diff --git a/docs/algorithms/AdjustedPhaseOneFunction2.ipynb b/docs/algorithms/AdjustedPhaseOneFunction2.ipynb index 5d26159f65729942566be6f33be79b939bff3f39..1d1cdbe9d68e0e73c50148eca6ed0173480f1a17 100644 --- a/docs/algorithms/AdjustedPhaseOneFunction2.ipynb +++ b/docs/algorithms/AdjustedPhaseOneFunction2.ipynb @@ -1,5502 +1,6317 @@ { - "metadata": { - "name": "", - "signature": "sha256:70ba993e68b55207eaf6b357fb1c7ef3ebe4ae2643324d0a92c40c56ee14262b" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": true, - "input": [ - "import matplotlib as mp\n", - "\n", - "import pandas as pd\n", - "\n", - "import numpy as np\n", - "\n", - "import scipy as sp\n", - "\n", - "import scipy.linalg as spl\n", - "\n", - "import glob\n", - "\n", - "import json\n", - "\n", - "import urllib2\n", - "\n", - "from datetime import datetime \n", - "\n", - "import matplotlib.pyplot as pl\n", - "\n", - "import re\n", - "\n", - "import obspy\n", - "\n", - "from obspy.core import UTCDateTime\n", - "\n", - "import geomagio\n", - "\n", - "from geomagio.edge import EdgeFactory\n", - "\n", - "#from geomagio.Algorithm import DeltaFAlgorithm" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 31 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "factory = EdgeFactory(host='137.227.224.97', port=2060)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 32 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "class Baselines:\n", - "\n", - " \n", - "\n", - " def __init__(self):\n", - "\n", - " self.absH = []\n", - "\n", - " self.absD = []\n", - "\n", - " self.absZ = []\n", - "\n", - " self.baseH = []\n", - "\n", - " self.baseD = []\n", - "\n", - " self.baseZ = []\n", - "\n", - " self.begin = []\n", - "\n", - "\n", - "\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "class AbsOrds:\n", - "\n", - " \n", - "\n", - " def __init__(self):\n", - "\n", - " self.ordp1 = []\n", - "\n", - " self.absp1 = []" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 34 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "def get_baselines_from_file(filename):\n", - "\n", - " f = open(filename)\n", - "\n", - " json12 = f.read()\n", - "\n", - " parsed_json12 = json.loads(json12)\n", - "\n", - " f.close()\n", - "\n", - " bns = Baselines()\n", - "\n", - " for i1 in range(len(parsed_json12)):\n", - "\n", - " if ((parsed_json12[i1]['horizontal_intensity_valid'] == 'Y') & (parsed_json12[i1]['vertical_intensity_valid'] == 'Y') & (parsed_json12[i1]['declination_valid'] == 'Y')):\n", - "\n", - " if ((parsed_json12[i1]['absH'] != None) & (parsed_json12[i1]['absD'] != None) & (parsed_json12[i1]['absZ'] != None) & (parsed_json12[i1]['baseH'] != None) & (parsed_json12[i1]['baseD'] != None) & (parsed_json12[i1]['baseZ'] != None)):\n", - "\n", - " bns.absH.append(parsed_json12[i1]['absH'])\n", - "\n", - " bns.absD.append(parsed_json12[i1]['absD']*np.pi/180)\n", - "\n", - " bns.absZ.append(parsed_json12[i1]['absZ'])\n", - "\n", - " #bns.baseH.append(parsed_json12[i1]['baseH'])\n", - "\n", - " #alternate d undoing small angle approximation in web absolutes\n", - "\n", - " db1 = parsed_json12[i1]['baseD']*np.pi/180\n", - "\n", - " da1 = parsed_json12[i1]['absD']*np.pi/180\n", - "\n", - " do1 = da1 - db1\n", - "\n", - " do2 = np.arctan(do1)\n", - "\n", - " if (do2 >np.pi/2):\n", - "\n", - " do2 = do2 - np.pi/2\n", - "\n", - " if (do2 < -np.pi/2):\n", - "\n", - " do2 = do2 + np.pi/2\n", - "\n", - " db2 = da1 - do2\n", - "\n", - " bns.baseD.append(db2)\n", - "\n", - " #alternate h undoing small angle approximation in web absolutes\n", - "\n", - " hb1 = parsed_json12[i1]['baseH']\n", - "\n", - " ha1 = parsed_json12[i1]['absH']\n", - "\n", - " ho1 = ha1 - hb1\n", - "\n", - " ho2 = ho1/np.cos(do2)\n", - "\n", - " hb2 = ha1 - ho2\n", - "\n", - " bns.baseH.append(hb2)\n", - "\n", - " #bns.baseD.append(parsed_json12[i1]['baseD']*np.pi/180)\n", - "\n", - " bns.baseZ.append(parsed_json12[i1]['baseZ'])\n", - "\n", - " bns.begin.append(parsed_json12[i1]['begin'])\n", - "\n", - " return bns" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 35 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "def get_bns_between_dates(a,b,baselines):\n", - "\n", - " d = next(idx for idx, value in enumerate(baselines.begin) if value > a)\n", - "\n", - " e = next(idx for idx, value in enumerate(baselines.begin) if value > b)\n", - "\n", - " baselines.absH = np.asarray(baselines.absH[d:e])\n", - "\n", - " baselines.absD = np.asarray(baselines.absD[d:e])\n", - "\n", - " baselines.absZ = np.asarray(baselines.absZ[d:e])\n", - "\n", - " baselines.baseH = np.asarray(baselines.baseH[d:e])\n", - "\n", - " baselines.baseD = np.asarray(baselines.baseD[d:e])\n", - "\n", - " baselines.baseZ = np.asarray(baselines.baseZ[d:e])\n", - "\n", - " baselines.begin = np.asarray(baselines.begin[d:e])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 36 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "def remove_outlier_baselines(baselines):\n", - "\n", - " baseHstd = baselines.baseH.std()\n", - "\n", - " baseHave = baselines.baseH.mean()\n", - "\n", - " maskH = (abs(baselines.baseH - baseHave) < baseHstd)\n", - "\n", - " baseDstd = baselines.baseD.std()\n", - "\n", - " baseDave = baselines.baseD.mean()\n", - "\n", - " maskD = (abs(baselines.baseD - baseDave) < baseDstd)\n", - "\n", - " baseZstd = baselines.baseZ.std()\n", - "\n", - " baseZave = baselines.baseZ.mean()\n", - "\n", - " maskZ = (abs(baselines.baseZ - baseZave) < baseZstd)\n", - "\n", - " maskbase = maskH & maskD & maskZ\n", - "\n", - " baselines.absH = baselines.absH[maskbase]\n", - "\n", - " baselines.absD = baselines.absD[maskbase]\n", - "\n", - " baselines.absZ = baselines.absZ[maskbase]\n", - "\n", - " baselines.baseH = baselines.baseH[maskbase]\n", - "\n", - " baselines.baseD = baselines.baseD[maskbase]\n", - "\n", - " baselines.baseZ = baselines.baseZ[maskbase]\n", - "\n", - " baselines.begin = baselines.begin[maskbase]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 37 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "def get_ord_abs_from_baselines(baselines):\n", - "\n", - " absolutes_ordinates = AbsOrds()\n", - "\n", - " ordH = baselines.absH - baselines.baseH\n", - "\n", - " ordD = baselines.absD - baselines.baseD\n", - "\n", - " ordZ = baselines.absZ - baselines.baseZ\n", - "\n", - " ordh = ordH*np.cos(ordD)\n", - "\n", - " orde = ordH*np.sin(ordD)\n", - "\n", - " ordz = ordZ\n", - "\n", - " absh = baselines.absH*np.cos(baselines.absD)\n", - "\n", - " abse = baselines.absH*np.sin(baselines.absD)\n", - "\n", - " absz = baselines.absZ\n", - "\n", - " absolutes_ordinates.absp1 = np.vstack([absh,abse,absz])\n", - "\n", - " absolutes_ordinates.ordp1 = np.vstack([ordh,orde,ordz])\n", - "\n", - " return absolutes_ordinates" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 38 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "def get_transform_from_abs_ords(abs_ords):\n", - "\n", - " tol = 1e-15\n", - " \n", - " ones = np.ones(abs_ords.ordp1[1].shape)\n", - "\n", - " ordp2 = np.vstack([abs_ords.ordp1,ones])\n", - "\n", - " absp2 = np.vstack([abs_ords.absp1,ones])\n", - "\n", - " M, res, rank, sigma = np.linalg.lstsq(ordp2.T,absp2.T)\n", - "\n", - " maskM = np.abs(M) > tol\n", - " \n", - " M = maskM * M\n", - " \n", - " return M.T, res, rank, sigma" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 39 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "def make_adjusted_from_transform_and_raw(M,raw):\n", - "\n", - " h = raw[0]\n", - "\n", - " hd = h.data\n", - "\n", - " e = raw[1]\n", - "\n", - " ed = e.data\n", - "\n", - " z = raw[2]\n", - "\n", - " zd = z.data\n", - "\n", - " raws = np.vstack([hd,ed,zd,np.ones(hd.shape)])\n", - "\n", - " adj = np.dot(M,raws)\n", - "\n", - " return adj" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 40 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - " def save_state(filename, M, PC):\n", - " \"\"\"Save algorithm state to a file.\n", - " File name is filename.\n", - " \"\"\"\n", - " \n", - " data = {\n", - " 'M11': M[0,0],\n", - " 'M12': M[0,1],\n", - " 'M13': M[0,2],\n", - " 'M14': M[0,3],\n", - " 'M21': M[1,0],\n", - " 'M22': M[1,1],\n", - " 'M23': M[1,2],\n", - " 'M24': M[1,3],\n", - " 'M31': M[2,0],\n", - " 'M32': M[2,1],\n", - " 'M33': M[2,2],\n", - " 'M34': M[2,3],\n", - " 'M41': M[3,0],\n", - " 'M42': M[3,1],\n", - " 'M43': M[3,2],\n", - " 'M44': M[3,3],\n", - " 'PC': PC\n", - " }\n", - " with open(filename, 'w') as f:\n", - " f.write(json.dumps(data))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 126 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "bou_bns = get_baselines_from_file('/users/aclaycomb/Documents/boujson12.txt')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 104 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(bou_bns.baseZ)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 105, - "text": [ - "[<matplotlib.lines.Line2D at 0x9f31b30>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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"cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(bou_bns.baseH)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 109, - "text": [ - "[<matplotlib.lines.Line2D at 0xa7df1f0>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "<matplotlib.figure.Figure at 0xa7fb8b0>" - ] - } - ], - "prompt_number": 111 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "bou_abs_ords = get_ord_abs_from_baselines(bou_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 112 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "remove_outlier_baselines(bou_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 113 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "bou_abs_ords.ordp1" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 114, - "text": [ - "array([[ 20819.0225 , 20822.6725 , 20835.5725 , 20830.505 ,\n", - " 20834.92 , 20838.195 , 20839.1875 , 20841.3375 ,\n", - " 20842.5775 , 20855.4525 , 20856.925 , 20853.8875 ,\n", - " 20831.53 , 20832.8425 , 20826.38 , 20828.645 ,\n", - " 20843.1925 , 20832.9675 , 20837.4525 , 20846.0975 ,\n", - " 20830.6325 , 20834.1125 , 20769.14 , 20794.5625 ,\n", - " 20859.625 , 20864.105 , 20833.81 , 20833.975 ,\n", - " 20839.245 , 20840.4875 , 20816.2625 , 20812.6225 ,\n", - " 20812.0275 , 20856.985 , 20866.515 ],\n", - " [ -66.05552143, -69.56509653, -69.58531975, -70.39066131,\n", - " -77.32957443, -98.58777987, -60.17731356, -66.21031332,\n", - " -73.53311902, -101.7679456 , -99.69692926, -103.47704148,\n", - " -75.13988255, -80.45152894, -55.17681569, -65.26578657,\n", - " -111.69252708, -74.91308838, -86.73110189, -106.15381897,\n", - " -107.58356323, -108.28667639, -104.51786586, -102.19585427,\n", - " -107.14591108, -100.2047143 , -96.81981031, -106.12597753,\n", - " -108.38802327, -114.06326124, -90.660629 , -93.93875212,\n", - " -96.43670871, -99.84246126, -98.05981425],\n", - " [ 47390.9975 , 47391.1575 , 47388.755 , 47385.1925 ,\n", - " 47381.635 , 47375.7125 , 47370.3 , 47369.2175 ,\n", - " 47369.5525 , 47371.2225 , 47374.06 , 47378.91 ,\n", - " 47375.375 , 47375.2475 , 47379.9475 , 47377.25 ,\n", - " 47383.3625 , 47377.49 , 47374.835 , 47377.4675 ,\n", - " 47377.0675 , 47379.205 , 47372.7525 , 47376.0625 ,\n", - " 47385.835 , 47388.29 , 47366.6525 , 47366.8075 ,\n", - " 47367.555 , 47369.59 , 47374.9675 , 47375.1025 ,\n", - " 47376.87 , 47387.67 , 47388.185 ]])" - ] - } - ], - "prompt_number": 114 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mbou, resbou, rankbou, sigbou = get_transform_from_abs_ords(bou_abs_ords)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 115 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mbou" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 116, - "text": [ - "array([[ 9.83427577e-01, -1.54730742e-01, 2.73849863e-02,\n", - " -1.27616468e+03],\n", - " [ 1.66801730e-01, 9.87916201e-01, -4.98683323e-03,\n", - " -8.45819261e-01],\n", - " [ -6.72505308e-03, -1.18093515e-02, 9.96186901e-01,\n", - " 9.05380089e+02],\n", - " [ -0.00000000e+00, -0.00000000e+00, 0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 116 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "resbou" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 117, - "text": [ - "array([ 8.12387327e+00, 6.62182726e+00, 2.38802764e+00,\n", - " 2.33828740e-31])" - ] - } - ], - "prompt_number": 117 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rankbou" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 118, - "text": [ - "4" - ] - } - ], - "prompt_number": 118 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfbouJan16 = factory.get_timeseries(observatory='BOU',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 56 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "bouJan16adj = make_adjusted_from_transform_and_raw(Mbou,hezfbouJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 119 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "bouJan16adj[1,1:5]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 120, - "text": [ - "array([ 3127.49144074, 3131.6261902 , 3135.43180642, 3137.51476505])" - ] - } - ], - "prompt_number": 120 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "bouh_pqqm = np.mean(bou_abs_ords.absp1[0] - bou_abs_ords.ordp1[0])\n", - "\n", - "boue_pqqm = np.mean(bou_abs_ords.absp1[1] - bou_abs_ords.ordp1[1])\n", - "\n", - "bouz_pqqm = np.mean(bou_abs_ords.absp1[2] - bou_abs_ords.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 121 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfbouJan16[0].data+bouh_pqqm)**2 + (hezfbouJan16[1].data+boue_pqqm)**2 + (hezfbouJan16[2].data+bouz_pqqm)**2)**(0.5) - hezfbouJan16[3].data + 23.1,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((bouJan16adj[0]**2 + bouJan16adj[1]**2 + bouJan16adj[2]**2)**(0.5) - hezfbouJan16[3].data + 23.1,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 122, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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odbloGVRUmAh8+mlohDOROWQZ5BidOtmPzg9s2FDfDXTwwVaPqFUrGD8eSkvd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\n1LKlTUII7yIxEAnTqhWsXy/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- "text": [ - "<matplotlib.figure.Figure at 0xaf31530>" - ] - } - ], - "prompt_number": 136 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "brw_abs_ord = get_ord_abs_from_baselines(brw_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 137 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mbrw, resbrw, rankbrw, sigbrw = get_transform_from_abs_ords(brw_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 138 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mbrw" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 139, - "text": [ - "array([[ 9.34857870e-01, -3.26009140e-01, 1.26923853e-02,\n", - " -6.01772088e+02],\n", - " [ 3.29840807e-01, 1.08450379e+00, -4.38199362e-02,\n", - " 1.96667652e+03],\n", - " [ -9.26036364e-04, -5.46591946e-05, 9.98670684e-01,\n", - " -6.15282786e+01],\n", - " [ 0.00000000e+00, 0.00000000e+00, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 139 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "resbrw" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 140, - "text": [ - "array([ 5.04178763e+01, 8.74894816e+02, 8.79660086e-01,\n", - " 3.69127163e-31])" - ] - } - ], - "prompt_number": 140 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rankbrw" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 141, - "text": [ - "4" - ] - } - ], - "prompt_number": 141 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sigbrw" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 142, - "text": [ - "array([ 2.99444530e+05, 9.36118621e+02, 1.18394564e+02,\n", - " 2.08605176e-03])" - ] - } - ], - "prompt_number": 142 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfbrwJan16 = factory.get_timeseries(observatory='BRW',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-30T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 143 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "brwJan16adj = make_adjusted_from_transform_and_raw(Mbrw,hezfbrwJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 144 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "brwh_pqqm = np.mean(brw_abs_ord.absp1[0] - brw_abs_ord.ordp1[0])\n", - "\n", - "brwe_pqqm = np.mean(brw_abs_ord.absp1[1] - brw_abs_ord.ordp1[1])\n", - "\n", - "brwz_pqqm = np.mean(brw_abs_ord.absp1[2] - brw_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 145 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfbrwJan16[0].data+brwh_pqqm)**2 + (hezfbrwJan16[1].data+brwe_pqqm)**2 + (hezfbrwJan16[2].data+brwz_pqqm)**2)**(0.5) - hezfbrwJan16[3].data + 1,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((brwJan16adj[0]**2 + brwJan16adj[1]**2 + brwJan16adj[2]**2)**(0.5) - hezfbrwJan16[3].data + 1.0,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 146, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "<matplotlib.figure.Figure at 0xaf31230>" - ] - } - ], - "prompt_number": 149 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "a = UTCDateTime(2015,7,1,0,0,0)\n", - "\n", - "b = UTCDateTime(2015,9,1,0,0,0)\n", - "\n", - "get_bns_between_dates(a,b,cmo_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 150 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(cmo_bns.baseH)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 151, - "text": [ - "[<matplotlib.lines.Line2D at 0xbd44690>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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8MU6lAAAAAElFTkSuQmCC\n", - "text": [ - "<matplotlib.figure.Figure at 0xbd5e390>" - ] - } - ], - "prompt_number": 153 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "cmo_abs_ord = get_ord_abs_from_baselines(cmo_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 154 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mcmo, rescmo, rankcmo, sigcmo = get_transform_from_abs_ords(cmo_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 155 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mcmo" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 156, - "text": [ - "array([[ 9.39880017e-01, -3.08098629e-01, 8.11979835e-03,\n", - " -2.01433612e+02],\n", - " [ 3.22786162e-01, 9.68250360e-01, 1.65168860e-02,\n", - " -8.65385130e+02],\n", - " [ -6.29637516e-03, -4.05108544e-03, 9.91887061e-01,\n", - " 4.72928486e+02],\n", - " [ 2.57949354e-15, -3.27115993e-15, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 156 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rescmo" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 157, - "text": [ - "array([ 4.43079439e-01, 5.71490493e-01, 2.15290921e-01,\n", - " 6.65075443e-32])" - ] - } - ], - "prompt_number": 157 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rankcmo" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 158, - "text": [ - "4" - ] - } - ], - "prompt_number": 158 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sigcmo" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 159, - "text": [ - "array([ 2.34191239e+05, 6.16124599e+01, 4.15099818e+01,\n", - " 6.83659777e-04])" - ] - } - ], - "prompt_number": 159 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfcmoJan16 = factory.get_timeseries(observatory='CMO',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 160 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "cmoJan16adj = make_adjusted_from_transform_and_raw(Mcmo,hezfcmoJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 161 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "cmoh_pqqm = np.mean(cmo_abs_ord.absp1[0] - cmo_abs_ord.ordp1[0])\n", - "\n", - "cmoe_pqqm = np.mean(cmo_abs_ord.absp1[1] - cmo_abs_ord.ordp1[1])\n", - "\n", - "cmoz_pqqm = np.mean(cmo_abs_ord.absp1[2] - cmo_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 162 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfcmoJan16[0].data+cmoh_pqqm)**2 + (hezfcmoJan16[1].data+cmoe_pqqm)**2 + (hezfcmoJan16[2].data+cmoz_pqqm)**2)**(0.5) - hezfcmoJan16[3].data - 7.9,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((cmoJan16adj[0]**2 + cmoJan16adj[1]**2 + cmoJan16adj[2]**2)**(0.5) - hezfcmoJan16[3].data - 7.9,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 163, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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HyrAO/CUh8rcHXsU66Ztho7wOZdday1agrs/zjsC/gdOBlUHK6zQXSrpdc43N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\njwJ/i+N9xWTqVGjY0LYbTrZ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- "text": [ - "<matplotlib.figure.Figure at 0xbab3230>" - ] - } - ], - "prompt_number": 170 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "frd_abs_ord = get_ord_abs_from_baselines(frd_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 171 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mfrd, resfrd, rankfrd, sigfrd = get_transform_from_abs_ords(frd_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 172 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mfrd" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 173, - "text": [ - "array([[ -9.82142394e-01, -1.83524785e-01, 1.28706175e-02,\n", - " -5.26638206e+02],\n", - " [ 1.79292243e-01, -1.00656618e+00, 1.13500368e-01,\n", - " -5.12726686e+03],\n", - " [ 3.98204985e-03, 6.04692528e-03, 1.01122495e+00,\n", - " 9.93776529e+01],\n", - " [ 0.00000000e+00, 0.00000000e+00, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 173 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "resfrd" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 174, - "text": [ - "array([ 1.65574426e+00, 1.93399773e+01, 2.49780320e-01,\n", - " 3.69823701e-31])" - ] - } - ], - "prompt_number": 174 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rankfrd" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 175, - "text": [ - "4" - ] - } - ], - "prompt_number": 175 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sigfrd" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 176, - "text": [ - "array([ 2.53434501e+05, 8.05438116e+01, 1.80397682e+01,\n", - " 5.10446006e-04])" - ] - } - ], - "prompt_number": 176 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezffrdJan16 = factory.get_timeseries(observatory='FRD',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 177 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "frdJan16adj = make_adjusted_from_transform_and_raw(Mfrd,hezffrdJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 178 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "frdh_pqqm = np.mean(frd_abs_ord.absp1[0] - frd_abs_ord.ordp1[0])\n", - "\n", - "frde_pqqm = np.mean(frd_abs_ord.absp1[1] - frd_abs_ord.ordp1[1])\n", - "\n", - "frdz_pqqm = np.mean(frd_abs_ord.absp1[2] - frd_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 179 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezffrdJan16[0].data+frdh_pqqm)**2 + (hezffrdJan16[1].data+frde_pqqm)**2 + (hezffrdJan16[2].data+frdz_pqqm)**2)**(0.5) - hezffrdJan16[3].data + 4.6,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((frdJan16adj[0]**2 + frdJan16adj[1]**2 + frdJan16adj[2]**2)**(0.5) - hezffrdJan16[3].data + 4.6,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 180, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Apu5iWU0VBA2FKoz8FmPp/ds2Ekil3Ba/4/EtYkromIYw0sFKh+mic/ZFpsKg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\nlNb+sB6yOD2YqeuTPplQXQfyypXJ98L98/3BnJf87u5kJz8Og5uOAmo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- "text": [ - "<matplotlib.figure.Figure at 0xbf87130>" - ] - } - ], - "prompt_number": 185 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "frn_abs_ord = get_ord_abs_from_baselines(frn_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 186 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mfrn, resfrn, rankfrn, sigfrn = get_transform_from_abs_ords(frn_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 187 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mfrn" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 188, - "text": [ - "array([[ 9.68565555e-01, -2.00310287e-01, -2.76807061e-02,\n", - " 1.17417624e+03],\n", - " [ 2.42370058e-01, 1.02706749e+00, -1.21082597e-01,\n", - " 4.70604971e+03],\n", - " [ -2.33058122e-02, -1.65549754e-02, 1.02128327e+00,\n", - " -3.03542290e+02],\n", - " [ 0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 188 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "resfrn" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 189, - "text": [ - "array([ 3.27533547e-02, 4.12836189e-01, 2.23735118e-02,\n", - " 1.93869090e-32])" - ] - } - ], - "prompt_number": 189 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rankfrn" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 190, - "text": [ - "4" - ] - } - ], - "prompt_number": 190 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sigfrn" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 191, - "text": [ - "array([ 1.45113868e+05, 3.65063445e+01, 2.10145476e+01,\n", - " 1.23018540e-04])" - ] - } - ], - "prompt_number": 191 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezffrnJan16 = factory.get_timeseries(observatory='FRN',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 192 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "frnJan16adj = make_adjusted_from_transform_and_raw(Mfrn,hezffrnJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 193 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "frnh_pqqm = np.mean(frn_abs_ord.absp1[0] - frn_abs_ord.ordp1[0])\n", - "\n", - "frne_pqqm = np.mean(frn_abs_ord.absp1[1] - frn_abs_ord.ordp1[1])\n", - "\n", - "frnz_pqqm = np.mean(frn_abs_ord.absp1[2] - frn_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 194 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezffrnJan16[0].data+frnh_pqqm)**2 + (hezffrnJan16[1].data+frne_pqqm)**2 + (hezffrnJan16[2].data+frnz_pqqm)**2)**(0.5) - hezffrnJan16[3].data + 6,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((frnJan16adj[0]**2 + frnJan16adj[1]**2 + frnJan16adj[2]**2)**(0.5) - hezffrnJan16[3].data + 6,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 195, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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KLNtZdg9cuZI5e/bYOu/TxcX8UGXl2mG2zUinufv3U9mMrC1dNxuDtxFOGQvr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\nSTZKU9FEhzwe7v3mGxZXVcW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KfBtBC/EyTseOYUci0rTlkuyXBn+/A14AegOLgeeD5VOBGmBr4OBgnepg/Xex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\n8qMDN7ARzgAAAABJRU5ErkJggg==\n", - "text": [ - "<matplotlib.figure.Figure at 0xc5db6d0>" - ] - } - ], - "prompt_number": 198 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "a = UTCDateTime(2015,10,15,0,0,0)\n", - "\n", - "b = UTCDateTime(2015,12,15,0,0,0)\n", - "\n", - "get_bns_between_dates(a,b,gua_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 199 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(gua_bns.baseH)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 200, - "text": [ - "[<matplotlib.lines.Line2D at 0xc761470>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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EREREREREREREREREREREREREREREvPf/Acb6bzM+TQg/AAAAAElF\nTkSuQmCC\n", - "text": [ - "<matplotlib.figure.Figure at 0xc85a370>" - ] - } - ], - "prompt_number": 202 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "gua_abs_ord = get_ord_abs_from_baselines(gua_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 203 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mgua, resgua, rankgua, siggua = get_transform_from_abs_ords(gua_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 204 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mgua" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 205, - "text": [ - "array([[ 9.98443843e-01, -1.93733450e-02, -4.25390474e-03,\n", - " 6.52567915e+02],\n", - " [ 2.02166513e-02, 1.00429017e+00, 7.01363090e-03,\n", - " -6.73172625e+01],\n", - " [ 6.93024175e-04, -4.44958958e-03, 1.00187292e+00,\n", - " 2.19933652e+02],\n", - " [ -0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 205 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "resgua" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 206, - "text": [ - "array([ 1.38786609e-01, 8.45265521e+00, 1.15363688e+00,\n", - " 1.42634942e-30])" - ] - } - ], - "prompt_number": 206 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfguaJan16 = factory.get_timeseries(observatory='GUA',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 207 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "guaJan16adj = make_adjusted_from_transform_and_raw(Mgua,hezfguaJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 208 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "guah_pqqm = np.mean(gua_abs_ord.absp1[0] - gua_abs_ord.ordp1[0])\n", - "\n", - "guae_pqqm = np.mean(gua_abs_ord.absp1[1] - gua_abs_ord.ordp1[1])\n", - "\n", - "guaz_pqqm = np.mean(gua_abs_ord.absp1[2] - gua_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 209 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfguaJan16[0].data+guah_pqqm)**2 + (hezfguaJan16[1].data+guae_pqqm)**2 + (hezfguaJan16[2].data+guaz_pqqm)**2)**(0.5) - hezfguaJan16[3].data - 7.9,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((guaJan16adj[0]**2 + guaJan16adj[1]**2 + guaJan16adj[2]**2)**(0.5) - hezfguaJan16[3].data - 7.9,'k')\n", - "\n", - "pl.ylim(-10,10)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 210, - "text": [ - "(-10, 10)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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ibDTEAdj//yngZ4SnjZ2p2ZHr165Ih14zrKy2lsydcZ+MzlRt5wBs\nRNRh1R4XtnbG8uvIDUs7rwfGesvHY+VIyH47kzYQ6zz7EOuICIuzsD3oIqx0shD78B6CdZqGZYhh\nrD5ER++EsZ1FWGYSO3QvbO28jeiQzaewo70wtPFZrJ+hHOsHuyZBu+7EvlOrgG9lsZ3XYkMIPyb6\nPZoYonaWEX0/Y62j6pDNMLWzKXY0vxQ7GikOQTtFRERERERERERERERERERERERERERERERERPLH\n/weAWL6TwV5uqgAAAABJRU5ErkJggg==\n", - "text": [ - "<matplotlib.figure.Figure at 0xc60a150>" - ] - } - ], - "prompt_number": 213 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "a = UTCDateTime(2015,10,15,0,0,0)\n", - "\n", - "b = UTCDateTime(2015,12,15,0,0,0)\n", - "\n", - "get_bns_between_dates(a,b,hon_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 214 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(hon_bns.baseH)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 215, - "text": [ - "[<matplotlib.lines.Line2D at 0xd2456b0>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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u0EMtCZnIXPrRKiiA/HwYNCj57xVWderYWg4FBZGf//hjm4+oV6/UlitWCgYi\nIZSqpqLhw2Ho0OD2gAmK6vIG06fDgAHBmqE0EgUDkRBKRY+i+fMtUX3zzcl9n3RQVTAoLrapvoPc\ni6hUIsHgQWAFUADMAkqHwNQDngM+AZYDd1dx/khgA7DY3fonUBaRjJLsHkWly1mOHGnLO0r1qkoi\nv/02tGkDRx+d+jLFKpFgMBfoAnQDVgFD3eOlC+B1BY4HbgYOj3C+A/wF6OFurydQFpGMkuyawdy5\nsGULXH118t4jnRx3HKxaZVNalxfUGUojSSQY5AEl7v4CoI27vwloANRyf+4DdlZxjYC3ookEU+fO\nsHbtT28+XiitFdx7r/+L6IRFvXrQqVPF2tqePTYxXVjWh/YqZzAQmOPuv4Hd/DcB67DmpB1VnPc7\nrJlpMnCIR2URSXvZ2bY4yooV3l971iz7edFF3l87nVXOG8yebdN8h2WgXk1xPw+I9KsMA2a7+8Ox\nb/8z3MdXAfWBlkAT4D/AW8DaStd4HPizu38vMA64PlIhRo4c+eN+bm4uuZm8vJKIq7SpqEcP765Z\nVGRTTvz1r5m5yH0iKgeDVM9Qmp+fT35+ftznJ9pMcy1wI/AzoLTCOgF4D5jmPp6M5QNmVnOddlhw\nyYnwnAadiUQwZozNhDlunHfXnDoVnnrKxhYEvStk0MyfD4MHw8KFNjDwqKNsTqdIA9FSIZWDzvoD\nQ4ALKAsEACuBM9z9BkBvrNdRZS3L7V8I+LyYn0i4eJ1E/uEH6z10330KBPHo1s1mk92/H2bOhP79\n/QsE8UgkGDwCNMSakhZjNQKAJ4Bs7Oa+EJgClE64OxHo6e6PxbqfFgB9gcEJlEUk43jdvfTJJ61X\nzKmnenfNTNKwIRx+uOVxwtSLqFQY4r+aiUQicBxbUGXlSmjePLFr7d5tzRqvvWbz80t8BgywXkWP\nPWYLEGVn+1cWzU0kkiGysryrHTz8MOTmKhAkqmdPeOghuOQSfwNBPNSLWCTEuneHW2+11c+aNYu8\nNW9uNYiqegdt3w7jx1sCVBLTo4fVssLWRARqJhIJtV27LIm8ZYtthYVl++W3b7+Fpk0jB4uCAjj4\nYJg40e/fJvx27oRbbrFVzfxOwsfaTKRgIJIB9u+HbdsiB42dO21sQcuWNV9HwkPBQERElEAWEZHY\nKRiIiIiCgYiIKBiIiAgKBiIigoKBiIigYCAiIigYiIgICgYiIoKCgYiIoGAgIiIoGIiICAoGIiKC\ngoGIiKA5vA7ZAAADb0lEQVRgICIiKBiIiAgKBiIigoKBiIigYCAiIigYiIgICgYiIoKCgYiIoGAg\nIiIoGIiICAoGIiKCgoGIiKBgICIiKBiIiAgKBiIigoKBiIiQWDB4EFgBFACzgEbu8WzgKeATYAnQ\nt4rzmwB5wCpgLnBIAmUREZEEJBIM5gJdgG7YDX2oe/xGoAToCvQDxgFZEc6/GwsGnYC33MeSZPn5\n+X4XIW3os/SWPk9/JRIM8rCbPsACoI27fwwwz93fCuwATohw/vnA0+7+08AvEyiLREn/4byjz9Jb\n+jz95VXOYCAwx90vwG70tYD2wPGUBYrymgOF7n6h+1hERHxQu4bn84AWEY4PA2a7+8OBfcAM9/EU\nrHbwEfAF8B5QXMP7OO4mIiIhdC0wH6hXzWvmA50jHF9JWaBp6T6O5HPKgoU2bdq0aYtu+5wU6Q98\nChxa6Xh9oIG73w/Ir+L8B4C73P27gTEel09ERFLgM6wZaLG7TXCPt8O+5S/Hehy1LXfORCyHANa1\n9E3UtVRERERERGrSH6tlfEZZk5LEZx02EHAxsNDfooTSFKzX29JyxzRwMn6RPs+RwAbKWhv6p75Y\nodQW687/KbAMGOQeT5u/z1pYAqQdUAcbzXyMnwUKubXYH4fEpw/Qg4o3rweAO939u1DeKxaRPs8R\nwO/9KU6otQC6u/sNgf9i98q0+fs8GXi93OO70SjlRKwFmvpdiJBrR8Wb10rKxse0oOoecRJZO34a\nDP7gT1HSyj+BM4nx7zPIE9W1BtaXe7zBPSbxcbCE/UfYlCGSOA2c9N7vsIGrkwlxs4aP2mE1rgXE\n+PcZ5GDg+F2ANHMq9kdyNnArVk0X75T27Zb4PY7NWtAd2ITNaybRawi8CPwvsKvSczX+fQY5GGyk\nYrfUtljtQOKzyf25FXgJOMnHsqSLQioOnNziY1nSwRbKblqT0N9oLOpggeBZrJkIYvz7DHIw+Ajo\niFV7soHLgFf8LFCIHQgc5O43AH5OxbZaic8rwDXu/jWU/SeU+LQst38h+huNVhbWrLYc+Gu542n1\n93k2lhn/nLIpsiV27bHeWEuwrmf6LGP3HPAVNg/XeuA6NHAyEZU/z4HAM1j35wLsxqUcTHROw2aQ\nXkLFbrn6+xQRERERERERERERERERERERERERERERERERkfj8P+4sU9UVmDSQAAAAAElFTkSuQmCC\n", - "text": [ - "<matplotlib.figure.Figure at 0xd55bc90>" - ] - } - ], - "prompt_number": 218 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hon_abs_ord = get_ord_abs_from_baselines(hon_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 219 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mhon, reshon, rankhon, sighon = get_transform_from_abs_ords(hon_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 220 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mhon" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 221, - "text": [ - "array([[ 9.99678600e-01, -1.74029673e-01, 2.63717245e-02,\n", - " -9.82568114e+02],\n", - " [ 1.69668389e-01, 1.01995732e+00, -5.55170451e-03,\n", - " 1.74349733e+02],\n", - " [ 1.23732740e-03, -1.69453292e-03, 1.05273966e+00,\n", - " -1.02140068e+03],\n", - " [ 0.00000000e+00, 2.98201676e-15, -1.09662984e-15,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 221 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "reshon" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 222, - "text": [ - "array([ 9.16388677e+00, 4.33161926e+00, 3.69640761e+00,\n", - " 2.63248229e-31])" - ] - } - ], - "prompt_number": 222 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfhonJan16 = factory.get_timeseries(observatory='HON',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 223 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "honJan16adj = make_adjusted_from_transform_and_raw(Mhon,hezfhonJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 224 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "honh_pqqm = np.mean(hon_abs_ord.absp1[0] - hon_abs_ord.ordp1[0])\n", - "\n", - "hone_pqqm = np.mean(hon_abs_ord.absp1[1] - hon_abs_ord.ordp1[1])\n", - "\n", - "honz_pqqm = np.mean(hon_abs_ord.absp1[2] - hon_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 225 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfhonJan16[0].data+honh_pqqm)**2 + (hezfhonJan16[1].data+hone_pqqm)**2 + (hezfhonJan16[2].data+honz_pqqm)**2)**(0.5) - hezfhonJan16[3].data - 14.5,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((honJan16adj[0]**2 + honJan16adj[1]**2 + honJan16adj[2]**2)**(0.5) - hezfhonJan16[3].data - 14.5,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 226, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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EJBwOR84hr1fuXLdOjh0yJPpea8UmEArJyqoqmbR5s+D1SqnfL9XBoPCb3wgf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\n+V4brevxsKm2tlGXZqzFVVU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- "text": [ - "<matplotlib.figure.Figure at 0xd690290>" - ] - } - ], - "prompt_number": 229 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "a = UTCDateTime(2015,10,15,0,0,0)\n", - "\n", - "b = UTCDateTime(2015,12,15,0,0,0)\n", - "\n", - "get_bns_between_dates(a,b,new_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 230 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(new_bns.baseH)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 231, - "text": [ - "[<matplotlib.lines.Line2D at 0xdb47250>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "<matplotlib.figure.Figure at 0xdb246f0>" - ] - } - ], - "prompt_number": 232 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "new_abs_ord = get_ord_abs_from_baselines(new_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 233 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mnew, resnew, ranknew, signew = get_transform_from_abs_ords(new_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 234 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mnew" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 235, - "text": [ - "array([[ 9.70381886e-01, -2.61357358e-01, -5.88339193e-03,\n", - " 3.31498500e+02],\n", - " [ 2.78701300e-01, 9.69226963e-01, 4.60085193e-02,\n", - " -2.56585110e+03],\n", - " [ -1.91365970e-02, 6.40280253e-03, 9.26542509e-01,\n", - " 4.80701668e+03],\n", - " [ 0.00000000e+00, 0.00000000e+00, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 235 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "resnew" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 236, - "text": [ - "array([ 2.77548347e-02, 5.00419296e-01, 2.95001807e-01,\n", - " 5.43664606e-32])" - ] - } - ], - "prompt_number": 236 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfnewJan16 = factory.get_timeseries(observatory='NEW',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 237 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "newJan16adj = make_adjusted_from_transform_and_raw(Mnew,hezfnewJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 238 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "newh_pqqm = np.mean(new_abs_ord.absp1[0] - new_abs_ord.ordp1[0])\n", - "\n", - "newe_pqqm = np.mean(new_abs_ord.absp1[1] - new_abs_ord.ordp1[1])\n", - "\n", - "newz_pqqm = np.mean(new_abs_ord.absp1[2] - new_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 239 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfnewJan16[0].data+newh_pqqm)**2 + (hezfnewJan16[1].data+newe_pqqm)**2 + (hezfnewJan16[2].data+newz_pqqm)**2)**(0.5) - hezfnewJan16[3].data + 7.1,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((newJan16adj[0]**2 + newJan16adj[1]**2 + newJan16adj[2]**2)**(0.5) - hezfnewJan16[3].data + 7.1,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 240, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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NIRpS3t7CA1u3ct6qVYH7cSYQveYq3/Wu/2sfJwAVFRH36af4pk/H55y/hYX8zdVYeWjb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\n5vTpw8bTTw+kEEN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RRcjAgfDZZ/Cf/7iOREQSRVKukD7hBHjrLete+usv19GEx+fLmb4qIhJrCZUc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\nERGRePX/gYyW1rwwkrgAAAAASUVORK5CYII=\n", - "text": [ - "<matplotlib.figure.Figure at 0xd8b9ff0>" - ] - } - ], - "prompt_number": 246 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "shu_abs_ord = get_ord_abs_from_baselines(shu_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 247 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mshu, resshu, rankshu, sigshu = get_transform_from_abs_ords(shu_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 248 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mshu" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 249, - "text": [ - "array([[ 9.88588472e-01, -2.08072460e-01, -6.35567167e-03,\n", - " -1.47139029e+01],\n", - " [ 2.10268162e-01, 9.69217999e-01, -1.82802376e-02,\n", - " 5.17502020e+02],\n", - " [ 1.51436210e-02, 4.47899422e-03, 1.01739086e+00,\n", - " -1.48503633e+03],\n", - " [ 1.97553040e-15, -2.48472033e-15, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 249 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "resshu" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 250, - "text": [ - "array([ 3.73523464e-01, 8.51217195e-01, 1.34919961e-01,\n", - " 1.70566200e-31])" - ] - } - ], - "prompt_number": 250 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfshuJan16 = factory.get_timeseries(observatory='SHU',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 251 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "shuJan16adj = make_adjusted_from_transform_and_raw(Mshu,hezfshuJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 252 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "shuh_pqqm = np.mean(shu_abs_ord.absp1[0] - shu_abs_ord.ordp1[0])\n", - "\n", - "shue_pqqm = np.mean(shu_abs_ord.absp1[1] - shu_abs_ord.ordp1[1])\n", - "\n", - "shuz_pqqm = np.mean(shu_abs_ord.absp1[2] - shu_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 253 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfshuJan16[0].data+shuh_pqqm)**2 + (hezfshuJan16[1].data+shue_pqqm)**2 + (hezfshuJan16[2].data+shuz_pqqm)**2)**(0.5) - hezfshuJan16[3].data - 13.1,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((shuJan16adj[0]**2 + shuJan16adj[1]**2 + shuJan16adj[2]**2)**(0.5) - hezfshuJan16[3].data - 13.1,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 254, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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MM4wxxkyaNMlkZ2ebo4cPt5bbjz59+tS8nj7dXP2XvxjApJx8ssHnM6WlpYabbzZn33tv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\nRtnpdnNPJXTPtT8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- "text": [ - "<matplotlib.figure.Figure at 0xe273570>" - ] - } - ], - "prompt_number": 261 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "sit_abs_ord = get_ord_abs_from_baselines(sit_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 262 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Msit, ressit, ranksit, sigsit = get_transform_from_abs_ords(sit_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 263 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Msit" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 264, - "text": [ - "array([[ 9.59545352e-01, -3.66675295e-01, -1.06533425e-01,\n", - " 5.29374275e+03],\n", - " [ 2.40650589e-01, 1.03242483e+00, -2.66118481e-02,\n", - " 2.86265553e+03],\n", - " [ 2.24111073e-02, 2.01999547e-02, 1.00408997e+00,\n", - " 2.41162824e+02],\n", - " [ 1.07816191e-15, -0.00000000e+00, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 264 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ressit" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 265, - "text": [ - "array([ 1.18381961e+00, 5.83420751e+00, 6.22840607e-01,\n", - " 1.48489761e-31])" - ] - } - ], - "prompt_number": 265 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfsitJan16 = factory.get_timeseries(observatory='SIT',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-30T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 266 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "sitJan16adj = make_adjusted_from_transform_and_raw(Msit,hezfsitJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 267 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "sith_pqqm = np.mean(sit_abs_ord.absp1[0] - sit_abs_ord.ordp1[0])\n", - "\n", - "site_pqqm = np.mean(sit_abs_ord.absp1[1] - sit_abs_ord.ordp1[1])\n", - "\n", - "sitz_pqqm = np.mean(sit_abs_ord.absp1[2] - sit_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 268 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfsitJan16[0].data+sith_pqqm)**2 + (hezfsitJan16[1].data+site_pqqm)**2 + (hezfsitJan16[2].data+sitz_pqqm)**2)**(0.5) - hezfsitJan16[3].data - 3.2,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((sitJan16adj[0]**2 + sitJan16adj[1]**2 + sitJan16adj[2]**2)**(0.5) - hezfsitJan16[3].data - 3.2,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 269, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "<matplotlib.figure.Figure at 0xe896910>" - ] - } - ], - "prompt_number": 276 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "sjg_abs_ord = get_ord_abs_from_baselines(sjg_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 277 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Msjg, ressjg, ranksjg, sigsjg = get_transform_from_abs_ords(sjg_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 278 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Msjg" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 279, - "text": [ - "array([[ 9.90536307e-01, 2.04242590e-01, 3.87150502e-02,\n", - " -1.50422264e+03],\n", - " [ -2.27532793e-01, 9.62960561e-01, 5.14425894e-03,\n", - " 8.62310605e+01],\n", - " [ -6.93279286e-03, 2.77582644e-02, 9.44616720e-01,\n", - " 1.70847335e+03],\n", - " [ 0.00000000e+00, -0.00000000e+00, 0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 279 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ressjg" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 280, - "text": [ - "array([ 1.76264961e+01, 7.71329740e+00, 1.34822012e+01,\n", - " 8.54387814e-31])" - ] - } - ], - "prompt_number": 280 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfsjgJan16 = factory.get_timeseries(observatory='SJG',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 281 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "sjgJan16adj = make_adjusted_from_transform_and_raw(Msjg,hezfsjgJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 282 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "sjgh_pqqm = np.mean(sjg_abs_ord.absp1[0] - sjg_abs_ord.ordp1[0])\n", - "\n", - "sjge_pqqm = np.mean(sjg_abs_ord.absp1[1] - sjg_abs_ord.ordp1[1])\n", - "\n", - "sjgz_pqqm = np.mean(sjg_abs_ord.absp1[2] - sjg_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 283 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfsjgJan16[0].data+sjgh_pqqm)**2 + (hezfsjgJan16[1].data+sjge_pqqm)**2 + (hezfsjgJan16[2].data+sjgz_pqqm)**2)**(0.5) - hezfsjgJan16[3].data + 55,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((sjgJan16adj[0]**2 + sjgJan16adj[1]**2 + sjgJan16adj[2]**2)**(0.5) - hezfsjgJan16[3].data + 55,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 284, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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DAl3XZPtLp7iimpYdPerMrQCrfHLtwWaP6+/QFo+nGGg31ZSCAk8xsMvHT2hvYyzG+Lw8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\naovTtkwB4nPmOM/0tdrqXao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- "text": [ - "<matplotlib.figure.Figure at 0x11f460f0>" - ] - } - ], - "prompt_number": 356 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = UTCDateTime(2015,10,1,0,0,0)\n", - "\n", - "b = UTCDateTime(2016,1,1,0,0,0)\n", - "\n", - "get_bns_between_dates(a,b,bsl_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 357 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(bsl_bns.baseH)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 358, - "text": [ - "[<matplotlib.lines.Line2D at 0x122ab270>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "<matplotlib.figure.Figure at 0x120dcc70>" - ] - } - ], - "prompt_number": 358 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "bsl_abs_ord = get_ord_abs_from_baselines(bsl_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 359 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mbsl, resbsl, rankbsl, sigbsl = get_transform_from_abs_ords(bsl_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 360 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mbsl" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 361, - "text": [ - "array([[ 1.05069413e+00, 1.10714328e-01, -2.11533688e-01,\n", - " 8.16799353e+03],\n", - " [ 2.79831733e-02, 1.06881857e+00, -1.29195735e-01,\n", - " 4.37112281e+03],\n", - " [ -3.85811324e-02, -5.23226381e-02, 1.12178759e+00,\n", - " -4.01891646e+03],\n", - " [ 0.00000000e+00, 0.00000000e+00, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 361 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "resbsl" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 362, - "text": [ - "array([ 2.28382379e+00, 2.91445223e+00, 7.70382134e-01,\n", - " 1.73969738e-32])" - ] - } - ], - "prompt_number": 362 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "hezfbslJan16 = factory.get_timeseries(observatory='BSL',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 363 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "bslJan16adj = make_adjusted_from_transform_and_raw(Mbsl,hezfbslJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 364 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "bslh_pqqm = np.mean(bsl_abs_ord.absp1[0] - bsl_abs_ord.ordp1[0])\n", - "\n", - "bsle_pqqm = np.mean(bsl_abs_ord.absp1[1] - bsl_abs_ord.ordp1[1])\n", - "\n", - "bslz_pqqm = np.mean(bsl_abs_ord.absp1[2] - bsl_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 365 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfbslJan16[0].data+bslh_pqqm)**2 + (hezfbslJan16[1].data+bsle_pqqm)**2 + (hezfbslJan16[2].data+bslz_pqqm)**2)**(0.5) - hezfbslJan16[3].data + 4.2,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((bslJan16adj[0]**2 + bslJan16adj[1]**2 + bslJan16adj[2]**2)**(0.5) - hezfbslJan16[3].data + 4.2,'k')\n", - "\n", - "pl.ylim(-10,10)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 368, - "text": [ - "(-10, 10)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "<matplotlib.figure.Figure at 0xee1b550>" - ] - } - ], - "prompt_number": 287 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "a = UTCDateTime(2015,12,1,0,0,0)\n", - "\n", - "b = UTCDateTime(2016,1,1,0,0,0)\n", - "\n", - "get_bns_between_dates(a,b,tuc_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 288 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(tuc_bns.baseH)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 289, - "text": [ - "[<matplotlib.lines.Line2D at 0x104ea350>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "<matplotlib.figure.Figure at 0xeaee7d0>" - ] - } - ], - "prompt_number": 289 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "tuc_abs_ord = get_ord_abs_from_baselines(tuc_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 290 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mtuc, restuc, ranktuc, sigtuc = get_transform_from_abs_ords(tuc_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 291 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mtuc" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 292, - "text": [ - "array([[ 9.83650649e-01, -2.14063605e-01, 8.68640018e-02,\n", - " -3.67288828e+03],\n", - " [ 2.12187743e-01, 8.89843829e-01, 2.22513804e-01,\n", - " -1.02132303e+04],\n", - " [ -1.15735139e-02, 3.83529111e-02, 8.93747080e-01,\n", - " 4.91693773e+03],\n", - " [ 0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 292 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "restuc" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 293, - "text": [ - "array([ 9.16746608e-01, 2.68686591e+00, 4.59761366e-01,\n", - " 1.26574587e-31])" - ] - } - ], - "prompt_number": 293 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezftucJan16 = factory.get_timeseries(observatory='TUC',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 294 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "tucJan16adj = make_adjusted_from_transform_and_raw(Mtuc,hezftucJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 295 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "tuch_pqqm = np.mean(tuc_abs_ord.absp1[0] - tuc_abs_ord.ordp1[0])\n", - "\n", - "tuce_pqqm = np.mean(tuc_abs_ord.absp1[1] - tuc_abs_ord.ordp1[1])\n", - "\n", - "tucz_pqqm = np.mean(tuc_abs_ord.absp1[2] - tuc_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 296 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezftucJan16[0].data+tuch_pqqm)**2 + (hezftucJan16[1].data+tuce_pqqm)**2 + (hezftucJan16[2].data+tucz_pqqm)**2)**(0.5) - hezftucJan16[3].data + 4.7,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((tucJan16adj[0]**2 + tucJan16adj[1]**2 + tucJan16adj[2]**2)**(0.5) - hezftucJan16[3].data + 4.7,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 297, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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1266.3, 1265.7, 1265.1, 1265.1, 1265.9, 1265.8])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 301 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "ded_bz = np.array([22.4, 22.6, 22.8, 22.5, 22.7, 23.1, 22.6, 23.1, 23.0, 22.7])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 302 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "ded_ah = np.array([8931.4, 8922.9, 8970.2, 8880.4, 8992.0, 9038.4, 8943.7, 8985.0, 8944.5, 8960.9])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 303 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "ded_ad = np.pi/180/60*np.array([1121.7, 1127.8, 1121.7, 1143.9, 1128.7, 1130.9, 1132.0, 1123.5, 1127.0, 1119.4])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 304 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "ded_az = np.array([56850.0, 56825.0, 56840.4, 56834.9, 56856.0, 56816.0, 56832.4, 56831.0, 56809.0, 56834.3])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 305 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "ded_bns = Baselines()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 306 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "ded_bns.baseH = ded_bh\n", - "\n", - "ded_bns.baseD = ded_bd\n", - "\n", - "ded_bns.baseZ = ded_bz\n", - "\n", - "ded_bns.absH = ded_ah\n", - "\n", - "ded_bns.absD = ded_ad\n", - "\n", - "ded_bns.absZ = ded_az" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 307 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(ded_bns.baseH)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 308, - "text": [ - "[<matplotlib.lines.Line2D at 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XXAK33+7P+SLUASRvsdjzOZOPPoIjj4SNGxt+gxJ//eQnsGEDPPhgw49bu9b20NiwIVo9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\nvTbgXMhKSqwNd9T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- "text": [ - "<matplotlib.figure.Figure at 0x105bc770>" - ] - } - ], - "prompt_number": 310 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "ded_abs_ord = get_ord_abs_from_baselines(ded_bns)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 311 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "Mded, resded, rankded, sigded = get_transform_from_abs_ords(ded_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 312 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mded" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 313, - "text": [ - "array([[ 9.17300296e-01, -3.73211099e-01, -5.14226627e-03,\n", - " 2.98482986e+02],\n", - " [ 3.53096520e-01, 9.06733279e-01, 6.61541037e-04,\n", - " -2.98943984e+01],\n", - " [ 3.68482513e-03, -3.82402473e-04, 9.90805880e-01,\n", - " 5.11440783e+02],\n", - " [ 0.00000000e+00, 1.60361633e-15, -0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 313 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "resded" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 314, - "text": [ - "array([ 4.44991815e+00, 1.17540091e+01, 1.07493093e-01,\n", - " 1.95935269e-31])" - ] - } - ], - "prompt_number": 314 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "hezfdedJan16 = factory.get_timeseries(observatory='DED',\n", - "\n", - " interval='minute',\n", - "\n", - " type='variation',\n", - "\n", - " channels=('H', 'E', 'Z', 'F'),\n", - "\n", - " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", - "\n", - " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 315 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "dedJan16adj = make_adjusted_from_transform_and_raw(Mded,hezfdedJan16)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 316 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "dedh_pqqm = np.mean(ded_abs_ord.absp1[0] - ded_abs_ord.ordp1[0])\n", - "\n", - "dede_pqqm = np.mean(ded_abs_ord.absp1[1] - ded_abs_ord.ordp1[1])\n", - "\n", - "dedz_pqqm = np.mean(ded_abs_ord.absp1[2] - ded_abs_ord.ordp1[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 317 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(((hezfdedJan16[0].data+dedh_pqqm)**2 + (hezfdedJan16[1].data+dede_pqqm)**2 + (hezfdedJan16[2].data+dedz_pqqm)**2)**(0.5) - hezfdedJan16[3].data + 0.5,'c')\n", - "\n", - "pl.hold(True)\n", - "\n", - "pl.plot((dedJan16adj[0]**2 + dedJan16adj[1]**2 + dedJan16adj[2]**2)**(0.5) - hezfdedJan16[3].data + 0.5,'k')\n", - "\n", - "pl.ylim(-30,30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 318, - "text": [ - "(-30, 30)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Yly1WM8WaGhg1Krp+rNixA66/nuXLl/OonoerymQAnTlzZvzr6KuLQiXbLhNi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\n0SJi86SDlJVS8dSpBCortQR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mHFjaK1pSfx+ylXOmreqxXc7ZyMnWtPHGxiQB2tFhJ15aIk67iCttvLQX7ixj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\n+IsWL8/EWMR49RgvUefC5cs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relQUeNatHpsKHJJ/Ps96CZv16oqX5LebPl1mGUlm3EmJP2kdf5Jrzzbxp1HZ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\nY8S/Zo14/CMjpceKRThwQDY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pU/CsZ56Rrzl0CH08PR0kbBy5a+neX36JaOCpUzE2Zs9GrqGFCxFdrH+fe/eI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DAAAd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\nAABJRU5ErkJggg==\n", - "text": [ - "<matplotlib.figure.Figure at 0x864b530>" - ] - } - ], - "prompt_number": 65 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "\n", - "fig = pl.figure()\n", - "\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "\n", - "ax.plot(x2,y2,z2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 66, - "text": [ - "[<mpl_toolkits.mplot3d.art3d.Line3D at 0x882add0>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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P1jPbwhNLknHttQrvvmsDYiM3NA3Wr5f4z3+srF5t4aqrdEUfN87HrbfGV/eH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\n5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- "text": [ - "<matplotlib.figure.Figure at 0x8600cb0>" - ] - } - ], - "prompt_number": 66 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "variations2 = np.vstack([x2,y2,z2])\n", - "variations2e = np.vstack([variations2,np.ones(np.shape(x2))])\n", - "\n", - "def make_axis_rotation_matrix(direction, angle):\n", - " \"\"\"\n", - " Create a rotation matrix corresponding to the rotation around a general\n", - " axis by a specified angle.\n", - "\n", - " R = dd^T + cos(a) (I - dd^T) + sin(a) skew(d)\n", - "\n", - " Parameters:\n", - "\n", - " angle : float a\n", - " direction : array d\n", - " \"\"\"\n", - " d = np.array(direction, dtype=np.float64)\n", - " d /= np.linalg.norm(d)\n", - "\n", - " eye = np.eye(3, dtype=np.float64)\n", - " ddt = np.outer(d, d)\n", - " skew = np.array([[ 0, d[2], -d[1]],\n", - " [-d[2], 0, d[0]],\n", - " [d[1], -d[0], 0]], dtype=np.float64)\n", - "\n", - " mtx = ddt + np.cos(angle) * (eye - ddt) + np.sin(angle) * skew\n", - " return mtx\n", - " \n", - "Mrot = make_axis_rotation_matrix(np.array([0.01,0.01,1]),10*np.pi/180)\n", - "Mrot" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 69, - "text": [ - "array([[ 0.98480927, 0.17363233, -0.00158442],\n", - " [-0.1736293 , 0.98480927, 0.0018882 ],\n", - " [ 0.0018882 , -0.00158442, 0.99999696]])" - ] - } - ], - "prompt_number": 69 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mscale = np.array([[1.5, 0, 0], [0, 0.95, 0], [0, 0, 1.2]])\n", - "Mscale" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 70, - "text": [ - "array([[ 1.5 , 0. , 0. ],\n", - " [ 0. , 0.95, 0. ],\n", - " [ 0. , 0. , 1.2 ]])" - ] - } - ], - "prompt_number": 70 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Vtranslate = np.array([[5],[10],[7]])\n", - "Vtranslate" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 71, - "text": [ - "array([[ 5],\n", - " [10],\n", - " [ 7]])" - ] - } - ], - "prompt_number": 71 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Maugmented = np.hstack([np.dot(Mscale,Mrot), Vtranslate])\n", - "Maugmented" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 72, - "text": [ - "array([[ 1.47721391e+00, 2.60448502e-01, -2.37662409e-03,\n", - " 5.00000000e+00],\n", - " [ -1.64947832e-01, 9.35568808e-01, 1.79379023e-03,\n", - " 1.00000000e+01],\n", - " [ 2.26584030e-03, -1.90129928e-03, 1.19999635e+00,\n", - " 7.00000000e+00]])" - ] - } - ], - "prompt_number": 72 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Maffine = np.vstack([Maugmented, np.array([0,0,0,1])])\n", - "Maffine" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 73, - "text": [ - "array([[ 1.47721391e+00, 2.60448502e-01, -2.37662409e-03,\n", - " 5.00000000e+00],\n", - " [ -1.64947832e-01, 9.35568808e-01, 1.79379023e-03,\n", - " 1.00000000e+01],\n", - " [ 2.26584030e-03, -1.90129928e-03, 1.19999635e+00,\n", - " 7.00000000e+00],\n", - " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 73 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "hez2 = np.dot(Maffine,variations2e)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 74 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig2 = pl.figure()\n", - "\n", - "ax2 = fig2.add_subplot(111,projection='3d')\n", - "ax2.set_xlim3d(-2,12)\n", - "ax2.set_ylim3d(-2,12)\n", - "ax2.set_zlim3d(-2,12)\n", - "ax2.plot(hez2[0,:],hez2[1,:],hez2[2,:],'r')\n", - "ax2.hold(True)\n", - "ax2.plot(x2,y2,z2,'b')\n", - "pl.savefig('hezftrial.png')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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8663gvv8egWuuSR+zcyc8c+ci+swzcB1JH+a+/x6Bm25C7E9/gv9IdlpywACI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\n7Ys8a02rsy+Lujpg5UoeK1bwWLxYzfJ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XjQ2D2NXFEtAwUT8hsmczQfz5zwfrSiUSvA9tTn5bvJgett+acertDwwA//Zv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\nxvs9doyfx803668HZL6HIi+P3rZMdLlgAb1700h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4wCgOWfyIu5vAOuFnlBBOUhIDDSJNaFr3aVB+ZisTODExOGu4Qwdv9f1DIdaB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\n4Ua/fnkxwl45dYpDXrf4XRlSUrg/u8/7uusY9x4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11bJKVPfu0Apv3MjtO3Qs7t5FX2jWzDZX/cQJaNUNGpgrWdlBXByI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v9HR+PXkyUWioffamTeMcbm1kNGgQz6nYhRa579xJtGULSxqHD9tnj4hozx6i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\nObo6eNCcveXLOVrWS7NmvMD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- "text": [ - "<matplotlib.figure.Figure at 0x9225070>" - ] - } - ], - "prompt_number": 82 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#ex_bns = Baselines()\n", - "#ex_bns.baseH = x2[:250:] - hez2[0,:250:]\n", - "#ex_bns.baseD = y2[:250:]\n", - "ex_abs_ord = AbsOrds()\n", - "ex_abs_ord.ordp1 = hez2[0:3,::250]\n", - "ex_abs_ord.absp1 = variations2e[0:3,::250]\n", - "ex_abs_ord.absp1" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 93, - "text": [ - "array([[ -1.00000000e+00, -8.99979996e-01, -7.99959992e-01,\n", - " -6.99939988e-01, -5.99919984e-01, -4.99899980e-01,\n", - " -3.99879976e-01, -2.99859972e-01, -1.99839968e-01,\n", - " -9.98199640e-02, 2.00040008e-04, 1.00220044e-01,\n", - " 2.00240048e-01, 3.00260052e-01, 4.00280056e-01,\n", - " 5.00300060e-01, 6.00320064e-01, 7.00340068e-01,\n", - " 8.00360072e-01, 9.00380076e-01],\n", - " [ 0.00000000e+00, -3.52482410e-01, 1.84565725e-01,\n", - " 2.22236611e-01, -6.48677759e-01, 8.66076986e-01,\n", - " -7.39073400e-01, 2.90003412e-01, 3.08400175e-01,\n", - " -8.08927289e-01, 9.99971544e-01, -8.00064792e-01,\n", - " 2.94313784e-01, 3.03637505e-01, -7.47022884e-01,\n", - " 8.65796698e-01, -6.41300219e-01, 2.11858001e-01,\n", - " 1.92984248e-01, -3.55634973e-01],\n", - " [ 0.00000000e+00, -2.56499819e-01, 5.70963663e-01,\n", - " -6.78745094e-01, 4.68308849e-01, 3.26570193e-03,\n", - " -5.42094563e-01, 9.08835529e-01, -9.30028666e-01,\n", - " 5.79372605e-01, 7.54125900e-03, -5.91483112e-01,\n", - " 9.34496292e-01, -9.04238999e-01, 5.30784973e-01,\n", - " 9.79430651e-03, -4.77859655e-01, 6.81645052e-01,\n", - " -5.67609756e-01, 2.50678049e-01]])" - ] - } - ], - "prompt_number": 93 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mex, resex, rankx, sigx = get_transform_from_abs_ords(ex_abs_ord)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 94 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mex" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 95, - "text": [ - "array([[ 6.56539515e-01, -1.82767681e-01, 1.57350020e-03,\n", - " -1.46603527e+00],\n", - " [ 1.15754890e-01, 1.03664134e+00, -1.32034672e-03,\n", - " -1.09359454e+01],\n", - " [ -1.05627738e-03, 1.98757921e-03, 8.33330802e-01,\n", - " -5.84791002e+00],\n", - " [ 0.00000000e+00, -0.00000000e+00, 0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 95 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mexi = np.linalg.inv(Mex)\n", - "Mexi" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 96, - "text": [ - "array([[ 1.47721391e+00, 2.60448502e-01, -2.37662409e-03,\n", - " 5.00000000e+00],\n", - " [ -1.64947832e-01, 9.35568808e-01, 1.79379023e-03,\n", - " 1.00000000e+01],\n", - " [ 2.26584030e-03, -1.90129928e-03, 1.19999635e+00,\n", - " 7.00000000e+00],\n", - " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 1.00000000e+00]])" - ] - } - ], - "prompt_number": 96 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Mdifference = Mexi - Maffine\n", - "Mdifference" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 97, - "text": [ - "array([[ 1.33226763e-15, -1.11022302e-16, 9.28944421e-16,\n", - " 1.06581410e-14],\n", - " [ 9.15933995e-16, -2.22044605e-16, 1.26005976e-15,\n", - " 1.42108547e-14],\n", - " [ 5.52943108e-16, -3.27429056e-16, 1.77635684e-15,\n", - " 8.88178420e-15],\n", - " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 1.11022302e-15]])" - ] - } - ], - "prompt_number": 97 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "h2Trace = obspy.Trace(data=hez2[0])\n", - "e2Trace = obspy.Trace(data=hez2[1])\n", - "z2Trace = obspy.Trace(data=hez2[2])\n", - "hez2Stream = obspy.Stream(traces=(h2Trace,e2Trace,z2Trace))\n", - "adj_ex = make_adjusted_from_transform_and_raw(Mex,hez2Stream)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 98 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.plot(hez2[0],'r')\n", - "pl.plot(x2,'b')\n", - "pl.plot(adj_ex[0],'k')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 99, - "text": [ - "[<matplotlib.lines.Line2D at 0x9beaef0>]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Zz7+fOJGoZ8/A2Vu8mMNfLhc/nj2bqGXLwNn76CPO4dZHRsOG8ZxKoNA99127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\nBEEQBEEQBEEQcvL/alkqoXQtdWwAAAAASUVORK5CYII=\n", - "text": [ - "<matplotlib.figure.Figure at 0xa5aa350>" - ] - } - ], - "prompt_number": 102 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adjusted Data Phase One, Function-based" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "import applicable libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import matplotlib as mp\n", + "\n", + "import pandas as pd\n", + "\n", + "import numpy as np\n", + "\n", + "import scipy as sp\n", + "\n", + "import scipy.linalg as spl\n", + "\n", + "import glob\n", + "\n", + "import json\n", + "\n", + "import urllib2\n", + "\n", + "from datetime import datetime \n", + "\n", + "import matplotlib.pyplot as pl\n", + "\n", + "import re\n", + "\n", + "import obspy\n", + "\n", + "from obspy.core import UTCDateTime\n", + "\n", + "import geomagio\n", + "\n", + "from geomagio.edge import EdgeFactory\n", + "\n", + "#from geomagio.Algorithm import DeltaFAlgorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set up edge factory" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "factory = EdgeFactory(host='137.227.224.97', port=2060)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Class used as a container for absolute $[ H, D, Z ]^T$, baseline $[ H, D, Z ]^T$, and start times of absolute measurements" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Baselines:\n", + "\n", + " \n", + "\n", + " def __init__(self):\n", + "\n", + " self.absH = []\n", + "\n", + " self.absD = []\n", + "\n", + " self.absZ = []\n", + "\n", + " self.baseH = []\n", + "\n", + " self.baseD = []\n", + "\n", + " self.baseZ = []\n", + "\n", + " self.begin = []\n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Class containing absolutes ($[ X, Y, Z ]^T$) and ordinates ($[ h, e, z]^T$). Conversion of coordinates happens in <pre>get_ord_abs_from_baselines</pre>" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class AbsOrds:\n", + "\n", + " \n", + "\n", + " def __init__(self):\n", + "\n", + " self.ordp1 = []\n", + "\n", + " self.absp1 = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function that reads a json file output from the <pre>geomag-baseline-calculator</pre> webservice (currently requires login, url <pre>https://geohazards.usgs.gov/baselines/baseline_data.php?observatoryId=3&startTime=1419630615&endTime=1453326615</pre>" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_baselines_from_file(filename):\n", + "\n", + " f = open(filename)\n", + "\n", + " json12 = f.read()\n", + "\n", + " parsed_json12 = json.loads(json12)\n", + "\n", + " f.close()\n", + "\n", + " bns = Baselines()\n", + "\n", + " for i1 in range(len(parsed_json12)):\n", + "\n", + " if ((parsed_json12[i1]['horizontal_intensity_valid'] == 'Y') & (parsed_json12[i1]['vertical_intensity_valid'] == 'Y') & (parsed_json12[i1]['declination_valid'] == 'Y')):\n", + "\n", + " if ((parsed_json12[i1]['absH'] != None) & (parsed_json12[i1]['absD'] != None) & (parsed_json12[i1]['absZ'] != None) & (parsed_json12[i1]['baseH'] != None) & (parsed_json12[i1]['baseD'] != None) & (parsed_json12[i1]['baseZ'] != None)):\n", + "\n", + " bns.absH.append(parsed_json12[i1]['absH'])\n", + "\n", + " bns.absD.append(parsed_json12[i1]['absD']*np.pi/180)\n", + "\n", + " bns.absZ.append(parsed_json12[i1]['absZ'])\n", + "\n", + " #bns.baseH.append(parsed_json12[i1]['baseH'])\n", + "\n", + " #alternate d undoing small angle approximation in web absolutes\n", + "\n", + " db1 = parsed_json12[i1]['baseD']*np.pi/180\n", + "\n", + " da1 = parsed_json12[i1]['absD']*np.pi/180\n", + "\n", + " do1 = da1 - db1\n", + "\n", + " do2 = np.arctan(do1)\n", + "\n", + " if (do2 >np.pi/2):\n", + "\n", + " do2 = do2 - np.pi/2\n", + "\n", + " if (do2 < -np.pi/2):\n", + "\n", + " do2 = do2 + np.pi/2\n", + "\n", + " db2 = da1 - do2\n", + "\n", + " bns.baseD.append(db2)\n", + "\n", + " #alternate h undoing small angle approximation in web absolutes\n", + "\n", + " hb1 = parsed_json12[i1]['baseH']\n", + "\n", + " ha1 = parsed_json12[i1]['absH']\n", + "\n", + " ho1 = ha1 - hb1\n", + "\n", + " ho2 = ho1/np.cos(do2)\n", + "\n", + " hb2 = ha1 - ho2\n", + "\n", + " bns.baseH.append(hb2)\n", + "\n", + " #bns.baseD.append(parsed_json12[i1]['baseD']*np.pi/180)\n", + "\n", + " bns.baseZ.append(parsed_json12[i1]['baseZ'])\n", + "\n", + " bns.begin.append(parsed_json12[i1]['begin'])\n", + "\n", + " return bns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function that gets baselines between a given date pair" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_bns_between_dates(a,b,baselines):\n", + "\n", + " d = next(idx for idx, value in enumerate(baselines.begin) if value > a)\n", + "\n", + " e = next(idx for idx, value in enumerate(baselines.begin) if value > b)\n", + "\n", + " baselines.absH = np.asarray(baselines.absH[d:e])\n", + "\n", + " baselines.absD = np.asarray(baselines.absD[d:e])\n", + "\n", + " baselines.absZ = np.asarray(baselines.absZ[d:e])\n", + "\n", + " baselines.baseH = np.asarray(baselines.baseH[d:e])\n", + "\n", + " baselines.baseD = np.asarray(baselines.baseD[d:e])\n", + "\n", + " baselines.baseZ = np.asarray(baselines.baseZ[d:e])\n", + "\n", + " baselines.begin = np.asarray(baselines.begin[d:e])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function that removes baselines above and below one standard deviation from the mean" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def remove_outlier_baselines(baselines):\n", + "\n", + " baseHstd = baselines.baseH.std()\n", + "\n", + " baseHave = baselines.baseH.mean()\n", + "\n", + " maskH = (abs(baselines.baseH - baseHave) < baseHstd)\n", + "\n", + " baseDstd = baselines.baseD.std()\n", + "\n", + " baseDave = baselines.baseD.mean()\n", + "\n", + " maskD = (abs(baselines.baseD - baseDave) < baseDstd)\n", + "\n", + " baseZstd = baselines.baseZ.std()\n", + "\n", + " baseZave = baselines.baseZ.mean()\n", + "\n", + " maskZ = (abs(baselines.baseZ - baseZave) < baseZstd)\n", + "\n", + " maskbase = maskH & maskD & maskZ\n", + "\n", + " baselines.absH = baselines.absH[maskbase]\n", + "\n", + " baselines.absD = baselines.absD[maskbase]\n", + "\n", + " baselines.absZ = baselines.absZ[maskbase]\n", + "\n", + " baselines.baseH = baselines.baseH[maskbase]\n", + "\n", + " baselines.baseD = baselines.baseD[maskbase]\n", + "\n", + " baselines.baseZ = baselines.baseZ[maskbase]\n", + "\n", + " baselines.begin = baselines.begin[maskbase]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function that converts $[ H, D, Z ]^T$ in ordinate, absolute (absolute = ordinate + baseline) to $[ h, e, z ]^T$ and $[ X, Y, Z ]^T$" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_ord_abs_from_baselines(baselines):\n", + "\n", + " absolutes_ordinates = AbsOrds()\n", + "\n", + " ordH = baselines.absH - baselines.baseH\n", + "\n", + " ordD = baselines.absD - baselines.baseD\n", + "\n", + " ordZ = baselines.absZ - baselines.baseZ\n", + "\n", + " ordh = ordH*np.cos(ordD)\n", + "\n", + " orde = ordH*np.sin(ordD)\n", + "\n", + " ordz = ordZ\n", + "\n", + " absh = baselines.absH*np.cos(baselines.absD)\n", + "\n", + " abse = baselines.absH*np.sin(baselines.absD)\n", + "\n", + " absz = baselines.absZ\n", + "\n", + " absolutes_ordinates.absp1 = np.vstack([absh,abse,absz])\n", + "\n", + " absolutes_ordinates.ordp1 = np.vstack([ordh,orde,ordz])\n", + "\n", + " return absolutes_ordinates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function that computes the affine transform matrix from the absolutes using numpy's least squares function of the linear algebra library. More robust than, but similar to the matrix multiplication method that would compute the 2-norm-minimizing solution $[M]$ of:\n", + "$$ \\left[\\begin{array}{cccc} X_1 & X_2 & \\ldots & X_n \\\\ \n", + " Y_1 & Y_2 & \\ldots & Y_n \\\\\n", + " Z_1 & Z_2 & \\ldots & Z_n \\\\\n", + " 1 & 1 & \\ldots & 1 \\end{array}\\right] = [M] \\left[ \\begin{array}{cccc} h_1 & h_2 & \\ldots & h_n \\\\ \n", + " e_1 & e_2 & \\ldots & e_n \\\\\n", + " z_1 & z_2 & \\ldots & z_n \\\\\n", + " 1 & 1 & \\ldots & 1 \\end{array}\\right] $$" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_transform_from_abs_ords(abs_ords):\n", + "\n", + " tol = 1e-15\n", + " \n", + " ones = np.ones(abs_ords.ordp1[1].shape)\n", + "\n", + " ordp2 = np.vstack([abs_ords.ordp1,ones])\n", + "\n", + " absp2 = np.vstack([abs_ords.absp1,ones])\n", + "\n", + " M, res, rank, sigma = np.linalg.lstsq(ordp2.T,absp2.T)\n", + "\n", + " maskM = np.abs(M) > tol\n", + " \n", + " M = maskM * M\n", + " \n", + " return M.T, res, rank, sigma" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function that applies the first equation (matrix-multiplies $[M]$ by the array of $[ h, e, z, 1]^T$ vectors\n", + "in the previous markdown comment block)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_adjusted_from_transform_and_raw(M,raw):\n", + "\n", + " h = raw[0]\n", + "\n", + " hd = h.data\n", + "\n", + " e = raw[1]\n", + "\n", + " ed = e.data\n", + "\n", + " z = raw[2]\n", + "\n", + " zd = z.data\n", + "\n", + " raws = np.vstack([hd,ed,zd,np.ones(hd.shape)])\n", + "\n", + " adj = np.dot(M,raws)\n", + "\n", + " return adj" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function that saves the computed matrix, as well as the pier correction, in json format" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + " def save_state(filename, M, PC):\n", + " \"\"\"Save algorithm state to a file.\n", + " File name is filename.\n", + " \"\"\"\n", + " \n", + " data = {\n", + " 'M11': M[0,0],\n", + " 'M12': M[0,1],\n", + " 'M13': M[0,2],\n", + " 'M14': M[0,3],\n", + " 'M21': M[1,0],\n", + " 'M22': M[1,1],\n", + " 'M23': M[1,2],\n", + " 'M24': M[1,3],\n", + " 'M31': M[2,0],\n", + " 'M32': M[2,1],\n", + " 'M33': M[2,2],\n", + " 'M34': M[2,3],\n", + " 'M41': M[3,0],\n", + " 'M42': M[3,1],\n", + " 'M43': M[3,2],\n", + " 'M44': M[3,3],\n", + " 'PC': PC\n", + " }\n", + " with open(filename, 'w') as f:\n", + " f.write(json.dumps(data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example Problem Demonstrating the Adjusted Data Algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example function. x1 takes the place of time, x2, y2, and z2 take the place of magnetic measurements in the three axes. Chosen to show how different affine linear transformations effect orientation, location, stretch, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x1 = np.linspace(0,np.pi, 5000)\n", + "\n", + "x2 = (2*x1 - np.pi)/np.pi" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y2 = np.sqrt(1 - x2**2)*np.cos(24*x1)\n", + "\n", + "z2 = np.sqrt(1 - x2**2)*np.sin(24*x1)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0xcc923c8>]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JuCb4+c+BBx8E7r0XeOYZOzFc2kFxSWHAvRIIJkmp1V2aSbm3V26Xay0rgf5+VoxRqhGw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\nLcCWLXx7eBj4n/8BbrtNNkY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T9IsCzBZGEeQg00ggz3KQspfGUarEqe7ODqDHpk18u3xvae0ViQTSRAImUX8a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\np8zpJF2SnK69MkcCnCTQ3k7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hiBLS1aohb6BKFeXf9aNHYOgbN6LWUJcu6DPRrZvyPR9iYtDfYvFiPM+HH+I+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NAAAf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\nr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aePttP3fcIZOfD/3765lt4YklhXHTTQoffmgDYiM3NA1Wr5b473+tLF9u4frr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6a/URFcXLIWZINz8MEvOzMqvtLW29yEea+Zah+3282bb77J0aNHWbx4McWj9cH8B37cVJGu1+tF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\nIAD4P4iwz5yW0mmLAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0xcbc3080>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "fig = pl.figure()\n", + "\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "ax.plot(x2,y2,z2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to rotate axes. Makes a rotation matrix $M_{rot}$" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.80473785, 0.50587936, -0.31061722],\n", + " [-0.31061722, 0.80473785, 0.50587936],\n", + " [ 0.50587936, -0.31061722, 0.80473785]])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variations2 = np.vstack([x2,y2,z2])\n", + "variations2e = np.vstack([variations2,np.ones(np.shape(x2))])\n", + "\n", + "def make_axis_rotation_matrix(direction, angle):\n", + " \"\"\"\n", + " Create a rotation matrix corresponding to the rotation around a general\n", + " axis by a specified angle.\n", + "\n", + " R = dd^T + cos(a) (I - dd^T) + sin(a) skew(d)\n", + "\n", + " Parameters:\n", + "\n", + " angle : float a\n", + " direction : array d\n", + " \"\"\"\n", + " d = np.array(direction, dtype=np.float64)\n", + " d /= np.linalg.norm(d)\n", + "\n", + " eye = np.eye(3, dtype=np.float64)\n", + " ddt = np.outer(d, d)\n", + " skew = np.array([[ 0, d[2], -d[1]],\n", + " [-d[2], 0, d[0]],\n", + " [d[1], -d[0], 0]], dtype=np.float64)\n", + "\n", + " mtx = ddt + np.cos(angle) * (eye - ddt) + np.sin(angle) * skew\n", + " return mtx\n", + " \n", + "Mrot = make_axis_rotation_matrix(np.array([1,1,1]),45*np.pi/180)\n", + "Mrot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matrix to change the scale (stretch) of the data (think scale value, but nanotesla to nanotesla scale). An entry of 1.5 means the sensor reports 1.5 nT for a field value of 1 nT $M_{scale}$" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.5 , 0. , 0. ],\n", + " [ 0. , 0.95, 0. ],\n", + " [ 0. , 0. , 1.2 ]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mscale = np.array([[1.5, 0, 0], [0, 0.95, 0], [0, 0, 1.2]])\n", + "Mscale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Translation vector, or offset. This says that at a field of 0 nT, the sensor reports a non-zero value of the field strength. $V_{trans}$" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5],\n", + " [10],\n", + " [ 7]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Vtranslate = np.array([[5],[10],[7]])\n", + "Vtranslate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final matrix combines these operations. Here I've taken the shortcut to put the translation in, and multiply the 3x3 matrices $M_{3x3} = M_{rot}*M_{scale}$, then this is augmented by $V_{trans}$ on the right, and $[0, 0, 0, 1]$ on the bottom. this is the form of an affine transformation matrix in 3 dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.20710678, 0.75881905, -0.46592583, 5. ],\n", + " [ -0.29508636, 0.76450096, 0.4805854 , 10. ],\n", + " [ 0.60705524, -0.37274066, 0.96568542, 7. ]])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Maugmented = np.hstack([np.dot(Mscale,Mrot), Vtranslate])\n", + "Maugmented" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.20710678, 0.75881905, -0.46592583, 5. ],\n", + " [ -0.29508636, 0.76450096, 0.4805854 , 10. ],\n", + " [ 0.60705524, -0.37274066, 0.96568542, 7. ],\n", + " [ 0. , 0. , 0. , 1. ]])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Maffine = np.vstack([Maugmented, np.array([0,0,0,1])])\n", + "Maffine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here I apply the transformation the sensor might (the rotation is a bit much but it's illustrative)." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "hez2 = np.dot(Maffine,variations2e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is a visual representation of the true field values (blue) and the sensor's field values (red)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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nTx88//zzep9nxZtXkKQbjUZRX18vPhh6iRNIlcnTUxqS9aNRyUOtZRYJVDpR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\nfb/7MMGzomvMxSp+pLPpvqmF329DXxn7yq8SAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0xce3ca58>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig2 = pl.figure()\n", + "\n", + "ax2 = fig2.add_subplot(111,projection='3d')\n", + "ax2.set_xlim3d(-2,12)\n", + "ax2.set_ylim3d(-2,12)\n", + "ax2.set_zlim3d(-2,12)\n", + "ax2.plot(hez2[0,:],hez2[1,:],hez2[2,:],'r')\n", + "ax2.hold(True)\n", + "ax2.plot(x2,y2,z2,'b')\n", + "pl.savefig('hezftrial.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.79289322, 3.8146376 , 3.82363296, 3.83043869, 3.83607063])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hez2[0,0:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the sensor data viewed from the true axes:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0xd480898>]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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I7hQzk40bod0qhdR6YsYM/fXh5XyFb74RM8l16cI8b57+/WNjYX/Onz99mWh3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fXxhc53D977+RtNWSyz5zxt63RarkMTIS12fxYuX7XbMG33vhQu3pm927cR5r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\nYRMI7HZ+z1deSduBu1pv1iyOR2OwbZtL0M+ZE5jJZutWLoT8wW6n8fi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Thn78UeqYw4cz37lj+TXpQ2IiBkLv3oiH7t8fsc5ZmW2bGdLSmG/fhuAYMQK0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+ "text/plain": [ + "<matplotlib.figure.Figure at 0xd8bdb00>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(hez2[1,:],hez2[2,:],'r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the time series are shown, true data in blue, sensor data in red" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0xd7c0dd8>]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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XlStBEASXIApfEATBJYjCFwRBcAmi8AVBEFyCKHxBEASXIApfEATBJYjCFwRB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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0xdea5080>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(hez2[2],'r')\n", + "pl.plot(z2,'b')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we are making the absolutes and ordinates by sub-sampling, similar to taking absolutes" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#ex_bns = Baselines()\n", + "#ex_bns.baseH = x2[:250:] - hez2[0,:250:]\n", + "#ex_bns.baseD = y2[:250:]\n", + "ex_abs_ord = AbsOrds()\n", + "ex_abs_ord.ordp1 = hez2[0:3,::250]\n", + "ex_abs_ord.absp1 = variations2e[0:3,::250]\n", + "ex_abs_ord.absp1\n", + "irange = np.linspace(0,4999,5000)[::250]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we make the transform from the ordinates and absolutes" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Mex, resex, rankx, sigx = get_transform_from_abs_ords(ex_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.5364919 , -0.32696549, 0.42156614, -2.36376755],\n", + " [ 0.33725291, 0.84709248, -0.25884768, -8.34525556],\n", + " [-0.20707815, 0.53250459, 0.67061488, -8.98395935],\n", + " [ 0. , 0. , -0. , 1. ]])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This transform should be close to the inverse of the example scaling, translating and rotating we did earlier. Changing to orthogonal coordinates is also a linear affine transformation, even though we did not include it in our example." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.20710678, 0.75881905, -0.46592583, 5. ],\n", + " [-0.29508636, 0.76450096, 0.4805854 , 10. ],\n", + " [ 0.60705524, -0.37274066, 0.96568542, 7. ],\n", + " [ 0. , 0. , 0. , 1. ]])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mexi = np.linalg.inv(Mex)\n", + "Mexi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can see that to within floating point math error, we have essentially un-done the transformation the sensor applies to the field" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ -1.33226763e-15, 1.22124533e-15, -1.05471187e-15,\n", + " -8.88178420e-16],\n", + " [ -1.27675648e-15, 7.77156117e-16, -4.44089210e-16,\n", + " -1.77635684e-15],\n", + " [ 1.11022302e-16, 1.22124533e-15, -5.55111512e-16,\n", + " -3.55271368e-15],\n", + " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " -2.22044605e-16]])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mdifference = Mexi - Maffine\n", + "Mdifference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we apply the inverse transform we found to make adjusted data from the sensor data" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "h2Trace = obspy.Trace(data=hez2[0])\n", + "e2Trace = obspy.Trace(data=hez2[1])\n", + "z2Trace = obspy.Trace(data=hez2[2])\n", + "hez2Stream = obspy.Stream(traces=(h2Trace,e2Trace,z2Trace))\n", + "adj_ex = make_adjusted_from_transform_and_raw(Mex,hez2Stream)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we visualise the absolutes (blue), ordinates(red), and adjusted data (black). absolutes are taken at the times shown by stars in the plot" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0xe4ec400>]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Xwlm7Fvr2ZerkybzyyCMc/d13TKyq4o9jxzLpzjsZff31XJF8t3LYYfDcc3Xjb9EiPDMz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\nij2OVkycKlCsscXl7iYbGMXezLDa3y5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62YzB6XlXRKQF+kN/QSn1RjaPDaCU2gkUASOy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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0xdf52400>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.subplot(1,2,1)\n", + "pl.plot(hez2[0],'r')\n", + "pl.plot(irange, ex_abs_ord.ordp1[0],'r*')\n", + "pl.plot(x2,'b')\n", + "pl.plot(adj_ex[0],'k')\n", + "pl.plot(irange,ex_abs_ord.absp1[0],'b*')\n", + "pl.subplot(1,2,2)\n", + "pl.plot(ex_abs_ord.ordp1[0] - ex_abs_ord.absp1[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we plot the error in the x component" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0xe711e80>]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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TnClWnqavjxW+JSZM8N5/5pnqZZWK3RqEpUvVpBOUgQPtyA3KgQfKyVvVxGlI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\nG9QXdxyIi8LfbTfbOVDDoYc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VypV7CE2bTshx26BByxAe3uR6ElHl6NELECIK27adyHH78uU/ISiolPLEsWjR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\nlkqVHka3brMNx3MdQP2d5rv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Swq6W+fN5711/8csvvGh67BjRyy9z1qggCDedPCf4hgzdDoJ/ozl/nn36FSv6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\n/wPXofRt7ZTjeAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0xe820240>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(hez2[2],'r')\n", + "pl.plot(z2,'b')\n", + "pl.plot(adj_ex[2],'k')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we show the absolute error as a function of how many absolutes are taken" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.text.Text at 0x10509f60>" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RdpebWo/eia6U6p4OdSf6tt0/G2PSgZODm9+ISHl7B9YVxLs8NAZ0TRClVPcUypro44Bv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\nTQwd4GBQehpDMjPJ6dufAekpJCUZEhO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+ "text/plain": [ + "<matplotlib.figure.Figure at 0xf027f28>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = np.arange(30)\n", + "max_error_x = np.ones_like(np.float64(a))\n", + "max_error_y = np.ones_like(np.float64(a))\n", + "max_error_z = np.ones_like(np.float64(a))\n", + "max_error_fc = np.ones_like(np.float64(a))\n", + "\n", + "for n_abs in a:\n", + " skip = 5000/(n_abs + 1)\n", + " ex_abs_ord = AbsOrds()\n", + " ex_abs_ord.ordp1 = hez2[0:3,::skip]\n", + " ex_abs_ord.absp1 = variations2e[0:3,::skip]\n", + " ex_abs_ord.absp1\n", + " irange = np.linspace(0,4999,5000)[::skip]\n", + " Mex, resex, rankx, sigx = get_transform_from_abs_ords(ex_abs_ord)\n", + " adj_ex = make_adjusted_from_transform_and_raw(Mex,hez2Stream)\n", + " max_error_x[n_abs] = np.max(np.abs(np.float64(adj_ex[0] - x2)))\n", + " max_error_y[n_abs] = np.max(np.abs(np.float64(adj_ex[1] - y2)))\n", + " max_error_z[n_abs] = np.max(np.abs(np.float64(adj_ex[2] - z2)))\n", + " max_error_fc[n_abs] = np.max(np.abs(np.float64(adj_ex[0]**2 + adj_ex[1]**2 + adj_ex[2]**2 - x2**2 - y2**2 - z2**2)))\n", + "\n", + "#pl.ylim(0,0.00002)\n", + "#pl.xlim(3,20)\n", + "pl.semilogy(a+1, max_error_x, 'r', a+1, max_error_y, 'g', a+1, max_error_z, 'b', a+1, max_error_fc, 'c')\n", + "pl.xlabel('number of absolutes taken')\n", + "pl.ylabel('maximum absolute error')" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.06799438e+00, 1.02560749e+00, 2.14203655e-14,\n", + " 3.68281794e-13, 4.55191440e-15, 2.63261635e-14,\n", + " 2.55351296e-15, 5.77315973e-15, 2.55351296e-15,\n", + " 5.74540415e-15, 3.38618023e-15, 4.86208296e-14,\n", + " 4.10782519e-15, 3.44169138e-15, 3.88578059e-15,\n", + " 1.06581410e-14, 4.77395901e-15, 2.99760217e-15,\n", + " 5.55111512e-15, 3.55271368e-15, 2.88657986e-15,\n", + " 5.32907052e-15, 3.05831749e-15, 8.24340596e-15,\n", + " 3.10862447e-15, 3.21964677e-15, 3.23352456e-15,\n", + " 3.10862447e-15, 3.99680289e-15, 3.55271368e-15])" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_error_x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\\alpha = 1$$" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x10a48390>]" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0xed4e4e0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(adj_ex[0] - x2)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3.5527136788005009e-15" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.abs(adj_ex[0]-x2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the matrices are calculated for different observatories. One thing we have not done in phase one is correct for the effect of temperature on scale and offset (part of the physics of fluxgate magnetometers)." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bou_bns = get_baselines_from_file('/users/aclaycomb/Documents/boujson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x10f204e0>]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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6bW3cP/PM+GmKWcy4bsnqrxtskF6bSaSQ1NNvR047LU7a/ehH0Qs96qgIqq++\nmnt6Z9KkmPVZLLvsEj1liBTFnDmt28+kSZEK6dgxLkSzZEn6sc8+i5TJPffENQ1Sxxs9OoLxW29F\n3vtf/0pf0OYLX4iVTFMjau66KyY5HX54pFhmzYqZv8uWRbrmiitiZvQBB8Tfe/bsqNP3vhffYO67\nL9qaq27dcv/QEikmBf12pGfP6I0ee2ykP4YMiaD10EMxaiMz6D/zTIz4iCtTpk2cGKt8tiTt8umn\nuZWrr4/gmFow7v334yTsm2+2/GphL70UQR9ifHpmimfMmBgJM25cBPMDDoiAf+mlMXlp3rw4AZ45\ngc+s4aJ9994bufcPP4xrNtxwQ4ysMWs4aen666MNhx0WwyWPOira85WvtKw9ImUjn2st5vNDo2vk\nSu7WrnXfdlv3F190X7nS/e9/j2v2/vnP7kcf7f7ss+69erkPGOB+1VUNr2166aXuPXu6X311bsea\nPdu9Sxf3m25qvuzcuXFN41Gj4n6/fu4bbOB+ww1xzPr6+Hnqqbju8V13ub/+evZ9bbddHNvdffhw\n9wcfTD82fLj7H/6Qvv/kk+5bbeX+1a+6H3po1HfVqnX3+eMfu59+elyvuEeP+Ns995z7N77RfNuG\nD3fv2NH900+bLytSTOR5jdxcA/R8YBowFZiUbNsTmJjaBuyTbN8OWAHUJT+jm9hn8f86FWzx4nUv\nav300xH0DjrI/YEH4qLsO+zg/swz6TJHHeV+223um2/uPn9+88e5+GL3b37Tfcst3efMSW+/4w73\nkSMblh0/Pt5RJ58cddtwwwj2u+8egfjOO6M+e+8dF48/5piox513NmzLBx+4b7qp+5o1cf/Pf44P\nsfPOc//HPyJgNw6+a9a4v/tuBOZ+/bK35ZNP4nhnnBF1bIl//tP9+ONb9hyRYihV0J8H9Gy07Wlg\neHL7SGCCp4P+qznss4h/lur00kvxih56aLqne/PN7hdckC6z007u06dH7/vww9f94Ej59NPoiW+6\nqfvChe4nnOA+Zkz68ZNOcu/d23316vS2e+6JgL7TTu5Ll7p37+5eUxN1uvJKd7M4bqaZM9333DMC\n6t13u//yl+5jx0bPOtNHH7lfdpn7rru6X3999jqvXRsfDl/+ctN/o/HjI+A//XTTZUTKWb5BP9ec\nvrFu/n8t0CO5vSmwsFF5KbF9941c/7PPpof6nXBCjF5Jje+fPz/y3VdeGXny3/624T4iRMeEooce\nilx8794wW2fXAAAIZUlEQVRx/mDKlHS5yZNj9Mtzz8X9Vati38OHx4nVefNi9MmOO8bxrroqlgq4\n+uqGxxs0KEbF7LhjjF+/7rrYZ+PL9fXsCbfdFmPwR43K3n6zmNnav3/Tf6Nhw2IM//DhzfwxRSpU\nrkHfgb+Z2WQz+1ay7TvAz8zsHeAnwLUZ5bc3szozm2BmBxawvrIeZusuuztwYAztrK2NCUWbbRYj\nfjp3joD+ve/F2PSUm26K+QBTpsQww9RFsffeOx30Fy+OYZRXXBGB+JFHYm2kN96I4w0cGOPMt946\nhl0efHDU4frrs1/7YIMNYljn00/HiJg77kifxG2po4/W+jMi65PrOP0D3P19M9sCGGdms4GTgcvd\n/XEzOxm4FzgMeB/o6+4fm9kQ4HEzG+zuyxvvdOTIkZ/frqmpoaamJr/WSFajRsUM0BtuiCWqU4YM\ngSOOiMC9eHGMYpk2Lcqdc076esQQQb+uLr4FvPxyjIO/5BL461/jimd9+8Y3ivPOi1UeH3wwevEX\nXtiyuQPHHgv//GfrL8x90UWte55IuaqtraU2NSmkEFqaDwJGAN8FPm60/ZMmyk8AhmTZXpyEl2R1\n663unTu7n3lmw+2zZsXJzw4d3H/968jJv/hi9n306RPnDa680v2aa2Lb8uXudXVxMhbc33wzRtOA\n++WXt7yeb7zhvv/+LX+eSLUgz5x+sz19M+sKdHD35WbWDRgOjALeM7ND3P3vZjYMeCMpvznwkbuv\nNbP+wA7EiWBpQ5dfHmPMO3ZsuH3nnSM988orsbLj3LmxXG82P/lJfDPYZBP4299iW7duscTB9tvH\n4mB9+kRaZ8MNW7dO0sCBMZdARIojl/ROL+AxM/Ok/EPuPs7MzgNuM7OOwL+B85LyBwM/NLN64mTv\n+e6+NNuOpbQGDsy+/YQT4lzAtdfGeYHNNste7rTTInc/YEBMmMrUs2d6ZizEidJ8L3EoIoVn8W2h\nDQ5s5m11bFnXO+9EQN9nnxiZk69//ztmCLf3i9aLlBszw91bPUJSC64JECs+brxxwxO9+dDFPETK\nk/phAqQvyt1UPl9EKoOCvnzusMNaPz5eRNoH5fRFRNqRfHP66umLiFQRBX0RkSqioC8iUkUU9EVE\nqoiCvohIFVHQFxGpIgr6IiJVREFfRKSKKOiLiFQRBX0RkSqioC8iUkUU9EVEqoiCvohIFVHQFxGp\nIgr6IiJVREFfRKSKKOiLiFQRBX0RkSqSU9A3s/lmNs3MpprZpGTbnmY2MbXNzPbJKH+tmc0xs1lm\nNrxYlRcRkZbJtae/Fqhx973cfWiy7WZghLvvBYwAfgpgZoOBU4BBwJHAaDNr9fUc26va2tq2rkJR\nqX3tWyW3r5LbVgi5Bn3LUnYt0CO5vSmwMLl9LDDG3Ve7+3xgDjCUKlPpbzy1r32r5PZVctsKoVOO\n5Rz4m5mtAe5097uA7wBPm9nPiQ+FLyVltwEmZjx3YbJNRETaWK5B/wB3f9/MtgDGmdls4GTgcnd/\n3MxOBu4FDitWRUVEJH/m7i17gtkIYDnwA3fvmbF9qbtvambXAO7uNyfbnyJy/y812k/LDiwiIgC4\ne6vPkzbb0zezrkAHd19uZt2A4cAo4D0zO8Td/25mw4jcPcBY4CEz+wWR1tkBmFTISouISOvkkt7p\nBTyW9Mw7AQ+5+zgzOw+4zcw6Av8GzgNw95lm9jAwE1gFXOQt/TohIiJF0eL0joiItF9tMiPXzI4w\ns9lm9oaZXd0WdSi0Jiaw9TSzcWb2upk9bWY9mttPOTCze8xskZm9mrGtyba0t8l4TbRvhJm9a2Z1\nyc8RGY+1t/b1MbNnzWyGmb1mZpcl2yviNczSvkuT7e3+NTSzDczspSSOvJacQy3sa+fuJf0hPmjm\nAtsBnYFXgJ1LXY8itGse0LPRtpuBq5LbVwM3tXU9c2zLgcCewKvNtQUYDEwlUn/bJ6+ttXUbWtG+\nEcAVWcoOaoft2wrYM7m9MfA6sHOlvIbraV9FvIZA1+R3R+BFYp5TwV67tujpDwXmuPvb7r4KGAMc\n1wb1KLRsE9iOA+5Pbt8PHF/SGrWSuz8PfNxoc1NtaXeT8ZpoH8Rr2NhxtL/2feDuryS3lwOzgD5U\nyGvYRPtSc4Ha/Wvo7iuSmxsQwdwp4GvXFkF/G2BBxv13qYzJW6kJbJPN7JvJtl7uvgjijQps2Wa1\ny9+WTbSl8evZnifjXWJmr5jZ3Rlfn9t1+8xse+JbzYs0/X5st23MaF9qSHi7fw3NrIOZTQU+AP7m\n7pMp4GunVTYL5wB3HwIcBVxsZgcRHwSZKumseSW1BWA00N/d9yT+2X7exvXJm5ltDPyRmES5nAp7\nP2ZpX0W8hu6+1mNNsz7AUDPbhQK+dm0R9BcCfTPu9yG9bk+75e7vJ7+XAI8TX7EWmVkvADPbCljc\ndjXMW1NtWQhsm1GuXb6e7r7EkyQpcBfpr8jtsn1m1okIiL919yeSzRXzGmZrX6W9hu6+DKgFjqCA\nr11bBP3JwA5mtp2ZdQFOJSZ0tVtm1jXpdZAxge01ol1nJcXOBJ7IuoPyZDTMjzbVlrHAqWbWxcz6\n0cRkvDLUoH3JP1LKicD05HZ7bd+9wEx3vy1jWyW9huu0rxJeQzPbPJWWMrONiKVtZlHI166Nzk4f\nQZxxnwNc09ZnywvQnn7EKKSpRLC/Jtm+GTA+aes4YNO2rmuO7fkd8B6wEngHOBvo2VRbgGuJUQOz\ngOFtXf9Wtu8B4NXkdXycyKG21/YdAKzJeE/WJf9zTb4f21Mb19O+dv8aArsl7Xklact/JtsL9tpp\ncpaISBXRiVwRkSqioC8iUkUU9EVEqoiCvohIFVHQFxGpIgr6IiJVREFfRKSKKOiLiFSR/w8+y8nH\nnKPecgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x10d727b8>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(bou_bns.baseZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "585.5477061" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bou_bns.baseZ[81]" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = UTCDateTime(bou_bns.begin[50])\n", + "\n", + "a.month" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,7,11,0,0,0)\n", + "\n", + "b = UTCDateTime(2015,9,15,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,bou_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x115b1eb8>]" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.49668069e+10, 2.29654469e+09, 3.49755539e+10,\n", + " 7.29213470e+05],\n", + " [ -6.45768758e+07, -9.89864120e+06, -1.50900417e+08,\n", + " -3.14617119e+03],\n", + " [ 3.40342627e+10, 5.22230716e+09, 7.95338758e+10,\n", + " 1.65822030e+06],\n", + " [ 7.18360022e+05, 1.10227043e+05, 1.67871880e+06,\n", + " 3.50000000e+01]])" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m2 = np.dot(np.vstack([bou_abs_ords.ordp1,np.ones_like(bou_abs_ords.ordp1[0])]),np.vstack([bou_abs_ords.absp1,np.ones_like(bou_abs_ords.absp1[0])]).T)\n", + "m2" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\aclaycomb\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\geomagio\\edge\\EdgeFactory.py:520: ObsPyDeprecationWarning: 'getWaveform' has been renamed to 'get_waveforms'. Use that instead.\n", + " edge_channel, starttime, endtime)\n" + ] + } + ], + "source": [ + "hezfbouJan16 = factory.get_timeseries(observatory='BOU',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bouJan16adj = make_adjusted_from_transform_and_raw(Mbou,hezfbouJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3127.49144074, 3131.6261902 , 3135.43180642, 3137.51476505])" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bouJan16adj[1,1:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bouh_pqqm = np.mean(bou_abs_ords.absp1[0] - bou_abs_ords.ordp1[0])\n", + "\n", + "boue_pqqm = np.mean(bou_abs_ords.absp1[1] - bou_abs_ords.ordp1[1])\n", + "\n", + "bouz_pqqm = np.mean(bou_abs_ords.absp1[2] - bou_abs_ords.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Z4pv+0Mkn47UBA3Cd30Vn1/vd/ni+acfoaBrR8XSfPgCAq43vBMCXFACgU0IC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\nERFZ/H+0CVRDKgo6mQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x117ec630>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure()\n", + "pl.plot(((hezfbouJan16[0].data+bouh_pqqm)**2 + (hezfbouJan16[1].data+boue_pqqm)**2 + (hezfbouJan16[2].data+bouz_pqqm)**2)**(0.5) - hezfbouJan16[3].data + 23.1,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((bouJan16adj[0]**2 + bouJan16adj[1]**2 + bouJan16adj[2]**2)**(0.5) - hezfbouJan16[3].data + 23.1,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjbou_state_.json', Mbou, -22)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "hezfbouJan16[0].stats.channel = 'H'\n", + "file1 = open('/users/aclaycomb/Documents/BOU201601vmin.min','w')\n", + "a = geomagio.iaga2002.IAGA2002Writer()\n", + "a.write(file1,hezfbouJan16,['H', 'E', 'Z', 'F'])\n", + "stats1 = hezfbouJan16[0].stats\n", + "stats1.channel\n", + "stats1.channel = 'X'\n", + "x = obspy.core.Trace(bouJan16adj[0], stats1)\n", + "stats1.channel = 'Y'\n", + "y = obspy.Trace(bouJan16adj[1], stats1)\n", + "stats1.channel = 'Z'\n", + "z = obspy.Trace(bouJan16adj[2], stats1)\n", + "stats1.channel = 'F'\n", + "f = obspy.Trace(hezfbouJan16[3].data - 22, stats1)\n", + "adjbouJan16 = obspy.Stream([x,y,z,f])\n", + "a = geomagio.iaga2002.IAGA2002Writer()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "file2 = open('/users/aclaycomb/Documents/BOU201601adj.min', 'w')\n", + "a.write(file2,adjbouJan16,['X', 'Y', 'Z', 'F'])" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "brw_bns = get_baselines_from_file('/users/aclaycomb/Documents/brwjson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x12035b38>]" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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HrCSW3fYeXkZCfEy+WQx+Ps/WhJeREB/DrCSSDD6+XQgh+WQx0JXk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\nBIWBEEJIEP8PdorL7Pr0LX4AAAAASUVORK5CYII=\n", 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x124f6f98>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(brw_bns.baseZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,7,1,0,0,0)\n", + "\n", + "b = UTCDateTime(2015,9,1,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,brw_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "remove_outlier_baselines(brw_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x125e3cc0>]" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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VBwwRESmfak5JiYhIGSlgiIhIThQwREQkJwoYIiKSEwUMERHJiQKG\niIjkRAFDRERyooAhIiI5+f+3SSe5pr3XvgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x12850c88>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(brw_bns.baseZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "brw_abs_ord = get_ord_abs_from_baselines(brw_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mbrw, resbrw, rankbrw, sigbrw = get_transform_from_abs_ords(brw_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.34857870e-01, -3.26009140e-01, 1.26923853e-02,\n", + " -6.01772088e+02],\n", + " [ 3.29840807e-01, 1.08450379e+00, -4.38199362e-02,\n", + " 1.96667652e+03],\n", + " [ -9.26036364e-04, -5.46591946e-05, 9.98670684e-01,\n", + " -6.15282786e+01],\n", + " [ 0.00000000e+00, 0.00000000e+00, -0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mbrw" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5.04178763e+01, 8.74894816e+02, 8.79660086e-01,\n", + " 1.34495954e-37])" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resbrw" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rankbrw" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.99444530e+05, 9.36118621e+02, 1.18394564e+02,\n", + " 2.08605176e-03])" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sigbrw" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezfbrwJan16 = factory.get_timeseries(observatory='BRW',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-30T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "brwJan16adj = make_adjusted_from_transform_and_raw(Mbrw,hezfbrwJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "brwh_pqqm = np.mean(brw_abs_ord.absp1[0] - brw_abs_ord.ordp1[0])\n", + "\n", + "brwe_pqqm = np.mean(brw_abs_ord.absp1[1] - brw_abs_ord.ordp1[1])\n", + "\n", + "brwz_pqqm = np.mean(brw_abs_ord.absp1[2] - brw_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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BChvRQ3p+CJM1Vanovo6wHrFZi3OlA8LXdNkyPBRlSOnec+dMP3tizx7bcw685nRlZcR0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\nA8MwDKOBhYFhGIbR8P8ByZeMebDgmxgAAAAASUVORK5CYII=\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x129f4e10>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezfbrwJan16[0].data+brwh_pqqm)**2 + (hezfbrwJan16[1].data+brwe_pqqm)**2 + (hezfbrwJan16[2].data+brwz_pqqm)**2)**(0.5) - hezfbrwJan16[3].data + 1,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((brwJan16adj[0]**2 + brwJan16adj[1]**2 + brwJan16adj[2]**2)**(0.5) - hezfbrwJan16[3].data + 1.0,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjbrw_state_.json', Mbrw, -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cmo_bns = get_baselines_from_file('/users/aclaycomb/Documents/cmojson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x1313c6d8>]" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x129ebbe0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(cmo_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,7,1,0,0,0)\n", + "\n", + "b = UTCDateTime(2015,9,1,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,cmo_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x13409d68>]" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x135b8a58>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(cmo_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cmo_abs_ord = get_ord_abs_from_baselines(cmo_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Mcmo, rescmo, rankcmo, sigcmo = get_transform_from_abs_ords(cmo_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.39880017e-01, -3.08098629e-01, 8.11979835e-03,\n", + " -2.01433612e+02],\n", + " [ 3.22786162e-01, 9.68250360e-01, 1.65168860e-02,\n", + " -8.65385130e+02],\n", + " [ -6.29637516e-03, -4.05108544e-03, 9.91887061e-01,\n", + " 4.72928486e+02],\n", + " [ 2.57918802e-15, -3.27337577e-15, -0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mcmo" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4.43079439e-01, 5.71490493e-01, 2.15290921e-01,\n", + " 2.83800382e-38])" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rescmo" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rankcmo" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.34191239e+05, 6.16124599e+01, 4.15099818e+01,\n", + " 6.83659777e-04])" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sigcmo" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezfcmoJan16 = factory.get_timeseries(observatory='CMO',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cmoJan16adj = make_adjusted_from_transform_and_raw(Mcmo,hezfcmoJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cmoh_pqqm = np.mean(cmo_abs_ord.absp1[0] - cmo_abs_ord.ordp1[0])\n", + "\n", + "cmoe_pqqm = np.mean(cmo_abs_ord.absp1[1] - cmo_abs_ord.ordp1[1])\n", + "\n", + "cmoz_pqqm = np.mean(cmo_abs_ord.absp1[2] - cmo_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0pQfVJ926+Z/vLy31X+TVqqwJQXJwGT/8gNHr16OlcsE0Q14v/k/JcVZUVeFB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\ni1c1e1rZl2WzmDm7Vy/dIB3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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x12a3b240>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezfcmoJan16[0].data+cmoh_pqqm)**2 + (hezfcmoJan16[1].data+cmoe_pqqm)**2 + (hezfcmoJan16[2].data+cmoz_pqqm)**2)**(0.5) - hezfcmoJan16[3].data - 7.9,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((cmoJan16adj[0]**2 + cmoJan16adj[1]**2 + cmoJan16adj[2]**2)**(0.5) - hezfcmoJan16[3].data - 7.9,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjcmo_state_.json', Mcmo, 7.9)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "frd_bns = get_baselines_from_file('/users/aclaycomb/Documents/frdjson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x1312c438>]" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0xe655a90>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(frd_bns.baseD)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,8,1,0,0,0)\n", + "\n", + "b = UTCDateTime(2015,10,1,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,frd_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "remove_outlier_baselines(frd_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x138281d0>]" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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69WFC6EEH5a5czsURN8gMBCZEjycAyfbn6wGUm9lyM9sATIrypcs/gLBN80Yz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\nfh5gnHO54zWZHFm6FI45Bp5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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x11ceecc0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(frd_bns.baseD)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "frd_abs_ord = get_ord_abs_from_baselines(frd_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mfrd, resfrd, rankfrd, sigfrd = get_transform_from_abs_ords(frd_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ -9.82142394e-01, -1.83524785e-01, 1.28706175e-02,\n", + " -5.26638206e+02],\n", + " [ 1.79292243e-01, -1.00656618e+00, 1.13500368e-01,\n", + " -5.12726686e+03],\n", + " [ 3.98204985e-03, 6.04692528e-03, 1.01122495e+00,\n", + " 9.93776528e+01],\n", + " [ 0.00000000e+00, 0.00000000e+00, -0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mfrd" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.65574426e+00, 1.93399773e+01, 2.49780320e-01,\n", + " 4.92759318e-39])" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resfrd" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rankfrd" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.53434501e+05, 8.05438116e+01, 1.80397682e+01,\n", + " 5.10446006e-04])" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sigfrd" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezffrdJan16 = factory.get_timeseries(observatory='FRD',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "frdJan16adj = make_adjusted_from_transform_and_raw(Mfrd,hezffrdJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "frdh_pqqm = np.mean(frd_abs_ord.absp1[0] - frd_abs_ord.ordp1[0])\n", + "\n", + "frde_pqqm = np.mean(frd_abs_ord.absp1[1] - frd_abs_ord.ordp1[1])\n", + "\n", + "frdz_pqqm = np.mean(frd_abs_ord.absp1[2] - frd_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MyGNGjjSWtVmYdYhq1TOEow05gArQno+aN36uhYOW+vxWPoaQdj7c3p6zSFAp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\nAQEBATGEiSEgICAgIIb/AdrMvwzEX9WaAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x138e45c0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezffrdJan16[0].data+frdh_pqqm)**2 + (hezffrdJan16[1].data+frde_pqqm)**2 + (hezffrdJan16[2].data+frdz_pqqm)**2)**(0.5) - hezffrdJan16[3].data + 4.6,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((frdJan16adj[0]**2 + frdJan16adj[1]**2 + frdJan16adj[2]**2)**(0.5) - hezffrdJan16[3].data + 4.6,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjfrd_state_.json', Mfrd, -4.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "frn_bns = get_baselines_from_file('/users/aclaycomb/Documents/frnjson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x13ffb320>]" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x12df0be0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(frn_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,5,1,0,0,0)\n", + "\n", + "b = UTCDateTime(2015,6,10,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,frn_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x142bc0b8>]" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x12df0f98>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(frn_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "frn_abs_ord = get_ord_abs_from_baselines(frn_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mfrn, resfrn, rankfrn, sigfrn = get_transform_from_abs_ords(frn_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.68565555e-01, -2.00310287e-01, -2.76807061e-02,\n", + " 1.17417624e+03],\n", + " [ 2.42370058e-01, 1.02706749e+00, -1.21082597e-01,\n", + " 4.70604971e+03],\n", + " [ -2.33058122e-02, -1.65549754e-02, 1.02128327e+00,\n", + " -3.03542290e+02],\n", + " [ 0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mfrn" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.27533547e-02, 4.12836189e-01, 2.23735118e-02,\n", + " 1.61860062e-39])" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resfrn" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rankfrn" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.45113868e+05, 3.65063445e+01, 2.10145476e+01,\n", + " 1.23018540e-04])" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sigfrn" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezffrnJan16 = factory.get_timeseries(observatory='FRN',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "frnJan16adj = make_adjusted_from_transform_and_raw(Mfrn,hezffrnJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "frnh_pqqm = np.mean(frn_abs_ord.absp1[0] - frn_abs_ord.ordp1[0])\n", + "\n", + "frne_pqqm = np.mean(frn_abs_ord.absp1[1] - frn_abs_ord.ordp1[1])\n", + "\n", + "frnz_pqqm = np.mean(frn_abs_ord.absp1[2] - frn_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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19fWce+65XHPNNSld8+jFioqEU+yhpXIot31xk1liI95EHPP3vdYYQHzRmH2Z\njERLHie6iotVjvvbccewWJ4vL+ekTz9Nat+1+/bxxxQfH1paTWvasTBayPadTKY99cD27Vywdi1X\nr19vVcJvJAjm221/J2aF/OshQzi7Tx9G2FrDkS2Ww4wMOm9ExT0oLY1p2dm4lCLDCAw/XrOG+UaX\nyzaj2ynDWLBvj/G9n56dbXUlLayp4bYhQ5L4jfe3SlrzpfE7Dk7Q7dTD7ebRsjK2JBhY7uFyUR8K\n8Vl9PeMyMujl9TI6I4Oz+vSx9jnd6Coyu5IuWLs2KlDPj8hWy7WNsXSGbhUYwlqD281Pr7nGqtjN\nq9ef/vSneDweXC6XtaDXj3/8Y8fVvcnj8eD1eh2zOk3vGWuuhMNhwuEwO3fujJkuaf470oj2AJdf\nfrl9/oUlJyfH0W2USmeuWhUzUyLSLzZscAzIPfrooyxLsPhXvCsQ8/e70LiCgcT92OYV5Q0JllJe\n1IbbKZrLATS1sZWRyB+Mij6Z1suYpUv52fr1Sb3v5V98kVSmjXncl5MMuH/YupVgREC1X1UH23B+\nNPsr8tNizRK3+cLWpetzuQiGw/xq82Y8Ed2ioYjjn2x0w5h95a3Z2dxsLaZnzyJSJSVW95fX5bIq\nxhHp6ZxgmzEfiy4qiptxF09kqm5kx+/i2loe2rGDwRED8ZHMFsPmxkZG2Fohz48bx66ZMwnPnu3Y\nty4UYkZ2NjdHzDRPiwiqPT2epJfzToVuFRjMr3pTOIzb7eaiiy6yVoZ88MEHATj77LOprKykqqoq\natB1/fr1VFdXU1BQwJVXXhmzwg+FQo6B4oKCgqjB4Hh8bbgNX2sC4bD1x6S1RpWUtFpJDU9wNQNw\nv5HqZq5CmZ+fz9SpU+PuPz07O24euRmMB6WlcY7R95qoH9u8mtmaYOmCFTG64uIx89eLkkg5vX/b\ntqTHLcwKL9Xrzzy4Y0fCtEbYf++AZOYNvLl7N9du2ECO0Tdtsre8WgsMkZWJBkfLsjVmAP3FwIEc\n07On1fV4RkSFH/ndcCuFLioiM0Gr9WzjKrrA5+PBkSNb3suWkmzvo8/zeKgMBKgJBhNe3bfViATd\nSAC7Zs7k7mHDrOU14ulhzGN4Y/duxxW+Wyn6+HyOsUb74PPhEZmJoyLK1NPjSWqsL1W61TwGs7KM\ntcTupZdeGrXKY7yVPGOtuWNKNghEmjVrFqeffvoBvTaS7733yHC5qD/mGOuPenuMCtVM3ZvZs2fS\n772qvp40dqSaAAAgAElEQVTJrQxKm+e4t9fLvgSDz1WBAMOML2hzOBy3T9eeipdoTRezsh+WxB+3\neWWaTC7V1Ule1QMckZnJ0ro6fr15M/cZFVIiYa1xJVGRZyfRfWFWsLE+80hPG0kZJ0fMI2kIhxng\n9xPWutXAENmC08AXMVYKtV/1lzc303fxYv48ciSFfj/pLhdrGxp4wvi7ihyoXTBxYlLdWZEeGjWK\nv1dU8FJFBT82FtTzuFxsnT6dFXv38q3PPrP2HZiWxn+rq9kTDLbpPsmJ/HnkyKS+i318Pn4xaFDC\n/XI8HqoDATxK8Z0ELSYzXXVrUxODIrKiJmZlOVo9PSMmw3W0bhUYwq0Ehq72fsQVW6SQ1pQ1NTEg\nyasZs1JuNn7nJTGupJca2xL128P+PsjGBOfuJuMqLNOWPRHJDAw7m5uZbmQwNdsqjvpQiHSXy6oo\n7dkaEyOufCKZk6iS+YzN2ajFRtZGqpjV+5IEueH2VlxlIEB+KxWS2dVTm8RndbMxmL49xpX7zqYm\nPq2v50QjEJiVVuS9Cz7Zu5dtTU1kut1sbWyMOZkLogN1ZMt0c0MDQ9LTedf2/maX5Ed1dfTx+Vrm\nBoRC/C7OfJbpbbhwscs10jBrQiHHnIMBaWkMSEtzVIw9XC7erKpigDEYmyqXFRbG3O49wGOku92k\nuVx8tHevNZYQT5bbTWUgkFSwy+nkwNBtupLera7m4i++AFLfp9wZbti4kYEJ+h9jMSvIWF1J5q0P\nkxl0GpWeTu8YmRPfKi21VqgEOCori3SXix4ulyPg7GxqsroYzK6ksuZm+vl89PJ4HBV55sKFuG1L\neZhdMsfl5CQc8DuvoICz+vSJ+owf2LYtqtvDHLA7KUGfst2eJAbqKwMBpmZl8a0Ef7j2SU07Elzd\n/9NI5wRnCyqWx8rKrHKYqgIBLv/iC+Zs3OgYGN/S1MT3+vRhhhGgTWcYV9N7QyFmGMkTVYEAn0UM\nYj4XMecgsvvMzKCJ1Xp8fOdOltbWMr5HD96prk4qXfNAJVpWe7bRnZjfCTn9xUccwTHtmDFfEwqx\nrK6OIQkuErM9Hlbt20f/JIJdnseT0vtTJ9JtAsN3PvuMl8zBxm7YYkjkngOYHay1tibNxAqF5gBU\nMoGhOhhkaFpaVD/kK7t3819bamZlIMD38/MdyxMDHLZsGdONdDp7i6Gvz8fuYNC6Yv2LLR/e3uV0\nXE4OJ+blxe2eMvVwu5mSlRU1vnHV+vW8FjEY+4JR2SabWgnJZVBtaGxkV3NzwvN6hzFecFJubsKb\nqpj3Ixjs9yc9S9b+h95r0SIe3LGD+balD7TWPFNezoAk703ca9Eix9pBsayL6EaasHw5czdtcnTb\n2M3OycGjlHX8cSlOslgyeTLQcqXdGvP5WOs0pVpRbm5KWiWJxgYH+P00hsNJVfi9k7ipUCp1m8Bg\nr9AOxsBgipzSH8neMmjW2uqi2Rmj4pmZnU0PlyupL05VMMhAv9/RPfRjY7blZbYlOt6oqqKP10uO\nbTDrpo0bqQoG2dzYSCAcJrdPH9J79mRdQwP9jCu5z4xZ3P9ntOoALjV+PvHTT/nfnj2ku1wJu7Ka\nwmFyPR4aw+GoL3rkIObUrCyOzMpKuqJNd7kSLoNgnv+ZPXvy+wRpqGMzMpiSmUk/v58dCQLDs0aF\nXuDzJcx3n5aVxQvjxrX6h661ttbwOTUvL+a+v4lY/O9AtXZRc2JurjUhEmB1iiefHpWdnXQG0VWF\nhVyb4jlNHeH/+vXjidGjSUsQ7MxMvnhjd3Ya+FdlJZ920i0+u01gsOtOgaGtE7IS3dbQfkX9bHl5\nq7/rkLQ0LuzXL2H2TFhrqgMBBtgCwztVVVZlZaoNBnm5spJ94bCjz/J223R833vvUfbHP9LwxBPA\n/sHU2mCQsRGD/Y+UlTkygfwulyON8v/WruVGozssrDWjP/yQx3fu5MvGRrLd7qixk8hz4VPKmtzU\nGrMLbEpWVsyUPvu1X3UwSI7Hw1l9+nBanDV/TBWBACfl5dHf50t44/pmrZmalUW+z0d5gkC2ramJ\nwzMzo1oBmbaKZObKlVbgHp2RwaaIZSkG+v1J3QGtvTLdbkd20XsTJ3b4MeO5f+RI7h0xosuOn6yH\nR4/mgn79Eu5nZijdmMSgdkBrNjc2ckSSM9bbq3sGhm4yxqC1xrVgAZPa8GHEu8H5opoaSvfudUxW\n+7y+3moxmN1G9nS2f1dVsamxkX22mZQr6+ocqzAC1IVCZLjd5Hg8VmAw0yb/bkyv3x0I8C+jaybd\n5bKyHMxslDfsa9nk5EBeHgXG7T2hZcB5rdEaKojRz3tZ//6kuVzWZ7epoYG/lJVx55Yt/HPXLupD\nISsb5v2aGgp8Pivl0uyTj/zcC/1+JmZmUmcrZ6SVdXW8t2cPI9LTGZmennDw9x0js6W31xu1iFt9\nKOQIdFuNxc76+XwxWwyBcNiqrDVwTn4+BQlmyAbCYSoDAYanpdEYDlMXDLLTCDo3DhrE0cZA7pLa\nWuuzNPvf7Xf7Mm9ob7ojQZpsYcTg5sajjora5/L+/WmOWMF2fEQywdHtXK1WOOmiIq5OYrn0H0Rk\ngnW07hkYzMHPpiaWtGFCVKqZzeZErQC7eDnRX1u5ksOXL3fk+R+ZnW2NMVgZGrbKqqfbzej0dHK9\nXqs//DdffskVEau3VgcC5Hk8Vg417L/huLkU8mmlpdbSvb8eOtRqMfzdGJw8IjOTuojlm+1XvvZK\n6YPJkx2T3wD+OHIkfpfL+uy+s2qV9dz3Vq92vP6JMWPIsA1+32ZUalevX291maiSEioDAbLcbrIj\nMjI2NzSwtLaW8uZmJq9YwVmrV7fs53bHTZc112c6e/Vqhqal0csWGMJa8/utW8k0Ztya5Xq0rIzD\ne/Sgn98fNcbQGArhe+89Ti8ttYLJrJ49W1oMxr6le/dy1qpVjrTUsuZm8r1ePC4XipbgY050+2m/\nfrxnW2b7TqMlp5RiZHo6O5qa+NxYs782FCLbdiU/N8HEutsiup2Gxvie3jtiRNRsZdE9DEtirkUq\ndYt01ci1VszK8oI1a/hvdXWbZzGmSoXxB/6rVu5/G8msAH0LFrBr5kxyIq6u64xUz4Zw2JG/bP7O\no5cu5TG3m4v69aOf38/YHj2sVNR8n8/K7d8XClHW3Mzw9HSqgkFyvV5ctNyb1t5F09OoPMzUzG/k\n5VkrRu4JBq0JZ/HSHaFl2d9jcnJ4dfduHhgxgqHp6VxRWGjltYOxdo1SVmCIzK03B7ahpYssw+22\nutXs/ee9Fy1ihTGJqHjPHm4aPNjKyOjl9VK0cqUV9Oyy3G56uN2OK3uzP/aUiBm+w40MLjMwuCNu\nltTDCBDQkn5bWl9vra10+5dfWim/0DJmYxqXkcEHPh8bGhpoCIU43Ghp/qOigt8MHcqs7GyW1tVZ\naaon5uayqbGRS41AnxXRZWZPmVzX0MCMlSsdAdKlVEswND7vNJeL5nCYYDiMx+XiYVvLsk+MVp6b\n/ZNKbxsypNW+7rIZMxJ26YmO98To0Z1ynG5xeRCZyWJ2KfzX6JbZmGBAF1oq8WTX1Llk7dqkZsma\nf4TJDP6af8TLjYo2oHXUDcz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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x1401c2e8>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezffrnJan16[0].data+frnh_pqqm)**2 + (hezffrnJan16[1].data+frne_pqqm)**2 + (hezffrnJan16[2].data+frnz_pqqm)**2)**(0.5) - hezffrnJan16[3].data + 6,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((frnJan16adj[0]**2 + frnJan16adj[1]**2 + frnJan16adj[2]**2)**(0.5) - hezffrnJan16[3].data + 6,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjfrn_state_.json', Mfrn, -6)" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "gua_bns = get_baselines_from_file('/users/aclaycomb/Documents/guajson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x14b648d0>]" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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bHiTBdYcCGKWqJQCgqlu97QqggYjUBlAfQBGAXVmIleBmpR9p75SUsL1DFDS/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\n1SD/D0KaEsPCygenAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x1402feb8>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(gua_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,10,15,0,0,0)\n", + "\n", + "b = UTCDateTime(2015,12,15,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,gua_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x14d9c898>]" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0PqgaHUGexSOA+aq6UFWbgXHAcQXajwHuCqNzcUDVxT0ILu7JpRy/PY2nQ0ZHEHHvAyzK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\nPQKQXMIObcW91jQ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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x14db9f28>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(gua_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "gua_abs_ord = get_ord_abs_from_baselines(gua_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mgua, resgua, rankgua, siggua = get_transform_from_abs_ords(gua_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.98443843e-01, -1.93733450e-02, -4.25390474e-03,\n", + " 6.52567915e+02],\n", + " [ 2.02166513e-02, 1.00429017e+00, 7.01363090e-03,\n", + " -6.73172625e+01],\n", + " [ 6.93024175e-04, -4.44958958e-03, 1.00187292e+00,\n", + " 2.19933652e+02],\n", + " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mgua" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.38786609e-01, 8.45265521e+00, 1.15363688e+00,\n", + " 7.95410191e-37])" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resgua" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezfguaJan16 = factory.get_timeseries(observatory='GUA',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "guaJan16adj = make_adjusted_from_transform_and_raw(Mgua,hezfguaJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "guah_pqqm = np.mean(gua_abs_ord.absp1[0] - gua_abs_ord.ordp1[0])\n", + "\n", + "guae_pqqm = np.mean(gua_abs_ord.absp1[1] - gua_abs_ord.ordp1[1])\n", + "\n", + "guaz_pqqm = np.mean(gua_abs_ord.absp1[2] - gua_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-10, 10)" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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IQ8EgkkLaX5DmSMEgIiIOBYNICukYgzRHKQsGY8wDxphiY8yn3u38VC1LRESS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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x15123630>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezfguaJan16[0].data+guah_pqqm)**2 + (hezfguaJan16[1].data+guae_pqqm)**2 + (hezfguaJan16[2].data+guaz_pqqm)**2)**(0.5) - hezfguaJan16[3].data - 7.9,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((guaJan16adj[0]**2 + guaJan16adj[1]**2 + guaJan16adj[2]**2)**(0.5) - hezfguaJan16[3].data - 7.9,'k')\n", + "\n", + "pl.ylim(-10,10)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjgua_state_.json', Mgua, 7.9)" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hon_bns = get_baselines_from_file('/users/aclaycomb/Documents/honjson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x13c403c8>]" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x14b9f048>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(hon_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,10,15,0,0,0)\n", + "\n", + "b = UTCDateTime(2015,12,15,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,hon_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x1199fcc0>]" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x152362b0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "remove_outlier_baselines(hon_bns)\n", + "\n", + "pl.plot(hon_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hon_abs_ord = get_ord_abs_from_baselines(hon_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mhon, reshon, rankhon, sighon = get_transform_from_abs_ords(hon_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.99678600e-01, -1.74029673e-01, 2.63717245e-02,\n", + " -9.82568114e+02],\n", + " [ 1.69668389e-01, 1.01995732e+00, -5.55170451e-03,\n", + " 1.74349733e+02],\n", + " [ 1.23732740e-03, -1.69453292e-03, 1.05273966e+00,\n", + " -1.02140068e+03],\n", + " [ 0.00000000e+00, 2.98072927e-15, -1.09299776e-15,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mhon" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 9.16388677e+00, 4.33161926e+00, 3.69640761e+00,\n", + " 4.59177481e-41])" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reshon" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezfhonJan16 = factory.get_timeseries(observatory='HON',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "honJan16adj = make_adjusted_from_transform_and_raw(Mhon,hezfhonJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "honh_pqqm = np.mean(hon_abs_ord.absp1[0] - hon_abs_ord.ordp1[0])\n", + "\n", + "hone_pqqm = np.mean(hon_abs_ord.absp1[1] - hon_abs_ord.ordp1[1])\n", + "\n", + "honz_pqqm = np.mean(hon_abs_ord.absp1[2] - hon_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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58mxurrHPUpYvl9PXrpUtFRUiIvKPf/xDcPnlAkDcSUnylamxUV9WVtbx//Xd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\nmwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x150d3c18>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezfhonJan16[0].data+honh_pqqm)**2 + (hezfhonJan16[1].data+hone_pqqm)**2 + (hezfhonJan16[2].data+honz_pqqm)**2)**(0.5) - hezfhonJan16[3].data - 14.5,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((honJan16adj[0]**2 + honJan16adj[1]**2 + honJan16adj[2]**2)**(0.5) - hezfhonJan16[3].data - 14.5,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjhon_state_.json', Mhon, 14.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "new_bns = get_baselines_from_file('/users/aclaycomb/Documents/newjson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x15931048>]" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x13cfde10>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(new_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,10,15,0,0,0)\n", + "\n", + "b = UTCDateTime(2015,12,15,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,new_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x15b24a58>]" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KKZVhBM61cfzxsHZt9PNEDShD9gUhqaI0DytOK8/SpbBrFxw4kPRM6h8ThJRSya8fV2A5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\nOuesV0wQjMxw5JFw1FGuyZ2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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x15b41748>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "remove_outlier_baselines(new_bns)\n", + "\n", + "pl.plot(new_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "new_abs_ord = get_ord_abs_from_baselines(new_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mnew, resnew, ranknew, signew = get_transform_from_abs_ords(new_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.70381886e-01, -2.61357358e-01, -5.88339193e-03,\n", + " 3.31498500e+02],\n", + " [ 2.78701300e-01, 9.69226963e-01, 4.60085193e-02,\n", + " -2.56585110e+03],\n", + " [ -1.91365970e-02, 6.40280253e-03, 9.26542509e-01,\n", + " 4.80701668e+03],\n", + " [ 0.00000000e+00, 0.00000000e+00, -0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 221, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mnew" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.77548347e-02, 5.00419296e-01, 2.95001807e-01,\n", + " 1.51528569e-39])" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resnew" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezfnewJan16 = factory.get_timeseries(observatory='NEW',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "newJan16adj = make_adjusted_from_transform_and_raw(Mnew,hezfnewJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "newh_pqqm = np.mean(new_abs_ord.absp1[0] - new_abs_ord.ordp1[0])\n", + "\n", + "newe_pqqm = np.mean(new_abs_ord.absp1[1] - new_abs_ord.ordp1[1])\n", + "\n", + "newz_pqqm = np.mean(new_abs_ord.absp1[2] - new_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 226, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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57tRTYdUqWLiQZnfe6ezrq7amNGrE+nXrnEwhPR3cUhS33gpe2/utt5YkaNUq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\nICIiAQoMIiISoMAgIiIB/w+MbZKWF/bP+gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x15cbd278>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezfnewJan16[0].data+newh_pqqm)**2 + (hezfnewJan16[1].data+newe_pqqm)**2 + (hezfnewJan16[2].data+newz_pqqm)**2)**(0.5) - hezfnewJan16[3].data + 7.1,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((newJan16adj[0]**2 + newJan16adj[1]**2 + newJan16adj[2]**2)**(0.5) - hezfnewJan16[3].data + 7.1,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjnew_state_.json', Mnew, -7.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "shu_bns = get_baselines_from_file('/users/aclaycomb/Documents/shujson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x166727f0>]" + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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K2Qa4+4qcU9jvYkdpQ+syBjMrISSFB939yag5tfgsxsRQBZSaWQ8zaw2cCzzV\nxH1qVsxs/2hvAjNrBwwFZhLW4+hoth8CT9a5ANnOyK+DF1p/TwHnmllrM/sKUAq8vbc62Uzkrcto\nw7Xd2cCs6LnWZTz3ArPd/d9z2lKLz6K7UY+7bzOznwEvEBLXPe4+p4m71dx0AR6PflKkBHjI3V8w\ns3eAiWZ2AeHnSc5pyk4WMzP7E1ABHGRmfyX8pMtNwH/vvP4auKCzxSuwLk8zs4GEs+cWAheC1mUc\nZvY14PvATDN7l1Ay+gXwG+r4/92YdaoL3EREJE8xlpJERKQJKTGIiEgeJQYREcmjxCAiInmUGERE\nJI8Sg4iI5FFiEBGRPEoMIiKS5/8BBsUfFKPK6REAAAAASUVORK5CYII=\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x15b41630>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(shu_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,10,25,0,0,0)\n", + "\n", + "b = UTCDateTime(2016,1,1,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,shu_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "remove_outlier_baselines(shu_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x1682f470>]" + ] + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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U/3ob4PykYwFw93fN7EWiO+f71OdHko3qB/82s42JuC6qbOJBwU6B\nFRGRulfI3U0iIlLHlCRERCQtJQkREUlLSUJERNJSkhARkbSUJEREJC0lCRERSUtJQkRE0vp/5TSk\nohkZyuIAAAAASUVORK5CYII=\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x15b41710>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(shu_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "shu_abs_ord = get_ord_abs_from_baselines(shu_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mshu, resshu, rankshu, sigshu = get_transform_from_abs_ords(shu_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.88588472e-01, -2.08072460e-01, -6.35567167e-03,\n", + " -1.47139029e+01],\n", + " [ 2.10268162e-01, 9.69217999e-01, -1.82802376e-02,\n", + " 5.17502020e+02],\n", + " [ 1.51436210e-02, 4.47899422e-03, 1.01739086e+00,\n", + " -1.48503633e+03],\n", + " [ 1.99237003e-15, -2.49062245e-15, -0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mshu" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.73523464e-01, 8.51217195e-01, 1.34919961e-01,\n", + " 1.48084738e-39])" + ] + }, + "execution_count": 236, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resshu" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezfshuJan16 = factory.get_timeseries(observatory='SHU',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "shuJan16adj = make_adjusted_from_transform_and_raw(Mshu,hezfshuJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "shuh_pqqm = np.mean(shu_abs_ord.absp1[0] - shu_abs_ord.ordp1[0])\n", + "\n", + "shue_pqqm = np.mean(shu_abs_ord.absp1[1] - shu_abs_ord.ordp1[1])\n", + "\n", + "shuz_pqqm = np.mean(shu_abs_ord.absp1[2] - shu_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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GOnDVVRHezGGW/va39HNGsrmNCW6ne/dAc9uECQCc/cwzFSaF1YMHe04mXt2x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\nRU5ErkJggg==\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x167dd400>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezfshuJan16[0].data+shuh_pqqm)**2 + (hezfshuJan16[1].data+shue_pqqm)**2 + (hezfshuJan16[2].data+shuz_pqqm)**2)**(0.5) - hezfshuJan16[3].data - 13.1,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((shuJan16adj[0]**2 + shuJan16adj[1]**2 + shuJan16adj[2]**2)**(0.5) - hezfshuJan16[3].data - 13.1,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjshu_state_.json', Mshu, 13.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sit_bns = get_baselines_from_file('/users/aclaycomb/Documents/sitjson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x15828fd0>]" + ] + }, + "execution_count": 243, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x167d2128>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(sit_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,10,1,0,0,0)\n", + "\n", + "b = UTCDateTime(2016,1,1,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,sit_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x144972e8>]" + ] + }, + "execution_count": 245, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x1443bf60>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(sit_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sit_abs_ord = get_ord_abs_from_baselines(sit_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Msit, ressit, ranksit, sigsit = get_transform_from_abs_ords(sit_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.59545352e-01, -3.66675295e-01, -1.06533425e-01,\n", + " 5.29374275e+03],\n", + " [ 2.40650589e-01, 1.03242483e+00, -2.66118481e-02,\n", + " 2.86265553e+03],\n", + " [ 2.24111073e-02, 2.01999547e-02, 1.00408997e+00,\n", + " 2.41162824e+02],\n", + " [ 1.08442241e-15, -0.00000000e+00, -0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 250, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Msit" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.18381961e+00, 5.83420751e+00, 6.22840607e-01,\n", + " 1.57555273e-39])" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ressit" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezfsitJan16 = factory.get_timeseries(observatory='SIT',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-30T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sitJan16adj = make_adjusted_from_transform_and_raw(Msit,hezfsitJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sith_pqqm = np.mean(sit_abs_ord.absp1[0] - sit_abs_ord.ordp1[0])\n", + "\n", + "site_pqqm = np.mean(sit_abs_ord.absp1[1] - sit_abs_ord.ordp1[1])\n", + "\n", + "sitz_pqqm = np.mean(sit_abs_ord.absp1[2] - sit_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 255, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1/++ffQann1723gmgtr179/L0zp3gjGdVrx787nfQoQPXtWrF3ePGsXvECAD+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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x1443b6a0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezfsitJan16[0].data+sith_pqqm)**2 + (hezfsitJan16[1].data+site_pqqm)**2 + (hezfsitJan16[2].data+sitz_pqqm)**2)**(0.5) - hezfsitJan16[3].data - 3.2,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((sitJan16adj[0]**2 + sitJan16adj[1]**2 + sitJan16adj[2]**2)**(0.5) - hezfsitJan16[3].data - 3.2,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjsit_state_.json', Msit, 3.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sjg_bns = get_baselines_from_file('/users/aclaycomb/Documents/sjgjson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x16f84ac8>]" + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x15e7d940>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(sjg_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,10,1,0,0,0)\n", + "\n", + "b = UTCDateTime(2015,12,1,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,sjg_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x17180e10>]" + ] + }, + "execution_count": 260, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x171de198>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(sjg_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sjg_abs_ord = get_ord_abs_from_baselines(sjg_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Msjg, ressjg, ranksjg, sigsjg = get_transform_from_abs_ords(sjg_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.90536307e-01, 2.04242590e-01, 3.87150502e-02,\n", + " -1.50422264e+03],\n", + " [ -2.27532793e-01, 9.62960561e-01, 5.14425894e-03,\n", + " 8.62310605e+01],\n", + " [ -6.93279286e-03, 2.77582644e-02, 9.44616720e-01,\n", + " 1.70847335e+03],\n", + " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Msjg" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.76264961e+01, 7.71329740e+00, 1.34822012e+01,\n", + " 3.73081703e-39])" + ] + }, + "execution_count": 266, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ressjg" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezfsjgJan16 = factory.get_timeseries(observatory='SJG',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sjgJan16adj = make_adjusted_from_transform_and_raw(Msjg,hezfsjgJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sjgh_pqqm = np.mean(sjg_abs_ord.absp1[0] - sjg_abs_ord.ordp1[0])\n", + "\n", + "sjge_pqqm = np.mean(sjg_abs_ord.absp1[1] - sjg_abs_ord.ordp1[1])\n", + "\n", + "sjgz_pqqm = np.mean(sjg_abs_ord.absp1[2] - sjg_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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I/BG/I9LOf9XW0lXr19Mf9u+PG/YhIsr73vdsN952/t5//33a4/WSc8AAAkAF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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x171d0dd8>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezfsjgJan16[0].data+sjgh_pqqm)**2 + (hezfsjgJan16[1].data+sjge_pqqm)**2 + (hezfsjgJan16[2].data+sjgz_pqqm)**2)**(0.5) - hezfsjgJan16[3].data + 55,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((sjgJan16adj[0]**2 + sjgJan16adj[1]**2 + sjgJan16adj[2]**2)**(0.5) - hezfsjgJan16[3].data + 55,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjsjg_state_.json', Msjg, -55)" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bsl_bns = get_baselines_from_file('/users/aclaycomb/Documents/bsljson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x17e9fb70>]" + ] + }, + "execution_count": 273, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x172ec080>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(bsl_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,10,1,0,0,0)\n", + "\n", + "b = UTCDateTime(2016,1,1,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,bsl_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x180ddd68>]" + ] + }, + "execution_count": 275, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KP+SQ2NHEp7WDMshVV8Hy5aGWKSLV89BD0KqVEkBVqSWQJt5+G446Ct57D3baKXY0Ipnl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\n94S//S3sSPbFF7G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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x17f5d278>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(bsl_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bsl_abs_ord = get_ord_abs_from_baselines(bsl_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Mbsl, resbsl, rankbsl, sigbsl = get_transform_from_abs_ords(bsl_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.05069413e+00, 1.10714328e-01, -2.11533688e-01,\n", + " 8.16799353e+03],\n", + " [ 2.79831733e-02, 1.06881857e+00, -1.29195735e-01,\n", + " 4.37112281e+03],\n", + " [ -3.85811324e-02, -5.23226381e-02, 1.12178759e+00,\n", + " -4.01891646e+03],\n", + " [ 0.00000000e+00, -0.00000000e+00, 0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mbsl" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.28382379e+00, 2.91445223e+00, 7.70382134e-01,\n", + " 3.85709084e-39])" + ] + }, + "execution_count": 279, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resbsl" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "hezfbslJan16 = factory.get_timeseries(observatory='BSL',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bslJan16adj = make_adjusted_from_transform_and_raw(Mbsl,hezfbslJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bslh_pqqm = np.mean(bsl_abs_ord.absp1[0] - bsl_abs_ord.ordp1[0])\n", + "\n", + "bsle_pqqm = np.mean(bsl_abs_ord.absp1[1] - bsl_abs_ord.ordp1[1])\n", + "\n", + "bslz_pqqm = np.mean(bsl_abs_ord.absp1[2] - bsl_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-10, 10)" + ] + }, + "execution_count": 283, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x1809aeb8>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezfbslJan16[0].data+bslh_pqqm)**2 + (hezfbslJan16[1].data+bsle_pqqm)**2 + (hezfbslJan16[2].data+bslz_pqqm)**2)**(0.5) - hezfbslJan16[3].data + 4.2,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((bslJan16adj[0]**2 + bslJan16adj[1]**2 + bslJan16adj[2]**2)**(0.5) - hezfbslJan16[3].data + 4.2,'k')\n", + "\n", + "pl.ylim(-10,10)" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjbsl_state_.json', Mbsl, -4.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "tuc_bns = get_baselines_from_file('/users/aclaycomb/Documents/tucjson12.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x18c8ba20>]" + ] + }, + "execution_count": 286, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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g7corPSgk0qKF/RVJXmrYvt3Wuy4rg8ceg4ce8qDgahYqMKjqLFWtCDbnAN1i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\naSh27LAF69etq/25sTN8ilh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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x18105630>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(tuc_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a = UTCDateTime(2015,12,1,0,0,0)\n", + "\n", + "b = UTCDateTime(2016,1,1,0,0,0)\n", + "\n", + "get_bns_between_dates(a,b,tuc_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x18e459e8>]" + ] + }, + "execution_count": 288, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Wt92msBCRwqMWBmEuRdeuYUG/Xr1yU1eNlSvDkh6ffgr33BNaMcm89hqccEKY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\nhoiIpESBISIiKfn/+VRr6z775yAAAAAASUVORK5CYII=\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x18cbcf60>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(tuc_bns.baseH)" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "tuc_abs_ord = get_ord_abs_from_baselines(tuc_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mtuc, restuc, ranktuc, sigtuc = get_transform_from_abs_ords(tuc_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.83650649e-01, -2.14063605e-01, 8.68640018e-02,\n", + " -3.67288828e+03],\n", + " [ 2.12187743e-01, 8.89843829e-01, 2.22513804e-01,\n", + " -1.02132303e+04],\n", + " [ -1.15735139e-02, 3.83529111e-02, 8.93747080e-01,\n", + " 4.91693773e+03],\n", + " [ 0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mtuc" + ] + }, + { + "cell_type": "code", + "execution_count": 292, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 9.16746608e-01, 2.68686591e+00, 4.59761366e-01,\n", + " 1.91204373e-39])" + ] + }, + "execution_count": 292, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "restuc" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezftucJan16 = factory.get_timeseries(observatory='TUC',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "tucJan16adj = make_adjusted_from_transform_and_raw(Mtuc,hezftucJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "tuch_pqqm = np.mean(tuc_abs_ord.absp1[0] - tuc_abs_ord.ordp1[0])\n", + "\n", + "tuce_pqqm = np.mean(tuc_abs_ord.absp1[1] - tuc_abs_ord.ordp1[1])\n", + "\n", + "tucz_pqqm = np.mean(tuc_abs_ord.absp1[2] - tuc_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 296, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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P5vnycl4+epRZISs6zaZ0nd/PsqoqfjF4MP28Xg43NzMoMTHsownKW1qsGz/b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\ngg==\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x18cc81d0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(((hezftucJan16[0].data+tuch_pqqm)**2 + (hezftucJan16[1].data+tuce_pqqm)**2 + (hezftucJan16[2].data+tucz_pqqm)**2)**(0.5) - hezftucJan16[3].data + 4.7,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((tucJan16adj[0]**2 + tucJan16adj[1]**2 + tucJan16adj[2]**2)**(0.5) - hezftucJan16[3].data + 4.7,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjtuc_state_.json', Mtuc, -4.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.5707963267948966" + ] + }, + "execution_count": 298, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.pi/2" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ded_bh = np.array([-137.8, -137.7, -140.1, -139.2, -138.2, -140.5, -138.4, -139.4, -137.4, -138.7])" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ded_bd = np.pi/180/60*np.array([1265.5, 1266.3, 1265.7, 1265.6, 1266.3, 1265.7, 1265.1, 1265.1, 1265.9, 1265.8])" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ded_bz = np.array([22.4, 22.6, 22.8, 22.5, 22.7, 23.1, 22.6, 23.1, 23.0, 22.7])" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ded_ah = np.array([8931.4, 8922.9, 8970.2, 8880.4, 8992.0, 9038.4, 8943.7, 8985.0, 8944.5, 8960.9])" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ded_ad = np.pi/180/60*np.array([1121.7, 1127.8, 1121.7, 1143.9, 1128.7, 1130.9, 1132.0, 1123.5, 1127.0, 1119.4])" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ded_az = np.array([56850.0, 56825.0, 56840.4, 56834.9, 56856.0, 56816.0, 56832.4, 56831.0, 56809.0, 56834.3])" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ded_bns = Baselines()" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ded_bns.baseH = ded_bh\n", + "\n", + "ded_bns.baseD = ded_bd\n", + "\n", + "ded_bns.baseZ = ded_bz\n", + "\n", + "ded_bns.absH = ded_ah\n", + "\n", + "ded_bns.absD = ded_ad\n", + "\n", + "ded_bns.absZ = ded_az" + ] + }, + { + "cell_type": "code", + "execution_count": 307, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x18fcd9b0>]" + ] + }, + "execution_count": 307, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x17ad3fd0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot(ded_bns.baseZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ded_abs_ord = get_ord_abs_from_baselines(ded_bns)" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mded, resded, rankded, sigded = get_transform_from_abs_ords(ded_abs_ord)" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.17300296e-01, -3.73211099e-01, -5.14226627e-03,\n", + " 2.98482986e+02],\n", + " [ 3.53096520e-01, 9.06733279e-01, 6.61541037e-04,\n", + " -2.98943984e+01],\n", + " [ 3.68482513e-03, -3.82402473e-04, 9.90805880e-01,\n", + " 5.11440783e+02],\n", + " [ 0.00000000e+00, 1.61017236e-15, -0.00000000e+00,\n", + " 1.00000000e+00]])" + ] + }, + "execution_count": 312, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Mded" + ] + }, + { + "cell_type": "code", + "execution_count": 313, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4.44991815e+00, 1.17540091e+01, 1.07493093e-01,\n", + " 1.63926361e-38])" + ] + }, + "execution_count": 313, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resded" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hezfdedJan16 = factory.get_timeseries(observatory='DED',\n", + "\n", + " interval='minute',\n", + "\n", + " type='variation',\n", + "\n", + " channels=('H', 'E', 'Z', 'F'),\n", + "\n", + " starttime=UTCDateTime('2016-01-01T00:00:00Z'),\n", + "\n", + " endtime=UTCDateTime('2016-01-31T23:59:59Z'))" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dedJan16adj = make_adjusted_from_transform_and_raw(Mded,hezfdedJan16)" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dedh_pqqm = np.mean(ded_abs_ord.absp1[0] - ded_abs_ord.ordp1[0])\n", + "\n", + "dede_pqqm = np.mean(ded_abs_ord.absp1[1] - ded_abs_ord.ordp1[1])\n", + "\n", + "dedz_pqqm = np.mean(ded_abs_ord.absp1[2] - ded_abs_ord.ordp1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-30, 30)" + ] + }, + "execution_count": 317, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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455aRq/S/t2wxtQnHSnnYPat2/TITxRDqlbZ7/mvXrw/5eyy8kiIt07YLFwZWMAt17ykx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\nYRgdrBgYhmEYHf8PC7KB4QT7HCsAAAAASUVORK5CYII=\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x16ce82e8>" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "pl.plot(((hezfdedJan16[0].data+dedh_pqqm)**2 + (hezfdedJan16[1].data+dede_pqqm)**2 + (hezfdedJan16[2].data+dedz_pqqm)**2)**(0.5) - hezfdedJan16[3].data + 0.5,'c')\n", + "\n", + "pl.hold(True)\n", + "\n", + "pl.plot((dedJan16adj[0]**2 + dedJan16adj[1]**2 + dedJan16adj[2]**2)**(0.5) - hezfdedJan16[3].data + 0.5,'k')\n", + "\n", + "pl.ylim(-30,30)" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "save_state('/users/aclaycomb/Documents/adjded_state_.json', Mded, -0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" } - ] + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/docs/algorithms/Adjusted_usage.md b/docs/algorithms/Adjusted_usage.md index e08df6bd057e27e116a494ffe3be69ac5e490ca3..4db49e5c509c398b8aa61f6675dd28781a47326e 100644 --- a/docs/algorithms/Adjusted_usage.md +++ b/docs/algorithms/Adjusted_usage.md @@ -28,6 +28,19 @@ contained in the statefile. --output-iaga-stdout +### Statefile Example Content + +This is the content of /etc/adjusted/adjbou_state_.json: + +{"M32": -0.011809351484171948, "M41": -0.0, "M44": 1.0, "PC": -22, + "M24": -0.84581925813504188, "M43": 0.0, + "M34": 905.38008857968441, "M33": 0.99618690124939757, + "M21": 0.16680172992706568, "M22": 0.98791620101212796, + "M23": -0.0049868332295851525, "M11": 0.98342757670906167, + "M42": -0.0, "M13": 0.027384986324932026, + "M12": -0.15473074200902157, "M14": -1276.1646811919759, + "M31": -0.006725053082782385} + ### Library Notes > Note: this library internally represents data gaps as NaN, and diff --git a/geomagio/algorithm/AdjustedAlgorithm.py b/geomagio/algorithm/AdjustedAlgorithm.py index 0f27d14c956a47397bdc43bbc96432365c635696..5e950a68cec307552de9061cf356fccf1e5e91cf 100644 --- a/geomagio/algorithm/AdjustedAlgorithm.py +++ b/geomagio/algorithm/AdjustedAlgorithm.py @@ -12,13 +12,16 @@ import sys class AdjustedAlgorithm(Algorithm): """Adjusted Data Algorithm""" - def __init__(self, matrix=None, pier_correction=None, statefile=None): + def __init__(self, matrix=None, pier_correction=None, statefile=None, + data_type=None, location=None): Algorithm.__init__(self, inchannels=('H', 'E', 'Z', 'F'), outchannels=('X', 'Y', 'Z', 'F')) # state variables self.matrix = matrix self.pier_correction = pier_correction self.statefile = statefile + self.data_type = data_type + self.location = location if (matrix is None): self.load_state() @@ -85,8 +88,7 @@ class AdjustedAlgorithm(Algorithm): with open(self.statefile, 'w') as f: f.write(json.dumps(data)) - @classmethod - def create_trace(cls, channel, stats, data): + def create_trace(self, channel, stats, data): """Utility to create a new trace object. Parameters @@ -104,9 +106,16 @@ class AdjustedAlgorithm(Algorithm): trace containing data and metadata. """ stats = Stats(stats) - stats.data_type = 'adjusted' - stats.location = 'A0' - trace = super(AdjustedAlgorithm, cls).create_trace(channel, stats, + if self.data_type is None: + stats.data_type = 'adjusted' + else: + stats.data_type = self.data_type + if self.data_type is None: + stats.location = 'A0' + else: + stats.location = self.location + + trace = super(AdjustedAlgorithm, self).create_trace(channel, stats, data) return trace diff --git a/test/algorithm_test/AdjustedAlgorithm_test.py b/test/algorithm_test/AdjustedAlgorithm_test.py index f7d051aa68346f9137e50ec8845a01ef5cefb962..f412efa7cc2bf44807e79f2c1f7c076a5d1fbcd0 100644 --- a/test/algorithm_test/AdjustedAlgorithm_test.py +++ b/test/algorithm_test/AdjustedAlgorithm_test.py @@ -10,6 +10,7 @@ def test_construct(): """ matrix = None pier_correction = None + # load adjusted data transform matrix and pier correction a = adj(matrix, pier_correction, 'etc/adjusted/adjbou_state_.json') assert_almost_equals(a.matrix[0, 0], 9.83427577e-01, 6) @@ -24,8 +25,10 @@ def test_process(): """ matrix = None pier_correction = None + # load adjusted data transform matrix and pier correction a = adj(matrix, pier_correction, 'etc/adjusted/adjbou_state_.json') + # load boulder Jan 16 files from /etc/ directory hezf_iaga2002_file = open('etc/adjusted/BOU201601vmin.min') hezf_iaga2002_string = hezf_iaga2002_file.read() xyzf_iaga2002_file = open('etc/adjusted/BOU201601adj.min') @@ -34,11 +37,15 @@ def test_process(): hezf = factory.parse_string(hezf_iaga2002_string) xyzf = factory.parse_string(xyzf_iaga2002_string) + # process hezf (raw) channels with loaded transform adj_bou = a.process(hezf) + + # unpack channels from loaded adjusted data file x = xyzf.select(channel='X')[0] y = xyzf.select(channel='Y')[0] z = xyzf.select(channel='Z')[0] f = xyzf.select(channel='F')[0] + # unpack channels from adjusted processing of raw data x_adj = adj_bou.select(channel='X')[0] y_adj = adj_bou.select(channel='Y')[0] z_adj = adj_bou.select(channel='Z')[0]