mirror of
https://github.com/Derisis13/SeismStart.git
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feat: finished autoencoder and classifier by LTD
This commit is contained in:
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preprocessed/lunar/data/Autoencoder.ipynb
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preprocessed/lunar/data/Autoencoder.ipynb
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preprocessed/lunar/data/Classifier.ipynb
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preprocessed/lunar/data/Classifier.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "9f49724a-a1cc-4973-8919-68d31c6186f1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"from tensorflow.keras import layers, models\n",
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"from tensorflow.keras.utils import to_categorical\n",
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"import scipy\n",
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"import pandas as pd\n",
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"from obspy import read\n",
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"from datetime import datetime, timedelta\n",
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"import matplotlib.pyplot as plt\n",
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"import os\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "8a233e36-5810-4cfb-b6d1-452acf432793",
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"metadata": {},
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"outputs": [],
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"source": [
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"# three types of fake data for testing\n",
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"\n",
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"# def signal1(A, t):\n",
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"# return A * np.sin(30*t) * np.exp(-t) + np.random.randn(t.shape[0])\n",
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"\n",
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"# def signal2(A, t):\n",
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"# return A * np.sin(30.5*t) * np.exp(-t)+ np.random.randn(t.shape[0])\n",
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"\n",
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"# def signal3(A, t):\n",
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"# return A * np.sin(31*t) * np.exp(-t) + np.random.randn(t.shape[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "f9d9086a-538e-4410-afd3-333093722ff1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# tests\n",
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"A = 2\n",
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"# t = np.linspace(0.0001, 5, 40000)\n",
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"# s1 = signal1(A, t)\n",
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"# s2 = signal2(A, t)\n",
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"# s3 = signal3(A, t)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "d7510935-dd2c-4484-96b5-eefec9837f33",
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"metadata": {},
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"outputs": [],
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"source": [
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"# plot fn for convenience\n",
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"def plot(x, y, title):\n",
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" plt.figure()\n",
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" plt.plot(x, y, linewidth=0.7, c='blue')\n",
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" plt.title(title)\n",
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" plt.savefig(title + '.png')\n",
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" plt.show()\n",
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"\n",
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"# plot(t, s1, 'Example1')\n",
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"# plot(t, s2, 'Example2')\n",
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"# plot(t, s3, 'Example3')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "3cfb129b-0ce8-4790-94ae-cb31fa3e0ec4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# # Number of samples and length of each signal\n",
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"# n_samples = 200\n",
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"# signal_length = 40000\n",
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"\n",
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"# # Initialize arrays\n",
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"# X_data = np.zeros((n_samples, signal_length))\n",
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"# y_labels = np.zeros(n_samples)\n",
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"\n",
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"# # time array\n",
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"# t = np.linspace(0, 1, signal_length)\n",
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"\n",
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"# # Generate samples\n",
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"# for i in range(n_samples):\n",
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"# if i < n_samples // 3:\n",
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"# X_data[i] = signal1(A, t)\n",
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"# y_labels[i] = 0\n",
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"# elif i < 2 * n_samples // 3:\n",
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"# X_data[i] = signal2(A, t)\n",
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"# y_labels[i] = 1\n",
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"# else:\n",
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"# X_data[i] = signal3(A, t)\n",
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"# y_labels[i] = 2\n",
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"\n",
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"# # One-hot encode the labels\n",
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"# y_labels_one_hot = to_categorical(y_labels, num_classes=3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"id": "f09275a3-2673-4d61-9903-8719b9e1b161",
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"metadata": {},
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"outputs": [],
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"source": [
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"test_filename = 'xa.s12.00.mhz.1970-03-25HR00_evid00003_trimmed_7000_sec'\n",
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"\n",
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"data_directory = './'\n",
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"mseed_file = f'{data_directory}{test_filename}.mseed'\n",
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"st = read(mseed_file)\n",
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"st\n",
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"\n",
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"tr = st.traces[0].copy()\n",
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"tr_times = tr.times()\n",
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"tr_data = tr.data\n",
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"\n",
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"# plot(tr_times, tr_data, 'Mseed Example')\n",
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"\n",
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"# print(tr_times.shape)\n",
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"\n",
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"def read_all_mseed_files(data_directory, target_length=None):\n",
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" # List all files in the directory with \".mseed\" extension\n",
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" mseed_files = [f for f in os.listdir(data_directory) if f.endswith('.mseed')]\n",
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" \n",
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" data_matrix = []\n",
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" \n",
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" # Loop through all the mseed files and extract time and data series\n",
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" for filename in mseed_files:\n",
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" st = read(os.path.join(data_directory, filename))\n",
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" tr = st.traces[0].copy() \n",
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" tr_data = tr.data \n",
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" \n",
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" if target_length is None:\n",
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" target_length = len(tr_data) # Set target length to the first trace's length\n",
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" \n",
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" # Pad or trim the data to the target length\n",
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" if len(tr_data) < target_length:\n",
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" # Pad with zeros if shorter\n",
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" tr_data = np.pad(tr_data, (0, target_length - len(tr_data)), mode='constant')\n",
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" else:\n",
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" # Trim if longer\n",
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" tr_data = tr_data[:target_length]\n",
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" \n",
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" data_matrix.append(tr_data)\n",
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" \n",
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" # Convert the list to a numpy matrix\n",
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" data_matrix = np.array(data_matrix)\n",
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" \n",
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" return data_matrix\n",
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"\n",
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"X_data = read_all_mseed_files(data_directory, 46376)\n",
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"\n",
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"# plot(tr_times, data[3], 'Example 3')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"id": "47c0ecea-e0fa-4ebf-99db-f6c8e77a72e0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1., 0., 0.])"
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]
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},
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"execution_count": 48,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"from sklearn.preprocessing import OneHotEncoder\n",
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"\n",
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"# read csv\n",
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"df = pd.read_csv('catalog.csv', header=None, names=['data'], skiprows=1)\n",
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"\n",
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"# split cells\n",
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"df['label'] = df['data'].apply(lambda x: x.split(',')[-1].strip())\n",
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"\n",
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"# Step 3: One-hot encode the labels\n",
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"encoder = OneHotEncoder(sparse_output=False)\n",
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"y_label = encoder.fit_transform(df['label'].values.reshape(-1, 1))\n",
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"\n",
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"\n",
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"\n",
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"y_label[4]\n",
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"\n",
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"# one-hot format: [deep, impact, shallow]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"id": "092a184e-a79f-462a-8017-741471a05ae9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_2\"</span>\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1mModel: \"functional_2\"\u001b[0m\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ input_layer_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46376</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">11,872,512</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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"</pre>\n"
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],
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"text/plain": [
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46376\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m11,872,512\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m32,896\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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||||
"│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │\n",
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||||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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||||
"│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m195\u001b[0m │\n",
|
||||
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
|
||||
]
|
||||
},
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">11,913,859</span> (45.45 MB)\n",
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"</pre>\n"
|
||||
],
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"text/plain": [
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"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m11,913,859\u001b[0m (45.45 MB)\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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{
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">11,913,859</span> (45.45 MB)\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m11,913,859\u001b[0m (45.45 MB)\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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||||
"input_shape = (46376,)\n",
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"\n",
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"# Build the classifier model\n",
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"def build_classifier(input_shape):\n",
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" inputs = layers.Input(shape=input_shape)\n",
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" x = layers.Dense(256, activation='relu')(inputs)\n",
|
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" x = layers.Dense(128, activation='relu')(x)\n",
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" x = layers.Dense(64, activation='relu')(x)\n",
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||||
" \n",
|
||||
" # Output layer (one hot encoding)\n",
|
||||
" outputs = layers.Dense(3, activation='softmax')(x)\n",
|
||||
" \n",
|
||||
" model = models.Model(inputs, outputs)\n",
|
||||
" return model\n",
|
||||
"\n",
|
||||
"classifier = build_classifier(input_shape)\n",
|
||||
"\n",
|
||||
"# Compile the model\n",
|
||||
"classifier.compile(optimizer='adam', \n",
|
||||
" loss='categorical_crossentropy', \n",
|
||||
" metrics=['CategoricalAccuracy'])\n",
|
||||
"\n",
|
||||
"# Display model\n",
|
||||
"classifier.summary()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"id": "ec34ffe0-d4ed-484d-b82a-c3270083f082",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 374ms/step - CategoricalAccuracy: 0.3743 - loss: 1.0963 - val_CategoricalAccuracy: 0.8750 - val_loss: 1.0815\n",
|
||||
"Epoch 2/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - CategoricalAccuracy: 0.8576 - loss: 1.0794 - val_CategoricalAccuracy: 0.8750 - val_loss: 1.0604\n",
|
||||
"Epoch 3/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - CategoricalAccuracy: 0.8160 - loss: 1.0603 - val_CategoricalAccuracy: 0.8750 - val_loss: 1.0354\n",
|
||||
"Epoch 4/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - CategoricalAccuracy: 0.8576 - loss: 1.0334 - val_CategoricalAccuracy: 0.8750 - val_loss: 1.0057\n",
|
||||
"Epoch 5/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - CategoricalAccuracy: 0.8264 - loss: 1.0080 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.9703\n",
|
||||
"Epoch 6/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - CategoricalAccuracy: 0.8264 - loss: 0.9747 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.9285\n",
|
||||
"Epoch 7/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - CategoricalAccuracy: 0.8264 - loss: 0.9361 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.8798\n",
|
||||
"Epoch 8/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - CategoricalAccuracy: 0.8368 - loss: 0.8870 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.8243\n",
|
||||
"Epoch 9/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - CategoricalAccuracy: 0.8368 - loss: 0.8350 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.7626\n",
|
||||
"Epoch 10/10\n",
|
||||
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - CategoricalAccuracy: 0.8368 - loss: 0.7783 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.6969\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<keras.src.callbacks.history.History at 0x179aad80c50>"
|
||||
]
|
||||
},
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# training\n",
|
||||
"classifier.fit(X_data, y_label, epochs=10, batch_size=32, validation_split=0.2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"id": "2bd36515-2777-4d49-82ec-b50f1e0904a0",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
|
||||
"[[0.23078983 0.55988955 0.20932065]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# test\n",
|
||||
"\n",
|
||||
"test = X_data[8]\n",
|
||||
"test = test.reshape(1, -1)\n",
|
||||
"\n",
|
||||
"# Now call predict\n",
|
||||
"prediction = classifier.predict(test)\n",
|
||||
"\n",
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "931bb66b-7171-4630-9753-d9ff7fa3cb78",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "077d77a2-c3bc-4606-b4bb-f420ff123817",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "711329a8-0b67-4afe-b455-3f8a296a622e",
|
||||
"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": "3.12.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
77
preprocessed/lunar/data/catalog.csv
Normal file
77
preprocessed/lunar/data/catalog.csv
Normal file
@@ -0,0 +1,77 @@
|
||||
filename,time_abs(%Y-%m-%dT%H:%M:%S.%f),time_rel(sec),evid,mq_type
|
||||
xa.s12.00.mhz.1970-01-19HR00_evid00002,1970-01-19T20:25:00.000000,73500.0,evid00002,impact_mq
|
||||
xa.s12.00.mhz.1970-03-25HR00_evid00003,1970-03-25T03:32:00.000000,12720.0,evid00003,impact_mq
|
||||
xa.s12.00.mhz.1970-03-26HR00_evid00004,1970-03-26T20:17:00.000000,73020.0,evid00004,impact_mq
|
||||
xa.s12.00.mhz.1970-04-25HR00_evid00006,1970-04-25T01:14:00.000000,4440.0,evid00006,impact_mq
|
||||
xa.s12.00.mhz.1970-04-26HR00_evid00007,1970-04-26T14:29:00.000000,52140.0,evid00007,deep_mq
|
||||
xa.s12.00.mhz.1970-06-15HR00_evid00008,1970-06-15T19:00:00.000000,68400.0,evid00008,impact_mq
|
||||
xa.s12.00.mhz.1970-06-26HR00_evid00009,1970-06-26T20:01:00.000000,72060.0,evid00009,impact_mq
|
||||
xa.s12.00.mhz.1970-07-20HR00_evid00010,1970-07-20T05:06:00.000000,18360.0,evid00010,impact_mq
|
||||
xa.s12.00.mhz.1970-07-20HR00_evid00011,1970-07-20T11:44:00.000000,42240.0,evid00011,deep_mq
|
||||
xa.s12.00.mhz.1970-09-26HR00_evid00013,1970-09-26T19:57:00.000000,71820.0,evid00013,impact_mq
|
||||
xa.s12.00.mhz.1970-10-24HR00_evid00014,1970-10-24T11:31:00.000000,41460.0,evid00014,impact_mq
|
||||
xa.s12.00.mhz.1970-11-12HR00_evid00015,1970-11-12T12:50:00.000000,46200.0,evid00015,impact_mq
|
||||
xa.s12.00.mhz.1970-12-11HR00_evid00017,1970-12-11T07:22:00.000000,26520.0,evid00017,impact_mq
|
||||
xa.s12.00.mhz.1970-12-27HR00_evid00019,1970-12-27T20:34:00.000000,74040.0,evid00019,deep_mq
|
||||
xa.s12.00.mhz.1970-12-31HR00_evid00021,1970-12-31T15:41:00.000000,56460.0,evid00021,deep_mq
|
||||
xa.s12.00.mhz.1971-01-15HR00_evid00022,1971-01-15T12:40:00.000000,45600.0,evid00022,impact_mq
|
||||
xa.s12.00.mhz.1971-01-28HR00_evid00023,1971-01-28T14:59:00.000000,53940.0,evid00023,deep_mq
|
||||
xa.s12.00.mhz.1971-01-29HR00_evid00024,1971-01-29T18:21:00.000000,66060.0,evid00024,impact_mq
|
||||
xa.s12.00.mhz.1971-02-09HR00_evid00026,1971-02-09T03:42:00.000000,13320.0,evid00026,impact_mq
|
||||
xa.s12.00.mhz.1971-03-25HR00_evid00028,1971-03-25T15:18:00.000000,55080.0,evid00028,impact_mq
|
||||
xa.s12.00.mhz.1971-04-13HR00_evid00029,1971-04-13T12:55:00.000000,46500.0,evid00029,impact_mq
|
||||
xa.s12.00.mhz.1971-04-17HR00_evid00030,1971-04-17T07:04:00.000000,25440.0,evid00030,shallow_mq
|
||||
xa.s12.00.mhz.1971-05-12HR00_evid00031,1971-05-12T08:05:00.000000,29100.0,evid00031,impact_mq
|
||||
xa.s12.00.mhz.1971-05-12HR00_evid00032,1971-05-12T09:45:00.000000,35100.0,evid00032,impact_mq
|
||||
xa.s12.00.mhz.1971-05-13HR00_evid00033,1971-05-13T03:00:00.000000,10800.0,evid00033,impact_mq
|
||||
xa.s12.00.mhz.1971-05-23HR00_evid00034,1971-05-23T22:20:00.000000,80400.0,evid00034,impact_mq
|
||||
xa.s12.00.mhz.1971-06-12HR00_evid00035,1971-06-12T10:51:00.000000,39060.0,evid00035,impact_mq
|
||||
xa.s12.00.mhz.1971-09-25HR00_evid00042,1971-09-25T08:57:00.000000,32220.0,evid00042,impact_mq
|
||||
xa.s12.00.mhz.1971-10-18HR00_evid00043,1971-10-18T03:19:00.000000,11940.0,evid00043,impact_mq
|
||||
xa.s12.00.mhz.1971-10-20HR00_evid00044,1971-10-20T18:08:00.000000,65280.0,evid00044,impact_mq
|
||||
xa.s12.00.mhz.1971-10-31HR00_evid00045,1971-10-31T05:30:00.000000,19800.0,evid00045,impact_mq
|
||||
xa.s12.00.mhz.1971-11-14HR00_evid00046,1971-11-14T04:17:00.000000,15420.0,evid00046,impact_mq
|
||||
xa.s12.00.mhz.1972-01-04HR00_evid00049,1972-01-04T06:35:00.000000,23700.0,evid00049,impact_mq
|
||||
xa.s12.00.mhz.1972-03-12HR00_evid00052,1972-03-12T18:00:00.000000,64800.0,evid00052,impact_mq
|
||||
xa.s12.00.mhz.1972-05-11HR00_evid00055,1972-05-11T13:35:00.000000,48900.0,evid00055,impact_mq
|
||||
xa.s12.00.mhz.1972-06-16HR00_evid00060,1972-06-16T16:11:00.000000,58260.0,evid00060,impact_mq
|
||||
xa.s12.00.mhz.1972-07-17HR00_evid00067,1972-07-17T07:50:00.000000,28200.0,evid00067,impact_mq
|
||||
xa.s12.00.mhz.1972-07-17HR00_evid00068,1972-07-17T21:56:00.000000,78960.0,evid00068,impact_mq
|
||||
xa.s12.00.mhz.1972-07-28HR00_evid00070,1972-07-28T02:23:00.000000,8580.0,evid00070,impact_mq
|
||||
xa.s12.00.mhz.1972-07-31HR00_evid00071,1972-07-31T18:08:00.000000,65280.0,evid00071,impact_mq
|
||||
xa.s12.00.mhz.1972-12-02HR00_evid00083,1972-12-02T07:58:00.000000,28680.0,evid00083,impact_mq
|
||||
xa.s12.00.mhz.1972-12-03HR00_evid00084,1972-12-03T02:38:00.000000,9480.0,evid00084,impact_mq
|
||||
xa.s12.00.mhz.1973-01-18HR00_evid00088,1973-01-18T23:01:00.000000,82860.0,evid00088,impact_mq
|
||||
xa.s12.00.mhz.1973-01-31HR00_evid00091,1973-01-31T00:44:00.000000,2640.0,evid00091,impact_mq
|
||||
xa.s12.00.mhz.1973-03-01HR00_evid00093,1973-03-01T07:15:00.000000,26100.0,evid00093,deep_mq
|
||||
xa.s12.00.mhz.1973-03-13HR00_evid00094,1973-03-13T08:01:00.000000,28860.0,evid00094,shallow_mq
|
||||
xa.s12.00.mhz.1973-03-24HR00_evid00097,1973-03-24T19:23:00.000000,69780.0,evid00097,impact_mq
|
||||
xa.s12.00.mhz.1973-05-14HR00_evid00104,1973-05-14T00:44:00.000000,2640.0,evid00104,impact_mq
|
||||
xa.s12.00.mhz.1973-06-05HR00_evid00107,1973-06-05T02:38:00.000000,9480.0,evid00107,impact_mq
|
||||
xa.s12.00.mhz.1973-06-05HR00_evid00108,1973-06-05T11:10:00.000000,40200.0,evid00108,deep_mq
|
||||
xa.s12.00.mhz.1973-06-18HR00_evid00109,1973-06-18T01:42:00.000000,6120.0,evid00109,impact_mq
|
||||
xa.s12.00.mhz.1973-06-27HR00_evid00112,1973-06-27T18:36:00.000000,66960.0,evid00112,impact_mq
|
||||
xa.s12.00.mhz.1973-07-03HR00_evid00113,1973-07-03T18:15:00.000000,65700.0,evid00113,impact_mq
|
||||
xa.s12.00.mhz.1973-07-04HR00_evid00114,1973-07-04T02:46:00.000000,9960.0,evid00114,impact_mq
|
||||
xa.s12.00.mhz.1973-07-20HR00_evid00117,1973-07-20T19:02:00.000000,68520.0,evid00117,deep_mq
|
||||
xa.s12.00.mhz.1973-07-28HR00_evid00120,1973-07-28T00:52:00.000000,3120.0,evid00120,impact_mq
|
||||
xa.s12.00.mhz.1973-07-29HR00_evid00121,1973-07-29T23:31:00.000000,84660.0,evid00121,impact_mq
|
||||
xa.s12.00.mhz.1973-08-21HR00_evid00127,1973-08-21T12:17:00.000000,44220.0,evid00127,impact_mq
|
||||
xa.s12.00.mhz.1974-01-10HR00_evid00136,1974-01-10T23:21:00.000000,84060.0,evid00136,impact_mq
|
||||
xa.s12.00.mhz.1974-02-07HR00_evid00137,1974-02-07T06:21:00.000000,22860.0,evid00137,impact_mq
|
||||
xa.s12.00.mhz.1974-02-12HR00_evid00138,1974-02-12T22:30:00.000000,81000.0,evid00138,impact_mq
|
||||
xa.s12.00.mhz.1974-03-25HR00_evid00140,1974-03-25T16:58:00.000000,61080.0,evid00140,impact_mq
|
||||
xa.s12.00.mhz.1974-04-08HR00_evid00141,1974-04-08T08:40:00.000000,31200.0,evid00141,impact_mq
|
||||
xa.s12.00.mhz.1974-04-19HR00_evid00142,1974-04-19T18:34:00.000000,66840.0,evid00142,impact_mq
|
||||
xa.s12.00.mhz.1974-04-26HR00_evid00144,1974-04-26T09:18:00.000000,33480.0,evid00144,deep_mq
|
||||
xa.s12.00.mhz.1974-04-27HR00_evid00145,1974-04-27T14:18:00.000000,51480.0,evid00145,impact_mq
|
||||
xa.s12.00.mhz.1974-06-25HR00_evid00149,1974-06-25T00:23:00.000000,1380.0,evid00149,impact_mq
|
||||
xa.s12.00.mhz.1974-07-06HR00_evid00150,1974-07-06T02:57:00.000000,10620.0,evid00150,impact_mq
|
||||
xa.s12.00.mhz.1974-07-06HR00_evid00151,1974-07-06T14:14:00.000000,51240.0,evid00151,impact_mq
|
||||
xa.s12.00.mhz.1974-07-11HR00_evid00152,1974-07-11T00:52:00.000000,3120.0,evid00152,shallow_mq
|
||||
xa.s12.00.mhz.1974-07-17HR00_evid00153,1974-07-17T12:05:00.000000,43500.0,evid00153,impact_mq
|
||||
xa.s12.00.mhz.1974-10-14HR00_evid00156,1974-10-14T17:43:00.000000,63780.0,evid00156,impact_mq
|
||||
xa.s12.00.mhz.1975-04-12HR00_evid00191,1975-04-12T18:15:00.000000,65700.0,evid00191,impact_mq
|
||||
xa.s12.00.mhz.1975-05-04HR00_evid00192,1975-05-04T10:05:00.000000,36300.0,evid00192,impact_mq
|
||||
xa.s12.00.mhz.1975-06-24HR00_evid00196,1975-06-24T16:03:00.000000,57780.0,evid00196,impact_mq
|
||||
xa.s12.00.mhz.1975-06-26HR00_evid00198,1975-06-26T03:24:00.000000,12240.0,evid00198,impact_mq
|
||||
|
Reference in New Issue
Block a user