Merge pull request #2 from mpecchi/add_my_figure

Separated plotting functions, fixed bugs, added lower level testing

example/example_gcms_data_analysis.py

@@ -1,23 +1,24 @@
 # 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
+from gcms_data_analysis.plotting import plot_ave_std
 
 # 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, "example\data")
 folder_path = plib.Path(
     r"C:\Users\mp933\OneDrive - Cornell University\Python\gcms_data_analysis\example\data"
 )
-
+# folder_path: plib.Path = plib.Path(
+#     r"C:\Users\mp933\OneDrive - Cornell University\Python\GCMS\NNDNDD"
+# )
 # 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_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_auto_save_to_excel(False)
 # Initialize a Project instance to manage and analyze GCMS data
 gcms = Project()
 
@@ -41,8 +42,8 @@
 list_of_all_deriv_compounds = gcms.create_list_of_all_deriv_compounds()
 
 # Load properties for standard and derivatized compounds from provided files
-compounds_properties = gcms.load_compounds_properties()
-deriv_compounds_properties = gcms.load_deriv_compounds_properties()
+compounds_properties = gcms.create_compounds_properties()
+deriv_compounds_properties = gcms.create_deriv_compounds_properties()
 
 # Flag indicating whether new compounds have been added, triggering a need to regenerate properties data
 new_files_with_new_compounds_added = False
@@ -56,7 +57,7 @@
 # Extract specific files for detailed analysis or further operations
 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
+# # 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()
 
 # Create a samples_info DataFrame without applying calibration data, for initial analysis
@@ -90,15 +91,17 @@
 # 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(
+plot_ave_std(
+    gcms,
     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(
+plot_ave_std(
+    gcms,
     param="fraction_of_sample_fr",
     aggr=True,
     files_or_samples="files",
@@ -107,17 +110,61 @@
     color_palette="Set2",
 )
 
-gcms.plot_ave_std(
+plot_ave_std(
+    gcms,
     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(
+plot_ave_std(
+    gcms,
     param="fraction_of_sample_fr",
     aggr=True,
     min_y_thresh=0.01,
     y_lim=[0, 0.5],
     color_palette="Set2",
 )
+
+# %%
+# import pickle
+
+# folder_path: plib.Path = plib.Path(r"C:\Users\mp933\Desktop\New folder")
+# pickle_path: plib.Path = plib.Path(folder_path, "pickle_object.pkl")
+# with open(pickle_path, "wb") as output_file:
+#     pickle.dump(gcms, output_file)
+# %%
+# import pickle
+# 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
+
+# folder_path: plib.Path = plib.Path(r"C:\Users\mp933\Desktop\New folder")
+# pickle_path: plib.Path = plib.Path(folder_path, "pickle_object.pkl")
+# with open(pickle_path, "rb") as input_file:
+#     gcms: Project = pickle.load(input_file)
+# from gcms_data_analysis.plotting import plot_pave_std
+
+# # %%
+# myfig = plot_pave_std(
+#     gcms,
+#     files_or_samples="files",
+#     width=12,
+#     height=5,
+#     legend_location="outside",
+#     y_lim=[0, 100],
+# )
+# # %%
+# myfig = plot_pave_std(
+#     gcms,
+#     files_or_samples="samples",
+#     width=6,
+#     height=6,
+#     legend_location="best",
+#     y_lim=[0, 100],
+#     min_y_thresh=10,
+# )
+
+# # %%

example/example_minimal_case.py

@@ -0,0 +1,220 @@
+# %% Import necessary libraries
+import pathlib as plib  # Used for handling file and directory paths
+from gcms_data_analysis import Project
+from gcms_data_analysis.plotting import plot_ave_std
+
+# 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, "example\data")
+folder_path = plib.Path(
+    r"C:\Users\mp933\OneDrive - Cornell University\Python\gcms_data_analysis\tests\data_minimal_case"
+)
+# folder_path: plib.Path = plib.Path(
+#     r"C:\Users\mp933\OneDrive - Cornell University\Python\GCMS\NNDNDD"
+# )
+# 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_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_auto_save_to_excel(False)
+# Initialize a Project instance to manage and analyze GCMS data
+gcms = Project()
+
+# Load metadata from a user-provided 'files_info.xlsx' file, or generate it from .txt GC-MS files if not provided
+files_info = gcms.load_files_info()
+# Load individual GCMS .txt files as pandas DataFrames
+files = gcms.load_all_files()
+files = gcms.add_iupac_to_files()
+list_of_all_compounds = gcms.create_list_of_all_compounds()
+files, is_files_deriv = gcms.apply_calibration_to_files()
+samples_info, samples_info_std = gcms.create_samples_info()
+samples, samples_std = gcms.create_samples_from_files()
+
+params = [
+    "height",
+    "area",
+    "area_if_undiluted",
+    "conc_vial_mg_L",
+    "conc_vial_if_undiluted_mg_L",
+    "fraction_of_sample_fr",
+    "fraction_of_feedstock_fr",
+]
+for param in params:
+    _ = gcms.create_files_param_report(param)
+    _ = gcms.create_files_param_aggrrep(param)
+
+    _, _ = gcms.create_samples_param_report(param)
+    _, _ = gcms.create_samples_param_aggrrep(param)
+
+# %%
+for param in params:
+    print(f"'{param}': ")
+    print_checked_df_to_script_text_with_arrays(gcms.files_reports[param])
+# %%
+
+for param in params:
+    print(f"'{param}': ")
+    print_checked_df_to_script_text_with_arrays(gcms.files_aggrreps[param])
+# %%
+for param in params:
+    print(f"'{param}': ")
+    print_checked_df_to_script_text_with_arrays(gcms.samples_reports[param])
+# %%
+for param in params:
+    print(f"'{param}': ")
+    print_checked_df_to_script_text_with_arrays(gcms.samples_reports_std[param])
+# %%
+
+for param in params:
+    print(f"'{param}': ")
+    print_checked_df_to_script_text_with_arrays(gcms.samples_aggrreps[param])
+# %%
+
+for param in params:
+    print(f"'{param}': ")
+    print_checked_df_to_script_text_with_arrays(gcms.samples_aggrreps_std[param])
+# %%
+
+
+# Load classification codes and mass fractions for functional groups from a provided file
+class_code_frac = gcms.load_class_code_frac()
+
+# 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"]
+
+# Generate a comprehensive list of all compounds found across samples
+list_of_all_compounds = gcms.create_list_of_all_compounds()
+
+# Similarly, create a list of all derivatized compounds found across samples
+list_of_all_deriv_compounds = gcms.create_list_of_all_deriv_compounds()
+
+# Load properties for standard and derivatized compounds from provided files
+compounds_properties = gcms.create_compounds_properties()
+deriv_compounds_properties = gcms.create_deriv_compounds_properties()
+
+# Flag indicating whether new compounds have been added, triggering a need to regenerate properties data
+new_files_with_new_compounds_added = False
+if new_files_with_new_compounds_added:
+    compounds_properties = gcms.create_compounds_properties()
+    deriv_compounds_properties = gcms.create_deriv_compounds_properties()
+
+# Apply calibration data to all loaded files, adjusting compound concentrations based on calibration curves
+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"]
+
+# # 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()
+
+# Create a samples_info DataFrame without applying calibration data, for initial analysis
+samples_info_0 = gcms.create_samples_info()
+
+# 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"]
+
+# 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"
+)
+
+# 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"
+)
+
+# 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
+
+plot_ave_std(
+    gcms,
+    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
+plot_ave_std(
+    gcms,
+    param="fraction_of_sample_fr",
+    aggr=True,
+    files_or_samples="files",
+    min_y_thresh=0.01,
+    y_lim=[0, 0.5],
+    color_palette="Set2",
+)
+
+plot_ave_std(
+    gcms,
+    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
+plot_ave_std(
+    gcms,
+    param="fraction_of_sample_fr",
+    aggr=True,
+    min_y_thresh=0.01,
+    y_lim=[0, 0.5],
+    color_palette="Set2",
+)
+
+# %%
+# import pickle
+
+# folder_path: plib.Path = plib.Path(r"C:\Users\mp933\Desktop\New folder")
+# pickle_path: plib.Path = plib.Path(folder_path, "pickle_object.pkl")
+# with open(pickle_path, "wb") as output_file:
+#     pickle.dump(gcms, output_file)
+# %%
+# import pickle
+# 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
+
+# folder_path: plib.Path = plib.Path(r"C:\Users\mp933\Desktop\New folder")
+# pickle_path: plib.Path = plib.Path(folder_path, "pickle_object.pkl")
+# with open(pickle_path, "rb") as input_file:
+#     gcms: Project = pickle.load(input_file)
+# from gcms_data_analysis.plotting import plot_pave_std
+
+# # %%
+# myfig = plot_pave_std(
+#     gcms,
+#     files_or_samples="files",
+#     width=12,
+#     height=5,
+#     legend_location="outside",
+#     y_lim=[0, 100],
+# )
+# # %%
+# myfig = plot_pave_std(
+#     gcms,
+#     files_or_samples="samples",
+#     width=6,
+#     height=6,
+#     legend_location="best",
+#     y_lim=[0, 100],
+#     min_y_thresh=10,
+# )
+
+# # %%