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updated layout
Browse files- Gradio_app.ipynb +204 -60
- app.py +71 -56
- data/cached/BK_MCCM_2023-04-01T16:43:30.541616Z.mseed +0 -0
- data/cached/BK_SAO_2023-04-01T16:43:36.718558Z.mseed +0 -0
- data/cached/NC_C006_2023-04-01T16:43:22.900707Z.mseed +0 -0
- data/cached/NC_C031_2023-04-01T16:43:22.656861Z.mseed +0 -0
- data/cached/NC_C050_2023-04-01T16:43:24.524689Z.mseed +0 -0
- data/cached/NC_C055_2023-04-01T16:43:24.930990Z.mseed +0 -0
- data/cached/NC_C056_2023-04-01T16:43:22.066458Z.mseed +0 -0
- data/cached/NC_G004_2023-04-01T16:43:40.683359Z.mseed +0 -0
- data/cached/NC_J010_2023-04-01T16:43:25.779606Z.mseed +0 -0
- data/cached/NC_J068_2023-04-01T16:43:26.681315Z.mseed +0 -0
- data/cached/NC_RWSVT_2023-04-01T16:43:23.543745Z.mseed +0 -0
- data/cached/PB_B054_2023-04-01T16:43:22.147061Z.mseed +0 -0
- data/cached/PB_B057_2023-04-01T16:43:26.668494Z.mseed +0 -0
- data/cached/PB_B058_2023-04-01T16:43:35.559392Z.mseed +0 -0
- data/cached/PB_B065_2023-04-01T16:43:36.828013Z.mseed +0 -0
- data/cached/PB_B066_2023-04-01T16:43:34.748830Z.mseed +0 -0
- data/cached/PB_B067_2023-04-01T16:43:36.084437Z.mseed +0 -0
- data/cached/PB_P176_2023-04-01T16:43:25.099350Z.mseed +0 -0
- data/cached/PB_P181_2023-04-01T16:43:23.984589Z.mseed +0 -0
- data/cached/PB_P197_2023-04-01T16:43:32.497268Z.mseed +0 -0
- data/cached/PB_P199_2023-04-01T16:43:28.886780Z.mseed +0 -0
- data/cached/PB_P201_2023-04-01T16:43:33.477717Z.mseed +0 -0
- data/cached/PB_P224_2023-04-01T16:43:22.286686Z.mseed +0 -0
- data/cached/PB_P248_2023-04-01T16:43:24.960417Z.mseed +0 -0
- data/cached/PB_P262_2023-04-01T16:43:24.445973Z.mseed +0 -0
Gradio_app.ipynb
CHANGED
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"cells": [
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{
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running on local URL: http://127.0.0.1:
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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]
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{
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"data": {
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"text/html": [
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"<div><iframe src=\"http://127.0.0.1:
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"data": {
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"text/plain": []
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},
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"execution_count":
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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@@ -153,7 +282,7 @@
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" return coeff\n",
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"\n",
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"def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source_depth_km, velocity_model, max_waveforms):\n",
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" distances, t0s, st_lats, st_lons, waveforms = [], [], [], [], []\n",
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" \n",
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" taup_model = TauPyModel(model=velocity_model)\n",
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" client = Client(client_name)\n",
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" st_lats.append(station.latitude)\n",
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" st_lons.append(station.longitude)\n",
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" waveforms.append(waveform)\n",
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"\n",
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" print(f\"Added {network.code}.{station.code} to the list of waveforms\")\n",
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"\n",
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" t0s = np.array(t0s)[selection_indexes]\n",
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" st_lats = np.array(st_lats)[selection_indexes]\n",
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" st_lons = np.array(st_lons)[selection_indexes]\n",
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"\n",
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" waveforms = [torch.tensor(waveform) for waveform in waveforms]\n",
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"\n",
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" topo_map.da.plot(ax = ax[2], cmap='Greys', add_colorbar=False, add_labels=False)\n",
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" ax[1].imshow(hillshade, cmap=\"Greys\", alpha=0.5)\n",
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"\n",
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" for i in range(len(waveforms)):\n",
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" print(f\"Plotting waveform {i+1}/{len(waveforms)}\")\n",
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" current_P = p_phases[i::len(waveforms)]\n",
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" velocity_s = (distances[i]*111.2)/(delta_t+current_S.mean()*60).item()\n",
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"\n",
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" print(f\"Station {st_lats[i]}, {st_lons[i]} has P velocity {velocity_p} and S velocity {velocity_s}\")\n",
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" \n",
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" # Generate an array from st_lat to eq_lat and from st_lon to eq_lon\n",
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" x = np.linspace(st_lons[i], eq_lon, 50)\n",
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" cbar.set_label('S Velocity (km/s)')\n",
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" ax[2].set_title('S Velocity')\n",
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"\n",
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"\n",
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"\n",
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" plt.subplots_adjust(hspace=0., wspace=0.5)\n",
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"\n",
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" fig.canvas.draw();\n",
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" image = np.array(fig.canvas.renderer.buffer_rgba())\n",
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" plt.close(fig)\n",
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"\n",
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" return image\n",
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"\n",
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"\n",
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"model = Onset_picker.load_from_checkpoint(\"./weights.ckpt\",\n",
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" with gr.Tab(\"Select earthquake from catalogue\"):\n",
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" 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",
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" \"\"\")\n",
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" \n",
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" client_inputs = gr.Dropdown(\n",
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" choices = list(URL_MAPPINGS.keys()), \n",
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" label=\"FDSN Client\", \n",
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" info=\"Select one of the available FDSN clients\",\n",
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" value = \"IRIS\",\n",
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" interactive=True\n",
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" )\n",
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"\n",
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" with gr.Row(): \n",
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"\n",
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" timestamp_inputs = gr.Textbox(value='2019-07-04 17:33:49',\n",
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" placeholder='YYYY-MM-DD HH:MM:SS',\n",
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" label=\"Timestamp\",\n",
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" info=\"Timestamp of the earthquake\",\n",
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" max_lines=1,\n",
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" interactive=True)\n",
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" \n",
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" eq_lat_inputs = gr.Number(value=35.766, \n",
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" label=\"Latitude\", \n",
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" info=\"Latitude of the earthquake\",\n",
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" interactive=True)\n",
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" \n",
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" eq_lon_inputs = gr.Number(value=-117.605,\n",
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" label=\"Longitude\",\n",
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" info=\"Longitude of the earthquake\",\n",
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" interactive=True)\n",
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" \n",
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" source_depth_inputs = gr.Number(value=10,\n",
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" label=\"Source depth (km)\",\n",
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" info=\"Depth of the earthquake\",\n",
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" interactive=True)\n",
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" \n",
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" radius_inputs = gr.Slider(minimum=1, \n",
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" maximum=150, \n",
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" value=50, label=\"Radius (km)\", \n",
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" step=10,\n",
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" info=\"\"\"Select the radius around the earthquake to download data from.\\n \n",
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" Note that the larger the radius, the longer the app will take to run.\"\"\",\n",
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" interactive=True)\n",
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" \n",
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" velocity_inputs = gr.Dropdown(\n",
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" choices = ['1066a', '1066b', 'ak135', \n",
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"
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"
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" label=\"1D velocity model\", \n",
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" info=\"Velocity model for station selection\",\n",
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" value = \"1066a\",\n",
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" interactive=True\n",
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" )\n",
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"\n",
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" \n",
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" button = gr.Button(\"Predict phases\")\n",
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"
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" \n",
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" button.click(predict_on_section, \n",
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" inputs=[client_inputs, timestamp_inputs, \n",
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" eq_lat_inputs, eq_lon_inputs, \n",
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" radius_inputs, source_depth_inputs, \n",
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" velocity_inputs, max_waveforms_inputs],\n",
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" outputs=
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"\n",
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"demo.launch()"
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]
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 83,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running on local URL: http://127.0.0.1:7923\n",
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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]
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{
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"data": {
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"text/html": [
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"<div><iframe src=\"http://127.0.0.1:7923/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"data": {
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"text/plain": []
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},
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"execution_count": 83,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Starting to download inventory\n",
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"Finished downloading inventory\n",
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"Processing CI.CCC...\n",
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"Downloading waveform\n",
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"Skipping CI_CCC_2019-07-04T17:33:40.494920Z\n",
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"Processing CI.CLC...\n",
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"Processing CI.JRC2...\n",
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"Reading cached waveform\n",
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"Added CI.JRC2 to the list of waveforms\n",
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"Processing CI.LRL...\n",
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"Reading cached waveform\n",
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"Added CI.LRL to the list of waveforms\n",
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"Processing CI.MPM...\n",
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"Reading cached waveform\n",
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"Processing CI.Q0072...\n",
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"Reading cached waveform\n",
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"Processing CI.SLA...\n",
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"Reading cached waveform\n",
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"Added CI.SLA to the list of waveforms\n",
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"Processing CI.SRT...\n",
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"Reading cached waveform\n",
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"Added CI.SRT to the list of waveforms\n",
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"Processing CI.TOW2...\n",
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"Reading cached waveform\n",
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"Added CI.TOW2 to the list of waveforms\n",
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"Processing CI.WBM...\n",
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"Downloading waveform\n",
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"Skipping CI_WBM_2019-07-04T17:33:40.063616Z\n",
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"Processing CI.WCS2...\n",
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"Downloading waveform\n",
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"Skipping CI_WCS2_2019-07-04T17:33:40.200958Z\n",
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"Processing CI.WMF...\n",
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"Reading cached waveform\n",
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"Added CI.WMF to the list of waveforms\n",
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"Processing CI.WNM...\n",
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"Reading cached waveform\n",
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"Processing CI.WRC2...\n",
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"Downloading waveform\n",
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"Skipping CI_WRC2_2019-07-04T17:33:38.698099Z\n",
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"Processing CI.WRV2...\n",
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"Reading cached waveform\n",
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"Processing CI.WVP2...\n",
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"Downloading waveform\n",
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"Skipping CI_WVP2_2019-07-04T17:33:39.650402Z\n",
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"Processing NP.1809...\n",
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"Reading cached waveform\n",
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"Processing NP.5419...\n",
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"Reading cached waveform\n",
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"Processing PB.B916...\n",
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"Reading cached waveform\n",
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"Processing PB.B917...\n",
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"Reading cached waveform\n",
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"Processing PB.B918...\n",
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"Reading cached waveform\n",
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"Processing PB.B921...\n",
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"Reading cached waveform\n",
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"Starting to run predictions\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_17878/2982466024.py:225: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
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" waveforms = np.array(waveforms)[selection_indexes]\n",
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"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_17878/2982466024.py:225: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
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" waveforms = np.array(waveforms)[selection_indexes]\n",
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"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_17878/2982466024.py:232: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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" waveforms = [torch.tensor(waveform) for waveform in waveforms]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Starting plotting 3 waveforms\n",
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"Fetching topography\n",
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"Plotting topo\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/anovosel/miniconda3/envs/phasehunter/lib/python3.11/site-packages/bmi_topography/api_key.py:49: UserWarning: You are using a demo key to fetch data from OpenTopography, functionality will be limited. See https://bmi-topography.readthedocs.io/en/latest/#api-key for more information.\n",
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" warnings.warn(\n"
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]
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+
},
|
129 |
+
{
|
130 |
+
"name": "stdout",
|
131 |
+
"output_type": "stream",
|
132 |
+
"text": [
|
133 |
+
"Plotting waveform 1/3\n",
|
134 |
+
"Station 36.11758, -117.85486 has P velocity 4.96342368856812 and S velocity 3.0001093626503503\n",
|
135 |
+
"Plotting waveform 2/3\n"
|
136 |
+
]
|
137 |
+
},
|
138 |
+
{
|
139 |
+
"name": "stderr",
|
140 |
+
"output_type": "stream",
|
141 |
+
"text": [
|
142 |
+
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_17878/2982466024.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
|
143 |
+
" output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])],\n"
|
144 |
+
]
|
145 |
+
},
|
146 |
+
{
|
147 |
+
"name": "stdout",
|
148 |
+
"output_type": "stream",
|
149 |
+
"text": [
|
150 |
+
"Station 35.98249, -117.80885 has P velocity 4.229153346955616 and S velocity 2.3118595983254937\n",
|
151 |
+
"Plotting waveform 3/3\n",
|
152 |
+
"Station 35.69235, -117.75051 has P velocity 2.9537452996413585 and S velocity 1.3863453902284213\n",
|
153 |
+
"Plotting stations\n"
|
154 |
+
]
|
155 |
+
},
|
156 |
+
{
|
157 |
+
"name": "stderr",
|
158 |
+
"output_type": "stream",
|
159 |
+
"text": [
|
160 |
+
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_17878/2982466024.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
|
161 |
+
" output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])],\n",
|
162 |
+
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_17878/2982466024.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
|
163 |
+
" output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])],\n"
|
164 |
+
]
|
165 |
}
|
166 |
],
|
167 |
"source": [
|
|
|
282 |
" return coeff\n",
|
283 |
"\n",
|
284 |
"def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source_depth_km, velocity_model, max_waveforms):\n",
|
285 |
+
" distances, t0s, st_lats, st_lons, waveforms, names = [], [], [], [], [], []\n",
|
286 |
" \n",
|
287 |
" taup_model = TauPyModel(model=velocity_model)\n",
|
288 |
" client = Client(client_name)\n",
|
|
|
364 |
" st_lats.append(station.latitude)\n",
|
365 |
" st_lons.append(station.longitude)\n",
|
366 |
" waveforms.append(waveform)\n",
|
367 |
+
" names.append(f\"{network.code}.{station.code}\")\n",
|
368 |
"\n",
|
369 |
" print(f\"Added {network.code}.{station.code} to the list of waveforms\")\n",
|
370 |
"\n",
|
|
|
394 |
" t0s = np.array(t0s)[selection_indexes]\n",
|
395 |
" st_lats = np.array(st_lats)[selection_indexes]\n",
|
396 |
" st_lons = np.array(st_lons)[selection_indexes]\n",
|
397 |
+
" names = np.array(names)[selection_indexes]\n",
|
398 |
"\n",
|
399 |
" waveforms = [torch.tensor(waveform) for waveform in waveforms]\n",
|
400 |
"\n",
|
|
|
433 |
" topo_map.da.plot(ax = ax[2], cmap='Greys', add_colorbar=False, add_labels=False)\n",
|
434 |
" ax[1].imshow(hillshade, cmap=\"Greys\", alpha=0.5)\n",
|
435 |
"\n",
|
436 |
+
" output_picks = pd.DataFrame({'station_name' : [], 'starttime' : [], \n",
|
437 |
+
" 'p_phase' : [], 'p_uncertainty' : [], 's_phase' : [], 's_uncertainty' : [],\n",
|
438 |
+
" 'velocity_p' : [], 'velocity_s' : []})\n",
|
439 |
+
" \n",
|
440 |
+
" \n",
|
441 |
" for i in range(len(waveforms)):\n",
|
442 |
" print(f\"Plotting waveform {i+1}/{len(waveforms)}\")\n",
|
443 |
" current_P = p_phases[i::len(waveforms)]\n",
|
|
|
465 |
" velocity_s = (distances[i]*111.2)/(delta_t+current_S.mean()*60).item()\n",
|
466 |
"\n",
|
467 |
" print(f\"Station {st_lats[i]}, {st_lons[i]} has P velocity {velocity_p} and S velocity {velocity_s}\")\n",
|
468 |
+
"\n",
|
469 |
+
" output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])], \n",
|
470 |
+
" 'p_phase' : [(delta_t+current_P.mean()*60).item()], 'p_uncertainty' : [current_P.std().item()*60], \n",
|
471 |
+
" 's_phase' : [(delta_t+current_S.mean()*60).item()], 's_uncertainty' : [current_S.std().item()*60],\n",
|
472 |
+
" 'velocity_p' : [velocity_p], 'velocity_s' : [velocity_s]}))\n",
|
473 |
" \n",
|
474 |
" # Generate an array from st_lat to eq_lat and from st_lon to eq_lon\n",
|
475 |
" x = np.linspace(st_lons[i], eq_lon, 50)\n",
|
|
|
498 |
" cbar.set_label('S Velocity (km/s)')\n",
|
499 |
" ax[2].set_title('S Velocity')\n",
|
500 |
"\n",
|
|
|
|
|
501 |
" plt.subplots_adjust(hspace=0., wspace=0.5)\n",
|
502 |
"\n",
|
503 |
" fig.canvas.draw();\n",
|
504 |
" image = np.array(fig.canvas.renderer.buffer_rgba())\n",
|
505 |
" plt.close(fig)\n",
|
506 |
"\n",
|
507 |
+
" return image, output_picks\n",
|
508 |
"\n",
|
509 |
"\n",
|
510 |
"model = Onset_picker.load_from_checkpoint(\"./weights.ckpt\",\n",
|
|
|
551 |
" with gr.Tab(\"Select earthquake from catalogue\"):\n",
|
552 |
" 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",
|
553 |
" \"\"\")\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
554 |
" with gr.Row(): \n",
|
555 |
+
" client_inputs = gr.Dropdown(\n",
|
556 |
+
" choices = list(URL_MAPPINGS.keys()), \n",
|
557 |
+
" label=\"FDSN Client\", \n",
|
558 |
+
" info=\"Select one of the available FDSN clients\",\n",
|
559 |
+
" value = \"IRIS\",\n",
|
560 |
+
" interactive=True\n",
|
561 |
+
" )\n",
|
562 |
"\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
563 |
" velocity_inputs = gr.Dropdown(\n",
|
564 |
" choices = ['1066a', '1066b', 'ak135', \n",
|
565 |
+
" 'ak135f', 'herrin', 'iasp91', \n",
|
566 |
+
" 'jb', 'prem', 'pwdk'], \n",
|
567 |
" label=\"1D velocity model\", \n",
|
568 |
" info=\"Velocity model for station selection\",\n",
|
569 |
" value = \"1066a\",\n",
|
570 |
" interactive=True\n",
|
571 |
" )\n",
|
572 |
"\n",
|
573 |
+
" with gr.Column(scale=4):\n",
|
574 |
+
" with gr.Row(): \n",
|
575 |
+
" timestamp_inputs = gr.Textbox(value='2019-07-04 17:33:49',\n",
|
576 |
+
" placeholder='YYYY-MM-DD HH:MM:SS',\n",
|
577 |
+
" label=\"Timestamp\",\n",
|
578 |
+
" info=\"Timestamp of the earthquake\",\n",
|
579 |
+
" max_lines=1,\n",
|
580 |
+
" interactive=True)\n",
|
581 |
+
" \n",
|
582 |
+
" eq_lat_inputs = gr.Number(value=35.766, \n",
|
583 |
+
" label=\"Latitude\", \n",
|
584 |
+
" info=\"Latitude of the earthquake\",\n",
|
585 |
+
" interactive=True)\n",
|
586 |
+
" \n",
|
587 |
+
" eq_lon_inputs = gr.Number(value=-117.605,\n",
|
588 |
+
" label=\"Longitude\",\n",
|
589 |
+
" info=\"Longitude of the earthquake\",\n",
|
590 |
+
" interactive=True)\n",
|
591 |
+
" \n",
|
592 |
+
" source_depth_inputs = gr.Number(value=10,\n",
|
593 |
+
" label=\"Source depth (km)\",\n",
|
594 |
+
" info=\"Depth of the earthquake\",\n",
|
595 |
+
" interactive=True)\n",
|
596 |
+
" \n",
|
597 |
+
"\n",
|
598 |
+
" \n",
|
599 |
+
" with gr.Column(scale=2):\n",
|
600 |
+
" with gr.Row(): \n",
|
601 |
+
" radius_inputs = gr.Slider(minimum=1, \n",
|
602 |
+
" maximum=150, \n",
|
603 |
+
" value=50, label=\"Radius (km)\", \n",
|
604 |
+
" step=10,\n",
|
605 |
+
" info=\"\"\"Select the radius around the earthquake to download data from.\\n \n",
|
606 |
+
" Note that the larger the radius, the longer the app will take to run.\"\"\",\n",
|
607 |
+
" interactive=True)\n",
|
608 |
+
" \n",
|
609 |
+
" max_waveforms_inputs = gr.Slider(minimum=1,\n",
|
610 |
+
" maximum=100,\n",
|
611 |
+
" value=10,\n",
|
612 |
+
" label=\"Max waveforms per section\",\n",
|
613 |
+
" step=1,\n",
|
614 |
+
" info=\"Maximum number of waveforms to show per section\\n (to avoid long prediction times)\",\n",
|
615 |
+
" interactive=True,\n",
|
616 |
+
" )\n",
|
617 |
" \n",
|
618 |
" button = gr.Button(\"Predict phases\")\n",
|
619 |
+
" output_image = gr.Image(label='Waveforms with Phases Marked', type='numpy', interactive=False)\n",
|
620 |
+
" output_picks = gr.Dataframe(label='# Pick data', type='pandas', interactive=False)\n",
|
621 |
+
"\n",
|
622 |
" button.click(predict_on_section, \n",
|
623 |
" inputs=[client_inputs, timestamp_inputs, \n",
|
624 |
" eq_lat_inputs, eq_lon_inputs, \n",
|
625 |
" radius_inputs, source_depth_inputs, \n",
|
626 |
" velocity_inputs, max_waveforms_inputs],\n",
|
627 |
+
" outputs=[output_image, output_picks])\n",
|
628 |
"\n",
|
629 |
"demo.launch()"
|
630 |
]
|
app.py
CHANGED
@@ -115,7 +115,7 @@ def variance_coefficient(residuals):
|
|
115 |
return coeff
|
116 |
|
117 |
def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source_depth_km, velocity_model, max_waveforms):
|
118 |
-
distances, t0s, st_lats, st_lons, waveforms = [], [], [], [], []
|
119 |
|
120 |
taup_model = TauPyModel(model=velocity_model)
|
121 |
client = Client(client_name)
|
@@ -197,6 +197,7 @@ def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source
|
|
197 |
st_lats.append(station.latitude)
|
198 |
st_lons.append(station.longitude)
|
199 |
waveforms.append(waveform)
|
|
|
200 |
|
201 |
print(f"Added {network.code}.{station.code} to the list of waveforms")
|
202 |
|
@@ -226,6 +227,7 @@ def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source
|
|
226 |
t0s = np.array(t0s)[selection_indexes]
|
227 |
st_lats = np.array(st_lats)[selection_indexes]
|
228 |
st_lons = np.array(st_lons)[selection_indexes]
|
|
|
229 |
|
230 |
waveforms = [torch.tensor(waveform) for waveform in waveforms]
|
231 |
|
@@ -264,6 +266,11 @@ def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source
|
|
264 |
topo_map.da.plot(ax = ax[2], cmap='Greys', add_colorbar=False, add_labels=False)
|
265 |
ax[1].imshow(hillshade, cmap="Greys", alpha=0.5)
|
266 |
|
|
|
|
|
|
|
|
|
|
|
267 |
for i in range(len(waveforms)):
|
268 |
print(f"Plotting waveform {i+1}/{len(waveforms)}")
|
269 |
current_P = p_phases[i::len(waveforms)]
|
@@ -291,6 +298,11 @@ def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source
|
|
291 |
velocity_s = (distances[i]*111.2)/(delta_t+current_S.mean()*60).item()
|
292 |
|
293 |
print(f"Station {st_lats[i]}, {st_lons[i]} has P velocity {velocity_p} and S velocity {velocity_s}")
|
|
|
|
|
|
|
|
|
|
|
294 |
|
295 |
# Generate an array from st_lat to eq_lat and from st_lon to eq_lon
|
296 |
x = np.linspace(st_lons[i], eq_lon, 50)
|
@@ -319,15 +331,13 @@ def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source
|
|
319 |
cbar.set_label('S Velocity (km/s)')
|
320 |
ax[2].set_title('S Velocity')
|
321 |
|
322 |
-
|
323 |
-
|
324 |
plt.subplots_adjust(hspace=0., wspace=0.5)
|
325 |
|
326 |
fig.canvas.draw();
|
327 |
image = np.array(fig.canvas.renderer.buffer_rgba())
|
328 |
plt.close(fig)
|
329 |
|
330 |
-
return image
|
331 |
|
332 |
|
333 |
model = Onset_picker.load_from_checkpoint("./weights.ckpt",
|
@@ -374,74 +384,79 @@ with gr.Blocks() as demo:
|
|
374 |
with gr.Tab("Select earthquake from catalogue"):
|
375 |
gr.Markdown("""Select an earthquake from the global earthquake catalogue and the app will download the waveform from the FDSN client of your choice.
|
376 |
""")
|
377 |
-
|
378 |
-
client_inputs = gr.Dropdown(
|
379 |
-
choices = list(URL_MAPPINGS.keys()),
|
380 |
-
label="FDSN Client",
|
381 |
-
info="Select one of the available FDSN clients",
|
382 |
-
value = "IRIS",
|
383 |
-
interactive=True
|
384 |
-
)
|
385 |
-
|
386 |
with gr.Row():
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
387 |
|
388 |
-
timestamp_inputs = gr.Textbox(value='2019-07-04 17:33:49',
|
389 |
-
placeholder='YYYY-MM-DD HH:MM:SS',
|
390 |
-
label="Timestamp",
|
391 |
-
info="Timestamp of the earthquake",
|
392 |
-
max_lines=1,
|
393 |
-
interactive=True)
|
394 |
-
|
395 |
-
eq_lat_inputs = gr.Number(value=35.766,
|
396 |
-
label="Latitude",
|
397 |
-
info="Latitude of the earthquake",
|
398 |
-
interactive=True)
|
399 |
-
|
400 |
-
eq_lon_inputs = gr.Number(value=-117.605,
|
401 |
-
label="Longitude",
|
402 |
-
info="Longitude of the earthquake",
|
403 |
-
interactive=True)
|
404 |
-
|
405 |
-
source_depth_inputs = gr.Number(value=10,
|
406 |
-
label="Source depth (km)",
|
407 |
-
info="Depth of the earthquake",
|
408 |
-
interactive=True)
|
409 |
-
|
410 |
-
radius_inputs = gr.Slider(minimum=1,
|
411 |
-
maximum=150,
|
412 |
-
value=50, label="Radius (km)",
|
413 |
-
step=10,
|
414 |
-
info="""Select the radius around the earthquake to download data from.\n
|
415 |
-
Note that the larger the radius, the longer the app will take to run.""",
|
416 |
-
interactive=True)
|
417 |
-
|
418 |
velocity_inputs = gr.Dropdown(
|
419 |
choices = ['1066a', '1066b', 'ak135',
|
420 |
-
|
421 |
-
|
422 |
label="1D velocity model",
|
423 |
info="Velocity model for station selection",
|
424 |
value = "1066a",
|
425 |
interactive=True
|
426 |
)
|
427 |
|
428 |
-
|
429 |
-
|
430 |
-
|
431 |
-
|
432 |
-
|
433 |
-
|
434 |
-
|
435 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
436 |
|
437 |
button = gr.Button("Predict phases")
|
438 |
-
|
439 |
-
|
|
|
440 |
button.click(predict_on_section,
|
441 |
inputs=[client_inputs, timestamp_inputs,
|
442 |
eq_lat_inputs, eq_lon_inputs,
|
443 |
radius_inputs, source_depth_inputs,
|
444 |
velocity_inputs, max_waveforms_inputs],
|
445 |
-
outputs=
|
446 |
|
447 |
demo.launch()
|
|
|
115 |
return coeff
|
116 |
|
117 |
def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source_depth_km, velocity_model, max_waveforms):
|
118 |
+
distances, t0s, st_lats, st_lons, waveforms, names = [], [], [], [], [], []
|
119 |
|
120 |
taup_model = TauPyModel(model=velocity_model)
|
121 |
client = Client(client_name)
|
|
|
197 |
st_lats.append(station.latitude)
|
198 |
st_lons.append(station.longitude)
|
199 |
waveforms.append(waveform)
|
200 |
+
names.append(f"{network.code}.{station.code}")
|
201 |
|
202 |
print(f"Added {network.code}.{station.code} to the list of waveforms")
|
203 |
|
|
|
227 |
t0s = np.array(t0s)[selection_indexes]
|
228 |
st_lats = np.array(st_lats)[selection_indexes]
|
229 |
st_lons = np.array(st_lons)[selection_indexes]
|
230 |
+
names = np.array(names)[selection_indexes]
|
231 |
|
232 |
waveforms = [torch.tensor(waveform) for waveform in waveforms]
|
233 |
|
|
|
266 |
topo_map.da.plot(ax = ax[2], cmap='Greys', add_colorbar=False, add_labels=False)
|
267 |
ax[1].imshow(hillshade, cmap="Greys", alpha=0.5)
|
268 |
|
269 |
+
output_picks = pd.DataFrame({'station_name' : [], 'starttime' : [],
|
270 |
+
'p_phase' : [], 'p_uncertainty' : [], 's_phase' : [], 's_uncertainty' : [],
|
271 |
+
'velocity_p' : [], 'velocity_s' : []})
|
272 |
+
|
273 |
+
|
274 |
for i in range(len(waveforms)):
|
275 |
print(f"Plotting waveform {i+1}/{len(waveforms)}")
|
276 |
current_P = p_phases[i::len(waveforms)]
|
|
|
298 |
velocity_s = (distances[i]*111.2)/(delta_t+current_S.mean()*60).item()
|
299 |
|
300 |
print(f"Station {st_lats[i]}, {st_lons[i]} has P velocity {velocity_p} and S velocity {velocity_s}")
|
301 |
+
|
302 |
+
output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])],
|
303 |
+
'p_phase' : [(delta_t+current_P.mean()*60).item()], 'p_uncertainty' : [current_P.std().item()*60],
|
304 |
+
's_phase' : [(delta_t+current_S.mean()*60).item()], 's_uncertainty' : [current_S.std().item()*60],
|
305 |
+
'velocity_p' : [velocity_p], 'velocity_s' : [velocity_s]}))
|
306 |
|
307 |
# Generate an array from st_lat to eq_lat and from st_lon to eq_lon
|
308 |
x = np.linspace(st_lons[i], eq_lon, 50)
|
|
|
331 |
cbar.set_label('S Velocity (km/s)')
|
332 |
ax[2].set_title('S Velocity')
|
333 |
|
|
|
|
|
334 |
plt.subplots_adjust(hspace=0., wspace=0.5)
|
335 |
|
336 |
fig.canvas.draw();
|
337 |
image = np.array(fig.canvas.renderer.buffer_rgba())
|
338 |
plt.close(fig)
|
339 |
|
340 |
+
return image, output_picks
|
341 |
|
342 |
|
343 |
model = Onset_picker.load_from_checkpoint("./weights.ckpt",
|
|
|
384 |
with gr.Tab("Select earthquake from catalogue"):
|
385 |
gr.Markdown("""Select an earthquake from the global earthquake catalogue and the app will download the waveform from the FDSN client of your choice.
|
386 |
""")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
387 |
with gr.Row():
|
388 |
+
client_inputs = gr.Dropdown(
|
389 |
+
choices = list(URL_MAPPINGS.keys()),
|
390 |
+
label="FDSN Client",
|
391 |
+
info="Select one of the available FDSN clients",
|
392 |
+
value = "IRIS",
|
393 |
+
interactive=True
|
394 |
+
)
|
395 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
396 |
velocity_inputs = gr.Dropdown(
|
397 |
choices = ['1066a', '1066b', 'ak135',
|
398 |
+
'ak135f', 'herrin', 'iasp91',
|
399 |
+
'jb', 'prem', 'pwdk'],
|
400 |
label="1D velocity model",
|
401 |
info="Velocity model for station selection",
|
402 |
value = "1066a",
|
403 |
interactive=True
|
404 |
)
|
405 |
|
406 |
+
with gr.Column(scale=4):
|
407 |
+
with gr.Row():
|
408 |
+
timestamp_inputs = gr.Textbox(value='2019-07-04 17:33:49',
|
409 |
+
placeholder='YYYY-MM-DD HH:MM:SS',
|
410 |
+
label="Timestamp",
|
411 |
+
info="Timestamp of the earthquake",
|
412 |
+
max_lines=1,
|
413 |
+
interactive=True)
|
414 |
+
|
415 |
+
eq_lat_inputs = gr.Number(value=35.766,
|
416 |
+
label="Latitude",
|
417 |
+
info="Latitude of the earthquake",
|
418 |
+
interactive=True)
|
419 |
+
|
420 |
+
eq_lon_inputs = gr.Number(value=-117.605,
|
421 |
+
label="Longitude",
|
422 |
+
info="Longitude of the earthquake",
|
423 |
+
interactive=True)
|
424 |
+
|
425 |
+
source_depth_inputs = gr.Number(value=10,
|
426 |
+
label="Source depth (km)",
|
427 |
+
info="Depth of the earthquake",
|
428 |
+
interactive=True)
|
429 |
+
|
430 |
+
|
431 |
+
|
432 |
+
with gr.Column(scale=2):
|
433 |
+
with gr.Row():
|
434 |
+
radius_inputs = gr.Slider(minimum=1,
|
435 |
+
maximum=150,
|
436 |
+
value=50, label="Radius (km)",
|
437 |
+
step=10,
|
438 |
+
info="""Select the radius around the earthquake to download data from.\n
|
439 |
+
Note that the larger the radius, the longer the app will take to run.""",
|
440 |
+
interactive=True)
|
441 |
+
|
442 |
+
max_waveforms_inputs = gr.Slider(minimum=1,
|
443 |
+
maximum=100,
|
444 |
+
value=10,
|
445 |
+
label="Max waveforms per section",
|
446 |
+
step=1,
|
447 |
+
info="Maximum number of waveforms to show per section\n (to avoid long prediction times)",
|
448 |
+
interactive=True,
|
449 |
+
)
|
450 |
|
451 |
button = gr.Button("Predict phases")
|
452 |
+
output_image = gr.Image(label='Waveforms with Phases Marked', type='numpy', interactive=False)
|
453 |
+
output_picks = gr.Dataframe(label='# Pick data', type='pandas', interactive=False)
|
454 |
+
|
455 |
button.click(predict_on_section,
|
456 |
inputs=[client_inputs, timestamp_inputs,
|
457 |
eq_lat_inputs, eq_lon_inputs,
|
458 |
radius_inputs, source_depth_inputs,
|
459 |
velocity_inputs, max_waveforms_inputs],
|
460 |
+
outputs=[output_image, output_picks])
|
461 |
|
462 |
demo.launch()
|
data/cached/BK_MCCM_2023-04-01T16:43:30.541616Z.mseed
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data/cached/BK_SAO_2023-04-01T16:43:36.718558Z.mseed
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data/cached/NC_C006_2023-04-01T16:43:22.900707Z.mseed
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data/cached/NC_C031_2023-04-01T16:43:22.656861Z.mseed
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data/cached/NC_C050_2023-04-01T16:43:24.524689Z.mseed
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data/cached/NC_C055_2023-04-01T16:43:24.930990Z.mseed
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data/cached/NC_C056_2023-04-01T16:43:22.066458Z.mseed
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data/cached/NC_G004_2023-04-01T16:43:40.683359Z.mseed
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data/cached/NC_J010_2023-04-01T16:43:25.779606Z.mseed
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data/cached/NC_J068_2023-04-01T16:43:26.681315Z.mseed
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data/cached/NC_RWSVT_2023-04-01T16:43:23.543745Z.mseed
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data/cached/PB_B054_2023-04-01T16:43:22.147061Z.mseed
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data/cached/PB_B057_2023-04-01T16:43:26.668494Z.mseed
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data/cached/PB_B058_2023-04-01T16:43:35.559392Z.mseed
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data/cached/PB_B065_2023-04-01T16:43:36.828013Z.mseed
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data/cached/PB_B066_2023-04-01T16:43:34.748830Z.mseed
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data/cached/PB_B067_2023-04-01T16:43:36.084437Z.mseed
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data/cached/PB_P176_2023-04-01T16:43:25.099350Z.mseed
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data/cached/PB_P181_2023-04-01T16:43:23.984589Z.mseed
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data/cached/PB_P262_2023-04-01T16:43:24.445973Z.mseed
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