Merge pull request #275 from jkittner/check-custom-bins

check the provided bins arguments and raise an Error if wrong

notebooks/usage.ipynb

@@ -483,7 +483,7 @@
     "ws = np.random.random(N) * 6\n",
     "wd = np.random.random(N) * 360\n",
     "df = pd.DataFrame({\"speed\": ws, \"direction\": wd})\n",
-    "plot_windrose(df, kind=\"contour\", bins=np.arange(0.01, 8, 1), cmap=cm.hot, lw=3)"
+    "plot_windrose(df, kind=\"contour\", bins=np.arange(0, 8, 1), cmap=cm.hot, lw=3)"
    ]
   },
   {

pyproject.toml

@@ -10,7 +10,7 @@ requires = [
 target-version = "py38"
 line-length = 79
 
-select = [
+lint.select = [
   "A",   # flake8-builtins
   "B",   # flake8-bugbear
   "C4",  # flake8-comprehensions
@@ -19,13 +19,13 @@ select = [
   "T20", # flake8-print
   "UP",  # upgrade
 ]
-per-file-ignores."docs/conf.py" = [
+lint.per-file-ignores."docs/conf.py" = [
   "A001",
 ]
-per-file-ignores."samples/example_by.py" = [
+lint.per-file-ignores."samples/example_by.py" = [
   "T201",
 ]
-per-file-ignores."samples/example_pdf_by.py" = [
+lint.per-file-ignores."samples/example_pdf_by.py" = [
   "T201",
 ]
 

tests/test_windrose.py

@@ -2,6 +2,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import pytest
 from numpy.testing import assert_allclose
 from pandas.testing import assert_frame_equal
 
@@ -73,3 +74,26 @@ def test_theta_labels():
     theta_labels = [t.get_text() for t in ax.get_xticklabels()]
     assert theta_labels == ["a", "b", "c", "d", "e", "f", "g", "h"]
     plt.close()
+
+
+def test_windrose_incorrect_bins_specified():
+    ax = WindroseAxes.from_ax()
+    with pytest.raises(ValueError) as exc_info:
+        ax.bar(direction=wd, var=ws, bins=[1, 2, 3])
+
+    (msg,) = exc_info.value.args
+    assert msg == (
+        "the first value provided in bins must be less than or equal to the minimum "
+        "value of the wind speed data. Did you mean: bins=(0, 1, 2, 3) ? "
+        "If you want to exclude values below a certain threshold, "
+        "try setting calm_limit=1."
+    )
+
+
+def test_windrose_incorrect_bins_in_combination_with_calm_limit():
+    ax = WindroseAxes.from_ax()
+    with pytest.raises(ValueError) as exc_info:
+        ax.bar(direction=wd, var=ws, bins=[1, 2, 3], calm_limit=2)
+
+    (msg,) = exc_info.value.args
+    assert msg == "the lowest value in bins must be >= 2 (=calm_limits)"

tests/test_windrose_np_mpl_oo.py

@@ -49,7 +49,8 @@ def test_windrose_stacked_histogram_not_normed_binned():
 def test_windrose_stacked_histogram_not_normed_binned_calm_limit():
     # Another stacked histogram representation, not normed, with bins limits and a calm limit
     ax = WindroseAxes.from_ax()
-    ax.box(wd, ws, bins=bins, calm_limit=0.2)
+    # the bins most not be below the calm_limit
+    ax.box(wd, ws, bins=[0.2, 1, 2, 3, 4, 5, 6, 7], calm_limit=0.2)
     ax.set_legend()
     return ax.figure
 
@@ -76,7 +77,8 @@ def test_filled_with_colormap():
 def test_filled_with_colormap_calm_limit():
     # A windrose in filled representation, with a controlled colormap and a calm limit
     ax = WindroseAxes.from_ax()
-    ax.contourf(wd, ws, bins=bins, cmap=cm.hot, calm_limit=0.2)
+    # the bins most not be below the calm_limit
+    ax.contourf(wd, ws, bins=[0.2, 1, 2, 3, 4, 5, 6, 7], cmap=cm.hot, calm_limit=0.2)
     ax.set_legend()
     return ax.figure
 
@@ -95,6 +97,8 @@ def test_filled_with_colormap_contours():
 def test_filled_with_colormap_contours_calm_limit():
     # Same as above, but with contours over each filled region...
     ax = WindroseAxes.from_ax()
+    # the bins most not be below the calm_limit
+    bins = [0.2, 1, 2, 3, 4, 5, 6, 7]
     ax.contourf(wd, ws, bins=bins, cmap=cm.hot, calm_limit=0.2)
     ax.contour(wd, ws, bins=bins, colors="black", calm_limit=0.2)
     ax.set_legend()
@@ -112,7 +116,15 @@ def test_without_filled_with_colormap_contours():
 @pytest.mark.mpl_image_compare(baseline_dir="output/oo")
 def test_without_filled_with_colormap_contours_calm_limit():
     ax = WindroseAxes.from_ax()
-    ax.contour(wd, ws, bins=bins, cmap=cm.hot, lw=3, calm_limit=0.2)
+    # the bins most not be below the calm_limit
+    ax.contour(
+        wd,
+        ws,
+        bins=[0.2, 1, 2, 3, 4, 5, 6, 7],
+        cmap=cm.hot,
+        lw=3,
+        calm_limit=0.2,
+    )
     ax.set_legend()
     return ax.figure
 

tests/test_windrose_pandas.py

@@ -16,7 +16,7 @@
 N = 500
 ws = np.random.random(N) * 6
 wd = np.random.random(N) * 360
-bins = np.arange(0.01, 8, 1)
+bins = np.arange(0, 8, 1)
 
 df = pd.DataFrame({"speed": ws, "direction": wd})
 
@@ -45,21 +45,21 @@ def test_box():
 @pytest.mark.mpl_image_compare(baseline_dir="output/df")
 def test_contourf():
     kind = "contourf"
-    ax = plot_windrose(df, kind=kind, bins=bins, cmap=cm.hot)
+    ax = plot_windrose(df, kind=kind, bins=np.arange(0.01, 8, 1), cmap=cm.hot)
     return ax.figure
 
 
 @pytest.mark.mpl_image_compare(baseline_dir="output/df")
 def test_contour():
     kind = "contour"
-    ax = plot_windrose(df, kind=kind, bins=bins, cmap=cm.hot, lw=3)
+    ax = plot_windrose(df, kind=kind, bins=np.arange(0.01, 8, 1), cmap=cm.hot, lw=3)
     return ax.figure
 
 
 @pytest.mark.mpl_image_compare(baseline_dir="output/df")
 def test_pdf():
     kind = "pdf"
-    ax, params = plot_windrose(df, kind=kind, bins=bins)
+    ax, params = plot_windrose(df, kind=kind, bins=np.arange(0.01, 8, 1))
     return ax.figure
 
 

windrose/windrose.py

@@ -371,6 +371,19 @@ def _init_plot(self, direction, var, **kwargs):
         bins = kwargs.pop("bins", None)
         if bins is None:
             bins = np.linspace(np.min(var), np.max(var), 6)
+        if isinstance(bins, (list, tuple, np.ndarray)):
+            if len(bins) > 0 and bins[0] > np.min(var) and not calm_limit:
+                raise ValueError(
+                    f"the first value provided in bins must be less than or equal "
+                    f"to the minimum value of the wind speed data. "
+                    f"Did you mean: bins={(0, *bins)!r} ? "
+                    f"If you want to exclude values below a certain threshold, "
+                    f"try setting calm_limit={min(bins)}.",
+                )
+            elif len(bins) > 0 and calm_limit is not None and min(bins) < calm_limit:
+                raise ValueError(
+                    f"the lowest value in bins must be >= {calm_limit} (=calm_limits)",
+                )
         if isinstance(bins, int):
             bins = np.linspace(np.min(var), np.max(var), bins)
         bins = np.asarray(bins)