""" Analyze Results | New York Copyright (c) 2024 Cannlytics Authors: Keegan Skeate Created: 6/26/2024 Updated: 6/26/2024 License: MIT License """ # Standard imports: import base64 from datetime import datetime import json import os import shutil import tempfile from typing import List, Optional # External imports: from cannlytics.data.cache import Bogart from cannlytics.data.coas import CoADoc from cannlytics.data.coas import standardize_results from cannlytics.data.coas.parsing import get_coa_files, parse_coa_pdfs from cannlytics.firebase import initialize_firebase from cannlytics.compounds import cannabinoids, terpenes from dotenv import dotenv_values import pandas as pd import pdfplumber #----------------------------------------------------------------------- # Find all COA PDFs. #----------------------------------------------------------------------- # Constants: pdf_dir = 'D://data/new-york' # Get all of the PDFs. pdfs = get_coa_files(pdf_dir) pdfs.sort(key=os.path.getmtime) print('Found %i PDFs.' % len(pdfs)) # Initialize COA parsing. parser = CoADoc() cache = Bogart('D://data/.cache/results-ny.jsonl') verbose = True all_results = [] #----------------------------------------------------------------------- # DEV: Identify all labs #----------------------------------------------------------------------- # Extract text from all PDFs. extracted_data = [] for pdf_file in pdfs: try: with pdfplumber.open(pdf_file) as pdf: text = pdf.pages[0].extract_text() + '\n' extracted_data.append({'file': pdf_file, 'text': text}) except: pass # Find all COAs from a specific lab. coas = {} unidentified_coas = [] labs = [ 'Phyto-farma Labs', 'Phyto-Farma Labs', 'Kaycha Labs', 'Keystone State Testing', 'Green Analytics', ] for data in extracted_data: for lab in labs: if lab in data['text']: lab_coas = coas.get(lab, []) lab_coas.append(data['file']) coas[lab] = lab_coas break else: unidentified_coas.append(data['file']) print('Number of unidentified COAs:', len(unidentified_coas)) # DEV: Look at the first page of a PDF. if unidentified_coas: pdf = pdfplumber.open(unidentified_coas[0]) page = pdf.pages[0] im = page.to_image(resolution=300) im.debug_tablefinder() # Count COAs per lab. for lab, lab_coas in coas.items(): print(lab, len(lab_coas)) #----------------------------------------------------------------------- # Parse Kaycha Labs COAs. #----------------------------------------------------------------------- from cannlytics.data.coas.algorithms.kaycha import parse_kaycha_coa # Parse COAs. lab_coas = coas['Kaycha Labs'] for pdf in lab_coas: if not os.path.exists(pdf): if verbose: print(f'PDF not found: {pdf}') continue pdf_hash = cache.hash_file(pdf) if cache is not None: if cache.get(pdf_hash): if verbose: print('Cached:', pdf) all_results.append(cache.get(pdf_hash)) continue try: coa_data = parse_kaycha_coa(parser, pdf) all_results.append(coa_data) if cache is not None: cache.set(pdf_hash, coa_data) print('Parsed:', pdf) except: print('Error:', pdf) #----------------------------------------------------------------------- # Parse Keystone State Testing COAs. #----------------------------------------------------------------------- from cannlytics.data.coas.algorithms.keystone import parse_keystone_coa lab_coas = coas['Keystone State Testing'] for pdf in lab_coas: pdf_hash = cache.hash_file(pdf) if cache is not None: if cache.get(pdf_hash): if verbose: print('Cached:', pdf) all_results.append(cache.get(pdf_hash)) continue try: coa_data = parse_keystone_coa(parser, pdf) all_results.append(coa_data) if cache is not None: cache.set(pdf_hash, coa_data) print('Parsed:', pdf) except Exception as e: print('Error:', pdf) print(e) #----------------------------------------------------------------------- # Parse Phyto-farma Labs COAs. #----------------------------------------------------------------------- from cannlytics.data.coas.algorithms.phytofarma import parse_phyto_farma_coa # Parse Phyto-Farma Labs COAs. lab_coas = coas['Phyto-Farma Labs'] + coas['Phyto-farma Labs'] for pdf in lab_coas: pdf_hash = cache.hash_file(pdf) if cache is not None: if cache.get(pdf_hash): if verbose: print('Cached:', pdf) all_results.append(cache.get(pdf_hash)) continue try: coa_data = parse_phyto_farma_coa(parser, pdf) all_results.append(coa_data) if cache is not None: cache.set(pdf_hash, coa_data) print('Parsed:', pdf) except Exception as e: print('Error:', pdf) print(e) #----------------------------------------------------------------------- # TODO: Parse Green Analytics COAs. #----------------------------------------------------------------------- # lab_coas = coas['Green Analytics'] lab_coas = [ 'D://data/new-york\\NYSCannabis\\pdfs\\1c3sh5h-coa-1.pdf', 'D://data/new-york\\NYSCannabis\\pdfs\\1cp4tdr-coa-1.pdf', 'D://data/new-york\\NYSCannabis\\pdfs\\1c91onw-coa-1.pdf' ] #----------------------------------------------------------------------- # Optional: Parse the COAs with AI. #----------------------------------------------------------------------- def encode_image(image_path): """Encode an image as a base64 string.""" with open(image_path, 'rb') as image_file: return base64.b64encode(image_file.read()).decode('utf-8') #----------------------------------------------------------------------- # TODO: Analyze results. #----------------------------------------------------------------------- from cannlytics.data.coas import standardize_results from cannlytics.compounds import cannabinoids, terpenes import matplotlib.pyplot as plt from matplotlib.dates import MonthLocator, DateFormatter import seaborn as sns # 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, }) def format_date(x, pos): try: return pd.to_datetime(x).strftime('%b %d, %Y') except ValueError: return '' # Read results. cache = Bogart('D://data/.cache/results-ny.jsonl') results = cache.to_df() print('Number of results:', len(results)) # Standardize results. compounds = list(cannabinoids.keys()) + list(terpenes.keys()) # # DEV: # compounds = [ # 'delta_9_thc', # 'thca', # 'alpha_humulene', # 'beta_caryophyllene', # 'beta_pinene', # 'd_limonene', # ] 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 = standardize_results(results, compounds) # Sort the results by date. results = results.sort_values('date') # TODO: Look at values of any terpenes not yet observed. #----------------------------------------------------------------------- # Lab analysis #----------------------------------------------------------------------- # # FIXME: Visualize the number of results by lab over time. # weekly_tests = results.groupby(['week', 'lab']).size().reset_index(name='count') # pivot_table = weekly_tests.pivot_table(values='count', index='week', columns='lab', aggfunc='sum').fillna(0) # plt.figure(figsize=(15, 8)) # colors = sns.color_palette('tab20', n_colors=len(pivot_table.columns)) # bottom = pd.Series([0] * len(pivot_table.index), index=pivot_table.index) # for lab, color in zip(pivot_table.columns, colors): # plt.bar( # pivot_table.index, # pivot_table[lab], # bottom=bottom, # label=lab, # color=color, # edgecolor='grey', # Add border # alpha=0.8, # Add transparency # ) # bottom += pivot_table[lab] # plt.title('Number of Lab Results by Lab', pad=10) # plt.xlabel('Week') # plt.ylabel('Number of Results') # plt.xticks(rotation=45) # ticks = plt.gca().get_xticks() # plt.gca().set_xticks(ticks[::4]) # Show every 4th xtick # plt.legend(loc='upper right', title='Lab', ncol=2) # plt.tight_layout() # plt.savefig(os.path.join(assets_dir, 'lab-timeseries.png')) # plt.show() # This one is good: sample = results.dropna(subset=['date']) plt.figure(figsize=(18, 8)) ax = sns.countplot(data=sample, x='week', hue='lab', palette='tab10') plt.title('Number of Lab Results by Lab', pad=10) plt.xlabel('') plt.ylabel('Number of Results') plt.xticks(rotation=45) ticks = ax.get_xticks() ax.set_xticks(ticks[::4]) ax.set_xticklabels([format_date(item.get_text(), None) for item in ax.get_xticklabels()]) plt.legend(loc='upper right') plt.tight_layout() plt.savefig(os.path.join(assets_dir, 'lab-timeseries.png')) plt.show() #----------------------------------------------------------------------- # Producer analysis #----------------------------------------------------------------------- # Assign standard producer names. producer_names = { 'Hudson Valley Cannabis LLC': 'Hudson Cannabis', 'MFNY Processor LLC': 'MFNY', '': 'Unknown', 'Hepworth Ag, INC': 'Hepworth Ag', 'Processing': 'Unknown', 'MFNY PROCESSOR LLC': 'MFNY', 'Hudson Valley Hemp Company': 'Hudson Cannabis', 'Hepworth Ag, Inc.': 'Hepworth Ag', 'NYHO Labs LLC': 'NYHO Labs', 'Hudson Valley Hemp Company, LLC': 'Hudson Cannabis', 'Cirona Labs': 'Cirona Labs', 'Hudson Cannabis c/o Hudson Valley Hemp Company, LLC': 'Hudson Cannabis', 'Milton, NY, 12547, US': 'Unknown', } results['producer_dba'] = results['producer'].map(producer_names) # FIXME: Visualize the number of results by producer over time. # results['week'] = results['date'].dt.to_period('W').dt.start_time # weekly_tests = results.groupby(['week', 'dba']).size().reset_index(name='count') # pivot_table = weekly_tests.pivot_table(values='count', index='week', columns='dba', aggfunc='sum').fillna(0) # plt.figure(figsize=(21, 9)) # colors = sns.color_palette('tab20', n_colors=len(pivot_table.columns)) # bottom = None # for dba, color in zip(pivot_table.columns, colors): # plt.bar( # pivot_table.index, # pivot_table[dba], # bottom=bottom, # label=dba, # color=color, # edgecolor='grey', # Add border # alpha=0.8, # Add transparency # ) # if bottom is None: # bottom = pivot_table[dba] # else: # bottom += pivot_table[dba] # plt.title('Number of Lab Results by Producer', pad=10) # plt.xlabel('Week') # plt.ylabel('Number of Results') # plt.xticks(rotation=45) # ticks = plt.gca().get_xticks() # plt.gca().set_xticks(ticks[::4]) # Show every 4th xtick # plt.legend(loc='upper right', title='Producer', ncol=2) # plt.tight_layout() # plt.savefig(os.path.join(assets_dir, 'producer-timeseries.png')) # plt.show() # This one is good. sample = results.dropna(subset=['date']) plt.figure(figsize=(18, 8)) ax = sns.countplot(data=sample, x='week', hue='producer_dba', palette='tab10') plt.title('Number of Lab Results by Producer', pad=10) plt.xlabel('') plt.ylabel('Number of Results') plt.xticks(rotation=45) ticks = ax.get_xticks() ax.set_xticks(ticks[::4]) ax.set_xticklabels([format_date(item.get_text(), None) for item in ax.get_xticklabels()]) plt.legend(loc='upper right') plt.tight_layout() plt.savefig(os.path.join(assets_dir, 'producer-timeseries.png')) plt.show() #----------------------------------------------------------------------- # Product type analysis #----------------------------------------------------------------------- # Assign product types. flower_types = [ 'Plant, Flower - Cured', 'Flower', ] preroll_types = [ 'Plant, Preroll', ] infused_preroll_types = [ 'Plant, Enhanced Preroll', ] concentrate_types = [ 'Concentrate', 'Derivative', 'Concentrates & Extract, Vape Cartridge', 'Concentrates & Extract, Live Rosin', 'Concentrates & Extract, Concentrate', 'Concentrates & Extract, Rosin', 'Concentrates & Extract, Distillate' ] edible_types = [ 'Edible', 'Ingestible, Gummy', 'Ingestible, Edibles', ] def assign_product_type(x): if x in flower_types: return 'Flower' if x in concentrate_types: return 'Concentrate' if x in edible_types: return 'Edible' if x in preroll_types: return 'Preroll' if x in infused_preroll_types: return 'Infused Preroll' return 'Other' # Assign standard product type. results['standard_product_type'] = results['product_type'].apply(assign_product_type) # Define a consistent color palette. product_type_palette = { 'Flower': '#2ca02c', # green 'Concentrate': '#ff7f0e', # orange 'Edible': '#8c564b', # brown 'Preroll': '#1f77b4', # blue 'Infused Preroll': '#9467bd', # purple 'Other': '#d62728' # red } # Visualize the number of results by product type over time sample = results.dropna(subset=['date']) sample.sort_values('date', inplace=True) plt.figure(figsize=(18, 8)) ax = sns.countplot(data=sample, x='week', hue='standard_product_type', palette=product_type_palette) plt.title('Number of Lab Results by Product Type', pad=10) plt.xlabel('') plt.ylabel('Number of Results') plt.xticks(rotation=45) ticks = ax.get_xticks() ax.set_xticks(ticks[::4]) # FIXME: ax.set_xticklabels([format_date(item.get_text(), None) for item in ax.get_xticklabels()]) plt.legend(loc='upper right') plt.tight_layout() plt.savefig(os.path.join(assets_dir, 'product-type-timeseries.png')) plt.show() # Visualize the proportions of product types in a pie chart plt.figure(figsize=(12, 12)) results['standard_product_type'].value_counts().plot.pie( autopct='%1.1f%%', startangle=90, colors=[product_type_palette[key] for key in results['standard_product_type'].value_counts().index] ) plt.title('Proportions of Product Types') plt.ylabel('') plt.tight_layout() plt.savefig(os.path.join(assets_dir, 'product-type-pie.png')) plt.show() # Visualize total cannabinoids and total terpenes in a scatter plot sample = results.loc[results['standard_product_type'] != 'Other'] plt.figure(figsize=(18, 8)) ax = sns.scatterplot( data=sample, y='total_cannabinoids', x='total_terpenes', hue='standard_product_type', palette=product_type_palette, s=200 ) plt.title('Total Cannabinoids to Total Terpenes', pad=10) plt.ylabel('Total Cannabinoids (%)') plt.xlabel('Total Terpenes (%)') plt.xlim(0, 10.5) plt.ylim(0, 100) 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]) plt.tight_layout() plt.savefig(os.path.join(assets_dir, 'cannabinoids-to-terpenes.png')) plt.show() # Optional: Visualize total THC to CBD. #----------------------------------------------------------------------- # Timeseries analysis. #----------------------------------------------------------------------- import statsmodels.api as sm # Look at the trend in THCA in flower. compound = 'thca' sample = results[results['standard_product_type'] == 'Flower'] avg = results.groupby(['month', 'standard_product_type'])[compound].mean().reset_index() avg['month'] = pd.to_datetime(avg['month'], errors='coerce') flower_data = avg[avg['standard_product_type'] == 'Flower'] flower_data = flower_data.dropna(subset=[compound, 'month']) flower_data['month_num'] = range(len(flower_data)) X = sm.add_constant(flower_data['month_num']) y = flower_data[compound] model = sm.OLS(y, X).fit() slope = model.params['month_num'] direction = '+' if slope > 0 else '-' plt.figure(figsize=(13, 8)) plt.plot(flower_data['month'], flower_data[compound], 'bo-', label='Avg. THCA by month', linewidth=2) plt.plot(flower_data['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 Flower in New York', pad=10) plt.xlabel('') plt.ylabel('THCA') plt.legend() plt.xticks(rotation=45) plt.tight_layout() plt.savefig(os.path.join(assets_dir, 'average-thca-by-month.png')) plt.show() #----------------------------------------------------------------------- # Visualize terpene ratios. #----------------------------------------------------------------------- # Function to create scatter plots def create_scatter_plot(x_col, y_col, title, x_label, y_label, filename): plt.figure(figsize=(18, 8)) ax = sns.scatterplot( data=results, x=x_col, y=y_col, hue='standard_product_type', palette=product_type_palette, 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]) plt.tight_layout() plt.savefig(os.path.join(assets_dir, filename)) plt.show() # 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 by Product Type', y_label='Alpha-Humulene', x_label='Beta-Caryophyllene', filename='alpha_humulene_to_beta_caryophyllene.png' ) # 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 by Product Type', y_label='Beta-Pinene', x_label='D-Limonene', filename='beta_pinene_to_d_limonene.png' ) #----------------------------------------------------------------------- # Regression analysis on THCA. #----------------------------------------------------------------------- from patsy import dmatrices # Run a regression on THCA in flower. compound = 'thca' product_type = 'Flower' sample = results[results['standard_product_type'] == product_type] sample['month'] = pd.to_datetime(sample['month'], errors='coerce') sample = sample.dropna(subset=['month']) sample['month_num'] = sample['month'].rank(method='dense').astype(int) - 1 y, X = dmatrices('thca ~ month_num + C(lab) + C(dba)', data=sample, return_type='dataframe') model = sm.OLS(y, X).fit() print(model.summary().as_latex()) #----------------------------------------------------------------------- # TODO: Save the results. #----------------------------------------------------------------------- # Save the results. last_test_date = results['date'].max().strftime('%Y-%m-%d') outfile = f'D://data/new-york/ny-results-{last_test_date}.xlsx' results.to_excel(outfile, index=False) print('Saved:', outfile)