Add type assertions and casts to fix mypy issues

src/vasp/automatedPostprocessing/log_plotter.py

@@ -19,7 +19,7 @@
 import argparse
 import logging
 from pathlib import Path
-from typing import Dict, Any, List, Optional, Tuple
+from typing import Dict, Any, List, Optional, Tuple, cast
 import pickle
 
 import numpy as np
@@ -401,7 +401,9 @@ def plot_multiple_variables_comparison(variable_mean: np.ndarray, variable_min:
     split_variable_max_data = np.array_split(variable_max, num_cycles)
 
     # Create subplots for mean, min, and max
-    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=figure_size, sharex=True)
+    fig, axes = plt.subplots(3, 1, figsize=figure_size, sharex=True)
+    assert isinstance(axes, np.ndarray) and axes.shape == (3,)
+    ax1, ax2, ax3 = axes
 
     for cycle in range(first_cycle - 1, last_cycle):
         cycle_variable_mean_data = split_variable_mean_data[cycle]
@@ -503,13 +505,14 @@ def plot_probe_points(time: np.ndarray, probe_points: Dict[int, Dict[str, np.nda
     # Create subplots based on the number of selected probe points
     if num_rows == 1 and num_cols == 1:
         # If only one probe point is selected, create a single figure
-        fig, axes = plt.subplots(figsize=figure_size)
+        fig, _ = plt.subplots(figsize=figure_size)
         axes = [fig.gca()]  # Get the current axis as a list
     else:
-        fig, axes = plt.subplots(num_rows, num_cols, figsize=figure_size)
+        fig, axes_array = plt.subplots(num_rows, num_cols, figsize=figure_size)
 
         # Flatten the axes array for easier iteration
-        axes = axes.flatten()
+        assert isinstance(axes_array, np.ndarray)
+        axes = axes_array.flatten().tolist()
 
     for i, (probe_point, data) in enumerate(selected_probe_data.items()):
         ax = axes[i]
@@ -525,7 +528,8 @@ def plot_probe_points(time: np.ndarray, probe_points: Dict[int, Dict[str, np.nda
         ax.grid(True)
         ax.tick_params(axis='y', which='major', labelsize=12, labelcolor='b')
 
-        ax2 = ax.twinx()
+        # Create a twin Axes sharing the xaxis and cast it to Axes
+        ax2 = cast(plt.Axes, ax.twinx())
         l2, = ax2.plot(time[start:end], pressure_data, color='r')
         ax2.set_ylabel("Pressure [Pa]", color='r')
         ax2.legend([l1, l2], ["Velocity Magnitude", "Pressure"], loc="upper right")
@@ -583,13 +587,14 @@ def plot_probe_points_displacement(time: np.ndarray, probe_points: Dict[int, Dic
     # Create subplots based on the number of selected probe points
     if num_rows == 1 and num_cols == 1:
         # If only one probe point is selected, create a single figure
-        fig, axes = plt.subplots(figsize=figure_size)
+        fig, _ = plt.subplots(figsize=figure_size)
         axes = [fig.gca()]
     else:
-        fig, axes = plt.subplots(num_rows, num_cols, figsize=figure_size)
+        fig, axes_array = plt.subplots(num_rows, num_cols, figsize=figure_size)
 
         # Flatten the axes array for easier iteration
-        axes = axes.flatten()
+        assert isinstance(axes_array, np.ndarray)
+        axes = axes_array.flatten().tolist()
 
     for i, (probe_point, data) in enumerate(selected_probe_data.items()):
         ax = axes[i]
@@ -658,9 +663,9 @@ def plot_probe_points_comparison(probe_points: Dict[int, Dict[str, np.ndarray]],
                      f"Comparing from cycle {first_cycle} to cycle {last_cycle}")
 
         # Create subplots for magnitude and pressure
-        fig, axs = plt.subplots(2, 1, figsize=figure_size)
-
-        ax, ax2 = axs
+        fig, axes = plt.subplots(2, 1, figsize=figure_size)
+        assert isinstance(axes, np.ndarray) and axes.shape == (2,)
+        ax, ax2 = axes
 
         # Split the data into separate cycles
         split_magnitude_data = np.array_split(data["magnitude"], num_cycles)
@@ -1011,13 +1016,14 @@ def plot_probe_points_tke(tke_data: Dict[int, Tuple[np.ndarray, np.ndarray, np.n
     # Create subplots for each probe point
     if num_rows == 1 and num_cols == 1:
         # If only one probe point is selected, create a single figure
-        fig, axes = plt.subplots(figsize=figure_size)
-        axes = [axes]
+        fig, _ = plt.subplots(figsize=figure_size)
+        axes = [fig.gca()]
     else:
-        fig, axes = plt.subplots(num_rows, num_cols, figsize=figure_size)
+        fig, axes_array = plt.subplots(num_rows, num_cols, figsize=figure_size)
 
         # Flatten the axes array for easier iteration
-        axes = axes.flatten()
+        assert isinstance(axes_array, np.ndarray)
+        axes = axes_array.flatten().tolist()
 
     # Add common title
     fig.suptitle("Turbulent Kinetic Energy (TKE) for Probe Points", fontsize=16)

src/vasp/automatedPostprocessing/postprocessing_h5py/create_spectrograms_chromagrams.py

@@ -79,13 +79,21 @@ def create_spectrogram_composite(case_name: str, quantity: str, df: pd.DataFrame
 
     # Create composite figure
     if amplitude_file and flow_rate_file:
-        fig1, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, sharex=True, gridspec_kw={'height_ratios': [1, 3, 1, 1, 1]})
+        fig1, axes = plt.subplots(5, sharex=True, gridspec_kw={'height_ratios': [1, 3, 1, 1, 1]})
+        assert isinstance(axes, np.ndarray) and axes.shape == (5,)
+        ax1, ax2, ax3, ax4, ax5 = axes
     elif flow_rate_file:
-        fig1, (ax1, ax2, ax3, ax4) = plt.subplots(4, sharex=True, gridspec_kw={'height_ratios': [1, 3, 1, 1]})
+        fig1, axes = plt.subplots(4, sharex=True, gridspec_kw={'height_ratios': [1, 3, 1, 1]})
+        assert isinstance(axes, np.ndarray) and axes.shape == (4,)
+        ax1, ax2, ax3, ax4 = axes
     elif amplitude_file:
-        fig1, (ax2, ax3, ax4, ax5) = plt.subplots(4, sharex=True, gridspec_kw={'height_ratios': [3, 1, 1, 1]})
+        fig1, axes = plt.subplots(4, sharex=True, gridspec_kw={'height_ratios': [3, 1, 1, 1]})
+        assert isinstance(axes, np.ndarray) and axes.shape == (4,)
+        ax2, ax3, ax4, ax5 = axes
     else:
-        fig1, (ax2, ax3, ax4) = plt.subplots(3, sharex=True, gridspec_kw={'height_ratios': [3, 1, 1]})
+        fig1, axes = plt.subplots(3, sharex=True, gridspec_kw={'height_ratios': [3, 1, 1]})
+        assert isinstance(axes, np.ndarray) and axes.shape == (3,)
+        ax2, ax3, ax4 = axes
 
     fig1.set_size_inches(7.5, 9)