AI-generated Key Takeaways
-
The
Array.erfc()method computes the complementary error function for each element in an input array and returns an array.
| Usage | Returns |
|---|---|
Array.
erfc
()
|
Array |
| Argument | Type | Details |
|---|---|---|
|
this:
input
|
Array | The input array. |
Examples
Code Editor (JavaScript)
print ( ee . Array ([ - 6 ]). erfc ()); // [2] print ( ee . Array ([ 0 ]). erfc ()); // [1] print ( ee . Array ([ 28 ]). erfc ()); // [0] var start = - 3 ; var end = 3 ; var points = ee . Array ( ee . List . sequence ( start , end , null , 50 )); var values = points . erfc (); // Plot erfc() defined above. var chart = ui . Chart . array . values ( values , 0 , points ) . setOptions ({ viewWindow : { min : start , max : end }, hAxis : { title : 'x' , viewWindowMode : 'maximized' , ticks : [ { v : start }, { v : 0 }, { v : end }] }, vAxis : { title : 'erfc(x)' , ticks : [ { v : 0 }, { v : 1 }, { v : 2 }] }, lineWidth : 1 , pointSize : 0 , }); print ( chart );
import ee import geemap.core as geemap
Colab (Python)
import altair as alt import pandas as pd display ( ee . Array ([ - 6 ]) . erfc ()) # [2] display ( ee . Array ([ 0 ]) . erfc ()) # [1] display ( ee . Array ([ 28 ]) . erfc ()) # [0] start = - 3 end = 3 points = ee . Array ( ee . List . sequence ( start , end , None , 50 )) values = points . erfc () df = pd . DataFrame ({ 'x' : points . getInfo (), 'erfc(x)' : values . getInfo ()}) # Plot erfc() defined above. alt . Chart ( df ) . mark_line () . encode ( x = alt . X ( 'x' , axis = alt . Axis ( values = [ start , 0 , end ])), y = alt . Y ( 'erfc(x)' , axis = alt . Axis ( values = [ 0 , 1 , 2 ])) )
