Modified the .gitignore and removed dummy files

bf16c724 · Mickael Bonnin · b71138e8 · bf16c724 · b71138e8 · b71138e8
--- a/.gitignore
+++ b/.gitignore
-__pycache__
\ No newline at end of file
+__pycache__
+.ipynb_checkpoints
\ No newline at end of file
--- a/example_demo/.ipynb_checkpoints/prediction-checkpoint.ipynb
+++ b/example_demo/.ipynb_checkpoints/prediction-checkpoint.ipynb
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0f96ee4d-3f8a-4a95-b815-4da29fbf1e8e",
-   "metadata": {},
-   "source": [
-    "# Prediction Mode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0bd9e000-a2ee-4e96-a29f-c105ceec4c62",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>This mode is used if the nature/label of the event is not known in advance. </b>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "826842f6-e7ce-4d55-86f3-ce97b39b24b2",
-   "metadata": {},
-   "source": [
-    "## Download data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c02a24e2-742c-4ac8-b11d-8964284db1c3",
-   "metadata": {},
-   "source": [
-    "Input data should be in **mseed** format and correspond to raw **60 second 3-component seismograms**. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a245431a-78d6-4fa1-9550-b52412a04c87",
-   "metadata": {},
-   "source": [
-    "Demo data is available in the \"mseed\" folder. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eb0b3464-7061-49a8-8eee-da130ad17bae",
-   "metadata": {},
-   "source": [
-    "To apply the algorithm, we need a folder architecture:\n",
-    "* mseed_demo\n",
-    "   * 2022004T134407\n",
-    "        * FR_CHLF_2022004T134407.mseed\n",
-    "        * FR_GARF_2022004T134407.mseed\n",
-    "        * FR_GNEF_2022004T134407.mseed\n",
-    "        * FR_VERF_2022004T134407.mseed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "631cae95-a1bc-46f8-8596-b11f0a6ee1b9",
-   "metadata": {},
-   "source": [
-    "## Run prediction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "263b8345-4e16-47a6-b60f-8cba04e9cb69",
-   "metadata": {},
-   "source": [
-    "### From a terminal:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36d73373-310d-4557-ba3b-3cc62544c703",
-   "metadata": {},
-   "source": [
-    "```\n",
-    " python run.py --mode pred --data_dir ./mseed_demo --spectro_dir ./spectro_demo --output_dir ./output_demo\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f928f030-d567-4cd3-98bc-aba6d0c922ec",
-   "metadata": {},
-   "source": [
-    "### From a notebook: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "93f08547-af04-429d-9dd4-220fd3669d30",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../')\n",
-    "from data_process import spectro_extract_pred\n",
-    "from prediction import pred"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1228c624-48f4-401d-9d0f-e76397188218",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of events : 9\n",
-      "*****************\n",
-      "EVENT 1 / 9\n",
-      "Number of stream : 14\n",
-      "*****************\n",
-      "EVENT 2 / 9\n",
-      "Number of stream : 10\n",
-      "*****************\n",
-      "EVENT 3 / 9\n",
-      "Number of stream : 8\n",
-      "Stream 5 / 8\r"
-     ]
-    }
-   ],
-   "source": [
-    "data = spectro_extract_pred(data_dir=\"./../mseed_demo\", spectro_dir=\"./../spectro_demo\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "26d663ad-ab6b-4c21-8c04-93f147e48256",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2022-09-12 17:51:33.630086: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
-      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of events : 9\n",
-      "*****************\n",
-      "EVENT 1 / 9\n",
-      "Number of station : 15\n",
-      "*****************\n",
-      "EVENT 2 / 9\n",
-      "Number of station : 10\n",
-      "*****************\n",
-      "EVENT 3 / 9\n",
-      "Number of station : 16\n",
-      "*****************\n",
-      "EVENT 4 / 9\n",
-      "Number of station : 8\n",
-      "*****************\n",
-      "EVENT 5 / 9\n",
-      "Number of station : 11\n",
-      "*****************\n",
-      "EVENT 6 / 9\n",
-      "Number of station : 12\n",
-      "*****************\n",
-      "EVENT 7 / 9\n",
-      "Number of station : 7\n",
-      "*****************\n",
-      "EVENT 8 / 9\n",
-      "Number of station : 4\n",
-      "*****************\n",
-      "EVENT 9 / 9\n",
-      "Number of station : 10\n",
-      "Station 10 / 10\r"
-     ]
-    }
-   ],
-   "source": [
-    "pred(model_dir=\"./../model/model_2021354T1554.h5\", spectro_dir='./../spectro_demo', output_dir=\"./../output_demo\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a059bc71-912e-4b2b-ab4a-0f7bf5c56b5c",
-   "metadata": {},
-   "source": [
-    "## Read csv file "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "29e9f0ed-331c-41be-bad4-b9539a132baa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e9c9f987-e2c7-4273-ad37-67aa2dab0d7b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>event</th>\n",
-       "      <th>prob_nat</th>\n",
-       "      <th>prob_ant</th>\n",
-       "      <th>pred</th>\n",
-       "      <th>nature</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2022004T111040</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.994</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Anthropogenic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2022001T213524</td>\n",
-       "      <td>0.961</td>\n",
-       "      <td>0.039</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Natural</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2022003T080315</td>\n",
-       "      <td>0.043</td>\n",
-       "      <td>0.957</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Anthropogenic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2022003T084110</td>\n",
-       "      <td>0.019</td>\n",
-       "      <td>0.981</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Anthropogenic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2022004T134407</td>\n",
-       "      <td>0.770</td>\n",
-       "      <td>0.230</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Natural</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            event  prob_nat  prob_ant  pred         nature\n",
-       "0  2022004T111040     0.006     0.994     1  Anthropogenic\n",
-       "1  2022001T213524     0.961     0.039     0        Natural\n",
-       "2  2022003T080315     0.043     0.957     1  Anthropogenic\n",
-       "3  2022003T084110     0.019     0.981     1  Anthropogenic\n",
-       "4  2022004T134407     0.770     0.230     0        Natural"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pred_net_csv = pd.read_csv(\"../output_demo/prediction_network_level.csv\")\n",
-    "pred_net_csv = pd.DataFrame(pred_net_csv)\n",
-    "pred_net_csv.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b2943f25-c309-488c-8e45-2ef919e6da57",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>file_name</th>\n",
-       "      <th>station</th>\n",
-       "      <th>prob_nat</th>\n",
-       "      <th>prob_ant</th>\n",
-       "      <th>pred</th>\n",
-       "      <th>nature</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>FR_DAUF_2022004T111040</td>\n",
-       "      <td>DAUF</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Anthropogenic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>FR_LEUC_2022004T111040</td>\n",
-       "      <td>LEUC</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.998</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Anthropogenic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>FR_CRNF_2022004T111040</td>\n",
-       "      <td>CRNF</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.986</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Anthropogenic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>FR_AGO_2022004T111040</td>\n",
-       "      <td>AGO</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Anthropogenic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>FR_ABJF_2022004T111040</td>\n",
-       "      <td>ABJF</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Anthropogenic</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                file_name station  prob_nat  prob_ant  pred         nature\n",
-       "0  FR_DAUF_2022004T111040    DAUF     0.001     0.999     1  Anthropogenic\n",
-       "1  FR_LEUC_2022004T111040    LEUC     0.002     0.998     1  Anthropogenic\n",
-       "2  FR_CRNF_2022004T111040    CRNF     0.014     0.986     1  Anthropogenic\n",
-       "3   FR_AGO_2022004T111040     AGO     0.001     0.999     1  Anthropogenic\n",
-       "4  FR_ABJF_2022004T111040    ABJF     0.000     1.000     1  Anthropogenic"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pred_sta_csv = pd.read_csv(\"../output_demo/prediction_station_level.csv\")\n",
-    "pred_sta_csv = pd.DataFrame(pred_sta_csv)\n",
-    "pred_sta_csv.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8ca9d8ef-2026-4839-abcf-c142c7876eaf",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "006f1a8d-f5fc-4783-92ee-f375aea5c03d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.8.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
-%% Cell type:markdown id:0f96ee4d-3f8a-4a95-b815-4da29fbf1e8e tags:
-
-# Prediction Mode
-
-%% Cell type:markdown id:0bd9e000-a2ee-4e96-a29f-c105ceec4c62 tags:
-
-<div class="alert alert-block alert-info">
-<b>This mode is used if the nature/label of the event is not known in advance. </b>
-</div>
-
-%% Cell type:markdown id:826842f6-e7ce-4d55-86f3-ce97b39b24b2 tags:
-
-## Download data
-
-%% Cell type:markdown id:c02a24e2-742c-4ac8-b11d-8964284db1c3 tags:
-
-Input data should be in **mseed** format and correspond to raw **60 second 3-component seismograms**.
-
-%% Cell type:markdown id:a245431a-78d6-4fa1-9550-b52412a04c87 tags:
-
-Demo data is available in the "mseed" folder.
-
-%% Cell type:markdown id:eb0b3464-7061-49a8-8eee-da130ad17bae tags:
-
-To apply the algorithm, we need a folder architecture:
-* mseed_demo
-   * 2022004T134407
-        * FR_CHLF_2022004T134407.mseed
-        * FR_GARF_2022004T134407.mseed
-        * FR_GNEF_2022004T134407.mseed
-        * FR_VERF_2022004T134407.mseed
-
-%% Cell type:markdown id:631cae95-a1bc-46f8-8596-b11f0a6ee1b9 tags:
-
-## Run prediction
-
-%% Cell type:markdown id:263b8345-4e16-47a6-b60f-8cba04e9cb69 tags:
-
-### From a terminal:
-
-%% Cell type:markdown id:36d73373-310d-4557-ba3b-3cc62544c703 tags:
-
-```
- python run.py --mode pred --data_dir ./mseed_demo --spectro_dir ./spectro_demo --output_dir ./output_demo
-```
-
-%% Cell type:markdown id:f928f030-d567-4cd3-98bc-aba6d0c922ec tags:
-
-### From a notebook:
-
-%% Cell type:code id:93f08547-af04-429d-9dd4-220fd3669d30 tags:
-
-``` python
-import sys
-sys.path.append('../')
-from data_process import spectro_extract_pred
-from prediction import pred
-```
-
-%% Cell type:code id:1228c624-48f4-401d-9d0f-e76397188218 tags:
-
-``` python
-data = spectro_extract_pred(data_dir="./../mseed_demo", spectro_dir="./../spectro_demo")
-```
-
-%% Output
-
-    Number of events : 9
-    *****************
-    EVENT 1 / 9
-    Number of stream : 14
-    *****************
-    EVENT 2 / 9
-    Number of stream : 10
-    *****************
-    EVENT 3 / 9
-    Number of stream : 8
-    Stream 5 / 8
-
-%% Cell type:code id:26d663ad-ab6b-4c21-8c04-93f147e48256 tags:
-
-``` python
-pred(model_dir="./../model/model_2021354T1554.h5", spectro_dir='./../spectro_demo', output_dir="./../output_demo")
-```
-
-%% Output
-
-    2022-09-12 17:51:33.630086: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
-    To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
-
-    Number of events : 9
-    *****************
-    EVENT 1 / 9
-    Number of station : 15
-    *****************
-    EVENT 2 / 9
-    Number of station : 10
-    *****************
-    EVENT 3 / 9
-    Number of station : 16
-    *****************
-    EVENT 4 / 9
-    Number of station : 8
-    *****************
-    EVENT 5 / 9
-    Number of station : 11
-    *****************
-    EVENT 6 / 9
-    Number of station : 12
-    *****************
-    EVENT 7 / 9
-    Number of station : 7
-    *****************
-    EVENT 8 / 9
-    Number of station : 4
-    *****************
-    EVENT 9 / 9
-    Number of station : 10
-    Station 10 / 10
-
-%% Cell type:markdown id:a059bc71-912e-4b2b-ab4a-0f7bf5c56b5c tags:
-
-## Read csv file
-
-%% Cell type:code id:29e9f0ed-331c-41be-bad4-b9539a132baa tags:
-
-``` python
-import pandas as pd
-```
-
-%% Cell type:code id:e9c9f987-e2c7-4273-ad37-67aa2dab0d7b tags:
-
-``` python
-pred_net_csv = pd.read_csv("../output_demo/prediction_network_level.csv")
-pred_net_csv = pd.DataFrame(pred_net_csv)
-pred_net_csv.head()
-```
-
-%% Output
-
-                event  prob_nat  prob_ant  pred         nature
-    0  2022004T111040     0.006     0.994     1  Anthropogenic
-    1  2022001T213524     0.961     0.039     0        Natural
-    2  2022003T080315     0.043     0.957     1  Anthropogenic
-    3  2022003T084110     0.019     0.981     1  Anthropogenic
-    4  2022004T134407     0.770     0.230     0        Natural
-
-%% Cell type:code id:b2943f25-c309-488c-8e45-2ef919e6da57 tags:
-
-``` python
-pred_sta_csv = pd.read_csv("../output_demo/prediction_station_level.csv")
-pred_sta_csv = pd.DataFrame(pred_sta_csv)
-pred_sta_csv.head()
-```
-
-%% Output
-
-                    file_name station  prob_nat  prob_ant  pred         nature
-    0  FR_DAUF_2022004T111040    DAUF     0.001     0.999     1  Anthropogenic
-    1  FR_LEUC_2022004T111040    LEUC     0.002     0.998     1  Anthropogenic
-    2  FR_CRNF_2022004T111040    CRNF     0.014     0.986     1  Anthropogenic
-    3   FR_AGO_2022004T111040     AGO     0.001     0.999     1  Anthropogenic
-    4  FR_ABJF_2022004T111040    ABJF     0.000     1.000     1  Anthropogenic
-
-%% Cell type:code id:8ca9d8ef-2026-4839-abcf-c142c7876eaf tags:
-
-``` python
-```
-
-%% Cell type:code id:006f1a8d-f5fc-4783-92ee-f375aea5c03d tags:
-
-``` python
-```
--- a/example_demo/.ipynb_checkpoints/validation-checkpoint.ipynb
+++ b/example_demo/.ipynb_checkpoints/validation-checkpoint.ipynb
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0f96ee4d-3f8a-4a95-b815-4da29fbf1e8e",
-   "metadata": {},
-   "source": [
-    "# Validation Mode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0bd9e000-a2ee-4e96-a29f-c105ceec4c62",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>This mode is used if the nature/label of the event is known in advance. </b>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "826842f6-e7ce-4d55-86f3-ce97b39b24b2",
-   "metadata": {},
-   "source": [
-    "## Download data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c02a24e2-742c-4ac8-b11d-8964284db1c3",
-   "metadata": {},
-   "source": [
-    "Input data should be in **mseed** format and correspond to raw **60 second 3-component seismograms**. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a245431a-78d6-4fa1-9550-b52412a04c87",
-   "metadata": {},
-   "source": [
-    "Demo data is available in the \"mseed\" folder. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eb0b3464-7061-49a8-8eee-da130ad17bae",
-   "metadata": {},
-   "source": [
-    "To apply the algorithm, we need a folder architecture:\n",
-    "* mseed_demo\n",
-    "   * 2022004T134407\n",
-    "        * FR_CHLF_2022004T134407.mseed\n",
-    "        * FR_GARF_2022004T134407.mseed\n",
-    "        * FR_GNEF_2022004T134407.mseed\n",
-    "        * FR_VERF_2022004T134407.mseed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b61cddaf-cf4d-4d46-afc6-85e99d5c7514",
-   "metadata": {},
-   "source": [
-    "To apply this mode, a **csv file** import pandas as pd is also required with the associated label for each event. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9bcac75b-0166-4346-975b-66b226902bb5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec2b7b4d-ccb3-4884-97a6-7e7de00010bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred_net_csv = pd.read_csv(\"../demo_file.csv\")\n",
-    "pred_net_csv = pd.DataFrame(pred_net_csv)\n",
-    "pred_net_csv.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "631cae95-a1bc-46f8-8596-b11f0a6ee1b9",
-   "metadata": {},
-   "source": [
-    "## Run validation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "263b8345-4e16-47a6-b60f-8cba04e9cb69",
-   "metadata": {},
-   "source": [
-    "### From a terminal:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36d73373-310d-4557-ba3b-3cc62544c703",
-   "metadata": {},
-   "source": [
-    "```\n",
-    " python run.py --mode pred --data_dir ./mseed_demo --spectro_dir ./spectro_demo --output_dir ./output_demo --csv_file ./demo_file.csv\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f928f030-d567-4cd3-98bc-aba6d0c922ec",
-   "metadata": {},
-   "source": [
-    "### From a notebook: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "93f08547-af04-429d-9dd4-220fd3669d30",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../')\n",
-    "from data_process import spectro_extract_valid\n",
-    "from prediction import valid\n",
-    "\n",
-    "import numpy as np "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8ee5e6f3-68f5-4a5b-8d1b-246ef60c32a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "events = np.genfromtxt(f'./../demo_file.csv', delimiter=',', skip_header = 1, dtype=str)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1228c624-48f4-401d-9d0f-e76397188218",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = spectro_extract_valid(data_dir=\"./../mseed_demo\", spectro_dir=\"./../spectro_demo\", events_list=events)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "26d663ad-ab6b-4c21-8c04-93f147e48256",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "valid(model_dir=\"./../model/model_2021354T1554.h5\", spectro_dir='./../spectro_demo', output_dir=\"./../output_demo\", event_label=events)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a059bc71-912e-4b2b-ab4a-0f7bf5c56b5c",
-   "metadata": {},
-   "source": [
-    "## Read csv file "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "29e9f0ed-331c-41be-bad4-b9539a132baa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e9c9f987-e2c7-4273-ad37-67aa2dab0d7b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred_net_csv = pd.read_csv(\"../output_demo/validation_network_level.csv\")\n",
-    "pred_net_csv = pd.DataFrame(pred_net_csv)\n",
-    "pred_net_csv.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b2943f25-c309-488c-8e45-2ef919e6da57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred_sta_csv = pd.read_csv(\"../output_demo/validation_station_level.csv\")\n",
-    "pred_sta_csv = pd.DataFrame(pred_sta_csv)\n",
-    "pred_sta_csv.head()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.8.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
-%% Cell type:markdown id:0f96ee4d-3f8a-4a95-b815-4da29fbf1e8e tags:
-
-# Validation Mode
-
-%% Cell type:markdown id:0bd9e000-a2ee-4e96-a29f-c105ceec4c62 tags:
-
-<div class="alert alert-block alert-info">
-<b>This mode is used if the nature/label of the event is known in advance. </b>
-</div>
-
-%% Cell type:markdown id:826842f6-e7ce-4d55-86f3-ce97b39b24b2 tags:
-
-## Download data
-
-%% Cell type:markdown id:c02a24e2-742c-4ac8-b11d-8964284db1c3 tags:
-
-Input data should be in **mseed** format and correspond to raw **60 second 3-component seismograms**.
-
-%% Cell type:markdown id:a245431a-78d6-4fa1-9550-b52412a04c87 tags:
-
-Demo data is available in the "mseed" folder.
-
-%% Cell type:markdown id:eb0b3464-7061-49a8-8eee-da130ad17bae tags:
-
-To apply the algorithm, we need a folder architecture:
-* mseed_demo
-   * 2022004T134407
-        * FR_CHLF_2022004T134407.mseed
-        * FR_GARF_2022004T134407.mseed
-        * FR_GNEF_2022004T134407.mseed
-        * FR_VERF_2022004T134407.mseed
-
-%% Cell type:markdown id:b61cddaf-cf4d-4d46-afc6-85e99d5c7514 tags:
-
-To apply this mode, a **csv file** import pandas as pd is also required with the associated label for each event.
-
-%% Cell type:code id:9bcac75b-0166-4346-975b-66b226902bb5 tags:
-
-``` python
-import pandas as pd
-```
-
-%% Cell type:code id:ec2b7b4d-ccb3-4884-97a6-7e7de00010bb tags:
-
-``` python
-pred_net_csv = pd.read_csv("../demo_file.csv")
-pred_net_csv = pd.DataFrame(pred_net_csv)
-pred_net_csv.head()
-```
-
-%% Cell type:markdown id:631cae95-a1bc-46f8-8596-b11f0a6ee1b9 tags:
-
-## Run validation
-
-%% Cell type:markdown id:263b8345-4e16-47a6-b60f-8cba04e9cb69 tags:
-
-### From a terminal:
-
-%% Cell type:markdown id:36d73373-310d-4557-ba3b-3cc62544c703 tags:
-
-```
- python run.py --mode pred --data_dir ./mseed_demo --spectro_dir ./spectro_demo --output_dir ./output_demo --csv_file ./demo_file.csv
-```
-
-%% Cell type:markdown id:f928f030-d567-4cd3-98bc-aba6d0c922ec tags:
-
-### From a notebook:
-
-%% Cell type:code id:93f08547-af04-429d-9dd4-220fd3669d30 tags:
-
-``` python
-import sys
-sys.path.append('../')
-from data_process import spectro_extract_valid
-from prediction import valid
-
-import numpy as np
-```
-
-%% Cell type:code id:8ee5e6f3-68f5-4a5b-8d1b-246ef60c32a7 tags:
-
-``` python
-events = np.genfromtxt(f'./../demo_file.csv', delimiter=',', skip_header = 1, dtype=str)
-```
-
-%% Cell type:code id:1228c624-48f4-401d-9d0f-e76397188218 tags:
-
-``` python
-data = spectro_extract_valid(data_dir="./../mseed_demo", spectro_dir="./../spectro_demo", events_list=events)
-```
-
-%% Cell type:code id:26d663ad-ab6b-4c21-8c04-93f147e48256 tags:
-
-``` python
-valid(model_dir="./../model/model_2021354T1554.h5", spectro_dir='./../spectro_demo', output_dir="./../output_demo", event_label=events)
-```
-
-%% Cell type:markdown id:a059bc71-912e-4b2b-ab4a-0f7bf5c56b5c tags:
-
-## Read csv file
-
-%% Cell type:code id:29e9f0ed-331c-41be-bad4-b9539a132baa tags:
-
-``` python
-import pandas as pd
-```
-
-%% Cell type:code id:e9c9f987-e2c7-4273-ad37-67aa2dab0d7b tags:
-
-``` python
-pred_net_csv = pd.read_csv("../output_demo/validation_network_level.csv")
-pred_net_csv = pd.DataFrame(pred_net_csv)
-pred_net_csv.head()
-```
-
-%% Cell type:code id:b2943f25-c309-488c-8e45-2ef919e6da57 tags:
-
-``` python
-pred_sta_csv = pd.read_csv("../output_demo/validation_station_level.csv")
-pred_sta_csv = pd.DataFrame(pred_sta_csv)
-pred_sta_csv.head()
-```
--- a/example_demo/.ipynb_checkpoints/visualization-checkpoint.ipynb
+++ b/example_demo/.ipynb_checkpoints/visualization-checkpoint.ipynb
--- a/mseed_demo/.DS_Store
+++ b/mseed_demo/.DS_Store
--- a/output_demo/.ipynb_checkpoints/prediction_network_level-checkpoint.csv
+++ b/output_demo/.ipynb_checkpoints/prediction_network_level-checkpoint.csv
-event,prob_nat,prob_ant,pred,nature
-2022004T111040,0.005,0.995,1,Anthropogenic
-2022001T213524,0.961,0.039,0,Natural
-2022003T080315,0.043,0.957,1,Anthropogenic
-2022003T084110,0.019,0.981,1,Anthropogenic
-2022004T134407,0.77,0.23,0,Natural
-2022004T105235,0.011,0.989,1,Anthropogenic
-2022004T101445,0.015,0.985,1,Anthropogenic
-2022004T111745,0.005,0.995,1,Anthropogenic
-2022003T041502,0.988,0.012,0,Natural
--- a/output_demo/.ipynb_checkpoints/prediction_station_level-checkpoint.csv
+++ b/output_demo/.ipynb_checkpoints/prediction_station_level-checkpoint.csv
-file_name,station,prob_nat,prob_ant,pred,nature
-FR_DAUF_2022004T111040,DAUF,0.001,0.999,1,Anthropogenic
-FR_LEUC_2022004T111040,LEUC,0.002,0.998,1,Anthropogenic
-FR_CRNF_2022004T111040,CRNF,0.014,0.986,1,Anthropogenic
-FR_AGO_2022004T111040,AGO,0.001,0.999,1,Anthropogenic
-FR_ABJF_2022004T111040,ABJF,0.0,1.0,1,Anthropogenic
-FR_GIZF_2022004T111040,GIZF,0.002,0.998,1,Anthropogenic
-FR_CHIF_2022004T111040,CHIF,0.0,1.0,1,Anthropogenic
-FR_GARF_2022004T111040,GARF,0.006,0.994,1,Anthropogenic
-FR_GNEF_2022004T111040,GNEF,0.013,0.987,1,Anthropogenic
-FR_GZNF_2022004T111040,GZNF,0.012,0.988,1,Anthropogenic
-FR_VERF_2022004T111040,VERF,0.014,0.986,1,Anthropogenic
-FR_SLVF_2022004T111040,SLVF,0.001,0.999,1,Anthropogenic
-FR_BRGF_2022004T111040,BRGF,0.005,0.995,1,Anthropogenic
-FR_HRSF_2022004T111040,HRSF,0.003,0.997,1,Anthropogenic
-FR_MTNF_2022001T213524,MTNF,0.924,0.076,0,Natural
-FR_SROF_2022001T213524,SROF,0.961,0.039,0,Natural
-FR_PLOF_2022001T213524,PLOF,0.949,0.051,0,Natural
-FR_DAUF_2022001T213524,DAUF,0.996,0.004,0,Natural
-FR_PLEF_2022001T213524,PLEF,0.989,0.011,0,Natural
-FR_RIAF_2022001T213524,RIAF,0.999,0.001,0,Natural
-FR_LOCF_2022001T213524,LOCF,0.866,0.134,0,Natural
-FR_SOMF_2022001T213524,SOMF,0.944,0.056,0,Natural
-FR_LOUF_2022001T213524,LOUF,0.992,0.008,0,Natural
-FR_BESN_2022001T213524,BESN,0.991,0.009,0,Natural
-FR_CRNF_2022003T080315,CRNF,0.031,0.969,1,Anthropogenic
-FR_CHIF_2022003T080315,CHIF,0.047,0.953,1,Anthropogenic
-FR_ABJF_2022003T080315,ABJF,0.04,0.96,1,Anthropogenic
-FR_GNEF_2022003T080315,GNEF,0.014,0.986,1,Anthropogenic
-FR_GARF_2022003T080315,GARF,0.008,0.992,1,Anthropogenic
-FR_LGIF_2022003T080315,LGIF,0.014,0.986,1,Anthropogenic
-FR_BSCF_2022003T080315,BSCF,0.03,0.97,1,Anthropogenic
-FR_BRGF_2022003T080315,BRGF,0.162,0.838,1,Anthropogenic
-FR_CHIF_2022003T084110,CHIF,0.006,0.994,1,Anthropogenic
-FR_RIAF_2022003T084110,RIAF,0.039,0.961,1,Anthropogenic
-FR_CRNF_2022003T084110,CRNF,0.014,0.986,1,Anthropogenic
-FR_BOUF_2022003T084110,BOUF,0.054,0.946,1,Anthropogenic
-FR_DAUF_2022003T084110,DAUF,0.011,0.989,1,Anthropogenic
-FR_GNEF_2022003T084110,GNEF,0.011,0.989,1,Anthropogenic
-FR_LGIF_2022003T084110,LGIF,0.012,0.988,1,Anthropogenic
-FR_BSCF_2022003T084110,BSCF,0.004,0.996,1,Anthropogenic
-FR_ABJF_2022004T134407,ABJF,0.044,0.956,1,Anthropogenic
-FR_CHLF_2022004T134407,CHLF,0.962,0.038,0,Natural
-FR_LBL_2022004T134407,LBL,0.216,0.784,1,Anthropogenic
-FR_GARF_2022004T134407,GARF,0.982,0.018,0,Natural
-FR_GNEF_2022004T134407,GNEF,0.914,0.086,0,Natural
-FR_VERF_2022004T134407,VERF,0.985,0.015,0,Natural
-FR_GZNF_2022004T134407,GZNF,0.985,0.015,0,Natural
-FR_COLF_2022004T134407,COLF,0.808,0.192,0,Natural
-FR_HRSF_2022004T134407,HRSF,0.989,0.011,0,Natural
-FR_BRGF_2022004T134407,BRGF,0.637,0.363,0,Natural
-FR_FRNF_2022004T134407,FRNF,0.952,0.048,0,Natural
-FR_DAUF_2022004T105235,DAUF,0.003,0.997,1,Anthropogenic
-FR_BEGF_2022004T105235,BEGF,0.009,0.991,1,Anthropogenic
-FR_BOUF_2022004T105235,BOUF,0.022,0.978,1,Anthropogenic
-FR_CRNF_2022004T105235,CRNF,0.026,0.974,1,Anthropogenic
-FR_LGIF_2022004T105235,LGIF,0.002,0.998,1,Anthropogenic
-FR_GNEF_2022004T105235,GNEF,0.006,0.994,1,Anthropogenic
-FR_PLOF_2022004T101445,PLOF,0.017,0.983,1,Anthropogenic
-FR_BEGF_2022004T101445,BEGF,0.002,0.998,1,Anthropogenic
-FR_RIAF_2022004T101445,RIAF,0.01,0.99,1,Anthropogenic
-FR_SROF_2022004T101445,SROF,0.061,0.939,1,Anthropogenic
-FR_MTNF_2022004T101445,MTNF,0.006,0.994,1,Anthropogenic
-FR_GUEF_2022004T101445,GUEF,0.002,0.998,1,Anthropogenic
-FR_OLIV_2022004T101445,OLIV,0.004,0.996,1,Anthropogenic
-FR_PLEF_2022004T111745,PLEF,0.011,0.989,1,Anthropogenic
-FR_RIAF_2022004T111745,RIAF,0.006,0.994,1,Anthropogenic
-FR_BEGF_2022004T111745,BEGF,0.001,0.999,1,Anthropogenic
-FR_MTNF_2022004T111745,MTNF,0.001,0.999,1,Anthropogenic
-FR_PLDF_2022003T041502,PLDF,0.989,0.011,0,Natural
-FR_CHLF_2022003T041502,CHLF,0.985,0.015,0,Natural
-FR_AGO_2022003T041502,AGO,0.999,0.001,0,Natural
-FR_BRGF_2022003T041502,BRGF,0.988,0.012,0,Natural
-FR_FRNF_2022003T041502,FRNF,0.985,0.015,0,Natural
-FR_COLF_2022003T041502,COLF,0.986,0.014,0,Natural
-FR_HRSF_2022003T041502,HRSF,0.987,0.013,0,Natural
-FR_GZNF_2022003T041502,GZNF,0.997,0.003,0,Natural
-FR_VERF_2022003T041502,VERF,0.985,0.015,0,Natural
-FR_LBL_2022003T041502,LBL,0.977,0.023,0,Natural
--- a/spectro_demo/.DS_Store
+++ b/spectro_demo/.DS_Store