require.txt

#  require.txt :: mock requirements.txt for fecon236          Date : 2018-06-13
#
#    "$ pip install -r requirements.txt" is the orthodox way, BUT
#    "$ pip install -r require.txt"      ONLY after reading below.
#
#  When we do continuous integration testing, we prefer the environment
#  created by conda, rather than by pip. Travis CI automatically installs
#  from requirements.txt when the language is Python -- which is something
#  we want to intentionally avoid for that reason. This is the developers'
#  preference, but the user may have different practices. So...
#  
#       =>  require.txt can be COPIED as requirements.txt,
#                       or be used directly, upon understanding this advisory,
#
#       for the bare essentials, which EXCLUDES software optimizations
#       such as MLK (Math Kernel Library) and LLVM, as well as
#       Jupyter notebook and IPython console.
#  
#  Please DISREGARD this file, if you already using the Anaconda distribution
#  for the full stack -- those optimizations are included effortlessly.
#
#  =>  TIP: get the conda-aware version of pip to INSTALL fecon236:
#  
#       conda install pip virtualenv
$
#       pip install --pre fecon236
#       pip install --upgrade fecon236
#       pip uninstall fecon236
#  
#
#  _______________ Virtual conda environment / Installing dependencies
#  
#  The --file flag trick can be used to create a specific environment:
#  
#       conda create --name feenv --file PATHTO/require.txt
#  
#  That trick will also work to build on an existing environment fooenv:
#  
#       conda install --name fooenv --file PATHTO/require.txt
#  
#  Note that this trick ONLY works because each package in this require.txt
#  is installable via conda.
#  
#  Then after activating your virtual environment, install fecon236
#  per above by pip. For more details, see
#       https://conda.io/docs/user-guide/tasks/manage-environments.html
#
#
# =================================== At the bare MINIMUM, require... =========

pandas==0.22.0
numpy==1.14.3
scipy==1.1.0
statsmodels==0.8.0
matplotlib==2.2.2
sympy==1.1.1
pandas-datareader==0.6.0

#  pandas is the CORNERSTONE which should dictate the best numpy version.
#  All else should be compatible with those two package.
#
#  Above tested as of fecon236:                         10.6.3a35.post180530
#        - Python 2.7 environment
#        - Python 3.4 environment
#  Versions are inter-compatible (dependency resolution) tested per
#  conda (Boolean Satisfiability SAT solver) and Travis integration.
#
#  Written in LOCKFILE format, i.e. using "==" instead of ">="
#  since the resulting build will be DETERMINISTIC.


#  _______________ RECOMMENDED GOODNESS to supplement fecon236:
#                  IPython console  (far better than regular >>> Python IDE)
#                  Jupyter notebook (for research and development)
#  ----- but also...
#  lxml
#  html5lib
#  bs4
#  ----- pandas needs the previous three to best parse, see sec module.


# =========================================================== ENDNOTES ========
#
#  _______________ Tedious setup.py considerations
#  
#  Whereas "install_requires" requirements in setup.py are Abstract,
#  i.e. not associated with any particular index,
#  requirements files often contain pip options like --index-url
#  or --find-links to make requirements Concrete, i.e.
#  associated with a particular index or directory of packages.
#  Whereas "install_requires" metadata in setup.py is automatically
#  analyzed by pip during an install, requirements files are NOT,
#  and only are used when a user specifically installs them
#  using "pip install -r requirements.txt".
#
#  RTFM  https://pip.readthedocs.io/en/1.1/requirements.html
#  Donald Stufft, 2013, "setup.py vs requirements.txt"
#      https://caremad.io/posts/2013/07/setup-vs-requirement