python-xarray-0.10.2/0000755000175000017500000000000013252507216014604 5ustar alastairalastairpython-xarray-0.10.2/.github/0000755000175000017500000000000013252452413016142 5ustar alastairalastairpython-xarray-0.10.2/.github/PULL_REQUEST_TEMPLATE.md0000644000175000017500000000031013252452413021735 0ustar alastairalastair - [ ] Closes #xxxx - [ ] Tests added / passed - [ ] Passes ``git diff upstream/master | flake8 --diff`` - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API python-xarray-0.10.2/.travis.yml0000644000175000017500000000576613252452413016731 0ustar alastairalastair# Based on http://conda.pydata.org/docs/travis.html language: python sudo: false # use container based build notifications: email: false branches: except: - fix-docs matrix: fast_finish: true include: - python: 2.7 env: CONDA_ENV=py27-min - python: 2.7 env: CONDA_ENV=py27-cdat+iris+pynio - python: 3.4 env: CONDA_ENV=py34 - python: 3.5 env: CONDA_ENV=py35 - python: 3.6 env: CONDA_ENV=py36 - python: 3.6 env: - CONDA_ENV=py36 - EXTRA_FLAGS="--run-flaky --run-network-tests" - python: 3.6 env: CONDA_ENV=py36-netcdf4-dev addons: apt_packages: - libhdf5-serial-dev - netcdf-bin - libnetcdf-dev - python: 3.6 env: CONDA_ENV=py36-dask-dev - python: 3.6 env: CONDA_ENV=py36-pandas-dev - python: 3.6 env: CONDA_ENV=py36-bottleneck-dev - python: 3.6 env: CONDA_ENV=py36-condaforge-rc - python: 3.6 env: CONDA_ENV=py36-pynio-dev - python: 3.6 env: CONDA_ENV=py36-rasterio1.0alpha - python: 3.6 env: CONDA_ENV=py36-zarr-dev - python: 3.6 env: CONDA_ENV=py36-netcdftime-dev - python: 3.5 env: CONDA_ENV=docs allow_failures: - python: 3.6 env: - CONDA_ENV=py36 - EXTRA_FLAGS="--run-flaky --run-network-tests" - python: 3.6 env: CONDA_ENV=py36-netcdf4-dev addons: apt_packages: - libhdf5-serial-dev - netcdf-bin - libnetcdf-dev - python: 3.6 env: CONDA_ENV=py36-dask-dev - python: 3.6 env: CONDA_ENV=py36-pandas-dev - python: 3.6 env: CONDA_ENV=py36-bottleneck-dev - python: 3.6 env: CONDA_ENV=py36-condaforge-rc - python: 3.6 env: CONDA_ENV=py36-pynio-dev - python: 3.6 env: CONDA_ENV=py36-rasterio1.0alpha - python: 3.6 env: CONDA_ENV=py36-zarr-dev - python: 3.6 env: CONDA_ENV=py36-netcdftime-dev before_install: - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then wget http://repo.continuum.io/miniconda/Miniconda-3.16.0-Linux-x86_64.sh -O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-3.16.0-Linux-x86_64.sh -O miniconda.sh; fi - bash miniconda.sh -b -p $HOME/miniconda - export PATH="$HOME/miniconda/bin:$PATH" - hash -r - conda config --set always_yes yes --set changeps1 no --set show_channel_urls true - conda update -q conda - conda info -a install: - if [[ "$CONDA_ENV" == "docs" ]]; then conda env create -n test_env --file doc/environment.yml; else conda env create -n test_env --file ci/requirements-$CONDA_ENV.yml; fi - source activate test_env - conda list - pip install --no-deps -e . - python xarray/util/print_versions.py script: - flake8 -j auto xarray - python -OO -c "import xarray" - if [[ "$CONDA_ENV" == "docs" ]]; then conda install -c conda-forge sphinx_rtd_theme; sphinx-build -n -b html -d _build/doctrees doc _build/html; else py.test xarray --cov=xarray --cov-config ci/.coveragerc --cov-report term-missing --verbose $EXTRA_FLAGS; fi after_success: - coveralls python-xarray-0.10.2/conftest.py0000644000175000017500000000053413252452413017003 0ustar alastairalastair"""Configuration for pytest.""" def pytest_addoption(parser): """Add command-line flags for pytest.""" parser.addoption("--run-flaky", action="store_true", help="runs flaky tests") parser.addoption("--run-network-tests", action="store_true", help="runs tests requiring a network connection") python-xarray-0.10.2/.coveragerc0000644000175000017500000000003313252452413016717 0ustar alastairalastair[run] omit = xarray/test/* python-xarray-0.10.2/HOW_TO_RELEASE0000644000175000017500000000653513252452413016735 0ustar alastairalastairHow to issue an xarray release in 15 easy steps Time required: about an hour. 1. Ensure your master branch is synced to upstream: git pull upstream master 2. Look over whats-new.rst and the docs. Make sure "What's New" is complete (check the date!) and add a brief summary note describing the release at the top. 3. Update the version in setup.py and switch to `ISRELEASED = True`. 4. If you have any doubts, run the full test suite one final time! py.test 5. On the master branch, commit the release in git: git commit -a -m 'Release v0.X.Y' 6. Tag the release: git tag -a v0.X.Y -m 'v0.X.Y' 7. Build source and binary wheels for pypi: python setup.py bdist_wheel sdist 8. Use twine to register and upload the release on pypi. Be careful, you can't take this back! twine upload dist/xarray-0.X.Y* You will need to be listed as a package owner at https://pypi.python.org/pypi/xarray for this to work. 9. Push your changes to master: git push upstream master git push upstream --tags 9. Update the stable branch (used by ReadTheDocs) and switch back to master: git checkout stable git rebase master git push upstream stable git checkout master It's OK to force push to 'stable' if necessary. We also update the stable branch with `git cherrypick` for documentation only fixes that apply the current released version. 10. Revert ISRELEASED in setup.py back to False. Don't change the version number: in normal development, we keep the version number in setup.py as the last released version. 11. Add a section for the next release (v.X.(Y+1)) to doc/whats-new.rst. 12. Commit your changes and push to master again: git commit -a -m 'Revert to dev version' git push upstream master You're done pushing to master! 13. Issue the release on GitHub. Click on "Draft a new release" at https://github.com/pydata/xarray/releases and paste in the latest from whats-new.rst. 14. Update the docs. Login to https://readthedocs.org/projects/xray/versions/ and switch your new release tag (at the bottom) from "Inactive" to "Active". It should now build automatically. 15. Update conda-forge. Clone https://github.com/conda-forge/xarray-feedstock and update the version number and sha256 in meta.yaml. (On OS X, you can calculate sha256 with `shasum -a 256 xarray-0.X.Y.tar.gz`). Submit a pull request (and merge it, once CI passes). 16. Issue the release announcement! For bug fix releases, I usually only email xarray@googlegroups.com. For major/feature releases, I will email a broader list (no more than once every 3-6 months): pydata@googlegroups.com, xarray@googlegroups.com, numpy-discussion@scipy.org, scipy-user@scipy.org, pyaos@lists.johnny-lin.com Google search will turn up examples of prior release announcements (look for "ANN xarray"). Note on version numbering: We follow a rough approximation of semantic version. Only major releases (0.X.0) show include breaking changes. Minor releases (0.X.Y) are for bug fixes and backwards compatible new features, but if a sufficient number of new features have arrived we will issue a major release even if there are no compatibility breaks. Once the project reaches a sufficient level of maturity for a 1.0.0 release, we intend to follow semantic versioning more strictly. python-xarray-0.10.2/MANIFEST.in0000644000175000017500000000020313252452413016333 0ustar alastairalastairinclude LICENSE recursive-include licenses * recursive-include doc * prune doc/_build prune doc/generated global-exclude .DS_Store python-xarray-0.10.2/.gitignore0000644000175000017500000000114413252452413016572 0ustar alastairalastair*.py[cod] __pycache__ # temp files from docs build doc/auto_gallery doc/example.nc doc/savefig # C extensions *.so # Packages *.egg *.egg-info dist build eggs parts bin var sdist develop-eggs .installed.cfg lib lib64 # Installer logs pip-log.txt # Unit test / coverage reports .coverage .tox nosetests.xml .cache .ropeproject/ .tags* .testmondata .pytest_cache # asv environments .asv # Translations *.mo # Mr Developer .mr.developer.cfg .project .pydevproject # PyCharm and Vim .idea *.swp .DS_Store # xarray specific doc/_build doc/generated xarray/version.py # Sync tools Icon* .ipynb_checkpoints python-xarray-0.10.2/setup.cfg0000644000175000017500000000030213252452413016416 0ustar alastairalastair[wheel] universal = 1 [tool:pytest] python_files=test_*.py [flake8] max-line-length=79 ignore= exclude= doc/ [isort] default_section=THIRDPARTY known_first_party=xarray multi_line_output=4 python-xarray-0.10.2/examples/0000755000175000017500000000000013252452413016420 5ustar alastairalastairpython-xarray-0.10.2/examples/xarray_multidimensional_coords.ipynb0000644000175000017500000117003513252452413026006 0ustar alastairalastair{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Working with Multidimensional Coordinates\n", "\n", "Author: [Ryan Abernathey](http://github.org/rabernat)\n", "\n", "Many datasets have _physical coordinates_ which differ from their _logical coordinates_. Xarray provides several ways to plot and analyze such datasets." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('numpy version : ', '1.11.0')\n", "('pandas version : ', u'0.18.0')\n", "('xarray version : ', '0.7.2-32-gf957eb8')\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "import cartopy.crs as ccrs\n", "from matplotlib import pyplot as plt\n", "\n", "print(\"numpy version : \", np.__version__)\n", "print(\"pandas version : \", pd.__version__)\n", "print(\"xarray version : \", xr.version.version)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example, consider this dataset from the [xarray-data](https://github.com/pydata/xarray-data) repository." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Dimensions: (time: 36, x: 275, y: 205)\n", "Coordinates:\n", " * time (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ...\n", " yc (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ...\n", " xc (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ...\n", " * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n", " * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n", "Data variables:\n", " Tair (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ...\n", "Attributes:\n", " title: /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc\n", " institution: U.W.\n", " source: RACM R1002RBRxaaa01a\n", " output_frequency: daily\n", " output_mode: averaged\n", " convention: CF-1.4\n", " references: Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.\n", " comment: Output from the Variable Infiltration Capacity (VIC) model.\n", " nco_openmp_thread_number: 1\n", " NCO: 4.3.7\n", " history: history deleted for brevity" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = xr.tutorial.load_dataset('rasm')\n", "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, the _logical coordinates_ are `x` and `y`, while the _physical coordinates_ are `xc` and `yc`, which represent the latitudes and longitude of the data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OrderedDict([(u'long_name', u'longitude of grid cell center'), (u'units', u'degrees_east'), (u'bounds', u'xv')])\n", "OrderedDict([(u'long_name', u'latitude of grid cell center'), (u'units', u'degrees_north'), (u'bounds', u'yv')])\n" ] } ], "source": [ "print(ds.xc.attrs)\n", "print(ds.yc.attrs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting ##\n", "\n", "Let's examine these coordinate variables by plotting them." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/rpa/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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pemzmljU8D6a9Ydtc3ujjYp4akdfqNXFDz2L9xgYBVS+N9dkcm/T6GGcHNDe/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\nU9pPWpsyA1BaPDKujadGsJm8YUECFZE0hxdHrJ8BhsPNLBvPOxPzeqj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bjMNdA67u8pFm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lpnRNshslnpgsbm8VTJrTTe5cBwO+t5Wxd9pIUbgrWmJlhpcqtN6X4Gf3tiPRMtNS4/t9b0uKFJ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\nhSwnoEaZVmx9I82CGhT6E6DGF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aWpt1cPEDAnnHORNxFqav1eCjWAHQVtE6kMFO//s7fj\nkUffUS1GRF8O4L3M/DtE9MV21cx0KT/mXXJhRoIake6Bh4CZa2ngjkQo57CWZqm1Pe9/CTWuT3ZI\n57WhBqpuSH2fWNSvj6UepaGSw/8UzLTABsghhsRFVOGGATk+a8CJ1yk7XyiXIrO6ng4jOjlUiMpr\nsCHzivQ+kCnbNUDEaqMGLTo9lNF1W7qNupfCzNnIzL4w80MAHvLHHyGiNwP4ZABvBvDDAP4zAP/b\nSr005fKBpuPBfPIF/6Pl+mDG7PdJYKaeN2fOVHM167XORHCZ0s5ECZNpDTzXLb0BB2zryzIrzVyo\nkdKyeFwH1ADottasLpW1EU99/KfiqY//1Hj+lvf/mlb5QgAvJqIXAbgHwBOJ6KcBPExEn8TMDxHR\nMwC8d5N+7zJPBgYfeqw0Mx5cRtAADTVAABsbak6x4aaGkklQY+hnaQESGuVg5Ql4sfQieGiwMXRy\nK0xpfdF/8fFWabiBKq8AJ2RDlO+RJcNY1fqi2xfHBcSI4wxkDOhpWnEsnRbISH0DRjIo0fVPhJlo\n0eiAmZbMsc5sEpFshTV8RPQcAH8JwG8S0VfCBbL5Xdp4TcDlAk3nfTkb97JWW2cHM9zIq5cddTWT\nxTKwadfbKmvJmjBy6pfeU93Q7D1lpltp5rie1aBmSuSzNaEG6N2Hpt8FbVWZ/+brpQBeCgBE9HwA\n38HMX0tELwPwdQB+0H++ZqWe7nIqCetfpoZxXjAxcJDhZtJbhXKGz5P9rMNNBWp8Qgtqwq2ouaCh\nUkaeaytOoRvAhvNxuQAbWcYnRjBhFABTWG6Kjkc6SvrZgS09SzmbVegKtK4c5jWEKB3LGqPL9YBM\nLGPBiu6HrrMFJVb9NSAx6otr1lApo6deIW8FV7O4dm5tqXkSPPoOPPLRuidBEO9u9rNwQWwGuLHr\nS6XK8k7acrlAs8suu+xySbJeZJgw4vwAgFcT0TfBh21eq4Fddtlll13uQKkAzVMf/yw89fHPiudv\neeT/KXSI6AaA/xXA/8zMryGizwHwHAD/2ltnngm3tuZ5zLy6R8FlAs11upnNzbsNrDMtC0zX2pmR\nez41QMBY/poBAbZ2QeuxyEidfneytt7UtTRA/V5ct5XGtbP+mprVZAVTPjO/DsDr/PEjAF6wuNJd\nthH/BrXpdtZjaRl41lqaeK7cznqtO8GiMsdKAyNPmjF6rTQo9Cr5hGJNjbSeZMYNss8z9zFDt8tS\nIzsX9GrWGnFx0VqDsv7MagMUlhsts4cqq5xlYdB5NZ2GVcY6n2SZkceqTM2SU1hZjPrmWGes/mTT\nIW1ZWWCdOYkc541/5IjlnwF4EzP/EwBg5t+DCFZDRG8D8O/6sWt1uUygaci1gUwr32qzotsFM7UH\n2ZowU9StR4tKXrUCMQjPcDcbvfU9ZvbNDJ3zpQtkDFAZq6PX7axWpraWptBr1NmOwjYdaoK0NuDU\nfZ+qV9NdRfgE63R2uRyRE5SW21nvg8vXF6EGMDfc7F1LU2s3uKy1oAZAEfks6lZcySyoCZP9XC8f\nqWI6lXO+sTLy3AKgmGf0KQGLTxNluFZWw02WlwpQpYLqOpq5Y1tlOCge25XzUi+HmEzHAhmrnjE9\nAzKytgydAnJq6RWoKfO5yK/D1XSYqYl2N9tkDc38ceqLAPw9AL9LRL/j017KzP+nrH1J18bktgGa\nHr/R2etlxvK3sspYdZwKZjIry4TfYM06Y6mOtDHHYtNjSVlibVlrTU3PYv8x2OmdeC+10oz1bY2o\nZ66eNtRU9RZCDWAHClhdzimSzS7bS7DEjFlpzLI8I+JZR594eYCAYuyIcCL0ATOccwY1sGDFgJos\nfxxqQp9Y5/mCMV2dV3UVsBRgg1xP1h0hSeQlgCnhpgAXUQ5CzfoOVhGrHgtOgHzSoqcPLYix0mu6\nBizUIEPWY+qzuG2cp0u9qTBjXutUmFFiWWdMmNliSJm/1vP/xshTiJk/bVblnXLxQLMYZIB5sDKa\nV2mzF2YmWHVOCjMt60wNZirWGVMmWGd63M2aTa0EJ1NkqmuZlQe03L7aoLSWlaZV75SoZ+cANTXd\n1eWEO8DvcsYSJgytfWlohE4GjLqeQaRFK01jzJq9N01mNVD70LSgxnd0CdRA6BT6wmKj+UDXG8DD\n0o1t9IKNkhbcxHatAAA1yNENzbXOWHXpfmd6NsDUrDkFxFh5Nf1ekFH6Uk/nh+85q4vtNuqwY8BM\nre0xmMnu2bRAAJvBDHDR49TFAs3mINPKHy13RjAzKiPwU8lbCgNzrDM9dS2pZ0uZu3nmkvpHLTwV\nK00LagCYemXdfetpzPwTQI28jpbuqrJbaO4sYa5YaRoPrGiZGYl4VnvoNUAnQg0696bhEkRG19Mo\na0IX1IhjC2rQ0INxLLqfoEZcYmSFinUmux0BQmQ/OsAGoqwUMvJMwAHyhiHq1s/cymPFmlt0DUFW\nwRrA6OlDL8RY6RogfNoY6BQ6Mj+cV+qy4KeAnZi+DszI6zetNVM20NzE5exyx6mLBJrbwSoDLIMZ\ne+1JR9kJ1zYlDPNW1plqfwyZCww9+7ycQtqAYE/q5wQHGLPSzO2zrndtqAHK78qCGiCHlSn7z9R0\nV5ELHih2WUeYR1zPaqASrTpou5613u7wONRkrmeqrgAoPUECsmvhEajxJFGDGog0eSyhJIMRrxT1\nQjfk5Fa0ZcHFJLARHcjGdAk4nK4Tqq0s30gHVL0jQQFi81MfN9b0oQIhRd4YrNTSLRDx57NBRpSR\nAFm3uhj1F+l1mClhqAIzsNrtDwJQWGcsS88acsHj1EUCTVMuwCoDnAvM1IFlkjvXRJgZs85Uqu4u\nY9bTAcFbBA1Yw8XMyuu1yKxtpdF6WqYECZgKNbq/Tb0VXNBWl+Nx/Tp3OVvhYQAdDpmVpmqdGQsQ\nMMMiU6aVxR1ozAgSMAI1kGkBWBpQ4/RtqHF5fRHQINKjXoCRirUGyI91/VlaqMvXoZ90Vh8ygBF1\nZBeAVGehI8uaDfjTiY+s5rA0BjcNmDHr7QGZCpQ001r5/ty0ysi8mg57eLB0dZnYnwbMdEY0k5L1\nw4KZLeDjgsep2wdoeia5W1hlWm1bltsqMF0jzBRttIBlgz+gjjZq7Vr3o9XHrcMvT5HWepelLmpT\nrDR1608f1JQg0hckwNV7vlCzulzwm69dVpKB+wMEMGOO61lYCwPADOcsrTTFA5TbUKNDMAerTZGv\n00agJlgzLKhx2X3ranR6oaetNQJMxqwzWRryMpmOuJ7CcqPhBiqfDR0DYma7k41IC0as82qblo4F\nALXzKSCj7qVplTHOLWtLATMVXduSVMJM6lcHzMj71Vo3s5VlRvT1UuXygWYpyIzlTwGQkTJnCzOL\nYE4cnsA6M0XOZf3MWjLF7SzP74ejuZP73vU0lu65QA2w4R40wEUPFLvMEObSSmMCBCoBAqw3Yizy\nc1CJYg0Uwd1sbD1NZhUYDxIQ+8g5pPikaVAT9dVnKNcBNfFyRbrUC/Bg7luj6inAppVmtC8rNOFG\nFKoCTqUBKi5sgXRAidluQ7cGMVaeBopCx0iz8oH0HbdAyASiQodtcDHLc152KPObMAPdRmcQAGZs\nMlxd8Dh1uUBzXSAz1vYUmJlg2ele/L8QZuZsnrlYpkBOJX+qNWNrK85cC8sUt7FZLmYdVpqW6xmA\nWfvTtCKf9UjYh2YsWMASqKnpriYXHD1mlxWkN0CAgpYx17NokVFuZoCAHQOkqlCjZ+wDxiOfRegA\nwhoac5+aFtRkdZSg4o7rwQIkCMSyvoIqAAWwkZccoEe20QIbA1ZIdLwKN6rP6UIqw7tPK+YStceK\nNUb3TJt6HlMWoOi8MYgxdOeADInyUd8AEZmn68nb4Ha5TJ/zshPczKKMRDTLRLiaubY2GFMueJy6\nTKDZYaav7BKYKfowBhryRAysM60zPW33rnk5tw01t454FtvJIGW9NnvDOa+5nibVOR4B7VyhhveN\nNe8s8RHLopVGPYiqAQJMK07d9axq9QEKC046l/ruf9UgAb5+4g6oQXr81zbfDE1aUBPz1CeyNDtY\nQDiXdej6TL0AHqzyW2BjgJKsF7J8A27i/aoBTlFpJ3BkjS0UPW3gel4XxEg9BQymvgaQkAYjP+gU\nkKKOjfPqeplqWg4nU2GGGG2YQdBjf00GzGwwjbjkceoygaYl1wEyjbInC8u8Nsy04KMo29G/Dsng\nZyS/p44eOecIZ2aavzOt9THjbc230ky9jjXX06Q614Ua4EQba17wm69dlklXgAAZ5jlITS/LV+BS\nRCfjCtR4+4zQbwUJiBDEOYRkUBMagBsKzAhoRD6vhBrEFnOogThmf00OKEoXNAg9ZGWQICSAiT8O\nbmhQc8Qq2MR7mxrQa2jEhwk3GbxowAGKBpuL/ue+tGs8kqqWF+O8pjsKMSPp3SBj1aGOq9afABCW\nvpnGedqQ6l0DZqJUggBsZpmJ7V7uOHX7Ac0uu+yyyznKBfsm77LLLrvscgfIBY9TtxfQnNo60yhz\nsgAAVvmJb2vGLDBz6+5yNxupr8fiYr3Ft8pdd4SzLVzNxiw7rUhqS9ppuZ1tGSAg6q1opdHXs5lc\ncDjMXWYIN9a+eP/4LOJZNWAA1Foapedd27R7WRRZb3i7LHSdYcGbF4T/1dj+NNoCk1lpfCNVK02s\nNbfSAJZ+rM6w2HhtaX0J+iKt5oomupraGbPUGG3J9iB0M8sLVF6tjNDLPCW05QZKdy2x6tI/Kcvy\n0sqrWWX8cdU9rWKZiTqNcj1uZq4MV/XLNHvNTOqPst7MsM50h2hmZK5pq8kFj1OXDzQ9E+xFoHM7\nwMx8YFlj7UyPjLmbVdvcUE4RJc0Orzx9sb/T6Y92N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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14,4))\n", "ds.xc.plot(ax=ax1)\n", "ds.yc.plot(ax=ax2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the variables `xc` (longitude) and `yc` (latitude) are two-dimensional scalar fields.\n", "\n", "If we try to plot the data variable `Tair`, by default we get the logical coordinates." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1xWOCzPcKCtisC80drjrLE3JK3x0bPLbKGw2rDQvH+nSPLgOQLfbJFvsYHwHk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\nfivwdhH5fuAM4EVkGUL4P17qtd8MBicw4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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds.Tair[0].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to visualize the data on a conventional latitude-longitude grid, we can take advantage of xarray's ability to apply [cartopy](http://scitools.org.uk/cartopy/index.html) map projections." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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p38Sd117MJdfdwgnfOI+KNBYXgFD48BEbAI4kweH3+7nk2xfR3dPHtT/6MSed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\n8ArZgoAI8f7FYZOCrQtCxwrm+QCzl6OyopwtW7aycdNGamvryM5uT9u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xcMfN19K9cwei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gHg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\nGJH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UeBLJ7wdBQGlupm5XHf9dupbjurfntFFm/1d9qIWHX32Pfy/+hNvHD2XKq+994fu3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\nSgQJ0WYD49CCDIB3Kgb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RfWE0WSMRShKtjyHHFE4qCPDP4wdaj7mxrHM20MjytcjJycHl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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14,6))\n", "ax = plt.axes(projection=ccrs.PlateCarree())\n", "ax.set_global()\n", "ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='xc', y='yc', add_colorbar=False)\n", "ax.coastlines()\n", "ax.set_ylim([0,90]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multidimensional Groupby ##\n", "\n", "The above example allowed us to visualize the data on a regular latitude-longitude grid. But what if we want to do a calculation that involves grouping over one of these physical coordinates (rather than the logical coordinates), for example, calculating the mean temperature at each latitude. This can be achieved using xarray's `groupby` function, which accepts multidimensional variables. By default, `groupby` will use every unique value in the variable, which is probably not what we want. Instead, we can use the `groupby_bins` function to specify the output coordinates of the group. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# define two-degree wide latitude bins\n", "lat_bins = np.arange(0,91,2)\n", "# define a label for each bin corresponding to the central latitude\n", "lat_center = np.arange(1,90,2)\n", "# group according to those bins and take the mean\n", "Tair_lat_mean = ds.Tair.groupby_bins('xc', lat_bins, labels=lat_center).mean()\n", "# plot the result\n", "Tair_lat_mean.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the resulting coordinate for the `groupby_bins` operation got the `_bins` suffix appended: `xc_bins`. This help us distinguish it from the original multidimensional variable `xc`." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 } python-xarray-0.10.2/examples/xarray_seasonal_means.ipynb0000644000175000017500000216034013252452413024047 0ustar alastairalastair{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Calculating Seasonal Averages from Timeseries of Monthly Means \n", "=====\n", "\n", "Author: [Joe Hamman](https://github.com/jhamman/)\n", "\n", "The data used for this example can be found in the [xray-data](https://github.com/xray/xray-data) repository. You may need to change the path to `rasm.nc` below.\n", "\n", "Suppose we have a netCDF or xray Dataset of monthly mean data and we want to calculate the seasonal average. To do this properly, we need to calculate the weighted average considering that each month has a different number of days.\n", "\n", "Suppose we have a netCDF or `xarray.Dataset` of monthly mean data and we want to calculate the seasonal average. To do this properly, we need to calculate the weighted average considering that each month has a different number of days." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "numpy version : 1.11.1\n", "pandas version : 0.18.1\n", "xarray version : 0.8.2\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "from netCDF4 import num2date\n", "import matplotlib.pyplot as plt \n", "\n", "print(\"numpy version : \", np.__version__)\n", "print(\"pandas version : \", pd.__version__)\n", "print(\"xarray version : \", xr.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Some calendar information so we can support any netCDF calendar. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dpm = {'noleap': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],\n", " '365_day': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],\n", " 'standard': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],\n", " 'gregorian': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],\n", " 'proleptic_gregorian': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],\n", " 'all_leap': [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],\n", " '366_day': [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],\n", " '360_day': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30]} " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### A few calendar functions to determine the number of days in each month\n", "If you were just using the standard calendar, it would be easy to use the `calendar.month_range` function." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def leap_year(year, calendar='standard'):\n", " \"\"\"Determine if year is a leap year\"\"\"\n", " leap = False\n", " if ((calendar in ['standard', 'gregorian',\n", " 'proleptic_gregorian', 'julian']) and\n", " (year % 4 == 0)):\n", " leap = True\n", " if ((calendar == 'proleptic_gregorian') and\n", " (year % 100 == 0) and\n", " (year % 400 != 0)):\n", " leap = False\n", " elif ((calendar in ['standard', 'gregorian']) and\n", " (year % 100 == 0) and (year % 400 != 0) and\n", " (year < 1583)):\n", " leap = False\n", " return leap\n", "\n", "def get_dpm(time, calendar='standard'):\n", " \"\"\"\n", " return a array of days per month corresponding to the months provided in `months`\n", " \"\"\"\n", " month_length = np.zeros(len(time), dtype=np.int)\n", " \n", " cal_days = dpm[calendar]\n", " \n", " for i, (month, year) in enumerate(zip(time.month, time.year)):\n", " month_length[i] = cal_days[month]\n", " if leap_year(year, calendar=calendar):\n", " month_length[i] += 1\n", " return month_length" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Open the `Dataset`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dimensions: (time: 36, x: 275, y: 205)\n", "Coordinates:\n", " * time (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ...\n", " * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n", " * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n", "Data variables:\n", " Tair (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ...\n", " yc (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ...\n", " xc (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ...\n", "Attributes:\n", " title: /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc\n", " institution: U.W.\n", " source: RACM R1002RBRxaaa01a\n", " output_frequency: daily\n", " output_mode: averaged\n", " convention: CF-1.4\n", " references: Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.\n", " comment: Output from the Variable Infiltration Capacity (VIC) model.\n", " nco_openmp_thread_number: 1\n", " NCO: 4.3.7\n", " history: history deleted for brevity\n" ] } ], "source": [ "ds = xr.tutorial.load_dataset('rasm')\n", "print(ds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Now for the heavy lifting:\n", "We first have to come up with the weights,\n", "- calculate the month lengths for each monthly data record\n", "- calculate weights using `groupby('time.season')`\n", "\n", "Finally, we just need to multiply our weights by the `Dataset` and sum allong the time dimension. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Make a DataArray with the number of days in each month, size = len(time)\n", "month_length = xr.DataArray(get_dpm(ds.time.to_index(), calendar='noleap'),\n", " coords=[ds.time], name='month_length')\n", "\n", "# Calculate the weights by grouping by 'time.season'.\n", "# Conversion to float type ('astype(float)') only necessary for Python 2.x\n", "weights = month_length.groupby('time.season') / month_length.astype(float).groupby('time.season').sum()\n", "\n", "# Test that the sum of the weights for each season is 1.0\n", "np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4))\n", "\n", "# Calculate the weighted average\n", "ds_weighted = (ds * weights).groupby('time.season').sum(dim='time')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dimensions: (season: 4, x: 275, y: 205)\n", "Coordinates:\n", " * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n", " * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n", " * season (season) object 'DJF' 'JJA' 'MAM' 'SON'\n", "Data variables:\n", " Tair (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", " xc (season, y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 ...\n", " yc (season, y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 ...\n" ] } ], "source": [ "print(ds_weighted)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# only used for comparisons\n", "ds_unweighted = ds.groupby('time.season').mean('time')\n", "ds_diff = ds_weighted - ds_unweighted" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mxwyuoHTHjfa8Zgs56T27zT/9xdth5VJk+QmQZxCX7TzL2raCuJtSjCFKIOmz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\n7EQhcTE93kEESP+A5a9bM+IvvBAv+W4o3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/Q6XqNqfn4Y89tPWx448WLuzp8lesP/jJxs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vBx6bsM/LSTzfaK3vEULkhRB9WutTwKnG9rIQ4lFg24RjN5Totg3xCWSsVxPpO8jpIuXIaORI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\n6BYoAig0YmZ2lWhWfapTbfxWysB0m9P7KliZImuZSc6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2xTW51o75mHhhhpKCZ9/xqXUb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NzZVVus3y4WTzy2zt5eilSCrD+ifXA3qtnDzi8hhEAWKdp6pNqyOsrrNvTeOGd5lJEVhjhQ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\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oTsPAzeseYuC9tenu1bVlkysv37jtcv8mGkPP888VVfH0ORUlG4ekC8PaliUZzu3KtFDG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7kfEJHvAf4I+Keqem2XMzhSzBzxaVz4TiTWSZpyHEbrt7hJUm2jlSykV/qqI88RUPsSKxmCwek4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0qro+DeJVYyMYEUwIS4/D01tj8DnqYdxhYlMtqDhnWu3qvPKJ8HmNeW3aRGFQwE1etygLDnho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\no8nDsC0FzYGj2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HMEfX5X3jtGDEwFzdbGQC8m5/8vctDJ7fUwsFDhtZUYWCR961ZhICBz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\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mwGUOH9qXQVBTT9Kq7r2J/MkHyW561p6Nr7N4PR1gtHQW352r1lfXzXrIeM1E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\n0ug3xklGduq960RN11Eby4nmMYchUhqVXlMgrQOM5uOl0FE7z+RdTUDc5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edCDKQZDDSl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NmlLmuUchVLWU+XmjuvnllKemiD+XmlOrTqeZbbdz8+D5/5bXvNF34VOpu0vcsTTaWU9axo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\nOOsSEbnsvvpw2TF9kdrDzjsnVSUv6sFN/GyCVIN2o995E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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Quick plot to show the results\n", "notnull = pd.notnull(ds_unweighted['Tair'][0])\n", "\n", "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(14,12))\n", "for i, season in enumerate(('DJF', 'MAM', 'JJA', 'SON')):\n", " ds_weighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh(\n", " ax=axes[i, 0], vmin=-30, vmax=30, cmap='Spectral_r', \n", " add_colorbar=True, extend='both')\n", " \n", " ds_unweighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh(\n", " ax=axes[i, 1], vmin=-30, vmax=30, cmap='Spectral_r', \n", " add_colorbar=True, extend='both')\n", "\n", " ds_diff['Tair'].sel(season=season).where(notnull).plot.pcolormesh(\n", " ax=axes[i, 2], vmin=-0.1, vmax=.1, cmap='RdBu_r',\n", " add_colorbar=True, extend='both')\n", "\n", " axes[i, 0].set_ylabel(season)\n", " axes[i, 1].set_ylabel('')\n", " axes[i, 2].set_ylabel('')\n", "\n", "for ax in axes.flat:\n", " ax.axes.get_xaxis().set_ticklabels([])\n", " ax.axes.get_yaxis().set_ticklabels([])\n", " ax.axes.axis('tight')\n", " ax.set_xlabel('')\n", " \n", "axes[0, 0].set_title('Weighted by DPM')\n", "axes[0, 1].set_title('Equal Weighting')\n", "axes[0, 2].set_title('Difference')\n", " \n", "plt.tight_layout()\n", "\n", "fig.suptitle('Seasonal Surface Air Temperature', fontsize=16, y=1.02)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Wrap it into a simple function\n", "def season_mean(ds, calendar='standard'):\n", " # Make a DataArray of season/year groups\n", " year_season = xr.DataArray(ds.time.to_index().to_period(freq='Q-NOV').to_timestamp(how='E'),\n", " coords=[ds.time], name='year_season')\n", "\n", " # Make a DataArray with the number of days in each month, size = len(time)\n", " month_length = xr.DataArray(get_dpm(ds.time.to_index(), calendar=calendar),\n", " coords=[ds.time], name='month_length')\n", " # Calculate the weights by grouping by 'time.season'\n", " weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum()\n", "\n", " # Test that the sum of the weights for each season is 1.0\n", " np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4))\n", "\n", " # Calculate the weighted average\n", " return (ds * weights).groupby('time.season').sum(dim='time')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 } python-xarray-0.10.2/xarray/0000755000175000017500000000000013252452413016110 5ustar alastairalastairpython-xarray-0.10.2/xarray/ufuncs.py0000644000175000017500000000676113252452413017777 0ustar alastairalastair"""xarray specific universal functions Handles unary and binary operations for the following types, in ascending priority order: - scalars - numpy.ndarray - dask.array.Array - xarray.Variable - xarray.DataArray - xarray.Dataset - xarray.core.groupby.GroupBy Once NumPy 1.10 comes out with support for overriding ufuncs, this module will hopefully no longer be necessary. """ from __future__ import absolute_import, division, print_function import warnings as _warnings import numpy as _np from .core.dataarray import DataArray as _DataArray from .core.dataset import Dataset as _Dataset from .core.duck_array_ops import _dask_or_eager_func from .core.groupby import GroupBy as _GroupBy from .core.pycompat import dask_array_type as _dask_array_type from .core.variable import Variable as _Variable _xarray_types = (_Variable, _DataArray, _Dataset, _GroupBy) _dispatch_order = (_np.ndarray, _dask_array_type) + _xarray_types def _dispatch_priority(obj): for priority, cls in enumerate(_dispatch_order): if isinstance(obj, cls): return priority return -1 class _UFuncDispatcher(object): """Wrapper for dispatching ufuncs.""" def __init__(self, name): self._name = name def __call__(self, *args, **kwargs): _warnings.warn( 'xarray.ufuncs will be deprecated when xarray no longer supports ' 'versions of numpy older than v1.13. Instead, use numpy ufuncs ' 'directly.', PendingDeprecationWarning, stacklevel=2) new_args = args f = _dask_or_eager_func(self._name, n_array_args=len(args)) if len(args) > 2 or len(args) == 0: raise TypeError('cannot handle %s arguments for %r' % (len(args), self._name)) elif len(args) == 1: if isinstance(args[0], _xarray_types): f = args[0]._unary_op(self) else: # len(args) = 2 p1, p2 = map(_dispatch_priority, args) if p1 >= p2: if isinstance(args[0], _xarray_types): f = args[0]._binary_op(self) else: if isinstance(args[1], _xarray_types): f = args[1]._binary_op(self, reflexive=True) new_args = tuple(reversed(args)) res = f(*new_args, **kwargs) if res is NotImplemented: raise TypeError('%r not implemented for types (%r, %r)' % (self._name, type(args[0]), type(args[1]))) return res def _create_op(name): func = _UFuncDispatcher(name) func.__name__ = name doc = getattr(_np, name).__doc__ func.__doc__ = ('xarray specific variant of numpy.%s. Handles ' 'xarray.Dataset, xarray.DataArray, xarray.Variable, ' 'numpy.ndarray and dask.array.Array objects with ' 'automatic dispatching.\n\n' 'Documentation from numpy:\n\n%s' % (name, doc)) return func __all__ = """logaddexp logaddexp2 conj exp log log2 log10 log1p expm1 sqrt square sin cos tan arcsin arccos arctan arctan2 hypot sinh cosh tanh arcsinh arccosh arctanh deg2rad rad2deg logical_and logical_or logical_xor logical_not maximum minimum fmax fmin isreal iscomplex isfinite isinf isnan signbit copysign nextafter ldexp fmod floor ceil trunc degrees radians rint fix angle real imag fabs sign frexp fmod """.split() for name in __all__: globals()[name] = _create_op(name) python-xarray-0.10.2/xarray/plot/0000755000175000017500000000000013252452413017066 5ustar alastairalastairpython-xarray-0.10.2/xarray/plot/utils.py0000644000175000017500000002660313252452413020607 0ustar alastairalastairfrom __future__ import absolute_import, division, print_function import warnings import numpy as np import pandas as pd import pkg_resources from ..core.pycompat import basestring from ..core.utils import is_scalar ROBUST_PERCENTILE = 2.0 def _load_default_cmap(fname='default_colormap.csv'): """ Returns viridis color map """ from matplotlib.colors import LinearSegmentedColormap # Not sure what the first arg here should be f = pkg_resources.resource_stream(__name__, fname) cm_data = pd.read_csv(f, header=None).values return LinearSegmentedColormap.from_list('viridis', cm_data) def import_seaborn(): '''import seaborn and handle deprecation of apionly module''' with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") try: import seaborn.apionly as sns if (w and issubclass(w[-1].category, UserWarning) and ("seaborn.apionly module" in str(w[-1].message))): raise ImportError except ImportError: import seaborn as sns finally: warnings.resetwarnings() return sns _registered = False def register_pandas_datetime_converter_if_needed(): # based on https://github.com/pandas-dev/pandas/pull/17710 global _registered if not _registered: try: from pandas.plotting import register_matplotlib_converters register_matplotlib_converters() except ImportError: # register_matplotlib_converters new in pandas 0.22 from pandas.tseries import converter converter.register() _registered = True def import_matplotlib_pyplot(): """Import pyplot as register appropriate converters.""" register_pandas_datetime_converter_if_needed() import matplotlib.pyplot as plt return plt def _determine_extend(calc_data, vmin, vmax): extend_min = calc_data.min() < vmin extend_max = calc_data.max() > vmax if extend_min and extend_max: extend = 'both' elif extend_min: extend = 'min' elif extend_max: extend = 'max' else: extend = 'neither' return extend def _build_discrete_cmap(cmap, levels, extend, filled): """ Build a discrete colormap and normalization of the data. """ import matplotlib as mpl if not filled: # non-filled contour plots extend = 'max' if extend == 'both': ext_n = 2 elif extend in ['min', 'max']: ext_n = 1 else: ext_n = 0 n_colors = len(levels) + ext_n - 1 pal = _color_palette(cmap, n_colors) new_cmap, cnorm = mpl.colors.from_levels_and_colors( levels, pal, extend=extend) # copy the old cmap name, for easier testing new_cmap.name = getattr(cmap, 'name', cmap) return new_cmap, cnorm def _color_palette(cmap, n_colors): import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap colors_i = np.linspace(0, 1., n_colors) if isinstance(cmap, (list, tuple)): # we have a list of colors cmap = ListedColormap(cmap, N=n_colors) pal = cmap(colors_i) elif isinstance(cmap, basestring): # we have some sort of named palette try: # is this a matplotlib cmap? cmap = plt.get_cmap(cmap) pal = cmap(colors_i) except ValueError: # ValueError happens when mpl doesn't like a colormap, try seaborn try: from seaborn.apionly import color_palette pal = color_palette(cmap, n_colors=n_colors) except (ValueError, ImportError): # or maybe we just got a single color as a string cmap = ListedColormap([cmap], N=n_colors) pal = cmap(colors_i) else: # cmap better be a LinearSegmentedColormap (e.g. viridis) pal = cmap(colors_i) return pal # _determine_cmap_params is adapted from Seaborn: # https://github.com/mwaskom/seaborn/blob/v0.6/seaborn/matrix.py#L158 # Used under the terms of Seaborn's license, see licenses/SEABORN_LICENSE. def _determine_cmap_params(plot_data, vmin=None, vmax=None, cmap=None, center=None, robust=False, extend=None, levels=None, filled=True, norm=None): """ Use some heuristics to set good defaults for colorbar and range. Parameters ========== plot_data: Numpy array Doesn't handle xarray objects Returns ======= cmap_params : dict Use depends on the type of the plotting function """ import matplotlib as mpl calc_data = np.ravel(plot_data[~pd.isnull(plot_data)]) # Handle all-NaN input data gracefully if calc_data.size == 0: # Arbitrary default for when all values are NaN calc_data = np.array(0.0) # Setting center=False prevents a divergent cmap possibly_divergent = center is not False # Set center to 0 so math below makes sense but remember its state center_is_none = False if center is None: center = 0 center_is_none = True # Setting both vmin and vmax prevents a divergent cmap if (vmin is not None) and (vmax is not None): possibly_divergent = False # Setting vmin or vmax implies linspaced levels user_minmax = (vmin is not None) or (vmax is not None) # vlim might be computed below vlim = None if vmin is None: if robust: vmin = np.percentile(calc_data, ROBUST_PERCENTILE) else: vmin = calc_data.min() elif possibly_divergent: vlim = abs(vmin - center) if vmax is None: if robust: vmax = np.percentile(calc_data, 100 - ROBUST_PERCENTILE) else: vmax = calc_data.max() elif possibly_divergent: vlim = abs(vmax - center) if possibly_divergent: # kwargs not specific about divergent or not: infer defaults from data divergent = ((vmin < 0) and (vmax > 0)) or not center_is_none else: divergent = False # A divergent map should be symmetric around the center value if divergent: if vlim is None: vlim = max(abs(vmin - center), abs(vmax - center)) vmin, vmax = -vlim, vlim # Now add in the centering value and set the limits vmin += center vmax += center # Choose default colormaps if not provided if cmap is None: if divergent: cmap = "RdBu_r" else: cmap = "viridis" # Allow viridis before matplotlib 1.5 if cmap == "viridis": cmap = _load_default_cmap() # Handle discrete levels if levels is not None: if is_scalar(levels): if user_minmax or levels == 1: levels = np.linspace(vmin, vmax, levels) else: # N in MaxNLocator refers to bins, not ticks ticker = mpl.ticker.MaxNLocator(levels - 1) levels = ticker.tick_values(vmin, vmax) vmin, vmax = levels[0], levels[-1] if extend is None: extend = _determine_extend(calc_data, vmin, vmax) if levels is not None: cmap, norm = _build_discrete_cmap(cmap, levels, extend, filled) return dict(vmin=vmin, vmax=vmax, cmap=cmap, extend=extend, levels=levels, norm=norm) def _infer_xy_labels_3d(darray, x, y, rgb): """ Determine x and y labels for showing RGB images. Attempts to infer which dimension is RGB/RGBA by size and order of dims. """ assert rgb is None or rgb != x assert rgb is None or rgb != y # Start by detecting and reporting invalid combinations of arguments assert darray.ndim == 3 not_none = [a for a in (x, y, rgb) if a is not None] if len(set(not_none)) < len(not_none): raise ValueError( 'Dimension names must be None or unique strings, but imshow was ' 'passed x=%r, y=%r, and rgb=%r.' % (x, y, rgb)) for label in not_none: if label not in darray.dims: raise ValueError('%r is not a dimension' % (label,)) # Then calculate rgb dimension if certain and check validity could_be_color = [label for label in darray.dims if darray[label].size in (3, 4) and label not in (x, y)] if rgb is None and not could_be_color: raise ValueError( 'A 3-dimensional array was passed to imshow(), but there is no ' 'dimension that could be color. At least one dimension must be ' 'of size 3 (RGB) or 4 (RGBA), and not given as x or y.') if rgb is None and len(could_be_color) == 1: rgb = could_be_color[0] if rgb is not None and darray[rgb].size not in (3, 4): raise ValueError('Cannot interpret dim %r of size %s as RGB or RGBA.' % (rgb, darray[rgb].size)) # If rgb dimension is still unknown, there must be two or three dimensions # in could_be_color. We therefore warn, and use a heuristic to break ties. if rgb is None: assert len(could_be_color) in (2, 3) rgb = could_be_color[-1] warnings.warn( 'Several dimensions of this array could be colors. Xarray ' 'will use the last possible dimension (%r) to match ' 'matplotlib.pyplot.imshow. You can pass names of x, y, ' 'and/or rgb dimensions to override this guess.' % rgb) assert rgb is not None # Finally, we pick out the red slice and delegate to the 2D version: return _infer_xy_labels(darray.isel(**{rgb: 0}), x, y) def _infer_xy_labels(darray, x, y, imshow=False, rgb=None): """ Determine x and y labels. For use in _plot2d darray must be a 2 dimensional data array, or 3d for imshow only. """ assert x is None or x != y if imshow and darray.ndim == 3: return _infer_xy_labels_3d(darray, x, y, rgb) if x is None and y is None: if darray.ndim != 2: raise ValueError('DataArray must be 2d') y, x = darray.dims elif x is None: if y not in darray.dims: raise ValueError('y must be a dimension name if x is not supplied') x = darray.dims[0] if y == darray.dims[1] else darray.dims[1] elif y is None: if x not in darray.dims: raise ValueError('x must be a dimension name if y is not supplied') y = darray.dims[0] if x == darray.dims[1] else darray.dims[1] elif any(k not in darray.coords and k not in darray.dims for k in (x, y)): raise ValueError('x and y must be coordinate variables') return x, y def get_axis(figsize, size, aspect, ax): import matplotlib as mpl import matplotlib.pyplot as plt if figsize is not None: if ax is not None: raise ValueError('cannot provide both `figsize` and ' '`ax` arguments') if size is not None: raise ValueError('cannot provide both `figsize` and ' '`size` arguments') _, ax = plt.subplots(figsize=figsize) elif size is not None: if ax is not None: raise ValueError('cannot provide both `size` and `ax` arguments') if aspect is None: width, height = mpl.rcParams['figure.figsize'] aspect = width / height figsize = (size * aspect, size) _, ax = plt.subplots(figsize=figsize) elif aspect is not None: raise ValueError('cannot provide `aspect` argument without `size`') if ax is None: ax = plt.gca() return ax python-xarray-0.10.2/xarray/plot/plot.py0000644000175000017500000007706013252452413020430 0ustar alastairalastair""" Use this module directly: import xarray.plot as xplt Or use the methods on a DataArray: DataArray.plot._____ """ from __future__ import absolute_import, division, print_function import functools import warnings from datetime import datetime import numpy as np import pandas as pd from xarray.core.pycompat import basestring from .facetgrid import FacetGrid from .utils import ( ROBUST_PERCENTILE, _determine_cmap_params, _infer_xy_labels, get_axis, import_matplotlib_pyplot) def _valid_numpy_subdtype(x, numpy_types): """ Is any dtype from numpy_types superior to the dtype of x? """ # If any of the types given in numpy_types is understood as numpy.generic, # all possible x will be considered valid. This is probably unwanted. for t in numpy_types: assert not np.issubdtype(np.generic, t) return any(np.issubdtype(x.dtype, t) for t in numpy_types) def _valid_other_type(x, types): """ Do all elements of x have a type from types? """ return all(any(isinstance(el, t) for t in types) for el in np.ravel(x)) def _ensure_plottable(*args): """ Raise exception if there is anything in args that can't be plotted on an axis. """ numpy_types = [np.floating, np.integer, np.timedelta64, np.datetime64] other_types = [datetime] for x in args: if not (_valid_numpy_subdtype(np.array(x), numpy_types) or _valid_other_type(np.array(x), other_types)): raise TypeError('Plotting requires coordinates to be numeric ' 'or dates.') def _easy_facetgrid(darray, plotfunc, x, y, row=None, col=None, col_wrap=None, sharex=True, sharey=True, aspect=None, size=None, subplot_kws=None, **kwargs): """ Convenience method to call xarray.plot.FacetGrid from 2d plotting methods kwargs are the arguments to 2d plotting method """ ax = kwargs.pop('ax', None) figsize = kwargs.pop('figsize', None) if ax is not None: raise ValueError("Can't use axes when making faceted plots.") if aspect is None: aspect = 1 if size is None: size = 3 elif figsize is not None: raise ValueError('cannot provide both `figsize` and `size` arguments') g = FacetGrid(data=darray, col=col, row=row, col_wrap=col_wrap, sharex=sharex, sharey=sharey, figsize=figsize, aspect=aspect, size=size, subplot_kws=subplot_kws) return g.map_dataarray(plotfunc, x, y, **kwargs) def plot(darray, row=None, col=None, col_wrap=None, ax=None, rtol=0.01, subplot_kws=None, **kwargs): """ Default plot of DataArray using matplotlib.pyplot. Calls xarray plotting function based on the dimensions of darray.squeeze() =============== =========================== Dimensions Plotting function --------------- --------------------------- 1 :py:func:`xarray.plot.line` 2 :py:func:`xarray.plot.pcolormesh` Anything else :py:func:`xarray.plot.hist` =============== =========================== Parameters ---------- darray : DataArray row : string, optional If passed, make row faceted plots on this dimension name col : string, optional If passed, make column faceted plots on this dimension name col_wrap : integer, optional Use together with ``col`` to wrap faceted plots ax : matplotlib axes, optional If None, uses the current axis. Not applicable when using facets. rtol : number, optional Relative tolerance used to determine if the indexes are uniformly spaced. Usually a small positive number. subplot_kws : dict, optional Dictionary of keyword arguments for matplotlib subplots. Only applies to FacetGrid plotting. **kwargs : optional Additional keyword arguments to matplotlib """ darray = darray.squeeze() plot_dims = set(darray.dims) plot_dims.discard(row) plot_dims.discard(col) ndims = len(plot_dims) error_msg = ('Only 2d plots are supported for facets in xarray. ' 'See the package `Seaborn` for more options.') if ndims == 1: if row or col: raise ValueError(error_msg) plotfunc = line elif ndims == 2: # Only 2d can FacetGrid kwargs['row'] = row kwargs['col'] = col kwargs['col_wrap'] = col_wrap kwargs['subplot_kws'] = subplot_kws plotfunc = pcolormesh else: if row or col: raise ValueError(error_msg) plotfunc = hist kwargs['ax'] = ax return plotfunc(darray, **kwargs) # This function signature should not change so that it can use # matplotlib format strings def line(darray, *args, **kwargs): """ Line plot of DataArray index against values Wraps :func:`matplotlib:matplotlib.pyplot.plot` Parameters ---------- darray : DataArray Must be 1 dimensional figsize : tuple, optional A tuple (width, height) of the figure in inches. Mutually exclusive with ``size`` and ``ax``. aspect : scalar, optional Aspect ratio of plot, so that ``aspect * size`` gives the width in inches. Only used if a ``size`` is provided. size : scalar, optional If provided, create a new figure for the plot with the given size. Height (in inches) of each plot. See also: ``aspect``. ax : matplotlib axes object, optional Axis on which to plot this figure. By default, use the current axis. Mutually exclusive with ``size`` and ``figsize``. hue : string, optional Coordinate for which you want multiple lines plotted (2D DataArrays only). x, y : string, optional Coordinates for x, y axis. Only one of these may be specified. The other coordinate plots values from the DataArray on which this plot method is called. add_legend : boolean, optional Add legend with y axis coordinates (2D inputs only). *args, **kwargs : optional Additional arguments to matplotlib.pyplot.plot """ ndims = len(darray.dims) if ndims > 2: raise ValueError('Line plots are for 1- or 2-dimensional DataArrays. ' 'Passed DataArray has {ndims} ' 'dimensions'.format(ndims=ndims)) # Ensures consistency with .plot method figsize = kwargs.pop('figsize', None) aspect = kwargs.pop('aspect', None) size = kwargs.pop('size', None) ax = kwargs.pop('ax', None) hue = kwargs.pop('hue', None) x = kwargs.pop('x', None) y = kwargs.pop('y', None) add_legend = kwargs.pop('add_legend', True) ax = get_axis(figsize, size, aspect, ax) error_msg = ('must be either None or one of ({0:s})' .format(', '.join([repr(dd) for dd in darray.dims]))) if x is not None and x not in darray.dims: raise ValueError('x ' + error_msg) if y is not None and y not in darray.dims: raise ValueError('y ' + error_msg) if x is not None and y is not None: raise ValueError('You cannot specify both x and y kwargs' 'for line plots.') if ndims == 1: dim, = darray.dims # get the only dimension name if (x is None and y is None) or x == dim: xplt = darray.coords[dim] yplt = darray xlabel = dim ylabel = darray.name else: yplt = darray.coords[dim] xplt = darray xlabel = darray.name ylabel = dim else: if x is None and y is None and hue is None: raise ValueError('For 2D inputs, please specify either hue or x.') if y is None: xlabel, huelabel = _infer_xy_labels(darray=darray, x=x, y=hue) ylabel = darray.name xplt = darray.coords[xlabel] yplt = darray.transpose(xlabel, huelabel) else: ylabel, huelabel = _infer_xy_labels(darray=darray, x=y, y=hue) xlabel = darray.name xplt = darray.transpose(ylabel, huelabel) yplt = darray.coords[ylabel] _ensure_plottable(xplt) primitive = ax.plot(xplt, yplt, *args, **kwargs) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) ax.set_title(darray._title_for_slice()) if darray.ndim == 2 and add_legend: ax.legend(handles=primitive, labels=list(darray.coords[huelabel].values), title=huelabel) # Rotate dates on xlabels if np.issubdtype(xplt.dtype, np.datetime64): ax.get_figure().autofmt_xdate() return primitive def hist(darray, figsize=None, size=None, aspect=None, ax=None, **kwargs): """ Histogram of DataArray Wraps :func:`matplotlib:matplotlib.pyplot.hist` Plots N dimensional arrays by first flattening the array. Parameters ---------- darray : DataArray Can be any dimension figsize : tuple, optional A tuple (width, height) of the figure in inches. Mutually exclusive with ``size`` and ``ax``. aspect : scalar, optional Aspect ratio of plot, so that ``aspect * size`` gives the width in inches. Only used if a ``size`` is provided. size : scalar, optional If provided, create a new figure for the plot with the given size. Height (in inches) of each plot. See also: ``aspect``. ax : matplotlib axes object, optional Axis on which to plot this figure. By default, use the current axis. Mutually exclusive with ``size`` and ``figsize``. **kwargs : optional Additional keyword arguments to matplotlib.pyplot.hist """ ax = get_axis(figsize, size, aspect, ax) no_nan = np.ravel(darray.values) no_nan = no_nan[pd.notnull(no_nan)] primitive = ax.hist(no_nan, **kwargs) ax.set_ylabel('Count') if darray.name is not None: ax.set_title('Histogram of {0}'.format(darray.name)) return primitive def _update_axes_limits(ax, xincrease, yincrease): """ Update axes in place to increase or decrease For use in _plot2d """ if xincrease is None: pass elif xincrease: ax.set_xlim(sorted(ax.get_xlim())) elif not xincrease: ax.set_xlim(sorted(ax.get_xlim(), reverse=True)) if yincrease is None: pass elif yincrease: ax.set_ylim(sorted(ax.get_ylim())) elif not yincrease: ax.set_ylim(sorted(ax.get_ylim(), reverse=True)) # MUST run before any 2d plotting functions are defined since # _plot2d decorator adds them as methods here. class _PlotMethods(object): """ Enables use of xarray.plot functions as attributes on a DataArray. For example, DataArray.plot.imshow """ def __init__(self, darray): self._da = darray def __call__(self, **kwargs): return plot(self._da, **kwargs) @functools.wraps(hist) def hist(self, ax=None, **kwargs): return hist(self._da, ax=ax, **kwargs) @functools.wraps(line) def line(self, *args, **kwargs): return line(self._da, *args, **kwargs) def _rescale_imshow_rgb(darray, vmin, vmax, robust): assert robust or vmin is not None or vmax is not None # There's a cyclic dependency via DataArray, so we can't import from # xarray.ufuncs in global scope. from xarray.ufuncs import maximum, minimum # Calculate vmin and vmax automatically for `robust=True` if robust: if vmax is None: vmax = np.nanpercentile(darray, 100 - ROBUST_PERCENTILE) if vmin is None: vmin = np.nanpercentile(darray, ROBUST_PERCENTILE) # If not robust and one bound is None, calculate the default other bound # and check that an interval between them exists. elif vmax is None: vmax = 255 if np.issubdtype(darray.dtype, np.integer) else 1 if vmax < vmin: raise ValueError( 'vmin=%r is less than the default vmax (%r) - you must supply ' 'a vmax > vmin in this case.' % (vmin, vmax)) elif vmin is None: vmin = 0 if vmin > vmax: raise ValueError( 'vmax=%r is less than the default vmin (0) - you must supply ' 'a vmin < vmax in this case.' % vmax) # Scale interval [vmin .. vmax] to [0 .. 1], with darray as 64-bit float # to avoid precision loss, integer over/underflow, etc with extreme inputs. # After scaling, downcast to 32-bit float. This substantially reduces # memory usage after we hand `darray` off to matplotlib. darray = ((darray.astype('f8') - vmin) / (vmax - vmin)).astype('f4') return minimum(maximum(darray, 0), 1) def _plot2d(plotfunc): """ Decorator for common 2d plotting logic Also adds the 2d plot method to class _PlotMethods """ commondoc = """ Parameters ---------- darray : DataArray Must be 2 dimensional, unless creating faceted plots x : string, optional Coordinate for x axis. If None use darray.dims[1] y : string, optional Coordinate for y axis. If None use darray.dims[0] figsize : tuple, optional A tuple (width, height) of the figure in inches. Mutually exclusive with ``size`` and ``ax``. aspect : scalar, optional Aspect ratio of plot, so that ``aspect * size`` gives the width in inches. Only used if a ``size`` is provided. size : scalar, optional If provided, create a new figure for the plot with the given size. Height (in inches) of each plot. See also: ``aspect``. ax : matplotlib axes object, optional Axis on which to plot this figure. By default, use the current axis. Mutually exclusive with ``size`` and ``figsize``. row : string, optional If passed, make row faceted plots on this dimension name col : string, optional If passed, make column faceted plots on this dimension name col_wrap : integer, optional Use together with ``col`` to wrap faceted plots xincrease : None, True, or False, optional Should the values on the x axes be increasing from left to right? if None, use the default for the matplotlib function yincrease : None, True, or False, optional Should the values on the y axes be increasing from top to bottom? if None, use the default for the matplotlib function add_colorbar : Boolean, optional Adds colorbar to axis add_labels : Boolean, optional Use xarray metadata to label axes vmin, vmax : floats, optional Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments. When a diverging dataset is inferred, setting one of these values will fix the other by symmetry around ``center``. Setting both values prevents use of a diverging colormap. If discrete levels are provided as an explicit list, both of these values are ignored. cmap : matplotlib colormap name or object, optional The mapping from data values to color space. If not provided, this will be either be ``viridis`` (if the function infers a sequential dataset) or ``RdBu_r`` (if the function infers a diverging dataset). When `Seaborn` is installed, ``cmap`` may also be a `seaborn` color palette. If ``cmap`` is seaborn color palette and the plot type is not ``contour`` or ``contourf``, ``levels`` must also be specified. colors : discrete colors to plot, optional A single color or a list of colors. If the plot type is not ``contour`` or ``contourf``, the ``levels`` argument is required. center : float, optional The value at which to center the colormap. Passing this value implies use of a diverging colormap. Setting it to ``False`` prevents use of a diverging colormap. robust : bool, optional If True and ``vmin`` or ``vmax`` are absent, the colormap range is computed with 2nd and 98th percentiles instead of the extreme values. extend : {'neither', 'both', 'min', 'max'}, optional How to draw arrows extending the colorbar beyond its limits. If not provided, extend is inferred from vmin, vmax and the data limits. levels : int or list-like object, optional Split the colormap (cmap) into discrete color intervals. If an integer is provided, "nice" levels are chosen based on the data range: this can imply that the final number of levels is not exactly the expected one. Setting ``vmin`` and/or ``vmax`` with ``levels=N`` is equivalent to setting ``levels=np.linspace(vmin, vmax, N)``. infer_intervals : bool, optional Only applies to pcolormesh. If True, the coordinate intervals are passed to pcolormesh. If False, the original coordinates are used (this can be useful for certain map projections). The default is to always infer intervals, unless the mesh is irregular and plotted on a map projection. subplot_kws : dict, optional Dictionary of keyword arguments for matplotlib subplots. Only applies to FacetGrid plotting. cbar_ax : matplotlib Axes, optional Axes in which to draw the colorbar. cbar_kwargs : dict, optional Dictionary of keyword arguments to pass to the colorbar. **kwargs : optional Additional arguments to wrapped matplotlib function Returns ------- artist : The same type of primitive artist that the wrapped matplotlib function returns """ # Build on the original docstring plotfunc.__doc__ = '%s\n%s' % (plotfunc.__doc__, commondoc) @functools.wraps(plotfunc) def newplotfunc(darray, x=None, y=None, figsize=None, size=None, aspect=None, ax=None, row=None, col=None, col_wrap=None, xincrease=True, yincrease=True, add_colorbar=None, add_labels=True, vmin=None, vmax=None, cmap=None, center=None, robust=False, extend=None, levels=None, infer_intervals=None, colors=None, subplot_kws=None, cbar_ax=None, cbar_kwargs=None, **kwargs): # All 2d plots in xarray share this function signature. # Method signature below should be consistent. # Decide on a default for the colorbar before facetgrids if add_colorbar is None: add_colorbar = plotfunc.__name__ != 'contour' imshow_rgb = ( plotfunc.__name__ == 'imshow' and darray.ndim == (3 + (row is not None) + (col is not None))) if imshow_rgb: # Don't add a colorbar when showing an image with explicit colors add_colorbar = False # Matplotlib does not support normalising RGB data, so do it here. # See eg. https://github.com/matplotlib/matplotlib/pull/10220 if robust or vmax is not None or vmin is not None: darray = _rescale_imshow_rgb(darray, vmin, vmax, robust) vmin, vmax, robust = None, None, False # Handle facetgrids first if row or col: allargs = locals().copy() allargs.pop('imshow_rgb') allargs.update(allargs.pop('kwargs')) # Need the decorated plotting function allargs['plotfunc'] = globals()[plotfunc.__name__] return _easy_facetgrid(**allargs) plt = import_matplotlib_pyplot() # colors is mutually exclusive with cmap if cmap and colors: raise ValueError("Can't specify both cmap and colors.") # colors is only valid when levels is supplied or the plot is of type # contour or contourf if colors and (('contour' not in plotfunc.__name__) and (not levels)): raise ValueError("Can only specify colors with contour or levels") # we should not be getting a list of colors in cmap anymore # is there a better way to do this test? if isinstance(cmap, (list, tuple)): warnings.warn("Specifying a list of colors in cmap is deprecated. " "Use colors keyword instead.", DeprecationWarning, stacklevel=3) rgb = kwargs.pop('rgb', None) xlab, ylab = _infer_xy_labels( darray=darray, x=x, y=y, imshow=imshow_rgb, rgb=rgb) if rgb is not None and plotfunc.__name__ != 'imshow': raise ValueError('The "rgb" keyword is only valid for imshow()') elif rgb is not None and not imshow_rgb: raise ValueError('The "rgb" keyword is only valid for imshow()' 'with a three-dimensional array (per facet)') # better to pass the ndarrays directly to plotting functions xval = darray[xlab].values yval = darray[ylab].values # check if we need to broadcast one dimension if xval.ndim < yval.ndim: xval = np.broadcast_to(xval, yval.shape) if yval.ndim < xval.ndim: yval = np.broadcast_to(yval, xval.shape) # May need to transpose for correct x, y labels # xlab may be the name of a coord, we have to check for dim names if imshow_rgb: # For RGB[A] images, matplotlib requires the color dimension # to be last. In Xarray the order should be unimportant, so # we transpose to (y, x, color) to make this work. yx_dims = (ylab, xlab) dims = yx_dims + tuple(d for d in darray.dims if d not in yx_dims) if dims != darray.dims: darray = darray.transpose(*dims) elif darray[xlab].dims[-1] == darray.dims[0]: darray = darray.transpose() # Pass the data as a masked ndarray too zval = darray.to_masked_array(copy=False) _ensure_plottable(xval, yval) if 'contour' in plotfunc.__name__ and levels is None: levels = 7 # this is the matplotlib default cmap_kwargs = {'plot_data': zval.data, 'vmin': vmin, 'vmax': vmax, 'cmap': colors if colors else cmap, 'center': center, 'robust': robust, 'extend': extend, 'levels': levels, 'filled': plotfunc.__name__ != 'contour', } cmap_params = _determine_cmap_params(**cmap_kwargs) if 'contour' in plotfunc.__name__: # extend is a keyword argument only for contour and contourf, but # passing it to the colorbar is sufficient for imshow and # pcolormesh kwargs['extend'] = cmap_params['extend'] kwargs['levels'] = cmap_params['levels'] if 'pcolormesh' == plotfunc.__name__: kwargs['infer_intervals'] = infer_intervals # This allows the user to pass in a custom norm coming via kwargs kwargs.setdefault('norm', cmap_params['norm']) if 'imshow' == plotfunc.__name__ and isinstance(aspect, basestring): # forbid usage of mpl strings raise ValueError("plt.imshow's `aspect` kwarg is not available " "in xarray") ax = get_axis(figsize, size, aspect, ax) primitive = plotfunc(xval, yval, zval, ax=ax, cmap=cmap_params['cmap'], vmin=cmap_params['vmin'], vmax=cmap_params['vmax'], **kwargs) # Label the plot with metadata if add_labels: ax.set_xlabel(xlab) ax.set_ylabel(ylab) ax.set_title(darray._title_for_slice()) if add_colorbar: cbar_kwargs = {} if cbar_kwargs is None else dict(cbar_kwargs) cbar_kwargs.setdefault('extend', cmap_params['extend']) if cbar_ax is None: cbar_kwargs.setdefault('ax', ax) else: cbar_kwargs.setdefault('cax', cbar_ax) cbar = plt.colorbar(primitive, **cbar_kwargs) if darray.name and add_labels and 'label' not in cbar_kwargs: cbar.set_label(darray.name, rotation=90) elif cbar_ax is not None or cbar_kwargs is not None: # inform the user about keywords which aren't used raise ValueError("cbar_ax and cbar_kwargs can't be used with " "add_colorbar=False.") _update_axes_limits(ax, xincrease, yincrease) # Rotate dates on xlabels if np.issubdtype(xval.dtype, np.datetime64): ax.get_figure().autofmt_xdate() return primitive # For use as DataArray.plot.plotmethod @functools.wraps(newplotfunc) def plotmethod(_PlotMethods_obj, x=None, y=None, figsize=None, size=None, aspect=None, ax=None, row=None, col=None, col_wrap=None, xincrease=True, yincrease=True, add_colorbar=None, add_labels=True, vmin=None, vmax=None, cmap=None, colors=None, center=None, robust=False, extend=None, levels=None, infer_intervals=None, subplot_kws=None, cbar_ax=None, cbar_kwargs=None, **kwargs): """ The method should have the same signature as the function. This just makes the method work on Plotmethods objects, and passes all the other arguments straight through. """ allargs = locals() allargs['darray'] = _PlotMethods_obj._da allargs.update(kwargs) for arg in ['_PlotMethods_obj', 'newplotfunc', 'kwargs']: del allargs[arg] return newplotfunc(**allargs) # Add to class _PlotMethods setattr(_PlotMethods, plotmethod.__name__, plotmethod) return newplotfunc @_plot2d def imshow(x, y, z, ax, **kwargs): """ Image plot of 2d DataArray using matplotlib.pyplot Wraps :func:`matplotlib:matplotlib.pyplot.imshow` While other plot methods require the DataArray to be strictly two-dimensional, ``imshow`` also accepts a 3D array where some dimension can be interpreted as RGB or RGBA color channels and allows this dimension to be specified via the kwarg ``rgb=``. Unlike matplotlib, Xarray can apply ``vmin`` and ``vmax`` to RGB or RGBA data, by applying a single scaling factor and offset to all bands. Passing ``robust=True`` infers ``vmin`` and ``vmax`` :ref:`in the usual way `. .. note:: This function needs uniformly spaced coordinates to properly label the axes. Call DataArray.plot() to check. The pixels are centered on the coordinates values. Ie, if the coordinate value is 3.2 then the pixels for those coordinates will be centered on 3.2. """ if x.ndim != 1 or y.ndim != 1: raise ValueError('imshow requires 1D coordinates, try using ' 'pcolormesh or contour(f)') # Centering the pixels- Assumes uniform spacing try: xstep = (x[1] - x[0]) / 2.0 except IndexError: # Arbitrary default value, similar to matplotlib behaviour xstep = .1 try: ystep = (y[1] - y[0]) / 2.0 except IndexError: ystep = .1 left, right = x[0] - xstep, x[-1] + xstep bottom, top = y[-1] + ystep, y[0] - ystep defaults = {'extent': [left, right, bottom, top], 'origin': 'upper', 'interpolation': 'nearest', } if not hasattr(ax, 'projection'): # not for cartopy geoaxes defaults['aspect'] = 'auto' # Allow user to override these defaults defaults.update(kwargs) if z.ndim == 3: # matplotlib imshow uses black for missing data, but Xarray makes # missing data transparent. We therefore add an alpha channel if # there isn't one, and set it to transparent where data is masked. if z.shape[-1] == 3: alpha = np.ma.ones(z.shape[:2] + (1,), dtype=z.dtype) if np.issubdtype(z.dtype, np.integer): alpha *= 255 z = np.ma.concatenate((z, alpha), axis=2) else: z = z.copy() z[np.any(z.mask, axis=-1), -1] = 0 primitive = ax.imshow(z, **defaults) return primitive @_plot2d def contour(x, y, z, ax, **kwargs): """ Contour plot of 2d DataArray Wraps :func:`matplotlib:matplotlib.pyplot.contour` """ primitive = ax.contour(x, y, z, **kwargs) return primitive @_plot2d def contourf(x, y, z, ax, **kwargs): """ Filled contour plot of 2d DataArray Wraps :func:`matplotlib:matplotlib.pyplot.contourf` """ primitive = ax.contourf(x, y, z, **kwargs) return primitive def _is_monotonic(coord, axis=0): """ >>> _is_monotonic(np.array([0, 1, 2])) True >>> _is_monotonic(np.array([2, 1, 0])) True >>> _is_monotonic(np.array([0, 2, 1])) False """ if coord.shape[axis] < 3: return True else: n = coord.shape[axis] delta_pos = (coord.take(np.arange(1, n), axis=axis) >= coord.take(np.arange(0, n - 1), axis=axis)) delta_neg = (coord.take(np.arange(1, n), axis=axis) <= coord.take(np.arange(0, n - 1), axis=axis)) return np.all(delta_pos) or np.all(delta_neg) def _infer_interval_breaks(coord, axis=0): """ >>> _infer_interval_breaks(np.arange(5)) array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5]) >>> _infer_interval_breaks([[0, 1], [3, 4]], axis=1) array([[-0.5, 0.5, 1.5], [ 2.5, 3.5, 4.5]]) """ coord = np.asarray(coord) if not _is_monotonic(coord, axis=axis): raise ValueError("The input coordinate is not sorted in increasing " "order along axis %d. This can lead to unexpected " "results. Consider calling the `sortby` method on " "the input DataArray. To plot data with categorical " "axes, consider using the `heatmap` function from " "the `seaborn` statistical plotting library." % axis) deltas = 0.5 * np.diff(coord, axis=axis) if deltas.size == 0: deltas = np.array(0.0) first = np.take(coord, [0], axis=axis) - np.take(deltas, [0], axis=axis) last = np.take(coord, [-1], axis=axis) + np.take(deltas, [-1], axis=axis) trim_last = tuple(slice(None, -1) if n == axis else slice(None) for n in range(coord.ndim)) return np.concatenate([first, coord[trim_last] + deltas, last], axis=axis) @_plot2d def pcolormesh(x, y, z, ax, infer_intervals=None, **kwargs): """ Pseudocolor plot of 2d DataArray Wraps :func:`matplotlib:matplotlib.pyplot.pcolormesh` """ # decide on a default for infer_intervals (GH781) x = np.asarray(x) if infer_intervals is None: if hasattr(ax, 'projection'): if len(x.shape) == 1: infer_intervals = True else: infer_intervals = False else: infer_intervals = True if infer_intervals: if len(x.shape) == 1: x = _infer_interval_breaks(x) y = _infer_interval_breaks(y) else: # we have to infer the intervals on both axes x = _infer_interval_breaks(x, axis=1) x = _infer_interval_breaks(x, axis=0) y = _infer_interval_breaks(y, axis=1) y = _infer_interval_breaks(y, axis=0) primitive = ax.pcolormesh(x, y, z, **kwargs) # by default, pcolormesh picks "round" values for bounds # this results in ugly looking plots with lots of surrounding whitespace if not hasattr(ax, 'projection') and x.ndim == 1 and y.ndim == 1: # not a cartopy geoaxis ax.set_xlim(x[0], x[-1]) ax.set_ylim(y[0], y[-1]) return primitive python-xarray-0.10.2/xarray/plot/default_colormap.csv0000644000175000017500000002025513252452413023127 0ustar alastairalastair0.26700401,0.00487433,0.32941519 0.26851048,0.00960483,0.33542652 0.26994384,0.01462494,0.34137895 0.27130489,0.01994186,0.34726862 0.27259384,0.02556309,0.35309303 0.27380934,0.03149748,0.35885256 0.27495242,0.03775181,0.36454323 0.27602238,0.04416723,0.37016418 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0.92610579,0.89732977,0.10407067 0.93590444,0.8985704,0.10813094 0.94563626,0.899815,0.11283773 0.95529972,0.90106534,0.11812832 0.96489353,0.90232311,0.12394051 0.97441665,0.90358991,0.13021494 0.98386829,0.90486726,0.13689671 0.99324789,0.90615657,0.1439362 python-xarray-0.10.2/xarray/plot/__init__.py0000644000175000017500000000056113252452413021201 0ustar alastairalastairfrom __future__ import absolute_import from __future__ import division from __future__ import print_function from .plot import (plot, line, contourf, contour, hist, imshow, pcolormesh) from .facetgrid import FacetGrid __all__ = [ 'plot', 'line', 'contour', 'contourf', 'hist', 'imshow', 'pcolormesh', 'FacetGrid', ] python-xarray-0.10.2/xarray/plot/facetgrid.py0000644000175000017500000003570213252452413021377 0ustar alastairalastairfrom __future__ import absolute_import, division, print_function import functools import itertools import warnings import numpy as np from ..core.formatting import format_item from ..core.pycompat import getargspec from .utils import ( _determine_cmap_params, _infer_xy_labels, import_matplotlib_pyplot) # Overrides axes.labelsize, xtick.major.size, ytick.major.size # from mpl.rcParams _FONTSIZE = 'small' # For major ticks on x, y axes _NTICKS = 5 def _nicetitle(coord, value, maxchar, template): """ Put coord, value in template and truncate at maxchar """ prettyvalue = format_item(value, quote_strings=False) title = template.format(coord=coord, value=prettyvalue) if len(title) > maxchar: title = title[:(maxchar - 3)] + '...' return title class FacetGrid(object): """ Initialize the matplotlib figure and FacetGrid object. The :class:`FacetGrid` is an object that links a xarray DataArray to a matplotlib figure with a particular structure. In particular, :class:`FacetGrid` is used to draw plots with multiple Axes where each Axes shows the same relationship conditioned on different levels of some dimension. It's possible to condition on up to two variables by assigning variables to the rows and columns of the grid. The general approach to plotting here is called "small multiples", where the same kind of plot is repeated multiple times, and the specific use of small multiples to display the same relationship conditioned on one ore more other variables is often called a "trellis plot". The basic workflow is to initialize the :class:`FacetGrid` object with the DataArray and the variable names that are used to structure the grid. Then plotting functions can be applied to each subset by calling :meth:`FacetGrid.map_dataarray` or :meth:`FacetGrid.map`. Attributes ---------- axes : numpy object array Contains axes in corresponding position, as returned from plt.subplots fig : matplotlib.Figure The figure containing all the axes name_dicts : numpy object array Contains dictionaries mapping coordinate names to values. None is used as a sentinel value for axes which should remain empty, ie. sometimes the bottom right grid """ def __init__(self, data, col=None, row=None, col_wrap=None, sharex=True, sharey=True, figsize=None, aspect=1, size=3, subplot_kws=None): """ Parameters ---------- data : DataArray xarray DataArray to be plotted row, col : strings Dimesion names that define subsets of the data, which will be drawn on separate facets in the grid. col_wrap : int, optional "Wrap" the column variable at this width, so that the column facets sharex : bool, optional If true, the facets will share x axes sharey : bool, optional If true, the facets will share y axes figsize : tuple, optional A tuple (width, height) of the figure in inches. If set, overrides ``size`` and ``aspect``. aspect : scalar, optional Aspect ratio of each facet, so that ``aspect * size`` gives the width of each facet in inches size : scalar, optional Height (in inches) of each facet. See also: ``aspect`` subplot_kws : dict, optional Dictionary of keyword arguments for matplotlib subplots """ plt = import_matplotlib_pyplot() # Handle corner case of nonunique coordinates rep_col = col is not None and not data[col].to_index().is_unique rep_row = row is not None and not data[row].to_index().is_unique if rep_col or rep_row: raise ValueError('Coordinates used for faceting cannot ' 'contain repeated (nonunique) values.') # single_group is the grouping variable, if there is exactly one if col and row: single_group = False nrow = len(data[row]) ncol = len(data[col]) nfacet = nrow * ncol if col_wrap is not None: warnings.warn('Ignoring col_wrap since both col and row ' 'were passed') elif row and not col: single_group = row elif not row and col: single_group = col else: raise ValueError( 'Pass a coordinate name as an argument for row or col') # Compute grid shape if single_group: nfacet = len(data[single_group]) if col: # idea - could add heuristic for nice shapes like 3x4 ncol = nfacet if row: ncol = 1 if col_wrap is not None: # Overrides previous settings ncol = col_wrap nrow = int(np.ceil(nfacet / ncol)) # Set the subplot kwargs subplot_kws = {} if subplot_kws is None else subplot_kws if figsize is None: # Calculate the base figure size with extra horizontal space for a # colorbar cbar_space = 1 figsize = (ncol * size * aspect + cbar_space, nrow * size) fig, axes = plt.subplots(nrow, ncol, sharex=sharex, sharey=sharey, squeeze=False, figsize=figsize, subplot_kw=subplot_kws) # Set up the lists of names for the row and column facet variables col_names = list(data[col].values) if col else [] row_names = list(data[row].values) if row else [] if single_group: full = [{single_group: x} for x in data[single_group].values] empty = [None for x in range(nrow * ncol - len(full))] name_dicts = full + empty else: rowcols = itertools.product(row_names, col_names) name_dicts = [{row: r, col: c} for r, c in rowcols] name_dicts = np.array(name_dicts).reshape(nrow, ncol) # Set up the class attributes # --------------------------- # First the public API self.data = data self.name_dicts = name_dicts self.fig = fig self.axes = axes self.row_names = row_names self.col_names = col_names # Next the private variables self._single_group = single_group self._nrow = nrow self._row_var = row self._ncol = ncol self._col_var = col self._col_wrap = col_wrap self._x_var = None self._y_var = None self._cmap_extend = None self._mappables = [] @property def _left_axes(self): return self.axes[:, 0] @property def _bottom_axes(self): return self.axes[-1, :] def map_dataarray(self, func, x, y, **kwargs): """ Apply a plotting function to a 2d facet's subset of the data. This is more convenient and less general than ``FacetGrid.map`` Parameters ---------- func : callable A plotting function with the same signature as a 2d xarray plotting method such as `xarray.plot.imshow` x, y : string Names of the coordinates to plot on x, y axes kwargs : additional keyword arguments to func Returns ------- self : FacetGrid object """ cmapkw = kwargs.get('cmap') colorskw = kwargs.get('colors') # colors is mutually exclusive with cmap if cmapkw and colorskw: raise ValueError("Can't specify both cmap and colors.") # These should be consistent with xarray.plot._plot2d cmap_kwargs = {'plot_data': self.data.values, # MPL default 'levels': 7 if 'contour' in func.__name__ else None, 'filled': func.__name__ != 'contour', } cmap_args = getargspec(_determine_cmap_params).args cmap_kwargs.update((a, kwargs[a]) for a in cmap_args if a in kwargs) cmap_params = _determine_cmap_params(**cmap_kwargs) if colorskw is not None: cmap_params['cmap'] = None # Order is important func_kwargs = kwargs.copy() func_kwargs.update(cmap_params) func_kwargs.update({'add_colorbar': False, 'add_labels': False}) # Get x, y labels for the first subplot x, y = _infer_xy_labels( darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y, imshow=func.__name__ == 'imshow', rgb=kwargs.get('rgb', None)) for d, ax in zip(self.name_dicts.flat, self.axes.flat): # None is the sentinel value if d is not None: subset = self.data.loc[d] mappable = func(subset, x, y, ax=ax, **func_kwargs) self._mappables.append(mappable) self._cmap_extend = cmap_params.get('extend') self._finalize_grid(x, y) if kwargs.get('add_colorbar', True): self.add_colorbar() return self def _finalize_grid(self, *axlabels): """Finalize the annotations and layout.""" self.set_axis_labels(*axlabels) self.set_titles() self.fig.tight_layout() for ax, namedict in zip(self.axes.flat, self.name_dicts.flat): if namedict is None: ax.set_visible(False) def add_colorbar(self, **kwargs): """Draw a colorbar """ kwargs = kwargs.copy() if self._cmap_extend is not None: kwargs.setdefault('extend', self._cmap_extend) if getattr(self.data, 'name', None) is not None: kwargs.setdefault('label', self.data.name) self.cbar = self.fig.colorbar(self._mappables[-1], ax=list(self.axes.flat), **kwargs) return self def set_axis_labels(self, x_var=None, y_var=None): """Set axis labels on the left column and bottom row of the grid.""" if x_var is not None: self._x_var = x_var self.set_xlabels(x_var) if y_var is not None: self._y_var = y_var self.set_ylabels(y_var) return self def set_xlabels(self, label=None, **kwargs): """Label the x axis on the bottom row of the grid.""" if label is None: label = self._x_var for ax in self._bottom_axes: ax.set_xlabel(label, **kwargs) return self def set_ylabels(self, label=None, **kwargs): """Label the y axis on the left column of the grid.""" if label is None: label = self._y_var for ax in self._left_axes: ax.set_ylabel(label, **kwargs) return self def set_titles(self, template="{coord} = {value}", maxchar=30, **kwargs): """ Draw titles either above each facet or on the grid margins. Parameters ---------- template : string Template for plot titles containing {coord} and {value} maxchar : int Truncate titles at maxchar kwargs : keyword args additional arguments to matplotlib.text Returns ------- self: FacetGrid object """ import matplotlib as mpl kwargs["size"] = kwargs.pop("size", mpl.rcParams["axes.labelsize"]) nicetitle = functools.partial(_nicetitle, maxchar=maxchar, template=template) if self._single_group: for d, ax in zip(self.name_dicts.flat, self.axes.flat): # Only label the ones with data if d is not None: coord, value = list(d.items()).pop() title = nicetitle(coord, value, maxchar=maxchar) ax.set_title(title, **kwargs) else: # The row titles on the right edge of the grid for ax, row_name in zip(self.axes[:, -1], self.row_names): title = nicetitle(coord=self._row_var, value=row_name, maxchar=maxchar) ax.annotate(title, xy=(1.02, .5), xycoords="axes fraction", rotation=270, ha="left", va="center", **kwargs) # The column titles on the top row for ax, col_name in zip(self.axes[0, :], self.col_names): title = nicetitle(coord=self._col_var, value=col_name, maxchar=maxchar) ax.set_title(title, **kwargs) return self def set_ticks(self, max_xticks=_NTICKS, max_yticks=_NTICKS, fontsize=_FONTSIZE): """ Set and control tick behavior Parameters ---------- max_xticks, max_yticks : int, optional Maximum number of labeled ticks to plot on x, y axes fontsize : string or int Font size as used by matplotlib text Returns ------- self : FacetGrid object """ from matplotlib.ticker import MaxNLocator # Both are necessary x_major_locator = MaxNLocator(nbins=max_xticks) y_major_locator = MaxNLocator(nbins=max_yticks) for ax in self.axes.flat: ax.xaxis.set_major_locator(x_major_locator) ax.yaxis.set_major_locator(y_major_locator) for tick in itertools.chain(ax.xaxis.get_major_ticks(), ax.yaxis.get_major_ticks()): tick.label.set_fontsize(fontsize) return self def map(self, func, *args, **kwargs): """ Apply a plotting function to each facet's subset of the data. Parameters ---------- func : callable A plotting function that takes data and keyword arguments. It must plot to the currently active matplotlib Axes and take a `color` keyword argument. If faceting on the `hue` dimension, it must also take a `label` keyword argument. args : strings Column names in self.data that identify variables with data to plot. The data for each variable is passed to `func` in the order the variables are specified in the call. kwargs : keyword arguments All keyword arguments are passed to the plotting function. Returns ------- self : FacetGrid object """ plt = import_matplotlib_pyplot() for ax, namedict in zip(self.axes.flat, self.name_dicts.flat): if namedict is not None: data = self.data.loc[namedict] plt.sca(ax) innerargs = [data[a].values for a in args] # TODO: is it possible to verify that an artist is mappable? mappable = func(*innerargs, **kwargs) self._mappables.append(mappable) self._finalize_grid(*args[:2]) return self python-xarray-0.10.2/xarray/coding/0000755000175000017500000000000013252452413017353 5ustar alastairalastairpython-xarray-0.10.2/xarray/coding/times.py0000644000175000017500000003264313252452413021056 0ustar alastairalastairfrom __future__ import absolute_import, division, print_function import re import traceback import warnings from datetime import datetime from functools import partial import numpy as np import pandas as pd from ..core import indexing from ..core.formatting import first_n_items, format_timestamp, last_item from ..core.pycompat import PY3 from ..core.variable import Variable from .variables import ( SerializationWarning, VariableCoder, lazy_elemwise_func, pop_to, safe_setitem, unpack_for_decoding, unpack_for_encoding) try: from pandas.errors import OutOfBoundsDatetime except ImportError: # pandas < 0.20 from pandas.tslib import OutOfBoundsDatetime # standard calendars recognized by netcdftime _STANDARD_CALENDARS = set(['standard', 'gregorian', 'proleptic_gregorian']) _NS_PER_TIME_DELTA = {'us': int(1e3), 'ms': int(1e6), 's': int(1e9), 'm': int(1e9) * 60, 'h': int(1e9) * 60 * 60, 'D': int(1e9) * 60 * 60 * 24} TIME_UNITS = frozenset(['days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds']) def _import_netcdftime(): ''' helper function handle the transition to netcdftime as a stand-alone package ''' try: # Try importing netcdftime directly import netcdftime as nctime if not hasattr(nctime, 'num2date'): # must have gotten an old version from netcdf4-python raise ImportError except ImportError: # in netCDF4 the num2date/date2num function are top-level api try: import netCDF4 as nctime except ImportError: raise ImportError("Failed to import netcdftime") return nctime def _netcdf_to_numpy_timeunit(units): units = units.lower() if not units.endswith('s'): units = '%ss' % units return {'microseconds': 'us', 'milliseconds': 'ms', 'seconds': 's', 'minutes': 'm', 'hours': 'h', 'days': 'D'}[units] def _unpack_netcdf_time_units(units): # CF datetime units follow the format: "UNIT since DATE" # this parses out the unit and date allowing for extraneous # whitespace. matches = re.match('(.+) since (.+)', units) if not matches: raise ValueError('invalid time units: %s' % units) delta_units, ref_date = [s.strip() for s in matches.groups()] return delta_units, ref_date def _decode_datetime_with_netcdftime(num_dates, units, calendar): nctime = _import_netcdftime() dates = np.asarray(nctime.num2date(num_dates, units, calendar)) if (dates[np.nanargmin(num_dates)].year < 1678 or dates[np.nanargmax(num_dates)].year >= 2262): warnings.warn('Unable to decode time axis into full ' 'numpy.datetime64 objects, continuing using dummy ' 'netcdftime.datetime objects instead, reason: dates out' ' of range', SerializationWarning, stacklevel=3) else: try: dates = nctime_to_nptime(dates) except ValueError as e: warnings.warn('Unable to decode time axis into full ' 'numpy.datetime64 objects, continuing using ' 'dummy netcdftime.datetime objects instead, reason:' '{0}'.format(e), SerializationWarning, stacklevel=3) return dates def _decode_cf_datetime_dtype(data, units, calendar): # Verify that at least the first and last date can be decoded # successfully. Otherwise, tracebacks end up swallowed by # Dataset.__repr__ when users try to view their lazily decoded array. values = indexing.ImplicitToExplicitIndexingAdapter( indexing.as_indexable(data)) example_value = np.concatenate([first_n_items(values, 1) or [0], last_item(values) or [0]]) try: result = decode_cf_datetime(example_value, units, calendar) except Exception: calendar_msg = ('the default calendar' if calendar is None else 'calendar %r' % calendar) msg = ('unable to decode time units %r with %s. Try ' 'opening your dataset with decode_times=False.' % (units, calendar_msg)) if not PY3: msg += ' Full traceback:\n' + traceback.format_exc() raise ValueError(msg) else: dtype = getattr(result, 'dtype', np.dtype('object')) return dtype def decode_cf_datetime(num_dates, units, calendar=None): """Given an array of numeric dates in netCDF format, convert it into a numpy array of date time objects. For standard (Gregorian) calendars, this function uses vectorized operations, which makes it much faster than netcdftime.num2date. In such a case, the returned array will be of type np.datetime64. Note that time unit in `units` must not be smaller than microseconds and not larger than days. See also -------- netcdftime.num2date """ num_dates = np.asarray(num_dates) flat_num_dates = num_dates.ravel() if calendar is None: calendar = 'standard' delta, ref_date = _unpack_netcdf_time_units(units) try: if calendar not in _STANDARD_CALENDARS: raise OutOfBoundsDatetime delta = _netcdf_to_numpy_timeunit(delta) try: ref_date = pd.Timestamp(ref_date) except ValueError: # ValueError is raised by pd.Timestamp for non-ISO timestamp # strings, in which case we fall back to using netcdftime raise OutOfBoundsDatetime # fixes: https://github.com/pydata/pandas/issues/14068 # these lines check if the the lowest or the highest value in dates # cause an OutOfBoundsDatetime (Overflow) error pd.to_timedelta(flat_num_dates.min(), delta) + ref_date pd.to_timedelta(flat_num_dates.max(), delta) + ref_date # Cast input dates to integers of nanoseconds because `pd.to_datetime` # works much faster when dealing with integers flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(np.int64) dates = (pd.to_timedelta(flat_num_dates_ns_int, 'ns') + ref_date).values except (OutOfBoundsDatetime, OverflowError): dates = _decode_datetime_with_netcdftime( flat_num_dates.astype(np.float), units, calendar) return dates.reshape(num_dates.shape) def decode_cf_timedelta(num_timedeltas, units): """Given an array of numeric timedeltas in netCDF format, convert it into a numpy timedelta64[ns] array. """ num_timedeltas = np.asarray(num_timedeltas) units = _netcdf_to_numpy_timeunit(units) shape = num_timedeltas.shape num_timedeltas = num_timedeltas.ravel() result = pd.to_timedelta(num_timedeltas, unit=units, box=False) # NaT is returned unboxed with wrong units; this should be fixed in pandas if result.dtype != 'timedelta64[ns]': result = result.astype('timedelta64[ns]') return result.reshape(shape) def _infer_time_units_from_diff(unique_timedeltas): for time_unit in ['days', 'hours', 'minutes', 'seconds']: delta_ns = _NS_PER_TIME_DELTA[_netcdf_to_numpy_timeunit(time_unit)] unit_delta = np.timedelta64(delta_ns, 'ns') diffs = unique_timedeltas / unit_delta if np.all(diffs == diffs.astype(int)): return time_unit return 'seconds' def infer_datetime_units(dates): """Given an array of datetimes, returns a CF compatible time-unit string of the form "{time_unit} since {date[0]}", where `time_unit` is 'days', 'hours', 'minutes' or 'seconds' (the first one that can evenly divide all unique time deltas in `dates`) """ dates = pd.to_datetime(np.asarray(dates).ravel(), box=False) dates = dates[pd.notnull(dates)] unique_timedeltas = np.unique(np.diff(dates)) units = _infer_time_units_from_diff(unique_timedeltas) reference_date = dates[0] if len(dates) > 0 else '1970-01-01' return '%s since %s' % (units, pd.Timestamp(reference_date)) def infer_timedelta_units(deltas): """Given an array of timedeltas, returns a CF compatible time-unit from {'days', 'hours', 'minutes' 'seconds'} (the first one that can evenly divide all unique time deltas in `deltas`) """ deltas = pd.to_timedelta(np.asarray(deltas).ravel(), box=False) unique_timedeltas = np.unique(deltas[pd.notnull(deltas)]) units = _infer_time_units_from_diff(unique_timedeltas) return units def nctime_to_nptime(times): """Given an array of netcdftime.datetime objects, return an array of numpy.datetime64 objects of the same size""" times = np.asarray(times) new = np.empty(times.shape, dtype='M8[ns]') for i, t in np.ndenumerate(times): dt = datetime(t.year, t.month, t.day, t.hour, t.minute, t.second) new[i] = np.datetime64(dt) return new def _cleanup_netcdf_time_units(units): delta, ref_date = _unpack_netcdf_time_units(units) try: units = '%s since %s' % (delta, format_timestamp(ref_date)) except OutOfBoundsDatetime: # don't worry about reifying the units if they're out of bounds pass return units def _encode_datetime_with_netcdftime(dates, units, calendar): """Fallback method for encoding dates using netcdftime. This method is more flexible than xarray's parsing using datetime64[ns] arrays but also slower because it loops over each element. """ nctime = _import_netcdftime() if np.issubdtype(dates.dtype, np.datetime64): # numpy's broken datetime conversion only works for us precision dates = dates.astype('M8[us]').astype(datetime) def encode_datetime(d): return np.nan if d is None else nctime.date2num(d, units, calendar) return np.vectorize(encode_datetime)(dates) def cast_to_int_if_safe(num): int_num = np.array(num, dtype=np.int64) if (num == int_num).all(): num = int_num return num def encode_cf_datetime(dates, units=None, calendar=None): """Given an array of datetime objects, returns the tuple `(num, units, calendar)` suitable for a CF compliant time variable. Unlike `date2num`, this function can handle datetime64 arrays. See also -------- netcdftime.date2num """ dates = np.asarray(dates) if units is None: units = infer_datetime_units(dates) else: units = _cleanup_netcdf_time_units(units) if calendar is None: calendar = 'proleptic_gregorian' delta, ref_date = _unpack_netcdf_time_units(units) try: if calendar not in _STANDARD_CALENDARS or dates.dtype.kind == 'O': # parse with netcdftime instead raise OutOfBoundsDatetime assert dates.dtype == 'datetime64[ns]' delta_units = _netcdf_to_numpy_timeunit(delta) time_delta = np.timedelta64(1, delta_units).astype('timedelta64[ns]') ref_date = np.datetime64(pd.Timestamp(ref_date)) num = (dates - ref_date) / time_delta except (OutOfBoundsDatetime, OverflowError): num = _encode_datetime_with_netcdftime(dates, units, calendar) num = cast_to_int_if_safe(num) return (num, units, calendar) def encode_cf_timedelta(timedeltas, units=None): if units is None: units = infer_timedelta_units(timedeltas) np_unit = _netcdf_to_numpy_timeunit(units) num = 1.0 * timedeltas / np.timedelta64(1, np_unit) num = np.where(pd.isnull(timedeltas), np.nan, num) num = cast_to_int_if_safe(num) return (num, units) class CFDatetimeCoder(VariableCoder): def encode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_encoding(variable) if np.issubdtype(data.dtype, np.datetime64): (data, units, calendar) = encode_cf_datetime( data, encoding.pop('units', None), encoding.pop('calendar', None)) safe_setitem(attrs, 'units', units, name=name) safe_setitem(attrs, 'calendar', calendar, name=name) return Variable(dims, data, attrs, encoding) def decode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_decoding(variable) if 'units' in attrs and 'since' in attrs['units']: units = pop_to(attrs, encoding, 'units') calendar = pop_to(attrs, encoding, 'calendar') dtype = _decode_cf_datetime_dtype(data, units, calendar) transform = partial( decode_cf_datetime, units=units, calendar=calendar) data = lazy_elemwise_func(data, transform, dtype) return Variable(dims, data, attrs, encoding) class CFTimedeltaCoder(VariableCoder): def encode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_encoding(variable) if np.issubdtype(data.dtype, np.timedelta64): data, units = encode_cf_timedelta( data, encoding.pop('units', None)) safe_setitem(attrs, 'units', units, name=name) return Variable(dims, data, attrs, encoding) def decode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_decoding(variable) if 'units' in attrs and attrs['units'] in TIME_UNITS: units = pop_to(attrs, encoding, 'units') transform = partial(decode_cf_timedelta, units=units) dtype = np.dtype('timedelta64[ns]') data = lazy_elemwise_func(data, transform, dtype=dtype) return Variable(dims, data, attrs, encoding) python-xarray-0.10.2/xarray/coding/variables.py0000644000175000017500000002626713252452413021712 0ustar alastairalastair"""Coders for individual Variable objects.""" from __future__ import absolute_import, division, print_function import warnings from functools import partial import numpy as np import pandas as pd from ..core import dtypes, duck_array_ops, indexing, utils from ..core.pycompat import dask_array_type from ..core.variable import Variable class SerializationWarning(RuntimeWarning): """Warnings about encoding/decoding issues in serialization.""" class VariableCoder(object): """Base class for encoding and decoding transformations on variables. We use coders for transforming variables between xarray's data model and a format suitable for serialization. For example, coders apply CF conventions for how data should be represented in netCDF files. Subclasses should implement encode() and decode(), which should satisfy the identity ``coder.decode(coder.encode(variable)) == variable``. If any options are necessary, they should be implemented as arguments to the __init__ method. The optional name argument to encode() and decode() exists solely for the sake of better error messages, and should correspond to the name of variables in the underlying store. """ def encode(self, variable, name=None): # type: (Variable, Any) -> Variable """Convert an encoded variable to a decoded variable.""" raise NotImplementedError def decode(self, variable, name=None): # type: (Variable, Any) -> Variable """Convert an decoded variable to a encoded variable.""" raise NotImplementedError class _ElementwiseFunctionArray(indexing.ExplicitlyIndexedNDArrayMixin): """Lazily computed array holding values of elemwise-function. Do not construct this object directly: call lazy_elemwise_func instead. Values are computed upon indexing or coercion to a NumPy array. """ def __init__(self, array, func, dtype): assert not isinstance(array, dask_array_type) self.array = indexing.as_indexable(array) self.func = func self._dtype = dtype @property def dtype(self): return np.dtype(self._dtype) def __getitem__(self, key): return self.func(self.array[key]) def __repr__(self): return ("%s(%r, func=%r, dtype=%r)" % (type(self).__name__, self.array, self.func, self.dtype)) def lazy_elemwise_func(array, func, dtype): """Lazily apply an element-wise function to an array. Parameters ---------- array : any valid value of Variable._data func : callable Function to apply to indexed slices of an array. For use with dask, this should be a pickle-able object. dtype : coercible to np.dtype Dtype for the result of this function. Returns ------- Either a dask.array.Array or _ElementwiseFunctionArray. """ if isinstance(array, dask_array_type): return array.map_blocks(func, dtype=dtype) else: return _ElementwiseFunctionArray(array, func, dtype) def unpack_for_encoding(var): return var.dims, var.data, var.attrs.copy(), var.encoding.copy() def unpack_for_decoding(var): return var.dims, var._data, var.attrs.copy(), var.encoding.copy() def safe_setitem(dest, key, value, name=None): if key in dest: var_str = ' on variable {!r}'.format(name) if name else '' raise ValueError( 'failed to prevent overwriting existing key {} in attrs{}. ' 'This is probably an encoding field used by xarray to describe ' 'how a variable is serialized. To proceed, remove this key from ' "the variable's attributes manually.".format(key, var_str)) dest[key] = value def pop_to(source, dest, key, name=None): """ A convenience function which pops a key k from source to dest. None values are not passed on. If k already exists in dest an error is raised. """ value = source.pop(key, None) if value is not None: safe_setitem(dest, key, value, name=name) return value def _apply_mask(data, # type: np.ndarray encoded_fill_values, # type: list decoded_fill_value, # type: Any dtype, # type: Any ): # type: np.ndarray """Mask all matching values in a NumPy arrays.""" condition = False for fv in encoded_fill_values: condition |= data == fv data = np.asarray(data, dtype=dtype) return np.where(condition, decoded_fill_value, data) class CFMaskCoder(VariableCoder): """Mask or unmask fill values according to CF conventions.""" def encode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_encoding(variable) if encoding.get('_FillValue') is not None: fill_value = pop_to(encoding, attrs, '_FillValue', name=name) if not pd.isnull(fill_value): data = duck_array_ops.fillna(data, fill_value) return Variable(dims, data, attrs, encoding) def decode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_decoding(variable) if 'missing_value' in attrs: # missing_value is deprecated, but we still want to support it as # an alias for _FillValue. if ('_FillValue' in attrs and not utils.equivalent(attrs['_FillValue'], attrs['missing_value'])): raise ValueError("Conflicting _FillValue and missing_value " "attrs on a variable {!r}: {} vs. {}\n\n" "Consider opening the offending dataset " "using decode_cf=False, correcting the " "attrs and decoding explicitly using " "xarray.decode_cf()." .format(name, attrs['_FillValue'], attrs['missing_value'])) attrs['_FillValue'] = attrs.pop('missing_value') if '_FillValue' in attrs: raw_fill_value = pop_to(attrs, encoding, '_FillValue', name=name) encoded_fill_values = [ fv for fv in np.ravel(raw_fill_value) if not pd.isnull(fv)] if len(encoded_fill_values) > 1: warnings.warn("variable {!r} has multiple fill values {}, " "decoding all values to NaN." .format(name, encoded_fill_values), SerializationWarning, stacklevel=3) dtype, decoded_fill_value = dtypes.maybe_promote(data.dtype) if encoded_fill_values: transform = partial(_apply_mask, encoded_fill_values=encoded_fill_values, decoded_fill_value=decoded_fill_value, dtype=dtype) data = lazy_elemwise_func(data, transform, dtype) return Variable(dims, data, attrs, encoding) def _scale_offset_decoding(data, scale_factor, add_offset, dtype): data = np.array(data, dtype=dtype, copy=True) if scale_factor is not None: data *= scale_factor if add_offset is not None: data += add_offset return data def _choose_float_dtype(dtype, has_offset): """Return a float dtype that can losslessly represent `dtype` values.""" # Keep float32 as-is. Upcast half-precision to single-precision, # because float16 is "intended for storage but not computation" if dtype.itemsize <= 4 and np.issubdtype(dtype, np.floating): return np.float32 # float32 can exactly represent all integers up to 24 bits if dtype.itemsize <= 2 and np.issubdtype(dtype, np.integer): # A scale factor is entirely safe (vanishing into the mantissa), # but a large integer offset could lead to loss of precision. # Sensitivity analysis can be tricky, so we just use a float64 # if there's any offset at all - better unoptimised than wrong! if not has_offset: return np.float32 # For all other types and circumstances, we just use float64. # (safe because eg. complex numbers are not supported in NetCDF) return np.float64 class CFScaleOffsetCoder(VariableCoder): """Scale and offset variables according to CF conventions. Follows the formula: decode_values = encoded_values * scale_factor + add_offset """ def encode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_encoding(variable) if 'scale_factor' in encoding or 'add_offset' in encoding: dtype = _choose_float_dtype(data.dtype, 'add_offset' in encoding) data = data.astype(dtype=dtype, copy=True) if 'add_offset' in encoding: data -= pop_to(encoding, attrs, 'add_offset', name=name) if 'scale_factor' in encoding: data /= pop_to(encoding, attrs, 'scale_factor', name=name) return Variable(dims, data, attrs, encoding) def decode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_decoding(variable) if 'scale_factor' in attrs or 'add_offset' in attrs: scale_factor = pop_to(attrs, encoding, 'scale_factor', name=name) add_offset = pop_to(attrs, encoding, 'add_offset', name=name) dtype = _choose_float_dtype(data.dtype, 'add_offset' in attrs) transform = partial(_scale_offset_decoding, scale_factor=scale_factor, add_offset=add_offset, dtype=dtype) data = lazy_elemwise_func(data, transform, dtype) return Variable(dims, data, attrs, encoding) class UnsignedIntegerCoder(VariableCoder): def encode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_encoding(variable) if encoding.get('_Unsigned', False): pop_to(encoding, attrs, '_Unsigned') signed_dtype = np.dtype('i%s' % data.dtype.itemsize) if '_FillValue' in attrs: new_fill = signed_dtype.type(attrs['_FillValue']) attrs['_FillValue'] = new_fill data = duck_array_ops.around(data).astype(signed_dtype) return Variable(dims, data, attrs, encoding) def decode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_decoding(variable) if '_Unsigned' in attrs: unsigned = pop_to(attrs, encoding, '_Unsigned') if data.dtype.kind == 'i': if unsigned: unsigned_dtype = np.dtype('u%s' % data.dtype.itemsize) transform = partial(np.asarray, dtype=unsigned_dtype) data = lazy_elemwise_func(data, transform, unsigned_dtype) if '_FillValue' in attrs: new_fill = unsigned_dtype.type(attrs['_FillValue']) attrs['_FillValue'] = new_fill else: warnings.warn("variable %r has _Unsigned attribute but is not " "of integer type. Ignoring attribute." % name, SerializationWarning, stacklevel=3) return Variable(dims, data, attrs, encoding) python-xarray-0.10.2/xarray/coding/__init__.py0000644000175000017500000000000013252452413021452 0ustar alastairalastairpython-xarray-0.10.2/xarray/tests/0000755000175000017500000000000013252452413017252 5ustar alastairalastairpython-xarray-0.10.2/xarray/tests/test_backends.py0000644000175000017500000033154313252452413022446 0ustar alastairalastairfrom __future__ import absolute_import, division, print_function import contextlib import itertools import os.path import pickle import shutil import sys import tempfile import unittest import warnings from io import BytesIO import numpy as np import pandas as pd import pytest import xarray as xr from xarray import ( DataArray, Dataset, backends, open_dataarray, open_dataset, open_mfdataset, save_mfdataset) from xarray.backends.common import robust_getitem from xarray.backends.netCDF4_ import _extract_nc4_variable_encoding from xarray.backends.pydap_ import PydapDataStore from xarray.core import indexing from xarray.core.pycompat import ( PY2, ExitStack, basestring, dask_array_type, iteritems) from xarray.tests import mock from . import ( TestCase, assert_allclose, assert_array_equal, assert_equal, assert_identical, flaky, has_netCDF4, has_scipy, network, raises_regex, requires_dask, requires_h5netcdf, requires_netCDF4, requires_pathlib, requires_pydap, requires_pynio, requires_rasterio, requires_scipy, requires_scipy_or_netCDF4, requires_zarr) from .test_dataset import create_test_data try: import netCDF4 as nc4 except ImportError: pass try: import dask.array as da except ImportError: pass try: from pathlib import Path except ImportError: try: from pathlib2 import Path except ImportError: pass ON_WINDOWS = sys.platform == 'win32' def open_example_dataset(name, *args, **kwargs): return open_dataset(os.path.join(os.path.dirname(__file__), 'data', name), *args, **kwargs) def create_masked_and_scaled_data(): x = np.array([np.nan, np.nan, 10, 10.1, 10.2], dtype=np.float32) encoding = {'_FillValue': -1, 'add_offset': 10, 'scale_factor': np.float32(0.1), 'dtype': 'i2'} return Dataset({'x': ('t', x, {}, encoding)}) def create_encoded_masked_and_scaled_data(): attributes = {'_FillValue': -1, 'add_offset': 10, 'scale_factor': np.float32(0.1)} return Dataset({'x': ('t', [-1, -1, 0, 1, 2], attributes)}) def create_unsigned_masked_scaled_data(): encoding = {'_FillValue': 255, '_Unsigned': 'true', 'dtype': 'i1', 'add_offset': 10, 'scale_factor': np.float32(0.1)} x = np.array([10.0, 10.1, 22.7, 22.8, np.nan], dtype=np.float32) return Dataset({'x': ('t', x, {}, encoding)}) def create_encoded_unsigned_masked_scaled_data(): # These are values as written to the file: the _FillValue will # be represented in the signed form. attributes = {'_FillValue': -1, '_Unsigned': 'true', 'add_offset': 10, 'scale_factor': np.float32(0.1)} # Create signed data corresponding to [0, 1, 127, 128, 255] unsigned sb = np.asarray([0, 1, 127, -128, -1], dtype='i1') return Dataset({'x': ('t', sb, attributes)}) def create_boolean_data(): attributes = {'units': '-'} return Dataset({'x': ('t', [True, False, False, True], attributes)}) class TestCommon(TestCase): def test_robust_getitem(self): class UnreliableArrayFailure(Exception): pass class UnreliableArray(object): def __init__(self, array, failures=1): self.array = array self.failures = failures def __getitem__(self, key): if self.failures > 0: self.failures -= 1 raise UnreliableArrayFailure return self.array[key] array = UnreliableArray([0]) with pytest.raises(UnreliableArrayFailure): array[0] self.assertEqual(array[0], 0) actual = robust_getitem(array, 0, catch=UnreliableArrayFailure, initial_delay=0) self.assertEqual(actual, 0) class NetCDF3Only(object): pass class DatasetIOTestCases(object): autoclose = False engine = None file_format = None def create_store(self): raise NotImplementedError @contextlib.contextmanager def roundtrip(self, data, save_kwargs={}, open_kwargs={}, allow_cleanup_failure=False): with create_tmp_file( allow_cleanup_failure=allow_cleanup_failure) as path: self.save(data, path, **save_kwargs) with self.open(path, **open_kwargs) as ds: yield ds @contextlib.contextmanager def roundtrip_append(self, data, save_kwargs={}, open_kwargs={}, allow_cleanup_failure=False): with create_tmp_file( allow_cleanup_failure=allow_cleanup_failure) as path: for i, key in enumerate(data.variables): mode = 'a' if i > 0 else 'w' self.save(data[[key]], path, mode=mode, **save_kwargs) with self.open(path, **open_kwargs) as ds: yield ds # The save/open methods may be overwritten below def save(self, dataset, path, **kwargs): dataset.to_netcdf(path, engine=self.engine, format=self.file_format, **kwargs) @contextlib.contextmanager def open(self, path, **kwargs): with open_dataset(path, engine=self.engine, autoclose=self.autoclose, **kwargs) as ds: yield ds def test_zero_dimensional_variable(self): expected = create_test_data() expected['float_var'] = ([], 1.0e9, {'units': 'units of awesome'}) expected['bytes_var'] = ([], b'foobar') expected['string_var'] = ([], u'foobar') with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_write_store(self): expected = create_test_data() with self.create_store() as store: expected.dump_to_store(store) # we need to cf decode the store because it has time and # non-dimension coordinates with xr.decode_cf(store) as actual: assert_allclose(expected, actual) def check_dtypes_roundtripped(self, expected, actual): for k in expected.variables: expected_dtype = expected.variables[k].dtype if (isinstance(self, NetCDF3Only) and expected_dtype == 'int64'): # downcast expected_dtype = np.dtype('int32') actual_dtype = actual.variables[k].dtype # TODO: check expected behavior for string dtypes more carefully string_kinds = {'O', 'S', 'U'} assert (expected_dtype == actual_dtype or (expected_dtype.kind in string_kinds and actual_dtype.kind in string_kinds)) def test_roundtrip_test_data(self): expected = create_test_data() with self.roundtrip(expected) as actual: self.check_dtypes_roundtripped(expected, actual) assert_identical(expected, actual) def test_load(self): expected = create_test_data() @contextlib.contextmanager def assert_loads(vars=None): if vars is None: vars = expected with self.roundtrip(expected) as actual: for k, v in actual.variables.items(): # IndexVariables are eagerly loaded into memory self.assertEqual(v._in_memory, k in actual.dims) yield actual for k, v in actual.variables.items(): if k in vars: self.assertTrue(v._in_memory) assert_identical(expected, actual) with pytest.raises(AssertionError): # make sure the contextmanager works! with assert_loads() as ds: pass with assert_loads() as ds: ds.load() with assert_loads(['var1', 'dim1', 'dim2']) as ds: ds['var1'].load() # verify we can read data even after closing the file with self.roundtrip(expected) as ds: actual = ds.load() assert_identical(expected, actual) def test_dataset_compute(self): expected = create_test_data() with self.roundtrip(expected) as actual: # Test Dataset.compute() for k, v in actual.variables.items(): # IndexVariables are eagerly cached self.assertEqual(v._in_memory, k in actual.dims) computed = actual.compute() for k, v in actual.variables.items(): self.assertEqual(v._in_memory, k in actual.dims) for v in computed.variables.values(): self.assertTrue(v._in_memory) assert_identical(expected, actual) assert_identical(expected, computed) def test_pickle(self): expected = Dataset({'foo': ('x', [42])}) with self.roundtrip( expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped: raw_pickle = pickle.dumps(roundtripped) # windows doesn't like opening the same file twice roundtripped.close() unpickled_ds = pickle.loads(raw_pickle) assert_identical(expected, unpickled_ds) def test_pickle_dataarray(self): expected = Dataset({'foo': ('x', [42])}) with self.roundtrip( expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped: unpickled_array = pickle.loads(pickle.dumps(roundtripped['foo'])) assert_identical(expected['foo'], unpickled_array) def test_dataset_caching(self): expected = Dataset({'foo': ('x', [5, 6, 7])}) with self.roundtrip(expected) as actual: assert isinstance(actual.foo.variable._data, indexing.MemoryCachedArray) assert not actual.foo.variable._in_memory actual.foo.values # cache assert actual.foo.variable._in_memory with self.roundtrip(expected, open_kwargs={'cache': False}) as actual: assert isinstance(actual.foo.variable._data, indexing.CopyOnWriteArray) assert not actual.foo.variable._in_memory actual.foo.values # no caching assert not actual.foo.variable._in_memory def test_roundtrip_None_variable(self): expected = Dataset({None: (('x', 'y'), [[0, 1], [2, 3]])}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_object_dtype(self): floats = np.array([0.0, 0.0, 1.0, 2.0, 3.0], dtype=object) floats_nans = np.array([np.nan, np.nan, 1.0, 2.0, 3.0], dtype=object) bytes_ = np.array([b'ab', b'cdef', b'g'], dtype=object) bytes_nans = np.array([b'ab', b'cdef', np.nan], dtype=object) strings = np.array([u'ab', u'cdef', u'g'], dtype=object) strings_nans = np.array([u'ab', u'cdef', np.nan], dtype=object) all_nans = np.array([np.nan, np.nan], dtype=object) original = Dataset({'floats': ('a', floats), 'floats_nans': ('a', floats_nans), 'bytes': ('b', bytes_), 'bytes_nans': ('b', bytes_nans), 'strings': ('b', strings), 'strings_nans': ('b', strings_nans), 'all_nans': ('c', all_nans), 'nan': ([], np.nan)}) expected = original.copy(deep=True) with self.roundtrip(original) as actual: try: assert_identical(expected, actual) except AssertionError: # Most stores use '' for nans in strings, but some don't. # First try the ideal case (where the store returns exactly) # the original Dataset), then try a more realistic case. # This currently includes all netCDF files when encoding is not # explicitly set. # https://github.com/pydata/xarray/issues/1647 expected['bytes_nans'][-1] = b'' expected['strings_nans'][-1] = u'' assert_identical(expected, actual) def test_roundtrip_string_data(self): expected = Dataset({'x': ('t', ['ab', 'cdef'])}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_string_encoded_characters(self): expected = Dataset({'x': ('t', [u'ab', u'cdef'])}) expected['x'].encoding['dtype'] = 'S1' with self.roundtrip(expected) as actual: assert_identical(expected, actual) self.assertEqual(actual['x'].encoding['_Encoding'], 'utf-8') expected['x'].encoding['_Encoding'] = 'ascii' with self.roundtrip(expected) as actual: assert_identical(expected, actual) self.assertEqual(actual['x'].encoding['_Encoding'], 'ascii') def test_roundtrip_datetime_data(self): times = pd.to_datetime(['2000-01-01', '2000-01-02', 'NaT']) expected = Dataset({'t': ('t', times), 't0': times[0]}) kwds = {'encoding': {'t0': {'units': 'days since 1950-01-01'}}} with self.roundtrip(expected, save_kwargs=kwds) as actual: assert_identical(expected, actual) assert actual.t0.encoding['units'] == 'days since 1950-01-01' def test_roundtrip_timedelta_data(self): time_deltas = pd.to_timedelta(['1h', '2h', 'NaT']) expected = Dataset({'td': ('td', time_deltas), 'td0': time_deltas[0]}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_float64_data(self): expected = Dataset({'x': ('y', np.array([1.0, 2.0, np.pi], dtype='float64'))}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_example_1_netcdf(self): expected = open_example_dataset('example_1.nc') with self.roundtrip(expected) as actual: # we allow the attributes to differ since that # will depend on the encoding used. For example, # without CF encoding 'actual' will end up with # a dtype attribute. assert_equal(expected, actual) def test_roundtrip_coordinates(self): original = Dataset({'foo': ('x', [0, 1])}, {'x': [2, 3], 'y': ('a', [42]), 'z': ('x', [4, 5])}) with self.roundtrip(original) as actual: assert_identical(original, actual) def test_roundtrip_global_coordinates(self): original = Dataset({'x': [2, 3], 'y': ('a', [42]), 'z': ('x', [4, 5])}) with self.roundtrip(original) as actual: assert_identical(original, actual) def test_roundtrip_coordinates_with_space(self): original = Dataset(coords={'x': 0, 'y z': 1}) expected = Dataset({'y z': 1}, {'x': 0}) with pytest.warns(xr.SerializationWarning): with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_roundtrip_boolean_dtype(self): original = create_boolean_data() self.assertEqual(original['x'].dtype, 'bool') with self.roundtrip(original) as actual: assert_identical(original, actual) self.assertEqual(actual['x'].dtype, 'bool') def test_orthogonal_indexing(self): in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: indexers = {'dim1': [1, 2, 0], 'dim2': [3, 2, 0, 3], 'dim3': np.arange(5)} expected = in_memory.isel(**indexers) actual = on_disk.isel(**indexers) # make sure the array is not yet loaded into memory assert not actual['var1'].variable._in_memory assert_identical(expected, actual) # do it twice, to make sure we're switched from orthogonal -> numpy # when we cached the values actual = on_disk.isel(**indexers) assert_identical(expected, actual) def test_vectorized_indexing(self): in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: indexers = {'dim1': DataArray([0, 2, 0], dims='a'), 'dim2': DataArray([0, 2, 3], dims='a')} expected = in_memory.isel(**indexers) actual = on_disk.isel(**indexers) # make sure the array is not yet loaded into memory assert not actual['var1'].variable._in_memory assert_identical(expected, actual.load()) # do it twice, to make sure we're switched from # vectorized -> numpy when we cached the values actual = on_disk.isel(**indexers) assert_identical(expected, actual) def multiple_indexing(indexers): # make sure a sequence of lazy indexings certainly works. with self.roundtrip(in_memory) as on_disk: actual = on_disk['var3'] expected = in_memory['var3'] for ind in indexers: actual = actual.isel(**ind) expected = expected.isel(**ind) # make sure the array is not yet loaded into memory assert not actual.variable._in_memory assert_identical(expected, actual.load()) # two-staged vectorized-indexing indexers = [ {'dim1': DataArray([[0, 7], [2, 6], [3, 5]], dims=['a', 'b']), 'dim3': DataArray([[0, 4], [1, 3], [2, 2]], dims=['a', 'b'])}, {'a': DataArray([0, 1], dims=['c']), 'b': DataArray([0, 1], dims=['c'])} ] multiple_indexing(indexers) # vectorized-slice mixed indexers = [ {'dim1': DataArray([[0, 7], [2, 6], [3, 5]], dims=['a', 'b']), 'dim3': slice(None, 10)} ] multiple_indexing(indexers) # vectorized-integer mixed indexers = [ {'dim3': 0}, {'dim1': DataArray([[0, 7], [2, 6], [3, 5]], dims=['a', 'b'])}, {'a': slice(None, None, 2)} ] multiple_indexing(indexers) # vectorized-integer mixed indexers = [ {'dim3': 0}, {'dim1': DataArray([[0, 7], [2, 6], [3, 5]], dims=['a', 'b'])}, {'a': 1, 'b': 0} ] multiple_indexing(indexers) # with negative step slice. indexers = [ {'dim1': DataArray([[0, 7], [2, 6], [3, 5]], dims=['a', 'b']), 'dim3': slice(-1, 1, -1)}, ] multiple_indexing(indexers) # with negative step slice. indexers = [ {'dim1': DataArray([[0, 7], [2, 6], [3, 5]], dims=['a', 'b']), 'dim3': slice(-1, 1, -2)}, ] multiple_indexing(indexers) def test_isel_dataarray(self): # Make sure isel works lazily. GH:issue:1688 in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: expected = in_memory.isel(dim2=in_memory['dim2'] < 3) actual = on_disk.isel(dim2=on_disk['dim2'] < 3) assert_identical(expected, actual) def validate_array_type(self, ds): # Make sure that only NumpyIndexingAdapter stores a bare np.ndarray. def find_and_validate_array(obj): # recursively called function. obj: array or array wrapper. if hasattr(obj, 'array'): if isinstance(obj.array, indexing.ExplicitlyIndexed): find_and_validate_array(obj.array) else: if isinstance(obj.array, np.ndarray): assert isinstance(obj, indexing.NumpyIndexingAdapter) elif isinstance(obj.array, dask_array_type): assert isinstance(obj, indexing.DaskIndexingAdapter) elif isinstance(obj.array, pd.Index): assert isinstance(obj, indexing.PandasIndexAdapter) else: raise TypeError('{} is wrapped by {}'.format( type(obj.array), type(obj))) for k, v in ds.variables.items(): find_and_validate_array(v._data) def test_array_type_after_indexing(self): in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: self.validate_array_type(on_disk) indexers = {'dim1': [1, 2, 0], 'dim2': [3, 2, 0, 3], 'dim3': np.arange(5)} expected = in_memory.isel(**indexers) actual = on_disk.isel(**indexers) assert_identical(expected, actual) self.validate_array_type(actual) # do it twice, to make sure we're switched from orthogonal -> numpy # when we cached the values actual = on_disk.isel(**indexers) assert_identical(expected, actual) self.validate_array_type(actual) def test_dropna(self): # regression test for GH:issue:1694 a = np.random.randn(4, 3) a[1, 1] = np.NaN in_memory = xr.Dataset({'a': (('y', 'x'), a)}, coords={'y': np.arange(4), 'x': np.arange(3)}) assert_identical(in_memory.dropna(dim='x'), in_memory.isel(x=slice(None, None, 2))) with self.roundtrip(in_memory) as on_disk: self.validate_array_type(on_disk) expected = in_memory.dropna(dim='x') actual = on_disk.dropna(dim='x') assert_identical(expected, actual) def test_ondisk_after_print(self): """ Make sure print does not load file into memory """ in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: repr(on_disk) assert not on_disk['var1']._in_memory class CFEncodedDataTest(DatasetIOTestCases): def test_roundtrip_bytes_with_fill_value(self): values = np.array([b'ab', b'cdef', np.nan], dtype=object) encoding = {'_FillValue': b'X', 'dtype': 'S1'} original = Dataset({'x': ('t', values, {}, encoding)}) expected = original.copy(deep=True) with self.roundtrip(original) as actual: assert_identical(expected, actual) original = Dataset({'x': ('t', values, {}, {'_FillValue': b''})}) with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_roundtrip_string_with_fill_value_nchar(self): values = np.array([u'ab', u'cdef', np.nan], dtype=object) expected = Dataset({'x': ('t', values)}) encoding = {'dtype': 'S1', '_FillValue': b'X'} original = Dataset({'x': ('t', values, {}, encoding)}) # Not supported yet. with pytest.raises(NotImplementedError): with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_unsigned_roundtrip_mask_and_scale(self): decoded = create_unsigned_masked_scaled_data() encoded = create_encoded_unsigned_masked_scaled_data() with self.roundtrip(decoded) as actual: for k in decoded.variables: self.assertEqual(decoded.variables[k].dtype, actual.variables[k].dtype) assert_allclose(decoded, actual, decode_bytes=False) with self.roundtrip(decoded, open_kwargs=dict(decode_cf=False)) as actual: for k in encoded.variables: self.assertEqual(encoded.variables[k].dtype, actual.variables[k].dtype) assert_allclose(encoded, actual, decode_bytes=False) with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual: for k in encoded.variables: self.assertEqual(encoded.variables[k].dtype, actual.variables[k].dtype) assert_allclose(encoded, actual, decode_bytes=False) # make sure roundtrip encoding didn't change the # original dataset. assert_allclose( encoded, create_encoded_unsigned_masked_scaled_data()) with self.roundtrip(encoded) as actual: for k in decoded.variables: self.assertEqual(decoded.variables[k].dtype, actual.variables[k].dtype) assert_allclose(decoded, actual, decode_bytes=False) with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual: for k in encoded.variables: self.assertEqual(encoded.variables[k].dtype, actual.variables[k].dtype) assert_allclose(encoded, actual, decode_bytes=False) def test_roundtrip_mask_and_scale(self): decoded = create_masked_and_scaled_data() encoded = create_encoded_masked_and_scaled_data() with self.roundtrip(decoded) as actual: assert_allclose(decoded, actual, decode_bytes=False) with self.roundtrip(decoded, open_kwargs=dict(decode_cf=False)) as actual: # TODO: this assumes that all roundtrips will first # encode. Is that something we want to test for? assert_allclose(encoded, actual, decode_bytes=False) with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual: assert_allclose(encoded, actual, decode_bytes=False) # make sure roundtrip encoding didn't change the # original dataset. assert_allclose(encoded, create_encoded_masked_and_scaled_data(), decode_bytes=False) with self.roundtrip(encoded) as actual: assert_allclose(decoded, actual, decode_bytes=False) with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual: assert_allclose(encoded, actual, decode_bytes=False) def test_coordinates_encoding(self): def equals_latlon(obj): return obj == 'lat lon' or obj == 'lon lat' original = Dataset({'temp': ('x', [0, 1]), 'precip': ('x', [0, -1])}, {'lat': ('x', [2, 3]), 'lon': ('x', [4, 5])}) with self.roundtrip(original) as actual: assert_identical(actual, original) with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with open_dataset(tmp_file, decode_coords=False) as ds: self.assertTrue(equals_latlon(ds['temp'].attrs['coordinates'])) self.assertTrue( equals_latlon(ds['precip'].attrs['coordinates'])) self.assertNotIn('coordinates', ds.attrs) self.assertNotIn('coordinates', ds['lat'].attrs) self.assertNotIn('coordinates', ds['lon'].attrs) modified = original.drop(['temp', 'precip']) with self.roundtrip(modified) as actual: assert_identical(actual, modified) with create_tmp_file() as tmp_file: modified.to_netcdf(tmp_file) with open_dataset(tmp_file, decode_coords=False) as ds: self.assertTrue(equals_latlon(ds.attrs['coordinates'])) self.assertNotIn('coordinates', ds['lat'].attrs) self.assertNotIn('coordinates', ds['lon'].attrs) def test_roundtrip_endian(self): ds = Dataset({'x': np.arange(3, 10, dtype='>i2'), 'y': np.arange(3, 20, dtype='