1793 lines
576 KiB
Text
1793 lines
576 KiB
Text
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1a1ebc14",
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"metadata": {},
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"source": [
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"#Cosmic Ray and Earthquake analysis\n",
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"Last updated 21/9/21 J. Devine\n",
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"\n",
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"##This is what I was planning to do - I didn't get very far.\n",
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"\n",
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"Stage 1: Collect the data\n",
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"datasets - nmdb data (all stations, corrected rates) (done!)\n",
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"USGS Earthquake db (done!)\n",
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"Sunspot data - cross ref with cosmics and earthquakes.\n",
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"Weather data - nearest neighbour for each NMDB location (not done - would be nice in the future)\n",
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"\n",
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"\n",
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"Stage 2: Back-calc the pressure correction - not done!\n",
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" a - cross ref. air pressure with nearest neighbour\n",
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" b - compare raw and corrected counts\n",
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" c - compare delta with pressure data sets\n",
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" d - compute correction constant\n",
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" e - compute pressure /cosmic rate correlation\n",
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"\n",
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"Then repeat for sunspot data. sunspot correlation with cosmics (expected -ve and strong)\n",
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"\n",
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"Stage 3: Earthquake data\n",
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"structure 10 year data (2010-2020), inc. 6 hourly averages of seismic energy. \n",
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"plot all earthquake locations (lat/long) to see fault lines\n",
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"Find the nearest nmdb sensor for each (actually find nearest earthquakes for each nmdb..).\n",
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"Compute for each earthquake the distances to NMDB sites (triangulation)\n",
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"\n",
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"\n",
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"Stage 4:\n",
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"Windows. \n",
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"Window variables - averages from 1m to 4 weeks (compute for cosmic and seismic from raw)\n",
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"Window variable - W\n",
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"Offset variable - look at all offsets from 1m to 365 days (+ and -)\n",
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"In each case perform a regression across the dataset and select for highest correlations.\n",
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"\n",
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"\n",
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"Stage 5: Random variable\n",
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"Pick a random variable from the weather data and do a null analysis.\n",
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"\n",
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"##What I actually did (so far)\n",
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"Import data from NMDB monitors, for basically all the sites, all date ranges and build arrays (pandas), and rescale per period.\n",
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"Import data from USGS and compute average seismic activity per period\n",
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"Import sunspot data and compute average sunsport data per period\n",
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"\n",
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"Correlation between USGS and sunspot data, with p values, across a range of offsets up to +/-10k days- interesting patterns!\n",
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"\n",
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"##What is left to do to make this useful\n",
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"Wrangle the NMDB data properly to do a similar variable offset calc with USGS data. \n",
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"If the correlations and p values are reasonable at specific offsets then - \n",
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"Compare this analysis with c(P,t0,d,m,𝚫t) = A*B = (Sm(ti+𝚫t)/M(Sm(ti+𝚫t))-1))*(|𝚫ncr(ti,i-1)|/M(|𝚫ncr(ti,i-1)|)-1) \n",
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"Build a PDF for agreement etc.\n",
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"\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": 4,
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"id": "0beec68a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import argparse, csv, urllib.request, time\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from pathlib import Path\n",
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"from datetime import datetime\n",
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"from pandas.tseries.offsets import DateOffset\n",
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"import matplotlib.pyplot as plot"
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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": 5,
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"id": "587e52ff",
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"metadata": {},
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"outputs": [],
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"source": [
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"from scipy.stats import kendalltau, pearsonr, spearmanr\n",
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"\n",
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"def kendall_pval(x,y):\n",
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" return kendalltau(x,y)[1]\n",
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" \n",
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"def pearsonr_pval(x,y):\n",
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" return pearsonr(x,y)[1]\n",
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" \n",
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"def spearmanr_pval(x,y):\n",
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" return spearmanr(x,y)[1]"
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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": 6,
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"id": "a487bffb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3.9.5\n"
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]
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}
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],
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"source": [
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"from platform import python_version\n",
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"print(python_version())"
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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": 7,
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"id": "666c0823",
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"metadata": {},
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"outputs": [],
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"source": [
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"def log_avg(spl_arraylike):\n",
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" return 10*np.log10(np.mean(np.power(10, spl_arraylike/10)))"
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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": 8,
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"id": "0e58cadf",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Start with the interval 1D\n"
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]
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}
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],
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"source": [
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"average_interval = \"1D\"\n",
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"print (\"Start with the interval \", average_interval)"
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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": 9,
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"id": "3c6f0960",
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"metadata": {},
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"outputs": [],
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"source": [
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"station_names = [\n",
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" \"AATA\",\"AATB\",\"APTY\",\"ARNM\",\"ATHN\",\"BKSN\",\n",
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" \"CALM\",\"DJON\",\"DOMB\",\"DOMC\",\"DRBS\",\"FSMT\",\n",
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" \"HRMS\",\"INVK\",\"IRK2\",\"IRK3\",\"IRKT\",\"JBGO\",\n",
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" \"JUNG\",\"JUNG1\",\"KERG\",\"KIEL\",\"KIEL2\",\"LMKS\",\n",
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" \"MCRL\",\"MGDN\",\"MOSC\",\"MRNY\",\"MXCO\",\"NAIN\",\n",
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" \"NANM\",\"NEWK\",\"NRLK\",\"OULU\",\"PSNM\",\"PTFM\",\n",
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" \"PWNK\",\"ROME\",\"SNAE\",\"SOPB\",\"SOPO\",\"TERA\",\n",
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" \"THUL\",\"TSMB\"\n",
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"]"
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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": 10,
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"id": "d2cf6c10",
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"metadata": {},
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"outputs": [],
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"source": [
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"start_date = [ \"1\", \"1\", \"1960\", \"0\", \"0\"]\n",
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"end_date = [\"31\", \"12\", \"2019\", \"23\", \"59\"]"
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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": "fd0c14a5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"AATAfully completed\n",
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"AATBfully completed\n",
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"APTYfully completed\n",
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"ARNMfully completed\n",
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"ATHNfully completed\n",
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"BKSNfully completed\n",
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"CALMfully completed\n",
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"DJONfully completed\n",
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"DOMBfully completed\n",
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"DOMCfully completed\n",
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"DRBSfully completed\n",
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"FSMTfully completed\n",
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"HRMSfully completed\n",
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"INVKfully completed\n",
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"http://nest.nmdb.eu/draw_graph.php?wget=1&stations[]=IRK2&tabchoice=1h&dtype=corr_for_pressure&output=ascii&date_choice=bydate&last_label=days_label&tresolution=60&force=0&start_day=1&start_month=1&start_year=2015&start_hour=0&start_min=0&end_day=31&end_month=12&end_year=2015&end_hour=23&end_min=59&yunits=0\n",
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"An exception occurred with IRK22015.csv\n",
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"IRK2fully completed\n",
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"http://nest.nmdb.eu/draw_graph.php?wget=1&stations[]=IRK3&tabchoice=1h&dtype=corr_for_pressure&output=ascii&date_choice=bydate&last_label=days_label&tresolution=60&force=0&start_day=1&start_month=1&start_year=2015&start_hour=0&start_min=0&end_day=31&end_month=12&end_year=2015&end_hour=23&end_min=59&yunits=0\n",
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"An exception occurred with IRK32015.csv\n",
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"IRK3fully completed\n",
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"IRKTfully completed\n",
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"JBGOfully completed\n",
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"JUNGfully completed\n",
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"JUNG1fully completed\n",
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"KERGfully completed\n",
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"KIELfully completed\n",
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"KIEL2fully completed\n",
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"LMKSfully completed\n",
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"MCRLfully completed\n",
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"MGDNfully completed\n",
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"MOSCfully completed\n",
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"MRNYfully completed\n",
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"MXCOfully completed\n",
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"NAINfully completed\n",
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"NANMfully completed\n",
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"NEWKfully completed\n",
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"http://nest.nmdb.eu/draw_graph.php?wget=1&stations[]=NRLK&tabchoice=1h&dtype=corr_for_pressure&output=ascii&date_choice=bydate&last_label=days_label&tresolution=60&force=0&start_day=1&start_month=1&start_year=2015&start_hour=0&start_min=0&end_day=31&end_month=12&end_year=2015&end_hour=23&end_min=59&yunits=0\n",
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"An exception occurred with NRLK2015.csv\n",
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"NRLKfully completed\n",
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"OULUfully completed\n",
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"PSNMfully completed\n",
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"PTFMfully completed\n",
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"PWNKfully completed\n",
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"ROMEfully completed\n",
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"SNAEfully completed\n",
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"SOPBfully completed\n",
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"SOPOfully completed\n",
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"TERAfully completed\n",
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"THULfully completed\n",
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"TSMBfully completed\n",
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"finished\n"
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]
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}
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],
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"source": [
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"# load all the stations\n",
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"for current_station in station_names:\n",
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" for i in range(int(start_date[2]), int(end_date[2])+1):\n",
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" station_file = Path(current_station+str(i)+'.csv')\n",
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" if not station_file.exists():\n",
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" URL = \"http://nest.nmdb.eu/draw_graph.php?wget=1&stations[]=\"+current_station+\"&tabchoice=1h&dtype=corr_for_pressure&output=ascii&date_choice=bydate&last_label=days_label&tresolution=60&force=0&start_day=\"+start_date[0]+\"&start_month=\"+start_date[1]+\"&start_year=\"+str(i)+\"&start_hour=\"+start_date[3]+\"&start_min=\"+start_date[4]+\"&end_day=\"+end_date[0]+\"&end_month=\"+end_date[1]+\"&end_year=\"+str(i)+\"&end_hour=\"+end_date[3]+\"&end_min=\"+end_date[4]+\"&yunits=0\"\n",
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" print(URL)\n",
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" try:\n",
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" req = urllib.request.Request(URL)\n",
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" #req.add_header('User-Agent', 'urllib/0.1')\n",
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" with urllib.request.urlopen(req) as f:\n",
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" content = f.readlines()\n",
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" fromhere = False\n",
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" content_p = \"\"\n",
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" for line in content:\n",
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" if \"start_date_time\" in line.decode(\"utf-8\", \"strict\"):\n",
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" fromhere = True\n",
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" if fromhere:\n",
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" content_p = content_p+ line.decode()\n",
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" with station_file.open('wt') as fh:\n",
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" fh.write(content_p)\n",
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" fh.close\n",
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" time.sleep(1)\n",
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" #print(current_station+'completed ' + str(i))\n",
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" except:\n",
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" print(\"An exception occurred with \"+str(station_file))\n",
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" #time.sleep(6)\n",
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" print(current_station+'fully completed')\n",
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"print(\"finished\")\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": 12,
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"id": "2db1f566",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"An exception occurred, IRK22015.csv\n",
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"An exception occurred, IRK32015.csv\n",
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"An exception occurred, NRLK2015.csv\n",
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"finished\n"
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]
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}
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],
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"source": [
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"# Populate data structures\n",
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"start_date = [ \"1\", \"1\", \"1960\", \"0\", \"0\"]\n",
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"end_date = [\"31\", \"12\", \"2019\", \"23\", \"59\"]\n",
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"combineddata = []\n",
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"for current_station in station_names:\n",
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" listofcosmics = []\n",
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" for i in range(int(start_date[2]), int(end_date[2])+1):\n",
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" station_file = Path(current_station+str(i)+'.csv')\n",
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" try:\n",
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" readframe = (pd.read_csv(station_file,sep=';',skiprows=1,names = ['start_date_time', current_station],parse_dates=['start_date_time'], index_col=['start_date_time']))\n",
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" #print(\"Opened \"+str(station_file))\n",
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" except:\n",
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" print(\"An exception occurred, \"+str(station_file))\n",
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|
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" listofcosmics.append(readframe)\n",
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" listofcosmics[0] = pd.merge(listofcosmics[0],listofcosmics[1], how='outer', left_index=True, right_index=True)\n",
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" listofcosmics[0] = listofcosmics[0].fillna(0)\n",
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" listofcosmics[0][current_station] = listofcosmics[0][str(current_station+\"_x\")] + listofcosmics[0][str(current_station+\"_y\")]\n",
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" listofcosmics[0] = listofcosmics[0].drop(columns=[str(current_station+'_x'), str(current_station+'_y')])\n",
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" howmanyitems = len(listofcosmics)\n",
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" for i in range(2, howmanyitems):\n",
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" listofcosmics[0] = pd.merge(listofcosmics[0],listofcosmics[i], how='outer', left_index=True, right_index=True)\n",
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" listofcosmics[0] = listofcosmics[0].fillna(0)\n",
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|
|
" listofcosmics[0][current_station] = listofcosmics[0][str(current_station+\"_x\")] + listofcosmics[0][str(current_station+\"_y\")]\n",
|
||
|
|
" listofcosmics[0] = listofcosmics[0].drop(columns=[str(current_station+\"_x\"), str(current_station+\"_y\")])\n",
|
||
|
|
" combineddata.append(listofcosmics[0])\n",
|
||
|
|
"print(\"finished\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"id": "dab6b38c",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"['AATA', 'AATB', 'APTY', 'ARNM', 'ATHN', 'BKSN', 'CALM', 'DJON', 'DOMB', 'DOMC', 'DRBS', 'FSMT', 'HRMS', 'INVK', 'IRK2', 'IRK3', 'IRKT', 'JBGO', 'JUNG', 'JUNG1', 'KERG', 'KIEL', 'KIEL2', 'LMKS', 'MCRL', 'MGDN', 'MOSC', 'MRNY', 'MXCO', 'NAIN', 'NANM', 'NEWK', 'NRLK', 'OULU', 'PSNM', 'PTFM', 'PWNK', 'ROME', 'SNAE', 'SOPB', 'SOPO', 'TERA', 'THUL', 'TSMB']\n",
|
||
|
|
" AATA\n",
|
||
|
|
"start_date_time \n",
|
||
|
|
"2019-01-04 06:59:00 3775.33\n",
|
||
|
|
"2019-01-04 07:59:00 3781.68\n",
|
||
|
|
"2019-01-04 08:59:00 3773.55\n",
|
||
|
|
"2019-01-04 09:59:00 3831.31\n",
|
||
|
|
"2019-01-04 10:59:00 3784.70\n",
|
||
|
|
"... ...\n",
|
||
|
|
"2019-12-31 19:59:00 3756.13\n",
|
||
|
|
"2019-12-31 20:59:00 3774.63\n",
|
||
|
|
"2019-12-31 21:59:00 3747.15\n",
|
||
|
|
"2019-12-31 22:59:00 3781.48\n",
|
||
|
|
"2019-12-31 23:59:00 3761.70\n",
|
||
|
|
"\n",
|
||
|
|
"[8105 rows x 1 columns]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#check that thte sequences match\n",
|
||
|
|
"print(station_names)\n",
|
||
|
|
"print(combineddata[0])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"id": "e0a38083",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" TSMB\n",
|
||
|
|
"start_date_time \n",
|
||
|
|
"1976-12-04 00:00:00 330.556\n",
|
||
|
|
"1976-12-04 01:00:00 330.972\n",
|
||
|
|
"1976-12-04 02:00:00 330.694\n",
|
||
|
|
"1976-12-04 03:00:00 331.139\n",
|
||
|
|
"1976-12-04 04:00:00 330.500\n",
|
||
|
|
"... ...\n",
|
||
|
|
"2019-11-01 19:00:00 332.000\n",
|
||
|
|
"2019-11-01 20:00:00 332.139\n",
|
||
|
|
"2019-11-01 21:00:00 331.583\n",
|
||
|
|
"2019-11-01 22:00:00 331.639\n",
|
||
|
|
"2019-11-01 23:00:00 332.000\n",
|
||
|
|
"\n",
|
||
|
|
"[335841 rows x 1 columns]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# random sample test of one\n",
|
||
|
|
"print(combineddata[43])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"id": "746c7442",
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEFCAYAAAAPCDf9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAyG0lEQVR4nO3deXxU5dn/8c83YUdkF9kUBFywKipV61ZXxKVVSmvRVtHa0lq7P33qUq1Wxda2T/WnfdTaimgfC1IRoUJFVFRQEQJE2TWySBAhsiMESHL9/jj3hAkkIdtkTibX+/XKKzP3OXPmvjKTa87c515kZjjnnGscstJdAeecc/XHk75zzjUinvSdc64R8aTvnHONiCd955xrRDzpO+dcI9Ik3RWoTKdOnaxXr17proZzzjUoc+fO/czMOpe3LdZJv1evXuTk5KS7Gs4516BIWlXRNm/ecc65RsSTvnPONSKe9J1zrhHxpO+cc42IJ33nnGtEPOk75xqsz3cVkbd+W7qr0aB40nfONVjffSqHC/78Zrqr0aBUOelLypY0X9KL4X5vSe9KypP0rKRmobx5uJ8XtvdKOsatoXyZpIvqPBrnXKPyzvINAPi6IFVXnTP9nwJLku7fDzxgZn2BTcANofwGYFMofyDsh6T+wDDgWGAw8Iik7NpV3znnoCQNOb9wTzHDR81m2acNq3mpSklfUg/gUuDv4b6A84Dnwi5PAVeE25eH+4Tt54f9LwfGmtkuM1sB5AGn1EEMzrk0WPHZ50yYn5/uagDpOdPPXb2ZNz4o4I6JC5n38Sau/Os7fLJ5Z73Xo7qqeqb/IPAroCTc7whsNrOicD8f6B5udwdWA4TtW8L+peXlPKaUpBGSciTlFBQUVD0S51y9Gvzgm/z82ffSXQ0A0t2487VH3mb2io2c+6fX01yTAztg0pd0GbDezObWQ30ws8fNbKCZDezcudz5gpxzMbCrqOTAO9WTuDTpx+lvUpGqnOmfAXxV0kpgLFGzzv8D2klKTNjWA1gTbq8BegKE7W2BDcnl5TzGuQanqLiERZ9sSXc1HFASl6wPvLzo03RXoVIHTPpmdquZ9TCzXkQXYl8zs28B04Gvh92GAxPD7UnhPmH7axY1uE0ChoXePb2BfsDsOovEuXr2h6nLuPShmeSt357uqjR66cj5FT3niH/UrlFk+66ilF6jqE0//ZuBX0jKI2qzfyKUPwF0DOW/AG4BMLNFwDhgMfAScJOZFdfi+Ru1a554lykL1qbk2GZWqzfdxNw17NhdVO62v89YzorPPq/xsStTVFzC68vWp+TY5Zm7ahMAGz/fXW/P6cpXnMYzfdXhsfLWb+cLd05lXM7qA+9cQ9VK+mb2upldFm4vN7NTzKyvmX3DzHaF8sJwv2/Yvjzp8SPNrI+ZHWVm/6nbUParK9sK99TpMWct31BhMqutx974iOfm5vOPWav457sfs7VwD71umcxf3/gIoPR+4mfGh5/xw2fmUVJi7Cra+9n5zkcb+Gz7rtL78z7exNRFn7J2y052V7G9cdADb/KFO6eW3i8qLqHXLZPpc9uUMvuV98Ewe8VGfjo2lzteWLTftp27i7l38hKu/Os7VapHdf31zeVc9+SclCf+khLjjhcWsmTtVgCyG/kQx3dDX/mKvJX3GXdN2v/9UBeys6KUW1wcn+ad2vhwXdT987WlqXsPx3oRldr43tM5vLJkPbddcjQ3nHlE6ZtjX6s37uCsP0znwv5d+OPXj+fOSYu47vRenNCjHfNXb+bkw9sDMH3peq4fPYfLju/KX64+qcb1KtxTzPPz1rCnuIQ7Jy3iB1/uw2MhsSe7bcICAH73n6X87j9LKzzez8flMjH3E373teO4c9KiShP7mX078X/fPRWIEte6bYVMXfgpkhh+ei9+8Wwuz8/fe5ml1y2TueqUwxgz+2MAikuMf8xaReeDmlGwbRd3TFzEBcd04ZUl67jzK/3593ufMO/jzQCMn5fPnJUbefzak+nYujl7iks4+w/TASjYtoviEiv3Nflg3Tb6dj6IrApeL4DXlq7jubn53H5pf9q1akrLptlIIn9T1F1uzeadlJQYX3v0bX50bl8u6N+lwmPVxJrNO/nHrL1rVAx99B1W/v7SOn2OhmTsnNWcekTHCrd/6+/vAnDXV4+t8+cuDh30T7j75TKvQXGJMWrmCr46oBtdDm5R58+bLPkkqypGzVzBuJzVvPSzs/fblhhvUFG+qgsZm/RfWRJ9Ut43ZSn3TVnKlQN7cOJh7Xn09Y/4eOMOAGbfdj5nhUQ0bfE6Btw9DYCJuZ9UeNwFa2p34e7oO14qc7+8hF8dibre+vyCA+47M+8zet0ymT9feQK/GFe2q92dFZyJJRJ+wh0vLCxz/5Ul6wD47b8X7/fYjzfuYPCDM8o9buJbQ/d2LXnhpjP4cP02CvcU853ROZx8eHvG33h66b5TF33Kqb07MOSRt8s0DU1ZsPeC2crfX4qS/k927Ckmd/Vmfjp2PovuHlxuHWpq3seb9it75t1VfOvUw+v0eVLNzHhg2gdcdephdG3bslbHqQ+vLF7HqUd0oElWFntKSio9u3/x/U8YOWUJI6cs4aJju1Bi8Oi3TmLFZ5/TvX1LWjWrOPWZGVt27qFdq2Zlymev2EjhnmLOPjLqVWiho+hHBZU3V37/HzlMXbSOZfcO5oNPt3P3i/v/ryQkLkirThuNysrYpL+vcTn5jMspO5DklPterfZxVm3YUa39zYzPtu9m9aYdrN5Yvcemyr4JP53WbN7JF0e+UqZs7qpN/H3Gco46tA3XPFG1a/05KzdWuj1/0w7OvH86E286gxN6tqtpdQH46djc/cp+PWFhg0n6G7bv4rPtuykqKeGh1/J466MNZT5k69LUpJ4su4qKad6k/EH4qzfuYMzsj7l8QHeOOrTNfttfXbKO7z5d+dKpmz7fTbtWTZHEjt17mzynLopOTPr+em+L8srfX0re+u1Meu8Txs1ZzZ++cQJ567dxVzknL/t68cdn0rxJFrdPWFjhPr1umQxEZ+yJbyNH3V72hG9b4R7atGhapmztlujb6uQFa3mgqIRmTeq+7TAjk35dt+Xva3fSi5E4K1i9cSerN+0gf9OOpNs7yd+0g8I9ddd3N2/kxWXevFXxk/P78dCrH9ZZHerDvZOXHHinJI+9sZzObZoDMGv5Ri4fUHbc3/0vLQOipojaJv2quvvfizn60DZc+cWeB965Ds1avgFBhU0uFz34Jp9t382QE6O/UfI1oYR/5azmL9PzSk9yvnRER8aMOK3c46347HNyV29mQPi7Fu4pZtEnWxn66Ntl9jvq9pf43lm9+e5ZR3Ddk3P47pm9Wb1pB9OXrue9/Ogb9COvf8Qfhh7PZSd0pf9vpu77VJU68Z5p3HhOH75yfDfGz618pHAiKSd8+4l3q/w8lz08s8r7FlcyP8Rxd70MwENXncilx3Vl68493Ddlb1Pukbf/JyXNhorzREUDBw60miyMvunz3Zx4z7TS9ua6du2XDmftlkJWb4wS+/ZdZS/uHtyiCT07tKJn+1b0aN+Snh3K/i7Ytovc1Zu5/z9L+WRLIWf168To609h0ntreHf5RsbOia7cX3p8V246py+XPBQ1kYw4+whuu+QYILo4lmgrrcjc2y+gRdNsWjdvwq8nLOCZdz+udP+Ed249j1eWrN+vKac8V53SkzGzK+9pcGy3g1n0SXTRs1l2Fs2bZrGtsO4viPfu1LrcnkHnHNWZ15dFo7svO74rd1zWny4Ht+DvM5Zz7+QlnHf0Icz/eBM/+HIfrvnS4eSs3ESP9i0pMaNzmxaMn5tP/24Hk50lbnt+AR9W0EXzsW+fxOAvdC29n0gsH913CdlZ4oX5azjnqM6lzQYbtu+ifatmZa5fFJdEJxEdWkf7FBWXMOqtFRzesTUvvr+WkUO+QIsm2TTNFovXbuXYbm0B2LJjDyfc/XKZ+jw74jROPaIjPx07n4m5n9CuVVMev2ZguRfSJ//kTC59qOrJzNWPmiZ9SXPNbGC52zIx6SczM3YVlZS2pX/tpO48P6/smLDbLjm
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plot several stations to check it makes sense\n",
|
||
|
|
"for station in range (0, 3):\n",
|
||
|
|
" \n",
|
||
|
|
" combineddata[station].plot()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"id": "9e20af18",
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" latitude longitude depth mag\n",
|
||
|
|
"time \n",
|
||
|
|
"1960-01-02 12:21:58+00:00 -55.877 -1.89 15 6.3\n",
|
||
|
|
"1960-01-02 22:51:46.080000+00:00 35.5563333 -121.351 6 4.04\n",
|
||
|
|
"1960-01-03 11:24:05+00:00 43.7 84.542 15 5.7\n",
|
||
|
|
"1960-01-04 06:16:35+00:00 11.374 42.609 15 6.1\n",
|
||
|
|
"1960-01-04 12:52:00+00:00 45.069 26.829 40 5.4\n",
|
||
|
|
"... ... ... ... ...\n",
|
||
|
|
"2019-12-31 19:02:55.645000+00:00 44.0786 150.2151 10 4.8\n",
|
||
|
|
"2019-12-31 19:10:10.306000+00:00 24.074 121.6908 12.21 4.6\n",
|
||
|
|
"2019-12-31 23:07:42.730000+00:00 17.9226 -66.8408 5 4.2\n",
|
||
|
|
"2019-12-31 23:09:44.616000+00:00 9.9422 -84.4593 59.75 4\n",
|
||
|
|
"2019-12-31 23:56:59.933000+00:00 -20.5382 168.9571 21.46 4.3\n",
|
||
|
|
"\n",
|
||
|
|
"[427535 rows x 4 columns]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# seismic data read in\n",
|
||
|
|
"sfs = []\n",
|
||
|
|
"start_year = 1960\n",
|
||
|
|
"end_year = 2019\n",
|
||
|
|
"types_dict = {'time':float, 'latitude':\"string\", 'longitude':\"string\", 'depth':\"string\", 'mag':\"string\"}\n",
|
||
|
|
"for i in range(start_year, end_year+1):\n",
|
||
|
|
" sfs.append(pd.read_csv('siesmic-'+str(i)+'.csv',sep=',',skiprows=1,names = ['time', 'latitude', 'longitude', 'depth', 'mag'],parse_dates=['time'],index_col=['time'],usecols = [i for i in range(5)],dtype=types_dict))\n",
|
||
|
|
"sframe= pd.concat(sfs)\n",
|
||
|
|
"print(sframe)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"id": "5fead4ec",
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Index(['latitude', 'longitude', 'depth', 'mag'], dtype='object')\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# manipulation with seismic data\n",
|
||
|
|
"cols = sframe.columns\n",
|
||
|
|
"print(cols)\n",
|
||
|
|
"#cols.remove('time')\n",
|
||
|
|
"sframe['latitude'] = pd.to_numeric(sframe['latitude'], errors='coerce')\n",
|
||
|
|
"sframe['longitude'] = pd.to_numeric(sframe['longitude'], errors='coerce')\n",
|
||
|
|
"sframe['depth'] = pd.to_numeric(sframe['depth'], errors='coerce')\n",
|
||
|
|
"sframe['mag'] = pd.to_numeric(sframe['mag'], errors='coerce')\n",
|
||
|
|
"#print(sframe.dtypes)\n",
|
||
|
|
"#sframe=pd.convert_objects(convert_numeric=True)\n",
|
||
|
|
"#for col in cols:\n",
|
||
|
|
"# sframe[col] = sframe[col].astype(float)\n",
|
||
|
|
"#sframe.index.tz_convert(None)\n",
|
||
|
|
"#sframe.tz_localize(None)\n",
|
||
|
|
"sframe_notz = sframe.tz_convert(None)\n",
|
||
|
|
"daily_quakes = sframe_notz.resample(average_interval, origin='1960-01-01').apply(lambda spl: 10*np.log10(np.mean(np.power(10, spl/10))))\n",
|
||
|
|
"daily_quakes = daily_quakes.fillna(0)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"id": "2bdfb7a2",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='time'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plot the quake data\n",
|
||
|
|
"sframe['mag'].plot()\n",
|
||
|
|
"daily_quakes['mag'].plot()\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"id": "3cc505e8",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Start Sunspots!\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print (\"Start Sunspots!\")\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"id": "4a418246",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# load the sunspot data\n",
|
||
|
|
"sun_file = Path('sunspots.csv')\n",
|
||
|
|
"content_p = \"\"\n",
|
||
|
|
"if not sun_file.exists():\n",
|
||
|
|
" URL = \"http://cesar.kso.ac.at/sunspot_numbers/daily_sn.csv\"\n",
|
||
|
|
" print(URL)\n",
|
||
|
|
" req = urllib.request.Request(URL)\n",
|
||
|
|
" req.add_header('User-Agent', 'urllib/0.1')\n",
|
||
|
|
" with urllib.request.urlopen(req) as f:\n",
|
||
|
|
" content = f.readlines()\n",
|
||
|
|
" for line in content:\n",
|
||
|
|
" content_p = content_p+ line.decode()\n",
|
||
|
|
" with sun_file.open('wt') as fh:\n",
|
||
|
|
" fh.write(content_p)\n",
|
||
|
|
" fh.close"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"id": "79bbe9b8",
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" tot sn ss diff\n",
|
||
|
|
"time \n",
|
||
|
|
"1944-06-05 23 0 -23 -23 \n",
|
||
|
|
"1944-06-13 0 0 0 0 \n",
|
||
|
|
"1944-06-14 0 0 0 0 \n",
|
||
|
|
"1944-06-16 0 0 0 0 \n",
|
||
|
|
"1944-06-21 0 0 0 0 \n",
|
||
|
|
"... ... ... ... ...\n",
|
||
|
|
"2021-06-29 61 10 -51 -41\n",
|
||
|
|
"2021-06-30 69 21 -48 -26\n",
|
||
|
|
"2021-07-01 53 11 -42 -30\n",
|
||
|
|
"2021-07-02 67 21 -46 -25\n",
|
||
|
|
"2021-07-03 76 23 -53 -29\n",
|
||
|
|
"\n",
|
||
|
|
"[21022 rows x 4 columns]\n",
|
||
|
|
" tot sn ss diff\n",
|
||
|
|
"time \n",
|
||
|
|
"1944-06-05 23 0 -23 -23 \n",
|
||
|
|
"1944-06-13 0 0 0 0 \n",
|
||
|
|
"1944-06-14 0 0 0 0 \n",
|
||
|
|
"1944-06-16 0 0 0 0 \n",
|
||
|
|
"1944-06-21 0 0 0 0 \n",
|
||
|
|
"... ... ... ... ...\n",
|
||
|
|
"2021-06-29 61 10 -51 -41\n",
|
||
|
|
"2021-06-30 69 21 -48 -26\n",
|
||
|
|
"2021-07-01 53 11 -42 -30\n",
|
||
|
|
"2021-07-02 67 21 -46 -25\n",
|
||
|
|
"2021-07-03 76 23 -53 -29\n",
|
||
|
|
"\n",
|
||
|
|
"[21022 rows x 4 columns]\n",
|
||
|
|
"tot int64\n",
|
||
|
|
"sn string\n",
|
||
|
|
"ss string\n",
|
||
|
|
"diff string\n",
|
||
|
|
"dtype: object\n",
|
||
|
|
"tot int64\n",
|
||
|
|
"sn float64\n",
|
||
|
|
"ss float64\n",
|
||
|
|
"diff float64\n",
|
||
|
|
"dtype: object\n",
|
||
|
|
" tot sn ss diff\n",
|
||
|
|
"time \n",
|
||
|
|
"1944-06-05 23.0 0.0 -23.0 -23.0\n",
|
||
|
|
"1944-06-06 NaN NaN NaN NaN\n",
|
||
|
|
"1944-06-07 NaN NaN NaN NaN\n",
|
||
|
|
"1944-06-08 NaN NaN NaN NaN\n",
|
||
|
|
"1944-06-09 NaN NaN NaN NaN\n",
|
||
|
|
"... ... ... ... ...\n",
|
||
|
|
"2021-06-29 61.0 10.0 -51.0 -41.0\n",
|
||
|
|
"2021-06-30 69.0 21.0 -48.0 -26.0\n",
|
||
|
|
"2021-07-01 53.0 11.0 -42.0 -30.0\n",
|
||
|
|
"2021-07-02 67.0 21.0 -46.0 -25.0\n",
|
||
|
|
"2021-07-03 76.0 23.0 -53.0 -29.0\n",
|
||
|
|
"\n",
|
||
|
|
"[28153 rows x 4 columns]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# data manipulations with sunspots\n",
|
||
|
|
"sunframe = []\n",
|
||
|
|
"sun_types_dict = {'time':float, 'total':\"string\", 'sn':\"string\", 'ss':\"string\", 'diff':\"string\"}\n",
|
||
|
|
"sunframe.append(pd.read_csv(sun_file,sep=';',skiprows=0,names = ['time', 'tot', 'sn', 'ss', 'diff'],parse_dates=['time'],index_col=['time'],dtype=sun_types_dict))\n",
|
||
|
|
"sundf= (pd.read_csv(sun_file,sep=';',skiprows=0,names = ['time', 'tot', 'sn', 'ss', 'diff'],parse_dates=['time'],index_col=['time'],dtype=sun_types_dict))\n",
|
||
|
|
"\n",
|
||
|
|
"print(sunframe[0])\n",
|
||
|
|
"print(sundf)\n",
|
||
|
|
"print(sundf.dtypes)\n",
|
||
|
|
"sundf['sn'] = pd.to_numeric(sundf['sn'], errors='coerce')\n",
|
||
|
|
"sundf['ss'] = pd.to_numeric(sundf['ss'], errors='coerce')\n",
|
||
|
|
"sundf['diff'] = pd.to_numeric(sundf['diff'], errors='coerce')\n",
|
||
|
|
"print(sundf.dtypes)\n",
|
||
|
|
"\n",
|
||
|
|
"daily_sunspots = sundf.resample(average_interval, origin='1960-01-01').mean()\n",
|
||
|
|
"\n",
|
||
|
|
"print(daily_sunspots)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"id": "fd304ce1",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='time'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plot the sunspot progress\n",
|
||
|
|
"daily_sunspots['tot'].plot()\n",
|
||
|
|
"daily_sunspots['sn'].plot()\n",
|
||
|
|
"daily_sunspots['ss'].plot()\n",
|
||
|
|
"daily_sunspots['diff'].plot()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"id": "15486960",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='time'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# show sunspot cycle\n",
|
||
|
|
"daily_sunspots['tot'].plot()\n",
|
||
|
|
"daily_quakes['mag'].plot()\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "e3ee8722",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"If we wanted to add geomagnetic data we could get it from here: \n",
|
||
|
|
"https://www.swpc.noaa.gov/products/goes-magnetometer#:~:text=GOES%20magnetometer%20data%20have%20been,during%20geomagnetic%20storms%20and%20substorms.&text=The%20data%20have%20often%20been,decisions%20for%20research%20sounding%20rockets. \n",
|
||
|
|
"The GOES sats have info from 1996 onwards, but I don't understand the meaning of it!"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "757a9bc6",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"next stage:\n",
|
||
|
|
"Build a variable average filter for each/all data sets\n",
|
||
|
|
"average from 1 hour data to n hours\n",
|
||
|
|
"correlation function (i.e. dataset a to dataset b)\n",
|
||
|
|
"variable window + offset function (i.e. compare dataset a, offset 1 week, with dataset b, no offset)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"id": "2393d3dc",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7fe2faa25eb0>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABthUlEQVR4nO2dd5gUxdPHv3WJcOQoSUBABUkiiooBjIAgxp8Js2LO2decM5gjKmYFFRCUDAKSc84cHPFIdxx3XK73j+65nd2d2Z3Znd2ZvevP8xxM6OmuCdvVobqKmBkKhUKhUFglyW0BFAqFQpFYKMWhUCgUClsoxaFQKBQKWyjFoVAoFApbKMWhUCgUClsoxaFQKBQKWyjFYQIRvU5ED7pQbi8i2u5gfp2JaLZT+bkJEX1LRK+4UK6j7yREOT8T0SWxLsdLENF0IrrNYtpVRNRLbr9ARD/EUrZYQ0SfEdGzDuZn+fdBRPOJ6IRIy7KsOIjoDCKaTUQ5RHSAiP4jopMjLTjWEFEGEZ0X4bUNAdwA4HNnpYo/zLwcQDYRDYhlOUR0ExGVEtHhgL+mUeQ3y2k5vQoRdQbQBcBot2XxKsx8AjNPd1sOp2DmO5n5ZSB+jRMd7wB4KdKLLSkOIqoFYCyADwHUA9AMwIsACiMt2OPcBOBvZj7itiDRQEQpcvNHAHfEocg5zFwj4G+n3Ux0clcm7gDwI8d5RW4lfdYKYAyA3kR0VCQXW+1xHAsAzPwzM5cy8xFmnihbs0HdRiJqRUSsfZSyO/qy7KXkEtFEImogz1Uloh+IaD8RZRPRAiJqrLvuddmtOkREo4monq6ci2X3NVumbS+Pfw/gaAB/yVbv46HKMaAvgH915fQiou1E9AgRZRHRLiK6WXfer7sd2FqWz+JuItog7/9lImoje3CHiOg3IkrTC0BETxPRPtlzuk53vAoRvUNE24hoj+zuVguQ8wki2g3gG3nZdADnElGVwBsloquIaGHAsYeIaIzc7kdEq6XcO4joUZNnFhIiepKINsl8VhPRpQHP6z8iGkJE+wH8CuAzAKfJ95ety6ouEY2T+cwjoja6fM4norUkesUfEdG/2nux8I3eTERrZL6bichU0RLR/fIemod5Hw2IaKz83g4Q0UwiMvvNBX5zbYhoqvxe9xHRj0RUR557gohGBsj0PhF9ILdrE9Ew+Z3uIKJXiCjZ5Fm/EKoseU03Iloin80IIvqVdEMiRNSfiJbK+5xNovdk9uz83hEAsnLP8rzhKIL8Hu4LOLZc/43pjmvv/WYiyiSig0R0JxGdLK/JlnJZlcn02VD4euNb+W7SAfwDoCnpeuoUMPREAb0SIjqRiBbLsn8FUDXgXk3fCzMXAFgE4EKzdxUSZg77B6AWgP0AhkN84HUDzr8A4AfdfisADCBF7k8HsAlCAVWT+2/Ic3cA+AtAdQDJAE4CUEt33Q4AHQGkA/hdK0fmlQfgfACpAB4HsBFAmjyfAeA8nUym5Rjc714AJ+v2ewEogejapQLoByBfew5Sztt06W8CMEu3zxBDELUAnADRU5sC4BgAtQGsBnBjQFnvAagC4Gx5n8fJ80MgWgv1ANSU9/R6wLVvymur6WQ4BKCzwb1WB5ALoJ3u2AIAV8vtXQDOlNt1AXQzeWZ+92xw/koATSEaK1fJe2qiu7YEwH0AUuQ3EpQfgG8hvsNTZLofAfwizzWQ93GFfEcPyTxvs/iNXgSgDURFdrZ8v910z3W73H4OwGIADS28j9chFGCq/DsTABk8m3QpS0PdsbYQ33YVAA0BzAAwVJ5rKeWrKfeT5Xs6Ve7/CTHMmg6gEYD5AO4I8axDlZUGYCuAB+Q9XAagCMAr8vyJALIA9JBy3Ajx26ticJ/h3pGpHIG/af37BPA/APN06bpAfCdpBjJo7/0ziIr2AgAFAEbJZ9VM3s/ZFt5DuGfTC6HrjW8D0m43+N5fCaiHtgeU/ZDM+woAxXbeC4APALxnRQcEPUfLCYH28ka2y4cxBkBjG4rjGd35uwGMl9u3AJgN40ptOqSCkfsd5ItJBvAsgN9055IglEyvwI8sXDkG5RYDOD7ghR3R7kcey4Lvhzod4RVHT93+IgBP6Pbfhe9j1D62dN353+T9EkSF20Z37jQAW3TXFgGoanBPOwCcZXK/PwB4Tm63g/hxV5f72yCUrqGSDbjnEgDZur9NIdIvBTBQd+02g/yMFMdXuv1+ANbK7RsAzNWdI4hv1ZLiMJBvFIAHdM91B4QynwWgtq6MUO/jJYgGQ9swz66ZlCXovenSXAJgiW5/FoAb5Pb52rMG0BiiYaJvNFwDYJrZsw5VFoCz5L1TQNlaBfUpgJcDrl8HWfEGHA/5jizccwaMFUdVAAchGz8Q4/efmOSpvfdmumP7AVyl2/8dwIMOPJteCF1vfIvIFcdZAHYGlD3bznsB8CqAr0N9C2Z/lifHmXkNM9/EzM0hegBNAQy1ej2A3brtfAA15Pb3ACYA+IWIdhLRW0SUqkubqdveCqFdG8jyt+rkK5Npm5mUH64cPQchWo969jNzick9WGGPbvuIwb4+r4PMnKfb3wpxvw0hegiLZPczG8B4eVxjL4tuaCA1ISpzI36CqFwA4FoAo5g5X+5fDlFBbyUx9HOaSR6AqBTq6P70w0g36LrN2RDfUAPdtZmBmZlg9h011efB4pdhNU8QUV8imiuHlLIh7lkvXx0AgyF6EznyWLj38TZEL3giieGvJ02Kz5b/l39zRNSYiH6RQ02HIJS7Xp7Ad/aT3G4J8RvZpZPpc4jWtIbfcwlTVlMAO+TzNLq+JYBHtLJkeS3kdYGEfEcW7tkQ+b3/CmAQiaHAayB+76Gw9HuM8tkA0dcbZhiVvVW3beW9hKoTQhKROS4zr4XQhh3loTyIH5CG5QkXZi5m5heZuQOA0wH0h2iZaLTQbR8N0RvYB6FtW2oniIhk2h1a1jbL0bMccl7HIhHfvwl15binxtEQ97sP4qM+QVc512Zm/Yfod98AQETNILq260zKmwSgIRF1hfjRaZUQmHkBMw+EqHhGQfR+bEFELQF8CeBeAPWZuQ6AldCNbxvIHXQfYdgF3bei+x40TN8Ribmf3yFaqo2lfH8HyHcQ4pv5hoh6ymMh3wcz5zLzI8x8DICLATxMROcGCi4bCdpQrsZrEM+gEzPXAjAoQJ4RAHoRUXMAl8L3zjIhehwNdDLVYma96WXgsw1V1i4AzeTz1NA/10wArwY0GKoz88+B94nw7yjcPYdiOIDrAJwLIJ+Z51i8LhzRPBs7GH3voeoVo7KP1m1beS/tASyLRFirVlXHywme5nK/BUQFM1cmWQrgLCI6mohqA3jKqgBE1JuIOpGYvDsEoRjKdEkGEVEHIqoO0fUfycylEBXYRUR0ruw5PALxg9HWLOyBmEOwWo6evyHGua2yFMBlRFSdiNoCuNXGtWa8SERpRHQmRIU1QvaqvgQwhIgaAUIpEFG4Ca6zAUxlZkMrOGYuhqiI3oYYq58k804jouuIqLZMcwjmzywU2hj+XpnvzfA1OszYA6A5BRgNhGAcgBOI6DISE973w/+HthTm32gaxBj2XgAlRNQXYuzbDxamoNcB+IOITgn3PuTkZFv5484BUArr31xNAIcB5EjF/1iALHshhki/gRgaWyOP7wIwEcC7RFSLiJJITPCG+p5DlTVHyn0vEaUQ0UCIOSaNLwHcSUQ9SJBORBcRUWCPHQj/jkLecyikoiiDGPYN19uwQzTPxg57ANSX36bGUgD9iKgeCeunBwPKLgFwPxGlEtFlsPFeiKgqxDzvpEiEtdrjyIWYZJlHRHkQCmMlRGUNZp4E0VVcDjF+P9aGDEcBGAlRKa2BsCzRv/jvIXo3uyHGMu+XZa6D0P4fQrT8BgAYwMxF8rrXATwju2mPWihHz3cQL6yaxXsYAjG3sAei5fOjxevM2A3Rwt0p87pT9vIA4AmI4Y+5sus8GcBxYfK7DmIyMBQ/ATgPQkHpu9bXA8iQZd0p8zJDs4LS/53MzKshftBzIJ5RJwD/hZFnKoBVAHYT0b4wacHM+yAm4N+AGLNupy8j1DfKzLk
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#daily_sunspots[\"tot\"][\"1980-01-01\":\"2019-01-01\"].plot()\n",
|
||
|
|
"#daily_quakes['mag'] = 50 * daily_quakes['mag']\n",
|
||
|
|
"#daily_quakes['mag'][\"1980-01-01\":\"2019-01-01\"].plot()\n",
|
||
|
|
"#print(sframe.corrwith(sundf))\n",
|
||
|
|
"#print(sunframe[0].corrwith(sframe['mag']))\n",
|
||
|
|
"\n",
|
||
|
|
"ax = daily_quakes['mag'][\"1975-01-01\":\"2019-01-01\"].plot(title='Sunspots (number) vs Earthquakes (average daily magnitude)')\n",
|
||
|
|
"#daily_raycount[\"1975\":\"2019\"].plot(ax=ax1)\n",
|
||
|
|
"\n",
|
||
|
|
"#ax.plot(daily_quakes['mag'][\"1975\":\"2019\"])\n",
|
||
|
|
"ax1 = ax.twinx()\n",
|
||
|
|
"ax1.plot(daily_sunspots[\"tot\"][\"1975-01-01\":\"2019-01-01\"], color='red')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"id": "3606fbb9",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"0.12402736865827761"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# do statistical correlation of sunspots and earthquakes (zero offset)\n",
|
||
|
|
"daily_sunspots[\"tot\"][\"1975-01-01\":\"2018-01-01\"].corr(daily_quakes['mag'][\"1975-01-01\":\"2018-01-01\"], method='pearson')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"id": "dc1a5805",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# offset hunter 1 year - we look +/- 180 days either side of the 'day' to see how it changes correlation\n",
|
||
|
|
"# e.g. if earthquakes happened more frequently 7 days after a sunspot peak, we should see it\n",
|
||
|
|
"# and if sunspots happen more frequently 15 days after an earthquake, this would show up too\n",
|
||
|
|
"correl_matrix=[]\n",
|
||
|
|
"for var_offset in range (-180, 180):\n",
|
||
|
|
" offset_ds = daily_sunspots[\"tot\"][\"1975-01-01\":\"2018-01-01\"].shift(periods=var_offset)\n",
|
||
|
|
" #correl_matrix[monthoffset,dayoffset] = daily_quakes['mag'][\"1980-01-01\":\"2019-01-01\"].corr(offset_ds, method='pearson')\n",
|
||
|
|
" correl_matrix = correl_matrix + [(daily_quakes['mag'][\"1975-01-01\":\"2019-01-01\"].corr(offset_ds, method='pearson'))]\n",
|
||
|
|
" \n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"id": "d1ea606a",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Here we plot the general correlation vs offset shift (0 is same day)\n",
|
||
|
|
"#print(correl_matrix)\n",
|
||
|
|
"#data = np.array([\n",
|
||
|
|
"#correl_matrix\n",
|
||
|
|
"#])\n",
|
||
|
|
"#x, y = data.T\n",
|
||
|
|
"offset_range = []\n",
|
||
|
|
"offset_range.extend(range(-180,180))\n",
|
||
|
|
"#print(offset_range)\n",
|
||
|
|
"plot.title(\"Sunspot vs Earthquake Correlation, offset from -180 to +180 days, 1975-2019\")\n",
|
||
|
|
"plot.xlabel(\"Offset (days)\")\n",
|
||
|
|
"plot.ylabel(\"Correlation Coef\")\n",
|
||
|
|
"plot.scatter(offset_range,correl_matrix[:])\n",
|
||
|
|
"plot.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"id": "86f484cd",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#offset hunter 1985-2010\n",
|
||
|
|
"# looking for the same thing in a different part of the dataset\n",
|
||
|
|
"correl_matrix_sm=[]\n",
|
||
|
|
"for var_offset in range (-180, 180):\n",
|
||
|
|
" offset_ds = daily_sunspots[\"tot\"][\"1985-01-01\":\"2010-01-01\"].shift(periods=var_offset)\n",
|
||
|
|
" #correl_matrix[monthoffset,dayoffset] = daily_quakes['mag'][\"1980-01-01\":\"2019-01-01\"].corr(offset_ds, method='pearson')\n",
|
||
|
|
" correl_matrix_sm = correl_matrix_sm + [(daily_quakes['mag'][\"1985-01-01\":\"2010-01-01\"].corr(offset_ds, method='pearson'))]\n",
|
||
|
|
" \n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"id": "90901f46",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plot the different range\n",
|
||
|
|
"offset_range = []\n",
|
||
|
|
"offset_range.extend(range(-180,180))\n",
|
||
|
|
"#print(offset_range)\n",
|
||
|
|
"plot.title(\"Sunspot vs Earthquake Correlation, offset from -180 to +180 days, 1985-2010\")\n",
|
||
|
|
"plot.xlabel(\"Offset (days)\")\n",
|
||
|
|
"plot.ylabel(\"Correlation Coef\")\n",
|
||
|
|
"plot.scatter(offset_range,correl_matrix_sm[:])\n",
|
||
|
|
"plot.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"id": "b835434d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# offset hunter 10k days (takes a long time to run)\n",
|
||
|
|
"# we also calculate p values, a large p = low statistical/confidence significance\n",
|
||
|
|
"# a very low p = high confidence interval\n",
|
||
|
|
"# (we want a high correlation and a low p for something 'useful')\n",
|
||
|
|
"correl_matrix_smt = []\n",
|
||
|
|
"corr_p = []\n",
|
||
|
|
"for var_offset in range (-10000, 10000):\n",
|
||
|
|
" offset_ds = daily_sunspots[\"tot\"][\"1975\":\"2019\"].shift(periods=var_offset)\n",
|
||
|
|
" #correl_matrix[monthoffset,dayoffset] = daily_quakes['mag'][\"1980-01-01\":\"2019-01-01\"].corr(offset_ds, method='pearson')\n",
|
||
|
|
" correl_matrix_smt = correl_matrix_smt + [(daily_quakes['mag'][\"1975\":\"2019\"].corr(offset_ds, method='pearson'))]\n",
|
||
|
|
" corr_p = corr_p + [(daily_quakes['mag'][\"1975\":\"2019\"].corr(offset_ds, method=pearsonr_pval))] "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"id": "78aecb32",
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plot large scale offsets (correlation and p values)\n",
|
||
|
|
"offset_range = []\n",
|
||
|
|
"offset_range.extend(range(-10000,10000))\n",
|
||
|
|
"#print(offset_range)\n",
|
||
|
|
"plot.title(\"Sunspot vs Earthquake Correlation, offset from -10000 to +10000 days, 1975-2019\")\n",
|
||
|
|
"plot.xlabel(\"Offset (days)\")\n",
|
||
|
|
"plot.ylabel(\"Correlation Coef/p value\")\n",
|
||
|
|
"plot.scatter(offset_range,correl_matrix_smt[:])\n",
|
||
|
|
"plot.scatter(offset_range,corr_p[:])\n",
|
||
|
|
"\n",
|
||
|
|
"plot.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 33,
|
||
|
|
"id": "11548c3d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Maximum correlation coef = 0.3318872530602409\n",
|
||
|
|
"Lowest P value = 1.9197353657904475e-235\n",
|
||
|
|
"Offset for maximum correlation = -5267\n",
|
||
|
|
"Offset for lowest P value = 5267\n",
|
||
|
|
"Offset in years = 14.420260095824778\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# print a text summary of th emost significant values...\n",
|
||
|
|
"print(\"Maximum correlation coef =\", max(correl_matrix_smt[:]))\n",
|
||
|
|
"print(\"Lowest P value = \", min(corr_p[:]))\n",
|
||
|
|
"print(\"Offset for maximum correlation = \", -correl_matrix_smt.index(max(correl_matrix_smt[:])))\n",
|
||
|
|
"print(\"Offset for lowest P value = \", corr_p.index(min(corr_p[:])))\n",
|
||
|
|
"print(\"Offset in years =\", (correl_matrix_smt.index(max(correl_matrix_smt[:]))/365.25))\n",
|
||
|
|
"#years ago - max historical correlation\n",
|
||
|
|
"#sunspots from 9 years ago = 35% correlation with high confidence earthquake intensity today..."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"id": "ad5d54a7",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"44"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# how many stations in the dataset?\n",
|
||
|
|
"#print(combineddata[21].columns.values)\n",
|
||
|
|
"#columhead = combineddata[21].columns.values\n",
|
||
|
|
"len(combineddata)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"id": "e1447ee0",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" AATA\n",
|
||
|
|
"start_date_time \n",
|
||
|
|
"2019-01-04 NaN\n",
|
||
|
|
"2019-01-05 NaN\n",
|
||
|
|
"2019-01-06 NaN\n",
|
||
|
|
"2019-01-07 NaN\n",
|
||
|
|
"2019-01-08 NaN\n",
|
||
|
|
"... ...\n",
|
||
|
|
"2019-12-27 3767.550000\n",
|
||
|
|
"2019-12-28 3763.804167\n",
|
||
|
|
"2019-12-29 3764.147500\n",
|
||
|
|
"2019-12-30 3751.696250\n",
|
||
|
|
"2019-12-31 3769.609583\n",
|
||
|
|
"\n",
|
||
|
|
"[362 rows x 1 columns]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# megarayhunter - all stations; run the same thing across them to see if we see differences\n",
|
||
|
|
"# doesn't work yet! - This is about where I'm at in development.\n",
|
||
|
|
"correl_matrix_smt=[]\n",
|
||
|
|
"corr_p =[]\n",
|
||
|
|
"daily_raycount = []\n",
|
||
|
|
" \n",
|
||
|
|
"for station in range(0,43):\n",
|
||
|
|
" combineddata[station] = combineddata[station].resample(average_interval, origin='1960-01-01').mean()\n",
|
||
|
|
" #print(combineddata[station])\n",
|
||
|
|
" daily_raycount.append(combineddata[station])\n",
|
||
|
|
"print(daily_raycount[0][\"1975\":\"2019\"].shift(periods=10))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"id": "368ce9cc",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" BKSN\n",
|
||
|
|
"start_date_time \n",
|
||
|
|
"2009-05-18 NaN\n",
|
||
|
|
"2009-05-19 NaN\n",
|
||
|
|
"2009-05-20 NaN\n",
|
||
|
|
"2009-05-21 NaN\n",
|
||
|
|
"2009-05-22 NaN\n",
|
||
|
|
"... ...\n",
|
||
|
|
"2019-12-27 123.888208\n",
|
||
|
|
"2019-12-28 123.903042\n",
|
||
|
|
"2019-12-29 123.562125\n",
|
||
|
|
"2019-12-30 123.711542\n",
|
||
|
|
"2019-12-31 123.954917\n",
|
||
|
|
"\n",
|
||
|
|
"[3880 rows x 1 columns]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(daily_raycount[5][\"1975\":\"2019\"].shift(periods=10))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 83,
|
||
|
|
"id": "76526954",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"AATA\n",
|
||
|
|
"AATB\n",
|
||
|
|
"APTY\n",
|
||
|
|
"ARNM\n",
|
||
|
|
"ATHN\n",
|
||
|
|
"BKSN\n",
|
||
|
|
"CALM\n",
|
||
|
|
"DJON\n",
|
||
|
|
"DOMB\n",
|
||
|
|
"DOMC\n",
|
||
|
|
"DRBS\n",
|
||
|
|
"FSMT\n",
|
||
|
|
"HRMS\n",
|
||
|
|
"INVK\n",
|
||
|
|
"IRK2\n",
|
||
|
|
"IRK3\n",
|
||
|
|
"IRKT\n",
|
||
|
|
"JBGO\n",
|
||
|
|
"JUNG\n",
|
||
|
|
"JUNG1\n",
|
||
|
|
"KERG\n",
|
||
|
|
"KIEL\n",
|
||
|
|
"KIEL2\n",
|
||
|
|
"LMKS\n",
|
||
|
|
"MCRL\n",
|
||
|
|
"MGDN\n",
|
||
|
|
"MOSC\n",
|
||
|
|
"MRNY\n",
|
||
|
|
"MXCO\n",
|
||
|
|
"NAIN\n",
|
||
|
|
"NANM\n",
|
||
|
|
"NEWK\n",
|
||
|
|
"NRLK\n",
|
||
|
|
"OULU\n",
|
||
|
|
"PSNM\n",
|
||
|
|
"PTFM\n",
|
||
|
|
"PWNK\n",
|
||
|
|
"ROME\n",
|
||
|
|
"SNAE\n",
|
||
|
|
"SOPB\n",
|
||
|
|
"SOPO\n",
|
||
|
|
"TERA\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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"text/plain": [
|
||
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"<Figure size 432x288 with 1 Axes>"
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]
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},
|
||
|
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"metadata": {
|
||
|
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"needs_background": "light"
|
||
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},
|
||
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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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"del offset_ds\n",
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||
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"del correl_matrix_smf\n",
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||
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"del corr_pf\n",
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||
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"dayrange = 30\n",
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||
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"day_set_range = []\n",
|
||
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"day_set_range.extend(range(-dayrange,dayrange))\n",
|
||
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|
"\n",
|
||
|
|
"correl_matrix_smf = [[]]\n",
|
||
|
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"corr_pf = [[]]\n",
|
||
|
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"offset_ds = []\n",
|
||
|
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"cosmic_quake = [[0 for x in range (0,42)] for y in range (-dayrange, dayrange)]\n",
|
||
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"\n",
|
||
|
|
"for station in range(0,42): \n",
|
||
|
|
" print(station_names[station])\n",
|
||
|
|
" for var_offset in range (-dayrange, dayrange):\n",
|
||
|
|
" local_offset = []\n",
|
||
|
|
" local_offset = daily_raycount[station][\"1975\":\"2019\"].shift(periods=var_offset)\n",
|
||
|
|
" fs = pd.Series(local_offset[station_names[station]])\n",
|
||
|
|
" #correl_matrix_smf = correl_matrix_smf + \n",
|
||
|
|
" #print(var_offset, daily_quakes['mag'][\"1975\":\"2019\"].corr(fs, method='pearson'))\n",
|
||
|
|
" cosmic_quake[station][var_offset] = daily_quakes['mag'][\"1975\":\"2019\"].corr(fs, method='pearson')\n",
|
||
|
|
" #print(len(cosmic_quake)) \n",
|
||
|
|
" flagplot = pd.to_numeric(cosmic_quake[station][:], errors='coerce')\n",
|
||
|
|
" #flagplot.plot()\n",
|
||
|
|
" #print(len(flagplot[station]))\n",
|
||
|
|
" #plot.scatter(day_set_range,flagplot)\n",
|
||
|
|
" plot.plot(flagplot)\n",
|
||
|
|
" #plot.show()\n",
|
||
|
|
" #correl_matrix_smf[station] = correl_matrix_smf[station] + daily_quakes['mag'][\"1975\":\"2019\"].corr(fs, method='pearson')\n",
|
||
|
|
" #print(correl_matrix_smf) \n",
|
||
|
|
" #correl_matrix_smf[station] = correl_matrix_smf[station]+daily_quakes['mag'][\"1980\":\"2018\"].corr(fs, method='pearson')\n",
|
||
|
|
" #corr_pf[station] = corr_pf[station] + [daily_quakes['mag'][\"1975\":\"2019\"].corr(fs, method=pearsonr_pval)] \n",
|
||
|
|
"\n",
|
||
|
|
"#print('offset', offset_ds[:]) \n",
|
||
|
|
"#print('daily quakes', daily_quakes['mag'][:])\n",
|
||
|
|
"#sporkquake = daily_quakes['mag'].to_frame()\n",
|
||
|
|
"#print(sporkquake)\n",
|
||
|
|
"#ts = pd.Series(sporkquake['mag'])\n",
|
||
|
|
"#fs = pd.Series(offset_ds[station_names[1]])\n",
|
||
|
|
"#print(type(sporkquake.to_series()))\n",
|
||
|
|
"#print(type(offset_ds.to_series()))\n",
|
||
|
|
"#print(sporkquake[\"1975\":\"2019\"])\n",
|
||
|
|
"#print('offset again', offset_ds)\n",
|
||
|
|
"#print('print ts',ts)\n",
|
||
|
|
"#print('print fs', fs)\n",
|
||
|
|
"#print(sporkquake.corr(offset_ds, method='pearson'))\n",
|
||
|
|
"#correl_matrix_smf = ts[\"1975\":\"2019\"].corr(fs, method='pearson')\n",
|
||
|
|
"#print(correl_matrix_smf) \n",
|
||
|
|
"\n",
|
||
|
|
"#offset_range = []\n",
|
||
|
|
"#offset_range.extend(range(-10000,10000))\n",
|
||
|
|
"#print(offset_range)\n",
|
||
|
|
"\n",
|
||
|
|
"#plot.scatter(range (-30, 30),cosmic_quake[0][range (-30, 30)])\n",
|
||
|
|
"#plot.scatter(offset_range,corr_p[:])\n",
|
||
|
|
"\n",
|
||
|
|
"#flagplot = pd.to_numeric(cosmic_quake[station], errors='coerce')\n",
|
||
|
|
"#flagplot.plot()\n",
|
||
|
|
"#plot.plot(flagplot)\n",
|
||
|
|
"#plot.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 146,
|
||
|
|
"id": "9ad53a96",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "ValueError",
|
||
|
|
"evalue": "x and y must be the same size",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[0;32m<ipython-input-146-09681caf96b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffset_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcorrel_matrix_smf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Daily cosmics (THUL) correlated with average daily earthquake score (log-average/24h)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffset_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcorr_pf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"p value for correlation, (pearson)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Time shifted correlation between cosmic rays [THUL] and earthquakes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
|
|
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 2893\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2894\u001b[0m \u001b[0mplotnonfinite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplotnonfinite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2895\u001b[0;31m **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m 2896\u001b[0m \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2897\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
|
|
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1446\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1449\u001b[0m \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
|
|
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 410\u001b[0m **kwargs)\n\u001b[0;32m--> 411\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
|
|
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4439\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4440\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4441\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4443\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
|
|
"\u001b[0;31mValueError\u001b[0m: x and y must be the same size"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot.scatter(offset_range,correl_matrix_smf[:], label=\"Daily cosmics (THUL) correlated with average daily earthquake score (log-average/24h)\")\n",
|
||
|
|
"plot.scatter(offset_range,corr_pf[:], label = \"p value for correlation, (pearson)\")\n",
|
||
|
|
"\n",
|
||
|
|
"plot.legend()\n",
|
||
|
|
"plot.title('Time shifted correlation between cosmic rays [THUL] and earthquakes')\n",
|
||
|
|
"plot.xlabel(\"Timeshift (days)\")\n",
|
||
|
|
"plot.ylabel(\"Correlation and p-value\")\n",
|
||
|
|
"plot.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 63,
|
||
|
|
"id": "552f2fc1",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"-0.356109919072931\n",
|
||
|
|
"1.9058301817431175e-204\n",
|
||
|
|
"3993\n",
|
||
|
|
"3993\n",
|
||
|
|
"0.21851890422627762\n",
|
||
|
|
"-5209\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(min(correl_matrix_smf[:]))\n",
|
||
|
|
"print(min(corr_pf[:]))\n",
|
||
|
|
"print(10000-correl_matrix_smf.index(min(correl_matrix_smf[:])))\n",
|
||
|
|
"print(10000-corr_pf.index(min(corr_pf[:])))\n",
|
||
|
|
"print(max(correl_matrix_smf[:]))\n",
|
||
|
|
"print(10000-correl_matrix_smf.index(max(correl_matrix_smf[:])))\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 65,
|
||
|
|
"id": "23024181",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"correl_matrix_smf = []\n",
|
||
|
|
"corr_pf = []\n",
|
||
|
|
"for var_offset in range (-5000, 5000):\n",
|
||
|
|
" offset_ds = daily_raycount[\"1999\":\"2019\"].shift(periods=var_offset)\n",
|
||
|
|
" correl_matrix_smf = correl_matrix_smf + [daily_quakes['mag'][\"1999\":\"2019\"].corr(offset_ds['MXCO'], method='pearson')]\n",
|
||
|
|
" corr_pf = corr_pf + [daily_quakes['mag'][\"1999\":\"2019\"].corr(offset_ds['MXCO'], method=pearsonr_pval)] \n",
|
||
|
|
"#print(correl_matrix_smt) \n",
|
||
|
|
"\n",
|
||
|
|
"offset_range = []\n",
|
||
|
|
"offset_range.extend(range(-5000,5000))\n",
|
||
|
|
"#print(offset_range)\n",
|
||
|
|
"\n",
|
||
|
|
"plot.scatter(offset_range,correl_matrix_smf[:])\n",
|
||
|
|
"plot.scatter(offset_range,corr_pf[:])\n",
|
||
|
|
"\n",
|
||
|
|
"plot.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 66,
|
||
|
|
"id": "9fd372df",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"correl_matrix_smft = []\n",
|
||
|
|
"corr_pft = []\n",
|
||
|
|
"for var_offset in range (-10000, 10000):\n",
|
||
|
|
" offset_ds = daily_raycount[\"1975\":\"2019\"].shift(periods=var_offset)\n",
|
||
|
|
" correl_matrix_smft = correl_matrix_smft + [sundf[\"tot\"][\"1975\":\"2019\"].corr(offset_ds['MXCO'], method='pearson')]\n",
|
||
|
|
" corr_pft = corr_pft + [sundf[\"tot\"][\"1975\":\"2019\"].corr(offset_ds['MXCO'], method=pearsonr_pval)] \n",
|
||
|
|
"#print(correl_matrix_smt) \n",
|
||
|
|
"\n",
|
||
|
|
"offset_range = []\n",
|
||
|
|
"offset_range.extend(range(-10000,10000))\n",
|
||
|
|
"#print(offset_range)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 68,
|
||
|
|
"id": "d247e560",
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"-0.7123943441942397\n",
|
||
|
|
"8707\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"plot.scatter(offset_range,correl_matrix_smft[:],marker='.', label=\"Daily cosmics (THUL) correlated with daily sunspot count\")\n",
|
||
|
|
"plot.scatter(offset_range,corr_pft[:], marker='.', label = \"p value for correlation, (pearson)\")\n",
|
||
|
|
"plot.legend()\n",
|
||
|
|
"plot.title('Time shifted correlation between cosmic rays and sunspots')\n",
|
||
|
|
"plot.xlabel(\"Timeshift (days)\")\n",
|
||
|
|
"plot.ylabel(\"Correlation and p-value\")\n",
|
||
|
|
"plot.show()\n",
|
||
|
|
"\n",
|
||
|
|
"print(min(correl_matrix_smft[:]))\n",
|
||
|
|
"print(10000-correl_matrix_smft.index(min(correl_matrix_smft[:])))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 69,
|
||
|
|
"id": "a6913936",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:title={'center':'Daily earthquakes'}, xlabel='time'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 69,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"daily_quakes['mag'][\"1975\":\"2019\"].plot(title='Daily earthquakes')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 70,
|
||
|
|
"id": "e20cd960",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='time'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 70,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEGCAYAAABM7t/CAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAh5ElEQVR4nO3deZxcZZkv8N8DEUciI8jiAo4h4DjjBdlyuQwKyjjDSC6CF1EwII444lwQEEUNgTuiFxAhKluQbCxhCZCQEKA7ITvZupv0mu6kO+l9T+97p/dn/qhTSXV1LaeqzqnzVuX3/XzySfc5p855+vSpp0+9532fV1QVRERkrmO8DoCIiCJjoiYiMhwTNRGR4ZioiYgMx0RNRGS4aW7s9JRTTtEZM2a4sWsiorSUl5fXrqqnhlrnSqKeMWMGcnNz3dg1EVFaEpHacOvY9EFEZDgmaiIiwzFRExEZjomaiMhwTNRERIZjoiYiMhwTNRGR4ZioidLEe3ua0DM46nUY5AImaqI0UNcxiJ+9VoA7Xy/wOhRyga1ELSJ3i0iJiOwVkZ+7HBMRxWhobBwA0Nx9yONIyA1RE7WInAPgJwAuBnAegKtF5Gy3AyMiIh87d9T/CCBHVQdVdQzABwCuczcsIiLys5OoSwBcJiIni8jxAGYD+FzwRiJym4jkikhuW1ub03ESER21oiZqVS0F8EcA6wGsA1AIYDzEdotUdZaqzjr11JCV+oiIKA62Hiaq6lJVvUhVLwfQBeCAu2EREZGfrXrUInKaqraKyN/B1z59ibthERGRn92JA94SkZMBjAK4Q1W73QuJiIgC2UrUqnqZ24EQEVFoHJlIRGQ4JmoiIsMxURMRGY6JmojIcEzURESGY6ImIjIcEzVRGlGvAyBXMFETpQHxOgByFRM1EZHhmKiJiAzHRE1EZDgmaiIiwzFRExEZjomaiMhwTNRERIZjoiYiMhwTNRGR4ZioiYgMx0RNRGQ4JmoiIsMxURMRGY6JmojIcEzURGlElRWp0xETNVEaEBakTmtM1GSUfU29WJnX4HUYRLaVNPZgVb671+w0V/dOFKPZT20HAFx/0RkeR0Jkz9VP7wAAXHehe9cs76iJiAzHRE1EZDgmakp5awobUdLY43UYRK5hoqaUd++KImQWN3sdBpFrbCVqEblHRPaKSImILBeRv3E7MCIi8omaqEXkdAB3AZilqucAOBbAjW4HRu5SVTy9qRwtvUNeh0JEUdht+pgG4GMiMg3A8QCa4jnYlrJWfOvpHRif4Ogpr+1t6sWfNhzAncsLvA4lLou3VeGuFI2dKFZRE7WqNgKYD6AOQDOAHlVdH7ydiNwmIrkiktvW1hZyX794sxDFjT3oOTSaYNiUqAlrqPGhkXGPI4nPw5mleKcorvsFopRjp+njJADXAjgTwGcBTBeRm4O3U9VFqjpLVWedeuqpzkdKRHSUstP08S8AqlW1TVVHAawCcKm7YRERkZ+dRF0H4BIROV5EBMA3AJTa2Xlb3zBmzM3Ay9m1icQYl901nZgxNwM7ytuTfmwiIifZaaPOAbASQD6AYus1i1yOi4iILLaKMqnqbwH81uVYiChB7E+VnpI6MrFrkL09yGd8QjHhUTfNiQlNwy6iLEidzjiEnDzx5Qffx1f/uNmTY3/nuV04a16mJ8cmigfrUZMnBkbGMeBRH+6Cum5PjksUL95RExEZLmmJutWlmhKj4xPIqerAwZ7Q+59QRVP3IVeOTRRKU/ch5FR1pGE7OHklaYl6Q2mLK/sdGB7DDYuysbYkdJnLpzaV49JHN6O+c9CV4xMFe6eoCTcsysbI2ITXoVCaSPumj9zaLgDAQVaJI6IUlfaJmogo1bmSqLsHR+J+bUVrH97Ka8DwWOgeAbk1nWHb/tYUNqKovjvuY+fXdeG9Pc5VZDvQ4vtZRsf5EZgiK27owdsFjV6HcdSraO1De/+w12FM4Uqiru+K/+HdjvJ2/HJFEQaHpybqvNpOXP9cFp7cVB7ytQ+8XYK3C+O/2N/Ka8CD7+yN+/XBPtjfhl+uKMIw2yopiveKm/Cbt/Z4HcZR71/+vA1ff3yr12FMkVJNHy29vr905S19HkdCROmqf3jM6xCmSKlETUR0NPI0Ubf1DWNz2ZFue6qKpzZXeBiRuVblN+DN3HqvwyAbypp7vQ7BUwda+pBf1zVleV5tF57cWB72+VOyvbm7HqsLGhzd5/t7Dyb0jC4cTxP1nMXZuPXF3EkP2zoHnP8h08Gq/Ea8sZuJOhW8XXh0TxF25V+24bpnd01Znl/bhb9sPIDRcTMGAi3fXYdV+c49wG3tG8JPX87DT1/Oc2yffp4m6pqOAQCAmvF7IyKKm/8PkBuD69hGTZRO0vCmp6i+G9XtA16H4amUTdRLd1TjivlbHe1Ol4jnd1TjiY0HMD6h+NWKIizeVoWHM30zlq202pYXbKnAom2VIV9f3T6AV3OSP2VZqmjrG8aibZVQVbT1Ja+fa05VBzbsc6f8gZMkhnLUC7ZUYOEHoa/DeKwpbERJY49j+wt27YKduGL+Vtf2nwpSNlHvrGhHdfsA3ikyoz1wZ0U7Npa2QFWxIq8B28rbDq/zD2PfXNaKbQdCz+H47QU7cf/qkqTEmoruWl6ARzLLsK+5N6ndp25YlI2fLMtN2vGSYUtZ66TrM1F3v16Iq5/e4dj+aCqjE/Xw2Dh6Dh0ds8IcLT9nvPzJmRXp6GhkdKK+YWE2zvvdeq/DICLylNGJujCBuh1uWLClAve8UWhr2+3lR5o4sqs6Im6rDnR7OdDShzmLs7GnoXvS8rHxCcxZnO14HYmlO6rxnza7IZU29+IPmaWO/JzJZPd3nYjNZS14cWf14e/fLWrCnMXZGBoN3dd4w74WLMuqcT0uu374/IeHv65q68ecxdlx7+vN3fVY/mEd5izOxvt7DzoRXtrgVFwxKG/pQ0Ecfzza+yP3De8dSrzNtW9oFLsqO9AdNIGwAthV2YFLzzo54WMEqmrrR25tp61tv7cwC31DY7j9irPxiY99xNE43BRq0IbTbn3R1/7906/NBOCbdGBXZQcmwvxR87eX3/JPM1yPzY6sgJuQgeFx7KqMfFMSya8Dap382//4dEJxpRuj76gpTVg5J5aeCUR0BBM1EZHhkpao07Hr2bULdmL++/tdP87v3t2LHRXtqGrrx+WPbcHW/a0Rt7/njULc8Wp+wsfdVNqCyx/bgpoogw2e2VyObz6xLeHjJeLZrZWYMTdj0rKGrkFcMX9r2Pk0z56XiSXbq3D5Y1tQWN+NB9/Zi5uWxNbGurmsBdc9uxMTadYb5ZHMUjy6tsyx/fUOjR4eV5DumnqGHO/rzzvqBDR2HUKnCwVYgr2wswaAb4hqXecgDo1ELmrT2jeEFgemHhscGUdd5yDGJiLX0+4aHEVDAjXI3fJaTh2q2wfwVn7owjtjE4reQ6Oo6xzE8Og4OgZG0Nwd23m7a3kh8uu6MTBiXmnMRCzaVoXnHBwUk5VA23UqCjeHa7yYqImIDMdETURkOCMS9R/WlmLWQxvCru8dCj9qz980WNM+gL6hMexp8NUc8KIGiCpw9v1ro27389cLMPvJ7SHXfVjdiZn3ZWBnReih5sGe2lSOmfdlINYm0oXbqgAAh8L01w2noK4LM+/LwNb9zg1BDmdnRTtmzM3ArS/uRnGYWhIz5mbYKpu5YEsF/j7E7yZS/fPajvBV0PqGRjHzvoyYh7PftbwA37KGW1/we+cGc928JAcAUNU+gBsXZTm2Xzec89v3sSXKc5Z0U9HahxlzM+KuiRK1H7WIfBHAGwGLZgL4L1V9Iq4jhuBvg03E1gO+xJFn1dUw+dlOtHrFscSuGufPmsD5Sda5fSXbV6Rqc1nib+rHXXjoG895CKxN4+R5bA54YJpdZa9/OyXPequw13t7mnHO6Z+I+fVRE7Wq7gdwPgCIyLEAGgGsjvlIREQUl1ibPr4BoFJVY6rH2ROiZ8Tw2HjcMz30D4+hqXtyL4NwI7kOr5/QsEOYI62LZHxiAs3dhzCYwBN/1dDH9i8K1bVsaGwc9Z2DqG4fmNLbIHhP/hn
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"hour_quakes = sframe_notz.resample('1H', origin='1975-01-01').apply(lambda spl: 10*np.log10(np.mean(np.power(10, spl/10))))\n",
|
||
|
|
"hour_quakes['mag'][\"1975\":\"2019\"].plot()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "35181e89",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#morning to do\n",
|
||
|
|
"#add labels to all plots\n",
|
||
|
|
"#run for all cosmic ray stations\n",
|
||
|
|
"#extend seismic data to all events (not just cut off at 4)\n",
|
||
|
|
"#conduct dimensionless analysis (same thing with delta variable in time from frame-frame)\n",
|
||
|
|
"#change the timebase for analysis - 1h, 3h, 6h, 12h, 24h, 5d, 7d, 1m\n",
|
||
|
|
"#quality score for each cosmic ray station (period on, amount of data collected - as a rank)\n",
|
||
|
|
"#random series test correlation\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 71,
|
||
|
|
"id": "924ba397",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x12742eaf0>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 71,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#daily_quakes['mag'] = 1/2 * daily_quakes['mag']\n",
|
||
|
|
"ax = daily_quakes['mag'][\"1975\":\"2019\"].plot(title='Daily earthquakes')\n",
|
||
|
|
"#daily_raycount[\"1975\":\"2019\"].plot(ax=ax1)\n",
|
||
|
|
"\n",
|
||
|
|
"#ax.plot(daily_quakes['mag'][\"1975\":\"2019\"])\n",
|
||
|
|
"ax1 = ax.twinx()\n",
|
||
|
|
"ax1.plot(daily_raycount[\"1975\":\"2019\"], color='red')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 90,
|
||
|
|
"id": "e7013e5d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.collections.PathCollection at 0x17740e0a0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 90,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#plot at 0 intersection (day/day)\n",
|
||
|
|
"plot.scatter(daily_quakes['mag'][\"1990\":\"2019\"],daily_raycount[\"1990\":\"2019\"], marker='.',s=0.1, label=\"Daily cosmics (THUL) vs Earthquake magnitude\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 89,
|
||
|
|
"id": "db2de0da",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.collections.PathCollection at 0x16a475cd0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 89,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#plot at -5000 days intersection\n",
|
||
|
|
"offset_ds_plot = daily_raycount[\"1990\":\"2019\"].shift(periods=-4913)\n",
|
||
|
|
"plot.scatter(daily_quakes['mag'][\"1990\":\"2019\"],offset_ds_plot, marker='.', s=0.1,label=\"Daily cosmics (THUL) vs Earthquake magnitude\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 87,
|
||
|
|
"id": "1c56885f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" MXCO\n",
|
||
|
|
"start_date_time \n",
|
||
|
|
"1990-01-01 NaN\n",
|
||
|
|
"1990-01-02 NaN\n",
|
||
|
|
"1990-01-03 NaN\n",
|
||
|
|
"1990-01-04 NaN\n",
|
||
|
|
"1990-01-05 NaN\n",
|
||
|
|
"... ...\n",
|
||
|
|
"2018-12-27 216.433909\n",
|
||
|
|
"2018-12-28 216.217667\n",
|
||
|
|
"2018-12-29 215.789875\n",
|
||
|
|
"2018-12-30 215.544792\n",
|
||
|
|
"2018-12-31 216.536957\n",
|
||
|
|
"\n",
|
||
|
|
"[10592 rows x 1 columns]\n",
|
||
|
|
"time\n",
|
||
|
|
"1990-01-01 4.993210\n",
|
||
|
|
"1990-01-02 4.835273\n",
|
||
|
|
"1990-01-03 4.509206\n",
|
||
|
|
"1990-01-04 4.700446\n",
|
||
|
|
"1990-01-05 4.823279\n",
|
||
|
|
" ... \n",
|
||
|
|
"2018-12-27 4.570452\n",
|
||
|
|
"2018-12-28 4.559079\n",
|
||
|
|
"2018-12-29 4.673080\n",
|
||
|
|
"2018-12-30 4.510135\n",
|
||
|
|
"2018-12-31 4.512682\n",
|
||
|
|
"Freq: D, Name: mag, Length: 10592, dtype: float64\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#plot at -5000 days intersection (positive correlation - max future)\n",
|
||
|
|
"offset_ds_plot = daily_raycount[\"1990\":\"2018\"].shift(periods=8882)\n",
|
||
|
|
"plot.scatter(daily_quakes['mag'][\"1990\":\"2018\"],offset_ds_plot, marker='.',s=0.1, label=\"Daily cosmics (THUL) vs Earthquake magnitude\")\n",
|
||
|
|
"\n",
|
||
|
|
"print(offset_ds_plot)\n",
|
||
|
|
"print(daily_quakes['mag'][\"1990\":\"2018\"])\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 86,
|
||
|
|
"id": "bfbaf35b",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" MXCO\n",
|
||
|
|
"start_date_time \n",
|
||
|
|
"1992-01-01 NaN\n",
|
||
|
|
"1992-01-02 NaN\n",
|
||
|
|
"1992-01-03 NaN\n",
|
||
|
|
"1992-01-04 NaN\n",
|
||
|
|
"1992-01-05 NaN\n",
|
||
|
|
"... ...\n",
|
||
|
|
"2016-12-27 224.173625\n",
|
||
|
|
"2016-12-28 225.840208\n",
|
||
|
|
"2016-12-29 226.912375\n",
|
||
|
|
"2016-12-30 225.899125\n",
|
||
|
|
"2016-12-31 222.014833\n",
|
||
|
|
"\n",
|
||
|
|
"[9132 rows x 1 columns]\n",
|
||
|
|
" tot sn ss diff\n",
|
||
|
|
"time \n",
|
||
|
|
"1992-01-01 240.0 0.0 -240.0 -240.0\n",
|
||
|
|
"1992-01-02 241.0 39.0 -203.0 -164.0\n",
|
||
|
|
"1992-01-03 301.0 25.0 -277.0 -251.0\n",
|
||
|
|
"1992-01-04 310.0 69.0 -241.0 -171.0\n",
|
||
|
|
"1992-01-05 338.0 85.0 -253.0 -167.0\n",
|
||
|
|
"... ... ... ... ...\n",
|
||
|
|
"2016-12-27 27.0 27.0 0.0 27.0\n",
|
||
|
|
"2016-12-28 11.0 11.0 0.0 11.0\n",
|
||
|
|
"2016-12-29 11.0 11.0 0.0 11.0\n",
|
||
|
|
"2016-12-30 11.0 11.0 0.0 11.0\n",
|
||
|
|
"2016-12-31 41.0 21.0 -21.0 0.0\n",
|
||
|
|
"\n",
|
||
|
|
"[9132 rows x 4 columns]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.collections.PathCollection at 0x1771fbd60>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 86,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#plot at -5000 days intersection (positive correlation - max future)\n",
|
||
|
|
"offset_ds_plotf = daily_raycount[\"1992\":\"2016\"].shift(periods=2000)\n",
|
||
|
|
"print(offset_ds_plotf)\n",
|
||
|
|
"print(daily_sunspots[\"1992\":\"2016\"])\n",
|
||
|
|
"plot.scatter(daily_sunspots['tot'][\"1992\":\"2016\"],offset_ds_plotf, marker='.',s=0.1, label=\"Daily cosmics (THUL) vs Earthquake magnitude\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "6aceb4dc",
|
||
|
|
"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": "3.9.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|