Memory Issues

[oguzhannysr]

@MuellerSeb Hello, I want to use your kriging interpolation method but I'm getting a memory error. How can I overcome this? However, although I waited for the same data in the SmartMap plugin in QGIS, I got a result. Is there anything I can change or suggest I make?

import numpy as np
import geopandas as gpd
from pykrige.ok import OrdinaryKriging
import matplotlib.pyplot as plt

gdf = gpd.read_file(r"test_EPSG4326.geojson")  

X = np.array([gdf.geometry.x, gdf.geometry.y]).T
y = gdf['m'].values

# Ordinary Kriging 
OK = OrdinaryKriging(X[:, 0], X[:, 1], y, variogram_model='linear', verbose=True, enable_plotting=False)

# İnterpolasyon
gridx = np.linspace(X[:, 0].min(), X[:, 0].max(), 100)
gridy = np.linspace(X[:, 1].min(), X[:, 1].max(), 100)

# Grid 
grid_z, ss = OK.execute('grid', gridx, gridy)


plt.figure(figsize=(10, 6))
plt.contourf(gridx, gridy, grid_z, cmap='coolwarm')
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolor='k', cmap='coolwarm')
plt.colorbar(label='Maglev Değeri')
plt.title('Ordinary Kriging İnterpolasyonu')
plt.xlabel('X Koordinatı')
plt.ylabel('Y Koordinatı')
plt.show()

[mihtm13]

Hello,
in my case adding the parameter OK.execute('grid', gridx, gridy, backend='loop') or OK.execute('grid', gridx, gridy, backend='C')
helped to reduce the ammount of RAM needed. But with using the loop-backend the processing time increases.
Here is the explanation for the "backend" parameter:
backend : str, optional Specifies which approach to use in kriging. Specifying 'vectorized' will solve the entire kriging problem at once in a vectorized operation. This approach is faster but also can consume a significant amount of memory for large grids and/or large datasets. Specifying 'loop' will loop through each point at which the kriging system is to be solved. This approach is slower but also less memory-intensive. Default is 'vectorized'. Note that Cython backend is not supported for UK.

But there is another problem associated to this error, because you're not able to control which datatype, like numpy.float32, the Pykrige program is using. It seems to always default to numpy.float64, which is in most cases too memory expensive.

[oguzhannysr]

@mihtm13 , Thanks but I couldn't get to that line because I get an error on the line below.

OK = OrdinaryKriging(X[:, 0], X[:, 1], y, variogram_model='linear', verbose=True, enable_plotting=False)