Merge pull request #90 from NeuroDataDesign/development

v0.0.4

Dockerfile

@@ -71,34 +71,38 @@ RUN ln -s /usr/bin/python3.6 /usr/local/bin/python
 #  cd .. && \
 #  rm -rf *
 
-# change working directory
+# make a directory for mounting local files into docker
 RUN mkdir /root/workspace/
-WORKDIR /root/workspace/
+
+# add vim in docker
+RUN apt-get install -y vim
+
+# change working directory
+RUN mkdir /root/code/
+WORKDIR /root/code/
+
+# clone the mgcpy code into the container
+ARG SOURCE_BRANCH=master
+RUN git clone -b ${SOURCE_BRANCH} https://github.com/NeuroDataDesign/mgcpy.git .
 
 # install python requirements
-COPY requirements.txt /root/workspace/requirements.txt
 RUN pip install -r requirements.txt
-RUN pip install jupyter
-RUN pip install matplotlib
-RUN pip install pycodestyle
-
-# copy the mgcpy code into the container
-COPY . /root/workspace/
+RUN pip install matplotlib seaborn pandas jupyter pycodestyle
 
 # setup pep8 guidelines (restricts push when pep8 is violated)
-RUN rm ./.git/hooks/pre-commit
+RUN rm -f ./.git/hooks/pre-commit
 RUN chmod 777 install-hooks.sh
 RUN ./install-hooks.sh
 
 # install mgcpy
 RUN python setup.py build_ext --inplace
 
 # add mgcpy to PYTHONPATH for dev purposes
-RUN echo "export PYTHONPATH='${PYTHONPATH}:/root/workspace'" >> ~/.bashrc
+RUN echo "export PYTHONPATH='${PYTHONPATH}:/root/code'" >> ~/.bashrc
 
-# test if mgcpy is correctly installed
+# clean dir and test if mgcpy is correctly installed
 RUN py3clean .
-RUN pytest
+RUN python -c "import mgcpy"
 
 # launch terminal
 CMD ["/bin/bash"]

README.md

@@ -12,6 +12,8 @@
 
 `mgcpy` is a Python package containing tools for multiscale graph correlation and other statistical tests, that is capable of dealing with high dimensional and multivariate data.
 
+**Documentation:** https://mgcpy.readthedocs.io/en/latest/
+
 ## Installation Guide:
 
 ### Install from PyPi
@@ -29,10 +31,27 @@ python3 setup.py install
 - `python3 setup.py build_ext --inplace  # for cython`, if you want to test in-place, first execute this
 
 ## Setting up the development environment:
-- Install [docker](https://docs.docker.com/install/)
-- Build the docker image, `docker build -t mgcpy-dev:latest .`
-  - This takes 10 to 15 mins to build
-- Launch the container to go into mgcpy's dev env, `docker run -it --rm --name mgcpy-dev-env mgcpy-dev`
+- To build image and run from scratch:
+  - Install [docker](https://docs.docker.com/install/)
+  - Build the docker image, `docker build -t mgcpy:latest .`
+    - This takes 20-30 mins to build
+  - Launch the container to go into mgcpy's dev env, `docker run -it --rm --name mgcpy-env mgcpy:latest`
+- Pull image from Dockerhub and run:
+  - `docker pull tpsatish95/mgcpy:latest` or `docker pull tpsatish95/mgcpy:development`
+  - `docker run -it --rm -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:latest`
+
+
+- To run demo notebooks (from within Docker):
+  - `cd demos`
+  - `jupyter notebook --ip 0.0.0.0 --no-browser --allow-root`
+  - Then copy the url it generates, it looks something like this: `http://(0de284ecf0cd or 127.0.0.1):8888/?token=e5a2541812d85e20026b1d04983dc8380055f2d16c28a6ad`
+  - Edit this: `(0de284ecf0cd or 127.0.0.1)` to: `127.0.0.1`, in the above link and open it in your browser
+  - Then open `mgc.ipynb`
+
+- To mount/load local files into docker container:
+  - Do `docker run -it --rm -v <local_dir_path>:/root/workspace/ -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:latest`, replace `<local_dir_path>` with your local dir path.
+  - Do `cd ../workspace` when you are inside the container to view the mounted files. The **mgcpy** package code will be in `/root/code` directory.
+
 
 ## MGC Algorithm's Flow
 ![MGCPY Flow](MGCPY.png)

demos/MDMR.ipynb

@@ -0,0 +1,70 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "module_path = \"C:\\\\Users\\\\sunda\\\\Desktop\\\\AAA FA18 JHU\\\\NDD1\\\\gitscr\\\\mgcpy\"\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "    \n",
+    "import numpy as np\n",
+    "from mgcpy.independence_tests.mdmr.mdmr import MDMR\n",
+    "from mgcpy.independence_tests.mdmr.mdmrfunctions import compute_distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[25.03876688]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = np.genfromtxt('../mgcpy/independence_tests/unit_tests/mdmr/data/X_mdmr.csv', delimiter=\",\")\n",
+    "Y = np.genfromtxt('../mgcpy/independence_tests/unit_tests/mdmr/data/Y_mdmr.csv', delimiter=\",\")\n",
+    "\n",
+    "mdmr = MDMR(compute_distance_matrix)\n",
+    "a, _ = mdmr.test_statistic(X, Y)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}