""" Analyze Results | Utah Copyright (c) 2023-2024 Cannlytics Authors: Keegan Skeate Created: 7/4/2024 Updated: 7/9/2024 License: MIT License """ # Standard imports: import os from typing import List from zipfile import ZipFile # External imports: import matplotlib.pyplot as plt import pandas as pd import seaborn as sns # Internal imports: from cannlytics import __version__ from cannlytics.data.cache import Bogart from cannlytics.data.coas.parsing import get_coa_files from cannlytics.data.coas import CoADoc from cannlytics.data.coas.algorithms.utah import parse_utah_coa from cannlytics.data.coas import standardize_results from cannlytics.data.coas.parsing import find_unique_analytes #----------------------------------------------------------------------- # Find all of the COA PDFs. #----------------------------------------------------------------------- def unzip_folder(folder, destination, remove=True): """Unzip a folder. Args: pdf_dir (str): The directory where the folder is stored. folder (str): The name of the folder to unzip. """ os.makedirs(destination, exist_ok=True) with ZipFile(folder) as zip_ref: zip_ref.extractall(destination) if remove: os.remove(folder) # Unzip all of the folders. pdf_dir = r'D:\data\public-records\Utah' folders = [os.path.join(pdf_dir, x) for x in os.listdir(pdf_dir) if x.endswith('.zip')] for folder in folders: unzip_folder(folder, pdf_dir) print('Unzipped:', folder) # Get all of the PDFs. pdfs = get_coa_files(pdf_dir) pdfs.sort(key=os.path.getmtime) print('Found %i PDFs.' % len(pdfs)) #----------------------------------------------------------------------- # Parse Utah Department of Agriculture and Food COAs. #----------------------------------------------------------------------- def parse_coa_pdfs( pdfs, algorithm=None, parser=None, cache=None, data=None, verbose=True, ) -> List[dict]: """Parse a list of COA PDFs. Args: pdfs (List[str]): A list of PDFs to parse. algorithm (function): The parsing algorithm to use. parser (object): The parser object to use. cache (object): The cache object to use. data (List[dict]): The data to append to. verbose (bool): Whether to print verbose output. Returns: List[dict]: The parsed data. """ if data is None: data = [] if parser is None: parser = CoADoc() for pdf in pdfs: if not os.path.exists(pdf): if verbose: print(f'PDF not found: {pdf}') continue if cache is not None: pdf_hash = cache.hash_file(pdf) if cache is not None: if cache.get(pdf_hash): if verbose: print('Cached:', pdf) data.append(cache.get(pdf_hash)) continue try: if algorithm is not None: coa_data = algorithm(parser, pdf) else: coa_data = parser.parse(pdf) data.append(coa_data) if cache is not None: cache.set(pdf_hash, coa_data) print('Parsed:', pdf) except: print('Error:', pdf) return data # Initialize COA parsing. cache = Bogart('D://data/.cache/results-ut.jsonl') # DEV: Clear the cache. cache.clear() # Parse COAs. all_results = parse_coa_pdfs( pdfs, algorithm=parse_utah_coa, cache=cache, ) # Read results. results = cache.to_df() print('Number of results:', len(results)) # Standardize time. results['date'] = pd.to_datetime(results['date_tested'], format='mixed') results['week'] = results['date'].dt.to_period('W').astype(str) results['month'] = results['date'].dt.to_period('M').astype(str) results = results.sort_values('date') # Standardize compounds. # Note: Removes nuisance analytes. analytes = find_unique_analytes(results) nuisance_analytes = [ 'det_detected', 'global_shortages_of_laboratory_suppliesto', 'here_recorded_may_not_be_used_as_an_endorsement_for_a_product', 'information_see', 'information_see_https_totoag_utah_govto_2021_to_04_to_29_toudaf_temporarily_adjusts_medical_cannabis_testing_protocols_due_to', 'nd_not_detected', 'notes', 'notes_sample_was_tested_as_received_the_cannabinoid_results_were_not_adjusted_for_moisture_content', 'phtatpthso_togtoaegn_utetashti_nggo_vwto_2_a_0_s', 'recorded_the_results_here_recorded_may_not_be_used_as_an_endorsement_for_a_product', 'results_pertain_only_to_the_test_sample_listed_in_this_report', 'see_https_totoag_utah_govto_2021_to_04_to_29_toudaf_temporarily_adjusts_medical_cannabis_testing_protocols_due_to_global', 'shortages_of_laboratory_suppliesto', 'tac_2500000', 'tac_t', 'this_report_may_not_be_reproduced_except_in_its_entirety', 'total_cbd', 'total_thc', ] analytes = analytes - set(nuisance_analytes) # TODO: Alphabetize the analytes. analytes = sorted(list(analytes)) results = standardize_results(results, analytes) # Save the results. last_test_date = results['date'].max().strftime('%Y-%m-%d') outfile = f'D://data/utah/ut-results-{last_test_date}.xlsx' latest = f'D://data/utah/ut-results-latest.csv' results.to_excel(outfile, index=False) results.to_csv(latest, index=False) print('Saved:', outfile) print('Saved:', latest) # Print out features. features = {x: 'string' for x in results.columns} print('Number of features:', len(features)) print('Features:', features) #----------------------------------------------------------------------- # Analyze Utah results. #----------------------------------------------------------------------- def format_date(x, pos): try: return pd.to_datetime(x).strftime('%b %#d, %Y') except ValueError: return '' # Setup. assets_dir = r'C:\Users\keega\Documents\cannlytics\cannabis-data-science\season-4\165-labels\presentation\images\figures' plt.style.use('seaborn-v0_8-whitegrid') plt.rcParams.update({ 'font.family': 'Times New Roman', 'font.size': 24, }) # FIXME: Read the curated results. # # Read results. # results = cache.to_df() # print('Number of results:', len(results)) # # Standardize time. # results['date'] = pd.to_datetime(results['date_tested'], format='mixed') # results['week'] = results['date'].dt.to_period('W').astype(str) # results['month'] = results['date'].dt.to_period('M').astype(str) # results = results.sort_values('date') # # Standardize compounds. # analytes = find_unique_analytes(results) # results = standardize_results(results, analytes) #----------------------------------------------------------------------- # Lab analysis #----------------------------------------------------------------------- # Visualize the number of tests by date. plt.figure(figsize=(18, 8)) ax = sns.countplot(data=results.dropna(subset=['month']), x='month', color='skyblue') plt.title('Number of Tests by Date in Utah', pad=10) plt.xlabel('') plt.ylabel('Number of Tests') plt.xticks(rotation=45) ticks = ax.get_xticks() ax.set_xticks(ticks[::4]) # Adjust the interval as needed ax.set_xticklabels([format_date(item.get_text(), None) for item in ax.get_xticklabels()]) plt.tight_layout() plt.savefig(os.path.join(assets_dir, 'ut-lab-timeseries.png')) plt.show() #----------------------------------------------------------------------- # Producer analysis #----------------------------------------------------------------------- # Assign standard producer names. producer_names = { 'Harvest of Utah': 'Harvest', 'True North of Utah': 'True North', 'Tryke': 'Tryke', 'Standard Wellness of Utah': 'Standard Wellness', 'Tryke Companies of Utah': 'Tryke', 'Tryke Companies of\nUtah': 'Tryke', 'Pure Plan': 'Pure Plan', 'Riverside Farm': 'Riverside Farm', 'Riverside Farms': 'Riverside Farm', 'Wholesome Ag': 'Wholesome Ag', 'Zion Cultivars': 'Zion Cultivars', 'Zion Cultivators': 'Zion Cultivars', 'Dragonfly Greenhouse': 'Dragonfly', 'Dragonfly Processing': 'Dragonfly', 'UMC Program': 'UMC Program', 'Zion Alchemy': 'Zion Alchemy', 'Wasatch Extraction': 'Wasatch Extraction', 'Standard Wellness of Utah Great Salt Lake 1/8': 'Standard Wellness', 'Tryke Companies of Utah Who Dat Orange': 'Tryke', } results['producer_dba'] = results['producer'].map(producer_names) # Aggregate data by month and DBA monthly_tests = results.groupby(['month', 'producer_dba']).size().reset_index(name='count') # Plotting the time series line plot colors = sns.color_palette('tab20', n_colors=len(monthly_tests['producer_dba'].unique())) pivot_table = monthly_tests.pivot_table(values='count', index='month', columns='producer_dba', aggfunc='sum').fillna(0) plt.figure(figsize=(21, 9)) bottom = None for dba in pivot_table.columns: plt.bar( pivot_table.index, pivot_table[dba], bottom=bottom, label=dba, color=colors.pop(0), edgecolor='grey', alpha=0.8, ) if bottom is None: bottom = pivot_table[dba] else: bottom += pivot_table[dba] plt.title('Number of Tests by Producer by Month in Utah', pad=10) plt.xlabel('') plt.ylabel('Number of Tests') plt.xticks(rotation=45) ticks = plt.gca().get_xticks() plt.gca().set_xticks(ticks[::3]) # Show every 3rd xtick plt.legend(loc='upper left', title='Producer', ncol=2) plt.tight_layout() plt.savefig(os.path.join(assets_dir, 'ut-producer-timeseries.png')) plt.show() #----------------------------------------------------------------------- # Timeseries analysis. #----------------------------------------------------------------------- import statsmodels.api as sm # Look at the trend in THCA in flower. compound = 'thca' sample = results.loc[results['date'] >= pd.to_datetime('2024-01-01')] avg = sample.groupby(['month'])[compound].mean().reset_index() avg['month'] = pd.to_datetime(avg['month'], errors='coerce') avg = avg.dropna(subset=[compound, 'month']) avg['month_num'] = range(len(avg)) X = sm.add_constant(avg['month_num']) y = avg[compound] model = sm.OLS(y, X).fit() slope = model.params['month_num'] direction = '+' if slope > 0 else '-' plt.figure(figsize=(13, 8)) plt.plot(avg['month'], avg[compound], 'bo-', label='Avg. THCA by month', linewidth=2) plt.plot(avg['month'], model.predict(X), 'r-', label=f'Trend: {direction}{slope:.2f}% per month', linewidth=2) plt.scatter(sample['date'], sample[compound], color='lightblue', s=80) plt.title('Trend of THCA in Utah Cannabis Flower', pad=10) plt.xlabel('') plt.ylabel('THCA') plt.legend() plt.xticks(rotation=45) plt.tight_layout() plt.savefig(os.path.join(assets_dir, 'ut-average-thca-by-month.png')) plt.show() #----------------------------------------------------------------------- # Visualize terpene ratios. #----------------------------------------------------------------------- from adjustText import adjust_text # Read in NY data and compare to UT data. ny_cache = Bogart('D://data/.cache/results-ny.jsonl') ny_results = ny_cache.to_df() ny_flower_types = [ 'Plant, Flower - Cured', 'Flower', ] ny_flower = ny_results.loc[ny_results['product_type'].isin(ny_flower_types)] ny_flower = standardize_results(ny_flower, analytes) ny_flower['state'] = 'NY' ny_flower['date'] = pd.to_datetime(ny_flower['date_tested'], format='mixed') ny_flower['week'] = ny_flower['date'].dt.to_period('W').astype(str) ny_flower['month'] = ny_flower['date'].dt.to_period('M').astype(str) # Create the sample. results['state'] = 'UT' # sample = results.loc[results['date'] >= pd.to_datetime('2024-01-01')] sample = results.copy() sample = pd.concat([sample, ny_flower]) # sample = sample.loc[sample['date'] >= pd.to_datetime('2024-01-01')] # Function to create scatter plots def create_scatter_plot(x_col, y_col, title, x_label, y_label, filename, annotate=False): plt.figure(figsize=(18, 8)) ax = sns.scatterplot( data=sample, x=x_col, y=y_col, hue='state', s=200 ) plt.title(title, pad=10) plt.xlabel(x_label) plt.ylabel(y_label) legend = ax.legend(title='Product Type', bbox_to_anchor=(1.05, 1), loc='upper left') for leg_entry in legend.legendHandles: leg_entry.set_sizes([200]) # Annotate 3 samples with beta_pinene to d_limonene ratio greater than 0.5 if annotate: texts = [] random_state = 420_000 high_ratio_samples = sample[sample['beta_pinene'] / sample['d_limonene'] > 0.75] \ .sample(n=5, random_state=random_state) for i, row in high_ratio_samples.iterrows(): if pd.notna(row[x_col]) and pd.notna(row[y_col]): texts.append(ax.text( row[x_col], row[y_col], row['product_name'], fontsize=24, color='crimson', )) adjust_text(texts, only_move={'points': 'xy', 'texts': 'xy'}, arrowprops=dict(arrowstyle='-', color='grey')) plt.tight_layout() plt.savefig(os.path.join(assets_dir, filename)) plt.show() # Visualize the ratio of `beta_pinene` to `d_limonene` create_scatter_plot( y_col='beta_pinene', x_col='d_limonene', title='Ratio of Beta-Pinene to D-Limonene in New York and Utah Cannabis Flower', y_label='Beta-Pinene', x_label='D-Limonene', filename='ut-beta-pinene-to-d-limonene.png', annotate=True ) # Visualize the ratio of `alpha_humulene` to `beta_caryophyllene` create_scatter_plot( y_col='alpha_humulene', x_col='beta_caryophyllene', title='Ratio of Alpha-Humulene to Beta-Caryophyllene in New York and Utah Cannabis Flower', y_label='Alpha-Humulene', x_label='Beta-Caryophyllene', filename='ut-alpha-humulene-to-beta-caryophyllene.png', annotate=False )