""" Get Results Rhode Island Copyright (c) 2024 Cannlytics Authors: Keegan Skeate Created: 5/25/2024 Updated: 5/30/2024 License: CC-BY 4.0 Description: Curate Rhode Island lab result data obtained through public records requests. Data Sources: - Public records request """ # Standard imports: from datetime import datetime import os # External imports: from cannlytics.data import save_with_copyright from cannlytics.utils import snake_case from cannlytics.utils.constants import ANALYTES import numpy as np import pandas as pd # Define columns. columns = { 'Id': 'sample_id', 'TestingFacilityName': 'lab', 'ItemFromFacilityLicenseNumber': 'producer_license_number', 'SourcePackageLabels': 'label', 'TestPerformedDate': 'date_tested', 'TestTypeName': 'test_type', 'TestResultLevel': 'test_result', 'OverallPassed': 'status', } # Define the data types for each column. dtype_spec = { 'Id': str, 'TestingFacilityName': str, 'ItemFromFacilityLicenseNumber': str, 'SourcePackageLabels': str, 'TestPerformedDate': str, 'TestTypeName': str, 'TestResultLevel': float, 'OverallPassed': bool, } def collect_data(data_dir, columns, dtype_spec): """Collect data from a directory of CSV and Excel files.""" results = [] for root, _, files in os.walk(data_dir): for file in files: if 'no data' in file.lower(): continue print('Reading:', file) file_path = os.path.join(root, file) if file.endswith('.csv'): df = read_and_standardize_csv(file_path, columns, dtype_spec) elif file.endswith('.xlsx'): df = read_and_standardize_excel(file_path, columns) if not df.empty: results.append(df) return pd.concat(results, ignore_index=True) def read_and_standardize_csv(file_path, columns, dtype_spec): """Read a CSV file and standardize the column names.""" try: df = pd.read_csv(file_path, dtype=dtype_spec, usecols=columns.keys(), encoding='latin1') df.rename(columns=columns, inplace=True) return df except Exception as e: print(f"Error reading {file_path}: {e}") return pd.DataFrame() def read_and_standardize_excel(file_path, columns): """Read an Excel file and standardize the column names.""" try: df = pd.read_excel(file_path, usecols=columns.keys()) df.rename(columns=columns, inplace=True) return df except Exception as e: print(f"Error reading {file_path}: {e}") return pd.DataFrame() def extract_test_details(data): """Extract test_name, units, and product_type from test_type.""" data[['test_name', 'units', 'product_type']] = data['test_type'].str.extract(r'(.+?) \((.+?)\) (.+)') return data def pivot_data(data): """Pivot the data to get results for each sample.""" results = data.pivot_table( index=['sample_id', 'producer_license_number', 'lab', 'label', 'date_tested', 'product_type'], columns='test_name', values='test_result', aggfunc='first' ).reset_index() results['date_tested'] = pd.to_datetime(results['date_tested'], errors='coerce') results['month'] = results['date_tested'].dt.to_period('M') results['year'] = results['date_tested'].dt.year return results def augment_calculations( df, cannabinoids=None, terpenes=None, delta_9_thc='delta_9_thc', thca='thca', cbd='cbd', cbda='cbda', ): """Augment the DataFrame with additional calculated fields.""" # Calculate total cannabinoids. if cannabinoids is not None: df['total_cannabinoids'] = round(df[cannabinoids].sum(axis=1), 2) # Calculate total terpenes. if terpenes is not None: df['total_terpenes'] = round(df[terpenes].sum(axis=1), 2) # Calculate the total THC to total CBD ratio. df['total_thc'] = round(df[delta_9_thc] + 0.877 * df[thca], 2) df['total_cbd'] = round(df[cbd] + 0.877 * df[cbda], 2) df['thc_cbd_ratio'] = round(df['total_thc'] / df['total_cbd'], 2) # Calculate the total cannabinoids to total terpenes ratio. if cannabinoids is not None and terpenes is not None: df['cannabinoids_terpenes_ratio'] = round(df['total_cannabinoids'] / df['total_terpenes'], 2) # Return the augmented data. return df 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 combine_similar_columns(df, similar_columns): """Combine similar columns with different spellings or capitalization.""" for target_col, col_variants in similar_columns.items(): if target_col not in df.columns: df[target_col] = pd.NA for col in col_variants: if col in df.columns: df[target_col] = df[target_col].combine_first(df[col]) df.drop(columns=[col], inplace=True) return df def get_results_ri(data_dir: str, output_dir: str) -> pd.DataFrame: # Collect Rhode Island lab results data = collect_data(data_dir, columns, dtype_spec) print('Number of Rhode Island tests:', len(data)) # Extract test details data = extract_test_details(data) # Pivot the data to get results for each sample. results = pivot_data(data) print('Number of Rhode Island samples:', len(results)) # Combine similar names. similar_columns = { 'total_yeast_and_mold': ['Total Yeast and MOld', 'Total Yeast and Mold'], '1_2_dichloroethane': ['1,2 Dichlorethane', '1,2 Dichloroethane'], 'total_cbd': ['Total CBD'], 'total_thc': ['Total THC'], '3_methylpentane': ['3 Methylpetane', '3 Methylpentane'], 'n_methylpyrrolidone': ['N Methylpyrrolidone', 'N methylpyrrlidone'], 'n_n_dimethylacetamide': ['N,N Dimethyacetamide', 'N,N Dimethylacetamide'], } results = combine_similar_columns(results, similar_columns) # Standardize the analyte names results = standardize_analyte_names(results, ANALYTES) print('Standardized analyte names.') # Augment additional calculated metrics. cannabinoids = ['cbd', 'cbda', 'delta_9_thc', 'thca'] terpenes = [ 'alpha_bisabolol', 'alpha_humulene', 'alpha_pinene', 'alpha_terpinene', 'beta_caryophyllene', 'beta_myrcene', 'beta_pinene', 'caryophyllene_oxide', 'd_limonene', 'linalool', 'nerolidol', 'other_terpenes' ] results = augment_calculations(results) print('Augmented fields.') # Sort the columns. non_numeric = [ 'sample_id', 'producer_license_number', 'lab', 'label', 'date_tested', 'product_type', 'month', 'year' ] numeric_cols = results.columns.difference(non_numeric) numeric_cols_sorted = sorted(numeric_cols) results = results[non_numeric + numeric_cols_sorted] # # Save the results with copyright and sources sheets. # date = datetime.now().strftime('%Y-%m-%d') # if not os.path.exists(output_dir): os.makedirs(output_dir) # outfile = f'{output_dir}/ri-results-{date}.xlsx' # save_with_copyright( # results, # outfile, # dataset_name='Rhode Island Cannabis Lab Results', # author='Keegan Skeate', # publisher='Cannlytics', # sources=['Rhode Island Office Of Cannabis Regulation'], # source_urls=['https://dbr.ri.gov/office-cannabis-regulation'], # ) # print('Saved Rhode Island lab results:', outfile) # Save the results. outfile = os.path.join(output_dir, 'ri-results-latest.xlsx') outfile_csv = os.path.join(output_dir, 'ri-results-latest.csv') outfile_json = os.path.join(output_dir, 'ri-results-latest.jsonl') results.to_excel(outfile, index=False) results.to_csv(outfile_csv, index=False) # FIXME: This causes an OverflowError # 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 results # === Test === # [✓] Tested: 2024-07-10 by Keegan Skeate if __name__ == '__main__': # Define where the data lives. data_dir = 'D://data/public-records/Rhode Island/Rhode Island' output_dir = 'D://data/rhode-island/results/datasets' # Curate results. get_results_ri(data_dir=data_dir, output_dir=output_dir)