Added explanations

Gradio_app.ipynb

@@ -17,14 +17,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7871\n",
+      "Running on local URL:  http://127.0.0.1:7875\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -32,7 +32,7 @@
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7871/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+       "<div><iframe src=\"http://127.0.0.1:7875/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -45,7 +45,7 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 25,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -130,7 +130,7 @@
     "        a.axvline(s_phase.mean()*processed_input.shape[-1], color='b', linestyle='--', label='S')\n",
     "\n",
     "    ax[-1].set_xlabel('Time, samples')\n",
-    "    ax[-1].set_ylabel('Uncert.')\n",
+    "    ax[-1].set_ylabel('Uncert., samples')\n",
     "    ax[-1].legend()\n",
     "\n",
     "    plt.subplots_adjust(hspace=0., wspace=0.)\n",
@@ -319,8 +319,9 @@
     "    ax[1].imshow(hillshade, cmap=\"Greys\", alpha=0.5)\n",
     "\n",
     "    output_picks = pd.DataFrame({'station_name' : [], 'starttime' : [], \n",
-    "                                 'p_phase' : [], 'p_uncertainty' : [], 's_phase' : [], 's_uncertainty' : [],\n",
-    "                                 'velocity_p' : [], 'velocity_s' : []})\n",
+    "                                 'p_phase, s' : [], 'p_uncertainty, s' : [], \n",
+    "                                 's_phase, s' : [], 's_uncertainty, s' : [],\n",
+    "                                 'velocity_p, km/s' : [], 'velocity_s, km/s' : []})\n",
     "                                  \n",
     "                                  \n",
     "    for i in range(len(waveforms)):\n",
@@ -352,9 +353,9 @@
     "        print(f\"Station {st_lats[i]}, {st_lons[i]} has P velocity {velocity_p} and S velocity {velocity_s}\")\n",
     "\n",
     "        output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])], \n",
-    "                                                        'p_phase' : [(delta_t+current_P.mean()*60).item()], 'p_uncertainty' : [current_P.std().item()*60], \n",
-    "                                                        's_phase' : [(delta_t+current_S.mean()*60).item()], 's_uncertainty' : [current_S.std().item()*60],\n",
-    "                                                        'velocity_p' : [velocity_p], 'velocity_s' : [velocity_s]}))\n",
+    "                                                        'p_phase, s' : [(delta_t+current_P.mean()*60).item()], 'p_uncertainty, s' : [current_P.std().item()*60], \n",
+    "                                                        's_phase, s' : [(delta_t+current_S.mean()*60).item()], 's_uncertainty, s' : [current_S.std().item()*60],\n",
+    "                                                        'velocity_p, km/s' : [velocity_p], 'velocity_s, km/s' : [velocity_s]}))\n",
     "        \n",
     "        # Generate an array from st_lat to eq_lat and from st_lon to eq_lon\n",
     "        x = np.linspace(st_lons[i], eq_lon, 50)\n",
@@ -445,8 +446,12 @@
     "        button.click(mark_phases, inputs=[inputs, upload], outputs=outputs)\n",
     "        \n",
     "    with gr.Tab(\"Select earthquake from catalogue\"):\n",
-    "        gr.Markdown(\"\"\"Select an earthquake from the global earthquake catalogue and the app will download the waveform from the FDSN client of your choice.\n",
-    "                       \"\"\")\n",
+    "        gr.Markdown(\"\"\"\n",
+    "        Select an earthquake from the global earthquake catalogue and the app will download the waveform from the FDSN client of your choice.\n",
+    "        Pick data for each waveform is reported in seconds from the start of the waveform.\n",
+    "        Velocities are derived from distance and travel time determined by PhaseHunter picks ($v = \\mathrm{distance}/\\mathrm{predicted_pick_time}$).\n",
+    "        Backround of velocity plot is colored by DEM.\n",
+    "        \"\"\")\n",
     "        with gr.Row(): \n",
     "            client_inputs = gr.Dropdown(\n",
     "                choices = list(URL_MAPPINGS.keys()), \n",
@@ -513,7 +518,9 @@
     "            \n",
     "        button = gr.Button(\"Predict phases\")\n",
     "        output_image = gr.Image(label='Waveforms with Phases Marked', type='numpy', interactive=False)\n",
-    "        output_picks = gr.Dataframe(label='# Pick data', type='pandas', interactive=False)\n",
+    "        output_picks = gr.Dataframe(label='Pick data', \n",
+    "                                    type='pandas', \n",
+    "                                    interactive=False)\n",
     "\n",
     "        button.click(predict_on_section, \n",
     "                 inputs=[client_inputs, timestamp_inputs, \n",

app.py

@@ -77,7 +77,7 @@ def mark_phases(waveform, uploaded_file):
         a.axvline(s_phase.mean()*processed_input.shape[-1], color='b', linestyle='--', label='S')
 
     ax[-1].set_xlabel('Time, samples')
-    ax[-1].set_ylabel('Uncert.')
+    ax[-1].set_ylabel('Uncert., samples')
     ax[-1].legend()
 
     plt.subplots_adjust(hspace=0., wspace=0.)
@@ -266,8 +266,9 @@ def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source
     ax[1].imshow(hillshade, cmap="Greys", alpha=0.5)
 
     output_picks = pd.DataFrame({'station_name' : [], 'starttime' : [], 
-                                 'p_phase' : [], 'p_uncertainty' : [], 's_phase' : [], 's_uncertainty' : [],
-                                 'velocity_p' : [], 'velocity_s' : []})
+                                 'p_phase, s' : [], 'p_uncertainty, s' : [], 
+                                 's_phase, s' : [], 's_uncertainty, s' : [],
+                                 'velocity_p, km/s' : [], 'velocity_s, km/s' : []})
                                   
                                   
     for i in range(len(waveforms)):
@@ -299,9 +300,9 @@ def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source
         print(f"Station {st_lats[i]}, {st_lons[i]} has P velocity {velocity_p} and S velocity {velocity_s}")
 
         output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])], 
-                                                        'p_phase' : [(delta_t+current_P.mean()*60).item()], 'p_uncertainty' : [current_P.std().item()*60], 
-                                                        's_phase' : [(delta_t+current_S.mean()*60).item()], 's_uncertainty' : [current_S.std().item()*60],
-                                                        'velocity_p' : [velocity_p], 'velocity_s' : [velocity_s]}))
+                                                        'p_phase, s' : [(delta_t+current_P.mean()*60).item()], 'p_uncertainty, s' : [current_P.std().item()*60], 
+                                                        's_phase, s' : [(delta_t+current_S.mean()*60).item()], 's_uncertainty, s' : [current_S.std().item()*60],
+                                                        'velocity_p, km/s' : [velocity_p], 'velocity_s, km/s' : [velocity_s]}))
         
         # Generate an array from st_lat to eq_lat and from st_lon to eq_lon
         x = np.linspace(st_lons[i], eq_lon, 50)
@@ -392,8 +393,12 @@ with gr.Blocks() as demo:
         button.click(mark_phases, inputs=[inputs, upload], outputs=outputs)
         
     with gr.Tab("Select earthquake from catalogue"):
-        gr.Markdown("""Select an earthquake from the global earthquake catalogue and the app will download the waveform from the FDSN client of your choice.
-                       """)
+        gr.Markdown("""
+        Select an earthquake from the global earthquake catalogue and the app will download the waveform from the FDSN client of your choice.
+        Pick data for each waveform is reported in seconds from the start of the waveform.
+        Velocities are derived from distance and travel time determined by PhaseHunter picks ($v = \mathrm{distance}/\mathrm{predicted_pick_time}$).
+        Backround of velocity plot is colored by DEM.
+        """)
         with gr.Row(): 
             client_inputs = gr.Dropdown(
                 choices = list(URL_MAPPINGS.keys()), 
@@ -460,7 +465,9 @@ with gr.Blocks() as demo:
             
         button = gr.Button("Predict phases")
         output_image = gr.Image(label='Waveforms with Phases Marked', type='numpy', interactive=False)
-        output_picks = gr.Dataframe(label='# Pick data', type='pandas', interactive=False)
+        output_picks = gr.Dataframe(label='Pick data', 
+                                    type='pandas', 
+                                    interactive=False)
 
         button.click(predict_on_section, 
                  inputs=[client_inputs, timestamp_inputs,