arcgis.raster.functions module¶

Raster functions allow you to define processing operations that will be applied to one or more rasters. These functions are applied to the raster data on the fly as the data is accessed and viewed; therefore, they can be applied quickly without having to endure the time it would otherwise take to create a processed product on disk, for which raster analytics tools like arcgis.raster.analytics.generate_raster can be used.

Functions can be applied to various rasters (or images), including the following:

Imagery layers
Rasters within imagery layers

abs¶

arcgis.raster.functions.abs(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Abs operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

acos¶

arcgis.raster.functions.acos(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The acos operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

acosh¶

arcgis.raster.functions.acosh(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The ACosH operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

aggregate¶

arcgis.raster.functions.aggregate(raster, dimension=None, aggregation_function=None, aggregation_definition_type='ALL', interval_keyword=None, interval_value=None, interval_unit=None, interval_ranges=None, ignore_nodata=False)¶

Creates a new raster by applying an aggregation function

Parameters

raster – Input Raster.
aggregation_function –
Optional String. Specifies the mathematical method that will be used to combine the aggregated slices in an interval.
- MEAN : Calculates the mean of a pixel’s values across all slices in the interval. This is the default.
- MAXIMUM : Calculates the maximum value of a pixel across all slices in the interval.
- MAJORITY : Calculates the value that occurred most frequently for a pixel across all slices in the interval.
- MINIMUM : Calculates the minimum value of a pixel across all slices in the interval.
- MINORITY : Calculates the value that occurred least frequently for a pixel across all slices in the interval.
- MEDIAN : Calculates the median value of a pixel across all slices in the interval.
- RANGE : Calculates the range of values for a pixel across all slices in the interval.
- STD : Calculates the standard deviation of a pixel’s values across all slices in the interval.
- SUM : Calculates the sum of a pixel’s values across all slices in the interval.
- VARIETY : Calculates the number of unique values of a pixel across all slices in the interval.
You may also pass custom aggregaation function. Create an RFT object out of the raster function template item on the portal and specify that as the input to aggregation_function or directly specify the RFT in JSON format as the aggregation_method.
aggregation_definition_type –
Optional String. Specifies the dimension interval for which the data will be aggregated.
- ALL : The data values will be aggregated across all slices. This is the default.
- INTERVAL_KEYWORD : The variable data will be aggregated using a commonly known interval.
- INTERVAL_VALUE : The variable data will be aggregated using a user-specified interval and unit.
- INTERVAL_RANGES : The variable data will be aggregated between specified pairs of values or dates.
dimension – Optional String. This is the dimension along which the variables will be aggregated.
interval_keyword –
Optional String. Specifies the keyword interval that will be used when aggregating along the dimension. This parameter is required when the aggregation_def parameter is set to INTERVAL_KEYWORD, and the aggregation must be across time.
- HOURLY : The data values will be aggregated into hourly time steps, and the result will include every hour in the time series.
- DAILY : The data values will be aggregated into daily time steps, and the result will include every day in the time series.
- WEEKLY : The data values will be aggregated into weekly time steps, and the result will include every week in the time series.
- DEKADLY : Divides each month into 3 periods of 10 days each (last period might have more or less than 10 days) and each month would output 3 slices.
- PENTADLY : Divides each month into 6 periods of 5 days each (last period might have more or less than 5 days) and each month would output 6 slices.
- MONTHLY : The data values will be aggregated into monthly time steps, and the result will include every month in the time series.
- QUARTERLY : The data values will be aggregated into quarterly time steps, and the result will include every quarter in the time series.
- YEARLY : The data values will be aggregated into yearly time steps, and the result will include every year in the time series.
- RECURRING_DAILY : The data values will be aggregated into daily time steps, and the result includes each one aggregated value per day. The output will include, at most, 366 daily time slices
- RECURRING_WEEKLY : The data values will be aggregated into weekly time steps, and the result will include one aggregated value per week. The output will include, at most, 53 weekly time slices.
- RECURRING_MONTHLY : The data values will be aggregated into weekly time steps, and the result will include one aggregated value per month. The output will include, at most, 12 monthly time slices.
- RECURRING_QUARTERLY : The data values will be aggregated into weekly time steps, and the result will include one aggregated value per quarter. The output will include, at most, 4 quarterly time slices.
interval_value –
Optional String. The size of the interval that will be used for the aggregation. This parameter is required when the aggregation_def parameter is set to INTERVAL_VALUE.

For example, to aggregate 30 years of monthly temperature data into 5-year increments, enter 5 as the interval_value, and specify interval_unit as YEARS.
interval_unit –
Optional String. The unit that will be used for the interval value. This parameter is required when the dimension parameter is set to a time field and the aggregation_def parameter is set to INTERVAL_VALUE.

If you are aggregating over anything other than time, this option will not be available and the unit for the interval value will match the variable unit of the input multidimensional raster data.
- HOURS : The data values will be aggregated into hourly time slices at the interval provided.
- DAYS : The data values will be aggregated into daily time slices at the interval provided.
- WEEKS : The data values will be aggregated into weekly time slices at the interval provided.
- MONTHS : The data values will be aggregated into monthly time slices at the interval provided.
- YEARS : The data values will be aggregated into yearly time slices at the interval provided.
interval_ranges –
Optional List of dictionary objects. Interval ranges specified as list of dictionary objects that will be used to aggregate groups of values.

This parameter is required when the aggregation_definition parameter is set to INTERVAL_RANGE. If dimension is StdTime, then the value must be specified in human readable time format (YYYY-MM-DDTHH:MM:SS).

Syntax:
[{“minValue”:”<min value>”,”maxValue”:”<max value>”}, {“minValue”:”<min value>”,”maxValue”:”<max value>”}]

Example:
[{“minValue”:”2012-01-15T03:00:00”,”maxValue”:”2012-01-15T09:00:00”}, {“minValue”:”2012-01-15T12:00:00”,”maxValue”:”2012-01-15T21:00:00”}]
ignore_nodata –
Optional Boolean. Specifies whether NoData values are ignored.
- True : The function will include all valid pixels and ignore any NoData pixels. This is the default.
- False : The function will result in NoData if there are any NoData values.

Returns

the output raster with function applied on it

aggregate_cells¶

arcgis.raster.functions.aggregate_cells(raster, cell_factor=2, aggregation_type=9, extent_handling=False, ignore_nodata=False)¶

Generates a reduced-resolution version of a raster.

Parameters

raster – the input raster
cell_factor – The factor by which to multiply the cell size of the input raster to obtain the desired resolution for the output raster. For example, a cell factor value of three would result in an output cell size three times larger than that of the input raster. The value must be an integer greater than 1.
aggregation_type –
Optional int. Establishes how the value for each output cell will be determined. The values of the input cells encompassed by the coarser output cell are aggregated by one of the following statistics:
- 2 (MAXIMUM) : The largest value of the input cells.
- 3 (MEAN) : The average value of the input cells.
- 4 (MEDIAN) : The median value of the input cells.
- 5 (MINIMUM) : The smallest value of the input cells.
- 9 (SUM) : The sum (total) of the input cell values.This is the default.
extent_handling –
Optional boolean. Defines how to handle the boundaries of the input raster when its rows or columns are not a multiple of the cell factor.
- True : Expands the top or right boundaries of the input raster so the total number of cells in a row or column is a multiple of the cell factor. Those expanded cells are given a value of NoData. With this option, the output raster can cover a larger spatial extent than the input raster. This is the default.
- False : Reduces the number of rows or columns in the output raster by 1. This will truncate the remaining cells on the top or right boundaries of the input raster, making the number of rows or columns in the input raster a multiple of the cell factor. With this option, the output raster can cover a smaller spatial extent than the input raster.
ignore_nodata –
Optional boolean. Denotes whether NoData values are ignored by the aggregation calculation.
- True : Specifies that if NoData values exist for any of the cells that fall within the spatial extent of a larger cell on the output raster, the NoData values will be ignored when determining the value for output cell locations. Only input cells within the extent of the output cell that have data values will be used in determining the value of the output cell. This is the default.
- False : Specifies that if any cell that falls within the spatial extent of a larger cell on the output raster has a value of NoData, the value for that output cell location will be NoData.When the this option is used, it is implied that when cells within an aggregation contain the NoData value, there is insufficient information to perform the specified calculations necessary to determine an output value.

Returns

output raster

apparent_reflectance¶

arcgis.raster.functions.apparent_reflectance(raster, radiance_gain_values=None, radiance_bias_values=None, reflectance_gain_values=None, reflectance_bias_values=None, sun_elevation=None, albedo=False, scale_factor=None, offset=None)¶

Function calibrates the digital number (DN) values of imagery from some satellite sensors. The calibration uses sun elevation, acquisition date, sensor gain and bias for each band to derive Top of Atmosphere reflectance, plus sun angle correction.

Argument	Description
raster	Required ImageryLayer object.
radiance_gain_values	Optional list. The radiance gain values
radiance_bias_values	Optional list. The radiance bias values
reflectance_gain_values	Optional list. The reflectance gain values
reflectance_bias_values	Optional list. The reflectance bias values
sun_elevation	This is sun elevation value, expressed in degrees.
albedo	Optional bool The results of the Apparent Reflectance function can also be expressed as albedo, which is the percentage of the available energy reflected by the planetary surface. Albedo data is used by scientific users for complex modeling and technical remote-sensing applications. False - The function returns apparent reflectance values. This is the default. True - The function returns 32-bit floating-point values, which most commonly are in the range of 0.0 to 1.0. No data clipping is performed if this option is selected.
scale_factor	Optional int. Your apparent reflectance output value can be expressed as an integer. The scaling factor is multiplied by the albedo to convert all floating-point values into integer values. If the scale factor is either 0 or not specified, default scaling will be applied depending on the pixel type of the input data: For 16-bit unsigned data types, the default scale factor is 50,000. For 8-bit unsigned data types, default scale factor is 255. The scaling factor is always applied when the output is apparent reflectance. No scaling is applied when the output is albedo.
offset	Optional int. Your scaled albedo value can optionally have an offset value: For 16-bit unsigned data types, the default scale offset is 5,000. For 8-bit unsigned data types, the default scale offset is 0. No scaling is applied when the output is albedo.

Returns: Imagery layer

apply¶

arcgis.raster.functions.apply(raster, fn_name, **kwargs)¶

Applies a server side raster function template defined by the imagery layer (image service) The name of the raster function template is available in the imagery layer properties.rasterFunctionInfos.

Function arguments are optional; argument names and default values are created by the author of the raster function template and are not known through the API. A client can simply provide the name of the raster function template only or, optionally, provide arguments to overwrite the default values. For more information about authoring server-side raster function templates, see Server-side raster functions.

Parameters

raster – the input raster, or imagery layer
fn_name – name of the server side raster function template, See imagery layer properties.rasterFunctionInfos
kwargs – keyword arguments to override the default values of the raster function template, including astype

Returns

the output raster

arg_max¶

arcgis.raster.functions.arg_max(rasters, undefined_class=None, astype=None)¶

In the ArgMax method, all raster bands from every input raster are assigned a 0-based incremental band index, which is first ordered by the input raster index, as shown in the table below, and then by the relative band order within each input raster.

See http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/argstatistics-function.htm

Parameters

rasters – the imagery layers filtered by where clause, spatial and temporal filters
undefined_class – int, required

Returns

the output raster with this function applied to it

arg_median¶

arcgis.raster.functions.arg_median(rasters, undefined_class=None, astype=None)¶

The ArgMedian method returns the Band index for which the given pixel attains the median value of values from all bands.

Consider values from all bands as an array. After sorting the array in ascending order, the median is the one value separating the lower half of the array from the higher half. More specifically, if the ascend-sorted array has n values, the median is the ith (0-based) value, where: i = ( (n-1) / 2 )

See http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/argstatistics-function.htm

Parameters

rasters – the imagery layers filtered by where clause, spatial and temporal filters
undefined_class – int, required

Returns

the output raster with this function applied to it

arg_min¶

arcgis.raster.functions.arg_min(rasters, undefined_class=None, astype=None)¶

ArgMin is the argument of the minimum, which returns the Band index for which the given pixel attains its minimum value.

See http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/argstatistics-function.htm

Parameters

rasters – the imagery layers filtered by where clause, spatial and temporal filters
undefined_class – int, required

Returns

the output raster with this function applied to it

arg_statistics¶

arcgis.raster.functions.arg_statistics(rasters, stat_type=None, min_value=None, max_value=None, undefined_class=None, astype=None)¶

The arg_statistics function produces an output with a pixel value that represents a statistical metric from all bands of input rasters. The statistics can be the band index of the maximum, minimum, or median value, or the duration (number of bands) between a minimum and maximum value

See http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/argstatistics-function.htm

Parameters

rasters – the imagery layers filtered by where clause, spatial and temporal filters
stat_type – one of “max”, “min”, “median”, “duration”
min_value – double, required if the type is duration
max_value – double, required if the type is duration
undefined_class – int, required if the type is maximum or minimum

Returns

the output raster with this function applied to it

arithmetic¶

arcgis.raster.functions.arithmetic(raster1, raster2, extent_type='FirstOf', cellsize_type='FirstOf', astype=None, operation_type=1)¶

The Arithmetic function performs an arithmetic operation between two rasters or a raster and a scalar, and vice versa.

Parameters

raster1 – the first raster- imagery layers filtered by where clause, spatial and temporal filters
raster2 – the 2nd raster - imagery layers filtered by where clause, spatial and temporal filters
extent_type – one of “FirstOf”, “IntersectionOf” “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf “MeanOf”, “LastOf”
operation_type – int 1 = Plus, 2 = Minus, 3 = Multiply, 4=Divide, 5=Power, 6=Mode

Returns

the output raster with this function applied to it

asin¶

arcgis.raster.functions.asin(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The asin operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

asinh¶

arcgis.raster.functions.asinh(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The ASinH operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

aspect¶

arcgis.raster.functions.aspect(raster)¶

aspect identifies the downslope direction of the maximum rate of change in value from each cell to its neighbors. Aspect can be thought of as the slope direction. The values of the output raster will be the compass direction of the aspect. For more information, see Aspect function and How Aspect works.

Parameters: raster – the input raster / imagery layer
Returns: aspect applied to the input raster

aspect_slope¶

arcgis.raster.functions.aspect_slope(raster, z_factor=1)¶

Creates a raster layer that simultaneously displays the aspect and slope of a surface.

Argument	Description
raster	The input raster.
z_factor	A multiplication factor that converts the vertical (elevation) values to the linear units of the horizontal (x,y) coordinate system. “Use larger values to add vertical exaggeration.

Returns: the output raster

atan¶

arcgis.raster.functions.atan(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The ATan operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

atan2¶

arcgis.raster.functions.atan2(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The ATan2 operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

atanh¶

arcgis.raster.functions.atanh(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The ATanH operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

bai¶

arcgis.raster.functions.bai(raster, band_indexes='3 4', astype=None)¶

The Burn Area Index (BAI) uses the reflectance values in the red and NIR portion of the spectrum to identify the areas of the terrain affected by fire.

BAI = 1/((0.1 -RED)^2 + (0.06 - NIR)^2)

Parameters

raster – the input raster / imagery layer
band_indexes – “Red NIR”, e.g., “3 4” or [3,4]
astype – output pixel type

Returns

output raster

band_arithmetic¶

arcgis.raster.functions.band_arithmetic(raster, band_indexes=None, astype=None, method=0)¶

The band_arithmetic function performs an arithmetic operation on the bands of a raster. For more information, see Band Arithmetic function at http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/band-arithmetic-function.htm

Parameters

raster – the input raster / imagery layer
band_indexes – band indexes or expression. Band indexes can be given as a space seperated string or a list of integers or floating point values. e.g., “4 3” or [4,3]. For user defined methods the band index can be given as an expression such as “(B3 - B1)/(B3 + B1)”
astype – output pixel type
method – int. The type of band arithmetic algorithm you want to deploy. You can define your custom algorithm, or choose a predefined index. 0 = UserDefined, 1 = NDVI, 2 = SAVI, 3 = TSAVI, 4 = MSAVI, 5 = GEMI, 6 = PVI, 7 = GVITM, 8 = Sultan, 9 = VARI, 10 = GNDVI, 11 = SR, 12 = NDVIre, 13 = SRre, 14 = MTVI2, 15 = RTVICore, 16 = CIre, 17 = CIg, 18 = NDWI, 19 = EVI, 20 = IronOxide, 21 = FerrousMinerals, 22 = ClayMinerals, 23 = WNDWI, 24 = BAI, 25 = NBR, 26 = NDBI, 27 = NDMI, 28 = NDSI, 29 = MNDWI

Returns

band_arithmetic applied to the input raster

bitwise_and¶

arcgis.raster.functions.bitwise_and(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BitwiseAnd operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

bitwise_left_shift¶

arcgis.raster.functions.bitwise_left_shift(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BitwiseLeftShift operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

bitwise_not¶

arcgis.raster.functions.bitwise_not(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BitwiseNot operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

bitwise_or¶

arcgis.raster.functions.bitwise_or(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BitwiseOr operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

bitwise_right_shift¶

arcgis.raster.functions.bitwise_right_shift(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BitwiseRightShift operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

bitwise_xor¶

arcgis.raster.functions.bitwise_xor(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BitwiseXOr operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

boolean_and¶

arcgis.raster.functions.boolean_and(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BooleanAnd operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

boolean_not¶

arcgis.raster.functions.boolean_not(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BooleanNot operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

boolean_or¶

arcgis.raster.functions.boolean_or(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BooleanOr operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

boolean_xor¶

arcgis.raster.functions.boolean_xor(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The BooleanXOr operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

buffered¶

arcgis.raster.functions.buffered(raster)¶

The Buffered function is used to optimize the performance of complex function chains. It stores the output from the part of the function chain that comes before it in memory.

Parameters: raster – The input raster.
Returns: output raster

cire¶

arcgis.raster.functions.cire(raster, band_indexes='7 6', astype=None)¶

The Chlorophyll Index - Red-Edge (CIre) is a vegetation index for estimating the chlorophyll content in leaves using the ratio of reflectivity in the near-infrared (NIR) and red-edge bands.

CIre = [(NIR / RedEdge)-1]

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR RedEdge”, e.g., “7 6” or [7,6]
astype – output pixel type

Returns

output raster

cig¶

arcgis.raster.functions.cig(raster, band_indexes='7 3', astype=None)¶

The Chlorophyll Index - Green (CIg) is a vegetation index for estimating the chlorophyll content in leaves using the ratio of reflectivity in the near-infrared (NIR) and green bands.

CIg = [(NIR / Green)-1]

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Green”, e.g., “7 3” or [7,3]
astype – output pixel type

Returns

output raster

classify¶

arcgis.raster.functions.classify(raster1, raster2=None, classifier_definition=None, astype=None)¶

classifies a segmented raster to a categorical raster.

Parameters

raster1 – the first raster - imagery layers filtered by where clause, spatial and temporal filters
raster2 – Optional segmentation raster - If provided, pixels in each segment will get same class assignments. imagery layers filtered by where clause, spatial and temporal filters
classifier_definition – the classifier parameters as a Python dictionary / json format

Returns

the output raster with this function applied to it

clay_minerals¶

arcgis.raster.functions.clay_minerals(raster, band_indexes='6 7', astype=None)¶

The Clay Minerals (CM) ratio is a geological index for identifying mineral features containing clay and alunite using two shortwave infrared (SWIR) bands. CM is used in mineral composite mapping.

CM = SWIR1 / SWIR2

Parameters

raster – the input raster / imagery layer
band_indexes – “SWIR1 SWIR2”, e.g., “6 7” or [6,7]
astype – output pixel type

Returns

output raster

clip¶

arcgis.raster.functions.clip(raster, geometry=None, clip_outside=True, astype=None)¶

Clips a raster using a rectangular shape according to the extents defined or will clip a raster to the shape of an input polygon. The shape defining the clip can clip the extent of the raster or clip out an area within the raster.

Parameters

raster – input raster
geometry – clipping geometry
clip_outside – boolean, If True, the imagery outside the extents will be removed, else the imagery within the clipping_geometry will be removed.
astype – output pixel type

Returns

the clipped raster

colormap¶

arcgis.raster.functions.colormap(raster, colormap_name=None, colormap=None, colorramp=None, astype=None)¶

Transforms the pixel values to display the raster data as a color (RGB) image, based on specific colors in a color map. For more information, see Colormap function at http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/colormap-function.htm

Parameters

raster – input raster
colormap_name – colormap name, if one of Random | NDVI | Elevation | Gray
colormap –

[

[<value1>, <red1>, <green1>, <blue1>], //[int, int, int, int]

[<value2>, <red2>, <green2>, <blue2>]

],
colorramp – Can be a string specifiying color ramp name like <Black To White|Yellow To Red|Slope|more..> or a color ramp object. For more information about colorramp object, see color ramp object at https://developers.arcgis.com/documentation/common-data-types/color-ramp-objects.htm)
astype – output pixel type

Returns

the colorized raster

colormap_to_rgb¶

arcgis.raster.functions.colormap_to_rgb(raster)¶

The function is designed to work with single band image service that has internal colormap. It will convert the image into a three-band 8-bit RGB raster. This function takes no arguments except an input raster. For qualified image service, there are two situations when ColormapToRGB function is automatically applied: The “colormapToRGB” property of the image service is set to true; or, client asks to export image into jpg or png format. For more information, see http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/colormap-to-rgb-function.htm)

Parameters: raster – the input raster / imagery layer
Returns: Three band raster

colorspace_conversion¶

arcgis.raster.functions.colorspace_conversion(raster, conversion_type='rgb_to_hsv')¶

The ColorspaceConversion function converts the color model of a three-band unsigned 8-bit image from either the hue, saturation, and value (HSV) to red, green, and blue (RGB) or vice versa. An ExtractBand function and/or a Stretch function are sometimes used for converting the imagery into valid input of ColorspaceConversion function. For more information, see http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/color-model-conversion-function.htm

Parameters

raster – the input raster
conversion_type – sting type, one of “rgb_to_hsv” or “hsv_to_rgb”. Default is “rgb_to_hsv”

Returns

the output raster with this function applied to it

complex¶

arcgis.raster.functions.complex(raster)¶

Complex function computes magnitude from complex values. It is used when input raster has complex pixel type. It computes magnitude from complex value to convert the pixel type to floating point for each pixel. It takes no argument but an optional input raster. For more information, see http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/complex-function.htm

Parameters: raster – the input raster / imagery layer
Returns: Output raster obtained after applying the function

composite_band¶

arcgis.raster.functions.composite_band(rasters, astype=None, cellsize_type='MaxOf')¶

Combines multiple images to form a multiband image.

Parameters

rasters – input rasters
astype – output pixel type
cellsize_type – The cell size used to create the output raster. one of “FirstOf”, “MinOf”, “MaxOf “MeanOf”, “LastOf”

Returns

the multiband image

compute_change¶

arcgis.raster.functions.compute_change(raster1, raster2, method=0, from_class_values=[], to_class_values=[], filter_method=1, define_transition_colors=0, extent_type='IntersectionOf', cellsize_type='MaxOf')¶

Produce raster outputs representing of various changes. Function available in ArcGIS Image Server 10.8.1 and higher.

Argument	Description
raster1	Required ImageryLayer object. The first raster for compute change function. To evaluate change from time 1 (earlier) to time 2 (later), enter the time 1 raster here.
raster2	Required ImageryLayer object. The second raster for compute change function. To evaluate change from time 1 (earlier) to time 2 (later), enter the time 2 raster.
method	Optional string. Specifies the method to be used. DIFFERENCE : The mathematical difference, or subtraction, between the pixel values in the input rasters will be calculated. This is the default. RELATIVE_DIFFERENCE : The difference in pixel values, accounting for the magnitudes of the values being compared, will be calculated. CATEGORICAL_DIFFERENCE : The difference between two categorical or thematic rasters will be calculated, where the output contains class transitions that occurred between the two rasters. Example: “DIFFERENCE”
from_class_values	Optional list. List of integer values corresponding to the ClassValue field in raster1. Required if method is CATEGORICAL_DIFFERENCE.
to_class_values	Optional list. List of integer values corresponding to the ClassValue field in raster2. Required if method is CATEGORICAL_DIFFERENCE.
filter_method	Optional string. Default value is “CHANGED_PIXELS_ONLY” (1). Possible options are: ALL CHANGED_PIXELS_ONLY UNCHANGED_PIXELS_ONLY
define_transition_colors	Optional string. Defines the method used to assign color for the output classes AVERAGE : The color of the pixel will be the average of the color of its original class and the color of its final class. FROM_COLOR : The color of the pixel will be the color of its original class. TO_COLOR : The color of the pixel will be the color of its final class.
extent_type	Optional string. One of “FirstOf”, “IntersectionOf” “UnionOf”, “LastOf”
cellsize_type	Optional string. One of “FirstOf”, “MinOf”, “MaxOf “MeanOf”, “LastOf”

Returns: Imagery layer

con¶

arcgis.raster.functions.con(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The con operation.Performs a conditional if/else evaluation on each of the input cells of an input raster. For more information see, http://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/con-.htm

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

USAGE EXAMPLE: To extract raster from flow direction raster layer that only covers the watershed. 
               rasters: 
               ["Input raster representing the true or false result of the desired condition. It can be of integer or floating point type.", 
                "The input whose values will be used as the output cell values if the condition is true. It can be an integer or a floating point raster, or a constant value.", 
                "The input whose values will be used as the output cell values if the condition is false. It can be an integer or a floating point raster, or a constant value."]

con([stowe_watershed_lyr, Stowe_fill_flow_direction_lyr, 0])

constant_raster¶

arcgis.raster.functions.constant_raster(constant, raster_info, gis=None)¶

Creates a virtual raster with a single pixel value.

Parameters

constant – Required list. The value of the constant to be added to the virtual raster.
raster_info –
Required Raster info dictionary or arcgis.raster.RasterInfo object or ImageryLayer object to set the properties of the output raster. if ImageryLayer is specified then the raster information is obtained from the ImageryLayer specified.

Example for RasterInfo dict -

{‘bandCount’: 3, ‘extent’: {“xmin”: 4488761.95, “ymin”: 5478609.805, “xmax”: 4489727.05, “ymax”: 5479555.305, “spatialReference”: { “wkt”: “PROJCS[“Deutsches_Hauptdreiecksnetz_Transverse_Mercator”, GEOGCS[“GCS_Deutsches_Hauptdreiecksnetz”, DATUM[“D_Deutsches_Hauptdreiecksnetz”, SPHEROID[“Bessel_1841”, 6377397.155,299.1528128]], PRIMEM[“Greenwich”, 0.0], UNIT[“Degree”, 0.0174532925199433]], PROJECTION[“Transverse_Mercator”], PARAMETER[“false_easting”, 4500000.0], PARAMETER[“false_northing”, 0.0], PARAMETER[“central_meridian”, 12.0], PARAMETER[“scale_factor”, 1.0], PARAMETER[“latitude_of_origin”, 0.0], UNIT[“Meter”, 1.0]]” }}, ‘pixelSizeX’: 0.0999999999999614, ‘pixelSizeY’: 0.1, ‘pixelType’: ‘U8’}
gis – Optional gis. gis parameter can be specified to render the output raster dynamically using the raster rendering service of the gis. If not provided, active gis will be used to do this. If gis parameter is not specified the output of constant_raster() cannot be displayed.

Returns

output raster

contour¶

arcgis.raster.functions.contour(raster, adaptive_smoothing=2.5, contour_type='CONTOUR_LINES', z_base=0, number_of_contours=0, contour_interval=100, nth_contour_line_in_bold=5, z_factor=1)¶

Creates contour lines.

Parameters

raster – the raster from which the contour is created
adaptive_smoothing(double) – adaptive smooting value, e.g., 2.5
contour_type(string) – contour type. Available values could be “CONTOUR_LINES”, “CONTOUR_FILL” or “SMOOTH_SURFACE_ONLY”
z_base(double) – the z-base value, e.g., 0
number_of_contours(int) – the number of contours, e.g., 0
contour_interval(double) – the contour interval, e.g., 100
nth_contour_line_in_bold(int) – the nth contour line that would be rendered in bold, e.g., 5
z_factor(double) – the z-factor, e.g., 1

Returns

contrast_brightness¶

arcgis.raster.functions.contrast_brightness(raster, contrast_offset=2, brightness_offset=1, astype=None)¶

The ContrastBrightness function enhances the appearance of raster data (imagery) by modifying the brightness or contrast within the image. This function works on 8-bit input raster only.

Parameters

raster – input raster
contrast_offset – double, -100 to 100
brightness_offset – double, -100 to 100
astype – pixel type of result raster

Returns

output raster

convolution¶

arcgis.raster.functions.convolution(raster, kernel=None, astype=None)¶

The Convolution function performs filtering on the pixel values in an image, which can be used for sharpening an image, blurring an image, detecting edges within an image, or other kernel-based enhancements. For more information, see Convolution function at http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/convolution-function.htm

Parameters

raster – input raster
kernel – well known kernel from arcgis.raster.kernels or user defined kernel passed as a list of list
astype – pixel type of result raster

Returns

output raster

cos¶

arcgis.raster.functions.cos(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Cos operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

cosh¶

arcgis.raster.functions.cosh(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The CosH operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

curvature¶

arcgis.raster.functions.curvature(raster, curvature_type='standard', z_factor=1, astype=None)¶

The Curvature function displays the shape or curvature of the slope. A part of a surface can be concave or convex; you can tell that by looking at the curvature value. The curvature is calculated by computing the second derivative of the surface. Refer to this conceptual help on how it works.

http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/curvature-function.htm

Parameters

raster – input raster
curvature_type – ‘standard’, ‘planform’, ‘profile’
z_factor – double
astype – output pixel type

Returns

the output raster

detect_change_using_change_analysis_raster¶

arcgis.raster.functions.detect_change_using_change_analysis_raster(raster, change_type='TIME_OF_LATEST_CHANGE', max_number_of_changes=1, segment_date='BEGINNING_OF_SEGMENT', change_direction='ALL', filter_by_year=False, min_year=None, max_year=None, filter_by_duration=False, min_duration=None, max_duration=None, filter_by_magnitude=False, min_magnitude=None, max_magnitude=None, filter_by_start_value=False, min_start_value=None, max_start_value=None, filter_by_end_value=False, min_end_value=None, max_end_value=None)¶

Function generates a raster containing pixel change information using the output change analysis raster from the arcgis.raster.analytics.analyze_changes_using_ccdc function or arcgis.raster.analytics.analyze_changes_using_landtrendr function. Function available in ArcGIS Image Server 10.8.1 and higher.

Argument	Description
raster	Required ImageryLayer object. The raster generated from the analyze_changes_using_ccdc or analyze_changes_using_landtrendr.
change_type	Optional String. Specifies the change information to calculate. TIME_OF_LATEST_CHANGE (0) - Each pixel will contain the date of the most recent change for that pixel in the time series. TIME_OF_EARLIEST_CHANGE (1) - Each pixel will contain the date of the earliest change for that pixel in the time series. TIME_OF_LARGEST_CHANGE (2) - Each pixel will contain the date of the most significant change for that pixel in the time series. NUM_OF_CHANGES (3) - Each pixel will contain the total number of times the pixel changed in the time series. TIME_OF_LONGEST_CHANGE (4) - Each pixel will contain the date of change at the end of the longest transition segment in the time series. Option available in ArcGIS Image Server 10.9 and higher. TIME_OF_SHORTEST_CHANGE (5) - Each pixel will contain the date of change at the end of the shortest transition segment in the time series. Option available in ArcGIS Image Server 10.9 and higher. TIME_OF_FASTEST_CHANGE (6) - Each pixel will contain the date of change at the end of the transition that occurred most quickly. Option available in ArcGIS Image Server 10.9 and higher. TIME_OF_SLOWEST_CHANGE (7) - Each pixel will contain the date of change at the end of the transition that occurred most slowly. Option available in ArcGIS Image Server 10.9 and higher. Example: “TIME_OF_LATEST_CHANGE”
max_number_of_changes	Optional Integer. The maximum number of changes per pixel that will be calculated. This number corresponds to the number of bands in the output raster. The default is 1, meaning only one change date will be calculated, and the output raster will contain only one band. This parameter is not available when the change_type parameter is set to NUM_OF_CHANGES. Example: 3
segment_date	Optional string. Specifies whether to extract the date at the beginning of a change segment, or the end. This parameter is available only when the input change analysis raster is the output from the arcgis.raster.analytics.analyze_changes_using_landtrendr function. BEGINNING_OF_SEGMENT - Extract the date at the beginning of a change segment. This is the default. END_OF_SEGMENT - Extract the date at the end of a change segment. Example: “END_OF_SEGMENT” Parameter available in ArcGIS Image Server 10.9 and higher.
change_direction	Optional string. The direction of change to be included in the analysis. For example, choose Increasing to only extract date of change information for periods where the change is in the positive or increasing direction. This parameter is available only when the input change analysis raster is the output from the analyze_changes_using_landtrendr function. ALL - All change directions will be included in the output. This is the default. INCREASE - Only change in the positive or increasing direction will be included in the output. DECREASE - Only change in the negative or decreasing direction will be included in the output. Example: “DECREASE” Parameter available in ArcGIS Image Server 10.9 and higher.
filter_by_year	Optional boolean. Specifies whether to filter by a range of years. True - Filter results such that only changes that occurred within a specific range of years is included in the output. False - Do not filter results by year. This is the default. Example: True Parameter available in ArcGIS Image Server 10.9 and higher.
min_year	Optional int. The earliest year to use to filter results. This parameter is required if the filter_by_year parameter is set to True. Example: 2000 Parameter available in ArcGIS Image Server 10.9 and higher.
max_year	Optional int. The latest year to use to filter results. This parameter is required if the filter_by_year parameter is set to True. Example: 2005 Parameter available in ArcGIS Image Server 10.9 and higher.
filter_by_duration	Optional boolean. Specifies whether to filter by the change duration. This parameter is available only when the input change analysis raster is the output from the analyze_changes_using_landtrendr function. True - Filter results by duration such that only the changes that lasted a given amount of time will be included in the output. False - Do not filter results by duration. This is the default. Example: True Parameter available in ArcGIS Image Server 10.9 and higher.
min_duration	Optional float. The minimum number of consecutive years to include in the results. This parameter is required if the filter_by_duration parameter is set to True Example: 2 Parameter available in ArcGIS Image Server 10.9 and higher.
max_duration	Optional float. The maximum number of consecutive years to include in the results. This parameter is required if the filter_by_duration parameter is set to True Example: 4 Parameter available in ArcGIS Image Server 10.9 and higher.
filter_by_magnitude	Optional boolean. Specifies whether to filter by change magnitude. True - Filter results by magnitude such that only the changes of a given magnitude will be included in the output. False - Do not filter results by magnitude. This is the default. Example: True Parameter available in ArcGIS Image Server 10.9 and higher.
min_magnitude	Optional float. The minimum magnitude to include in the results. This parameter is required if the filter_by_magnitude parameter is set to True. Example: 0.25 Parameter available in ArcGIS Image Server 10.9 and higher.
max_magnitude	Optional float. The maximum magnitude to include in the results. This parameter is required if the filter_by_magnitude parameter is set to True. Example: 3 Parameter available in ArcGIS Image Server 10.9 and higher.
filter_by_start_value	Optional boolean. Specifies whether to filter by start value. This parameter is available only when the input change analysis raster is the output from the arcgis.raster.analytics.analyze_changes_using_landtrendr function. True - Filter results by start value so that only the change that starts with value defined by a range. False - Do not filter by start value. This is the default. Example: True Parameter available in ArcGIS Image Server 10.9 and higher.
min_start_value	Optional float. The minimum value that defines the range of start value. This parameter is required if the filter_by_start_value parameter is set to True. Example: 0.75 Parameter available in ArcGIS Image Server 10.9 and higher.
max_start_value	Optional float. The maximum value that defines the range of start value. This parameter is required if the filter_by_start_value parameter is set to True. Example: 0.9 Parameter available in ArcGIS Image Server 10.9 and higher.
filter_by_end_value	Optional boolean. Specifies whether to filter by end value. This parameter is available only when the input change analysis raster is the output from the arcgis.raster.analytics.analyze_changes_using_landtrendr function. True - Filter results by end value so that only the change that ends with value defined by a range. False - Do not filter results by end value. This is the default. Example: True Parameter available in ArcGIS Image Server 10.9 and higher.
min_end_value	Optional float. The minimum value that defines the range of end value. This parameter is required if the filter_by_end_value parameter is set to True. Example: -0.12 Parameter available in ArcGIS Image Server 10.9 and higher.
max_end_value	Optional float. The maximum value that defines the range of end value. This parameter is required if the filter_by_end_value parameter is set to True. Example: 0.35 Parameter available in ArcGIS Image Server 10.9 and higher.

Returns: Imagery layer

divide¶

arcgis.raster.functions.divide(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Divide operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

duration¶

arcgis.raster.functions.duration(rasters, min_value=None, max_value=None, undefined_class=None, astype=None)¶

Returns the duration (number of bands) between a minimum and maximum value. The Duration method finds the longest consecutive elements in the array, where each element has a value greater than or equal to min_value and less than or equal to max_value, and then returns its length.

See http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/argstatistics-function.htm

Parameters

rasters – the imagery layers filtered by where clause, spatial and temporal filters
undefined_class – int, required

Returns

the output raster with this function applied to it

elevation_void_fill¶

arcgis.raster.functions.elevation_void_fill(raster, max_void_width=0, astype=None)¶

The elevation_void_fill function is used to create pixels where holes exist in your elevation. Refer to this conceptual help on how it works. The arguments for the elevation_void_fill function are as follows:

Parameters

raster – input raster
max_void_width – number. Maximum void width to fill. 0: fill all
astype – output pixel type

Returns

the output raster

equal_to¶

arcgis.raster.functions.equal_to(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The EqualTo operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

evi¶

arcgis.raster.functions.evi(raster, band_indexes='5 4 2', astype=None)¶

The Enhanced Vegetation Index (EVI) is an optimized vegetation index that accounts for atmospheric influences and vegetation background signal. It’s similar to NDVI, but is less sensitive to background and atmospheric noise, and it does not become saturated NDVI when viewing areas with very dense green vegetation.

EVI = 2.5 * [(NIR - Red)/(NIR + (6*Red) - (7.5*Blue) + 1)]

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Red Blue”, e.g., “5 4 2” or [5,4,2]
astype – output pixel type

Returns

output raster

exp¶

arcgis.raster.functions.exp(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Exp operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

exp10¶

arcgis.raster.functions.exp10(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Exp10 operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

exp2¶

arcgis.raster.functions.exp2(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Exp2 operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

expression¶

arcgis.raster.functions.expression(raster, expression='(B3 - B1 / B3 + B1)', astype=None)¶

Use a single-line algebraic formula to create a single-band output. The supported operators are -, +, /, *, and unary -. To identify the bands, prepend the band number with a B or b. For example: “BandIndexes”:”(B1 + B2) / (B3 * B5)”

Parameters

raster – the input raster / imagery layer
expression – the algebric formula
astype – output pixel type

Returns

output raster

Returns

extract_band¶

arcgis.raster.functions.extract_band(raster, band_ids=None, band_names=None, band_wavelengths=None, missing_band_action=None, wavelength_match_tolerance=None, astype=None)¶

The extract_band function allows you to extract one or more bands from a raster, or it can reorder the bands in a multiband image. The arguments for the extract_band function are as follows:

Parameters

raster – input raster
band_ids – array of int, band_ids uses one-based indexing.
band_names – array of string
band_wavelengths – array of double
missing_band_action – int, 0 = esriMissingBandActionFindBestMatch, 1 = esriMissingBandActionFail
wavelength_match_tolerance – double
astype – output pixel type

Returns

the output raster

ferrous_minerals¶

arcgis.raster.functions.ferrous_minerals(raster, band_indexes='6 5', astype=None)¶

The Ferrous Minerals (FM) ratio is a geological index for identifying rock features containing some quantity of iron-bearing minerals using the shortwave infrared (SWIR) and near-infrared (NIR) bands. FM is used in mineral composite mapping.

FM = SWIR / NIR

Parameters

raster – the input raster / imagery layer
band_indexes – “SWIR NIR”, e.g., “6 5” or [6,5]
astype – output pixel type

Returns

output raster

FLOAT¶

arcgis.raster.functions.FLOAT(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Float operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

float_divide¶

arcgis.raster.functions.float_divide(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The FloatDivide operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

floor_divide¶

arcgis.raster.functions.floor_divide(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The FloorDivide operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

focal_stats¶

arcgis.raster.functions.focal_stats(raster, neighborhood_type=1, width=3, height=3, inner_radius=1, outer_radius=3, radius=3, start_angle=0, end_angle=90, neighborhood_values=None, stat_type=3, percentile_value=90, ignore_no_data=True)¶

Calculates for each input cell location a statistic of the values within a specified neighborhood around it. For more information see, https://pro.arcgis.com/en/pro-app/help/data/imagery/focal-statistics-function.htm

Parameters

raster – input raster
neighborhood_type – int, default is 1. The shape of the area around each cell used to calculate the statistic. 1 = Rectangle 2 = Circle 3 = Annulus 4 = Wedge 5 = Irregular 6 = Weight
width – int, default is 3. Specified when neighborhood_type is Rectangle
height – int, default is 3. Specified when neighborhood_type is Rectangle
inner_radius – int, default is 1. Specified when neighborhood_type is Annulus
outer_radius – int, default is 3. Specified when neighborhood_type is Annulus
radius – int, default is 3. Specified when neighborhood_type is Circle or Wedge
start_angle – float, default is 0. Specified when neighborhood_type is Wedge
end_angle – float, default is 90. Specified when neighborhood_type is Wedge
neighborhood_values – Specified when neighborhood_type is Irregular or Weight. It can be a list of lists, in which the width and height will be automatically set from the columns and rows of the two dimensional list, respectively. Alternatively, it can be a one dimensional list obtained from flattening a two dimensional list. In this case, the dimensions need to be specified explicitly with the width and height parameters.
stat_type –
int, default is 3(Mean)

There are 11 types of statistics available: 1=Majority, 2=Maximum, 3=Mean , 4=Median, 5= Minimum, 6 = Minority, 7=Range, 8=Standard deviation, 9=Sum, 10=Variety, 12=Percentile
- Majority = Calculates the majority (value that occurs most often) of the cells in the neighborhood.
- Maximum = Calculates the maximum (largest value) of the cells in the neighborhood.
- Mean = Calculates the mean (average value) of the cells in the neighborhood.
- Median = Calculates the median of the cells in the neighborhood.
- Minimum = Calculates the minimum (smallest value) of the cells in the neighborhood.
- Minority = Calculates the minority (value that occurs least often) of the cells in the neighborhood.
- Range = Calculates the range (difference between largest and smallest value) of the cells in the neighborhood.
- Standard deviation = Calculates the standard deviation of the cells in the neighborhood.
- Sum = Calculates the sum (total of all values) of the cells in the neighborhood.
- Variety = Calculates the variety (the number of unique values) of the cells in the neighborhood.
- Percentile = Calculates a specified percentile of the cells in the neighborhood.
ignore_no_data –
boolean, default is True.
- True - Specifies that if a NoData value exists within a neighborhood, the NoData value will be ignored. Only cells within the neighborhood that have data values will be used in determining the output value. This is the default.
- False - Specifies that if any cell in a neighborhood has a value of NoData, the output for the processing cell will be NoData.
percentile_value – float, default is 90. Denotes which percentile to calculate when the stat_type is Percentile. The value can range from 0 to 100.

Returns

the output raster

Note

The focal_stats() function is different from the focal_statistics() function in the following aspects:

The focal_stats() function supports Mean, Majority, Maximum, Median, Minimum, Minority, Percentile, Range, Standard deviation, Sum, and Variety. The focal_statistics() function supports only Minimum, Maximum, Mean, and Standard Deviation.

The focal_stats() function supports Rectangle, Circle, Annulus, Wedge, Irregular, and Weight neighborhoods. The focal_statistics() function supports only Rectangle.

The option to determine whether NoData values are ignored or not is available in the focal_stats() function by setting a bool value for ignore_no_data param. This option is not present in the focal_statistics() function.

The option to determine if NoData pixels are to be processed out is available in the focal_statistics() function by setting a bool value for fill_no_data_only param. This option is not present in the focal_stats() function.

gemi¶

arcgis.raster.functions.gemi(raster, band_indexes='4 3', astype=None)¶

Global Environmental Monitoring Index

GEMI = eta*(1-0.25*eta)-((Red-0.125)/(1-Red))

where eta = (2*(NIR^2-Red^2)+1.5*NIR+0.5*Red)/(NIR+Red+0.5)

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Red”, e.g., “4 3” or [4,3]
astype – output pixel type

Returns

output raster

generate_trend¶

arcgis.raster.functions.generate_trend(raster, dimension_name, regression_type=0, cycle_length=1, cycle_unit='YEARS', harmonic_frequency=1, polynomial_order=2, ignore_nodata=True, rmse=True, r2=False, slope_p_value=False, seasonal_period='DAYS')¶

Estimates the trend for each pixel along a dimension for one or more variables in a multidimensional raster.

Parameters

raster – The input multidimensional raster.
dimension_name – Required String. The dimension along which a trend will be extracted for the variable or variables selected in the analysis.
regression_type –
Optional Integer or String. Specifies the type of line to be used to fit to the pixel values along a dimension.
- 0 (LINEAR) : Fits the pixel values for a variable along a linear trend line. This is the default.
- 1 (HARMONIC) : Fits the pixel values for a variable along a harmonic trend line.
- 2 (POLYNOMIAL) : Fits the pixel values for a variable along a second-order polynomial trend line.
- 3 (MANN-KENDALL) : Variable pixel values will be evaluated using the Mann-Kendall trend test.
- 4 (SEASONAL-KENDALL) : Variable pixel values will be evaluated using the Seasonal-Kendall trend test.
cycle_length – Optional Integer. The length of periodic variation to model. This parameter is required when Trend Line Type is set to Harmonic. For example, leaf greenness often has one strong cycle of variation in a single year, so the cycle length is 1 year. Hourly temperature data has one strong cycle of variation throughout a single day, so the cycle length is 1 day. Default is 1.
cycle_unit – Optional String. Specifies the time unit to be used for the length of harmonic cycle. Options: “DAYS”, “YEARS” (This is the default).
harmonic_frequency – Optional Integer. The frequency number to use in the trend fitting. This parameter specifies the frequency of cycles in a year. The default value is 1, or one harmonic cycle per year. This parameter is only included in the trend analysis for a harmonic regression (regression_type=1).
polynomial_order – Optional Integer. The polynomial order number to use in the trend fitting. This parameter specifies the polynomial order. The default value is 2, or second-order polynomial. This parameter is only included in the trend analysis for a polynomial regression (regression_type=2).
ignore_nodata –
Optional Boolean. Specifies whether NoData values are ignored in the analysis.
- True : The analysis will include all valid pixels along a given dimension and ignore any NoData pixels. This is the default.
- False : The analysis will result in NoData if there are any NoData values for the pixels along the given dimension.
rmse –
Optional Boolean. Specifies whether to generate the root mean square error (RMSE) of the trend fit line.
- True : The RMSE will be calculated and displayed when the tool is finished running. This is the default.
- False : The RMSE will not be calculated.
r2 –
Optional Boolean. Specifies whether to calculate the R-squared goodness-of-fit statistic for the trend fit line.
- True : The R-squared will be calculated and displayed when the tool is finished running.
- False : The R-squared will not be calculated. This is the default.
slope_p_value –
Optional Boolean. Specifies whether to calculate the p-value statistic for the slope coefficient of the trend line.
- True : The p-value will be calculated and displayed when the tool is finished running.
- False : The p-value will not be calculated. This is the default.
seasonal_period – Optional String. Specifies the seasonal period. Default - “DAYS” Possible Options - “DAYS”, “MONTHS”

Returns

output raster

geometric¶

arcgis.raster.functions.geometric(raster, geodata_transforms=None, append_geodata_xform=None, z_factor=None, z_offset=None, constant_z=None, correct_geoid=None, astype=None, tolerance=None, dem=None)¶

The geometric function transforms the image (for example, orthorectification) based on a sensor definition and a terrain model.This function was added at 10.1.The arguments for the geometric function are as follows:

Parameters

raster – The input raster.
geodata_transforms – Please refer to the Geodata Transformations documentation for more details.
append_geodata_xform – Optional boolean. Indicates whether the geodata transformation is appended to the existing one from the input raster.
z_factor – Optional double. Satellite rational polynomial coefficients (RPCs) are scaled for elevation datasets with vertical units in meters. If your elevation uses other vertical units, enter a Z Factor to rescale to meters. For example, if your elevation units are in feet, you would use a value of 0.3048 to convert your elevation units from feet to meters.
z_offset – Optional double. The base value to be added to the elevation value in the DEM. This could be used to offset elevation values that do not start at sea level.
constant_z – Optional double. Specify a constant elevation to use for the Geometric function.
correct_geoid – Optional boolean. Set it to True to apply the geoid (EGM96) correction to the z-values, unless your DEM is already referenced to ellipsoidal heights.
tolerance – Optional double. Specify the maximum tolerable error in the geometric function, given in number of pixels.
dem – Optional Raster. Specify the DEM to use for the Geometric function

gndvi¶

arcgis.raster.functions.gndvi(raster, band_indexes='4 2', astype=None)¶

Green Normalized Difference Vegetation Index

GNDVI = (NIR-Green)/(NIR+Green)

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Green”, e.g., “5 3” or [5,3]
astype – output pixel type

Returns

output raster

grayscale¶

arcgis.raster.functions.grayscale(raster, conversion_parameters=None)¶

The Grayscale function converts a multi-band image into a single-band grayscale image. Specified weights are applied to each of the input bands, and a normalization is applied for output. For more information, see http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/grayscale-function.htm

Parameters

raster – the input raster
conversion_parameters – array of double (A length of N array representing weights for each band, where N=band count.)

Returns

the output raster with this function applied to it

greater_than¶

arcgis.raster.functions.greater_than(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The GreaterThan operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

greater_than_equal¶

arcgis.raster.functions.greater_than_equal(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The GreaterThanEqual operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

gvitm¶

arcgis.raster.functions.gvitm(raster, band_indexes='1 2 3 4 5 6', astype=None)¶

Green Vegetation Index - Landsat TM

GVITM = -0.2848*Band1-0.2435*Band2-0.5436*Band3+0.7243*Band4+0.0840*Band5-0.1800*Band7

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Red”, e.g., “4 3” or [4,3]
astype – output pixel type

Returns

output raster

heat_index¶

arcgis.raster.functions.heat_index(temperature_raster, relative_humidity_raster, temperature_units='Fahrenheit', heat_index_units='Fahrenheit')¶

Calculates apparent temperature based on ambient temperature and relative humidity. The apparent temperature is often described as how hot it feels to the human body.

Argument	Description
temperature_raster	The raster that represent temperature. A single-band raster where pixel values represent ambient air temperature.
relative_humidity_raster	The raster that represent relative humidity. A single-band raster where pixel values represent relative humidity as a percentage value between 0 and 100.
temperature_units	Optional String. The unit of measurement associated with the input temperature raster. Available input units are Celsius, Fahrenheit, and Kelvin.
heat_index_units	Optional String. The unit of measurement associated with the output raster. Available output units are Celsius, Fahrenheit, and Kelvin.

Returns: the output raster

hillshade¶

arcgis.raster.functions.hillshade(dem, azimuth=215.0, altitude=75.0, z_factor=0.3, slope_type=1, ps_power=None, psz_factor=None, remove_edge_effect=None, astype=None, hillshade_type=0)¶

A hillshade is a grayscale 3D model of the surface taking the sun’s relative position into account to shade the image. For more information, see hillshade function and How hillshade works.

The arguments for the hillshade function are as follows:

Parameters

dem – input DEM
azimuth – double (e.g. 215.0)
altitude – double (e.g. 75.0)
z_factor – double (e.g. 0.3)
slope_type – new at 10.2. 1=DEGREE, 2=PERCENTRISE, 3=SCALED. default is 1.
ps_power – new at 10.2. double, used together with SCALED slope type
psz_factor – new at 10.2. double, used together with SCALED slope type
remove_edge_effect – new at 10.2. boolean, true of false
astype – output pixel type
hillshade_type – new at 10.5.1 0 = traditional, 1 = multi - directional; default is 0

Returns

the output raster

identity¶

arcgis.raster.functions.identity(raster)¶

” The function is used to define the source raster as part of the default mosaicking behavior of the mosaic dataset. This function is a no-op function and takes no arguments except a raster. For more information, see (http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/identity-function.htm)

Parameters: raster – the input raster / imagery layer
Returns: the input raster

INT¶

arcgis.raster.functions.INT(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Int operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

interpolate_irregular_data¶

arcgis.raster.functions.interpolate_irregular_data(point_feature, value_field=None, cell_size=0, interpolation_method=0, radius=3, gis=None)¶

Interpolates from point clouds or irregular grids. (Supported from 10.8.1)

Argument	Description
point_feature_class	Required. The input point feature layer.
value_field	Optional string. The name of the field that contains the value of the points to be interpolated.
cell_size	Optional int. The cell size for the output raster dataset.
interpolation_method	Optional int or string. The resampling method to use for interpolation: 0 or NEAREST_NEIGBOR : Calculates pixel value using the nearest pixel. If no source pixel exists, no new pixel can be created in the output. This is the default. 2 or LINEAR_TINNING : Uses a triangular irregular network from the center points of each cell in the irregular raster to interpolate a surface that is then converted to a regular raster. 3 or NATURAL_NEIGHBOR : Finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas to interpolate a value. 4 or INVERSE_DISTANCE_WEIGHTED : Determines cell values using a linearly weighted combination of a set of sample points or cells. The weight is a function of the inverse of the distance from the known points or cells.
radius	Optional int. The number of pixels to be included for resampling. The default value is 3 pixels.
gis	Optional gis. gis parameter can be specified to render the output raster dynamically using the raster rendering service of the gis. If not provided, active gis will be used to do this. If gis parameter is not specified the output of interpolate_irregular_data() cannot be displayed.

Returns: output raster with function applied

iron_oxide¶

arcgis.raster.functions.iron_oxide(raster, band_indexes='4 2', astype=None)¶

The Iron Oxide (IO) ratio is a geological index for identifying rock features that have experienced oxidation of iron-bearing sulfides using the red and blue bands. IO is useful in identifying iron oxide features below vegetation canopies, and is used in mineral composite mapping.

IronOxide = Red / Blue

Parameters

raster – the input raster / imagery layer
band_indexes – “Red Blue”, e.g., “4 2” or [4,2]
astype – output pixel type

Returns

output raster

is_null¶

arcgis.raster.functions.is_null(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The IsNull operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

less_than¶

arcgis.raster.functions.less_than(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The LessThan operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

less_than_equal¶

arcgis.raster.functions.less_than_equal(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The LessThanEqual operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

linear_spectral_unmixing¶

arcgis.raster.functions.linear_spectral_unmixing(raster, spectral_profile_def=None, non_negative=False, sum_to_one=False)¶

Performs subpixel classification and calculates the fractional abundance of different land cover types for individual pixels.

Parameters

raster – The input raster.
spectral_profile_def – Optional dict. The class spectral profile information.
non_negative –
Optional bool. Specifies the options to define the output pixel values.
- True : There will be no negative output values.
- False : There can be negative values of fractional land cover. This is the default.
sum_to_one –
Optional bool. Specifies the options to define the output pixel values.
- True : Class values for each pixel are provided in decimal format with the sum of all classes equal to 1.
  
  Example:
  Class1 = 0.16; Class2 = 0.24; Class3 = 0.60.
- False : The sum of all classes in a pixel can exceed 1. This is the default.

Returns

output raster

ln¶

arcgis.raster.functions.ln(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Ln operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

local¶

arcgis.raster.functions.local(rasters, operation, extent_type='FirstOf', cellsize_type='FirstOf', astype=None, process_as_multiband=None)¶

The local function allows you to perform bitwise, conditional, logical, mathematical, and statistical operations on a pixel-by-pixel basis. For more information, see local function.

License:At 10.5, you must license your ArcGIS Server as ArcGIS Server 10.5.1 Enterprise Advanced or ArcGIS Image Server to use this resource. At versions prior to 10.5, the hosting ArcGIS Server needs to have a Spatial Analyst license.

The arguments for the local function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
operation – int. see reference here.
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type
process_as_multiband – True or False, set to True to process as multiband. Applicable for operations - Majority, Maximum, Mean, Median, Minimum, Minority, Range, Standard Deviation, Sum, and Variety.

Returns

the output raster

log10¶

arcgis.raster.functions.log10(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Log10 operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

log2¶

arcgis.raster.functions.log2(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Log2 operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

lookup¶

arcgis.raster.functions.lookup(raster, field=None)¶

Creates a new raster by looking up values found in another field in the table of the input raster. For more information see, https://pro.arcgis.com/en/pro-app/help/data/imagery/lookup-function.htm

Parameters

raster – The input raster that contains a field from which to create a new raster.
field – Field containing the desired values for the new raster.

Returns

the output raster with this function applied to it

majority¶

arcgis.raster.functions.majority(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Majority operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
ignore_nodata – True or False, set to True to ignore NoData values
astype – output pixel type
process_as_multiband – Set to True to process as multiband.

Returns

the output raster

mask¶

arcgis.raster.functions.mask(raster, no_data_values=None, included_ranges=None, no_data_interpretation=None, astype=None)¶

The mask function changes the image by specifying a certain pixel value or a range of pixel values as no data. The arguments for the mask function are as follows:

Parameters

raster – input raster
no_data_values –
list of strings [“band0_val”,”band1_val”,…]. The NoData values can be specified for each band. The index of each element in no_data_values list represents the no data value in the corresponding band.

You can specify more than one value by entering a space-delimited string for each index. e.g., [“band0_val1 band0_val2”, “band1_val1 band1_val2”,…]
included_ranges – list of floats [band0_lowerbound,band0_upperbound,band1_lowerbound,band1_upperbound, band2_…..], The included ranges can be specified for each band by specifying a minimum and maximum value.
no_data_interpretation –
int 0=MatchAny, 1=MatchAll. This parameter refers to how the NoData values will impact the output image.
- 0 (MatchAny) : If the NoData value you specify occurs for a cell in a specified band, then that cell in the output image will be NoData.
- 1 (MatchAll) : The NoData values you specify for each band must occur in the same cell for the output image to contain the NoData cell.
astype – output pixel type

Returns

the output raster

max¶

arcgis.raster.functions.max(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Max operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type
process_as_multiband – Set to True to process as multiband.

Returns

the output raster

mean¶

arcgis.raster.functions.mean(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Mean operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
ignore_nodata – True or False, set to True to ignore NoData values
astype – output pixel type
process_as_multiband – Set to True to process as multiband.

Returns

the output raster

med¶

arcgis.raster.functions.med(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Med operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
ignore_nodata – True or False, set to True to ignore NoData values
astype – output pixel type
process_as_multiband – Set to True to process as multiband.

Returns

the output raster

min¶

arcgis.raster.functions.min(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Min operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
ignore_nodata – True or False, set to True to ignore NoData values
astype – output pixel type
process_as_multiband – Set to True to process as multiband.

Returns

the output raster

minority¶

arcgis.raster.functions.minority(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Minority operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
ignore_nodata – True or False, set to True to ignore NoData values
astype – output pixel type
process_as_multiband – True or False, set to True to process as multiband.

Returns

the output raster

minus¶

arcgis.raster.functions.minus(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The binary Minus (subtraction,-) operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

ml_classify¶

arcgis.raster.functions.ml_classify(raster, signature, astype=None)¶

The ml_classify function allows you to perform a supervised classification using the maximum likelihood classification algorithm. The hosting ArcGIS Server needs to have a Spatial Analyst license.LicenseLicense:At 10.5, you must license your ArcGIS Server as ArcGIS Server 10.5.1 Enterprise Advanced or ArcGIS Image Server to use this resource. At versions prior to 10.5, the hosting ArcGIS Server needs to have a Spatial Analyst license.

The arguments for the ml_classify function are as follows:

Parameters

raster – input raster
signature – string. a signature string returned from computeClassStatistics (GSG)
astype – output pixel type

Returns

the output raster

mndwi¶

arcgis.raster.functions.mndwi(raster, band_indexes='3 6', astype=None)¶

The Modified Normalized Difference Water Index (MNDWI) uses green and SWIR bands for the enhancement of open water features. It also diminishes built-up area features that are often correlated with open water in other indices.

MNDWI = (Green - SWIR) / (Green + SWIR)

Parameters

raster – the input raster / imagery layer
band_indexes – “Green SWIR”, e.g., “3 6” or [3,6]
astype – output pixel type

Returns

output raster

mod¶

arcgis.raster.functions.mod(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Mod operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

monitor_vegetation¶

arcgis.raster.functions.monitor_vegetation(raster, method='NDVI', band_indexes=None, astype=None)¶

The monitor_vegetation function performs an arithmetic operation on the bands of a performs an arithmetic operation on the bands of a multiband raster layer to reveal vegetation coverage information of the study area. see Band Arithmetic function at http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/band-arithmetic-function.htm

Parameters

raster – the input raster / imagery layer
method – String. The method to create the vegetation index layer. The different vegetation indexes can help highlight certain features or reduce various noise. NDVI, SAVI, TSAVI, MSAVI, GEMI, PVI, GVITM, Sultan, VARI, GNDVI, SR, NDVIre, SRre, MTVI2, RTVICore, CIre, CIg, NDWI, EVI Default is NDVI.
band_indexes – band indexes
astype – output pixel type

Returns

output raster

msavi¶

arcgis.raster.functions.msavi(raster, band_indexes='4 3', astype=None)¶

Modified Soil Adjusted Vegetation Index

MSAVI2 = (1/2)*(2(NIR+1)-sqrt((2*NIR+1)^2-8(NIR-Red)))

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Red”, e.g., “4 3” or [4,3]
astype – output pixel type

Returns

output raster

mtvi2¶

arcgis.raster.functions.mtvi2(raster, band_indexes='7 5 3', astype=None)¶

The Modified Triangular Vegetation Index (MTVI2) is a vegetation index for detecting leaf chlorophyll content at the canopy scale while being relatively insensitive to leaf area index. It uses reflectance in the green, red, and near-infrared (NIR) bands

MTVI2 = (1.5*(1.2*(NIR-Green)-2.5*(Red-Green))/sqrt((2*NIR+1)^2-(6*NIR-5*sqrt(Red))-0.5))

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Red Green”, e.g., “7 5 3” or [7,5,3]
astype – output pixel type

Returns

output raster

multidimensional_filter¶

arcgis.raster.functions.multidimensional_filter(raster, variables=None, dimension_definition='ALL', dimension=None, dimension_ranges=None, dimension_values=None, start_of_first_iteration=None, end_of_first_iteration=None, iteration_step=3, iteration_unit=None, dimensionless=False)¶

Applies a filter on a multidimensional raster. Function creates a raster layer from a multidimensional raster by slicing data along defined variables and dimensions.

Parameters

raster – The input multidimensional raster
variables – Optional List. The list of variables that will be included in the output multidimensional raster layer. If no variable is specified, the first variable will be used.
dimension_definition –
Optional String. Specifies the method that will be used to slice the dimension.
- ALL : The full range for each dimension will be used. This is the default.
- BY_RANGES : The dimension will be sliced using a range or a list of ranges.
- BY_ITERATION : The dimension will be sliced over a specified interval.
- BY_VALUES : The dimension will be sliced using a list of dimension values.
dimension – Optional String. The dimension along which the variables will be sliced. This parameter is required when the dimension_definition is set to BY_ITERATION.
dimension_ranges –
Optional list of dicts. This slices the data based on the dimension name and the minimum and maximum values for the range. This parameter is required when the dimension_definition is set to BY_RANGE. If dimension is StdTime, then the min value and max value must be specified in human readable time format (YYYY-MM-DDTHH:MM:SS). The input should be specified as: [{“dimension”:”<dimension_name>”, “minValue”:”<dimension_min_value>”, “maxValue”:”<dimension_max_value>”},{“dimension”:”<dimension_name>”, “minValue”:”<dimension_min_value>”, “maxValue”:”<dimension_max_value>”}]

Example:
[{“dimension”:”StdTime”, “minValue”:”2013-05-17T00:00:00”, “maxValue”:”2013-05-17T03:00:00”},{“dimension”:”StdZ”, “minValue”:”-5000”, “maxValue”:”-4000”}]
dimension_values –
Optional List of Dicts. This slices the data based on the dimension name and the value specified. This parameter is required when the dimension_definition is set to BY_VALUE.

If dimension is StdTime, then the value must be specified in human readable time format (YYYY-MM-DDTHH:MM:SS). The input should be specified as: [{“dimension”:”<dimension_name>”, “value”:”<dimension_value>”},{“dimension”:”<dimension_name>”, “value”:”<dimension_value>”}]

Example:
[{“dimension”:”StdTime”, “value”:”2012-01-15T03:00:00”},{“dimension”:” StdZ “, “value”:”-4000”}]
start_of_first_iteration – Optional String. The beginning of the interval. This parameter is required when the dimension_definition is set to BY_ITERATION
end_of_first_iteration – Optional String. The end of the interval. This parameter is required when the dimension_definition is set to BY_ITERATION
iteration_step – Optional Float. The frequency with which the data will be sliced. This parameter is required when the dimension_definition is set to BY_ITERATION The default is 3.
iteration_unit –
Optional String. Specifies the iteration unit. This parameter is required when the dimension_definition is set to BY_ITERATION and the dimension parameter is set to StdTime.
- HOURS - Uses hours as the specified unit of time.
- DAYS - Uses days as the specified unit of time.
- WEEKS - Uses weeks as the specified unit of time.
- MONTHS - Uses months as the specified unit of time.
- YEARS -Uses years as the specified unit of time.
dimensionless –
Optional Boolean. Specifies whether the layer should have dimension values. This option is only available if a single slice is selected to create a layer.
- True - The layer will not have dimension values.
- False - The layer will have a dimension value. This is the default.

Returns

output raster

nbr¶

arcgis.raster.functions.nbr(raster, band_indexes='5 7', astype=None)¶

The Normalized Burn Ratio Index (NBRI) uses the NIR and SWIR bands to emphasize burned areas, while mitigating illumination and atmospheric effects. Your images should be corrected to reflectance values before using this index.

NBR = (NIR - SWIR) / (NIR+ SWIR)

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR SWIR”, e.g., “5 7” or [5,7]
astype – output pixel type

Returns

output raster

ndbi¶

arcgis.raster.functions.ndbi(raster, band_indexes='6 5', astype=None)¶

The Normalized Difference Built-up Index (NDBI) uses the NIR and SWIR bands to emphasize man-made built-up areas. It is ratio based to mitigate the effects of terrain illumination differences as well as atmospheric effects.

NDBI = (SWIR - NIR) / (SWIR + NIR)

Parameters

raster – the input raster / imagery layer
band_indexes – “SWIR NIR”, e.g., “6 5” or [6,5]
astype – output pixel type

Returns

output raster

ndmi¶

arcgis.raster.functions.ndmi(raster, band_indexes='5 6', astype=None)¶

The Normalized Difference Moisture Index (NDMI) is sensitive to the moisture levels in vegetation. It is used to monitor droughts as well as monitor fuel levels in fire-prone areas. It uses NIR and SWIR bands to create a ratio designed to mitigate illumination and atmospheric effects.

NDMI = (NIR - SWIR1)/(NIR + SWIR1)

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR SWIR1”, e.g., “5 6” or [5,6]
astype – output pixel type

Returns

output raster

ndsi¶

arcgis.raster.functions.ndsi(raster, band_indexes='4 6', astype=None)¶

The Normalized Difference Snow Index (NDSI) is designed to use MODIS (band 4 and band 6) and Landsat TM (band 2 and band 5) for identification of snow cover while ignoring cloud cover. Since it is ratio based, it also mitigates atmospheric effects.

NDSI = (Green - SWIR) / (Green + SWIR)

Parameters

raster – the input raster / imagery layer
band_indexes – “Green SWIR”, e.g., “4 6” or [4,6]
astype – output pixel type

Returns

output raster

ndvi¶

arcgis.raster.functions.ndvi(raster, band_indexes='4 3', astype=None)¶

Normalized Difference Vegetation Index NDVI = ((NIR - Red)/(NIR + Red))

Parameters

raster – the input raster / imagery layer
band_indexes – Band Indexes “NIR Red”, e.g., “4 3” or [4,3]
astype – output pixel type

Returns

Normalized Difference Vegetation Index raster

ndvire¶

arcgis.raster.functions.ndvire(raster, band_indexes='7 6', astype=None)¶

Red-Edge NDVI (NDVIre) The Red-Edge NDVI (NDVIre) is a vegetation index for estimating vegetation health using the red-edge band. It is especially useful for estimating crop health in the mid to late stages of growth where the chlorophyll concentration is relatively higher. Also, NDVIre can be used to map the within-field variability of nitrogen foliage to understand the fertilizer requirements of crops.

NDVIre = (NIR-RedEdge)/(NIR+RedEdge)

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR RedEdge”, e.g., “7 6” or [7,6]
astype – output pixel type

Returns

output raster

NDVI¶

arcgis.raster.functions.NDVI(raster, visible_band=2, ir_band=1, astype=None)¶

The Normalized Difference Vegetation Index (ndvi) is a standardized index that allows you to generate an image displaying greenness (relative biomass). This index takes advantage of the contrast of the characteristics of two bands from a multispectral raster dataset the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the near-infrared (NIR) band. For more information, see ndvi function. The arguments for the ndvi function are as follows:

Parameters

raster – input raster
visible_band_id – int (zero-based band id, e.g. 2)
infrared_band_id – int (zero-based band id, e.g. 1)
astype – output pixel type

Returns

the output raster

The following equation is used by the NDVI function to generate a 0 200 range 8 bit result: NDVI = ((IR - R)/(IR + R)) * 100 + 100 If you need the specific pixel values (-1.0 to 1.0), use the lowercase ndvi method.

ndwi¶

arcgis.raster.functions.ndwi(raster, band_indexes='5 3', astype=None)¶

The Normalized Difference Water Index (NDWI) is an index for delineating and monitoring content changes in surface water. It is computed with the near-infrared (NIR) and green bands.

NDWI = (Green - NIR)/(Green +NIR)

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Green”, e.g., “5 3” or [5,3]
astype – output pixel type

Returns

output raster

negate¶

arcgis.raster.functions.negate(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Negate operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

not_equal¶

arcgis.raster.functions.not_equal(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The NotEqual operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

pansharpen¶

arcgis.raster.functions.pansharpen(pan_raster, ms_raster, ir_raster=None, fourth_band_of_ms_is_ir=True, weights=[0.166, 0.167, 0.167, 0.5], type='ESRI', sensor=None)¶

The Pansharpening function uses a higher-resolution panchromatic raster to fuse with a lower-resolution, multiband raster. It can generate colorized multispectral image with higher resolution. For more information, see http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/pansharpening-function.htm

Parameters

pan_raster – raster, which is panchromatic
ms_raster – raster, which is multispectral
ir_raster – Optional, if fourth_band_of_ms_is_ir is true or selected pansharpening method doesn’t require near-infrared image
fourth_band_of_ms_is_ir – Boolean, “true” if “ms_raster” has near-infrared image on fourth band
weights – Weights applied for Red, Green, Blue, Near-Infrared bands. 4-elements array, Sum of values is 1
type – string, describes the Pansharpening method one of “IHS”, “Brovey” “ESRI”, “SimpleMean”, “Gram-Schmidt”. Default is “ESRI”
sensor – string, it is an optional parameter to specify the sensor name

Returns

output raster with function applied

plus¶

arcgis.raster.functions.plus(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The binary Plus (addition,+) operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

power¶

arcgis.raster.functions.power(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Power operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

predict_using_trend¶

arcgis.raster.functions.predict_using_trend(raster, dimension_definition_type=0, dimension_values=None, start=None, end=None, interval_value=1, interval_unit='HOURS')¶

Computes a forecasted multidimensional raster layer using the output trend raster from the generate_trend function.

Parameters

raster – The input raster.
dimension_definition_type –
Optional Integer or String. Specifies the method used to provide prediction dimension values.
- 0 (BY_VALUE) : The prediction will be calculated for a single dimension value. For example, you want to predict yearly precipitation for the years 2050, 2100, and 2150. This is the default.
- 1 (BY_INTERVAL) : The prediction will be calculated for an interval of the dimension defined by a start and an end value. For example, you want to predict yearly precipitation for every year between 2050 and 2150.
dimension_values – Optional List. The dimension value or values to be used in the prediction. The format of the time, depth, and height values must match the format of the dimension values used to generate the trend raster. If the trend raster was generated for the StdTime dimension, the format should be YYYY-MM-DDTHH:MM:SS, for example, 2050-01-01T00:00:00.
start – Optional String or Integer. The start date, height, or depth of the dimension interval to be used in the prediction. This parameter is required when the dimension_definition_type parameter is set to 1 (By Interval).
end – Optional String or Integer. The end date, height, or depth of the dimension interval to be used in the prediction. This parameter is required when the dimension_definition_type parameter is set to 1 (By Interval).
interval_value – Optional Integer. The number of steps between two dimension values to be included in the prediction. The default value is 1. This parameter is required when the dimension_definition_type parameter is set to 1 (By Interval).
interval_unit –
Optional String. The unit that will be used for the value interval. This parameter only applies when the dimension of analysis is a time dimension.
- HOURS - The prediction will be calculated for each hour in the range of time described by the start, end, and interval_value parameters.
- DAYS - The prediction will be calculated for each day in the range of time described by the start, end, and interval_value parameters.
- WEEKS - The prediction will be calculated for each week in the range of time described by the start, end, and interval_value parameters.
- MONTHS - The prediction will be calculated for each month in the range of time described by the start, end, and interval_value parameters.
- YEARS - The prediction will be calculated for each year in the range of time described by the start, end, and interval_value parameters.

Returns

output raster

pvi¶

arcgis.raster.functions.pvi(raster, band_indexes='4 3 0.3 0.5', astype=None)¶

Perpendicular Vegetation Index PVI = (NIR-a*Red-b)/(sqrt(1+a^2))

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Red a b”, e.g., “4 3 0.3 0.5” or [4,3,0.3,0.5]
astype – output pixel type

Returns

output raster

cellstats_range¶

arcgis.raster.functions.cellstats_range(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Range operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
ignore_nodata – True or False, set to True to ignore NoData values
astype – output pixel type
process_as_multiband – True or False, set to True to process as multiband.

Returns

the output raster

random_raster¶

arcgis.raster.functions.random_raster(raster_info, distribution=1, min_uniform=0.0, max_uniform=1.0, min_integer=1, max_integer=10, normal_mean=0.0, std_dev=1.0, exp_mean=1.0, poisson_mean=1.0, alpha=1.0, beta=1.0, N=10, r=10, probability=0.5, seed=1, generator_type=2, gis=None)¶

Creates a virtual raster with random values for each cell.

Parameters

raster_info –
Required Raster info dictionary or arcgis.raster.RasterInfo object or ImageryLayer object to set the properties of the output raster. if ImageryLayer is specified then the raster information is obtained from the ImageryLayer specified.

Example for RasterInfo dict -

{‘bandCount’: 3, ‘extent’: {“xmin”: 4488761.95, “ymin”: 5478609.805, “xmax”: 4489727.05, “ymax”: 5479555.305, “spatialReference”: { “wkt”: “PROJCS[“Deutsches_Hauptdreiecksnetz_Transverse_Mercator”, GEOGCS[“GCS_Deutsches_Hauptdreiecksnetz”, DATUM[“D_Deutsches_Hauptdreiecksnetz”, SPHEROID[“Bessel_1841”, 6377397.155,299.1528128]], PRIMEM[“Greenwich”,0.0], UNIT[“Degree”, 0.0174532925199433]], PROJECTION[“Transverse_Mercator”], PARAMETER[“false_easting”, 4500000.0], PARAMETER[“false_northing”, 0.0], PARAMETER[“central_meridian”, 12.0], PARAMETER[“scale_factor”, 1.0], PARAMETER[“latitude_of_origin”, 0.0], UNIT[“Meter”, 1.0]]” }}, ‘pixelSizeX’: 0.0999999999999614, ‘pixelSizeY’: 0.1, ‘pixelType’: ‘U8’}
distribution –
Optional int. Specify the random value distribution method to use. Default 1. i,e; Uniform
Choice list:
Uniform = 1 UniformInteger = 2 Normal = 3 Exponential = 4 Poisson = 5 Gamma = 6 Binomial = 7 Geometric = 8 NegativeBinomial = 9
- Uniform - A uniform distribution with the defined range.
- UniformInteger - An integer distribution with the defined range.
- Normal - A normal distribution with a defined {normal_mean} and {std_dev}.
- Exponential - An exponential distribution with a defined {exp_mean}.
- Poisson - A Poisson distribution with a defined {Mean}.
- Gamma - A gamma distribution with a defined {alpha} and {beta}.
- Binomial - A binomial distribution with a defined {N} and {probability}.
- Geometric - A geometric distribution with a defined {probability}.
- NegativeBinomial - A Pascal distribution with a defined {r} and {probability}.
min_uniform – Optional float. The default value is 0.0
max_uniform – Optional float. The default value is 1.0
min_integer – Optional int. The default value is 1
max_integer – Optional int. The default value is 10
normal_mean – Optional float. The default value is 0.0
std_dev – Optional float. The default value is 1.0
exp_mean – Optional float. The default value is 1.0
poisson_mean – Optional float. The default value is 1.0
alpha – Optional float. The default value is 1.0
beta – Optional float. The default value is 1.0
N – Optional int. The default value is 0.0
r – Optional int. The default value is 0.0
probability – Optional float. The default value is 0.5
seed – Optional int. The default value is 0.0
generator_type – Optional int. Default 2. i.e; MersenneTwister Choice list: Standard C Rand = 0 ACM collected algorithm 599 = 1 MersenneTwister = 2
gis – Optional gis. gis parameter can be specified to render the output raster dynamically using the raster rendering service of the gis. If not provided, active gis will be used to do this. If gis parameter is not specified the output of constant_raster() cannot be displayed.

Returns

output raster

raster_calculator¶

arcgis.raster.functions.raster_calculator(rasters, input_names, expression, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The RasterCalculator function provides access to all existing math functions so you can make calls to them when building your expressions. The calculator function requires single-band inputs. If you need to perform expressions on bands in a multispectral image as part of a function chain, you can use the Extract Bands Function before the RasterCalculator function. For more info including operators supported, see Calculator function http://pro.arcgis.com/en/pro-app/help/data/imagery/calculator-function.htm

Parameters

raster – array of rasters
input_names – array of strings for arbitrary raster names.
expression – string, expression to calculate output raster from input rasters
extent_type – string, one of “FirstOf”, “IntersectionOf” “UnionOf”, “LastOf”. Default is “FirstOf”.
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf “MeanOf”, “LastOf”. Default is “FirstOf”.
astype – output pixel type

Returns

output raster with function applied

raster_collection_function¶

arcgis.raster.functions.raster_collection_function(raster, item_function=None, aggregation_function=None, processing_function=None, aggregation_definition_type='ALL', dimension=None, interval_keyword=None, interval_value=None, interval_unit=None, interval_ranges=None)¶

Creates a new raster by applying item, aggregation and processing function

Parameters

raster – Input Imagery Layer. The image service the layer is based on should be a mosaic dataset
item_function – The raster function template to be applied on each item of the mosaic dataset. Create an RFT object out of the raster function template item on the portal and specify that as the input to item_function
aggregation_function – The aggregation function to be applied on the mosaic dataset. Create an RFT object out of the raster function template item on the portal and specify that as the input to aggregation_function
processing_function – The processing template to be applied on the imagery layer. Create an RFT object out of the raster function template item on the portal and specify that as the input to processing_function.
aggregation_definition_type –
Optional String. Specifies the dimension interval for which the data will be aggregated.
- ALL : The data values will be aggregated across all slices. This is the default.
- INTERVAL_KEYWORD : The variable data will be aggregated using a commonly known interval.
- INTERVAL_VALUE : The variable data will be aggregated using a user-specified interval and unit.
- INTERVAL_RANGES : The variable data will be aggregated between specified pairs of values or dates.
dimension – Optional String. This is the dimension along which the variables will be aggregated.
interval_keyword –
Optional String. Specifies the keyword interval that will be used when aggregating along the dimension. This parameter is required when the aggregation_def parameter is set to INTERVAL_KEYWORD, and the aggregation must be across time.
- HOURLY : The data values will be aggregated into hourly time steps, and the result will include every hour in the time series.
- DAILY : The data values will be aggregated into daily time steps, and the result will include every day in the time series.
- WEEKLY : The data values will be aggregated into weekly time steps, and the result will include every week in the time series.
- DEKADLY : Divides each month into 3 periods of 10 days each (last period might have more or less than 10 days) and each month would output 3 slices.
- PENTADLY : Divides each month into 6 periods of 5 days each (last period might have more or less than 5 days) and each month would output 6 slices.
- MONTHLY : The data values will be aggregated into monthly time steps, and the result will include every month in the time series.
- QUARTERLY : The data values will be aggregated into quarterly time steps, and the result will include every quarter in the time series.
- YEARLY : The data values will be aggregated into yearly time steps, and the result will include every year in the time series.
- RECURRING_DAILY : The data values will be aggregated into daily time steps, and the result includes each one aggregated value per day. The output will include, at most, 366 daily time slices
- RECURRING_WEEKLY : The data values will be aggregated into weekly time steps, and the result will include one aggregated value per week. The output will include, at most, 53 weekly time slices.
- RECURRING_MONTHLY : The data values will be aggregated into weekly time steps, and the result will include one aggregated value per month. The output will include, at most, 12 monthly time slices.
- RECURRING_QUARTERLY : The data values will be aggregated into weekly time steps, and the result will include one aggregated value per quarter. The output will include, at most, 4 quarterly time slices.
interval_value –
Optional String. The size of the interval that will be used for the aggregation. This parameter is required when the aggregation_def parameter is set to INTERVAL_VALUE.

For example, to aggregate 30 years of monthly temperature data into 5-year increments, enter 5 as the interval_value, and specify interval_unit as YEARS.
interval_unit –
Optional String. The unit that will be used for the interval value. This parameter is required when the dimension parameter is set to a time field and the aggregation_def parameter is set to INTERVAL_VALUE.

If you are aggregating over anything other than time, this option will not be available and the unit for the interval value will match the variable unit of the input multidimensional raster data.
- HOURS : The data values will be aggregated into hourly time slices at the interval provided.
- DAYS : The data values will be aggregated into daily time slices at the interval provided.
- WEEKS : The data values will be aggregated into weekly time slices at the interval provided.
- MONTHS : The data values will be aggregated into monthly time slices at the interval provided.
- YEARS : The data values will be aggregated into yearly time slices at the interval provided.
interval_ranges –
Optional List of dictionary objects. Interval ranges specified as list of dictionary objects that will be used to aggregate groups of values.

This parameter is required when the aggregation_definition parameter is set to INTERVAL_RANGE. If dimension is StdTime, then the value must be specified in human readable time format (YYYY-MM-DDTHH:MM:SS).

Syntax:
[{“minValue”:”<min value>”,”maxValue”:”<max value>”}, {“minValue”:”<min value>”,”maxValue”:”<max value>”}]

Example:
[{“minValue”:”2012-01-15T03:00:00”,”maxValue”:”2012-01-15T09:00:00”}, {“minValue”:”2012-01-15T12:00:00”,”maxValue”:”2012-01-15T21:00:00”}]

Returns

the output raster with function applied on it

rasterize_features¶

arcgis.raster.functions.rasterize_features(raster, feature_class, class_index_field=None, resolve_overlap_method=0)¶

Converts features to raster. Features are assigned pixel values based on the feature’s OBJECTID (default). Optionally, the pixel values can be based on a user defined value field in the input feature’s attribute table.

Argument	Description
raster	Required. The input Imagery Layer object.
feature_class	Required. The input point feature layer.
class_index_field	Optional string.Select the field to use to identify each feature.
resolve_overlap_method	Optional int or string. Determine how to manage features that overlap: FIRST - The overlapping areas will be assigned a value from the first dataset listed. LAST - The overlapping areas will be assigned a value from the last dataset listed. SMALLEST - The overlapping areas will be assigned a value from the smaller of the features. LARGEST - The overlapping areas will be assigned a value from the larger of the features.

Returns: output raster with function applied

remap¶

arcgis.raster.functions.remap(raster, input_ranges=None, output_values=None, geometry_type=None, geometries=None, no_data_ranges=None, allow_unmatched=None, astype=None)¶

The remap function allows you to change or reclassify the pixel values of the raster data. For more information, see remap function.

The arguments for the remap function are as follows:

Parameters

raster – input raster
input_ranges – [double, double,…], input ranges are specified in pairs: from (inclusive) and to (exclusive).
output_values – [double, …], output values of corresponding input ranges
geometry_type – added at 10.3
geometries – added at 10.3
no_data_ranges – [double, double, …], nodata ranges are specified in pairs: from (inclusive) and to (exclusive).
allow_unmatched – Boolean, specify whether to keep the unmatched values or turn into nodata.
astype – output pixel type

Returns

the output raster

reproject¶

arcgis.raster.functions.reproject(raster, spatial_reference=None, x_cell_size=0, y_cell_size=0, x_registration_point=0, y_registration_point=0)¶

Modifies the projection of a raster dataset, mosaic dataset, or raster item in a mosaic dataset. It can also resample the data to a new cell size and define an origin.

Argument	Description
raster	Required Imagery Layer object. The raster dataset to be reprojected or resampled.
spatial_reference	Optional dict. The coordinate system used to reproject the data. Example: { “wkid” : 4176, “latestWkid” : 4176 }
x_cell_size	Optional.The x-dimension to which the data should be resampled. This is optional. If the value is 0 or less, the output envelope (extent and cell sizes) is calculated from the input raster.
y_cell_size	Optional.The y-dimension to which the data should be resampled. This is optional. If the value is 0 or less, the output envelope (extent and cell sizes) is calculated from the input raster.
x_registration_point	Optional. The x-coordinate used to define the upper left corner of the dataset. This coordinate must be defined in the units of the new spatial reference. If both the x_cell_size and y_cell_size parameters are greater than 0, they are used along with the x_registration_point and y_registration_point parameters to define the output envelope.
y_registration_point	Optional. The y-coordinate used to define the upper left corner of the dataset. This coordinate must be defined in the units of the new spatial reference. If both the x_cell_size and y_cell_size parameters are greater than 0, they are used along with the x_registration_point and y_registration_point parameters to define the output envelope.

Returns: output raster with function applied

resample¶

arcgis.raster.functions.resample(raster, resampling_type=None, input_cellsize=None, output_cellsize=None, astype=None)¶

The resample function resamples pixel values from a given resolution.The arguments for the resample function are as follows:

Parameters

raster – input raster
resampling_type – one of NearestNeighbor,Bilinear,Cubic,Majority,BilinearInterpolationPlus,BilinearGaussBlur, BilinearGaussBlurPlus, Average, Minimum, Maximum,VectorAverage(require two bands)
input_cellsize – point that defines cellsize in source spatial reference
output_cellsize – point that defines output cellsize
astype – output pixel type

Returns

the output raster

round_down¶

arcgis.raster.functions.round_down(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The RoundDown operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

round_up¶

arcgis.raster.functions.round_up(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The RoundUp operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

rtvi_core¶

arcgis.raster.functions.rtvi_core(raster, band_indexes='7 6 3', astype=None)¶

The Red-Edge Triangulated Vegetation Index (RTVICore) is a vegetation index for estimating leaf area index and biomass. This index uses reflectance in the NIR, red-edge, and green spectral bands

RTVICore = [100(NIR-RedEdge)-10(NIR-Green)]

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR RedEdge Green”, e.g., “7 6 3” or [7,6,3]
astype – output pixel type

Returns

output raster

s1_radiometric_calibration¶

arcgis.raster.functions.s1_radiometric_calibration(raster, calibration_type=None)¶

Performs different types of radiometric calibration on Sentinel-1 data.

Parameters

raster –
The input raster. The Sentinel-1 Level-1 GRD or SLC input raster you want to process.

The function will use the LUT file either to apply the thermal correction or to remove the correction, depending on the contents of the LUT.
calibration_type –
Optional string or int. one of four calibration types:
- ”beta_nought” (0) - produces an output containing the radar brightness coefficient.
- ”sigma_nought” (1) - the backscatter returned to the antenna from a unit area on the ground, related to ground range.
- ”gamma” (2) - measurement of emitted and returned energy useful for determining antenna patterns.
- None - Specify None to not apply a correction. This is the default.

Returns

output raster

s1_thermal_noise_removal¶

arcgis.raster.functions.s1_thermal_noise_removal(raster, calibration_type=None)¶

Removes thermal noise from Sentinel-1 data.

Parameters

raster –

The input raster. The Sentinel-1 Level-1 GRD or SLC input raster you want to process.

The function will use the LUT file either to apply the thermal correction or to remove the correction, depending on the contents of the LUT.

Returns

output raster

savi¶

arcgis.raster.functions.savi(raster, band_indexes='4 3 0.33', astype=None)¶

Soil-Adjusted Vegetation Index SAVI = ((NIR - Red) / (NIR + Red + L)) x (1 + L) where L represents amount of green vegetative cover, e.g., 0.5

Parameters

raster – the input raster / imagery layer
band_indexes – “BandIndexes”: “NIR Red L”, for example, “4 3 0.33” or [4,3,0.33]
astype – output pixel type

Returns

output raster

segment_mean_shift¶

arcgis.raster.functions.segment_mean_shift(raster, spectral_detail=None, spatial_detail=None, spectral_radius=None, spatial_radius=None, min_num_pixels_per_segment=None, astype=None)¶

The segment_mean_shift function produces a segmented output. Pixel values in the output image represent the converged RGB colors of the segment. The input raster needs to be a 3-band 8-bit image. If the imagery layer is not a 3-band 8-bit unsigned image, you can use the Stretch function before the segment_mean_shift function.

License:At 10.5, you must license your ArcGIS Server as ArcGIS Server 10.5.1 Enterprise Advanced or ArcGIS Image Server to use this resource. At versions prior to 10.5, the hosting ArcGIS Server needs to have a Spatial Analyst license.

When specifying arguments for SegmentMeanShift, use either SpectralDetail,SpatialDetail as a pair, or use SpectralRadius, SpatialRadius. They have an inverse relationship. SpectralRadius = 21 - SpectralDetail, SpatialRadius = 21 - SpectralRadius

The arguments for the segment_mean_shift function are as follows:

Parameters

raster – input raster
spectral_detail – double 0-21. Bigger value is faster and has more segments.
spatial_detail – int 0-21. Bigger value is faster and has more segments.
spectral_radius – double. Bigger value is slower and has less segments.
spatial_radius – int. Bigger value is slower and has less segments.
min_num_pixels_per_segment – int
astype – output pixel type

Returns

the output raster

set_null¶

arcgis.raster.functions.set_null(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The SetNull operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

shaded_relief¶

arcgis.raster.functions.shaded_relief(raster, azimuth=None, altitude=None, z_factor=None, colormap=None, slope_type=None, ps_power=None, psz_factor=None, remove_edge_effect=None, astype=None, colorramp=None, hillshade_type=0)¶

Shaded relief is a color 3D model of the terrain, created by merging the images from the Elevation-coded and Hillshade methods. For more information, see Shaded relief function.

The arguments for the shaded_relief function are as follows:

Parameters

raster – input raster
azimuth – double (e.g. 215.0)
altitude – double (e.g. 75.0)
z_factor – double (e.g. 0.3)
colormap – [[<value1>, <red1>, <green1>, <blue1>], [<value2>, <red2>, <green2>, <blue2>]]
slope_type – 1=DEGREE, 2=PERCENTRISE, 3=SCALED. default is 1.
ps_power – double, used together with SCALED slope type
psz_factor – double, used together with SCALED slope type
remove_edge_effect – boolean, True or False
astype – output pixel type
colorramp – string, specifying color ramp name like <Black To White|Yellow To Red|Slope|more..> or a color ramp object. For more information about colorramp object, see color ramp object at https://developers.arcgis.com/documentation/common-data-types/color-ramp-objects.htm)
hillshade_type – new at 10.8.1. int, 0 = traditional, 1 = multi - directional; default is 0

Returns

the output raster

sin¶

arcgis.raster.functions.sin(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Sin operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

sinh¶

arcgis.raster.functions.sinh(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The SinH operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

slope¶

arcgis.raster.functions.slope(dem, z_factor=None, slope_type=None, ps_power=None, psz_factor=None, remove_edge_effect=None, astype=None)¶

slope represents the rate of change of elevation for each pixel. For more information, see slope function and How slope works.

The arguments for the slope function are as follows:

Parameters

dem – input DEM
z_factor – double (e.g. 0.3)
slope_type – new at 10.2. 1=DEGREE, 2=PERCENTRISE, 3=SCALED. default is 1.
ps_power – new at 10.2. double, used together with SCALED slope type
psz_factor – new at 10.2. double, used together with SCALED slope type
remove_edge_effect – new at 10.2. boolean, true of false
astype – output pixel type

Returns

the output raster

spectral_conversion¶

arcgis.raster.functions.spectral_conversion(raster, conversion_matrix)¶

The SpectralConversion function applies a matrix to a multi-band image to affect the spectral values of the output. In the matrix, different weights can be assigned to all the input bands to calculate each of the output bands. The column/row size of the matrix equals to the band count of input raster. For more information, see Spectral Conversion function http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/spectral-conversion-function.htm

Parameters

raster – the input raster
conversion_parameters – array of double (A NxN length one-dimension matrix, where N=band count.)

Returns

the output raster with this function applied to it

speckle¶

arcgis.raster.functions.speckle(raster, filter_type='Lee', filter_size='3x3', noise_model='Multiplicative', noise_var=None, additive_noise_mean=None, multiplicative_noise_mean=1, nlooks=1, damp_factor=None)¶

The Speckle function filters the speckled radar dataset to smooth out the noise while retaining the edges or sharp features in the image. Four speckle reduction filtering algorithms are provided through this function. For more information including required and optional parameters for each filter and the default parameter values, see Speckle function http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/speckle-function.htm

Parameters

raster – input raster type
filter_type – string, one of “Lee”, “EnhancedLee” “Frost”, “Kaun”. Default is “Lee”.
filter_size – string, kernel size. One of “3x3”, “5x5”, “7x7”, “9x9”, “11x11”. Default is “3x3”.
noise_model – string, For Lee filter only. One of “Multiplicative”, “Additive”, “AdditiveAndMultiplicative”
noise_var – double, for Lee filter with noise_model “Additive” or “AdditiveAndMultiplicative”
additive_noise_mean – string, for Lee filter witth noise_model “AdditiveAndMultiplicative” only
multiplicative_noise_mean – double, For Lee filter with noise_model “Additive” or “AdditiveAndMultiplicative”
nlooks – int, for Lee, EnhancedLee and Kuan Filters
damp_factor – double, for EnhancedLee and Frost filters

Returns

output raster with function applied

sqrt¶

arcgis.raster.functions.sqrt(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Square Root operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

square¶

arcgis.raster.functions.square(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Square operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

sr¶

arcgis.raster.functions.sr(raster, band_indexes='4 3', astype=None)¶

Simple Ratio (SR)

SR = NIR / Red

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Red”, e.g., “4 3” or [4,3]
astype – output pixel type

Returns

output raster

srre¶

arcgis.raster.functions.srre(raster, band_indexes='7 6', astype=None)¶

The Red-Edge Simple Ratio (SRre) is a vegetation index for estimating the amount of healthy and stressed vegetation. It is the ratio of light scattered in the NIR and red-edge bands, which reduces the effects of atmosphere and topography.

Values are high for vegetation with high canopy closure and healthy vegetation, lower for high canopy closure and stressed vegetation, and low for soil, water, and nonvegetated features. The range of values is from 0 to about 30, where healthy vegetation generally falls between values of 1 to 10.

SRre = NIR / RedEdge

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR RedEdge”, e.g., “7 6” or [7,6]
astype – output pixel type

Returns

output raster

focal_statistics¶

arcgis.raster.functions.focal_statistics(raster, kernel_columns=None, kernel_rows=None, stat_type=None, columns=None, rows=None, fill_no_data_only=None, astype=None)¶

The focal_statistics function calculates focal statistics for each pixel of an image based on a defined focal neighborhood. For more information, see statistics function.

The arguments for the statistics function are as follows:

Parameters

raster – input raster
kernel_columns – int (e.g. 3)
kernel_rows – int (e.g. 3)
stat_type –
int or string. There are four types of focal statistical functions:

1=Min, 2=Max, 3=Mean, 4=StandardDeviation, 5=Median, 6=Majority, 7=Minority
- Min - Calculates the minimum value of the pixels within the neighborhood
- Max - Calculates the maximum value of the pixels within the neighborhood
- Mean - Calculates the average value of the pixels within the neighborhood. This is the default.
- StandardDeviation - Calculates the standard deviation value of the pixels within the neighborhood
- Median - Calculates the median value of pixels within the neighborhood.
- Majority - Calculates the majority value, or the value that occurs most frequently, of the pixels within the neighborhood.
- Minority - Calculates the minority value, or the value that occurs least frequently, of the pixels within the neighborhood.
columns – int (e.g. 3). The number of pixel rows to use in your focal neighborhood dimension.
rows – int (e.g. 3). The number of pixel columns to use in your focal neighborhood dimension.
fill_no_data_only – bool
astype – output pixel type

Returns

the output raster

Note

The focal_statistics() function is different from the focal_stats() function in the following aspects:

The focal_statistics() function supports Minimum, Maximum, Mean, and Standard Deviation, Median, Majority, Minority. The focal_stats() function supports Mean, Majority, Maximum, Median, Minimum, Minority, Range, Standard deviation, Sum, and Variety.

The focal_statistics() function supports only Rectangle. The focal_stats() function supports Rectangle, Circle, Annulus, Wedge, Irregular, and Weight neighborhoods.

The option to determine if NoData pixels are to be processed out is available in the focal_statistics() function by setting a bool value for fill_no_data_only param. This option is not present in the focal_stats() function.

The option to determine whether NoData values are ignored or not is available in the focal_stats() function by setting a bool value for ignore_no_data param. This option is not present in the focal_statistics() function.

statistics_histogram¶

arcgis.raster.functions.statistics_histogram(raster, statistics=None, histograms=None)¶

” The function is used to define the statistics and histogram of a raster. It is normally used for control the default display of exported image. For more information, see Statistics and Histogram function, http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/statistics-and-histogram-function.htm

Parameters

raster – the input raster / imagery layer
statistics – array of statistics objects. (Predefined statistics for each band)
histograms – array of histogram objects. (Predefined histograms for each band)

Returns

Statistics and Histogram defined raster

std¶

arcgis.raster.functions.std(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Std operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
ignore_nodata – True or False, set to True to ignore NoData values
astype – output pixel type
process_as_multiband – True or False, set to True to process as multiband.

Returns

the output raster

stretch¶

arcgis.raster.functions.stretch(raster, stretch_type=0, min=None, max=None, num_stddev=None, statistics=None, dra=None, min_percent=None, max_percent=None, gamma=None, compute_gamma=None, sigmoid_strength_level=None, astype=None)¶

The stretch function enhances an image through multiple stretch types. For more information, see stretch function.

Gamma stretch works with all stretch types. The Gamma parameter is needed when UseGamma is set to true. Min and Max can be used to define output minimum and maximum. DRA is used to get statistics from the extent in the export_image request. ComputeGamma will automatically calculate best gamma value to render exported image based on empirical model.

Stretch type None does not require other parameters. Stretch type StdDev requires NumberOfStandardDeviations, Statistics, or DRA (true). Stretch type Histogram (Histogram Equalization) requires the source dataset to have histograms or additional DRA (true). Stretch type MinMax requires Statistics or DRA (true). Stretch type PercentClip requires MinPercent, MaxPercent, and DRA (true), or histograms from the source dataset. Stretch type Sigmoid does not require other parameters.

Optionally, set the SigmoidStrengthLevel (1 to 6) to adjust the curvature of Sigmoid curve used in color stretch.

The arguments for the stretch function are as follows:

Parameters

raster – input raster
stretch_type – str, one of None, StdDev, Histogram, MinMax, PercentClip, 9 = Sigmoid
min – double
max – double
num_stddev – double (e.g. 2.5)
statistics – double (e.g. 2.5)[<min1>, <max1>, <mean1>, <standardDeviation1>], //[double, double, double, double][<min2>, <max2>, <mean2>, <standardDeviation2>]],
dra – boolean. derive statistics from current request, Statistics parameter is ignored when DRA is true
min_percent – double (e.g. 0.25), applicable to PercentClip
max_percent – double (e.g. 0.5), applicable to PercentClip
gamma – array of doubles
compute_gamma – optional, applicable to any stretch type when “UseGamma” is “true”
sigmoid_strength_level – int (1~6), applicable to Sigmoid
astype – output pixel type

Returns

the output raster

sultan¶

arcgis.raster.functions.sultan(raster, band_indexes='1 2 3 4 5 6', astype=None)¶

Sultan’s Formula (transform to 3 band 8 bit image)

Band 1 = (Band5 / Band6) x 100

Band 2 = (Band5 / Band1) x 100

Band 3 = (Band3 / Band4) x (Band5 / Band4) x 100

Parameters

raster – the input raster / imagery layer
band_indexes – “Band1 Band2 Band3 Band4 Band5 Band6”, e.g., “1 2 3 4 5 6” or [1,2,3,4,5,6]
astype – output pixel type

Returns

output raster

sum¶

arcgis.raster.functions.sum(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Sum operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
ignore_nodata – True or False, set to True to ignore NoData values
astype – output pixel type
process_as_multiband – True or False, set to True to process as multiband.

Returns

the output raster

tan¶

arcgis.raster.functions.tan(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Tan operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

tanh¶

arcgis.raster.functions.tanh(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The TanH operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

tasseled_cap¶

arcgis.raster.functions.tasseled_cap(raster)¶

The function is designed to analyze and map vegetation and urban development changes detected by various satellite sensor systems. It is known as the Tasseled Cap transformation due to the shape of the graphical distribution of data. This function takes no arguments except a raster. The input for this function is the source raster of image service. There are no other parameters for this function because all the information is derived from the input’s properties and key metadata (bands, data type, and sensor name). Only imagery from the Landsat MSS, Landsat TM, Landsat ETM+, IKONOS, QuickBird, WorldView-2 and RapidEye sensors are supported. Prior to applying this function, there should not be any functions that would alter the pixel values in the function chain, such as the Stretch, Apparent Reflectance or Pansharpening function. The only exception is for Landsat ETM+; when using Landsat ETM+, the Apparent Reflectance function must precede the Tasseled Cap function. For more information, see http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/tasseled-cap-transformation.htm

Parameters: raster – the input raster / imagery layer
Returns: the output raster with TasseledCap function applied to it

threshold¶

arcgis.raster.functions.threshold(raster, astype=None)¶

The binary threshold function produces the binary image. It uses the Otsu method and assumes the input image to have a bi-modal histogram.

The arguments for the threshold function are as follows:

Parameters

raster – input raster
astype – output pixel type

Returns

the output raster

times¶

arcgis.raster.functions.times(rasters, extent_type='FirstOf', cellsize_type='FirstOf', astype=None)¶

The Times (multiplication,*) operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
astype – output pixel type

Returns

the output raster

transpose_bits¶

arcgis.raster.functions.transpose_bits(raster, input_bit_positions=None, output_bit_positions=None, constant_fill_check=None, constant_fill_value=None, fill_raster=None, astype=None)¶

The transpose_bits function performs a bit operation. It extracts bit values from the source data and assigns them to new bits in the output data.

The arguments for the transpose_bits function are as follows:

If constant_fill_check is False, it assumes there is an input fill_raster. If an input fill_raster is not given, it falls back constant_fill_check to True and looks for constant_fill_value. Filling is used to initialize pixel values of the output raster. Landsat 8 has a quality assessment band. The following are the example input and output bit positions to extract confidence levels by mapping them to 0-3:

Landsat 8 Water: {“input_bit_positions”:[4,5],”output_bit_positions”:[0,1]}
Landsat 8 Cloud Shadow: {“input_bit_positions”:[6,7],”output_bit_positions”:[0,1]}
Landsat 8 Vegetation: {“input_bit_positions”:[8,9],”output_bit_positions”:[0,1]}
Landsat 8 Snow/Ice: {“input_bit_positions”:[10,11],”output_bit_positions”:[0,1]}
Landsat 8 Cirrus: {“input_bit_positions”:[12,13],”output_bit_positions”:[0,1]}
Landsat 8 Cloud: {“input_bit_positions”:[14,15],”output_bit_positions”:[0,1]}
Landsat 8 Designated Fill: {“input_bit_positions”:[0],”output_bit_positions”:[0]}
Landsat 8 Dropped Frame: {“input_bit_positions”:[1],”output_bit_positions”:[0]}
Landsat 8 Terrain Occlusion: {“input_bit_positions”:[2],”output_bit_positions”:[0]}

Parameters

raster – input raster
input_bit_positions – array of long, required
output_bit_positions – array of long, required
constant_fill_check – bool, optional
constant_fill_value – int, required
fill_raster – optional, the fill raster
astype – output pixel type

Returns

the output raster

trend_to_rgb¶

arcgis.raster.functions.trend_to_rgb(raster, model_type=0)¶

Display the generate trend raster. Function available in ArcGIS Image Server 10.8.1 and higher.

Argument

Description

raster

Required ImageryLayer object.

model_type

Optional String. Specifies the model type.

“LINEAR” (0)

“HARMONIC” (1)

Example:: “LINEAR”

Returns: Imagery layer

tsavi¶

arcgis.raster.functions.tsavi(raster, band_indexes='4 3 0.33 0.50 1.50', astype=None)¶

Transformed Soil Adjusted Vegetation Index

TSAVI = (s(NIR-s*Red-a))/(a*NIR+Red-a*s+X*(1+s^2))

Parameters

raster – the input raster / imagery layer
band_indexes – “NIR Red s a X”, e.g., “4 3 0.33 0.50 1.50” or [4,3,0.33,0.50,1.50] where a = the soil line intercept, s = the soil line slope, X = an adjustment factor that is set to minimize soil noise
astype – output pixel type

Returns

output raster

unit_conversion¶

arcgis.raster.functions.unit_conversion(raster, from_unit=None, to_unit=None, astype=None)¶

The unit_conversion function performs unit conversions.

The arguments for the unit_conversion function are as follows:

from_unit and to_unit take the following str values: Speed Units: MetersPerSecond, KilometersPerHour, Knots, FeetPerSecond, MilesPerHour Temperature Units: Celsius,Fahrenheit,Kelvin Distance Units: str, one of Inches, Feet, Yards, Miles, NauticalMiles, Millimeters, Centimeters, Meters

Parameters

raster – input raster
from_unit – units constant listed below (int)
to_unit – units constant listed below (int)
astype – output pixel type

Returns

the output raster

vari¶

arcgis.raster.functions.vari(raster, band_indexes='3 2 1', astype=None)¶

Visible Atmospherically Resistant Index

VARI = (Green - Red)/(Green + Red - Blue)

Parameters

raster – the input raster / imagery layer
band_indexes – “Red Green Blue”, e.g., “3 2 1” or [3,2,1]
astype – output pixel type

Returns

output raster

variety¶

arcgis.raster.functions.variety(rasters, extent_type='FirstOf', cellsize_type='FirstOf', ignore_nodata=False, astype=None, process_as_multiband=None)¶

The Variety operation

The arguments for this function are as follows:

Parameters

rasters – array of rasters. If a scalar is needed for the operation, the scalar can be a double or string
extent_type – one of “FirstOf”, “IntersectionOf”, “UnionOf”, “LastOf”
cellsize_type – one of “FirstOf”, “MinOf”, “MaxOf, “MeanOf”, “LastOf”
ignore_nodata – True or False, set to True to ignore NoData values
astype – output pixel type
process_as_multiband – True or False, set to True to process as multiband.

Returns

the output raster

vector_field¶

arcgis.raster.functions.vector_field(raster_u_mag, raster_v_dir, input_data_type='Vector-UV', angle_reference_system='Geographic', output_data_type='Vector-UV', astype=None)¶

The VectorField function is used to composite two single-band rasters (each raster represents U/V or Magnitude/Direction) into a two-band raster (each band represents U/V or Magnitude/Direction). Data combination type (U-V or Magnitude-Direction) can also be converted interchangeably with this function. For more information, see Vector Field function (http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/vector-field-function.htm)

Parameters

raster_u_mag – raster item representing ‘U’ or ‘Magnitude’ - imagery layers filtered by where clause, spatial and temporal filters
raster_v_dir – raster item representing ‘V’ or ‘Direction’ - imagery layers filtered by where clause, spatial and temporal filters
input_data_type – string, ‘Vector-UV’ or ‘Vector-MagDir’ per input used in ‘raster_u_mag’ and ‘raster_v_dir’
angle_reference_system – string, optional when ‘input_data_type’ is ‘Vector-UV’, one of “Geographic”, “Arithmetic”
output_data_type – string, ‘Vector-UV’ or ‘Vector-MagDir’

Returns

the output raster with this function applied to it

vector_field_renderer¶

arcgis.raster.functions.vector_field_renderer(raster, is_uv_components=None, reference_system=None, mass_flow_angle_representation=None, calculation_method='Vector Average', symbology_name='Single Arrow', astype=None)¶

The vector_field_renderer function symbolizes a U-V or Magnitude-Direction raster.

The arguments for the vector_field_renderer function are as follows:

Parameters

raster – input raster
is_uv_components – bool
reference_system – int 1=Arithmetic, 2=Angular
mass_flow_angle_representation – int 0=from 1=to
calculation_method – string, “Vector Average” |
symbology_name – string, “Single Arrow” |
astype – output pixel type

Returns

the output raster

weighted_overlay¶

arcgis.raster.functions.weighted_overlay(rasters, fields, influences, remaps, eval_from, eval_to)¶

The WeightedOverlay function allows you to overlay several rasters using a common measurement scale and weights each according to its importance. For more information, see http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/weighted-overlay-function.htm

Parameters

raster – array of rasters
fields – array of string fields of the input rasters to be used for weighting.
influences – array of double, Each input raster is weighted according to its importance, or its influence. The sum of the influence weights must equal 1
remaps – array of strings, Each value in an input raster is assigned a new value based on the remap. The remap value can be a valid value or a NoData value.
eval_from – required, numeric value of evaluation scale from
eval_to – required, numeric value of evaluation scale to

Returns

output raster with function applied

weighted_sum¶

arcgis.raster.functions.weighted_sum(rasters, fields, weights)¶

The WeightedSum function allows you to overlay several rasters, multiplying each by their given weight and summing them together. For more information, see http://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/weighted-sum-function.htm

Parameters

raster – array of rasters
fields – array of string fields of the input rasters to be used for weighting.
weights – array of double, The weight value by which to multiply the raster. It can be any positive or negative decimal value.

Returns

output raster with function applied

wind_chill¶

arcgis.raster.functions.wind_chill(temperature_raster, wind_speed_raster, temperature_units='Fahrenheit', wind_speed_units='mph', wind_chill_units='Fahrenheit')¶

The Wind Chill function is useful for identifying dangerous winter conditions that, depending on exposure times to the elements, can result in frostbite or even hypothermia. Wind chill is a way to measure how cold an individual feels when wind is taken into account with already cold temperatures. The faster the wind speed, the more quickly the body will lose heat and the colder they will feel.

Argument	Description
temperature_raster	The raster that represent temperature. A single-band raster where pixel values represent ambient air temperature.
wind_speed_raster	The raster that represent relative humidity. A single-band raster where pixel values represent wind speed.
temperature_units	Optional String. The unit of measurement associated with the input temperature raster. Available input units are Celsius, Fahrenheit, and Kelvin.
wind_speed_units	Optional String. Defines the unit of measurement for the wind-speed raster. Available input units are mph, km/h, ft/s and kn. Each represents Miles Per Hour (mph) Kilometers Per Hour (km/h) Meters Per Second (m/s) Feet Per Second (ft/s) Knots (kn)
wind_chill_units	Optional String. The unit of measurement associated with the output raster. Available output units are Celsius, Fahrenheit, and Kelvin.

Returns: the output raster