Anomaly

Disponible con licencia de Image Analyst.

Disponible con una licencia de Spatial Analyst.

Resumen

Creates a raster object that contains the anomaly pixel values of the input multidimensional raster based on a time-dimension interval and the anomaly calculation method.

Debate

Use the Anomaly function to calculate pixel value anomalies for temporal variables in a multidimensional raster object. For example, if you have daily precipitation data, you can find the pixels that are highly different from the monthly average precipitation values throughout your dataset.

The three mathematical methods for calculating anomaly values with this function are as follows:

• Difference from mean = x - µ
  • x = pixel value in a slice
  • µ = mean of that pixel's values over a given time interval
• Percent difference from mean = |x - µ| /[(x + µ)/2]
  • x = pixel value in a slice
  • µ = mean of that pixel's values over a given time interval
  • |x - µ| = absolute value of the difference between the value and the mean
• Percent of average = x / µ
  • x = pixel value in a slice
  • µ = mean of that pixel's values over a given time interval

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

Sintaxis

Anomaly (in_raster, {dimension_name}, {anomaly_type}, {temporal_interval}, {ignore_nodata})

Parámetro	Explicación	Tipo de datos
in_raster	The input multidimensional raster.	Raster
dimension_name	The dimension name which the anomaly definition will be performed along. (El valor predeterminado es StdTime)	String
anomaly_type	Specifies the method used to calculate the anomaly. DIFFERENCE_FROM_MEAN—Calculates the difference between a pixel value and the mean of that pixel's values across slices defined by the interval. This is the default. PERCENT_DIFFERENCE_FROM_MEAN—Calculates the percent difference between a pixel value and the mean of that pixel's values across slices defined by the interval. PERCENT_OF_MEAN—Calculates the percent of the mean. (El valor predeterminado es DIFFERENCE_FROM_MEAN)	String
temporal_interval	Specifies the temporal interval to use to calculate the average. None—Calculates the average across all slices for each pixel. This is the default. HOURLY—Calculates the hourly average for each pixel. DAILY—Calculates the daily average for each pixel. WEEKLY—Calculates the weekly average for each pixel. MONTHLY—Calculates the monthly average for each pixel. YEARLY—Calculates the yearly average for each pixel. (El valor predeterminado es None)	String
ignore_nodata	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 pixel along the given dimension. (El valor predeterminado es True)	Boolean

Valor de retorno

Tipo de datos	Explicación
Raster	The output anomaly multidimensional raster.

Muestra de código

Anomaly example

Calculate precipitation anomalies across all slices and for monthly values.

import arcpy
from arcpy.sa import *

arcpy.CheckOutExtension("Spatial")
 
in_raster = Raster("C:/sapyexamples/data/ClimateData_Daily.nc", True)
 
# Choose the precipitation variable 
prcp_raster = Subset(in_raster, variables = 'prcp')
 
# Calculate percent of mean anomalies, 
# where the mean_value is the mean of all slices 
prcp_anomaly = Anomaly(prcp_raster, anomaly_type = 'PERCENT_OF_MEAN') 
 
# Calculate the difference from mean,
# where the mean_value is the mean of each year 
prcp_monthly_anomaly = Anomaly(prcp_raster, temporal_interval = 'YEARLY')