Merge pull request #814 from neuropsychology/dev

0.2.5

.codeclimate.yml

@@ -1,43 +1,43 @@
 engines:
-  pep8:
-    enabled: true
-    checks:
-      E501:
+    pep8:
+        enabled: true
+        checks:
+            E501:
+                enabled: false
+            E203:
+                enabled: false
+            E302:
+                enabled: false
+            E303:
+                enabled: false
+
+checks:
+    argument-count:
         enabled: false
-      E203:
+    complex-logic:
         enabled: false
-      E302:
+    file-lines:
         enabled: false
-      E303:
+    method-complexity:
+        enabled: false
+    method-count:
+        enabled: false
+    method-lines:
+        enabled: false
+    nested-control-flow:
+        enabled: true
+    return-statements:
+        enabled: true
+    similar-code:
+        enabled: false
+    identical-code:
         enabled: false
-
-checks:
-  argument-count:
-    enabled: false
-  complex-logic:
-    enabled: false
-  file-lines:
-    enabled: true
-  method-complexity:
-    enabled: false
-  method-count:
-    enabled: true
-  method-lines:
-    enabled: false
-  nested-control-flow:
-    enabled: true
-  return-statements:
-    enabled: true
-  similar-code:
-    enabled: false
-  identical-code:
-    enabled: false
 
 exclude_patterns:
-- "tests/"
-- "scripts/"
-- "benchmarks/"
-- "studies/"
-- "data/"
-- "docs/img/"
-- "docs/readme/"
+    - "tests/"
+    - "scripts/"
+    - "benchmarks/"
+    - "studies/"
+    - "data/"
+    - "docs/img/"
+    - "docs/readme/"

.github/workflows/docs-build.yml

@@ -27,10 +27,11 @@ jobs:
                   pip install sphinx-book-theme
                   pip install sphinxemoji
                   pip install sphinx-copybutton
+                  pip install ipykernel==6.23.2
                   pip install ipython
                   pip install myst-parser
                   pip install myst-nb
-                  pip install numpy
+                  pip install numpy==1.24.3
                   pip install pandas
                   pip install scipy
                   pip install scikit-learn

.github/workflows/docs-check.yml

@@ -22,10 +22,11 @@ jobs:
                   pip install sphinx-book-theme
                   pip install sphinxemoji
                   pip install sphinx-copybutton
+                  pip install ipykernel==6.23.2
                   pip install ipython
                   pip install myst-parser
                   pip install myst-nb
-                  pip install numpy
+                  pip install numpy==1.24.3
                   pip install pandas
                   pip install scipy
                   pip install scikit-learn

docs/conf.py

@@ -12,6 +12,8 @@
 import os
 import re
 import sys
+import asyncio
+import platform
 
 # -- Path setup --------------------------------------------------------------
 
@@ -105,6 +107,9 @@ def find_version():
 
 # googleanalytics_id = "G-DVXSEGN5M9"
 
+# Address asyncio warning
+if platform.system() == "Windows":
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
 
 # NumPyDoc configuration -----------------------------------------------------
 

docs/functions/signal.rst

@@ -19,6 +19,10 @@ Preprocessing
 """""""""""""""""""""
 .. autofunction:: neurokit2.signal_resample
 
+*signal_fillmissing()*
+""""""""""""""""""""
+.. autofunction:: neurokit2.signal_fillmissing
+
 
 Transformation
 ^^^^^^^^^^^^^^^^

neurokit2/__init__.py

@@ -32,7 +32,7 @@
 from .video import *
 
 # Info
-__version__ = "0.2.4"
+__version__ = "0.2.5"
 
 
 # Maintainer info

neurokit2/complexity/complexity.py

@@ -37,10 +37,10 @@ def complexity(signal, which="makowski2022", delay=1, dimension=2, tolerance="sd
         depending on what the literature suggests. We recommend using this function only for quick
         exploratory analyses, but then replacing it by the calls to the individual functions.
 
-        Check-out our open-access `study <https://onlinelibrary.wiley.com/doi/10.1111/ejn.15800>`_
+        Check-out our `open-access study <https://onlinelibrary.wiley.com/doi/10.1111/ejn.15800>`_
         explaining the selection of indices.
 
-    The categorization by "computation time" is based on our `study
+    The categorization by "computation time" is based on `our study
     <https://www.mdpi.com/1099-4300/24/8/1036>`_ results:
 
     .. figure:: https://raw.githubusercontent.com/DominiqueMakowski/ComplexityStructure/main/figures/time1-1.png

neurokit2/complexity/fractal_tmf.py

@@ -109,7 +109,7 @@ def fractal_tmf(signal, n=40, show=False, **kwargs):
     w = np.zeros(n)
     for i in range(n):
         surro = signal_surrogate(signal, method="IAAFT")
-        w[i] = fractal_dfa(surro, multifractal=True, show=False)[0]["Width"]
+        w[i] = float(fractal_dfa(surro, multifractal=True, show=False)[0]["Width"].iloc[0])
 
     # Run t-test
     # TODO: adjust in the future

neurokit2/complexity/optim_complexity_tolerance.py

@@ -157,7 +157,7 @@ def complexity_tolerance(
 
     .. ipython:: python
 
-      @savefig p_complexity_tolerance4.png scale=100%
+      @savefig p_complexity_tolerance5.png scale=100%
       r, info = nk.complexity_tolerance(signal, delay=1, dimension=3,
                                         method = 'bin', show=True)
       @suppress
@@ -168,7 +168,7 @@ def complexity_tolerance(
     .. ipython:: python
 
       # Slow method
-      @savefig p_complexity_tolerance5.png scale=100%
+      @savefig p_complexity_tolerance6.png scale=100%
       r, info = nk.complexity_tolerance(signal, delay=8, dimension=6,
                                         method = 'maxApEn', show=True)
       @suppress
@@ -184,7 +184,7 @@ def complexity_tolerance(
     .. ipython:: python
 
       # Narrower range
-      @savefig p_complexity_tolerance6.png scale=100%
+      @savefig p_complexity_tolerance7.png scale=100%
       r, info = nk.complexity_tolerance(signal, delay=8, dimension=6, method = 'maxApEn',
                                         r_range=np.linspace(0.002, 0.8, 30), show=True)
       @suppress

neurokit2/complexity/utils.py

@@ -2,6 +2,7 @@
 import numpy as np
 import sklearn.metrics
 import sklearn.neighbors
+from packaging import version
 
 from .utils_complexity_embedding import complexity_embedding
 
@@ -109,11 +110,16 @@ def _get_count(
     # Get neighbors count
     # -------------------
     # Sanity checks
-    if distance not in sklearn.neighbors.KDTree.valid_metrics + ["range"]:
+    sklearn_version = version.parse(sklearn.__version__)
+    if sklearn_version >= version.parse("1.3.0"):
+        valid_metrics = sklearn.neighbors.KDTree.valid_metrics() + ["range"]
+    else:
+        valid_metrics = sklearn.neighbors.KDTree.valid_metrics + ["range"]
+    if distance not in valid_metrics:
         raise ValueError(
             "The given metric (%s) is not valid."
             "The valid metric names are: %s"
-            % (distance, sklearn.neighbors.KDTree.valid_metrics + ["range"])
+            % (distance, valid_metrics)
         )
 
     if fuzzy is True:

neurokit2/complexity/utils_fractal_mandelbrot.py

@@ -260,7 +260,7 @@ def _buddhabrot_initialize(size=1000, iterations=100, real_range=(-2, 2), imagin
 def _mandelbrot_optimize(c):
     # Optimizations: most of the mset points lie within the
     # within the cardioid or in the period-2 bulb. (The two most
-    # prominant shapes in the mandelbrot set. We can eliminate these
+    # prominent shapes in the mandelbrot set. We can eliminate these
     # from our search straight away and save alot of time.
     # see: http://en.wikipedia.org/wiki/Mandelbrot_set#Optimizations