AI-generated Key Takeaways
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Array.erfc()
calculates the complementary error function for each element in an input array. -
The function returns a new array with the calculated complementary error function values.
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It operates on an element-by-element basis, applying the erfc function to each value in the input array.
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Usage examples are provided in both JavaScript and Python to demonstrate the function's application.
Usage | Returns |
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Array.erfc() | Array |
Argument | Type | Details |
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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])) )