pax_global_header00006660000000000000000000000064131511246410014510gustar00rootroot0000000000000052 comment=e48af9b2f990c333bc6c59c0f566cd919a3338d1 geopandas-0.3.0/000077500000000000000000000000001315112464100134515ustar00rootroot00000000000000geopandas-0.3.0/.coveragerc000066400000000000000000000004011315112464100155650ustar00rootroot00000000000000[report] exclude_lines = pragma: no cover def __repr__ raise AssertionError raise NotImplementedError if __name__ == .__main__.: omit = geopandas/tests/* geopandas/io/tests/* geopandas/tools/tests/* geopandas/_version.py geopandas-0.3.0/.gitattributes000066400000000000000000000000431315112464100163410ustar00rootroot00000000000000geopandas/_version.py export-subst geopandas-0.3.0/.gitignore000066400000000000000000000002041315112464100154350ustar00rootroot00000000000000.coverage *.pyc build dist doc/_build geopandas.egg-info geopandas/version.py *.py~ doc/_static/world_* examples/nybb_*.zip .cache/ geopandas-0.3.0/.travis.yml000066400000000000000000000034141315112464100155640ustar00rootroot00000000000000language: python sudo: false cache: pip addons: apt: packages: - libgdal1h - gdal-bin - libgdal-dev - libspatialindex-dev matrix: include: # Only one test for these Python versions - python: 3.4 env: PANDAS=0.18.1 MATPLOTLIB=1.4.3 SHAPELY=1.5.17 - python: 3.5 env: PANDAS=0.19.2 MATPLOTLIB=1.5.3 # Python 2.7 and 3.6 test all supported Pandas versions - python: 2.7 env: PANDAS=0.16.2 MATPLOTLIB=1.4.3 SHAPELY=1.5.17 - python: 2.7 env: PANDAS=0.17.1 MATPLOTLIB=1.4.3 SHAPELY=1.5.17 - python: 2.7 env: PANDAS=0.18.1 MATPLOTLIB=1.5.3 - python: 2.7 env: PANDAS=0.19.2 MATPLOTLIB=1.5.3 - python: 2.7 env: PANDAS=0.20.2 MATPLOTLIB=2.0.2 - python: 2.7 env: PANDAS=master MATPLOTLIB=master - python: 3.6 env: PANDAS=0.16.2 MATPLOTLIB=1.4.3 SHAPELY=1.5.17 - python: 3.6 env: PANDAS=0.17.1 MATPLOTLIB=1.4.3 SHAPELY=1.5.17 - python: 3.6 env: PANDAS=0.18.1 MATPLOTLIB=1.5.3 - python: 3.6 env: PANDAS=0.19.2 MATPLOTLIB=1.5.3 - python: 3.6 env: PANDAS=0.20.2 MATPLOTLIB=2.0.2 - python: 3.6 env: PANDAS=master MATPLOTLIB=master before_install: - pip install -U pip install: - pip install numpy - if [[ $MATPLOTLIB == 'master' ]]; then pip install git+https://github.com/matplotlib/matplotlib.git; else pip install matplotlib==$MATPLOTLIB; fi - if [ -n "$SHAPELY" ]; then pip install shapely==$SHAPELY; fi - pip install -r requirements.txt - pip install -r requirements.test.txt - if [[ $PANDAS == 'master' ]]; then pip install git+https://github.com/pydata/pandas.git; else pip install pandas==$PANDAS; fi script: - py.test geopandas --cov geopandas -v --cov-report term-missing after_success: - codecov geopandas-0.3.0/CHANGELOG.md000066400000000000000000000054611315112464100152700ustar00rootroot00000000000000Changes ======= Version 0.3.0 (August 29, 2017) ------------------------------- Improvements: * Improve plotting performance using ``matplotlib.collections`` (#267) * Improve default plotting appearance. The defaults now follow the new matplotlib defaults (#318, #502, #510) * Provide access to x/y coordinates as attributes for Point GeoSeries (#383) * Make the NYBB dataset available through ``geopandas.datasets`` (#384) * Enable ``sjoin`` on non-integer-index GeoDataFrames (#422) * Add ``cx`` indexer to GeoDataFrame (#482) * ``GeoDataFrame.from_features`` now also accepts a Feature Collection (#225, #507) * Use index label instead of integer id in output of ``iterfeatures`` and ``to_json`` (#421) * Return empty data frame rather than raising an error when performing a spatial join with non overlapping geodataframes (#335) Bug fixes: * Compatibility with shapely 1.6.0 (#512) * Fix ``fiona.filter`` results when bbox is not None (#372) * Fix ``dissolve`` to retain CRS (#389) * Fix ``cx`` behavior when using index of 0 (#478) * Fix display of lower bin in legend label of choropleth plots using a PySAL scheme (#450) Version 0.2.0 ------------- Improvements: * Complete overhaul of the documentation * Addition of ``overlay`` to perform spatial overlays with polygons (#142) * Addition of ``sjoin`` to perform spatial joins (#115, #145, #188) * Addition of ``__geo_interface__`` that returns a python data structure to represent the ``GeoSeries`` as a GeoJSON-like ``FeatureCollection`` (#116) and ``iterfeatures`` method (#178) * Addition of the ``explode`` (#146) and ``dissolve`` (#310, #311) methods. * Addition of the ``sindex`` attribute, a Spatial Index using the optional dependency ``rtree`` (``libspatialindex``) that can be used to speed up certain operations such as overlays (#140, #141). * Addition of the ``GeoSeries.ix`` coordinate indexer to slice a GeoSeries based on a bounding box of the coordinates (#55). * Improvements to plotting: ability to specify edge colors (#173), support for the ``vmin``, ``vmax``, ``figsize``, ``linewidth`` keywords (#207), legends for chloropleth plots (#210), color points by specifying a colormap (#186) or a single color (#238). * Larger flexibility of ``to_crs``, accepting both dicts and proj strings (#289) * Addition of embedded example data, accessible through ``geopandas.datasets.get_path``. API changes: * In the ``plot`` method, the ``axes`` keyword is renamed to ``ax`` for consistency with pandas, and the ``colormap`` keyword is renamed to ``cmap`` for consistency with matplotlib (#208, #228, #240). Bug fixes: * Properly handle rows with missing geometries (#139, #193). * Fix ``GeoSeries.to_json`` (#263). * Correctly serialize metadata when pickling (#199, #206). * Fix ``merge`` and ``concat`` to return correct GeoDataFrame (#247, #320, #322). geopandas-0.3.0/CONTRIBUTING.md000066400000000000000000000044001315112464100157000ustar00rootroot00000000000000Guidelines ========== Contributions to GeoPandas are very welcome. They are likely to be accepted more quickly if they follow these guidelines. At this stage of GeoPandas development, the priorities are to define a simple, usable, and stable API and to have clean, maintainable, readable code. Performance matters, but not at the expense of those goals. In general, GeoPandas follows the conventions of the pandas project where applicable. Please read the [pandas contributing guidelines](http://pandas.pydata.org/pandas-docs/stable/contributing.html). In particular, when submitting a pull request: - All existing tests should pass. Please make sure that the test suite passes, both locally and on [Travis CI](https://travis-ci.org/geopandas/geopandas). Status on Travis will be visible on a pull request. If you want to enable Travis CI on your own fork, please read the pandas guidelines link above or the [getting started docs](http://about.travis-ci.org/docs/user/getting-started/). - New functionality should include tests. Please write reasonable tests for your code and make sure that they pass on your pull request. - Classes, methods, functions, etc. should have docstrings. The first line of a docstring should be a standalone summary. Parameters and return values should be ducumented explicitly. Improving the documentation and testing for code already in GeoPandas is a great way to get started if you'd like to make a contribution. Style ----- - GeoPandas supports python 2 (2.6+) and python 3 (3.2+) with a single code base. Use modern python idioms when possible that are compatibile with both major versions, and use the [six](https://pythonhosted.org/six) library where helpful to smooth over the differences. Use `from __future__ import` statements where appropriate. Test code locally in both python 2 and python 3 when possible (all supported versions will be automatically tested on Travis CI). - Follow PEP 8 when possible. - Imports should be grouped with standard library imports first, 3rd-party libraries next, and geopandas imports third. Within each grouping, imports should be alphabetized. Always use absolute imports when possible, and explicit relative imports for local imports when necessary in tests. geopandas-0.3.0/LICENSE.txt000066400000000000000000000027401315112464100152770ustar00rootroot00000000000000Copyright (c) 2013-2016, GeoPandas developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Enthought, Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. geopandas-0.3.0/MANIFEST.in000066400000000000000000000000641315112464100152070ustar00rootroot00000000000000include versioneer.py include geopandas/_version.py geopandas-0.3.0/README.md000066400000000000000000000116531315112464100147360ustar00rootroot00000000000000GeoPandas [![build status](https://secure.travis-ci.org/geopandas/geopandas.png?branch=master)](https://travis-ci.org/geopandas/geopandas) [![Coverage Status](https://codecov.io/gh/geopandas/geopandas/branch/master/graph/badge.svg)](https://codecov.io/gh/geopandas/geopandas) [![Join the chat at https://gitter.im/geopandas/geopandas](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/geopandas/geopandas?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) ========= Python tools for geographic data Introduction ------------ GeoPandas is a project to add support for geographic data to [pandas](http://pandas.pydata.org) objects. It currently implements `GeoSeries` and `GeoDataFrame` types which are subclasses of `pandas.Series` and `pandas.DataFrame` respectively. GeoPandas objects can act on [shapely](http://toblerity.github.io/shapely) geometry objects and perform geometric operations. GeoPandas geometry operations are cartesian. The coordinate reference system (crs) can be stored as an attribute on an object, and is automatically set when loading from a file. Objects may be transformed to new coordinate systems with the `to_crs()` method. There is currently no enforcement of like coordinates for operations, but that may change in the future. Documentation is available at [geopandas.org](http://geopandas.org) (current release) and [Read the Docs](http://geopandas.readthedocs.io/en/latest/) (release and development versions). Install -------- **Requirements** For the installation of GeoPandas, the following packages are required: - ``pandas`` - ``shapely`` - ``fiona`` - ``descartes`` - ``pyproj`` Further, [``rtree``](https://github.com/Toblerity/rtree) is an optional dependency. ``rtree`` requires the C library [``libspatialindex``](https://github.com/libspatialindex/libspatialindex). If using brew, you can install using ``brew install Spatialindex``. **Install** Then, installation works as normal: ``pip install geopandas`` Examples -------- >>> p1 = Polygon([(0, 0), (1, 0), (1, 1)]) >>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)]) >>> g = GeoSeries([p1, p2, p3]) >>> g 0 POLYGON ((0.0000000000000000 0.000000000000000... 1 POLYGON ((0.0000000000000000 0.000000000000000... 2 POLYGON ((2.0000000000000000 0.000000000000000... dtype: object ![Example 1](examples/test.png) Some geographic operations return normal pandas object. The `area` property of a `GeoSeries` will return a `pandas.Series` containing the area of each item in the `GeoSeries`: >>> print g.area 0 0.5 1 1.0 2 1.0 dtype: float64 Other operations return GeoPandas objects: >>> g.buffer(0.5) Out[15]: 0 POLYGON ((-0.3535533905932737 0.35355339059327... 1 POLYGON ((-0.5000000000000000 0.00000000000000... 2 POLYGON ((1.5000000000000000 0.000000000000000... dtype: object ![Example 2](examples/test_buffer.png) GeoPandas objects also know how to plot themselves. GeoPandas uses [descartes](https://pypi.python.org/pypi/descartes) to generate a [matplotlib](http://matplotlib.org) plot. To generate a plot of our GeoSeries, use: >>> g.plot() GeoPandas also implements alternate constructors that can read any data format recognized by [fiona](http://toblerity.github.io/fiona). To read a zip file containing an ESRI shapefile with the [boroughs boundaries of New York City](https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm) (GeoPandas includes this as an example dataset): >>> nybb_path = geopandas.datasets.get_path('nybb') >>> boros = GeoDataFrame.from_file(nybb_path) >>> boros.set_index('BoroCode', inplace=True) >>> boros.sort() >>> boros BoroName Shape_Area Shape_Leng \ BoroCode 1 Manhattan 6.364422e+08 358532.956418 2 Bronx 1.186804e+09 464517.890553 3 Brooklyn 1.959432e+09 726568.946340 4 Queens 3.049947e+09 861038.479299 5 Staten Island 1.623853e+09 330385.036974 geometry BoroCode 1 (POLYGON ((981219.0557861328125000 188655.3157... 2 (POLYGON ((1012821.8057861328125000 229228.264... 3 (POLYGON ((1021176.4790039062500000 151374.796... 4 (POLYGON ((1029606.0765991210937500 156073.814... 5 (POLYGON ((970217.0223999023437500 145643.3322... ![New York City boroughs](examples/nyc.png) >>> boros['geometry'].convex_hull 0 POLYGON ((915517.6877458114176989 120121.88125... 1 POLYGON ((1000721.5317993164062500 136681.7761... 2 POLYGON ((988872.8212280273437500 146772.03179... 3 POLYGON ((977855.4451904296875000 188082.32238... 4 POLYGON ((1017949.9776000976562500 225426.8845... dtype: object ![Convex hulls of New York City boroughs](examples/nyc_hull.png) geopandas-0.3.0/appveyor.yml000066400000000000000000000030761315112464100160470ustar00rootroot00000000000000# With infos from # http://tjelvarolsson.com/blog/how-to-continuously-test-your-python-code-on-windows-using-appveyor/ # https://packaging.python.org/en/latest/appveyor/ # https://github.com/rmcgibbo/python-appveyor-conda-example matrix: fast_finish: true # immediately finish build once one of the jobs fails. environment: matrix: - PYTHON_VERSION: "2.7" MINICONDA: C:\Miniconda-x64 - PYTHON_VERSION: "3.5" MINICONDA: C:\Miniconda35-x64 # all our python builds have to happen in tests_script... build: false init: - "ECHO %PYTHON_VERSION% %MINICONDA%" install: # cancel older builds for the same PR - ps: if ($env:APPVEYOR_PULL_REQUEST_NUMBER -and $env:APPVEYOR_BUILD_NUMBER -ne ((Invoke-RestMethod ` https://ci.appveyor.com/api/projects/$env:APPVEYOR_ACCOUNT_NAME/$env:APPVEYOR_PROJECT_SLUG/history?recordsNumber=50).builds | ` Where-Object pullRequestId -eq $env:APPVEYOR_PULL_REQUEST_NUMBER)[0].buildNumber) { ` throw "There are newer queued builds for this pull request, failing early." } # set up environment - "set PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%" - conda config --set always_yes yes --set show_channel_urls true --set changeps1 no - conda update -q conda - conda config --add channels conda-forge - conda info -a - "conda create -q -n test-environment python=%PYTHON_VERSION%" - activate test-environment - conda install pandas shapely=1.5 fiona pyproj rtree matplotlib descartes - conda install pytest mock test_script: - activate test-environment - conda list - pytest geopandas -v geopandas-0.3.0/asv.conf.json000066400000000000000000000140701315112464100160630ustar00rootroot00000000000000{ // The version of the config file format. Do not change, unless // you know what you are doing. "version": 1, // The name of the project being benchmarked "project": "geopandas", // The project's homepage "project_url": "http://geopandas.org/", // The URL or local path of the source code repository for the // project being benchmarked "repo": ".", // List of branches to benchmark. If not provided, defaults to "master" // (for git) or "default" (for mercurial). // "branches": ["master"], // for git // "branches": ["default"], // for mercurial // The DVCS being used. If not set, it will be automatically // determined from "repo" by looking at the protocol in the URL // (if remote), or by looking for special directories, such as // ".git" (if local). // "dvcs": "git", // The tool to use to create environments. May be "conda", // "virtualenv" or other value depending on the plugins in use. // If missing or the empty string, the tool will be automatically // determined by looking for tools on the PATH environment // variable. "environment_type": "conda", // timeout in seconds for installing any dependencies in environment // defaults to 10 min //"install_timeout": 600, // the base URL to show a commit for the project. "show_commit_url": "http://github.com/geopandas/geopandas/commit/", // The Pythons you'd like to test against. If not provided, defaults // to the current version of Python used to run `asv`. // "pythons": ["2.7", "3.3"], // The matrix of dependencies to test. Each key is the name of a // package (in PyPI) and the values are version numbers. An empty // list or empty string indicates to just test against the default // (latest) version. null indicates that the package is to not be // installed. If the package to be tested is only available from // PyPi, and the 'environment_type' is conda, then you can preface // the package name by 'pip+', and the package will be installed via // pip (with all the conda available packages installed first, // followed by the pip installed packages). // "matrix": { "pandas": [], "shapely": [], "cython": [], "fiona": [], "pyproj": [], "six": [], "rtree": [], "matplotlib": [], "descartes": [] }, // "numpy": ["1.6", "1.7"], // "six": ["", null], // test with and without six installed // "pip+emcee": [""], // emcee is only available for install with pip. // }, // Combinations of libraries/python versions can be excluded/included // from the set to test. Each entry is a dictionary containing additional // key-value pairs to include/exclude. // // An exclude entry excludes entries where all values match. The // values are regexps that should match the whole string. // // An include entry adds an environment. Only the packages listed // are installed. The 'python' key is required. The exclude rules // do not apply to includes. // // In addition to package names, the following keys are available: // // - python // Python version, as in the *pythons* variable above. // - environment_type // Environment type, as above. // - sys_platform // Platform, as in sys.platform. Possible values for the common // cases: 'linux2', 'win32', 'cygwin', 'darwin'. // // "exclude": [ // {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows // {"environment_type": "conda", "six": null}, // don't run without six on conda // ], // // "include": [ // // additional env for python2.7 // {"python": "2.7", "numpy": "1.8"}, // // additional env if run on windows+conda // {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""}, // ], // The directory (relative to the current directory) that benchmarks are // stored in. If not provided, defaults to "benchmarks" // "benchmark_dir": "benchmarks", // The directory (relative to the current directory) to cache the Python // environments in. If not provided, defaults to "env" "env_dir": ".asv/env", // The directory (relative to the current directory) that raw benchmark // results are stored in. If not provided, defaults to "results". "results_dir": ".asv/results", // The directory (relative to the current directory) that the html tree // should be written to. If not provided, defaults to "html". "html_dir": ".asv/html", // The number of characters to retain in the commit hashes. // "hash_length": 8, // `asv` will cache wheels of the recent builds in each // environment, making them faster to install next time. This is // number of builds to keep, per environment. // "wheel_cache_size": 0 // The commits after which the regression search in `asv publish` // should start looking for regressions. Dictionary whose keys are // regexps matching to benchmark names, and values corresponding to // the commit (exclusive) after which to start looking for // regressions. The default is to start from the first commit // with results. If the commit is `null`, regression detection is // skipped for the matching benchmark. // // "regressions_first_commits": { // "some_benchmark": "352cdf", // Consider regressions only after this commit // "another_benchmark": null, // Skip regression detection altogether // } // The thresholds for relative change in results, after which `asv // publish` starts reporting regressions. Dictionary of the same // form as in ``regressions_first_commits``, with values // indicating the thresholds. If multiple entries match, the // maximum is taken. If no entry matches, the default is 5%. // // "regressions_thresholds": { // "some_benchmark": 0.01, // Threshold of 1% // "another_benchmark": 0.5, // Threshold of 50% // } } geopandas-0.3.0/benchmarks/000077500000000000000000000000001315112464100155665ustar00rootroot00000000000000geopandas-0.3.0/benchmarks/__init__.py000066400000000000000000000000011315112464100176660ustar00rootroot00000000000000 geopandas-0.3.0/benchmarks/geom_methods.py000066400000000000000000000073721315112464100206230ustar00rootroot00000000000000import random import numpy as np from geopandas import GeoSeries from shapely.geometry import Point, LineString, Polygon def with_attributes(**attrs): def decorator(func): for key, value in attrs.items(): setattr(func, key, value) return func return decorator class Bench: def setup(self, *args): self.points = GeoSeries([Point(i, i) for i in range(100000)]) triangles = GeoSeries([Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(1000)]) triangles2 = triangles.copy().iloc[np.random.choice(1000, 1000)] triangles3 = GeoSeries([Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(10000)]) triangle = Polygon([(random.random(), random.random()) for _ in range(3)]) self.triangles, self.triangles2 = triangles, triangles2 self.triangles_big = triangles3 self.triangle = triangle @with_attributes(param_names=['op'], params=[('contains', 'crosses', 'disjoint', 'intersects', 'overlaps', 'touches', 'within', 'geom_equals', 'geom_almost_equals', 'geom_equals_exact')]) def time_binary_predicate(self, op): getattr(self.triangles, op)(self.triangle) @with_attributes(param_names=['op'], params=[('contains', 'crosses', 'disjoint', 'intersects', 'overlaps', 'touches', 'within', 'geom_equals', 'geom_almost_equals')]) # 'geom_equals_exact')]) def time_binary_predicate_vector(self, op): getattr(self.triangles, op)(self.triangles2) @with_attributes(param_names=['op'], params=[('distance')]) def time_binary_float(self, op): getattr(self.triangles, op)(self.triangle) @with_attributes(param_names=['op'], params=[('distance')]) def time_binary_float_vector(self, op): getattr(self.triangles, op)(self.triangles2) @with_attributes(param_names=['op'], params=[('difference', 'symmetric_difference', 'union', 'intersection')]) def time_binary_geo(self, op): getattr(self.triangles, op)(self.triangle) @with_attributes(param_names=['op'], params=[('difference', 'symmetric_difference', 'union', 'intersection')]) def time_binary_geo_vector(self, op): getattr(self.triangles, op)(self.triangles2) @with_attributes(param_names=['op'], params=[('is_valid', 'is_empty', 'is_simple', 'is_ring')]) def time_unary_predicate(self, op): getattr(self.triangles, op) @with_attributes(param_names=['op'], params=[('area', 'length')]) def time_unary_float(self, op): getattr(self.triangles_big, op) @with_attributes(param_names=['op'], params=[('boundary', 'centroid', 'convex_hull', 'envelope', 'exterior', 'interiors')]) def time_unary_geo(self, op): getattr(self.triangles, op) def time_unary_geo_representative_point(self, *args): getattr(self.triangles, 'representative_point')() def time_geom_type(self, *args): self.triangles_big.geom_type def time_bounds(self, *args): self.triangles.bounds def time_unary_union(self, *args): self.triangles.unary_union def time_buffer(self, *args): self.points.buffer(2) # TODO # project, interpolate, translate, rotate, scale, skew, explode # cx indexer geopandas-0.3.0/benchmarks/overlay.py000066400000000000000000000030221315112464100176160ustar00rootroot00000000000000from geopandas import GeoDataFrame, GeoSeries, read_file, datasets, overlay from shapely.geometry import Polygon class Countries: param_names = ['op'] params = [('intersection', 'union', 'identity', 'symmetric_difference', 'difference')] def setup(self, *args): world = read_file(datasets.get_path('naturalearth_lowres')) capitals = read_file(datasets.get_path('naturalearth_cities')) countries = world[['geometry', 'name']] countries = countries.to_crs('+init=epsg:3395')[ countries.name != "Antarctica"] capitals = capitals.to_crs('+init=epsg:3395') capitals['geometry'] = capitals.buffer(500000) self.countries = countries self.capitals = capitals def time_overlay(self, op): overlay(self.countries, self.capitals, how=op) class Small: param_names = ['op'] params = [('intersection', 'union', 'identity', 'symmetric_difference', 'difference')] def setup(self, *args): polys1 = GeoSeries([Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)])]) polys2 = GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)])]) df1 = GeoDataFrame({'geometry': polys1, 'df1': [1, 2]}) df2 = GeoDataFrame({'geometry': polys2, 'df2': [1, 2]}) self.df1, self.df2 = df1, df2 def time_overlay(self, op): overlay(self.df1, self.df2, how=op) geopandas-0.3.0/benchmarks/plotting.py000066400000000000000000000041141315112464100200000ustar00rootroot00000000000000import random from geopandas import GeoDataFrame, GeoSeries from shapely.geometry import Point, LineString, Polygon, MultiPolygon import numpy as np class Bench: param_names = ['geom_type'] params = [('Point', 'LineString', 'Polygon', 'MultiPolygon', 'mixed')] def setup(self, geom_type): if geom_type == 'Point': geoms = GeoSeries([Point(i, i) for i in range(1000)]) elif geom_type == 'LineString': geoms = GeoSeries([LineString([(random.random(), random.random()) for _ in range(5)]) for _ in range(100)]) elif geom_type == 'Polygon': geoms = GeoSeries([Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(100)]) elif geom_type == 'MultiPolygon': geoms = GeoSeries( [MultiPolygon([Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(3)]) for _ in range(20)]) elif geom_type == 'mixed': g1 = GeoSeries([Point(i, i) for i in range(100)]) g2 = GeoSeries([LineString([(random.random(), random.random()) for _ in range(5)]) for _ in range(100)]) g3 = GeoSeries([Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(100)]) geoms = g1 geoms.iloc[np.random.randint(0, 100, 50)] = g2 geoms.iloc[np.random.randint(0, 100, 33)] = g3 print(geoms.geom_type.value_counts()) df = GeoDataFrame({'geometry': geoms, 'values': np.random.randn(len(geoms))}) self.geoms = geoms self.df = df def time_plot_series(self, *args): self.geoms.plot() def time_plot_values(self, *args): self.df.plot(column='values') geopandas-0.3.0/benchmarks/sjoin.py000066400000000000000000000016301315112464100172620ustar00rootroot00000000000000import random from geopandas import GeoDataFrame, GeoSeries, sjoin from shapely.geometry import Point, LineString, Polygon import numpy as np class Bench: param_names = ['op'] params = [('intersects', 'contains', 'within')] def setup(self, *args): triangles = GeoSeries( [Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(1000)]) points = GeoSeries( [Point(x, y) for x, y in zip(np.random.random(10000), np.random.random(10000))]) df1 = GeoDataFrame({'val1': np.random.randn(len(triangles)), 'geometry': triangles}) df2 = GeoDataFrame({'val1': np.random.randn(len(points)), 'geometry': points}) self.df1, self.df2 = df1, df2 def time_sjoin(self, op): sjoin(self.df1, self.df2, op=op) geopandas-0.3.0/constraints.txt000066400000000000000000000000131315112464100165530ustar00rootroot00000000000000rtree>=0.8 geopandas-0.3.0/doc/000077500000000000000000000000001315112464100142165ustar00rootroot00000000000000geopandas-0.3.0/doc/Makefile000066400000000000000000000127211315112464100156610ustar00rootroot00000000000000# Makefile for Sphinx documentation # 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The message catalogs are in $(BUILDDIR)/locale." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." geopandas-0.3.0/doc/environment.yml000066400000000000000000000003221315112464100173020ustar00rootroot00000000000000name: geopandas_docs channels: - conda-forge dependencies: - python=3.5 - pandas - shapely - fiona - pyproj - rtree - six - geopy - matplotlib - descartes - pysal - sphinx - sphinx_rtd_theme - ipython - pillow geopandas-0.3.0/doc/make.bat000066400000000000000000000117671315112464100156370ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source set I18NSPHINXOPTS=%SPHINXOPTS% source if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. texinfo to make Texinfo files echo. gettext to make PO message catalogs echo. changes to make an overview over all changed/added/deprecated items echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml if errorlevel 1 exit /b 1 echo. echo.Build finished. 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The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "texinfo" ( %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo if errorlevel 1 exit /b 1 echo. echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. goto end ) if "%1" == "gettext" ( %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale if errorlevel 1 exit /b 1 echo. echo.Build finished. The message catalogs are in %BUILDDIR%/locale. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) :end geopandas-0.3.0/doc/source/000077500000000000000000000000001315112464100155165ustar00rootroot00000000000000geopandas-0.3.0/doc/source/_static/000077500000000000000000000000001315112464100171445ustar00rootroot00000000000000geopandas-0.3.0/doc/source/_static/boros_with_holes.png000066400000000000000000004421401315112464100232300ustar00rootroot00000000000000PNG  IHDRxFsBIT|d pHYsaa?i IDATxyX9-TRJY2c؍J&La,cd>c!ɾ36QKR:=?:9vau]]?srMYT(!BlC; B!$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ$xB!ٌ^ x☙_3}tnܸ*vӦMtGGGʕ+9vXZZjaaaAժU1cFqqqqу%Kbcc+aaaiƞ>}lll(Y$={$>>>XTkf|B!5 r%zƍiѢ+WTR:uJ7aZlӧO6m{رcԭ[={9k,|||R AAA4mڔ3p@$ׯφ 4hK.EPn]BCCub?NݺuXldԫWFۿ ;AAATT ooo̙'B9RpTn߾)͛7^+V_EGG+FFFJ۶m^zXYY)>>>:FRt TT*rAׯ_+%JP\]]u;;;+%KT8T*%((HOѣu{{{+J|||B!gP$0(P+꼦:qs&Gim۶'OЩS'؎;2~x~W ʕ+qtt^zڸ9ry666İw^F6~.\+WҪU+~W4 ;vLUؾ};ÇٳGGGLLL2؄B!Y||}4RSǏh4:B!6;q1vXs/22ӬY3~(VgϞՉ"../B{hѢDDDKdd$* GGGSJΔؿYX1^x͛7S:B!6z'x~~~;QF1rT *-[tym۶agg7nnnXYYb +V`dd3LJ(:ڵkquuųٙ@!!!ܺu:hmCC4۷]ߏE!ⳣ<F ...6^:ys,]֭[ӢE ={ڵke֬Y9s2j(뇅΄G~tTjժnݚ &`cc {.:9r$5o{0bUF-qՋW_sN5k6+u^^^ (yM7{k,^zZVT*U/ZKLLTvܩSqttTʖ-Oʑ#GҬ7 @YbnnTREO3...N޽Rxq%o޼fSggg%o޼Jŕ={qӕʕ++JZիWg9)@m !B|7TIé\2aaaq'11]_2f̘O!BGVSvm޽;.\˗ d7oή]x$y"[O!GիWquudɒx7oҨQO۹7j(mrzO!$xB!>RT)011y:̙ӧO߿?fffYC8u?m O9xB!> ;;;:u]hh(}aԨQINNΒ=zׯ_k_nmڴݝƍ%)H's 6mϧnݺ9rwwwѣW^}CBB4lؐ RLw}Ç)S GβRBٽ{7?fTVjժv͛Yx1 .B 4jԈ!C`mmw3f gΜL2*Uhmۖ1c0n8vɓ'QTYB|D2'DEEèQljtFόpwwgҥl۶ ooobbb6m666|ozY&M}{{{/^+OׯO&?CFB|mrqY̍KJJJ~|ݻ7{&00kײ~n݊Zb(P U۷oV믿ΰ;w&117oIHHoߦ`B|L2'lڴ9sjvyu k֬Iw.w}ǖ-[ػw/Æ #O<̞=GGGj֬˵+RP!nܸ7|i? 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Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'GeoPandasdoc' # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'GeoPandas.tex', u'GeoPandas Documentation', u'Kelsey Jordahl', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'geopandas', u'GeoPandas Documentation', [u'Kelsey Jordahl'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'GeoPandas', u'GeoPandas Documentation', u'Kelsey Jordahl', 'GeoPandas', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' geopandas-0.3.0/doc/source/contributing.rst000066400000000000000000000255331315112464100207670ustar00rootroot00000000000000Contributing to GeoPandas ========================= (Contribution guidelines largely copied from `pandas `_) Overview -------- Contributions to GeoPandas are very welcome. They are likely to be accepted more quickly if they follow these guidelines. At this stage of GeoPandas development, the priorities are to define a simple, usable, and stable API and to have clean, maintainable, readable code. Performance matters, but not at the expense of those goals. In general, GeoPandas follows the conventions of the pandas project where applicable. In particular, when submitting a pull request: - All existing tests should pass. Please make sure that the test suite passes, both locally and on `Travis CI `_. Status on Travis will be visible on a pull request. If you want to enable Travis CI on your own fork, please read the pandas guidelines link above or the `getting started docs `_. - New functionality should include tests. Please write reasonable tests for your code and make sure that they pass on your pull request. - Classes, methods, functions, etc. should have docstrings. The first line of a docstring should be a standalone summary. Parameters and return values should be ducumented explicitly. - GeoPandas supports python 2 (2.6+) and python 3 (3.2+) with a single code base. Use modern python idioms when possible that are compatibile with both major versions, and use the `six `_ library where helpful to smooth over the differences. Use ``from __future__ import`` statements where appropriate. Test code locally in both python 2 and python 3 when possible (all supported versions will be automatically tested on Travis CI). - Follow PEP 8 when possible. - Imports should be grouped with standard library imports first, 3rd-party libraries next, and geopandas imports third. Within each grouping, imports should be alphabetized. Always use absolute imports when possible, and explicit relative imports for local imports when necessary in tests. Seven Steps for Contributing ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ There are seven basic steps to contributing to *geopandas*: 1) Fork the *geopandas* git repository 2) Create a development environment 3) Install *geopandas* dependencies 4) Make a ``development`` build of *geopandas* 5) Make changes to code and add tests 6) Update the documentation 7) Submit a Pull Request Each of these 7 steps is detailed below. 1) Forking the *geopandas* repository using Git ------------------------------------------------ To the new user, working with Git is one of the more daunting aspects of contributing to *geopandas**. It can very quickly become overwhelming, but sticking to the guidelines below will help keep the process straightforward and mostly trouble free. As always, if you are having difficulties please feel free to ask for help. The code is hosted on `GitHub `_. To contribute you will need to sign up for a `free GitHub account `_. We use `Git `_ for version control to allow many people to work together on the project. Some great resources for learning Git: * Software Carpentry's `Git Tutorial `_ * `Atlassian `_ * the `GitHub help pages `_. * Matthew Brett's `Pydagogue `_. Getting started with Git ~~~~~~~~~~~~~~~~~~~~~~~~~ `GitHub has instructions `__ for installing git, setting up your SSH key, and configuring git. All these steps need to be completed before you can work seamlessly between your local repository and GitHub. .. _contributing.forking: Forking ~~~~~~~~ You will need your own fork to work on the code. Go to the `geopandas project page `_ and hit the ``Fork`` button. You will want to clone your fork to your machine:: git clone git@github.com:your-user-name/geopandas.git geopandas-yourname cd geopandas-yourname git remote add upstream git://github.com/geopandas/geopandas.git This creates the directory `geopandas-yourname` and connects your repository to the upstream (main project) *geopandas* repository. The testing suite will run automatically on Travis-CI once your pull request is submitted. However, if you wish to run the test suite on a branch prior to submitting the pull request, then Travis-CI needs to be hooked up to your GitHub repository. Instructions for doing so are `here `__. Creating a branch ~~~~~~~~~~~~~~~~~~ You want your master branch to reflect only production-ready code, so create a feature branch for making your changes. For example:: git branch shiny-new-feature git checkout shiny-new-feature The above can be simplified to:: git checkout -b shiny-new-feature This changes your working directory to the shiny-new-feature branch. Keep any changes in this branch specific to one bug or feature so it is clear what the branch brings to *geopandas*. You can have many shiny-new-features and switch in between them using the git checkout command. To update this branch, you need to retrieve the changes from the master branch:: git fetch upstream git rebase upstream/master This will replay your commits on top of the latest geopandas git master. If this leads to merge conflicts, you must resolve these before submitting your pull request. If you have uncommitted changes, you will need to ``stash`` them prior to updating. This will effectively store your changes and they can be reapplied after updating. .. _contributing.dev_env: 2) Creating a development environment --------------------------------------- A development environment is a virtual space where you can keep an independent installation of *geopandas*. This makes it easy to keep both a stable version of python in one place you use for work, and a development version (which you may break while playing with code) in another. An easy way to create a *geopandas* development environment is as follows: - Install either `Anaconda `_ or `miniconda `_ - Make sure that you have :ref:`cloned the repository ` - ``cd`` to the *geopandas** source directory Tell conda to create a new environment, named ``geopandas_dev``, or any other name you would like for this environment, by running:: conda create -n geopandas_dev For a python 3 environment:: conda create -n geopandas_dev python=3.4 This will create the new environment, and not touch any of your existing environments, nor any existing python installation. To work in this environment, Windows users should ``activate`` it as follows:: activate geopandas_dev Mac OSX and Linux users should use:: source activate geopandas_dev You will then see a confirmation message to indicate you are in the new development environment. To view your environments:: conda info -e To return to you home root environment:: deactivate See the full conda docs `here `__. At this point you can easily do a *development* install, as detailed in the next sections. 3) Installing Dependencies -------------------------- To run *geopandas* in an development environment, you must first install *geopandas*'s dependencies. We suggest doing so using the following commands (executed after your development environment has been activated):: conda install -c conda-forge fiona shapely pyproj rtree conda install pandas This should install all necessary dependencies. 4) Making a development build ----------------------------- Once dependencies are in place, make an in-place build by navigating to the git clone of the *geopandas* repository and running:: python setup.py develop 5) Making changes and writing tests ------------------------------------- *geopandas* is serious about testing and strongly encourages contributors to embrace `test-driven development (TDD) `_. This development process "relies on the repetition of a very short development cycle: first the developer writes an (initially failing) automated test case that defines a desired improvement or new function, then produces the minimum amount of code to pass that test." So, before actually writing any code, you should write your tests. Often the test can be taken from the original GitHub issue. However, it is always worth considering additional use cases and writing corresponding tests. Adding tests is one of the most common requests after code is pushed to *geopandas*. Therefore, it is worth getting in the habit of writing tests ahead of time so this is never an issue. *geopandas* uses the `pytest testing system `_ and the convenient extensions in `numpy.testing `_. Writing tests ~~~~~~~~~~~~~ All tests should go into the ``tests`` directory. This folder contains many current examples of tests, and we suggest looking to these for inspiration. The ``.util`` module has some special ``assert`` functions that make it easier to make statements about whether GeoSeries or GeoDataFrame objects are equivalent. The easiest way to verify that your code is correct is to explicitly construct the result you expect, then compare the actual result to the expected correct result, using eg the function ``assert_geoseries_equal``. Running the test suite ~~~~~~~~~~~~~~~~~~~~~~ The tests can then be run directly inside your Git clone (without having to install *geopandas*) by typing:: pytest 6) Updating the Documentation ----------------------------- *geopandas* documentation resides in the `doc` folder. Changes to the docs are make by modifying the appropriate file in the `source` folder within `doc`. *geopandas* docs us reStructuredText syntax, `which is explained here `_ and the docstrings follow the `Numpy Docstring standard `_. Once you have made your changes, you can build the docs by navigating to the `doc` folder and typing:: make html The resulting html pages will be located in `doc/build/html`. 7) Submitting a Pull Request ------------------------------ Once you've made changes and pushed them to your forked repository, you then submit a pull request to have them integrated into the *geopandas* code base. You can find a pull request (or PR) tutorial in the `GitHub's Help Docs `_. geopandas-0.3.0/doc/source/data_structures.rst000066400000000000000000000147101315112464100214670ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas as gpd import matplotlib orig = matplotlib.rcParams['figure.figsize'] matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]] Data Structures ========================================= GeoPandas implements two main data structures, a ``GeoSeries`` and a ``GeoDataFrame``. These are subclasses of pandas ``Series`` and ``DataFrame``, respectively. GeoSeries --------- A ``GeoSeries`` is essentially a vector where each entry in the vector is a set of shapes corresponding to one observation. An entry may consist of only one shape (like a single polygon) or multiple shapes that are meant to be thought of as one observation (like the many polygons that make up the State of Hawaii or a country like Indonesia). *geopandas* has three basic classes of geometric objects (which are actually *shapely* objects): * Points / Multi-Points * Lines / Multi-Lines * Polygons / Multi-Polygons Note that all entries in a ``GeoSeries`` need not be of the same geometric type, although certain export operations will fail if this is not the case. Overview of Attributes and Methods ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The ``GeoSeries`` class implements nearly all of the attributes and methods of Shapely objects. When applied to a ``GeoSeries``, they will apply elementwise to all geometries in the series. Binary operations can be applied between two ``GeoSeries``, in which case the operation is carried out elementwise. The two series will be aligned by matching indices. Binary operations can also be applied to a single geometry, in which case the operation is carried out for each element of the series with that geometry. In either case, a ``Series`` or a ``GeoSeries`` will be returned, as appropriate. A short summary of a few attributes and methods for GeoSeries is presented here, and a full list can be found in the :doc:`all attributes and methods page `. There is also a family of methods for creating new shapes by expanding existing shapes or applying set-theoretic operations like "union" described in :doc:`geometric manipulations `. Attributes ^^^^^^^^^^^^^^^ * ``area``: shape area (units of projection -- see :doc:`projections `) * ``bounds``: tuple of max and min coordinates on each axis for each shape * ``total_bounds``: tuple of max and min coordinates on each axis for entire GeoSeries * ``geom_type``: type of geometry. * ``is_valid``: tests if coordinates make a shape that is reasonable geometric shape (`according to this `_). Basic Methods ^^^^^^^^^^^^^^ * ``distance(other)``: returns ``Series`` with minimum distance from each entry to ``other`` * ``centroid``: returns ``GeoSeries`` of centroids * ``representative_point()``: returns ``GeoSeries`` of points that are guaranteed to be within each geometry. It does **NOT** return centroids. * ``to_crs()``: change coordinate reference system. See :doc:`projections ` * ``plot()``: plot ``GeoSeries``. See :doc:`mapping `. Relationship Tests ^^^^^^^^^^^^^^^^^^^ * ``geom_almost_equals(other)``: is shape almost the same as ``other`` (good when floating point precision issues make shapes slightly different) * ``contains(other)``: is shape contained within ``other`` * ``intersects(other)``: does shape intersect ``other`` GeoDataFrame ------------ A ``GeoDataFrame`` is a tabular data structure that contains a ``GeoSeries``. The most important property of a ``GeoDataFrame`` is that it always has one ``GeoSeries`` column that holds a special status. This ``GeoSeries`` is referred to as the ``GeoDataFrame``'s "geometry". When a spatial method is applied to a ``GeoDataFrame`` (or a spatial attribute like ``area`` is called), this commands will always act on the "geometry" column. The "geometry" column -- no matter its name -- can be accessed through the ``geometry`` attribute (``gdf.geometry``), and the name of the ``geometry`` column can be found by typing ``gdf.geometry.name``. A ``GeoDataFrame`` may also contain other columns with geometrical (shapely) objects, but only one column can be the active geometry at a time. To change which column is the active geometry column, use the ``set_geometry`` method. An example using the ``worlds`` GeoDataFrame: .. ipython:: python world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) world.head() #Plot countries @savefig world_borders.png world.plot(); Currently, the column named "geometry" with country borders is the active geometry column: .. ipython:: python world.geometry.name We can also rename this column to "borders": .. ipython:: python world = world.rename(columns={'geometry': 'borders'}).set_geometry('borders') world.geometry.name Now, we create centroids and make it the geometry: .. ipython:: python world['centroid_column'] = world.centroid world = world.set_geometry('centroid_column') @savefig world_centroids.png world.plot(); **Note:** A ``GeoDataFrame`` keeps track of the active column by name, so if you rename the active geometry column, you must also reset the geometry:: gdf = gdf.rename(columns={'old_name': 'new_name'}).set_geometry('new_name') **Note 2:** Somewhat confusingly, by default when you use the ``read_file`` command, the column containing spatial objects from the file is named "geometry" by default, and will be set as the active geometry column. However, despite using the same term for the name of the column and the name of the special attribute that keeps track of the active column, they are distinct. You can easily shift the active geometry column to a different ``GeoSeries`` with the ``set_geometry`` command. Further, ``gdf.geometry`` will always return the active geometry column, *not* the column named ``geometry``. If you wish to call a column named "geometry", and a different column is the active geometry column, use ``gdf['geometry']``, not ``gdf.geometry``. Attributes and Methods ~~~~~~~~~~~~~~~~~~~~~~ Any of the attributes calls or methods described for a ``GeoSeries`` will work on a ``GeoDataFrame`` -- effectively, they are just applied to the "geometry" ``GeoSeries``. However, ``GeoDataFrames`` also have a few extra methods for input and output which are described on the :doc:`Input and Output ` page and for geocoding with are described in :doc:`Geocoding `. .. ipython:: python :suppress: matplotlib.rcParams['figure.figsize'] = orig geopandas-0.3.0/doc/source/geocoding.rst000066400000000000000000000010101315112464100201760ustar00rootroot00000000000000 Geocoding ========== [TO BE COMPLETED] .. function:: geopandas.geocode.geocode(strings, provider='googlev3', **kwargs) Geocode a list of strings and return a GeoDataFrame containing the resulting points in its ``geometry`` column. Available ``provider``s include ``googlev3``, ``bing``, ``google``, ``yahoo``, ``mapquest``, and ``openmapquest``. ``**kwargs`` will be passed as parameters to the appropriate geocoder. Requires `geopy`_. Please consult the Terms of Service for the chosen provider. geopandas-0.3.0/doc/source/geometric_manipulations.rst000066400000000000000000000163161315112464100232000ustar00rootroot00000000000000Geometric Manipulations ======================== *geopandas* makes available all the tools for geometric manipulations in the `*shapely* library `_. Note that documentation for all set-theoretic tools for creating new shapes using the relationship between two different spatial datasets -- like creating intersections, or differences -- can be found on the :doc:`set operations ` page. Constructive Methods ~~~~~~~~~~~~~~~~~~~~ .. method:: GeoSeries.buffer(distance, resolution=16) Returns a ``GeoSeries`` of geometries representing all points within a given `distance` of each geometric object. .. attribute:: GeoSeries.boundary Returns a ``GeoSeries`` of lower dimensional objects representing each geometries's set-theoretic `boundary`. .. attribute:: GeoSeries.centroid Returns a ``GeoSeries`` of points for each geometric centroid. .. attribute:: GeoSeries.convex_hull Returns a ``GeoSeries`` of geometries representing the smallest convex `Polygon` containing all the points in each object unless the number of points in the object is less than three. For two points, the convex hull collapses to a `LineString`; for 1, a `Point`. .. attribute:: GeoSeries.envelope Returns a ``GeoSeries`` of geometries representing the point or smallest rectangular polygon (with sides parallel to the coordinate axes) that contains each object. .. method:: GeoSeries.simplify(tolerance, preserve_topology=True) Returns a ``GeoSeries`` containing a simplified representation of each object. .. attribute:: GeoSeries.unary_union Return a geometry containing the union of all geometries in the ``GeoSeries``. Affine transformations ~~~~~~~~~~~~~~~~~~~~~~~~ .. method:: GeoSeries.rotate(self, angle, origin='center', use_radians=False) Rotate the coordinates of the GeoSeries. .. method:: GeoSeries.scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin='center') Scale the geometries of the GeoSeries along each (x, y, z) dimensio. .. method:: GeoSeries.skew(self, angle, origin='center', use_radians=False) Shear/Skew the geometries of the GeoSeries by angles along x and y dimensions. .. method:: GeoSeries.translate(self, angle, origin='center', use_radians=False) Shift the coordinates of the GeoSeries. Examples of Geometric Manipulations ------------------------------------ .. sourcecode:: python >>> p1 = Polygon([(0, 0), (1, 0), (1, 1)]) >>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)]) >>> g = GeoSeries([p1, p2, p3]) >>> g 0 POLYGON ((0.0000000000000000 0.000000000000000... 1 POLYGON ((0.0000000000000000 0.000000000000000... 2 POLYGON ((2.0000000000000000 0.000000000000000... dtype: object .. image:: _static/test.png Some geographic operations return normal pandas object. The ``area`` property of a ``GeoSeries`` will return a ``pandas.Series`` containing the area of each item in the ``GeoSeries``: .. sourcecode:: python >>> print g.area 0 0.5 1 1.0 2 1.0 dtype: float64 Other operations return GeoPandas objects: .. sourcecode:: python >>> g.buffer(0.5) Out[15]: 0 POLYGON ((-0.3535533905932737 0.35355339059327... 1 POLYGON ((-0.5000000000000000 0.00000000000000... 2 POLYGON ((1.5000000000000000 0.000000000000000... dtype: object .. image:: _static/test_buffer.png GeoPandas objects also know how to plot themselves. GeoPandas uses `descartes`_ to generate a `matplotlib`_ plot. To generate a plot of our GeoSeries, use: .. sourcecode:: python >>> g.plot() GeoPandas also implements alternate constructors that can read any data format recognized by `fiona`_. To read a zip file containing an ESRI shapefile with the `borough boundaries of New York City`_ (GeoPandas includes this as an example dataset): .. sourcecode:: python >>> nybb_path = geopandas.datasets.get_path('nybb') >>> boros = GeoDataFrame.from_file(nybb_path) >>> boros.set_index('BoroCode', inplace=True) >>> boros.sort() >>> boros BoroName Shape_Area Shape_Leng \ BoroCode 1 Manhattan 6.364422e+08 358532.956418 2 Bronx 1.186804e+09 464517.890553 3 Brooklyn 1.959432e+09 726568.946340 4 Queens 3.049947e+09 861038.479299 5 Staten Island 1.623853e+09 330385.036974 geometry BoroCode 1 (POLYGON ((981219.0557861328125000 188655.3157... 2 (POLYGON ((1012821.8057861328125000 229228.264... 3 (POLYGON ((1021176.4790039062500000 151374.796... 4 (POLYGON ((1029606.0765991210937500 156073.814... 5 (POLYGON ((970217.0223999023437500 145643.3322... .. image:: _static/nyc.png .. sourcecode:: python >>> boros['geometry'].convex_hull 0 POLYGON ((915517.6877458114176989 120121.88125... 1 POLYGON ((1000721.5317993164062500 136681.7761... 2 POLYGON ((988872.8212280273437500 146772.03179... 3 POLYGON ((977855.4451904296875000 188082.32238... 4 POLYGON ((1017949.9776000976562500 225426.8845... dtype: object .. image:: _static/nyc_hull.png To demonstrate a more complex operation, we'll generate a ``GeoSeries`` containing 2000 random points: .. sourcecode:: python >>> from shapely.geometry import Point >>> xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000 >>> xc = (xmax - xmin) * np.random.random(2000) + xmin >>> yc = (ymax - ymin) * np.random.random(2000) + ymin >>> pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)]) Now draw a circle with fixed radius around each point: .. sourcecode:: python >>> circles = pts.buffer(2000) We can collapse these circles into a single shapely MultiPolygon geometry with .. sourcecode:: python >>> mp = circles.unary_union To extract the part of this geometry contained in each borough, we can just use: .. sourcecode:: python >>> holes = boros['geometry'].intersection(mp) .. image:: _static/holes.png and to get the area outside of the holes: .. sourcecode:: python >>> boros_with_holes = boros['geometry'].difference(mp) .. image:: _static/boros_with_holes.png Note that this can be simplified a bit, since ``geometry`` is available as an attribute on a ``GeoDataFrame``, and the ``intersection`` and ``difference`` methods are implemented with the "&" and "-" operators, respectively. For example, the latter could have been expressed simply as ``boros.geometry - mp``. It's easy to do things like calculate the fractional area in each borough that are in the holes: .. sourcecode:: python >>> holes.area / boros.geometry.area BoroCode 1 0.602015 2 0.523457 3 0.585901 4 0.577020 5 0.559507 dtype: float64 .. _Descartes: https://pypi.python.org/pypi/descartes .. _matplotlib: http://matplotlib.org .. _fiona: http://toblerity.github.io/fiona .. _geopy: https://github.com/geopy/geopy .. _geo_interface: https://gist.github.com/sgillies/2217756 .. _borough boundaries of New York City: https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm .. toctree:: :maxdepth: 2 geopandas-0.3.0/doc/source/index.rst000066400000000000000000000031401315112464100173550ustar00rootroot00000000000000GeoPandas |version| =================== GeoPandas is an open source project to make working with geospatial data in python easier. GeoPandas extends the datatypes used by `pandas`_ to allow spatial operations on geometric types. Geometric operations are performed by `shapely`_. Geopandas further depends on `fiona`_ for file access and `descartes`_ and `matplotlib`_ for plotting. .. _pandas: http://pandas.pydata.org .. _shapely: http://toblerity.github.io/shapely .. _fiona: http://toblerity.github.io/fiona .. _Descartes: https://pypi.python.org/pypi/descartes .. _matplotlib: http://matplotlib.org Description ----------- The goal of GeoPandas is to make working with geospatial data in python easier. It combines the capabilities of pandas and shapely, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS. .. toctree:: :maxdepth: 2 Installation Data Structures Reading and Writing Files Indexing and Selecting Data Making Maps Managing Projections Geometric Manipulations Set Operations with overlay Aggregation with dissolve Merging Data Geocoding Reference to All Attributes and Methods Contributing to GeoPandas About Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` geopandas-0.3.0/doc/source/indexing.rst000066400000000000000000000021771315112464100200640ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas as gpd Indexing and Selecting Data =========================== GeoPandas inherits the standard ``pandas`` methods for indexing/selecting data. This includes label based indexing with ``.loc`` and integer position based indexing with ``.iloc``, which apply to both ``GeoSeries`` and ``GeoDataFrame`` objects. For more information on indexing/selecting, see the pandas_ documentation. .. _pandas: http://pandas.pydata.org/pandas-docs/stable/indexing.html In addition to the standard ``pandas`` methods, GeoPandas also provides coordinate based indexing with the ``cx`` indexer, which slices using a bounding box. Geometries in the ``GeoSeries`` or ``GeoDataFrame`` that intersect the bounding box will be returned. Using the ``world`` dataset, we can use this functionality to quickly select all countries whose boundaries extend into the southern hemisphere. .. ipython:: python world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) southern_world = world.cx[:, :0] @savefig world_southern.png southern_world.plot(figsize=(10, 3)); geopandas-0.3.0/doc/source/install.rst000066400000000000000000000040061315112464100177160ustar00rootroot00000000000000Installation ============ Installing GeoPandas --------------------- To install the released version, you can use pip:: pip install geopandas or you can install the conda package from the conda-forge channel:: conda install -c conda-forge geopandas You may install the latest development version by cloning the `GitHub` repository and using the setup script:: git clone https://github.com/geopandas/geopandas.git cd geopandas pip install . It is also possible to install the latest development version directly from the GitHub repository with:: pip install git+git://github.com/geopandas/geopandas.git Dependencies -------------- Installation via `conda` should also install all dependencies, but a complete list is as follows: - `numpy`_ - `pandas`_ (version 0.15.2 or later) - `shapely`_ - `fiona`_ - `six`_ - `pyproj`_ Further, optional dependencies are: - `geopy`_ 0.99 (optional; for geocoding) - `psycopg2`_ (optional; for PostGIS connection) - `rtree`_ (optional; spatial index to improve performance) For plotting, these additional packages may be used: - `matplotlib`_ - `descartes`_ - `pysal`_ These can be installed independently via the following set of commands:: conda install -c conda-forge fiona shapely pyproj rtree conda install pandas .. _PyPI: https://pypi.python.org/pypi/geopandas .. _GitHub: https://github.com/geopandas/geopandas .. _numpy: http://www.numpy.org .. _pandas: http://pandas.pydata.org .. _shapely: http://toblerity.github.io/shapely .. _fiona: http://toblerity.github.io/fiona .. _Descartes: https://pypi.python.org/pypi/descartes .. _matplotlib: http://matplotlib.org .. _geopy: https://github.com/geopy/geopy .. _six: https://pythonhosted.org/six .. _psycopg2: https://pypi.python.org/pypi/psycopg2 .. _pysal: http://pysal.org .. _pyproj: https://github.com/jswhit/pyproj .. _rtree: https://github.com/Toblerity/rtree .. _libspatialindex: https://github.com/libspatialindex/libspatialindex .. _Travis CI: https://travis-ci.org/geopandas/geopandas geopandas-0.3.0/doc/source/io.rst000066400000000000000000000025701315112464100166630ustar00rootroot00000000000000 Reading and Writing Files ========================================= Reading Spatial Data --------------------- *geopandas* can read almost any vector-based spatial data format including ESRI shapefile, GeoJSON files and more using the command:: gpd.read_file() which returns a GeoDataFrame object. (This is possible because *geopandas* makes use of the great `fiona `_ library, which in turn makes use of a massive open-source program called `GDAL/OGR `_ designed to facilitate spatial data transformations). Any arguments passed to ``read_file()`` after the file name will be passed directly to ``fiona.open``, which does the actual data importation. In general, ``read_file`` is pretty smart and should do what you want without extra arguments, but for more help, type:: import fiona; help(fiona.open) Among other things, one can explicitly set the driver (shapefile, GeoJSON) with the ``driver`` keyword, or pick a single layer from a multi-layered file with the ``layer`` keyword. *geopandas* can also get data from a PostGIS database using the ``read_postgis()`` command. Writing Spatial Data --------------------- GeoDataFrames can be exported to many different standard formats using the ``GeoDataFrame.to_file()`` method. For a full list of supported formats, type ``import fiona; fiona.supported_drivers``. geopandas-0.3.0/doc/source/mapping.rst000066400000000000000000000100101315112464100176730ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas as gpd import matplotlib orig = matplotlib.rcParams['figure.figsize'] matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]] Mapping Tools ========================================= *geopandas* provides a high-level interface to the ``matplotlib`` library for making maps. Mapping shapes is as easy as using the ``plot()`` method on a ``GeoSeries`` or ``GeoDataFrame``. Loading some example data: .. ipython:: python world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities')) We can now plot those GeoDataFrames: .. ipython:: python # Examine country GeoDataFrame world.head() # Basic plot, random colors @savefig world_randomcolors.png world.plot(); Note that in general, any options one can pass to `pyplot `_ in ``matplotlib`` (or `style options that work for lines `_) can be passed to the ``plot()`` method. Chloropleth Maps ----------------- *geopandas* makes it easy to create Chloropleth maps (maps where the color of each shape is based on the value of an associated variable). Simply use the plot command with the ``column`` argument set to the column whose values you want used to assign colors. .. ipython:: python # Plot by GDP per capta world = world[(world.pop_est>0) & (world.name!="Antarctica")] world['gdp_per_cap'] = world.gdp_md_est / world.pop_est @savefig world_gdp_per_cap.png world.plot(column='gdp_per_cap'); Choosing colors ~~~~~~~~~~~~~~~~ One can also modify the colors used by ``plot`` with the ``cmap`` option (for a full list of colormaps, see the `matplotlib website `_): .. ipython:: python @savefig world_gdp_per_cap_red.png world.plot(column='gdp_per_cap', cmap='OrRd'); The way color maps are scaled can also be manipulated with the ``scheme`` option (if you have ``pysal`` installed, which can be accomplished via ``conda install pysal``). By default, ``scheme`` is set to 'equal_intervals', but it can also be adjusted to any other `pysal option `_, like 'quantiles', 'percentiles', etc. .. ipython:: python @savefig world_gdp_per_cap_quantiles.png world.plot(column='gdp_per_cap', cmap='OrRd', scheme='quantiles'); Maps with Layers ----------------- There are two strategies for making a map with multiple layers -- one more succinct, and one that is a littel more flexible. Before combining maps, however, remember to always ensure they share a common CRS (so they will align). .. ipython:: python # Look at capitals # Note use of standard `pyplot` line style options @savefig capitals.png cities.plot(marker='*', color='green', markersize=5); # Check crs cities = cities.to_crs(world.crs) # Now we can overlay over country outlines # And yes, there are lots of island capitals # apparently in the middle of the ocean! **Method 1** .. ipython:: python base = world.plot(color='white', edgecolor='black') @savefig capitals_over_countries_1.png cities.plot(ax=base, marker='o', color='red', markersize=5); **Method 2: Using matplotlib objects** .. ipython:: python import matplotlib.pyplot as plt fig, ax = plt.subplots() # set aspect to equal. This is done automatically # when using *geopandas* plot on it's own, but not when # working with pyplot directly. ax.set_aspect('equal') world.plot(ax=ax, color='white', edgecolor='black') cities.plot(ax=ax, marker='o', color='red', markersize=5) @savefig capitals_over_countries_2.png plt.show(); Other Resources ----------------- Links to jupyter Notebooks for different mapping tasks: `Making Heat Maps `_ .. ipython:: python :suppress: matplotlib.rcParams['figure.figsize'] = orig geopandas-0.3.0/doc/source/mergingdata.rst000066400000000000000000000060161315112464100205350ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas as gpd Merging Data ========================================= There are two ways to combine datasets in *geopandas* -- attribute joins and spatial joins. In an attribute join, a ``GeoSeries`` or ``GeoDataFrame`` is combined with a regular *pandas* ``Series`` or ``DataFrame`` based on a common variable. This is analogous to normal merging or joining in *pandas*. In a Spatial Join, observations from to ``GeoSeries`` or ``GeoDataFrames`` are combined based on their spatial relationship to one another. In the following examples, we use these datasets: .. ipython:: python world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities')) # For attribute join country_shapes = world[['geometry', 'iso_a3']] country_names = world[['name', 'iso_a3']] # For spatial join countries = world[['geometry', 'name']] countries = countries.rename(columns={'name':'country'}) Attribute Joins ---------------- Attribute joins are accomplished using the ``merge`` method. In general, it is recommended to use the ``merge`` method called from the spatial dataset. With that said, the stand-alone ``merge`` function will work if the GeoDataFrame is in the ``left`` argument; if a DataFrame is in the ``left`` argument and a GeoDataFrame is in the ``right`` position, the result will no longer be a GeoDataFrame. For example, consider the following merge that adds full names to a ``GeoDataFrame`` that initially has only ISO codes for each country by merging it with a *pandas* ``DataFrame``. .. ipython:: python # `country_shapes` is GeoDataFrame with country shapes and iso codes country_shapes.head() # `country_names` is DataFrame with country names and iso codes country_names.head() # Merge with `merge` method on shared variable (iso codes): country_shapes = country_shapes.merge(country_names, on='iso_a3') country_shapes.head() Spatial Joins ---------------- In a Spatial Join, two geometry objects are merged based on their spatial relationship to one another. .. ipython:: python # One GeoDataFrame of countries, one of Cities. # Want to merge so we can get each city's country. countries.head() cities.head() # Execute spatial join cities_with_country = gpd.sjoin(cities, countries, how="inner", op='intersects') cities_with_country.head() The ``op`` options determines the type of join operation to apply. ``op`` can be set to "intersects", "within" or "contains" (these are all equivalent when joining points to polygons, but differ when joining polygons to other polygons or lines). Note more complicated spatial relationships can be studied by combining geometric operations with spatial join. To find all polygons within a given distance of a point, for example, one can first use the ``buffer`` method to expand each point into a circle of appropriate radius, then intersect those buffered circles with the polygons in question. geopandas-0.3.0/doc/source/projections.rst000066400000000000000000000103211315112464100206040ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas as gpd Managing Projections ========================================= Coordinate Reference Systems ----------------------------- CRS are important because the geometric shapes in a GeoSeries or GeoDataFrame object are simply a collection of coordinates in an arbitrary space. A CRS tells Python how those coordinates related to places on the Earth. CRS are referred to using codes called `proj4 strings `_. You can find the codes for most commonly used projections from `www.spatialreference.org `_ or `remotesensing.org `_. The same CRS can often be referred to in many ways. For example, one of the most commonly used CRS is the WGS84 latitude-longitude projection. One `proj4` representation of this projection is: ``"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"``. But common projections can also be referred to by `EPSG` codes, so this same projection can also called using the `proj4` string ``"+init=epsg:4326"``. *geopandas* can accept lots of representations of CRS, including the `proj4` string itself (``"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"``) or parameters broken out in a dictionary: ``{'proj': 'latlong', 'ellps': 'WGS84', 'datum': 'WGS84', 'no_defs': True}``). In addition, some functions will take `EPSG` codes directly. For reference, a few very common projections and their proj4 strings: * WGS84 Latitude/Longitude: ``"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"`` or ``"+init=epsg:4326"`` * UTM Zones (North): ``"+proj=utm +zone=33 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"`` * UTM Zones (South): ``"+proj=utm +zone=33 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +south"`` Setting a Projection ---------------------- There are two relevant operations for projections: setting a projection and re-projecting. Setting a projection may be necessary when for some reason *geopandas* has coordinate data (x-y values), but no information about how those coordinates refer to locations in the real world. Setting a projection is how one tells *geopandas* how to interpret coordinates. If no CRS is set, *geopandas* geometry operations will still work, but coordinate transformations will not be possible and exported files may not be interpreted correctly by other software. Be aware that **most of the time** you don't have to set a projection. Data loaded from a reputable source (using the ``from_file()`` command) *should* always include projection information. You can see an objects current CRS through the ``crs`` attribute: ``my_geoseries.crs``. From time to time, however, you may get data that does not include a projection. In this situation, you have to set the CRS so *geopandas* knows how to interpret the coordinates. For example, if you convert a spreadsheet of latitudes and longitudes into a GeoSeries by hand, you would set the projection by assigning the WGS84 latitude-longitude CRS to the ``crs`` attribute: .. sourcecode:: python my_geoseries.crs = {'init' :'epsg:4326'} Re-Projecting ---------------- Re-projecting is the process of changing the representation of locations from one coordinate system to another. All projections of locations on the Earth into a two-dimensional plane `are distortions `_, the projection that is best for your application may be different from the projection associated with the data you import. In these cases, data can be re-projected using the ``to_crs`` command: .. ipython:: python # load example data world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) # Check original projection # (it's Platte Carre! x-y are long and lat) world.crs # Visualize ax = world.plot() @savefig world_starting.png ax.set_title("WGS84 (lat/lon)"); # Reproject to Mercator (after dropping Antartica) world = world[(world.name != "Antarctica") & (world.name != "Fr. S. Antarctic Lands")] world = world.to_crs({'init': 'epsg:3395'}) # world.to_crs(epsg=3395) would also work ax = world.plot() @savefig world_reproj.png ax.set_title("Mercator"); geopandas-0.3.0/doc/source/reference.rst000066400000000000000000000253671315112464100202230ustar00rootroot00000000000000 Reference =========================== The following Shapely methods and attributes are available on ``GeoSeries`` objects: .. attribute:: GeoSeries.area Returns a ``Series`` containing the area of each geometry in the ``GeoSeries``. .. attribute:: GeoSeries.bounds Returns a ``DataFrame`` with columns ``minx``, ``miny``, ``maxx``, ``maxy`` values containing the bounds for each geometry. (see ``GeoSeries.total_bounds`` for the limits of the entire series). .. attribute:: GeoSeries.length Returns a ``Series`` containing the length of each geometry. .. attribute:: GeoSeries.geom_type Returns a ``Series`` of strings specifying the `Geometry Type` of each object. .. method:: GeoSeries.distance(other) Returns a ``Series`` containing the minimum distance to the `other` ``GeoSeries`` (elementwise) or geometric object. .. method:: GeoSeries.representative_point() Returns a ``GeoSeries`` of (cheaply computed) points that are guaranteed to be within each geometry. .. attribute:: GeoSeries.exterior Returns a ``GeoSeries`` of LinearRings representing the outer boundary of each polygon in the GeoSeries. (Applies to GeoSeries containing only Polygons). .. attribute:: GeoSeries.interiors Returns a ``GeoSeries`` of InteriorRingSequences representing the inner rings of each polygon in the GeoSeries. (Applies to GeoSeries containing only Polygons). `Unary Predicates` .. attribute:: GeoSeries.is_empty Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for empty geometries. .. attribute:: GeoSeries.is_ring Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for features that are closed. .. attribute:: GeoSeries.is_simple Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for geometries that do not cross themselves (meaningful only for `LineStrings` and `LinearRings`). .. attribute:: GeoSeries.is_valid Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for geometries that are valid. `Binary Predicates` .. method:: GeoSeries.almost_equals(other[, decimal=6]) Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if each object is approximately equal to the `other` at all points to specified `decimal` place precision. (See also :meth:`equals`) .. method:: GeoSeries.contains(other) Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if each object's `interior` contains the `boundary` and `interior` of the other object and their boundaries do not touch at all. .. method:: GeoSeries.crosses(other) Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if the `interior` of each object intersects the `interior` of the other but does not contain it, and the dimension of the intersection is less than the dimension of the one or the other. .. method:: GeoSeries.disjoint(other) Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if the `boundary` and `interior` of each object does not intersect at all with those of the other. .. method:: GeoSeries.equals(other) Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if if the set-theoretic `boundary`, `interior`, and `exterior` of each object coincides with those of the other. .. method:: GeoSeries.intersects(other) Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if if the `boundary` and `interior` of each object intersects in any way with those of the other. .. method:: GeoSeries.touches(other) Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if the objects have at least one point in common and their interiors do not intersect with any part of the other. .. method:: GeoSeries.within(other) Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if each object's `boundary` and `interior` intersect only with the `interior` of the other (not its `boundary` or `exterior`). (Inverse of :meth:`contains`) `Set-theoretic Methods` .. method:: GeoSeries.difference(other) Returns a ``GeoSeries`` of the points in each geometry that are not in the *other* object. .. method:: GeoSeries.intersection(other) Returns a ``GeoSeries`` of the intersection of each object with the `other` geometric object. .. method:: GeoSeries.symmetric_difference(other) Returns a ``GeoSeries`` of the points in each object not in the `other` geometric object, and the points in the `other` not in this object. .. method:: GeoSeries.union(other) Returns a ``GeoSeries`` of the union of points from each object and the `other` geometric object. `Constructive Methods` .. method:: GeoSeries.buffer(distance, resolution=16) Returns a ``GeoSeries`` of geometries representing all points within a given `distance` of each geometric object. .. attribute:: GeoSeries.boundary Returns a ``GeoSeries`` of lower dimensional objects representing each geometries's set-theoretic `boundary`. .. attribute:: GeoSeries.centroid Returns a ``GeoSeries`` of points for each geometric centroid. .. attribute:: GeoSeries.convex_hull Returns a ``GeoSeries`` of geometries representing the smallest convex `Polygon` containing all the points in each object unless the number of points in the object is less than three. For two points, the convex hull collapses to a `LineString`; for 1, a `Point`. .. attribute:: GeoSeries.envelope Returns a ``GeoSeries`` of geometries representing the point or smallest rectangular polygon (with sides parallel to the coordinate axes) that contains each object. .. method:: GeoSeries.simplify(tolerance, preserve_topology=True) Returns a ``GeoSeries`` containing a simplified representation of each object. `Affine transformations` .. method:: GeoSeries.rotate(self, angle, origin='center', use_radians=False) Rotate the coordinates of the GeoSeries. .. method:: GeoSeries.scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin='center') Scale the geometries of the GeoSeries along each (x, y, z) dimensio. .. method:: GeoSeries.skew(self, angle, origin='center', use_radians=False) Shear/Skew the geometries of the GeoSeries by angles along x and y dimensions. .. method:: GeoSeries.translate(self, angle, origin='center', use_radians=False) Shift the coordinates of the GeoSeries. `Aggregating methods` .. attribute:: GeoSeries.unary_union Return a geometry containing the union of all geometries in the ``GeoSeries``. Additionally, the following methods are implemented: .. method:: GeoSeries.from_file() Load a ``GeoSeries`` from a file from any format recognized by `fiona`_. .. method:: GeoSeries.to_crs(crs=None, epsg=None) Transform all geometries in a GeoSeries to a different coordinate reference system. The ``crs`` attribute on the current GeoSeries must be set. Either ``crs`` in dictionary form or an EPSG code may be specified for output. This method will transform all points in all objects. It has no notion or projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. .. method:: GeoSeries.plot(colormap='Set1', alpha=0.5, axes=None) Generate a plot of the geometries in the ``GeoSeries``. ``colormap`` can be any recognized by matplotlib, but discrete colormaps such as ``Accent``, ``Dark2``, ``Paired``, ``Pastel1``, ``Pastel2``, ``Set1``, ``Set2``, or ``Set3`` are recommended. Wraps the ``plot_series()`` function. .. attribute:: GeoSeries.total_bounds Returns a tuple containing ``minx``, ``miny``, ``maxx``, ``maxy`` values for the bounds of the series as a whole. See ``GeoSeries.bounds`` for the bounds of the geometries contained in the series. .. attribute:: GeoSeries.__geo_interface__ Implements the `geo_interface`_. Returns a python data structure to represent the ``GeoSeries`` as a GeoJSON-like ``FeatureCollection``. Note that the features will have an empty ``properties`` dict as they don't have associated attributes (geometry only). Methods of pandas ``Series`` objects are also available, although not all are applicable to geometric objects and some may return a ``Series`` rather than a ``GeoSeries`` result. The methods ``copy()``, ``align()``, ``isnull()`` and ``fillna()`` have been implemented specifically for ``GeoSeries`` and are expected to work correctly. GeoDataFrame ------------ A ``GeoDataFrame`` is a tablular data structure that contains a column called ``geometry`` which contains a `GeoSeries``. Currently, the following methods are implemented for a ``GeoDataFrame``: .. classmethod:: GeoDataFrame.from_file(filename, **kwargs) Load a ``GeoDataFrame`` from a file from any format recognized by `fiona`_. See ``read_file()``. .. classmethod:: GeoDataFrame.from_postgis(sql, con, geom_col='geom', crs=None, index_col=None, coerce_float=True, params=None) Load a ``GeoDataFrame`` from a file from a PostGIS database. See ``read_postgis()``. .. method:: GeoSeries.to_crs(crs=None, epsg=None, inplace=False) Transform all geometries in the ``geometry`` column of a GeoDataFrame to a different coordinate reference system. The ``crs`` attribute on the current GeoSeries must be set. Either ``crs`` in dictionary form or an EPSG code may be specified for output. If ``inplace=True`` the geometry column will be replaced in the current dataframe, otherwise a new GeoDataFrame will be returned. This method will transform all points in all objects. It has no notion or projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. .. method:: GeoSeries.to_file(filename, driver="ESRI Shapefile", **kwargs) Write the ``GeoDataFrame`` to a file. By default, an ESRI shapefile is written, but any OGR data source supported by Fiona can be written. ``**kwargs`` are passed to the Fiona driver. .. method:: GeoSeries.to_json(**kwargs) Returns a GeoJSON representation of the ``GeoDataFrame`` as a string. .. method:: GeoDataFrame.plot(column=None, colormap=None, alpha=0.5, categorical=False, legend=False, axes=None) Generate a plot of the geometries in the ``GeoDataFrame``. If the ``column`` parameter is given, colors plot according to values in that column, otherwise calls ``GeoSeries.plot()`` on the ``geometry`` column. Wraps the ``plot_dataframe()`` function. .. attribute:: GeoDataFrame.__geo_interface__ Implements the `geo_interface`_. Returns a python data structure to represent the ``GeoDataFrame`` as a GeoJSON-like ``FeatureCollection``. All pandas ``DataFrame`` methods are also available, although they may not operate in a meaningful way on the ``geometry`` column and may not return a ``GeoDataFrame`` result even when it would be appropriate to do so. geopandas-0.3.0/doc/source/set_operations.rst000066400000000000000000000153171315112464100213150ustar00rootroot00000000000000.. ipython:: python :suppress: import geopandas as gpd Set-Operations with Overlay ============================ When working with multiple spatial datasets -- especially multiple *polygon* or *line* datasets -- users often wish to create new shapes based on places where those datasets overlap (or don't overlap). These manipulations are often referred using the language of sets -- intersections, unions, and differences. These types of operations are made available in the *geopandas* library through the ``overlay`` function. The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the DataFrame level, not on individual geometries, and the properties from both are retained. In effect, for every shape in the first GeoDataFrame, this operation is executed against every other shape in the other GeoDataFrame: .. image:: _static/overlay_operations.png **Source: QGIS Documentation** (Note to users familiar with the *shapely* library: ``overlay`` can be thought of as offering versions of the standard *shapely* set-operations that deal with the complexities of applying set operations to two *GeoSeries*. The standard *shapely* set-operations are also available as ``GeoSeries`` methods.) The different Overlay operations -------------------------------- First, we create some example data: .. ipython:: python from shapely.geometry import Polygon polys1 = gpd.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), Polygon([(2,2), (4,2), (4,4), (2,4)])]) polys2 = gpd.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), Polygon([(3,3), (5,3), (5,5), (3,5)])]) df1 = gpd.GeoDataFrame({'geometry': polys1, 'df1':[1,2]}) df2 = gpd.GeoDataFrame({'geometry': polys2, 'df2':[1,2]}) These two GeoDataFrames have some overlapping areas: .. ipython:: python ax = df1.plot(color='red'); @savefig overlay_example.png width=5in df2.plot(ax=ax, color='green', alpha=0.5); We illustrate the different overlay modes with the above example. The ``overlay`` function will determine the set of all individual geometries from overlaying the two input GeoDataFrames. This result covers the area covered by the two input GeoDataFrames, and also preserves all unique regions defined by the combined boundaries of the two GeoDataFrames. When using ``how='union'``, all those possible geometries are returned: .. ipython:: python res_union = gpd.overlay(df1, df2, how='union') res_union ax = res_union.plot(alpha=0.5, cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_union.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); The other ``how`` operations will return different subsets of those geometries. With ``how='intersection'``, it returns only those geometries that are contained by both GeoDataFrames: .. ipython:: python res_intersection = gpd.overlay(df1, df2, how='intersection') res_intersection ax = res_intersection.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_intersection.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); ``how='symmetric_difference'`` is the opposite of ``'intersection'`` and returns the geometries that are only part of one of the GeoDataFrames but not of both: .. ipython:: python res_symdiff = gpd.overlay(df1, df2, how='symmetric_difference') res_symdiff ax = res_symdiff.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_symdiff.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); To obtain the geometries that are part of ``df1`` but are not contained in ``df2``, you can use ``how='difference'``: .. ipython:: python res_difference = gpd.overlay(df1, df2, how='difference') res_difference ax = res_difference.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_difference.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); Finally, with ``how='identity'``, the result consists of the surface of ``df1``, but with the geometries obtained from overlaying ``df1`` with ``df2``: .. ipython:: python res_identity = gpd.overlay(df1, df2, how='identity') res_identity ax = res_identity.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_identity.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); Overlay Countries Example ------------------------- First, we load the countries and cities example datasets and select : .. ipython:: python world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) capitals = gpd.read_file(gpd.datasets.get_path('naturalearth_cities')) # Select South Amarica and some columns countries = world[world['continent'] == "South America"] countries = countries[['geometry', 'name']] # Project to crs that uses meters as distance measure countries = countries.to_crs('+init=epsg:3395') capitals = capitals.to_crs('+init=epsg:3395') To illustrate the ``overlay`` function, consider the following case in which one wishes to identify the "core" portion of each country -- defined as areas within 500km of a capital -- using a ``GeoDataFrame`` of countries and a ``GeoDataFrame`` of capitals. .. ipython:: python # Look at countries: @savefig world_basic.png width=5in countries.plot(); # Now buffer cities to find area within 500km. # Check CRS -- World Mercator, units of meters. capitals.crs # make 500km buffer capitals['geometry']= capitals.buffer(500000) @savefig capital_buffers.png width=5in capitals.plot(); To select only the portion of countries within 500km of a capital, we specify the ``how`` option to be "intersect", which creates a new set of polygons where these two layers overlap: .. ipython:: python country_cores = gpd.overlay(countries, capitals, how='intersection') @savefig country_cores.png width=5in country_cores.plot(alpha=0.5, edgecolor='k', cmap='tab10'); Changing the "how" option allows for different types of overlay operations. For example, if we were interested in the portions of countries *far* from capitals (the peripheries), we would compute the difference of the two. .. ipython:: python country_peripheries = gpd.overlay(countries, capitals, how='difference') @savefig country_peripheries.png width=5in country_peripheries.plot(alpha=0.5, edgecolor='k', cmap='tab10'); More Examples ------------- A larger set of examples of the use of ``overlay`` can be found `here `_ .. toctree:: :maxdepth: 2 geopandas-0.3.0/examples/000077500000000000000000000000001315112464100152675ustar00rootroot00000000000000geopandas-0.3.0/examples/choropleths.ipynb000066400000000000000000007704561315112464100207070ustar00rootroot00000000000000{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Protoyping choropleth classification schemes from PySAL for use with GeoPandas\n", "\n", "\n", "Under the hood, if PySAL is not available or if an unsupported classification scheme is specified, the choropleth classification would fall back to GeoPandas defaults." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import geopandas as gp" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# we use PySAL for loading a test shapefile\n", "# replace this cell if you have a local shapefile and want to use GeoPandas readers\n", "import pysal as ps \n", "pth = ps.examples.get_path(\"columbus.shp\")\n", "tracts = gp.GeoDataFrame.from_file(pth)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "tracts.plot(column='CRIME', scheme='QUANTILES', k=3, colormap='OrRd')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Xhbi4ODw8PXj8CftuoNqjRVQUfx08Znc5L40rhVcxd/uJs2dp37zqVqBX047D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\n7vaA/n3p+thjdHv8cc6eO2fxC1EqlTIqKop9v/2OdxN/fH18OP7HQZu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"text": [ "" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "tracts.plot(column='CRIME', scheme='Unrecognized', k=3, colormap='OrRd')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Unrecognized scheme: Unrecognized\n", "Using Quantiles instead\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Xhbi4ODw8PXj8CftuoNqjRVQUfx08Znc5L40rhVcxd/uJs2dp37zqVqBX047D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\n7vaA/n3p+thjdHv8cc6eO2fxC1EqlTIqKop9v/2OdxN/fH18OP7HQZu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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "tracts.plot(column='CRIME', scheme='QUANTILES', k=1, colormap='OrRd')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Invalid k: 1\n", "2<=k<=9, setting k=5 (default)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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BuHKIYRzhqjZy1Cj+9a9/NXiO0WgkMzOLT79YzBtvvUtEkD/H/vthg2X+Ts/A2MKbgyvk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\nwt7WGt+mnrTu/STPDu5Fm1al10JQazT/2Vk9CM7eZMaNG8cff/xBp74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"text": [ "" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "tracts.plot(column='CRIME', scheme='fisher_jenks', k=8, colormap='OrRd')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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YCzeECRMm4OHpwVNPP+O0Nlo0b86uoyfsLuflpianuNgJParakdR02jd3Xupm\nW+w5fIK7+lYearnS3r17eejBsbwzcxJ33+q4/D0qF6XY1LwORLAXbghSqZTvvvueteu+ZcNPcU5p\n44FRI7iYX2h3OX8vDwqdPGPoSgXaUh4cfNc1a+9qBoOR42fOMWTIkGqvSU1NZdDAu5ky5l4eG+nY\nfWitVivyBvxUc6MSwV64YbRt25bX58/nkYmT2bFzFwAFBQW8tehdLA5Y2DT96SlYrFa2HzhiV7lQ\nPx9KDYZ6t2+LXw4exmq1Mn7YgGvSXlV+TTyEt5dXtcnOSktL6d+3L7d1actrTz/q0LaLS0pJPX+R\nFi3se64iiAe0wg1m6tNPU1hYyMB7hxHg7092TjbFxSU8+fik8ul8daVWq1EoFDyycBl3de2AzmhC\nbzCiNxrJLijkaEoaSrkcs8WC2WK9lFHTWjY98Br55NcduKtVKJw0K8kWP+9MpHOXqjdLsVgsDL1v\nCGq5lS8XznR426fTziOVyejTp4/D627sRLAXbjiz//tfRowcSXJyMmFhYfTp04fsnJx6Bfu0tHSi\nWrXFaDRyPs/Idwl7kEgkSC99lRoNmCxWuoQ3RaVQoFIqUSuVqF1cuJCfz5ajRx34Dqt3MCWVNpFh\n16St6vx+6DhjHn2synPTp0/jyN+HOPDtUqeMq+cXlojx+jqqMdhPmDCBuLg4AgICOHz4MAAvv/wy\nGzZsQCKR4Ovry6pVqwgLq/yXLz4+nunTp2M2m3nsscd44YUXnPMOhJtSdHQ00dHRQNmUvLzcPCKa\nNq213PoNG/ngo+UUl5Sg1ZZSqtOh1+m4mJWNXCJhbEwvvvz9Lw6/UnGx0diPV3AiN5f5Dz1Uqc6M\n3Fw2Hz2KyWRy+lhyXkkJowbULUWCI5jNZo6dSqlyfv3bb7/N56tWsX3VW/h5O2f3rIS9h8QQTh3V\nOGY/fvx44uPjKxx7/vnnOXToEAcPHmTo0KFV7oNoNpuZMmUK8fHxJCUlsXbtWo4dq1tWQUGojUrl\nSm6ebbtFTZj0BAm/befE4aNcPHsWQ3Y2rgY9rfx8+GrqJB64pSdGs7lS5sZcrRbXau4og318ADh1\nIat+b6QWO4+dwGK18sQD9zi1nZrs2HsYjUZTKeBu2bKFuXPm8P27/6V9y2ZOa//3g8fofYsYwqmL\nGm9DYmJiSElJqXDM3d29/Pvi4mL8/PwqlUtMTKR58+blD3BGjx7N+vXrad26df17LAhXkclkNabV\nNZlMzHyQxmJcAAAgAElEQVThJfYdOEBeXh6vDB/MtME1b0a+49Qp7mzVqvx1kV6PxxV/968mAf46\nfYZWIUF2999WH275DTeVCyobNnJxlrgdiXTq1KnCsYMHD9K/f386tmrGHT07VVPSMXQGIz6XfrkK\n9qnTbJzZs2cTHh7O6tWrmTVrVqXz6enpFYZ2QkNDSU9Pr3svBaEeJk5+ksVL3+fowYN0CAtmcr87\narzeRS7n5yMVx+BLjUa8a0jCJZNKSXLyJib7Tp+lZUSIU9uoze+H/iH2jn9X7r799tvceulhaZCv\n8xPBhQX6cexYktPbaYzqNMA4f/585s+fz8KFC3nmmWcqrWYrT75ko7lz55Z/HxsbS2xsbF26JQjl\nPl7xKf+cOIFeb+B/P6ynQ2gwCa++ZFNZf3c3jmZkVDimN5sJrGEDbaVczpks5w7j5BQXMa2vbblo\nnMFisXD0ZAqdzp3jm2++4ZMVH3Ng/z6+WfQSE19eRE6B/WsU7NU8PIidSalOb+d6lJCQQEJCQp3L\n1+tp0tixYxk0aFCl4yEhIaSm/vsfJDU1ldDQ0GrruTLYC0JdWCz/5pRPS0vn8aeeRimXIaEsXe/s\n4YNtrqtFYCB7z5ytcMxksZSPzVdFpVBwPq/A/o7baO/pM1gsVqY9NNxpbdRGKpXy8OA7ObpvD1vj\n44gMCeTojx/h5+2Fh0ZNToHzVxGX6g3Ib9LZOFffCFf1vLQmdgf75OTk8ocz69evr3JXmm7dupGc\nnExKSgrBwcGsW7eOtWvX2tuUINjE3d2dQUOGERgQgFwuI//SHWZkoD8ucgUqhZwPt+5g1Y4/cFO5\noHFxwUPtiqfaFU9XV7zc1Phq3PHz1OCvcSemdQu2H0+u0IbVauWTbdtYlZBAiI8Pnz75ZMU+uLiQ\nV+K8YPfBL7/i6qJE7dZw4/UAy/47pcrjvl6enDyb5vT2z2VcJDQs0untNEY1BvsxY8awfft2srOz\nCQsLY968eWzatInjx48jk8mIioriww8/BCAjI4NJkyYRFxeHXC5n2bJlDBgwALPZzMSJE8XDWcFp\npk+fzuTJk/GXSDAbTZgAk0xGUZGWXLMZk8WCyWLBbLViufzn1Rt7VFFvnlaLt1oNwHeTHuPP06dJ\nOH6CvVU8f/J2cyMlJ8dp73FP8mmahzvv4W99BQf4cvj4aae307lNc1Zv2uX0dhqjGoN9VXfjEyZM\nqPLa4OBg4uL+zVkycOBABg50bE4MQajKpEmTeGvhQoZGt+KRXr3qXV+JXk/n+a+z4eBBHrnlFgA6\nhIXRISwMXzc3/kqrfAcb4OnJP07cGD2rsIiJ99c8g6ghRQQHoDc6f6PxEX1v5dUPv3J6O42RyI0j\nNAoPPfIIP15a+Fdfbi4uuCmVbD95qtI5V6VLlWXC/Pwwmp0T7I6mpWO2WBp0vL420ZHhmM1mp7dj\nuJTi+lq01diIdAlCozB16lQWzJ/Pmewcmvn51ru+IHd3TlaxIYnGRVlhyGfBDz9wNDWVAq0Ws8VK\nxFMzAWv5nrBlw0Rl11qxcul/Za+v2Kj88vdXHCkvZzKbkUol+PpUPxuooX2zKQGFXOb0dqIjw9Go\nVWzbto3+/Rtml64blQj2QqPg7e1Nnz59WLXnD+YNtn3mTXXaBjXh538q70mruioB2Za//yZIo6Gp\nhwdJWVn0aBaGTCpDKpVcmgkkQSKRIJNIkUjKpiVLpdKyDb2RIJWCTCJDIgWZVIZEAjKJFJlUglQi\nRSaT8PnOPbg28IPZmuTm57P1z4M898gwp7dlMplwU6v46quv2LJlC1qtlvfff9/p7TYGItgLjcZT\nU6cy+dFHmTNokN07IF2tf5s2/HjkKF//tReT1YLFbMFstZCWW5aWYeVvv6HV6wH4edrTuLm40OKV\nOcweeR8dI2rP0WOPj3/dxV2d2zq0Tkca/NRcVEoFC5+d7NB6d+49zLr47SQeOc65jIsUFJVguLRS\nWrtxAx0im/Lr/r958803cathwZtQRgR7odG47777mCSTsXjbr3QND8PCv8Mjl2ffAJgtVqzWsrTE\n5vIhFjiXk4OPmxqVQoH50rz9l3/6CWkViwS/2rULCeCrdsXNpWwcXyKRcCgl1aHBvlSvp9Ro5PFR\nldezXA/Szmfx59//sHDao3WuY9f+o3wTn8Cffx/nbMaFCkFdKZfjrVET1cSP7jE9GRrTg15tWyG/\nNGQUOPJxkpOTK6VwECoTwV5oNKRSKUazmQ937qzyfG3rui+Pl1953X0d2vP2yJE2tS+XSvgn44JN\n19rqo19+RSqRcGfPyutZrgf3PPVf3NWuzHxsdK3X7jmUxNebEthz+B9S0i+SdcW+vQq5DG83Nc0C\n/ege0537+vSgT/vW5UH9amazme1/H0MmkXDixAkR7G0ggr3QqGzZsoW7YmNJnPkcCjvTDbd79f8Y\n0bMbyyaWpTFuPf1FTmZl21xeKZNzNtv2623xWcIuwoMCHFqnoxw7fY4jyWdZ/krFhVZ//v0P635O\n4I9Dx0hJv0B+cQkGQ9ksGoVCgbe3F5HNm1Nw8BARAX4c+PjNaoN6dTo9PoucIi09evemR48eDntP\njZkI9gIWi4WkpCQsFgsymQyFQkFwcHC9d35qCD169EDj7s725GT62rmQz2q14qr8dyl+VIAfSem2\nz513VSi44MD8MHklJaTnF/D5C44dC3eUAY+9iFIuY+WPW3nl/S/JKyquENS9vDxpFhlF9+7dGDJ4\nEHfeGYtc9m/ICQhpir+nu92BHsBgMvPZqlU17oMrVCSCvUBiYiK33HILXu4aLFYrRqOJZs0iOJJ0\nY+5BEBgQwMUi+1MXWK1WVFcE+z6tWvDn6bM1lKjIXakkt9hxKRNmrvoKpULeoJuLX3b4+Gk++Hoj\nuw4c4dz5LEq0pVgpGzo7lZFNRLNmDO/alcGDB9K/710Vgnq1rICdSRMv89K4iUy6dhLBXqB58+bI\nZTLS132ATCbjQl4+zR+aRmlpKa4NmDu9rhRyOaVG+zcAtwIuVyTZuv+WnrwVtxmtTo9aVfViqit5\nq105W+iYO/vTmRf5Yf8hRg24zSH12ePY6XN8+PVPbN/7N2czLlKs1WG1WlEq5AT6eHF717b88vt+\noqKi+OfIwXq0VFWSCtu4u6rIcWJ6isZIrKAV8PPzw8XFheS0snzsgd5eBPh48csvvzRwz+omqmVL\nki/an27YarXidsWdffOgQCRAXJJt+8sGaDToDPb/krlaqV5P7Lw38PZw56u3bEvLXF9arZYmtz2A\nosNA2t03mY+//Zm8giJiOrdh+ctTKEn8gdL9G0nZugaj0YQVCXt2JdSrTavVWuVMJ1sYTGZUKlW9\n2r/ZiDt7AYAmgQEcOHWW6KZlqag7RjZl8+bNDB06tIF7Zr/QsDA+3rChfB781Vo1CeSpKvZMsAKu\nLhXv4NUuSrYd+4f7u3SpvV0vLwym+i3jN5nNdHp+DkaLheQfV9SrLnsMn/4a+YXFLH3xCcYN6V9t\nds2tu/exec9B3lr4Gl5e9dtnth6jOBw/m8aaNWv4+++/KS0tpbRUi06nQyFX4OXtjZ+fH76+vgQG\nBtK9e3e6detWr742BiLYCwCEhzflWMq/Cb5ua9+Kr3//vQF7VHcSiYQinY4dpyvntrFYLMQnJVUb\n7NVX5UoP8vTg+AXbplM28/fHZLHYdG16dh6/JiWx//RZkjMvkpGXT36JlnytFgC5TEazu8chlZTl\n45dKpchkUuQyGXKZFLlcjkIuQ6GQ06FlM9bW4xNAcbGWrXsOMG3sEJ4YU/3mKGaTieHP/B9Rkc14\nbsYzdW7PEfKLi5GeO0vHNs3x9VChDvTE1dUVk9FETl4e6WdPcvTv/aSfz8RgNHPmTEqD9vd6IIK9\nAEBUixYkH/+7/PWgXp2Z+/n3mM1mZDLn5zxxpK5duxIVGkzSZ29XOpeRlUuzB6dgMpmQXz0102pF\n41pxaKB9aAjxh23bBi86KKhCvpv//bmXNzf8TIlej85gRGswYDCZyhdsSQCFTIZKocBdpSLC15ej\nej2uSgXj+sWUldMb0OoN6AxG9AYjOqMRg8mEwWjEYDJTkFfIt/E76hXs7506B6VCzqJZT9Z43Yjp\nr6LTG0j8fUed26rAav+udpcpFQoeGTOCRQtervG6+C0JPP7Mf+vURmMjgr0AQOvWrVmz87fy1y1D\ng/Fwc2X16tXVprW+XvXp04e0i9lV/qIK9i/bber4xYu0DQ6ucM4KqK8axrm7c3t+3H/Ipnajm5Tl\nmy8s0ZJw7DiPLV+Ft1qNp6srnh5qAj08iGzShPbh4bQOC6tyHcCYxYvx9XBj0ZTxNrU559OvWLhu\nI7L29Ut/PHvSA7Ve8/OufTz6yMMO2/Db19eH7YeOEfbAk9zbuytvTh6L5tL+AbWyWpErar8JqW/a\njMakxmA/YcIE4uLiCAgI4PCl9LEzZ85k48aNKJVKoqKiWLlyJZ5V7M0ZERGBh4dH+bztxMRE57wD\nwSE6dOhARnZuhWMvPzicWc8/z6hRo26oOfdhYWGoXFxIOptO+8jwSuelEgkH09IqBXugPPXBZYO7\ndcb66RqOZ2bSqkmTGttVKsr+Oa3cvptXv19PdFAQH0y2b468VCLBbONQUMK+wyxct5F7buvBstn/\nwWg0YzSZMBlNWLBgNJoxW6y1pl72VKtpFx1Va3symbROc+Krc/TvfXy66nPef/8jPvv5Nw6eTOH3\nZa/ZVFYikaDX1/4wXCqVYrXUfdZPY1JjsB8/fjxTp05l3Lhx5cf69+/PG2+8gVQqZdasWSxYsICF\nCxdWKiuRSEhISHDYXYDgXF26dCE7r6DC3fCkwXfx2S8JPPzQQ6z9+usbavaDt7cXp89frDLYK+Qy\n5v8czzvbtjEt9g7G9f53wxMP14rB3tXFBblUyg8HDzHr7pqDPZQNzcz7bj0Rvr52B3ooC05mq23B\nfmV82VaFG95/1e526kLlouTsOcdt9i2XyXl84gQenziBFm06UKgtsbmsRCpBW6qr/ToJWGz8eTZ2\nNX7GiYmJwdvbu8Kxfv36lX806tmzJ2lV7Npz2ZXjl8L1zdvbGze1mqSzFReqrH7hSZIPHyQsNIS3\n3nrrhtk0wsfHh7RqUh2s++807r+tB6UGI1uP/1PhnHsV6wq81K7sPVv74qo8rbZsRo9CwYonnqhT\nv2VSaYXN02tiMluQXcNhCndXFZmZjs39c5lUIsWecCGVSNDpqp5tdSWZTGZXvY1Zvf6mfPbZZwwa\nVHU2PolEQt++fenWrRsrVly7KWRC3TVpEsjBkykVjrUMDWb/Rwt4c+IDvP/uIkKDg5k4cQJ79uxp\nmE7aKC83jyBf7yrPDezdlZUvTkXtokRx1Zi+xqXyp5dmfr6k5udXOn61QcveRymT8ePzzyOzMy/P\nZfYM45gtljo/4KyrkhLb777tIZHYd3MolUrRlpbadJ1V3NkD9XhAO3/+fJRKJWPHjq3y/O7duwkK\nCiIrK4t+/foRHR1NTExMldfOnTu3/PvY2Fhiq5gWJzhfREQER1Oq/pj+YN8YxtzZh//tSuSrbb/T\nv29f3N3d6dmrJ7fdHss999xDixYtrnGPq2axWMjIzKR3m5Y1XieRSNBfSqVruvSnl7pysO/VMpID\n56r/BAswec2X5BQX8/GkSXYnYLuSTCrFYOP2hibztQv2B46eIO1iDl8vWuSU+iVSaa3BvlirZV3C\nH8QnHqRUp0dfzTqKShrJnX1CQgIJCQl1Ll+nv5WrVq1i06ZNbNu2rdprgoLKZib4+/szbNgwEhMT\nbQr2QsNpGd2a5H1/VHteKpUy8rZejLytF2azmQ1/7GPL3r9Z/eEyXnpxFq6uroSFhtLn1hje/+CD\na9jzipKSklAqFNXe2V/WLNCPUxfLhnpKLq18VV61ExXAyN49WLo5AYPRVP4Q9krr/trLbydOMPmO\nO4iq4qGvPaRSKWajbdHJYrWg1enZ8OvvDLnzlnq1W5tRz72On68PDzxwv1PqL7uzL/veZDKxKfEg\nP+5MZP/JM2Tk5FGs1ZV/knFTu9IsIowZUybVWq9eb6g8xfYGdfWN8Lx58+wqb/dPIT4+nrfeeovt\n27dX+8BOq9ViNptxd3enpKSEzZs3M2fOHHubEq6xtm3b8tumjTZdK5PJGHZrD4bdWpZe1mw289fx\nUxw8dZbnl3/CS7NnExISUm15Z87fj4uLo3loUK3X3daxNUt++IWWr9T8d7N903AkwOZjxxjcoX2F\ncwajiTlxcbQNDmb0bfXPYyOTSm0expnzyCh2H01mzMwFlOz7qd5tVyfx0DFOp2Xyv+/WOq0NrVZL\n6oUsNIMexnhpFbJK5YK/rw89u3fl9piejBk5hIimlR+416RUp0OhVNR+4U2gxmA/ZswYtm/fTnZ2\nNmFhYcybN48FCxZgMBjo168fAL179+aDDz4gIyODSZMmERcXR2ZmJsOHDwfKfks/+OCDYnPgG0Dn\nzp1Jz65bcimZTEavNi3p1aYlq37ZwZo1a5g5cyZpaWkkJycTEhJCdHQ0UHbDMGjQIDw0Gvz9/QgO\nDiY8IoKoqOa0atWKtm3bEh0dXeVdti22btnMbW1rHsIB+L8Jo3nvx19QKZQsGDuc9mFh1V6rUirY\nnJRUIdify8lh4PsfYLFYeGe8bfPiayOXSrHYOHbdsUUznhs5iLlr/ueQtqsz9vkF+Pv7McyJ6YSt\nlrLg/p9JDzNq2GC6denokHq12lIUChHsoZZgv3Zt5d/k1S2wCQ4OJi4uDoDIyEgOHqxPNjyhIXTo\n0IHiEi3JaedpYcOdcXX6d2nHsqXv8ebChWV5S/R6brv1VrZf2kFqx44d3NapLYsef4iks2kkp53n\n9Plz/HbkIF/m5JOVX0CxVovZbMHH24s2rVsTEhpGWHg4ERERREVF0bJlS5o2bVrlp4NjSceY+tTD\ntfbTxcWF3UvmETN9HnO+Wc/ml56t9tpAd3eSMv/Nbb9k26+8v2MHVqsVjYtLvcbprySVSGyejQMQ\n6OtdviLXGXbvP8yZjIv89ON3TmsDQOWqIjgokDf/b7ZD6y0t1Ylgf0njGMwSHEKlUjF06FCmLF3F\nL2+8WOd6Hu5/G+u2/8H7Ux9haJ/uzF31LfuzisrPa7Va9h0/zaJvNzLj/nt44I7K483aUj2bEg+g\n1etJz8rl3MUsjuw8ydYNBWQXFJJXWITeYMTDwx1fHx8CAwMJCg4hLDycCxcv0qm5bfvAdo1uQdLK\nRdw69RV6vfwao3v34pWRgwm8KslXm5Agfjt2glc3buTbAwfRGY0Mu6Urabn5nDx3vs4/q6vJZDKb\n7+yhbEWwM6c4PzTrTQIC/Bk8aKDT2oBLs5Cc8D50ej0uytrTU98MRLAXKnj/gw+Iiozku+1/MPL2\n3nWqo0VoEMdXLy5/bTCZkF2xmcXixYsZOnQoi999h9ufeZW+Xdvz7SvTK9ShdnVh5O29qElhiZbk\ntExOZmSSkpnF2QtZHNl1CpPZTGFJKU18an5Ae1lEUCBp3y3n4dcW8+3uRNb+vgelXE4TTw88XF1B\nAum5eZQajXz5115i2rVi1awpBPv70Os/L9U5TW9V5DbMSrlSeIC/w9q+2o7EQ5w7n0X8xh+d1sZl\nEonEKb+0tKVizP4yEeyFCvz9/Vm8ZAmPT52Cp5sb/bp1qHedvdu0ZPV7K8nOzsbPzw8om1nQsWNH\ncnNzade2DUajCUUVM11q4uGmpmurSLq2iqxwXDPoYT5aH887U+zL6fPFf6fzBXAiNYO3vvqRP/85\nSb6uLAtlgJcHw/p0ZcnTj6G8Ing4eq67PWP2ABGBZcFeq9WitjWvjI3GzX6LJk0C6d6tK6/OX8BP\nGzeRnHwSX18fTh23Lce/rZwV7A2GxjMbp77ET0GoZMKECRQXFzPmpRdZ/3/P0adddL3qu+/W7qza\nsoO2bVrzzbdlY7/Tpk7l8NGjtIluhdliYffR48R2auuI7hPo5cW2/XUPRi3Dglnxwn9surZsFasD\ng71MhsXG2ThA+S+eY2fS6GrDQ+namEwm/rd1Nx+t+4nUzGykEgm+TcKQSiR4uboS7ObGMSekC5bY\nuYLWHiIZWhkR7IUqPf300xQXFTHslQV8Pusp7u7RqV71/TDvWeav+R8D774bmVTC+AF38NN/n+SL\nzTtYZ9ChtWHpu616t2vJ+l1/Oay+mijlMi4UFjHgtddQKRRMvuMO7unRo871lS3vty/qSSQSkk6d\nrVOwP5dxkcVf/I/Nv+/jbMbF8v8OaoWCEI2GgW3bMLZ3b8Iu5bgymUy0fvX/+GXzFgb072d3e9W+\nB6lE5LBxMhHshWq9NHs2LioVD77yCgN7duKj6RPR1GNP2tkPDWfkbT3xcncj0LvsAehzDwzhuQcc\nO6Xvnf88zHfb/+DF5V+w4PHaZ+XUx2+L5vC/XYkk/nOSFZu2sf/MmXoFe7mdD2gBZFIJp1Jrf0hs\nMBj47IfNfL9lF4dPnCG3oAizxYJcKsXPTU2f8HCGd+5MbKtW1Q59yOVyVHI5n6763KHBXiqRNJqV\nrtcrEeyFGj377LMMGzaMMaMfoPX4Z1k2ZTz33dq9zvW1Cq9+oZWjBHh5cVfn9iz98ReeHz0cb083\np7WlVqt4qP9tPNT/NlbEbaNduH2Lfq5W9oDWzjIyGamZFffcNZvNxO/ey8ofNrP3yAkuZOdhMJmQ\nABoXF8K9PBl1S3se6t0bP3f70lcHubvz11977etkLZw1Zi/8SwR7oVaRkZH8mfgX7777LhNfeZk1\n23bz8YxJeLs7L4jW1w+vPkvgiMcJH/Mk2xfPoUvL2vO114fBYMBitdItqn7tyGUyrHbc4h5LScVi\nsbB+2x/8dXgyF3Lzyc4rLD/vqlAQ7O7O0LZtGdmtK52b2jYltSYdQ4KJTz5Z73quJIK984lgL9js\nmWeeYcSIETw0dixtJzzL6xNH8+jdsQ3drSoplUoufL+c6Edn0GfqKyx/5jHG3X2H09qTSqWolAqe\n+OQT1k2fbvuOS1dRVpOS91R6Jmu27mTX4WOcTL9ATmExBqOx/NeCsagYVyQ0cXUlm0Jev/dehnXu\n5JSZKMM6d+bHI0er3tqxjqRSqRjFcTIR7AW7hIeHs2PXLpYvX87sl17io43bWDr1Ebq3at7QXatE\nqVRy+qtl3DbtFSa9s4KXPv2a9f/3HF2jHZ+dUy6Xc2bN+4Q88Djrdu9mYr+6jWdr9XqMJhMDZr5G\ncnomOZcWj1kBmUSCxkVJE407PVu14o7oaO5q3bpScraWr8zBZLU6JBCn5uYy6uMVRPn60rNZBEO7\ndOGWS9lNf1i/gftHDK93G1C26Ysz7uztmdnU2IlgL9TJ448/zkMPPcSzM2bQ//nXGdijM6uefwLl\ndbg0fceSVzl65iyDZr3BLU/PYUCX9vzvtecdusUegI+XBqVCzsWCglqvTcvNZevff/P32bOk5+ZS\nqNViMJnK7273/3OKJu4aurVoQWyrVtzVujWuLratBHWRy9l37hxjetT92cplcqmUbK2WPJ2OvRkZ\nvLdjJ5cnmn697lvHBfs6zEKyhbZUh6qKPQpuRiLYC3Xm5ubGR8uX8+xzz9GzR3d+PXC03lM0naVt\ns6acXfcB87/4nvlf/oDmnnF4a9S0axrKiJiePHL3HWQVFLJu2052Hz3BXV3b8eR9A+3+heCiUJBb\nXFz++mxWFr8eOVIe1ItKS8tz6EslEtRKJX4aDR1atKBpQACfbd/OgqFDGdmlc53fq1qhIDUvr87l\nrxTk5UVsZCQ7UlJIP3MCjUbDV2vXEfdzPNOefsohbUDZz8IZwb6gsAiNnQ+gGyuJtYGfiogHM41D\neGgI7//nYQZ0d0y2QmcyGAx8+NNWftj5J8fOpVNQUlr+d1AmleLqoqSkVIevu4YTa5bgZsN00wPJ\nZ/h2+x6WfB+HxWxBLpdjuCKouymV+Lm7ExkQQK8WLbilTRtcr8rqOe3TTzl18SKH/lu/ZGAxb76F\nt0bDhv88Wa96LjOZTHRf+AYevj6knz3lkDqv1qtPLGfPpZBxwrHrIx55fAYSpYbVq1c7tN7rgb2x\nU9zZCw5hMVuQy26MlYpKpZJpIwYxbcS/W2oePXOWpoH+5Q9Wz2RcpOOkmUQ/8gyp33wEwMX8fH7+\n8yA/7v6L5LRM8otKKCjRlgf1sv1jLUiA21q25JboaHpHR+Ni49DW+fx8wr1ty+dTE4VMhs5orHc9\nl8nlcj5/9BGGf7yCp6ZO5/2li2svZCepTILVCdk7i0u0hPjVb0OZxkIEewGArKwszpw5Q2FhIX5+\nfnTqZN9wjMVqYe/x0wR4edI8uAmuqrrlom8obZtVnJLYLDiAvR8toP3E51ANGFs+Q0ZC2dofmVRK\nC39/ekdG0btVK7pFRSGTy/lu924+2raNQ2lpvHS/fbs6Gcxm3KvZEMgeLjIZOgdvDN8+NJTRnTrx\n4fIVPD55Ih3at6+9kB0kEoldU05tVVxSgoeHh8PrvRHVGOwnTJhAXFwcAQEBHD58GICZM2eyceNG\nlEolUVFRrFy5Ek9Pz0pl4+PjmT59Omazmccee4wXXnjBOe9AsEtycjK//fYb+/bt49ixJNLS0rh4\n4SIGoxFPD3eUSiWFRcUUFBTYlVPkrr59WfnrLhZ9v4mi4hLUKhU+Hu74eXkQ4OlOiK8X4QG+NG0S\nwJDeXXFzvf4fmrUMC+bOzm359cBR5o8aRaeoqEpDL1cb2acPfyYnc/zCBbvbM5nNeDjg5+Iil1No\nw2bc9vq/4cP49eRJYmL7UZCTWXsBO0jtzPZpq+JirQj2l9T4r3n8+PHEx8dXONa/f3+OHj3KoUOH\naNmyJQsWLKhUzmw2M2XKFOLj40lKSmLt2rUcO3bMsT0XbGY2m/n444/p2LEjHTt2ZOmSd8m5kMZd\nt3bjrXkvcHD3JkovHufi6f2k/bMHF6WCHTt22NXGF2u+5HTKWfILCtHpdOw7cID3V3zCI09OoU2f\nWAbSyBEAACAASURBVArVPmw5kcb0D79g8fdxTnqnjvfli9MAKDUaaw30lxXpdCjrMO3RZDbjVY90\nFOUklz9/ON53kydRXFzMvUNHOLReiYPTJeTn57N+42bOpqaj0YgHtFDLnX1MTAwpKSkVjvW7Yv5w\nz549+f777yuVS0xMpHnz5kRERAAwevRo1q9fT+vWrevfY8Fm6enpvP7666xb9zUaNzXjHxzJ9P98\niYeHe43lunZuz48//lhhc2N7KBQKWrZsScuWlRNzTZw4kWOn/6lTvQ3Bx0tDEx9PViUkcGcH29I9\nm81m8oqLeXjpUu7p0oXRffrYVs5qxdet/oHJbDEjkzjn+UmQlxfTb7+NdzbFE/dzPPcMvNsh9do6\njGMymdh34DC7//yLI0dPcOrMWc5fuEh+QRElWi0GgxHzpSEsyaX/u1CHT1mNUb3G7D/77DPGjBlT\n6Xh6ejphV+znGRoayp9//lmfpgQ7XN4U/vfff6dnt06s/OAtBt99l83l+90Rw5ff2bbxuL3atGnD\nuj27nFK3s+gMJvxcbV8R+96kSXy6eTM7jx/n461bMZhMjLv99lrLWa1WAjT1T0GRUVhEoV5Pzzfe\nxGq1lgdRq5VLrwGsZa8vnbCWHSk7f+k6y5XnrOWlyu/Ah40cjaEkv979hbJhHJPJzOovv2P/oSOc\nOHma9PMXyMnJo7ikBJ1ej8lkrjBrSqlUonFzxcvLkzatomjaNJS20S3p0bUjPbt1xtXVlX73PSQW\nVl1S52A/f/58lEolY8eOrXTO3s0c5s6dW/59bGxsne8ob2YWi4VHH32UL774Am8vT+4fPphPlmwl\nIjzU7rpG3DeQ//7f2+h0OlQOeGB4pY4dO7Ioq26bmjeEU+mZ5BeXMGe47cMWrkolUwYPZsrgwQxZ\nuJCDKSm2B3v3mj912UIukyEB2jQNRiqVIpNKkEqkSKUSpFIpUokEmbTstUwqLf9eLpOhVChQymWo\nlEpUCgWuLgpcXJS4KBS4ubjgqlSidnWhsLiEie+sYN++/XTt2qXefT58+Cj5BYVMeGomCoUcV5UK\nTw93QoIDCQsJomXzSDp3bEufXt0ICbZ9f+TgoADOnTtX7/5dDxISEkhISKhz+ToF+1WrVrFp0ya2\nbdtW5fmQkBBSU1PLX6emphIaWn3QuTLYC/aLj49n2rSnOXEimXFjRrBi6cJ6bbIcER6Kv58vcXFx\njBjh2LHZLl26kJWbj9lsrnKz8OvN/7d33nFVlm0c/54Jhz1kiICoqGzEvbUUTUHLlaNclDnSfBum\nvWpqliMrR1n6WqZZmmnuvdLcpmiOFAc4CVkCwgHOfP9AycE4B87hQD7fz+d8gIfnue7rwDm/5z73\nfY2Rc5dgLZMRXqtWma53trEhKcOw2a8e8Cwi2MFYfF1ckMok7J03rdy2SuKdRT8y+j/vcPTg/nLb\nCg8L4/SZ06Td+LP8jj2CTw0vjp/+y6Q2LcWTE+Fp04z7/xq9sPdwiWDjxo3FzvoaN27MlStXuH79\nOiqVitWrV9O9u2lrlgvAxYsXea59e15+uQ+9u3cmL+UyyxZ9Xi6hf0izxg3YssX0SzkuLi7Y2tjw\n1407JrdtDo5cuEyLclSy9HJ2JkupNPj8GiaIs3eyUZCn1pTbTmm82KIRp06dNoktqVSMOTaV6/vX\n5vDhwzRp0pjevXsTHR3FkCFDmDVrlsnHquyUKPb9+/enZcuWxMXF4ePjw9KlSxkzZgzZ2dlERkYS\nERHBqFEF7dsSExOJiooCCpIwvvrqKzp37kxQUBB9+/YVNmdNiE6nY+rUqTRp3Jjq7k5c+/MAH09+\nD7mB0SKG0KVjOw4fNv3auk6nw8nZidNXE0xu29RMXvozaq2Wsd26ldlGQI0a5BuQ4JT94IbgYoJo\nHFdbW9Qa84v9pyMGo9ZoWL16Tbltmast4St9X2L3xp94+cVOuNjLqedXnfyce3w8fTqLFi0y/YCV\nmBKXcVatWvXUsZiYops4e3l5sXXrPyF1Xbp0oUuXLuV0T+BJ4uPj6dOnN8l3k9j8y3e0b9PCLOP0\n6NaZUe9OIj09HZcHLenKy9GjR4kZMgRtXi4tgsrfL9WcLNuxn09/3kSnkBAcytHIu1m9eiw/eLDU\ncsC30wr2MUxRqdLd3h61iZOqisLZ0RYXe1u+/GYRffsal0D2JCIT9vF9FLFYTKvmjWnVvPFjx79f\n8QvvT5rIq6+++syEZlaN/HYBABYsWECDBuHUq+3DxT/2mE3oAZydnfCr6cu6desMvkan05GYmMjx\n48f59ddf+f7771GpVABs2rSJtm3a0CGoNhe//5y63oZvslU0+06fZ8QX/6OBjw8TyrlnUc+rIFU/\n7u+S2wbeSk3FVHJX3cERnRGlBzQaDdlKJbeTU7mQcINbySmlX/QATydHbt8u/5KcRGyeqpfFMXTg\ny9T282Hs2LEVNqalEcolVAHu3r1L/379OHfuLMu++Zwe3TqbfUyNRkNNn+osXLgQKysr0tLSuHfv\nHvfu3SMjI4PMjAzS0tPIuJdORkYm9+/fJydHiVQmxd7ODo1GQ25ePtHR0bi5udGmTRtq1fLjSuJd\npJV4Y/ZUXDxRH8zE19WVL4r5FGsM4gfRLscuXyb4kXDkJ0k2MmO5JOytrdDp9dh1GfggnPKRkEu9\nYUUJpBIx1V2cefm5lnw0pE+xnzhqelTjcFz5l+RE4ooviLhk/kxaRPZi7NixhBmYQ1GVEcS+kvPT\nTz8xZvRomjdtyKWTe3FxKf8GHhRUfty7/wiHjp3g/F+XiU+4QXJqGvezcwpa7D0yM5wxfSr2tjbY\n2SlwsLPFwc6Gmm52NA0Kx9PdFe/qHnh7uePr5YGtrQ2ZWdkEtXuZce+Px83NDQBnZ2eOHD1G0yaN\n6fPRPNZ8+B+TiZsh5OapiLt1hwZ1i4+q2XPqT7r991Pc7O35dsQIk41tLZNx8U7Js9+UrCwkRoYs\nF4e/uzsA7/buio2VFTYKOXbWChRyOXY2CuysrbC3tcbe1hYnOxvsrRUoFP/Uyk++l8EXqzez9Xgs\nc9dsYd7arbQMqseyCaPwdq/22FjO9rZoTLA/IDZTElhJhIUGMfiVXvTq1ZMLF/4y6Z5XZUQQ+0pK\nVlYWgwYNZP9v+/lixiSGDnzZoOuUSiWHjp7i5OmzXLh0mRs3b3M3OZXMrPsolbmoVCo0T6zn2tvZ\n4OxoT12/GgTU8aVpwxA6tW1OzTI2Bx8wahJ16wfw3//+97Hj1apV48jRYzRr0oRXZy7kxw/eNKvg\nn7l6nbUHjrH3z7+4EH8TrVZL3PJ5eLu7PnXu6n2HGDzra3xdXfl2xAgSUlKI+/tvxCIRCrkchVyO\ntVyOjVxOvlrN/bw87ufmcjstjUOXLuHn5oadQoGjQoGTrS3OtrY429nhbGuLg5UVienpJfqakZNj\nsk881WwLErOmvfZ0wqMhuDs7MWvEQGaNGIhKpebdr5fx097D1Hn1LewU1jQPrMv3E0bi7uSExEQ1\nbcQWmNkDfPnpNCJaF0TorFy5ssLHr0iEevaVkC1btjB06BCsreS8+nIPsnNySEpOITUtnYyMLLLu\nZ6PMzSMvLw+VWo1GrUH1RMSHRCLBSi7D1tYGZydHPNyq4evjRUDd2kQ0CKVl04Y8Hz2A1NRUbpw0\nbYilXZ22nIqNLTYC686dOzRr0oS2wf4se980NdcBVGo1P+45yNbjZzh+6Sq5+SoaNWzIC12j6Nev\nH3169SQ61J8PXunx2HUjvljC9zt+QyQSUdPDg7/T05HL5Xg/WG/PV6nIz89HrVajUqmQSguSfmxs\nbFCp1STcuIGtlRyNTodWp0On0z+Sqfo4Igpe82KRqHCJRyIWo8zPR6fXM6RFC4a2aI6Xk1OZ/w4a\njYbAj6Zzf8ty5HLTdQ47cu4iH//4K4cvXCFPpaa6qxN+7tU4nXCL3KySb2al0evlAezas4esOxdM\n5K3hxCfcoGHbaL76aiEDBw6s8PHLilDPvoozceJEZsyYART8M+csWIxUIkEmk2JlJUdhbY2drQ3V\nXD1xcXammqsLnh7VsLO1JbB+XTp3aGNwlT8He1tu30k0+XNwcrQnKSmpWLGvUaMGh44coUWzZgyf\nu4TFbw8zybjLd/7OhO9W0X/AAH6c8gkdOnR4LHEruvuLbF29slDsj/11mW+37eOn3QeRSCTExMTQ\nokUL2rdvTy0Dk6hUKhW2NjbEr1yIk33xpQ7SM+5zPTmZ28lpJKbdIzk9g5TMLO7dzyEjO4cD5+JQ\naTT8ePw4y44eRSIW42xjQ3D16vRt1JDn6tUzOFLn4XkZOTm4y8t+03iSlqGBbJs9CYCfdx9k6g9r\nOXrxqtEZ80UhFosxS+ylAdSuVZOv5nzE6DffpFWrVtSuXdsifpgbQewrEVu3buWLL74AQJd53ezj\nOTo6oDJD8o2nuyu7d+/mueeeK/YcPz8/Dh05QsvmzRj71TLmjx5S7nHrentiZ2vL//63pMjfDx06\nlJkzZvDanEXsPX2BnPx82rVrx4Hff6d169ZlGlMul+PuVo0/4q4R2bj4TT4XJ3tcnOxpWK/oBK1W\noydx485djox/n6SMDFYcPcbBq1f54/p1Dly5AoCNXI6vszPt6tZlSIvmVHtQWiFDqeRYfAKnb93i\nakoKdzILMnbTs+7j7mw6sX9Ibp6Kdg1D2FCvFlETZnA7LYOZs+cglUr/+bQilSCWSJBJpIhEYmQy\nKWKJGLFIjEwqRSyVIJFIkIglSGQSkpOT0Wi1rF2/hWxl7oNPrrkEB9Snc8fSS02Ul1f79WDH3gN0\n79aN02fOmCQxsbIhLONUEs6cOUO7tm2ZPvkdxr4/jaw7580e/zt05LusWb+V+1eNK2dcGrsPHKfX\n6++zZeu2UuscnT9/nrCwMBJWfkV11/JtPmdm5+DVZwTK3Nxi36zdo6ORSCQMGjKE7t27m6RkQ6vm\nzekS5Mf7/V8ss43Wb00m4dbfHC2i74NGo2H3XxdZf/o055OSSFcq0T7xnhFRUBNHYSXH2VZBTQ83\ntn86yWRN1V//bDFrDhwtnBzIZTLkchkymYy09HsFS1PiB1mw+idzYfUlTtofff8//JQgEokQAVqd\njuCAenSJbI9MLkMmkyKVSJFKJEikYpwdHXF2csTV1YlqLi54uFfD2tqKawm3uHz5GvE3b5H4912S\nklNIS79HNVcXVi9bWKQfKpWK0BYv0KZte7777rty/b0qAmEZpwpy9uxZOnfuxLAh/RkzfChj35/G\nmXMXad2iiVnHdavmgkZj+uSbyHbNeL51E9asWVOq2J86dQpvD7dyCz2Ao50tdjYKLl68WGwo3SYz\nlICoFxDA+etXymVDTPH13KVSKV3CQukS9k93qCOXrzD4xx/Z+NF7tAkPNKhPbnm4lZrO6DFv8dFH\nH6F4YqyQkGBGv/4Kw2NeMfm4v27cRsyb45i/aOmDm8gjN5MHFT0fhpY+ycMb0KPLoGnpGbw/dgSN\nIp7utCWXy9m48n80ff5FIiMj6devn8mfjyURkqoszPz582nZsiV9e0Qx5+OC6BWxWMTFuPKJR0m8\n8dYEPP0bMe/rpejM9KlKIhYbNGv+bskSerYy3U2tejUXYmNjTWbPEMLCw7n6t+GJSEVhbDSKk21B\nVu8LzRuaXegBRIhQKBRPCT1ASEgox06apkbOk/R6sSuZty+Qn3qV/LSrqNKuoUq/hjr9Gup78Wju\nJaDNSECXeR1d5nU++fB9JBIxuszraDMSUKdfIzflMlmJf5GScAZ7e1venTi92PEC6vszb+aHjBgx\n/KleHlUdYWZvIbKzs+nXty9Hjx1l5bfz6da1Y+HvJBIJ1xLMV5Z19a+bsZLLGPZKDwb2iTLLGDqd\nvsSwSrVazebNmzl2/DjfL/vCJGMePHuRvLz8Cu+K1qhRI2Ymp5bLhkgkQm/EPmd+BdS+eRSRiGLr\nwjdv3pzvliyuUH+KI/76TWTS4tfbI59rw+bte3l/0gxcnJ2o5uqMp6c7jRuE4ulZkJ8QM6gvu/cf\nonv3bpw+faZKVGc1BEHsLcCxY8fo07s33jU8OH9sJ54e7o/9XiaTcfOW+apC2tra4O1Zja9mvG+2\nMXR6/WNvkqSkJLZt28b+/fuJPXWSq9fisVFYodZocC3H3sSNpGQWbtjF+iOnyMjJoXPnF3j99ddN\n8RQMpkmTJtzLzCInN6/MvXUL1l8NP78iat88iriE9eHBgwczadIkjp44RYumjSrUrydxdXFCqyv+\nb/P5J5PY/dtB5i9a+iBEVleYQFijuidfffYR3bp2ZO7MyTRp351Ro0axeHHluJGVF2EZp4L56KOP\n6NihA6/2fZFDO9c+JfQACisr7qaUb6ZYEk6ODqTdyzSbfShojXf48GEiIztS3dODmjV9mTPzYzTZ\nybw56EUuHVxD6oU9SCRiPlz+i1G2c3Lz+HLddpqP+ZCwYe8TezeTj2bNJjUtnV/WrKFu3bpmelZF\no1AocHVx5tTl+LLbkBfE6RtKRVS1fJSSNgOdnZ3p27cvk6Z/XqE+FUXtWr6FbQmLoqavd+GykDr9\nGpp7BUtAG1ctQSIR0+OVN5C51MEvpBXZ2UpO/vFHBXpvXoSZfQWRnp5Ozx49iIu7WGq1ShtbBWnp\n98zmi3s1F/66ZL49AYAWjULZdeA44f7VeS/mJdq1iCgyHT3I34/Vvx3ls5GDSrSn0+nYeiyWJdt+\n4/ezf1HTtyb9BrzKrjffpFq1aiVeWxHU9PUl9koCbcODynR9kJ83B84YnlCkqmCxh6I3QR8yffp0\n/P39Of9XHCFB9SvQq8cJqFfHqCJwD+nWNZJuXSOp1/A5PpwyrUolVxmKMLOvAPbs2UNgQAAyiZ4L\nx3eXWq3Swc6OzKz7ZvPH29sLZW5ema5dt2UfLbvF4N2wK8oSmnJMHBvDgXWL+XTyW0S2a1Zs3ZH3\nRr1KckZmYXXMJzkXf5PhXyzBt/9oRn65HO/QCI4dP8HFuDimTJlSKYQeoG5AAOcSbpV+YjG0CQtE\nbcTMXqPTmaxKpiGIRKISe7l6eXkRHR3NyHcmWbTna8OwYIASZ/clYW9ny40bN0zpUqWhRLGPiYnB\nw8OD0NB/wpTWrFlDcHAwEomkxKgHPz8/wsLCiIiIoGnTpqbzuAqh1WqJiYnhpRdf5J03Y9i1YQXO\nBiS5uDg7kaPMNZtfdWrVfKq8QnFkZGTx1qQ51GwcjcynOX2GT+BKwi2SU+/R87WnY8KN5dXe0UjE\nYiYtXV14LDUji+krfiVs2Hjavj2Nu1jx7bLlJCUn8+2331XKCoUhIaFcSUwu8/UdG4QA8NoPK5i8\naRPz9+5j1Yk/OHjlCvEpKU8lv1X0zN6QG8uiRYu4nXiXmDfNtxdUGimpBX0BynrDGfpqH378cYUp\nXao0lLiMM3ToUMaMGcOgQf98xA4NDWX9+vUMHz68RMMikYj9+/ebrPFFVSM1NRV/f38yMzM58dsm\nGjc0XKDc3Fw4fc58fTODA+qi1Rb/Zth36ATT5y0l9uwlsnOUSKVS6tTyZVrMK7wz5g0UCgXvT/6E\nz7/8loyMLJycDCvPUBz1/X1Z9dsRQmr7snzXQU7GXSUoMIhR77zHa6+9hq1t8WUIKguNGzdmweef\nlfl6ubzgrXji5g30N/SFNXaKCo19uFmqBxy7DUHyIJ78YQNxqViCRCJGKin4XiaVIJdKkcukWMmk\nWMlkBQ3F5VKsraywtbbCxtoKWys59jYK7G0U2CkUONsqcLCzwcXeDpVGU6qAuri4sG/fbzRp0oSp\nM+cy9YO3y/z3KCufLViCTCYrcwZsWHAAmZnm3c+yFCWKfZs2bZ6KNQ0ICDDY+LOaGXv48GF69exJ\n00Zh/LJsIY6OxolhdU8P1AbOvMtC00bh6PX6wu5JSqWSGQuW8fPG3dy6k4RGq8XRwZ7WLZow4Z1R\ntG3V7Ckbn06fyML//UDUoP9weNPScvnzev8XeWfafD5evZVevfuwYuNW/Pz8ymWzomnatCkp9zLI\ny1dhbWV8qdzjl64CkLlleZG/z83N50ZyMjfvpnAn5R4bDv/BjpNneaVHJ3Lz8snNzyc3T4UqX0We\nWo0qX02+So1ao0al1pCnVqHJzUWj0aDV6tBodWh1WnRaHTq9Hp2u4Ku+8PH0+1fk5Fbq86hduzZb\nt24lMjKSmt41DK7WaipOxP6Jq0vZS0TY2tigUpnvvWdJzLZBKxKJ6NixIxKJhOHDhzNsmGmKXVV2\n5s6dy+RJk3h79Gt8NPHdMtmo5etjlszWh3jXKKjm2LHvKM5euErm/WwkYjG+vjV4e/RrTHxvtEHF\n1Ca9P4aJH33G7cRkvL2ejioylN+OnqZTp07s3LmzzDYsjYODA06ODpy5dp3mZWi5uDf2PNIS8hIU\nCisCavoQULOgAUp2vordsef53+cfltlnY5jwyVecuWxYOHDz5s1ZtmwZgwcPwq+mN8+1bWlm7/5B\nJpWhL8eewYlTZ4rdP6rqmE3sDx8+TPXq1UlJSSEyMpKAgADatGlT5LlTp04t/L59+/alpthXRlQq\nFQMGDOC3fXtZ88PXvBDZvsy26tT2LdcmV3Z2Nnv3H+HgsRNc+OsyN28nkpKWTk62knyVqtD2qbNx\nNG0Yxntjh9O10/NGj/PBu6OZ+fnXdBv8Nqd3/1QmX38/Gsuegyc4f77iS9uaGh8fX05dji+T2P95\n9ToKIz4R5KvVhi2km4hGYQGs3XbA4PN79epFfHw8vV4dyZHdvxJQ39+M3v2DQmFVronSuxM/YeHC\nomvnWJr9+/ezf//+Ml9vNrGvXr2gx6ibmxs9evTgxIkTBol9VSQ+Pp6orl2RSkScPrQNH2+vctkL\nCaxv0BKYSqXiwsUrHDr6B6f+PMcPK3997PcymRQbGwUuTo7Uq1OLev5+NI4Io13rFgTUq2OSzMAP\n3h1V5vjqg8dieXHoe3z44ZR/RVnZ2nXqsPOPP3nl+dY4OTydKPb6nG/YciwWmURCbr4KPeDh7IhU\nIuHG3RQcbQwve6DWaBBVoNq3ahLOncS/0Wq1Br9uxo0bx9WrV+nUYyCnft+CW7Wnm8aYGoW19VPN\neYxBq9XSo0eP0k+0AE9OhKdNm2bU9eUS++IESalUotVqsbe3Jycnh127djFlypTyDFVp2bBhA0OG\nDCH6hedZuvDTMm0MKZVKdu77nd8OHOXchUvceJA9a+cZiF7/cLOu4PFwTfUh1tbWBATUJzAwCGtr\na7p37ciiuR/jVI7mF8Yw8rVXmfjRZySnpOLuZlgYZErqPSZ9+g2r1u/kvxMnMWHCBDN7aV5SUlKY\nMmUK27ZtIzcvD4/ebwAF9YGsZFJsrK1wtrPlyp0kGgbUxkou4/bdNHLzVUhlUtRabcH/2Yg9LpVa\nW6Ezey9PNxTWVpw7d44GDRoYfN0333xDt263aNmxJ0f2rDO74NvYKMocdvlvp0Sx79+/PwcOHCA1\nNRUfHx+mTZuGi4sLY8aMITU1laioKCIiIti+fTuJiYkMGzaMrVu3kpSURM+ePYGC8qyvvPIKnTp1\nqpAnVFHodDrGjx/PN998zZzpHzDiNeOTMNzrNCQt7V6heMvlMuxtbXFzc8XT3Y1WLRrh5OCAg709\nDo52eLq74enhRnUPd06ePsfE6Z8THx9fGPEUHh7G821bVpjQAzg5OSEWi1m5fhf/eWNAiedmZmXz\nwYyv+HHdDho2bMjuPXtp0aLknIPKzJUrV5g8aRKbN2+mYZA/G778kI7NG5KXm8vRP//i+Nk4Lly9\nzvXEZJLSMmgeWp/DK+cXaWvAe5+w5cAJg8fOr+CZPUBN7+ocPXrUKLEXi8Vs2rSZ6OjoChF8mUxm\ntuJ+VZ0SxX7VqlVFHn/ppZeeOubl5cXWrVuBgh35M2fOmMC9yklmZibdu3Xj2rUr/LblZ6PCKh8l\nNTWdD94ZydiRr+HublxyUJ/Bo5k8efLjoa16Pfn5Fb+5ZGuj4LfDJ4sV+9zcPD5ZsJSvl/1KQEAg\nO3fuolWrVhXspWnR6XSEhobyfLNwDi6fQ4PAf9akrRUKnmveiOeaG14npnl4AGt2HzL4fLVGg4n6\nkxtM/dq+nDp1yujrJBIJW7ZsITo6mmbPvcTO9T9Q19+wTmACpkPIoDWSU6dOERwUBDoV54/tKrPQ\nP6Suf22jhR7gbnIygwcPfuxYVHQ3Ppg2mxMnK/ZG6+HuxsWr1586fuHSNQaO+RDPBl3YvPcEP61c\nxbHjx6u80EPBjNXWRsGUEa88JvRlJaptc3Q6vcGRICq1xiTtAI0hNMifixfLlv8hkUjYunUrz3Xo\nSNPnX2T3voMm9k6gNASxN4JFixbRrl07+veOZt+WVUbHzz9J/Xp1GPXOJKPXGHU6HWq15qnwyBkz\nZhAREcGuCn4jBdSrTXJKQcPpzKxsFq9YR9OuQ2gWPZT7aik7duzk3PkLREWZp5yypfD29ubkhcsm\nsVWnZg0A9pw6Z9D5aq22wsW+WcNgEuLLXuxNLBazdOlSRowYydjxxm0uCpQfQewNQK1WM3DgQCZM\nGM8Pi7/g0+n/LbFWu6H88dtG8vLyOXrcuGYbt+8kIZfLiqw3U7t2HeKuXiu3b8bQvnULspW5hHd8\nBc/wzsz9dg2dur7I7dt32LRp879iJl8Uder4c76ITzRlRS6TsstAsVdZYBmneUQIKalp5OaWr5TH\nW2+9Rfz1m5Uznv1fvN4vVL00gPbt2nHk6FFq+nrz32mf8u7Ej+nWpSPzZ5cvwsjOzg6RSFRYz+NJ\ndDoddSPak5ubh1QqQSqVIpPJyM/PJzgo+Knzs7Oz+eOPP2jV1PANNFMw4OXujJs8g5hhIxkwYAAe\nHh4VOr6lCAoO5siebSazp7CWs2rvIf68dh21VotGo+Xv9AzEIhF2Cms0Oh06rQ6tXkda5n00JZS8\nMAd2dra4Ojtx7NixEpvJl4aXlxcuzs7sO3CkXPkopmbw8LdRazTFFu2r6ghibwAjRo6kZ69ebTSt\npwAAF5VJREFU2NvbY2dnx5YtW7hw0TQf30UiESlpRYu9UpnLzZu32X/gAEqlkry8PPLy8sjNzS2y\nuFzHjh1wcbTj6y+Kb7tmDjw9PHBycqRNmzbPjNADREREsPKH701mz0omIy3zPleTkhGLxUjEYjKV\nSuQyGTb2NsgksoJG2xIx9/Py0ReT1p+UnMK5v+K5evMWN28l0bpZOFEdi85xMZZavl4cP368XGIP\nENGwIdt3/2ZxsV+9djMff/Ylf126UhgVV1TrxX8DgtgbwJO1rc+dO0d6cmK57SYnp6LX68m8X3Q5\n48ys+8itrGjdurVB9uztHVDl3uenXzbi5GhPg7Bg/Hy9y+1naeh0OnJz83B1NX/STGUiIiKCxORU\ndDqdSZb13FycsFZYk3Bic6nntu85jEMn/sS2dmvyionAKuhrC4tXrCP90r5SbWZn57Di1+3kq1So\n1Vo0Gg1qTcHXh99n3b/PmTPl7zfboUMHFi/6mry8PKyty9bdy1iys7P57Msl7Nh9gIQbt0i/l4FW\nq8WjmjOjBvfkw3eGUaNhtEn+l5URQeyNJDs7m7Vr19ClQ/lmSjdv3SagcQfs7GwYPqTokMWbt+9g\nZ2d4xcdly5bRr19fPv/qO67fuEl4aCCHdq4tl5+GEHvmPApra2rVenbC6Q4fPswrA/oTVLumyWza\n2yhIzcgy6NxPxo/iy6WrcXV2ZPGPG4gIrsucD8dS398PT/dqhZu3A0Z8wJa9Rwyy+cvmvUz4ZCFB\nQUFIpRIkkoKlQ4lUivTBo15gGFFR0WV+jg8ZNmwYK1b8QEizzgyPGcDI11416rVeGiqVimUr17L6\n182c/+symVlZqFRqJGIxLk4O1K7pxev9ovjvW0OxeVBV9W5K2r+m32xRCGJvJCtWrODWrdu8O+aN\nUs/1CWhGZtZ99Hp9YUVBHlQTVKnVODs5cOPCkWJL+KakpqNRa0hKSsLT07PU8WrUqMHBg4fIzs7G\n09OT0KD6pKSmmT1rcd+BwxXeCtCSpKen89KLLzIwqh2z33nNZDNBRztb8gysuNiqWQStmkUA8OO6\nHdSu6UX7Vk2eOs/WxtrgOkv3s3MIDAzg+AnDk7vKioODAydO/MH8+fNZvmwZ02bNo2GDUEKD6tOk\nYThenu7IZFIkEgkO9nYEB9ZDJpOh1WpJupvCrTuJ7P/9GPsPHeX6zdt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"text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "tracts.plot(column='CRIME', scheme='equal_interval', k=7, colormap='OrRd')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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FJRIJlTodGw41DLxmi4Xf9+49Z7BXK+oH+2BvD/afsG0Hqk7+/pgs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\nQQG1kMlkaLVaEh4kc/vuPfbuP8Le/Ue5cfsu6RmPyM1VkZmVhUajxaZ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"text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "tracts.plot(column='CRIME', scheme='equal_interval', k=12, colormap='OrRd')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Invalid k: 12\n", "2<=k<=9, setting k=5 (default)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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1oY+HG6EB/nVm2hUaJoK9cEWYPHkybu5uPDbTthuotoiMiOCPpKM2l/Nw0ZBX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\nN+o4e6OOExd/m/SMTPLytGRl5yt7Kr1bIBKBg8oWfy93mrdtxivPt6V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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notes\n", "\n", "This is only using a subset of the classifiers in PySAL. specifically those that take only an attribute and a value of k as an argument. This simplifies the number of new default parameters that would be required in GeoPandas.DataFrame.plot().\n", "\n", "It is of course possible to add other classifiers with the cost of additional kw args." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }geopandas-0.3.0/examples/null_geom.geojson000066400000000000000000000007721315112464100206440ustar00rootroot00000000000000{ "type": "FeatureCollection", "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, "features": [ { "type": "Feature", "properties": { "Name": "Null Geometry" }, "geometry": null }, { "type": "Feature", "properties": { "Name": "SF to NY" }, "geometry": { "type": "LineString", "coordinates": [ [ -122.4051293283311, 37.786780113640894 ], [ -73.859832357849271, 40.487594916296196 ] ] } } ] } 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TODO: autogenerate these from docs themselves Kelsey Jordahl Time-stamp: """ import numpy as np import matplotlib.pyplot as plt from shapely.geometry import Point from geopandas import GeoSeries, GeoDataFrame np.random.seed(1) DPI = 100 # http://www1.nyc.gov/assets/planning/download/zip/data-maps/open-data/nybb_16a.zip boros = GeoDataFrame.from_file('nybb.shp') boros.set_index('BoroCode', inplace=True) boros.sort() boros.plot() plt.xticks(rotation=90) plt.savefig('nyc.png', dpi=DPI, bbox_inches='tight') #plt.show() boros.geometry.convex_hull.plot() plt.xticks(rotation=90) plt.savefig('nyc_hull.png', dpi=DPI, bbox_inches='tight') #plt.show() N = 2000 # number of random points R = 2000 # radius of buffer in feet xmin, xmax = plt.gca().get_xlim() ymin, ymax = plt.gca().get_ylim() #xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000 xc = (xmax - xmin) * np.random.random(N) + xmin yc = (ymax - ymin) * np.random.random(N) + ymin pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)]) mp = pts.buffer(R).unary_union boros_with_holes = boros.geometry - mp boros_with_holes.plot() plt.xticks(rotation=90) plt.savefig('boros_with_holes.png', dpi=DPI, bbox_inches='tight') plt.show() holes = boros.geometry & mp holes.plot() plt.xticks(rotation=90) plt.savefig('holes.png', dpi=DPI, bbox_inches='tight') plt.show() geopandas-0.3.0/examples/nyc_hull.png000066400000000000000000006377651315112464100176420ustar00rootroot00000000000000PNG  IHDRk.sBIT|d pHYs.#.#x?v IDATxyXVu}#*Uf2''kfZil7G+[E\ w-M-]TpeAGxn9u5sޟ97v˫t PV"Za-X,@X   k`Za-X,@X   k`Za-X,@X   k`Za-X,\ÇÇܹs WxxBCCsgRJJRRR&M(44T Uퟚ={(##C'OTf(EEE]ޒw^FYm%2eK8q92dF[oZbs:N+jZ`֯_/Qk\Yݫ믿Ve95n8=m۶Ν;+g}>}TE4j(9r{5c uϞ=[>eF}BBB￯o:w\yմi+keamjjªZ 6ԩSuWzMԻwo] ~Ν[K,U\\\v]V$}'PZ` PkUio&MzjU>ӧWio6m~zU?M.ljҤbcc+ @޽{f;v̴S֭SΝ+<ɓ'յkWBBB4`>}ZҎ;b {W5nܸ ~}w7kݻo߮e˖iݦݵdׯR%iٲe6lJJJ uͦp%$$[n|r/kjݺuj߾}O6M?nԡC%$$(66VV]֏?VZUYAtA)$$D+VЊ+\>۱cGYF ք;v~aiӦ}Νӄ oPOl .ZfޤI7NO#NSK.wܡBȑ#_T?mvԩSua?ܠVki͚505%%EcǎPM6Vͦӧg)3ݻkŊҥp84y {zyy)99eP+IAAAJJJ29990aB{K> |iPŋ j_EBC>TFFF*))eP+k$OOOC}͚5JNNpoág}Tիf̘a j_klDm۶˞я?AU'OSO=e(//{}]6ƌ><:w'N\vo^^ յl={?pn j߾} kzzzjʕٳe߿怀:tB^nzm5kL6l(3(o|;vTWM8׆ *tĉ3j ŋ/,|ZIHC@͛7jÇTwZLO˺2{lOWV 'xP;> 6m{ｷBA L͝;B?CSsZWP۽{i+/ KX+ս{wñO?t}[n5l6WǎM+:yj ]_ѰZ`fjɓ'?j 4-R#:u2նlr}I[76sXPl+ջcǎ;o߮_~}ݻ F)!!Re-[LgϞ-w\C-44TqqqU}$]޽{_[E|XiU$t6;Nm߾/cL׾ԏ?hέkܦMJwt_UVj jذaz{yyBmذ+T$md#G.V_٠\56l0^p·+W4պuv}>YeCK;w`o׮]СC+ջ}>5Kl*D{pamnn6M]vx@k ꩧc?cUZp"ׯm6[w8ڷo^]Ne~@aŋp8 6mڨyZ^^.8pܽ?S]tѨQ.Uwt+վ=ieb:tȲiii*,,4ԼQm kPu6-((s=g9׸'ݽyfo^cǎ… {n={VZv>EGGg5\W^ZdInUʦMVlj K럝SW?_eʪph`UsÆZ@@`^Ξ=?\/V^]=ΝӛoW^yE;vT?+CWXr ZNuyC=66V~w^uQ=V^-Yfp^՛I&cǎz饗{Y޵?Ovu&=z 'N!!!Zd*}ͥKgϞ:t萡O>D{ٳgu8qBڸqƎ+777Þ &;Թs.O:U[]ޕu^Em^Չo֞zW]z֭u릱cꡇҖ-[JΝ`M4>>>-=v::uZjUC26{_XU sssURRrdXjuz͛RAAABCC%I)))eϨOQp?>~F} 3ꃬ,jWO6++K7|gSDDDϛÇkĉs*>>^gΜ)zU KJJ r2vssSÆ M>uTBKsrrt:Jݛ5k*] Kw@@C 3g'ϨOQp?˟կA>} ݻw͚5]ܹSZ@@>J]m۶h/EZUCW0\wiOV}oڴ)t:ή\Tkڜ%$$h톺/_*_~0 ͛WZ{)|tu*dΰ4&&wwww4tMo۶<== HZiРA u???-[L:uݻTj۰aCEFFjǎS~~~{.}$x+ѣ~mt:knwVH0=sM6꾾Zdvz=8`]Iv铔NS,ۛj-R jYsVNJKJJtRCA~V2d6l`7nX/V=fffVzYYYcVwKz5lP۲e9RIII}-wO>}Le˖}9-[P__m۶j tWڵkMAٳ+}-\{jMX[PPaÆEn^^ys*_oǎc???nݺ}||ԿSەj),,}O |J_|Ν;g 8Pׯ _~ \z1n5ksJ]׮Z֞={Vz֬Yc7jH .ԍ7X\v8\CC*TӝίhXhe hذa 4.\Ђ *S̈́(B~ uooo-X@{5khٕc=V_ꮻk޼y}AZÆ 5jԨ 衇L9s~ _~f͚e5o޼a#5z)___=ȑ#qrfeeiȐ!`qر 4fCPv~r:tHvƍOtM:tCݰaF)al}>x~oΛ7OO?foV7 Y^pAw}/^l{zzjׯU?c 5jP;}5yd7%%Ewqy?!CTh?Opܹs?ܓÇ@Ӌ/XKjܸC\aaa.2.o ?|p;v͛7nSaa~7jm[o?~egg.]ONӰN:up4u!Mu???mUF_ߦK/ UϞ=piڿV\"Ӟh[lQ>}TPP`Z VBBz꥝;wjٲeڽ{c$~}tBBbbbf-_\󂃃aUqov]ݺuSBBrJXSZvm*.Kff ]wuWt]J7%.~F} 3g'ϨOQԷj <aÆ={vZI3f믛vء;vI&Z`AZI0a>DCphƍ}%upp.\xA-=~6OwߩGUߨQ# JIIQǎtCjÆ j߾}mV}z*-[6mTj_||6nܨ^Q^{Mfͪp~Q߾}uV_QӧkԾںuZn}EjB]רQ#9N9NgyϨOQp?>~F} 3꫺|?[lTV~a͙3G_|}}˼fBBB?QkfkeuY?yu޽l6:t7xC;vP߾}E׎;4aƖ׻ﾫM6)..Zs=ڹsrSwss7߬?\Զmj?I۶mO<-Zyn6%%%iٕ,fmmwԩW!>|X UXXڶm+77?55Uwֱcǔ%)22Rڵ%i޽:p;l5o\AAA<۶mSjj222thBAAA/7LC?9tUV R.]wUzɓ'pU5-O!j @uoy5Za-X,@X   k`Za-X,@X   k`Za-X,@X   k`Za-X,@X   k`Za-X,@X   k`Za-XաPo: )""BڵS-0!@0x`ZZÆ ﯀lR[V۶m(kNM6p#@7o޼2ZI:?Ǐkǎ.TӦMhth^ Zy/_P(oo@UVnxxF],6N={ve5iDϟWaaa:{Ξ=Gy5kRdd<==u.M^~eqF IDAT$(--MVzz?'NԩSVnnΟ?_+??_iii.m67nf͚>뮻Nj׮Qs!@5gٳP#666Ve^#//4=zN8'Oyyy***N򔗗v5nX͛7W``J݈kNaaarssPlNiLj'OT@@EPuY[n-=nԨ-ZTߍ6<*нpBݮ&MyjѢEiEGG+$$DvF[“RSS}vCmJթS'COVjj9RfffԩSQ^^JJJm&álegg.qss_iۺukiӦ4m׮ZnM `ZIӧO(=nР~aKfj޼7onݺ縻-[4V`` .U@X :iņ㨨(Z4vhB-Z_áǏ+55UG5ϯ@X4}"+ 膅͛7seCW^MS}vT9%%%JOOWZZ9ǏO. tNgUTT'Nĉڹss<== nppڴipEFF*&&FM4Z9Waa6dYnnn QHHH)==]JOOױct eee:Waa;cǎ%|6mjJ QXX-j 6#@3w\q-Զm[}<<<29>Ç+==]:ydigV\ΝSzzӵuV4jHM6U``! 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PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PEN៺IENDB`geopandas-0.3.0/examples/overlays.ipynb000066400000000000000000010114201315112464100201750ustar00rootroot00000000000000{ "metadata": { "name": "", "signature": "sha256:f2cfcb83c09747f8bcf7a08abe582a16207e6bcfb0547e4cb69755830bcae172" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Spatial overlays allow you to compare two GeoDataFrames containing polygon or multipolygon geometries \n", "and create a new GeoDataFrame with the new geometries representing the spatial combination *and*\n", "merged properties. This allows you to answer questions like\n", "\n", "> What are the demographics of the census tracts within 1000 ft of the highway?\n", "\n", "The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the dataframe level, \n", "not on individual geometries, and the properties from both are retained" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import Image \n", "Image(url=\"http://docs.qgis.org/testing/en/_images/overlay_operations.png\") " ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can load up two GeoDataFrames containing (multi)polygon geometries..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import os\n", "from shapely.geometry import Point\n", "from geopandas import GeoDataFrame, read_file\n", "from geopandas.tools import overlay\n", "\n", "# NYC Boros\n", "zippath = os.path.abspath('../examples/nybb_14aav.zip')\n", "polydf = read_file('/nybb_14a_av/nybb.shp', vfs='zip://' + zippath)\n", "\n", "# Generate some circles\n", "b = [int(x) for x in polydf.total_bounds]\n", "N = 10\n", "polydf2 = GeoDataFrame([\n", " {'geometry' : Point(x, y).buffer(10000), 'value1': x + y, 'value2': x - y}\n", " for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)),\n", " range(b[1], b[3], int((b[3]-b[1])/N)))])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first dataframe contains multipolygons of the NYC boros" ] }, { "cell_type": "code", "collapsed": false, "input": [ "polydf.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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xauVE+LtwQoJCePv2LZWrSD5aGnRskO73si1tS2GbwpiamrJ48WKmTZtG6dJS\nb7hR90YolUo6jOrAgkULhAefdCDEUZBn2LRpE/6vXzJ5wOfFmq7WexV29uXw8DhCx6/tsC1szOap\n0vxhlwn/cO9FJAMHD8H5G2fu+99n9qLZWNtYU9q+NCtWrgCgb9++3Hv9ms0nT37UtlKpZHTLlnRz\ncuLnMWNSfL7nSU/K1yuPmbUZWlofzoL7P5McStSsXZMH9x6wN3gvddqkHVcmMQUtC9K/f39m/j6T\nfZ77qNe2HsOWDOPnzT9z6cglitgVITo2mh07d2So3S8Rsc9RkCdYuXIlQ4YMYf7I5qnGhUkP93zf\nolaDKi6OiiVN2fJrx4R77ud8OHbJlz1799G+Y3t6TOxBzwk9AQgNCeXfv/7lx59+xO+FH7/9+htz\n5s7llx9/5HVwMFVLlaJuuXIUNDAgNi6OgNBQAoODMTY2JlTe5H39+nX69e9HRHgErx69SmZbs77N\nUCgU3Lt4j4bWDTEskPLJmE8xefdkts7YyoBZA5LV/9+K/3F2n7S49FAO6iVIHbEgI8j1qFSqhB5W\n3PlJGTpDnRj/wDAaDN5EmfLVOH36FHd3fE8Ry4IAxMaqKNF+CUOGjWbp8mWYlTBjyfklydq44nGF\nX5r9wooVKxg0aBAbN27kiLs7Vy5f5umzZ+jr6fE+UZwXIMFtWfym7sXnF1OhdsaOO2YFcXFxNNdu\nzjeNvuHf4ylGHMnViAUZgSAJsbJD2rnDM+ZcIjEv37znq15rKe3wFX36fkdcXBzztpzl5kN/YmNV\nTF3jiQptDh91J4YY/jj5R7I29q/cz4zuMwApVs348ePp168fO3buxOfRI1atWY2hsTG7du1CT19a\n1U48bO7VqxcAK0as4MT2E5z6+xRxORg7W0tLi+VXlnP+wnlOnz6dYpmYmBiGuA3RaGCr3ILoOQpy\nPWN/+YWdW9fx8J+hGTpSl5iV/1xm0T93mb9wCS1btmTRokUc2L+Xs2fPYW5iQESMApuiRQmJDGHx\nxcUYFTD6qL7HZg/m9JmTrF21Wk1MTAwHDx7E1dWVsES9RgNDAy6cv0Dlyh8c4A8ePJjVq1cnfJ9x\ncAa1W6YcWzu72DR5E8fWH+PE8ROUK1fuo3vf9f+Obdu2oVQq+W36b4weNTpHbfsUoucoECSiRvXq\nzJk7l1Z1S2daGAHK2Jpy9/5DOnXqBEDPnj05cMidg4fd6eo6AMeqVQkKD2L+mfnJhBFAoVRQvUl1\njqmPsfU2/OMjAAAgAElEQVTJVtoM+bDHslefXvTs1RNTa1Oa92kurUIPbY8qToXHMY+PvIGvWrXq\no++WxS0z/U6Zpfe03gQGBeLg4EBMIn+RUVFR/LX1L+afns/kvyczbfo0uvfontBz/9JISxyLASeA\nW8BN4Ac5fx5wByk86z9I4VTjGYcULOsuUvTAeKojRS58ACxKlK8H7JDzzwMlEt3rA9yXr97pfCdB\nPqJKFcmB7bwfmqZR8tM0rlUKgOXLl3P27FlKlCxBwUIFWbh4IX9t/wu/ID/m/TuPQuYph0Vt4tqE\nOR5Sz9GquBXmttIex8mTJwNQr2M9Nvps5KeNP2FmbcawpcMYt30cM+bMoFadWjx+/Pij9ho0kLbo\nPPTO+YURhULBTxt+okDBAh8N+11cXIiOiqZc9XLUbF6T5VeXc97rPFWqVuHp06c5bqemSUscY4BR\nQEXACRgKlEcKm1oRqIIkXOPk8hWArvKnC7CcD93gFUB/pHCt9vJ95LwAOW8BED92MQMmA7XkawpS\nmFbBF0JAQAC7du1gz7yu6Ot9nsdupVKJXXEL3r9/z6Ahg3Du6cyv//uVC1cvULdLXZZcXELhIun3\nom1iKf0UPU974n3NO8XV5/rt67PRZyPGJYyp7FiZGTNnJMwxnjp1CocKDsRExiSrlxN8/e3XGBQw\n+Mgr0MCBAzG3/hCcy7qkNcuvLcfCwYIqVavg4eGhCVM1Rlri+ArwktOhSL1FW8ADiN9FegEoKqfb\nAduQRNUXKQRrbcAGKIAUYhVgM9BeTrcFNsnpv4HGcro5kggHy5cHHwRVkM9RKBQULlwYSxMD2jdM\n7isxo6zYfYngMMlH4q3rt+g7rS/Vm1Rny+MtDP59cIaH7HXa1MHA2IDTnqe5e/cuN/+7mWI5Q2ND\nJu6YyPgd41mwZAFVq1fF29sbADVqYmM0M2SN3xA+YdIEguVY2V27diXifQTeJ70Tyunq6TJp1yS6\nju9Kuw7tmDFjhkbs1QQZmXMsCVRFEsPE9APi3SnbAs8T3XsOFEkh30/OR/58JqdjgRCkONaptSXI\n51xP5OZrTI/PX6y49cifn5edQEdbh5EjR1KtejU6W3fmv73/ZbpNcxtztjzawtxjczkYfhAP1ad7\nVbVb1mbTw034vfajTp06dOjQgXu372Fd0vqT9bKTTqM6YetgS+OmjYmOjsa1tytxcXEUsUv+Z9Z5\nTGdmHJzBH4v+oE3bNkRERGjA4pwlvbtpjYHdwAikHmQ8E4Bo4K8stitDTJ06NSHt7OyMs7OzxmwR\nfB4qlSphnlFLqWDItzU/q72YmDi6TdqPkZExL19LJ1Dq1KnDlctXkoVNzSgmFiZUa1wt7YIyr31f\nE/gqkOYuzfnrr78wNDRk3nfz2Ply52fZkVmUSiW/HfoNt2pu9OzVE4eyDtiUskl1esGxoSMrvVcy\nqfUkKlepzJHDRyhTpky22efp6anRLUXpEUcdpOHun0Diw6J9gZZ8GAaD1CMsluh7UaQenx8fht6J\n8+PrFAdeyPYUQpqD9AOcE9UpBqS4czWxOAryNlOnTElIzxn2ec4lAMavPMGrwAjeBkhno/UN9Nn8\n52bcFrpRv0P9z24/vWybvY3rJ6Ue8feDv8fAwIDmLs3xe+eHWq3mXeA7dv2+iwGzBuSYTQB6+nrM\nPDyTgZUGcsrzFFqGnw5ta25jzpKLS1gwYAHVqldj659bad26dbbYlrSjM23atGx5TmqkNdGiQJoP\nDEBamInHBfgDaAi8TZRfAakXWQtpCHwMsAPUSMPxH5DmHQ8CiwF3wA2oDAwBuiHNRXZDWpC5DFST\n7bgip4OT2Cj2OeYTYmJiPvJyHXZyPIYGmV+IOev9jKY/bAWFkvDwCCytLClTqwwTdkxA3yBr41Wn\nRTPtZujq6HLs2LEEd2IPHz6kRq0aKLWVBPpL4r0veB9GhZJvJcpuzuw5w+Ihiwl8HciYdWNo0a9F\nmnX+XvA3a35Zw8SJE5kyeUqa5T+X3LbPsR7gCnwDXJOvFsASpKG2h5y3XC5/G9gpfx5GEr545XID\n1iJt2fFBEkaAdUhzjA+AkcBYOT8QmA5cQhLUaSQXRkE+YsiQIQnplnXtPksYIyJj6DFlHxaWloSH\nR2BlWZio2CjG/TUux4URYOyWsURGRlK/fn3mzpU8AJUpU4aXfi9Rx0h/Im4L3TQijAD1O9Rn56ud\nGBQw4I/+f6Tr5E6nUZ2Ye2wui5YuolPnTvluP6Q4ISPINcSvGJsW0Mf/yE9oa2f+jEK7H3dy3TcU\n36fP0NbWwsDQkCGLh9Csz+d7D88sTRTSNEHVqlWZMWMGAwcOpJxDOS5dvcTUvVOp8nUVjdkWT2R4\nJK2NpGHysovLKFezXBo14M3zN4xtOhZTQ1OOHT2Gubl5mnUyQ27rOQoEOcLUyZMS0m+Ofp4w/rb+\nFGduvOD1mzcA2NnbUbJySY0KI8CBsANUbVQV7+vetGzZEj8/P7xvebPh3oZcIYwA+ob6LDorndEY\nWmvoRydoUsOiqAXLry5H21ybyo6VP9ptkJcR4ijIFYTKZ5IXjGqOllbmf5YnLj9m9uZzlCldhoiI\nSMqVtcP3yRPGbEzZt2JOom+oz7zj83CPdmfslrFM2D6BHS93YGKRu842VKxTkb1B0trreJfx6aqj\nZ6DHTPeZ1Olch3oN6rFv377sNDFHEP4cBbmCZ8+eU6GMNSO6ZX5f4/uwKPr8+j8aN2nG/v370dHR\nJiI6mjZubShqVzTtBnIIpVJJE9fPX4nPToxNjGn+XXPiYtLvNUipVOK20I3i5YvTvWd3Ro4cyYzp\nMz7rTLwmyZtWf4yYc8zj7N+/n3bt2uGxtBdNapXOdDutx+zgWbAWN27eRq1W4+LSHK/b3mzw2fDJ\ngFeCrOf2+dtM6zANx0qOHNh3AEPDjDvuTYqYcxR8cbRr1w7gs4Tx7+O3OX3tGZaW1qjVarp07sTJ\nU6cYvX60EEYNUMGpAmtureHO/TusXbtW0+ZkCiGOAo1y48YNAMoUNc10G+/Dohj6x1HGjp/AsePH\nqVypIjt3/U2JSiUydIJFkLUUNCtIKcdSPHr0SNOmZAohjgKNMmvWLAAKGOpluo1Bsw5RtHgp2rVr\nx8qVK7EuYgvA3Yt3eRf4LkvsFGQOM1sz/F74adqMTCHEUaBRtm3bBsD78KhM1fe48Ij9p+/RrHkL\nKlasiKmpKR5HPJi4cyLdxnbD2MQ4K80VZBBzG3P83/pr2oxMIcRRkCvIzPYdtVpNs+FbaN68BTNn\nziQ6OpquXbsC8HWnrxkwa0CymDP/7f2PFw9fZInNgrQxsTQhOChvHmwT4ijQKFWrVgWghFXKHrg/\nxfUHrwFYsnQpAL6+vgB899t3KQbiiouLY0qHKQyolLPOHb5kzIuYExySN8VR7HMUaJRr164B0Mwp\n466vjl9+jH2ZUhQpIvkfjPfaYmptyoi6I4iNjaXDDx34pvs3AExsMxGAFVdXZIXpgnRgUdSCoMAg\nTZuRKYQ4CjSKqakpQUFB/OhaN8N1/QPDsLK2SfgeHw514eCFqOIkR/Vzes9hweAFRMlzmlUaVqFE\n+RLJGxNkC+5r3bGzs9O0GZlCDKsFGmXPnj2ZqvfvpcfM2fwfTxIFflq6ZClFihbHrkZzJh2LZcoJ\nNd1mHiQ68oO3mHod6n22zYL0ERoSyrE/jzH/9/maNiVTCHEUaAw/P79Me223LiytQisVH37CQ4YM\n4V1YBF2m70MpR9Ur69QCM9sPm8tXjFrBuyCxvScn2DJtCw4ODnnWM78QR4HGOHz4cKbrVihlgblp\nAVauWgXAixcv2LRpE2Hvg9FKciJmwIpL/LwvkMFrvFADexZmrrcqSD8RoREcWX+EGdPzbkAuIY4C\njeHnJ20OruOYuTlAp4pFWbpEcq9la2vLkSNHMC6Y3MONvnEhDAqaYm1XBTPrkmydsZWtM7by5tmb\nzBsv+CR/Tv+T0qVK4+KSdwOGCnEUaIxbN6Vwpo2qF89U/TXjW3Do8JEEr9XVq1cnIjQEVVzqHqkH\nrr6GqVUpNkzaSI8SPWhl1Iq9y/amWl6QfkKDQzmz9wxLhi/hwIoDTJ82XdMmfRZitVqgMQ67S5Ey\n/r3im6n6YRExGBrooyXPLz548ICY6Ggu/L0Yp86jUnSVpW9ciGFbfQh47sP9s/s5t+sPlo9Yjl1V\nOyrVrZTpd/lSeXbvGf8s+ofLhy7z9sVbLK0tcXR0ZP7v82nbtq2mzfssRM9RoDGOHj2Knp4ezWtn\nzhvPpdt+2FhZJHx3c3OT2l0xhqfXT3+yrnlRO+p0GU3fhadRoGBkvZE0UTThwOoDmbLlS0KtVnNs\n6zGGVhvKkKpDCHsQxvzZ8wkKCuL50+ccOnCIQYMGadrMzyYtcSwGnABuATeRogeCFBnQA7gPHAUS\nT/SMQwqWdRdI7Je+OnBDvrcoUb4esEPOPw8knoDqIz/jPtA7ne8kyCM4OTmhpaWkajmbtAungNf9\nV5SW4ya/ePGCwKAgytRoxoAVFylWOX1hV82KlMbU+sOw3ueqT6Zs+ZKY1X0WK39YSbe23Xj54iXH\nPY7TrVs3jIw0Exwsu0hLHGOQQrJWBJyAoUB5pAiBHkBZ4DgfIgZWALrKny5IUQnjxzYrgP6AvXzF\nz9T2Rwr9ag8sAObI+WbAZKQwr7WAKXwswoI8TsuWLQgPj6BO5cx56b71OIDyFR0BqFevHk98fWn7\n8zqKONRM8fhgapRxaotCoeD7P75n5MqRmbLlSyImJoZ2bdsxdepUTEzy759kWr+gV4CXnA4F7iDF\no26LFM8a+bO9nG4HbEMSVV+kEKy1ARugAFKIVYDNieokbutvoLGcbo7UKw2WLw8+CKogj/P777/j\n7n4EAAvTzPU4bj0OwMnJiV69euHr64tlma8oaJExoX15/yrPbp5FrVZzxeNKpuz40nBq48SZs2c0\nbUa2k5E5x5JAVeACYAW8lvNfy98BbIHnieo8RxLTpPl+cj7y5zM5HQuEIMWxTq0tQT7g32NHaVnP\njv1/dMtUfT//d7x8E0Lr1q0xNjZGS0ePwaszJm4xUZGsHlydF/cuAXDJ/RI75u7IlD1fErVa1uKp\n71Oio6M1bUq2kt7VamOkXt0I4H2Se2r50hhTp05NSDs7O+fZHflfEhcvXeaXHtVp0yDtuMgpsXbf\nVSpXqoCxsTG37tynZnu3DA2lAWa6GABQuVE3bnruRK1SUdCsYKbs+ZIwtTTFqKARFy9epH799M3t\nZgZPT088PT2zrf20SI846iAJ4xYgfkPYa8AaadhtA8R7s/RDWsSJpyhSj89PTifNj69THHgh21MI\naQ7SD3BOVKcY8G9KBiYWR0HeICAwiMW7LvFT78yddd7mcQe3URNQqVRcvnSRbrOmZtqWjpO2EfYu\ngEeXPSheMXN7Lr80itgV4fTp09kqjkk7OvFel3KKtP6rVQDrgNvAwkT5+5FWkpE/9ybK7wboAqWQ\nFlkuIonoO6T5RwXQC9iXQlvfIi3wgDTf2AxpEcYUaAocycjLCXInb9++BWDdxDafLBceEYPHhYf8\ntu4U/3k95eCZ+3Qet4vqfdYR8D6G/v3707t3byLCQynu2CBDNjy/ff6j773mHUWppc2CwQsy9jJf\nKMUqFuPKtfw9R5tWz7Ee4ApcB67JeeOA2cBOpJVmX6CLfO+2nH8baf7QjQ9DbjdgI2AAHALc5fx1\nSL3SB0g9xvhJqEBgOnBJ/j4NaWFGkMc5c+YMJgUNaVY7ZR+OS3dcYOHOqzx9GYC5mSnhEZFMWe1J\n0SLWmJia0bBJB/4aOhRnZ2cuX76MZQmHDNtQtIIT9XuM5cxfsxPytHR08Xvgx4vHL7AtZZvp9/sS\n0DfUJ/pt/p5zFHGrBTlOnTp1OH/+POqLUz7KD34XyeDZ/+PvE3dZtGgxPXv2TNgqEhISwpEjR5g9\nezbXrl1jwYIFjBo1KqHulBMZ/w2o1WqC/B5iVlTyNxjs/5zVAxyJCgth9PrRuPQRmyNSY83YNQR6\nBXLU/WiOPVPErRbke6rJoRFu+rxOyDtx+TGmTeaw89htbG1sGTBgABUqVKBoseIoFApMTEzo2rVr\ngufwxMIIcP/cwQzboVAoEoQRwMSyKKP/8adIBSd+7/s7v3b7NTOv90Wgb6RPRGSEps3IVoQ4CrIc\ntVrNp3rzEyZK4Qr2nLyLWq1m0op/aeS2OeG+l7c3+vr6vHz5EqNiVajX/RcGrb7GlBNqppxQ4/Rt\n8o3avtdSXKvLMNra2vRb8h92Tq04teOU8P2YCgZGBkRGRGrajGxFiKMgSwkLC0OpVKJUKhO85STF\n1taWLVu2MHmVJ8rav/LbBukc9O+//05cXBxjx0oHroxNrek+8380GTQbG/uvEuo3c0vuWbqZ2x9Z\n+h71u0s2xMedEXyMvrE+kZFCHAWCdFPF0TEhvX79+lTLubq6JqQrVKiAu7s7Y8aMwdvbmzVr1gAw\nZOPtFOsqFApqtv2eQlbStpuGvaekWO5zKOFYH10DI+6cu8PpPZ92YvElYljQMN8Pq4XLMkGWEif7\nUixUqBBdunT5ZNmUht4rV64EQKHUwrCgaap1W45aQcvPsDM9/LQ3kLVuNZnWcRoNuzVk0rZJ2fzE\nvIO+kT7RUfl7tVr0HAVZytVrXixZsoTAwEAKFUo9FnVsbGwycbx06RKrV68GYMCKy9lqZ3rQ1tXl\n+7XeWJauzMntJ4mLTXmaICnnD53HtYwrzbSb0dGiUzZbqRkMjAzy/fFBIY6CLMXU1JRhw4Z98ihf\nr57d0dHRQalUUqpkSa5evQqA25AhACi1tLEt+1Wq9XMa82LSEccZvdKOhzKz90wmtppI2DsF1vY1\neBcQkt3maQQDYyGOAkG62LJlCwqFAoVCwblz51It99dff7F33z4ubxrI2D718X3yhEGDBvHw4UMu\nX5FOXHwqzIEm6DJ1F859p3Fq+ym8PL1SLfddxX78++cJ6nUfy+hdL3CdcwTUEPg6MAetzRkMCxqK\nYbVAkB4uXryYkF4jD42T4uXlxeBBA1nxswvVy9syoW8D9PV0GDlyJF06d04oV7Fh5xTra5KGfSaD\nQsGxrcdSvN+vYj+e331On/knaDJoFgAGBU1QKJWc2HEiJ03NEQwLGBITE6NpM7IVIY6CLGHJkiVc\nuHCBiRMnsnbdumT3Q0JCaN3ShYHtquBSpwyKWtMo8M0sqlevTpkyZbgqb+5u2Gcq307dmdPmpwsd\nXX1O7Tr1UV5UVBQ/Nv2Rp3ee0mveMUp+1fCj+7r6hlw9djUnzcwRCpgVICY6BpVKpWlTsg2xWi3I\nMmrVqkWtWrVSvLd3717Cw95z0+c1Fs1+T8gvWaoMdevWRaGljUPdNjj3zfptOVlF69Gr2TOrF/MG\nzMNjowdWJa14+fAlSi1tGvScQKlq3ySrY2Rmy5NbTzRgbfaiZ6CHQqkgLCyMAgUKaNqcbEH0HAU5\nQqdOnfiqajU8Lj4C4N69e/wxfwFbt24FoN/Sc3T59R9Nmpgmjs1csSpdmSPrjqCKU/Hy4UvMi5Vj\nnHsEjfr/lmIdi5IV8H/iz/6V+3PY2uxHR0eH8PBwTZuRbQjHE4Ic5eTJk9SqVYsePV3Zu0cSw2Ll\na9J3yTmUcojV3Ex0eBgbRtSnz4KTaOsboq396cHXg/MH2Ta+DYYFDdkXvO+TZfMa7Qq2w+uqF3Z2\ndmkXzgKE4wlBvqZhw4bUrVuPvXv+od+Ss1Rt0Y9e80/mCWEE0DU0YvCaa+gbF0xTGAHsnVrRzG0B\nYSFhuG9yT7N8XkGtVqOKU+XrOUchjoIcx8tLWnwpUqE2bX9eh46+gYYtyl5qdhiGto4eRzbmH1/N\nD68/REtLK8d6jZpAiKMgx2nWTApnfmrzr7x7+0LD1mQ/WlpaWJSsxLM7z9MunEc4se0Ejl85Zjhu\nT14i/76ZINcSf3765KZpLOj8ZQSULPFVQ94H5B/3Z4+uPuLrel9r2oxsRYijIMeZNWv2R9+ve2wl\n4l0g++f2JyYygjWDq3Fk2WgNWZc9VG05gLjYOJoomnDv6j1Nm/PZRLyPoHDhwpo2I1sR+xwFOU6H\nDu1Zs+bDKZo9M10xKGhOxLsAtPUNeXH/Gmp1fthI8QHLkuXpOGk7+2a54r7enXLVMheSNrcQGRqJ\nmZmZps3IVtLTc1yPFIr1RqK8WkhRBa8hBcCqmejeOKRgWXeRogfGU11u4wGwKFG+HrBDzj8PlEh0\nrw9wX756p8NWQR6gUqVKAPzyyy/06SMFnox4FwDApT1LASheJf8N2So36opBIQtu/XdL06Z8NpHh\nkZibm2vajGwlPeK4AUgaaWguMAmoCkyWvwNUALrKny7Acj7sS1qBFK3QXr7i2+yPFHXQHlgAzJHz\nzeS2a8nXFKQwrYI8TpEi0jzjnDlz2LRpE7q6uh/dL2RVnGZDstazd27Bonh5Xvu+TrtgLicuLi5+\n32G+JT3ieBoISpL3Eoh31mcC+MnpdsA2IAYpZKsPUqxqG6AAUm8TYDPQXk63BTbJ6b+BxnK6OVLs\n6mD58iC5SAvyIEqlkhkzZgLQoUNHqlT52D1ZeNBrwkPeaMK0bOWW5y4eX/uXmKiUHTbcuXAH/2f+\nqdb3f+qfa5w9RIZH8vTpU02bka1kds5xLHAG+B1JYOvI+bZIQ+N4ngNFkMQy8T4GPzkf+fOZnI4F\nQgBzua3EdZ4nqiPI44wePYrz58+xZ0/yI4Ntf9mAsamVBqzKXoxMLABSPCkTGRHJcKfh1GtXj2l7\np31078mdJ0xsNZGXj18m5DXu2Zhxf47LXoM/gbmNOQEBARp7fk6QWXFcB/wA7AE6I81LNs0qozLK\n1KlTE9LOzs44OztryhRBOqlXvwFXr3zs7XvYlvsUtCiKjl7+2xS+caQzT7xPAvA+6D1mVh8vZujp\n6wHwLugdUZFRzOo5izP/nOGY+hg/OP1A2Lsw7KraoaOrw+Mbj/G54oNardbY0LZJnyZsXbuVyZMn\nZ9szPD098fT0zLb20yKz4lgLaCKndwNr5bQfUCxRuaJIPT4/OZ00P75OceCFbE8hpDlIP8A5UZ1i\nQIrxNxOLoyD3M2fOnARhdKjfnprth2JWxB4T6xJp1MybRIeHJQhjW7e2yYQREs4Nc/PMTXqV6kXg\nK8lB7q1ztwh7F0bl+pVZcHpBzhmdBq0Gt2L9+PX4+fklzCFnNUk7OtOmTUu9cDaQ2X2OPkC847pG\nSKvJAPuBboAuUAppkeUi8Ap4hzT/qAB6AfsS1ekjp78Fjsvpo0ir3SaAKVLPNP+cv/pC8fLySgi9\nCtBq9EpKV2+SL4QxNOg1PheT/0SVOjoolNLZ8WpNq6Vav3zt8qhVako7lk7Ie3jtIQCOzo6pVdMI\nBkYGFLYuzOXLmo/1k12kRxy3AWeBckhzg98Bg5BWqL2A3+TvALeBnfLnYcANiHeZ44bUw3yAJK7x\np/DXIc0xPgBGIs1nAgQC05G2Cl0EpiEtzAjyKJs3b6Zq1aoJ35sPXZCv5hYv/rOErb+48D7g5Uf5\n2jq69Jwj/dyndpyKj7dPivW/7ixtX2rwbQMAjEyMKO9UHoDn93Lf0UPzIuZ4e3tr2oxsIz+sxQuX\nZXmA2NhYdHR0Er4PWH6BIuVTdoybV4mNimSGiwHGZtaM+ftlsvuz25gRFRqEqZUpddvV5eTOk8w+\nMhuHWg4A+Hj58H3V7xn8x2BWjVlF456N+WnjT7jouGBmZcbOV5r3kB78JhgvTy8OrjiI9ylvdu3c\nRceOHXPk2cJlmUBjPH36lDlz5nD48GFu3ryZZe6oXr16lRBhEKCgRdF8IYybRzfif/MGJnzX1tOn\nQc8JhAa+4vntC8nKj/1fIAqlEofSDhxcfZDQ4FDMbT9spI4fTp/5+wwAx7cex0VH2r0W+DqQp3ez\nf+tMah2NE9tP8F3Z7+hetDsbf9pIdbvqPH3yNMeEURMIcRTg6+tL9+49KFu2HAsXrqJly5ZUrlyZ\nKlWq4uOT8hAwI6xevZratWsDYF7UnlE7n6VRI2/w8v5Vrh5ay4kNH0I7OPf7FYB1Q52SlX/31g8t\nLW3+/fdfLK0t6fZLNyyKWiTcVyqVKJVKbp2VTtCMGDEi4Z6JqQmTW08m/H32ed4OeBlAU2VTOph3\nIC7uQ4zu37r+xlK3pQzuO5iw0DCe+T5jzeo12bYQk1sQZ6s1gEqlYuXKlSxbtow2bdqgr69P+fLl\n6dq1a0KZ6OhooqOjMTY2/uzneXt7M2nSJHx8HhEVFUVsbCy2tjbo6uoSFBTMvXt3KVnyK/r0WYSN\nTVnUahWxsdHs3z8Pe3t7AN68eZMpRwOxsbG8fx+a8L2KS7/Pfp/cQqcpO9j6swunNv9KtVYDKGhR\nFKVSybdTdrF7Wmf2/NadduO3Jrj1Cg9+mzC1YGFpwZtnb/Dx9sH3pi9+9/3w8/FD10CX7wd+zx9/\n/IFSqWThwoWAdCLFuZEz3Yt0x6W/C65TXClgkrWxW0ytTAF4H/ielvot2fhgIzYlbfhv739c976O\ng4NDlj4vtyPmHHMYT09PvvkmeSAmgOHDhzN//nz8/f2ZOHEiGzZswMbGlufPn2XYb55arWb79u30\n6NEDACMjExo27IO2th5RUWH4+z9CW1sPM7MilC1bFzMz2xTbmDevHRER73FycvpkPOqUmDBhAjNn\nzkz43m7sJr5qnr+OyB9eMoKL/ywGoHrrAbQeswZVbCzTm+qgpaVFm582UKV5L+6fP8iDcwe4vH8l\nBw8exMHBgfpf1yc0NBRLS0tsbGwoUqQIJYuXZNCgQZQuXTrF5504cYKRo0by9PlT2g1vR7fx3T6a\ny80KNkzewNbpW7Etbcvmh5tpptWM0NBQDAw0u/80p+cchTjmIBcvXqRRoyYYG1vy+vVDzMyKEBjo\nh7GxOaGhH582MDeXTiCYmprx9u2bZOJ49+5dtm7dyrFjx3n69BlBQYEoFFC8eAmKFSuKh4dHQllb\nW76QBCMAACAASURBVAf691+WKcek7u7LuHBhN5D6fFRK3LhxA0dHR77uNYmyddtgUaICugZGGX5+\nXsD7yBb2zpZEv4HrRBr1n86ema5c99iKXfXG2JR34srexTRr0pT69esyatSoz3YSu2vXLn4Z9wth\nUWG4TnHFpZ9Lljqe/bXzr5zafYr5nvMZ7TyauLg4jTu2FeKYcfKEON68eZN69Rrw1Vdt+OYbaWgZ\nGxstzzNpM316E0qXrkG5cvU4eHB+Qr3KlSvToEED7O3tuXTpEnp6ely8eIlbt25SuHARrK3LUaRI\neS5c2E1w8AeHBjo6+qjVKlxd51KiRJVM233gwHyuXPkf9vb23L9/P+0KMiYmJoSEhDDhSCTaunqZ\nfn5e4ea/O/h7ejd09Q0ZuvkeOnqGzG33YbHl7Nmz1KlTJ1k9b29vrK2tsbL6sKXpxYsXHDhwgAED\nBhAdHY2BgQF79uyhffv2H9VVqVQsWbKEGTNnUMCiACNWj6Bi3YpZ8j77l+9n8dDF2FW1w+eaD/7+\n/lhYWKRdMRsR4phxcr04XrhwAScnJ2rX7oiLy/A0y8fERDJzZotU75cqVRV7eyfq1OnyUX5YWDAP\nHlzA0bFplv0vv3nzGB4/vsr8+QsYNWpkuutZW1vj/+Ytk4/HZokdeYF9s3rhdfRPDIwL0frHteya\n2hmAEiVKcOv/7Z1nWFRHF4DfpVcBK6Ci2Bs2NBasUaOfGjRqoth7V9DYNZaYaDSWaGKJlYCx9x6x\nYO8RUSwUQQVFBaR3lu/H7C4gGBFYKd73efbZu7P3zplb9uzMnDPneHlhaJix53zjxg2aNGlC1WrV\n8bzrgZ6eHgCOTo6sXrWaVm1acezIMYyNjfnyyy85c+ZMJpkg5qfHTxyPq4srlepWYtKmSVjXsc7V\nuawYuYLjG4+rPvcf0B9XF9dc1ZlbJFeeIsi0adMwNDTNlmIE0eubN+8ckyfvwd5+GhYWValTpx2N\nGnVj1KiNDBy4IpNiBDGvWL9+xzwd/rx8KXqLH5ufWENLh27Tt+ZZOwoD3Wa6Usa6Nga6WhxdOpha\ntWszc+YsAgICMilGgA0bNgLg4/0YT09PVXkLuxaYlTIjKDRI1aPUN3j/fJ+Ojg4b1m/gVfAr6lWu\nx/jG41k3ad1HTYOkJzQ4FI8zHgC0ai0c07e5bstRXYUZqeeoZkaMGMHWrc6MHLmB0qVz92+eH9y5\nc5xHj47w+PHDbB/j7u5Ol6+7M/lACJrZSF9aVLh9ZAOn103m3NnTNG2a2ZUHxChi/o8LCQwMIjAw\nkPCwEDp27MjJk2lpW+Pj4yldpjSz98xGU1OT4KfBuM5x5dWLD8eBvHnzJl98IXxIazetzS+nf0Hf\nMHuGlCDfIP6a+xdXD1+lUaNG7N29l5CQEIYOHYqdnR3Ll+dvjE2p51iEWLJkCdu2bcfeflqhVIwA\nUVGhFCtW7KOO+WPNWio17lgoFWNKcjIuji1w3zyL5MSEbB93bPlIzm2Ywo7t296rGF+/fk2Lli05\nf/EK9z09CA8LoVz5Chw/fjzDfnp6epgVNyMmMoYG7RrQsmdLQt+EEhUVxUTHiWzatCnL+lu2bKlS\njNu3b8dEywSHsg64ubplub+SIN8g5nWfx0ibkchCZJxxO8MF9wuULl2aWrVqce3atXxXjPmBpBzV\nyIwZMyhRogJ16+ZbNLdcI5PJuHXrBlZWVvj4+GTrmNNnztDw69FqbpmaSE3l+cObnN+2mKVfm+J1\n7r+X7KWmphLodY1bRzdy6eKFTEaT9AQEBJCclERKklC6traNeP3qJWFhYRn2i4yM5FnAM6o2FD6m\nRiZGlLIsxaxZs9iwcQMjRoxg3bp1meovU6YM8+fPJzU1FQcHBy5fvMzypctZMnAJbwIzBw+OiYxh\njO0YRtUdRbHkYnjc8eD0qdNZGo4+RyTlqEaMjU3o0GFUfjcjV1hbiygyz58/Z8GCHz+4f3R0NFGR\nEVjZtFB309SCprY2M45F0bSnI0mJ8ez9sTcbhtfltf/9TPsmJybi6tSKzeOFMrl06VKG7+Pi4vim\nZy/KWVXA399fFXQjJSkRu5atuX37lsrZv04dG2QyGYmJifz6668APLn7RFVXi14t2HdgHzLFqHLs\n2LGq7+RyOc3tmrNv3z6VUUeJhoYGpiVN8TzvycQvJrJl1hbVXOTcrnMJuBfAabfTHD96/LNz8v4Q\nknJUE3K5nKioCExMCnfUGZlMPCKpqals2/Zha6WPjw8GhsXQ1Mpbx+RPiaa2Dh3H/4bjDn8q1W/N\nS797rBtqw74F3xIbkeaPemTpQHSSwoiKimL16tUMHz5c9d3t27epVr0mt7z8CQ0L5+TJkwQEBCCT\nadC8z3RexGhRppINrVq3wcXFBS8voXyTkpKYOHEiAO673FX1DV00FGNzY+Lj4gFUSvDYsWPUqVuH\nq1eEg/6MGTMYOly4il2/fh1HJ0e+m/4dq0atwr6dPQdXHeQH+x944feCRzcf8fTpU+zs7NR3MQsx\nknJUE8nJwoWlsCchKl3aGm1t3WyHpnr+/Dl6hh83R1lQMTWvyICV7nQcK/xO77vvZU3/ypzbMpfT\nG2bgfeUoO7f/jZGRERMmTFAprFWrVtOiZWvKN+1B2xFLSElKYOzYsUyfPp2SpUrz+MxWwp7cIez5\nIxIT4pk5cyYdOnyFj48PhoaG1K8vcuq473InJioGAB1dHcpVFfGi9Qz0SEpO4siRI3Tt2pXilYtn\nCITredeTBw8e0LlrZ3p83wOPsx6079CexYsXs3jxYq4dvca4xuNo0KABFhYWn/KSFiok5agmlAv3\ndXUL96oQbW1d6tRpy5AhQ7MVpcfCwoK4mMhP0LJPR9NvJzFw+Wk0tbRIiIvmgutCnl3eyd/bXFSK\nTMn0GTOZMWs2Pebt4auxK5BpaKGhoYGBgQHz5s1jwfy5mJcpTUR4GElJSZQoIdar16tXnypVqvDo\n0SNevHihqs/QOO35uesu/qDiY+PRkGmwY+cOmnRpwoJDC9DV10VTSxObujbcvnUbm7o2NPumGVXq\nV8Hrkhdr/1gLoErQFfU26r1LFCUEknJUE8pexO3bR/K5Jbmnffsx+PkFUL68FYcOZU4OlR5ra2vi\noiNJSS4YWfLyCuuG7Ri86iJNe09Fz7AYK5YvyxSu68yZM/z+xxr6LztD1SbCid+6QRuMzEqxefNm\n6tWrx/wFC/C866E6Zv78eYo17CIj8YEDBwCQacg4lXIqQ/0/7PsBAC0tLZKSkrC2tiYsMAx/L3/G\nNhpLs+bNePjwIVM2T+FozFGq2lZlfo/5DB0yVBVBZ8qUKar6tv+9nbdv300sKqFEUo5qQiaToaen\nR0JCTH43JdcYGBRjxIhNvHgRRPfu3TNZV9NTokQJ9A2NeBvk9wlbqD4O/jKIf49tBqBcraa0H7EY\nPX3DLFOkHjx4kAr126piVXpfPcrqflWIfvtGFWzkwP79fP/995w9e5bAwEAaNWqUoQ4thfuTTRub\nTM78Dy4/oELFCvTp2weA/3X6H8+9nxPxJgIAebIceYqcTkM7oaOrg/cd4cAfFBikqqPr112xbWxL\njcY1MC1p+lFLQj83JOWoRvT1DdDXLxrzb0ZGZrRtOwTI2Pt4F5lMRomSJQn2K9zh8yNDXiCXy7n7\njwtHlg3PcD7RESFUrlw50zHm5ua8fHSDnTM7s3ZAFXbM+pq3L/z45+QJ1UqX5s2bs2zZMtq2bZsp\nHmJYWJjKI0BLS4vIt+9MT8jAyNiI6VOnA3D27FmqVa/G2b/PoqGhwZUrV+g8srNq9y7DugCwf/9+\nlZP5l22/5PbN21RrXI2I0IginQMmtxRua4GgQK6QOXfuHF26fM3EiTvR08t9TMaCwvPn9/n772m4\nu599r7Nzj17f8vh1Ct/+mDkndWFgQVvxs/hmliu3Dq3judcV9I1MmbzvFVo6Omwa2QBdeQyTJjky\nbuxYldEtJSWFVatW8erVK6ysrBg8eHCWywaVpKam8vr1a8zNzRk/fgJTpnxPczs7XgQFoaOrQ2JC\nIgAjFo+g94zevH7+mr5WIgRdcHAwpUqVYtr0aSxfthxzc3OCg4M5nnAcHR0dXvi/YFHfRTy69ghj\nM2Oi3kaRmprK5cuX6TegH8Evg0mIT0BXV5f4+Hg1X9G8oSCukNkCvALuvVM+AXgI3AeWpCufiUiW\n9QiRPVCJraIOH2BVunJdYJei/BqQPg3dIERmQ2+gUAUCPHjwIBUr1itSihGgfPk6aGrq0KxZs/ca\naH5Z9DMB/57mwfl9n7h1eUuNFt8w9I/LNOnpRFx0OD931CUuKpx+y84SGp3IhPHjmTJ1qmp/TU1N\nJk+ezJIlSxg3blyWijE2NpaAgABq166NhoYGmzeLIfsff/xOxYoVeREUxIoVK4iOiub69evYNrJl\n48yNTG41mdOupwERFbxMmTJoaGjQsmVLmrdoToJiNc+Tu09ITU1lpM1IfG/7YmRqRNTbKOy72QOw\nZesWnvo/xelPJxafXExKSgp2Lez4/fffAbF0cdWqVdS2qY1MJmP16tVqvcYFmewox61Ap3fK2gL2\nQF2gDrBMUV4L6K147wSsJU3TrwOGIdK1Vk1X5zBEnuqqwErSFG1xYC4iR/YXwDxEmtZCQUREBDo6\nRUsxKunRYw7Ae40z1apVY87sWVzcOjvP8tB8Kp55XlRta2mLUGudxq/E1EIs/9w6wQ79YmakyoWr\nluc9ryzrqVGzFk2bZ/YfHDt2LNbW1jx48ABIM8D0XrifSrYiFbympiZz5szB29sbCwsLvun5DZ4X\nPdkyewu6+rqEv01LwtnNvhuXL15m9qzZVK1WlYnNJtJBowPxMfEkJyVjbGgsYj9Om06Hjh3YsnkL\nABunbsTI1IhZO2dx5fIVJk6ciEwmQ19fn5XrVlL367pUrl+ZefPnkZiYmKtrWljJjnK8CLxr0hoD\nLAaUs9LKtUndEKlck4AARArWJoAFYIxIsQrgAijXWdkDfym29wHtFNsdEbmrwxUvNzIr6QJLbGwc\nGhqF1xH6v6hcuRF2dn2YPHnKe5XfpElOyJJiOLZ8ZJbfF1S2OoooNMVKW6GRbm24w0+HASheVsw1\nWlZvDICL85Ys60mRp3L96hVkMhldu3ZVlaefr9XR0eHefeH8ramtz5Pbp7GyssLR0ZGlS5cyYMAA\njh45Smy0iIhUwqIEvaf3plGTjEacZcuXMWXKFHy8fZCnpN0PuxZ2Yhli1ap0+l8nQpJCALC1tUVb\nQxuPsx606tmKLiO60HFIR1ZfXc2yc8vY/GgzwxcN5887f6JrrPvZ9h5zapCpCrRCDIPdAeXdsgTS\nJ9gNBMpmUR6kKEfxrsy4lAxEIPJYv6+uQkHFihWIjAzO72aojbZthxEUFISpqSkxMZkt8np6epw9\n48bjC7u5tH1xPrQwd5hXrktKUiIL2spY0FbGzcNiLXP4q2fsntsD/1uncHKahEwm4/Dhwzg7O2c4\nftqUyartkSPT/iDq1Kmj2pbL5SQo5vu2zxCuPwYGBgBUqV9FZbk2NRUDpumu03GZ70LIqxBVHdHR\n0Uydkja0B1i4cCHTpk/j4oWLxMfH06VrF9oNbMeys2KA17FjR6Kio2jerTkAkzZMYuqWqdRqWov6\nbTL6bdqPs2fR4kUEBQXxuZHTsClagBnQFGgM7AbyzaN0/vz5qu02bdrQpk2b/GqKioEDB7J69e/4\n+d2kcuXG+d2cPEdTU4vx47excuW3XLt2jXbt2mXap0aNGhw+dJBe3/Xm5r7fMLOwxqxcTcrXbUH9\nTkPyPez+S587XN7+C1/0mKBaCz7zeBS/fVcO76tH2Tmnm2rfJ5f3Uq5ceQL97qKbFI6T4wRevX5D\nRevKGJoUJ+xVIFpaWvTv3x8Ae3t7Ro4cSbXqNTh//jz29vaqupQGxLZt2+Lu7q4q19PTw9HRkRUr\nV1C5bGVmO85GJpMRHBzMTa+bvH0lBnAxsTGMGTuGlStWZlpLDeDg4EDlypW5ceMGBw4cICQkhLGr\nxhITKf7ELC0tiY+Lx6LKh1fH9J7WG++b3tg2suX+vfs5SrKWU9zd3TNcn09Ndi0/FYEjgI3i8wng\nF+C84rMvQlEqF5f+ong/iZgrfAqcA2oqyh0QPc8xin3mI3qhWsBLoBTQB2gDKMO7/AmcRRhv0lMg\nrdUACxYsYO3azYwe/VehX0b4PjZtGsHs2ZMZM2bMe/eJj4/n6tWr3Lhxgzt37uJ+4QKRUVGYW9eh\nWqvv+KL7uAxD2E+F6+S2PLnjTtkajRiy+grntv5AqQq1sKrbktV90/7rdXR00NbRJTlFTpkKNUiI\niUCeKse4ZDlaD/0ZK5sW/DmkJlMnjGDy5LQeo4GBAXFxcYwbNw5Ly7L8vmYttWrWpFTJkkyYMA47\nOzumTp3KsmXLWLBgAbNmzVL1FtPTp28fnsc8p6JNRS79fYmomCj0jPTo1LYTWzZv4fz589y8eZNi\nxYrRunVrqlevzvo/1zNmtLgnxcyKsS90HyPrjcT/nj8XLlygVatWuMndsvVcyuVyHJs60r199wwJ\n0z41BTVNQkUyKsdRiGHvPKAacBqwQhhitiMMKGUV5VWAVOA6MBEx73gMWI1QjGMV9Y5BKMTuivfi\nwC2goaKdtxXbabPRggKrHJOSkjAxMaVfv+WULVs0I54sX96DFi2acOLEiWwfk5yczK1btzh+/DjO\nLtuITZTzzdw9lK3xaXvYT+9ewNmpNQAamppoa+uQEB9HGeta1Gk/gDMbZwLQYfRSytVuTumKddAz\nMsmyruO/jcM42oczp8WqFrlcjqamJmvXrqVfv36ULFWa5g4zSElK5PUTTwLunOH4saNZ9rjTc+fO\nHRo2FJGRqttW56vmX7Fp8yZ0DHSICosiJSWF+Ph41q1bx5s3b9DW1ub2ndscO3JMVcc052msGLGC\n5CRhRKpfvz4RSRFsvL8x29dqTpc5NK/RPF/jOhZEV54dwBWEEnwODEG491RCuObsIM3N5gFiiP0A\n0bsci1CMKLY3IVx2fBGKEWAzYo7RB3ACZijKw4CFwE2EQl1AZsVYoNHW1qZ58+Z4ev53sNHCyoMH\n54mOfkvdunU/6jgtLS2aNm3Kjz/+SMATXwY69GTb919y6/Cfampp1lSo1wqnXc+wrNYQeUqKar7v\ndcAjru1exoyjEcw7l0rz3lOxqmP3XsUIUKedA7f//Vf1WZlWwsnJCX19fUzNzJAnJ9FuxCIcFh8l\nJSWFmTNnZ1nXmzdvcOjrQJs2bVSK0bKyJUG+QXTo0IGJEycSERLBV199hZeXFxUrVeSHeT+wftN6\ndh7ZSYRWhKquSRsn4XXZCw2ZBjKZjJpNa+Lh4YG/lz8Tm03M1nUK9Ank2vFrn53VuiiM9QpszxGg\nZs1aGBtXp3Nnx/xuSp4SHOyLq+skVq1amSFUV045ePAg/foPoNXQRTTpkb1cO3mFXC5n7w/dCfA4\ni1EpK6zqtubuyc3UaNmTnj/syFYdiXExLLU34/mzp6pIN6VLl6FhwwacPHmSq1ev0rx5cyrVb0Xk\nm0BCgp6wd+9eevbsCYiMg5O+n4SRoRFnz50lXh6PZWVLGrRrQKqif3HijxM8f/YcmUzGqVOnaNWq\nFQ0aNMDb25tTKadUc7gRIRH0LCXqPZ16mif3njChyQQGLRhE55GdOb7xOPEx8ZiWNsV+jH0WZ5Px\n2nylKdyVS5YsyZs3mYPmfioK6rC6IFNgleOpU6fo1u0bxo//G0PDQuOi+UEiI9/wxx/9GTVqFL//\nnnduHkePHuW7Pn3p/fNRipUuj5mFelNLKFfCTD8agbaOPhtH2BD+6hnGxS2Qp8oJfx3IrJOx2Y5N\nubp3OVy3/EmXLmLZXmJiIjKZDG1tcfylS5dwc3PDzMyM4cOHY2Qk/GB37tyJg4MD9dvUx8NdBKXQ\n1dcVwW/jE1XuUr8u+5Up3wtXoOjoaOzs7DIk5jqdKpzE4+Pi6WrQNUPZx3Lt2DV2/rQTX09f4mOF\nRb1ps6aquJH5wadWjoUvyUchYf/+/fTs2RM7uz5FSjEmJsbh7DyBWrVqsWrVb3lad9euXWlQv75q\nHnDuWfknMWT5Xj+OZY3GDPvzDvt/6ovvtWPIUyE1VU7I00eUqWzz4UqAVJkmv/66TKUcdXR0ePbs\nGdu3b6dv3760aNGCFi0yR0gPjxCzRR7uHkzaMImVI1eSEJeAn58flpaW6OjoEB0drcrls2PHDvr2\n7ZuhDsvKlqptPX09TiaeREPz/bNmcrmct6/e8vDaQ7xve6OppUk122pU/6I6+5bv4+j6o4wYNoKD\n2w6ir6/P48ePsbW1zdZ1KCpIPUc1EBERofJNmzfvXD63Jm/Zs+cHDAwSuH79mlpccQICArC2Fj3G\nqQfeYGCqPtcRZc+xzZAfcd86l86Oa7D9ehR/DrPh7Qs/9A2N6DR5EzVbfpOt+lwmt8P/zlmVq46f\nnx8tWrUhIUWDhOgwHj7wwsrKKstjT5w4QefOnbGwtuCl/0uATKlVExMTWbBggcpi7DDLgR2LdmBs\nZsyOwB3oGWR26wEIfx3OlcNX8Lnlw/OHzwl9EcqbF29IladS2rw0lawrIZfL8fb2Jiw0jLLlyuLi\n7ELLli2zdd6fCmlY/fEUOOU4cOBAXF1dcXLahYlJ6fxuTp5x794Z9u//icDAwEwRZfKS9L3FAb+6\nUalRe7XIcf9rAeed5wNQrFQ5nHY9QyaT4en2NwcW9UcmkzFwxTlePL5FlcZfcWbzHIJ9PXDc4Z/l\nH8PrAC/+HFaPpKRENDQ0sKnXALmJNT3n72XHtK+oX7kke3btfG974uLi+Lb3txw7coz2HdrjdkoY\n8nx8fAgPD2fQ4EE8fPCQuXvm0qqXWMkTHxePrp5uhmsWHxvP7VO38b7lza0Tt/D38qdCxQrUqFED\nm9o21K9fH1tbW6ysrLJ0HSqoSMPqIsDAgQM5cOBIkVKMISHPcHP7g/btO6g1tP6NGzcyfC5bs4na\nZIW/9Fdtp8pTcJ3SgcS4aIIeXldFuTm2fDghgb64rU877uru5dj1mZqpvlJWNTEsZsqxY8do1qwZ\nD73uM+WgOxoaGpSv15aA+0f/sz36+vocPZy2z61btxg0dBC+3r4kJiRi286W/Rf3U6x4Whg8PX09\nYqJiuHLwCrf+uYXfv34E+QVRqnQpypUtx6BegxhybAjm5ua5uFKfJ5JyVAORkZFoK4IWFAXi46PZ\nvn0a333Xi02bsu8blxOqVlWkIy1hwaTdgZxYPQEdHV06KPK4fCyJcTEEPrhK4IPrGJe0xLxKfUzK\nVEBbV5+vp2yiXO1mHFsxmqjQl0SFiuGsTCZDp3RVCA5meP9vGTZsKGXKlMHY2JjpM2bw98FdWSpH\nmYYG1Vp+y+JflnLxgjuaWlrER78lISaCe/9sZeywftlqs7e3N8uWL2PXrl3Ua1+Pn8//jI6+Drp6\n4pnyuePDw6sPeXLvCQ8vP+TZ42eULV+WZk2a8Z3Td3Tp0kWtPfvPBUk5qoEHDx4UKSPMpk1jMDDQ\nZsMG9fghyuVydu/eTWBgoGo5nEkJC/4cUpPXz7wpX6sp4cFPMTApgY5+9iMdKecUP4SWljbJirQO\neobGxMdEqaLzdOrUkSpVqqj2/X7yZJb9uozEuBh09DOHJAu6f4HRg3qjqalJ6TLmbJnQgqiQIFq3\nbcecOXNU+928eZMNGzagq6tLo0aNiImJYd/+fXjc9SA2Npa6LeviuNGRFj1aoKmpSfibcHYu3sm5\n7ed4G/yWSlUqYV3BmrGDx9K3b1+pZ6gGJOWoBvbtO0D58vXyuxl5wqVLOwgNDWTKlCmZ5tkiIyPp\n378/P/3000c7giu5du0aDv368zYiBjNzK2LevgIgyPtfatSoQXKJUoS/CmCVQ0VaDfiBtkM/nDtb\niU07B1KSk0lJiOb1E08iQoPRNzAkJTmZ+DjhpO3s7MzAgQORyWTExcWxbds2Fv70M8+fPcXAwDBT\nTMbo6Gi0dXTR0sk4MpDL5Vzbs5LYsJdMnz4NgLV/rGbgwEGMGDGCDRs2AJCQkMCYcWPYuXMnDds3\nJDU1lWNnjqFnoIehmSHj1o6jUcdGGJkYIZfLuXzwMkfXHOX+lfvUrF2TGU4zGDZsWJZrqiXyFkk5\nqoHAwEA6dXLI72bkmlOn1nP16i7at2+vSjSfHldXV44cOYK5ubnqx/8xyOVyvuvTlzL1OtJ//Go0\nFf6AKUlJXPx7Ec/vnCI6OoDEBOFnZ/v1qI+qv8ec7Rk+R4e9IjYyFBkykMk4vKgvgYGBKmOGvr4+\nI0aMYMSIEe+t08zMDEglNNCHiFfPCHp0g9CnD3h0+SBJCfE4O/+lUlxff/01b9+m5du5ePEi/Qf2\nR9NQkz9u/EGFWhWylBHoE8jGqRu5dvgaslQZvXr2Ys+mPSorvsSnQVKOecyNGzcICXmNlVX2fOMK\nKnfunODq1V0YGhri5OSU5T7KtKTR0dE5kpGQkMCLwOd8t3yeSjECaGpr02bwPBgsMvOFv/RHLpdT\nrFTO59Fe+XkSF/UWo+LmxEa8ITEuGsOS5bl0+eOcms3MzGj8RRP+HF4XExMzrCtVom61qszd5oq9\nvT06OjqZjklKSsLRyRFnZ2e6O3ZnyE9ZRyQK9Ankz+//5F+3f2nZsiUb122kW7du+R696HNFUo55\nzM2bNylTxqpQG2Tu3v2Hw4eX0rx5cy5fvvze/ezs7IiNjUVfXz9HcvT19algXQl353k06eVEqQo1\nM+0jk8kws/xwNLyUpCTCgnwIefYQ35uneOXrgYFpKTRIJdj3DhEhwthiVMyE6Eix9lhLW5v/jc/e\n+uL07Ny+jeDg4Gw5RV+/fl1kC9SBlZdXUqV+lUz7+Hr44jzHGY+zHnTs1JEHXg+knNIFAEk55jFX\nr16jdOnMP4DCgo/PdU6eXMW2bdvo1avXB/fPqWJUsvaP1UyeOp0tYxphUKwEjXt9T9NeWa9De+cz\nkAAAFSFJREFUl6ekEPHqKUEPb/A6wIvHlw/x2j8ttZGhUTF0dXUJC32DTCbjyy/bYWNTh4Zj+9Cs\nWTMqV66cIRmWpqZmjtpctmzZD1qDk5KScJrkxFbnrXQd1ZVhS4Zl8im8d/EeznOc8b7lTbdu3fj7\n/t+SUixASMoxj7l715MyZdTnm6dOXr/258CBn1iy5Bf69cue20lu6dixI14dO5KUlISz81+MHDmC\nV34eqp5gQmwkkW+eY2BoTHhI1pHVhwwZws8///xR/pc5VYzZ4dy5cwwZNgSZnowVF1dQtUFV1Xep\nqalcOnCJnYt2EugdiIODA6f2nKJ06aLjE1tUkFbI5CEhISGUKVOGceNcKF68cPmZRUWFsmJFL2rU\nqMHDhw/zrR0ymYxGjRpTqnRpoqOj0NHWxsqqAt27d6Nbt25Uq1admzdvkJqaionJ+0OIqZtNmzZx\n8dJFbOrYsHbdWvyf+GPb2Jbk5GS8vb35xvEbBv04KIMSPrvjLNsWbCPyTSTDhw5nzpw5+XoOhQ1p\nhUwhxtnZGXPzioVOMQYEePDXX5MoWbIU69ev//AB72Bra0vbtm1ZtmzZh3f+AP/1R/ep/wTv3btH\nnTp1MgW/mDV7FosXLabFNy24uu0qEbFiDrN80/KYVzRnxnczKFWuFACeFzxxXeDKnbN3MDQyZNq0\nacyYPiNLw41EwULqOeYhjRo1wcCgOl9+OTS/m5Jt5PIU1q8fTNeuHfj999//Mwn9+1Aqj4JyH3JL\ncnIyS39dyuxZIhjt3bt3VX6chw4dYsDAAXRz7MbgHwe/9/i/5v3FjkVpsSDHjRvH7Nmz1br0sqgj\nBZ74eAqMcixRojTduv1QaNx4QkOfs3XrBIyMDAgMfJbjIARyuZyoqKhCO0RMTU3lxo0beHh4sHr1\naqJjo0mQJ9CgfQNObjmJrq4uVhWtCA8PJzYulv5z+/Pt999mqicxIRGX+S78s/kf3r4RybBOnTpF\nhw4dPvUpFUmkYXUhJj4+Fh0dg/xuRrY5enQptWpVZ+3aNbmKzqKhoVFoFWN0dDT23e25cfMGJcxL\n8Mz7GRN+n0CX0V3Q0tJi8sbJnHI5RUpSCvpG+jTu1BhjM+MMddw9f5fdS3dz1/0ulpaW/DDzBxwd\nHSX/xEJOdn4RW4AuwGvSEmwp+R74FSiJyPkCMBMYCqQgEmqdUpTbAs6AHnAcUPpr6AIuiORZoUBv\nRLZCgEGAMtHGT4r9CiTXr19HLk+lRImCP98olyezY8dMAgLuc/t2KMWLF8/vJuUL/v7+tP+qPbrF\nddkWsC2T0gOh+DsN7pSp/NnjZ+z/bT9XD1wlLjqO//3vfyz7Zxl2dnZFNtPk50Z2lONW4HcyK6by\nQAfSFBmI7IO9Fe/K7INVEUm21gHDEMmyjgOdEEm2hiGUYlXFsUtIyz44F6FUQWQfPEw+JtlKSUnB\n1dUVX19fLCwsqFevHv7+/mhra+Pg4ECVKrZoaxf8Na9nzmzm7dun7N2797NUjKtWr2LpsqWEvA6h\n9betmeI85T9deyJCIzi4+iD+nv5EhUZx9+JdNLU0+aLpF/y6+FcGDBhQqOIiSmSP7NzRi4jUrO+y\nApgGHEpX1g2RjTAJCEBkGWyCUKDGCMUIQtF2RyhHe0SKV4B9wB+K7Y6IXqdSGbohFOr7o4Wqma+/\ntufEieNYWlYhOvotkZGhqu/09YvRteu0/Gpatnjz5ikBAR5cubKTFi1aqpI7fU6sWrWKuQvmMnL5\nSBq0a0AZqzLv3TcsOIyN0zZyce9FatauSaMGjUgpnkKfzn3o27fve6N6SxQNcvp31w0IBDzfKbcE\nrqX7HIjoQSYptpUEKcpRvD9XbCcDEYhUrZbvHBOY7phPyp49e1i48Ge8vb1xctqNiYlw04iJecvh\nw7/SuvUgLC2r50fTss3Ll95s3TqRpKQEnJycWLEiZ/ERCztr1q9h0MJBdBqSeaisJPJtJFtmbuHM\ntjM0adKEa1evUa9e0YiyJJF9cqIcDYBZiCG1kiI7ySKXy5k3bz5RUTK++WaOSjECGBqa4eCwKB9b\nlz3k8hT27/+RMWNG0aNHD2xsbD7bebGw0DCqNMx6eWdsVCwu8104sekEdWrX4YzbGZo1a/aJWyhR\nUMiJcqyMGGbfVXwuh5gPbILoEZZPt285RI8vSLH9bjmK76yAF4r2mCDmIIOANumOKQ+czapB8+fP\nV223adOGNm3aZLXbR5GYmEhERATTp88gMPAlgwatokSJ8h8+sICRkBDLuXNb0NfXZOXKlZ+9BVVD\nQ4PkxOQMZZFhkbjOd8XNxY3KlSqzf89+vvrqq3xqoYQSd3d33N3d801+drsPFYEjZLZWA/gjjCZh\nCEPMduAL0gwyVRAGmesI6/UN4BiwGjHnOFZR7xiEIaY7aQaZWwgrtgyhgBuS2SCT536OsbGxKmdo\nE5OSDBiwokAqxqSkBO7dO83Tpx6YmlrQrNl36OmJSNlyeTKurpMICLgPiKmB7ASSKOqYmJqw9NxS\nqjSoQmRYJC7zXXBzdqNmzZos/nkx7durJ5mXRO4piH6OO4DWiHnA5wgL8tZ036fXTA+A3Yr3ZITi\nU34/FuHKo4+wVp9UlG8GXAEfRI+xj6I8DFgI3FR8XsAnslQ7OPSjbNlqNG36HVWrNkNXt+D5LiYl\nJbBokZg369y5C0+f3mHr1vOMGLERP79bnD+/GX19GYGBgVhYWHz2PUYl8fHxyFPkLB28lIt7L1LH\npg6HDhyiXbt2+d00iQJGUZh4ytOeo7+/P5UqVWLw4FVUqJCz0P/qRi5PYeHCtB5OTEwMurq6aGlp\nYWRUnJSUeJycHJkyZcpn6arzPuRyOZqamujq69K8WXMW/byIpk2b5nezJLLJp+45St2Jd7CysqJ1\n6zZcvbrjwzvnEwEBHqrt+Ph4DAwM0NDQwM7OjujoMHr27MmiRYskxfgOiYmJAPxz4h/OnjkrKUaJ\n/0TyXH0HTU1NfvxxAZ07dyUlJQlNTe0PH/SJUS5RVPYYU1JSMjghr127Jr+aVqDR09NDLpd/tpZ6\niY9D6jlmQbVq1YiJicLP71Z+NyVLjh4Vya7c3NwAMijG5cuXY2yceRmchEBSjBLZpSg8KXlurY6P\nj6dhw4aEhsYyatSWAmPM8PG5zqlTvxMbG05sbAwVK1bC1NQUD49/AbC2royvr3eBaa+ERF4izTkW\nAPT09PDw8OD166e8ePE4v5sDwL//HmP37h8YPXoIL14EMWTIUCIi4ihTphmdOzuip2fAunVrJMUo\nIZFHSL+k96Cjo4OhoRFhYUH53RSOHVvJiROrGDduLAsXLsTExITx48cRG/uWWrXacOPGXvr370vH\njh3zu6kSEkUGaVj9HhISEjAyMmbsWGfMzCzzvP7stSGW337rTXx8NNu3b8fBwSHD96amZkREhNOl\ny9ccOnRArUmjJCTym4LoBP5ZcvbsWQwMjPNFMYaHB3PmzEZ8fK6RmprCmjVrMilGgAcPvHBzc2Pg\nwIGSoUFCIo+RlON7OHToEAYGpp9c7pUru7h40YWWLVswcuQvjB49Gm3trN2JLC0tGTRo0CduoYTE\n54GkHN9D+fLlSU6O/ySykpLiOX58JcHBPiQkRLBv3x46d+78SWRLSEhkjWSQeQ82NjaEhLzg8eMr\naqk/MjKER48uER4ezMaNI0lKeom9fQfu378nKUYJiQJAUZioUotB5vbt2zRq1AhdXX1mzDieZ/XK\n5XLc3NZy585xDA0NCAsL5auvOnL8+DHJoCIh8R9IBpl8ply5cgQFCfedUqXK0rr18Dyt/8iRJbx6\ndZ8LF9yxtbXlt99+Y+zYsZJilJAoYEg9x3Rs376dfv36AWBmVpaJE7flSb1K9u1byP37Z/Hz86NS\npUp5WreERFFH6jnmI4sW/QLAwIErsLZukON6UlKSePrUk3v3TmNhUZUvvuhBQIAH9++fZd68eZJi\nlJAoBEjKUcGSJUvw8rrHpEl7KFasZI7qCAl5xr59CwgLC8LIyJiwsBA8PE5Ss2ZrDh36mQULFjB3\n7tw8brmEhIQ6kIbVCpRO1PPmnctxHUuX2pOYGIuvry8VK1bEy8sLB4e+PHjgRYsWrTh37ozkrC0h\nkUOkYXU+EBkZCUCtWq1zdHxqaiqnT29AT0+HV6+CVCHDateuzZ07/+Lp6Um9evUkxSghUYiQlCNi\nHbWGhibffjs/R8cfPryEJ0+ucfTokUyxFDU1NWnQIOfzlxISEvlDdpzAtwCvgHvpyn4FHiLSs+5H\npFNVMhORLOsRkD6/pa2iDh9gVbpyXWCXovwaUCHdd4MAb8VrYDbamiNkMhkaGhrEx0dn+xi5PIWX\nL7355581+PhcwdPzLi1btlRXEyUkJD4x2VGOW4FO75SdAmoD9RCKa6aivBbQW/HeCVhL2hzBOmAY\nUFXxUtY5DJF1sCqwEliiKC+OyHT4heI1D1DLYueSJUtStWo1rl/fn+X3qalyfHyu4eLiyJIlXVi6\ntCsrV/bE1XUyiYkBuLg4U6FChSyPzS2fOm9vUZZXlM/tc5D3qcmOcrwIvH2nzA2QK7avA+UU290Q\nqVyTgADAF2gCWADGiJzVAC6I/NQA9sBfiu19gDJHZkeEEg5XvNzIrKTzjAkTxnH79sFMwW1fvHjM\npk0jOHx4Ef/7X0v69+/LlSuXOHnyGDExUdy+fZMePXqoq1lF/oGXlKMkr6CSF3OOQxEKEcASMTRW\nEgiURSjLwHTlQYpyFO/PFdvJQAQiR7blO8cEpjsmzxk1ahTnz5/n0KHF9O27lEuXXHn7NoigoMd8\n+20v1q1bi6GhIfPnz6d+/frqaoaEhEQBIbeBJ2YDicD2PGhLvqKhoYGzszMlShixerUDhobRODh0\nYc2a33Fx+QtDQ8P8bqKEhEQBpCIZDTIAg4HLgF66shmKl5KTiGG1OcKAo8QBMQep3EeZQFgLeKPY\n7gOsT3fMn4j5zHfxBVKll/SSXkX+5UsBpCIZlWMnwAt4dylJLcAD0AGsAT/SDDLXEYpSBhwnbf5w\nLGmKsg+wU7FdHHiCMMKYpduWkJCQKBDsAF4ghs/PEXOMPsBT4I7itTbd/rMQGv4RwqiiROnK4wus\nTleuC+wmzZWnYrrvhijKfRBuPRISEhISEhISEp8rjoje5H3FNojhtBvCf/IUGYfSuXEwD1Acp5Q1\nCAgDEoBn5L0z+/x057aNjM7s3yNcoYp/AnkuiPne+6T5kKpL3jPEVMod4CbQOBfynIF4xP1RLg5Q\nPhvBQAxiFKJcHJDb89lF2mKHQYrz8VWcn/JZrIuYHgpEPDuewC2grZrl+SCmmyoB0YjnR93yTiiO\nu684Tx01yvNFjEQ9gQdktFvk5eIRazJez6yTMxUA6iBOWg/QRDz0lYGlwDTFPtOBXxTbyvlMbcTQ\n25e0+cwbCEdxyDyfuVYh6xli+K4JnEPcjO6I+Uw/hAN6XsgCmILwzdRDzMvGAvURij4AOA34k6Yc\n1SWvq2JbOTdcSs3yLiGmYEyB/yGuc07lLUfcr3sIQ9xOxLMxD3G/5iHumR9iLjs359MbcU8aIH6c\nfopzWIVYoGCCeBYfAt8pztUZGI1YDJHe5Uwd8kDMy/+LUAjplaM65GkBIcCPiuPNSPNsUYe8wQiF\nNRrQR/w2rD5SXnpbhVKeqWJb2enZTcbrOZoCSi9gU7rPcxBK8RFQRlFmrvgMomcwPd3+Siu3BRkt\n4emt3EpreS9gM2mW8F1A+uQw6xE/RmV029zIAnED4hTbDoh/q6mKz36K80yvHNUlbxdwWHFcetQl\nbzuiR9BHUZ7b69kdoRyVXgyPEA/0OtKejfWIefHcnI+y/oqIP1GlgfARYoFCH4W8ZNKURFNFHTLE\nD15bzfKmIYyS80hTjuqS1xnRczxJRtQlryNisclJxJ/tY4Ri+1h5kNETBsX+fRD36Q2Z7997yc8E\nW/eBlggFYYC4IeUQivGVYp9XpCnK9zmFv1uelYP5faAFEKWQ0YSMBCL+kZTJYnIjC8TwQBPRE66A\neBDKI1YQBQMp78hXl7xqgBFiOO0ONFKTPAvEdZ2BuLbrEOvvlctKcyrvhWJbuTj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"text": [ "" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the second GeoDataFrame is a randomly generated set of circles in the same geographic space (TODO ...use real data)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "polydf2.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `geopandas.tools.overlay` function takes three arguments:\n", "\n", "* df1\n", "* df2\n", "* how\n", "\n", "Where `how` can be one of:\n", "\n", " ['intersection',\n", " 'union',\n", " 'identity',\n", " 'symmetric_difference',\n", " 'difference']\n", "\n", "So let's identify the areas (and attributes) where both dataframes intersect using the `overlay` tool. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from geopandas.tools import overlay\n", "newdf = overlay(polydf, polydf2, how=\"intersection\")\n", "newdf.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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VYUzQGUZdPsknO9bjOLAHx8Lv0KZNG4yMjPD09ESlUlGtWjVOnDjBk6gnPI16+lo74TfD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\nLi4u2NnZ4ezsrJM9oSIiIrh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BoroCodeBoroNameShape_AreaShape_Lenggeometry
0 5 Staten Island 1.623847e+09 330454.175933 (POLYGON ((970217.0223999023 145643.3322143555...
1 3 Brooklyn 1.937810e+09 741227.337073 (POLYGON ((1021176.479003906 151374.7969970703...
2 4 Queens 3.045079e+09 896875.396449 (POLYGON ((1029606.076599121 156073.8142089844...
3 1 Manhattan 6.364308e+08 358400.912836 (POLYGON ((981219.0557861328 188655.3157958984...
4 2 Bronx 1.186822e+09 464475.145651 (POLYGON ((1012821.805786133 229228.2645874023...
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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ " BoroCode BoroName Shape_Area Shape_Leng \\\n", "0 5 Staten Island 1.623847e+09 330454.175933 \n", "1 3 Brooklyn 1.937810e+09 741227.337073 \n", "2 4 Queens 3.045079e+09 896875.396449 \n", "3 1 Manhattan 6.364308e+08 358400.912836 \n", "4 2 Bronx 1.186822e+09 464475.145651 \n", "\n", " geometry \n", "0 (POLYGON ((970217.0223999023 145643.3322143555... \n", "1 (POLYGON ((1021176.479003906 151374.7969970703... \n", "2 (POLYGON ((1029606.076599121 156073.8142089844... \n", "3 (POLYGON ((981219.0557861328 188655.3157958984... \n", "4 (POLYGON ((1012821.805786133 229228.2645874023... " ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "polydf2.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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geometryvalue1value2
0 POLYGON ((923175 120121, 923126.847266722 1191... 1033296 793054
1 POLYGON ((938595 135393, 938546.847266722 1344... 1063988 793202
2 POLYGON ((954015 150665, 953966.847266722 1496... 1094680 793350
3 POLYGON ((969435 165937, 969386.847266722 1649... 1125372 793498
4 POLYGON ((984855 181209, 984806.847266722 1802... 1156064 793646
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " geometry value1 value2\n", "0 POLYGON ((923175 120121, 923126.847266722 1191... 1033296 793054\n", "1 POLYGON ((938595 135393, 938546.847266722 1344... 1063988 793202\n", "2 POLYGON ((954015 150665, 953966.847266722 1496... 1094680 793350\n", "3 POLYGON ((969435 165937, 969386.847266722 1649... 1125372 793498\n", "4 POLYGON ((984855 181209, 984806.847266722 1802... 1156064 793646" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "newdf.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
BoroCodeBoroNameShape_AreaShape_Lengvalue1value2geometry
0 5 Staten Island 1.623847e+09 330454.175933 1033296 793054 POLYGON ((916755.4256330276 129447.9617643995,...
1 5 Staten Island 1.623847e+09 330454.175933 1063988 793202 POLYGON ((938595 135393, 938546.847266722 1344...
2 5 Staten Island 1.623847e+09 330454.175933 1125372 793498 POLYGON ((961436.3049926758 175473.0296020508,...
3 5 Staten Island 1.623847e+09 330454.175933 1094680 793350 POLYGON ((954015 150665, 953966.847266722 1496...
4 2 Bronx 1.186822e+09 464475.145651 1309524 794386 POLYGON ((1043287.193237305 260300.0289916992,...
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " BoroCode BoroName Shape_Area Shape_Leng value1 value2 \\\n", "0 5 Staten Island 1.623847e+09 330454.175933 1033296 793054 \n", "1 5 Staten Island 1.623847e+09 330454.175933 1063988 793202 \n", "2 5 Staten Island 1.623847e+09 330454.175933 1125372 793498 \n", "3 5 Staten Island 1.623847e+09 330454.175933 1094680 793350 \n", "4 2 Bronx 1.186822e+09 464475.145651 1309524 794386 \n", "\n", " geometry \n", "0 POLYGON ((916755.4256330276 129447.9617643995,... \n", "1 POLYGON ((938595 135393, 938546.847266722 1344... \n", "2 POLYGON ((961436.3049926758 175473.0296020508,... \n", "3 POLYGON ((954015 150665, 953966.847266722 1496... \n", "4 POLYGON ((1043287.193237305 260300.0289916992,... " ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at the other `how` operations:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "newdf = overlay(polydf, polydf2, how=\"union\")\n", "newdf.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SE4mMDGfV/9rjtmUol9cO4Ps5x6hWsy43b90FrGjY8DciI6OIi4tkydLuNG7c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\nHz2mUef8HZ+dv+rMpYOXSI6NxSSPjF1UYgJDaNmveZ7HNBo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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "newdf = overlay(polydf, polydf2, how=\"identity\")\n", "newdf.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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pKf36/ZGu7O7dE+jqqti6dTOtxD2NTZs05eiJozh86YCmpiazZs1K12b1qpVMnz49Jd/d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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "newdf = overlay(polydf, polydf2, how=\"symmetric_difference\")\n", "newdf.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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pKSnMnDmTmzdv4urqSmxCAk4eHgCM79Il1/H2lSYc69C+PeZVq7JxoyTTTKc6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\nB7Nhw980bPgeLVv2zxXU9jjXr59i//5fsbQ0xdd3L87Ozs8oywoKzw9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"text": [ "" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "newdf = overlay(polydf, polydf2, how=\"difference\")\n", "newdf.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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sjL6JPradbLNc34HlB/gY+pHg4GBu37jNlSNXWD56OTVr16R7t+60rN2Szi06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\nyUAEIlVrqbeOCc5wTIGyc+dOZs6chbf3bXR1dTl8OH/dbqpWrcrq1Sv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"text": [ "" ] } ], "prompt_number": 12 } ], "metadata": {} } ] }geopandas-0.3.0/examples/spatial_joins.ipynb000066400000000000000000002411251315112464100211760ustar00rootroot00000000000000{ "metadata": { "name": "", "signature": "sha256:48efe66abdde37459952c96981b795279e6c34f5d4ebbec82ba5614f7e90d199" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Spatial Joins\n", "\n", "A *spatial join* uses [binary predicates](http://toblerity.org/shapely/manual.html#binary-predicates) \n", "such as `intersects` and `crosses` to combine two `GeoDataFrames` based on the spatial relationship \n", "between their geometries.\n", "\n", "A common use case might be a spatial join between a point layer and a polygon layer where you want to retain the point geometries and grab the attributes of the intersecting polygons.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import Image \n", "Image(url=\"https://dl.dropboxusercontent.com/u/6769420/sjoin_test.png\") " ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Types of spatial joins\n", "\n", "We currently support the following methods of spatial joins. We refer to the *left_df* and *right_df* which are the correspond to the two dataframes passed in as args.\n", "\n", "### Left outer join\n", "\n", "In a LEFT OUTER JOIN (`how='left'`), we keep *all* rows from the left and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right if they intersect and lose right rows that don't intersect. A left outer join implies that we are interested in retaining the geometries of the left. \n", "\n", "This is equivalent to the PostGIS query:\n", "```\n", "SELECT pts.geom, pts.id as ptid, polys.id as polyid \n", "FROM pts\n", "LEFT OUTER JOIN polys\n", "ON ST_Intersects(pts.geom, polys.geom);\n", "\n", " geom | ptid | polyid \n", "--------------------------------------------+------+--------\n", " 010100000040A9FBF2D88AD03F349CD47D796CE9BF | 4 | 10\n", " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 10\n", " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 20\n", " 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF | 2 | 20\n", " 0101000000818693BA2F8FF7BF4ADD97C75604E9BF | 1 | \n", "(5 rows)\n", "```\n", "\n", "### Right outer join\n", "\n", "In a RIGHT OUTER JOIN (`how='right'`), we keep *all* rows from the right and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the left if they intersect and lose left rows that don't intersect. A right outer join implies that we are interested in retaining the geometries of the right. \n", "\n", "This is equivalent to the PostGIS query:\n", "```\n", "SELECT polys.geom, pts.id as ptid, polys.id as polyid \n", "FROM pts\n", "RIGHT OUTER JOIN polys\n", "ON ST_Intersects(pts.geom, polys.geom);\n", "\n", " geom | ptid | polyid \n", "----------+------+--------\n", " 01...9BF | 4 | 10\n", " 01...9BF | 3 | 10\n", " 02...7BF | 3 | 20\n", " 02...7BF | 2 | 20\n", " 00...5BF | | 30\n", "(5 rows)\n", "```\n", "\n", "### Inner join\n", "\n", "In an INNER JOIN (`how='inner'`), we keep rows from the right and left only where their binary predicate is `True`. We duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right and left only if they intersect and lose all rows that do not. An inner join implies that we are interested in retaining the geometries of the left. \n", "\n", "This is equivalent to the PostGIS query:\n", "```\n", "SELECT pts.geom, pts.id as ptid, polys.id as polyid \n", "FROM pts\n", "INNER JOIN polys\n", "ON ST_Intersects(pts.geom, polys.geom);\n", "\n", " geom | ptid | polyid \n", "--------------------------------------------+------+--------\n", " 010100000040A9FBF2D88AD03F349CD47D796CE9BF | 4 | 10\n", " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 10\n", " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 20\n", " 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF | 2 | 20\n", "(4 rows) \n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spatial Joins between two GeoDataFrames\n", "\n", "Let's take a look at how we'd implement these using `GeoPandas`. First, load up the NYC test data into `GeoDataFrames`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "from shapely.geometry import Point\n", "from geopandas import GeoDataFrame, read_file\n", "from geopandas.tools import overlay\n", "\n", "# NYC Boros\n", "zippath = os.path.abspath('../examples/nybb_14aav.zip')\n", "polydf = read_file('/nybb_14a_av/nybb.shp', vfs='zip://' + zippath)\n", "\n", "# Generate some points\n", "b = [int(x) for x in polydf.total_bounds]\n", "N = 8\n", "pointdf = GeoDataFrame([\n", " {'geometry' : Point(x, y), 'value1': x + y, 'value2': x - y}\n", " for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)),\n", " range(b[1], b[3], int((b[3]-b[1])/N)))])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": true, "input": [ "pointdf" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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geometryvalue1value2
0 POINT (913175 120121) 1033296 793054
1 POINT (932450 139211) 1071661 793239
2 POINT (951725 158301) 1110026 793424
3 POINT (971000 177391) 1148391 793609
4 POINT (990275 196481) 1186756 793794
5 POINT (1009550 215571) 1225121 793979
6 POINT (1028825 234661) 1263486 794164
7 POINT (1048100 253751) 1301851 794349
8 POINT (1067375 272841) 1340216 794534
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BoroCodeBoroNameShape_AreaShape_Lenggeometry
0 5 Staten Island 1.623847e+09 330454.175933 (POLYGON ((970217.0223999023 145643.3322143555...
1 3 Brooklyn 1.937810e+09 741227.337073 (POLYGON ((1021176.479003906 151374.7969970703...
2 4 Queens 3.045079e+09 896875.396449 (POLYGON ((1029606.076599121 156073.8142089844...
3 1 Manhattan 6.364308e+08 358400.912836 (POLYGON ((981219.0557861328 188655.3157958984...
4 2 Bronx 1.186822e+09 464475.145651 (POLYGON ((1012821.805786133 229228.2645874023...
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CgizNyZMncXZ2Zninqj8kgAFBn3gV8IHyDkUIe/8Sl3kd0NOTBkC3vF6x8dAd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"text": [ "" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Joins" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from geopandas.tools import sjoin\n", "join_left_df = sjoin(pointdf, polydf, how=\"left\")\n", "join_left_df\n", "# Note the NaNs where the point did not intersect a boro" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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geometryvalue1value2BoroCodeBoroNameShape_AreaShape_Leng
0 POINT (913175 120121) 1033296 793054NaN None NaN NaN
1 POINT (932450 139211) 1071661 793239 5 Staten Island 1.623847e+09 330454.175933
2 POINT (951725 158301) 1110026 793424 5 Staten Island 1.623847e+09 330454.175933
3 POINT (971000 177391) 1148391 793609NaN None NaN NaN
4 POINT (990275 196481) 1186756 793794NaN None NaN NaN
5 POINT (1009550 215571) 1225121 793979 4 Queens 3.045079e+09 896875.396449
6 POINT (1028825 234661) 1263486 794164 2 Bronx 1.186822e+09 464475.145651
7 POINT (1048100 253751) 1301851 794349NaN None NaN NaN
8 POINT (1067375 272841) 1340216 794534NaN None NaN NaN
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ " geometry value1 value2 BoroCode BoroName \\\n", "0 POINT (913175 120121) 1033296 793054 NaN None \n", "1 POINT (932450 139211) 1071661 793239 5 Staten Island \n", "2 POINT (951725 158301) 1110026 793424 5 Staten Island \n", "3 POINT (971000 177391) 1148391 793609 NaN None \n", "4 POINT (990275 196481) 1186756 793794 NaN None \n", "5 POINT (1009550 215571) 1225121 793979 4 Queens \n", "6 POINT (1028825 234661) 1263486 794164 2 Bronx \n", "7 POINT (1048100 253751) 1301851 794349 NaN None \n", "8 POINT (1067375 272841) 1340216 794534 NaN None \n", "\n", " Shape_Area Shape_Leng \n", "0 NaN NaN \n", "1 1.623847e+09 330454.175933 \n", "2 1.623847e+09 330454.175933 \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 3.045079e+09 896875.396449 \n", "6 1.186822e+09 464475.145651 \n", "7 NaN NaN \n", "8 NaN NaN " ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "join_right_df = sjoin(pointdf, polydf, how=\"right\")\n", "join_right_df\n", "# Note Staten Island is repeated" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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BoroCodeBoroNameShape_AreaShape_Lenggeometryvalue1value2
0 5 Staten Island 1.623847e+09 330454.175933 (POLYGON ((970217.0223999023 145643.3322143555... 1071661 793239
1 5 Staten Island 1.623847e+09 330454.175933 (POLYGON ((970217.0223999023 145643.3322143555... 1110026 793424
2 3 Brooklyn 1.937810e+09 741227.337073 (POLYGON ((1021176.479003906 151374.7969970703... NaN NaN
3 4 Queens 3.045079e+09 896875.396449 (POLYGON ((1029606.076599121 156073.8142089844... 1225121 793979
4 1 Manhattan 6.364308e+08 358400.912836 (POLYGON ((981219.0557861328 188655.3157958984... NaN NaN
5 2 Bronx 1.186822e+09 464475.145651 (POLYGON ((1012821.805786133 229228.2645874023... 1263486 794164
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ " BoroCode BoroName Shape_Area Shape_Leng \\\n", "0 5 Staten Island 1.623847e+09 330454.175933 \n", "1 5 Staten Island 1.623847e+09 330454.175933 \n", "2 3 Brooklyn 1.937810e+09 741227.337073 \n", "3 4 Queens 3.045079e+09 896875.396449 \n", "4 1 Manhattan 6.364308e+08 358400.912836 \n", "5 2 Bronx 1.186822e+09 464475.145651 \n", "\n", " geometry value1 value2 \n", "0 (POLYGON ((970217.0223999023 145643.3322143555... 1071661 793239 \n", "1 (POLYGON ((970217.0223999023 145643.3322143555... 1110026 793424 \n", "2 (POLYGON ((1021176.479003906 151374.7969970703... NaN NaN \n", "3 (POLYGON ((1029606.076599121 156073.8142089844... 1225121 793979 \n", "4 (POLYGON ((981219.0557861328 188655.3157958984... NaN NaN \n", "5 (POLYGON ((1012821.805786133 229228.2645874023... 1263486 794164 " ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "join_inner_df = sjoin(pointdf, polydf, how=\"inner\")\n", "join_inner_df\n", "# Note the lack of NaNs; dropped anything that didn't intersect" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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geometryvalue1value2BoroCodeBoroNameShape_AreaShape_Leng
0 POINT (932450 139211) 1071661 793239 5 Staten Island 1.623847e+09 330454.175933
1 POINT (951725 158301) 1110026 793424 5 Staten Island 1.623847e+09 330454.175933
2 POINT (1009550 215571) 1225121 793979 4 Queens 3.045079e+09 896875.396449
3 POINT (1028825 234661) 1263486 794164 2 Bronx 1.186822e+09 464475.145651
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ " geometry value1 value2 BoroCode BoroName \\\n", "0 POINT (932450 139211) 1071661 793239 5 Staten Island \n", "1 POINT (951725 158301) 1110026 793424 5 Staten Island \n", "2 POINT (1009550 215571) 1225121 793979 4 Queens \n", "3 POINT (1028825 234661) 1263486 794164 2 Bronx \n", "\n", " Shape_Area Shape_Leng \n", "0 1.623847e+09 330454.175933 \n", "1 1.623847e+09 330454.175933 \n", "2 3.045079e+09 896875.396449 \n", "3 1.186822e+09 464475.145651 " ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're not limited to using the `intersection` binary predicate. Any of the `Shapely` geometry methods that return a Boolean can be used by specifying the `op` kwarg." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sjoin(pointdf, polydf, how=\"left\", op=\"within\")" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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geometryvalue1value2BoroCodeBoroNameShape_AreaShape_Leng
0 POINT (913175 120121) 1033296 793054NaN None NaN NaN
1 POINT (932450 139211) 1071661 793239 5 Staten Island 1.623847e+09 330454.175933
2 POINT (951725 158301) 1110026 793424 5 Staten Island 1.623847e+09 330454.175933
3 POINT (971000 177391) 1148391 793609NaN None NaN NaN
4 POINT (990275 196481) 1186756 793794NaN None NaN NaN
5 POINT (1009550 215571) 1225121 793979 4 Queens 3.045079e+09 896875.396449
6 POINT (1028825 234661) 1263486 794164 2 Bronx 1.186822e+09 464475.145651
7 POINT (1048100 253751) 1301851 794349NaN None NaN NaN
8 POINT (1067375 272841) 1340216 794534NaN None NaN NaN
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ " geometry value1 value2 BoroCode BoroName \\\n", "0 POINT (913175 120121) 1033296 793054 NaN None \n", "1 POINT (932450 139211) 1071661 793239 5 Staten Island \n", "2 POINT (951725 158301) 1110026 793424 5 Staten Island \n", "3 POINT (971000 177391) 1148391 793609 NaN None \n", "4 POINT (990275 196481) 1186756 793794 NaN None \n", "5 POINT (1009550 215571) 1225121 793979 4 Queens \n", "6 POINT (1028825 234661) 1263486 794164 2 Bronx \n", "7 POINT (1048100 253751) 1301851 794349 NaN None \n", "8 POINT (1067375 272841) 1340216 794534 NaN None \n", "\n", " Shape_Area Shape_Leng \n", "0 NaN NaN \n", "1 1.623847e+09 330454.175933 \n", "2 1.623847e+09 330454.175933 \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 3.045079e+09 896875.396449 \n", "6 1.186822e+09 464475.145651 \n", "7 NaN NaN \n", "8 NaN NaN " ] } ], "prompt_number": 32 } ], "metadata": {} } ] }geopandas-0.3.0/examples/test.png000066400000000000000000000774341315112464100167730ustar00rootroot00000000000000PNG  IHDR$%dЎksBIT|d pHYs.#.#x?v IDATxuu##|5զb-eokKgԧe6*jњ1\Flmc5?< B8>sc7zkr^W 5=P> 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'HǏ͛K̙3U=V?q?ufXΝ;cӦMuزeK_,!VƟ %Hz(V^Pw bܸq1s̚|x\s5`ǰao_ZA %Aw}rhnn{+ 2eJ#@]j{| @r 9Aܰ`h⋱bŊx'=:;;cرqUWU,XSL{\\ݝ58p,ܹs9NW#F|;bŊ1b L@#$ PقwjժXdI޽{+ƏMMԷ8~xW\Æ5 'o@YŰabԩIiooKƝw޽q5$ ꫯ{_K*˲Ȳ,̙<@?18tP1cb1cƌʹk.wH;$PJַ#DDqcǎɓq]w%رccݺuq-ĻoO͛[oyWĉkHԩSqGDcԨQ9O r}BF\PlQ(GV1H * .e˖ի+ַm6 'NI&ռ'}:uA}%Hʽ{X1bDp 9M@$… _~_ɓa*Fy r}BF\PlQ^xd8$ F eY#ގW׾s̉~:zxg !,={v,]4-[ÇY/J;v,=ǎYfٳc1rA3ZZZ*֎9&Ms &Vs0s̘9sfc@(A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r 9A HN$'H$  @r ayjoo]vEGGG9r$&LӦM+2ʼ$ }tزe֭[Ua ;sL7Gy$y^7o޼7cC8!euwww=Dܹ36m[n-[D{{Eɲ,Ν;7ömK_RܹǴƺuoL:KgggT9r$&M<C!=P^:1ŋٳgu\{{{|/y %m۶;zOԧn۷͛=]]]=f̘7|P @I9Ν ,lj'[oK,_b%6mЇo=zkgϞ}swޘ0a  @y4=@QeYÇWXfMk֬1c=f~_/\6r_c1mڴxGc'N6+uwww=D+1nܸhnn=q_gYΝzuɘ5kV?~ZeaÆXtE߻wo|c89^xᅘ1cƀf쌖#GĤI/@|Zz1eʔ EaÆ "oSqW+zXn]hٸqcŋGOӟTL4ABkb-˲XdI?~Vm߾=^z饁@$ȿ8sLڵ^]vY. 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(error)" % dispcmd) return None return stdout def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. """ dirname = os.path.basename(root) if not dirname.startswith(parentdir_prefix): if verbose: print("guessing rootdir is '%s', but '%s' doesn't start with " "prefix '%s'" % (root, dirname, parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None} @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = set([r.strip() for r in refnames.strip("()").split(",")]) # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r'\d', r)]) if verbose: print("discarding '%s', no digits" % ",".join(refs-tags)) if verbose: print("likely tags: %s" % ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] if verbose: print("picking %s" % r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags"} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ if not os.path.exists(os.path.join(root, ".git")): if verbose: print("no .git in %s" % root) raise NotThisMethod("no .git directory") GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out = run_command(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%s*" % tag_prefix], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%s'" % describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" % (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits return pieces def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post.dev%d" % pieces["distance"] else: # exception #1 rendered = "0.post.dev%d" % pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%s" % pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Eexceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"]} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%s'" % style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None} def get_versions(): """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which # case we can only use expanded keywords. cfg = get_config() verbose = cfg.verbose try: return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass try: root = os.path.realpath(__file__) # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. for i in cfg.versionfile_source.split('/'): root = os.path.dirname(root) except NameError: return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to find root of source tree"} try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) return render(pieces, cfg.style) except NotThisMethod: pass try: if cfg.parentdir_prefix: return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) except NotThisMethod: pass return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version"} geopandas-0.3.0/geopandas/base.py000066400000000000000000000443711315112464100167070ustar00rootroot00000000000000from warnings import warn import numpy as np import pandas as pd from pandas import Series, DataFrame, MultiIndex from pandas.core.indexing import _NDFrameIndexer from shapely.geometry import box, MultiPoint, MultiLineString, MultiPolygon from shapely.ops import cascaded_union, unary_union import shapely.affinity as affinity import geopandas as gpd try: from rtree.core import RTreeError HAS_SINDEX = True except ImportError: class RTreeError(Exception): pass HAS_SINDEX = False def _geo_op(this, other, op): """Operation that returns a GeoSeries""" if isinstance(other, GeoPandasBase): this = this.geometry crs = this.crs if crs != other.crs: warn('GeoSeries crs mismatch: {0} and {1}'.format(this.crs, other.crs)) this, other = this.align(other.geometry) return gpd.GeoSeries([getattr(this_elem, op)(other_elem) for this_elem, other_elem in zip(this, other)], index=this.index, crs=crs) else: return gpd.GeoSeries([getattr(s, op)(other) for s in this.geometry], index=this.index, crs=this.crs) # TODO: think about merging with _geo_op def _series_op(this, other, op, **kwargs): """Geometric operation that returns a pandas Series""" null_val = False if op != 'distance' else np.nan if isinstance(other, GeoPandasBase): this = this.geometry this, other = this.align(other.geometry) return Series([getattr(this_elem, op)(other_elem, **kwargs) if not this_elem.is_empty | other_elem.is_empty else null_val for this_elem, other_elem in zip(this, other)], index=this.index) else: return Series([getattr(s, op)(other, **kwargs) if s else null_val for s in this.geometry], index=this.index) def _geo_unary_op(this, op): """Unary operation that returns a GeoSeries""" return gpd.GeoSeries([getattr(geom, op) for geom in this.geometry], index=this.index, crs=this.crs) def _series_unary_op(this, op, null_value=False): """Unary operation that returns a Series""" return Series([getattr(geom, op, null_value) for geom in this.geometry], index=this.index) class GeoPandasBase(object): _sindex = None _sindex_generated = False def _generate_sindex(self): if not HAS_SINDEX: warn("Cannot generate spatial index: Missing package `rtree`.") else: from geopandas.sindex import SpatialIndex stream = ((i, item.bounds, idx) for i, (idx, item) in enumerate(self.geometry.iteritems()) if pd.notnull(item) and not item.is_empty) try: self._sindex = SpatialIndex(stream) # What we really want here is an empty generator error, or # for the bulk loader to log that the generator was empty # and move on. See https://github.com/Toblerity/rtree/issues/20. except RTreeError: pass self._sindex_generated = True def _invalidate_sindex(self): """ Indicates that the spatial index should be re-built next time it's requested. """ self._sindex = None self._sindex_generated = False @property def area(self): """Return the area of each geometry in the GeoSeries""" return _series_unary_op(self, 'area', null_value=np.nan) @property def geom_type(self): """Return the geometry type of each geometry in the GeoSeries""" return _series_unary_op(self, 'geom_type', null_value=None) @property def type(self): """Return the geometry type of each geometry in the GeoSeries""" return self.geom_type @property def length(self): """Return the length of each geometry in the GeoSeries""" return _series_unary_op(self, 'length', null_value=np.nan) @property def is_valid(self): """Return True for each valid geometry, else False""" return _series_unary_op(self, 'is_valid', null_value=False) @property def is_empty(self): """Return True for each empty geometry, False for non-empty""" return _series_unary_op(self, 'is_empty', null_value=False) @property def is_simple(self): """Return True for each simple geometry, else False""" return _series_unary_op(self, 'is_simple', null_value=False) @property def is_ring(self): """Return True for each geometry that is a closed ring, else False""" # operates on the exterior, so can't use _series_unary_op() return Series([geom.exterior.is_ring for geom in self.geometry], index=self.index) # # Unary operations that return a GeoSeries # @property def boundary(self): """Return the bounding geometry for each geometry""" return _geo_unary_op(self, 'boundary') @property def centroid(self): """Return the centroid of each geometry in the GeoSeries""" return _geo_unary_op(self, 'centroid') @property def convex_hull(self): """Return the convex hull of each geometry""" return _geo_unary_op(self, 'convex_hull') @property def envelope(self): """Return a bounding rectangle for each geometry""" return _geo_unary_op(self, 'envelope') @property def exterior(self): """Return the outer boundary of each polygon""" # TODO: return empty geometry for non-polygons return _geo_unary_op(self, 'exterior') @property def interiors(self): """Return the interior rings of each polygon""" # TODO: return empty list or None for non-polygons return _series_unary_op(self, 'interiors', null_value=False) def representative_point(self): """Return a GeoSeries of points guaranteed to be in each geometry""" return gpd.GeoSeries([geom.representative_point() for geom in self.geometry], index=self.index) # # Reduction operations that return a Shapely geometry # @property def cascaded_union(self): """Deprecated: Return the unary_union of all geometries""" return cascaded_union(self.geometry.values) @property def unary_union(self): """Return the union of all geometries""" return unary_union(self.geometry.values) # # Binary operations that return a pandas Series # def contains(self, other): """Return True for all geometries that contain *other*, else False""" return _series_op(self, other, 'contains') def geom_equals(self, other): """Return True for all geometries that equal *other*, else False""" return _series_op(self, other, 'equals') def geom_almost_equals(self, other, decimal=6): """Return True for all geometries that is approximately equal to *other*, else False""" # TODO: pass precision argument return _series_op(self, other, 'almost_equals', decimal=decimal) def geom_equals_exact(self, other, tolerance): """Return True for all geometries that equal *other* to a given tolerance, else False""" # TODO: pass tolerance argument. return _series_op(self, other, 'equals_exact', tolerance=tolerance) def crosses(self, other): """Return True for all geometries that cross *other*, else False""" return _series_op(self, other, 'crosses') def disjoint(self, other): """Return True for all geometries that are disjoint with *other*, else False""" return _series_op(self, other, 'disjoint') def intersects(self, other): """Return True for all geometries that intersect *other*, else False""" return _series_op(self, other, 'intersects') def overlaps(self, other): """Return True for all geometries that overlap *other*, else False""" return _series_op(self, other, 'overlaps') def touches(self, other): """Return True for all geometries that touch *other*, else False""" return _series_op(self, other, 'touches') def within(self, other): """Return True for all geometries that are within *other*, else False""" return _series_op(self, other, 'within') def distance(self, other): """Return distance of each geometry to *other*""" return _series_op(self, other, 'distance') # # Binary operations that return a GeoSeries # def difference(self, other): """Return the set-theoretic difference of each geometry with *other*""" return _geo_op(self, other, 'difference') def symmetric_difference(self, other): """Return the symmetric difference of each geometry with *other*""" return _geo_op(self, other, 'symmetric_difference') def union(self, other): """Return the set-theoretic union of each geometry with *other*""" return _geo_op(self, other, 'union') def intersection(self, other): """Return the set-theoretic intersection of each geometry with *other*""" return _geo_op(self, other, 'intersection') # # Other operations # @property def bounds(self): """Return a DataFrame of minx, miny, maxx, maxy values of geometry objects""" bounds = np.array([geom.bounds for geom in self.geometry]) return DataFrame(bounds, columns=['minx', 'miny', 'maxx', 'maxy'], index=self.index) @property def total_bounds(self): """Return a single bounding box (minx, miny, maxx, maxy) for all geometries This is a shortcut for calculating the min/max x and y bounds individually. """ b = self.bounds return np.array((b['minx'].min(), b['miny'].min(), b['maxx'].max(), b['maxy'].max())) @property def sindex(self): if not self._sindex_generated: self._generate_sindex() return self._sindex def buffer(self, distance, resolution=16): return gpd.GeoSeries([geom.buffer(distance, resolution) for geom in self.geometry], index=self.index, crs=self.crs) def simplify(self, *args, **kwargs): return gpd.GeoSeries([geom.simplify(*args, **kwargs) for geom in self.geometry], index=self.index, crs=self.crs) def relate(self, other): raise NotImplementedError def project(self, other, normalized=False): """ Return the distance along each geometry nearest to *other* Parameters ---------- other : BaseGeometry or GeoSeries The *other* geometry to computed projected point from. normalized : boolean If normalized is True, return the distance normalized to the length of the object. The project method is the inverse of interpolate. """ return _series_op(self, other, 'project', normalized=normalized) def interpolate(self, distance, normalized=False): """ Return a point at the specified distance along each geometry Parameters ---------- distance : float or Series of floats Distance(s) along the geometries at which a point should be returned normalized : boolean If normalized is True, distance will be interpreted as a fraction of the geometric object's length. """ return gpd.GeoSeries([s.interpolate(distance, normalized) for s in self.geometry], index=self.index, crs=self.crs) def translate(self, xoff=0.0, yoff=0.0, zoff=0.0): """ Shift the coordinates of the GeoSeries. Parameters ---------- xoff, yoff, zoff : float, float, float Amount of offset along each dimension. xoff, yoff, and zoff for translation along the x, y, and z dimensions respectively. See shapely manual for more information: http://toblerity.org/shapely/manual.html#affine-transformations """ return gpd.GeoSeries([affinity.translate(s, xoff, yoff, zoff) for s in self.geometry], index=self.index, crs=self.crs) def rotate(self, angle, origin='center', use_radians=False): """ Rotate the coordinates of the GeoSeries. Parameters ---------- angle : float The angle of rotation can be specified in either degrees (default) or radians by setting use_radians=True. Positive angles are counter-clockwise and negative are clockwise rotations. origin : string, Point, or tuple (x, y) The point of origin can be a keyword 'center' for the bounding box center (default), 'centroid' for the geometry's centroid, a Point object or a coordinate tuple (x, y). use_radians : boolean Whether to interpret the angle of rotation as degrees or radians See shapely manual for more information: http://toblerity.org/shapely/manual.html#affine-transformations """ return gpd.GeoSeries([affinity.rotate(s, angle, origin=origin, use_radians=use_radians) for s in self.geometry], index=self.index, crs=self.crs) def scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin='center'): """ Scale the geometries of the GeoSeries along each (x, y, z) dimension. Parameters ---------- xfact, yfact, zfact : float, float, float Scaling factors for the x, y, and z dimensions respectively. origin : string, Point, or tuple The point of origin can be a keyword 'center' for the 2D bounding box center (default), 'centroid' for the geometry's 2D centroid, a Point object or a coordinate tuple (x, y, z). Note: Negative scale factors will mirror or reflect coordinates. See shapely manual for more information: http://toblerity.org/shapely/manual.html#affine-transformations """ return gpd.GeoSeries([affinity.scale(s, xfact, yfact, zfact, origin=origin) for s in self.geometry], index=self.index, crs=self.crs) def skew(self, xs=0.0, ys=0.0, origin='center', use_radians=False): """ Shear/Skew the geometries of the GeoSeries by angles along x and y dimensions. Parameters ---------- xs, ys : float, float The shear angle(s) for the x and y axes respectively. These can be specified in either degrees (default) or radians by setting use_radians=True. origin : string, Point, or tuple (x, y) The point of origin can be a keyword 'center' for the bounding box center (default), 'centroid' for the geometry's centroid, a Point object or a coordinate tuple (x, y). use_radians : boolean Whether to interpret the shear angle(s) as degrees or radians See shapely manual for more information: http://toblerity.org/shapely/manual.html#affine-transformations """ return gpd.GeoSeries([affinity.skew(s, xs, ys, origin=origin, use_radians=use_radians) for s in self.geometry], index=self.index, crs=self.crs) def explode(self): """ Explode multi-part geometries into multiple single geometries. Single rows can become multiple rows. This is analogous to PostGIS's ST_Dump(). The 'path' index is the second level of the returned MultiIndex Returns ------ A GeoSeries with a MultiIndex. The levels of the MultiIndex are the original index and an integer. Example ------- >>> gdf # gdf is GeoSeries of MultiPoints 0 (POINT (0 0), POINT (1 1)) 1 (POINT (2 2), POINT (3 3), POINT (4 4)) >>> gdf.explode() 0 0 POINT (0 0) 1 POINT (1 1) 1 0 POINT (2 2) 1 POINT (3 3) 2 POINT (4 4) dtype: object """ index = [] geometries = [] for idx, s in self.geometry.iteritems(): if s.type.startswith('Multi') or s.type == 'GeometryCollection': geoms = s.geoms idxs = [(idx, i) for i in range(len(geoms))] else: geoms = [s] idxs = [(idx, 0)] index.extend(idxs) geometries.extend(geoms) return gpd.GeoSeries(geometries, index=MultiIndex.from_tuples(index)).__finalize__(self) class _CoordinateIndexer(_NDFrameIndexer): """ Coordinate based indexer to select by intersection with bounding box. Format of input should be ``.cx[xmin:xmax, ymin:ymax]``. Any of ``xmin``, ``xmax``, ``ymin``, and ``ymax`` can be provided, but input must include a comma separating x and y slices. That is, ``.cx[:, :]`` will return the full series/frame, but ``.cx[:]`` is not implemented. """ def _getitem_tuple(self, tup): obj = self.obj xs, ys = tup # handle numeric values as x and/or y coordinate index if type(xs) is not slice: xs = slice(xs, xs) if type(ys) is not slice: ys = slice(ys, ys) # don't know how to handle step; should this raise? if xs.step is not None or ys.step is not None: warn("Ignoring step - full interval is used.") xmin, ymin, xmax, ymax = obj.total_bounds bbox = box(xs.start if xs.start is not None else xmin, ys.start if ys.start is not None else ymin, xs.stop if xs.stop is not None else xmax, ys.stop if ys.stop is not None else ymax) idx = obj.intersects(bbox) return obj[idx] def _array_input(arr): if isinstance(arr, (MultiPoint, MultiLineString, MultiPolygon)): # Prevent against improper length detection when input is a # Multi* geom = arr arr = np.empty(1, dtype=object) arr[0] = geom return arr geopandas-0.3.0/geopandas/datasets/000077500000000000000000000000001315112464100172225ustar00rootroot00000000000000geopandas-0.3.0/geopandas/datasets/README.md000066400000000000000000000026051315112464100205040ustar00rootroot00000000000000# Datasets included with geopandas - `'nybb'`: Borough boundaries of New York City, data provided Department of City Planning (DCP), https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm - `'naturalearth_cities'`: capital cities, based on http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ - `'naturalearth_lowres'`: country boundaries, based on http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/ ## Data sources and licenses ### `nybb` #### Summary These districts were created by the Department of City Planning to aid city agencies in administering public services. #### Description The borough boundaries of New York City clipped to the shoreline at mean high tide. #### Credits Department of City Planning. #### Use limitations This dataset is being provided by the Department of City Planning (DCP) on DCP’s website for informational purposes only. DCP does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of the dataset, nor are any such warranties to be implied or inferred with respect to the dataset as furnished on the website. DCP and the City are not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use the dataset, or applications utilizing the dataset, provided by any third party. geopandas-0.3.0/geopandas/datasets/__init__.py000066400000000000000000000016411315112464100213350ustar00rootroot00000000000000import os __all__ = ['available', 'get_path'] _module_path = os.path.dirname(__file__) _available_dir = [p for p in next(os.walk(_module_path))[1] if not p.startswith('__')] _available_zip = {'nybb': 'nybb_16a.zip'} available = _available_dir + list(_available_zip.keys()) def get_path(dataset): """ Get the path to the data file. Parameters ---------- dataset : str The name of the dataset. See ``geopandas.datasets.available`` for all options. """ if dataset in _available_dir: return os.path.abspath( os.path.join(_module_path, dataset, dataset + '.shp')) elif dataset in _available_zip: fpath = os.path.abspath( os.path.join(_module_path, _available_zip[dataset])) return 'zip://' + fpath else: msg = "The dataset '{data}' is not available".format(data=dataset) raise ValueError(msg) geopandas-0.3.0/geopandas/datasets/naturalearth_cities/000077500000000000000000000000001315112464100232545ustar00rootroot00000000000000geopandas-0.3.0/geopandas/datasets/naturalearth_cities/naturalearth_cities.README.html000066400000000000000000000765571315112464100311540ustar00rootroot00000000000000 Populated Places | Natural Earth
« 1:10m Cultural Vectors 

Populated Places

pop_thumb
City and town points, from Tokyo to Wasilla, Cairo to Kandahar
Download populated places (1.51 MB) version 2.0.0
Download simple (less columns) (719.87 KB) version 2.0.0

About

Point symbols with name attributes. Includes all admin-0 and many admin-1 capitals, major cities and towns, plus a sampling of smaller towns in sparsely inhabited regions. We favor regional significance over population census in determining our selection of places. Use the scale rankings to filter the number of towns that appear on your map.

pop_banner

LandScan derived population estimates are provided for 90% of our cities. Those lacking population estimates are often in sparsely inhabited areas. We provide a range of population values that account for the total “metropolitan” population rather than it’s administrative boundary population. Use the PopMax column to size your town labels. Starting in version 1.1, popMax has been throttled down to the UN estimated metro population for the ~500 largest urban areas in the world. This affects towns in China, India, and parts of Africa where our Landscan counting method usually over estimated.

Population estimates were derived from the LANDSCAN dataset maintained and distributed by the Oak Ridge National Laboratory. These data were converted from raster to vector and pixels with fewer than 200 persons per square kilometer were removed from the dataset as they were classified as rural. Once urban pixels were selected, these pixels were aggregated into contiguous units. Concurrently Thiessen polygons were created based on the selected city points. The Thiessen polygons were used to intersect the contiguous city boundaries to produce bounded areas for the cities. As a result, our estimates capture a metropolitan and micropolitan populations per city regardless of administrative units.

Once intersected, the contiguous polygons were recalculated, using aerial interpolation assuming uniform population distribution within each pixel, to determine the population total. This process was conducted multiple times, for each scale level, to produce population estimates for each city at nested scales of 1:300 million, 1:110 million, 1:50 million, 1:20 million, and 1:10 million.

Download landscan urban areas (13.01 MB) version 2.0.0

Population ranks

Are calculated as rank_max and rank_min using this general VB formula that can be pasted into ArcMap Field Calculator advanced area (set your output to x):

a = [pop_max]

if( a > 10000000 ) then x = 14 elseif( a > 5000000 ) then x = 13 elseif( a > 1000000 ) then x = 12 elseif( a > 500000 ) then x = 11 elseif( a > 200000 ) then x = 10 elseif( a > 100000 ) then x = 9 elseif( a > 50000 ) then x = 8 elseif( a > 20000 ) then x = 7 elseif( a > 10000 ) then x = 6 elseif( a > 5000 ) then x = 5 elseif( a > 2000 ) then x = 4 elseif( a > 1000 ) then x = 3 elseif( a > 200 ) then x = 2 elseif( a > 0 ) then x = 1 else x = 0 end if

Issues

While we don’t want to show every admin-1 capital, for those countries where we show most admin-1 capitals, we should have a complete set. If you find we are missing one, please log it in the Cx tool at right.

Version History

  • 2.0.0
  • 1.4.0
  • 1.3.0
  • 1.1.0
  • 0.9.0

The master changelog is available on Github »

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In addition to the standard DataFrame constructor arguments, GeoDataFrame also accepts the following keyword arguments: Keyword Arguments ----------------- crs : str (optional) Coordinate system geometry : str or array (optional) If str, column to use as geometry. If array, will be set as 'geometry' column on GeoDataFrame. """ # XXX: This will no longer be necessary in pandas 0.17 _internal_names = ['_data', '_cacher', '_item_cache', '_cache', 'is_copy', '_subtyp', '_index', '_default_kind', '_default_fill_value', '_metadata', '__array_struct__', '__array_interface__'] _metadata = ['crs', '_geometry_column_name'] _geometry_column_name = DEFAULT_GEO_COLUMN_NAME def __init__(self, *args, **kwargs): crs = kwargs.pop('crs', None) geometry = kwargs.pop('geometry', None) super(GeoDataFrame, self).__init__(*args, **kwargs) self.crs = crs if geometry is not None: self.set_geometry(geometry, inplace=True) self._invalidate_sindex() # Serialize metadata (will no longer be necessary in pandas 0.17+) # See https://github.com/pydata/pandas/pull/10557 def __getstate__(self): meta = dict((k, getattr(self, k, None)) for k in self._metadata) return dict(_data=self._data, _typ=self._typ, _metadata=self._metadata, **meta) def __setattr__(self, attr, val): # have to special case geometry b/c pandas tries to use as column... if attr == 'geometry': object.__setattr__(self, attr, val) else: super(GeoDataFrame, self).__setattr__(attr, val) def _get_geometry(self): if self._geometry_column_name not in self: raise AttributeError("No geometry data set yet (expected in" " column '%s'." % self._geometry_column_name) return self[self._geometry_column_name] def _set_geometry(self, col): # TODO: Use pandas' core.common.is_list_like() here. if not isinstance(col, (list, np.ndarray, Series)): raise ValueError("Must use a list-like to set the geometry" " property") self.set_geometry(col, inplace=True) geometry = property(fget=_get_geometry, fset=_set_geometry, doc="Geometry data for GeoDataFrame") def set_geometry(self, col, drop=False, inplace=False, crs=None): """ Set the GeoDataFrame geometry using either an existing column or the specified input. By default yields a new object. The original geometry column is replaced with the input. Parameters ---------- keys : column label or array drop : boolean, default True Delete column to be used as the new geometry inplace : boolean, default False Modify the GeoDataFrame in place (do not create a new object) crs : str/result of fion.get_crs (optional) Coordinate system to use. If passed, overrides both DataFrame and col's crs. Otherwise, tries to get crs from passed col values or DataFrame. Examples -------- >>> df1 = df.set_geometry([Point(0,0), Point(1,1), Point(2,2)]) >>> df2 = df.set_geometry('geom1') Returns ------- geodataframe : GeoDataFrame """ # Most of the code here is taken from DataFrame.set_index() if inplace: frame = self else: frame = self.copy() if not crs: crs = getattr(col, 'crs', self.crs) to_remove = None geo_column_name = self._geometry_column_name if isinstance(col, (Series, list, np.ndarray)): level = col elif hasattr(col, 'ndim') and col.ndim != 1: raise ValueError("Must pass array with one dimension only.") else: try: level = frame[col].values except KeyError: raise ValueError("Unknown column %s" % col) except: raise if drop: to_remove = col geo_column_name = self._geometry_column_name else: geo_column_name = col if to_remove: del frame[to_remove] if isinstance(level, GeoSeries) and level.crs != crs: # Avoids caching issues/crs sharing issues level = level.copy() level.crs = crs # Check that we are using a listlike of geometries if not all(isinstance(item, BaseGeometry) or not item for item in level): raise TypeError("Input geometry column must contain valid geometry objects.") frame[geo_column_name] = level frame._geometry_column_name = geo_column_name frame.crs = crs frame._invalidate_sindex() if not inplace: return frame @classmethod def from_file(cls, filename, **kwargs): """ Alternate constructor to create a GeoDataFrame from a file. Example: df = geopandas.GeoDataFrame.from_file('nybb.shp') Wraps geopandas.read_file(). For additional help, see read_file() """ return geopandas.io.file.read_file(filename, **kwargs) @classmethod def from_features(cls, features, crs=None): """ Alternate constructor to create GeoDataFrame from an iterable of features or a feature collection. Parameters ---------- features - Iterable of features, where each element must be a feature dictionary or implement the __geo_interface__. - Feature collection, where the 'features' key contains an iterable of features. - Object holding a feature collection that implements the ``__geo_interface__``. crs : str or dict (optional) Coordinate reference system to set on the resulting frame. Returns ------- GeoDataFrame Notes ----- For more information about the ``__geo_interface__``, see https://gist.github.com/sgillies/2217756 """ # Handle feature collections if hasattr(features, "__geo_interface__"): fs = features.__geo_interface__ else: fs = features if isinstance(fs, dict) and fs.get('type') == 'FeatureCollection': features_lst = fs['features'] else: features_lst = features rows = [] for f in features_lst: if hasattr(f, "__geo_interface__"): f = f.__geo_interface__ else: f = f d = {'geometry': shape(f['geometry']) if f['geometry'] else None} d.update(f['properties']) rows.append(d) df = GeoDataFrame.from_dict(rows) df.crs = crs return df @classmethod def from_postgis(cls, sql, con, geom_col='geom', crs=None, index_col=None, coerce_float=True, params=None): """ Alternate constructor to create a GeoDataFrame from a sql query containing a geometry column. Example: df = geopandas.GeoDataFrame.from_postgis(con, "SELECT geom, highway FROM roads;") Wraps geopandas.read_postgis(). For additional help, see read_postgis() """ return geopandas.io.sql.read_postgis(sql, con, geom_col, crs, index_col, coerce_float, params) def to_json(self, na='null', show_bbox=False, **kwargs): """ Returns a GeoJSON string representation of the GeoDataFrame. Parameters ---------- na : {'null', 'drop', 'keep'}, default 'null' Indicates how to output missing (NaN) values in the GeoDataFrame * null: output the missing entries as JSON null * drop: remove the property from the feature. This applies to each feature individually so that features may have different properties * keep: output the missing entries as NaN show_bbox : include bbox (bounds) in the geojson The remaining *kwargs* are passed to json.dumps(). """ return json.dumps(self._to_geo(na=na, show_bbox=show_bbox), **kwargs) @property def __geo_interface__(self): """ Returns a python feature collection (i.e. the geointerface) representation of the GeoDataFrame. This differs from `_to_geo()` only in that it is a property with default args instead of a method """ return self._to_geo(na='null', show_bbox=True) def iterfeatures(self, na='null', show_bbox=False): """ Returns an iterator that yields feature dictionaries that comply with __geo_interface__ Parameters ---------- na : {'null', 'drop', 'keep'}, default 'null' Indicates how to output missing (NaN) values in the GeoDataFrame * null: ouput the missing entries as JSON null * drop: remove the property from the feature. This applies to each feature individually so that features may have different properties * keep: output the missing entries as NaN show_bbox : include bbox (bounds) in the geojson. default False """ def fill_none(row): """ Takes in a Series, converts to a dictionary with null values set to None """ na_keys = row.index[row.isnull()] d = row.to_dict() for k in na_keys: d[k] = None return d # na_methods must take in a Series and return dict na_methods = {'null': fill_none, 'drop': lambda row: row.dropna().to_dict(), 'keep': lambda row: row.to_dict()} if na not in na_methods: raise ValueError('Unknown na method {0}'.format(na)) f = na_methods[na] for name, row in self.iterrows(): properties = f(row) del properties[self._geometry_column_name] feature = { 'id': str(name), 'type': 'Feature', 'properties': properties, 'geometry': mapping(row[self._geometry_column_name]) if row[self._geometry_column_name] else None } if show_bbox: feature['bbox'] = row.geometry.bounds yield feature def _to_geo(self, **kwargs): """ Returns a python feature collection (i.e. the geointerface) representation of the GeoDataFrame. """ geo = {'type': 'FeatureCollection', 'features': list(self.iterfeatures(**kwargs))} if kwargs.get('show_bbox', False): geo['bbox'] = tuple(self.total_bounds) return geo def to_file(self, filename, driver="ESRI Shapefile", schema=None, **kwargs): """ Write this GeoDataFrame to an OGR data source A dictionary of supported OGR providers is available via: >>> import fiona >>> fiona.supported_drivers Parameters ---------- filename : string File path or file handle to write to. driver : string, default 'ESRI Shapefile' The OGR format driver used to write the vector file. schema : dict, default None If specified, the schema dictionary is passed to Fiona to better control how the file is written. The *kwargs* are passed to fiona.open and can be used to write to multi-layer data, store data within archives (zip files), etc. """ from geopandas.io.file import to_file to_file(self, filename, driver, schema, **kwargs) def to_crs(self, crs=None, epsg=None, inplace=False): """Transform geometries to a new coordinate reference system This method will transform all points in all objects. It has no notion or projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. `to_crs` passes the `crs` argument to the `Proj` function from the `pyproj` library (with the option `preserve_units=True`). It can therefore accept proj4 projections in any format supported by `Proj`, including dictionaries, or proj4 strings. """ if inplace: df = self else: df = self.copy() geom = df.geometry.to_crs(crs=crs, epsg=epsg) df.geometry = geom df.crs = geom.crs if not inplace: return df def __getitem__(self, key): """ If the result is a column containing only 'geometry', return a GeoSeries. If it's a DataFrame with a 'geometry' column, return a GeoDataFrame. """ result = super(GeoDataFrame, self).__getitem__(key) geo_col = self._geometry_column_name if isinstance(key, string_types) and key == geo_col: result.__class__ = GeoSeries result.crs = self.crs result._invalidate_sindex() elif isinstance(result, DataFrame) and geo_col in result: result.__class__ = GeoDataFrame result.crs = self.crs result._geometry_column_name = geo_col result._invalidate_sindex() elif isinstance(result, DataFrame) and geo_col not in result: result.__class__ = DataFrame return result # # Implement pandas methods # def merge(self, *args, **kwargs): result = DataFrame.merge(self, *args, **kwargs) geo_col = self._geometry_column_name if isinstance(result, DataFrame) and geo_col in result: result.__class__ = GeoDataFrame result.crs = self.crs result._geometry_column_name = geo_col result._invalidate_sindex() elif isinstance(result, DataFrame) and geo_col not in result: result.__class__ = DataFrame return result @property def _constructor(self): return GeoDataFrame def __finalize__(self, other, method=None, **kwargs): """propagate metadata from other to self """ # merge operation: using metadata of the left object if method == 'merge': for name in self._metadata: object.__setattr__(self, name, getattr(other.left, name, None)) # concat operation: using metadata of the first object elif method == 'concat': for name in self._metadata: object.__setattr__(self, name, getattr(other.objs[0], name, None)) else: for name in self._metadata: object.__setattr__(self, name, getattr(other, name, None)) return self def copy(self, deep=True): """ Make a copy of this GeoDataFrame object Parameters ---------- deep : boolean, default True Make a deep copy, i.e. also copy data Returns ------- copy : GeoDataFrame """ # FIXME: this will likely be unnecessary in pandas >= 0.13 data = self._data if deep: data = data.copy() return GeoDataFrame(data).__finalize__(self) def plot(self, *args, **kwargs): return plot_dataframe(self, *args, **kwargs) plot.__doc__ = plot_dataframe.__doc__ def dissolve(self, by=None, aggfunc='first', as_index=True): """ Dissolve geometries within `groupby` into single observation. This is accomplished by applying the `unary_union` method to all geometries within a groupself. Observations associated with each `groupby` group will be aggregated using the `aggfunc`. Parameters ---------- by : string, default None Column whose values define groups to be dissolved aggfunc : function or string, default "first" Aggregation function for manipulation of data associated with each group. Passed to pandas `groupby.agg` method. as_index : boolean, default True If true, groupby columns become index of result. Returns ------- GeoDataFrame """ # Process non-spatial component data = self.drop(labels=self.geometry.name, axis=1) aggregated_data = data.groupby(by=by).agg(aggfunc) # Process spatial component def merge_geometries(block): merged_geom = block.unary_union return merged_geom g = self.groupby(by=by, group_keys=False)[self.geometry.name].agg(merge_geometries) # Aggregate aggregated_geometry = GeoDataFrame(g, geometry=self.geometry.name, crs=self.crs) # Recombine aggregated = aggregated_geometry.join(aggregated_data) # Reset if requested if not as_index: aggregated = aggregated.reset_index() return aggregated def _dataframe_set_geometry(self, col, drop=False, inplace=False, crs=None): if inplace: raise ValueError("Can't do inplace setting when converting from" " DataFrame to GeoDataFrame") gf = GeoDataFrame(self) # this will copy so that BlockManager gets copied return gf.set_geometry(col, drop=drop, inplace=False, crs=crs) if PY3: DataFrame.set_geometry = _dataframe_set_geometry else: import types DataFrame.set_geometry = types.MethodType(_dataframe_set_geometry, None, DataFrame) GeoDataFrame._create_indexer('cx', _CoordinateIndexer) geopandas-0.3.0/geopandas/geoseries.py000066400000000000000000000244101315112464100177520ustar00rootroot00000000000000from functools import partial import json import numpy as np from pandas import Series import pyproj from shapely.geometry import shape, Point from shapely.geometry.base import BaseGeometry from shapely.ops import transform from geopandas.plotting import plot_series from geopandas.base import GeoPandasBase, _series_unary_op, _CoordinateIndexer def _is_empty(x): try: return x.is_empty except: return False class GeoSeries(GeoPandasBase, Series): """A Series object designed to store shapely geometry objects.""" _metadata = ['name', 'crs'] def __new__(cls, *args, **kwargs): kwargs.pop('crs', None) arr = Series.__new__(cls) if type(arr) is GeoSeries: return arr else: return arr.view(GeoSeries) def __init__(self, *args, **kwargs): # fix problem for scalar geometries passed if len(args) == 1 and isinstance(args[0], BaseGeometry): args = ([args[0]],) crs = kwargs.pop('crs', None) super(GeoSeries, self).__init__(*args, **kwargs) self.crs = crs self._invalidate_sindex() def append(self, *args, **kwargs): return self._wrapped_pandas_method('append', *args, **kwargs) @property def geometry(self): return self @property def x(self): """Return the x location of point geometries in a GeoSeries""" if (self.geom_type == "Point").all(): return _series_unary_op(self, 'x', null_value=np.nan) else: message = "x attribute access only provided for Point geometries" raise ValueError(message) @property def y(self): """Return the y location of point geometries in a GeoSeries""" if (self.geom_type == "Point").all(): return _series_unary_op(self, 'y', null_value=np.nan) else: message = "y attribute access only provided for Point geometries" raise ValueError(message) @classmethod def from_file(cls, filename, **kwargs): """ Alternate constructor to create a GeoSeries from a file Parameters ---------- filename : str File path or file handle to read from. Depending on which kwargs are included, the content of filename may vary, see: http://toblerity.github.io/fiona/README.html#usage for usage details. kwargs : key-word arguments These arguments are passed to fiona.open, and can be used to access multi-layer data, data stored within archives (zip files), etc. """ import fiona geoms = [] with fiona.open(filename, **kwargs) as f: crs = f.crs for rec in f: geoms.append(shape(rec['geometry'])) g = GeoSeries(geoms) g.crs = crs return g @property def __geo_interface__(self): """Returns a GeoSeries as a python feature collection """ from geopandas import GeoDataFrame return GeoDataFrame({'geometry': self}).__geo_interface__ def to_file(self, filename, driver="ESRI Shapefile", **kwargs): from geopandas import GeoDataFrame data = GeoDataFrame({"geometry": self, "id":self.index.values}, index=self.index) data.crs = self.crs data.to_file(filename, driver, **kwargs) # # Implement pandas methods # @property def _constructor(self): return GeoSeries def _wrapped_pandas_method(self, mtd, *args, **kwargs): """Wrap a generic pandas method to ensure it returns a GeoSeries""" val = getattr(super(GeoSeries, self), mtd)(*args, **kwargs) if type(val) == Series: val.__class__ = GeoSeries val.crs = self.crs val._invalidate_sindex() return val def __getitem__(self, key): return self._wrapped_pandas_method('__getitem__', key) def sort_index(self, *args, **kwargs): return self._wrapped_pandas_method('sort_index', *args, **kwargs) def take(self, *args, **kwargs): return self._wrapped_pandas_method('take', *args, **kwargs) def select(self, *args, **kwargs): return self._wrapped_pandas_method('select', *args, **kwargs) @property def _can_hold_na(self): return False def __finalize__(self, other, method=None, **kwargs): """ propagate metadata from other to self """ # NOTE: backported from pandas master (upcoming v0.13) for name in self._metadata: object.__setattr__(self, name, getattr(other, name, None)) return self def copy(self, order='C'): """ Make a copy of this GeoSeries object Parameters ---------- deep : boolean, default True Make a deep copy, i.e. also copy data Returns ------- copy : GeoSeries """ # FIXME: this will likely be unnecessary in pandas >= 0.13 return GeoSeries(self.values.copy(order), index=self.index, name=self.name).__finalize__(self) def isna(self): """ N/A values in a GeoSeries can be represented by empty geometric objects, in addition to standard representations such as None and np.nan. Returns ------- A boolean pandas Series of the same size as the GeoSeries, True where a value is N/A. See Also -------- GeoSereies.notna : inverse of isna """ non_geo_null = super(GeoSeries, self).isnull() val = self.apply(_is_empty) return np.logical_or(non_geo_null, val) def isnull(self): """Alias for `isna` method. See `isna` for more detail.""" return self.isna() def notna(self): """ N/A values in a GeoSeries can be represented by empty geometric objects, in addition to standard representations such as None and np.nan. Returns ------- A boolean pandas Series of the same size as the GeoSeries, False where a value is N/A. See Also -------- GeoSeries.isna : inverse of notna """ return ~self.isna() def notnull(self): """Alias for `notna` method. See `notna` for more detail.""" return self.notna() def fillna(self, value=None, method=None, inplace=False, **kwargs): """Fill NA/NaN values with a geometry (empty polygon by default). "method" is currently not implemented for pandas <= 0.12. """ if value is None: value = BaseGeometry() return super(GeoSeries, self).fillna(value=value, method=method, inplace=inplace, **kwargs) def align(self, other, join='outer', level=None, copy=True, fill_value=None, **kwargs): if fill_value is None: fill_value = BaseGeometry() left, right = super(GeoSeries, self).align(other, join=join, level=level, copy=copy, fill_value=fill_value, **kwargs) if isinstance(other, GeoSeries): return GeoSeries(left), GeoSeries(right) else: # It is probably a Series, let's keep it that way return GeoSeries(left), right def __contains__(self, other): """Allow tests of the form "geom in s" Tests whether a GeoSeries contains a geometry. Note: This is not the same as the geometric method "contains". """ if isinstance(other, BaseGeometry): return np.any(self.geom_equals(other)) else: return False def plot(self, *args, **kwargs): return plot_series(self, *args, **kwargs) plot.__doc__ = plot_series.__doc__ # # Additional methods # def to_crs(self, crs=None, epsg=None): """Transform geometries to a new coordinate reference system This method will transform all points in all objects. It has no notion or projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. `to_crs` passes the `crs` argument to the `Proj` function from the `pyproj` library (with the option `preserve_units=True`). It can therefore accept proj4 projections in any format supported by `Proj`, including dictionaries, or proj4 strings. """ from fiona.crs import from_epsg if self.crs is None: raise ValueError('Cannot transform naive geometries. ' 'Please set a crs on the object first.') if crs is None: try: crs = from_epsg(epsg) except TypeError: raise TypeError('Must set either crs or epsg for output.') proj_in = pyproj.Proj(self.crs, preserve_units=True) proj_out = pyproj.Proj(crs, preserve_units=True) project = partial(pyproj.transform, proj_in, proj_out) result = self.apply(lambda geom: transform(project, geom)) result.__class__ = GeoSeries result.crs = crs result._invalidate_sindex() return result def to_json(self, **kwargs): """ Returns a GeoJSON string representation of the GeoSeries. Parameters ---------- *kwargs* that will be passed to json.dumps(). """ return json.dumps(self.__geo_interface__, **kwargs) # # Implement standard operators for GeoSeries # def __xor__(self, other): """Implement ^ operator as for builtin set type""" return self.symmetric_difference(other) def __or__(self, other): """Implement | operator as for builtin set type""" return self.union(other) def __and__(self, other): """Implement & operator as for builtin set type""" return self.intersection(other) def __sub__(self, other): """Implement - operator as for builtin set type""" return self.difference(other) GeoSeries._create_indexer('cx', _CoordinateIndexer) geopandas-0.3.0/geopandas/io/000077500000000000000000000000001315112464100160215ustar00rootroot00000000000000geopandas-0.3.0/geopandas/io/__init__.py000066400000000000000000000000001315112464100201200ustar00rootroot00000000000000geopandas-0.3.0/geopandas/io/file.py000066400000000000000000000065701315112464100173220ustar00rootroot00000000000000import os import fiona import numpy as np from geopandas import GeoDataFrame def read_file(filename, **kwargs): """ Returns a GeoDataFrame from a file. *filename* is either the absolute or relative path to the file to be opened and *kwargs* are keyword args to be passed to the `open` method in the fiona library when opening the file. For more information on possible keywords, type: ``import fiona; help(fiona.open)`` """ bbox = kwargs.pop('bbox', None) with fiona.open(filename, **kwargs) as f: crs = f.crs if bbox is not None: assert len(bbox)==4 f_filt = f.filter(bbox=bbox) else: f_filt = f gdf = GeoDataFrame.from_features(f_filt, crs=crs) # re-order with column order from metadata, with geometry last columns = list(f.meta["schema"]["properties"]) + ["geometry"] gdf = gdf[columns] return gdf def to_file(df, filename, driver="ESRI Shapefile", schema=None, **kwargs): """ Write this GeoDataFrame to an OGR data source A dictionary of supported OGR providers is available via: >>> import fiona >>> fiona.supported_drivers Parameters ---------- df : GeoDataFrame to be written filename : string File path or file handle to write to. driver : string, default 'ESRI Shapefile' The OGR format driver used to write the vector file. schema : dict, default None If specified, the schema dictionary is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the schema based on each column's dtype The *kwargs* are passed to fiona.open and can be used to write to multi-layer data, store data within archives (zip files), etc. """ if schema is None: schema = infer_schema(df) filename = os.path.abspath(os.path.expanduser(filename)) with fiona.drivers(): with fiona.open(filename, 'w', driver=driver, crs=df.crs, schema=schema, **kwargs) as colxn: for feature in df.iterfeatures(): colxn.write(feature) def infer_schema(df): try: from collections import OrderedDict except ImportError: from ordereddict import OrderedDict def convert_type(in_type): if in_type == object: return 'str' out_type = type(np.asscalar(np.zeros(1, in_type))).__name__ if out_type == 'long': out_type = 'int' return out_type properties = OrderedDict([ (col, convert_type(_type)) for col, _type in zip(df.columns, df.dtypes) if col != df._geometry_column_name ]) geom_type = _common_geom_type(df) if not geom_type: raise ValueError("Geometry column cannot contain mutiple " "geometry types when writing to file.") schema = {'geometry': geom_type, 'properties': properties} return schema def _common_geom_type(df): # Need to check geom_types before we write to file... # Some (most?) providers expect a single geometry type: # Point, LineString, or Polygon geom_types = df.geometry.geom_type.unique() from os.path import commonprefix # To find longest common prefix geom_type = commonprefix([g[::-1] for g in geom_types if g])[::-1] # Reverse if not geom_type: geom_type = None return geom_type geopandas-0.3.0/geopandas/io/sql.py000066400000000000000000000024261315112464100171760ustar00rootroot00000000000000import binascii from pandas import read_sql import shapely.wkb from geopandas import GeoSeries, GeoDataFrame def read_postgis(sql, con, geom_col='geom', crs=None, index_col=None, coerce_float=True, params=None): """ Returns a GeoDataFrame corresponding to the result of the query string, which must contain a geometry column. Examples: sql = "SELECT geom, kind FROM polygons;" df = geopandas.read_postgis(sql, con) Parameters ---------- sql: string con: DB connection object or SQLAlchemy engine geom_col: string, default 'geom' column name to convert to shapely geometries crs: optional CRS to use for the returned GeoDataFrame See the documentation for pandas.read_sql for further explanation of the following parameters: index_col, coerce_float, params """ df = read_sql(sql, con, index_col=index_col, coerce_float=coerce_float, params=params) if geom_col not in df: raise ValueError("Query missing geometry column '{0}'".format( geom_col)) wkb_geoms = df[geom_col] s = wkb_geoms.apply(lambda x: shapely.wkb.loads(binascii.unhexlify(x.encode()))) df[geom_col] = GeoSeries(s) return GeoDataFrame(df, crs=crs, geometry=geom_col) geopandas-0.3.0/geopandas/io/tests/000077500000000000000000000000001315112464100171635ustar00rootroot00000000000000geopandas-0.3.0/geopandas/io/tests/__init__.py000066400000000000000000000000001315112464100212620ustar00rootroot00000000000000geopandas-0.3.0/geopandas/io/tests/test_io.py000066400000000000000000000040711315112464100212050ustar00rootroot00000000000000from __future__ import absolute_import import fiona import geopandas from geopandas import read_postgis, read_file import pytest from geopandas.tests.util import connect, create_db, validate_boro_df class TestIO: def setup_method(self): nybb_zip_path = geopandas.datasets.get_path('nybb') self.df = read_file(nybb_zip_path) with fiona.open(nybb_zip_path) as f: self.crs = f.crs self.columns = list(f.meta["schema"]["properties"].keys()) def test_read_postgis_default(self): con = connect('test_geopandas') if con is None or not create_db(self.df): raise pytest.skip() try: sql = "SELECT * FROM nybb;" df = read_postgis(sql, con) finally: con.close() validate_boro_df(df) def test_read_postgis_custom_geom_col(self): con = connect('test_geopandas') if con is None or not create_db(self.df): raise pytest.skip() try: sql = """SELECT borocode, boroname, shape_leng, shape_area, geom AS __geometry__ FROM nybb;""" df = read_postgis(sql, con, geom_col='__geometry__') finally: con.close() validate_boro_df(df) def test_read_file(self): df = self.df.rename(columns=lambda x: x.lower()) validate_boro_df(df) assert df.crs == self.crs # get lower case columns, and exclude geometry column from comparison lower_columns = [c.lower() for c in self.columns] assert (df.columns[:-1] == lower_columns).all() def test_filtered_read_file(self): full_df_shape = self.df.shape nybb_filename = geopandas.datasets.get_path('nybb') bbox = (1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244) filtered_df = read_file(nybb_filename, bbox=bbox) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) geopandas-0.3.0/geopandas/plotting.py000066400000000000000000000416031315112464100176300ustar00rootroot00000000000000from __future__ import print_function from distutils.version import LooseVersion import warnings import numpy as np def _flatten_multi_geoms(geoms, colors=None): """ Returns Series like geoms and colors, except that any Multi geometries are split into their components and colors are repeated for all component in the same Multi geometry. Maintains 1:1 matching of geometry to color. Passing `color` is optional, and when no `color` is passed a list of None values is returned as `component_colors`. "Colors" are treated opaquely and so can actually contain any values. Returns ------- components : list of geometry component_colors : list of whatever type `colors` contains """ if colors is None: colors = [None] * len(geoms) components, component_colors = [], [] # precondition, so zip can't short-circuit assert len(geoms) == len(colors) for geom, color in zip(geoms, colors): if geom.type.startswith('Multi'): for poly in geom: components.append(poly) # repeat same color for all components component_colors.append(color) else: components.append(geom) component_colors.append(color) return components, component_colors def plot_polygon_collection(ax, geoms, values=None, color=None, cmap=None, vmin=None, vmax=None, **kwargs): """ Plots a collection of Polygon and MultiPolygon geometries to `ax` Parameters ---------- ax : matplotlib.axes.Axes where shapes will be plotted geoms : a sequence of `N` Polygons and/or MultiPolygons (can be mixed) values : a sequence of `N` values, optional Values will be mapped to colors using vmin/vmax/cmap. They should have 1:1 correspondence with the geometries (not their components). Otherwise follows `color` / `facecolor` kwargs. edgecolor : single color or sequence of `N` colors Color for the edge of the polygons facecolor : single color or sequence of `N` colors Color to fill the polygons. Cannot be used together with `values`. color : single color or sequence of `N` colors Sets both `edgecolor` and `facecolor` **kwargs Additional keyword arguments passed to the collection Returns ------- collection : matplotlib.collections.Collection that was plotted """ from descartes.patch import PolygonPatch from matplotlib.collections import PatchCollection geoms, values = _flatten_multi_geoms(geoms, values) if None in values: values = None # PatchCollection does not accept some kwargs. if 'markersize' in kwargs: del kwargs['markersize'] # color=None overwrites specified facecolor/edgecolor with default color if color is not None: kwargs['color'] = color collection = PatchCollection([PolygonPatch(poly) for poly in geoms], **kwargs) if values is not None: collection.set_array(np.asarray(values)) collection.set_cmap(cmap) collection.set_clim(vmin, vmax) ax.add_collection(collection, autolim=True) ax.autoscale_view() return collection def plot_linestring_collection(ax, geoms, values=None, color=None, cmap=None, vmin=None, vmax=None, **kwargs): """ Plots a collection of LineString and MultiLineString geometries to `ax` Parameters ---------- ax : matplotlib.axes.Axes where shapes will be plotted geoms : a sequence of `N` LineStrings and/or MultiLineStrings (can be mixed) values : a sequence of `N` values, optional Values will be mapped to colors using vmin/vmax/cmap. They should have 1:1 correspondence with the geometries (not their components). color : single color or sequence of `N` colors Cannot be used together with `values`. Returns ------- collection : matplotlib.collections.Collection that was plotted """ from matplotlib.collections import LineCollection geoms, values = _flatten_multi_geoms(geoms, values) if None in values: values = None # LineCollection does not accept some kwargs. if 'markersize' in kwargs: del kwargs['markersize'] # color=None gives black instead of default color cycle if color is not None: kwargs['color'] = color segments = [np.array(linestring)[:, :2] for linestring in geoms] collection = LineCollection(segments, **kwargs) if values is not None: collection.set_array(np.asarray(values)) collection.set_cmap(cmap) collection.set_clim(vmin, vmax) ax.add_collection(collection, autolim=True) ax.autoscale_view() return collection def plot_point_collection(ax, geoms, values=None, color=None, cmap=None, vmin=None, vmax=None, marker='o', markersize=None, **kwargs): """ Plots a collection of Point geometries to `ax` Parameters ---------- ax : matplotlib.axes.Axes where shapes will be plotted geoms : sequence of `N` Points values : a sequence of `N` values, optional Values mapped to colors using vmin, vmax, and cmap. Cannot be specified together with `color`. markersize : scalar or array-like, optional Size of the markers. Note that under the hood ``scatter`` is used, so the specified value will be proportional to the area of the marker (size in points^2). Returns ------- collection : matplotlib.collections.Collection that was plotted """ if values is not None and color is not None: raise ValueError("Can only specify one of 'values' and 'color' kwargs") x = geoms.x.values y = geoms.y.values # matplotlib 1.4 does not support c=None, and < 2.0 does not support s=None if values is not None: kwargs['c'] = values if markersize is not None: kwargs['s'] = markersize collection = ax.scatter(x, y, color=color, vmin=vmin, vmax=vmax, cmap=cmap, marker=marker, **kwargs) return collection def plot_series(s, cmap=None, color=None, ax=None, figsize=None, **style_kwds): """ Plot a GeoSeries. Generate a plot of a GeoSeries geometry with matplotlib. Parameters ---------- s : Series The GeoSeries to be plotted. Currently Polygon, MultiPolygon, LineString, MultiLineString and Point geometries can be plotted. cmap : str (default None) The name of a colormap recognized by matplotlib. Any colormap will work, but categorical colormaps are generally recommended. Examples of useful discrete colormaps include: tab10, tab20, Accent, Dark2, Paired, Pastel1, Set1, Set2 color : str (default None) If specified, all objects will be colored uniformly. ax : matplotlib.pyplot.Artist (default None) axes on which to draw the plot figsize : pair of floats (default None) Size of the resulting matplotlib.figure.Figure. If the argument ax is given explicitly, figsize is ignored. **style_kwds : dict Color options to be passed on to the actual plot function, such as ``edgecolor``, ``facecolor``, ``linewidth``, ``markersize``, ``alpha``. Returns ------- matplotlib axes instance """ if 'colormap' in style_kwds: warnings.warn("'colormap' is deprecated, please use 'cmap' instead " "(for consistency with matplotlib)", FutureWarning) cmap = style_kwds.pop('colormap') if 'axes' in style_kwds: warnings.warn("'axes' is deprecated, please use 'ax' instead " "(for consistency with pandas)", FutureWarning) ax = style_kwds.pop('axes') import matplotlib.pyplot as plt if ax is None: fig, ax = plt.subplots(figsize=figsize) ax.set_aspect('equal') # if cmap is specified, create range of colors based on cmap values = None if cmap is not None: values = np.arange(len(s)) if hasattr(cmap, 'N'): values = values % cmap.N style_kwds['vmin'] = style_kwds.get('vmin', values.min()) style_kwds['vmax'] = style_kwds.get('vmax', values.max()) geom_types = s.geometry.type poly_idx = np.asarray((geom_types == 'Polygon') | (geom_types == 'MultiPolygon')) line_idx = np.asarray((geom_types == 'LineString') | (geom_types == 'MultiLineString')) point_idx = np.asarray(geom_types == 'Point') # plot all Polygons and all MultiPolygon components in the same collection polys = s.geometry[poly_idx] if not polys.empty: # color overrides both face and edgecolor. As we want people to be # able to use edgecolor as well, pass color to facecolor facecolor = style_kwds.pop('facecolor', None) if color is not None: facecolor = color values_ = values[poly_idx] if cmap else None plot_polygon_collection(ax, polys, values_, facecolor=facecolor, cmap=cmap, **style_kwds) # plot all LineStrings and MultiLineString components in same collection lines = s.geometry[line_idx] if not lines.empty: values_ = values[line_idx] if cmap else None plot_linestring_collection(ax, lines, values_, color=color, cmap=cmap, **style_kwds) # plot all Points in the same collection points = s.geometry[point_idx] if not points.empty: values_ = values[point_idx] if cmap else None plot_point_collection(ax, points, values_, color=color, cmap=cmap, **style_kwds) plt.draw() return ax def plot_dataframe(df, column=None, cmap=None, color=None, ax=None, categorical=False, legend=False, scheme=None, k=5, vmin=None, vmax=None, figsize=None, **style_kwds): """ Plot a GeoDataFrame. Generate a plot of a GeoDataFrame with matplotlib. If a column is specified, the plot coloring will be based on values in that column. Parameters ---------- df : GeoDataFrame The GeoDataFrame to be plotted. Currently Polygon, MultiPolygon, LineString, MultiLineString and Point geometries can be plotted. column : str (default None) The name of the column to be plotted. Ignored if `color` is also set. cmap : str (default None) The name of a colormap recognized by matplotlib. categorical : bool (default False) If False, cmap will reflect numerical values of the column being plotted. For non-numerical columns, this will be set to True. color : str (default None) If specified, all objects will be colored uniformly. legend : bool (default False) Plot a legend. Ignored if no `column` is given, or if `color` is given. ax : matplotlib.pyplot.Artist (default None) axes on which to draw the plot scheme : str (default None) Name of a choropleth classification scheme (requires PySAL). A pysal.esda.mapclassify.Map_Classifier object will be used under the hood. Supported schemes: 'Equal_interval', 'Quantiles', 'Fisher_Jenks' k : int (default 5) Number of classes (ignored if scheme is None) vmin : None or float (default None) Minimum value of cmap. If None, the minimum data value in the column to be plotted is used. vmax : None or float (default None) Maximum value of cmap. If None, the maximum data value in the column to be plotted is used. figsize Size of the resulting matplotlib.figure.Figure. If the argument axes is given explicitly, figsize is ignored. **style_kwds : dict Color options to be passed on to the actual plot function, such as ``edgecolor``, ``facecolor``, ``linewidth``, ``markersize``, ``alpha``. Returns ------- matplotlib axes instance """ if 'colormap' in style_kwds: warnings.warn("'colormap' is deprecated, please use 'cmap' instead " "(for consistency with matplotlib)", FutureWarning) cmap = style_kwds.pop('colormap') if 'axes' in style_kwds: warnings.warn("'axes' is deprecated, please use 'ax' instead " "(for consistency with pandas)", FutureWarning) ax = style_kwds.pop('axes') if column and color: warnings.warn("Only specify one of 'column' or 'color'. Using " "'color'.", UserWarning) column = None import matplotlib import matplotlib.pyplot as plt if column is None: return plot_series(df.geometry, cmap=cmap, color=color, ax=ax, figsize=figsize, **style_kwds) if df[column].dtype is np.dtype('O'): categorical = True # Define `values` as a Series if categorical: if cmap is None: if LooseVersion(matplotlib.__version__) >= '2.0': cmap = 'tab10' else: cmap = 'Set1' categories = list(set(df[column].values)) categories.sort() valuemap = dict([(k, v) for (v, k) in enumerate(categories)]) values = np.array([valuemap[k] for k in df[column]]) else: values = df[column] if scheme is not None: binning = __pysal_choro(values, scheme, k=k) # set categorical to True for creating the legend categorical = True binedges = [values.min()] + binning.bins.tolist() categories = ['{0:.2f} - {1:.2f}'.format(binedges[i], binedges[i+1]) for i in range(len(binedges)-1)] values = np.array(binning.yb) if ax is None: fig, ax = plt.subplots(figsize=figsize) ax.set_aspect('equal') mn = values.min() if vmin is None else vmin mx = values.max() if vmax is None else vmax geom_types = df.geometry.type poly_idx = np.asarray((geom_types == 'Polygon') | (geom_types == 'MultiPolygon')) line_idx = np.asarray((geom_types == 'LineString') | (geom_types == 'MultiLineString')) point_idx = np.asarray(geom_types == 'Point') # plot all Polygons and all MultiPolygon components in the same collection polys = df.geometry[poly_idx] if not polys.empty: plot_polygon_collection(ax, polys, values[poly_idx], vmin=mn, vmax=mx, cmap=cmap, **style_kwds) # plot all LineStrings and MultiLineString components in same collection lines = df.geometry[line_idx] if not lines.empty: plot_linestring_collection(ax, lines, values[line_idx], vmin=mn, vmax=mx, cmap=cmap, **style_kwds) # plot all Points in the same collection points = df.geometry[point_idx] if not points.empty: plot_point_collection(ax, points, values[point_idx], vmin=mn, vmax=mx, cmap=cmap, **style_kwds) if legend and not color: from matplotlib.lines import Line2D from matplotlib.colors import Normalize from matplotlib import cm norm = Normalize(vmin=mn, vmax=mx) n_cmap = cm.ScalarMappable(norm=norm, cmap=cmap) if categorical: patches = [] for value, cat in enumerate(categories): patches.append( Line2D([0], [0], linestyle="none", marker="o", alpha=style_kwds.get('alpha', 1), markersize=10, markerfacecolor=n_cmap.to_rgba(value))) ax.legend(patches, categories, numpoints=1, loc='best') else: n_cmap.set_array([]) ax.get_figure().colorbar(n_cmap) plt.draw() return ax def __pysal_choro(values, scheme, k=5): """ Wrapper for choropleth schemes from PySAL for use with plot_dataframe Parameters ---------- values Series to be plotted scheme pysal.esda.mapclassify classificatin scheme ['Equal_interval'|'Quantiles'|'Fisher_Jenks'] k number of classes (2 <= k <=9) Returns ------- binning Binning objects that holds the Series with values replaced with class identifier and the bins. """ try: from pysal.esda.mapclassify import ( Quantiles, Equal_Interval, Fisher_Jenks) schemes = {} schemes['equal_interval'] = Equal_Interval schemes['quantiles'] = Quantiles schemes['fisher_jenks'] = Fisher_Jenks scheme = scheme.lower() if scheme not in schemes: raise ValueError("Invalid scheme. Scheme must be in the" " set: %r" % schemes.keys()) binning = schemes[scheme](values, k) return binning except ImportError: raise ImportError("PySAL is required to use the 'scheme' keyword") geopandas-0.3.0/geopandas/sindex.py000066400000000000000000000010621315112464100172550ustar00rootroot00000000000000from geopandas import base if base.HAS_SINDEX: from rtree.index import Index as RTreeIndex class SpatialIndex(RTreeIndex): """ A simple wrapper around rtree's RTree Index """ def __init__(self, *args): if not base.HAS_SINDEX: raise ImportError("SpatialIndex needs `rtree`") RTreeIndex.__init__(self, *args) @property def size(self): return len(self.leaves()[0][1]) @property def is_empty(self): if len(self.leaves()) > 1: return False return self.size < 1 geopandas-0.3.0/geopandas/tests/000077500000000000000000000000001315112464100165545ustar00rootroot00000000000000geopandas-0.3.0/geopandas/tests/__init__.py000066400000000000000000000000001315112464100206530ustar00rootroot00000000000000geopandas-0.3.0/geopandas/tests/test_datasets.py000066400000000000000000000007321315112464100217770ustar00rootroot00000000000000from __future__ import absolute_import from geopandas import read_file, GeoDataFrame from geopandas.datasets import get_path class TestDatasets: def test_read_paths(self): gdf = read_file(get_path('naturalearth_lowres')) assert isinstance(gdf, GeoDataFrame) gdf = read_file(get_path('naturalearth_cities')) assert isinstance(gdf, GeoDataFrame) gdf = read_file(get_path('nybb')) assert isinstance(gdf, GeoDataFrame) geopandas-0.3.0/geopandas/tests/test_dissolve.py000066400000000000000000000057001315112464100220170ustar00rootroot00000000000000from __future__ import absolute_import import numpy as np import pandas as pd import geopandas from geopandas import GeoDataFrame, read_file from pandas.util.testing import assert_frame_equal class TestDataFrame: def setup_method(self): nybb_filename = geopandas.datasets.get_path('nybb') self.polydf = read_file(nybb_filename) self.polydf = self.polydf[['geometry', 'BoroName', 'BoroCode']] self.polydf = self.polydf.rename(columns={'geometry': 'myshapes'}) self.polydf = self.polydf.set_geometry('myshapes') self.polydf['manhattan_bronx'] = 5 self.polydf.loc[3:4, 'manhattan_bronx'] = 6 # Merged geometry manhattan_bronx = self.polydf.loc[3:4, ] others = self.polydf.loc[0:2, ] collapsed = [others.geometry.unary_union, manhattan_bronx.geometry.unary_union] merged_shapes = GeoDataFrame( {'myshapes': collapsed}, geometry='myshapes', index=pd.Index([5, 6], name='manhattan_bronx')) # Different expected results self.first = merged_shapes.copy() self.first['BoroName'] = ['Staten Island', 'Manhattan'] self.first['BoroCode'] = [5, 1] self.mean = merged_shapes.copy() self.mean['BoroCode'] = [4, 1.5] def test_geom_dissolve(self): test = self.polydf.dissolve('manhattan_bronx') assert test.geometry.name == 'myshapes' assert test.geom_almost_equals(self.first).all() def test_dissolve_retains_existing_crs(self): assert self.polydf.crs is not None test = self.polydf.dissolve('manhattan_bronx') assert test.crs is not None def test_dissolve_retains_nonexisting_crs(self): self.polydf.crs = None test = self.polydf.dissolve('manhattan_bronx') assert test.crs is None def test_first_dissolve(self): test = self.polydf.dissolve('manhattan_bronx') assert_frame_equal(self.first, test, check_column_type=False) def test_mean_dissolve(self): test = self.polydf.dissolve('manhattan_bronx', aggfunc='mean') assert_frame_equal(self.mean, test, check_column_type=False) test = self.polydf.dissolve('manhattan_bronx', aggfunc=np.mean) assert_frame_equal(self.mean, test, check_column_type=False) def test_multicolumn_dissolve(self): multi = self.polydf.copy() multi['dup_col'] = multi.manhattan_bronx multi_test = multi.dissolve(['manhattan_bronx', 'dup_col'], aggfunc='first') first = self.first.copy() first['dup_col'] = first.index first = first.set_index([first.index, 'dup_col']) assert_frame_equal(multi_test, first, check_column_type=False) def test_reset_index(self): test = self.polydf.dissolve('manhattan_bronx', as_index=False) comparison = self.first.reset_index() assert_frame_equal(comparison, test, check_column_type=False) geopandas-0.3.0/geopandas/tests/test_geocode.py000066400000000000000000000115011315112464100215700ustar00rootroot00000000000000from __future__ import absolute_import import numpy as np import pandas as pd from fiona.crs import from_epsg from shapely.geometry import Point from geopandas import GeoSeries, GeoDataFrame from geopandas.tools import geocode, reverse_geocode from geopandas.tools.geocoding import _prepare_geocode_result import pytest from pandas.util.testing import assert_series_equal from geopandas.tests.util import mock, assert_geoseries_equal def _skip_if_no_geopy(): try: import geopy except ImportError: raise pytest.skip("Geopy not installed. Skipping tests.") except SyntaxError: raise pytest.skip("Geopy is known to be broken on Python 3.2. " "Skipping tests.") class ForwardMock(mock.MagicMock): """ Mock the forward geocoding function. Returns the passed in address and (p, p+.5) where p increases at each call """ def __init__(self, *args, **kwargs): super(ForwardMock, self).__init__(*args, **kwargs) self._n = 0.0 def __call__(self, *args, **kwargs): self.return_value = args[0], (self._n, self._n + 0.5) self._n += 1 return super(ForwardMock, self).__call__(*args, **kwargs) class ReverseMock(mock.MagicMock): """ Mock the reverse geocoding function. Returns the passed in point and 'address{p}' where p increases at each call """ def __init__(self, *args, **kwargs): super(ReverseMock, self).__init__(*args, **kwargs) self._n = 0 def __call__(self, *args, **kwargs): self.return_value = 'address{0}'.format(self._n), args[0] self._n += 1 return super(ReverseMock, self).__call__(*args, **kwargs) class TestGeocode: def setup_method(self): _skip_if_no_geopy() self.locations = ['260 Broadway, New York, NY', '77 Massachusetts Ave, Cambridge, MA'] self.points = [Point(-71.0597732, 42.3584308), Point(-77.0365305, 38.8977332)] def test_prepare_result(self): # Calls _prepare_result with sample results from the geocoder call # loop p0 = Point(12.3, -45.6) # Treat these as lat/lon p1 = Point(-23.4, 56.7) d = {'a': ('address0', p0.coords[0]), 'b': ('address1', p1.coords[0])} df = _prepare_geocode_result(d) assert type(df) is GeoDataFrame assert from_epsg(4326) == df.crs assert len(df) == 2 assert 'address' in df coords = df.loc['a']['geometry'].coords[0] test = p0.coords[0] # Output from the df should be lon/lat assert coords[0] == pytest.approx(test[1]) assert coords[1] == pytest.approx(test[0]) coords = df.loc['b']['geometry'].coords[0] test = p1.coords[0] assert coords[0] == pytest.approx(test[1]) assert coords[1] == pytest.approx(test[0]) def test_prepare_result_none(self): p0 = Point(12.3, -45.6) # Treat these as lat/lon d = {'a': ('address0', p0.coords[0]), 'b': (None, None)} df = _prepare_geocode_result(d) assert type(df) is GeoDataFrame assert from_epsg(4326) == df.crs assert len(df) == 2 assert 'address' in df row = df.loc['b'] assert len(row['geometry'].coords) == 0 assert np.isnan(row['address']) def test_bad_provider_forward(self): with pytest.raises(ValueError): geocode(['cambridge, ma'], 'badprovider') def test_bad_provider_reverse(self): with pytest.raises(ValueError): reverse_geocode(['cambridge, ma'], 'badprovider') def test_forward(self): with mock.patch('geopy.geocoders.googlev3.GoogleV3.geocode', ForwardMock()) as m: g = geocode(self.locations, provider='googlev3', timeout=2) assert len(self.locations) == m.call_count n = len(self.locations) assert isinstance(g, GeoDataFrame) expected = GeoSeries( [Point(float(x) + 0.5, float(x)) for x in range(n)], crs=from_epsg(4326)) assert_geoseries_equal(expected, g['geometry']) assert_series_equal(g['address'], pd.Series(self.locations, name='address')) def test_reverse(self): with mock.patch('geopy.geocoders.googlev3.GoogleV3.reverse', ReverseMock()) as m: g = reverse_geocode(self.points, provider='googlev3', timeout=2) assert len(self.points) == m.call_count assert isinstance(g, GeoDataFrame) expected = GeoSeries(self.points, crs=from_epsg(4326)) assert_geoseries_equal(expected, g['geometry']) address = pd.Series( ['address' + str(x) for x in range(len(self.points))], name='address') assert_series_equal(g['address'], address) geopandas-0.3.0/geopandas/tests/test_geodataframe.py000066400000000000000000000457511315112464100226200ustar00rootroot00000000000000from __future__ import absolute_import import json import os import tempfile import shutil import numpy as np import pandas as pd from shapely.geometry import Point, Polygon import fiona import geopandas from geopandas import GeoDataFrame, read_file, GeoSeries from geopandas.tests.util import ( assert_geoseries_equal, connect, create_db, PACKAGE_DIR, validate_boro_df) from pandas.util.testing import assert_frame_equal import pytest class TestDataFrame: def setup_method(self): N = 10 nybb_filename = geopandas.datasets.get_path('nybb') self.df = read_file(nybb_filename) self.tempdir = tempfile.mkdtemp() self.boros = self.df['BoroName'] self.crs = {'init': 'epsg:4326'} self.df2 = GeoDataFrame([ {'geometry': Point(x, y), 'value1': x + y, 'value2': x * y} for x, y in zip(range(N), range(N))], crs=self.crs) self.df3 = read_file( os.path.join(PACKAGE_DIR, 'examples', 'null_geom.geojson')) self.line_paths = self.df3['Name'] def teardown_method(self): shutil.rmtree(self.tempdir) def test_df_init(self): assert type(self.df2) is GeoDataFrame assert self.df2.crs == self.crs def test_different_geo_colname(self): data = {"A": range(5), "B": range(-5, 0), "location": [Point(x, y) for x, y in zip(range(5), range(5))]} df = GeoDataFrame(data, crs=self.crs, geometry='location') locs = GeoSeries(data['location'], crs=self.crs) assert_geoseries_equal(df.geometry, locs) assert 'geometry' not in df assert df.geometry.name == 'location' # internal implementation detail assert df._geometry_column_name == 'location' geom2 = [Point(x, y) for x, y in zip(range(5, 10), range(5))] df2 = df.set_geometry(geom2, crs='dummy_crs') assert 'location' in df2 assert df2.crs == 'dummy_crs' assert df2.geometry.crs == 'dummy_crs' # reset so it outputs okay df2.crs = df.crs assert_geoseries_equal(df2.geometry, GeoSeries(geom2, crs=df2.crs)) def test_geo_getitem(self): data = {"A": range(5), "B": range(-5, 0), "location": [Point(x, y) for x, y in zip(range(5), range(5))]} df = GeoDataFrame(data, crs=self.crs, geometry='location') assert isinstance(df.geometry, GeoSeries) df['geometry'] = df["A"] assert isinstance(df.geometry, GeoSeries) assert df.geometry[0] == data['location'][0] # good if this changed in the future assert not isinstance(df['geometry'], GeoSeries) assert isinstance(df['location'], GeoSeries) data["geometry"] = [Point(x + 1, y - 1) for x, y in zip(range(5), range(5))] df = GeoDataFrame(data, crs=self.crs) assert isinstance(df.geometry, GeoSeries) assert isinstance(df['geometry'], GeoSeries) # good if this changed in the future assert not isinstance(df['location'], GeoSeries) def test_geometry_property(self): assert_geoseries_equal(self.df.geometry, self.df['geometry'], check_dtype=True, check_index_type=True) df = self.df.copy() new_geom = [Point(x, y) for x, y in zip(range(len(self.df)), range(len(self.df)))] df.geometry = new_geom new_geom = GeoSeries(new_geom, index=df.index, crs=df.crs) assert_geoseries_equal(df.geometry, new_geom) assert_geoseries_equal(df['geometry'], new_geom) # new crs gs = GeoSeries(new_geom, crs="epsg:26018") df.geometry = gs assert df.crs == "epsg:26018" def test_geometry_property_errors(self): with pytest.raises(AttributeError): df = self.df.copy() del df['geometry'] df.geometry # list-like error with pytest.raises(ValueError): df = self.df2.copy() df.geometry = 'value1' # list-like error with pytest.raises(ValueError): df = self.df.copy() df.geometry = 'apple' # non-geometry error with pytest.raises(TypeError): df = self.df.copy() df.geometry = list(range(df.shape[0])) with pytest.raises(KeyError): df = self.df.copy() del df['geometry'] df['geometry'] # ndim error with pytest.raises(ValueError): df = self.df.copy() df.geometry = df def test_set_geometry(self): geom = GeoSeries([Point(x, y) for x, y in zip(range(5), range(5))]) original_geom = self.df.geometry df2 = self.df.set_geometry(geom) assert self.df is not df2 assert_geoseries_equal(df2.geometry, geom) assert_geoseries_equal(self.df.geometry, original_geom) assert_geoseries_equal(self.df['geometry'], self.df.geometry) # unknown column with pytest.raises(ValueError): self.df.set_geometry('nonexistent-column') # ndim error with pytest.raises(ValueError): self.df.set_geometry(self.df) # new crs - setting should default to GeoSeries' crs gs = GeoSeries(geom, crs="epsg:26018") new_df = self.df.set_geometry(gs) assert new_df.crs == "epsg:26018" # explicit crs overrides self and dataframe new_df = self.df.set_geometry(gs, crs="epsg:27159") assert new_df.crs == "epsg:27159" assert new_df.geometry.crs == "epsg:27159" # Series should use dataframe's new_df = self.df.set_geometry(geom.values) assert new_df.crs == self.df.crs assert new_df.geometry.crs == self.df.crs def test_set_geometry_col(self): g = self.df.geometry g_simplified = g.simplify(100) self.df['simplified_geometry'] = g_simplified df2 = self.df.set_geometry('simplified_geometry') # Drop is false by default assert 'simplified_geometry' in df2 assert_geoseries_equal(df2.geometry, g_simplified) # If True, drops column and renames to geometry df3 = self.df.set_geometry('simplified_geometry', drop=True) assert 'simplified_geometry' not in df3 assert_geoseries_equal(df3.geometry, g_simplified) def test_set_geometry_inplace(self): geom = [Point(x, y) for x, y in zip(range(5), range(5))] ret = self.df.set_geometry(geom, inplace=True) assert ret is None geom = GeoSeries(geom, index=self.df.index, crs=self.df.crs) assert_geoseries_equal(self.df.geometry, geom) def test_set_geometry_series(self): # Test when setting geometry with a Series that # alignment will occur # # Reverse the index order # Set the Series to be Point(i,i) where i is the index self.df.index = range(len(self.df)-1, -1, -1) d = {} for i in range(len(self.df)): d[i] = Point(i, i) g = GeoSeries(d) # At this point, the DataFrame index is [4,3,2,1,0] and the # GeoSeries index is [0,1,2,3,4]. Make sure set_geometry aligns # them to match indexes df = self.df.set_geometry(g) for i, r in df.iterrows(): assert i == r['geometry'].x assert i == r['geometry'].y def test_to_json(self): text = self.df.to_json() data = json.loads(text) assert data['type'] == 'FeatureCollection' assert len(data['features']) == 5 def test_to_json_geom_col(self): df = self.df.copy() df['geom'] = df['geometry'] df['geometry'] = np.arange(len(df)) df.set_geometry('geom', inplace=True) text = df.to_json() data = json.loads(text) assert data['type'] == 'FeatureCollection' assert len(data['features']) == 5 def test_to_json_na(self): # Set a value as nan and make sure it's written self.df.loc[self.df['BoroName'] == 'Queens', 'Shape_Area'] = np.nan text = self.df.to_json() data = json.loads(text) assert len(data['features']) == 5 for f in data['features']: props = f['properties'] assert len(props) == 4 if props['BoroName'] == 'Queens': assert props['Shape_Area'] is None def test_to_json_bad_na(self): # Check that a bad na argument raises error with pytest.raises(ValueError): self.df.to_json(na='garbage') def test_to_json_dropna(self): self.df.loc[self.df['BoroName'] == 'Queens', 'Shape_Area'] = np.nan self.df.loc[self.df['BoroName'] == 'Bronx', 'Shape_Leng'] = np.nan text = self.df.to_json(na='drop') data = json.loads(text) assert len(data['features']) == 5 for f in data['features']: props = f['properties'] if props['BoroName'] == 'Queens': assert len(props) == 3 assert 'Shape_Area' not in props # Just make sure setting it to nan in a different row # doesn't affect this one assert 'Shape_Leng' in props elif props['BoroName'] == 'Bronx': assert len(props) == 3 assert 'Shape_Leng' not in props assert 'Shape_Area' in props else: assert len(props) == 4 def test_to_json_keepna(self): self.df.loc[self.df['BoroName'] == 'Queens', 'Shape_Area'] = np.nan self.df.loc[self.df['BoroName'] == 'Bronx', 'Shape_Leng'] = np.nan text = self.df.to_json(na='keep') data = json.loads(text) assert len(data['features']) == 5 for f in data['features']: props = f['properties'] assert len(props) == 4 if props['BoroName'] == 'Queens': assert np.isnan(props['Shape_Area']) # Just make sure setting it to nan in a different row # doesn't affect this one assert 'Shape_Leng' in props elif props['BoroName'] == 'Bronx': assert np.isnan(props['Shape_Leng']) assert 'Shape_Area' in props def test_copy(self): df2 = self.df.copy() assert type(df2) is GeoDataFrame assert self.df.crs == df2.crs def test_to_file(self): """ Test to_file and from_file """ tempfilename = os.path.join(self.tempdir, 'boros.shp') self.df.to_file(tempfilename) # Read layer back in df = GeoDataFrame.from_file(tempfilename) assert 'geometry' in df assert len(df) == 5 assert np.alltrue(df['BoroName'].values == self.boros) # Write layer with null geometry out to file tempfilename = os.path.join(self.tempdir, 'null_geom.shp') self.df3.to_file(tempfilename) # Read layer back in df3 = GeoDataFrame.from_file(tempfilename) assert 'geometry' in df3 assert len(df3) == 2 assert np.alltrue(df3['Name'].values == self.line_paths) def test_to_file_types(self): """ Test various integer type columns (GH#93) """ tempfilename = os.path.join(self.tempdir, 'int.shp') int_types = [np.int, np.int8, np.int16, np.int32, np.int64, np.intp, np.uint8, np.uint16, np.uint32, np.uint64, np.long] geometry = self.df2.geometry data = dict((str(i), np.arange(len(geometry), dtype=dtype)) for i, dtype in enumerate(int_types)) df = GeoDataFrame(data, geometry=geometry) df.to_file(tempfilename) def test_mixed_types_to_file(self): """ Test that mixed geometry types raise error when writing to file """ tempfilename = os.path.join(self.tempdir, 'test.shp') s = GeoDataFrame({'geometry': [Point(0, 0), Polygon([(0, 0), (1, 0), (1, 1)])]}) with pytest.raises(ValueError): s.to_file(tempfilename) def test_to_file_schema(self): """ Ensure that the file is written according to the schema if it is specified """ from collections import OrderedDict tempfilename = os.path.join(self.tempdir, 'test.shp') properties = OrderedDict([ ('Shape_Leng', 'float:19.11'), ('BoroName', 'str:40'), ('BoroCode', 'int:10'), ('Shape_Area', 'float:19.11'), ]) schema = {'geometry': 'Polygon', 'properties': properties} # Take the first 2 features to speed things up a bit self.df.iloc[:2].to_file(tempfilename, schema=schema) with fiona.open(tempfilename) as f: result_schema = f.schema assert result_schema == schema def test_bool_index(self): # Find boros with 'B' in their name df = self.df[self.df['BoroName'].str.contains('B')] assert len(df) == 2 boros = df['BoroName'].values assert 'Brooklyn' in boros assert 'Bronx' in boros assert type(df) is GeoDataFrame def test_coord_slice_points(self): assert self.df2.cx[-2:-1, -2:-1].empty assert_frame_equal(self.df2, self.df2.cx[:, :]) assert_frame_equal(self.df2.loc[5:], self.df2.cx[5:, :]) assert_frame_equal(self.df2.loc[5:], self.df2.cx[:, 5:]) assert_frame_equal(self.df2.loc[5:], self.df2.cx[5:, 5:]) def test_transform(self): df2 = self.df2.copy() df2.crs = {'init': 'epsg:26918', 'no_defs': True} lonlat = df2.to_crs(epsg=4326) utm = lonlat.to_crs(epsg=26918) assert all(df2['geometry'].geom_almost_equals(utm['geometry'], decimal=2)) def test_to_crs_geo_column_name(self): # Test to_crs() with different geometry column name (GH#339) df2 = self.df2.copy() df2.crs = {'init': 'epsg:26918', 'no_defs': True} df2 = df2.rename(columns={'geometry': 'geom'}) df2.set_geometry('geom', inplace=True) lonlat = df2.to_crs(epsg=4326) utm = lonlat.to_crs(epsg=26918) assert lonlat.geometry.name == 'geom' assert utm.geometry.name == 'geom' assert all(df2.geometry.geom_almost_equals(utm.geometry, decimal=2)) def test_from_features(self): nybb_filename = geopandas.datasets.get_path('nybb') with fiona.open(nybb_filename) as f: features = list(f) crs = f.crs df = GeoDataFrame.from_features(features, crs=crs) df.rename(columns=lambda x: x.lower(), inplace=True) validate_boro_df(df) assert df.crs == crs def test_from_features_unaligned_properties(self): p1 = Point(1, 1) f1 = {'type': 'Feature', 'properties': {'a': 0}, 'geometry': p1.__geo_interface__} p2 = Point(2, 2) f2 = {'type': 'Feature', 'properties': {'b': 1}, 'geometry': p2.__geo_interface__} p3 = Point(3, 3) f3 = {'type': 'Feature', 'properties': {'a': 2}, 'geometry': p3.__geo_interface__} df = GeoDataFrame.from_features([f1, f2, f3]) result = df[['a', 'b']] expected = pd.DataFrame.from_dict([{'a': 0, 'b': np.nan}, {'a': np.nan, 'b': 1}, {'a': 2, 'b': np.nan}]) assert_frame_equal(expected, result) def test_from_feature_collection(self): data = {'name': ['a', 'b', 'c'], 'lat': [45, 46, 47.5], 'lon': [-120, -121.2, -122.9]} df = pd.DataFrame(data) geometry = [Point(xy) for xy in zip(df['lon'], df['lat'])] gdf = GeoDataFrame(df, geometry=geometry) # from_features returns sorted columns expected = gdf[['geometry', 'lat', 'lon', 'name']] # test FeatureCollection res = GeoDataFrame.from_features(gdf.__geo_interface__) assert_frame_equal(res, expected) # test list of Features res = GeoDataFrame.from_features(gdf.__geo_interface__['features']) assert_frame_equal(res, expected) # test __geo_interface__ attribute (a GeoDataFrame has one) res = GeoDataFrame.from_features(gdf) assert_frame_equal(res, expected) def test_from_postgis_default(self): con = connect('test_geopandas') if con is None or not create_db(self.df): raise pytest.skip() try: sql = "SELECT * FROM nybb;" df = GeoDataFrame.from_postgis(sql, con) finally: con.close() validate_boro_df(df) def test_from_postgis_custom_geom_col(self): con = connect('test_geopandas') if con is None or not create_db(self.df): raise pytest.skip() try: sql = """SELECT borocode, boroname, shape_leng, shape_area, geom AS __geometry__ FROM nybb;""" df = GeoDataFrame.from_postgis(sql, con, geom_col='__geometry__') finally: con.close() validate_boro_df(df) def test_dataframe_to_geodataframe(self): df = pd.DataFrame({"A": range(len(self.df)), "location": list(self.df.geometry)}, index=self.df.index) gf = df.set_geometry('location', crs=self.df.crs) assert isinstance(df, pd.DataFrame) assert isinstance(gf, GeoDataFrame) assert_geoseries_equal(gf.geometry, self.df.geometry) assert gf.geometry.name == 'location' assert 'geometry' not in gf gf2 = df.set_geometry('location', crs=self.df.crs, drop=True) assert isinstance(df, pd.DataFrame) assert isinstance(gf2, GeoDataFrame) assert gf2.geometry.name == 'geometry' assert 'geometry' in gf2 assert 'location' not in gf2 assert 'location' in df # should be a copy df.ix[0, "A"] = 100 assert gf.ix[0, "A"] == 0 assert gf2.ix[0, "A"] == 0 with pytest.raises(ValueError): df.set_geometry('location', inplace=True) def test_geodataframe_geointerface(self): assert self.df.__geo_interface__['type'] == 'FeatureCollection' assert len(self.df.__geo_interface__['features']) == self.df.shape[0] def test_geodataframe_geojson_no_bbox(self): geo = self.df._to_geo(na="null", show_bbox=False) assert 'bbox' not in geo.keys() for feature in geo['features']: assert 'bbox' not in feature.keys() def test_geodataframe_geojson_bbox(self): geo = self.df._to_geo(na="null", show_bbox=True) assert 'bbox' in geo.keys() assert len(geo['bbox']) == 4 assert isinstance(geo['bbox'], tuple) for feature in geo['features']: assert 'bbox' in feature.keys() def test_pickle(self): filename = os.path.join(self.tempdir, 'df.pkl') self.df.to_pickle(filename) unpickled = pd.read_pickle(filename) assert_frame_equal(self.df, unpickled) assert self.df.crs == unpickled.crs geopandas-0.3.0/geopandas/tests/test_geom_methods.py000066400000000000000000000435161315112464100226500ustar00rootroot00000000000000from __future__ import absolute_import import string import numpy as np from pandas import Series, DataFrame, MultiIndex from shapely.geometry import ( Point, LinearRing, LineString, Polygon, MultiPoint) from shapely.geometry.collection import GeometryCollection from shapely.ops import unary_union from geopandas import GeoSeries, GeoDataFrame from geopandas.base import GeoPandasBase from geopandas.tests.util import ( geom_equals, geom_almost_equals, assert_geoseries_equal) import pytest from numpy.testing import assert_array_equal from pandas.util.testing import assert_series_equal, assert_frame_equal class TestGeomMethods: def setup_method(self): self.t1 = Polygon([(0, 0), (1, 0), (1, 1)]) self.t2 = Polygon([(0, 0), (1, 1), (0, 1)]) self.t3 = Polygon([(2, 0), (3, 0), (3, 1)]) self.sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) self.inner_sq = Polygon([(0.25, 0.25), (0.75, 0.25), (0.75, 0.75), (0.25, 0.75)]) self.nested_squares = Polygon(self.sq.boundary, [self.inner_sq.boundary]) self.p0 = Point(5, 5) self.g0 = GeoSeries([self.t1, self.t2, self.sq, self.inner_sq, self.nested_squares, self.p0]) self.g1 = GeoSeries([self.t1, self.sq]) self.g2 = GeoSeries([self.sq, self.t1]) self.g3 = GeoSeries([self.t1, self.t2]) self.g3.crs = {'init': 'epsg:4326', 'no_defs': True} self.g4 = GeoSeries([self.t2, self.t1]) self.na = GeoSeries([self.t1, self.t2, Polygon()]) self.na_none = GeoSeries([self.t1, None]) self.a1 = self.g1.copy() self.a1.index = ['A', 'B'] self.a2 = self.g2.copy() self.a2.index = ['B', 'C'] self.esb = Point(-73.9847, 40.7484) self.sol = Point(-74.0446, 40.6893) self.landmarks = GeoSeries([self.esb, self.sol], crs={'init': 'epsg:4326', 'no_defs': True}) self.l1 = LineString([(0, 0), (0, 1), (1, 1)]) self.l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1)]) self.g5 = GeoSeries([self.l1, self.l2]) self.g6 = GeoSeries([self.p0, self.t3]) # Crossed lines self.l3 = LineString([(0, 0), (1, 1)]) self.l4 = LineString([(0, 1), (1, 0)]) self.crossed_lines = GeoSeries([self.l3, self.l4]) # Placeholder for testing, will just drop in different geometries # when needed self.gdf1 = GeoDataFrame({'geometry': self.g1, 'col0': [1.0, 2.0], 'col1': ['geo', 'pandas']}) self.gdf2 = GeoDataFrame({'geometry': self.g1, 'col3': [4, 5], 'col4': ['rand', 'string']}) def _test_unary_real(self, op, expected, a): """ Tests for 'area', 'length', 'is_valid', etc. """ fcmp = assert_series_equal self._test_unary(op, expected, a, fcmp) def _test_unary_topological(self, op, expected, a): if isinstance(expected, GeoPandasBase): fcmp = assert_geoseries_equal else: def fcmp(a, b): assert a.equals(b) self._test_unary(op, expected, a, fcmp) def _test_binary_topological(self, op, expected, a, b, *args, **kwargs): """ Tests for 'intersection', 'union', 'symmetric_difference', etc. """ if isinstance(expected, GeoPandasBase): fcmp = assert_geoseries_equal else: def fcmp(a, b): assert geom_equals(a, b) if isinstance(b, GeoPandasBase): right_df = True else: right_df = False self._binary_op_test(op, expected, a, b, fcmp, True, right_df, *args, **kwargs) def _test_binary_real(self, op, expected, a, b, *args, **kwargs): fcmp = assert_series_equal self._binary_op_test(op, expected, a, b, fcmp, True, False, *args, **kwargs) def _test_binary_operator(self, op, expected, a, b): """ The operators only have GeoSeries on the left, but can have GeoSeries or GeoDataFrame on the right. """ if isinstance(expected, GeoPandasBase): fcmp = assert_geoseries_equal else: def fcmp(a, b): assert geom_equals(a, b) if isinstance(b, GeoPandasBase): right_df = True else: right_df = False self._binary_op_test(op, expected, a, b, fcmp, False, right_df) def _binary_op_test(self, op, expected, left, right, fcmp, left_df, right_df, *args, **kwargs): """ This is a helper to call a function on GeoSeries and GeoDataFrame arguments. For example, 'intersection' is a member of both GeoSeries and GeoDataFrame and can take either GeoSeries or GeoDataFrame inputs. This function has the ability to test all four combinations of input types. Parameters ---------- expected : str The operation to be tested. e.g., 'intersection' left: GeoSeries right: GeoSeries fcmp: function Called with the result of the operation and expected. It should assert if the result is incorrect left_df: bool If the left input should also be called with a GeoDataFrame right_df: bool Indicates whether the right input should be called with a GeoDataFrame """ def _make_gdf(s): n = len(s) col1 = string.ascii_lowercase[:n] col2 = range(n) return GeoDataFrame({'geometry': s.values, 'col1': col1, 'col2': col2}, index=s.index, crs=s.crs) # Test GeoSeries.op(GeoSeries) result = getattr(left, op)(right, *args, **kwargs) fcmp(result, expected) if left_df: # Test GeoDataFrame.op(GeoSeries) gdf_left = _make_gdf(left) result = getattr(gdf_left, op)(right, *args, **kwargs) fcmp(result, expected) if right_df: # Test GeoSeries.op(GeoDataFrame) gdf_right = _make_gdf(right) result = getattr(left, op)(gdf_right, *args, **kwargs) fcmp(result, expected) if left_df: # Test GeoDataFrame.op(GeoDataFrame) result = getattr(gdf_left, op)(gdf_right, *args, **kwargs) fcmp(result, expected) def _test_unary(self, op, expected, a, fcmp): # GeoSeries, (GeoSeries or geometry) result = getattr(a, op) fcmp(result, expected) # GeoDataFrame, (GeoSeries or geometry) gdf = self.gdf1.set_geometry(a) result = getattr(gdf, op) fcmp(result, expected) def test_intersection(self): self._test_binary_topological('intersection', self.t1, self.g1, self.g2) def test_union_series(self): self._test_binary_topological('union', self.sq, self.g1, self.g2) def test_union_polygon(self): self._test_binary_topological('union', self.sq, self.g1, self.t2) def test_symmetric_difference_series(self): self._test_binary_topological('symmetric_difference', self.sq, self.g3, self.g4) def test_symmetric_difference_poly(self): expected = GeoSeries([GeometryCollection(), self.sq], crs=self.g3.crs) self._test_binary_topological('symmetric_difference', expected, self.g3, self.t1) def test_difference_series(self): expected = GeoSeries([GeometryCollection(), self.t2]) self._test_binary_topological('difference', expected, self.g1, self.g2) def test_difference_poly(self): expected = GeoSeries([self.t1, self.t1]) self._test_binary_topological('difference', expected, self.g1, self.t2) def test_boundary(self): l1 = LineString([(0, 0), (1, 0), (1, 1), (0, 0)]) l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)]) expected = GeoSeries([l1, l2], index=self.g1.index, crs=self.g1.crs) self._test_unary_topological('boundary', expected, self.g1) def test_area(self): expected = Series(np.array([0.5, 1.0]), index=self.g1.index) self._test_unary_real('area', expected, self.g1) expected = Series(np.array([0.5, np.nan]), index=self.na_none.index) self._test_unary_real('area', expected, self.na_none) def test_bounds(self): # Set columns to get the order right expected = DataFrame({'minx': [0.0, 0.0], 'miny': [0.0, 0.0], 'maxx': [1.0, 1.0], 'maxy': [1.0, 1.0]}, index=self.g1.index, columns=['minx', 'miny', 'maxx', 'maxy']) result = self.g1.bounds assert_frame_equal(expected, result) gdf = self.gdf1.set_geometry(self.g1) result = gdf.bounds assert_frame_equal(expected, result) def test_unary_union(self): p1 = self.t1 p2 = Polygon([(2, 0), (3, 0), (3, 1)]) expected = unary_union([p1, p2]) g = GeoSeries([p1, p2]) self._test_unary_topological('unary_union', expected, g) def test_contains(self): expected = [True, False, True, False, False, False] assert_array_equal(expected, self.g0.contains(self.t1)) def test_length(self): expected = Series(np.array([2 + np.sqrt(2), 4]), index=self.g1.index) self._test_unary_real('length', expected, self.g1) expected = Series( np.array([2 + np.sqrt(2), np.nan]), index=self.na_none.index) self._test_unary_real('length', expected, self.na_none) def test_crosses(self): expected = [False, False, False, False, False, False] assert_array_equal(expected, self.g0.crosses(self.t1)) expected = [False, True] assert_array_equal(expected, self.crossed_lines.crosses(self.l3)) def test_disjoint(self): expected = [False, False, False, False, False, True] assert_array_equal(expected, self.g0.disjoint(self.t1)) def test_distance(self): expected = Series(np.array([np.sqrt((5 - 1)**2 + (5 - 1)**2), np.nan]), self.na_none.index) assert_array_equal(expected, self.na_none.distance(self.p0)) expected = Series(np.array([np.sqrt(4**2 + 4**2), np.nan]), self.g6.index) assert_array_equal(expected, self.g6.distance(self.na_none)) def test_intersects(self): expected = [True, True, True, True, True, False] assert_array_equal(expected, self.g0.intersects(self.t1)) expected = [True, False] assert_array_equal(expected, self.na_none.intersects(self.t2)) def test_overlaps(self): expected = [True, True, False, False, False, False] assert_array_equal(expected, self.g0.overlaps(self.inner_sq)) expected = [False, False] assert_array_equal(expected, self.g4.overlaps(self.t1)) def test_touches(self): expected = [False, True, False, False, False, False] assert_array_equal(expected, self.g0.touches(self.t1)) def test_within(self): expected = [True, False, False, False, False, False] assert_array_equal(expected, self.g0.within(self.t1)) expected = [True, True, True, True, True, False] assert_array_equal(expected, self.g0.within(self.sq)) def test_is_valid(self): expected = Series(np.array([True] * len(self.g1)), self.g1.index) self._test_unary_real('is_valid', expected, self.g1) def test_is_empty(self): expected = Series(np.array([False] * len(self.g1)), self.g1.index) self._test_unary_real('is_empty', expected, self.g1) def test_is_ring(self): expected = Series(np.array([True] * len(self.g1)), self.g1.index) self._test_unary_real('is_ring', expected, self.g1) def test_is_simple(self): expected = Series(np.array([True] * len(self.g1)), self.g1.index) self._test_unary_real('is_simple', expected, self.g1) def test_xy_points(self): expected_x = [-73.9847, -74.0446] expected_y = [40.7484, 40.6893] assert_array_equal(expected_x, self.landmarks.geometry.x) assert_array_equal(expected_y, self.landmarks.geometry.y) def test_xy_polygons(self): # accessing x attribute in polygon geoseries should raise an error with pytest.raises(ValueError): _ = self.gdf1.geometry.x # and same for accessing y attribute in polygon geoseries with pytest.raises(ValueError): _ = self.gdf1.geometry.y def test_exterior(self): exp_exterior = GeoSeries([LinearRing(p.boundary) for p in self.g3]) for expected, computed in zip(exp_exterior, self.g3.exterior): assert computed.equals(expected) def test_interiors(self): square_series = GeoSeries(self.nested_squares) exp_interiors = GeoSeries([LinearRing(self.inner_sq.boundary)]) for expected, computed in zip(exp_interiors, square_series.interiors): assert computed[0].equals(expected) def test_interpolate(self): expected = GeoSeries([Point(0.5, 1.0), Point(0.75, 1.0)]) self._test_binary_topological('interpolate', expected, self.g5, 0.75, normalized=True) expected = GeoSeries([Point(0.5, 1.0), Point(1.0, 0.5)]) self._test_binary_topological('interpolate', expected, self.g5, 1.5) def test_project(self): expected = Series([2.0, 1.5], index=self.g5.index) p = Point(1.0, 0.5) self._test_binary_real('project', expected, self.g5, p) expected = Series([1.0, 0.5], index=self.g5.index) self._test_binary_real('project', expected, self.g5, p, normalized=True) def test_translate_tuple(self): trans = self.sol.x - self.esb.x, self.sol.y - self.esb.y assert self.landmarks.translate(*trans)[0].equals(self.sol) res = self.gdf1.set_geometry(self.landmarks).translate(*trans)[0] assert res.equals(self.sol) def test_rotate(self): angle = 98 expected = self.g4 o = Point(0, 0) res = self.g4.rotate(angle, origin=o).rotate(-angle, origin=o) assert geom_almost_equals(self.g4, res) res = self.gdf1.set_geometry(self.g4).rotate(angle, origin=Point(0, 0)) assert geom_almost_equals(expected, res.rotate(-angle, origin=o)) def test_scale(self): expected = self.g4 scale = 2., 1. inv = tuple(1./i for i in scale) o = Point(0, 0) res = self.g4.scale(*scale, origin=o).scale(*inv, origin=o) assert geom_almost_equals(expected, res) res = self.gdf1.set_geometry(self.g4).scale(*scale, origin=o) res = res.scale(*inv, origin=o) assert geom_almost_equals(expected, res) def test_skew(self): expected = self.g4 skew = 45. o = Point(0, 0) # Test xs res = self.g4.skew(xs=skew, origin=o).skew(xs=-skew, origin=o) assert geom_almost_equals(expected, res) res = self.gdf1.set_geometry(self.g4).skew(xs=skew, origin=o) res = res.skew(xs=-skew, origin=o) assert geom_almost_equals(expected, res) # Test ys res = self.g4.skew(ys=skew, origin=o).skew(ys=-skew, origin=o) assert geom_almost_equals(expected, res) res = self.gdf1.set_geometry(self.g4).skew(ys=skew, origin=o) res = res.skew(ys=-skew, origin=o) assert geom_almost_equals(expected, res) def test_envelope(self): e = self.g3.envelope assert np.all(e.geom_equals(self.sq)) assert isinstance(e, GeoSeries) assert self.g3.crs == e.crs def test_total_bounds(self): bbox = self.sol.x, self.sol.y, self.esb.x, self.esb.y assert isinstance(self.landmarks.total_bounds, np.ndarray) assert tuple(self.landmarks.total_bounds) == bbox df = GeoDataFrame({'geometry': self.landmarks, 'col1': range(len(self.landmarks))}) assert tuple(df.total_bounds) == bbox def test_explode(self): s = GeoSeries([MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])]) index = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 2)] expected = GeoSeries([Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3), Point(4, 4)], index=MultiIndex.from_tuples(index)) assert_geoseries_equal(expected, s.explode()) df = self.gdf1[:2].set_geometry(s) assert_geoseries_equal(expected, df.explode()) # # Test '&', '|', '^', and '-' # The left can only be a GeoSeries. The right hand side can be a # GeoSeries, GeoDataFrame or Shapely geometry # def test_intersection_operator(self): self._test_binary_operator('__and__', self.t1, self.g1, self.g2) def test_union_operator(self): self._test_binary_operator('__or__', self.sq, self.g1, self.g2) def test_union_operator_polygon(self): self._test_binary_operator('__or__', self.sq, self.g1, self.t2) def test_symmetric_difference_operator(self): self._test_binary_operator('__xor__', self.sq, self.g3, self.g4) def test_difference_series2(self): expected = GeoSeries([GeometryCollection(), self.t2]) self._test_binary_operator('__sub__', expected, self.g1, self.g2) def test_difference_poly2(self): expected = GeoSeries([self.t1, self.t1]) self._test_binary_operator('__sub__', expected, self.g1, self.t2) geopandas-0.3.0/geopandas/tests/test_geoseries.py000066400000000000000000000173101315112464100221540ustar00rootroot00000000000000from __future__ import absolute_import import os import json import shutil import tempfile import numpy as np import pandas as pd from shapely.geometry import (Polygon, Point, LineString, MultiPoint, MultiLineString, MultiPolygon) from shapely.geometry.base import BaseGeometry from geopandas import GeoSeries import pytest from geopandas.tests.util import geom_equals from numpy.testing import assert_array_equal from pandas.util.testing import assert_series_equal class TestSeries: def setup_method(self): self.tempdir = tempfile.mkdtemp() self.t1 = Polygon([(0, 0), (1, 0), (1, 1)]) self.t2 = Polygon([(0, 0), (1, 1), (0, 1)]) self.sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) self.g1 = GeoSeries([self.t1, self.sq]) self.g2 = GeoSeries([self.sq, self.t1]) self.g3 = GeoSeries([self.t1, self.t2]) self.g3.crs = {'init': 'epsg:4326', 'no_defs': True} self.g4 = GeoSeries([self.t2, self.t1]) self.na = GeoSeries([self.t1, self.t2, Polygon()]) self.na_none = GeoSeries([self.t1, self.t2, None]) self.a1 = self.g1.copy() self.a1.index = ['A', 'B'] self.a2 = self.g2.copy() self.a2.index = ['B', 'C'] self.esb = Point(-73.9847, 40.7484) self.sol = Point(-74.0446, 40.6893) self.landmarks = GeoSeries([self.esb, self.sol], crs={'init': 'epsg:4326', 'no_defs': True}) self.l1 = LineString([(0, 0), (0, 1), (1, 1)]) self.l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1)]) self.g5 = GeoSeries([self.l1, self.l2]) def teardown_method(self): shutil.rmtree(self.tempdir) def test_single_geom_constructor(self): p = Point(1, 2) line = LineString([(2, 3), (4, 5), (5, 6)]) poly = Polygon([(0, 0), (1, 0), (1, 1)], [[(.1, .1), (.9, .1), (.9, .9)]]) mp = MultiPoint([(1, 2), (3, 4), (5, 6)]) mline = MultiLineString([[(1, 2), (3, 4), (5, 6)], [(7, 8), (9, 10)]]) poly2 = Polygon([(1, 1), (1, -1), (-1, -1), (-1, 1)], [[(.5, .5), (.5, -.5), (-.5, -.5), (-.5, .5)]]) mpoly = MultiPolygon([poly, poly2]) geoms = [p, line, poly, mp, mline, mpoly] index = ['a', 'b', 'c', 'd'] for g in geoms: gs = GeoSeries(g) assert len(gs) == 1 assert gs.iloc[0] is g gs = GeoSeries(g, index=index) assert len(gs) == len(index) for x in gs: assert x is g def test_copy(self): gc = self.g3.copy() assert type(gc) is GeoSeries assert self.g3.name == gc.name assert self.g3.crs == gc.crs def test_in(self): assert self.t1 in self.g1 assert self.sq in self.g1 assert self.t1 in self.a1 assert self.t2 in self.g3 assert self.sq not in self.g3 assert 5 not in self.g3 def test_geom_equals(self): assert np.all(self.g1.geom_equals(self.g1)) assert_array_equal(self.g1.geom_equals(self.sq), [False, True]) def test_geom_equals_align(self): a = self.a1.geom_equals(self.a2) exp = pd.Series([False, True, False], index=['A', 'B', 'C']) assert_series_equal(a, exp) def test_align(self): a1, a2 = self.a1.align(self.a2) assert a2['A'].is_empty assert a1['B'].equals(a2['B']) assert a1['C'].is_empty def test_geom_almost_equals(self): # TODO: test decimal parameter assert np.all(self.g1.geom_almost_equals(self.g1)) assert_array_equal(self.g1.geom_almost_equals(self.sq), [False, True]) def test_geom_equals_exact(self): # TODO: test tolerance parameter assert np.all(self.g1.geom_equals_exact(self.g1, 0.001)) assert_array_equal(self.g1.geom_equals_exact(self.sq, 0.001), [False, True]) def test_equal_comp_op(self): s = GeoSeries([Point(x, x) for x in range(3)]) res = s == Point(1, 1) exp = pd.Series([False, True, False]) assert_series_equal(res, exp) def test_to_file(self): """ Test to_file and from_file """ tempfilename = os.path.join(self.tempdir, 'test.shp') self.g3.to_file(tempfilename) # Read layer back in? s = GeoSeries.from_file(tempfilename) assert all(self.g3.geom_equals(s)) # TODO: compare crs def test_to_json(self): """ Test whether GeoSeries.to_json works and returns an actual json file. """ json_str = self.g3.to_json() json_dict = json.loads(json_str) # TODO : verify the output is a valid GeoJSON. def test_representative_point(self): assert np.all(self.g1.contains(self.g1.representative_point())) assert np.all(self.g2.contains(self.g2.representative_point())) assert np.all(self.g3.contains(self.g3.representative_point())) assert np.all(self.g4.contains(self.g4.representative_point())) def test_transform(self): utm18n = self.landmarks.to_crs(epsg=26918) lonlat = utm18n.to_crs(epsg=4326) assert np.all(self.landmarks.geom_almost_equals(lonlat)) with pytest.raises(ValueError): self.g1.to_crs(epsg=4326) with pytest.raises(TypeError): self.landmarks.to_crs(crs=None, epsg=None) def test_fillna(self): # default is to fill with empty geometry na = self.na_none.fillna() assert isinstance(na[2], BaseGeometry) assert na[2].is_empty assert geom_equals(self.na_none[:2], na[:2]) # XXX: method works inconsistently for different pandas versions # self.na_none.fillna(method='backfill') def test_coord_slice(self): """ Test CoordinateSlicer """ # need some better test cases assert geom_equals(self.g3, self.g3.cx[:, :]) assert geom_equals(self.g3[[True, False]], self.g3.cx[0.9:, :0.1]) assert geom_equals(self.g3[[False, True]], self.g3.cx[0:0.1, 0.9:1.0]) def test_coord_slice_with_zero(self): # Test that CoordinateSlice correctly handles zero slice (#GH477). gs = GeoSeries([Point(x, x) for x in range(-3, 4)]) assert geom_equals(gs.cx[:0, :0], gs.loc[:3]) assert geom_equals(gs.cx[:, :0], gs.loc[:3]) assert geom_equals(gs.cx[:0, :], gs.loc[:3]) assert geom_equals(gs.cx[0:, 0:], gs.loc[3:]) assert geom_equals(gs.cx[0:, :], gs.loc[3:]) assert geom_equals(gs.cx[:, 0:], gs.loc[3:]) def test_geoseries_geointerface(self): assert self.g1.__geo_interface__['type'] == 'FeatureCollection' assert len(self.g1.__geo_interface__['features']) == self.g1.shape[0] def test_proj4strings(self): # As string reprojected = self.g3.to_crs('+proj=utm +zone=30N') reprojected_back = reprojected.to_crs(epsg=4326) assert np.all(self.g3.geom_almost_equals(reprojected_back)) # As dict reprojected = self.g3.to_crs({'proj': 'utm', 'zone': '30N'}) reprojected_back = reprojected.to_crs(epsg=4326) assert np.all(self.g3.geom_almost_equals(reprojected_back)) # Set to equivalent string, convert, compare to original copy = self.g3.copy() copy.crs = '+init=epsg:4326' reprojected = copy.to_crs({'proj': 'utm', 'zone': '30N'}) reprojected_back = reprojected.to_crs(epsg=4326) assert np.all(self.g3.geom_almost_equals(reprojected_back)) # Conversions by different format reprojected_string = self.g3.to_crs('+proj=utm +zone=30N') reprojected_dict = self.g3.to_crs({'proj': 'utm', 'zone': '30N'}) assert np.all(reprojected_string.geom_almost_equals(reprojected_dict)) geopandas-0.3.0/geopandas/tests/test_merge.py000066400000000000000000000036271315112464100212740ustar00rootroot00000000000000from __future__ import absolute_import import pandas as pd from shapely.geometry import Point from geopandas import GeoDataFrame, GeoSeries class TestMerging: def setup_method(self): self.gseries = GeoSeries([Point(i, i) for i in range(3)]) self.series = pd.Series([1, 2, 3]) self.gdf = GeoDataFrame({'geometry': self.gseries, 'values': range(3)}) self.df = pd.DataFrame({'col1': [1, 2, 3], 'col2': [0.1, 0.2, 0.3]}) def _check_metadata(self, gdf, geometry_column_name='geometry', crs=None): assert gdf._geometry_column_name == geometry_column_name assert gdf.crs == crs def test_merge(self): res = self.gdf.merge(self.df, left_on='values', right_on='col1') # check result is a GeoDataFrame assert isinstance(res, GeoDataFrame) # check geometry property gives GeoSeries assert isinstance(res.geometry, GeoSeries) # check metadata self._check_metadata(res) ## test that crs and other geometry name are preserved self.gdf.crs = {'init' :'epsg:4326'} self.gdf = (self.gdf.rename(columns={'geometry': 'points'}) .set_geometry('points')) res = self.gdf.merge(self.df, left_on='values', right_on='col1') assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) self._check_metadata(res, 'points', self.gdf.crs) def test_concat_axis0(self): res = pd.concat([self.gdf, self.gdf]) assert res.shape == (6, 2) assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) self._check_metadata(res) def test_concat_axis1(self): res = pd.concat([self.gdf, self.df], axis=1) assert res.shape == (3, 4) assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) self._check_metadata(res) geopandas-0.3.0/geopandas/tests/test_overlay.py000066400000000000000000000072041315112464100216510ustar00rootroot00000000000000from __future__ import absolute_import from shapely.geometry import Point import geopandas from geopandas import GeoDataFrame, read_file, overlay import pytest class TestDataFrame: def setup_method(self): N = 10 nybb_filename = geopandas.datasets.get_path('nybb') self.polydf = read_file(nybb_filename) self.crs = {'init': 'epsg:4326'} b = [int(x) for x in self.polydf.total_bounds] self.polydf2 = GeoDataFrame( [{'geometry': Point(x, y).buffer(10000), 'value1': x + y, 'value2': x - y} for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)), range(b[1], b[3], int((b[3]-b[1])/N)))], crs=self.crs) self.pointdf = GeoDataFrame( [{'geometry': Point(x, y), 'value1': x + y, 'value2': x - y} for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)), range(b[1], b[3], int((b[3]-b[1])/N)))], crs=self.crs) # TODO this appears to be necessary; # why is the sindex not generated automatically? self.polydf2._generate_sindex() self.union_shape = (180, 7) def test_union(self): df = overlay(self.polydf, self.polydf2, how="union") assert type(df) is GeoDataFrame assert df.shape == self.union_shape assert 'value1' in df.columns and 'Shape_Area' in df.columns def test_union_no_index(self): # explicitly ignore indicies dfB = overlay(self.polydf, self.polydf2, how="union", use_sindex=False) assert dfB.shape == self.union_shape # remove indicies from df self.polydf._sindex = None self.polydf2._sindex = None dfC = overlay(self.polydf, self.polydf2, how="union") assert dfC.shape == self.union_shape def test_intersection(self): df = overlay(self.polydf, self.polydf2, how="intersection") assert df['BoroName'][0] is not None assert df.shape == (68, 7) def test_identity(self): df = overlay(self.polydf, self.polydf2, how="identity") assert df.shape == (154, 7) def test_symmetric_difference(self): df = overlay(self.polydf, self.polydf2, how="symmetric_difference") assert df.shape == (122, 7) def test_difference(self): df = overlay(self.polydf, self.polydf2, how="difference") assert df.shape == (86, 7) def test_bad_how(self): with pytest.raises(ValueError): overlay(self.polydf, self.polydf, how="spandex") def test_nonpoly(self): with pytest.raises(TypeError): overlay(self.pointdf, self.polydf, how="union") def test_duplicate_column_name(self): polydf2r = self.polydf2.rename(columns={'value2': 'Shape_Area'}) df = overlay(self.polydf, polydf2r, how="union") assert 'Shape_Area_2' in df.columns and 'Shape_Area' in df.columns def test_geometry_not_named_geometry(self): # Issue #306 # Add points and flip names polydf3 = self.polydf.copy() polydf3 = polydf3.rename(columns={'geometry': 'polygons'}) polydf3 = polydf3.set_geometry('polygons') polydf3['geometry'] = self.pointdf.geometry.loc[0:4] assert polydf3.geometry.name == 'polygons' df = overlay(polydf3, self.polydf2, how="union") assert type(df) is GeoDataFrame df2 = overlay(self.polydf, self.polydf2, how="union") assert df.geom_almost_equals(df2).all() def test_geoseries_warning(self): # Issue #305 with pytest.raises(NotImplementedError): overlay(self.polydf, self.polydf2.geometry, how="union") geopandas-0.3.0/geopandas/tests/test_pandas_methods.py000066400000000000000000000161271315112464100231650ustar00rootroot00000000000000from __future__ import absolute_import from distutils.version import LooseVersion from six import PY3 import numpy as np import pandas as pd import shapely from shapely.geometry import Point, Polygon from geopandas import GeoDataFrame, GeoSeries from geopandas.tests.util import assert_geoseries_equal import pytest from numpy.testing import assert_array_equal from pandas.util.testing import assert_frame_equal, assert_series_equal @pytest.fixture def s(): return GeoSeries([Point(x, y) for x, y in zip(range(3), range(3))]) @pytest.fixture def df(): return GeoDataFrame({'geometry': [Point(x, x) for x in range(3)], 'value1': np.arange(3, dtype='int64'), 'value2': np.array([1, 2, 1], dtype='int64')}) def test_repr(s, df): assert 'POINT' in repr(s) assert 'POINT' in repr(df) def test_indexing(s, df): # accessing scalar from the geometry (colunm) exp = Point(1, 1) assert s[1] == exp assert s.loc[1] == exp assert s.iloc[1] == exp assert df.loc[1, 'geometry'] == exp assert df.iloc[1, 0] == exp # multiple values exp = GeoSeries([Point(2, 2), Point(0, 0)], index=[2, 0]) assert_geoseries_equal(s.loc[[2, 0]], exp) assert_geoseries_equal(s.iloc[[2, 0]], exp) assert_geoseries_equal(s.reindex([2, 0]), exp) assert_geoseries_equal(df.loc[[2, 0], 'geometry'], exp) # TODO here iloc does not return a GeoSeries assert_series_equal(df.iloc[[2, 0], 0], exp, check_series_type=False, check_names=False) # boolean indexing exp = GeoSeries([Point(0, 0), Point(2, 2)], index=[0, 2]) mask = np.array([True, False, True]) assert_geoseries_equal(s[mask], exp) assert_geoseries_equal(s.loc[mask], exp) assert_geoseries_equal(df[mask]['geometry'], exp) assert_geoseries_equal(df.loc[mask, 'geometry'], exp) def test_assignment(s, df): exp = GeoSeries([Point(10, 10), Point(1, 1), Point(2, 2)]) s2 = s.copy() s2[0] = Point(10, 10) assert_geoseries_equal(s2, exp) s2 = s.copy() s2.loc[0] = Point(10, 10) assert_geoseries_equal(s2, exp) s2 = s.copy() s2.iloc[0] = Point(10, 10) assert_geoseries_equal(s2, exp) df2 = df.copy() df2.loc[0, 'geometry'] = Point(10, 10) assert_geoseries_equal(df2['geometry'], exp) df2 = df.copy() df2.iloc[0, 0] = Point(10, 10) assert_geoseries_equal(df2['geometry'], exp) def test_assign(df): res = df.assign(new=1) exp = df.copy() exp['new'] = 1 assert isinstance(res, GeoDataFrame) assert_frame_equal(res, exp, ) def test_astype(s): with pytest.raises(TypeError): s.astype(int) assert s.astype(str)[0] == 'POINT (0 0)' def test_to_csv(df): exp = ('geometry,value1,value2\nPOINT (0 0),0,1\nPOINT (1 1),1,2\n' 'POINT (2 2),2,1\n') assert df.to_csv(index=False) == exp @pytest.mark.skipif(str(pd.__version__) < LooseVersion('0.17'), reason="s.max() does not raise on 0.16") def test_numerical_operations(s, df): # df methods ignore the geometry column exp = pd.Series([3, 4], index=['value1', 'value2']) assert_series_equal(df.sum(), exp) # series methods raise error with pytest.raises(TypeError): s.sum() if PY3: # in python 2, objects are still orderable with pytest.raises(TypeError): s.max() with pytest.raises(TypeError): s.idxmax() # numerical ops raise an error with pytest.raises(TypeError): df + 1 with pytest.raises(TypeError): s + 1 # boolean comparisons work res = df == 100 exp = pd.DataFrame(False, index=df.index, columns=df.columns) assert_frame_equal(res, exp) def test_where(s): res = s.where(np.array([True, False, True])) exp = s.copy() exp[1] = np.nan assert_series_equal(res, exp) def test_select_dtypes(df): res = df.select_dtypes(include=[np.number]) exp = df[['value1', 'value2']] assert_frame_equal(res, exp) # Missing values @pytest.mark.xfail def test_fillna(): # this currently does not work (it seems to fill in the second coordinate # of the point s2 = GeoSeries([Point(0, 0), None, Point(2, 2)]) res = s2.fillna(Point(1, 1)) assert_geoseries_equal(res, s) @pytest.mark.xfail def test_dropna(): # this currently does not work (doesn't drop) s2 = GeoSeries([Point(0, 0), None, Point(2, 2)]) res = s2.dropna() exp = s2.loc[[0, 2]] assert_geoseries_equal(res, exp) @pytest.mark.parametrize("NA", [None, np.nan, Point(), Polygon()]) def test_isna(NA): s2 = GeoSeries([Point(0, 0), NA, Point(2, 2)]) exp = pd.Series([False, True, False]) res = s2.isnull() assert_series_equal(res, exp) res = s2.isna() assert_series_equal(res, exp) res = s2.notnull() assert_series_equal(res, ~exp) res = s2.notna() assert_series_equal(res, ~exp) # Groupby / algos @pytest.mark.xfail def test_unique(): # this currently raises a TypeError s = GeoSeries([Point(0, 0), Point(0, 0), Point(2, 2)]) exp = np.array([Point(0, 0), Point(2, 2)]) assert_array_equal(s.unique(), exp) @pytest.mark.xfail def test_value_counts(): # each object is considered unique s = GeoSeries([Point(0, 0), Point(1, 1), Point(0, 0)]) res = s.value_counts() exp = pd.Series([2, 1], index=[Point(0, 0), Point(1, 1)]) assert_series_equal(res, exp) @pytest.mark.xfail def test_drop_duplicates_series(): # currently, geoseries with identical values are not recognized as # duplicates dups = GeoSeries([Point(0, 0), Point(0, 0)]) dropped = dups.drop_duplicates() assert len(dropped) == 1 @pytest.mark.xfail def test_drop_duplicates_frame(): # currently, dropping duplicates in a geodataframe produces a TypeError # better behavior would be dropping the duplicated points gdf_len = 3 dup_gdf = GeoDataFrame({'geometry': [Point(0, 0) for _ in range(gdf_len)], 'value1': range(gdf_len)}) dropped_geometry = dup_gdf.drop_duplicates(subset="geometry") assert len(dropped_geometry) == 1 dropped_all = dup_gdf.drop_duplicates() assert len(dropped_all) == gdf_len def test_groupby(df): # counts work fine res = df.groupby('value2').count() exp = pd.DataFrame({'geometry': [2, 1], 'value1': [2, 1], 'value2': [1, 2]}).set_index('value2') assert_frame_equal(res, exp) # reductions ignore geometry column res = df.groupby('value2').sum() exp = pd.DataFrame({'value1': [2, 1], 'value2': [1, 2]}, dtype='int64').set_index('value2') assert_frame_equal(res, exp) # applying on the geometry column res = df.groupby('value2')['geometry'].apply(lambda x: x.cascaded_union) exp = pd.Series([shapely.geometry.MultiPoint([(0, 0), (2, 2)]), Point(1, 1)], index=pd.Index([1, 2], name='value2'), name='geometry') assert_series_equal(res, exp) def test_groupby_groups(df): g = df.groupby('value2') res = g.get_group(1) assert isinstance(res, GeoDataFrame) exp = df.loc[[0, 2]] assert_frame_equal(res, exp) geopandas-0.3.0/geopandas/tests/test_plotting.py000066400000000000000000000601501315112464100220270ustar00rootroot00000000000000from __future__ import absolute_import, division from distutils.version import LooseVersion import itertools import warnings import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from shapely.affinity import rotate from shapely.geometry import MultiPolygon, Polygon, LineString, Point from geopandas import GeoSeries, GeoDataFrame, read_file import pytest @pytest.fixture(autouse=True) def close_figures(request): yield plt.close('all') try: cycle = matplotlib.rcParams['axes.prop_cycle'].by_key() MPL_DFT_COLOR = cycle['color'][0] except KeyError: MPL_DFT_COLOR = matplotlib.rcParams['axes.color_cycle'][0] class TestPointPlotting: def setup_method(self): self.N = 10 self.points = GeoSeries(Point(i, i) for i in range(self.N)) values = np.arange(self.N) self.df = GeoDataFrame({'geometry': self.points, 'values': values}) def test_figsize(self): ax = self.points.plot(figsize=(1, 1)) np.testing.assert_array_equal(ax.figure.get_size_inches(), (1, 1)) ax = self.df.plot(figsize=(1, 1)) np.testing.assert_array_equal(ax.figure.get_size_inches(), (1, 1)) def test_default_colors(self): # # without specifying values -> uniform color # GeoSeries ax = self.points.plot() _check_colors(self.N, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * self.N) # GeoDataFrame ax = self.df.plot() _check_colors(self.N, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * self.N) # # with specifying values -> different colors for all 10 values ax = self.df.plot(column='values') cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N)/(self.N-1)) _check_colors(self.N, ax.collections[0].get_facecolors(), expected_colors) def test_colormap(self): # without specifying values but cmap specified -> no uniform color # but different colors for all points # GeoSeries ax = self.points.plot(cmap='RdYlGn') cmap = plt.get_cmap('RdYlGn') exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) ax = self.df.plot(cmap='RdYlGn') _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) # # with specifying values -> different colors for all 10 values ax = self.df.plot(column='values', cmap='RdYlGn') cmap = plt.get_cmap('RdYlGn') _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) # when using a cmap with specified lut -> limited number of different # colors ax = self.points.plot(cmap=plt.get_cmap('Set1', lut=5)) cmap = plt.get_cmap('Set1', lut=5) exp_colors = cmap(list(range(5))*3) _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) def test_single_color(self): ax = self.points.plot(color='green') _check_colors(self.N, ax.collections[0].get_facecolors(), ['green']*self.N) ax = self.df.plot(color='green') _check_colors(self.N, ax.collections[0].get_facecolors(), ['green']*self.N) with warnings.catch_warnings(record=True) as _: # don't print warning # 'color' overrides 'column' ax = self.df.plot(column='values', color='green') _check_colors(self.N, ax.collections[0].get_facecolors(), ['green']*self.N) def test_style_kwargs(self): ax = self.points.plot(markersize=10) assert ax.collections[0].get_sizes() == [10] ax = self.df.plot(markersize=10) assert ax.collections[0].get_sizes() == [10] ax = self.df.plot(column='values', markersize=10) assert ax.collections[0].get_sizes() == [10] def test_legend(self): with warnings.catch_warnings(record=True) as _: # don't print warning # legend ignored if color is given. ax = self.df.plot(column='values', color='green', legend=True) assert len(ax.get_figure().axes) == 1 # no separate legend axis # legend ignored if no column is given. ax = self.df.plot(legend=True) assert len(ax.get_figure().axes) == 1 # no separate legend axis # # Continuous legend # the colorbar matches the Point colors ax = self.df.plot(column='values', cmap='RdYlGn', legend=True) point_colors = ax.collections[0].get_facecolors() cbar_colors = ax.get_figure().axes[1].collections[0].get_facecolors() # first point == bottom of colorbar np.testing.assert_array_equal(point_colors[0], cbar_colors[0]) # last point == top of colorbar np.testing.assert_array_equal(point_colors[-1], cbar_colors[-1]) # # Categorical legend # the colorbar matches the Point colors ax = self.df.plot(column='values', categorical=True, legend=True) point_colors = ax.collections[0].get_facecolors() cbar_colors = ax.get_legend().axes.collections[0].get_facecolors() # first point == bottom of colorbar np.testing.assert_array_equal(point_colors[0], cbar_colors[0]) # last point == top of colorbar np.testing.assert_array_equal(point_colors[-1], cbar_colors[-1]) class TestPointZPlotting: def setup_method(self): self.N = 10 self.points = GeoSeries(Point(i, i, i) for i in range(self.N)) values = np.arange(self.N) self.df = GeoDataFrame({'geometry': self.points, 'values': values}) def test_plot(self): # basic test that points with z coords don't break plotting self.df.plot() class TestLineStringPlotting: def setup_method(self): self.N = 10 values = np.arange(self.N) self.lines = GeoSeries([LineString([(0, i), (4, i+0.5), (9, i)]) for i in range(self.N)], index=list('ABCDEFGHIJ')) self.df = GeoDataFrame({'geometry': self.lines, 'values': values}) def test_single_color(self): ax = self.lines.plot(color='green') _check_colors(self.N, ax.collections[0].get_colors(), ['green']*self.N) ax = self.df.plot(color='green') _check_colors(self.N, ax.collections[0].get_colors(), ['green']*self.N) with warnings.catch_warnings(record=True) as _: # don't print warning # 'color' overrides 'column' ax = self.df.plot(column='values', color='green') _check_colors(self.N, ax.collections[0].get_colors(), ['green']*self.N) def test_style_kwargs(self): # linestyle (style patterns depend on linewidth, therefore pin to 1) linestyle = 'dashed' ax = self.lines.plot(linestyle=linestyle, linewidth=1) exp_ls = _style_to_linestring_onoffseq(linestyle) for ls in ax.collections[0].get_linestyles(): assert ls[0] == exp_ls[0] assert tuple(ls[1]) == exp_ls[1] ax = self.df.plot(linestyle=linestyle, linewidth=1) for ls in ax.collections[0].get_linestyles(): assert ls[0] == exp_ls[0] assert tuple(ls[1]) == exp_ls[1] ax = self.df.plot(column='values', linestyle=linestyle, linewidth=1) for ls in ax.collections[0].get_linestyles(): assert ls[0] == exp_ls[0] assert tuple(ls[1]) == exp_ls[1] class TestPolygonPlotting: def setup_method(self): t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(1, 0), (2, 0), (2, 1)]) self.polys = GeoSeries([t1, t2], index=list('AB')) self.df = GeoDataFrame({'geometry': self.polys, 'values': [0, 1]}) multipoly1 = MultiPolygon([t1, t2]) multipoly2 = rotate(multipoly1, 180) self.df2 = GeoDataFrame({'geometry': [multipoly1, multipoly2], 'values': [0, 1]}) def test_single_color(self): ax = self.polys.plot(color='green') _check_colors(2, ax.collections[0].get_facecolors(), ['green']*2) # color only sets facecolor _check_colors(2, ax.collections[0].get_edgecolors(), ['k'] * 2) ax = self.df.plot(color='green') _check_colors(2, ax.collections[0].get_facecolors(), ['green']*2) _check_colors(2, ax.collections[0].get_edgecolors(), ['k'] * 2) with warnings.catch_warnings(record=True) as _: # don't print warning # 'color' overrides 'values' ax = self.df.plot(column='values', color='green') _check_colors(2, ax.collections[0].get_facecolors(), ['green']*2) def test_vmin_vmax(self): # when vmin == vmax, all polygons should be the same color # non-categorical ax = self.df.plot(column='values', categorical=False, vmin=0, vmax=0) actual_colors = ax.collections[0].get_facecolors() np.testing.assert_array_equal(actual_colors[0], actual_colors[1]) # categorical ax = self.df.plot(column='values', categorical=True, vmin=0, vmax=0) actual_colors = ax.collections[0].get_facecolors() np.testing.assert_array_equal(actual_colors[0], actual_colors[1]) def test_style_kwargs(self): # facecolor overrides default cmap when color is not set ax = self.polys.plot(facecolor='k') _check_colors(2, ax.collections[0].get_facecolors(), ['k']*2) # facecolor overrides more general-purpose color when both are set ax = self.polys.plot(color='red', facecolor='k') # TODO with new implementation, color overrides facecolor # _check_colors(2, ax.collections[0], ['k']*2, alpha=0.5) # edgecolor ax = self.polys.plot(edgecolor='red') np.testing.assert_array_equal([(1, 0, 0, 1)], ax.collections[0].get_edgecolors()) ax = self.df.plot('values', edgecolor='red') np.testing.assert_array_equal([(1, 0, 0, 1)], ax.collections[0].get_edgecolors()) # alpha sets both edge and face ax = self.polys.plot(facecolor='g', edgecolor='r', alpha=0.4) _check_colors(2, ax.collections[0].get_facecolors(), ['g'] * 2, alpha=0.4) _check_colors(2, ax.collections[0].get_edgecolors(), ['r'] * 2, alpha=0.4) def test_multipolygons(self): # MultiPolygons ax = self.df2.plot() assert len(ax.collections[0].get_paths()) == 4 _check_colors(4, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR]*4) ax = self.df2.plot('values') cmap = plt.get_cmap(lut=2) # colors are repeated for all components within a MultiPolygon expected_colors = [cmap(0), cmap(0), cmap(1), cmap(1)] _check_colors(4, ax.collections[0].get_facecolors(), expected_colors) class TestPolygonZPlotting: def setup_method(self): t1 = Polygon([(0, 0, 0), (1, 0, 0), (1, 1, 1)]) t2 = Polygon([(1, 0, 0), (2, 0, 0), (2, 1, 1)]) self.polys = GeoSeries([t1, t2], index=list('AB')) self.df = GeoDataFrame({'geometry': self.polys, 'values': [0, 1]}) multipoly1 = MultiPolygon([t1, t2]) multipoly2 = rotate(multipoly1, 180) self.df2 = GeoDataFrame({'geometry': [multipoly1, multipoly2], 'values': [0, 1]}) def test_plot(self): # basic test that points with z coords don't break plotting self.df.plot() class TestNonuniformGeometryPlotting: def setup_method(self): pytest.importorskip('matplotlib', '1.5.0') poly = Polygon([(1, 0), (2, 0), (2, 1)]) line = LineString([(0.5, 0.5), (1, 1), (1, 0.5), (1.5, 1)]) point = Point(0.75, 0.25) self.series = GeoSeries([poly, line, point]) self.df = GeoDataFrame({'geometry': self.series, 'values': [1, 2, 3]}) def test_colors(self): # default uniform color ax = self.series.plot() _check_colors(1, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR]) _check_colors(1, ax.collections[1].get_edgecolors(), [MPL_DFT_COLOR]) _check_colors(1, ax.collections[2].get_facecolors(), [MPL_DFT_COLOR]) # colormap: different colors ax = self.series.plot(cmap='RdYlGn') cmap = plt.get_cmap('RdYlGn') exp_colors = cmap(np.arange(3) / (3 - 1)) _check_colors(1, ax.collections[0].get_facecolors(), [exp_colors[0]]) _check_colors(1, ax.collections[1].get_edgecolors(), [exp_colors[1]]) _check_colors(1, ax.collections[2].get_facecolors(), [exp_colors[2]]) def test_style_kwargs(self): ax = self.series.plot(markersize=10) assert ax.collections[2].get_sizes() == [10] ax = self.df.plot(markersize=10) assert ax.collections[2].get_sizes() == [10] class TestPySALPlotting: @classmethod def setup_class(cls): try: import pysal as ps except ImportError: raise pytest.skip("PySAL is not installed") pth = ps.examples.get_path("columbus.shp") cls.df = read_file(pth) cls.df['NEGATIVES'] = np.linspace(-10, 10, len(cls.df.index)) def test_legend(self): ax = self.df.plot(column='CRIME', scheme='QUANTILES', k=3, cmap='OrRd', legend=True) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [u'0.18 - 26.07', u'26.07 - 41.97', u'41.97 - 68.89'] assert labels == expected def test_negative_legend(self): ax = self.df.plot(column='NEGATIVES', scheme='FISHER_JENKS', k=3, cmap='OrRd', legend=True) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [u'-10.00 - -3.33', u'-3.33 - 3.33', u'3.33 - 10.00'] assert labels == expected def test_invalid_scheme(self): with pytest.raises(ValueError): scheme = 'invalid_scheme_*#&)(*#' self.df.plot(column='CRIME', scheme=scheme, k=3, cmap='OrRd', legend=True) class TestPlotCollections: def setup_method(self): self.N = 3 self.values = np.arange(self.N) self.points = GeoSeries(Point(i, i) for i in range(self.N)) self.lines = GeoSeries([LineString([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)]) self.polygons = GeoSeries([Polygon([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)]) def test_points(self): # failing with matplotlib 1.4.3 (edge stays black even when specified) pytest.importorskip('matplotlib', '1.5.0') from geopandas.plotting import plot_point_collection from matplotlib.collections import PathCollection fig, ax = plt.subplots() coll = plot_point_collection(ax, self.points) assert isinstance(coll, PathCollection) ax.cla() # default: single default matplotlib color coll = plot_point_collection(ax, self.points) _check_colors(self.N, coll.get_facecolors(), [MPL_DFT_COLOR] * self.N) # edgecolor depends on matplotlib version # _check_colors(self.N, coll.get_edgecolors(), [MPL_DFT_COLOR]*self.N) ax.cla() # specify single other color coll = plot_point_collection(ax, self.points, color='g') _check_colors(self.N, coll.get_facecolors(), ['g'] * self.N) _check_colors(self.N, coll.get_edgecolors(), ['g'] * self.N) ax.cla() # specify edgecolor/facecolor coll = plot_point_collection(ax, self.points, facecolor='g', edgecolor='r') _check_colors(self.N, coll.get_facecolors(), ['g'] * self.N) _check_colors(self.N, coll.get_edgecolors(), ['r'] * self.N) ax.cla() # list of colors coll = plot_point_collection(ax, self.points, color=['r', 'g', 'b']) _check_colors(self.N, coll.get_facecolors(), ['r', 'g', 'b']) _check_colors(self.N, coll.get_edgecolors(), ['r', 'g', 'b']) ax.cla() def test_points_values(self): from geopandas.plotting import plot_point_collection # default colormap fig, ax = plt.subplots() coll = plot_point_collection(ax, self.points, self.values) fig.canvas.draw_idle() cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolors(), expected_colors) # edgecolor depends on matplotlib version # _check_colors(self.N, coll.get_edgecolors(), expected_colors) def test_linestrings(self): from geopandas.plotting import plot_linestring_collection from matplotlib.collections import LineCollection fig, ax = plt.subplots() coll = plot_linestring_collection(ax, self.lines) assert isinstance(coll, LineCollection) ax.cla() # default: single default matplotlib color coll = plot_linestring_collection(ax, self.lines) _check_colors(self.N, coll.get_color(), [MPL_DFT_COLOR] * self.N) ax.cla() # specify single other color coll = plot_linestring_collection(ax, self.lines, color='g') _check_colors(self.N, coll.get_colors(), ['g'] * self.N) ax.cla() # specify edgecolor / facecolor coll = plot_linestring_collection(ax, self.lines, facecolor='g', edgecolor='r') _check_colors(self.N, coll.get_facecolors(), ['g'] * self.N) _check_colors(self.N, coll.get_edgecolors(), ['r'] * self.N) ax.cla() # list of colors coll = plot_linestring_collection(ax, self.lines, color=['r', 'g', 'b']) _check_colors(self.N, coll.get_colors(), ['r', 'g', 'b']) ax.cla() # pass through of kwargs coll = plot_linestring_collection(ax, self.lines, linestyle='--', linewidth=1) exp_ls = _style_to_linestring_onoffseq('dashed') res_ls = coll.get_linestyle()[0] assert res_ls[0] == exp_ls[0] assert tuple(res_ls[1]) == exp_ls[1] ax.cla() def test_linestrings_values(self): from geopandas.plotting import plot_linestring_collection fig, ax = plt.subplots() # default colormap coll = plot_linestring_collection(ax, self.lines, self.values) fig.canvas.draw_idle() cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_color(), expected_colors) ax.cla() # specify colormap coll = plot_linestring_collection(ax, self.lines, self.values, cmap='RdBu') fig.canvas.draw_idle() cmap = plt.get_cmap('RdBu') expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_color(), expected_colors) ax.cla() # specify vmin/vmax coll = plot_linestring_collection(ax, self.lines, self.values, vmin=3, vmax=5) fig.canvas.draw_idle() cmap = plt.get_cmap() expected_colors = cmap([0]) _check_colors(self.N, coll.get_color(), expected_colors) ax.cla() def test_polygons(self): from geopandas.plotting import plot_polygon_collection from matplotlib.collections import PatchCollection fig, ax = plt.subplots() coll = plot_polygon_collection(ax, self.polygons) assert isinstance(coll, PatchCollection) ax.cla() # default: single default matplotlib color coll = plot_polygon_collection(ax, self.polygons) _check_colors(self.N, coll.get_facecolor(), [MPL_DFT_COLOR] * self.N) _check_colors(self.N, coll.get_edgecolor(), ['k'] * self.N) ax.cla() # default: color sets both facecolor and edgecolor coll = plot_polygon_collection(ax, self.polygons, color='g') _check_colors(self.N, coll.get_facecolor(), ['g'] * self.N) _check_colors(self.N, coll.get_edgecolor(), ['g'] * self.N) ax.cla() # only setting facecolor keeps default for edgecolor coll = plot_polygon_collection(ax, self.polygons, facecolor='g') _check_colors(self.N, coll.get_facecolor(), ['g'] * self.N) _check_colors(self.N, coll.get_edgecolor(), ['k'] * self.N) ax.cla() # custom facecolor and edgecolor coll = plot_polygon_collection(ax, self.polygons, facecolor='g', edgecolor='r') _check_colors(self.N, coll.get_facecolor(), ['g'] * self.N) _check_colors(self.N, coll.get_edgecolor(), ['r'] * self.N) ax.cla() def test_polygons_values(self): from geopandas.plotting import plot_polygon_collection fig, ax = plt.subplots() # default colormap, edge is still black by default coll = plot_polygon_collection(ax, self.polygons, self.values) fig.canvas.draw_idle() cmap = plt.get_cmap() exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolor(), exp_colors) # edgecolor depends on matplotlib version #_check_colors(self.N, coll.get_edgecolor(), ['k'] * self.N) ax.cla() # specify colormap coll = plot_polygon_collection(ax, self.polygons, self.values, cmap='RdBu') fig.canvas.draw_idle() cmap = plt.get_cmap('RdBu') exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolor(), exp_colors) ax.cla() # specify vmin/vmax coll = plot_polygon_collection(ax, self.polygons, self.values, vmin=3, vmax=5) fig.canvas.draw_idle() cmap = plt.get_cmap() exp_colors = cmap([0]) _check_colors(self.N, coll.get_facecolor(), exp_colors) ax.cla() # override edgecolor coll = plot_polygon_collection(ax, self.polygons, self.values, edgecolor='g') fig.canvas.draw_idle() cmap = plt.get_cmap() exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolor(), exp_colors) _check_colors(self.N, coll.get_edgecolor(), ['g'] * self.N) ax.cla() def _check_colors(N, actual_colors, expected_colors, alpha=None): """ Asserts that the members of `collection` match the `expected_colors` (in order) Parameters ---------- N : int The number of geometries believed to be in collection. matplotlib.collection is implemented such that the number of geoms in `collection` doesn't have to match the number of colors assignments in the collection: the colors will cycle to meet the needs of the geoms. `N` helps us resolve this. collection : matplotlib.collections.Collection The colors of this collection's patches are read from `collection.get_facecolors()` expected_colors : sequence of RGBA tuples alpha : float (optional) If set, this alpha transparency will be applied to the `expected_colors`. (Any transparency on the `collecton` is assumed to be set in its own facecolor RGBA tuples.) """ import matplotlib.colors as colors conv = colors.colorConverter # Convert 2D numpy array to a list of RGBA tuples. actual_colors = map(tuple, actual_colors) all_actual_colors = list(itertools.islice( itertools.cycle(actual_colors), N)) for actual, expected in zip(all_actual_colors, expected_colors): assert actual == conv.to_rgba(expected, alpha=alpha), \ '{} != {}'.format(actual, conv.to_rgba(expected, alpha=alpha)) def _style_to_linestring_onoffseq(linestyle): """ Converts a linestyle string representation, namely one of: ['dashed', 'dotted', 'dashdot', 'solid'], documented in `Collections.set_linestyle`, to the form `onoffseq`. """ if LooseVersion(matplotlib.__version__) >= '2.0': return matplotlib.lines._get_dash_pattern(linestyle) else: from matplotlib.backend_bases import GraphicsContextBase return GraphicsContextBase.dashd[linestyle] geopandas-0.3.0/geopandas/tests/test_sindex.py000066400000000000000000000104461315112464100214640ustar00rootroot00000000000000import sys from shapely.geometry import Polygon, Point import geopandas from geopandas import GeoSeries, GeoDataFrame, base, read_file import pytest @pytest.mark.skipif(sys.platform.startswith("win"), reason="fails on AppVeyor") @pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping') class TestSeriesSindex: def test_empty_index(self): assert GeoSeries().sindex is None def test_point(self): s = GeoSeries([Point(0, 0)]) assert s.sindex.size == 1 hits = s.sindex.intersection((-1, -1, 1, 1)) assert len(list(hits)) == 1 hits = s.sindex.intersection((-2, -2, -1, -1)) assert len(list(hits)) == 0 def test_empty_point(self): s = GeoSeries([Point()]) assert s.sindex is None assert s._sindex_generated is True def test_empty_geo_series(self): assert GeoSeries().sindex is None def test_polygons(self): t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(0, 0), (1, 1), (0, 1)]) sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) s = GeoSeries([t1, t2, sq]) assert s.sindex.size == 3 def test_polygons_append(self): t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(0, 0), (1, 1), (0, 1)]) sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) s = GeoSeries([t1, t2, sq]) t = GeoSeries([t1, t2, sq], [3, 4, 5]) s = s.append(t) assert len(s) == 6 assert s.sindex.size == 6 def test_lazy_build(self): s = GeoSeries([Point(0, 0)]) assert s._sindex is None assert s.sindex.size == 1 assert s._sindex is not None @pytest.mark.skipif(sys.platform.startswith("win"), reason="fails on AppVeyor") @pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping') class TestFrameSindex: def setup_method(self): data = {"A": range(5), "B": range(-5, 0), "location": [Point(x, y) for x, y in zip(range(5), range(5))]} self.df = GeoDataFrame(data, geometry='location') def test_sindex(self): self.df.crs = {'init': 'epsg:4326'} assert self.df.sindex.size == 5 hits = list(self.df.sindex.intersection((2.5, 2.5, 4, 4), objects=True)) assert len(hits) == 2 assert hits[0].object == 3 def test_lazy_build(self): assert self.df._sindex is None assert self.df.sindex.size == 5 assert self.df._sindex is not None def test_sindex_rebuild_on_set_geometry(self): # First build the sindex assert self.df.sindex is not None self.df.set_geometry( [Point(x, y) for x, y in zip(range(5, 10), range(5, 10))], inplace=True) assert self.df._sindex_generated is False # Skip to accommodate Shapely geometries being unhashable @pytest.mark.skip class TestJoinSindex: def setup_method(self): nybb_filename = geopandas.datasets.get_path('nybb') self.boros = read_file(nybb_filename) def test_merge_geo(self): # First check that we gets hits from the boros frame. tree = self.boros.sindex hits = tree.intersection((1012821.80, 229228.26), objects=True) res = [self.boros.ix[hit.object]['BoroName'] for hit in hits] assert res == ['Bronx', 'Queens'] # Check that we only get the Bronx from this view. first = self.boros[self.boros['BoroCode'] < 3] tree = first.sindex hits = tree.intersection((1012821.80, 229228.26), objects=True) res = [first.ix[hit.object]['BoroName'] for hit in hits] assert res == ['Bronx'] # Check that we only get Queens from this view. second = self.boros[self.boros['BoroCode'] >= 3] tree = second.sindex hits = tree.intersection((1012821.80, 229228.26), objects=True) res = [second.ix[hit.object]['BoroName'] for hit in hits], assert res == ['Queens'] # Get both the Bronx and Queens again. merged = first.merge(second, how='outer') assert len(merged) == 5 assert merged.sindex.size == 5 tree = merged.sindex hits = tree.intersection((1012821.80, 229228.26), objects=True) res = [merged.ix[hit.object]['BoroName'] for hit in hits] assert res == ['Bronx', 'Queens'] geopandas-0.3.0/geopandas/tests/test_types.py000066400000000000000000000050651315112464100213370ustar00rootroot00000000000000from __future__ import absolute_import from pandas import Series, DataFrame from shapely.geometry import Point from geopandas import GeoSeries, GeoDataFrame class TestSeries: def setup_method(self): N = self.N = 10 r = 0.5 self.pts = GeoSeries([Point(x, y) for x, y in zip(range(N), range(N))]) self.polys = self.pts.buffer(r) def test_slice(self): assert type(self.pts[:2]) is GeoSeries assert type(self.pts[::2]) is GeoSeries assert type(self.polys[:2]) is GeoSeries def test_head(self): assert type(self.pts.head()) is GeoSeries def test_tail(self): assert type(self.pts.tail()) is GeoSeries def test_sort_index(self): assert type(self.pts.sort_index()) is GeoSeries def test_loc(self): assert type(self.pts.loc[5:]) is GeoSeries def test_iloc(self): assert type(self.pts.iloc[5:]) is GeoSeries def test_fancy(self): idx = (self.pts.index.to_series() % 2).astype(bool) assert type(self.pts[idx]) is GeoSeries def test_take(self): assert type(self.pts.take(list(range(0, self.N, 2)))) is GeoSeries def test_select(self): assert type(self.pts.select(lambda x: x % 2 == 0)) is GeoSeries def test_groupby(self): for f, s in self.pts.groupby(lambda x: x % 2): assert type(s) is GeoSeries class TestDataFrame: def setup_method(self): N = 10 self.df = GeoDataFrame([ {'geometry': Point(x, y), 'value1': x + y, 'value2': x*y} for x, y in zip(range(N), range(N))]) def test_geometry(self): assert type(self.df.geometry) is GeoSeries # still GeoSeries if different name df2 = GeoDataFrame({"coords": [Point(x, y) for x, y in zip(range(5), range(5))], "nums": range(5)}, geometry="coords") assert type(df2.geometry) is GeoSeries assert type(df2['coords']) is GeoSeries def test_nongeometry(self): assert type(self.df['value1']) is Series def test_geometry_multiple(self): assert type(self.df[['geometry', 'value1']]) is GeoDataFrame def test_nongeometry_multiple(self): assert type(self.df[['value1', 'value2']]) is DataFrame def test_slice(self): assert type(self.df[:2]) is GeoDataFrame assert type(self.df[::2]) is GeoDataFrame def test_fancy(self): idx = (self.df.index.to_series() % 2).astype(bool) assert type(self.df[idx]) is GeoDataFrame geopandas-0.3.0/geopandas/tests/util.py000066400000000000000000000132321315112464100201040ustar00rootroot00000000000000import os.path from geopandas import GeoDataFrame, GeoSeries HERE = os.path.abspath(os.path.dirname(__file__)) PACKAGE_DIR = os.path.dirname(os.path.dirname(HERE)) try: import psycopg2 from psycopg2 import OperationalError except ImportError: class OperationalError(Exception): pass try: import unittest.mock as mock except ImportError: import mock def validate_boro_df(df): """ Tests a GeoDataFrame that has been read in from the nybb dataset.""" assert isinstance(df, GeoDataFrame) # Make sure all the columns are there and the geometries # were properly loaded as MultiPolygons assert len(df) == 5 columns = ('borocode', 'boroname', 'shape_leng', 'shape_area') for col in columns: assert col in df.columns assert all(df.geometry.type == 'MultiPolygon') def connect(dbname): try: con = psycopg2.connect(dbname=dbname) except (NameError, OperationalError): return None return con def create_db(df): """ Create a nybb table in the test_geopandas PostGIS database. Returns a boolean indicating whether the database table was successfully created """ # Try to create the database, skip the db tests if something goes # wrong # If you'd like these tests to run, create a database called # 'test_geopandas' and enable postgis in it: # > createdb test_geopandas # > psql -c "CREATE EXTENSION postgis" -d test_geopandas con = connect('test_geopandas') if con is None: return False try: cursor = con.cursor() cursor.execute("DROP TABLE IF EXISTS nybb;") sql = """CREATE TABLE nybb ( geom geometry, borocode integer, boroname varchar(40), shape_leng float, shape_area float );""" cursor.execute(sql) for i, row in df.iterrows(): sql = """INSERT INTO nybb VALUES ( ST_GeometryFromText(%s), %s, %s, %s, %s );""" cursor.execute(sql, (row['geometry'].wkt, row['BoroCode'], row['BoroName'], row['Shape_Leng'], row['Shape_Area'])) finally: cursor.close() con.commit() con.close() return True def assert_seq_equal(left, right): """ Poor man's version of assert_almost_equal which isn't working with Shapely objects right now """ assert (len(left) == len(right), "Mismatched lengths: %d != %d" % (len(left), len(right))) for elem_left, elem_right in zip(left, right): assert elem_left == elem_right, "%r != %r" % (left, right) def geom_equals(this, that): """Test for geometric equality. Empty geometries are considered equal. Parameters ---------- this, that : arrays of Geo objects (or anything that has an `is_empty` attribute) """ return (this.geom_equals(that) | (this.is_empty & that.is_empty)).all() def geom_almost_equals(this, that): """Test for 'almost' geometric equality. Empty geometries considered equal. Parameters ---------- this, that : arrays of Geo objects (or anything that has an `is_empty` property) """ return (this.geom_almost_equals(that) | (this.is_empty & that.is_empty)).all() def assert_geoseries_equal(left, right, check_dtype=False, check_index_type=False, check_series_type=True, check_less_precise=False, check_geom_type=False, check_crs=True): """Test util for checking that two GeoSeries are equal. Parameters ---------- left, right : two GeoSeries check_dtype : bool, default False if True, check geo dtype [only included so it's a drop-in replacement for assert_series_equal] check_index_type : bool, default False check that index types are equal check_series_type : bool, default True check that both are same type (*and* are GeoSeries). If False, will attempt to convert both into GeoSeries. check_less_precise : bool, default False if True, use geom_almost_equals. if False, use geom_equals. check_geom_type : bool, default False if True, check that all the geom types are equal. check_crs: bool, default True if check_series_type is True, then also check that the crs matches """ assert len(left) == len(right), "%d != %d" % (len(left), len(right)) if check_index_type: assert isinstance(left.index, type(right.index)) if check_dtype: assert left.dtype == right.dtype, "dtype: %s != %s" % (left.dtype, right.dtype) if check_series_type: assert isinstance(left, GeoSeries) assert isinstance(left, type(right)) if check_crs: assert(left.crs == right.crs) else: if not isinstance(left, GeoSeries): left = GeoSeries(left) if not isinstance(right, GeoSeries): right = GeoSeries(right, index=left.index) assert left.index.equals(right.index), "index: %s != %s" % (left.index, right.index) if check_geom_type: assert (left.type == right.type).all(), "type: %s != %s" % (left.type, right.type) if check_less_precise: assert geom_almost_equals(left, right) else: assert geom_equals(left, right) geopandas-0.3.0/geopandas/tools/000077500000000000000000000000001315112464100165525ustar00rootroot00000000000000geopandas-0.3.0/geopandas/tools/__init__.py000066400000000000000000000004101315112464100206560ustar00rootroot00000000000000from __future__ import absolute_import from .geocoding import geocode, reverse_geocode from .overlay import overlay from .sjoin import sjoin from .util import collect __all__ = [ 'overlay', 'sjoin', 'geocode', 'reverse_geocode', 'collect', ] geopandas-0.3.0/geopandas/tools/geocoding.py000066400000000000000000000117601315112464100210670ustar00rootroot00000000000000from collections import defaultdict import time from fiona.crs import from_epsg import numpy as np import pandas as pd from shapely.geometry import Point from six import iteritems import geopandas as gpd def _throttle_time(provider): """ Amount of time to wait between requests to a geocoding API. Currently implemented for Nominatim, as their terms of service require a maximum of 1 request per second. https://wiki.openstreetmap.org/wiki/Nominatim_usage_policy """ if provider == 'nominatim': return 1 else: return 0 def geocode(strings, provider='googlev3', **kwargs): """ Geocode a set of strings and get a GeoDataFrame of the resulting points. Parameters ---------- strings : list or Series of addresses to geocode provider : geopy geocoder to use, default 'googlev3' Some providers require additional arguments such as access keys See each geocoder's specific parameters in geopy.geocoders * googlev3, default * bing * google * yahoo * mapquest * openmapquest Ensure proper use of the results by consulting the Terms of Service for your provider. Geocoding requires geopy. Install it using 'pip install geopy'. See also https://github.com/geopy/geopy Example ------- >>> df = geocode(['boston, ma', '1600 pennsylvania ave. washington, dc']) address \ 0 Boston, MA, USA 1 1600 Pennsylvania Avenue Northwest, President'... geometry 0 POINT (-71.0597732 42.3584308) 1 POINT (-77.0365305 38.8977332) """ return _query(strings, True, provider, **kwargs) def reverse_geocode(points, provider='googlev3', **kwargs): """ Reverse geocode a set of points and get a GeoDataFrame of the resulting addresses. The points Parameters ---------- points : list or Series of Shapely Point objects. x coordinate is longitude y coordinate is latitude provider : geopy geocoder to use, default 'googlev3' These are the same options as the geocode() function Some providers require additional arguments such as access keys See each geocoder's specific parameters in geopy.geocoders * googlev3, default * bing * google * yahoo * mapquest * openmapquest Ensure proper use of the results by consulting the Terms of Service for your provider. Reverse geocoding requires geopy. Install it using 'pip install geopy'. See also https://github.com/geopy/geopy Example ------- >>> df = reverse_geocode([Point(-71.0594869, 42.3584697), Point(-77.0365305, 38.8977332)]) address \ 0 29 Court Square, Boston, MA 02108, USA 1 1600 Pennsylvania Avenue Northwest, President'... geometry 0 POINT (-71.0594869 42.3584697) 1 POINT (-77.0365305 38.8977332) """ return _query(points, False, provider, **kwargs) def _query(data, forward, provider, **kwargs): import geopy from geopy.geocoders.base import GeocoderQueryError if not isinstance(data, pd.Series): data = pd.Series(data) # workaround changed name in 0.96 try: Yahoo = geopy.geocoders.YahooPlaceFinder except AttributeError: Yahoo = geopy.geocoders.Yahoo coders = {'googlev3': geopy.geocoders.GoogleV3, 'bing': geopy.geocoders.Bing, 'yahoo': Yahoo, 'openmapquest': geopy.geocoders.OpenMapQuest, 'nominatim': geopy.geocoders.Nominatim} if provider not in coders: raise ValueError('Unknown geocoding provider: {0}'.format(provider)) coder = coders[provider](**kwargs) results = {} for i, s in iteritems(data): try: if forward: results[i] = coder.geocode(s) else: results[i] = coder.reverse((s.y, s.x), exactly_one=True) except (GeocoderQueryError, ValueError): results[i] = (None, None) time.sleep(_throttle_time(provider)) df = _prepare_geocode_result(results) return df def _prepare_geocode_result(results): """ Helper function for the geocode function Takes a dict where keys are index entries, values are tuples containing: (address, (lat, lon)) """ # Prepare the data for the DataFrame as a dict of lists d = defaultdict(list) index = [] for i, s in iteritems(results): address, loc = s # loc is lat, lon and we want lon, lat if loc is None: p = Point() else: p = Point(loc[1], loc[0]) if address is None: address = np.nan d['geometry'].append(p) d['address'].append(address) index.append(i) df = gpd.GeoDataFrame(d, index=index) df.crs = from_epsg(4326) return df geopandas-0.3.0/geopandas/tools/overlay.py000066400000000000000000000136551315112464100206170ustar00rootroot00000000000000import pandas as pd from shapely.ops import unary_union, polygonize from shapely.geometry import MultiLineString from geopandas import GeoDataFrame, GeoSeries def _uniquify(columns): ucols = [] for col in columns: inc = 1 newcol = col while newcol in ucols: inc += 1 newcol = "{0}_{1}".format(col, inc) ucols.append(newcol) return ucols def _extract_rings(df): """Collects all inner and outer linear rings from a GeoDataFrame with (multi)Polygon geometeries Parameters ---------- df: GeoDataFrame with MultiPolygon or Polygon geometry column Returns ------- rings: list of LinearRings """ poly_msg = "overlay only takes GeoDataFrames with (multi)polygon geometries" rings = [] geometry_column = df.geometry.name for i, feat in df.iterrows(): geom = feat[geometry_column] if geom.type not in ['Polygon', 'MultiPolygon']: raise TypeError(poly_msg) if hasattr(geom, 'geoms'): for poly in geom.geoms: # if it's a multipolygon if not poly.is_valid: # geom from layer is not valid attempting fix by buffer 0" poly = poly.buffer(0) rings.append(poly.exterior) rings.extend(poly.interiors) else: if not geom.is_valid: # geom from layer is not valid attempting fix by buffer 0" geom = geom.buffer(0) rings.append(geom.exterior) rings.extend(geom.interiors) return rings def overlay(df1, df2, how, use_sindex=True): """Perform spatial overlay between two polygons. Currently only supports data GeoDataFrames with polygons. Implements several methods that are all effectively subsets of the union. Parameters ---------- df1 : GeoDataFrame with MultiPolygon or Polygon geometry column df2 : GeoDataFrame with MultiPolygon or Polygon geometry column how : string Method of spatial overlay: 'intersection', 'union', 'identity', 'symmetric_difference' or 'difference'. use_sindex : boolean, default True Use the spatial index to speed up operation if available. Returns ------- df : GeoDataFrame GeoDataFrame with new set of polygons and attributes resulting from the overlay """ allowed_hows = [ 'intersection', 'union', 'identity', 'symmetric_difference', 'difference', # aka erase ] if how not in allowed_hows: raise ValueError("`how` was \"%s\" but is expected to be in %s" % \ (how, allowed_hows)) if isinstance(df1, GeoSeries) or isinstance(df2, GeoSeries): raise NotImplementedError("overlay currently only implemented for GeoDataFrames") # Collect the interior and exterior rings rings1 = _extract_rings(df1) rings2 = _extract_rings(df2) mls1 = MultiLineString(rings1) mls2 = MultiLineString(rings2) # Union and polygonize try: # calculating union (try the fast unary_union) mm = unary_union([mls1, mls2]) except: # unary_union FAILED # see https://github.com/Toblerity/Shapely/issues/47#issuecomment-18506767 # calculating union again (using the slow a.union(b)) mm = mls1.union(mls2) newpolys = polygonize(mm) # determine spatial relationship collection = [] for fid, newpoly in enumerate(newpolys): cent = newpoly.representative_point() # Test intersection with original polys # FIXME there should be a higher-level abstraction to search by bounds # and fall back in the case of no index? if use_sindex and df1.sindex is not None: candidates1 = [x.object for x in df1.sindex.intersection(newpoly.bounds, objects=True)] else: candidates1 = [i for i, x in df1.iterrows()] if use_sindex and df2.sindex is not None: candidates2 = [x.object for x in df2.sindex.intersection(newpoly.bounds, objects=True)] else: candidates2 = [i for i, x in df2.iterrows()] df1_hit = False df2_hit = False prop1 = None prop2 = None for cand_id in candidates1: cand = df1.ix[cand_id] if cent.intersects(cand[df1.geometry.name]): df1_hit = True prop1 = cand break # Take the first hit for cand_id in candidates2: cand = df2.ix[cand_id] if cent.intersects(cand[df2.geometry.name]): df2_hit = True prop2 = cand break # Take the first hit # determine spatial relationship based on type of overlay hit = False if how == "intersection" and (df1_hit and df2_hit): hit = True elif how == "union" and (df1_hit or df2_hit): hit = True elif how == "identity" and df1_hit: hit = True elif how == "symmetric_difference" and not (df1_hit and df2_hit): hit = True elif how == "difference" and (df1_hit and not df2_hit): hit = True if not hit: continue # gather properties if prop1 is None: prop1 = pd.Series(dict.fromkeys(df1.columns, None)) if prop2 is None: prop2 = pd.Series(dict.fromkeys(df2.columns, None)) # Concat but don't retain the original geometries out_series = pd.concat([prop1.drop(df1._geometry_column_name), prop2.drop(df2._geometry_column_name)]) out_series.index = _uniquify(out_series.index) # Create a geoseries and add it to the collection out_series['geometry'] = newpoly collection.append(out_series) # Return geodataframe with new indicies return GeoDataFrame(collection, index=range(len(collection))) geopandas-0.3.0/geopandas/tools/sjoin.py000066400000000000000000000136501315112464100202530ustar00rootroot00000000000000import numpy as np import pandas as pd from shapely import prepared def sjoin(left_df, right_df, how='inner', op='intersects', lsuffix='left', rsuffix='right'): """Spatial join of two GeoDataFrames. Parameters ---------- left_df, right_df : GeoDataFrames how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column op : string, default 'intersection' Binary predicate, one of {'intersects', 'contains', 'within'}. See http://toblerity.org/shapely/manual.html#binary-predicates. lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). """ import rtree allowed_hows = ['left', 'right', 'inner'] if how not in allowed_hows: raise ValueError("`how` was \"%s\" but is expected to be in %s" % \ (how, allowed_hows)) allowed_ops = ['contains', 'within', 'intersects'] if op not in allowed_ops: raise ValueError("`op` was \"%s\" but is expected to be in %s" % \ (op, allowed_ops)) if left_df.crs != right_df.crs: print('Warning: CRS does not match!') index_left = 'index_%s' % lsuffix index_right = 'index_%s' % rsuffix # due to GH 352 if (any(left_df.columns.isin([index_left, index_right])) or any(right_df.columns.isin([index_left, index_right]))): raise ValueError("'{0}' and '{1}' cannot be names in the frames being" " joined".format(index_left, index_right)) # the rtree spatial index only allows limited (numeric) index types, but an # index in geopandas may be any arbitrary dtype. so reset both indices now # and store references to the original indices, to be reaffixed later. # GH 352 left_df.index.name = index_left right_df.index.name = index_right left_df = left_df.reset_index() right_df = right_df.reset_index() if op == "within": # within implemented as the inverse of contains; swap names left_df, right_df = right_df, left_df # insert the bounds in the rtree spatial index right_df_bounds = right_df.geometry.apply(lambda x: x.bounds) stream = ((i, b, None) for i, b in enumerate(right_df_bounds)) tree_idx = rtree.index.Index(stream) idxmatch = (left_df.geometry.apply(lambda x: x.bounds) .apply(lambda x: list(tree_idx.intersection(x)))) idxmatch = idxmatch[idxmatch.apply(len) > 0] if idxmatch.shape[0] > 0: # if output from join has overlapping geometries r_idx = np.concatenate(idxmatch.values) l_idx = np.concatenate([[i] * len(v) for i, v in idxmatch.iteritems()]) # Vectorize predicate operations def find_intersects(a1, a2): return a1.intersects(a2) def find_contains(a1, a2): return a1.contains(a2) predicate_d = {'intersects': find_intersects, 'contains': find_contains, 'within': find_contains} check_predicates = np.vectorize(predicate_d[op]) result = ( pd.DataFrame( np.column_stack( [l_idx, r_idx, check_predicates( left_df.geometry .apply(lambda x: prepared.prep(x))[l_idx], right_df[right_df.geometry.name][r_idx]) ])) ) result.columns = ['_key_left', '_key_right', 'match_bool'] result = ( pd.DataFrame(result[result['match_bool']==1]) .drop('match_bool', axis=1) ) else: # when output from the join has no overlapping geometries result = pd.DataFrame(columns=['_key_left', '_key_right']) if op == "within": # within implemented as the inverse of contains; swap names left_df, right_df = right_df, left_df result = result.rename(columns={'_key_left': '_key_right', '_key_right': '_key_left'}) if how == 'inner': result = result.set_index('_key_left') joined = ( left_df .merge(result, left_index=True, right_index=True) .merge(right_df.drop(right_df.geometry.name, axis=1), left_on='_key_right', right_index=True, suffixes=('_%s' % lsuffix, '_%s' % rsuffix)) ) joined = joined.set_index(index_left).drop(['_key_right'], axis=1) joined.index.name = None elif how == 'left': result = result.set_index('_key_left') joined = ( left_df .merge(result, left_index=True, right_index=True, how='left') .merge(right_df.drop(right_df.geometry.name, axis=1), how='left', left_on='_key_right', right_index=True, suffixes=('_%s' % lsuffix, '_%s' % rsuffix)) ) joined = joined.set_index(index_left).drop(['_key_right'], axis=1) joined.index.name = None else: # how == 'right': joined = ( left_df .drop(left_df.geometry.name, axis=1) .merge(result.merge(right_df, left_on='_key_right', right_index=True, how='right'), left_index=True, right_on='_key_left', how='right') .set_index(index_right) ) joined = joined.drop(['_key_left', '_key_right'], axis=1) return joined geopandas-0.3.0/geopandas/tools/tests/000077500000000000000000000000001315112464100177145ustar00rootroot00000000000000geopandas-0.3.0/geopandas/tools/tests/__init__.py000066400000000000000000000000001315112464100220130ustar00rootroot00000000000000geopandas-0.3.0/geopandas/tools/tests/test_sjoin.py000066400000000000000000000240571315112464100224570ustar00rootroot00000000000000from __future__ import absolute_import from distutils.version import LooseVersion import numpy as np import pandas as pd from shapely.geometry import Point, Polygon import geopandas from geopandas import GeoDataFrame, GeoSeries, read_file, base from geopandas import sjoin import pytest from pandas.util.testing import assert_frame_equal pandas_0_18_problem = 'fails under pandas < 0.19 due to pandas issue 15692,'\ 'not problem with sjoin.' @pytest.fixture() def dfs(request): polys1 = GeoSeries( [Polygon([(0, 0), (5, 0), (5, 5), (0, 5)]), Polygon([(5, 5), (6, 5), (6, 6), (5, 6)]), Polygon([(6, 0), (9, 0), (9, 3), (6, 3)])]) polys2 = GeoSeries( [Polygon([(1, 1), (4, 1), (4, 4), (1, 4)]), Polygon([(4, 4), (7, 4), (7, 7), (4, 7)]), Polygon([(7, 7), (10, 7), (10, 10), (7, 10)])]) df1 = GeoDataFrame({'geometry': polys1, 'df1': [0, 1, 2]}) df2 = GeoDataFrame({'geometry': polys2, 'df2': [3, 4, 5]}) if request.param == 'string-index': df1.index = ['a', 'b', 'c'] df2.index = ['d', 'e', 'f'] # construction expected frames expected = {} part1 = df1.copy().reset_index().rename( columns={'index': 'index_left'}) part2 = df2.copy().iloc[[0, 1, 1, 2]].reset_index().rename( columns={'index': 'index_right'}) part1['_merge'] = [0, 1, 2] part2['_merge'] = [0, 0, 1, 3] exp = pd.merge(part1, part2, on='_merge', how='outer') expected['intersects'] = exp.drop('_merge', axis=1).copy() part1 = df1.copy().reset_index().rename( columns={'index': 'index_left'}) part2 = df2.copy().reset_index().rename( columns={'index': 'index_right'}) part1['_merge'] = [0, 1, 2] part2['_merge'] = [0, 3, 3] exp = pd.merge(part1, part2, on='_merge', how='outer') expected['contains'] = exp.drop('_merge', axis=1).copy() part1['_merge'] = [0, 1, 2] part2['_merge'] = [3, 1, 3] exp = pd.merge(part1, part2, on='_merge', how='outer') expected['within'] = exp.drop('_merge', axis=1).copy() return [request.param, df1, df2, expected] @pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping') class TestSpatialJoin: @pytest.mark.parametrize('dfs', ['default-index', 'string-index'], indirect=True) @pytest.mark.parametrize('op', ['intersects', 'contains', 'within']) def test_inner(self, op, dfs): index, df1, df2, expected = dfs res = sjoin(df1, df2, how='inner', op=op) exp = expected[op].dropna().copy() exp = exp.drop('geometry_y', axis=1).rename( columns={'geometry_x': 'geometry'}) exp[['df1', 'df2']] = exp[['df1', 'df2']].astype('int64') if index == 'default-index': exp[['index_left', 'index_right']] = \ exp[['index_left', 'index_right']].astype('int64') exp = exp.set_index('index_left') exp.index.name = None assert_frame_equal(res, exp) @pytest.mark.parametrize('dfs', ['default-index', 'string-index'], indirect=True) @pytest.mark.parametrize('op', ['intersects', 'contains', 'within']) def test_left(self, op, dfs): index, df1, df2, expected = dfs res = sjoin(df1, df2, how='left', op=op) exp = expected[op].dropna(subset=['index_left']).copy() exp = exp.drop('geometry_y', axis=1).rename( columns={'geometry_x': 'geometry'}) exp['df1'] = exp['df1'].astype('int64') if index == 'default-index': exp['index_left'] = exp['index_left'].astype('int64') # TODO: in result the dtype is object res['index_right'] = res['index_right'].astype(float) exp = exp.set_index('index_left') exp.index.name = None assert_frame_equal(res, exp) @pytest.mark.parametrize('dfs', ['default-index', 'string-index'], indirect=True) @pytest.mark.parametrize('op', ['intersects', 'contains', 'within']) def test_right(self, op, dfs): index, df1, df2, expected = dfs res = sjoin(df1, df2, how='right', op=op) exp = expected[op].dropna(subset=['index_right']).copy() exp = exp.drop('geometry_x', axis=1).rename( columns={'geometry_y': 'geometry'}) exp['df2'] = exp['df2'].astype('int64') if index == 'default-index': exp['index_right'] = exp['index_right'].astype('int64') res['index_left'] = res['index_left'].astype(float) exp = exp.set_index('index_right') exp = exp.reindex(columns=res.columns) assert_frame_equal(res, exp, check_index_type=False) @pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping') class TestSpatialJoinNYBB: def setup_method(self): nybb_filename = geopandas.datasets.get_path('nybb') self.polydf = read_file(nybb_filename) self.crs = self.polydf.crs N = 20 b = [int(x) for x in self.polydf.total_bounds] self.pointdf = GeoDataFrame( [{'geometry': Point(x, y), 'pointattr1': x + y, 'pointattr2': x - y} for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)), range(b[1], b[3], int((b[3]-b[1])/N)))], crs=self.crs) def test_geometry_name(self): # test sjoin is working with other geometry name polydf_original_geom_name = self.polydf.geometry.name self.polydf = (self.polydf.rename(columns={'geometry': 'new_geom'}) .set_geometry('new_geom')) assert polydf_original_geom_name != self.polydf.geometry.name res = sjoin(self.polydf, self.pointdf, how="left") assert self.polydf.geometry.name == res.geometry.name def test_sjoin_left(self): df = sjoin(self.pointdf, self.polydf, how='left') assert df.shape == (21, 8) for i, row in df.iterrows(): assert row.geometry.type == 'Point' assert 'pointattr1' in df.columns assert 'BoroCode' in df.columns def test_sjoin_right(self): # the inverse of left df = sjoin(self.pointdf, self.polydf, how="right") df2 = sjoin(self.polydf, self.pointdf, how="left") assert df.shape == (12, 8) assert df.shape == df2.shape for i, row in df.iterrows(): assert row.geometry.type == 'MultiPolygon' for i, row in df2.iterrows(): assert row.geometry.type == 'MultiPolygon' def test_sjoin_inner(self): df = sjoin(self.pointdf, self.polydf, how="inner") assert df.shape == (11, 8) def test_sjoin_op(self): # points within polygons df = sjoin(self.pointdf, self.polydf, how="left", op="within") assert df.shape == (21, 8) assert df.ix[1]['BoroName'] == 'Staten Island' # points contain polygons? never happens so we should have nulls df = sjoin(self.pointdf, self.polydf, how="left", op="contains") assert df.shape == (21, 8) assert np.isnan(df.ix[1]['Shape_Area']) def test_sjoin_bad_op(self): # AttributeError: 'Point' object has no attribute 'spandex' with pytest.raises(ValueError): sjoin(self.pointdf, self.polydf, how="left", op="spandex") def test_sjoin_duplicate_column_name(self): pointdf2 = self.pointdf.rename(columns={'pointattr1': 'Shape_Area'}) df = sjoin(pointdf2, self.polydf, how="left") assert 'Shape_Area_left' in df.columns assert 'Shape_Area_right' in df.columns def test_sjoin_values(self): # GH190 self.polydf.index = [1, 3, 4, 5, 6] df = sjoin(self.pointdf, self.polydf, how='left') assert df.shape == (21, 8) df = sjoin(self.polydf, self.pointdf, how='left') assert df.shape == (12, 8) @pytest.mark.skipif(str(pd.__version__) < LooseVersion('0.19'), reason=pandas_0_18_problem) @pytest.mark.xfail def test_no_overlapping_geometry(self): # Note: these tests are for correctly returning GeoDataFrame # when result of the join is empty df_inner = sjoin(self.pointdf.iloc[17:], self.polydf, how='inner') df_left = sjoin(self.pointdf.iloc[17:], self.polydf, how='left') df_right = sjoin(self.pointdf.iloc[17:], self.polydf, how='right') # Recent Pandas development has introduced a new way of handling merges # this change has altered the output when no overlapping geometries if str(pd.__version__) > LooseVersion('0.18.1'): right_idxs = pd.Series(range(0, 5), name='index_right', dtype='int64') else: right_idxs = pd.Series(name='index_right', dtype='int64') expected_inner_df = pd.concat( [self.pointdf.iloc[:0], pd.Series(name='index_right', dtype='int64'), self.polydf.drop('geometry', axis=1).iloc[:0]], axis=1) expected_inner = GeoDataFrame( expected_inner_df, crs={'init': 'epsg:4326', 'no_defs': True}) expected_right_df = pd.concat( [self.pointdf.drop('geometry', axis=1).iloc[:0], pd.concat([pd.Series(name='index_left', dtype='int64'), right_idxs], axis=1), self.polydf], axis=1) expected_right = GeoDataFrame( expected_right_df, crs={'init': 'epsg:4326', 'no_defs': True})\ .set_index('index_right') expected_left_df = pd.concat( [self.pointdf.iloc[17:], pd.Series(name='index_right', dtype='int64'), self.polydf.iloc[:0].drop('geometry', axis=1)], axis=1) expected_left = GeoDataFrame( expected_left_df, crs={'init': 'epsg:4326', 'no_defs': True}) assert expected_inner.equals(df_inner) assert expected_right.equals(df_right) assert expected_left.equals(df_left) @pytest.mark.skip("Not implemented") def test_sjoin_outer(self): df = sjoin(self.pointdf, self.polydf, how="outer") assert df.shape == (21, 8) geopandas-0.3.0/geopandas/tools/tests/test_tools.py000066400000000000000000000027631315112464100224750ustar00rootroot00000000000000from __future__ import absolute_import from shapely.geometry import Point, MultiPoint, LineString from geopandas import GeoSeries from geopandas.tools import collect import pytest class TestTools: def setup_method(self): self.p1 = Point(0, 0) self.p2 = Point(1, 1) self.p3 = Point(2, 2) self.mpc = MultiPoint([self.p1, self.p2, self.p3]) self.mp1 = MultiPoint([self.p1, self.p2]) self.line1 = LineString([(3, 3), (4, 4)]) def test_collect_single(self): result = collect(self.p1) assert self.p1.equals(result) def test_collect_single_force_multi(self): result = collect(self.p1, multi=True) expected = MultiPoint([self.p1]) assert expected.equals(result) def test_collect_multi(self): result = collect(self.mp1) assert self.mp1.equals(result) def test_collect_multi_force_multi(self): result = collect(self.mp1) assert self.mp1.equals(result) def test_collect_list(self): result = collect([self.p1, self.p2, self.p3]) assert self.mpc.equals(result) def test_collect_GeoSeries(self): s = GeoSeries([self.p1, self.p2, self.p3]) result = collect(s) assert self.mpc.equals(result) def test_collect_mixed_types(self): with pytest.raises(ValueError): collect([self.p1, self.line1]) def test_collect_mixed_multi(self): with pytest.raises(ValueError): collect([self.mpc, self.mp1]) geopandas-0.3.0/geopandas/tools/util.py000066400000000000000000000026701315112464100201060ustar00rootroot00000000000000import pandas as pd from shapely.geometry import MultiPoint, MultiLineString, MultiPolygon from shapely.geometry.base import BaseGeometry _multi_type_map = { 'Point': MultiPoint, 'LineString': MultiLineString, 'Polygon': MultiPolygon } def collect(x, multi=False): """ Collect single part geometries into their Multi* counterpart Parameters ---------- x : an iterable or Series of Shapely geometries, a GeoSeries, or a single Shapely geometry multi : boolean, default False if True, force returned geometries to be Multi* even if they only have one component. """ if isinstance(x, BaseGeometry): x = [x] elif isinstance(x, pd.Series): x = list(x) # We cannot create GeometryCollection here so all types # must be the same. If there is more than one element, # they cannot be Multi*, i.e., can't pass in combination of # Point and MultiPoint... or even just MultiPoint t = x[0].type if not all(g.type == t for g in x): raise ValueError('Geometry type must be homogenous') if len(x) > 1 and t.startswith('Multi'): raise ValueError( 'Cannot collect {0}. Must have single geometries'.format(t)) if len(x) == 1 and (t.startswith('Multi') or not multi): # If there's only one single part geom and we're not forcing to # multi, then just return it return x[0] return _multi_type_map[t](x) geopandas-0.3.0/readthedocs.yml000066400000000000000000000001531315112464100164600ustar00rootroot00000000000000formats: - none conda: file: doc/environment.yml python: version: 3 setup_py_install: true geopandas-0.3.0/requirements.test.txt000066400000000000000000000002711315112464100177130ustar00rootroot00000000000000psycopg2>=2.5.1 SQLAlchemy>=0.8.3 geopy==1.10.0 matplotlib>=1.2.1 descartes>=1.0 mock>=1.0.1 # technically not need for python >= 3.3 pytest>=3.1.0 pytest-cov codecov rtree>=0.8 pysal geopandas-0.3.0/requirements.txt000066400000000000000000000001031315112464100167270ustar00rootroot00000000000000Cython>=0.16 shapely>=1.2.18 fiona>=1.0.1 pyproj>=1.9.3 six>=1.3.0 geopandas-0.3.0/setup.cfg000066400000000000000000000005561315112464100153000ustar00rootroot00000000000000[bdist_wheel] universal = 1 # See the docstring in versioneer.py for instructions. Note that you must # re-run 'versioneer.py setup' after changing this section, and commit the # resulting files. [versioneer] VCS = git style = pep440 versionfile_source = geopandas/_version.py versionfile_build = geopandas/_version.py tag_prefix = v parentdir_prefix = geopandas- geopandas-0.3.0/setup.py000066400000000000000000000035001315112464100151610ustar00rootroot00000000000000#!/usr/bin/env/python """Installation script """ import os try: from setuptools import setup except ImportError: from distutils.core import setup import versioneer LONG_DESCRIPTION = """GeoPandas is a project to add support for geographic data to `pandas`_ objects. The goal of GeoPandas is to make working with geospatial data in python easier. It combines the capabilities of `pandas`_ and `shapely`_, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS. .. _pandas: http://pandas.pydata.org .. _shapely: http://toblerity.github.io/shapely """ if os.environ.get('READTHEDOCS', False) == 'True': INSTALL_REQUIRES = [] else: INSTALL_REQUIRES = ['pandas', 'shapely', 'fiona', 'descartes', 'pyproj'] # get all data dirs in the datasets module data_files = [] for item in os.listdir("geopandas/datasets"): if not item.startswith('__'): if os.path.isdir(os.path.join("geopandas/datasets/", item)): data_files.append(os.path.join("datasets", item, '*')) elif item.endswith('.zip'): data_files.append(os.path.join("datasets", item)) setup(name='geopandas', version=versioneer.get_version(), description='Geographic pandas extensions', license='BSD', author='GeoPandas contributors', author_email='kjordahl@alum.mit.edu', url='http://geopandas.org', long_description=LONG_DESCRIPTION, packages=['geopandas', 'geopandas.io', 'geopandas.tools', 'geopandas.datasets', 'geopandas.tests', 'geopandas.tools.tests'], package_data={'geopandas': data_files}, install_requires=INSTALL_REQUIRES, cmdclass=versioneer.get_cmdclass()) geopandas-0.3.0/versioneer.py000066400000000000000000002003231315112464100162040ustar00rootroot00000000000000 # Version: 0.16 """The Versioneer - like a rocketeer, but for versions. The Versioneer ============== * like a rocketeer, but for versions! * https://github.com/warner/python-versioneer * Brian Warner * License: Public Domain * Compatible With: python2.6, 2.7, 3.3, 3.4, 3.5, and pypy * [![Latest Version] (https://pypip.in/version/versioneer/badge.svg?style=flat) ](https://pypi.python.org/pypi/versioneer/) * [![Build Status] (https://travis-ci.org/warner/python-versioneer.png?branch=master) ](https://travis-ci.org/warner/python-versioneer) This is a tool for managing a recorded version number in distutils-based python projects. The goal is to remove the tedious and error-prone "update the embedded version string" step from your release process. Making a new release should be as easy as recording a new tag in your version-control system, and maybe making new tarballs. ## Quick Install * `pip install versioneer` to somewhere to your $PATH * add a `[versioneer]` section to your setup.cfg (see below) * run `versioneer install` in your source tree, commit the results ## Version Identifiers Source trees come from a variety of places: * a version-control system checkout (mostly used by developers) * a nightly tarball, produced by build automation * a snapshot tarball, produced by a web-based VCS browser, like github's "tarball from tag" feature * a release tarball, produced by "setup.py sdist", distributed through PyPI Within each source tree, the version identifier (either a string or a number, this tool is format-agnostic) can come from a variety of places: * ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows about recent "tags" and an absolute revision-id * the name of the directory into which the tarball was unpacked * an expanded VCS keyword ($Id$, etc) * a `_version.py` created by some earlier build step For released software, the version identifier is closely related to a VCS tag. Some projects use tag names that include more than just the version string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool needs to strip the tag prefix to extract the version identifier. For unreleased software (between tags), the version identifier should provide enough information to help developers recreate the same tree, while also giving them an idea of roughly how old the tree is (after version 1.2, before version 1.3). Many VCS systems can report a description that captures this, for example `git describe --tags --dirty --always` reports things like "0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the 0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has uncommitted changes. The version identifier is used for multiple purposes: * to allow the module to self-identify its version: `myproject.__version__` * to choose a name and prefix for a 'setup.py sdist' tarball ## Theory of Operation Versioneer works by adding a special `_version.py` file into your source tree, where your `__init__.py` can import it. This `_version.py` knows how to dynamically ask the VCS tool for version information at import time. `_version.py` also contains `$Revision$` markers, and the installation process marks `_version.py` to have this marker rewritten with a tag name during the `git archive` command. As a result, generated tarballs will contain enough information to get the proper version. To allow `setup.py` to compute a version too, a `versioneer.py` is added to the top level of your source tree, next to `setup.py` and the `setup.cfg` that configures it. This overrides several distutils/setuptools commands to compute the version when invoked, and changes `setup.py build` and `setup.py sdist` to replace `_version.py` with a small static file that contains just the generated version data. ## Installation First, decide on values for the following configuration variables: * `VCS`: the version control system you use. Currently accepts "git". * `style`: the style of version string to be produced. See "Styles" below for details. Defaults to "pep440", which looks like `TAG[+DISTANCE.gSHORTHASH[.dirty]]`. * `versionfile_source`: A project-relative pathname into which the generated version strings should be written. This is usually a `_version.py` next to your project's main `__init__.py` file, so it can be imported at runtime. If your project uses `src/myproject/__init__.py`, this should be `src/myproject/_version.py`. This file should be checked in to your VCS as usual: the copy created below by `setup.py setup_versioneer` will include code that parses expanded VCS keywords in generated tarballs. The 'build' and 'sdist' commands will replace it with a copy that has just the calculated version string. This must be set even if your project does not have any modules (and will therefore never import `_version.py`), since "setup.py sdist" -based trees still need somewhere to record the pre-calculated version strings. Anywhere in the source tree should do. If there is a `__init__.py` next to your `_version.py`, the `setup.py setup_versioneer` command (described below) will append some `__version__`-setting assignments, if they aren't already present. * `versionfile_build`: Like `versionfile_source`, but relative to the build directory instead of the source directory. These will differ when your setup.py uses 'package_dir='. If you have `package_dir={'myproject': 'src/myproject'}`, then you will probably have `versionfile_build='myproject/_version.py'` and `versionfile_source='src/myproject/_version.py'`. If this is set to None, then `setup.py build` will not attempt to rewrite any `_version.py` in the built tree. If your project does not have any libraries (e.g. if it only builds a script), then you should use `versionfile_build = None`. To actually use the computed version string, your `setup.py` will need to override `distutils.command.build_scripts` with a subclass that explicitly inserts a copy of `versioneer.get_version()` into your script file. See `test/demoapp-script-only/setup.py` for an example. * `tag_prefix`: a string, like 'PROJECTNAME-', which appears at the start of all VCS tags. If your tags look like 'myproject-1.2.0', then you should use tag_prefix='myproject-'. If you use unprefixed tags like '1.2.0', this should be an empty string, using either `tag_prefix=` or `tag_prefix=''`. * `parentdir_prefix`: a optional string, frequently the same as tag_prefix, which appears at the start of all unpacked tarball filenames. If your tarball unpacks into 'myproject-1.2.0', this should be 'myproject-'. To disable this feature, just omit the field from your `setup.cfg`. This tool provides one script, named `versioneer`. That script has one mode, "install", which writes a copy of `versioneer.py` into the current directory and runs `versioneer.py setup` to finish the installation. To versioneer-enable your project: * 1: Modify your `setup.cfg`, adding a section named `[versioneer]` and populating it with the configuration values you decided earlier (note that the option names are not case-sensitive): ```` [versioneer] VCS = git style = pep440 versionfile_source = src/myproject/_version.py versionfile_build = myproject/_version.py tag_prefix = parentdir_prefix = myproject- ```` * 2: Run `versioneer install`. This will do the following: * copy `versioneer.py` into the top of your source tree * create `_version.py` in the right place (`versionfile_source`) * modify your `__init__.py` (if one exists next to `_version.py`) to define `__version__` (by calling a function from `_version.py`) * modify your `MANIFEST.in` to include both `versioneer.py` and the generated `_version.py` in sdist tarballs `versioneer install` will complain about any problems it finds with your `setup.py` or `setup.cfg`. Run it multiple times until you have fixed all the problems. * 3: add a `import versioneer` to your setup.py, and add the following arguments to the setup() call: version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), * 4: commit these changes to your VCS. To make sure you won't forget, `versioneer install` will mark everything it touched for addition using `git add`. Don't forget to add `setup.py` and `setup.cfg` too. ## Post-Installation Usage Once established, all uses of your tree from a VCS checkout should get the current version string. All generated tarballs should include an embedded version string (so users who unpack them will not need a VCS tool installed). If you distribute your project through PyPI, then the release process should boil down to two steps: * 1: git tag 1.0 * 2: python setup.py register sdist upload If you distribute it through github (i.e. users use github to generate tarballs with `git archive`), the process is: * 1: git tag 1.0 * 2: git push; git push --tags Versioneer will report "0+untagged.NUMCOMMITS.gHASH" until your tree has at least one tag in its history. ## Version-String Flavors Code which uses Versioneer can learn about its version string at runtime by importing `_version` from your main `__init__.py` file and running the `get_versions()` function. From the "outside" (e.g. in `setup.py`), you can import the top-level `versioneer.py` and run `get_versions()`. Both functions return a dictionary with different flavors of version information: * `['version']`: A condensed version string, rendered using the selected style. This is the most commonly used value for the project's version string. The default "pep440" style yields strings like `0.11`, `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section below for alternative styles. * `['full-revisionid']`: detailed revision identifier. For Git, this is the full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". * `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that this is only accurate if run in a VCS checkout, otherwise it is likely to be False or None * `['error']`: if the version string could not be computed, this will be set to a string describing the problem, otherwise it will be None. It may be useful to throw an exception in setup.py if this is set, to avoid e.g. creating tarballs with a version string of "unknown". Some variants are more useful than others. Including `full-revisionid` in a bug report should allow developers to reconstruct the exact code being tested (or indicate the presence of local changes that should be shared with the developers). `version` is suitable for display in an "about" box or a CLI `--version` output: it can be easily compared against release notes and lists of bugs fixed in various releases. The installer adds the following text to your `__init__.py` to place a basic version in `YOURPROJECT.__version__`: from ._version import get_versions __version__ = get_versions()['version'] del get_versions ## Styles The setup.cfg `style=` configuration controls how the VCS information is rendered into a version string. The default style, "pep440", produces a PEP440-compliant string, equal to the un-prefixed tag name for actual releases, and containing an additional "local version" section with more detail for in-between builds. For Git, this is TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags --dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and that this commit is two revisions ("+2") beyond the "0.11" tag. For released software (exactly equal to a known tag), the identifier will only contain the stripped tag, e.g. "0.11". Other styles are available. See details.md in the Versioneer source tree for descriptions. ## Debugging Versioneer tries to avoid fatal errors: if something goes wrong, it will tend to return a version of "0+unknown". To investigate the problem, run `setup.py version`, which will run the version-lookup code in a verbose mode, and will display the full contents of `get_versions()` (including the `error` string, which may help identify what went wrong). ## Updating Versioneer To upgrade your project to a new release of Versioneer, do the following: * install the new Versioneer (`pip install -U versioneer` or equivalent) * edit `setup.cfg`, if necessary, to include any new configuration settings indicated by the release notes * re-run `versioneer install` in your source tree, to replace `SRC/_version.py` * commit any changed files ### Upgrading to 0.16 Nothing special. ### Upgrading to 0.15 Starting with this version, Versioneer is configured with a `[versioneer]` section in your `setup.cfg` file. Earlier versions required the `setup.py` to set attributes on the `versioneer` module immediately after import. The new version will refuse to run (raising an exception during import) until you have provided the necessary `setup.cfg` section. In addition, the Versioneer package provides an executable named `versioneer`, and the installation process is driven by running `versioneer install`. In 0.14 and earlier, the executable was named `versioneer-installer` and was run without an argument. ### Upgrading to 0.14 0.14 changes the format of the version string. 0.13 and earlier used hyphen-separated strings like "0.11-2-g1076c97-dirty". 0.14 and beyond use a plus-separated "local version" section strings, with dot-separated components, like "0.11+2.g1076c97". PEP440-strict tools did not like the old format, but should be ok with the new one. ### Upgrading from 0.11 to 0.12 Nothing special. ### Upgrading from 0.10 to 0.11 You must add a `versioneer.VCS = "git"` to your `setup.py` before re-running `setup.py setup_versioneer`. This will enable the use of additional version-control systems (SVN, etc) in the future. ## Future Directions This tool is designed to make it easily extended to other version-control systems: all VCS-specific components are in separate directories like src/git/ . The top-level `versioneer.py` script is assembled from these components by running make-versioneer.py . In the future, make-versioneer.py will take a VCS name as an argument, and will construct a version of `versioneer.py` that is specific to the given VCS. It might also take the configuration arguments that are currently provided manually during installation by editing setup.py . Alternatively, it might go the other direction and include code from all supported VCS systems, reducing the number of intermediate scripts. ## License To make Versioneer easier to embed, all its code is dedicated to the public domain. The `_version.py` that it creates is also in the public domain. Specifically, both are released under the Creative Commons "Public Domain Dedication" license (CC0-1.0), as described in https://creativecommons.org/publicdomain/zero/1.0/ . """ from __future__ import print_function try: import configparser except ImportError: import ConfigParser as configparser import errno import json import os import re import subprocess import sys class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_root(): """Get the project root directory. We require that all commands are run from the project root, i.e. the directory that contains setup.py, setup.cfg, and versioneer.py . """ root = os.path.realpath(os.path.abspath(os.getcwd())) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): # allow 'python path/to/setup.py COMMAND' root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): err = ("Versioneer was unable to run the project root directory. " "Versioneer requires setup.py to be executed from " "its immediate directory (like 'python setup.py COMMAND'), " "or in a way that lets it use sys.argv[0] to find the root " "(like 'python path/to/setup.py COMMAND').") raise VersioneerBadRootError(err) try: # Certain runtime workflows (setup.py install/develop in a setuptools # tree) execute all dependencies in a single python process, so # "versioneer" may be imported multiple times, and python's shared # module-import table will cache the first one. So we can't use # os.path.dirname(__file__), as that will find whichever # versioneer.py was first imported, even in later projects. me = os.path.realpath(os.path.abspath(__file__)) if os.path.splitext(me)[0] != os.path.splitext(versioneer_py)[0]: print("Warning: build in %s is using versioneer.py from %s" % (os.path.dirname(me), versioneer_py)) except NameError: pass return root def get_config_from_root(root): """Read the project setup.cfg file to determine Versioneer config.""" # This might raise EnvironmentError (if setup.cfg is missing), or # configparser.NoSectionError (if it lacks a [versioneer] section), or # configparser.NoOptionError (if it lacks "VCS="). See the docstring at # the top of versioneer.py for instructions on writing your setup.cfg . setup_cfg = os.path.join(root, "setup.cfg") parser = configparser.SafeConfigParser() with open(setup_cfg, "r") as f: parser.readfp(f) VCS = parser.get("versioneer", "VCS") # mandatory def get(parser, name): if parser.has_option("versioneer", name): return parser.get("versioneer", name) return None cfg = VersioneerConfig() cfg.VCS = VCS cfg.style = get(parser, "style") or "" cfg.versionfile_source = get(parser, "versionfile_source") cfg.versionfile_build = get(parser, "versionfile_build") cfg.tag_prefix = get(parser, "tag_prefix") if cfg.tag_prefix in ("''", '""'): cfg.tag_prefix = "" cfg.parentdir_prefix = get(parser, "parentdir_prefix") cfg.verbose = get(parser, "verbose") return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" # these dictionaries contain VCS-specific tools LONG_VERSION_PY = {} HANDLERS = {} def register_vcs_handler(vcs, method): # decorator """Decorator to mark a method as the handler for a particular VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): """Call the given command(s).""" assert isinstance(commands, list) p = None for c in commands: try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except EnvironmentError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %s" % dispcmd) print(e) return None else: if verbose: print("unable to find command, tried %s" % (commands,)) return None stdout = p.communicate()[0].strip() if sys.version_info[0] >= 3: stdout = stdout.decode() if p.returncode != 0: if verbose: print("unable to run %s (error)" % dispcmd) return None return stdout LONG_VERSION_PY['git'] = ''' # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build # directories (produced by setup.py build) will contain a much shorter file # that just contains the computed version number. # This file is released into the public domain. Generated by # versioneer-0.16 (https://github.com/warner/python-versioneer) """Git implementation of _version.py.""" import errno import os import re import subprocess import sys def get_keywords(): """Get the keywords needed to look up the version information.""" # these strings will be replaced by git during git-archive. # setup.py/versioneer.py will grep for the variable names, so they must # each be defined on a line of their own. _version.py will just call # get_keywords(). git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" keywords = {"refnames": git_refnames, "full": git_full} return keywords class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_config(): """Create, populate and return the VersioneerConfig() object.""" # these strings are filled in when 'setup.py versioneer' creates # _version.py cfg = VersioneerConfig() cfg.VCS = "git" cfg.style = "%(STYLE)s" cfg.tag_prefix = "%(TAG_PREFIX)s" cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" cfg.verbose = False return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" LONG_VERSION_PY = {} HANDLERS = {} def register_vcs_handler(vcs, method): # decorator """Decorator to mark a method as the handler for a particular VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): """Call the given command(s).""" assert isinstance(commands, list) p = None for c in commands: try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except EnvironmentError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %%s" %% dispcmd) print(e) return None else: if verbose: print("unable to find command, tried %%s" %% (commands,)) return None stdout = p.communicate()[0].strip() if sys.version_info[0] >= 3: stdout = stdout.decode() if p.returncode != 0: if verbose: print("unable to run %%s (error)" %% dispcmd) return None return stdout def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. """ dirname = os.path.basename(root) if not dirname.startswith(parentdir_prefix): if verbose: print("guessing rootdir is '%%s', but '%%s' doesn't start with " "prefix '%%s'" %% (root, dirname, parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None} @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = set([r.strip() for r in refnames.strip("()").split(",")]) # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %%d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r'\d', r)]) if verbose: print("discarding '%%s', no digits" %% ",".join(refs-tags)) if verbose: print("likely tags: %%s" %% ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] if verbose: print("picking %%s" %% r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags"} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ if not os.path.exists(os.path.join(root, ".git")): if verbose: print("no .git in %%s" %% root) raise NotThisMethod("no .git directory") GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out = run_command(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%%s*" %% tag_prefix], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%%s'" %% describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%%s' doesn't start with prefix '%%s'" print(fmt %% (full_tag, tag_prefix)) pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" %% (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits return pieces def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post.dev%%d" %% pieces["distance"] else: # exception #1 rendered = "0.post.dev%%d" %% pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%%s" %% pieces["short"] else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%%s" %% pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Eexceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"]} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%%s'" %% style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None} def get_versions(): """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which # case we can only use expanded keywords. cfg = get_config() verbose = cfg.verbose try: return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass try: root = os.path.realpath(__file__) # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. for i in cfg.versionfile_source.split('/'): root = os.path.dirname(root) except NameError: return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to find root of source tree"} try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) return render(pieces, cfg.style) except NotThisMethod: pass try: if cfg.parentdir_prefix: return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) except NotThisMethod: pass return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version"} ''' @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = set([r.strip() for r in refnames.strip("()").split(",")]) # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r'\d', r)]) if verbose: print("discarding '%s', no digits" % ",".join(refs-tags)) if verbose: print("likely tags: %s" % ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] if verbose: print("picking %s" % r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags"} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ if not os.path.exists(os.path.join(root, ".git")): if verbose: print("no .git in %s" % root) raise NotThisMethod("no .git directory") GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out = run_command(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%s*" % tag_prefix], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%s'" % describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" % (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits return pieces def do_vcs_install(manifest_in, versionfile_source, ipy): """Git-specific installation logic for Versioneer. For Git, this means creating/changing .gitattributes to mark _version.py for export-time keyword substitution. """ GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] files = [manifest_in, versionfile_source] if ipy: files.append(ipy) try: me = __file__ if me.endswith(".pyc") or me.endswith(".pyo"): me = os.path.splitext(me)[0] + ".py" versioneer_file = os.path.relpath(me) except NameError: versioneer_file = "versioneer.py" files.append(versioneer_file) present = False try: f = open(".gitattributes", "r") for line in f.readlines(): if line.strip().startswith(versionfile_source): if "export-subst" in line.strip().split()[1:]: present = True f.close() except EnvironmentError: pass if not present: f = open(".gitattributes", "a+") f.write("%s export-subst\n" % versionfile_source) f.close() files.append(".gitattributes") run_command(GITS, ["add", "--"] + files) def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. """ dirname = os.path.basename(root) if not dirname.startswith(parentdir_prefix): if verbose: print("guessing rootdir is '%s', but '%s' doesn't start with " "prefix '%s'" % (root, dirname, parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None} SHORT_VERSION_PY = """ # This file was generated by 'versioneer.py' (0.16) from # revision-control system data, or from the parent directory name of an # unpacked source archive. Distribution tarballs contain a pre-generated copy # of this file. import json import sys version_json = ''' %s ''' # END VERSION_JSON def get_versions(): return json.loads(version_json) """ def versions_from_file(filename): """Try to determine the version from _version.py if present.""" try: with open(filename) as f: contents = f.read() except EnvironmentError: raise NotThisMethod("unable to read _version.py") mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", contents, re.M | re.S) if not mo: raise NotThisMethod("no version_json in _version.py") return json.loads(mo.group(1)) def write_to_version_file(filename, versions): """Write the given version number to the given _version.py file.""" os.unlink(filename) contents = json.dumps(versions, sort_keys=True, indent=1, separators=(",", ": ")) with open(filename, "w") as f: f.write(SHORT_VERSION_PY % contents) print("set %s to '%s'" % (filename, versions["version"])) def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post.dev%d" % pieces["distance"] else: # exception #1 rendered = "0.post.dev%d" % pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%s" % pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Eexceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"]} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%s'" % style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None} class VersioneerBadRootError(Exception): """The project root directory is unknown or missing key files.""" def get_versions(verbose=False): """Get the project version from whatever source is available. Returns dict with two keys: 'version' and 'full'. """ if "versioneer" in sys.modules: # see the discussion in cmdclass.py:get_cmdclass() del sys.modules["versioneer"] root = get_root() cfg = get_config_from_root(root) assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" handlers = HANDLERS.get(cfg.VCS) assert handlers, "unrecognized VCS '%s'" % cfg.VCS verbose = verbose or cfg.verbose assert cfg.versionfile_source is not None, \ "please set versioneer.versionfile_source" assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" versionfile_abs = os.path.join(root, cfg.versionfile_source) # extract version from first of: _version.py, VCS command (e.g. 'git # describe'), parentdir. This is meant to work for developers using a # source checkout, for users of a tarball created by 'setup.py sdist', # and for users of a tarball/zipball created by 'git archive' or github's # download-from-tag feature or the equivalent in other VCSes. get_keywords_f = handlers.get("get_keywords") from_keywords_f = handlers.get("keywords") if get_keywords_f and from_keywords_f: try: keywords = get_keywords_f(versionfile_abs) ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) if verbose: print("got version from expanded keyword %s" % ver) return ver except NotThisMethod: pass try: ver = versions_from_file(versionfile_abs) if verbose: print("got version from file %s %s" % (versionfile_abs, ver)) return ver except NotThisMethod: pass from_vcs_f = handlers.get("pieces_from_vcs") if from_vcs_f: try: pieces = from_vcs_f(cfg.tag_prefix, root, verbose) ver = render(pieces, cfg.style) if verbose: print("got version from VCS %s" % ver) return ver except NotThisMethod: pass try: if cfg.parentdir_prefix: ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) if verbose: print("got version from parentdir %s" % ver) return ver except NotThisMethod: pass if verbose: print("unable to compute version") return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version"} def get_version(): """Get the short version string for this project.""" return get_versions()["version"] def get_cmdclass(): """Get the custom setuptools/distutils subclasses used by Versioneer.""" if "versioneer" in sys.modules: del sys.modules["versioneer"] # this fixes the "python setup.py develop" case (also 'install' and # 'easy_install .'), in which subdependencies of the main project are # built (using setup.py bdist_egg) in the same python process. Assume # a main project A and a dependency B, which use different versions # of Versioneer. A's setup.py imports A's Versioneer, leaving it in # sys.modules by the time B's setup.py is executed, causing B to run # with the wrong versioneer. Setuptools wraps the sub-dep builds in a # sandbox that restores sys.modules to it's pre-build state, so the # parent is protected against the child's "import versioneer". By # removing ourselves from sys.modules here, before the child build # happens, we protect the child from the parent's versioneer too. # Also see https://github.com/warner/python-versioneer/issues/52 cmds = {} # we add "version" to both distutils and setuptools from distutils.core import Command class cmd_version(Command): description = "report generated version string" user_options = [] boolean_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): vers = get_versions(verbose=True) print("Version: %s" % vers["version"]) print(" full-revisionid: %s" % vers.get("full-revisionid")) print(" dirty: %s" % vers.get("dirty")) if vers["error"]: print(" error: %s" % vers["error"]) cmds["version"] = cmd_version # we override "build_py" in both distutils and setuptools # # most invocation pathways end up running build_py: # distutils/build -> build_py # distutils/install -> distutils/build ->.. # setuptools/bdist_wheel -> distutils/install ->.. # setuptools/bdist_egg -> distutils/install_lib -> build_py # setuptools/install -> bdist_egg ->.. # setuptools/develop -> ? # we override different "build_py" commands for both environments if "setuptools" in sys.modules: from setuptools.command.build_py import build_py as _build_py else: from distutils.command.build_py import build_py as _build_py class cmd_build_py(_build_py): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() _build_py.run(self) # now locate _version.py in the new build/ directory and replace # it with an updated value if cfg.versionfile_build: target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) cmds["build_py"] = cmd_build_py if "cx_Freeze" in sys.modules: # cx_freeze enabled? from cx_Freeze.dist import build_exe as _build_exe class cmd_build_exe(_build_exe): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() target_versionfile = cfg.versionfile_source print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) _build_exe.run(self) os.unlink(target_versionfile) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) cmds["build_exe"] = cmd_build_exe del cmds["build_py"] # we override different "sdist" commands for both environments if "setuptools" in sys.modules: from setuptools.command.sdist import sdist as _sdist else: from distutils.command.sdist import sdist as _sdist class cmd_sdist(_sdist): def run(self): versions = get_versions() self._versioneer_generated_versions = versions # unless we update this, the command will keep using the old # version self.distribution.metadata.version = versions["version"] return _sdist.run(self) def make_release_tree(self, base_dir, files): root = get_root() cfg = get_config_from_root(root) _sdist.make_release_tree(self, base_dir, files) # now locate _version.py in the new base_dir directory # (remembering that it may be a hardlink) and replace it with an # updated value target_versionfile = os.path.join(base_dir, cfg.versionfile_source) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, self._versioneer_generated_versions) cmds["sdist"] = cmd_sdist return cmds CONFIG_ERROR = """ setup.cfg is missing the necessary Versioneer configuration. You need a section like: [versioneer] VCS = git style = pep440 versionfile_source = src/myproject/_version.py versionfile_build = myproject/_version.py tag_prefix = parentdir_prefix = myproject- You will also need to edit your setup.py to use the results: import versioneer setup(version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), ...) Please read the docstring in ./versioneer.py for configuration instructions, edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. """ SAMPLE_CONFIG = """ # See the docstring in versioneer.py for instructions. Note that you must # re-run 'versioneer.py setup' after changing this section, and commit the # resulting files. [versioneer] #VCS = git #style = pep440 #versionfile_source = #versionfile_build = #tag_prefix = #parentdir_prefix = """ INIT_PY_SNIPPET = """ from ._version import get_versions __version__ = get_versions()['version'] del get_versions """ def do_setup(): """Main VCS-independent setup function for installing Versioneer.""" root = get_root() try: cfg = get_config_from_root(root) except (EnvironmentError, configparser.NoSectionError, configparser.NoOptionError) as e: if isinstance(e, (EnvironmentError, configparser.NoSectionError)): print("Adding sample versioneer config to setup.cfg", file=sys.stderr) with open(os.path.join(root, "setup.cfg"), "a") as f: f.write(SAMPLE_CONFIG) print(CONFIG_ERROR, file=sys.stderr) return 1 print(" creating %s" % cfg.versionfile_source) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py") if os.path.exists(ipy): try: with open(ipy, "r") as f: old = f.read() except EnvironmentError: old = "" if INIT_PY_SNIPPET not in old: print(" appending to %s" % ipy) with open(ipy, "a") as f: f.write(INIT_PY_SNIPPET) else: print(" %s unmodified" % ipy) else: print(" %s doesn't exist, ok" % ipy) ipy = None # Make sure both the top-level "versioneer.py" and versionfile_source # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so # they'll be copied into source distributions. Pip won't be able to # install the package without this. manifest_in = os.path.join(root, "MANIFEST.in") simple_includes = set() try: with open(manifest_in, "r") as f: for line in f: if line.startswith("include "): for include in line.split()[1:]: simple_includes.add(include) except EnvironmentError: pass # That doesn't cover everything MANIFEST.in can do # (http://docs.python.org/2/distutils/sourcedist.html#commands), so # it might give some false negatives. Appending redundant 'include' # lines is safe, though. if "versioneer.py" not in simple_includes: print(" appending 'versioneer.py' to MANIFEST.in") with open(manifest_in, "a") as f: f.write("include versioneer.py\n") else: print(" 'versioneer.py' already in MANIFEST.in") if cfg.versionfile_source not in simple_includes: print(" appending versionfile_source ('%s') to MANIFEST.in" % cfg.versionfile_source) with open(manifest_in, "a") as f: f.write("include %s\n" % cfg.versionfile_source) else: print(" versionfile_source already in MANIFEST.in") # Make VCS-specific changes. For git, this means creating/changing # .gitattributes to mark _version.py for export-time keyword # substitution. do_vcs_install(manifest_in, cfg.versionfile_source, ipy) return 0 def scan_setup_py(): """Validate the contents of setup.py against Versioneer's expectations.""" found = set() setters = False errors = 0 with open("setup.py", "r") as f: for line in f.readlines(): if "import versioneer" in line: found.add("import") if "versioneer.get_cmdclass()" in line: found.add("cmdclass") if "versioneer.get_version()" in line: found.add("get_version") if "versioneer.VCS" in line: setters = True if "versioneer.versionfile_source" in line: setters = True if len(found) != 3: print("") print("Your setup.py appears to be missing some important items") print("(but I might be wrong). Please make sure it has something") print("roughly like the following:") print("") print(" import versioneer") print(" setup( version=versioneer.get_version(),") print(" cmdclass=versioneer.get_cmdclass(), ...)") print("") errors += 1 if setters: print("You should remove lines like 'versioneer.VCS = ' and") print("'versioneer.versionfile_source = ' . This configuration") print("now lives in setup.cfg, and should be removed from setup.py") print("") errors += 1 return errors if __name__ == "__main__": cmd = sys.argv[1] if cmd == "setup": errors = do_setup() errors += scan_setup_py() if errors: sys.exit(1)