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Merge pull request #21 from KrishnaswamyLab/dev
scprep v0.9.0
2 parents 2f960d4 + 643d296 commit 1f62152

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-351
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18 files changed

+2403
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lines changed

.travis.yml

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language: python
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python:
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- "2.7"
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- "3.5"
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- "3.6"
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- "3.7-dev"
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sudo: required
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cache: packages
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dist: xenial
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addons:
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apt:
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packages:
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- libhdf5-dev
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- ffmpeg
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cache:
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- pip
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- apt
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install:
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- cd python; python setup.py install; cd ..
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script:
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- cd python; pip install -U .[test,doc]
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- python setup.py test
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- travis_wait 30 python setup.py test
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- cd doc; make html; cd ../..
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deploy:
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provider: pypi
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user: scottgigante
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password: ${PYPI_PASSWORD}
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distributions: sdist bdist_wheel
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skip_existing: true
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skip_cleanup: true
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on:
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tags: true
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branch: master
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after_success:
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- cd python; coveralls; cd ..

python/doc/source/conf.py

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# General information about the project.
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project = 'scprep'
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copyright = '2018 Krishnaswamy Lab, Yale University'
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author = 'Jay Stanley and Scott Gigante, Krishnaswamy Lab, Yale University'
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author = 'Scott Gigante, Jay Stanley, Daniel Burkhardt'
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# The version info for the project you're documenting, acts as replacement for
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# |version| and |release|, also used in various other places throughout the
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# built documents.
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#
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# The short X.Y version.
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version = '0.1.0'
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version = '0.7.2'
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# The full version, including alpha/beta/rc tags.
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release = '0.1.0'
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release = '0.7.2'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.
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# author, documentclass [howto, manual, or own class]).
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latex_documents = [
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(master_doc, 'scprep.tex', 'scprep Documentation',
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'Jay Stanley and Scott Gigante, Krishnaswamy Lab, Yale University', 'manual'),
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'Scott Gigante, Jay Stanley, Daniel Burkhardt', 'manual'),
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]
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python/requirements.txt

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numpy>=1.10.0
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scipy>=0.18.0
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scipy>=0.18.0,!=1.2.0.*
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scikit-learn>=0.19.1
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future
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pandas>=0.19.0

python/scprep/hdf5.py

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@@ -154,7 +154,7 @@ def get_values(dataset):
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Data read from HDF5 dataset
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"""
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if _is_h5py(dataset, allow_file=False, allow_group=False):
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return dataset.value
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return dataset[()]
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elif _is_tables(dataset, allow_file=False, allow_group=False):
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return dataset.read()
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else:

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