rasterio.io module

Classes capable of reading and writing datasets

Instances of these classes are called dataset objects.

class rasterio.io.BufferedDatasetWriter

Bases: rasterio._io.BufferedDatasetWriterBase, rasterio.windows.WindowMethodsMixin, rasterio.transform.TransformMethodsMixin

Maintains data and metadata in a buffer, writing to disk or network only when close() is called.

This allows incremental updates to datasets using formats that don’t otherwise support updates, such as JPEG.

block_shapes

An ordered list of block shapes for each bands

Shapes are tuples and have the same ordering as the dataset’s shape: (count of image rows, count of image columns).

Returns

Return type

list

block_size()

Returns the size in bytes of a particular block

Only useful for TIFF formatted datasets.

Parameters

  • bidx (int) – Band index, starting with 1.

  • i (int) – Row index of the block, starting with 0.

  • j (int) – Column index of the block, starting with 0.

Returns

Return type

int

block_window()

Returns the window for a particular block

Parameters

  • bidx (int) – Band index, starting with 1.

  • i (int) – Row index of the block, starting with 0.

  • j (int) – Column index of the block, starting with 0.

Returns

Return type

Window

block_windows()

Iterator over a band’s blocks and their windows

The primary use of this method is to obtain windows to pass to read() for highly efficient access to raster block data.

The positional parameter bidx takes the index (starting at 1) of the desired band. This iterator yields blocks “left to right” and “top to bottom” and is similar to Python’s enumerate() in that the first element is the block index and the second is the dataset window.

Blocks are built-in to a dataset and describe how pixels are grouped within each band and provide a mechanism for efficient I/O. A window is a range of pixels within a single band defined by row start, row stop, column start, and column stop. For example, ((0, 2), (0, 2)) defines a 2 x 2 window at the upper left corner of a raster band. Blocks are referenced by an (i, j) tuple where (0, 0) would be a band’s upper left block.

Raster I/O is performed at the block level, so accessing a window spanning multiple rows in a striped raster requires reading each row. Accessing a 2 x 2 window at the center of a 1800 x 3600 image requires reading 2 rows, or 7200 pixels just to get the target 4. The same image with internal 256 x 256 blocks would require reading at least 1 block (if the window entire window falls within a single block) and at most 4 blocks, or at least 512 pixels and at most 2048.

Given an image that is 512 x 512 with blocks that are 256 x 256, its blocks and windows would look like:

Blocks:

        0       256     512
      0 +--------+--------+
        |        |        |
        | (0, 0) | (0, 1) |
        |        |        |
    256 +--------+--------+
        |        |        |
        | (1, 0) | (1, 1) |
        |        |        |
    512 +--------+--------+


Windows:

    UL: ((0, 256), (0, 256))
    UR: ((0, 256), (256, 512))
    LL: ((256, 512), (0, 256))
    LR: ((256, 512), (256, 512))
Parameters

bidx (int, optional) – The band index (using 1-based indexing) from which to extract windows. A value less than 1 uses the first band if all bands have homogeneous windows and raises an exception otherwise.

Yields

block, window

bounds

Returns the lower left and upper right bounds of the dataset in the units of its coordinate reference system.

The returned value is a tuple: (lower left x, lower left y, upper right x, upper right y)

build_overviews()

Build overviews at one or more decimation factors for all bands of the dataset.

checksum()

Compute an integer checksum for the stored band

Parameters

  • bidx (int) – The band’s index (1-indexed).

  • window (tuple, optional) – A window of the band. Default is the entire extent of the band.

Returns

Return type

An int.

close()

Close the dataset

closed

Test if the dataset is closed

Returns

Return type

bool

colorinterp

Returns a sequence of ColorInterp.<enum> representing color interpretation in band order.

To set color interpretation, provide a sequence of ColorInterp.<enum>:

import rasterio from rasterio.enums import ColorInterp

with rasterio.open(‘rgba.tif’, ‘r+’) as src:
src.colorinterp = (

ColorInterp.red, ColorInterp.green, ColorInterp.blue, ColorInterp.alpha)

Returns

Return type

tuple

colormap()

Returns a dict containing the colormap for a band or None.

compression
count

The number of raster bands in the dataset

Returns

Return type

int

crs

The dataset’s coordinate reference system

In setting this property, the value may be a CRS object or an EPSG:nnnn or WKT string.

Returns

Return type

CRS

dataset_mask()

Calculate the dataset’s 2D mask. Derived from the individual band masks provided by read_masks().

Parameters

out_shape, window, boundless and resampling are passed directly to read_masks() (out,) –

Returns

  • ndarray, shape=(self.height, self.width), dtype=’uint8’

  • 0 = nodata, 255 = valid data

  • The dataset mask is calculate based on the individual band masks according to

  • the following logic, in order of precedence

  • 1. If a .msk file, dataset-wide alpha or internal mask exists, – it will be used as the dataset mask.

  • 2. If an 4-band RGBA with a shadow nodata value, – band 4 will be used as the dataset mask.

  • 3. If a nodata value exists, use the binary OR (|) of the band masks

  • 4. If no nodata value exists, return a mask filled with 255

  • Note that this differs from read_masks and GDAL RFC15

  • in that it applies per-dataset, not per-band

  • (see https (//trac.osgeo.org/gdal/wiki/rfc15_nodatabitmask))

descriptions

Descriptions for each dataset band

To set descriptions, one for each band is required.

Returns

Return type

list of str

driver
dtypes

The data types of each band in index order

Returns

Return type

list of str

files

Returns a sequence of files associated with the dataset.

Returns

Return type

tuple

gcps

ground control points and their coordinate reference system.

This property is a 2-tuple, or pair: (gcps, crs).

gcpslist of GroundControlPoint

Zero or more ground control points.

crs: CRS

The coordinate reference system of the ground control points.

get_gcps()

Get GCPs and their associated CRS.

get_nodatavals()
get_tag_item()

Returns tag item value

Parameters

  • ns (str) – The key for the metadata item to fetch.

  • dm (str) – The domain to fetch for.

  • bidx (int) – Band index, starting with 1.

  • ovr (int) – Overview level

Returns

Return type

str

get_transform()

Returns a GDAL geotransform in its native form.

height
index(x, y, op=<built-in function floor>, precision=None)

Returns the (row, col) index of the pixel containing (x, y) given a coordinate reference system.

Use an epsilon, magnitude determined by the precision parameter and sign determined by the op function:

positive for floor, negative for ceil.

Parameters

  • x (float) – x value in coordinate reference system

  • y (float) – y value in coordinate reference system

  • op (function, optional (default: math.floor)) – Function to convert fractional pixels to whole numbers (floor, ceiling, round)

  • precision (int, optional (default: None)) – Decimal places of precision in indexing, as in round().

Returns

(row index, col index)

Return type

tuple

indexes

The 1-based indexes of each band in the dataset

For a 3-band dataset, this property will be [1, 2, 3].

Returns

Return type

list of int

interleaving
is_tiled
lnglat()
mask_flag_enums

Sets of flags describing the sources of band masks.

all_valid: There are no invalid pixels, all mask values will be

  1. When used this will normally be the only flag set.

per_dataset: The mask band is shared between all bands on the

dataset.

alpha: The mask band is actually an alpha band and may have

values other than 0 and 255.

nodata: Indicates the mask is actually being generated from

nodata values (mutually exclusive of “alpha”).

Returns

One list of rasterio.enums.MaskFlags members per band.

Return type

list [, list*]

Examples

For a 3 band dataset that has masks derived from nodata values:

>>> dataset.mask_flag_enums
([<MaskFlags.nodata: 8>], [<MaskFlags.nodata: 8>], [<MaskFlags.nodata: 8>])
>>> band1_flags = dataset.mask_flag_enums[0]
>>> rasterio.enums.MaskFlags.nodata in band1_flags
True
>>> rasterio.enums.MaskFlags.alpha in band1_flags
False
meta

The basic metadata of this dataset.

mode
name
nodata

The dataset’s single nodata value

Notes

May be set.

Returns

Return type

float

nodatavals

Nodata values for each band

Notes

This may not be set.

Returns

Return type

list of float

offsets

Raster offset for each dataset band

To set offsets, one for each band is required.

Returns

Return type

list of float

options
overviews()
photometric
profile

Basic metadata and creation options of this dataset.

May be passed as keyword arguments to rasterio.open() to create a clone of this dataset.

read()

Read a dataset’s raw pixels as an N-d array

This data is read from the dataset’s band cache, which means that repeated reads of the same windows may avoid I/O.

Parameters

  • indexes (list of ints or a single int, optional) – If indexes is a list, the result is a 3D array, but is a 2D array if it is a band index number.

  • out (numpy ndarray, optional) –

    As with Numpy ufuncs, this is an optional reference to an output array into which data will be placed. If the height and width of out differ from that of the specified window (see below), the raster image will be decimated or replicated using the specified resampling method (also see below).

    Note: the method’s return value may be a view on this array. In other words, out is likely to be an incomplete representation of the method’s results.

    This parameter cannot be combined with out_shape.

  • out_dtype (str or numpy dtype) – The desired output data type. For example: ‘uint8’ or rasterio.uint16.

  • out_shape (tuple, optional) –

    A tuple describing the shape of a new output array. See out (above) for notes on image decimation and replication.

    Cannot combined with out.

  • window (a pair (tuple) of pairs of ints or Window, optional) – The optional window argument is a 2 item tuple. The first item is a tuple containing the indexes of the rows at which the window starts and stops and the second is a tuple containing the indexes of the columns at which the window starts and stops. For example, ((0, 2), (0, 2)) defines a 2x2 window at the upper left of the raster dataset.

  • masked (bool, optional) – If masked is True the return value will be a masked array. Otherwise (the default) the return value will be a regular array. Masks will be exactly the inverse of the GDAL RFC 15 conforming arrays returned by read_masks().

  • boundless (bool, optional (default False)) – If True, windows that extend beyond the dataset’s extent are permitted and partially or completely filled arrays will be returned as appropriate.

  • resampling (Resampling) – By default, pixel values are read raw or interpolated using a nearest neighbor algorithm from the band cache. Other resampling algorithms may be specified. Resampled pixels are not cached.

  • fill_value (scalar) – Fill value applied in the boundless=True case only.

Returns

  • Numpy ndarray or a view on a Numpy ndarray

  • Note (as with Numpy ufuncs, an object is returned even if you)

  • use the optional out argument and the return value shall be

  • preferentially used by callers.

read_crs()

Return the GDAL dataset’s stored CRS

read_masks()

Read raster band masks as a multidimensional array

This data is read from the dataset’s band cache, which means that repeated reads of the same windows may avoid I/O.

Parameters

  • indexes (list of ints or a single int, optional) – If indexes is a list, the result is a 3D array, but is a 2D array if it is a band index number.

  • out (numpy ndarray, optional) –

    As with Numpy ufuncs, this is an optional reference to an output array with the same dimensions and shape into which data will be placed.

    Note: the method’s return value may be a view on this array. In other words, out is likely to be an incomplete representation of the method’s results.

    Cannot combine with out_shape.

  • out_shape (tuple, optional) –

    A tuple describing the output array’s shape. Allows for decimated reads without constructing an output Numpy array.

    Cannot combined with out.

  • window (a pair (tuple) of pairs of ints or Window, optional) – The optional window argument is a 2 item tuple. The first item is a tuple containing the indexes of the rows at which the window starts and stops and the second is a tuple containing the indexes of the columns at which the window starts and stops. For example, ((0, 2), (0, 2)) defines a 2x2 window at the upper left of the raster dataset.

  • boundless (bool, optional (default False)) – If True, windows that extend beyond the dataset’s extent are permitted and partially or completely filled arrays will be returned as appropriate.

  • resampling (Resampling) – By default, pixel values are read raw or interpolated using a nearest neighbor algorithm from the band cache. Other resampling algorithms may be specified. Resampled pixels are not cached.

Returns

  • Numpy ndarray or a view on a Numpy ndarray

  • Note (as with Numpy ufuncs, an object is returned even if you)

  • use the optional out argument and the return value shall be

  • preferentially used by callers.

read_transform()

Return the stored GDAL GeoTransform

res

Returns the (width, height) of pixels in the units of its coordinate reference system.

sample()

Get the values of a dataset at certain positions

Values are from the nearest pixel. They are not interpolated.

Parameters

  • xy (iterable) – Pairs of x, y coordinates (floats) in the dataset’s reference system.

  • indexes (int or list of int) – Indexes of dataset bands to sample.

  • masked (bool, default: False) – Whether to mask samples that fall outside the extent of the dataset.

Returns

Arrays of length equal to the number of specified indexes containing the dataset values for the bands corresponding to those indexes.

Return type

iterable

scales

Raster scale for each dataset band

To set scales, one for each band is required.

Returns

Return type

list of float

set_band_description()

Sets the description of a dataset band.

Parameters

  • bidx (int) – Index of the band (starting with 1).

  • value (string) – A description of the band.

Returns

Return type

None

set_band_unit()

Sets the unit of measure of a dataset band.

Parameters

  • bidx (int) – Index of the band (starting with 1).

  • value (string) – A label for the band’s unit of measure such as ‘meters’ or ‘degC’. See the Pint project for a suggested list of units.

Returns

Return type

None

shape
start()
stop()

Ends the dataset’s life cycle

subdatasets

Sequence of subdatasets

tag_namespaces()

Get a list of the dataset’s metadata domains.

Returned items may be passed as ns to the tags method.

Parameters

int, optional (bidx) – Can be used to select a specific band, otherwise the dataset’s general metadata domains are returned.

Returns

Return type

list of str

tags()

Returns a dict containing copies of the dataset or band’s tags.

Tags are pairs of key and value strings. Tags belong to namespaces. The standard namespaces are: default (None) and ‘IMAGE_STRUCTURE’. Applications can create their own additional namespaces.

The optional bidx argument can be used to select the tags of a specific band. The optional ns argument can be used to select a namespace other than the default.

transform

The dataset’s georeferencing transformation matrix

This transform maps pixel row/column coordinates to coordinates in the dataset’s coordinate reference system.

Returns

Return type

Affine

units

one units string for each dataset band

Possible values include ‘meters’ or ‘degC’. See the Pint project for a suggested list of units.

To set units, one for each band is required.

Returns

Return type

list of str

Type

A list of str

update_tags()

Updates the tags of a dataset or one of its bands.

Tags are pairs of key and value strings. Tags belong to namespaces. The standard namespaces are: default (None) and ‘IMAGE_STRUCTURE’. Applications can create their own additional namespaces.

The optional bidx argument can be used to select the dataset band. The optional ns argument can be used to select a namespace other than the default.

width
window(left, bottom, right, top, precision=None)

Get the window corresponding to the bounding coordinates.

The resulting window is not cropped to the row and column limits of the dataset.

Parameters

  • left (float) – Left (west) bounding coordinate

  • bottom (float) – Bottom (south) bounding coordinate

  • right (float) – Right (east) bounding coordinate

  • top (float) – Top (north) bounding coordinate

  • precision (int, optional) – Number of decimal points of precision when computing inverse transform.

Returns

window

Return type

Window

window_bounds(window)

Get the bounds of a window

Parameters

window (rasterio.windows.Window) – Dataset window

Returns

bounds – x_min, y_min, x_max, y_max for the given window

Return type

tuple

window_transform(window)

Get the affine transform for a dataset window.

Parameters

window (rasterio.windows.Window) – Dataset window

Returns

transform – The affine transform matrix for the given window

Return type

Affine

write()

Write the src array into indexed bands of the dataset.

If indexes is a list, the src must be a 3D array of matching shape. If an int, the src must be a 2D array.

See read() for usage of the optional window argument.

write_band()

Write the src array into the bidx band.

Band indexes begin with 1: read_band(1) returns the first band.

The optional window argument takes a tuple like:

((row_start, row_stop), (col_start, col_stop))

specifying a raster subset to write into.

write_colormap()

Write a colormap for a band to the dataset.

write_mask()

Write the valid data mask src array into the dataset’s band mask.

The optional window argument takes a tuple like:

((row_start, row_stop), (col_start, col_stop))

specifying a raster subset to write into.

write_transform()
xy(row, col, offset='center')

Returns the coordinates (x, y) of a pixel at row and col. The pixel’s center is returned by default, but a corner can be returned by setting offset to one of ul, ur, ll, lr.

Parameters

  • row (int) – Pixel row.

  • col (int) – Pixel column.

  • offset (str, optional) – Determines if the returned coordinates are for the center of the pixel or for a corner.

Returns

(x, y)

Return type

tuple

class rasterio.io.DatasetReader

Bases: rasterio._io.DatasetReaderBase, rasterio.windows.WindowMethodsMixin, rasterio.transform.TransformMethodsMixin

An unbuffered data and metadata reader

block_shapes

An ordered list of block shapes for each bands

Shapes are tuples and have the same ordering as the dataset’s shape: (count of image rows, count of image columns).

Returns

Return type

list

block_size()

Returns the size in bytes of a particular block

Only useful for TIFF formatted datasets.

Parameters

  • bidx (int) – Band index, starting with 1.

  • i (int) – Row index of the block, starting with 0.

  • j (int) – Column index of the block, starting with 0.

Returns

Return type

int

block_window()

Returns the window for a particular block

Parameters

  • bidx (int) – Band index, starting with 1.

  • i (int) – Row index of the block, starting with 0.

  • j (int) – Column index of the block, starting with 0.

Returns

Return type

Window

block_windows()

Iterator over a band’s blocks and their windows

The primary use of this method is to obtain windows to pass to read() for highly efficient access to raster block data.

The positional parameter bidx takes the index (starting at 1) of the desired band. This iterator yields blocks “left to right” and “top to bottom” and is similar to Python’s enumerate() in that the first element is the block index and the second is the dataset window.

Blocks are built-in to a dataset and describe how pixels are grouped within each band and provide a mechanism for efficient I/O. A window is a range of pixels within a single band defined by row start, row stop, column start, and column stop. For example, ((0, 2), (0, 2)) defines a 2 x 2 window at the upper left corner of a raster band. Blocks are referenced by an (i, j) tuple where (0, 0) would be a band’s upper left block.

Raster I/O is performed at the block level, so accessing a window spanning multiple rows in a striped raster requires reading each row. Accessing a 2 x 2 window at the center of a 1800 x 3600 image requires reading 2 rows, or 7200 pixels just to get the target 4. The same image with internal 256 x 256 blocks would require reading at least 1 block (if the window entire window falls within a single block) and at most 4 blocks, or at least 512 pixels and at most 2048.

Given an image that is 512 x 512 with blocks that are 256 x 256, its blocks and windows would look like:

Blocks:

        0       256     512
      0 +--------+--------+
        |        |        |
        | (0, 0) | (0, 1) |
        |        |        |
    256 +--------+--------+
        |        |        |
        | (1, 0) | (1, 1) |
        |        |        |
    512 +--------+--------+


Windows:

    UL: ((0, 256), (0, 256))
    UR: ((0, 256), (256, 512))
    LL: ((256, 512), (0, 256))
    LR: ((256, 512), (256, 512))
Parameters

bidx (int, optional) – The band index (using 1-based indexing) from which to extract windows. A value less than 1 uses the first band if all bands have homogeneous windows and raises an exception otherwise.

Yields

block, window

bounds

Returns the lower left and upper right bounds of the dataset in the units of its coordinate reference system.

The returned value is a tuple: (lower left x, lower left y, upper right x, upper right y)

checksum()

Compute an integer checksum for the stored band

Parameters

  • bidx (int) – The band’s index (1-indexed).

  • window (tuple, optional) – A window of the band. Default is the entire extent of the band.

Returns

Return type

An int.

close()

Close the dataset

closed

Test if the dataset is closed

Returns

Return type

bool

colorinterp

Returns a sequence of ColorInterp.<enum> representing color interpretation in band order.

To set color interpretation, provide a sequence of ColorInterp.<enum>:

import rasterio from rasterio.enums import ColorInterp

with rasterio.open(‘rgba.tif’, ‘r+’) as src:
src.colorinterp = (

ColorInterp.red, ColorInterp.green, ColorInterp.blue, ColorInterp.alpha)

Returns

Return type

tuple

colormap()

Returns a dict containing the colormap for a band or None.

compression
count

The number of raster bands in the dataset

Returns

Return type

int

crs

The dataset’s coordinate reference system

In setting this property, the value may be a CRS object or an EPSG:nnnn or WKT string.

Returns

Return type

CRS

dataset_mask()

Calculate the dataset’s 2D mask. Derived from the individual band masks provided by read_masks().

Parameters

out_shape, window, boundless and resampling are passed directly to read_masks() (out,) –

Returns

  • ndarray, shape=(self.height, self.width), dtype=’uint8’

  • 0 = nodata, 255 = valid data

  • The dataset mask is calculate based on the individual band masks according to

  • the following logic, in order of precedence

  • 1. If a .msk file, dataset-wide alpha or internal mask exists, – it will be used as the dataset mask.

  • 2. If an 4-band RGBA with a shadow nodata value, – band 4 will be used as the dataset mask.

  • 3. If a nodata value exists, use the binary OR (|) of the band masks

  • 4. If no nodata value exists, return a mask filled with 255

  • Note that this differs from read_masks and GDAL RFC15

  • in that it applies per-dataset, not per-band

  • (see https (//trac.osgeo.org/gdal/wiki/rfc15_nodatabitmask))

descriptions

Descriptions for each dataset band

To set descriptions, one for each band is required.

Returns

Return type

list of str

driver
dtypes

The data types of each band in index order

Returns

Return type

list of str

files

Returns a sequence of files associated with the dataset.

Returns

Return type

tuple

gcps

ground control points and their coordinate reference system.

This property is a 2-tuple, or pair: (gcps, crs).

gcpslist of GroundControlPoint

Zero or more ground control points.

crs: CRS

The coordinate reference system of the ground control points.

get_gcps()

Get GCPs and their associated CRS.

get_nodatavals()
get_tag_item()

Returns tag item value

Parameters

  • ns (str) – The key for the metadata item to fetch.

  • dm (str) – The domain to fetch for.

  • bidx (int) – Band index, starting with 1.

  • ovr (int) – Overview level

Returns

Return type

str

get_transform()

Returns a GDAL geotransform in its native form.

height
index(x, y, op=<built-in function floor>, precision=None)

Returns the (row, col) index of the pixel containing (x, y) given a coordinate reference system.

Use an epsilon, magnitude determined by the precision parameter and sign determined by the op function:

positive for floor, negative for ceil.

Parameters

  • x (float) – x value in coordinate reference system

  • y (float) – y value in coordinate reference system

  • op (function, optional (default: math.floor)) – Function to convert fractional pixels to whole numbers (floor, ceiling, round)

  • precision (int, optional (default: None)) – Decimal places of precision in indexing, as in round().

Returns

(row index, col index)

Return type

tuple

indexes

The 1-based indexes of each band in the dataset

For a 3-band dataset, this property will be [1, 2, 3].

Returns

Return type

list of int

interleaving
is_tiled
lnglat()
mask_flag_enums

Sets of flags describing the sources of band masks.

all_valid: There are no invalid pixels, all mask values will be

  1. When used this will normally be the only flag set.

per_dataset: The mask band is shared between all bands on the

dataset.

alpha: The mask band is actually an alpha band and may have

values other than 0 and 255.

nodata: Indicates the mask is actually being generated from

nodata values (mutually exclusive of “alpha”).

Returns

One list of rasterio.enums.MaskFlags members per band.

Return type

list [, list*]

Examples

For a 3 band dataset that has masks derived from nodata values:

>>> dataset.mask_flag_enums
([<MaskFlags.nodata: 8>], [<MaskFlags.nodata: 8>], [<MaskFlags.nodata: 8>])
>>> band1_flags = dataset.mask_flag_enums[0]
>>> rasterio.enums.MaskFlags.nodata in band1_flags
True
>>> rasterio.enums.MaskFlags.alpha in band1_flags
False
meta

The basic metadata of this dataset.

mode
name
nodata

The dataset’s single nodata value

Notes

May be set.

Returns

Return type

float

nodatavals

Nodata values for each band

Notes

This may not be set.

Returns

Return type

list of float

offsets

Raster offset for each dataset band

To set offsets, one for each band is required.

Returns

Return type

list of float

options
overviews()
photometric
profile

Basic metadata and creation options of this dataset.

May be passed as keyword arguments to rasterio.open() to create a clone of this dataset.

read()

Read a dataset’s raw pixels as an N-d array

This data is read from the dataset’s band cache, which means that repeated reads of the same windows may avoid I/O.

Parameters

  • indexes (list of ints or a single int, optional) – If indexes is a list, the result is a 3D array, but is a 2D array if it is a band index number.

  • out (numpy ndarray, optional) –

    As with Numpy ufuncs, this is an optional reference to an output array into which data will be placed. If the height and width of out differ from that of the specified window (see below), the raster image will be decimated or replicated using the specified resampling method (also see below).

    Note: the method’s return value may be a view on this array. In other words, out is likely to be an incomplete representation of the method’s results.

    This parameter cannot be combined with out_shape.

  • out_dtype (str or numpy dtype) – The desired output data type. For example: ‘uint8’ or rasterio.uint16.

  • out_shape (tuple, optional) –

    A tuple describing the shape of a new output array. See out (above) for notes on image decimation and replication.

    Cannot combined with out.

  • window (a pair (tuple) of pairs of ints or Window, optional) – The optional window argument is a 2 item tuple. The first item is a tuple containing the indexes of the rows at which the window starts and stops and the second is a tuple containing the indexes of the columns at which the window starts and stops. For example, ((0, 2), (0, 2)) defines a 2x2 window at the upper left of the raster dataset.

  • masked (bool, optional) – If masked is True the return value will be a masked array. Otherwise (the default) the return value will be a regular array. Masks will be exactly the inverse of the GDAL RFC 15 conforming arrays returned by read_masks().

  • boundless (bool, optional (default False)) – If True, windows that extend beyond the dataset’s extent are permitted and partially or completely filled arrays will be returned as appropriate.

  • resampling (Resampling) – By default, pixel values are read raw or interpolated using a nearest neighbor algorithm from the band cache. Other resampling algorithms may be specified. Resampled pixels are not cached.

  • fill_value (scalar) – Fill value applied in the boundless=True case only.

Returns

  • Numpy ndarray or a view on a Numpy ndarray

  • Note (as with Numpy ufuncs, an object is returned even if you)

  • use the optional out argument and the return value shall be

  • preferentially used by callers.

read_crs()

Return the GDAL dataset’s stored CRS

read_masks()

Read raster band masks as a multidimensional array

This data is read from the dataset’s band cache, which means that repeated reads of the same windows may avoid I/O.

Parameters

  • indexes (list of ints or a single int, optional) – If indexes is a list, the result is a 3D array, but is a 2D array if it is a band index number.

  • out (numpy ndarray, optional) –

    As with Numpy ufuncs, this is an optional reference to an output array with the same dimensions and shape into which data will be placed.

    Note: the method’s return value may be a view on this array. In other words, out is likely to be an incomplete representation of the method’s results.

    Cannot combine with out_shape.

  • out_shape (tuple, optional) –

    A tuple describing the output array’s shape. Allows for decimated reads without constructing an output Numpy array.

    Cannot combined with out.

  • window (a pair (tuple) of pairs of ints or Window, optional) – The optional window argument is a 2 item tuple. The first item is a tuple containing the indexes of the rows at which the window starts and stops and the second is a tuple containing the indexes of the columns at which the window starts and stops. For example, ((0, 2), (0, 2)) defines a 2x2 window at the upper left of the raster dataset.

  • boundless (bool, optional (default False)) – If True, windows that extend beyond the dataset’s extent are permitted and partially or completely filled arrays will be returned as appropriate.

  • resampling (Resampling) – By default, pixel values are read raw or interpolated using a nearest neighbor algorithm from the band cache. Other resampling algorithms may be specified. Resampled pixels are not cached.

Returns

  • Numpy ndarray or a view on a Numpy ndarray

  • Note (as with Numpy ufuncs, an object is returned even if you)

  • use the optional out argument and the return value shall be

  • preferentially used by callers.

read_transform()

Return the stored GDAL GeoTransform

res

Returns the (width, height) of pixels in the units of its coordinate reference system.

sample()

Get the values of a dataset at certain positions

Values are from the nearest pixel. They are not interpolated.

Parameters

  • xy (iterable) – Pairs of x, y coordinates (floats) in the dataset’s reference system.

  • indexes (int or list of int) – Indexes of dataset bands to sample.

  • masked (bool, default: False) – Whether to mask samples that fall outside the extent of the dataset.

Returns

Arrays of length equal to the number of specified indexes containing the dataset values for the bands corresponding to those indexes.

Return type

iterable

scales

Raster scale for each dataset band

To set scales, one for each band is required.

Returns

Return type

list of float

shape
stop()

Ends the dataset’s life cycle

subdatasets

Sequence of subdatasets

tag_namespaces()

Get a list of the dataset’s metadata domains.

Returned items may be passed as ns to the tags method.

Parameters

int, optional (bidx) – Can be used to select a specific band, otherwise the dataset’s general metadata domains are returned.

Returns

Return type

list of str

tags()

Returns a dict containing copies of the dataset or band’s tags.

Tags are pairs of key and value strings. Tags belong to namespaces. The standard namespaces are: default (None) and ‘IMAGE_STRUCTURE’. Applications can create their own additional namespaces.

The optional bidx argument can be used to select the tags of a specific band. The optional ns argument can be used to select a namespace other than the default.

transform

The dataset’s georeferencing transformation matrix

This transform maps pixel row/column coordinates to coordinates in the dataset’s coordinate reference system.

Returns

Return type

Affine

units

one units string for each dataset band

Possible values include ‘meters’ or ‘degC’. See the Pint project for a suggested list of units.

To set units, one for each band is required.

Returns

Return type

list of str

Type

A list of str

width
window(left, bottom, right, top, precision=None)

Get the window corresponding to the bounding coordinates.

The resulting window is not cropped to the row and column limits of the dataset.

Parameters

  • left (float) – Left (west) bounding coordinate

  • bottom (float) – Bottom (south) bounding coordinate

  • right (float) – Right (east) bounding coordinate

  • top (float) – Top (north) bounding coordinate

  • precision (int, optional) – Number of decimal points of precision when computing inverse transform.

Returns

window

Return type

Window

window_bounds(window)

Get the bounds of a window

Parameters

window (rasterio.windows.Window) – Dataset window

Returns

bounds – x_min, y_min, x_max, y_max for the given window

Return type

tuple

window_transform(window)

Get the affine transform for a dataset window.

Parameters

window (rasterio.windows.Window) – Dataset window

Returns

transform – The affine transform matrix for the given window

Return type

Affine

write_transform()
xy(row, col, offset='center')

Returns the coordinates (x, y) of a pixel at row and col. The pixel’s center is returned by default, but a corner can be returned by setting offset to one of ul, ur, ll, lr.

Parameters

  • row (int) – Pixel row.

  • col (int) – Pixel column.

  • offset (str, optional) – Determines if the returned coordinates are for the center of the pixel or for a corner.

Returns

(x, y)

Return type

tuple

class rasterio.io.DatasetWriter

Bases: rasterio._io.DatasetWriterBase, rasterio.windows.WindowMethodsMixin, rasterio.transform.TransformMethodsMixin

An unbuffered data and metadata writer. Its methods write data directly to disk.

block_shapes

An ordered list of block shapes for each bands

Shapes are tuples and have the same ordering as the dataset’s shape: (count of image rows, count of image columns).

Returns

Return type

list

block_size()

Returns the size in bytes of a particular block

Only useful for TIFF formatted datasets.

Parameters

  • bidx (int) – Band index, starting with 1.

  • i (int) – Row index of the block, starting with 0.

  • j (int) – Column index of the block, starting with 0.

Returns

Return type

int

block_window()

Returns the window for a particular block

Parameters

  • bidx (int) – Band index, starting with 1.

  • i (int) – Row index of the block, starting with 0.

  • j (int) – Column index of the block, starting with 0.

Returns

Return type

Window

block_windows()

Iterator over a band’s blocks and their windows

The primary use of this method is to obtain windows to pass to read() for highly efficient access to raster block data.

The positional parameter bidx takes the index (starting at 1) of the desired band. This iterator yields blocks “left to right” and “top to bottom” and is similar to Python’s enumerate() in that the first element is the block index and the second is the dataset window.

Blocks are built-in to a dataset and describe how pixels are grouped within each band and provide a mechanism for efficient I/O. A window is a range of pixels within a single band defined by row start, row stop, column start, and column stop. For example, ((0, 2), (0, 2)) defines a 2 x 2 window at the upper left corner of a raster band. Blocks are referenced by an (i, j) tuple where (0, 0) would be a band’s upper left block.

Raster I/O is performed at the block level, so accessing a window spanning multiple rows in a striped raster requires reading each row. Accessing a 2 x 2 window at the center of a 1800 x 3600 image requires reading 2 rows, or 7200 pixels just to get the target 4. The same image with internal 256 x 256 blocks would require reading at least 1 block (if the window entire window falls within a single block) and at most 4 blocks, or at least 512 pixels and at most 2048.

Given an image that is 512 x 512 with blocks that are 256 x 256, its blocks and windows would look like:

Blocks:

        0       256     512
      0 +--------+--------+
        |        |        |
        | (0, 0) | (0, 1) |
        |        |        |
    256 +--------+--------+
        |        |        |
        | (1, 0) | (1, 1) |
        |        |        |
    512 +--------+--------+


Windows:

    UL: ((0, 256), (0, 256))
    UR: ((0, 256), (256, 512))
    LL: ((256, 512), (0, 256))
    LR: ((256, 512), (256, 512))
Parameters

bidx (int, optional) – The band index (using 1-based indexing) from which to extract windows. A value less than 1 uses the first band if all bands have homogeneous windows and raises an exception otherwise.

Yields

block, window

bounds

Returns the lower left and upper right bounds of the dataset in the units of its coordinate reference system.

The returned value is a tuple: (lower left x, lower left y, upper right x, upper right y)

build_overviews()

Build overviews at one or more decimation factors for all bands of the dataset.

checksum()

Compute an integer checksum for the stored band

Parameters

  • bidx (int) – The band’s index (1-indexed).

  • window (tuple, optional) – A window of the band. Default is the entire extent of the band.

Returns

Return type

An int.

close()

Close the dataset

closed

Test if the dataset is closed

Returns

Return type

bool

colorinterp

Returns a sequence of ColorInterp.<enum> representing color interpretation in band order.

To set color interpretation, provide a sequence of ColorInterp.<enum>:

import rasterio from rasterio.enums import ColorInterp

with rasterio.open(‘rgba.tif’, ‘r+’) as src:
src.colorinterp = (

ColorInterp.red, ColorInterp.green, ColorInterp.blue, ColorInterp.alpha)

Returns

Return type

tuple

colormap()

Returns a dict containing the colormap for a band or None.

compression
count

The number of raster bands in the dataset

Returns

Return type

int

crs

The dataset’s coordinate reference system

In setting this property, the value may be a CRS object or an EPSG:nnnn or WKT string.

Returns

Return type

CRS

dataset_mask()

Calculate the dataset’s 2D mask. Derived from the individual band masks provided by read_masks().

Parameters

out_shape, window, boundless and resampling are passed directly to read_masks() (out,) –

Returns

  • ndarray, shape=(self.height, self.width), dtype=’uint8’

  • 0 = nodata, 255 = valid data

  • The dataset mask is calculate based on the individual band masks according to

  • the following logic, in order of precedence

  • 1. If a .msk file, dataset-wide alpha or internal mask exists, – it will be used as the dataset mask.

  • 2. If an 4-band RGBA with a shadow nodata value, – band 4 will be used as the dataset mask.

  • 3. If a nodata value exists, use the binary OR (|) of the band masks

  • 4. If no nodata value exists, return a mask filled with 255

  • Note that this differs from read_masks and GDAL RFC15

  • in that it applies per-dataset, not per-band

  • (see https (//trac.osgeo.org/gdal/wiki/rfc15_nodatabitmask))

descriptions

Descriptions for each dataset band

To set descriptions, one for each band is required.

Returns

Return type

list of str

driver
dtypes

The data types of each band in index order

Returns

Return type

list of str

files

Returns a sequence of files associated with the dataset.

Returns

Return type

tuple

gcps

ground control points and their coordinate reference system.

This property is a 2-tuple, or pair: (gcps, crs).

gcpslist of GroundControlPoint

Zero or more ground control points.

crs: CRS

The coordinate reference system of the ground control points.

get_gcps()

Get GCPs and their associated CRS.

get_nodatavals()
get_tag_item()

Returns tag item value

Parameters

  • ns (str) – The key for the metadata item to fetch.

  • dm (str) – The domain to fetch for.

  • bidx (int) – Band index, starting with 1.

  • ovr (int) – Overview level

Returns

Return type

str

get_transform()

Returns a GDAL geotransform in its native form.

height
index(x, y, op=<built-in function floor>, precision=None)

Returns the (row, col) index of the pixel containing (x, y) given a coordinate reference system.

Use an epsilon, magnitude determined by the precision parameter and sign determined by the op function:

positive for floor, negative for ceil.

Parameters

  • x (float) – x value in coordinate reference system

  • y (float) – y value in coordinate reference system

  • op (function, optional (default: math.floor)) – Function to convert fractional pixels to whole numbers (floor, ceiling, round)

  • precision (int, optional (default: None)) – Decimal places of precision in indexing, as in round().

Returns

(row index, col index)

Return type

tuple

indexes

The 1-based indexes of each band in the dataset

For a 3-band dataset, this property will be [1, 2, 3].

Returns

Return type

list of int

interleaving
is_tiled
lnglat()
mask_flag_enums

Sets of flags describing the sources of band masks.

all_valid: There are no invalid pixels, all mask values will be

  1. When used this will normally be the only flag set.

per_dataset: The mask band is shared between all bands on the

dataset.

alpha: The mask band is actually an alpha band and may have

values other than 0 and 255.

nodata: Indicates the mask is actually being generated from

nodata values (mutually exclusive of “alpha”).

Returns

One list of rasterio.enums.MaskFlags members per band.

Return type

list [, list*]

Examples

For a 3 band dataset that has masks derived from nodata values:

>>> dataset.mask_flag_enums
([<MaskFlags.nodata: 8>], [<MaskFlags.nodata: 8>], [<MaskFlags.nodata: 8>])
>>> band1_flags = dataset.mask_flag_enums[0]
>>> rasterio.enums.MaskFlags.nodata in band1_flags
True
>>> rasterio.enums.MaskFlags.alpha in band1_flags
False
meta

The basic metadata of this dataset.

mode
name
nodata

The dataset’s single nodata value

Notes

May be set.

Returns

Return type

float

nodatavals

Nodata values for each band

Notes

This may not be set.

Returns

Return type

list of float

offsets

Raster offset for each dataset band

To set offsets, one for each band is required.

Returns

Return type

list of float

options
overviews()
photometric
profile

Basic metadata and creation options of this dataset.

May be passed as keyword arguments to rasterio.open() to create a clone of this dataset.

read()

Read a dataset’s raw pixels as an N-d array

This data is read from the dataset’s band cache, which means that repeated reads of the same windows may avoid I/O.

Parameters

  • indexes (list of ints or a single int, optional) – If indexes is a list, the result is a 3D array, but is a 2D array if it is a band index number.

  • out (numpy ndarray, optional) –

    As with Numpy ufuncs, this is an optional reference to an output array into which data will be placed. If the height and width of out differ from that of the specified window (see below), the raster image will be decimated or replicated using the specified resampling method (also see below).

    Note: the method’s return value may be a view on this array. In other words, out is likely to be an incomplete representation of the method’s results.

    This parameter cannot be combined with out_shape.

  • out_dtype (str or numpy dtype) – The desired output data type. For example: ‘uint8’ or rasterio.uint16.

  • out_shape (tuple, optional) –

    A tuple describing the shape of a new output array. See out (above) for notes on image decimation and replication.

    Cannot combined with out.

  • window (a pair (tuple) of pairs of ints or Window, optional) – The optional window argument is a 2 item tuple. The first item is a tuple containing the indexes of the rows at which the window starts and stops and the second is a tuple containing the indexes of the columns at which the window starts and stops. For example, ((0, 2), (0, 2)) defines a 2x2 window at the upper left of the raster dataset.

  • masked (bool, optional) – If masked is True the return value will be a masked array. Otherwise (the default) the return value will be a regular array. Masks will be exactly the inverse of the GDAL RFC 15 conforming arrays returned by read_masks().

  • boundless (bool, optional (default False)) – If True, windows that extend beyond the dataset’s extent are permitted and partially or completely filled arrays will be returned as appropriate.

  • resampling (Resampling) – By default, pixel values are read raw or interpolated using a nearest neighbor algorithm from the band cache. Other resampling algorithms may be specified. Resampled pixels are not cached.

  • fill_value (scalar) – Fill value applied in the boundless=True case only.

Returns

  • Numpy ndarray or a view on a Numpy ndarray

  • Note (as with Numpy ufuncs, an object is returned even if you)

  • use the optional out argument and the return value shall be

  • preferentially used by callers.

read_crs()

Return the GDAL dataset’s stored CRS

read_masks()

Read raster band masks as a multidimensional array

This data is read from the dataset’s band cache, which means that repeated reads of the same windows may avoid I/O.

Parameters

  • indexes (list of ints or a single int, optional) – If indexes is a list, the result is a 3D array, but is a 2D array if it is a band index number.

  • out (numpy ndarray, optional) –

    As with Numpy ufuncs, this is an optional reference to an output array with the same dimensions and shape into which data will be placed.

    Note: the method’s return value may be a view on this array. In other words, out is likely to be an incomplete representation of the method’s results.

    Cannot combine with out_shape.

  • out_shape (tuple, optional) –

    A tuple describing the output array’s shape. Allows for decimated reads without constructing an output Numpy array.

    Cannot combined with out.

  • window (a pair (tuple) of pairs of ints or Window, optional) – The optional window argument is a 2 item tuple. The first item is a tuple containing the indexes of the rows at which the window starts and stops and the second is a tuple containing the indexes of the columns at which the window starts and stops. For example, ((0, 2), (0, 2)) defines a 2x2 window at the upper left of the raster dataset.

  • boundless (bool, optional (default False)) – If True, windows that extend beyond the dataset’s extent are permitted and partially or completely filled arrays will be returned as appropriate.

  • resampling (Resampling) – By default, pixel values are read raw or interpolated using a nearest neighbor algorithm from the band cache. Other resampling algorithms may be specified. Resampled pixels are not cached.

Returns

  • Numpy ndarray or a view on a Numpy ndarray

  • Note (as with Numpy ufuncs, an object is returned even if you)

  • use the optional out argument and the return value shall be

  • preferentially used by callers.

read_transform()

Return the stored GDAL GeoTransform

res

Returns the (width, height) of pixels in the units of its coordinate reference system.

sample()

Get the values of a dataset at certain positions

Values are from the nearest pixel. They are not interpolated.

Parameters

  • xy (iterable) – Pairs of x, y coordinates (floats) in the dataset’s reference system.

  • indexes (int or list of int) – Indexes of dataset bands to sample.

  • masked (bool, default: False) – Whether to mask samples that fall outside the extent of the dataset.

Returns

Arrays of length equal to the number of specified indexes containing the dataset values for the bands corresponding to those indexes.

Return type

iterable

scales

Raster scale for each dataset band

To set scales, one for each band is required.

Returns

Return type

list of float

set_band_description()

Sets the description of a dataset band.

Parameters

  • bidx (int) – Index of the band (starting with 1).

  • value (string) – A description of the band.

Returns

Return type

None

set_band_unit()

Sets the unit of measure of a dataset band.

Parameters

  • bidx (int) – Index of the band (starting with 1).

  • value (string) – A label for the band’s unit of measure such as ‘meters’ or ‘degC’. See the Pint project for a suggested list of units.

Returns

Return type

None

shape
start()
stop()

Ends the dataset’s life cycle

subdatasets

Sequence of subdatasets

tag_namespaces()

Get a list of the dataset’s metadata domains.

Returned items may be passed as ns to the tags method.

Parameters

int, optional (bidx) – Can be used to select a specific band, otherwise the dataset’s general metadata domains are returned.

Returns

Return type

list of str

tags()

Returns a dict containing copies of the dataset or band’s tags.

Tags are pairs of key and value strings. Tags belong to namespaces. The standard namespaces are: default (None) and ‘IMAGE_STRUCTURE’. Applications can create their own additional namespaces.

The optional bidx argument can be used to select the tags of a specific band. The optional ns argument can be used to select a namespace other than the default.

transform

The dataset’s georeferencing transformation matrix

This transform maps pixel row/column coordinates to coordinates in the dataset’s coordinate reference system.

Returns

Return type

Affine

units

one units string for each dataset band

Possible values include ‘meters’ or ‘degC’. See the Pint project for a suggested list of units.

To set units, one for each band is required.

Returns

Return type

list of str

Type

A list of str

update_tags()

Updates the tags of a dataset or one of its bands.

Tags are pairs of key and value strings. Tags belong to namespaces. The standard namespaces are: default (None) and ‘IMAGE_STRUCTURE’. Applications can create their own additional namespaces.

The optional bidx argument can be used to select the dataset band. The optional ns argument can be used to select a namespace other than the default.

width
window(left, bottom, right, top, precision=None)

Get the window corresponding to the bounding coordinates.

The resulting window is not cropped to the row and column limits of the dataset.

Parameters

  • left (float) – Left (west) bounding coordinate

  • bottom (float) – Bottom (south) bounding coordinate

  • right (float) – Right (east) bounding coordinate

  • top (float) – Top (north) bounding coordinate

  • precision (int, optional) – Number of decimal points of precision when computing inverse transform.

Returns

window

Return type

Window

window_bounds(window)

Get the bounds of a window

Parameters

window (rasterio.windows.Window) – Dataset window

Returns

bounds – x_min, y_min, x_max, y_max for the given window

Return type

tuple

window_transform(window)

Get the affine transform for a dataset window.

Parameters

window (rasterio.windows.Window) – Dataset window

Returns

transform – The affine transform matrix for the given window

Return type

Affine

write()

Write the src array into indexed bands of the dataset.

If indexes is a list, the src must be a 3D array of matching shape. If an int, the src must be a 2D array.

See read() for usage of the optional window argument.

write_band()

Write the src array into the bidx band.

Band indexes begin with 1: read_band(1) returns the first band.

The optional window argument takes a tuple like:

((row_start, row_stop), (col_start, col_stop))

specifying a raster subset to write into.

write_colormap()

Write a colormap for a band to the dataset.

write_mask()

Write the valid data mask src array into the dataset’s band mask.

The optional window argument takes a tuple like:

((row_start, row_stop), (col_start, col_stop))

specifying a raster subset to write into.

write_transform()
xy(row, col, offset='center')

Returns the coordinates (x, y) of a pixel at row and col. The pixel’s center is returned by default, but a corner can be returned by setting offset to one of ul, ur, ll, lr.

Parameters

  • row (int) – Pixel row.

  • col (int) – Pixel column.

  • offset (str, optional) – Determines if the returned coordinates are for the center of the pixel or for a corner.

Returns

(x, y)

Return type

tuple

class rasterio.io.MemoryFile(file_or_bytes=None, filename=None, ext='')

Bases: rasterio._io.MemoryFileBase

A BytesIO-like object, backed by an in-memory file.

This allows formatted files to be read and written without I/O.

A MemoryFile created with initial bytes becomes immutable. A MemoryFile created without initial bytes may be written to using either file-like or dataset interfaces.

Examples

A GeoTIFF can be loaded in memory and accessed using the GeoTIFF format driver

>>> with open('tests/data/RGB.byte.tif', 'rb') as f, MemoryFile(f) as memfile:
...     with memfile.open() as src:
...         pprint.pprint(src.profile)
...
{'count': 3,
 'crs': CRS({'init': 'epsg:32618'}),
 'driver': 'GTiff',
 'dtype': 'uint8',
 'height': 718,
 'interleave': 'pixel',
 'nodata': 0.0,
 'tiled': False,
 'transform': Affine(300.0379266750948, 0.0, 101985.0,
       0.0, -300.041782729805, 2826915.0),
 'width': 791}
close()

Close MemoryFile and release allocated memory.

exists()

Test if the in-memory file exists.

Returns

True if the in-memory file exists.

Return type

bool

getbuffer()

Return a view on bytes of the file.

open(driver=None, width=None, height=None, count=None, crs=None, transform=None, dtype=None, nodata=None, sharing=False, **kwargs)

Open the file and return a Rasterio dataset object.

If data has already been written, the file is opened in ‘r’ mode. Otherwise, the file is opened in ‘w’ mode.

Parameters

  • well that there is no path parameter (Note) –

  • a single dataset and there is no need to specify a (contains) –

  • path. –

  • parameters are optional and have the same semantics as the (Other) –

  • of rasterio.open() (parameters) –

read()

Read size bytes from MemoryFile.

seek()

Seek to position in MemoryFile.

tell()

Tell current position in MemoryFile.

write()

Write data bytes to MemoryFile

class rasterio.io.ZipMemoryFile(file_or_bytes=None)

Bases: rasterio.io.MemoryFile

A read-only BytesIO-like object backed by an in-memory zip file.

This allows a zip file containing formatted files to be read without I/O.

close()

Close MemoryFile and release allocated memory.

exists()

Test if the in-memory file exists.

Returns

True if the in-memory file exists.

Return type

bool

getbuffer()

Return a view on bytes of the file.

open(path, driver=None, sharing=False, **kwargs)

Open a dataset within the zipped stream.

Parameters

  • path (str) – Path to a dataset in the zip file, relative to the root of the archive.

  • parameters are optional and have the same semantics as the (Other) –

  • of rasterio.open() (parameters) –

Returns

Return type

A Rasterio dataset object

read()

Read size bytes from MemoryFile.

seek()

Seek to position in MemoryFile.

tell()

Tell current position in MemoryFile.

write()

Write data bytes to MemoryFile

rasterio.io.get_writer_for_driver(driver)

Return the writer class appropriate for the specified driver.

rasterio.io.get_writer_for_path(path)

Return the writer class appropriate for the existing dataset.