert -delay 100 *.png less-is-more.gif
+
+import re
+import sys
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+from matplotlib.artist import Artist
+
+
+def smooth1d(x, window_len):
+ s = np.r_[2 * x[0] - x[window_len:1:-1], x, 2 * x[-1] - x[-1:-window_len:-1]]
+ w = np.hanning(window_len)
+ y = np.convolve(w / w.sum(), s, mode="same")
+ return y[window_len - 1 : -window_len + 1]
+
+
+def smooth2d(A, sigma=3):
+ window_len = max(int(sigma), 3) * 2 + 1
+ A1 = np.array([smooth1d(x, window_len) for x in np.asarray(A)])
+ A2 = np.transpose(A1)
+ A3 = np.array([smooth1d(x, window_len) for x in A2])
+ A4 = np.transpose(A3)
+ return A4
+
+
+class BaseFilter(object):
+ def prepare_image(self, src_image, dpi, pad):
+ ny, nx, depth = src_image.shape
+ padded_src = np.zeros([pad * 2 + ny, pad * 2 + nx, depth], dtype="d")
+ padded_src[pad:-pad, pad:-pad, :] = src_image[:, :, :]
+ return padded_src
+
+ def get_pad(self, dpi):
+ return 0
+
+ def __call__(self, im, dpi):
+ pad = self.get_pad(dpi)
+ padded_src = self.prepare_image(im, dpi, pad)
+ tgt_image = self.process_image(padded_src, dpi)
+ return tgt_image, -pad, -pad
+
+
+class OffsetFilter(BaseFilter):
+ def __init__(self, offsets=None):
+ if offsets is None:
+ self.offsets = (0, 0)
+ else:
+ self.offsets = offsets
+
+ def get_pad(self, dpi):
+ return int(max(*self.offsets) / 72.0 * dpi)
+
+ def process_image(self, padded_src, dpi):
+ ox, oy = self.offsets
+ a1 = np.roll(padded_src, int(ox / 72.0 * dpi), axis=1)
+ a2 = np.roll(a1, -int(oy / 72.0 * dpi), axis=0)
+ return a2
+
+
+class GaussianFilter(BaseFilter):
+ def __init__(self, sigma, alpha=0.5, color=None):
+ self.sigma = sigma
+ self.alpha = alpha
+ if color is None:
+ self.color = (0, 0, 0)
+ else:
+ self.color = color
+
+ def get_pad(self, dpi):
+ return int(self.sigma * 3 / 72.0 * dpi)
+
+ def process_image(self, padded_src, dpi):
+ tgt_image = np.zeros_like(padded_src)
+ aa = smooth2d(padded_src[:, :, -1] * self.alpha, self.sigma / 72.0 * dpi)
+ tgt_image[:, :, -1] = aa
+ tgt_image[:, :, :-1] = self.color
+ return tgt_image
+
+
+class DropShadowFilter(BaseFilter):
+ def __init__(self, sigma, alpha=0.3, color=None, offsets=None):
+ self.gauss_filter = GaussianFilter(sigma, alpha, color)
+ self.offset_filter = OffsetFilter(offsets)
+
+ def get_pad(self, dpi):
+ return max(self.gauss_filter.get_pad(dpi), self.offset_filter.get_pad(dpi))
+
+ def process_image(self, padded_src, dpi):
+ t1 = self.gauss_filter.process_image(padded_src, dpi)
+ t2 = self.offset_filter.process_image(t1, dpi)
+ return t2
+
+
+class FilteredArtistList(Artist):
+ def __init__(self, artist_list, filter):
+ self._artist_list = artist_list
+ self._filter = filter
+ Artist.__init__(self)
+
+ def draw(self, renderer):
+ renderer.start_rasterizing()
+ renderer.start_filter()
+ for a in self._artist_list:
+ a.draw(renderer)
+ renderer.stop_filter(self._filter)
+ renderer.stop_rasterizing()
+
+
+# -----------------------------------------------------------------------------
+class RangeDict(dict):
+ def __getitem__(self, item):
+ if type(item) != range:
+ for key in self:
+ if item in key:
+ return self[key]
+ else:
+ return super().__getitem__(item)
+
+
+_ax1_title = RangeDict(
+ {
+ range(1, 2): "Less is More",
+ range(2, 5): "Remove Backgrounds",
+ range(5, 10): "Remove redundant labels",
+ range(10, 14): "Remove borders",
+ range(14, 16): "Reduce colors",
+ range(16, 19): "Remove special effects",
+ range(19, 22): "Remove bolding",
+ range(22, 25): "Lighten labels",
+ range(25, 29): "Ligthen lines",
+ range(29, 33): "Or remove lines",
+ range(33, 35): "Direct label",
+ range(35, 36): "Less is More",
+ }
+)
+_bar_facecolors = RangeDict(
+ {
+ range(1, 15): ["#22befc", "#1dB95d", "#ca5a56", "#8b6eaa", "#fcfc38"],
+ range(15, 36): ["#b2b2b2", "#b2b2b2", "#cb5958", "#b2b2b2", "#b2b2b2"],
+ }
+)
+_bar_edgecolor = RangeDict({range(1, 13): "#000000", range(13, 36): "None"})
+_bar_shadows = RangeDict({range(1, 18): True, range(18, 36): False})
+_xticklabels = RangeDict(
+ {
+ range(1, 36): [
+ "French\nFries",
+ "Potato\nChips",
+ "Bacon\n",
+ "Pizza\n",
+ "Chili Dog\n",
+ ]
+ }
+)
+_xticklines_color = RangeDict(
+ {range(1, 27): "#000000", range(27, 31): "#b2b2b2", range(34, 36): "None"}
+)
+_yticklabels = RangeDict(
+ {
+ range(1, 34): ["0", "100", "200", "300", "400", "500", "600", "700"],
+ range(34, 36): ["", "", "", "", "", "", "", ""],
+ }
+)
+_yticklabels_weight = RangeDict({range(1, 21): "bold", range(21, 36): "normal"})
+_yticklabels_color = RangeDict(
+ {range(1, 24): "#000000", range(24, 34): "#b2b2b2", range(34, 36): "None"}
+)
+_yticklines_color = RangeDict(
+ {range(1, 28): "#000000", range(28, 34): "#b2b2b2", range(34, 36): "None"}
+)
+_grid_color = RangeDict(
+ {range(1, 26): "#000000", range(26, 30): "#b2b2b2", range(30, 36): "None"}
+)
+_ax3_title = RangeDict(
+ {
+ range(1, 7): "Calories per 100g for different foods",
+ range(7, 36): "Calories per 100g",
+ }
+)
+_ax3_title_weight = RangeDict({range(1, 20): "bold", range(20, 36): "normal"})
+_ax3_title_color = RangeDict({range(1, 23): "#000000", range(23, 36): "#b2b2b2"})
+_xlabel = RangeDict({range(1, 9): "Type of Food", range(9, 36): ""})
+_ylabel = RangeDict({range(1, 8): "Number of Calories", range(8, 36): ""})
+_ax2_background = RangeDict({range(1, 3): True, range(3, 36): False})
+_ax3_background = RangeDict({range(1, 4): True, range(4, 36): False})
+_legend = RangeDict({range(1, 6): True, range(6, 36): False})
+_ax2_spines_linewidth = RangeDict(
+ {range(1, 11): (2, 2, 2, 2), range(11, 36): (0, 0, 0, 0)}
+)
+_ax3_spines_linewidth = RangeDict(
+ {
+ range(1, 12): (2, 2, 2, 2),
+ range(12, 31): (0, 0.75, 0.75, 0),
+ range(31, 32): (0, 0, 0.75, 0),
+ range(32, 36): (0, 0, 0, 0),
+ }
+)
+_ax3_spines_color = RangeDict(
+ {
+ range(1, 27): ("k", "k", "k", "k"),
+ range(27, 28): ("k", "#b2b2b2", "k", "k"),
+ range(28, 36): ("k", "#b2b2b2", "#b2b2b2", "k"),
+ }
+)
+_direct_label = RangeDict({range(1, 35): False, range(35, 36): True})
+
+
+if len(sys.argv) > 1:
+ frame = int(sys.argv[1])
+else:
+ frame = 1
+frame = max(min(frame, 36), 1)
+
+
+dpi = 100
+width, height = 640, 460
+values = [607, 542, 533, 296, 260]
+
+facecolors = _bar_facecolors[frame]
+edgecolor = _bar_edgecolor[frame]
+labels = _xticklabels[frame]
+shadow = _bar_shadows[frame]
+legend = _legend[frame]
+ax3_background = _ax3_background[frame]
+ax2_background = _ax2_background[frame]
+ax2_spines_linewidth = _ax2_spines_linewidth[frame]
+ax3_spines_linewidth = _ax3_spines_linewidth[frame]
+ax3_spines_color = _ax3_spines_color[frame]
+ax1_title = _ax1_title[frame]
+ax3_title = _ax3_title[frame]
+ax3_title_weight = _ax3_title_weight[frame]
+ax3_title_color = _ax3_title_color[frame]
+xlabel = _xlabel[frame]
+ylabel = _ylabel[frame]
+grid_color = _grid_color[frame]
+yticklabels = _yticklabels[frame]
+yticklabels_weight = _yticklabels_weight[frame]
+yticklabels_color = _yticklabels_color[frame]
+yticklines_color = _yticklines_color[frame]
+xticklines_color = _xticklines_color[frame]
+direct_label = _direct_label[frame]
+
+
+fig = plt.figure(figsize=(width / dpi, height / dpi), dpi=dpi)
+
+ax1 = fig.add_axes([0.0, 0.0, 1.0, 1.0], frameon=False)
+ax1.text(
+ 0.05,
+ 0.05,
+ "A remake of www.darkhorseanalytics.com",
+ ha="left",
+ va="top",
+ family="Source Sans Pro",
+ weight="light",
+)
+ax1.text(
+ 0.95, 0.05, "Made with matplotlib", ha="right", va="top", family="Source Sans Pro"
+)
+
+x, y = 0.050, 0.075
+w, h = 1 - 2 * x, 1 - 2 * y - 0.1
+ax2 = fig.add_axes([x, y, w, h])
+ax2.set_xticks([])
+ax2.set_yticks([])
+
+x, y = 0.165, 0.25
+w, h = 0.585, 0.45
+ax3 = fig.add_axes([x, y, w, h])
+ax3.set_ylim(0, 701)
+ax3.set_yticks(range(0, 701, 100))
+ax3.set_xlim(0, 10)
+ax3.tick_params("x", length=0, which="major", pad=12.5)
+
+
+# -----------------------------------------------------------------------------
+ax1.text(
+ 0.05,
+ 0.95,
+ ax1_title,
+ ha="left",
+ va="top",
+ family="Source Sans Pro",
+ fontsize=32,
+ weight="light",
+)
+
+
+# -----------------------------------------------------------------------------
+if ax2_background:
+ ax2.imshow(mpimg.imread("texture.jpg"))
+for linewidth, axis in zip(ax2_spines_linewidth, ["top", "bottom", "left", "right"]):
+ ax2.spines[axis].set_linewidth(linewidth)
+
+# -----------------------------------------------------------------------------
+if ax3_background:
+ ax3.set_facecolor(".75")
+
+for color, linewidth, axis in zip(
+ ax3_spines_color, ax3_spines_linewidth, ["top", "bottom", "left", "right"]
+):
+ ax3.spines[axis].set_linewidth(linewidth)
+ ax3.spines[axis].set_color(color)
+
+ax3.set_xticks(range(1, 10, 2))
+ax3.set_xticklabels(labels)
+if ax3_spines_linewidth[1] > 0:
+ locator = matplotlib.ticker.FixedLocator(range(0, 10, 2))
+ ax3.xaxis.set_minor_locator(locator)
+ ax3.tick_params("x", length=3.5, width=0.8, which="minor", color=xticklines_color)
+
+
+for label in ax3.get_yticklabels():
+ label.set_fontweight(yticklabels_weight)
+ label.set_color(yticklabels_color)
+ax3.tick_params(axis="y", color=yticklines_color)
+
+
+ax3.set_xlabel(xlabel, weight="bold")
+ax3.set_ylabel(ylabel, weight="bold")
+
+ax3.set_title(
+ ax3_title, weight=ax3_title_weight, size=12, loc="left", color=ax3_title_color
+)
+
+plt.grid(axis="y", color=grid_color)
+
+X, Y = range(1, 10, 2), values
+bars = ax3.bar(X, Y, 0.8, facecolor="w", edgecolor="k", linewidth=1.5, zorder=10)
+for bar, color in zip(bars, facecolors):
+ bar.set_facecolor(color)
+ bar.set_edgecolor(edgecolor)
+if direct_label:
+ for x, y in zip(X, Y):
+ ax3.text(
+ x,
+ y - 25,
+ "%d" % y,
+ ha="center",
+ va="top",
+ color="white",
+ zorder=20,
+ transform=ax3.transData,
+ fontsize=9,
+ )
+
+if shadow:
+ gauss = DropShadowFilter(5, alpha=0.65, offsets=(3, 1))
+ shadow = FilteredArtistList(bars, gauss)
+ ax3.add_artist(shadow)
+ shadow.set_zorder(bars[0].get_zorder() - 0.1)
+
+if legend:
+ ax3.legend(bars, labels, edgecolor="k", loc="center left", bbox_to_anchor=(1, 0.65))
+
+plt.savefig("frame-%02d.png" % frame, dpi=dpi)
+# plt.show()
diff --git a/code/animation/lissajous.py b/code/animation/lissajous.py
new file mode 100644
index 0000000..2c7e3c1
--- /dev/null
+++ b/code/animation/lissajous.py
@@ -0,0 +1,58 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+
+fig = plt.figure(figsize=(8, 4), dpi=100)
+
+frames = 500
+n = 8
+
+X, Y, L = [], [], []
+for row in range(n // 2):
+ T2 = np.linspace(0, row * 2 * np.pi, frames) if row > 0 else np.zeros(frames)
+
+ for col in range(n):
+ T1 = np.linspace(0, col * 2 * np.pi, frames) if col > 0 else np.ones(frames)
+
+ index = n * row + col
+
+ ax = plt.subplot(n // 2, n, 1 + index, aspect=1, frameon=False)
+ ax.set_xlim([-1, +1])
+ ax.set_xticks([])
+ ax.set_ylim([-1, +1])
+ ax.set_yticks([])
+
+ X.append(np.cos(T1))
+ Y.append(np.sin(T2))
+
+ ax.plot(X[-1], Y[-1], color="0.95", clip_on=False, linewidth=0.75)
+ (l,) = ax.plot(
+ [],
+ [],
+ "-o",
+ clip_on=False,
+ markevery=[-1],
+ markeredgewidth=2,
+ markerfacecolor="C0",
+ markeredgecolor="white",
+ )
+ L.append(l)
+
+
+plt.subplots_adjust(left=0.0, bottom=None, right=0.95, top=None)
+
+
+def animate(frame):
+ for i in range(len(L)):
+ L[i].set_data(X[i][:frame], Y[i][:frame])
+ if frame == 150:
+ plt.savefig("../../figures/animation/lissajous.pdf")
+
+
+ani = animation.FuncAnimation(fig, animate, interval=5, frames=frames)
+plt.show()
diff --git a/code/animation/platecarree.py b/code/animation/platecarree.py
new file mode 100644
index 0000000..7a8fa98
--- /dev/null
+++ b/code/animation/platecarree.py
@@ -0,0 +1,16 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(10, 5))
+ax = plt.axes(projection=ccrs.PlateCarree())
+ax.coastlines()
+
+plt.tight_layout()
+plt.savefig("../../figures/animation/platecarree.pdf")
+plt.show()
diff --git a/code/animation/rain.py b/code/animation/rain.py
new file mode 100644
index 0000000..de444b8
--- /dev/null
+++ b/code/animation/rain.py
@@ -0,0 +1,47 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+
+
+def rain_update(frame):
+ global R, scatter
+
+ R["color"][:, 3] = np.maximum(0, R["color"][:, 3] - 1 / len(R))
+ R["size"] += 1 / len(R)
+
+ i = frame % len(R)
+ R["position"][i] = np.random.uniform(0, 1, 2)
+ R["size"][i] = 0
+ R["color"][i, 3] = 1
+
+ scatter.set_edgecolors(R["color"])
+ scatter.set_sizes(1000 * R["size"].ravel())
+ scatter.set_offsets(R["position"])
+
+ if frame == 50:
+ plt.savefig("../../figures/chapter-13/rain.pdf")
+ return (scatter,)
+
+
+fig = plt.figure(figsize=(6, 2), facecolor="white", dpi=100)
+ax = fig.add_axes([0, 0, 1, 1], frameon=False) # , aspect=1)
+scatter = ax.scatter([], [], s=[], linewidth=0.5, edgecolors=[], facecolors="None")
+
+n = 100
+R = np.zeros(
+ n, dtype=[("position", float, (2,)), ("size", float, (1,)), ("color", float, (4,))]
+)
+R["position"] = np.random.uniform(0, 1, (n, 2))
+R["size"] = np.linspace(0, 1, n).reshape(n, 1)
+R["color"][:, 3] = np.linspace(0, 1, n)
+
+ax.set_xlim(0, 1), ax.set_xticks([])
+ax.set_ylim(0, 1), ax.set_yticks([])
+
+animation = animation.FuncAnimation(fig, rain_update, interval=10, frames=200)
+plt.show()
diff --git a/code/animation/sine-cosine-mp4.py b/code/animation/sine-cosine-mp4.py
new file mode 100644
index 0000000..122f7ea
--- /dev/null
+++ b/code/animation/sine-cosine-mp4.py
@@ -0,0 +1,43 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+
+fig = plt.figure(figsize=(7, 2))
+ax = plt.subplot()
+
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+(line1,) = ax.plot(X, C, marker="o", markevery=[-1], markeredgecolor="white")
+(line2,) = ax.plot(X, S, marker="o", markevery=[-1], markeredgecolor="white")
+text = ax.text(0.01, 0.95, "Test", ha="left", va="top", transform=ax.transAxes)
+ax.set_xticks([])
+ax.set_yticks([])
+
+
+def update(frame):
+ line1.set_data(X[:frame], C[:frame])
+ line2.set_data(X[:frame], S[:frame])
+ text.set_text("Frame %d" % frame)
+ if frame in [1, 32, 128, 255]:
+ plt.savefig("../../figures/animation/sine-cosine-frame-%03d.pdf" % frame)
+ return line1, line2, text
+
+
+plt.tight_layout()
+writer = animation.FFMpegWriter(fps=30)
+anim = animation.FuncAnimation(fig, update, interval=10, frames=len(X))
+from tqdm.autonotebook import tqdm
+
+bar = tqdm(total=len(X))
+anim.save(
+ "../../figures/animation/sine-cosine.mp4",
+ writer=writer,
+ dpi=100,
+ progress_callback=lambda i, n: bar.update(1),
+)
+bar.close()
diff --git a/code/animation/sine-cosine.py b/code/animation/sine-cosine.py
new file mode 100644
index 0000000..88a9377
--- /dev/null
+++ b/code/animation/sine-cosine.py
@@ -0,0 +1,27 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+
+fig = plt.figure(figsize=(7, 2))
+ax = plt.subplot()
+
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+(line1,) = ax.plot(X, C, marker="o", markevery=[-1], markeredgecolor="white")
+(line2,) = ax.plot(X, S, marker="o", markevery=[-1], markeredgecolor="white")
+
+
+def update(frame):
+ line1.set_data(X[:frame], C[:frame])
+ line2.set_data(X[:frame], S[:frame])
+
+
+plt.tight_layout()
+ani = animation.FuncAnimation(fig, update, interval=10)
+plt.savefig("../../figures/animation/sine-cosine.pdf")
+plt.show()
diff --git a/code/beyond/__pycache__/bluenoise.cpython-38.pyc b/code/beyond/__pycache__/bluenoise.cpython-38.pyc
new file mode 100644
index 0000000..ddea5bd
Binary files /dev/null and b/code/beyond/__pycache__/bluenoise.cpython-38.pyc differ
diff --git a/code/beyond/bluenoise.py b/code/beyond/bluenoise.py
new file mode 100644
index 0000000..10135cd
--- /dev/null
+++ b/code/beyond/bluenoise.py
@@ -0,0 +1,122 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+from math import cos, sin, floor, sqrt, pi, ceil
+
+
+def generate(shape, radius, k=32, seed=None):
+ """
+ Generate blue noise over a two-dimensional rectangle of size (width,height)
+
+ Parameters
+ ----------
+
+ shape : tuple
+ Two-dimensional domain (width x height)
+ radius : float
+ Minimum distance between samples
+ k : int, optional
+ Limit of samples to choose before rejection (typically k = 30)
+ seed : int, optional
+ If provided, this will set the random seed before generating noise,
+ for valid pseudo-random comparisons.
+
+ References
+ ----------
+
+ .. [1] Fast Poisson Disk Sampling in Arbitrary Dimensions, Robert Bridson,
+ Siggraph, 2007. :DOI:`10.1145/1278780.1278807`
+ """
+
+ def sqdist(a, b):
+ """ Squared Euclidean distance """
+ dx, dy = a[0] - b[0], a[1] - b[1]
+ return dx * dx + dy * dy
+
+ def grid_coords(p):
+ """ Return index of cell grid corresponding to p """
+ return int(floor(p[0] / cellsize)), int(floor(p[1] / cellsize))
+
+ def fits(p, radius):
+ """ Check whether p can be added to the queue """
+
+ radius2 = radius * radius
+ gx, gy = grid_coords(p)
+ for x in range(max(gx - 2, 0), min(gx + 3, grid_width)):
+ for y in range(max(gy - 2, 0), min(gy + 3, grid_height)):
+ g = grid[x + y * grid_width]
+ if g is None:
+ continue
+ if sqdist(p, g) <= radius2:
+ return False
+ return True
+
+ # When given a seed, we use a private random generator in order to not
+ # disturb the default global random generator
+ if seed is not None:
+ from numpy.random.mtrand import RandomState
+
+ rng = RandomState(seed=seed)
+ else:
+ rng = np.random
+
+ width, height = shape
+ cellsize = radius / sqrt(2)
+ grid_width = int(ceil(width / cellsize))
+ grid_height = int(ceil(height / cellsize))
+ grid = [None] * (grid_width * grid_height)
+
+ p = rng.uniform(0, shape, 2)
+ queue = [p]
+ grid_x, grid_y = grid_coords(p)
+ grid[grid_x + grid_y * grid_width] = p
+
+ while queue:
+ qi = rng.randint(len(queue))
+ qx, qy = queue[qi]
+ queue[qi] = queue[-1]
+ queue.pop()
+ for _ in range(k):
+ theta = rng.uniform(0, 2 * pi)
+ r = radius * np.sqrt(rng.uniform(1, 4))
+ p = qx + r * cos(theta), qy + r * sin(theta)
+ if not (0 <= p[0] < width and 0 <= p[1] < height) or not fits(p, radius):
+ continue
+ queue.append(p)
+ gx, gy = grid_coords(p)
+ grid[gx + gy * grid_width] = p
+
+ return np.array([p for p in grid if p is not None])
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+ import matplotlib.pyplot as plt
+
+ np.random.seed(1)
+
+ fig = plt.figure(figsize=(9, 3.25))
+
+ ax = plt.subplot(1, 3, 1, aspect=1, xlim=[0, 5], xticks=[], ylim=[0, 5], yticks=[])
+ V = np.random.uniform(0, 5, (1600, 2))
+ ax.scatter(V[:, 0], V[:, 1], s=5, edgecolor="None", facecolor="black")
+ ax.set_title("Uniform distribution (n=1600)")
+
+ ax = plt.subplot(1, 3, 2, aspect=1, xlim=[0, 5], xticks=[], ylim=[0, 5], yticks=[])
+ X, Y = np.meshgrid(np.linspace(0, 5, 40), np.linspace(0, 5, 40))
+ X += np.random.normal(0, 0.04, X.shape)
+ Y += np.random.normal(0, 0.04, Y.shape)
+ ax.scatter(X, Y, s=5, edgecolor="None", facecolor="black")
+ ax.set_title("Jittered (n=1600)")
+
+ ax = plt.subplot(1, 3, 3, aspect=1, xlim=[0, 5], xticks=[], ylim=[0, 5], yticks=[])
+ V = generate([5, 5], 0.099)
+ ax.scatter(V[:, 0], V[:, 1], s=5, edgecolor="None", facecolor="black")
+ ax.set_title("Blue noise ditribution (n=%d)" % len(V))
+
+ plt.tight_layout()
+ plt.savefig("../../figures/beyond/bluenoise.pdf")
+ plt.show()
diff --git a/code/beyond/dungeon.py b/code/beyond/dungeon.py
new file mode 100644
index 0000000..47b6ebc
--- /dev/null
+++ b/code/beyond/dungeon.py
@@ -0,0 +1,246 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import scipy.spatial
+import shapely.geometry
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+from matplotlib.patches import Polygon, Ellipse
+import bluenoise
+
+# This is important because "cities" have been manually positionned
+np.random.seed(1)
+
+
+# Hatch pattern with given orientation (or random if None given)
+def hatch(n=4, theta=None):
+ theta = theta or np.random.uniform(0, np.pi)
+ P = np.zeros((n, 2, 2))
+ X = np.linspace(-0.5, +0.5, n, endpoint=True)
+ P[:, 0, 1] = -0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 1] = +0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 0] = X + np.random.normal(0, 0.025, n)
+ P[:, 0, 0] = X + np.random.normal(0, 0.025, n)
+ c, s = np.cos(theta), np.sin(theta)
+ Z = np.array([[c, s], [-s, c]])
+ return P @ Z.T
+
+
+def seg_dists(p, a, b):
+ """Cartesian distance from point to line segment
+
+ Args:
+ - p: np.array of single point, shape (2,) or 2D array, shape (x, 2)
+ - a: np.array of shape (x, 2)
+ - b: np.array of shape (x, 2)
+ """
+ # normalized tangent vectors
+ d_ba = b - a
+ d = np.divide(d_ba, (np.hypot(d_ba[:, 0], d_ba[:, 1]).reshape(-1, 1)))
+
+ # signed parallel distance components
+ # rowwise dot products of 2D vectors
+ s = np.multiply(a - p, d).sum(axis=1)
+ t = np.multiply(p - b, d).sum(axis=1)
+
+ # clamped parallel distance
+ h = np.maximum.reduce([s, t, np.zeros(len(s))])
+
+ # perpendicular distance component
+ # rowwise cross products of 2D vectors
+ d_pa = p - a
+ c = d_pa[:, 0] * d[:, 1] - d_pa[:, 1] * d[:, 0]
+
+ return np.hypot(h, c)
+
+
+# Actual drawing
+fig = plt.figure(figsize=(7, 7))
+fig.patch.set_facecolor("#ffffff")
+ax = plt.subplot(1, 1, 1, aspect=1, frameon=False)
+
+
+# Figure border using the hatch pattern. They are first spread according to
+# a blue noise distribution, scaled according to the distance to the nearest
+# neighbour and then lines composing the hatch are clipped against the
+# corresponding Voronoi cell.
+h = 4 # Number of line segments composing a hatch
+radius = 0.2 # Minimum radius between points
+# (the smaller, the longer to compute)
+
+P = bluenoise.generate((15, 15), radius=radius) - (0.5, 0.5)
+Walls = np.array(
+ [
+ [1, 1],
+ [5, 1],
+ [5, 3],
+ [8, 3],
+ [8, 2],
+ [11, 2],
+ [11, 5],
+ [10, 5],
+ [10, 6],
+ [12, 6],
+ [12, 8],
+ [13, 8],
+ [13, 10],
+ [11, 10],
+ [11, 12],
+ [2, 12],
+ [2, 10],
+ [1, 10],
+ [1, 7],
+ [4, 7],
+ [4, 10],
+ [3, 10],
+ [3, 11],
+ [10, 11],
+ [10, 10],
+ [9, 10],
+ [9, 8],
+ [11, 8],
+ [11, 7],
+ [9, 7],
+ [9, 5],
+ [8, 5],
+ [8, 4],
+ [5, 4],
+ [5, 6],
+ [1, 6],
+ [1, 1],
+ ]
+)
+walls = Polygon(
+ Walls,
+ closed=True,
+ zorder=10,
+ facecolor="white",
+ edgecolor="None",
+ lw=3,
+ joinstyle="round",
+)
+ax.add_patch(walls)
+
+for i in range(-5, 15):
+ ax.axhline(
+ i,
+ color=".5",
+ clip_path=walls,
+ zorder=20,
+ linestyle=(0, (1, 4)),
+ linewidth=1,
+ dash_capstyle="round",
+ )
+ ax.axvline(
+ i,
+ color=".5",
+ clip_path=walls,
+ zorder=20,
+ linestyle=(0, (1, 4)),
+ linewidth=1,
+ dash_capstyle="round",
+ )
+
+walls = Polygon(
+ Walls,
+ closed=True,
+ zorder=30,
+ facecolor="None",
+ edgecolor="black",
+ lw=3,
+ joinstyle="round",
+)
+ax.add_patch(walls)
+
+# ax.scatter([3.5], [7.5], s=250, marker="x", zorder=100, color="black", linewidth=5)
+# ax.text(3.5, 7.5, "X",
+# family="Morris Roman", size=24, zorder=20, ha="center", va="center")
+
+for i in range(30):
+ ellipse = Ellipse(
+ xy=np.random.uniform(1, 12, 2),
+ width=np.random.uniform(0.05, 0.15),
+ height=np.random.uniform(0.05, 0.15),
+ zorder=100,
+ facecolor="white",
+ edgecolor="black",
+ linewidth=1.25,
+ clip_on=True,
+ angle=np.random.uniform(0, 360),
+ )
+ ax.add_artist(ellipse)
+ ellipse.set_clip_path(walls)
+
+for i in range(20):
+ ellipse = Ellipse(
+ xy=np.random.normal(2, 0.2, 2),
+ width=np.random.uniform(0.05, 0.15),
+ height=np.random.uniform(0.05, 0.15),
+ zorder=100,
+ facecolor="white",
+ edgecolor="black",
+ linewidth=1.25,
+ clip_on=True,
+ angle=np.random.uniform(0, 360),
+ )
+ ax.add_artist(ellipse)
+ ellipse.set_clip_path(walls)
+
+
+D = scipy.spatial.distance.cdist(P, P)
+D.sort(axis=1)
+S = []
+vor = scipy.spatial.Voronoi(P)
+for i in range(len(vor.point_region)):
+ region = vor.regions[vor.point_region[i]]
+ if not -1 in region and min(seg_dists(vor.points[i], Walls[:-1], Walls[1:])) < 0.35:
+ verts = np.array([vor.vertices[i] for i in region])
+ poly = shapely.geometry.Polygon(verts)
+ H = 1.25 * D[i, 1] * hatch(h) + P[i]
+ for i in range(len(H)):
+ line = shapely.geometry.LineString(H[i])
+ intersect = poly.intersection(line)
+ if intersect:
+ S.append(intersect.coords)
+
+# Grey background using thick lines
+hatches = LineCollection(S, color="#eeeeee", linewidth=7, capstyle="round", zorder=-20)
+ax.add_collection(hatches)
+
+# Actual hatches
+hatches = LineCollection(S, color="black", linewidth=1.5, capstyle="round", zorder=-10)
+ax.add_collection(hatches)
+
+ax.text(
+ 6,
+ 0.75,
+ "Matplotlib Dungeon",
+ clip_on=False,
+ family="Morris Roman",
+ size=32,
+ zorder=20,
+ ha="left",
+ va="center",
+)
+ax.text(
+ 6,
+ 0.1,
+ "A brand new adventure in Scientific Python",
+ clip_on=False,
+ family="Morris Roman",
+ size=16,
+ zorder=20,
+ ha="left",
+ va="center",
+)
+
+
+ax.set_xlim(0, 14), ax.set_xticks([])
+ax.set_ylim(-0, 12.5), ax.set_yticks([])
+
+plt.tight_layout()
+plt.savefig("../../figures/beyond/dungeon.pdf")
+plt.show()
diff --git a/code/beyond/dungeon.svg b/code/beyond/dungeon.svg
new file mode 100644
index 0000000..646ec2c
--- /dev/null
+++ b/code/beyond/dungeon.svg
@@ -0,0 +1,17 @@
+
+
+
diff --git a/code/beyond/dyson-hatching.py b/code/beyond/dyson-hatching.py
new file mode 100644
index 0000000..a64ef51
--- /dev/null
+++ b/code/beyond/dyson-hatching.py
@@ -0,0 +1,85 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import bluenoise
+import numpy as np
+import scipy.spatial
+import shapely.geometry
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+
+
+# Hatch pattern with given orientation (or random if None given)
+def hatch(n=4, theta=None):
+ theta = theta or np.random.uniform(0, np.pi)
+ P = np.zeros((n, 2, 2))
+ X = np.linspace(-0.5, +0.5, n, endpoint=True)
+ P[:, 0, 1] = -0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 1] = +0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 0] = X + np.random.normal(0, 0.025, n)
+ P[:, 0, 0] = X + np.random.normal(0, 0.025, n)
+ c, s = np.cos(theta), np.sin(theta)
+ Z = np.array([[c, s], [-s, c]])
+ return P @ Z.T
+
+
+# Points (blue noise distribution)
+P = bluenoise.generate((10, 10), radius=0.5)
+
+# Voronoi cells
+V = scipy.spatial.Voronoi(P)
+
+# Sorted distances between pojnts
+D = scipy.spatial.distance.cdist(P, P)
+D.sort(axis=1)
+
+
+# Actual drawing
+fig = plt.figure(figsize=(9, 3.25))
+
+
+# Point + Voronoi cells
+ax = plt.subplot(1, 3, 1, aspect=1, xlim=[0, 10], xticks=[], ylim=[0, 10], yticks=[])
+for i in range(len(V.point_region)):
+ region = V.regions[V.point_region[i]]
+ if not -1 in region:
+ verts = np.array([V.vertices[i] for i in region])
+ cell = plt.Polygon(verts, edgecolor=".25", facecolor="None")
+ ax.add_artist(cell)
+ax.scatter(P[:, 0], P[:, 1], s=5, color="black", zorder=10)
+ax.set_title("Random points (blue noise)")
+
+
+# Hatches (unclipped)
+ax = plt.subplot(1, 3, 2, aspect=1, xlim=[0, 10], xticks=[], ylim=[0, 10], yticks=[])
+S = []
+for i in range(len(V.point_region)):
+ region = V.regions[V.point_region[i]]
+ if not -1 in region:
+ verts = np.array([V.vertices[i] for i in region])
+ S.extend((1.25 * D[i, 1] * hatch(5) + P[i]))
+ax.add_collection(LineCollection(S, color="black"))
+ax.set_title("Unclipped hatches")
+
+
+# Hatches (clipped)
+ax = plt.subplot(1, 3, 3, aspect=1, xlim=[0, 10], xticks=[], ylim=[0, 10], yticks=[])
+S = []
+for i in range(len(V.point_region)):
+ region = V.regions[V.point_region[i]]
+ if not -1 in region:
+ verts = np.array([V.vertices[i] for i in region])
+ poly = shapely.geometry.Polygon(verts)
+ for l in 1.25 * D[i, 1] * hatch(5) + P[i]:
+ line = shapely.geometry.LineString(l)
+ intersect = poly.intersection(line)
+ if intersect:
+ S.append(intersect.coords)
+ax.add_collection(LineCollection(S, color="black"))
+ax.set_title("Clipped hatches")
+
+plt.tight_layout()
+plt.savefig("../../figures/beyond/dyson-hatching.pdf")
+plt.show()
diff --git a/code/beyond/interactive-loupe.py b/code/beyond/interactive-loupe.py
new file mode 100644
index 0000000..ede8ddd
--- /dev/null
+++ b/code/beyond/interactive-loupe.py
@@ -0,0 +1,107 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+from matplotlib.patches import Circle
+from matplotlib.figure import Figure
+from matplotlib.backends.backend_agg import FigureCanvas
+
+
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+P = np.random.uniform(0, 5, (500, 2))
+C = np.random.uniform(5, 25, 500)
+
+
+def plot(ax):
+ ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
+ ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ ax.yaxis.set_major_locator(ticker.MultipleLocator(1.0))
+ ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ ax.yaxis.set_minor_formatter(ticker.FormatStrFormatter("%.2f"))
+ for i, label in enumerate(ax.get_yticklabels(which="minor")):
+ label.set_size(7)
+ ax.xaxis.set_minor_formatter(ticker.FormatStrFormatter("%.2f"))
+ for i, label in enumerate(ax.get_xticklabels(which="minor")):
+ label.set_size(7)
+ ax.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ ax.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+
+ ax.scatter(P[:, 0], P[:, 1], C, color="black", zorder=10)
+
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_subplot(xlim=(0.0, 5), ylim=(0.0, 5), aspect=1)
+plot(ax)
+
+ifig = Figure(figsize=(6, 6), dpi=100, frameon=False)
+canvas = FigureCanvas(ifig)
+iax = ifig.add_subplot(xlim=(0.0, 5), ylim=(0.0, 5), aspect=1)
+plot(iax)
+plt.tight_layout()
+canvas.draw()
+Z = np.array(canvas.renderer.buffer_rgba())
+del ifig
+
+
+background = fig.canvas.copy_from_bbox(ax.bbox)
+
+
+def on_motion(event):
+ x, y = event.xdata, event.ydata
+ if x is None or y is None:
+ return
+ fig.canvas.restore_region(background)
+
+ circle_bg.set_center((x, y))
+ ax.draw_artist(circle_bg)
+
+ circle_fg.set_center((x, y))
+ ax.draw_artist(circle_fg)
+
+ image.set_extent([0 - x, 10 - x, 0 - y, 10 - y])
+ image.set_clip_path(circle_bg)
+ ax.draw_artist(image)
+
+ fig.canvas.blit(ax.bbox)
+
+
+cid = fig.canvas.mpl_connect("motion_notify_event", on_motion)
+
+circle_bg = Circle(
+ (1, 1),
+ 1,
+ transform=ax.transData,
+ zorder=20,
+ clip_on=False,
+ edgecolor="none",
+ facecolor="white",
+)
+ax.add_artist(circle_bg)
+
+circle_fg = Circle(
+ (1, 1),
+ 1,
+ transform=ax.transData,
+ zorder=30,
+ clip_on=False,
+ linewidth=2,
+ edgecolor="black",
+ facecolor="None",
+)
+ax.add_artist(circle_fg)
+
+image = ax.imshow(Z, extent=[0, 5, 0, 5], zorder=25, clip_on=True)
+image.set_extent([0, 10, 0, 10])
+image.set_clip_path(circle_bg)
+
+plt.tight_layout()
+plt.show()
diff --git a/code/beyond/polygon-clipping.py b/code/beyond/polygon-clipping.py
new file mode 100644
index 0000000..6526d6a
--- /dev/null
+++ b/code/beyond/polygon-clipping.py
@@ -0,0 +1,194 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate polygon and clipping
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.path as mpath
+import matplotlib.patches as mpatches
+
+
+def circle(center, radius):
+ """ Regular circle path """
+
+ T = np.arange(0, np.pi * 2.0, 0.01)
+ T = T.reshape(-1, 1)
+ X = center[0] + radius * np.cos(T)
+ Y = center[1] + radius * np.sin(T)
+ vertices = np.hstack((X, Y))
+ codes = np.ones(len(vertices), dtype=mpath.Path.code_type) * mpath.Path.LINETO
+ codes[0] = mpath.Path.MOVETO
+ return vertices, codes
+
+
+def rectangle(center, size):
+ """ Regular rectangle path """
+
+ (x, y), (w, h) = center, size
+ vertices = np.array([(x, y), (x + w, y), (x + w, y + h), (x, y + h), (x, y)])
+ codes = np.array(
+ [
+ mpath.Path.MOVETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ ]
+ )
+ return vertices, codes
+
+
+def patch(
+ ax, path, facecolor="0.9", edgecolor="black", linewidth=1, linestyle="-", clip=None
+):
+ """ Build a patch with potential clipping path """
+ patch = mpatches.PathPatch(
+ path,
+ linewidth=linewidth,
+ linestyle=linestyle,
+ facecolor=facecolor,
+ edgecolor=edgecolor,
+ )
+ if clip:
+ # If the path is not drawn, clipping doesn't work
+ clip_patch = mpatches.PathPatch(
+ clip, linewidth=0, facecolor="None", edgecolor="None"
+ )
+ ax.add_patch(clip_patch)
+ patch.set_clip_path(clip_patch)
+ ax.add_patch(patch)
+
+
+def subplot(cols, rows, index, title):
+ """ Shortcut to subplot to factorize options"""
+ ax = plt.subplot(cols, rows, index, aspect=1)
+ ax.text(-1.25, 0.75, "A", size="large", ha="center", va="center")
+ ax.text(+1.25, 0.75, "B", size="large", ha="center", va="center")
+ ax.set_title(title, weight="bold")
+ ax.set_xlim(-1.5, +1.5), ax.set_xticks([])
+ ax.set_ylim(-1, +1), ax.set_yticks([])
+ return ax
+
+
+V1, C1 = rectangle((-1.5, -1), size=(3, 2))
+V2, C2 = circle((-0.5, 0), radius=0.75)
+# For A, we use clock-wise circle vertices
+A = mpath.Path(V2, C2)
+# For ¬A, we use counter clock-wise circle vertices
+A_ = mpath.Path(np.concatenate([V2[::-1], V1]), np.concatenate([C2, C1]))
+
+V2, C2 = circle((+0.5, 0), radius=0.75)
+# For B, we use clock-wise circle vertices
+B = mpath.Path(V2, C2)
+# For ¬B, we use counter clock-wise circle vertices
+B_ = mpath.Path(np.concatenate([V2[::-1], V1]), np.concatenate([C2, C1]))
+
+
+fig = plt.figure(figsize=(9, 5))
+rows, cols = 3, 4
+
+# A
+ax = subplot(rows, cols, 1, "A")
+patch(ax, A, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="-", linewidth=1.5)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+
+# B
+ax = subplot(rows, cols, 2, "B")
+patch(ax, B, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="-", linewidth=1.4)
+
+# ¬A
+ax = subplot(rows, cols, 3, "¬A")
+patch(ax, A_, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="-", linewidth=1.5)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# ¬B
+ax = subplot(rows, cols, 4, "¬B")
+patch(ax, B_, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="-", linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# A ∪ B
+ax = subplot(rows, cols, 5, "A ∪ B")
+patch(ax, A, edgecolor="None")
+patch(ax, B, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A_, linewidth=1.5)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B_, linewidth=1.5)
+
+# A ∪ ¬B
+ax = subplot(rows, cols, 6, "A ∪ ¬B")
+patch(ax, A, edgecolor="None")
+patch(ax, B_, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A_, linewidth=1.5)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B, linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# ¬A ∪ B
+ax = subplot(rows, cols, 7, "¬A ∪ B")
+patch(ax, A_, edgecolor="None")
+patch(ax, B, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B_, linewidth=1.5)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A, linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# ¬A ∪ ¬B
+ax = subplot(rows, cols, 8, "¬A ∪ ¬B")
+patch(ax, A_, edgecolor="None")
+patch(ax, B_, edgecolor="None")
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B, linewidth=1.5)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A, linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+# A ∩ B
+ax = subplot(rows, cols, 9, "A ∩ B")
+patch(ax, A, edgecolor="None", clip=B)
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B, linewidth=1.5)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A, linewidth=1.5)
+
+# A ∩ ¬B
+ax = subplot(rows, cols, 10, "A ∩ ¬B")
+patch(ax, A, edgecolor="None", clip=B_)
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B_, linewidth=1.5)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A, linewidth=1.5)
+
+
+# ¬A ∩ B
+ax = subplot(rows, cols, 11, "¬A ∩ B")
+patch(ax, B, edgecolor="None", clip=A_)
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A_, linewidth=1.5)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B, linewidth=1.5)
+
+# ¬A ∩ ¬B
+ax = subplot(rows, cols, 12, "¬A ∩ ¬B")
+patch(ax, A_, edgecolor="None", clip=B_)
+patch(ax, A, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", linestyle="--", linewidth=1)
+patch(ax, B, facecolor="None", edgecolor="black", clip=A_, linewidth=1.5)
+patch(ax, A, facecolor="None", edgecolor="black", clip=B_, linewidth=1.5)
+plt.setp(ax.spines.values(), linewidth=1.5)
+
+plt.tight_layout()
+plt.savefig("../../figures/beyond/polygon-clipping.pdf")
+plt.show()
diff --git a/code/beyond/radial-maze-path.npy b/code/beyond/radial-maze-path.npy
new file mode 100644
index 0000000..3d631af
Binary files /dev/null and b/code/beyond/radial-maze-path.npy differ
diff --git a/code/beyond/radial-maze.py b/code/beyond/radial-maze.py
new file mode 100644
index 0000000..5eaa019
--- /dev/null
+++ b/code/beyond/radial-maze.py
@@ -0,0 +1,165 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import matplotlib.path as path
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+# Maze parameters
+n_arms = 8
+n_squares = 5
+square_width = 1.5
+square_height = 1.5
+inner_radius = 3
+outer_radius = inner_radius + n_squares * square_height
+delta = 0.1 # cosmetic to widen a bit maze arms
+
+W, C = [], []
+for i in range(n_arms):
+ theta = i / n_arms * 2 * np.pi
+
+ # Build external walls
+ N = (square_width / 2 + delta) * np.array([np.sin(theta), -np.cos(theta)])
+ V_in = inner_radius * np.array([np.cos(theta), np.sin(theta)])
+ V_out = (outer_radius + delta) * np.array([np.cos(theta), np.sin(theta)])
+ W.extend([V_in + N, V_out + N, V_out - N, V_in - N])
+
+ # Build arm start (pie)
+ t0 = theta - 0.5 / n_arms * 2 * np.pi
+ t1 = theta + 0.5 / n_arms * 2 * np.pi
+ V = []
+ for t in np.linspace(t0, t1, 25):
+ V.append(1.00 * inner_radius * np.array([np.cos(t), np.sin(t)]))
+ for t in np.linspace(t1, t0, 25):
+ V.append(0.25 * inner_radius * np.array([np.cos(t), np.sin(t)]))
+ V.append(V[-1])
+ C.append(V)
+
+ # Build arms squares
+ N = square_width / 2 * np.array([np.sin(theta), -np.cos(theta)])
+ T = square_height / 2 * np.array([np.cos(theta), np.sin(theta)])
+ for j in range(n_squares):
+ r = inner_radius + (j + 0.5) / n_squares * (outer_radius - inner_radius)
+ V = r * np.array([np.cos(theta), np.sin(theta)])
+ C.append([V - T + N, V + T + N, V + T - N, V - T - N])
+
+W.append(W[0])
+W = np.array(W)
+
+
+class PathTracer:
+ """ Path tracer on a figure
+
+ You can trace a path using your mouse:
+ 1. Click to start
+ 2. Move th mouse
+ 3. Click to stop and save (if save=True)
+ """
+
+ def __init__(self, line=None, load=True, save=True, filename="path.npy"):
+ self.line = line
+ self.save = save
+ self.filename = filename
+ self.xs = []
+ self.ys = []
+ self.active = False
+ if load and os.path.exists(self.filename):
+ P = np.load(self.filename)
+ self.xs = P[:, 0].tolist()
+ self.ys = P[:, 1].tolist()
+ self.line.set_data(self.xs, self.ys)
+ self.line.figure.canvas.draw()
+ line.figure.canvas.mpl_connect("button_press_event", self.on_press)
+ line.figure.canvas.mpl_connect("motion_notify_event", self.on_motion)
+
+ def on_press(self, event):
+ if event.inaxes != self.line.axes:
+ return
+ if not self.active:
+ self.active = True
+ self.xs = [event.xdata]
+ self.ys = [event.ydata]
+ else:
+ self.active = False
+ if self.save:
+ P = np.c_[self.xs, self.ys]
+ np.save(self.filename, P)
+ self.line.set_data(self.xs, self.ys)
+ self.line.figure.canvas.draw()
+
+ def on_motion(self, event):
+ if event.inaxes != self.line.axes or not self.active:
+ return
+ x, y = event.xdata, event.ydata
+ d = np.sqrt((x - self.xs[-1]) ** 2 + (y - self.ys[-1]) ** 2)
+ if d < 0.1:
+ return
+ self.xs.append(event.xdata)
+ self.ys.append(event.ydata)
+ self.line.set_data(self.xs, self.ys)
+ self.line.figure.canvas.draw()
+
+
+# The maze-path as been drawn by hand using the path tracer above
+# Here we count the number of path points contained in each part
+# of the same such as to set the color usng a colormap
+P = np.load("radial-maze-path.npy")
+N = np.zeros(len(C))
+for i, vertices in enumerate(C):
+ codes = [path.Path.MOVETO] + [path.Path.LINETO,] * (len(vertices) - 1)
+ p = path.Path(vertices, codes)
+ N[i] = p.contains_points(P).sum()
+N = 1 - (N - N.min()) / (N.max() - N.min())
+cmap = plt.cm.get_cmap("viridis")
+colors = cmap(N)
+
+# Alternatively, you can also set colors on each arm
+# colors = [] # this will be a list of n_arms * n_squares colors
+# for arm in range(n_arms):
+# for square in range(n_squares+1):
+# if arm < 6:
+# color = cmap(square/n_squares)
+# else:
+# color = 0.00, 0.0, 0.00, 0.05
+# colors.append(color)
+
+# -------------------------------------------------------
+fig = plt.figure(figsize=(8, 8))
+ax = plt.subplot(1, 1, 1, aspect=1, frameon=False, xticks=[], yticks=[])
+
+# Borders
+plt.plot(W[:, 0], W[:, 1], color="black", linewidth=2, zorder=10)
+
+# Squares
+collection = PolyCollection(
+ C, closed=True, linewidth=2, facecolors=colors, edgecolors="white"
+)
+ax.add_collection(collection)
+
+# Letters
+for i in range(n_arms):
+ theta = i / n_arms * 2 * np.pi
+ x = (outer_radius + 1) * np.cos(theta)
+ y = (outer_radius + 1) * np.sin(theta)
+ ax.text(
+ x,
+ y,
+ chr(ord("A") + i),
+ weight="bold",
+ rotation=i / n_arms * 360 - 90,
+ va="center",
+ ha="center",
+ size="xx-large",
+ )
+
+# Path tracker click to start and end (clear previous path)
+(line,) = ax.plot([], [], linestyle="-", color="black", linewidth=1.0, alpha=0.75)
+tracer = PathTracer(line, save=False, filename="radial-maze-path.npy")
+
+plt.tight_layout()
+plt.savefig("../../figures/beyond/radial-maze.pdf")
+plt.show()
diff --git a/code/beyond/stamp.py b/code/beyond/stamp.py
new file mode 100644
index 0000000..f48f5bd
--- /dev/null
+++ b/code/beyond/stamp.py
@@ -0,0 +1,53 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import FancyBboxPatch
+
+I = imageio.imread("../data/mona-lisa.png")
+height, width = np.array(I.shape)
+
+cmap = plt.get_cmap("cividis")
+fig = plt.figure(figsize=(width / 150, height / 150))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ aspect=1,
+ frameon=False,
+ xlim=[0, width],
+ xticks=[],
+ ylim=[0, height],
+ yticks=[],
+)
+
+box = FancyBboxPatch(
+ (0, 0),
+ width,
+ height,
+ zorder=40,
+ boxstyle="roundtooth,tooth_size=24",
+ lw=8,
+ ec="white",
+ fc="None",
+)
+ax.add_patch(box)
+box = FancyBboxPatch(
+ (0, 0),
+ width,
+ height,
+ zorder=50,
+ boxstyle="roundtooth,tooth_size=24",
+ lw=3,
+ ec="black",
+ fc="None",
+)
+ax.add_patch(box)
+
+im = ax.imshow(I, extent=[0, width, 0, height], zorder=30, cmap=cmap)
+im.set_clip_path(box)
+
+plt.savefig("../../figures/beyond/stamp.png", dpi=300)
+plt.show()
diff --git a/code/beyond/tikz-dashes.py b/code/beyond/tikz-dashes.py
new file mode 100644
index 0000000..88fe404
--- /dev/null
+++ b/code/beyond/tikz-dashes.py
@@ -0,0 +1,136 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def style(index, e=0.001):
+ patterns = [
+ ("densely dotted", (-0.5, (e, 2 - e))),
+ ("dotted", (-0.5, (e, 3 - e))),
+ ("loosely dotted", (-0.5, (e, 5 - e))),
+ ("densely dashed", (-0.5, (2, 3))),
+ ("dashed", (-0.5, (2, 4))),
+ ("loosely dashed", (-0.5, (2, 7))),
+ ("densely dashdotted", (-0.5, (2, 2, e, 2 - e))),
+ ("dashdotted", (-0.5, (2, 3, e, 2 - e))),
+ ("loosely dashdotted", (-0.5, (2, 5, e, 5 - e))),
+ ("densely dashdotdotted", (-0.5, (2, 2, e, 2 - e, e, 2 - e))),
+ ("dashdotdotted", (-0.5, (2, 3, e, 3 - e, e, 3 - e))),
+ ("loosely dashdotdotted", (-0.5, (2, 5, e, 5 - e, e, 5 - e))),
+ ]
+ return patterns[index]
+
+
+fig = plt.figure(figsize=(4.25, 1.5))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ xlim=[-1, 23],
+ ylim=[-1, 7],
+ frameon=False,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+)
+
+for i in range(4):
+ for j in range(3):
+ name, pattern = style(i * 3 + j)
+ X = np.array([j * 8, j * 8 + 6])
+ Y = np.array([i * 2, i * 2])
+ plt.plot(
+ X,
+ Y + 0.25,
+ color="0.4",
+ linewidth=2.0,
+ dash_capstyle="projecting",
+ linestyle=pattern,
+ )
+ plt.plot(
+ X, Y, color="0.3", linewidth=2.0, dash_capstyle="round", linestyle=pattern
+ )
+ plt.text(j * 8 + 3, i * 2 - 0.5, name, ha="center", va="top", size="x-small")
+
+ # Because of butt capstlye, we have to modify patterns a bit
+ name, pattern = style(i * 3 + j, e=0.25)
+ plt.plot(
+ X,
+ Y + 0.5,
+ color="0.5",
+ linewidth=2.0,
+ dash_capstyle="butt",
+ linestyle=pattern,
+ )
+
+plt.savefig("tikz-dashes.pdf")
+plt.show()
+
+
+# # Line width
+# # -----------------------------------------------------------------------------
+# y = 18
+# for i,linewidth in enumerate([1,2,3,4,5]):
+# X,Y = [.5+4.25*i, .5+4.25*i+3], [y,y]
+# plt.plot(X, Y, color="black", linewidth=linewidth, solid_capstyle="round")
+# plt.text((X[0]+X[1])/2, y-.75, "%.1f" % linewidth,
+# size="x-small", ha="center", va="top")
+# plt.text(0.5, y+0.75, "Line width", size="small", ha="left", va="bottom")
+
+# # Solid cap style
+# # -----------------------------------------------------------------------------
+# y = 14
+# for i,capstyle in enumerate(["butt", "round", "projecting"]):
+# X,Y = [.75+7*i, .75+7*i+5.5], [y,y]
+# plt.plot(X, Y, color=".85", linewidth=8,
+# solid_capstyle="projecting")
+# plt.plot(X, Y, color="black", linewidth=8,
+# solid_capstyle=capstyle)
+# plt.plot(X, Y, color="white", linewidth=1, marker="|", markersize=5.0)
+# plt.text((X[0]+X[1])/2, y-.75, capstyle,
+# size="x-small", ha="center", va="top")
+# plt.text(0.5, y+0.75, "Solid cap style", size="small", ha="left", va="bottom")
+
+# # Dash cap style
+# # -----------------------------------------------------------------------------
+# y = 10
+# for i,capstyle in enumerate(["butt", "round", "projecting"]):
+# X,Y = [.75+7*i, .75+7*i+5.5], [y,y]
+# plt.plot(X, Y, color=".85", linewidth=8, linestyle="--",
+# solid_capstyle=capstyle, dash_capstyle="projecting")
+# plt.plot(X, Y, color="black", linewidth=8, linestyle="--",
+# solid_capstyle=capstyle, dash_capstyle=capstyle)
+# plt.text((X[0]+X[1])/2, y-.75, capstyle,
+# size="x-small", ha="center", va="top")
+# plt.text(0.5, y+0.75, "Dash cap style", size="small", ha="left", va="bottom")
+
+
+# # Dash style
+# # -----------------------------------------------------------------------------
+# y = 6
+# for i,linestyle in enumerate(["-","--",":","-.", (0,(0.01,2))]):
+# X,Y = [.5+4.25*i, .5+4.25*i+3], [y,y]
+# plt.plot(X, Y, color="black", linewidth=2.0, linestyle=linestyle,
+# solid_capstyle="round", dash_capstyle="round")
+# if isinstance(linestyle,str):
+# plt.text((X[0]+X[1])/2, y-.75, '"%s"' % str(linestyle),
+# size="x-small", ha="center", va="top", family="monospace")
+# else:
+# plt.text((X[0]+X[1])/2, y-.75, "(0,(0.01,2))",
+# size="x-small", ha="center", va="top")
+# plt.text(0.5, y+0.75, "Classic dash style", size="small", ha="left", va="bottom")
+
+
+# # Color cycle
+# # -----------------------------------------------------------------------------
+# y = 2
+# X = np.linspace(1,20,10)
+# Y = np.ones(len(X))*y
+# C = ["C%d" % i for i in range(10)]
+# plt.scatter(X, Y, s=200, color=C)
+# for x,c in zip(X,C):
+# plt.text(x, y-0.75, '"%s"' % c,
+# size="x-small", ha="center", va="top", family="monospace")
+# plt.text(0.5, y+0.75, "Color cycle", size="small", ha="left", va="bottom")
+
+
+# plt.tight_layout()
+# plt.savefig("reference-lines.pdf", dpi=600)
+# plt.show()
diff --git a/code/beyond/tinybot.py b/code/beyond/tinybot.py
new file mode 100644
index 0000000..e912f82
--- /dev/null
+++ b/code/beyond/tinybot.py
@@ -0,0 +1,285 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.path import Path
+from matplotlib.patches import PathPatch
+from matplotlib.ticker import MultipleLocator
+from matplotlib.patches import Circle, Rectangle
+from matplotlib.collections import LineCollection
+
+
+def line_intersect(p1, p2, P3, P4):
+
+ p1 = np.atleast_2d(p1)
+ p2 = np.atleast_2d(p2)
+ P3 = np.atleast_2d(P3)
+ P4 = np.atleast_2d(P4)
+
+ x1, y1 = p1[:, 0], p1[:, 1]
+ x2, y2 = p2[:, 0], p2[:, 1]
+ X3, Y3 = P3[:, 0], P3[:, 1]
+ X4, Y4 = P4[:, 0], P4[:, 1]
+
+ D = (Y4 - Y3) * (x2 - x1) - (X4 - X3) * (y2 - y1)
+
+ # Colinearity test
+ C = D != 0
+ UA = (X4 - X3) * (y1 - Y3) - (Y4 - Y3) * (x1 - X3)
+ UA = np.divide(UA, D, where=C)
+ UB = (x2 - x1) * (y1 - Y3) - (y2 - y1) * (x1 - X3)
+ UB = np.divide(UB, D, where=C)
+
+ # Test if intersections are inside each segment
+ C = C * (UA > 0) * (UA < 1) * (UB > 0) * (UB < 1)
+
+ X = np.where(C, x1 + UA * (x2 - x1), np.inf)
+ Y = np.where(C, y1 + UA * (y2 - y1), np.inf)
+ return np.stack([X, Y], axis=1)
+
+
+class Maze:
+ """
+ A simple 8-maze made of straight walls (line segments)
+ """
+
+ def __init__(self):
+ self.walls = np.array(
+ [
+ # Surrounding walls
+ [(0, 0), (0, 500)],
+ [(0, 500), (300, 500)],
+ [(300, 500), (300, 0)],
+ [(300, 0), (0, 0)],
+ # Bottom hole
+ [(100, 100), (200, 100)],
+ [(200, 100), (200, 200)],
+ [(200, 200), (100, 200)],
+ [(100, 200), (100, 100)],
+ # Top hole
+ [(100, 300), (200, 300)],
+ [(200, 300), (200, 400)],
+ [(200, 400), (100, 400)],
+ [(100, 400), (100, 300)],
+ # Moving walls (invisibles) to constraing bot path
+ [(0, 250), (100, 200)],
+ [(200, 300), (300, 250)],
+ ]
+ )
+
+ def draw(self, ax, grid=True, margin=5):
+ """
+ Render the maze
+ """
+
+ # Buidling a filled patch from walls
+ V, C, S = [], [], self.walls
+ V.extend(S[0 + i, 0] for i in [0, 1, 2, 3, 0])
+ V.extend(S[4 + i, 0] for i in [0, 1, 2, 3, 0])
+ V.extend(S[8 + i, 0] for i in [0, 1, 2, 3, 0])
+ C = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY] * 3
+ path = Path(V, C)
+ patch = PathPatch(
+ path, clip_on=False, linewidth=1.5, edgecolor="black", facecolor="white"
+ )
+
+ # Set figure limits, grid and ticks
+ ax.set_axisbelow(True)
+ ax.add_artist(patch)
+ ax.set_xlim(0 - margin, 300 + margin)
+ ax.set_ylim(0 - margin, 500 + margin)
+ if grid:
+ ax.xaxis.set_major_locator(MultipleLocator(100))
+ ax.xaxis.set_minor_locator(MultipleLocator(10))
+ ax.yaxis.set_major_locator(MultipleLocator(100))
+ ax.yaxis.set_minor_locator(MultipleLocator(10))
+ ax.grid(True, "major", color="0.75", linewidth=1.00, clip_on=False)
+ ax.grid(True, "minor", color="0.75", linewidth=0.50, clip_on=False)
+ ax.set_xticklabels([])
+ ax.set_yticklabels([])
+ ax.tick_params(axis="both", which="major", size=0)
+ ax.tick_params(axis="both", which="minor", size=0)
+
+
+class Bot:
+ def __init__(self):
+ self.size = 10
+ self.position = 150, 250
+ self.orientation = 0
+ self.n_sensors = 8
+ A = np.linspace(-np.pi / 2, +np.pi / 2, self.n_sensors + 2, endpoint=True)[1:-1]
+ self.sensors = {
+ "angle": A,
+ "range": 75 * np.ones((self.n_sensors, 1)),
+ "value": np.ones((self.n_sensors, 1)),
+ }
+
+ def draw(self, ax):
+
+ # Sensors
+ n = 2 * len(self.sensors["angle"])
+ sensors = LineCollection(
+ np.zeros((n, 2, 2)),
+ colors=["0.75", "0.00"] * n,
+ linewidths=[0.75, 1.00] * n,
+ linestyles=["--", "-"] * n,
+ )
+ # Body
+ body = Circle(
+ self.position,
+ self.size,
+ zorder=20,
+ edgecolor="black",
+ facecolor=(1, 1, 1, 0.75),
+ )
+
+ # Head
+ P = np.zeros((1, 2, 2))
+ P[0, 0] = self.position
+ P[0, 1] = P[0, 1] + self.size * np.array(
+ [np.cos(self.orientation), np.sin(self.orientation)]
+ )
+ head = LineCollection(P, colors="black", zorder=30)
+
+ # List of artists to be rendered (sensors, body, head)
+ self.artists = [sensors, body, head]
+ ax.add_collection(sensors)
+ ax.add_artist(body)
+ ax.add_artist(head)
+
+ def update(self, maze):
+
+ sensors, body, head = self.artists
+
+ # Sensors
+ verts = sensors.get_segments()
+ linewidths = sensors.get_linewidth()
+ A = self.sensors["angle"] + self.orientation
+ T = np.stack([np.cos(A), np.sin(A)], axis=1)
+ P1 = self.position + self.size * T
+ P2 = P1 + self.sensors["range"] * T
+ P3, P4 = maze.walls[:, 0], maze.walls[:, 1]
+ for i, (p1, p2) in enumerate(zip(P1, P2)):
+ verts[2 * i] = verts[2 * i + 1] = p1, p2
+ linewidths[2 * i + 1] = 1
+ C = line_intersect(p1, p2, P3, P4)
+ index = np.argmin(((C - p1) ** 2).sum(axis=1))
+ p = C[index]
+ if p[0] < np.inf:
+ verts[2 * i + 1] = p1, p
+ self.sensors["value"][i] = np.sqrt(((p1 - p) ** 2).sum())
+ self.sensors["value"][i] /= self.sensors["range"][i]
+ else:
+ self.sensors["value"][i] = 1
+ sensors.set_verts(verts)
+ sensors.set_linewidths(linewidths)
+
+ # Body
+ body.set_center(self.position)
+
+ # Head
+ P = np.zeros((1, 2, 2))
+ P[0, 0] = self.position
+ P[0, 1] = P[0, 0] + self.size * np.array(
+ [np.cos(self.orientation), np.sin(self.orientation)]
+ )
+ head.set_verts(P)
+
+
+# ------------------------------------------------------------------------------
+if __name__ == "__main__":
+ from matplotlib.gridspec import GridSpec
+ import matplotlib.animation as animation
+
+ maze = Maze()
+ bot = Bot()
+ bot.position = 150, 250
+ bot.orientation = 0
+ bot.sensors["range"][3:5] *= 1.25
+
+ fig = plt.figure(figsize=(10, 5), frameon=False)
+ G = GridSpec(8, 2, width_ratios=(1, 2))
+ ax = plt.subplot(G[:, 0], aspect=1, frameon=False)
+
+ # Maze + bot
+ maze.draw(ax, grid=True, margin=15)
+ bot.draw(ax)
+
+ # Sensor plots
+ plots = []
+ P = np.zeros((500, 2))
+ (trace,) = ax.plot([], [], color="0.5", zorder=10, linewidth=1, linestyle=":")
+ for i in range(bot.n_sensors):
+ ax = plt.subplot(G[i, 1])
+ ax.set_xticks([]), ax.set_yticks([])
+ ax.set_ylabel("Sensor %d" % (i + 1), fontsize="x-small")
+ (plot,) = ax.plot([], [], linewidth=0.75)
+ ax.set_xlim(0, 500)
+ ax.set_ylim(0, 1.1)
+ plots.append(plot)
+ X = np.arange(500)
+ Y = np.zeros((bot.n_sensors, 500))
+
+ def update(frame):
+
+ # Update bot position according to sensors
+ dv = (bot.sensors["value"].ravel() * [-4.0, -3, -2, -1, 1, 2, 3, 4]).sum()
+ if abs(dv) > 0.5:
+ bot.orientation += 0.01 * dv
+ bot.position += 2 * np.array([np.cos(bot.orientation), np.sin(bot.orientation)])
+ bot.update(maze)
+
+ # Alternate wall moving to bias bot behavior
+ if bot.position[1] < 100:
+ maze.walls[12:] = [[(0, 250), (100, 300)], [(200, 200), (300, 250)]]
+ elif bot.position[1] > 400:
+ maze.walls[12:] = [[(0, 250), (100, 200)], [(200, 300), (300, 250)]]
+
+ # Sensor plots
+ n = len(bot.sensors["angle"])
+ if frame < 500:
+ P[frame] = bot.position
+ trace.set_xdata(P[:frame, 0])
+ trace.set_ydata(P[:frame, 1])
+ for i in range(n):
+ Y[i, frame] = bot.sensors["value"][i]
+ plots[i].set_ydata(Y[i, :frame])
+ plots[i].set_xdata(X[:frame])
+ else:
+ P[:-1] = P[1:]
+ P[-1] = bot.position
+ trace.set_xdata(P[:, 0])
+ trace.set_ydata(P[:, 1])
+ Y[:, 0:-1] = Y[:, 1:]
+ for i in range(n):
+ Y[i, -1] = bot.sensors["value"][i]
+ plots[i].set_ydata(Y[i])
+ plots[i].set_xdata(X)
+
+ return bot.artists, trace, plots
+
+ anim = animation.FuncAnimation(fig, update, frames=100_000, interval=10)
+ plt.tight_layout()
+
+ # Animation saving
+ if 0:
+ fig.set_frameon(True)
+ anim = animation.FuncAnimation(fig, update, frames=1000, interval=10)
+ import tqdm
+
+ writer = animation.FFMpegWriter(fps=30)
+ print("Encoding animation (maze.mp4")
+ bar = tqdm.tqdm(total=1000)
+ anim.save(
+ "maze.mp4",
+ writer=writer,
+ dpi=100,
+ progress_callback=lambda i, n: bar.update(1),
+ )
+ bar.close()
+ fig.set_frameon(False)
+
+ plt.show()
diff --git a/code/colors/alpha-scatter.py b/code/colors/alpha-scatter.py
new file mode 100644
index 0000000..66dd778
--- /dev/null
+++ b/code/colors/alpha-scatter.py
@@ -0,0 +1,64 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from matplotlib.backends.backend_agg import FigureCanvas
+from scipy.ndimage import gaussian_filter
+
+# Initialize random generator
+np.random.seed(123)
+
+# Create a figure that pyplot does not know about.
+fig = Figure(figsize=(10, 10))
+# attach a non-interactive Agg canvas to the figure
+# (as a side-effect of the ``__init__``)
+canvas = FigureCanvas(fig)
+ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+
+n = 50_000
+X = np.random.normal(0, 1, n)
+Y = np.random.normal(0, 1, n)
+ax.scatter(X, Y, s=75, facecolors="C0", alpha=0.10, linewidth=0)
+ax.set_xlim(-2, 2), ax.set_ylim(-2, 2)
+
+# ax.plot([1, 2, 3])
+canvas.draw()
+I = np.array(canvas.renderer.buffer_rgba())[..., 0]
+I = 1.0 - I / I.max()
+
+# now display the array X as an Axes in a new figure
+fig = plt.figure(figsize=(12, 4.5))
+
+ax = plt.subplot(1, 3, 1, aspect=1)
+ax.scatter(X, Y, s=10, facecolors="black", alpha=0.10, linewidth=0, rasterized=True)
+ax.set_xlim(-2, 2), ax.set_ylim(-2, 2)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_title("Scatter plot (n=50,000)")
+
+ax = plt.subplot(1, 3, 2)
+ax.imshow(I, cmap="gray_r", extent=[-2, 2, -2, 2])
+ax.set_xlim(-2, 2), ax.set_ylim(-2, 2)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_title("Scatter/Image plot (n=50,000)")
+
+ax = plt.subplot(1, 3, 3)
+image = ax.imshow(gaussian_filter(I, 25), cmap="gray_r", extent=[-2, 2, -2, 2])
+ax.contour(
+ gaussian_filter(I, 25),
+ extent=[-2, 2, -2, 2],
+ linestyles="--",
+ linewidths=1.0,
+ colors="white",
+ alpha=0.5,
+)
+ax.set_xlim(-2, 2), ax.set_ylim(-2, 2)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_title("Density plot")
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/alpha-scatter.pdf", dpi=600)
+plt.show()
diff --git a/code/colors/alpha-vs-color.py b/code/colors/alpha-vs-color.py
new file mode 100644
index 0000000..d85f495
--- /dev/null
+++ b/code/colors/alpha-vs-color.py
@@ -0,0 +1,219 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Open-color colors from https://yeun.github.io/open-color/
+colors = {
+ "gray": {
+ 0: "#f8f9fa",
+ 1: "#f1f3f5",
+ 2: "#e9ecef",
+ 3: "#dee2e6",
+ 4: "#ced4da",
+ 5: "#adb5bd",
+ 6: "#868e96",
+ 7: "#495057",
+ 8: "#343a40",
+ 9: "#212529",
+ },
+ "red": {
+ 0: "#fff5f5",
+ 1: "#ffe3e3",
+ 2: "#ffc9c9",
+ 3: "#ffa8a8",
+ 4: "#ff8787",
+ 5: "#ff6b6b",
+ 6: "#fa5252",
+ 7: "#f03e3e",
+ 8: "#e03131",
+ 9: "#c92a2a",
+ },
+ "pink": {
+ 0: "#fff0f6",
+ 1: "#ffdeeb",
+ 2: "#fcc2d7",
+ 3: "#faa2c1",
+ 4: "#f783ac",
+ 5: "#f06595",
+ 6: "#e64980",
+ 7: "#d6336c",
+ 8: "#c2255c",
+ 9: "#a61e4d",
+ },
+ "grape": {
+ 0: "#f8f0fc",
+ 1: "#f3d9fa",
+ 2: "#eebefa",
+ 3: "#e599f7",
+ 4: "#da77f2",
+ 5: "#cc5de8",
+ 6: "#be4bdb",
+ 7: "#ae3ec9",
+ 8: "#9c36b5",
+ 9: "#862e9c",
+ },
+ "violet": {
+ 0: "#f3f0ff",
+ 1: "#e5dbff",
+ 2: "#d0bfff",
+ 3: "#b197fc",
+ 4: "#9775fa",
+ 5: "#845ef7",
+ 6: "#7950f2",
+ 7: "#7048e8",
+ 8: "#6741d9",
+ 9: "#5f3dc4",
+ },
+ "indigo": {
+ 0: "#edf2ff",
+ 1: "#dbe4ff",
+ 2: "#bac8ff",
+ 3: "#91a7ff",
+ 4: "#748ffc",
+ 5: "#5c7cfa",
+ 6: "#4c6ef5",
+ 7: "#4263eb",
+ 8: "#3b5bdb",
+ 9: "#364fc7",
+ },
+ "blue": {
+ 0: "#e7f5ff",
+ 1: "#d0ebff",
+ 2: "#a5d8ff",
+ 3: "#74c0fc",
+ 4: "#4dabf7",
+ 5: "#339af0",
+ 6: "#228be6",
+ 7: "#1c7ed6",
+ 8: "#1971c2",
+ 9: "#1864ab",
+ },
+ "cyan": {
+ 0: "#e3fafc",
+ 1: "#c5f6fa",
+ 2: "#99e9f2",
+ 3: "#66d9e8",
+ 4: "#3bc9db",
+ 5: "#22b8cf",
+ 6: "#15aabf",
+ 7: "#1098ad",
+ 8: "#0c8599",
+ 9: "#0b7285",
+ },
+ "teal": {
+ 0: "#e6fcf5",
+ 1: "#c3fae8",
+ 2: "#96f2d7",
+ 3: "#63e6be",
+ 4: "#38d9a9",
+ 5: "#20c997",
+ 6: "#12b886",
+ 7: "#0ca678",
+ 8: "#099268",
+ 9: "#087f5b",
+ },
+ "green": {
+ 0: "#ebfbee",
+ 1: "#d3f9d8",
+ 2: "#b2f2bb",
+ 3: "#8ce99a",
+ 4: "#69db7c",
+ 5: "#51cf66",
+ 6: "#40c057",
+ 7: "#37b24d",
+ 8: "#2f9e44",
+ 9: "#2b8a3e",
+ },
+ "lime": {
+ 0: "#f4fce3",
+ 1: "#e9fac8",
+ 2: "#d8f5a2",
+ 3: "#c0eb75",
+ 4: "#a9e34b",
+ 5: "#94d82d",
+ 6: "#82c91e",
+ 7: "#74b816",
+ 8: "#66a80f",
+ 9: "#5c940d",
+ },
+ "yellow": {
+ 0: "#fff9db",
+ 1: "#fff3bf",
+ 2: "#ffec99",
+ 3: "#ffe066",
+ 4: "#ffd43b",
+ 5: "#fcc419",
+ 6: "#fab005",
+ 7: "#f59f00",
+ 8: "#f08c00",
+ 9: "#e67700",
+ },
+ "orange": {
+ 0: "#fff4e6",
+ 1: "#ffe8cc",
+ 2: "#ffd8a8",
+ 3: "#ffc078",
+ 4: "#ffa94d",
+ 5: "#ff922b",
+ 6: "#fd7e14",
+ 7: "#f76707",
+ 8: "#e8590c",
+ 9: "#d9480f",
+ },
+}
+
+
+# Setup
+fig = plt.figure(figsize=(6, 2))
+
+X = np.linspace(0, 2 * np.pi, 256)
+Y1 = np.zeros((50, len(X)))
+Y2 = np.zeros((len(Y1), len(X)))
+noise = 0.5
+for i in range(len(Y1)):
+ Y1[i] = np.cos(X) + noise * np.random.uniform(-1, 1, len(X))
+ Y2[i] = np.sin(X) + noise * np.random.uniform(-1, 1, len(X))
+
+
+# Usage of transparency
+ax = plt.subplot(1, 2, 1)
+
+Y, SD = Y1.mean(axis=0), Y1.std(axis=0)
+ax.fill_between(X, Y + SD, Y - SD, facecolor="C0", alpha=0.25, zorder=-40)
+ax.plot(X, Y, color="C0", zorder=-30)
+
+Y, SD = Y2.mean(axis=0), Y2.std(axis=0)
+ax.fill_between(X, Y + SD, Y - SD, facecolor="C1", alpha=0.25, zorder=-20)
+ax.plot(X, Y, color="C1", zorder=-10)
+
+ax.set_xticks([]), ax.set_yticks([])
+
+
+# Usage of explicite colors
+ax = plt.subplot(1, 2, 2)
+
+Y, SD = Y1.mean(axis=0), Y1.std(axis=0)
+ax.fill_between(X, Y + SD, Y - SD, facecolor=colors["blue"][1], zorder=-40)
+ax.plot(X, Y, color=colors["blue"][7], zorder=-30)
+
+Y, SD = Y2.mean(axis=0), Y2.std(axis=0)
+ax.fill_between(
+ X,
+ Y + SD,
+ Y - SD,
+ facecolor=colors["orange"][1],
+ edgecolor="white",
+ linewidth=0.75,
+ zorder=-20,
+)
+ax.plot(X, Y, color=colors["orange"][7], zorder=-10)
+
+ax.set_xticks([]), ax.set_yticks([])
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/alpha-vs-color.pdf")
+plt.show()
diff --git a/code/colors/color-gradients.py b/code/colors/color-gradients.py
new file mode 100644
index 0000000..56ff512
--- /dev/null
+++ b/code/colors/color-gradients.py
@@ -0,0 +1,129 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate the different colorspaces (sRGB, RGB and Lab)
+# ----------------------------------------------------------------------------
+import math
+import numpy as np
+from skimage.color import rgb2lab, lab2rgb, rgb2xyz, xyz2rgb
+import matplotlib.pyplot as plt
+
+plt.rc("font", family="Roboto")
+
+
+def sRGB_to_Lab(color):
+ color = np.asarray(color, dtype=float)
+ shape = color.shape
+ if len(shape) == 1:
+ color = color.reshape(1, 1, shape[0])
+ elif len(shape) == 2:
+ color = color.reshape(1, shape[0], shape[1])
+ return rgb2lab(color)
+
+
+def Lab_to_sRGB(color):
+ color = np.asarray(color, dtype=float)
+ shape = color.shape
+ if len(shape) == 1:
+ color = color.reshape(1, 1, shape[0])
+ elif len(shape) == 2:
+ color = color.reshape(1, shape[0], shape[1])
+ return lab2rgb(color)
+
+
+def sRGB_to_RGB(color):
+ color = np.asarray(color, dtype=float).reshape(-1, 3)
+ R, G, B = color[..., 0], color[..., 1], color[..., 2]
+ R = np.where(R > 0.04045, np.power((R + 0.055) / 1.055, 2.4), R / 12.92)
+ G = np.where(G > 0.04045, np.power((G + 0.055) / 1.055, 2.4), G / 12.92)
+ B = np.where(B > 0.04045, np.power((B + 0.055) / 1.055, 2.4), B / 12.92)
+ return np.c_[R, G, B]
+
+
+def RGB_to_sRGB(color):
+ color = np.asarray(color, dtype=float).reshape(-1, 3)
+ R, G, B = color[..., 0], color[..., 1], color[..., 2]
+ R = np.where(R > 0.0031308, 1.055 * np.power(R, 1 / 2.4) - 0.055, R * 12.92)
+ G = np.where(G > 0.0031308, 1.055 * np.power(G, 1 / 2.4) - 0.055, G * 12.92)
+ B = np.where(B > 0.0031308, 1.055 * np.power(B, 1 / 2.4) - 0.055, B * 12.92)
+ return np.c_[R, G, B]
+
+
+def gradient(color0, color1, mode="sRGB", n=256):
+ T = np.linspace(0, 1, n).reshape(n, 1)
+ if mode == "Lab":
+ C = (1 - T) * sRGB_to_Lab(color0) + T * sRGB_to_Lab(color1)
+ return Lab_to_sRGB(C)
+ elif mode == "RGB":
+ C = (1 - T) * sRGB_to_RGB(color0) + T * sRGB_to_RGB(color1)
+ return RGB_to_sRGB(C)
+ else:
+ return (1 - T) * color0 + T * color1
+
+
+def hex(color):
+ color = (np.asarray(color) * 255).astype(int)
+ r, g, b = color
+ return ("#%02x%02x%02x" % (r, g, b)).upper()
+
+
+def plot(ax, color0, color1, yticks=True):
+ rows, cols = 16, 256
+ Z = np.zeros((3, rows, cols, 3))
+ Z[0] = gradient(color0, color1, "sRGB")
+ Z[2] = gradient(color0, color1, "RGB")
+ Z[1] = gradient(color0, color1, "Lab")
+
+ ax.tick_params(axis="both", length=0, labelsize="xx-small")
+ ax.imshow(Z.reshape(3 * rows, cols, 3), extent=[0, cols, 0, 3 * rows])
+
+ if yticks:
+ ax.set_yticks([rows // 2, rows // 2 + rows, rows // 2 + 2 * rows])
+ ax.set_yticklabels(["Lab", "RGB", "sRGB"])
+ else:
+ ax.set_yticks([])
+ ax.set_xticks([])
+ plt.text(0, -2, hex(color0), ha="left", va="top", fontsize="xx-small")
+ plt.text(cols, -2, hex(color1), ha="right", va="top", fontsize="xx-small")
+
+
+fig = plt.figure(figsize=(4.25, 3.5))
+plt.rcParams["axes.linewidth"] = 0.5
+
+rows, cols = 6, 2
+
+ax = plt.subplot(rows, cols, 1)
+plot(ax, (1, 1, 1), (1, 0, 0))
+ax = plt.subplot(rows, cols, 2)
+plot(ax, (1, 0, 0), (0, 0, 0), False)
+
+ax = plt.subplot(rows, cols, 3)
+plot(ax, (1, 1, 1), (0, 1, 0))
+ax = plt.subplot(rows, cols, 4)
+plot(ax, (0, 1, 0), (0, 0, 0), False)
+
+ax = plt.subplot(rows, cols, 5)
+plot(ax, (1, 1, 1), (0, 0, 1))
+ax = plt.subplot(rows, cols, 6)
+plot(ax, (0, 0, 1), (0, 0, 0), False)
+
+ax = plt.subplot(rows, cols, 7)
+plot(ax, (1, 0, 0), (0, 1, 0))
+ax = plt.subplot(rows, cols, 8)
+plot(ax, (0, 1, 0), (0, 0, 1), False)
+
+ax = plt.subplot(rows, cols, 9)
+plot(ax, (1, 0, 1), (1, 1, 0))
+ax = plt.subplot(rows, cols, 10)
+plot(ax, (1, 1, 0), (0, 1, 1), False)
+
+ax = plt.subplot(rows, cols, 11)
+plot(ax, (1, 1, 1), (0, 0, 0))
+ax = plt.subplot(rows, cols, 12)
+plot(ax, (0, 0, 0), (1, 1, 1), False)
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/color-gradients.pdf", dpi=600)
+plt.show()
diff --git a/code/colors/color-wheel.py b/code/colors/color-wheel.py
new file mode 100644
index 0000000..48c6195
--- /dev/null
+++ b/code/colors/color-wheel.py
@@ -0,0 +1,222 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Color wheel in HSV
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.collections import QuadMesh
+from matplotlib.font_manager import FontProperties
+
+# Curved label (polar coordinates)
+def polar_text(text, angle, radius=1, scale=0.005, family="sans"):
+ prop = FontProperties(family=family, weight="regular")
+ path = TextPath((0, 0), text, size=1, prop=prop)
+ V = path.vertices
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ V[:, 0] = angle - (V[:, 0] - (xmin + xmax) / 2) * scale
+ V[:, 1] = radius + V[:, 1] * scale
+ patch = PathPatch(path, facecolor="black", linewidth=0, clip_on=False)
+ ax.add_artist(patch)
+
+
+# Polar imshow using quadmesh
+def polar_imshow(
+ ax, Z, extents=[0, 1, 0, 2 * np.pi], vmin=None, vmax=None, cmap="viridis"
+):
+
+ Z = np.atleast_3d(Z)
+ nr, nt, d = Z.shape
+ rmin, rmax, tmin, tmax = extents
+
+ if d == 1:
+ cmap = plt.get_cmap(cmap)
+ vmin = vmin or Z.min()
+ vmax = vmax or Z.max()
+ norm = colors.Normalize(vmin=vmin, vmax=vmax)
+ facecolors = cmap(norm(Z))
+ else:
+ facecolors = Z.reshape(nr, nt, 3).reshape(-1, 3)
+
+ R = np.linspace(rmin, rmax, nr + 1)
+ T = np.linspace(tmin, tmax, nt + 1)
+ T, R = np.meshgrid(T, R)
+ nr, nt = R.shape
+ R, T = R.ravel(), T.ravel()
+ coords = np.column_stack((T, R))
+ collection = QuadMesh(
+ nt - 1,
+ nr - 1,
+ coords,
+ rasterized=True,
+ facecolors=facecolors,
+ edgecolors="None",
+ linewidth=0,
+ antialiased=False,
+ )
+ ax.add_collection(collection)
+ return collection
+
+
+radius = 1.05
+scale = 0.085
+family = "Yanone Kaffeesatz"
+
+fig = plt.figure(figsize=(8, 4))
+
+n = 50
+R = np.linspace(0, 1, 2 * n)
+T = np.linspace(0, 1, 10 * n)
+T, R = np.meshgrid(T, R)
+H, S, V = T, R, np.ones_like(T)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(2, 4, 1, polar=True, frameon=True)
+Z = colors.hsv_to_rgb(np.dstack([H, S, V]))
+ax.set_xticks([]), ax.set_yticks([])
+polar_text(
+ "<––– hue –––", family=family, angle=np.pi / 4, radius=radius, scale=2 * scale
+)
+ax.text(
+ 3 * np.pi / 4,
+ 1.5,
+ "value = 1.00",
+ family=family,
+ size=10,
+ bbox={
+ "pad": 1.5,
+ "linewidth": 0.5,
+ "boxstyle": "round,pad=.2",
+ "edgecolor": "black",
+ "facecolor": "white",
+ },
+)
+
+ax.text(np.pi, 0.0, "––– saturation –––> ", size=8, family=family)
+polar_imshow(ax, Z)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(2, 4, 2, polar=True, frameon=True)
+Z = colors.hsv_to_rgb(np.dstack([H, S, 0.75 * V]))
+ax.set_xticks([]), ax.set_yticks([])
+polar_text(
+ "<––– hue –––", family=family, angle=np.pi / 4, radius=radius, scale=2 * scale
+)
+ax.text(
+ 3 * np.pi / 4,
+ 1.5,
+ "value = 0.75",
+ family=family,
+ size=10,
+ bbox={
+ "pad": 1.5,
+ "linewidth": 0.5,
+ "boxstyle": "round,pad=.2",
+ "edgecolor": "None",
+ "facecolor": "0.75",
+ },
+)
+ax.text(np.pi, 0.0, "––– saturation –––> ", size=8, family=family)
+polar_imshow(ax, Z)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(2, 4, 5, polar=True, frameon=True)
+Z = colors.hsv_to_rgb(np.dstack([H, S, 0.5 * V]))
+ax.set_xticks([]), ax.set_yticks([])
+polar_text(
+ "<––– hue –––", family=family, angle=np.pi / 4, radius=radius, scale=2 * scale
+)
+ax.text(
+ 3 * np.pi / 4,
+ 1.5,
+ "value = 0.50",
+ family=family,
+ size=10,
+ color="white",
+ bbox={
+ "pad": 1.5,
+ "linewidth": 0.5,
+ "boxstyle": "round,pad=.2",
+ "edgecolor": "None",
+ "facecolor": "0.5",
+ },
+)
+ax.text(np.pi, 0.0, "––– saturation –––> ", size=8, family=family, color="1.0")
+polar_imshow(ax, Z)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(2, 4, 6, polar=True, frameon=True)
+Z = colors.hsv_to_rgb(np.dstack([H, S, 0.25 * V]))
+ax.set_xticks([]), ax.set_yticks([])
+polar_text(
+ "<––– hue –––", family=family, angle=np.pi / 4, radius=radius, scale=2 * scale
+)
+ax.text(
+ 3 * np.pi / 4,
+ 1.5,
+ "value = 0.25",
+ family=family,
+ size=10,
+ color="white",
+ bbox={
+ "pad": 1.5,
+ "linewidth": 0.5,
+ "boxstyle": "round,pad=.2",
+ "edgecolor": "None",
+ "facecolor": "0.25",
+ },
+)
+ax.text(np.pi, 0.0, "-–– saturation –––> ", size=8, family=family, color="1.0")
+polar_imshow(ax, Z)
+
+# -----------------------------------------------------------------------------
+ax = fig.add_subplot(1, 2, 2, polar=True, frameon=False)
+n = 100
+R = np.linspace(0, 1, n)
+R -= R % (1 / 5.99)
+T = np.linspace(0, 1, 10 * n)
+T -= T % (1 / 11.99)
+T, R = np.meshgrid(T, R)
+H, S, V = T, R, np.ones_like(T)
+Z = colors.hsv_to_rgb(np.dstack([H, S, V]))
+polar_imshow(ax, Z)
+ax.set_xticks(np.linspace(0, 2 * np.pi, 13))
+ax.set_yticks(np.linspace(0, 1, 7))
+ax.set_yticklabels([])
+ax.set_xticklabels([])
+ax.grid(linewidth=1, color="white")
+labels = [
+ "red",
+ "orange",
+ "yellow",
+ "lime",
+ "green",
+ "turquoise",
+ "cyan",
+ "skyblue",
+ "blue",
+ "violet",
+ "purple",
+ "magenta",
+]
+for label, x in zip(labels, np.linspace(0.5, 12.5, 12)):
+ label = label.upper()
+ angle = x / 13 * 2 * np.pi
+ polar_text(
+ label.upper(), family=family, angle=angle, radius=radius, scale=1.25 * scale
+ )
+ax.set_theta_offset(-np.pi / 12)
+
+R = np.ones(100)
+T = np.linspace(0, 2 * np.pi, 100)
+ax.plot(T, R, color="white")
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/color-wheel.png", dpi=600)
+plt.savefig("../../figures/colors/color-wheel.pdf", dpi=600)
+plt.show()
diff --git a/code/colors/colored-hist.py b/code/colors/colored-hist.py
new file mode 100644
index 0000000..cf26f44
--- /dev/null
+++ b/code/colors/colored-hist.py
@@ -0,0 +1,296 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+material = {
+ "red": {
+ 0: "#ffebee",
+ 1: "#ffcdd2",
+ 2: "#ef9a9a",
+ 3: "#e57373",
+ 4: "#ef5350",
+ 5: "#f44336",
+ 6: "#e53935",
+ 7: "#d32f2f",
+ 8: "#c62828",
+ 9: "#b71c1c",
+ },
+ "pink": {
+ 0: "#fce4ec",
+ 1: "#f8bbd0",
+ 2: "#f48fb1",
+ 3: "#f06292",
+ 4: "#ec407a",
+ 5: "#e91e63",
+ 6: "#d81b60",
+ 7: "#c2185b",
+ 8: "#ad1457",
+ 9: "#880e4f",
+ },
+ "purple": {
+ 0: "#f3e5f5",
+ 1: "#e1bee7",
+ 2: "#ce93d8",
+ 3: "#ba68c8",
+ 4: "#ab47bc",
+ 5: "#9c27b0",
+ 6: "#8e24aa",
+ 7: "#7b1fa2",
+ 8: "#6a1b9a",
+ 9: "#4a148c",
+ },
+ "deep purple": {
+ 0: "#ede7f6",
+ 1: "#d1c4e9",
+ 2: "#b39ddb",
+ 3: "#9575cd",
+ 4: "#7e57c2",
+ 5: "#673ab7",
+ 6: "#5e35b1",
+ 7: "#512da8",
+ 8: "#4527a0",
+ 9: "#311b92",
+ },
+ "indigo": {
+ 0: "#e8eaf6",
+ 1: "#c5cae9",
+ 2: "#9fa8da",
+ 3: "#7986cb",
+ 4: "#5c6bc0",
+ 5: "#3f51b5",
+ 6: "#3949ab",
+ 7: "#303f9f",
+ 8: "#283593",
+ 9: "#1a237e",
+ },
+ "blue": {
+ 0: "#e3f2fd",
+ 1: "#bbdefb",
+ 2: "#90caf9",
+ 3: "#64b5f6",
+ 4: "#42a5f5",
+ 5: "#2196f3",
+ 6: "#1e88e5",
+ 7: "#1976d2",
+ 8: "#1565c0",
+ 9: "#0d47a1",
+ },
+ "light blue": {
+ 0: "#e1f5fe",
+ 1: "#b3e5fc",
+ 2: "#81d4fa",
+ 3: "#4fc3f7",
+ 4: "#29b6f6",
+ 5: "#03a9f4",
+ 6: "#039be5",
+ 7: "#0288d1",
+ 8: "#0277bd",
+ 9: "#01579b",
+ },
+ "cyan": {
+ 0: "#e0f7fa",
+ 1: "#b2ebf2",
+ 2: "#80deea",
+ 3: "#4dd0e1",
+ 4: "#26c6da",
+ 5: "#00bcd4",
+ 6: "#00acc1",
+ 7: "#0097a7",
+ 8: "#00838f",
+ 9: "#006064",
+ },
+ "teal": {
+ 0: "#e0f2f1",
+ 1: "#b2dfdb",
+ 2: "#80cbc4",
+ 3: "#4db6ac",
+ 4: "#26a69a",
+ 5: "#009688",
+ 6: "#00897b",
+ 7: "#00796b",
+ 8: "#00695c",
+ 9: "#004d40",
+ },
+ "green": {
+ 0: "#e8f5e9",
+ 1: "#c8e6c9",
+ 2: "#a5d6a7",
+ 3: "#81c784",
+ 4: "#66bb6a",
+ 5: "#4caf50",
+ 6: "#43a047",
+ 7: "#388e3c",
+ 8: "#2e7d32",
+ 9: "#1b5e20",
+ },
+ "light green": {
+ 0: "#f1f8e9",
+ 1: "#dcedc8",
+ 2: "#c5e1a5",
+ 3: "#aed581",
+ 4: "#9ccc65",
+ 5: "#8bc34a",
+ 6: "#7cb342",
+ 7: "#689f38",
+ 8: "#558b2f",
+ 9: "#33691e",
+ },
+ "lime": {
+ 0: "#f9fbe7",
+ 1: "#f0f4c3",
+ 2: "#e6ee9c",
+ 3: "#dce775",
+ 4: "#d4e157",
+ 5: "#cddc39",
+ 6: "#c0ca33",
+ 7: "#afb42b",
+ 8: "#9e9d24",
+ 9: "#827717",
+ },
+ "yellow": {
+ 0: "#fffde7",
+ 1: "#fff9c4",
+ 2: "#fff59d",
+ 3: "#fff176",
+ 4: "#ffee58",
+ 5: "#ffeb3b",
+ 6: "#fdd835",
+ 7: "#fbc02d",
+ 8: "#f9a825",
+ 9: "#f57f17",
+ },
+ "amber": {
+ 0: "#fff8e1",
+ 1: "#ffecb3",
+ 2: "#ffe082",
+ 3: "#ffd54f",
+ 4: "#ffca28",
+ 5: "#ffc107",
+ 6: "#ffb300",
+ 7: "#ffa000",
+ 8: "#ff8f00",
+ 9: "#ff6f00",
+ },
+ "orange": {
+ 0: "#fff3e0",
+ 1: "#ffe0b2",
+ 2: "#ffcc80",
+ 3: "#ffb74d",
+ 4: "#ffa726",
+ 5: "#ff9800",
+ 6: "#fb8c00",
+ 7: "#f57c00",
+ 8: "#ef6c00",
+ 9: "#e65100",
+ },
+ "deep orange": {
+ 0: "#fbe9e7",
+ 1: "#ffccbc",
+ 2: "#ffab91",
+ 3: "#ff8a65",
+ 4: "#ff7043",
+ 5: "#ff5722",
+ 6: "#f4511e",
+ 7: "#e64a19",
+ 8: "#d84315",
+ 9: "#bf360c",
+ },
+ "brown": {
+ 0: "#efebe9",
+ 1: "#d7ccc8",
+ 2: "#bcaaa4",
+ 3: "#a1887f",
+ 4: "#8d6e63",
+ 5: "#795548",
+ 6: "#6d4c41",
+ 7: "#5d4037",
+ 8: "#4e342e",
+ 9: "#3e2723",
+ },
+ "grey": {
+ 0: "#fafafa",
+ 1: "#f5f5f5",
+ 2: "#eeeeee",
+ 3: "#e0e0e0",
+ 4: "#bdbdbd",
+ 5: "#9e9e9e",
+ 6: "#757575",
+ 7: "#616161",
+ 8: "#424242",
+ 9: "#212121",
+ },
+ "blue grey": {
+ 0: "#eceff1",
+ 1: "#cfd8dc",
+ 2: "#b0bec5",
+ 3: "#90a4ae",
+ 4: "#78909c",
+ 5: "#607d8b",
+ 6: "#546e7a",
+ 7: "#455a64",
+ 8: "#37474f",
+ 9: "#263238",
+ },
+}
+
+
+np.random.seed(123)
+plt.figure(figsize=(8, 4))
+ax = plt.subplot(1, 1, 1, frameon=False)
+
+
+def bars(origin, color, n, index):
+ X = origin + np.arange(n)
+ H = 20 * np.random.uniform(1.0, 5.0, n)
+ H.sort()
+
+ ax.bar(
+ X,
+ H,
+ width=1.0,
+ align="edge",
+ color=[material[color][1 + 2 * i] for i in range(n)],
+ )
+ ax.plot([origin - 0.5, origin + n + 0.5], [0, 0], color="black", lw=2.5)
+ ax.text(origin + n / 2, -1, "Group " + index, va="top", ha="center")
+
+
+ax.axhline(50, 0.05, 1, color="0.5", linewidth=0.5, linestyle="--", zorder=-10)
+ax.axhline(100, 0.05, 1, color="0.5", linewidth=0.5, linestyle="--", zorder=-10)
+
+n = 3
+bars(0 * (n + 2), "red", n, "A")
+bars(1 * (n + 2), "indigo", n, "B")
+bars(2 * (n + 2), "orange", n, "C")
+bars(3 * (n + 2), "teal", n, "D")
+bars(4 * (n + 2), "pink", n, "E")
+bars(5 * (n + 2), "cyan", n, "F")
+
+ax.set_xlim(-2, 6 * (n + 2) - 1.5)
+ax.set_xticks([])
+ax.set_ylim(0, 111)
+ax.set_yticks([0, 50, 100])
+ax.set_yticklabels(["0%", "50%", "100%"])
+
+
+plt.legend(
+ # bbox_to_anchor=(0.0, 1.0, 1.0, 0.1),
+ loc="upper right",
+ borderaxespad=0.0,
+ ncol=3,
+ handles=[
+ mpatches.Patch(color=material["blue grey"][1], label="2018"),
+ mpatches.Patch(color=material["blue grey"][3], label="2019"),
+ mpatches.Patch(color=material["blue grey"][5], label="2020"),
+ ],
+)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/colored-hist.pdf")
+plt.show()
diff --git a/code/colors/colored-plot.py b/code/colors/colored-plot.py
new file mode 100644
index 0000000..55f3099
--- /dev/null
+++ b/code/colors/colored-plot.py
@@ -0,0 +1,33 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate colored plots
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+
+
+def plot(ax, X, Y, cmap, alpha):
+ P = np.array([X, Y]).T.reshape(-1, 1, 2)
+ S = np.concatenate([P[:-1], P[1:]], axis=1)
+ C = cmap(np.linspace(0, 1, len(S)))
+ L = LineCollection(S, color=C, alpha=alpha, linewidth=1.25)
+ ax.add_collection(L)
+
+
+fig = plt.figure(figsize=(12, 3))
+fig.patch.set_facecolor("black")
+ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+X = np.linspace(-5 * np.pi, +5 * np.pi, 2500)
+for d in np.linspace(0, 1, 15):
+ dy = d / 2 + (1 - np.abs(X) / X.max()) ** 2
+ dx = 1 + d / 3
+ Y = dy * np.sin(dx * X) + 0.1 * np.cos(3 + 5 * X)
+ plot(ax, X, Y, plt.get_cmap("rainbow"), d)
+ax.set_xlim(X.min(), X.max())
+ax.set_ylim(-2.0, 2.0)
+plt.savefig("../../figures/colors/colored-plot.pdf")
+plt.show()
diff --git a/code/colors/flower-polar.py b/code/colors/flower-polar.py
new file mode 100644
index 0000000..3ecd57d
--- /dev/null
+++ b/code/colors/flower-polar.py
@@ -0,0 +1,124 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Flower polar / flower power (poly collection and polar projection)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+from matplotlib.collections import PolyCollection
+
+
+def flower(ax, n_branches=24, n_sections=4, lw=1):
+ R = np.linspace(0.1, 1.0, 25)
+ paths = []
+ facecolors = []
+ n_sections += 1
+
+ for i in range(n_branches):
+ for j in range(n_sections - 1):
+ R_, T_ = [], []
+ T = np.linspace(
+ i * 2 * np.pi / n_branches,
+ (i + n_sections / 2) * 2 * np.pi / n_branches,
+ len(R),
+ )
+ t = np.interp(
+ np.linspace(j + 1, j + 2, 20), np.linspace(0, n_sections, len(T)), T
+ )
+ r = np.interp(
+ np.linspace(j + 1, j + 2, 20), np.linspace(0, n_sections, len(R)), R
+ )
+ R_.extend(r[::1].tolist())
+ T_.extend(t[::1].tolist())
+
+ T = np.linspace(
+ (i + 1 + j + 1) * 2 * np.pi / n_branches,
+ (i + 1 + j + 1 - n_sections / 2) * 2 * np.pi / n_branches,
+ len(R),
+ )
+ t = np.interp(
+ np.linspace(j + 1, j + 2, 20), np.linspace(0, n_sections, len(T)), T
+ )
+ r = np.interp(
+ np.linspace(j + 1, j + 2, 20), np.linspace(0, n_sections, len(R)), R
+ )
+ R_.extend(r[::-1].tolist())
+ T_.extend(t[::-1].tolist())
+
+ T = np.linspace(
+ (i + 1) * 2 * np.pi / n_branches,
+ (i + 1 + n_sections / 2) * 2 * np.pi / n_branches,
+ len(R),
+ )
+ t = np.interp(
+ np.linspace(j, j + 1, 20), np.linspace(0, n_sections, len(T)), T
+ )
+ r = np.interp(
+ np.linspace(j, j + 1, 20), np.linspace(0, n_sections, len(R)), R
+ )
+ R_.extend(r[::-1].tolist())
+ T_.extend(t[::-1].tolist())
+
+ T = np.linspace(
+ (i + 1 + j) * 2 * np.pi / n_branches,
+ (i + 1 + j - n_sections / 2) * 2 * np.pi / n_branches,
+ len(R),
+ )
+ t = np.interp(
+ np.linspace(j, j + 1, 20), np.linspace(0, n_sections, len(T)), T
+ )
+ r = np.interp(
+ np.linspace(j, j + 1, 20), np.linspace(0, n_sections, len(R)), R
+ )
+ R_.extend(r[::1].tolist())
+ T_.extend(t[::1].tolist())
+
+ P = np.dstack([T_, R_]).squeeze()
+ paths.append(P)
+ h = i / n_branches
+ s = 0.5 + 0.5 * j / (n_sections - 1)
+ v = 1.00
+ facecolors.append(colors.hsv_to_rgb([h, s, v]))
+
+ collection = PolyCollection(
+ paths, linewidths=5.5 * lw, facecolors="None", edgecolors="black"
+ )
+ ax.add_collection(collection)
+
+ collection = PolyCollection(
+ paths, linewidths=4 * lw, facecolors="None", edgecolors="white"
+ )
+ ax.add_collection(collection)
+
+ ax.fill_between(np.linspace(0, 2 * np.pi, 100), 0.0, 0.5, facecolor="white")
+
+ collection = PolyCollection(
+ paths, linewidths=lw, facecolors=facecolors, edgecolors="white"
+ )
+ ax.add_collection(collection)
+
+
+fig = plt.figure(figsize=(12, 4))
+
+ax = fig.add_subplot(1, 3, 1, polar=True, frameon=False)
+flower(ax, 6, 3, lw=2.0)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_rlim(0, 1.1)
+
+ax = fig.add_subplot(1, 3, 2, polar=True, frameon=False)
+flower(ax, 12, 4, lw=1.5)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_rlim(0, 1.1)
+
+ax = fig.add_subplot(1, 3, 3, polar=True, frameon=False)
+flower(ax, 24, 5, lw=1.0)
+ax.set_xticks([]), ax.set_yticks([])
+ax.set_rlim(0, 1.1)
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/flower-polar.pdf", dpi=600)
+# plt.savefig("../../figures/colors/flower-polar.png", dpi=300)
+plt.show()
diff --git a/code/colors/material-colors.py b/code/colors/material-colors.py
new file mode 100644
index 0000000..3306276
--- /dev/null
+++ b/code/colors/material-colors.py
@@ -0,0 +1,294 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatch
+from matplotlib.patches import FancyBboxPatch
+
+colors = {
+ "red": {
+ 0: "#ffebee",
+ 1: "#ffcdd2",
+ 2: "#ef9a9a",
+ 3: "#e57373",
+ 4: "#ef5350",
+ 5: "#f44336",
+ 6: "#e53935",
+ 7: "#d32f2f",
+ 8: "#c62828",
+ 9: "#b71c1c",
+ },
+ "pink": {
+ 0: "#fce4ec",
+ 1: "#f8bbd0",
+ 2: "#f48fb1",
+ 3: "#f06292",
+ 4: "#ec407a",
+ 5: "#e91e63",
+ 6: "#d81b60",
+ 7: "#c2185b",
+ 8: "#ad1457",
+ 9: "#880e4f",
+ },
+ "purple": {
+ 0: "#f3e5f5",
+ 1: "#e1bee7",
+ 2: "#ce93d8",
+ 3: "#ba68c8",
+ 4: "#ab47bc",
+ 5: "#9c27b0",
+ 6: "#8e24aa",
+ 7: "#7b1fa2",
+ 8: "#6a1b9a",
+ 9: "#4a148c",
+ },
+ "d.purple": {
+ 0: "#ede7f6",
+ 1: "#d1c4e9",
+ 2: "#b39ddb",
+ 3: "#9575cd",
+ 4: "#7e57c2",
+ 5: "#673ab7",
+ 6: "#5e35b1",
+ 7: "#512da8",
+ 8: "#4527a0",
+ 9: "#311b92",
+ },
+ "indigo": {
+ 0: "#e8eaf6",
+ 1: "#c5cae9",
+ 2: "#9fa8da",
+ 3: "#7986cb",
+ 4: "#5c6bc0",
+ 5: "#3f51b5",
+ 6: "#3949ab",
+ 7: "#303f9f",
+ 8: "#283593",
+ 9: "#1a237e",
+ },
+ "blue": {
+ 0: "#e3f2fd",
+ 1: "#bbdefb",
+ 2: "#90caf9",
+ 3: "#64b5f6",
+ 4: "#42a5f5",
+ 5: "#2196f3",
+ 6: "#1e88e5",
+ 7: "#1976d2",
+ 8: "#1565c0",
+ 9: "#0d47a1",
+ },
+ "l.blue": {
+ 0: "#e1f5fe",
+ 1: "#b3e5fc",
+ 2: "#81d4fa",
+ 3: "#4fc3f7",
+ 4: "#29b6f6",
+ 5: "#03a9f4",
+ 6: "#039be5",
+ 7: "#0288d1",
+ 8: "#0277bd",
+ 9: "#01579b",
+ },
+ "cyan": {
+ 0: "#e0f7fa",
+ 1: "#b2ebf2",
+ 2: "#80deea",
+ 3: "#4dd0e1",
+ 4: "#26c6da",
+ 5: "#00bcd4",
+ 6: "#00acc1",
+ 7: "#0097a7",
+ 8: "#00838f",
+ 9: "#006064",
+ },
+ "teal": {
+ 0: "#e0f2f1",
+ 1: "#b2dfdb",
+ 2: "#80cbc4",
+ 3: "#4db6ac",
+ 4: "#26a69a",
+ 5: "#009688",
+ 6: "#00897b",
+ 7: "#00796b",
+ 8: "#00695c",
+ 9: "#004d40",
+ },
+ "green": {
+ 0: "#e8f5e9",
+ 1: "#c8e6c9",
+ 2: "#a5d6a7",
+ 3: "#81c784",
+ 4: "#66bb6a",
+ 5: "#4caf50",
+ 6: "#43a047",
+ 7: "#388e3c",
+ 8: "#2e7d32",
+ 9: "#1b5e20",
+ },
+ "l.green": {
+ 0: "#f1f8e9",
+ 1: "#dcedc8",
+ 2: "#c5e1a5",
+ 3: "#aed581",
+ 4: "#9ccc65",
+ 5: "#8bc34a",
+ 6: "#7cb342",
+ 7: "#689f38",
+ 8: "#558b2f",
+ 9: "#33691e",
+ },
+ "lime": {
+ 0: "#f9fbe7",
+ 1: "#f0f4c3",
+ 2: "#e6ee9c",
+ 3: "#dce775",
+ 4: "#d4e157",
+ 5: "#cddc39",
+ 6: "#c0ca33",
+ 7: "#afb42b",
+ 8: "#9e9d24",
+ 9: "#827717",
+ },
+ "yellow": {
+ 0: "#fffde7",
+ 1: "#fff9c4",
+ 2: "#fff59d",
+ 3: "#fff176",
+ 4: "#ffee58",
+ 5: "#ffeb3b",
+ 6: "#fdd835",
+ 7: "#fbc02d",
+ 8: "#f9a825",
+ 9: "#f57f17",
+ },
+ "amber": {
+ 0: "#fff8e1",
+ 1: "#ffecb3",
+ 2: "#ffe082",
+ 3: "#ffd54f",
+ 4: "#ffca28",
+ 5: "#ffc107",
+ 6: "#ffb300",
+ 7: "#ffa000",
+ 8: "#ff8f00",
+ 9: "#ff6f00",
+ },
+ "orange": {
+ 0: "#fff3e0",
+ 1: "#ffe0b2",
+ 2: "#ffcc80",
+ 3: "#ffb74d",
+ 4: "#ffa726",
+ 5: "#ff9800",
+ 6: "#fb8c00",
+ 7: "#f57c00",
+ 8: "#ef6c00",
+ 9: "#e65100",
+ },
+ "d.orange": {
+ 0: "#fbe9e7",
+ 1: "#ffccbc",
+ 2: "#ffab91",
+ 3: "#ff8a65",
+ 4: "#ff7043",
+ 5: "#ff5722",
+ 6: "#f4511e",
+ 7: "#e64a19",
+ 8: "#d84315",
+ 9: "#bf360c",
+ },
+ "brown": {
+ 0: "#efebe9",
+ 1: "#d7ccc8",
+ 2: "#bcaaa4",
+ 3: "#a1887f",
+ 4: "#8d6e63",
+ 5: "#795548",
+ 6: "#6d4c41",
+ 7: "#5d4037",
+ 8: "#4e342e",
+ 9: "#3e2723",
+ },
+ "grey": {
+ 0: "#fafafa",
+ 1: "#f5f5f5",
+ 2: "#eeeeee",
+ 3: "#e0e0e0",
+ 4: "#bdbdbd",
+ 5: "#9e9e9e",
+ 6: "#757575",
+ 7: "#616161",
+ 8: "#424242",
+ 9: "#212121",
+ },
+ "blue grey": {
+ 0: "#eceff1",
+ 1: "#cfd8dc",
+ 2: "#b0bec5",
+ 3: "#90a4ae",
+ 4: "#78909c",
+ 5: "#607d8b",
+ 6: "#546e7a",
+ 7: "#455a64",
+ 8: "#37474f",
+ 9: "#263238",
+ },
+}
+
+nx, dx = 10, 5
+ny, dy = 19, 5
+
+fig = plt.figure(figsize=(8, 10), dpi=100)
+ax = plt.subplot(
+ 1,
+ 1,
+ 1,
+ frameon=False,
+ xlim=(-1, nx * dx),
+ ylim=(-1, (ny - 1) * dy + 2),
+ xticks=[],
+ yticks=[],
+)
+
+
+for palette, y in zip(colors.keys(), range(0, ny * dy, dy)):
+ for level, x in zip(range(10), range(0, 10 * dx, dx)):
+ fancy = FancyBboxPatch(
+ (x, y),
+ dx - 1,
+ 0.3 * dy,
+ facecolor=colors[palette][level],
+ edgecolor="black",
+ linewidth=0,
+ )
+ ax.add_patch(fancy)
+
+ name = "%s %s" % (palette.upper(), level)
+ ax.text(
+ x,
+ y - 0.15 * dy,
+ name,
+ size=8,
+ family="Roboto",
+ weight="regular",
+ ha="left",
+ va="top",
+ )
+
+ value = colors[palette][level].upper()
+ ax.text(
+ x,
+ y - 0.375 * dy,
+ value,
+ size=7.5,
+ color="0.5",
+ family="Roboto",
+ weight="regular",
+ ha="left",
+ va="top",
+ )
+
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/material-colors.pdf")
+plt.savefig("../../figures/colors/material-colors.png", dpi=300)
+plt.show()
diff --git a/code/colors/mona-lisa.py b/code/colors/mona-lisa.py
new file mode 100644
index 0000000..6b8ef75
--- /dev/null
+++ b/code/colors/mona-lisa.py
@@ -0,0 +1,57 @@
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+
+I = imageio.imread("../data/mona-lisa.png")
+
+
+def plot(ax, cmap, name=None):
+ ax.imshow(I, cmap=plt.get_cmap(cmap), rasterized=True)
+ ax.text(
+ 0.5,
+ 0.025,
+ name or cmap,
+ transform=ax.transAxes,
+ color="white",
+ ha="center",
+ va="bottom",
+ size="large",
+ family="Roboto Slab",
+ )
+ ax.set_xticks([]), ax.set_yticks([])
+
+
+rows, cols = 3, 4
+plt.figure(figsize=(9, 10.25))
+
+ax = plt.subplot(rows, cols, 1, frameon=False)
+plot(ax, "cividis", "Cividis")
+ax = plt.subplot(rows, cols, 2, frameon=False)
+plot(ax, "viridis", "Viridis")
+ax = plt.subplot(rows, cols, 3, frameon=False)
+plot(ax, "magma", "Magma")
+ax = plt.subplot(rows, cols, 4, frameon=False)
+plot(ax, "inferno", "Inferno")
+
+ax = plt.subplot(rows, cols, 5, frameon=False)
+plot(ax, "bone")
+ax = plt.subplot(rows, cols, 6, frameon=False)
+plot(ax, "YlGn_r", "YlGn\n(Yellow Green)")
+ax = plt.subplot(rows, cols, 7, frameon=False)
+plot(ax, "RdPu_r", "RdPu\n(Red Purple)")
+ax = plt.subplot(rows, cols, 8, frameon=False)
+plot(ax, "OrRd_r", "OrRd\n(Orange Red)")
+
+
+ax = plt.subplot(rows, cols, 9, frameon=False)
+plot(ax, "Greys_r", "Greys")
+ax = plt.subplot(rows, cols, 10, frameon=False)
+plot(ax, "GnBu_r", "GnBu\n(Green Blue)")
+ax = plt.subplot(rows, cols, 11, frameon=False)
+plot(ax, "BuPu_r", "BuPu\n(Blue Purple)")
+ax = plt.subplot(rows, cols, 12, frameon=False)
+plot(ax, "YlOrBr_r", "YlOrBr\n(Yellow Orange Brown)")
+
+plt.subplots_adjust(left=0, bottom=0, right=1, top=1, hspace=0.00, wspace=0.00)
+plt.savefig("../../figures/colors/mona-lisa.pdf", dpi=300)
+plt.show()
diff --git a/code/colors/open-colors.py b/code/colors/open-colors.py
new file mode 100644
index 0000000..c6e2398
--- /dev/null
+++ b/code/colors/open-colors.py
@@ -0,0 +1,224 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatch
+from matplotlib.patches import FancyBboxPatch
+
+# Open-color colors from https://yeun.github.io/open-color/
+colors = {
+ "gray": {
+ 0: "#f8f9fa",
+ 1: "#f1f3f5",
+ 2: "#e9ecef",
+ 3: "#dee2e6",
+ 4: "#ced4da",
+ 5: "#adb5bd",
+ 6: "#868e96",
+ 7: "#495057",
+ 8: "#343a40",
+ 9: "#212529",
+ },
+ "red": {
+ 0: "#fff5f5",
+ 1: "#ffe3e3",
+ 2: "#ffc9c9",
+ 3: "#ffa8a8",
+ 4: "#ff8787",
+ 5: "#ff6b6b",
+ 6: "#fa5252",
+ 7: "#f03e3e",
+ 8: "#e03131",
+ 9: "#c92a2a",
+ },
+ "pink": {
+ 0: "#fff0f6",
+ 1: "#ffdeeb",
+ 2: "#fcc2d7",
+ 3: "#faa2c1",
+ 4: "#f783ac",
+ 5: "#f06595",
+ 6: "#e64980",
+ 7: "#d6336c",
+ 8: "#c2255c",
+ 9: "#a61e4d",
+ },
+ "grape": {
+ 0: "#f8f0fc",
+ 1: "#f3d9fa",
+ 2: "#eebefa",
+ 3: "#e599f7",
+ 4: "#da77f2",
+ 5: "#cc5de8",
+ 6: "#be4bdb",
+ 7: "#ae3ec9",
+ 8: "#9c36b5",
+ 9: "#862e9c",
+ },
+ "violet": {
+ 0: "#f3f0ff",
+ 1: "#e5dbff",
+ 2: "#d0bfff",
+ 3: "#b197fc",
+ 4: "#9775fa",
+ 5: "#845ef7",
+ 6: "#7950f2",
+ 7: "#7048e8",
+ 8: "#6741d9",
+ 9: "#5f3dc4",
+ },
+ "indigo": {
+ 0: "#edf2ff",
+ 1: "#dbe4ff",
+ 2: "#bac8ff",
+ 3: "#91a7ff",
+ 4: "#748ffc",
+ 5: "#5c7cfa",
+ 6: "#4c6ef5",
+ 7: "#4263eb",
+ 8: "#3b5bdb",
+ 9: "#364fc7",
+ },
+ "blue": {
+ 0: "#e7f5ff",
+ 1: "#d0ebff",
+ 2: "#a5d8ff",
+ 3: "#74c0fc",
+ 4: "#4dabf7",
+ 5: "#339af0",
+ 6: "#228be6",
+ 7: "#1c7ed6",
+ 8: "#1971c2",
+ 9: "#1864ab",
+ },
+ "cyan": {
+ 0: "#e3fafc",
+ 1: "#c5f6fa",
+ 2: "#99e9f2",
+ 3: "#66d9e8",
+ 4: "#3bc9db",
+ 5: "#22b8cf",
+ 6: "#15aabf",
+ 7: "#1098ad",
+ 8: "#0c8599",
+ 9: "#0b7285",
+ },
+ "teal": {
+ 0: "#e6fcf5",
+ 1: "#c3fae8",
+ 2: "#96f2d7",
+ 3: "#63e6be",
+ 4: "#38d9a9",
+ 5: "#20c997",
+ 6: "#12b886",
+ 7: "#0ca678",
+ 8: "#099268",
+ 9: "#087f5b",
+ },
+ "green": {
+ 0: "#ebfbee",
+ 1: "#d3f9d8",
+ 2: "#b2f2bb",
+ 3: "#8ce99a",
+ 4: "#69db7c",
+ 5: "#51cf66",
+ 6: "#40c057",
+ 7: "#37b24d",
+ 8: "#2f9e44",
+ 9: "#2b8a3e",
+ },
+ "lime": {
+ 0: "#f4fce3",
+ 1: "#e9fac8",
+ 2: "#d8f5a2",
+ 3: "#c0eb75",
+ 4: "#a9e34b",
+ 5: "#94d82d",
+ 6: "#82c91e",
+ 7: "#74b816",
+ 8: "#66a80f",
+ 9: "#5c940d",
+ },
+ "yellow": {
+ 0: "#fff9db",
+ 1: "#fff3bf",
+ 2: "#ffec99",
+ 3: "#ffe066",
+ 4: "#ffd43b",
+ 5: "#fcc419",
+ 6: "#fab005",
+ 7: "#f59f00",
+ 8: "#f08c00",
+ 9: "#e67700",
+ },
+ "orange": {
+ 0: "#fff4e6",
+ 1: "#ffe8cc",
+ 2: "#ffd8a8",
+ 3: "#ffc078",
+ 4: "#ffa94d",
+ 5: "#ff922b",
+ 6: "#fd7e14",
+ 7: "#f76707",
+ 8: "#e8590c",
+ 9: "#d9480f",
+ },
+}
+
+
+nx, dx = 10, 5
+ny, dy = 13, 5
+
+fig = plt.figure(figsize=(8, 8), dpi=100)
+ax = plt.subplot(
+ 1,
+ 1,
+ 1,
+ frameon=False,
+ xlim=(-1, nx * dx),
+ ylim=(-1, (ny - 1) * dy + 2),
+ xticks=[],
+ yticks=[],
+)
+
+
+for palette, y in zip(colors.keys(), range(0, ny * dy, dy)):
+ for level, x in zip(range(10), range(0, nx * dx, dx)):
+ fancy = FancyBboxPatch(
+ (x, y),
+ dx - 1,
+ 0.3 * dy,
+ facecolor=colors[palette][level],
+ edgecolor="black",
+ linewidth=0,
+ )
+ ax.add_patch(fancy)
+
+ name = "%s %d" % (palette.upper(), level)
+ ax.text(
+ x,
+ y - 0.15 * dy,
+ name,
+ size=8,
+ family="Roboto",
+ weight="regular",
+ ha="left",
+ va="top",
+ )
+
+ value = colors[palette][level].upper()
+ ax.text(
+ x,
+ y - 0.35 * dy,
+ value,
+ size=8,
+ color="0.5",
+ family="Roboto",
+ weight="regular",
+ ha="left",
+ va="top",
+ )
+
+
+plt.tight_layout()
+plt.savefig("../../figures/colors/open-colors.pdf")
+plt.savefig("../../figures/colors/open-colors.png", dpi=300)
+plt.show()
diff --git a/code/colors/stacked-plots.py b/code/colors/stacked-plots.py
new file mode 100644
index 0000000..7513a5a
--- /dev/null
+++ b/code/colors/stacked-plots.py
@@ -0,0 +1,277 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.ndimage import gaussian_filter1d
+
+material = {
+ "red": {
+ 0: "#ffebee",
+ 1: "#ffcdd2",
+ 2: "#ef9a9a",
+ 3: "#e57373",
+ 4: "#ef5350",
+ 5: "#f44336",
+ 6: "#e53935",
+ 7: "#d32f2f",
+ 8: "#c62828",
+ 9: "#b71c1c",
+ },
+ "pink": {
+ 0: "#fce4ec",
+ 1: "#f8bbd0",
+ 2: "#f48fb1",
+ 3: "#f06292",
+ 4: "#ec407a",
+ 5: "#e91e63",
+ 6: "#d81b60",
+ 7: "#c2185b",
+ 8: "#ad1457",
+ 9: "#880e4f",
+ },
+ "purple": {
+ 0: "#f3e5f5",
+ 1: "#e1bee7",
+ 2: "#ce93d8",
+ 3: "#ba68c8",
+ 4: "#ab47bc",
+ 5: "#9c27b0",
+ 6: "#8e24aa",
+ 7: "#7b1fa2",
+ 8: "#6a1b9a",
+ 9: "#4a148c",
+ },
+ "deep purple": {
+ 0: "#ede7f6",
+ 1: "#d1c4e9",
+ 2: "#b39ddb",
+ 3: "#9575cd",
+ 4: "#7e57c2",
+ 5: "#673ab7",
+ 6: "#5e35b1",
+ 7: "#512da8",
+ 8: "#4527a0",
+ 9: "#311b92",
+ },
+ "indigo": {
+ 0: "#e8eaf6",
+ 1: "#c5cae9",
+ 2: "#9fa8da",
+ 3: "#7986cb",
+ 4: "#5c6bc0",
+ 5: "#3f51b5",
+ 6: "#3949ab",
+ 7: "#303f9f",
+ 8: "#283593",
+ 9: "#1a237e",
+ },
+ "blue": {
+ 0: "#e3f2fd",
+ 1: "#bbdefb",
+ 2: "#90caf9",
+ 3: "#64b5f6",
+ 4: "#42a5f5",
+ 5: "#2196f3",
+ 6: "#1e88e5",
+ 7: "#1976d2",
+ 8: "#1565c0",
+ 9: "#0d47a1",
+ },
+ "light blue": {
+ 0: "#e1f5fe",
+ 1: "#b3e5fc",
+ 2: "#81d4fa",
+ 3: "#4fc3f7",
+ 4: "#29b6f6",
+ 5: "#03a9f4",
+ 6: "#039be5",
+ 7: "#0288d1",
+ 8: "#0277bd",
+ 9: "#01579b",
+ },
+ "cyan": {
+ 0: "#e0f7fa",
+ 1: "#b2ebf2",
+ 2: "#80deea",
+ 3: "#4dd0e1",
+ 4: "#26c6da",
+ 5: "#00bcd4",
+ 6: "#00acc1",
+ 7: "#0097a7",
+ 8: "#00838f",
+ 9: "#006064",
+ },
+ "teal": {
+ 0: "#e0f2f1",
+ 1: "#b2dfdb",
+ 2: "#80cbc4",
+ 3: "#4db6ac",
+ 4: "#26a69a",
+ 5: "#009688",
+ 6: "#00897b",
+ 7: "#00796b",
+ 8: "#00695c",
+ 9: "#004d40",
+ },
+ "green": {
+ 0: "#e8f5e9",
+ 1: "#c8e6c9",
+ 2: "#a5d6a7",
+ 3: "#81c784",
+ 4: "#66bb6a",
+ 5: "#4caf50",
+ 6: "#43a047",
+ 7: "#388e3c",
+ 8: "#2e7d32",
+ 9: "#1b5e20",
+ },
+ "light green": {
+ 0: "#f1f8e9",
+ 1: "#dcedc8",
+ 2: "#c5e1a5",
+ 3: "#aed581",
+ 4: "#9ccc65",
+ 5: "#8bc34a",
+ 6: "#7cb342",
+ 7: "#689f38",
+ 8: "#558b2f",
+ 9: "#33691e",
+ },
+ "lime": {
+ 0: "#f9fbe7",
+ 1: "#f0f4c3",
+ 2: "#e6ee9c",
+ 3: "#dce775",
+ 4: "#d4e157",
+ 5: "#cddc39",
+ 6: "#c0ca33",
+ 7: "#afb42b",
+ 8: "#9e9d24",
+ 9: "#827717",
+ },
+ "yellow": {
+ 0: "#fffde7",
+ 1: "#fff9c4",
+ 2: "#fff59d",
+ 3: "#fff176",
+ 4: "#ffee58",
+ 5: "#ffeb3b",
+ 6: "#fdd835",
+ 7: "#fbc02d",
+ 8: "#f9a825",
+ 9: "#f57f17",
+ },
+ "amber": {
+ 0: "#fff8e1",
+ 1: "#ffecb3",
+ 2: "#ffe082",
+ 3: "#ffd54f",
+ 4: "#ffca28",
+ 5: "#ffc107",
+ 6: "#ffb300",
+ 7: "#ffa000",
+ 8: "#ff8f00",
+ 9: "#ff6f00",
+ },
+ "orange": {
+ 0: "#fff3e0",
+ 1: "#ffe0b2",
+ 2: "#ffcc80",
+ 3: "#ffb74d",
+ 4: "#ffa726",
+ 5: "#ff9800",
+ 6: "#fb8c00",
+ 7: "#f57c00",
+ 8: "#ef6c00",
+ 9: "#e65100",
+ },
+ "deep orange": {
+ 0: "#fbe9e7",
+ 1: "#ffccbc",
+ 2: "#ffab91",
+ 3: "#ff8a65",
+ 4: "#ff7043",
+ 5: "#ff5722",
+ 6: "#f4511e",
+ 7: "#e64a19",
+ 8: "#d84315",
+ 9: "#bf360c",
+ },
+ "brown": {
+ 0: "#efebe9",
+ 1: "#d7ccc8",
+ 2: "#bcaaa4",
+ 3: "#a1887f",
+ 4: "#8d6e63",
+ 5: "#795548",
+ 6: "#6d4c41",
+ 7: "#5d4037",
+ 8: "#4e342e",
+ 9: "#3e2723",
+ },
+ "grey": {
+ 0: "#fafafa",
+ 1: "#f5f5f5",
+ 2: "#eeeeee",
+ 3: "#e0e0e0",
+ 4: "#bdbdbd",
+ 5: "#9e9e9e",
+ 6: "#757575",
+ 7: "#616161",
+ 8: "#424242",
+ 9: "#212121",
+ },
+ "blue grey": {
+ 0: "#eceff1",
+ 1: "#cfd8dc",
+ 2: "#b0bec5",
+ 3: "#90a4ae",
+ 4: "#78909c",
+ 5: "#607d8b",
+ 6: "#546e7a",
+ 7: "#455a64",
+ 8: "#37474f",
+ 9: "#263238",
+ },
+}
+
+
+np.random.seed(1)
+plt.figure(figsize=(8, 4))
+ax = plt.subplot(1, 1, 1)
+
+X = np.linspace(0, 1, 500)
+
+Y0 = np.ones(len(X))
+for i in range(10):
+ Y = Y0 + np.random.uniform(0, 1 / (i + 1), len(X))
+ Y = gaussian_filter1d(Y, 3)
+ ax.fill_between(
+ X,
+ Y,
+ Y0,
+ edgecolor="white",
+ linewidth=0.25,
+ facecolor=material["blue grey"][9 - i],
+ )
+ ax.fill_between(
+ X[325:425], Y[325:425], Y0[325:425], facecolor=material["yellow"][9 - i]
+ )
+ ax.plot(X[325:425], Y[325:425], color="black", linewidth=0.25)
+ Y0 = Y
+
+ax.axvline(X[325], color="black", linestyle="--")
+ax.axvline(X[424], color="black", linestyle="--")
+
+ax.set_xlim(0, 1)
+ax.set_xticks([])
+
+ax.set_ylim(1, 3)
+ax.set_yticks([])
+
+plt.tight_layout()
+plt.savefig("../figures/stacked-plots.pdf")
+plt.show()
diff --git a/code/coordinates/collage.py b/code/coordinates/collage.py
new file mode 100644
index 0000000..b60ad64
--- /dev/null
+++ b/code/coordinates/collage.py
@@ -0,0 +1,52 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.transforms as transforms
+
+
+def imshow(ax, I, position=(0, 0), scale=1, angle=0, zorder=10):
+ height, width = I.shape
+ extent = scale * np.array([-width / 2, width / 2, -height / 2, height / 2])
+ im = ax.imshow(I, extent=extent, zorder=zorder, cmap="cividis")
+ transform = transforms.Affine2D().rotate_deg(angle).translate(*position)
+ trans_data = transform + ax.transData
+ im.set_transform(trans_data)
+ x1, x2, y1, y2 = im.get_extent()
+ ax.plot(
+ [x1, x2, x2, x1, x1],
+ [y1, y1, y2, y2, y1],
+ "white",
+ linewidth=25 * scale,
+ transform=trans_data,
+ zorder=zorder - 0.1,
+ )
+ ax.plot(
+ [x1, x2, x2, x1, x1],
+ [y1, y1, y2, y2, y1],
+ "black",
+ alpha=0.25,
+ linewidth=40 * scale,
+ transform=trans_data,
+ zorder=zorder - 0.2,
+ )
+
+
+fig = plt.figure(figsize=(5, 5))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, frameon=False, xlim=[0, 1000], ylim=[0, 1000])
+
+
+np.random.seed(123)
+I = imageio.imread("../data/mona-lisa.png")
+for i in range(200):
+ position = np.random.uniform(-100, 1100, 2)
+ scale = np.random.uniform(0.20, 0.25)
+ angle = np.random.uniform(-75, +75)
+ imshow(ax, I, position, scale, angle, zorder=10 + i)
+
+plt.savefig("../../figures/coordinates/collage.png", dpi=300)
+plt.show()
diff --git a/code/coordinates/transforms-blend.py b/code/coordinates/transforms-blend.py
new file mode 100644
index 0000000..2c00c8a
--- /dev/null
+++ b/code/coordinates/transforms-blend.py
@@ -0,0 +1,29 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.transforms import blended_transform_factory, ScaledTranslation
+
+fig = plt.figure(figsize=(6, 4))
+
+ax = fig.add_subplot(1, 1, 1, aspect=1)
+ax.set_xlim(0, 10)
+ax.set_xticks(range(11))
+ax.set_ylim(0, 5)
+ax.set_xticks(range(11))
+
+point = 1 / 72
+fontsize = 12
+dx, dy = 0, -1.5 * fontsize * point
+offset = ScaledTranslation(dx, dy, fig.dpi_scale_trans)
+transform = blended_transform_factory(ax.transData, ax.transAxes + offset)
+
+for x in range(11):
+ plt.text(x, 0, "↑", transform=transform, ha="center", va="top", fontsize=fontsize)
+
+plt.tight_layout()
+plt.savefig("../../figures/coordinates/transforms-blend.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-exercise-1.py b/code/coordinates/transforms-exercise-1.py
new file mode 100644
index 0000000..1910dac
--- /dev/null
+++ b/code/coordinates/transforms-exercise-1.py
@@ -0,0 +1,30 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(8, 2))
+
+ymin, ymax = [0, 2]
+xmin, xmax = [0, 8]
+ax = plt.subplot(1, 1, 1, aspect=1, xlim=[xmin, xmax], ylim=[ymin, ymax])
+point = fig.dpi / 72
+X = 0.5 + np.arange(8)
+Y = np.ones(len(X))
+
+# Marker size is expressed in point^2 and a point is defined as fig.dpi/72
+# This means that a size of 10 really means (10*point)^2
+# Problem is thus to convert points into data coordinates:
+# PT_to_DC = lambda x: x * ax.get_window_extent().width / (xmax-xmin)
+# Note that the DC_to_PR is validonly for a given window size
+
+DC_to_PT = lambda x: x * ax.get_window_extent().width / (xmax - xmin) / point
+S = DC_to_PT(1) ** 2
+plt.scatter(X, Y, s=S, facecolor="none", edgecolor="black", linewidth=1)
+
+plt.savefig("../../figures/coordinates/transforms-exercise-1.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-floating-axis.py b/code/coordinates/transforms-floating-axis.py
new file mode 100644
index 0000000..4596191
--- /dev/null
+++ b/code/coordinates/transforms-floating-axis.py
@@ -0,0 +1,76 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate rotated & translated axis (using axisartists toolkit)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.transforms import Affine2D
+from matplotlib.ticker import MultipleLocator
+import mpl_toolkits.axisartist.floating_axes as floating_axes
+
+
+fig = plt.figure(figsize=(8, 8))
+
+# Main axis
+ax1 = plt.subplot(1, 1, 1, aspect=1, xlim=[0, 10], ylim=[0, 10])
+ax1.xaxis.set_major_locator(MultipleLocator(1.00))
+ax1.xaxis.set_minor_locator(MultipleLocator(0.50))
+ax1.yaxis.set_major_locator(MultipleLocator(1.00))
+ax1.yaxis.set_minor_locator(MultipleLocator(0.50))
+ax1.grid(linewidth=0.75, linestyle="--")
+
+# Floating axis
+center = np.array([5, 5]) # data coordinates
+size = np.array([5, 3]) # data coordinates
+orientation = -30 # degrees
+T = size / 2 * [(-1, -1), (+1, -1), (+1, +1), (-1, +1), (-1, -1)]
+rotation = Affine2D().rotate_deg(orientation)
+P = center + rotation.transform(T)
+
+# Floating axis bounding box visualization
+# T = rotation.transform(T)
+# xmin, xmax = T[:,0].min(), T[:,0].max()
+# ymin, ymax = T[:,1].min(), T[:,1].max()
+# T = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)]
+# P = center + T
+# plt.plot(P[:,0], P[:,1], color="black", linewidth=0.75)
+
+# Actual floating axis
+DC_to_FC = ax1.transData.transform
+FC_to_NFC = fig.transFigure.inverted().transform
+DC_to_NFC = lambda x: FC_to_NFC(DC_to_FC(x))
+xmin, ymin = DC_to_NFC((P[:, 0].min(), P[:, 1].min()))
+xmax, ymax = DC_to_NFC((P[:, 0].max(), P[:, 1].max()))
+transform = Affine2D().scale(1, 1).rotate_deg(orientation)
+helper = floating_axes.GridHelperCurveLinear(transform, (0, size[0], 0, size[1]))
+ax2 = floating_axes.FloatingSubplot(fig, 111, grid_helper=helper, zorder=0)
+ax2.set_position((xmin, ymin, xmax - xmin, ymax - xmin))
+fig.add_subplot(ax2)
+
+
+# Cosmetic changes
+ax2.axis["bottom"].major_ticks.set_tick_out(True)
+ax2.axis["bottom"].major_ticklabels.set_visible(False)
+ax2.axis["top"].major_ticks.set_visible(False)
+ax2.axis["left"].major_ticks.set_tick_out(True)
+ax2.axis["left"].major_ticklabels.set_visible(False)
+ax2.axis["right"].major_ticks.set_visible(False)
+aux = ax2.get_aux_axes(transform)
+aux.text(
+ 0.1,
+ 0.1,
+ "Floating & rotated axis",
+ ha="left",
+ va="bottom",
+ size=10,
+ rotation=orientation,
+ rotation_mode="anchor",
+)
+
+aux.set_xticks([0, 1])
+
+plt.savefig("../../figures/coordinates/transform-example.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-hist.py b/code/coordinates/transforms-hist.py
new file mode 100644
index 0000000..e5b2889
--- /dev/null
+++ b/code/coordinates/transforms-hist.py
@@ -0,0 +1,163 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate rotated & translated axis (using axisartists toolkit)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+from matplotlib.transforms import Affine2D
+import mpl_toolkits.axisartist.floating_axes as floating_axes
+
+
+# Reroducibility seed
+np.random.seed(123)
+
+# Generate some data
+Z = np.random.normal(0, (1.25, 0.75), (150, 2))
+Z = Affine2D().rotate_deg(35).transform(Z)
+Zm = Z.mean(axis=0)
+
+
+# Principal components analysis
+# Note that for some seeds, the PC1 and PC2 needs to be inverted
+# It could be fixed by looking at the orientation but I'm lazy
+W, V = np.linalg.eig(np.cov(Z.T))
+PC1, PC2 = V[np.argsort(abs(W))]
+rotation = 180 * np.arctan2(*PC1) / np.pi
+T = np.array([PC1[1], PC1[0]]) # tangent to PCA1
+O = np.array([T[1], -T[0]]) # orthogonal to PCA1
+
+
+# Draw
+fig = plt.figure(figsize=(5, 5))
+ax1 = fig.add_axes([0.05, 0.05, 0.9, 0.9], aspect=1)
+
+# Main scatter plot
+ax1.scatter(Z[:, 0], Z[:, 1], s=50, fc="C0", ec="white", lw=0.75)
+ax1.set_xlim([-3, 6])
+ax1.set_xticks([-3, 6])
+ax1.set_xticklabels([])
+ax1.set_ylim([-3, 6])
+ax1.set_yticks([-3, 6])
+ax1.set_yticklabels([])
+ax1.spines["top"].set_visible(False)
+ax1.spines["right"].set_visible(False)
+
+
+# Draw main PCA axis
+P = np.vstack([Zm - T * 10, Zm + T * 10])
+ax1.plot(P[:, 0], P[:, 1], color="black", linestyle="--", linewidth=0.75, zorder=10)
+
+# Compute the width of the distribution along orthogonal direction to the PCA
+# main axis. This is made by rotating points and taking max on the Y axis.
+transform = Affine2D().rotate_deg(-rotation)
+P = transform.transform(Z - Z.mean(axis=0))
+d = np.abs(P[:, 1]).max()
+
+# Draw a rectangle surrounding the distribution & oriented along PCA main axis
+P = np.vstack(
+ [
+ Zm - 10 * T - d * O,
+ Zm + (6 - d) * T - d * O,
+ Zm + (6 - d) * T + d * O,
+ Zm - 10 * T + d * O,
+ ]
+)
+ax1.add_patch(
+ Polygon(
+ P,
+ closed=True,
+ fill=True,
+ edgecolor="None",
+ facecolor="C0",
+ alpha=0.1,
+ zorder=-50,
+ )
+)
+P = np.vstack([Zm - 10 * T, Zm + (6 - d) * T]) - d * O
+plt.plot(P[:, 0], P[:, 1], color="C0", linestyle="-", linewidth=0.75, alpha=0.25)
+P = np.vstack([Zm - 10 * T, Zm + (6 - d) * T]) + d * O
+plt.plot(P[:, 0], P[:, 1], color="C0", linestyle="-", linewidth=0.75, alpha=0.25)
+
+# Some markers on the axis to show the mean (we could compute exactly the delta
+# for placing the marker but it is not the point of this example)
+ax1.scatter(Zm[0], -2.85, s=50, color="black", marker="v", clip_on=False)
+ax1.scatter(-2.85, Zm[1], s=50, color="black", marker="<", clip_on=False)
+
+
+# Now the complicated stuff to orientate and translate the secondary axis
+
+# 1. Compute the center of the histogram
+C = Zm + 6 * T
+
+# 2. Compute the coordinate and the size in normalized figure coordinates
+x, y = fig.transFigure.inverted().transform(ax1.transData.transform(C))
+xo, yo = fig.transFigure.inverted().transform(ax1.transData.transform(C + 2 * d * O))
+h = w = np.sqrt((xo - x) ** 2 + (yo - y) ** 2)
+
+# 3. Create the secondary axis
+# Warning: it must be squared, ie. xmax-xmin = ymax-ymin
+# It is possible to have non squared axis, but it would complicate things.
+xmin, xmax = -16, 16
+ymin, ymax = 0, xmax - xmin
+transform = Affine2D().rotate_deg(rotation - 90)
+helper = floating_axes.GridHelperCurveLinear(transform, (xmin, xmax, ymin, ymax))
+ax2 = floating_axes.FloatingSubplot(fig, 111, grid_helper=helper, zorder=0)
+
+# This auxiliary axis is necessary to draw stuff (no real idea why)
+ax2_aux = ax2.get_aux_axes(transform)
+
+# 4. We know the size of the axis we want but it is rotated. When we specify
+# the size and position, it related to the non-rotate axis and we thus need
+# to compute the bounding box. To do that, we rotate the four coordinates
+# from which we deduce the bounding box coordinates.
+transform = Affine2D().rotate_deg(rotation - 90)
+R = transform.transform(
+ [
+ (x - w / 2, y - h / 2),
+ (x + w / 2, y - h / 2),
+ (x - w / 2, y + h / 2),
+ (x + w / 2, y + h / 2),
+ ]
+)
+w = R[:, 0].max() - R[:, 0].min()
+h = R[:, 1].max() - R[:, 1].min()
+ax2.set_position((x - w / 2, y - h / 2, w, h))
+fig.add_subplot(ax2)
+
+# 5. Some decoration the secondary axis
+ax2.axis["left"].major_ticklabels.set_visible(False)
+ax2.axis["bottom"].major_ticklabels.set_visible(False)
+ax2.axis["bottom"].major_ticks.set_tick_out(True)
+ax2.axis["left"].set_visible(False)
+ax2.axis["right"].set_visible(False)
+ax2.axis["top"].set_visible(False)
+ax2.set_xticks([0, 1])
+ax2.patch.set_visible(False)
+
+# 6. Display the histogram, taking care of the extents of the X axis
+counts, bins = np.histogram(-Z @ PC1, bins=12)
+X = (bins - bins[0]) / (bins[-1] - bins[0])
+X = xmin + (xmax - xmin) * X
+Y = np.array(counts)
+ax2_aux.hist(X[:-1], X, weights=Y, facecolor="C0", edgecolor="white", linewidth=0.25)
+
+# 7. Adding some labels
+dx, dy = (X[1] - X[0]) / 2, 0.75
+for x, y in zip(X, Y):
+ ax2_aux.text(
+ x + dx,
+ y + dy,
+ "%d" % y,
+ ha="center",
+ va="center",
+ size=8,
+ rotation=rotation - 90,
+ )
+
+# Save
+plt.savefig("../../figures/coordinates/transforms-hist.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-letter.py b/code/coordinates/transforms-letter.py
new file mode 100644
index 0000000..96c4f0f
--- /dev/null
+++ b/code/coordinates/transforms-letter.py
@@ -0,0 +1,41 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.transforms import ScaledTranslation
+
+fig = plt.figure(figsize=(6, 4))
+
+ax = fig.add_subplot(2, 1, 1)
+plt.text(
+ 0.1,
+ 0.1,
+ "A",
+ fontsize="x-large",
+ weight="bold",
+ ha="left",
+ va="baseline",
+ transform=ax.transAxes,
+)
+
+ax = fig.add_subplot(2, 1, 2)
+dx, dy = 10 / fig.dpi, 10 / fig.dpi
+offset = ScaledTranslation(dx, dy, fig.dpi_scale_trans)
+
+plt.text(
+ 0,
+ 0,
+ "B",
+ fontsize="x-large",
+ weight="bold",
+ ha="left",
+ va="baseline",
+ transform=ax.transAxes + offset,
+)
+
+plt.tight_layout()
+plt.savefig("../../figures/coordinates/transforms-letter.pdf")
+plt.show()
diff --git a/code/coordinates/transforms-polar.py b/code/coordinates/transforms-polar.py
new file mode 100644
index 0000000..a469060
--- /dev/null
+++ b/code/coordinates/transforms-polar.py
@@ -0,0 +1,32 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.transforms as transforms
+
+fig = plt.figure(figsize=(5, 5), dpi=100)
+ax = fig.add_subplot(1, 1, 1, projection="polar")
+ax.set_ylim(-1, 1), ax.set_yticks([-1, -0.5, 0, 0.5, 1])
+
+FC_to_DC = ax.transData.inverted().transform
+NDC_to_FC = ax.transAxes.transform
+NDC_to_DC = lambda x: FC_to_DC(NDC_to_FC(x))
+
+P = NDC_to_DC([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]])
+plt.plot(
+ P[:, 0],
+ P[:, 1],
+ clip_on=False,
+ color="k",
+ linewidth=1.0,
+ linestyle="--",
+ zorder=-10,
+)
+plt.scatter(P[:-1, 0], P[:-1, 1], clip_on=False, facecolor="w", edgecolor="k")
+
+plt.tight_layout()
+plt.savefig("../../figures/coordinates/transforms-polar.pdf")
+plt.show()
diff --git a/code/coordinates/transforms.py b/code/coordinates/transforms.py
new file mode 100644
index 0000000..f043eac
--- /dev/null
+++ b/code/coordinates/transforms.py
@@ -0,0 +1,34 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.transforms as transforms
+
+fig = plt.figure(figsize=(6, 5), dpi=100)
+ax = fig.add_subplot(1, 1, 1)
+
+ax.set_xlim(0, 360)
+ax.set_ylim(-1, 1)
+
+DC_to_FC = ax.transData.transform
+FC_to_DC = ax.transData.inverted().transform
+
+NDC_to_FC = ax.transAxes.transform
+FC_to_NDC = ax.transAxes.inverted().transform
+
+NFC_to_FC = fig.transFigure.transform
+FC_to_NFC = fig.transFigure.inverted().transform
+
+
+print(NFC_to_FC([1, 1])) # (600,500)
+print(NDC_to_FC([1, 1])) # (540,440)
+print(DC_to_FC([360, 1])) # (540,440)
+
+DC_to_NDC = lambda x: FC_to_NDC(DC_to_FC(x))
+
+print(DC_to_NDC([0, -1])) # (0.0, 0.0)
+print(DC_to_NDC([180, 0])) # (0.5, 0.5)
+print(DC_to_NDC([360, 1])) # (1.0, 1.0)
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diff --git a/code/data/mona-lisa.png b/code/data/mona-lisa.png
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diff --git a/code/defaults/defaults-exercice-1.py b/code/defaults/defaults-exercice-1.py
new file mode 100644
index 0000000..aea2e79
--- /dev/null
+++ b/code/defaults/defaults-exercice-1.py
@@ -0,0 +1,66 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Custom defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+p = plt.rcParams
+
+p["figure.figsize"] = 6, 2.5
+p["figure.facecolor"] = "#fff"
+
+p["axes.axisbelow"] = True
+p["axes.linewidth"] = 1
+p["axes.facecolor"] = "#f9f9f9"
+p["axes.ymargin"] = 0.0
+
+p["axes.grid"] = True
+p["axes.grid.axis"] = "x"
+p["grid.color"] = "#999999"
+p["grid.linestyle"] = "--"
+
+p["axes.spines.bottom"] = True
+p["axes.spines.left"] = True
+p["axes.spines.right"] = False
+p["axes.spines.top"] = False
+p["font.sans-serif"] = ["Fira Sans Condensed"]
+
+p["xtick.bottom"] = True
+p["xtick.top"] = False
+p["xtick.direction"] = "out"
+p["xtick.major.size"] = 0
+p["xtick.major.width"] = 1
+p["xtick.major.pad"] = 65
+
+p["ytick.left"] = True
+p["ytick.right"] = False
+p["ytick.direction"] = "out"
+p["ytick.major.size"] = 5
+p["ytick.major.width"] = 1
+
+p["lines.linewidth"] = 2
+p["lines.marker"] = "o"
+p["lines.markeredgewidth"] = 1.5
+p["lines.markeredgecolor"] = "auto"
+p["lines.markerfacecolor"] = "white"
+p["lines.markersize"] = 6
+
+
+fig = plt.figure()
+ax = plt.subplot(1, 1, 1, aspect=1)
+ax.plot(X, C, markevery=(0, 64), clip_on=False, zorder=10)
+ax.plot(X, S, markevery=(0, 64), clip_on=False, zorder=10)
+ax.set_yticks([-1, 0, 1])
+ax.set_xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
+ax.set_xticklabels(["-π", "-π/2", "0", "+π/2", "+π"])
+ax.spines["bottom"].set_position(("data", 0))
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-exercice-1.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-1.py b/code/defaults/defaults-step-1.py
new file mode 100644
index 0000000..b9f0bd4
--- /dev/null
+++ b/code/defaults/defaults-step-1.py
@@ -0,0 +1,18 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Implicit defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+plt.plot(X, C)
+plt.plot(X, S)
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-1.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-2.py b/code/defaults/defaults-step-2.py
new file mode 100644
index 0000000..267dc13
--- /dev/null
+++ b/code/defaults/defaults-step-2.py
@@ -0,0 +1,59 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / explicit defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+p = plt.rcParams
+
+fig = plt.figure(
+ figsize=p["figure.figsize"],
+ dpi=p["figure.dpi"],
+ facecolor=p["figure.facecolor"],
+ edgecolor=p["figure.edgecolor"],
+ frameon=p["figure.frameon"],
+)
+
+ax = plt.subplot(1, 1, 1)
+
+ax.plot(
+ X, C, color="C0", linewidth=p["lines.linewidth"], linestyle=p["lines.linestyle"]
+)
+
+ax.plot(
+ X, S, color="C1", linewidth=p["lines.linewidth"], linestyle=p["lines.linestyle"]
+)
+
+xmin, xmax = X.min(), X.max()
+xmargin = p["axes.xmargin"] * (xmax - xmin)
+ax.set_xlim(xmin - xmargin, xmax + xmargin)
+
+ymin, ymax = min(C.min(), S.min()), max(C.max(), S.max())
+ymargin = p["axes.ymargin"] * (ymax - ymin)
+ax.set_ylim(ymin - ymargin, ymax + ymargin)
+
+ax.tick_params(
+ axis="x",
+ which="major",
+ direction=p["xtick.direction"],
+ length=p["xtick.major.size"],
+ width=p["xtick.major.width"],
+)
+
+ax.tick_params(
+ axis="y",
+ which="major",
+ direction=p["ytick.direction"],
+ length=p["ytick.major.size"],
+ width=p["ytick.major.width"],
+)
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-2.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-3.py b/code/defaults/defaults-step-3.py
new file mode 100644
index 0000000..dd6e8fd
--- /dev/null
+++ b/code/defaults/defaults-step-3.py
@@ -0,0 +1,65 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Custom defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+p = plt.rcParams
+
+p["figure.figsize"] = 6, 2.5
+p["figure.edgecolor"] = "black"
+p["figure.facecolor"] = "#f9f9f9"
+
+p["axes.linewidth"] = 1
+p["axes.facecolor"] = "#f9f9f9"
+p["axes.ymargin"] = 0.1
+p["axes.spines.bottom"] = True
+p["axes.spines.left"] = True
+p["axes.spines.right"] = False
+p["axes.spines.top"] = False
+p["font.sans-serif"] = ["Fira Sans Condensed"]
+
+p["axes.grid"] = False
+p["grid.color"] = "black"
+p["grid.linewidth"] = 0.1
+
+p["xtick.bottom"] = True
+p["xtick.top"] = False
+p["xtick.direction"] = "out"
+p["xtick.major.size"] = 5
+p["xtick.major.width"] = 1
+p["xtick.minor.size"] = 3
+p["xtick.minor.width"] = 0.5
+p["xtick.minor.visible"] = True
+
+p["ytick.left"] = True
+p["ytick.right"] = False
+p["ytick.direction"] = "out"
+p["ytick.major.size"] = 5
+p["ytick.major.width"] = 1
+p["ytick.minor.size"] = 3
+p["ytick.minor.width"] = 0.5
+p["ytick.minor.visible"] = True
+
+p["lines.linewidth"] = 2
+p["lines.marker"] = "o"
+p["lines.markeredgewidth"] = 1.5
+p["lines.markeredgecolor"] = "auto"
+p["lines.markerfacecolor"] = "white"
+p["lines.markersize"] = 6
+
+fig = plt.figure(linewidth=1)
+ax = plt.subplot(1, 1, 1, aspect=1)
+ax.plot(X, C, markevery=(0, 32))
+ax.plot(X, S, markevery=(0, 32))
+ax.set_yticks([-1, 0, 1])
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-3.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-4.py b/code/defaults/defaults-step-4.py
new file mode 100644
index 0000000..f864192
--- /dev/null
+++ b/code/defaults/defaults-step-4.py
@@ -0,0 +1,24 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Custom defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.style.use("./mystyle.txt")
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure(linewidth=1)
+ax = plt.subplot(1, 1, 1, aspect=1)
+ax.plot(X, C, markevery=(0, 32))
+ax.plot(X, S, markevery=(0, 32))
+ax.set_yticks([-1, 0, 1])
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-4.pdf")
+plt.show()
diff --git a/code/defaults/defaults-step-5.py b/code/defaults/defaults-step-5.py
new file mode 100644
index 0000000..20d394c
--- /dev/null
+++ b/code/defaults/defaults-step-5.py
@@ -0,0 +1,34 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Defaults settings / Custom defaults
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.style.use("./mystyle.txt")
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure()
+ax = plt.subplot(1, 1, 1, aspect=1)
+ax.plot(X, C)
+ax.plot(X, S)
+
+ax.set_yticks([-1, 1])
+ax.set_yticklabels(["-1", "+1"])
+ax.set_xticks([-np.pi, -np.pi / 2, np.pi / 2, np.pi])
+ax.set_xticklabels(["-π", "-π/2", "+π/2", "+π"])
+
+ax.spines["bottom"].set_position(("data", 0))
+ax.spines["left"].set_position(("data", 0))
+
+ax.plot(1, 0, ">k", transform=ax.get_yaxis_transform(), clip_on=False)
+ax.plot(0, 1, "^k", transform=ax.get_xaxis_transform(), clip_on=False)
+
+plt.tight_layout()
+plt.savefig("../../figures/defaults/defaults-step-5.pdf")
+plt.show()
diff --git a/code/defaults/mystyle.txt b/code/defaults/mystyle.txt
new file mode 100644
index 0000000..1581872
--- /dev/null
+++ b/code/defaults/mystyle.txt
@@ -0,0 +1,36 @@
+
+figure.figsize: 6,2.5
+
+axes.linewidth: 1
+axes.axisbelow: True
+axes.ymargin: 0.1
+axes.spines.bottom: True
+axes.spines.left: True
+axes.spines.right: False
+axes.spines.top: False
+font.sans-serif: Fira Sans Condensed
+
+axes.grid: False
+grid.color: black
+grid.linewidth: .1
+
+xtick.bottom: True
+xtick.top: False
+xtick.direction: out
+xtick.major.size: 5
+xtick.major.width: 1
+xtick.minor.size: 3
+xtick.minor.width: .5
+xtick.minor.visible: True
+
+ytick.left: True
+ytick.right: False
+ytick.direction: out
+ytick.major.size: 5
+ytick.major.width: 1
+ytick.minor.size: 3
+ytick.minor.width: .5
+ytick.minor.visible: True
+
+lines.linewidth: 2
+lines.markersize: 5
diff --git a/code/introduction/matplotlib-timeline.py b/code/introduction/matplotlib-timeline.py
new file mode 100644
index 0000000..84d4a22
--- /dev/null
+++ b/code/introduction/matplotlib-timeline.py
@@ -0,0 +1,86 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def annotate(ax, x, y, text, fc="#ff7777", y0=0):
+ y = y - 0.5
+ ax.annotate(
+ " " + text + " ",
+ xy=(x, y),
+ xycoords="data",
+ xytext=(0, 12),
+ textcoords="offset points",
+ color="white",
+ size="x-small",
+ va="center",
+ ha="center",
+ weight="bold",
+ bbox=dict(boxstyle="round", fc=fc, ec="none"),
+ arrowprops=dict(
+ arrowstyle="wedge,tail_width=1.", fc=fc, ec="none", patchA=None
+ ),
+ )
+ plt.plot([x, x], [y, y0], color="black", linestyle=":", linewidth=0.75)
+
+
+fig = plt.figure(figsize=(5, 2))
+ax = fig.add_subplot(111, xlim=(2002.5, 2021.5), ylim=(0, 6.5), yticks=([]))
+ax.tick_params("x", labelsize="x-small", which="major")
+plt.plot([2002.5, 2021.5], [0, 0], color="black", linewidth=1.0, clip_on=False)
+X = np.arange(2003, 2022)
+Y = np.zeros(len(X))
+plt.scatter(
+ X,
+ Y,
+ s=50,
+ linewidth=1.0,
+ zorder=10,
+ clip_on=False,
+ edgecolor="black",
+ facecolor="white",
+)
+
+annotate(ax, 2021, 4, "3.4")
+annotate(ax, 2020, 3, "3.3")
+annotate(ax, 2019, 4, "3.2")
+annotate(ax, 2019, 2, "3.1")
+annotate(ax, 2018, 3, "3.0", y0=1.5)
+annotate(ax, 2018, 1, "2.2", fc="#777777")
+annotate(ax, 2017, 4, "2.1", y0=2.5)
+annotate(ax, 2017, 2, "2.0")
+annotate(ax, 2015, 2, "1.5")
+annotate(ax, 2014, 1, "1.4")
+annotate(ax, 2013, 2, "1.3")
+annotate(ax, 2012, 1, "1.2")
+annotate(ax, 2011, 3, "1.1", y0=2.5)
+annotate(ax, 2011, 2, "1.0")
+annotate(ax, 2009, 1, "0.99")
+annotate(ax, 2003, 1, "0.10")
+
+x0, x1 = 2002.5, 2011.9
+ax.plot([x0, x1], [5, 5], color="black", linewidth=1, marker="|", clip_on=False)
+ax.text((x0 + x1) / 2, 5.1, "J.D. Hunter", ha="center", va="bottom", size="x-small")
+
+x0, x1 = 2012.1, 2017.9
+ax.plot([x0, x1], [5, 5], color="black", linewidth=1, marker="|", clip_on=False)
+ax.text((x0 + x1) / 2, 5.1, "M. Droettboom", ha="center", va="bottom", size="x-small")
+
+x0, x1 = 2014.1, 2021.5
+ax.plot([x0, x1 + 1], [6, 6], color="black", linewidth=1, marker="|")
+ax.text((x0 + x1) / 2, 6.1, "T. Caswell", ha="center", va="bottom", size="x-small")
+
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["bottom"].set_visible(False)
+ax.set_xticks(np.arange(2003, 2022, 2))
+
+plt.tight_layout()
+plt.savefig("../../figures/introduction/matplotlib-timeline.pdf")
+plt.savefig("../../figures/introduction/matplotlib-timeline.png", dpi=300)
+plt.show()
diff --git a/code/introduction/visualization-landscape.graffle b/code/introduction/visualization-landscape.graffle
new file mode 100644
index 0000000..876fd1c
Binary files /dev/null and b/code/introduction/visualization-landscape.graffle differ
diff --git a/code/layout/complex-layout-bare.py b/code/layout/complex-layout-bare.py
new file mode 100644
index 0000000..ae4770d
--- /dev/null
+++ b/code/layout/complex-layout-bare.py
@@ -0,0 +1,58 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+
+fig = plt.figure(figsize=(6.5, 6))
+gspec = gridspec.GridSpec(
+ ncols=7, nrows=7, figure=fig, height_ratios=[1, 0.25, 1, 1, 1, 1, 1]
+)
+
+plt.rc("axes", edgecolor=".75")
+ax = plt.subplot(
+ gspec[1, :2], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+ax = plt.subplot(
+ gspec[0, 2:4], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+ax = plt.subplot(
+ gspec[0, 5:7], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+plt.rc("axes", edgecolor="black")
+
+for col in range(2, 7):
+ ax = plt.subplot(
+ gspec[1, col], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+ )
+
+for row in range(2, 7):
+ ax = plt.subplot(
+ gspec[row, :2], frameon=True, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+ )
+ for col in range(2, 7):
+ ax = plt.subplot(
+ gspec[row, col],
+ aspect=1,
+ frameon=True,
+ xlim=[0, 1],
+ xticks=[],
+ ylim=[0, 1],
+ yticks=[],
+ )
+
+
+ax = plt.subplot(
+ gspec[0, 4], frameon=True, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+
+ax = plt.subplot(
+ gspec[0, :2], frameon=True, xlim=[0, 10], xticks=[], ylim=[0, 4], yticks=[]
+)
+
+plt.savefig("../../figures/layout/complex-layout-bare.pdf")
+plt.show()
diff --git a/code/layout/complex-layout.py b/code/layout/complex-layout.py
new file mode 100644
index 0000000..fccd95c
--- /dev/null
+++ b/code/layout/complex-layout.py
@@ -0,0 +1,185 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+
+fig = plt.figure(figsize=(6.5, 6))
+gspec = gridspec.GridSpec(
+ ncols=7, nrows=7, figure=fig, height_ratios=[1, 0.25, 1, 1, 1, 1, 1]
+)
+cmap = plt.get_cmap("Blues")
+
+
+for row in range(2, 7):
+ ax = plt.subplot(
+ gspec[row, :2], frameon=False, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+ )
+ ax.text(
+ 1,
+ 1,
+ "Very long text using\n newlines and small font",
+ family="Roboto Condensed",
+ horizontalalignment="right",
+ verticalalignment="top",
+ )
+
+ for col in range(2, 7):
+ ax = plt.subplot(
+ gspec[row, col],
+ aspect=1,
+ frameon=False,
+ xlim=[0, 1],
+ xticks=[],
+ ylim=[0, 1],
+ yticks=[],
+ )
+ ax.axhline(0.5, color="white", lw=1)
+ ax.axvline(0.5, color="white", lw=1)
+
+ Z = np.random.uniform(0.25, 0.75, (2, 2))
+ ax.imshow(Z, extent=[0, 1, 0, 1], cmap=cmap, vmin=0, vmax=1)
+ if row == 2:
+ ax.text(
+ 0.5,
+ 1.1,
+ "Subject %d" % (col - 1),
+ ha="center",
+ va="bottom",
+ size="small",
+ weight="bold",
+ )
+
+
+ax = plt.subplot(
+ gspec[0, 4], frameon=True, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[]
+)
+ax.axhline(0.5, color="black", lw=0.75)
+ax.axvline(0.5, color="black", lw=0.75)
+
+
+# First quadrant
+ax.text(
+ -0.5, 0.75, "First quadrant", ha="right", va="center", size="small", weight="bold"
+)
+ax.text(
+ -0.5,
+ 0.55,
+ "Explanation of first quadrant",
+ ha="right",
+ va="center",
+ size="x-small",
+ family="Roboto Condensed",
+)
+ax.plot(
+ [-0.25, 0.25],
+ [0.75, 0.75],
+ color="black",
+ marker="o",
+ markevery=[-1],
+ clip_on=False,
+ linewidth=0.75,
+ markersize=2,
+)
+
+# Second quadrant
+ax.text(
+ -0.5, 0.25, "Second quadrant", ha="right", va="center", size="small", weight="bold"
+)
+ax.text(
+ -0.5,
+ 0.05,
+ "Explanation of second quadrant",
+ ha="right",
+ va="center",
+ size="x-small",
+ family="Roboto Condensed",
+)
+ax.plot(
+ [-0.25, 0.25],
+ [0.25, 0.25],
+ color="black",
+ marker="o",
+ markevery=[-1],
+ clip_on=False,
+ linewidth=0.75,
+ markersize=2,
+)
+
+# Third quadrant
+ax.text(
+ 1.5, 0.75, "Third quadrant", ha="left", va="center", size="small", weight="bold"
+)
+ax.text(
+ 1.5,
+ 0.55,
+ "Explanation of third quadrant",
+ ha="left",
+ va="center",
+ size="x-small",
+ family="Roboto Condensed",
+)
+ax.plot(
+ [1.25, 0.75],
+ [0.75, 0.75],
+ color="black",
+ marker="o",
+ markevery=[-1],
+ clip_on=False,
+ linewidth=0.75,
+ markersize=2,
+)
+
+# Fourth quadrant
+ax.text(
+ 1.5, 0.25, "Fourth quadrant", ha="left", va="center", size="small", weight="bold"
+)
+ax.text(
+ 1.5,
+ 0.05,
+ "Explanation of fourth quadrant",
+ ha="left",
+ va="center",
+ size="x-small",
+ family="Roboto Condensed",
+)
+ax.plot(
+ [1.25, 0.75],
+ [0.25, 0.25],
+ color="black",
+ marker="o",
+ markevery=[-1],
+ clip_on=False,
+ linewidth=0.75,
+ markersize=2,
+)
+
+
+# Legend
+ax = plt.subplot(
+ gspec[0, :2], frameon=False, xlim=[0, 10], xticks=[], ylim=[0, 4], yticks=[]
+)
+ax.scatter([1, 1, 1], [3, 2, 1], color=[cmap(0.25), cmap(0.50), cmap(0.75)])
+ax.text(
+ 2, 1, "Large", ha="left", va="center", size="small", weight="bold", family="Roboto"
+)
+ax.text(
+ 2, 2, "Medium", ha="left", va="center", size="small", weight="bold", family="Roboto"
+)
+ax.text(
+ 2,
+ 3,
+ "Limited",
+ ha="left",
+ va="center",
+ size="small",
+ weight="bold",
+ family="Roboto",
+)
+
+plt.savefig("../../figures/layout/complex-layout.pdf")
+plt.show()
diff --git a/code/layout/layout-aspect.py b/code/layout/layout-aspect.py
new file mode 100644
index 0000000..3dcce82
--- /dev/null
+++ b/code/layout/layout-aspect.py
@@ -0,0 +1,64 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["figure.dpi"] = 100
+p["figure.facecolor"] = "#ffffff"
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+p["axes.grid"] = True
+p["grid.color"] = "0.5"
+p["grid.linewidth"] = 0.5
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols = 2, 2
+gspec = gridspec.GridSpec(
+ ncols=ncols, nrows=nrows, figure=fig, width_ratios=[1, 2], height_ratios=[1, 2]
+)
+
+
+def plot(ax, xmax=1, ymax=1):
+ ax.set_xlim(0, xmax)
+ ax.set_xticks(np.linspace(0, xmax, 4 * xmax + 1))
+ ax.set_xlabel("X Label")
+ ax.set_ylim(0, ymax)
+ ax.set_yticks(np.linspace(0, ymax, 4 * ymax + 1))
+ ax.set_ylabel("Y Label")
+ ax.set_title("Title", family="Roboto", weight=500)
+
+
+if 0: # No constraints
+ plot(plt.subplot(gspec[0, 0]))
+ plot(plt.subplot(gspec[1, 0]))
+ plot(plt.subplot(gspec[0, 1]))
+ plot(plt.subplot(gspec[1, 1]))
+
+if 0: # Aspect is constrained
+ plot(plt.subplot(gspec[0, 0]))
+ plot(plt.subplot(gspec[1, 0], aspect=1))
+ plot(plt.subplot(gspec[0, 1], aspect=1))
+ plot(plt.subplot(gspec[1, 1], aspect=1))
+
+if 1: # Aspect is constrained but limits fit
+ plot(plt.subplot(gspec[0, 0]))
+ plot(plt.subplot(gspec[1, 0], aspect=1), ymax=2)
+ plot(plt.subplot(gspec[0, 1], aspect=1), xmax=2)
+ plot(plt.subplot(gspec[1, 1], aspect=1), xmax=2, ymax=2)
+
+plt.savefig("../../figures/layout/layout-aspect-3.pdf")
+plt.show()
diff --git a/code/layout/layout-classical.py b/code/layout/layout-classical.py
new file mode 100644
index 0000000..f79edad
--- /dev/null
+++ b/code/layout/layout-classical.py
@@ -0,0 +1,49 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+p["axes.grid"] = True
+p["grid.color"] = "0.5"
+p["grid.linewidth"] = 0.5
+
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure()
+nrows, ncols = 3, 3
+
+
+def plot(ax, text):
+ ax.set_xlim(0, 1)
+ ax.set_xticks(np.linspace(0, 1, 5))
+ ax.set_xlabel("X Label")
+ ax.set_ylim(0, 1)
+ ax.set_yticks(np.linspace(0, 1, 5))
+ ax.set_ylabel("Y Label")
+ ax.text(
+ 0.5, 0.5, text, alpha=0.75, ha="center", va="center", weight="bold", size=12
+ )
+ ax.set_title("Title", family="Roboto", weight=500)
+
+
+for i in range(1, nrows * ncols + 1):
+ plot(
+ plt.subplot(nrows, ncols, i, aspect=1), "subplot(%d,%d,%d)" % (nrows, ncols, i)
+ )
+
+plt.tight_layout()
+plt.savefig("../../figures/layout/layout-classical.pdf")
+plt.show()
diff --git a/code/layout/layout-gridspec.py b/code/layout/layout-gridspec.py
new file mode 100644
index 0000000..ec5057d
--- /dev/null
+++ b/code/layout/layout-gridspec.py
@@ -0,0 +1,51 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+p["axes.grid"] = True
+p["grid.color"] = "0.5"
+p["grid.linewidth"] = 0.5
+
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols = 3, 3
+gspec = gridspec.GridSpec(ncols=ncols, nrows=nrows, figure=fig)
+
+
+def plot(ax, text):
+ ax.set_xlim(0, 1)
+ ax.set_xticks(np.linspace(0, 1, 5))
+ ax.set_xlabel("X Label")
+ ax.set_ylim(0, 1)
+ ax.set_yticks(np.linspace(0, 1, 5))
+ ax.set_ylabel("Y Label")
+ ax.text(
+ 0.5, 0.5, text, alpha=0.75, ha="center", va="center", weight="bold", size=12
+ )
+ ax.set_title("Title", family="Roboto", weight=500)
+
+
+for i in range(ncols):
+ plot(plt.subplot(gspec[0, i]), "subplot(gspec[0,%d])" % i)
+for i in range(1, nrows):
+ plot(plt.subplot(gspec[i, 0]), "subplot(gspec[%d,0])" % i)
+plot(plt.subplot(gspec[1:, 1:]), "subplot(gspec[1:,1:])")
+
+plt.savefig("../../figures/layout/layout-gridspec.pdf")
+plt.show()
diff --git a/code/layout/standard-layout-1.py b/code/layout/standard-layout-1.py
new file mode 100644
index 0000000..2b5edb8
--- /dev/null
+++ b/code/layout/standard-layout-1.py
@@ -0,0 +1,56 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+
+# Some data
+n = 64
+X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+)
+Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+
+def f(x, y):
+ return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-(x ** 2) - y ** 2)
+
+
+x = np.linspace(-3, 3, 100)
+y = np.linspace(-3, 3, 100)
+X, Y = np.meshgrid(x, y)
+Z = 0.5 * f(X, Y)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols = 1, 2
+w1, w2 = 20, 1
+gspec = gridspec.GridSpec(ncols=ncols, nrows=nrows, figure=fig, width_ratios=[w1, w2])
+
+ax = plt.subplot(gspec[0, 0], aspect=1)
+ax.set_xlim(0, 1)
+ax.set_xticks(np.linspace(0, 1, 4 + 1))
+ax.set_xlabel("X Label")
+ax.set_ylim(0, 1)
+ax.set_yticks(np.linspace(0, 1, 4 + 1))
+ax.set_ylabel("Y Label")
+ax.set_title("Title", family="Roboto", weight=500)
+I = ax.imshow(Z, extent=[0, 1, 0, 1])
+
+ax = plt.subplot(gspec[0, 1], aspect=w1)
+plt.colorbar(I, cax=ax)
+
+plt.savefig("../../figures/layout/standard-layout-1.pdf")
+plt.show()
diff --git a/code/layout/standard-layout-2.py b/code/layout/standard-layout-2.py
new file mode 100644
index 0000000..55236b6
--- /dev/null
+++ b/code/layout/standard-layout-2.py
@@ -0,0 +1,63 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+#
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 6, 6
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+
+X = np.random.normal(0.5, 0.15, 5000)
+Y = np.random.normal(0.5, 0.15, 5000)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols, ratio = 2, 2, 5
+gspec = gridspec.GridSpec(
+ ncols=ncols,
+ nrows=nrows,
+ figure=fig,
+ height_ratios=[1, ratio],
+ width_ratios=[ratio, 1],
+)
+
+ax = plt.subplot(gspec[1, 0])
+ax.scatter(X, Y, s=15, facecolor="black", linewidth=0, alpha=0.25)
+ax.set_xlim(0, 1), ax.set_xticks(np.linspace(0, 1, 5))
+ax.set_ylim(0, 1), ax.set_yticks(np.linspace(0, 1, 5))
+
+ax = plt.subplot(gspec[0, 0])
+ax.set_xlim(0, 1), ax.set_xticks(np.linspace(0, 1, 5))
+ax.set_xticklabels([]), ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.hist(
+ X, bins=np.linspace(0, 1, 21), facecolor="0.75", edgecolor="white", linewidth=0.5
+)
+
+ax = plt.subplot(gspec[1, 1])
+ax.set_ylim(0, 1), ax.set_yticks(np.linspace(0, 1, 5))
+ax.set_yticklabels([]), ax.set_xticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["bottom"].set_visible(False)
+ax.spines["top"].set_visible(False)
+H = ax.hist(
+ Y,
+ bins=np.linspace(0, 1, 21),
+ facecolor="0.75",
+ edgecolor="white",
+ orientation="horizontal",
+ linewidth=0.5,
+)
+
+plt.savefig("../../figures/layout/standard-layout-2.pdf")
+plt.show()
diff --git a/code/optimization/line-benchmark.py b/code/optimization/line-benchmark.py
new file mode 100644
index 0000000..03d73ae
--- /dev/null
+++ b/code/optimization/line-benchmark.py
@@ -0,0 +1,49 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from timeit import default_timer as timer
+
+n_lines, n_points = 1_000, 2
+X = [np.random.uniform(0, 1, n_points) for i in range(n_lines)]
+Y = [np.random.uniform(0, 1, n_points) for i in range(n_lines)]
+
+fig = plt.figure(figsize=(9, 3.5))
+
+# -----------------------------
+ax = fig.add_subplot(1, 3, 1, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[])
+start = timer()
+for x, y in zip(X, Y):
+ ax.plot(x, y, color="blue", alpha=0.1, linewidth=0.5)
+end = timer()
+ax.set_title("Individual plots: %.4fs" % (end - start))
+
+
+# -----------------------------
+ax = fig.add_subplot(1, 3, 2, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[])
+start = timer()
+X_, Y_ = [], []
+for x, y in zip(X, Y):
+ X_.extend(x), X_.extend([None])
+ Y_.extend(y), Y_.extend([None])
+ax.plot(X_, Y_, color="blue", alpha=0.1, linewidth=0.5)
+end = timer()
+ax.set_title("Unified plot: %.4fs" % (end - start))
+
+# -----------------------------
+from matplotlib.collections import LineCollection
+
+ax = fig.add_subplot(1, 3, 3, aspect=1, xlim=[0, 1], xticks=[], ylim=[0, 1], yticks=[])
+start = timer()
+V = [np.stack([x, y], axis=1) for x, y in zip(X, Y)]
+lines = LineCollection(V, color="blue", alpha=0.1, linewidth=0.5)
+ax.add_collection(lines)
+end = timer()
+ax.set_title("Line collection: %.4fs" % (end - start))
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/line-benchmark.png", dpi=600)
+plt.show()
diff --git a/code/optimization/multisample.py b/code/optimization/multisample.py
new file mode 100644
index 0000000..fe82591
--- /dev/null
+++ b/code/optimization/multisample.py
@@ -0,0 +1,133 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate multisample on data or screen space (using imshow)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+
+plt.rc("font", family="Roboto")
+
+fig = plt.figure(figsize=(2 * 4.25, 2 * 2.5 * 4.25 / 4), dpi=100)
+
+xmin, xmax = 0 * np.pi, 5 * np.pi
+ymin, ymax = -1.1, +1.1
+
+
+def f(x):
+ return np.sin(np.power(x, 3)) * np.sin(x)
+
+
+# --- X linear space ----------------------------------------------------------
+ax = plt.subplot(
+ 311,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+ frameon=False,
+ xlim=[xmin, xmax],
+ ylim=[ymin, ymax],
+)
+
+X = np.linspace(xmin, xmax, 10000)
+Y = f(X)
+plt.plot(X, Y, linewidth=0.5, alpha=0.25, color="black")
+ax.set_title("Line plot", ha="left", loc="left")
+
+# --- X linear space ----------------------------------------------------------
+ax = plt.subplot(
+ 312,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+ frameon=False,
+ xlim=[xmin, xmax],
+ ylim=[ymin, ymax],
+)
+
+X = np.linspace(xmin, xmax, 10000)
+Y = f(X)
+
+segments = np.zeros((len(X) - 1, 2, 2))
+segments[:, 0, 0], segments[:, 0, 1] = X[:-1], Y[:-1]
+segments[:, 1, 0], segments[:, 1, 1] = X[1:], Y[1:]
+
+ax.add_collection(LineCollection(segments, linewidths=0.5, alpha=0.25, colors="black"))
+ax.set_title("Line collection", ha="left", loc="left")
+
+
+# --- Multisample (screen space) ----------------------------------------------
+n_samples = 8
+ax = plt.subplot(
+ 313,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+ frameon=False,
+ xlim=[xmin, xmax],
+ ylim=[ymin, ymax],
+)
+x0, y0 = ax.transAxes.transform((0, 0)).astype(int)
+x1, y1 = ax.transAxes.transform((1, 1)).astype(int)
+rows, cols = y1 - y0, x1 - x0
+shape = n_samples * rows, n_samples * cols
+Z = np.zeros(shape)
+
+I, J = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]))
+# N = np.random.uniform(-0.5,0.5,I.shape)
+N = np.random.normal(0, 0.5, I.shape)
+X = xmin + ((I + N) / (shape[1] - 1)) * (xmax - xmin)
+Y = np.floor(((f(X) - ymin) / (ymax - ymin)) * shape[0]).astype(int)
+Z[Y, I] = np.minimum(np.abs(Y - J), 1)
+ax.imshow(
+ Z,
+ extent=[xmin, xmax, ymin, ymax],
+ origin="lower",
+ cmap="gray_r",
+ interpolation="lanczos",
+ vmin=0,
+ vmax=1.5,
+)
+ax.set_title("Multisample (imshow)", ha="left", loc="left")
+
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/multisample.png", dpi=600)
+plt.show()
+
+
+# # --- Multisample (data space) ------------------------------------------------
+# ax = plt.subplot(412, xticks=[], yticks=[], aspect=1,
+# xlim=[xmin, xmax], ylim=[ymin, ymax])
+
+# x0, y0 = ax.transAxes.transform((0,0)).astype(int)
+# x1, y1 = ax.transAxes.transform((1,1)).astype(int)
+# rows, cols = y1-y0, x1-x0
+
+# X = np.linspace(0, cols-1, n_samples*n_samples*cols) #1000)
+# #X += np.random.normal(0,.5)
+# X = xmin + (X/(cols-1))*(xmax-xmin)
+# Y = f(X)
+
+
+# #dx = (xmax-xmin)/((x1-x0) * n_samples * n_samples)
+# #for i in range(n_samples*n_samples):
+# # X = np.linspace(xmin,xmax,1000)
+# # Y = f(X+dx*i/n_samples)
+# plt.plot(X,Y, linewidth=0.1, alpha=1.0, color="black")
+# ax.set_title("Multisample (data space)")
+
+
+# # --- X log space -------------------------------------------------------------
+# ax = plt.subplot(413, xticks=[], yticks=[], aspect=1,
+# xlim=[xmin, xmax], ylim=[ymin, ymax])
+
+# X = 1 - np.logspace(0,3,10000,endpoint=True)/1000
+# X = xmin + X*(xmax-xmin)
+# Y = f(X)
+# plt.plot(X,Y, linewidth=0.25, alpha=0.5, color="black")
+# plt.plot(X,Y, linewidth=0.25, alpha=0.5, color="black")
+# ax.set_title("X log space")
diff --git a/code/optimization/multithread.py b/code/optimization/multithread.py
new file mode 100644
index 0000000..6a59215
--- /dev/null
+++ b/code/optimization/multithread.py
@@ -0,0 +1,57 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from matplotlib.backends.backend_agg import FigureCanvas
+
+X = np.random.normal(4.5, 2, 5_000_000)
+Y = np.random.normal(4.5, 2, 5_000_000)
+
+
+def plot(extent):
+ """ Offline rendering """
+
+ xmin, xmax, ymin, ymax = extent
+ fig = Figure(figsize=(2, 2))
+ canvas = FigureCanvas(fig)
+ ax = fig.add_axes(
+ [0, 0, 1, 1],
+ frameon=False,
+ xlim=[xmin, xmax],
+ xticks=[],
+ ylim=[ymin, ymax],
+ yticks=[],
+ )
+ epsilon = 0.1
+ I = np.argwhere(
+ (X >= (xmin - epsilon))
+ & (X <= (xmax + epsilon))
+ & (Y >= (ymin - epsilon))
+ & (Y <= (ymax + epsilon))
+ )
+ ax.scatter(
+ X[I], Y[I], 3, clip_on=False, color="black", edgecolor="None", alpha=0.0025
+ )
+ canvas.draw()
+ return np.array(canvas.renderer.buffer_rgba())
+
+
+if __name__ == "__main__":
+ from multiprocessing import Pool
+
+ extents = [[x, x + 3, y, y + 3] for x in range(0, 9, 3) for y in range(0, 9, 3)]
+ pool = Pool()
+ images = pool.map(plot, extents)
+ pool.close()
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = plt.subplot(xlim=[0, 9], ylim=[0, 9])
+ for img, extent in zip(images, extents):
+ ax.imshow(img, extent=extent, interpolation="None")
+
+ plt.savefig("../../figures/optimization/multithread.png", dpi=600)
+ plt.show()
diff --git a/code/optimization/scatter-benchmark.py b/code/optimization/scatter-benchmark.py
new file mode 100644
index 0000000..4097c73
--- /dev/null
+++ b/code/optimization/scatter-benchmark.py
@@ -0,0 +1,46 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from timeit import default_timer as timer
+
+n_points = 100_000
+np.random.seed(1)
+X = np.random.normal(0, 1, n_points)
+Y = np.random.normal(0, 1, n_points)
+
+fig = plt.figure(figsize=(9, 3.5))
+
+# -----------------------------
+ax = fig.add_subplot(
+ 1, 3, 2, aspect=1, xlim=[-5, 5], xticks=[], ylim=[-5, 5], yticks=[]
+)
+start = timer()
+ax.scatter(X, Y, s=4, color="black", alpha=0.008, linewidth=0)
+end = timer()
+ax.set_title("Scatter: %.4fs" % (end - start))
+
+# -----------------------------
+ax = fig.add_subplot(
+ 1, 3, 3, aspect=1, xlim=[-5, 5], xticks=[], ylim=[-5, 5], yticks=[]
+)
+start = timer()
+ax.plot(X, Y, "o", markersize=2, color="black", alpha=0.008, markeredgewidth=0)
+end = timer()
+ax.set_title("Plot: %.4fs" % (end - start))
+
+# -----------------------------
+ax = fig.add_subplot(
+ 1, 3, 1, aspect=1, xlim=[-5, 5], xticks=[], ylim=[-5, 5], yticks=[]
+)
+start = timer()
+ax.hist2d(X, Y, 128, cmap="gray_r")
+end = timer()
+ax.set_title("Hist2d: %.4fs" % (end - start))
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/scatter-benchmark.png", dpi=600)
+plt.show()
diff --git a/code/optimization/scatters.py b/code/optimization/scatters.py
new file mode 100644
index 0000000..b5da809
--- /dev/null
+++ b/code/optimization/scatters.py
@@ -0,0 +1,65 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+n = [10_000, 100_000, 1_000_000]
+alpha = [0.2, 0.02, 0.002]
+fig = plt.figure(figsize=(9, 9))
+
+index = 1
+for i in range(3):
+
+ X = np.random.normal(0, 2, n[i])
+ Y = np.random.normal(0, 2, n[i])
+
+ ax = plt.subplot(3, 3, index, aspect=1)
+ ax.scatter(
+ X, Y, 5, facecolor="black", edgecolor="None", alpha=alpha[i], antialiased=True
+ )
+ ax.set_xlim(-5, 5), ax.set_xticks([])
+ ax.set_ylim(-5, 5), ax.set_yticks([])
+ ax.text(
+ -5,
+ 4.75,
+ " scatter, n={:,}, alpha={:.3f}".format(n[i], alpha[i]),
+ ha="left",
+ va="top",
+ size="small",
+ )
+ index += 1
+
+ ax = plt.subplot(3, 3, index, aspect=1)
+ ax.hist2d(X, Y, 128, cmap="gray_r")
+ ax.set_xlim(-5, 5), ax.set_xticks([])
+ ax.set_ylim(-5, 5), ax.set_yticks([])
+ ax.text(
+ -5,
+ 4.75,
+ " hist2d (128x128 bins), n={:,}".format(n[i]),
+ ha="left",
+ va="top",
+ size="small",
+ )
+ index += 1
+
+ ax = plt.subplot(3, 3, index, aspect=1)
+ ax.hexbin(X, Y, gridsize=64, cmap="gray_r", linewidth=0, antialiased=0)
+ ax.set_xlim(-5, 5), ax.set_xticks([])
+ ax.set_ylim(-5, 5), ax.set_yticks([])
+ ax.text(
+ -5,
+ 4.75,
+ " hexbin (64x64 bins), n={:,}".format(n[i]),
+ ha="left",
+ va="top",
+ size="small",
+ )
+ index += 1
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/scatters.png", dpi=600)
+plt.show()
diff --git a/code/optimization/self-cover.py b/code/optimization/self-cover.py
new file mode 100644
index 0000000..6cb73dd
--- /dev/null
+++ b/code/optimization/self-cover.py
@@ -0,0 +1,56 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.path import Path
+import matplotlib.patches as patches
+
+fig = plt.figure(figsize=(8, 1.5))
+
+n = 3
+X = np.append(np.linspace(0, n * 2 * np.pi, 500), [0])
+Y = np.sin(X)
+
+ax = plt.subplot(
+ 1,
+ 2,
+ 1,
+ frameon=False,
+ xlim=(0, n * 2 * np.pi + 1),
+ xticks=[],
+ ylim=(-1, 1.1),
+ yticks=[],
+)
+ax.plot(X, Y, "C0", linewidth=8, alpha=0.5, solid_capstyle="round", clip_on=False)
+ax.set_title("No self covering")
+
+ax = plt.subplot(
+ 1,
+ 2,
+ 2,
+ frameon=False,
+ xlim=(0, n * 2 * np.pi),
+ xticks=[],
+ ylim=(-1, 1.1),
+ yticks=[],
+)
+ax.plot(
+ X[:-1], Y[:-1], "C0", linewidth=8, alpha=0.5, solid_capstyle="round", clip_on=False
+)
+ax.plot(
+ [X[-2], X[-1]],
+ [Y[-2], Y[-1]],
+ "C0",
+ linewidth=8,
+ alpha=0.5,
+ solid_capstyle="round",
+ clip_on=False,
+)
+ax.set_title("Simulated self covering")
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/self-cover.pdf")
+plt.show()
diff --git a/code/optimization/transparency.py b/code/optimization/transparency.py
new file mode 100644
index 0000000..b3801b2
--- /dev/null
+++ b/code/optimization/transparency.py
@@ -0,0 +1,60 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(7, 1.25))
+ax = plt.subplot(frameon=False)
+ax.set_xlim(0.5, 7.5), ax.set_xticks([])
+ax.set_ylim(0, 1.5), ax.set_yticks([])
+
+
+X, Y = [1], [1]
+alpha = 1
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point\nalpha={:.2f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [2], [1]
+alpha = 0.1
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [3] * 10, [1] * 10
+alpha = 0.1
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [4] * 25, [1] * 25
+alpha = 0.1
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [5] * 10, [1] * 10
+alpha = 0.002
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [6] * 64, [1] * 64
+alpha = 0.002
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+X, Y = [7] * 512, [1] * 512
+alpha = 0.002
+ax.scatter(X, Y, 256, marker="o", facecolor="black", edgecolor="None", alpha=alpha)
+text = "{:d} point(s)\nalpha={:.3f}".format(len(X), alpha)
+ax.text(X[0], 0.75, text, ha="center", va="top", size="small")
+
+
+plt.tight_layout()
+plt.savefig("../../figures/optimization/transparency.pdf")
+plt.show()
diff --git a/code/ornaments/annotate-regular.py b/code/ornaments/annotate-regular.py
new file mode 100644
index 0000000..7ead5ae
--- /dev/null
+++ b/code/ornaments/annotate-regular.py
@@ -0,0 +1,65 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(6, 4))
+ax = plt.subplot(
+ 1,
+ 1,
+ 1,
+ xlim=[-1, +1],
+ xticks=[-1, 0, 1],
+ ylim=[-1, +1],
+ yticks=[-1, 0, 1],
+ aspect=1,
+)
+
+np.random.seed(123)
+X = np.random.normal(0, 0.35, 1000)
+Y = np.random.normal(0, 0.35, 1000)
+
+ax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+
+
+I = np.random.choice(len(X), size=8, replace=False)
+Px, Py = X[I], Y[I]
+I = np.argsort(Y[I])[::-1]
+Px, Py = Px[I], Py[I]
+
+ax.scatter(Px, Py, edgecolor="black", facecolor="white", zorder=20)
+ax.scatter(Px, Py, edgecolor="None", facecolor="C1", alpha=0.5, zorder=30)
+
+y, dy = 1.0, 0.125
+style = "arc,angleA=-0,angleB=0,armA=-100,armB=0,rad=0"
+
+for i in range(len(I)):
+ ax.annotate(
+ "Point " + chr(ord("A") + i),
+ xy=(Px[i], Py[i]),
+ xycoords="data",
+ xytext=(1.25, y - i * dy),
+ textcoords="data",
+ arrowprops=dict(
+ arrowstyle="->",
+ color="black",
+ linewidth=0.75,
+ shrinkA=25,
+ shrinkB=5,
+ patchA=None,
+ patchB=None,
+ connectionstyle=style,
+ ),
+ )
+
+ax.spines["right"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["left"].set_position(("data", -1.1))
+ax.spines["bottom"].set_position(("data", -1.1))
+
+# plt.tight_layout()
+plt.savefig("../../figures/ornaments/legend-regular.pdf")
+plt.show()
diff --git a/code/ornaments/annotation-direct.py b/code/ornaments/annotation-direct.py
new file mode 100644
index 0000000..f0a4917
--- /dev/null
+++ b/code/ornaments/annotation-direct.py
@@ -0,0 +1,52 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as path_effects
+
+fig = plt.figure(figsize=(4, 4))
+ax = plt.subplot(1, 1, 1, xlim=[-1, +1], xticks=[], ylim=[-1, +1], yticks=[], aspect=1)
+
+np.random.seed(123)
+X = np.random.normal(0, 0.35, 1000)
+Y = np.random.normal(0, 0.35, 1000)
+
+ax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+
+I = np.random.choice(len(X), size=5, replace=False)
+Px, Py = X[I], Y[I]
+I = np.argsort(Y[I])[::-1]
+Px, Py = Px[I], Py[I]
+
+ax.scatter(Px, Py, edgecolor="black", facecolor="white", zorder=20)
+ax.scatter(Px, Py, edgecolor="None", facecolor="C1", alpha=0.5, zorder=30)
+
+y, dy = 1.0, 0.125
+style = "arc,angleA=-0,angleB=0,armA=-100,armB=0,rad=0"
+
+for i in range(len(I)):
+ text = ax.annotate(
+ "Point " + chr(ord("A") + i),
+ xy=(Px[i], Py[i]),
+ xycoords="data",
+ xytext=(0, 18),
+ textcoords="offset points",
+ ha="center",
+ size="small",
+ arrowprops=dict(
+ arrowstyle="->", shrinkA=0, shrinkB=5, color="black", linewidth=0.75
+ ),
+ )
+ text.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+ )
+ text.arrow_patch.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+ )
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/annotation-direct.pdf")
+plt.show()
diff --git a/code/ornaments/annotation-side.py b/code/ornaments/annotation-side.py
new file mode 100644
index 0000000..a034759
--- /dev/null
+++ b/code/ornaments/annotation-side.py
@@ -0,0 +1,55 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as path_effects
+
+
+fig = plt.figure(figsize=(5.5, 4))
+ax = plt.subplot(1, 1, 1, xlim=[-1, +1], xticks=[], ylim=[-1, +1], yticks=[], aspect=1)
+
+np.random.seed(123)
+X = np.random.normal(0, 0.35, 1000)
+Y = np.random.normal(0, 0.35, 1000)
+
+ax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+
+I = np.random.choice(len(X), size=5, replace=False)
+Px, Py = X[I], Y[I]
+I = np.argsort(Y[I])[::-1]
+Px, Py = Px[I], Py[I]
+
+ax.scatter(Px, Py, edgecolor="black", facecolor="white", zorder=20)
+ax.scatter(Px, Py, edgecolor="None", facecolor="C1", alpha=0.5, zorder=30)
+
+y, dy = 0.25, 0.125
+style = "arc,angleA=-0,angleB=0,armA=-100,armB=0,rad=0"
+for i in range(len(I)):
+ text = ax.annotate(
+ "Point " + chr(ord("A") + i),
+ xy=(Px[i], Py[i]),
+ xycoords="data",
+ xytext=(1.25, y - i * dy),
+ textcoords="data",
+ arrowprops=dict(
+ arrowstyle="->",
+ color="black",
+ linewidth=0.75,
+ shrinkA=20,
+ shrinkB=5,
+ patchA=None,
+ patchB=None,
+ connectionstyle=style,
+ ),
+ )
+ text.arrow_patch.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+ )
+
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/annotation-side.pdf")
+plt.show()
diff --git a/code/ornaments/annotation-zoom.py b/code/ornaments/annotation-zoom.py
new file mode 100644
index 0000000..75d5c23
--- /dev/null
+++ b/code/ornaments/annotation-zoom.py
@@ -0,0 +1,84 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+from matplotlib.patches import Rectangle
+from matplotlib.patches import ConnectionPatch
+
+
+fig = plt.figure(figsize=(5, 4))
+
+n = 5
+gs = GridSpec(n, n + 1)
+
+ax = plt.subplot(
+ gs[:n, :n], xlim=[-1, +1], xticks=[], ylim=[-1, +1], yticks=[], aspect=1
+)
+
+np.random.seed(123)
+X = np.random.normal(0, 0.35, 1000)
+Y = np.random.normal(0, 0.35, 1000)
+ax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+
+I = np.random.choice(len(X), size=n, replace=False)
+Px, Py = X[I], Y[I]
+I = np.argsort(Y[I])[::-1]
+Px, Py = Px[I], Py[I]
+
+ax.scatter(Px, Py, edgecolor="black", facecolor="None", linewidth=0.75)
+
+dx, dy = 0.075, 0.075
+for i, (x, y) in enumerate(zip(Px, Py)):
+ sax = plt.subplot(
+ gs[i, n],
+ xlim=[x - dx, x + dx],
+ xticks=[],
+ ylim=[y - dy, y + dy],
+ yticks=[],
+ aspect=1,
+ )
+ sax.scatter(X, Y, edgecolor="None", facecolor="C1", alpha=0.5)
+ sax.scatter(Px, Py, edgecolor="black", facecolor="None", linewidth=0.75)
+
+ sax.text(
+ 1.1,
+ 0.5,
+ "Point " + chr(ord("A") + i),
+ rotation=90,
+ size=8,
+ ha="left",
+ va="center",
+ transform=sax.transAxes,
+ )
+
+ rect = Rectangle(
+ (x - dx, y - dy),
+ 2 * dx,
+ 2 * dy,
+ edgecolor="black",
+ facecolor="None",
+ linestyle="--",
+ linewidth=0.75,
+ )
+ ax.add_patch(rect)
+
+ con = ConnectionPatch(
+ xyA=(x, y),
+ coordsA=ax.transData,
+ xyB=(0, 0.5),
+ coordsB=sax.transAxes,
+ linestyle="--",
+ linewidth=0.75,
+ patchA=rect,
+ arrowstyle="->",
+ )
+ fig.add_artist(con)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/annotation-zoom.pdf")
+plt.show()
diff --git a/code/ornaments/bessel-functions.py b/code/ornaments/bessel-functions.py
new file mode 100644
index 0000000..3616e09
--- /dev/null
+++ b/code/ornaments/bessel-functions.py
@@ -0,0 +1,110 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+from scipy.special import jn, jn_zeros
+import matplotlib.pyplot as plt
+
+plt.rc("font", family="Roboto Condensed")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+
+fig = plt.figure(figsize=(6, 3), dpi=100)
+ax = plt.subplot(xlim=[0, 20], ylim=[-0.5, 1])
+
+X = np.linspace(0, 20, 1000, endpoint=True)
+n = 6
+for i in range(n):
+ Ji = jn(i, X)
+ linewidth = 1.5 if i == 0 else 1
+ linestyle = "-" if i == 0 else "-"
+ color = "C1" if i == 0 else "%.2f" % (i / n)
+ label = r"$J_%d$" % i
+
+ ax.plot(X, Ji, color="white", clip_on=False, zorder=10 - i, linewidth=2.5)
+
+ ax.plot(
+ X,
+ Ji,
+ color=color,
+ clip_on=False,
+ zorder=10 - i,
+ linewidth=linewidth,
+ linestyle=linestyle,
+ label=label,
+ )
+
+ k = np.argmax(Ji)
+ ax.text(
+ X[k],
+ Ji[k] + 0.05,
+ label,
+ color=color,
+ usetex=True,
+ ha="center",
+ va="bottom",
+ size="small",
+ )
+ # k = np.argmin(Ji)
+ # ax.text(X[k], Ji[k]-0.05, label, color=color,
+ # ha="center", va="top", size="small")
+
+ Zx = [x for x in jn_zeros(i, 6) if x < 20]
+ Zy = np.zeros(len(Zx))
+ ax.scatter(Zx, Zy, s=15, zorder=20, edgecolor=color, facecolor="white", linewidth=1)
+
+ if i == 0:
+ ax.annotate(
+ "Root",
+ (Zx[0], Zy[0]),
+ size="small",
+ xytext=(-30, -30),
+ textcoords="offset points",
+ arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=-0.3"),
+ )
+
+ Zy = -0.6 * np.ones(len(Zx))
+ ax.scatter(
+ Zx,
+ Zy,
+ s=30,
+ zorder=20,
+ clip_on=False,
+ marker="|",
+ facecolor="black",
+ linewidth=0.5,
+ )
+
+ax.set_title(
+ "Bessel functions",
+ x=1,
+ weight="light",
+ family="Roboto Condensed",
+ transform=ax.transAxes,
+ ha="right",
+)
+ax.text(
+ 1,
+ 0.98,
+ r"$J_n(x) = \frac{1}{2\pi} \int_{-\pi}^\pi e^{i(x \sin \tau -n \tau)} \,d\tau$",
+ va="top",
+ transform=ax.transAxes,
+ size=12,
+ ha="right",
+ usetex=True,
+)
+
+ax.set_yticks([-0.5, 0, 0.5, 1])
+ax.set_xticks([0, 10, 20])
+ax.axhline(0, color="0.5", linewidth=0.5)
+
+ax.spines["right"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["left"].set_position(("data", -1))
+ax.spines["bottom"].set_position(("data", -0.6))
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/bessel-functions.pdf")
+plt.show()
diff --git a/code/ornaments/elegant-scatter.py b/code/ornaments/elegant-scatter.py
new file mode 100644
index 0000000..297c209
--- /dev/null
+++ b/code/ornaments/elegant-scatter.py
@@ -0,0 +1,87 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.size"] = 10.0
+plt.rcParams["font.serif"] = ["Source Serif Pro"]
+plt.rcParams["font.sans-serif"] = ["Source Sans Pro"]
+plt.rcParams["font.monospace"] = ["Source Code Pro"]
+
+
+fig = plt.figure(figsize=(5, 5))
+ax = plt.axes()
+
+ax.set_xlabel("Height (m)", weight="medium")
+ax.set_ylabel("Weight (kg)", weight="medium")
+ax.set_title(
+ """Distribution of height & weight\n""" """according to sex & age (fake data)""",
+ family="serif",
+)
+
+n = 250
+np.random.seed(1)
+X, Y = np.zeros(2 * n), np.zeros(2 * n)
+S, C = np.zeros(2 * n), np.zeros(2 * n)
+
+X[:n] = np.random.normal(1.60, 0.1, n)
+Y[:n] = np.random.normal(50, 10, n)
+S[:n] = np.random.uniform(25, 50, n)
+C[:n] = 0
+
+X[n:] = np.random.normal(1.75, 0.1, n)
+Y[n:] = np.random.normal(75, 10, n)
+S[n:] = np.random.uniform(25, 50, n)
+C[n:] = 1
+
+cmap = plt.get_cmap("RdYlBu")
+scatter = plt.scatter(X, Y, s=S, edgecolor="black", linewidth=0.75, zorder=-20)
+scatter = plt.scatter(X, Y, s=S, edgecolor="None", facecolor="white", zorder=-10)
+scatter = plt.scatter(X, Y, c=C, s=S, cmap=cmap, edgecolor="None", alpha=0.25)
+
+handles, labels = scatter.legend_elements(
+ num=3,
+ prop="sizes",
+ alpha=1,
+ markeredgewidth=0.5,
+ markeredgecolor="black",
+ markerfacecolor="None",
+)
+legend = plt.legend(
+ handles,
+ labels,
+ title=" Age",
+ loc=(0.6, 0.05),
+ handletextpad=0.1,
+ labelspacing=0.25,
+ facecolor="None",
+ edgecolor="None",
+)
+legend.get_children()[0].align = "left"
+legend.get_title().set_fontweight("medium")
+ax.add_artist(legend)
+
+handles, labels = scatter.legend_elements(markeredgewidth=0.0, markeredgecolor="black")
+labels = ["Female", "Male"]
+legend = plt.legend(
+ handles,
+ labels,
+ title=" Sex",
+ loc=(0.75, 0.05),
+ handletextpad=0.1,
+ labelspacing=0.25,
+ facecolor="None",
+ edgecolor="None",
+)
+legend.get_children()[0].align = "left"
+legend.get_title().set_fontweight("medium")
+
+
+plt.scatter(X, [19] * len(X), marker="|", color=cmap(C), linewidth=0.5, alpha=0.25)
+plt.scatter([1.3] * len(X), Y, marker="_", color=cmap(C), linewidth=0.5, alpha=0.25)
+
+plt.savefig("../../figures/ornaments/elegant-scatter.pdf")
+plt.show()
diff --git a/code/ornaments/label-alternatives.py b/code/ornaments/label-alternatives.py
new file mode 100644
index 0000000..b0552ca
--- /dev/null
+++ b/code/ornaments/label-alternatives.py
@@ -0,0 +1,70 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(6, 6))
+
+n = 5
+for i in range(n):
+ index = i + 1
+ ax = plt.subplot(
+ n,
+ 1,
+ index,
+ facecolor="None",
+ xlim=[0, 10.5],
+ xticks=[],
+ ylim=[0, 10],
+ yticks=[],
+ )
+
+ for j in range(5):
+ ax.plot(
+ [0, 10],
+ [1 + j * 1.5, 1 + j * 1.5],
+ clip_on=False,
+ linewidth=5,
+ color=".9",
+ solid_capstyle="round",
+ )
+
+ x = np.random.uniform(1, 9)
+ ax.plot(
+ [0, x],
+ [1 + j * 1.5, 1 + j * 1.5],
+ clip_on=False,
+ linewidth=5,
+ color="C1",
+ solid_capstyle="round",
+ )
+
+ ax.text(
+ 10.5,
+ 0,
+ " %d " % (2000 + index),
+ font="Roboto Condensed",
+ weight="medium",
+ color="black",
+ ha="center",
+ va="center",
+ size="small",
+ bbox=dict(
+ facecolor="white",
+ edgecolor="black",
+ linewidth=0.75,
+ boxstyle="round,pad=0.25",
+ ),
+ )
+
+ ax.spines["right"].set_visible(False)
+ ax.spines["top"].set_visible(False)
+ ax.spines["left"].set_visible(False)
+ # ax.spines['bottom'].set_position(('data', -1.25))
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/legend-regular.pdf")
+plt.show()
diff --git a/code/ornaments/latex-text-box.py b/code/ornaments/latex-text-box.py
new file mode 100644
index 0000000..d05a38d
--- /dev/null
+++ b/code/ornaments/latex-text-box.py
@@ -0,0 +1,134 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import matplotlib.patches as patches
+import matplotlib.patheffects as path_effects
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+
+rows, cols = 2, 3
+fig = plt.figure(figsize=(cols * 2, rows * 2.45))
+
+for row in range(rows):
+ for col in range(cols):
+ index = row * cols + col + 1
+
+ if index == cols * rows:
+ ax = plt.subplot(rows, cols, index, aspect=1, frameon=False)
+ ax.set_xticks([]), ax.set_yticks([])
+
+ plt.rcParams.update(
+ {"font.family": "sans-serif", "font.sans-serif": ["Helvetica"]}
+ )
+
+ ax.text(
+ 0,
+ 1.06,
+ r"\begin{minipage}{4.3cm}\small "
+ r"\textbf{Figure caption} "
+ r"can be directly inserted from within matplotlib using the full \LaTeX~engine. To fit the axes, you can use a minipage and adjust its width to the axes. Of course, the width can be also computed automatically but from time to time it is ok to do it by trials and errors (I did). Fun part is that we could also include some tikz rendering inside the figure. "
+ r"\end{minipage}\vspace{-5mm}",
+ va="top",
+ transform=ax.transAxes,
+ clip_on=False,
+ usetex=True,
+ )
+ continue
+
+ ax = plt.subplot(rows, cols, index, aspect=1, frameon=True)
+ ax.tick_params(which="both", direction="in")
+ ax.tick_params(which="both", right=True)
+ ax.set_axisbelow(True)
+
+ ax.set_xlim(0, 10)
+ ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ ax.set_xticklabels([])
+
+ ax.set_ylim(0, 10)
+ ax.yaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ ax.set_yticklabels([])
+
+ ax.grid(color=".9", linestyle="--")
+
+ n = 50
+ x0, y0 = np.random.uniform(2, 8, 2)
+ s = np.random.uniform(0.5, 2.0)
+ X = np.random.normal(x0, s, n)
+ Y = np.random.normal(y0, s, n)
+ S = np.random.uniform(100, 200, n)
+ ax.scatter(X, Y, alpha=0.5, facecolor="C0", edgecolor="None")
+
+ Xm, Ym = X.mean(), Y.mean()
+ ax.axvline(Xm, linewidth=0.5, color="black")
+ text = ax.text(
+ Xm,
+ 9.5,
+ "%.1f" % Xm,
+ transform=ax.transData,
+ size=7,
+ rotation=90,
+ ha="center",
+ va="top",
+ )
+ text.set_path_effects(
+ [
+ path_effects.Stroke(linewidth=2, foreground="white"),
+ path_effects.Normal(),
+ ]
+ )
+
+ ax.axhline(Ym, linewidth=0.5, color="black")
+ text = ax.text(
+ 9.5,
+ Ym,
+ "%.1f" % Ym,
+ transform=ax.transData,
+ size=7,
+ rotation=0,
+ ha="right",
+ va="center",
+ )
+ text.set_path_effects(
+ [
+ path_effects.Stroke(linewidth=2, foreground="white"),
+ path_effects.Normal(),
+ ]
+ )
+
+ ax.scatter([Xm], [Ym], s=10, facecolor="black", edgecolor="None")
+
+ divider = make_axes_locatable(ax)
+ cax = divider.append_axes("top", size="15%", pad=0)
+ cax.get_xaxis().set_visible(False)
+ cax.get_yaxis().set_visible(False)
+ cax.set_facecolor("black")
+ cax.text(
+ 0.05,
+ 0.45,
+ "SESSION %2d" % index,
+ size=10,
+ color="white",
+ ha="left",
+ va="center",
+ )
+ cax.text(
+ 0.95,
+ 0.45,
+ "σ=%.1f" % s,
+ size=10,
+ color="white",
+ ha="right",
+ va="center",
+ weight="bold",
+ )
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/latex-text-box.png", dpi=600)
+plt.show()
diff --git a/code/ornaments/legend-alternatives.py b/code/ornaments/legend-alternatives.py
new file mode 100644
index 0000000..5b63b63
--- /dev/null
+++ b/code/ornaments/legend-alternatives.py
@@ -0,0 +1,161 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def plot(ax):
+ ax.set_xlim([-np.pi, np.pi])
+ ax.set_xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
+ ax.set_xticklabels(["-π", "-π/2", "0", "+π/2", "+π"])
+ ax.set_ylim([-1, 1])
+ ax.set_yticks([-1, 0, 1])
+ ax.set_yticklabels(["-1", "0", "+1"])
+
+ ax.spines["right"].set_visible(False)
+ ax.spines["top"].set_visible(False)
+ ax.spines["left"].set_position(("data", -3.25))
+ ax.spines["bottom"].set_position(("data", -1.25))
+
+ (plot1,) = ax.plot(X, C, label="cosine", clip_on=False)
+ (plot2,) = ax.plot(X, S, label="sine", clip_on=False)
+
+ return plot1, plot2
+
+
+n = 4
+fig = plt.figure(figsize=(6, n * 1.9))
+
+X = np.linspace(-np.pi, np.pi, 400, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+ax = plt.subplot(n, 1, 1)
+plot1, plot2 = plot(ax)
+ax.text(
+ X[-1],
+ C[-1],
+ " — " + plot1.get_label(),
+ size="small",
+ color=plot1.get_color(),
+ ha="left",
+ va="center",
+)
+ax.text(
+ X[-1],
+ S[-1],
+ " — " + plot2.get_label(),
+ size="small",
+ color=plot2.get_color(),
+ ha="left",
+ va="center",
+)
+
+ax = plt.subplot(n, 1, 2)
+plot1, plot2 = plot(ax)
+ax.text(
+ X[100],
+ C[100],
+ " " + plot1.get_label(),
+ family="Roboto Condensed",
+ size="small",
+ bbox=dict(facecolor="white", edgecolor="None", alpha=0.85),
+ color=plot1.get_color(),
+ ha="center",
+ va="center",
+ rotation=42.5,
+)
+ax.text(
+ X[200],
+ S[200],
+ " " + plot2.get_label(),
+ rotation=42.5,
+ family="Roboto Condensed",
+ size="small",
+ bbox=dict(facecolor="white", edgecolor="None", alpha=0.85),
+ color=plot2.get_color(),
+ ha="center",
+ va="center",
+)
+
+ax = plt.subplot(n, 1, 3)
+plot1, plot2 = plot(ax)
+
+ax.annotate(
+ "cosine",
+ (X[100], C[100]),
+ size="small",
+ color=plot1.get_color(),
+ xytext=(-50, +10),
+ textcoords="offset points",
+ arrowprops=dict(
+ arrowstyle="->", color=plot1.get_color(), connectionstyle="arc3,rad=-0.3"
+ ),
+)
+ax.annotate(
+ "sine",
+ (X[200], S[200]),
+ size="small",
+ color=plot2.get_color(),
+ xytext=(-50, +10),
+ textcoords="offset points",
+ arrowprops=dict(
+ arrowstyle="->", color=plot2.get_color(), connectionstyle="arc3,rad=-0.3"
+ ),
+)
+
+ax = plt.subplot(n, 1, 4)
+plot1, plot2 = plot(ax)
+index = 10
+ax.scatter(
+ [X[index]],
+ [C[index]],
+ s=100,
+ marker="o",
+ zorder=10,
+ clip_on=False,
+ linewidth=1,
+ edgecolor=plot1.get_color(),
+ facecolor="white",
+)
+ax.text(
+ X[index],
+ 1.01 * C[index],
+ "A",
+ zorder=20,
+ color=plot1.get_color(),
+ ha="center",
+ va="center",
+ size="x-small",
+ clip_on=False,
+)
+
+ax.scatter(
+ [X[index]],
+ [S[index]],
+ s=100,
+ marker="o",
+ zorder=10,
+ clip_on=False,
+ linewidth=1,
+ edgecolor=plot2.get_color(),
+ facecolor="white",
+)
+ax.text(
+ X[index],
+ 1.05 * S[index],
+ "B",
+ zorder=20,
+ color=plot2.get_color(),
+ ha="center",
+ va="center",
+ size="x-small",
+ clip_on=False,
+)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/legend-alternatives.pdf")
+plt.show()
diff --git a/code/ornaments/legend-regular.py b/code/ornaments/legend-regular.py
new file mode 100644
index 0000000..ff9b022
--- /dev/null
+++ b/code/ornaments/legend-regular.py
@@ -0,0 +1,33 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(6, 2.5))
+ax = plt.subplot(
+ xlim=[-np.pi, np.pi],
+ xticks=[-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],
+ xticklabels=["-π", "-π/2", "0", "+π/2", "+π"],
+ ylim=[-1, 1],
+ yticks=[-1, 0, 1],
+ yticklabels=["-1", "0", "+1"],
+)
+
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+ax.plot(X, C, label="cosine", clip_on=False)
+ax.plot(X, S, label="sine", clip_on=False)
+
+ax.spines["right"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["left"].set_position(("data", -3.25))
+ax.spines["bottom"].set_position(("data", -1.25))
+ax.legend(edgecolor="None")
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/legend-regular.pdf")
+plt.show()
diff --git a/code/ornaments/title-regular.py b/code/ornaments/title-regular.py
new file mode 100644
index 0000000..ecad9d8
--- /dev/null
+++ b/code/ornaments/title-regular.py
@@ -0,0 +1,47 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(6, 3))
+ax = plt.subplot(
+ xlim=[-np.pi, np.pi],
+ xticks=[-np.pi, -np.pi / 2, np.pi / 2, np.pi],
+ xticklabels=["-π", "-π/2", "+π/2", "+π"],
+ ylim=[-1, 1],
+ yticks=[-1, 1],
+ yticklabels=["-1", "+1"],
+)
+
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+ax.plot(X, C, label="cosine", clip_on=False)
+ax.plot(X, S, label="sine", clip_on=False)
+
+ax.spines["right"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["left"].set_position(("data", -3.25))
+ax.spines["bottom"].set_position(("data", -1.25))
+ax.legend(
+ edgecolor="None",
+ ncol=2,
+ loc="upper right",
+ bbox_to_anchor=(1.01, 1.225),
+ borderaxespad=0,
+)
+ax.set_title("Trigonometric functions", x=1, y=1.2, ha="right")
+
+ax.set_xlabel("Angle", va="center", weight="bold")
+ax.xaxis.set_label_coords(0.5, -0.25)
+
+ax.set_ylabel("Value", ha="center", weight="bold")
+ax.yaxis.set_label_coords(-0.025, 0.5)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/ornaments/title-regular.pdf")
+plt.show()
diff --git a/code/reference/axes-adjustment.py b/code/reference/axes-adjustment.py
new file mode 100644
index 0000000..4128844
--- /dev/null
+++ b/code/reference/axes-adjustment.py
@@ -0,0 +1,213 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from matplotlib.collections import PatchCollection
+
+
+fig = plt.figure(figsize=(4.25, 4.25 * 95 / 115))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ frameon=False,
+ aspect=1,
+ xlim=(0 - 5, 100 + 10),
+ ylim=(-10, 80 + 5),
+ xticks=[],
+ yticks=[],
+)
+
+
+box = mpatches.FancyBboxPatch(
+ (0, 0),
+ 100,
+ 83,
+ mpatches.BoxStyle("Round", pad=0, rounding_size=2),
+ linewidth=1.0,
+ facecolor="0.9",
+ edgecolor="black",
+)
+ax.add_artist(box)
+
+box = mpatches.FancyBboxPatch(
+ (0, 0),
+ 100,
+ 75,
+ mpatches.BoxStyle("Round", pad=0, rounding_size=0),
+ linewidth=1.0,
+ facecolor="white",
+ edgecolor="black",
+)
+ax.add_artist(box)
+
+
+box = mpatches.Rectangle(
+ (5, 5), 45, 30, zorder=10, linewidth=1.0, facecolor="white", edgecolor="black"
+)
+ax.add_artist(box)
+
+box = mpatches.Rectangle(
+ (5, 40), 45, 30, zorder=10, linewidth=1.0, facecolor="white", edgecolor="black"
+)
+ax.add_artist(box)
+
+box = mpatches.Rectangle(
+ (55, 5), 40, 65, zorder=10, linewidth=1.0, facecolor="white", edgecolor="black"
+)
+ax.add_artist(box)
+
+# Window button
+X, Y = [5, 10, 15], [79, 79, 79]
+plt.scatter(X, Y, s=75, zorder=10, edgecolor="black", facecolor="white", linewidth=1)
+
+
+# Window size extension
+X, Y = [0, 0], [0, -8]
+plt.plot(X, Y, color="black", linestyle=":", linewidth=1, clip_on=False)
+
+X, Y = [100, 100], [0, -8]
+plt.plot(X, Y, color="black", linestyle=":", linewidth=1, clip_on=False)
+
+X, Y = [100, 108], [0, 0]
+plt.plot(X, Y, color="black", linestyle=":", linewidth=1, clip_on=False)
+
+X, Y = [100, 108], [75, 75]
+plt.plot(X, Y, color="black", linestyle=":", linewidth=1, clip_on=False)
+
+
+def ext_arrow(p0, p1, p2, p3):
+ p0, p1 = np.asarray(p0), np.asarray(p1)
+ p2, p3 = np.asarray(p2), np.asarray(p3)
+ ax.arrow(
+ *p0,
+ *(p1 - p0),
+ zorder=20,
+ linewidth=0,
+ length_includes_head=True,
+ width=0.4,
+ head_width=2,
+ head_length=2,
+ color="black"
+ )
+ ax.arrow(
+ *p3,
+ *(p2 - p3),
+ zorder=20,
+ linewidth=0,
+ length_includes_head=True,
+ width=0.4,
+ head_width=2,
+ head_length=2,
+ color="black"
+ )
+ plt.plot([p1[0], p2[0]], [p1[1], p2[1]], linewidth=0.9, color="black")
+
+
+def int_arrow(p0, p1):
+ p0, p1 = np.asarray(p0), np.asarray(p1)
+ ax.arrow(
+ *((p0 + p1) / 2),
+ *((p1 - p0) / 2),
+ zorder=20,
+ linewidth=0,
+ length_includes_head=True,
+ width=0.4,
+ head_width=2,
+ head_length=2,
+ color="black"
+ )
+ ax.arrow(
+ *((p0 + p1) / 2),
+ *(-(p1 - p0) / 2),
+ zorder=20,
+ linewidth=0,
+ length_includes_head=True,
+ width=0.4,
+ head_width=2,
+ head_length=2,
+ color="black"
+ )
+
+
+x = 0
+y = 10
+ext_arrow((x - 4, y), (x, y), (x + 5, y), (x + 9, y))
+ax.text(x + 9.5, y, "left", ha="left", va="center", size="x-small", zorder=20)
+
+x += 50
+ext_arrow((x - 4, y), (x, y), (x + 5, y), (x + 9, y))
+ax.text(x - 4.5, y, "wspace", ha="right", va="center", size="x-small", zorder=20)
+
+x += 45
+ext_arrow((x - 4, y), (x, y), (x + 5, y), (x + 9, y))
+ax.text(x - 4.5, y, "right", ha="right", va="center", size="x-small", zorder=20)
+
+y = 0
+x = 25
+ext_arrow((x, y - 4), (x, y), (x, y + 5), (x, y + 9))
+ax.text(x, y + 9.5, "bottom", ha="center", va="bottom", size="x-small", zorder=20)
+
+y += 35
+ext_arrow((x, y - 4), (x, y), (x, y + 5), (x, y + 9))
+ax.text(x, y - 4.5, "hspace", ha="center", va="top", size="x-small", zorder=20)
+
+y += 35
+ext_arrow((x, y - 4), (x, y), (x, y + 5), (x, y + 9))
+ax.text(x, y - 4.5, "top", ha="center", va="top", size="x-small", zorder=20)
+
+int_arrow((0, -5), (100, -5))
+ax.text(
+ 50,
+ -5,
+ "figure width",
+ backgroundcolor="white",
+ zorder=30,
+ ha="center",
+ va="center",
+ size="x-small",
+)
+
+int_arrow((105, 0), (105, 75))
+ax.text(
+ 105,
+ 75 / 2,
+ "figure height",
+ backgroundcolor="white",
+ zorder=30,
+ rotation="vertical",
+ ha="center",
+ va="center",
+ size="x-small",
+)
+
+int_arrow((55, 62.5), (95, 62.5))
+ax.text(
+ 75,
+ 62.5,
+ "axes width",
+ backgroundcolor="white",
+ zorder=30,
+ ha="center",
+ va="center",
+ size="x-small",
+)
+
+int_arrow((62.5, 5), (62.5, 70))
+ax.text(
+ 62.5,
+ 35,
+ "axes height",
+ backgroundcolor="white",
+ zorder=30,
+ rotation="vertical",
+ ha="center",
+ va="center",
+ size="x-small",
+)
+
+
+plt.savefig("reference-axes-adjustment.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/collection.py b/code/reference/collection.py
new file mode 100644
index 0000000..df5016c
--- /dev/null
+++ b/code/reference/collection.py
@@ -0,0 +1,337 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.path as mpath
+from matplotlib.collections import LineCollection
+from matplotlib.collections import PathCollection
+from matplotlib.collections import CircleCollection
+from matplotlib.collections import PolyCollection
+from matplotlib.collections import EllipseCollection
+from matplotlib.collections import RegularPolyCollection
+from matplotlib.collections import StarPolygonCollection
+from matplotlib.collections import AsteriskPolygonCollection
+
+
+fig = plt.figure(figsize=(4.25, 8 * 0.4))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ xlim=[0, 11],
+ ylim=[0.5, 8.5],
+ frameon=False,
+ xticks=[],
+ yticks=[],
+ aspect=1,
+)
+y = 8
+
+
+# Line collection
+# ----------------------------------------------------------------------------
+n = 50
+segments = np.zeros((n, 2, 2))
+segments[:, 0, 0] = np.linspace(1, 10.25, n) - 0.2
+segments[:, 0, 1] = y - 0.2
+segments[:, 1, 0] = segments[:, 0, 0] + 0.2
+segments[:, 1, 1] = y + 0.15
+linewidths = np.linspace(0.5, 2.5, n)
+
+collection = LineCollection(segments, linewidths=linewidths, edgecolor="black")
+ax.add_collection(collection)
+
+ax.text(1 - 0.25, y + 0.25, "Line collection", size="small", ha="left", va="baseline")
+ax.text(
+ 10 + 0.25,
+ y + 0.25,
+ "LineCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Circle collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+sizes = np.linspace(25, 100, n)
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+
+collection = CircleCollection(
+ sizes,
+ # linewidths = linewidths,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+
+ax.text(
+ X[0] - 0.25, y + 0.35, "Circle collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "CircleCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Ellipse collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+angles = np.linspace(0, 45, n)
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = EllipseCollection(
+ widths,
+ heights,
+ angles,
+ # linewidths = linewidths,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ X[0] - 0.25, y + 0.35, "Ellipse collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "EllipseCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Polygon collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n) - 0.2, y + 0.1
+X, Y = offsets[:, 0], offsets[:, 1]
+verts = np.zeros((n, 4, 2))
+verts[:] = [0, 0], [1, 0], [1, 1], [0, 1]
+sizes = np.linspace(0.25, 0.50, n)
+verts *= sizes.reshape(n, 1, 1)
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+numsides = 5
+rotation = np.pi / 4
+
+offsets[:, 1] -= sizes / 2 - 0.25
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = PolyCollection(
+ verts,
+ # linewidths = linewidths,
+ sizes=None,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ 1 - 0.25, y + 0.35, "Polygon collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ 10 + 0.25,
+ y + 0.35,
+ "PolyCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Path collection
+# ----------------------------------------------------------------------------
+n = 10
+paths = []
+for i in range(n):
+ angle1 = np.random.randint(0, 180)
+ angle2 = angle1 + np.random.randint(180, 270)
+ path = mpath.Path.wedge(angle1, angle2)
+ verts, codes = path.vertices * 0.25, path.codes
+ path = mpath.Path(verts, codes)
+ paths.append(path)
+
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n) + 0.2, y
+X, Y = offsets[:, 0], offsets[:, 1]
+sizes = np.ones(n) / 100
+offsets[:, 1] -= sizes / 2 - 0.125
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = PathCollection(
+ paths,
+ sizes=None,
+ linewidths=1.0,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets - (0.2, -0.25),
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(1 - 0.25, y + 0.35, "Path collection", size="small", ha="left", va="baseline")
+ax.text(
+ 10 + 0.25,
+ y + 0.35,
+ "PathCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Regular collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+numsides = 5
+rotation = np.pi / 4
+sizes = np.linspace(100, 200, n)
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = RegularPolyCollection(
+ numsides,
+ rotation,
+ # linewidths = linewidths,
+ sizes=sizes,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ X[0] - 0.25,
+ y + 0.35,
+ "Regular polygon collection",
+ size="small",
+ ha="left",
+ va="baseline",
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "RegularPolyCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Star collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+numsides = 5
+rotation = np.pi / 4
+sizes = np.linspace(100, 200, n)
+linewidths = np.linspace(1, 2, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.25, 0.75, n)]
+collection = StarPolygonCollection(
+ numsides,
+ rotation,
+ # linewidths = linewidths,
+ sizes=sizes,
+ facecolors=facecolors,
+ edgecolor="black",
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ X[0] - 0.25, y + 0.35, "Star collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "StarPolygonCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Asterisk collection
+# ----------------------------------------------------------------------------
+n = 10
+offsets = np.ones((n, 2))
+offsets[:, 0], offsets[:, 1] = np.linspace(1, 10, n), y
+X, Y = offsets[:, 0], offsets[:, 1]
+widths, heights = 15 * np.ones(n), 10 * np.ones(n)
+numsides = 8
+rotation = np.pi / 4
+sizes = np.linspace(50, 150, n)
+linewidths = np.linspace(0.5, 2.5, n)
+facecolors = ["%.1f" % c for c in np.linspace(0.35, 0.65, n)]
+collection = AsteriskPolygonCollection(
+ numsides,
+ rotation,
+ linewidths=linewidths,
+ sizes=sizes,
+ edgecolor=facecolors,
+ offsets=offsets,
+ transOffset=ax.transData,
+)
+ax.add_collection(collection)
+ax.text(
+ X[0] - 0.25, y + 0.35, "Asterisk collection", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.35,
+ "AsteriskPolygonCollection",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+plt.savefig("reference-collection.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-diverging.py b/code/reference/colormap-diverging.py
new file mode 100644
index 0000000..2b13115
--- /dev/null
+++ b/code/reference/colormap-diverging.py
@@ -0,0 +1,45 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+cmaps = (
+ "PRGn",
+ "PiYG",
+ "RdYlGn",
+ "BrBG",
+ "RdGy",
+ "PuOr",
+ "RdBu",
+ "RdYlBu",
+ "Spectral",
+ "coolwarm_r",
+ "bwr_r",
+ "seismic_r",
+)
+
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.22), dpi=100)
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.18, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps[::-1]:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-diverging.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-qualitative.py b/code/reference/colormap-qualitative.py
new file mode 100644
index 0000000..eab2f34
--- /dev/null
+++ b/code/reference/colormap-qualitative.py
@@ -0,0 +1,45 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+cmaps = (
+ "tab10",
+ "tab20",
+ "tab20b",
+ "tab20c",
+ "Pastel1",
+ "Pastel2",
+ "Paired",
+ "Set1",
+ "Set2",
+ "Set3",
+ "Accent",
+ "Dark2",
+)
+
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.22))
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.18, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps[::-1]:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-qualitative.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-sequential-1.py b/code/reference/colormap-sequential-1.py
new file mode 100644
index 0000000..a7460c6
--- /dev/null
+++ b/code/reference/colormap-sequential-1.py
@@ -0,0 +1,51 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+cmaps = (
+ "Greys",
+ "Reds",
+ "Oranges",
+ "YlOrBr",
+ "YlOrRd",
+ "OrRd",
+ "PuRd",
+ "RdPu",
+ "BuPu",
+ "Purples",
+ "YlGnBu",
+ "Blues",
+ "PuBu",
+ "GnBu",
+ "PuBuGn",
+ "BuGn",
+ "Greens",
+ "YlGn",
+)
+
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.22))
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.15, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps[::-1]:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-sequential-1.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-sequential-2.py b/code/reference/colormap-sequential-2.py
new file mode 100644
index 0000000..5c679ae
--- /dev/null
+++ b/code/reference/colormap-sequential-2.py
@@ -0,0 +1,46 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+cmaps = (
+ "bone",
+ "gray",
+ "pink",
+ "afmhot",
+ "hot",
+ "gist_heat",
+ "copper",
+ "Wistia",
+ "autumn_r",
+ "summer_r",
+ "spring_r",
+ "cool",
+ "winter_r",
+)
+
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.22))
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.15, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps[::-1]:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-sequential-2.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colormap-uniform.py b/code/reference/colormap-uniform.py
new file mode 100644
index 0000000..4dd3fd8
--- /dev/null
+++ b/code/reference/colormap-uniform.py
@@ -0,0 +1,32 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+cmaps = "viridis", "plasma", "inferno", "magma"
+n = len(cmaps)
+
+fig = plt.figure(figsize=(4.25, n * 0.23))
+ax = plt.subplot(1, 1, 1, frameon=False, xlim=[0, 10], xticks=[], yticks=[])
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.15, right=0.99)
+
+y, dy, pad = 0, 0.5, 0.1
+ticks, labels = [], []
+for cmap in cmaps:
+ Z = np.linspace(0, 1, 512).reshape(1, 512)
+ plt.imshow(Z, extent=[0, 10, y, y + dy], cmap=plt.get_cmap(cmap))
+ ticks.append(y + dy / 2)
+ labels.append(cmap)
+ y = y + dy + pad
+
+ax.set_ylim(-pad, y)
+ax.set_yticks(ticks)
+ax.set_yticklabels(labels)
+ax.tick_params(axis="y", which="both", length=0, labelsize="small")
+
+plt.savefig("reference-colormap-uniform.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/colorspec.py b/code/reference/colorspec.py
new file mode 100644
index 0000000..de1bf9e
--- /dev/null
+++ b/code/reference/colorspec.py
@@ -0,0 +1,56 @@
+# -----------------------------------------------------------------------------
+# Matplotlib cheat sheet
+# Released under the BSD License
+# -----------------------------------------------------------------------------
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(5.5, 1.5))
+ax = fig.add_axes([0, 0, 1, 1], frameon=False) # , aspect=1)
+# ymin, ymax= 0, 7
+# xmin, xmax = 0, figsize[0]/figsize[1]
+# ax.set_xlim(xmin, xmax), ax.set_xticks([])
+# ax.set_ylim(ymin, ymax), ax.set_yticks([])
+# ax = plt.subplot()
+
+palettes = {
+ "raw": ["b", "g", "r", "c", "m", "y", "k", "w"],
+ "rgba": [(1, 0, 0), (1, 0, 0, 0.75), (1, 0, 0, 0.50), (1, 0, 0, 0.25)],
+ "HexRGBA": ["#FF0000", "#FF0000BB", "#FF000088", "#FF000044"],
+ "cycle": ["C%d" % i for i in range(10)],
+ "grey": ["%1.1f" % (i / 10) for i in range(11)],
+ "name": ["DarkRed", "Firebrick", "Crimson", "IndianRed", "Salmon"],
+}
+
+
+ymin, ymax = 0, len(palettes)
+xmin, xmax = 0, 22
+ax.set_xlim(xmin, xmax), ax.set_xticks([])
+ax.set_ylim(ymin, ymax), ax.set_yticks([])
+
+
+for y, (name, colors) in enumerate(palettes.items()):
+ C = mpl.colors.to_rgba_array(colors).reshape((1, len(colors), 4))
+ ax.imshow(C, extent=[xmin, xmax, y + 0.025, y + 0.95])
+ dx = (xmax - xmin) / len(colors)
+ for i in range(len(colors)):
+ color = "white"
+ if colors[i] in ["1.0", "w"]:
+ color = "black"
+ text = str(colors[i]).replace(" ", "")
+ ax.text(
+ (i + 0.5) * dx,
+ y + 0.5,
+ text,
+ color=color,
+ zorder=10,
+ family="Source Code Pro",
+ size=9,
+ ha="center",
+ va="center",
+ )
+
+plt.savefig("../../figures/reference/colorspec.pdf")
+plt.show()
diff --git a/code/reference/font.py b/code/reference/font.py
new file mode 100644
index 0000000..b074993
--- /dev/null
+++ b/code/reference/font.py
@@ -0,0 +1,171 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import matplotlib.pyplot as plt
+from matplotlib.font_manager import FontProperties
+
+fig = plt.figure(figsize=(4.25, 3.8))
+ax = fig.add_axes(
+ [0, 0, 1, 1], frameon=False, xticks=[], yticks=[], xlim=[0, 40], ylim=[0, 38]
+)
+
+y = 1
+
+# -----------------------------------------------------------------------------
+variants = {
+ "normal": "/Users/rougier/Library/Fonts/Delicious-Roman.otf",
+ "small-caps": "/Users/rougier/Library/Fonts/Delicious-SmallCaps.otf",
+}
+
+text = "The quick brown fox jumps over the lazy dog"
+for i, variant in enumerate(variants.keys()):
+ ax.text(
+ 1,
+ y,
+ text,
+ size=9,
+ va="center",
+ fontproperties=FontProperties(fname=variants[variant]),
+ )
+
+ ax.text(
+ 39,
+ y,
+ variant,
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65
+y += 1
+
+# -----------------------------------------------------------------------------
+styles = ["normal", "italic"]
+
+text = "The quick brown fox jumps over the lazy dog"
+for i, style in enumerate(styles):
+ ax.text(1, y, text, size=9, va="center", style=style, family="Source Sans Pro")
+
+ ax.text(
+ 39,
+ y,
+ style,
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65
+y += 1
+
+
+# -----------------------------------------------------------------------------
+families = {
+ "Pacifico": "cursive",
+ "Source Sans Pro": "sans",
+ "Source Serif Pro": "serif",
+ "Source Code Pro": "monospace",
+}
+
+text = "The quick brown fox jumps over the lazy dog"
+for i, family in enumerate(families):
+ ax.text(1, y, text, va="center", size=9, family=family, weight="regular")
+
+ ax.text(
+ 39,
+ y,
+ "%s" % (families[family]),
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65
+y += 1
+
+
+# -----------------------------------------------------------------------------
+weights = {
+ "ultralight": 100,
+ "light": 200,
+ "normal": 400,
+ "regular": 400,
+ "book": 400,
+ "medium": 500,
+ "roman": 500,
+ "semibold": 600,
+ "demibold": 600,
+ "demi": 600,
+ "bold": 700,
+ "heavy": 800,
+ "extra bold": 800,
+ "black": 900,
+}
+
+text = "The quick brown fox jumps over the lazy dog"
+for i, weight in enumerate(["ultralight", "normal", "semibold", "bold", "black"]):
+ ax.text(1, y, text, size=9, va="center", family="Source Sans Pro", weight=weight)
+
+ ax.text(
+ 39,
+ y,
+ "%s (%d)" % (weight, weights[weight]),
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65
+y += 1
+
+# -----------------------------------------------------------------------------
+sizes = {
+ "xx-small": 0.579,
+ "x-small": 0.694,
+ "small": 0.833,
+ "medium": 1.0,
+ "large": 1.200,
+ "x-large": 1.440,
+ "xx-large": 1.728,
+}
+
+text = "The quick brown fox"
+for i, size in enumerate(sizes.keys()):
+ ax.text(
+ 1,
+ y,
+ text,
+ size=size,
+ ha="left",
+ va="center",
+ family="Source Sans Pro",
+ weight="light",
+ )
+
+ ax.text(
+ 39,
+ y,
+ "%s (%.2f)" % (size, sizes[size]),
+ color="0.25",
+ va="center",
+ ha="right",
+ size="small",
+ family="Source Code Pro",
+ weight=400,
+ )
+ y += 1.65 * max(sizes[size], sizes["small"])
+
+
+plt.savefig("reference-font.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/hatch.py b/code/reference/hatch.py
new file mode 100644
index 0000000..81f5927
--- /dev/null
+++ b/code/reference/hatch.py
@@ -0,0 +1,132 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+
+fig = plt.figure(figsize=(4.25, 4.5 * 0.55))
+ax = fig.add_axes(
+ [0, 0, 1, 1], xlim=[0, 11], ylim=[0.5, 4.5], frameon=False, xticks=[], yticks=[]
+) # , aspect=1)
+y = 3.75
+
+
+# Hatch pattern
+# ----------------------------------------------------------------------------
+patterns = "/", "\\", "|", "-", "+", "x", "o", "O", ".", "*"
+w, h = 0.75 * 10 / len(patterns), 0.5
+X = np.linspace(1, 10 - w, len(patterns))
+
+for x, pattern in zip(X, patterns):
+ rect = Rectangle(
+ (x, y),
+ w,
+ h,
+ hatch=pattern * 2,
+ facecolor="0.85",
+ edgecolor="0.00",
+ linewidth=1.0,
+ )
+ ax.add_patch(rect)
+ plt.text(
+ x + w / 2,
+ y - 0.125,
+ '"%s"' % pattern,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+
+plt.text(X[0] - 0.25, y + 0.65, "Hatch pattern", size="small", ha="left", va="baseline")
+plt.text(
+ X[-1] + w + 0.25,
+ y + 0.65,
+ "hatch",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1.5
+
+
+# Hatch density
+# ----------------------------------------------------------------------------
+patterns = "/", "//", "///", "////"
+w, h = 0.75 * 10 / len(patterns), 0.5
+X = np.linspace(1, 10 - w, len(patterns))
+
+for x, pattern in zip(X, patterns):
+ rect = Rectangle(
+ (x, y), w, h, hatch=pattern, facecolor="0.85", edgecolor="0.00", linewidth=1.0
+ )
+ ax.add_patch(rect)
+ plt.text(
+ x + w / 2,
+ y - 0.125,
+ '"%s"' % pattern,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+
+plt.text(X[0] - 0.25, y + 0.65, "Hatch density", size="small", ha="left", va="baseline")
+plt.text(
+ X[-1] + w + 0.25,
+ y + 0.65,
+ "hatch",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1.5
+
+
+# Hatch linewidth (hack)
+# ----------------------------------------------------------------------------
+# We cannot have different hatch linewidth in a single figure and the solution
+# was to generate images and to show them here. Images were generated from the
+# make-hatch-linewidth.py script.
+widths = 1, 2, 3, 4, 5, 6
+w, h = 0.75 * 10 / len(widths), 0.5
+X = np.linspace(1, 10 - w, len(widths))
+for (x, width) in zip(X, widths):
+ image = imageio.imread("hatch-%d.png" % width)
+ ax.imshow(image, extent=[x, x + w, y, y + h])
+ rect = Rectangle((x, y), w, h, facecolor="none", edgecolor="black", linewidth=1.0)
+ ax.add_patch(rect)
+ plt.text(
+ x + w / 2,
+ y - 0.125,
+ "%d" % width,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.65, "Hatch linewidth", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + w + 0.25,
+ y + 0.65,
+ "rcParams['hatch.linewidth']",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1.5
+
+plt.savefig("reference-hatch.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/line.py b/code/reference/line.py
new file mode 100644
index 0000000..a2d2dd5
--- /dev/null
+++ b/code/reference/line.py
@@ -0,0 +1,224 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(4.25, 6 * 0.55))
+ax = fig.add_axes(
+ [0, 0, 1, 1], xlim=[0, 11], ylim=[0.5, 6.5], frameon=False, xticks=[], yticks=[]
+)
+y = 6
+
+
+def split(n_segment):
+ width = 9
+ segment_width = 0.75 * (width / n_segment)
+ segment_pad = (width - n_segment * segment_width) / (n_segment - 1)
+ X0 = 1 + np.arange(n_segment) * (segment_width + segment_pad)
+ X1 = X0 + segment_width
+ return X0, X1
+
+
+# Color
+# ----------------------------------------------------------------------------
+X0, X1 = split(10)
+for i, (x0, x1) in enumerate(zip(X0, X1)):
+ ax.plot([x0, x1], [y, y], color="C%d" % i, linewidth=10, solid_capstyle="butt")
+ plt.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ '"C%d"' % i,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+ax.text(X0[0] - 0.25, y + 0.2, "Line color", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "color / c",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+
+y -= 1
+
+# Line width
+# ----------------------------------------------------------------------------
+X0, X1 = split(5)
+LW = np.arange(1, 6)
+for x0, x1, lw in zip(X0, X1, LW):
+ ax.plot([x0, x1], [y, y], color="black", linewidth=lw, solid_capstyle="round")
+ plt.text((x0 + x1) / 2, y - 0.2, "%d" % lw, size="x-small", ha="center", va="top")
+ax.text(X0[0] - 0.25, y + 0.2, "Line width", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "linewidth / lw",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Solid capstyle
+# ----------------------------------------------------------------------------
+X0, X1 = split(3)
+styles = "butt", "round", "projecting"
+for x0, x1, style in zip(X0, X1, styles):
+ ax.plot([x0, x1], [y, y], color=".85", solid_capstyle="projecting", linewidth=7)
+ ax.plot([x0, x1], [y, y], color="black", solid_capstyle=style, linewidth=7)
+ ax.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ '"%s"' % style,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+
+ax.text(X0[0] - 0.25, y + 0.2, "Cap style", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "solid_capstyle",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Dash capstyle
+# ----------------------------------------------------------------------------
+X0, X1 = split(3)
+styles = "butt", "round", "projecting"
+for x0, x1, style in zip(X0, X1, styles):
+ ax.plot(
+ [x0, x1],
+ [y, y],
+ color=".85",
+ dash_capstyle="projecting",
+ linewidth=7,
+ linestyle="--",
+ )
+ ax.plot(
+ [x0, x1],
+ [y, y],
+ color="black",
+ linewidth=7,
+ linestyle="--",
+ dash_capstyle=style,
+ )
+ ax.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ '"%s"' % style,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+ax.text(X0[0] - 0.25, y + 0.2, "Dash cap style", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "dash_capstyle",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Line style
+# ----------------------------------------------------------------------------
+X0, X1 = split(5)
+styles = "-", ":", "--", "-.", (0, (0.01, 2))
+
+for x0, x1, style in zip(X0, X1, styles):
+ ax.plot(
+ [x0, x1],
+ [y, y],
+ color="black",
+ linestyle=style,
+ solid_capstyle="round",
+ dash_capstyle="round",
+ linewidth=3,
+ )
+ if isinstance(style, str):
+ text = '"%s"' % style
+ else:
+ text = "%s" % str(style)
+
+ ax.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ text,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+ax.text(X0[0] - 0.25, y + 0.2, "Line style", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "linestyle / ls",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Color
+# ----------------------------------------------------------------------------
+X0, X1 = split(2)
+for i, (x0, x1) in enumerate(zip(X0, X1)):
+
+ X = np.linspace(x0, x1, 500)
+ Y = y + 0.1 * np.cos(5 * np.pi * (X - x0) / (x1 - x0))
+
+ ax.plot(X, Y, color="black", linewidth=0.75, antialiased=i, rasterized=1 - i)
+ plt.text(
+ (x0 + x1) / 2,
+ y - 0.2,
+ "%s" % bool(i),
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+
+ax.text(X0[0] - 0.25, y + 0.2, "Antialias", size="small", ha="left", va="baseline")
+ax.text(
+ X1[-1] + 0.25,
+ y + 0.2,
+ "antialiased",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+
+y -= 1
+
+plt.savefig("reference-line.pdf", dpi=200)
+plt.show()
diff --git a/code/reference/marker.py b/code/reference/marker.py
new file mode 100644
index 0000000..682dff8
--- /dev/null
+++ b/code/reference/marker.py
@@ -0,0 +1,288 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(4.25, 8 * 0.55))
+ax = fig.add_axes(
+ [0, 0, 1, 1], xlim=[0, 11], ylim=[0.5, 8.5], frameon=False, xticks=[], yticks=[]
+) # , aspect=1)
+
+X = np.linspace(1, 10, 10)
+Y = np.zeros(len(X))
+y = 8
+
+# Marker edge color
+# ----------------------------------------------------------------------------
+C = ["C%d" % i for i in range(10)]
+plt.scatter(X, y + Y, s=200, facecolor="white", edgecolor=C, linewidth=1.5)
+for x, c in zip(X, C):
+ plt.text(
+ x,
+ y - 0.25,
+ '"%s"' % c,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Marker edge color", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "mec / ec",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+# Marker face color
+# ----------------------------------------------------------------------------
+C = ["C%d" % i for i in range(10)]
+plt.scatter(X, y + Y, s=200, facecolor=C, edgecolor="None")
+for x, c in zip(X, C):
+ plt.text(
+ x,
+ y - 0.25,
+ '"%s"' % c,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Marker face color", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "mfc / fc",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Marker edge width
+# ----------------------------------------------------------------------------
+LW = 1 + np.arange(10) / 2
+plt.scatter(X, y + Y, s=100, facecolor="white", edgecolor="black", linewidth=LW)
+for x, lw in zip(X, LW):
+ plt.text(
+ x,
+ y - 0.25,
+ "%.1f" % lw,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Marker edge width", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "mew / lw",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Marker edge width
+# ----------------------------------------------------------------------------
+S = (1 + np.arange(10)) * 25
+plt.scatter(X, y + Y, s=S, facecolor="black", edgecolor="None")
+for x, s in zip(X, S):
+ plt.text(
+ x, y - 0.25, "%d" % s, size="x-small", ha="center", va="top", family="monospace"
+ )
+plt.text(X[0] - 0.25, y + 0.25, "Marker size", size="small", ha="left", va="baseline")
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "ms / s",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+
+X = np.linspace(1, 10, 12)
+
+# Filled markers
+# -----------------------------------------------------------------------------
+M = [".", "o", "s", "P", "X", "*", "p", "D", "<", ">", "^", "v"]
+for x, marker in zip(X, M):
+ plt.scatter(x, y, s=256, color="black", marker="s", fc=".9", ec="none")
+ plt.scatter(
+ x,
+ y,
+ s=100,
+ color="black",
+ marker=marker,
+ fc="white",
+ ec="black",
+ linewidth=0.75,
+ )
+ plt.text(
+ x,
+ y - 0.25,
+ '"%s"' % marker,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Filled markers", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "marker",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Unfilled markers
+# -----------------------------------------------------------------------------
+M = ["1", "2", "3", "4", "+", "x", "|", "_", 4, 5, 6, 7]
+for x, marker in zip(X, M):
+ if isinstance(marker, str):
+ text = '"%s"' % marker
+ else:
+ text = "%s" % marker
+
+ plt.scatter(x, y, s=256, color="black", marker="s", fc=".9", ec="none")
+ plt.scatter(
+ x, y, s=100, color="black", marker=marker, fc="none", ec="black", linewidth=0.75
+ )
+ plt.text(
+ x, y - 0.25, text, size="x-small", ha="center", va="top", family="monospace"
+ )
+plt.text(
+ X[0] - 0.25, y + 0.25, "Unfilled markers", size="small", ha="left", va="baseline"
+)
+plt.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "marker",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Unicode markers
+# -----------------------------------------------------------------------------
+M = ["♠", "♣", "♥", "♦", "→", "←", "↑", "↓", "◐", "◑", "◒", "◓"]
+for x, marker in zip(X, M):
+ ax.scatter(x, y, s=256, color="black", marker="s", fc=".9", ec="none")
+ ax.scatter(
+ x,
+ y,
+ s=100,
+ color="black",
+ marker="$" + marker + "$",
+ fc="black",
+ ec="none",
+ linewidth=0.5,
+ )
+ ax.text(
+ x,
+ y - 0.25,
+ '"\$%s\$"' % marker,
+ size="x-small",
+ ha="center",
+ va="top",
+ family="monospace",
+ )
+ax.text(
+ X[0] - 0.25, y + 0.25, "Unicode markers", size="small", ha="left", va="baseline"
+)
+ax.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "marker",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+y -= 1
+
+# Spacing
+# -----------------------------------------------------------------------------
+n_segment = 4
+width = 9
+segment_width = 0.75 * (width / n_segment)
+segment_pad = (width - n_segment * segment_width) / (n_segment - 1)
+X0 = 1 + np.arange(n_segment) * (segment_width + segment_pad)
+marks = [10, [0, -1], (25, 5), [0, 25, -1]]
+
+for x0, mark in zip(X0, marks):
+ X = np.linspace(x0, x0 + segment_width, 50)
+ Y = y * np.ones(len(X))
+ ax.plot(
+ X,
+ Y,
+ linewidth=1,
+ color="black",
+ marker=".",
+ mfc="white",
+ mec="black",
+ mew="1",
+ markevery=mark,
+ )
+
+ ax.text(
+ (X[0] + X[-1]) / 2,
+ y - 0.1,
+ "%s" % str(mark),
+ size="x-small",
+ ha="center",
+ va="top",
+ )
+
+
+ax.text(1 - 0.25, y + 0.25, "Marker spacing", size="small", ha="left", va="baseline")
+ax.text(
+ X[-1] + 0.25,
+ y + 0.25,
+ "markevery",
+ color="blue",
+ size="small",
+ ha="right",
+ va="baseline",
+ family="monospace",
+)
+
+
+plt.savefig("reference-marker.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/scale.py b/code/reference/scale.py
new file mode 100644
index 0000000..dc12ad5
--- /dev/null
+++ b/code/reference/scale.py
@@ -0,0 +1,192 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter, SymmetricalLogLocator
+
+
+X = np.linspace(-2, 2, 1001)
+Y = np.cos(X * 5 * 2 * np.pi)
+
+
+figure = plt.figure(figsize=(6, 6))
+
+# Style
+# -----------------------------------------------------------------------------
+# plt.rc('font', family="Roboto Condensed")
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+# Linear scale
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 1, ylim=(-3, 2))
+plt.plot(X, Y, color="C1", linewidth=1.5)
+ax.set_yticks([])
+ax.text(
+ 0.01,
+ 0.95,
+ "Linear scale",
+ transform=ax.transAxes,
+ weight=600,
+ horizontalalignment="left",
+ verticalalignment="top",
+ size="medium",
+)
+
+# Respective domains for scales
+plt.plot([-2, 2], [-1.5, -1.5], color="black", linewidth=1, marker="|")
+ax.text(
+ 0,
+ -1.5,
+ "SymLog scale domain",
+ transform=ax.transData,
+ size="x-small",
+ bbox=dict(facecolor="white", edgecolor="None", pad=1.0),
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+
+plt.plot([0, 2], [-2.0, -2.0], color="black", linewidth=1, marker="|")
+ax.text(
+ 1,
+ -2,
+ "Log scale domain",
+ transform=ax.transData,
+ size="x-small",
+ bbox=dict(facecolor="white", edgecolor="None", pad=1.0),
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+
+plt.plot([0, 1], [-2.5, -2.5], color="black", linewidth=1, marker="|")
+ax.text(
+ 0.5,
+ -2.5,
+ "Logit scale domain",
+ transform=ax.transData,
+ size="x-small",
+ bbox=dict(facecolor="white", edgecolor="None", pad=1.0),
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+
+
+# Log scale (X > 0)
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 2, ylim=(-2, 2))
+ax.set_xscale("log", basex=10)
+plt.plot(X, Y, color="C1", linewidth=1.5)
+ax.set_yticks([])
+ax.text(
+ 0.01,
+ 0.95,
+ "Log scale (x > 0)",
+ transform=ax.transAxes,
+ weight=600,
+ horizontalalignment="left",
+ verticalalignment="top",
+ size="medium",
+)
+
+
+# SymLog scale
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 3, ylim=(-2, 2))
+t = 0.05
+ax.set_xscale("symlog", basex=10, linthreshx=t, linscalex=1)
+plt.plot(X, Y, color="C1", linewidth=1.5)
+ax.set_yticks([])
+ax.xaxis.set_minor_locator(
+ SymmetricalLogLocator(base=10, linthresh=t, subs=np.arange(2, 10))
+)
+
+ax.text(
+ 0.01,
+ 0.95,
+ "Symlog scale",
+ transform=ax.transAxes,
+ weight=600,
+ horizontalalignment="left",
+ verticalalignment="top",
+ size="medium",
+)
+ax.fill_betweenx([-2, 2], [-t, -t], [+t, +t], facecolor="black", alpha=0.1)
+ax.text(
+ 0.50,
+ 0.05,
+ "Linear",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.text(
+ 0.15,
+ 0.05,
+ "Logarithmic",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.text(
+ 0.85,
+ 0.05,
+ "Logarithmic",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+
+
+# Logit (0 < X < 1)
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 4, ylim=(-2, 2))
+ax.set_xscale("logit")
+plt.plot(X, Y, color="C1", linewidth=1.5)
+ax.set_yticks([])
+ax.xaxis.set_minor_formatter(NullFormatter())
+ax.text(
+ 0.01,
+ 0.95,
+ "Logit scale (0 < x < 1)",
+ transform=ax.transAxes,
+ weight=600,
+ horizontalalignment="left",
+ verticalalignment="top",
+ size="medium",
+)
+ax.fill_betweenx([-2, 2], [0.2, 0.2], [0.8, 0.8], facecolor="black", alpha=0.1)
+ax.text(
+ 0.50,
+ 0.05,
+ "Quasi linear",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.text(
+ 0.2,
+ 0.05,
+ "Logarithmic",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.text(
+ 0.8,
+ 0.05,
+ "Logarithmic",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+
+# Show
+plt.tight_layout()
+plt.savefig("reference-scale.pdf")
+plt.show()
diff --git a/code/reference/text-alignment.py b/code/reference/text-alignment.py
new file mode 100644
index 0000000..1d19c08
--- /dev/null
+++ b/code/reference/text-alignment.py
@@ -0,0 +1,91 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+dpi = 100
+fig = plt.figure(figsize=(4.25, 1.5), dpi=dpi)
+ax = fig.add_axes(
+ [0, 0, 1, 1], frameon=False, xlim=(0, 4.25), ylim=(0, 1.5), xticks=[], yticks=[]
+)
+
+fontsize = 48
+renderer = fig.canvas.get_renderer()
+horizontalalignment = "left"
+verticalalignment = "center"
+position = (0.25, 1.5 / 2)
+color = "0.25"
+
+# Compute vertical and horizontal alignment offsets
+text = ax.text(0, 0, "Matplotlib", fontsize=fontsize)
+yoffset = {}
+for alignment in ["top", "center", "baseline", "bottom"]:
+ text.set_verticalalignment(alignment)
+ y = text.get_window_extent(renderer).y0 / dpi
+ yoffset[alignment] = y
+
+xoffset = {}
+for alignment in ["left", "center", "right"]:
+ text.set_horizontalalignment(alignment)
+ x = text.get_window_extent(renderer).x0 / dpi
+ xoffset[alignment] = x
+
+# Actual positioning of the text
+text.set_horizontalalignment(horizontalalignment)
+text.set_verticalalignment(verticalalignment)
+text.set_position(position)
+
+
+for name, y in yoffset.items():
+ y = position[1] - y + yoffset[verticalalignment]
+ plt.plot([0.1, 3.75], [y, y], linewidth=0.5, color=color)
+ plt.text(3.75, y, " " + name, color=color, ha="left", va="center", size="x-small")
+
+for name, x in xoffset.items():
+ x = position[0] - x + xoffset[horizontalalignment]
+ plt.plot([x, x], [0.25, 1.25], linewidth=0.5, color=color)
+ plt.text(x, 0.24, name, color=color, ha="center", va="top", size="x-small")
+
+P = []
+for x in xoffset.values():
+ x = position[0] - x + xoffset[horizontalalignment]
+ for y in yoffset.values():
+ y = position[1] - y + yoffset[verticalalignment]
+ P.append((x, y))
+P = np.array(P)
+
+ax.scatter(
+ P[:, 0],
+ P[:, 1],
+ s=10,
+ zorder=10,
+ facecolor="white",
+ edgecolor=color,
+ linewidth=0.75,
+)
+
+epsilon = 0.05
+plt.text(
+ P[3, 0] + epsilon,
+ P[3, 1] - epsilon,
+ "(0,0)",
+ color=color,
+ ha="left",
+ va="top",
+ size="x-small",
+)
+plt.text(
+ P[8, 0] - epsilon,
+ P[8, 1] + epsilon,
+ "(1,1)",
+ color=color,
+ ha="right",
+ va="bottom",
+ size="x-small",
+)
+
+plt.savefig("reference-text-alignment.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/tick-formatter.py b/code/reference/tick-formatter.py
new file mode 100644
index 0000000..3afe31b
--- /dev/null
+++ b/code/reference/tick-formatter.py
@@ -0,0 +1,109 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+# Setup a plot such that only the bottom spine is shown
+def setup(ax):
+ ax.spines["right"].set_color("none")
+ ax.spines["left"].set_color("none")
+ ax.yaxis.set_major_locator(ticker.NullLocator())
+ ax.spines["top"].set_color("none")
+ ax.xaxis.set_ticks_position("bottom")
+ ax.tick_params(which="major", width=1.00, length=5)
+ ax.tick_params(which="minor", width=0.75, length=2.5, labelsize=10)
+ ax.set_xlim(0, 5)
+ ax.set_ylim(0, 1)
+ ax.patch.set_alpha(0.0)
+
+
+fig = plt.figure(figsize=(8, 6))
+n = 7
+
+# Null formatter
+ax = fig.add_subplot(n, 1, 1)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(ticker.NullFormatter())
+ax.xaxis.set_minor_formatter(ticker.NullFormatter())
+ax.text(0.0, 0.1, "NullFormatter()", fontsize=16, transform=ax.transAxes)
+
+# Fixed formatter
+ax = fig.add_subplot(n, 1, 2)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+majors = ["", "0", "1", "2", "3", "4", "5"]
+ax.xaxis.set_major_formatter(ticker.FixedFormatter(majors))
+minors = [""] + [
+ "%.2f" % (x - int(x)) if (x - int(x)) else "" for x in np.arange(0, 5, 0.25)
+]
+ax.xaxis.set_minor_formatter(ticker.FixedFormatter(minors))
+ax.text(
+ 0.0, 0.1, "FixedFormatter(['', '0', '1', ...])", fontsize=15, transform=ax.transAxes
+)
+
+
+# FuncFormatter can be used as a decorator
+@ticker.FuncFormatter
+def major_formatter(x, pos):
+ return "[%.2f]" % x
+
+
+ax = fig.add_subplot(n, 1, 3)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(major_formatter)
+ax.text(
+ 0.0,
+ 0.1,
+ 'FuncFormatter(lambda x, pos: "[%.2f]" % x)',
+ fontsize=15,
+ transform=ax.transAxes,
+)
+
+
+# FormatStr formatter
+ax = fig.add_subplot(n, 1, 4)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter(">%d<"))
+ax.text(0.0, 0.1, "FormatStrFormatter('>%d<')", fontsize=15, transform=ax.transAxes)
+
+# Scalar formatter
+ax = fig.add_subplot(n, 1, 5)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.AutoLocator())
+ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ax.xaxis.set_major_formatter(ticker.ScalarFormatter(useMathText=True))
+ax.text(0.0, 0.1, "ScalarFormatter()", fontsize=15, transform=ax.transAxes)
+
+# StrMethod formatter
+ax = fig.add_subplot(n, 1, 6)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(ticker.StrMethodFormatter("{x}"))
+ax.text(0.0, 0.1, "StrMethodFormatter('{x}')", fontsize=15, transform=ax.transAxes)
+
+# Percent formatter
+ax = fig.add_subplot(n, 1, 7)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))
+ax.xaxis.set_major_formatter(ticker.PercentFormatter(xmax=5))
+ax.text(0.0, 0.1, "PercentFormatter(xmax=5)", fontsize=15, transform=ax.transAxes)
+
+# Push the top of the top axes outside the figure because we only show the
+# bottom spine.
+fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=1.05)
+
+plt.savefig("reference-tick-formatter.pdf", dpi=600)
+plt.show()
diff --git a/code/reference/tick-locator.py b/code/reference/tick-locator.py
new file mode 100644
index 0000000..44fff8f
--- /dev/null
+++ b/code/reference/tick-locator.py
@@ -0,0 +1,97 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+# Setup a plot such that only the bottom spine is shown
+def setup(ax):
+ ax.spines["right"].set_color("none")
+ ax.spines["left"].set_color("none")
+ ax.yaxis.set_major_locator(ticker.NullLocator())
+ ax.spines["top"].set_color("none")
+ ax.xaxis.set_ticks_position("bottom")
+ ax.tick_params(which="major", width=1.00)
+ ax.tick_params(which="major", length=5)
+ ax.tick_params(which="minor", width=0.75)
+ ax.tick_params(which="minor", length=2.5)
+ ax.set_xlim(0, 5)
+ ax.set_ylim(0, 1)
+ ax.patch.set_alpha(0.0)
+
+
+plt.figure(figsize=(8, 6))
+n = 8
+
+# Null Locator
+ax = plt.subplot(n, 1, 1)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.NullLocator())
+ax.xaxis.set_minor_locator(ticker.NullLocator())
+ax.text(0.0, 0.1, "NullLocator()", fontsize=14, transform=ax.transAxes)
+
+# Multiple Locator
+ax = plt.subplot(n, 1, 2)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.5))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.1))
+ax.text(0.0, 0.1, "MultipleLocator(0.5)", fontsize=14, transform=ax.transAxes)
+
+# Fixed Locator
+ax = plt.subplot(n, 1, 3)
+setup(ax)
+majors = [0, 1, 5]
+ax.xaxis.set_major_locator(ticker.FixedLocator(majors))
+minors = np.linspace(0, 1, 11)[1:-1]
+ax.xaxis.set_minor_locator(ticker.FixedLocator(minors))
+ax.text(0.0, 0.1, "FixedLocator([0, 1, 5])", fontsize=14, transform=ax.transAxes)
+
+# Linear Locator
+ax = plt.subplot(n, 1, 4)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.LinearLocator(3))
+ax.xaxis.set_minor_locator(ticker.LinearLocator(31))
+ax.text(0.0, 0.1, "LinearLocator(numticks=3)", fontsize=14, transform=ax.transAxes)
+
+# Index Locator
+ax = plt.subplot(n, 1, 5)
+setup(ax)
+ax.plot(range(0, 5), [0] * 5, color="white")
+ax.xaxis.set_major_locator(ticker.IndexLocator(base=0.5, offset=0.25))
+ax.text(
+ 0.0, 0.1, "IndexLocator(base=0.5, offset=0.25)", fontsize=14, transform=ax.transAxes
+)
+
+# Auto Locator
+ax = plt.subplot(n, 1, 6)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.AutoLocator())
+ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ax.text(0.0, 0.1, "AutoLocator()", fontsize=14, transform=ax.transAxes)
+
+# MaxN Locator
+ax = plt.subplot(n, 1, 7)
+setup(ax)
+ax.xaxis.set_major_locator(ticker.MaxNLocator(4))
+ax.xaxis.set_minor_locator(ticker.MaxNLocator(40))
+ax.text(0.0, 0.1, "MaxNLocator(n=4)", fontsize=14, transform=ax.transAxes)
+
+# Log Locator
+ax = plt.subplot(n, 1, 8)
+setup(ax)
+ax.set_xlim(10 ** 3, 10 ** 10)
+ax.set_xscale("log")
+ax.xaxis.set_major_locator(ticker.LogLocator(base=10.0, numticks=15))
+ax.text(
+ 0.0, 0.1, "LogLocator(base=10, numticks=15)", fontsize=15, transform=ax.transAxes
+)
+
+# Push the top of the top axes outside the figure because we only show the
+# bottom spine.
+plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=1.05)
+
+plt.savefig("reference-tick-locator.pdf", dpi=600)
+plt.show()
diff --git a/code/rules/graphics.py b/code/rules/graphics.py
new file mode 100644
index 0000000..1cc732d
--- /dev/null
+++ b/code/rules/graphics.py
@@ -0,0 +1,229 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import ImageGrid
+from mpl_toolkits.axes_grid1.inset_locator import mark_inset
+from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
+
+from projections import *
+
+# -----------------------------------------------------------------------------
+def polar_frame(ax, title=None, legend=False, zoom=False, labels=True):
+ """ Draw a polar frame """
+
+ for rho in [0, 2, 5, 10, 20, 40, 60, 80, 90]:
+ lw, color, alpha = 1, "0.00", 0.25
+ if rho == 90 and not zoom:
+ color, lw, alpha = "0.00", 2, 1
+
+ n = 500
+ R = np.ones(n) * rho / 90.0
+ T = np.linspace(-np.pi / 2, np.pi / 2, n)
+ X, Y = polar_to_cartesian(R, T)
+ ax.plot(X, Y, color=color, lw=lw, alpha=alpha)
+
+ if not zoom and rho in [0, 10, 20, 40, 80] and labels:
+ ax.text(
+ X[-1] * 1.0 - 0.075,
+ Y[-1],
+ u"%d°" % rho,
+ color="k", # size=15,
+ horizontalalignment="center",
+ verticalalignment="center",
+ )
+
+ for theta in [-90, -60, -30, 0, +30, +60, +90]:
+ lw, color, alpha = 1, "0.00", 0.25
+ if theta in [-90, +90] and not zoom:
+ color, lw, alpha = "0.00", 2, 1
+ angle = theta / 90.0 * np.pi / 2
+
+ n = 500
+ R = np.linspace(0, 1, n)
+ T = np.ones(n) * angle
+ X, Y = polar_to_cartesian(R, T)
+ ax.plot(X, Y, color=color, lw=lw, alpha=alpha)
+
+ if not zoom and theta in [-90, -60, -30, +30, +60, +90] and labels:
+ ax.text(
+ X[-1] * 1.05,
+ Y[-1] * 1.05,
+ u"%d°" % theta,
+ color="k", # size=15,
+ horizontalalignment="left",
+ verticalalignment="center",
+ )
+ d = 0.01
+ ax.set_xlim(0.0 - d, 1.0 + d)
+ ax.set_ylim(-1.0 - d, 1.0 + d)
+ ax.set_xticks([])
+ ax.set_yticks([])
+
+ if legend:
+ ax.set_frame_on(True)
+ ax.spines["left"].set_color("none")
+ ax.spines["right"].set_color("none")
+ ax.spines["top"].set_color("none")
+ ax.xaxis.set_ticks_position("bottom")
+ ax.spines["bottom"].set_position(("data", -1.2))
+ ax.set_xticks([])
+ ax.text(
+ 0.0,
+ -1.1,
+ "$\longleftarrow$ Foveal",
+ verticalalignment="top",
+ horizontalalignment="left",
+ size=12,
+ )
+ ax.text(
+ 1.0,
+ -1.1,
+ "Peripheral $\longrightarrow$",
+ verticalalignment="top",
+ horizontalalignment="right",
+ size=12,
+ )
+ else:
+ ax.set_frame_on(False)
+ if title:
+ ax.title(title)
+
+
+# -----------------------------------------------------------------------------
+def logpolar_frame(ax, title=None, legend=False, labels=True):
+ """ Draw a log polar frame """
+
+ for rho in [2, 5, 10, 20, 40, 60, 80, 90]:
+ lw, color, alpha = 1, "0.00", 0.25
+ if rho == 90:
+ color, lw, alpha = "0.00", 2, 1
+
+ n = 500
+ R = np.ones(n) * rho / 90.0
+ T = np.linspace(-np.pi / 2, np.pi / 2, n)
+ X, Y = polar_to_logpolar(R, T)
+ X, Y = X * 2, 2 * Y - 1
+ ax.plot(X, Y, color=color, lw=lw, alpha=alpha)
+ if labels and rho in [2, 5, 10, 20, 40, 80]:
+ ax.text(
+ X[-1],
+ Y[-1] + 0.05,
+ u"%d°" % rho,
+ color="k", # size=15,
+ horizontalalignment="right",
+ verticalalignment="bottom",
+ )
+
+ for theta in [-90, -60, -30, 0, +30, +60, +90]:
+ lw, color, alpha = 1, "0.00", 0.25
+ if theta in [-90, +90]:
+ color, lw, alpha = "0.00", 2, 1
+ angle = theta / 90.0 * np.pi / 2
+
+ n = 500
+ R = np.linspace(0, 1, n)
+ T = np.ones(n) * angle
+ X, Y = polar_to_logpolar(R, T)
+ X, Y = X * 2, 2 * Y - 1
+ ax.plot(X, Y, color=color, lw=lw, alpha=alpha)
+ if labels:
+ ax.text(
+ X[-1] * 1.0 + 0.05,
+ Y[-1] * 1.0,
+ u"%d°" % theta,
+ color="k", # size=15,
+ horizontalalignment="left",
+ verticalalignment="center",
+ )
+
+ d = 0.01
+ ax.set_xlim(0.0 - d, 2.0 + d)
+ ax.set_ylim(-1.0 - d, 1.0 + d)
+ ax.set_xticks([])
+ ax.set_yticks([])
+ if legend:
+ ax.set_frame_on(True)
+ ax.spines["left"].set_color("none")
+ ax.spines["right"].set_color("none")
+ ax.spines["top"].set_color("none")
+ ax.xaxis.set_ticks_position("bottom")
+ ax.spines["bottom"].set_position(("data", -1.2))
+ ax.set_xticks([0, 2])
+ ax.set_xticklabels(["0", "4.8 (mm)"])
+ ax.text(
+ 0.0,
+ -1.1,
+ "$\longleftarrow$ Rostral",
+ verticalalignment="top",
+ horizontalalignment="left",
+ size=12,
+ )
+ ax.text(
+ 2,
+ -1.1,
+ "Caudal $\longrightarrow$",
+ verticalalignment="top",
+ horizontalalignment="right",
+ size=12,
+ )
+ else:
+ ax.set_frame_on(False)
+ if title:
+ ax.title(title)
+
+
+# -----------------------------------------------------------------------------
+def polar_imshow(axis, Z, *args, **kwargs):
+ kwargs["interpolation"] = kwargs.get("interpolation", "nearest")
+ kwargs["cmap"] = kwargs.get("cmap", plt.cm.gray_r)
+ # kwargs['vmin'] = kwargs.get('vmin', Z.min())
+ # kwargs['vmax'] = kwargs.get('vmax', Z.max())
+ kwargs["vmin"] = kwargs.get("vmin", 0)
+ kwargs["vmax"] = kwargs.get("vmax", 1)
+ kwargs["origin"] = kwargs.get("origin", "lower")
+ axis.imshow(Z, extent=[0, 1, -1, 1], *args, **kwargs)
+
+
+# -----------------------------------------------------------------------------
+def logpolar_imshow(axis, Z, *args, **kwargs):
+ kwargs["interpolation"] = kwargs.get("interpolation", "nearest")
+ kwargs["cmap"] = kwargs.get("cmap", plt.cm.gray_r)
+ # kwargs['vmin'] = kwargs.get('vmin', Z.min())
+ # kwargs['vmax'] = kwargs.get('vmax', Z.max())
+ kwargs["vmin"] = kwargs.get("vmin", 0)
+ kwargs["vmax"] = kwargs.get("vmax", 1)
+ kwargs["origin"] = kwargs.get("origin", "lower")
+ im = axis.imshow(Z, extent=[0, 2, -1, 1], *args, **kwargs)
+ # axins = inset_axes(axis, width='25%', height='5%', loc=3)
+ # vmin, vmax = Z.min(), Z.max()
+ # plt.colorbar(im, cax=axins, orientation='horizontal', ticks=[vmin,vmax], format = '%.2f')
+ # axins.xaxis.set_ticks_position('bottom')
diff --git a/code/rules/helper.py b/code/rules/helper.py
new file mode 100644
index 0000000..777a294
--- /dev/null
+++ b/code/rules/helper.py
@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import os
+import numpy as np
+
+from parameters import *
+
+
+def disc(shape=(1024, 1024), center=(512, 512), radius=512):
+ """ Generate a numpy array containing a disc. """
+
+ def distance(x, y):
+ return (x - center[0]) ** 2 + (y - center[1]) ** 2
+
+ D = np.fromfunction(distance, shape)
+ return np.where(D < radius * radius, 1.0, 0.0)
+
+
+def gaussian(shape=(25, 25), width=0.5, center=0.0):
+ """ Generate a gaussian of the form g(x) = height*exp(-(x-center)**2/width**2). """
+ if type(shape) in [float, int]:
+ shape = (shape,)
+ if type(width) in [float, int]:
+ width = (width,) * len(shape)
+ if type(center) in [float, int]:
+ center = (center,) * len(shape)
+ grid = []
+ for size in shape:
+ grid.append(slice(0, size))
+ C = np.mgrid[tuple(grid)]
+ R = np.zeros(shape)
+ for i, size in enumerate(shape):
+ if shape[i] > 1:
+ R += (((C[i] / float(size - 1)) * 2 - 1 - center[i]) / width[i]) ** 2
+ return np.exp(-R / 2)
+
+
+def stimulus(position, size, intensity):
+ """
+ Parameters
+ ----------
+
+ position : (rho,theta) (degrees)
+ size : float (degrees)
+ intensity: float
+ """
+
+ x, y = cartesian(position[0] / 90.0, np.pi * position[1] / 180.0)
+ Y, X = np.mgrid[0 : shape[0], 0 : shape[1]]
+ X = X / float(shape[1])
+ Y = 2 * Y / float(shape[0]) - 1
+ R = (X - x) ** 2 + (Y - y) ** 2
+ return np.exp(-0.5 * R / (size / 90.0))
+
+
+def best_fft_shape(shape):
+ """
+ This function returns the best shape for computing a fft
+
+ From fftw.org:
+ FFTW is best at handling sizes of the form 2^a*3^b*5^c*7^d*11^e*13^f,
+ where e+f is either 0 or 1,
+
+ From http://www.netlib.org/fftpack/doc
+ "the method is most efficient when n is a product of small primes."
+ -> What is small ?
+ """
+
+ # fftpack (not sure of the base)
+ base = [13, 11, 7, 5, 3, 2]
+ # fftw
+ # base = [13,11,7,5,3,2]
+
+ def factorize(n):
+ if n == 0:
+ raise (RuntimeError, "Length n must be positive integer")
+ elif n == 1:
+ return [
+ 1,
+ ]
+ factors = []
+ for b in base:
+ while n % b == 0:
+ n /= b
+ factors.append(b)
+ if n == 1:
+ return factors
+ return []
+
+ def is_optimal(n):
+ factors = factorize(n)
+ # fftpack
+ return len(factors) > 0
+ # fftw
+ # return len(factors) > 0 and factors[:2] not in [[13,13],[13,11],[11,11]]
+
+ shape = np.atleast_1d(np.array(shape))
+ for i in range(shape.size):
+ while not is_optimal(shape[i]):
+ shape[i] += 1
+ return shape.astype(int)
diff --git a/code/rules/parameters.py b/code/rules/parameters.py
new file mode 100644
index 0000000..f64346f
--- /dev/null
+++ b/code/rules/parameters.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import numpy as np
+
+second = 1.0
+millisecond = 0.001
+dt = 5 * millisecond
+duration = 10 * second
+noise = 0.01
+
+retina_shape = np.array([4096, 2048]).astype(int)
+projection_shape = np.array([512, 512]).astype(int)
+n = 128
+colliculus_shape = np.array([n, n]).astype(int)
+
+# Default stimulus
+stimulus_size = 1.5 # in degrees
+stimulus_intensity = 1.5
+
+# DNF parameters (linear)
+sigma_e = 0.10
+A_e = 1.30
+sigma_i = 1.00
+A_i = 0.65
+alpha = 12.5
+tau = 10 * millisecond
+scale = 40.0 * 40.0 / (n * n)
diff --git a/code/rules/projections.py b/code/rules/projections.py
new file mode 100644
index 0000000..6ae510e
--- /dev/null
+++ b/code/rules/projections.py
@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import os
+import numpy as np
+from parameters import *
+
+
+def cartesian_to_polar(x, y):
+ """ Cartesian to polar coordinates. """
+
+ rho = np.sqrt(x ** 2 + y ** 2)
+ theta = np.arctan2(y, x)
+ return rho, theta
+
+
+def polar_to_cartesian(rho, theta):
+ """ Polar to cartesian coordinates. """
+
+ x = rho * np.cos(theta)
+ y = rho * np.sin(theta)
+ return x, y
+
+
+def polar_to_logpolar(rho, theta):
+ """ Polar to logpolar coordinates. """
+
+ # Shift in the SC mapping function in deg
+ A = 3.0
+ # Collicular magnification along u axe in mm/rad
+ Bx = 1.4
+ # Collicular magnification along v axe in mm/rad
+ By = 1.8
+ xmin, xmax = 0.0, 4.80743279742
+ ymin, ymax = -2.76745559565, 2.76745559565
+ rho = rho * 90.0
+ x = Bx * np.log(np.sqrt(rho * rho + 2 * A * rho * np.cos(theta) + A * A) / A)
+ y = By * np.arctan(rho * np.sin(theta) / (rho * np.cos(theta) + A))
+ x = (x - xmin) / (xmax - xmin)
+ y = (y - ymin) / (ymax - ymin)
+ return x, y
+
+
+def retina_projection(Rs=retina_shape, Ps=projection_shape):
+ """
+ Compute the projection indices from retina to colliculus
+
+ Parameters
+ ----------
+
+ Rs : (int,int)
+ Half-retina shape
+
+ Ps : (int,int)
+ Retina projection shape (might be different from colliculus)
+ """
+
+ filename = "retina (%d,%d) - colliculus (%d,%d).npy" % (Rs[0], Rs[1], Ps[0], Ps[1])
+ if os.path.exists(filename):
+ return np.load(filename)
+
+ s = 4
+ rho = (np.logspace(start=0, stop=1, num=s * Rs[1], base=10) - 1) / 9.0
+ theta = np.linspace(start=-np.pi / 2, stop=np.pi / 2, num=s * Rs[0])
+
+ rho = rho.reshape((s * Rs[1], 1))
+ rho = np.repeat(rho, s * Rs[0], axis=1)
+
+ theta = theta.reshape((1, s * Rs[0]))
+ theta = np.repeat(theta, s * Rs[1], axis=0)
+
+ y, x = polar_to_cartesian(rho, theta)
+
+ xmin, xmax = x.min(), x.max()
+ x = (x - xmin) / (xmax - xmin)
+
+ ymin, ymax = y.min(), y.max()
+ y = (y - ymin) / (ymax - ymin)
+
+ P = np.zeros((Ps[0], Ps[1], 2), dtype=int)
+ xi = np.rint(x * (Rs[0] - 1)).astype(int)
+ yi = np.rint((0.0 + 1.0 * y) * (Rs[1] - 1)).astype(int)
+
+ yc, xc = polar_to_logpolar(rho, theta)
+ xmin, xmax = xc.min(), xc.max()
+ xc = (xc - xmin) / (xmax - xmin)
+ ymin, ymax = yc.min(), yc.max()
+ yc = (yc - ymin) / (ymax - ymin)
+ xc = np.rint(xc * (Ps[0] - 1)).astype(int)
+ yc = np.rint((0.0 + yc * 1.0) * (Ps[1] - 1)).astype(int)
+
+ P[xc, yc, 0] = xi
+ P[xc, yc, 1] = yi
+ np.save(filename, P)
+ return P
diff --git a/code/rules/rule-1.py b/code/rules/rule-1.py
new file mode 100644
index 0000000..04f2171
--- /dev/null
+++ b/code/rules/rule-1.py
@@ -0,0 +1,300 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright (c) 2014, Matplotlib Development Team. All Rights Reserved.
+# Distributed under the (new) BSD License. See LICENSE.txt for more info.
+#
+# Author: Nicolas P. Rougier
+# Source: New York Times graphics, 2007
+# -> http://www.nytimes.com/imagepages/2007/07/29/health/29cancer.graph.web.html
+# -----------------------------------------------------------------------------
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+# ----------
+# Data to be represented
+diseases = [
+ "Kidney Cancer",
+ "Bladder Cancer",
+ "Esophageal Cancer",
+ "Ovarian Cancer",
+ "Liver Cancer",
+ "Non-Hodgkin's\nlymphoma",
+ "Leukemia",
+ "Prostate Cancer",
+ "Pancreatic Cancer",
+ "Breast Cancer",
+ "Colorectal Cancer",
+ "Lung Cancer",
+]
+men_deaths = [
+ 10000,
+ 12000,
+ 13000,
+ 0,
+ 14000,
+ 12000,
+ 16000,
+ 25000,
+ 20000,
+ 500,
+ 25000,
+ 80000,
+]
+men_cases = [
+ 30000,
+ 50000,
+ 13000,
+ 0,
+ 16000,
+ 30000,
+ 25000,
+ 220000,
+ 22000,
+ 600,
+ 55000,
+ 115000,
+]
+women_deaths = [
+ 6000,
+ 5500,
+ 5000,
+ 20000,
+ 9000,
+ 12000,
+ 13000,
+ 0,
+ 19000,
+ 40000,
+ 30000,
+ 70000,
+]
+women_cases = [
+ 20000,
+ 18000,
+ 5000,
+ 25000,
+ 9000,
+ 29000,
+ 24000,
+ 0,
+ 21000,
+ 160000,
+ 55000,
+ 97000,
+]
+
+# ----------
+# Choose some nice colors
+matplotlib.rc("axes", facecolor="white")
+matplotlib.rc("figure.subplot", wspace=0.65)
+matplotlib.rc("grid", color="white")
+matplotlib.rc("grid", linewidth=1)
+
+# Make figure background the same colors as axes
+fig = plt.figure(figsize=(12, 7), facecolor="white")
+
+
+# ---WOMEN data ---
+axes_left = plt.subplot(121)
+
+# Keep only top and right spines
+axes_left.spines["left"].set_color("none")
+axes_left.spines["right"].set_zorder(10)
+axes_left.spines["bottom"].set_color("none")
+axes_left.xaxis.set_ticks_position("top")
+axes_left.yaxis.set_ticks_position("right")
+axes_left.spines["top"].set_position(("data", len(diseases) + 0.25))
+axes_left.spines["top"].set_color("w")
+
+# Set axes limits
+plt.xlim(200000, 0)
+plt.ylim(0, len(diseases))
+
+# Set ticks labels
+plt.xticks([150000, 100000, 50000, 0], ["150,000", "100,000", "50,000", "WOMEN"])
+axes_left.get_xticklabels()[-1].set_weight("bold")
+axes_left.get_xticklines()[-1].set_markeredgewidth(0)
+for label in axes_left.get_xticklabels():
+ label.set_fontsize(10)
+plt.yticks([])
+
+
+# Plot data
+for i in range(len(women_deaths)):
+ H, h = 0.8, 0.55
+ # Death
+ value = women_cases[i]
+ p = patches.Rectangle(
+ (0, i + (1 - H) / 2.0),
+ value,
+ H,
+ fill=True,
+ transform=axes_left.transData,
+ lw=0,
+ facecolor="red",
+ alpha=0.1,
+ )
+ axes_left.add_patch(p)
+ # New cases
+ value = women_deaths[i]
+ p = patches.Rectangle(
+ (0, i + (1 - h) / 2.0),
+ value,
+ h,
+ fill=True,
+ transform=axes_left.transData,
+ lw=0,
+ facecolor="red",
+ alpha=0.5,
+ )
+ axes_left.add_patch(p)
+
+# Add a grid
+axes_left.grid()
+
+plt.text(165000, 8.2, "Leading Causes\nOf Cancer Deaths", fontsize=18, va="top")
+plt.text(
+ 165000,
+ 7,
+ """In 2007, there were more\n"""
+ """than 1.4 million new cases\n"""
+ """of cancer in the United States.""",
+ va="top",
+ fontsize=10,
+)
+
+# --- MEN data ---
+axes_right = plt.subplot(122, sharey=axes_left)
+
+# Keep only top and left spines
+axes_right.spines["right"].set_color("none")
+axes_right.spines["left"].set_zorder(10)
+axes_right.spines["bottom"].set_color("none")
+axes_right.xaxis.set_ticks_position("top")
+axes_right.yaxis.set_ticks_position("left")
+axes_right.spines["top"].set_position(("data", len(diseases) + 0.25))
+axes_right.spines["top"].set_color("w")
+
+
+# Set axes limits
+plt.xlim(0, 200000)
+plt.ylim(0, len(diseases))
+
+# Set ticks labels
+plt.xticks(
+ [0, 50000, 100000, 150000, 200000],
+ ["MEN", "50,000", "100,000", "150,000", "200,000"],
+)
+axes_right.get_xticklabels()[0].set_weight("bold")
+for label in axes_right.get_xticklabels():
+ label.set_fontsize(10)
+axes_right.get_xticklines()[1].set_markeredgewidth(0)
+plt.yticks([])
+
+# Plot data
+for i in range(len(men_deaths)):
+ H, h = 0.8, 0.55
+ # Death
+ value = men_cases[i]
+ p = patches.Rectangle(
+ (0, i + (1 - H) / 2.0),
+ value,
+ H,
+ fill=True,
+ transform=axes_right.transData,
+ lw=0,
+ facecolor="blue",
+ alpha=0.1,
+ )
+ axes_right.add_patch(p)
+ # New cases
+ value = men_deaths[i]
+ p = patches.Rectangle(
+ (0, i + (1 - h) / 2.0),
+ value,
+ h,
+ fill=True,
+ transform=axes_right.transData,
+ lw=0,
+ facecolor="blue",
+ alpha=0.5,
+ )
+ axes_right.add_patch(p)
+
+# Add a grid
+axes_right.grid()
+
+# Y axis labels
+# We want them to be exactly in the middle of the two y spines
+# and it requires some computations
+for i in range(len(diseases)):
+ x1, y1 = axes_left.transData.transform_point((0, i + 0.5))
+ x2, y2 = axes_right.transData.transform_point((0, i + 0.5))
+ x, y = fig.transFigure.inverted().transform_point(((x1 + x2) / 2, y1))
+ plt.text(
+ x,
+ y,
+ diseases[i],
+ transform=fig.transFigure,
+ fontsize=10,
+ horizontalalignment="center",
+ verticalalignment="center",
+ )
+
+
+# Devil hides in the details...
+arrowprops = dict(arrowstyle="-", connectionstyle="angle,angleA=0,angleB=90,rad=0")
+x = women_cases[-1]
+axes_left.annotate(
+ "NEW CASES",
+ xy=(0.9 * x, 11.5),
+ xycoords="data",
+ horizontalalignment="right",
+ fontsize=10,
+ xytext=(-40, -3),
+ textcoords="offset points",
+ arrowprops=arrowprops,
+)
+
+x = women_deaths[-1]
+axes_left.annotate(
+ "DEATHS",
+ xy=(0.85 * x, 11.5),
+ xycoords="data",
+ horizontalalignment="right",
+ fontsize=10,
+ xytext=(-50, -25),
+ textcoords="offset points",
+ arrowprops=arrowprops,
+)
+
+x = men_cases[-1]
+axes_right.annotate(
+ "NEW CASES",
+ xy=(0.9 * x, 11.5),
+ xycoords="data",
+ horizontalalignment="left",
+ fontsize=10,
+ xytext=(+40, -3),
+ textcoords="offset points",
+ arrowprops=arrowprops,
+)
+
+x = men_deaths[-1]
+axes_right.annotate(
+ "DEATHS",
+ xy=(0.9 * x, 11.5),
+ xycoords="data",
+ horizontalalignment="left",
+ fontsize=10,
+ xytext=(+50, -25),
+ textcoords="offset points",
+ arrowprops=arrowprops,
+)
+
+
+# Done
+plt.savefig("../../figures/rules/rule-1.pdf")
+plt.show()
diff --git a/code/rules/rule-2.py b/code/rules/rule-2.py
new file mode 100644
index 0000000..68c79e6
--- /dev/null
+++ b/code/rules/rule-2.py
@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+# -----------------------------------------------------------------------------
+# Copyright INRIA
+# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
+# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
+#
+# This software is governed by the CeCILL license under French law and abiding
+# by the rules of distribution of free software. You can use, modify and/ or
+# redistribute the software under the terms of the CeCILL license as circulated
+# by CEA, CNRS and INRIA at the following URL
+# http://www.cecill.info/index.en.html.
+#
+# As a counterpart to the access to the source code and rights to copy, modify
+# and redistribute granted by the license, users are provided only with a
+# limited warranty and the software's author, the holder of the economic
+# rights, and the successive licensors have only limited liability.
+#
+# In this respect, the user's attention is drawn to the risks associated with
+# loading, using, modifying and/or developing or reproducing the software by
+# the user in light of its specific status of free software, that may mean that
+# it is complicated to manipulate, and that also therefore means that it is
+# reserved for developers and experienced professionals having in-depth
+# computer knowledge. Users are therefore encouraged to load and test the
+# software's suitability as regards their requirements in conditions enabling
+# the security of their systems and/or data to be ensured and, more generally,
+# to use and operate it in the same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+# -----------------------------------------------------------------------------
+import os
+import numpy as np
+
+from helper import *
+from graphics import *
+from projections import *
+
+
+if __name__ == "__main__":
+ import matplotlib.pyplot as plt
+ from matplotlib.patches import Polygon
+ from mpl_toolkits.axes_grid1 import ImageGrid
+ from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
+ from mpl_toolkits.axes_grid1.inset_locator import mark_inset
+
+ P = retina_projection()
+
+ # Checkerboard pattern for retina
+ grid = 2 * 32
+ even = int(grid / 2) * [0, 1]
+ odd = int(grid / 2) * [1, 0]
+ R = np.row_stack(int(grid / 2) * (even, odd))
+ R = R.repeat(grid, axis=0).repeat(grid, axis=1)
+
+ # Mask with a disc
+ R = R * disc(
+ (retina_shape[0], retina_shape[0]),
+ (retina_shape[0] // 2, retina_shape[0] // 2),
+ retina_shape[0] // 2,
+ )
+
+ # Take half-retina
+ R = R[:, retina_shape[1] :]
+
+ # Project to colliculus
+ SC = R[P[..., 0], P[..., 1]]
+
+ fig = plt.figure(figsize=(10, 8), facecolor="w")
+ ax1, ax2 = ImageGrid(fig, 111, nrows_ncols=(1, 2), axes_pad=0.5)
+ polar_frame(ax1, legend=True)
+ polar_imshow(ax1, R, vmin=0, vmax=5)
+ logpolar_frame(ax2, legend=True)
+ logpolar_imshow(ax2, SC, vmin=0, vmax=5)
+
+ plt.savefig("../../figures/rules/rule-2.pdf")
+ plt.show()
diff --git a/code/rules/rule-3.py b/code/rules/rule-3.py
new file mode 100644
index 0000000..3b2baad
--- /dev/null
+++ b/code/rules/rule-3.py
@@ -0,0 +1,111 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
+from mpl_toolkits.axes_grid1.inset_locator import mark_inset
+
+
+def simulate():
+ d = 0.005
+ x = np.random.uniform(0, d)
+ y = d - x
+ x, y = np.random.uniform(0, d, 2)
+
+ dt = 0.05
+ t = 35.0
+ alpha = 0.25
+ n = int(t / dt)
+ X = np.zeros(n)
+ Y = np.zeros(n)
+ C = np.random.randint(0, 2, n)
+
+ for i in range(n):
+ # Asynchronous
+ if 0:
+ if C[i]:
+ x += (alpha + (x - y)) * (1 - x) * dt
+ x = max(x, 0.0)
+ y += (alpha + (y - x)) * (1 - y) * dt
+ y = max(y, 0.0)
+ else:
+ y += (alpha + (y - x)) * (1 - y) * dt
+ y = max(y, 0.0)
+ x += (alpha + (x - y)) * (1 - x) * dt
+ x = max(x, 0.0)
+ # Synchronous
+ else:
+ dx = (alpha + (x - y)) * (1 - x) * dt
+ dy = (alpha + (y - x)) * (1 - y) * dt
+ x = max(x + dx, 0.0)
+ y = max(y + dy, 0.0)
+ X[i] = x
+ Y[i] = y
+ return X, Y
+
+
+np.random.seed(11)
+S = []
+n = 250
+for i in range(n):
+ S.append(simulate())
+
+
+plt.figure(figsize=(20, 10))
+ax = plt.subplot(121, aspect=1)
+axins = zoomed_inset_axes(ax, 25, loc=3)
+for i in range(n):
+ X, Y = S[i]
+ if X[-1] > 0.9 and Y[-1] > 0.9:
+ c = "r"
+ lw = 1.0
+ axins.scatter(X[0], Y[0], c="r", edgecolor="w", zorder=10)
+ else:
+ c = "b"
+ lw = 1.0
+ ax.plot(X, Y, c=c, alpha=0.25, lw=lw)
+ axins.plot(X, Y, c=c, alpha=0.25, lw=lw)
+
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_xlabel("x position")
+ax.set_ylabel("y position")
+ax.set_title("%d trajectories of a dual particle system (x,y)" % n)
+axins.set_xlim(0.01, 0.02)
+axins.set_xticks([])
+axins.set_ylim(0.01, 0.02)
+axins.set_yticks([])
+
+ax = plt.subplot(122, aspect=1)
+axins = zoomed_inset_axes(ax, 50, loc=3)
+axins.set_facecolor((1, 1, 0.9))
+n = 9
+for i in range(n):
+ X, Y = S[i]
+ ls = "-"
+ if i == 2:
+ ls = "--"
+ if X[-1] > 0.9 and Y[-1] > 0.9:
+ c = "r"
+ lw = 2.0
+ axins.scatter(X[0], Y[0], s=150, c="r", edgecolor="w", zorder=10, lw=2)
+ else:
+ c = "b"
+ lw = 2.0
+ ax.plot(X, Y, c=c, alpha=0.75, lw=lw, ls=ls)
+ axins.plot(X, Y, c=c, alpha=0.75, lw=lw, ls=ls)
+
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_xticks([0, 1])
+ax.set_yticks([0, 1])
+ax.set_xticklabels(["0", "1"], fontsize=16)
+ax.set_yticklabels(["0", "1"], fontsize=16)
+ax.set_xlabel("x position", fontsize=20)
+ax.set_ylabel("y position", fontsize=20)
+# ax.set_title('%d trajectories of a dual particle system (x,y)' % n)
+axins.set_xlim(0.01, 0.02)
+axins.set_xticks([])
+axins.set_ylim(0.01, 0.02)
+axins.set_yticks([])
+
+plt.savefig("../../figures/rules/rule-3.pdf")
+plt.show()
diff --git a/code/rules/rule-5-left.py b/code/rules/rule-5-left.py
new file mode 100644
index 0000000..63e1624
--- /dev/null
+++ b/code/rules/rule-5-left.py
@@ -0,0 +1,11 @@
+from pylab import *
+
+fig = plt.figure(figsize=(8, 4))
+
+n = 256
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+plot(X, C), plot(X, S)
+
+savefig("../../figures/rules/rule-5-left.pdf")
+show()
diff --git a/code/rules/rule-5-right.py b/code/rules/rule-5-right.py
new file mode 100644
index 0000000..12299e3
--- /dev/null
+++ b/code/rules/rule-5-right.py
@@ -0,0 +1,63 @@
+from pylab import *
+
+figure(figsize=(8, 5), dpi=80)
+subplot(111)
+
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
+plot(X, S, color="red", linewidth=2.5, linestyle="-", label="sine")
+
+ax = gca()
+ax.spines["right"].set_color("none")
+ax.spines["top"].set_color("none")
+ax.xaxis.set_ticks_position("bottom")
+ax.spines["bottom"].set_position(("data", 0))
+ax.yaxis.set_ticks_position("left")
+ax.spines["left"].set_position(("data", 0))
+
+xlim(X.min() * 1.1, X.max() * 1.1)
+xticks(
+ [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],
+ [r"$-\pi$", r"$-\pi/2$", r"$0$", r"$+\pi/2$", r"$+\pi$"],
+)
+
+ylim(C.min() * 1.1, C.max() * 1.1)
+yticks([-1, +1], [r"$-1$", r"$+1$"])
+
+
+legend(loc="upper left")
+
+t = 2 * np.pi / 3
+plot([t, t], [0, np.cos(t)], color="blue", linewidth=1.5, linestyle="--")
+scatter([t,], [np.cos(t),], 50, color="blue")
+annotate(
+ r"$sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$",
+ xy=(t, np.sin(t)),
+ xycoords="data",
+ xytext=(+10, +30),
+ textcoords="offset points",
+ fontsize=16,
+ arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"),
+)
+
+plot([t, t], [0, np.sin(t)], color="red", linewidth=1.5, linestyle="--")
+scatter([t,], [np.sin(t),], 50, color="red")
+annotate(
+ r"$cos(\frac{2\pi}{3})=-\frac{1}{2}$",
+ xy=(t, np.cos(t)),
+ xycoords="data",
+ xytext=(-90, -50),
+ textcoords="offset points",
+ fontsize=16,
+ arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"),
+)
+
+for label in ax.get_xticklabels() + ax.get_yticklabels():
+ label.set_fontsize(16)
+ label.set_bbox(dict(facecolor="white", edgecolor="None", alpha=0.65))
+
+
+savefig("../../figures/rules/rule-5-right.pdf")
+show()
diff --git a/code/rules/rule-5.tex b/code/rules/rule-5.tex
new file mode 100644
index 0000000..8a73908
--- /dev/null
+++ b/code/rules/rule-5.tex
@@ -0,0 +1,6 @@
+\documentclass[varwidth]{standalone}
+\usepackage{graphicx}
+\begin{document}
+\includegraphics[height=3cm]{../../figures/rules/rule-5-left.pdf}
+\includegraphics[height=3cm]{../../figures/rules/rule-5-right.pdf}
+\end{document}
diff --git a/code/rules/rule-6.py b/code/rules/rule-6.py
new file mode 100644
index 0000000..5e0786b
--- /dev/null
+++ b/code/rules/rule-6.py
@@ -0,0 +1,71 @@
+import numpy as np
+import matplotlib
+
+import matplotlib.pylab as plt
+import matplotlib.patheffects as PathEffects
+from matplotlib.ticker import MultipleLocator
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+import matplotlib.gridspec as gridspec
+
+
+def make(ax1, ax2, cmap, title, y, color="k"):
+ # -----------------
+ ax1.set_xlim(0, 1)
+ ax1.set_ylim(0, 1)
+ ax1.set_xticks([])
+ ax1.set_yticks([0, 0.5, 1])
+ ax1.get_yaxis().tick_left()
+
+ ax1.axhline(y, lw=1, c=color, xmin=0, xmax=1)
+ ax1.text(0.025, y + 0.015, "Slice y=%.2f" % y, fontsize=10, color=color)
+ ax1.imshow(Z, cmap=cmap, origin="upper", extent=[0, 1, 0, 1])
+ ax1.set_xticks([]), ax1.set_yticks([])
+ ax1.set_title(title)
+
+ ax2.set_xlim(0, 1)
+ ax2.set_ylim(-0.1, +1.1)
+ ax2.set_xticks([0, 0.5, 1])
+ ax2.get_xaxis().tick_bottom()
+ ax2.set_yticks([0, 1])
+ ax2.get_yaxis().tick_left()
+ ax2.plot(T / np.pi, Z[int(1024 * (1 - y))], c="k", lw=0.5)
+ ax2.axis("off")
+ ax2.text(0.025, 1.25, "Slice detail")
+
+
+if __name__ == "__main__":
+ fg = 0.0, 0.0, 0.0
+ bg = 1.0, 1.0, 1.0
+ matplotlib.rcParams["xtick.direction"] = "out"
+ matplotlib.rcParams["ytick.direction"] = "out"
+ matplotlib.rcParams["font.size"] = 12.0
+ matplotlib.rc("axes", facecolor=bg)
+ matplotlib.rc("axes", edgecolor=fg)
+ matplotlib.rc("xtick", color=fg)
+ matplotlib.rc("ytick", color=fg)
+ matplotlib.rc("figure", facecolor=bg)
+ matplotlib.rc("savefig", facecolor=bg)
+
+ plt.figure(figsize=(18, 6))
+
+ G = gridspec.GridSpec(2, 3, width_ratios=[1, 1, 1], height_ratios=[15, 1])
+
+ T = np.linspace(0, np.pi, 2 * 512)
+ X, Y = np.meshgrid(T, T)
+ Z = np.power(Y / 2, 5) * np.sin(np.exp(np.pi / 2 * X))
+ Z = (Z - Z.min()) / (Z.max() - Z.min())
+
+ ax1 = plt.subplot(G[0, 0], aspect=1)
+ ax2 = plt.subplot(G[1, 0])
+ make(ax1, ax2, plt.cm.rainbow, "Rainbow colormap (qualitative)", y=0.3)
+
+ ax1 = plt.subplot(G[0, 1], aspect=1)
+ ax2 = plt.subplot(G[1, 1])
+ make(ax1, ax2, plt.cm.seismic, "Seismic colormap (diverging)", y=0.2)
+
+ ax1 = plt.subplot(G[0, 2], aspect=1)
+ ax2 = plt.subplot(G[1, 2])
+ make(ax1, ax2, plt.cm.Purples, "Purples colormap (sequential)", 0.1, "white")
+
+ plt.savefig("../../figures/rules/rule-6.pdf")
+ plt.show()
diff --git a/code/rules/rule-7.py b/code/rules/rule-7.py
new file mode 100644
index 0000000..d6a4cce
--- /dev/null
+++ b/code/rules/rule-7.py
@@ -0,0 +1,71 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+n = 20
+Z = np.linspace(0, 1, n * n).reshape(n, n)
+Z = (Z - Z.min()) / (Z.max() - Z.min())
+Z += np.random.uniform(0, 1, (n, n))
+
+plt.figure(figsize=(20, 8))
+
+ax = plt.subplot(1, 2, 1, aspect=1)
+values = [30.0, 20.0, 15.0, 10.0]
+x, y = 0.0, 0.5
+for value in values:
+ r1 = 0.5 * (value / values[0])
+ r2 = 0.5 * ((np.sqrt(value / np.pi)) / (np.sqrt(values[0] / np.pi)))
+ ax.add_artist(plt.Circle((x + r2, y), r1, color="r"))
+ ax.add_artist(plt.Circle((x + r2, 1.5 + y), r2, color="k"))
+ fontsize = 2 * value
+ # plt.text(x+r2, y, "%d" % value, color="white", family = "Times New Roman",
+ # fontsize=fontsize, ha="center", va="center")
+ # plt.text(x+r2, 1.5+y, "%d" % value, color="white", family = "Times New Roman",
+ # fontsize=fontsize, ha="center", va="center")
+ x += 2 * r2 + 0.05
+plt.axhline(1.25, c="k")
+plt.text(
+ 0.0,
+ 1.25 + 0.05,
+ "Relative size using disc area",
+ ha="left",
+ va="bottom",
+ color=".25",
+)
+plt.text(
+ 0.0,
+ 1.25 - 0.05,
+ "Relative size using disc radius",
+ ha="left",
+ va="top",
+ color=".25",
+)
+plt.xlim(-0.05, 3.5)
+plt.ylim(-0.05, 2.6)
+plt.axis("off")
+
+
+ax = plt.subplot(1, 2, 2, aspect=1)
+
+plt.axhline(5, c="k")
+plt.text(
+ 0.0, 5 + 0.15, "Relative size using full range", ha="left", va="bottom", color=".25"
+)
+plt.text(
+ 0.0, 5 - 0.15, "Relative size using partial range", ha="left", va="top", color=".25"
+)
+
+
+n = 10
+np.random.seed(123)
+X = 0.25 + np.arange(n)
+Y = 2 + np.random.uniform(0.75, 0.85, n)
+
+plt.bar(X, 5 * (Y - 2.2), 0.9, color="k", ec="None", bottom=6)
+plt.bar(X, 20 * (Y - 2.75), 0.9, color="r", ec="None", bottom=1)
+
+plt.xlim(-0.05, 10.5)
+plt.ylim(-0.05, 10.5)
+plt.axis("off")
+
+plt.savefig("../../figures/rules/rule-7.pdf")
+plt.show()
diff --git a/code/rules/rule-8.py b/code/rules/rule-8.py
new file mode 100644
index 0000000..b4d8720
--- /dev/null
+++ b/code/rules/rule-8.py
@@ -0,0 +1,55 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Data
+# -----------------------------------------------------------------------------
+p, n = 7, 32
+X = np.linspace(0, 2, n)
+Y = np.random.uniform(-0.75, 0.5, (p, n))
+
+# -----------------------------------------------------------------------------
+fig = plt.figure(figsize=(20, 8))
+ax = plt.subplot(1, 2, 1, aspect=1)
+ax.patch.set_facecolor((1, 1, 0.75))
+for i in range(p):
+ plt.plot(X, Y[i], label="Series %d " % (1 + i), lw=2)
+plt.xlim(0, 2)
+plt.ylim(-1, 1)
+plt.yticks(np.linspace(-1, 1, 18))
+plt.xticks(np.linspace(0, 2, 18))
+plt.legend()
+plt.grid()
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(1, 2, 2, aspect=1)
+Yy = p - (np.arange(p) + 0.5)
+Xx = [p,] * p
+rects = plt.barh(
+ Yy, Xx, align="center", height=0.75, color=".95", ec="None", zorder=-20
+)
+plt.xlim(0, p), plt.ylim(0, p)
+
+for i in range(p):
+ label = "Series %d" % (1 + i)
+ plt.text(-0.1, Yy[i], label, ha="right", fontsize=16)
+ plt.axvline(0, (Yy[i] - 0.4) / p, (Yy[i] + 0.4) / p, c="k", lw=3)
+ plt.axvline(
+ 0.25 * p, (Yy[i] - 0.375) / p, (Yy[i] + 0.375) / p, c=".5", lw=0.5, zorder=-15
+ )
+ plt.axvline(
+ 0.50 * p, (Yy[i] - 0.375) / p, (Yy[i] + 0.375) / p, c=".5", lw=0.5, zorder=-15
+ )
+ plt.axvline(
+ 0.75 * p, (Yy[i] - 0.375) / p, (Yy[i] + 0.375) / p, c=".5", lw=0.5, zorder=-15
+ )
+ plt.plot(X * p / 2, i + 0.5 + 2 * Y[i] / p, c="k", lw=2)
+ for j in range(p):
+ if i != j:
+ plt.plot(X * p / 2, i + 0.5 + 2 * Y[j] / p, c=".5", lw=0.5, zorder=-10)
+plt.text(0.25 * p, 0, "0.5", va="top", ha="center", fontsize=10)
+plt.text(0.50 * p, 0, "1.0", va="top", ha="center", fontsize=10)
+plt.text(0.75 * p, 0, "1.5", va="top", ha="center", fontsize=10)
+plt.axis("off")
+
+plt.savefig("../../figures/rules/rule-8.pdf")
+plt.show()
diff --git a/code/rules/rule-9.py b/code/rules/rule-9.py
new file mode 100644
index 0000000..0bbfc62
--- /dev/null
+++ b/code/rules/rule-9.py
@@ -0,0 +1,60 @@
+import numpy as np
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+plt.xkcd()
+
+X = np.linspace(0, 2.32, 100)
+Y = X * X - 5 * np.exp(-5 * (X - 2) * (X - 2))
+
+fig = plt.figure(figsize=(12, 5), dpi=72, facecolor="white")
+axes = plt.subplot(111)
+
+plt.plot(X, Y, color="k", linewidth=2, linestyle="-", zorder=+10)
+
+axes.set_xlim(X.min(), X.max())
+axes.set_ylim(1.01 * Y.min(), 1.01 * Y.max())
+
+axes.spines["right"].set_color("none")
+axes.spines["top"].set_color("none")
+axes.xaxis.set_ticks_position("bottom")
+axes.spines["bottom"].set_position(("data", 0))
+axes.yaxis.set_ticks_position("left")
+axes.spines["left"].set_position(("data", X.min()))
+
+axes.set_xticks([])
+axes.set_yticks([])
+axes.set_xlim(1.05 * X.min(), 1.10 * X.max())
+axes.set_ylim(1.15 * Y.min(), 1.05 * Y.max())
+
+t = [10, 40, 82, 88, 93, 99]
+plt.scatter(X[t], Y[t], s=50, zorder=+12, c="k")
+
+plt.text(X[t[0]] - 0.1, Y[t[0]] + 0.1, "Industrial\nRobot", ha="left", va="bottom")
+plt.text(X[t[1]] - 0.15, Y[t[1]] + 0.1, "Humanoid\nRobot", ha="left", va="bottom")
+plt.text(X[t[2]] - 0.25, Y[t[2]], "Zombie", ha="left", va="center")
+plt.text(X[t[3]] + 0.05, Y[t[3]], "Prosthetic\nHand", ha="left", va="center")
+plt.text(X[t[4]] + 0.05, Y[t[4]], "Bunraku\nPuppet", ha="left", va="center")
+plt.text(X[t[5]] + 0.05, Y[t[5]], "Human", ha="left", va="center")
+plt.text(X[t[2]] - 0.05, 1.5, "Uncanny\nValley", ha="center", va="center", fontsize=24)
+
+plt.ylabel("- Comfort Level +", y=0.5, fontsize=20)
+plt.text(0.05, -0.1, "Human Likeness ->", ha="left", va="top", color="r", fontsize=20)
+
+X = np.linspace(0, 1.1 * 2.32, 100)
+axes.fill_between(X, 0, -10, color="0.85", zorder=-1)
+axes.fill_between(X, 0, +10, color=(1.0, 1.0, 0.9), zorder=-1)
+
+# X = np.linspace(1.652,2.135,100)
+X = np.linspace(1.5, 2.25, 100)
+Y = X * X - 5 * np.exp(-5 * (X - 2) * (X - 2))
+axes.fill_between(X, Y, +10, color=(1, 1, 1), zorder=-1)
+
+axes.axvline(x=1.5, ymin=0, ymax=1, color=".5", ls="--")
+axes.axvline(x=2.25, ymin=0, ymax=1, color=".5", ls="--")
+
+plt.savefig("../../figures/rules/rule-9.pdf")
+plt.show()
diff --git a/code/scales-projections/geo-projections.py b/code/scales-projections/geo-projections.py
new file mode 100644
index 0000000..50a88ef
--- /dev/null
+++ b/code/scales-projections/geo-projections.py
@@ -0,0 +1,51 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import cartopy.crs
+
+mpl.rcParams["font.serif"] = "Roboto Slab"
+mpl.rcParams["font.size"] = 10
+mpl.rcParams["font.weight"] = 400
+mpl.rcParams["font.family"] = "serif"
+
+dpi = 100
+inch = 2.54
+fig_width = 2 * 10.85 / inch
+fig_height = fig_width * 1.25
+
+fig = plt.figure(figsize=(fig_width, fig_height), dpi=dpi)
+
+projections = [
+ (cartopy.crs.Mercator(), "Mercator",),
+ (cartopy.crs.PlateCarree(), "Plate Carree"),
+ (cartopy.crs.LambertCylindrical(), "Lambert Cylindrical"),
+ (cartopy.crs.Orthographic(), "Orthographic"),
+ (cartopy.crs.Mollweide(), "Mollweide"),
+ (cartopy.crs.EqualEarth(), "Equal Earth"),
+ (cartopy.crs.LambertConformal(), "Lambert Conformal"),
+ (cartopy.crs.EquidistantConic(), "Equidistant Conic"),
+ (cartopy.crs.AlbersEqualArea(), "Albers Equal Area"),
+ (
+ cartopy.crs.NearsidePerspective(
+ central_latitude=50.72, central_longitude=-3.53, satellite_height=10000000.0
+ ),
+ "Nearside Perspective",
+ ),
+ (cartopy.crs.NorthPolarStereo(), "North Polar Stereo"),
+ (cartopy.crs.SouthPolarStereo(), "South Polar Stereo"),
+]
+
+
+for i, (projection, name) in enumerate(projections):
+ ax = plt.subplot(4, 3, i + 1, projection=projection, frameon="False")
+ ax.coastlines(resolution="110m", linewidth=0.5)
+ ax.stock_img()
+ ax.set_title(name, weight=400)
+
+plt.savefig("../../figures/scales-projections/geo-projections.png", dpi=300)
+plt.show()
diff --git a/code/scales-projections/polar-patterns.py b/code/scales-projections/polar-patterns.py
new file mode 100644
index 0000000..5741d80
--- /dev/null
+++ b/code/scales-projections/polar-patterns.py
@@ -0,0 +1,131 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def shotgun_pattern(T, a, b, c=1, d=1, e=10000):
+ """
+ Equations taken from [1] or [2] + some modifications by Matthieu LEROY
+ in order to squish the pattern and make a log-based pattern.
+
+ [1] https://math.stackexchange.com/questions/1808380/non-symmetrical-lemniscate-curve-parameterization
+ [2] https://www.jneurosci.org/content/22/18/8201
+
+ Parameters
+ ----------
+ T: array-like of floats
+ Angle between -pi and pi.
+ a: float
+ Amplitude.
+ b: float
+ Asymetric parameter.
+ c: float
+ Squish parameter??
+ d: float
+ Not sure what it does.
+ e: float
+ Not sure what it does.
+
+ Returns
+ -------
+ Shotgun pattern.
+ """
+ x = a * (np.cos(T) + b) * np.cos(T) / (c + np.sin(T) ** 2)
+ y = d * x * np.sin(T)
+ res = np.sqrt(x ** 2 + y ** 2) # to polar coordinates
+ return np.exp(res) / e # to get log-based plots after
+
+
+def plot(ax, title, alpha=1, shotgun=False):
+ T = np.linspace(-np.pi, np.pi, 5000)
+ if shotgun:
+ R = shotgun_pattern(T, 4, 0.2, 0.1, 4) + shotgun_pattern(
+ T - np.pi / 2, 3.5, 0, c=0.15, d=1
+ )
+ else:
+ R = alpha + (1 - alpha) * np.cos(T)
+ R = np.log(1 + np.abs(50 * R)) / np.log(10)
+ R = 1000 * (R / R.max())
+
+ ax.set_theta_offset(np.pi / 2)
+ ax.set_thetalim(0, 2 * np.pi)
+ ax.set_rorigin(0)
+ ax.set_rlabel_position(np.pi / 2)
+
+ ax.fill(T, R, zorder=20, color="C1", clip_on=True, alpha=0.25)
+ ax.plot(
+ T,
+ R,
+ zorder=30,
+ alpha=0.75,
+ color="C1",
+ linewidth=1.0,
+ linestyle=":",
+ clip_on=False,
+ )
+ ax.plot(T, R, zorder=40, color="C1", linewidth=1.5, clip_on=True)
+ ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2])
+ ax.xaxis.set_tick_params("major", pad=-2.5)
+ ax.set_xticklabels(
+ ["0°", "", "180°", ""],
+ family="Roboto",
+ size="small",
+ horizontalalignment="center",
+ verticalalignment="center",
+ )
+ ax.set_yticks([200, 400, 600, 800, 1010])
+
+ for y, label in zip([390, 590, 790], ["-20 dB", "-15 dB", "-10 dB"]):
+ ax.text(
+ 0,
+ y,
+ label,
+ zorder=10,
+ family="Roboto Condensed",
+ size="small",
+ horizontalalignment="center",
+ verticalalignment="center",
+ bbox=dict(facecolor="white", edgecolor="None", pad=1.0),
+ )
+ ax.set_yticklabels([])
+
+ ax.set_ylim(200, 1010)
+ ax.set_title(title, family="Roboto", weight="bold", size="large", y=-0.2)
+
+
+fig = plt.figure(figsize=(8, 7))
+fig.suptitle(
+ "Microphone polar patterns", family="Roboto", weight="bold", size="xx-large"
+)
+
+# Omnidirectional
+ax = plt.subplot(2, 3, 1, projection="polar")
+plot(ax, "Omnidirectional", 1.00)
+
+# Subcardioid
+ax = plt.subplot(2, 3, 2, projection="polar")
+plot(ax, "Subcardioid", 0.75)
+
+# Cardioid
+ax = plt.subplot(2, 3, 3, projection="polar")
+plot(ax, "Cardioid", 0.50)
+
+# Supercardioid
+ax = plt.subplot(2, 3, 4, projection="polar")
+plot(ax, "Supercardioid", 0.25)
+
+# Bidirectional
+ax = plt.subplot(2, 3, 5, projection="polar")
+plot(ax, "Bidirectional", 0.00)
+
+# Shotgun (not quite right yet)
+ax = plt.subplot(2, 3, 6, projection="polar")
+plot(ax, "Shotgun", shotgun=True)
+
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/polar-patterns.pdf")
+plt.show()
diff --git a/code/scales-projections/projection-3d-frame.py b/code/scales-projections/projection-3d-frame.py
new file mode 100644
index 0000000..b8e6770
--- /dev/null
+++ b/code/scales-projections/projection-3d-frame.py
@@ -0,0 +1,145 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from mpl_toolkits.mplot3d import Axes3D, proj3d, art3d
+import matplotlib.patheffects as path_effects
+
+
+# Style
+# -----------------------------------------------------------------------------
+# plt.rc('font', family="Roboto Condensed")
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, projection="3d")
+ax.set_axis_off()
+ax.set_xlim(0, 10)
+ax.set_ylim(10, 0)
+ax.set_zlim(0, 10)
+
+for d in np.arange(0, 11):
+ color, linewidth, zorder = "0.75", 0.5, -100
+ if d in [0, 5, 10]:
+ color, linewidth, zorder = "0.5", 0.75, -50
+ ax.plot([0, 0], [d, d], [0, 10], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([0, 0], [0, 10], [d, d], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([0, 10], [0, 0], [d, d], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([d, d], [0, 10], [0, 0], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([0, 10], [d, d], [0, 0], linewidth=linewidth, color=color, zorder=zorder)
+ ax.plot([d, d], [0, 0], [0, 10], linewidth=linewidth, color=color, zorder=zorder)
+
+size, color = "small", "0.75"
+for d in np.arange(1, 11):
+ ax.text(d, 10.75, 0, "%d" % d, color=color, size=size, ha="center", va="center")
+ ax.text(10.5, d, 0, "%d" % d, color=color, size=size, ha="center", va="center")
+ ax.text(0, 10.75, d, "%d" % d, color=color, size=size, ha="center", va="center")
+ ax.text(10.5, 0, d, "%d" % d, color=color, size=size, ha="center", va="center")
+ ax.text(d, 0, 10.5, "%d" % d, color=color, size=size, ha="center", va="center")
+
+ ax.text(0, d, 10.5, "%d" % d, color=color, size=size, ha="center", va="center")
+
+
+plt.plot([0, 10], [0, 0], [0, 0], linewidth=1.5, color="red")
+ax.text(10.5, 0, 0, "X", color="black", ha="center", va="center")
+
+plt.plot([0, 0], [0, 10], [0, 0], linewidth=1.5, color="green")
+ax.text(0, 10.5, 0, "Y", color="black", ha="center", va="center")
+
+plt.plot([0, 0], [0, 0], [0, 10], linewidth=1.5, color="blue")
+ax.text(0, 0, 10.5, "Z", color="black", ha="center", va="center")
+
+
+text = ax.text(0, 0, 0, "O", color="black", size="large", ha="center", va="center")
+text.set_path_effects(
+ [path_effects.Stroke(linewidth=3, foreground="white"), path_effects.Normal()]
+)
+
+
+rect = mpatches.Rectangle((0, 0), 7, 5, facecolor="k", alpha=0.03, zorder=50)
+ax.add_patch(rect)
+art3d.pathpatch_2d_to_3d(rect, z=6, zdir="z")
+
+rect = mpatches.Rectangle((0, 0), 7, 6, facecolor="k", alpha=0.06, zorder=50)
+ax.add_patch(rect)
+art3d.pathpatch_2d_to_3d(rect, z=5, zdir="y")
+
+rect = mpatches.Rectangle((0, 0), 5, 6, facecolor="k", alpha=0.09, zorder=50)
+ax.add_patch(rect)
+art3d.pathpatch_2d_to_3d(rect, z=7, zdir="x")
+
+
+class Arrow3D(mpatches.FancyArrowPatch):
+ def __init__(self, xs, ys, zs, *args, **kwargs):
+ mpatches.FancyArrowPatch.__init__(self, (0, 0), (0, 0), *args, **kwargs)
+ self._verts3d = xs, ys, zs
+
+ def draw(self, renderer):
+ xs3d, ys3d, zs3d = self._verts3d
+ xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)
+ self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))
+ mpatches.FancyArrowPatch.draw(self, renderer)
+
+
+ax.add_artist(
+ Arrow3D(
+ [7, 0],
+ [5, 5],
+ [6, 6],
+ mutation_scale=10,
+ lw=1.5,
+ arrowstyle="-|>",
+ color="black",
+ )
+)
+ax.add_artist(
+ Arrow3D(
+ [7, 7],
+ [5, 0],
+ [6, 6],
+ mutation_scale=10,
+ lw=1.5,
+ arrowstyle="-|>",
+ color="black",
+ )
+)
+ax.add_artist(
+ Arrow3D(
+ [7, 7],
+ [5, 5],
+ [6, 0],
+ mutation_scale=10,
+ lw=1.5,
+ arrowstyle="-|>",
+ color="black",
+ )
+)
+
+ax.scatter([7], [5], [6], s=50, facecolor="black", edgecolor="black", linewidth=1.5)
+
+ax.text(
+ 7.5, 5, 6, "(7.0, 5.0, 6.0)", color="black", size="small", ha="left", va="center"
+)
+
+ax.plot([7, 0], [5, 0], [6, 6], color="blue", linewidth=1.5, linestyle="--", marker="o")
+ax.text(0.5, 0.5, 6.5, "6", color="black", size="small", ha="left", va="bottom")
+
+ax.plot([7, 7], [5, 0], [6, 0], color="red", linewidth=1.5, linestyle="--", marker="o")
+ax.text(0, 5.5, 0, "7", color="black", size="small", ha="right", va="bottom")
+
+ax.plot(
+ [7, 0], [5, 5], [6, 0], color="green", linewidth=1.5, linestyle="--", marker="o"
+)
+ax.text(7.5, 0, 0, "5", color="black", size="small", ha="left", va="bottom")
+
+
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/projection-3d-frame.pdf")
+plt.show()
diff --git a/code/scales-projections/projection-polar-config.py b/code/scales-projections/projection-polar-config.py
new file mode 100644
index 0000000..46853fc
--- /dev/null
+++ b/code/scales-projections/projection-polar-config.py
@@ -0,0 +1,44 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def polar(ax, r0, rmin, rmax, rticks, tmin, tmax, tticks):
+ ax.set_yticks(np.linspace(rmin, rmax, rticks))
+ ax.set_yticklabels([])
+ ax.set_rorigin(r0)
+ ax.set_rmin(rmin)
+ ax.set_rmax(rmax)
+
+ ax.set_xticks(np.linspace(np.pi * tmin / 180, np.pi * tmax / 180, tticks))
+ ax.set_xticklabels([])
+ ax.set_thetamin(tmin)
+ ax.set_thetamax(tmax)
+
+ text = r"""$r_{0}=%.2f,r_{min}=%.2f,r_{max}=%.2f$""" % (r0, rmin, rmax)
+ text += "\n"
+ text += r"""$t_{min}=%.2f,t_{max}=%.2f$""" % (tmin, tmax)
+
+ plt.text(
+ 0.5, -0.15, text, size="small", ha="center", va="bottom", transform=ax.transAxes
+ )
+
+
+fig = plt.figure(figsize=(8, 4))
+
+ax = fig.add_subplot(1, 3, 1, aspect=1, projection="polar")
+polar(ax, 0.00, 0.00, 1.00, 4, 0, 360, 16)
+
+ax = fig.add_subplot(1, 3, 2, aspect=1, projection="polar")
+polar(ax, 0.00, 0.25, 1.00, 8, 0, 360, 32)
+
+ax = fig.add_subplot(1, 3, 3, aspect=1, projection="polar")
+polar(ax, 0.00, 0.50, 1.00, 10, 0, 90, 20)
+
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/projection-polar-config.pdf", dpi=600)
+plt.show()
diff --git a/code/scales-projections/projection-polar-histogram.py b/code/scales-projections/projection-polar-histogram.py
new file mode 100644
index 0000000..baa395d
--- /dev/null
+++ b/code/scales-projections/projection-polar-histogram.py
@@ -0,0 +1,137 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as path_effects
+
+# Setup
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, projection="polar", frameon=True)
+ax.set_thetalim(0, 2 * np.pi)
+ax.set_rlim(0, 1000)
+ax.set_xticks([])
+ax.set_xticklabels([])
+ax.set_yticks(np.linspace(100, 1000, 10))
+ax.set_yticklabels([])
+ax.tick_params("both", grid_alpha=0.50, grid_zorder=-10, grid_linewidth=0.5)
+
+
+# Theta ticks
+radius = ax.get_rmax()
+length = 0.025 * radius
+for i in range(360):
+ angle = np.pi * i / 180
+ plt.plot(
+ [angle, angle],
+ [radius, radius - length],
+ linewidth=0.50,
+ color="0.75",
+ clip_on=False,
+ )
+for i in range(0, 360, 5):
+ angle = np.pi * i / 180
+ plt.plot(
+ [angle, angle],
+ [radius, radius - 2 * length],
+ linewidth=0.75,
+ color="0.75",
+ clip_on=False,
+ )
+for i in range(0, 360, 15):
+ angle = np.pi * i / 180
+ plt.plot([angle, angle], [radius, 100], linewidth=0.5, color="0.75")
+ plt.plot(
+ [angle, angle],
+ [radius + length, radius],
+ zorder=500,
+ linewidth=1.0,
+ color="0.00",
+ clip_on=False,
+ )
+ plt.text(
+ angle,
+ radius + 4 * length,
+ "%d°" % i,
+ zorder=500,
+ rotation=i - 90,
+ rotation_mode="anchor",
+ va="top",
+ ha="center",
+ size="small",
+ family="Roboto",
+ color="black",
+ )
+for i in range(0, 360, 90):
+ angle = np.pi * i / 180
+ plt.plot([angle, angle], [radius, 0], zorder=500, linewidth=1.00, color="0.0")
+
+
+# Radius ticks
+def polar_to_cartesian(theta, radius):
+ x = radius * np.cos(theta)
+ y = radius * np.sin(theta)
+ return np.array([x, y])
+
+
+def cartesian_to_polar(x, y):
+ radius = np.sqrt(x ** 2 + y ** 2)
+ theta = np.arctan2(y, x)
+ return np.array([theta, radius])
+
+
+for i in range(0, 1000, 10):
+ P0 = 0, i
+ P1 = cartesian_to_polar(*(polar_to_cartesian(*P0) + [0, 0.75 * length]))
+ plt.plot([P0[0], P1[0]], [P0[1], P1[1]], linewidth=0.50, color="0.75")
+
+for i in range(100, 1000, 100):
+ P0 = 0, i
+ P1 = cartesian_to_polar(*(polar_to_cartesian(*P0) + [0, +1.0 * length]))
+ plt.plot([P0[0], P1[0]], [P0[1], P1[1]], zorder=500, linewidth=0.75, color="0.0")
+ P1 = cartesian_to_polar(*(polar_to_cartesian(*P0) + [0, -1.0 * length]))
+ text = ax.text(
+ P1[0],
+ P1[1],
+ "%d" % i,
+ zorder=500,
+ va="top",
+ ha="center",
+ size="x-small",
+ family="Roboto",
+ color="black",
+ )
+ text.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+ )
+
+# Circular bands
+n = 1000
+T = np.linspace(0, 2 * np.pi, n)
+color = "0.95"
+ax.fill_between(T, 0, 100, color=color, zorder=-50)
+ax.fill_between(T, 200, 300, color=color, zorder=-50)
+ax.fill_between(T, 400, 500, color=color, zorder=-50)
+ax.fill_between(T, 600, 700, color=color, zorder=-50)
+ax.fill_between(T, 800, 900, color=color, zorder=-50)
+plt.scatter([0], [0], 20, facecolor="white", edgecolor="black", zorder=1000)
+
+
+# Actual plot
+np.random.seed(123)
+n = 145
+T = 2 * np.pi / n + np.linspace(0, 2 * np.pi, n)
+T[1::2] = T[0:-1:2]
+R = np.random.uniform(400, 800, n)
+R[-1] = R[0]
+R[1:-1:2] = R[2::2]
+ax.fill(T, R, color="C1", zorder=150, alpha=0.1)
+ax.plot(T, R, color="white", zorder=200, linewidth=3.5)
+ax.plot(T, R, color="C1", zorder=250, linewidth=1.5)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/projection-polar-histogram.pdf")
+plt.show()
diff --git a/code/scales-projections/scales-check.py b/code/scales-projections/scales-check.py
new file mode 100644
index 0000000..ae67b72
--- /dev/null
+++ b/code/scales-projections/scales-check.py
@@ -0,0 +1,54 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter, SymmetricalLogLocator
+
+
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, aspect=1, xlim=[0, 100], ylim=[0, 100])
+P0, P1, P2, P3 = (0.1, 0.1), (1, 1), (10, 10), (100, 100)
+transform = ax.transData.transform
+print("Linear scale")
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P0[0], P1[0], abs((transform(P1) - transform(P0))[0])
+ )
+)
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P1[0], P2[0], abs((transform(P2) - transform(P1))[0])
+ )
+)
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P2[0], P3[0], abs((transform(P3) - transform(P2))[0])
+ )
+)
+print()
+
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, aspect=1, xlim=[0.1, 100], ylim=[0.1, 100])
+ax.set_xscale("log")
+transform = ax.transData.transform
+
+P0, P1, P2, P3 = (0.1, 0.1), (1, 1), (10, 10), (100, 100)
+print("Log scale")
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P0[0], P1[0], abs((transform(P1) - transform(P0))[0])
+ )
+)
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P1[0], P2[0], abs((transform(P2) - transform(P1))[0])
+ )
+)
+print(
+ " -> distance({0:5.1f},{1:5.1f}) = {2:.2f}".format(
+ P2[0], P3[0], abs((transform(P3) - transform(P2))[0])
+ )
+)
diff --git a/code/scales-projections/scales-comparison.py b/code/scales-projections/scales-comparison.py
new file mode 100644
index 0000000..cb1e66e
--- /dev/null
+++ b/code/scales-projections/scales-comparison.py
@@ -0,0 +1,92 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+
+# Style
+# -----------------------------------------------------------------------------
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+fig = plt.figure(figsize=(6, 2))
+
+# Linear axis
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 1, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "linear scale (default)",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+ax.tick_params(axis="both", which="major")
+
+
+# Log axis
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 2, xlim=[0.1, 1.0], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "log scale ('log')",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_xscale("log")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+# Logit axis
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 3, xlim=[0.1, 0.9], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "logit scale ('logit')",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_xscale("logit")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+# Show
+# -----------------------------------------------------------------------------
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/scales-comparison.pdf")
+plt.show()
diff --git a/code/scales-projections/scales-custom.py b/code/scales-projections/scales-custom.py
new file mode 100644
index 0000000..eba2ded
--- /dev/null
+++ b/code/scales-projections/scales-custom.py
@@ -0,0 +1,100 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+
+# Style
+# -----------------------------------------------------------------------------
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+fig = plt.figure(figsize=(6, 2))
+
+# Linear axis
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 1, xlim=[0.0, 1.0], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "linear scale",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+ax.tick_params(axis="both", which="major")
+
+
+def forward(x):
+ return x ** 2
+
+
+def inverse(x):
+ return x ** (1 / 2)
+
+
+# x**2 scale
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 2, xlim=[0.1, 1.0], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "$x^2$ scale",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_xscale("function", functions=(forward, inverse))
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+# sqrt(x)
+# -----------------------------------------------------------------------------
+ax = plt.subplot(3, 1, 3, xlim=[0.1, 1.0], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.text(
+ 0.5,
+ 0.1,
+ "$\sqrt{x}$ scale",
+ transform=ax.transAxes,
+ horizontalalignment="center",
+ verticalalignment="bottom",
+)
+ax.set_xscale("function", functions=(inverse, forward))
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.10))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.1f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.02))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+# Show
+# -----------------------------------------------------------------------------
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/scales-custom.pdf")
+plt.show()
diff --git a/code/scales-projections/scales-log-log.py b/code/scales-projections/scales-log-log.py
new file mode 100644
index 0000000..1097bf7
--- /dev/null
+++ b/code/scales-projections/scales-log-log.py
@@ -0,0 +1,88 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter, MultipleLocator
+
+X = np.linspace(0.001, 90, 5000)
+figure = plt.figure(figsize=(6, 6))
+
+# Style
+# -----------------------------------------------------------------------------
+plt.rc("font", family="Roboto")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+
+# X-linear Y-linear
+# -----------------------------------------------------------------------------
+ax1 = plt.subplot(2, 2, 1, xlim=(0.0, 10), ylim=(0.0, 10))
+ax1.plot(X, 10 ** X, color="C0")
+ax1.plot(X, X, color="C1")
+ax1.plot(X, np.log10(X), color="C2")
+ax1.set_ylabel("Linear")
+ax1.xaxis.set_major_locator(MultipleLocator(2.0))
+ax1.xaxis.set_minor_locator(MultipleLocator(0.4))
+ax1.yaxis.set_major_locator(MultipleLocator(2.0))
+ax1.yaxis.set_minor_locator(MultipleLocator(0.4))
+ax1.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ax1.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+ax1.tick_params(which="both", labelbottom=False, bottom=False)
+
+ax1.text(1.25, 8.50, "$f(x) = 10^x$", color="C0")
+ax1.text(5.75, 5.00, "$f(x) = x$", color="C1")
+ax1.text(5.50, 1.50, "$f(x) = log_{10}(x)$", color="C2")
+ax1.set_title("X linear - Y linear")
+
+
+# X-log Y-linear
+# -----------------------------------------------------------------------------
+ax2 = plt.subplot(2, 2, 2, xlim=(0.001, 100), ylim=(0.0, 10), sharey=ax1)
+ax2.set_xscale("log")
+ax2.tick_params(which="both", labelbottom=False, bottom=False)
+ax2.tick_params(which="both", labelleft=False, left=False)
+ax2.plot(X, 10 ** X, color="C0")
+ax2.plot(X, X, color="C1")
+ax2.plot(X, np.log10(X), color="C2")
+ax2.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ax2.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+ax2.set_title("X logarithmic - Y linear")
+
+
+# X-linear Y-log
+# -----------------------------------------------------------------------------
+ax3 = plt.subplot(2, 2, 3, xlim=(0.0, 10), ylim=(0.001, 100), sharex=ax1)
+ax3.set_yscale("log")
+ax3.plot(X, 10 ** X, color="C0")
+ax3.plot(X, X, color="C1")
+ax3.plot(X, np.log10(X), color="C2")
+ax3.set_ylabel("Logarithmic")
+ax3.set_xlabel("Linear")
+ax3.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ax3.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+ax3.set_title("X linear - Y logarithmic")
+
+# X-log Y-log
+# -----------------------------------------------------------------------------
+ax4 = plt.subplot(2, 2, 4, xlim=(0.001, 100), ylim=(0.001, 100), sharex=ax2, sharey=ax3)
+ax4.set_xscale("log")
+ax4.set_yscale("log")
+ax4.tick_params(which="both", labelleft=False, left=False)
+ax4.plot(X, 10 ** X, color="C0")
+ax4.plot(X, X, color="C1")
+ax4.plot(X, np.log10(X), color="C2")
+ax4.set_xlabel("Logarithmic")
+ax4.grid(True, "minor", color="0.85", linewidth=0.50, zorder=-20)
+ax4.grid(True, "major", color="0.65", linewidth=0.75, zorder=-10)
+ax4.set_title("X logarithmic - Y logarithmic")
+
+
+# Show
+# -----------------------------------------------------------------------------
+plt.tight_layout()
+plt.savefig("../../figures/scales-projections/scales-log-log.pdf")
+plt.show()
diff --git a/code/scales-projections/text-polar.py b/code/scales-projections/text-polar.py
new file mode 100644
index 0000000..13610a3
--- /dev/null
+++ b/code/scales-projections/text-polar.py
@@ -0,0 +1,111 @@
+# -----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# -----------------------------------------------------------------------------
+# Illustrate the use of polar projection and rotated text (with transformation)
+# -----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.patches import Ellipse
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1)
+
+
+size = 0.1
+vals = np.ones(12)
+np.random.seed(123)
+
+# A nice set of colors for seasons
+cmap20c = plt.get_cmap("tab20c")
+cmap20b = plt.get_cmap("tab20b")
+colors = [
+ cmap20c(0),
+ cmap20c(1),
+ cmap20c(2), # Winter
+ cmap20c(10),
+ cmap20c(9),
+ cmap20c(8), # Spring
+ cmap20c(4),
+ cmap20c(5),
+ cmap20c(6), # Summer
+ cmap20b(15),
+ cmap20b(14),
+ cmap20b(13),
+] # Autumn
+
+# Simple pie
+ax.pie(np.ones(12), radius=1, colors=colors, wedgeprops=dict(width=size, edgecolor="w"))
+
+
+# Rotated and transformed label
+def label(text, angle, radius=1, scale=0.005):
+ path = TextPath((0, 0), text, size=10)
+ V = path.vertices
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ ymin, ymax = V[:, 1].min(), V[:, 1].max()
+ V -= (xmin + xmax) / 2, (ymin + ymax) / 2
+ V *= scale
+ for i in range(len(V)):
+ a = angle - V[i, 0]
+ V[i, 0] = (radius + V[i, 1]) * np.cos(a)
+ V[i, 1] = (radius + V[i, 1]) * np.sin(a)
+ patch = PathPatch(path, facecolor="k", linewidth=0)
+ ax.add_artist(patch)
+
+
+# This could be made through a list but it is easier to red this way
+label("JANUARY", 0.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("FEBRUARY", 1.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("MARCH", 2.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("WINTER", 1.5 * 2 * np.pi / 12, 1 + size, 0.0125)
+
+label("APRIL", 3.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("MAY", 4.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("JUNE", 5.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("SPRING", 4.5 * 2 * np.pi / 12, 1 + size, 0.0125)
+
+label("JULY", 6.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("AUGUST", 7.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("SEPTEMBER", 8.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("SUMMER", 7.5 * 2 * np.pi / 12, 1 + size, 0.0125)
+
+label("OCTOBER", 9.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("NOVEMBER", 10.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("DECEMBER", 11.5 * 2 * np.pi / 12, 1 - 0.5 * size)
+label("AUTUMN", 10.5 * 2 * np.pi / 12, 1 + size, 0.0125)
+
+
+# Add a polar projection on top of the previous one
+ax = fig.add_axes([0.15, 0.15, 0.7, 0.7], projection="polar")
+
+# Some ellipses that will enforce polar projection
+for i in range(250):
+ p = np.random.uniform(0, 2 * np.pi), np.random.uniform(0.05, 0.95)
+ w = h = 0.01 + 0.05 * np.random.uniform(1, 2)
+ color = colors[int(np.floor((p[0] / (2 * np.pi)) * 12))]
+ ellipse = Ellipse(
+ p,
+ width=2 * w,
+ height=h,
+ zorder=10,
+ facecolor=color,
+ edgecolor="none",
+ alpha=0.5,
+ )
+ ax.add_artist(ellipse)
+ax.set_xlim(0, 2 * np.pi)
+ax.set_xticks(np.linspace(0, 2 * np.pi, 12, endpoint=False))
+ax.set_xticklabels([])
+ax.set_ylim(0, 1)
+ax.set_yticks(np.linspace(0, 1, 6))
+ax.set_yticklabels([])
+ax.set_rorigin(-0.25)
+
+
+# Save results and show it
+plt.savefig("../../figures/scales-projections/text-polar.pdf")
+plt.show()
diff --git a/code/showcases/contour-dropshadow.py b/code/showcases/contour-dropshadow.py
new file mode 100644
index 0000000..00f22f0
--- /dev/null
+++ b/code/showcases/contour-dropshadow.py
@@ -0,0 +1,96 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Contour with drop shadows
+# ----------------------------------------------------------------------------
+import scipy
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from matplotlib.backends.backend_agg import FigureCanvas
+from matplotlib.patheffects import Stroke, Normal
+from scipy.ndimage import gaussian_filter
+
+# Some data
+n = 64
+X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+)
+Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+
+def f(x, y):
+ return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-(x ** 2) - y ** 2)
+
+
+x = np.linspace(-3, 3, 100)
+y = np.linspace(-3, 3, 100)
+X, Y = np.meshgrid(x, y)
+Z = 0.5 * f(X, Y)
+
+n = 11 # Number of levels
+dz = (Z.max() - Z.min()) / n
+levels = np.linspace(Z.min(), Z.max(), n, endpoint=True)
+cmap = plt.get_cmap("viridis")
+
+
+def drop_shadow(Z, l0, l1, sigma=5, alpha=0.5):
+ """Compute the drop shadow for a contour between l0 and l1.
+
+ This works by first:
+ 1. render the contour in black using an offline image
+ 2. blue the contour using a Guassian filter
+ 3. set the alpha channel accordingly
+ """
+ fig = Figure(figsize=(5, 5))
+ canvas = FigureCanvas(fig)
+ ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+ ax.contourf(
+ Z,
+ vmin=Z.min(),
+ vmax=Z.max(),
+ levels=[l0, l1],
+ origin="lower",
+ colors="black",
+ extent=[-1, 1, -1, 1],
+ )
+ ax.set_xlim(-1, 1), ax.set_ylim(-1, 1)
+ canvas.draw()
+ A = np.array(canvas.renderer.buffer_rgba())[:, :, 0]
+ del fig
+ A = gaussian_filter(A, sigma)
+ A = (A - A.min()) / (A.max() - A.min())
+ I = np.zeros((A.shape[0], A.shape[1], 4))
+ I[:, :, 3] = (1 - A) * alpha
+ return I
+
+
+fig = plt.figure(figsize=(8, 8), dpi=100)
+ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+
+zorder = -100
+for i in range(len(levels) - 1):
+ l0, l1 = levels[i], levels[i + 1]
+ I = drop_shadow(Z, l0, l1)
+ ax.imshow(I, extent=[-3, 3, -3, 3], origin="upper", zorder=zorder)
+ zorder += 1
+ ax.contourf(
+ Z,
+ vmin=Z.min(),
+ vmax=Z.max(),
+ levels=[l0, l1],
+ origin="lower",
+ cmap=cmap,
+ extent=[-3, 3, -3, 3],
+ zorder=zorder,
+ )
+ zorder += 1
+ax.set_xlim(-2.9, 2.9)
+ax.set_ylim(-2.9, 2.9)
+
+plt.savefig("../../figures/showcases/contour-dropshadow.png", dpi=600)
+# plt.savefig("../figures/contour-dropshadow.pdf", dpi=300)
+plt.show()
diff --git a/code/showcases/domain-coloring.py b/code/showcases/domain-coloring.py
new file mode 100644
index 0000000..782f87a
--- /dev/null
+++ b/code/showcases/domain-coloring.py
@@ -0,0 +1,43 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patheffects import Stroke, Normal
+
+
+def normalize(Z):
+ zmin, zmax = Z.min(), Z.max()
+ return (Z - zmin) / (zmax - zmin)
+
+
+T = np.linspace(-2.5, 2.5, 2048)
+X, Y = np.meshgrid(T, T)
+Z = X + 1j * Y
+Z = Z + 1 / Z
+A = normalize(np.angle(Z))
+N = normalize(np.abs(Z)) * 2 * np.pi * 200
+
+
+fig = plt.figure(figsize=(8, 8))
+e = 0.001
+ax = fig.add_axes([e, e, 1 - 2 * e, 1 - 2 * e], frameon=True, facecolor="black")
+
+ax.imshow(
+ A,
+ interpolation="bicubic",
+ cmap="Spectral",
+ rasterized=True,
+ alpha=1 - (N < 1.5 * np.pi) * 0.25 * abs(np.cos(N % (np.pi / 2))) ** 2,
+)
+ax.contour(np.abs(Z.real - np.round(Z.real)), 1, colors="black", linewidths=0.25)
+ax.contour(np.abs(Z.imag - np.round(Z.imag)), 1, colors="black", linewidths=0.25)
+
+
+ax.set_xticks([])
+ax.set_yticks([])
+plt.savefig("../../figures/showcases/domain-coloring.png", dpi=600)
+plt.savefig("../../figures/showcases/domain-coloring.pdf")
+plt.show()
diff --git a/code/showcases/escher-movie.py b/code/showcases/escher-movie.py
new file mode 100644
index 0000000..c33f3a6
--- /dev/null
+++ b/code/showcases/escher-movie.py
@@ -0,0 +1,72 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+
+
+def line(A, B, thickness=0.005, n=100):
+ T = (B - A) / np.linalg.norm(B - A)
+ O = np.array([-T[1], T[0]])
+ P0 = A + thickness * O
+ P1 = B + thickness * O
+ P2 = B - thickness * O
+ P3 = A - thickness * O
+ P = np.concatenate(
+ [
+ np.linspace(P0, P1, n),
+ np.linspace(P1, P2, n // 10),
+ np.linspace(P2, P3, n),
+ np.linspace(P3, P0, n // 10),
+ ]
+ )
+ X, Y = P[:, 0], P[:, 1]
+ return np.dstack([np.exp(X) * np.cos(Y), np.exp(X) * np.sin(Y)]).squeeze()
+
+
+n = 250
+X = np.exp(-5 * np.linspace(-1, 1, n, endpoint=True) ** 2)
+A = X * 7 + (1 - X) * 13
+B = X * 3 + (1 - X) * 11
+
+for k, (a, b) in enumerate(zip(A, B)):
+ y = 2 * np.pi / (a + b * b / a)
+ x = b * y / a
+ v1, v2 = np.array([x, y]), np.array([-y, x])
+
+ fig = plt.figure(figsize=(8, 8))
+ ax = plt.subplot(1, 1, 1, aspect=1)
+
+ for i, z in enumerate(np.linspace(0, 1, 3 * 10, endpoint=False)):
+ zorder = 0
+ color = "0.5"
+ thickness = 0.001
+ if i % 10 == 0:
+ thickness = 0.0025
+ color = "0.0"
+ zorder = 10
+ P0, P1 = (2 + z) * b * v2, (2 + z) * b * v2 + 4 * a * v1
+ L = line(P0, P1, thickness=thickness, n=500)
+ ax.add_patch(Polygon(L, facecolor=color, zorder=zorder))
+
+ for i, z in enumerate(np.linspace(0, 1, 7 * 10, endpoint=False)):
+ zorder = 0
+ color = "0.5"
+ thickness = 0.001
+ if i % 10 == 0:
+ thickness = 0.0025
+ color = "0.0"
+ zorder = 10
+
+ P0, P1 = (1 + z) * a * v1, (1 + z) * a * v1 + 4 * b * v2
+ L = line(P0, P1, thickness=thickness, n=500)
+ ax.add_patch(Polygon(L, facecolor=color, zorder=zorder))
+
+ ax.set_xlim(-np.pi, np.pi), ax.set_xticks([])
+ ax.set_ylim(-np.pi, np.pi), ax.set_yticks([])
+ plt.tight_layout()
+ # plt.show()
+ plt.savefig("escher-frame-%03d.png" % k, dpi=100)
+ print("Frame escher-frame-%03d.png" % k)
+ del fig
+
+# Encode with (slow):
+# ffmpeg -i escher-frame-%03d.png -framerate 30 -vcodec libvpx-vp9 -b:v 1M output.webm
diff --git a/code/showcases/escher.py b/code/showcases/escher.py
new file mode 100644
index 0000000..f4966e6
--- /dev/null
+++ b/code/showcases/escher.py
@@ -0,0 +1,68 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+
+
+def line(A, B, thickness=0.005, n=100):
+ T = (B - A) / np.linalg.norm(B - A)
+ O = np.array([-T[1], T[0]])
+ P0 = A + thickness * O
+ P1 = B + thickness * O
+ P2 = B - thickness * O
+ P3 = A - thickness * O
+ P = np.concatenate(
+ [
+ np.linspace(P0, P1, n),
+ np.linspace(P1, P2, n // 10),
+ np.linspace(P2, P3, n),
+ np.linspace(P3, P0, n // 10),
+ ]
+ )
+ X, Y = P[:, 0], P[:, 1]
+ return np.dstack([np.exp(X) * np.cos(Y), np.exp(X) * np.sin(Y)]).squeeze()
+
+
+a, b = 11, 9
+y = 2 * np.pi / (a + b * b / a)
+x = b * y / a
+v1, v2 = np.array([x, y]), np.array([-y, x])
+
+
+fig = plt.figure(figsize=(8, 8))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, frameon=False)
+
+for i, z in enumerate(np.linspace(0, 1, b * 10, endpoint=False)):
+ zorder = 0
+ color = "0.5"
+ thickness = 0.001
+ if i % 10 == 0:
+ thickness = 0.0025
+ color = "0.0"
+ zorder = 10
+ P0, P1 = (2 + z) * b * v2, (2 + z) * b * v2 + 4 * a * v1
+ L = line(P0, P1, thickness=thickness, n=500)
+ ax.add_patch(Polygon(L, facecolor=color, zorder=zorder))
+
+for i, z in enumerate(np.linspace(0, 1, a * 10, endpoint=False)):
+ zorder = 0
+ color = "0.5"
+ thickness = 0.001
+ if i % 10 == 0:
+ thickness = 0.0025
+ color = "0.0"
+ zorder = 10
+
+ P0, P1 = (1 + z) * a * v1, (1 + z) * a * v1 + 4 * b * v2
+ L = line(P0, P1, thickness=thickness, n=500)
+ ax.add_patch(Polygon(L, facecolor=color, zorder=zorder))
+
+
+ax.set_xlim(-np.pi, np.pi), ax.set_xticks([])
+ax.set_ylim(-np.pi, np.pi), ax.set_yticks([])
+plt.savefig("../../figures/showcases/escher.pdf")
+plt.show()
diff --git a/code/showcases/mandelbrot.py b/code/showcases/mandelbrot.py
new file mode 100644
index 0000000..d24d4a9
--- /dev/null
+++ b/code/showcases/mandelbrot.py
@@ -0,0 +1,52 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+from matplotlib import colors
+import matplotlib.pyplot as plt
+
+
+def mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon=2.0):
+ X = np.linspace(xmin, xmax, xn, dtype=np.float32)
+ Y = np.linspace(ymin, ymax, yn, dtype=np.float32)
+ C = X + Y[:, None] * 1j
+ N = np.zeros(C.shape, dtype=int)
+ Z = np.zeros(C.shape, np.complex64)
+ for n in range(maxiter):
+ I = np.less(abs(Z), horizon)
+ N[I] = n
+ Z[I] = Z[I] ** 2 + C[I]
+ N[N == maxiter - 1] = 0
+ return Z, N
+
+
+xmin, xmax, xn = -2.75, +1.25, 4000
+ymin, ymax, yn = -1.5, +1.5, 3000
+maxiter = 200
+horizon = 2.0 ** 40
+log_horizon = np.log(np.log(horizon)) / np.log(2)
+Z, N = mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon)
+
+# Normalized recount as explained in:
+# https://linas.org/art-gallery/escape/smooth.html
+# https://www.ibm.com/developerworks/community/blogs/jfp/entry/My_Christmas_Gift
+with np.errstate(invalid="ignore"):
+ M = np.nan_to_num(N + 1 - np.log(np.log(abs(Z))) / np.log(2) + log_horizon)
+
+width = 10
+height = 10 * yn / xn
+fig = plt.figure(figsize=(width, height), dpi=100)
+ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect=1)
+
+# Shaded rendering
+light = colors.LightSource(azdeg=315, altdeg=10)
+M = light.shade(
+ M, cmap=plt.cm.hot, vert_exag=1.5, norm=colors.PowerNorm(0.3), blend_mode="hsv"
+)
+plt.imshow(M, extent=[xmin, xmax, ymin, ymax], interpolation="bicubic")
+ax.set_xticks([])
+ax.set_yticks([])
+plt.savefig("mandelbrot.png", dpi=600)
+plt.show()
diff --git a/code/showcases/mosaic.py b/code/showcases/mosaic.py
new file mode 100644
index 0000000..d1149cc
--- /dev/null
+++ b/code/showcases/mosaic.py
@@ -0,0 +1,127 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.spatial import Voronoi
+from math import sqrt, ceil, floor, pi, cos, sin
+from matplotlib.collections import PolyCollection
+
+
+def blue_noise(shape, radius, k=30, seed=None):
+ """
+ Generate blue noise over a two-dimensional rectangle of size (width,height)
+
+ Parameters
+ ----------
+
+ shape : tuple
+ Two-dimensional domain (width x height)
+ radius : float
+ Minimum distance between samples
+ k : int, optional
+ Limit of samples to choose before rejection (typically k = 30)
+ seed : int, optional
+ If provided, this will set the random seed before generating noise,
+ for valid pseudo-random comparisons.
+
+ References
+ ----------
+
+ .. [1] Fast Poisson Disk Sampling in Arbitrary Dimensions, Robert Bridson,
+ Siggraph, 2007. :DOI:`10.1145/1278780.1278807`
+ """
+
+ def sqdist(a, b):
+ """ Squared Euclidean distance """
+ dx, dy = a[0] - b[0], a[1] - b[1]
+ return dx * dx + dy * dy
+
+ def grid_coords(p):
+ """ Return index of cell grid corresponding to p """
+ return int(floor(p[0] / cellsize)), int(floor(p[1] / cellsize))
+
+ def fits(p, radius):
+ """ Check whether p can be added to the queue """
+
+ radius2 = radius * radius
+ gx, gy = grid_coords(p)
+ for x in range(max(gx - 2, 0), min(gx + 3, grid_width)):
+ for y in range(max(gy - 2, 0), min(gy + 3, grid_height)):
+ g = grid[x + y * grid_width]
+ if g is None:
+ continue
+ if sqdist(p, g) <= radius2:
+ return False
+ return True
+
+ # When given a seed, we use a private random generator in order to not
+ # disturb the default global random generator
+ if seed is not None:
+ from numpy.random.mtrand import RandomState
+
+ rng = RandomState(seed=seed)
+ else:
+ rng = np.random
+
+ width, height = shape
+ cellsize = radius / sqrt(2)
+ grid_width = int(ceil(width / cellsize))
+ grid_height = int(ceil(height / cellsize))
+ grid = [None] * (grid_width * grid_height)
+
+ p = rng.uniform(0, shape, 2)
+ queue = [p]
+ grid_x, grid_y = grid_coords(p)
+ grid[grid_x + grid_y * grid_width] = p
+
+ while queue:
+ qi = rng.randint(len(queue))
+ qx, qy = queue[qi]
+ queue[qi] = queue[-1]
+ queue.pop()
+ for _ in range(k):
+ theta = rng.uniform(0, 2 * pi)
+ r = radius * np.sqrt(rng.uniform(1, 4))
+ p = qx + r * cos(theta), qy + r * sin(theta)
+ if not (0 <= p[0] < width and 0 <= p[1] < height) or not fits(p, radius):
+ continue
+ queue.append(p)
+ gx, gy = grid_coords(p)
+ grid[gx + gy * grid_width] = p
+
+ return np.array([p for p in grid if p is not None])
+
+
+I = plt.imread("../data/poppy.png")
+P = blue_noise((1, 1), radius=0.005)
+P = np.append(P, [[+999, +999], [-999, +999], [+999, -999], [-999, -999]], axis=0)
+voronoi = Voronoi(P)
+
+
+polys, colors = [], []
+for i, region in enumerate(voronoi.regions):
+ if -1 in region:
+ continue
+ poly = [list(voronoi.vertices[i]) for i in region]
+ if len(poly) <= 0:
+ continue
+ polys.append(poly)
+ index = np.where(voronoi.point_region == i)[0][0]
+ row, col = int((1 - P[index, 1]) * I.shape[1]), int(P[index, 0] * I.shape[0])
+ colors.append(I[row, col])
+colors = np.array(colors)
+collection = PolyCollection(
+ polys, linewidth=0.5, facecolor=colors, edgecolors=colors * 0.5
+)
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[0, 1], ylim=[0, 1], aspect=1)
+ax.axis("off")
+
+ax.add_collection(collection)
+
+plt.savefig("../../figures/showcases/mosaic.pdf")
+plt.show()
diff --git a/code/showcases/recursive-voronoi.py b/code/showcases/recursive-voronoi.py
new file mode 100644
index 0000000..786c064
--- /dev/null
+++ b/code/showcases/recursive-voronoi.py
@@ -0,0 +1,284 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import scipy.spatial
+import matplotlib.pyplot as plt
+import matplotlib.path as mpath
+from shapely.geometry import Polygon
+from matplotlib.collections import PolyCollection
+from math import sqrt, ceil, floor, pi, cos, sin
+
+
+def blue_noise(shape, radius, k=30, seed=None):
+ """
+ Generate blue noise over a two-dimensional rectangle of size (width,height)
+
+ Parameters
+ ----------
+
+ shape : tuple
+ Two-dimensional domain (width x height)
+ radius : float
+ Minimum distance between samples
+ k : int, optional
+ Limit of samples to choose before rejection (typically k = 30)
+ seed : int, optional
+ If provided, this will set the random seed before generating noise,
+ for valid pseudo-random comparisons.
+
+ References
+ ----------
+
+ .. [1] Fast Poisson Disk Sampling in Arbitrary Dimensions, Robert Bridson,
+ Siggraph, 2007. :DOI:`10.1145/1278780.1278807`
+ """
+
+ def sqdist(a, b):
+ """ Squared Euclidean distance """
+ dx, dy = a[0] - b[0], a[1] - b[1]
+ return dx * dx + dy * dy
+
+ def grid_coords(p):
+ """ Return index of cell grid corresponding to p """
+ return int(floor(p[0] / cellsize)), int(floor(p[1] / cellsize))
+
+ def fits(p, radius):
+ """ Check whether p can be added to the queue """
+
+ radius2 = radius * radius
+ gx, gy = grid_coords(p)
+ for x in range(max(gx - 2, 0), min(gx + 3, grid_width)):
+ for y in range(max(gy - 2, 0), min(gy + 3, grid_height)):
+ g = grid[x + y * grid_width]
+ if g is None:
+ continue
+ if sqdist(p, g) <= radius2:
+ return False
+ return True
+
+ # When given a seed, we use a private random generator in order to not
+ # disturb the default global random generator
+ if seed is not None:
+ from numpy.random.mtrand import RandomState
+
+ rng = RandomState(seed=seed)
+ else:
+ rng = np.random
+
+ width, height = shape
+ cellsize = radius / sqrt(2)
+ grid_width = int(ceil(width / cellsize))
+ grid_height = int(ceil(height / cellsize))
+ grid = [None] * (grid_width * grid_height)
+
+ p = rng.uniform(0, shape, 2)
+ queue = [p]
+ grid_x, grid_y = grid_coords(p)
+ grid[grid_x + grid_y * grid_width] = p
+
+ while queue:
+ qi = rng.randint(len(queue))
+ qx, qy = queue[qi]
+ queue[qi] = queue[-1]
+ queue.pop()
+ for _ in range(k):
+ theta = rng.uniform(0, 2 * pi)
+ r = radius * np.sqrt(rng.uniform(1, 4))
+ p = qx + r * cos(theta), qy + r * sin(theta)
+ if not (0 <= p[0] < width and 0 <= p[1] < height) or not fits(p, radius):
+ continue
+ queue.append(p)
+ gx, gy = grid_coords(p)
+ grid[gx + gy * grid_width] = p
+
+ return np.array([p for p in grid if p is not None])
+
+
+def bounded_voronoi(points):
+ """
+ Reconstruct infinite voronoi regions in a 2D diagram to finite regions.
+
+ Parameters
+ ----------
+ vor : Voronoi
+ Input diagram
+
+ Returns
+ -------
+ regions : list of tuples
+ Indices of vertices in each revised Voronoi regions.
+ vertices : list of tuples
+ Coordinates for revised Voronoi vertices. Same as coordinates
+ of input vertices, with 'points at infinity' appended to the
+ end.
+ """
+
+ vor = scipy.spatial.Voronoi(points)
+ new_regions = []
+ new_vertices = vor.vertices.tolist()
+ center = vor.points.mean(axis=0)
+ radius = vor.points.ptp().max() * 2
+
+ # Construct a map containing all ridges for a given point
+ all_ridges = {}
+ for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):
+ all_ridges.setdefault(p1, []).append((p2, v1, v2))
+ all_ridges.setdefault(p2, []).append((p1, v1, v2))
+
+ # Reconstruct infinite regions
+ for p1, region in enumerate(vor.point_region):
+ vertices = vor.regions[region]
+
+ if all(v >= 0 for v in vertices):
+ # finite region
+ new_regions.append(vertices)
+ continue
+
+ # reconstruct a non-finite region
+ ridges = all_ridges[p1]
+ new_region = [v for v in vertices if v >= 0]
+
+ for p2, v1, v2 in ridges:
+ if v2 < 0:
+ v1, v2 = v2, v1
+ if v1 >= 0:
+ # finite ridge: already in the region
+ continue
+
+ # Compute the missing endpoint of an infinite ridge
+ t = vor.points[p2] - vor.points[p1] # tangent
+ t /= np.linalg.norm(t)
+ n = np.array([-t[1], t[0]]) # normal
+
+ midpoint = vor.points[[p1, p2]].mean(axis=0)
+ direction = np.sign(np.dot(midpoint - center, n)) * n
+ far_point = vor.vertices[v2] + direction * radius
+
+ new_region.append(len(new_vertices))
+ new_vertices.append(far_point.tolist())
+
+ # sort region counterclockwise
+ vs = np.asarray([new_vertices[v] for v in new_region])
+ c = vs.mean(axis=0)
+ angles = np.arctan2(vs[:, 1] - c[1], vs[:, 0] - c[0])
+ new_region = np.array(new_region)[np.argsort(angles)]
+
+ # finish
+ new_regions.append(new_region.tolist())
+ return new_regions, np.asarray(new_vertices)
+
+
+def poly_random_points_safe(V, n=10):
+ def random_point_inside_triangle(A, B, C):
+ r1 = np.sqrt(np.random.uniform(0, 1))
+ r2 = np.random.uniform(0, 1)
+ return (1 - r1) * A + r1 * (1 - r2) * B + r1 * r2 * C
+
+ def triangle_area(A, B, C):
+ return 0.5 * np.abs(
+ (B[0] - A[0]) * (C[1] - A[1]) - (C[0] - A[0]) * (B[1] - A[1])
+ )
+
+ C = V.mean(axis=0)
+ T = [(C, V[i], V[i + 1]) for i in range(len(V) - 1)]
+ A = np.array([triangle_area(*t) for t in T])
+ A /= A.sum()
+
+ points = [C]
+ for i in np.random.choice(len(A), size=n - 1, p=A):
+ points.append(random_point_inside_triangle(*T[i]))
+ return points
+
+
+def poly_random_points(V, n=10):
+ path = mpath.Path(V)
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ ymin, ymax = V[:, 1].min(), V[:, 1].max()
+ xscale, yscale = xmax - xmin, ymax - ymin
+ if xscale > yscale:
+ xscale, yscale = 1, yscale / xscale
+ else:
+ xscale, yscale = xscale / yscale, 1
+ radius = 0.85 * np.sqrt(2 * xscale * yscale / (n * np.pi))
+ points = blue_noise((xscale, yscale), radius)
+ points = [xmin, ymin] + points * [xmax - xmin, ymax - ymin]
+ inside = path.contains_points(points)
+
+ P = points[inside]
+ if len(P) < 5:
+ return poly_random_points_safe(V, n)
+ np.random.shuffle(P)
+ return P[:n]
+
+
+def voronoi(V, npoints, level, maxlevel, color=None):
+ if level == maxlevel:
+ return []
+
+ linewidths = [1.50, 1.00, 0.75, 0.50, 0.25, 0.10]
+ edgecolors = [
+ (0, 0, 0, 1.00),
+ (0, 0, 0, 0.50),
+ (0, 0, 0, 0.25),
+ (0, 0, 0, 0.10),
+ (0, 0, 0, 0.10),
+ (0, 0, 0, 0.10),
+ ]
+
+ if level == 1:
+ color = np.random.uniform(0, 1, 4)
+ color[3] = 0.5
+
+ points = poly_random_points(V, npoints - level)
+ regions, vertices = bounded_voronoi(points)
+ clip = Polygon(V)
+ cells = []
+ for region in regions:
+ polygon = Polygon(vertices[region]).intersection(clip)
+ polygon = np.array([point for point in polygon.exterior.coords])
+ linewidth = linewidths[level]
+ edgecolor = edgecolors[level]
+ facecolor = "none"
+ if level > 1:
+ alpha = color[3] + (1 / (level + 1)) * 0.25 * np.random.uniform(-1, 0.5)
+ color = color[0], color[1], color[2], min(max(alpha, 0.1), 1)
+ if level == maxlevel - 1:
+ facecolor = color
+ zorder = -level
+ cells.append((polygon, linewidth, edgecolor, facecolor, zorder))
+ cells.extend(voronoi(polygon, npoints, level + 1, maxlevel, color))
+ return cells
+
+
+np.random.seed(12345)
+T = np.linspace(0, 2 * np.pi, 100, endpoint=False)
+R = 100
+X, Y = R * np.cos(T), R * np.sin(T)
+V = np.c_[X, Y]
+
+V = 100 * np.array([[-1, -1], [-1, 1], [1, 1], [1, -1]])
+
+fig = plt.figure(figsize=(8, 8))
+d = R - 1
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, xlim=[-d, d], ylim=[-d, d])
+ax.axis("off")
+
+cells = voronoi(V, 11, level=0, maxlevel=5)
+zorder = [cell[-1] for cell in cells]
+cells = [cells[i] for i in np.argsort(zorder)]
+polygons = [cell[0] for cell in cells]
+linewidths = [cell[1] for cell in cells]
+edgecolors = [cell[2] for cell in cells]
+facecolors = [cell[3] for cell in cells]
+
+collection = PolyCollection(
+ polygons, linewidth=linewidths, edgecolor=edgecolors, facecolor=facecolors
+)
+ax.add_collection(collection)
+
+plt.savefig("../../figures/showcases/recursive-voronoi.pdf")
+plt.savefig("../../figures/showcases/recursive-voronoi.png", dpi=600)
+plt.show()
diff --git a/code/showcases/text-shadow.py b/code/showcases/text-shadow.py
new file mode 100644
index 0000000..cf316fe
--- /dev/null
+++ b/code/showcases/text-shadow.py
@@ -0,0 +1,87 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.collections import PolyCollection
+from matplotlib.font_manager import FontProperties
+
+red = np.array([233, 77, 85, 255]) / 255
+darkred = np.array([130, 60, 71, 255]) / 255
+prop = FontProperties(family="Source Sans Pro", weight=100)
+
+fig = plt.figure(figsize=(14.8 / 2.54, 21 / 2.54))
+
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, xlim=[-10, 10], ylim=[-14.2, 14.2])
+ax.axis("off")
+
+# Text path
+path = TextPath((0, 0), "MATPLOTLIB", size=2.5, prop=prop)
+
+# Text centering
+V = path.vertices
+xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ymin, ymax = V[:, 1].min(), V[:, 1].max()
+V -= (xmin + xmax) / 2 + 1, (ymin + ymax) / 2
+
+
+# Compute shadow by iterating over text path segments
+polys = []
+for (point, code) in path.iter_segments(curves=False):
+ if code == path.MOVETO:
+ points = [point]
+ elif code == path.LINETO:
+ points.append(point)
+ elif code == path.CLOSEPOLY:
+ points.append(points[0])
+ points = np.array(points)
+ for i in range(len(points) - 1):
+ p0, p1 = points[i], points[i + 1]
+ polys.append([p0, p1, p1 + (+20, -20), p0 + (+20, -20)])
+
+# Display shadow
+collection = PolyCollection(
+ polys, closed=True, linewidth=0.0, facecolor=darkred, zorder=-10
+)
+ax.add_collection(collection)
+
+# Display text
+patch = PathPatch(path, facecolor="white", edgecolor="none", zorder=10)
+ax.add_artist(patch)
+
+# Transparent gradient to fade out shadow
+I = np.zeros((200, 1, 4)) + red
+ax.imshow(I, extent=[-11, 11, -15, 15], zorder=-20, clip_on=False)
+I[:, 0, 3] = np.linspace(0, 1, len(I))
+ax.imshow(I, extent=[-11, 11, -15, 15], zorder=0, clip_on=False)
+
+
+ax.text(
+ 6.5,
+ -1.75,
+ "a versatile scientific visualization library ",
+ color="white",
+ ha="right",
+ va="baseline",
+ size=10,
+ family="Pacifico",
+ zorder=30,
+)
+
+# Save and show result
+plt.savefig("../../figures/showcases/text-shadow.pdf")
+plt.savefig("../../figures/showcases/text-shadow.png", dpi=600)
+plt.show()
+
+
+# Bad alternative (slow and memory hungry)
+# ----------------------------------------
+# ax.text(0, 0, "MATPLOTLIB", color="white", ha="center", va="center",
+# size=48, family="Source Sans Pro", weight=100, zorder=10)
+# for x in np.linspace(0,7,500):
+# ax.text(x, -x, "MATPLOTLIB", color="darkred", ha="center", va="center",
+# size=48, family="Source Sans Pro", weight=100, zorder=-10)
diff --git a/code/showcases/text-spiral.py b/code/showcases/text-spiral.py
new file mode 100644
index 0000000..ed30b00
--- /dev/null
+++ b/code/showcases/text-spiral.py
@@ -0,0 +1,60 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import PathPatch
+from matplotlib.textpath import TextPath
+from matplotlib.collections import PolyCollection
+from matplotlib.font_manager import FontProperties
+
+n = 100
+A = np.linspace(np.pi, n * 2 * np.pi, 10_000)
+R = 5 + np.linspace(np.pi, n * 2 * np.pi, 10_000)
+T = np.stack([R * np.cos(A), R * np.sin(A)], axis=1)
+dx = np.cos(A) - R * np.sin(A)
+dy = np.sin(A) + R * np.cos(A)
+O = np.stack([-dy, dx], axis=1)
+O = O / (np.linalg.norm(O, axis=1)).reshape(len(O), 1)
+
+L = np.zeros(len(T))
+np.cumsum(np.sqrt(((T[1:] - T[:-1]) ** 2).sum(axis=1)), out=L[1:])
+
+import mpmath
+
+mpmath.mp.dps = 15000
+text = str(mpmath.pi)
+
+path = TextPath((0, 0), text, size=6, prop=FontProperties(family="Source Serif Pro"))
+Vx, Vy = path.vertices[:, 0], path.vertices[:, 1]
+X = np.interp(Vx, L, T[:, 0]) + Vy * np.interp(Vx, L, O[:, 0])
+Y = np.interp(Vx, L, T[:, 1]) + Vy * np.interp(Vx, L, O[:, 1])
+Vx[...], Vy[...] = X, Y
+
+fig = plt.figure(figsize=(8, 8))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1)
+patch = PathPatch(path, facecolor="k", linewidth=0)
+ax.add_artist(patch)
+
+# from matplotlib.patheffects import Stroke, Normal
+# text = fig.text(.05, .060, "Scientific Visualization — Python & Matplotlib",
+# va="bottom", size="16", color="white",
+# transform=ax.transAxes,
+# weight="bold", family="Roboto Condensed")
+# text.set_path_effects([Stroke(linewidth=2, foreground="black"), Normal()])
+# text = fig.text(.05, .055, "github.com/rougier/scientific-visualization-book",
+# size = 10.2, transform=ax.transAxes,
+# va="top", color="blue", family="Roboto Mono", weight="bold")
+# text.set_path_effects([Stroke(linewidth=5, foreground="white"), Normal()])
+
+plt.rcParams["text.usetex"] = True
+ax.text(-3, 0, "$\pi$", ha="center", va="center", size=500, color="white", alpha=0.6)
+
+ax.set_xlim(-200, 200), ax.set_xticks([])
+ax.set_ylim(-200, 200), ax.set_yticks([])
+
+# plt.savefig("/Users/rougier/Desktop/spiral-pi.png", dpi=150)
+plt.savefig("../../figures/showcases/text-spiral.pdf")
+plt.show()
diff --git a/code/showcases/waterfall-3d.py b/code/showcases/waterfall-3d.py
new file mode 100644
index 0000000..cf9a4d6
--- /dev/null
+++ b/code/showcases/waterfall-3d.py
@@ -0,0 +1,153 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from shapely.geometry import box, Polygon
+from scipy.ndimage import gaussian_filter1d
+from matplotlib.collections import PolyCollection
+from matplotlib.transforms import Affine2D
+from matplotlib.text import TextPath
+from matplotlib.patches import PathPatch
+from mpl_toolkits.mplot3d import Axes3D, art3d
+from matplotlib.font_manager import FontProperties
+
+
+def text3d(ax, xyz, s, zdir="z", size=0.1, angle=0, **kwargs):
+ x, y, z = xyz
+ if zdir == "y":
+ xy, z = (x, z), y
+ elif zdir == "x":
+ xy, z = (y, z), x
+ else:
+ xy, z = (x, y), z
+ path = TextPath((0, 0), s, size=size, prop=FontProperties(family="Roboto"))
+ V = path.vertices
+ V[:, 0] -= (V[:, 0].max() - V[:, 0].min()) / 2
+ trans = Affine2D().rotate(angle).translate(xy[0], xy[1])
+ path = PathPatch(trans.transform_path(path), **kwargs)
+ ax.add_patch(path)
+ art3d.pathpatch_2d_to_3d(path, z=z, zdir=zdir)
+
+
+# Some nice but random curves
+def random_curve(n=100):
+ Y = np.random.uniform(0, 1, n)
+ Y = gaussian_filter1d(Y, 1)
+ X = np.linspace(-1, 1, len(Y))
+ Y *= np.exp(-2 * (X * X))
+ return Y
+
+
+def cmap_plot(Y, ymin=0, ymax=1, n=50, cmap="magma", y0=0):
+ X = np.linspace(0.3, 0.7, len(Y))
+ Y = gaussian_filter1d(Y, 2)
+
+ verts = []
+ colors = []
+ P = Polygon([(X[0], 0), *zip(X, Y), (X[-1], 0)])
+
+ dy = (ymax - ymin) / n
+ cmap = plt.cm.get_cmap(cmap)
+ cnorm = matplotlib.colors.Normalize(vmin=ymin, vmax=ymax)
+
+ for y in np.arange(Y.min(), Y.max(), dy):
+ B = box(0, y, 10, y + dy)
+ I = P.intersection(B)
+ if hasattr(I, "geoms"):
+ for p in I.geoms:
+ V = np.array(p.exterior.coords)
+ V[:, 1] += y0
+ verts.append(V)
+ colors.append(cmap(cnorm(y)))
+ else:
+ if I.exterior.coords:
+ V = np.array(I.exterior.coords)
+ V[:, 1] += y0
+ verts.append(V)
+ colors.append(cmap(cnorm(y)))
+
+ return verts, colors
+
+
+fig = plt.figure(figsize=(10, 10))
+fig.patch.set_facecolor("black")
+ax = fig.gca(projection="3d", proj_type="ortho")
+ax.patch.set_facecolor("black")
+
+# Make panes transparent
+ax.xaxis.pane.fill = False # Left pane
+ax.yaxis.pane.fill = False # Right pane
+ax.zaxis.pane.fill = False # Right pane
+
+# Remove grid lines
+ax.grid(False)
+
+# Remove tick labels
+ax.set_xticklabels([])
+ax.set_yticklabels([])
+ax.set_zticklabels([])
+
+# Transparent spines
+ax.w_xaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
+ax.w_yaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
+ax.w_zaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
+
+# Transparent panes
+ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
+ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
+ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
+
+# No ticks
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_zticks([])
+
+
+np.random.seed(1)
+
+# text3d(ax, (1.05, 0.5, 0), "Series A", zdir="x",
+# size=0.05, facecolor="white", edgecolor="None", alpha=.25)
+
+# text3d(ax, (1.05, 0.5, 0.6), "Series B", zdir="x",
+# size=0.05, facecolor="white", edgecolor="None", alpha=.25)
+
+# text3d(ax, (1.05, 0.5, 1.2), "Series C", zdir="x",
+# size=0.05, facecolor="white", edgecolor="None", alpha=.25)
+
+
+for zs in np.linspace(0, 1, 50):
+ Y = 0.1 * random_curve()
+ verts, colors = cmap_plot(Y, ymin=0, ymax=0.075, n=50, cmap="magma", y0=-0.2)
+ collection = PolyCollection(
+ verts, antialiased=False, edgecolors="None", facecolor=colors
+ )
+ ax.add_collection3d(collection, zdir="x", zs=zs)
+
+ Y = 0.1 * random_curve()
+ verts, colors = cmap_plot(Y, ymin=0, ymax=0.075, n=50, cmap="magma", y0=0.4)
+ collection = PolyCollection(
+ verts, antialiased=False, edgecolors="None", facecolor=colors
+ )
+ ax.add_collection3d(collection, zdir="x", zs=zs)
+
+ Y = 0.1 * random_curve()
+ verts, colors = cmap_plot(Y, ymin=0, ymax=0.075, n=50, cmap="magma", y0=1.0)
+ collection = PolyCollection(
+ verts, antialiased=False, edgecolors="None", facecolor=colors
+ )
+ ax.add_collection3d(collection, zdir="x", zs=zs)
+
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_zlim(0, 1)
+
+ax.view_init(elev=40, azim=-40)
+
+plt.tight_layout()
+plt.savefig("../../figures/showcases/waterfall-3d.pdf")
+# plt.savefig("./waterfall-3d.png", dpi=300)
+plt.show()
diff --git a/code/showcases/windmap.py b/code/showcases/windmap.py
new file mode 100644
index 0000000..649a301
--- /dev/null
+++ b/code/showcases/windmap.py
@@ -0,0 +1,242 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation, writers
+from matplotlib.collections import LineCollection
+
+
+class Streamlines(object):
+ """
+ Copyright (c) 2011 Raymond Speth.
+
+ Permission is hereby granted, free of charge, to any person obtaining a
+ copy of this software and associated documentation files (the "Software"),
+ to deal in the Software without restriction, including without limitation
+ the rights to use, copy, modify, merge, publish, distribute, sublicense,
+ and/or sell copies of the Software, and to permit persons to whom the
+ Software is furnished to do so, subject to the following conditions:
+
+ The above copyright notice and this permission notice shall be included in
+ all copies or substantial portions of the Software.
+
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+ DEALINGS IN THE SOFTWARE.
+
+ See: http://web.mit.edu/speth/Public/streamlines.py
+ """
+
+ def __init__(
+ self, X, Y, U, V, res=0.125, spacing=2, maxLen=2500, detectLoops=False
+ ):
+ """
+ Compute a set of streamlines covering the given velocity field.
+
+ X and Y - 1D or 2D (e.g. generated by np.meshgrid) arrays of the
+ grid points. The mesh spacing is assumed to be uniform
+ in each dimension.
+ U and V - 2D arrays of the velocity field.
+ res - Sets the distance between successive points in each
+ streamline (same units as X and Y)
+ spacing - Sets the minimum density of streamlines, in grid points.
+ maxLen - The maximum length of an individual streamline segment.
+ detectLoops - Determines whether an attempt is made to stop extending
+ a given streamline before reaching maxLen points if
+ it forms a closed loop or reaches a velocity node.
+
+ Plots are generated with the 'plot' or 'plotArrows' methods.
+ """
+
+ self.spacing = spacing
+ self.detectLoops = detectLoops
+ self.maxLen = maxLen
+ self.res = res
+
+ xa = np.asanyarray(X)
+ ya = np.asanyarray(Y)
+ self.x = xa if xa.ndim == 1 else xa[0]
+ self.y = ya if ya.ndim == 1 else ya[:, 0]
+ self.u = U
+ self.v = V
+ self.dx = (self.x[-1] - self.x[0]) / (self.x.size - 1) # assume a regular grid
+ self.dy = (self.y[-1] - self.y[0]) / (self.y.size - 1) # assume a regular grid
+ self.dr = self.res * np.sqrt(self.dx * self.dy)
+
+ # marker for which regions have contours
+ self.used = np.zeros(self.u.shape, dtype=bool)
+ self.used[0] = True
+ self.used[-1] = True
+ self.used[:, 0] = True
+ self.used[:, -1] = True
+
+ # Don't try to compute streamlines in regions where there is no velocity data
+ for i in range(self.x.size):
+ for j in range(self.y.size):
+ if self.u[j, i] == 0.0 and self.v[j, i] == 0.0:
+ self.used[j, i] = True
+
+ # Make the streamlines
+ self.streamlines = []
+ while not self.used.all():
+ nz = np.transpose(np.logical_not(self.used).nonzero())
+ # Make a streamline starting at the first unrepresented grid point
+ self.streamlines.append(
+ self._makeStreamline(self.x[nz[0][1]], self.y[nz[0][0]])
+ )
+
+ def _interp(self, x, y):
+ """ Compute the velocity at point (x,y) """
+ i = (x - self.x[0]) / self.dx
+ ai = i % 1
+
+ j = (y - self.y[0]) / self.dy
+ aj = j % 1
+
+ i, j = int(i), int(j)
+
+ # Bilinear interpolation
+ u = (
+ self.u[j, i] * (1 - ai) * (1 - aj)
+ + self.u[j, i + 1] * ai * (1 - aj)
+ + self.u[j + 1, i] * (1 - ai) * aj
+ + self.u[j + 1, i + 1] * ai * aj
+ )
+
+ v = (
+ self.v[j, i] * (1 - ai) * (1 - aj)
+ + self.v[j, i + 1] * ai * (1 - aj)
+ + self.v[j + 1, i] * (1 - ai) * aj
+ + self.v[j + 1, i + 1] * ai * aj
+ )
+
+ self.used[j : j + self.spacing, i : i + self.spacing] = True
+
+ return u, v
+
+ def _makeStreamline(self, x0, y0):
+ """
+ Compute a streamline extending in both directions from the given point.
+ """
+
+ sx, sy = self._makeHalfStreamline(x0, y0, 1) # forwards
+ rx, ry = self._makeHalfStreamline(x0, y0, -1) # backwards
+
+ rx.reverse()
+ ry.reverse()
+
+ return rx + [x0] + sx, ry + [y0] + sy
+
+ def _makeHalfStreamline(self, x0, y0, sign):
+ """
+ Compute a streamline extending in one direction from the given point.
+ """
+
+ xmin = self.x[0]
+ xmax = self.x[-1]
+ ymin = self.y[0]
+ ymax = self.y[-1]
+
+ sx = []
+ sy = []
+
+ x = x0
+ y = y0
+ i = 0
+ while xmin < x < xmax and ymin < y < ymax:
+ u, v = self._interp(x, y)
+ theta = np.arctan2(v, u)
+
+ x += sign * self.dr * np.cos(theta)
+ y += sign * self.dr * np.sin(theta)
+ sx.append(x)
+ sy.append(y)
+
+ i += 1
+
+ if self.detectLoops and i % 10 == 0 and self._detectLoop(sx, sy):
+ break
+
+ if i > self.maxLen / 2:
+ break
+
+ return sx, sy
+
+ def _detectLoop(self, xVals, yVals):
+ """ Detect closed loops and nodes in a streamline. """
+ x = xVals[-1]
+ y = yVals[-1]
+ D = np.array(
+ [np.hypot(x - xj, y - yj) for xj, yj in zip(xVals[:-1], yVals[:-1])]
+ )
+ return (D < 0.9 * self.dr).any()
+
+
+# See https://stackoverflow.com/questions/11578760/
+# matplotlib-control-capstyle-of-line-collection-large-number-of-lines
+# -----------------------------------------------------------------------------
+import types
+from matplotlib.backend_bases import GraphicsContextBase, RendererBase
+
+
+class GC(GraphicsContextBase):
+ def __init__(self):
+ super().__init__()
+ self._capstyle = "round"
+
+
+def custom_new_gc(self):
+ return GC()
+
+
+RendererBase.new_gc = types.MethodType(custom_new_gc, RendererBase)
+
+
+# -----------------------------------------------------------------------------
+Y, X = np.mgrid[-3:3:100j, -3:3:100j]
+U, V = -1 - X ** 2 + Y, 1 + X - X * Y ** 2
+speed = np.sqrt(U * U + V * V)
+
+fig = plt.figure(figsize=(8, 8))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, frameon=False)
+
+lengths = []
+colors = []
+lines = []
+
+cmap = plt.get_cmap("Blues_r")
+s = Streamlines(X, Y, U, V)
+for streamline in s.streamlines:
+ x, y = streamline
+ points = np.array([x, y]).T.reshape(-1, 1, 2)
+ segments = np.concatenate([points[:-1], points[1:]], axis=1)
+ n = len(segments)
+
+ D = np.sqrt(((points[1:] - points[:-1]) ** 2).sum(axis=-1))
+ L = D.cumsum().reshape(n, 1) + np.random.uniform(0, 1)
+ C = np.zeros((n, 3))
+ C[:] = (L * 1.5) % 1
+
+ C = cmap(((L * 1.5) % 1).ravel())
+
+ linewidths = np.zeros(n)
+ linewidths[:] = 2 - ((L.reshape(n) * 1.5) % 1)
+ line = LineCollection(segments, color=C, linewidth=linewidths)
+
+ lengths.append(L)
+ colors.append(C)
+ lines.append(line)
+
+ ax.add_collection(line)
+
+ax.set_xlim(-3, +3), ax.set_xticks([])
+ax.set_ylim(-3, +3), ax.set_yticks([])
+plt.savefig("../../figures/showcases/windmap.png", dpi=600)
+plt.show()
diff --git a/code/threed/bunnies.py b/code/threed/bunnies.py
new file mode 100644
index 0000000..c0a0a6d
--- /dev/null
+++ b/code/threed/bunnies.py
@@ -0,0 +1,134 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def ortho(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=float)
+ M[0, 0] = +2.0 / (right - left)
+ M[1, 1] = +2.0 / (top - bottom)
+ M[2, 2] = -2.0 / (zfar - znear)
+ M[3, 3] = 1.0
+ M[0, 2] = -(right + left) / float(right - left)
+ M[1, 3] = -(top + bottom) / float(top - bottom)
+ M[2, 3] = -(zfar + znear) / float(zfar - znear)
+ return M
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+
+def mesh(MVP, V, F, cmap=None, clip=True):
+ V = np.c_[V, np.ones(len(V))] @ MVP.T
+ V /= V[:, 3].reshape(-1, 1)
+ V = V[F]
+ T = V[:, :, :2]
+ Z = -V[:, :, 2].mean(axis=1)
+ zmin, zmax = Z.min(), Z.max()
+ Z = (Z - zmin) / (zmax - zmin)
+ I = np.argsort(Z)
+ T = T[I, :]
+ if cmap is not None:
+ C = plt.get_cmap(cmap)(Z)
+ C = C[I, :]
+ else:
+ C = 1.0, 1.0, 1.0, 0.5
+ return PolyCollection(
+ T, closed=True, linewidth=0.1, clip_on=clip, facecolor=C, edgecolor="black"
+ )
+
+
+fig = plt.figure(figsize=(6, 6))
+
+ax = plt.subplot(
+ 2, 2, 1, xlim=[-1, +1], ylim=[-1, +1], xticks=[], yticks=[], aspect=1, frameon=False
+)
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -2.5)
+proj = perspective(25, 1, 1, 100)
+ax.add_collection(mesh(proj @ view @ model, V, F, "magma", False))
+
+ax = plt.subplot(
+ 2, 2, 2, xlim=[-1, +1], ylim=[-1, +1], xticks=[], yticks=[], aspect=1, frameon=False
+)
+model = xrotate(90)
+view = np.eye(4)
+proj = ortho(-1, 1, -1, 1, 0, 100)
+ax.add_collection(mesh(proj @ view @ model, 2 * V, F))
+
+ax = plt.subplot(
+ 2, 2, 3, xlim=[-1, +1], ylim=[-1, +1], xticks=[], yticks=[], aspect=1, frameon=False
+)
+model = yrotate(90)
+view = np.eye(4)
+proj = ortho(-1, 1, -1, 1, 0, 100)
+ax.add_collection(mesh(proj @ view @ model, 2 * V, F))
+
+ax = plt.subplot(
+ 2, 2, 4, xlim=[-1, +1], ylim=[-1, +1], xticks=[], yticks=[], aspect=1, frameon=False
+)
+model = view = np.eye(4)
+proj = ortho(-1, 1, -1, 1, 0, 100)
+ax.add_collection(mesh(proj @ view @ model, 2 * V, F))
+
+plt.tight_layout()
+plt.savefig("../../figures/threed/bunnies.pdf")
+plt.show()
diff --git a/code/threed/bunny-1.py b/code/threed/bunny-1.py
new file mode 100644
index 0000000..ef25d3e
--- /dev/null
+++ b/code/threed/bunny-1.py
@@ -0,0 +1,35 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-1.pdf")
+plt.show()
diff --git a/code/threed/bunny-2.py b/code/threed/bunny-2.py
new file mode 100644
index 0000000..5e0a82b
--- /dev/null
+++ b/code/threed/bunny-2.py
@@ -0,0 +1,57 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+V = np.c_[V, np.ones(len(V))] @ perspective(25, 1, 1, 100).T
+V /= V[:, 3].reshape(-1, 1)
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-2.pdf")
+plt.show()
diff --git a/code/threed/bunny-3.py b/code/threed/bunny-3.py
new file mode 100644
index 0000000..bb867c7
--- /dev/null
+++ b/code/threed/bunny-3.py
@@ -0,0 +1,58 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+V += (0, 0, -3.5)
+V = np.c_[V, np.ones(len(V))] @ perspective(25, 1, 1, 100).T
+V /= V[:, 3].reshape(-1, 1)
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-3.pdf")
+plt.show()
diff --git a/code/threed/bunny-4.py b/code/threed/bunny-4.py
new file mode 100644
index 0000000..5d25aa0
--- /dev/null
+++ b/code/threed/bunny-4.py
@@ -0,0 +1,85 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V = np.c_[V, np.ones(len(V))] @ MVP.T
+V /= V[:, 3].reshape(-1, 1)
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-4.pdf")
+plt.show()
diff --git a/code/threed/bunny-5.py b/code/threed/bunny-5.py
new file mode 100644
index 0000000..faac679
--- /dev/null
+++ b/code/threed/bunny-5.py
@@ -0,0 +1,95 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V_ = np.c_[V, np.ones(len(V))] @ MVP.T
+V_ /= V_[:, 3].reshape(-1, 1)
+T = V_[F][..., :2]
+
+fig = plt.figure(figsize=(8, 8))
+for i, (fovy, z) in enumerate([(25, -3), (40, -2.0), (65, -1.25), (80, -1.0)]):
+ view = translate(0, 0, z)
+ proj = perspective(fovy, 1, 1, 100)
+ MVP = proj @ view @ model
+ V_ = np.c_[V, np.ones(len(V))] @ MVP.T
+ V_ /= V_[:, 3].reshape(-1, 1)
+ T = V_[F][..., :2]
+ ax = plt.subplot(2, 2, i + 1, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+ ax.axis("off")
+ collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="None", edgecolor="black"
+ )
+ ax.add_collection(collection)
+ ax.set_title("Aperture: %d" % fovy)
+
+plt.tight_layout()
+plt.savefig("../../figures/threed/bunny-5.pdf")
+plt.show()
diff --git a/code/threed/bunny-6.py b/code/threed/bunny-6.py
new file mode 100644
index 0000000..1555256
--- /dev/null
+++ b/code/threed/bunny-6.py
@@ -0,0 +1,85 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V = np.c_[V, np.ones(len(V))] @ MVP.T
+V /= V[:, 3].reshape(-1, 1)
+T = V[F][..., :2]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="0.9", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-6.pdf")
+plt.show()
diff --git a/code/threed/bunny-7.py b/code/threed/bunny-7.py
new file mode 100644
index 0000000..9b14610
--- /dev/null
+++ b/code/threed/bunny-7.py
@@ -0,0 +1,91 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V = np.c_[V, np.ones(len(V))] @ MVP.T
+V /= V[:, 3].reshape(-1, 1)
+V = V[F]
+
+T = V[:, :, :2]
+Z = -V[:, :, 2].mean(axis=1)
+I = np.argsort(Z)
+T = T[I, :]
+
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor="0.9", edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-7.pdf")
+plt.show()
diff --git a/code/threed/bunny-8.py b/code/threed/bunny-8.py
new file mode 100644
index 0000000..13f0bf3
--- /dev/null
+++ b/code/threed/bunny-8.py
@@ -0,0 +1,93 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * np.radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+# Data processing
+V, F = [], []
+with open("bunny.obj") as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ F.append([int(x) for x in values[1:4]])
+V, F = np.array(V), np.array(F) - 1
+
+V = (V - (V.max(0) + V.min(0)) / 2) / max(V.max(0) - V.min(0))
+
+model = xrotate(20) @ yrotate(45)
+view = translate(0, 0, -3.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+V = np.c_[V, np.ones(len(V))] @ MVP.T
+V /= V[:, 3].reshape(-1, 1)
+V = V[F]
+
+T = V[:, :, :2]
+Z = -V[:, :, 2].mean(axis=1)
+zmin, zmax = Z.min(), Z.max()
+Z = (Z - zmin) / (zmax - zmin)
+C = plt.get_cmap("magma")(Z)
+I = np.argsort(Z)
+T, C = T[I, :], C[I, :]
+
+# Rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1, frameon=False)
+collection = PolyCollection(
+ T, closed=True, linewidth=0.1, facecolor=C, edgecolor="black"
+)
+ax.add_collection(collection)
+plt.savefig("../../figures/threed/bunny-8.pdf")
+plt.show()
diff --git a/code/threed/bunny.obj b/code/threed/bunny.obj
new file mode 100644
index 0000000..18abc08
--- /dev/null
+++ b/code/threed/bunny.obj
@@ -0,0 +1,7474 @@
+# OBJ file format with ext .obj
+# vertex count = 2503
+# face count = 4968
+v -3.4101800e-003 1.3031957e-001 2.1754370e-002
+v -8.1719160e-002 1.5250145e-001 2.9656090e-002
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+v -2.4901590e-002 1.1211138e-001 3.7560240e-002
+v -1.8405680e-002 1.7843055e-001 -2.4219580e-002
+v 1.9067940e-002 1.2144925e-001 3.1968440e-002
+v 6.0412000e-003 1.2494359e-001 3.2652890e-002
+v -1.3469030e-002 1.6299355e-001 -1.2000020e-002
+v -3.4393240e-002 1.7236688e-001 -9.8213000e-004
+v -8.4314160e-002 1.0957263e-001 3.7097300e-003
+v -4.2233540e-002 1.7211574e-001 -4.1799800e-003
+v -6.3308390e-002 1.5660615e-001 -1.3838790e-002
+v -7.6903950e-002 1.6708033e-001 -2.6931360e-002
+v -7.2253920e-002 1.1539550e-001 5.1670300e-002
+v 1.2981330e-002 1.1366375e-001 3.8302950e-002
+v -3.7857280e-002 1.7010102e-001 1.4236000e-003
+v 4.8689400e-003 3.7962370e-002 4.5867630e-002
+v -5.7180550e-002 4.0918830e-002 4.6301340e-002
+v -4.5209070e-002 3.8839100e-002 4.4503770e-002
+v -3.3761490e-002 1.2617876e-001 1.7132300e-003
+v -5.0242270e-002 1.5773747e-001 9.3944500e-003
+v -2.1216950e-002 1.5887938e-001 -4.6923700e-003
+v -5.6472950e-002 1.5778406e-001 8.1786500e-003
+v -5.2802060e-002 4.1319860e-002 4.6169800e-002
+v -4.9960340e-002 4.3101950e-002 4.4462650e-002
+v -2.9748750e-002 3.6539860e-002 5.2493310e-002
+v -3.5438900e-003 4.2659770e-002 4.7541530e-002
+v 4.9304900e-003 4.1982660e-002 4.5723390e-002
+v -3.9088180e-002 1.6872020e-001 -1.1924680e-002
+v -5.6901000e-002 4.5437000e-002 4.3236960e-002
+v -4.1244880e-002 4.3098890e-002 4.2129560e-002
+v -2.6471980e-002 4.5034530e-002 5.1219460e-002
+v -2.1866970e-002 4.4022930e-002 5.3243800e-002
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+v 4.9760060e-002 7.0235220e-002 2.3732020e-002
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+v -2.2491950e-002 4.6513620e-002 5.1885310e-002
+v -6.5174200e-003 4.5036200e-002 4.7502780e-002
+v 3.7699000e-004 4.4935790e-002 4.6519930e-002
+v 3.4023920e-002 1.1353879e-001 2.4595280e-002
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+v -1.7533470e-002 4.7964250e-002 4.8829630e-002
+v -7.0012600e-003 4.6416520e-002 4.7485540e-002
+v 5.9862300e-003 4.6689140e-002 4.9073620e-002
+v 9.1007200e-003 4.8474490e-002 4.9353190e-002
+v -3.5453700e-002 1.1244769e-001 3.5055410e-002
+v -7.5983200e-002 1.3820800e-001 4.9216580e-002
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+v -1.4354710e-002 5.4428200e-002 5.0710310e-002
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diff --git a/code/threed/bunny.py b/code/threed/bunny.py
new file mode 100644
index 0000000..1632660
--- /dev/null
+++ b/code/threed/bunny.py
@@ -0,0 +1,135 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4))
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[2, 0] = (right + left) / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[3, 2] = -2.0 * znear * zfar / (zfar - znear)
+ M[2, 3] = -1.0
+ return M.T
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(fovy / 360.0 * np.pi) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def scale(x, y, z):
+ return np.array(
+ [[x, 0, 0, 0], [0, y, 0, 0], [0, 0, z, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def zoom(z):
+ return scale(z, z, z)
+
+
+def translate(x, y, z):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def xrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def yrotate(theta):
+ t = np.pi * theta / 180
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=float
+ )
+
+
+def obj_load(filename):
+ V, Vi = [], []
+ with open(filename) as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ Vi.append([int(x) for x in values[1:4]])
+ return np.array(V), np.array(Vi) - 1
+
+
+# -----------------------------------------------------------------------------
+
+# Loading and centering
+V, Vi = obj_load("bunny.obj")
+V = (V - (V.max(axis=0) + V.min(axis=0)) / 2) / max(V.max(axis=0) - V.min(axis=0))
+
+# Computing model-view-projection matrix
+model = zoom(1.5) @ xrotate(20) @ yrotate(45)
+view = translate(0, 0, -4.5)
+proj = perspective(25, 1, 1, 100)
+MVP = proj @ view @ model
+
+# Applying MVP
+VH = np.c_[V, np.ones(len(V))] # Homogenous coordinates
+VT = VH @ MVP.T # Transformed coordinates
+VN = VT / VT[:, 3].reshape(-1, 1) # Normalization
+VS = VN[:, :3] # Normalized device coordinates
+
+# Actual faces
+V = VS[Vi]
+
+# Backface culling
+CW = (
+ (V[:, 1, 0] - V[:, 0, 0]) * (V[:, 1, 1] + V[:, 0, 1])
+ + (V[:, 2, 0] - V[:, 1, 0]) * (V[:, 2, 1] + V[:, 1, 1])
+ + (V[:, 0, 0] - V[:, 2, 0]) * (V[:, 0, 1] + V[:, 2, 1])
+)
+V = V[CW < 0]
+
+# Rendering as a collection of polygons (triangles)
+segments = V[:, :, :2]
+zbuffer = -V[:, :, 2].mean(axis=1)
+
+# Color according to depth
+zmin, zmax = zbuffer.min(), zbuffer.max()
+zbuffer = (zbuffer - zmin) / (zmax - zmin)
+colors = plt.get_cmap("magma")(zbuffer)
+
+# Sort triangles according to z buffer
+I = np.argsort(zbuffer)
+segments, colors = segments[I, :], colors[I, :]
+
+# Actual rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+ax.axis("off")
+
+for fc, ec, lw in [
+ ("None", "black", 6.0),
+ ("None", "white", 3.0),
+ (colors, "black", 0.25),
+]:
+ collection = PolyCollection(
+ segments, closed=True, linewidth=lw, facecolor=fc, edgecolor=ec
+ )
+ ax.add_collection(collection)
+
+plt.savefig("../../figures/threed/bunny.pdf", transparent=True)
+plt.show()
diff --git a/code/typography/projection-3d-gaussian.py b/code/typography/projection-3d-gaussian.py
new file mode 100644
index 0000000..b660b32
--- /dev/null
+++ b/code/typography/projection-3d-gaussian.py
@@ -0,0 +1,331 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.text import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.colors import LightSource
+from matplotlib.transforms import Affine2D
+from mpl_toolkits.mplot3d import Axes3D, art3d
+from matplotlib.font_manager import FontProperties
+
+
+def text3d(ax, xyz, s, zdir="z", size=0.1, angle=0, **kwargs):
+ x, y, z = xyz
+ if zdir == "y":
+ xy, z = (x, z), y
+ elif zdir == "x":
+ xy, z = (y, z), x
+ else:
+ xy, z = (x, y), z
+ path = TextPath((0, 0), s, size=size, prop=FontProperties(family="Roboto"))
+ V = path.vertices
+ V[:, 0] -= (V[:, 0].max() - V[:, 0].min()) / 2
+ trans = Affine2D().rotate(angle).translate(xy[0], xy[1])
+ path = PathPatch(trans.transform_path(path), **kwargs)
+ ax.add_patch(path)
+ art3d.pathpatch_2d_to_3d(path, z=z, zdir=zdir)
+
+
+fig = plt.figure(figsize=(6, 6))
+ax = plt.subplot(1, 1, 1, projection="3d")
+ax.view_init(elev=30, azim=-55)
+ax.set_axis_off()
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_zlim(0, 1)
+
+xrange = 0, 5
+yrange = 0, 5
+zrange = 0, 2
+xticks = (1.0, 0.2)
+yticks = (1.0, 0.2)
+zticks = (1.0, 0.2)
+
+maxrange = max(xrange[1] - xrange[0], yrange[1] - yrange[0], zrange[1] - zrange[0])
+xtransform = lambda x: (x - xrange[0]) / maxrange
+ytransform = lambda y: (y - yrange[0]) / maxrange
+ztransform = lambda z: (z - zrange[0]) / maxrange
+xmin, xmax = xtransform(xrange[0]), xtransform(xrange[1])
+ymin, ymax = ytransform(yrange[0]), ytransform(yrange[1])
+zmin, zmax = ztransform(zrange[0]), ztransform(zrange[1])
+
+
+# Minor gridlines
+linewidth, color = 0.25, "0.75"
+for x in np.arange(xrange[0], xrange[1] + xticks[1], xticks[1]):
+ x = xtransform(x)
+ ax.plot(
+ [x, x, x],
+ [ymin, ymax, ymax],
+ [zmin, zmin, zmax],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+for y in np.arange(yrange[0], yrange[1] + yticks[1], yticks[1]):
+ y = ytransform(y)
+ ax.plot(
+ [xmax, xmin, xmin],
+ [y, y, y],
+ [zmin, zmin, zmax],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+for z in np.arange(zrange[0], zrange[1] + zticks[1], zticks[1]):
+ z = ztransform(z)
+ ax.plot(
+ [xmax, xmin, xmin],
+ [ymax, ymax, ymin],
+ [z, z, z],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+
+# Major gridlines
+linewidth, color = 0.50, "0.50"
+for x in np.arange(xrange[0], xrange[1] + xticks[0], xticks[0]):
+ x = xtransform(x)
+ ax.plot(
+ [x, x, x],
+ [ymin, ymax, ymax],
+ [zmin, zmin, zmax],
+ linewidth=linewidth,
+ color=color,
+ zorder=-25,
+ )
+for y in np.arange(yrange[0], yrange[1] + yticks[0], yticks[0]):
+ y = ytransform(y)
+ ax.plot(
+ [xmax, xmin, xmin],
+ [y, y, y],
+ [zmin, zmin, zmax],
+ linewidth=linewidth,
+ color=color,
+ zorder=-25,
+ )
+for z in np.arange(zrange[0], zrange[1] + zticks[0], zticks[0]):
+ z = ztransform(z)
+ ax.plot(
+ [xmax, xmin, xmin],
+ [ymax, ymax, ymin],
+ [z, z, z],
+ linewidth=linewidth,
+ color=color,
+ zorder=-25,
+ )
+
+# Frame
+linewidth, color = 1.00, "0.00"
+ax.plot(
+ [xmin, xmin, xmax, xmax, xmin],
+ [ymin, ymax, ymax, ymin, ymin],
+ [zmin, zmin, zmin, zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-10,
+)
+ax.plot(
+ [xmin, xmin, xmax, xmax, xmin],
+ [ymax, ymax, ymax, ymax, ymax],
+ [zmin, zmax, zmax, zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-10,
+)
+ax.plot(
+ [xmin, xmin, xmin, xmin, xmin],
+ [ymin, ymin, ymax, ymax, ymin],
+ [zmin, zmax, zmax, zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-10,
+)
+
+# Minor ticks
+linewidth, length, color = 0.25, 0.02, "0.5"
+for x in np.arange(xrange[0], xrange[1] + xticks[1], xticks[1]):
+ x = xtransform(x)
+ ax.plot(
+ [x, x],
+ [ymin, ymin - length],
+ [zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+for y in np.arange(yrange[0], yrange[1] + yticks[1], yticks[1]):
+ y = ytransform(y)
+ ax.plot(
+ [xmax, xmax + length],
+ [y, y],
+ [zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+for z in np.arange(zrange[0], zrange[1] + zticks[1], zticks[1]):
+ z = ztransform(z)
+ ax.plot(
+ [xmin, xmin - length],
+ [ymin, ymin],
+ [z, z],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+
+# Major ticks & labels
+linewidth, length, color = 0.75, 0.04, "0.0"
+for x in np.arange(xrange[0], xrange[1] + xticks[0], xticks[0]):
+ x_ = xtransform(x)
+ ax.plot(
+ [x_, x_],
+ [ymin, ymin - length],
+ [zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+ text3d(
+ ax,
+ (x_, ymin - 2 * length, zmin),
+ "%.1f" % x,
+ size=0.04,
+ facecolor="black",
+ edgecolor="None",
+ )
+for y in np.arange(yrange[0], yrange[1] + yticks[0], yticks[0]):
+ y_ = ytransform(y)
+ ax.plot(
+ [xmax, xmax + length],
+ [y_, y_],
+ [zmin, zmin],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+ text3d(
+ ax,
+ (xmax + 2 * length, y_, zmin),
+ "%.1f" % y,
+ angle=np.pi / 2,
+ size=0.04,
+ facecolor="black",
+ edgecolor="None",
+ )
+for z in np.arange(zrange[0], zrange[1] + zticks[1], zticks[0]):
+ z_ = ztransform(z)
+ ax.plot(
+ [xmin, xmin - length],
+ [ymin, ymin],
+ [z_, z_],
+ linewidth=linewidth,
+ color=color,
+ zorder=-50,
+ )
+ text3d(
+ ax,
+ (xmin - 2 * length, ymin, z_),
+ "%.1f" % z,
+ zdir="y",
+ size=0.04,
+ facecolor="black",
+ edgecolor="None",
+ )
+
+
+# Data
+n = 101
+X = Y = np.linspace(-1, 1, n)
+X, Y = np.meshgrid(X, Y)
+R = np.sqrt(5 * (X ** 2 + Y ** 2))
+Z = 0.5 * np.exp(-0.5 * R * R)
+X, Y = (1 + X) / 2, (1 + Y) / 2
+
+# Plot
+C = np.ones((n, n, 3))
+I1 = LightSource(azdeg=0, altdeg=25).hillshade(Z).reshape(n, n, 1)
+I2 = LightSource(azdeg=120, altdeg=25).hillshade(Z).reshape(n, n, 1)
+I3 = LightSource(azdeg=240, altdeg=25).hillshade(Z).reshape(n, n, 1)
+C = 0.5 * C + (I1 * (1, 0, 0) + I2 * (0, 1, 0) + I3 * (0, 0, 1))
+C = np.minimum(C, (1, 1, 1))
+C[::2, ::2] = C[1::2, 1::2] = 0.0, 0.0, 0.0
+
+
+# ax.plot_wireframe(X, Y, Z, rstride=n-1, cstride=n-1, color="black",
+# linewidth=3, antialiased=True, zorder=25)
+ax.plot_wireframe(
+ X,
+ Y,
+ Z,
+ rstride=1,
+ cstride=1,
+ color="black",
+ linewidth=5,
+ antialiased=True,
+ zorder=10,
+)
+ax.plot_wireframe(
+ X,
+ Y,
+ Z,
+ rstride=1,
+ cstride=1,
+ color="white",
+ linewidth=2,
+ antialiased=True,
+ zorder=20,
+)
+ax.plot_surface(
+ X,
+ Y,
+ Z,
+ rstride=5,
+ cstride=5,
+ facecolors=C,
+ shade=False,
+ edgecolor="None",
+ linewidth=0,
+ antialiased=True,
+ zorder=30,
+)
+
+text3d(
+ ax,
+ ((xmin + xmax) / 2, ymax, 1.05 * zmax),
+ "X axis",
+ zdir="y",
+ size=0.05,
+ facecolor="black",
+ edgecolor="None",
+)
+text3d(
+ ax,
+ (xmin, (ymin + ymax) / 2, 1.05 * zmax),
+ "Y axis",
+ zdir="x",
+ size=0.05,
+ facecolor="black",
+ edgecolor="None",
+)
+text3d(
+ ax,
+ (1.05 * xmax, ymax, (zmin + zmax) / 2),
+ "Z axis",
+ angle=np.pi / 2,
+ zdir="y",
+ size=0.05,
+ facecolor="black",
+ edgecolor="None",
+)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/projection-3d-gaussian.pdf")
+plt.show()
diff --git a/code/typography/text-outline.py b/code/typography/text-outline.py
new file mode 100644
index 0000000..7fc8f21
--- /dev/null
+++ b/code/typography/text-outline.py
@@ -0,0 +1,58 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patheffects import Stroke, Normal
+
+fig = plt.figure(figsize=(8, 3))
+ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+family = "Pacifico"
+size = 100
+cmap = plt.cm.magma
+text = "Matplotlib"
+
+for x in np.linspace(0, 1, 20):
+ lw, color = x * 225, cmap(1 - x)
+ t = ax.text(
+ 0.5,
+ 0.45,
+ text,
+ size=size,
+ color="none",
+ weight="bold",
+ va="center",
+ ha="center",
+ family=family,
+ zorder=-lw,
+ )
+ t.set_path_effects([Stroke(linewidth=lw + 1, foreground="black")])
+ t = ax.text(
+ 0.5,
+ 0.45,
+ text,
+ size=size,
+ color="black",
+ weight="bold",
+ va="center",
+ ha="center",
+ family=family,
+ zorder=-lw + 1,
+ )
+ t.set_path_effects([Stroke(linewidth=lw, foreground=color)])
+t = ax.text(
+ 1.0,
+ 0.01,
+ "https://matplotlib.org ",
+ va="bottom",
+ ha="right",
+ size=10,
+ color="white",
+ family="Roboto",
+ alpha=0.50,
+)
+
+plt.savefig("../../figures/typography/text-outline.pdf")
+plt.show()
diff --git a/code/typography/text-starwars.py b/code/typography/text-starwars.py
new file mode 100644
index 0000000..f40ba0d
--- /dev/null
+++ b/code/typography/text-starwars.py
@@ -0,0 +1,57 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate the use of TextPath and access to raw vertices
+# ----------------------------------------------------------------------------
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+
+fig = plt.figure(figsize=(4.25, 2))
+ax = fig.add_axes([0, 0, 1, 1], aspect=1, xlim=[-40, 40], ylim=[-1, 25])
+ax.axis("off")
+
+text = [
+ "Beautiful is better than ugly.",
+ "Explicit is better than implicit.",
+ "Simple is better than complex.",
+ "Complex is better than complicated.",
+ "Flat is better than nested.",
+ "Sparse is better than dense.",
+ "Readability counts.",
+]
+
+y = 0
+size = 6
+xfactor = 1 / 50
+yfactor = 1 / 120
+
+for i, line in enumerate(text[::-1]):
+ path = TextPath((0, 0), line, size=size)
+ V = path.vertices
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ ymin, ymax = V[:, 1].min(), V[:, 1].max()
+
+ # X centering
+ V[:, 0] -= (xmax + xmin) / 2
+
+ # Moving whole text at y coordinates
+ V[:, 1] += y
+
+ # Rescaling along y
+ V[:, 1] *= 1 - (V[:, 1] * yfactor)
+
+ # Rescaling along x
+ V[:, 0] *= 1 - (V[:, 1] * xfactor)
+
+ # Update interlines
+ y += size * (1 - ymin * yfactor)
+
+ # Display
+ patch = PathPatch(path, facecolor="%.2f" % (i / 10), linewidth=0, clip_on=False)
+ ax.add_artist(patch)
+
+plt.savefig("../../figures/typography/text-starwars.pdf")
+plt.show()
diff --git a/code/typography/tick-labels-variation.py b/code/typography/tick-labels-variation.py
new file mode 100644
index 0000000..672d154
--- /dev/null
+++ b/code/typography/tick-labels-variation.py
@@ -0,0 +1,105 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+
+# Style
+# -----------------------------------------------------------------------------
+plt.rc("font", family="Roboto Condensed")
+plt.rc("xtick", labelsize="small")
+plt.rc("ytick", labelsize="small")
+plt.rc("axes", labelsize="medium", titlesize="medium")
+
+fig = plt.figure(figsize=(8, 2))
+
+#
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 1, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.05))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.2f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.01))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+ax.tick_params(axis="both", which="major")
+labels = ax.get_xticklabels()
+for i, label in enumerate(labels):
+ if i in [1, 6, 11, 16, 21]:
+ label.set_font("Roboto")
+ label.set_size(8.5)
+
+#
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 2, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.05))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.2f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.01))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+labels = ax.get_xticklabels()
+for i, label in enumerate(labels):
+ if i not in [1, 6, 11, 16, 21]:
+ label.set_size(7)
+
+#
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 3, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.05))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.2f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.01))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+labels = ax.get_xticklabels()
+for i, label in enumerate(labels):
+ if i not in [1, 6, 11, 16, 21]:
+ label.set_weight(200)
+
+
+#
+# -----------------------------------------------------------------------------
+ax = plt.subplot(4, 1, 4, xlim=[0, 1], ylim=[0, 1])
+ax.patch.set_facecolor("none")
+ax.set_yticks([])
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(0.05))
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%.2f"))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.01))
+ax.xaxis.set_minor_formatter(plt.NullFormatter())
+
+labels = ax.get_xticklabels()
+for i, label in enumerate(labels):
+ if i in [1, 6, 11, 16, 21]:
+ label.set_color("white")
+ label.set_weight(600)
+ label.set_bbox(
+ dict(
+ boxstyle="round,pad=.25",
+ linewidth=0.5,
+ facecolor="black",
+ edgecolor="black",
+ )
+ )
+
+# Show
+# -----------------------------------------------------------------------------
+plt.tight_layout()
+plt.savefig("../../figures/typography/tick-labels-variation.pdf")
+plt.show()
diff --git a/code/typography/typography-font-stacks.py b/code/typography/typography-font-stacks.py
new file mode 100644
index 0000000..5d76c3d
--- /dev/null
+++ b/code/typography/typography-font-stacks.py
@@ -0,0 +1,70 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.font_manager import findfont, FontProperties
+
+
+def font_text(x, y, text, family, dy=0):
+
+ ax.text(x, y + dy, text, family=family, size=size, va="bottom")
+ font = findfont(FontProperties(family=family))
+ filename = os.path.basename(font)
+ ax.text(
+ x,
+ y,
+ filename,
+ va="top",
+ family="Roboto Condensed",
+ weight=400,
+ size=10,
+ color="0.5",
+ )
+
+
+plt.figure(figsize=(8, 2), dpi=100)
+size = 20
+dx = 15
+xmin, xmax = -1, 4 * dx - 5
+
+ax = plt.subplot(
+ 3, 1, 1, frameon=False, xlim=(xmin, xmax), ylim=(-1, 1), xticks=[], yticks=[]
+)
+font_text(0 * dx, 0, "Serif", "Serif")
+font_text(1 * dx, 0, "Sans", "Sans")
+font_text(2 * dx, 0, "Monospace", "Monospace")
+font_text(3 * dx, 0, "Cursive", "Cursive", -0.25)
+
+
+ax = plt.subplot(
+ 3, 1, 2, frameon=False, xlim=(xmin, xmax), ylim=(-1, 1), xticks=[], yticks=[]
+)
+font_text(0 * dx, 0, "Serif", "Roboto Slab")
+font_text(1 * dx, 0, "Sans", "Roboto Condensed")
+font_text(2 * dx, 0, "Monospace", "Roboto Mono")
+font_text(3 * dx, 0, "Cursive", "Merienda", -0.25)
+
+
+ax = plt.subplot(
+ 3, 1, 3, frameon=False, xlim=(xmin, xmax), ylim=(-1, 1), xticks=[], yticks=[]
+)
+font_text(0 * dx, 0, "Serif", "Source Serif Pro")
+font_text(1 * dx, 0, "Sans", "Source Sans Pro")
+font_text(2 * dx, 0, "Monospace", "Source Code Pro")
+font_text(3 * dx, 0, "Cursive", "ITC Zapf Chancery")
+
+# ax.text(0*dx, 0, "Serif", family="Roboto Slab", size=size)
+# ax.text(0*dx, -0.5, find("Roboto Slab"),
+# family="Roboto Slab", weight=300, size=8, color="0.25")
+
+# ax.text(1*dx, 0, "Sans-serif", family="Roboto Condensed", size=size)
+# ax.text(2*dx, 0, "Monospace", family="Roboto Mono", size=size)
+# ax.text(3*dx, 0, "Cursive", family="Pacifico", size=size)
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/typography-font-stacks.pdf", dpi=100)
+plt.show()
diff --git a/code/typography/typography-legibility.py b/code/typography/typography-legibility.py
new file mode 100644
index 0000000..ea01adc
--- /dev/null
+++ b/code/typography/typography-legibility.py
@@ -0,0 +1,74 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patheffects import Stroke, Normal
+
+dpi = 200
+figsize = (4.25, 3.0)
+
+shape = 20, 80
+I = np.random.uniform(0, 1, shape)
+cmap = plt.cm.get_cmap("gray")
+x, y = 0.5, 2
+
+fig = plt.figure(figsize=figsize, dpi=dpi)
+
+# Regular
+ax = fig.add_subplot(4, 2, 1, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(x, y, "Read me", color="black", transform=ax.transData)
+
+ax = fig.add_subplot(4, 2, 2, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(x, y, "Read me", color="white", transform=ax.transData)
+
+# Bold
+ax = fig.add_subplot(4, 2, 3, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(x, y, "Read me", color="black", weight="bold", transform=ax.transData)
+
+ax = fig.add_subplot(4, 2, 4, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(x, y, "Read me", color="white", weight="bold", transform=ax.transData)
+
+# Boxes
+ax = fig.add_subplot(4, 2, 5, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(
+ x,
+ y,
+ "Read me",
+ color="black",
+ transform=ax.transData,
+ bbox={"facecolor": "white", "edgecolor": "None", "pad": 1, "alpha": 0.75},
+)
+
+ax = fig.add_subplot(4, 2, 6, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+ax.text(
+ x,
+ y,
+ "Read me",
+ color="white",
+ transform=ax.transData,
+ bbox={"facecolor": "black", "edgecolor": "None", "pad": 1, "alpha": 0.75},
+)
+
+ax = fig.add_subplot(4, 2, 7, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+text = ax.text(x, y, "Read me", color="black", transform=ax.transData)
+text.set_path_effects([Stroke(linewidth=1.5, foreground="white"), Normal()])
+
+ax = fig.add_subplot(4, 2, 8, xticks=[], yticks=[])
+ax.imshow(I, cmap=cmap, origin="lower")
+text = ax.text(x, y, "Read me", color="white", transform=ax.transData)
+text.set_path_effects([Stroke(linewidth=1.5, foreground="black"), Normal()])
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/typography-legibility.pdf", dpi=600)
+plt.show()
diff --git a/code/typography/typography-math-stacks.py b/code/typography/typography-math-stacks.py
new file mode 100644
index 0000000..c2e47a9
--- /dev/null
+++ b/code/typography/typography-math-stacks.py
@@ -0,0 +1,51 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.rcParams["text.usetex"] = False
+
+
+def plot(family):
+ plt.rcParams["mathtext.fontset"] = family
+ fig = plt.figure(figsize=(3, 1.75), dpi=100)
+ ax = plt.subplot(
+ 1, 1, 1, frameon=False, xlim=(-1, 1), ylim=(-0.5, 1.5), xticks=[], yticks=[]
+ )
+ ax.text(
+ 0,
+ 0.0,
+ r"$\frac{\pi}{4} = \sum_{k=0}^\infty\frac{(-1)^k}{2k+1}$",
+ size=32,
+ ha="center",
+ va="bottom",
+ )
+ ax.text(
+ 0,
+ -0.1,
+ 'mathtext.fontset = "%s"' % family,
+ size=14,
+ ha="center",
+ va="top",
+ family="Roboto Condensed",
+ color="0.5",
+ )
+
+ # plt.tight_layout()
+ plt.savefig("../../figures/typography/typography-math-%s.pdf" % family, dpi=600)
+ plt.show()
+
+
+plot("cm")
+plot("stix")
+plot("stixsans")
+plot("dejavusans")
+plot("dejavuserif")
+plot("custom")
+
+# Process result with
+# pdfjam --nup 3x2 ../figures/typography-math-cm.pdf ../figures/typography-math-custom.pdf ../figures/typography-math-dejavusans.pdf ../figures/typography-math-dejavuserif.pdf ../figures/typography-math-stix.pdf ../figures/typography-math-stixsans.pdf --outfile ../figures/typography-math-stacks.pdf; pdfcrop ../figures/typography-math-stacks.pdf ../figures/typography-math-stacks.pdf
diff --git a/code/typography/typography-matters.py b/code/typography/typography-matters.py
new file mode 100644
index 0000000..76ab14a
--- /dev/null
+++ b/code/typography/typography-matters.py
@@ -0,0 +1,95 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib
+from matplotlib import ticker
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as path_effects
+
+plt.figure(figsize=(8, 5), dpi=100)
+
+ax = plt.subplot(2, 1, 1, aspect=0.25)
+ax.text(
+ 0.5,
+ 0.5,
+ "Typography matters",
+ family="Times New Roman",
+ size=60,
+ color=".85",
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+ax.set_title(
+ "Typography is an important dimension of a scientific figure",
+ x=0,
+ y=0.965,
+ horizontalalignment="left",
+ verticalalignment="baseline",
+)
+ax.set_xticks(np.linspace(0, 1, 21))
+
+
+ax = plt.subplot(2, 1, 2, aspect=0.25)
+font = matplotlib.font_manager.FontProperties(
+ family="Roboto Condensed", weight="regular", size=9
+)
+ax.set_xticks(np.linspace(0, 1, 21))
+ax.set_yticks(np.linspace(0, 1, 5))
+
+for label in ax.get_xticklabels():
+ label.set_fontproperties(font)
+for label in ax.get_yticklabels():
+ label.set_fontproperties(font)
+
+for label in ax.get_xticklabels()[1:-1:2]:
+ label.set_size(7.5)
+ label.set_weight("light")
+
+for label in ax.get_yticklabels()[1:-1:]:
+ label.set_size(7.5)
+ label.set_weight("light")
+
+ax.text(
+ 0.5,
+ 0.5,
+ "Typography",
+ family="Times New Roman",
+ size=80,
+ color=".85",
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+ax.text(
+ 0.8375,
+ 0.725,
+ "matters",
+ family="Times New Roman",
+ size=30,
+ color="0.25",
+ horizontalalignment="center",
+ verticalalignment="center",
+)
+
+font = matplotlib.font_manager.FontProperties(
+ family="Roboto Slab", weight="regular", size=15
+)
+text = ax.set_title(
+ "Typography is an important dimension of a scientific figure",
+ fontproperties=font,
+ x=0,
+ y=0.975,
+ horizontalalignment="left",
+ verticalalignment="baseline",
+)
+text.set_path_effects(
+ [path_effects.Stroke(linewidth=2, foreground="white"), path_effects.Normal()]
+)
+
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/typography-matters.pdf", dpi=100)
+plt.savefig("../../figures/typography/typography-matters.png", dpi=300)
+plt.show()
diff --git a/code/typography/typography-text-path.py b/code/typography/typography-text-path.py
new file mode 100644
index 0000000..01300b5
--- /dev/null
+++ b/code/typography/typography-text-path.py
@@ -0,0 +1,113 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import scipy
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.font_manager import FontProperties
+
+
+def interpolate(X, Y, T):
+ dR = (np.diff(X) ** 2 + np.diff(Y) ** 2) ** 0.5
+ R = np.zeros_like(X)
+ R[1:] = np.cumsum(dR)
+ return np.interp(T, R, X), np.interp(T, R, Y), R[-1]
+
+
+def contour(X, Y, text, offset=0):
+
+ # Interpolate text along curve
+ # X0,Y0 for position + X1,Y1 for normal vectors
+ path = TextPath(
+ (0, -0.75), text, prop=FontProperties(size=2, family="Roboto", weight="bold")
+ )
+ V = path.vertices
+ X0, Y0, D = interpolate(X, Y, offset + V[:, 0])
+ X1, Y1, _ = interpolate(X, Y, offset + V[:, 0] + 0.1)
+
+ # Here we interpolate the original path to get the "remainder"
+ # (path minus text)
+ X, Y, _ = interpolate(X, Y, np.linspace(V[:, 0].max() + 1, D - 1, 200))
+ plt.plot(
+ X, Y, color="black", linewidth=0.5, markersize=1, marker="o", markevery=[0, -1]
+ )
+
+ # Transform text vertices
+ dX, dY = X1 - X0, Y1 - Y0
+ norm = np.sqrt(dX ** 2 + dY ** 2)
+ dX, dY = dX / norm, dY / norm
+ X0 += -V[:, 1] * dY
+ Y0 += +V[:, 1] * dX
+ V[:, 0], V[:, 1] = X0, Y0
+
+ # Faint outline
+ patch = PathPatch(
+ path,
+ facecolor="white",
+ zorder=10,
+ alpha=0.25,
+ edgecolor="white",
+ linewidth=1.25,
+ )
+ ax.add_artist(patch)
+
+ # Actual text
+ patch = PathPatch(
+ path, facecolor="black", zorder=30, edgecolor="black", linewidth=0.0
+ )
+ ax.add_artist(patch)
+
+
+# Some data
+n = 64
+X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+)
+Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+
+def f(x, y):
+ return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-(x ** 2) - y ** 2)
+
+
+n = 100
+x = np.linspace(-3, 3, n)
+y = np.linspace(-3, 3, n)
+X, Y = np.meshgrid(x, y)
+Z = 0.5 * f(X, Y)
+
+
+fig = plt.figure(figsize=(10, 5), dpi=100)
+levels = 10
+
+# Regular contour with straight labels
+ax = fig.add_subplot(1, 2, 1, aspect=1, xticks=[], yticks=[])
+CF = plt.contourf(Z, origin="lower", levels=levels)
+CS = plt.contour(Z, origin="lower", levels=levels, colors="black", linewidths=0.5)
+ax.clabel(CS, CS.levels)
+
+# Regular contour with curved labels
+# ! aspect=1 is critical here, else text path would be deformed
+ax = fig.add_subplot(1, 2, 2, aspect=1, xticks=[], yticks=[])
+CF = plt.contourf(Z, origin="lower", levels=levels)
+CS = plt.contour(
+ Z, origin="lower", levels=levels, alpha=0, colors="black", linewidths=0.5
+)
+
+for level, collection in zip(CS.levels[:], CS.collections[:]):
+ for path in collection.get_paths():
+ V = np.array(path.vertices)
+ text = "%.3f" % level
+ if level == 0.0:
+ text = " DO NOT CROSS •••" * 8
+ contour(V[:, 0], V[:, 1], text)
+
+plt.tight_layout()
+plt.savefig("../../figures/typography/typography-text-path.png", dpi=600)
+plt.savefig("../../figures/typography/typography-text-path.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/3d/README.md b/code/unsorted/3d/README.md
new file mode 100644
index 0000000..01e73ff
--- /dev/null
+++ b/code/unsorted/3d/README.md
@@ -0,0 +1,2 @@
+
+Temporary non commented / documented /cleaned code !!!
diff --git a/code/unsorted/3d/__pycache__/glm.cpython-37.pyc b/code/unsorted/3d/__pycache__/glm.cpython-37.pyc
new file mode 100644
index 0000000..6f7bf00
Binary files /dev/null and b/code/unsorted/3d/__pycache__/glm.cpython-37.pyc differ
diff --git a/code/unsorted/3d/__pycache__/plot.cpython-37.pyc b/code/unsorted/3d/__pycache__/plot.cpython-37.pyc
new file mode 100644
index 0000000..bbc1570
Binary files /dev/null and b/code/unsorted/3d/__pycache__/plot.cpython-37.pyc differ
diff --git a/code/unsorted/3d/axis.py b/code/unsorted/3d/axis.py
new file mode 100644
index 0000000..16d6d69
--- /dev/null
+++ b/code/unsorted/3d/axis.py
@@ -0,0 +1,13 @@
+import glm
+import plot
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+ plt.show()
diff --git a/code/unsorted/3d/bar.py b/code/unsorted/3d/bar.py
new file mode 100644
index 0000000..4d5cf23
--- /dev/null
+++ b/code/unsorted/3d/bar.py
@@ -0,0 +1,29 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+
+ n = 24
+ X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ )
+ Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+ cmap = plt.get_cmap("magma")
+ norm = mpl.colors.Normalize(vmin=-0.25, vmax=0.75)
+ facecolors = cmap(norm(Y))[..., :3]
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+ plot.bar(ax, camera, Y, facecolors=facecolors, edgecolors=facecolors * 0.25)
+
+ plt.show()
diff --git a/code/unsorted/3d/bunnies.py b/code/unsorted/3d/bunnies.py
new file mode 100644
index 0000000..950b598
--- /dev/null
+++ b/code/unsorted/3d/bunnies.py
@@ -0,0 +1,60 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+# Wavefront loader (only vertices and faces)
+def obj_load(filename):
+ V, Vi = [], []
+ with open(filename) as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ Vi.append([int(x) for x in values[1:4]])
+ return np.array(V), np.array(Vi) - 1
+
+
+# Model loading
+V, F = obj_load("bunny.obj")
+V = glm.fit_unit_cube(V)
+
+
+fig = plt.figure(figsize=(8, 8))
+
+ax = plt.subplot(221)
+camera = glm.camera(20, 45, 1.25, "perspective")
+plot.axis(ax, camera, ticks=False)
+plot.mesh(ax, camera, V, F, facecolor="black", edgecolor="black", linewidth=2.5)
+plot.mesh(ax, camera, V, F, cmap=plt.get_cmap("magma"), edgecolor="none")
+ax.text(0.99, 0.99, "Perpective", transform=ax.transAxes, ha="right", va="top")
+
+white = (1.0, 1.0, 1.0, 0.75)
+black = (0.0, 0.0, 0.0, 1.00)
+
+ax = plt.subplot(222)
+camera = glm.camera(90, 0, 2, "ortho")
+plot.axis(ax, camera, ticks=False)
+plot.mesh(ax, camera, V, F, linewidth=0.25, facecolor=white, edgecolor=black)
+ax.text(0.99, 0.99, "Orthographic (XZ)", transform=ax.transAxes, ha="right", va="top")
+
+ax = plt.subplot(223)
+camera = glm.camera(0, 90, 2, "ortho")
+plot.axis(ax, camera, ticks=False)
+plot.mesh(ax, camera, V, F, linewidth=0.25, facecolor=white, edgecolor=black)
+ax.text(0.99, 0.99, "Orthographic (XY)", transform=ax.transAxes, ha="right", va="top")
+
+ax = plt.subplot(224)
+camera = glm.camera(0, 0, 2, "ortho")
+plot.axis(ax, camera, ticks=False)
+plot.mesh(ax, camera, V, F, linewidth=0.25, facecolor=white, edgecolor=black)
+ax.text(0.99, 0.99, "Orthographic (ZY)", transform=ax.transAxes, ha="right", va="top")
+
+plt.tight_layout()
+plt.show()
diff --git a/code/unsorted/3d/bunny.obj b/code/unsorted/3d/bunny.obj
new file mode 100644
index 0000000..18abc08
--- /dev/null
+++ b/code/unsorted/3d/bunny.obj
@@ -0,0 +1,7474 @@
+# OBJ file format with ext .obj
+# vertex count = 2503
+# face count = 4968
+v -3.4101800e-003 1.3031957e-001 2.1754370e-002
+v -8.1719160e-002 1.5250145e-001 2.9656090e-002
+v -3.0543480e-002 1.2477885e-001 1.0983400e-003
+v -2.4901590e-002 1.1211138e-001 3.7560240e-002
+v -1.8405680e-002 1.7843055e-001 -2.4219580e-002
+v 1.9067940e-002 1.2144925e-001 3.1968440e-002
+v 6.0412000e-003 1.2494359e-001 3.2652890e-002
+v -1.3469030e-002 1.6299355e-001 -1.2000020e-002
+v -3.4393240e-002 1.7236688e-001 -9.8213000e-004
+v -8.4314160e-002 1.0957263e-001 3.7097300e-003
+v -4.2233540e-002 1.7211574e-001 -4.1799800e-003
+v -6.3308390e-002 1.5660615e-001 -1.3838790e-002
+v -7.6903950e-002 1.6708033e-001 -2.6931360e-002
+v -7.2253920e-002 1.1539550e-001 5.1670300e-002
+v 1.2981330e-002 1.1366375e-001 3.8302950e-002
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diff --git a/code/unsorted/3d/bunny.py b/code/unsorted/3d/bunny.py
new file mode 100644
index 0000000..3f5989c
--- /dev/null
+++ b/code/unsorted/3d/bunny.py
@@ -0,0 +1,43 @@
+import glm
+import plot
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# Wavefront loader (only vertices and faces)
+def obj_load(filename):
+ V, Vi = [], []
+ with open(filename) as f:
+ for line in f.readlines():
+ if line.startswith("#"):
+ continue
+ values = line.split()
+ if not values:
+ continue
+ if values[0] == "v":
+ V.append([float(x) for x in values[1:4]])
+ elif values[0] == "f":
+ Vi.append([int(x) for x in values[1:4]])
+ return np.array(V), np.array(Vi) - 1
+
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+ax.axis("off")
+
+camera = glm.camera(20, 45, 1.15, "perspective")
+plot.axis(ax, camera)
+
+V, F = obj_load("bunny.obj")
+V = glm.fit_unit_cube(V)
+plot.mesh(
+ ax,
+ camera,
+ V,
+ F,
+ cmap=plt.get_cmap("magma"),
+ linewidth=0.5,
+ edgecolor=(0, 0, 0, 0.5),
+)
+
+plt.show()
diff --git a/code/unsorted/3d/contour.py b/code/unsorted/3d/contour.py
new file mode 100644
index 0000000..5ff6cf0
--- /dev/null
+++ b/code/unsorted/3d/contour.py
@@ -0,0 +1,34 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+
+ n = 32
+ X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ )
+ Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+ def f(x, y):
+ return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-(x ** 2) - y ** 2)
+
+ n = 100
+ x = np.linspace(-3, 3, n)
+ y = np.linspace(-3, 3, n)
+ X, Y = np.meshgrid(x, y)
+ Z = 0.5 * f(X, Y)
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+ plot.contour(ax, camera, Z)
+
+plt.show()
diff --git a/code/unsorted/3d/glm.py b/code/unsorted/3d/glm.py
new file mode 100644
index 0000000..0156edf
--- /dev/null
+++ b/code/unsorted/3d/glm.py
@@ -0,0 +1,138 @@
+import numpy as np
+
+
+def normalize(X):
+ return X / (1e-16 + np.sqrt((np.array(X) ** 2).sum(axis=-1)))[..., np.newaxis]
+
+
+def rescale(V, vmin=0, vmax=1):
+ Vmin, Vmax = V.min(), V.max()
+ return vmin + (vmax - vmin) * (V - Vmin) / (Vmax - Vmin)
+
+
+def center(V):
+ return V - (V.min(axis=-1) + V.max(axis=-1)) / 2
+
+
+def clip(V, vmin=0, vmax=1):
+ return np.minimum(np.maximum(V, vmin), vmax)
+
+
+def degrees(angle):
+ return 180 * angle / np.pi
+
+
+def radians(angle):
+ return np.pi * angle / 180
+
+
+def frustum(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 * znear / (right - left)
+ M[1, 1] = +2.0 * znear / (top - bottom)
+ M[2, 2] = -(zfar + znear) / (zfar - znear)
+ M[0, 2] = (right + left) / (right - left)
+ M[2, 1] = (top + bottom) / (top - bottom)
+ M[2, 3] = -2.0 * znear * zfar / (zfar - znear)
+ M[3, 2] = -1.0
+ return M
+
+
+def perspective(fovy, aspect, znear, zfar):
+ h = np.tan(0.5 * radians(fovy)) * znear
+ w = h * aspect
+ return frustum(-w, w, -h, h, znear, zfar)
+
+
+def ortho(left, right, bottom, top, znear, zfar):
+ M = np.zeros((4, 4), dtype=np.float32)
+ M[0, 0] = +2.0 / (right - left)
+ M[1, 1] = +2.0 / (top - bottom)
+ M[2, 2] = -2.0 / (zfar - znear)
+ M[3, 3] = 1.0
+ M[0, 2] = -(right + left) / float(right - left)
+ M[1, 3] = -(top + bottom) / float(top - bottom)
+ M[2, 3] = -(zfar + znear) / float(zfar - znear)
+ return M
+
+
+def scale(x=1, y=1, z=1):
+ return np.array(
+ [[x, 0, 0, 0], [0, y, 0, 0], [0, 0, z, 0], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def translate(x=0, y=0, z=0):
+ return np.array(
+ [[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def xrotate(theta=0):
+ t = radians(theta)
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def yrotate(theta=0):
+ t = radians(theta)
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, 0, s, 0], [0, 1, 0, 0], [-s, 0, c, 0], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def zrotate(theta=0):
+ t = radians(theta)
+ c, s = np.cos(t), np.sin(t)
+ return np.array(
+ [[c, -s, 0, 0], [s, c, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=np.float32
+ )
+
+
+def fit_unit_cube(V):
+ xmin, xmax = V[:, 0].min(), V[:, 0].max()
+ ymin, ymax = V[:, 1].min(), V[:, 1].max()
+ zmin, zmax = V[:, 2].min(), V[:, 2].max()
+ scale = max([xmax - xmin, ymax - ymin, zmax - zmin])
+ V /= scale
+ V[:, 0] -= (xmax + xmin) / 2 / scale
+ V[:, 1] -= (ymax + ymin) / 2 / scale
+ V[:, 2] -= (zmax + zmin) / 2 / scale
+ return V
+
+
+def transform(V, mvp):
+ V = np.asarray(V)
+ shape = V.shape
+ V = V.reshape(-1, 3)
+ ones = np.ones(len(V), dtype=np.float32)
+ V = np.c_[V.astype(np.float32), ones] # Homogenous coordinates
+ V = V @ mvp.T # Transformed coordinates
+ V = V / V[:, 3].reshape(-1, 1) # Normalization
+ V = V[:, :3] # Normalized device coordinates
+ return V.reshape(shape)
+
+
+def frontback(T):
+ Z = (
+ (T[:, 1, 0] - T[:, 0, 0]) * (T[:, 1, 1] + T[:, 0, 1])
+ + (T[:, 2, 0] - T[:, 1, 0]) * (T[:, 2, 1] + T[:, 1, 1])
+ + (T[:, 0, 0] - T[:, 2, 0]) * (T[:, 0, 1] + T[:, 2, 1])
+ )
+ return Z < 0, Z >= 0
+
+
+def camera(xrotation=25, yrotation=45, zoom=1, mode="perspective"):
+ xrotation = min(max(xrotation, 0), 90)
+ yrotation = min(max(yrotation, 0), 90)
+ zoom = max(0.1, zoom)
+ model = scale(zoom, zoom, zoom) @ xrotate(xrotation) @ yrotate(yrotation)
+ view = translate(0, 0, -4.5)
+ if mode == "ortho":
+ proj = ortho(-1, +1, -1, +1, 1, 100)
+ else:
+ proj = perspective(25, 1, 1, 100)
+ return proj @ view @ model
diff --git a/code/unsorted/3d/platonic-solids.pdf b/code/unsorted/3d/platonic-solids.pdf
new file mode 100644
index 0000000..7f2bd13
Binary files /dev/null and b/code/unsorted/3d/platonic-solids.pdf differ
diff --git a/code/unsorted/3d/platonic-solids.png b/code/unsorted/3d/platonic-solids.png
new file mode 100644
index 0000000..620af2e
Binary files /dev/null and b/code/unsorted/3d/platonic-solids.png differ
diff --git a/code/unsorted/3d/platonic-solids.py b/code/unsorted/3d/platonic-solids.py
new file mode 100644
index 0000000..dc6c94c
--- /dev/null
+++ b/code/unsorted/3d/platonic-solids.py
@@ -0,0 +1,334 @@
+import glm
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from matplotlib.collections import PolyCollection
+
+
+def tetrahedron():
+ """ Tetrahedron with 4 faces, 6 edges and 4 vertices """
+ a = 2 * np.pi / 3
+ vertices = [
+ (0, 0.5, 0),
+ (0.5 * np.cos(0 * a), -0.25, 0.5 * np.sin(0 * a)),
+ (0.5 * np.cos(1 * a), -0.25, 0.5 * np.sin(1 * a)),
+ (0.5 * np.cos(2 * a), -0.25, 0.5 * np.sin(2 * a)),
+ ]
+ faces = [(1, 2, 3), (1, 2, 0), (2, 3, 0), (3, 1, 0)]
+ return np.array(vertices), np.array(faces)
+
+
+def octahedron():
+ """ Octahedron with 8 faces, 12 edges and 6 vertices """
+
+ r = 0.5 * 1 / np.sqrt(2)
+ vertices = [
+ (0, 0.5, 0),
+ (0, -0.5, 0),
+ (-r, 0, -r),
+ (r, 0, -r),
+ (r, 0, r),
+ (-r, 0, r),
+ ]
+ faces = [
+ (2, 3, 0),
+ (3, 4, 0),
+ (4, 5, 0),
+ (5, 2, 0),
+ (3, 2, 1),
+ (4, 3, 1),
+ (5, 4, 1),
+ (2, 5, 1),
+ ]
+ return np.array(vertices), np.array(faces)
+
+
+def dodecahedron():
+ """ Regular dodecahedron with 12 faces, 30 edges and 20 vertices """
+
+ r = (1 + np.sqrt(5)) / 2
+ vertices = [
+ (-1, -1, +1),
+ (r, 1 / r, 0),
+ (r, -1 / r, 0),
+ (-r, 1 / r, 0),
+ (-r, -1 / r, 0),
+ (0, r, 1 / r),
+ (0, r, -1 / r),
+ (1 / r, 0, -r),
+ (-1 / r, 0, -r),
+ (0, -r, -1 / r),
+ (0, -r, 1 / r),
+ (1 / r, 0, r),
+ (-1 / r, 0, r),
+ (+1, +1, -1),
+ (+1, +1, +1),
+ (-1, +1, -1),
+ (-1, +1, +1),
+ (+1, -1, -1),
+ (+1, -1, +1),
+ (-1, -1, -1),
+ ]
+ # faces = [ (19, 3, 2), (12, 19, 2), (15, 12, 2),
+ # (8, 14, 2), (18, 8, 2), (3, 18, 2),
+ # (20, 5, 4), (9, 20, 4), (16, 9, 4),
+ # (13, 17, 4), (1, 13, 4), (5, 1, 4),
+ # (7, 16, 4), (6, 7, 4), (17, 6, 4),
+ # (6, 15, 2), (7, 6, 2), (14, 7, 2),
+ # (10, 18, 3), (11, 10, 3), (19, 11, 3),
+ # (11, 1, 5), (10, 11, 5), (20, 10, 5),
+ # (20, 9, 8), (10, 20, 8), (18, 10, 8),
+ # (9, 16, 7), (8, 9, 7), (14, 8, 7),
+ # (12, 15, 6), (13, 12, 6), (17, 13, 6),
+ # (13, 1, 11), (12, 13, 11), (19, 12, 11) ]
+ faces = [
+ (19, 3, 2, 15, 12),
+ (8, 14, 2, 3, 18),
+ (20, 5, 4, 16, 9),
+ (13, 17, 4, 5, 1),
+ (7, 16, 4, 17, 6),
+ (6, 15, 2, 14, 7),
+ (10, 18, 3, 19, 11),
+ (11, 1, 5, 20, 10),
+ (20, 9, 8, 18, 10),
+ (9, 16, 7, 14, 8),
+ (12, 15, 6, 17, 13),
+ (13, 1, 11, 19, 12),
+ ]
+ vertices = np.array(vertices) / np.sqrt(3) / 2
+ faces = np.array(faces) - 1
+ return vertices, faces
+
+
+def icosahedron():
+ """ Regular icosahedron with 20 faces, 30 edges and 12 vertices """
+
+ a = (1 + np.sqrt(5)) / 2
+ vertices = [
+ (-1, a, 0),
+ (1, a, 0),
+ (-1, -a, 0),
+ (1, -a, 0),
+ (0, -1, a),
+ (0, 1, a),
+ (0, -1, -a),
+ (0, 1, -a),
+ (a, 0, -1),
+ (a, 0, 1),
+ (-a, 0, -1),
+ (-a, 0, 1),
+ ]
+ faces = [
+ [0, 11, 5],
+ [0, 5, 1],
+ [0, 1, 7],
+ [0, 7, 10],
+ [0, 10, 11],
+ [1, 5, 9],
+ [5, 11, 4],
+ [11, 10, 2],
+ [10, 7, 6],
+ [7, 1, 8],
+ [3, 9, 4],
+ [3, 4, 2],
+ [3, 2, 6],
+ [3, 6, 8],
+ [3, 8, 9],
+ [4, 9, 5],
+ [2, 4, 11],
+ [6, 2, 10],
+ [8, 6, 7],
+ [9, 8, 1],
+ ]
+ vertices = np.array(vertices) / np.sqrt(a + 2) / 2
+ faces = np.array(faces)
+ return vertices, faces
+
+
+def cube():
+ vertices = [
+ (0, 0, 0),
+ (1, 0, 0),
+ (1, 0, 1),
+ (0, 0, 1),
+ (0, 1, 0),
+ (1, 1, 0),
+ (1, 1, 1),
+ (0, 1, 1),
+ ]
+ faces = [
+ [0, 1, 2, 3],
+ [4, 5, 6, 7],
+ [0, 1, 5, 4],
+ [1, 2, 6, 5],
+ [2, 3, 7, 6],
+ [3, 0, 4, 7],
+ ]
+ return (np.array(vertices) - 0.5) / np.sqrt(2), np.array(faces)
+
+
+def plot(ax, camera, V, F):
+ ax.axis("off")
+
+ T = glm.transform(V[F], camera)
+ V, Z = T[..., :2], T[..., 2].mean(axis=-1)
+ V = V[np.argsort(-Z)]
+ collection = PolyCollection(
+ V,
+ antialiased=True,
+ linewidth=1.0,
+ facecolor=(0.9, 0.9, 1, 0.75),
+ edgecolor=(0, 0, 0.75, 0.25),
+ )
+ ax.add_collection(collection)
+
+
+fig = plt.figure(figsize=(8, 5.5))
+camera = glm.camera(25, 35, 1.5, "perspective")
+
+ax = plt.subplot(2, 3, 1, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+ax.axis("off")
+ax.text(
+ 0.5,
+ 0.6,
+ "PLATONIC",
+ transform=ax.transAxes,
+ weight="black",
+ size=24,
+ va="bottom",
+ ha="center",
+ family="Source Sans Pro",
+)
+ax.text(
+ 0.5,
+ 0.6,
+ "S O L I D S",
+ transform=ax.transAxes,
+ weight="light",
+ size=22,
+ va="top",
+ ha="center",
+ family="Source Sans Pro",
+)
+ax.text(
+ 0.5,
+ 0.475,
+ "matplotlib.org",
+ transform=ax.transAxes,
+ weight="light",
+ size=13,
+ va="top",
+ ha="center",
+ family="Source Code Pro",
+)
+
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 2, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *tetrahedron())
+ax.text(
+ 0.5,
+ 1.0,
+ "Tetrahedron",
+ transform=ax.transAxes,
+ weight="bold",
+ va="bottom",
+ ha="center",
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "4 faces, 4 vertices, 6 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 3, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *cube())
+ax.text(
+ 0.5, 1.0, "Cube", transform=ax.transAxes, weight="bold", va="bottom", ha="center"
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "6 faces, 8 vertices, 12 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 4, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *octahedron())
+ax.text(
+ 0.5,
+ 1.0,
+ "Octahedron",
+ transform=ax.transAxes,
+ weight="bold",
+ va="bottom",
+ ha="center",
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "8 faces, 6 vertices, 12 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 5, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *dodecahedron())
+ax.text(
+ 0.5,
+ 1.0,
+ "Dodecahedron",
+ transform=ax.transAxes,
+ weight="bold",
+ va="bottom",
+ ha="center",
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "12 faces, 20 vertices, 30 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+# -----------------------------------------------------------------------------
+ax = plt.subplot(2, 3, 6, xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+plot(ax, camera, *icosahedron())
+ax.text(
+ 0.5,
+ 1.0,
+ "Icosahedron",
+ transform=ax.transAxes,
+ weight="bold",
+ va="bottom",
+ ha="center",
+)
+ax.text(
+ 0.5,
+ 1.0,
+ "20 faces, 12 vertices, 30 edges",
+ transform=ax.transAxes,
+ va="top",
+ ha="center",
+ size="x-small",
+)
+
+
+plt.tight_layout()
+plt.savefig("platonic-solids.png", dpi=300)
+plt.savefig("platonic-solids.pdf")
+plt.show()
diff --git a/code/unsorted/3d/plot.py b/code/unsorted/3d/plot.py
new file mode 100644
index 0000000..01b08f8
--- /dev/null
+++ b/code/unsorted/3d/plot.py
@@ -0,0 +1,595 @@
+import glm
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from matplotlib.textpath import TextPath
+from matplotlib.patches import PathPatch
+from matplotlib.collections import PolyCollection
+
+
+# -----------------------------------------------------------------------------
+def axis(ax, camera, ticks=True):
+ """
+ Draw three dimension axis with ticks and tick labels.
+
+ Parameters:
+ -----------
+
+ ax : matplotlib.axes instance
+ The regulmat axes where to draw
+
+ camera : 4x4 numpy array
+ A transformation matrix in homogenous coordinates (4x4)
+
+ ticks : bool
+ Whether to draw tick and tick labels
+
+ Note:
+ -----
+
+ The axis is drawn inside the unit cube centered on the origin:
+
+ - XZ lies on the plane Y = -0.5
+ - XY lies on the plane Z = -0.5
+ - YZ lies on the plane X = +0.5
+ - xticks goes from (-0.5, -0.5, +0.5) to (+0.5, -0.5, +0.5)
+ - zticks goes from (-0.5, -0.5, -0.5) to (-0.5, -0.5, +0.5)
+ - yticks goes from (-0.5, -0.5, -0.5) to (-0.5, +0.5, -0.5)
+ """
+
+ # Mandatory settings for matplotlib axes
+ # --------------------------------------
+ ax.set_xlim(-1, 1)
+ ax.set_ylim(-1, 1)
+ ax.set_aspect(1)
+ ax.axis("off")
+
+ # Parameters
+ # ---------------------------------
+ axis_linewidth = 1.00
+ grid_linewidth = 0.50
+ ticks_linewidth = 0.75
+
+ grid_color = "0.5"
+ axis_color = "0.0"
+ ticks_color = "0.0"
+ ticks_length = 0.025
+ ticks_pad = 0.05
+
+ # ticks and ticklabels
+ nx = 11
+ xticks = np.linspace(-0.5, +0.5, nx, endpoint=True)
+ xticklabels = ["%.1f" % x for x in xticks]
+
+ ny = 11
+ yticks = np.linspace(-0.5, +0.5, ny, endpoint=True)
+ yticklabels = ["%.1f" % y for y in yticks]
+
+ nz = 11
+ zticks = np.linspace(-0.5, +0.5, nz, endpoint=True)
+ zticklabels = ["%.1f" % z for z in zticks]
+
+ colors = []
+ segments = []
+ linewidths = []
+
+ # XZ axis
+ # ---------------------------------
+ X0 = np.linspace([0, 0, 0], [1, 0, 0], nx) - 0.5
+ X1 = X0 + [0, 0, 1]
+ X2 = X1 + [0, 0, ticks_length]
+ X3 = X2 + [0, 0, ticks_pad]
+ X0 = glm.transform(X0, camera)[..., :2]
+ X1 = glm.transform(X1, camera)[..., :2]
+ X2 = glm.transform(X2, camera)[..., :2]
+ X3 = glm.transform(X3, camera)[..., :2]
+
+ Z0 = np.linspace([0, 0, 0], [0, 0, 1], nz) - 0.5
+ Z1 = Z0 + [1, 0, 0]
+ Z2 = Z0 - [ticks_length, 0, 0]
+ Z3 = Z2 - [ticks_pad, 0, 0]
+ Z0 = glm.transform(Z0, camera)[..., :2]
+ Z1 = glm.transform(Z1, camera)[..., :2]
+ Z2 = glm.transform(Z2, camera)[..., :2]
+ Z3 = glm.transform(Z3, camera)[..., :2]
+
+ for p0, p1, p2, p3, label in zip(X0, X1, X2, X3, xticklabels):
+ # grid line
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ if not ticks:
+ continue
+
+ # tick
+ segments.append([p1, p2])
+ linewidths.append(ticks_linewidth)
+ colors.append(ticks_color)
+
+ # label
+ ax.text(p3[0], p3[1], label, ha="center", va="center", size="x-small")
+
+ for p0, p1, p2, p3, label in zip(Z0, Z1, Z2, Z3, zticklabels):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ if not ticks:
+ continue
+
+ # ticks
+ segments.append([p0, p2])
+ linewidths.append(ticks_linewidth)
+ colors.append(ticks_color)
+
+ # label
+ ax.text(p3[0], p3[1], label, ha="center", va="center", size="x-small")
+
+ # axis border
+ segments.append([X0[0], X0[-1], X1[-1], X1[0], X0[0]])
+ linewidths.append(axis_linewidth)
+ colors.append(axis_color)
+
+ # XY axis
+ # ---------------------------------
+ X0 = np.linspace([0, 0, 0], [1, 0, 0], nx) - 0.5
+ X1 = X0 + [0, 1, 0]
+ X0 = glm.transform(X0, camera)[..., :2]
+ X1 = glm.transform(X1, camera)[..., :2]
+ Y0 = np.linspace([0, 0, 0], [0, 1, 0], ny) - 0.5
+ Y1 = Y0 + [1, 0, 0]
+ Y2 = Y0 - [ticks_length, 0, 0]
+ Y3 = Y2 - [ticks_pad, 0, 0]
+ Y0 = glm.transform(Y0, camera)[..., :2]
+ Y1 = glm.transform(Y1, camera)[..., :2]
+ Y2 = glm.transform(Y2, camera)[..., :2]
+ Y3 = glm.transform(Y3, camera)[..., :2]
+
+ for p0, p1 in zip(X0, X1):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ for p0, p1, p2, p3, label in zip(Y0, Y1, Y2, Y3, yticklabels):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ if not ticks:
+ continue
+ # ticks
+ segments.append([p0, p2])
+ linewidths.append(ticks_linewidth)
+ colors.append(ticks_color)
+
+ # label
+ ax.text(p3[0], p3[1], label, ha="center", va="center", size="x-small")
+
+ # axis border
+ segments.append([X0[0], X0[-1], X1[-1], X1[0], X0[0]])
+ linewidths.append(axis_linewidth)
+ colors.append(axis_color)
+
+ # ZY axis
+ # ---------------------------------
+ Z0 = np.linspace([1, 0, 0], [1, 0, 1], nx) - 0.5
+ Z1 = Z0 + [0, 1, 0]
+ Z0 = glm.transform(Z0, camera)[..., :2]
+ Z1 = glm.transform(Z1, camera)[..., :2]
+
+ Y0 = np.linspace([1, 0, 0], [1, 1, 0], ny) - 0.5
+ Y1 = Y0 + [0, 0, 1]
+ Y0 = glm.transform(Y0, camera)[..., :2]
+ Y1 = glm.transform(Y1, camera)[..., :2]
+
+ for p0, p1 in zip(Z0, Z1):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ for p0, p1 in zip(Y0, Y1):
+ # grid lines
+ segments.append([p0, p1])
+ linewidths.append(grid_linewidth)
+ colors.append(grid_color)
+
+ # axis border
+ segments.append([Y0[0], Y0[-1], Y1[-1], Y1[0], Y0[0]])
+ linewidths.append(axis_linewidth)
+ colors.append(axis_color)
+
+ # Actual rendering
+ collection = PolyCollection(
+ segments,
+ closed=False,
+ clip_on=False,
+ linewidths=linewidths,
+ facecolors="None",
+ edgecolor=colors,
+ )
+ ax.add_collection(collection)
+
+
+# -----------------------------------------------------------------------------
+def mesh(
+ ax,
+ camera,
+ vertices,
+ faces,
+ cmap=None,
+ facecolor="white",
+ edgecolor="none",
+ linewidth=1.0,
+ mode="all",
+):
+
+ # Mandatory settings for matplotlib axes
+ # --------------------------------------
+ ax.set_xlim(-1, 1)
+ ax.set_ylim(-1, 1)
+ ax.set_aspect(1)
+ ax.axis("off")
+
+ facecolor = mpl.colors.to_rgba_array(facecolor)
+ edgecolor = mpl.colors.to_rgba_array(edgecolor)
+
+ T = glm.transform(vertices, camera)[faces]
+ Z = -T[:, :, 2].mean(axis=1)
+
+ # Facecolor using depth buffer
+ if cmap is not None:
+ cmap = plt.get_cmap("magma")
+ norm = mpl.colors.Normalize(vmin=Z.min(), vmax=Z.max())
+ facecolor = cmap(norm(Z))
+
+ # Back face culling
+ if mode == "front":
+ front, back = glm.frontback(T)
+ T, Z = T[front], Z[front]
+ if len(facecolor) == len(faces):
+ facecolor = facecolor[front]
+ if len(edgecolor) == len(faces):
+ facecolor = edgecolor[front]
+ # Front face culling
+ elif mode == "back":
+ front, back = glm.frontback(T)
+ T, Z = T[back], Z[back]
+ if len(facecolor) == len(faces):
+ facecolor = facecolor[back]
+ if len(edgecolor) == len(faces):
+ facecolor = edgecolor[back]
+
+ # Separate 2d triangles from zbuffer
+ triangles = T[:, :, :2]
+ antialiased = True
+ if linewidth == 0.0:
+ antialiased = False
+
+ # Sort triangles according to z buffer
+ I = np.argsort(Z)
+ triangles = triangles[I, :]
+ if len(facecolor) == len(I):
+ facecolor = facecolor[I, :]
+ if len(edgecolor) == len(I):
+ edgecolor = edgecolor[I, :]
+
+ collection = PolyCollection(
+ triangles,
+ linewidth=linewidth,
+ antialiased=antialiased,
+ facecolor=facecolor,
+ edgecolor=edgecolor,
+ )
+ ax.add_collection(collection)
+
+
+# -----------------------------------------------------------------------------
+def surf(
+ ax,
+ camera,
+ Y,
+ facecolor="white",
+ edgecolor="black",
+ facecolors=None,
+ edgecolors=None,
+ linewidth=0.5,
+ shading=(1.00, 1.00, 1.00, 0.75, 0.50, 1.00),
+):
+
+ # Mandatory settings for matplotlib axes
+ ax.set_xlim(-1, 1)
+ ax.set_ylim(-1, 1)
+ ax.set_aspect(1)
+ ax.axis("off")
+
+ # Facecolor
+ if facecolors is None:
+ facecolors = np.zeros((Y.shape[0], Y.shape[1], 3))
+ facecolors[...] = mpl.colors.to_rgb(facecolor)
+ facecolors[...] = facecolors[..., :3]
+
+ # Facecolor
+ if edgecolors is None:
+ edgecolors = np.zeros((Y.shape[0], Y.shape[1], 3))
+ edgecolors[...] = mpl.colors.to_rgb(edgecolor)
+ edgecolors[...] = edgecolors[..., :3]
+
+ # Surface
+ n = Y.shape[0]
+ T = np.linspace(-0.5, +0.5, n)
+ X, Z = np.meshgrid(T, T)
+ V = np.c_[X.ravel(), Y.ravel() - 0.5, Z.ravel()]
+ F = (np.arange((n - 1) * (n)).reshape(n - 1, n))[:, :-1].T
+ F = np.repeat(F.ravel(), 6).reshape(n - 1, n - 1, 6)
+ F[:, :] += np.array([0, n + 1, 1, 0, n, n + 1])
+ F = F.reshape(-1, 3)
+ V = glm.transform(V, camera)
+ T = V[F]
+
+ # Create list from array such that we can add lines
+ polys = T[..., :2].tolist()
+ zbuffer = (-T[..., 2].mean(axis=1)).tolist()
+ edgecolors = ["none",] * len(polys)
+ antialiased = [False,] * len(polys)
+ linewidths = [1.0,] * len(polys)
+ facecolors = ((facecolors.reshape(-1, 3))[F].mean(axis=1)).tolist()
+
+ # Helper function for creating a line segment between two points
+ def segment(p0, p1, linewidth=1.5, epsilon=0.01):
+ polys.append([p0[:2], p1[:2]])
+ facecolors.append("none")
+ edgecolors.append("black")
+ antialiased.append(True)
+ linewidths.append(linewidth)
+ zbuffer.append(-(p0[2] + p1[2]) / 2 + epsilon)
+
+ # Border + grid over the surface
+ V = V.reshape(n, n, -1)
+
+ # Border
+ for i in range(0, n - 1):
+ segment(V[i, 0], V[i + 1, 0])
+ segment(V[i, -1], V[i + 1, -1])
+ segment(V[0, i], V[0, i + 1])
+ segment(V[-1, i], V[-1, i + 1])
+
+ for i in range(0, n - 1):
+ for j in range(0, n - 1):
+ segment(V[i, j], V[i + 1, j], 0.5)
+ segment(V[j, i], V[j, i + 1], 0.5)
+
+ # Sort everything
+ I = np.argsort(zbuffer)
+ polys = [polys[i] for i in I]
+ facecolors = [facecolors[i] for i in I]
+ edgecolors = [edgecolors[i] for i in I]
+ linewidths = [linewidths[i] for i in I]
+ antialiased = [antialiased[i] for i in I]
+
+ # Display
+ collection = PolyCollection(
+ polys,
+ linewidth=linewidths,
+ antialiased=antialiased,
+ facecolors=facecolors,
+ edgecolors=edgecolors,
+ )
+ ax.add_collection(collection)
+
+
+# -----------------------------------------------------------------------------
+def bar(
+ ax,
+ camera,
+ Y,
+ facecolor="white",
+ edgecolor="black",
+ facecolors=None,
+ edgecolors=None,
+ linewidth=0.5,
+ shading=(1.00, 1.00, 1.00, 0.75, 0.50, 1.00),
+):
+
+ # Mandatory settings for matplotlib axes
+ ax.set_xlim(-1, 1)
+ ax.set_ylim(-1, 1)
+ ax.set_aspect(1)
+ ax.axis("off")
+
+ # Facecolor
+ if facecolors is None:
+ facecolors = np.zeros((Y.shape[0], Y.shape[1], 3))
+ facecolors[...] = mpl.colors.to_rgb(facecolor)
+ facecolors[...] = facecolors[..., :3]
+
+ # Facecolor
+ if edgecolors is None:
+ edgecolors = np.zeros((Y.shape[0], Y.shape[1], 3))
+ edgecolors[...] = mpl.colors.to_rgb(edgecolor)
+ edgecolors[...] = edgecolors[..., :3]
+
+ # Here we compute the eight 3D vertices necessary to render a bar
+ shape = Y.shape
+ dx, dz = 0.5 / shape[0], 0.5 / shape[1]
+ X, Z = np.meshgrid(
+ np.linspace(-0.5 + dx, 0.5 - dx, shape[0]),
+ np.linspace(-0.5 + dz, 0.5 - dz, shape[1]),
+ )
+ P = np.c_[X.ravel(), np.zeros(X.size), Z.ravel()]
+ P = P.reshape(shape[0], shape[1], 3)
+ dx = np.array([dx, 0, 0])
+ dz = np.array([0, 0, dz])
+ dy = np.zeros((shape[0], shape[1], 3))
+ V = np.zeros((8, shape[0], shape[1], 3))
+
+ dy[..., 1] = -0.5
+ V[0] = P + dx - dz + dy
+ V[1] = P - dx - dz + dy
+ V[2] = P + dx + dz + dy
+ V[3] = P - dx + dz + dy
+
+ dy[..., 1] = -0.5 + Y
+ V[4] = P + dx - dz + dy
+ V[5] = P - dx - dz + dy
+ V[6] = P + dx + dz + dy
+ V[7] = P - dx + dz + dy
+
+ # Transformation of the vertices in 2D + z
+ T = glm.transform(V, camera)
+ V = T[:, :2].reshape(8, shape[0], shape[1], 2)
+ Z = -T[:, 2].reshape(8 * shape[0] * shape[1])
+
+ # Normalization of the z value such that we can maipulate zbar / zface
+ # Drawback is that it cannot be sorted anymore with other 3d objects
+ Z = glm.rescale(Z, 0, 1)
+
+ # Building of individual bars (without bottom face)
+ # and a new z buffer that is a combination of the bar and face mean z
+ faces, colors, zbuffer = [], [], []
+ indices = (
+ [4, 5, 7, 6], # +Y
+ [0, 1, 3, 2], # -Y
+ [0, 1, 5, 4], # +X
+ [1, 3, 7, 5], # -X
+ [2, 3, 7, 6], # -Z
+ [0, 2, 6, 4],
+ ) # +Z
+ FC = np.zeros((shape[0], shape[1], len(indices), 3))
+ EC = np.zeros((shape[0], shape[1], len(indices), 3))
+ F = np.zeros((shape[0], shape[1], len(indices), 4, 2))
+ Z_ = np.zeros((shape[0], shape[1], len(indices)))
+
+ # This could probably be vectorized but it might be tricky
+ for i in range(shape[0]):
+ for j in range(shape[1]):
+ index = np.arange(8) * shape[0] * shape[1] + i * shape[1] + j
+ zbar = 1 * Z[index].mean()
+ for k, ((v0, v1, v2, v3), shade) in enumerate(zip(indices, shading)):
+ zface = (Z[index[v0]] + Z[index[v1]] + Z[index[v2]] + Z[index[v3]]) / 4
+ F[i, j, k] = V[v0, i, j], V[v1, i, j], V[v2, i, j], V[v3, i, j]
+ Z_[i, j, k] = 10 * zbar + zface
+ FC[i, j, k] = facecolors[i, j] * shade
+ EC[i, j, k] = edgecolors[i, j]
+
+ # Final reshape for sorting quads
+ Z = Z_.reshape(shape[0] * shape[1] * len(indices))
+ F = F.reshape(shape[0] * shape[1] * len(indices), 4, 2)
+ FC = FC.reshape(shape[0] * shape[1] * len(indices), 3)
+ EC = EC.reshape(shape[0] * shape[1] * len(indices), 3)
+ I = np.argsort(Z)
+
+ collection = PolyCollection(
+ F[I], linewidth=0.25, facecolors=FC[I], edgecolors=EC[I]
+ )
+ ax.add_collection(collection)
+
+
+# -----------------------------------------------------------------------------
+def contour(ax, camera, Y, n_levels=32):
+
+ n = Y.shape[0]
+ T = np.linspace(-0.5, +0.5, n)
+ X, Z = np.meshgrid(T, T)
+ C = ax.contour(X, Z, Y, n_levels)
+
+ cmap = plt.get_cmap("magma")
+ ymin, ymax = Y.min(), Y.max()
+ norm = mpl.colors.Normalize(vmin=2 * ymin, vmax=ymax)
+
+ dy = 0.99 * (ymax - ymin) / n_levels
+
+ segments = []
+ facecolors = []
+ edgecolors = []
+ closed = []
+ antialiased = []
+ epsilon = 0.0025
+
+ for level, collection in zip(C.levels, C.collections):
+
+ local_segments = []
+ local_zbuffer = []
+ local_antialiased = []
+ local_edgecolors = []
+ local_facecolors = []
+ local_closed = []
+
+ # Collect and transform all paths
+ paths = []
+ for path in collection.get_paths():
+ V = np.array(path.vertices)
+ V = np.c_[V[:, 0], level * np.ones(len(path)), V[:, 1]]
+ V0 = V
+ V1 = V0 - [0, dy, 0]
+ T0 = glm.transform(V0, camera)
+ T1 = glm.transform(V1, camera)
+ V0, Z0 = T0[:, :2], T0[:, 2]
+ V1, Z1 = T1[:, :2], T1[:, 2]
+ paths.append([V, V0, Z0, V1, Z1])
+
+ for (V, V0, Z0, V1, Z1) in paths:
+
+ for i in range(len(V0) - 1):
+ local_segments.append([V0[i], V0[i + 1], V1[i + 1], V1[i]])
+ local_zbuffer.append(-(Z0[i] + Z0[i + 1] + Z1[i + 1] + Z1[i]) / 4)
+ local_antialiased.append(False)
+ local_edgecolors.append("none")
+ facecolor = np.array(cmap(norm(level))[:3])
+ local_facecolors.append(0.75 * facecolor)
+ local_closed.append(True)
+
+ local_segments.append([V1[i + 1], V1[i]])
+ local_zbuffer.append(-(Z1[i + 1] + Z1[i]) / 2 + epsilon)
+ local_antialiased.append(True)
+ facecolor = np.array(cmap(norm(level))[:3])
+ local_edgecolors.append(facecolor * 0.25)
+ local_facecolors.append("none")
+ local_closed.append(False)
+
+ if ((V[0] - V[-1]) ** 2).sum() > 0.00001:
+ local_segments.append([V0[i + 1], V0[i]])
+ local_zbuffer.append(-(Z0[i + 1] + Z0[i]) / 2 + epsilon)
+ local_antialiased.append(True)
+ facecolor = np.array(cmap(norm(level))[:3])
+ local_edgecolors.append(facecolor * 0.25)
+ local_facecolors.append("none")
+ local_closed.append(False)
+
+ I = np.argsort(local_zbuffer)
+ local_segments = [local_segments[i] for i in I]
+ local_facecolors = [local_facecolors[i] for i in I]
+ local_edgecolors = [local_edgecolors[i] for i in I]
+ local_closed = [local_closed[i] for i in I]
+ local_antialiased = [local_antialiased[i] for i in I]
+
+ segments.extend(local_segments)
+ facecolors.extend(local_facecolors)
+ edgecolors.extend(local_edgecolors)
+ closed.extend(local_closed)
+ antialiased.extend(local_antialiased)
+
+ for (V, V0, Z0, V1, Z1) in paths:
+ if ((V[0] - V[-1]) ** 2).sum() < 0.00001:
+ segments.append(V0)
+ antialiased.append(True)
+ facecolor = cmap(norm(level))[:3]
+ facecolors.append(facecolor)
+ edgecolors.append("black")
+ closed.append(True)
+
+ collection.remove()
+
+ collection = PolyCollection(
+ segments,
+ linewidth=0.5,
+ antialiased=antialiased,
+ closed=closed,
+ facecolors=facecolors,
+ edgecolors=edgecolors,
+ )
+ ax.add_collection(collection)
diff --git a/code/unsorted/3d/scatter.py b/code/unsorted/3d/scatter.py
new file mode 100644
index 0000000..872afb0
--- /dev/null
+++ b/code/unsorted/3d/scatter.py
@@ -0,0 +1,76 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+ from matplotlib.patches import Ellipse
+ from matplotlib.collections import PolyCollection
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+
+ np.random.seed(1)
+ n = 1024
+ P = 0.2 * np.random.normal(0, 1, (n, 3))
+
+ # Bottom shadow
+ V = glm.transform(P * [1, 0, 1] - [0, 0.5, 0], camera)
+ T = np.linspace(0, 2 * np.pi, 12)
+ radius = 0.015
+ X, Y, Z = radius * np.cos(T), np.zeros(len(T)), radius * np.sin(T)
+ C = np.c_[X, Y, Z]
+ polys = []
+ for i in range(n):
+ V = glm.transform(C + [P[i, 0], -0.5, P[i, 2]], camera)[:, :2]
+ polys.append(V)
+
+ collection = PolyCollection(
+ polys, linewidths=0, alpha=0.5, zorder=+10, facecolors="0.5", edgecolor="none"
+ )
+ ax.add_collection(collection)
+
+ # Actual scatter
+ V = glm.transform(P, camera)
+ X, Y, Z = V[:, 0], V[:, 1], V[:, 2]
+ I = np.argsort(Z)
+ X, Y = X[I], Y[I]
+ facecolor = [
+ (0, 0, 0, 0),
+ (0, 0, 0, 0),
+ (0, 0, 0, 0),
+ mpl.colors.to_rgba("C4"),
+ (1, 1, 1, 0.25),
+ (1, 1, 1, 1),
+ ] * len(X)
+ edgecolor = [
+ (0, 0, 0, 0.05),
+ (0, 0, 0, 0.10),
+ (0, 0, 0, 0.15),
+ (0, 0, 0, 1.00),
+ (0, 0, 0, 0.00),
+ (0, 0, 0, 0.00),
+ ] * len(X)
+ linewidth = [6, 4, 2, 0.5, 0.0, 0.0] * len(X)
+ dX = (0, 0, 0, 0, -0.0035, -0.0035) * len(X)
+ dY = (0, 0, 0, 0, +0.0025, +0.0025) * len(Y)
+ size = np.array((1, 1, 1, 1, 0.25, 0.05) * len(X)) * 50
+ X, Y = np.repeat(X, 6), np.repeat(Y, 6)
+ ax.scatter(
+ X + dX,
+ Y + dY,
+ s=size,
+ linewidth=linewidth,
+ zorder=10,
+ facecolor=facecolor,
+ edgecolor=edgecolor,
+ )
+
+ plt.savefig("scatter.png", dpi=300)
+ plt.savefig("scatter.pdf")
+ plt.show()
diff --git a/code/unsorted/3d/sphere.py b/code/unsorted/3d/sphere.py
new file mode 100644
index 0000000..bb1db4a
--- /dev/null
+++ b/code/unsorted/3d/sphere.py
@@ -0,0 +1,72 @@
+import glm
+import plot
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def sphere(radius=1.0, slices=32, stacks=32):
+ slices += 1
+ stacks += 1
+ n = slices * stacks
+ vertices = np.zeros((n, 3))
+ theta1 = np.repeat(np.linspace(0, np.pi, stacks, endpoint=True), slices)
+ theta2 = np.tile(np.linspace(0, 2 * np.pi, slices, endpoint=True), stacks)
+
+ vertices[:, 1] = np.sin(theta1) * np.cos(theta2) * radius
+ vertices[:, 2] = np.cos(theta1) * radius
+ vertices[:, 0] = np.sin(theta1) * np.sin(theta2) * radius
+
+ indices = []
+ for i in range(stacks - 1):
+ for j in range(slices - 1):
+ indices.append(i * (slices) + j)
+ indices.append(i * (slices) + j + 1)
+ indices.append(i * (slices) + j + slices + 1)
+
+ indices.append(i * (slices) + j + slices + 1)
+ indices.append(i * (slices) + j + slices)
+ indices.append(i * (slices) + j)
+
+ indices = np.array(indices)
+ indices = indices.reshape(len(indices) // 3, 3)
+ return vertices, indices
+
+
+def lighting(F, direction=(1, 1, 1), color=(1, 0, 0), specular=False):
+
+ # Faces center
+ C = F.mean(axis=1)
+ # Faces normal
+ N = glm.normalize(np.cross(F[:, 2] - F[:, 0], F[:, 1] - F[:, 0]))
+ # Relative light direction
+ D = glm.normalize(C - direction)
+ # Diffuse term
+ diffuse = glm.clip((N * D).sum(-1).reshape(-1, 1))
+
+ # Specular term
+ if specular:
+ specular = np.power(diffuse, 24)
+ return np.maximum(diffuse * color, specular)
+
+ return diffuse * color
+
+
+V, F = sphere(0.5, 2 * 32, 2 * 32)
+facecolor = lighting(V[F], (-1, 1, 1), (1, 0, 0), True)
+
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1)
+
+camera = glm.camera(20, 45, 1.15, "perspective")
+plot.axis(ax, camera)
+plot.mesh(
+ ax,
+ camera,
+ V,
+ F,
+ mode="front",
+ linewidth=0,
+ facecolor=facecolor,
+ edgecolor=(0, 0, 0, 0),
+)
+plt.show()
diff --git a/code/unsorted/3d/surf.py b/code/unsorted/3d/surf.py
new file mode 100644
index 0000000..4ad366b
--- /dev/null
+++ b/code/unsorted/3d/surf.py
@@ -0,0 +1,29 @@
+import glm
+import plot
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+
+ n = 24
+ X, Z = np.meshgrid(
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ np.linspace(-0.5 + 0.5 / n, +0.5 - 0.5 / n, n),
+ )
+ Y = 0.75 * np.exp(-10 * (X ** 2 + Z ** 2))
+
+ cmap = plt.get_cmap("magma")
+ norm = mpl.colors.Normalize(vmin=-0.25, vmax=0.75)
+ facecolors = cmap(norm(Y))[..., :3]
+
+ fig = plt.figure(figsize=(6, 6))
+ ax = fig.add_axes([0, 0, 1, 1])
+
+ camera = glm.camera(25, 45, 1, "perspective")
+ plot.axis(ax, camera)
+ plot.surf(ax, camera, Y, facecolors=facecolors)
+
+plt.show()
diff --git a/code/unsorted/advanced-linestyles.pdf b/code/unsorted/advanced-linestyles.pdf
new file mode 100644
index 0000000..34bbf35
Binary files /dev/null and b/code/unsorted/advanced-linestyles.pdf differ
diff --git a/code/unsorted/advanced-linestyles.py b/code/unsorted/advanced-linestyles.py
new file mode 100644
index 0000000..81315a9
--- /dev/null
+++ b/code/unsorted/advanced-linestyles.py
@@ -0,0 +1,204 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(figsize=(4.25, 6))
+ax = plt.subplot(111, aspect=1, frameon=False, xticks=[], yticks=[])
+
+X = np.linspace(0, 2 * np.pi, 100)
+Y = 0.5 * np.sin(X)
+yticks, ylabels = [], []
+
+
+# 1
+# -----------------------------------------------------------------------------
+y = 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(X, y + Y, "black", linewidth=1)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=10,
+ solid_capstyle="butt",
+ dash_capstyle="butt",
+ linestyle=(0.0, (0.1, 1.0)),
+)
+
+# 2
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X,
+ y + Y,
+ ".25",
+ linewidth=5,
+ solid_capstyle="butt",
+ dash_capstyle="butt",
+ linestyle=(0.0, (0.1, 0.45)),
+)
+ax.plot(X, y + Y, "black", linewidth=1)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=10,
+ solid_capstyle="butt",
+ dash_capstyle="butt",
+ linestyle=(0.0, (0.1, 1.0)),
+)
+
+# 3
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(X, y + Y, "black", linewidth=8, solid_capstyle="round")
+ax.plot(X, y + Y, "white", linewidth=6, solid_capstyle="round")
+ax.plot(X, y + Y, "black", linewidth=1, solid_capstyle="round")
+
+# 4
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=8,
+ solid_capstyle="round",
+ dash_capstyle="round",
+ linestyle=(0.0, (0.01, 1.5)),
+)
+
+# 5
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X, y + Y, "black", linewidth=1.5, marker="o", markevery=10, mec="black", mfc="white"
+)
+
+# 6
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X, y + Y, "black", linewidth=1.5, marker="o", markevery=10, mec="white", mfc="black"
+)
+
+# 7
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=1.5,
+ markersize=10,
+ mew=2.5,
+ mec="white",
+ mfc="white",
+ marker="$↑$",
+ markevery=10,
+)
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=0.0,
+ markersize=10,
+ mew=0.5,
+ mec="black",
+ mfc="black",
+ marker="$↑$",
+ markevery=10,
+)
+
+# 8
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+for i in range(9):
+ ax.plot(
+ X,
+ y + Y,
+ "%.2f" % (i / 10),
+ linewidth=4,
+ solid_capstyle="round",
+ dash_capstyle="round",
+ linestyle=(i + 1, (0.01, 9.0)),
+ )
+
+# 9
+# -----------------------------------------------------------------------------
+y += 1
+yticks.append(y), ylabels.append("%d" % y)
+Y = Y.mean() + 0 * Y
+ax.plot(
+ X,
+ y + Y,
+ marker=(3, 0, -90),
+ mew=2.5,
+ mec="black",
+ mfc="black",
+ markersize=7,
+ linestyle="None",
+ markevery=2,
+)
+ax.plot(
+ X,
+ y + Y,
+ marker=(3, 0, -90),
+ mew=1,
+ mec="white",
+ mfc="white",
+ markersize=7,
+ linestyle="None",
+ markevery=2,
+)
+
+# 10
+# -----------------------------------------------------------------------------
+y += 0.5
+yticks.append(y), ylabels.append("%d" % (y + 0.5))
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=6,
+ solid_capstyle="butt",
+ ms=10,
+ mew=1.5,
+ mec="white",
+ mfc="None",
+ marker=5,
+ markevery=(5, 10),
+)
+
+# 11
+# -----------------------------------------------------------------------------
+y += 0.5
+yticks.append(y), ylabels.append("%d" % (y + 1))
+ax.plot(
+ X,
+ y + Y,
+ "black",
+ linewidth=1,
+ linestyle="--",
+ marker="$✁$",
+ markevery=(10, 1000),
+ ms=20,
+ mew=0.75,
+ mec="black",
+ mfc="white",
+)
+
+ax.set_yticks(yticks)
+ax.set_yticklabels(ylabels)
+ax.tick_params(axis="both", which="both", length=0)
+
+plt.tight_layout()
+plt.savefig("advanced-linestyles.pdf")
+plt.show()
diff --git a/code/unsorted/alpha-compositing.py b/code/unsorted/alpha-compositing.py
new file mode 100644
index 0000000..bb4cd95
--- /dev/null
+++ b/code/unsorted/alpha-compositing.py
@@ -0,0 +1,193 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Illustrate alpha compositing (simulated)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.path as mpath
+import matplotlib.patches as mpatches
+
+
+def circle(center, radius):
+ """ Regular circle path """
+
+ T = np.arange(0, np.pi * 2.0, 0.01)
+ T = T.reshape(-1, 1)
+ X = center[0] + radius * np.cos(T)
+ Y = center[1] + radius * np.sin(T)
+ vertices = np.hstack((X, Y))
+ codes = np.ones(len(vertices), dtype=mpath.Path.code_type) * mpath.Path.LINETO
+ codes[0] = mpath.Path.MOVETO
+ return vertices, codes
+
+
+def rectangle(center, size):
+ """ Regular rectangle path """
+
+ (x, y), (w, h) = center, size
+ vertices = np.array([(x, y), (x + w, y), (x + w, y + h), (x, y + h), (x, y)])
+ codes = np.array(
+ [
+ mpath.Path.MOVETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ mpath.Path.LINETO,
+ ]
+ )
+ return vertices, codes
+
+
+def patch(
+ ax,
+ path,
+ facecolor="0.9",
+ edgecolor="black",
+ linewidth=0,
+ linestyle="-",
+ antialiased=True,
+ clip=None,
+):
+ """ Build a patch with potential clipping path """
+ patch = mpatches.PathPatch(
+ path,
+ antialiased=antialiased,
+ linewidth=linewidth,
+ linestyle=linestyle,
+ facecolor=facecolor,
+ edgecolor=edgecolor,
+ )
+ if clip:
+ # If the path is not drawn, clipping doesn't work
+ clip_patch = mpatches.PathPatch(
+ clip, linewidth=0, facecolor="None", edgecolor="None"
+ )
+ ax.add_patch(clip_patch)
+ patch.set_clip_path(clip_patch)
+ ax.add_patch(patch)
+
+
+def subplot(cols, rows, index, title):
+ """ Shortcut to subplot to factorize options"""
+ ax = plt.subplot(cols, rows, index, aspect=1)
+ # ax.text(-1.25, 0.75, "A", size="large", ha="center", va="center")
+ # ax.text(+1.25, 0.75, "B", size="large", ha="center", va="center")
+ ax.set_title(title, weight="bold")
+ ax.set_xlim(-1.5, +1.5), ax.set_xticks([])
+ ax.set_ylim(-1, +1), ax.set_yticks([])
+ return ax
+
+
+def blend(A, B, f):
+ xA, xB = np.array(A), np.array(B)
+ aA, aB = xA[3], xB[3]
+ aR = aA + aB * (1 - aA)
+ xaA, xaB = aA * xA, aB * xB
+ xR = 1.0 / aR * ((1 - aB) * xaA + (1 - aA) * xaB + aA * aB * f(xA, xB))
+ return xR[:3]
+
+
+def blend_multiply(A, B):
+ return blend(A, B, lambda x, y: x * y)
+
+
+def blend_screen(A, B):
+ return blend(A, B, lambda x, y: x + y - x * y)
+
+
+def blend_darken(A, B):
+ return blend(A, B, lambda x, y: np.minimum(x, y))
+
+
+def blend_lighten(A, B):
+ return blend(A, B, lambda x, y: np.minimum(x, y))
+
+
+def blend_color_dodge(A, B):
+ return blend(A, B, lambda x, y: np.where(A < 1, np.minimum(1, B / (1 - A)), 1))
+
+
+def blend_color_burn(A, B):
+ return blend(A, B, lambda x, y: np.where(A > 0, 1 - np.minimum(1, (1 - B) / A), 0))
+
+
+V1, C1 = rectangle((-1.5, -1), size=(3, 2))
+V2, C2 = circle((-0.5, 0), radius=0.75)
+A = mpath.Path(V2, C2)
+V2, C2 = circle((+0.5, 0), radius=0.75)
+B = mpath.Path(V2, C2)
+
+
+fig = plt.figure(figsize=(9, 5))
+rows, cols = 3, 4
+
+cA = np.array([0.7, 0.0, 0.0, 0.8])
+cB = np.array([0.0, 0.0, 0.9, 0.4])
+
+# No Blend
+ax = subplot(rows, cols, 1, "No blend")
+patch(ax, B, facecolor=cB)
+patch(ax, B, clip=A, facecolor="white", antialiased=False)
+patch(ax, A, facecolor=cA)
+
+# Default blend
+ax = subplot(rows, cols, 2, "Default blend")
+patch(ax, A, facecolor=cA)
+patch(ax, B, facecolor=cB)
+
+# Multiply
+ax = subplot(rows, cols, 3, "Multiply")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_multiply(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Screen
+ax = subplot(rows, cols, 4, "Screen")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_screen(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Darken
+ax = subplot(rows, cols, 5, "Darken")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_darken(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Lighten
+ax = subplot(rows, cols, 6, "Lighten")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_lighten(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Color dodge
+ax = subplot(rows, cols, 7, "Color dodge")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_color_dodge(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# Color burn
+ax = subplot(rows, cols, 8, "Color burn")
+patch(ax, A, facecolor=cA)
+patch(ax, B, clip=A, facecolor=blend_color_burn(cA, cB))
+patch(ax, B, facecolor=cB)
+
+# A ∩ B
+ax = subplot(rows, cols, 9, "")
+
+# A ∩ ¬B
+ax = subplot(rows, cols, 10, "")
+
+# ¬A ∩ B
+ax = subplot(rows, cols, 11, "")
+
+# ¬A ∩ ¬B
+ax = subplot(rows, cols, 12, "")
+
+
+plt.tight_layout()
+# plt.savefig("polygon-clipping.png", dpi=600)
+# plt.savefig("polygon-clipping.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/alpha-gradient.py b/code/unsorted/alpha-gradient.py
new file mode 100644
index 0000000..01649a2
--- /dev/null
+++ b/code/unsorted/alpha-gradient.py
@@ -0,0 +1,35 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Setup
+fig = plt.figure(figsize=(6, 3))
+
+X = np.linspace(0, 4 * np.pi, 256)
+Y1 = np.zeros((50, len(X)))
+Y2 = np.zeros((len(Y1), len(X)))
+for i in range(len(Y1)):
+ Y1[i] = np.cos(X) + np.random.uniform(-1, 1)
+ Y2[i] = np.sin(X) + np.random.uniform(-1, 1)
+
+
+# Usage of transparency
+ax = plt.subplot(1, 1, 1)
+
+Y, SD, VAR = Y1.mean(axis=0), Y1.std(axis=0), Y1.var(axis=0)
+
+ax.fill_between(X, Y + VAR, Y - VAR, facecolor="C0", alpha=0.25, zorder=-40)
+ax.fill_between(X, Y + SD, Y - SD, facecolor="C0", alpha=0.25, zorder=-30)
+ax.plot(X, Y + VAR, color="C0", linestyle="--", linewidth=1)
+ax.plot(X, Y - VAR, color="C0", linestyle="--", linewidth=1)
+ax.plot(X, Y, color="C0", zorder=-20)
+
+ax.set_xticks([]), ax.set_yticks([])
+
+plt.tight_layout()
+# plt.savefig("../figures/alpha-gradient.pdf")
+plt.show()
diff --git a/code/unsorted/dyson-hatching.py b/code/unsorted/dyson-hatching.py
new file mode 100644
index 0000000..4aad409
--- /dev/null
+++ b/code/unsorted/dyson-hatching.py
@@ -0,0 +1,248 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Requirements:
+# - noise library at https://pypi.org/project/noise/
+# - shapely library at https://pypi.org/project/Shapely/
+# - "Celtic Garamond" font at https://www.dafont.com/celtic-garamond-2nd.font
+# - "Morris Roman" font at https://www.dafont.com/morris-roman.font
+# - Nessy image from Irina Miroshnichenko (Sudowoodo)
+# (not included in the book because of rights)
+#
+# Note: After having installed font, you might need to erase your font cache
+# such that newly installed font can be found. Have a look at
+# https://matplotlib.org/faq/troubleshooting_faq.html
+# ----------------------------------------------------------------------------
+
+import noise
+import numpy as np
+import scipy.spatial
+import shapely.geometry
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+from matplotlib.collections import PolyCollection
+from math import cos, sin, floor, sqrt, pi, ceil
+
+
+# This is important because "cities" have been manually positionned
+np.random.seed(1)
+
+
+# Blue noise sampling
+# -------------------
+def blue_noise(shape, radius, k=32, seed=None):
+ """
+ Generate blue noise over a two-dimensional rectangle of size (width,height)
+
+ Parameters
+ ----------
+
+ shape : tuple
+ Two-dimensional domain (width x height)
+ radius : float
+ Minimum distance between samples
+ k : int, optional
+ Limit of samples to choose before rejection (typically k = 30)
+ seed : int, optional
+ If provided, this will set the random seed before generating noise,
+ for valid pseudo-random comparisons.
+
+ References
+ ----------
+
+ .. [1] Fast Poisson Disk Sampling in Arbitrary Dimensions, Robert Bridson,
+ Siggraph, 2007. :DOI:`10.1145/1278780.1278807`
+ """
+
+ def sqdist(a, b):
+ """ Squared Euclidean distance """
+ dx, dy = a[0] - b[0], a[1] - b[1]
+ return dx * dx + dy * dy
+
+ def grid_coords(p):
+ """ Return index of cell grid corresponding to p """
+ return int(floor(p[0] / cellsize)), int(floor(p[1] / cellsize))
+
+ def fits(p, radius):
+ """ Check whether p can be added to the queue """
+
+ radius2 = radius * radius
+ gx, gy = grid_coords(p)
+ for x in range(max(gx - 2, 0), min(gx + 3, grid_width)):
+ for y in range(max(gy - 2, 0), min(gy + 3, grid_height)):
+ g = grid[x + y * grid_width]
+ if g is None:
+ continue
+ if sqdist(p, g) <= radius2:
+ return False
+ return True
+
+ # When given a seed, we use a private random generator in order to not
+ # disturb the default global random generator
+ if seed is not None:
+ from numpy.random.mtrand import RandomState
+
+ rng = RandomState(seed=seed)
+ else:
+ rng = np.random
+
+ width, height = shape
+ cellsize = radius / sqrt(2)
+ grid_width = int(ceil(width / cellsize))
+ grid_height = int(ceil(height / cellsize))
+ grid = [None] * (grid_width * grid_height)
+
+ p = rng.uniform(0, shape, 2)
+ queue = [p]
+ grid_x, grid_y = grid_coords(p)
+ grid[grid_x + grid_y * grid_width] = p
+
+ while queue:
+ qi = rng.randint(len(queue))
+ qx, qy = queue[qi]
+ queue[qi] = queue[-1]
+ queue.pop()
+ for _ in range(k):
+ theta = rng.uniform(0, 2 * pi)
+ r = radius * np.sqrt(rng.uniform(1, 4))
+ p = qx + r * cos(theta), qy + r * sin(theta)
+ if not (0 <= p[0] < width and 0 <= p[1] < height) or not fits(p, radius):
+ continue
+ queue.append(p)
+ gx, gy = grid_coords(p)
+ grid[gx + gy * grid_width] = p
+
+ return np.array([p for p in grid if p is not None])
+
+
+# Hatch pattern with given orientation (or random if None given)
+def hatch(n=4, theta=None):
+ theta = theta or np.random.uniform(0, np.pi)
+ P = np.zeros((n, 2, 2))
+ X = np.linspace(-0.5, +0.5, n, endpoint=True)
+ P[:, 0, 1] = -0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 1] = +0.5 + np.random.normal(0, 0.05, n)
+ P[:, 1, 0] = X + np.random.normal(0, 0.025, n)
+ P[:, 0, 0] = X + np.random.normal(0, 0.025, n)
+ c, s = np.cos(theta), np.sin(theta)
+ Z = np.array([[c, s], [-s, c]])
+ return P @ Z.T
+
+
+# Actual drawing
+fig = plt.figure(figsize=(6, 6))
+fig.patch.set_facecolor("#ffffff")
+ax = plt.subplot(1, 1, 1, aspect=1, frameon=False)
+
+
+# Figure border using the hatch pattern. They are first spread according to
+# a blue noise distribution, scaled according to the distance to the nearest
+# neighbour and then lines composing the hatch are clipped against the
+# corresponding Voronoi cell.
+h = 4 # Number of line segments composing a hatch
+radius = 0.2 # Minimum radius between points
+# (the smaller, the longer to compute)
+
+P = blue_noise((11, 11), radius=radius) - (0.5, 0.5)
+D = scipy.spatial.distance.cdist(P, P)
+D.sort(axis=1)
+S = []
+vor = scipy.spatial.Voronoi(P)
+for i in range(len(vor.point_region)):
+ region = vor.regions[vor.point_region[i]]
+ if not -1 in region:
+ verts = np.array([vor.vertices[i] for i in region])
+ poly = shapely.geometry.Polygon(verts)
+ H = 1.25 * D[i, 1] * hatch(h) + P[i]
+ for i in range(len(H)):
+ line = shapely.geometry.LineString(H[i])
+ intersect = poly.intersection(line)
+ if intersect:
+ S.append(intersect.coords)
+
+# Grey background using thick lines
+hatches = LineCollection(S, color="#eeeeee", linewidth=7, capstyle="round", zorder=-20)
+ax.add_collection(hatches)
+
+# Actual hatches
+hatches = LineCollection(S, color="black", linewidth=1.5, capstyle="round", zorder=-10)
+ax.add_collection(hatches)
+
+# Plain rectangle
+rectangle = plt.Rectangle((0, 0), 10, 10, fc="none", ec="white", lw=3.5)
+ax.add_patch(rectangle)
+rectangle = plt.Rectangle((0, 0), 10, 10, fc="white", ec="black", lw=2.5)
+ax.add_patch(rectangle)
+
+
+# A cheap map using Perlin noise and contour/contourf
+shape = 256, 256
+scale = 150
+octaves = 5
+persistence = 0.5
+lacunarity = 2.5
+Z = np.zeros(shape)
+for i in range(shape[0]):
+ for j in range(shape[1]):
+ Z[i][j] = noise.pnoise2(
+ i / scale,
+ j / scale,
+ octaves=octaves,
+ persistence=persistence,
+ lacunarity=lacunarity,
+ repeatx=1024,
+ repeaty=1024,
+ base=0,
+ )
+X = np.linspace(0, 10, 256)
+Y = np.linspace(0, 10, 256)
+plt.contourf(X, Y, Z, 2, colors=["#eeeeee", "#ffffff"], zorder=10)
+plt.contour(X, Y, Z, 2, colors="black", linestyles="-", zorder=10)
+
+
+# Text (could be factorized)
+plt.text(1.0, 5.25, "MATPLOTLIB", family="Celtic Garamond the 2nd", size=16, zorder=20)
+
+plt.scatter([6.5], [6.15], s=25, color="black", zorder=20)
+plt.text(6.65, 6.20, "Beautiful", family="Morris Roman", size=12, zorder=20)
+
+plt.scatter([1], [7.5], s=25, color="black", zorder=20)
+plt.text(1, 7.75, "Versatile", ha="center", family="Morris Roman", size=12, zorder=20)
+
+plt.scatter([1.7], [1.7], s=25, color="black", zorder=20)
+plt.text(1.7, 1.35, "Powerful", ha="center", family="Morris Roman", size=12, zorder=20)
+
+plt.scatter([6.2], [3.2], s=25, color="black", zorder=20)
+plt.text(6.2, 2.8, "Scalable", ha="center", family="Morris Roman", size=12, zorder=20)
+
+
+# Wind rose at the bottom right
+V = np.zeros((8, 2, 3, 2))
+for i in range(4):
+ theta = np.pi / 4 + i * np.pi / 2
+ c, s = np.cos(theta), np.sin(theta)
+ Z = np.array([[c, s], [-s, c]])
+ V[i, 0] = [(0, 0), (-1, 1), (0, 5)] @ Z.T
+ V[i, 1] = [(0, 0), (+1, 1), (0, 5)] @ Z.T
+ theta -= np.pi / 4
+ c, s = np.cos(theta), np.sin(theta)
+ Z = np.array([[c, s], [-s, c]])
+ V[4 + i, 0] = [(0, 0), (-1, 1), (0, 5)] @ Z.T
+ V[4 + i, 1] = [(0, 0), (+1, 1), (0, 5)] @ Z.T
+V = V.reshape(16, 3, 2)
+V = 0.9 * V / 5 + (9, 1)
+FC = np.zeros((16, 4))
+FC[0::2] = 0, 0, 0, 1
+FC[1::2] = 1, 1, 1, 1
+collection = PolyCollection(V, edgecolors="black", facecolors=FC, lw=0.75, zorder=20)
+ax.add_collection(collection)
+
+
+# Done
+ax.set_xlim(-1, 11), ax.set_xticks([])
+ax.set_ylim(-1, 11), ax.set_yticks([])
+plt.tight_layout()
+# plt.savefig("dyson-hatching.pdf")
+plt.show()
diff --git a/code/unsorted/earthquakes.py b/code/unsorted/earthquakes.py
new file mode 100644
index 0000000..c56ce29
--- /dev/null
+++ b/code/unsorted/earthquakes.py
@@ -0,0 +1,108 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import urllib
+import numpy as np
+import cartopy
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+
+# -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
+feed = "http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"
+
+# Significant earthquakes in the past 30 days
+# url = urllib.request.urlopen(feed + "significant_month.csv")
+
+# Earthquakes of magnitude > 4.5 in the past 30 days
+url = urllib.request.urlopen(feed + "4.5_month.csv")
+
+# Earthquakes of magnitude > 2.5 in the past 30 days
+# url = urllib.request.urlopen(feed + "2.5_month.csv")
+
+# Earthquakes of magnitude > 1.0 in the past 30 days
+# url = urllib.request.urlopen(feed + "1.0_month.csv")
+
+# Read data
+data = (url.read().decode()).split("\n")
+E = np.genfromtxt(
+ data, delimiter=",", names=True, usecols=("latitude", "longitude", "mag")
+)
+X, Y, M = E["longitude"], E["latitude"], E["mag"]
+M = 25 * (M - 4) ** 3
+
+# Plot data
+fig = plt.figure(figsize=(8, 6))
+ax = plt.subplot(1, 1, 1, projection=cartopy.crs.EqualEarth())
+ax.set_global()
+
+ax.add_feature(cartopy.feature.OCEAN, zorder=0, facecolor="blue", alpha=0.1)
+ax.add_feature(
+ cartopy.feature.LAND, zorder=0, facecolor="white", edgecolor="0.25", linewidth=0.5
+)
+
+# Visual effect for a more salient information
+ax.scatter(
+ X, Y, M, lw=1, ec="black", alpha=1, zorder=10, transform=cartopy.crs.PlateCarree()
+)
+ax.scatter(
+ X,
+ Y,
+ M - 1,
+ ec="none",
+ fc="white",
+ alpha=1,
+ zorder=20,
+ transform=cartopy.crs.PlateCarree(),
+)
+ax.scatter(
+ X,
+ Y,
+ M - 1,
+ ec="none",
+ fc="red",
+ alpha=0.25,
+ zorder=20,
+ transform=cartopy.crs.PlateCarree(),
+)
+ax.scatter(
+ X,
+ Y,
+ 2,
+ ec="none",
+ fc="black",
+ alpha=1,
+ zorder=30,
+ transform=cartopy.crs.PlateCarree(),
+)
+
+# Title & subtitles
+ax.text(
+ 0.5,
+ 1.01,
+ "Earthquakes with magnitude > 4.5 in the past 30 days",
+ va="bottom",
+ ha="center",
+ transform=ax.transAxes,
+ family="Source Serif Pro",
+ size=12,
+ weight=600,
+)
+ax.text(
+ 0.5,
+ -0.01,
+ datetime.now().strftime("%m/%d/%Y, %H:%M:%S")
+ + " - data from http://earthquake.usgs.gov",
+ va="top",
+ ha="center",
+ transform=ax.transAxes,
+ family="Source Serif Pro",
+ size=12,
+ weight=400,
+)
+
+plt.tight_layout()
+plt.savefig("earthquakes.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/git-commits.py b/code/unsorted/git-commits.py
new file mode 100644
index 0000000..f88b822
--- /dev/null
+++ b/code/unsorted/git-commits.py
@@ -0,0 +1,125 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import git
+import numpy as np
+import matplotlib
+
+matplotlib.use("module://imgcat")
+import matplotlib.pyplot as plt
+
+from matplotlib.patches import Polygon
+from datetime import date, datetime
+from dateutil.relativedelta import relativedelta
+
+
+def calmap(ax, year, data, origin="upper", weekstart="sun"):
+ ax.tick_params("x", length=0, labelsize="medium", which="major")
+ ax.tick_params("y", length=0, labelsize="x-small", which="major")
+
+ # Month borders
+ xticks, labels = [], []
+
+ start = datetime(year, 1, 1).weekday()
+
+ _data = np.zeros(7 * 53) * np.nan
+ _data[start : start + len(data)] = data
+ data = _data.reshape(53, 7).T
+
+ for month in range(1, 13):
+ first = datetime(year, month, 1)
+ last = first + relativedelta(months=1, days=-1)
+ if origin == "lower":
+ y0 = first.weekday()
+ y1 = last.weekday()
+ x0 = (int(first.strftime("%j")) + start - 1) // 7
+ x1 = (int(last.strftime("%j")) + start - 1) // 7
+ P = [
+ (x0, y0),
+ (x0, 7),
+ (x1, 7),
+ (x1, y1 + 1),
+ (x1 + 1, y1 + 1),
+ (x1 + 1, 0),
+ (x0 + 1, 0),
+ (x0 + 1, y0),
+ ]
+ else:
+ y0 = 6 - first.weekday()
+ y1 = 6 - last.weekday()
+ x0 = (int(first.strftime("%j")) + start - 1) // 7
+ x1 = (int(last.strftime("%j")) + start - 1) // 7
+ P = [
+ (x0, y0 + 1),
+ (x0, 0),
+ (x1, 0),
+ (x1, y1),
+ (x1 + 1, y1),
+ (x1 + 1, 7),
+ (x0 + 1, 7),
+ (x0 + 1, y0 + 1),
+ ]
+
+ xticks.append(x0 + (x1 - x0 + 1) / 2)
+ labels.append(first.strftime("%b"))
+ poly = Polygon(
+ P,
+ edgecolor="black",
+ facecolor="None",
+ linewidth=1,
+ zorder=20,
+ clip_on=False,
+ )
+ ax.add_artist(poly)
+
+ ax.set_xticks(xticks)
+ ax.set_xticklabels(labels)
+ ax.set_yticks(0.5 + np.arange(7))
+
+ labels = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
+ if origin == "upper":
+ labels = labels[::-1]
+ ax.set_yticklabels(labels)
+
+ ax.text(
+ 1.01,
+ 0.5,
+ "{}".format(year),
+ rotation=90,
+ ha="left",
+ va="center",
+ transform=ax.transAxes,
+ size="28",
+ weight="bold",
+ alpha=0.5,
+ )
+
+ # Showing data
+ cmap = plt.cm.get_cmap("Purples")
+ im = ax.imshow(
+ data, extent=[0, 53, 0, 7], zorder=10, vmin=0, vmax=8, cmap=cmap, origin=origin
+ )
+
+
+def get_commits(year=2021, path="../.."):
+ "Collect commit dates for a given year"
+
+ n = 1 + (date(year, 12, 31) - date(year, 1, 1)).days
+ C = np.zeros(n, dtype=int)
+ repo = git.Repo(path)
+ for commit in repo.iter_commits("master"):
+ timestamp = datetime.fromtimestamp(commit.committed_date)
+ if timestamp.year == year:
+ day = timestamp.timetuple().tm_yday
+ C[day - 1] += 1
+ return C
+
+
+for year in [2019, 2020, 2021]:
+ fig = plt.figure(figsize=(10, 2), dpi=250, frameon=False)
+ ax = plt.subplot(xticks=[], yticks=[], frameon=False)
+ calmap(ax, year, get_commits(year), origin="upper", weekstart="sun")
+ plt.tight_layout()
+ fig.show()
diff --git a/code/unsorted/github-activity.py b/code/unsorted/github-activity.py
new file mode 100644
index 0000000..3a420c9
--- /dev/null
+++ b/code/unsorted/github-activity.py
@@ -0,0 +1,139 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import os
+import numpy as np
+import html.parser
+import urllib.request
+import dateutil.parser
+from datetime import date, datetime
+from dateutil.relativedelta import relativedelta
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+
+
+def github_contrib(user, year):
+ """ Get GitHub user daily contribution """
+
+ # Check for a cached version (file)
+ filename = "github-{0}-{1}.html".format(user, year)
+ if os.path.exists(filename):
+ with open(filename) as file:
+ contents = file.read()
+ # Else get file from GitHub
+ else:
+ url = "https://github.com/users/{0}/contributions?to={1}-12-31"
+ url = url.format(user, year)
+ contents = str(urllib.request.urlopen(url).read())
+ with open(filename, "w") as file:
+ file.write(contents)
+
+ # Parse result (html)
+ n = 1 + (date(year, 12, 31) - date(year, 1, 1)).days
+ C = -np.ones(n, dtype=int)
+
+ class HTMLParser(html.parser.HTMLParser):
+ def handle_starttag(self, tag, attrs):
+ if tag == "rect":
+ data = {key: value for (key, value) in attrs}
+ date = dateutil.parser.parse(data["data-date"])
+ count = int(data["data-count"])
+ day = date.timetuple().tm_yday - 1
+ if count > 0:
+ C[day] = count
+
+ parser = HTMLParser()
+ parser.feed(contents)
+ return C
+
+
+def calmap(ax, year, data, origin="upper", weekstart="sun"):
+ ax.tick_params("x", length=0, labelsize="medium", which="major")
+ ax.tick_params("y", length=0, labelsize="x-small", which="major")
+
+ # Month borders
+ xticks, labels = [], []
+
+ start = datetime(year, 1, 1).weekday()
+
+ _data = np.zeros(7 * 53) * np.nan
+ _data[start : start + len(data)] = data
+ data = _data.reshape(53, 7).T
+
+ for month in range(1, 13):
+ first = datetime(year, month, 1)
+ last = first + relativedelta(months=1, days=-1)
+ if origin == "lower":
+ y0 = first.weekday()
+ y1 = last.weekday()
+ x0 = (int(first.strftime("%j")) + start - 1) // 7
+ x1 = (int(last.strftime("%j")) + start - 1) // 7
+ P = [
+ (x0, y0),
+ (x0, 7),
+ (x1, 7),
+ (x1, y1 + 1),
+ (x1 + 1, y1 + 1),
+ (x1 + 1, 0),
+ (x0 + 1, 0),
+ (x0 + 1, y0),
+ ]
+ else:
+ y0 = 6 - first.weekday()
+ y1 = 6 - last.weekday()
+ x0 = (int(first.strftime("%j")) + start - 1) // 7
+ x1 = (int(last.strftime("%j")) + start - 1) // 7
+ P = [
+ (x0, y0 + 1),
+ (x0, 0),
+ (x1, 0),
+ (x1, y1),
+ (x1 + 1, y1),
+ (x1 + 1, 7),
+ (x0 + 1, 7),
+ (x0 + 1, y0 + 1),
+ ]
+
+ xticks.append(x0 + (x1 - x0 + 1) / 2)
+ labels.append(first.strftime("%b"))
+ poly = Polygon(
+ P,
+ edgecolor="black",
+ facecolor="None",
+ linewidth=1,
+ zorder=20,
+ clip_on=False,
+ )
+ ax.add_artist(poly)
+
+ ax.set_xticks(xticks)
+ ax.set_xticklabels(labels)
+ ax.set_yticks(0.5 + np.arange(7))
+
+ labels = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
+ if origin == "upper":
+ labels = labels[::-1]
+ ax.set_yticklabels(labels)
+ ax.set_title("{}".format(year), size="medium", weight="bold")
+
+ # Showing data
+ cmap = plt.cm.get_cmap("Purples")
+ ax.imshow(
+ data, extent=[0, 53, 0, 7], zorder=10, vmin=0, vmax=10, cmap=cmap, origin=origin
+ )
+
+
+fig = plt.figure(figsize=(8, 7.5), dpi=100)
+year = 2014
+n = 5
+for i in range(n):
+ ax = plt.subplot(n, 1, i + 1, xlim=[0, 53], ylim=[0, 7], frameon=False, aspect=1)
+ calmap(ax, year + i, github_contrib("rougier", year + i), origin="upper")
+
+plt.tight_layout()
+
+# plt.savefig("github-activity.png", dpi=300)
+# plt.savefig("github-activity.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/hatched-bars.py b/code/unsorted/hatched-bars.py
new file mode 100644
index 0000000..02e2c08
--- /dev/null
+++ b/code/unsorted/hatched-bars.py
@@ -0,0 +1,73 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+
+
+def lighten_color(color, amount=0.66):
+ import matplotlib.colors as mc
+ import colorsys
+
+ try:
+ c = mc.cnames[color]
+ except:
+ c = color
+ c = np.array(colorsys.rgb_to_hls(*mc.to_rgb(c)))
+ return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
+
+
+cmap = plt.get_cmap("tab10")
+color = cmap(0)
+plt.rcParams["hatch.color"] = lighten_color(color)
+plt.rcParams["hatch.linewidth"] = 8
+
+
+fig = plt.figure(figsize=(4.25, 2))
+ax = plt.subplot(1, 1, 1)
+np.random.seed(123)
+
+x1, y1 = 3 * np.arange(4), np.random.randint(25, 50, 4)
+x2, y2 = x1 + 1, np.random.randint(25, 75, 4)
+
+ax.bar(x1, y1, color=color)
+for i in range(len(x1)):
+ plt.annotate(
+ "%d%%" % y1[i],
+ (x1[i], y1[i]),
+ xytext=(0, 1),
+ fontsize="x-small",
+ color=color,
+ textcoords="offset points",
+ va="bottom",
+ ha="center",
+ )
+
+ax.bar(x2, y2, color=color, hatch="/")
+for i in range(len(x2)):
+ plt.annotate(
+ "%d%%" % y2[i],
+ (x2[i], y2[i]),
+ xytext=(0, 1),
+ fontsize="x-small",
+ color=color,
+ textcoords="offset points",
+ va="bottom",
+ ha="center",
+ )
+
+ax.set_yticks([])
+ax.set_xticks(0.5 + np.arange(0, 12, 3))
+ax.set_xticklabels(["2016", "2017", "2018", "2019"])
+ax.tick_params("x", length=0, labelsize="small", which="major")
+
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.spines["top"].set_visible(False)
+
+plt.tight_layout()
+# plt.savefig("hatched-bars.pdf")
+plt.show()
diff --git a/code/unsorted/layout-weird.py b/code/unsorted/layout-weird.py
new file mode 100644
index 0000000..e1143e3
--- /dev/null
+++ b/code/unsorted/layout-weird.py
@@ -0,0 +1,81 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+# Weird axes layout (just for fun)
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+p = plt.rcParams
+p["figure.figsize"] = 7, 7
+p["font.sans-serif"] = ["Roboto Condensed"]
+p["font.weight"] = "light"
+p["ytick.minor.visible"] = True
+p["xtick.minor.visible"] = True
+p["axes.grid"] = True
+p["grid.color"] = "0.5"
+p["grid.linewidth"] = 0.5
+
+
+X = np.linspace(-np.pi, np.pi, 257, endpoint=True)
+C, S = np.cos(X), np.sin(X)
+
+fig = plt.figure(constrained_layout=True)
+nrows, ncols = 2, 2
+gspec = gridspec.GridSpec(ncols=ncols, nrows=nrows, figure=fig)
+
+
+ax = plt.subplot(1, 1, 1)
+ax.set_xlim(0, 1)
+ax.set_xticks(np.linspace(0, 1, 5))
+ax.set_xlabel("X Label")
+ax.set_ylim(0, 1)
+ax.set_yticks(np.linspace(0, 1, 5))
+ax.set_ylabel("Y Label")
+ax.set_title("Close-up", x=0.25, family="Roboto", weight=500)
+
+# Manual edit for the axes limits
+ax2 = fig.add_axes([0.53, 0.515, 0.4485, 0.4505])
+ax2.spines["top"].set_visible(False)
+ax2.spines["right"].set_visible(False)
+ax2.grid(False)
+ax2.set_xticks([])
+ax2.set_yticks([])
+
+n = 10000
+X = np.random.normal(0.25, 1, n)
+Y = np.random.normal(0.25, 1, n)
+
+# Manual edit for the axes limits
+ax3 = fig.add_axes([0.575, 0.56, 0.4, 0.4])
+ax3.set_title("Distribution", family="Roboto", weight=500)
+ax3.set_xlim(-3, 3)
+ax3.set_ylim(-3, 3)
+S = ax3.scatter(X, Y, s=0.5, linewidths=0, color=".5")
+S = ax3.scatter(X, Y, s=0.5, linewidths=0, color="black")
+from matplotlib.patches import Polygon
+
+p = Polygon(
+ [(0, 0), (0, 1), (0.5, 1), (0.5, 0.5), (1, 0.5), (1, 0), (0, 0)],
+ transform=ax3.transData,
+ closed=True,
+ facecolor="None",
+ edgecolor="black",
+ linewidth=0.75,
+ zorder=50,
+)
+S.set_clip_path(p)
+ax3.add_artist(p)
+
+
+S = ax.scatter(X, Y, s=100, linewidths=1, color="black")
+S = ax.scatter(X, Y, s=100, linewidths=0, color="white")
+for s in np.linspace(5, 100, 10):
+ S = ax.scatter(X, Y, s=s, linewidths=0, alpha=0.05, color="red")
+S = ax.scatter(X, Y, s=3, linewidths=0, alpha=0.75, color="black")
+
+# plt.savefig("../figures/layout-weird.pdf")
+plt.show()
diff --git a/code/unsorted/make-hatch-linewidth.py b/code/unsorted/make-hatch-linewidth.py
new file mode 100644
index 0000000..7bb52f5
--- /dev/null
+++ b/code/unsorted/make-hatch-linewidth.py
@@ -0,0 +1,35 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+from matplotlib.backends.backend_agg import FigureCanvasAgg
+
+figsize = 4.25, 5 * 0.55
+xlim = 0.0, 11.0
+ylim = 0.5, 5.0
+dx = figsize[0] / (xlim[1] - xlim[0])
+dy = figsize[1] / (ylim[1] - ylim[0])
+widths = 1, 2, 3, 4, 5, 6
+w, h = 0.75 * 10 / len(widths), 0.5
+figsize = w * dx, h * dy
+dpi = 600
+
+for width in widths:
+ plt.rcParams["hatch.linewidth"] = width
+ fig = plt.figure(figsize=figsize, dpi=dpi)
+ ax = fig.add_axes([0, 0, 1, 1], xlim=[0, 1], ylim=[0, 1])
+ ax.axis("off")
+ canvas = FigureCanvasAgg(fig)
+ rect = Rectangle(
+ (0, 0), 1, 1, hatch="/", facecolor="0.85", edgecolor="0.00", linewidth=0.0
+ )
+ ax.add_patch(rect)
+ canvas.draw()
+ image = np.frombuffer(canvas.tostring_rgb(), dtype="uint8")
+ image = image.reshape(int(figsize[1] * dpi), int(figsize[0] * dpi), 3)
+ imageio.imwrite("hatch-%d.png" % width, image)
diff --git a/code/unsorted/metropolis.py b/code/unsorted/metropolis.py
new file mode 100644
index 0000000..bfeed00
--- /dev/null
+++ b/code/unsorted/metropolis.py
@@ -0,0 +1,110 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import tqdm
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from scipy.ndimage import gaussian_filter
+from matplotlib.collections import LineCollection
+from matplotlib.backends.backend_agg import FigureCanvas
+
+
+# Nothing complicated here.
+# Directly translated from https://github.com/marcusvolz/metropolis
+# See also https://github.com/marcusvolz
+#
+# Don't use n > 11000 or you'll be stuck forever...
+def metropolis(width=10000, height=10000, n=11000, step=75, branching=0.1, noise=2.0):
+ delta = noise * np.pi / 180
+ points = np.zeros(
+ n, dtype=[("x", float), ("y", float), ("dir", float), ("level", int)]
+ )
+ edges = np.zeros(
+ n,
+ dtype=[
+ ("x0", float),
+ ("y0", float),
+ ("x1", float),
+ ("y1", float),
+ ("level", int),
+ ],
+ )
+ points[0] = width / 2, height / 2, np.random.uniform(-2 * np.pi, 2 * np.pi), 1
+
+ for i in tqdm.trange(1, n):
+ while True:
+ point = np.random.choice(points[:i])
+ branch = 1 if np.random.uniform(0, 1) <= branching else 0
+ alpha = point["dir"] + delta * np.random.uniform(-1, +1)
+ alpha += branch * np.random.choice([-np.pi / 2, +np.pi / 2])
+ v = np.array((np.cos(alpha), np.sin(alpha)))
+ v = v * step * (1 + 1 / (point["level"] + branch))
+ x = point["x"] + v[0]
+ y = point["y"] + v[1]
+ level = point["level"] + branch
+ if x < 0 or x > width or y < 0 or y > height:
+ continue
+ dist = np.sqrt((points["x"] - x) ** 2 + (points["y"] - y) ** 2)
+ if dist.min() >= step:
+ points[i] = x, y, alpha, level
+ edges[i] = x, y, point["x"], point["y"], level
+ break
+ return edges[edges["level"] > 0]
+
+
+np.random.seed(12345)
+width, height, border = 10000, 10000, 500
+if 1: # Set to 0 after computation
+ edges = metropolis(width, height)
+ np.save("egdes.npy", edges)
+else:
+ edges = np.load("egdes.npy")
+
+
+segments = []
+for edge in edges:
+ x0, y0, x1, y1, level = edge
+ segments.append([(x0, y0), (x1, y1)])
+
+# Drop shadow pre-processing
+# We render into an array and we apply Gaussian blur
+fig = Figure(figsize=(6, 6))
+canvas = FigureCanvas(fig)
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ aspect=1,
+ frameon=False,
+ xticks=[],
+ yticks=[],
+ xlim=[0, width],
+ ylim=[0, height],
+)
+sigma = 2.0
+collection = LineCollection(segments, linewidths=1.5, colors="black", capstyle="round")
+ax.add_collection(collection)
+canvas.draw()
+I = np.array(canvas.renderer.buffer_rgba())[..., :3]
+I[:, :, 0] = gaussian_filter(I[:, :, 0], sigma=sigma)
+I[:, :, 1] = gaussian_filter(I[:, :, 1], sigma=sigma)
+I[:, :, 2] = gaussian_filter(I[:, :, 2], sigma=sigma)
+
+
+# Actual rendering
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ aspect=1,
+ frameon=False,
+ xticks=[],
+ xlim=[border, width - border],
+ yticks=[],
+ ylim=[border, height - border],
+)
+ax.imshow(I, extent=[0, width, 0, height], alpha=0.5)
+collection = LineCollection(segments, linewidths=1.0, colors="black", capstyle="round")
+ax.add_collection(collection)
+plt.savefig("metropolis.pdf", dpi=600)
+plt.show()
diff --git a/code/unsorted/poster-layout.py b/code/unsorted/poster-layout.py
new file mode 100644
index 0000000..91627f1
--- /dev/null
+++ b/code/unsorted/poster-layout.py
@@ -0,0 +1,177 @@
+# -----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# -----------------------------------------------------------------------------
+# Complex layout and text effects
+# -----------------------------------------------------------------------------
+import numpy as np
+import imageio
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+from matplotlib.text import TextPath
+import matplotlib.patches as mpatches
+import matplotlib.patheffects as path_effects
+from matplotlib.font_manager import FontProperties
+
+
+def box(ax, index, x, y, width, height):
+ rectangle = mpatches.Rectangle(
+ (x, y), width, height, zorder=10, facecolor="none", edgecolor="black"
+ )
+ ax.add_artist(rectangle)
+
+ ax.text(
+ x + (width - 1) / 2,
+ y + (height - 1) / 2,
+ "%d" % index,
+ weight="bold",
+ zorder=50,
+ size="32",
+ va="center",
+ ha="center",
+ color="k",
+ alpha=0.25,
+ family="GlassJaw BB",
+ )
+
+
+fig = plt.figure(figsize=(6.5, 9.3))
+
+ax = fig.add_axes(
+ [0, 0, 1, 1],
+ aspect=1,
+ frameon=False,
+ xlim=(0, 65),
+ ylim=(0, 93),
+ xticks=[],
+ yticks=[],
+)
+
+ax.xaxis.set_major_locator(ticker.MultipleLocator(10))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(1))
+ax.yaxis.set_major_locator(ticker.MultipleLocator(10))
+ax.yaxis.set_minor_locator(ticker.MultipleLocator(1))
+# ax.grid(which="both", linewidth=0.5, color="0.85", zorder=-10)
+
+boxes = [
+ (1, 1, 15, 21),
+ (17, 1, 15, 10),
+ (17, 12, 15, 10),
+ (33, 1, 15, 15),
+ (49, 1, 15, 15),
+ (1, 23, 31, 8),
+ (1, 32, 8, 14),
+ (10, 32, 22, 14),
+ (1, 47, 15, 13),
+ (17, 47, 15, 13),
+ (33, 45, 15, 15),
+ (33, 32, 15, 12),
+ (49, 32, 15, 18),
+ (49, 51, 15, 9),
+ (1, 61, 15, 15),
+ (17, 61, 36, 15),
+ (54, 61, 10, 15),
+ (49, 77, 15, 15),
+]
+
+for index, (x, y, width, height) in enumerate(boxes):
+ box(ax, index, x, y, width, height)
+
+# fig.add_axes([33/65, 17/93, 31/65, 14/93], xticks=[], yticks=[])
+
+ax.text(
+ 33,
+ 17.25,
+ """Matplotlib can also be used to layout\n"""
+ """a poster where each box can be filled\n"""
+ """with different subplot.\n"""
+ """\n"""
+ """To compute the boxes, just draw them\n"""
+ """on a piece of graph of paper and use\n"""
+ """the measure to get the bounds.\n"""
+ """\n"""
+ """The cartoon fonts used in this example\n"""
+ """are "Lint McCree" and "GlassJaw"\n"""
+ """""",
+ size=6,
+ color="k",
+ family="Lint McCree Intl BB",
+)
+
+
+# Title
+# -----------------------------------------------------------------------------
+textpath = TextPath(
+ (8, 80), "MATPLOTLIB", size=8, prop=FontProperties(family="GlassJaw BB")
+)
+patch = mpatches.PathPatch(
+ textpath, facecolor="none", edgecolor="none", zorder=5, joinstyle="round"
+)
+patch.set_path_effects(
+ [
+ path_effects.Stroke(linewidth=8, foreground="black"),
+ path_effects.Stroke(linewidth=6, foreground="yellow"),
+ path_effects.Stroke(linewidth=3, foreground="black"),
+ ]
+)
+
+transform = ax.transData + mpl.transforms.Affine2D().rotate_deg(2.5)
+patch.set_transform(transform)
+
+ax.add_artist(patch)
+Z = np.linspace(1, 0, 100).reshape(100, 1)
+im = ax.imshow(Z, cmap="autumn", extent=[1, 100, 79, 87], zorder=15)
+im.set_transform(transform)
+im.set_clip_path(patch._path, patch.get_transform())
+
+ax.text(
+ 47,
+ 79,
+ "Scientific visualization made simple & beautiful",
+ color="black",
+ zorder=20,
+ family="Lint McCree Intl BB",
+ weight="bold",
+ size="xx-small",
+ ha="right",
+ va="baseline",
+)
+
+ax.text(
+ 8,
+ 87,
+ " BSD Licensed ",
+ color="white",
+ zorder=30,
+ rotation=-1.5,
+ family="Lint McCree Intl BB",
+ ha="center",
+ va="center",
+ size=7,
+ bbox=dict(boxstyle="roundtooth", fc="k", ec="w", lw=1, pad=0.75),
+)
+
+
+# Box 18
+# -----------------------------------------------------------------------------
+I = imageio.imread("../data/John-Hunter-comic.png")
+ax.imshow(I, extent=[49, 49 + 15, 77, 77 + 15], zorder=0, interpolation="bicubic")
+
+ax.text(
+ 49.7,
+ 77.5,
+ "John D. Hunter III",
+ color="black",
+ zorder=30,
+ family="Lint McCree Intl BB",
+ weight="bold",
+ ha="left",
+ va="bottom",
+ size=5,
+ bbox=dict(boxstyle="square", fc="w", ec="k", lw=1, pad=0.5),
+)
+
+# plt.savefig("poster-layout.png", dpi=300)
+plt.show()
diff --git a/code/unsorted/scale-logit.py b/code/unsorted/scale-logit.py
new file mode 100644
index 0000000..cb935c6
--- /dev/null
+++ b/code/unsorted/scale-logit.py
@@ -0,0 +1,43 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: BSD
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+from matplotlib.ticker import NullFormatter
+
+X = np.random.uniform(0, 1, 1000)
+Y = np.random.uniform(0, 1, 1000)
+S = np.sqrt((X - 0.5) ** 2 + (Y - 0.5) ** 2)
+C = np.ones((1000, 4)) * colors.to_rgba("C0")
+C[S > 0.5] = colors.to_rgba("C1")
+
+figure = plt.figure(figsize=(8, 4))
+
+# Linear scale
+ax = plt.subplot(1, 2, 1, xlim=(0, 1), ylim=(0, 1))
+ax.scatter(X, Y, s=25, facecolor=C, edgecolor="none", alpha=0.5)
+T = np.linspace(0, 2 * np.pi, 100)
+X2 = 0.5 + 0.5 * np.cos(T)
+Y2 = 0.5 + 0.5 * np.sin(T)
+ax.plot(X2, Y2, color="black", linestyle="--", linewidth=0.75)
+
+#
+ax = plt.subplot(1, 2, 2, xlim=(0.01, 0.99), ylim=(0.01, 0.99))
+ax.set_xscale("logit")
+ax.set_yscale("logit")
+ax.scatter(X, Y, s=25, facecolor=C, edgecolor="none", alpha=0.5)
+ax.yaxis.set_minor_formatter(NullFormatter())
+ax.xaxis.set_minor_formatter(NullFormatter())
+T = np.linspace(0, 2 * np.pi, 256)
+X2 = 0.5 + 0.5 * np.cos(T)
+Y2 = 0.5 + 0.5 * np.sin(T)
+ax.plot(X2, Y2, color="black", linestyle="--", linewidth=0.75)
+
+
+# Show
+plt.tight_layout()
+# plt.savefig("scale-logit.pdf")
+plt.show()
diff --git a/code/unsorted/stacked-bars.py b/code/unsorted/stacked-bars.py
new file mode 100644
index 0000000..66f207f
--- /dev/null
+++ b/code/unsorted/stacked-bars.py
@@ -0,0 +1,71 @@
+# ----------------------------------------------------------------------------
+# Title: Scientific Visualisation - Python & Matplotlib
+# Author: Nicolas P. Rougier
+# License: Creative Commons BY-NC-SA International 4.0
+# ----------------------------------------------------------------------------
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+
+
+def lighten_color(color, amount=0.66):
+ import colorsys
+
+ try:
+ c = mcolors.cnames[color]
+ except:
+ c = color
+ c = np.array(colorsys.rgb_to_hls(*mcolors.to_rgb(c)))
+ return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
+
+
+# cmap = plt.get_cmap("Blues")
+cmap = plt.get_cmap("tab10")
+
+
+V = np.array(
+ [
+ [50, 23, 20, 7],
+ [45, 33, 12, 10],
+ [35, 43, 10, 12],
+ [54, 23, 15, 8],
+ [65, 23, 7, 5],
+ ]
+)
+
+
+fig = plt.figure(figsize=(6, 2), dpi=100)
+ax = plt.subplot(111, xticks=[], ylim=[-0.5, len(V) - 0.5])
+
+ratings = ["Excellent", "Good", "Average", "Awful"]
+Y = np.arange(len(V))
+L = np.zeros(len(V))
+
+for i in range(4):
+ color = lighten_color(cmap(3), 1.00 - i / 4)
+ ax.barh(Y, V[:, i], left=L, color=color, label=ratings[i])
+ for j in range(len(V)):
+ ax.text(
+ L[j] + V[j, i] / 2,
+ Y[j],
+ "%d%%" % V[j, i],
+ zorder=10,
+ ha="center",
+ va="center",
+ color="white",
+ size="x-small",
+ )
+ L += V[:, i]
+
+
+ax.legend(frameon=False, bbox_to_anchor=(1.0, 1), loc=2, borderaxespad=0)
+ax.set_yticks(Y)
+ax.set_yticklabels(["Rating %d" % (i + 1) for i in Y])
+
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+ax.spines["bottom"].set_visible(False)
+
+plt.tight_layout()
+plt.savefig("stacked-bars.pdf")
+plt.show()
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