Added timezone input

Gradio_app.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7872\n",
+      "Running on local URL:  http://127.0.0.1:7873\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -17,7 +17,7 @@
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7872/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+       "<div><iframe src=\"http://127.0.0.1:7873/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -30,7 +30,7 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 44,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -38,7 +38,187 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded (1, 28412)\n"
+      "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_3502/3370384716.py:299: 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_3502/3370384716.py:299: 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_3502/3370384716.py:306: 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_3502/3370384716.py:391: 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_3502/3370384716.py:391: 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 36.11758, -117.85486 has P velocity 4.743224278343435 and S velocity 2.6493777937777434\n",
+      "Plotting waveform 3/3\n",
+      "Station 35.69235, -117.75051 has P velocity 3.4168555245020182 and S velocity 1.6801806885131727\n",
+      "Plotting stations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/3370384716.py:391: 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_3502/3370384716.py:411: 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.WMF  36.11758 -117.85486  2019-07-04T17:33:41.867962Z    9.509396   \n",
+      "2       CI.SRT  35.69235 -117.75051  2019-07-04T17:33:38.029990Z    4.530285   \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          0.017238   17.024826          0.043195          4.743224   \n",
+      "2          0.013012    9.212895          0.018260          3.416856   \n",
+      "\n",
+      "   velocity_s, km/s  \n",
+      "0          2.262277  \n",
+      "1          2.649378  \n",
+      "2          1.680181  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/3370384716.py:529: 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.WMF  36.11758 -117.85486  2019-07-04T17:33:41.867962Z    9.509396   \n",
+      "2       CI.SRT  35.69235 -117.75051  2019-07-04T17:33:38.029990Z    4.530285   \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          0.017238   17.024826          0.043195          4.743224   \n",
+      "2          0.013012    9.212895          0.018260          3.416856   \n",
+      "\n",
+      "   velocity_s, km/s  \n",
+      "0          2.262277  \n",
+      "1          2.649378  \n",
+      "2          1.680181  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_3502/3370384716.py:529: 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"
      ]
     }
    ],
@@ -693,8 +873,8 @@
     "                )\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",
+    "                timestamp_inputs = gr.Textbox(value='2019-07-04T17:33:49-00',\n",
+    "                                    placeholder='YYYY-MM-DDTHH:MM:SS-TZ',\n",
     "                                    label=\"Timestamp\",\n",
     "                                    info=\"Timestamp of the earthquake\",\n",
     "                                    max_lines=1,\n",
@@ -835,34 +1015,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ccb9acd0>]"
+       "2023-04-10T00:16:56.000000Z"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "a = np.load(\"data/sample/sample_1.npy\")    \n",
-    "np.save('test_no_p.npy', a[:, 3000:])\n",
-    "plt.plot(a[0])\n"
+    "obspy.UTCDateTime('2023-04-09T17:16:56-07')"
    ]
   },
   {

app.py

@@ -648,8 +648,8 @@ with gr.Blocks() as demo:
                 )
 
             with gr.Column(scale=2):
-                timestamp_inputs = gr.Textbox(value='2019-07-04 17:33:49',
-                                    placeholder='YYYY-MM-DD HH:MM:SS',
+                timestamp_inputs = gr.Textbox(value='2019-07-04T17:33:49-00',
+                                    placeholder='YYYY-MM-DDTHH:MM:SS-TZ',
                                     label="Timestamp",
                                     info="Timestamp of the earthquake",
                                     max_lines=1,

data/velocity/35.766_-117.605_10.0_2019-07-04T17:33:49-00_3.csv

@@ -0,0 +1,4 @@
+station_name,st_lat,st_lon,starttime,"p_phase, s","p_uncertainty, s","s_phase, s","s_uncertainty, s","velocity_p, km/s","velocity_s, km/s"
+CI.JRC2,35.98249,-117.80885,2019-07-04T17:33:39.947494Z,7.320212364196777,0.020417090272530913,13.38510799407959,0.028438671142794192,4.13660431013202,2.2622770044299756
+CI.WMF,36.11758,-117.85486,2019-07-04T17:33:41.867962Z,9.509395599365234,0.017237872816622257,17.024826049804688,0.04319542204029858,4.743224278343435,2.6493777937777434
+CI.SRT,35.69235,-117.75051,2019-07-04T17:33:38.029990Z,4.530284881591797,0.01301152427913621,9.212895393371582,0.01826027117203921,3.4168555245020182,1.6801806885131727