Added download for data

Gradio_app.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7897\n",
+      "Running on local URL:  http://127.0.0.1:7901\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -17,7 +17,7 @@
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7897/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+       "<div><iframe src=\"http://127.0.0.1:7901/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -30,140 +30,9 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 51,
+     "execution_count": 55,
      "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_27324/1938231065.py:224: 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_27324/1938231065.py:224: 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_27324/1938231065.py:231: 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 4.987805380766392 and S velocity 2.9782985042350987\n",
-      "Plotting waveform 2/3\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_27324/1938231065.py:299: 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_27324/1938231065.py:299: 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 4.255522557803516 and S velocity 2.2929437916670583\n",
-      "Plotting waveform 3/3\n",
-      "Station 35.69235, -117.75051 has P velocity 2.979034174961547 and S velocity 1.3728192788753049\n",
-      "Plotting stations\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/_g/3q5q8_dj0ydcpktxlwxb5vrh0000gq/T/ipykernel_27324/1938231065.py:299: 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_27324/1938231065.py:324: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
-      "  ax[i].set_xticklabels(ax[i].get_xticks(), rotation = 50)\n"
-     ]
     }
    ],
    "source": [
@@ -174,6 +43,7 @@
     "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 bmi_topography import Topography\n",
@@ -510,7 +380,16 @@
     "    image = np.array(fig.canvas.renderer.buffer_rgba())\n",
     "    plt.close(fig)\n",
     "\n",
-    "    return image, output_picks\n",
+    "    output_picks.to_csv('data/picks.csv', index=False)\n",
+    "    output_csv = 'data/picks.csv'\n",
+    "\n",
+    "    return image, output_picks, output_csv\n",
+    "\n",
+    "def download_picks(output_picks):\n",
+    "    output_csv = io.BytesIO()\n",
+    "    output_picks.to_csv(output_csv, index=False)\n",
+    "    output_csv.seek(0)\n",
+    "    return output_csv\n",
     "\n",
     "model = torch.jit.load(\"model.pt\")\n",
     "\n",
@@ -638,65 +517,23 @@
     "            \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', \n",
-    "                                    type='pandas', \n",
-    "                                    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",
-    "                 outputs=[output_image, output_picks])\n",
+    "                 outputs=[output_image, output_picks, output_csv])\n",
     "\n",
     "demo.launch()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<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>\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%html\n",
-    "\n",
-    "<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>\n",
-    "\n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

app.py

@@ -5,6 +5,7 @@ import numpy as np
 import pandas as pd
 from phasehunter.data_preparation import prepare_waveform
 import torch
+import io
 
 from scipy.stats import gaussian_kde
 from bmi_topography import Topography
@@ -341,7 +342,16 @@ def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source
     image = np.array(fig.canvas.renderer.buffer_rgba())
     plt.close(fig)
 
-    return image, output_picks
+    output_picks.to_csv('data/picks.csv', index=False)
+    output_csv = 'data/picks.csv'
+
+    return image, output_picks, output_csv
+
+def download_picks(output_picks):
+    output_csv = io.BytesIO()
+    output_picks.to_csv(output_csv, index=False)
+    output_csv.seek(0)
+    return output_csv
 
 model = torch.jit.load("model.pt")
 
@@ -469,15 +479,18 @@ with gr.Blocks() as demo:
             
         button = gr.Button("Predict phases")
         output_image = gr.Image(label='Waveforms with Phases Marked', type='numpy', interactive=False)
-        output_picks = gr.Dataframe(label='Pick data', 
-                                    type='pandas', 
-                                    interactive=False)
+
+        with gr.Row():
+            output_picks = gr.Dataframe(label='Pick data', 
+                                        type='pandas', 
+                                        interactive=False)
+            output_csv = gr.File(label="Output File", file_types=[".csv"])
 
         button.click(predict_on_section, 
                  inputs=[client_inputs, timestamp_inputs, 
                          eq_lat_inputs, eq_lon_inputs, 
                          radius_inputs, source_depth_inputs, 
                          velocity_inputs, max_waveforms_inputs],
-                 outputs=[output_image, output_picks])
+                 outputs=[output_image, output_picks, output_csv])
 
 demo.launch()

data/picks.csv

@@ -0,0 +1,4 @@
+station_name,starttime,"p_phase, s","p_uncertainty, s","s_phase, s","s_uncertainty, s","velocity_p, km/s","velocity_s, km/s"
+CI.SRT,2019-07-04T17:33:38.029990Z,5.195954322814941,0.4883878491818905,11.277575492858887,1.6350263357162476,2.9791118172972846,1.3725759526093728
+CI.JRC2,2019-07-04T17:33:39.947494Z,7.11562442779541,0.4898720048367977,13.208794593811035,1.644418090581894,4.255539668245224,2.29247429065224
+CI.WMF,2019-07-04T17:33:41.867962Z,9.043024063110352,0.4864034615457058,15.146604537963867,1.6339519992470741,4.987844305676596,2.9779080827144124