abstract plotting functions

pyproject.toml

@@ -56,4 +56,5 @@ omit = [
   "src/spikeanalysis/analysis_utils/*", # utils are all numba
   "src/spikeanalysis/spike_plotter.py", # no testing for actual plotting yet
   "src/spikeanalysis/intrinsic_plotter.py", # no testing for plotting yet
+  "src/spikeanalysis/plotting.functions.py", # no test for plotting yet
 ]

src/spikeanalysis/__init__.py

@@ -6,6 +6,7 @@
 from .analog_analysis import AnalogAnalysis
 from .curated_spike_analysis import CuratedSpikeAnalysis, read_responsive_neurons
 from .stats_functions import kolmo_smir_stats
+from .plotting_functions import plot_piechart
 
 import importlib.metadata
 

src/spikeanalysis/plotting_functions.py

@@ -0,0 +1,46 @@
+from typing import Optional, Sequence
+import matplotlib.pyplot as plt
+
+
+def plot_piechart(self, wedges: Sequence, counts: Sequence, colors: Optional[Sequence] = None):
+    """Plots a piechart"""
+
+    assert len(wedges) == len(counts), "each wedge needs a corresponding count"
+    assert not counts.index(0), "counts with 0 will display incorrectly"
+    assert counts[0] != 0, "counts with 0 will display incorrectly"
+    import numpy as np
+
+    if self.figsize[0] <= 10:
+        fontsize = 10
+    else:
+        fontsize = 14
+
+    f, ax = plt.subplots(figsize=self.figsize)
+
+    if colors is None:
+        colors = [
+            "#ff9999",
+            "#66b3ff",
+            "#99ff99",
+            "#FEC8D8",
+            "#ffcc99",
+            "#F6BF85",
+            "#B7ADED",
+        ]
+
+    ax.pie(
+        counts,
+        labels=wedges,
+        autopct=lambda pct: "{:.1f}%\n(n={:d})".format(pct, int(np.round(pct / 100 * np.sum(counts)))),
+        shadow=False,
+        startangle=90,
+        colors=colors,
+        textprops={"fontsize": fontsize},
+    )
+    ax.axis("equal")
+    if self.title:
+        plt.title(self.title)
+    plt.tight_layout()
+    plt.figure(dpi=self.dpi)
+
+    plt.show()