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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'hi!'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'hi!'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/anovosel/miniconda3/envs/phasehunter/lib/python3.11/site-packages/gradio/outputs.py:43: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7862\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
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"<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
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{
"data": {
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},
"execution_count": 4,
"metadata": {},
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{
"name": "stdout",
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"text": [
"Error in callback <function _draw_all_if_interactive at 0x1774d0ea0> (for post_execute):\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/miniconda3/envs/phasehunter/lib/python3.11/site-packages/matplotlib/pyplot.py:120\u001b[0m, in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_draw_all_if_interactive\u001b[39m():\n\u001b[1;32m 119\u001b[0m \u001b[39mif\u001b[39;00m matplotlib\u001b[39m.\u001b[39mis_interactive():\n\u001b[0;32m--> 120\u001b[0m draw_all()\n",
"File \u001b[0;32m~/miniconda3/envs/phasehunter/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:132\u001b[0m, in \u001b[0;36mGcf.draw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[39mfor\u001b[39;00m manager \u001b[39min\u001b[39;00m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39mget_all_fig_managers():\n\u001b[1;32m 131\u001b[0m \u001b[39mif\u001b[39;00m force \u001b[39mor\u001b[39;00m manager\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mstale:\n\u001b[0;32m--> 132\u001b[0m manager\u001b[39m.\u001b[39;49mcanvas\u001b[39m.\u001b[39;49mdraw_idle()\n",
"File \u001b[0;32m~/miniconda3/envs/phasehunter/lib/python3.11/site-packages/matplotlib/backend_bases.py:2082\u001b[0m, in \u001b[0;36mFigureCanvasBase.draw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2080\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_is_idle_drawing:\n\u001b[1;32m 2081\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_idle_draw_cntx():\n\u001b[0;32m-> 2082\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdraw(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
"File \u001b[0;32m~/miniconda3/envs/phasehunter/lib/python3.11/site-packages/matplotlib/backends/backend_agg.py:397\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 395\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrenderer\u001b[39m.\u001b[39mclear()\n\u001b[1;32m 396\u001b[0m \u001b[39m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[0;32m--> 397\u001b[0m \u001b[39mwith\u001b[39;49;00m RendererAgg\u001b[39m.\u001b[39;49mlock, \\\n\u001b[1;32m 398\u001b[0m (\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtoolbar\u001b[39m.\u001b[39;49m_wait_cursor_for_draw_cm() \u001b[39mif\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtoolbar\n\u001b[1;32m 399\u001b[0m \u001b[39melse\u001b[39;49;00m nullcontext()):\n\u001b[1;32m 400\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfigure\u001b[39m.\u001b[39;49mdraw(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrenderer)\n\u001b[1;32m 401\u001b[0m \u001b[39m# A GUI class may be need to update a window using this draw, so\u001b[39;49;00m\n\u001b[1;32m 402\u001b[0m \u001b[39m# don't forget to call the superclass.\u001b[39;49;00m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# Gradio app that takes seismic waveform as input and marks 2 phases on the waveform as output.\n",
"\n",
"import gradio as gr\n",
"import numpy as np\n",
"import pandas as pd\n",
"from phasehunter.model import Onset_picker, Updated_onset_picker\n",
"from phasehunter.data_preparation import prepare_waveform\n",
"import torch\n",
"\n",
"from scipy.stats import gaussian_kde\n",
"\n",
"import obspy\n",
"from obspy.clients.fdsn import Client\n",
"from obspy.clients.fdsn.header import FDSNNoDataException, FDSNTimeoutException, FDSNInternalServerException\n",
"from obspy.geodetics.base import locations2degrees\n",
"from obspy.taup import TauPyModel\n",
"from obspy.taup.helper_classes import SlownessModelError\n",
"\n",
"from obspy.clients.fdsn.header import URL_MAPPINGS\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.dates as mdates\n",
"\n",
"from glob import glob\n",
"\n",
"def make_prediction(waveform):\n",
" waveform = np.load(waveform)\n",
" processed_input = prepare_waveform(waveform)\n",
" \n",
" # Make prediction\n",
" with torch.no_grad():\n",
" output = model(processed_input)\n",
"\n",
" p_phase = output[:, 0]\n",
" s_phase = output[:, 1]\n",
"\n",
" return processed_input, p_phase, s_phase\n",
"\n",
"def mark_phases(waveform):\n",
" processed_input, p_phase, s_phase = make_prediction(waveform)\n",
"\n",
" # Create a plot of the waveform with the phases marked\n",
" if sum(processed_input[0][2] == 0): #if input is 1C\n",
" fig, ax = plt.subplots(nrows=2, figsize=(10, 2), sharex=True)\n",
"\n",
" ax[0].plot(processed_input[0][0])\n",
" ax[0].set_ylabel('Norm. Ampl.')\n",
"\n",
" else: #if input is 3C\n",
" fig, ax = plt.subplots(nrows=4, figsize=(10, 6), sharex=True)\n",
" ax[0].plot(processed_input[0][0])\n",
" ax[1].plot(processed_input[0][1])\n",
" ax[2].plot(processed_input[0][2])\n",
"\n",
" ax[0].set_ylabel('Z')\n",
" ax[1].set_ylabel('N')\n",
" ax[2].set_ylabel('E')\n",
"\n",
" p_phase_plot = p_phase*processed_input.shape[-1]\n",
" p_kde = gaussian_kde(p_phase_plot)\n",
" p_dist_space = np.linspace( min(p_phase_plot)-10, max(p_phase_plot)+10, 500 )\n",
" ax[-1].plot( p_dist_space, p_kde(p_dist_space), color='r')\n",
"\n",
" s_phase_plot = s_phase*processed_input.shape[-1]\n",
" s_kde = gaussian_kde(s_phase_plot)\n",
" s_dist_space = np.linspace( min(s_phase_plot)-10, max(s_phase_plot)+10, 500 )\n",
" ax[-1].plot( s_dist_space, s_kde(s_dist_space), color='b')\n",
"\n",
" for a in ax:\n",
" a.axvline(p_phase.mean()*processed_input.shape[-1], color='r', linestyle='--', label='P')\n",
" 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].legend()\n",
"\n",
" plt.subplots_adjust(hspace=0., wspace=0.)\n",
"\n",
" # Convert the plot to an image and return it\n",
" fig.canvas.draw()\n",
" image = np.array(fig.canvas.renderer.buffer_rgba())\n",
" plt.close(fig)\n",
" return image\n",
"\n",
"def variance_coefficient(residuals):\n",
" # calculate the variance of the residuals\n",
" var = residuals.var()\n",
" \n",
" # scale the variance to a coefficient between 0 and 1\n",
" coeff = 1 - (var / (residuals.max() - residuals.min()))\n",
" \n",
" return coeff\n",
"\n",
"def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source_depth_km, velocity_model):\n",
" distances, t0s, st_lats, st_lons, waveforms = [], [], [], [], []\n",
" \n",
" taup_model = TauPyModel(model=velocity_model)\n",
" client = Client(client_name)\n",
"\n",
" window = radius_km / 111.2\n",
"\n",
" assert eq_lat - window > -90 and eq_lat + window < 90, \"Latitude out of bounds\"\n",
" assert eq_lon - window > -180 and eq_lon + window < 180, \"Longitude out of bounds\"\n",
"\n",
" starttime = obspy.UTCDateTime(timestamp)\n",
" endtime = starttime + 120\n",
"\n",
" inv = client.get_stations(network=\"*\", station=\"*\", location=\"*\", channel=\"*H*\", \n",
" starttime=starttime, endtime=endtime, \n",
" minlatitude=(eq_lat-window), maxlatitude=(eq_lat+window),\n",
" minlongitude=(eq_lon-window), maxlongitude=(eq_lon+window), \n",
" level='station')\n",
" \n",
" waveforms = []\n",
" cached_waveforms = glob(\"data/cached/*.mseed\")\n",
"\n",
" for network in inv:\n",
" for station in network:\n",
" try:\n",
" distance = locations2degrees(eq_lat, eq_lon, station.latitude, station.longitude)\n",
"\n",
" arrivals = taup_model.get_travel_times(source_depth_in_km=source_depth_km, \n",
" distance_in_degree=distance, \n",
" phase_list=[\"P\", \"S\"])\n",
"\n",
" if len(arrivals) > 0:\n",
"\n",
" starttime = obspy.UTCDateTime(timestamp) + arrivals[0].time - 15\n",
" endtime = starttime + 60\n",
"\n",
" if f\"data/cached/{network.code}_{station.code}_{starttime}.mseed\" not in cached_waveforms:\n",
" waveform = client.get_waveforms(network=network.code, station=station.code, location=\"*\", channel=\"*\", \n",
" starttime=starttime, endtime=endtime)\n",
" waveform.write(f\"data/cached/{network.code}_{station.code}_{starttime}.mseed\", format=\"MSEED\")\n",
" else:\n",
" waveform = obspy.read(f\"data/cached/{network.code}_{station.code}_{starttime}.mseed\")\n",
" \n",
" waveform = waveform.select(channel=\"H[BH][ZNE]\")\n",
" waveform = waveform.merge(fill_value=0)\n",
" waveform = waveform[:3]\n",
" \n",
" len_check = [len(x.data) for x in waveform]\n",
" if len(set(len_check)) > 1:\n",
" continue\n",
"\n",
" if len(waveform) == 3:\n",
" try:\n",
" waveform = prepare_waveform(np.stack([x.data for x in waveform]))\n",
" except:\n",
" continue\n",
" \n",
" distances.append(distance)\n",
" t0s.append(starttime)\n",
" st_lats.append(station.latitude)\n",
" st_lons.append(station.longitude)\n",
" waveforms.append(waveform)\n",
"\n",
" except (IndexError, FDSNNoDataException, FDSNTimeoutException):\n",
" continue\n",
"\n",
" with torch.no_grad():\n",
" waveforms_torch = torch.vstack(waveforms)\n",
" output = model(waveforms_torch)\n",
"\n",
" p_phases = output[:, 0]\n",
" s_phases = output[:, 1]\n",
"\n",
" # Max confidence - min variance \n",
" p_max_confidence = np.min([p_phases[i::len(waveforms)].std() for i in range(len(waveforms))]) \n",
" s_max_confidence = np.min([s_phases[i::len(waveforms)].std() for i in range(len(waveforms))])\n",
"\n",
" fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 3), sharex=True)\n",
" for i in range(len(waveforms)):\n",
" current_P = p_phases[i::len(waveforms)]\n",
" current_S = s_phases[i::len(waveforms)]\n",
"\n",
" x = [t0s[i] + pd.Timedelta(seconds=k/100) for k in np.linspace(0,6000,6000)]\n",
" x = mdates.date2num(x)\n",
"\n",
" # Normalize confidence for the plot\n",
" p_conf = 1/(current_P.std()/p_max_confidence).item()\n",
" s_conf = 1/(current_S.std()/s_max_confidence).item()\n",
"\n",
" ax[0].plot(x, waveforms[i][0, 0]*10+distances[i]*111.2, color='black', alpha=0.5, lw=1)\n",
"\n",
" ax[0].scatter(x[int(current_P.mean()*waveforms[i][0].shape[-1])], waveforms[i][0, 0].mean()+distances[i]*111.2, color='r', alpha=p_conf, marker='|')\n",
" ax[0].scatter(x[int(current_S.mean()*waveforms[i][0].shape[-1])], waveforms[i][0, 0].mean()+distances[i]*111.2, color='b', alpha=s_conf, marker='|')\n",
" ax[0].set_ylabel('Z')\n",
"\n",
" ax[0].xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))\n",
" ax[0].xaxis.set_major_locator(mdates.SecondLocator(interval=5))\n",
" \n",
" ax[0].scatter(None, None, color='r', marker='|', label='P')\n",
" ax[0].scatter(None, None, color='b', marker='|', label='S')\n",
" ax[0].legend()\n",
"\n",
" ax[1].scatter(st_lats, st_lons, color='b', marker='d', label='Stations')\n",
" ax[1].scatter(eq_lat, eq_lon, color='r', marker='*', label='Earthquake')\n",
" ax[1].legend()\n",
" plt.subplots_adjust(hspace=0., wspace=0.)\n",
" \n",
" fig.canvas.draw();\n",
" image = np.array(fig.canvas.renderer.buffer_rgba())\n",
" plt.close(fig)\n",
"\n",
" return image\n",
"\n",
"\n",
"model = Onset_picker.load_from_checkpoint(\"./weights.ckpt\",\n",
" picker=Updated_onset_picker(),\n",
" learning_rate=3e-4)\n",
"model.eval()\n",
"\n",
"with gr.Blocks() as demo:\n",
" gr.Markdown(\"# PhaseHunter\")\n",
" gr.Markdown(\"\"\"This app allows one to detect P and S seismic phases along with uncertainty of the detection. \n",
" The app can be used in three ways: either by selecting one of the sample waveforms;\n",
" or by selecting an earthquake from the global earthquake catalogue;\n",
" or by uploading a waveform of interest.\n",
" \"\"\")\n",
" with gr.Tab(\"Default example\"):\n",
" # Define the input and output types for Gradio\n",
" inputs = gr.Dropdown(\n",
" [\"data/sample/sample_0.npy\", \n",
" \"data/sample/sample_1.npy\", \n",
" \"data/sample/sample_2.npy\"], \n",
" label=\"Sample waveform\", \n",
" info=\"Select one of the samples\",\n",
" value = \"data/sample/sample_0.npy\"\n",
" )\n",
"\n",
" button = gr.Button(\"Predict phases\")\n",
" outputs = gr.outputs.Image(label='Waveform with Phases Marked', type='numpy')\n",
" \n",
" button.click(mark_phases, inputs=inputs, outputs=outputs)\n",
" \n",
" with gr.Tab(\"Select earthquake from catalogue\"):\n",
" gr.Markdown('TEST')\n",
" \n",
" client_inputs = gr.Dropdown(\n",
" choices = list(URL_MAPPINGS.keys()), \n",
" label=\"FDSN Client\", \n",
" info=\"Select one of the available FDSN clients\",\n",
" value = \"IRIS\",\n",
" interactive=True\n",
" )\n",
" with gr.Row(): \n",
"\n",
" timestamp_inputs = gr.Textbox(value='2019-07-04 17:33:49',\n",
" placeholder='YYYY-MM-DD HH:MM:SS',\n",
" label=\"Timestamp\",\n",
" info=\"Timestamp of the earthquake\",\n",
" max_lines=1,\n",
" interactive=True)\n",
" \n",
" eq_lat_inputs = gr.Number(value=35.766, \n",
" label=\"Latitude\", \n",
" info=\"Latitude of the earthquake\",\n",
" interactive=True)\n",
" \n",
" eq_lon_inputs = gr.Number(value=-117.605,\n",
" label=\"Longitude\",\n",
" info=\"Longitude of the earthquake\",\n",
" interactive=True)\n",
" \n",
" source_depth_inputs = gr.Number(value=10,\n",
" label=\"Source depth (km)\",\n",
" info=\"Depth of the earthquake\",\n",
" interactive=True)\n",
" \n",
" radius_inputs = gr.Slider(minimum=1, \n",
" maximum=150, \n",
" value=50, label=\"Radius (km)\", \n",
" step=10,\n",
" info=\"\"\"Select the radius around the earthquake to download data from.\\n \n",
" Note that the larger the radius, the longer the app will take to run.\"\"\",\n",
" interactive=True)\n",
" \n",
" velocity_inputs = gr.Dropdown(\n",
" choices = ['1066a', '1066b', 'ak135', 'ak135f', 'herrin', 'iasp91', 'jb', 'prem', 'pwdk'], \n",
" label=\"1D velocity model\", \n",
" info=\"Velocity model for station selection\",\n",
" value = \"1066a\",\n",
" interactive=True\n",
" )\n",
" \n",
" \n",
" button = gr.Button(\"Predict phases\")\n",
" outputs_section = gr.Image(label='Waveforms with Phases Marked', type='numpy', interactive=False)\n",
" \n",
" button.click(predict_on_section, \n",
" inputs=[client_inputs, timestamp_inputs, \n",
" eq_lat_inputs, eq_lon_inputs, \n",
" radius_inputs, source_depth_inputs, velocity_inputs],\n",
" outputs=outputs_section)\n",
"\n",
" with gr.Tab(\"Predict on your own waveform\"):\n",
" gr.Markdown(\"\"\"\n",
" Please upload your waveform in .npy (numpy) format. \n",
" Your waveform should be sampled at 100 sps and have 3 (Z, N, E) or 1 (Z) channels.\n",
" \"\"\")\n",
"\n",
"demo.launch()"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": []
}
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