Welcome to GeoRasters’s documentation!

georasters.get_geo_info(filename, band=1)

Gets information from a Raster data set

georasters.map_pixel(point_x, point_y, cellx, celly, xmin, ymax)

Usage:

map_pixel(xcoord, ycoord, x_cell_size, y_cell_size, xmin, ymax)

where:

xmin is leftmost X coordinate in system

ymax is topmost Y coordinate in system

Example:

raster = HMISea.tif

ndv, xsize, ysize, geot, projection, datatype = get_geo_info(raster)

row, col = map_pixel(x,y,geot[1],geot[-1], geot[0],geot[3])

georasters.map_pixel_inv(row, col, cellx, celly, xmin, ymax)

Usage:

map_pixel(xcoord, ycoord, x_cell_size, y_cell_size, xmin, ymax)

where:

xmin is leftmost X coordinate in system

ymax is topmost Y coordinate in system

Example:

raster = HMISea.tif

ndv, xsize, ysize, geot, projection, datatype = get_geo_info(raster)

row, col = map_pixel(x,y,geot[1],geot[-1], geot[0],geot[3])

georasters.aggregate(raster, ndv, block_size)

Aggregate raster to smaller resolution, by adding cells. Usage:

aggregate(raster, ndv, block_size)

where:

raster is a Numpy array created by importing the raster (e.g. geotiff)

ndv is the NoData Value for the raster (can be read using the get_geo_info function)

block_size is a duple of factors by which the raster will be shrinked

Example:

raster = HMISea.tif

ndv, xsize, ysize, geot, projection, datatype = get_geo_info(raster)

costs = load_tiff(raster)

costs2=aggregate(costs, ndv, (10,10))

georasters.create_geotiff(name, Array, driver, ndv, xsize, ysize, geot, projection, datatype, band=1)

Creates new geotiff from array

georasters.align_rasters(raster, alignraster, how=<numpy.ma.core._frommethod object>, cxsize=None, cysize=None, masked=False)

Align two rasters so that data overlaps by geographical location Usage: (alignedraster_o, alignedraster_a, geot_a) = AlignRasters(raster, alignraster, how=np.mean)

where:

raster: string with location of raster to be aligned

alignraster: string with location of raster to which raster will be aligned

how: function used to aggregate cells (if the rasters have different sizes)

It is assumed that both rasters have the same size

georasters.load_tiff(file)

Load a geotiff raster keeping ndv values using a masked array

Usage:

data = load_tiff(file)

georasters.union(rasters)

Union of rasters

Usage:

union(rasters)

where:

rasters is a list of GeoRaster objects

class georasters.GeoRaster(raster, geot, nodata_value=nan, fill_value=-10000000000.0, projection=None, datatype=None)

GeoRaster class to create and handle GIS rasters. Eash GeoRaster object is a numpy masked array + geotransfrom + nodata_value

Usage:

geo=GeoRaster(raster, geot, nodata_value=ndv)

where

raster: Numpy masked array with the raster data,

which could be loaded with the load_tiff(file)

geot: GDAL Geotransformation nodata_value: No data value in raster, optional

aggregate(block_size)

Returns copy of raster aggregated to smaller resolution, by adding cells.

align(geo2, how=np.mean)

Returns both georasters aligned and with the same pixelsize

apply(func, *args, **kwargs)

Returns the value of applying function func on the raster data

func: Python function *args: Arguments of function **kwargs: Additional arguments of function

argmax(axis=None, out=None)

Return indices of the maximum values along the given axis.

Refer to numpy.argmax for full documentation.

numpy.argmax : equivalent function

argmin(axis=None, out=None)

Return indices of the minimum values along the given axis of a.

Refer to numpy.argmin for detailed documentation.

numpy.argmin : equivalent function

block_reduce(block_size, how=func)

Returns copy of raster aggregated to smaller resolution, by adding cells. Default: func=np.ma.mean

clip(shp, keep=False, *args, **kwargs)

Clip raster using shape, where shape is either a GeoPandas DataFrame, shapefile, or some other geometry format used by python-raster-stats

Returns list of GeoRasters or Pandas DataFrame with GeoRasters and additional information

Usage:

clipped = geo.clip(shape, keep=False)

where: keep: Boolean (Default False), returns Georasters and Geometry information

copy()

Returns copy of itself

count(axis=None)

Count the non-masked elements of the array along the given axis.

distance(sources, destinations, x='x', y='y', isolation=True, export_raster=False, export_shape=False, routes=False, path='./')

Compute cost distance measured from each start point to all end points. The function returns the distances between the start point and the end points as a Pandas dataframe. Additionally, for each start point it computes the level of isolation, i.e. its average travel distance to all other locations

extract(x, y, radius=r)

Return subraster of raster geo around location (x,y) with radius r where (x,y) and r are in the same coordinate system as geo

flatten(order='C')

Return a copy of the array collapsed into one dimension.

order : {‘C’, ‘F’, ‘A’}, optional

Whether to flatten in C (row-major), Fortran (column-major) order, or preserve the C/Fortran ordering from a. The default is ‘C’.

gini()

Return computed Gini coefficient.

map_pixel(point_x, point_y)

Return value of raster in location Note: (point_x, point_y) must belong to the geographic coordinate system and the coverage of the raster

map_pixel_location(point_x, point_y)

geo.map_pixel(point_x, point_y)

Return value of raster in location

map_vector(x, **kvars)

Create new GeoRaster, which has its data replaced by x Useful to map output of PySal analyses, e.g. spatial autocorrelation values, etc.

Usage: raster2 = map_vector(x, raster) where

raster: GeoRaster x: Numpy array of data with same length as non-missing values in raster,

i.e., len(x) == np.sum(raster.mask==False)

max(axis=None, out=None)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

numpy.amax : equivalent function

mcp(*args, **kwargs)

Setup MCP_Geometric object from skimage for optimal travel time computations

mean(axis=None, dtype=None, out=None)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

numpy.mean : equivalent function

median(axis=None, out=None, overwrite_input=False)

axis : int, optional

Axis along which the medians are computed. The default (axis=None) is to compute the median along a flattened version of the array.

out : ndarray, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.

overwrite_input : bool, optional

If True, then allow use of memory of input array (a) for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. Note that, if overwrite_input is True and the input is not already an ndarray, an error will be raised.

merge(Georaster)

Returns merge of GeoRaster with another one

min(axis=None, out=None)

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

numpy.amin : equivalent function

plot()

Returns plot of raster data

prod(axis=None, dtype=None, out=None)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

numpy.prod : equivalent function

pysal_G(**kwargs)

Compute Getis and Ord’s G for GeoRaster

Usage: geo.pysal_G(permutations = 1000, rook=True)

arguments passed to raster_weights() and pysal.G See help(gr.raster_weights), help(pysal.G) for options

pysal_G_Local(star=False, **kwargs)

Compute Local G or G* measures of local spatial autocorrelation for GeoRaster

Usage: geo.pysal_Moran(permutations = 1000, rook=True)

arguments passed to raster_weights() and pysal.G_Local See help(gr.raster_weights), help(pysal.G_Local) for options

pysal_Gamma(**kwargs)

Compute Gamma Index of Spatial Autocorrelation for GeoRaster

Usage: geo.pysal_Gamma(permutations = 1000, rook=True, operation=’c’)

arguments passed to raster_weights() and pysal.Gamma See help(gr.raster_weights), help(pysal.Gamma) for options

pysal_Geary(**kwargs)

Compute Geary’s C for GeoRaster

Usage: geo.pysal_C(permutations = 1000, rook=True)

arguments passed to raster_weights() and pysal.Geary See help(gr.raster_weights), help(pysal.Geary) for options

pysal_Join_Counts(**kwargs)

Compute join count statistics for GeoRaster

Usage: geo.pysal_Join_Counts(permutations = 1000, rook=True)

arguments passed to raster_weights() and pysal.Join_Counts See help(gr.raster_weights), help(pysal.Join_Counts) for options

pysal_Moran(**kwargs)

Compute Moran’s I measure of global spatial autocorrelation for GeoRaster

Usage: geo.pysal_Moran(permutations = 1000, rook=True)

arguments passed to raster_weights() and pysal.Moran See help(gr.raster_weights), help(pysal.Moran) for options

pysal_Moran_Local(**kwargs)

Compute Local Moran’s I measure of local spatial autocorrelation for GeoRaster

Usage: geo.pysal_Moran_Local(permutations = 1000, rook=True)

arguments passed to raster_weights() and pysal.Moran_Local See help(gr.raster_weights), help(pysal.Moran_Local) for options

raster_weights(**kwargs)

Compute neighbor weights for GeoRaster. See help(gr.raster_weights) for options

Usage: geo.raster_weights(rook=True)

resize(new_shape, order=0, mode='constant', cval=np.nan, preserve_range=True)

Returns resized georaster

resize_old(block_size, order=0, mode='constant', cval=False)

geo.resize(new_shape, order=0, mode=’constant’, cval=np.nan, preserve_range=True)

Returns resized georaster

stats(shp, stats='mean', add_stats=None, raster_out=True, *args, **kwargs)

Compute raster statistics for a given geometry in shape, where shape is either a GeoPandas DataFrame, shapefile, or some other geometry format used by python-raster-stats. Runs python-raster-stats in background (additional help and info can be found there)

Returns dataframe with statistics and clipped raster

Usage:

df = geo.stats(shape, stats=stats, add_stats=add_stats)

where: raster_out: If True (Default), returns clipped Georasters

std(axis=None, dtype=None, out=None, ddof=0)

Returns the standard deviation of the array elements along given axis.

Refer to numpy.std for full documentation.

numpy.std : equivalent function

sum(axis=None, dtype=None, out=None)

Return the sum of the array elements over the given axis.

Refer to numpy.sum for full documentation.

numpy.sum : equivalent function

to_geopandas(**kwargs)

Convert GeoRaster to GeoPandas DataFrame, which can be easily exported to other types of files and used to do other types of operations. The DataFrame has the geometry (Polygon), row, col, value, x, and y values for each cell

to_pandas(**kwargs)

Convert GeoRaster to Pandas DataFrame, which can be easily exported to other types of files The DataFrame has the row, col, value, x, and y values for each cell

to_tiff(filename)

Saves GeoRaster as geotiff filename.tif with type datatype

If GeoRaster does not have datatype, then it tries to assign a type. You can assign the type yourself by setting

geo.datatype = ‘gdal.GDT_’+type

union(Georaster)

Returns union of GeoRaster with another one

var(axis=None, dtype=None, out=None, ddof=0)

Returns the variance of the array elements, along given axis.

Refer to numpy.var for full documentation.

numpy.var : equivalent function

exception georasters.RasterGeoError

exception georasters.RasterGeoTError

exception georasters.RasterGeoTWarning

georasters.merge(rasters)

Merges GeoRasters, same as union Usage:

merge(rasters)

where

rasters is a list of GeoRaster objects

georasters.from_file(filename, **kwargs)

Create a GeoRaster object from a file

georasters.to_pandas(raster, name='value')

Convert GeoRaster to Pandas DataFrame, which can be easily exported to other types of files The DataFrame has the row, col, value, x, and y values for each cell Usage:

df = gr.to_pandas(raster)

georasters.to_geopandas(raster, **kwargs)

Convert GeoRaster to GeoPandas DataFrame, which can be easily exported to other types of files and used to do other types of operations. The DataFrame has the geometry (Polygon), row, col, value, x, and y values for each cell Usage:

df = gr.to_geopandas(raster)

georasters.from_pandas(df, value='value', x='x', y='y', cellx=None, celly=None, xmin=None, ymax=None, geot=None, nodata_value=None, projection=None, datatype=None)

Creates a GeoRaster from a Pandas DataFrame. Useful to plot or export data to rasters. Usage:

raster = from_pandas(df, value=’value’, x=’x’, y=’y’, cellx= cellx, celly=celly,

xmin=xmin, ymax=ymax, geot=geot, nodata_value=ndv, projection=projection, datatype=datatype)

Although it does not require all the inputs, it is highly recommended to include the geographical information, so that the GeoRaster is properly constructed. As usual, the information can be added afterwards directly to the GeoRaster.

georasters.raster_weights(raster, rook=False, transform='r', **kwargs)

Construct PySal weights for rasters It drops weights for all cells that have no data or are Inf/NaN Usage:

w = raster_weights(raster, rook=False, transform=’r’, **kwargs)

where

raster: (Masked) Numpy array for which weights are to be constructed rook: Boolean, type of contiguity. Default is queen. For rook, rook = True **kwargs are defined by and passed to pysal.lat2W. See help(pysal.lat2W)

georasters.map_vector(x, raster, **kvars)

Create new GeoRaster, which has its data replaced by x Useful to map output of PySal analyses, e.g. spatial autocorrelation values, etc.

Usage: raster2 = map_vector(x, raster) where

raster: GeoRaster x: Numpy array of data with same length as non-missing values in raster,

i.e., len(x) == np.sum(raster.mask==False)

georasters.align_georasters(raster, alignraster, how=<function mean>, cxsize=None, cysize=None)

Align two rasters so that data overlaps by geographical location Usage: (alignedraster_o, alignedraster_a) = AlignRasters(raster, alignraster, how=np.mean) where

raster: string with location of raster to be aligned alignraster: string with location of raster to which raster will be aligned how: function used to aggregate cells (if the rasters have different sizes)

It is assumed that both rasters have the same size

Indices and tables

• Index
• Module Index
• Search Page