| 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() | |