Mishra_MetabEng2023

Benchmark-Models/Mishra_MetabEng2023/Mishra_MetabEng2023.yaml

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+format_version: 1
+parameter_file: parameters.tsv
+problems:
+  - condition_files:
+    - experimentalConditions.tsv
+    measurement_files:
+    - measurements.tsv
+    observable_files:
+    - observables.tsv
+    sbml_files:
+    - lipid_model.xml
+

Benchmark-Models/Mishra_MetabEng2023/README-petab-files.md

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+```python
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib import cm
+
+from matplotlib import cm  # Import cm for colormaps
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score, mean_squared_error
+
+
+def plot_measurements_vs_simulation(simulation_df, measurement_df, output_file=None):
+    """
+    Generates a logarithmic scatter plot comparing measurements and simulations, 
+    with R² and MSE displayed on the plot.
+
+    Parameters:
+        simulation_df (pd.DataFrame): DataFrame containing simulation data.
+        measurement_df (pd.DataFrame): DataFrame containing measurement data.
+        output_file (str, optional): If provided, saves the plot to this file. Defaults to None.
+
+    Returns:
+        None
+    """
+    # Merge the dataframes on relevant columns
+    df_merged = pd.merge(
+        pd.DataFrame(simulation_df),
+        pd.DataFrame(measurement_df),
+        on=['observableId', 'simulationConditionId', 'time', 'datasetId']
+    )
+
+    # Modify datasetID
+    df_merged['datasetId'] = df_merged['datasetId'].str.extract(r'^[^_]*_([^_]*)_')[0]
+
+    # Map unique colors to each datasetId
+    unique_datasets = df_merged['datasetId'].unique()
+    colors = colormaps['tab10']  # Use the updated colormap API
+    color_list = [colors(i) for i in range(len(unique_datasets))]
+
+    # Plotting
+    plt.figure(figsize=(8, 8))
+    plt.xscale('log')
+    plt.yscale('log')
+
+    for i, dataset in enumerate(unique_datasets):
+        subset = df_merged[df_merged['datasetId'] == dataset]
+        plt.scatter(subset['measurement'], subset['simulation'], color=color_list[i], label=dataset)
+
+    # Calculate R² and MSE
+    r2 = r2_score(df_merged['measurement'], df_merged['simulation'])
+    mse = mean_squared_error(df_merged['measurement'], df_merged['simulation'])
+
+    # Add R² and MSE to the plot
+    x_min, x_max = df_merged['measurement'].min(), df_merged['measurement'].max()
+    plt.plot([x_min, x_max], [x_min, x_max], 'k--')
+    plt.text(
+        x_min * 1.2, x_max / 2,
+        f"$R^2$: {r2:.3f}\nMSE: {mse:.3e}",
+        fontsize=10, bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray')
+    )
+
+    # Add labels and legend
+    plt.xlabel("Measurement (log scale)")
+    plt.ylabel("Simulation (log scale)")
+    plt.legend(title="Dataset ID")
+    plt.show()
+
+    # Show or save the plot
+    if output_file:
+        plt.savefig(output_file, dpi=300, bbox_inches='tight')
+    else:
+        plt.show()
+
+
+# Assuming `simulation_df` and `measurement_df` are predefined dataframes
+plot_measurements_vs_simulation(simulation_df, measurement_df)
+
+# To save the plot instead of showing it
+plot_measurements_vs_simulation(simulation_df, measurement_df, output_file="measurements_vs_simulation.png")
+
+
+```