"""
How to get started with the calculation of crowding indices
===========================================================

.. sectionauthor:: Julian Heinze (Helmholtz Centre for Environmental Research GmbH - UFZ)

This example demonstrates how to get the crowding indices.
"""

# %%
# 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()