restructured using black

example/example_gcms_data_analysis.py

@@ -1,15 +1,19 @@
 # Import necessary libraries
 import pathlib as plib  # Used for handling file and directory paths
-from gcms_data_analysis import Project  # Import the Project class from the gcms_data_analysis package
+from gcms_data_analysis import (
+    Project,
+)  # Import the Project class from the gcms_data_analysis package
 
 # Define the folder path where your data is located. Change this path to where you've stored your data files.
-folder_path = plib.Path(plib.Path(__file__).parent, 'data')
+folder_path = plib.Path(plib.Path(__file__).parent, "data")
 
 # Set global configurations for the Project class.
 # These configurations affect all instances of the class.
-Project.set_folder_path(folder_path)  # Set the base folder path for the project's data files
+Project.set_folder_path(
+    folder_path
+)  # Set the base folder path for the project's data files
 Project.set_plot_grid(False)  # Disable grid lines in plots for a cleaner look
-Project.set_plot_font('Sans')  # Set the font style for plots to 'Sans'
+Project.set_plot_font("Sans")  # Set the font style for plots to 'Sans'
 
 # Initialize a Project instance to manage and analyze GCMS data
 gcms = Project()
@@ -25,7 +29,7 @@
 
 # Load calibration data for standard and derivatized samples, and determine if they are derivatized
 calibrations, is_calibr_deriv = gcms.load_calibrations()
-c1, c2 = calibrations['calibration'], calibrations['deriv_calibration']
+c1, c2 = calibrations["calibration"], calibrations["deriv_calibration"]
 
 # Generate a comprehensive list of all compounds found across samples
 list_of_all_compounds = gcms.create_list_of_all_compounds()
@@ -47,7 +51,7 @@
 files, is_files_deriv = gcms.apply_calibration_to_files()
 
 # Extract specific files for detailed analysis or further operations
-f11, f22, f33 = files['A_1'], files['Ader_1'], files['B_1']
+f11, f22, f33 = files["A_1"], files["Ader_1"], files["B_1"]
 
 # Add statistical information to the files_info DataFrame, such as mean, median, and standard deviation for each file
 files_info = gcms.add_stats_to_files_info()
@@ -57,39 +61,63 @@
 
 # Create samples and their standard deviations from the files, storing the results in dictionaries
 samples, samples_std = gcms.create_samples_from_files()
-s1, s2, s3 = samples['A'], samples['Ader'], samples['B']
-sd1, sd2, sd3 = samples_std['A'], samples_std['Ader'], samples_std['B']
+s1, s2, s3 = samples["A"], samples["Ader"], samples["B"]
+sd1, sd2, sd3 = samples_std["A"], samples_std["Ader"], samples_std["B"]
 
 # Add statistical information to the samples_info DataFrame, enhancing the initial analysis with statistical data
 samples_info = gcms.add_stats_to_samples_info()
 
 # Generate reports for specific parameters (e.g., concentration, mass fraction) for files and samples
-rep_files_conc = gcms.create_files_param_report(param='conc_vial_mg_L')
-rep_files_fr = gcms.create_files_param_report(param='fraction_of_sample_fr')
-rep_samples_conc, rep_samples_conc_std = gcms.create_samples_param_report(param='conc_vial_mg_L')
-rep_samples_fr, rep_samples_fr_std = gcms.create_samples_param_report(param='fraction_of_sample_fr')
+rep_files_conc = gcms.create_files_param_report(param="conc_vial_mg_L")
+rep_files_fr = gcms.create_files_param_report(param="fraction_of_sample_fr")
+rep_samples_conc, rep_samples_conc_std = gcms.create_samples_param_report(
+    param="conc_vial_mg_L"
+)
+rep_samples_fr, rep_samples_fr_std = gcms.create_samples_param_report(
+    param="fraction_of_sample_fr"
+)
 
 # Generate aggregated reports based on functional groups for files and samples, for specific parameters
-agg_files_conc = gcms.create_files_param_aggrrep(param='conc_vial_mg_L')
-agg_files_fr = gcms.create_files_param_aggrrep(param='fraction_of_sample_fr')
-agg_samples_conc, agg_samples_conc_std = gcms.create_samples_param_aggrrep(param='conc_vial_mg_L')
-agg_samples_fr, agg_samples_fr_std = gcms.create_samples_param_aggrrep(param='fraction_of_sample_fr')
+agg_files_conc = gcms.create_files_param_aggrrep(param="conc_vial_mg_L")
+agg_files_fr = gcms.create_files_param_aggrrep(param="fraction_of_sample_fr")
+agg_samples_conc, agg_samples_conc_std = gcms.create_samples_param_aggrrep(
+    param="conc_vial_mg_L"
+)
+agg_samples_fr, agg_samples_fr_std = gcms.create_samples_param_aggrrep(
+    param="fraction_of_sample_fr"
+)
 
 # Plotting results based on the generated reports, allowing for visual comparison of average values and standard deviations
 # Plot results for individual files or samples based
 
-gcms.plot_ave_std(param='fraction_of_sample_fr', min_y_thresh=0, files_or_samples='files',
-    legend_location='outside',
-    only_samples_to_plot=['A_1', 'A_2', 'Ader_1', 'Ader_2'], #y_lim=[0, 5000]
-            )
+gcms.plot_ave_std(
+    param="fraction_of_sample_fr",
+    min_y_thresh=0,
+    files_or_samples="files",
+    legend_location="outside",
+    only_samples_to_plot=["A_1", "A_2", "Ader_1", "Ader_2"],  # y_lim=[0, 5000]
+)
 # plot results bases on aggreport
-gcms.plot_ave_std(param='fraction_of_sample_fr', aggr=True, files_or_samples='files',
-                min_y_thresh=0.01,
-    y_lim=[0, .5], color_palette='Set2')
-
-gcms.plot_ave_std(param='fraction_of_sample_fr', min_y_thresh=0,
-    legend_location='outside', only_samples_to_plot=['A', 'Ader'], #y_lim=[0, 5000]
-            )
+gcms.plot_ave_std(
+    param="fraction_of_sample_fr",
+    aggr=True,
+    files_or_samples="files",
+    min_y_thresh=0.01,
+    y_lim=[0, 0.5],
+    color_palette="Set2",
+)
+
+gcms.plot_ave_std(
+    param="fraction_of_sample_fr",
+    min_y_thresh=0,
+    legend_location="outside",
+    only_samples_to_plot=["A", "Ader"],  # y_lim=[0, 5000]
+)
 # plot results bases on aggreport
-gcms.plot_ave_std(param='fraction_of_sample_fr', aggr=True, min_y_thresh=0.01,
-    y_lim=[0, .5], color_palette='Set2')
+gcms.plot_ave_std(
+    param="fraction_of_sample_fr",
+    aggr=True,
+    min_y_thresh=0.01,
+    y_lim=[0, 0.5],
+    color_palette="Set2",
+)