ee.Image.arrayFlatten

यह, एक ही बैंड वाली ऐसी इमेज को स्केलर पिक्सल वाली इमेज में बदलता है जिसमें एक ही आकार के मल्टीडाइमेंशनल पिक्सल होते हैं. इसमें, हर एलिमेंट के लिए एक बैंड होता है.

इस्तेमालरिटर्न
Image.arrayFlatten(coordinateLabels, separator)इमेज
आर्ग्यूमेंटटाइपविवरण
this: imageइमेजमल्टीडाइमेंशनल पिक्सल वाली इमेज को फ़्लैट करना.
coordinateLabelsसूचीहर ऐक्सिस के साथ, हर पोज़िशन का नाम. उदाहरण के लिए, 'दिन' और 'रंग' का मतलब बताने वाले ऐक्सिस के साथ 2x2 वाले कलेक्शन के लेबल, [['monday', 'tuesday'], ['red', 'green']] हो सकते हैं. इससे बैंड के नाम 'monday_red', 'monday_green', 'tuesday_red' और 'tuesday_green' बन सकते हैं.
separatorस्ट्रिंग, डिफ़ॉल्ट: "_"हर बैंड के नाम में, कलेक्शन के लेबल के बीच सेपरेटर.

उदाहरण

कोड एडिटर (JavaScript)

// A function to print arrays for a selected pixel in the following examples.
function sampArrImg(arrImg) {
  var point = ee.Geometry.Point([-121, 42]);
  return arrImg.sample(point, 500).first().get('array');
}

// A 1D array image.
var arrayImg1D = ee.Image([0, 1, 2]).toArray();
print('1D array image (pixel)', sampArrImg(arrayImg1D));
// [0, 1, 2]

// Define image band names for a 1D array image with 3 rows. You are labeling
// all rows and columns using a list of lists; the 1st sub list defines labels
// for array rows and the 2nd (if applicable) defines labels for array columns.
var bandNames1D = [['row0', 'row1', 'row2']];

// Flatten the 1D array image into an image with n bands equal to all
// combinations of rows and columns. Here, we have 3 rows and 0 columns,
// so the result will be a 3-band image.
var imgFrom1Darray = arrayImg1D.arrayFlatten(bandNames1D);
print('Image from 1D array', imgFrom1Darray);

// Make a 2D array image by repeating the 1D array on 2-axis.
var arrayImg2D = arrayImg1D.arrayRepeat(1, 2);
print('2D array image (pixel)', sampArrImg(arrayImg2D));
// [[0, 0],
//  [1, 1],
//  [2, 2]]

// Define image band names for a 2D array image with 3 rows and 2 columns.
// Recall that you are labeling all rows and columns using a list of lists;
// The 1st sub list defines labels for array rows and the 2nd (if applicable)
// defines labels for array columns.
var bandNames2D = [['row0', 'row1', 'row2'], ['col0', 'col1']];

// Flatten the 2D array image into an image with n bands equal to all
// combinations of rows and columns. Here, we have 3 rows and 2 columns,
// so the result will be a 6-band image.
var imgFrom2Darray = arrayImg2D.arrayFlatten(bandNames2D);
print('Image from 2D array', imgFrom2Darray);

Python सेटअप

Python API और इंटरैक्टिव डेवलपमेंट के लिए geemap का इस्तेमाल करने के बारे में जानने के लिए, Python एनवायरमेंट पेज देखें.

import ee
import geemap.core as geemap

Colab (Python)

# A function to print arrays for a selected pixel in the following examples.
def samp_arr_img(arr_img):
  point = ee.Geometry.Point([-121, 42])
  return arr_img.sample(point, 500).first().get('array')

# A 1D array image.
array_img_1d = ee.Image([0, 1, 2]).toArray()
display('1D array image (pixel):', samp_arr_img(array_img_1d))
# [0, 1, 2]

# Define image band names for a 1D array image with 3 rows. You are labeling
# all rows and columns using a list of lists; the 1st sub list defines labels
# for array rows and the 2nd (if applicable) defines labels for array columns.
band_names_1d = [['row0', 'row1', 'row2']]

# Flatten the 1D array image into an image with n bands equal to all
# combinations of rows and columns. Here, we have 3 rows and 0 columns,
# so the result will be a 3-band image.
img_from_1d_array = array_img_1d.arrayFlatten(band_names_1d)
display('Image from 1D array:', img_from_1d_array)

# Make a 2D array image by repeating the 1D array on 2-axis.
array_img_2d = array_img_1d.arrayRepeat(1, 2)
display('2D array image (pixel):', samp_arr_img(array_img_2d))
# [[0, 0],
#  [1, 1],
#  [2, 2]]

# Define image band names for a 2D array image with 3 rows and 2 columns.
# Recall that you are labeling all rows and columns using a list of lists;
# The 1st sub list defines labels for array rows and the 2nd (if applicable)
# defines labels for array columns.
band_names_2d = [['row0', 'row1', 'row2'], ['col0', 'col1']]

# Flatten the 2D array image into an image with n bands equal to all
# combinations of rows and columns. Here, we have 3 rows and 2 columns,
# so the result will be a 6-band image.
img_from_2d_array = array_img_2d.arrayFlatten(band_names_2d)
display('Image from 2D array:', img_from_2d_array)