Now original waveform is displayed

Gradio_app.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7866\n",
+      "Running on local URL:  http://127.0.0.1:7869\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -17,7 +17,7 @@
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7866/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+       "<div><iframe src=\"http://127.0.0.1:7869/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -30,22 +30,16 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 13,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.13119522482156754\n"
+      "4\n",
+      "0.02744414610788226\n"
      ]
     }
    ],
@@ -83,8 +77,9 @@
     "    if len(waveform.shape) == 1:\n",
     "        waveform = waveform.reshape(1, waveform.shape[0])\n",
     "\n",
+    "    orig_waveform = waveform[:, :6000].copy()\n",
     "    processed_input = prepare_waveform(waveform)\n",
-    "    \n",
+    "\n",
     "    # Make prediction\n",
     "    with torch.inference_mode():\n",
     "        output = model(processed_input)\n",
@@ -92,33 +87,34 @@
     "    p_phase = output[:, 0]\n",
     "    s_phase = output[:, 1]\n",
     "\n",
-    "    return processed_input, p_phase, s_phase\n",
+    "    return processed_input, p_phase, s_phase, orig_waveform\n",
+    "\n",
     "\n",
     "def mark_phases(waveform, uploaded_file, p_thres, s_thres):\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",
+    "    processed_input, p_phase, s_phase, orig_waveform = 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].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(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",
+    "        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",
-    "    print(p_phase.std().item()*60)\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",

app.py

@@ -36,6 +36,7 @@ def make_prediction(waveform):
     if len(waveform.shape) == 1:
         waveform = waveform.reshape(1, waveform.shape[0])
 
+    orig_waveform = waveform[:, :6000].copy()
     processed_input = prepare_waveform(waveform)
 
     # Make prediction
@@ -45,7 +46,7 @@ def make_prediction(waveform):
     p_phase = output[:, 0]
     s_phase = output[:, 1]
 
-    return processed_input, p_phase, s_phase
+    return processed_input, p_phase, s_phase, orig_waveform
 
 
 def mark_phases(waveform, uploaded_file, p_thres, s_thres):
@@ -53,20 +54,20 @@ def mark_phases(waveform, uploaded_file, p_thres, s_thres):
     if uploaded_file is not None:
         waveform = uploaded_file.name
 
-    processed_input, p_phase, s_phase = make_prediction(waveform)
+    processed_input, p_phase, s_phase, orig_waveform = make_prediction(waveform)
 
     # Create a plot of the waveform with the phases marked
     if sum(processed_input[0][2] == 0):  # if input is 1C
         fig, ax = plt.subplots(nrows=2, figsize=(10, 2), sharex=True)
 
-        ax[0].plot(processed_input[0][0], color="black", lw=1)
+        ax[0].plot(orig_waveform[0], color="black", lw=1)
         ax[0].set_ylabel("Norm. Ampl.")
 
     else:  # if input is 3C
         fig, ax = plt.subplots(nrows=4, figsize=(10, 6), sharex=True)
-        ax[0].plot(processed_input[0][0], color="black", lw=1)
-        ax[1].plot(processed_input[0][1], color="black", lw=1)
-        ax[2].plot(processed_input[0][2], color="black", lw=1)
+        ax[0].plot(orig_waveform[0], color="black", lw=1)
+        ax[1].plot(orig_waveform[1], color="black", lw=1)
+        ax[2].plot(orig_waveform[2], color="black", lw=1)
 
         ax[0].set_ylabel("Z")
         ax[1].set_ylabel("N")