added apply_filters_ui.py

src/sdstools/apply_filters_ui.py

@@ -0,0 +1,149 @@
+import matplotlib.pyplot as plt
+import geopandas as gpd
+import numpy as np
+import contextily as cx
+from matplotlib.widgets import Slider, Button
+from matplotlib_scalebar.scalebar import ScaleBar
+import tkinter as tk
+from tkinter import filedialog
+import os
+
+global shorelines_path
+
+root = tk.Tk()
+root.shorelines_path = tk.filedialog.askopenfilename(title="Select extracted shorelines joined with model scores")
+shorelines_path = root.shorelines_path
+root.withdraw()
+
+
+def min_max_normalize(arr):
+    min_val = np.min(arr)
+    max_val = np.max(arr)
+    return (arr - min_val) / (max_val - min_val)
+
+def add_north_arrow(ax, north_arrow_params):
+    x,y,arrow_length = north_arrow_params
+    ax.annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
+                arrowprops=dict(facecolor='white', width=2, headwidth=4),
+                ha='center', va='center', fontsize=8, color='white',
+                xycoords=ax.transAxes)
+
+def plot_shorelines_by_column(ax,
+                              cax,
+                              shorelines,
+                              site,
+                              north_arrow_params,
+                              scale_bar_loc,
+                              column='year',
+                              image_score=0.0,
+                              seg_score=0.0,
+                              kde_score=0.0,
+                              ):
+    global plotting_shorelines
+    shorelines['year'] = shorelines['dates'].dt.year
+    shorelines = shorelines.sort_values(by=column)
+    plotting_shorelines = shorelines[shorelines['model_scores']>image_score]
+    plotting_shorelines = plotting_shorelines[plotting_shorelines['model_scores_seg']>seg_score]
+    plotting_shorelines = plotting_shorelines[plotting_shorelines['kde_value']>kde_score]
+    lines = plotting_shorelines.to_crs(epsg=3857)
+    map_plot = lines.plot(ax=ax,
+                          cax=cax,
+                    column=column,
+                    legend=True,
+                    legend_kwds={"label": column, "orientation": "vertical", 'shrink': 0.3},
+                    cmap='viridis',
+                    linewidth=1
+                    )
+    ax.set_title(site)
+    cx.add_basemap(ax,
+                   source=cx.providers.Esri.WorldImagery,
+                   attribution=False)
+    add_north_arrow(ax, north_arrow_params)
+    ax.add_artist(ScaleBar(1, location=scale_bar_loc))
+    ax.set_axis_off()
+
+# Create the figure and the line that will be manipulated
+shorelines = gpd.read_file(shorelines_path)
+weighted_overall_score = (0.2*shorelines['kde_value']+
+                          0.3*shorelines['model_scores']+
+                          0.5*shorelines['model_scores_seg'])/3
+weighted_overall_score = min_max_normalize(weighted_overall_score)
+shorelines['overall_score'] = weighted_overall_score
+init_score = 0
+fig, ax = plt.subplots()
+cax = ax.inset_axes([1.03, 0, 0.1, 1], transform=ax.transAxes) 
+plot_shorelines_by_column(ax,
+                          cax,
+                          shorelines,
+                          'Elwha',
+                          (0.23, 0.92, 0.2),
+                          'upper left',
+                          column='year',
+                          )
+# Adjust the main plot to make room for the sliders
+fig.subplots_adjust(left=0.25)
+
+# Create a vertical slider to control the amplitude
+ax_im_score = fig.add_axes([0.25, 0.1, 0.65, 0.03])
+im_score_slider = Slider(ax=ax_im_score,
+                      label="Image Suitability Score",
+                      valmin=0.0,
+                      valmax=0.99,
+                      valinit=0,
+                      valstep=0.01,
+                      orientation="horizontal",
+                      )
+ax_seg_score = fig.add_axes([0.25, 0.05, 0.65, 0.03])
+seg_score_slider = Slider(ax=ax_seg_score,
+                      label="Segmentation Score",
+                      valmin=0.0,
+                      valmax=0.99,
+                      valinit=0,
+                      valstep=0.01,
+                      orientation="horizontal",
+                      )
+ax_kde_score = fig.add_axes([0.25, 0, 0.65, 0.03])
+kde_score_slider = Slider(ax=ax_kde_score,
+                      label="KDE Score",
+                      valmin=0.0,
+                      valmax=0.99,
+                      valinit=0,
+                      valstep=0.01,
+                      orientation="horizontal",
+                      )
+
+# Define the update function to be called anytime a slider's value changes
+def update(val):
+    cax.clear()
+    ax.clear()
+    plot_shorelines_by_column(ax,
+                              cax,
+                          shorelines,
+                          'Elwha',
+                          (0.23, 0.92, 0.2),
+                          'upper left',
+                          column='year',
+                          image_score=im_score_slider.val,
+                          seg_score=seg_score_slider.val,
+                          kde_score=kde_score_slider.val,
+                          )
+    fig.canvas.draw_idle()
+
+
+
+# Register the update function with each slider
+im_score_slider.on_changed(update)
+seg_score_slider.on_changed(update)
+kde_score_slider.on_changed(update)
+
+saveax = fig.add_axes([0.1, 0.9, 0.1, 0.04])
+button = Button(saveax, 'Save', hovercolor='0.975')
+
+def save(event):
+    save_path = os.path.splitext(shorelines_path)[0]+'_filtered.geojson'
+    print('saving filtered shorelines to: ' + save_path)
+    plotting_shorelines.to_file(os.path.splitext(shorelines_path)[0]+'_filtered.geojson')
+
+button.on_clicked(save)
+
+plt.show()