algorithms/get_results_md.py

"""
Get Results | Maryland
Copyright (c) 2023-2024 Cannlytics

Authors:
    Keegan Skeate <https://github.com/keeganskeate>
Created: 9/26/2023
Updated: 7/10/2024
License: CC-BY 4.0 <https://huggingface.co/datasets/cannlytics/cannabis_tests/blob/main/LICENSE>

Description:

    Collect all public Maryland lab result data.

Data Sources:
    
    - Public records request from the Maryland Medical Cannabis Commission (MMCC).

"""
# Standard imports:
import os

# External imports:
from cannlytics.utils import snake_case, camel_to_snake
from cannlytics.utils.constants import ANALYTES
import pandas as pd

def combine_redundant_columns(df, product_types=None, verbose=False):
    """Combine redundant columns and extract units and product types."""
    combined_results = {}
    for col in df.columns:
        matched = False
        if product_types is not None:
            for product_type in product_types:
                if product_type in col and '(' not in col:
                    base_name = col.split(product_type)[0].strip()
                    if base_name not in combined_results:
                        combined_results[base_name] = df[col]
                        if verbose:
                            print('New column:', base_name)
                    else:
                        combined_results[base_name] = combined_results[base_name].fillna(df[col])
                        if verbose:
                            print('Combined column:', base_name)
                    matched = True
        if matched:
            continue
        if '(' in col and ')' in col:
            base_name = col.split('(')[0].strip()
            if base_name not in combined_results:
                combined_results[base_name] = df[col]
                if verbose:
                    print('New column:', base_name)
            else:
                combined_results[base_name] = combined_results[base_name].fillna(df[col])
                if verbose:
                    print('Combined column:', base_name)
        elif col not in combined_results:
            if verbose:
                print('New column:', col)
            combined_results[col] = df[col]
    return pd.DataFrame(combined_results)

def standardize_analyte_names(df, analyte_mapping):
    """Standardize analyte names."""
    df.columns = [analyte_mapping.get(snake_case(col), snake_case(col)) for col in df.columns]
    return df

def get_results_md(data_dir: str, output_dir: str) -> pd.DataFrame:
    """Get results for Maryland."""

    # Get the data files.
    datafiles = [os.path.join(data_dir, x) for x in os.listdir(data_dir)]

    # Read all of the data.
    all_data = []
    for datafile in datafiles:

        # Read the segment of data.
        df = pd.read_csv(datafile)

        # Pivot the dataframe.
        results = df.pivot_table(
            index=[
                'TestPerformedDate',
                'PackageId',
                'StrainName',
                'TestingFacilityId',
                'ProductCategoryName',
            ],
            columns='TestTypeName',
            values='TestResultLevel',
            aggfunc='first'
        ).reset_index()
        results = pd.DataFrame(results)

        # Determine the "status" based on the "TestPassed" column.
        status = df.groupby('PackageId')['TestPassed'].apply(lambda x: 'Fail' if False in x.values else 'Pass')
        results = results.merge(status, left_on='PackageId', right_index=True)

        # Combine redundant columns
        product_types = [
            'Infused Edible',
            'Infused Non-Edible',
            'Non-Solvent Concentrate',
            'R&D Testing',
            'Raw Plant Material',
            'Solvent Based Concentrate',
            'Sub-Contract',
            'Whole Wet Plant',
        ]
        results = combine_redundant_columns(results, product_types=product_types)

        # Standardize the analyte names
        results = standardize_analyte_names(results, ANALYTES)
        columns = {
            'testpassed': 'status',
            'testperformeddate': 'date_tested',
            'packageid': 'package_id',
            'strainname': 'strain_name',
            'testingfacilityid': 'lab_id',
            'productcategoryname': 'product_type'
        }
        results = results.rename(columns=columns)

        # Drop duplicates.
        results = results.drop_duplicates(subset=['package_id'])

        # Record the data.
        all_data.extend(results.to_dict(orient='records'))
        print('Read %i MD lab results from %s.' % (len(results), datafile))

    # Aggregate all lab results.
    all_results = pd.DataFrame(all_data)
    print('Aggregated %i MD lab results.' % len(all_results))

    # Save the results.
    outfile = os.path.join(output_dir, 'md-results-latest.xlsx')
    outfile_csv = os.path.join(output_dir, 'md-results-latest.csv')
    outfile_json = os.path.join(output_dir, 'md-results-latest.jsonl')
    all_results.to_excel(outfile, index=False)
    all_results.to_csv(outfile_csv, index=False)
    all_results.to_json(outfile_json, orient='records', lines=True)
    print('Saved Excel:', outfile)
    print('Saved CSV:', outfile_csv)
    print('Saved JSON:', outfile_json)

    # Return the results.
    return all_results

# === Tests ===
# [✓] Tested: 2024-07-10 by Keegan Skeate <[email protected]>
if __name__ == '__main__':

    # Define where the data lives.
    data_dir = r'D:\data\public-records\Maryland\md-prr-2024-01-02\md-prr-2024-01-02'
    output_dir = 'D://data/maryland'

    # Curate results.
    get_results_md(data_dir=data_dir, output_dir=output_dir)