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توضیحات

Data visualization toolchain based on aggregating into a grid

ویژگی	مقدار
سیستم عامل	-
نام فایل	datashader-0.9.0
نام	datashader
نسخه کتابخانه	0.9.0
نگهدارنده	['Datashader developers']
ایمیل نگهدارنده	['dev@datashader.org']
نویسنده	-
ایمیل نویسنده	-
آدرس صفحه اصلی	https://datashader.org
آدرس اینترنتی	https://pypi.org/project/datashader/
مجوز	New BSD

<img src="https://github.com/holoviz/datashader/raw/main/doc/_static/logo_stacked.png" data-canonical-src="https://github.com/holoviz/datashader/raw/main/doc/_static/logo_stacked.png" width="200"/><br> ----------------- # Turn even the largest data into images, accurately | | | | --- | --- | | Build Status | [![Build Status](https://github.com/holoviz/datashader/workflows/tests/badge.svg)](https://github.com/holoviz/datashader/actions?query=workflow%3Atests) | | Coverage | [![codecov](https://codecov.io/gh/holoviz/datashader/branch/main/graph/badge.svg)](https://codecov.io/gh/holoviz/datashader) | | Latest dev release | [![Github tag](https://img.shields.io/github/tag/holoviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/datashader/tags) [![dev-site](https://img.shields.io/website-up-down-green-red/https/pyviz-dev.github.io/datashader.svg?label=dev%20website)](https://pyviz-dev.github.io/datashader/) | | Latest release | [![Github release](https://img.shields.io/github/release/holoviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/datashader/releases) [![PyPI version](https://img.shields.io/pypi/v/datashader.svg?colorB=cc77dd)](https://pypi.python.org/pypi/datashader) [![datashader version](https://img.shields.io/conda/v/pyviz/datashader.svg?colorB=4488ff&style=flat)](https://anaconda.org/pyviz/datashader) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/datashader.svg?label=conda%7Cconda-forge&colorB=4488ff)](https://anaconda.org/conda-forge/datashader) [![defaults version](https://img.shields.io/conda/v/anaconda/datashader.svg?label=conda%7Cdefaults&style=flat&colorB=4488ff)](https://anaconda.org/anaconda/datashader) | | Python | [![Python support](https://img.shields.io/pypi/pyversions/datashader.svg)](https://pypi.org/project/datashader/) | Docs | [![gh-pages](https://img.shields.io/github/last-commit/holoviz/datashader/gh-pages.svg)](https://github.com/holoviz/datashader/tree/gh-pages) [![site](https://img.shields.io/website-up-down-green-red/http/datashader.org.svg)](http://datashader.org) | | Support | [![Discourse](https://img.shields.io/discourse/status?server=https%3A%2F%2Fdiscourse.holoviz.org)](https://discourse.holoviz.org/) | ------- [![History of OS GIS Timeline](examples/assets/images/featured-badge-gh.svg)](https://makepath.com/history-of-open-source-gis/) ------- ## What is it? Datashader is a data rasterization pipeline for automating the process of creating meaningful representations of large amounts of data. Datashader breaks the creation of images of data into 3 main steps: 1. Projection Each record is projected into zero or more bins of a nominal plotting grid shape, based on a specified glyph. 2. Aggregation Reductions are computed for each bin, compressing the potentially large dataset into a much smaller *aggregate* array. 3. Transformation These aggregates are then further processed, eventually creating an image. Using this very general pipeline, many interesting data visualizations can be created in a performant and scalable way. Datashader contains tools for easily creating these pipelines in a composable manner, using only a few lines of code. Datashader can be used on its own, but it is also designed to work as a pre-processing stage in a plotting library, allowing that library to work with much larger datasets than it would otherwise. ## Installation Datashader supports Python 3.7, 3.8, 3.9 and 3.10 on Linux, Windows, or Mac and can be installed with conda: conda install datashader or with pip: pip install datashader For the best performance, we recommend using conda so that you are sure to get numerical libraries optimized for your platform. The latest releases are avalailable on the pyviz channel `conda install -c pyviz datashader` and the latest pre-release versions are avalailable on the dev-labelled channel `conda install -c pyviz/label/dev datashader`. ## Fetching Examples Once you've installed datashader as above you can fetch the examples: datashader examples cd datashader-examples This will create a new directory called <span class="title-ref">datashader-examples</span> with all the data needed to run the examples. To run all the examples you will need some extra dependencies. If you installed datashader **within a conda environment**, with that environment active run: conda env update --file environment.yml Otherwise create a new environment: conda env create --name datashader --file environment.yml conda activate datashader ## Developer Instructions 1. Install Python 3 [miniconda](https://docs.conda.io/en/latest/miniconda.html) or [anaconda](https://www.anaconda.com/distribution/), if you don't already have it on your system. 2. Clone the datashader git repository if you do not already have it: git clone git://github.com/holoviz/datashader.git 3. Set up a new conda environment with all of the dependencies needed to run the examples: cd datashader conda env create --name datashader --file ./examples/environment.yml conda activate datashader 4. Put the datashader directory into the Python path in this environment: pip install --no-deps -e . ## Learning more After working through the examples, you can find additional resources linked from the [datashader documentation](http://datashader.org), including API documentation and papers and talks about the approach. ## Some Examples ![USA census](examples/assets/images/usa_census.jpg) ![NYC races](examples/assets/images/nyc_races.jpg) ![NYC taxi](examples/assets/images/nyc_pickups_vs_dropoffs.jpg)

نیازمندی

مقدار	نام
-	colorcet
-	dask
-	datashape
>=0.51	numba
-	numpy
-	pandas
-	param
-	pillow
-	pyct
-	requests
-	scipy
-	toolz
-	xarray
<3.0	bokeh
-	codecov
-	fastparquet
-	flake8
-	geopandas
-	graphviz
-	holoviews
-	matplotlib
-	nbconvert
-	nbformat
>=0.7.1	nbsite
>0.5	nbsmoke[verify]
-	netcdf4
-	networkx
-	numpydoc
<11	pyarrow
<0.9.0	pydata-sphinx-theme
-	pytest
-	pytest-benchmark
-	pytest-cov
-	python-graphviz
-	python-snappy
-	rasterio
-	rioxarray
-	scikit-image
-	snappy
-	spatialpandas
-	sphinx-copybutton
-	streamz
<3.0	bokeh
-	geopandas
-	holoviews
-	matplotlib
-	scikit-image
-	spatialpandas
-	networkx
-	streamz
-	fastparquet
-	graphviz
-	python-graphviz
-	python-snappy
-	rasterio
-	snappy
>=0.7.1	nbsite
-	numpydoc
<0.9.0	pydata-sphinx-theme
-	sphinx-copybutton
<3.0	bokeh
-	geopandas
-	holoviews
-	matplotlib
-	scikit-image
-	spatialpandas
<3.0	bokeh
-	geopandas
-	holoviews
-	matplotlib
-	scikit-image
-	spatialpandas
-	networkx
-	streamz
-	fastparquet
-	graphviz
-	python-graphviz
-	python-snappy
-	rasterio
-	snappy
-	cupy
-	cudf
-	dask-cudf
-	codecov
-	fastparquet
-	flake8
-	nbconvert
-	nbformat
>0.5	nbsmoke[verify]
-	netcdf4
<11	pyarrow
-	pytest
-	pytest-benchmark
-	pytest-cov
-	rasterio
-	rioxarray
-	spatialpandas

زبان مورد نیاز

مقدار	نام
>=3.7	Python

نحوه نصب

نصب پکیج whl datashader-0.9.0:

pip install datashader-0.9.0.whl

نصب پکیج tar.gz datashader-0.9.0:

pip install datashader-0.9.0.tar.gz