Aggregate

Mit der Image Analyst-Lizenz verfügbar.

Zusammenfassung

Creates a raster object by combining slices from the input multidimensional raster based on a dimension interval and an aggregation method.

Auswertung

Use the Aggregate function to aggregate variable values from a multidimensional raster over a given variable. For example, if you have daily precipitation data, you can determine the monthly average precipitation value.

This function creates a raster object that is the aggregation of the input multidimensional raster. By default, aggregation will be computed for all variables that are associated with the given dimension. For example, if precipitation, temperature, and wind speed all have a time dimension, all three variables will be aggregated separately if dimension_name is set to the time dimension. If you only want to aggregate one variable, use the Subset function before you use Aggregate.

The referenced raster dataset for the raster object is temporary. To make it permanent, you can call the raster object's save method.

Syntax

Aggregate (in_raster, dimension_name, raster_function, {raster_function_arguments}, {aggregation_definition})

Parameter	Erklärung	Datentyp
in_raster	The input multidimensional raster to be aggregated.	Raster
dimension_name	The aggregation dimension. This is the dimension along which the variables will be aggregated.	String
raster_function	The name of a raster function as a string (for example, Mean, Max, Min, Majority, Minority, Sum, and so on) or the path to a custom raster function (.rft.xml file) used to compute the aggregated pixel values.	String
raster_function_arguments	The parameters and values associated with the raster function or function chain. If not specified, default values will be used.	Dictionary
aggregation_definition	The dimension interval for which the data will be aggregated. The key-value pairs must follow one of the following formats: 'interval':<interval_keyword> Use the interval keyword only when dimension_name is a time dimension. Interval keywords include HOURLY, DAILY, WEEKLY, MONTHLY, QUARTERLY, and YEARLY. For example, use this option to aggregate daily temperature data into monthly temperature data for each year. The output will include no more than 12 slices for each year. 'recurrent_interval':<recurrent_interval_keyword> Use the recurrent interval keyword only when dimension_name is a time dimension. Recurrent interval keywords include DAILY, WEEKLY, MONTHLY, and QUARTERLY. For example, use this option to calculate the average temperature value for each month across all the years in the dataset. The output will include no more than 12 slices (one slice for each month of the year). 'interval_value':<value>, 'interval_unit':<unit> Use the interval value to define recurring intervals. The interval unit is required when dimension_name is a time dimension. Interval unit keywords include HOUR, DAY, WEEK, MONTH, QUARTER, and YEAR. For example, use this option to aggregate daily temperature into temperature every 10 days. 'interval_ranges':[(min, max),(min, max), ...] Use the interval ranges option to define custom intervals for aggregation. For example, use this option to aggregate daily temperature into temperature across specific seasonal dates. If not specified, all slices across the dimension will be aggregated.	Dictionary

Rückgabewert

Datentyp	Erklärung
Raster	The output aggregated multidimensional raster.

Codebeispiel

Aggregate example

Aggregate precipitation data by calculating the overall average, monthly average, minimum over 10 days, and maximum over 2 ranges.

import arcpy
from arcpy.ia import *

arcpy.CheckOutExtension("ImageAnalyst")

in_raster = Raster('ClimateData_Daily.nc', True)

# Choose the precipitation variable
prcp_raster = Subset(in_raster, variables = 'prcp')

# Calculate the average precipitation across the whole time period
avg_prcp = Aggregate(prcp_raster, 'StdTime', 'Mean')

# Calculate the monthly mean precipitation 
monthly_avg_prcp = Aggregate(prcp_raster, 'StdTime', 
	'Mean', {'interval': 'monthly'})

# Calculate the minimum precipitation every 10 days
min_precip_10day = Aggregate(prcp_raster, 'StdTime', 'Min', 
	{'interval_value' : 10, 'interval_unit': 'day'})

# Calculate the maximum precipitation in two separate time ranges
max_prcp_range = Aggregate(prcp_raster, 'StdTime', 'Max', 
	{'interval_ranges':  [('2001-01-01T00:00:00', '2001-02-01T00:00:00'), 
	('2001-12-01T00:00:00', '2001-12-24T00:00:00')]})