Apply bootstrapit.sh

This commit is contained in:
Manuel Barkhau 2018-11-04 21:34:53 +01:00
parent 6416df7094
commit d951483a83
26 changed files with 1260 additions and 80 deletions

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# These dependencies are installed using:
#
# conda install --channel conda-forge --name <env>
#
# Conda should be used for
#
# 1. Binary python packages (numpy, pandas, pillow).
# The pypi may not always have binary packages for all platforms
# and architectures you want to support. For example, pyblake2 only
# has binary wheels for windows on pypi, whereas there are binary
# packages on conda-forge (as of Sep 2018).
# Binary wheels are becomming more common on the pypi this is
# becomming, so this is less and less of an issue. Most of the time
# it should be fine to add the dependency to pypi.txt instead.
#
# 2. Non python packages (nodejs, typescript).
# Using conda for these kinds of dependencies minimizes
# installation overhead for developers.
# https://pypi.org/project/ujson/
# UltraJSON is an ultra fast JSON encoder and decoder written
# in pure C with bindings for Python 2.5+ and 3.
ujson
# The hot new pkdf on the block is argon2, winner of
# the https://password-hashing.net/ competition.
argon2_cffi
# https://blake2.net/
# BLAKE2 is a cryptographic hash function faster than MD5, SHA-1,
# SHA-2, and SHA-3, yet is at least as secure as the latest standard
# SHA-3. BLAKE2 has been adopted by many projects due to its high
# speed, security, and simplicity.
pyblake2

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# These dependencies are installed using:
#
# pip install --upgrade
#
# This list should only contain packages related to
# local development and debugging. It should not contain
# any packages required for production, building or packaging
# PuDB is a full-screen, console-based visual debugger for Python.
# https://documen.tician.de/pudb/
pudb
# Py-Spy: A sampling profiler for Python programs.
# https://github.com/benfred/py-spy
# This is good for coarse grained profiling (even on production)
py-spy
# SNAKEVIZ : A browser based viewer for the output of Pythons cProfile.
# https://jiffyclub.github.io/snakeviz/
# This is good for fine grained profiling (function level/micro optimizations)
snakeviz
# I've yet to find a decent memory profiler for python, feel free to
# add one after you've tested it and found it to be actually useful.
ipython # nuff said

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# These dependencies are installed using:
#
# pip install --upgrade
#
# This file should only declare dependencies related to code
# formatting, linting, testing and packaging.
#
# No dependencies required for production should be listed here.
flake8
flake8-bugbear
flake8-docstrings
flake8-builtins
flake8-comprehensions
pylint
mypy
pytest
pytest-cov
pylint
twine
straitjacket
pycalver

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# These dependencies are installed using:
#
# pip install --upgrade
#
# This list is the default package list. All pure python packages
# for the production environment at runtime should be listed here.
# Binary (non-pure) packages may also be listed here, but you
# should see if there is a conda package that suits your needs.
pathlib2
typing
click

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# These dependencies are installed using:
#
# pip install --upgrade
# pip install --upgrade --no-deps --target vendor/
#
# Vendored dependencies are installed both in the virtual
# environment as well as in the vendor/ directory. This way:
#
# 1. All transitive dependencies of a package are installed in
# the virtualenv (in the first installation step)
# 2. If there is a binary version of the package available, it
# will be installed into the virtualenv
# 3. In the third step only (--no-deps) the source version of
# the (--no-binary) package is installed to vendor/
#
# This allows us to:
#
# 1. Easily navigate to the source of a vendored dependency
# 2. Use binary versions packages instead of source versions of
# packages, simply by not including the vendor/ directory in
# the PYTHONPATH. The version from the virtualenv will then
# be loaded instead.