{ "cells": [ { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running on local URL: http://127.0.0.1:7923\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Starting to download inventory\n", "Finished downloading inventory\n", "Processing CI.CCC...\n", "Downloading waveform\n", "Skipping CI_CCC_2019-07-04T17:33:40.494920Z\n", "Processing CI.CLC...\n", "Processing CI.JRC2...\n", "Reading cached waveform\n", "Added CI.JRC2 to the list of waveforms\n", "Processing CI.LRL...\n", "Reading cached waveform\n", "Added CI.LRL to the list of waveforms\n", "Processing CI.MPM...\n", "Reading cached waveform\n", "Processing CI.Q0072...\n", "Reading cached waveform\n", "Processing CI.SLA...\n", "Reading cached waveform\n", "Added CI.SLA to the list of waveforms\n", "Processing CI.SRT...\n", "Reading cached waveform\n", "Added CI.SRT to the list of waveforms\n", "Processing CI.TOW2...\n", "Reading cached waveform\n", "Added CI.TOW2 to the list of waveforms\n", "Processing CI.WBM...\n", "Downloading waveform\n", "Skipping CI_WBM_2019-07-04T17:33:40.063616Z\n", "Processing CI.WCS2...\n", "Downloading waveform\n", "Skipping CI_WCS2_2019-07-04T17:33:40.200958Z\n", "Processing CI.WMF...\n", "Reading cached waveform\n", "Added CI.WMF to the list of waveforms\n", "Processing CI.WNM...\n", "Reading cached waveform\n", "Processing CI.WRC2...\n", "Downloading waveform\n", "Skipping CI_WRC2_2019-07-04T17:33:38.698099Z\n", "Processing CI.WRV2...\n", "Reading cached waveform\n", "Processing CI.WVP2...\n", "Downloading waveform\n", "Skipping CI_WVP2_2019-07-04T17:33:39.650402Z\n", "Processing NP.1809...\n", "Reading cached waveform\n", "Processing NP.5419...\n", "Reading cached waveform\n", "Processing PB.B916...\n", "Reading cached waveform\n", "Processing PB.B917...\n", "Reading cached waveform\n", "Processing PB.B918...\n", "Reading cached waveform\n", "Processing PB.B921...\n", "Reading cached waveform\n", "Starting to run predictions\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/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", " waveforms = np.array(waveforms)[selection_indexes]\n", "/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", " waveforms = np.array(waveforms)[selection_indexes]\n", "/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", " waveforms = [torch.tensor(waveform) for waveform in waveforms]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Starting plotting 3 waveforms\n", "Fetching topography\n", "Plotting topo\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/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", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Plotting waveform 1/3\n", "Station 36.11758, -117.85486 has P velocity 4.96342368856812 and S velocity 3.0001093626503503\n", "Plotting waveform 2/3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/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", " output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])],\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Station 35.98249, -117.80885 has P velocity 4.229153346955616 and S velocity 2.3118595983254937\n", "Plotting waveform 3/3\n", "Station 35.69235, -117.75051 has P velocity 2.9537452996413585 and S velocity 1.3863453902284213\n", "Plotting stations\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/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", " output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])],\n", "/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", " output_picks = output_picks.append(pd.DataFrame({'station_name': [names[i]], 'starttime' : [str(t0s[i])],\n" ] } ], "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", "from bmi_topography import Topography\n", "import earthpy.spatial as es\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", "from matplotlib.colors import LightSource\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, uploaded_file):\n", "\n", " if uploaded_file is not None:\n", " waveform = uploaded_file.name\n", "\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], color='black', lw=1)\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], color='black', lw=1)\n", " ax[1].plot(processed_input[0][1], color='black', lw=1)\n", " ax[2].plot(processed_input[0][2], color='black', lw=1)\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 bin_distances(distances, bin_size=10):\n", " # Bin the distances into groups of `bin_size` kilometers\n", " binned_distances = {}\n", " for i, distance in enumerate(distances):\n", " bin_index = distance // bin_size\n", " if bin_index not in binned_distances:\n", " binned_distances[bin_index] = (distance, i)\n", " elif i < binned_distances[bin_index][1]:\n", " binned_distances[bin_index] = (distance, i)\n", "\n", " # Select the first distance in each bin and its index\n", " first_distances = []\n", " for bin_index in binned_distances:\n", " first_distance, first_distance_index = binned_distances[bin_index]\n", " first_distances.append(first_distance_index)\n", " \n", " return first_distances\n", "\n", "def variance_coefficient(residuals):\n", " # calculate the variance of the residuals\n", " var = residuals.var()\n", " # scale the variance to a coefficient between 0 and 1\n", " coeff = 1 - (var / (residuals.max() - residuals.min()))\n", " return coeff\n", "\n", "def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source_depth_km, velocity_model, max_waveforms):\n", " distances, t0s, st_lats, st_lons, waveforms, names = [], [], [], [], [], []\n", " \n", " taup_model = TauPyModel(model=velocity_model)\n", " client = Client(client_name)\n", "\n", " window = radius_km / 111.2\n", " max_waveforms = int(max_waveforms)\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", " try:\n", " print('Starting to download inventory')\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", " print('Finished downloading inventory')\n", " except (IndexError, FDSNNoDataException, FDSNTimeoutException, FDSNInternalServerException):\n", " fig, ax = plt.subplots()\n", " ax.text(0.5,0.5,'Something is wrong with the data provider, try another')\n", " fig.canvas.draw();\n", " image = np.array(fig.canvas.renderer.buffer_rgba())\n", " plt.close(fig)\n", " return image\n", " \n", " waveforms = []\n", " cached_waveforms = glob(\"data/cached/*.mseed\")\n", "\n", " for network in inv:\n", " # Skip the SYntetic networks\n", " if network.code == 'SY':\n", " continue\n", " for station in network:\n", " print(f\"Processing {network.code}.{station.code}...\")\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", " try:\n", " if f\"data/cached/{network.code}_{station.code}_{starttime}.mseed\" not in cached_waveforms:\n", " print('Downloading waveform')\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", " print('Finished downloading and caching waveform')\n", " else:\n", " print('Reading cached waveform')\n", " waveform = obspy.read(f\"data/cached/{network.code}_{station.code}_{starttime}.mseed\")\n", " \n", "\n", " except (IndexError, FDSNNoDataException, FDSNTimeoutException, FDSNInternalServerException):\n", " print(f'Skipping {network.code}_{station.code}_{starttime}')\n", " continue\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", "\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", " names.append(f\"{network.code}.{station.code}\")\n", "\n", " print(f\"Added {network.code}.{station.code} to the list of waveforms\")\n", "\n", " except:\n", " continue\n", " \n", " \n", " # If there are no waveforms, return an empty plot\n", " if len(waveforms) == 0:\n", " fig, ax = plt.subplots()\n", " ax.text(0.5,0.5,'No waveforms found')\n", " fig.canvas.draw();\n", " image = np.array(fig.canvas.renderer.buffer_rgba())\n", " plt.close(fig)\n", " return image\n", " \n", "\n", " first_distances = bin_distances(distances, bin_size=10/111.2)\n", "\n", " # Edge case when there are way too many waveforms to process\n", " selection_indexes = np.random.choice(first_distances, \n", " np.min([len(first_distances), max_waveforms]),\n", " replace=False)\n", "\n", " waveforms = np.array(waveforms)[selection_indexes]\n", " distances = np.array(distances)[selection_indexes]\n", " t0s = np.array(t0s)[selection_indexes]\n", " st_lats = np.array(st_lats)[selection_indexes]\n", " st_lons = np.array(st_lons)[selection_indexes]\n", " names = np.array(names)[selection_indexes]\n", "\n", " waveforms = [torch.tensor(waveform) for waveform in waveforms]\n", "\n", " print('Starting to run predictions')\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", " print(f\"Starting plotting {len(waveforms)} waveforms\")\n", " fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(10, 3))\n", "\n", " # Plot topography\n", " print('Fetching topography')\n", " params = Topography.DEFAULT.copy()\n", " extra_window = 0.5\n", " params[\"south\"] = np.min([st_lats.min(), eq_lat])-extra_window\n", " params[\"north\"] = np.max([st_lats.max(), eq_lat])+extra_window\n", " params[\"west\"] = np.min([st_lons.min(), eq_lon])-extra_window\n", " params[\"east\"] = np.max([st_lons.max(), eq_lon])+extra_window\n", "\n", " topo_map = Topography(**params)\n", " topo_map.fetch()\n", " topo_map.load()\n", "\n", " print('Plotting topo')\n", " hillshade = es.hillshade(topo_map.da[0], altitude=10)\n", " \n", " topo_map.da.plot(ax = ax[1], cmap='Greys', add_colorbar=False, add_labels=False)\n", " topo_map.da.plot(ax = ax[2], cmap='Greys', add_colorbar=False, add_labels=False)\n", " 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", " \n", " \n", " for i in range(len(waveforms)):\n", " print(f\"Plotting waveform {i+1}/{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=20))\n", "\n", " delta_t = t0s[i].timestamp - obspy.UTCDateTime(timestamp).timestamp\n", "\n", " velocity_p = (distances[i]*111.2)/(delta_t+current_P.mean()*60).item()\n", " velocity_s = (distances[i]*111.2)/(delta_t+current_S.mean()*60).item()\n", "\n", " 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", " \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", " y = np.linspace(st_lats[i], eq_lat, 50)\n", " \n", " # Plot the array\n", " ax[1].scatter(x, y, c=np.zeros_like(x)+velocity_p, alpha=0.5, vmin=0, vmax=8)\n", " ax[2].scatter(x, y, c=np.zeros_like(x)+velocity_s, alpha=0.5, vmin=0, vmax=8)\n", "\n", " # Add legend\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", " print('Plotting stations')\n", " for i in range(1,3):\n", " ax[i].scatter(st_lons, st_lats, color='b', label='Stations')\n", " ax[i].scatter(eq_lon, eq_lat, color='r', marker='*', label='Earthquake')\n", "\n", " # Generate colorbar for the velocity plot\n", " cbar = plt.colorbar(ax[1].scatter(None, None, c=velocity_p, alpha=0.5, vmin=0, vmax=8), ax=ax[1])\n", " cbar.set_label('P Velocity (km/s)')\n", " ax[1].set_title('P Velocity')\n", "\n", " cbar = plt.colorbar(ax[2].scatter(None, None, c=velocity_s, alpha=0.5, vmin=0, vmax=8), ax=ax[2])\n", " cbar.set_label('S Velocity (km/s)')\n", " ax[2].set_title('S Velocity')\n", "\n", " plt.subplots_adjust(hspace=0., wspace=0.5)\n", "\n", " fig.canvas.draw();\n", " image = np.array(fig.canvas.renderer.buffer_rgba())\n", " plt.close(fig)\n", "\n", " return image, output_picks\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.HTML(\"\"\"

PhaseHunter

\n", "

This app allows one to detect P and S seismic phases along with \n", " uncertainty\n", " of the detection.

\n", "
    \n", "
  1. By selecting one of the sample waveforms.
    2. \n", "
  2. By uploading your own waveform.
    3. \n", "
  3. By selecting an earthquake from the global earthquake catalogue.
    4. \n", "
\n", "

Please upload your waveform in .npy (numpy) format.

\n", "

Your waveform should be sampled at 100 samples per second and have 3 (Z, N, E) or 1 (Z) channels. If your file is longer than 60 seconds, the app will only use the first 60 seconds of the waveform.

\n", " \"\"\")\n", " with gr.Tab(\"Try on a single station\"):\n", " with gr.Row(): \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", " upload = gr.File(label=\"Or upload your own waveform\")\n", "\n", " button = gr.Button(\"Predict phases\")\n", " outputs = gr.Image(label='Waveform with Phases Marked', type='numpy', interactive=False)\n", " \n", " 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", " with gr.Row(): \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", "\n", " velocity_inputs = gr.Dropdown(\n", " choices = ['1066a', '1066b', 'ak135', \n", " 'ak135f', 'herrin', 'iasp91', \n", " 'jb', 'prem', 'pwdk'], \n", " label=\"1D velocity model\", \n", " info=\"Velocity model for station selection\",\n", " value = \"1066a\",\n", " interactive=True\n", " )\n", "\n", " with gr.Column(scale=4):\n", " with gr.Row(): \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", "\n", " \n", " with gr.Column(scale=2):\n", " with gr.Row(): \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", " max_waveforms_inputs = gr.Slider(minimum=1,\n", " maximum=100,\n", " value=10,\n", " label=\"Max waveforms per section\",\n", " step=1,\n", " info=\"Maximum number of waveforms to show per section\\n (to avoid long prediction times)\",\n", " interactive=True,\n", " )\n", " \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", "\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, \n", " velocity_inputs, max_waveforms_inputs],\n", " outputs=[output_image, output_picks])\n", "\n", "demo.launch()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "phasehunter", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "6bf57068982d7b420bddaaf1d0614a7795947176033057024cf47d8ca2c1c4cd" } } }, "nbformat": 4, "nbformat_minor": 2 }