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thomastuthomastu
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boilerplate + scaffolding
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‎.dvc/.gitignore

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/config.local
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/updater
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/lock
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/updater.lock
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/tmp
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/state-journal
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/state-wal
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/state
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/cache

‎.dvc/config

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‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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*.egg-info/
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.installed.cfg
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*.egg
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# DotEnv configuration
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.env
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# Database
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*.db
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*.rdb
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# Pycharm
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.idea
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# VS Code
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.vscode/
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# Spyder
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.spyproject/
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# Jupyter NB Checkpoints
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.ipynb_checkpoints/
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# exclude data from source control by default
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/data/
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# Mac OS-specific storage files
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.DS_Store
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# vim
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*.swp
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*.swo
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# Mypy cache
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.mypy_cache/

‎LICENSE

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The MIT License (MIT)
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Copyright (c) 2020, Thomas Tu
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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‎Makefile

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.PHONY: clean data lint requirements sync_data_to_s3 sync_data_from_s3
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#################################################################################
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# GLOBALS #
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#################################################################################
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PROJECT_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST))))
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BUCKET = [OPTIONAL] your-bucket-for-syncing-data (do not include 's3://')
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PROFILE = default
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PROJECT_NAME = CaReCur
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PYTHON_INTERPRETER = python3
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ifeq (,$(shell which conda))
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HAS_CONDA=False
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else
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HAS_CONDA=True
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endif
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#################################################################################
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# COMMANDS #
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#################################################################################
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## Install Python Dependencies
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requirements: test_environment
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$(PYTHON_INTERPRETER) -m pip install -U pip setuptools wheel
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$(PYTHON_INTERPRETER) -m pip install -r requirements.txt
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## Make Dataset
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data: requirements
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$(PYTHON_INTERPRETER) src/data/make_dataset.py data/raw data/processed
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## Delete all compiled Python files
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clean:
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find . -type f -name "*.py[co]" -delete
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find . -type d -name "__pycache__" -delete
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## Lint using flake8
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lint:
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flake8 src
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## Upload Data to S3
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sync_data_to_s3:
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ifeq (default,$(PROFILE))
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aws s3 sync data/ s3://$(BUCKET)/data/
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else
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aws s3 sync data/ s3://$(BUCKET)/data/ --profile $(PROFILE)
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endif
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## Download Data from S3
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sync_data_from_s3:
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ifeq (default,$(PROFILE))
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aws s3 sync s3://$(BUCKET)/data/ data/
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else
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aws s3 sync s3://$(BUCKET)/data/ data/ --profile $(PROFILE)
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endif
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## Set up python interpreter environment
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create_environment:
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ifeq (True,$(HAS_CONDA))
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@echo ">>> Detected conda, creating conda environment."
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ifeq (3,$(findstring 3,$(PYTHON_INTERPRETER)))
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conda create --name $(PROJECT_NAME) python=3
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else
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conda create --name $(PROJECT_NAME) python=2.7
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endif
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@echo ">>> New conda env created. Activate with:\nsource activate $(PROJECT_NAME)"
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else
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$(PYTHON_INTERPRETER) -m pip install -q virtualenv virtualenvwrapper
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@echo ">>> Installing virtualenvwrapper if not already installed.\nMake sure the following lines are in shell startup file\n\
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export WORKON_HOME=$$HOME/.virtualenvs\nexport PROJECT_HOME=$$HOME/Devel\nsource /usr/local/bin/virtualenvwrapper.sh\n"
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@bash -c "source `which virtualenvwrapper.sh`;mkvirtualenv $(PROJECT_NAME) --python=$(PYTHON_INTERPRETER)"
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@echo ">>> New virtualenv created. Activate with:\nworkon $(PROJECT_NAME)"
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endif
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## Test python environment is setup correctly
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test_environment:
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$(PYTHON_INTERPRETER) test_environment.py
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#################################################################################
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# PROJECT RULES #
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#################################################################################
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#################################################################################
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# Self Documenting Commands #
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#################################################################################
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.DEFAULT_GOAL := help
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# Inspired by <http://marmelab.com/blog/2016/02/29/auto-documented-makefile.html>
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# sed script explained:
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# /^##/:
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# * save line in hold space
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# * purge line
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# * Loop:
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# * append newline + line to hold space
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# * go to next line
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# * if line starts with doc comment, strip comment character off and loop
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# * remove target prerequisites
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# * append hold space (+ newline) to line
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# * replace newline plus comments by `---`
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# * print line
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# Separate expressions are necessary because labels cannot be delimited by
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# semicolon; see <http://stackoverflow.com/a/11799865/1968>
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.PHONY: help
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help:
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@echo "$$(tput bold)Available rules:$$(tput sgr0)"
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@echo
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@sed -n -e "/^## / { \
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h; \
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s/.*//; \
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:doc" \
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-e "H; \
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n; \
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s/^## //; \
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t doc" \
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-e "s/:.*//; \
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G; \
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s/\\n## /---/; \
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s/\\n/ /g; \
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p; \
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}" ${MAKEFILE_LIST} \
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| LC_ALL='C' sort --ignore-case \
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| awk -F '---' \
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-v ncol=$$(tput cols) \
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-v indent=19 \
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-v col_on="$$(tput setaf 6)" \
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-v col_off="$$(tput sgr0)" \
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'{ \
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printf "%s%*s%s ", col_on, -indent, $$1, col_off; \
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n = split($$2, words, " "); \
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line_length = ncol - indent; \
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for (i = 1; i <= n; i++) { \
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line_length -= length(words[i]) + 1; \
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if (line_length <= 0) { \
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line_length = ncol - indent - length(words[i]) - 1; \
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printf "\n%*s ", -indent, " "; \
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} \
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printf "%s ", words[i]; \
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} \
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printf "\n"; \
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}' \
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| more $(shell test $(shell uname) = Darwin && echo '--no-init --raw-control-chars')

‎README.md

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carecur
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==============================
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California Renewables Curtailment project.
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Project Organization
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------------
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├── LICENSE
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├── Makefile <- Makefile with commands like `make data` or `make train`
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├── README.md <- The top-level README for developers using this project.
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├── data
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│   ├── external <- Data from third party sources.
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│   ├── interim <- Intermediate data that has been transformed.
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│   ├── processed <- The final, canonical data sets for modeling.
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│   └── raw <- The original, immutable data dump.
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│
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├── docs <- A default Sphinx project; see sphinx-doc.org for details
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│
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├── models <- Trained and serialized models, model predictions, or model summaries
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│
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
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│ the creator's initials, and a short `-` delimited description, e.g.
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│ `1.0-jqp-initial-data-exploration`.
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│
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├── references <- Data dictionaries, manuals, and all other explanatory materials.
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│
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├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
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│   └── figures <- Generated graphics and figures to be used in reporting
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│
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
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│ generated with `pip freeze > requirements.txt`
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│
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├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
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├── src <- Source code for use in this project.
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│   ├── __init__.py <- Makes src a Python module
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│ │
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│   ├── data <- Scripts to download or generate data
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│   │   └── make_dataset.py
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│ │
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│   ├── features <- Scripts to turn raw data into features for modeling
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│   │   └── build_features.py
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│ │
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│   ├── models <- Scripts to train models and then use trained models to make
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│ │ │ predictions
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│   │   ├── predict_model.py
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│   │   └── train_model.py
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│ │
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│   └── visualization <- Scripts to create exploratory and results oriented visualizations
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│   └── visualize.py
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│
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└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
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--------
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<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>

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