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csv_2_plot: refine data plotting logic to improve clarity and handling of unfiltered data

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Signed-off-by: YoungSoo Shin <shinys000114@gmail.com>
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YoungSoo Shin
2025-12-11 18:15:37 +09:00
parent a15996e493
commit ce569b9410
1 changed files with 8 additions and 7 deletions
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15
example/logger/csv_2_plot.py
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@@ -117,14 +117,15 @@ def plot_power_data(csv_path, output_path, plot_types, sources,
max_data_value = 0 max_data_value = 0
for j, col_name in enumerate(config['cols']): for j, col_name in enumerate(config['cols']):
if col_name in df.columns: if col_name in df.columns:
# Plot original data
ax.plot(x_axis_data, df[col_name], label=f'{channel_labels[j]} (Raw)', color=channel_colors[j], alpha=0.5, zorder=2)
max_col_value = df[col_name].max() max_col_value = df[col_name].max()
if max_col_value > max_data_value: if max_col_value > max_data_value:
max_data_value = max_col_value max_data_value = max_col_value
# --- Apply and plot filtered data --- # --- Apply and plot filtered data ---
if filter_type and window_size: if filter_type and window_size:
# Plot original data (lightly)
ax.plot(x_axis_data, df[col_name], label=f'{channel_labels[j]} (Raw)', color=channel_colors[j], alpha=0.5, zorder=2)
# Calculate window size in samples # Calculate window size in samples
if avg_interval_s > 0: if avg_interval_s > 0:
window_samples = int(window_size / avg_interval_s) window_samples = int(window_size / avg_interval_s)
@@ -135,11 +136,10 @@ def plot_power_data(csv_path, output_path, plot_types, sources,
filter_label = "" filter_label = ""
if filter_type == 'savgol': if filter_type == 'savgol':
# Window size for savgol must be odd
if window_samples % 2 == 0: if window_samples % 2 == 0:
window_samples += 1 window_samples += 1
if window_samples > 2: # Polyorder must be less than window_length if window_samples > 2:
filtered_data = savgol_filter(df[col_name], window_samples, 2) # polynomial order 2 filtered_data = savgol_filter(df[col_name], window_samples, 2)
filter_label = "Savitzky-Golay" filter_label = "Savitzky-Golay"
elif filter_type == 'moving_average': elif filter_type == 'moving_average':
filtered_data = df[col_name].rolling(window=window_samples, center=True).mean() filtered_data = df[col_name].rolling(window=window_samples, center=True).mean()
@@ -148,14 +148,15 @@ def plot_power_data(csv_path, output_path, plot_types, sources,
filtered_data = df[col_name].ewm(span=window_samples, adjust=False).mean() filtered_data = df[col_name].ewm(span=window_samples, adjust=False).mean()
filter_label = "Exponential Moving Average" filter_label = "Exponential Moving Average"
elif filter_type == 'gaussian': elif filter_type == 'gaussian':
# Sigma is a fraction of the window size
sigma = window_samples / 4.0 sigma = window_samples / 4.0
filtered_data = gaussian_filter1d(df[col_name], sigma=sigma) filtered_data = gaussian_filter1d(df[col_name], sigma=sigma)
filter_label = "Gaussian" filter_label = "Gaussian"
if filtered_data is not None: if filtered_data is not None:
ax.plot(x_axis_data, filtered_data, label=f'{channel_labels[j]} ({filter_label})', color=channel_colors[j], linestyle='-', linewidth=1.5, zorder=3) ax.plot(x_axis_data, filtered_data, label=f'{channel_labels[j]} ({filter_label})', color=channel_colors[j], linestyle='-', linewidth=1.5, zorder=3)
else:
# No filter, plot original data (boldly)
ax.plot(x_axis_data, df[col_name], label=channel_labels[j], color=channel_colors[j], linewidth=1.5, zorder=2)
else: else:
print(f"Warning: Column '{col_name}' not found in CSV. Skipping.") print(f"Warning: Column '{col_name}' not found in CSV. Skipping.")