{ "info": { "author": "Simone Murzilli; Guido Barbaglia", "author_email": "geobrickspy@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "Raster Correlation\n====================\n\nThe library provides an easy way correlate raster of the same size. It returns a json containing statistical outputs and frequencies information to be directly used with Highcharts JS or Matplotlib chart libraries.\n\n# Installation\n\n## Dependencies\n\nThe library has different dependencies (see also requirements.txt) click, watchdog, flask, flask-cors, numpy, scipy, pysal, brewer2mpl, rasterio, GeobricksCommon.\n\n## On Ubuntu\n\n```bash\nsudo add-apt-repository ppa:ubuntugis/ppa\nsudo apt-get update\nsudo apt-get install python-numpy libgdal1h gdal-bin libgdal-dev\n```\n\nIn case of compiling errors for numpy\n```bash\nsudo apt-get install libblas3gf libc6 libgcc1 libgfortran3 liblapack3gf libstdc++6 build-essential gfortran python-all-dev libatlas-base-dev python-dev\n```\n\nIn case of compiling errors for scipy\n```bash\nsudo apt-get install libblas-dev liblapack-dev\n```\n\n## Installation\n\nThe library is distributed through PyPi and can be installed by typing the following commands in the console:\n```\npip -r https://raw.githubusercontent.com/geobricks/geobricks_raster_correlation/master/requirements.txt\n\npip install GeobricksRasterCorrelation\n```\n\n**N.B.** Due to a well known PyPi issue it's not possible to install scipy and pysal through setup.py or requirements.txt \n\nIn order to install pysal run the following command\n```bash\npip install pysal\n```\n\n\n# Examples\n\n## Library usage\n\n```python\nfrom geobricks_raster_correlation.core.raster_correlation_core import get_correlation\n\nraster_path1 = \"path_to_raster1.tif\"\nraster_path2 = \"path_to_raster2.tif\"\n# Number of bins to be applied to the scatter chart\nbins = 300\ncorr = get_correlation(raster_path1, raster_path2, bins)\nprint corr\n```\n\n## Example with matplotlib\n\nThis example generate a correlation chart with matplotlib\n\n```python\nfrom geobricks_raster_correlation.core.raster_correlation_core import get_correlation\nfrom matplotlib import pyplot as plt\nfrom matplotlib.pylab import polyfit, polyval\n\n# input to your raster files\nraster_path1 = \"path_to_raster1.tif\"\nraster_path2 = \"path_to_raster2.tif\"\n\n# Number of sampling bins\nbins = 150\n\ncorr = get_correlation(raster_path1, raster_path2, bins)\nx = []\ny = []\ncolors = []\n# print corr['series']\nfor serie in corr['series']:\n colors.append(serie['color'])\n for data in serie['data']:\n x.append(data[0])\n y.append(data[1])\n\n# Adding regression line\n(m, b) = polyfit(x, y, 1)\nyp = polyval([m, b], x)\nplt.plot(x, yp)\n\n# plotting scatter\nplt.scatter(x, y, c=colors)\nplt.show()\n```\n\nThe returned json:\n \n * corr['stats'] contains the statistics: slope, p_value, std_err, intercept, r_value\n * corr['series'] contains the output series that can be used directly as an Highcharts input or with Matplotlib.", "description_content_type": null, "docs_url": null, "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": 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