Use pyplot.get_cmap instead of cm.get_cmap

matplotlib.cm.get_cmap has been removed in matplotlib 3.9.0

docs/_gen_figures/gen_datasets_repository.py

@@ -1,7 +1,7 @@
 import numpy as np
 from light_dark import mk_light_dark
-from matplotlib.cm import get_cmap
-from wildboar import iseos
+from matplotlib.pyplot import get_cmap
+from wildboar.utils.variable_len import is_end_of_series
 from wildboar.datasets import load_dataset, load_gun_point
 from wildboar.datasets.preprocess import minmax_scale, truncate
 

docs/_gen_figures/gen_outlier.py

@@ -37,11 +37,11 @@ def plot_inlier_outlier(
 
     ax[1].set_title("Distribution")
 
-    ax[1].bar(label, value, color=plt.cm.get_cmap(lut=2).colors)
+    ax[1].bar(label, value, color=plt.get_cmap(lut=2).colors)
 
     ax[2].set_title("Time series")
     plot.plot_time_domain(
-        x_outlier, y=y_outlier, ax=ax[2], n_samples=10, cmap=plt.cm.get_cmap(lut=2)
+        x_outlier, y=y_outlier, ax=ax[2], n_samples=10, cmap=plt.get_cmap(lut=2)
     )
 
 
@@ -53,7 +53,7 @@ def make_projection(*, x, x_outlier):
 
 
 def plot_projection(*, original_projection, outlier_projection, y_outlier, ax):
-    cmap = plt.cm.get_cmap(lut=3)
+    cmap = plt.get_cmap(lut=3)
     ax.scatter(
         original_projection[:, 0],
         original_projection[:, 1],

src/wildboar/ensemble/_ensemble.py

@@ -839,10 +839,7 @@ def fit(self, x, y, sample_weight=None):
             max_samples=self.max_samples,
             max_depth=self.max_depth,
             sample_weight=sample_weight,
-<<<<<<< HEAD
-=======
             check_input=False,
->>>>>>> c7ab42773 (Fix bugs introduced with scikit-learn 1.5.)
         )
         return self
 

src/wildboar/explain/_importance.py

@@ -21,9 +21,8 @@
 from ..utils.validation import check_array, check_option
 
 try:
-    from matplotlib.axes import Axes
-    from matplotlib.cm import ScalarMappable, get_cmap
-    from matplotlib.pylab import subplots
+    from matplotlib.cm import ScalarMappable
+    from matplotlib.pyplot import get_cmap, subplots
 
     from ..utils.plot import MidpointNormalize, plot_frequency_domain, plot_time_domain
 except ModuleNotFoundError as e:
@@ -35,7 +34,6 @@
     subplots = matplotlib_missing
     MidpointNormalize = matplotlib_missing
     plot_time_domain = matplotlib_missing
-    Axes = matplotlib_missing
 
 Importance = namedtuple("Importance", ["mean", "std", "full"])
 

src/wildboar/utils/plot.py

@@ -1,17 +1,19 @@
 # Authors: Isak Samsten
 # License: BSD 3 clause
+"""
+Plotting utilities.
+"""
 
 import numpy as np
 from sklearn.utils import column_or_1d, resample
 
 from ..utils.validation import check_array
 
 try:
-    import matplotlib.pylab as plt
-    from matplotlib.cm import get_cmap
     from matplotlib.collections import LineCollection
     from matplotlib.colors import Normalize
     from matplotlib.lines import Line2D
+    from matplotlib.pyplot import get_cmap, subplots
 except ModuleNotFoundError as e:
     from ..utils import DependencyMissing
 
@@ -102,7 +104,7 @@ def plot_time_domain(
     >>> plot_time_domain(X, y, n_sample=10)
     """
     if ax is None:
-        fig, ax = plt.subplots()
+        fig, ax = subplots()
 
     x = check_array(np.atleast_2d(x), input_name="x", allow_3d=False, order=None)
     if y is not None:
@@ -199,7 +201,7 @@ def plot_frequency_domain(
     >>> plot_frequency_domain(X, y, n_sample=10)
     """
     if ax is None:
-        fig, ax = plt.subplots()
+        fig, ax = subplots()
 
     x = check_array(x, allow_3d=False, order=None)
     if y is not None: