cannlytics / cannlytics

🔥 Cannlytics = cannabis + analytics. Data pipelines, user interfaces, and the best statistics in the game. Made with ❤️

Cannabis data science and analytics.

https://cannlytics.com

🔥 Cannlytics is a suite of tools that you can use to wrangle, standardize, and analyze cannabis data. We believe that people in the cannabis space would benefit from organized, decentralized, and accessible data. This repository contains each core component of Cannlytics and detailed instructions on how to build, develop, publish, and use each tool. You can find more information about each tool below:

Component	Status	Production URL
🦾 Cannlytics AI	🟢	https://data.cannlytics.com
📡 Cannlytics API	🟢	https://cannlytics.com/api
📱 Cannlytics App	🟡	https://app.cannlytics.com
📜 Cannlytics Documentation	🟡	https://docs.cannlytics.com
🐍 Cannlytics Python SDK	🟢	https://pypi.org/project/cannlytics/
🌐 Cannlytics Website	🟢	https://cannlytics.com

🍁 Datasets

Cannlytics Datasets is a collection of cannabis data from around the world. The data is archived locally, stored in a cloud database, and available for download from Hugging Face.

Dataset	Description
cannabis_licenses	Cannabis license data for each state with permitted adult-use cannabis.
cannabis_tests	Public cannabis lab test results.

🧑‍🚀 Cannabis Data Science

Do you want to join a team of data scientists from around the world who are advancing cannabis science, molecule by molecule? 🧬 Come to the Cannabis Data Science Meetup Group! At the meetup, you will be introduced to many useful notes, notebooks, and video tutorials to help you get, wrangle, and analyze cannabis data with the best of them. Join the fun on Wednesdays at 8:30am PST / 9:30am MT / 10:30am CT / 11:30am EST. You are always welcome to use the code, watch the videos, and make contributions of your own! Please tune in 🚀

👨‍🏭 Contributing

Contributions are always welcome! You are encouraged to submit issues, functionality, and features that you want to be addressed. See the contributing guide to get started. Anyone is welcome to contribute anything. Currently, the project could greatly benefit from:

• 👨‍💻 Code
• 💡 Ideas
• 👷 Tests
• 🖊️ Documentation
• 🏹 Examples

💖 Support

Cannlytics is made available with hard work and your good will. Please consider making a contribution to help us continue crafting useful tools and data pipelines for you. Thank you 🙏

Provider	Link
👐 OpenCollective	https://opencollective.com/cannlytics-company

🏛️ License

Copyright (c) 2020-2023 Cannlytics and The Cannabis Data Science Team

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

cannlytics's Issues

Integrate the user interface with the Metrc module.

The ability to interface with the Metrc module from the user interface is needed. The fields needed to interface with Metrc need to be included in the user interface and the corresponding data save to Firestore.

CoA Generation

Certificate of analysis (CoA) generation utilizing user-editable templates is needed.

• CoA generation should include the logic necessary for calculation of results from data collected scientific instruments.
• CoA generation should be able to differentiate between sample types and output corresponding analyses and data on to the certificate by sample type and analyses performed.
• The user interface of the console needs a CoA Review tab, where generated C0As will be reviewed and signed by
  one user (an analyst), then by the chief scientific officer or quality assurance level manager.
• Fields output onto CoA should be contained within the Firestore database.
• The user needs the ability to alter the fields for the CoA.
• Users should be able to upload CoA templates that match saved fields to produce final PDFs.

Implement `parse_veda_coa`

Parse a Veda Scientific CoA PDF.

Create user interface to send and receive data to and from Firestore for main LIMS models

A user interface is needed to create, read, update, and delete data (C.R.U.D.). Firestore, a NoSQL database, will serve as the database. The user interface is built with Django templates and Bootstrap v5 CSS and JS. Forms, even hard-coded, are needed to send and display data to and from Firestore. Tables, potentially built with AG Grid are need to list data read from Fierstore.

The minimal models needed are:

• Projects (Orders)
• Samples
• Contacts (Clients)
• Analyses (Tests)
• Analytes

These interfaces need to be finished ASAP.

Password Reset

Uncaught ReferenceError: cannabisdata is not defined

https://console.cannlytics.com/account/password-reset

Implement `parse_anresco_pdf`

Parse a Anresco Laboratories CoA URL.

Result calculations

User-defined logic for result calculations is needed.

• User-defined calculations should be able to be applied to any data collected from scientific instruments. See this article for an example of how to save calculations as strings and apply them to Pandas DataFrames.
• Certain calculations, such as for microbiological, residual solvent, pesticide, and heavy metal screening, will require comparison to a defined limit for each analyte and overall to determine if the analyte and sample passes (status='Pass') or fails (status='Fail') screening. If any analyte in a analyses fails, then status is set to Fail in the sample results. Each failing analyte is recorded with the analyte's results in a metrics field in a sample's results model.

State cannabis data

Data sources need to be found and automatic (where possible) data collection routines need to be written. Where automatic data collection routines are not possible, formal instructions need to be written for how to collect data for a given state.

Scientific instrument parsing logic

Data from scientific instruments needs to be parsed and uploaded to the Firestore database to allow for easy access.

• All parsing should be done in a reliable manner, with 99% or higher
  success rate, assuming that there is no change in the data directory or file
  structure.
• It is assumed that the data directory and accompanying files will exist on at least one
  scientific instrument computer and all recently modified data files in specified directories should be parsed and uploaded to Firestore. For example:

  {
      "instruments": [
          {
              "name": "HPLC 001",
              "data_directories": [
                  "path/to/instrument/data",
                  "optional/additional/path/to/control/data",
              ],
          }
      ]
  }

• Data parsing should happen automatically with a way to recognize when files are modified within
  the directory. All newly created and modified data files should be read, parsed, and uploaded to Firestore every x
  minutes.

Ability to add specific fields/keys to forms

A list of additional fields can be added to each model type, so users can specify additional fields (data points) that they would like to track.

For example, a user could add a Hubspot client ID:

{
    "additional_fields": [
        {
            "name": "Hubspot Client ID",
            "key": "hubspot_client_id",
            "required": false,
            "type": "text",
        }
    ]
}