Quickstart

Installation

Install package with conda:

conda install -c conda-forge pyinterpolate

Ordinary Kriging

The package has multiple spatial interpolation functions. The flow of analysis is usually the same for each method. The interpolation of missing value from points is the basic case. We use for it Ordinary Kriging.

[1.] Read and prepare data.

import geopandas as gpd


point_data = gpd.read_file('dem.gpkg')

[2.] Analyze data, calculate the experimental variogram.

from pyinterpolate import calculate_semivariance


search_radius = 500
max_range = 40000

experimental_semivariogram = calculate_semivariance(ds=point_data,
                                                    step_size=search_radius,
                                                    max_range=max_range)

[3.] Data transformation, fit theoretical variogram.

from pyinterpolate import build_theoretical_variogram


semivar = build_theoretical_variogram(experimental_variogram=experimental_semivariogram,
                                      models_group='spherical',
                                      sill=400,
                                      rang=20000,
                                      nugget=0)

[4.] Interpolation.

from pyinterpolate import ordinary_kriging


unknown_point = (20000, 65000)
prediction = ordinary_kriging(theoretical_model=semivar,
                              known_locations=point_data,
                              unknown_location=unknown_point,
                              no_neighbors=32)

[5.] Error and uncertainty analysis.

print(prediction)  # [predicted, variance error, lon, lat]

>> [211.23, 0.89, 20000, 60000]

[6.] Full code.

import geopandas as gpd
from pyinterpolate import calculate_semivariance
from pyinterpolate import build_theoretical_variogram
from pyinterpolate import ordinary_kriging


point_data = gpd.read_file('dem.gpkg')  # x, y, value
search_radius = 500
max_range = 40000

experimental_semivariogram = calculate_semivariance(ds=point_data,
                                                    step_size=search_radius,
                                                    max_range=max_range)
semivar = build_theoretical_variogram(experimental_variogram=experimental_semivariogram,
                                      models_group='spherical',
                                      sill=400,
                                      rang=20000,
                                      nugget=0)
unknown_point = (20000, 65000)
prediction = ordinary_kriging(theoretical_model=semivar,
                              known_locations=point_data,
                              unknown_location=unknown_point,
                              no_neighbors=32)