Pass numpy-nDarray directly to spectralpython about spectral HOT 4 CLOSED

Most of the spectral functions can operate on numpy.ndarray objects so you should be able to stack the GRASS layers into an ndarray and pass it to the spectral functions. If you need to read the data from a file, rather than interface GRASS directly from python, you can use numpy.save to write the array to a .npy file, then use numpy.load to read it.

from spectral.

It worked as aspected :)
i first stored the image name in a list
then i read the dataset as a numpy array and I append each array in a empty list
then i concatenate all the layer from the list of arrays in a single one

#img = ['layername1', 'layername2', ... , ....]
import numpy as np
arraylist = []
for i in img[]:
    rl = garray.array()
    rl.read(i)
    arraylist.append(rl)
imagegroup = np.concatenate([aux[..., np.newaxis] for aux in arraylist], axis=2)

maybe there is abetter way to concatenate my layers, but as it is works great!
Thank you so much!!!

from spectral.

The way you are doing it looks fine. If you know the shape, data type, and number layers, you could save some memory by pre-allocating imagegroup, then setting the bands directly (e.g., imagegroup[:, :, i] = rl). That way, you wouldn't have all the layers in memory twice (in both arraylist and imagegroup).

from spectral.

👍

i changed the code to :

rl = garray.array()
imagegroup = np.empty((rl.shape[0], rl.shape[1], 8), dtype='float')
for i,v in enumerate(img[3:]):
    rl.read(v)
    imagegroup[:, :, i] = rl

Thanks a lot!

from spectral.