Command Line User Guide

Rasterio's command line interface (CLI) is a program named "rio" [1].

The CLI allows you to build workflows using shell commands, either interactively at the command prompt or with a script. Many common cases are covered by CLI sub-commands and it is often more convenient to use a ready-made command as opposed to implementing similar functionality as a python script.

The rio program is developed using the Click framework. Its plugin system allows external modules to share a common namespace and handling of context variables.

$ rio --help                                                                                                                         ⏎
Usage: rio [OPTIONS] COMMAND [ARGS]...

  Rasterio command line interface.

Options:
  -v, --verbose           Increase verbosity.
  -q, --quiet             Decrease verbosity.
  --aws-profile TEXT      Select a profile from the AWS credentials file
  --aws-no-sign-requests  Make requests anonymously
  --aws-requester-pays    Requester pays data transfer costs
  --version               Show the version and exit.
  --gdal-version
  --help                  Show this message and exit.

Commands:
  blocks     Write dataset blocks as GeoJSON features.
  bounds     Write bounding boxes to stdout as GeoJSON.
  calc       Raster data calculator.
  clip       Clip a raster to given bounds.
  convert    Copy and convert raster dataset.
  edit-info  Edit dataset metadata.
  env        Print information about the Rasterio environment.
  gcps       Print ground control points as GeoJSON.
  info       Print information about a data file.
  insp       Open a data file and start an interpreter.
  mask       Mask in raster using features.
  merge      Merge a stack of raster datasets.
  overview   Construct overviews in an existing dataset.
  rasterize  Rasterize features.
  rm         Delete a dataset.
  sample     Sample a dataset.
  shapes     Write shapes extracted from bands or masks.
  stack      Stack a number of bands into a multiband dataset.
  transform  Transform coordinates.
  warp       Warp a raster dataset.

Commands are shown below. See --help of individual commands for more details.

creation options

For commands that create new datasets, format specific creation options may also be passed using --co. For example, to tile a new GeoTIFF output file, add the following.

--co tiled=true --co blockxsize=256 --co blockysize=256

To compress it using the LZW method, add

--co compress=LZW

bounds

Added in 0.10.

The bounds command writes the bounding boxes of raster datasets to GeoJSON for use with, e.g., geojsonio-cli.

$ rio bounds tests/data/RGB.byte.tif --indent 2
{
  "features": [
    {
      "geometry": {
        "coordinates": [
          [
            [
              -78.898133,
              23.564991
            ],
            [
              -76.599438,
              23.564991
            ],
            [
              -76.599438,
              25.550874
            ],
            [
              -78.898133,
              25.550874
            ],
            [
              -78.898133,
              23.564991
            ]
          ]
        ],
        "type": "Polygon"
      },
      "properties": {
        "id": "0",
        "title": "tests/data/RGB.byte.tif"
      },
      "type": "Feature"
    }
  ],
  "type": "FeatureCollection"
}

Shoot the GeoJSON into a Leaflet map using geojsonio-cli by typing rio bounds tests/data/RGB.byte.tif | geojsonio.

calc

Added in 0.19

The calc command reads files as arrays, evaluates lisp-like expressions in their context, and writes the result as a new file. Members of the numpy module and arithmetic and logical operators are available builtin functions and operators. It is intended for simple calculations; any calculations requiring multiple steps is better done in Python using the Rasterio and Numpy APIs.

Input files may have different numbers of bands but should have the same number of rows and columns. The output file will have the same number of rows and columns as the inputs and one band per element of the expression result. An expression involving arithmetic operations on N-D arrays will produce a N-D array and result in an N-band output file.

The following produces a 3-band GeoTIFF with all values scaled by 0.95 and incremented by 2. In the expression, (read 1) evaluates to the first input dataset (3 bands) as a 3-D array.

$ rio calc "(+ 2 (* 0.95 (read 1)))" tests/data/RGB.byte.tif /tmp/out.tif

The following produces a 3-band GeoTIFF in which the first band is copied from the first band of the input and the next two bands are scaled (down) by the ratio of the first band's mean to their own means. The --name option is used to bind datasets to a name within the expression. (take a 1) gets the first band of the dataset named a as a 2-D array and (asarray ...) collects a sequence of 2-D arrays into a 3-D array for output.

$ rio calc "(asarray (take a 1) (* (take a 2) (/ (mean (take a 1)) (mean (take a 2)))) (* (take a 3) (/ (mean (take a 1)) (mean (take a 3)))))" \
> --name a=tests/data/RGB.byte.tif /tmp/out.rgb.tif

The command above is also an example of a calculation that is far beyond the design of the calc command and something that could be done much more efficiently in Python.

clip

Added in 0.27

The clip command clips a raster using bounds input directly or from a template raster.

$ rio clip input.tif output.tif --bounds xmin ymin xmax ymax
$ rio clip input.tif output.tif --like template.tif

If using --bounds, values must be in coordinate reference system of input. If using --like, bounds will automatically be transformed to match the coordinate reference system of the input.

It can also be combined to read bounds of a feature dataset using Fiona:

$ rio clip input.tif output.tif --bounds $(fio info features.shp --bounds)

convert

Added in 0.25

The convert command copies and converts raster datasets to other data types and formats (similar to gdal_translate).

Data values may be linearly scaled when copying by using the --scale-ratio and --scale-offset options. Destination raster values are calculated as

dst = scale_ratio * src + scale_offset

For example, to scale uint16 data with an actual range of 0-4095 to 0-255 as uint8:

$ rio convert in16.tif out8.tif --dtype uint8 --scale-ratio 0.0625

You can use --rgb as shorthand for --co photometric=rgb.

edit-info

Added in 0.24

The edit-info command allows you edit a raster dataset's metadata, namely

coordinate reference system
affine transformation matrix
nodata value
tags
color interpretation

A TIFF created by spatially-unaware image processing software like Photoshop or Imagemagick can be turned into a GeoTIFF by editing these metadata items.

For example, you can set or change a dataset's coordinate reference system to Web Mercator (EPSG:3857),

$ rio edit-info --crs EPSG:3857 example.tif

set its :ref:`affine transformation matrix <coordinate-transformation>`,

$ rio edit-info --transform "[300.0, 0.0, 101985.0, 0.0, -300.0, 2826915.0]" example.tif

or set its nodata value to, e.g., 0:

$ rio edit-info --nodata 0 example.tif

or set its color interpretation to red, green, blue, and alpha:

$ rio edit-info --colorinterp 1=red,2=green,3=blue,4=alpha example.tif

which can also be expressed as:

$ rio edit-info --colorinterp RGBA example.tif

See :class:`rasterio.enums.ColorInterp` for a full list of supported color interpretations and the color docs for more information.

info

Added in 0.13

The info command prints structured information about a dataset.

$ rio info tests/data/RGB.byte.tif --indent 2
{
  "count": 3,
  "crs": "EPSG:32618",
  "dtype": "uint8",
  "driver": "GTiff",
  "bounds": [
    101985.0,
    2611485.0,
    339315.0,
    2826915.0
  ],
  "lnglat": [
    -77.75790625255473,
    24.561583285327067
  ],
  "height": 718,
  "width": 791,
  "shape": [
    718,
    791
  ],
  "res": [
    300.0379266750948,
    300.041782729805
  ],
  "nodata": 0.0
}

More information, such as band statistics, can be had using the --verbose option.

$ rio info tests/data/RGB.byte.tif --indent 2 --verbose
{
  "count": 3,
  "crs": "EPSG:32618",
  "stats": [
    {
      "max": 255.0,
      "mean": 44.434478650699106,
      "min": 1.0
    },
    {
      "max": 255.0,
      "mean": 66.02203484105824,
      "min": 1.0
    },
    {
      "max": 255.0,
      "mean": 71.39316199120559,
      "min": 1.0
    }
  ],
  "dtype": "uint8",
  "driver": "GTiff",
  "bounds": [
    101985.0,
    2611485.0,
    339315.0,
    2826915.0
  ],
  "lnglat": [
    -77.75790625255473,
    24.561583285327067
  ],
  "height": 718,
  "width": 791,
  "shape": [
    718,
    791
  ],
  "res": [
    300.0379266750948,
    300.041782729805
  ],
  "nodata": 0.0
}

insp

The insp command opens a dataset and an interpreter.

$ rio insp --ipython tests/data/RGB.byte.tif
Rasterio 0.32.0 Interactive Inspector (Python 2.7.10)
Type "src.meta", "src.read(1)", or "help(src)" for more information.
In [1]: print(src.name)
/path/rasterio/tests/data/RGB.byte.tif

In [2]: print(src.bounds)
BoundingBox(left=101985.0, bottom=2611485.0, right=339315.0, top=2826915.0)

mask

Added in 0.21

The mask command masks in pixels from all bands of a raster using features (masking out all areas not covered by features) and optionally crops the output raster to the extent of the features. Features are assumed to be in the same coordinate reference system as the input raster.

A common use case is masking in raster data by political or other boundaries.

$ rio mask input.tif output.tif --geojson-mask input.geojson

GeoJSON features may be provided using stdin or specified directly as first argument, and output can be cropped to the extent of the features.

$ rio mask input.tif output.tif --crop --geojson-mask - < input.geojson

The feature mask can be inverted to mask out pixels covered by features and keep pixels not covered by features.

$ rio mask input.tif output.tif --invert --geojson-mask input.geojson

merge

Added in 0.12.1

The merge command can be used to flatten a stack of identically structured datasets.

$ rio merge rasterio/tests/data/R*.tif merged.tif

overview

New in 0.25

The overview command creates overviews stored in the dataset, which can improve performance in some applications.

The decimation levels at which to build overviews can be specified as a comma separated list

$ rio overview --build 2,4,8,16

or a base and range of exponents.

$ rio overview --build 2^1..4

Note that overviews can not currently be removed and are not automatically updated when the dataset's primary bands are modified.

Information about existing overviews can be printed using the --ls option.

$ rio overview --ls

The block size (tile width and height) used for overviews (internal or external) can be specified by setting the GDAL_TIFF_OVR_BLOCKSIZE environment variable to a power-of-two value between 64 and 4096. The default value is 128.

$ GDAL_TIFF_OVR_BLOCKSIZE=256 rio overview --build 2^1..4

rasterize

New in 0.18.

The rasterize command rasterizes GeoJSON features into a new or existing raster.

$ rio rasterize test.tif --res 0.0167 < input.geojson

The resulting file will have an upper left coordinate determined by the bounds of the GeoJSON (in EPSG:4326, which is the default), with a pixel size of approximately 30 arc seconds. Pixels whose center is within the polygon or that are selected by Bresenham's line algorithm will be burned in with a default value of 1.

It is possible to rasterize into an existing raster and use an alternative default value:

$ rio rasterize existing.tif --default_value 10 < input.geojson

It is also possible to rasterize using a template raster, which will be used to determine the transform, dimensions, and coordinate reference system of the output raster:

$ rio rasterize test.tif --like tests/data/shade.tif < input.geojson

GeoJSON features may be provided using stdin or specified directly as first argument, and dimensions may be provided in place of pixel resolution:

$ rio rasterize input.geojson test.tif --dimensions 1024 1024

Other options are available, see:

$ rio rasterize --help

rm

New in 1.0

Invoking the shell's $ rm <path> on a dataset can be used to delete a dataset referenced by a file path, but it won't handle deleting side car files. This command is aware of datasets and their sidecar files.

sample

New in 0.18.

The sample command reads x, y positions from stdin and writes the dataset values at that position to stdout.

$ cat << EOF | rio sample tests/data/RGB.byte.tif
> [220649.99999832606, 2719199.999999095]
> EOF
[18, 25, 14]

The output of the transform command (see below) makes good input for sample.

shapes

New in 0.11.

The shapes command extracts and writes features of a specified dataset band out as GeoJSON.

$ rio shapes tests/data/shade.tif --bidx 1 --precision 6 --collection > shade.geojson

The resulting file looks like this.

Using the --mask option you can write out the shapes of a dataset's valid data region.

$ rio shapes tests/data/RGB.byte.tif --mask --precision 6 --collection > mask.geojson

The output of which looks like this.

Note: rio shapes returns line-delimited GeoJSONs by default. Use the --collection flag as shown here to return a single GeoJSON feature collection.

stack

New in 0.15.

The stack command stacks a number of bands from one or more input files into a multiband dataset. Input datasets must be of a kind: same data type, dimensions, etc. The output is cloned from the first input. By default, stack will take all bands from each input and write them in same order to the output. Optionally, bands for each input may be specified using the following syntax:

--bidx N takes the Nth band from the input (first band is 1).
--bidx M,N,O takes bands M, N, and O.
--bidx M..O takes bands M-O, inclusive.
--bidx ..N takes all bands up to and including N.
--bidx N.. takes all bands from N to the end.

Examples using the Rasterio testing dataset that produce a copy of it.

$ rio stack RGB.byte.tif stacked.tif
$ rio stack RGB.byte.tif --bidx 1,2,3 stacked.tif
$ rio stack RGB.byte.tif --bidx 1..3 stacked.tif
$ rio stack RGB.byte.tif --bidx ..2 RGB.byte.tif --bidx 3.. stacked.tif

You can use --rgb as shorthand for --co photometric=rgb.

transform

New in 0.10.

The transform command reads a JSON array of coordinates, interleaved, and writes another array of transformed coordinates to stdout.

To transform a longitude, latitude point (EPSG:4326 is the default) to another coordinate system with 2 decimal places of output precision, do the following.

$ echo "[-78.0, 23.0]" | rio transform - --dst-crs EPSG:32618 --precision 2
[192457.13, 2546667.68]

To transform a longitude, latitude bounding box to the coordinate system of a raster dataset, do the following.

$ echo "[-78.0, 23.0, -76.0, 25.0]" | rio transform - --dst-crs tests/data/RGB.byte.tif --precision 2
[192457.13, 2546667.68, 399086.97, 2765319.94]

warp

New in 0.25

The warp command warps (reprojects) a raster based on parameters that can be obtained from a template raster, or input directly. The output is always overwritten.

To copy coordinate reference system, transform, and dimensions from a template raster, do the following:

$ rio warp input.tif output.tif --like template.tif

You can specify an output coordinate system using a PROJ.4 or EPSG:nnnn string, or a JSON text-encoded PROJ.4 object:

$ rio warp input.tif output.tif --dst-crs EPSG:4326

$ rio warp input.tif output.tif --dst-crs '+proj=longlat +ellps=WGS84 +datum=WGS84'

You can also specify dimensions, which will automatically calculate appropriate resolution based on the relationship between the bounds in the target crs and these dimensions:

$ rio warp input.tif output.tif --dst-crs EPSG:4326 --dimensions 100 200

Or provide output bounds (in source crs) and resolution:

$ rio warp input.tif output.tif --dst-crs EPSG:4326 --bounds -78 22 -76 24 --res 0.1

Previous command in case of south-up image, -- escapes the next -:

$ rio warp input.tif output.tif --dst-crs EPSG:4326 --bounds -78 22 -76 24 --res 0.1 -- -0.1

Other options are available, see:

$ rio warp --help

Rio Plugins

Rio uses click-plugins to provide the ability to create additional subcommands using plugins developed outside rasterio. This is ideal for commands that require additional dependencies beyond those used by rasterio, or that provide functionality beyond the intended scope of rasterio.

For example, rio-mbtiles provides a command rio mbtiles to export a raster to an MBTiles file.

See click-plugins for more information on how to build these plugins in general.

To use these plugins with rio, add the commands to the rasterio.rio_plugins entry point in your setup.py file, as described here and in rasterio/rio/main.py.

See the plugin registry for a list of available plugins.

Other commands?

Suggestions for other commands are welcome!

[1]	In some Linux distributions "rio" may instead refer to the command line Diamond Rio MP3 player controller. This conflict can be avoided by installing Rasterio in an isolated Python environment.

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Command Line User Guide

creation options

bounds

calc

clip

convert

edit-info

info

insp

mask

merge

overview

rasterize

rm

sample

shapes

stack

transform

warp

Rio Plugins

Other commands?

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Command Line User Guide

creation options

bounds

calc

clip

convert

edit-info

info

insp

mask

merge

overview

rasterize

rm

sample

shapes

stack

transform

warp

Rio Plugins

Other commands?