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"""
Get Cannabis Results | Hawaii
Copyright (c) 2024 Cannlytics
Authors:
Keegan Skeate <https://github.com/keeganskeate>
Created: 7/10/2024
Updated: 7/10/2024
License: CC-BY 4.0 <https://huggingface.co/datasets/cannlytics/cannabis_tests/blob/main/LICENSE>
Data Source:
- Public records request
"""
# Standard imports:
import json
import os
# External imports:
from cannlytics.data.coas.coas import CoADoc
from cannlytics.data.coas import standardize_results
from cannlytics.data.coas.parsing import find_unique_analytes
from cannlytics.utils.utils import convert_to_numeric, snake_case
import pandas as pd
def extract_samples(datafile, verbose=True):
"""Extract samples from CSV layout."""
# Read the datafile, line by line.
if verbose: print('Processing:', datafile)
with open(datafile, 'r') as file:
lines = file.readlines()
# Define rows to skip.
skip_rows = [
'"',
"Date",
"Report:",
"LicenseNum:",
"Start:",
"End:",
"Status:",
"Report Timestamp"
]
# Extract the data for each sample.
parser = CoADoc()
samples = []
obs = None
analyses, results = [], []
for i, line in enumerate(lines):
# Skip nuisance rows.
skip = False
for skip_row in skip_rows:
if line.startswith(skip_row):
skip = True
break
if skip:
continue
# Get all values.
values = line.replace('\n', '').split(',')
# Skip blank rows.
if all([x == '' for x in values]):
continue
# Identify samples as rows that start with a date.
try: date = pd.to_datetime(values[0])
except: date = None
if date and values[0] != '':
# Record results for any existing observation.
if obs is not None:
obs['analyses'] = json.dumps(analyses)
obs['results'] = json.dumps(results)
samples.append(obs)
analyses, results = [], []
# Get the details for each sample.
try:
ids = values[4]
batch_number = ids.split('(')[0].split(':')[-1].strip()
sample_id = ids.split(':')[-1].replace(')', '').strip()
obs = {
'date_tested': date.isoformat(),
'product_type': values[1],
'strain_name': values[2],
'product_name': values[3],
'batch_number': batch_number,
'sample_id': sample_id,
'status': values[-3],
'producer': values[-2],
'producer_license_number': values[-1],
}
# Handle long product names.
except:
try:
row = lines[i + 1].replace('\n', '').split(',')
ids = row[1]
except:
row = lines[i + 1].replace('\n', '').split(',') + lines[i + 2].replace('\n', '').split(',')
ids = row[-4]
batch_number = ids.split('(')[0].split(':')[-1].strip()
sample_id = ids.split(':')[-1].replace(')', '').strip()
obs = {
'date_tested': date.isoformat(),
'product_type': values[1],
'strain_name': values[2],
'product_name': ' '.join([values[3], row[0]]),
'batch_number': batch_number,
'sample_id': sample_id,
'status': row[-3],
'producer': row[-2],
'producer_license_number': row[-1],
}
continue
# Get the cannabinoid results.
if values[0] == 'Potency Analysis Test':
analyses.append('cannabinoids')
n_analytes = 5
for n in range(1, n_analytes + 1):
row = lines[i + n].replace('\n', '').split(',')
name = row[0]
key = parser.analytes.get(snake_case(name), snake_case(name))
if name == 'Total':
obs['total_cannabinoids'] = convert_to_numeric(row[1].replace('%', ''))
continue
results.append({
'analysis': 'cannabinoids',
'key': key,
'name': name,
'value': convert_to_numeric(row[1].replace('%', '')),
'status': row[2],
})
continue
# Get the foreign matter results.
if values[0] == 'Foreign Matter Inspection Test':
analyses.append('foreign_matter')
row = lines[i + 1].replace('\n', '').split(',')
results.append({
'analysis': 'foreign_matter',
'key': 'foreign_matter',
'name': 'Foreign Matter',
'value': convert_to_numeric(row[1].replace('%', '')),
'status': row[2],
})
continue
# Get the microbe results.
if values[0] == 'Microbiological Screening Test':
analyses.append('microbes')
n_analytes = 6
for n in range(1, n_analytes + 1):
row = lines[i + n].replace('\n', '').split(',')
name = row[0]
key = parser.analytes.get(snake_case(name), snake_case(name))
if name == '': continue
results.append({
'analysis': 'microbes',
'key': key,
'name': name,
'value': convert_to_numeric(row[1].split(' ')[0]),
'units': row[1].split(' ')[-1],
'status': row[2],
})
continue
# Get the mycotoxin results.
if values[0] == 'Mycotoxin Screening Test':
analyses.append('mycotoxins')
row = lines[i + 1].replace('\n', '').split(',')
name = row[0]
key = parser.analytes.get(snake_case(name), snake_case(name))
results.append({
'analysis': 'mycotoxins',
'key': key,
'name': name,
'value': convert_to_numeric(row[1].split(' ')[0]),
'status': row[2],
})
continue
# Get the moisture content result.
if values[0] == 'Moisture Content Test':
analyses.append('moisture_content')
row = lines[i + 1].replace('\n', '').split(',')
value = convert_to_numeric(row[1].replace('%', ''))
obs['moisture_content'] = value
continue
# Get the residual solvent results.
# See: https://health.hawaii.gov/medicalcannabis/files/2022/05/Chapter-11-850-Hawaii-Administrative-Interim-Rules-Effective-April-29-2022.pdf
if values[0] == 'Residual Solvent Test':
analyses.append('residual_solvents')
solvents = [
'Benzene',
'Butane',
'Ethanol',
'Heptane',
'Hexane',
'Pentane',
'Toluene',
'Total xylenes'
]
for n in range(1, len(solvents) + 1):
row = lines[i + n].replace('\n', '').split(',')
name = solvents[n - 1]
key = parser.analytes.get(snake_case(name), snake_case(name))
results.append({
'analysis': 'residual_solvents',
'key': key,
'name': name,
'value': convert_to_numeric(row[1].split(' ')[0]),
'units': row[1].split(' ')[-1],
'status': row[2],
})
continue
# Record the last sample's results.
obs['analyses'] = json.dumps(analyses)
obs['results'] = json.dumps(results)
samples.append(obs)
# Return the samples.
return pd.DataFrame(samples)
# === Tests ===
# [✓] Tested: 2024-07-10 by Keegan Skeate <keegan@cannlytics.com>
if __name__ == '__main__':
# Define where the data lives.
data_dir = 'D://data/hawaii/public-records'
datafiles = [os.path.join(data_dir, x) for x in os.listdir(data_dir) if x.endswith('.csv')]
# Process each CSV file.
data = [extract_samples(file) for file in datafiles]
# Aggregate all samples.
results = pd.concat(data, ignore_index=True)
print('Number of results:', len(results))
# Standardize the results.
analytes = find_unique_analytes(results)
analytes = sorted(list(analytes))
results = standardize_results(results, analytes)
# 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')
# Save the results.
outfile = 'D://data/hawaii/hi-results-latest.xlsx'
outfile_csv = 'D://data/hawaii/hi-results-latest.csv'
outfile_json = 'D://data/hawaii/hi-results-latest.jsonl'
results.to_excel(outfile, index=False)
results.to_csv(outfile_csv, index=False)
results.to_json(outfile_json, orient='records', lines=True)
print('Saved Excel:', outfile)
print('Saved CSV:', outfile_csv)
print('Saved JSON:', outfile_json)
# Print out the features.
features = {x: 'string' for x in results.columns}
print('Number of features:', len(features))
print('Features:', features)
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