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Add type assertions and casts to fix mypy issues #168

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Jul 17, 2024
Merged

Add type assertions and casts to fix mypy issues #168

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38 changes: 22 additions & 16 deletions src/vasp/automatedPostprocessing/log_plotter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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]
Expand Down Expand Up @@ -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]
Expand All @@ -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")
Expand Down Expand Up @@ -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]
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down
16 changes: 12 additions & 4 deletions src/vasp/automatedPostprocessing/postprocessing_h5py/create_spectrograms_chromagrams.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)

Expand Down
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