src package

Submodules

src.AppConfig module

This module contains the configuration settings for the Streamlit application.

Functions:

  • streamlit_appconfig: Function to set the Streamlit page configuration.

  • session_policy_and_style: Function to reset the session state and set the page style.

Dependencies:

  • pandas

  • streamlit

src.AppConfig.session_policy_and_style()

Function to reset the session state and set the page style.

Returns:

None

src.AppConfig.streamlit_appconfig()

Function to set the Streamlit page configuration.

Returns:

None

src.Synthesis_main module

src.Synthetify module

This file contains the main code for the Synthetify application and act as the entry point for the Streamlit application.

Functions:

  • upload_and_process_dataset: Function to upload and process the dataset.

  • process_and_display_dataset_info: Function to process and display the dataset information.

  • display_data_preprocessing: Function to display data preprocessing options.

  • synthesize_main: Function to display data generation options.

  • imputation_button: Function to handle imputation button click.

  • synthesize_button: Function to handle data generation button click.

Dependencies:

  • sys

  • streamlit

  • data_desc

  • data_properties_utils

  • data_preprocess

  • preprocessing_button

  • AppConfig

  • Synthesis_main

To run the application, execute the following command: `streamlit run Synthetify.py`

Work Flow: 1. Upload the dataset for processing. 2. Select the page for data information, data preprocessing, or data generation. 3. Perform data preprocessing operations. 4. Generate synthetic data based on the selected options.

src.Synthetify.convert_future_days(future_days, inferred_freq, df_length)

Convert future days based on the inferred frequency.

Parameters:

future_daysint

Number of days to generate future data.

inferred_freqstr

Inferred frequency of the dataset.

df_lengthint

Length of the dataset.

Returns:

int

Adjusted number of days based on the inferred frequency.

src.Synthetify.display_data_preprocessing(uploaded_file)

Function to display data preprocessing options.

Parameters:

uploaded_filefile

Uploaded file for processing.

Results:

  • Perform data preprocessing operations.

src.Synthetify.imputation_button(impute_button)

Function to handle imputation button click.

Parameters:

impute_buttonbool

Boolean flag to trigger the imputation operations.

Results:

  • Evaluate models for imputation.

  • Impute missing data.

src.Synthetify.process_and_display_dataset_info(uploaded_file)

Function to process and display the dataset information.

Parameters:

uploaded_filefile

Uploaded file for processing.

Results:

  • Display the dataset information.

  • Display data preprocessing options.

  • Display data generation options.

src.Synthetify.synthesize_button(generation_button, handle_range, future_days, plot_data)

Function to handle data generation button click.

Parameters:

generation_buttonbool

Boolean flag to trigger the data generation operations.

handle_rangebool

Boolean flag to calculate the range for the generated data.

future_daysint

Number of days to generate future data.

plot_databool

Boolean flag to plot the original vs generated data distributions.

Results:

  • Evaluate models for data generation.

  • Generate future data.

src.Synthetify.synthesize_main()

Function to display data generation options.

Results:

  • Display data generation options.

  • Perform data generation operations.

src.Synthetify.upload_and_process_dataset()

Function to upload and process the dataset.

Results:

  • Display the dataset information.

  • Display data preprocessing options.

  • Display data generation options.

src.preprocessing_button module

This file contains the code to perform data preprocessing operations on the dataset.

Functions:

  • perform_data_preprocessing: Function to perform data preprocessing operations on the dataset.

Dependencies:

  • sys

  • streamlit

  • data_desc

  • data_properties_utils

  • data_preprocess

  • evaluate_models

src.preprocessing_button.perform_data_preprocessing(df, onclick, handle_missing=False, clean_outliers=False)

Function to perform data preprocessing operations on the dataset.

Parameters:

dfpandas DataFrame

Input dataset to perform preprocessing operations.

onclickbool

Boolean flag to trigger the preprocessing operations.

handle_missingbool

Boolean flag to handle missing timestamps.

Returns:

None

Module contents