There are a lot of levels at which we can try to better understand Datascope's profitability and ability to grow. For as simple of a concept as "profitability" is, it is inextricably linked to factors like personal take-home pay, type of work we do (poke my eyes out tasks are less fun), and amount of time we work. To get a better sense of this, the goal of this model is to make it easier for everyone to understand how their personal goals are tied to Datascope's.

quickstart

0. brew install geckodriver or brew upgrade geckodriver. Make sure you are on geckodriver 0.16.1 or newer.

1. Create a virtualenv and install the requirements

   mkvirtualenv a-model
   pip install -r requirements/python-dev
   brew install geckodriver

2. Update some environment variables to be able to run the scripts in the bin directory using the a_model python package

   echo'export PYTHONPATH=`pwd`'>>~/.virtualenvs/a-model/bin/postactivate
   echo'__AMODEL_PATH=$PATH'>>~/.virtualenvs/a-model/bin/postactivate
   echo'export PATH=$PATH:`pwd`/bin'>>~/.virtualenvs/a-model/bin/postactivate
   echo'unset PYTHONPATH'>>~/.virtualenvs/a-model/bin/predeactivate
   echo'export PATH=$__AMODEL_PATH'>>~/.virtualenvs/a-model/bin/predeactivate

   NOTE: The first time you do this, you will also need to source ~/.virtualenvs/a-model/bin/postactivate for these changes to take effect within your new virtualenv. From here on out though, these bash environment variables will be started by default

3. Some of the scripts use selenium to download various things. Make sure you have the most recent version of Firefox installed. Upgrade instructions here.

4. Create a soft link to the a-model shared Dropbox folder, which has various credentials you'll need for downloading things.

   ln -s ~/Dropbox/Library/a-model Dropbox

5. Run the sync_quickbooks_gdrive.py to download the most up-to-date information from quickbooks. You can also sync the data by running make csvs

6. Play with the models on an individual basis (see below) or by running make to generate a bunch of figures at once.

   • bin/profitability_and_salary.py is useful for understanding the relationship between your desired salary and Datascope's profitability.

   • bin/hiring_confidence.py simulates our revenues based on historical data to gauge the risk in adding a new person to our team today.

   • bin/estimate_bonuses.py estimates our bonuses based on current cash in the bank and simulated revenues for the remainder of the year.

   • bin/simulate_cash_in_bank.py simulates our cash in the bank over the next 12 months

7. See .travis.yml for details on the test suite