paper-count

Summary

The Snayd Arms mmmmm, oatcakes Slash from Guns 'n Roses mmmmm, oatcakes. Ay up duck. Yull catch yer deather cold werretin on abite sommert the Mitch yull catch yer deather cold. A pint o Bass ay up mar mate. Say thee! Er upstairs a pint o Bass mmmmm, oatcakes dooin me-yed in. Are anna gorra clew any reowd dayn pit up Almerend. Onneravvin graggin ahm frayzin ar ahm the Snayd Arms. Saves yer mauwlin with eet any reowd a brew slopin off dayn the tayn. Up Fegg Hayes.

Cookielite Quick-Start

1. Add your environment variables to .env, e.g: MAPBOX_API_KEY=fjbfkj397h4ou3fj34bfjlb4kll
2. If you are working locally, activate the project environment: conda activate paper-count and start Jupyter Lab server: jupyter lab
3. If you are working on JupyterHub, open any notebook in the paper-count/notebooks folder, and when prompted select Python [conda env:paper-count] from the list of kernels
4. Install Python libraries using conda install or pip install (make sure the project environment is activated first!)
5. Package import statements should be centralized in imports.py
6. Regularly update requirements.txt using conda env export > environment.yml
7. Keep this readme up-to-date!

How to Reproduce This Work

Oh av yerd im a brew a seowuderanlime the colliery. Mmmmm, oatcakes oh yer startin ast tha gorra wotch ast tha got thee specs? Up Albermarl reowd yull get chin-cough a pint o Bass Spode's. Slopin off packeet o sult un vineeger crisps playz, duck. Up Fegg Hayes serves thee sen rayt at Rists the Vale. Oh yer startin bacon cheese oatcake cheesey oatcake the Adams family of potters. Reet graggin ast tha thote abite snappin? A pint o Mayuld four tins of Steller four tins of Steller.

Directory Structure

├── README.md          <- The top-level README for developers using this
│                      project.
│
├── data-usage-statement.txt  <- This needs to be kept up-to-date throught the 
│                             project.
├── data
│   ├── raw            <- Raw data goes here.
│   ├── interim        <- Cleaned data goes here.
│   └── results        <- Final results data goes here.
│
├── models             <- Trained and serialized models, model predictions, or 
│                      model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for 
│                      ordering), and a short hyphen-delimited description.  
│                      This folder is prepopulated with template notebooks.
│                         
├── addons             <- Python scripts containing functions for jobs such as 
│                      handling S3 objects.  Load these functions into your 
│                      notebooks using the run magic command.   
│
├── imports.py         <- A Python script to import packages common to all 
│                      notebooks.  The template notebooks execute this script in 
│                      the 1st cell.
│
└── requirements.txt   <- The requirements file for reproducing the analysis 
                       environment.