Shapely github

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Shapely github

GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Summary: Python package for manipulation and analysis of geometric objects in the Cartesian plane.

Installing shapely from the conda-forge channel can be achieved by adding conda-forge to your channels with:.

Once the conda-forge channel has been enabled, shapely can be installed with:. It is possible to list all of the versions of shapely available on your platform with:. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages.

Such a repository is known as a feedstock. A feedstock is made up of a conda recipe the instructions on what and how to build the package and the necessary configurations for automatic building using freely available continuous integration services. To manage the continuous integration and simplify feedstock maintenance conda-smithy has been developed. Using the conda-forge.

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For more information please check the conda-forge documentation. Its primary use is in the construction of the CI.

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If you would like to improve the shapely recipe or build a new package version, please fork this repository and submit a PR. Upon submission, your changes will be run on the appropriate platforms to give the reviewer an opportunity to confirm that the changes result in a successful build. Once merged, the recipe will be re-built and uploaded automatically to the conda-forge channel, whereupon the built conda packages will be available for everybody to install and use from the conda-forge channel.

Skip to content. A conda-smithy repository for shapely. BSDClause License. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Latest commit. Git stats commits 1 branch 0 tags. Failed to load latest commit information. View code. Terminology feedstock - the conda recipe raw materialsupporting scripts and CI configuration.

Feedstock Maintainers ocefpaf pelson phobson xylar. About A conda-smithy repository for shapely. Resources Readme. Releases No releases published. Sponsor this project. Contributors 8.This work is licensed under a Creative Commons Attribution 3. Deterministic spatial analysis is an important component of computational approaches to problems in agriculture, ecology, epidemiology, sociology, and many other fields.

Which properties in this town intersect with the year flood contour from this new flooding model? These are just a few of the possible questions addressable using non-statistical spatial analysis, and more specifically, computational geometry. Shapely is thereby deeply rooted in the conventions of the geographic information systems GIS world, but aspires to be equally useful to programmers working on non-conventional problems.

The second premise is that the persistence, serialization, and map projection of features are significant, but orthogonal problems. If you enjoy and profit from idiomatic Python, appreciate packages that do one thing well, and agree that a spatially enabled RDBMS is often enough the wrong tool for your computational geometry job, Shapely might be for you. The fundamental types of geometric objects implemented by Shapely are points, curves, and surfaces.

Each is associated with three sets of possibly infinite points in the plane. The interiorboundaryand exterior sets of a feature are mutually exclusive and their union coincides with the entire plane 2.

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A Point has an interior set of exactly one point, a boundary set of exactly no points, and an exterior set of all other points. A Point has a topological dimension of 0. A Curve has an interior set consisting of the infinitely many points along its length imagine a Point dragged in spacea boundary set consisting of its two end points, and an exterior set of all other points.

A Curve has a topological dimension of 1. A Surface has an interior set consisting of the infinitely many points within imagine a Curve dragged in space to cover an areaa boundary set consisting of one or more Curvesand an exterior set of all other points including those within holes that might exist in the surface.

A Surface has a topological dimension of 2. The point type is implemented by a Point class; curve by the LineString and LinearRing classes; and surface by a Polygon class.

Shapely implements no smooth i. All curves must be approximated by linear splines. All rounded patches must be approximated by regions bounded by linear splines. Collections of points are implemented by a MultiPoint class, collections of curves by a MultiLineString class, and collections of surfaces by a MultiPolygon class. A Y-shaped line feature, for example, is well modeled as a whole by a MultiLineString. The standard data model has additional constraints specific to certain types of geometric objects that will be discussed in following sections of this manual.

The spatial data model is accompanied by a group of natural language relationships between geometric objects — containsintersectsoverlapstouchesetc.

A comprehensive review of the relationships in terms of the DE-9IM is found in 4 and will not be reiterated in this manual.

Following the JTS technical specs 5this manual will make a distinction between constructive bufferconvex hull and set-theoretic operations intersectionunionetc. The individual operations will be fully described in a following section of the manual. This practice is as old as the tradition of accurate paper maps.

Shapely does not support coordinate system transformations. All operations on two or more features presume that the features exist in the same Cartesian plane. Geometric objects are created in the typical Python fashion, using the classes themselves as instance factories. A few of their intrinsic properties will be discussed in this sections, others in the following sections on operations and serializations.

Instances of PointLineStringand LinearRing have as their most important attribute a finite sequence of coordinates that determines their interior, boundary, and exterior point sets.

A line string can be determined by as few as 2 points, but contains an infinite number of points. Coordinate sequences are immutable. A third z coordinate value may be used when constructing instances, but has no effect on geometric analysis. All operations are performed in the x-y plane.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Shapely is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. Shapely is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are. For more details, see:. Shapely may be installed from a source distribution or one of several kinds of built distribution.

Built distributions are the only option for users who do not have or do not know how to use their platform's compiler and Python SDK, and a good option for users who would rather not bother. Shapely is available via system package management tools like apt, yum, and Homebrew, and is also provided by popular Python distributions like Canopy and Anaconda. If you use the Conda package manager to install Shapely, be sure to use the conda-forge channel. These can be installed using pip by specifying the entire URL.

If you want to build Shapely from source for compatibility with other modules that depend on GEOS such as cartopy or osgeo. If you've installed GEOS to a standard location, the geos-config program will be used to get compiler and linker options.

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Shapely does not read or write data files, but it can serialize and deserialize using several well known formats and protocols. The shapely. Dependencies for developing Shapely are listed in requirements-dev. Cython and Numpy are not required for production installations, only for development. Use of a virtual environment is strongly recommended.

Skip to content. Manipulation and analysis of geometric objects shapely. View license.

Shapely 1.7.0

Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master.I want to play, mimic, learn. Thanks to both of you! Both of these functions were very useful to me The original is much easier to read, and the vectorized version is much faster. That's definitely not the case.

Can we use this if the points are in 3D x,y,z? I am trying to calculate the volume after fitting a concave hull to the points.

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Thanks, this was really helpful! I would suggest adding a parameter for a default distance between coordinates, e. In my case the points were on average units apart and it was easier to adjust this parameter than to rescale the data. Skip to content. Instantly share code, notes, and snippets. Code Revisions 1 Stars 24 Forks 1.

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Learn more about clone URLs. Download ZIP. Sorry, something went wrong. This comment has been minimized. Sign in to view. Copy link Quote reply. Never mind.

I found the data and recreated the examples. Thank you! On to the "learn" part. Nice Job. Going to use this as a reference. Smaller numbers don't fall inward as much as larger numbers. Too large, and you lose everything! MultiPoint list points. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window. Reload to refresh your session.

You signed out in another tab or window.Shapely is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. Shapely is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are.

For more details, see:. Shapely GitHub repository. Shapely documentation and manual. Shapely may be installed from a source distribution or one of several kinds of built distribution.

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Shapely is available via system package management tools like apt, yum, and Homebrew, and is also provided by popular Python distributions like Canopy and Anaconda. If you use the Conda package manager to install Shapely, be sure to use the conda-forge channel.

These can be installed using pip by specifying the entire URL. If you want to build Shapely from source for compatibility with other modules that depend on GEOS such as cartopy or osgeo. Shapely does not read or write data files, but it can serialize and deserialize using several well known formats and protocols. The shapely. Dependencies for developing Shapely are listed in requirements-dev.

Cython and Numpy are not required for production installations, only for development. Use of a virtual environment is strongly recommended. Steve M. Taro Matsuzawa aka. Major portions of this work were supported by a grant for Pleiades from the U. Remove assertion on the number of version-like strings found in the GEOS version string.

It could be 2 or 3. Disallow sequences of MultiPolygons as arguments to the MultiPolygon constructor, resolving Polygons can now be constructed from sequences of point objects as well as sequences of x, y sequences The bounds property of an empty point object now returns an empty tuple, consistent with other geometry types Failure of ops.

A missing special case for rectangular polygons has been added to the polylabel algorithm The prepared geoemtry validation condition has been tightened in to fix the bug reported in Attempting to interpolate an empty geometry no longer results in a segmentation fault, raising ValueError instead A Python version check is made by the package setup script.

Shapely 1. An exception is raised when an attempt is made to prepare a PreparedGeometry Handle a TypeError that can occur when geometries are torn down Please note that asShape is likely to be deprecated in a future version of Shapely.

Check for length of LineString coordinates in speed mode, preventing crashes when using LineStrings with only one coordinate Splitting a linestring by one of its end points will now succeed instead of failing with a ValueErrorReleased: Jan 28, View statistics for this project via Libraries.

Tags geometry, topology, gis. Shapely is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. Shapely is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are.

shapely github

For more details, see:. Shapely may be installed from a source distribution or one of several kinds of built distribution. Shapely is available via system package management tools like apt, yum, and Homebrew, and is also provided by popular Python distributions like Canopy and Anaconda.

If you use the Conda package manager to install Shapely, be sure to use the conda-forge channel. These can be installed using pip by specifying the entire URL. If you want to build Shapely from source for compatibility with other modules that depend on GEOS such as cartopy or osgeo.

Shapely does not read or write data files, but it can serialize and deserialize using several well known formats and protocols. The shapely. Dependencies for developing Shapely are listed in requirements-dev. Cython and Numpy are not required for production installations, only for development. Use of a virtual environment is strongly recommended.

Major portions of this work were supported by a grant for Pleiades from the U. Shapely 1. Exceptions are consolidated in a shapely.

shapely github

Much of the work on 1. Shapely now vendorizes packaging to use during builds only and never again invokes the geos-config utility at run-time.

In addition to the changes listed under the alpha and beta pre-releases below, the following change has been made to the project:. Jan 28, Jan 13, Dec 31, Jun 22, Jul 29, Jul 18, Jan 24, Dec 9, Oct 31, Oct 26, Sep 1, Aug 22, A game theoretic approach to explain the output of any machine learning model. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions see papers for details and citations. Shap can be installed from either PyPI or conda-forge :. While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods see our Nature MI paper.

The above explanation shows features each contributing to push the model output from the base value the average model output over the training dataset we passed to the model output.

shapely github

Features pushing the prediction higher are shown in red, those pushing the prediction lower are in blue these force plots are introduced in our Nature BME paper. If we take many explanations such as the one shown above, rotate them 90 degrees, and then stack them horizontally, we can see explanations for an entire dataset in the notebook this plot is interactive :.

To understand how a single feature effects the output of the model we can plot the SHAP value of that feature vs. Since SHAP values represent a feature's responsibility for a change in the model output, the plot below represents the change in predicted house price as RM the average number of rooms per house in an area changes. Vertical dispersion at a single value of RM represents interaction effects with other features.

In this case coloring by RAD index of accessibility to radial highways highlights that the average number of rooms per house has less impact on home price for areas with a high RAD value.

To get an overview of which features are most important for a model we can plot the SHAP values of every feature for every sample. The plot below sorts features by the sum of SHAP value magnitudes over all samples, and uses SHAP values to show the distribution of the impacts each feature has on the model output.

The color represents the feature value red high, blue low. We can also just take the mean absolute value of the SHAP values for each feature to get a standard bar plot produces stacked bars for multi-class outputs :. The implementation here differs from the original DeepLIFT by using a distribution of background samples instead of a single reference value, and using Shapley equations to linearize components such as max, softmax, products, divisions, etc.

TensorFlow models and Keras models using the TensorFlow backend are supported there is also preliminary support for PyTorch :. The plot above explains ten outputs digits for four different images.

Red pixels increase the model's output while blue pixels decrease the output. The input images are shown on the left, and as nearly transparent grayscale backings behind each of the explanations. The sum of the SHAP values equals the difference between the expected model output averaged over the background dataset and the current model output. Note that for the 'zero' image the blank middle is important, while for the 'four' image the lack of a connection on top makes it a four instead of a nine.


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