Merge branch 'wesleybowman:master' into master

.github/workflows/pre-commit.yml

@@ -3,12 +3,22 @@ name: pre-commit
 on:
   pull_request:
   push:
-    branches: [master]
+    branches: [master, main]
 
 jobs:
   pre-commit:
     runs-on: ubuntu-latest
+    env:
+      FORCE_COLOR: "1"
     steps:
     - uses: actions/checkout@v2
     - uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+    - id: file_changes
+      uses: trilom/[email protected]
+      with:
+        output: ' '
     - uses: pre-commit/[email protected]
+      with:
+        extra_args: --files ${{ steps.file_changes.outputs.files}}

.github/workflows/publish.yml

@@ -14,7 +14,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v1
       with:
-        python-version: 3.8
+        python-version: "3.x"
 
     - name: Get tags
       run: git fetch --depth=1 origin +refs/tags/*:refs/tags/*
@@ -41,7 +41,7 @@ jobs:
       shell: bash
 
     - name: Publish a Python distribution to PyPI
-      uses: pypa/gh-action-pypi-publish@master
+      uses: pypa/gh-action-pypi-publish@main
       with:
         user: __token__
         password: ${{ secrets.PYPI_PASSWORD }}

.github/workflows/tests.yml

@@ -3,18 +3,21 @@ name: Tests
 on:
   pull_request:
   push:
-    branches: [master]
+    branches: [master, main]
 
 jobs:
   run:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        python-version: [ "3.7", "3.8", "3.9"]
+        python-version: [ "3.7", "3.8", "3.9", "3.10"]
         os: [windows-latest, ubuntu-latest, macos-latest]
         # Oldest one based on NEP-29 and latest one.
         # See https://numpy.org/neps/nep-0029-deprecation_policy.html
-        numpy-version: ["1.17", "1.20"]
+        numpy-version: ["1.19", "1.22"]
+        exclude:
+          - python-version: "3.10"
+            numpy-version: "1.19"
 
     steps:
     - uses: actions/checkout@v2

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v3.4.0
+  rev: v4.2.0
   hooks:
     - id: check-ast
     - id: end-of-file-fixer
@@ -15,33 +15,33 @@ repos:
     - id: debug-statements
 
 - repo: https://gitlab.com/pycqa/flake8
-  rev: 3.8.4
+  rev: 3.9.2
   hooks:
     - id: flake8
       exclude: docs/source/conf.py
       args: [--max-line-length=105]
 
 - repo: https://github.com/pre-commit/mirrors-isort
-  rev: v5.7.0
+  rev: v5.10.1
   hooks:
   - id: isort
     additional_dependencies: [toml]
     args: [--project=utide, --multi-line=3, --lines-after-imports=2, --lines-between-types=1, --trailing-comma, --force-grid-wrap=0, --use-parentheses, --line-width=88]
 
 - repo: https://github.com/psf/black
-  rev: 20.8b1
+  rev: 22.3.0
   hooks:
   - id: black
     language_version: python3
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: v2.10.0
+  rev: v2.32.0
   hooks:
     - id: pyupgrade
       args:
         - --py36-plus
 
 - repo: https://github.com/asottile/add-trailing-comma
-  rev: v2.1.0
+  rev: v2.2.2
   hooks:
     - id: add-trailing-comma

utide/astronomy.py

@@ -65,7 +65,7 @@ def ut_astron(jd):
     d = jd - daten
     D = d / 10000
 
-    args = np.vstack((np.ones(jd.shape), d, D * D, D ** 3))
+    args = np.vstack((np.ones(jd.shape), d, D * D, D**3))
 
     astro = np.fmod((np.dot(_coefs, args) / 360), 1)
 

utide/confidence.py

@@ -365,11 +365,11 @@ def _confidence(
                     varImap += 0.25 * varcov[nNR + k, 2, 2]
                 rp = ref.I.Rp
                 rm = ref.I.Rm
-                varXuHH = (rp.real ** 2 + rm.real ** 2) * varReap + (
-                    rp.imag ** 2 + rm.imag ** 2
+                varXuHH = (rp.real**2 + rm.real**2) * varReap + (
+                    rp.imag**2 + rm.imag**2
                 ) * varImap
-                varYuHH = (rp.real ** 2 + rm.real ** 2) * varImap + (
-                    rp.imag ** 2 + rm.imag ** 2
+                varYuHH = (rp.real**2 + rm.real**2) * varImap + (
+                    rp.imag**2 + rm.imag**2
                 ) * varReap
                 for varX, varY in zip(varXuHH, varYuHH):
                     if not opt.twodim:
@@ -438,10 +438,10 @@ def ut_linci(X, Y, sigX, sigY):
 
     rp = 0.5 * np.sqrt((Xu + Yv) ** 2 + (Xv - Yu) ** 2)
     rm = 0.5 * np.sqrt((Xu - Yv) ** 2 + (Xv + Yu) ** 2)
-    sigXu2 = sigXu ** 2
-    sigYu2 = sigYu ** 2
-    sigXv2 = sigXv ** 2
-    sigYv2 = sigYv ** 2
+    sigXu2 = sigXu**2
+    sigYu2 = sigYu**2
+    sigXv2 = sigXv**2
+    sigYv2 = sigYv**2
 
     ex = (Xu + Yv) / rp
     fx = (Xu - Yv) / rm
@@ -457,8 +457,8 @@ def ut_linci(X, Y, sigX, sigY):
 
     # phase
     rn = 2 * (Xu * Yu + Xv * Yv)
-    rd = Xu ** 2 - Yu ** 2 + Xv ** 2 - Yv ** 2
-    den = rn ** 2 + rd ** 2
+    rd = Xu**2 - Yu**2 + Xv**2 - Yv**2
+    den = rn**2 + rd**2
     dXu2 = ((rd * Yu - rn * Xu) / den) ** 2
     dYu2 = ((rd * Xu + rn * Yu) / den) ** 2
     dXv2 = ((rd * Yv - rn * Xv) / den) ** 2
@@ -480,8 +480,8 @@ def ut_linci(X, Y, sigX, sigY):
 
         # Orientation.
         rn = 2.0 * (Xu * Xv + Yu * Yv)
-        rd = Xu ** 2 + Yu ** 2 - (Xv ** 2 + Yv ** 2)
-        den = rn ** 2 + rd ** 2
+        rd = Xu**2 + Yu**2 - (Xv**2 + Yv**2)
+        den = rn**2 + rd**2
         dXu2 = ((rd * Xv - rn * Xu) / den) ** 2
         dYu2 = ((rd * Yv - rn * Yu) / den) ** 2
         dXv2 = ((rd * Xu + rn * Xv) / den) ** 2

utide/harmonics.py

@@ -126,7 +126,7 @@ def FUV(t, tref, lind, lat, ngflgs):
         rr = sat.amprat.copy()
 
         j = sat.ilatfac == 1
-        rr[j] *= 0.36309 * (1.0 - 5.0 * slat ** 2) / slat
+        rr[j] *= 0.36309 * (1.0 - 5.0 * slat**2) / slat
 
         j = sat.ilatfac == 2
         rr[j] *= 2.59808 * slat

utide/periodogram.py

@@ -167,15 +167,16 @@ def _psd_lomb(t, x, window=None, freq=None, ofac=1):
     PSD units are [`x`-units^2 per cycle per unit time]
 
     """
-    out = Bunch()
 
-    # copy inputs
-    x = np.array(x)
-    t = np.array(t, dtype=float)
+    # In scipy 1.7.x, the new pythran-based lombscargle requires contiguous
+    # array arguments.
+    def _lombscargle(*args):
+        newargs = [np.ascontiguousarray(a, dtype=np.float64) for a in args]
+        return signal.lombscargle(*newargs)
 
-    # remove mean
-    x -= x.mean()
+    out = Bunch()
 
+    xdm = x - x.mean()
     n = len(x)
 
     if window is None:
@@ -184,8 +185,7 @@ def _psd_lomb(t, x, window=None, freq=None, ofac=1):
         # interpolate window from uniform grid to nonuniform t
         t_uniform = np.linspace(np.min(t), np.max(t), n)
         w = np.interp(t, t_uniform, window)
-
-        x *= w
+        xdm *= w
 
     # Estimated record length as n delta-t, where delta-t
     # is the *average* time per sample.
@@ -201,24 +201,22 @@ def _psd_lomb(t, x, window=None, freq=None, ofac=1):
 
     out.F = freq
 
-    xr = np.real(x)
-
     # signal.lombscargle returns "(A**2) * N/4 for a harmonic signal
     # with amplitude A for sufficiently large N."
     # It takes *angular* frequencies as 3rd argument.
     freq_radian = freq * 2 * np.pi
-    psdnorm = 2 * delta_t * n / (w ** 2).sum()
-    out.Pxx = psdnorm * signal.lombscargle(t, xr, freq_radian)
+    psdnorm = 2 * delta_t * n / (w**2).sum()
+    out.Pxx = psdnorm * _lombscargle(t, np.real(xdm), freq_radian)
 
     if x.dtype.kind == "f":
         return out
 
-    out.Pyy = psdnorm * signal.lombscargle(t, x.imag, freq_radian)
+    out.Pyy = psdnorm * _lombscargle(t, xdm.imag, freq_radian)
 
     # If we need to limit memory usage and don't want to use
     # Cython, we can segment the frequencies and loop over the
     # segments.  The speed penalty will be minimal.
-    out.Pxy = psdnorm * _ls_cross(t, x, freq_radian)
+    out.Pxy = psdnorm * _ls_cross(t, xdm, freq_radian)
 
     return out
 
@@ -247,14 +245,14 @@ def _ls_cross(t, x, fr):
     tmpy = np.empty(fr.shape, dtype=complex)
 
     c = np.cos(arg)
-    a = 1 / np.sqrt(sum(c ** 2))
+    a = 1 / np.sqrt(sum(c**2))
     tmpx.real = a * (xr[:, np.newaxis] * c).sum(axis=0)
     tmpy.real = a * (xi[:, np.newaxis] * c).sum(axis=0)
 
     del c
 
     s = np.sin(arg)
-    b = 1 / np.sqrt(sum(s ** 2))
+    b = 1 / np.sqrt(sum(s**2))
     tmpx.imag = b * (xr[:, np.newaxis] * s).sum(axis=0)
     tmpy.imag = -b * (xi[:, np.newaxis] * s).sum(axis=0)
 
@@ -289,7 +287,7 @@ def _psd(e, window, fs):
 
     # cs = cs[:iny]
     cs[1:-1] *= 2
-    psdnorm = (1 / fs) * (1 / (window ** 2).sum())  # dt / sum of win squared.
+    psdnorm = (1 / fs) * (1 / (window**2).sum())  # dt / sum of win squared.
     return cs * psdnorm
 
 

utide/robustfit.py

@@ -18,13 +18,13 @@ def andrews(r):
 
 def bisquare(r):
     r = np.abs(r)
-    w = (r < 1) * (1 - r ** 2) ** 2
+    w = (r < 1) * (1 - r**2) ** 2
     return w
 
 
 def cauchy(r):
     r = np.abs(r)
-    w = 1 / (1 + r ** 2)
+    w = 1 / (1 + r**2)
     return w
 
 
@@ -57,7 +57,7 @@ def talwar(r):
 
 def welsch(r):
     r = np.abs(r)
-    w = np.exp(-(r ** 2))
+    w = np.exp(-(r**2))
     return w
 
 

utide/utilities.py

@@ -254,7 +254,7 @@ def _showmatbunch(b, elements=None, origin=None):
                 else:
                     entry = f"ndarray, shape {v.shape}, dtype {v.dtype}"
             else:
-                entry = "{} {}".format(type(v).__name__, v)
+                entry = f"{type(v).__name__} {v}"
             elements.append((_origin, entry))
     elements.sort()
     return elements
@@ -273,7 +273,7 @@ def showmatbunch(b):
         b = loadbunch(b)
     elist = _showmatbunch(b)
     names = [n for n, v in elist]
-    namelen = min(40, max([len(n) for n in names]))
+    namelen = min(40, max(len(n) for n in names))
     str_fmt = "{0!s:<{namelen}} : {1!s}\n"
     strlist = [str_fmt.format(n[1:], v, namelen=namelen) for (n, v) in elist]
     return "".join(strlist)