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from turtle import title
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_species_distributions
from sklearn.neighbors import KernelDensity
import gradio as gr
def construct_grids(batch):
xmin = batch.x_left_lower_corner + batch.grid_size
xmax = xmin + (batch.Nx * batch.grid_size)
ymin = batch.y_left_lower_corner + batch.grid_size
ymax = ymin + (batch.Ny * batch.grid_size)
xgrid = np.arange(xmin, xmax, batch.grid_size)
ygrid = np.arange(ymin, ymax, batch.grid_size)
return (xgrid, ygrid)
def plot_species_distributions(bandwidth):
data = fetch_species_distributions()
species_names = ["Bradypus Variegatus", "Microryzomys Minutus"]
Xtrain = np.vstack([data["train"]["dd lat"], data["train"]["dd long"]]).T
ytrain = np.array(
[d.decode("ascii").startswith("micro") for d in data["train"]["species"]],
dtype="int",
)
Xtrain *= np.pi / 180.0
xgrid, ygrid = construct_grids(data)
X, Y = np.meshgrid(xgrid[::5], ygrid[::5][::-1])
land_reference = data.coverages[6][::5, ::5]
land_mask = (land_reference > -9999).ravel()
xy = np.vstack([Y.ravel(), X.ravel()]).T
xy = xy[land_mask]
xy *= np.pi / 180.0
fig = plt.figure()
fig.subplots_adjust(left=0.05, right=0.95, wspace=0.05)
for i in range(2):
plt.subplot(1, 2, i + 1)
print(" - computing KDE in spherical coordinates")
kde = KernelDensity(
bandwidth=bandwidth, metric="haversine", kernel="gaussian", algorithm="ball_tree"
)
kde.fit(Xtrain[ytrain == i])
Z = np.full(land_mask.shape[0], -9999, dtype="int")
Z[land_mask] = np.exp(kde.score_samples(xy))
Z = Z.reshape(X.shape)
levels = np.linspace(0, Z.max(), 25)
plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)
plt.contour(
X, Y, land_reference, levels=[-9998], colors="k", linestyles="solid"
)
plt.xticks([])
plt.yticks([])
plt.title(species_names[i])
return plt
bandwidth_input = gr.inputs.Slider(minimum=0.01, maximum=0.3, default=0.01, step=0.01, label="Bandwidth")
title="Kernel Density Estimate of Species Distributions"
description="This shows an example of a neighbors-based query (in particular a kernel density estimate) on geospatial data, using a Ball Tree built upon the Haversine distance metric – i.e. distances over points in latitude/longitude. The dataset is provided by Phillips et. al. (2006). If available, the example uses basemap to plot the coast lines and national boundaries of South America. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html"
iface = gr.Interface(fn=plot_species_distributions, title = title, description=description, inputs=bandwidth_input, outputs="plot")
iface.launch()
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