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| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # --- QUICK VIEW CALLBACK (new, lightweight) --- | |
| def run_quick_view(file, turn_col, mag_col, rolling_window): | |
| if file is None: | |
| return None, "Upload a CSV first." | |
| df = pd.read_csv(file.name) | |
| if df.empty: | |
| return None, "CSV is empty." | |
| # Resolve turn axis | |
| use_turn = (turn_col not in [None, "None"] and turn_col in df.columns and pd.api.types.is_numeric_dtype(df[turn_col])) | |
| if use_turn: | |
| d = df.sort_values(turn_col).reset_index(drop=True) | |
| x = d[turn_col].to_numpy() | |
| else: | |
| d = df.reset_index(drop=True) | |
| x = np.arange(len(d)) + 1 # friendlier 1-based index | |
| # Validate magnitude | |
| if mag_col not in d.columns or not pd.api.types.is_numeric_dtype(d[mag_col]): | |
| return None, f"'{mag_col}' is not a numeric magnitude column." | |
| y = d[mag_col].astype(float).to_numpy() | |
| w = int(rolling_window) | |
| w = max(3, min(w, len(d))) | |
| roll = pd.Series(y).rolling(w, min_periods=max(3, w // 3)).mean().to_numpy() | |
| fig = plt.figure(figsize=(8, 4.5)) | |
| ax = fig.add_subplot(111) | |
| ax.scatter(x, y, s=8, alpha=0.6, label="Turns") | |
| ax.plot(x, roll, label=f"Rolling mean (w={w})") | |
| ax.set_title(f"Quick View — {mag_col}") | |
| ax.set_xlabel(turn_col if use_turn else "Turn (row order)") | |
| ax.set_ylabel("Magnitude") | |
| ax.legend() | |
| fig.tight_layout() | |
| msg = "Quick View: response magnitude over time. Use Advanced tabs for stability bands + perturbations." | |
| return fig, msg | |
| # --- UPLOAD CALLBACK --- | |
| def on_upload_all(file): | |
| if file is None: | |
| # Ensure 'None' is always a possible choice for value=None updates | |
| return ( | |
| gr.Dropdown.update(choices=[None], value=None), | |
| gr.Dropdown.update(choices=[None], value=None), | |
| gr.Dropdown.update(choices=[None], value=None), | |
| gr.Dropdown.update(choices=[None], value=None), | |
| gr.Dropdown.update(choices=["None"], value="None"), | |
| "Please upload a CSV file.", | |
| pd.DataFrame(), | |
| ) | |
| try: | |
| df = pd.read_csv(file.name) | |
| if df.empty: | |
| return ( | |
| gr.Dropdown.update(choices=[None], value=None), | |
| gr.Dropdown.update(choices=[None], value=None), | |
| gr.Dropdown.update(choices=[None], value=None), | |
| gr.Dropdown.update(choices=[None], value=None), | |
| gr.Dropdown.update(choices=["None"], value="None"), | |
| "CSV is empty.", | |
| pd.DataFrame(), | |
| ) | |
| columns = list(df.columns) | |
| numeric_cols = [col for col in columns if pd.api.types.is_numeric_dtype(df[col])] | |
| # Ensure 'None' is a selectable option for optional columns where value is None | |
| dropdown_choices_for_none_value = [None] + columns | |
| dropdown_choices_for_numeric_none_value = [None] + numeric_cols | |
| dropdown_choices_for_string_none_value = ["None"] + columns | |
| # Set default mag_col to the first numeric column if available | |
| default_mag_col = numeric_cols[0] if numeric_cols else None | |
| return ( | |
| gr.Dropdown.update(choices=dropdown_choices_for_none_value, value=None), | |
| gr.Dropdown.update(choices=dropdown_choices_for_none_value, value=None), | |
| gr.Dropdown.update(choices=dropdown_choices_for_numeric_none_value, value=default_mag_col), | |
| gr.Dropdown.update(choices=dropdown_choices_for_numeric_none_value, value=None), | |
| gr.Dropdown.update(choices=dropdown_choices_for_string_none_value, value="None"), | |
| f |