[docs] installation

README.md

@@ -3,12 +3,13 @@
 [![Documentation Status](https://readthedocs.org/projects/daart/badge/?version=latest)](https://daart.readthedocs.io/en/latest/?badge=latest)
 [![DOI](https://zenodo.org/badge/334987729.svg)](https://zenodo.org/badge/latestdoi/334987729)
 
-A collection of tools for the discrete classification of animal behaviors using low-dimensional representations of videos (such as skeletons provided by tracking algorithms). Our approach combines strong supervision, weak supervision, and self-supervision to improve model performance. See the preprint [here](https://www.biorxiv.org/content/10.1101/2021.06.16.448685v1) for more details. This repo currently supports fitting the 
-following types of base models on behavioral time series data:
+A collection of tools for the discrete classification of animal behaviors using low-dimensional representations of videos (such as skeletons provided by tracking algorithms). Our approach combines strong supervision, weak supervision, and self-supervision to improve model performance. See the preprint [here](https://www.biorxiv.org/content/10.1101/2021.06.16.448685v1) for more details. This repo currently supports fitting the following types of base models on behavioral time series data:
 * Dense MLP network with initial 1D convolutional layer
 * RNNs - both LSTMs and GRUs
 * Temporal Convolutional Networks (TCNs)
 
+See the [documentation](https://daart.readthedocs.io/) to get started!
+
 If you use daart in your analysis of behavioral data, please cite our preprint!
 
     @inproceedings{whiteway2021semi,
@@ -21,39 +22,7 @@ If you use daart in your analysis of behavioral data, please cite our preprint!
 
 ## Installation
 
-First you'll have to install the `git` package in order to access the code on github. Follow the 
-directions [here](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) 
-for your specific OS.
-Then, in the command line, navigate to where you'd like to install the `daart` package and move 
-into that directory:
-```
-$: git clone https://github.com/themattinthehatt/daart
-$: cd daart
-```
-
-Next, follow the directions 
-[here](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) 
-to install the `conda` package for managing development environments.
-Then, create a conda environment:
-
-```
-$: conda create --name=daart python=3.6
-$: conda activate daart
-(daart) $: pip install -r requirements.txt 
-```
-
-To make the package modules visible to the python interpreter, locally run pip 
-install from inside the main `daart` directory:
-
-```
-(daart) $: pip install -e .
-```
 
-To be able to use this environment for jupyter notebooks:
-
-```
-(daart) $: python -m ipykernel install --user --name daart
-```
 
 ## Getting started
 

docs/README.md

@@ -72,6 +72,7 @@ extensions = [
     'sphinx.ext.napoleon',  # parsing of different docstring styles
     'sphinx_automodapi.automodapi',
     'sphinx_copybutton',  # add copy button to code blocks
+    'sphinx_rtd_dark_mode',
 ]
 ```
 

docs/conf.py

@@ -28,6 +28,7 @@
     'sphinx.ext.napoleon',  # parsing of different docstring styles
     'sphinx_automodapi.automodapi',
     'sphinx_copybutton',  # add copy button to code blocks
+    'sphinx_rtd_dark_mode',
 ]
 
 templates_path = ['_templates']
@@ -40,6 +41,9 @@
 html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 html_static_path = ['_static']
 
+# user starts in dark mode
+default_dark_mode = True
+
 
 # document constructors
 def skip(app, what, name, obj, skip, options):

docs/index.rst

@@ -6,6 +6,19 @@
 Welcome to daart's documentation!
 =================================
 
+The ``daart`` package is a collection of tools for the discrete classification of animal behaviors
+using low-dimensional representations of videos (such as skeletons provided by tracking algorithms).
+``daart`` combines strong supervision, weak supervision, and self-supervision to improve model
+performance.
+See the `preprint <https://www.biorxiv.org/content/10.1101/2021.06.16.448685v1>`_ for more details.
+
+This repo currently supports fitting the following types of base models on behavioral time series
+data:
+
+* dense MLP network with initial 1D convolutional layer
+* RNNs - both LSTMs and GRUs
+* temporal convolutional networks (TCNs)
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents:

docs/source/installation.rst

@@ -1,4 +1,77 @@
 Installation
 ============
 
-TODO
+First you'll have to install the ``git`` package in order to access the code on github.
+Follow the
+`git installion instructions <https://git-scm.com/book/en/v2/Getting-Started-Installing-Git>`_
+for your specific OS.
+
+**Install ffmpeg**
+
+First, check to see you have ``ffmpeg`` installed by typing the following into the terminal:
+
+.. code-block:: console
+
+    ffmpeg -version
+
+If not, install:
+
+.. code-block:: console
+
+    sudo apt install ffmpeg
+
+**Set up a conda environment**
+
+Next, follow the
+`conda installation instructions <https://docs.conda.io/projects/conda/en/latest/user-guide/install/>`_
+to install the ``conda`` package for managing development environments.
+Then, create a conda environment:
+
+.. code-block:: console
+
+    conda create --name daart python=3.6
+
+Active the new environment:
+
+.. code-block:: console
+
+    conda activate daart
+
+**Install daart**
+
+.. note::
+
+    Make sure your conda environment is activated during the following steps.
+
+1. Move into the directory where you want to place the repository folder, and download it from
+   github.
+
+   .. code-block:: console
+
+       git clone https://github.com/themattinthehatt/daart
+
+2. Move into the newly created folder:
+
+   .. code-block:: console
+
+       cd daart
+
+   and install the package and all dependencies:
+
+   .. code-block::
+
+       pip install -e .
+
+3. Verify that all the unit tests are passing on your machine by running
+
+   .. code-block:: console
+
+       pytest
+
+   The tests will take a minute or two to run.
+
+4. To be able to use this environment for jupyter notebooks:
+
+   .. code-block:: console
+
+       python -m ipykernel install --user --name daart

setup.py

@@ -1,12 +1,32 @@
 from distutils.core import setup
 
 
-VERSION = '1.0.1'
+VERSION = '1.0.2'
 
 # add the README.md file to the long_description
 with open('README.md', 'r') as fh:
     long_description = fh.read()
 
+install_requires = [
+    'h5py',
+    'ipykernel',
+    'jupyter',
+    'matplotlib',
+    'numpy',
+    'opencv-python',
+    'pandas',
+    'pytest',
+    'pyyaml',
+    'scikit-learn',
+    'scipy>=1.2.0',
+    'seaborn',
+    'tables',
+    'test-tube',
+    'torch==1.8.0',
+    'tqdm',
+    'typeguard',
+]
+
 extras_require = {
     'dev': {
         'sphinx',
@@ -16,6 +36,7 @@
         'sphinx-copybutton',
     }
 }
+
 setup(
     name='daart',
     packages=['daart'],
@@ -26,6 +47,7 @@
     author='matt whiteway',
     author_email='',
     url='http://www.github.com/themattinthehatt/daart',
+    install_requires=install_requires,
     extras_require=extras_require,
-    keywords=['machine learning', 'action segmentation', 'computer_vision'],
+    keywords=['machine learning', 'action segmentation', 'computer vision'],
 )