Calculate the crowding indices

This example shows how to calculate crowding indices from arrays. The corresponding distribution will be plotted later on.

Necessary imports

from spatiocoexistence.crowding import crowding_indices
import numpy as np
import matplotlib.pyplot as plt

Generate random point data

n = 100000
x_dimension = 1000
y_dimension = 2000
x = np.round(np.random.uniform(0, x_dimension, n), 2)
y = np.round(np.random.uniform(0, y_dimension, n), 2)
species = np.random.randint(0, 10, n)
dbh = np.random.uniform(1, 10, n)
status = np.random.randint(-2, 1, n).astype(np.int32)
radius = 10

Calculate the crowding indices in serial computation

CI, HI, CI_D, HI_D = crowding_indices(
    x,
    y,
    species,
    status,
    radius,
    cell_size=radius,
    dbh=dbh,
    domain_x=x_dimension,
    domain_y=y_dimension,
    num_threads=1,
)

Plot the conspecific crowding indices

plt.figure()
plt.hist(CI)
plt.show()

Plot the conspecific crowding indices

plt.figure()
plt.hist(HI)
plt.show()

Plot the conspecific crowding indices

plt.figure()
plt.hist(CI_D)
plt.show()

Plot the conspecific crowding indices

plt.figure()
plt.hist(HI_D)
plt.show()