Updated pycsep requirements for pip installation & ga using pip (#250)

* ga: added build test from pypi

* build: Added Dockerfile for a container installation

* ga: reverted branch ga test to upstream ga only

* docs: updated and sync'd readme and docs with the installation guide using correct dependencies

* updated manifest

.github/workflows/build-test.yml

@@ -45,3 +45,32 @@ jobs:
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'
         run: |
           bash <(curl -s https://codecov.io/bash) || echo 'Codecov failed to upload'
+
+
+  build_pip:
+    if: github.repository == 'SCECcode/pycsep'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ['3.9']
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+
+      - name: Install pyCSEP
+        run: |
+          pip install -e .
+          python -c "import csep; print('Version: ', csep.__version__)"
+
+      - name: Test with pytest
+        run: |
+          pip install vcrpy==4.3.1 pytest pytest-cov
+          pytest --cov=./ --cov-config=.coveragerc

Dockerfile

@@ -0,0 +1,38 @@
+## Build the docker image using:
+# docker build --build-arg USERNAME=$USER --build-arg USER_UID --no-cache -t pycsep .
+
+## Docker instructions
+
+# Install Docker image from trusted source
+FROM python:3.9.18-slim-bullseye
+
+# Setup user id and permissions.
+ARG USERNAME=modeler
+ARG USER_UID=1100
+ARG USER_GID=$USER_UID
+RUN groupadd --non-unique -g $USER_GID $USERNAME \
+    && useradd -u $USER_UID -g $USER_GID -s /bin/sh -m $USERNAME
+
+# Install git
+RUN apt update
+RUN apt install -y git
+
+# Set up work directory in the Docker container.
+WORKDIR /usr/src/
+
+# Set up and create python virtual environment
+ENV VIRTUAL_ENV=/opt/venv
+RUN python3 -m venv $VIRTUAL_ENV
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+# Install pycsep.
+RUN git clone https://github.com/SCECcode/pycsep.git
+RUN pip install --no-cache-dir --upgrade pip
+RUN cd pycsep && pip install .
+
+# Docker can now be initialized as user
+USER $USERNAME
+
+## Run the docker image in interactive mode from the command line
+# docker run -it --rm --name pycsep pycsep:latest
+

MANIFEST.in

@@ -1,6 +1,7 @@
 include README.md
 include CONTRIBUTING.md
 include CODE_OF_CONDUCT.md
+include Dockerfile
 include LICENSE.txt
 include CHANGELOG.md
 include CREDITS.md

README.md

@@ -36,13 +36,11 @@ pyCSEP can be installed in several ways. It can be installed using conda or pip
 source code found in the pyCSEP github repo. Researchers interested in contributing to pyCSEP development should 
 install pyCSEP from source code. pyCSEP depends on the following software packages. 
 These which may be installed automatically, or manually, depending on the installation method used.
-* Python 3.7 or later (https://python.org)
+* Python 3.9 or later (https://python.org)
 * NumPy 1.21.3 or later (https://numpy.org)
 * SciPy 1.7.1 or later (https://scipy.org)
 * pandas 1.3.4 or later (https://pandas.pydata.org)
-* cartopy 0.20.0 or later (https://scitools.org.uk/cartopy/docs/latest)
-* GEOS 3.7.2 or later (https://trac.osgeo.org/geos/)
-* PROJ 8.0.0 or later (https://proj.org/)
+* cartopy 0.22.0 or later (https://scitools.org.uk/cartopy/docs/latest)
 
 Please see the [requirements file](https://github.com/SCECcode/pycsep/blob/master/requirements.yml) for a complete list 
 of requirements. These are installed automatically when using the `conda` distribution.

docs/getting_started/installing.rst

@@ -5,7 +5,7 @@ We are working on a ``conda-forge`` recipe and PyPI distribution.
 If you plan on contributing to this package, visit the
 `contribution guidelines <https://github.com/SCECcode/pycsep/blob/master/CONTRIBUTING.md>`_ for installation instructions.
 
-.. note:: This package requires >=Python 3.7.
+.. note:: This package requires >=Python 3.9.
 
 The easiest way to install PyCSEP is using ``conda``. It can also be installed using ``pip`` or built from source.
 
@@ -22,29 +22,25 @@ Before this installation will work, you must **first** install the following sys
 should be installed by the installation script. To help manage dependency issues, we recommend using virtual environments
 like `virtualenv`.
 
-| Python 3.7 or later (https://python.org)
+| Python 3.9 or later (https://python.org)
 |
-| NumPy 1.10 or later (https://numpy.org)
+| NumPy 1.21.3 or later (https://numpy.org)
 |     Python package for scientific computing and numerical calculations.
 |
-| GEOS 3.3.3 or later (https://trac.osgeo.org/geos/)
-|     C++ library for processing geometry.
+| SciPy 1.7.1 or later (https://scipy.org)
+|     Python package that extends NumPy tools.
 |
-| PROJ 4.9.0 or later (https://proj4.org/)
-|     Library for cartographic projections.
-
-Example for Ubuntu: ::
-
-    sudo apt-get install libproj-dev proj-data proj-bin
-    sudo apt-get install libgeos-dev
-    pip install --upgrade pip
-    pip install numpy
+| Pandas 1.3.4 or later (https://pandas.pydata.org)
+|     Python package for data analysis and manipulation.
+|
+| Cartopy 0.22.0 or later (https://scitools.org.uk/cartopy/)
+|     Python package for geospatial data processing.
 
-Example for MacOS: ::
+Example for Ubuntu and MacOS: ::
 
-    brew install proj geos
+    git clone https://github.com/sceccode/pycsep
     pip install --upgrade pip
-    pip install numpy
+    pip install -e .
 
 Installing from Source
 ----------------------
@@ -54,13 +50,13 @@ the latest GitHub commit.
 
 We recommend using virtual environments when installing python packages from source to avoid any dependency conflicts. We prefer
 ``conda`` as the package manager over ``pip``, because ``conda`` does a good job of handling binary distributions of packages
-across multiple platforms. Also, we recommend using the ``miniconda`` installer, because it is lightweight and only includes
+across multiple platforms. Also, we recommend using the ``miniconda`` or the ``miniforge`` (which uses mamba for a faster dependency handling) installers, because it is lightweight and only includes
 necessary pacakages like ``pip`` and ``zlib``.
 
 Using Conda
 ***********
 
-If you don't have ``conda`` on your machine, download and install `Miniconda <https://docs.conda.io/en/latest/miniconda.html>`_. ::
+If you don't have ``conda`` on your machine, download and install `Miniconda <https://docs.conda.io/en/latest/miniconda.html>`_ or `Miniforge <https://github.com/conda-forge/miniforge>`_ ::
 
     git clone https://github.com/SCECcode/pycsep
     cd pycsep