N-Dim array manipulation in a loop, getting IndexError: too many indices for array

[cesardepaula]

Hello people, good morning!!

In my implementation I`m trying to manipulation N-Dim. array on a looping to calculate a covariance between 2 variables from this N-Dim. array.

To explain better, I have one raster file containing 4 bands (R, G, B, Nir). On my implementation I read raster file, extract rows, columns and number of bands. After that I read the raster file as array, according code below.

My goal on this algorithm is to calculate the covariance for R, G, B bands in relation Nir band in the same looping. For example:
Cov_1 = np.cov(R, Nir)

Cov_2 = np.cov(G, Nir)

Cov_3 = np.cov(B, Nir)

The algorithm has implemented in python is:

from osgeo import gdal import numpy as np ds = gdal.Open('Sun_Glint_Sample_.tif') rows = ds.RasterYSize cols = ds.RasterXSize bands = ds.RasterCount cov = [] for i in range(bands): i += 1 data_ds = ds.GetRasterBand(i).ReadAsArray(0, 0, cols, rows).ravel() cov = np.cov(data_ds[:, i], data_ds[:,4], bias = True) print('Dimension: ', data_ds.ndim) print('Shape: ', data_ds.shape) print('Array: ', data_ds[:, i]) print('Covariance: ', cov)

Below the information printed before to calculate the covariance:

Output:
runfile('D:/CESAR_PHD/10.Lyzenga/Sun_glint_removal_v04.py', wdir='D:/CESAR_PHD/10.Lyzenga') Dimension: 1 Shape: (417944,) Array: [135 123 94 ... 31 57 77] Dimension: 1 Shape: (417944,) Array: [191 181 139 ... 49 93 128] Dimension: 1 Shape: (417944,) Array: [176 164 126 ... 42 78 107] Dimension: 1 Shape: (417944,) Array: [91 98 69 ... 12 19 33]

Below the error printed when I tried calculated the covariance:

Error:
 File "D:/CESAR_PHD/10.Lyzenga/Sun_glint_removal_v04.py", line 22, in <module> cov = np.cov(data_ds[:, i], data_ds[:,4], bias = True) IndexError: too many indices for array

Thank you for help me!!

[scidam]

Try the following code:

nir_flattened = ds.GetRasterBand(bands - 1).ReadAsArray(0, 0, cols, rows).ravel() for i in range(bands - 1): data_ds_flattened = ds.GetRasterBand(i).ReadAsArray(0, 0, cols, rows).ravel() cov = np.cov(data_ds_flattened, nir_flattened, bias=True) print('Covariance: ', cov) # expected cov is 2 x 2 matrix

But, I am not sure that: 1) bands counting starts from 0, 2) would it possible to read bands in arbitrary order, as I did; probably something like .seek method should be applied first.