API Documentation

Raster Class

class spatialist.raster.Raster(filename, list_separate=True, timestamps=None)[source]

This is intended as a raster meta information handler with options for reading and writing raster data in a convenient manner by simplifying the numerous options provided by the GDAL python binding. Several methods are provided along with this class to directly modify the raster object in memory or directly write a newly created file to disk (without modifying the raster object itself). Upon initializing a Raster object, only metadata is loaded. The actual data can be, for example, loaded to memory by calling methods matrix() or load().

Parameters:

  • filename (str or list or osgeo.gdal.Dataset) – the raster file(s)/object to read

  • list_separate (bool) – treat a list of files as separate layers or otherwise as a single layer? The former is intended for single layers of a stack, the latter for tiles of a mosaic.

  • timestamps (list[str] or function or None) – the time information for each layer or a function converting band names to a datetime.datetime object

__getitem__(index)[source]

subset the object by slices or vector geometry. If slices are provided, one slice for each raster dimension needs to be defined. I.e., if the raster object contains several image bands, three slices are necessary. Integer slices are treated as pixel coordinates and float slices as map coordinates. If a Vector geometry is defined, it is internally projected to the raster CRS if necessary, its extent derived, and the extent converted to raster pixel slices, which are then used for subsetting.

Parameters:

index (tuple of slice or Vector) – the subsetting indices to be used

Returns:

a new raster object referenced through an in-memory GDAL VRT file

Return type:

Raster

Examples

>>> filename = 'test'
>>> with Raster(filename) as ras:
>>>     print(ras)
class      : spatialist Raster object
dimensions : 2908, 2069, 115 (rows, cols, bands)
resolution : 20.0, -20.0 (x, y)
extent     : 713315.198, 754695.198, 4068985.595, 4127145.595 (xmin, xmax, ymin, ymax)
coord. ref.: +proj=utm +zone=29 +datum=WGS84 +units=m +no_defs
data source: test
>>>
>>>
>>> xmin = 0
>>> xmax = 100
>>> ymin = 4068985.595
>>> ymax = 4088985.595
>>> with Raster(filename)[ymin:ymax, xmin:xmax, :] as ras:
>>>     print(ras)
class      : spatialist Raster object
dimensions : 1000, 100, 115 (rows, cols, bands)
resolution : 20.0, -20.0 (x, y)
extent     : 713315.198, 715315.198, 4068985.595, 4088985.595 (xmin, xmax, ymin, ymax)
coord. ref.: +proj=utm +zone=29 +datum=WGS84 +units=m +no_defs
data source: /tmp/tmpk5weyhhq.vrt
>>>
>>>
>>> ext = {'xmin': 713315.198, 'xmax': 715315.198, 'ymin': ymin, 'ymax': ymax}
>>>
>>> with bbox(ext, crs=32629) as vec:
>>>     with Raster(filename)[vec] as ras:
>>>         print(ras)
class      : spatialist Raster object
dimensions : 1000, 100, 115 (rows, cols, bands)
resolution : 20.0, -20.0 (x, y)
extent     : 713315.198, 715315.198, 4068985.595, 4088985.595 (xmin, xmax, ymin, ymax)
coord. ref.: +proj=utm +zone=29 +datum=WGS84 +units=m +no_defs
data source: /tmp/tmps4rc9o09.vrt
allstats(approximate=False)[source]

Compute some basic raster statistics

Parameters:

approximate (bool) – approximate statistics from overviews or a subset of all tiles?

Returns:

a list with a dictionary of statistics for each band. Keys: min, max, mean, sdev. See osgeo.gdal.Band.ComputeStatistics().

Return type:

list[dict]

array()[source]

read all raster bands into a numpy ndarray

Returns:

the array containing all raster data

Return type:

numpy.ndarray

assign(array, band)[source]

assign an array to an existing Raster object

Parameters:

  • array (numpy.ndarray) – the array to be assigned to the Raster object

  • band (int) – the index of the band to assign to

property bandnames
Returns:

the names of the bands

Return type:

list

property bands
Returns:

the number of image bands

Return type:

int

bbox(outname=None, driver=None, overwrite=True, source='image')[source]
Parameters:

  • outname (str or None) – the name of the file to write; If None, the bounding box is returned as Vector object

  • driver (str or None) – The file format to write. None: auto-detect from filename extension.

  • overwrite (bool) – overwrite an already existing file?

  • source (str) – get the bounding box of either the image (‘image’) or the ground control points (‘gcp’).

Returns:

the bounding box vector object if outname is not None and None otherwise.

Return type:

Vector or None

See also

spatialist.vector.bbox()

close()[source]

closes the GDAL raster file connection

property cols
Returns:

the number of image columns

Return type:

int

coord_img2map(x=None, y=None)[source]

convert image pixel coordinates to map coordinates in the raster CRS. Either x, y or both must be defined.

Parameters:
Returns:

the converted coordinate for either x, y or both

Return type:

float or tuple

coord_map2img(x=None, y=None)[source]

convert map coordinates in the raster CRS to image pixel coordinates. Either x, y or both must be defined.

Parameters:
Returns:

the converted coordinate for either x, y or both

Return type:

int or tuple

property dim
Returns:

(rows, columns, bands)

Return type:

tuple

property driver
Returns:

a GDAL raster driver object.

Return type:

osgeo.gdal.Driver

property dtype
Returns:

the data type description; e.g. Float32

Return type:

str

property epsg
Returns:

the CRS EPSG code

Return type:

int

property extent
Returns:

the extent of the image

Return type:

dict

extract(px, py, radius=1, nodata=None)[source]

extract weighted average of pixels intersecting with a defined radius to a point.

Parameters:

  • px (int or float) – the x coordinate in units of the Raster SRS

  • py (int or float) – the y coordinate in units of the Raster SRS

  • radius (int or float) – the radius around the point to extract pixel values from; defined as multiples of the pixel resolution

  • nodata (int) – a value to ignore from the computations; If None, the nodata value of the Raster object is used

Returns:

the the weighted average of all pixels within the defined radius

Return type:

int or float

property files
Returns:

a list of all absolute names of files associated with this raster data set

Return type:

list of str

property format
Returns:

the name of the image format

Return type:

str

property geo

General image geo information.

Returns:

a dictionary with keys xmin, xmax, xres, rotation_x, ymin, ymax, yres, rotation_y

Return type:

dict

property geogcs
Returns:

an identifier of the geographic coordinate system

Return type:

str or None

is_valid()[source]

Check image integrity. Tries to compute the checksum for each raster layer and returns False if this fails. See this forum entry: How to check if image is valid?.

Returns:

is the file valid?

Return type:

bool

layers()[source]
Returns:

a list containing a osgeo.gdal.Band object for each image band

Return type:

list[osgeo.gdal.Band]

load()[source]

load all raster data to internal memory arrays. This shortens the read time of other methods like matrix().

matrix(band=1, mask_nan=True)[source]

read a raster band (subset) into a numpy ndarray

Parameters:

  • band (int) – the band to read the matrix from; 1-based indexing

  • mask_nan (bool) – convert nodata values to numpy.nan? As numpy.nan requires at least float values, any integer array is cast to float32.

Returns:

the matrix (subset) of the selected band

Return type:

numpy.ndarray

property nodata
Returns:

the raster nodata value(s)

Return type:

float or list

property proj4
Returns:

the CRS PROJ4 description

Return type:

str

property proj4args
Returns:

the PROJ4 string arguments as a dictionary

Return type:

dict

property projcs
Returns:

an identifier of the projected coordinate system; If the CRS is not projected None is returned

Return type:

str or None

property projection
Returns:

the CRS Well Known Text (WKT) description

Return type:

str

property res

the raster resolution in x and y direction

Returns:

(xres, yres)

Return type:

tuple

rescale(fun)[source]

perform raster computations with custom functions and assign them to the existing raster object in memory

Parameters:

fun (function) – the custom function to compute on the data

Examples

>>> with Raster('filename') as ras:
>>>     ras.rescale(lambda x: 10 * x)
property rows
Returns:

the number of image rows

Return type:

int

property srs
Returns:

the spatial reference system of the data set.

Return type:

osgeo.osr.SpatialReference

write(outname, dtype='default', format='GTiff', nodata='default', overwrite=False, cmap=None, update=False, xoff=0, yoff=0, array=None, options=None, overviews=None, overview_resampling='AVERAGE')[source]

write the raster object to a file.

Parameters:

  • outname (str) – the file to be written

  • dtype (str) – the data type of the written file; data type notations of GDAL (e.g. Float32) and numpy (e.g. int8) are supported.

  • format (str) – the file format; e.g. ‘GTiff’

  • nodata (int or float) – the nodata value to write to the file

  • overwrite (bool) – overwrite an already existing file? Only applies if update is False.

  • cmap (osgeo.gdal.ColorTable) – a color map to apply to each band. Can for example be created with function cmap_mpl2gdal().

  • update (bool) – open the output file for update or only for writing?

  • xoff (int) – the x/column offset

  • yoff (int) – the y/row offset

  • array (numpy.ndarray) – write different data than that associated with the Raster object

  • options (list[str] or None) – a list of options for creating the output dataset via osgeo.gdal.Driver.Create(). For drivers GTiff and COG, TIFF tags can also be defined, which are then written to the file using osgeo.gdal.MajorObject.SetMetadataItem(). For example TIFFTAG_SOFTWARE=spatialist.

  • overviews (list[int] or None) – a list of integer overview levels to be created; see osgeo.gdal.Dataset.BuildOverviews().

  • overview_resampling (str) – the resampling to use for creating the overviews

Raster Tools

apply_along_time

Apply a time series computation to a 3D raster stack using multiple CPUs.

png

convert a raster image to png.

rasterize

rasterize a vector object

stack

function for mosaicking, resampling and stacking of multiple raster files

subset_tolerance

this parameter can be set to increase the pixel tolerance in percent when subsetting Raster objects with the extent of other spatial objects.

Dtype

Convert between different data type representations.
class spatialist.raster.Dtype(dtype)[source]

Convert between different data type representations. After initialization, other representations can be obtained from the object attributes gdalint, gdalstr and numpystr.

Parameters:

dtype (int or str) –

the input data type. Currently supported:

  • GDAL integer, e.g. 1 as obtained from from osgeo.gdalconst import GDT_Byte

  • GDAL string, e.g. ‘Byte’

  • numpy string, e.g. ‘uint8’

Examples

>>> from spatialist.raster import Dtype
>>> print(Dtype('Byte').numpystr)
'uint8'
property gdalint2gdalstr
property gdalint2numpystr
property gdalstr2gdalint
property numpy2gdalint

create a dictionary for mapping numpy data types to GDAL data type codes

Returns:

the type map

Return type:

dict

spatialist.raster.apply_along_time(src, dst, func1d, nodata, format, cmap=None, maxlines=None, cores=8, *args, **kwargs)[source]

Apply a time series computation to a 3D raster stack using multiple CPUs. The stack is read in chunks of maxlines x columns x time steps, for which the result is computed and stored in a 2D output array. After finishing the computation for all chunks, the output array is written to the specified file.

Notes

It is intended to directly write the computation result of each chunk to the output file respectively so that no unnecessary memory is used for storing the complete result. This however first requires some restructuring of the method spatialist.Raster.write().

Parameters:

  • src (Raster) – the source raster data

  • dst (str) – the output file in GeoTiff format

  • func1d (function) – the function to be applied over a single time series 1D array

  • nodata (int) – the nodata value to write to the output file

  • format (str) – the output file format, e.g. ‘GTiff’

  • cmap (gdal.ColorTable) – a color table to write to the resulting file; see spatialist.auxil.cmap_mpl2gdal() for creation options.

  • maxlines (int) – the maximum number of lines to read at once. Controls the amount of memory used.

  • cores (int) – the number of parallel cores

  • args (any) – Additional arguments to func1d.

  • kwargs (any) – Additional named arguments to func1d.

See also

spatialist.ancillary.parallel_apply_along_axis()

spatialist.raster.png(src, dst, percent=10, scale=(2, 98), vmin=None, vmax=None, worldfile=False, nodata=None)[source]

convert a raster image to png. The input raster must either have one or three bands to create a grey scale or RGB image respectively.

Parameters:

  • src (Raster) – the input raster image to be converted

  • dst (str) – the output png file name

  • percent (int) – the size of the png relative to src

  • scale (tuple or None) – the percentile bounds as (min, max) for scaling the values of the input image or None to not apply any scaling. Overridden by vmin and vmax if both are not None.

  • vmin (int or float or None) – the minimum value used for image scaling.

  • vmax (int or float or None) – the maximum value used for image scaling.

  • worldfile (bool) – create a world file (extension .wld)?

  • nodata (int or None) – The no data value to write to the file. All pixels with this value will be transparent.

Notes

Currently it is not possible to control what happens with values outside of the percentile range defined by scale. Therefore, if e.g. nodata is set to 0, all values below the lower percentile will be marked as 0 and will thus be transparent in the image. On the other hand if nodata is 255, all values higher than the upper percentile will be transparent. This is addressed in GDAL issue #1825.

Examples

>>> from spatialist.raster import Raster, png
>>> src = 'src.tif'
>>> dst = 'dst.png'
>>> with Raster(src) as ras:
>>>     png(src=ras, dst=dst, percent=10, scale=(2, 98), worldfile=True)
spatialist.raster.rasterize(vectorobject, reference, outname=None, burn_values=1, expressions=None, nodata=0, append=False)[source]

rasterize a vector object

Parameters:

  • vectorobject (Vector) – the vector object to be rasterized

  • reference (Raster) – a reference Raster object to retrieve geo information and extent from

  • outname (str or None) – the name of the GeoTiff output file; if None, an in-memory object of type Raster is returned and parameter outname is ignored

  • burn_values (int or list) – the values to be written to the raster file

  • expressions (list) – SQL expressions to filter the vector object by attributes

  • nodata (int or float or None) – the nodata value of the target raster file

  • append (bool) – if the output file already exists, update this file with new rasterized values? If True and the output file exists, parameters reference and nodata are ignored.

Returns:

if outname is None, a raster object pointing to an in-memory dataset else None

Return type:

Raster or None

Example

>>> from spatialist import Vector, Raster, rasterize
>>> outname1 = 'target1.tif'
>>> outname2 = 'target2.tif'
>>> with Vector('source.shp') as vec:
>>>     with Raster('reference.tif') as ref:
>>>         burn_values = [1, 2]
>>>         expressions = ['ATTRIBUTE=1', 'ATTRIBUTE=2']
>>>         rasterize(vec, reference, outname1, burn_values, expressions)
>>>         expressions = ["ATTRIBUTE2='a'", "ATTRIBUTE2='b'"]
>>>         rasterize(vec, reference, outname2, burn_values, expressions)
spatialist.raster.stack(srcfiles, dstfile, resampling, targetres, dstnodata, srcnodata=None, shapefile=None, layernames=None, sortfun=None, separate=False, overwrite=False, compress=True, cores=4, pbar=False)[source]

function for mosaicking, resampling and stacking of multiple raster files

Parameters:

  • srcfiles (list) – a list of file names or a list of lists; each sub-list is treated as a task to mosaic its containing files

  • dstfile (str) – the destination file or a directory (if separate is True)

  • resampling ({near, bilinear, cubic, cubicspline, lanczos, average, mode, max, min, med, Q1, Q3}) – the resampling method; see documentation of gdalwarp.

  • targetres (tuple or list) – two entries for x and y spatial resolution in units of the source CRS

  • srcnodata (int, float or None) – the nodata value of the source files; if left at the default (None), the nodata values are read from the files

  • dstnodata (int or float) – the nodata value of the destination file(s)

  • shapefile (str, Vector or None) – a shapefile for defining the spatial extent of the destination files

  • layernames (list) – the names of the output layers; if None, the basenames of the input files are used; overrides sortfun

  • sortfun (function) – a function for sorting the input files; not used if layernames is not None. This is first used for sorting the items in each sub-list of srcfiles; the basename of the first item in a sub-list will then be used as the name for the mosaic of this group. After mosaicing, the function is again used for sorting the names in the final output (only relevant if separate is False)

  • separate (bool) – should the files be written to a single raster stack (ENVI format) or separate files (GTiff format)?

  • overwrite (bool) – overwrite the file if it already exists?

  • compress (bool) – compress the geotiff files?

  • cores (int) – the number of CPU threads to use

  • pbar (bool) – add a progressbar? This is currently only used if separate==False

Raises:

RuntimeError –

Notes

This function does not reproject any raster files. Thus, the CRS must be the same for all input raster files. This is checked prior to executing gdalwarp. In case a shapefile is defined, it is internally reprojected to the raster CRS prior to retrieving its extent.

Examples

from pyroSAR.ancillary import groupbyTime, find_datasets, seconds
from spatialist.raster import stack

# find pyroSAR files by metadata attributes
archive_s1 = '/.../sentinel1/GRD/processed'
scenes_s1 = find_datasets(archive_s1, sensor=('S1A', 'S1B'), acquisition_mode='IW')

# group images by acquisition time
groups = groupbyTime(images=scenes_s1, function=seconds, time=30)

# mosaic individual groups and stack the mosaics to a single ENVI file
# only files overlapping with the shapefile are selected and resampled to its extent
stack(srcfiles=groups, dstfile='stack', resampling='bilinear', targetres=(20, 20),
      srcnodata=-99, dstnodata=-99, shapefile='site.shp', separate=False)
spatialist.raster.subset_tolerance = 0

this parameter can be set to increase the pixel tolerance in percent when subsetting Raster objects with the extent of other spatial objects.

Examples

Coordinates are in EPSG:32632, pixel resolution of the image to be subsetted is 90 m:
(subsetting extent)
{‘xmin’: 534093.341, ‘xmax’: 830103.341, ‘ymin’: 5030609.645, ‘ymax’: 5250929.645}
subset_tolerance = 0
{‘xmin’: 534003.341, ‘xmax’: 830103.341, ‘ymin’: 5030519.645, ‘ymax’: 5250929.645}
subset_tolerance = 0.02
{‘xmin’: 534093.341, ‘xmax’: 830103.341, ‘ymin’: 5030609.645, ‘ymax’: 5250929.645}

Vector Class

class spatialist.vector.Vector(filename=None, driver=None)[source]

This is intended as a vector meta information handler with options for reading and writing vector data in a convenient manner by simplifying the numerous options provided by the OGR python binding.

Parameters:

  • filename (str or None) –

    the vector file to read; if filename is None, a new in-memory Vector object is created. In this case driver is overridden and set to ‘Memory’. The following file extensions are auto-detected:

    • .geojson (GeoJSON)

    • .gpkg (GPKG)

    • .kml (KML)

    • .shp (ESRI Shapefile)

  • driver (str) – the vector file format; needs to be defined if the format cannot be auto-detected from the filename extension

__getitem__(expression)[source]

subset the vector object by index or attribute.

Parameters:

expression (int or str) – the key or expression to be used for subsetting. See osgeo.ogr.Layer.SetAttributeFilter() for details on the expression syntax.

Returns:

a vector object matching the specified criteria

Return type:

Vector

Examples

Assuming we have a shapefile called testsites.shp, which has an attribute sitename, we can subset individual sites and write them to new files like so:

>>> from spatialist import Vector
>>> filename = 'testsites.shp'
>>> with Vector(filename)["sitename='site1'"] as site1:
>>>     site1.write('site1.shp')
addfeature(geometry, fields=None)[source]

add a feature to the vector object from a geometry

Parameters:

  • geometry (osgeo.ogr.Geometry) – the geometry to add as a feature

  • fields (dict or None) – the field names and values to assign to the new feature

addfield(name, type, width=10)[source]

add a field to the vector layer

Parameters:

  • name (str) – the field name

  • type (int) – the OGR Field Type (OFT), e.g. ogr.OFTString. See Module ogr.

  • width (int) – the width of the new field (only for ogr.OFTString fields)

addlayer(name, srs, geomType)[source]

add a layer to the vector layer

Parameters:
addvector(vec)[source]

add a vector object to the layer of the current Vector object

Parameters:

  • vec (Vector) – the vector object to add

  • merge (bool) – merge overlapping polygons?

bbox(outname=None, driver=None, overwrite=True)[source]

create a bounding box from the extent of the Vector object

Parameters:

  • outname (str or None) – the name of the vector file to be written; if None, a Vector object is returned

  • driver (str) – the name of the file format to write

  • overwrite (bool) – overwrite an already existing file?

Returns:

if outname is None, the bounding box Vector object

Return type:

Vector or None

close()[source]

closes the OGR vector file connection

convert2wkt(set3D=True)[source]

export the geometry of each feature as a wkt string

Parameters:

set3D (bool) – keep the third (height) dimension?

property extent

the extent of the vector object

Returns:

a dictionary with keys xmin, xmax, ymin, ymax

Return type:

dict

property fieldDefs
Returns:

the field definition for each field of the Vector object

Return type:

list[osgeo.ogr.FieldDefn]

property fieldnames
Returns:

the names of the fields

Return type:

list of str

property geomType
Returns:

the layer geometry type

Return type:

int

property geomTypes
Returns:

the geometry type of each feature

Return type:

list

getArea()[source]
Returns:

the area of the vector geometries

Return type:

float

getFeatureByAttribute(fieldname, attribute)[source]

get features by field attribute

Parameters:

  • fieldname (str) – the name of the queried field

  • attribute (int or str) – the field value of interest

Returns:

the feature(s) matching the search query

Return type:

list[osgeo.ogr.Feature] or osgeo.ogr.Feature

getFeatureByIndex(index)[source]

get features by numerical (positional) index

Parameters:

index (int) – the queried index

Returns:

the requested feature

Return type:

osgeo.ogr.Feature

getProjection(type)[source]

get the CRS of the Vector object. See spatialist.auxil.crsConvert().

Parameters:

type (str) – the type of projection required.

Returns:

the output CRS

Return type:

int or str or osgeo.osr.SpatialReference

getUniqueAttributes(fieldname)[source]
Parameters:

fieldname (str) – the name of the field of interest

Returns:

the unique attributes of the field

Return type:

list of str or int

getfeatures()[source]
Returns:

a list of cloned features

Return type:

list[osgeo.ogr.Feature]

init_features()[source]

delete all in-memory features

init_layer()[source]

initialize a layer object

property layerdef
Returns:

the layer’s feature definition

Return type:

osgeo.ogr.FeatureDefn

property layername
Returns:

the name of the layer

Return type:

str

load()[source]

load all feature into memory

property nfeatures
Returns:

the number of features

Return type:

int

property nfields
Returns:

the number of fields

Return type:

int

property nlayers
Returns:

the number of layers

Return type:

int

property proj4
Returns:

the CRS in PRO4 format

Return type:

str

reproject(projection)[source]

in-memory reprojection

Parameters:

projection (int or str or osgeo.osr.SpatialReference) – the target CRS. See spatialist.auxil.crsConvert().

setCRS(crs)[source]

directly reset the spatial reference system of the vector object. This is not going to reproject the Vector object, see reproject() instead.

Parameters:

crs (int or str or osgeo.osr.SpatialReference) – the input CRS

Example

>>> site = Vector('shape.shp')
>>> site.setCRS('+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs ')
property srs
Returns:

the geometry’s spatial reference system

Return type:

osgeo.osr.SpatialReference

write(outfile, driver=None, overwrite=True)[source]

write the Vector object to a file

Parameters:

  • outfile –

    the name of the file to write; the following extensions are automatically detected for determining the format driver:

    • .geojson (GeoJSON)

    • .gpkg (GPKG)

    • .kml (KML)

    • .shp (ESRI Shapefile)

  • driver (str) – the output file format; needs to be defined if the format cannot be auto-detected from the filename extension

  • overwrite (bool) – overwrite an already existing file?

Vector Tools

bbox

create a bounding box vector object or file.

boundary

Get the boundary of the largest geometry as new vector object.

feature2vector

create a Vector object from ogr features

intersect

intersect two Vector objects

vectorize

Vectorization of an array using osgeo.gdal.Polygonize().

wkt2vector

convert a well-known text string geometry to a Vector object
spatialist.vector.bbox(coordinates, crs, outname=None, driver=None, overwrite=True)[source]

create a bounding box vector object or file. The CRS can be in either WKT, EPSG or PROJ4 format

Parameters:

  • coordinates (dict) – a dictionary containing numerical variables with keys xmin, xmax, ymin and ymax

  • crs (int or str or osgeo.osr.SpatialReference) – the coordinate reference system of the coordinates. See crsConvert() for options.

  • outname (str) – the file to write to. If None, the bounding box is returned as Vector object

  • driver (str) –

    the output file format; needs to be defined if the format cannot

    be auto-detected from the filename extension

  • overwrite (bool) – overwrite an existing file?

Returns:

the bounding box Vector object

Return type:

Vector or None

spatialist.vector.boundary(vectorobject, expression=None, outname=None)[source]

Get the boundary of the largest geometry as new vector object. The following steps are performed:

  • find the largest geometry matching the expression

  • compute the geometry’s boundary using osgeo.ogr.Geometry.Boundary(), returning a MULTILINE geometry

  • select the longest line of the MULTILINE geometry

  • create a closed linear ring from this longest line

  • create a polygon from this linear ring

  • create a new Vector object and add the newly created polygon

Parameters:

  • vectorobject (Vector) – the vector object containing multiple polygon geometries, e.g. all geometries with a certain value in one field.

  • expression (str or None) – the SQL expression to select the candidates for the largest geometry.

  • outname (str or None) – the name of the output vector file; if None, an in-memory object of type Vector is returned.

Returns:

if outname is None, a vector object pointing to an in-memory dataset else None

Return type:

Vector or None

spatialist.vector.feature2vector(feature, ref, layername=None)[source]

create a Vector object from ogr features

Parameters:

  • feature (list[osgeo.ogr.Feature] or osgeo.ogr.Feature) – a single feature or a list of features

  • ref (Vector) – a reference Vector object to retrieve geo information from

  • layername (str or None) – the name of the output layer; retrieved from ref if None

Returns:

the new Vector object

Return type:

Vector

spatialist.vector.intersect(obj1, obj2)[source]

intersect two Vector objects

Parameters:

  • obj1 (Vector) – the first vector object; this object is reprojected to the CRS of obj2 if necessary

  • obj2 (Vector) – the second vector object

Returns:

the intersection of obj1 and obj2 if both intersect and None otherwise

Return type:

Vector or None

spatialist.vector.vectorize(target, reference, outname=None, layername='layer', fieldname='value', driver=None)[source]

Vectorization of an array using osgeo.gdal.Polygonize().

Parameters:

  • target (numpy.ndarray) – the input array. Each identified object of pixels with the same value will be converted into a vector feature.

  • outname (str or None) – the name of the vector file. If None a vector object is returned.

  • reference (Raster) – a reference Raster object to retrieve geo information and extent from.

  • layername (str) – the name of the vector object layer.

  • fieldname (str) – the name of the field to contain the raster value for the respective vector feature.

  • driver (str or None) – the vector file type of outname. Several extensions are read automatically (see Vector.write()). Is ignored if outname=None.

spatialist.vector.wkt2vector(wkt, srs, layername='wkt')[source]

convert a well-known text string geometry to a Vector object

Parameters:
Returns:

the vector representation

Return type:

Vector

Examples

>>> from spatialist.vector import wkt2vector
>>> wkt = 'POLYGON ((0. 0., 0. 1., 1. 1., 1. 0., 0. 0.))'
>>> with wkt2vector(wkt, srs=4326) as vec:
>>>     print(vec.getArea())
1.0

General Spatial Tools

cmap_mpl2gdal

convert a matplotlib color table to a GDAL representation.

coordinate_reproject

reproject a coordinate from one CRS to another

crsConvert

convert between different types of spatial reference representations

gdalbuildvrt

a simple wrapper for osgeo.gdal.BuildVRT()

gdal_rasterize

a simple wrapper for osgeo.gdal.Rasterize()

gdal_translate

a simple wrapper for osgeo.gdal.Translate()

gdalwarp

a simple wrapper for osgeo.gdal.Warp()

haversine

compute the distance in meters between two points in latlon

ogr2ogr

a simple wrapper for osgeo.gdal.VectorTranslate() aka ogr2ogr

utm_autodetect

get the UTM CRS for a spatial object
spatialist.auxil.cmap_mpl2gdal(mplcolor, values)[source]

convert a matplotlib color table to a GDAL representation.

Parameters:

  • mplcolor (str) – a color table code

  • values (list[int] or range) – the integer data values for which to retrieve colors

Returns:

the color table in GDAL format

Return type:

osgeo.gdal.ColorTable

Note

This function is currently only developed for handling discrete integer data values in an 8 Bit file. Colors are thus scaled between 0 and 255.

Examples

>>> from osgeo import gdal
>>> from spatialist.auxil import cmap_mpl2gdal
>>> values = list(range(0, 100))
>>> cmap = cmap_mpl2gdal(mplcolor='YlGnBu', values=values)
>>> print(isinstance(cmap, gdal.ColorTable))
True
spatialist.auxil.coordinate_reproject(x, y, s_crs, t_crs)[source]

reproject a coordinate from one CRS to another

Parameters:
Return type:

tuple

spatialist.auxil.crsConvert(crsIn, crsOut, wkt_format='DEFAULT')[source]

convert between different types of spatial reference representations

Parameters:
Returns:

the output CRS

Return type:

int or str or osgeo.osr.SpatialReference

Examples

convert an integer EPSG code to PROJ.4:

>>> crsConvert(4326, 'proj4')
'+proj=longlat +datum=WGS84 +no_defs '

convert the opengis URL back to EPSG:

>>> crsConvert('https://www.opengis.net/def/crs/EPSG/0/4326', 'epsg')
4326

convert an EPSG compound CRS (WGS84 horizontal + EGM96 vertical) to PROJ.4

>>> crsConvert('EPSG:4326+5773', 'proj4')
'+proj=longlat +datum=WGS84 +geoidgrids=us_nga_egm96_15.tif +vunits=m +no_defs'
spatialist.auxil.gdal_rasterize(src, dst, **kwargs)[source]

a simple wrapper for osgeo.gdal.Rasterize()

Parameters:
spatialist.auxil.gdal_translate(src, dst, **kwargs)[source]

a simple wrapper for osgeo.gdal.Translate()

Parameters:
spatialist.auxil.gdalbuildvrt(src, dst, void=True, **kwargs)[source]

a simple wrapper for osgeo.gdal.BuildVRT()

Parameters:
spatialist.auxil.gdalwarp(src, dst, pbar=False, **kwargs)[source]

a simple wrapper for osgeo.gdal.Warp()

Parameters:
spatialist.auxil.haversine(lat1, lon1, lat2, lon2)[source]

compute the distance in meters between two points in latlon

Parameters:

  • lat1 (int or float) – the latitude of point 1

  • lon1 (int or float) – the longitude of point 1

  • lat2 (int or float) – the latitude of point 2

  • lon2 (int or float) – the longitude of point 2

Returns:

the distance between point 1 and point 2 in meters

Return type:

float

spatialist.auxil.ogr2ogr(src, dst, **kwargs)[source]

a simple wrapper for osgeo.gdal.VectorTranslate() aka ogr2ogr

Parameters:
spatialist.auxil.utm_autodetect(spatial, crsOut)[source]

get the UTM CRS for a spatial object

The bounding box of the object is extracted, reprojected to EPSG:4326 and its center coordinate used for computing the best UTM zone fit.

Parameters:

  • spatial (Raster or Vector) – a spatial object in an arbitrary CRS

  • crsOut (str) – the output CRS type; see function crsConvert() for options

Returns:

the output CRS

Return type:

int or str or osgeo.osr.SpatialReference

Database Tools

spatialist.sqlite_util.sqlite_setup(driver=':memory:', extensions=None, verbose=False)[source]

Setup a sqlite3 connection and load extensions to it. This function intends to simplify the process of loading extensions to sqlite3, which can be quite difficult depending on the version used. Particularly loading spatialite has caused quite some trouble. In recent distributions of Ubuntu this has become much easier due to a new apt package libsqlite3-mod-spatialite. For use in Windows, spatialist comes with its own spatialite DLL distribution. See here for more details on loading spatialite as an sqlite3 extension.

Parameters:

  • driver (str) – the database file or (by default) an in-memory database

  • extensions (list) – a list of extensions to load

  • verbose (bool) – print loading information?

Returns:

the database connection

Return type:

sqlite3.Connection

Example

>>> from spatialist.sqlite_util import sqlite_setup
>>> conn = sqlite_setup(extensions=['spatialite'])

Ancillary Functions

This script gathers central functions and classes for general applications

class spatialist.ancillary.HiddenPrints[source]

Bases: object

Suppress console stdout prints, i.e. redirect them to a temporary string object.
Adapted from https://stackoverflow.com/questions/8391411/suppress-calls-to-print-python

Examples

>>> with HiddenPrints():
>>>     print('foobar')
>>> print('foobar')
spatialist.ancillary.dissolve(inlist)[source]

list and tuple flattening

Parameters:

inlist (list) – the list with sub-lists or tuples to be flattened

Returns:

the flattened result

Return type:

list

Examples

>>> dissolve([[1, 2], [3, 4]])
[1, 2, 3, 4]

>>> dissolve([(1, 2, (3, 4)), [5, (6, 7)]])
[1, 2, 3, 4, 5, 6, 7]
spatialist.ancillary.finder(target, matchlist, foldermode=0, regex=False, recursive=True)[source]

function for finding files/folders in folders and their subdirectories

Parameters:

  • target (str or list[str]) – a directory, zip- or tar-archive or a list of them to be searched

  • matchlist (list[str]) – a list of search patterns

  • foldermode (int) –

    • 0: only files

    • 1: files and folders

    • 2: only folders

  • regex (bool) – are the search patterns in matchlist regular expressions or unix shell standard (default)?

  • recursive (bool) – search target recursively into all subdirectories or only in the top level? This is currently only implemented for parameter target being a directory.

Returns:

the absolute names of files/folders matching the patterns

Return type:

list[str]

spatialist.ancillary.multicore(function, cores, multiargs, pbar=False, **singleargs)[source]

wrapper for multicore process execution

Parameters:

  • function – individual function to be applied to each process item

  • cores (int) – the number of subprocesses started/CPUs used; this value is reduced in case the number of subprocesses is smaller

  • multiargs (dict) – a dictionary containing sub-function argument names as keys and lists of arguments to be distributed among the processes as values

  • pbar (bool) – add a progress bar? Does not yet work on Windows.

  • singleargs – all remaining arguments which are invariant among the subprocesses

Returns:

the return of the function for all subprocesses

Return type:

None or list

Notes

  • all multiargs value lists must be of same length, i.e. all argument keys must be explicitly defined for each subprocess

  • all function arguments passed via singleargs must be provided with the full argument name and its value (i.e. argname=argval); default function args are not accepted

  • if the processes return anything else than None, this function will return a list of results

  • if all processes return None, this function will be of type void

Examples

>>> def add(x, y, z):
>>>     return x + y + z
>>> multicore(add, cores=2, multiargs={'x': [1, 2]}, y=5, z=9)
[15, 16]
>>> multicore(add, cores=2, multiargs={'x': [1, 2], 'y': [5, 6]}, z=9)
[15, 17]

See also

pathos.multiprocessing

spatialist.ancillary.parallel_apply_along_axis(func1d, axis, arr, cores=4, *args, **kwargs)[source]

Like numpy.apply_along_axis() but using multiple threads. Adapted from here.

Parameters:

  • func1d (function) – the function to be applied

  • axis (int) – the axis along which to apply func1d

  • arr (numpy.ndarray) – the input array

  • cores (int) – the number of parallel cores

  • args (any) – Additional arguments to func1d.

  • kwargs (any) – Additional named arguments to func1d.

Return type:

numpy.ndarray

spatialist.ancillary.parse_literal(x)[source]

return the smallest possible data type for a string or list of strings

Parameters:

x (str or list) – a string to be parsed

Returns:

the parsing result

Return type:

int, float or str

Examples

>>> isinstance(parse_literal('1.5'), float)
True

>>> isinstance(parse_literal('1'), int)
True

>>> isinstance(parse_literal('foobar'), str)
True
spatialist.ancillary.run(cmd, outdir=None, logfile=None, inlist=None, void=True, errorpass=False, env=None)[source]
wrapper for subprocess execution including logfile writing and command prompt piping
this is a convenience wrapper around the subprocess module and calls its class Popen internally.
Parameters:

  • cmd (list) – the command arguments

  • outdir (str or None) – the directory to execute the command in

  • logfile (str or None) – a file to write stdout to

  • inlist (list or None) – a list of arguments passed to stdin, i.e. arguments passed to interactive input of the program

  • void (bool) – return stdout and stderr?

  • errorpass (bool) – if False, a subprocess.CalledProcessError is raised if the command fails

  • env (dict or None) – the environment to be passed to the subprocess

Returns:

a tuple of (stdout, stderr) if void is False otherwise None

Return type:

None or Tuple

spatialist.ancillary.sampler(mask, samples=None, dim=1, replace=False, seed=42)[source]

General function to select random sample indexes from arrays. Adapted from package S1_ARD.

Parameters:

  • mask (numpy.ndarray) – A 2D boolean mask to limit the sample selection.

  • samples (int or None) – The number of samples to select. If None, the positions of all matching values are returned. If there are fewer values than required samples, the positions of all values are returned.

  • dim (int) – The dimensions of the output array and its indexes. If 1, the returned array has one dimension and the indexes refer to the one-dimensional (i.e., flattened) representation of the input mask. If 2, the output array is of shape (2, samples) with two separate 2D arrays for y (index 0) and x respectively, which reference positions in the original 2D shape of the input array.

  • replace (bool) – Draw samples with or without replacement?

  • seed (int) – Seed used to initialize the pseudo-random number generator.

Returns:

The index positions of the generated random samples as 1D or 2D array.

Return type:

numpy.ndarray

Examples

>>> import numpy as np
>>> from spatialist.ancillary import sampler
>>> array = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> mask = array > 2
>>> s1d = sampler(mask=mask, samples=2, dim=1)
>>> s2d = sampler(mask=mask, samples=2, dim=2)
>>> print(s1d)
[2 3]
>>> print(s2d)
[[1 1]
 [0 1]]
>>> print(array.flatten()[s1d] == array[s2d[0], s2d[1]])
[ True  True]

See also

numpy.random.seed, numpy.random.choice

spatialist.ancillary.which(program, mode=1)[source]
mimics UNIX’s which
taken from this post: http://stackoverflow.com/questions/377017/test-if-executable-exists-in-python
can be replaced by shutil.which() starting from Python 3.3
Parameters:

  • program (str) – the program to be found

  • mode (os.F_OK or os.X_OK) – the mode of the found file, i.e. file exists or file is executable; see os.access()

Returns:

the full path and name of the command

Return type:

str or None

ENVI HDR file manipulation

This module offers functionality for editing ENVI header files

class spatialist.envi.HDRobject(data=None)[source]

ENVI HDR info handler

Parameters:

data (str, dict or None) – the file or dictionary to get the info from; If None (default), an object with default values for an empty raster file is returned

Examples

>>> from spatialist.envi import HDRobject
>>> with HDRobject('E:/test.hdr') as hdr:
>>>     hdr.band_names = ['one', 'two']
>>>     print(hdr)
>>>     hdr.write()
write(filename='same')[source]

write object to an ENVI header file

spatialist.envi.hdr(data, filename)[source]

write ENVI header files

Parameters:

  • data (str or dict) – the file or dictionary to get the info from

  • filename (str) – the HDR file to write

Some general examples

in-memory vector object rasterization

Here we create a new raster data set with the same geo-information and extent as a reference data set and burn the geometries from a shapefile into it.
In this example, the shapefile contains an attribute Site_name and one of the geometries in the shapefile has a value of my_testsite for this attribute.
We use the expressions parameter to subset the shapefile and burn a value of 1 in the raster at all locations where the geometry selection overlaps. Multiple expressions can be defined together with multiple burn values.
Also, burn values can be appended to an already existing raster data set. In this case, the rasterization is performed in-memory to further use it for e.g. plotting. Alternatively, an outname can be defined to directly write the result to disk as a GeoTiff.
See spatialist.raster.rasterize() for further reference.
>>> from spatialist import Vector, Raster
>>> from spatialist.raster import rasterize
>>> import matplotlib.pyplot as plt
>>>
>>> shapefile = 'testsites.shp'
>>> rasterfile = 'extent.tif'
>>>
>>> with Raster(rasterfile) as ras:
>>>     with Vector(shapefile) as vec:
>>>         mask = rasterize(vec, reference=ras, burn_values=1, expressions=["Site_Name='my testsite'"])
>>>         plt.imshow(mask.matrix())
>>>         plt.show()