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File size: 115,943 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7864\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 17,
"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 for CI_CCC_2019-07-04T17:33:40.494920Z\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 for CI_WBM_2019-07-04T17:33:40.063616Z\n",
"Skipping CI_WBM_2019-07-04T17:33:40.063616Z\n",
"Processing CI.WCS2...\n",
"Downloading waveform for CI_WCS2_2019-07-04T17:33:40.200958Z\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 for CI_WRC2_2019-07-04T17:33:38.698099Z\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 for CI_WVP2_2019-07-04T17:33:39.650402Z\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_3502/4124724611.py:273: 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_3502/4124724611.py:273: 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_3502/4124724611.py:280: 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 35.98249, -117.80885 has P velocity 4.13660431013202 and S velocity 2.2622770044299756\n",
"Plotting waveform 2/3\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/4124724611.py:365: 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]],\n",
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/4124724611.py:365: 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]],\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Station 35.69235, -117.75051 has P velocity 3.4155476453388767 and S velocity 1.67967367867923\n",
"Plotting waveform 3/3\n",
"Station 36.11758, -117.85486 has P velocity 4.745724852828504 and S velocity 2.6483289549749593\n",
"Plotting stations\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/4124724611.py:365: 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]],\n",
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/4124724611.py:385: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
" ax[i].set_xticklabels(ax[i].get_xticks(), rotation = 50)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" station_name st_lat st_lon starttime p_phase, s \\\n",
"0 CI.JRC2 35.98249 -117.80885 2019-07-04T17:33:39.947494Z 7.320212 \n",
"1 CI.SRT 35.69235 -117.75051 2019-07-04T17:33:38.029990Z 4.532020 \n",
"2 CI.WMF 36.11758 -117.85486 2019-07-04T17:33:41.867962Z 9.504385 \n",
"\n",
" p_uncertainty, s s_phase, s s_uncertainty, s velocity_p, km/s \\\n",
"0 0.020417 13.385108 0.028439 4.136604 \n",
"1 0.017490 9.215676 0.019568 3.415548 \n",
"2 0.015920 17.031569 0.046738 4.745725 \n",
"\n",
" velocity_s, km/s \n",
"0 2.262277 \n",
"1 1.679674 \n",
"2 2.648329 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/4124724611.py:503: MatplotlibDeprecationWarning: Unable to determine Axes to steal space for Colorbar. Using gca(), but will raise in the future. Either provide the *cax* argument to use as the Axes for the Colorbar, provide the *ax* argument to steal space from it, or add *mappable* to an Axes.\n",
" plt.colorbar(m)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" station_name st_lat st_lon starttime p_phase, s \\\n",
"0 CI.JRC2 35.98249 -117.80885 2019-07-04T17:33:39.947494Z 7.320212 \n",
"1 CI.SRT 35.69235 -117.75051 2019-07-04T17:33:38.029990Z 4.532020 \n",
"2 CI.WMF 36.11758 -117.85486 2019-07-04T17:33:41.867962Z 9.504385 \n",
"\n",
" p_uncertainty, s s_phase, s s_uncertainty, s velocity_p, km/s \\\n",
"0 0.020417 13.385108 0.028439 4.136604 \n",
"1 0.017490 9.215676 0.019568 3.415548 \n",
"2 0.015920 17.031569 0.046738 4.745725 \n",
"\n",
" velocity_s, km/s \n",
"0 2.262277 \n",
"1 1.679674 \n",
"2 2.648329 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/4124724611.py:503: MatplotlibDeprecationWarning: Unable to determine Axes to steal space for Colorbar. Using gca(), but will raise in the future. Either provide the *cax* argument to use as the Axes for the Colorbar, provide the *ax* argument to steal space from it, or add *mappable* to an Axes.\n",
" plt.colorbar(m)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" station_name st_lat st_lon starttime p_phase, s \\\n",
"0 CI.JRC2 35.98249 -117.80885 2019-07-04T17:33:39.947494Z 7.320212 \n",
"1 CI.SRT 35.69235 -117.75051 2019-07-04T17:33:38.029990Z 4.532020 \n",
"2 CI.WMF 36.11758 -117.85486 2019-07-04T17:33:41.867962Z 9.504385 \n",
"\n",
" p_uncertainty, s s_phase, s s_uncertainty, s velocity_p, km/s \\\n",
"0 0.020417 13.385108 0.028439 4.136604 \n",
"1 0.017490 9.215676 0.019568 3.415548 \n",
"2 0.015920 17.031569 0.046738 4.745725 \n",
"\n",
" velocity_s, km/s \n",
"0 2.262277 \n",
"1 1.679674 \n",
"2 2.648329 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/4124724611.py:503: MatplotlibDeprecationWarning: Unable to determine Axes to steal space for Colorbar. Using gca(), but will raise in the future. Either provide the *cax* argument to use as the Axes for the Colorbar, provide the *ax* argument to steal space from it, or add *mappable* to an Axes.\n",
" plt.colorbar(m)\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.data_preparation import prepare_waveform\n",
"import torch\n",
"import io\n",
"\n",
"from scipy.stats import gaussian_kde\n",
"from scipy.signal import resample\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 mpl_toolkits.axes_grid1 import ImageGrid\n",
"\n",
"from glob import glob\n",
"\n",
"\n",
"def resample_waveform(waveform, original_freq, target_freq):\n",
" \"\"\"\n",
" Resample a waveform from original frequency to target frequency using SciPy's resample function.\n",
" \n",
" Args:\n",
" waveform (numpy.ndarray): The input waveform as a 1D array.\n",
" original_freq (float): The original sampling frequency of the waveform.\n",
" target_freq (float): The target sampling frequency of the waveform.\n",
" \n",
" Returns:\n",
" resampled_waveform (numpy.ndarray): The resampled waveform as a 1D array.\n",
" \"\"\"\n",
" # Calculate the resampling ratio\n",
" resampling_ratio = target_freq / original_freq\n",
" # Calculate the new length of the resampled waveform\n",
" resampled_length = int(waveform.shape[-1] * resampling_ratio)\n",
" # Resample the waveform using SciPy's resample function\n",
" resampled_waveform = resample(waveform, resampled_length, axis=-1)\n",
" \n",
" return resampled_waveform\n",
"\n",
"def make_prediction(waveform, sampling_rate):\n",
" waveform = np.load(waveform)\n",
" print('Loaded', waveform.shape)\n",
"\n",
" if len(waveform.shape) == 1:\n",
" waveform = waveform.reshape(1, waveform.shape[0])\n",
" print('Reshaped', waveform.shape)\n",
" if sampling_rate != 100:\n",
" waveform = resample_waveform(waveform, sampling_rate, 100)\n",
" print('Resampled', waveform.shape)\n",
"\n",
" orig_waveform = waveform[:, :6000].copy()\n",
" processed_input = prepare_waveform(waveform)\n",
"\n",
" # Make prediction\n",
" with torch.inference_mode():\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, orig_waveform\n",
"\n",
"\n",
"def mark_phases(waveform, uploaded_file, p_thres, s_thres, sampling_rate):\n",
"\n",
" if uploaded_file is not None:\n",
" waveform = uploaded_file.name\n",
"\n",
" processed_input, p_phase, s_phase, orig_waveform = make_prediction(waveform, sampling_rate)\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(orig_waveform[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(orig_waveform[0], color='black', lw=1)\n",
" ax[1].plot(orig_waveform[1], color='black', lw=1)\n",
" ax[2].plot(orig_waveform[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",
"\n",
" do_we_have_p = (p_phase.std().item()*60 < p_thres)\n",
" if do_we_have_p:\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",
" else:\n",
" ax[-1].text(0.5, 0.75, 'No P phase detected', horizontalalignment='center', verticalalignment='center', transform=ax[-1].transAxes)\n",
"\n",
" do_we_have_s = (s_phase.std().item()*60 < s_thres)\n",
" if do_we_have_s:\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', alpha=do_we_have_p)\n",
" a.axvline(s_phase.mean()*processed_input.shape[-1], color='b', linestyle='--', label='S', alpha=do_we_have_s)\n",
" else:\n",
" ax[-1].text(0.5, 0.25, 'No S phase detected', horizontalalignment='center', verticalalignment='center', transform=ax[-1].transAxes)\n",
"\n",
" ax[-1].set_xlabel('Time, samples')\n",
" ax[-1].set_ylabel('Uncert., samples')\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, conf_thres_P, conf_thres_S):\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",
" \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",
" 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",
" filename=f'{network.code}_{station.code}_{starttime}'\n",
" if f\"data/cached/{filename}.mseed\" not in cached_waveforms:\n",
" print(f'Downloading waveform for {filename}')\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].sort(keys=['channel'], reverse=True)\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",
" print('No waveforms found')\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",
" output_picks = pd.DataFrame()\n",
" output_picks.to_csv('data/picks.csv', index=False)\n",
" output_csv = 'data/picks.csv'\n",
" return image, output_picks, output_csv\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",
" p_phases = p_phases.reshape(len(waveforms),-1)\n",
" s_phases = s_phases.reshape(len(waveforms),-1)\n",
"\n",
" # Max confidence - min variance \n",
" p_max_confidence = p_phases.std(axis=-1).min()\n",
" s_max_confidence = s_phases.std(axis=-1).min()\n",
"\n",
" print(f\"Starting plotting {len(waveforms)} waveforms\")\n",
" fig, ax = plt.subplots(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' : [], \n",
" 'st_lat' : [], 'st_lon' : [],\n",
" 'starttime' : [], \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",
" for i in range(len(waveforms)):\n",
" print(f\"Plotting waveform {i+1}/{len(waveforms)}\")\n",
" current_P = p_phases[i]\n",
" current_S = s_phases[i]\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",
" delta_t = t0s[i].timestamp - obspy.UTCDateTime(timestamp).timestamp\n",
"\n",
" ax[0].plot(x, waveforms[i][0, 0]*10+distances[i]*111.2, color='black', alpha=0.5, lw=1)\n",
"\n",
" if (current_P.std().item()*60 < conf_thres_P) or (current_S.std().item()*60 < conf_thres_S):\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",
" \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",
" # 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=0.1, vmin=0, vmax=8)\n",
" ax[2].scatter(x, y, c=np.zeros_like(x)+velocity_s, alpha=0.1, vmin=0, vmax=8)\n",
"\n",
" else:\n",
" velocity_p = np.nan\n",
" velocity_s = np.nan\n",
" \n",
" ax[0].set_ylabel('Z')\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]], \n",
" 'st_lat' : [st_lats[i]], 'st_lon' : [st_lons[i]],\n",
" 'starttime' : [str(t0s[i])], \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",
" \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].xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))\n",
" ax[0].xaxis.set_major_locator(mdates.SecondLocator(interval=20))\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",
" ax[i].set_aspect('equal')\n",
" ax[i].set_xticklabels(ax[i].get_xticks(), rotation = 50)\n",
"\n",
" fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,\n",
" wspace=0.02, hspace=0.02)\n",
" \n",
" cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8])\n",
" cbar = fig.colorbar(ax[2].scatter(None, None, c=velocity_p, alpha=0.5, vmin=0, vmax=8), cax=cb_ax)\n",
"\n",
" cbar.set_label('Velocity (km/s)')\n",
" ax[1].set_title('P Velocity')\n",
" ax[2].set_title('S Velocity')\n",
"\n",
" for a in ax:\n",
" a.tick_params(axis='both', which='major', labelsize=8)\n",
" \n",
" plt.subplots_adjust(hspace=0., wspace=0.5)\n",
" fig.canvas.draw();\n",
" image = np.array(fig.canvas.renderer.buffer_rgba())\n",
" plt.close(fig)\n",
"\n",
" output_csv = f'data/velocity/{eq_lat}_{eq_lon}_{source_depth_km}_{timestamp}_{len(waveforms)}.csv'\n",
" output_picks.to_csv(output_csv, index=False)\n",
" \n",
" return image, output_picks, output_csv\n",
"\n",
"import numpy as np\n",
"from matplotlib import colors, cm\n",
"\n",
"# Function to find the closest index for a given value in an array\n",
"def find_closest_index(array, value):\n",
" return np.argmin(np.abs(array - value))\n",
"\n",
"def compute_velocity_model(azimuth, elevation):\n",
" filename = list(output_csv.temp_files)[0]\n",
" \n",
" df = pd.read_csv(filename)\n",
" print(df)\n",
" filename = filename.split('/')[-1]\n",
" \n",
" # Current EQ location\n",
" eq_lat = float(filename.split(\"_\")[0])\n",
" eq_lon = float(filename.split(\"_\")[1])\n",
" eq_depth = float(filename.split(\"_\")[2])\n",
"\n",
" # Define the region of interest (latitude, longitude, and depth ranges)\n",
" lat_range = (np.min([df.st_lat.min(), eq_lat]), np.max([df.st_lat.max(), eq_lat]))\n",
" lon_range = (np.min([df.st_lon.min(), eq_lon]), np.max([df.st_lon.max(), eq_lon]))\n",
" depth_range = (0, 50)\n",
"\n",
" # Define the number of nodes in each dimension\n",
" n_lat = 10\n",
" n_lon = 10\n",
" n_depth = 10\n",
" num_points = 100\n",
"\n",
" # Create the grid\n",
" lat_values = np.linspace(lat_range[0], lat_range[1], n_lat)\n",
" lon_values = np.linspace(lon_range[0], lon_range[1], n_lon)\n",
" depth_values = np.linspace(depth_range[0], depth_range[1], n_depth)\n",
"\n",
" # Initialize the velocity model with constant values\n",
" initial_velocity = 0 # km/s, this can be P-wave or S-wave velocity\n",
" velocity_model = np.full((n_lat, n_lon, n_depth), initial_velocity, dtype=float)\n",
"\n",
" # Loop through the stations and update the velocity model\n",
" for i in range(len(df)):\n",
" if ~np.isnan(df['velocity_p, km/s'].iloc[i]):\n",
" # Interpolate coordinates along the great circle path between the earthquake and the station\n",
" lon_deg = np.linspace(df.st_lon.iloc[i], eq_lon, num_points)\n",
" lat_deg = np.linspace(df.st_lat.iloc[i], eq_lat, num_points)\n",
" depth_interpolated = np.interp(np.linspace(0, 1, num_points), [0, 1], [eq_depth, 0])\n",
"\n",
" # Loop through the interpolated coordinates and update the grid cells with the average P-wave velocity\n",
" for lat, lon, depth in zip(lat_deg, lon_deg, depth_interpolated):\n",
" lat_index = find_closest_index(lat_values, lat)\n",
" lon_index = find_closest_index(lon_values, lon)\n",
" depth_index = find_closest_index(depth_values, depth)\n",
" \n",
" if velocity_model[lat_index, lon_index, depth_index] == initial_velocity:\n",
" velocity_model[lat_index, lon_index, depth_index] = df['velocity_p, km/s'].iloc[i]\n",
" else:\n",
" velocity_model[lat_index, lon_index, depth_index] = (velocity_model[lat_index, lon_index, depth_index] +\n",
" df['velocity_p, km/s'].iloc[i]) / 2\n",
" \n",
" # Create the figure and axis\n",
" fig = plt.figure(figsize=(8, 8))\n",
" ax = fig.add_subplot(111, projection='3d')\n",
"\n",
" # Set the plot limits\n",
" ax.set_xlim3d(lat_range[0], lat_range[1])\n",
" ax.set_ylim3d(lon_range[0], lon_range[1])\n",
" ax.set_zlim3d(depth_range[1], depth_range[0])\n",
"\n",
" ax.set_xlabel('Latitude')\n",
" ax.set_ylabel('Longitude')\n",
" ax.set_zlabel('Depth (km)')\n",
" ax.set_title('Velocity Model')\n",
" \n",
" # Create the meshgrid\n",
" x, y, z = np.meshgrid(\n",
" np.linspace(lat_range[0], lat_range[1], velocity_model.shape[0]+1),\n",
" np.linspace(lon_range[0], lon_range[1], velocity_model.shape[1]+1),\n",
" np.linspace(depth_range[0], depth_range[1], velocity_model.shape[2]+1),\n",
" indexing='ij'\n",
" )\n",
"\n",
" # Create the color array\n",
" norm = plt.Normalize(vmin=velocity_model.min(), vmax=velocity_model.max())\n",
" colors = plt.cm.plasma(norm(velocity_model))\n",
"\n",
" # Plot the voxels\n",
" ax.voxels(x, y, z, velocity_model > 0, facecolors=colors, alpha=0.5, edgecolor='k')\n",
"\n",
" # Set the view angle\n",
" ax.view_init(elev=elevation, azim=azimuth)\n",
"\n",
" m = cm.ScalarMappable(cmap=plt.cm.plasma, norm=norm)\n",
" m.set_array([])\n",
" plt.colorbar(m)\n",
"\n",
" # Show the plot\n",
" fig.canvas.draw();\n",
" image = np.array(fig.canvas.renderer.buffer_rgba())\n",
" plt.close(fig)\n",
"\n",
" return image\n",
"\n",
"model = torch.jit.load(\"model.pt\")\n",
"\n",
"with gr.Blocks() as demo:\n",
" gr.HTML(\"\"\"\n",
"<div style=\"padding: 20px; border-radius: 10px;\">\n",
" <h1 style=\"font-size: 30px; text-align: center; margin-bottom: 20px;\">PhaseHunter <span style=\"animation: arrow-anim 10s linear infinite; display: inline-block; transform: rotate(45deg) translateX(-20px);\">🏹</span>\n",
"\n",
"<style>\n",
" @keyframes arrow-anim {\n",
" 0% { transform: translateX(-20px); }\n",
" 50% { transform: translateX(20px); }\n",
" 100% { transform: translateX(-20px); }\n",
" }\n",
"</style></h1> \n",
" \n",
" <p style=\"font-size: 16px; margin-bottom: 20px;\">Detect <span style=\"background-image: linear-gradient(to right, #ED213A, #93291E); \n",
" -webkit-background-clip: text;\n",
" -webkit-text-fill-color: transparent;\n",
" background-clip: text;\">P</span> and <span style=\"background-image: linear-gradient(to right, #00B4DB, #0083B0); \n",
" -webkit-background-clip: text;\n",
" -webkit-text-fill-color: transparent;\n",
" background-clip: text;\">S</span> seismic phases with <span style=\"background-image: linear-gradient(to right, #f12711, #f5af19); \n",
" -webkit-background-clip: text;\n",
" -webkit-text-fill-color: transparent;\n",
" background-clip: text;\">uncertainty</span></p>\n",
" <ul style=\"font-size: 16px; margin-bottom: 40px;\">\n",
" <li>Detect seismic phases by selecting a sample waveform or uploading your own waveform in <code>.npy</code> format.</li>\n",
" <li>Select an earthquake from the global earthquake catalogue and PhaseHunter will analyze seismic stations in the given radius.</li>\n",
" <li>Waveforms should be sampled at 100 samples/sec and have 3 (Z, N, E) or 1 (Z) channels. PhaseHunter analyzes the first 6000 samples of your file.</li>\n",
" </ul>\n",
"</div>\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",
" with gr.Column(scale=1):\n",
" P_thres_inputs = gr.Slider(minimum=0.01,\n",
" maximum=1,\n",
" value=0.1,\n",
" label=\"P uncertainty threshold, s\",\n",
" step=0.01,\n",
" info=\"Acceptable uncertainty for P picks expressed in std() seconds\",\n",
" interactive=True,\n",
" )\n",
" \n",
" S_thres_inputs = gr.Slider(minimum=0.01,\n",
" maximum=1,\n",
" value=0.2,\n",
" label=\"S uncertainty threshold, s\",\n",
" step=0.01,\n",
" info=\"Acceptable uncertainty for S picks expressed in std() seconds\",\n",
" interactive=True,\n",
" )\n",
" with gr.Column(scale=1):\n",
" upload = gr.File(label=\"Or upload your own waveform\")\n",
" sampling_rate_inputs = gr.Slider(minimum=10,\n",
" maximum=1000,\n",
" value=100,\n",
" label=\"Samlping rate, Hz\",\n",
" step=10,\n",
" info=\"Sampling rate of the waveform\",\n",
" interactive=True,\n",
" )\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, \n",
" P_thres_inputs, S_thres_inputs,\n",
" sampling_rate_inputs], \n",
" outputs=outputs) \n",
" with gr.Tab(\"Select earthquake from catalogue\"):\n",
"\n",
" gr.HTML(\"\"\"\n",
" <div style=\"padding: 20px; border-radius: 10px; font-size: 16px;\">\n",
" <p style=\"font-weight: bold; font-size: 24px; margin-bottom: 20px;\">Using PhaseHunter to Analyze Seismic Waveforms</p>\n",
" <p>Select an earthquake from the global earthquake catalogue (e.g. <a href=\"https://earthquake.usgs.gov/earthquakes/map\">USGS</a>) and the app will download the waveform from the FDSN client of your choice. The app will use a velocity model of your choice to select appropriate time windows for each station within a specified radius of the earthquake.</p>\n",
" <p>The app will then analyze the waveforms and mark the detected phases on the waveform. Pick data for each waveform is reported in seconds from the start of the waveform.</p>\n",
" <p>Velocities are derived from distance and travel time determined by PhaseHunter picks (<span style=\"font-style: italic;\">v = distance/predicted_pick_time</span>). The background of the velocity plot is colored by DEM.</p>\n",
" </div>\n",
" \"\"\")\n",
" with gr.Row(): \n",
" with gr.Column(scale=2):\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=2):\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",
" source_depth_inputs = gr.Number(value=10,\n",
" label=\"Source depth (km)\",\n",
" info=\"Depth of the earthquake\",\n",
" interactive=True)\n",
" \n",
" with gr.Column(scale=2):\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",
" with gr.Column(scale=2):\n",
" radius_inputs = gr.Slider(minimum=1, \n",
" maximum=200, \n",
" value=50, \n",
" 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",
" with gr.Column(scale=2):\n",
" P_thres_inputs = gr.Slider(minimum=0.01,\n",
" maximum=1,\n",
" value=0.1,\n",
" label=\"P uncertainty threshold, s\",\n",
" step=0.01,\n",
" info=\"Acceptable uncertainty for P picks expressed in std() seconds\",\n",
" interactive=True,\n",
" )\n",
" S_thres_inputs = gr.Slider(minimum=0.01,\n",
" maximum=1,\n",
" value=0.2,\n",
" label=\"S uncertainty threshold, s\",\n",
" step=0.01,\n",
" info=\"Acceptable uncertainty for S picks expressed in std() seconds\",\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",
"\n",
" with gr.Row():\n",
" output_picks = gr.Dataframe(label='Pick data', \n",
" type='pandas', \n",
" interactive=False)\n",
" output_csv = gr.File(label=\"Output File\", file_types=[\".csv\"])\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",
" P_thres_inputs, S_thres_inputs],\n",
" outputs=[output_image, output_picks, output_csv])\n",
" \n",
" with gr.Row():\n",
" with gr.Column(scale=2):\n",
" inputs_vel_model = [\n",
" ## FIX FILE NAME ISSUE\n",
" gr.Slider(minimum=-180, maximum=180, value=0, step=5, label=\"Azimuth\", interactive=True),\n",
" gr.Slider(minimum=-90, maximum=90, value=30, step=5, label=\"Elevation\", interactive=True)\n",
" ]\n",
" button = gr.Button(\"Look at 3D Velocities\")\n",
" outputs_vel_model = gr.Image(label=\"3D Velocity Model\")\n",
"\n",
" button.click(compute_velocity_model, \n",
" inputs=inputs_vel_model, \n",
" outputs=outputs_vel_model)\n",
"\n",
"\n",
" \n",
"\n",
"\n",
"demo.launch()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x14eb2da90>]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/usr/local/lib/python3.9/site-packages/gradio/routes.py\", line 393, in run_predict\n",
" output = await app.get_blocks().process_api(\n",
" File \"/usr/local/lib/python3.9/site-packages/gradio/blocks.py\", line 1108, in process_api\n",
" result = await self.call_function(\n",
" File \"/usr/local/lib/python3.9/site-packages/gradio/blocks.py\", line 915, in call_function\n",
" prediction = await anyio.to_thread.run_sync(\n",
" File \"/usr/local/lib/python3.9/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
" return await get_asynclib().run_sync_in_worker_thread(\n",
" File \"/usr/local/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"/usr/local/lib/python3.9/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
" result = context.run(func, *args)\n",
" File \"/var/folders/ky/4j6xbvhs5m583jflkhyzxf9h0000gn/T/ipykernel_9385/3876498698.py\", line 76, in mark_phases\n",
" waveform = resample_waveform(waveform, sampling_rate, 100)\n",
" File \"/var/folders/ky/4j6xbvhs5m583jflkhyzxf9h0000gn/T/ipykernel_9385/3876498698.py\", line 46, in resample_waveform\n",
" resampled_length = int(waveform.shape[-1] * resampling_ratio)\n",
"AttributeError: 'str' object has no attribute 'shape'\n"
]
}
],
"source": [
"a = np.load(\"test.npy\") \n",
"plt.plot(a)\n",
"\n",
"b = resample_waveform(a, 200, 100)\n",
"plt.plot(b)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"np.save(\"test.npy\", np.random.randint(0,10, size=(6000)))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['DEFAULT_TEMP_DIR',\n",
" '__abstractmethods__',\n",
" '__class__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__getstate__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__init_subclass__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__module__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__slots__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '__weakref__',\n",
" '_abc_impl',\n",
" '_id',\n",
" '_skip_init_processing',\n",
" '_style',\n",
" 'as_example',\n",
" 'attach_load_event',\n",
" 'base64_to_temp_file_if_needed',\n",
" 'change',\n",
" 'clear',\n",
" 'deserialize',\n",
" 'download_temp_copy_if_needed',\n",
" 'elem_classes',\n",
" 'elem_id',\n",
" 'file_count',\n",
" 'file_types',\n",
" 'get_block_name',\n",
" 'get_config',\n",
" 'get_expected_parent',\n",
" 'get_load_fn_and_initial_value',\n",
" 'get_specific_update',\n",
" 'hash_base64',\n",
" 'hash_file',\n",
" 'hash_url',\n",
" 'info',\n",
" 'interactive',\n",
" 'label',\n",
" 'load_event',\n",
" 'load_event_to_attach',\n",
" 'make_temp_copy_if_needed',\n",
" 'parent',\n",
" 'postprocess',\n",
" 'preprocess',\n",
" 'render',\n",
" 'root',\n",
" 'root_url',\n",
" 'save_uploaded_file',\n",
" 'select',\n",
" 'selectable',\n",
" 'serialize',\n",
" 'set_event_trigger',\n",
" 'share_token',\n",
" 'show_label',\n",
" 'style',\n",
" 'temp_files',\n",
" 'test_input',\n",
" 'type',\n",
" 'unrender',\n",
" 'update',\n",
" 'upload',\n",
" 'value',\n",
" 'visible']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(output_csv)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"KeyboardInterrupt\n",
"\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"filename.split(\"/\")[-1]\n",
" eq_lat = float(filename.split(\"_\")[0])\n",
" eq_lon = float(filename.split(\"_\")[1])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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
},
"nbformat": 4,
"nbformat_minor": 2
}
|