Updated formatting

magnipy/diversipy.py

@@ -238,7 +238,9 @@ def get_magnitude_functions(self):
     #  │ Diversity Summaries                                      │
     #  ╰──────────────────────────────────────────────────────────╯
 
-    def MagAreas(self, integration="trapz", absolute_area=True, scale=True, plot=False):
+    def MagAreas(
+        self, integration="trapz", absolute_area=True, scale=True, plot=False
+    ):
         """
         Compute the areas under the magnitude functions for all datasets.
 
@@ -366,7 +368,9 @@ def plot_MagDiffs_heatmap(self):
         import matplotlib.pyplot as plt
         import pandas as pd
 
-        df = pd.DataFrame(self._MagDiffs, columns=self._names, index=self._names)
+        df = pd.DataFrame(
+            self._MagDiffs, columns=self._names, index=self._names
+        )
         sns.heatmap(df, annot=False, cmap="rocket_r")
         plt.show()
         return None

magnipy/utils/tutorial_data.py

@@ -32,7 +32,9 @@ def get_Xs():
     # Sample 100 points each from Gaussians at (0.5, 0.5) and (1.5, 1.5)
     mean2 = [[0.5, 0.5], [1.5, 1.5]]
     cov2 = np.eye(2) * 0.02
-    X3 = np.concatenate([sample_points_gaussian(mean, cov2, 100) for mean in mean2])
+    X3 = np.concatenate(
+        [sample_points_gaussian(mean, cov2, 100) for mean in mean2]
+    )
 
     # Sample 200 points from a Gaussian centered at (0, 0.5)
     mean1 = [0.5, 0.5]
@@ -134,7 +136,9 @@ def show_magnitude_function(df, ts):
 def plot_df(df, title):
     fig = plt.figure()
     ax = fig.add_subplot(111, projection="3d")
-    ax.scatter(df["x"], df["y"], df["z"], c=df["z"], cmap="viridis", marker="o", s=5)
+    ax.scatter(
+        df["x"], df["y"], df["z"], c=df["z"], cmap="viridis", marker="o", s=5
+    )
     plt.title(title)
     plt.show()
 
@@ -143,7 +147,9 @@ def plot_dfs(dfs, titles):
     n = len(dfs)
 
     # Create a figure with 1 row and n columns of 3D subplots
-    fig, axes = plt.subplots(1, n, figsize=(18, 6), subplot_kw={"projection": "3d"})
+    fig, axes = plt.subplots(
+        1, n, figsize=(18, 6), subplot_kw={"projection": "3d"}
+    )
 
     for idx in range(0, n):
         df = dfs[idx]
@@ -188,12 +194,18 @@ def plot_matrix_heatmaps(matrices, distance=True, metric="Euclidean Distance"):
     vmin = min(matrices[0].min(), matrices[1].min(), matrices[2].min())
     vmax = max(matrices[0].max(), matrices[1].max(), matrices[2].max())
 
-    rando_heatmap = axs[0].imshow(matrices[0], cmap="viridis", vmin=vmin, vmax=vmax)
+    rando_heatmap = axs[0].imshow(
+        matrices[0], cmap="viridis", vmin=vmin, vmax=vmax
+    )
     axs[0].set_title("Random")
     axs[0].set_ylabel("Index of Datapoint")
-    blob_heatmap = axs[1].imshow(matrices[1], cmap="viridis", vmin=vmin, vmax=vmax)
+    blob_heatmap = axs[1].imshow(
+        matrices[1], cmap="viridis", vmin=vmin, vmax=vmax
+    )
     axs[1].set_title("Blobs / Clusters")
-    swiss_heatmap = axs[2].imshow(matrices[2], cmap="viridis", vmin=vmin, vmax=vmax)
+    swiss_heatmap = axs[2].imshow(
+        matrices[2], cmap="viridis", vmin=vmin, vmax=vmax
+    )
     axs[2].set_title("Swiss Roll")
 
     fig.colorbar(
@@ -208,7 +220,9 @@ def plot_matrix_heatmaps(matrices, distance=True, metric="Euclidean Distance"):
 
 def plot_weights(dfs, ts, weights, titles):
     # scaling colorbar
-    vmin = min(weights[0][:, 0].min(), weights[1][:, 0].min(), weights[2][:, 0].min())
+    vmin = min(
+        weights[0][:, 0].min(), weights[1][:, 0].min(), weights[2][:, 0].min()
+    )
     vmax = max(
         weights[0][:, -1].max(),
         weights[1][:, -1].max(),
@@ -272,7 +286,9 @@ def plot_weights(dfs, ts, weights, titles):
                         "$t=1/2 * t_{{conv}}$ \n1/2 of the Convergence Scale"
                     )
                 else:
-                    axes[idx, t_idx].set_title("$t=t_{{conv}}$ \nConvergence Scale")
+                    axes[idx, t_idx].set_title(
+                        "$t=t_{{conv}}$ \nConvergence Scale"
+                    )
 
     # Adjust layout and show the figure
     cbar = fig.colorbar(
@@ -430,7 +446,9 @@ def plot_simulation_progression(Xs, colors, size):
 
     fig, ax = plt.subplots(1, 3, figsize=(15, 5))
 
-    ax[0].scatter(Xs[0][:, 0], Xs[0][:, 1], c=colors[0], cmap="viridis", alpha=0.6)
+    ax[0].scatter(
+        Xs[0][:, 0], Xs[0][:, 1], c=colors[0], cmap="viridis", alpha=0.6
+    )
     ax[0].set_xlim(x_int)
     ax[0].set_ylim(y_int)
     ax[0].set_title("Beginning of Simulation (X0)")
@@ -444,7 +462,9 @@ def plot_simulation_progression(Xs, colors, size):
     ax[1].set_title("Midway Through Simulation")
     ax[1].set_xlim(x_int)
     ax[1].set_ylim(y_int)
-    ax[2].scatter(Xs[-1][:, 0], Xs[-1][:, 1], c=colors[-1], cmap="viridis", alpha=0.6)
+    ax[2].scatter(
+        Xs[-1][:, 0], Xs[-1][:, 1], c=colors[-1], cmap="viridis", alpha=0.6
+    )
     ax[2].set_title("End of Simulation")
     ax[2].set_xlim(x_int)
     ax[2].set_ylim(y_int)
@@ -482,7 +502,9 @@ def create_animation(Xs, colors, div, is_dropping: bool, metric="magdiff"):
 
     # Initial figure
     fig, ax = plt.subplots(figsize=(10, 5))
-    scat = ax.scatter(Xs[0][:, 0], Xs[0][:, 1], c=colors[0], cmap="viridis", alpha=0.6)
+    scat = ax.scatter(
+        Xs[0][:, 0], Xs[0][:, 1], c=colors[0], cmap="viridis", alpha=0.6
+    )
 
     def update(frame):
         fig.clear()