Merge pull request #101 from ocefpaf/numpy20

Fix for numpy 2.0...

Author: ocefpaf

.github/workflows/tests.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        python-version: ["3.10", "3.11", "3.12"]
+        python-version: [ "3.10", "3.11", "3.12" ]
         os: [ubuntu-latest]
       fail-fast: false
 

.pre-commit-config.yaml

@@ -28,18 +28,18 @@ repos:
     - id: blackdoc
 
 - repo: https://github.com/charliermarsh/ruff-pre-commit
-  rev: v0.3.5
+  rev: v0.5.5
   hooks:
     - id: ruff
 
 - repo: https://github.com/psf/black
-  rev: 24.3.0
+  rev: 24.4.2
   hooks:
   - id: black
     language_version: python3
 
 - repo: https://github.com/codespell-project/codespell
-  rev: v2.2.6
+  rev: v2.3.0
   hooks:
     - id: codespell
       args:
@@ -51,6 +51,6 @@ repos:
     - id: add-trailing-comma
 
 - repo: https://github.com/tox-dev/pyproject-fmt
-  rev: "1.7.0"
+  rev: "2.2.0"
   hooks:
     - id: pyproject-fmt

oceans/RPSstuff.py

@@ -13,7 +13,7 @@ def h2hms(hours):
     Examples
     --------
     >>> h2hms(12.51)
-    (12.0, 30.0, 36.0)
+    (np.float64(12.0), np.float64(30.0), np.float64(36.0))
 
     """
     hour = np.floor(hours)
@@ -33,7 +33,7 @@ def hms2h(h, m=None, s=None):
     12.51
     >>> # Or,
     >>> hms2h(123036)
-    12.51
+    np.float64(12.51)
 
     """
     if not m and not s:
@@ -55,7 +55,7 @@ def ms2hms(millisecs):
     Examples
     --------
     >>> ms2hms(1e3 * 60)
-    (0.0, 1.0, 0.0)
+    (np.float64(0.0), np.float64(1.0), np.float64(0.0))
 
     """
     sec = np.round(millisecs / 1000)
@@ -233,7 +233,7 @@ def s2hms(secs):
     Examples
     --------
     >>> s2hms(3600 + 60 + 1)
-    (1.0, 1.0, 1)
+    (np.float64(1.0), np.float64(1.0), np.int64(1))
 
     """
     hr = np.floor(secs / 3600)
@@ -509,7 +509,7 @@ def fixcoast(coast):
     """
 
     ind = coast == -99999.0
-    coast[ind] = np.NaN
+    coast[ind] = np.nan
 
     ind = np.where(np.isnan(coast[:, 0]))[0]
     dind = np.diff(ind)
@@ -518,9 +518,9 @@ def fixcoast(coast):
     coast = np.delete(coast, ind[idup], axis=0)
 
     if not np.isnan(coast[0, 0]):
-        coast = np.insert(coast, 0, np.NaN, axis=0)
+        coast = np.insert(coast, 0, np.nan, axis=0)
 
     if not np.isnan(coast[-1, -1]):
-        coast = np.append(coast, np.c_[np.NaN, np.NaN], axis=0)
+        coast = np.append(coast, np.c_[np.nan, np.nan], axis=0)
 
     return coast

oceans/colormaps.py

@@ -142,15 +142,15 @@ def load_cmap(fname):
 }
 
 # Data colormaps.
-for fname in glob("%s/*.dat" % cmap_path):
+for fname in glob(f"{cmap_path}/*.dat"):
     cmap = os.path.basename(fname).split(".")[0]
     data = load_cmap(fname)
     arrays.update({cmap: data})
 
 cm = Bunch()
 for key, value in arrays.items():
     cm.update({key: cmat2cmpl(value)})
-    cm.update({"%s_r" % key: cmat2cmpl(value, reverse=True)})
+    cm.update({f"{key}_r": cmat2cmpl(value, reverse=True)})
 
 
 def demo():

oceans/datasets.py

@@ -28,7 +28,7 @@ def _woa_variable(variable):
 
 
 def _woa_url(variable, time_period, resolution):
-    base = "https://data.nodc.noaa.gov/thredds/dodsC"
+    base = "https://www.ncei.noaa.gov/thredds-ocean/dodsC"
 
     v = _woa_variable(variable)
 

oceans/filters.py

@@ -178,7 +178,7 @@ def smoo2(A, hei, wid, kind="hann", badflag=-9999, beta=14):
     # Checking window type and dimensions
     kinds = ["hann", "hamming", "blackman", "bartlett", "kaiser"]
     if kind not in kinds:
-        raise ValueError("Invalid window type requested: %s" % kind)
+        raise ValueError(f"Invalid window type requested: {kind}")
 
     if (np.mod(hei, 2) == 0) or (np.mod(wid, 2) == 0):
         raise ValueError("Window dimensions must be odd")
@@ -205,7 +205,7 @@ def smoo2(A, hei, wid, kind="hann", badflag=-9999, beta=14):
     A = np.asanyarray(A)
     Fnan = np.isnan(A)
     imax, jmax = A.shape
-    As = np.NaN * np.ones((imax, jmax))
+    As = np.nan * np.ones((imax, jmax))
 
     for i in range(imax):
         for j in range(jmax):
@@ -257,7 +257,7 @@ def smoo2(A, hei, wid, kind="hann", badflag=-9999, beta=14):
             a = Ac * wdwc
             As[i, j] = a.sum() / wdwc.sum()
     # Assigning NaN to the positions holding NaNs in the original array.
-    As[Fnan] = np.NaN
+    As[Fnan] = np.nan
 
     return As
 
@@ -302,7 +302,7 @@ def weim(x, N, kind="hann", badflag=-9999, beta=14):
     # Checking window type and dimensions.
     kinds = ["hann", "hamming", "blackman", "bartlett", "kaiser"]
     if kind not in kinds:
-        raise ValueError("Invalid window type requested: %s" % kind)
+        raise ValueError(f"Invalid window type requested: {kind}")
 
     if np.mod(N, 2) == 0:
         raise ValueError("Window size must be odd")
@@ -329,7 +329,7 @@ def weim(x, N, kind="hann", badflag=-9999, beta=14):
     ln = (N - 1) / 2
     lx = x.size
     lf = lx - ln
-    xs = np.NaN * np.ones(lx)
+    xs = np.nan * np.ones(lx)
 
     # Eliminating bad data from mean computation.
     fbad = x == badflag
@@ -459,7 +459,7 @@ def medfilt1(x, L=3):
         msg = "Input sequence has to be 1d: ndim = {}".format
         raise ValueError(msg(xin.ndim))
 
-    xout = np.zeros_like(xin) + np.NaN
+    xout = np.zeros_like(xin) + np.nan
 
     Lwing = (L - 1) // 2
 
@@ -538,7 +538,7 @@ def md_trenberth(x):
     >>> filtered = md_trenberth(x)
     >>> fig, ax = plt.subplots()
     >>> (l1,) = ax.plot(t, x, label="original")
-    >>> pad = [np.NaN] * 5
+    >>> pad = [np.nan] * 5
     >>> (l2,) = ax.plot(t, np.r_[pad, filtered, pad], label="filtered")
     >>> legend = ax.legend()
 
@@ -598,9 +598,9 @@ def pl33tn(x, dt=1.0, T=33.0, mode="valid", t=None):
     >>> filtered_33d3 = pl33tn(x, dt=4.0, T=72.0)  # 3 day filter
     >>> fig, ax = plt.subplots()
     >>> (l1,) = ax.plot(t, x, label="original")
-    >>> pad = [np.NaN] * 8
+    >>> pad = [np.nan] * 8
     >>> (l2,) = ax.plot(t, np.r_[pad, filtered_33, pad], label="33 hours")
-    >>> pad = [np.NaN] * 17
+    >>> pad = [np.nan] * 17
     >>> (l3,) = ax.plot(t, np.r_[pad, filtered_33d3, pad], label="3 days")
     >>> legend = ax.legend()
 

oceans/ocfis.py

@@ -4,6 +4,7 @@
 import gsw
 import numpy as np
 import numpy.ma as ma
+import pandas as pd
 
 
 def spdir2uv(spd, ang, deg=False):
@@ -583,7 +584,7 @@ def bin_dates(self, freq, tz=None):
     >>> from pandas import Series, date_range
     >>> n = 24 * 30
     >>> sig = np.random.rand(n) + 2 * np.cos(2 * np.pi * np.arange(n))
-    >>> dates = date_range(start="1/1/2000", periods=n, freq="H")
+    >>> dates = date_range(start="1/1/2000", periods=n, freq="h")
     >>> series = Series(data=sig, index=dates)
     >>> new_series = bin_dates(series, freq="D", tz=None)
 
@@ -593,7 +594,7 @@ def bin_dates(self, freq, tz=None):
     new_index = date_range(start=self.index[0], end=self.index[-1], freq=freq, tz=tz)
     new_series = self.groupby(new_index.asof).mean()
     # Averages at the center.
-    secs = new_index.freq.delta.total_seconds()
+    secs = pd.Timedelta(new_index.freq).total_seconds()
     new_series.index = new_series.index.values + int(secs // 2)
     return new_series
 
@@ -625,7 +626,7 @@ def series_spline(self):
 
 def despike(self, n=3, recursive=False):
     """
-    Replace spikes with np.NaN.
+    Replace spikes with np.nan.
     Removing spikes that are >= n * std.
     default n = 3.
 
@@ -638,12 +639,12 @@ def despike(self, n=3, recursive=False):
     )
 
     removed = np.count_nonzero(outliers)
-    result[outliers] = np.NaN
+    result[outliers] = np.nan
 
     counter = 0
     if recursive:
         while outliers.any():
-            result[outliers] = np.NaN
+            result[outliers] = np.nan
             base = np.abs(result - np.nanmean(result))
             outliers = base >= n * np.nanstd(result)
             counter += 1
@@ -658,7 +659,7 @@ def pol2cart(theta, radius, units="deg"):
     Examples
     --------
     >>> pol2cart(0, 1, units="deg")
-    (1.0, 0.0)
+    (np.float64(1.0), np.float64(0.0))
 
     """
     if units in ["deg", "degs"]:
@@ -781,7 +782,7 @@ def get_profile(x, y, f, xi, yi, mode="nearest", order=3):
     return map_coordinates(f, coords, mode=mode, order=order)
 
 
-def strip_mask(arr, fill_value=np.NaN):
+def strip_mask(arr, fill_value=np.nan):
     """
     Take a masked array and return its data(filled) + mask.
 

oceans/plotting.py

@@ -130,7 +130,7 @@ def level_colormap(levels, cmap=None):
     cdict = {"red": tuple(R), "green": tuple(G), "blue": tuple(B)}
 
     return matplotlib.colors.LinearSegmentedColormap(
-        "%s_levels" % cmap.name,
+        f"{cmap.name}_levels",
         cdict,
         256,
     )

oceans/sw_extras/gamma_GP_from_SP_pt.py

@@ -445,7 +445,7 @@ def gamma_GP_from_SP_pt(SP, pt, p, lon, lat):
     gamma_Pac = gamma_G_pacific(SP, pt)
     gamma_Ind = gamma_G_indian(SP, pt)
     gamma_SOce = gamma_G_southern_ocean(SP, pt, p)
-    # gamma_Arc = np.zeros_like(SP) * np.NaN
+    # gamma_Arc = np.zeros_like(SP) * np.nan
 
     # Definition of the Indian part.
     io_lon = np.array(
@@ -590,7 +590,7 @@ def gamma_GP_from_SP_pt(SP, pt, p, lon, lat):
     gamma_GP = w_so * gamma_SOce + (1.0 - w_so) * gamma_middle
 
     # Set NaN in the arctic region.
-    gamma_GP[lat > 66.0] = np.NaN
+    gamma_GP[lat > 66.0] = np.nan
 
     # De-normalization.
     gamma_GP = 20.0 * gamma_GP - 20

oceans/sw_extras/sw_extras.py

@@ -321,7 +321,7 @@ def cor_beta(lat):
     --------
     >>> import oceans.sw_extras.sw_extras as swe
     >>> swe.cor_beta(0)
-    2.2891225867210798e-11
+    np.float64(2.2891225867210798e-11)
 
     References
     ----------
@@ -358,7 +358,7 @@ def inertial_period(lat):
     >>> import oceans.sw_extras.sw_extras as swe
     >>> lat = 30.0
     >>> swe.inertial_period(lat) / 3600
-    23.93484986278565
+    np.float64(23.93484986278565)
 
     """
     lat = np.asanyarray(lat)
@@ -427,7 +427,7 @@ def visc(s, t, p):
     --------
     >>> import oceans.sw_extras.sw_extras as swe
     >>> swe.visc(40.0, 40.0, 1000.0)
-    8.200192496633804e-07
+    np.float64(8.200192496633804e-07)
 
     Modifications: Original 1998/01/19 - Ayal Anis 1998
 
@@ -466,7 +466,7 @@ def tcond(s, t, p):
     --------
     >>> import oceans.sw_extras.sw_extras as swe
     >>> swe.tcond(35, 20, 0)
-    0.5972445569999999
+    np.float64(0.5972445569999999)
 
     References
     ----------