diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/INSTALLER b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/METADATA b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..0e1c52b79a79c396d377bffbb00edebd49bd5635 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/METADATA @@ -0,0 +1,79 @@ +Metadata-Version: 2.4 +Name: pyarrow +Version: 23.0.1 +Summary: Python library for Apache Arrow +Maintainer-email: Apache Arrow Developers +License-Expression: Apache-2.0 +Project-URL: Homepage, https://arrow.apache.org/ +Project-URL: Documentation, https://arrow.apache.org/docs/python +Project-URL: Repository, https://github.com/apache/arrow +Project-URL: Issues, https://github.com/apache/arrow/issues +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3.14 +Classifier: Programming Language :: Python :: Free Threading :: 2 - Beta +Requires-Python: >=3.10 +Description-Content-Type: text/markdown +License-File: LICENSE.txt +License-File: NOTICE.txt +Dynamic: license-file + + + +## Python library for Apache Arrow + +[![pypi](https://img.shields.io/pypi/v/pyarrow.svg)](https://pypi.org/project/pyarrow/) [![conda-forge](https://anaconda.org/conda-forge/pyarrow/badges/version.svg)](https://anaconda.org/conda-forge/pyarrow) + +This library provides a Python API for functionality provided by the Arrow C++ +libraries, along with tools for Arrow integration and interoperability with +pandas, NumPy, and other software in the Python ecosystem. + +## Installing + +Across platforms, you can install a recent version of pyarrow with the conda +package manager: + +```shell +conda install pyarrow -c conda-forge +``` + +On Linux, macOS, and Windows, you can also install binary wheels from PyPI with +pip: + +```shell +pip install pyarrow +``` + +If you encounter any issues importing the pip wheels on Windows, you may need +to install the latest [Visual C++ Redistributable for Visual Studio][3]. + +## Development + +See [Python Development][2] in the documentation subproject. + +### Building the documentation + +See [documentation build instructions][1] in the documentation subproject. + +[1]: https://github.com/apache/arrow/blob/main/docs/source/developers/documentation.rst +[2]: https://arrow.apache.org/docs/developers/python/index.html +[3]: https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170#latest-microsoft-visual-c-redistributable-version diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/RECORD b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..ffec8d3bc1e4124fb06e5ef2689e7617732ac5b9 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/RECORD @@ -0,0 +1,856 @@ +pyarrow-23.0.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +pyarrow-23.0.1.dist-info/METADATA,sha256=85PGLmy5cV6C3G58pQNpUXRyFyv2jhWE4pSUlJas290,3053 +pyarrow-23.0.1.dist-info/RECORD,, +pyarrow-23.0.1.dist-info/WHEEL,sha256=yzF9ixp0XVYLhnovZSdud9vspTPdVe52BzwI7Tv3jTM,113 +pyarrow-23.0.1.dist-info/licenses/LICENSE.txt,sha256=I_xF3OF2nZ3fSqxNbNrz9_DRT8xNkw3Q1K---i6jMio,113501 +pyarrow-23.0.1.dist-info/licenses/NOTICE.txt,sha256=r9iV1hoAEB9tWkHPUYyDhl2BXoEJ6Vb1EqDOTqlzjAY,2916 +pyarrow-23.0.1.dist-info/top_level.txt,sha256=Zuk_c1WeinXdMz20fXlEtGC67zfKOWuwU8adpEEU_nI,18 +pyarrow/__init__.pxd,sha256=Wnar1phFqM_ZHnZmtbuqm6wJHsXlBoYKhV7Qmo2jUHA,2195 +pyarrow/__init__.py,sha256=k2XINbsRZlOTmT0dYr7otJbKtGUJM5kFYKNYDk1f-30,18592 +pyarrow/__pycache__/__init__.cpython-310.pyc,, +pyarrow/__pycache__/_compute_docstrings.cpython-310.pyc,, +pyarrow/__pycache__/_generated_version.cpython-310.pyc,, +pyarrow/__pycache__/acero.cpython-310.pyc,, +pyarrow/__pycache__/benchmark.cpython-310.pyc,, +pyarrow/__pycache__/cffi.cpython-310.pyc,, +pyarrow/__pycache__/compute.cpython-310.pyc,, +pyarrow/__pycache__/conftest.cpython-310.pyc,, +pyarrow/__pycache__/csv.cpython-310.pyc,, +pyarrow/__pycache__/cuda.cpython-310.pyc,, +pyarrow/__pycache__/dataset.cpython-310.pyc,, +pyarrow/__pycache__/feather.cpython-310.pyc,, +pyarrow/__pycache__/flight.cpython-310.pyc,, +pyarrow/__pycache__/fs.cpython-310.pyc,, +pyarrow/__pycache__/ipc.cpython-310.pyc,, +pyarrow/__pycache__/json.cpython-310.pyc,, +pyarrow/__pycache__/jvm.cpython-310.pyc,, +pyarrow/__pycache__/orc.cpython-310.pyc,, +pyarrow/__pycache__/pandas_compat.cpython-310.pyc,, +pyarrow/__pycache__/substrait.cpython-310.pyc,, +pyarrow/__pycache__/types.cpython-310.pyc,, +pyarrow/__pycache__/util.cpython-310.pyc,, +pyarrow/_acero.cpython-310-x86_64-linux-gnu.so,sha256=xdMgalbyoZVu-x1MFgBO3yv1OqACASvzaJ9AyxQKd3o,282872 +pyarrow/_acero.pxd,sha256=5ish_GgGWvit4ebhzoZil7b-m0r2RuG5JwYoxsH34FI,1440 +pyarrow/_acero.pyx,sha256=7Qo0ePiMF2tsytAj4t334f-32KA9XdJKU6LKye87usw,21586 +pyarrow/_azurefs.cpython-310-x86_64-linux-gnu.so,sha256=V4kMp1cWfTunMtDnAGS2kmEBxOLGfBvZyQKYPU5-P1g,144928 +pyarrow/_azurefs.pyx,sha256=mlieD3Ze2LeiQFAaq4ykjLAW7jCUiEtK_brgX5dvGCo,9148 +pyarrow/_compute.cpython-310-x86_64-linux-gnu.so,sha256=RwQ03PEi4xv0GHgECquSs2VWf0UJTWii-QVI2jYA9kU,1288712 +pyarrow/_compute.pxd,sha256=fhi87jkN6x4qG8JbAtRksx-4YTn3sFIw2zZxuK1flpM,2022 +pyarrow/_compute.pyx,sha256=27FWJcYyU2xY6tKhWljYQlzIz5o0TJyPlJfM1LQq8vk,114180 +pyarrow/_compute_docstrings.py,sha256=7_S5bqdASMSrndaEKSbr--ofVW4sbhT2iM24bGvvOYw,4936 +pyarrow/_csv.cpython-310-x86_64-linux-gnu.so,sha256=dejRTRhDCRpqGVLvtMA7CICV0q3h6m23-BBXAfMG5CQ,353720 +pyarrow/_csv.pxd,sha256=1Zk3Zpvvhy-Tb7c79Aqd4e7bBM21kc1JxWJkl02Y4DE,1638 +pyarrow/_csv.pyx,sha256=MQ_b87VV18O9MifhdUm7TbBIdmnwhARfG4unidu1EYg,55449 +pyarrow/_cuda.pxd,sha256=VzhM6j9dpNgrABlvFJKoMpRC0As55im-M3tgPTOuwEk,1922 +pyarrow/_cuda.pyx,sha256=XPM0q9Cy-I0bcB5j9dpSkLfkxYvdfLv2UTJym9YAM8Q,35328 +pyarrow/_dataset.cpython-310-x86_64-linux-gnu.so,sha256=_zArNF_dalTnUejSNjf5IXblJbLpiiFa9_rimL8sQSU,1007192 +pyarrow/_dataset.pxd,sha256=Ag9rUhoBySU6ba3wFLeuZyWMJnz9VkAf9TQEzWG4hUU,4944 +pyarrow/_dataset.pyx,sha256=tW56kG-2kD3jTTxwXjDGwfJ9wxpPhL7fWtsxnMBzueA,164256 +pyarrow/_dataset_orc.cpython-310-x86_64-linux-gnu.so,sha256=BbBTJoGXShi4fPgdfKWIRpNwAF0ls77QnAEx1oB9Jdg,79544 +pyarrow/_dataset_orc.pyx,sha256=JSFoRI0pfHtL2jeIuPg5TJHodcfuCNYmj_iEZ4xY87w,1499 +pyarrow/_dataset_parquet.cpython-310-x86_64-linux-gnu.so,sha256=ACOpBFx6-88rqMwj-LS0kqwyIEv5w2fRjZo7W22ALig,354880 +pyarrow/_dataset_parquet.pxd,sha256=y-3iKehyB_eB_oeqjtt4aQRbUpVGVN1oUMFGIY13brE,1572 +pyarrow/_dataset_parquet.pyx,sha256=wdcEST6sLbbfKTrqjJpZEXpSZK0RA0h9sxkgsscNuHo,41608 +pyarrow/_dataset_parquet_encryption.cpython-310-x86_64-linux-gnu.so,sha256=q3xqb-FoinZPpI-OGOXrufFQ8Z6N8Jh31SZG5UGLwkY,120336 +pyarrow/_dataset_parquet_encryption.pyx,sha256=p7LDNUsp3jMVcWDcbOFp8a3CYJjASVPI_tfATpY-ePg,7229 +pyarrow/_dlpack.pxi,sha256=clw0FkGoyZMEtUU8zPpO_DMtl2X-27kb2UtyhQuIc1s,1832 +pyarrow/_feather.cpython-310-x86_64-linux-gnu.so,sha256=W0UcIxSe3qMd3PVicOI0lLq_fJE3xGDX9QUzzZOKg8M,108600 +pyarrow/_feather.pyx,sha256=DWQI4U0uAWE1ZYUwPreBPJg1TGLEGmF3wPEIRL-PhPw,3773 +pyarrow/_flight.cpython-310-x86_64-linux-gnu.so,sha256=Cjv2ENTUnUVS3t2ScK4IXFZ33G7OwS3dx0rvRrThAyI,1210952 +pyarrow/_flight.pyx,sha256=DjaT1iEGJyX543tYv5MpgZ4rEGUhkAhQ9-exrgbjcnQ,114770 +pyarrow/_fs.cpython-310-x86_64-linux-gnu.so,sha256=cWez-p27EbxkFyILN_RknjW7vI9BRGMe-i3takFqFc8,433528 +pyarrow/_fs.pxd,sha256=SmHS31eyYU7VUZlVuP613HKgpd7bENnQGApvX_g2Lfw,2439 +pyarrow/_fs.pyx,sha256=kLqIoSXeovlPrWe8EShzzPC3z3EgSCaeq8cSNH_ZWm4,53271 +pyarrow/_gcsfs.cpython-310-x86_64-linux-gnu.so,sha256=Tb5ptGO9pXrPCOC2oP20X-ahAHUKJP2o1IpIz7dVwGo,136080 +pyarrow/_gcsfs.pyx,sha256=sooI3ztzEQXVVKcahgin0UGm_6Sgy-uwfcMrACP690w,9046 +pyarrow/_generated_version.py,sha256=0Mp6jhnPtGCgtUSwlvLLlUXsE-uaoh2ar69PMiRCvNA,706 +pyarrow/_hdfs.cpython-310-x86_64-linux-gnu.so,sha256=nnbYNQ6FfroBvNNQ0mcwDw-STxUsD0hvIb-0YXirBxo,138128 +pyarrow/_hdfs.pyx,sha256=QVkMqzz2iGoipaNufk0GPZRzjy1JKJzuuqqk88MdYA4,5802 +pyarrow/_json.cpython-310-x86_64-linux-gnu.so,sha256=6am50l5d844DLPI-4BLemO9Qw8pUEfRICp8JQnrpAVI,128720 +pyarrow/_json.pxd,sha256=tECTP14M12-b_ja5QI3snQbd0uWPWmmC9FwkWq23Vg0,1206 +pyarrow/_json.pyx,sha256=VCdkaxuO0Q0SzAp7zxqoG2e5GImXp1ZERYMM6jG_5EI,12532 +pyarrow/_orc.cpython-310-x86_64-linux-gnu.so,sha256=G-RXK0gONsEsDzyIF9JqcM27hja5crk_EBN-4BvzbGI,213848 +pyarrow/_orc.pxd,sha256=6hL0cq1RufqQD-B_bV3ne1rhu2g-h4rDOFNQsSb6qps,5689 +pyarrow/_orc.pyx,sha256=Pn7r4dzagWaqMf8rymbXBIWisxonBaStZgXCi7pfrZI,15556 +pyarrow/_parquet.cpython-310-x86_64-linux-gnu.so,sha256=jnPQZzt4QHwJv27kYof4IsUd_jknw9i0Sg4CBZLN0H8,612480 +pyarrow/_parquet.pxd,sha256=pCqIo5UzwVmo2qe6Uo4zQwnRp8nRBjA8NJNxhhompQI,4947 +pyarrow/_parquet.pyx,sha256=q0OcpBOR4WzcQqKxb3u-MiLxvBLcPK6h_eOSJUVOosg,80049 +pyarrow/_parquet_encryption.cpython-310-x86_64-linux-gnu.so,sha256=ViAN_4O7WBsHjuaGVpmecgqJFvUeqG5S6nEjdTrBSf0,342392 +pyarrow/_parquet_encryption.pxd,sha256=WUPm2Kj50f06y5IYzb9lVF64XREzqIZwqJSWPs-1VSE,2871 +pyarrow/_parquet_encryption.pyx,sha256=Am9cy5u732B5KmcNLVb_ARy6cxiq2MIXUut6JU3e6X8,26983 +pyarrow/_pyarrow_cpp_tests.cpython-310-x86_64-linux-gnu.so,sha256=iPoGRnYBNqqmoSN3ocnV7DhjiFYqWXtgVQECbKVZ3vA,88160 +pyarrow/_pyarrow_cpp_tests.pxd,sha256=nPyRmNtFbOUvSXCwegAApQFfh8UI_K9Hq5dN4oPAxdo,1199 +pyarrow/_pyarrow_cpp_tests.pyx,sha256=gLeMzB9RWodZgXEpipX65_0aqWu12SjMld0JZmZVRP0,1753 +pyarrow/_s3fs.cpython-310-x86_64-linux-gnu.so,sha256=dE0bV0GML01TUd4_w6ZUtsnAbM106xaie4CLgi5rNMo,228512 +pyarrow/_s3fs.pyx,sha256=I5MF6knp3tYnS0VlWK0enMavJ9r54RcjgHJMoi2t6EQ,20584 +pyarrow/_substrait.cpython-310-x86_64-linux-gnu.so,sha256=WFwJGuH5OCjbZHRwMmo3nyu_aeny3HF-C_UFfAwm8HE,205216 +pyarrow/_substrait.pyx,sha256=fZT496mzp1ssSKIzqtmPeGBKeYq5f0vm8cWlWqaC408,15666 +pyarrow/acero.py,sha256=yus6kVAtfYVoSenfPZPHgmeEmnaySb5UCM1lVMkzF1M,15743 +pyarrow/array.pxi,sha256=RqssOhLS8qkLpny_KpE98KTPNIF-QJ23p0dGPxYCPwo,159297 +pyarrow/benchmark.pxi,sha256=DYXdu-jMSH7XcTohbc8x8NiKRLtpX9IULfY20ohkffA,869 +pyarrow/benchmark.py,sha256=k9Z3yQyoojpYz4lTA6DkCfqT6fPG3N2fJtsHKjpbYFo,856 +pyarrow/builder.pxi,sha256=9QE4KAiA4JpA7-2JLgX3xo32jRtuWZ3YqC-T9GzUVDc,4634 +pyarrow/cffi.py,sha256=hEcrPH9KeG6NES3ZCpSbOVYhOgDOuBB_2LgMMucgw-8,2396 +pyarrow/compat.pxi,sha256=Sq5c3CKq0uj5aDyOoHHkPEO_VsSpZ90JRaL2rAKHk5I,1920 +pyarrow/compute.py,sha256=EweboAC1SgLDPyrY7zgMV6YThGp2pqhqT-C-w7QxhwQ,24379 +pyarrow/config.pxi,sha256=59eukT2QdWZSHuOMJhXEUsXGqVcN9g68ZFPZXCMb-dw,3926 +pyarrow/conftest.py,sha256=54Oim0WylDduwtVx7l7fTKSQsJyPUbP_duPSYicHXs8,9891 +pyarrow/csv.py,sha256=S6tm31Bra9HPf9IsYwBLltZBLMvNzypWfeCLySsjmds,974 +pyarrow/cuda.py,sha256=j--8HcBAm5Ib-kbhK4d2M6SVQmDWkr7Mt5fnwU2LzdQ,1087 +pyarrow/dataset.py,sha256=S3g4O9G9IhjWByr7tZ5q8HwMC35InoBmgKsiX2aGCYc,40507 +pyarrow/device.pxi,sha256=kx9WnmI2bAKgt9txyGVQWackkRj9NPZLsE5q5H1RFgU,5563 +pyarrow/error.pxi,sha256=Wj7-NGUfdvlEwAwd8Ta_JqRC8IUOUpm_PmpvizCFvfY,8909 +pyarrow/feather.py,sha256=fOJRqGWBWrVchVSwruTcbNqN_KHDpueQ4xdoaTaEVm8,9943 +pyarrow/flight.py,sha256=HLB04A0SZ35MZJumPIuBu5I2dpetjEc-CGMEdjQeQRQ,2177 +pyarrow/fs.py,sha256=qL7lXu2QSbD3JbOwJEQwf642Y5Nne56tpW5nQ8O9t5A,14972 +pyarrow/gandiva.pyx,sha256=sNXorsDoKVbr7iI7fwXmQZxorZA77WyCRiJtSdF_Ev8,24280 +pyarrow/include/arrow/acero/accumulation_queue.h,sha256=fz0okH6sZqcSpBl2w3vOU9wSa9QlOIAXzikmb3mINPE,5985 +pyarrow/include/arrow/acero/aggregate_node.h,sha256=9fhmBQGUphsqLd7bNmiOJaEg5NJxVyWA5RGs9UaTxR0,2201 +pyarrow/include/arrow/acero/api.h,sha256=fRuKEHbKDYWRCwSHLc7vSD-6mQavyOsztluCR7evFCk,1151 +pyarrow/include/arrow/acero/asof_join_node.h,sha256=Ko6r1wDjxg01FE9-xKkttx7WzCAzf43GxbpvGHgKZp8,1490 +pyarrow/include/arrow/acero/backpressure_handler.h,sha256=mbJYVYeFMQlg40j_k7f0jFXZ8kRjo5lrE2dndC54Z7s,2395 +pyarrow/include/arrow/acero/benchmark_util.h,sha256=T5bNabF1TDAp28S7V_vt_VIDn6l5Be0zOVCHhcTcFf8,1943 +pyarrow/include/arrow/acero/bloom_filter.h,sha256=bFzzAzQrs9ePp2tCPQIuk1Oa9gG_Nyp72M_HM0dhakM,11978 +pyarrow/include/arrow/acero/exec_plan.h,sha256=U0KA3tnNvVb75G0XQFLVbGzXCGdddGyRhW3zMa8oWJc,35909 +pyarrow/include/arrow/acero/hash_join.h,sha256=zjljUjH2AQlI54Vz2_7hvJIAjTgaT0BPC_LVD7xNd2k,3022 +pyarrow/include/arrow/acero/hash_join_dict.h,sha256=_BKJmK3Z_KdJuYHh4KQCuT_1rXlUohrtEgGLtEJ4fgQ,15360 +pyarrow/include/arrow/acero/hash_join_node.h,sha256=FXT-aeXL7nNTuV75f9oXgdGyqMK_72GnqGUm9cmBnko,4378 +pyarrow/include/arrow/acero/map_node.h,sha256=Bd1HcW0N5azoIVth2ATeHxgTKd9XmmEkz42YBNw5eK0,2628 +pyarrow/include/arrow/acero/options.h,sha256=sLrKayGLs0MVJKcopQrO3pAOrqmvd1j3EfloUCMKECI,37654 +pyarrow/include/arrow/acero/order_by_impl.h,sha256=dQqplP-AZWPZRKio8LmTjYWlCYz9VmW-usUrtaLpd_w,1691 +pyarrow/include/arrow/acero/partition_util.h,sha256=xS8hayuTRimyrOHzf1xRm1IVjkyIeVCGXY78B2rJaJc,7437 +pyarrow/include/arrow/acero/query_context.h,sha256=D364aGRS3uWe8lgYqCNRjVvs5sKetLOOXzACdp5GZeg,6212 +pyarrow/include/arrow/acero/schema_util.h,sha256=KA_hV2xy2TRccMyksSzQrdH9_rdGo3tQyHOIvrWWYBQ,7961 +pyarrow/include/arrow/acero/task_util.h,sha256=6pqILuYfcVwt9HqVhRfXFVJoOC-Q_dtk8mQ5SxjgwbY,3706 +pyarrow/include/arrow/acero/test_nodes.h,sha256=xKeLWZZC8iokveVXPjseO1MOvWMcby-0xiMISy0qw8E,2877 +pyarrow/include/arrow/acero/time_series_util.h,sha256=W9yzoaTGkB2jtYm8w2CYknSw1EjMbsdTfmEuuL2zMtk,1210 +pyarrow/include/arrow/acero/tpch_node.h,sha256=l3zocxHTfGmXTjywJxwoXCIk9tjzURgWdYKSgSk8DAQ,2671 +pyarrow/include/arrow/acero/type_fwd.h,sha256=4zLhtLJf_7MSXgrhQIZVGeLxjT7JrEDAn9yW75DTFlc,1103 +pyarrow/include/arrow/acero/util.h,sha256=byhMEj5XoAUy-93AjLrx_p9_iUZdYn5uJ_cDkCJQt5Q,6121 +pyarrow/include/arrow/acero/visibility.h,sha256=E-4G2O4F2YabXnFNJYnsI2VbVoKBtO7AXqh_SPuJi6k,1616 +pyarrow/include/arrow/adapters/orc/adapter.h,sha256=G5SSGGYMSREILC43kqL5fqo94c4tKgukitO15m217tY,11031 +pyarrow/include/arrow/adapters/orc/options.h,sha256=FMxda5YSskRrB6h9FvcAuMxl5qdavWrNYHPlanjtk48,3696 +pyarrow/include/arrow/adapters/tensorflow/convert.h,sha256=ZGFAodnwTJK0ZoXfgYJdjgi_F4vfEhI9E87zejxVb6E,3465 +pyarrow/include/arrow/api.h,sha256=Gs6HiRBYU5N7-a79hjTl9WMSda551XdUKpWthFY2v1s,2491 +pyarrow/include/arrow/array.h,sha256=P5oW6hvD2j97bLaSTE4_UHuV6Y38DTwJVww3Eb3xdTQ,1981 +pyarrow/include/arrow/array/array_base.h,sha256=kq08hubpDlrIVgDRhL4MEqPyaEftgmoznQ91ZVrLnAQ,12371 +pyarrow/include/arrow/array/array_binary.h,sha256=JvtB8DoR0_tqfSFS_9nMRrJ39lt1cTm5yXh-DLkhqjU,11247 +pyarrow/include/arrow/array/array_decimal.h,sha256=xRfrZ1IFO09EmkHEolCwrJ4lsXjLo5DXdfH5_v2gSyw,3105 +pyarrow/include/arrow/array/array_dict.h,sha256=6AMbSnZoMj-nhQhZhG4RNnxy9VVPk2DvZjVblwIUhgY,7611 +pyarrow/include/arrow/array/array_nested.h,sha256=MRFllX_LO6AQXNkyAujEdax2gEYrnQALySwB9tuP85U,37717 +pyarrow/include/arrow/array/array_primitive.h,sha256=_U8_pTHg-aHY5jaSFjHZ4hqRPXdYiyKTpr2y3OcJcdg,8184 +pyarrow/include/arrow/array/array_run_end.h,sha256=4zs3tcUrIgDOhSEOywJ1vGY2lsH-5QuEBn87mxnDbi8,5101 +pyarrow/include/arrow/array/builder_adaptive.h,sha256=92DpiIZDXSI_yOrMftj7P60zlCLjNmwfGM5ubdbXWM4,6861 +pyarrow/include/arrow/array/builder_base.h,sha256=CP9kS8pDFd4XyJQdgIlBp3pTIX9mND1Lvh85re4IC8w,13723 +pyarrow/include/arrow/array/builder_binary.h,sha256=cfrm-LnXOZ73pQpn_kXSXw0Bh252DpsfP4KGHlPNy3g,33744 +pyarrow/include/arrow/array/builder_decimal.h,sha256=DFxyFlpzWRZS9zdBhsjII5fFUOMY9bXHn3EIrIvmOMo,5051 +pyarrow/include/arrow/array/builder_dict.h,sha256=FZjvCRIDmVuwmzx_HCcDK6ZjNoZKCEsSV-fGI0K974Y,27899 +pyarrow/include/arrow/array/builder_nested.h,sha256=_VB9EUamHQcxQtJM0FiSy8Rs4vhzpOuqFMRfKa4qE5U,31231 +pyarrow/include/arrow/array/builder_primitive.h,sha256=WX_3Y-FeCvTtoE2kHJ9L2cew_IN5W4uyQFfjsJ2lMLk,26031 +pyarrow/include/arrow/array/builder_run_end.h,sha256=SZIdsUKK1qAc9pdonPGf0A_aikZHcxxzicezRGR5hLs,11416 +pyarrow/include/arrow/array/builder_time.h,sha256=eLhc7UP9ttNJDbTeDdsf-ihMFORksmh7A0ScA5hLROI,2503 +pyarrow/include/arrow/array/builder_union.h,sha256=8BF532sAMc7JxWIbSN-yX6Z9fqY9jmmsIa054DPvbWE,10144 +pyarrow/include/arrow/array/concatenate.h,sha256=wBy-CBTz9MeRCmcnfXGvkXnvSRApvPOcfCf64A42ys8,2059 +pyarrow/include/arrow/array/data.h,sha256=65s-2w9lskJAzQYio59NBAa9TH78JU95mcTX3_XRCoc,28949 +pyarrow/include/arrow/array/diff.h,sha256=bYNKy2oLAxtt6VYDWvCfq2bnJTVNjG5KMTsGl-gT_kM,3344 +pyarrow/include/arrow/array/statistics.h,sha256=f0_4nJyk9Su-7u4B-HR6lfyKLe7e5E9J1HNqhgnRU28,6914 +pyarrow/include/arrow/array/util.h,sha256=qVHvCaVlALz8WJwAjyMwsBm5J2iN89CSgj7NpmmqlkI,3652 +pyarrow/include/arrow/array/validate.h,sha256=JdDb3XJg4TmAfpv_zgu2ITfL2H9no10TQit-HPj9Myw,1710 +pyarrow/include/arrow/buffer.h,sha256=aDZgA8fFnizg61OPvgpWxftsGRAGNd-EW2BMjE233ZA,23226 +pyarrow/include/arrow/buffer_builder.h,sha256=qaMgLKbycOSsn5e5_nlOj3tdPHu0i45if78ZrFN2Lmc,17468 +pyarrow/include/arrow/builder.h,sha256=mBxMko271lJ7Xbku0hCixj943Yx-d2i4Q5Hm2WfwiGM,1546 +pyarrow/include/arrow/c/abi.h,sha256=GCjxzmA2jNWQ0ZWVkQ-hnbCbMHKPikO1-JkzYj96cnY,20318 +pyarrow/include/arrow/c/bridge.h,sha256=6e6ZoIsG-pE8FQ5aU4nRJ71vVL3U9k697g7PYKDsVz4,21789 +pyarrow/include/arrow/c/dlpack.h,sha256=IXIuMdfqbHJ_AdT_25tzp0z48mWC65w4xZMjTP3yl7Q,1983 +pyarrow/include/arrow/c/dlpack_abi.h,sha256=mjp9WWq8qv6gkGirT4y0o3BL_ZI9VyHQpJ5aEpPFetI,9920 +pyarrow/include/arrow/c/helpers.h,sha256=f0Q519PwoliFHpxsHp-QvbP6fpVMN2Ha35Tk-RBK6Ws,6279 +pyarrow/include/arrow/chunk_resolver.h,sha256=oVm7DpbWRmjJhexj_h6BtispCBxFZPIHK1kl9goBGG8,12841 +pyarrow/include/arrow/chunked_array.h,sha256=z6LA9OB3uhtmn7ZZe5wfi3Am3icVQ-L_e8s3KEMuq18,10647 +pyarrow/include/arrow/compare.h,sha256=yp4ganiaR6xXokExmFAauUyfSPwOM8DuBmmAtyK0NJg,8059 +pyarrow/include/arrow/compute/api.h,sha256=6z6HdNH6S0Amop2GUfrsifxOvgupfTqFDZf76YREt-4,2139 +pyarrow/include/arrow/compute/api_aggregate.h,sha256=GCPpIA6qz6XL2UI4SyxXjyjPeC68OoT1tttpiue8eMY,21945 +pyarrow/include/arrow/compute/api_scalar.h,sha256=qR-K4zRkfJmINCF0vTp3rMWf7hdi4PU4haD59wGO9Xg,70099 +pyarrow/include/arrow/compute/api_vector.h,sha256=axkl_2Kz-VqeZkQgAsGCy9spxV62PkgJKhTklQyWWWs,34507 +pyarrow/include/arrow/compute/cast.h,sha256=y8v-EYs-1zJ5NhulMGNzagKc9CkU14ld36VIbYdV94g,4245 +pyarrow/include/arrow/compute/exec.h,sha256=2baXvq1FWwyfQfTV4r6qhF818LEYOxyn_DiTqE5AMCk,17975 +pyarrow/include/arrow/compute/expression.h,sha256=LBwjf1LTtfCfTT3RGUq_3PI_V0LRf2HxtZ-Ovqslw-c,11164 +pyarrow/include/arrow/compute/function.h,sha256=rtEKbrMURIqOWo3JgdOxHsDMk1F-Kk5_Jz_xcpXavxg,16419 +pyarrow/include/arrow/compute/function_options.h,sha256=Q9rjkXPrU9-Xi64_fMLPbBbW_byhjJFsvHppP1CumdA,3088 +pyarrow/include/arrow/compute/initialize.h,sha256=ms8mtCSHTaStTrWdapbNyNSpM0PO4ox32O9colbBR7k,1193 +pyarrow/include/arrow/compute/kernel.h,sha256=d3BIi5NtSBqPsh2gyCsfT03LvRmO2vM_XgoXip_HuKA,32283 +pyarrow/include/arrow/compute/ordering.h,sha256=8Vw3VzDi1mGgVwKGQZakz9TVj0A40wxcL13EvuqNVjU,4129 +pyarrow/include/arrow/compute/registry.h,sha256=x7LHiaNEVvZ0VUssZFsasB52Z1AxRflkdI5tR1hhzqc,4837 +pyarrow/include/arrow/compute/row/grouper.h,sha256=ID9sFjFawulQC5T_f-TQttvohhsSAmNkHIj1iXzDY-0,7520 +pyarrow/include/arrow/compute/type_fwd.h,sha256=tiG7MrLEfXAPF53JKlnFdJ7kzjhNP9a_xCb2Ept3ozc,1555 +pyarrow/include/arrow/compute/util.h,sha256=l1y6P1hHsI-mQ7lB6KNn_8AzYCxCxuhaSBOOG_wqjcs,9376 +pyarrow/include/arrow/compute/visibility.h,sha256=_h6gB6GG5YSF3aak6oAerylqtJvjhTN9Tq8MZccpO0M,1597 +pyarrow/include/arrow/config.h,sha256=8liyKI0CJO0G-Fz5I--QjIAwh0m4hosfyAOwvVVs0sU,3044 +pyarrow/include/arrow/csv/api.h,sha256=LbwWhPyIsi_73hvsSr77RNR9uUxrVyXM__hp7QcSom0,907 +pyarrow/include/arrow/csv/chunker.h,sha256=nTs8hdy4D3Nz3oZWm2JMuA02noY_0pWRYWq_RptqzHY,1171 +pyarrow/include/arrow/csv/column_builder.h,sha256=5t3842eXFsAHtrLGJ4LQkH2epUAr06PbnF7POwlgQQw,2889 +pyarrow/include/arrow/csv/column_decoder.h,sha256=10idcPJE2V_TbvgjzPqmFy1dd_qSGWvu9eDkenTuCz0,2358 +pyarrow/include/arrow/csv/converter.h,sha256=cjtnz_hZFxm_dWjAMjr1iqqk1egXI2Yb8Bd0xC8md5E,2789 +pyarrow/include/arrow/csv/invalid_row.h,sha256=gTHjEbjkpee6syLGA8hFY7spx1ROMJmtMcwhXv21x5Q,1889 +pyarrow/include/arrow/csv/options.h,sha256=0ShMt1PduP3K-n20HjxhWRQMORKP_q4rsIRYeWzoDR0,8202 +pyarrow/include/arrow/csv/parser.h,sha256=8PplRh3Qxckk8VPyM70P_f1MBb4WMGnNVpoeJ9kOdHU,8616 +pyarrow/include/arrow/csv/reader.h,sha256=416pt3yNQsgn4RhIyRMsmSJmvv1sw3ouQotubXG91gQ,4606 +pyarrow/include/arrow/csv/test_common.h,sha256=uEYzw8EROvd1QMBQ98d4MaZ7BqMlw2e0flAyz-du0Z4,1972 +pyarrow/include/arrow/csv/type_fwd.h,sha256=ptVbengmY_a7Yz1w0SKmKL16yyw9yEeym0Q0cnRCSV4,984 +pyarrow/include/arrow/csv/writer.h,sha256=Y1zErZ5H1r2QzjAta3TXpFrdl2btoardCF8USCAGtGg,3549 +pyarrow/include/arrow/dataset/api.h,sha256=p7i-bncJLhmfBkfjJWS7684vD9Lke1m6tb7HQq7Tpn4,1322 +pyarrow/include/arrow/dataset/dataset.h,sha256=NsXdSFYrO4BpJSEV2e4yJRrN-hdLxMJepwAyyqFWt-0,20327 +pyarrow/include/arrow/dataset/dataset_writer.h,sha256=TQV75b_UigfGjIpBnPk8teOncM5WroKfKV15oicBRRY,4589 +pyarrow/include/arrow/dataset/discovery.h,sha256=x7-5NBAyEeQWGlWanJDLZAoWksKiMwM96tlDx_M6n5c,11236 +pyarrow/include/arrow/dataset/file_base.h,sha256=nUMWLbhh7lt5c3iOw5edAtcy4jrCzpBUX5JZ29FW0Tk,20385 +pyarrow/include/arrow/dataset/file_csv.h,sha256=7PlvQW_2FJ5RRN-VH4-OBw5cZ6nkd0KE0sj1TQvCZeo,5016 +pyarrow/include/arrow/dataset/file_ipc.h,sha256=6-btvXhflZsAH90T3wMkwzZkte6T4ixzeCEUn_5uYW8,4083 +pyarrow/include/arrow/dataset/file_json.h,sha256=sPjOeMOtbZZbvOivnOdb4MvYKHltpTnY8fONkhB9PZs,3523 +pyarrow/include/arrow/dataset/file_orc.h,sha256=P7nAD9nacVngDEjH8ChQRt0AQmDg4Z1wBx360LDOoSg,2452 +pyarrow/include/arrow/dataset/file_parquet.h,sha256=_mhpCrrLGn9XPKSt9lsdbG4wosPOkMiqkjXDz-2D_So,16935 +pyarrow/include/arrow/dataset/parquet_encryption_config.h,sha256=Upo0k5MijZaMaRZjPp5Xg8TRt1p8Zwh2c2tdimjVe1A,3425 +pyarrow/include/arrow/dataset/partition.h,sha256=3wrNekD_-fPO1YW91Za-T4muCfQeAX7SZRIcsCN_czI,16815 +pyarrow/include/arrow/dataset/plan.h,sha256=IjuR9K2sWD85_2HpVVoJ-3YUCq--UPblHU46exX5qRg,1181 +pyarrow/include/arrow/dataset/projector.h,sha256=KfZijq09Ht0Z2cJHsrjg-sE3SiZ4TKainflReK-39cg,1135 +pyarrow/include/arrow/dataset/scanner.h,sha256=fWeW3NZF5zQYAjSIDU6wJsitzaVrSRbOmrMUnNQE1RA,26176 +pyarrow/include/arrow/dataset/type_fwd.h,sha256=YOUSRwdNAlXJ7meFLolpAFQ_mSlObs2F81zcOy0DoI4,3170 +pyarrow/include/arrow/dataset/visibility.h,sha256=ckmf_sEI0WBo4W7DIgH1QrOq82skOHtoksl9B3yYvzU,1586 +pyarrow/include/arrow/datum.h,sha256=XYaZ_URrAtVqHMq-_2YtXk_ETeQ4yZWLVAnsi-k2Mac,11511 +pyarrow/include/arrow/device.h,sha256=mLz99tb74VdjxXtKt6RZCYKJQ8TYz93uaCFJ1ZiItMw,15344 +pyarrow/include/arrow/device_allocation_type_set.h,sha256=ynoZ-XyFlOAjh01PU-R11mE_EOxuw3xzc94v5OXa0u4,3306 +pyarrow/include/arrow/engine/api.h,sha256=ORM0M5KQeurjEG8Eoa5IeV_ZgKBRPlWyicyv3ORWkAY,886 +pyarrow/include/arrow/engine/substrait/api.h,sha256=W9NB1RAm0ZVxztRXYA-GD7H8XLQNXFoYT7TdGFHoNTE,1079 +pyarrow/include/arrow/engine/substrait/extension_set.h,sha256=z1IIQVd4bat5kRngg91IyoE5UYTv7bar1JQ5ck63YbY,21552 +pyarrow/include/arrow/engine/substrait/extension_types.h,sha256=x5ZIuynNh6WFt3wRjW--zUsuC3SeDLk1qRg9_xhswWM,3075 +pyarrow/include/arrow/engine/substrait/options.h,sha256=dtvUty_zoDmcFwVflppiDzelYkeOhCO74uRF6izQSzk,5820 +pyarrow/include/arrow/engine/substrait/relation.h,sha256=V3VKFlDdE61e1OS8LbJiwvm5w0uq5bzBLhKqmgmKaws,2385 +pyarrow/include/arrow/engine/substrait/serde.h,sha256=mjxfuFo4aPhCiwefpKAJMIlknF4UOHSr6gWU__1SwCc,16528 +pyarrow/include/arrow/engine/substrait/test_plan_builder.h,sha256=REFa79D1AOIIjp2Iez73iw5gEnzG9Rac9t8WwiGLsuI,3003 +pyarrow/include/arrow/engine/substrait/test_util.h,sha256=IHZeYrk50Sx9anJfC25DWP6XesItKEywDWUqvUJcjEQ,1517 +pyarrow/include/arrow/engine/substrait/type_fwd.h,sha256=P9YRjAQpSgoIjDC0siYyxoQzcPVo3r9y85qjiMtudBs,1028 +pyarrow/include/arrow/engine/substrait/util.h,sha256=_dRiQBaIMWNbsYG7kuXhs3dMk4dI63-pM0uSxYPOvgE,3570 +pyarrow/include/arrow/engine/substrait/visibility.h,sha256=GRzH6U-UCPT8d60cywOkFfcanPSgiZKCDP6X2rIpbMs,1740 +pyarrow/include/arrow/extension/bool8.h,sha256=VsHTtVyrqk6UKgvifad7LouuieoAZuZs_uVvegdGq4Q,2145 +pyarrow/include/arrow/extension/fixed_shape_tensor.h,sha256=VOqvTSnwDIvnhbstYX5nnqWfhtZ7MaD-lSF89BEqlhE,5610 +pyarrow/include/arrow/extension/json.h,sha256=bNh6R9aP3MK-rbTON8IzVn7FIt9RGMmUHGpQkFGfOMw,2026 +pyarrow/include/arrow/extension/opaque.h,sha256=uMVqSScey_13Ho6V86vfkuoByZni9ufh5BGKgX4bTZk,2920 +pyarrow/include/arrow/extension/uuid.h,sha256=DBzTD83Mh3oacfZzfU6fQuGC87gvtnGGaDue8s9M0Ws,2364 +pyarrow/include/arrow/extension_type.h,sha256=YUvUzPz-0QiG-jOjs__TfyPEpsHWGkGO5PMVG1vMjkY,6639 +pyarrow/include/arrow/filesystem/api.h,sha256=Xgy2GOZtBVwDjTaXPDyPPlS9Bwt9gjWXm5I_QbyRbFo,1383 +pyarrow/include/arrow/filesystem/azurefs.h,sha256=M-7jR8AqyPu8uTXbw4INLb9RsZKrPmZlPO7T7MskQ_8,15299 +pyarrow/include/arrow/filesystem/filesystem.h,sha256=tjRP-ZEWzES6bMfmDQ2MwnYr_3OGV5ZyfbZr3JqWZXM,29604 +pyarrow/include/arrow/filesystem/filesystem_library.h,sha256=f2_p4Odzvq7zrMePwNcWFFet8oolnDI8SF0t55Z6T_Y,1765 +pyarrow/include/arrow/filesystem/gcsfs.h,sha256=5HSHqA9a1T2zVnZO1rNZKnXjLVyq1aAm6DRmQmg_v5w,10372 +pyarrow/include/arrow/filesystem/hdfs.h,sha256=Jn91pjfk6RMx-MuAWsEAKLTyKQ7bDPNA5jMEVzafSgc,4133 +pyarrow/include/arrow/filesystem/localfs.h,sha256=eIhPrpABheQz21WE845ULleTk83e4EtJnES4jALW6mM,4972 +pyarrow/include/arrow/filesystem/mockfs.h,sha256=kohu7s9s9xtd75sGTE2K_rsHW89swDOtSSSFxBixMcc,4768 +pyarrow/include/arrow/filesystem/path_util.h,sha256=hrDVHk4F9M7oGABB4x2wKfQMjSlSAIS0IaLVv2jHrl4,5698 +pyarrow/include/arrow/filesystem/s3_test_util.h,sha256=22B600fr84Gq4rGALdidIP0IzDs9G3aOqkLN4y-6xrI,2962 +pyarrow/include/arrow/filesystem/s3fs.h,sha256=q79pFbxFmsfA30LE78bOh6hj5Iszh01Vtw8LUpIa6T8,18221 +pyarrow/include/arrow/filesystem/test_util.h,sha256=IcdXbz8kXaZDvUPN6Oc77UpWI2Psv1f_CKZxB3UGg-I,11738 +pyarrow/include/arrow/filesystem/type_fwd.h,sha256=zztDER55Wbt4rVnkd-ReeDO-YnrpemftFeFtZ7ZGidY,1462 +pyarrow/include/arrow/flight/api.h,sha256=YotLTQn-KCl6y5BIg8coEFZ9n7PMtJ02ly7Pc5gmX7U,1257 +pyarrow/include/arrow/flight/client.h,sha256=MdHgubO7ExcWgp8S-CP9B4UfJ_aRLgLat0yz0pXQEdg,17936 +pyarrow/include/arrow/flight/client_auth.h,sha256=a3Dkm_jPOuqzNsDA4eejuMUwCEBMavM8uS7w81ihbRY,2216 +pyarrow/include/arrow/flight/client_cookie_middleware.h,sha256=5zkCP2SxMFQuTX8N9NHxOve5J_ef2rFO6-xY4Tfnygk,1204 +pyarrow/include/arrow/flight/client_middleware.h,sha256=aAZwCahuiBhP85iMPe7xNWvidBR9KeHGto2YAqJioI4,2948 +pyarrow/include/arrow/flight/client_tracing_middleware.h,sha256=d0sTmUOfq5M9FMliIKK-flJkR6-7r69NjU2TpxhfqWo,1217 +pyarrow/include/arrow/flight/middleware.h,sha256=JPQd8JnIVcwjTH6yOBck4BWR-WV95fpnAdhHyEYvfKE,2254 +pyarrow/include/arrow/flight/otel_logging.h,sha256=riS9sZM2C3mH6VMbESizJ6lGmudqdJhfdCY9_cJJqMA,1139 +pyarrow/include/arrow/flight/platform.h,sha256=1ZfzVaollAZosGyH_1JvzEA8iNR0hi9cUGz5eyLT1zc,1209 +pyarrow/include/arrow/flight/server.h,sha256=GAcV0-THuBuj-bXfwqYrZ1P2bwZgKQSJLbu8ToltRvU,13185 +pyarrow/include/arrow/flight/server_auth.h,sha256=xXkot_fmnEZ0yXHEBmiezAQCxM7vQem1Vo1V0vEwcW4,4457 +pyarrow/include/arrow/flight/server_middleware.h,sha256=kRXm15c1mjPduoS7N61m0TG8nGIhN_ielsjK9yFlsgs,3155 +pyarrow/include/arrow/flight/server_tracing_middleware.h,sha256=NzyufqTwzcTZ32FNuY-rwse4KmH8zoyoc1RhZxUMwFs,2186 +pyarrow/include/arrow/flight/test_auth_handlers.h,sha256=XkvMWucv9GQjlt2ttvYxshym4kUubUdMh-timlQIt1I,3315 +pyarrow/include/arrow/flight/test_definitions.h,sha256=U_HhBnNdNZmojraYzICBmBvuef0bTT5eYzFXp8iGFzc,13110 +pyarrow/include/arrow/flight/test_flight_server.h,sha256=SbRhZP0U4ILnbg7lYQvGeXmvPM_B6bai12FTM_HD4RQ,3930 +pyarrow/include/arrow/flight/test_util.h,sha256=1Tv0Yl5lth2j3fIZT7iI8n_kexzGgJHGxplV4xeu7TE,6668 +pyarrow/include/arrow/flight/transport.h,sha256=ZDXc-f8o00TFWESwsGU1My7rR9OfM3X7OZjDcGXTwIA,12181 +pyarrow/include/arrow/flight/transport_server.h,sha256=iVdXmrb2pemh4o6BxwvB7OZAV4UeoWrbhe4ePZ5Pi4s,5268 +pyarrow/include/arrow/flight/type_fwd.h,sha256=tQFAM3QNKPdzB4VqUGdEUFjNPYXVZLApwGnSus2GQx8,1797 +pyarrow/include/arrow/flight/types.h,sha256=BwMWMwRPSxdyYPcR1ocdi2KXpJvAXRepkz6vUfZVCmc,47383 +pyarrow/include/arrow/flight/types_async.h,sha256=3nIQqwCYO4Ir3Mt2bG7BNntXxuNHYQNNpz-Yl3EaFTQ,2599 +pyarrow/include/arrow/flight/visibility.h,sha256=N1k74cwyRvOaYFa_tCjdgUjiSdPBhmy20UuVGu0wTg0,1596 +pyarrow/include/arrow/io/api.h,sha256=Pn4jZSTsLW8MAlMyXUokmJdupX54u154GYI5AvD5ByA,996 +pyarrow/include/arrow/io/buffered.h,sha256=PHaMwCMaXu3vAjhDpZMC2xt5hKooJXVeMffhoDzTxNc,5912 +pyarrow/include/arrow/io/caching.h,sha256=AAjoyKwQ06m2XiglFS6Ch_cdg2p4-wkA7GakGI_eX1E,6708 +pyarrow/include/arrow/io/compressed.h,sha256=3JxIOo1q8VhjIErfwVM5ZLVkwwQKXd-FT5517j58etA,3774 +pyarrow/include/arrow/io/concurrency.h,sha256=SmIr0OWCgMUR3j9ngVbjMJhWOUrU15jQf_jz2rUw7r4,7934 +pyarrow/include/arrow/io/file.h,sha256=-ZEklW1Q0sj3pYCQLQ1ebirKd3s2GI3vUEIszFr8mVU,7625 +pyarrow/include/arrow/io/hdfs.h,sha256=2s3f49ggAYgSCsX5SoqnomwsXd24_IZhW-VSBJclqTg,8559 +pyarrow/include/arrow/io/interfaces.h,sha256=QIBHTJUobEkwcqnKMT_GEKu5ArzpeGmK-8v7z4qGHIQ,13428 +pyarrow/include/arrow/io/memory.h,sha256=SY535DEFEOIXojttrsGmMtK5PClwGx5sHK8E_TlCGCQ,6321 +pyarrow/include/arrow/io/mman.h,sha256=qoLBAGFcvpYTy96Ga7FNWDJKT3uhxpFAF3hbXIaDSiY,4111 +pyarrow/include/arrow/io/slow.h,sha256=8-ZjQJq49EQJ4esQ6qHHjlKCeZNg4BSND7ire-ZtLYQ,3942 +pyarrow/include/arrow/io/stdio.h,sha256=dqMTHoJbmiXcyNa2fN60tSWQsx0GPphZVCLdGiZNt8I,2095 +pyarrow/include/arrow/io/test_common.h,sha256=SF59odb0E3rtGju5jABsQzZcUi9gl5TwHuKg4f0EQ5Y,2146 +pyarrow/include/arrow/io/transform.h,sha256=W9XWonw69VymQAaQptfW7jD-6ry7VCpfPXlkB7aZzOE,1890 +pyarrow/include/arrow/io/type_fwd.h,sha256=Pi7EFpFvBXsFN1xKOyZjTSP95xNDs6W5hxb5GucoVVE,2315 +pyarrow/include/arrow/ipc/api.h,sha256=sbbb-uYHZtqE59AUlwNnAzUkP41h3Et9Rnu20gNKm9w,972 +pyarrow/include/arrow/ipc/dictionary.h,sha256=UTjZPIG8mLZOk9IW2QnR9RZGr1npexZOp103fv-O70E,6104 +pyarrow/include/arrow/ipc/feather.h,sha256=uCnxwO7eUH18kJ-lWz9IWwSj6AjfejqqLdoifJ-UBDo,4918 +pyarrow/include/arrow/ipc/message.h,sha256=KtMCbIC2J4-5iyPG5Sijqu_MALxiuKWBYZhGnw0jxOQ,20011 +pyarrow/include/arrow/ipc/options.h,sha256=rAnPbL2wXRg4tZpnC3FNkxWxPdhRR5xUM2tYkc1crZ4,7900 +pyarrow/include/arrow/ipc/reader.h,sha256=NqdrqqAEItO1ecYUINRO7-qhKlYy-CHSJKGI2hdXlRQ,24106 +pyarrow/include/arrow/ipc/test_common.h,sha256=kd6-NmmaT__0cDYIwes8lnOqbQDSqFZn8eg2N-SHfE4,6559 +pyarrow/include/arrow/ipc/type_fwd.h,sha256=Ty8ET7nLI4JJeTqDMyP0pEH9QVj9xs7BpJkZrnrpaPY,1440 +pyarrow/include/arrow/ipc/util.h,sha256=wTkfC9YFKZlAAjyzlmQVZcW90oOj_JatjDN4qz0IxHg,1414 +pyarrow/include/arrow/ipc/writer.h,sha256=hum8E_orkG_X38vgyfyKhGbyvcLJ3AkXEykyBjAXIYg,18870 +pyarrow/include/arrow/json/api.h,sha256=XRW1fP43zVqwy1yabaKctNK9MDZqnxkoHDH1fx5B3Y4,879 +pyarrow/include/arrow/json/chunked_builder.h,sha256=DDuMwrImMECw6Mhfncn2xMOjkFcKUV1O1597_fSFSAs,2365 +pyarrow/include/arrow/json/chunker.h,sha256=dkZOcxsF1Q3ek58P7IoA8f3lQyBQpFvGSFeynNV2Olc,1119 +pyarrow/include/arrow/json/converter.h,sha256=3lXsP3BSdpLPIkFAJnYW9vP8BbX3neVYR_W0zFKClQ0,3134 +pyarrow/include/arrow/json/from_string.h,sha256=y--AK08e-EivbrilSGU6xtPH1VpqBNZIYvCMUT2_V_E,4106 +pyarrow/include/arrow/json/object_parser.h,sha256=Y_6Oceya06aUyeo-1k047dm2-JUMJa2_w9iyZ-goIRQ,1627 +pyarrow/include/arrow/json/object_writer.h,sha256=UrIrjCkIz7Q5n_FpV5NNPD96gHHdTkvTJaekuGBHwTo,1428 +pyarrow/include/arrow/json/options.h,sha256=EypQgDwLZQbrPnAh45nSPfpGGYrxvLgfp1eAG_l0p3Q,2227 +pyarrow/include/arrow/json/parser.h,sha256=3oIzO5kUs2Takc7t_d5mH7bp1uIcc1M-qbuHmPoSI34,3383 +pyarrow/include/arrow/json/rapidjson_defs.h,sha256=lBJlfuYWIeQQ8awPd3bk4jJc81efr_KzKwG8Klw7t1s,1474 +pyarrow/include/arrow/json/reader.h,sha256=KNO9dCyc2RZs7WxUSEW7bpCYBh_h1C3U52YHYxBnP0M,5212 +pyarrow/include/arrow/json/test_common.h,sha256=YiiY_jswpp7Nu6IW1Y2lBhqWSFRoNaNEy1jHd5qkYHQ,10874 +pyarrow/include/arrow/json/type_fwd.h,sha256=o9aigB5losknJFFei1k25pDVYZgkC2elmRMX1C6aTjo,942 +pyarrow/include/arrow/memory_pool.h,sha256=sKBdu-Aidm_X3qbPnfpur5LX7nuwlsnCraPsGR2Up70,12911 +pyarrow/include/arrow/memory_pool_test.h,sha256=qv7csk6hZiO2ELFF-1yukpppjETDDX0nuBFBbPFHtMU,3350 +pyarrow/include/arrow/pretty_print.h,sha256=Z36O7Rp3wp8gCG51YHh-x8ytrrENDpFZSPmSSXs9TAo,5716 +pyarrow/include/arrow/python/api.h,sha256=2XyYOFAmorPGXrTVYZkiKL67nuH8w1ha6D1reU7K-Vs,1179 +pyarrow/include/arrow/python/arrow_to_pandas.h,sha256=jQiSTN8IL95onwaclaWqCR5Nb2RHrBtubRyEOJR9eRE,6215 +pyarrow/include/arrow/python/async.h,sha256=C0f8YYmgwBGgDau4xEFsdjukiZB4YvpylETHEZryHOo,2352 +pyarrow/include/arrow/python/benchmark.h,sha256=f-kzyMOlPKDse2bcLWhyMrDEMZrG_JHAPpDJgGW0bXU,1192 +pyarrow/include/arrow/python/common.h,sha256=9IQC__JUqTRXv5MfAqKM6ivMwuQ-uMZKEguGDEODkiA,14389 +pyarrow/include/arrow/python/config.h,sha256=SQa6SuM3oAfQ7wEbwVJqlPfqslqlq2DXK_FK6-K08Kg,1150 +pyarrow/include/arrow/python/csv.h,sha256=QxU3B-Hv_RsoEcMGS9-1434ugouL2ygC64Lq6FgviNM,1397 +pyarrow/include/arrow/python/datetime.h,sha256=Bny_THGi2tyUeHxcOuw01O7hNE8B_gave5ABAZQtwTQ,7931 +pyarrow/include/arrow/python/decimal.h,sha256=K9ScmzSa7XCuuwpXKnq0GPbRDTuRU5yXfWr9zGtOchc,6362 +pyarrow/include/arrow/python/extension_type.h,sha256=0gzb42y_mbw4fsYs3u8cwPFLBRlG-kkHQLgbvGtrY0U,3181 +pyarrow/include/arrow/python/filesystem.h,sha256=FG0AcLekqaDf9IQPqKixAfIcY_ZLgIKP5NvvXdtBVUM,5126 +pyarrow/include/arrow/python/flight.h,sha256=t4ZD8gWaubbO_mBDv0XZ58TCo1VQl0hzHn1_7w4U64I,14450 +pyarrow/include/arrow/python/gdb.h,sha256=H-qvM-nU8a_3Z5tk8PvppTwQtBMSZhQKQIVgRAsRfFg,972 +pyarrow/include/arrow/python/helpers.h,sha256=Q3TmwyffFpVYQEp55ZoWTDDhnte7zQKiXylsfbEMCuI,5479 +pyarrow/include/arrow/python/inference.h,sha256=FUFvB4Zy7V-tueXdmbDcqTeLK4xj5GZEeRW5yhiJlsU,2038 +pyarrow/include/arrow/python/io.h,sha256=4jGnodpSUlnVqAVh9fWId7H4WldlLPkXyroABpdaW6w,3858 +pyarrow/include/arrow/python/ipc.h,sha256=SZbw6jCCqLiLNCY3k632GmwHeD_r_xrDS0dhqV49VhY,2259 +pyarrow/include/arrow/python/iterators.h,sha256=Ugfm3JvetAH0l-oAjjpZfhrUBqRimVMaw4-xusvqLSg,7327 +pyarrow/include/arrow/python/lib.h,sha256=NSqX1jl5ZWssVm0SmNFHp4PckJYy8YCJxlL02MKa4Uc,4562 +pyarrow/include/arrow/python/lib_api.h,sha256=rm6EBrvNbQHNzt_TJGbbdIo5T_1IC2WBAXIQnpMzsNA,20132 +pyarrow/include/arrow/python/numpy_convert.h,sha256=y13eHwfe1lJKzadoTr2-GyX6xPsE6Z7FN31s7PN-2Rk,4870 +pyarrow/include/arrow/python/numpy_init.h,sha256=FniVHP7W2YBlenoMYhQrODvoqqvDMSls2JANGtNPQts,999 +pyarrow/include/arrow/python/numpy_interop.h,sha256=rI6ek8JTOYtjo7gEADSDBS6QuAOHa2A0YQPZ2GeypFw,3418 +pyarrow/include/arrow/python/numpy_to_arrow.h,sha256=z9KapsuoOSpWILPt9bea7GR4BL6AQ28T6DUO0mSkh3k,2760 +pyarrow/include/arrow/python/parquet_encryption.h,sha256=7KjqRuxbO0UxxHikTDvrLH733UVe0uvTf_bQMiI_xCA,5596 +pyarrow/include/arrow/python/platform.h,sha256=AtkGgpUVqPHs9Z8Tm07yb2zN_wOtb2cvlf-5xFBkc4k,1268 +pyarrow/include/arrow/python/pyarrow.h,sha256=TK3BtD9n3QKOQ9dX3LXbQc0hu9alWcufV0O93iQW7B0,2761 +pyarrow/include/arrow/python/pyarrow_api.h,sha256=7l0G4-_m9yALYoifsY8Z6qh3HHD0PgkpVSgCn_JaGU4,867 +pyarrow/include/arrow/python/pyarrow_lib.h,sha256=-70_Ckj3_0ImlzaXSJOE_d3w9pGM66lXiGPyln9c96Y,863 +pyarrow/include/arrow/python/python_test.h,sha256=ea32mM20uHySlygi9MtVxr26O-ydTZHCUQIlxaIMjT4,1195 +pyarrow/include/arrow/python/python_to_arrow.h,sha256=BoVytf6P7PBYXyznchElKZSFvEsFyimB-tLFdw0AUNo,2521 +pyarrow/include/arrow/python/type_traits.h,sha256=U7dW4sajVVmjXALsP-B8ZZwI4GgIUG3t-rbp2fwrHWI,10236 +pyarrow/include/arrow/python/udf.h,sha256=de3R8PhNJO5lT9oCqRxe8e2_SE3jBpHOkwbNqCrlgjQ,3104 +pyarrow/include/arrow/python/util.h,sha256=sP2CEGG7NNqwjrEeNKB-VFqkKxn5P2Tsc0jPUMXQuLg,1732 +pyarrow/include/arrow/python/vendored/pythoncapi_compat.h,sha256=bzMnlHTCfjk5DQRIxwytunYh5aQxU3iSElaaDyNnAY8,40900 +pyarrow/include/arrow/python/visibility.h,sha256=hwJw5sGrWJckQkNaAuLe4Tf-VDjQbXknyzNOVgZI3FI,1381 +pyarrow/include/arrow/record_batch.h,sha256=DZ_GZUGTEBv7uJeNnxPoY1ZnB-m7tfSYcHuNdL6iXVU,18947 +pyarrow/include/arrow/result.h,sha256=sh0Yp4pFT07H-ro8CFzK-Iye_0PF-unu-vz4C0hcoHA,18357 +pyarrow/include/arrow/scalar.h,sha256=rsZGG1akIQJRRTw-_dlXLjkPT6NTONiwv70L_1zPxho,37320 +pyarrow/include/arrow/sparse_tensor.h,sha256=1EetIzwI_JvNITcy3LS9Ra0wFiDWLB0hXjy26VOCg2s,25205 +pyarrow/include/arrow/status.h,sha256=r9B963ikSEuSQpbnuGBV-pl1Dtfi5Co0gFd7hI0AMUE,17392 +pyarrow/include/arrow/stl.h,sha256=2PEb_N_ZOWrwBJnFWah96GuLmEBc5Vl4_IFws9NCF2k,19343 +pyarrow/include/arrow/stl_allocator.h,sha256=TBbvjbuQIH9y88FI2SaqAL7pOIt3wZ1xMKwXqeKNiJE,4956 +pyarrow/include/arrow/stl_iterator.h,sha256=RelNQrADHupKWTuFBCCkqVlyuGHXU3yB6gcsDpQpra8,9953 +pyarrow/include/arrow/table.h,sha256=0RFiXqJcNfcdH9jcDcBiRunPdWcHZn4A0blWdSR-MqE,15300 +pyarrow/include/arrow/table_builder.h,sha256=LRcLCL2iUrj6vF4f9AjPswVjqtqlMw7z_8VBAfUJeCo,3763 +pyarrow/include/arrow/tensor.h,sha256=t5qdeufYoYG1Tl4DCiDzeZkOhyiQQPOGbMKCLa0u7WM,9089 +pyarrow/include/arrow/tensor/converter.h,sha256=RZq0Try_kiZ085_d_CvhewMsd57InGb2TCeiveaf-Oo,2891 +pyarrow/include/arrow/testing/async_test_util.h,sha256=IrHWfPeIyhrgeTGHUPLt92LdsofmFX6khjngWsZv3dY,2262 +pyarrow/include/arrow/testing/builder.h,sha256=4x0bWOedaVomWU0m7dF99irOv3flR-_p-IMofTDZtwo,8556 +pyarrow/include/arrow/testing/executor_util.h,sha256=38_rF-V_9zF1ttJMspkPiI-34VU1RDjg1ADBS8lUFHk,1885 +pyarrow/include/arrow/testing/extension_type.h,sha256=2xHmtD1bnK-u8Or38keTBvDuXhgKvsVyiiO-exlWHXg,8113 +pyarrow/include/arrow/testing/fixed_width_test_util.h,sha256=g6yB7RkziU7HEhNJnxOhkn2nE5HeXaFX3tbBX3q9_sE,3091 +pyarrow/include/arrow/testing/future_util.h,sha256=qIhi417OGMWSMUSDHjkGTYd-ihZbqw8ZSIRwJ01vbKg,6246 +pyarrow/include/arrow/testing/generator.h,sha256=zwEI_ZS-nxOIoYZUW_X9VE0wg5ILk8PzBpeo6kqXUDM,13525 +pyarrow/include/arrow/testing/gtest_compat.h,sha256=0NqH39my7m1FMpsrQYnxQx4bdEE10SCXZaysN6yjQFA,1311 +pyarrow/include/arrow/testing/gtest_util.h,sha256=s6hzot0wcsaVvajwRlBYmWGUjSvJ13o8AlNGLhEtPr4,24381 +pyarrow/include/arrow/testing/matchers.h,sha256=Z6jJW4fGlvtpc7oWpPlx5ynLk2tnLwF-dhv5fQIcvqc,16832 +pyarrow/include/arrow/testing/math.h,sha256=YLDNNqYpSHBT1jZo06pYN-TG_RtOJFOJ29YJ0hSP8g0,1424 +pyarrow/include/arrow/testing/process.h,sha256=AzPW3Lh2R4sTm-RUUi4Od3aSba9zoLcS_zHBxztv4zI,1372 +pyarrow/include/arrow/testing/random.h,sha256=U17nEG1fl4vAMGULYVMWUtdIv15R87EoK-SKZirujAc,38331 +pyarrow/include/arrow/testing/uniform_real.h,sha256=-G_2J9cvevoCtB55vsCsWtJkMUHLIMyOwdT6G8ZW45Y,2970 +pyarrow/include/arrow/testing/util.h,sha256=bvrOS5Uvcfn4Crb-bf_4_02Jb5MPQsItf9AnWC6CKmA,5662 +pyarrow/include/arrow/testing/visibility.h,sha256=-wjc00QIhygXJa7tknbIL685AQ1wnyCPr-EtVzkzmq0,1606 +pyarrow/include/arrow/type.h,sha256=6X9yEP8xA0EmrVo96fG0w4QsGfafqgy9333qinsS4YU,97136 +pyarrow/include/arrow/type_fwd.h,sha256=04ZjJ5UYWHsFofNPN5yx94VEeFh3__906P5J1QHQaqc,23665 +pyarrow/include/arrow/type_traits.h,sha256=nKDVrC4_4EBfyEbHQdVr4mgmAaSvdp6cJE2XPZfUwaA,56056 +pyarrow/include/arrow/util/algorithm.h,sha256=045EVzsC9rThlRVFaCoBmmtWZmFy5y28PR9yapn9sXY,1229 +pyarrow/include/arrow/util/align_util.h,sha256=DG2L24KReTiU8nFpXLigbflkKouKWTPUf6osQs6mxiY,10669 +pyarrow/include/arrow/util/aligned_storage.h,sha256=e0pCTBZ9hn9VAVTnENubbEii_witF73xUeBZNIQd73I,4254 +pyarrow/include/arrow/util/async_generator.h,sha256=avVH3JPaDk_vrEh8uhPLopVN3LSn-29yst23GoptZR8,78200 +pyarrow/include/arrow/util/async_generator_fwd.h,sha256=Q8sueYZWwdxkaVDabuglHM3XuP--NerYgq8UxsVHgFY,1728 +pyarrow/include/arrow/util/async_util.h,sha256=1nnAJZ22iK7wSzmvZDo3PMhuWqJIt2qKdlXzTyhoCK4,19759 +pyarrow/include/arrow/util/base64.h,sha256=qzcBE98cg8Tx5iPJAvQ4Pdf2yc6R2r-4yGJS1_DEIeY,1095 +pyarrow/include/arrow/util/basic_decimal.h,sha256=Q2zfksUdYc92TL8po81I_aQOmwHTwZGPjqXvJM7cgek,33569 +pyarrow/include/arrow/util/benchmark_util.h,sha256=SG3gfwE-wGNZAwpL3TvffnSiZGM2cztV5xRBnbqy2Mw,7641 +pyarrow/include/arrow/util/binary_view_util.h,sha256=-sFAQX9cnfWmmZJo8stFX5vkJki7T2UloAvDzYO0MN8,4625 +pyarrow/include/arrow/util/bit_block_counter.h,sha256=iSIemzizxVokwC0Ze6SjSi-al_nrP2ViXF6JPoIVUWc,20162 +pyarrow/include/arrow/util/bit_run_reader.h,sha256=1G9jN387hUCm0q9LvoSPEjY5v4fBOC8Aq_-riu-Sklw,17290 +pyarrow/include/arrow/util/bit_util.h,sha256=gABDSW-EqgHu559EFfYvPkR7feICKR7YDbLx-Kh10uU,16397 +pyarrow/include/arrow/util/bitmap.h,sha256=eGjGlh6s4Dubhfrk-Kp931YgR8KyaHNcxbTW1rqtqbI,17459 +pyarrow/include/arrow/util/bitmap_builders.h,sha256=28rjB9AGwG-VMzuen9FARssdMul0RJx2ZmPFQL9SV1E,1596 +pyarrow/include/arrow/util/bitmap_generate.h,sha256=m6ZsNwx1GhsEktQr63NxXHQkX2B7Nti011XYsPg2xfo,3661 +pyarrow/include/arrow/util/bitmap_ops.h,sha256=fPPD_cvXebYLrAeQOCCYydU1AwF0faN5DwQKV8op-Es,10877 +pyarrow/include/arrow/util/bitmap_reader.h,sha256=-EePBZDJyoF1GDQLHi5a9GK6rPQ-NRZS8iZZUSw6WmE,8392 +pyarrow/include/arrow/util/bitmap_visit.h,sha256=myn8k66VrvZnL6R6VW6IDPTfO68VxjbJ8Up5IuSjFL4,3470 +pyarrow/include/arrow/util/bitmap_writer.h,sha256=a4goXhLlY0qcfvYxbfbGD_HZ8Au1wFcbV1tVF3BPaXs,9383 +pyarrow/include/arrow/util/byte_size.h,sha256=Pd2c_3a0IeSOUevhPIlXNkDmgoB06g4c9YCsuRwwSKM,3997 +pyarrow/include/arrow/util/cancel.h,sha256=SgAy5S0TBISnCypp4R_OI-jPUQ997qtsLOSdXo8l5bM,3659 +pyarrow/include/arrow/util/checked_cast.h,sha256=SR9Qg8NuLSBJw2w1UfgeGvCfT8k7wrbN7BzADQOZfAU,2076 +pyarrow/include/arrow/util/compare.h,sha256=OLrSSyllkY4Sv00IK-37A2d68gr4OwnWJsxn1aF9xTU,1982 +pyarrow/include/arrow/util/compression.h,sha256=fvlURoWJsgO8Hr6Xs_VNaqiOatmIGn9ktVUkYv7pIu4,8427 +pyarrow/include/arrow/util/concurrent_map.h,sha256=wMi9WDHfRuJ_aSFgcJPpsVwGJ9vIJ5agaZ3rVUlwGe4,1775 +pyarrow/include/arrow/util/config.h,sha256=WjSomLcG1dvYoc0O6r_ePvTfEEgU_a6DpbScKs_OOy4,2090 +pyarrow/include/arrow/util/converter.h,sha256=PILfos6VlnLK6fOFMfLIUhiKl3o1dJo9T4HJXeR7V5E,14637 +pyarrow/include/arrow/util/cpu_info.h,sha256=MqLdJabBZkzDjiScaQ7if9dmoAGvXT2QavGoGkho3lU,3964 +pyarrow/include/arrow/util/crc32.h,sha256=4gN0M-SRnxaGKci2ATPbMWZG2TG3YULXjaTpadV0Udk,1337 +pyarrow/include/arrow/util/debug.h,sha256=CPB_oDOuZ_u89e9wM8bGn88mGvClgfa7UDxDph6v9sY,971 +pyarrow/include/arrow/util/decimal.h,sha256=cEQRH5x5kN_MAsWNOPCLjz57QjW4ErfppMHG4b-0EOA,20291 +pyarrow/include/arrow/util/delimiting.h,sha256=JYe9YcWMeFT_ISuojx_VgVqOYLvZ2TiiR2sNn-WdeBQ,7317 +pyarrow/include/arrow/util/endian.h,sha256=PPai9ig8DtGVouKksjTq7H_anm0BjgoTMaVTkWlMKWY,8236 +pyarrow/include/arrow/util/float16.h,sha256=3RfWoQ0sIPCk2yXniIYw-g7X-EEmaos_IG9z8cnXCmQ,7454 +pyarrow/include/arrow/util/formatting.h,sha256=3D0xE7sTcJR6XEUb12LKSqL-A3bX_Pyp1oVkPScjCwQ,22516 +pyarrow/include/arrow/util/functional.h,sha256=4ljKXSWX3G_lBT2BfLXuG44pzZwVKeaojpLWCniqKyc,5612 +pyarrow/include/arrow/util/future.h,sha256=tsSVDEH2dhXKyvIKl6R9BVBolpPdZXoRRf2-YRbtdxg,32296 +pyarrow/include/arrow/util/hash_util.h,sha256=CjiNVPUJPxXvVJy7ys79aIb7YB6Bm-5nTJAR4DHsxcs,1918 +pyarrow/include/arrow/util/hashing.h,sha256=2rIuTiAhMVsIt52fc9RR6vVM9NNfzeFJPsAPWCd6vX8,34224 +pyarrow/include/arrow/util/int_util.h,sha256=zTOAq57M4pUe469WpnW6I5hNtxe3vGRHlZWhngA1DzM,4859 +pyarrow/include/arrow/util/int_util_overflow.h,sha256=7eLu26N5xY2VYM8aSJOZyfmwqIc3IdMXsshsjmuge74,8218 +pyarrow/include/arrow/util/io_util.h,sha256=2s53VCM_Rd7j7IsXzDHK6-Hwqx5bp6BlKFUybn75ebM,13762 +pyarrow/include/arrow/util/iterator.h,sha256=FymwfXvx9akIj1hAvLgrFQ01BWJbvAv4xx06TNDc9dc,18345 +pyarrow/include/arrow/util/key_value_metadata.h,sha256=wjU6uQGcSmy-YFqMs6rwLP7E4X-0IFBjPrWZstistzQ,3590 +pyarrow/include/arrow/util/launder.h,sha256=C3rNBRh4reuUp8YuRdGQU95WPc8vl4bAY-z5LXgDiuA,1046 +pyarrow/include/arrow/util/list_util.h,sha256=_OmtsDqe-mnZ_7tVWxB2yHdgCJhpiME_RP3nXHzKbdI,2028 +pyarrow/include/arrow/util/logger.h,sha256=p9i4dNgne36LWpFmNSYBYgTQ4kFSao20dJ40LgRRZKQ,6693 +pyarrow/include/arrow/util/logging.h,sha256=63wlnOug1zSd1ur7caDp0F0z2IG7QjHbUFGChE8f-nA,9139 +pyarrow/include/arrow/util/macros.h,sha256=a5_NbnOnb2z4MYLM3mXSRk0oMH-CfkrOa7lzNJKjNy0,9884 +pyarrow/include/arrow/util/math_constants.h,sha256=2sfWoVc8syHz8X26XgBmejzXStl7hmvKiOh9622oUZA,1112 +pyarrow/include/arrow/util/mutex.h,sha256=n4bsrHK2Q8zbYsQEyNaFqNu__vvqgwo1AfrLLCxfkpU,2554 +pyarrow/include/arrow/util/parallel.h,sha256=_43wTVSrtJwOkRt3KJcJF1DyYHwhoWbWPsrAswftP-Q,3817 +pyarrow/include/arrow/util/pcg_random.h,sha256=nbXowfCJFiy4GjVfF9I8VvB6fxkyR5zNB1FKdnFsYTQ,1252 +pyarrow/include/arrow/util/prefetch.h,sha256=vaE4FPdscbtO0cPbzl8F1PzB1NDO18ytYlEmZCHDjHs,1251 +pyarrow/include/arrow/util/queue.h,sha256=X9vRZQX3YL_a2Lzwe-zcNNHguR7FoGYmD-Q0THqsCBM,1017 +pyarrow/include/arrow/util/range.h,sha256=WJ1v4BaQ4iMLuh-fgdGyWqY88rzR6Z9rqDQj5RC84XE,8725 +pyarrow/include/arrow/util/ree_util.h,sha256=NO71naM7twCfCrX52vPmWoNhL90XP4PFEdc6AI9-OCs,22397 +pyarrow/include/arrow/util/regex.h,sha256=Tj92CttOh2HxS0EKQ_9-sxMBAsQrDOUKNP0ngIJFdP8,1742 +pyarrow/include/arrow/util/rows_to_batches.h,sha256=PZNoLeMCfJJdeHVvUny0UHc5AtS0hctUCi7zUztJpeE,7120 +pyarrow/include/arrow/util/secure_string.h,sha256=0T9JxwkqXpGszGfcrstxJyRZztV9VRsFqVcGhDcEmhM,2476 +pyarrow/include/arrow/util/simd.h,sha256=PpKm-aWpZYIYP0NnyGrQceOO9m3_7JbN4uro0IhIT9w,1679 +pyarrow/include/arrow/util/small_vector.h,sha256=hXkuEMONoYd7vFS_6lLPwP49SVdBpAhSlvMK-1d3PrE,14439 +pyarrow/include/arrow/util/span.h,sha256=FjFcz8VzZDSF-nR-dFbyd1wzL5XQpupI0db2-r0FyP4,4298 +pyarrow/include/arrow/util/string.h,sha256=UStQlZ3Fkhmwm5IcKDsbUABENDRnjV1cU5dRXnEv8IQ,5296 +pyarrow/include/arrow/util/string_util.h,sha256=0IUIcjSf2zMP8rVoVx5y1satijO7cr0PvOgG32d0lFA,2364 +pyarrow/include/arrow/util/task_group.h,sha256=fI330NoJT8u84AEUA6pSxWrE7UBKn2LaM4DfPFoalqA,4362 +pyarrow/include/arrow/util/test_common.h,sha256=OqhWyOEVmWbzhr2JfiaMn1flC84pU6kHL5vYZV1bhUM,2981 +pyarrow/include/arrow/util/thread_pool.h,sha256=Q2KzKTxcikTEgdNAouFwjwsorFa3wE9IVkKqYd8o_Lw,25530 +pyarrow/include/arrow/util/time.h,sha256=_s6yyeTNkNPiLjtaWceItyD8_yGTx2GMiXj1ICVlzyU,3439 +pyarrow/include/arrow/util/tracing.h,sha256=sVfC_Rj2gwkWKVSKT0l0FOO5c2EGsfYwlkZX4d9ncxA,1286 +pyarrow/include/arrow/util/type_fwd.h,sha256=JeZ1muIiVKxHcAXzBikMCmk_7SxFz_IqmzuT6yY1F-A,1803 +pyarrow/include/arrow/util/type_traits.h,sha256=iRrIf4YhuMOGL5DJWIJoV2bUEhZRHWuce8TXm-JMkns,2155 +pyarrow/include/arrow/util/ubsan.h,sha256=dJiINEVEdpLSr5-Z_a7BxoYGvA1-pCfHia_xiF0TfYo,3228 +pyarrow/include/arrow/util/union_util.h,sha256=PSssBiw-v-PDen_q75c6OkNO5PwyIPhGbf9PMJj7P2M,1211 +pyarrow/include/arrow/util/unreachable.h,sha256=O1TG4ozCYT3_xvDpJouKWrlFADIEpIemQ28y4DqIwu4,1070 +pyarrow/include/arrow/util/uri.h,sha256=D24zebazFcrKGt7iGpkcGQ87DuF-2AbjPKVkDlq9Nuk,3886 +pyarrow/include/arrow/util/utf8.h,sha256=flGZ786kHo33Xg_zw0zVA9GAT8jYdPUHTVhIPHGjOj8,2031 +pyarrow/include/arrow/util/value_parsing.h,sha256=p_7p_fUUw3jfUkiVnKADdHb3KCdJ7Xg2JQnc39uQHMI,30654 +pyarrow/include/arrow/util/vector.h,sha256=OKnWECz72otbpw6UZSzZIZHJXCXGb9IsPiFI-l3wODY,5609 +pyarrow/include/arrow/util/visibility.h,sha256=zuHLTwNcR34LcCtbhj1YXzfNL8NxO-3XHY_UgUJgdZY,3238 +pyarrow/include/arrow/util/windows_compatibility.h,sha256=Chme9fWRqYRzfIbLw7V_yeiIWd3F4dFeG6ImHHr4Xqw,1255 +pyarrow/include/arrow/util/windows_fixup.h,sha256=hjoh6zvB8u8OVUQqLtdcrmohMzoAoLy6XJFLxcfFhK0,1435 +pyarrow/include/arrow/vendored/ProducerConsumerQueue.h,sha256=Bz1ks3NDgXXLfT8TMUkE38RpMOSwKRRtwU1e37Y1CUw,6101 +pyarrow/include/arrow/vendored/datetime.h,sha256=tsFbz8LKBFzRzTEOAKZyWRbdFLfnCnZRCK9Tyi1PANs,1103 +pyarrow/include/arrow/vendored/datetime/date.h,sha256=fa_tmkMHwmx9vZHjH5lZVhZFCYEcHi5xmueW3F1tDE4,237808 +pyarrow/include/arrow/vendored/datetime/ios.h,sha256=Qnu0iuy2-ein9KkVoSL1t71_W_VFZkdjDVsOnYTnP38,1641 +pyarrow/include/arrow/vendored/datetime/tz.h,sha256=m5JJv7LE7Vukp8h50r90sCfbOSAD2bMVIVQUUxNZeDQ,85347 +pyarrow/include/arrow/vendored/datetime/tz_private.h,sha256=pDkKXYdzfzQ5uh-jcUhURBLqHo00t0UnlimUdiM53Cs,10706 +pyarrow/include/arrow/vendored/datetime/visibility.h,sha256=VCGKzhQOgL1zwGXKl_7lLULfSy0OsPt8FLWHwA4sOtU,1002 +pyarrow/include/arrow/vendored/double-conversion/bignum-dtoa.h,sha256=imGhcg0RywMsFNMYTqp6rlXw2HZCIAla8SC_n92gCqE,4358 +pyarrow/include/arrow/vendored/double-conversion/bignum.h,sha256=RnQ2CPL8Pt6fVCGh_8VDF11e_GyrrwO0IH0uMnTcsEs,5949 +pyarrow/include/arrow/vendored/double-conversion/cached-powers.h,sha256=jjwfR3bue7mNlE5lbTrFR2KlgjRew2OkmjBa7oQO0Qg,3079 +pyarrow/include/arrow/vendored/double-conversion/diy-fp.h,sha256=J-RgqH27jspT5Ubth9pTA9NAZH6e7n1OVhxModgi8Sc,5088 +pyarrow/include/arrow/vendored/double-conversion/double-conversion.h,sha256=J1Tl5-8aFY0A9SnaA9z5Q90jnMxw55illPIuE-jdD5Q,1804 +pyarrow/include/arrow/vendored/double-conversion/double-to-string.h,sha256=C-tKRi0IuLycXgS6CC1oiFkCroOo_-AO0VOjmfe0tlE,23925 +pyarrow/include/arrow/vendored/double-conversion/fast-dtoa.h,sha256=ZAho25fqeP3t2RM0XgqfhTBXQIIicACLpdyHHMRX3JU,4122 +pyarrow/include/arrow/vendored/double-conversion/fixed-dtoa.h,sha256=HLnpxkHjKldm-FBiDRbADYljJBSYbQGP4Gz-sVbiSJU,2828 +pyarrow/include/arrow/vendored/double-conversion/ieee.h,sha256=CVKA9RXSjv4ZygqDHMiF-H2hUh3QHQvp1GZYC3MAhkE,15281 +pyarrow/include/arrow/vendored/double-conversion/string-to-double.h,sha256=Ul6b-2R0pjUaAWNM3Ki4kH933LqrW6_XfPz4BSiE2v8,10906 +pyarrow/include/arrow/vendored/double-conversion/strtod.h,sha256=6xCRm47vmcghYJug5mhhTVbsZ3m3Y6tQfMehEyVZNx0,3096 +pyarrow/include/arrow/vendored/double-conversion/utils.h,sha256=wFRb5cGABiNoUSCnvKmdv_KIMcBtX1PX89tPFfvgbQI,15614 +pyarrow/include/arrow/vendored/pcg/pcg_extras.hpp,sha256=FEYzq8NFxPfdJyLs4kVtTBLkaD6iO71INz9EJnaxTdc,19784 +pyarrow/include/arrow/vendored/pcg/pcg_random.hpp,sha256=7TaV3nZhcwpf6XxlZ6cod1GaW5gm-iUn67t2fiMPNbA,73501 +pyarrow/include/arrow/vendored/pcg/pcg_uint128.hpp,sha256=DyavmKgq9ob13z--Lee8WKfiMBk1cvKhkZlDxQyQNOA,28750 +pyarrow/include/arrow/vendored/portable-snippets/debug-trap.h,sha256=9KphJ9gRtDT9DXR9iZ7aS23xa2T8tLmLsFEJMg0pLDQ,3081 +pyarrow/include/arrow/vendored/safeint/safe_math.h,sha256=HgcHut9A9VZGoZe6A-669uv1N8ydMJE9a3bJzZiOwFA,78099 +pyarrow/include/arrow/vendored/safeint/safe_math_impl.h,sha256=wzI4AvijLrtEg9aTtqoY1SQNOZ1XxsP5GyuN3juC6K4,61119 +pyarrow/include/arrow/vendored/strptime.h,sha256=q1IZi5CvyUp_PNzbQ4_XLroAV24VEovBEz2TkpwUJ9c,1212 +pyarrow/include/arrow/vendored/xxhash.h,sha256=MUwtyzu7xjkx9mBcS65SaDcCK7tgeqQgj-KYEMxcHWc,844 +pyarrow/include/arrow/vendored/xxhash/xxhash.h,sha256=PDFiHmNL4ul4A0wiSLDpY9JSrDS73RMulr1W9aNEW8s,279426 +pyarrow/include/arrow/visit_array_inline.h,sha256=XuQjuME8XZeJp7W86YuCsuoVVgmG1NulXAA0KJkmmB0,2446 +pyarrow/include/arrow/visit_data_inline.h,sha256=4MkdFVsrjhMyTDNrScQtOYV_nwzqR2ddSS2yYnbyLt0,12460 +pyarrow/include/arrow/visit_scalar_inline.h,sha256=KvNY0j8nE9gs_805LXMV3ATgvxvUqW4UeKpXUxR3rMA,2419 +pyarrow/include/arrow/visit_type_inline.h,sha256=O0BURx2TowqdO61W9DJEr5cKP5BsVkUt8Bpc8VuiXec,4387 +pyarrow/include/arrow/visitor.h,sha256=NKos98j54uY9tdXzctI_n_nwFRrXNOwanxLDqDZONw4,8690 +pyarrow/include/arrow/visitor_generate.h,sha256=n2YKZW-5hY7ICQSwEUBZIYh2eg9ZoTfD54XRd9OlNDo,3324 +pyarrow/include/parquet/api/io.h,sha256=Ricq0d2R4QXHiGZCbjxZ_0F_QmKq0IrfTidNu5NoXPI,847 +pyarrow/include/parquet/api/reader.h,sha256=vnM5XDPn1TVsDJk4SDgb3ZU2Ta4vdrRzCpDWO90rYHk,1204 +pyarrow/include/parquet/api/schema.h,sha256=KsNJ529pEh7bGUa0rLUCcfanI9rW2uSTirgpvKq0hdc,855 +pyarrow/include/parquet/api/writer.h,sha256=UJZbY8QGVRMtAmozzjoM9TnI4gssqlNFUKCXBw2IfuI,1007 +pyarrow/include/parquet/arrow/reader.h,sha256=oZXb2Oof-MH37vKuq58SucwtWnBlZ57M5PsrgUnURBw,16361 +pyarrow/include/parquet/arrow/schema.h,sha256=Mi56ul7itNS6NDbMpKOJCufjHVqaSY5_rbsNRNLE560,6204 +pyarrow/include/parquet/arrow/test_util.h,sha256=qCUHfC-7P2D32n2__AV35MbzKoL5KEgNHJQaiElFx6M,18849 +pyarrow/include/parquet/arrow/writer.h,sha256=0edx0fgyze6uxTgpDyacSljKMUay86pRqvIRQ7AZpXk,7005 +pyarrow/include/parquet/benchmark_util.h,sha256=emDpaIbtfUNlK0M2zMP-oTy2S2raQ-LpQULK_G74OtM,1758 +pyarrow/include/parquet/bloom_filter.h,sha256=TC3OxK0J2v6tHxT_Bbw7mlYtM0603KXgBoHRvmzM9aA,14999 +pyarrow/include/parquet/bloom_filter_reader.h,sha256=63kpHYKs5TPrbRamkBLZsDYbD-I9UeVhF-R8d7JHeLg,2892 +pyarrow/include/parquet/column_page.h,sha256=7w2hQDqkIhqpHLRh2LHk0ujnuknSPHVtuvJ21LS--n0,6942 +pyarrow/include/parquet/column_reader.h,sha256=HQPo-owohtevjPItqajvEDWj8OR1u6oW6i9xvjP9Yiw,19255 +pyarrow/include/parquet/column_scanner.h,sha256=HecBvh-z0n_1HJsD-GIdcGHQAvDOHKlLzppB9RBsD9s,8863 +pyarrow/include/parquet/column_writer.h,sha256=lDOsSuqIxI8e8hTdAvZfBm-b0D6uMfpC1L5HLykE3bo,12452 +pyarrow/include/parquet/encoding.h,sha256=SDGAMKCvNwOz92WiadNmhVO2bPG-IVUJJ4vb_pShtpo,16872 +pyarrow/include/parquet/encryption/crypto_factory.h,sha256=NutbypTw9971ju95UkWtW-g8TYnR94TWZAnx5-dznPA,7064 +pyarrow/include/parquet/encryption/encryption.h,sha256=pXcGO8WhWVzaS8hDrphLS_M2sHX0mtGMCs4G9d3ZA1c,16552 +pyarrow/include/parquet/encryption/file_key_material_store.h,sha256=YzAVO3M2H5v5Fz2b_WlmB3GE5wVbMEnFTL3S9XPH6k0,2200 +pyarrow/include/parquet/encryption/file_key_unwrapper.h,sha256=VBuBwYeLqYHq_WGnjL2tfwB6mH3jjFVb-TpPKa6lb50,4717 +pyarrow/include/parquet/encryption/file_key_wrapper.h,sha256=xXlS-yyYhUW20S31REsSj5JxbVbij_td0WlPbgxNaYg,3778 +pyarrow/include/parquet/encryption/file_system_key_material_store.h,sha256=xAccp-7Il2UrnItxLmOE4ywhQKgeapyxVNwshDpan0Y,3695 +pyarrow/include/parquet/encryption/key_encryption_key.h,sha256=6dJNBYA_4Z_dyMph71Q5KI0axe-yzdjTnRgG_zj2OX0,2318 +pyarrow/include/parquet/encryption/key_material.h,sha256=kPTSIuRFYOnH4BCPIB33zG9hp5D2Ba-5kZVlq3rFnRI,6221 +pyarrow/include/parquet/encryption/key_metadata.h,sha256=Pc0nA9LW3Fc9NLMMxz7osbw8si2jSiOVTES-J-9R0y0,4003 +pyarrow/include/parquet/encryption/key_toolkit.h,sha256=xLHCsTK6bcrXd_MQv7GDTrLoZNeE_-WFb192LdYpcH0,4645 +pyarrow/include/parquet/encryption/kms_client.h,sha256=MM870qRC_yc4hkzx7xVPaPm12ErR8sqFgDTHLWHcRX0,3222 +pyarrow/include/parquet/encryption/kms_client_factory.h,sha256=VZ97CMgDQxx5oZWFGprjXsaM1hZ0wNudPmFU1_lniAc,1293 +pyarrow/include/parquet/encryption/local_wrap_kms_client.h,sha256=dW5Fe3lUHP8dmsSPDGMbldGl2GlnER4I9zWhCU6g_78,4037 +pyarrow/include/parquet/encryption/test_encryption_util.h,sha256=rYdlDq1nmZu3gsc7vPcBDVOwdxESyVcnVTDCNMPdmoA,5349 +pyarrow/include/parquet/encryption/test_in_memory_kms.h,sha256=c6GA-eObXQtCtzehYKr4uGMpjtLRUW0C5VIQQk8EynM,3743 +pyarrow/include/parquet/encryption/two_level_cache_with_expiration.h,sha256=2kNvixV36bYjUlUJquqnaaOO-NI7e8O3Vm1x-xDPyDA,4818 +pyarrow/include/parquet/encryption/type_fwd.h,sha256=dL8snyUwNjhTQE2FQ2dXAUjTboEXhH2JOehQovHfixc,955 +pyarrow/include/parquet/exception.h,sha256=TJbx6X_khxV6e61rUfHY0Pr65EoxKTu2xpvOAdh9BMo,6104 +pyarrow/include/parquet/file_reader.h,sha256=Z1RiOP74Y9tMv7kZ7FWX05IR05qzgSOTRKrY71pLOJE,11185 +pyarrow/include/parquet/file_writer.h,sha256=6fK6Mn-MdiQ-J4oo8BTi_eVVVshlffoQiJzFaLRrqco,9343 +pyarrow/include/parquet/geospatial/statistics.h,sha256=77ieKxQcY46FB7jgCZhk03fa2G_J2S9NJ8R0_JU-qGs,8892 +pyarrow/include/parquet/hasher.h,sha256=z_U9ojA6y_f4AQtYP6DhuUWp-MX3uywCHd4aWDXAqyE,5242 +pyarrow/include/parquet/level_comparison.h,sha256=5z4fUJJPWq9W60l2CsAI7T7E2auGYD7m0fpR5rfLmsw,1306 +pyarrow/include/parquet/level_comparison_inc.h,sha256=r20_6Rv5L7UmFGJ68f-JaZ5hLXb87wvZa80hZNQoF-I,2494 +pyarrow/include/parquet/level_conversion.h,sha256=OsuqK1xiUnEnOLPKwfm9X-pXTaXRMlDIkj3lwGb2ggI,9432 +pyarrow/include/parquet/level_conversion_inc.h,sha256=Q_ZZ-Ody9s8rSZlBzIwNcgcPikXtnAcv475gCgqRk5o,14248 +pyarrow/include/parquet/metadata.h,sha256=HA2wE85mSzg37qqsxGmolMYpBLaSSwdiwxkeQOP1n_g,21191 +pyarrow/include/parquet/page_index.h,sha256=AqvLT6M81jY13YbpX-s-hOzCVz0AGNkZJ9om3_tp4Vk,17307 +pyarrow/include/parquet/parquet_version.h,sha256=57JJTZUgqXX1m2WHAKXRKKn15LVMK1X-Q8zSXhnYJkg,1164 +pyarrow/include/parquet/platform.h,sha256=DUf6vCMAV4eS-dOZzHDE7G_EAGIwDaQ-J3X3bpY8rgk,3939 +pyarrow/include/parquet/printer.h,sha256=hXqzwuj5p1oKY2awZkVOq7qXx-Rsoo6QPH1oTE94AHU,1548 +pyarrow/include/parquet/properties.h,sha256=YNL9gevyuQMsCjo3znl5xyleCDsI-KnFA8_G6JWn_n8,57689 +pyarrow/include/parquet/schema.h,sha256=CjZh2i9WN5VeoDbLqy7M1AZtopZ43_C9blWG3OT2IfU,18222 +pyarrow/include/parquet/size_statistics.h,sha256=Ahr8qyIVre8U_JVRLHL0BbSoYpdJjo2OUeE1UD4I1tw,4327 +pyarrow/include/parquet/statistics.h,sha256=3LFLy_k0KuIjsYBZWFJJShrkHIEm8Zk_QRDfRKj_9XA,18005 +pyarrow/include/parquet/stream_reader.h,sha256=1WmN0vYCqTz1Lwb_Di4xPWTE-VbCQQuzZralSpWQm3U,8791 +pyarrow/include/parquet/stream_writer.h,sha256=NIC3WYnGNPZxJm3k8Bms5A5wtg8OXj1li9JPbdPSGxA,7831 +pyarrow/include/parquet/test_util.h,sha256=VHF8cjNduIW8P6Kw2sLUJmvbL7tmTG_kWIWOaC7G5OA,33538 +pyarrow/include/parquet/type_fwd.h,sha256=xmvMW2wSIFvoEZBwVql7hzcPBzOHZnmTlEtIjwp_pzM,3065 +pyarrow/include/parquet/types.h,sha256=mOx1d2y3BhZv5CXdAtylBFBvkMflOdJG_uWNamBFJKQ,27304 +pyarrow/include/parquet/windows_compatibility.h,sha256=xIEGHW354URgdIP9A4V303TJL8A1IkCEvp08bMKsHTU,897 +pyarrow/include/parquet/windows_fixup.h,sha256=DpyWCywx8YIqouun6BJcgMrHFMTCBgowWdJ1mnJnQ2s,1052 +pyarrow/include/parquet/xxhasher.h,sha256=QAa7ZE7S3UFtU_Voz3oi3YclIYhbhviJkafLOYgiuWg,2074 +pyarrow/includes/__init__.pxd,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +pyarrow/includes/common.pxd,sha256=me4htiESPFmZok1M8eDXk9OS-7p9Om_1pazNowvttuc,4950 +pyarrow/includes/libarrow.pxd,sha256=fP5_tqqVqGtS1my5j9-UxTv3NHNI4oBDPjbBcikTu2A,121041 +pyarrow/includes/libarrow_acero.pxd,sha256=c84RdYfIuFWW_36-1RELJsowfQwXhgUxbdC_xKQyFCI,5298 +pyarrow/includes/libarrow_cuda.pxd,sha256=0fRcHbCZY_gFdwIXIElnpGvTxeA5xVxZH1-vwZh16SM,4942 +pyarrow/includes/libarrow_dataset.pxd,sha256=cqV1LQvfsKtRwePFaWUh8dCFlb6JED8tLGCsrtS_Ml8,17270 +pyarrow/includes/libarrow_dataset_parquet.pxd,sha256=PW8g6CD2yjwgwoJgYQ9Ntg2e2v6Vfu4AYL5kiM4sq0Q,4584 +pyarrow/includes/libarrow_feather.pxd,sha256=MTJUDQbfKP8Ir700Fobl7xcbjX7WcrsUV4mxFXlfwn0,2140 +pyarrow/includes/libarrow_flight.pxd,sha256=QO7RJ5TUVA65gtkJLenjIOEW2eIbPuKLw-nGWD9oHRY,24724 +pyarrow/includes/libarrow_fs.pxd,sha256=PHdXeBI1oD-fLV_POryp-kcyt5084X0DdP3N0aLRmNQ,15379 +pyarrow/includes/libarrow_python.pxd,sha256=6RxtLurxWwDzN-GtJ7Mo0qXoNnP3mOU8DcavIdumXM4,11647 +pyarrow/includes/libarrow_substrait.pxd,sha256=PtgwVFqsCQBzCHzK-VqhQOdTyU9J4SziQ_lnxRwChfs,4061 +pyarrow/includes/libgandiva.pxd,sha256=o28v4Q43dYXQEcwldKO6RYs0tke2U6GZVxQmjq4rAt4,11530 +pyarrow/includes/libparquet.pxd,sha256=dDV_i74prFbqBrYQ8bn2Jfp1CCChHznNAckD81VJZkw,26321 +pyarrow/includes/libparquet_encryption.pxd,sha256=oyvw0ZLyLWrHwMiOt6mbWYyTKb3rN9kmjOMMW1P2EsM,8690 +pyarrow/interchange/__init__.py,sha256=DH0bwbKpdjD1WCW1VinnXEuVLY098uHKkirv7DFc9JM,845 +pyarrow/interchange/__pycache__/__init__.cpython-310.pyc,, +pyarrow/interchange/__pycache__/buffer.cpython-310.pyc,, +pyarrow/interchange/__pycache__/column.cpython-310.pyc,, +pyarrow/interchange/__pycache__/dataframe.cpython-310.pyc,, +pyarrow/interchange/__pycache__/from_dataframe.cpython-310.pyc,, +pyarrow/interchange/buffer.py,sha256=NF_GU1uQ6INqHqCwzY6XQQqRxKDh6znEeDHiRqaEIQ0,3359 +pyarrow/interchange/column.py,sha256=n9f3mlViqUFOa3Kofdwoo52qC-WYVpyF2YilRsl4uaw,19350 +pyarrow/interchange/dataframe.py,sha256=tmSMmBvBAc-ZSUzE8tBNbvQLHuuxLuBkMkK6KYwtS8M,8405 +pyarrow/interchange/from_dataframe.py,sha256=JfkP4wuY_9x76H6RDtmsOzs6B6qe-1WS7zxpKeD481s,19709 +pyarrow/io.pxi,sha256=gDtFPrwdBrxT88jpNwddg9mM-KSt_XFv333W2IbF3I4,86228 +pyarrow/ipc.pxi,sha256=NTZxt0qAu-HIjDbbczp91sKUJoJQBz48z-IxcKn9b6w,44752 +pyarrow/ipc.py,sha256=ssicn1x2jRZt2m1nQZ4sB70sa1FB3d8PoqiOnWO-tzg,9603 +pyarrow/json.py,sha256=JMEXB5jNZp-DOYmGBrzs2RtwOCLdKCRa3E5kUfH0Ceg,869 +pyarrow/jvm.py,sha256=_cyen6Bi-cBUlg-OAb_RTG6nj2W57kMrR1HuAWVcEQs,9577 +pyarrow/lib.cpython-310-x86_64-linux-gnu.so,sha256=weFjiFPhFN9tT2nNPpxEW_6Gu8tpRp0TGhiQhj6TtyI,4283368 +pyarrow/lib.h,sha256=NSqX1jl5ZWssVm0SmNFHp4PckJYy8YCJxlL02MKa4Uc,4562 +pyarrow/lib.pxd,sha256=HHKBlMGhU4NkQKqPcTCkYPojbExu2jSt8C4YTeCWdck,17838 +pyarrow/lib.pyx,sha256=cssmzMbq2cOOgMLKQUSkLwvXRmS46ED6U-8d4B91bUo,6174 +pyarrow/lib_api.h,sha256=rm6EBrvNbQHNzt_TJGbbdIo5T_1IC2WBAXIQnpMzsNA,20132 +pyarrow/libarrow.so.2300,sha256=0wrfak57rAFFaA5nzu0-nS_0ospXmhR8-IFaaUmixC4,52334816 +pyarrow/libarrow_acero.so.2300,sha256=bpsqCjL8Fp1diKCNfJH5FvZzVff9Ks_jlv9FEzic4T4,1899056 +pyarrow/libarrow_compute.so.2300,sha256=K0xSJnlFmhYopvdcPEojr1iSJutmPhzCAh6f6sr2vEQ,18887496 +pyarrow/libarrow_dataset.so.2300,sha256=KRgRLNGSuhCKjqdqPdIYOw0vozKKIqDHC5rqBbjzlnU,2331344 +pyarrow/libarrow_flight.so.2300,sha256=FHZ-B9W3uPrH0z9WFoCjLQFpW4h5CNeGW9PTaLARleA,26444648 +pyarrow/libarrow_python.so,sha256=IUO7OhzaWJk7G0ptfwsdECSFUlfv723dv2h_F1EEKm0,2342392 +pyarrow/libarrow_python.so.2300,sha256=IUO7OhzaWJk7G0ptfwsdECSFUlfv723dv2h_F1EEKm0,2342392 +pyarrow/libarrow_python.so.2300.1.0,sha256=IUO7OhzaWJk7G0ptfwsdECSFUlfv723dv2h_F1EEKm0,2342392 +pyarrow/libarrow_python_flight.so,sha256=B52L6RjU5pvT7_o2AmoeV2Tr1uPJmhl-A6vGO6YJy8I,104024 +pyarrow/libarrow_python_flight.so.2300,sha256=B52L6RjU5pvT7_o2AmoeV2Tr1uPJmhl-A6vGO6YJy8I,104024 +pyarrow/libarrow_python_flight.so.2300.1.0,sha256=B52L6RjU5pvT7_o2AmoeV2Tr1uPJmhl-A6vGO6YJy8I,104024 +pyarrow/libarrow_python_parquet_encryption.so,sha256=e3JVUNOPI0fCLFXewNtovNzMTMXXZAnAy9vSgb9x7H4,38448 +pyarrow/libarrow_python_parquet_encryption.so.2300,sha256=e3JVUNOPI0fCLFXewNtovNzMTMXXZAnAy9vSgb9x7H4,38448 +pyarrow/libarrow_python_parquet_encryption.so.2300.1.0,sha256=e3JVUNOPI0fCLFXewNtovNzMTMXXZAnAy9vSgb9x7H4,38448 +pyarrow/libarrow_substrait.so.2300,sha256=RBpdd1C8B9s5t7wDcr8_OOe34qEcW45r0-g1NO5aXuo,5761616 +pyarrow/libparquet.so.2300,sha256=BUZNufKfPjxyvaUGJbLDrhWTGyp13-o1KghADaTxoU8,12416216 +pyarrow/memory.pxi,sha256=cZSlJ8Brteb9aWa7VzvCrpnw26wRkplF9KtF2nqtH8M,8806 +pyarrow/orc.py,sha256=yDp3vD4gOdOsOBmXKFe24Kq3xO3FDBfIOHvsvzQUkKY,12602 +pyarrow/pandas-shim.pxi,sha256=RM38cacj1TlQ5IN79CprIr4AUbbY5GiWdiLsnx0zn3g,8817 +pyarrow/pandas_compat.py,sha256=y1dLv-lrnpJleFwWnSO5_rBM-Uwky6ZgtKUMYTPhbN8,45916 +pyarrow/parquet/__init__.py,sha256=4W64CbvwvO60tG58nfNtyCwMVCfuPumtu82p-kiGPaE,822 +pyarrow/parquet/__pycache__/__init__.cpython-310.pyc,, +pyarrow/parquet/__pycache__/core.cpython-310.pyc,, +pyarrow/parquet/__pycache__/encryption.cpython-310.pyc,, +pyarrow/parquet/core.py,sha256=0-Te7kjeh7zKT3jd4VBK2IMOycQ3uHwjzqgqRFY6aro,95541 +pyarrow/parquet/encryption.py,sha256=-XW7Qcbl-jQhpZsR610uQ8-z9ZVE_NL045Jdnp1TZ9M,1153 +pyarrow/public-api.pxi,sha256=G6tk0gOWMBoQaQGo4lyp0OLSkqEZqMhB0z27MoQR3rU,14064 +pyarrow/scalar.pxi,sha256=2-ttwuILFVqcf8gcMBWbEkwA-3vdnxGN-8ah6YDvrlA,52384 +pyarrow/src/arrow/python/CMakeLists.txt,sha256=D4Ypror_508aAd_juYkrS9Qu2maeirK4QXzwGEZEj0M,855 +pyarrow/src/arrow/python/api.h,sha256=2XyYOFAmorPGXrTVYZkiKL67nuH8w1ha6D1reU7K-Vs,1179 +pyarrow/src/arrow/python/arrow_to_pandas.cc,sha256=0lQ5wG_eqXa76QfuHdCr7Uex1yDM7nneD5G62ykDCzg,96642 +pyarrow/src/arrow/python/arrow_to_pandas.h,sha256=jQiSTN8IL95onwaclaWqCR5Nb2RHrBtubRyEOJR9eRE,6215 +pyarrow/src/arrow/python/arrow_to_python_internal.h,sha256=nQXPZTL3xa4Sm-a-Gv-8bpFs-qAOZHkqWmA_m-dSLVw,1740 +pyarrow/src/arrow/python/async.h,sha256=C0f8YYmgwBGgDau4xEFsdjukiZB4YvpylETHEZryHOo,2352 +pyarrow/src/arrow/python/benchmark.cc,sha256=z6qYRx4qMuNXPaC8fuPJlQd92aosMN85u1aD50R1-UU,1293 +pyarrow/src/arrow/python/benchmark.h,sha256=f-kzyMOlPKDse2bcLWhyMrDEMZrG_JHAPpDJgGW0bXU,1192 +pyarrow/src/arrow/python/common.cc,sha256=HExYdSkfggXu96XWNXBvK1XukCfPotLVFtk8D9FVaOw,7603 +pyarrow/src/arrow/python/common.h,sha256=9IQC__JUqTRXv5MfAqKM6ivMwuQ-uMZKEguGDEODkiA,14389 +pyarrow/src/arrow/python/config.cc,sha256=yRQYqXioit9N4uAVyLrBbb70Bto79wh6ZAwvyeX9HBs,1097 +pyarrow/src/arrow/python/config.h,sha256=SQa6SuM3oAfQ7wEbwVJqlPfqslqlq2DXK_FK6-K08Kg,1150 +pyarrow/src/arrow/python/config_internal.h.cmake,sha256=kU0GNa9lLgI7_eJseYzfo-C9s8762Tb1sep47Oux3hk,855 +pyarrow/src/arrow/python/csv.cc,sha256=ql5AY76AqiFksWsrmzSl551k5s9vS8YcmypM2A9rhw8,1803 +pyarrow/src/arrow/python/csv.h,sha256=QxU3B-Hv_RsoEcMGS9-1434ugouL2ygC64Lq6FgviNM,1397 +pyarrow/src/arrow/python/datetime.cc,sha256=WQoJT6a1ocd3SItRC_q8EA5-4Z9sWNdGFxXen3m0xeU,23013 +pyarrow/src/arrow/python/datetime.h,sha256=Bny_THGi2tyUeHxcOuw01O7hNE8B_gave5ABAZQtwTQ,7931 +pyarrow/src/arrow/python/decimal.cc,sha256=4CNaLDOWZelTllSIPdIKDlIhUy6AgKJxKG0WmzJyq94,9699 +pyarrow/src/arrow/python/decimal.h,sha256=K9ScmzSa7XCuuwpXKnq0GPbRDTuRU5yXfWr9zGtOchc,6362 +pyarrow/src/arrow/python/extension_type.cc,sha256=SOhG7u7MNnJkSe-dWi5-ZsGuzQC-M7PihaYFAr6x6hA,7033 +pyarrow/src/arrow/python/extension_type.h,sha256=0gzb42y_mbw4fsYs3u8cwPFLBRlG-kkHQLgbvGtrY0U,3181 +pyarrow/src/arrow/python/filesystem.cc,sha256=0twavI91TE20Otq5kkVUwnN5sindU_mBWoVAvz1ZMgI,6152 +pyarrow/src/arrow/python/filesystem.h,sha256=FG0AcLekqaDf9IQPqKixAfIcY_ZLgIKP5NvvXdtBVUM,5126 +pyarrow/src/arrow/python/flight.cc,sha256=tL3nFVTjDlmiuqDOSxf7HODdpqIMUSbSsNc1vaveP1w,14192 +pyarrow/src/arrow/python/flight.h,sha256=t4ZD8gWaubbO_mBDv0XZ58TCo1VQl0hzHn1_7w4U64I,14450 +pyarrow/src/arrow/python/gdb.cc,sha256=hkATQV6YlpxkBaLfY9_C24XJ6qYSg2T0DfQX4XfpvFY,22556 +pyarrow/src/arrow/python/gdb.h,sha256=H-qvM-nU8a_3Z5tk8PvppTwQtBMSZhQKQIVgRAsRfFg,972 +pyarrow/src/arrow/python/helpers.cc,sha256=MqeJ1PD1g97tKL9suvE1LWD2cO-MWMwo8-ag3FXAauY,16934 +pyarrow/src/arrow/python/helpers.h,sha256=Q3TmwyffFpVYQEp55ZoWTDDhnte7zQKiXylsfbEMCuI,5479 +pyarrow/src/arrow/python/inference.cc,sha256=Wz_zlalsCa4_BWntagf6l7QLb_nONJZKl5TQa4YVMWE,26293 +pyarrow/src/arrow/python/inference.h,sha256=FUFvB4Zy7V-tueXdmbDcqTeLK4xj5GZEeRW5yhiJlsU,2038 +pyarrow/src/arrow/python/io.cc,sha256=k2pgFsCFXaI7naqXcxDtZJJp7K06NnEhsDEoMQvbaxc,11948 +pyarrow/src/arrow/python/io.h,sha256=4jGnodpSUlnVqAVh9fWId7H4WldlLPkXyroABpdaW6w,3858 +pyarrow/src/arrow/python/ipc.cc,sha256=3D9iMbOFHlhNXX4432wsfbfjWvDryZWgdA0Ak19V_8Q,4472 +pyarrow/src/arrow/python/ipc.h,sha256=SZbw6jCCqLiLNCY3k632GmwHeD_r_xrDS0dhqV49VhY,2259 +pyarrow/src/arrow/python/iterators.h,sha256=Ugfm3JvetAH0l-oAjjpZfhrUBqRimVMaw4-xusvqLSg,7327 +pyarrow/src/arrow/python/numpy_convert.cc,sha256=166BIW7zVTRMKogxLUuhV4e5jOevmonvRtXDydNujgc,21194 +pyarrow/src/arrow/python/numpy_convert.h,sha256=y13eHwfe1lJKzadoTr2-GyX6xPsE6Z7FN31s7PN-2Rk,4870 +pyarrow/src/arrow/python/numpy_init.cc,sha256=cJKOH946T7VCcB-gVIoGgfbWTrbj3FPkI4TgnsLTf7s,1178 +pyarrow/src/arrow/python/numpy_init.h,sha256=FniVHP7W2YBlenoMYhQrODvoqqvDMSls2JANGtNPQts,999 +pyarrow/src/arrow/python/numpy_internal.h,sha256=1R_tNINnHYMGNTA5k_BMbGWFlA297f3Yj4ufrhm-yiE,5320 +pyarrow/src/arrow/python/numpy_interop.h,sha256=rI6ek8JTOYtjo7gEADSDBS6QuAOHa2A0YQPZ2GeypFw,3418 +pyarrow/src/arrow/python/numpy_to_arrow.cc,sha256=uuQo_rewpuc882cuFy-uMmtRLR1iMT3Ew3JystLBXfc,31961 +pyarrow/src/arrow/python/numpy_to_arrow.h,sha256=z9KapsuoOSpWILPt9bea7GR4BL6AQ28T6DUO0mSkh3k,2760 +pyarrow/src/arrow/python/parquet_encryption.cc,sha256=5ilofycsTj6IP0RGCUZFHucHN53oZAmx32L6el7RQh0,4408 +pyarrow/src/arrow/python/parquet_encryption.h,sha256=7KjqRuxbO0UxxHikTDvrLH733UVe0uvTf_bQMiI_xCA,5596 +pyarrow/src/arrow/python/platform.h,sha256=AtkGgpUVqPHs9Z8Tm07yb2zN_wOtb2cvlf-5xFBkc4k,1268 +pyarrow/src/arrow/python/pyarrow.cc,sha256=vukhk0aEmqI2QZ54XMwyC3930mJHGUVSSW49GSgifWw,3683 +pyarrow/src/arrow/python/pyarrow.h,sha256=TK3BtD9n3QKOQ9dX3LXbQc0hu9alWcufV0O93iQW7B0,2761 +pyarrow/src/arrow/python/pyarrow_api.h,sha256=7l0G4-_m9yALYoifsY8Z6qh3HHD0PgkpVSgCn_JaGU4,867 +pyarrow/src/arrow/python/pyarrow_lib.h,sha256=-70_Ckj3_0ImlzaXSJOE_d3w9pGM66lXiGPyln9c96Y,863 +pyarrow/src/arrow/python/python_test.cc,sha256=7WZnh-qh0SvzAin20DHAAd_vdcj2EZQYCNhgqyECdN0,33047 +pyarrow/src/arrow/python/python_test.h,sha256=ea32mM20uHySlygi9MtVxr26O-ydTZHCUQIlxaIMjT4,1195 +pyarrow/src/arrow/python/python_to_arrow.cc,sha256=DjnP866Q8RUY9WnE8moCI5nExs5Rni4oYz2kuytZ0-8,47894 +pyarrow/src/arrow/python/python_to_arrow.h,sha256=BoVytf6P7PBYXyznchElKZSFvEsFyimB-tLFdw0AUNo,2521 +pyarrow/src/arrow/python/type_traits.h,sha256=U7dW4sajVVmjXALsP-B8ZZwI4GgIUG3t-rbp2fwrHWI,10236 +pyarrow/src/arrow/python/udf.cc,sha256=tRd7cPh66N5tU4DdO70F8x1FqODcGZ3HtrYjF4BSs9w,29820 +pyarrow/src/arrow/python/udf.h,sha256=de3R8PhNJO5lT9oCqRxe8e2_SE3jBpHOkwbNqCrlgjQ,3104 +pyarrow/src/arrow/python/util.cc,sha256=Tkgz17XmYFzVybvQ9Vo9LChVo98R5BlbjqCNMHk86DM,1852 +pyarrow/src/arrow/python/util.h,sha256=sP2CEGG7NNqwjrEeNKB-VFqkKxn5P2Tsc0jPUMXQuLg,1732 +pyarrow/src/arrow/python/vendored/CMakeLists.txt,sha256=02XvDJAdKiajCEBOmnMKBpmzbRU7FPkNdlNXtw0-A24,837 +pyarrow/src/arrow/python/vendored/pythoncapi_compat.h,sha256=bzMnlHTCfjk5DQRIxwytunYh5aQxU3iSElaaDyNnAY8,40900 +pyarrow/src/arrow/python/visibility.h,sha256=hwJw5sGrWJckQkNaAuLe4Tf-VDjQbXknyzNOVgZI3FI,1381 +pyarrow/substrait.py,sha256=yOMgCb-A5XW9t2DKgutRydDHpFvQkiNNq6ZIu0hIUB8,1230 +pyarrow/table.pxi,sha256=UY25fBtFThwdRT87cNAadUFJmVYHjSwAxcT7qVVBc4c,208768 +pyarrow/tensor.pxi,sha256=vWs4_2-xeSkFsvL0VMVciIntRWiaH-6QxZyx8j00v1U,43661 +pyarrow/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +pyarrow/tests/__pycache__/__init__.cpython-310.pyc,, +pyarrow/tests/__pycache__/arrow_16597.cpython-310.pyc,, +pyarrow/tests/__pycache__/arrow_39313.cpython-310.pyc,, +pyarrow/tests/__pycache__/arrow_7980.cpython-310.pyc,, +pyarrow/tests/__pycache__/conftest.cpython-310.pyc,, +pyarrow/tests/__pycache__/pandas_examples.cpython-310.pyc,, +pyarrow/tests/__pycache__/pandas_threaded_import.cpython-310.pyc,, +pyarrow/tests/__pycache__/read_record_batch.cpython-310.pyc,, +pyarrow/tests/__pycache__/strategies.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_acero.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_adhoc_memory_leak.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_array.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_builder.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_cffi.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_compute.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_convert_builtin.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_cpp_internals.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_csv.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_cuda.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_cuda_numba_interop.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_cython.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_dataset.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_dataset_encryption.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_deprecations.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_device.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_dlpack.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_exec_plan.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_extension_type.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_feather.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_flight.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_flight_async.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_fs.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_gandiva.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_gdb.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_io.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_ipc.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_json.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_jvm.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_memory.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_misc.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_orc.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_pandas.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_scalars.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_schema.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_sparse_tensor.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_strategies.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_substrait.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_table.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_tensor.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_types.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_udf.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_util.cpython-310.pyc,, +pyarrow/tests/__pycache__/test_without_numpy.cpython-310.pyc,, +pyarrow/tests/__pycache__/util.cpython-310.pyc,, +pyarrow/tests/__pycache__/wsgi_examples.cpython-310.pyc,, +pyarrow/tests/arrow_16597.py,sha256=DNb41h9E3ITGvAJJu86i5SfsKrwstQJ0E5gT_bpTS_k,1354 +pyarrow/tests/arrow_39313.py,sha256=0pyBixoX38fldTPO1Vwshi_H0XBACrz8esYoL4o71KI,1431 +pyarrow/tests/arrow_7980.py,sha256=tZKb_tRLfxHaosDk9Yu2GLEsJjMaruXD5CKhbK_6Hq8,1094 +pyarrow/tests/bound_function_visit_strings.pyx,sha256=vDEFoNYR8BWNkCntKDuBUT8sXNRBex_5G2bFKogr1Bs,2026 +pyarrow/tests/conftest.py,sha256=yhbHWUARnpRonmxVtXkPv7YBOr_K-B0DRWfkrW4wnCE,10386 +pyarrow/tests/data/feather/v0.17.0.version.2-compression.lz4.feather,sha256=qzcc7Bo4OWBXYsyyKdDJwdTRstMqB1Zz0GiGYtndBnE,594 +pyarrow/tests/data/orc/README.md,sha256=_4X5XszZqQtWAVEz5N1Va4VyyayGQgNDKrcdMX2Ib4s,932 +pyarrow/tests/data/orc/TestOrcFile.emptyFile.jsn.gz,sha256=xLjAXd-3scx3DCyeAsmxTO3dv1cj9KRvYopKe5rQNiI,50 +pyarrow/tests/data/orc/TestOrcFile.emptyFile.orc,sha256=zj0579dQBXhF7JuB-ZphkmQ81ybLo6Ca4zPV4HXoImY,523 +pyarrow/tests/data/orc/TestOrcFile.test1.jsn.gz,sha256=kLxmwMVHtfzpHqBztFjfY_PTCloaXpfHq9DDDszb8Wk,323 +pyarrow/tests/data/orc/TestOrcFile.test1.orc,sha256=A4JxgMCffTkz9-XT1QT1tg2TlYZRRz1g7iIMmqzovqA,1711 +pyarrow/tests/data/orc/TestOrcFile.testDate1900.jsn.gz,sha256=oWf7eBR3ZtOA91OTvdeQJYos1an56msGsJwhGOan3lo,182453 +pyarrow/tests/data/orc/TestOrcFile.testDate1900.orc,sha256=nYsVYhUGGOL80gHj37si_vX0dh8QhIMSeU4sHjNideM,30941 +pyarrow/tests/data/orc/decimal.jsn.gz,sha256=kTEyYdPDAASFUX8Niyry5mRDF-Y-LsrhSAjbu453mvA,19313 +pyarrow/tests/data/orc/decimal.orc,sha256=W5cV2WdLy4OrSTnd_Qv5ntphG4TcB-MyG4UpRFwSxJY,16337 +pyarrow/tests/data/parquet/v0.7.1.all-named-index.parquet,sha256=YPGUXtw-TsOPbiNDieZHobNp3or7nHhAxJGjmIDAyqE,3948 +pyarrow/tests/data/parquet/v0.7.1.column-metadata-handling.parquet,sha256=7sebZgpfdcP37QksT3FhDL6vOA9gR6GBaq44NCVtOYw,2012 +pyarrow/tests/data/parquet/v0.7.1.parquet,sha256=vmdzhIzpBbmRkq3Gjww7KqurfSFNtQuSpSIDeQVmqys,4372 +pyarrow/tests/data/parquet/v0.7.1.some-named-index.parquet,sha256=VGgSjqihCRtdBxlUcfP5s3BSR7aUQKukW-bGgJLf_HY,4008 +pyarrow/tests/extensions.pyx,sha256=UVfIEnGYy2SDiYkMW6jp6zTo1tvO7gUREH4OGEWjU2k,3046 +pyarrow/tests/interchange/__init__.py,sha256=9hdXHABrVpkbpjZgUft39kOFL2xSGeG4GEua0Hmelus,785 +pyarrow/tests/interchange/__pycache__/__init__.cpython-310.pyc,, +pyarrow/tests/interchange/__pycache__/test_conversion.cpython-310.pyc,, +pyarrow/tests/interchange/__pycache__/test_interchange_spec.cpython-310.pyc,, +pyarrow/tests/interchange/test_conversion.py,sha256=23e5tpKBL-ekA5uWpM6-f6HVPF937Hnzfune0Ty9moo,18609 +pyarrow/tests/interchange/test_interchange_spec.py,sha256=AwqwD8b2gV1f5HERtzG6UtOIshFGafUI_9CQOVLPnho,9381 +pyarrow/tests/pandas_examples.py,sha256=SDPL87Ea997nGi7afOqlceq6wH4Bh5IR5S0fHZV3Acs,5107 +pyarrow/tests/pandas_threaded_import.py,sha256=b_ubLr5dj4dWJht9552qc3S3Yt3fQQgaUH6208oZvHg,1429 +pyarrow/tests/parquet/__init__.py,sha256=dKsXU9M-sJyz2wYIuqwsKM9meOlK_qY6qhmQzIvEpCE,931 +pyarrow/tests/parquet/__pycache__/__init__.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/common.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/conftest.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/encryption.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_basic.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_compliant_nested_type.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_data_types.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_dataset.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_datetime.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_encryption.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_metadata.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_pandas.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_parquet_file.cpython-310.pyc,, +pyarrow/tests/parquet/__pycache__/test_parquet_writer.cpython-310.pyc,, +pyarrow/tests/parquet/common.py,sha256=ezd4yAsRXI1R-MxE1iIbyHHIlEGyKumckQzVHAx46rM,6149 +pyarrow/tests/parquet/conftest.py,sha256=CyKJlVT-pQNb2f2BwlUaIAoWtobcZ65Y0vgpGX2ttnA,3157 +pyarrow/tests/parquet/encryption.py,sha256=Nr232E2ueexYXGWNZQOfQpw3LkPTbSNHWSfwf1DuBVI,5218 +pyarrow/tests/parquet/test_basic.py,sha256=0tZ7Y8-Df2O4e-QavU2s0RR8xHuIVf3cIdAAWrJPH_0,36873 +pyarrow/tests/parquet/test_compliant_nested_type.py,sha256=Lz7tCPrSpv9GrKPMS-eu1LehsCTwz7KdUdCYJ8tF8dE,3901 +pyarrow/tests/parquet/test_data_types.py,sha256=Y_RqQBfDgrxQnOc0IjuHA7ueIOmotH8CMkmqQ4prz9I,19319 +pyarrow/tests/parquet/test_dataset.py,sha256=U144J3RrIA3rCQIJXxpgy5PQVn5-7gYsy-f3pD20nPs,42020 +pyarrow/tests/parquet/test_datetime.py,sha256=A3ZaRj88u0IrlhCNp2KY_A8txrb7y2pKPgEVvI7e7bU,16398 +pyarrow/tests/parquet/test_encryption.py,sha256=eULJdOgRNMrd6klGgW5ElotrejsL9ev_dBz3LG2P9L8,28357 +pyarrow/tests/parquet/test_metadata.py,sha256=340mr8FxLTVfLMSDDDRm8WWblCzRz7FD77mr4BoJodg,27732 +pyarrow/tests/parquet/test_pandas.py,sha256=4r9sip2N0XbkBb6SZuh51SUHJn9BlAtfjbzB3d3Gk_M,23543 +pyarrow/tests/parquet/test_parquet_file.py,sha256=VlsY4JwZUckF5uxDrVlrJRBVnvTmuuajd0h37BMkbOo,13872 +pyarrow/tests/parquet/test_parquet_writer.py,sha256=9-BjKiXjLzgKC0IodiT8KnCtSPBo1ZKinS_spFX8uWs,18215 +pyarrow/tests/pyarrow_cython_example.pyx,sha256=fx6zT1bUb2-cDnwKoG71K3ozpmrNJ53kKQHHJTExGz8,2115 +pyarrow/tests/read_record_batch.py,sha256=9Y0X0h03hUXwOKZz7jBBZSwgIrjxT-FkWIw6pu38Frc,953 +pyarrow/tests/strategies.py,sha256=eJkCeVCPB714DisrR1Q1INIUqIBX9v1rF2lNtBamlTY,15581 +pyarrow/tests/test_acero.py,sha256=t1TIMZJ9zfCYu9V_Mg_i6SvtoIXFo2GZbxDJh3Luu60,18009 +pyarrow/tests/test_adhoc_memory_leak.py,sha256=Pn4PcIbOBRtSJuz9Ar_ocubco0QOMZ-eAE9Bs7Wp4mA,1453 +pyarrow/tests/test_array.py,sha256=m42aqjvL-_hLP2faOY4asW6k7Uy-L4WfhT5vyqx922o,146212 +pyarrow/tests/test_builder.py,sha256=zNEcslLwyb40oYbG7lInQcI81QHMKDOzi1zthw1Je7c,2803 +pyarrow/tests/test_cffi.py,sha256=ZkOKn6qfySL7LPqR-1FAlRS7n8h3npF5WmqSV8eBVg8,26339 +pyarrow/tests/test_compute.py,sha256=cdgZXDHpTVKkuhuGP1ONc4wo_RUavcrY0hSp4V61T9U,157487 +pyarrow/tests/test_convert_builtin.py,sha256=yhFtMOj6Sqx5TyLs6dxQQl9_ceX_Qbm5MmTyEulpDk0,82323 +pyarrow/tests/test_cpp_internals.py,sha256=Xg4CUB6zohQkcYG64Lj_Uf2BscI27Vv0JC_CqNkDQuE,2006 +pyarrow/tests/test_csv.py,sha256=45yAVGgLS7cS5tJpnzz-yEZbrp5PXCbDIVtv-oVZX8w,77863 +pyarrow/tests/test_cuda.py,sha256=81lyEjUUd2UWbeFW-JJECYJ9UZKAXOTys55uMd2i0so,36153 +pyarrow/tests/test_cuda_numba_interop.py,sha256=iHP_FE4sWbsKwNNXRcYnVozp3Wd1o0Mg6BDymx710G4,8794 +pyarrow/tests/test_cython.py,sha256=p3Pz_w8Ysd6pMQfvjGPsBA-fYjX7os9M9B9K9UB42m8,7040 +pyarrow/tests/test_dataset.py,sha256=TA9e4J0nL05TpISLgCzAOyY8g7vAmM4j8PYNaTkCw7U,218325 +pyarrow/tests/test_dataset_encryption.py,sha256=EpD2zXVcLS_3i4fwli7Z45ibDFSgXCFFQ2qiXtAxBsg,18023 +pyarrow/tests/test_deprecations.py,sha256=W_rneq4jC6zqCNoGhBDf1F28Q-0LHI7YKLgtsbV6LHM,891 +pyarrow/tests/test_device.py,sha256=qe9Wiwo-XVazt9pdxyqQJUz6fNR0jTs9CHiyaoppNA4,2550 +pyarrow/tests/test_dlpack.py,sha256=sdubFfaU6ZCjZFoj-vbqnT-Yf79kI2ZBOe9Qi_J0Vfk,5974 +pyarrow/tests/test_exec_plan.py,sha256=pjOkSaWeqjN6celKxUEH3tBGXLh8kKbmSSsvKOWsbQQ,10096 +pyarrow/tests/test_extension_type.py,sha256=q-Y3BI469ePC0aLG5LUSTObdeMqkeQtnD-oRLBx_9kM,67567 +pyarrow/tests/test_feather.py,sha256=QMFIyOh2bGwatHq3gE1OZWIgIdfJ2i5UjhNTN0DVK5U,25616 +pyarrow/tests/test_flight.py,sha256=kZsX-0YWYtrIrcqkdg8yNmjxMuVQSo_Ly5rqD6y2H80,102280 +pyarrow/tests/test_flight_async.py,sha256=kP2wIrUz1HIbrr4CE0SxSg4Zc56ta3xX6EdibAI2yVM,2848 +pyarrow/tests/test_fs.py,sha256=G97d6CLdiFCnWo18H_OHCWt-JHeSqYCv7rs5quosI3U,72758 +pyarrow/tests/test_gandiva.py,sha256=AEf9ln-j5MmIMQ0JTQPhnZwbNh82ynSURsWPaKaNing,15623 +pyarrow/tests/test_gdb.py,sha256=mircGfK7H3PQgn4dVYfDUYbC03CzuAXqGW4oOmd-uZw,45052 +pyarrow/tests/test_io.py,sha256=fBEyAu9zTMHu9ovgwOwv7kEslYhY38sd0GNR78d-ECg,63736 +pyarrow/tests/test_ipc.py,sha256=KFK4E6Ee_zZ52k9hbxS6nMFNo41asuoppiFsJW8PGEI,46053 +pyarrow/tests/test_json.py,sha256=yTOKNdiCcJxmPCAiUfNojAOqsytj_w9uZ0YPJd3yNWU,23003 +pyarrow/tests/test_jvm.py,sha256=QLRHeFx8Gbz1UywlnDi2akV6DBtqGHReQEuNl2vSbcE,15475 +pyarrow/tests/test_memory.py,sha256=j8fs5HqguRobzbcHSvyRzd0ICT7IMeonMvcs147DEd4,9860 +pyarrow/tests/test_misc.py,sha256=1febCUPe9YTPl7QU5jH8Tlo2q_5UpoYoqsg_esZpCiU,7626 +pyarrow/tests/test_orc.py,sha256=UrDyNHJoTX72KFGVLf-WRzzC8e6nsP-GsCm97x0l43o,21565 +pyarrow/tests/test_pandas.py,sha256=NuZGxTh6PNJM2cV2fOrEWLZexc4OSn8G10hMcEJbqyw,195140 +pyarrow/tests/test_scalars.py,sha256=8LPIZy1LmqOTK6-e_RZjiRM9owLodVvMFtRfFSiWBrc,30289 +pyarrow/tests/test_schema.py,sha256=x5MSVt6kOhvSPTCIMIFVTm27Lp6znJ4emk9L5ziqJwc,23347 +pyarrow/tests/test_sparse_tensor.py,sha256=bGO7Es4D65zmGNRN013G9VUCQ_v-mPUb90sAk--3yyw,17666 +pyarrow/tests/test_strategies.py,sha256=ZPJiWvj26qQPsR9aZCF-VUsT6PyWPrJsr6q2oXJshSs,1829 +pyarrow/tests/test_substrait.py,sha256=rsLPNlDPDAxZ1OIu-LAINSD4q_UZJhOvtTzPfXjrWM8,32223 +pyarrow/tests/test_table.py,sha256=aRgarP_oVThWXp41TBe6MLVPgmoZqCgwdKMVhq2KHig,123867 +pyarrow/tests/test_tensor.py,sha256=LYSEYGUjtdnsbL0WAir9jFindo-r0bLySiDA1uAXL8E,6643 +pyarrow/tests/test_types.py,sha256=XaGZSzq556PewtGRcUEQ6T5T6D5QidchC7RrmRQYI-8,44212 +pyarrow/tests/test_udf.py,sha256=WA9E5skUqh7uMr_zH3rQ11LRx0SK2G3WO8HjVHGWyQY,29792 +pyarrow/tests/test_util.py,sha256=ozTlooHBMOP3nbX5b3dG2aanrXwxXHx1giicm0QQyPM,5030 +pyarrow/tests/test_without_numpy.py,sha256=ysbB-jML318I04ViQT4Ok7iMg1cI-NU8kguPu-FTSl4,1855 +pyarrow/tests/util.py,sha256=o_opaXvKIDveE2cf_jjo6fHRuRk2CjLGu6OsFFbt-mM,13665 +pyarrow/tests/wsgi_examples.py,sha256=vQIDb5989sRVLsELw-fRHhfX-dE96sTl5J2lEuEKup8,1348 +pyarrow/types.pxi,sha256=M1_lSb4uNHl2akZkQlgOf21SnfvrWQz6rybtCIVAdYQ,162774 +pyarrow/types.py,sha256=d79cmgr2MDz79UQy6ChoKf4MScKCtDvtOBVGIbV1644,9734 +pyarrow/util.py,sha256=8cQQMKLvvoQitDnz1Tv-w2zJgsIKJUNvtbqupk5TJZs,8679 +pyarrow/vendored/__init__.py,sha256=9hdXHABrVpkbpjZgUft39kOFL2xSGeG4GEua0Hmelus,785 +pyarrow/vendored/__pycache__/__init__.cpython-310.pyc,, +pyarrow/vendored/__pycache__/docscrape.cpython-310.pyc,, +pyarrow/vendored/__pycache__/version.cpython-310.pyc,, +pyarrow/vendored/docscrape.py,sha256=phTjwuzoO5hB88QerZk3uGu9c5OrZwjFzI7vEIIbCUQ,22975 +pyarrow/vendored/version.py,sha256=5-Vo4Q3kPJrm1DSGusnMlTxuA8ynI4hAryApBd6MnpQ,14345 diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/WHEEL b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..296667c0520a9eb33ddac307bd393b232c1b8ee2 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: setuptools (80.9.0) +Root-Is-Purelib: false +Tag: cp310-cp310-manylinux_2_28_x86_64 + diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/licenses/LICENSE.txt b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/licenses/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..794ee9ea37fbaedde82edd4176511f5d23229d94 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/licenses/LICENSE.txt @@ -0,0 +1,2331 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + 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. + +-------------------------------------------------------------------------------- + +src/arrow/util (some portions): Apache 2.0, and 3-clause BSD + +Some portions of this module are derived from code in the Chromium project, +copyright (c) Google inc and (c) The Chromium Authors and licensed under the +Apache 2.0 License or the under the 3-clause BSD license: + + Copyright (c) 2013 The Chromium Authors. 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 Google Inc. 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. + +-------------------------------------------------------------------------------- + +This project includes code from Daniel Lemire's FrameOfReference project. + +https://github.com/lemire/FrameOfReference/blob/6ccaf9e97160f9a3b299e23a8ef739e711ef0c71/src/bpacking.cpp +https://github.com/lemire/FrameOfReference/blob/146948b6058a976bc7767262ad3a2ce201486b93/scripts/turbopacking64.py + +Copyright: 2013 Daniel Lemire +Home page: http://lemire.me/en/ +Project page: https://github.com/lemire/FrameOfReference +License: Apache License Version 2.0 http://www.apache.org/licenses/LICENSE-2.0 + +-------------------------------------------------------------------------------- + +This project includes code from the TensorFlow project + +Copyright 2015 The TensorFlow Authors. All Rights Reserved. + +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. + +-------------------------------------------------------------------------------- + +This project includes code from the NumPy project. + +https://github.com/numpy/numpy/blob/e1f191c46f2eebd6cb892a4bfe14d9dd43a06c4e/numpy/core/src/multiarray/multiarraymodule.c#L2910 + +https://github.com/numpy/numpy/blob/68fd82271b9ea5a9e50d4e761061dfcca851382a/numpy/core/src/multiarray/datetime.c + +Copyright (c) 2005-2017, NumPy Developers. +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 NumPy Developers nor the names of any + 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. + +-------------------------------------------------------------------------------- + +This project includes code from the Boost project + +Boost Software License - Version 1.0 - August 17th, 2003 + +Permission is hereby granted, free of charge, to any person or organization +obtaining a copy of the software and accompanying documentation covered by +this license (the "Software") to use, reproduce, display, distribute, +execute, and transmit the Software, and to prepare derivative works of the +Software, and to permit third-parties to whom the Software is furnished to +do so, all subject to the following: + +The copyright notices in the Software and this entire statement, including +the above license grant, this restriction and the following disclaimer, +must be included in all copies of the Software, in whole or in part, and +all derivative works of the Software, unless such copies or derivative +works are solely in the form of machine-executable object code generated by +a source language processor. + +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, TITLE AND NON-INFRINGEMENT. IN NO EVENT +SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE +FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +DEALINGS IN THE SOFTWARE. + +-------------------------------------------------------------------------------- + +This project includes code from the FlatBuffers project + +Copyright 2014 Google Inc. + +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. + +-------------------------------------------------------------------------------- + +This project includes code from the tslib project + +Copyright 2015 Microsoft Corporation. All rights reserved. + +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. + +-------------------------------------------------------------------------------- + +This project includes code from the jemalloc project + +https://github.com/jemalloc/jemalloc + +Copyright (C) 2002-2017 Jason Evans . +All rights reserved. +Copyright (C) 2007-2012 Mozilla Foundation. All rights reserved. +Copyright (C) 2009-2017 Facebook, Inc. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: +1. Redistributions of source code must retain the above copyright notice(s), + this list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice(s), + 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 HOLDER(S) ``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(S) 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. +-------------------------------------------------------------------------------- + +This project includes code from the Go project, BSD 3-clause license + PATENTS +weak patent termination clause +(https://github.com/golang/go/blob/master/PATENTS). + +Copyright (c) 2009 The Go Authors. 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 Google Inc. 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. + +-------------------------------------------------------------------------------- + +This project includes code from the hs2client + +https://github.com/cloudera/hs2client + +Copyright 2016 Cloudera Inc. + +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. + +-------------------------------------------------------------------------------- + +The script r/configure has the following license (MIT) + +Copyright (c) 2017, Jeroen Ooms and Jim Hester + +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. + +-------------------------------------------------------------------------------- + +cpp/src/arrow/util/logging.cc, cpp/src/arrow/util/logging.h and +cpp/src/arrow/util/logging-test.cc are adapted from +Ray Project (https://github.com/ray-project/ray) (Apache 2.0). + +Copyright (c) 2016 Ray Project (https://github.com/ray-project/ray) + +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. + +-------------------------------------------------------------------------------- +The files cpp/src/arrow/vendored/datetime/date.h, cpp/src/arrow/vendored/datetime/tz.h, +cpp/src/arrow/vendored/datetime/tz_private.h, cpp/src/arrow/vendored/datetime/ios.h, +cpp/src/arrow/vendored/datetime/ios.mm, +cpp/src/arrow/vendored/datetime/tz.cpp are adapted from +Howard Hinnant's date library (https://github.com/HowardHinnant/date) +It is licensed under MIT license. + +The MIT License (MIT) +Copyright (c) 2015, 2016, 2017 Howard Hinnant +Copyright (c) 2016 Adrian Colomitchi +Copyright (c) 2017 Florian Dang +Copyright (c) 2017 Paul Thompson +Copyright (c) 2018 Tomasz Kamiński + +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. + +-------------------------------------------------------------------------------- + +The file cpp/src/arrow/util/utf8.h includes code adapted from the page + https://bjoern.hoehrmann.de/utf-8/decoder/dfa/ +with the following license (MIT) + +Copyright (c) 2008-2009 Bjoern Hoehrmann + +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. + +-------------------------------------------------------------------------------- + +The files in cpp/src/arrow/vendored/xxhash/ have the following license +(BSD 2-Clause License) + +xxHash Library +Copyright (c) 2012-2014, Yann Collet +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. + +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. + +You can contact the author at : +- xxHash homepage: http://www.xxhash.com +- xxHash source repository : https://github.com/Cyan4973/xxHash + +-------------------------------------------------------------------------------- + +The files in cpp/src/arrow/vendored/double-conversion/ have the following license +(BSD 3-Clause License) + +Copyright 2006-2011, the V8 project authors. 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 Google Inc. 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. + +-------------------------------------------------------------------------------- + +The files in cpp/src/arrow/vendored/uriparser/ have the following license +(BSD 3-Clause License) + +uriparser - RFC 3986 URI parsing library + +Copyright (C) 2007, Weijia Song +Copyright (C) 2007, Sebastian Pipping +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 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. + +-------------------------------------------------------------------------------- + +The files under dev/tasks/conda-recipes have the following license + +BSD 3-clause license +Copyright (c) 2015-2018, conda-forge +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +1. 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. + +3. Neither the name of the copyright holder 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 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. + +-------------------------------------------------------------------------------- + +The files in cpp/src/arrow/vendored/utfcpp/ have the following license + +Copyright 2006-2018 Nemanja Trifunovic + +Permission is hereby granted, free of charge, to any person or organization +obtaining a copy of the software and accompanying documentation covered by +this license (the "Software") to use, reproduce, display, distribute, +execute, and transmit the Software, and to prepare derivative works of the +Software, and to permit third-parties to whom the Software is furnished to +do so, all subject to the following: + +The copyright notices in the Software and this entire statement, including +the above license grant, this restriction and the following disclaimer, +must be included in all copies of the Software, in whole or in part, and +all derivative works of the Software, unless such copies or derivative +works are solely in the form of machine-executable object code generated by +a source language processor. + +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, TITLE AND NON-INFRINGEMENT. IN NO EVENT +SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE +FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +DEALINGS IN THE SOFTWARE. + +-------------------------------------------------------------------------------- + +This project includes code from Apache Kudu. + + * cpp/cmake_modules/CompilerInfo.cmake is based on Kudu's cmake_modules/CompilerInfo.cmake + +Copyright: 2016 The Apache Software Foundation. +Home page: https://kudu.apache.org/ +License: http://www.apache.org/licenses/LICENSE-2.0 + +-------------------------------------------------------------------------------- + +This project includes code from Apache Impala (incubating), formerly +Impala. The Impala code and rights were donated to the ASF as part of the +Incubator process after the initial code imports into Apache Parquet. + +Copyright: 2012 Cloudera, Inc. +Copyright: 2016 The Apache Software Foundation. +Home page: http://impala.apache.org/ +License: http://www.apache.org/licenses/LICENSE-2.0 + +-------------------------------------------------------------------------------- + +This project includes code from Apache Aurora. + +* dev/release/{release,changelog,release-candidate} are based on the scripts from + Apache Aurora + +Copyright: 2016 The Apache Software Foundation. +Home page: https://aurora.apache.org/ +License: http://www.apache.org/licenses/LICENSE-2.0 + +-------------------------------------------------------------------------------- + +This project includes code from Snappy. + +* cpp/cmake_modules/{SnappyCMakeLists.txt,SnappyConfig.h} are based on code + from Google's Snappy project. + +Copyright: 2009 Google Inc. All rights reserved. +Homepage: https://github.com/google/snappy +License: 3-clause BSD + +-------------------------------------------------------------------------------- + +This project includes code from the manylinux project. + +* python/manylinux1/scripts/{build_python.sh,python-tag-abi-tag.py, + requirements.txt} are based on code from the manylinux project. + +Copyright: 2016 manylinux +Homepage: https://github.com/pypa/manylinux +License: The MIT License (MIT) + +-------------------------------------------------------------------------------- + +This project includes code from the cymove project: + +* python/pyarrow/includes/common.pxd includes code from the cymove project + +The MIT License (MIT) +Copyright (c) 2019 Omer Ozarslan + +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. + +-------------------------------------------------------------------------------- + +The projects includes code from the Ursabot project under the dev/archery +directory. + +License: BSD 2-Clause + +Copyright 2019 RStudio, Inc. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. 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. + +-------------------------------------------------------------------------------- + +This project include code from mingw-w64. + +* cpp/src/arrow/util/cpu-info.cc has a polyfill for mingw-w64 < 5 + +Copyright (c) 2009 - 2013 by the mingw-w64 project +Homepage: https://mingw-w64.org +License: Zope Public License (ZPL) Version 2.1. + +--------------------------------------------------------------------------------- + +This project include code from Google's Asylo project. + +* cpp/src/arrow/result.h is based on status_or.h + +Copyright (c) Copyright 2017 Asylo authors +Homepage: https://asylo.dev/ +License: Apache 2.0 + +-------------------------------------------------------------------------------- + +This project includes code from Google's protobuf project + +* cpp/src/arrow/result.h ARROW_ASSIGN_OR_RAISE is based off ASSIGN_OR_RETURN +* cpp/src/arrow/util/bit_stream_utils.h contains code from wire_format_lite.h + +Copyright 2008 Google Inc. All rights reserved. +Homepage: https://developers.google.com/protocol-buffers/ +License: + +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 Google Inc. 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. + +Code generated by the Protocol Buffer compiler is owned by the owner +of the input file used when generating it. This code is not +standalone and requires a support library to be linked with it. This +support library is itself covered by the above license. + +-------------------------------------------------------------------------------- + +3rdparty dependency LLVM is statically linked in certain binary distributions. +Additionally some sections of source code have been derived from sources in LLVM +and have been clearly labeled as such. LLVM has the following license: + +============================================================================== +The LLVM Project is under the Apache License v2.0 with LLVM Exceptions: +============================================================================== + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + 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. + + +---- LLVM Exceptions to the Apache 2.0 License ---- + +As an exception, if, as a result of your compiling your source code, portions +of this Software are embedded into an Object form of such source code, you +may redistribute such embedded portions in such Object form without complying +with the conditions of Sections 4(a), 4(b) and 4(d) of the License. + +In addition, if you combine or link compiled forms of this Software with +software that is licensed under the GPLv2 ("Combined Software") and if a +court of competent jurisdiction determines that the patent provision (Section +3), the indemnity provision (Section 9) or other Section of the License +conflicts with the conditions of the GPLv2, you may retroactively and +prospectively choose to deem waived or otherwise exclude such Section(s) of +the License, but only in their entirety and only with respect to the Combined +Software. + +============================================================================== +Software from third parties included in the LLVM Project: +============================================================================== +The LLVM Project contains third party software which is under different license +terms. All such code will be identified clearly using at least one of two +mechanisms: +1) It will be in a separate directory tree with its own `LICENSE.txt` or + `LICENSE` file at the top containing the specific license and restrictions + which apply to that software, or +2) It will contain specific license and restriction terms at the top of every + file. + +-------------------------------------------------------------------------------- + +3rdparty dependency gRPC is statically linked in certain binary +distributions, like the python wheels. gRPC has the following license: + +Copyright 2014 gRPC 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. + +-------------------------------------------------------------------------------- + +3rdparty dependency Apache Thrift is statically linked in certain binary +distributions, like the python wheels. Apache Thrift has the following license: + +Apache Thrift +Copyright (C) 2006 - 2019, The Apache Software Foundation + +This product includes software developed at +The Apache Software Foundation (http://www.apache.org/). + +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. + +-------------------------------------------------------------------------------- + +3rdparty dependency Apache ORC is statically linked in certain binary +distributions, like the python wheels. Apache ORC has the following license: + +Apache ORC +Copyright 2013-2019 The Apache Software Foundation + +This product includes software developed by The Apache Software +Foundation (http://www.apache.org/). + +This product includes software developed by Hewlett-Packard: +(c) Copyright [2014-2015] Hewlett-Packard Development Company, L.P + +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. + +-------------------------------------------------------------------------------- + +3rdparty dependency zstd is statically linked in certain binary +distributions, like the python wheels. ZSTD has the following license: + +BSD License + +For Zstandard software + +Copyright (c) 2016-present, Facebook, Inc. 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 Facebook 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 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. + +-------------------------------------------------------------------------------- + +3rdparty dependency lz4 is statically linked in certain binary +distributions, like the python wheels. lz4 has the following license: + +LZ4 Library +Copyright (c) 2011-2016, Yann Collet +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. + +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. + +-------------------------------------------------------------------------------- + +3rdparty dependency Brotli is statically linked in certain binary +distributions, like the python wheels. Brotli has the following license: + +Copyright (c) 2009, 2010, 2013-2016 by the Brotli Authors. + +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. + +-------------------------------------------------------------------------------- + +3rdparty dependency rapidjson is statically linked in certain binary +distributions, like the python wheels. rapidjson and its dependencies have the +following licenses: + +Tencent is pleased to support the open source community by making RapidJSON +available. + +Copyright (C) 2015 THL A29 Limited, a Tencent company, and Milo Yip. +All rights reserved. + +If you have downloaded a copy of the RapidJSON binary from Tencent, please note +that the RapidJSON binary is licensed under the MIT License. +If you have downloaded a copy of the RapidJSON source code from Tencent, please +note that RapidJSON source code is licensed under the MIT License, except for +the third-party components listed below which are subject to different license +terms. Your integration of RapidJSON into your own projects may require +compliance with the MIT License, as well as the other licenses applicable to +the third-party components included within RapidJSON. To avoid the problematic +JSON license in your own projects, it's sufficient to exclude the +bin/jsonchecker/ directory, as it's the only code under the JSON license. +A copy of the MIT License is included in this file. + +Other dependencies and licenses: + + Open Source Software Licensed Under the BSD License: + -------------------------------------------------------------------- + + The msinttypes r29 + Copyright (c) 2006-2013 Alexander Chemeris + 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 copyright holder 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 REGENTS 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 REGENTS AND 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. + + Terms of the MIT License: + -------------------------------------------------------------------- + + 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. + +-------------------------------------------------------------------------------- + +3rdparty dependency snappy is statically linked in certain binary +distributions, like the python wheels. snappy has the following license: + +Copyright 2011, Google Inc. +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 Google Inc. 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. + +=== + +Some of the benchmark data in testdata/ is licensed differently: + + - fireworks.jpeg is Copyright 2013 Steinar H. Gunderson, and + is licensed under the Creative Commons Attribution 3.0 license + (CC-BY-3.0). See https://creativecommons.org/licenses/by/3.0/ + for more information. + + - kppkn.gtb is taken from the Gaviota chess tablebase set, and + is licensed under the MIT License. See + https://sites.google.com/site/gaviotachessengine/Home/endgame-tablebases-1 + for more information. + + - paper-100k.pdf is an excerpt (bytes 92160 to 194560) from the paper + “Combinatorial Modeling of Chromatin Features Quantitatively Predicts DNA + Replication Timing in _Drosophila_” by Federico Comoglio and Renato Paro, + which is licensed under the CC-BY license. See + http://www.ploscompbiol.org/static/license for more ifnormation. + + - alice29.txt, asyoulik.txt, plrabn12.txt and lcet10.txt are from Project + Gutenberg. The first three have expired copyrights and are in the public + domain; the latter does not have expired copyright, but is still in the + public domain according to the license information + (http://www.gutenberg.org/ebooks/53). + +-------------------------------------------------------------------------------- + +3rdparty dependency gflags is statically linked in certain binary +distributions, like the python wheels. gflags has the following license: + +Copyright (c) 2006, Google Inc. +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 Google Inc. 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. + +-------------------------------------------------------------------------------- + +3rdparty dependency glog is statically linked in certain binary +distributions, like the python wheels. glog has the following license: + +Copyright (c) 2008, Google Inc. +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 Google Inc. 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. + + +A function gettimeofday in utilities.cc is based on + +http://www.google.com/codesearch/p?hl=en#dR3YEbitojA/COPYING&q=GetSystemTimeAsFileTime%20license:bsd + +The license of this code is: + +Copyright (c) 2003-2008, Jouni Malinen and contributors +All Rights Reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + +1. 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. + +3. Neither the name(s) of the above-listed copyright holder(s) 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. + +-------------------------------------------------------------------------------- + +3rdparty dependency re2 is statically linked in certain binary +distributions, like the python wheels. re2 has the following license: + +Copyright (c) 2009 The RE2 Authors. 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 Google Inc. 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. + +-------------------------------------------------------------------------------- + +3rdparty dependency c-ares is statically linked in certain binary +distributions, like the python wheels. c-ares has the following license: + +# c-ares license + +Copyright (c) 2007 - 2018, Daniel Stenberg with many contributors, see AUTHORS +file. + +Copyright 1998 by the Massachusetts Institute of Technology. + +Permission to use, copy, modify, and distribute this software and its +documentation for any purpose and without fee is hereby granted, provided that +the above copyright notice appear in all copies and that both that copyright +notice and this permission notice appear in supporting documentation, and that +the name of M.I.T. not be used in advertising or publicity pertaining to +distribution of the software without specific, written prior permission. +M.I.T. makes no representations about the suitability of this software for any +purpose. It is provided "as is" without express or implied warranty. + +-------------------------------------------------------------------------------- + +3rdparty dependency zlib is redistributed as a dynamically linked shared +library in certain binary distributions, like the python wheels. In the future +this will likely change to static linkage. zlib has the following license: + +zlib.h -- interface of the 'zlib' general purpose compression library + version 1.2.11, January 15th, 2017 + + Copyright (C) 1995-2017 Jean-loup Gailly and Mark Adler + + This software is provided 'as-is', without any express or implied + warranty. In no event will the authors be held liable for any damages + arising from the use of this software. + + Permission is granted to anyone to use this software for any purpose, + including commercial applications, and to alter it and redistribute it + freely, subject to the following restrictions: + + 1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. + 3. This notice may not be removed or altered from any source distribution. + + Jean-loup Gailly Mark Adler + jloup@gzip.org madler@alumni.caltech.edu + +-------------------------------------------------------------------------------- + +3rdparty dependency openssl is redistributed as a dynamically linked shared +library in certain binary distributions, like the python wheels. openssl +preceding version 3 has the following license: + + LICENSE ISSUES + ============== + + The OpenSSL toolkit stays under a double license, i.e. both the conditions of + the OpenSSL License and the original SSLeay license apply to the toolkit. + See below for the actual license texts. + + OpenSSL License + --------------- + +/* ==================================================================== + * Copyright (c) 1998-2019 The OpenSSL Project. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. 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. + * + * 3. All advertising materials mentioning features or use of this + * software must display the following acknowledgment: + * "This product includes software developed by the OpenSSL Project + * for use in the OpenSSL Toolkit. (http://www.openssl.org/)" + * + * 4. The names "OpenSSL Toolkit" and "OpenSSL Project" must not be used to + * endorse or promote products derived from this software without + * prior written permission. For written permission, please contact + * openssl-core@openssl.org. + * + * 5. Products derived from this software may not be called "OpenSSL" + * nor may "OpenSSL" appear in their names without prior written + * permission of the OpenSSL Project. + * + * 6. Redistributions of any form whatsoever must retain the following + * acknowledgment: + * "This product includes software developed by the OpenSSL Project + * for use in the OpenSSL Toolkit (http://www.openssl.org/)" + * + * THIS SOFTWARE IS PROVIDED BY THE OpenSSL PROJECT ``AS IS'' AND ANY + * EXPRESSED 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 OpenSSL PROJECT OR + * ITS 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. + * ==================================================================== + * + * This product includes cryptographic software written by Eric Young + * (eay@cryptsoft.com). This product includes software written by Tim + * Hudson (tjh@cryptsoft.com). + * + */ + + Original SSLeay License + ----------------------- + +/* Copyright (C) 1995-1998 Eric Young (eay@cryptsoft.com) + * All rights reserved. + * + * This package is an SSL implementation written + * by Eric Young (eay@cryptsoft.com). + * The implementation was written so as to conform with Netscapes SSL. + * + * This library is free for commercial and non-commercial use as long as + * the following conditions are aheared to. The following conditions + * apply to all code found in this distribution, be it the RC4, RSA, + * lhash, DES, etc., code; not just the SSL code. The SSL documentation + * included with this distribution is covered by the same copyright terms + * except that the holder is Tim Hudson (tjh@cryptsoft.com). + * + * Copyright remains Eric Young's, and as such any Copyright notices in + * the code are not to be removed. + * If this package is used in a product, Eric Young should be given attribution + * as the author of the parts of the library used. + * This can be in the form of a textual message at program startup or + * in documentation (online or textual) provided with the package. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * 1. Redistributions of source code must retain the 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. + * 3. All advertising materials mentioning features or use of this software + * must display the following acknowledgement: + * "This product includes cryptographic software written by + * Eric Young (eay@cryptsoft.com)" + * The word 'cryptographic' can be left out if the rouines from the library + * being used are not cryptographic related :-). + * 4. If you include any Windows specific code (or a derivative thereof) from + * the apps directory (application code) you must include an acknowledgement: + * "This product includes software written by Tim Hudson (tjh@cryptsoft.com)" + * + * THIS SOFTWARE IS PROVIDED BY ERIC YOUNG ``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 AUTHOR 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. + * + * The licence and distribution terms for any publically available version or + * derivative of this code cannot be changed. i.e. this code cannot simply be + * copied and put under another distribution licence + * [including the GNU Public Licence.] + */ + +-------------------------------------------------------------------------------- + +This project includes code from the rtools-backports project. + +* ci/scripts/PKGBUILD and ci/scripts/r_windows_build.sh are based on code + from the rtools-backports project. + +Copyright: Copyright (c) 2013 - 2019, Алексей and Jeroen Ooms. +All rights reserved. +Homepage: https://github.com/r-windows/rtools-backports +License: 3-clause BSD + +-------------------------------------------------------------------------------- + +Some code from pandas has been adapted for the pyarrow codebase. pandas is +available under the 3-clause BSD license, which follows: + +pandas license +============== + +Copyright (c) 2011-2012, Lambda Foundry, Inc. and PyData Development Team +All rights reserved. + +Copyright (c) 2008-2011 AQR Capital Management, LLC +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 copyright holder nor the names of any + 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 HOLDER 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. + +-------------------------------------------------------------------------------- + +Some bits from DyND, in particular aspects of the build system, have been +adapted from libdynd and dynd-python under the terms of the BSD 2-clause +license + +The BSD 2-Clause License + + Copyright (C) 2011-12, Dynamic NDArray Developers + 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. + + 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. + +Dynamic NDArray Developers list: + + * Mark Wiebe + * Continuum Analytics + +-------------------------------------------------------------------------------- + +Some source code from Ibis (https://github.com/cloudera/ibis) has been adapted +for PyArrow. Ibis is released under the Apache License, Version 2.0. + +-------------------------------------------------------------------------------- + +dev/tasks/homebrew-formulae/apache-arrow.rb has the following license: + +BSD 2-Clause License + +Copyright (c) 2009-present, Homebrew contributors +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. + +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. + +---------------------------------------------------------------------- + +cpp/src/arrow/vendored/base64.cpp has the following license + +ZLIB License + +Copyright (C) 2004-2017 René Nyffenegger + +This source code is provided 'as-is', without any express or implied +warranty. In no event will the author be held liable for any damages arising +from the use of this software. + +Permission is granted to anyone to use this software for any purpose, including +commercial applications, and to alter it and redistribute it freely, subject to +the following restrictions: + +1. The origin of this source code must not be misrepresented; you must not + claim that you wrote the original source code. If you use this source code + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + +2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original source code. + +3. This notice may not be removed or altered from any source distribution. + +René Nyffenegger rene.nyffenegger@adp-gmbh.ch + +-------------------------------------------------------------------------------- + +This project includes code from Folly. + + * cpp/src/arrow/vendored/ProducerConsumerQueue.h + +is based on Folly's + + * folly/Portability.h + * folly/lang/Align.h + * folly/ProducerConsumerQueue.h + +Copyright: Copyright (c) Facebook, Inc. and its affiliates. +Home page: https://github.com/facebook/folly +License: http://www.apache.org/licenses/LICENSE-2.0 + +-------------------------------------------------------------------------------- + +The file cpp/src/arrow/vendored/musl/strptime.c has the following license + +Copyright © 2005-2020 Rich Felker, et al. + +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. + +-------------------------------------------------------------------------------- + +The file cpp/cmake_modules/BuildUtils.cmake contains code from + +https://gist.github.com/cristianadam/ef920342939a89fae3e8a85ca9459b49 + +which is made available under the MIT license + +Copyright (c) 2019 Cristian Adam + +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. + +-------------------------------------------------------------------------------- + +The files in cpp/src/arrow/vendored/portable-snippets/ contain code from + +https://github.com/nemequ/portable-snippets + +and have the following copyright notice: + +Each source file contains a preamble explaining the license situation +for that file, which takes priority over this file. With the +exception of some code pulled in from other repositories (such as +µnit, an MIT-licensed project which is used for testing), the code is +public domain, released using the CC0 1.0 Universal dedication (*). + +(*) https://creativecommons.org/publicdomain/zero/1.0/legalcode + +-------------------------------------------------------------------------------- + +The files in cpp/src/arrow/vendored/fast_float/ contain code from + +https://github.com/lemire/fast_float + +which is made available under the Apache License 2.0. + +-------------------------------------------------------------------------------- + +The file python/pyarrow/vendored/docscrape.py contains code from + +https://github.com/numpy/numpydoc/ + +which is made available under the BSD 2-clause license. + +-------------------------------------------------------------------------------- + +The file python/pyarrow/vendored/version.py contains code from + +https://github.com/pypa/packaging/ + +which is made available under both the Apache license v2.0 and the +BSD 2-clause license. + +-------------------------------------------------------------------------------- + +The files in cpp/src/arrow/vendored/pcg contain code from + +https://github.com/imneme/pcg-cpp + +and have the following copyright notice: + +Copyright 2014-2019 Melissa O'Neill , + and the PCG Project contributors. + +SPDX-License-Identifier: (Apache-2.0 OR MIT) + +Licensed under the Apache License, Version 2.0 (provided in +LICENSE-APACHE.txt and at http://www.apache.org/licenses/LICENSE-2.0) +or under the MIT license (provided in LICENSE-MIT.txt and at +http://opensource.org/licenses/MIT), at your option. This file may not +be copied, modified, or distributed except according to those terms. + +Distributed on an "AS IS" BASIS, WITHOUT WARRANTY OF ANY KIND, either +express or implied. See your chosen license for details. + +-------------------------------------------------------------------------------- +r/R/dplyr-count-tally.R (some portions) + +Some portions of this file are derived from code from + +https://github.com/tidyverse/dplyr/ + +which is made available under the MIT license + +Copyright (c) 2013-2019 RStudio and others. + +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. + +-------------------------------------------------------------------------------- + +The file src/arrow/util/io_util.cc contains code from the CPython project +which is made available under the Python Software Foundation License Version 2. + +-------------------------------------------------------------------------------- + +3rdparty dependency opentelemetry-cpp is statically linked in certain binary +distributions. opentelemetry-cpp is made available under the Apache License 2.0. + +Copyright The OpenTelemetry Authors +SPDX-License-Identifier: Apache-2.0 + +-------------------------------------------------------------------------------- + +ci/conan/ is based on code from Conan Package and Dependency Manager. + +Copyright (c) 2019 Conan.io + +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. + +-------------------------------------------------------------------------------- + +3rdparty dependency UCX is redistributed as a dynamically linked shared +library in certain binary distributions. UCX has the following license: + +Copyright (c) 2014-2015 UT-Battelle, LLC. All rights reserved. +Copyright (C) 2014-2020 Mellanox Technologies Ltd. All rights reserved. +Copyright (C) 2014-2015 The University of Houston System. All rights reserved. +Copyright (C) 2015 The University of Tennessee and The University + of Tennessee Research Foundation. All rights reserved. +Copyright (C) 2016-2020 ARM Ltd. All rights reserved. +Copyright (c) 2016 Los Alamos National Security, LLC. All rights reserved. +Copyright (C) 2016-2020 Advanced Micro Devices, Inc. All rights reserved. +Copyright (C) 2019 UChicago Argonne, LLC. All rights reserved. +Copyright (c) 2018-2020 NVIDIA CORPORATION. All rights reserved. +Copyright (C) 2020 Huawei Technologies Co., Ltd. All rights reserved. +Copyright (C) 2016-2020 Stony Brook University. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + +1. 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. +3. Neither the name of the copyright holder 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 +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. + +-------------------------------------------------------------------------------- + +The file dev/tasks/r/github.packages.yml contains code from + +https://github.com/ursa-labs/arrow-r-nightly + +which is made available under the Apache License 2.0. + +-------------------------------------------------------------------------------- +.github/actions/sync-nightlies/action.yml (some portions) + +Some portions of this file are derived from code from + +https://github.com/JoshPiper/rsync-docker + +which is made available under the MIT license + +Copyright (c) 2020 Joshua Piper + +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. + +-------------------------------------------------------------------------------- +.github/actions/sync-nightlies/action.yml (some portions) + +Some portions of this file are derived from code from + +https://github.com/burnett01/rsync-deployments + +which is made available under the MIT license + +Copyright (c) 2019-2022 Contention +Copyright (c) 2019-2022 Burnett01 + +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. + +-------------------------------------------------------------------------------- +java/vector/src/main/java/org/apache/arrow/vector/util/IntObjectHashMap.java +java/vector/src/main/java/org/apache/arrow/vector/util/IntObjectMap.java + +These files are derived from code from Netty, which is made available under the +Apache License 2.0. + +-------------------------------------------------------------------------------- +cpp/src/arrow/util/math_internal.cc (some portions) + +Some portions of this file are derived from + +https://github.com/ankane/dist-rust/ + +which is made available under the MIT license + +The MIT License (MIT) + +Copyright (c) 2021-2023 Contributors + +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. + +-------------------------------------------------------------------------------- +The files cpp/src/arrow/vendored/whereami/whereami.h, +cpp/src/arrow/vendored/whereami/whereami.cc are adapted from +Grégory Pakosz's whereami library (https://github.com/gpakosz/whereami) +It is dual licensed under both the WTFPLv2 and MIT licenses. + +The WTFPLv2 License + DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE + Version 2, December 2004 + + Copyright (C) 2004 Sam Hocevar + + Everyone is permitted to copy and distribute verbatim or modified + copies of this license document, and changing it is allowed as long + as the name is changed. + + DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE + TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION + + 0. You just DO WHAT THE FUCK YOU WANT TO. + 1. Bla bla bla + 2. Montesqieu et camembert, vive la France, zut alors! + +The MIT License (MIT) +Copyright Gregory Pakosz + +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. + +-------------------------------------------------------------------------------- + +The files in cpp/src/arrow/vendored/safeint/ contain code from + +https://github.com/dcleblanc/SafeInt + +and are made available under the MIT license. + +MIT License + +Copyright (c) 2018 Microsoft + +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/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/licenses/NOTICE.txt b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/licenses/NOTICE.txt new file mode 100644 index 0000000000000000000000000000000000000000..8046f20a0b919dba562dee3d5097fdee7bdd4010 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/licenses/NOTICE.txt @@ -0,0 +1,81 @@ +Apache Arrow +Copyright 2016-2026 The Apache Software Foundation + +This product includes software developed at +The Apache Software Foundation (http://www.apache.org/). + +This product includes software from the SFrame project (BSD, 3-clause). +* Copyright (C) 2015 Dato, Inc. +* Copyright (c) 2009 Carnegie Mellon University. + +This product includes software from the Feather project (Apache 2.0) +https://github.com/wesm/feather + +This product includes software from the DyND project (BSD 2-clause) +https://github.com/libdynd + +This product includes software from the LLVM project + * distributed under the University of Illinois Open Source + +This product includes software from the mman-win32 project + * Copyright https://code.google.com/p/mman-win32/ + * Licensed under the MIT License; + +This product includes software from the LevelDB project + * Copyright (c) 2011 The LevelDB Authors. All rights reserved. + * Use of this source code is governed by a BSD-style license that can be + * Moved from Kudu http://github.com/cloudera/kudu + +This product includes software from the CMake project + * Copyright 2001-2009 Kitware, Inc. + * Copyright 2012-2014 Continuum Analytics, Inc. + * All rights reserved. + +This product includes software from https://github.com/matthew-brett/multibuild (BSD 2-clause) + * Copyright (c) 2013-2016, Matt Terry and Matthew Brett; all rights reserved. + +This product includes software from the Ibis project (Apache 2.0) + * Copyright (c) 2015 Cloudera, Inc. + * https://github.com/cloudera/ibis + +This product includes software from Dremio (Apache 2.0) + * Copyright (C) 2017-2018 Dremio Corporation + * https://github.com/dremio/dremio-oss + +This product includes software from Google Guava (Apache 2.0) + * Copyright (C) 2007 The Guava Authors + * https://github.com/google/guava + +This product include software from CMake (BSD 3-Clause) + * CMake - Cross Platform Makefile Generator + * Copyright 2000-2019 Kitware, Inc. and Contributors + +The web site includes files generated by Jekyll. + +-------------------------------------------------------------------------------- + +This product includes code from Apache Kudu, which includes the following in +its NOTICE file: + + Apache Kudu + Copyright 2016 The Apache Software Foundation + + This product includes software developed at + The Apache Software Foundation (http://www.apache.org/). + + Portions of this software were developed at + Cloudera, Inc (http://www.cloudera.com/). + +-------------------------------------------------------------------------------- + +This product includes code from Apache ORC, which includes the following in +its NOTICE file: + + Apache ORC + Copyright 2013-2019 The Apache Software Foundation + + This product includes software developed by The Apache Software + Foundation (http://www.apache.org/). + + This product includes software developed by Hewlett-Packard: + (c) Copyright [2014-2015] Hewlett-Packard Development Company, L.P diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/top_level.txt b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..652a7f20a026b7151711b81e752207ae1bcfce96 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow-23.0.1.dist-info/top_level.txt @@ -0,0 +1,2 @@ +__dummy__ +pyarrow diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_acero.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_acero.pxd new file mode 100644 index 0000000000000000000000000000000000000000..dc9babee190e110583d9d2cc24b71567203290af --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_acero.pxd @@ -0,0 +1,118 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from pyarrow.includes.common cimport * +from pyarrow.includes.libarrow cimport * + + +cdef extern from "arrow/acero/options.h" namespace "arrow::acero" nogil: + cdef enum CJoinType "arrow::acero::JoinType": + CJoinType_LEFT_SEMI "arrow::acero::JoinType::LEFT_SEMI" + CJoinType_RIGHT_SEMI "arrow::acero::JoinType::RIGHT_SEMI" + CJoinType_LEFT_ANTI "arrow::acero::JoinType::LEFT_ANTI" + CJoinType_RIGHT_ANTI "arrow::acero::JoinType::RIGHT_ANTI" + CJoinType_INNER "arrow::acero::JoinType::INNER" + CJoinType_LEFT_OUTER "arrow::acero::JoinType::LEFT_OUTER" + CJoinType_RIGHT_OUTER "arrow::acero::JoinType::RIGHT_OUTER" + CJoinType_FULL_OUTER "arrow::acero::JoinType::FULL_OUTER" + + cdef cppclass CExecNodeOptions "arrow::acero::ExecNodeOptions": + pass + + cdef cppclass CSourceNodeOptions "arrow::acero::SourceNodeOptions"(CExecNodeOptions): + pass + + cdef cppclass CTableSourceNodeOptions "arrow::acero::TableSourceNodeOptions"(CExecNodeOptions): + CTableSourceNodeOptions(shared_ptr[CTable] table) + CTableSourceNodeOptions(shared_ptr[CTable] table, int64_t max_batch_size) + + cdef cppclass CSinkNodeOptions "arrow::acero::SinkNodeOptions"(CExecNodeOptions): + pass + + cdef cppclass CFilterNodeOptions "arrow::acero::FilterNodeOptions"(CExecNodeOptions): + CFilterNodeOptions(CExpression) + + cdef cppclass CProjectNodeOptions "arrow::acero::ProjectNodeOptions"(CExecNodeOptions): + CProjectNodeOptions(vector[CExpression] expressions) + CProjectNodeOptions(vector[CExpression] expressions, + vector[c_string] names) + + cdef cppclass CAggregateNodeOptions "arrow::acero::AggregateNodeOptions"(CExecNodeOptions): + CAggregateNodeOptions(vector[CAggregate] aggregates, vector[CFieldRef] names) + + cdef cppclass COrderByNodeOptions "arrow::acero::OrderByNodeOptions"(CExecNodeOptions): + COrderByNodeOptions(COrdering ordering) + + cdef cppclass CHashJoinNodeOptions "arrow::acero::HashJoinNodeOptions"(CExecNodeOptions): + CHashJoinNodeOptions(CJoinType, vector[CFieldRef] in_left_keys, + vector[CFieldRef] in_right_keys) + CHashJoinNodeOptions(CJoinType, vector[CFieldRef] in_left_keys, + vector[CFieldRef] in_right_keys, + CExpression filter, + c_string output_suffix_for_left, + c_string output_suffix_for_right) + CHashJoinNodeOptions(CJoinType join_type, + vector[CFieldRef] left_keys, + vector[CFieldRef] right_keys, + vector[CFieldRef] left_output, + vector[CFieldRef] right_output, + CExpression filter, + c_string output_suffix_for_left, + c_string output_suffix_for_right) + + cdef struct CAsofJoinKeys "arrow::acero::AsofJoinNodeOptions::Keys": + CFieldRef on_key + vector[CFieldRef] by_key + + cdef cppclass CAsofJoinNodeOptions "arrow::acero::AsofJoinNodeOptions"(CExecNodeOptions): + CAsofJoinNodeOptions(vector[CAsofJoinKeys] keys, int64_t tolerance) + + +cdef extern from "arrow/acero/exec_plan.h" namespace "arrow::acero" nogil: + cdef cppclass CDeclaration "arrow::acero::Declaration": + cppclass Input: + Input(CExecNode*) + Input(CDeclaration) + + c_string label + vector[Input] inputs + + CDeclaration() + CDeclaration(c_string factory_name, CExecNodeOptions options) + CDeclaration(c_string factory_name, vector[Input] inputs, shared_ptr[CExecNodeOptions] options) + + @staticmethod + CDeclaration Sequence(vector[CDeclaration] decls) + + cdef cppclass CExecNode "arrow::acero::ExecNode": + const vector[CExecNode*]& inputs() const + const shared_ptr[CSchema]& output_schema() const + + CResult[shared_ptr[CTable]] DeclarationToTable( + CDeclaration declaration, c_bool use_threads + ) + CResult[shared_ptr[CTable]] DeclarationToTable( + CDeclaration declaration, c_bool use_threads, + CMemoryPool* memory_pool, CFunctionRegistry* function_registry + ) + CResult[unique_ptr[CRecordBatchReader]] DeclarationToReader( + CDeclaration declaration, c_bool use_threads + ) + + CResult[c_string] DeclarationToString(const CDeclaration& declaration) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_cuda.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_cuda.pxd new file mode 100644 index 0000000000000000000000000000000000000000..39ca53908a68b14966583d3568703da2c0ab408c --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_cuda.pxd @@ -0,0 +1,109 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from pyarrow.includes.libarrow cimport * + +cdef extern from "arrow/gpu/cuda_api.h" namespace "arrow::cuda" nogil: + + cdef cppclass CCudaDeviceManager" arrow::cuda::CudaDeviceManager": + @staticmethod + CResult[CCudaDeviceManager*] Instance() + CResult[shared_ptr[CCudaContext]] GetContext(int gpu_number) + CResult[shared_ptr[CCudaContext]] GetSharedContext(int gpu_number, + void* handle) + CStatus AllocateHost(int device_number, int64_t nbytes, + shared_ptr[CCudaHostBuffer]* buffer) + int num_devices() const + + cdef cppclass CCudaContext" arrow::cuda::CudaContext": + CResult[shared_ptr[CCudaBuffer]] Allocate(int64_t nbytes) + CResult[shared_ptr[CCudaBuffer]] View(uint8_t* data, int64_t nbytes) + CResult[shared_ptr[CCudaBuffer]] OpenIpcBuffer( + const CCudaIpcMemHandle& ipc_handle) + CStatus Synchronize() + int64_t bytes_allocated() const + const void* handle() const + int device_number() const + CResult[uintptr_t] GetDeviceAddress(uintptr_t addr) + shared_ptr[CDevice] device() const + shared_ptr[CMemoryManager] memory_manager() const + + cdef cppclass CCudaIpcMemHandle" arrow::cuda::CudaIpcMemHandle": + @staticmethod + CResult[shared_ptr[CCudaIpcMemHandle]] FromBuffer( + const void* opaque_handle) + CResult[shared_ptr[CBuffer]] Serialize(CMemoryPool* pool) const + + cdef cppclass CCudaBuffer" arrow::cuda::CudaBuffer"(CBuffer): + CCudaBuffer(uint8_t* data, int64_t size, + const shared_ptr[CCudaContext]& context, + c_bool own_data=false, c_bool is_ipc=false) + CCudaBuffer(const shared_ptr[CCudaBuffer]& parent, + const int64_t offset, const int64_t size) + + @staticmethod + CResult[shared_ptr[CCudaBuffer]] FromBuffer(shared_ptr[CBuffer] buf) + + CStatus CopyToHost(const int64_t position, const int64_t nbytes, + void* out) const + CStatus CopyFromHost(const int64_t position, const void* data, + int64_t nbytes) + CStatus CopyFromDevice(const int64_t position, const void* data, + int64_t nbytes) + CStatus CopyFromAnotherDevice(const shared_ptr[CCudaContext]& src_ctx, + const int64_t position, const void* data, + int64_t nbytes) + CResult[shared_ptr[CCudaIpcMemHandle]] ExportForIpc() + shared_ptr[CCudaContext] context() const + + cdef cppclass \ + CCudaHostBuffer" arrow::cuda::CudaHostBuffer"(CMutableBuffer): + pass + + cdef cppclass \ + CCudaBufferReader" arrow::cuda::CudaBufferReader"(CBufferReader): + CCudaBufferReader(const shared_ptr[CBuffer]& buffer) + CResult[int64_t] Read(int64_t nbytes, void* buffer) + CResult[shared_ptr[CBuffer]] Read(int64_t nbytes) + + cdef cppclass \ + CCudaBufferWriter" arrow::cuda::CudaBufferWriter"(WritableFile): + CCudaBufferWriter(const shared_ptr[CCudaBuffer]& buffer) + CStatus Close() + CStatus Write(const void* data, int64_t nbytes) + CStatus WriteAt(int64_t position, const void* data, int64_t nbytes) + CStatus SetBufferSize(const int64_t buffer_size) + int64_t buffer_size() + int64_t num_bytes_buffered() const + + CResult[shared_ptr[CCudaHostBuffer]] AllocateCudaHostBuffer( + int device_number, const int64_t size) + + # Cuda prefix is added to avoid picking up arrow::cuda functions + # from arrow namespace. + CResult[shared_ptr[CCudaBuffer]] \ + CudaSerializeRecordBatch" arrow::cuda::SerializeRecordBatch"\ + (const CRecordBatch& batch, + CCudaContext* ctx) + CResult[shared_ptr[CRecordBatch]] \ + CudaReadRecordBatch" arrow::cuda::ReadRecordBatch"\ + (const shared_ptr[CSchema]& schema, + CDictionaryMemo* dictionary_memo, + const shared_ptr[CCudaBuffer]& buffer, + CMemoryPool* pool) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_dataset.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_dataset.pxd new file mode 100644 index 0000000000000000000000000000000000000000..12cf82733935870b592e882d27412cbbdf293784 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_dataset.pxd @@ -0,0 +1,423 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from libcpp.unordered_map cimport unordered_map +from libcpp cimport bool as c_bool + +from pyarrow.includes.common cimport * +from pyarrow.includes.libarrow cimport * +from pyarrow.includes.libarrow_acero cimport * +from pyarrow.includes.libarrow_fs cimport * + + +cdef extern from "arrow/dataset/plan.h" namespace "arrow::dataset::internal" nogil: + + cdef void Initialize() + + +ctypedef CStatus cb_writer_finish_internal(CFileWriter*) +ctypedef void cb_writer_finish(dict, CFileWriter*) + +cdef extern from "arrow/dataset/api.h" namespace "arrow::dataset" nogil: + + cdef enum ExistingDataBehavior" arrow::dataset::ExistingDataBehavior": + ExistingDataBehavior_DELETE_MATCHING" \ + arrow::dataset::ExistingDataBehavior::kDeleteMatchingPartitions" + ExistingDataBehavior_OVERWRITE_OR_IGNORE" \ + arrow::dataset::ExistingDataBehavior::kOverwriteOrIgnore" + ExistingDataBehavior_ERROR" \ + arrow::dataset::ExistingDataBehavior::kError" + + cdef cppclass CScanOptions "arrow::dataset::ScanOptions": + shared_ptr[CSchema] dataset_schema + shared_ptr[CSchema] projected_schema + c_bool use_threads + c_bool cache_metadata + CExpression filter + + cdef cppclass CScanNodeOptions "arrow::dataset::ScanNodeOptions"(CExecNodeOptions): + CScanNodeOptions(shared_ptr[CDataset] dataset, shared_ptr[CScanOptions] scan_options, bint require_sequenced_output, bint implicit_ordering) + + shared_ptr[CScanOptions] scan_options + + cdef cppclass CFragmentScanOptions "arrow::dataset::FragmentScanOptions": + c_string type_name() const + + ctypedef CIterator[shared_ptr[CScanTask]] CScanTaskIterator \ + "arrow::dataset::ScanTaskIterator" + + cdef cppclass CScanTask" arrow::dataset::ScanTask": + CResult[CRecordBatchIterator] Execute() + + cdef cppclass CFragment "arrow::dataset::Fragment": + CResult[shared_ptr[CSchema]] ReadPhysicalSchema() + CResult[CScanTaskIterator] Scan(shared_ptr[CScanOptions] options) + c_bool splittable() const + c_string type_name() const + const CExpression& partition_expression() const + + ctypedef vector[shared_ptr[CFragment]] CFragmentVector \ + "arrow::dataset::FragmentVector" + + ctypedef CIterator[shared_ptr[CFragment]] CFragmentIterator \ + "arrow::dataset::FragmentIterator" + + cdef cppclass CInMemoryFragment "arrow::dataset::InMemoryFragment"( + CFragment): + CInMemoryFragment(vector[shared_ptr[CRecordBatch]] record_batches, + CExpression partition_expression) + + cdef cppclass CTaggedRecordBatch "arrow::dataset::TaggedRecordBatch": + shared_ptr[CRecordBatch] record_batch + shared_ptr[CFragment] fragment + + ctypedef CIterator[CTaggedRecordBatch] CTaggedRecordBatchIterator \ + "arrow::dataset::TaggedRecordBatchIterator" + + cdef cppclass CScanner "arrow::dataset::Scanner": + CScanner(shared_ptr[CDataset], shared_ptr[CScanOptions]) + CScanner(shared_ptr[CFragment], shared_ptr[CScanOptions]) + CResult[CScanTaskIterator] Scan() + CResult[CTaggedRecordBatchIterator] ScanBatches() + CResult[shared_ptr[CTable]] ToTable() + CResult[shared_ptr[CTable]] TakeRows(const CArray& indices) + CResult[shared_ptr[CTable]] Head(int64_t num_rows) + CResult[int64_t] CountRows() + CResult[CFragmentIterator] GetFragments() + CResult[shared_ptr[CRecordBatchReader]] ToRecordBatchReader() + const shared_ptr[CScanOptions]& options() + + cdef cppclass CScannerBuilder "arrow::dataset::ScannerBuilder": + CScannerBuilder(shared_ptr[CDataset], + shared_ptr[CScanOptions] scan_options) + CScannerBuilder(shared_ptr[CSchema], shared_ptr[CFragment], + shared_ptr[CScanOptions] scan_options) + + @staticmethod + shared_ptr[CScannerBuilder] FromRecordBatchReader( + shared_ptr[CRecordBatchReader] reader) + CStatus ProjectColumns "Project"(const vector[c_string]& columns) + CStatus Project(vector[CExpression]& exprs, vector[c_string]& columns) + CStatus Filter(CExpression filter) + CStatus UseThreads(c_bool use_threads) + CStatus CacheMetadata(c_bool cache_metadata) + CStatus Pool(CMemoryPool* pool) + CStatus BatchSize(int64_t batch_size) + CStatus BatchReadahead(int32_t batch_readahead) + CStatus FragmentReadahead(int32_t fragment_readahead) + CStatus FragmentScanOptions( + shared_ptr[CFragmentScanOptions] fragment_scan_options) + CResult[shared_ptr[CScanOptions]] GetScanOptions() + CResult[shared_ptr[CScanner]] Finish() + shared_ptr[CSchema] schema() const + + ctypedef vector[shared_ptr[CDataset]] CDatasetVector \ + "arrow::dataset::DatasetVector" + + cdef cppclass CDataset "arrow::dataset::Dataset": + const shared_ptr[CSchema] & schema() + CResult[CFragmentIterator] GetFragments() + CResult[CFragmentIterator] GetFragments(CExpression predicate) + const CExpression & partition_expression() + c_string type_name() + + CResult[shared_ptr[CDataset]] ReplaceSchema(shared_ptr[CSchema]) + + CResult[shared_ptr[CScannerBuilder]] NewScan() + + cdef cppclass CInMemoryDataset "arrow::dataset::InMemoryDataset"( + CDataset): + CInMemoryDataset(shared_ptr[CRecordBatchReader]) + CInMemoryDataset(shared_ptr[CTable]) + + cdef cppclass CUnionDataset "arrow::dataset::UnionDataset"( + CDataset): + @staticmethod + CResult[shared_ptr[CUnionDataset]] Make(shared_ptr[CSchema] schema, + CDatasetVector children) + + const CDatasetVector& children() const + + cdef cppclass CInspectOptions "arrow::dataset::InspectOptions": + int fragments + CField.CMergeOptions field_merge_options + + cdef cppclass CFinishOptions "arrow::dataset::FinishOptions": + shared_ptr[CSchema] schema + CInspectOptions inspect_options + c_bool validate_fragments + + cdef cppclass CDatasetFactory "arrow::dataset::DatasetFactory": + CResult[vector[shared_ptr[CSchema]]] InspectSchemas(CInspectOptions) + CResult[shared_ptr[CSchema]] Inspect(CInspectOptions) + CResult[shared_ptr[CDataset]] FinishWithSchema "Finish"( + const shared_ptr[CSchema]& schema) + CResult[shared_ptr[CDataset]] Finish() + const CExpression& root_partition() + CStatus SetRootPartition(CExpression partition) + + cdef cppclass CUnionDatasetFactory "arrow::dataset::UnionDatasetFactory": + @staticmethod + CResult[shared_ptr[CDatasetFactory]] Make( + vector[shared_ptr[CDatasetFactory]] factories) + + cdef cppclass CFileSource "arrow::dataset::FileSource": + const c_string& path() const + const shared_ptr[CFileSystem]& filesystem() const + const shared_ptr[CBuffer]& buffer() const + const int64_t size() const + CResult[shared_ptr[CRandomAccessFile]] Open() const + # HACK: Cython can't handle all the overloads so don't declare them. + # This means invalid construction of CFileSource won't be caught in + # the C++ generation phase (though it will still be caught when + # the generated C++ is compiled). + CFileSource(...) + + cdef cppclass CFileWriteOptions \ + "arrow::dataset::FileWriteOptions": + const shared_ptr[CFileFormat]& format() const + c_string type_name() const + + cdef cppclass CFileWriter \ + "arrow::dataset::FileWriter": + const shared_ptr[CFileFormat]& format() const + const shared_ptr[CSchema]& schema() const + const shared_ptr[CFileWriteOptions]& options() const + const CFileLocator& destination() const + CResult[int64_t] GetBytesWritten() + + cdef cppclass CFileFormat "arrow::dataset::FileFormat": + shared_ptr[CFragmentScanOptions] default_fragment_scan_options + c_string type_name() const + CResult[shared_ptr[CSchema]] Inspect(const CFileSource&) const + CResult[shared_ptr[CFileFragment]] MakeFragment( + CFileSource source, + CExpression partition_expression, + shared_ptr[CSchema] physical_schema) + shared_ptr[CFileWriteOptions] DefaultWriteOptions() + + cdef cppclass CFileFragment "arrow::dataset::FileFragment"( + CFragment): + const CFileSource& source() const + const shared_ptr[CFileFormat]& format() const + + cdef cppclass CFileSystemDatasetWriteOptions \ + "arrow::dataset::FileSystemDatasetWriteOptions": + shared_ptr[CFileWriteOptions] file_write_options + shared_ptr[CFileSystem] filesystem + c_string base_dir + shared_ptr[CPartitioning] partitioning + c_bool preserve_order + int max_partitions + c_string basename_template + function[cb_writer_finish_internal] writer_pre_finish + function[cb_writer_finish_internal] writer_post_finish + ExistingDataBehavior existing_data_behavior + c_bool create_dir + uint32_t max_open_files + uint64_t max_rows_per_file + uint64_t min_rows_per_group + uint64_t max_rows_per_group + + cdef cppclass CFileSystemDataset \ + "arrow::dataset::FileSystemDataset"(CDataset): + @staticmethod + CResult[shared_ptr[CDataset]] Make( + shared_ptr[CSchema] schema, + CExpression source_partition, + shared_ptr[CFileFormat] format, + shared_ptr[CFileSystem] filesystem, + vector[shared_ptr[CFileFragment]] fragments) + + @staticmethod + CStatus Write( + const CFileSystemDatasetWriteOptions& write_options, + shared_ptr[CScanner] scanner) + + c_string type() + vector[c_string] files() + const shared_ptr[CFileFormat]& format() const + const shared_ptr[CFileSystem]& filesystem() const + const shared_ptr[CPartitioning]& partitioning() const + + cdef cppclass CIpcFileWriteOptions \ + "arrow::dataset::IpcFileWriteOptions"(CFileWriteOptions): + shared_ptr[CIpcWriteOptions] options + + cdef cppclass CIpcFileFormat "arrow::dataset::IpcFileFormat"( + CFileFormat): + pass + + cdef cppclass COrcFileFormat "arrow::dataset::OrcFileFormat"( + CFileFormat): + pass + + cdef cppclass CCsvFileWriteOptions \ + "arrow::dataset::CsvFileWriteOptions"(CFileWriteOptions): + shared_ptr[CCSVWriteOptions] write_options + CMemoryPool* pool + + cdef cppclass CCsvFileFormat "arrow::dataset::CsvFileFormat"( + CFileFormat): + CCSVParseOptions parse_options + + cdef cppclass CCsvFragmentScanOptions \ + "arrow::dataset::CsvFragmentScanOptions"(CFragmentScanOptions): + CCSVConvertOptions convert_options + CCSVReadOptions read_options + function[StreamWrapFunc] stream_transform_func + + cdef cppclass CJsonFileFormat "arrow::dataset::JsonFileFormat"(CFileFormat): + pass + + cdef cppclass CJsonFragmentScanOptions "arrow::dataset::JsonFragmentScanOptions"(CFragmentScanOptions): + CJSONParseOptions parse_options + CJSONReadOptions read_options + + cdef struct CPartitionPathFormat "arrow::dataset::PartitionPathFormat": + c_string directory + c_string filename + + cdef cppclass CPartitioning "arrow::dataset::Partitioning": + c_string type_name() const + CResult[CExpression] Parse(const c_string & path) const + CResult[CPartitionPathFormat] Format(const CExpression & expr) const + const shared_ptr[CSchema] & schema() + c_bool Equals(const CPartitioning& other) const + + cdef cppclass CSegmentEncoding" arrow::dataset::SegmentEncoding": + bint operator==(CSegmentEncoding) + + CSegmentEncoding CSegmentEncoding_None\ + " arrow::dataset::SegmentEncoding::None" + CSegmentEncoding CSegmentEncoding_Uri\ + " arrow::dataset::SegmentEncoding::Uri" + + cdef cppclass CKeyValuePartitioningOptions \ + "arrow::dataset::KeyValuePartitioningOptions": + CSegmentEncoding segment_encoding + + cdef cppclass CHivePartitioningOptions \ + "arrow::dataset::HivePartitioningOptions": + CSegmentEncoding segment_encoding + c_string null_fallback + + cdef cppclass CPartitioningFactoryOptions \ + "arrow::dataset::PartitioningFactoryOptions": + c_bool infer_dictionary + shared_ptr[CSchema] schema + CSegmentEncoding segment_encoding + + cdef cppclass CHivePartitioningFactoryOptions \ + "arrow::dataset::HivePartitioningFactoryOptions": + c_bool infer_dictionary + c_string null_fallback + shared_ptr[CSchema] schema + CSegmentEncoding segment_encoding + + cdef cppclass CPartitioningFactory "arrow::dataset::PartitioningFactory": + c_string type_name() const + + cdef cppclass CKeyValuePartitioning \ + "arrow::dataset::KeyValuePartitioning"(CPartitioning): + CKeyValuePartitioning(shared_ptr[CSchema] schema, + vector[shared_ptr[CArray]] dictionaries, + CKeyValuePartitioningOptions options) + + vector[shared_ptr[CArray]] dictionaries() const + CSegmentEncoding segment_encoding() + + cdef cppclass CDirectoryPartitioning \ + "arrow::dataset::DirectoryPartitioning"(CPartitioning): + CDirectoryPartitioning(shared_ptr[CSchema] schema, + vector[shared_ptr[CArray]] dictionaries) + + @staticmethod + shared_ptr[CPartitioningFactory] MakeFactory( + vector[c_string] field_names, CPartitioningFactoryOptions) + + vector[shared_ptr[CArray]] dictionaries() const + + cdef cppclass CHivePartitioning \ + "arrow::dataset::HivePartitioning"(CPartitioning): + CHivePartitioning(shared_ptr[CSchema] schema, + vector[shared_ptr[CArray]] dictionaries, + CHivePartitioningOptions options) + + @staticmethod + shared_ptr[CPartitioningFactory] MakeFactory( + CHivePartitioningFactoryOptions) + + vector[shared_ptr[CArray]] dictionaries() const + c_string null_fallback() const + + cdef cppclass CFilenamePartitioning \ + "arrow::dataset::FilenamePartitioning"(CPartitioning): + CFilenamePartitioning(shared_ptr[CSchema] schema, + vector[shared_ptr[CArray]] dictionaries) + + @staticmethod + shared_ptr[CPartitioningFactory] MakeFactory( + vector[c_string] field_names, CPartitioningFactoryOptions) + + vector[shared_ptr[CArray]] dictionaries() const + + cdef cppclass CPartitioningOrFactory \ + "arrow::dataset::PartitioningOrFactory": + CPartitioningOrFactory(shared_ptr[CPartitioning]) + CPartitioningOrFactory(shared_ptr[CPartitioningFactory]) + CPartitioningOrFactory & operator = (shared_ptr[CPartitioning]) + CPartitioningOrFactory & operator = ( + shared_ptr[CPartitioningFactory]) + shared_ptr[CPartitioning] partitioning() const + shared_ptr[CPartitioningFactory] factory() const + + cdef cppclass CFileSystemFactoryOptions \ + "arrow::dataset::FileSystemFactoryOptions": + CPartitioningOrFactory partitioning + c_string partition_base_dir + c_bool exclude_invalid_files + vector[c_string] selector_ignore_prefixes + + cdef cppclass CFileSystemDatasetFactory \ + "arrow::dataset::FileSystemDatasetFactory"( + CDatasetFactory): + @staticmethod + CResult[shared_ptr[CDatasetFactory]] MakeFromPaths "Make"( + shared_ptr[CFileSystem] filesystem, + vector[c_string] paths, + shared_ptr[CFileFormat] format, + CFileSystemFactoryOptions options + ) + + @staticmethod + CResult[shared_ptr[CDatasetFactory]] MakeFromSelector "Make"( + shared_ptr[CFileSystem] filesystem, + CFileSelector, + shared_ptr[CFileFormat] format, + CFileSystemFactoryOptions options + ) + + @staticmethod + CResult[shared_ptr[CDatasetFactory]] MakeFromFileInfos "Make"( + shared_ptr[CFileSystem] filesystem, + vector[CFileInfo] files, + shared_ptr[CFileFormat] format, + CFileSystemFactoryOptions options + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd new file mode 100644 index 0000000000000000000000000000000000000000..8f4917f6c0c80ad969d79a3ce71e035ddf0b0c57 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd @@ -0,0 +1,107 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from pyarrow.includes.libarrow_dataset cimport * +from pyarrow.includes.libparquet_encryption cimport * + +from pyarrow._parquet cimport * + + +cdef extern from "arrow/dataset/parquet_encryption_config.h" namespace "arrow::dataset" nogil: + cdef cppclass CParquetEncryptionConfig "arrow::dataset::ParquetEncryptionConfig": + shared_ptr[CCryptoFactory] crypto_factory + shared_ptr[CKmsConnectionConfig] kms_connection_config + shared_ptr[CEncryptionConfiguration] encryption_config + + cdef cppclass CParquetDecryptionConfig "arrow::dataset::ParquetDecryptionConfig": + shared_ptr[CCryptoFactory] crypto_factory + shared_ptr[CKmsConnectionConfig] kms_connection_config + shared_ptr[CDecryptionConfiguration] decryption_config + + +cdef extern from "arrow/dataset/api.h" namespace "arrow::dataset" nogil: + + cdef cppclass CParquetFileWriter \ + "arrow::dataset::ParquetFileWriter"(CFileWriter): + const shared_ptr[FileWriter]& parquet_writer() const + + cdef cppclass CParquetFileWriteOptions \ + "arrow::dataset::ParquetFileWriteOptions"(CFileWriteOptions): + shared_ptr[WriterProperties] writer_properties + shared_ptr[ArrowWriterProperties] arrow_writer_properties + shared_ptr[CParquetEncryptionConfig] parquet_encryption_config + + cdef cppclass CParquetFileFragment "arrow::dataset::ParquetFileFragment"( + CFileFragment): + const vector[int]& row_groups() const + shared_ptr[CFileMetaData] metadata() const + CResult[vector[shared_ptr[CFragment]]] SplitByRowGroup( + CExpression predicate) + CResult[shared_ptr[CFragment]] SubsetWithFilter "Subset"( + CExpression predicate) + CResult[shared_ptr[CFragment]] SubsetWithIds "Subset"( + vector[int] row_group_ids) + CStatus EnsureCompleteMetadata() + + cdef cppclass CParquetFileFormatReaderOptions \ + "arrow::dataset::ParquetFileFormat::ReaderOptions": + unordered_set[c_string] dict_columns + TimeUnit coerce_int96_timestamp_unit + Type binary_type + Type list_type + + cdef cppclass CParquetFileFormat "arrow::dataset::ParquetFileFormat"( + CFileFormat): + CParquetFileFormatReaderOptions reader_options + CResult[shared_ptr[CFileFragment]] MakeFragment( + CFileSource source, + CExpression partition_expression, + shared_ptr[CSchema] physical_schema, + vector[int] row_groups) + + cdef cppclass CParquetFragmentScanOptions \ + "arrow::dataset::ParquetFragmentScanOptions"(CFragmentScanOptions): + shared_ptr[CReaderProperties] reader_properties + shared_ptr[ArrowReaderProperties] arrow_reader_properties + shared_ptr[CParquetDecryptionConfig] parquet_decryption_config + + cdef cppclass CParquetFactoryOptions \ + "arrow::dataset::ParquetFactoryOptions": + CPartitioningOrFactory partitioning + c_string partition_base_dir + c_bool validate_column_chunk_paths + + cdef cppclass CParquetDatasetFactory \ + "arrow::dataset::ParquetDatasetFactory"(CDatasetFactory): + @staticmethod + CResult[shared_ptr[CDatasetFactory]] MakeFromMetaDataPath "Make"( + const c_string& metadata_path, + shared_ptr[CFileSystem] filesystem, + shared_ptr[CParquetFileFormat] format, + CParquetFactoryOptions options + ) + + @staticmethod + CResult[shared_ptr[CDatasetFactory]] MakeFromMetaDataSource "Make"( + const CFileSource& metadata_path, + const c_string& base_path, + shared_ptr[CFileSystem] filesystem, + shared_ptr[CParquetFileFormat] format, + CParquetFactoryOptions options + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_feather.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_feather.pxd new file mode 100644 index 0000000000000000000000000000000000000000..722e947bfeca238af0bd6ee002e7f3f9f1063ed6 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_feather.pxd @@ -0,0 +1,50 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from pyarrow.includes.libarrow cimport (CCompressionType, CStatus, CTable, + COutputStream, CResult, shared_ptr, + vector, CRandomAccessFile, CSchema, + c_string, CIpcReadOptions) + + +cdef extern from "arrow/ipc/api.h" namespace "arrow::ipc" nogil: + int kFeatherV1Version" arrow::ipc::feather::kFeatherV1Version" + int kFeatherV2Version" arrow::ipc::feather::kFeatherV2Version" + + cdef cppclass CFeatherProperties" arrow::ipc::feather::WriteProperties": + int version + int chunksize + CCompressionType compression + int compression_level + + CStatus WriteFeather" arrow::ipc::feather::WriteTable" \ + (const CTable& table, COutputStream* out, + CFeatherProperties properties) + + cdef cppclass CFeatherReader" arrow::ipc::feather::Reader": + @staticmethod + CResult[shared_ptr[CFeatherReader]] Open( + const shared_ptr[CRandomAccessFile]& file, + const CIpcReadOptions& options) + int version() + shared_ptr[CSchema] schema() + + CStatus Read(shared_ptr[CTable]* out) + CStatus Read(const vector[int] indices, shared_ptr[CTable]* out) + CStatus Read(const vector[c_string] names, shared_ptr[CTable]* out) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_flight.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_flight.pxd new file mode 100644 index 0000000000000000000000000000000000000000..a89137b8455ee3ae25630ce0f125654610f8c351 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_flight.pxd @@ -0,0 +1,621 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from pyarrow.includes.common cimport * +from pyarrow.includes.libarrow cimport * +from pyarrow.includes.libarrow_python cimport CTimePoint + +from libcpp.map cimport multimap + + +cdef extern from "arrow/flight/api.h" namespace "arrow" nogil: + cdef char* CTracingServerMiddlewareName\ + " arrow::flight::TracingServerMiddleware::kMiddlewareName" + + cdef cppclass CActionType" arrow::flight::ActionType": + c_string type + c_string description + bint operator==(CActionType) + CResult[c_string] SerializeToString() + + @staticmethod + CResult[CActionType] Deserialize(const c_string& serialized) + + cdef cppclass CAction" arrow::flight::Action": + c_string type + shared_ptr[CBuffer] body + bint operator==(CAction) + CResult[c_string] SerializeToString() + c_string ToString() + + @staticmethod + CResult[CAction] Deserialize(const c_string& serialized) + + cdef cppclass CFlightResult" arrow::flight::Result": + CFlightResult() + CFlightResult(CFlightResult) + shared_ptr[CBuffer] body + bint operator==(CFlightResult) + CResult[c_string] SerializeToString() + c_string ToString() + + @staticmethod + CResult[CFlightResult] Deserialize(const c_string& serialized) + + cdef cppclass CBasicAuth" arrow::flight::BasicAuth": + CBasicAuth() + CBasicAuth(CBuffer) + CBasicAuth(CBasicAuth) + c_string username + c_string password + bint operator==(CBasicAuth) + CResult[c_string] SerializeToString() + c_string ToString() + + @staticmethod + CResult[CBasicAuth] Deserialize(const c_string& serialized) + + cdef cppclass CResultStream" arrow::flight::ResultStream": + CResult[unique_ptr[CFlightResult]] Next() + + cdef cppclass CDescriptorType \ + " arrow::flight::FlightDescriptor::DescriptorType": + bint operator==(CDescriptorType) + + CDescriptorType CDescriptorTypeUnknown\ + " arrow::flight::FlightDescriptor::UNKNOWN" + CDescriptorType CDescriptorTypePath\ + " arrow::flight::FlightDescriptor::PATH" + CDescriptorType CDescriptorTypeCmd\ + " arrow::flight::FlightDescriptor::CMD" + + cdef cppclass CFlightDescriptor" arrow::flight::FlightDescriptor": + CDescriptorType type + c_string cmd + vector[c_string] path + bint operator==(CFlightDescriptor) + CResult[c_string] SerializeToString() + c_string ToString() + + @staticmethod + CResult[CFlightDescriptor] Deserialize(const c_string& serialized) + + cdef cppclass CTicket" arrow::flight::Ticket": + CTicket() + c_string ticket + bint operator==(CTicket) + CResult[c_string] SerializeToString() + c_string ToString() + + @staticmethod + CResult[CTicket] Deserialize(const c_string& serialized) + + cdef cppclass CCriteria" arrow::flight::Criteria": + CCriteria() + c_string expression + bint operator==(CCriteria) + CResult[c_string] SerializeToString() + + @staticmethod + CResult[CCriteria] Deserialize(const c_string& serialized) + + cdef cppclass CLocation" arrow::flight::Location": + CLocation() + c_string ToString() + c_bool Equals(const CLocation& other) + + @staticmethod + CResult[CLocation] Parse(const c_string& uri_string) + + @staticmethod + CResult[CLocation] ForGrpcTcp(const c_string& host, int port) + + @staticmethod + CResult[CLocation] ForGrpcTls(const c_string& host, int port) + + @staticmethod + CResult[CLocation] ForGrpcUnix(const c_string& path) + + cdef cppclass CFlightEndpoint" arrow::flight::FlightEndpoint": + CFlightEndpoint() + + CTicket ticket + vector[CLocation] locations + optional[CTimePoint] expiration_time + c_string app_metadata + + bint operator==(CFlightEndpoint) + CResult[c_string] SerializeToString() + c_string ToString() + + @staticmethod + CResult[CFlightEndpoint] Deserialize(const c_string& serialized) + + cdef cppclass CFlightInfo" arrow::flight::FlightInfo": + CFlightInfo(CFlightInfo info) + int64_t total_records() + int64_t total_bytes() + c_bool ordered() + c_string app_metadata() + CResult[shared_ptr[CSchema]] GetSchema(CDictionaryMemo* memo) + CFlightDescriptor& descriptor() + const vector[CFlightEndpoint]& endpoints() + CResult[c_string] SerializeToString() + c_string ToString() + bint operator==(CFlightInfo) + + @staticmethod + CResult[unique_ptr[CFlightInfo]] Deserialize( + const c_string& serialized) + + cdef cppclass CSchemaResult" arrow::flight::SchemaResult": + CSchemaResult() + CSchemaResult(CSchemaResult result) + CResult[shared_ptr[CSchema]] GetSchema(CDictionaryMemo* memo) + bint operator==(CSchemaResult) + CResult[c_string] SerializeToString() + c_string ToString() + + @staticmethod + CResult[CSchemaResult] Deserialize(const c_string& serialized) + + cdef cppclass CFlightListing" arrow::flight::FlightListing": + CResult[unique_ptr[CFlightInfo]] Next() + + cdef cppclass CSimpleFlightListing" arrow::flight::SimpleFlightListing": + # This doesn't work with Cython >= 3 + # CSimpleFlightListing(vector[CFlightInfo]&& info) + CSimpleFlightListing(const vector[CFlightInfo]& info) + + cdef cppclass CFlightPayload" arrow::flight::FlightPayload": + shared_ptr[CBuffer] descriptor + shared_ptr[CBuffer] app_metadata + CIpcPayload ipc_message + + cdef cppclass CFlightDataStream" arrow::flight::FlightDataStream": + shared_ptr[CSchema] schema() + CResult[CFlightPayload] Next() + + cdef cppclass CFlightStreamChunk" arrow::flight::FlightStreamChunk": + CFlightStreamChunk() + shared_ptr[CRecordBatch] data + shared_ptr[CBuffer] app_metadata + + cdef cppclass CMetadataRecordBatchReader \ + " arrow::flight::MetadataRecordBatchReader": + CResult[shared_ptr[CSchema]] GetSchema() + CResult[CFlightStreamChunk] Next() + CResult[shared_ptr[CTable]] ToTable() + CIpcReadStats stats() const + + CResult[shared_ptr[CRecordBatchReader]] MakeRecordBatchReader\ + " arrow::flight::MakeRecordBatchReader"( + shared_ptr[CMetadataRecordBatchReader]) + + cdef cppclass CMetadataRecordBatchWriter \ + " arrow::flight::MetadataRecordBatchWriter"(CRecordBatchWriter): + CStatus Begin(shared_ptr[CSchema] schema, + const CIpcWriteOptions& options) + CStatus WriteMetadata(shared_ptr[CBuffer] app_metadata) + CStatus WriteWithMetadata(const CRecordBatch& batch, + shared_ptr[CBuffer] app_metadata) + + cdef cppclass CFlightStreamReader \ + " arrow::flight::FlightStreamReader"(CMetadataRecordBatchReader): + void Cancel() + CResult[shared_ptr[CTable]] ToTableWithStopToken" ToTable"\ + (const CStopToken& stop_token) + + cdef cppclass CFlightMessageReader \ + " arrow::flight::FlightMessageReader"(CMetadataRecordBatchReader): + CFlightDescriptor& descriptor() + + cdef cppclass CFlightMessageWriter \ + " arrow::flight::FlightMessageWriter"(CMetadataRecordBatchWriter): + pass + + cdef cppclass CFlightStreamWriter \ + " arrow::flight::FlightStreamWriter"(CMetadataRecordBatchWriter): + CStatus DoneWriting() + + cdef cppclass CRecordBatchStream \ + " arrow::flight::RecordBatchStream"(CFlightDataStream): + CRecordBatchStream(shared_ptr[CRecordBatchReader]& reader, + const CIpcWriteOptions& options) + + cdef cppclass CFlightMetadataReader" arrow::flight::FlightMetadataReader": + CStatus ReadMetadata(shared_ptr[CBuffer]* out) + + cdef cppclass CFlightMetadataWriter" arrow::flight::FlightMetadataWriter": + CStatus WriteMetadata(const CBuffer& message) + + cdef cppclass CServerAuthReader" arrow::flight::ServerAuthReader": + CStatus Read(c_string* token) + + cdef cppclass CServerAuthSender" arrow::flight::ServerAuthSender": + CStatus Write(c_string& token) + + cdef cppclass CClientAuthReader" arrow::flight::ClientAuthReader": + CStatus Read(c_string* token) + + cdef cppclass CClientAuthSender" arrow::flight::ClientAuthSender": + CStatus Write(c_string& token) + + cdef cppclass CServerAuthHandler" arrow::flight::ServerAuthHandler": + pass + + cdef cppclass CClientAuthHandler" arrow::flight::ClientAuthHandler": + pass + + cdef cppclass CServerCallContext" arrow::flight::ServerCallContext": + c_string& peer_identity() + c_string& peer() + c_bool is_cancelled() + void AddHeader(const c_string& key, const c_string& value) + void AddTrailer(const c_string& key, const c_string& value) + CServerMiddleware* GetMiddleware(const c_string& key) + + cdef cppclass CTimeoutDuration" arrow::flight::TimeoutDuration": + CTimeoutDuration(double) + double count() + + cdef cppclass CFlightCallOptions" arrow::flight::FlightCallOptions": + CFlightCallOptions() + CTimeoutDuration timeout + CIpcWriteOptions write_options + CIpcReadOptions read_options + vector[pair[c_string, c_string]] headers + CStopToken stop_token + + cdef cppclass CCertKeyPair" arrow::flight::CertKeyPair": + CCertKeyPair() + c_string pem_cert + c_string pem_key + + cdef cppclass CFlightMethod" arrow::flight::FlightMethod": + bint operator==(CFlightMethod) + + CFlightMethod CFlightMethodInvalid\ + " arrow::flight::FlightMethod::Invalid" + CFlightMethod CFlightMethodHandshake\ + " arrow::flight::FlightMethod::Handshake" + CFlightMethod CFlightMethodListFlights\ + " arrow::flight::FlightMethod::ListFlights" + CFlightMethod CFlightMethodGetFlightInfo\ + " arrow::flight::FlightMethod::GetFlightInfo" + CFlightMethod CFlightMethodGetSchema\ + " arrow::flight::FlightMethod::GetSchema" + CFlightMethod CFlightMethodDoGet\ + " arrow::flight::FlightMethod::DoGet" + CFlightMethod CFlightMethodDoPut\ + " arrow::flight::FlightMethod::DoPut" + CFlightMethod CFlightMethodDoAction\ + " arrow::flight::FlightMethod::DoAction" + CFlightMethod CFlightMethodListActions\ + " arrow::flight::FlightMethod::ListActions" + CFlightMethod CFlightMethodDoExchange\ + " arrow::flight::FlightMethod::DoExchange" + + cdef cppclass CCallInfo" arrow::flight::CallInfo": + CFlightMethod method + + ctypedef multimap[cpp_string_view, cpp_string_view] CCallHeaders\ + " arrow::flight::CallHeaders" + + cdef cppclass CAddCallHeaders" arrow::flight::AddCallHeaders": + void AddHeader(const c_string& key, const c_string& value) + + cdef cppclass CServerMiddleware" arrow::flight::ServerMiddleware": + c_string name() + + cdef cppclass CServerMiddlewareFactory\ + " arrow::flight::ServerMiddlewareFactory": + pass + + cdef cppclass CClientMiddleware" arrow::flight::ClientMiddleware": + pass + + cdef cppclass CClientMiddlewareFactory\ + " arrow::flight::ClientMiddlewareFactory": + pass + + cpdef cppclass CTracingServerMiddlewareTraceKey\ + " arrow::flight::TracingServerMiddleware::TraceKey": + CTracingServerMiddlewareTraceKey() + c_string key + c_string value + + cdef cppclass CTracingServerMiddleware\ + " arrow::flight::TracingServerMiddleware"(CServerMiddleware): + vector[CTracingServerMiddlewareTraceKey] GetTraceContext() + + cdef shared_ptr[CServerMiddlewareFactory] \ + MakeTracingServerMiddlewareFactory\ + " arrow::flight::MakeTracingServerMiddlewareFactory"() + + cdef cppclass CFlightServerOptions" arrow::flight::FlightServerOptions": + CFlightServerOptions(const CLocation& location) + CLocation location + unique_ptr[CServerAuthHandler] auth_handler + vector[CCertKeyPair] tls_certificates + c_bool verify_client + c_string root_certificates + vector[pair[c_string, shared_ptr[CServerMiddlewareFactory]]] middleware + + cdef cppclass CFlightClientOptions" arrow::flight::FlightClientOptions": + c_string tls_root_certs + c_string cert_chain + c_string private_key + c_string override_hostname + vector[shared_ptr[CClientMiddlewareFactory]] middleware + int64_t write_size_limit_bytes + vector[pair[c_string, CIntStringVariant]] generic_options + c_bool disable_server_verification + + @staticmethod + CFlightClientOptions Defaults() + + cdef cppclass CDoPutResult" arrow::flight::FlightClient::DoPutResult": + unique_ptr[CFlightStreamWriter] writer + unique_ptr[CFlightMetadataReader] reader + + cdef cppclass CDoExchangeResult" arrow::flight::FlightClient::DoExchangeResult": + unique_ptr[CFlightStreamWriter] writer + unique_ptr[CFlightStreamReader] reader + + cdef cppclass CFlightClient" arrow::flight::FlightClient": + @staticmethod + CResult[unique_ptr[CFlightClient]] Connect(const CLocation& location, + const CFlightClientOptions& options) + + c_bool supports_async() + CStatus CheckAsyncSupport() + + CStatus Authenticate(CFlightCallOptions& options, + unique_ptr[CClientAuthHandler] auth_handler) + + CResult[pair[c_string, c_string]] AuthenticateBasicToken( + CFlightCallOptions& options, + const c_string& username, + const c_string& password) + + CResult[unique_ptr[CResultStream]] DoAction(CFlightCallOptions& options, CAction& action) + CResult[vector[CActionType]] ListActions(CFlightCallOptions& options) + + CResult[unique_ptr[CFlightListing]] ListFlights(CFlightCallOptions& options, CCriteria criteria) + CResult[unique_ptr[CFlightInfo]] GetFlightInfo(CFlightCallOptions& options, + CFlightDescriptor& descriptor) + CFuture[CFlightInfo] GetFlightInfoAsync(CFlightCallOptions& options, + CFlightDescriptor& descriptor) + CResult[unique_ptr[CSchemaResult]] GetSchema(CFlightCallOptions& options, + CFlightDescriptor& descriptor) + CResult[unique_ptr[CFlightStreamReader]] DoGet(CFlightCallOptions& options, CTicket& ticket) + CResult[CDoPutResult] DoPut(CFlightCallOptions& options, + CFlightDescriptor& descriptor, + shared_ptr[CSchema]& schema) + CResult[CDoExchangeResult] DoExchange(CFlightCallOptions& options, + CFlightDescriptor& descriptor) + CStatus Close() + + cdef cppclass CFlightStatusCode" arrow::flight::FlightStatusCode": + bint operator==(CFlightStatusCode) + + CFlightStatusCode CFlightStatusInternal \ + " arrow::flight::FlightStatusCode::Internal" + CFlightStatusCode CFlightStatusTimedOut \ + " arrow::flight::FlightStatusCode::TimedOut" + CFlightStatusCode CFlightStatusCancelled \ + " arrow::flight::FlightStatusCode::Cancelled" + CFlightStatusCode CFlightStatusUnauthenticated \ + " arrow::flight::FlightStatusCode::Unauthenticated" + CFlightStatusCode CFlightStatusUnauthorized \ + " arrow::flight::FlightStatusCode::Unauthorized" + CFlightStatusCode CFlightStatusUnavailable \ + " arrow::flight::FlightStatusCode::Unavailable" + CFlightStatusCode CFlightStatusFailed \ + " arrow::flight::FlightStatusCode::Failed" + + cdef cppclass FlightStatusDetail" arrow::flight::FlightStatusDetail": + CFlightStatusCode code() + c_string extra_info() + + @staticmethod + shared_ptr[FlightStatusDetail] UnwrapStatus(const CStatus& status) + + cdef cppclass FlightWriteSizeStatusDetail\ + " arrow::flight::FlightWriteSizeStatusDetail": + int64_t limit() + int64_t actual() + + @staticmethod + shared_ptr[FlightWriteSizeStatusDetail] UnwrapStatus( + const CStatus& status) + + cdef CStatus MakeFlightError" arrow::flight::MakeFlightError" \ + (CFlightStatusCode code, const c_string& message) + + cdef CStatus MakeFlightError" arrow::flight::MakeFlightError" \ + (CFlightStatusCode code, + const c_string& message, + const c_string& extra_info) + +# Callbacks for implementing Flight servers +# Use typedef to emulate syntax for std::function +ctypedef CStatus cb_list_flights(object, const CServerCallContext&, + const CCriteria*, + unique_ptr[CFlightListing]*) +ctypedef CStatus cb_get_flight_info(object, const CServerCallContext&, + const CFlightDescriptor&, + unique_ptr[CFlightInfo]*) +ctypedef CStatus cb_get_schema(object, const CServerCallContext&, + const CFlightDescriptor&, + unique_ptr[CSchemaResult]*) +ctypedef CStatus cb_do_put(object, const CServerCallContext&, + unique_ptr[CFlightMessageReader], + unique_ptr[CFlightMetadataWriter]) +ctypedef CStatus cb_do_get(object, const CServerCallContext&, + const CTicket&, + unique_ptr[CFlightDataStream]*) +ctypedef CStatus cb_do_exchange(object, const CServerCallContext&, + unique_ptr[CFlightMessageReader], + unique_ptr[CFlightMessageWriter]) +ctypedef CStatus cb_do_action(object, const CServerCallContext&, + const CAction&, + unique_ptr[CResultStream]*) +ctypedef CStatus cb_list_actions(object, const CServerCallContext&, + vector[CActionType]*) +ctypedef CStatus cb_result_next(object, unique_ptr[CFlightResult]*) +ctypedef CStatus cb_data_stream_next(object, CFlightPayload*) +ctypedef CStatus cb_server_authenticate(object, CServerAuthSender*, + CServerAuthReader*) +ctypedef CStatus cb_is_valid(object, const c_string&, c_string*) +ctypedef CStatus cb_client_authenticate(object, CClientAuthSender*, + CClientAuthReader*) +ctypedef CStatus cb_get_token(object, c_string*) + +ctypedef CStatus cb_middleware_sending_headers(object, CAddCallHeaders*) +ctypedef CStatus cb_middleware_call_completed(object, const CStatus&) +ctypedef CStatus cb_client_middleware_received_headers( + object, const CCallHeaders&) +ctypedef CStatus cb_server_middleware_start_call( + object, + const CCallInfo&, + const CCallHeaders&, + shared_ptr[CServerMiddleware]*) +ctypedef CStatus cb_client_middleware_start_call( + object, + const CCallInfo&, + unique_ptr[CClientMiddleware]*) + +cdef extern from "arrow/python/flight.h" namespace "arrow::py::flight" nogil: + cdef char* CPyServerMiddlewareName\ + " arrow::py::flight::kPyServerMiddlewareName" + + cdef cppclass PyFlightServerVtable: + PyFlightServerVtable() + function[cb_list_flights] list_flights + function[cb_get_flight_info] get_flight_info + function[cb_get_schema] get_schema + function[cb_do_put] do_put + function[cb_do_get] do_get + function[cb_do_exchange] do_exchange + function[cb_do_action] do_action + function[cb_list_actions] list_actions + + cdef cppclass PyServerAuthHandlerVtable: + PyServerAuthHandlerVtable() + function[cb_server_authenticate] authenticate + function[cb_is_valid] is_valid + + cdef cppclass PyClientAuthHandlerVtable: + PyClientAuthHandlerVtable() + function[cb_client_authenticate] authenticate + function[cb_get_token] get_token + + cdef cppclass PyFlightServer: + PyFlightServer(object server, PyFlightServerVtable vtable) + + CStatus Init(CFlightServerOptions& options) + int port() + CStatus ServeWithSignals() except * + CStatus Shutdown() + CStatus Wait() + + cdef cppclass PyServerAuthHandler\ + " arrow::py::flight::PyServerAuthHandler"(CServerAuthHandler): + PyServerAuthHandler(object handler, PyServerAuthHandlerVtable vtable) + + cdef cppclass PyClientAuthHandler\ + " arrow::py::flight::PyClientAuthHandler"(CClientAuthHandler): + PyClientAuthHandler(object handler, PyClientAuthHandlerVtable vtable) + + cdef cppclass CPyFlightResultStream\ + " arrow::py::flight::PyFlightResultStream"(CResultStream): + CPyFlightResultStream(object generator, + function[cb_result_next] callback) + + cdef cppclass CPyFlightDataStream\ + " arrow::py::flight::PyFlightDataStream"(CFlightDataStream): + CPyFlightDataStream(object data_source, + unique_ptr[CFlightDataStream] stream) + + cdef cppclass CPyGeneratorFlightDataStream\ + " arrow::py::flight::PyGeneratorFlightDataStream"\ + (CFlightDataStream): + CPyGeneratorFlightDataStream(object generator, + shared_ptr[CSchema] schema, + function[cb_data_stream_next] callback, + const CIpcWriteOptions& options) + + cdef cppclass PyServerMiddlewareVtable\ + " arrow::py::flight::PyServerMiddleware::Vtable": + PyServerMiddlewareVtable() + function[cb_middleware_sending_headers] sending_headers + function[cb_middleware_call_completed] call_completed + + cdef cppclass PyClientMiddlewareVtable\ + " arrow::py::flight::PyClientMiddleware::Vtable": + PyClientMiddlewareVtable() + function[cb_middleware_sending_headers] sending_headers + function[cb_client_middleware_received_headers] received_headers + function[cb_middleware_call_completed] call_completed + + cdef cppclass CPyServerMiddleware\ + " arrow::py::flight::PyServerMiddleware"(CServerMiddleware): + CPyServerMiddleware(object middleware, PyServerMiddlewareVtable vtable) + void* py_object() + + cdef cppclass CPyServerMiddlewareFactory\ + " arrow::py::flight::PyServerMiddlewareFactory"\ + (CServerMiddlewareFactory): + CPyServerMiddlewareFactory( + object factory, + function[cb_server_middleware_start_call] start_call) + + cdef cppclass CPyClientMiddleware\ + " arrow::py::flight::PyClientMiddleware"(CClientMiddleware): + CPyClientMiddleware(object middleware, PyClientMiddlewareVtable vtable) + + cdef cppclass CPyClientMiddlewareFactory\ + " arrow::py::flight::PyClientMiddlewareFactory"\ + (CClientMiddlewareFactory): + CPyClientMiddlewareFactory( + object factory, + function[cb_client_middleware_start_call] start_call) + + cdef CStatus CreateFlightInfo" arrow::py::flight::CreateFlightInfo"( + shared_ptr[CSchema] schema, + CFlightDescriptor& descriptor, + vector[CFlightEndpoint] endpoints, + int64_t total_records, + int64_t total_bytes, + c_bool ordered, + const c_string& app_metadata, + unique_ptr[CFlightInfo]* out) + + cdef CStatus CreateSchemaResult" arrow::py::flight::CreateSchemaResult"( + shared_ptr[CSchema] schema, + unique_ptr[CSchemaResult]* out) + + +cdef extern from "" namespace "std" nogil: + cdef cppclass CIntStringVariant" std::variant": + CIntStringVariant() + CIntStringVariant(int) + CIntStringVariant(c_string) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_fs.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_fs.pxd new file mode 100644 index 0000000000000000000000000000000000000000..af01c47c8c7b037b6a9f799f367872391f755343 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_fs.pxd @@ -0,0 +1,364 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from pyarrow.includes.common cimport * +from pyarrow.includes.libarrow cimport * +from pyarrow.includes.libarrow_python cimport CTimePoint + +cdef extern from "arrow/filesystem/api.h" namespace "arrow::fs" nogil: + + ctypedef enum CFileType "arrow::fs::FileType": + CFileType_NotFound "arrow::fs::FileType::NotFound" + CFileType_Unknown "arrow::fs::FileType::Unknown" + CFileType_File "arrow::fs::FileType::File" + CFileType_Directory "arrow::fs::FileType::Directory" + + cdef cppclass CFileInfo "arrow::fs::FileInfo": + CFileInfo() + CFileInfo(CFileInfo) + CFileInfo& operator=(CFileInfo) + CFileInfo(const CFileInfo&) + CFileInfo& operator=(const CFileInfo&) + + CFileType type() + void set_type(CFileType type) + c_string path() + void set_path(const c_string& path) + c_string base_name() + int64_t size() + void set_size(int64_t size) + c_string extension() + CTimePoint mtime() + void set_mtime(CTimePoint mtime) + + cdef cppclass CFileSelector "arrow::fs::FileSelector": + CFileSelector() + c_string base_dir + c_bool allow_not_found + c_bool recursive + + cdef cppclass CFileLocator "arrow::fs::FileLocator": + shared_ptr[CFileSystem] filesystem + c_string path + + cdef cppclass CFileSystem "arrow::fs::FileSystem": + shared_ptr[CFileSystem] shared_from_this() + c_string type_name() const + CResult[c_string] NormalizePath(c_string path) + CResult[c_string] MakeUri(c_string path) + CResult[CFileInfo] GetFileInfo(const c_string& path) + CResult[vector[CFileInfo]] GetFileInfo( + const vector[c_string]& paths) + CResult[vector[CFileInfo]] GetFileInfo(const CFileSelector& select) + CStatus CreateDir(const c_string& path, c_bool recursive) + CStatus DeleteDir(const c_string& path) + CStatus DeleteDirContents(const c_string& path, c_bool missing_dir_ok) + CStatus DeleteRootDirContents() + CStatus DeleteFile(const c_string& path) + CStatus DeleteFiles(const vector[c_string]& paths) + CStatus Move(const c_string& src, const c_string& dest) + CStatus CopyFile(const c_string& src, const c_string& dest) + CResult[shared_ptr[CInputStream]] OpenInputStream( + const c_string& path) + CResult[shared_ptr[CRandomAccessFile]] OpenInputFile( + const c_string& path) + CResult[shared_ptr[COutputStream]] OpenOutputStream( + const c_string& path, const shared_ptr[const CKeyValueMetadata]&) + CResult[shared_ptr[COutputStream]] OpenAppendStream( + const c_string& path, const shared_ptr[const CKeyValueMetadata]&) + c_bool Equals(const CFileSystem& other) + c_bool Equals(shared_ptr[CFileSystem] other) + + CResult[shared_ptr[CFileSystem]] CFileSystemFromUri \ + "arrow::fs::FileSystemFromUri"(const c_string& uri) + CResult[shared_ptr[CFileSystem]] CFileSystemFromUri \ + "arrow::fs::FileSystemFromUri"(const c_string& uri, c_string* out_path) + CResult[shared_ptr[CFileSystem]] CFileSystemFromUriOrPath \ + "arrow::fs::FileSystemFromUriOrPath"(const c_string& uri, + c_string* out_path) + + cdef cppclass CFileSystemGlobalOptions \ + "arrow::fs::FileSystemGlobalOptions": + c_string tls_ca_file_path + c_string tls_ca_dir_path + + CStatus CFileSystemsInitialize "arrow::fs::Initialize" \ + (const CFileSystemGlobalOptions& options) + + cdef cppclass CSubTreeFileSystem \ + "arrow::fs::SubTreeFileSystem"(CFileSystem): + CSubTreeFileSystem(const c_string& base_path, + shared_ptr[CFileSystem] base_fs) + c_string base_path() + shared_ptr[CFileSystem] base_fs() + + ctypedef enum CS3LogLevel "arrow::fs::S3LogLevel": + CS3LogLevel_Off "arrow::fs::S3LogLevel::Off" + CS3LogLevel_Fatal "arrow::fs::S3LogLevel::Fatal" + CS3LogLevel_Error "arrow::fs::S3LogLevel::Error" + CS3LogLevel_Warn "arrow::fs::S3LogLevel::Warn" + CS3LogLevel_Info "arrow::fs::S3LogLevel::Info" + CS3LogLevel_Debug "arrow::fs::S3LogLevel::Debug" + CS3LogLevel_Trace "arrow::fs::S3LogLevel::Trace" + + cdef struct CS3GlobalOptions "arrow::fs::S3GlobalOptions": + CS3LogLevel log_level + int num_event_loop_threads + + cdef cppclass CS3ProxyOptions "arrow::fs::S3ProxyOptions": + c_string scheme + c_string host + int port + c_string username + c_string password + c_bool Equals(const CS3ProxyOptions& other) + + @staticmethod + CResult[CS3ProxyOptions] FromUriString "FromUri"( + const c_string& uri_string) + + ctypedef enum CS3CredentialsKind "arrow::fs::S3CredentialsKind": + CS3CredentialsKind_Anonymous "arrow::fs::S3CredentialsKind::Anonymous" + CS3CredentialsKind_Default "arrow::fs::S3CredentialsKind::Default" + CS3CredentialsKind_Explicit "arrow::fs::S3CredentialsKind::Explicit" + CS3CredentialsKind_Role "arrow::fs::S3CredentialsKind::Role" + CS3CredentialsKind_WebIdentity \ + "arrow::fs::S3CredentialsKind::WebIdentity" + + cdef cppclass CS3RetryStrategy "arrow::fs::S3RetryStrategy": + @staticmethod + shared_ptr[CS3RetryStrategy] GetAwsDefaultRetryStrategy(int64_t max_attempts) + + @staticmethod + shared_ptr[CS3RetryStrategy] GetAwsStandardRetryStrategy(int64_t max_attempts) + + cdef cppclass CS3Options "arrow::fs::S3Options": + c_string region + double connect_timeout + double request_timeout + c_string endpoint_override + c_string scheme + c_bool background_writes + c_bool allow_delayed_open + c_bool allow_bucket_creation + c_bool allow_bucket_deletion + c_bool check_directory_existence_before_creation + c_bool force_virtual_addressing + c_string tls_ca_file_path + shared_ptr[const CKeyValueMetadata] default_metadata + c_string role_arn + c_string session_name + c_string external_id + int load_frequency + CS3ProxyOptions proxy_options + CS3CredentialsKind credentials_kind + shared_ptr[CS3RetryStrategy] retry_strategy + void ConfigureDefaultCredentials() + void ConfigureAccessKey(const c_string& access_key, + const c_string& secret_key, + const c_string& session_token) + c_string GetAccessKey() + c_string GetSecretKey() + c_string GetSessionToken() + c_bool Equals(const CS3Options& other) + + @staticmethod + CS3Options Defaults() + + @staticmethod + CS3Options Anonymous() + + @staticmethod + CS3Options FromAccessKey(const c_string& access_key, + const c_string& secret_key, + const c_string& session_token) + + @staticmethod + CS3Options FromAssumeRole(const c_string& role_arn, + const c_string& session_name, + const c_string& external_id, + const int load_frequency) + + cdef cppclass CS3FileSystem "arrow::fs::S3FileSystem"(CFileSystem): + @staticmethod + CResult[shared_ptr[CS3FileSystem]] Make(const CS3Options& options) + CS3Options options() + c_string region() + + cdef CStatus CInitializeS3 "arrow::fs::InitializeS3"( + const CS3GlobalOptions& options) + cdef CStatus CEnsureS3Initialized "arrow::fs::EnsureS3Initialized"() + cdef CStatus CFinalizeS3 "arrow::fs::FinalizeS3"() + cdef CStatus CEnsureS3Finalized "arrow::fs::EnsureS3Finalized"() + + cdef CResult[c_string] ResolveS3BucketRegion(const c_string& bucket) + + cdef cppclass CGcsCredentials "arrow::fs::GcsCredentials": + c_bool anonymous() + CTimePoint expiration() + c_string access_token() + c_string target_service_account() + + cdef cppclass CGcsOptions "arrow::fs::GcsOptions": + CGcsCredentials credentials + c_string endpoint_override + c_string scheme + c_string default_bucket_location + optional[c_string] project_id + optional[double] retry_limit_seconds + shared_ptr[const CKeyValueMetadata] default_metadata + c_bool Equals(const CS3Options& other) + + @staticmethod + CGcsOptions Defaults() + + @staticmethod + CGcsOptions Anonymous() + + @staticmethod + CGcsOptions FromAccessToken(const c_string& access_token, + CTimePoint expiration) + + @staticmethod + CGcsOptions FromImpersonatedServiceAccount(const CGcsCredentials& base_credentials, + c_string& target_service_account) + + cdef cppclass CGcsFileSystem "arrow::fs::GcsFileSystem": + @staticmethod + CResult[shared_ptr[CGcsFileSystem]] Make(const CGcsOptions& options) + CGcsOptions options() + + cdef cppclass CAzureOptions "arrow::fs::AzureOptions": + c_string account_name + c_string blob_storage_authority + c_string dfs_storage_authority + c_string blob_storage_scheme + c_string dfs_storage_scheme + + c_bool Equals(const CAzureOptions& other) + CStatus ConfigureDefaultCredential() + CStatus ConfigureAccountKeyCredential(c_string account_key) + CStatus ConfigureSASCredential(c_string sas_token) + CStatus ConfigureManagedIdentityCredential(c_string client_id) + CStatus ConfigureClientSecretCredential(c_string tenant_id, + c_string client_id, + c_string client_secret) + + cdef cppclass CAzureFileSystem "arrow::fs::AzureFileSystem": + @staticmethod + CResult[shared_ptr[CAzureFileSystem]] Make(const CAzureOptions& options) + CAzureOptions options() + + cdef cppclass CHdfsOptions "arrow::fs::HdfsOptions": + HdfsConnectionConfig connection_config + int32_t buffer_size + int16_t replication + int64_t default_block_size + + @staticmethod + CResult[CHdfsOptions] FromUriString "FromUri"( + const c_string& uri_string) + void ConfigureEndPoint(c_string host, int port) + void ConfigureDriver(c_bool use_hdfs3) + void ConfigureReplication(int16_t replication) + void ConfigureUser(c_string user_name) + void ConfigureBufferSize(int32_t buffer_size) + void ConfigureBlockSize(int64_t default_block_size) + void ConfigureKerberosTicketCachePath(c_string path) + void ConfigureExtraConf(c_string key, c_string value) + + cdef cppclass CHadoopFileSystem "arrow::fs::HadoopFileSystem"(CFileSystem): + @staticmethod + CResult[shared_ptr[CHadoopFileSystem]] Make( + const CHdfsOptions& options) + CHdfsOptions options() + + cdef cppclass CMockFileSystem "arrow::fs::internal::MockFileSystem"( + CFileSystem): + CMockFileSystem(CTimePoint current_time) + + CStatus CCopyFiles "arrow::fs::CopyFiles"( + const vector[CFileLocator]& sources, + const vector[CFileLocator]& destinations, + const CIOContext& io_context, + int64_t chunk_size, c_bool use_threads) + CStatus CCopyFilesWithSelector "arrow::fs::CopyFiles"( + const shared_ptr[CFileSystem]& source_fs, + const CFileSelector& source_sel, + const shared_ptr[CFileSystem]& destination_fs, + const c_string& destination_base_dir, + const CIOContext& io_context, + int64_t chunk_size, c_bool use_threads) + + +# Callbacks for implementing Python filesystems +# Use typedef to emulate syntax for std::function +ctypedef void CallbackGetTypeName(object, c_string*) +ctypedef c_bool CallbackEquals(object, const CFileSystem&) + +ctypedef void CallbackGetFileInfo(object, const c_string&, CFileInfo*) +ctypedef void CallbackGetFileInfoVector(object, const vector[c_string]&, + vector[CFileInfo]*) +ctypedef void CallbackGetFileInfoSelector(object, const CFileSelector&, + vector[CFileInfo]*) +ctypedef void CallbackCreateDir(object, const c_string&, c_bool) +ctypedef void CallbackDeleteDir(object, const c_string&) +ctypedef void CallbackDeleteDirContents(object, const c_string&, c_bool) +ctypedef void CallbackDeleteRootDirContents(object) +ctypedef void CallbackDeleteFile(object, const c_string&) +ctypedef void CallbackMove(object, const c_string&, const c_string&) +ctypedef void CallbackCopyFile(object, const c_string&, const c_string&) + +ctypedef void CallbackOpenInputStream(object, const c_string&, + shared_ptr[CInputStream]*) +ctypedef void CallbackOpenInputFile(object, const c_string&, + shared_ptr[CRandomAccessFile]*) +ctypedef void CallbackOpenOutputStream( + object, const c_string&, const shared_ptr[const CKeyValueMetadata]&, + shared_ptr[COutputStream]*) +ctypedef void CallbackNormalizePath(object, const c_string&, c_string*) + +cdef extern from "arrow/python/filesystem.h" namespace "arrow::py::fs" nogil: + + cdef cppclass CPyFileSystemVtable "arrow::py::fs::PyFileSystemVtable": + PyFileSystemVtable() + function[CallbackGetTypeName] get_type_name + function[CallbackEquals] equals + function[CallbackGetFileInfo] get_file_info + function[CallbackGetFileInfoVector] get_file_info_vector + function[CallbackGetFileInfoSelector] get_file_info_selector + function[CallbackCreateDir] create_dir + function[CallbackDeleteDir] delete_dir + function[CallbackDeleteDirContents] delete_dir_contents + function[CallbackDeleteRootDirContents] delete_root_dir_contents + function[CallbackDeleteFile] delete_file + function[CallbackMove] move + function[CallbackCopyFile] copy_file + function[CallbackOpenInputStream] open_input_stream + function[CallbackOpenInputFile] open_input_file + function[CallbackOpenOutputStream] open_output_stream + function[CallbackOpenOutputStream] open_append_stream + function[CallbackNormalizePath] normalize_path + + cdef cppclass CPyFileSystem "arrow::py::fs::PyFileSystem": + @staticmethod + shared_ptr[CPyFileSystem] Make(object handler, + CPyFileSystemVtable vtable) + + PyObject* handler() diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_python.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_python.pxd new file mode 100644 index 0000000000000000000000000000000000000000..4724c52ccb53460f738ada3fd5d17d1226ad9373 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_python.pxd @@ -0,0 +1,296 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from pyarrow.includes.common cimport * +from pyarrow.includes.libarrow cimport * + + +ctypedef CInvalidRowResult PyInvalidRowCallback(object, + const CCSVInvalidRow&) + + +cdef extern from "arrow/python/csv.h" namespace "arrow::py::csv": + + function[CInvalidRowHandler] MakeInvalidRowHandler( + function[PyInvalidRowCallback], object handler) + + +cdef extern from "arrow/python/api.h" namespace "arrow::py": + # Requires GIL + CResult[shared_ptr[CDataType]] InferArrowType( + object obj, object mask, c_bool pandas_null_sentinels) + + +cdef extern from "arrow/python/api.h" namespace "arrow::py::internal": + object NewMonthDayNanoTupleType() + CResult[PyObject*] MonthDayNanoIntervalArrayToPyList( + const CMonthDayNanoIntervalArray& array) + CResult[PyObject*] MonthDayNanoIntervalScalarToPyObject( + const CMonthDayNanoIntervalScalar& scalar) + + +cdef extern from "arrow/python/arrow_to_pandas.h" namespace "arrow::py::MapConversionType": + cdef enum MapConversionType "arrow::py::MapConversionType": + DEFAULT, + LOSSY, + STRICT_ + + +cdef extern from "arrow/python/api.h" namespace "arrow::py" nogil: + shared_ptr[CDataType] GetPrimitiveType(Type type) + + object PyFloat_FromHalf(uint16_t value) + + cdef cppclass PyConversionOptions: + PyConversionOptions() + + shared_ptr[CDataType] type + int64_t size + CMemoryPool* pool + c_bool from_pandas + c_bool ignore_timezone + c_bool strict + + # TODO Some functions below are not actually "nogil" + + CResult[shared_ptr[CChunkedArray]] ConvertPySequence( + object obj, object mask, const PyConversionOptions& options, + CMemoryPool* pool) + + CResult[shared_ptr[CArray]] Arange(int64_t start, int64_t stop, + int64_t step, CMemoryPool* pool) + + CResult[shared_ptr[CDataType]] NumPyDtypeToArrow(object dtype) + + CStatus NdarrayToArrow(CMemoryPool* pool, object ao, object mo, + c_bool from_pandas, + const shared_ptr[CDataType]& type, + shared_ptr[CChunkedArray]* out) + + CStatus NdarrayToArrow(CMemoryPool* pool, object ao, object mo, + c_bool from_pandas, + const shared_ptr[CDataType]& type, + const CCastOptions& cast_options, + shared_ptr[CChunkedArray]* out) + + CStatus NdarrayToTensor(CMemoryPool* pool, object ao, + const vector[c_string]& dim_names, + shared_ptr[CTensor]* out) + + CStatus TensorToNdarray(const shared_ptr[CTensor]& tensor, object base, + PyObject** out) + + CStatus SparseCOOTensorToNdarray( + const shared_ptr[CSparseCOOTensor]& sparse_tensor, object base, + PyObject** out_data, PyObject** out_coords) + + CStatus SparseCSRMatrixToNdarray( + const shared_ptr[CSparseCSRMatrix]& sparse_tensor, object base, + PyObject** out_data, PyObject** out_indptr, PyObject** out_indices) + + CStatus SparseCSCMatrixToNdarray( + const shared_ptr[CSparseCSCMatrix]& sparse_tensor, object base, + PyObject** out_data, PyObject** out_indptr, PyObject** out_indices) + + CStatus SparseCSFTensorToNdarray( + const shared_ptr[CSparseCSFTensor]& sparse_tensor, object base, + PyObject** out_data, PyObject** out_indptr, PyObject** out_indices) + + CStatus NdarraysToSparseCOOTensor(CMemoryPool* pool, object data_ao, + object coords_ao, + const vector[int64_t]& shape, + const vector[c_string]& dim_names, + shared_ptr[CSparseCOOTensor]* out) + + CStatus NdarraysToSparseCSRMatrix(CMemoryPool* pool, object data_ao, + object indptr_ao, object indices_ao, + const vector[int64_t]& shape, + const vector[c_string]& dim_names, + shared_ptr[CSparseCSRMatrix]* out) + + CStatus NdarraysToSparseCSCMatrix(CMemoryPool* pool, object data_ao, + object indptr_ao, object indices_ao, + const vector[int64_t]& shape, + const vector[c_string]& dim_names, + shared_ptr[CSparseCSCMatrix]* out) + + CStatus NdarraysToSparseCSFTensor(CMemoryPool* pool, object data_ao, + object indptr_ao, object indices_ao, + const vector[int64_t]& shape, + const vector[int64_t]& axis_order, + const vector[c_string]& dim_names, + shared_ptr[CSparseCSFTensor]* out) + + CStatus TensorToSparseCOOTensor(shared_ptr[CTensor], + shared_ptr[CSparseCOOTensor]* out) + + CStatus TensorToSparseCSRMatrix(shared_ptr[CTensor], + shared_ptr[CSparseCSRMatrix]* out) + + CStatus TensorToSparseCSCMatrix(shared_ptr[CTensor], + shared_ptr[CSparseCSCMatrix]* out) + + CStatus TensorToSparseCSFTensor(shared_ptr[CTensor], + shared_ptr[CSparseCSFTensor]* out) + + CStatus ConvertArrayToPandas(const PandasOptions& options, + shared_ptr[CArray] arr, + object py_ref, PyObject** out) + + CStatus ConvertChunkedArrayToPandas(const PandasOptions& options, + shared_ptr[CChunkedArray] arr, + object py_ref, PyObject** out) + + CStatus ConvertTableToPandas(const PandasOptions& options, + shared_ptr[CTable] table, + PyObject** out) + + void c_set_default_memory_pool \ + " arrow::py::set_default_memory_pool"(CMemoryPool* pool)\ + + CMemoryPool* c_get_memory_pool \ + " arrow::py::get_memory_pool"() + + cdef cppclass PyBuffer(CBuffer): + @staticmethod + CResult[shared_ptr[CBuffer]] FromPyObject(object obj) + + cdef cppclass PyForeignBuffer(CBuffer): + @staticmethod + CStatus Make(const uint8_t* data, int64_t size, object base, + shared_ptr[CBuffer]* out) + + cdef cppclass PyReadableFile(CRandomAccessFile): + PyReadableFile(object fo) + + cdef cppclass PyOutputStream(COutputStream): + PyOutputStream(object fo) + + cdef cppclass PandasOptions: + CMemoryPool* pool + c_bool strings_to_categorical + c_bool zero_copy_only + c_bool integer_object_nulls + c_bool date_as_object + c_bool timestamp_as_object + c_bool use_threads + c_bool coerce_temporal_nanoseconds + c_bool ignore_timezone + c_bool deduplicate_objects + c_bool safe_cast + c_bool split_blocks + c_bool self_destruct + MapConversionType maps_as_pydicts + c_bool decode_dictionaries + shared_ptr[const unordered_set[c_string]] categorical_columns + shared_ptr[const unordered_set[c_string]] extension_columns + c_bool to_numpy + + +cdef extern from "arrow/python/api.h" namespace "arrow::py::internal" nogil: + cdef cppclass CTimePoint "arrow::py::internal::TimePoint": + pass + + CTimePoint PyDateTime_to_TimePoint(PyDateTime_DateTime* pydatetime) + int64_t TimePoint_to_ns(CTimePoint val) + CTimePoint TimePoint_from_s(double val) + CTimePoint TimePoint_from_ns(int64_t val) + + CResult[c_string] TzinfoToString(PyObject* pytzinfo) + CResult[PyObject*] StringToTzinfo(c_string) + + +cdef extern from "arrow/python/numpy_init.h" namespace "arrow::py": + int arrow_init_numpy() except -1 + + +cdef extern from "arrow/python/pyarrow.h" namespace "arrow::py": + int import_pyarrow() except -1 + + +cdef extern from "arrow/python/common.h" namespace "arrow::py": + c_bool IsPyError(const CStatus& status) + void RestorePyError(const CStatus& status) except * + + +cdef extern from "arrow/python/common.h" namespace "arrow::py" nogil: + cdef cppclass SharedPtrNoGIL[T](shared_ptr[T]): + # This looks like the only way to satisfy both Cython 2 and Cython 3 + SharedPtrNoGIL& operator=(...) + cdef cppclass UniquePtrNoGIL[T, DELETER=*](unique_ptr[T, DELETER]): + UniquePtrNoGIL& operator=(...) + + +cdef extern from "arrow/python/inference.h" namespace "arrow::py": + c_bool IsPyBool(object o) + c_bool IsPyInt(object o) + c_bool IsPyFloat(object o) + + +cdef extern from "arrow/python/ipc.h" namespace "arrow::py": + cdef cppclass CPyRecordBatchReader" arrow::py::PyRecordBatchReader" \ + (CRecordBatchReader): + @staticmethod + CResult[shared_ptr[CRecordBatchReader]] Make(shared_ptr[CSchema], + object) + + +cdef extern from "arrow/python/ipc.h" namespace "arrow::py" nogil: + cdef cppclass CCastingRecordBatchReader" arrow::py::CastingRecordBatchReader" \ + (CRecordBatchReader): + @staticmethod + CResult[shared_ptr[CRecordBatchReader]] Make(shared_ptr[CRecordBatchReader], + shared_ptr[CSchema]) + + +cdef extern from "arrow/python/extension_type.h" namespace "arrow::py": + cdef cppclass CPyExtensionType \ + " arrow::py::PyExtensionType"(CExtensionType): + @staticmethod + CStatus FromClass(const shared_ptr[CDataType] storage_type, + const c_string extension_name, object typ, + shared_ptr[CExtensionType]* out) + + @staticmethod + CStatus FromInstance(shared_ptr[CDataType] storage_type, + object inst, shared_ptr[CExtensionType]* out) + + object GetInstance() + CStatus SetInstance(object) + + c_string PyExtensionName() + CStatus RegisterPyExtensionType(shared_ptr[CDataType]) + CStatus UnregisterPyExtensionType(c_string type_name) + + +cdef extern from "arrow/python/benchmark.h" namespace "arrow::py::benchmark": + void Benchmark_PandasObjectIsNull(object lst) except * + + +cdef extern from "arrow/python/gdb.h" namespace "arrow::gdb" nogil: + void GdbTestSession "arrow::gdb::TestSession"() + +cdef extern from "arrow/python/helpers.h" namespace "arrow::py::internal": + c_bool IsThreadingEnabled() + +cdef extern from "arrow/python/config.h" namespace "arrow::py": + cdef cppclass CBuildInfo "arrow::py::BuildInfo": + c_string build_type + + const CBuildInfo& GetBuildInfo "arrow::py::GetBuildInfo"() diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_substrait.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_substrait.pxd new file mode 100644 index 0000000000000000000000000000000000000000..865568e2ba6f141c29271527093e33547bafb263 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libarrow_substrait.pxd @@ -0,0 +1,100 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from libcpp.vector cimport vector as std_vector + +from pyarrow.includes.common cimport * +from pyarrow.includes.libarrow cimport * +from pyarrow.includes.libarrow_acero cimport * + +ctypedef CResult[CDeclaration] CNamedTableProvider(const std_vector[c_string]&, const CSchema&) + +cdef extern from "arrow/engine/substrait/options.h" namespace "arrow::engine" nogil: + cdef enum ConversionStrictness \ + "arrow::engine::ConversionStrictness": + EXACT_ROUNDTRIP \ + "arrow::engine::ConversionStrictness::EXACT_ROUNDTRIP" + PRESERVE_STRUCTURE \ + "arrow::engine::ConversionStrictness::PRESERVE_STRUCTURE" + BEST_EFFORT \ + "arrow::engine::ConversionStrictness::BEST_EFFORT" + + cdef cppclass CConversionOptions \ + "arrow::engine::ConversionOptions": + CConversionOptions() + ConversionStrictness strictness + function[CNamedTableProvider] named_table_provider + c_bool allow_arrow_extensions + +cdef extern from "arrow/engine/substrait/extension_set.h" \ + namespace "arrow::engine" nogil: + + cdef struct CSubstraitId "arrow::engine::Id": + cpp_string_view uri + cpp_string_view name + + cdef struct CExtensionSetTypeRecord "arrow::engine::ExtensionSet::TypeRecord": + CSubstraitId id + shared_ptr[CDataType] type + + cdef cppclass CExtensionSet "arrow::engine::ExtensionSet": + CExtensionSet() + unordered_map[uint32_t, cpp_string_view]& uris() + CResult[uint32_t] EncodeType(const CDataType&) + CResult[CExtensionSetTypeRecord] DecodeType(uint32_t) + + cdef cppclass ExtensionIdRegistry: + std_vector[c_string] GetSupportedSubstraitFunctions() + + ExtensionIdRegistry* default_extension_id_registry() + +cdef extern from "arrow/engine/substrait/relation.h" namespace "arrow::engine" nogil: + + cdef cppclass CNamedExpression "arrow::engine::NamedExpression": + CExpression expression + c_string name + + cdef cppclass CBoundExpressions "arrow::engine::BoundExpressions": + std_vector[CNamedExpression] named_expressions + shared_ptr[CSchema] schema + +cdef extern from "arrow/engine/substrait/serde.h" namespace "arrow::engine" nogil: + + CResult[shared_ptr[CBuffer]] SerializeExpressions( + const CBoundExpressions& bound_expressions, const CConversionOptions& conversion_options) + + CResult[CBoundExpressions] DeserializeExpressions( + const CBuffer& serialized_expressions) + + CResult[shared_ptr[CBuffer]] SerializeSchema( + const CSchema &schema, CExtensionSet* extension_set, + const CConversionOptions& conversion_options) + + CResult[shared_ptr[CSchema]] DeserializeSchema( + const CBuffer& serialized_schema, const CExtensionSet& extension_set, + const CConversionOptions& conversion_options) + + +cdef extern from "arrow/engine/substrait/util.h" namespace "arrow::engine" nogil: + CResult[shared_ptr[CRecordBatchReader]] ExecuteSerializedPlan( + const CBuffer& substrait_buffer, const ExtensionIdRegistry* registry, + CFunctionRegistry* func_registry, const CConversionOptions& conversion_options, + c_bool use_threads) + + CResult[shared_ptr[CBuffer]] SerializeJsonPlan(const c_string& substrait_json) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libgandiva.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libgandiva.pxd new file mode 100644 index 0000000000000000000000000000000000000000..782d83423e827fd1fbfcf66fa73fe4f3ab3d1a1c --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libgandiva.pxd @@ -0,0 +1,298 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from libcpp.string cimport string as c_string +from libcpp.unordered_set cimport unordered_set as c_unordered_set +from libc.stdint cimport int64_t, int32_t, uint8_t, uintptr_t + +from pyarrow.includes.common cimport * +from pyarrow.includes.libarrow cimport * + +cdef extern from "gandiva/node.h" namespace "gandiva" nogil: + + cdef cppclass CNode" gandiva::Node": + c_string ToString() + shared_ptr[CDataType] return_type() + + cdef cppclass CGandivaExpression" gandiva::Expression": + c_string ToString() + shared_ptr[CNode] root() + shared_ptr[CField] result() + + ctypedef vector[shared_ptr[CNode]] CNodeVector" gandiva::NodeVector" + + ctypedef vector[shared_ptr[CGandivaExpression]] \ + CExpressionVector" gandiva::ExpressionVector" + +cdef extern from "gandiva/selection_vector.h" namespace "gandiva" nogil: + + cdef cppclass CSelectionVector" gandiva::SelectionVector": + + shared_ptr[CArray] ToArray() + + enum CSelectionVector_Mode" gandiva::SelectionVector::Mode": + CSelectionVector_Mode_NONE" gandiva::SelectionVector::Mode::MODE_NONE" + CSelectionVector_Mode_UINT16" \ + gandiva::SelectionVector::Mode::MODE_UINT16" + CSelectionVector_Mode_UINT32" \ + gandiva::SelectionVector::Mode::MODE_UINT32" + CSelectionVector_Mode_UINT64" \ + gandiva::SelectionVector::Mode::MODE_UINT64" + + cdef CStatus SelectionVector_MakeInt16\ + "gandiva::SelectionVector::MakeInt16"( + int64_t max_slots, CMemoryPool* pool, + shared_ptr[CSelectionVector]* selection_vector) + + cdef CStatus SelectionVector_MakeInt32\ + "gandiva::SelectionVector::MakeInt32"( + int64_t max_slots, CMemoryPool* pool, + shared_ptr[CSelectionVector]* selection_vector) + + cdef CStatus SelectionVector_MakeInt64\ + "gandiva::SelectionVector::MakeInt64"( + int64_t max_slots, CMemoryPool* pool, + shared_ptr[CSelectionVector]* selection_vector) + +cdef inline CSelectionVector_Mode _ensure_selection_mode(str name) except *: + uppercase = name.upper() + if uppercase == 'NONE': + return CSelectionVector_Mode_NONE + elif uppercase == 'UINT16': + return CSelectionVector_Mode_UINT16 + elif uppercase == 'UINT32': + return CSelectionVector_Mode_UINT32 + elif uppercase == 'UINT64': + return CSelectionVector_Mode_UINT64 + else: + raise ValueError(f'Invalid value for Selection Mode: {name!r}') + +cdef inline str _selection_mode_name(CSelectionVector_Mode ctype): + if ctype == CSelectionVector_Mode_NONE: + return 'NONE' + elif ctype == CSelectionVector_Mode_UINT16: + return 'UINT16' + elif ctype == CSelectionVector_Mode_UINT32: + return 'UINT32' + elif ctype == CSelectionVector_Mode_UINT64: + return 'UINT64' + else: + raise RuntimeError('Unexpected CSelectionVector_Mode value') + +cdef extern from "gandiva/condition.h" namespace "gandiva" nogil: + + cdef cppclass CCondition" gandiva::Condition": + c_string ToString() + shared_ptr[CNode] root() + shared_ptr[CField] result() + +cdef extern from "gandiva/arrow.h" namespace "gandiva" nogil: + + ctypedef vector[shared_ptr[CArray]] CArrayVector" gandiva::ArrayVector" + + +cdef extern from "gandiva/tree_expr_builder.h" namespace "gandiva" nogil: + + cdef shared_ptr[CNode] TreeExprBuilder_MakeBoolLiteral \ + "gandiva::TreeExprBuilder::MakeLiteral"(c_bool value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeUInt8Literal \ + "gandiva::TreeExprBuilder::MakeLiteral"(uint8_t value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeUInt16Literal \ + "gandiva::TreeExprBuilder::MakeLiteral"(uint16_t value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeUInt32Literal \ + "gandiva::TreeExprBuilder::MakeLiteral"(uint32_t value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeUInt64Literal \ + "gandiva::TreeExprBuilder::MakeLiteral"(uint64_t value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInt8Literal \ + "gandiva::TreeExprBuilder::MakeLiteral"(int8_t value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInt16Literal \ + "gandiva::TreeExprBuilder::MakeLiteral"(int16_t value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInt32Literal \ + "gandiva::TreeExprBuilder::MakeLiteral"(int32_t value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInt64Literal \ + "gandiva::TreeExprBuilder::MakeLiteral"(int64_t value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeFloatLiteral \ + "gandiva::TreeExprBuilder::MakeLiteral"(float value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeDoubleLiteral \ + "gandiva::TreeExprBuilder::MakeLiteral"(double value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeStringLiteral \ + "gandiva::TreeExprBuilder::MakeStringLiteral"(const c_string& value) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeBinaryLiteral \ + "gandiva::TreeExprBuilder::MakeBinaryLiteral"(const c_string& value) + + cdef shared_ptr[CGandivaExpression] TreeExprBuilder_MakeExpression\ + "gandiva::TreeExprBuilder::MakeExpression"( + shared_ptr[CNode] root_node, shared_ptr[CField] result_field) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeFunction \ + "gandiva::TreeExprBuilder::MakeFunction"( + const c_string& name, const CNodeVector& children, + shared_ptr[CDataType] return_type) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeField \ + "gandiva::TreeExprBuilder::MakeField"(shared_ptr[CField] field) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeIf \ + "gandiva::TreeExprBuilder::MakeIf"( + shared_ptr[CNode] condition, shared_ptr[CNode] this_node, + shared_ptr[CNode] else_node, shared_ptr[CDataType] return_type) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeAnd \ + "gandiva::TreeExprBuilder::MakeAnd"(const CNodeVector& children) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeOr \ + "gandiva::TreeExprBuilder::MakeOr"(const CNodeVector& children) + + cdef shared_ptr[CCondition] TreeExprBuilder_MakeCondition \ + "gandiva::TreeExprBuilder::MakeCondition"( + shared_ptr[CNode] condition) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInExpressionInt32 \ + "gandiva::TreeExprBuilder::MakeInExpressionInt32"( + shared_ptr[CNode] node, const c_unordered_set[int32_t]& values) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInExpressionInt64 \ + "gandiva::TreeExprBuilder::MakeInExpressionInt64"( + shared_ptr[CNode] node, const c_unordered_set[int64_t]& values) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInExpressionTime32 \ + "gandiva::TreeExprBuilder::MakeInExpressionTime32"( + shared_ptr[CNode] node, const c_unordered_set[int32_t]& values) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInExpressionTime64 \ + "gandiva::TreeExprBuilder::MakeInExpressionTime64"( + shared_ptr[CNode] node, const c_unordered_set[int64_t]& values) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInExpressionDate32 \ + "gandiva::TreeExprBuilder::MakeInExpressionDate32"( + shared_ptr[CNode] node, const c_unordered_set[int32_t]& values) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInExpressionDate64 \ + "gandiva::TreeExprBuilder::MakeInExpressionDate64"( + shared_ptr[CNode] node, const c_unordered_set[int64_t]& values) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInExpressionTimeStamp \ + "gandiva::TreeExprBuilder::MakeInExpressionTimeStamp"( + shared_ptr[CNode] node, const c_unordered_set[int64_t]& values) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInExpressionString \ + "gandiva::TreeExprBuilder::MakeInExpressionString"( + shared_ptr[CNode] node, const c_unordered_set[c_string]& values) + + cdef shared_ptr[CNode] TreeExprBuilder_MakeInExpressionBinary \ + "gandiva::TreeExprBuilder::MakeInExpressionBinary"( + shared_ptr[CNode] node, const c_unordered_set[c_string]& values) + +cdef extern from "gandiva/projector.h" namespace "gandiva" nogil: + + cdef cppclass CProjector" gandiva::Projector": + + CStatus Evaluate( + const CRecordBatch& batch, CMemoryPool* pool, + const CArrayVector* output) + + CStatus Evaluate( + const CRecordBatch& batch, + const CSelectionVector* selection, + CMemoryPool* pool, + const CArrayVector* output) + + c_string DumpIR() + + cdef CStatus Projector_Make \ + "gandiva::Projector::Make"( + shared_ptr[CSchema] schema, const CExpressionVector& children, + shared_ptr[CProjector]* projector) + + cdef CStatus Projector_Make \ + "gandiva::Projector::Make"( + shared_ptr[CSchema] schema, const CExpressionVector& children, + CSelectionVector_Mode mode, + shared_ptr[CConfiguration] configuration, + shared_ptr[CProjector]* projector) + +cdef extern from "gandiva/filter.h" namespace "gandiva" nogil: + + cdef cppclass CFilter" gandiva::Filter": + + CStatus Evaluate( + const CRecordBatch& batch, + shared_ptr[CSelectionVector] out_selection) + + c_string DumpIR() + + cdef CStatus Filter_Make \ + "gandiva::Filter::Make"( + shared_ptr[CSchema] schema, shared_ptr[CCondition] condition, + shared_ptr[CConfiguration] configuration, + shared_ptr[CFilter]* filter) + +cdef extern from "gandiva/function_signature.h" namespace "gandiva" nogil: + + cdef cppclass CFunctionSignature" gandiva::FunctionSignature": + + CFunctionSignature(const c_string& base_name, + vector[shared_ptr[CDataType]] param_types, + shared_ptr[CDataType] ret_type) + + shared_ptr[CDataType] ret_type() const + + const c_string& base_name() const + + vector[shared_ptr[CDataType]] param_types() const + + c_string ToString() const + +cdef extern from "gandiva/expression_registry.h" namespace "gandiva" nogil: + + cdef vector[shared_ptr[CFunctionSignature]] \ + GetRegisteredFunctionSignatures() + +cdef extern from "gandiva/configuration.h" namespace "gandiva" nogil: + + cdef cppclass CConfiguration" gandiva::Configuration": + + CConfiguration() + + CConfiguration(bint optimize, bint dump_ir) + + void set_optimize(bint optimize) + + void set_dump_ir(bint dump_ir) + + cdef cppclass CConfigurationBuilder \ + " gandiva::ConfigurationBuilder": + @staticmethod + shared_ptr[CConfiguration] DefaultConfiguration() + + CConfigurationBuilder() + + shared_ptr[CConfiguration] build() diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libparquet.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libparquet.pxd new file mode 100644 index 0000000000000000000000000000000000000000..81901a00acd55c6c00f64b9e1611ca3fe5a2e9f0 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libparquet.pxd @@ -0,0 +1,634 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from pyarrow.includes.common cimport * +from pyarrow.includes.libarrow cimport (Type, CChunkedArray, CScalar, CSchema, + CStatus, CTable, CMemoryPool, CBuffer, + CKeyValueMetadata, CRandomAccessFile, + COutputStream, CCacheOptions, + TimeUnit, CRecordBatchReader) + + +cdef extern from "parquet/api/schema.h" namespace "parquet::schema" nogil: + cdef cppclass Node: + pass + + cdef cppclass GroupNode(Node): + pass + + cdef cppclass PrimitiveNode(Node): + pass + + cdef cppclass ColumnPath: + c_string ToDotString() + vector[c_string] ToDotVector() + + +cdef extern from "parquet/api/schema.h" namespace "parquet" nogil: + enum ParquetType" parquet::Type::type": + ParquetType_BOOLEAN" parquet::Type::BOOLEAN" + ParquetType_INT32" parquet::Type::INT32" + ParquetType_INT64" parquet::Type::INT64" + ParquetType_INT96" parquet::Type::INT96" + ParquetType_FLOAT" parquet::Type::FLOAT" + ParquetType_DOUBLE" parquet::Type::DOUBLE" + ParquetType_BYTE_ARRAY" parquet::Type::BYTE_ARRAY" + ParquetType_FIXED_LEN_BYTE_ARRAY" parquet::Type::FIXED_LEN_BYTE_ARRAY" + + enum ParquetLogicalTypeId" parquet::LogicalType::Type::type": + ParquetLogicalType_UNDEFINED" parquet::LogicalType::Type::UNDEFINED" + ParquetLogicalType_STRING" parquet::LogicalType::Type::STRING" + ParquetLogicalType_MAP" parquet::LogicalType::Type::MAP" + ParquetLogicalType_LIST" parquet::LogicalType::Type::LIST" + ParquetLogicalType_ENUM" parquet::LogicalType::Type::ENUM" + ParquetLogicalType_DECIMAL" parquet::LogicalType::Type::DECIMAL" + ParquetLogicalType_DATE" parquet::LogicalType::Type::DATE" + ParquetLogicalType_TIME" parquet::LogicalType::Type::TIME" + ParquetLogicalType_TIMESTAMP" parquet::LogicalType::Type::TIMESTAMP" + ParquetLogicalType_INT" parquet::LogicalType::Type::INT" + ParquetLogicalType_FLOAT16" parquet::LogicalType::Type::FLOAT16" + ParquetLogicalType_JSON" parquet::LogicalType::Type::JSON" + ParquetLogicalType_BSON" parquet::LogicalType::Type::BSON" + ParquetLogicalType_UUID" parquet::LogicalType::Type::UUID" + ParquetLogicalType_GEOMETRY" parquet::LogicalType::Type::GEOMETRY" + ParquetLogicalType_GEOGRAPHY" parquet::LogicalType::Type::GEOGRAPHY" + ParquetLogicalType_NONE" parquet::LogicalType::Type::NONE" + + enum ParquetTimeUnit" parquet::LogicalType::TimeUnit::unit": + ParquetTimeUnit_UNKNOWN" parquet::LogicalType::TimeUnit::UNKNOWN" + ParquetTimeUnit_MILLIS" parquet::LogicalType::TimeUnit::MILLIS" + ParquetTimeUnit_MICROS" parquet::LogicalType::TimeUnit::MICROS" + ParquetTimeUnit_NANOS" parquet::LogicalType::TimeUnit::NANOS" + + enum ParquetEdgeInterpolationAlgorithm" parquet::LogicalType::EdgeInterpolationAlgorithm": + ParquetEdgeInterpolationAlgorithm_UNKNOWN" parquet::LogicalType::EdgeInterpolationAlgorithm::UNKNOWN" + ParquetEdgeInterpolationAlgorithm_SPHERICAL" parquet::LogicalType::EdgeInterpolationAlgorithm::SPHERICAL" + ParquetEdgeInterpolationAlgorithm_VINCENTY" parquet::LogicalType::EdgeInterpolationAlgorithm::VINCENTY" + ParquetEdgeInterpolationAlgorithm_THOMAS" parquet::LogicalType::EdgeInterpolationAlgorithm::THOMAS" + ParquetEdgeInterpolationAlgorithm_ANDOYER" parquet::LogicalType::EdgeInterpolationAlgorithm::ANDOYER" + ParquetEdgeInterpolationAlgorithm_KARNEY" parquet::LogicalType::EdgeInterpolationAlgorithm::KARNEY" + + enum ParquetConvertedType" parquet::ConvertedType::type": + ParquetConvertedType_NONE" parquet::ConvertedType::NONE" + ParquetConvertedType_UTF8" parquet::ConvertedType::UTF8" + ParquetConvertedType_MAP" parquet::ConvertedType::MAP" + ParquetConvertedType_MAP_KEY_VALUE \ + " parquet::ConvertedType::MAP_KEY_VALUE" + ParquetConvertedType_LIST" parquet::ConvertedType::LIST" + ParquetConvertedType_ENUM" parquet::ConvertedType::ENUM" + ParquetConvertedType_DECIMAL" parquet::ConvertedType::DECIMAL" + ParquetConvertedType_DATE" parquet::ConvertedType::DATE" + ParquetConvertedType_TIME_MILLIS" parquet::ConvertedType::TIME_MILLIS" + ParquetConvertedType_TIME_MICROS" parquet::ConvertedType::TIME_MICROS" + ParquetConvertedType_TIMESTAMP_MILLIS \ + " parquet::ConvertedType::TIMESTAMP_MILLIS" + ParquetConvertedType_TIMESTAMP_MICROS \ + " parquet::ConvertedType::TIMESTAMP_MICROS" + ParquetConvertedType_UINT_8" parquet::ConvertedType::UINT_8" + ParquetConvertedType_UINT_16" parquet::ConvertedType::UINT_16" + ParquetConvertedType_UINT_32" parquet::ConvertedType::UINT_32" + ParquetConvertedType_UINT_64" parquet::ConvertedType::UINT_64" + ParquetConvertedType_INT_8" parquet::ConvertedType::INT_8" + ParquetConvertedType_INT_16" parquet::ConvertedType::INT_16" + ParquetConvertedType_INT_32" parquet::ConvertedType::INT_32" + ParquetConvertedType_INT_64" parquet::ConvertedType::INT_64" + ParquetConvertedType_JSON" parquet::ConvertedType::JSON" + ParquetConvertedType_BSON" parquet::ConvertedType::BSON" + ParquetConvertedType_INTERVAL" parquet::ConvertedType::INTERVAL" + + enum ParquetRepetition" parquet::Repetition::type": + ParquetRepetition_REQUIRED" parquet::REPETITION::REQUIRED" + ParquetRepetition_OPTIONAL" parquet::REPETITION::OPTIONAL" + ParquetRepetition_REPEATED" parquet::REPETITION::REPEATED" + + enum ParquetEncoding" parquet::Encoding::type": + ParquetEncoding_PLAIN" parquet::Encoding::PLAIN" + ParquetEncoding_PLAIN_DICTIONARY" parquet::Encoding::PLAIN_DICTIONARY" + ParquetEncoding_RLE" parquet::Encoding::RLE" + ParquetEncoding_BIT_PACKED" parquet::Encoding::BIT_PACKED" + ParquetEncoding_DELTA_BINARY_PACKED \ + " parquet::Encoding::DELTA_BINARY_PACKED" + ParquetEncoding_DELTA_LENGTH_BYTE_ARRAY \ + " parquet::Encoding::DELTA_LENGTH_BYTE_ARRAY" + ParquetEncoding_DELTA_BYTE_ARRAY" parquet::Encoding::DELTA_BYTE_ARRAY" + ParquetEncoding_RLE_DICTIONARY" parquet::Encoding::RLE_DICTIONARY" + ParquetEncoding_BYTE_STREAM_SPLIT \ + " parquet::Encoding::BYTE_STREAM_SPLIT" + + enum ParquetCompression" parquet::Compression::type": + ParquetCompression_UNCOMPRESSED" parquet::Compression::UNCOMPRESSED" + ParquetCompression_SNAPPY" parquet::Compression::SNAPPY" + ParquetCompression_GZIP" parquet::Compression::GZIP" + ParquetCompression_LZO" parquet::Compression::LZO" + ParquetCompression_BROTLI" parquet::Compression::BROTLI" + ParquetCompression_LZ4" parquet::Compression::LZ4" + ParquetCompression_ZSTD" parquet::Compression::ZSTD" + + enum ParquetVersion" parquet::ParquetVersion::type": + ParquetVersion_V1" parquet::ParquetVersion::PARQUET_1_0" + ParquetVersion_V2_4" parquet::ParquetVersion::PARQUET_2_4" + ParquetVersion_V2_6" parquet::ParquetVersion::PARQUET_2_6" + + enum ParquetSortOrder" parquet::SortOrder::type": + ParquetSortOrder_SIGNED" parquet::SortOrder::SIGNED" + ParquetSortOrder_UNSIGNED" parquet::SortOrder::UNSIGNED" + ParquetSortOrder_UNKNOWN" parquet::SortOrder::UNKNOWN" + + cdef cppclass CParquetLogicalType" parquet::LogicalType": + c_string ToString() const + c_string ToJSON() const + ParquetLogicalTypeId type() const + + cdef cppclass CParquetDecimalType \ + " parquet::DecimalLogicalType"(CParquetLogicalType): + int32_t precision() const + int32_t scale() const + + cdef cppclass CParquetIntType \ + " parquet::IntLogicalType"(CParquetLogicalType): + int bit_width() const + c_bool is_signed() const + + cdef cppclass CParquetTimeType \ + " parquet::TimeLogicalType"(CParquetLogicalType): + c_bool is_adjusted_to_utc() const + ParquetTimeUnit time_unit() const + + cdef cppclass CParquetTimestampType \ + " parquet::TimestampLogicalType"(CParquetLogicalType): + c_bool is_adjusted_to_utc() const + ParquetTimeUnit time_unit() const + + cdef cppclass CParquetGeometryType \ + " parquet::GeometryLogicalType"(CParquetLogicalType): + c_string crs() const + + cdef cppclass CParquetGeographyType \ + " parquet::GeographyLogicalType"(CParquetLogicalType): + c_string crs() const + ParquetEdgeInterpolationAlgorithm algorithm() const + + cdef cppclass ColumnDescriptor" parquet::ColumnDescriptor": + c_bool Equals(const ColumnDescriptor& other) + + shared_ptr[ColumnPath] path() + int16_t max_definition_level() + int16_t max_repetition_level() + + ParquetType physical_type() + const shared_ptr[const CParquetLogicalType]& logical_type() + ParquetConvertedType converted_type() + const c_string& name() + int type_length() + int type_precision() + int type_scale() + + cdef cppclass SchemaDescriptor: + const ColumnDescriptor* Column(int i) + shared_ptr[Node] schema() + GroupNode* group() + c_bool Equals(const SchemaDescriptor& other) + c_string ToString() + int num_columns() + + cdef c_string FormatStatValue(ParquetType parquet_type, c_string val) + + enum ParquetCipher" parquet::ParquetCipher::type": + ParquetCipher_AES_GCM_V1" parquet::ParquetCipher::AES_GCM_V1" + ParquetCipher_AES_GCM_CTR_V1" parquet::ParquetCipher::AES_GCM_CTR_V1" + + struct AadMetadata: + c_string aad_prefix + c_string aad_file_unique + c_bool supply_aad_prefix + + struct EncryptionAlgorithm: + ParquetCipher algorithm + AadMetadata aad + + +# Specific array<> types needed for GeoStatistics +cdef extern from "" namespace "std" nogil: + cdef cppclass double_array4 "std::array": + double_array4() except + + double& operator[](size_t) + + cdef cppclass bool_array4 "std::array": + bool_array4() except + + c_bool& operator[](size_t) + + +cdef extern from "parquet/geospatial/statistics.h" namespace "parquet" nogil: + + cdef cppclass CParquetGeoStatistics" parquet::geospatial::GeoStatistics": + c_bool is_valid() const + + double_array4 lower_bound() const + double_array4 upper_bound() const + bool_array4 dimension_valid() const + bool_array4 dimension_empty() const + + optional[vector[int32_t]] geometry_types() const + + c_string ToString() const + + +cdef extern from "parquet/api/reader.h" namespace "parquet" nogil: + cdef cppclass ColumnReader: + pass + + cdef cppclass BoolReader(ColumnReader): + pass + + cdef cppclass Int32Reader(ColumnReader): + pass + + cdef cppclass Int64Reader(ColumnReader): + pass + + cdef cppclass Int96Reader(ColumnReader): + pass + + cdef cppclass FloatReader(ColumnReader): + pass + + cdef cppclass DoubleReader(ColumnReader): + pass + + cdef cppclass ByteArrayReader(ColumnReader): + pass + + cdef cppclass RowGroupReader: + pass + + cdef cppclass CEncodedStatistics" parquet::EncodedStatistics": + const c_string& max() const + const c_string& min() const + int64_t null_count + int64_t distinct_count + bint has_min + bint has_max + bint has_null_count + bint has_distinct_count + + cdef cppclass ParquetByteArray" parquet::ByteArray": + uint32_t len + const uint8_t* ptr + + cdef cppclass ParquetFLBA" parquet::FLBA": + const uint8_t* ptr + + cdef cppclass CStatistics" parquet::Statistics": + int64_t null_count() const + int64_t distinct_count() const + int64_t num_values() const + bint HasMinMax() + bint HasNullCount() + bint HasDistinctCount() + c_bool Equals(const CStatistics&) const + void Reset() + c_string EncodeMin() + c_string EncodeMax() + CEncodedStatistics Encode() + void SetComparator() + ParquetType physical_type() const + const ColumnDescriptor* descr() const + + cdef cppclass CBoolStatistics" parquet::BoolStatistics"(CStatistics): + c_bool min() + c_bool max() + + cdef cppclass CInt32Statistics" parquet::Int32Statistics"(CStatistics): + int32_t min() + int32_t max() + + cdef cppclass CInt64Statistics" parquet::Int64Statistics"(CStatistics): + int64_t min() + int64_t max() + + cdef cppclass CFloatStatistics" parquet::FloatStatistics"(CStatistics): + float min() + float max() + + cdef cppclass CDoubleStatistics" parquet::DoubleStatistics"(CStatistics): + double min() + double max() + + cdef cppclass CByteArrayStatistics \ + " parquet::ByteArrayStatistics"(CStatistics): + ParquetByteArray min() + ParquetByteArray max() + + cdef cppclass CFLBAStatistics" parquet::FLBAStatistics"(CStatistics): + ParquetFLBA min() + ParquetFLBA max() + + cdef cppclass CColumnCryptoMetaData" parquet::ColumnCryptoMetaData": + shared_ptr[ColumnPath] path_in_schema() const + c_bool encrypted_with_footer_key() const + const c_string& key_metadata() const + + cdef cppclass ParquetIndexLocation" parquet::IndexLocation": + int64_t offset + int32_t length + + cdef cppclass CColumnChunkMetaData" parquet::ColumnChunkMetaData": + int64_t file_offset() const + const c_string& file_path() const + + c_bool is_metadata_set() const + ParquetType type() const + int64_t num_values() const + shared_ptr[ColumnPath] path_in_schema() const + bint is_stats_set() const + shared_ptr[CStatistics] statistics() const + c_bool is_geo_stats_set() const + shared_ptr[CParquetGeoStatistics] geo_statistics() const + ParquetCompression compression() const + const vector[ParquetEncoding]& encodings() const + c_bool Equals(const CColumnChunkMetaData&) const + + int64_t has_dictionary_page() const + int64_t dictionary_page_offset() const + int64_t data_page_offset() const + int64_t index_page_offset() const + int64_t total_compressed_size() const + int64_t total_uncompressed_size() const + unique_ptr[CColumnCryptoMetaData] crypto_metadata() const + optional[ParquetIndexLocation] GetColumnIndexLocation() const + optional[ParquetIndexLocation] GetOffsetIndexLocation() const + shared_ptr[const CKeyValueMetadata] key_value_metadata() const + + struct CSortingColumn" parquet::SortingColumn": + int column_idx + c_bool descending + c_bool nulls_first + + cdef cppclass CRowGroupMetaData" parquet::RowGroupMetaData": + c_bool Equals(const CRowGroupMetaData&) const + int num_columns() const + int64_t num_rows() const + int64_t total_byte_size() const + vector[CSortingColumn] sorting_columns() const + unique_ptr[CColumnChunkMetaData] ColumnChunk(int i) const + + cdef cppclass CFileMetaData" parquet::FileMetaData": + c_bool Equals(const CFileMetaData&) const + uint32_t size() + int num_columns() + int64_t num_rows() + int num_row_groups() + ParquetVersion version() + const c_string created_by() + int num_schema_elements() + + void set_file_path(const c_string& path) + void AppendRowGroups(const CFileMetaData& other) except + + + unique_ptr[CRowGroupMetaData] RowGroup(int i) + const SchemaDescriptor* schema() + shared_ptr[const CKeyValueMetadata] key_value_metadata() const + void WriteTo(COutputStream* dst) const + + inline c_bool is_encryption_algorithm_set() const + inline EncryptionAlgorithm encryption_algorithm() const + inline const c_string& footer_signing_key_metadata() const + + cdef shared_ptr[CFileMetaData] CFileMetaData_Make \ + " parquet::FileMetaData::Make"(const void* serialized_metadata, + uint32_t* metadata_len) + + cdef cppclass CReaderProperties" parquet::ReaderProperties": + c_bool is_buffered_stream_enabled() const + void enable_buffered_stream() + void disable_buffered_stream() + + void set_buffer_size(int64_t buf_size) + int64_t buffer_size() const + + void set_thrift_string_size_limit(int32_t size) + int32_t thrift_string_size_limit() const + + void set_thrift_container_size_limit(int32_t size) + int32_t thrift_container_size_limit() const + + void file_decryption_properties(shared_ptr[CFileDecryptionProperties] + decryption) + shared_ptr[CFileDecryptionProperties] file_decryption_properties() \ + const + + c_bool page_checksum_verification() const + void set_page_checksum_verification(c_bool check_crc) + + CReaderProperties default_reader_properties() + + cdef cppclass ArrowReaderProperties: + ArrowReaderProperties() + void set_binary_type(Type binary_type) + Type binary_type() + void set_list_type(Type list_type) + Type list_type() + void set_read_dictionary(int column_index, c_bool read_dict) + c_bool read_dictionary(int column_index) + void set_batch_size(int64_t batch_size) + int64_t batch_size() + void set_pre_buffer(c_bool pre_buffer) + c_bool pre_buffer() const + void set_cache_options(CCacheOptions options) + CCacheOptions cache_options() const + void set_coerce_int96_timestamp_unit(TimeUnit unit) + TimeUnit coerce_int96_timestamp_unit() const + void set_arrow_extensions_enabled(c_bool extensions_enabled) + c_bool get_arrow_extensions_enabled() const + + ArrowReaderProperties default_arrow_reader_properties() + + cdef cppclass ParquetFileReader: + shared_ptr[CFileMetaData] metadata() + + +cdef extern from "parquet/api/writer.h" namespace "parquet" nogil: + cdef cppclass CdcOptions: + int64_t min_chunk_size + int64_t max_chunk_size + int norm_level + + cdef cppclass WriterProperties: + cppclass Builder: + Builder* data_page_version(ParquetDataPageVersion version) + Builder* version(ParquetVersion version) + Builder* compression(ParquetCompression codec) + Builder* compression(const c_string& path, + ParquetCompression codec) + Builder* compression_level(int compression_level) + Builder* compression_level(const c_string& path, + int compression_level) + Builder* encryption( + shared_ptr[CFileEncryptionProperties] + file_encryption_properties) + Builder* disable_dictionary() + Builder* enable_dictionary() + Builder* enable_dictionary(const c_string& path) + Builder* set_sorting_columns(vector[CSortingColumn] sorting_columns) + Builder* disable_statistics() + Builder* enable_statistics() + Builder* enable_statistics(const c_string& path) + Builder* enable_store_decimal_as_integer() + Builder* disable_store_decimal_as_integer() + Builder* data_pagesize(int64_t size) + Builder* max_rows_per_page(int64_t max_rows) + Builder* encoding(ParquetEncoding encoding) + Builder* encoding(const c_string& path, + ParquetEncoding encoding) + Builder* max_row_group_length(int64_t size) + Builder* write_batch_size(int64_t batch_size) + Builder* dictionary_pagesize_limit(int64_t dictionary_pagesize_limit) + Builder* enable_write_page_index() + Builder* disable_write_page_index() + Builder* enable_page_checksum() + Builder* disable_page_checksum() + Builder* enable_content_defined_chunking() + Builder* disable_content_defined_chunking() + Builder* content_defined_chunking_options(CdcOptions options) + shared_ptr[WriterProperties] build() + + cdef cppclass ArrowWriterProperties: + cppclass Builder: + Builder() + Builder* disable_deprecated_int96_timestamps() + Builder* enable_deprecated_int96_timestamps() + Builder* coerce_timestamps(TimeUnit unit) + Builder* allow_truncated_timestamps() + Builder* disallow_truncated_timestamps() + Builder* store_schema() + Builder* enable_compliant_nested_types() + Builder* disable_compliant_nested_types() + Builder* set_engine_version(ArrowWriterEngineVersion version) + Builder* set_time_adjusted_to_utc(c_bool adjusted) + shared_ptr[ArrowWriterProperties] build() + c_bool support_deprecated_int96_timestamps() + +cdef extern from "parquet/arrow/reader.h" namespace "parquet::arrow" nogil: + cdef cppclass FileReader: + FileReader(CMemoryPool* pool, unique_ptr[ParquetFileReader] reader) + + CStatus GetSchema(shared_ptr[CSchema]* out) + + CStatus ReadColumn(int i, shared_ptr[CChunkedArray]* out) + CStatus ReadSchemaField(int i, shared_ptr[CChunkedArray]* out) + + int num_row_groups() + CStatus ReadRowGroup(int i, shared_ptr[CTable]* out) + CStatus ReadRowGroup(int i, const vector[int]& column_indices, + shared_ptr[CTable]* out) + + CStatus ReadRowGroups(const vector[int]& row_groups, + shared_ptr[CTable]* out) + CStatus ReadRowGroups(const vector[int]& row_groups, + const vector[int]& column_indices, + shared_ptr[CTable]* out) + + CResult[unique_ptr[CRecordBatchReader]] GetRecordBatchReader(const vector[int]& row_group_indices, + const vector[int]& column_indices) + CResult[unique_ptr[CRecordBatchReader]] GetRecordBatchReader(const vector[int]& row_group_indices) + + CStatus ReadTable(shared_ptr[CTable]* out) + CStatus ReadTable(const vector[int]& column_indices, + shared_ptr[CTable]* out) + + CStatus ScanContents(vector[int] columns, int32_t column_batch_size, + int64_t* num_rows) + + const ParquetFileReader* parquet_reader() + + void set_use_threads(c_bool use_threads) + + void set_batch_size(int64_t batch_size) + + cdef cppclass FileReaderBuilder: + FileReaderBuilder() + CStatus Open(const shared_ptr[CRandomAccessFile]& file, + const CReaderProperties& properties, + const shared_ptr[CFileMetaData]& metadata) + + ParquetFileReader* raw_reader() + FileReaderBuilder* memory_pool(CMemoryPool*) + FileReaderBuilder* properties(const ArrowReaderProperties&) + CStatus Build(unique_ptr[FileReader]* out) + + CStatus FromParquetSchema( + const SchemaDescriptor* parquet_schema, + const ArrowReaderProperties& properties, + const shared_ptr[const CKeyValueMetadata]& key_value_metadata, + shared_ptr[CSchema]* out) + + CStatus StatisticsAsScalars(const CStatistics& Statistics, + shared_ptr[CScalar]* min, + shared_ptr[CScalar]* max) + +cdef extern from "parquet/arrow/schema.h" namespace "parquet::arrow" nogil: + + CStatus ToParquetSchema( + const CSchema* arrow_schema, + const WriterProperties& properties, + const ArrowWriterProperties& arrow_properties, + shared_ptr[SchemaDescriptor]* out) + + +cdef extern from "parquet/properties.h" namespace "parquet" nogil: + cdef enum ArrowWriterEngineVersion: + V1 "parquet::ArrowWriterProperties::V1", + V2 "parquet::ArrowWriterProperties::V2" + + cdef cppclass ParquetDataPageVersion: + pass + + cdef ParquetDataPageVersion ParquetDataPageVersion_V1 \ + " parquet::ParquetDataPageVersion::V1" + cdef ParquetDataPageVersion ParquetDataPageVersion_V2 \ + " parquet::ParquetDataPageVersion::V2" + +cdef extern from "parquet/arrow/writer.h" namespace "parquet::arrow" nogil: + cdef cppclass FileWriter: + + @staticmethod + CResult[unique_ptr[FileWriter]] Open(const CSchema& schema, CMemoryPool* pool, + const shared_ptr[COutputStream]& sink, + const shared_ptr[WriterProperties]& properties, + const shared_ptr[ArrowWriterProperties]& arrow_properties) + + CStatus WriteTable(const CTable& table, int64_t chunk_size) + CStatus NewRowGroup() + CStatus Close() + CStatus AddKeyValueMetadata(const shared_ptr[const CKeyValueMetadata]& key_value_metadata) + + const shared_ptr[CFileMetaData] metadata() const + + CStatus WriteMetaDataFile( + const CFileMetaData& file_metadata, + const COutputStream* sink) + +cdef extern from "parquet/encryption/encryption.h" namespace "parquet" nogil: + cdef cppclass CFileDecryptionProperties\ + " parquet::FileDecryptionProperties": + pass + + cdef cppclass CFileEncryptionProperties\ + " parquet::FileEncryptionProperties": + pass diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libparquet_encryption.pxd b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libparquet_encryption.pxd new file mode 100644 index 0000000000000000000000000000000000000000..7024f14ac27e8ba1c65ac6a06d3bbb35bd75d886 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/includes/libparquet_encryption.pxd @@ -0,0 +1,187 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# distutils: language = c++ + +from pyarrow.includes.common cimport * +from pyarrow.includes.libarrow cimport CSecureString +from pyarrow.includes.libarrow_fs cimport CFileSystem +from pyarrow._parquet cimport (ParquetCipher, + CFileEncryptionProperties, + CFileDecryptionProperties, + ParquetCipher_AES_GCM_V1, + ParquetCipher_AES_GCM_CTR_V1) + + +cdef extern from "parquet/encryption/kms_client.h" \ + namespace "parquet::encryption" nogil: + cdef cppclass CKmsClient" parquet::encryption::KmsClient": + c_string WrapKey(const CSecureString& key, + const c_string& master_key_identifier) except + + CSecureString UnwrapKey(const c_string& wrapped_key, + const c_string& master_key_identifier) except + + + cdef cppclass CKeyAccessToken" parquet::encryption::KeyAccessToken": + CKeyAccessToken(const c_string value) + void Refresh(const c_string& new_value) + const c_string& value() const + + cdef cppclass CKmsConnectionConfig \ + " parquet::encryption::KmsConnectionConfig": + CKmsConnectionConfig() + c_string kms_instance_id + c_string kms_instance_url + shared_ptr[CKeyAccessToken] refreshable_key_access_token + unordered_map[c_string, c_string] custom_kms_conf + +# Callbacks for implementing Python kms clients +# Use typedef to emulate syntax for std::function +ctypedef void CallbackWrapKey( + object, const CSecureString&, const c_string&, c_string*) +ctypedef void CallbackUnwrapKey( + object, const c_string&, const c_string&, CSecureString*) + +cdef extern from "parquet/encryption/kms_client_factory.h" \ + namespace "parquet::encryption" nogil: + cdef cppclass CKmsClientFactory" parquet::encryption::KmsClientFactory": + shared_ptr[CKmsClient] CreateKmsClient( + const CKmsConnectionConfig& kms_connection_config) except + + +# Callbacks for implementing Python kms client factories +# Use typedef to emulate syntax for std::function +ctypedef void CallbackCreateKmsClient( + object, + const CKmsConnectionConfig&, shared_ptr[CKmsClient]*) + +cdef extern from "parquet/encryption/crypto_factory.h" \ + namespace "parquet::encryption" nogil: + cdef cppclass CEncryptionConfiguration\ + " parquet::encryption::EncryptionConfiguration": + CEncryptionConfiguration(const c_string& footer_key) except + + c_string footer_key + c_string column_keys + c_bool uniform_encryption + ParquetCipher encryption_algorithm + c_bool plaintext_footer + c_bool double_wrapping + double cache_lifetime_seconds + c_bool internal_key_material + int32_t data_key_length_bits + + cdef cppclass CDecryptionConfiguration\ + " parquet::encryption::DecryptionConfiguration": + CDecryptionConfiguration() except + + double cache_lifetime_seconds + + cdef cppclass CCryptoFactory" parquet::encryption::CryptoFactory": + void RegisterKmsClientFactory( + shared_ptr[CKmsClientFactory] kms_client_factory) except + + shared_ptr[CFileEncryptionProperties] GetFileEncryptionProperties( + const CKmsConnectionConfig& kms_connection_config, + const CEncryptionConfiguration& encryption_config, + const c_string parquet_file_path, + const shared_ptr[CFileSystem] file_system) except +* + shared_ptr[CFileDecryptionProperties] GetFileDecryptionProperties( + const CKmsConnectionConfig& kms_connection_config, + const CDecryptionConfiguration& decryption_config, + const c_string parquet_file_path, + const shared_ptr[CFileSystem] file_system) except +* + void RemoveCacheEntriesForToken(const c_string& access_token) except + + void RemoveCacheEntriesForAllTokens() except + + void RotateMasterKeys(const CKmsConnectionConfig& kms_connection_config, + const c_string parquet_file_path, + const shared_ptr[CFileSystem] file_system, + c_bool double_wrapping, + double cache_lifetime_seconds) + +cdef extern from "parquet/encryption/file_key_material_store.h" \ + namespace "parquet::encryption" nogil: + cdef cppclass CFileKeyMaterialStore\ + "parquet::encryption::FileKeyMaterialStore": + @staticmethod + c_string GetKeyMaterial(c_string key_id_in_file) except + + vector[c_string] GetKeyIDSet() except + + +cdef extern from "parquet/encryption/file_system_key_material_store.h" \ + namespace "parquet::encryption" nogil: + cdef cppclass CFileSystemKeyMaterialStore\ + "parquet::encryption::FileSystemKeyMaterialStore": + + @staticmethod + shared_ptr[CFileSystemKeyMaterialStore] Make(c_string parquet_file_path, + shared_ptr[CFileSystem] file_system, + c_bool use_tmp_prefix) except + + + c_string GetKeyMaterial(c_string key_id_in_file) except + + + vector[c_string] GetKeyIDSet() except + + +cdef extern from "parquet/encryption/key_material.h" \ + namespace "parquet::encryption" nogil: + cdef cppclass CKeyMaterial "parquet::encryption::KeyMaterial": + @staticmethod + CKeyMaterial Parse(const c_string& key_material_string) + c_bool is_footer_key() + c_bool is_double_wrapped() + const c_string& master_key_id() + const c_string& wrapped_dek() + const c_string& kek_id() + const c_string& wrapped_kek() + const c_string& kms_instance_id() + const c_string& kms_instance_url() + +cdef extern from "arrow/python/parquet_encryption.h" \ + namespace "arrow::py::parquet::encryption" nogil: + cdef cppclass CPyKmsClientVtable \ + " arrow::py::parquet::encryption::PyKmsClientVtable": + CPyKmsClientVtable() + function[CallbackWrapKey] wrap_key + function[CallbackUnwrapKey] unwrap_key + + cdef cppclass CPyKmsClient\ + " arrow::py::parquet::encryption::PyKmsClient"(CKmsClient): + CPyKmsClient(object handler, CPyKmsClientVtable vtable) + + cdef cppclass CPyKmsClientFactoryVtable\ + " arrow::py::parquet::encryption::PyKmsClientFactoryVtable": + CPyKmsClientFactoryVtable() + function[CallbackCreateKmsClient] create_kms_client + + cdef cppclass CPyKmsClientFactory\ + " arrow::py::parquet::encryption::PyKmsClientFactory"( + CKmsClientFactory): + CPyKmsClientFactory(object handler, CPyKmsClientFactoryVtable vtable) + + cdef cppclass CPyCryptoFactory\ + " arrow::py::parquet::encryption::PyCryptoFactory"(CCryptoFactory): + CResult[shared_ptr[CFileEncryptionProperties]] \ + SafeGetFileEncryptionProperties( + const CKmsConnectionConfig& kms_connection_config, + const CEncryptionConfiguration& encryption_config, + const c_string parquet_file_path, + const shared_ptr[CFileSystem] filesystem) + CResult[shared_ptr[CFileDecryptionProperties]] \ + SafeGetFileDecryptionProperties( + const CKmsConnectionConfig& kms_connection_config, + const CDecryptionConfiguration& decryption_config, + const c_string parquet_file_path, + const shared_ptr[CFileSystem] filesystem) + CStatus SafeRotateMasterKeys(const CKmsConnectionConfig& kms_connection_config, + const c_string parquet_file_path, + const shared_ptr[CFileSystem] filesystem, + c_bool double_wrapping, + double cache_lifetime_seconds) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7ebe59b499c214dd82954bff84824cfea574b415 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/__init__.py @@ -0,0 +1,20 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# flake8: noqa + +from .from_dataframe import from_dataframe diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/buffer.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..1f537798130b9a77bc50e1040ea8046557974894 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/buffer.py @@ -0,0 +1,107 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +from __future__ import annotations +import enum + +import pyarrow as pa + + +class DlpackDeviceType(enum.IntEnum): + """Integer enum for device type codes matching DLPack.""" + + CPU = 1 + CUDA = 2 + CPU_PINNED = 3 + OPENCL = 4 + VULKAN = 7 + METAL = 8 + VPI = 9 + ROCM = 10 + + +class _PyArrowBuffer: + """ + Data in the buffer is guaranteed to be contiguous in memory. + + Note that there is no dtype attribute present, a buffer can be thought of + as simply a block of memory. However, if the column that the buffer is + attached to has a dtype that's supported by DLPack and ``__dlpack__`` is + implemented, then that dtype information will be contained in the return + value from ``__dlpack__``. + + This distinction is useful to support both data exchange via DLPack on a + buffer and (b) dtypes like variable-length strings which do not have a + fixed number of bytes per element. + """ + + def __init__(self, x: pa.Buffer, allow_copy: bool = True) -> None: + """ + Handle PyArrow Buffers. + """ + self._x = x + + @property + def bufsize(self) -> int: + """ + Buffer size in bytes. + """ + return self._x.size + + @property + def ptr(self) -> int: + """ + Pointer to start of the buffer as an integer. + """ + return self._x.address + + def __dlpack__(self): + """ + Produce DLPack capsule (see array API standard). + + Raises: + - TypeError : if the buffer contains unsupported dtypes. + - NotImplementedError : if DLPack support is not implemented + + Useful to have to connect to array libraries. Support optional because + it's not completely trivial to implement for a Python-only library. + """ + raise NotImplementedError("__dlpack__") + + def __dlpack_device__(self) -> tuple[DlpackDeviceType, int | None]: + """ + Device type and device ID for where the data in the buffer resides. + Uses device type codes matching DLPack. + Note: must be implemented even if ``__dlpack__`` is not. + """ + if self._x.is_cpu: + return (DlpackDeviceType.CPU, None) + else: + raise NotImplementedError("__dlpack_device__") + + def __repr__(self) -> str: + return ( + "PyArrowBuffer(" + + str( + { + "bufsize": self.bufsize, + "ptr": self.ptr, + "device": self.__dlpack_device__()[0].name, + } + ) + + ")" + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/column.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/column.py new file mode 100644 index 0000000000000000000000000000000000000000..ddbceabcb00ade7a3d760f4e1663819571584d8e --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/column.py @@ -0,0 +1,529 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +from __future__ import annotations + +import enum +from typing import ( + Any, + Dict, + Iterable, + Optional, + Tuple, +) + +import sys +if sys.version_info >= (3, 8): + from typing import TypedDict +else: + from typing_extensions import TypedDict + +import pyarrow as pa +import pyarrow.compute as pc +from pyarrow.interchange.buffer import _PyArrowBuffer + + +class DtypeKind(enum.IntEnum): + """ + Integer enum for data types. + + Attributes + ---------- + INT : int + Matches to signed integer data type. + UINT : int + Matches to unsigned integer data type. + FLOAT : int + Matches to floating point data type. + BOOL : int + Matches to boolean data type. + STRING : int + Matches to string data type (UTF-8 encoded). + DATETIME : int + Matches to datetime data type. + CATEGORICAL : int + Matches to categorical data type. + """ + + INT = 0 + UINT = 1 + FLOAT = 2 + BOOL = 20 + STRING = 21 # UTF-8 + DATETIME = 22 + CATEGORICAL = 23 + + +Dtype = Tuple[DtypeKind, int, str, str] # see Column.dtype + + +_PYARROW_KINDS = { + pa.int8(): (DtypeKind.INT, "c"), + pa.int16(): (DtypeKind.INT, "s"), + pa.int32(): (DtypeKind.INT, "i"), + pa.int64(): (DtypeKind.INT, "l"), + pa.uint8(): (DtypeKind.UINT, "C"), + pa.uint16(): (DtypeKind.UINT, "S"), + pa.uint32(): (DtypeKind.UINT, "I"), + pa.uint64(): (DtypeKind.UINT, "L"), + pa.float16(): (DtypeKind.FLOAT, "e"), + pa.float32(): (DtypeKind.FLOAT, "f"), + pa.float64(): (DtypeKind.FLOAT, "g"), + pa.bool_(): (DtypeKind.BOOL, "b"), + pa.string(): (DtypeKind.STRING, "u"), + pa.large_string(): (DtypeKind.STRING, "U"), +} + + +class ColumnNullType(enum.IntEnum): + """ + Integer enum for null type representation. + + Attributes + ---------- + NON_NULLABLE : int + Non-nullable column. + USE_NAN : int + Use explicit float NaN value. + USE_SENTINEL : int + Sentinel value besides NaN. + USE_BITMASK : int + The bit is set/unset representing a null on a certain position. + USE_BYTEMASK : int + The byte is set/unset representing a null on a certain position. + """ + + NON_NULLABLE = 0 + USE_NAN = 1 + USE_SENTINEL = 2 + USE_BITMASK = 3 + USE_BYTEMASK = 4 + + +class ColumnBuffers(TypedDict): + # first element is a buffer containing the column data; + # second element is the data buffer's associated dtype + data: Tuple[_PyArrowBuffer, Dtype] + + # first element is a buffer containing mask values indicating missing data; + # second element is the mask value buffer's associated dtype. + # None if the null representation is not a bit or byte mask + validity: Optional[Tuple[_PyArrowBuffer, Dtype]] + + # first element is a buffer containing the offset values for + # variable-size binary data (e.g., variable-length strings); + # second element is the offsets buffer's associated dtype. + # None if the data buffer does not have an associated offsets buffer + offsets: Optional[Tuple[_PyArrowBuffer, Dtype]] + + +class CategoricalDescription(TypedDict): + # whether the ordering of dictionary indices is semantically meaningful + is_ordered: bool + # whether a dictionary-style mapping of categorical values to other objects + # exists + is_dictionary: bool + # Python-level only (e.g. ``{int: str}``). + # None if not a dictionary-style categorical. + categories: Optional[_PyArrowColumn] + + +class Endianness: + """Enum indicating the byte-order of a data-type.""" + + LITTLE = "<" + BIG = ">" + NATIVE = "=" + NA = "|" + + +class NoBufferPresent(Exception): + """Exception to signal that there is no requested buffer.""" + + +class _PyArrowColumn: + """ + A column object, with only the methods and properties required by the + interchange protocol defined. + + A column can contain one or more chunks. Each chunk can contain up to three + buffers - a data buffer, a mask buffer (depending on null representation), + and an offsets buffer (if variable-size binary; e.g., variable-length + strings). + + TBD: Arrow has a separate "null" dtype, and has no separate mask concept. + Instead, it seems to use "children" for both columns with a bit mask, + and for nested dtypes. Unclear whether this is elegant or confusing. + This design requires checking the null representation explicitly. + + The Arrow design requires checking: + 1. the ARROW_FLAG_NULLABLE (for sentinel values) + 2. if a column has two children, combined with one of those children + having a null dtype. + + Making the mask concept explicit seems useful. One null dtype would + not be enough to cover both bit and byte masks, so that would mean + even more checking if we did it the Arrow way. + + TBD: there's also the "chunk" concept here, which is implicit in Arrow as + multiple buffers per array (= column here). Semantically it may make + sense to have both: chunks were meant for example for lazy evaluation + of data which doesn't fit in memory, while multiple buffers per column + could also come from doing a selection operation on a single + contiguous buffer. + + Given these concepts, one would expect chunks to be all of the same + size (say a 10,000 row dataframe could have 10 chunks of 1,000 rows), + while multiple buffers could have data-dependent lengths. Not an issue + in pandas if one column is backed by a single NumPy array, but in + Arrow it seems possible. + Are multiple chunks *and* multiple buffers per column necessary for + the purposes of this interchange protocol, or must producers either + reuse the chunk concept for this or copy the data? + + Note: this Column object can only be produced by ``__dataframe__``, so + doesn't need its own version or ``__column__`` protocol. + """ + + def __init__( + self, column: pa.Array | pa.ChunkedArray, allow_copy: bool = True + ) -> None: + """ + Handles PyArrow Arrays and ChunkedArrays. + """ + # Store the column as a private attribute + if isinstance(column, pa.ChunkedArray): + if column.num_chunks == 1: + column = column.chunk(0) + else: + if not allow_copy: + raise RuntimeError( + "Chunks will be combined and a copy is required which " + "is forbidden by allow_copy=False" + ) + column = column.combine_chunks() + + self._allow_copy = allow_copy + + if pa.types.is_boolean(column.type): + if not allow_copy: + raise RuntimeError( + "Boolean column will be casted to uint8 and a copy " + "is required which is forbidden by allow_copy=False" + ) + self._dtype = self._dtype_from_arrowdtype(column.type, 8) + self._col = pc.cast(column, pa.uint8()) + else: + self._col = column + dtype = self._col.type + try: + bit_width = dtype.bit_width + except ValueError: + # in case of a variable-length strings, considered as array + # of bytes (8 bits) + bit_width = 8 + self._dtype = self._dtype_from_arrowdtype(dtype, bit_width) + + def size(self) -> int: + """ + Size of the column, in elements. + + Corresponds to DataFrame.num_rows() if column is a single chunk; + equal to size of this current chunk otherwise. + + Is a method rather than a property because it may cause a (potentially + expensive) computation for some dataframe implementations. + """ + return len(self._col) + + @property + def offset(self) -> int: + """ + Offset of first element. + + May be > 0 if using chunks; for example for a column with N chunks of + equal size M (only the last chunk may be shorter), + ``offset = n * M``, ``n = 0 .. N-1``. + """ + return self._col.offset + + @property + def dtype(self) -> Tuple[DtypeKind, int, str, str]: + """ + Dtype description as a tuple ``(kind, bit-width, format string, + endianness)``. + + Bit-width : the number of bits as an integer + Format string : data type description format string in Apache Arrow C + Data Interface format. + Endianness : current only native endianness (``=``) is supported + + Notes: + - Kind specifiers are aligned with DLPack where possible (hence the + jump to 20, leave enough room for future extension) + - Masks must be specified as boolean with either bit width 1 (for + bit masks) or 8 (for byte masks). + - Dtype width in bits was preferred over bytes + - Endianness isn't too useful, but included now in case in the + future we need to support non-native endianness + - Went with Apache Arrow format strings over NumPy format strings + because they're more complete from a dataframe perspective + - Format strings are mostly useful for datetime specification, and + for categoricals. + - For categoricals, the format string describes the type of the + categorical in the data buffer. In case of a separate encoding of + the categorical (e.g. an integer to string mapping), this can + be derived from ``self.describe_categorical``. + - Data types not included: complex, Arrow-style null, binary, + decimal, and nested (list, struct, map, union) dtypes. + """ + return self._dtype + + def _dtype_from_arrowdtype( + self, dtype: pa.DataType, bit_width: int + ) -> Tuple[DtypeKind, int, str, str]: + """ + See `self.dtype` for details. + """ + # Note: 'c' (complex) not handled yet (not in array spec v1). + # 'b', 'B' (bytes), 'S', 'a', (old-style string) 'V' (void) + # not handled datetime and timedelta both map to datetime + # (is timedelta handled?) + + if pa.types.is_timestamp(dtype): + kind = DtypeKind.DATETIME + ts = dtype.unit[0] + tz = dtype.tz if dtype.tz else "" + f_string = f"ts{ts}:{tz}" + return kind, bit_width, f_string, Endianness.NATIVE + elif pa.types.is_dictionary(dtype): + kind = DtypeKind.CATEGORICAL + arr = self._col + indices_dtype = arr.indices.type + _, f_string = _PYARROW_KINDS.get(indices_dtype) + return kind, bit_width, f_string, Endianness.NATIVE + else: + kind, f_string = _PYARROW_KINDS.get(dtype, (None, None)) + if kind is None: + raise ValueError( + f"Data type {dtype} not supported by interchange protocol") + + return kind, bit_width, f_string, Endianness.NATIVE + + @property + def describe_categorical(self) -> CategoricalDescription: + """ + If the dtype is categorical, there are two options: + - There are only values in the data buffer. + - There is a separate non-categorical Column encoding categorical + values. + + Raises TypeError if the dtype is not categorical + + Returns the dictionary with description on how to interpret the + data buffer: + - "is_ordered" : bool, whether the ordering of dictionary indices + is semantically meaningful. + - "is_dictionary" : bool, whether a mapping of + categorical values to other objects exists + - "categories" : Column representing the (implicit) mapping of + indices to category values (e.g. an array of + cat1, cat2, ...). None if not a dictionary-style + categorical. + + TBD: are there any other in-memory representations that are needed? + """ + arr = self._col + if not pa.types.is_dictionary(arr.type): + raise TypeError( + "describe_categorical only works on a column with " + "categorical dtype!" + ) + + return { + "is_ordered": self._col.type.ordered, + "is_dictionary": True, + "categories": _PyArrowColumn(arr.dictionary), + } + + @property + def describe_null(self) -> Tuple[ColumnNullType, Any]: + """ + Return the missing value (or "null") representation the column dtype + uses, as a tuple ``(kind, value)``. + + Value : if kind is "sentinel value", the actual value. If kind is a bit + mask or a byte mask, the value (0 or 1) indicating a missing value. + None otherwise. + """ + # In case of no missing values, we need to set ColumnNullType to + # non nullable as in the current __dataframe__ protocol bit/byte masks + # cannot be None + if self.null_count == 0: + return ColumnNullType.NON_NULLABLE, None + else: + return ColumnNullType.USE_BITMASK, 0 + + @property + def null_count(self) -> int: + """ + Number of null elements, if known. + + Note: Arrow uses -1 to indicate "unknown", but None seems cleaner. + """ + arrow_null_count = self._col.null_count + n = arrow_null_count if arrow_null_count != -1 else None + return n + + @property + def metadata(self) -> Dict[str, Any]: + """ + The metadata for the column. See `DataFrame.metadata` for more details. + """ + pass + + def num_chunks(self) -> int: + """ + Return the number of chunks the column consists of. + """ + return 1 + + def get_chunks( + self, n_chunks: Optional[int] = None + ) -> Iterable[_PyArrowColumn]: + """ + Return an iterator yielding the chunks. + + See `DataFrame.get_chunks` for details on ``n_chunks``. + """ + if n_chunks and n_chunks > 1: + chunk_size = self.size() // n_chunks + if self.size() % n_chunks != 0: + chunk_size += 1 + + array = self._col + i = 0 + for start in range(0, chunk_size * n_chunks, chunk_size): + yield _PyArrowColumn( + array.slice(start, chunk_size), self._allow_copy + ) + i += 1 + else: + yield self + + def get_buffers(self) -> ColumnBuffers: + """ + Return a dictionary containing the underlying buffers. + + The returned dictionary has the following contents: + + - "data": a two-element tuple whose first element is a buffer + containing the data and whose second element is the data + buffer's associated dtype. + - "validity": a two-element tuple whose first element is a buffer + containing mask values indicating missing data and + whose second element is the mask value buffer's + associated dtype. None if the null representation is + not a bit or byte mask. + - "offsets": a two-element tuple whose first element is a buffer + containing the offset values for variable-size binary + data (e.g., variable-length strings) and whose second + element is the offsets buffer's associated dtype. None + if the data buffer does not have an associated offsets + buffer. + """ + buffers: ColumnBuffers = { + "data": self._get_data_buffer(), + "validity": None, + "offsets": None, + } + + try: + buffers["validity"] = self._get_validity_buffer() + except NoBufferPresent: + pass + + try: + buffers["offsets"] = self._get_offsets_buffer() + except NoBufferPresent: + pass + + return buffers + + def _get_data_buffer( + self, + ) -> Tuple[_PyArrowBuffer, Any]: # Any is for self.dtype tuple + """ + Return the buffer containing the data and the buffer's + associated dtype. + """ + array = self._col + dtype = self.dtype + + # In case of dictionary arrays, use indices + # to define a buffer, codes are transferred through + # describe_categorical() + if pa.types.is_dictionary(array.type): + array = array.indices + dtype = _PyArrowColumn(array).dtype + + n = len(array.buffers()) + if n == 2: + return _PyArrowBuffer(array.buffers()[1]), dtype + elif n == 3: + return _PyArrowBuffer(array.buffers()[2]), dtype + + def _get_validity_buffer(self) -> Tuple[_PyArrowBuffer, Any]: + """ + Return the buffer containing the mask values indicating missing data + and the buffer's associated dtype. + Raises NoBufferPresent if null representation is not a bit or byte + mask. + """ + # Define the dtype of the returned buffer + dtype = (DtypeKind.BOOL, 1, "b", Endianness.NATIVE) + array = self._col + buff = array.buffers()[0] + if buff: + return _PyArrowBuffer(buff), dtype + else: + raise NoBufferPresent( + "There are no missing values so " + "does not have a separate mask") + + def _get_offsets_buffer(self) -> Tuple[_PyArrowBuffer, Any]: + """ + Return the buffer containing the offset values for variable-size binary + data (e.g., variable-length strings) and the buffer's associated dtype. + Raises NoBufferPresent if the data buffer does not have an associated + offsets buffer. + """ + array = self._col + n = len(array.buffers()) + if n == 2: + raise NoBufferPresent( + "This column has a fixed-length dtype so " + "it does not have an offsets buffer" + ) + elif n == 3: + # Define the dtype of the returned buffer + dtype = self._col.type + if pa.types.is_large_string(dtype): + dtype = (DtypeKind.INT, 64, "l", Endianness.NATIVE) + else: + dtype = (DtypeKind.INT, 32, "i", Endianness.NATIVE) + return _PyArrowBuffer(array.buffers()[1]), dtype diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/dataframe.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/dataframe.py new file mode 100644 index 0000000000000000000000000000000000000000..59ba765c175ad471274a99bf857c8880a072e0b8 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/dataframe.py @@ -0,0 +1,217 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +from __future__ import annotations +from typing import ( + Any, + Iterable, + Optional, + Sequence, +) + +import pyarrow as pa + +from pyarrow.interchange.column import _PyArrowColumn + + +class _PyArrowDataFrame: + """ + A data frame class, with only the methods required by the interchange + protocol defined. + + A "data frame" represents an ordered collection of named columns. + A column's "name" must be a unique string. + Columns may be accessed by name or by position. + + This could be a public data frame class, or an object with the methods and + attributes defined on this DataFrame class could be returned from the + ``__dataframe__`` method of a public data frame class in a library adhering + to the dataframe interchange protocol specification. + """ + + def __init__( + self, df: pa.Table | pa.RecordBatch, + nan_as_null: bool = False, + allow_copy: bool = True + ) -> None: + """ + Constructor - an instance of this (private) class is returned from + `pa.Table.__dataframe__` or `pa.RecordBatch.__dataframe__`. + """ + self._df = df + # ``nan_as_null`` is a keyword intended for the consumer to tell the + # producer to overwrite null values in the data with ``NaN`` (or + # ``NaT``). + if nan_as_null is True: + raise RuntimeError( + "nan_as_null=True currently has no effect, " + "use the default nan_as_null=False" + ) + self._nan_as_null = nan_as_null + self._allow_copy = allow_copy + + def __dataframe__( + self, nan_as_null: bool = False, allow_copy: bool = True + ) -> _PyArrowDataFrame: + """ + Construct a new exchange object, potentially changing the parameters. + ``nan_as_null`` is a keyword intended for the consumer to tell the + producer to overwrite null values in the data with ``NaN``. + It is intended for cases where the consumer does not support the bit + mask or byte mask that is the producer's native representation. + ``allow_copy`` is a keyword that defines whether or not the library is + allowed to make a copy of the data. For example, copying data would be + necessary if a library supports strided buffers, given that this + protocol specifies contiguous buffers. + """ + return _PyArrowDataFrame(self._df, nan_as_null, allow_copy) + + @property + def metadata(self) -> dict[str, Any]: + """ + The metadata for the data frame, as a dictionary with string keys. The + contents of `metadata` may be anything, they are meant for a library + to store information that it needs to, e.g., roundtrip losslessly or + for two implementations to share data that is not (yet) part of the + interchange protocol specification. For avoiding collisions with other + entries, please add name the keys with the name of the library + followed by a period and the desired name, e.g, ``pandas.indexcol``. + """ + # The metadata for the data frame, as a dictionary with string keys. + # Add schema metadata here (pandas metadata or custom metadata) + if self._df.schema.metadata: + schema_metadata = {"pyarrow." + k.decode('utf8'): v.decode('utf8') + for k, v in self._df.schema.metadata.items()} + return schema_metadata + else: + return {} + + def num_columns(self) -> int: + """ + Return the number of columns in the DataFrame. + """ + return self._df.num_columns + + def num_rows(self) -> int: + """ + Return the number of rows in the DataFrame, if available. + """ + return self._df.num_rows + + def num_chunks(self) -> int: + """ + Return the number of chunks the DataFrame consists of. + """ + if isinstance(self._df, pa.RecordBatch): + return 1 + else: + # pyarrow.Table can have columns with different number + # of chunks so we take the number of chunks that + # .to_batches() returns as it takes the min chunk size + # of all the columns (to_batches is a zero copy method) + batches = self._df.to_batches() + return len(batches) + + def column_names(self) -> Iterable[str]: + """ + Return an iterator yielding the column names. + """ + return self._df.schema.names + + def get_column(self, i: int) -> _PyArrowColumn: + """ + Return the column at the indicated position. + """ + return _PyArrowColumn(self._df.column(i), + allow_copy=self._allow_copy) + + def get_column_by_name(self, name: str) -> _PyArrowColumn: + """ + Return the column whose name is the indicated name. + """ + return _PyArrowColumn(self._df.column(name), + allow_copy=self._allow_copy) + + def get_columns(self) -> Iterable[_PyArrowColumn]: + """ + Return an iterator yielding the columns. + """ + return [ + _PyArrowColumn(col, allow_copy=self._allow_copy) + for col in self._df.columns + ] + + def select_columns(self, indices: Sequence[int]) -> _PyArrowDataFrame: + """ + Create a new DataFrame by selecting a subset of columns by index. + """ + return _PyArrowDataFrame( + self._df.select(list(indices)), self._nan_as_null, self._allow_copy + ) + + def select_columns_by_name( + self, names: Sequence[str] + ) -> _PyArrowDataFrame: + """ + Create a new DataFrame by selecting a subset of columns by name. + """ + return _PyArrowDataFrame( + self._df.select(list(names)), self._nan_as_null, self._allow_copy + ) + + def get_chunks( + self, n_chunks: Optional[int] = None + ) -> Iterable[_PyArrowDataFrame]: + """ + Return an iterator yielding the chunks. + + By default (None), yields the chunks that the data is stored as by the + producer. If given, ``n_chunks`` must be a multiple of + ``self.num_chunks()``, meaning the producer must subdivide each chunk + before yielding it. + + Note that the producer must ensure that all columns are chunked the + same way. + """ + # Subdivide chunks + if n_chunks and n_chunks > 1: + chunk_size = self.num_rows() // n_chunks + if self.num_rows() % n_chunks != 0: + chunk_size += 1 + if isinstance(self._df, pa.Table): + batches = self._df.to_batches(max_chunksize=chunk_size) + else: + batches = [] + for start in range(0, chunk_size * n_chunks, chunk_size): + batches.append(self._df.slice(start, chunk_size)) + # In case when the size of the chunk is such that the resulting + # list is one less chunk then n_chunks -> append an empty chunk + if len(batches) == n_chunks - 1: + batches.append(pa.record_batch([[]], schema=self._df.schema)) + # yields the chunks that the data is stored as + else: + if isinstance(self._df, pa.Table): + batches = self._df.to_batches() + else: + batches = [self._df] + + # Create an iterator of RecordBatches + iterator = [_PyArrowDataFrame(batch, + self._nan_as_null, + self._allow_copy) + for batch in batches] + return iterator diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/from_dataframe.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/from_dataframe.py new file mode 100644 index 0000000000000000000000000000000000000000..fcaec41e3dcdf982e19bd45ba4a1941fab5ec34e --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/interchange/from_dataframe.py @@ -0,0 +1,614 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +from __future__ import annotations + +from typing import ( + Any, + Tuple, +) + +from pyarrow.interchange.column import ( + DtypeKind, + ColumnBuffers, + ColumnNullType, +) + +import pyarrow as pa +import re + +import pyarrow.compute as pc +from pyarrow.interchange.column import Dtype + + +# A typing protocol could be added later to let Mypy validate code using +# `from_dataframe` better. +DataFrameObject = Any +ColumnObject = Any +BufferObject = Any + + +_PYARROW_DTYPES: dict[DtypeKind, dict[int, Any]] = { + DtypeKind.INT: {8: pa.int8(), + 16: pa.int16(), + 32: pa.int32(), + 64: pa.int64()}, + DtypeKind.UINT: {8: pa.uint8(), + 16: pa.uint16(), + 32: pa.uint32(), + 64: pa.uint64()}, + DtypeKind.FLOAT: {16: pa.float16(), + 32: pa.float32(), + 64: pa.float64()}, + DtypeKind.BOOL: {1: pa.bool_(), + 8: pa.uint8()}, + DtypeKind.STRING: {8: pa.string()}, +} + + +def from_dataframe(df: DataFrameObject, allow_copy=True) -> pa.Table: + """ + Build a ``pa.Table`` from any DataFrame supporting the interchange protocol. + + Parameters + ---------- + df : DataFrameObject + Object supporting the interchange protocol, i.e. `__dataframe__` + method. + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.Table + + Examples + -------- + >>> import pyarrow + >>> from pyarrow.interchange import from_dataframe + + Convert a pandas dataframe to a pyarrow table: + + >>> import pandas as pd + >>> df = pd.DataFrame({ + ... "n_attendees": [100, 10, 1], + ... "country": ["Italy", "Spain", "Slovenia"], + ... }) + >>> df + n_attendees country + 0 100 Italy + 1 10 Spain + 2 1 Slovenia + >>> from_dataframe(df) + pyarrow.Table + n_attendees: int64 + country: large_string + ---- + n_attendees: [[100,10,1]] + country: [["Italy","Spain","Slovenia"]] + """ + if isinstance(df, pa.Table): + return df + elif isinstance(df, pa.RecordBatch): + return pa.Table.from_batches([df]) + + if not hasattr(df, "__dataframe__"): + raise ValueError("`df` does not support __dataframe__") + + return _from_dataframe(df.__dataframe__(allow_copy=allow_copy), + allow_copy=allow_copy) + + +def _from_dataframe(df: DataFrameObject, allow_copy=True): + """ + Build a ``pa.Table`` from the DataFrame interchange object. + + Parameters + ---------- + df : DataFrameObject + Object supporting the interchange protocol, i.e. `__dataframe__` + method. + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.Table + """ + batches = [] + for chunk in df.get_chunks(): + batch = protocol_df_chunk_to_pyarrow(chunk, allow_copy) + batches.append(batch) + + if not batches: + batch = protocol_df_chunk_to_pyarrow(df) + batches.append(batch) + + return pa.Table.from_batches(batches) + + +def protocol_df_chunk_to_pyarrow( + df: DataFrameObject, + allow_copy: bool = True +) -> pa.RecordBatch: + """ + Convert interchange protocol chunk to ``pa.RecordBatch``. + + Parameters + ---------- + df : DataFrameObject + Object supporting the interchange protocol, i.e. `__dataframe__` + method. + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.RecordBatch + """ + # We need a dict of columns here, with each column being a pa.Array + columns: dict[str, pa.Array] = {} + for name in df.column_names(): + if not isinstance(name, str): + raise ValueError(f"Column {name} is not a string") + if name in columns: + raise ValueError(f"Column {name} is not unique") + col = df.get_column_by_name(name) + dtype = col.dtype[0] + if dtype in ( + DtypeKind.INT, + DtypeKind.UINT, + DtypeKind.FLOAT, + DtypeKind.STRING, + DtypeKind.DATETIME, + ): + columns[name] = column_to_array(col, allow_copy) + elif dtype == DtypeKind.BOOL: + columns[name] = bool_column_to_array(col, allow_copy) + elif dtype == DtypeKind.CATEGORICAL: + columns[name] = categorical_column_to_dictionary(col, allow_copy) + else: + raise NotImplementedError(f"Data type {dtype} not handled yet") + + return pa.RecordBatch.from_pydict(columns) + + +def column_to_array( + col: ColumnObject, + allow_copy: bool = True, +) -> pa.Array: + """ + Convert a column holding one of the primitive dtypes to a PyArrow array. + A primitive type is one of: int, uint, float, bool (1 bit). + + Parameters + ---------- + col : ColumnObject + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.Array + """ + buffers = col.get_buffers() + data_type = col.dtype + data = buffers_to_array(buffers, data_type, + col.size(), + col.describe_null, + col.offset, + allow_copy) + return data + + +def bool_column_to_array( + col: ColumnObject, + allow_copy: bool = True, +) -> pa.Array: + """ + Convert a column holding boolean dtype to a PyArrow array. + + Parameters + ---------- + col : ColumnObject + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.Array + """ + buffers = col.get_buffers() + size = buffers["data"][1][1] + + # If booleans are byte-packed a copy to bit-packed will be made + if size == 8 and not allow_copy: + raise RuntimeError( + "Boolean column will be casted from uint8 and a copy " + "is required which is forbidden by allow_copy=False" + ) + + data_type = col.dtype + data = buffers_to_array(buffers, data_type, + col.size(), + col.describe_null, + col.offset) + if size == 8: + data = pc.cast(data, pa.bool_()) + + return data + + +def categorical_column_to_dictionary( + col: ColumnObject, + allow_copy: bool = True, +) -> pa.DictionaryArray: + """ + Convert a column holding categorical data to a pa.DictionaryArray. + + Parameters + ---------- + col : ColumnObject + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.DictionaryArray + """ + if not allow_copy: + raise RuntimeError( + "Categorical column will be casted from uint8 and a copy " + "is required which is forbidden by allow_copy=False" + ) + + categorical = col.describe_categorical + + if not categorical["is_dictionary"]: + raise NotImplementedError( + "Non-dictionary categoricals not supported yet") + + # We need to first convert the dictionary column + cat_column = categorical["categories"] + dictionary = column_to_array(cat_column) + # Then we need to convert the indices + # Here we need to use the buffer data type! + buffers = col.get_buffers() + _, data_type = buffers["data"] + indices = buffers_to_array(buffers, data_type, + col.size(), + col.describe_null, + col.offset) + + # Constructing a pa.DictionaryArray + dict_array = pa.DictionaryArray.from_arrays(indices, dictionary) + + return dict_array + + +def parse_datetime_format_str(format_str): + """Parse datetime `format_str` to interpret the `data`.""" + + # timestamp 'ts{unit}:tz' + timestamp_meta = re.match(r"ts([smun]):(.*)", format_str) + if timestamp_meta: + unit, tz = timestamp_meta.group(1), timestamp_meta.group(2) + if unit != "s": + # the format string describes only a first letter of the unit, so + # add one extra letter to convert the unit to numpy-style: + # 'm' -> 'ms', 'u' -> 'us', 'n' -> 'ns' + unit += "s" + + return unit, tz + + raise NotImplementedError(f"DateTime kind is not supported: {format_str}") + + +def map_date_type(data_type): + """Map column date type to pyarrow date type. """ + kind, bit_width, f_string, _ = data_type + + if kind == DtypeKind.DATETIME: + unit, tz = parse_datetime_format_str(f_string) + return pa.timestamp(unit, tz=tz) + else: + pa_dtype = _PYARROW_DTYPES.get(kind, {}).get(bit_width, None) + + # Error if dtype is not supported + if pa_dtype: + return pa_dtype + else: + raise NotImplementedError( + f"Conversion for {data_type} is not yet supported.") + + +def buffers_to_array( + buffers: ColumnBuffers, + data_type: Tuple[DtypeKind, int, str, str], + length: int, + describe_null: ColumnNullType, + offset: int = 0, + allow_copy: bool = True, +) -> pa.Array: + """ + Build a PyArrow array from the passed buffer. + + Parameters + ---------- + buffer : ColumnBuffers + Dictionary containing tuples of underlying buffers and + their associated dtype. + data_type : Tuple[DtypeKind, int, str, str], + Dtype description of the column as a tuple ``(kind, bit-width, format string, + endianness)``. + length : int + The number of values in the array. + describe_null: ColumnNullType + Null representation the column dtype uses, + as a tuple ``(kind, value)`` + offset : int, default: 0 + Number of elements to offset from the start of the buffer. + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.Array + + Notes + ----- + The returned array doesn't own the memory. The caller of this function + is responsible for keeping the memory owner object alive as long as + the returned PyArrow array is being used. + """ + data_buff, _ = buffers["data"] + try: + validity_buff, validity_dtype = buffers["validity"] + except TypeError: + validity_buff = None + try: + offset_buff, offset_dtype = buffers["offsets"] + except TypeError: + offset_buff = None + + # Construct a pyarrow Buffer + data_pa_buffer = pa.foreign_buffer(data_buff.ptr, data_buff.bufsize, + base=data_buff) + + # Construct a validity pyarrow Buffer, if applicable + if validity_buff: + validity_pa_buff = validity_buffer_from_mask(validity_buff, + validity_dtype, + describe_null, + length, + offset, + allow_copy) + else: + validity_pa_buff = validity_buffer_nan_sentinel(data_pa_buffer, + data_type, + describe_null, + length, + offset, + allow_copy) + + # Construct a pyarrow Array from buffers + data_dtype = map_date_type(data_type) + + if offset_buff: + _, offset_bit_width, _, _ = offset_dtype + # If an offset buffer exists, construct an offset pyarrow Buffer + # and add it to the construction of an array + offset_pa_buffer = pa.foreign_buffer(offset_buff.ptr, + offset_buff.bufsize, + base=offset_buff) + + if data_type[2] == 'U': + string_type = pa.large_string() + else: + if offset_bit_width == 64: + string_type = pa.large_string() + else: + string_type = pa.string() + array = pa.Array.from_buffers( + string_type, + length, + [validity_pa_buff, offset_pa_buffer, data_pa_buffer], + offset=offset, + ) + else: + array = pa.Array.from_buffers( + data_dtype, + length, + [validity_pa_buff, data_pa_buffer], + offset=offset, + ) + + return array + + +def validity_buffer_from_mask( + validity_buff: BufferObject, + validity_dtype: Dtype, + describe_null: ColumnNullType, + length: int, + offset: int = 0, + allow_copy: bool = True, +) -> pa.Buffer: + """ + Build a PyArrow buffer from the passed mask buffer. + + Parameters + ---------- + validity_buff : BufferObject + Tuple of underlying validity buffer and associated dtype. + validity_dtype : Dtype + Dtype description as a tuple ``(kind, bit-width, format string, + endianness)``. + describe_null : ColumnNullType + Null representation the column dtype uses, + as a tuple ``(kind, value)`` + length : int + The number of values in the array. + offset : int, default: 0 + Number of elements to offset from the start of the buffer. + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.Buffer + """ + null_kind, sentinel_val = describe_null + validity_kind, _, _, _ = validity_dtype + assert validity_kind == DtypeKind.BOOL + + if null_kind == ColumnNullType.NON_NULLABLE: + # Sliced array can have a NON_NULLABLE ColumnNullType due + # to no missing values in that slice of an array though the bitmask + # exists and validity_buff must be set to None in this case + return None + + elif null_kind == ColumnNullType.USE_BYTEMASK or ( + null_kind == ColumnNullType.USE_BITMASK and sentinel_val == 1 + ): + buff = pa.foreign_buffer(validity_buff.ptr, + validity_buff.bufsize, + base=validity_buff) + + if null_kind == ColumnNullType.USE_BYTEMASK: + if not allow_copy: + raise RuntimeError( + "To create a bitmask a copy of the data is " + "required which is forbidden by allow_copy=False" + ) + mask = pa.Array.from_buffers(pa.int8(), length, + [None, buff], + offset=offset) + mask_bool = pc.cast(mask, pa.bool_()) + else: + mask_bool = pa.Array.from_buffers(pa.bool_(), length, + [None, buff], + offset=offset) + + if sentinel_val == 1: + mask_bool = pc.invert(mask_bool) + + return mask_bool.buffers()[1] + + elif null_kind == ColumnNullType.USE_BITMASK and sentinel_val == 0: + return pa.foreign_buffer(validity_buff.ptr, + validity_buff.bufsize, + base=validity_buff) + else: + raise NotImplementedError( + f"{describe_null} null representation is not yet supported.") + + +def validity_buffer_nan_sentinel( + data_pa_buffer: BufferObject, + data_type: Dtype, + describe_null: ColumnNullType, + length: int, + offset: int = 0, + allow_copy: bool = True, +) -> pa.Buffer: + """ + Build a PyArrow buffer from NaN or sentinel values. + + Parameters + ---------- + data_pa_buffer : pa.Buffer + PyArrow buffer for the column data. + data_type : Dtype + Dtype description as a tuple ``(kind, bit-width, format string, + endianness)``. + describe_null : ColumnNullType + Null representation the column dtype uses, + as a tuple ``(kind, value)`` + length : int + The number of values in the array. + offset : int, default: 0 + Number of elements to offset from the start of the buffer. + allow_copy : bool, default: True + Whether to allow copying the memory to perform the conversion + (if false then zero-copy approach is requested). + + Returns + ------- + pa.Buffer + """ + kind, bit_width, _, _ = data_type + data_dtype = map_date_type(data_type) + null_kind, sentinel_val = describe_null + + # Check for float NaN values + if null_kind == ColumnNullType.USE_NAN: + if not allow_copy: + raise RuntimeError( + "To create a bitmask a copy of the data is " + "required which is forbidden by allow_copy=False" + ) + + if kind == DtypeKind.FLOAT and bit_width == 16: + # 'pyarrow.compute.is_nan' kernel not yet implemented + # for float16 + raise NotImplementedError( + f"{data_type} with {null_kind} is not yet supported.") + else: + pyarrow_data = pa.Array.from_buffers( + data_dtype, + length, + [None, data_pa_buffer], + offset=offset, + ) + mask = pc.is_nan(pyarrow_data) + mask = pc.invert(mask) + return mask.buffers()[1] + + # Check for sentinel values + elif null_kind == ColumnNullType.USE_SENTINEL: + if not allow_copy: + raise RuntimeError( + "To create a bitmask a copy of the data is " + "required which is forbidden by allow_copy=False" + ) + + if kind == DtypeKind.DATETIME: + sentinel_dtype = pa.int64() + else: + sentinel_dtype = data_dtype + pyarrow_data = pa.Array.from_buffers(sentinel_dtype, + length, + [None, data_pa_buffer], + offset=offset) + sentinel_arr = pc.equal(pyarrow_data, sentinel_val) + mask_bool = pc.invert(sentinel_arr) + return mask_bool.buffers()[1] + + elif null_kind == ColumnNullType.NON_NULLABLE: + pass + else: + raise NotImplementedError( + f"{describe_null} null representation is not yet supported.") diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/parquet/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/parquet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..134f3c097ef004f83fdc8e24e5cb45166c17577e --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/parquet/__init__.py @@ -0,0 +1,20 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# flake8: noqa + +from .core import * diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/parquet/core.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/parquet/core.py new file mode 100644 index 0000000000000000000000000000000000000000..676bc445238e1bead69620272f2405673a48e854 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/parquet/core.py @@ -0,0 +1,2454 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + + +from collections import defaultdict +from contextlib import nullcontext +from functools import reduce + +import inspect +import json +import os +import re +import operator + +import pyarrow as pa + +try: + import pyarrow._parquet as _parquet +except ImportError as exc: + raise ImportError( + "The pyarrow installation is not built with support " + f"for the Parquet file format ({str(exc)})" + ) from None + +from pyarrow._parquet import (ParquetReader, Statistics, # noqa + FileMetaData, RowGroupMetaData, + ColumnChunkMetaData, + ParquetSchema, ColumnSchema, + ParquetLogicalType, + FileEncryptionProperties, + FileDecryptionProperties, + SortingColumn) +from pyarrow.fs import (LocalFileSystem, FileType, _resolve_filesystem_and_path, + _ensure_filesystem) +from pyarrow.util import guid, _is_path_like, _stringify_path, _deprecate_api + + +def _check_contains_null(val): + if isinstance(val, bytes): + for byte in val: + if isinstance(byte, bytes): + compare_to = chr(0) + else: + compare_to = 0 + if byte == compare_to: + return True + elif isinstance(val, str): + return '\x00' in val + return False + + +def _check_filters(filters, check_null_strings=True): + """ + Check if filters are well-formed. + """ + if filters is not None: + if len(filters) == 0 or any(len(f) == 0 for f in filters): + raise ValueError("Malformed filters") + if isinstance(filters[0][0], str): + # We have encountered the situation where we have one nesting level + # too few: + # We have [(,,), ..] instead of [[(,,), ..]] + filters = [filters] + if check_null_strings: + for conjunction in filters: + for col, op, val in conjunction: + if ( + isinstance(val, list) and + all(_check_contains_null(v) for v in val) or + _check_contains_null(val) + ): + raise NotImplementedError( + "Null-terminated binary strings are not supported " + "as filter values." + ) + return filters + + +_DNF_filter_doc = """Predicates are expressed using an ``Expression`` or using + the disjunctive normal form (DNF), like ``[[('x', '=', 0), ...], ...]``. + DNF allows arbitrary boolean logical combinations of single column predicates. + The innermost tuples each describe a single column predicate. The list of inner + predicates is interpreted as a conjunction (AND), forming a more selective and + multiple column predicate. Finally, the most outer list combines these filters + as a disjunction (OR). + + Predicates may also be passed as List[Tuple]. This form is interpreted + as a single conjunction. To express OR in predicates, one must + use the (preferred) List[List[Tuple]] notation. + + Each tuple has format: (``key``, ``op``, ``value``) and compares the + ``key`` with the ``value``. + The supported ``op`` are: ``=`` or ``==``, ``!=``, ``<``, ``>``, ``<=``, + ``>=``, ``in`` and ``not in``. If the ``op`` is ``in`` or ``not in``, the + ``value`` must be a collection such as a ``list``, a ``set`` or a + ``tuple``. + + Examples: + + Using the ``Expression`` API: + + .. code-block:: python + + import pyarrow.compute as pc + pc.field('x') = 0 + pc.field('y').isin(['a', 'b', 'c']) + ~pc.field('y').isin({'a', 'b'}) + + Using the DNF format: + + .. code-block:: python + + ('x', '=', 0) + ('y', 'in', ['a', 'b', 'c']) + ('z', 'not in', {'a','b'}) + + """ + + +def filters_to_expression(filters): + """ + Check if filters are well-formed and convert to an ``Expression``. + + Parameters + ---------- + filters : List[Tuple] or List[List[Tuple]] + + Notes + ----- + See internal ``pyarrow._DNF_filter_doc`` attribute for more details. + + Examples + -------- + + >>> filters_to_expression([('foo', '==', 'bar')]) + + + Returns + ------- + pyarrow.compute.Expression + An Expression representing the filters + """ + import pyarrow.dataset as ds + + if isinstance(filters, ds.Expression): + return filters + + filters = _check_filters(filters, check_null_strings=False) + + def convert_single_predicate(col, op, val): + field = ds.field(col) + + if op == "=" or op == "==": + return field == val + elif op == "!=": + return field != val + elif op == '<': + return field < val + elif op == '>': + return field > val + elif op == '<=': + return field <= val + elif op == '>=': + return field >= val + elif op == 'in': + return field.isin(val) + elif op == 'not in': + return ~field.isin(val) + else: + raise ValueError(f'"{col}" is not a valid operator in predicates.') + + disjunction_members = [] + + for conjunction in filters: + conjunction_members = [ + convert_single_predicate(col, op, val) + for col, op, val in conjunction + ] + + disjunction_members.append(reduce(operator.and_, conjunction_members)) + + return reduce(operator.or_, disjunction_members) + + +_filters_to_expression = _deprecate_api( + "_filters_to_expression", "filters_to_expression", + filters_to_expression, "10.0.0", DeprecationWarning) + + +# ---------------------------------------------------------------------- +# Reading a single Parquet file + + +class ParquetFile: + """ + Reader interface for a single Parquet file. + + Parameters + ---------- + source : str, pathlib.Path, pyarrow.NativeFile, or file-like object + Readable source. For passing bytes or buffer-like file containing a + Parquet file, use pyarrow.BufferReader. + metadata : FileMetaData, default None + Use existing metadata object, rather than reading from file. + common_metadata : FileMetaData, default None + Will be used in reads for pandas schema metadata if not found in the + main file's metadata, no other uses at the moment. + read_dictionary : list + List of column names to read directly as DictionaryArray. + binary_type : pyarrow.DataType, default None + If given, Parquet binary columns will be read as this datatype. + This setting is ignored if a serialized Arrow schema is found in + the Parquet metadata. + list_type : subclass of pyarrow.DataType, default None + If given, non-MAP repeated columns will be read as an instance of + this datatype (either pyarrow.ListType or pyarrow.LargeListType). + This setting is ignored if a serialized Arrow schema is found in + the Parquet metadata. + memory_map : bool, default False + If the source is a file path, use a memory map to read file, which can + improve performance in some environments. + buffer_size : int, default 0 + If positive, perform read buffering when deserializing individual + column chunks. Otherwise IO calls are unbuffered. + pre_buffer : bool, default False + Coalesce and issue file reads in parallel to improve performance on + high-latency filesystems (e.g. S3). If True, Arrow will use a + background I/O thread pool. + coerce_int96_timestamp_unit : str, default None + Cast timestamps that are stored in INT96 format to a particular + resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' + and therefore INT96 timestamps will be inferred as timestamps + in nanoseconds. + decryption_properties : FileDecryptionProperties, default None + File decryption properties for Parquet Modular Encryption. + thrift_string_size_limit : int, default None + If not None, override the maximum total string size allocated + when decoding Thrift structures. The default limit should be + sufficient for most Parquet files. + thrift_container_size_limit : int, default None + If not None, override the maximum total size of containers allocated + when decoding Thrift structures. The default limit should be + sufficient for most Parquet files. + filesystem : FileSystem, default None + If nothing passed, will be inferred based on path. + Path will try to be found in the local on-disk filesystem otherwise + it will be parsed as an URI to determine the filesystem. + page_checksum_verification : bool, default False + If True, verify the checksum for each page read from the file. + arrow_extensions_enabled : bool, default True + If True, read Parquet logical types as Arrow extension types where possible, + (e.g., read JSON as the canonical `arrow.json` extension type or UUID as + the canonical `arrow.uuid` extension type). + + Examples + -------- + + Generate an example PyArrow Table and write it to Parquet file: + + >>> import pyarrow as pa + >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, 'example.parquet') + + Create a ``ParquetFile`` object from the Parquet file: + + >>> parquet_file = pq.ParquetFile('example.parquet') + + Read the data: + + >>> parquet_file.read() + pyarrow.Table + n_legs: int64 + animal: string + ---- + n_legs: [[2,2,4,4,5,100]] + animal: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]] + + Create a ParquetFile object with "animal" column as DictionaryArray: + + >>> parquet_file = pq.ParquetFile('example.parquet', + ... read_dictionary=["animal"]) + >>> parquet_file.read() + pyarrow.Table + n_legs: int64 + animal: dictionary + ---- + n_legs: [[2,2,4,4,5,100]] + animal: [ -- dictionary: + ["Flamingo","Parrot",...,"Brittle stars","Centipede"] -- indices: + [0,1,2,3,4,5]] + """ + + def __init__(self, source, *, metadata=None, common_metadata=None, + read_dictionary=None, binary_type=None, list_type=None, + memory_map=False, buffer_size=0, pre_buffer=False, + coerce_int96_timestamp_unit=None, + decryption_properties=None, thrift_string_size_limit=None, + thrift_container_size_limit=None, filesystem=None, + page_checksum_verification=False, arrow_extensions_enabled=True): + + self._close_source = getattr(source, 'closed', True) + + filesystem, source = _resolve_filesystem_and_path( + source, filesystem, memory_map=memory_map) + if filesystem is not None: + source = filesystem.open_input_file(source) + self._close_source = True # We opened it here, ensure we close it. + + self.reader = ParquetReader() + self.reader.open( + source, use_memory_map=memory_map, + buffer_size=buffer_size, pre_buffer=pre_buffer, + read_dictionary=read_dictionary, metadata=metadata, + binary_type=binary_type, list_type=list_type, + coerce_int96_timestamp_unit=coerce_int96_timestamp_unit, + decryption_properties=decryption_properties, + thrift_string_size_limit=thrift_string_size_limit, + thrift_container_size_limit=thrift_container_size_limit, + page_checksum_verification=page_checksum_verification, + arrow_extensions_enabled=arrow_extensions_enabled, + ) + self.common_metadata = common_metadata + self._nested_paths_by_prefix = self._build_nested_paths() + + def __enter__(self): + return self + + def __exit__(self, *args, **kwargs): + self.close() + + def _build_nested_paths(self): + paths = self.reader.column_paths + + result = defaultdict(list) + + for i, path in enumerate(paths): + key = path[0] + rest = path[1:] + while True: + result[key].append(i) + + if not rest: + break + + key = '.'.join((key, rest[0])) + rest = rest[1:] + + return result + + @property + def metadata(self): + """ + Return the Parquet metadata. + """ + return self.reader.metadata + + @property + def schema(self): + """ + Return the Parquet schema, unconverted to Arrow types + """ + return self.metadata.schema + + @property + def schema_arrow(self): + """ + Return the inferred Arrow schema, converted from the whole Parquet + file's schema + + Examples + -------- + Generate an example Parquet file: + + >>> import pyarrow as pa + >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, 'example.parquet') + >>> parquet_file = pq.ParquetFile('example.parquet') + + Read the Arrow schema: + + >>> parquet_file.schema_arrow + n_legs: int64 + animal: string + """ + return self.reader.schema_arrow + + @property + def num_row_groups(self): + """ + Return the number of row groups of the Parquet file. + + Examples + -------- + >>> import pyarrow as pa + >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, 'example.parquet') + >>> parquet_file = pq.ParquetFile('example.parquet') + + >>> parquet_file.num_row_groups + 1 + """ + return self.reader.num_row_groups + + def close(self, force: bool = False): + if self._close_source or force: + self.reader.close() + + @property + def closed(self) -> bool: + return self.reader.closed + + def read_row_group(self, i, columns=None, use_threads=True, + use_pandas_metadata=False): + """ + Read a single row group from a Parquet file. + + Parameters + ---------- + i : int + Index of the individual row group that we want to read. + columns : list + If not None, only these columns will be read from the row group. A + column name may be a prefix of a nested field, e.g. 'a' will select + 'a.b', 'a.c', and 'a.d.e'. + use_threads : bool, default True + Perform multi-threaded column reads. + use_pandas_metadata : bool, default False + If True and file has custom pandas schema metadata, ensure that + index columns are also loaded. + + Returns + ------- + pyarrow.table.Table + Content of the row group as a table (of columns) + + Examples + -------- + >>> import pyarrow as pa + >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, 'example.parquet') + >>> parquet_file = pq.ParquetFile('example.parquet') + + >>> parquet_file.read_row_group(0) + pyarrow.Table + n_legs: int64 + animal: string + ---- + n_legs: [[2,2,4,4,5,100]] + animal: [["Flamingo","Parrot",...,"Brittle stars","Centipede"]] + """ + column_indices = self._get_column_indices( + columns, use_pandas_metadata=use_pandas_metadata) + return self.reader.read_row_group(i, column_indices=column_indices, + use_threads=use_threads) + + def read_row_groups(self, row_groups, columns=None, use_threads=True, + use_pandas_metadata=False): + """ + Read a multiple row groups from a Parquet file. + + Parameters + ---------- + row_groups : list + Only these row groups will be read from the file. + columns : list + If not None, only these columns will be read from the row group. A + column name may be a prefix of a nested field, e.g. 'a' will select + 'a.b', 'a.c', and 'a.d.e'. + use_threads : bool, default True + Perform multi-threaded column reads. + use_pandas_metadata : bool, default False + If True and file has custom pandas schema metadata, ensure that + index columns are also loaded. + + Returns + ------- + pyarrow.table.Table + Content of the row groups as a table (of columns). + + Examples + -------- + >>> import pyarrow as pa + >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, 'example.parquet') + >>> parquet_file = pq.ParquetFile('example.parquet') + + >>> parquet_file.read_row_groups([0,0]) + pyarrow.Table + n_legs: int64 + animal: string + ---- + n_legs: [[2,2,4,4,5,...,2,4,4,5,100]] + animal: [["Flamingo","Parrot","Dog",...,"Brittle stars","Centipede"]] + """ + column_indices = self._get_column_indices( + columns, use_pandas_metadata=use_pandas_metadata) + return self.reader.read_row_groups(row_groups, + column_indices=column_indices, + use_threads=use_threads) + + def iter_batches(self, batch_size=65536, row_groups=None, columns=None, + use_threads=True, use_pandas_metadata=False): + """ + Read streaming batches from a Parquet file. + + Parameters + ---------- + batch_size : int, default 64K + Maximum number of records to yield per batch. Batches may be + smaller if there aren't enough rows in the file. + row_groups : list + Only these row groups will be read from the file. + columns : list + If not None, only these columns will be read from the file. A + column name may be a prefix of a nested field, e.g. 'a' will select + 'a.b', 'a.c', and 'a.d.e'. + use_threads : boolean, default True + Perform multi-threaded column reads. + use_pandas_metadata : boolean, default False + If True and file has custom pandas schema metadata, ensure that + index columns are also loaded. + + Yields + ------ + pyarrow.RecordBatch + Contents of each batch as a record batch + + Examples + -------- + Generate an example Parquet file: + + >>> import pyarrow as pa + >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, 'example.parquet') + >>> parquet_file = pq.ParquetFile('example.parquet') + >>> for i in parquet_file.iter_batches(): + ... print("RecordBatch") + ... print(i.to_pandas()) + ... + RecordBatch + n_legs animal + 0 2 Flamingo + 1 2 Parrot + 2 4 Dog + 3 4 Horse + 4 5 Brittle stars + 5 100 Centipede + """ + if batch_size <= 0: + raise ValueError("batch_size must be greater than zero") + + if row_groups is None: + row_groups = range(0, self.metadata.num_row_groups) + column_indices = self._get_column_indices( + columns, use_pandas_metadata=use_pandas_metadata) + + batches = self.reader.iter_batches(batch_size, + row_groups=row_groups, + column_indices=column_indices, + use_threads=use_threads) + return batches + + def read(self, columns=None, use_threads=True, use_pandas_metadata=False): + """ + Read a Table from Parquet format. + + Parameters + ---------- + columns : list + If not None, only these columns will be read from the file. A + column name may be a prefix of a nested field, e.g. 'a' will select + 'a.b', 'a.c', and 'a.d.e'. + use_threads : bool, default True + Perform multi-threaded column reads. + use_pandas_metadata : bool, default False + If True and file has custom pandas schema metadata, ensure that + index columns are also loaded. + + Returns + ------- + pyarrow.table.Table + Content of the file as a table (of columns). + + Examples + -------- + Generate an example Parquet file: + + >>> import pyarrow as pa + >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, 'example.parquet') + >>> parquet_file = pq.ParquetFile('example.parquet') + + Read a Table: + + >>> parquet_file.read(columns=["animal"]) + pyarrow.Table + animal: string + ---- + animal: [["Flamingo","Parrot",...,"Brittle stars","Centipede"]] + """ + column_indices = self._get_column_indices( + columns, use_pandas_metadata=use_pandas_metadata) + return self.reader.read_all(column_indices=column_indices, + use_threads=use_threads) + + def scan_contents(self, columns=None, batch_size=65536): + """ + Read contents of file for the given columns and batch size. + + Notes + ----- + This function's primary purpose is benchmarking. + The scan is executed on a single thread. + + Parameters + ---------- + columns : list of integers, default None + Select columns to read, if None scan all columns. + batch_size : int, default 64K + Number of rows to read at a time internally. + + Returns + ------- + num_rows : int + Number of rows in file + + Examples + -------- + >>> import pyarrow as pa + >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, 'example.parquet') + >>> parquet_file = pq.ParquetFile('example.parquet') + + >>> parquet_file.scan_contents() + 6 + """ + column_indices = self._get_column_indices(columns) + return self.reader.scan_contents(column_indices, + batch_size=batch_size) + + def _get_column_indices(self, column_names, use_pandas_metadata=False): + if column_names is None: + return None + + indices = [] + + for name in column_names: + if name in self._nested_paths_by_prefix: + indices.extend(self._nested_paths_by_prefix[name]) + + if use_pandas_metadata: + file_keyvalues = self.metadata.metadata + common_keyvalues = (self.common_metadata.metadata + if self.common_metadata is not None + else None) + + if file_keyvalues and b'pandas' in file_keyvalues: + index_columns = _get_pandas_index_columns(file_keyvalues) + elif common_keyvalues and b'pandas' in common_keyvalues: + index_columns = _get_pandas_index_columns(common_keyvalues) + else: + index_columns = [] + + if indices is not None and index_columns: + indices += [self.reader.column_name_idx(descr) + for descr in index_columns + if not isinstance(descr, dict)] + + return indices + + +_SPARK_DISALLOWED_CHARS = re.compile('[ ,;{}()\n\t=]') + + +def _sanitized_spark_field_name(name): + return _SPARK_DISALLOWED_CHARS.sub('_', name) + + +def _sanitize_schema(schema, flavor): + if 'spark' in flavor: + sanitized_fields = [] + + schema_changed = False + + for field in schema: + name = field.name + sanitized_name = _sanitized_spark_field_name(name) + + if sanitized_name != name: + schema_changed = True + sanitized_field = pa.field(sanitized_name, field.type, + field.nullable, field.metadata) + sanitized_fields.append(sanitized_field) + else: + sanitized_fields.append(field) + + new_schema = pa.schema(sanitized_fields, metadata=schema.metadata) + return new_schema, schema_changed + else: + return schema, False + + +def _sanitize_table(table, new_schema, flavor): + # TODO: This will not handle prohibited characters in nested field names + if 'spark' in flavor: + column_data = [table[i] for i in range(table.num_columns)] + return pa.Table.from_arrays(column_data, schema=new_schema) + else: + return table + + +_parquet_writer_arg_docs = """version : {"1.0", "2.4", "2.6"}, default "2.6" + Determine which Parquet logical types are available for use, whether the + reduced set from the Parquet 1.x.x format or the expanded logical types + added in later format versions. + Files written with version='2.4' or '2.6' may not be readable in all + Parquet implementations, so version='1.0' is likely the choice that + maximizes file compatibility. + UINT32 and some logical types are only available with version '2.4'. + Nanosecond timestamps are only available with version '2.6'. + Other features such as compression algorithms or the new serialized + data page format must be enabled separately (see 'compression' and + 'data_page_version'). +use_dictionary : bool or list, default True + Specify if we should use dictionary encoding in general or only for + some columns. + When encoding the column, if the dictionary size is too large, the + column will fallback to ``PLAIN`` encoding. Specially, ``BOOLEAN`` type + doesn't support dictionary encoding. +compression : str or dict, default 'snappy' + Specify the compression codec, either on a general basis or per-column. + Valid values: {'NONE', 'SNAPPY', 'GZIP', 'BROTLI', 'LZ4', 'ZSTD'}. +write_statistics : bool or list, default True + Specify if we should write statistics in general (default is True) or only + for some columns. +use_deprecated_int96_timestamps : bool, default None + Write timestamps to INT96 Parquet format. Defaults to False unless enabled + by flavor argument. This take priority over the coerce_timestamps option. +coerce_timestamps : str, default None + Cast timestamps to a particular resolution. If omitted, defaults are chosen + depending on `version`. For ``version='1.0'`` and ``version='2.4'``, + nanoseconds are cast to microseconds ('us'), while for + ``version='2.6'`` (the default), they are written natively without loss + of resolution. Seconds are always cast to milliseconds ('ms') by default, + as Parquet does not have any temporal type with seconds resolution. + If the casting results in loss of data, it will raise an exception + unless ``allow_truncated_timestamps=True`` is given. + Valid values: {None, 'ms', 'us'} +allow_truncated_timestamps : bool, default False + Allow loss of data when coercing timestamps to a particular + resolution. E.g. if microsecond or nanosecond data is lost when coercing to + 'ms', do not raise an exception. Passing ``allow_truncated_timestamp=True`` + will NOT result in the truncation exception being ignored unless + ``coerce_timestamps`` is not None. +data_page_size : int, default None + Set a target threshold for the approximate encoded size of data + pages within a column chunk (in bytes). If None, use the default data page + size of 1MByte. +max_rows_per_page : int, default None + Maximum number of rows per page within a column chunk. + If None, use the default of 20000. + Smaller values reduce memory usage during reads but increase metadata overhead. +flavor : {'spark'}, default None + Sanitize schema or set other compatibility options to work with + various target systems. +filesystem : FileSystem, default None + If nothing passed, will be inferred from `where` if path-like, else + `where` is already a file-like object so no filesystem is needed. +compression_level : int or dict, default None + Specify the compression level for a codec, either on a general basis or + per-column. If None is passed, arrow selects the compression level for + the compression codec in use. The compression level has a different + meaning for each codec, so you have to read the documentation of the + codec you are using. + An exception is thrown if the compression codec does not allow specifying + a compression level. +use_byte_stream_split : bool or list, default False + Specify if the byte_stream_split encoding should be used in general or + only for some columns. If both dictionary and byte_stream_stream are + enabled, then dictionary is preferred. + The byte_stream_split encoding is valid for integer, floating-point + and fixed-size binary data types (including decimals); it should be + combined with a compression codec so as to achieve size reduction. +column_encoding : string or dict, default None + Specify the encoding scheme on a per column basis. + Can only be used when ``use_dictionary`` is set to False, and + cannot be used in combination with ``use_byte_stream_split``. + Currently supported values: {'PLAIN', 'BYTE_STREAM_SPLIT', + 'DELTA_BINARY_PACKED', 'DELTA_LENGTH_BYTE_ARRAY', 'DELTA_BYTE_ARRAY'}. + Certain encodings are only compatible with certain data types. + Please refer to the encodings section of `Reading and writing Parquet + files `_. +data_page_version : {"1.0", "2.0"}, default "1.0" + The serialized Parquet data page format version to write, defaults to + 1.0. This does not impact the file schema logical types and Arrow to + Parquet type casting behavior; for that use the "version" option. +use_compliant_nested_type : bool, default True + Whether to write compliant Parquet nested type (lists) as defined + `here `_, defaults to ``True``. + For ``use_compliant_nested_type=True``, this will write into a list + with 3-level structure where the middle level, named ``list``, + is a repeated group with a single field named ``element``:: + + group (LIST) { + repeated group list { + element; + } + } + + For ``use_compliant_nested_type=False``, this will also write into a list + with 3-level structure, where the name of the single field of the middle + level ``list`` is taken from the element name for nested columns in Arrow, + which defaults to ``item``:: + + group (LIST) { + repeated group list { + item; + } + } +encryption_properties : FileEncryptionProperties, default None + File encryption properties for Parquet Modular Encryption. + If None, no encryption will be done. + The encryption properties can be created using: + ``CryptoFactory.file_encryption_properties()``. +write_batch_size : int, default None + Number of values to write to a page at a time. If None, use the default of + 1024. ``write_batch_size`` is complementary to ``data_page_size``. If pages + are exceeding the ``data_page_size`` due to large column values, lowering + the batch size can help keep page sizes closer to the intended size. +dictionary_pagesize_limit : int, default None + Specify the dictionary page size limit per row group. If None, use the + default 1MB. +store_schema : bool, default True + By default, the Arrow schema is serialized and stored in the Parquet + file metadata (in the "ARROW:schema" key). When reading the file, + if this key is available, it will be used to more faithfully recreate + the original Arrow data. For example, for tz-aware timestamp columns + it will restore the timezone (Parquet only stores the UTC values without + timezone), or columns with duration type will be restored from the int64 + Parquet column. +write_page_index : bool, default False + Whether to write a page index in general for all columns. + Writing statistics to the page index disables the old method of writing + statistics to each data page header. The page index makes statistics-based + filtering more efficient than the page header, as it gathers all the + statistics for a Parquet file in a single place, avoiding scattered I/O. + Note that the page index is not yet used on the read size by PyArrow. +write_page_checksum : bool, default False + Whether to write page checksums in general for all columns. + Page checksums enable detection of data corruption, which might occur during + transmission or in the storage. +sorting_columns : Sequence of SortingColumn, default None + Specify the sort order of the data being written. The writer does not sort + the data nor does it verify that the data is sorted. The sort order is + written to the row group metadata, which can then be used by readers. +store_decimal_as_integer : bool, default False + Allow decimals with 1 <= precision <= 18 to be stored as integers. + In Parquet, DECIMAL can be stored in any of the following physical types: + - int32: for 1 <= precision <= 9. + - int64: for 10 <= precision <= 18. + - fixed_len_byte_array: precision is limited by the array size. + Length n can store <= floor(log_10(2^(8*n - 1) - 1)) base-10 digits. + - binary: precision is unlimited. The minimum number of bytes to store the + unscaled value is used. + + By default, this is DISABLED and all decimal types annotate fixed_len_byte_array. + When enabled, the writer will use the following physical types to store decimals: + - int32: for 1 <= precision <= 9. + - int64: for 10 <= precision <= 18. + - fixed_len_byte_array: for precision > 18. + + As a consequence, decimal columns stored in integer types are more compact. +use_content_defined_chunking : bool or dict, default False + Optimize parquet files for content addressable storage (CAS) systems by writing + data pages according to content-defined chunk boundaries. This allows for more + efficient deduplication of data across files, hence more efficient network + transfers and storage. The chunking is based on a rolling hash algorithm that + identifies chunk boundaries based on the actual content of the data. + + Note that it is an experimental feature and the API may change in the future. + + If set to ``True``, a default configuration is used with `min_chunk_size=256 KiB` + and `max_chunk_size=1024 KiB`. The chunk size distribution approximates a normal + distribution between `min_chunk_size` and `max_chunk_size` (sizes are accounted + before any Parquet encodings). + + A `dict` can be passed to adjust the chunker parameters with the following keys: + - `min_chunk_size`: minimum chunk size in bytes, default 256 KiB + The rolling hash will not be updated until this size is reached for each chunk. + Note that all data sent through the hash function is counted towards the chunk + size, including definition and repetition levels if present. + - `max_chunk_size`: maximum chunk size in bytes, default is 1024 KiB + The chunker will create a new chunk whenever the chunk size exceeds this value. + Note that the parquet writer has a related `data_pagesize` property that controls + the maximum size of a parquet data page after encoding. While setting + `data_page_size` to a smaller value than `max_chunk_size` doesn't affect the + chunking effectiveness, it results in more small parquet data pages. + - `norm_level`: normalization level to center the chunk size around the average + size more aggressively, default 0 + Increasing the normalization level increases the probability of finding a chunk, + improving the deduplication ratio, but also increasing the number of small chunks + resulting in many small parquet data pages. The default value provides a good + balance between deduplication ratio and fragmentation. Use norm_level=1 or + norm_level=2 to reach a higher deduplication ratio at the expense of + fragmentation. +write_time_adjusted_to_utc : bool, default False + Set the value of isAdjustedTOUTC when writing a TIME column. + If True, this tells the Parquet reader that the TIME columns + are expressed in reference to midnight in the UTC timezone. + If False (the default), the TIME columns are assumed to be expressed + in reference to midnight in an unknown, presumably local, timezone. +""" + +_parquet_writer_example_doc = """\ +Generate an example PyArrow Table and RecordBatch: + +>>> import pyarrow as pa +>>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], +... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", +... "Brittle stars", "Centipede"]}) +>>> batch = pa.record_batch([[2, 2, 4, 4, 5, 100], +... ["Flamingo", "Parrot", "Dog", "Horse", +... "Brittle stars", "Centipede"]], +... names=['n_legs', 'animal']) + +create a ParquetWriter object: + +>>> import pyarrow.parquet as pq +>>> writer = pq.ParquetWriter('example.parquet', table.schema) + +and write the Table into the Parquet file: + +>>> writer.write_table(table) +>>> writer.close() + +>>> pq.read_table('example.parquet').to_pandas() + n_legs animal +0 2 Flamingo +1 2 Parrot +2 4 Dog +3 4 Horse +4 5 Brittle stars +5 100 Centipede + +create a ParquetWriter object for the RecordBatch: + +>>> writer2 = pq.ParquetWriter('example2.parquet', batch.schema) + +and write the RecordBatch into the Parquet file: + +>>> writer2.write_batch(batch) +>>> writer2.close() + +>>> pq.read_table('example2.parquet').to_pandas() + n_legs animal +0 2 Flamingo +1 2 Parrot +2 4 Dog +3 4 Horse +4 5 Brittle stars +5 100 Centipede +""" + + +class ParquetWriter: + + __doc__ = f""" +Class for incrementally building a Parquet file for Arrow tables. + +Parameters +---------- +where : path or file-like object +schema : pyarrow.Schema +{_parquet_writer_arg_docs} +writer_engine_version : unused +**options : dict + If options contains a key `metadata_collector` then the + corresponding value is assumed to be a list (or any object with + `.append` method) that will be filled with the file metadata instance + of the written file. + +Examples +-------- +{_parquet_writer_example_doc} +""" + + def __init__(self, where, schema, filesystem=None, + flavor=None, + version='2.6', + use_dictionary=True, + compression='snappy', + write_statistics=True, + use_deprecated_int96_timestamps=None, + compression_level=None, + use_byte_stream_split=False, + column_encoding=None, + writer_engine_version=None, + data_page_version='1.0', + use_compliant_nested_type=True, + encryption_properties=None, + write_batch_size=None, + dictionary_pagesize_limit=None, + store_schema=True, + write_page_index=False, + write_page_checksum=False, + sorting_columns=None, + store_decimal_as_integer=False, + write_time_adjusted_to_utc=False, + max_rows_per_page=None, + **options): + if use_deprecated_int96_timestamps is None: + # Use int96 timestamps for Spark + if flavor is not None and 'spark' in flavor: + use_deprecated_int96_timestamps = True + else: + use_deprecated_int96_timestamps = False + + self.flavor = flavor + if flavor is not None: + schema, self.schema_changed = _sanitize_schema(schema, flavor) + else: + self.schema_changed = False + + self.schema = schema + self.where = where + + # If we open a file using a filesystem, store file handle so we can be + # sure to close it when `self.close` is called. + self.file_handle = None + + filesystem, path = _resolve_filesystem_and_path(where, filesystem) + if filesystem is not None: + # ARROW-10480: do not auto-detect compression. While + # a filename like foo.parquet.gz is nonconforming, it + # shouldn't implicitly apply compression. + sink = self.file_handle = filesystem.open_output_stream( + path, compression=None) + else: + sink = where + self._metadata_collector = options.pop('metadata_collector', None) + engine_version = 'V2' + self.writer = _parquet.ParquetWriter( + sink, schema, + version=version, + compression=compression, + use_dictionary=use_dictionary, + write_statistics=write_statistics, + use_deprecated_int96_timestamps=use_deprecated_int96_timestamps, + compression_level=compression_level, + use_byte_stream_split=use_byte_stream_split, + column_encoding=column_encoding, + writer_engine_version=engine_version, + data_page_version=data_page_version, + use_compliant_nested_type=use_compliant_nested_type, + encryption_properties=encryption_properties, + write_batch_size=write_batch_size, + dictionary_pagesize_limit=dictionary_pagesize_limit, + store_schema=store_schema, + write_page_index=write_page_index, + write_page_checksum=write_page_checksum, + sorting_columns=sorting_columns, + store_decimal_as_integer=store_decimal_as_integer, + write_time_adjusted_to_utc=write_time_adjusted_to_utc, + max_rows_per_page=max_rows_per_page, + **options) + self.is_open = True + + def __del__(self): + if getattr(self, 'is_open', False): + self.close() + + def __enter__(self): + return self + + def __exit__(self, *args, **kwargs): + self.close() + # return false since we want to propagate exceptions + return False + + def write(self, table_or_batch, row_group_size=None): + """ + Write RecordBatch or Table to the Parquet file. + + Parameters + ---------- + table_or_batch : {RecordBatch, Table} + row_group_size : int, default None + Maximum number of rows in each written row group. If None, the row + group size will be the minimum of the number of rows in the + Table/RecordBatch and 1024 * 1024. + """ + if isinstance(table_or_batch, pa.RecordBatch): + self.write_batch(table_or_batch, row_group_size) + elif isinstance(table_or_batch, pa.Table): + self.write_table(table_or_batch, row_group_size) + else: + raise TypeError(type(table_or_batch)) + + def write_batch(self, batch, row_group_size=None): + """ + Write RecordBatch to the Parquet file. + + Parameters + ---------- + batch : RecordBatch + row_group_size : int, default None + Maximum number of rows in written row group. If None, the + row group size will be the minimum of the RecordBatch + size (in rows) and 1024 * 1024. If set larger than 64 * 1024 * 1024 + then 64 * 1024 * 1024 will be used instead. + """ + table = pa.Table.from_batches([batch], batch.schema) + self.write_table(table, row_group_size) + + def write_table(self, table, row_group_size=None): + """ + Write Table to the Parquet file. + + Parameters + ---------- + table : Table + row_group_size : int, default None + Maximum number of rows in each written row group. If None, + the row group size will be the minimum of the Table size (in rows) + and 1024 * 1024. If set larger than 64 * 1024 * 1024 then + 64 * 1024 * 1024 will be used instead. + + """ + if self.schema_changed: + table = _sanitize_table(table, self.schema, self.flavor) + assert self.is_open + + if not table.schema.equals(self.schema, check_metadata=False): + msg = ( + "Table schema does not match schema used to create file: \n" + f"table:\n{table.schema!s} vs. \nfile:\n{self.schema!s}" + ) + raise ValueError(msg) + + self.writer.write_table(table, row_group_size=row_group_size) + + def close(self): + """ + Close the connection to the Parquet file. + """ + if self.is_open: + self.writer.close() + self.is_open = False + if self._metadata_collector is not None: + self._metadata_collector.append(self.writer.metadata) + if self.file_handle is not None: + self.file_handle.close() + + def add_key_value_metadata(self, key_value_metadata): + """ + Add key-value metadata to the file. + This will overwrite any existing metadata with the same key. + + Parameters + ---------- + key_value_metadata : dict + Keys and values must be string-like / coercible to bytes. + """ + assert self.is_open + self.writer.add_key_value_metadata(key_value_metadata) + + +def _get_pandas_index_columns(keyvalues): + return (json.loads(keyvalues[b'pandas'].decode('utf8')) + ['index_columns']) + + +EXCLUDED_PARQUET_PATHS = {'_SUCCESS'} + + +_read_docstring_common = """\ +read_dictionary : list, default None + List of names or column paths (for nested types) to read directly + as DictionaryArray. Only supported for BYTE_ARRAY storage. To read + a flat column as dictionary-encoded pass the column name. For + nested types, you must pass the full column "path", which could be + something like level1.level2.list.item. Refer to the Parquet + file's schema to obtain the paths. +binary_type : pyarrow.DataType, default None + If given, Parquet binary columns will be read as this datatype. + This setting is ignored if a serialized Arrow schema is found in + the Parquet metadata. +list_type : subclass of pyarrow.DataType, default None + If given, non-MAP repeated columns will be read as an instance of + this datatype (either pyarrow.ListType or pyarrow.LargeListType). + This setting is ignored if a serialized Arrow schema is found in + the Parquet metadata. +memory_map : bool, default False + If the source is a file path, use a memory map to read file, which can + improve performance in some environments. +buffer_size : int, default 0 + If positive, perform read buffering when deserializing individual + column chunks. Otherwise IO calls are unbuffered. +partitioning : pyarrow.dataset.Partitioning or str or list of str, \ +default "hive" + The partitioning scheme for a partitioned dataset. The default of "hive" + assumes directory names with key=value pairs like "/year=2009/month=11". + In addition, a scheme like "/2009/11" is also supported, in which case + you need to specify the field names or a full schema. See the + ``pyarrow.dataset.partitioning()`` function for more details.""" + + +_parquet_dataset_example = """\ +Generate an example PyArrow Table and write it to a partitioned dataset: + +>>> import pyarrow as pa +>>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], +... 'n_legs': [2, 2, 4, 4, 5, 100], +... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", +... "Brittle stars", "Centipede"]}) +>>> import pyarrow.parquet as pq +>>> pq.write_to_dataset(table, root_path='dataset_v2', +... partition_cols=['year']) + +create a ParquetDataset object from the dataset source: + +>>> dataset = pq.ParquetDataset('dataset_v2/') + +and read the data: + +>>> dataset.read().to_pandas() + n_legs animal year +0 5 Brittle stars 2019 +1 2 Flamingo 2020 +2 4 Dog 2021 +3 100 Centipede 2021 +4 2 Parrot 2022 +5 4 Horse 2022 + +create a ParquetDataset object with filter: + +>>> dataset = pq.ParquetDataset('dataset_v2/', +... filters=[('n_legs','=',4)]) +>>> dataset.read().to_pandas() + n_legs animal year +0 4 Dog 2021 +1 4 Horse 2022 +""" + + +class ParquetDataset: + __doc__ = f""" +Encapsulates details of reading a complete Parquet dataset possibly +consisting of multiple files and partitions in subdirectories. + +Parameters +---------- +path_or_paths : str or List[str] + A directory name, single file name, or list of file names. +filesystem : FileSystem, default None + If nothing passed, will be inferred based on path. + Path will try to be found in the local on-disk filesystem otherwise + it will be parsed as an URI to determine the filesystem. +schema : pyarrow.parquet.Schema + Optionally provide the Schema for the Dataset, in which case it will + not be inferred from the source. +filters : pyarrow.compute.Expression or List[Tuple] or List[List[Tuple]], default None + Rows which do not match the filter predicate will be removed from scanned + data. Partition keys embedded in a nested directory structure will be + exploited to avoid loading files at all if they contain no matching rows. + Within-file level filtering and different partitioning schemes are supported. + + {_DNF_filter_doc} +{_read_docstring_common} +ignore_prefixes : list, optional + Files matching any of these prefixes will be ignored by the + discovery process. + This is matched to the basename of a path. + By default this is ['.', '_']. + Note that discovery happens only if a directory is passed as source. +pre_buffer : bool, default True + Coalesce and issue file reads in parallel to improve performance on + high-latency filesystems (e.g. S3, GCS). If True, Arrow will use a + background I/O thread pool. If using a filesystem layer that itself + performs readahead (e.g. fsspec's S3FS), disable readahead for best + results. Set to False if you want to prioritize minimal memory usage + over maximum speed. +coerce_int96_timestamp_unit : str, default None + Cast timestamps that are stored in INT96 format to a particular resolution + (e.g. 'ms'). Setting to None is equivalent to 'ns' and therefore INT96 + timestamps will be inferred as timestamps in nanoseconds. +decryption_properties : FileDecryptionProperties or None + File-level decryption properties. + The decryption properties can be created using + ``CryptoFactory.file_decryption_properties()``. +thrift_string_size_limit : int, default None + If not None, override the maximum total string size allocated + when decoding Thrift structures. The default limit should be + sufficient for most Parquet files. +thrift_container_size_limit : int, default None + If not None, override the maximum total size of containers allocated + when decoding Thrift structures. The default limit should be + sufficient for most Parquet files. +page_checksum_verification : bool, default False + If True, verify the page checksum for each page read from the file. +arrow_extensions_enabled : bool, default True + If True, read Parquet logical types as Arrow extension types where possible, + (e.g., read JSON as the canonical `arrow.json` extension type or UUID as + the canonical `arrow.uuid` extension type). + +Examples +-------- +{_parquet_dataset_example} +""" + + def __init__(self, path_or_paths, filesystem=None, schema=None, *, filters=None, + read_dictionary=None, binary_type=None, list_type=None, + memory_map=False, buffer_size=None, partitioning="hive", + ignore_prefixes=None, + pre_buffer=True, coerce_int96_timestamp_unit=None, + decryption_properties=None, thrift_string_size_limit=None, + thrift_container_size_limit=None, + page_checksum_verification=False, + arrow_extensions_enabled=True): + import pyarrow.dataset as ds + + # map format arguments + read_options = { + "pre_buffer": pre_buffer, + "coerce_int96_timestamp_unit": coerce_int96_timestamp_unit, + "thrift_string_size_limit": thrift_string_size_limit, + "thrift_container_size_limit": thrift_container_size_limit, + "page_checksum_verification": page_checksum_verification, + "arrow_extensions_enabled": arrow_extensions_enabled, + "binary_type": binary_type, + "list_type": list_type, + } + if buffer_size: + read_options.update(use_buffered_stream=True, + buffer_size=buffer_size) + if read_dictionary is not None: + read_options.update(dictionary_columns=read_dictionary) + + if decryption_properties is not None: + read_options.update(decryption_properties=decryption_properties) + + self._filter_expression = None + if filters is not None: + self._filter_expression = filters_to_expression(filters) + + # map old filesystems to new one + if filesystem is not None: + filesystem = _ensure_filesystem( + filesystem, use_mmap=memory_map) + elif filesystem is None and memory_map: + # if memory_map is specified, assume local file system (string + # path can in principle be URI for any filesystem) + filesystem = LocalFileSystem(use_mmap=memory_map) + + # This needs to be checked after _ensure_filesystem, because that + # handles the case of an fsspec LocalFileSystem + if ( + hasattr(path_or_paths, "__fspath__") and + filesystem is not None and + not isinstance(filesystem, LocalFileSystem) + ): + raise TypeError( + "Path-like objects with __fspath__ must only be used with " + f"local file systems, not {type(filesystem)}" + ) + + # check for single fragment dataset or dataset directory + single_file = None + self._base_dir = None + if not isinstance(path_or_paths, list): + if _is_path_like(path_or_paths): + filesystem, path_or_paths = _resolve_filesystem_and_path( + path_or_paths, filesystem, memory_map=memory_map + ) + finfo = filesystem.get_file_info(path_or_paths) + if finfo.type == FileType.Directory: + self._base_dir = path_or_paths + else: + single_file = path_or_paths + + parquet_format = ds.ParquetFileFormat(**read_options) + + if single_file is not None: + fragment = parquet_format.make_fragment(single_file, filesystem) + + self._dataset = ds.FileSystemDataset( + [fragment], schema=schema or fragment.physical_schema, + format=parquet_format, + filesystem=fragment.filesystem + ) + return + + # check partitioning to enable dictionary encoding + if partitioning == "hive": + partitioning = ds.HivePartitioning.discover( + infer_dictionary=True) + + self._dataset = ds.dataset(path_or_paths, filesystem=filesystem, + schema=schema, format=parquet_format, + partitioning=partitioning, + ignore_prefixes=ignore_prefixes) + + def equals(self, other): + if not isinstance(other, ParquetDataset): + raise TypeError('`other` must be an instance of ParquetDataset') + + return (self.schema == other.schema and + self._dataset.format == other._dataset.format and + self.filesystem == other.filesystem and + # self.fragments == other.fragments and + self.files == other.files) + + def __eq__(self, other): + try: + return self.equals(other) + except TypeError: + return NotImplemented + + @property + def schema(self): + """ + Schema of the Dataset. + + Examples + -------- + Generate an example dataset: + + >>> import pyarrow as pa + >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], + ... 'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_to_dataset(table, root_path='dataset_v2_schema', + ... partition_cols=['year']) + >>> dataset = pq.ParquetDataset('dataset_v2_schema/') + + Read the schema: + + >>> dataset.schema + n_legs: int64 + animal: string + year: dictionary + """ + return self._dataset.schema + + def read(self, columns=None, use_threads=True, use_pandas_metadata=False): + """ + Read (multiple) Parquet files as a single pyarrow.Table. + + Parameters + ---------- + columns : List[str] + Names of columns to read from the dataset. The partition fields + are not automatically included. + use_threads : bool, default True + Perform multi-threaded column reads. + use_pandas_metadata : bool, default False + If True and file has custom pandas schema metadata, ensure that + index columns are also loaded. + + Returns + ------- + pyarrow.Table + Content of the file as a table (of columns). + + Examples + -------- + Generate an example dataset: + + >>> import pyarrow as pa + >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], + ... 'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_to_dataset(table, root_path='dataset_v2_read', + ... partition_cols=['year']) + >>> dataset = pq.ParquetDataset('dataset_v2_read/') + + Read the dataset: + + >>> dataset.read(columns=["n_legs"]) + pyarrow.Table + n_legs: int64 + ---- + n_legs: [[5],[2],[4,100],[2,4]] + """ + # if use_pandas_metadata, we need to include index columns in the + # column selection, to be able to restore those in the pandas DataFrame + metadata = self.schema.metadata or {} + + if use_pandas_metadata: + # if the dataset schema metadata itself doesn't have pandas + # then try to get this from common file (for backwards compat) + if b"pandas" not in metadata: + common_metadata = self._get_common_pandas_metadata() + if common_metadata: + metadata = common_metadata + + if columns is not None and use_pandas_metadata: + if metadata and b'pandas' in metadata: + # RangeIndex can be represented as dict instead of column name + index_columns = [ + col for col in _get_pandas_index_columns(metadata) + if not isinstance(col, dict) + ] + columns = ( + list(columns) + list(set(index_columns) - set(columns)) + ) + + table = self._dataset.to_table( + columns=columns, filter=self._filter_expression, + use_threads=use_threads + ) + + # if use_pandas_metadata, restore the pandas metadata (which gets + # lost if doing a specific `columns` selection in to_table) + if use_pandas_metadata: + if metadata and b"pandas" in metadata: + new_metadata = table.schema.metadata or {} + new_metadata.update({b"pandas": metadata[b"pandas"]}) + table = table.replace_schema_metadata(new_metadata) + + return table + + def _get_common_pandas_metadata(self): + + if not self._base_dir: + return None + + metadata = None + for name in ["_common_metadata", "_metadata"]: + metadata_path = os.path.join(str(self._base_dir), name) + finfo = self.filesystem.get_file_info(metadata_path) + if finfo.is_file: + pq_meta = read_metadata( + metadata_path, filesystem=self.filesystem) + metadata = pq_meta.metadata + if metadata and b'pandas' in metadata: + break + + return metadata + + def read_pandas(self, **kwargs): + """ + Read dataset including pandas metadata, if any. Other arguments passed + through to :func:`read`, see docstring for further details. + + Parameters + ---------- + **kwargs : optional + Additional options for :func:`read` + + Examples + -------- + Generate an example parquet file: + + >>> import pyarrow as pa + >>> import pandas as pd + >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], + ... 'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> table = pa.Table.from_pandas(df) + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, 'table_V2.parquet') + >>> dataset = pq.ParquetDataset('table_V2.parquet') + + Read the dataset with pandas metadata: + + >>> dataset.read_pandas(columns=["n_legs"]) + pyarrow.Table + n_legs: int64 + ---- + n_legs: [[2,2,4,4,5,100]] + + >>> dataset.read_pandas(columns=["n_legs"]).schema.pandas_metadata + {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, ...} + """ + return self.read(use_pandas_metadata=True, **kwargs) + + @property + def fragments(self): + """ + A list of the Dataset source fragments or pieces with absolute + file paths. + + Examples + -------- + Generate an example dataset: + + >>> import pyarrow as pa + >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], + ... 'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_to_dataset(table, root_path='dataset_v2_fragments', + ... partition_cols=['year']) + >>> dataset = pq.ParquetDataset('dataset_v2_fragments/') + + List the fragments: + + >>> dataset.fragments + [>> import pyarrow as pa + >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], + ... 'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> import pyarrow.parquet as pq + >>> pq.write_to_dataset(table, root_path='dataset_v2_files', + ... partition_cols=['year']) + >>> dataset = pq.ParquetDataset('dataset_v2_files/') + + List the files: + + >>> dataset.files + ['dataset_v2_files/year=2019/...-0.parquet', ... + """ + return self._dataset.files + + @property + def filesystem(self): + """ + The filesystem type of the Dataset source. + """ + return self._dataset.filesystem + + @property + def partitioning(self): + """ + The partitioning of the Dataset source, if discovered. + """ + return self._dataset.partitioning + + +_read_table_docstring = """ +{0} + +Parameters +---------- +source : str, list of str, pyarrow.NativeFile, or file-like object + If a string is passed, can be a single file name or directory name. If a + list of strings is passed, should be file names. For file-like objects, + only read a single file. Use pyarrow.BufferReader to read a file contained + in a bytes or buffer-like object. +columns : list + If not None, only these columns will be read from the file. A column + name may be a prefix of a nested field, e.g. 'a' will select 'a.b', + 'a.c', and 'a.d.e'. If empty, no columns will be read. Note + that the table will still have the correct num_rows set despite having + no columns. +use_threads : bool, default True + Perform multi-threaded column reads. +schema : Schema, optional + Optionally provide the Schema for the parquet dataset, in which case it + will not be inferred from the source. +{1} +filesystem : FileSystem, default None + If nothing passed, will be inferred based on path. + Path will try to be found in the local on-disk filesystem otherwise + it will be parsed as an URI to determine the filesystem. +filters : pyarrow.compute.Expression or List[Tuple] or List[List[Tuple]], default None + Rows which do not match the filter predicate will be removed from scanned + data. Partition keys embedded in a nested directory structure will be + exploited to avoid loading files at all if they contain no matching rows. + Within-file level filtering and different partitioning schemes are supported. + + {3} +ignore_prefixes : list, optional + Files matching any of these prefixes will be ignored by the + discovery process. + This is matched to the basename of a path. + By default this is ['.', '_']. + Note that discovery happens only if a directory is passed as source. +pre_buffer : bool, default True + Coalesce and issue file reads in parallel to improve performance on + high-latency filesystems (e.g. S3). If True, Arrow will use a + background I/O thread pool. If using a filesystem layer that itself + performs readahead (e.g. fsspec's S3FS), disable readahead for best + results. +coerce_int96_timestamp_unit : str, default None + Cast timestamps that are stored in INT96 format to a particular + resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' + and therefore INT96 timestamps will be inferred as timestamps + in nanoseconds. +decryption_properties : FileDecryptionProperties or None + File-level decryption properties. + The decryption properties can be created using + ``CryptoFactory.file_decryption_properties()``. +thrift_string_size_limit : int, default None + If not None, override the maximum total string size allocated + when decoding Thrift structures. The default limit should be + sufficient for most Parquet files. +thrift_container_size_limit : int, default None + If not None, override the maximum total size of containers allocated + when decoding Thrift structures. The default limit should be + sufficient for most Parquet files. +page_checksum_verification : bool, default False + If True, verify the checksum for each page read from the file. +arrow_extensions_enabled : bool, default True + If True, read Parquet logical types as Arrow extension types where possible, + (e.g., read JSON as the canonical `arrow.json` extension type or UUID as + the canonical `arrow.uuid` extension type). + +Returns +------- +{2} + +{4} +""" + +_read_table_example = """\ + +Examples +-------- + +Generate an example PyArrow Table and write it to a partitioned dataset: + +>>> import pyarrow as pa +>>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], +... 'n_legs': [2, 2, 4, 4, 5, 100], +... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", +... "Brittle stars", "Centipede"]}) +>>> import pyarrow.parquet as pq +>>> pq.write_to_dataset(table, root_path='dataset_name_2', +... partition_cols=['year']) + +Read the data: + +>>> pq.read_table('dataset_name_2').to_pandas() + n_legs animal year +0 5 Brittle stars 2019 +1 2 Flamingo 2020 +2 4 Dog 2021 +3 100 Centipede 2021 +4 2 Parrot 2022 +5 4 Horse 2022 + + +Read only a subset of columns: + +>>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"]) +pyarrow.Table +n_legs: int64 +animal: string +---- +n_legs: [[5],[2],[4,100],[2,4]] +animal: [["Brittle stars"],["Flamingo"],["Dog","Centipede"],["Parrot","Horse"]] + +Read a subset of columns and read one column as DictionaryArray: + +>>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"], +... read_dictionary=["animal"]) +pyarrow.Table +n_legs: int64 +animal: dictionary +---- +n_legs: [[5],[2],[4,100],[2,4]] +animal: [ -- dictionary: +["Brittle stars"] -- indices: +[0], -- dictionary: +["Flamingo"] -- indices: +[0], -- dictionary: +["Dog","Centipede"] -- indices: +[0,1], -- dictionary: +["Parrot","Horse"] -- indices: +[0,1]] + +Read the table with filter: + +>>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"], +... filters=[('n_legs','<',4)]).to_pandas() + n_legs animal +0 2 Flamingo +1 2 Parrot + +Read data from a single Parquet file: + +>>> pq.write_table(table, 'example.parquet') +>>> pq.read_table('dataset_name_2').to_pandas() + n_legs animal year +0 5 Brittle stars 2019 +1 2 Flamingo 2020 +2 4 Dog 2021 +3 100 Centipede 2021 +4 2 Parrot 2022 +5 4 Horse 2022 +""" + + +def read_table(source, *, columns=None, use_threads=True, + schema=None, use_pandas_metadata=False, read_dictionary=None, + binary_type=None, list_type=None, memory_map=False, buffer_size=0, + partitioning="hive", filesystem=None, filters=None, + ignore_prefixes=None, pre_buffer=True, + coerce_int96_timestamp_unit=None, + decryption_properties=None, thrift_string_size_limit=None, + thrift_container_size_limit=None, + page_checksum_verification=False, + arrow_extensions_enabled=True): + + try: + dataset = ParquetDataset( + source, + schema=schema, + filesystem=filesystem, + partitioning=partitioning, + memory_map=memory_map, + read_dictionary=read_dictionary, + binary_type=binary_type, + list_type=list_type, + buffer_size=buffer_size, + filters=filters, + ignore_prefixes=ignore_prefixes, + pre_buffer=pre_buffer, + coerce_int96_timestamp_unit=coerce_int96_timestamp_unit, + decryption_properties=decryption_properties, + thrift_string_size_limit=thrift_string_size_limit, + thrift_container_size_limit=thrift_container_size_limit, + page_checksum_verification=page_checksum_verification, + arrow_extensions_enabled=arrow_extensions_enabled, + ) + except ImportError: + # fall back on ParquetFile for simple cases when pyarrow.dataset + # module is not available + if filters is not None: + raise ValueError( + "the 'filters' keyword is not supported when the " + "pyarrow.dataset module is not available" + ) + if partitioning != "hive": + raise ValueError( + "the 'partitioning' keyword is not supported when the " + "pyarrow.dataset module is not available" + ) + if schema is not None: + raise ValueError( + "the 'schema' argument is not supported when the " + "pyarrow.dataset module is not available" + ) + if isinstance(source, list): + raise ValueError( + "the 'source' argument cannot be a list of files " + "when the pyarrow.dataset module is not available" + ) + + filesystem, path = _resolve_filesystem_and_path(source, filesystem) + if filesystem is not None: + if not filesystem.get_file_info(path).is_file: + raise ValueError( + "the 'source' argument should be " + "an existing parquet file and not a directory " + "when the pyarrow.dataset module is not available" + ) + + source = filesystem.open_input_file(path) + + dataset = ParquetFile( + source, read_dictionary=read_dictionary, + binary_type=binary_type, + list_type=list_type, + memory_map=memory_map, buffer_size=buffer_size, + pre_buffer=pre_buffer, + coerce_int96_timestamp_unit=coerce_int96_timestamp_unit, + decryption_properties=decryption_properties, + thrift_string_size_limit=thrift_string_size_limit, + thrift_container_size_limit=thrift_container_size_limit, + page_checksum_verification=page_checksum_verification, + ) + + return dataset.read(columns=columns, use_threads=use_threads, + use_pandas_metadata=use_pandas_metadata) + + +read_table.__doc__ = _read_table_docstring.format( + """Read a Table from Parquet format""", + "\n".join(("""use_pandas_metadata : bool, default False + If True and file has custom pandas schema metadata, ensure that + index columns are also loaded.""", _read_docstring_common)), + """pyarrow.Table + Content of the file as a table (of columns)""", + _DNF_filter_doc, _read_table_example) + + +def read_pandas(source, columns=None, **kwargs): + return read_table( + source, columns=columns, use_pandas_metadata=True, **kwargs + ) + + +read_pandas.__doc__ = _read_table_docstring.format( + 'Read a Table from Parquet format, also reading DataFrame\n' + 'index values if known in the file metadata', + "\n".join((_read_docstring_common, + """**kwargs + additional options for :func:`read_table`""")), + """pyarrow.Table + Content of the file as a Table of Columns, including DataFrame + indexes as columns""", + _DNF_filter_doc, "") + + +def write_table(table, where, row_group_size=None, version='2.6', + use_dictionary=True, compression='snappy', + write_statistics=True, + use_deprecated_int96_timestamps=None, + coerce_timestamps=None, + allow_truncated_timestamps=False, + data_page_size=None, flavor=None, + filesystem=None, + compression_level=None, + use_byte_stream_split=False, + column_encoding=None, + data_page_version='1.0', + use_compliant_nested_type=True, + encryption_properties=None, + write_batch_size=None, + dictionary_pagesize_limit=None, + store_schema=True, + write_page_index=False, + write_page_checksum=False, + sorting_columns=None, + store_decimal_as_integer=False, + write_time_adjusted_to_utc=False, + max_rows_per_page=None, + **kwargs): + # Implementor's note: when adding keywords here / updating defaults, also + # update it in write_to_dataset and _dataset_parquet.pyx ParquetFileWriteOptions + row_group_size = kwargs.pop('chunk_size', row_group_size) + use_int96 = use_deprecated_int96_timestamps + try: + with ParquetWriter( + where, table.schema, + filesystem=filesystem, + version=version, + flavor=flavor, + use_dictionary=use_dictionary, + write_statistics=write_statistics, + coerce_timestamps=coerce_timestamps, + data_page_size=data_page_size, + allow_truncated_timestamps=allow_truncated_timestamps, + compression=compression, + use_deprecated_int96_timestamps=use_int96, + compression_level=compression_level, + use_byte_stream_split=use_byte_stream_split, + column_encoding=column_encoding, + data_page_version=data_page_version, + use_compliant_nested_type=use_compliant_nested_type, + encryption_properties=encryption_properties, + write_batch_size=write_batch_size, + dictionary_pagesize_limit=dictionary_pagesize_limit, + store_schema=store_schema, + write_page_index=write_page_index, + write_page_checksum=write_page_checksum, + sorting_columns=sorting_columns, + store_decimal_as_integer=store_decimal_as_integer, + write_time_adjusted_to_utc=write_time_adjusted_to_utc, + max_rows_per_page=max_rows_per_page, + **kwargs) as writer: + writer.write_table(table, row_group_size=row_group_size) + except Exception: + if _is_path_like(where): + try: + os.remove(_stringify_path(where)) + except os.error: + pass + raise + + +_write_table_example = """\ +Generate an example PyArrow Table: + +>>> import pyarrow as pa +>>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], +... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", +... "Brittle stars", "Centipede"]}) + +and write the Table into Parquet file: + +>>> import pyarrow.parquet as pq +>>> pq.write_table(table, 'example.parquet') + +Defining row group size for the Parquet file: + +>>> pq.write_table(table, 'example.parquet', row_group_size=3) + +Defining row group compression (default is Snappy): + +>>> pq.write_table(table, 'example.parquet', compression='none') + +Defining row group compression and encoding per-column: + +>>> pq.write_table(table, 'example.parquet', +... compression={'n_legs': 'snappy', 'animal': 'gzip'}, +... use_dictionary=['n_legs', 'animal']) + +Defining column encoding per-column: + +>>> pq.write_table(table, 'example.parquet', +... column_encoding={'animal':'PLAIN'}, +... use_dictionary=False) +""" + +write_table.__doc__ = f""" +Write a Table to Parquet format. + +Parameters +---------- +table : pyarrow.Table +where : string or pyarrow.NativeFile +row_group_size : int, default None + Maximum number of rows in each written row group. If None, the + row group size will be the minimum of the Table size (in rows) + and 1024 * 1024. If set larger than 64 * 1024 * 1024 then + 64 * 1024 * 1024 will be used instead. +{_parquet_writer_arg_docs} +**kwargs : optional + Additional options for ParquetWriter + +Examples +-------- +{_write_table_example} +""" + + +def write_to_dataset(table, root_path, partition_cols=None, + filesystem=None, schema=None, partitioning=None, + basename_template=None, use_threads=None, + file_visitor=None, existing_data_behavior=None, + **kwargs): + """Wrapper around dataset.write_dataset for writing a Table to + Parquet format by partitions. + For each combination of partition columns and values, + a subdirectories are created in the following + manner: + + root_dir/ + group1=value1 + group2=value1 + .parquet + group2=value2 + .parquet + group1=valueN + group2=value1 + .parquet + group2=valueN + .parquet + + Parameters + ---------- + table : pyarrow.Table + root_path : str, pathlib.Path + The root directory of the dataset. + partition_cols : list, + Column names by which to partition the dataset. + Columns are partitioned in the order they are given. + filesystem : FileSystem, default None + If nothing passed, will be inferred based on path. + Path will try to be found in the local on-disk filesystem otherwise + it will be parsed as an URI to determine the filesystem. + schema : Schema, optional + This Schema of the dataset. + partitioning : Partitioning or list[str], optional + The partitioning scheme specified with the + ``pyarrow.dataset.partitioning()`` function or a list of field names. + When providing a list of field names, you can use + ``partitioning_flavor`` to drive which partitioning type should be + used. + basename_template : str, optional + A template string used to generate basenames of written data files. + The token '{i}' will be replaced with an automatically incremented + integer. If not specified, it defaults to "guid-{i}.parquet". + use_threads : bool, default True + Write files in parallel. If enabled, then maximum parallelism will be + used determined by the number of available CPU cores. + file_visitor : function + If set, this function will be called with a WrittenFile instance + for each file created during the call. This object will have both + a path attribute and a metadata attribute. + + The path attribute will be a string containing the path to + the created file. + + The metadata attribute will be the parquet metadata of the file. + This metadata will have the file path attribute set and can be used + to build a _metadata file. The metadata attribute will be None if + the format is not parquet. + + Example visitor which simple collects the filenames created:: + + visited_paths = [] + + def file_visitor(written_file): + visited_paths.append(written_file.path) + + existing_data_behavior : 'overwrite_or_ignore' | 'error' | \ +'delete_matching' + Controls how the dataset will handle data that already exists in + the destination. The default behaviour is 'overwrite_or_ignore'. + + 'overwrite_or_ignore' will ignore any existing data and will + overwrite files with the same name as an output file. Other + existing files will be ignored. This behavior, in combination + with a unique basename_template for each write, will allow for + an append workflow. + + 'error' will raise an error if any data exists in the destination. + + 'delete_matching' is useful when you are writing a partitioned + dataset. The first time each partition directory is encountered + the entire directory will be deleted. This allows you to overwrite + old partitions completely. + **kwargs : dict, + Used as additional kwargs for :func:`pyarrow.dataset.write_dataset` + function for matching kwargs, and remainder to + :func:`pyarrow.dataset.ParquetFileFormat.make_write_options`. + See the docstring of :func:`write_table` and + :func:`pyarrow.dataset.write_dataset` for the available options. + Using `metadata_collector` in kwargs allows one to collect the + file metadata instances of dataset pieces. The file paths in the + ColumnChunkMetaData will be set relative to `root_path`. + + Examples + -------- + Generate an example PyArrow Table: + + >>> import pyarrow as pa + >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], + ... 'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + + and write it to a partitioned dataset: + + >>> import pyarrow.parquet as pq + >>> pq.write_to_dataset(table, root_path='dataset_name_3', + ... partition_cols=['year']) + >>> pq.ParquetDataset('dataset_name_3').files + ['dataset_name_3/year=2019/...-0.parquet', ... + + Write a single Parquet file into the root folder: + + >>> pq.write_to_dataset(table, root_path='dataset_name_4') + >>> pq.ParquetDataset('dataset_name_4/').files + ['dataset_name_4/...-0.parquet'] + """ + metadata_collector = kwargs.pop('metadata_collector', None) + + # Check for conflicting keywords + msg_confl = ( + "The '{1}' argument is not supported. " + "Use only '{0}' instead." + ) + if partition_cols is not None and partitioning is not None: + raise ValueError(msg_confl.format("partitioning", + "partition_cols")) + + if metadata_collector is not None and file_visitor is not None: + raise ValueError(msg_confl.format("file_visitor", + "metadata_collector")) + + import pyarrow.dataset as ds + + # extract write_dataset specific options + # reset assumed to go to make_write_options + write_dataset_kwargs = dict() + for key in inspect.signature(ds.write_dataset).parameters: + if key in kwargs: + write_dataset_kwargs[key] = kwargs.pop(key) + write_dataset_kwargs['max_rows_per_group'] = kwargs.pop( + 'row_group_size', kwargs.pop("chunk_size", None) + ) + + if metadata_collector is not None: + def file_visitor(written_file): + metadata_collector.append(written_file.metadata) + + # map format arguments + parquet_format = ds.ParquetFileFormat() + write_options = parquet_format.make_write_options(**kwargs) + + # map old filesystems to new one + if filesystem is not None: + filesystem = _ensure_filesystem(filesystem) + + if partition_cols: + part_schema = table.select(partition_cols).schema + partitioning = ds.partitioning(part_schema, flavor="hive") + + if basename_template is None: + basename_template = guid() + '-{i}.parquet' + + if existing_data_behavior is None: + existing_data_behavior = 'overwrite_or_ignore' + + ds.write_dataset( + table, root_path, filesystem=filesystem, + format=parquet_format, file_options=write_options, schema=schema, + partitioning=partitioning, use_threads=use_threads, + file_visitor=file_visitor, + basename_template=basename_template, + existing_data_behavior=existing_data_behavior, + **write_dataset_kwargs) + return + + +def write_metadata(schema, where, metadata_collector=None, filesystem=None, + **kwargs): + """ + Write metadata-only Parquet file from schema. This can be used with + `write_to_dataset` to generate `_common_metadata` and `_metadata` sidecar + files. + + Parameters + ---------- + schema : pyarrow.Schema + where : string or pyarrow.NativeFile + metadata_collector : list + where to collect metadata information. + filesystem : FileSystem, default None + If nothing passed, will be inferred from `where` if path-like, else + `where` is already a file-like object so no filesystem is needed. + **kwargs : dict, + Additional kwargs for ParquetWriter class. See docstring for + `ParquetWriter` for more information. + + Examples + -------- + Generate example data: + + >>> import pyarrow as pa + >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + + Write a dataset and collect metadata information. + + >>> metadata_collector = [] + >>> import pyarrow.parquet as pq + >>> pq.write_to_dataset( + ... table, 'dataset_metadata', + ... metadata_collector=metadata_collector) + + Write the `_common_metadata` parquet file without row groups statistics. + + >>> pq.write_metadata( + ... table.schema, 'dataset_metadata/_common_metadata') + + Write the `_metadata` parquet file with row groups statistics. + + >>> pq.write_metadata( + ... table.schema, 'dataset_metadata/_metadata', + ... metadata_collector=metadata_collector) + """ + filesystem, where = _resolve_filesystem_and_path(where, filesystem) + + if hasattr(where, "seek"): # file-like + cursor_position = where.tell() + + writer = ParquetWriter(where, schema, filesystem, **kwargs) + writer.close() + + if metadata_collector is not None: + # ParquetWriter doesn't expose the metadata until it's written. Write + # it and read it again. + metadata = read_metadata(where, filesystem=filesystem) + if hasattr(where, "seek"): + where.seek(cursor_position) # file-like, set cursor back. + + for m in metadata_collector: + metadata.append_row_groups(m) + if filesystem is not None: + with filesystem.open_output_stream(where) as f: + metadata.write_metadata_file(f) + else: + metadata.write_metadata_file(where) + + +def read_metadata(where, memory_map=False, decryption_properties=None, + filesystem=None): + """ + Read FileMetaData from footer of a single Parquet file. + + Parameters + ---------- + where : str (file path) or file-like object + memory_map : bool, default False + Create memory map when the source is a file path. + decryption_properties : FileDecryptionProperties, default None + Decryption properties for reading encrypted Parquet files. + filesystem : FileSystem, default None + If nothing passed, will be inferred based on path. + Path will try to be found in the local on-disk filesystem otherwise + it will be parsed as an URI to determine the filesystem. + + Returns + ------- + metadata : FileMetaData + The metadata of the Parquet file + + Examples + -------- + >>> import pyarrow as pa + >>> import pyarrow.parquet as pq + >>> table = pa.table({'n_legs': [4, 5, 100], + ... 'animal': ["Dog", "Brittle stars", "Centipede"]}) + >>> pq.write_table(table, 'example.parquet') + + >>> pq.read_metadata('example.parquet') + + created_by: parquet-cpp-arrow version ... + num_columns: 2 + num_rows: 3 + num_row_groups: 1 + format_version: 2.6 + serialized_size: ... + """ + filesystem, where = _resolve_filesystem_and_path(where, filesystem) + file_ctx = nullcontext() + if filesystem is not None: + file_ctx = where = filesystem.open_input_file(where) + + with file_ctx: + file = ParquetFile(where, memory_map=memory_map, + decryption_properties=decryption_properties) + return file.metadata + + +def read_schema(where, memory_map=False, decryption_properties=None, + filesystem=None): + """ + Read effective Arrow schema from Parquet file metadata. + + Parameters + ---------- + where : str (file path) or file-like object + memory_map : bool, default False + Create memory map when the source is a file path. + decryption_properties : FileDecryptionProperties, default None + Decryption properties for reading encrypted Parquet files. + filesystem : FileSystem, default None + If nothing passed, will be inferred based on path. + Path will try to be found in the local on-disk filesystem otherwise + it will be parsed as an URI to determine the filesystem. + + Returns + ------- + schema : pyarrow.Schema + The schema of the Parquet file + + Examples + -------- + >>> import pyarrow as pa + >>> import pyarrow.parquet as pq + >>> table = pa.table({'n_legs': [4, 5, 100], + ... 'animal': ["Dog", "Brittle stars", "Centipede"]}) + >>> pq.write_table(table, 'example.parquet') + + >>> pq.read_schema('example.parquet') + n_legs: int64 + animal: string + """ + filesystem, where = _resolve_filesystem_and_path(where, filesystem) + file_ctx = nullcontext() + if filesystem is not None: + file_ctx = where = filesystem.open_input_file(where) + + with file_ctx: + file = ParquetFile( + where, memory_map=memory_map, + decryption_properties=decryption_properties) + return file.schema.to_arrow_schema() + + +__all__ = ( + "ColumnChunkMetaData", + "ColumnSchema", + "FileDecryptionProperties", + "FileEncryptionProperties", + "FileMetaData", + "ParquetDataset", + "ParquetFile", + "ParquetLogicalType", + "ParquetReader", + "ParquetSchema", + "ParquetWriter", + "RowGroupMetaData", + "SortingColumn", + "Statistics", + "read_metadata", + "read_pandas", + "read_schema", + "read_table", + "write_metadata", + "write_table", + "write_to_dataset", + "_filters_to_expression", + "filters_to_expression", +) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/parquet/encryption.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/parquet/encryption.py new file mode 100644 index 0000000000000000000000000000000000000000..df6eed913fa52da8c8c9f497f12953b6178439b6 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/parquet/encryption.py @@ -0,0 +1,23 @@ +# pylint: disable=unused-wildcard-import, unused-import + +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. +from pyarrow._parquet_encryption import (CryptoFactory, # noqa + EncryptionConfiguration, + DecryptionConfiguration, + KmsConnectionConfig, + KmsClient) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/CMakeLists.txt b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..67508982eab82eacb6d00bb28986b79f6fed5078 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/CMakeLists.txt @@ -0,0 +1,19 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +arrow_install_all_headers("arrow/python") +add_subdirectory(vendored) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/api.h b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/api.h new file mode 100644 index 0000000000000000000000000000000000000000..2af0963a9c0444bb858f10323f914e21747cebaf --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/api.h @@ -0,0 +1,29 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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. + +#pragma once + +#include "arrow/python/arrow_to_pandas.h" +#include "arrow/python/common.h" +#include "arrow/python/datetime.h" +#include "arrow/python/helpers.h" +#include "arrow/python/inference.h" +#include "arrow/python/io.h" +#include "arrow/python/numpy_convert.h" +#include "arrow/python/numpy_to_arrow.h" +#include "arrow/python/python_to_arrow.h" +#include "arrow/python/util.h" diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/arrow_to_pandas.cc b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/arrow_to_pandas.cc new file mode 100644 index 0000000000000000000000000000000000000000..f163266f3b876796f8447d0eb356cc28652c9bac --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/arrow_to_pandas.cc @@ -0,0 +1,2659 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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. + +// Functions for pandas conversion via NumPy + +#include "arrow/python/arrow_to_pandas.h" +#include "arrow/python/numpy_interop.h" // IWYU pragma: expand + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "arrow/array.h" +#include "arrow/buffer.h" +#include "arrow/datum.h" +#include "arrow/status.h" +#include "arrow/table.h" +#include "arrow/type.h" +#include "arrow/type_traits.h" +#include "arrow/util/checked_cast.h" +#include "arrow/util/hashing.h" +#include "arrow/util/int_util.h" +#include "arrow/util/logging.h" +#include "arrow/util/macros.h" +#include "arrow/util/parallel.h" +#include "arrow/visit_type_inline.h" + +#include "arrow/compute/api.h" + +#include "arrow/python/arrow_to_python_internal.h" +#include "arrow/python/common.h" +#include "arrow/python/datetime.h" +#include "arrow/python/decimal.h" +#include "arrow/python/helpers.h" +#include "arrow/python/numpy_convert.h" +#include "arrow/python/numpy_internal.h" +#include "arrow/python/pyarrow.h" +#include "arrow/python/python_to_arrow.h" +#include "arrow/python/type_traits.h" + +namespace arrow { + +class MemoryPool; + +using internal::checked_cast; +using internal::CheckIndexBounds; +using internal::OptionalParallelFor; + +namespace py { +namespace { + +// Fix options for conversion of an inner (child) array. +PandasOptions MakeInnerOptions(PandasOptions options) { + // Make sure conversion of inner dictionary arrays always returns an array, + // not a dict {'indices': array, 'dictionary': array, 'ordered': bool} + options.decode_dictionaries = true; + options.categorical_columns.reset(); + options.strings_to_categorical = false; + + // In ARROW-7723, we found as a result of ARROW-3789 that second + // through microsecond resolution tz-aware timestamps were being promoted to + // use the DATETIME_NANO_TZ conversion path, yielding a datetime64[ns] NumPy + // array in this function. PyArray_GETITEM returns datetime.datetime for + // units second through microsecond but PyLong for nanosecond (because + // datetime.datetime does not support nanoseconds). + // We force the object conversion to preserve the value of the timezone. + // Nanoseconds are returned as integers. + options.coerce_temporal_nanoseconds = false; + + return options; +} + +// ---------------------------------------------------------------------- +// PyCapsule code for setting ndarray base to reference C++ object + +struct ArrayCapsule { + std::shared_ptr array; +}; + +struct BufferCapsule { + std::shared_ptr buffer; +}; + +void ArrayCapsule_Destructor(PyObject* capsule) { + delete reinterpret_cast(PyCapsule_GetPointer(capsule, "arrow::Array")); +} + +void BufferCapsule_Destructor(PyObject* capsule) { + delete reinterpret_cast(PyCapsule_GetPointer(capsule, "arrow::Buffer")); +} + +// ---------------------------------------------------------------------- +// pandas 0.x DataFrame conversion internals + +using internal::arrow_traits; +using internal::npy_traits; + +template +struct WrapBytes {}; + +template <> +struct WrapBytes { + static inline PyObject* Wrap(const char* data, int64_t length) { + return PyUnicode_FromStringAndSize(data, length); + } +}; + +template <> +struct WrapBytes { + static inline PyObject* Wrap(const char* data, int64_t length) { + return PyUnicode_FromStringAndSize(data, length); + } +}; + +template <> +struct WrapBytes { + static inline PyObject* Wrap(const char* data, int64_t length) { + return PyUnicode_FromStringAndSize(data, length); + } +}; + +template <> +struct WrapBytes { + static inline PyObject* Wrap(const char* data, int64_t length) { + return PyBytes_FromStringAndSize(data, length); + } +}; + +template <> +struct WrapBytes { + static inline PyObject* Wrap(const char* data, int64_t length) { + return PyBytes_FromStringAndSize(data, length); + } +}; + +template <> +struct WrapBytes { + static inline PyObject* Wrap(const char* data, int64_t length) { + return PyBytes_FromStringAndSize(data, length); + } +}; + +template <> +struct WrapBytes { + static inline PyObject* Wrap(const char* data, int64_t length) { + return PyBytes_FromStringAndSize(data, length); + } +}; + +static inline bool ListTypeSupported(const DataType& type) { + switch (type.id()) { + case Type::BOOL: + case Type::UINT8: + case Type::INT8: + case Type::UINT16: + case Type::INT16: + case Type::UINT32: + case Type::INT32: + case Type::INT64: + case Type::UINT64: + case Type::HALF_FLOAT: + case Type::FLOAT: + case Type::DOUBLE: + case Type::DECIMAL128: + case Type::DECIMAL256: + case Type::BINARY: + case Type::LARGE_BINARY: + case Type::STRING: + case Type::LARGE_STRING: + case Type::DATE32: + case Type::DATE64: + case Type::STRUCT: + case Type::MAP: + case Type::TIME32: + case Type::TIME64: + case Type::TIMESTAMP: + case Type::DURATION: + case Type::DICTIONARY: + case Type::INTERVAL_MONTH_DAY_NANO: + case Type::NA: // empty list + // The above types are all supported. + return true; + case Type::FIXED_SIZE_LIST: + case Type::LIST: + case Type::LARGE_LIST: + case Type::LIST_VIEW: + case Type::LARGE_LIST_VIEW: { + const auto& list_type = checked_cast(type); + return ListTypeSupported(*list_type.value_type()); + } + case Type::EXTENSION: { + const auto& ext = checked_cast(*type.GetSharedPtr()); + return ListTypeSupported(*(ext.storage_type())); + } + default: + break; + } + return false; +} + +Status CapsulizeArray(const std::shared_ptr& arr, PyObject** out) { + auto capsule = new ArrayCapsule{{arr}}; + *out = PyCapsule_New(reinterpret_cast(capsule), "arrow::Array", + &ArrayCapsule_Destructor); + if (*out == nullptr) { + delete capsule; + RETURN_IF_PYERROR(); + } + return Status::OK(); +} + +Status CapsulizeBuffer(const std::shared_ptr& buffer, PyObject** out) { + auto capsule = new BufferCapsule{{buffer}}; + *out = PyCapsule_New(reinterpret_cast(capsule), "arrow::Buffer", + &BufferCapsule_Destructor); + if (*out == nullptr) { + delete capsule; + RETURN_IF_PYERROR(); + } + return Status::OK(); +} + +Status SetNdarrayBase(PyArrayObject* arr, PyObject* base) { + if (PyArray_SetBaseObject(arr, base) == -1) { + // Error occurred, trust that SetBaseObject sets the error state + Py_XDECREF(base); + RETURN_IF_PYERROR(); + } + return Status::OK(); +} + +Status SetBufferBase(PyArrayObject* arr, const std::shared_ptr& buffer) { + PyObject* base; + RETURN_NOT_OK(CapsulizeBuffer(buffer, &base)); + return SetNdarrayBase(arr, base); +} + +inline void set_numpy_metadata(int type, const DataType* datatype, PyArray_Descr* out) { + auto metadata = + reinterpret_cast(PyDataType_C_METADATA(out)); + if (type == NPY_DATETIME) { + if (datatype->id() == Type::TIMESTAMP) { + const auto& timestamp_type = checked_cast(*datatype); + metadata->meta.base = internal::NumPyFrequency(timestamp_type.unit()); + } else { + ARROW_DCHECK(false) + << "NPY_DATETIME views only supported for Arrow TIMESTAMP types"; + } + } else if (type == NPY_TIMEDELTA) { + ARROW_DCHECK_EQ(datatype->id(), Type::DURATION); + const auto& duration_type = checked_cast(*datatype); + metadata->meta.base = internal::NumPyFrequency(duration_type.unit()); + } +} + +Status PyArray_NewFromPool(int nd, npy_intp* dims, PyArray_Descr* descr, MemoryPool* pool, + PyObject** out) { + // ARROW-6570: Allocate memory from MemoryPool for a couple reasons + // + // * Track allocations + // * Get better performance through custom allocators + int64_t total_size = PyDataType_ELSIZE(descr); + for (int i = 0; i < nd; ++i) { + total_size *= dims[i]; + } + + ARROW_ASSIGN_OR_RAISE(auto buffer, AllocateBuffer(total_size, pool)); + *out = PyArray_NewFromDescr(&PyArray_Type, descr, nd, dims, + /*strides=*/nullptr, + /*data=*/buffer->mutable_data(), + /*flags=*/NPY_ARRAY_CARRAY | NPY_ARRAY_WRITEABLE, + /*obj=*/nullptr); + if (*out == nullptr) { + RETURN_IF_PYERROR(); + // Trust that error set if NULL returned + } + return SetBufferBase(reinterpret_cast(*out), std::move(buffer)); +} + +template +inline const T* GetPrimitiveValues(const Array& arr) { + if (arr.length() == 0) { + return nullptr; + } + const int elsize = arr.type()->byte_width(); + const auto& prim_arr = checked_cast(arr); + return reinterpret_cast(prim_arr.values()->data() + arr.offset() * elsize); +} + +Status MakeNumPyView(std::shared_ptr arr, PyObject* py_ref, int npy_type, int ndim, + npy_intp* dims, PyObject** out) { + PyAcquireGIL lock; + + PyArray_Descr* descr = internal::GetSafeNumPyDtype(npy_type); + set_numpy_metadata(npy_type, arr->type().get(), descr); + PyObject* result = PyArray_NewFromDescr( + &PyArray_Type, descr, ndim, dims, /*strides=*/nullptr, + const_cast(GetPrimitiveValues(*arr)), /*flags=*/0, nullptr); + PyArrayObject* np_arr = reinterpret_cast(result); + if (np_arr == nullptr) { + // Error occurred, trust that error set + return Status::OK(); + } + + PyObject* base; + if (py_ref == nullptr) { + // Capsule will be owned by the ndarray, no incref necessary. See + // ARROW-1973 + RETURN_NOT_OK(CapsulizeArray(arr, &base)); + } else { + Py_INCREF(py_ref); + base = py_ref; + } + RETURN_NOT_OK(SetNdarrayBase(np_arr, base)); + + // Do not allow Arrow data to be mutated + PyArray_CLEARFLAGS(np_arr, NPY_ARRAY_WRITEABLE); + *out = result; + return Status::OK(); +} + +class PandasWriter { + public: + enum type { + OBJECT, + UINT8, + INT8, + UINT16, + INT16, + UINT32, + INT32, + UINT64, + INT64, + HALF_FLOAT, + FLOAT, + DOUBLE, + BOOL, + DATETIME_DAY, + DATETIME_SECOND, + DATETIME_MILLI, + DATETIME_MICRO, + DATETIME_NANO, + DATETIME_SECOND_TZ, + DATETIME_MILLI_TZ, + DATETIME_MICRO_TZ, + DATETIME_NANO_TZ, + TIMEDELTA_SECOND, + TIMEDELTA_MILLI, + TIMEDELTA_MICRO, + TIMEDELTA_NANO, + CATEGORICAL, + EXTENSION + }; + + PandasWriter(const PandasOptions& options, int64_t num_rows, int num_columns) + : options_(options), num_rows_(num_rows), num_columns_(num_columns) { + PyAcquireGIL lock; + internal::InitPandasStaticData(); + } + virtual ~PandasWriter() {} + + void SetBlockData(PyObject* arr) { + block_arr_.reset(arr); + block_data_ = + reinterpret_cast(PyArray_DATA(reinterpret_cast(arr))); + } + + /// \brief Either copy or wrap single array to create pandas-compatible array + /// for Series or DataFrame. num_columns_ can only be 1. Will try to zero + /// copy if possible (or error if not possible and zero_copy_only=True) + virtual Status TransferSingle(std::shared_ptr data, PyObject* py_ref) = 0; + + /// \brief Copy ChunkedArray into a multi-column block + virtual Status CopyInto(std::shared_ptr data, int64_t rel_placement) = 0; + + Status EnsurePlacementAllocated() { + std::lock_guard guard(allocation_lock_); + if (placement_data_ != nullptr) { + return Status::OK(); + } + PyAcquireGIL lock; + npy_intp placement_dims[1] = {num_columns_}; + PyObject* placement_arr = PyArray_SimpleNew(1, placement_dims, NPY_INT64); + RETURN_IF_PYERROR(); + placement_arr_.reset(placement_arr); + placement_data_ = reinterpret_cast( + PyArray_DATA(reinterpret_cast(placement_arr))); + return Status::OK(); + } + + Status EnsureAllocated() { + std::lock_guard guard(allocation_lock_); + if (block_data_ != nullptr) { + return Status::OK(); + } + RETURN_NOT_OK(Allocate()); + return Status::OK(); + } + + virtual bool CanZeroCopy(const ChunkedArray& data) const { return false; } + + virtual Status Write(std::shared_ptr data, int64_t abs_placement, + int64_t rel_placement) { + RETURN_NOT_OK(EnsurePlacementAllocated()); + if (num_columns_ == 1 && options_.allow_zero_copy_blocks) { + RETURN_NOT_OK(TransferSingle(data, /*py_ref=*/nullptr)); + } else { + RETURN_NOT_OK( + CheckNoZeroCopy("Cannot do zero copy conversion into " + "multi-column DataFrame block")); + RETURN_NOT_OK(EnsureAllocated()); + RETURN_NOT_OK(CopyInto(data, rel_placement)); + } + placement_data_[rel_placement] = abs_placement; + return Status::OK(); + } + + virtual Status GetDataFrameResult(PyObject** out) { + PyObject* result = PyDict_New(); + RETURN_IF_PYERROR(); + + PyObject* block; + RETURN_NOT_OK(GetResultBlock(&block)); + + PyDict_SetItemString(result, "block", block); + PyDict_SetItemString(result, "placement", placement_arr_.obj()); + + RETURN_NOT_OK(AddResultMetadata(result)); + *out = result; + return Status::OK(); + } + + // Caller steals the reference to this object + virtual Status GetSeriesResult(PyObject** out) { + RETURN_NOT_OK(MakeBlock1D()); + // Caller owns the object now + *out = block_arr_.detach(); + return Status::OK(); + } + + protected: + virtual Status AddResultMetadata(PyObject* result) { return Status::OK(); } + + Status MakeBlock1D() { + // For Series or for certain DataFrame block types, we need to shape to a + // 1D array when there is only one column + PyAcquireGIL lock; + + ARROW_DCHECK_EQ(1, num_columns_); + + npy_intp new_dims[1] = {static_cast(num_rows_)}; + PyArray_Dims dims; + dims.ptr = new_dims; + dims.len = 1; + + PyObject* reshaped = PyArray_Newshape( + reinterpret_cast(block_arr_.obj()), &dims, NPY_ANYORDER); + RETURN_IF_PYERROR(); + + // ARROW-8801: Here a PyArrayObject is created that is not being managed by + // any OwnedRef object. This object is then put in the resulting object + // with PyDict_SetItemString, which increments the reference count, so a + // memory leak ensues. There are several ways to fix the memory leak but a + // simple one is to put the reshaped 1D block array in this OwnedRefNoGIL + // so it will be correctly decref'd when this class is destructed. + block_arr_.reset(reshaped); + return Status::OK(); + } + + virtual Status GetResultBlock(PyObject** out) { + *out = block_arr_.obj(); + return Status::OK(); + } + + Status CheckNoZeroCopy(const std::string& message) { + if (options_.zero_copy_only) { + return Status::Invalid(message); + } + return Status::OK(); + } + + Status CheckNotZeroCopyOnly(const ChunkedArray& data) { + if (options_.zero_copy_only) { + return Status::Invalid("Needed to copy ", data.num_chunks(), " chunks with ", + data.null_count(), " nulls, but zero_copy_only was True"); + } + return Status::OK(); + } + + virtual Status Allocate() { + return Status::NotImplemented("Override Allocate in subclasses"); + } + + Status AllocateNDArray(int npy_type, int ndim = 2) { + PyAcquireGIL lock; + + PyObject* block_arr = nullptr; + npy_intp block_dims[2] = {0, 0}; + + if (ndim == 2) { + block_dims[0] = num_columns_; + block_dims[1] = num_rows_; + } else { + block_dims[0] = num_rows_; + } + PyArray_Descr* descr = internal::GetSafeNumPyDtype(npy_type); + if (PyDataType_REFCHK(descr)) { + // ARROW-6876: if the array has refcounted items, let Numpy + // own the array memory so as to decref elements on array destruction + block_arr = PyArray_SimpleNewFromDescr(ndim, block_dims, descr); + RETURN_IF_PYERROR(); + } else { + RETURN_NOT_OK( + PyArray_NewFromPool(ndim, block_dims, descr, options_.pool, &block_arr)); + } + + SetBlockData(block_arr); + return Status::OK(); + } + + void SetDatetimeUnit(NPY_DATETIMEUNIT unit) { + PyAcquireGIL lock; + auto date_dtype = + reinterpret_cast(PyDataType_C_METADATA( + PyArray_DESCR(reinterpret_cast(block_arr_.obj())))); + date_dtype->meta.base = unit; + } + + PandasOptions options_; + + std::mutex allocation_lock_; + + int64_t num_rows_; + int num_columns_; + + OwnedRefNoGIL block_arr_; + uint8_t* block_data_ = nullptr; + + // ndarray + OwnedRefNoGIL placement_arr_; + int64_t* placement_data_ = nullptr; + + private: + ARROW_DISALLOW_COPY_AND_ASSIGN(PandasWriter); +}; + +template +inline void ConvertIntegerWithNulls(const PandasOptions& options, + const ChunkedArray& data, OutType* out_values) { + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = *data.chunk(c); + const InType* in_values = GetPrimitiveValues(arr); + // Upcast to double, set NaN as appropriate + + for (int i = 0; i < arr.length(); ++i) { + *out_values++ = + arr.IsNull(i) ? static_cast(NAN) : static_cast(in_values[i]); + } + } +} + +template +inline void ConvertIntegerNoNullsSameType(const PandasOptions& options, + const ChunkedArray& data, T* out_values) { + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = *data.chunk(c); + if (arr.length() > 0) { + const T* in_values = GetPrimitiveValues(arr); + memcpy(out_values, in_values, sizeof(T) * arr.length()); + out_values += arr.length(); + } + } +} + +template +inline void ConvertIntegerNoNullsCast(const PandasOptions& options, + const ChunkedArray& data, OutType* out_values) { + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = *data.chunk(c); + const InType* in_values = GetPrimitiveValues(arr); + for (int64_t i = 0; i < arr.length(); ++i) { + *out_values = in_values[i]; + } + } +} + +template +struct MemoizationTraits { + using Scalar = typename T::c_type; +}; + +template +struct MemoizationTraits> { + // For binary, we memoize string_view as a scalar value to avoid having to + // unnecessarily copy the memory into the memo table data structure + using Scalar = std::string_view; +}; + +// Generic Array -> PyObject** converter that handles object deduplication, if +// requested +template +inline Status ConvertAsPyObjects(const PandasOptions& options, const ChunkedArray& data, + WrapFunction&& wrap_func, PyObject** out_values) { + using ArrayType = typename TypeTraits::ArrayType; + using Scalar = typename MemoizationTraits::Scalar; + + auto convert_chunks = [&](auto&& wrap_func) -> Status { + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = arrow::internal::checked_cast(*data.chunk(c)); + RETURN_NOT_OK(internal::WriteArrayObjects(arr, wrap_func, out_values)); + out_values += arr.length(); + } + return Status::OK(); + }; + + if (options.deduplicate_objects) { + // GH-40316: only allocate a memo table if deduplication is enabled. + ::arrow::internal::ScalarMemoTable memo_table(options.pool); + std::vector unique_values; + int32_t memo_size = 0; + + auto WrapMemoized = [&](const Scalar& value, PyObject** out_values) { + int32_t memo_index; + RETURN_NOT_OK(memo_table.GetOrInsert(value, &memo_index)); + if (memo_index == memo_size) { + // New entry + RETURN_NOT_OK(wrap_func(value, out_values)); + unique_values.push_back(*out_values); + ++memo_size; + } else { + // Duplicate entry + Py_INCREF(unique_values[memo_index]); + *out_values = unique_values[memo_index]; + } + return Status::OK(); + }; + return convert_chunks(std::move(WrapMemoized)); + } else { + return convert_chunks(std::forward(wrap_func)); + } +} + +Status ConvertStruct(PandasOptions options, const ChunkedArray& data, + PyObject** out_values) { + if (data.num_chunks() == 0) { + return Status::OK(); + } + // ChunkedArray has at least one chunk + auto arr = checked_cast(data.chunk(0).get()); + // Use it to cache the struct type and number of fields for all chunks + int32_t num_fields = arr->num_fields(); + auto array_type = arr->type(); + std::vector fields_data(num_fields * data.num_chunks()); + OwnedRef dict_item; + + // See notes in MakeInnerOptions. + options = MakeInnerOptions(std::move(options)); + // Don't blindly convert because timestamps in lists are handled differently. + options.timestamp_as_object = true; + + for (int c = 0; c < data.num_chunks(); c++) { + auto fields_data_offset = c * num_fields; + auto arr = checked_cast(data.chunk(c).get()); + // Convert the struct arrays first + for (int32_t i = 0; i < num_fields; i++) { + auto field = arr->field(static_cast(i)); + // In case the field is an extension array, use .storage() to convert to Pandas + if (field->type()->id() == Type::EXTENSION) { + const ExtensionArray& arr_ext = checked_cast(*field); + field = arr_ext.storage(); + } + RETURN_NOT_OK(ConvertArrayToPandas(options, field, nullptr, + fields_data[i + fields_data_offset].ref())); + ARROW_DCHECK(PyArray_Check(fields_data[i + fields_data_offset].obj())); + } + + // Construct a dictionary for each row + const bool has_nulls = data.null_count() > 0; + for (int64_t i = 0; i < arr->length(); ++i) { + if (has_nulls && arr->IsNull(i)) { + Py_INCREF(Py_None); + *out_values = Py_None; + } else { + // Build the new dict object for the row + dict_item.reset(PyDict_New()); + RETURN_IF_PYERROR(); + for (int32_t field_idx = 0; field_idx < num_fields; ++field_idx) { + OwnedRef field_value; + auto name = array_type->field(static_cast(field_idx))->name(); + if (!arr->field(static_cast(field_idx))->IsNull(i)) { + // Value exists in child array, obtain it + auto array = reinterpret_cast( + fields_data[field_idx + fields_data_offset].obj()); + auto ptr = reinterpret_cast(PyArray_GETPTR1(array, i)); + field_value.reset(PyArray_GETITEM(array, ptr)); + RETURN_IF_PYERROR(); + } else { + // Translate the Null to a None + Py_INCREF(Py_None); + field_value.reset(Py_None); + } + // PyDict_SetItemString increments reference count + auto setitem_result = + PyDict_SetItemString(dict_item.obj(), name.c_str(), field_value.obj()); + RETURN_IF_PYERROR(); + ARROW_DCHECK_EQ(setitem_result, 0); + } + *out_values = dict_item.obj(); + // Grant ownership to the resulting array + Py_INCREF(*out_values); + } + ++out_values; + } + } + return Status::OK(); +} + +Status DecodeDictionaries(MemoryPool* pool, const std::shared_ptr& dense_type, + ArrayVector* arrays) { + compute::ExecContext ctx(pool); + compute::CastOptions options; + for (size_t i = 0; i < arrays->size(); ++i) { + ARROW_ASSIGN_OR_RAISE((*arrays)[i], + compute::Cast(*(*arrays)[i], dense_type, options, &ctx)); + } + return Status::OK(); +} + +Status DecodeDictionaries(MemoryPool* pool, const std::shared_ptr& dense_type, + std::shared_ptr* array) { + auto chunks = (*array)->chunks(); + RETURN_NOT_OK(DecodeDictionaries(pool, dense_type, &chunks)); + *array = std::make_shared(std::move(chunks), dense_type); + return Status::OK(); +} + +template +enable_if_list_like ConvertListsLike(PandasOptions options, + const ChunkedArray& data, + PyObject** out_values) { + using ListArrayT = typename TypeTraits::ArrayType; + // Get column of underlying value arrays + ArrayVector value_arrays; + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = checked_cast(*data.chunk(c)); + // values() does not account for offsets, so we need to slice into it. + // We can't use Flatten(), because it removes the values behind a null list + // value, and that makes the offsets into original list values and our + // flattened_values array different. + std::shared_ptr flattened_values = arr.values()->Slice( + arr.value_offset(0), arr.value_offset(arr.length()) - arr.value_offset(0)); + if (arr.value_type()->id() == Type::EXTENSION) { + const auto& arr_ext = checked_cast(*flattened_values); + value_arrays.emplace_back(arr_ext.storage()); + } else { + value_arrays.emplace_back(flattened_values); + } + } + + using ListArrayType = typename ListArrayT::TypeClass; + const auto& list_type = checked_cast(*data.type()); + auto value_type = list_type.value_type(); + if (value_type->id() == Type::EXTENSION) { + value_type = checked_cast(*value_type).storage_type(); + } + + auto flat_column = std::make_shared(value_arrays, value_type); + + options = MakeInnerOptions(std::move(options)); + + OwnedRefNoGIL owned_numpy_array; + RETURN_NOT_OK(ConvertChunkedArrayToPandas(options, flat_column, nullptr, + owned_numpy_array.ref())); + PyObject* numpy_array = owned_numpy_array.obj(); + ARROW_DCHECK(PyArray_Check(numpy_array)); + + int64_t chunk_offset = 0; + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = checked_cast(*data.chunk(c)); + const bool has_nulls = data.null_count() > 0; + for (int64_t i = 0; i < arr.length(); ++i) { + if (has_nulls && arr.IsNull(i)) { + Py_INCREF(Py_None); + *out_values = Py_None; + } else { + // Need to subtract value_offset(0) since the original chunk might be a slice + // into another array. + OwnedRef start(PyLong_FromLongLong(arr.value_offset(i) + chunk_offset - + arr.value_offset(0))); + OwnedRef end(PyLong_FromLongLong(arr.value_offset(i + 1) + chunk_offset - + arr.value_offset(0))); + OwnedRef slice(PySlice_New(start.obj(), end.obj(), nullptr)); + + if (ARROW_PREDICT_FALSE(slice.obj() == nullptr)) { + // Fall out of loop, will return from RETURN_IF_PYERROR + break; + } + *out_values = PyObject_GetItem(numpy_array, slice.obj()); + + if (*out_values == nullptr) { + // Fall out of loop, will return from RETURN_IF_PYERROR + break; + } + } + ++out_values; + } + RETURN_IF_PYERROR(); + + chunk_offset += arr.value_offset(arr.length()) - arr.value_offset(0); + } + + return Status::OK(); +} + +// TODO GH-40579: optimize ListView conversion to avoid unnecessary copies +template +enable_if_list_view ConvertListsLike(PandasOptions options, + const ChunkedArray& data, + PyObject** out_values) { + using ListViewArrayType = typename TypeTraits::ArrayType; + using NonViewType = + std::conditional_t; + using NonViewClass = typename TypeTraits::ArrayType; + ArrayVector list_arrays; + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = checked_cast(*data.chunk(c)); + ARROW_ASSIGN_OR_RAISE(auto non_view_array, + NonViewClass::FromListView(arr, options.pool)); + list_arrays.emplace_back(non_view_array); + } + auto chunked_array = std::make_shared(list_arrays); + return ConvertListsLike(options, *chunked_array, out_values); +} + +template +Status ConvertMapHelper(F1 resetRow, F2 addPairToRow, F3 stealRow, + const ChunkedArray& data, PyArrayObject* py_keys, + PyArrayObject* py_items, + // needed for null checks in items + const std::vector> item_arrays, + PyObject** out_values) { + OwnedRef key_value; + OwnedRef item_value; + + int64_t chunk_offset = 0; + for (int c = 0; c < data.num_chunks(); ++c) { + const auto& arr = checked_cast(*data.chunk(c)); + const bool has_nulls = data.null_count() > 0; + + // Make a list of key/item pairs for each row in array + for (int64_t i = 0; i < arr.length(); ++i) { + if (has_nulls && arr.IsNull(i)) { + Py_INCREF(Py_None); + *out_values = Py_None; + } else { + int64_t entry_offset = arr.value_offset(i); + int64_t num_pairs = arr.value_offset(i + 1) - entry_offset; + + // Build the new list object for the row of Python pairs + RETURN_NOT_OK(resetRow(num_pairs)); + + // Add each key/item pair in the row + for (int64_t j = 0; j < num_pairs; ++j) { + // Get key value, key is non-nullable for a valid row + auto ptr_key = reinterpret_cast( + PyArray_GETPTR1(py_keys, chunk_offset + entry_offset + j)); + key_value.reset(PyArray_GETITEM(py_keys, ptr_key)); + RETURN_IF_PYERROR(); + + if (item_arrays[c]->IsNull(entry_offset + j)) { + // Translate the Null to a None + Py_INCREF(Py_None); + item_value.reset(Py_None); + } else { + // Get valid value from item array + auto ptr_item = reinterpret_cast( + PyArray_GETPTR1(py_items, chunk_offset + entry_offset + j)); + item_value.reset(PyArray_GETITEM(py_items, ptr_item)); + RETURN_IF_PYERROR(); + } + + // Add the key/item pair to the row + RETURN_NOT_OK(addPairToRow(j, key_value, item_value)); + } + + // Pass ownership to the resulting array + *out_values = stealRow(); + } + ++out_values; + } + RETURN_IF_PYERROR(); + + chunk_offset += arr.values()->length(); + } + + return Status::OK(); +} + +// A more helpful error message around TypeErrors that may stem from unhashable keys +Status CheckMapAsPydictsTypeError() { + if (ARROW_PREDICT_TRUE(!PyErr_Occurred())) { + return Status::OK(); + } + if (PyErr_ExceptionMatches(PyExc_TypeError)) { + // Modify the error string directly, so it is re-raised + // with our additional info. + // + // There are not many interesting things happening when this + // is hit. This is intended to only be called directly after + // PyDict_SetItem, where a finite set of errors could occur. + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + std::string message; + RETURN_NOT_OK(internal::PyObject_StdStringStr(value, &message)); + message += + ". If keys are not hashable, then you must use the option " + "[maps_as_pydicts=None (default)]"; + + // resets the error + PyErr_SetString(PyExc_TypeError, message.c_str()); + } + return ConvertPyError(); +} + +Status CheckForDuplicateKeys(bool error_on_duplicate_keys, Py_ssize_t total_dict_len, + Py_ssize_t total_raw_len) { + if (total_dict_len < total_raw_len) { + const char* message = + "[maps_as_pydicts] " + "After conversion of Arrow maps to pydicts, " + "detected data loss due to duplicate keys. " + "Original input length is [%lld], total converted pydict length is [%lld]."; + std::array buf; + std::snprintf(buf.data(), buf.size(), message, total_raw_len, total_dict_len); + + if (error_on_duplicate_keys) { + return Status::UnknownError(buf.data()); + } else { + ARROW_LOG(WARNING) << buf.data(); + } + } + return Status::OK(); +} + +Status ConvertMap(PandasOptions options, const ChunkedArray& data, + PyObject** out_values) { + // Get columns of underlying key/item arrays + std::vector> key_arrays; + std::vector> item_arrays; + for (int c = 0; c < data.num_chunks(); ++c) { + const auto& map_arr = checked_cast(*data.chunk(c)); + key_arrays.emplace_back(map_arr.keys()); + item_arrays.emplace_back(map_arr.items()); + } + + const auto& map_type = checked_cast(*data.type()); + auto key_type = map_type.key_type(); + auto item_type = map_type.item_type(); + + // ARROW-6899: Convert dictionary-encoded children to dense instead of + // failing below. A more efficient conversion than this could be done later + if (key_type->id() == Type::DICTIONARY) { + auto dense_type = checked_cast(*key_type).value_type(); + RETURN_NOT_OK(DecodeDictionaries(options.pool, dense_type, &key_arrays)); + key_type = dense_type; + } + if (item_type->id() == Type::DICTIONARY) { + auto dense_type = checked_cast(*item_type).value_type(); + RETURN_NOT_OK(DecodeDictionaries(options.pool, dense_type, &item_arrays)); + item_type = dense_type; + } + + // See notes in MakeInnerOptions. + options = MakeInnerOptions(std::move(options)); + // Don't blindly convert because timestamps in lists are handled differently. + options.timestamp_as_object = true; + + auto flat_keys = std::make_shared(key_arrays, key_type); + auto flat_items = std::make_shared(item_arrays, item_type); + OwnedRefNoGIL owned_numpy_keys; + RETURN_NOT_OK( + ConvertChunkedArrayToPandas(options, flat_keys, nullptr, owned_numpy_keys.ref())); + OwnedRefNoGIL owned_numpy_items; + RETURN_NOT_OK( + ConvertChunkedArrayToPandas(options, flat_items, nullptr, owned_numpy_items.ref())); + PyArrayObject* py_keys = reinterpret_cast(owned_numpy_keys.obj()); + PyArrayObject* py_items = reinterpret_cast(owned_numpy_items.obj()); + + if (options.maps_as_pydicts == MapConversionType::DEFAULT) { + // The default behavior to express an Arrow MAP as a list of [(key, value), ...] pairs + OwnedRef list_item; + return ConvertMapHelper( + [&list_item](int64_t num_pairs) { + list_item.reset(PyList_New(num_pairs)); + return CheckPyError(); + }, + [&list_item](int64_t idx, OwnedRef& key_value, OwnedRef& item_value) { + PyList_SET_ITEM(list_item.obj(), idx, + PyTuple_Pack(2, key_value.obj(), item_value.obj())); + return CheckPyError(); + }, + [&list_item] { return list_item.detach(); }, data, py_keys, py_items, item_arrays, + out_values); + } else { + // Use a native pydict + OwnedRef dict_item; + Py_ssize_t total_dict_len{0}; + Py_ssize_t total_raw_len{0}; + + bool error_on_duplicate_keys; + if (options.maps_as_pydicts == MapConversionType::LOSSY) { + error_on_duplicate_keys = false; + } else if (options.maps_as_pydicts == MapConversionType::STRICT_) { + error_on_duplicate_keys = true; + } else { + auto val = std::underlying_type_t(options.maps_as_pydicts); + return Status::UnknownError("Received unknown option for maps_as_pydicts: " + + std::to_string(val)); + } + + auto status = ConvertMapHelper( + [&dict_item, &total_raw_len](int64_t num_pairs) { + total_raw_len += num_pairs; + dict_item.reset(PyDict_New()); + return CheckPyError(); + }, + [&dict_item]([[maybe_unused]] int64_t idx, OwnedRef& key_value, + OwnedRef& item_value) { + auto setitem_result = + PyDict_SetItem(dict_item.obj(), key_value.obj(), item_value.obj()); + ARROW_RETURN_NOT_OK(CheckMapAsPydictsTypeError()); + // returns -1 if there are internal errors around hashing/resizing + return setitem_result == 0 ? Status::OK() + : Status::UnknownError( + "[maps_as_pydicts] " + "Unexpected failure inserting Arrow (key, " + "value) pair into Python dict"); + }, + [&dict_item, &total_dict_len] { + total_dict_len += PyDict_Size(dict_item.obj()); + return dict_item.detach(); + }, + data, py_keys, py_items, item_arrays, out_values); + + ARROW_RETURN_NOT_OK(status); + // If there were no errors generating the pydicts, + // then check if we detected any data loss from duplicate keys. + return CheckForDuplicateKeys(error_on_duplicate_keys, total_dict_len, total_raw_len); + } +} + +template +inline void ConvertNumericNullable(const ChunkedArray& data, InType na_value, + OutType* out_values) { + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = *data.chunk(c); + const InType* in_values = GetPrimitiveValues(arr); + + if (arr.null_count() > 0) { + for (int64_t i = 0; i < arr.length(); ++i) { + *out_values++ = arr.IsNull(i) ? na_value : in_values[i]; + } + } else { + memcpy(out_values, in_values, sizeof(InType) * arr.length()); + out_values += arr.length(); + } + } +} + +template +inline void ConvertNumericNullableCast(const ChunkedArray& data, InType na_value, + OutType* out_values) { + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = *data.chunk(c); + const InType* in_values = GetPrimitiveValues(arr); + + for (int64_t i = 0; i < arr.length(); ++i) { + *out_values++ = arr.IsNull(i) ? static_cast(na_value) + : static_cast(in_values[i]); + } + } +} + +template +class TypedPandasWriter : public PandasWriter { + public: + using T = typename npy_traits::value_type; + + using PandasWriter::PandasWriter; + + Status TransferSingle(std::shared_ptr data, PyObject* py_ref) override { + if (CanZeroCopy(*data)) { + PyObject* wrapped; + npy_intp dims[2] = {static_cast(num_columns_), + static_cast(num_rows_)}; + RETURN_NOT_OK( + MakeNumPyView(data->chunk(0), py_ref, NPY_TYPE, /*ndim=*/2, dims, &wrapped)); + SetBlockData(wrapped); + return Status::OK(); + } else { + RETURN_NOT_OK(CheckNotZeroCopyOnly(*data)); + RETURN_NOT_OK(EnsureAllocated()); + return CopyInto(data, /*rel_placement=*/0); + } + } + + Status CheckTypeExact(const DataType& type, Type::type expected) { + if (type.id() != expected) { + return Status::NotImplemented("Cannot write Arrow data of type ", type.ToString(), + " to pandas block with NumPy type ", + GetNumPyTypeName(NPY_TYPE)); + } + return Status::OK(); + } + + T* GetBlockColumnStart(int64_t rel_placement) { + return reinterpret_cast(block_data_) + rel_placement * num_rows_; + } + + protected: + Status Allocate() override { return AllocateNDArray(NPY_TYPE); } +}; + +struct ObjectWriterVisitor { + const PandasOptions& options; + const ChunkedArray& data; + PyObject** out_values; + + Status Visit(const NullType& type) { + for (int c = 0; c < data.num_chunks(); c++) { + std::shared_ptr arr = data.chunk(c); + + for (int64_t i = 0; i < arr->length(); ++i) { + // All values are null + Py_INCREF(Py_None); + *out_values = Py_None; + ++out_values; + } + } + return Status::OK(); + } + + Status Visit(const BooleanType& type) { + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = checked_cast(*data.chunk(c)); + + for (int64_t i = 0; i < arr.length(); ++i) { + if (arr.IsNull(i)) { + Py_INCREF(Py_None); + *out_values++ = Py_None; + } else if (arr.Value(i)) { + // True + Py_INCREF(Py_True); + *out_values++ = Py_True; + } else { + // False + Py_INCREF(Py_False); + *out_values++ = Py_False; + } + } + } + return Status::OK(); + } + + template + enable_if_integer Visit(const Type& type) { + using T = typename Type::c_type; + auto WrapValue = [](T value, PyObject** out) { + *out = std::is_signed::value ? PyLong_FromLongLong(value) + : PyLong_FromUnsignedLongLong(value); + RETURN_IF_PYERROR(); + return Status::OK(); + }; + return ConvertAsPyObjects(options, data, WrapValue, out_values); + } + + template + enable_if_t::value || is_binary_view_like_type::value || + is_fixed_size_binary_type::value, + Status> + Visit(const Type& type) { + auto WrapValue = [](const std::string_view& view, PyObject** out) { + *out = WrapBytes::Wrap(view.data(), view.length()); + if (*out == nullptr) { + PyErr_Clear(); + return Status::UnknownError("Wrapping ", view, " failed"); + } + return Status::OK(); + }; + return ConvertAsPyObjects(options, data, WrapValue, out_values); + } + + template + enable_if_date Visit(const Type& type) { + auto WrapValue = [](typename Type::c_type value, PyObject** out) { + RETURN_NOT_OK(internal::PyDate_from_int(value, Type::UNIT, out)); + RETURN_IF_PYERROR(); + return Status::OK(); + }; + return ConvertAsPyObjects(options, data, WrapValue, out_values); + } + + template + enable_if_time Visit(const Type& type) { + const TimeUnit::type unit = type.unit(); + auto WrapValue = [unit](typename Type::c_type value, PyObject** out) { + RETURN_NOT_OK(internal::PyTime_from_int(value, unit, out)); + RETURN_IF_PYERROR(); + return Status::OK(); + }; + return ConvertAsPyObjects(options, data, WrapValue, out_values); + } + + template + enable_if_timestamp Visit(const Type& type) { + const TimeUnit::type unit = type.unit(); + OwnedRef tzinfo; + + auto ConvertTimezoneNaive = [&](typename Type::c_type value, PyObject** out) { + RETURN_NOT_OK(internal::PyDateTime_from_int(value, unit, out)); + RETURN_IF_PYERROR(); + return Status::OK(); + }; + auto ConvertTimezoneAware = [&](typename Type::c_type value, PyObject** out) { + PyObject* naive_datetime; + RETURN_NOT_OK(ConvertTimezoneNaive(value, &naive_datetime)); + + // convert the timezone naive datetime object to timezone aware + // two step conversion of the datetime mimics Python's code: + // dt.replace(tzinfo=datetime.timezone.utc).astimezone(tzinfo) + // first step: replacing timezone with timezone.utc (replace method) + OwnedRef args(PyTuple_New(0)); + OwnedRef keywords(PyDict_New()); + PyDict_SetItemString(keywords.obj(), "tzinfo", PyDateTime_TimeZone_UTC); + OwnedRef naive_datetime_replace(PyObject_GetAttrString(naive_datetime, "replace")); + OwnedRef datetime_utc( + PyObject_Call(naive_datetime_replace.obj(), args.obj(), keywords.obj())); + // second step: adjust the datetime to tzinfo timezone (astimezone method) + *out = PyObject_CallMethod(datetime_utc.obj(), "astimezone", "O", tzinfo.obj()); + + // the timezone naive object is no longer required + Py_DECREF(naive_datetime); + RETURN_IF_PYERROR(); + + return Status::OK(); + }; + + if (!type.timezone().empty() && !options.ignore_timezone) { + // convert timezone aware + PyObject* tzobj; + ARROW_ASSIGN_OR_RAISE(tzobj, internal::StringToTzinfo(type.timezone())); + tzinfo.reset(tzobj); + RETURN_IF_PYERROR(); + RETURN_NOT_OK( + ConvertAsPyObjects(options, data, ConvertTimezoneAware, out_values)); + } else { + // convert timezone naive + RETURN_NOT_OK( + ConvertAsPyObjects(options, data, ConvertTimezoneNaive, out_values)); + } + + return Status::OK(); + } + + template + enable_if_t::value, Status> Visit( + const Type& type) { + OwnedRef args(PyTuple_New(0)); + OwnedRef kwargs(PyDict_New()); + RETURN_IF_PYERROR(); + auto to_date_offset = [&](const MonthDayNanoIntervalType::MonthDayNanos& interval, + PyObject** out) { + ARROW_DCHECK(internal::BorrowPandasDataOffsetType() != nullptr); + // DateOffset objects do not add nanoseconds component to pd.Timestamp. + // as of Pandas 1.3.3 + // (https://github.com/pandas-dev/pandas/issues/43892). + // So convert microseconds and remainder to preserve data + // but give users more expected results. + int64_t microseconds = interval.nanoseconds / 1000; + int64_t nanoseconds; + if (interval.nanoseconds >= 0) { + nanoseconds = interval.nanoseconds % 1000; + } else { + nanoseconds = -((-interval.nanoseconds) % 1000); + } + + PyDict_SetItemString(kwargs.obj(), "months", PyLong_FromLong(interval.months)); + PyDict_SetItemString(kwargs.obj(), "days", PyLong_FromLong(interval.days)); + PyDict_SetItemString(kwargs.obj(), "microseconds", + PyLong_FromLongLong(microseconds)); + PyDict_SetItemString(kwargs.obj(), "nanoseconds", PyLong_FromLongLong(nanoseconds)); + *out = + PyObject_Call(internal::BorrowPandasDataOffsetType(), args.obj(), kwargs.obj()); + RETURN_IF_PYERROR(); + return Status::OK(); + }; + return ConvertAsPyObjects(options, data, to_date_offset, + out_values); + } + + template + Status VisitDecimal(const DecimalT& type) { + OwnedRef decimal; + OwnedRef Decimal; + RETURN_NOT_OK(internal::ImportModule("decimal", &decimal)); + RETURN_NOT_OK(internal::ImportFromModule(decimal.obj(), "Decimal", &Decimal)); + PyObject* decimal_constructor = Decimal.obj(); + + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = checked_cast(*data.chunk(c)); + + for (int64_t i = 0; i < arr.length(); ++i) { + if (arr.IsNull(i)) { + Py_INCREF(Py_None); + *out_values++ = Py_None; + } else { + *out_values++ = + internal::DecimalFromString(decimal_constructor, arr.FormatValue(i)); + RETURN_IF_PYERROR(); + } + } + } + + return Status::OK(); + } + + Status Visit(const Decimal32Type& type) { + return VisitDecimal(type); + } + + Status Visit(const Decimal64Type& type) { + return VisitDecimal(type); + } + + Status Visit(const Decimal128Type& type) { + return VisitDecimal(type); + } + + Status Visit(const Decimal256Type& type) { + return VisitDecimal(type); + } + + template + enable_if_t::value || is_list_view_type::value, Status> Visit( + const T& type) { + if (!ListTypeSupported(*type.value_type())) { + return Status::NotImplemented( + "Not implemented type for conversion from List to Pandas: ", + type.value_type()->ToString()); + } + return ConvertListsLike(options, data, out_values); + } + + Status Visit(const MapType& type) { return ConvertMap(options, data, out_values); } + + Status Visit(const StructType& type) { + return ConvertStruct(options, data, out_values); + } + + template + enable_if_t::value || + std::is_same::value || + std::is_same::value || + std::is_same::value || + std::is_same::value || + (std::is_base_of::value && + !std::is_same::value) || + std::is_base_of::value, + Status> + Visit(const Type& type) { + return Status::NotImplemented("No implemented conversion to object dtype: ", + type.ToString()); + } +}; + +class ObjectWriter : public TypedPandasWriter { + public: + using TypedPandasWriter::TypedPandasWriter; + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + PyAcquireGIL lock; + ObjectWriterVisitor visitor{this->options_, *data, + this->GetBlockColumnStart(rel_placement)}; + return VisitTypeInline(*data->type(), &visitor); + } +}; + +static inline bool IsNonNullContiguous(const ChunkedArray& data) { + return data.num_chunks() == 1 && data.null_count() == 0; +} + +template +class IntWriter : public TypedPandasWriter { + public: + using ArrowType = typename npy_traits::TypeClass; + using TypedPandasWriter::TypedPandasWriter; + + bool CanZeroCopy(const ChunkedArray& data) const override { + return IsNonNullContiguous(data); + } + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + RETURN_NOT_OK(this->CheckTypeExact(*data->type(), ArrowType::type_id)); + ConvertIntegerNoNullsSameType( + this->options_, *data, this->GetBlockColumnStart(rel_placement)); + return Status::OK(); + } +}; + +template +class FloatWriter : public TypedPandasWriter { + public: + using ArrowType = typename npy_traits::TypeClass; + using TypedPandasWriter::TypedPandasWriter; + using T = typename ArrowType::c_type; + + bool CanZeroCopy(const ChunkedArray& data) const override { + return IsNonNullContiguous(data) && data.type()->id() == ArrowType::type_id; + } + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + Type::type in_type = data->type()->id(); + auto out_values = this->GetBlockColumnStart(rel_placement); + +#define INTEGER_CASE(IN_TYPE) \ + ConvertIntegerWithNulls(this->options_, *data, out_values); \ + break; + + switch (in_type) { + case Type::UINT8: + INTEGER_CASE(uint8_t); + case Type::INT8: + INTEGER_CASE(int8_t); + case Type::UINT16: + INTEGER_CASE(uint16_t); + case Type::INT16: + INTEGER_CASE(int16_t); + case Type::UINT32: + INTEGER_CASE(uint32_t); + case Type::INT32: + INTEGER_CASE(int32_t); + case Type::UINT64: + INTEGER_CASE(uint64_t); + case Type::INT64: + INTEGER_CASE(int64_t); + case Type::HALF_FLOAT: + ConvertNumericNullableCast(*data, npy_traits::na_sentinel, out_values); + case Type::FLOAT: + ConvertNumericNullableCast(*data, npy_traits::na_sentinel, out_values); + break; + case Type::DOUBLE: + ConvertNumericNullableCast(*data, npy_traits::na_sentinel, out_values); + break; + default: + return Status::NotImplemented("Cannot write Arrow data of type ", + data->type()->ToString(), + " to a Pandas floating point block"); + } + +#undef INTEGER_CASE + + return Status::OK(); + } +}; + +using UInt8Writer = IntWriter; +using Int8Writer = IntWriter; +using UInt16Writer = IntWriter; +using Int16Writer = IntWriter; +using UInt32Writer = IntWriter; +using Int32Writer = IntWriter; +using UInt64Writer = IntWriter; +using Int64Writer = IntWriter; +using Float16Writer = FloatWriter; +using Float32Writer = FloatWriter; +using Float64Writer = FloatWriter; + +class BoolWriter : public TypedPandasWriter { + public: + using TypedPandasWriter::TypedPandasWriter; + + Status TransferSingle(std::shared_ptr data, PyObject* py_ref) override { + RETURN_NOT_OK( + CheckNoZeroCopy("Zero copy conversions not possible with " + "boolean types")); + RETURN_NOT_OK(EnsureAllocated()); + return CopyInto(data, /*rel_placement=*/0); + } + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + RETURN_NOT_OK(this->CheckTypeExact(*data->type(), Type::BOOL)); + auto out_values = this->GetBlockColumnStart(rel_placement); + for (int c = 0; c < data->num_chunks(); c++) { + const auto& arr = checked_cast(*data->chunk(c)); + for (int64_t i = 0; i < arr.length(); ++i) { + *out_values++ = static_cast(arr.Value(i)); + } + } + return Status::OK(); + } +}; + +// ---------------------------------------------------------------------- +// Date / timestamp types + +template +inline void ConvertDatetime(const ChunkedArray& data, int64_t* out_values) { + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = *data.chunk(c); + const T* in_values = GetPrimitiveValues(arr); + + for (int64_t i = 0; i < arr.length(); ++i) { + *out_values++ = arr.IsNull(i) ? kPandasTimestampNull + : (static_cast(in_values[i]) * SHIFT); + } + } +} + +template +void ConvertDatesShift(const ChunkedArray& data, int64_t* out_values) { + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = *data.chunk(c); + const T* in_values = GetPrimitiveValues(arr); + for (int64_t i = 0; i < arr.length(); ++i) { + *out_values++ = arr.IsNull(i) ? kPandasTimestampNull + : static_cast(in_values[i]) / SHIFT; + } + } +} + +class DatetimeDayWriter : public TypedPandasWriter { + public: + using TypedPandasWriter::TypedPandasWriter; + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + int64_t* out_values = this->GetBlockColumnStart(rel_placement); + const auto& type = checked_cast(*data->type()); + switch (type.unit()) { + case DateUnit::DAY: + ConvertDatesShift(*data, out_values); + break; + case DateUnit::MILLI: + ConvertDatesShift(*data, out_values); + break; + } + return Status::OK(); + } + + protected: + Status Allocate() override { + RETURN_NOT_OK(this->AllocateNDArray(NPY_DATETIME)); + SetDatetimeUnit(NPY_FR_D); + return Status::OK(); + } +}; + +template +class DatetimeWriter : public TypedPandasWriter { + public: + using TypedPandasWriter::TypedPandasWriter; + + bool CanZeroCopy(const ChunkedArray& data) const override { + if (data.type()->id() == Type::TIMESTAMP) { + const auto& type = checked_cast(*data.type()); + return IsNonNullContiguous(data) && type.unit() == UNIT; + } else { + return false; + } + } + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + const auto& ts_type = checked_cast(*data->type()); + ARROW_DCHECK_EQ(UNIT, ts_type.unit()) << "Should only call instances of this writer " + << "with arrays of the correct unit"; + ConvertNumericNullable(*data, kPandasTimestampNull, + this->GetBlockColumnStart(rel_placement)); + return Status::OK(); + } + + protected: + Status Allocate() override { + RETURN_NOT_OK(this->AllocateNDArray(NPY_DATETIME)); + SetDatetimeUnit(internal::NumPyFrequency(UNIT)); + return Status::OK(); + } +}; + +using DatetimeSecondWriter = DatetimeWriter; + +class DatetimeMilliWriter : public DatetimeWriter { + public: + using DatetimeWriter::DatetimeWriter; + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + Type::type type = data->type()->id(); + int64_t* out_values = this->GetBlockColumnStart(rel_placement); + if (type == Type::DATE32) { + // Convert from days since epoch to datetime64[ms] + ConvertDatetime(*data, out_values); + } else if (type == Type::DATE64) { + ConvertNumericNullable(*data, kPandasTimestampNull, out_values); + } else { + const auto& ts_type = checked_cast(*data->type()); + ARROW_DCHECK_EQ(TimeUnit::MILLI, ts_type.unit()) + << "Should only call instances of this writer " + << "with arrays of the correct unit"; + ConvertNumericNullable(*data, kPandasTimestampNull, out_values); + } + return Status::OK(); + } +}; + +using DatetimeMicroWriter = DatetimeWriter; + +class DatetimeNanoWriter : public DatetimeWriter { + public: + using DatetimeWriter::DatetimeWriter; + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + Type::type type = data->type()->id(); + int64_t* out_values = this->GetBlockColumnStart(rel_placement); + compute::ExecContext ctx(options_.pool); + compute::CastOptions options; + if (options_.safe_cast) { + options = compute::CastOptions::Safe(); + } else { + options = compute::CastOptions::Unsafe(); + } + Datum out; + auto target_type = timestamp(TimeUnit::NANO); + + if (type == Type::DATE32) { + // Convert from days since epoch to datetime64[ns] + ConvertDatetime(*data, out_values); + } else if (type == Type::DATE64) { + // Date64Type is millisecond timestamp stored as int64_t + // TODO(wesm): Do we want to make sure to zero out the milliseconds? + ConvertDatetime(*data, out_values); + } else if (type == Type::TIMESTAMP) { + const auto& ts_type = checked_cast(*data->type()); + + if (ts_type.unit() == TimeUnit::NANO) { + ConvertNumericNullable(*data, kPandasTimestampNull, out_values); + } else if (ts_type.unit() == TimeUnit::MICRO || ts_type.unit() == TimeUnit::MILLI || + ts_type.unit() == TimeUnit::SECOND) { + ARROW_ASSIGN_OR_RAISE(out, compute::Cast(data, target_type, options, &ctx)); + ConvertNumericNullable(*out.chunked_array(), kPandasTimestampNull, + out_values); + } else { + return Status::NotImplemented("Unsupported time unit"); + } + } else { + return Status::NotImplemented("Cannot write Arrow data of type ", + data->type()->ToString(), + " to a Pandas datetime block."); + } + return Status::OK(); + } +}; + +template +class DatetimeTZWriter : public BASE { + public: + DatetimeTZWriter(const PandasOptions& options, const std::string& timezone, + int64_t num_rows) + : BASE(options, num_rows, 1), timezone_(timezone) {} + + protected: + Status GetResultBlock(PyObject** out) override { + RETURN_NOT_OK(this->MakeBlock1D()); + *out = this->block_arr_.obj(); + return Status::OK(); + } + + Status AddResultMetadata(PyObject* result) override { + PyObject* py_tz = PyUnicode_FromStringAndSize( + timezone_.c_str(), static_cast(timezone_.size())); + RETURN_IF_PYERROR(); + PyDict_SetItemString(result, "timezone", py_tz); + Py_DECREF(py_tz); + return Status::OK(); + } + + private: + std::string timezone_; +}; + +using DatetimeSecondTZWriter = DatetimeTZWriter; +using DatetimeMilliTZWriter = DatetimeTZWriter; +using DatetimeMicroTZWriter = DatetimeTZWriter; +using DatetimeNanoTZWriter = DatetimeTZWriter; + +template +class TimedeltaWriter : public TypedPandasWriter { + public: + using TypedPandasWriter::TypedPandasWriter; + + Status AllocateTimedelta(int ndim) { + RETURN_NOT_OK(this->AllocateNDArray(NPY_TIMEDELTA, ndim)); + SetDatetimeUnit(internal::NumPyFrequency(UNIT)); + return Status::OK(); + } + + bool CanZeroCopy(const ChunkedArray& data) const override { + const auto& type = checked_cast(*data.type()); + return IsNonNullContiguous(data) && type.unit() == UNIT; + } + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + const auto& type = checked_cast(*data->type()); + ARROW_DCHECK_EQ(UNIT, type.unit()) << "Should only call instances of this writer " + << "with arrays of the correct unit"; + ConvertNumericNullable(*data, kPandasTimestampNull, + this->GetBlockColumnStart(rel_placement)); + return Status::OK(); + } + + protected: + Status Allocate() override { return AllocateTimedelta(2); } +}; + +using TimedeltaSecondWriter = TimedeltaWriter; +using TimedeltaMilliWriter = TimedeltaWriter; +using TimedeltaMicroWriter = TimedeltaWriter; + +class TimedeltaNanoWriter : public TimedeltaWriter { + public: + using TimedeltaWriter::TimedeltaWriter; + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + Type::type type = data->type()->id(); + int64_t* out_values = this->GetBlockColumnStart(rel_placement); + if (type == Type::DURATION) { + const auto& ts_type = checked_cast(*data->type()); + if (ts_type.unit() == TimeUnit::NANO) { + ConvertNumericNullable(*data, kPandasTimestampNull, out_values); + } else if (ts_type.unit() == TimeUnit::MICRO) { + ConvertDatetime(*data, out_values); + } else if (ts_type.unit() == TimeUnit::MILLI) { + ConvertDatetime(*data, out_values); + } else if (ts_type.unit() == TimeUnit::SECOND) { + ConvertDatetime(*data, out_values); + } else { + return Status::NotImplemented("Unsupported time unit"); + } + } else { + return Status::NotImplemented("Cannot write Arrow data of type ", + data->type()->ToString(), + " to a Pandas timedelta block."); + } + return Status::OK(); + } +}; + +Status MakeZeroLengthArray(const std::shared_ptr& type, + std::shared_ptr* out) { + std::unique_ptr builder; + RETURN_NOT_OK(MakeBuilder(default_memory_pool(), type, &builder)); + RETURN_NOT_OK(builder->Resize(0)); + return builder->Finish(out); +} + +bool NeedDictionaryUnification(const ChunkedArray& data) { + if (data.num_chunks() < 2) { + return false; + } + const auto& arr_first = checked_cast(*data.chunk(0)); + for (int c = 1; c < data.num_chunks(); c++) { + const auto& arr = checked_cast(*data.chunk(c)); + if (!(arr_first.dictionary()->Equals(arr.dictionary()))) { + return true; + } + } + return false; +} + +template +class CategoricalWriter + : public TypedPandasWriter::npy_type> { + public: + using TRAITS = arrow_traits; + using ArrayType = typename TypeTraits::ArrayType; + using T = typename TRAITS::T; + + explicit CategoricalWriter(const PandasOptions& options, int64_t num_rows) + : TypedPandasWriter(options, num_rows, 1), + ordered_(false), + needs_copy_(false) {} + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + return Status::NotImplemented("categorical type"); + } + + Status TransferSingle(std::shared_ptr data, PyObject* py_ref) override { + const auto& dict_type = checked_cast(*data->type()); + std::shared_ptr dict; + if (data->num_chunks() == 0) { + // no dictionary values => create empty array + RETURN_NOT_OK(this->AllocateNDArray(TRAITS::npy_type, 1)); + RETURN_NOT_OK(MakeZeroLengthArray(dict_type.value_type(), &dict)); + } else { + ARROW_DCHECK_EQ(IndexType::type_id, dict_type.index_type()->id()); + RETURN_NOT_OK(WriteIndices(*data, &dict)); + } + + PyObject* pydict; + RETURN_NOT_OK(ConvertArrayToPandas(this->options_, dict, nullptr, &pydict)); + dictionary_.reset(pydict); + ordered_ = dict_type.ordered(); + return Status::OK(); + } + + Status Write(std::shared_ptr data, int64_t abs_placement, + int64_t rel_placement) override { + RETURN_NOT_OK(this->EnsurePlacementAllocated()); + RETURN_NOT_OK(TransferSingle(data, /*py_ref=*/nullptr)); + this->placement_data_[rel_placement] = abs_placement; + return Status::OK(); + } + + Status GetSeriesResult(PyObject** out) override { + PyAcquireGIL lock; + + PyObject* result = PyDict_New(); + RETURN_IF_PYERROR(); + + // Expected single array dictionary layout + PyDict_SetItemString(result, "indices", this->block_arr_.obj()); + RETURN_IF_PYERROR(); + RETURN_NOT_OK(AddResultMetadata(result)); + + *out = result; + return Status::OK(); + } + + protected: + Status AddResultMetadata(PyObject* result) override { + PyDict_SetItemString(result, "dictionary", dictionary_.obj()); + PyObject* py_ordered = ordered_ ? Py_True : Py_False; + Py_INCREF(py_ordered); + PyDict_SetItemString(result, "ordered", py_ordered); + return Status::OK(); + } + + Status WriteIndicesUniform(const ChunkedArray& data) { + // For unsigned types, upcast to signed since pandas uses -1 for nulls + // uint8 to int16, uint16 to int32, uint32 to int64, signed types unchanged + using OutputType = std::conditional_t< + std::is_same::value, int16_t, + std::conditional_t< + std::is_same::value, int32_t, + std::conditional_t::value, int64_t, T>>>; + const int npy_output_type = std::is_same::value ? NPY_INT16 + : std::is_same::value ? NPY_INT32 + : std::is_same::value + ? NPY_INT64 + : TRAITS::npy_type; + + RETURN_NOT_OK(this->AllocateNDArray(npy_output_type, 1)); + auto out_values = reinterpret_cast(this->block_data_); + + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = checked_cast(*data.chunk(c)); + const auto& indices = checked_cast(*arr.indices()); + auto values = reinterpret_cast(indices.raw_values()); + + RETURN_NOT_OK(CheckIndexBounds(*indices.data(), arr.dictionary()->length())); + // Null is -1 in CategoricalBlock + for (int i = 0; i < arr.length(); ++i) { + if (indices.IsValid(i)) { + *out_values++ = static_cast(values[i]); + } else { + *out_values++ = -1; + } + } + } + return Status::OK(); + } + + Status WriteIndicesVarying(const ChunkedArray& data, std::shared_ptr* out_dict) { + // Yield int32 indices to allow for dictionary outgrowing the current index + // type + RETURN_NOT_OK(this->AllocateNDArray(NPY_INT32, 1)); + auto out_values = reinterpret_cast(this->block_data_); + + const auto& dict_type = checked_cast(*data.type()); + + ARROW_ASSIGN_OR_RAISE(auto unifier, DictionaryUnifier::Make(dict_type.value_type(), + this->options_.pool)); + for (int c = 0; c < data.num_chunks(); c++) { + const auto& arr = checked_cast(*data.chunk(c)); + const auto& indices = checked_cast(*arr.indices()); + auto values = reinterpret_cast(indices.raw_values()); + + std::shared_ptr transpose_buffer; + RETURN_NOT_OK(unifier->Unify(*arr.dictionary(), &transpose_buffer)); + + auto transpose = reinterpret_cast(transpose_buffer->data()); + int64_t dict_length = arr.dictionary()->length(); + + RETURN_NOT_OK(CheckIndexBounds(*indices.data(), dict_length)); + + // Null is -1 in CategoricalBlock + for (int i = 0; i < arr.length(); ++i) { + if (indices.IsValid(i)) { + *out_values++ = transpose[values[i]]; + } else { + *out_values++ = -1; + } + } + } + + std::shared_ptr unused_type; + return unifier->GetResult(&unused_type, out_dict); + } + + Status WriteIndices(const ChunkedArray& data, std::shared_ptr* out_dict) { + ARROW_DCHECK_GT(data.num_chunks(), 0); + + // Sniff the first chunk + const auto& arr_first = checked_cast(*data.chunk(0)); + const auto indices_first = std::static_pointer_cast(arr_first.indices()); + + // For unsigned types, we need to convert to signed for pandas compatibility + // even when there are no nulls, so we skip the fast path + const bool is_unsigned = std::is_unsigned::value; + + if (data.num_chunks() == 1 && indices_first->null_count() == 0 && !is_unsigned) { + RETURN_NOT_OK( + CheckIndexBounds(*indices_first->data(), arr_first.dictionary()->length())); + + PyObject* wrapped; + npy_intp dims[1] = {static_cast(this->num_rows_)}; + RETURN_NOT_OK(MakeNumPyView(indices_first, /*py_ref=*/nullptr, TRAITS::npy_type, + /*ndim=*/1, dims, &wrapped)); + this->SetBlockData(wrapped); + *out_dict = arr_first.dictionary(); + } else { + RETURN_NOT_OK(this->CheckNotZeroCopyOnly(data)); + if (NeedDictionaryUnification(data)) { + RETURN_NOT_OK(WriteIndicesVarying(data, out_dict)); + } else { + RETURN_NOT_OK(WriteIndicesUniform(data)); + *out_dict = arr_first.dictionary(); + } + } + return Status::OK(); + } + + OwnedRefNoGIL dictionary_; + bool ordered_; + bool needs_copy_; +}; + +class ExtensionWriter : public PandasWriter { + public: + using PandasWriter::PandasWriter; + + Status Allocate() override { + // no-op + return Status::OK(); + } + + Status TransferSingle(std::shared_ptr data, PyObject* py_ref) override { + PyAcquireGIL lock; + PyObject* py_array; + py_array = wrap_chunked_array(data); + py_array_.reset(py_array); + + return Status::OK(); + } + + Status CopyInto(std::shared_ptr data, int64_t rel_placement) override { + return TransferSingle(data, nullptr); + } + + Status GetDataFrameResult(PyObject** out) override { + PyAcquireGIL lock; + PyObject* result = PyDict_New(); + RETURN_IF_PYERROR(); + + PyDict_SetItemString(result, "py_array", py_array_.obj()); + PyDict_SetItemString(result, "placement", placement_arr_.obj()); + *out = result; + return Status::OK(); + } + + Status GetSeriesResult(PyObject** out) override { + *out = py_array_.detach(); + return Status::OK(); + } + + protected: + OwnedRefNoGIL py_array_; +}; + +Status MakeWriter(const PandasOptions& options, PandasWriter::type writer_type, + const DataType& type, int64_t num_rows, int num_columns, + std::shared_ptr* writer) { +#define BLOCK_CASE(NAME, TYPE) \ + case PandasWriter::NAME: \ + *writer = std::make_shared(options, num_rows, num_columns); \ + break; + +#define CATEGORICAL_CASE(TYPE) \ + case TYPE::type_id: \ + *writer = std::make_shared>(options, num_rows); \ + break; + +#define TZ_CASE(NAME, TYPE) \ + case PandasWriter::NAME: { \ + const auto& ts_type = checked_cast(type); \ + *writer = std::make_shared(options, ts_type.timezone(), num_rows); \ + } break; + + switch (writer_type) { + case PandasWriter::CATEGORICAL: { + const auto& index_type = *checked_cast(type).index_type(); + switch (index_type.id()) { + CATEGORICAL_CASE(Int8Type); + CATEGORICAL_CASE(Int16Type); + CATEGORICAL_CASE(Int32Type); + CATEGORICAL_CASE(Int64Type); + CATEGORICAL_CASE(UInt8Type); + CATEGORICAL_CASE(UInt16Type); + CATEGORICAL_CASE(UInt32Type); + case Type::UINT64: + return Status::TypeError( + "Converting UInt64 dictionary indices to pandas is not supported."); + default: + // Unreachable + ARROW_DCHECK(false); + break; + } + } break; + case PandasWriter::EXTENSION: + *writer = std::make_shared(options, num_rows, num_columns); + break; + BLOCK_CASE(OBJECT, ObjectWriter); + BLOCK_CASE(UINT8, UInt8Writer); + BLOCK_CASE(INT8, Int8Writer); + BLOCK_CASE(UINT16, UInt16Writer); + BLOCK_CASE(INT16, Int16Writer); + BLOCK_CASE(UINT32, UInt32Writer); + BLOCK_CASE(INT32, Int32Writer); + BLOCK_CASE(UINT64, UInt64Writer); + BLOCK_CASE(INT64, Int64Writer); + BLOCK_CASE(HALF_FLOAT, Float16Writer); + BLOCK_CASE(FLOAT, Float32Writer); + BLOCK_CASE(DOUBLE, Float64Writer); + BLOCK_CASE(BOOL, BoolWriter); + BLOCK_CASE(DATETIME_DAY, DatetimeDayWriter); + BLOCK_CASE(DATETIME_SECOND, DatetimeSecondWriter); + BLOCK_CASE(DATETIME_MILLI, DatetimeMilliWriter); + BLOCK_CASE(DATETIME_MICRO, DatetimeMicroWriter); + BLOCK_CASE(DATETIME_NANO, DatetimeNanoWriter); + BLOCK_CASE(TIMEDELTA_SECOND, TimedeltaSecondWriter); + BLOCK_CASE(TIMEDELTA_MILLI, TimedeltaMilliWriter); + BLOCK_CASE(TIMEDELTA_MICRO, TimedeltaMicroWriter); + BLOCK_CASE(TIMEDELTA_NANO, TimedeltaNanoWriter); + TZ_CASE(DATETIME_SECOND_TZ, DatetimeSecondTZWriter); + TZ_CASE(DATETIME_MILLI_TZ, DatetimeMilliTZWriter); + TZ_CASE(DATETIME_MICRO_TZ, DatetimeMicroTZWriter); + TZ_CASE(DATETIME_NANO_TZ, DatetimeNanoTZWriter); + default: + return Status::NotImplemented("Unsupported block type"); + } + +#undef BLOCK_CASE +#undef CATEGORICAL_CASE + + return Status::OK(); +} + +static Status GetPandasWriterType(const ChunkedArray& data, const PandasOptions& options, + PandasWriter::type* output_type) { +#define INTEGER_CASE(NAME) \ + *output_type = \ + data.null_count() > 0 \ + ? options.integer_object_nulls ? PandasWriter::OBJECT : PandasWriter::DOUBLE \ + : PandasWriter::NAME; \ + break; + + switch (data.type()->id()) { + case Type::BOOL: + *output_type = data.null_count() > 0 ? PandasWriter::OBJECT : PandasWriter::BOOL; + break; + case Type::UINT8: + INTEGER_CASE(UINT8); + case Type::INT8: + INTEGER_CASE(INT8); + case Type::UINT16: + INTEGER_CASE(UINT16); + case Type::INT16: + INTEGER_CASE(INT16); + case Type::UINT32: + INTEGER_CASE(UINT32); + case Type::INT32: + INTEGER_CASE(INT32); + case Type::UINT64: + INTEGER_CASE(UINT64); + case Type::INT64: + INTEGER_CASE(INT64); + case Type::HALF_FLOAT: + *output_type = PandasWriter::HALF_FLOAT; + break; + case Type::FLOAT: + *output_type = PandasWriter::FLOAT; + break; + case Type::DOUBLE: + *output_type = PandasWriter::DOUBLE; + break; + case Type::STRING: // fall through + case Type::LARGE_STRING: // fall through + case Type::STRING_VIEW: // fall through + case Type::BINARY: // fall through + case Type::LARGE_BINARY: + case Type::BINARY_VIEW: + case Type::NA: // fall through + case Type::FIXED_SIZE_BINARY: // fall through + case Type::STRUCT: // fall through + case Type::TIME32: // fall through + case Type::TIME64: // fall through + case Type::DECIMAL32: // fall through + case Type::DECIMAL64: // fall through + case Type::DECIMAL128: // fall through + case Type::DECIMAL256: // fall through + case Type::INTERVAL_MONTH_DAY_NANO: // fall through + *output_type = PandasWriter::OBJECT; + break; + case Type::DATE32: + if (options.date_as_object) { + *output_type = PandasWriter::OBJECT; + } else if (options.coerce_temporal_nanoseconds) { + *output_type = PandasWriter::DATETIME_NANO; + } else if (options.to_numpy) { + // Numpy supports Day, but Pandas does not + *output_type = PandasWriter::DATETIME_DAY; + } else { + *output_type = PandasWriter::DATETIME_MILLI; + } + break; + case Type::DATE64: + if (options.date_as_object) { + *output_type = PandasWriter::OBJECT; + } else if (options.coerce_temporal_nanoseconds) { + *output_type = PandasWriter::DATETIME_NANO; + } else { + *output_type = PandasWriter::DATETIME_MILLI; + } + break; + case Type::TIMESTAMP: { + const auto& ts_type = checked_cast(*data.type()); + if (options.timestamp_as_object && ts_type.unit() != TimeUnit::NANO) { + // Nanoseconds are never out of bounds for pandas, so in that case + // we don't convert to object + *output_type = PandasWriter::OBJECT; + } else if (options.coerce_temporal_nanoseconds) { + if (!ts_type.timezone().empty()) { + *output_type = PandasWriter::DATETIME_NANO_TZ; + } else { + *output_type = PandasWriter::DATETIME_NANO; + } + } else { + if (!ts_type.timezone().empty()) { + switch (ts_type.unit()) { + case TimeUnit::SECOND: + *output_type = PandasWriter::DATETIME_SECOND_TZ; + break; + case TimeUnit::MILLI: + *output_type = PandasWriter::DATETIME_MILLI_TZ; + break; + case TimeUnit::MICRO: + *output_type = PandasWriter::DATETIME_MICRO_TZ; + break; + case TimeUnit::NANO: + *output_type = PandasWriter::DATETIME_NANO_TZ; + break; + } + } else { + switch (ts_type.unit()) { + case TimeUnit::SECOND: + *output_type = PandasWriter::DATETIME_SECOND; + break; + case TimeUnit::MILLI: + *output_type = PandasWriter::DATETIME_MILLI; + break; + case TimeUnit::MICRO: + *output_type = PandasWriter::DATETIME_MICRO; + break; + case TimeUnit::NANO: + *output_type = PandasWriter::DATETIME_NANO; + break; + } + } + } + } break; + case Type::DURATION: { + const auto& dur_type = checked_cast(*data.type()); + if (options.coerce_temporal_nanoseconds) { + *output_type = PandasWriter::TIMEDELTA_NANO; + } else { + switch (dur_type.unit()) { + case TimeUnit::SECOND: + *output_type = PandasWriter::TIMEDELTA_SECOND; + break; + case TimeUnit::MILLI: + *output_type = PandasWriter::TIMEDELTA_MILLI; + break; + case TimeUnit::MICRO: + *output_type = PandasWriter::TIMEDELTA_MICRO; + break; + case TimeUnit::NANO: + *output_type = PandasWriter::TIMEDELTA_NANO; + break; + } + } + } break; + case Type::FIXED_SIZE_LIST: + case Type::LIST: + case Type::LARGE_LIST: + case Type::LIST_VIEW: + case Type::LARGE_LIST_VIEW: + case Type::MAP: { + auto list_type = std::static_pointer_cast(data.type()); + if (!ListTypeSupported(*list_type->value_type())) { + return Status::NotImplemented("Not implemented type for Arrow list to pandas: ", + list_type->value_type()->ToString()); + } + *output_type = PandasWriter::OBJECT; + } break; + case Type::DICTIONARY: + *output_type = PandasWriter::CATEGORICAL; + break; + case Type::EXTENSION: + *output_type = PandasWriter::EXTENSION; + break; + default: + return Status::NotImplemented( + "No known equivalent Pandas block for Arrow data of type ", + data.type()->ToString(), " is known."); + } + return Status::OK(); +} + +// Construct the exact pandas "BlockManager" memory layout +// +// * For each column determine the correct output pandas type +// * Allocate 2D blocks (ncols x nrows) for each distinct data type in output +// * Allocate block placement arrays +// * Write Arrow columns out into each slice of memory; populate block +// * placement arrays as we go +class PandasBlockCreator { + public: + using WriterMap = std::unordered_map>; + + explicit PandasBlockCreator(const PandasOptions& options, FieldVector fields, + ChunkedArrayVector arrays) + : options_(options), fields_(std::move(fields)), arrays_(std::move(arrays)) { + num_columns_ = static_cast(arrays_.size()); + if (num_columns_ > 0) { + num_rows_ = arrays_[0]->length(); + } + column_block_placement_.resize(num_columns_); + } + virtual ~PandasBlockCreator() = default; + + virtual Status Convert(PyObject** out) = 0; + + Status AppendBlocks(const WriterMap& blocks, PyObject* list) { + for (const auto& it : blocks) { + PyObject* item; + RETURN_NOT_OK(it.second->GetDataFrameResult(&item)); + if (PyList_Append(list, item) < 0) { + RETURN_IF_PYERROR(); + } + + // ARROW-1017; PyList_Append increments object refcount + Py_DECREF(item); + } + return Status::OK(); + } + + protected: + PandasOptions options_; + + FieldVector fields_; + ChunkedArrayVector arrays_; + int num_columns_; + int64_t num_rows_; + + // column num -> relative placement within internal block + std::vector column_block_placement_; +}; + +// Helper function for extension chunked arrays +// Constructing a storage chunked array of an extension chunked array +std::shared_ptr GetStorageChunkedArray(std::shared_ptr arr) { + auto value_type = checked_cast(*arr->type()).storage_type(); + ArrayVector storage_arrays; + for (int c = 0; c < arr->num_chunks(); c++) { + const auto& arr_ext = checked_cast(*arr->chunk(c)); + storage_arrays.emplace_back(arr_ext.storage()); + } + return std::make_shared(std::move(storage_arrays), value_type); +}; + +// Helper function to decode RunEndEncodedArray +Result> GetDecodedChunkedArray( + std::shared_ptr arr) { + ARROW_ASSIGN_OR_RAISE(Datum decoded, compute::RunEndDecode(arr)); + ARROW_DCHECK(decoded.is_chunked_array()); + return decoded.chunked_array(); +}; + +class ConsolidatedBlockCreator : public PandasBlockCreator { + public: + using PandasBlockCreator::PandasBlockCreator; + + Status Convert(PyObject** out) override { + column_types_.resize(num_columns_); + RETURN_NOT_OK(CreateBlocks()); + RETURN_NOT_OK(WriteTableToBlocks()); + PyAcquireGIL lock; + + PyObject* result = PyList_New(0); + RETURN_IF_PYERROR(); + + RETURN_NOT_OK(AppendBlocks(blocks_, result)); + RETURN_NOT_OK(AppendBlocks(singleton_blocks_, result)); + + *out = result; + return Status::OK(); + } + + Status GetBlockType(int column_index, PandasWriter::type* out) { + if (options_.IsExtensionColumn(fields_[column_index]->name())) { + *out = PandasWriter::EXTENSION; + return Status::OK(); + } else { + // In case of an extension array default to the storage type + if (arrays_[column_index]->type()->id() == Type::EXTENSION) { + arrays_[column_index] = GetStorageChunkedArray(arrays_[column_index]); + } + // In case of a RunEndEncodedArray default to the values type + else if (arrays_[column_index]->type()->id() == Type::RUN_END_ENCODED) { + ARROW_ASSIGN_OR_RAISE(arrays_[column_index], + GetDecodedChunkedArray(arrays_[column_index])); + } + return GetPandasWriterType(*arrays_[column_index], options_, out); + } + } + + Status CreateBlocks() { + for (int i = 0; i < num_columns_; ++i) { + const DataType& type = *arrays_[i]->type(); + PandasWriter::type output_type; + RETURN_NOT_OK(GetBlockType(i, &output_type)); + + int block_placement = 0; + std::shared_ptr writer; + if (output_type == PandasWriter::CATEGORICAL || + output_type == PandasWriter::DATETIME_SECOND_TZ || + output_type == PandasWriter::DATETIME_MILLI_TZ || + output_type == PandasWriter::DATETIME_MICRO_TZ || + output_type == PandasWriter::DATETIME_NANO_TZ || + output_type == PandasWriter::EXTENSION) { + RETURN_NOT_OK(MakeWriter(options_, output_type, type, num_rows_, + /*num_columns=*/1, &writer)); + singleton_blocks_[i] = writer; + } else { + auto it = block_sizes_.find(output_type); + if (it != block_sizes_.end()) { + block_placement = it->second; + // Increment count + ++it->second; + } else { + // Add key to map + block_sizes_[output_type] = 1; + } + } + column_types_[i] = output_type; + column_block_placement_[i] = block_placement; + } + + // Create normal non-categorical blocks + for (const auto& it : this->block_sizes_) { + PandasWriter::type output_type = static_cast(it.first); + std::shared_ptr block; + RETURN_NOT_OK(MakeWriter(this->options_, output_type, /*unused*/ *null(), num_rows_, + it.second, &block)); + this->blocks_[output_type] = block; + } + return Status::OK(); + } + + Status GetWriter(int i, std::shared_ptr* block) { + PandasWriter::type output_type = this->column_types_[i]; + switch (output_type) { + case PandasWriter::CATEGORICAL: + case PandasWriter::DATETIME_SECOND_TZ: + case PandasWriter::DATETIME_MILLI_TZ: + case PandasWriter::DATETIME_MICRO_TZ: + case PandasWriter::DATETIME_NANO_TZ: + case PandasWriter::EXTENSION: { + auto it = this->singleton_blocks_.find(i); + if (it == this->singleton_blocks_.end()) { + return Status::KeyError("No block allocated"); + } + *block = it->second; + } break; + default: + auto it = this->blocks_.find(output_type); + if (it == this->blocks_.end()) { + return Status::KeyError("No block allocated"); + } + *block = it->second; + break; + } + return Status::OK(); + } + + Status WriteTableToBlocks() { + auto WriteColumn = [this](int i) { + std::shared_ptr block; + RETURN_NOT_OK(this->GetWriter(i, &block)); + // ARROW-3789 Use std::move on the array to permit self-destructing + return block->Write(std::move(arrays_[i]), i, this->column_block_placement_[i]); + }; + + return OptionalParallelFor(options_.use_threads, num_columns_, WriteColumn); + } + + private: + // column num -> block type id + std::vector column_types_; + + // block type -> type count + std::unordered_map block_sizes_; + std::unordered_map block_types_; + + // block type -> block + WriterMap blocks_; + + WriterMap singleton_blocks_; +}; + +/// \brief Create blocks for pandas.DataFrame block manager using one block per +/// column strategy. This permits some zero-copy optimizations as well as the +/// ability for the table to "self-destruct" if selected by the user. +class SplitBlockCreator : public PandasBlockCreator { + public: + using PandasBlockCreator::PandasBlockCreator; + + Status GetWriter(int i, std::shared_ptr* writer) { + PandasWriter::type output_type = PandasWriter::OBJECT; + const DataType& type = *arrays_[i]->type(); + if (options_.IsExtensionColumn(fields_[i]->name())) { + output_type = PandasWriter::EXTENSION; + } else { + // Null count needed to determine output type + RETURN_NOT_OK(GetPandasWriterType(*arrays_[i], options_, &output_type)); + } + return MakeWriter(this->options_, output_type, type, num_rows_, 1, writer); + } + + Status Convert(PyObject** out) override { + PyAcquireGIL lock; + + PyObject* result = PyList_New(0); + RETURN_IF_PYERROR(); + + for (int i = 0; i < num_columns_; ++i) { + std::shared_ptr writer; + RETURN_NOT_OK(GetWriter(i, &writer)); + // ARROW-3789 Use std::move on the array to permit self-destructing + RETURN_NOT_OK(writer->Write(std::move(arrays_[i]), i, /*rel_placement=*/0)); + + PyObject* item; + RETURN_NOT_OK(writer->GetDataFrameResult(&item)); + if (PyList_Append(result, item) < 0) { + RETURN_IF_PYERROR(); + } + // PyList_Append increments object refcount + Py_DECREF(item); + } + + *out = result; + return Status::OK(); + } + + private: + std::vector> writers_; +}; + +Status ConvertCategoricals(const PandasOptions& options, ChunkedArrayVector* arrays, + FieldVector* fields) { + std::vector columns_to_encode; + + // For Categorical conversions + auto EncodeColumn = [&](int j) { + int i = columns_to_encode[j]; + if (options.zero_copy_only) { + return Status::Invalid("Need to dictionary encode a column, but ", + "only zero-copy conversions allowed"); + } + compute::ExecContext ctx(options.pool); + ARROW_ASSIGN_OR_RAISE( + Datum out, DictionaryEncode((*arrays)[i], + compute::DictionaryEncodeOptions::Defaults(), &ctx)); + (*arrays)[i] = out.chunked_array(); + (*fields)[i] = (*fields)[i]->WithType((*arrays)[i]->type()); + return Status::OK(); + }; + + if (options.HasCategoricalColumns()) { + for (int i = 0; i < static_cast(arrays->size()); i++) { + if ((*arrays)[i]->type()->id() != Type::DICTIONARY && + options.IsCategoricalColumn((*fields)[i]->name())) { + columns_to_encode.push_back(i); + } + } + } + if (options.strings_to_categorical) { + for (int i = 0; i < static_cast(arrays->size()); i++) { + if (is_base_binary_like((*arrays)[i]->type()->id()) || + is_binary_view_like((*arrays)[i]->type()->id())) { + columns_to_encode.push_back(i); + } + } + } + return OptionalParallelFor(options.use_threads, + static_cast(columns_to_encode.size()), EncodeColumn); +} + +} // namespace + +Status ConvertArrayToPandas(const PandasOptions& options, std::shared_ptr arr, + PyObject* py_ref, PyObject** out) { + return ConvertChunkedArrayToPandas( + options, std::make_shared(std::move(arr)), py_ref, out); +} + +Status ConvertChunkedArrayToPandas(const PandasOptions& options, + std::shared_ptr arr, PyObject* py_ref, + PyObject** out) { + if (options.decode_dictionaries && arr->type()->id() == Type::DICTIONARY) { + // XXX we should return an error as below if options.zero_copy_only + // is true, but that would break compatibility with existing tests. + const auto& dense_type = + checked_cast(*arr->type()).value_type(); + RETURN_NOT_OK(DecodeDictionaries(options.pool, dense_type, &arr)); + ARROW_DCHECK_NE(arr->type()->id(), Type::DICTIONARY); + + // The original Python DictionaryArray won't own the memory anymore + // as we actually built a new array when we decoded the DictionaryArray + // thus let the final resulting numpy array own the memory through a Capsule + py_ref = nullptr; + } + + if (options.strings_to_categorical && (is_base_binary_like(arr->type()->id()) || + is_binary_view_like(arr->type()->id()))) { + if (options.zero_copy_only) { + return Status::Invalid("Need to dictionary encode a column, but ", + "only zero-copy conversions allowed"); + } + compute::ExecContext ctx(options.pool); + ARROW_ASSIGN_OR_RAISE( + Datum out, + DictionaryEncode(arr, compute::DictionaryEncodeOptions::Defaults(), &ctx)); + arr = out.chunked_array(); + } + + PandasOptions modified_options = options; + modified_options.strings_to_categorical = false; + + // ARROW-7596: We permit the hybrid Series/DataFrame code path to do zero copy + // optimizations that we do not allow in the default case when converting + // Table->DataFrame + modified_options.allow_zero_copy_blocks = true; + + // In case of an extension array default to the storage type + if (arr->type()->id() == Type::EXTENSION) { + arr = GetStorageChunkedArray(arr); + } + // In case of a RunEndEncodedArray decode the array + else if (arr->type()->id() == Type::RUN_END_ENCODED) { + if (options.zero_copy_only) { + return Status::Invalid("Need to dencode a RunEndEncodedArray, but ", + "only zero-copy conversions allowed"); + } + ARROW_ASSIGN_OR_RAISE(arr, GetDecodedChunkedArray(arr)); + + // Because we built a new array when we decoded the RunEndEncodedArray + // the final resulting numpy array should own the memory through a Capsule + py_ref = nullptr; + } + + PandasWriter::type output_type; + RETURN_NOT_OK(GetPandasWriterType(*arr, modified_options, &output_type)); + if (options.decode_dictionaries) { + ARROW_DCHECK_NE(output_type, PandasWriter::CATEGORICAL); + } + + std::shared_ptr writer; + RETURN_NOT_OK(MakeWriter(modified_options, output_type, *arr->type(), arr->length(), + /*num_columns=*/1, &writer)); + RETURN_NOT_OK(writer->TransferSingle(std::move(arr), py_ref)); + return writer->GetSeriesResult(out); +} + +Status ConvertTableToPandas(const PandasOptions& options, std::shared_ptr table, + PyObject** out) { + ChunkedArrayVector arrays = table->columns(); + FieldVector fields = table->fields(); + + // ARROW-3789: allow "self-destructing" by releasing references to columns as + // we convert them to pandas + table = nullptr; + + RETURN_NOT_OK(ConvertCategoricals(options, &arrays, &fields)); + + PandasOptions modified_options = options; + modified_options.strings_to_categorical = false; + modified_options.categorical_columns.reset(); + + if (options.split_blocks) { + modified_options.allow_zero_copy_blocks = true; + SplitBlockCreator helper(modified_options, std::move(fields), std::move(arrays)); + return helper.Convert(out); + } else { + ConsolidatedBlockCreator helper(modified_options, std::move(fields), + std::move(arrays)); + return helper.Convert(out); + } +} + +} // namespace py +} // namespace arrow diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/arrow_to_pandas.h b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/arrow_to_pandas.h new file mode 100644 index 0000000000000000000000000000000000000000..b4e91e6cf5ab4469236c7eec3c8174d4b0d47427 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/arrow_to_pandas.h @@ -0,0 +1,165 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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. + +// Functions for converting between pandas's NumPy-based data representation +// and Arrow data structures + +#pragma once + +#include "arrow/python/platform.h" + +#include +#include +#include + +#include "arrow/memory_pool.h" +#include "arrow/python/visibility.h" + +namespace arrow { + +class Array; +class ChunkedArray; +class Column; +class DataType; +class MemoryPool; +class Status; +class Table; + +namespace py { + +enum class MapConversionType { + DEFAULT, // convert arrow maps to assoc lists (list of kev-value tuples) in Pandas + LOSSY, // report warnings when lossiness is encountered due to duplicate keys + STRICT_, // raise a Python exception when lossiness is encountered due to duplicate + // keys +}; + +struct PandasOptions { + bool HasCategoricalColumns() const { + return categorical_columns && !categorical_columns->empty(); + } + + bool IsCategoricalColumn(const std::string& name) const { + return categorical_columns && categorical_columns->count(name); + } + + bool HasExtensionColumns() const { + return extension_columns && !extension_columns->empty(); + } + + bool IsExtensionColumn(const std::string& name) const { + return extension_columns && extension_columns->count(name); + } + + /// arrow::MemoryPool to use for memory allocations + MemoryPool* pool = default_memory_pool(); + + /// If true, we will convert all string columns to categoricals + bool strings_to_categorical = false; + bool zero_copy_only = false; + bool integer_object_nulls = false; + bool date_as_object = false; + bool timestamp_as_object = false; + bool use_threads = false; + + /// Coerce all date and timestamp to datetime64[ns] + bool coerce_temporal_nanoseconds = false; + + /// Used to maintain backwards compatibility for + /// timezone bugs (see ARROW-9528). Should be removed + /// after Arrow 2.0 release. + bool ignore_timezone = false; + + /// \brief If true, do not create duplicate PyObject versions of equal + /// objects. This only applies to immutable objects like strings or datetime + /// objects + bool deduplicate_objects = false; + + /// \brief For certain data types, a cast is needed in order to store the + /// data in a pandas DataFrame or Series (e.g. timestamps are always stored + /// as nanoseconds in pandas). This option controls whether it is a safe + /// cast or not. + bool safe_cast = true; + + /// \brief If true, create one block per column rather than consolidated + /// blocks (1 per data type). Do zero-copy wrapping when there are no + /// nulls. pandas currently will consolidate the blocks on its own, causing + /// increased memory use, so keep this in mind if you are working on a + /// memory-constrained situation. + bool split_blocks = false; + + /// \brief If true, allow non-writable zero-copy views to be created for + /// single column blocks. This option is also used to provide zero copy for + /// Series data + bool allow_zero_copy_blocks = false; + + /// \brief If true, attempt to deallocate buffers in passed Arrow object if + /// it is the only remaining shared_ptr copy of it. See ARROW-3789 for + /// original context for this feature. Only currently implemented for Table + /// conversions + bool self_destruct = false; + + /// \brief The default behavior (DEFAULT), is to convert Arrow Map arrays to + /// Python association lists (list-of-tuples) in the same order as the Arrow + /// Map, as in [(key1, value1), (key2, value2), ...] + /// If LOSSY or STRICT, convert Arrow Map arrays to native Python dicts. + /// This can change the ordering of (key, value) pairs, and will deduplicate + /// multiple keys, resulting in a possible loss of data. + /// If 'lossy', this key deduplication results in a warning printed + /// when detected. If 'strict', this instead results in an exception + /// being raised when detected. + MapConversionType maps_as_pydicts = MapConversionType::DEFAULT; + + // Used internally for nested arrays. + bool decode_dictionaries = false; + + // Columns that should be casted to categorical + // + // This is wrapped in a shared_ptr because this struct is copied internally for + // each column or nested field (see GH-47861). + std::shared_ptr> categorical_columns; + + // Columns that should be passed through to be converted to + // ExtensionArray/Block + std::shared_ptr> extension_columns; + + // Used internally to decipher between to_numpy() and to_pandas() when + // the expected output differs + bool to_numpy = false; +}; + +ARROW_PYTHON_EXPORT +Status ConvertArrayToPandas(const PandasOptions& options, std::shared_ptr arr, + PyObject* py_ref, PyObject** out); + +ARROW_PYTHON_EXPORT +Status ConvertChunkedArrayToPandas(const PandasOptions& options, + std::shared_ptr col, PyObject* py_ref, + PyObject** out); + +// Convert a whole table as efficiently as possible to a pandas.DataFrame. +// +// The returned Python object is a list of tuples consisting of the exact 2D +// BlockManager structure of the pandas.DataFrame used as of pandas 0.19.x. +// +// tuple item: (indices: ndarray[int32], block: ndarray[TYPE, ndim=2]) +ARROW_PYTHON_EXPORT +Status ConvertTableToPandas(const PandasOptions& options, std::shared_ptr
table, + PyObject** out); + +} // namespace py +} // namespace arrow diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/arrow_to_python_internal.h b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/arrow_to_python_internal.h new file mode 100644 index 0000000000000000000000000000000000000000..514cda320012316b1f9bc04a76c45159dc5bd181 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/arrow_to_python_internal.h @@ -0,0 +1,49 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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. + +#pragma once + +#include "arrow/array.h" +#include "arrow/python/platform.h" + +namespace arrow { +namespace py { +namespace internal { +// TODO(ARROW-12976): See if we can refactor Pandas ObjectWriter logic +// to the .cc file and move this there as well if we can. + +// Converts array to a sequency of python objects. +template +inline Status WriteArrayObjects(const ArrayType& arr, WriteValue&& write_func, + Assigner out_values) { + // TODO(ARROW-12976): Use visitor here? + const bool has_nulls = arr.null_count() > 0; + for (int64_t i = 0; i < arr.length(); ++i) { + if (has_nulls && arr.IsNull(i)) { + Py_INCREF(Py_None); + *out_values = Py_None; + } else { + RETURN_NOT_OK(write_func(arr.GetView(i), out_values)); + } + ++out_values; + } + return Status::OK(); +} + +} // namespace internal +} // namespace py +} // namespace arrow diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/async.h b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/async.h new file mode 100644 index 0000000000000000000000000000000000000000..1568d21938e6e79e724d957120e68a7576ba9c2a --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/async.h @@ -0,0 +1,60 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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. + +#pragma once + +#include + +#include "arrow/python/common.h" +#include "arrow/status.h" +#include "arrow/util/future.h" + +namespace arrow::py { + +/// \brief Bind a Python callback to an arrow::Future. +/// +/// If the Future finishes successfully, py_wrapper is called with its +/// result value and should return a PyObject*. If py_wrapper is successful, +/// py_cb is called with its return value. +/// +/// If either the Future or py_wrapper fails, py_cb is called with the +/// associated Python exception. +/// +/// \param future The future to bind to. +/// \param py_cb The Python callback function. Will be passed the result of +/// py_wrapper, or a Python exception if the future failed or one was +/// raised by py_wrapper. +/// \param py_wrapper A function (likely defined in Cython) to convert the C++ +/// result of the future to a Python object. +template +void BindFuture(Future future, PyObject* py_cb, PyWrapper py_wrapper) { + Py_INCREF(py_cb); + OwnedRefNoGIL cb_ref(py_cb); + + auto future_cb = [cb_ref = std::move(cb_ref), + py_wrapper = std::move(py_wrapper)](Result result) { + SafeCallIntoPythonVoid([&]() { + OwnedRef py_value_or_exc{WrapResult(std::move(result), std::move(py_wrapper))}; + Py_XDECREF( + PyObject_CallFunctionObjArgs(cb_ref.obj(), py_value_or_exc.obj(), NULLPTR)); + ARROW_WARN_NOT_OK(CheckPyError(), "Internal error in async call"); + }); + }; + future.AddCallback(std::move(future_cb)); +} + +} // namespace arrow::py diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/benchmark.cc b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/benchmark.cc new file mode 100644 index 0000000000000000000000000000000000000000..6dcc959ed221247eb93a80179e61a1f40a726e29 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/benchmark.cc @@ -0,0 +1,38 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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. + +#include "arrow/python/benchmark.h" +#include "arrow/python/helpers.h" + +namespace arrow { +namespace py { +namespace benchmark { + +void Benchmark_PandasObjectIsNull(PyObject* list) { + if (!PyList_CheckExact(list)) { + PyErr_SetString(PyExc_TypeError, "expected a list"); + return; + } + Py_ssize_t i, n = PyList_GET_SIZE(list); + for (i = 0; i < n; i++) { + internal::PandasObjectIsNull(PyList_GET_ITEM(list, i)); + } +} + +} // namespace benchmark +} // namespace py +} // namespace arrow diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/benchmark.h b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/benchmark.h new file mode 100644 index 0000000000000000000000000000000000000000..8060dd33722a08eb0935687ea5cb306dbd38a9f0 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/benchmark.h @@ -0,0 +1,36 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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. + +#pragma once + +#include "arrow/python/platform.h" + +#include "arrow/python/visibility.h" + +namespace arrow { +namespace py { +namespace benchmark { + +// Micro-benchmark routines for use from ASV + +// Run PandasObjectIsNull() once over every object in *list* +ARROW_PYTHON_EXPORT +void Benchmark_PandasObjectIsNull(PyObject* list); + +} // namespace benchmark +} // namespace py +} // namespace arrow diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/common.cc b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/common.cc new file mode 100644 index 0000000000000000000000000000000000000000..a6d963b9b4e43ace79d686f9739baf002bf1a3a7 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/common.cc @@ -0,0 +1,246 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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. + +#include "arrow/python/common.h" + +#include +#include +#include +#include + +#include "arrow/memory_pool.h" +#include "arrow/status.h" +#include "arrow/util/checked_cast.h" +#include "arrow/util/logging.h" + +#include "arrow/python/helpers.h" + +namespace arrow { + +using internal::checked_cast; + +namespace py { + +static std::mutex memory_pool_mutex; +static MemoryPool* default_python_pool = nullptr; + +void set_default_memory_pool(MemoryPool* pool) { + std::lock_guard guard(memory_pool_mutex); + default_python_pool = pool; +} + +MemoryPool* get_memory_pool() { + std::lock_guard guard(memory_pool_mutex); + if (default_python_pool) { + return default_python_pool; + } else { + return default_memory_pool(); + } +} + +// ---------------------------------------------------------------------- +// PythonErrorDetail + +namespace { + +const char kErrorDetailTypeId[] = "arrow::py::PythonErrorDetail"; + +// Try to match the Python exception type with an appropriate Status code +StatusCode MapPyError(PyObject* exc_type) { + StatusCode code; + + if (PyErr_GivenExceptionMatches(exc_type, PyExc_MemoryError)) { + code = StatusCode::OutOfMemory; + } else if (PyErr_GivenExceptionMatches(exc_type, PyExc_IndexError)) { + code = StatusCode::IndexError; + } else if (PyErr_GivenExceptionMatches(exc_type, PyExc_KeyError)) { + code = StatusCode::KeyError; + } else if (PyErr_GivenExceptionMatches(exc_type, PyExc_TypeError)) { + code = StatusCode::TypeError; + } else if (PyErr_GivenExceptionMatches(exc_type, PyExc_ValueError) || + PyErr_GivenExceptionMatches(exc_type, PyExc_OverflowError)) { + code = StatusCode::Invalid; + } else if (PyErr_GivenExceptionMatches(exc_type, PyExc_EnvironmentError)) { + code = StatusCode::IOError; + } else if (PyErr_GivenExceptionMatches(exc_type, PyExc_NotImplementedError)) { + code = StatusCode::NotImplemented; + } else { + code = StatusCode::UnknownError; + } + return code; +} + +// PythonErrorDetail indicates a Python exception was raised. +class PythonErrorDetail : public StatusDetail { + public: + const char* type_id() const override { return kErrorDetailTypeId; } + + std::string ToString() const override { + // This is simple enough not to need the GIL + Result result = FormatImpl(); + + if (result.ok()) { + return result.ValueOrDie(); + } else { + // Fallback to just the exception type + const auto ty = reinterpret_cast(exc_type_.obj()); + return std::string("Python exception: ") + ty->tp_name; + } + } + + void RestorePyError() const { + Py_INCREF(exc_type_.obj()); + Py_INCREF(exc_value_.obj()); + Py_INCREF(exc_traceback_.obj()); + PyErr_Restore(exc_type_.obj(), exc_value_.obj(), exc_traceback_.obj()); + } + + PyObject* exc_type() const { return exc_type_.obj(); } + + PyObject* exc_value() const { return exc_value_.obj(); } + + static std::shared_ptr FromPyError() { + PyObject* exc_type = nullptr; + PyObject* exc_value = nullptr; + PyObject* exc_traceback = nullptr; + + PyErr_Fetch(&exc_type, &exc_value, &exc_traceback); + PyErr_NormalizeException(&exc_type, &exc_value, &exc_traceback); + ARROW_CHECK(exc_type) + << "PythonErrorDetail::FromPyError called without a Python error set"; + ARROW_DCHECK(PyType_Check(exc_type)); + ARROW_DCHECK(exc_value); // Ensured by PyErr_NormalizeException, double-check + if (exc_traceback == nullptr) { + // Needed by PyErr_Restore() + Py_INCREF(Py_None); + exc_traceback = Py_None; + } + + std::shared_ptr detail(new PythonErrorDetail); + detail->exc_type_.reset(exc_type); + detail->exc_value_.reset(exc_value); + detail->exc_traceback_.reset(exc_traceback); + return detail; + } + + protected: + Result FormatImpl() const { + PyAcquireGIL lock; + + // Use traceback.format_exception() + OwnedRef traceback_module; + RETURN_NOT_OK(internal::ImportModule("traceback", &traceback_module)); + + OwnedRef fmt_exception; + RETURN_NOT_OK(internal::ImportFromModule(traceback_module.obj(), "format_exception", + &fmt_exception)); + + OwnedRef formatted; + formatted.reset(PyObject_CallFunctionObjArgs(fmt_exception.obj(), exc_type_.obj(), + exc_value_.obj(), exc_traceback_.obj(), + NULL)); + RETURN_IF_PYERROR(); + + std::stringstream ss; + ss << "Python exception: "; + Py_ssize_t num_lines = PySequence_Length(formatted.obj()); + RETURN_IF_PYERROR(); + + for (Py_ssize_t i = 0; i < num_lines; ++i) { + Py_ssize_t line_size; + + PyObject* line = PySequence_GetItem(formatted.obj(), i); + RETURN_IF_PYERROR(); + + const char* data = PyUnicode_AsUTF8AndSize(line, &line_size); + RETURN_IF_PYERROR(); + + ss << std::string_view(data, line_size); + } + return ss.str(); + } + + PythonErrorDetail() = default; + + OwnedRefNoGIL exc_type_, exc_value_, exc_traceback_; +}; + +} // namespace + +// ---------------------------------------------------------------------- +// Python exception <-> Status + +Status ConvertPyError(StatusCode code) { + auto detail = PythonErrorDetail::FromPyError(); + if (code == StatusCode::UnknownError) { + code = MapPyError(detail->exc_type()); + } + + std::string message; + RETURN_NOT_OK(internal::PyObject_StdStringStr(detail->exc_value(), &message)); + return Status(code, message, detail); +} + +bool IsPyError(const Status& status) { + if (status.ok()) { + return false; + } + auto detail = status.detail(); + bool result = detail != nullptr && detail->type_id() == kErrorDetailTypeId; + return result; +} + +void RestorePyError(const Status& status) { + ARROW_CHECK(IsPyError(status)); + const auto& detail = checked_cast(*status.detail()); + detail.RestorePyError(); +} + +// ---------------------------------------------------------------------- +// PyBuffer + +PyBuffer::PyBuffer() : Buffer(nullptr, 0) {} + +Status PyBuffer::Init(PyObject* obj) { + if (!PyObject_GetBuffer(obj, &py_buf_, PyBUF_ANY_CONTIGUOUS)) { + data_ = reinterpret_cast(py_buf_.buf); + ARROW_CHECK_NE(data_, nullptr) << "Null pointer in Py_buffer"; + size_ = py_buf_.len; + capacity_ = py_buf_.len; + is_mutable_ = !py_buf_.readonly; + return Status::OK(); + } else { + return ConvertPyError(StatusCode::Invalid); + } +} + +Result> PyBuffer::FromPyObject(PyObject* obj) { + PyBuffer* buf = new PyBuffer(); + std::shared_ptr res(buf); + RETURN_NOT_OK(buf->Init(obj)); + return res; +} + +PyBuffer::~PyBuffer() { + if (data_ != nullptr) { + PyAcquireGIL lock; + PyBuffer_Release(&py_buf_); + } +} + +} // namespace py +} // namespace arrow diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/common.h b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/common.h new file mode 100644 index 0000000000000000000000000000000000000000..affefe2859b65f04f711de1f90d14f642641c5fb --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/pyarrow/src/arrow/python/common.h @@ -0,0 +1,457 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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. + +#pragma once + +#include +#include +#include +#include + +#include "arrow/buffer.h" +#include "arrow/python/pyarrow.h" +#include "arrow/python/visibility.h" +#include "arrow/result.h" +#include "arrow/util/macros.h" + +namespace arrow { + +class MemoryPool; +template +class Result; + +namespace py { + +// Convert current Python error to a Status. The Python error state is cleared +// and can be restored with RestorePyError(). +ARROW_PYTHON_EXPORT Status ConvertPyError(StatusCode code = StatusCode::UnknownError); +// Query whether the given Status is a Python error (as wrapped by ConvertPyError()). +ARROW_PYTHON_EXPORT bool IsPyError(const Status& status); +// Restore a Python error wrapped in a Status. +ARROW_PYTHON_EXPORT void RestorePyError(const Status& status); + +// Catch a pending Python exception and return the corresponding Status. +// If no exception is pending, Status::OK() is returned. +inline Status CheckPyError(StatusCode code = StatusCode::UnknownError) { + if (ARROW_PREDICT_TRUE(!PyErr_Occurred())) { + return Status::OK(); + } else { + return ConvertPyError(code); + } +} + +#define RETURN_IF_PYERROR() ARROW_RETURN_NOT_OK(CheckPyError()) + +#define PY_RETURN_IF_ERROR(CODE) ARROW_RETURN_NOT_OK(CheckPyError(CODE)) + +// For Cython, as you can't define template C++ functions in Cython, only use them. +// This function can set a Python exception. It assumes that T has a (cheap) +// default constructor. +template +T GetResultValue(Result result) { + if (ARROW_PREDICT_TRUE(result.ok())) { + return *std::move(result); + } else { + int r = internal::check_status(result.status()); // takes the GIL + assert(r == -1); // should have errored out + ARROW_UNUSED(r); + return {}; + } +} + +/// \brief Wrap a Result and return the corresponding Python object. +/// +/// If the Result is successful, py_wrapper is called with its result value +/// and should return a PyObject*. If py_wrapper is successful (returns +/// a non-NULL value), its return value is returned. +/// +/// If either the Result or py_wrapper fails, the associated Python exception +/// is raised and NULL is returned. +// +/// \param result The Result whose value to wrap in a Python object. +/// \param py_wrapper A function (likely defined in Cython) to convert the C++ +/// value of the Result to a Python object. +/// \return A new Python reference, or NULL if an exception occurred +template +PyObject* WrapResult(Result result, PyWrapper&& py_wrapper) { + static_assert(std::is_same_v()))>, + "PyWrapper argument to WrapResult should return a PyObject* " + "when called with a T*"); + Status st = result.status(); + if (st.ok()) { + PyObject* py_value = py_wrapper(result.MoveValueUnsafe()); + st = CheckPyError(); + if (st.ok()) { + return py_value; + } + Py_XDECREF(py_value); // should be null, but who knows + } + // Status is an error, convert it to an exception. + return internal::convert_status(st); +} + +// A RAII-style helper that ensures the GIL is acquired inside a lexical block. +class ARROW_PYTHON_EXPORT PyAcquireGIL { + public: + PyAcquireGIL() : acquired_gil_(false) { acquire(); } + + ~PyAcquireGIL() { release(); } + + void acquire() { + if (!acquired_gil_) { + state_ = PyGILState_Ensure(); + acquired_gil_ = true; + } + } + + // idempotent + void release() { + if (acquired_gil_) { + PyGILState_Release(state_); + acquired_gil_ = false; + } + } + + private: + bool acquired_gil_; + PyGILState_STATE state_; + ARROW_DISALLOW_COPY_AND_ASSIGN(PyAcquireGIL); +}; + +// A RAII-style helper that releases the GIL until the end of a lexical block +class ARROW_PYTHON_EXPORT PyReleaseGIL { + public: + PyReleaseGIL() : ptr_(PyEval_SaveThread(), &unique_ptr_deleter) {} + + private: + static void unique_ptr_deleter(PyThreadState* state) { + if (state) { + PyEval_RestoreThread(state); + } + } + std::unique_ptr ptr_; +}; + +// A helper to call safely into the Python interpreter from arbitrary C++ code. +// The GIL is acquired, and the current thread's error status is preserved. +template +auto SafeCallIntoPython(Function&& func) -> decltype(func()) { + PyAcquireGIL lock; + PyObject* exc_type; + PyObject* exc_value; + PyObject* exc_traceback; + PyErr_Fetch(&exc_type, &exc_value, &exc_traceback); + auto maybe_status = std::forward(func)(); + // If the return Status is a "Python error", the current Python error status + // describes the error and shouldn't be clobbered. + if (!IsPyError(::arrow::ToStatus(maybe_status)) && exc_type != NULLPTR) { + PyErr_Restore(exc_type, exc_value, exc_traceback); + } + return maybe_status; +} + +template +auto SafeCallIntoPythonVoid(Function&& func) -> decltype(func()) { + PyAcquireGIL lock; + PyObject* exc_type; + PyObject* exc_value; + PyObject* exc_traceback; + PyErr_Fetch(&exc_type, &exc_value, &exc_traceback); + func(); + if (exc_type != NULLPTR) { + PyErr_Restore(exc_type, exc_value, exc_traceback); + } +} + +// A RAII primitive that DECREFs the underlying PyObject* when it +// goes out of scope. +class ARROW_PYTHON_EXPORT OwnedRef { + public: + OwnedRef() : obj_(NULLPTR) {} + OwnedRef(OwnedRef&& other) : OwnedRef(other.detach()) {} + explicit OwnedRef(PyObject* obj) : obj_(obj) {} + + OwnedRef& operator=(OwnedRef&& other) { + obj_ = other.detach(); + return *this; + } + + ~OwnedRef() { + // GH-38626: destructor may be called after the Python interpreter is finalized. + if (Py_IsInitialized()) { + reset(); + } + } + + void reset(PyObject* obj) { + Py_XDECREF(obj_); + obj_ = obj; + } + + void reset() { reset(NULLPTR); } + + PyObject* detach() { + PyObject* result = obj_; + obj_ = NULLPTR; + return result; + } + + PyObject* obj() const { return obj_; } + + PyObject** ref() { return &obj_; } + + operator bool() const { return obj_ != NULLPTR; } + + private: + ARROW_DISALLOW_COPY_AND_ASSIGN(OwnedRef); + + PyObject* obj_; +}; + +// Same as OwnedRef, but ensures the GIL is taken when it goes out of scope. +// This is for situations where the GIL is not always known to be held +// (e.g. if it is released in the middle of a function for performance reasons) +class ARROW_PYTHON_EXPORT OwnedRefNoGIL : public OwnedRef { + public: + OwnedRefNoGIL() : OwnedRef() {} + OwnedRefNoGIL(OwnedRefNoGIL&& other) : OwnedRef(other.detach()) {} + explicit OwnedRefNoGIL(PyObject* obj) : OwnedRef(obj) {} + + ~OwnedRefNoGIL() { + // GH-38626: destructor may be called after the Python interpreter is finalized. + if (Py_IsInitialized() && obj() != NULLPTR) { + PyAcquireGIL lock; + reset(); + } + } +}; + +template