Loading Data

Python

Data can be loaded in several ways.

To load from disk, if GDAL is available on your system, almost any form of raster data can be easily loaded, like so:

GDAL

import richdem as rd
beau = rd.LoadGDAL("beauford.tif")

NumPy

Data can also be loaded from a NumPy array:

import numpy as np
import richdem as rd

npa = np.random.random(size=(50,50))
rda = rd.rdarray(npa, no_data=-9999)

Note that !`rd.rdarray()` creates a view of the data stored in !`npa`. Modifying rda will modify npa. This prevents unwanted memory from being used. If you instead want rda to be a new copy of the data, use:

rda = rd.rdarray(a, no_data=-9999)

Saved NumPy Arrays

It is possible to save, and load, data to and from a NumPy array like so:

import numpy as np
import richdem as rd

npa = np.random.random(size=(50,50))
rda = rd.rdarray(npa, no_data=-9999)
np.save('out.npy', rda)
loaded = rd.rdarray(np.load('out.npy'), no_data=-9999)

This can be done in a compressed format like so:

np.savez('rda', rda=rda)
np.load('rda.npz')['rda']

Note that there is not yet a way to save the metadata of an rdarray. (TODO)

C++

TODO