diff --git a/env-llmeval/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/INSTALLER b/env-llmeval/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/LICENSE b/env-llmeval/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..268c00e0364506fa384d9b019001de22beee5332 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/LICENSE @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2016 Tsuyoshi Hombashi + +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: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +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. diff --git a/env-llmeval/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..4f7b94510223515d0c0fb8f7c7feaae4cff7509f --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/METADATA @@ -0,0 +1,276 @@ +Metadata-Version: 2.1 +Name: DataProperty +Version: 1.0.1 +Summary: Python library for extract property from data. +Home-page: https://github.com/thombashi/DataProperty +Author: Tsuyoshi Hombashi +Author-email: tsuyoshi.hombashi@gmail.com +Maintainer: Tsuyoshi Hombashi +Maintainer-email: tsuyoshi.hombashi@gmail.com +License: MIT License +Project-URL: Source, https://github.com/thombashi/DataProperty +Project-URL: Tracker, https://github.com/thombashi/DataProperty/issues +Keywords: data,library,property +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Information Technology +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Software Development :: Libraries +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Requires-Python: >=3.7 +Description-Content-Type: text/x-rst +License-File: LICENSE +Requires-Dist: mbstrdecoder (<2,>=1.0.0) +Requires-Dist: typepy[datetime] (<2,>=1.2.0) +Provides-Extra: logging +Requires-Dist: loguru (<1,>=0.4.1) ; extra == 'logging' +Provides-Extra: test +Requires-Dist: pytest (>=6.0.1) ; extra == 'test' +Requires-Dist: pytest-md-report (>=0.3) ; extra == 'test' +Requires-Dist: tcolorpy (>=0.1.2) ; extra == 'test' + +.. contents:: **DataProperty** + :backlinks: top + :local: + + +Summary +======= +A Python library for extract property from data. + + +.. image:: https://badge.fury.io/py/DataProperty.svg + :target: https://badge.fury.io/py/DataProperty + :alt: PyPI package version + +.. image:: https://anaconda.org/conda-forge/DataProperty/badges/version.svg + :target: https://anaconda.org/conda-forge/DataProperty + :alt: conda-forge package version + +.. image:: https://img.shields.io/pypi/pyversions/DataProperty.svg + :target: https://pypi.org/project/DataProperty + :alt: Supported Python versions + +.. image:: https://img.shields.io/pypi/implementation/DataProperty.svg + :target: https://pypi.org/project/DataProperty + :alt: Supported Python implementations + +.. image:: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml/badge.svg + :target: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml + :alt: CI status of Linux/macOS/Windows + +.. image:: https://coveralls.io/repos/github/thombashi/DataProperty/badge.svg?branch=master + :target: https://coveralls.io/github/thombashi/DataProperty?branch=master + :alt: Test coverage + +.. image:: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql/badge.svg + :target: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql + :alt: CodeQL + + +Installation +============ + +Installation: pip +------------------------------ +:: + + pip install DataProperty + +Installation: conda +------------------------------ +:: + + conda install -c conda-forge dataproperty + +Installation: apt +------------------------------ +:: + + sudo add-apt-repository ppa:thombashi/ppa + sudo apt update + sudo apt install python3-dataproperty + + +Usage +===== + +Extract property of data +------------------------ + +e.g. Extract a ``float`` value property +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code:: python + + >>> from dataproperty import DataProperty + >>> DataProperty(-1.1) + data=-1.1, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=1, extra_len=1 + +e.g. Extract a ``int`` value property +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code:: python + + >>> from dataproperty import DataProperty + >>> DataProperty(123456789) + data=123456789, type=INTEGER, align=right, ascii_width=9, int_digits=9, decimal_places=0, extra_len=0 + +e.g. Extract a ``str`` (ascii) value property +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code:: python + + >>> from dataproperty import DataProperty + >>> DataProperty("sample string") + data=sample string, type=STRING, align=left, length=13, ascii_width=13, extra_len=0 + +e.g. Extract a ``str`` (multi-byte) value property +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code:: python + + >>> from dataproperty import DataProperty + >>> str(DataProperty("吾輩は猫である")) + data=吾輩は猫である, type=STRING, align=left, length=7, ascii_width=14, extra_len=0 + +e.g. Extract a time (``datetime``) value property +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code:: python + + >>> import datetime + >>> from dataproperty import DataProperty + >>> DataProperty(datetime.datetime(2017, 1, 1, 0, 0, 0)) + data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0 + +e.g. Extract a ``bool`` value property +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code:: python + + >>> from dataproperty import DataProperty + >>> DataProperty(True) + data=True, type=BOOL, align=left, ascii_width=4, extra_len=0 + + +Extract data property for each element from a matrix +---------------------------------------------------- +``DataPropertyExtractor.to_dp_matrix`` method returns a matrix of ``DataProperty`` instances from a data matrix. +An example data set and the result are as follows: + +:Sample Code: + .. code:: python + + import datetime + from dataproperty import DataPropertyExtractor + + dp_extractor = DataPropertyExtractor() + dt = datetime.datetime(2017, 1, 1, 0, 0, 0) + inf = float("inf") + nan = float("nan") + + dp_matrix = dp_extractor.to_dp_matrix([ + [1, 1.1, "aa", 1, 1, True, inf, nan, dt], + [2, 2.2, "bbb", 2.2, 2.2, False, "inf", "nan", dt], + [3, 3.33, "cccc", -3, "ccc", "true", inf, "NAN", "2017-01-01T01:23:45+0900"], + ]) + + for row, dp_list in enumerate(dp_matrix): + for col, dp in enumerate(dp_list): + print("row={:d}, col={:d}, {}".format(row, col, str(dp))) + +:Output: + :: + + row=0, col=0, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0 + row=0, col=1, data=1.1, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0 + row=0, col=2, data=aa, type=STRING, align=left, ascii_width=2, length=2, extra_len=0 + row=0, col=3, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0 + row=0, col=4, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0 + row=0, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0 + row=0, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0 + row=0, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0 + row=0, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0 + row=1, col=0, data=2, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0 + row=1, col=1, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0 + row=1, col=2, data=bbb, type=STRING, align=left, ascii_width=3, length=3, extra_len=0 + row=1, col=3, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0 + row=1, col=4, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0 + row=1, col=5, data=False, type=BOOL, align=left, ascii_width=5, extra_len=0 + row=1, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0 + row=1, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0 + row=1, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0 + row=2, col=0, data=3, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0 + row=2, col=1, data=3.33, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=2, extra_len=0 + row=2, col=2, data=cccc, type=STRING, align=left, ascii_width=4, length=4, extra_len=0 + row=2, col=3, data=-3, type=INTEGER, align=right, ascii_width=2, int_digits=1, decimal_places=0, extra_len=1 + row=2, col=4, data=ccc, type=STRING, align=left, ascii_width=3, length=3, extra_len=0 + row=2, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0 + row=2, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0 + row=2, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0 + row=2, col=8, data=2017-01-01T01:23:45+0900, type=STRING, align=left, ascii_width=24, length=24, extra_len=0 + + +Full example source code can be found at *examples/py/to_dp_matrix.py* + + +Extract properties for each column from a matrix +------------------------------------------------------ +``DataPropertyExtractor.to_column_dp_list`` method returns a list of ``DataProperty`` instances from a data matrix. The list represents the properties for each column. +An example data set and the result are as follows: + +Example data set and result are as follows: + +:Sample Code: + .. code:: python + + import datetime + from dataproperty import DataPropertyExtractor + + dp_extractor = DataPropertyExtractor() + dt = datetime.datetime(2017, 1, 1, 0, 0, 0) + inf = float("inf") + nan = float("nan") + + data_matrix = [ + [1, 1.1, "aa", 1, 1, True, inf, nan, dt], + [2, 2.2, "bbb", 2.2, 2.2, False, "inf", "nan", dt], + [3, 3.33, "cccc", -3, "ccc", "true", inf, "NAN", "2017-01-01T01:23:45+0900"], + ] + + dp_extractor.headers = ["int", "float", "str", "num", "mix", "bool", "inf", "nan", "time"] + col_dp_list = dp_extractor.to_column_dp_list(dp_extractor.to_dp_matrix(dp_matrix)) + + for col_idx, col_dp in enumerate(col_dp_list): + print(str(col_dp)) + +:Output: + :: + + column=0, type=INTEGER, align=right, ascii_width=3, bit_len=2, int_digits=1, decimal_places=0 + column=1, type=REAL_NUMBER, align=right, 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0000000000000000000000000000000000000000..100b4bffb00abd785f61fca42fea2ab74a70d7f7 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/LICENSE.txt @@ -0,0 +1,37 @@ +NetworkX is distributed with the 3-clause BSD license. + +:: + + Copyright (C) 2004-2024, NetworkX Developers + Aric Hagberg + Dan Schult + Pieter Swart + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + + * Neither the name of the NetworkX Developers nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..fa5e70540b98a3d0ef30c33afa40a045ca60afbc --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/METADATA @@ -0,0 +1,133 @@ +Metadata-Version: 2.1 +Name: networkx +Version: 3.3 +Summary: Python package for creating and manipulating graphs and networks +Author-email: Aric Hagberg +Maintainer-email: NetworkX Developers +Project-URL: Homepage, https://networkx.org/ +Project-URL: Bug Tracker, https://github.com/networkx/networkx/issues +Project-URL: Documentation, https://networkx.org/documentation/stable/ +Project-URL: Source Code, https://github.com/networkx/networkx +Keywords: Networks,Graph Theory,Mathematics,network,graph,discrete mathematics,math +Platform: Linux +Platform: Mac OSX +Platform: Windows +Platform: Unix +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: BSD License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Topic :: Scientific/Engineering :: Bio-Informatics +Classifier: Topic :: Scientific/Engineering :: Information Analysis +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Physics +Requires-Python: >=3.10 +Description-Content-Type: text/x-rst +License-File: LICENSE.txt +Provides-Extra: default +Requires-Dist: numpy >=1.23 ; extra == 'default' +Requires-Dist: scipy !=1.11.0,!=1.11.1,>=1.9 ; extra == 'default' +Requires-Dist: matplotlib >=3.6 ; extra == 'default' +Requires-Dist: pandas >=1.4 ; extra == 'default' +Provides-Extra: developer +Requires-Dist: changelist ==0.5 ; extra == 'developer' +Requires-Dist: pre-commit >=3.2 ; extra == 'developer' +Requires-Dist: mypy >=1.1 ; extra == 'developer' +Requires-Dist: rtoml ; extra == 'developer' +Provides-Extra: doc +Requires-Dist: sphinx >=7 ; extra == 'doc' +Requires-Dist: pydata-sphinx-theme >=0.14 ; extra == 'doc' +Requires-Dist: sphinx-gallery >=0.14 ; extra == 'doc' +Requires-Dist: numpydoc >=1.7 ; extra == 'doc' +Requires-Dist: pillow >=9.4 ; extra == 'doc' +Requires-Dist: texext >=0.6.7 ; extra == 'doc' +Requires-Dist: myst-nb >=1.0 ; extra == 'doc' +Provides-Extra: extra +Requires-Dist: lxml >=4.6 ; extra == 'extra' +Requires-Dist: pygraphviz >=1.12 ; extra == 'extra' +Requires-Dist: pydot >=2.0 ; extra == 'extra' +Requires-Dist: sympy >=1.10 ; extra == 'extra' +Provides-Extra: test +Requires-Dist: pytest >=7.2 ; extra == 'test' +Requires-Dist: pytest-cov >=4.0 ; extra == 'test' + +NetworkX +======== + + +.. image:: https://github.com/networkx/networkx/workflows/test/badge.svg?branch=main + :target: https://github.com/networkx/networkx/actions?query=workflow%3A%22test%22 + +.. image:: https://codecov.io/gh/networkx/networkx/branch/main/graph/badge.svg + :target: https://app.codecov.io/gh/networkx/networkx/branch/main + +.. image:: https://img.shields.io/github/labels/networkx/networkx/Good%20First%20Issue?color=green&label=Contribute%20&style=flat-square + :target: https://github.com/networkx/networkx/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+First+Issue%22 + + +NetworkX is a Python package for the creation, manipulation, +and study of the structure, dynamics, and functions +of complex networks. + +- **Website (including documentation):** https://networkx.org +- **Mailing list:** https://groups.google.com/forum/#!forum/networkx-discuss +- **Source:** https://github.com/networkx/networkx +- **Bug reports:** https://github.com/networkx/networkx/issues +- **Report a security vulnerability:** https://tidelift.com/security +- **Tutorial:** https://networkx.org/documentation/latest/tutorial.html +- **GitHub Discussions:** https://github.com/networkx/networkx/discussions + +Simple example +-------------- + +Find the shortest path between two nodes in an undirected graph: + +.. code:: pycon + + >>> import networkx as nx + >>> G = nx.Graph() + >>> G.add_edge("A", "B", weight=4) + >>> G.add_edge("B", "D", weight=2) + >>> G.add_edge("A", "C", weight=3) + >>> G.add_edge("C", "D", weight=4) + >>> nx.shortest_path(G, "A", "D", weight="weight") + ['A', 'B', 'D'] + +Install +------- + +Install the latest version of NetworkX:: + + $ pip install networkx + +Install with all optional dependencies:: + + $ pip install networkx[all] + +For additional details, please see `INSTALL.rst`. + +Bugs +---- + +Please report any bugs that you find `here `_. +Or, even better, fork the repository on `GitHub `_ +and create a pull request (PR). We welcome all changes, big or small, and we +will help you make the PR if you are new to `git` (just ask on the issue and/or +see `CONTRIBUTING.rst`). + +License +------- + +Released under the 3-Clause BSD license (see `LICENSE.txt`):: + + Copyright (C) 2004-2024 NetworkX Developers + Aric Hagberg + Dan Schult + Pieter Swart diff --git a/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..3587db22ea69bf0ea0bb543aa14af6ddf738768f --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/RECORD @@ -0,0 +1,1149 @@ +networkx-3.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +networkx-3.3.dist-info/LICENSE.txt,sha256=W0M7kPdV65u9Bv7_HRpPXyMsUgihhWlBmeRfqV12J5I,1763 +networkx-3.3.dist-info/METADATA,sha256=YQezeWnohXGh2TPdJ8pc1uuJaJ0gu8Q6rifuJxSHL1A,5131 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b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: bdist_wheel (0.43.0) +Root-Is-Purelib: true +Tag: py3-none-any + diff --git a/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/entry_points.txt b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/entry_points.txt new file mode 100644 index 0000000000000000000000000000000000000000..2170e9f4285422f4f95b05fa682a9a479c19bf24 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/entry_points.txt @@ -0,0 +1,2 @@ +[networkx.backends] +nx-loopback = networkx.classes.tests.dispatch_interface:dispatcher diff --git a/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/top_level.txt b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d07dfe2f85d6849d7f416dcce756b2501ba847e --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/networkx-3.3.dist-info/top_level.txt @@ -0,0 +1 @@ +networkx diff --git a/env-llmeval/lib/python3.10/site-packages/nvidia_cudnn_cu12-8.9.2.26.dist-info/INSTALLER b/env-llmeval/lib/python3.10/site-packages/nvidia_cudnn_cu12-8.9.2.26.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/nvidia_cudnn_cu12-8.9.2.26.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/nvidia_cudnn_cu12-8.9.2.26.dist-info/License.txt b/env-llmeval/lib/python3.10/site-packages/nvidia_cudnn_cu12-8.9.2.26.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..f0d485c1c82d2c86b62ac0deeb8568fcdb58e0bb --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/nvidia_cudnn_cu12-8.9.2.26.dist-info/License.txt @@ -0,0 +1,154 @@ +LICENSE AGREEMENT FOR NVIDIA 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The following portions of the SDK are distributable under the Agreement: the runtime files .so and .h, cudnn64_7.dll, and cudnn.lib. + +In addition to the rights above, for parties that are developing software intended solely for use on Jetson development kits or Jetson modules and running Linux for Tegra software the following shall apply: the SDK may be distributed in its entirety, as provided by NVIDIA and without separation of its components, for you and/or your licensees to create software development kits for use only on the Jetson platform and running Linux for Tegra software. + +3. Licensing. If the distribution terms in this Agreement are not suitable for your organization, or for any questions regarding this Agreement, please contact NVIDIA at nvidia-compute-license-questions@nvidia.com. + (v. January 28, 2020) + diff --git a/env-llmeval/lib/python3.10/site-packages/nvidia_cudnn_cu12-8.9.2.26.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/nvidia_cudnn_cu12-8.9.2.26.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..3a2c0024c90df509291fce505ecbff159bc6f3c3 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/nvidia_cudnn_cu12-8.9.2.26.dist-info/METADATA @@ -0,0 +1,36 @@ +Metadata-Version: 2.1 +Name: nvidia-cudnn-cu12 +Version: 8.9.2.26 +Summary: cuDNN runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: cuda_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: Other/Proprietary License +Classifier: Natural Language :: English +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Requires-Python: >=3 +License-File: License.txt +Requires-Dist: nvidia-cublas-cu12 + +cuDNN runtime 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software is made available under the terms of *either* of the licenses +found in LICENSE.APACHE or LICENSE.BSD. Contributions to this software is made +under the terms of *both* these licenses. diff --git a/env-llmeval/lib/python3.10/site-packages/packaging-24.0.dist-info/LICENSE.APACHE b/env-llmeval/lib/python3.10/site-packages/packaging-24.0.dist-info/LICENSE.APACHE new file mode 100644 index 0000000000000000000000000000000000000000..f433b1a53f5b830a205fd2df78e2b34974656c7b --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/packaging-24.0.dist-info/LICENSE.APACHE @@ -0,0 +1,177 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/env-llmeval/lib/python3.10/site-packages/packaging-24.0.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/packaging-24.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..10ab4390a96923dea26efffa4c42eaacb502536b --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/packaging-24.0.dist-info/METADATA @@ -0,0 +1,102 @@ +Metadata-Version: 2.1 +Name: packaging +Version: 24.0 +Summary: Core utilities for Python packages +Author-email: Donald Stufft +Requires-Python: >=3.7 +Description-Content-Type: text/x-rst +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: Apache Software License +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Typing :: Typed +Project-URL: Documentation, https://packaging.pypa.io/ +Project-URL: Source, https://github.com/pypa/packaging + +packaging +========= + +.. start-intro + +Reusable core utilities for various Python Packaging +`interoperability specifications `_. + +This library provides utilities that implement the interoperability +specifications which have clearly one correct behaviour (eg: :pep:`440`) +or benefit greatly from having a single shared implementation (eg: :pep:`425`). + +.. end-intro + +The ``packaging`` project includes the following: version handling, specifiers, +markers, requirements, tags, utilities. + +Documentation +------------- + +The `documentation`_ provides information and the API for the following: + +- Version Handling +- Specifiers +- Markers +- Requirements +- Tags +- Utilities + +Installation +------------ + +Use ``pip`` to install these utilities:: + + pip install packaging + +The ``packaging`` library uses calendar-based versioning (``YY.N``). + +Discussion +---------- + +If you run into bugs, you can file them in our `issue tracker`_. + +You can also join ``#pypa`` on Freenode to ask questions or get involved. + + +.. _`documentation`: https://packaging.pypa.io/ +.. _`issue tracker`: https://github.com/pypa/packaging/issues + + +Code of Conduct +--------------- + +Everyone interacting in the packaging project's codebases, issue trackers, chat +rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_. + +.. _PSF Code of Conduct: https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md + +Contributing +------------ + +The ``CONTRIBUTING.rst`` file outlines how to contribute to this project as +well as how to report a potential security issue. The documentation for this +project also covers information about `project development`_ and `security`_. + +.. _`project development`: https://packaging.pypa.io/en/latest/development/ +.. _`security`: https://packaging.pypa.io/en/latest/security/ + +Project History +--------------- + +Please review the ``CHANGELOG.rst`` file or the `Changelog documentation`_ for +recent changes and project history. + +.. _`Changelog documentation`: https://packaging.pypa.io/en/latest/changelog/ + diff --git a/env-llmeval/lib/python3.10/site-packages/packaging-24.0.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/packaging-24.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..a5419e4d96ad680b02d9d92bac5f0655b6e795af --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/packaging-24.0.dist-info/RECORD @@ -0,0 +1,36 @@ +packaging-24.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 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a/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/INSTALLER b/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/LICENSE b/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..268c00e0364506fa384d9b019001de22beee5332 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/LICENSE @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2016 Tsuyoshi Hombashi + +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: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +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. diff --git a/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..e94d542bda0af891f0762350f90ed4361e75d07c --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/METADATA @@ -0,0 +1,929 @@ +Metadata-Version: 2.1 +Name: pytablewriter +Version: 1.2.0 +Summary: pytablewriter is a Python library to write a table in various formats: AsciiDoc / CSV / Elasticsearch / HTML / JavaScript / JSON / LaTeX / LDJSON / LTSV / Markdown / MediaWiki / NumPy / Excel / Pandas / Python / reStructuredText / SQLite / TOML / TSV / YAML. +Home-page: https://github.com/thombashi/pytablewriter +Author: Tsuyoshi Hombashi +Author-email: tsuyoshi.hombashi@gmail.com +License: MIT License +Project-URL: Changlog, https://github.com/thombashi/pytablewriter/releases +Project-URL: Documentation, https://pytablewriter.rtfd.io/ +Project-URL: Funding, https://github.com/sponsors/thombashi +Project-URL: Source, https://github.com/thombashi/pytablewriter +Project-URL: Tracker, https://github.com/thombashi/pytablewriter/issues +Keywords: AsciiDoc,table,CSV,Excel,JavaScript,JSON,LaTeX,LTSV,Markdown,MediaWiki,HTML,pandas,reStructuredText,SQLite,TSV,TOML +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Information Technology +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Software Development :: Code Generators +Classifier: Topic :: Software Development :: Libraries +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Topic :: Text Processing +Classifier: Topic :: Text Processing :: Markup :: HTML +Classifier: Topic :: Text Processing :: Markup :: LaTeX +Classifier: Topic :: Text Processing :: Markup :: Markdown +Classifier: Topic :: Text Processing :: Markup :: reStructuredText +Classifier: Typing :: Typed +Requires-Python: >=3.7 +Description-Content-Type: text/x-rst +License-File: LICENSE +Requires-Dist: setuptools >=38.3.0 +Requires-Dist: DataProperty <2,>=1.0.1 +Requires-Dist: mbstrdecoder <2,>=1.0.0 +Requires-Dist: pathvalidate <4,>=2.3.0 +Requires-Dist: tabledata <2,>=1.3.1 +Requires-Dist: tcolorpy <1,>=0.0.5 +Requires-Dist: typepy[datetime] <2,>=1.3.2 +Provides-Extra: all +Requires-Dist: xlwt ; extra == 'all' +Requires-Dist: XlsxWriter <4,>=0.9.6 ; extra == 'all' +Requires-Dist: elasticsearch <9,>=8.0.1 ; extra == 'all' +Requires-Dist: pytablereader <2,>=0.31.3 ; extra == 'all' +Requires-Dist: dominate <3,>=2.1.5 ; extra == 'all' +Requires-Dist: loguru <1,>=0.4.1 ; extra == 'all' +Requires-Dist: SimpleSQLite <2,>=1.3.2 ; extra == 'all' +Requires-Dist: pytablewriter-altrow-theme <1,>=0.2.0 ; extra == 'all' +Requires-Dist: pytablewriter-altcol-theme <1,>=0.1.0 ; extra == 'all' +Requires-Dist: toml <1,>=0.9.3 ; extra == 'all' +Requires-Dist: PyYAML <7,>=3.11 ; extra == 'all' +Requires-Dist: simplejson <4,>=3.8.1 ; extra == 'all' +Requires-Dist: pandas <3,>=0.25.3 ; extra == 'all' +Provides-Extra: docs +Requires-Dist: sphinx-rtd-theme >=1.2.2 ; extra == 'docs' +Requires-Dist: Sphinx >=2.4 ; extra == 'docs' +Requires-Dist: xlwt ; extra == 'docs' +Requires-Dist: XlsxWriter <4,>=0.9.6 ; extra == 'docs' +Requires-Dist: elasticsearch <9,>=8.0.1 ; extra == 'docs' +Requires-Dist: pytablereader <2,>=0.31.3 ; extra == 'docs' +Requires-Dist: dominate <3,>=2.1.5 ; extra == 'docs' +Requires-Dist: loguru <1,>=0.4.1 ; extra == 'docs' +Requires-Dist: SimpleSQLite <2,>=1.3.2 ; extra == 'docs' +Requires-Dist: pytablewriter-altrow-theme <1,>=0.2.0 ; extra == 'docs' +Requires-Dist: pytablewriter-altcol-theme <1,>=0.1.0 ; extra == 'docs' +Requires-Dist: toml <1,>=0.9.3 ; extra == 'docs' +Requires-Dist: PyYAML <7,>=3.11 ; extra == 'docs' +Requires-Dist: simplejson <4,>=3.8.1 ; extra == 'docs' +Requires-Dist: pandas <3,>=0.25.3 ; extra == 'docs' +Provides-Extra: es +Requires-Dist: elasticsearch <9,>=8.0.1 ; extra == 'es' +Provides-Extra: es8 +Requires-Dist: elasticsearch <9,>=8.0.1 ; extra == 'es8' +Provides-Extra: excel +Requires-Dist: xlwt ; extra == 'excel' +Requires-Dist: XlsxWriter <4,>=0.9.6 ; extra == 'excel' +Provides-Extra: from +Requires-Dist: pytablereader <2,>=0.31.3 ; extra == 'from' +Provides-Extra: html +Requires-Dist: dominate <3,>=2.1.5 ; extra == 'html' +Provides-Extra: logging +Requires-Dist: loguru <1,>=0.4.1 ; extra == 'logging' +Provides-Extra: pandas +Requires-Dist: pandas <3,>=0.25.3 ; extra == 'pandas' +Provides-Extra: sqlite +Requires-Dist: SimpleSQLite <2,>=1.3.2 ; extra == 'sqlite' +Provides-Extra: test +Requires-Dist: pandas <3,>=0.25.3 ; extra == 'test' +Requires-Dist: XlsxWriter <4,>=0.9.6 ; extra == 'test' +Requires-Dist: beautifulsoup4 >=4.10 ; extra == 'test' +Requires-Dist: toml <1,>=0.9.3 ; extra == 'test' +Requires-Dist: pytablewriter-altcol-theme <1,>=0.1.0 ; extra == 'test' +Requires-Dist: pytest-md-report >=0.4.1 ; extra == 'test' +Requires-Dist: pytablereader <2,>=0.31.3 ; extra == 'test' +Requires-Dist: SimpleSQLite <2,>=1.3.2 ; extra == 'test' +Requires-Dist: dominate <3,>=2.1.5 ; extra == 'test' +Requires-Dist: pytablewriter-altrow-theme <1,>=0.2.0 ; extra == 'test' +Requires-Dist: loguru <1,>=0.4.1 ; extra == 'test' +Requires-Dist: xlwt ; extra == 'test' +Requires-Dist: PyYAML <7,>=3.11 ; extra == 'test' +Requires-Dist: elasticsearch <9,>=8.0.1 ; extra == 'test' +Requires-Dist: tablib >=3.2.0 ; extra == 'test' +Requires-Dist: pytablereader[excel,sqlite] >=0.31.3 ; extra == 'test' +Requires-Dist: simplejson <4,>=3.8.1 ; extra == 'test' +Requires-Dist: sqliteschema >=1.3.0 ; extra == 'test' +Requires-Dist: pytest >=6.0.1 ; extra == 'test' +Provides-Extra: theme +Requires-Dist: pytablewriter-altrow-theme <1,>=0.2.0 ; extra == 'theme' +Requires-Dist: pytablewriter-altcol-theme <1,>=0.1.0 ; extra == 'theme' +Provides-Extra: toml +Requires-Dist: toml <1,>=0.9.3 ; extra == 'toml' +Provides-Extra: yaml +Requires-Dist: PyYAML <7,>=3.11 ; extra == 'yaml' + +.. contents:: **pytablewriter** + :backlinks: top + :depth: 2 + +Summary +========= +`pytablewriter `__ is a Python library to write a table in various formats: AsciiDoc / CSV / Elasticsearch / HTML / JavaScript / JSON / LaTeX / LDJSON / LTSV / Markdown / MediaWiki / NumPy / Excel / Pandas / Python / reStructuredText / SQLite / TOML / TSV / YAML. + +.. image:: https://badge.fury.io/py/pytablewriter.svg + :target: https://badge.fury.io/py/pytablewriter + :alt: PyPI package version + +.. image:: https://anaconda.org/conda-forge/pytablewriter/badges/version.svg + :target: https://anaconda.org/conda-forge/pytablewriter + :alt: conda-forge package version + +.. image:: https://img.shields.io/pypi/pyversions/pytablewriter.svg + :target: https://pypi.org/project/pytablewriter/ + :alt: Supported Python versions + +.. image:: https://img.shields.io/pypi/implementation/pytablewriter.svg + :target: https://pypi.org/project/pytablewriter + :alt: Supported Python implementations + +.. image:: https://github.com/thombashi/pytablewriter/actions/workflows/ci.yml/badge.svg + :target: https://github.com/thombashi/pytablewriter/actions/workflows/ci.yml + :alt: CI status of Linux/macOS/Windows + +.. image:: https://coveralls.io/repos/github/thombashi/pytablewriter/badge.svg?branch=master + :target: https://coveralls.io/github/thombashi/pytablewriter?branch=master + :alt: Test coverage + +.. image:: https://github.com/thombashi/pytablewriter/actions/workflows/github-code-scanning/codeql/badge.svg + :target: https://github.com/thombashi/pytablewriter/actions/workflows/github-code-scanning/codeql + :alt: CodeQL + +Features +-------- +- Write a table in various formats: + - Text formats: + - `AsciiDoc `__ + - CSV / Tab-separated values (TSV) / Space-separated values (SSV) + - HTML / CSS + - JSON / `Line-delimited JSON(LDJSON) `__ + - `Labeled Tab-separated Values (LTSV) `__ + - LaTeX: ``tabular``/``array`` environment + - Markdown: CommonMark / `GitHub Flavored Markdown (GFM) `__ / `kramdown `__ + - `MediaWiki `__ + - reStructuredText: `Grid Tables `__/`Simple Tables `__/`CSV Table `__ + - Source code (definition of a variable that represents tabular data) + - JavaScript / `NumPy `__ (`numpy.array `__) / `Pandas `__ (`pandas.DataFrame `__) / Python + - `TOML `__ + - `YAML `__ + - Unicode + - Binary file formats: + - Microsoft Excel :superscript:`TM` (``.xlsx``/``.xls`` file format) + - `pandas.DataFrame `__ pickle file + - `SQLite `__ database + - Application-specific formats: + - `Elasticsearch `__ +- Automatic table cell formatting: + - Alignment + - Padding + - Decimal places of numbers +- Customize table cell styles: + - Text/Background color + - Text alignment + - Font size/weight + - Thousand separator for numbers: e.g. ``1,000``/``1 000`` +- Configure output: + - Write a table to a stream such as a file/standard-output/string-buffer/Jupyter-Notebook + - Get rendered tabular text +- Data sources: + - nested list + - CSV + - `pandas.DataFrame `__ / `pandas.Series `__ + - etc. +- Multibyte character support +- ANSI color support + +Installation +============ + +Installation: pip +------------------------------ +:: + + pip install pytablewriter + +Some of the formats require additional dependency packages, you can install these packages as follows: + +.. csv-table:: Installation of optional dependencies + :header: Installation example, Remark + + ``pip install pytablewriter[es]``, Elasticsearch + ``pip install pytablewriter[excel]``, Excel + ``pip install pytablewriter[html]``, HTML + ``pip install pytablewriter[sqlite]``, SQLite database + ``pip install pytablewriter[toml]``, TOML + ``pip install pytablewriter[theme]``, pytablewriter theme plugins + ``pip install pytablewriter[all]``, Install all of the optional dependencies + +Installation: conda +------------------------------ +:: + + conda install -c conda-forge pytablewriter + +Installation: apt +------------------------------ +:: + + sudo add-apt-repository ppa:thombashi/ppa + sudo apt update + sudo apt install python3-pytablewriter + +Examples +========== +Write tables +-------------- +Write a Markdown table +~~~~~~~~~~~~~~~~~~~~~~~~ +:Sample Code: + .. code-block:: python + + from pytablewriter import MarkdownTableWriter + + def main(): + writer = MarkdownTableWriter( + table_name="example_table", + headers=["int", "float", "str", "bool", "mix", "time"], + value_matrix=[ + [0, 0.1, "hoge", True, 0, "2017-01-01 03:04:05+0900"], + [2, "-2.23", "foo", False, None, "2017-12-23 45:01:23+0900"], + [3, 0, "bar", "true", "inf", "2017-03-03 33:44:55+0900"], + [-10, -9.9, "", "FALSE", "nan", "2017-01-01 00:00:00+0900"], + ], + ) + writer.write_table() + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: + + # example_table + |int|float|str |bool | mix | time | + |--:|----:|----|-----|-------:|------------------------| + | 0| 0.10|hoge|True | 0|2017-01-01 03:04:05+0900| + | 2|-2.23|foo |False| |2017-12-23 12:34:51+0900| + | 3| 0.00|bar |True |Infinity|2017-03-03 22:44:55+0900| + |-10|-9.90| |False| NaN|2017-01-01 00:00:00+0900| + +:Rendering Result: + .. figure:: https://cdn.jsdelivr.net/gh/thombashi/pytablewriter@master/docs/pages/examples/table_format/text/ss/markdown.png + :scale: 80% + :alt: https://github.com/thombashi/pytablewriter/blob/master/docs/pages/examples/table_format/text/ss/markdown.png + + Rendered markdown at GitHub + +Write a Markdown table with margins +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:Sample Code: + .. code-block:: python + + from pytablewriter import MarkdownTableWriter + + def main(): + writer = MarkdownTableWriter( + table_name="write a table with margins", + headers=["int", "float", "str", "bool", "mix", "time"], + value_matrix=[ + [0, 0.1, "hoge", True, 0, "2017-01-01 03:04:05+0900"], + [2, "-2.23", "foo", False, None, "2017-12-23 45:01:23+0900"], + [3, 0, "bar", "true", "inf", "2017-03-03 33:44:55+0900"], + [-10, -9.9, "", "FALSE", "nan", "2017-01-01 00:00:00+0900"], + ], + margin=1 # add a whitespace for both sides of each cell + ) + writer.write_table() + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: + + # write a table with margins + | int | float | str | bool | mix | time | + | --: | ----: | ---- | ----- | -------: | ------------------------ | + | 0 | 0.10 | hoge | True | 0 | 2017-01-01 03:04:05+0900 | + | 2 | -2.23 | foo | False | | 2017-12-23 12:34:51+0900 | + | 3 | 0.00 | bar | True | Infinity | 2017-03-03 22:44:55+0900 | + | -10 | -9.90 | | False | NaN | 2017-01-01 00:00:00+0900 | + +``margin`` attribute can be available for all of the text format writer classes. + +Write a GitHub Flavored Markdown (GFM) table +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +If you set ``flavor`` keyword argument of ``MarkdownTableWriter`` class to ``"github"`` or ``"gfm"``, the writer will output markdown tables with GitHub flavor. +GFM can apply some additional styles to tables such as ``fg_color`` (text color). + +:Sample Code: + .. code-block:: python + + from pytablewriter import MarkdownTableWriter + from pytablewriter.style import Style + + writer = MarkdownTableWriter( + column_styles=[ + Style(fg_color="red"), + Style(fg_color="green", decoration_line="underline"), + ], + headers=["A", "B"], + value_matrix=[ + ["abc", 1], + ["efg", 2], + ], + margin=1, + flavor="github", + enable_ansi_escape=False, + ) + writer.write_table() + +Rendered results can be found at `here `__ + +Apply styles to GFM table with programmatically +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Applying style filters to GFM allows for more flexible style settings for cells. +See also the `example <#style-filter>`_ + +Write a Markdown table to a stream or a file +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +`Refer an example `__ + +Write a table to an Excel sheet +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +:Sample Code: + .. code-block:: python + + from pytablewriter import ExcelXlsxTableWriter + + def main(): + writer = ExcelXlsxTableWriter() + writer.table_name = "example" + writer.headers = ["int", "float", "str", "bool", "mix", "time"] + writer.value_matrix = [ + [0, 0.1, "hoge", True, 0, "2017-01-01 03:04:05+0900"], + [2, "-2.23", "foo", False, None, "2017-12-23 12:34:51+0900"], + [3, 0, "bar", "true", "inf", "2017-03-03 22:44:55+0900"], + [-10, -9.9, "", "FALSE", "nan", "2017-01-01 00:00:00+0900"], + ] + writer.dump("sample.xlsx") + + if __name__ == "__main__": + main() + +:Output: + .. figure:: https://cdn.jsdelivr.net/gh/thombashi/pytablewriter@master/docs/pages/examples/table_format/binary/spreadsheet/ss/excel_single.png + :scale: 100% + :alt: https://github.com/thombashi/pytablewriter/blob/master/docs/pages/examples/table_format/binary/spreadsheet/ss/excel_single.png + + Output excel file (``sample_single.xlsx``) + +Write a Unicode table +~~~~~~~~~~~~~~~~~~~~~~~ +:Sample Code: + .. code-block:: python + + from pytablewriter import UnicodeTableWriter + + def main(): + writer = UnicodeTableWriter( + table_name="example_table", + headers=["int", "float", "str", "bool", "mix", "time"], + value_matrix=[ + [0, 0.1, "hoge", True, 0, "2017-01-01 03:04:05+0900"], + [2, "-2.23", "foo", False, None, "2017-12-23 45:01:23+0900"], + [3, 0, "bar", "true", "inf", "2017-03-03 33:44:55+0900"], + [-10, -9.9, "", "FALSE", "nan", "2017-01-01 00:00:00+0900"], + ] + ) + writer.write_table() + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: + + ┌───┬─────┬────┬─────┬────────┬────────────────────────┐ + │int│float│str │bool │ mix │ time │ + ├───┼─────┼────┼─────┼────────┼────────────────────────┤ + │ 0│ 0.10│hoge│True │ 0│2017-01-01 03:04:05+0900│ + ├───┼─────┼────┼─────┼────────┼────────────────────────┤ + │ 2│-2.23│foo │False│ │2017-12-23 12:34:51+0900│ + ├───┼─────┼────┼─────┼────────┼────────────────────────┤ + │ 3│ 0.00│bar │True │Infinity│2017-03-03 22:44:55+0900│ + ├───┼─────┼────┼─────┼────────┼────────────────────────┤ + │-10│-9.90│ │False│ NaN│2017-01-01 00:00:00+0900│ + └───┴─────┴────┴─────┴────────┴────────────────────────┘ + +Write a table with JavaScript format (as a nested list variable definition) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +:Sample Code: + .. code-block:: python + + import pytablewriter as ptw + + + def main(): + writer = ptw.JavaScriptTableWriter( + table_name="js_variable", + headers=["int", "float", "str", "bool", "mix", "time"], + value_matrix=[ + [0, 0.1, "hoge", True, 0, "2017-01-01 03:04:05+0900"], + [2, "-2.23", "foo", False, None, "2017-12-23 45:01:23+0900"], + [3, 0, "bar", "true", "inf", "2017-03-03 33:44:55+0900"], + [-10, -9.9, "", "FALSE", "nan", "2017-01-01 00:00:00+0900"], + ], + ) + + writer.write_table() + + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: js + + const js_variable = [ + ["int", "float", "str", "bool", "mix", "time"], + [0, 0.1, "hoge", true, 0, "2017-01-01 03:04:05+0900"], + [2, -2.23, "foo", false, null, "2017-12-23 45:01:23+0900"], + [3, 0, "bar", true, Infinity, "2017-03-03 33:44:55+0900"], + [-10, -9.9, "", "FALSE", NaN, "2017-01-01 00:00:00+0900"] + ]; + +Write a Markdown table from ``pandas.DataFrame`` instance +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +``from_dataframe`` method of writer classes will set up tabular data from ``pandas.DataFrame``: + +:Sample Code: + .. code-block:: python + + from textwrap import dedent + import pandas as pd + import io + from pytablewriter import MarkdownTableWriter + + def main(): + csv_data = io.StringIO(dedent("""\ + "i","f","c","if","ifc","bool","inf","nan","mix_num","time" + 1,1.10,"aa",1.0,"1",True,Infinity,NaN,1,"2017-01-01 00:00:00+09:00" + 2,2.20,"bbb",2.2,"2.2",False,Infinity,NaN,Infinity,"2017-01-02 03:04:05+09:00" + 3,3.33,"cccc",-3.0,"ccc",True,Infinity,NaN,NaN,"2017-01-01 00:00:00+09:00" + """)) + df = pd.read_csv(csv_data, sep=',') + + writer = MarkdownTableWriter(dataframe=df) + writer.write_table() + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: + + | i | f | c | if |ifc|bool | inf |nan|mix_num | time | + |--:|---:|----|---:|---|-----|--------|---|-------:|-------------------------| + | 1|1.10|aa | 1.0| 1|True |Infinity|NaN| 1|2017-01-01 00:00:00+09:00| + | 2|2.20|bbb | 2.2|2.2|False|Infinity|NaN|Infinity|2017-01-02 03:04:05+09:00| + | 3|3.33|cccc|-3.0|ccc|True |Infinity|NaN| NaN|2017-01-01 00:00:00+09:00| + + +Adding a column of the DataFrame index if you specify ``add_index_column=True``: + +:Sample Code: + .. code-block:: python + + import pandas as pd + import pytablewriter as ptw + + def main(): + writer = ptw.MarkdownTableWriter(table_name="add_index_column") + writer.from_dataframe( + pd.DataFrame({"A": [1, 2], "B": [10, 11]}, index=["a", "b"]), + add_index_column=True, + ) + writer.write_table() + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: + + # add_index_column + | | A | B | + |---|--:|--:| + |a | 1| 10| + |b | 2| 11| + +Write a Markdown table from space-separated values +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +:Sample Code: + .. code-block:: python + + import pytablewriter as ptw + + + def main(): + writer = ptw.MarkdownTableWriter(table_name="ps") + writer.from_csv( + """ + USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND + root 1 0.0 0.4 77664 8784 ? Ss May11 0:02 /sbin/init + root 2 0.0 0.0 0 0 ? S May11 0:00 [kthreadd] + root 4 0.0 0.0 0 0 ? I< May11 0:00 [kworker/0:0H] + root 6 0.0 0.0 0 0 ? I< May11 0:00 [mm_percpu_wq] + root 7 0.0 0.0 0 0 ? S May11 0:01 [ksoftirqd/0] + """, + delimiter=" ", + ) + writer.write_table() + + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: + + # ps + |USER|PID|%CPU|%MEM| VSZ |RSS |TTY|STAT|START|TIME| COMMAND | + |----|--:|---:|---:|----:|---:|---|----|-----|----|--------------| + |root| 1| 0| 0.4|77664|8784|? |Ss |May11|0:02|/sbin/init | + |root| 2| 0| 0.0| 0| 0|? |S |May11|0:00|[kthreadd] | + |root| 4| 0| 0.0| 0| 0|? |I< |May11|0:00|[kworker/0:0H]| + |root| 6| 0| 0.0| 0| 0|? |I< |May11|0:00|[mm_percpu_wq]| + |root| 7| 0| 0.0| 0| 0|? |S |May11|0:01|[ksoftirqd/0] | + +Get rendered tabular text as str +---------------------------------- +``dumps`` method returns rendered tabular text. +``dumps`` only available for text format writers. + +:Sample Code: + .. code-block:: python + + import pytablewriter as ptw + + + def main(): + writer = ptw.MarkdownTableWriter( + headers=["int", "float", "str", "bool", "mix", "time"], + value_matrix=[ + [0, 0.1, "hoge", True, 0, "2017-01-01 03:04:05+0900"], + [2, "-2.23", "foo", False, None, "2017-12-23 45:01:23+0900"], + [3, 0, "bar", "true", "inf", "2017-03-03 33:44:55+0900"], + [-10, -9.9, "", "FALSE", "nan", "2017-01-01 00:00:00+0900"], + ], + ) + + print(writer.dumps()) + + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: + + |int|float|str |bool | mix | time | + |--:|----:|----|-----|-------:|------------------------| + | 0| 0.10|hoge|True | 0|2017-01-01 03:04:05+0900| + | 2|-2.23|foo |False| |2017-12-23 45:01:23+0900| + | 3| 0.00|bar |True |Infinity|2017-03-03 33:44:55+0900| + |-10|-9.90| |False| NaN|2017-01-01 00:00:00+0900| + +Configure table styles +------------------------ +Column styles +~~~~~~~~~~~~~~~ +Writers can specify +`Style `__ +for each column by ``column_styles`` attribute of writer classes. + +:Sample Code: + .. code-block:: python + + import pytablewriter as ptw + from pytablewriter.style import Style + + + def main(): + writer = ptw.MarkdownTableWriter( + table_name="set style by column_styles", + headers=[ + "auto align", + "left align", + "center align", + "bold", + "italic", + "bold italic ts", + ], + value_matrix=[ + [11, 11, 11, 11, 11, 11], + [1234, 1234, 1234, 1234, 1234, 1234], + ], + column_styles=[ + Style(), + Style(align="left"), + Style(align="center"), + Style(font_weight="bold"), + Style(font_style="italic"), + Style(font_weight="bold", font_style="italic", thousand_separator=","), + ], # specify styles for each column + ) + writer.write_table() + + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: + + # set style by styles + |auto align|left align|center align| bold |italic|bold italic ts| + |---------:|----------|:----------:|-------:|-----:|-------------:| + | 11|11 | 11 | **11**| _11_| _**11**_| + | 1234|1234 | 1234 |**1234**|_1234_| _**1,234**_| + + `Rendering result `__ + + +You can also set ``Style`` to a specific column with an index or header by using ``set_style`` method: + +:Sample Code: + .. code-block:: python + + from pytablewriter import MarkdownTableWriter + from pytablewriter.style import Style + + def main(): + writer = MarkdownTableWriter() + writer.headers = ["A", "B", "C",] + writer.value_matrix = [[11, 11, 11], [1234, 1234, 1234]] + + writer.table_name = "set style by column index" + writer.set_style(1, Style(align="center", font_weight="bold")) + writer.set_style(2, Style(thousand_separator=" ")) + writer.write_table() + writer.write_null_line() + + writer.table_name = "set style by header" + writer.set_style("B", Style(font_style="italic")) + writer.write_table() + + if __name__ == "__main__": + main() + +:Output: + .. code-block:: + + # set style by column index + | A | B | C | + |---:|:------:|----:| + | 11| **11** | 11| + |1234|**1234**|1 234| + + # set style by header + | A | B | C | + |---:|-----:|----:| + | 11| _11_| 11| + |1234|_1234_|1 234| + +Style filter +~~~~~~~~~~~~~~ +You can apply styles to specific cells by using style filters. +Style filters will be written as Python functions. +Examples of a style filter function and how you apply it are as follows: + +:Sample Code: + .. code-block:: python + + from typing import Any, Optional + + from pytablewriter import MarkdownTableWriter + from pytablewriter.style import Cell, Style + + + def style_filter(cell: Cell, **kwargs: Any) -> Optional[Style]: + if cell.is_header_row(): + return None + + if cell.col == 0: + return Style(font_weight="bold") + + value = int(cell.value) + + if value > 80: + return Style(fg_color="red", font_weight="bold", decoration_line="underline") + elif value > 50: + return Style(fg_color="yellow", font_weight="bold") + elif value > 20: + return Style(fg_color="green") + + return Style(fg_color="lightblue") + + + writer = MarkdownTableWriter( + table_name="style filter example", + headers=["Key", "Value 1", "Value 2"], + value_matrix=[ + ["A", 95, 40], + ["B", 55, 5], + ["C", 30, 85], + ["D", 0, 69], + ], + flavor="github", + enable_ansi_escape=False, + ) + writer.add_style_filter(style_filter) + writer.write_table() + +Rendered results can be found at `here `__ + +Theme +~~~~~~~ +Themes consists of a set of style filters. +The following command will install external predefined themes: + +:: + + pip install pytablewriter[theme] + +Themes can be set via the constructor of the writer classes or the ``set_theme`` method. +The following is an example of setting the ``altrow`` theme via the constructor. +``altrow`` theme will be colored rows alternatively: + +:Sample Code: + .. code-block:: python + + import pytablewriter as ptw + + writer = ptw.TableWriterFactory.create_from_format_name( + "markdown", + headers=["INT", "STR"], + value_matrix=[[1, "hoge"], [2, "foo"], [3, "bar"]], + margin=1, + theme="altrow", + ) + writer.write_table() + +:Output: + .. figure:: https://cdn.jsdelivr.net/gh/thombashi/pytablewriter-altrow-theme@master/ss/ptw-altrow-theme_example_default.png + :scale: 100% + :alt: https://github.com/thombashi/pytablewriter-altrow-theme/blob/master/ss/ptw-altrow-theme_example_default.png + +`[theme]` extras includes the following themes: + +- `pytablewriter-altrow-theme `__ + - `Generated HTML table example `__ +- `pytablewriter-altcol-theme `__ + - `Generated HTML table example `__ + +Make tables for specific applications +--------------------------------------- +Render a table on Jupyter Notebook +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +All table writer class instances in ``pytablewriter`` can render in Jupyter Notebook. +To render writers at notebook cells, you will require the dependency packages to be installed either by: + +- ``pip install pytablewriter[html]`` or +- ``pip install pytablewriter[all]`` + +Jupyter Notebook code examples can be found `here `__: + +.. figure:: https://cdn.jsdelivr.net/gh/thombashi/pytablewriter@master/ss/jupyter_notebook.png + :scale: 100% + :alt: https://github.com/thombashi/pytablewriter/blob/master/ss/jupyter_notebook.png + + Table rendering results of Jupyter Notebook + +Multibyte character support +----------------------------- +Write a table using multibyte character +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +You can use multibyte characters as table data. +Multibyte characters are also properly padded and aligned. + +:Sample Code: + .. code-block:: python + + import pytablewriter as ptw + + + def main(): + writer = ptw.RstSimpleTableWriter( + table_name="生成に関するパターン", + headers=["パターン名", "概要", "GoF", "Code Complete[1]"], + value_matrix=[ + ["Abstract Factory", "関連する一連のインスタンスを状況に応じて、適切に生成する方法を提供する。", "Yes", "Yes"], + ["Builder", "複合化されたインスタンスの生成過程を隠蔽する。", "Yes", "No"], + ["Factory Method", "実際に生成されるインスタンスに依存しない、インスタンスの生成方法を提供する。", "Yes", "Yes"], + ["Prototype", "同様のインスタンスを生成するために、原型のインスタンスを複製する。", "Yes", "No"], + ["Singleton", "あるクラスについて、インスタンスが単一であることを保証する。", "Yes", "Yes"], + ], + ) + writer.write_table() + + + if __name__ == "__main__": + main() + +:Output: + .. figure:: https://cdn.jsdelivr.net/gh/thombashi/pytablewriter@master/docs/pages/examples/multibyte/ss/multi_byte_char.png + :scale: 100% + :alt: https://github.com/thombashi/pytablewriter/blob/master/docs/pages/examples/multibyte/ss/multi_byte_char.png + + Output of multi-byte character table + +Multiprocessing +----------------- +You can increase the number of workers to process table data via ``max_workers`` attribute of a writer. +The more ``max_workers`` the less processing time when tabular data is large and the execution environment has available cores. + +If you increase ``max_workers`` larger than one, recommend using main guarded as follows to avoid problems caused by multi-processing: + +.. code-block:: python + + from multiprocessing import cpu_count + import pytablewriter as ptw + + def main(): + writer = ptw.MarkdownTableWriter() + writer.max_workers = cpu_count() + ... + + if __name__ == "__main__": + main() + +For more information +---------------------- +More examples are available at +https://pytablewriter.rtfd.io/en/latest/pages/examples/index.html + +Dependencies +============ +- Python 3.7+ +- `Python package dependencies (automatically installed) `__ + + +Optional dependencies +--------------------- +- ``logging`` extras + - `loguru `__: Used for logging if the package installed +- ``from`` extras + - `pytablereader `__ +- ``es`` extra + - `elasticsearch `__ +- ``excel`` extras + - `xlwt `__ + - `XlsxWriter `__ +- ``html`` extras + - `dominate `__ +- ``sqlite`` extras + - `SimpleSQLite `__ +- ``theme`` extras + - `pytablewriter-altrow-theme `__ + - `pytablewriter-altcol-theme `__ +- ``toml`` extras + - `toml `__ + +Documentation +=============== +https://pytablewriter.rtfd.io/ + +Projects using pytablewriter +================================== +- `pytest-md-report `__ + + +Related Projects +================================== +- `pytablereader `__ + - Tabular data loaded by ``pytablereader`` can be written another tabular data format with ``pytablewriter``. + +Sponsors +==================================== +.. image:: https://avatars.githubusercontent.com/u/44389260?s=48&u=6da7176e51ae2654bcfd22564772ef8a3bb22318&v=4 + :target: https://github.com/chasbecker + :alt: Charles Becker (chasbecker) +.. image:: https://avatars.githubusercontent.com/u/46711571?s=48&u=57687c0e02d5d6e8eeaf9177f7b7af4c9f275eb5&v=4 + :target: https://github.com/Arturi0 + :alt: onetime: Arturi0 +.. image:: https://avatars.githubusercontent.com/u/3658062?s=48&v=4 + :target: https://github.com/b4tman + :alt: onetime: Dmitry Belyaev (b4tman) + +`Become a sponsor `__ + diff --git a/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..fe5ba01796a258832cdfd58e244dea7070f0c515 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pytablewriter-1.2.0.dist-info/RECORD @@ -0,0 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a/env-llmeval/lib/python3.10/site-packages/rouge_score/__pycache__/tokenizers_test.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/rouge_score/__pycache__/tokenizers_test.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3b6c1d0bc4dc34c01142883baa168bdd1c5a155b Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/rouge_score/__pycache__/tokenizers_test.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/rouge_score/io.py b/env-llmeval/lib/python3.10/site-packages/rouge_score/io.py new file mode 100644 index 0000000000000000000000000000000000000000..ffd96ef5859861fe5a3c6d145fa435dbbfcf8857 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/rouge_score/io.py @@ -0,0 +1,182 @@ +# Copyright 2022 The rouge_score Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Library for reading/writing input and score files.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import glob + +from absl import logging +import six +from six.moves import zip +from six.moves import zip_longest + + + +def compute_scores_and_write_to_csv(target_filepattern, + prediction_filepattern, + output_filename, + scorer, + aggregator, + delimiter="\n"): + """Runs aggregate score calculations and outputs results to a CSV file. + + Args: + target_filepattern: Pattern for files containing target text. + prediction_filepattern: Pattern for files containing prediction text. + output_filename: Name of file to write results to. + scorer: A BaseScorer object to compute scores. + aggregator: An aggregator to aggregate scores. If None, outputs are + per-example scores. + delimiter: Record delimiter. + """ + + target_filenames = _glob(target_filepattern) + prediction_filenames = _glob(prediction_filepattern) + if (len(target_filenames) < 1 or + len(target_filenames) != len(prediction_filenames)): + raise ValueError("Must have equal and positive number of target and " + "prediction files. Found: %d target files (%s)," + " %d prediction files (%s)." % + (len(target_filenames), target_filepattern, + len(prediction_filenames), prediction_filepattern)) + + scores = _compute_scores(target_filenames, prediction_filenames, scorer, + delimiter) + if aggregator: + for score in scores: + aggregator.add_scores(score) + _write_aggregates_to_csv(output_filename, aggregator.aggregate()) + else: + _write_scores_to_csv(output_filename, scores) + + +def _glob(filepattern): + return glob.glob(filepattern) # pylint: disable=unreachable + + +def _open(filepattern, mode="r"): + return open(filepattern, mode) # pylint: disable=unreachable + + +def _record_gen(filename, delimiter): + """Opens file and yields records separated by delimiter.""" + with _open(filename) as f: + records = f.read().split(six.ensure_str(delimiter)) + if records[-1]: + # Need a final delimiter at end of file to be able to detect an empty last + # record. + logging.warn("Expected delimiter at end of file") + else: + records = records[:-1] + for record in records: + yield record + + +def _compute_scores(target_filenames, prediction_filenames, scorer, delimiter): + """Computes aggregates scores across the given target and prediction files. + + Args: + target_filenames: List of filenames from which to read target lines. + prediction_filenames: List of filenames from which to read prediction lines. + scorer: A BaseScorer object to compute scores. + delimiter: string delimiter between each record in input files + + Returns: + A list of dicts mapping score_type to Score objects. + Raises: + ValueError: If invalid targets or predictions are provided. + """ + + scores = [] + for target_filename, prediction_filename in zip( + sorted(target_filenames), sorted(prediction_filenames)): + logging.info("Reading targets from %s.", target_filename) + logging.info("Reading predictions from %s.", prediction_filename) + targets = _record_gen(target_filename, delimiter) + preds = _record_gen(prediction_filename, delimiter) + for target_rec, prediction_rec in zip_longest(targets, preds): + if target_rec is None or prediction_rec is None: + raise ValueError("Must have equal number of lines across target and " + "prediction files. Mismatch between files: %s, %s." % + (target_filename, prediction_filename)) + scores.append(scorer.score(target_rec, prediction_rec)) + + return scores + + +def _write_aggregates_to_csv(output_filename, aggregates): + """Writes aggregate scores to an output CSV file. + + Output file is a comma separated where each line has the format: + score_type-(P|R|F),low_ci,mean,high_ci + + P/R/F indicates whether the score is a precision, recall or f-measure. + + Args: + output_filename: Name of file to write results to. + aggregates: A dict mapping each score_type to a AggregateScore object. + """ + + logging.info("Writing results to %s.", output_filename) + with _open(output_filename, "w") as output_file: + output_file.write("score_type,low,mid,high\n") + for score_type, aggregate in sorted(aggregates.items()): + output_file.write("%s-R,%f,%f,%f\n" % + (score_type, aggregate.low.recall, aggregate.mid.recall, + aggregate.high.recall)) + output_file.write("%s-P,%f,%f,%f\n" % + (score_type, aggregate.low.precision, + aggregate.mid.precision, aggregate.high.precision)) + output_file.write("%s-F,%f,%f,%f\n" % + (score_type, aggregate.low.fmeasure, + aggregate.mid.fmeasure, aggregate.high.fmeasure)) + logging.info("Finished writing results.") + + +def _write_scores_to_csv(output_filename, scores): + """Writes scores for each individual example to an output CSV file. + + Output file is a comma separated where each line has the format: + id,score1,score2,score3,... + + The header row indicates the type of each score column. + + Args: + output_filename: Name of file to write results to. + scores: A list of dicts mapping each score_type to a Score object. + """ + + if len(scores) < 1: + logging.warn("No scores to write") + return + rouge_types = sorted(scores[0].keys()) + + logging.info("Writing results to %s.", output_filename) + with _open(output_filename, "w") as out_file: + out_file.write("id") + for rouge_type in rouge_types: + out_file.write(",{t}-P,{t}-R,{t}-F".format(t=rouge_type)) + out_file.write("\n") + for i, result in enumerate(scores): + out_file.write("%d" % i) + for rouge_type in rouge_types: + out_file.write(",%f,%f,%f" % + (result[rouge_type].precision, result[rouge_type].recall, + result[rouge_type].fmeasure)) + out_file.write("\n") + logging.info("Finished writing results.") diff --git a/env-llmeval/lib/python3.10/site-packages/rouge_score/rouge.py b/env-llmeval/lib/python3.10/site-packages/rouge_score/rouge.py new file mode 100644 index 0000000000000000000000000000000000000000..a9c45781dc44c1319e5c3bf00e3f9e99a6abd4c6 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/rouge_score/rouge.py @@ -0,0 +1,89 @@ +# Copyright 2022 The rouge_score Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r"""Main routine to calculate ROUGE scores across text files. + +Designed to replicate scores computed by the ROUGE perl implementation as +closely as possible. + +Output is a text file in CSV format. + +Sample usage: + +rouge ---rouge_types=rouge1,rouge2,rougeL \ + --target_filepattern=*.targets \ + --prediction_fliepattern=*.decodes \ + --output_filename=scores.csv \ + --use_stemmer + +Which is equivalent to calling the perl ROUGE script as: + +ROUGE-1.5.5.pl -m -e ./data -n 2 -a /tmp/rouge/settings.xml + +Where settings.xml provides target and decode text. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from absl import app +from absl import flags +from rouge_score import io +from rouge_score import rouge_scorer +from rouge_score import scoring + +flags.DEFINE_string("target_filepattern", None, + "Files containing target text.") +flags.DEFINE_string("prediction_filepattern", None, + "Files containing prediction text.") +flags.DEFINE_string("output_filename", None, + "File in which to write calculated ROUGE scores as a CSV.") +flags.DEFINE_string("delimiter", "\n", + "Record delimiter in files.") +flags.DEFINE_list("rouge_types", ["rouge1", "rouge2", "rougeL"], + "List of ROUGE types to calculate.") +flags.DEFINE_boolean("use_stemmer", False, + "Whether to use Porter stemmer to remove common suffixes.") +flags.DEFINE_boolean("aggregate", True, + "Write aggregates if this is set to True") +flags.DEFINE_boolean("split_summaries", False, + ("Whether to split references and candidates into" + " sentences before computing RougeLsum.")) + +FLAGS = flags.FLAGS + + +def main(argv): + if len(argv) > 1: + raise app.UsageError("Too many command-line arguments.") + scorer = rouge_scorer.RougeScorer( + FLAGS.rouge_types, + use_stemmer=FLAGS.use_stemmer, + split_summaries=FLAGS.split_summaries) + aggregator = scoring.BootstrapAggregator() if FLAGS.aggregate else None + io.compute_scores_and_write_to_csv( + FLAGS.target_filepattern, + FLAGS.prediction_filepattern, + FLAGS.output_filename, + scorer, + aggregator, + delimiter=FLAGS.delimiter) + + +if __name__ == "__main__": + flags.mark_flag_as_required("target_filepattern") + flags.mark_flag_as_required("prediction_filepattern") + flags.mark_flag_as_required("output_filename") + app.run(main) diff --git a/env-llmeval/lib/python3.10/site-packages/rouge_score/rouge_scorer_test.py b/env-llmeval/lib/python3.10/site-packages/rouge_score/rouge_scorer_test.py new file mode 100644 index 0000000000000000000000000000000000000000..faff191e2c3b7fb319bc60703ce25fe8388570f8 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/rouge_score/rouge_scorer_test.py @@ -0,0 +1,314 @@ +# Copyright 2022 The rouge_score Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for rouge scorer. + +Tests for both correctness and for consistency with the official ROUGE-1.5.5 +implementation. + +"Ground truth" scores are taken from manual runs of ROUGE-1.5.5. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from absl.testing import absltest +from absl.testing import parameterized +from rouge_score import rouge_scorer +from rouge_score import test_util +from rouge_score import tokenizers + + +class RougeScorerTest(parameterized.TestCase): + + def setUp(self): + super(RougeScorerTest, self).setUp() + with open(test_util.TARGETS_FILE) as f: + self.targets = f.readlines() + with open(test_util.PREDICTIONS_FILE) as f: + self.predictions = f.readlines() + + @parameterized.parameters(["rougen", "rouge0", "rouge10"]) + def testInvalidRougeTypes(self, rouge_type): + with self.assertRaises(ValueError): + scorer = rouge_scorer.RougeScorer([rouge_type]) + scorer.score("testing one two", "testing") + + @parameterized.parameters(["rouge1", "rouge9", "rougeL", "rougeLsum"]) + def testValidRougeTypes(self, rouge_type): + scorer = rouge_scorer.RougeScorer([rouge_type]) + result = scorer.score("testing one two", "testing") + self.assertSameElements(list(result.keys()), [rouge_type]) + + def testRouge1(self): + scorer = rouge_scorer.RougeScorer(["rouge1"]) + result = scorer.score("testing one two", "testing") + self.assertAlmostEqual(1, result["rouge1"].precision) + self.assertAlmostEqual(1 / 3, result["rouge1"].recall) + self.assertAlmostEqual(1 / 2, result["rouge1"].fmeasure) + + def testRouge1Multi(self): + scorer = rouge_scorer.RougeScorer(["rouge1"]) + result = scorer.score_multi(["testing one two"], "testing") + self.assertAlmostEqual(1, result["rouge1"].precision) + self.assertAlmostEqual(1 / 3, result["rouge1"].recall) + self.assertAlmostEqual(1 / 2, result["rouge1"].fmeasure) + + def testRougeAllMulti(self): + scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"]) + result = scorer.score_multi(["first text", "first something"], "text first") + self.assertAlmostEqual(1, result["rouge1"].fmeasure) + self.assertAlmostEqual(0, result["rouge2"].fmeasure) + self.assertAlmostEqual(0.5, result["rougeL"].fmeasure) + + @parameterized.parameters(["rouge1", "rouge2", "rougeL", "rougeLsum"]) + def testRougeEmpty(self, rouge_type): + scorer = rouge_scorer.RougeScorer([rouge_type]) + result = scorer.score("testing one two", "") + self.assertAlmostEqual(0, result[rouge_type].precision) + self.assertAlmostEqual(0, result[rouge_type].recall) + self.assertAlmostEqual(0, result[rouge_type].fmeasure) + + def testRouge2(self): + scorer = rouge_scorer.RougeScorer(["rouge2"]) + result = scorer.score("testing one two", "testing one") + self.assertAlmostEqual(1, result["rouge2"].precision) + self.assertAlmostEqual(1 / 2, result["rouge2"].recall) + self.assertAlmostEqual(2 / 3, result["rouge2"].fmeasure) + + def testRougeLConsecutive(self): + scorer = rouge_scorer.RougeScorer(["rougeL"]) + result = scorer.score("testing one two", "testing one") + self.assertAlmostEqual(1, result["rougeL"].precision) + self.assertAlmostEqual(2 / 3, result["rougeL"].recall) + self.assertAlmostEqual(4 / 5, result["rougeL"].fmeasure) + + def testRougeLNonConsecutive(self): + scorer = rouge_scorer.RougeScorer(["rougeL"]) + result = scorer.score("testing one two", "testing two") + self.assertAlmostEqual(1, result["rougeL"].precision) + self.assertAlmostEqual(2 / 3, result["rougeL"].recall) + self.assertAlmostEqual(4 / 5, result["rougeL"].fmeasure) + + def testMultipleRougeTypes(self): + scorer = rouge_scorer.RougeScorer(["rouge1", "rougeL"]) + result = scorer.score("testing one two", "testing one") + self.assertSameElements(list(result.keys()), ["rouge1", "rougeL"]) + self.assertAlmostEqual(1, result["rouge1"].precision) + self.assertAlmostEqual(2 / 3, result["rouge1"].recall) + self.assertAlmostEqual(4 / 5, result["rouge1"].fmeasure) + self.assertAlmostEqual(1, result["rougeL"].precision) + self.assertAlmostEqual(2 / 3, result["rougeL"].recall) + self.assertAlmostEqual(4 / 5, result["rougeL"].fmeasure) + + def testRouge1AgainstRouge155(self): + scorer = rouge_scorer.RougeScorer(["rouge1"]) + result = scorer.score(self.targets[0], self.predictions[0]) + self.assertAlmostEqual(0.40741, result["rouge1"].recall, 5) + self.assertAlmostEqual(0.68750, result["rouge1"].precision, 5) + self.assertAlmostEqual(0.51163, result["rouge1"].fmeasure, 5) + result = scorer.score(self.targets[1], self.predictions[1]) + self.assertAlmostEqual(0.40476, result["rouge1"].recall, 5) + self.assertAlmostEqual(0.65385, result["rouge1"].precision, 5) + self.assertAlmostEqual(0.50000, result["rouge1"].fmeasure, 5) + + def testRouge1AgainstRouge155WithStemming(self): + scorer = rouge_scorer.RougeScorer(["rouge1"], use_stemmer=True) + result = scorer.score(self.targets[0], self.predictions[0]) + self.assertAlmostEqual(0.40741, result["rouge1"].recall, 5) + self.assertAlmostEqual(0.68750, result["rouge1"].precision, 5) + self.assertAlmostEqual(0.51163, result["rouge1"].fmeasure, 5) + result = scorer.score(self.targets[1], self.predictions[1]) + self.assertAlmostEqual(0.42857, result["rouge1"].recall, 5) + self.assertAlmostEqual(0.69231, result["rouge1"].precision, 5) + self.assertAlmostEqual(0.52941, result["rouge1"].fmeasure, 5) + + def testRouge2AgainstRouge155(self): + scorer = rouge_scorer.RougeScorer(["rouge2"]) + result = scorer.score(self.targets[0], self.predictions[0]) + self.assertAlmostEqual(0.30769, result["rouge2"].recall, 5) + self.assertAlmostEqual(0.53333, result["rouge2"].precision, 5) + self.assertAlmostEqual(0.39024, result["rouge2"].fmeasure, 5) + result = scorer.score(self.targets[1], self.predictions[1]) + self.assertAlmostEqual(0.29268, result["rouge2"].recall, 5) + self.assertAlmostEqual(0.48000, result["rouge2"].precision, 5) + self.assertAlmostEqual(0.36364, result["rouge2"].fmeasure, 5) + + def testRouge2AgainstRouge155WithStemming(self): + scorer = rouge_scorer.RougeScorer(["rouge2"], use_stemmer=True) + result = scorer.score(self.targets[0], self.predictions[0]) + self.assertAlmostEqual(0.30769, result["rouge2"].recall, 5) + self.assertAlmostEqual(0.53333, result["rouge2"].precision, 5) + self.assertAlmostEqual(0.39024, result["rouge2"].fmeasure, 5) + result = scorer.score(self.targets[1], self.predictions[1]) + self.assertAlmostEqual(0.29268, result["rouge2"].recall, 5) + self.assertAlmostEqual(0.48000, result["rouge2"].precision, 5) + self.assertAlmostEqual(0.36364, result["rouge2"].fmeasure, 5) + + def testRougeLAgainstRouge155(self): + scorer = rouge_scorer.RougeScorer(["rougeL"]) + result = scorer.score(self.targets[0], self.predictions[0]) + self.assertAlmostEqual(0.40741, result["rougeL"].recall, 5) + self.assertAlmostEqual(0.68750, result["rougeL"].precision, 5) + self.assertAlmostEqual(0.51163, result["rougeL"].fmeasure, 5) + result = scorer.score(self.targets[1], self.predictions[1]) + self.assertAlmostEqual(0.40476, result["rougeL"].recall, 5) + self.assertAlmostEqual(0.65385, result["rougeL"].precision, 5) + self.assertAlmostEqual(0.50000, result["rougeL"].fmeasure, 5) + + def testRougeLSumAgainstRouge155WithStemming(self): + scorer = rouge_scorer.RougeScorer(["rougeLsum"], use_stemmer=True) + + target = test_util.get_text( + os.path.join(test_util.PYROUGE_DIR, "target_multi.0.txt")) + prediction = test_util.get_text( + os.path.join(test_util.PYROUGE_DIR, "prediction_multi.0.txt")) + result = scorer.score(target, prediction) + + self.assertAlmostEqual(0.36538, result["rougeLsum"].recall, places=5) + self.assertAlmostEqual(0.66667, result["rougeLsum"].precision, places=5) + self.assertAlmostEqual(0.47205, result["rougeLsum"].fmeasure, places=5) + + def testRougeLSumSentenceSplitting(self): + scorer = rouge_scorer.RougeScorer(["rougeLsum"], use_stemmer=True) + + target = "First sentence.\nSecond Sentence." + prediction = "Second sentence.\nFirst Sentence." + result = scorer.score(target, prediction) + self.assertAlmostEqual(1.0, result["rougeLsum"].fmeasure, places=5) + + scorer = rouge_scorer.RougeScorer(["rougeLsum"], + use_stemmer=True, + split_summaries=False) + result = scorer.score(target, prediction) + + # Without newlines, summaries are treated as single sentences. + target = target.replace("\n", " ") + prediction = prediction.replace("\n", " ") + result = scorer.score(target, prediction) + self.assertAlmostEqual(0.50, result["rougeLsum"].fmeasure, places=5) + + # Split summaries into sentences using nltk + scorer = rouge_scorer.RougeScorer(["rougeLsum"], + use_stemmer=True, + split_summaries=True) + result = scorer.score(target, prediction) + + self.assertAlmostEqual(1.0, result["rougeLsum"].fmeasure, places=5) + + def testLcsTable(self): + ref = [1, 2, 3, 4, 5] + c1 = [2, 5, 3, 4] + t = rouge_scorer._lcs_table(ref, c1) + self.assertEqual(3, t[len(ref)][len(c1)]) + def _read_lcs(t, ref, can): + return rouge_scorer._backtrack_norec(t, ref, can) + # Indices + self.assertEqual([1, 2, 3], + _read_lcs(t, ref, c1)) + # Values + self.assertEqual([2, 3, 4], + [ref[i] for i in _read_lcs(t, ref, c1)]) + + # No common subsequence. + c2 = [8, 9] + t = rouge_scorer._lcs_table(ref, c2) + self.assertEqual(0, t[len(ref)][len(c2)]) + self.assertEqual([], + _read_lcs(t, ref, c2)) + + def testUnionLcs(self): + # Example in Section 3.2 of https://www.aclweb.org/anthology/W04-1013, + # except using indices into ref. + + # First test helper. + lcs1 = [0, 1] # lcs [1, 2] + lcs2 = [0, 2, 4] + self.assertEqual([0, 1, 2, 4], rouge_scorer._find_union([lcs1, lcs2])) + self.assertEqual([0, 1, 2, 4], rouge_scorer._find_union([lcs2, lcs1])) + + ref = [1, 2, 3, 4, 5] + c1 = [1, 2, 6, 7, 8] # lcs = [1, 2] + c2 = [1, 3, 8, 9, 5] # lcs = [1, 3, 5] + self.assertEqual([1, 2, 3, 5], + rouge_scorer._union_lcs(ref, [c1, c2])) + self.assertEqual([1, 2, 3, 5], + rouge_scorer._union_lcs(ref, [c1, c2])) + + def testSummaryLevelLcs(self): + refs = [ + [1, 2, 3, 4, 5] + ] + cans = [ + [1, 2, 6, 7, 8], # lcs = [1, 2] + [1, 3, 8, 9, 5] # lcs = [1, 3, 5] + ] + score = rouge_scorer._summary_level_lcs(refs, cans) + self.assertEqual(0.8, score.recall) # 4 / 5 + self.assertEqual(0.4, score.precision) # 4 / 10 + # 0.4*0.8 / (0.4 + 0.8) + self.assertAlmostEqual(0.5333, score.fmeasure, places=3) + + # Tokenizer may drop all tokens, resulting in empty candidate list. + score = rouge_scorer._summary_level_lcs([["reference"]], [[]]) + self.assertEqual(0.0, score.recall) + + def testRougeLsum(self): + scorer = rouge_scorer.RougeScorer(["rougeLsum"]) + result = scorer.score("w1 w2 w3 w4 w5", "w1 w2 w6 w7 w8\nw1 w3 w8 w9 w5") + self.assertAlmostEqual(0.8, result["rougeLsum"].recall) + self.assertAlmostEqual(0.4, result["rougeLsum"].precision) + self.assertAlmostEqual(0.5333, result["rougeLsum"].fmeasure, places=3) + + # Empty case + result = scorer.score("w1 w2 w3 w4 w5", "") + self.assertAlmostEqual(0.0, result["rougeLsum"].fmeasure, places=3) + self.assertAlmostEqual(0.0, result["rougeLsum"].recall, places=3) + self.assertAlmostEqual(0.0, result["rougeLsum"].precision, places=3) + + result = scorer.score("", "w1") + self.assertAlmostEqual(0.0, result["rougeLsum"].fmeasure, places=3) + self.assertAlmostEqual(0.0, result["rougeLsum"].recall, places=3) + self.assertAlmostEqual(0.0, result["rougeLsum"].precision, places=3) + + # Case in which summary is all non-word characters. + result = scorer.score("w1 w2 w3 w4 w5", "/") + self.assertAlmostEqual(0.0, result["rougeLsum"].fmeasure, places=3) + self.assertAlmostEqual(0.0, result["rougeLsum"].recall, places=3) + self.assertAlmostEqual(0.0, result["rougeLsum"].precision, places=3) + + def testRougeLsumLarge(self): + with open(test_util.LARGE_PREDICTIONS_FILE) as f: + prediction = f.read() + with open(test_util.LARGE_TARGETS_FILE) as f: + target = f.read() + scorer = rouge_scorer.RougeScorer(["rougeLsum"]) + result = scorer.score(target, prediction) + self.assertAlmostEqual(0.533, result["rougeLsum"].fmeasure, places=3) + + def testRougeTokenizerInit(self): + scorer = rouge_scorer.RougeScorer(["rouge1"], + tokenizer=tokenizers.DefaultTokenizer()) + + target = "this is a test" + prediction = target + result = scorer.score(target, prediction) + self.assertEqual(1.0, result["rouge1"].fmeasure) + + +if __name__ == "__main__": + absltest.main() diff --git a/env-llmeval/lib/python3.10/site-packages/rouge_score/scoring.py b/env-llmeval/lib/python3.10/site-packages/rouge_score/scoring.py new file mode 100644 index 0000000000000000000000000000000000000000..d7d018c4cf57102ed68611830fb6bd571a34c862 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/rouge_score/scoring.py @@ -0,0 +1,167 @@ +# Copyright 2022 The rouge_score Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Library for scoring and evaluation of text samples. + +Aggregation functions use bootstrap resampling to compute confidence intervals +as per the original ROUGE perl implementation. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import abc +import collections +from typing import Dict + +import numpy as np +import six +from six.moves import range + + +class Score( + collections.namedtuple("Score", ["precision", "recall", "fmeasure"])): + """Tuple containing precision, recall, and f-measure values.""" + + +class BaseScorer(object, metaclass=abc.ABCMeta): + """Base class for Scorer objects.""" + + @abc.abstractmethod + def score(self, target: str, prediction: str) -> Dict[str, Score]: + """Calculates score between the target and prediction. + + Args: + target: Text containing the target (ground truth) text. + prediction: Text containing the predicted text. + + Returns: + A dict mapping each score_type (string) to Score object. + """ + + +class AggregateScore( + collections.namedtuple("AggregateScore", ["low", "mid", "high"])): + """Tuple containing confidence intervals for scores.""" + + +class BootstrapAggregator(object): + """Aggregates scores to provide confidence intervals. + + Sample usage: + scorer = rouge_scorer.RougeScorer(['rouge1', 'rougeL']) + aggregator = Aggregator() + aggregator.add_scores(scorer.score("one two three", "one two")) + aggregator.add_scores(scorer.score("one two five six", "seven eight")) + result = aggregator.aggregate() + print result + {'rougeL': AggregateScore( + low=Score(precision=0.0, recall=0.0, fmeasure=0.0), + mid=Score(precision=0.5, recall=0.33, fmeasure=0.40), + high=Score(precision=1.0, recall=0.66, fmeasure=0.80)), + 'rouge1': AggregateScore( + low=Score(precision=0.0, recall=0.0, fmeasure=0.0), + mid=Score(precision=0.5, recall=0.33, fmeasure=0.40), + high=Score(precision=1.0, recall=0.66, fmeasure=0.80))} + """ + + def __init__(self, confidence_interval=0.95, n_samples=1000): + """Initializes a BootstrapAggregator object. + + Args: + confidence_interval: Confidence interval to compute on the mean as a + decimal. + n_samples: Number of samples to use for bootstrap resampling. + + Raises: + ValueError: If invalid argument is given. + """ + + if confidence_interval < 0 or confidence_interval > 1: + raise ValueError("confidence_interval must be in range [0, 1]") + if n_samples <= 0: + raise ValueError("n_samples must be positive") + + self._n_samples = n_samples + self._confidence_interval = confidence_interval + self._scores = collections.defaultdict(list) + + def add_scores(self, scores): + """Adds a sample for future aggregation. + + Args: + scores: Dict mapping score_type strings to a namedtuple object/class + representing a score. + """ + + for score_type, score in six.iteritems(scores): + self._scores[score_type].append(score) + + def aggregate(self): + """Aggregates scores previously added using add_scores. + + Returns: + A dict mapping score_type to AggregateScore objects. + """ + + result = {} + for score_type, scores in six.iteritems(self._scores): + # Stack scores into a 2-d matrix of (sample, measure). + score_matrix = np.vstack(tuple(scores)) + # Percentiles are returned as (interval, measure). + percentiles = self._bootstrap_resample(score_matrix) + # Extract the three intervals (low, mid, high). + intervals = tuple( + (scores[0].__class__(*percentiles[j, :]) for j in range(3))) + result[score_type] = AggregateScore( + low=intervals[0], mid=intervals[1], high=intervals[2]) + return result + + def _bootstrap_resample(self, matrix): + """Performs bootstrap resampling on a matrix of scores. + + Args: + matrix: A 2-d matrix of (sample, measure). + + Returns: + A 2-d matrix of (bounds, measure). There are three bounds: low (row 0), + mid (row 1) and high (row 2). Mid is always the mean, while low and high + bounds are specified by self._confidence_interval (which defaults to 0.95 + meaning it will return the 2.5th and 97.5th percentiles for a 95% + confidence interval on the mean). + """ + + # Matrix of (bootstrap sample, measure). + sample_mean = np.zeros((self._n_samples, matrix.shape[1])) + for i in range(self._n_samples): + sample_idx = np.random.choice( + np.arange(matrix.shape[0]), size=matrix.shape[0]) + sample = matrix[sample_idx, :] + sample_mean[i, :] = np.mean(sample, axis=0) + + # Take percentiles on the estimate of the mean using bootstrap samples. + # Final result is a (bounds, measure) matrix. + percentile_delta = (1 - self._confidence_interval) / 2 + q = 100 * np.array([percentile_delta, 0.5, 1 - percentile_delta]) + return np.percentile(sample_mean, q, axis=0) + + +def fmeasure(precision, recall): + """Computes f-measure given precision and recall values.""" + + if precision + recall > 0: + return 2 * precision * recall / (precision + recall) + else: + return 0.0 diff --git a/env-llmeval/lib/python3.10/site-packages/rouge_score/scoring_test.py b/env-llmeval/lib/python3.10/site-packages/rouge_score/scoring_test.py new file mode 100644 index 0000000000000000000000000000000000000000..4cb80838a4c4bf320dbfbb201860755444c55476 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/rouge_score/scoring_test.py @@ -0,0 +1,182 @@ +# Copyright 2022 The rouge_score Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for rouge scoring and aggregation. + +Checks for both correctness, and for consistency with values from the perl ROUGE +implementation which this package replicates. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from absl.testing import absltest +import numpy as np +from six.moves import range +from six.moves import zip +from rouge_score import rouge_scorer +from rouge_score import scoring +from rouge_score import test_util + +# Delta for matching against ground truth rouge values. Must be relatively +# high compared to the individual rouge tests since bootstrap sampling +# introduces randomness. +_DELTA = 0.002 + +# Use a fixed random seed, or tests may fail with nonzero probability. +_RANDOM_SEED = 123 + + +class BootstrapAggregatorTest(absltest.TestCase): + + def setUp(self): + super(BootstrapAggregatorTest, self).setUp() + np.random.seed(_RANDOM_SEED) + with open(test_util.LARGE_TARGETS_FILE) as f: + self.targets = f.readlines() + with open(test_util.LARGE_PREDICTIONS_FILE) as f: + self.predictions = f.readlines() + + def assertSimilarAggregates(self, precision, recall, fmeasure, aggregate, + delta=_DELTA): + """Helper method for asserting matching aggregate scores. + + Args: + precision: Tuple of (low, mid, high) precision scores. + recall: Tuple of (low, mid, high) recall scores. + fmeasure: Tuple of (low, mid, high) fmeasure scores. + aggregate: An AggregateScore object. + delta: Tolerance delta for matching values. + """ + + self.assertAlmostEqual(precision[0], aggregate.low.precision, delta=delta) + self.assertAlmostEqual(precision[1], aggregate.mid.precision, delta=delta) + self.assertAlmostEqual(precision[2], aggregate.high.precision, delta=delta) + self.assertAlmostEqual(recall[0], aggregate.low.recall, delta=delta) + self.assertAlmostEqual(recall[1], aggregate.mid.recall, delta=delta) + self.assertAlmostEqual(recall[2], aggregate.high.recall, delta=delta) + self.assertAlmostEqual(fmeasure[0], aggregate.low.fmeasure, delta=delta) + self.assertAlmostEqual(fmeasure[1], aggregate.mid.fmeasure, delta=delta) + self.assertAlmostEqual(fmeasure[2], aggregate.high.fmeasure, delta=delta) + + def testConsistentPercentiles(self): + aggregator = scoring.BootstrapAggregator(confidence_interval=0.9) + aggregator.add_scores({ + "rouge1": scoring.Score(precision=1, recall=1 / 3, fmeasure=1 / 2) + }) + aggregator.add_scores({ + "rouge1": scoring.Score(precision=0, recall=0, fmeasure=0) + }) + aggregator.add_scores({ + "rouge1": scoring.Score(precision=1, recall=1, fmeasure=1) + }) + result = aggregator.aggregate() + + self.assertSimilarAggregates((1 / 3, 2 / 3, 3 / 3), + (1 / 9, 4 / 9, 7 / 9), + (1 / 6, 3 / 6, 5 / 6), + result["rouge1"], delta=1e-8) + + def testLargeConfidence(self): + aggregator = scoring.BootstrapAggregator(confidence_interval=0.0) + aggregator.add_scores({ + "rouge1": scoring.Score(precision=1, recall=1 / 3, fmeasure=1 / 2) + }) + aggregator.add_scores({ + "rouge1": scoring.Score(precision=0, recall=0, fmeasure=0) + }) + aggregator.add_scores({ + "rouge1": scoring.Score(precision=1, recall=1, fmeasure=1) + }) + result = aggregator.aggregate() + + self.assertSimilarAggregates((2 / 3, 2 / 3, 2 / 3), + (4 / 9, 4 / 9, 4 / 9), + (3 / 6, 3 / 6, 3 / 6), + result["rouge1"], delta=1e-8) + + def testMultipleRougeTypes(self): + scorer = rouge_scorer.RougeScorer(["rouge1", "rougeL"], use_stemmer=False) + aggregator = scoring.BootstrapAggregator() + for target, prediction in zip(self.targets[:5], self.predictions[:5]): + aggregator.add_scores(scorer.score(target, prediction)) + result = aggregator.aggregate() + + self.assertSameElements(list(result.keys()), ["rouge1", "rougeL"]) + + def testConfidenceIntervalsAgainstRouge155(self): + scorer = rouge_scorer.RougeScorer(["rouge1"], use_stemmer=False) + aggregator = scoring.BootstrapAggregator() + for target, prediction in zip(self.targets, self.predictions): + aggregator.add_scores(scorer.score(target, prediction)) + result = aggregator.aggregate() + + self.assertSimilarAggregates((0.48695, 0.49879, 0.51131), + (0.31106, 0.31950, 0.32849), + (0.37614, 0.38554, 0.39581), + result["rouge1"]) + + def testConfidenceIntervalsAgainstRouge155WithStemming(self): + scorer = rouge_scorer.RougeScorer(["rouge1", "rougeL"], use_stemmer=True) + aggregator = scoring.BootstrapAggregator() + for target, prediction in zip(self.targets, self.predictions): + aggregator.add_scores(scorer.score(target, prediction)) + result = aggregator.aggregate() + + self.assertSimilarAggregates((0.51027, 0.52434, 0.53788), + (0.32563, 0.33580, 0.34548), + (0.39380, 0.40524, 0.41661), + result["rouge1"]) + self.assertSimilarAggregates((0.50759, 0.52104, 0.53382), # P + (0.32418, 0.33377, 0.34362), # R + (0.39157, 0.40275, 0.41383), # F + result["rougeL"]) + + def testConfidenceIntervalsAgainstRouge155WithStemmingMultiLine(self): + scorer = rouge_scorer.RougeScorer( + ["rouge1", "rouge2", "rougeLsum"], use_stemmer=True) + aggregator = scoring.BootstrapAggregator() + t_files = [os.path.join(test_util.PYROUGE_DIR, 'target_multi.%d.txt' % i) for i in range(0, 250)] + p_files = [os.path.join(test_util.PYROUGE_DIR, 'prediction_multi.%d.txt' % i) for i in range(0, 250)] + + targets = [test_util.get_text(x) for x in t_files] + predictions = [test_util.get_text(x) for x in p_files] + assert len(targets) == len(predictions) + assert len(targets) == 250 + for target, prediction in zip(targets, predictions): + aggregator.add_scores(scorer.score(target, prediction)) + result = aggregator.aggregate() + + # DIR = testdata/pyrouge_evaluate_plain_text_files + # pyrouge_evaluate_plain_text_files -s $DIR -sfp "prediction_multi.(.*).txt" + # -m $DIR -mfp target_multi.#ID#.txt + self.assertSimilarAggregates((0.58963, 0.59877, 0.60822), # P + (0.37327, 0.38091, 0.38914), # R + (0.45607, 0.46411, 0.47244), # F + result["rouge1"]) + self.assertSimilarAggregates((0.35429, 0.36516, 0.37665), # P + (0.22341, 0.23109, 0.23916), # R + (0.27312, 0.28209, 0.29133), # F + result["rouge2"]) + self.assertSimilarAggregates((0.58604, 0.59491, 0.60444), # P + (0.37084, 0.37846, 0.38671), # R + (0.45305, 0.46113, 0.46946), # F + result["rougeLsum"]) + + +if __name__ == "__main__": + absltest.main() diff --git a/env-llmeval/lib/python3.10/site-packages/tabulate/__init__.py b/env-llmeval/lib/python3.10/site-packages/tabulate/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..503df34848d710b62099e514dee3202de29c3f2a --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/tabulate/__init__.py @@ -0,0 +1,2716 @@ +"""Pretty-print tabular data.""" + +from collections import namedtuple +from collections.abc import Iterable, Sized +from html import escape as htmlescape +from itertools import chain, zip_longest as izip_longest +from functools import reduce, partial +import io +import re +import math +import textwrap +import dataclasses + +try: + import wcwidth # optional wide-character (CJK) support +except ImportError: + wcwidth = None + + +def _is_file(f): + return isinstance(f, io.IOBase) + + +__all__ = ["tabulate", "tabulate_formats", "simple_separated_format"] +try: + from .version import version as __version__ # noqa: F401 +except ImportError: + pass # running __init__.py as a script, AppVeyor pytests + + +# minimum extra space in headers +MIN_PADDING = 2 + +# Whether or not to preserve leading/trailing whitespace in data. +PRESERVE_WHITESPACE = False + +_DEFAULT_FLOATFMT = "g" +_DEFAULT_INTFMT = "" +_DEFAULT_MISSINGVAL = "" +# default align will be overwritten by "left", "center" or "decimal" +# depending on the formatter +_DEFAULT_ALIGN = "default" + + +# if True, enable wide-character (CJK) support +WIDE_CHARS_MODE = wcwidth is not None + +# Constant that can be used as part of passed rows to generate a separating line +# It is purposely an unprintable character, very unlikely to be used in a table +SEPARATING_LINE = "\001" + +Line = namedtuple("Line", ["begin", "hline", "sep", "end"]) + + +DataRow = namedtuple("DataRow", ["begin", "sep", "end"]) + + +# A table structure is supposed to be: +# +# --- lineabove --------- +# headerrow +# --- linebelowheader --- +# datarow +# --- linebetweenrows --- +# ... (more datarows) ... +# --- linebetweenrows --- +# last datarow +# --- linebelow --------- +# +# TableFormat's line* elements can be +# +# - either None, if the element is not used, +# - or a Line tuple, +# - or a function: [col_widths], [col_alignments] -> string. +# +# TableFormat's *row elements can be +# +# - either None, if the element is not used, +# - or a DataRow tuple, +# - or a function: [cell_values], [col_widths], [col_alignments] -> string. +# +# padding (an integer) is the amount of white space around data values. +# +# with_header_hide: +# +# - either None, to display all table elements unconditionally, +# - or a list of elements not to be displayed if the table has column headers. +# +TableFormat = namedtuple( + "TableFormat", + [ + "lineabove", + "linebelowheader", + "linebetweenrows", + "linebelow", + "headerrow", + "datarow", + "padding", + "with_header_hide", + ], +) + + +def _is_separating_line(row): + row_type = type(row) + is_sl = (row_type == list or row_type == str) and ( + (len(row) >= 1 and row[0] == SEPARATING_LINE) + or (len(row) >= 2 and row[1] == SEPARATING_LINE) + ) + return is_sl + + +def _pipe_segment_with_colons(align, colwidth): + """Return a segment of a horizontal line with optional colons which + indicate column's alignment (as in `pipe` output format).""" + w = colwidth + if align in ["right", "decimal"]: + return ("-" * (w - 1)) + ":" + elif align == "center": + return ":" + ("-" * (w - 2)) + ":" + elif align == "left": + return ":" + ("-" * (w - 1)) + else: + return "-" * w + + +def _pipe_line_with_colons(colwidths, colaligns): + """Return a horizontal line with optional colons to indicate column's + alignment (as in `pipe` output format).""" + if not colaligns: # e.g. printing an empty data frame (github issue #15) + colaligns = [""] * len(colwidths) + segments = [_pipe_segment_with_colons(a, w) for a, w in zip(colaligns, colwidths)] + return "|" + "|".join(segments) + "|" + + +def _mediawiki_row_with_attrs(separator, cell_values, colwidths, colaligns): + alignment = { + "left": "", + "right": 'align="right"| ', + "center": 'align="center"| ', + "decimal": 'align="right"| ', + } + # hard-coded padding _around_ align attribute and value together + # rather than padding parameter which affects only the value + values_with_attrs = [ + " " + alignment.get(a, "") + c + " " for c, a in zip(cell_values, colaligns) + ] + colsep = separator * 2 + return (separator + colsep.join(values_with_attrs)).rstrip() + + +def _textile_row_with_attrs(cell_values, colwidths, colaligns): + cell_values[0] += " " + alignment = {"left": "<.", "right": ">.", "center": "=.", "decimal": ">."} + values = (alignment.get(a, "") + v for a, v in zip(colaligns, cell_values)) + return "|" + "|".join(values) + "|" + + +def _html_begin_table_without_header(colwidths_ignore, colaligns_ignore): + # this table header will be suppressed if there is a header row + return "\n" + + +def _html_row_with_attrs(celltag, unsafe, cell_values, colwidths, colaligns): + alignment = { + "left": "", + "right": ' style="text-align: right;"', + "center": ' style="text-align: center;"', + "decimal": ' style="text-align: right;"', + } + if unsafe: + values_with_attrs = [ + "<{0}{1}>{2}".format(celltag, alignment.get(a, ""), c) + for c, a in zip(cell_values, colaligns) + ] + else: + values_with_attrs = [ + "<{0}{1}>{2}".format(celltag, alignment.get(a, ""), htmlescape(c)) + for c, a in zip(cell_values, colaligns) + ] + rowhtml = "{}".format("".join(values_with_attrs).rstrip()) + if celltag == "th": # it's a header row, create a new table header + rowhtml = f"
\n\n{rowhtml}\n\n" + return rowhtml + + +def _moin_row_with_attrs(celltag, cell_values, colwidths, colaligns, header=""): + alignment = { + "left": "", + "right": '', + "center": '', + "decimal": '', + } + values_with_attrs = [ + "{}{} {} ".format(celltag, alignment.get(a, ""), header + c + header) + for c, a in zip(cell_values, colaligns) + ] + return "".join(values_with_attrs) + "||" + + +def _latex_line_begin_tabular(colwidths, colaligns, booktabs=False, longtable=False): + alignment = {"left": "l", "right": "r", "center": "c", "decimal": "r"} + tabular_columns_fmt = "".join([alignment.get(a, "l") for a in colaligns]) + return "\n".join( + [ + ("\\begin{tabular}{" if not longtable else "\\begin{longtable}{") + + tabular_columns_fmt + + "}", + "\\toprule" if booktabs else "\\hline", + ] + ) + + +def _asciidoc_row(is_header, *args): + """handle header and data rows for asciidoc format""" + + def make_header_line(is_header, colwidths, colaligns): + # generate the column specifiers + + alignment = {"left": "<", "right": ">", "center": "^", "decimal": ">"} + # use the column widths generated by tabulate for the asciidoc column width specifiers + asciidoc_alignments = zip( + colwidths, [alignment[colalign] for colalign in colaligns] + ) + asciidoc_column_specifiers = [ + "{:d}{}".format(width, align) for width, align in asciidoc_alignments + ] + header_list = ['cols="' + (",".join(asciidoc_column_specifiers)) + '"'] + + # generate the list of options (currently only "header") + options_list = [] + + if is_header: + options_list.append("header") + + if options_list: + header_list += ['options="' + ",".join(options_list) + '"'] + + # generate the list of entries in the table header field + + return "[{}]\n|====".format(",".join(header_list)) + + if len(args) == 2: + # two arguments are passed if called in the context of aboveline + # print the table header with column widths and optional header tag + return make_header_line(False, *args) + + elif len(args) == 3: + # three arguments are passed if called in the context of dataline or headerline + # print the table line and make the aboveline if it is a header + + cell_values, colwidths, colaligns = args + data_line = "|" + "|".join(cell_values) + + if is_header: + return make_header_line(True, colwidths, colaligns) + "\n" + data_line + else: + return data_line + + else: + raise ValueError( + " _asciidoc_row() requires two (colwidths, colaligns) " + + "or three (cell_values, colwidths, colaligns) arguments) " + ) + + +LATEX_ESCAPE_RULES = { + r"&": r"\&", + r"%": r"\%", + r"$": r"\$", + r"#": r"\#", + r"_": r"\_", + r"^": r"\^{}", + r"{": r"\{", + r"}": r"\}", + r"~": r"\textasciitilde{}", + "\\": r"\textbackslash{}", + r"<": r"\ensuremath{<}", + r">": r"\ensuremath{>}", +} + + +def _latex_row(cell_values, colwidths, colaligns, escrules=LATEX_ESCAPE_RULES): + def escape_char(c): + return escrules.get(c, c) + + escaped_values = ["".join(map(escape_char, cell)) for cell in cell_values] + rowfmt = DataRow("", "&", "\\\\") + return _build_simple_row(escaped_values, rowfmt) + + +def _rst_escape_first_column(rows, headers): + def escape_empty(val): + if isinstance(val, (str, bytes)) and not val.strip(): + return ".." + else: + return val + + new_headers = list(headers) + new_rows = [] + if headers: + new_headers[0] = escape_empty(headers[0]) + for row in rows: + new_row = list(row) + if new_row: + new_row[0] = escape_empty(row[0]) + new_rows.append(new_row) + return new_rows, new_headers + + +_table_formats = { + "simple": TableFormat( + lineabove=Line("", "-", " ", ""), + linebelowheader=Line("", "-", " ", ""), + linebetweenrows=None, + linebelow=Line("", "-", " ", ""), + headerrow=DataRow("", " ", ""), + datarow=DataRow("", " ", ""), + padding=0, + with_header_hide=["lineabove", "linebelow"], + ), + "plain": TableFormat( + lineabove=None, + linebelowheader=None, + linebetweenrows=None, + linebelow=None, + headerrow=DataRow("", " ", ""), + datarow=DataRow("", " ", ""), + padding=0, + with_header_hide=None, + ), + "grid": TableFormat( + lineabove=Line("+", "-", "+", "+"), + linebelowheader=Line("+", "=", "+", "+"), + linebetweenrows=Line("+", "-", "+", "+"), + linebelow=Line("+", "-", "+", "+"), + headerrow=DataRow("|", "|", "|"), + datarow=DataRow("|", "|", "|"), + padding=1, + with_header_hide=None, + ), + "simple_grid": TableFormat( + lineabove=Line("┌", "─", "┬", "┐"), + linebelowheader=Line("├", "─", "┼", "┤"), + linebetweenrows=Line("├", "─", "┼", "┤"), + linebelow=Line("└", "─", "┴", "┘"), + headerrow=DataRow("│", "│", "│"), + datarow=DataRow("│", "│", "│"), + padding=1, + with_header_hide=None, + ), + "rounded_grid": TableFormat( + lineabove=Line("╭", "─", "┬", "╮"), + linebelowheader=Line("├", "─", "┼", "┤"), + linebetweenrows=Line("├", "─", "┼", "┤"), + linebelow=Line("╰", "─", "┴", "╯"), + headerrow=DataRow("│", "│", "│"), + datarow=DataRow("│", "│", "│"), + padding=1, + with_header_hide=None, + ), + "heavy_grid": TableFormat( + lineabove=Line("┏", "━", "┳", "┓"), + linebelowheader=Line("┣", "━", "╋", "┫"), + linebetweenrows=Line("┣", "━", "╋", "┫"), + linebelow=Line("┗", "━", "┻", "┛"), + headerrow=DataRow("┃", "┃", "┃"), + datarow=DataRow("┃", "┃", "┃"), + padding=1, + with_header_hide=None, + ), + "mixed_grid": TableFormat( + lineabove=Line("┍", "━", "┯", "┑"), + linebelowheader=Line("┝", "━", "┿", "┥"), + linebetweenrows=Line("├", "─", "┼", "┤"), + linebelow=Line("┕", "━", "┷", "┙"), + headerrow=DataRow("│", "│", "│"), + datarow=DataRow("│", "│", "│"), + padding=1, + with_header_hide=None, + ), + "double_grid": TableFormat( + lineabove=Line("╔", "═", "╦", "╗"), + linebelowheader=Line("╠", "═", "╬", "╣"), + linebetweenrows=Line("╠", "═", "╬", "╣"), + linebelow=Line("╚", "═", "╩", "╝"), + headerrow=DataRow("║", "║", "║"), + datarow=DataRow("║", "║", "║"), + padding=1, + with_header_hide=None, + ), + "fancy_grid": TableFormat( + lineabove=Line("╒", "═", "╤", "╕"), + linebelowheader=Line("╞", "═", "╪", "╡"), + linebetweenrows=Line("├", "─", "┼", "┤"), + linebelow=Line("╘", "═", "╧", "╛"), + headerrow=DataRow("│", "│", "│"), + datarow=DataRow("│", "│", "│"), + padding=1, + with_header_hide=None, + ), + "outline": TableFormat( + lineabove=Line("+", "-", "+", "+"), + linebelowheader=Line("+", "=", "+", "+"), + linebetweenrows=None, + linebelow=Line("+", "-", "+", "+"), + headerrow=DataRow("|", "|", "|"), + datarow=DataRow("|", "|", "|"), + padding=1, + with_header_hide=None, + ), + "simple_outline": TableFormat( + lineabove=Line("┌", "─", "┬", "┐"), + linebelowheader=Line("├", "─", "┼", "┤"), + linebetweenrows=None, + linebelow=Line("└", "─", "┴", "┘"), + headerrow=DataRow("│", "│", "│"), + datarow=DataRow("│", "│", "│"), + padding=1, + with_header_hide=None, + ), + "rounded_outline": TableFormat( + lineabove=Line("╭", "─", "┬", "╮"), + linebelowheader=Line("├", "─", "┼", "┤"), + linebetweenrows=None, + linebelow=Line("╰", "─", "┴", "╯"), + headerrow=DataRow("│", "│", "│"), + datarow=DataRow("│", "│", "│"), + padding=1, + with_header_hide=None, + ), + "heavy_outline": TableFormat( + lineabove=Line("┏", "━", "┳", "┓"), + linebelowheader=Line("┣", "━", "╋", "┫"), + linebetweenrows=None, + linebelow=Line("┗", "━", "┻", "┛"), + headerrow=DataRow("┃", "┃", "┃"), + datarow=DataRow("┃", "┃", "┃"), + padding=1, + with_header_hide=None, + ), + "mixed_outline": TableFormat( + lineabove=Line("┍", "━", "┯", "┑"), + linebelowheader=Line("┝", "━", "┿", "┥"), + linebetweenrows=None, + linebelow=Line("┕", "━", "┷", "┙"), + headerrow=DataRow("│", "│", "│"), + datarow=DataRow("│", "│", "│"), + padding=1, + with_header_hide=None, + ), + "double_outline": TableFormat( + lineabove=Line("╔", "═", "╦", "╗"), + linebelowheader=Line("╠", "═", "╬", "╣"), + linebetweenrows=None, + linebelow=Line("╚", "═", "╩", "╝"), + headerrow=DataRow("║", "║", "║"), + datarow=DataRow("║", "║", "║"), + padding=1, + with_header_hide=None, + ), + "fancy_outline": TableFormat( + lineabove=Line("╒", "═", "╤", "╕"), + linebelowheader=Line("╞", "═", "╪", "╡"), + linebetweenrows=None, + linebelow=Line("╘", "═", "╧", "╛"), + headerrow=DataRow("│", "│", "│"), + datarow=DataRow("│", "│", "│"), + padding=1, + with_header_hide=None, + ), + "github": TableFormat( + lineabove=Line("|", "-", "|", "|"), + linebelowheader=Line("|", "-", "|", "|"), + linebetweenrows=None, + linebelow=None, + headerrow=DataRow("|", "|", "|"), + datarow=DataRow("|", "|", "|"), + padding=1, + with_header_hide=["lineabove"], + ), + "pipe": TableFormat( + lineabove=_pipe_line_with_colons, + linebelowheader=_pipe_line_with_colons, + linebetweenrows=None, + linebelow=None, + headerrow=DataRow("|", "|", "|"), + datarow=DataRow("|", "|", "|"), + padding=1, + with_header_hide=["lineabove"], + ), + "orgtbl": TableFormat( + lineabove=None, + linebelowheader=Line("|", "-", "+", "|"), + linebetweenrows=None, + linebelow=None, + headerrow=DataRow("|", "|", "|"), + datarow=DataRow("|", "|", "|"), + padding=1, + with_header_hide=None, + ), + "jira": TableFormat( + lineabove=None, + linebelowheader=None, + linebetweenrows=None, + linebelow=None, + headerrow=DataRow("||", "||", "||"), + datarow=DataRow("|", "|", "|"), + padding=1, + with_header_hide=None, + ), + "presto": TableFormat( + lineabove=None, + linebelowheader=Line("", "-", "+", ""), + linebetweenrows=None, + linebelow=None, + headerrow=DataRow("", "|", ""), + datarow=DataRow("", "|", ""), + padding=1, + with_header_hide=None, + ), + "pretty": TableFormat( + lineabove=Line("+", "-", "+", "+"), + linebelowheader=Line("+", "-", "+", "+"), + linebetweenrows=None, + linebelow=Line("+", "-", "+", "+"), + headerrow=DataRow("|", "|", "|"), + datarow=DataRow("|", "|", "|"), + padding=1, + with_header_hide=None, + ), + "psql": TableFormat( + lineabove=Line("+", "-", "+", "+"), + linebelowheader=Line("|", "-", "+", "|"), + linebetweenrows=None, + linebelow=Line("+", "-", "+", "+"), + headerrow=DataRow("|", "|", "|"), + datarow=DataRow("|", "|", "|"), + padding=1, + with_header_hide=None, + ), + "rst": TableFormat( + lineabove=Line("", "=", " ", ""), + linebelowheader=Line("", "=", " ", ""), + linebetweenrows=None, + linebelow=Line("", "=", " ", ""), + headerrow=DataRow("", " ", ""), + datarow=DataRow("", " ", ""), + padding=0, + with_header_hide=None, + ), + "mediawiki": TableFormat( + lineabove=Line( + '{| class="wikitable" style="text-align: left;"', + "", + "", + "\n|+ \n|-", + ), + linebelowheader=Line("|-", "", "", ""), + linebetweenrows=Line("|-", "", "", ""), + linebelow=Line("|}", "", "", ""), + headerrow=partial(_mediawiki_row_with_attrs, "!"), + datarow=partial(_mediawiki_row_with_attrs, "|"), + padding=0, + with_header_hide=None, + ), + "moinmoin": TableFormat( + lineabove=None, + linebelowheader=None, + linebetweenrows=None, + linebelow=None, + headerrow=partial(_moin_row_with_attrs, "||", header="'''"), + datarow=partial(_moin_row_with_attrs, "||"), + padding=1, + with_header_hide=None, + ), + "youtrack": TableFormat( + lineabove=None, + linebelowheader=None, + linebetweenrows=None, + linebelow=None, + headerrow=DataRow("|| ", " || ", " || "), + datarow=DataRow("| ", " | ", " |"), + padding=1, + with_header_hide=None, + ), + "html": TableFormat( + lineabove=_html_begin_table_without_header, + linebelowheader="", + linebetweenrows=None, + linebelow=Line("\n
", "", "", ""), + headerrow=partial(_html_row_with_attrs, "th", False), + datarow=partial(_html_row_with_attrs, "td", False), + padding=0, + with_header_hide=["lineabove"], + ), + "unsafehtml": TableFormat( + lineabove=_html_begin_table_without_header, + linebelowheader="", + linebetweenrows=None, + linebelow=Line("\n", "", "", ""), + headerrow=partial(_html_row_with_attrs, "th", True), + datarow=partial(_html_row_with_attrs, "td", True), + padding=0, + with_header_hide=["lineabove"], + ), + "latex": TableFormat( + lineabove=_latex_line_begin_tabular, + linebelowheader=Line("\\hline", "", "", ""), + linebetweenrows=None, + linebelow=Line("\\hline\n\\end{tabular}", "", "", ""), + headerrow=_latex_row, + datarow=_latex_row, + padding=1, + with_header_hide=None, + ), + "latex_raw": TableFormat( + lineabove=_latex_line_begin_tabular, + linebelowheader=Line("\\hline", "", "", ""), + linebetweenrows=None, + linebelow=Line("\\hline\n\\end{tabular}", "", "", ""), + headerrow=partial(_latex_row, escrules={}), + datarow=partial(_latex_row, escrules={}), + padding=1, + with_header_hide=None, + ), + "latex_booktabs": TableFormat( + lineabove=partial(_latex_line_begin_tabular, booktabs=True), + linebelowheader=Line("\\midrule", "", "", ""), + linebetweenrows=None, + linebelow=Line("\\bottomrule\n\\end{tabular}", "", "", ""), + headerrow=_latex_row, + datarow=_latex_row, + padding=1, + with_header_hide=None, + ), + "latex_longtable": TableFormat( + lineabove=partial(_latex_line_begin_tabular, longtable=True), + linebelowheader=Line("\\hline\n\\endhead", "", "", ""), + linebetweenrows=None, + linebelow=Line("\\hline\n\\end{longtable}", "", "", ""), + headerrow=_latex_row, + datarow=_latex_row, + padding=1, + with_header_hide=None, + ), + "tsv": TableFormat( + lineabove=None, + linebelowheader=None, + linebetweenrows=None, + linebelow=None, + headerrow=DataRow("", "\t", ""), + datarow=DataRow("", "\t", ""), + padding=0, + with_header_hide=None, + ), + "textile": TableFormat( + lineabove=None, + linebelowheader=None, + linebetweenrows=None, + linebelow=None, + headerrow=DataRow("|_. ", "|_.", "|"), + datarow=_textile_row_with_attrs, + padding=1, + with_header_hide=None, + ), + "asciidoc": TableFormat( + lineabove=partial(_asciidoc_row, False), + linebelowheader=None, + linebetweenrows=None, + linebelow=Line("|====", "", "", ""), + headerrow=partial(_asciidoc_row, True), + datarow=partial(_asciidoc_row, False), + padding=1, + with_header_hide=["lineabove"], + ), +} + + +tabulate_formats = list(sorted(_table_formats.keys())) + +# The table formats for which multiline cells will be folded into subsequent +# table rows. The key is the original format specified at the API. The value is +# the format that will be used to represent the original format. +multiline_formats = { + "plain": "plain", + "simple": "simple", + "grid": "grid", + "simple_grid": "simple_grid", + "rounded_grid": "rounded_grid", + "heavy_grid": "heavy_grid", + "mixed_grid": "mixed_grid", + "double_grid": "double_grid", + "fancy_grid": "fancy_grid", + "pipe": "pipe", + "orgtbl": "orgtbl", + "jira": "jira", + "presto": "presto", + "pretty": "pretty", + "psql": "psql", + "rst": "rst", +} + +# TODO: Add multiline support for the remaining table formats: +# - mediawiki: Replace \n with
+# - moinmoin: TBD +# - youtrack: TBD +# - html: Replace \n with
+# - latex*: Use "makecell" package: In header, replace X\nY with +# \thead{X\\Y} and in data row, replace X\nY with \makecell{X\\Y} +# - tsv: TBD +# - textile: Replace \n with
(must be well-formed XML) + +_multiline_codes = re.compile(r"\r|\n|\r\n") +_multiline_codes_bytes = re.compile(b"\r|\n|\r\n") + +# Handle ANSI escape sequences for both control sequence introducer (CSI) and +# operating system command (OSC). Both of these begin with 0x1b (or octal 033), +# which will be shown below as ESC. +# +# CSI ANSI escape codes have the following format, defined in section 5.4 of ECMA-48: +# +# CSI: ESC followed by the '[' character (0x5b) +# Parameter Bytes: 0..n bytes in the range 0x30-0x3f +# Intermediate Bytes: 0..n bytes in the range 0x20-0x2f +# Final Byte: a single byte in the range 0x40-0x7e +# +# Also include the terminal hyperlink sequences as described here: +# https://gist.github.com/egmontkob/eb114294efbcd5adb1944c9f3cb5feda +# +# OSC 8 ; params ; uri ST display_text OSC 8 ;; ST +# +# Example: \x1b]8;;https://example.com\x5ctext to show\x1b]8;;\x5c +# +# Where: +# OSC: ESC followed by the ']' character (0x5d) +# params: 0..n optional key value pairs separated by ':' (e.g. foo=bar:baz=qux:abc=123) +# URI: the actual URI with protocol scheme (e.g. https://, file://, ftp://) +# ST: ESC followed by the '\' character (0x5c) +_esc = r"\x1b" +_csi = rf"{_esc}\[" +_osc = rf"{_esc}\]" +_st = rf"{_esc}\\" + +_ansi_escape_pat = rf""" + ( + # terminal colors, etc + {_csi} # CSI + [\x30-\x3f]* # parameter bytes + [\x20-\x2f]* # intermediate bytes + [\x40-\x7e] # final byte + | + # terminal hyperlinks + {_osc}8; # OSC opening + (\w+=\w+:?)* # key=value params list (submatch 2) + ; # delimiter + ([^{_esc}]+) # URI - anything but ESC (submatch 3) + {_st} # ST + ([^{_esc}]+) # link text - anything but ESC (submatch 4) + {_osc}8;;{_st} # "closing" OSC sequence + ) +""" +_ansi_codes = re.compile(_ansi_escape_pat, re.VERBOSE) +_ansi_codes_bytes = re.compile(_ansi_escape_pat.encode("utf8"), re.VERBOSE) +_ansi_color_reset_code = "\033[0m" + +_float_with_thousands_separators = re.compile( + r"^(([+-]?[0-9]{1,3})(?:,([0-9]{3}))*)?(?(1)\.[0-9]*|\.[0-9]+)?$" +) + + +def simple_separated_format(separator): + """Construct a simple TableFormat with columns separated by a separator. + + >>> tsv = simple_separated_format("\\t") ; \ + tabulate([["foo", 1], ["spam", 23]], tablefmt=tsv) == 'foo \\t 1\\nspam\\t23' + True + + """ + return TableFormat( + None, + None, + None, + None, + headerrow=DataRow("", separator, ""), + datarow=DataRow("", separator, ""), + padding=0, + with_header_hide=None, + ) + + +def _isnumber_with_thousands_separator(string): + """ + >>> _isnumber_with_thousands_separator(".") + False + >>> _isnumber_with_thousands_separator("1") + True + >>> _isnumber_with_thousands_separator("1.") + True + >>> _isnumber_with_thousands_separator(".1") + True + >>> _isnumber_with_thousands_separator("1000") + False + >>> _isnumber_with_thousands_separator("1,000") + True + >>> _isnumber_with_thousands_separator("1,0000") + False + >>> _isnumber_with_thousands_separator("1,000.1234") + True + >>> _isnumber_with_thousands_separator(b"1,000.1234") + True + >>> _isnumber_with_thousands_separator("+1,000.1234") + True + >>> _isnumber_with_thousands_separator("-1,000.1234") + True + """ + try: + string = string.decode() + except (UnicodeDecodeError, AttributeError): + pass + + return bool(re.match(_float_with_thousands_separators, string)) + + +def _isconvertible(conv, string): + try: + conv(string) + return True + except (ValueError, TypeError): + return False + + +def _isnumber(string): + """ + >>> _isnumber("123.45") + True + >>> _isnumber("123") + True + >>> _isnumber("spam") + False + >>> _isnumber("123e45678") + False + >>> _isnumber("inf") + True + """ + if not _isconvertible(float, string): + return False + elif isinstance(string, (str, bytes)) and ( + math.isinf(float(string)) or math.isnan(float(string)) + ): + return string.lower() in ["inf", "-inf", "nan"] + return True + + +def _isint(string, inttype=int): + """ + >>> _isint("123") + True + >>> _isint("123.45") + False + """ + return ( + type(string) is inttype + or isinstance(string, (bytes, str)) + and _isconvertible(inttype, string) + ) + + +def _isbool(string): + """ + >>> _isbool(True) + True + >>> _isbool("False") + True + >>> _isbool(1) + False + """ + return type(string) is bool or ( + isinstance(string, (bytes, str)) and string in ("True", "False") + ) + + +def _type(string, has_invisible=True, numparse=True): + """The least generic type (type(None), int, float, str, unicode). + + >>> _type(None) is type(None) + True + >>> _type("foo") is type("") + True + >>> _type("1") is type(1) + True + >>> _type('\x1b[31m42\x1b[0m') is type(42) + True + >>> _type('\x1b[31m42\x1b[0m') is type(42) + True + + """ + + if has_invisible and isinstance(string, (str, bytes)): + string = _strip_ansi(string) + + if string is None: + return type(None) + elif hasattr(string, "isoformat"): # datetime.datetime, date, and time + return str + elif _isbool(string): + return bool + elif _isint(string) and numparse: + return int + elif _isnumber(string) and numparse: + return float + elif isinstance(string, bytes): + return bytes + else: + return str + + +def _afterpoint(string): + """Symbols after a decimal point, -1 if the string lacks the decimal point. + + >>> _afterpoint("123.45") + 2 + >>> _afterpoint("1001") + -1 + >>> _afterpoint("eggs") + -1 + >>> _afterpoint("123e45") + 2 + >>> _afterpoint("123,456.78") + 2 + + """ + if _isnumber(string) or _isnumber_with_thousands_separator(string): + if _isint(string): + return -1 + else: + pos = string.rfind(".") + pos = string.lower().rfind("e") if pos < 0 else pos + if pos >= 0: + return len(string) - pos - 1 + else: + return -1 # no point + else: + return -1 # not a number + + +def _padleft(width, s): + """Flush right. + + >>> _padleft(6, '\u044f\u0439\u0446\u0430') == ' \u044f\u0439\u0446\u0430' + True + + """ + fmt = "{0:>%ds}" % width + return fmt.format(s) + + +def _padright(width, s): + """Flush left. + + >>> _padright(6, '\u044f\u0439\u0446\u0430') == '\u044f\u0439\u0446\u0430 ' + True + + """ + fmt = "{0:<%ds}" % width + return fmt.format(s) + + +def _padboth(width, s): + """Center string. + + >>> _padboth(6, '\u044f\u0439\u0446\u0430') == ' \u044f\u0439\u0446\u0430 ' + True + + """ + fmt = "{0:^%ds}" % width + return fmt.format(s) + + +def _padnone(ignore_width, s): + return s + + +def _strip_ansi(s): + r"""Remove ANSI escape sequences, both CSI (color codes, etc) and OSC hyperlinks. + + CSI sequences are simply removed from the output, while OSC hyperlinks are replaced + with the link text. Note: it may be desirable to show the URI instead but this is not + supported. + + >>> repr(_strip_ansi('\x1B]8;;https://example.com\x1B\\This is a link\x1B]8;;\x1B\\')) + "'This is a link'" + + >>> repr(_strip_ansi('\x1b[31mred\x1b[0m text')) + "'red text'" + + """ + if isinstance(s, str): + return _ansi_codes.sub(r"\4", s) + else: # a bytestring + return _ansi_codes_bytes.sub(r"\4", s) + + +def _visible_width(s): + """Visible width of a printed string. ANSI color codes are removed. + + >>> _visible_width('\x1b[31mhello\x1b[0m'), _visible_width("world") + (5, 5) + + """ + # optional wide-character support + if wcwidth is not None and WIDE_CHARS_MODE: + len_fn = wcwidth.wcswidth + else: + len_fn = len + if isinstance(s, (str, bytes)): + return len_fn(_strip_ansi(s)) + else: + return len_fn(str(s)) + + +def _is_multiline(s): + if isinstance(s, str): + return bool(re.search(_multiline_codes, s)) + else: # a bytestring + return bool(re.search(_multiline_codes_bytes, s)) + + +def _multiline_width(multiline_s, line_width_fn=len): + """Visible width of a potentially multiline content.""" + return max(map(line_width_fn, re.split("[\r\n]", multiline_s))) + + +def _choose_width_fn(has_invisible, enable_widechars, is_multiline): + """Return a function to calculate visible cell width.""" + if has_invisible: + line_width_fn = _visible_width + elif enable_widechars: # optional wide-character support if available + line_width_fn = wcwidth.wcswidth + else: + line_width_fn = len + if is_multiline: + width_fn = lambda s: _multiline_width(s, line_width_fn) # noqa + else: + width_fn = line_width_fn + return width_fn + + +def _align_column_choose_padfn(strings, alignment, has_invisible): + if alignment == "right": + if not PRESERVE_WHITESPACE: + strings = [s.strip() for s in strings] + padfn = _padleft + elif alignment == "center": + if not PRESERVE_WHITESPACE: + strings = [s.strip() for s in strings] + padfn = _padboth + elif alignment == "decimal": + if has_invisible: + decimals = [_afterpoint(_strip_ansi(s)) for s in strings] + else: + decimals = [_afterpoint(s) for s in strings] + maxdecimals = max(decimals) + strings = [s + (maxdecimals - decs) * " " for s, decs in zip(strings, decimals)] + padfn = _padleft + elif not alignment: + padfn = _padnone + else: + if not PRESERVE_WHITESPACE: + strings = [s.strip() for s in strings] + padfn = _padright + return strings, padfn + + +def _align_column_choose_width_fn(has_invisible, enable_widechars, is_multiline): + if has_invisible: + line_width_fn = _visible_width + elif enable_widechars: # optional wide-character support if available + line_width_fn = wcwidth.wcswidth + else: + line_width_fn = len + if is_multiline: + width_fn = lambda s: _align_column_multiline_width(s, line_width_fn) # noqa + else: + width_fn = line_width_fn + return width_fn + + +def _align_column_multiline_width(multiline_s, line_width_fn=len): + """Visible width of a potentially multiline content.""" + return list(map(line_width_fn, re.split("[\r\n]", multiline_s))) + + +def _flat_list(nested_list): + ret = [] + for item in nested_list: + if isinstance(item, list): + for subitem in item: + ret.append(subitem) + else: + ret.append(item) + return ret + + +def _align_column( + strings, + alignment, + minwidth=0, + has_invisible=True, + enable_widechars=False, + is_multiline=False, +): + """[string] -> [padded_string]""" + strings, padfn = _align_column_choose_padfn(strings, alignment, has_invisible) + width_fn = _align_column_choose_width_fn( + has_invisible, enable_widechars, is_multiline + ) + + s_widths = list(map(width_fn, strings)) + maxwidth = max(max(_flat_list(s_widths)), minwidth) + # TODO: refactor column alignment in single-line and multiline modes + if is_multiline: + if not enable_widechars and not has_invisible: + padded_strings = [ + "\n".join([padfn(maxwidth, s) for s in ms.splitlines()]) + for ms in strings + ] + else: + # enable wide-character width corrections + s_lens = [[len(s) for s in re.split("[\r\n]", ms)] for ms in strings] + visible_widths = [ + [maxwidth - (w - l) for w, l in zip(mw, ml)] + for mw, ml in zip(s_widths, s_lens) + ] + # wcswidth and _visible_width don't count invisible characters; + # padfn doesn't need to apply another correction + padded_strings = [ + "\n".join([padfn(w, s) for s, w in zip((ms.splitlines() or ms), mw)]) + for ms, mw in zip(strings, visible_widths) + ] + else: # single-line cell values + if not enable_widechars and not has_invisible: + padded_strings = [padfn(maxwidth, s) for s in strings] + else: + # enable wide-character width corrections + s_lens = list(map(len, strings)) + visible_widths = [maxwidth - (w - l) for w, l in zip(s_widths, s_lens)] + # wcswidth and _visible_width don't count invisible characters; + # padfn doesn't need to apply another correction + padded_strings = [padfn(w, s) for s, w in zip(strings, visible_widths)] + return padded_strings + + +def _more_generic(type1, type2): + types = { + type(None): 0, + bool: 1, + int: 2, + float: 3, + bytes: 4, + str: 5, + } + invtypes = { + 5: str, + 4: bytes, + 3: float, + 2: int, + 1: bool, + 0: type(None), + } + moregeneric = max(types.get(type1, 5), types.get(type2, 5)) + return invtypes[moregeneric] + + +def _column_type(strings, has_invisible=True, numparse=True): + """The least generic type all column values are convertible to. + + >>> _column_type([True, False]) is bool + True + >>> _column_type(["1", "2"]) is int + True + >>> _column_type(["1", "2.3"]) is float + True + >>> _column_type(["1", "2.3", "four"]) is str + True + >>> _column_type(["four", '\u043f\u044f\u0442\u044c']) is str + True + >>> _column_type([None, "brux"]) is str + True + >>> _column_type([1, 2, None]) is int + True + >>> import datetime as dt + >>> _column_type([dt.datetime(1991,2,19), dt.time(17,35)]) is str + True + + """ + types = [_type(s, has_invisible, numparse) for s in strings] + return reduce(_more_generic, types, bool) + + +def _format(val, valtype, floatfmt, intfmt, missingval="", has_invisible=True): + """Format a value according to its type. + + Unicode is supported: + + >>> hrow = ['\u0431\u0443\u043a\u0432\u0430', '\u0446\u0438\u0444\u0440\u0430'] ; \ + tbl = [['\u0430\u0437', 2], ['\u0431\u0443\u043a\u0438', 4]] ; \ + good_result = '\\u0431\\u0443\\u043a\\u0432\\u0430 \\u0446\\u0438\\u0444\\u0440\\u0430\\n------- -------\\n\\u0430\\u0437 2\\n\\u0431\\u0443\\u043a\\u0438 4' ; \ + tabulate(tbl, headers=hrow) == good_result + True + + """ # noqa + if val is None: + return missingval + + if valtype is str: + return f"{val}" + elif valtype is int: + return format(val, intfmt) + elif valtype is bytes: + try: + return str(val, "ascii") + except (TypeError, UnicodeDecodeError): + return str(val) + elif valtype is float: + is_a_colored_number = has_invisible and isinstance(val, (str, bytes)) + if is_a_colored_number: + raw_val = _strip_ansi(val) + formatted_val = format(float(raw_val), floatfmt) + return val.replace(raw_val, formatted_val) + else: + return format(float(val), floatfmt) + else: + return f"{val}" + + +def _align_header( + header, alignment, width, visible_width, is_multiline=False, width_fn=None +): + "Pad string header to width chars given known visible_width of the header." + if is_multiline: + header_lines = re.split(_multiline_codes, header) + padded_lines = [ + _align_header(h, alignment, width, width_fn(h)) for h in header_lines + ] + return "\n".join(padded_lines) + # else: not multiline + ninvisible = len(header) - visible_width + width += ninvisible + if alignment == "left": + return _padright(width, header) + elif alignment == "center": + return _padboth(width, header) + elif not alignment: + return f"{header}" + else: + return _padleft(width, header) + + +def _remove_separating_lines(rows): + if type(rows) == list: + separating_lines = [] + sans_rows = [] + for index, row in enumerate(rows): + if _is_separating_line(row): + separating_lines.append(index) + else: + sans_rows.append(row) + return sans_rows, separating_lines + else: + return rows, None + + +def _reinsert_separating_lines(rows, separating_lines): + if separating_lines: + for index in separating_lines: + rows.insert(index, SEPARATING_LINE) + + +def _prepend_row_index(rows, index): + """Add a left-most index column.""" + if index is None or index is False: + return rows + if isinstance(index, Sized) and len(index) != len(rows): + raise ValueError( + "index must be as long as the number of data rows: " + + "len(index)={} len(rows)={}".format(len(index), len(rows)) + ) + sans_rows, separating_lines = _remove_separating_lines(rows) + new_rows = [] + index_iter = iter(index) + for row in sans_rows: + index_v = next(index_iter) + new_rows.append([index_v] + list(row)) + rows = new_rows + _reinsert_separating_lines(rows, separating_lines) + return rows + + +def _bool(val): + "A wrapper around standard bool() which doesn't throw on NumPy arrays" + try: + return bool(val) + except ValueError: # val is likely to be a numpy array with many elements + return False + + +def _normalize_tabular_data(tabular_data, headers, showindex="default"): + """Transform a supported data type to a list of lists, and a list of headers. + + Supported tabular data types: + + * list-of-lists or another iterable of iterables + + * list of named tuples (usually used with headers="keys") + + * list of dicts (usually used with headers="keys") + + * list of OrderedDicts (usually used with headers="keys") + + * list of dataclasses (Python 3.7+ only, usually used with headers="keys") + + * 2D NumPy arrays + + * NumPy record arrays (usually used with headers="keys") + + * dict of iterables (usually used with headers="keys") + + * pandas.DataFrame (usually used with headers="keys") + + The first row can be used as headers if headers="firstrow", + column indices can be used as headers if headers="keys". + + If showindex="default", show row indices of the pandas.DataFrame. + If showindex="always", show row indices for all types of data. + If showindex="never", don't show row indices for all types of data. + If showindex is an iterable, show its values as row indices. + + """ + + try: + bool(headers) + is_headers2bool_broken = False # noqa + except ValueError: # numpy.ndarray, pandas.core.index.Index, ... + is_headers2bool_broken = True # noqa + headers = list(headers) + + index = None + if hasattr(tabular_data, "keys") and hasattr(tabular_data, "values"): + # dict-like and pandas.DataFrame? + if hasattr(tabular_data.values, "__call__"): + # likely a conventional dict + keys = tabular_data.keys() + rows = list( + izip_longest(*tabular_data.values()) + ) # columns have to be transposed + elif hasattr(tabular_data, "index"): + # values is a property, has .index => it's likely a pandas.DataFrame (pandas 0.11.0) + keys = list(tabular_data) + if ( + showindex in ["default", "always", True] + and tabular_data.index.name is not None + ): + if isinstance(tabular_data.index.name, list): + keys[:0] = tabular_data.index.name + else: + keys[:0] = [tabular_data.index.name] + vals = tabular_data.values # values matrix doesn't need to be transposed + # for DataFrames add an index per default + index = list(tabular_data.index) + rows = [list(row) for row in vals] + else: + raise ValueError("tabular data doesn't appear to be a dict or a DataFrame") + + if headers == "keys": + headers = list(map(str, keys)) # headers should be strings + + else: # it's a usual iterable of iterables, or a NumPy array, or an iterable of dataclasses + rows = list(tabular_data) + + if headers == "keys" and not rows: + # an empty table (issue #81) + headers = [] + elif ( + headers == "keys" + and hasattr(tabular_data, "dtype") + and getattr(tabular_data.dtype, "names") + ): + # numpy record array + headers = tabular_data.dtype.names + elif ( + headers == "keys" + and len(rows) > 0 + and isinstance(rows[0], tuple) + and hasattr(rows[0], "_fields") + ): + # namedtuple + headers = list(map(str, rows[0]._fields)) + elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"): + # dict-like object + uniq_keys = set() # implements hashed lookup + keys = [] # storage for set + if headers == "firstrow": + firstdict = rows[0] if len(rows) > 0 else {} + keys.extend(firstdict.keys()) + uniq_keys.update(keys) + rows = rows[1:] + for row in rows: + for k in row.keys(): + # Save unique items in input order + if k not in uniq_keys: + keys.append(k) + uniq_keys.add(k) + if headers == "keys": + headers = keys + elif isinstance(headers, dict): + # a dict of headers for a list of dicts + headers = [headers.get(k, k) for k in keys] + headers = list(map(str, headers)) + elif headers == "firstrow": + if len(rows) > 0: + headers = [firstdict.get(k, k) for k in keys] + headers = list(map(str, headers)) + else: + headers = [] + elif headers: + raise ValueError( + "headers for a list of dicts is not a dict or a keyword" + ) + rows = [[row.get(k) for k in keys] for row in rows] + + elif ( + headers == "keys" + and hasattr(tabular_data, "description") + and hasattr(tabular_data, "fetchone") + and hasattr(tabular_data, "rowcount") + ): + # Python Database API cursor object (PEP 0249) + # print tabulate(cursor, headers='keys') + headers = [column[0] for column in tabular_data.description] + + elif ( + dataclasses is not None + and len(rows) > 0 + and dataclasses.is_dataclass(rows[0]) + ): + # Python 3.7+'s dataclass + field_names = [field.name for field in dataclasses.fields(rows[0])] + if headers == "keys": + headers = field_names + rows = [[getattr(row, f) for f in field_names] for row in rows] + + elif headers == "keys" and len(rows) > 0: + # keys are column indices + headers = list(map(str, range(len(rows[0])))) + + # take headers from the first row if necessary + if headers == "firstrow" and len(rows) > 0: + if index is not None: + headers = [index[0]] + list(rows[0]) + index = index[1:] + else: + headers = rows[0] + headers = list(map(str, headers)) # headers should be strings + rows = rows[1:] + elif headers == "firstrow": + headers = [] + + headers = list(map(str, headers)) + # rows = list(map(list, rows)) + rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows)) + + # add or remove an index column + showindex_is_a_str = type(showindex) in [str, bytes] + if showindex == "default" and index is not None: + rows = _prepend_row_index(rows, index) + elif isinstance(showindex, Sized) and not showindex_is_a_str: + rows = _prepend_row_index(rows, list(showindex)) + elif isinstance(showindex, Iterable) and not showindex_is_a_str: + rows = _prepend_row_index(rows, showindex) + elif showindex == "always" or (_bool(showindex) and not showindex_is_a_str): + if index is None: + index = list(range(len(rows))) + rows = _prepend_row_index(rows, index) + elif showindex == "never" or (not _bool(showindex) and not showindex_is_a_str): + pass + + # pad with empty headers for initial columns if necessary + if headers and len(rows) > 0: + nhs = len(headers) + ncols = len(rows[0]) + if nhs < ncols: + headers = [""] * (ncols - nhs) + headers + + return rows, headers + + +def _wrap_text_to_colwidths(list_of_lists, colwidths, numparses=True): + numparses = _expand_iterable(numparses, len(list_of_lists[0]), True) + + result = [] + + for row in list_of_lists: + new_row = [] + for cell, width, numparse in zip(row, colwidths, numparses): + if _isnumber(cell) and numparse: + new_row.append(cell) + continue + + if width is not None: + wrapper = _CustomTextWrap(width=width) + # Cast based on our internal type handling + # Any future custom formatting of types (such as datetimes) + # may need to be more explicit than just `str` of the object + casted_cell = ( + str(cell) if _isnumber(cell) else _type(cell, numparse)(cell) + ) + wrapped = wrapper.wrap(casted_cell) + new_row.append("\n".join(wrapped)) + else: + new_row.append(cell) + result.append(new_row) + + return result + + +def _to_str(s, encoding="utf8", errors="ignore"): + """ + A type safe wrapper for converting a bytestring to str. This is essentially just + a wrapper around .decode() intended for use with things like map(), but with some + specific behavior: + + 1. if the given parameter is not a bytestring, it is returned unmodified + 2. decode() is called for the given parameter and assumes utf8 encoding, but the + default error behavior is changed from 'strict' to 'ignore' + + >>> repr(_to_str(b'foo')) + "'foo'" + + >>> repr(_to_str('foo')) + "'foo'" + + >>> repr(_to_str(42)) + "'42'" + + """ + if isinstance(s, bytes): + return s.decode(encoding=encoding, errors=errors) + return str(s) + + +def tabulate( + tabular_data, + headers=(), + tablefmt="simple", + floatfmt=_DEFAULT_FLOATFMT, + intfmt=_DEFAULT_INTFMT, + numalign=_DEFAULT_ALIGN, + stralign=_DEFAULT_ALIGN, + missingval=_DEFAULT_MISSINGVAL, + showindex="default", + disable_numparse=False, + colalign=None, + maxcolwidths=None, + rowalign=None, + maxheadercolwidths=None, +): + """Format a fixed width table for pretty printing. + + >>> print(tabulate([[1, 2.34], [-56, "8.999"], ["2", "10001"]])) + --- --------- + 1 2.34 + -56 8.999 + 2 10001 + --- --------- + + The first required argument (`tabular_data`) can be a + list-of-lists (or another iterable of iterables), a list of named + tuples, a dictionary of iterables, an iterable of dictionaries, + an iterable of dataclasses (Python 3.7+), a two-dimensional NumPy array, + NumPy record array, or a Pandas' dataframe. + + + Table headers + ------------- + + To print nice column headers, supply the second argument (`headers`): + + - `headers` can be an explicit list of column headers + - if `headers="firstrow"`, then the first row of data is used + - if `headers="keys"`, then dictionary keys or column indices are used + + Otherwise a headerless table is produced. + + If the number of headers is less than the number of columns, they + are supposed to be names of the last columns. This is consistent + with the plain-text format of R and Pandas' dataframes. + + >>> print(tabulate([["sex","age"],["Alice","F",24],["Bob","M",19]], + ... headers="firstrow")) + sex age + ----- ----- ----- + Alice F 24 + Bob M 19 + + By default, pandas.DataFrame data have an additional column called + row index. To add a similar column to all other types of data, + use `showindex="always"` or `showindex=True`. To suppress row indices + for all types of data, pass `showindex="never" or `showindex=False`. + To add a custom row index column, pass `showindex=some_iterable`. + + >>> print(tabulate([["F",24],["M",19]], showindex="always")) + - - -- + 0 F 24 + 1 M 19 + - - -- + + + Column alignment + ---------------- + + `tabulate` tries to detect column types automatically, and aligns + the values properly. By default it aligns decimal points of the + numbers (or flushes integer numbers to the right), and flushes + everything else to the left. Possible column alignments + (`numalign`, `stralign`) are: "right", "center", "left", "decimal" + (only for `numalign`), and None (to disable alignment). + + + Table formats + ------------- + + `intfmt` is a format specification used for columns which + contain numeric data without a decimal point. This can also be + a list or tuple of format strings, one per column. + + `floatfmt` is a format specification used for columns which + contain numeric data with a decimal point. This can also be + a list or tuple of format strings, one per column. + + `None` values are replaced with a `missingval` string (like + `floatfmt`, this can also be a list of values for different + columns): + + >>> print(tabulate([["spam", 1, None], + ... ["eggs", 42, 3.14], + ... ["other", None, 2.7]], missingval="?")) + ----- -- ---- + spam 1 ? + eggs 42 3.14 + other ? 2.7 + ----- -- ---- + + Various plain-text table formats (`tablefmt`) are supported: + 'plain', 'simple', 'grid', 'pipe', 'orgtbl', 'rst', 'mediawiki', + 'latex', 'latex_raw', 'latex_booktabs', 'latex_longtable' and tsv. + Variable `tabulate_formats`contains the list of currently supported formats. + + "plain" format doesn't use any pseudographics to draw tables, + it separates columns with a double space: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "plain")) + strings numbers + spam 41.9999 + eggs 451 + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="plain")) + spam 41.9999 + eggs 451 + + "simple" format is like Pandoc simple_tables: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "simple")) + strings numbers + --------- --------- + spam 41.9999 + eggs 451 + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="simple")) + ---- -------- + spam 41.9999 + eggs 451 + ---- -------- + + "grid" is similar to tables produced by Emacs table.el package or + Pandoc grid_tables: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "grid")) + +-----------+-----------+ + | strings | numbers | + +===========+===========+ + | spam | 41.9999 | + +-----------+-----------+ + | eggs | 451 | + +-----------+-----------+ + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="grid")) + +------+----------+ + | spam | 41.9999 | + +------+----------+ + | eggs | 451 | + +------+----------+ + + "simple_grid" draws a grid using single-line box-drawing + characters: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "simple_grid")) + ┌───────────┬───────────┐ + │ strings │ numbers │ + ├───────────┼───────────┤ + │ spam │ 41.9999 │ + ├───────────┼───────────┤ + │ eggs │ 451 │ + └───────────┴───────────┘ + + "rounded_grid" draws a grid using single-line box-drawing + characters with rounded corners: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "rounded_grid")) + ╭───────────┬───────────╮ + │ strings │ numbers │ + ├───────────┼───────────┤ + │ spam │ 41.9999 │ + ├───────────┼───────────┤ + │ eggs │ 451 │ + ╰───────────┴───────────╯ + + "heavy_grid" draws a grid using bold (thick) single-line box-drawing + characters: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "heavy_grid")) + ┏━━━━━━━━━━━┳━━━━━━━━━━━┓ + ┃ strings ┃ numbers ┃ + ┣━━━━━━━━━━━╋━━━━━━━━━━━┫ + ┃ spam ┃ 41.9999 ┃ + ┣━━━━━━━━━━━╋━━━━━━━━━━━┫ + ┃ eggs ┃ 451 ┃ + ┗━━━━━━━━━━━┻━━━━━━━━━━━┛ + + "mixed_grid" draws a grid using a mix of light (thin) and heavy (thick) lines + box-drawing characters: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "mixed_grid")) + ┍━━━━━━━━━━━┯━━━━━━━━━━━┑ + │ strings │ numbers │ + ┝━━━━━━━━━━━┿━━━━━━━━━━━┥ + │ spam │ 41.9999 │ + ├───────────┼───────────┤ + │ eggs │ 451 │ + ┕━━━━━━━━━━━┷━━━━━━━━━━━┙ + + "double_grid" draws a grid using double-line box-drawing + characters: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "double_grid")) + ╔═══════════╦═══════════╗ + ║ strings ║ numbers ║ + ╠═══════════╬═══════════╣ + ║ spam ║ 41.9999 ║ + ╠═══════════╬═══════════╣ + ║ eggs ║ 451 ║ + ╚═══════════╩═══════════╝ + + "fancy_grid" draws a grid using a mix of single and + double-line box-drawing characters: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "fancy_grid")) + ╒═══════════╤═══════════╕ + │ strings │ numbers │ + ╞═══════════╪═══════════╡ + │ spam │ 41.9999 │ + ├───────────┼───────────┤ + │ eggs │ 451 │ + ╘═══════════╧═══════════╛ + + "outline" is the same as the "grid" format but doesn't draw lines between rows: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "outline")) + +-----------+-----------+ + | strings | numbers | + +===========+===========+ + | spam | 41.9999 | + | eggs | 451 | + +-----------+-----------+ + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="outline")) + +------+----------+ + | spam | 41.9999 | + | eggs | 451 | + +------+----------+ + + "simple_outline" is the same as the "simple_grid" format but doesn't draw lines between rows: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "simple_outline")) + ┌───────────┬───────────┐ + │ strings │ numbers │ + ├───────────┼───────────┤ + │ spam │ 41.9999 │ + │ eggs │ 451 │ + └───────────┴───────────┘ + + "rounded_outline" is the same as the "rounded_grid" format but doesn't draw lines between rows: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "rounded_outline")) + ╭───────────┬───────────╮ + │ strings │ numbers │ + ├───────────┼───────────┤ + │ spam │ 41.9999 │ + │ eggs │ 451 │ + ╰───────────┴───────────╯ + + "heavy_outline" is the same as the "heavy_grid" format but doesn't draw lines between rows: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "heavy_outline")) + ┏━━━━━━━━━━━┳━━━━━━━━━━━┓ + ┃ strings ┃ numbers ┃ + ┣━━━━━━━━━━━╋━━━━━━━━━━━┫ + ┃ spam ┃ 41.9999 ┃ + ┃ eggs ┃ 451 ┃ + ┗━━━━━━━━━━━┻━━━━━━━━━━━┛ + + "mixed_outline" is the same as the "mixed_grid" format but doesn't draw lines between rows: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "mixed_outline")) + ┍━━━━━━━━━━━┯━━━━━━━━━━━┑ + │ strings │ numbers │ + ┝━━━━━━━━━━━┿━━━━━━━━━━━┥ + │ spam │ 41.9999 │ + │ eggs │ 451 │ + ┕━━━━━━━━━━━┷━━━━━━━━━━━┙ + + "double_outline" is the same as the "double_grid" format but doesn't draw lines between rows: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "double_outline")) + ╔═══════════╦═══════════╗ + ║ strings ║ numbers ║ + ╠═══════════╬═══════════╣ + ║ spam ║ 41.9999 ║ + ║ eggs ║ 451 ║ + ╚═══════════╩═══════════╝ + + "fancy_outline" is the same as the "fancy_grid" format but doesn't draw lines between rows: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "fancy_outline")) + ╒═══════════╤═══════════╕ + │ strings │ numbers │ + ╞═══════════╪═══════════╡ + │ spam │ 41.9999 │ + │ eggs │ 451 │ + ╘═══════════╧═══════════╛ + + "pipe" is like tables in PHP Markdown Extra extension or Pandoc + pipe_tables: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "pipe")) + | strings | numbers | + |:----------|----------:| + | spam | 41.9999 | + | eggs | 451 | + + "presto" is like tables produce by the Presto CLI: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "presto")) + strings | numbers + -----------+----------- + spam | 41.9999 + eggs | 451 + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="pipe")) + |:-----|---------:| + | spam | 41.9999 | + | eggs | 451 | + + "orgtbl" is like tables in Emacs org-mode and orgtbl-mode. They + are slightly different from "pipe" format by not using colons to + define column alignment, and using a "+" sign to indicate line + intersections: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "orgtbl")) + | strings | numbers | + |-----------+-----------| + | spam | 41.9999 | + | eggs | 451 | + + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="orgtbl")) + | spam | 41.9999 | + | eggs | 451 | + + "rst" is like a simple table format from reStructuredText; please + note that reStructuredText accepts also "grid" tables: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], + ... ["strings", "numbers"], "rst")) + ========= ========= + strings numbers + ========= ========= + spam 41.9999 + eggs 451 + ========= ========= + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="rst")) + ==== ======== + spam 41.9999 + eggs 451 + ==== ======== + + "mediawiki" produces a table markup used in Wikipedia and on other + MediaWiki-based sites: + + >>> print(tabulate([["strings", "numbers"], ["spam", 41.9999], ["eggs", "451.0"]], + ... headers="firstrow", tablefmt="mediawiki")) + {| class="wikitable" style="text-align: left;" + |+ + |- + ! strings !! align="right"| numbers + |- + | spam || align="right"| 41.9999 + |- + | eggs || align="right"| 451 + |} + + "html" produces HTML markup as an html.escape'd str + with a ._repr_html_ method so that Jupyter Lab and Notebook display the HTML + and a .str property so that the raw HTML remains accessible + the unsafehtml table format can be used if an unescaped HTML format is required: + + >>> print(tabulate([["strings", "numbers"], ["spam", 41.9999], ["eggs", "451.0"]], + ... headers="firstrow", tablefmt="html")) + + + + + + + + +
strings numbers
spam 41.9999
eggs 451
+ + "latex" produces a tabular environment of LaTeX document markup: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="latex")) + \\begin{tabular}{lr} + \\hline + spam & 41.9999 \\\\ + eggs & 451 \\\\ + \\hline + \\end{tabular} + + "latex_raw" is similar to "latex", but doesn't escape special characters, + such as backslash and underscore, so LaTeX commands may embedded into + cells' values: + + >>> print(tabulate([["spam$_9$", 41.9999], ["\\\\emph{eggs}", "451.0"]], tablefmt="latex_raw")) + \\begin{tabular}{lr} + \\hline + spam$_9$ & 41.9999 \\\\ + \\emph{eggs} & 451 \\\\ + \\hline + \\end{tabular} + + "latex_booktabs" produces a tabular environment of LaTeX document markup + using the booktabs.sty package: + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="latex_booktabs")) + \\begin{tabular}{lr} + \\toprule + spam & 41.9999 \\\\ + eggs & 451 \\\\ + \\bottomrule + \\end{tabular} + + "latex_longtable" produces a tabular environment that can stretch along + multiple pages, using the longtable package for LaTeX. + + >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="latex_longtable")) + \\begin{longtable}{lr} + \\hline + spam & 41.9999 \\\\ + eggs & 451 \\\\ + \\hline + \\end{longtable} + + + Number parsing + -------------- + By default, anything which can be parsed as a number is a number. + This ensures numbers represented as strings are aligned properly. + This can lead to weird results for particular strings such as + specific git SHAs e.g. "42992e1" will be parsed into the number + 429920 and aligned as such. + + To completely disable number parsing (and alignment), use + `disable_numparse=True`. For more fine grained control, a list column + indices is used to disable number parsing only on those columns + e.g. `disable_numparse=[0, 2]` would disable number parsing only on the + first and third columns. + + Column Widths and Auto Line Wrapping + ------------------------------------ + Tabulate will, by default, set the width of each column to the length of the + longest element in that column. However, in situations where fields are expected + to reasonably be too long to look good as a single line, tabulate can help automate + word wrapping long fields for you. Use the parameter `maxcolwidth` to provide a + list of maximal column widths + + >>> print(tabulate( \ + [('1', 'John Smith', \ + 'This is a rather long description that might look better if it is wrapped a bit')], \ + headers=("Issue Id", "Author", "Description"), \ + maxcolwidths=[None, None, 30], \ + tablefmt="grid" \ + )) + +------------+------------+-------------------------------+ + | Issue Id | Author | Description | + +============+============+===============================+ + | 1 | John Smith | This is a rather long | + | | | description that might look | + | | | better if it is wrapped a bit | + +------------+------------+-------------------------------+ + + Header column width can be specified in a similar way using `maxheadercolwidth` + + """ + + if tabular_data is None: + tabular_data = [] + + list_of_lists, headers = _normalize_tabular_data( + tabular_data, headers, showindex=showindex + ) + list_of_lists, separating_lines = _remove_separating_lines(list_of_lists) + + if maxcolwidths is not None: + num_cols = len(list_of_lists[0]) + if isinstance(maxcolwidths, int): # Expand scalar for all columns + maxcolwidths = _expand_iterable(maxcolwidths, num_cols, maxcolwidths) + else: # Ignore col width for any 'trailing' columns + maxcolwidths = _expand_iterable(maxcolwidths, num_cols, None) + + numparses = _expand_numparse(disable_numparse, num_cols) + list_of_lists = _wrap_text_to_colwidths( + list_of_lists, maxcolwidths, numparses=numparses + ) + + if maxheadercolwidths is not None: + num_cols = len(list_of_lists[0]) + if isinstance(maxheadercolwidths, int): # Expand scalar for all columns + maxheadercolwidths = _expand_iterable( + maxheadercolwidths, num_cols, maxheadercolwidths + ) + else: # Ignore col width for any 'trailing' columns + maxheadercolwidths = _expand_iterable(maxheadercolwidths, num_cols, None) + + numparses = _expand_numparse(disable_numparse, num_cols) + headers = _wrap_text_to_colwidths( + [headers], maxheadercolwidths, numparses=numparses + )[0] + + # empty values in the first column of RST tables should be escaped (issue #82) + # "" should be escaped as "\\ " or ".." + if tablefmt == "rst": + list_of_lists, headers = _rst_escape_first_column(list_of_lists, headers) + + # PrettyTable formatting does not use any extra padding. + # Numbers are not parsed and are treated the same as strings for alignment. + # Check if pretty is the format being used and override the defaults so it + # does not impact other formats. + min_padding = MIN_PADDING + if tablefmt == "pretty": + min_padding = 0 + disable_numparse = True + numalign = "center" if numalign == _DEFAULT_ALIGN else numalign + stralign = "center" if stralign == _DEFAULT_ALIGN else stralign + else: + numalign = "decimal" if numalign == _DEFAULT_ALIGN else numalign + stralign = "left" if stralign == _DEFAULT_ALIGN else stralign + + # optimization: look for ANSI control codes once, + # enable smart width functions only if a control code is found + # + # convert the headers and rows into a single, tab-delimited string ensuring + # that any bytestrings are decoded safely (i.e. errors ignored) + plain_text = "\t".join( + chain( + # headers + map(_to_str, headers), + # rows: chain the rows together into a single iterable after mapping + # the bytestring conversino to each cell value + chain.from_iterable(map(_to_str, row) for row in list_of_lists), + ) + ) + + has_invisible = _ansi_codes.search(plain_text) is not None + + enable_widechars = wcwidth is not None and WIDE_CHARS_MODE + if ( + not isinstance(tablefmt, TableFormat) + and tablefmt in multiline_formats + and _is_multiline(plain_text) + ): + tablefmt = multiline_formats.get(tablefmt, tablefmt) + is_multiline = True + else: + is_multiline = False + width_fn = _choose_width_fn(has_invisible, enable_widechars, is_multiline) + + # format rows and columns, convert numeric values to strings + cols = list(izip_longest(*list_of_lists)) + numparses = _expand_numparse(disable_numparse, len(cols)) + coltypes = [_column_type(col, numparse=np) for col, np in zip(cols, numparses)] + if isinstance(floatfmt, str): # old version + float_formats = len(cols) * [ + floatfmt + ] # just duplicate the string to use in each column + else: # if floatfmt is list, tuple etc we have one per column + float_formats = list(floatfmt) + if len(float_formats) < len(cols): + float_formats.extend((len(cols) - len(float_formats)) * [_DEFAULT_FLOATFMT]) + if isinstance(intfmt, str): # old version + int_formats = len(cols) * [ + intfmt + ] # just duplicate the string to use in each column + else: # if intfmt is list, tuple etc we have one per column + int_formats = list(intfmt) + if len(int_formats) < len(cols): + int_formats.extend((len(cols) - len(int_formats)) * [_DEFAULT_INTFMT]) + if isinstance(missingval, str): + missing_vals = len(cols) * [missingval] + else: + missing_vals = list(missingval) + if len(missing_vals) < len(cols): + missing_vals.extend((len(cols) - len(missing_vals)) * [_DEFAULT_MISSINGVAL]) + cols = [ + [_format(v, ct, fl_fmt, int_fmt, miss_v, has_invisible) for v in c] + for c, ct, fl_fmt, int_fmt, miss_v in zip( + cols, coltypes, float_formats, int_formats, missing_vals + ) + ] + + # align columns + aligns = [numalign if ct in [int, float] else stralign for ct in coltypes] + if colalign is not None: + assert isinstance(colalign, Iterable) + for idx, align in enumerate(colalign): + aligns[idx] = align + minwidths = ( + [width_fn(h) + min_padding for h in headers] if headers else [0] * len(cols) + ) + cols = [ + _align_column(c, a, minw, has_invisible, enable_widechars, is_multiline) + for c, a, minw in zip(cols, aligns, minwidths) + ] + + if headers: + # align headers and add headers + t_cols = cols or [[""]] * len(headers) + t_aligns = aligns or [stralign] * len(headers) + minwidths = [ + max(minw, max(width_fn(cl) for cl in c)) + for minw, c in zip(minwidths, t_cols) + ] + headers = [ + _align_header(h, a, minw, width_fn(h), is_multiline, width_fn) + for h, a, minw in zip(headers, t_aligns, minwidths) + ] + rows = list(zip(*cols)) + else: + minwidths = [max(width_fn(cl) for cl in c) for c in cols] + rows = list(zip(*cols)) + + if not isinstance(tablefmt, TableFormat): + tablefmt = _table_formats.get(tablefmt, _table_formats["simple"]) + + ra_default = rowalign if isinstance(rowalign, str) else None + rowaligns = _expand_iterable(rowalign, len(rows), ra_default) + _reinsert_separating_lines(rows, separating_lines) + + return _format_table( + tablefmt, headers, rows, minwidths, aligns, is_multiline, rowaligns=rowaligns + ) + + +def _expand_numparse(disable_numparse, column_count): + """ + Return a list of bools of length `column_count` which indicates whether + number parsing should be used on each column. + If `disable_numparse` is a list of indices, each of those indices are False, + and everything else is True. + If `disable_numparse` is a bool, then the returned list is all the same. + """ + if isinstance(disable_numparse, Iterable): + numparses = [True] * column_count + for index in disable_numparse: + numparses[index] = False + return numparses + else: + return [not disable_numparse] * column_count + + +def _expand_iterable(original, num_desired, default): + """ + Expands the `original` argument to return a return a list of + length `num_desired`. If `original` is shorter than `num_desired`, it will + be padded with the value in `default`. + If `original` is not a list to begin with (i.e. scalar value) a list of + length `num_desired` completely populated with `default will be returned + """ + if isinstance(original, Iterable) and not isinstance(original, str): + return original + [default] * (num_desired - len(original)) + else: + return [default] * num_desired + + +def _pad_row(cells, padding): + if cells: + pad = " " * padding + padded_cells = [pad + cell + pad for cell in cells] + return padded_cells + else: + return cells + + +def _build_simple_row(padded_cells, rowfmt): + "Format row according to DataRow format without padding." + begin, sep, end = rowfmt + return (begin + sep.join(padded_cells) + end).rstrip() + + +def _build_row(padded_cells, colwidths, colaligns, rowfmt): + "Return a string which represents a row of data cells." + if not rowfmt: + return None + if hasattr(rowfmt, "__call__"): + return rowfmt(padded_cells, colwidths, colaligns) + else: + return _build_simple_row(padded_cells, rowfmt) + + +def _append_basic_row(lines, padded_cells, colwidths, colaligns, rowfmt, rowalign=None): + # NOTE: rowalign is ignored and exists for api compatibility with _append_multiline_row + lines.append(_build_row(padded_cells, colwidths, colaligns, rowfmt)) + return lines + + +def _align_cell_veritically(text_lines, num_lines, column_width, row_alignment): + delta_lines = num_lines - len(text_lines) + blank = [" " * column_width] + if row_alignment == "bottom": + return blank * delta_lines + text_lines + elif row_alignment == "center": + top_delta = delta_lines // 2 + bottom_delta = delta_lines - top_delta + return top_delta * blank + text_lines + bottom_delta * blank + else: + return text_lines + blank * delta_lines + + +def _append_multiline_row( + lines, padded_multiline_cells, padded_widths, colaligns, rowfmt, pad, rowalign=None +): + colwidths = [w - 2 * pad for w in padded_widths] + cells_lines = [c.splitlines() for c in padded_multiline_cells] + nlines = max(map(len, cells_lines)) # number of lines in the row + # vertically pad cells where some lines are missing + # cells_lines = [ + # (cl + [" " * w] * (nlines - len(cl))) for cl, w in zip(cells_lines, colwidths) + # ] + + cells_lines = [ + _align_cell_veritically(cl, nlines, w, rowalign) + for cl, w in zip(cells_lines, colwidths) + ] + lines_cells = [[cl[i] for cl in cells_lines] for i in range(nlines)] + for ln in lines_cells: + padded_ln = _pad_row(ln, pad) + _append_basic_row(lines, padded_ln, colwidths, colaligns, rowfmt) + return lines + + +def _build_line(colwidths, colaligns, linefmt): + "Return a string which represents a horizontal line." + if not linefmt: + return None + if hasattr(linefmt, "__call__"): + return linefmt(colwidths, colaligns) + else: + begin, fill, sep, end = linefmt + cells = [fill * w for w in colwidths] + return _build_simple_row(cells, (begin, sep, end)) + + +def _append_line(lines, colwidths, colaligns, linefmt): + lines.append(_build_line(colwidths, colaligns, linefmt)) + return lines + + +class JupyterHTMLStr(str): + """Wrap the string with a _repr_html_ method so that Jupyter + displays the HTML table""" + + def _repr_html_(self): + return self + + @property + def str(self): + """add a .str property so that the raw string is still accessible""" + return self + + +def _format_table(fmt, headers, rows, colwidths, colaligns, is_multiline, rowaligns): + """Produce a plain-text representation of the table.""" + lines = [] + hidden = fmt.with_header_hide if (headers and fmt.with_header_hide) else [] + pad = fmt.padding + headerrow = fmt.headerrow + + padded_widths = [(w + 2 * pad) for w in colwidths] + if is_multiline: + pad_row = lambda row, _: row # noqa do it later, in _append_multiline_row + append_row = partial(_append_multiline_row, pad=pad) + else: + pad_row = _pad_row + append_row = _append_basic_row + + padded_headers = pad_row(headers, pad) + padded_rows = [pad_row(row, pad) for row in rows] + + if fmt.lineabove and "lineabove" not in hidden: + _append_line(lines, padded_widths, colaligns, fmt.lineabove) + + if padded_headers: + append_row(lines, padded_headers, padded_widths, colaligns, headerrow) + if fmt.linebelowheader and "linebelowheader" not in hidden: + _append_line(lines, padded_widths, colaligns, fmt.linebelowheader) + + if padded_rows and fmt.linebetweenrows and "linebetweenrows" not in hidden: + # initial rows with a line below + for row, ralign in zip(padded_rows[:-1], rowaligns): + append_row( + lines, row, padded_widths, colaligns, fmt.datarow, rowalign=ralign + ) + _append_line(lines, padded_widths, colaligns, fmt.linebetweenrows) + # the last row without a line below + append_row( + lines, + padded_rows[-1], + padded_widths, + colaligns, + fmt.datarow, + rowalign=rowaligns[-1], + ) + else: + separating_line = ( + fmt.linebetweenrows + or fmt.linebelowheader + or fmt.linebelow + or fmt.lineabove + or Line("", "", "", "") + ) + for row in padded_rows: + # test to see if either the 1st column or the 2nd column (account for showindex) has + # the SEPARATING_LINE flag + if _is_separating_line(row): + _append_line(lines, padded_widths, colaligns, separating_line) + else: + append_row(lines, row, padded_widths, colaligns, fmt.datarow) + + if fmt.linebelow and "linebelow" not in hidden: + _append_line(lines, padded_widths, colaligns, fmt.linebelow) + + if headers or rows: + output = "\n".join(lines) + if fmt.lineabove == _html_begin_table_without_header: + return JupyterHTMLStr(output) + else: + return output + else: # a completely empty table + return "" + + +class _CustomTextWrap(textwrap.TextWrapper): + """A custom implementation of CPython's textwrap.TextWrapper. This supports + both wide characters (Korea, Japanese, Chinese) - including mixed string. + For the most part, the `_handle_long_word` and `_wrap_chunks` functions were + copy pasted out of the CPython baseline, and updated with our custom length + and line appending logic. + """ + + def __init__(self, *args, **kwargs): + self._active_codes = [] + self.max_lines = None # For python2 compatibility + textwrap.TextWrapper.__init__(self, *args, **kwargs) + + @staticmethod + def _len(item): + """Custom len that gets console column width for wide + and non-wide characters as well as ignores color codes""" + stripped = _strip_ansi(item) + if wcwidth: + return wcwidth.wcswidth(stripped) + else: + return len(stripped) + + def _update_lines(self, lines, new_line): + """Adds a new line to the list of lines the text is being wrapped into + This function will also track any ANSI color codes in this string as well + as add any colors from previous lines order to preserve the same formatting + as a single unwrapped string. + """ + code_matches = [x for x in _ansi_codes.finditer(new_line)] + color_codes = [ + code.string[code.span()[0] : code.span()[1]] for code in code_matches + ] + + # Add color codes from earlier in the unwrapped line, and then track any new ones we add. + new_line = "".join(self._active_codes) + new_line + + for code in color_codes: + if code != _ansi_color_reset_code: + self._active_codes.append(code) + else: # A single reset code resets everything + self._active_codes = [] + + # Always ensure each line is color terminted if any colors are + # still active, otherwise colors will bleed into other cells on the console + if len(self._active_codes) > 0: + new_line = new_line + _ansi_color_reset_code + + lines.append(new_line) + + def _handle_long_word(self, reversed_chunks, cur_line, cur_len, width): + """_handle_long_word(chunks : [string], + cur_line : [string], + cur_len : int, width : int) + Handle a chunk of text (most likely a word, not whitespace) that + is too long to fit in any line. + """ + # Figure out when indent is larger than the specified width, and make + # sure at least one character is stripped off on every pass + if width < 1: + space_left = 1 + else: + space_left = width - cur_len + + # If we're allowed to break long words, then do so: put as much + # of the next chunk onto the current line as will fit. + if self.break_long_words: + # Tabulate Custom: Build the string up piece-by-piece in order to + # take each charcter's width into account + chunk = reversed_chunks[-1] + i = 1 + while self._len(chunk[:i]) <= space_left: + i = i + 1 + cur_line.append(chunk[: i - 1]) + reversed_chunks[-1] = chunk[i - 1 :] + + # Otherwise, we have to preserve the long word intact. Only add + # it to the current line if there's nothing already there -- + # that minimizes how much we violate the width constraint. + elif not cur_line: + cur_line.append(reversed_chunks.pop()) + + # If we're not allowed to break long words, and there's already + # text on the current line, do nothing. Next time through the + # main loop of _wrap_chunks(), we'll wind up here again, but + # cur_len will be zero, so the next line will be entirely + # devoted to the long word that we can't handle right now. + + def _wrap_chunks(self, chunks): + """_wrap_chunks(chunks : [string]) -> [string] + Wrap a sequence of text chunks and return a list of lines of + length 'self.width' or less. (If 'break_long_words' is false, + some lines may be longer than this.) Chunks correspond roughly + to words and the whitespace between them: each chunk is + indivisible (modulo 'break_long_words'), but a line break can + come between any two chunks. Chunks should not have internal + whitespace; ie. a chunk is either all whitespace or a "word". + Whitespace chunks will be removed from the beginning and end of + lines, but apart from that whitespace is preserved. + """ + lines = [] + if self.width <= 0: + raise ValueError("invalid width %r (must be > 0)" % self.width) + if self.max_lines is not None: + if self.max_lines > 1: + indent = self.subsequent_indent + else: + indent = self.initial_indent + if self._len(indent) + self._len(self.placeholder.lstrip()) > self.width: + raise ValueError("placeholder too large for max width") + + # Arrange in reverse order so items can be efficiently popped + # from a stack of chucks. + chunks.reverse() + + while chunks: + + # Start the list of chunks that will make up the current line. + # cur_len is just the length of all the chunks in cur_line. + cur_line = [] + cur_len = 0 + + # Figure out which static string will prefix this line. + if lines: + indent = self.subsequent_indent + else: + indent = self.initial_indent + + # Maximum width for this line. + width = self.width - self._len(indent) + + # First chunk on line is whitespace -- drop it, unless this + # is the very beginning of the text (ie. no lines started yet). + if self.drop_whitespace and chunks[-1].strip() == "" and lines: + del chunks[-1] + + while chunks: + chunk_len = self._len(chunks[-1]) + + # Can at least squeeze this chunk onto the current line. + if cur_len + chunk_len <= width: + cur_line.append(chunks.pop()) + cur_len += chunk_len + + # Nope, this line is full. + else: + break + + # The current line is full, and the next chunk is too big to + # fit on *any* line (not just this one). + if chunks and self._len(chunks[-1]) > width: + self._handle_long_word(chunks, cur_line, cur_len, width) + cur_len = sum(map(self._len, cur_line)) + + # If the last chunk on this line is all whitespace, drop it. + if self.drop_whitespace and cur_line and cur_line[-1].strip() == "": + cur_len -= self._len(cur_line[-1]) + del cur_line[-1] + + if cur_line: + if ( + self.max_lines is None + or len(lines) + 1 < self.max_lines + or ( + not chunks + or self.drop_whitespace + and len(chunks) == 1 + and not chunks[0].strip() + ) + and cur_len <= width + ): + # Convert current line back to a string and store it in + # list of all lines (return value). + self._update_lines(lines, indent + "".join(cur_line)) + else: + while cur_line: + if ( + cur_line[-1].strip() + and cur_len + self._len(self.placeholder) <= width + ): + cur_line.append(self.placeholder) + self._update_lines(lines, indent + "".join(cur_line)) + break + cur_len -= self._len(cur_line[-1]) + del cur_line[-1] + else: + if lines: + prev_line = lines[-1].rstrip() + if ( + self._len(prev_line) + self._len(self.placeholder) + <= self.width + ): + lines[-1] = prev_line + self.placeholder + break + self._update_lines(lines, indent + self.placeholder.lstrip()) + break + + return lines + + +def _main(): + """\ + Usage: tabulate [options] [FILE ...] + + Pretty-print tabular data. + See also https://github.com/astanin/python-tabulate + + FILE a filename of the file with tabular data; + if "-" or missing, read data from stdin. + + Options: + + -h, --help show this message + -1, --header use the first row of data as a table header + -o FILE, --output FILE print table to FILE (default: stdout) + -s REGEXP, --sep REGEXP use a custom column separator (default: whitespace) + -F FPFMT, --float FPFMT floating point number format (default: g) + -I INTFMT, --int INTFMT integer point number format (default: "") + -f FMT, --format FMT set output table format; supported formats: + plain, simple, grid, fancy_grid, pipe, orgtbl, + rst, mediawiki, html, latex, latex_raw, + latex_booktabs, latex_longtable, tsv + (default: simple) + """ + import getopt + import sys + import textwrap + + usage = textwrap.dedent(_main.__doc__) + try: + opts, args = getopt.getopt( + sys.argv[1:], + "h1o:s:F:A:f:", + ["help", "header", "output", "sep=", "float=", "int=", "align=", "format="], + ) + except getopt.GetoptError as e: + print(e) + print(usage) + sys.exit(2) + headers = [] + floatfmt = _DEFAULT_FLOATFMT + intfmt = _DEFAULT_INTFMT + colalign = None + tablefmt = "simple" + sep = r"\s+" + outfile = "-" + for opt, value in opts: + if opt in ["-1", "--header"]: + headers = "firstrow" + elif opt in ["-o", "--output"]: + outfile = value + elif opt in ["-F", "--float"]: + floatfmt = value + elif opt in ["-I", "--int"]: + intfmt = value + elif opt in ["-C", "--colalign"]: + colalign = value.split() + elif opt in ["-f", "--format"]: + if value not in tabulate_formats: + print("%s is not a supported table format" % value) + print(usage) + sys.exit(3) + tablefmt = value + elif opt in ["-s", "--sep"]: + sep = value + elif opt in ["-h", "--help"]: + print(usage) + sys.exit(0) + files = [sys.stdin] if not args else args + with (sys.stdout if outfile == "-" else open(outfile, "w")) as out: + for f in files: + if f == "-": + f = sys.stdin + if _is_file(f): + _pprint_file( + f, + headers=headers, + tablefmt=tablefmt, + sep=sep, + floatfmt=floatfmt, + intfmt=intfmt, + file=out, + colalign=colalign, + ) + else: + with open(f) as fobj: + _pprint_file( + fobj, + headers=headers, + tablefmt=tablefmt, + sep=sep, + floatfmt=floatfmt, + intfmt=intfmt, + file=out, + colalign=colalign, + ) + + +def _pprint_file(fobject, headers, tablefmt, sep, floatfmt, intfmt, file, colalign): + rows = fobject.readlines() + table = [re.split(sep, r.rstrip()) for r in rows if r.strip()] + print( + tabulate( + table, + headers, + tablefmt, + floatfmt=floatfmt, + intfmt=intfmt, + colalign=colalign, + ), + file=file, + ) + + +if __name__ == "__main__": + _main() diff --git a/env-llmeval/lib/python3.10/site-packages/tabulate/__pycache__/__init__.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/tabulate/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e338de90fc8a9d2b7c7e241975a2a7538f2299d5 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/tabulate/__pycache__/__init__.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/tabulate/__pycache__/version.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/tabulate/__pycache__/version.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1b90c3baf790534e491c36023ef53507d66a1dd3 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/tabulate/__pycache__/version.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/tabulate/version.py b/env-llmeval/lib/python3.10/site-packages/tabulate/version.py new file mode 100644 index 0000000000000000000000000000000000000000..1dd234cadb9062a0be1c43f22b3c0b4c10bbba5d --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/tabulate/version.py @@ -0,0 +1,5 @@ +# coding: utf-8 +# file generated by setuptools_scm +# don't change, don't track in version control +__version__ = version = '0.9.0' +__version_tuple__ = version_tuple = (0, 9, 0) diff --git a/env-llmeval/lib/python3.10/site-packages/tokenizers-0.15.2.dist-info/INSTALLER b/env-llmeval/lib/python3.10/site-packages/tokenizers-0.15.2.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/tokenizers-0.15.2.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/tokenizers-0.15.2.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/tokenizers-0.15.2.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..11afade06b707d5efa82dfbb8354d243ad6b9f4f --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/tokenizers-0.15.2.dist-info/METADATA @@ -0,0 +1,208 @@ +Metadata-Version: 2.1 +Name: tokenizers +Version: 0.15.2 +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Requires-Dist: huggingface_hub >=0.16.4, <1.0 +Requires-Dist: pytest ; extra == 'testing' +Requires-Dist: requests ; extra == 'testing' +Requires-Dist: numpy ; extra == 'testing' +Requires-Dist: datasets ; extra == 'testing' +Requires-Dist: black ==22.3 ; extra == 'testing' +Requires-Dist: sphinx ; extra == 'docs' +Requires-Dist: sphinx_rtd_theme ; extra == 'docs' +Requires-Dist: setuptools_rust ; extra == 'docs' +Requires-Dist: tokenizers[testing] ; extra == 'dev' +Provides-Extra: testing +Provides-Extra: docs +Provides-Extra: dev +Keywords: NLP,tokenizer,BPE,transformer,deep learning +Author: Anthony MOI +Author-email: Nicolas Patry , Anthony Moi +Requires-Python: >=3.7 +Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM +Project-URL: Homepage, https://github.com/huggingface/tokenizers +Project-URL: Source, https://github.com/huggingface/tokenizers + +

+
+ +
+

+

+ + Build + + + GitHub + +

+
+ +# Tokenizers + +Provides an implementation of today's most used tokenizers, with a focus on performance and +versatility. + +Bindings over the [Rust](https://github.com/huggingface/tokenizers/tree/master/tokenizers) implementation. +If you are interested in the High-level design, you can go check it there. + +Otherwise, let's dive in! + +## Main features: + + - Train new vocabularies and tokenize using 4 pre-made tokenizers (Bert WordPiece and the 3 + most common BPE versions). + - Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes + less than 20 seconds to tokenize a GB of text on a server's CPU. + - Easy to use, but also extremely versatile. + - Designed for research and production. + - Normalization comes with alignments tracking. It's always possible to get the part of the + original sentence that corresponds to a given token. + - Does all the pre-processing: Truncate, Pad, add the special tokens your model needs. + +### Installation + +#### With pip: + +```bash +pip install tokenizers +``` + +#### From sources: + +To use this method, you need to have the Rust installed: + +```bash +# Install with: +curl https://sh.rustup.rs -sSf | sh -s -- -y +export PATH="$HOME/.cargo/bin:$PATH" +``` + +Once Rust is installed, you can compile doing the following + +```bash +git clone https://github.com/huggingface/tokenizers +cd tokenizers/bindings/python + +# Create a virtual env (you can use yours as well) +python -m venv .env +source .env/bin/activate + +# Install `tokenizers` in the current virtual env +pip install -e . +``` + +### Load a pretrained tokenizer from the Hub + +```python +from tokenizers import Tokenizer + +tokenizer = Tokenizer.from_pretrained("bert-base-cased") +``` + +### Using the provided Tokenizers + +We provide some pre-build tokenizers to cover the most common cases. You can easily load one of +these using some `vocab.json` and `merges.txt` files: + +```python +from tokenizers import CharBPETokenizer + +# Initialize a tokenizer +vocab = "./path/to/vocab.json" +merges = "./path/to/merges.txt" +tokenizer = CharBPETokenizer(vocab, merges) + +# And then encode: +encoded = tokenizer.encode("I can feel the magic, can you?") +print(encoded.ids) +print(encoded.tokens) +``` + +And you can train them just as simply: + +```python +from tokenizers import CharBPETokenizer + +# Initialize a tokenizer +tokenizer = CharBPETokenizer() + +# Then train it! +tokenizer.train([ "./path/to/files/1.txt", "./path/to/files/2.txt" ]) + +# Now, let's use it: +encoded = tokenizer.encode("I can feel the magic, can you?") + +# And finally save it somewhere +tokenizer.save("./path/to/directory/my-bpe.tokenizer.json") +``` + +#### Provided Tokenizers + + - `CharBPETokenizer`: The original BPE + - `ByteLevelBPETokenizer`: The byte level version of the BPE + - `SentencePieceBPETokenizer`: A BPE implementation compatible with the one used by SentencePiece + - `BertWordPieceTokenizer`: The famous Bert tokenizer, using WordPiece + +All of these can be used and trained as explained above! + +### Build your own + +Whenever these provided tokenizers don't give you enough freedom, you can build your own tokenizer, +by putting all the different parts you need together. +You can check how we implemented the [provided tokenizers](https://github.com/huggingface/tokenizers/tree/master/bindings/python/py_src/tokenizers/implementations) and adapt them easily to your own needs. + +#### Building a byte-level BPE + +Here is an example showing how to build your own byte-level BPE by putting all the different pieces +together, and then saving it to a single file: + +```python +from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers, processors + +# Initialize a tokenizer +tokenizer = Tokenizer(models.BPE()) + +# Customize pre-tokenization and decoding +tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=True) +tokenizer.decoder = decoders.ByteLevel() +tokenizer.post_processor = processors.ByteLevel(trim_offsets=True) + +# And then train +trainer = trainers.BpeTrainer( + vocab_size=20000, + min_frequency=2, + initial_alphabet=pre_tokenizers.ByteLevel.alphabet() +) +tokenizer.train([ + "./path/to/dataset/1.txt", + "./path/to/dataset/2.txt", + "./path/to/dataset/3.txt" +], trainer=trainer) + +# And Save it +tokenizer.save("byte-level-bpe.tokenizer.json", pretty=True) +``` + +Now, when you want to use this tokenizer, this is as simple as: + +```python +from tokenizers import Tokenizer + +tokenizer = Tokenizer.from_file("byte-level-bpe.tokenizer.json") + +encoded = tokenizer.encode("I can feel the magic, can you?") +``` + diff --git a/env-llmeval/lib/python3.10/site-packages/tokenizers-0.15.2.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/tokenizers-0.15.2.dist-info/RECORD new file mode 100644 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