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platform-demo-scripts/notebooks/Explore igf data.ipynb

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initial commit
2023-07-05 09:58:06 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c65c7056-5b31-42f8-8278-9c9c2b636187",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"from obspy import read\n",
"from matplotlib import pyplot as plt, dates \n",
"\n",
"import glob"
]
},
{
"cell_type": "markdown",
"id": "ea411d66-aaf1-48e0-9d19-ed5560432002",
"metadata": {},
"source": [
"# Explore data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ac416bcb-6e94-49dd-be68-cedef82dcefc",
"metadata": {},
"outputs": [],
"source": [
"data_path=\"../data/igf/\"\n",
"data = pd.read_excel(data_path + \"Catalog_20_21.xlsx\", index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "41064808-0bd0-45ee-9c1e-21e465c823cf",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Datetime</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>Depth</th>\n",
" <th>Mw</th>\n",
" <th>Phases</th>\n",
" <th>mseed_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-01-01 10:09:42.200</td>\n",
" <td>5.582503e+06</td>\n",
" <td>5.702646e+06</td>\n",
" <td>0.7</td>\n",
" <td>2.469231</td>\n",
" <td>Pg BRDW 2020-01-01 10:09:44.400, Sg BRDW 2020-...</td>\n",
" <td>20200101100941.mseed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-01-02 08:03:06.300</td>\n",
" <td>5.577930e+06</td>\n",
" <td>5.705644e+06</td>\n",
" <td>0.7</td>\n",
" <td>1.916667</td>\n",
" <td>Pg BRDW 2020-01-02 08:03:07.118, Sg BRDW 2020-...</td>\n",
" <td>20200102080305.mseed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-01-03 14:36:51.500</td>\n",
" <td>5.582597e+06</td>\n",
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" <td>0.7</td>\n",
" <td>1.916667</td>\n",
" <td>Pg DWOL 2020-01-03 14:36:54.525, Sg DWOL 2020-...</td>\n",
" <td>20200103143651.mseed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-01-04 05:01:01.700</td>\n",
" <td>5.575319e+06</td>\n",
" <td>5.716387e+06</td>\n",
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" <td>Pg DWOL 2020-01-04 05:01:03.089, Sg DWOL 2020-...</td>\n",
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" </tr>\n",
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" <th>4</th>\n",
" <td>2020-01-04 14:52:08.100</td>\n",
" <td>5.577217e+06</td>\n",
" <td>5.705684e+06</td>\n",
" <td>0.7</td>\n",
" <td>1.844444</td>\n",
" <td>Pg BRDW 2020-01-04 14:52:08.488, Sg BRDW 2020-...</td>\n",
" <td>20200104145207.mseed</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Datetime X Y Depth Mw \n",
"0 2020-01-01 10:09:42.200 5.582503e+06 5.702646e+06 0.7 2.469231 \\\n",
"1 2020-01-02 08:03:06.300 5.577930e+06 5.705644e+06 0.7 1.916667 \n",
"2 2020-01-03 14:36:51.500 5.582597e+06 5.701562e+06 0.7 1.916667 \n",
"3 2020-01-04 05:01:01.700 5.575319e+06 5.716387e+06 0.7 1.600000 \n",
"4 2020-01-04 14:52:08.100 5.577217e+06 5.705684e+06 0.7 1.844444 \n",
"\n",
" Phases mseed_name \n",
"0 Pg BRDW 2020-01-01 10:09:44.400, Sg BRDW 2020-... 20200101100941.mseed \n",
"1 Pg BRDW 2020-01-02 08:03:07.118, Sg BRDW 2020-... 20200102080305.mseed \n",
"2 Pg DWOL 2020-01-03 14:36:54.525, Sg DWOL 2020-... 20200103143651.mseed \n",
"3 Pg DWOL 2020-01-04 05:01:03.089, Sg DWOL 2020-... 20200104050102.mseed \n",
"4 Pg BRDW 2020-01-04 14:52:08.488, Sg BRDW 2020-... 20200104145207.mseed "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "668150d2-1a3c-4bbf-9b56-1e47ede6dfd4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2198, 7)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f8cc5794-216b-4105-aa96-42c55af43306",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<DatetimeArray>\n",
"['2020-01-01 10:09:42.200000', '2020-01-02 08:03:06.300000',\n",
" '2020-01-03 14:36:51.500000', '2020-01-04 05:01:01.700000',\n",
" '2020-01-04 14:52:08.100000', '2020-01-04 19:47:26',\n",
" '2020-01-05 06:27:21.300000', '2020-01-05 10:42:54',\n",
" '2020-01-05 20:56:35.700000', '2020-01-06 05:35:10.100000',\n",
" ...\n",
" '2021-10-29 22:13:28.200000', '2021-10-30 09:01:58.100000',\n",
" '2021-10-30 10:55:19.100000', '2021-10-30 20:49:21.200000',\n",
" '2021-10-30 21:01:09.800000', '2021-10-30 22:12:27.100000',\n",
" '2021-10-31 00:03:17.800000', '2021-10-31 01:13:04.100000',\n",
" '2021-10-31 18:12:38.700000', '2021-10-31 19:11:48.200000']\n",
"Length: 2198, dtype: datetime64[ns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.Datetime.unique()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "36d27c72-df99-4b23-962c-6eab122eeef1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1720, 7)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.dropna().shape"
]
},
{
"cell_type": "markdown",
"id": "fae3e69a-a5c5-42c9-9575-da54244e7994",
"metadata": {},
"source": [
"## Check missing data"
]
},
{
"cell_type": "markdown",
"id": "8f7d97da-74c8-40d7-bbae-0776379d39b3",
"metadata": {},
"source": [
"#### Missing mseed_name"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "01e7c1bd-506b-4b3a-9378-b9ce7f91f54c",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Datetime</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>Depth</th>\n",
" <th>Mw</th>\n",
" <th>Phases</th>\n",
" <th>mseed_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2020-01-06 18:46:05.900</td>\n",
" <td>5.583057e+06</td>\n",
" <td>5.701401e+06</td>\n",
" <td>0.700000</td>\n",
" <td>1.880000</td>\n",
" <td>Pg GROD 2020-01-06 18:46:08.904, Sg GROD 2020-...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2020-01-07 19:24:16.900</td>\n",
" <td>5.583143e+06</td>\n",
" <td>5.701039e+06</td>\n",
" <td>0.700000</td>\n",
" <td>1.820000</td>\n",
" <td>Pg JEDR 2020-01-07 19:24:19.289, Sg JEDR 2020-...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2020-01-14 03:58:31.700</td>\n",
" <td>5.582596e+06</td>\n",
" <td>5.701750e+06</td>\n",
" <td>0.700000</td>\n",
" <td>1.850000</td>\n",
" <td>Pg JEDR 2020-01-14 03:58:33.990, Sg JEDR 2020-...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>2020-01-15 09:01:16.700</td>\n",
" <td>5.582595e+06</td>\n",
" <td>5.703723e+06</td>\n",
" <td>0.800000</td>\n",
" <td>2.071429</td>\n",
" <td>Pg GROD 2020-01-15 09:01:19.262, Sg GROD 2020-...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>2020-01-16 04:55:53.600</td>\n",
" <td>5.576011e+06</td>\n",
" <td>5.706832e+06</td>\n",
" <td>0.700000</td>\n",
" <td>1.640000</td>\n",
" <td>Pg MOSK2 2020-01-16 04:55:55.566, Sg MOSK2 202...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2166</th>\n",
" <td>2021-10-25 00:30:29.400</td>\n",
" <td>5.574834e+06</td>\n",
" <td>5.706510e+06</td>\n",
" <td>0.700000</td>\n",
" <td>1.600000</td>\n",
" <td>Pg BRDW 2021-10-25 00:30:30.324, Sg BRDW 2021-...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2173</th>\n",
" <td>2021-10-26 08:05:39.300</td>\n",
" <td>5.575470e+06</td>\n",
" <td>5.715877e+06</td>\n",
" <td>0.900000</td>\n",
" <td>1.366667</td>\n",
" <td>Sg DWOL 2021-10-26 08:05:40.992, Pg DWOL 2021-...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2184</th>\n",
" <td>2021-10-28 15:15:41.600</td>\n",
" <td>5.575855e+06</td>\n",
" <td>5.715674e+06</td>\n",
" <td>0.900000</td>\n",
" <td>1.300000</td>\n",
" <td>Pg DWOL 2021-10-28 15:15:42.729, Sg DWOL 2021-...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2186</th>\n",
" <td>2021-10-28 23:28:26.400</td>\n",
" <td>5.577772e+06</td>\n",
" <td>5.707358e+06</td>\n",
" <td>0.500000</td>\n",
" <td>1.400000</td>\n",
" <td>Pg DABR 2021-10-28 23:28:28.434, Sg DABR 2021-...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2193</th>\n",
" <td>2021-10-30 22:12:27.100</td>\n",
" <td>5.580585e+06</td>\n",
" <td>5.713451e+06</td>\n",
" <td>0.919473</td>\n",
" <td>1.483333</td>\n",
" <td>Pg ZUKW2 2021-10-30 22:12:27.983, Sg ZUKW2 202...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>478 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Datetime X Y Depth Mw \n",
"11 2020-01-06 18:46:05.900 5.583057e+06 5.701401e+06 0.700000 1.880000 \\\n",
"14 2020-01-07 19:24:16.900 5.583143e+06 5.701039e+06 0.700000 1.820000 \n",
"31 2020-01-14 03:58:31.700 5.582596e+06 5.701750e+06 0.700000 1.850000 \n",
"36 2020-01-15 09:01:16.700 5.582595e+06 5.703723e+06 0.800000 2.071429 \n",
"38 2020-01-16 04:55:53.600 5.576011e+06 5.706832e+06 0.700000 1.640000 \n",
"... ... ... ... ... ... \n",
"2166 2021-10-25 00:30:29.400 5.574834e+06 5.706510e+06 0.700000 1.600000 \n",
"2173 2021-10-26 08:05:39.300 5.575470e+06 5.715877e+06 0.900000 1.366667 \n",
"2184 2021-10-28 15:15:41.600 5.575855e+06 5.715674e+06 0.900000 1.300000 \n",
"2186 2021-10-28 23:28:26.400 5.577772e+06 5.707358e+06 0.500000 1.400000 \n",
"2193 2021-10-30 22:12:27.100 5.580585e+06 5.713451e+06 0.919473 1.483333 \n",
"\n",
" Phases mseed_name \n",
"11 Pg GROD 2020-01-06 18:46:08.904, Sg GROD 2020-... NaN \n",
"14 Pg JEDR 2020-01-07 19:24:19.289, Sg JEDR 2020-... NaN \n",
"31 Pg JEDR 2020-01-14 03:58:33.990, Sg JEDR 2020-... NaN \n",
"36 Pg GROD 2020-01-15 09:01:19.262, Sg GROD 2020-... NaN \n",
"38 Pg MOSK2 2020-01-16 04:55:55.566, Sg MOSK2 202... NaN \n",
"... ... ... \n",
"2166 Pg BRDW 2021-10-25 00:30:30.324, Sg BRDW 2021-... NaN \n",
"2173 Sg DWOL 2021-10-26 08:05:40.992, Pg DWOL 2021-... NaN \n",
"2184 Pg DWOL 2021-10-28 15:15:42.729, Sg DWOL 2021-... NaN \n",
"2186 Pg DABR 2021-10-28 23:28:28.434, Sg DABR 2021-... NaN \n",
"2193 Pg ZUKW2 2021-10-30 22:12:27.983, Sg ZUKW2 202... NaN \n",
"\n",
"[478 rows x 7 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[(data.mseed_name.isna())]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "28b1a93f-8342-4eba-86b2-a3724a5c038c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing mseed fname in catalog: 478\n"
]
}
],
"source": [
"print(\"Missing mseed fname in catalog: \", data[(data.mseed_name.isna())].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9bbe4ea7-c86c-4874-8b6d-93578d30e991",
"metadata": {},
"outputs": [],
"source": [
"def extract_data_from_catalog(fname): \n",
" targets, frames = [], []\n",
" data = data = pd.read_excel(fname, index_col=0)\n",
" for row in data: \n",
" _targets, _frames = extract_data_from_event(row.Phases, row.mseed_fname)\n",
" targets.extend(_targets)\n",
" frames.extend(_frames)\n",
" return targets, frames\n",
"\n",
"\n",
"def extract_data_from_event(phases, mseed_fname): \n",
" pass"
]
},
{
"cell_type": "markdown",
"id": "e1f9caf3-caa0-4a87-a450-8025eee85e77",
"metadata": {},
"source": [
"### Calculate stats"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "95691a54-46be-4d46-a779-3d5dfebc0d2e",
"metadata": {},
"outputs": [],
"source": [
"def calculate_stats(phases, mseed_fname, stats={}, path=data_path+\"mseeds\"): \n",
" \n",
" phases = [ann.split(' ') for ann in phases.split(', ')]\n",
" phases = pd.DataFrame(phases, columns = ['phase', 'station', 'date', 'hour'])\n",
" phases.index = pd.DatetimeIndex(phases.date + ' ' + phases.hour, tz= \"UTC\")\n",
"\n",
" \n",
" mseed_path = f\"{path}/mseeds_2020/{mseed_fname}\"\n",
" if not os.path.exists(mseed_path): \n",
" mseed_path = f\"{path}/mseeds_2021/{mseed_fname}\"\n",
"\n",
" mseed_exisits = os.path.exists(mseed_path)\n",
"\n",
" missing_traces_count = 0 # phases.shape[0]/2\n",
" missing_annotations_count = 0\n",
" missing_traces = {}\n",
"\n",
" if mseed_exisits: \n",
"\n",
" stream = read(mseed_path)\n",
" traces_count = len(stream.traces)\n",
" \n",
" mseed_stations = set()\n",
" \n",
" for i, trace in enumerate(stream.traces): \n",
" \n",
" station = trace.stats.station\n",
" mseed_stations.add(station)\n",
" station_annotations = phases[phases.station==station]\n",
" p_timestamps = station_annotations[station_annotations.phase=='Pg'].index\n",
" s_timestamps = station_annotations[station_annotations.phase=='Sg'].index\n",
"\n",
"\n",
" if station not in stats: \n",
" stats[station] = {'p':0, 's':0}\n",
" stats[station]['p'] += len(p_timestamps)\n",
" stats[station]['s'] += len(s_timestamps)\n",
"\n",
"\n",
" annotated_stations = set(phases.station.values)\n",
" stations_with_missing_traces = annotated_stations - mseed_stations\n",
" missing_traces_count = len(stations_with_missing_traces)\n",
" \n",
" stations_with_missing_annotations = mseed_stations - annotated_stations\n",
" missing_annotations_count = len(stations_with_missing_annotations)\n",
" \n",
" missing_data = {\n",
" mseed_path: {\n",
" 'stations_with_missing_traces': stations_with_missing_traces, \n",
" 'stations_with_missing_annotations': stations_with_missing_annotations,\n",
" 'stations_with_available_data': mseed_stations.intersection(annotated_stations)\n",
" }\n",
" }\n",
"\n",
" return stats, mseed_exisits, missing_annotations_count, missing_traces_count, missing_data\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7b4a17f1-65a0-4636-96fd-670ac1fb8505",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stats\n",
"Missing files: 0\n",
"Missing annotations: 12286\n",
"Missing traces: 142\n",
"Missing mseed fname in catalog: 478\n"
]
}
],
"source": [
"stats = {}\n",
"missing_files = []\n",
"missing_traces = 0\n",
"missing_annotations = 0 \n",
"missing_data = {}\n",
"\n",
"for phases, fname in zip(data.Phases, data.mseed_name): \n",
" \n",
" if not pd.isna(fname): \n",
" stats, mseed_exisits, missing_annotations_, missing_traces_, missing_data_ = calculate_stats(phases, fname, stats)\n",
" missing_annotations += missing_annotations_\n",
" missing_traces += missing_traces_\n",
" missing_data.update(missing_data_)\n",
" if not mseed_exisits: \n",
" missing_files.append(fname)\n",
" \n",
"stats = pd.DataFrame(stats)/3 \n",
"missing_data = pd.DataFrame(missing_data).transpose()\n",
"\n",
"print(\"Stats\")\n",
"print(\"Missing files: \", len(missing_files))\n",
"print(\"Missing annotations: \", missing_annotations)\n",
"print(\"Missing traces: \", missing_traces)\n",
"print(\"Missing mseed fname in catalog: \", data[(data.mseed_name.isna())].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "30139ddc-727d-4494-b33b-ec4068e6061d",
"metadata": {},
"outputs": [],
"source": [
"missing_data.stations_with_missing_traces = missing_data.stations_with_missing_traces.apply(lambda x: list(x) if len(x)>0 else None)\n",
"missing_data.stations_with_missing_annotations = missing_data.stations_with_missing_annotations.apply(lambda x: list(x) if len(x)>0 else None)\n",
"missing_data['path'] = missing_data.index\n",
"missing_data.index = missing_data.path.apply(lambda x: os.path.basename(x))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c8ecb508-430c-4c45-800c-285f3b6eef91",
"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>stations_with_missing_traces</th>\n",
" <th>stations_with_missing_annotations</th>\n",
" <th>stations_with_available_data</th>\n",
" <th>path</th>\n",
" </tr>\n",
" <tr>\n",
" <th>path</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20200109224901.mseed</th>\n",
" <td>[RYNR]</td>\n",
" <td>[RZEC, TRBC2, LUBW, TRZS, PPOL, OBIS, GROD, RU...</td>\n",
" <td>{MOSK2, JEDR, DWOL, BRDW, GUZI, NWLU, TRN2, PC...</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200109224901....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20200111165827.mseed</th>\n",
" <td>[TRN2]</td>\n",
" <td>[RZEC, TRBC2, LUBW, TRZS, OBIS, RUDN, LUBZ]</td>\n",
" <td>{MOSK2, JEDR, DWOL, RYNR, BRDW, GUZI, NWLU, PP...</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200111165827....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20200112055037.mseed</th>\n",
" <td>[TRZS]</td>\n",
" <td>[RZEC, TRBC2, MOSK2, LUBW, BRDW, SGOR, GUZI, P...</td>\n",
" <td>{JEDR, DWOL, RYNR, NWLU, RUDN, TRN2, GROD, ZMST}</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200112055037....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20200114011249.mseed</th>\n",
" <td>[PCHB]</td>\n",
" <td>[RZEC, TRBC2, LUBW, DWOL, BRDW, TRZS, SGOR, GU...</td>\n",
" <td>{MOSK2, JEDR, RYNR, PPOL, RUDN, TRN2, GROD, ZMST}</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200114011249....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20200123060736.mseed</th>\n",
" <td>[GROD]</td>\n",
" <td>[RZEC, TRBC2, LUBW, BRDW, OBIS, PCHB, LUBZ]</td>\n",
" <td>{MOSK2, JEDR, RYNR, TRZS, NWLU, GUZI, PPOL, RU...</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200123060736....</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stations_with_missing_traces \n",
"path \n",
"20200109224901.mseed [RYNR] \\\n",
"20200111165827.mseed [TRN2] \n",
"20200112055037.mseed [TRZS] \n",
"20200114011249.mseed [PCHB] \n",
"20200123060736.mseed [GROD] \n",
"\n",
" stations_with_missing_annotations \n",
"path \n",
"20200109224901.mseed [RZEC, TRBC2, LUBW, TRZS, PPOL, OBIS, GROD, RU... \\\n",
"20200111165827.mseed [RZEC, TRBC2, LUBW, TRZS, OBIS, RUDN, LUBZ] \n",
"20200112055037.mseed [RZEC, TRBC2, MOSK2, LUBW, BRDW, SGOR, GUZI, P... \n",
"20200114011249.mseed [RZEC, TRBC2, LUBW, DWOL, BRDW, TRZS, SGOR, GU... \n",
"20200123060736.mseed [RZEC, TRBC2, LUBW, BRDW, OBIS, PCHB, LUBZ] \n",
"\n",
" stations_with_available_data \n",
"path \n",
"20200109224901.mseed {MOSK2, JEDR, DWOL, BRDW, GUZI, NWLU, TRN2, PC... \\\n",
"20200111165827.mseed {MOSK2, JEDR, DWOL, RYNR, BRDW, GUZI, NWLU, PP... \n",
"20200112055037.mseed {JEDR, DWOL, RYNR, NWLU, RUDN, TRN2, GROD, ZMST} \n",
"20200114011249.mseed {MOSK2, JEDR, RYNR, PPOL, RUDN, TRN2, GROD, ZMST} \n",
"20200123060736.mseed {MOSK2, JEDR, RYNR, TRZS, NWLU, GUZI, PPOL, RU... \n",
"\n",
" path \n",
"path \n",
"20200109224901.mseed ../data/igf/mseeds/mseeds_2020/20200109224901.... \n",
"20200111165827.mseed ../data/igf/mseeds/mseeds_2020/20200111165827.... \n",
"20200112055037.mseed ../data/igf/mseeds/mseeds_2020/20200112055037.... \n",
"20200114011249.mseed ../data/igf/mseeds/mseeds_2020/20200114011249.... \n",
"20200123060736.mseed ../data/igf/mseeds/mseeds_2020/20200123060736.... "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_data[~missing_data.stations_with_missing_traces.isna()].head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7c2b99e9-9428-460c-87e9-c1704a701bd8",
"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>stations_with_missing_traces</th>\n",
" <th>stations_with_missing_annotations</th>\n",
" <th>stations_with_available_data</th>\n",
" <th>path</th>\n",
" </tr>\n",
" <tr>\n",
" <th>path</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20200101100941.mseed</th>\n",
" <td>None</td>\n",
" <td>[OBIS, LUBZ]</td>\n",
" <td>{RZEC, TRBC2, MOSK2, JEDR, RYNR, LUBW, BRDW, T...</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200101100941....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20200102080305.mseed</th>\n",
" <td>None</td>\n",
" <td>[RZEC, LUBW, RYNR, TRZS, SGOR, OBIS, TRN2, PCH...</td>\n",
" <td>{TRBC2, MOSK2, JEDR, BRDW, NWLU, GUZI, PPOL, Z...</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200102080305....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20200103143651.mseed</th>\n",
" <td>None</td>\n",
" <td>[RZEC, TRBC2, MOSK2, LUBW, BRDW, TRZS, SGOR, G...</td>\n",
" <td>{JEDR, DWOL, RYNR, TRN2, GROD, RUDN}</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200103143651....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20200104050102.mseed</th>\n",
" <td>None</td>\n",
" <td>[RZEC, TRBC2, LUBW, JEDR, RYNR, BRDW, TRZS, NW...</td>\n",
" <td>{MOSK2, DWOL, GUZI, GROD, ZMST, SGOR}</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200104050102....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20200104145207.mseed</th>\n",
" <td>None</td>\n",
" <td>[RZEC, TRBC2, LUBW, TRZS, NWLU, OBIS, PCHB, GR...</td>\n",
" <td>{MOSK2, JEDR, DWOL, RYNR, BRDW, GUZI, PPOL, TR...</td>\n",
" <td>../data/igf/mseeds/mseeds_2020/20200104145207....</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stations_with_missing_traces \n",
"path \n",
"20200101100941.mseed None \\\n",
"20200102080305.mseed None \n",
"20200103143651.mseed None \n",
"20200104050102.mseed None \n",
"20200104145207.mseed None \n",
"\n",
" stations_with_missing_annotations \n",
"path \n",
"20200101100941.mseed [OBIS, LUBZ] \\\n",
"20200102080305.mseed [RZEC, LUBW, RYNR, TRZS, SGOR, OBIS, TRN2, PCH... \n",
"20200103143651.mseed [RZEC, TRBC2, MOSK2, LUBW, BRDW, TRZS, SGOR, G... \n",
"20200104050102.mseed [RZEC, TRBC2, LUBW, JEDR, RYNR, BRDW, TRZS, NW... \n",
"20200104145207.mseed [RZEC, TRBC2, LUBW, TRZS, NWLU, OBIS, PCHB, GR... \n",
"\n",
" stations_with_available_data \n",
"path \n",
"20200101100941.mseed {RZEC, TRBC2, MOSK2, JEDR, RYNR, LUBW, BRDW, T... \\\n",
"20200102080305.mseed {TRBC2, MOSK2, JEDR, BRDW, NWLU, GUZI, PPOL, Z... \n",
"20200103143651.mseed {JEDR, DWOL, RYNR, TRN2, GROD, RUDN} \n",
"20200104050102.mseed {MOSK2, DWOL, GUZI, GROD, ZMST, SGOR} \n",
"20200104145207.mseed {MOSK2, JEDR, DWOL, RYNR, BRDW, GUZI, PPOL, TR... \n",
"\n",
" path \n",
"path \n",
"20200101100941.mseed ../data/igf/mseeds/mseeds_2020/20200101100941.... \n",
"20200102080305.mseed ../data/igf/mseeds/mseeds_2020/20200102080305.... \n",
"20200103143651.mseed ../data/igf/mseeds/mseeds_2020/20200103143651.... \n",
"20200104050102.mseed ../data/igf/mseeds/mseeds_2020/20200104050102.... \n",
"20200104145207.mseed ../data/igf/mseeds/mseeds_2020/20200104145207.... "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_data[~missing_data.stations_with_missing_annotations.isna()].head()"
]
},
{
"cell_type": "markdown",
"id": "8acde36d-dbd6-4ddc-95fd-085a3dd14be4",
"metadata": {},
"source": [
"### Inspect cases with missing annotations"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "4c7b36d1-441b-427e-9485-900172e086d2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stations with missing annotations: ['OBIS', 'LUBZ']\n"
]
},
{
"data": {
"text/plain": [
"57 Trace(s) in Stream:\n",
"\n",
"PL.BRDW..EHE | 2020-01-01T10:09:36.480000Z - 2020-01-01T10:09:56.480000Z | 100.0 Hz, 2001 samples\n",
"...\n",
"(55 other traces)\n",
"...\n",
"PL.ZMST..EHZ | 2020-01-01T10:09:36.480000Z - 2020-01-01T10:09:56.480000Z | 100.0 Hz, 2001 samples\n",
"\n",
"[Use \"print(Stream.__str__(extended=True))\" to print all Traces]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fname = missing_data[~missing_data.stations_with_missing_annotations.isna()].index[0]\n",
"missing_stations = missing_data.loc[fname].stations_with_missing_annotations\n",
"print(\"Stations with missing annotations: \", missing_stations)\n",
"st = read(missing_data.loc[fname].path)\n",
"st"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "23819d30-bc55-4263-b374-bb6541bc4ac9",
"metadata": {},
"outputs": [],
"source": [
"def plot_traces_from_mseed(phases, mseed_fname, path=data_path+\"mseeds/mseeds_2020\", channel=None): \n",
"\n",
" phases = [ann.split(' ') for ann in phases.split(', ')]\n",
" phases = pd.DataFrame(phases, columns = ['phase', 'station', 'date', 'hour'])\n",
" phases.index = pd.DatetimeIndex(phases.date + ' ' + phases.hour, tz= \"UTC\")\n",
"\n",
" stream = read(f\"{path}/{mseed_fname}\")\n",
" traces_count = len(stream.traces)\n",
" if channel is not None: \n",
" traces_count = int(traces_count/3) \n",
" \n",
" fig, axes = plt.subplots(traces_count, figsize=(20, traces_count*1.5))\n",
" \n",
" for i, trace in enumerate(stream.traces): \n",
" trace_series = pd.Series(trace.data)\n",
" \n",
" if channel is not None: \n",
" idx = int(i/3)\n",
" if trace.stats.channel[-1]!=channel: \n",
" continue\n",
" else: \n",
" idx = i\n",
" \n",
" freq = 1000 / trace.stats.sampling_rate\n",
" trace_series.index = pd.date_range(start=str(trace.stats.starttime), end=str(trace.stats.endtime), freq=f'{freq}ms')\n",
"\n",
" trace_series.plot(title=f\"{trace.stats.station} {trace.stats.channel}\", ax=axes[idx])\n",
"\n",
" station = trace.stats.station\n",
" station_annotations = phases[phases.station==station]\n",
" p_timestamps = station_annotations[station_annotations.phase=='Pg'].index\n",
" s_timestamps = station_annotations[station_annotations.phase=='Sg'].index\n",
"\n",
" if len(p_timestamps) == 0: \n",
" print(\"No P annotations for station\", station, trace.stats.starttime)\n",
" if len(s_timestamps) == 0: \n",
" print(\"No S annotations for station\", station, trace.stats.starttime)\n",
"\n",
" for pt in p_timestamps: \n",
" if pt < trace_series.index[0] or pt > trace_series.index[-1]:\n",
" print(f\"Pg {pt} out of scope for trace {trace}\")\n",
" else: \n",
" axes[idx].axvline(x=pt, color='blue', linestyle='--', label='P')\n",
"\n",
" for st in s_timestamps: \n",
" if st < trace_series.index[0] or st > trace_series.index[-1]:\n",
" print(f\"Sg {st} out of scope for trace {trace}\")\n",
" else:\n",
" axes[idx].axvline(x=st, color='orange', linestyle='--', label='S')\n",
" \n",
" axes[idx].legend(loc='upper left')\n",
"\n",
" plt.savefig(f'{data_path}annotated_traces/{mseed_fname}.pdf')\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "71c4c255-a2ba-42f1-9b63-b349ecb7fb1b",
"metadata": {},
"outputs": [
{
"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",
"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": [
"No P annotations for station LUBZ 2020-01-01T10:09:36.480000Z\n",
"No S annotations for station LUBZ 2020-01-01T10:09:36.480000Z\n",
"No P annotations for station OBIS 2020-01-01T10:09:36.480000Z\n",
"No S annotations for station OBIS 2020-01-01T10:09:36.480000Z\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2000x2850 with 19 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"phases = data[data.mseed_name == fname].Phases[0]\n",
"plot_traces_from_mseed(phases, fname, channel='Z')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4cce54bb-c15b-4e64-826e-be1604c56de5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "4f7e1a21-bded-43db-b787-c765d6dacc0b",
"metadata": {},
"source": [
"## Plot statistics"
]
},
{
"cell_type": "markdown",
"id": "b5f84b79-d088-4a6a-842f-c4f48be95713",
"metadata": {},
"source": [
"### Annotations per station"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "29900509-0657-43cb-bf7c-be81a7a9a696",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'Phases occurences per station'}>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stats.transpose().plot(kind='bar', title = 'Phases occurences per station', figsize=(15,5))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "a6049a83-cbba-42ef-b756-3a83f4511bb9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"18002.0"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.loc['p'].sum()"
]
},
{
"cell_type": "markdown",
"id": "71873d25-1e39-4392-bc85-85beece78dfa",
"metadata": {},
"source": [
"### Missing annotations per station"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "b0f5988a-f6db-4319-8e0a-c780d7106f0c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'Missing annotations per station'}>"
]
},
"execution_count": 20,
"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": [
"missing_station_list = []\n",
"missing_station_list.extend(st[0] if st is not None else None for st in missing_data.stations_with_missing_annotations.values)\n",
"pd.Series(missing_station_list).value_counts().plot(kind='barh', title='Missing annotations per station')"
]
},
{
"cell_type": "markdown",
"id": "a58fa9d0-3078-4bc3-bdde-0b456c2f7273",
"metadata": {},
"source": [
"### Missing traces per station"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "79f22b4f-530b-47a0-9247-310384d7181c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'Missing traces'}>"
]
},
"execution_count": 21,
"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": [
"missing_traces_list = []\n",
"missing_traces_list.extend(st[0] if st is not None else None for st in missing_data.stations_with_missing_traces.values)\n",
"pd.Series(missing_traces_list).value_counts().plot(kind='barh', title='Missing traces')"
]
},
{
"cell_type": "markdown",
"id": "9082b5fd-2789-469c-82aa-29cbbbe654e5",
"metadata": {},
"source": [
"### Annotations over time"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "fec57a0e-6455-47b1-91be-016c20cb8e6e",
"metadata": {},
"outputs": [],
"source": [
"data = pd.merge(data, missing_data, left_on='mseed_name', right_index=True, how='outer')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "68195c50-81fa-495a-86ce-ecfed4a068ab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'Cumulative number of traces with Pg annotations'}, xlabel='Datetime'>"
]
},
"execution_count": 23,
"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": [
"data['Pg_count'] = data.stations_with_available_data.apply(lambda x: len(x) if not pd.isna(x) else 0)\n",
"data['Pg_count_cumsum']= data.Pg_count.cumsum()\n",
"data.index = data.Datetime\n",
"data.Pg_count_cumsum.plot(title=\"Cumulative number of traces with Pg annotations\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "b8b3525e-5133-43fd-bba4-829c7bf3004f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(data.stations_with_missing_annotations.values[0])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "6ae59f8b-4971-42a3-8b04-7a107085f7d0",
"metadata": {},
"outputs": [],
"source": [
"data['missing_annotations'] = data.stations_with_missing_annotations.apply(lambda x: len(x) if type(x)==list else 0)\n",
"data['missing_traces'] = data.stations_with_missing_traces.apply(lambda x: len(x) if type(x)==list else 0)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "f78fb182-6155-4a3c-8ccc-c3595dbf3c7d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"ax = data.groupby([pd.Grouper(key='Datetime', freq='M')])['missing_annotations'].sum().plot(kind='bar', \n",
" title=\"Missing annotations per month\",\n",
" rot=45, \n",
" figsize=(15,5))\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "45d9e44d-24cf-4b29-b78e-74573731c91d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'Missing traces in mseeds over time'}, xlabel='Datetime'>"
]
},
"execution_count": 27,
"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": [
"data.missing_traces.plot(marker='.', linestyle='none', title='Missing traces in mseeds over time')"
]
},
{
"cell_type": "markdown",
"id": "265a514b-e0b6-4d3c-9087-cbe418d7c4a7",
"metadata": {},
"source": [
"# Plot traces and save figs"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "9501545c-abd9-42c5-b515-ccc6479e94ea",
"metadata": {
"scrolled": true
},
"outputs": [
{
"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",
"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",
"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": [
"No P annotations for station LUBZ 2020-01-01T10:09:36.480000Z\n",
"No S annotations for station LUBZ 2020-01-01T10:09:36.480000Z\n",
"No P annotations for station LUBZ 2020-01-01T10:09:36.480000Z\n",
"No S annotations for station LUBZ 2020-01-01T10:09:36.480000Z\n",
"No P annotations for station LUBZ 2020-01-01T10:09:36.480000Z\n",
"No S annotations for station LUBZ 2020-01-01T10:09:36.480000Z\n"
]
},
{
"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",
"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",
"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": [
"No P annotations for station OBIS 2020-01-01T10:09:36.478000Z\n",
"No S annotations for station OBIS 2020-01-01T10:09:36.478000Z\n",
"No P annotations for station OBIS 2020-01-01T10:09:36.476000Z\n",
"No S annotations for station OBIS 2020-01-01T10:09:36.476000Z\n",
"No P annotations for station OBIS 2020-01-01T10:09:36.480000Z\n",
"No S annotations for station OBIS 2020-01-01T10:09:36.480000Z\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2000x8550 with 57 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"for phases, fname in zip(data.Phases, data.mseed_name): \n",
" if not pd.isna(fname): \n",
" plot_traces_from_mseed(phases, fname)\n",
" break\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "a2f222f4-f7d7-4427-bb3a-15af7b75dad0",
"metadata": {},
"outputs": [],
"source": [
"with pd.ExcelWriter(data_path+'catalog with missing data.xlsx') as writer: \n",
" data.to_excel(writer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4763f667-476f-4029-a392-b4d5093840d4",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink