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venv/Lib/site-packages/joblib-0.17.0.dist-info/INSTALLER
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venv/Lib/site-packages/joblib-0.17.0.dist-info/INSTALLER
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pip
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venv/Lib/site-packages/joblib-0.17.0.dist-info/LICENSE.txt
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venv/Lib/site-packages/joblib-0.17.0.dist-info/LICENSE.txt
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BSD 3-Clause License
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Copyright (c) 2008-2016, The joblib developers.
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of the copyright holder nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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117
venv/Lib/site-packages/joblib-0.17.0.dist-info/METADATA
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venv/Lib/site-packages/joblib-0.17.0.dist-info/METADATA
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Metadata-Version: 2.1
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Name: joblib
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Version: 0.17.0
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Summary: Lightweight pipelining: using Python functions as pipeline jobs.
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Home-page: https://joblib.readthedocs.io
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Author: Gael Varoquaux
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Author-email: gael.varoquaux@normalesup.org
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License: BSD
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Platform: any
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Environment :: Console
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Classifier: Intended Audience :: Developers
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Classifier: Intended Audience :: Science/Research
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Classifier: Intended Audience :: Education
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Classifier: License :: OSI Approved :: BSD License
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Classifier: Operating System :: OS Independent
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.6
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Classifier: Programming Language :: Python :: 3.7
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Classifier: Programming Language :: Python :: 3.8
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Classifier: Topic :: Scientific/Engineering
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Classifier: Topic :: Utilities
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Classifier: Topic :: Software Development :: Libraries
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Requires-Python: >=3.6
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Joblib is a set of tools to provide **lightweight pipelining in
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Python**. In particular:
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1. transparent disk-caching of functions and lazy re-evaluation
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(memoize pattern)
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2. easy simple parallel computing
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Joblib is optimized to be **fast** and **robust** on large
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data in particular and has specific optimizations for `numpy` arrays. It is
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**BSD-licensed**.
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==================== ===============================================
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**Documentation:** https://joblib.readthedocs.io
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**Download:** https://pypi.python.org/pypi/joblib#downloads
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**Source code:** https://github.com/joblib/joblib
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**Report issues:** https://github.com/joblib/joblib/issues
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==================== ===============================================
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Vision
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--------
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The vision is to provide tools to easily achieve better performance and
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reproducibility when working with long running jobs.
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* **Avoid computing the same thing twice**: code is often rerun again and
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again, for instance when prototyping computational-heavy jobs (as in
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scientific development), but hand-crafted solutions to alleviate this
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issue are error-prone and often lead to unreproducible results.
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* **Persist to disk transparently**: efficiently persisting
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arbitrary objects containing large data is hard. Using
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joblib's caching mechanism avoids hand-written persistence and
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implicitly links the file on disk to the execution context of
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the original Python object. As a result, joblib's persistence is
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good for resuming an application status or computational job, eg
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after a crash.
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Joblib addresses these problems while **leaving your code and your flow
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control as unmodified as possible** (no framework, no new paradigms).
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Main features
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------------------
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1) **Transparent and fast disk-caching of output value:** a memoize or
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make-like functionality for Python functions that works well for
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arbitrary Python objects, including very large numpy arrays. Separate
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persistence and flow-execution logic from domain logic or algorithmic
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code by writing the operations as a set of steps with well-defined
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inputs and outputs: Python functions. Joblib can save their
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computation to disk and rerun it only if necessary::
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>>> from joblib import Memory
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>>> cachedir = 'your_cache_dir_goes_here'
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>>> mem = Memory(cachedir)
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>>> import numpy as np
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>>> a = np.vander(np.arange(3)).astype(np.float)
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>>> square = mem.cache(np.square)
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>>> b = square(a) # doctest: +ELLIPSIS
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________________________________________________________________________________
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[Memory] Calling square...
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square(array([[0., 0., 1.],
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[1., 1., 1.],
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[4., 2., 1.]]))
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___________________________________________________________square - 0...s, 0.0min
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>>> c = square(a)
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>>> # The above call did not trigger an evaluation
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2) **Embarrassingly parallel helper:** to make it easy to write readable
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parallel code and debug it quickly::
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>>> from joblib import Parallel, delayed
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>>> from math import sqrt
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>>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10))
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[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
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3) **Fast compressed Persistence**: a replacement for pickle to work
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efficiently on Python objects containing large data (
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*joblib.dump* & *joblib.load* ).
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..
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>>> import shutil ; shutil.rmtree(cachedir)
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231
venv/Lib/site-packages/joblib-0.17.0.dist-info/RECORD
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venv/Lib/site-packages/joblib-0.17.0.dist-info/RECORD
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joblib-0.17.0.dist-info/LICENSE.txt,sha256=-OWIkGu9oHPojgnkwRnCbbbHLJsFncP-e-fgi-_0y60,1527
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joblib-0.17.0.dist-info/top_level.txt,sha256=P0LsoZ45gBL7ckL4lqQt7tdbrHD4xlVYhffmhHeeT_U,7
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joblib/pool.py,sha256=JuG9uuE3KJwR8gJZIGzwJqObJD0xFuUhQP2IYvgQab0,14334
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joblib/test/__init__.py,sha256=bkIwY5OneyPcRn2VuzQlIFdtW5Cwo1mUJ7IfSztDO9c,73
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joblib/test/__pycache__/__init__.cpython-36.pyc,,
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joblib/test/__pycache__/test_backports.cpython-36.pyc,,
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joblib/test/__pycache__/test_dask.cpython-36.pyc,,
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joblib/test/__pycache__/test_deprecated_objects.cpython-36.pyc,,
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joblib/test/__pycache__/test_func_inspect.cpython-36.pyc,,
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joblib/test/__pycache__/test_func_inspect_special_encoding.cpython-36.pyc,,
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joblib/test/__pycache__/test_hashing.cpython-36.pyc,,
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joblib/test/__pycache__/test_init.cpython-36.pyc,,
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joblib/test/__pycache__/test_logger.cpython-36.pyc,,
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joblib/test/__pycache__/test_memmapping.cpython-36.pyc,,
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joblib/test/__pycache__/test_memory.cpython-36.pyc,,
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joblib/test/__pycache__/test_module.cpython-36.pyc,,
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joblib/test/__pycache__/test_my_exceptions.cpython-36.pyc,,
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joblib/test/__pycache__/test_numpy_pickle.cpython-36.pyc,,
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joblib/test/__pycache__/test_numpy_pickle_compat.cpython-36.pyc,,
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joblib/test/__pycache__/test_numpy_pickle_utils.cpython-36.pyc,,
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joblib/test/__pycache__/test_parallel.cpython-36.pyc,,
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joblib/test/__pycache__/test_store_backends.cpython-36.pyc,,
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joblib/test/__pycache__/test_testing.cpython-36.pyc,,
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joblib/test/__pycache__/testutils.cpython-36.pyc,,
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joblib/test/common.py,sha256=gWDIvGl8Ns6vPTBvItuSFoEWigI0EPXSoPkSvqSM4zM,3283
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joblib/test/data/__pycache__/__init__.cpython-36.pyc,,
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joblib/test/data/create_numpy_pickle.py,sha256=8cmbXZ3azzwMN_OzwPObt6K10IPmxXQgDAJkFjRiNU4,3458
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joblib/test/data/joblib_0.10.0_pickle_py27_np17.pkl,sha256=icRQjj374B-AHk5znxre0T9oWUHokoHIBQ8MqKo8l-U,986
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joblib/test/data/joblib_0.10.0_pickle_py34_np19.pkl.gzip,sha256=os8NJjQI9FhnlZM-Ay9dX_Uo35gZnoJCgQSIVvcBPfE,831
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joblib/test/data/joblib_0.10.0_pickle_py34_np19.pkl.lzma,sha256=Q_0y43qU7_GqAabJ8y3PWVhOisurnCAq3GzuCu04V58,697
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joblib/test/data/joblib_0.10.0_pickle_py34_np19.pkl.xz,sha256=BNfmiQfpeLVpdfkwlJK4hJ5Cpgl0vreVyekyc5d_PNM,752
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joblib/test/data/joblib_0.10.0_pickle_py35_np19.pkl,sha256=l7nvLolhBDIdPFznOz3lBHiMOPBPCMi1bXop1tFSCpY,1068
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joblib/test/data/joblib_0.10.0_pickle_py35_np19.pkl.lzma,sha256=Bf7gCUeTuTjCkbcIdyZYz69irblX4SAVQEzxCnMQhNU,701
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joblib/test/data/joblib_0.10.0_pickle_py35_np19.pkl.xz,sha256=As8w2LGWwwNmKy3QNdKljK63Yq46gjRf_RJ0lh5_WqA,752
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||||
joblib/test/data/joblib_0.11.0_compressed_pickle_py36_np111.gz,sha256=1WrnXDqDoNEPYOZX1Q5Wr2463b8vVV6fw4Wm5S4bMt4,800
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||||
joblib/test/data/joblib_0.11.0_pickle_py36_np111.pkl,sha256=XmsOFxeC1f1aYdGETclG6yfF9rLoB11DayOAhDMULrw,1068
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||||
joblib/test/data/joblib_0.11.0_pickle_py36_np111.pkl.bz2,sha256=vI2yWb50LKL_NgZyd_XkoD5teIg93uI42mWnx9ee-AQ,991
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||||
joblib/test/data/joblib_0.11.0_pickle_py36_np111.pkl.gzip,sha256=1WrnXDqDoNEPYOZX1Q5Wr2463b8vVV6fw4Wm5S4bMt4,800
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joblib/test/data/joblib_0.11.0_pickle_py36_np111.pkl.lzma,sha256=IWA0JlZG2ur53HgTUDl1m7q79dcVq6b0VOq33gKoJU0,715
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joblib/test/data/joblib_0.11.0_pickle_py36_np111.pkl.xz,sha256=3Xh_NbMZdBjYx7ynfJ3Fyke28izSRSSzzNB0z5D4k9Y,752
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joblib/test/data/joblib_0.8.4_compressed_pickle_py27_np17.gz,sha256=Sp-ZT7i6pj5on2gbptszu7RarzJpOmHJ67UKOmCPQMg,659
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||||
joblib/test/data/joblib_0.9.2_compressed_pickle_py27_np16.gz,sha256=NLtDrvo2XIH0KvUUAvhOqMeoXEjGW0IuTk_osu5XiDw,658
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joblib/test/data/joblib_0.9.2_compressed_pickle_py27_np17.gz,sha256=NLtDrvo2XIH0KvUUAvhOqMeoXEjGW0IuTk_osu5XiDw,658
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joblib/test/data/joblib_0.9.2_compressed_pickle_py35_np19.gz,sha256=nzO9iiGkG3KbBdrF3usOho8higkrDj_lmICUzxZyF_Y,673
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joblib/test/data/joblib_0.9.2_pickle_py27_np16.pkl,sha256=naijdk2xIeKdIa3mfJw0JlmOdtiN6uRM1yOJg6-M73M,670
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joblib/test/data/joblib_0.9.2_pickle_py27_np16.pkl_01.npy,sha256=DvvX2c5-7DpuCg20HnleA5bMo9awN9rWxhtGSEPSiAk,120
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||||
joblib/test/data/joblib_0.9.2_pickle_py27_np16.pkl_02.npy,sha256=HBzzbLeB-8whuVO7CgtF3wktoOrg52WILlljzNcBBbE,120
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joblib/test/data/joblib_0.9.2_pickle_py27_np16.pkl_03.npy,sha256=oMRa4qKJhBy-uiRDt-uqOzHAqencxzKUrKVynaAJJAU,236
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||||
joblib/test/data/joblib_0.9.2_pickle_py27_np16.pkl_04.npy,sha256=PsviRClLqT4IR5sWwbmpQR41af9mDtBFncodJBOB3wU,104
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joblib/test/data/joblib_0.9.2_pickle_py27_np17.pkl,sha256=LynX8dLOygfxDfFywOgm7wgWOhSxLG7z-oDsU6X83Dw,670
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||||
joblib/test/data/joblib_0.9.2_pickle_py27_np17.pkl_01.npy,sha256=DvvX2c5-7DpuCg20HnleA5bMo9awN9rWxhtGSEPSiAk,120
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joblib/test/data/joblib_0.9.2_pickle_py27_np17.pkl_02.npy,sha256=HBzzbLeB-8whuVO7CgtF3wktoOrg52WILlljzNcBBbE,120
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joblib/test/data/joblib_0.9.2_pickle_py27_np17.pkl_04.npy,sha256=PsviRClLqT4IR5sWwbmpQR41af9mDtBFncodJBOB3wU,104
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joblib/test/data/joblib_0.9.2_pickle_py33_np18.pkl,sha256=w9TLxpDTzp5TI6cU6lRvMsAasXEChcQgGE9s30sm_CU,691
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joblib/test/data/joblib_0.9.2_pickle_py33_np18.pkl_01.npy,sha256=DvvX2c5-7DpuCg20HnleA5bMo9awN9rWxhtGSEPSiAk,120
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joblib/test/data/joblib_0.9.2_pickle_py33_np18.pkl_02.npy,sha256=HBzzbLeB-8whuVO7CgtF3wktoOrg52WILlljzNcBBbE,120
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joblib/test/data/joblib_0.9.2_pickle_py33_np18.pkl_03.npy,sha256=jt6aZKUrJdfbMJUJVsl47As5MrfRSs1avGMhbmS6vec,307
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joblib/test/data/joblib_0.9.2_pickle_py33_np18.pkl_04.npy,sha256=PsviRClLqT4IR5sWwbmpQR41af9mDtBFncodJBOB3wU,104
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joblib/test/data/joblib_0.9.2_pickle_py34_np19.pkl,sha256=ilOBAOaulLFvKrD32S1NfnpiK-LfzA9rC3O2I7xROuI,691
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joblib/test/data/joblib_0.9.2_pickle_py34_np19.pkl_02.npy,sha256=HBzzbLeB-8whuVO7CgtF3wktoOrg52WILlljzNcBBbE,120
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joblib/test/data/joblib_0.9.2_pickle_py34_np19.pkl_04.npy,sha256=PsviRClLqT4IR5sWwbmpQR41af9mDtBFncodJBOB3wU,104
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joblib/test/data/joblib_0.9.2_pickle_py35_np19.pkl,sha256=WfDVIqKcMzzh1gSAshIfzBoIpdLdZQuG79yYf5kfpOo,691
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joblib/test/data/joblib_0.9.2_pickle_py35_np19.pkl_01.npy,sha256=DvvX2c5-7DpuCg20HnleA5bMo9awN9rWxhtGSEPSiAk,120
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joblib/test/data/joblib_0.9.2_pickle_py35_np19.pkl_02.npy,sha256=HBzzbLeB-8whuVO7CgtF3wktoOrg52WILlljzNcBBbE,120
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joblib/test/data/joblib_0.9.2_pickle_py35_np19.pkl_03.npy,sha256=jt6aZKUrJdfbMJUJVsl47As5MrfRSs1avGMhbmS6vec,307
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joblib/test/data/joblib_0.9.2_pickle_py35_np19.pkl_04.npy,sha256=PsviRClLqT4IR5sWwbmpQR41af9mDtBFncodJBOB3wU,104
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joblib/test/data/joblib_0.9.4.dev0_compressed_cache_size_pickle_py35_np19.gz,sha256=8jYfWJsx0oY2J-3LlmEigK5cClnJSW2J2rfeSTZw-Ts,802
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||||
joblib/test/data/joblib_0.9.4.dev0_compressed_cache_size_pickle_py35_np19.gz_01.npy.z,sha256=YT9VvT3sEl2uWlOyvH2CkyE9Sok4od9O3kWtgeuUUqE,43
|
||||
joblib/test/data/joblib_0.9.4.dev0_compressed_cache_size_pickle_py35_np19.gz_02.npy.z,sha256=txA5RDI0PRuiU_UNKY8pGp-zQgQQ9vaVvMi60hOPaVs,43
|
||||
joblib/test/data/joblib_0.9.4.dev0_compressed_cache_size_pickle_py35_np19.gz_03.npy.z,sha256=d3AwICvU2MpSNjh2aPIsdJeGZLlDjANAF1Soa6uM0Po,37
|
||||
joblib/test/test_backports.py,sha256=Y9bhGa6H-K_FgLkDyXaSHzpaWk148Rjn8R9IKCKdy-k,1175
|
||||
joblib/test/test_dask.py,sha256=iOiStymVbzdtKe-wSxOfoOTEkSCX-BttFTmSHOSsF1s,16814
|
||||
joblib/test/test_deprecated_objects.py,sha256=7eTuMu0A-uSoHuBywBFmSS7YUctL03n8AVgyxvznHc4,1249
|
||||
joblib/test/test_disk.py,sha256=wJd1o9nLzqEjLqxxkgB9S7-UcKjHPQ8qK5l0czcNp0o,2205
|
||||
joblib/test/test_format_stack.py,sha256=wTtjRlp0edNv7_NzxZU6DAVJQoebL-lnGsUEMwVZXpM,4250
|
||||
joblib/test/test_func_inspect.py,sha256=KDvLtRziyA_r6GiNEh05_zhlx9ntJW7abSuQ6cqJajU,8935
|
||||
joblib/test/test_func_inspect_special_encoding.py,sha256=oHbMTPOK3XI0YVoS0GsouJ-GfM_neP4GOIJC-TKnNgU,146
|
||||
joblib/test/test_hashing.py,sha256=hUCcSQKtfz8hrc4vTxPEKBQR6OkPGKVmVTq79iOzml8,13655
|
||||
joblib/test/test_init.py,sha256=bgNF-9CIJl1MFNA75LBWOaiNtvduVfuvglz_u9Tt8Uc,422
|
||||
joblib/test/test_logger.py,sha256=WjxDzpRmdwj_Uyt2R-S3DFRT9CGTk7G2DWucU3sqbu8,985
|
||||
joblib/test/test_memmapping.py,sha256=ADfy92kxC5anaWjCf43swvEUGrJ_4VW5A7_q0pih36E,42154
|
||||
joblib/test/test_memory.py,sha256=169E8KmLLtUTdqBmPxOzB4DNRByinqkJXMDTxz_Zgwg,43755
|
||||
joblib/test/test_module.py,sha256=MQESEUZv2rq8bEoPpoAf0KkN7bsQSq9b3EKIxvsQ6FM,1832
|
||||
joblib/test/test_my_exceptions.py,sha256=4_1xlIwbgEt6_bqaQ8lGyTfUAV7RhLMRRbQKIyYOTA8,2066
|
||||
joblib/test/test_numpy_pickle.py,sha256=ruXWIAZeUF9t1ng-lpWhkeV955NjSS841sK71xPHTxk,36786
|
||||
joblib/test/test_numpy_pickle_compat.py,sha256=C5OiaFrqmxYD57fr_LpmItd6OOZPeOMfo9RVr6ZZIkk,624
|
||||
joblib/test/test_numpy_pickle_utils.py,sha256=PJVVgr-v3so9oAf9LblASRCpt-wXAo19FvsUpw-fZjI,421
|
||||
joblib/test/test_parallel.py,sha256=MEx53epk2AvwFyuBnVQn9oA1CCCDVNbWHfVj9ZeqGnk,60485
|
||||
joblib/test/test_store_backends.py,sha256=fZh0_E5Rj5VTJ_UzH3autHpWwEaWQvWTiQB8felVAN4,1942
|
||||
joblib/test/test_testing.py,sha256=I-EkdKHWdHu8m5fo2NnyB0AqR8zAOJ01WKKvyZYRneY,2467
|
||||
joblib/test/testutils.py,sha256=6a7zVJm1kg6M-t4CH9tz8A6rMdC6ZY9sU6wBB8C7Zzo,251
|
||||
joblib/testing.py,sha256=rCkEMsUeNQocS2Ns-Gmlg4ldanDyDFUODBmgMyuOJ8M,2180
|
||||
5
venv/Lib/site-packages/joblib-0.17.0.dist-info/WHEEL
Normal file
5
venv/Lib/site-packages/joblib-0.17.0.dist-info/WHEEL
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
Wheel-Version: 1.0
|
||||
Generator: bdist_wheel (0.35.1)
|
||||
Root-Is-Purelib: true
|
||||
Tag: py3-none-any
|
||||
|
||||
|
|
@ -0,0 +1 @@
|
|||
joblib
|
||||
Loading…
Add table
Add a link
Reference in a new issue