Added 👨‍🌾 Raw Garden lab result data + algorithm

README.md

@@ -56,21 +56,29 @@ The dataset is partitioned into the various sources of lab results.
 
 | Source | Observations |
 |--------|--------------|
-| Raw Gardens | 2,810 |
+| Raw Gardens | 2,667 |
 | MCR Labs | Coming soon! |
 | PSI Labs | Coming soon! |
 | SC Labs | Coming soon! |
 
 ### Data Instances
 
-You can load the lab test data from the various sources:
+You can load `details`, `results`, and `values` for each of the dataset files. For example:
 
 ```py
 from datasets import load_dataset
 
-# Load Raw Garden lab test data.
+# Specify the Cannabis Tests dataset.
 dataset = 'cannlytics/cannabis_tests'
-rawgarden_data = load_dataset(dataset, 'rawgarden')
+
+# Load Raw Garden lab test details.
+rawgarden_details = load_dataset(dataset, 'rawgarden_details')
+
+# Load Raw Garden lab test results.
+rawgarden_results = load_dataset(dataset, 'rawgarden_results')
+
+# Load Raw Garden lab test values.
+rawgarden_values = load_dataset(dataset, 'rawgarden_values')
 ```
 
 ### Data Fields
@@ -199,6 +207,7 @@ The data represents only a subset of the population of cannabis lab results. Non
 | `'N/A'` | `None` |
 | `'na'` | `None` |
 | `'NT'` | `None` |
+
 ## Additional Information
 
 ### Dataset Curators

algorithms/get_all_rawgarden_data.py

@@ -0,0 +1,440 @@
+"""
+Get Raw Garden Test Result Data
+Copyright (c) 2022 Cannlytics
+
+Authors:
+    Keegan Skeate <https://github.com/keeganskeate>
+    Candace O'Sullivan-Sutherland <https://github.com/candy-o>
+Created: 8/23/2022
+Updated: 9/13/2022
+License: <https://github.com/cannlytics/cannlytics/blob/main/LICENSE>
+
+Description:
+
+    Curate Raw Garden's publicly published lab results by:
+
+        1. Finding products and their COA URLS on Raw Garden's website.
+        2. Downloading COA PDFs from their URLs.
+        3. Using CoADoc to parse the COA PDFs (with OCR).
+        4. Archiving the COA data in Firestore.
+
+Data Source:
+
+    - Raw Garden Lab Results
+    URL: <https://rawgarden.farm/lab-results/>
+
+Command line usage:
+
+    python ai/curation/get_rawgarden_data/get_rawgarden_data.py \
+        --days_ago=1 --get_all=False
+
+"""
+# Standard imports.
+from datetime import datetime, timedelta
+import gc
+import os
+from time import sleep
+from typing import Any, List, Optional, Tuple
+
+# External imports.
+from bs4 import BeautifulSoup
+import pandas as pd
+import requests
+
+# Internal imports.
+from cannlytics.data.coas import CoADoc
+from cannlytics.data.data import create_hash
+from cannlytics.firebase import (
+    get_document,
+    initialize_firebase,
+    update_documents,
+    upload_file,
+)
+from cannlytics.utils import kebab_case, rmerge
+from cannlytics.utils.constants import DEFAULT_HEADERS
+
+# Specify where your data lives.
+BUCKET_NAME = 'cannlytics-company.appspot.com'
+COLLECTION = 'public/data/lab_results'
+STORAGE_REF = 'data/lab_results/raw_garden'
+
+# Create directories if they don't already exist.
+# TODO: Edit `ENV_FILE` and `DATA_DIR` as needed for your desired setup.
+ENV_FILE = '../.env'
+DATA_DIR = '.././'
+COA_DATA_DIR = f'{DATA_DIR}/raw_garden'
+COA_PDF_DIR = f'{COA_DATA_DIR}/pdfs'
+TEMP_PATH = f'{COA_DATA_DIR}/tmp'
+if not os.path.exists(DATA_DIR): os.makedirs(DATA_DIR)
+if not os.path.exists(COA_DATA_DIR): os.makedirs(COA_DATA_DIR)
+if not os.path.exists(COA_PDF_DIR): os.makedirs(COA_PDF_DIR)
+if not os.path.exists(TEMP_PATH): os.makedirs(TEMP_PATH)
+
+# Define constants.
+BASE = 'https://rawgarden.farm/lab-results/'
+
+
+def get_rawgarden_products(
+        start: Optional[Any] = None,
+        end: Optional[Any] = None,
+    ) -> pd.DataFrame:
+    """Get Raw Garden's lab results page. Then get all of the product
+    categories. Finally, get all product data, including: `coa_pdf`, 
+    `lab_results_url`, `product_name`, `product_subtype`, `date_retail`.
+    Args:
+        start (str or datetime): A point in time to begin restricting
+            the product list by `date_retail` (optional).
+        end (str or datetime): A point in time to end restricting
+            the product list by `date_retail` (optional).
+    Returns:
+        (DataFrame): Returns a DataFrame of product data.
+    """
+
+    # Get the website.
+    response = requests.get(BASE, headers=DEFAULT_HEADERS)
+    soup = BeautifulSoup(response.content, 'html.parser')
+
+    # Get all product data listed on the website.
+    observations = []
+    categories = soup.find_all('div', attrs={'class': 'category-content'})
+    for category in categories:
+        subtype = category.find('h3').text
+        dates = category.findAll('h5', attrs={'class': 'result-date'})
+        names = category.findAll('h5')
+        names = [div for div in names if div.get('class') is None]
+        links = category.findAll('a')
+        for i, link in enumerate(links):
+            try:
+                href = link.get('href')
+                date = pd.to_datetime(dates[i].text)
+                name = names[i].text
+                if href.endswith('.pdf'):
+                    observations.append({
+                        'coa_pdf': href.split('/')[-1],
+                        'lab_results_url': href,
+                        'product_name': name,
+                        'product_subtype': subtype,
+                        'date_retail': date,
+                    })
+            except AttributeError:
+                continue
+    
+    # Restrict the observations to the desired time frame.
+    results = pd.DataFrame(observations)
+    dates = results['date_retail']
+    if start:
+        if isinstance(start, str):
+            latest = pd.to_datetime(start)
+        else:
+            latest = start
+        results = results.loc[dates >= latest]
+    if end:
+        if isinstance(end, str):
+            earliest = pd.to_datetime(end)
+        else:
+            earliest = end
+        results = results.loc[dates <= earliest]
+    results['date_retail'] = dates.apply(lambda x: x.isoformat()[:19])
+    return results
+
+
+def download_rawgarden_coas(
+        items: pd.DataFrame,
+        pause: Optional[float] = 0.24,
+        verbose: Optional[bool] = True,
+    ) -> None:
+    """Download Raw Garden product COAs to `product_subtype` folders.
+    Args:
+        items: (DataFrame): A DataFrame of products with `product_subtype`
+            and `lab_results_url` to download.
+        pause (float): A pause to respect the server serving the PDFs,
+            `0.24` seconds by default (optional).
+        verbose (bool): Whether or not to print status, `True` by
+            default (optional).
+    """
+    if verbose:
+        total = len(items)
+        print('Downloading %i PDFs, ETA > %.2fs' % (total, total * pause))
+
+    # Create a folder of each of the subtypes.
+    subtypes = list(items['product_subtype'].unique())
+    for subtype in subtypes:
+        folder = kebab_case(subtype)
+        subtype_folder = f'{COA_PDF_DIR}/{folder}'
+        if not os.path.exists(subtype_folder):
+            os.makedirs(subtype_folder)
+
+    # Download each COA PDF from its URL to a `product_subtype` folder.
+    for i, row in enumerate(items.iterrows()):
+        item = row[1]
+        url = item['lab_results_url']
+        subtype = item['product_subtype']
+        filename = url.split('/')[-1]
+        folder = kebab_case(subtype)
+        outfile = os.path.join(COA_PDF_DIR, folder, filename)
+        response = requests.get(url, headers=DEFAULT_HEADERS)
+        with open(outfile, 'wb') as pdf:
+            pdf.write(response.content)
+        if verbose:
+            message = 'Downloaded {}/{} | {}/{}'
+            message = message.format(str(i +  1), str(total), folder, filename)
+            print(message)
+        sleep(pause)
+
+
+def parse_rawgarden_coas(
+        directory: str,
+        filenames: Optional[list] = None,
+        temp_path: Optional[str] = '/tmp',
+        verbose: Optional[bool] = True,
+        **kwargs,
+    ) -> Tuple[list]:
+    """Parse Raw Garden lab results with CoADoc.
+    Args:
+        directory (str): The directory of files to parse.
+        filenames (list): A list of files to parse (optional).
+        temp_path (str): A temporary directory to use for any OCR (optional).
+        verbose (bool): Whether or not to print status, `True` by
+            default (optional).
+    Returns:
+        (tuple): Returns both a list of parsed and unidentified COA data.
+    """
+    parsed, unidentified = [], []
+    started = False
+    for path, _, files in os.walk(directory):
+        if verbose and not started:
+            started = True
+            if filenames:
+                total = len(filenames)
+            else:
+                total = len(files)
+            print('Parsing %i COAs, ETA > %.2fm' % (total, total * 25 / 60))
+        for filename in files:
+            if not filename.endswith('.pdf'):
+                continue
+            if filenames is not None:
+                if filename not in filenames:
+                    continue
+            file_path = os.path.join(path, filename)
+
+            # Parse the COA, by any means necessary!
+            parser = CoADoc()
+            try:
+                new_data = parser.parse_pdf(
+                    file_path,
+                    temp_path=temp_path,
+                    **kwargs
+                )
+            except:
+                try:
+                    # FIXME: This should work without directly calling OCR.
+                    temp_file = f'{temp_path}/ocr_coa.pdf'
+                    parser.pdf_ocr(
+                        file_path,
+                        temp_file,
+                        temp_path,
+                        resolution=180,
+                    )
+                    new_data = parser.parse_pdf(
+                        temp_file,
+                        temp_path=temp_path,
+                        **kwargs
+                    )
+                except Exception as e:
+                    # Hot-fix: Remove temporary `magick-*` files.
+                    for i in os.listdir(temp_path):
+                        magick_path = os.path.join(temp_path, i)
+                        if os.path.isfile(magick_path) and i.startswith('magick-'):
+                            os.remove(magick_path)
+                    unidentified.append({'coa_pdf': filename})
+                    if verbose:
+                        print('Error:', filename)
+                        print(e)
+                    continue
+
+            # Add the subtype key and record the data.
+            subtype = path.split('\\')[-1]
+            if isinstance(new_data, dict):
+                new_data = [new_data]
+            new_data[0]['product_subtype'] = subtype
+            parsed.extend(new_data)
+            parser.quit()
+            gc.collect()
+            if verbose:
+                print('Parsed:', filename)
+
+    return parsed, unidentified
+
+
+def upload_lab_results(
+        observations: List[dict],
+        collection: Optional[str] = None,
+        database: Optional[Any] = None,
+        update: Optional[bool] = True,
+        verbose: Optional[bool] = True,
+    ) -> None:
+    """Upload lab results to Firestore.
+    Args:
+        observations (list): A list of lab results to upload.
+        collection (str): The Firestore collection where lab results live,
+            `'public/data/lab_results'` by default (optional).
+        database (Client): A Firestore database instance (optional).
+        update (bool): Whether or not to update existing entries, `True`
+            by default (optional).
+        verbose (bool): Whether or not to print status, `True` by
+            default (optional).
+    """
+    if collection is None:
+        collection = COLLECTION
+    if database is None:
+        database = initialize_firebase()
+    refs, updates = [], []
+    for obs in observations:
+        sample_id = obs['sample_id']
+        ref = f'{collection}/{sample_id}'
+        if not update:
+            doc = get_document(ref)
+            if doc is not None:
+                continue
+        refs.append(ref)
+        updates.append(obs)
+    if updates:
+        if verbose:
+            print('Uploading %i lab results.' % len(refs))
+        update_documents(refs, updates, database=database)
+    if verbose:
+        print('Uploaded %i lab results.' % len(refs))
+
+
+#-----------------------------------------------------------------------
+# EXAMPLE: Collect Raw Garden lab results data by:
+#
+#    1. Finding products and their COA URLS.
+#    2. Downloading COA PDFs from their URLs.
+#    3. Using CoADoc to parse the COA PDFs (with OCR).
+#    4. Saving the data to datafiles, Firebase Storage, and Firestore.
+#
+#-----------------------------------------------------------------------
+if __name__ == '__main__':
+
+    # === Setup ===
+
+    # Support command line usage.
+    # Future work: Allow data dirs to be specified from the command line.
+    import argparse
+    try:
+        parser = argparse.ArgumentParser()
+        parser.add_argument('--days_ago', dest='days_ago', type=int)
+        parser.add_argument('--get_all', dest='get_all', type=bool)
+        args = parser.parse_args()
+    except SystemExit:
+        args = {}
+
+    # Specify collection period.
+    DAYS_AGO = args.get('days_ago', 1)
+    GET_ALL =  args.get('get_all', True)
+
+    # === Data Collection ===
+
+    # Get the most recent Raw Garden products.
+    start = datetime.now() - timedelta(days=DAYS_AGO)
+    if GET_ALL:
+        start = datetime(year=2018, month=1, day=1)
+    products = get_rawgarden_products(start=start)
+    filenames = products['coa_pdf'].to_list()
+
+    # Download Raw Garden product COAs to `product_subtype` folders.
+    download_rawgarden_coas(products, pause=0.24, verbose=True)
+
+    # === Data Curation ===
+
+    # Parse COA PDFs with CoADoc.
+    coa_data, unidentified_coas = parse_rawgarden_coas(
+        COA_PDF_DIR,
+        filenames=filenames,
+        temp_path=TEMP_PATH,
+        verbose=True,
+    )
+
+    # Merge the `products`'s `product_subtype` with the COA data.
+    coa_df = rmerge(
+        pd.DataFrame(coa_data),
+        products,
+        on='coa_pdf',
+        how='left',
+        replace='right',
+    )
+
+    # Create hashes.
+    coa_df = coa_df.where(pd.notnull(coa_df), None)
+    coa_df['results_hash'] = coa_df['results'].apply(
+        lambda x: create_hash(x),
+    )
+    coa_df['sample_hash'] = coa_df.loc[:, coa_df.columns != 'sample_hash'].apply(
+        lambda x: create_hash(x.to_dict()),
+        axis=1,
+    )
+    datafile_hash = create_hash(coa_df)
+
+    # === Data Archiving ===
+
+    # Create custom column order.
+    column_order = ['sample_hash', 'results_hash']
+    column_order += list(parser.column_order)
+    index = column_order.index('product_type') + 1
+    column_order.insert(index, 'product_subtype')
+
+    # Optional: Save the COA data to a workbook.
+    parser = CoADoc()
+    datafile = f'{COA_DATA_DIR}/{datafile_hash}.xlsx'
+    parser.save(coa_df, datafile, column_order=column_order)
+
+    # Optional: Save the unidentified COA data.
+    errors = [x['coa_pdf'] for x in unidentified_coas]
+    timestamp = datetime.now().isoformat()[:19].replace(':', '-')
+    error_file = f'{COA_DATA_DIR}/rawgarden-unidentified-coas-{timestamp}.xlsx'
+    products.loc[products['coa_pdf'].isin(errors)].to_excel(error_file)
+
+    # === Firebase Database and Storage ===
+
+    # Optional: Initialize Firebase.
+    initialize_firebase(ENV_FILE)
+
+    # Optional: Upload the lab results to Firestore.
+    upload_lab_results(
+        coa_df.to_dict(orient='records'),
+        update=True,
+        verbose=True
+    )
+
+    # Optional: Upload datafiles to Firebase Storage.
+    storage_datafile = '/'.join([STORAGE_REF, datafile.split('/')[-1]])
+    storage_error_file = '/'.join([STORAGE_REF, error_file.split('/')[-1]])
+    upload_file(storage_datafile, datafile, bucket_name=BUCKET_NAME)
+    upload_file(storage_error_file, error_file, bucket_name=BUCKET_NAME)
+
+    # == Data Aggregation ===
+
+    # # Initialize the COA parser.
+    # parser = CoADoc()
+
+    # # Stack COA datafiles, re-hash, and re-save!
+    # datafiles = [
+    #     f'{COA_DATA_DIR}/d7815fd2a097d06b719aadcc00233026f86076a680db63c532a11b67d7c8bc70.xlsx',
+    #     f'{COA_DATA_DIR}/01880e30f092cf5739f9f2b58de705fc4c245d6859c00b50505a3a802ff7c2b2.xlsx',
+    #     f'{COA_DATA_DIR}/154de9b1992a1bfd9a07d2e52c702e8437596923f34bee43f62f3e24f042b81c.xlsx',
+    # ]
+
+    # # Create custom column order.
+    # column_order = ['sample_hash', 'results_hash']
+    # column_order += list(parser.column_order)
+    # index = column_order.index('product_type') + 1
+    # column_order.insert(index, 'product_subtype')
+
+    # # Aggregate the datafiles.
+    # master_data = parser.aggregate(
+    #     datafiles,
+    #     output=COA_DATA_DIR,
+    #     sheet_name='Details',
+    #     column_order=column_order,
+    # )

cannabis_tests.py

@@ -6,11 +6,11 @@ Authors:
     Keegan Skeate <https://github.com/keeganskeate>
     Candace O'Sullivan-Sutherland <https://github.com/candy-o>
 Created: 9/10/2022
-Updated: 9/10/2022
+Updated: 9/13/2022
 License: <https://github.com/cannlytics/cannlytics/blob/main/LICENSE>
 """
-import json
 import datasets # pip install datasets
+# from datasets import Sequence, datasets.Value
 
 # Citation for using the dataset.
 CANNABIS_TESTS_CITATION = """\
@@ -31,7 +31,10 @@ dataset of curated cannabis lab test results.
 CANNABIS_TESTS_URL = 'https://huggingface.co/datasets/cannlytics/cannabis_tests'
 
 # Raw Garden constants.
-RAWGARDEN_DATA_URL = 'https://cannlytics.page.link/rawgarden'
+# RAWGARDEN_DATA_URL = 'https://cannlytics.page.link/rawgarden'
+RAWGARDEN_DETAILS_URL = 'https://cannlytics.page.link/rawgarden-details'
+RAWGARDEN_RESULTS_URL = 'https://cannlytics.page.link/rawgarden-results'
+RAWGARDEN_VALUES_URL = 'https://cannlytics.page.link/rawgarden-values'
 RAWGARDEN_URL = 'https://github.com/cannlytics/cannlytics/tree/main/ai/curation/get_rawgarden_data'
 RAWGARDEN_DESCRIPTION = """\
 Raw Garden lab test results (https://cannlytics.com/data/tests) is a
@@ -39,6 +42,131 @@ dataset of curated cannabis lab test results from Raw Garden, a large
 cannabis processor in California.
 """
 RAWGARDEN_FEATURES = []
+RAWGARDEN_RESULTS = {
+    'sample_hash': datasets.Value(dtype='string', id=None),
+    'results_hash': datasets.Value(dtype='string', id=None),
+    'sample_id': datasets.Value(dtype='string', id=None),
+    'product_name': datasets.Value(dtype='string', id=None),
+    'producer': datasets.Value(dtype='string', id=None),
+    'product_type': datasets.Value(dtype='string', id=None),
+    'product_subtype': datasets.Value(dtype='string', id=None),
+    'date_tested': datasets.Value(dtype='string', id=None),
+    'analysis': datasets.Value(dtype='string', id=None),
+    'key': datasets.Value(dtype='string', id=None),
+    'limit': datasets.Value(dtype='double', id=None),
+    'lod': datasets.Value(dtype='double', id=None),
+    'lodloq': datasets.Value(dtype='double', id=None),
+    'loq': datasets.Value(dtype='double', id=None),
+    'margin_of_error': datasets.Value(dtype='double', id=None),
+    'mg_g': datasets.Value(dtype='double', id=None),
+    'name': datasets.Value(dtype='string', id=None),
+    'status': datasets.Value(dtype='string', id=None),
+    'units': datasets.Value(dtype='string', id=None),
+    'value': datasets.Value(dtype='double', id=None),
+}
+RAWGARDEN_DETAILS = {
+    'sample_hash': datasets.Value(dtype='string', id=None),
+    'results_hash': datasets.Value(dtype='string', id=None),
+    'sample_id': datasets.Value(dtype='string', id=None),
+    'product_name': datasets.Value(dtype='string', id=None),
+    'producer': datasets.Value(dtype='string', id=None),
+    'product_type': datasets.Value(dtype='string', id=None),
+    'product_subtype': datasets.Value(dtype='string', id=None),
+    'date_tested': datasets.Value(dtype='string', id=None),
+    'analyses': datasets.Value(dtype='string', id=None),
+    'batch_number': datasets.Value(dtype='string', id=None),
+    'batch_size': datasets.Value(dtype='string', id=None),
+    'batch_units': datasets.Value(dtype='string', id=None),
+    'cannabinoids_method': datasets.Value(dtype='string', id=None),
+    'cannabinoids_status': datasets.Value(dtype='string', id=None),
+    'coa_algorithm': datasets.Value(dtype='string', id=None),
+    'coa_algorithm_entry_point': datasets.Value(dtype='string', id=None),
+    'coa_parsed_at': datasets.Value(dtype='string', id=None),
+    'coa_pdf': datasets.Value(dtype='string', id=None),
+    'coa_urls': datasets.Value(dtype='string', id=None),
+    'date_collected': datasets.Value(dtype='string', id=None),
+    'date_produced': datasets.Value(dtype='string', id=None),
+    'date_received': datasets.Value(dtype='string', id=None),
+    'date_retail': datasets.Value(dtype='string', id=None),
+    'delta_9_thc_per_unit': datasets.Value(dtype='string', id=None),
+    'distributor': datasets.Value(dtype='string', id=None),
+    'distributor_address': datasets.Value(dtype='string', id=None),
+    'distributor_city': datasets.Value(dtype='string', id=None),
+    'distributor_license_number': datasets.Value(dtype='string', id=None),
+    'distributor_state': datasets.Value(dtype='string', id=None),
+    'distributor_street': datasets.Value(dtype='string', id=None),
+    'distributor_zipcode': datasets.Value(dtype='float64', id=None),
+    'foreign_matter_method': datasets.Value(dtype='string', id=None),
+    'foreign_matter_status': datasets.Value(dtype='string', id=None),
+    'heavy_metals_method': datasets.Value(dtype='string', id=None),
+    'heavy_metals_status': datasets.Value(dtype='string', id=None),
+    'images': datasets.Value(dtype='string', id=None),
+    'lab': datasets.Value(dtype='string', id=None),
+    'lab_address': datasets.Value(dtype='string', id=None),
+    'lab_city': datasets.Value(dtype='string', id=None),
+    'lab_county': datasets.Value(dtype='string', id=None),
+    'lab_email': datasets.Value(dtype='string', id=None),
+    'lab_id': datasets.Value(dtype='string', id=None),
+    'lab_image_url': datasets.Value(dtype='string', id=None),
+    'lab_latitude': datasets.Value(dtype='float64', id=None),
+    'lab_license_number': datasets.Value(dtype='string', id=None),
+    'lab_longitude': datasets.Value(dtype='float64', id=None),
+    'lab_phone': datasets.Value(dtype='string', id=None),
+    'lab_results_url': datasets.Value(dtype='string', id=None),
+    'lab_state': datasets.Value(dtype='string', id=None),
+    'lab_street': datasets.Value(dtype='string', id=None),
+    'lab_website': datasets.Value(dtype='string', id=None),
+    'lab_zipcode': datasets.Value(dtype='int64', id=None),
+    'lims': datasets.Value(dtype='string', id=None),
+    'metrc_ids': datasets.Value(dtype='string', id=None),
+    'metrc_lab_id': datasets.Value(dtype='string', id=None),
+    'metrc_source_id': datasets.Value(dtype='string', id=None),
+    'microbes_method': datasets.Value(dtype='string', id=None),
+    'microbes_status': datasets.Value(dtype='string', id=None),
+    'moisture_content': datasets.Value(dtype='string', id=None),
+    'moisture_method': datasets.Value(dtype='string', id=None),
+    'mycotoxins_method': datasets.Value(dtype='string', id=None),
+    'mycotoxins_status': datasets.Value(dtype='string', id=None),
+    'notes': datasets.Value(dtype='string', id=None),
+    'pesticides_method': datasets.Value(dtype='string', id=None),
+    'pesticides_status': datasets.Value(dtype='string', id=None),
+    'producer_address': datasets.Value(dtype='string', id=None),
+    'producer_city': datasets.Value(dtype='string', id=None),
+    'producer_image_url': datasets.Value(dtype='string', id=None),
+    'producer_license_number': datasets.Value(dtype='string', id=None),
+    'producer_state': datasets.Value(dtype='string', id=None),
+    'producer_street': datasets.Value(dtype='string', id=None),
+    'producer_url': datasets.Value(dtype='string', id=None),
+    'producer_zipcode': datasets.Value(dtype='float64', id=None),
+    'product_size': datasets.Value(dtype='string', id=None),
+    'public': datasets.Value(dtype='float64', id=None),
+    'residual_solvents_method': datasets.Value(dtype='string', id=None),
+    'residual_solvents_status': datasets.Value(dtype='string', id=None),
+    'results': datasets.Value(dtype='string', id=None),
+    'sample_number': datasets.Value(dtype='float64', id=None),
+    'sample_size': datasets.Value(dtype='string', id=None),
+    'sampling_method': datasets.Value(dtype='string', id=None),
+    'serving_size': datasets.Value(dtype='string', id=None),
+    'status': datasets.Value(dtype='string', id=None),
+    'sum_of_cannabinoids': datasets.Value(dtype='float64', id=None),
+    'terpenes_method': datasets.Value(dtype='string', id=None),
+    'terpenes_status': datasets.Value(dtype='string', id=None),
+    'total_cannabinoids': datasets.Value(dtype='float64', id=None),
+    'total_cbc': datasets.Value(dtype='float64', id=None),
+    'total_cbd': datasets.Value(dtype='float64', id=None),
+    'total_cbdv': datasets.Value(dtype='float64', id=None),
+    'total_cbg': datasets.Value(dtype='float64', id=None),
+    'total_terpenes': datasets.Value(dtype='float64', id=None),
+    'total_terpenes_mg_g': datasets.Value(dtype='float64', id=None),
+    'total_thc': datasets.Value(dtype='float64', id=None),
+    'total_thcv': datasets.Value(dtype='float64', id=None),
+    'url': datasets.Value(dtype='string', id=None),
+    'water_activity_method': datasets.Value(dtype='string', id=None),
+    'water_activity_status': datasets.Value(dtype='string', id=None)
+}
+# RAWGARDEN_VALUES = {
+#     'delta_9_thc': datasets.Value(dtype='float64', id=None),
+# }
 
 # MCR Labs constants.
 MCRLABS_DATA_URL = 'https://cannlytics.page.link/mcrlabs'
@@ -110,72 +238,94 @@ class CannabisTests(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
         CannabisTestsConfig(
-            name='rawgarden',
+            name='rawgarden_details',
             description=RAWGARDEN_DESCRIPTION,
-            features=RAWGARDEN_FEATURES,
-            data_url=RAWGARDEN_DATA_URL,
+            features=RAWGARDEN_DETAILS,
+            data_url=RAWGARDEN_DETAILS_URL,
             citation=CANNABIS_TESTS_CITATION,
             url=RAWGARDEN_URL,
         ),
-        CannabisTestsConfig(
-            name='mcrlabs',
-            description=MCRLABS_DESCRIPTION,
-            features=MCRLABS_FEATURES,
-            data_url=MCRLABS_DATA_URL,
-            citation=CANNABIS_TESTS_CITATION,
-            url=MCRLABS_URL,
-        ),
-        CannabisTestsConfig(
-            name='psilabs',
-            description=PSILABS_DESCRIPTION,
-            features=PSILABS_FEATURES,
-            data_url=PSILABS_DATA_URL,
-            citation=CANNABIS_TESTS_CITATION,
-            url=PSILABS_URL,
-        ),
-        CannabisTestsConfig(
-            name='sclabs',
-            description=SCLABS_DESCRIPTION,
-            features=SCLABS_FEATURES,
-            data_url=SCLABS_DATA_URL,
-            citation=CANNABIS_TESTS_CITATION,
-            url=SCLABS_URL,
-        ),
+        # CannabisTestsConfig(
+        #     name='rawgarden_results',
+        #     description=RAWGARDEN_DESCRIPTION,
+        #     features=RAWGARDEN_RESULTS,
+        #     data_url=RAWGARDEN_RESULTS_URL,
+        #     citation=CANNABIS_TESTS_CITATION,
+        #     url=RAWGARDEN_URL,
+        # ),
+        # CannabisTestsConfig(
+        #     name='rawgarden_values',
+        #     description=RAWGARDEN_DESCRIPTION,
+        #     features=RAWGARDEN_VALUES,
+        #     data_url=RAWGARDEN_VALUES_URL,
+        #     citation=CANNABIS_TESTS_CITATION,
+        #     url=RAWGARDEN_URL,
+        # ),
+        # CannabisTestsConfig(
+        #     name='mcrlabs',
+        #     description=MCRLABS_DESCRIPTION,
+        #     features=MCRLABS_FEATURES,
+        #     data_url=MCRLABS_DATA_URL,
+        #     citation=CANNABIS_TESTS_CITATION,
+        #     url=MCRLABS_URL,
+        # ),
+        # CannabisTestsConfig(
+        #     name='psilabs',
+        #     description=PSILABS_DESCRIPTION,
+        #     features=PSILABS_FEATURES,
+        #     data_url=PSILABS_DATA_URL,
+        #     citation=CANNABIS_TESTS_CITATION,
+        #     url=PSILABS_URL,
+        # ),
+        # CannabisTestsConfig(
+        #     name='sclabs',
+        #     description=SCLABS_DESCRIPTION,
+        #     features=SCLABS_FEATURES,
+        #     data_url=SCLABS_DATA_URL,
+        #     citation=CANNABIS_TESTS_CITATION,
+        #     url=SCLABS_URL,
+        # ),
     ]
 
-    DEFAULT_CONFIG_NAME = 'rawgarden'
+    DEFAULT_CONFIG_NAME = 'rawgarden_details'
 
     def _info(self):
-        features = {feature: datasets.Value('string') for feature in self.config.features}
+        # features = {feature: datasets.datasets.Value('string') for feature in self.config.features}
 
         # TODO: Define all numeric features.
-        # features['span1_index'] = datasets.Value('int32')
+        # features['span1_index'] = datasets.datasets.Value('int32')
 
         # TODO: Define all image features.
 
         # TODO: Define all sequence features.
         # features["answers"] = datasets.features.Sequence(
-        #     {"text": datasets.Value("string"), "answer_start": datasets.Value("int32"),}
+        #     {"text": datasets.datasets.Value("string"), "answer_start": datasets.datasets.Value("int32"),}
         # )
 
         return datasets.DatasetInfo(
             citation=CANNABIS_TESTS_CITATION,
             description=CANNABIS_TESTS_DESCRIPTION,
-            features=features,
+            features=RAWGARDEN_DETAILS,
             homepage=CANNABIS_TESTS_URL,
             supervised_keys=None,
         )    
 
-    def _generate_examples(self, filepath):
-        """This function returns the examples in raw (text) form."""
-        with open(filepath,  encoding='utf-8') as f:
-            for line in f:
-                row = json.loads(line)
-                product_name = row.get('product_name', '').strip()
-                for i, result in enumerate(row['results']):
-                    _id = str(i)
-                    yield _id, {
-                        'product_name': product_name,
-                        'analyte': result.get('key', ''),
-                        'value': result.get('value', 0),
-                    }
+    # def _generate_examples(self, filepath):
+    #     """This function returns the examples in raw (text) form."""
+    #     with open(filepath,  encoding='utf-8') as f:
+    #         for line in f:
+    #             row = json.loads(line)
+    #             product_name = row.get('product_name', '').strip()
+    #             for i, result in enumerate(row['results']):
+    #                 _id = str(i)
+    #                 yield _id, {
+    #                     'product_name': product_name,
+    #                     'analyte': result.get('key', ''),
+    #                     'value': result.get('value', 0),
+    #                 }
+
+
+# if __name__ == '__main__':
+
+#     from datasets import load_dataset
+#     dataset = load_dataset('cannabis_tests.py')

dataset_infos.json

@@ -0,0 +1 @@
+{".": {"description": "", "citation": "", "homepage": "", "license": "", "features": {"sample_hash": {"dtype": "string", "id": null, "_type": "Value"}, "results_hash": {"dtype": "string", "id": null, "_type": "Value"}, "sample_id": {"dtype": "string", "id": null, "_type": "Value"}, "product_name": {"dtype": "string", "id": null, "_type": "Value"}, "producer": {"dtype": "string", "id": null, "_type": "Value"}, "product_type": {"dtype": "string", "id": null, "_type": "Value"}, "product_subtype": {"dtype": "string", "id": null, "_type": "Value"}, "date_tested": {"dtype": "string", "id": null, "_type": "Value"}, "analyses": {"dtype": "string", "id": null, "_type": "Value"}, "batch_number": {"dtype": "string", "id": null, "_type": "Value"}, "batch_size": {"dtype": "string", "id": null, "_type": "Value"}, "batch_units": {"dtype": "string", "id": null, "_type": "Value"}, "cannabinoids_method": {"dtype": "string", "id": null, "_type": "Value"}, "cannabinoids_status": {"dtype": "string", "id": null, "_type": "Value"}, "coa_algorithm": {"dtype": "string", "id": null, "_type": "Value"}, "coa_algorithm_entry_point": {"dtype": "string", "id": null, "_type": "Value"}, "coa_parsed_at": {"dtype": "string", "id": null, "_type": "Value"}, "coa_pdf": {"dtype": "string", "id": null, "_type": "Value"}, "coa_urls": {"dtype": "string", "id": null, "_type": "Value"}, "date_collected": {"dtype": "string", "id": null, "_type": "Value"}, "date_produced": {"dtype": "string", "id": null, "_type": "Value"}, "date_received": {"dtype": "string", "id": null, "_type": "Value"}, "date_retail": {"dtype": "string", "id": null, "_type": "Value"}, "delta_9_thc_per_unit": {"dtype": "string", "id": null, "_type": "Value"}, "distributor": {"dtype": "string", "id": null, "_type": "Value"}, "distributor_address": {"dtype": "string", "id": null, "_type": "Value"}, "distributor_city": {"dtype": "string", "id": null, "_type": "Value"}, "distributor_license_number": {"dtype": "string", "id": null, "_type": "Value"}, "distributor_state": {"dtype": "string", "id": null, "_type": "Value"}, "distributor_street": {"dtype": "string", "id": null, "_type": "Value"}, "distributor_zipcode": {"dtype": "float64", "id": null, "_type": "Value"}, "foreign_matter_method": {"dtype": "string", "id": null, "_type": "Value"}, "foreign_matter_status": {"dtype": "string", "id": null, "_type": "Value"}, "heavy_metals_method": {"dtype": "string", "id": null, "_type": "Value"}, "heavy_metals_status": {"dtype": "string", "id": null, "_type": "Value"}, "images": {"dtype": "string", "id": null, "_type": "Value"}, "lab": {"dtype": "string", "id": null, "_type": "Value"}, "lab_address": {"dtype": "string", "id": null, "_type": "Value"}, "lab_city": {"dtype": "string", "id": null, "_type": "Value"}, "lab_county": {"dtype": "string", "id": null, "_type": "Value"}, "lab_email": {"dtype": "string", "id": null, "_type": "Value"}, "lab_id": {"dtype": "string", "id": null, "_type": "Value"}, "lab_image_url": {"dtype": "string", "id": null, "_type": "Value"}, "lab_latitude": {"dtype": "float64", "id": null, "_type": "Value"}, "lab_license_number": {"dtype": "string", "id": null, "_type": "Value"}, "lab_longitude": {"dtype": "float64", "id": null, "_type": "Value"}, "lab_phone": {"dtype": "string", "id": null, "_type": "Value"}, "lab_results_url": {"dtype": "string", "id": null, "_type": "Value"}, "lab_state": {"dtype": "string", "id": null, "_type": "Value"}, "lab_street": {"dtype": "string", "id": null, "_type": "Value"}, "lab_website": {"dtype": "string", "id": null, "_type": "Value"}, "lab_zipcode": {"dtype": "int64", "id": null, "_type": "Value"}, "lims": {"dtype": "string", "id": null, "_type": "Value"}, "metrc_ids": {"dtype": "string", "id": null, "_type": "Value"}, "metrc_lab_id": {"dtype": "string", "id": null, "_type": "Value"}, "metrc_source_id": {"dtype": "string", "id": null, "_type": "Value"}, "microbes_method": {"dtype": "string", "id": null, "_type": "Value"}, "microbes_status": {"dtype": "string", "id": null, "_type": "Value"}, "moisture_content": {"dtype": "string", "id": null, "_type": "Value"}, "moisture_method": {"dtype": "string", "id": null, "_type": "Value"}, "mycotoxins_method": {"dtype": "string", "id": null, "_type": "Value"}, "mycotoxins_status": {"dtype": "string", "id": null, "_type": "Value"}, "notes": {"dtype": "string", "id": null, "_type": "Value"}, "pesticides_method": {"dtype": "string", "id": null, "_type": "Value"}, "pesticides_status": {"dtype": "string", "id": null, "_type": "Value"}, "producer_address": {"dtype": "string", "id": null, "_type": "Value"}, "producer_city": {"dtype": "string", "id": null, "_type": "Value"}, "producer_image_url": {"dtype": "string", "id": null, "_type": "Value"}, "producer_license_number": {"dtype": "string", "id": null, "_type": "Value"}, "producer_state": {"dtype": "string", "id": null, "_type": "Value"}, "producer_street": {"dtype": "string", "id": null, "_type": "Value"}, "producer_url": {"dtype": "string", "id": null, "_type": "Value"}, "producer_zipcode": {"dtype": "float64", "id": null, "_type": "Value"}, "product_size": {"dtype": "string", "id": null, "_type": "Value"}, "public": {"dtype": "float64", "id": null, "_type": "Value"}, "residual_solvents_method": {"dtype": "string", "id": null, "_type": "Value"}, "residual_solvents_status": {"dtype": "string", "id": null, "_type": "Value"}, "results": {"dtype": "string", "id": null, "_type": "Value"}, "sample_number": {"dtype": "float64", "id": null, "_type": "Value"}, "sample_size": {"dtype": "string", "id": null, "_type": "Value"}, "sampling_method": {"dtype": "string", "id": null, "_type": "Value"}, "serving_size": {"dtype": "string", "id": null, "_type": "Value"}, "status": {"dtype": "string", "id": null, "_type": "Value"}, "sum_of_cannabinoids": {"dtype": "float64", "id": null, "_type": "Value"}, "terpenes_method": {"dtype": "string", "id": null, "_type": "Value"}, "terpenes_status": {"dtype": "string", "id": null, "_type": "Value"}, "total_cannabinoids": {"dtype": "float64", "id": null, "_type": "Value"}, "total_cbc": {"dtype": "float64", "id": null, "_type": "Value"}, "total_cbd": {"dtype": "float64", "id": null, "_type": "Value"}, "total_cbdv": {"dtype": "float64", "id": null, "_type": "Value"}, "total_cbg": {"dtype": "float64", "id": null, "_type": "Value"}, "total_terpenes": {"dtype": "float64", "id": null, "_type": "Value"}, "total_terpenes_mg_g": {"dtype": "float64", "id": null, "_type": "Value"}, "total_thc": {"dtype": "float64", "id": null, "_type": "Value"}, "total_thcv": {"dtype": "float64", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "water_activity_method": {"dtype": "string", "id": null, "_type": "Value"}, "water_activity_status": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "csv", "config_name": ".", "version": {"version_str": "0.0.0", "description": 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