pyWBE.reporting

Module Contents

Functions

get_html_table(df)

This function takes a pandas DataFrame and returns an HTML table.

create_doc_report(doc_path, time_series_plot, ...)

This function takes the paths to the plots and tables and creates a DOCX report.

plot_trends(data, trend_plot_pth)

This function takes a time-series data, plots the trend of the data and saves it.

plot_conc_change(data, conc_change_plot_pth)

This function calculates the weekly concentration percentage change, plots it and saves it.

plot_change_pt_detect(data, change_pt_detect_plot_pth)

This function detects change points in the time-series data, plots it and saves it.

plot_seasonality(data, seasonality_plot_pth[, model_type])

This function detects the seasonality in the time-series data, plots it and saves it.

plot_normalize(data, normalize_plot_pth, to_normalize, ...)

This function normalizes the time-series data, plots it and saves it.

plot_forecast(data, forecast_plot_pth, window)

This function forecasts the time-series data, plots it and saves it.

generate_report_from_data(data_1, data_2, value_col, ...)

This function generates a PDF report from the given data.
pyWBE.reporting.get_html_table(df: pandas.DataFrame)

This function takes a pandas DataFrame and returns an HTML table.

Parameters:

df – pandas DataFrame.

: type df: pd.DataFrame.

Returns:

str.

pyWBE.reporting.create_doc_report(doc_path: str, time_series_plot: str, trend_plot: str, conc_change_plot: str, change_pt_detect_plot: str, seasonality_plot: str, normalize_plot: str, forecast_plot: str, lead_lag_plot: str, lead_table: pandas.DataFrame, lag_table: pandas.DataFrame)

This function takes the paths to the plots and tables and creates a DOCX report.

Parameters:

  • doc_path (str.) – Path to save the DOCX report.

  • time_series_plot (str.) – Path to the time-series plot.

  • trend_plot (str.) – Path to the trend plot.

  • conc_change_plot (str.) – Path to the concentration change plot.

  • change_pt_detect_plot (str.) – Path to the change point detection plot.

  • seasonality_plot (str.) – Path to the seasonality plot.

  • normalize_plot (str.) – Path to the normalized plot.

  • forecast_plot (str.) – Path to the forecast plot.

  • lead_lag_plot (str.) – Path to the lead-lag plot.

  • lead_table (pd.DataFrame.) – Lead correlations table.

  • lag_table (pd.DataFrame.) – Lag correlations table.

This function takes a time-series data, plots the trend of the data and saves it.

Parameters:

  • data (pd.Series.) – Time-series data.

  • trend_plot_pth (str.) – Path to save the trend plot.

pyWBE.reporting.plot_conc_change(data: pandas.Series, conc_change_plot_pth: str)

This function calculates the weekly concentration percentage change, plots it and saves it.

Parameters:

  • data (pd.Series.) – Time-series data.

  • conc_change_plot_pth (str.) – Path to save the weekly concentration percentage change plot.

pyWBE.reporting.plot_change_pt_detect(data: pandas.Series, change_pt_detect_plot_pth: str, model: str = 'l2', min_size: int = 28, penalty: int = 1)

This function detects change points in the time-series data, plots it and saves it.

Parameters:

  • data (pd.Series.) – Time-series data.

  • change_pt_detect_plot_pth (str.) – Path to save the change point detection plot.

  • model (str.) – Change point detection model.

  • min_size – The minimum separation (time steps) between

two consecutive change points detected by the model.

Parameters:

penalty (int.) – The penalty to be used in the model.

pyWBE.reporting.plot_seasonality(data: pandas.Series, seasonality_plot_pth: str, model_type: str = 'additive')

This function detects the seasonality in the time-series data, plots it and saves it.

Parameters:

  • data (pd.Series.) – Time-series data.

  • seasonality_plot_pth (str.) – Path to save the seasonality plot.

  • model_type (str.) – Seasonality model type.

pyWBE.reporting.plot_normalize(data: pandas.DataFrame, normalize_plot_pth: str, to_normalize: str, normalize_by: str | int)

This function normalizes the time-series data, plots it and saves it.

Parameters:

  • data (pd.DataFrame.) – Time-series data.

  • normalize_plot_pth (str.) – Path to save the normalized plot.

  • to_normalize (str.) – Column to normalize.

  • normalize_by (str.) – Column or integet value to normalize by.

pyWBE.reporting.plot_forecast(data: pandas.Series, forecast_plot_pth: str, window: pandas.DatetimeIndex)

This function forecasts the time-series data, plots it and saves it.

Parameters:

  • data (pd.Series.) – Time-series data.

  • forecast_plot_pth (str.) – Path to save the forecast plot.

  • window (pd.DatetimeIndex.) – Forecast window.

pyWBE.reporting.generate_report_from_data(data_1: pandas.DataFrame, data_2: pandas.Series, value_col: str, normalize_using_col: str, forecast_window: pandas.DatetimeIndex, lead_lag_time_instances: int, lead_lag_max_lag: int, pdf_path: str, time_series_plot_pth: str, trend_plot_pth: str, conc_change_plot_pth: str, change_pt_detect_plot_pth: str, seasonality_plot_pth: str, normalize_plot_pth: str, forecast_plot_pth: str, lead_lag_plot_pth: str, plot_type: str = 'linear', change_pt_model: str = 'l2', change_pt_min_size: int = 28, change_pt_penalty: int = 1, seasonality_model: str = 'additive')

This function generates a PDF report from the given data.

Parameters:

  • data_1 (pd.DataFrame.) – Primary time-series data to perform analysis on.

  • data_2 (pd.Series.) – Secondary time-series data to be used for correlation analysis.

  • value_col (str.) – Column name indicating values of interest in data_1.

  • normalize_using_col (str.) – Column name to normalize “values” in data_1 by.

  • forecast_window (pd.DatetimeIndex.) – Forecast window for the time-series data.

  • lead_lag_time_instances (int.) – Number of time instances to consider for lead-lag correlation.

  • lead_lag_max_lag (int.) – Maximum lag to consider for lead-lag correlation.

  • pdf_path (str.) – Path to save the PDF report.

  • time_series_plot_pth (str.) – Path to save the time-series plot.

  • trend_plot_pth (str.) – Path to save the trend plot.

  • conc_change_plot_pth (str.) – Path to save the concentration change plot.

  • change_pt_detect_plot_pth (str.) – Path to save the change point detection plot.

  • seasonality_plot_pth (str.) – Path to save the seasonality plot.

  • normalize_plot_pth (str.) – Path to save the normalized plot.

  • forecast_plot_pth (str.) – Path to save the forecast plot.

  • lead_lag_plot_pth (str.) – Path to save the lead-lag plot.

  • plot_type (str.) – Type of plot to be used for time-series data.

  • change_pt_model (str.) – Change point detection model.

  • change_pt_min_size – The minimum separation (time steps) between

two consecutive change points detected by the model.

Parameters:

  • change_pt_penalty (int.) – The penalty to be used in the model.

  • seasonality_model (str.) – Seasonality model type.

Returns:

HTML of generated report.

Return type:

str.