Create app.py

app.py

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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()