{ "metadata": { "name": "", "signature": "sha256:7738873759cfd7e8f686a03c823f88e733ada3dafa395978a3631e114beb6538" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of Simpson Island paleomagnetic data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Corresponding Author: Nicholas L. Swanson-Hysell (swanson-hysell@berkeley.edu)" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This IPython notebook contains data analysis on paleomagnetic data developed from ca. 1.1 billion year old lava flows of the Osler Volcanic Group that are part of the North American Midcontinent Rift as well as supporting analyses on other data sets. These analyses accompany a **Geochemistry, Geophysics, Geosystems** manuscript entitled \"Confirmation of progressive plate motion during the Midcontinent Rift's early magmatic stage from the Osler Volcanic Group, Ontario, Canada\" by N. L. Swanson-Hysell, A. A. Vaughan, M. R. Mustain and K. Asp. This notebook is part of the supporting online materials and is available at [https://github.com/Swanson-Hysell/2014_Swanson-Hysell-et-al_Osler](https://github.com/Swanson-Hysell/2014_Swanson-Hysell-et-al_Osler) along with the necessary files to execute all of the code. Within this github repository are the following folders:\n", "\n", "1. [2014_Osler_Code](https://github.com/Swanson-Hysell/2014_Swanson-Hysell-et-al_Osler/tree/master/2014_Osler_Code) This folder contains this notebook file as well as necessary libraries.\n", "2. [2014_Osler_Data](https://github.com/Swanson-Hysell/2014_Swanson-Hysell-et-al_Osler/tree/master/2014_Osler_Data) This folder contains the data generated in the study at the specimen level as well as the flow means that are imported into this notebook for data analysis.\n", "3. [2014_Osler_Manuscript](https://github.com/Swanson-Hysell/2014_Swanson-Hysell-et-al_Osler/tree/master/2014_Osler_Manuscript) This folder contains the manuscript text and figures.\n", "\n", "If you are viewing this supporting material as a PDF document and want to run the code in the notebook you will need to download the code from the github repository and you will also need a Python distribution that includes IPython. There are good instructions for installing IPython here: http://ipython.org/install.html. Alternatively you can view the notebook online with the IPython nbviewer at this link: http://bit.ly/19XRdjJ" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Import libraries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This code blocks imports necessary libraries that define functions that will be used in the data analysis below. The code below uses the pmag.py and pmagplotlib.py files of the the PmagPy software package (version pmagpy-2.206) authored by Lisa Tauxe (https://github.com/ltauxe/PmagPy). The check_updates and get_version functions of the PmagPy libraries cause errors in the interactive environment of IPython so the code below calls a slightly modified version of those libraries that is included in the Github repository for this work. There are other functions that are necessary for the interactive plotting of paleomagnetic data (some of them edited from PmagPy) that are within the IPmag.py library that is imported in the code block below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#import paleomagnetic specific libraries\n", "import pmag, pmagplotlib, IPmag\n", "import pandas\n", "pandas.set_option('display.max_columns', 500)\n", "from IPython.core.display import HTML\n", "from mpl_toolkits.basemap import Basemap" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook runs with pylab inline which imports the numpy, scipy and matplotlib functions and allows for the plots to be viewed inline in the IPython notebook (instead of opening up in another window). " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 2 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Import Simpson Island paleomagnetic data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data file FlowDataAll.csv has data in this format:\n", "\n", "SITE, STRAT_HEIGHT, DEC_GEO, INC_GEO, Dec_TC, INC_TC, A95, n, Plat, ABS_Plat, VGP_lat, VGP_long\n", "\n", "where: \n", "\n", "**SITE** is the name of the site and corresponds to an individual lava flow where in the first part of the site name (e.g. SI1) corresponds to the name of a stratigraphic section while the second part (e.g. (11.8 to 26.4)) corresponds to the stratigraphic ranges within the measured section over which the flows is exposed.\n", "\n", "**STRAT_HEIGHT** is the cumulative stratigraphic height of the Simpson Island stratigraphy starting with the base of the SI1 section (see map in the main manuscript).\n", "\n", "**DEC_GEO** is the site mean declination in geographic (i.e. *in situ*) coordinates prior to tilt-correction.\n", "\n", "**INC_GEO** is the site mean inclination in geographic (i.e. *in situ*) coordinates prior to tilt-correction.\n", "\n", "**Dec_TC** is the site mean declination in tilt-corrected coordinates following correction for bedding tilt.\n", "\n", "**INC_TC** is the site mean inclination in tilt-corrected coordinates following correction for bedding tilt.\n", "\n", "**A95** is the 95$\\%$ Fisher confidence ellipse around the calculated site mean.\n", "\n", "**n** is the number of individually oriented samples that are being included to calculate the site mean.\n", "\n", "**Plat** is the paleolatitude calculated from the site mean using the dipole equation.\n", "\n", "**ABS_Plat** is the absolute value of this paleolatitude.\n", "\n", "**VGP_lat** is the latitude of the virtual geomagnetic pole calculated using the site location and the site mean direction.\n", "\n", "**VGP_long** is the longitude of the virtual geomagnetic pole calculated using the site location and the site mean direction.\n", "\n", "These data are displayed in a table below and imported by variable in the cell below the table." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Table of Simpson Island paleomagnetic data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = pandas.read_csv('../2014_Osler_Data/SimpsonIsland_OslerData.csv')\n", "#write these data to a latex file for the supplemental PDF\n", "with open('OslerData.txt','w') as f:\n", " f.write(data.to_latex(index=False,columns=['Site','Strat_Height','Site_Lat','Site_Long','Dec_Geo','Inc_Geo','Dec_TC','Inc_TC','n','VGP_lat','VGP_long']))\n", "display(HTML(data.to_html()))" ], "language": "python", "metadata": {}, "outputs": [ 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SiteStrat_HeightSite_LatSite_LongDec_GeoInc_GeoDec_TCInc_TCA95nPlatABS_PlatVGP_latVGP_long
0 SI1(11.8 to 26.4) 11.8 48.8122-87.6620 120.3-77.1 79.7-70.5 2.7 7-54.7 54.7 33.1 229.6
1 SI1(28.3 to 29.2) 28.3 48.8107-87.6623 141.0-67.4 109.8-66.8 3.9 7-49.4 49.4 45.8 210.9
2 SI1(29.2 to 29.7) 29.2 48.8104-87.6622 131.2-69.1 99.7-66.2 5.7 3-48.6 48.6 39.6 214.5
3 SI1(33.3 to 37.1) 33.3 48.8100-87.6623 127.7-72.3 92.6-68.1 7.7 5-51.2 51.2 37.2 220.5
4 SI1(40.6 to 42.5) 40.6 48.8095-87.6626 134.9-77.7 85.4-73.3 3.5 6-59.0 59.0 38.2 231.6
5 SI1(42.5 to 44.4) 42.5 48.8095-87.6627 103.0-71.6 77.8-63.5 4.5 6-45.1 45.1 25.8 222.3
6 SI1(58.1 to 64.1) 58.1 48.8086-87.6622 114.4-70.8 86.1-64.4 8.7 5-46.2 46.2 30.8 218.8
7 SI1(87.6 to 88.5) 87.6 48.8073-87.6620 134.3-75.8 89.9-71.9 1.9 6-56.9 56.9 39.0 227.7
8 SI1(88.5 to 89.2) 88.5 48.8071-87.6619 128.2-74.6 88.9-70.0 5.0 5-53.9 53.9 36.9 224.9
9 SI1(89.2 to 91.5) 89.2 48.8070-87.6618 120.1-72.8 87.0-67.2 7.6 5-49.9 49.9 33.6 221.8
10 SI1(91.5 to 92.6) 91.5 48.8069-87.6618 118.4-74.8 82.8-68.5 3.0 6-51.8 51.8 32.7 225.5
11 SI1(94.3 to 94.9) 94.3 48.8068-87.6620 145.5-76.5 94.1-74.4 4.3 5-60.8 60.8 42.8 230.8
12 SI1(94.9 to 96.6) 94.9 48.8066-87.6621 136.6-80.0 79.1-75.0 2.3 5-61.7 61.7 37.2 236.6
13 SI1(116.3 to 118.8) 116.3 48.8062-87.6634 112.9-78.2 74.1-70.3 6.4 6-54.4 54.4 30.4 231.8
14 SI1(119.7 to 122.1) 119.7 48.8060-87.6634 124.2-74.0 87.5-68.9 6.8 5-52.3 52.3 35.3 223.9
15 SI1(122.1 to 123.7) 122.1 48.8058-87.6633 111.3-76.8 75.7-69.1 4.9 6-52.6 52.6 30.0 229.6
16 SI2a (1.1 to 1.6) 442.1 48.7943-87.6556 139.0-54.1 117.1-58.1 4.1 6-38.8 38.8 44.8 194.2
17 SI2a (10.2 to 12.8) 451.2 48.7940-87.6555 171.8-57.3 152.3-68.8 4.8 6-52.1 52.1 72.2 203.7
18 SI2b (2.2 to 2.6) 519.2 48.7921-87.6546 144.7-57.3 120.0-62.4 4.2 8-43.7 43.7 49.3 198.7
19 SI2b (10.5 to 10.6) 527.5 48.7916-87.6539 140.7-64.3 107.4-67.3 4.4 5-50.0 50.0 44.7 212.8
20 SI2c (0.0 to 2.1) 592.0 48.7903-87.6545 120.9-38.7 108.7-39.2 4.4 6-22.2 22.2 28.7 184.0
21 SI2c (2.1 to 18.6) 594.1 48.7902-87.6545 127.2-45.6 111.3-47.3 2.4 6-28.5 28.5 34.7 187.5
22 SI3(2.3 to 5.5) 745.3 48.7828-87.6326 156.0-56.8 135.4-69.3 4.5 6-52.9 52.9 62.0 208.0
23 SI3(5.5 to 12.5) 748.5 48.7826-87.6323 154.3-60.9 127.5-72.5 3.6 10-57.8 57.8 58.2 218.9
24 SI3(12.5 to 20.1) 755.5 48.7823-87.6318 145.9-64.8 109.1-73.7 6.2 6-59.6 59.6 49.3 225.3
25 SI3(20.1 to 28.9) 763.1 48.7821-87.6316 140.1-61.4 108.1-69.3 1.9 9-52.9 52.9 46.7 216.0
26 SI3(36.9 to 48.4) 779.9 48.7817-87.6310 136.0-64.7 99.6-70.5 4.3 6-54.7 54.7 42.6 221.6
27 SI3(55.8 to 60.9) 798.8 48.7812-87.6304 139.9-63.2 105.6-70.7 4.3 6-55.0 55.0 45.9 219.8
28 SI3(103.3 to 110.3) 846.3 48.7799-87.6295 122.4-65.9 84.0-68.1 6.1 6-51.2 51.2 32.9 224.5
29 SI3(124.8 to 130.4) 867.8 48.7791-87.6292 114.1-63.1 81.5-63.7 5.0 5-45.3 45.3 27.8 220.5
30 SI9(0.0 to 9.2) 1183.0 48.7721-87.6222 104.4-73.3 54.5-67.3 3.2 7-50.1 50.1 19.4 238.8
31 SI9(12.0 to 21.2) 1195.0 48.7718-87.6221 107.7-69.0 64.4-65.3 4.1 8-47.4 47.4 21.1 231.5
32 SI9(66.9 to 84.0) 1249.9 48.7700-87.6203 116.0-70.0 67.4-68.1 3.6 7-51.3 51.3 25.3 232.7
33 SI9(84.0 to 88.9) 1267.0 48.7696-87.6204 104.7-76.7 47.1-69.2 4.2 8-52.8 52.8 19.1 244.4
34 SI9(139.8 to 160.0) 1322.8 48.7680-87.6192 117.6-48.6 96.0-51.8 5.4 8-32.4 32.4 27.5 201.2
35 SI9(160.0 to 166.5) 1343.0 48.7676-87.6189 120.6-49.2 98.5-53.2 4.7 8-33.8 33.8 29.9 200.8
36 SI9(166.5 to 171.4) 1349.5 48.7674-87.6185 129.3-45.9 109.6-53.0 9.3 8-33.5 33.5 36.9 193.5
37 SI9(171.4 to 188.8) 1354.4 48.7672-87.6190 127.2-39.1 111.7-46.1 6.9 6-27.5 27.5 34.3 186.3
38 SI9(312.1 to 321.0) 1495.1 48.7662-87.6181 143.6-50.4 121.8-61.2 2.8 8-42.3 42.3 49.7 195.8
39 SI9(336.4 to 353.8) 1519.4 48.7630-87.6151 135.7-47.6 115.2-56.4 3.9 8-37.0 37.0 42.6 193.4
40 SI9(375.9 to 381.0) 1558.9 48.7623-87.6142 136.1-46.3 116.6-55.3 7.3 4-35.9 35.9 42.8 191.3
41 SI9(387.6 to 395.3) 1570.6 48.7620-87.6140 137.6-46.8 117.8-56.2 6.9 7-36.7 36.7 44.1 191.4
42 SI9(395.3 to 399.3) 1578.3 48.7617-87.6138 143.2-53.4 118.3-63.8 4.1 7-45.4 45.4 49.0 201.9
43 SI4(0.0 to 13.8) 1983.0 48.7525-87.5971 152.7-60.8 119.4-72.7 3.0 6-58.1 58.1 54.1 220.8
44 SI4(13.8 to 20.2) 1996.8 48.7524-87.5973 148.6-62.2 111.4-72.6 2.2 6-57.9 57.9 49.9 222.2
45 SI4(21.4 to 30.0) 2004.4 48.7520-87.5972 140.7-63.1 100.7-70.8 3.4 7-55.2 55.2 43.4 221.9
46 SI4(39.7 to 44.7) 2022.7 48.7516-87.5967 165.5-58.5 143.2-73.9 5.1 6-60.0 60.0 66.2 224.4
47 SI4(72.3 to 74.6) 2055.3 48.7508-87.5957 159.1-58.6 132.4-72.7 3.3 6-58.0 58.0 60.9 219.0
48 SI4(74.6 to 80.0) 2057.6 48.7507-87.5953 152.5-52.0 131.4-65.0 1.8 6-47.0 47.0 57.9 198.0
49 SI4(80.2 to 100.7) 2063.2 48.7505-87.5951 145.1-54.1 119.8-64.8 4.7 8-46.8 46.8 50.5 203.1
50 SI4(106.0 to 121.4) 2089.0 48.7499-87.5953 140.0-46.6 120.7-56.8 4.3 7-37.4 37.4 46.4 190.3
51 SI4(121.4 to 127.3) 2104.4 48.7494-87.5954 129.6-43.6 111.5-50.9 4.1 7-31.6 31.6 36.9 190.4
52 SI4(133.8 to 143.1) 2116.8 48.7490-87.5955 134.8-52.7 109.6-60.6 4.7 7-41.6 41.6 41.7 201.8
53 SI4(160.4 to 171.1) 2143.4 48.7485-87.5958 163.7-46.6 151.1-62.4 4.0 8-43.7 43.7 69.5 179.1
54 SI8(0.0 to 3.9) 2336.0 48.7466-87.6194 152.4-56.4 126.1-68.9 2.2 8-52.3 52.3 56.4 209.4
55 SI8(3.9 to 19.3) 2339.9 48.7465-87.6195 153.2-59.3 123.0-71.6 5.3 7-56.4 56.4 55.6 217.1
56 SI8(19.3 to 45.0) 2355.3 48.7462-87.6200 132.9-51.6 108.7-59.1 5.5 8-39.9 39.9 40.1 200.5
57 SI8(47.5 to 56.7) 2383.5 48.7458-87.6211 145.6-53.4 121.2-64.4 5.6 6-46.2 46.2 51.2 201.6
58 SI8(62.9 to 84.4) 2398.9 48.7456-87.6223 140.2-61.5 103.3-69.6 2.7 8-53.4 53.4 43.9 218.6
59 SI8(84.4 to 102.6) 2420.4 48.7453-87.6239 121.2-64.7 80.9-66.0 4.3 8-48.3 48.3 29.5 223.4
60 SI8(106.6 to 115.4) 2442.6 48.7449-87.6276 135.2-58.0 103.8-65.2 7.2 8-47.3 47.3 41.2 211.2
61 SI6(0.0 to 3.0) 2451.0 48.7462-87.6393 137.0-55.3 103.2-63.3 6.1 5-44.8 44.8 39.5 208.7
62 SI6(12.0 to 28.4) 2463.0 48.7460-87.6394 120.8-62.8 78.3-63.0 2.3 8-44.5 44.5 25.5 221.6
63 SI6(40.6 to 57.8) 2491.6 48.7451-87.6393 134.8-59.1 95.2-65.3 5.3 6-47.4 47.4 36.5 215.5
64 SI6(76.5 to 94.1) 2527.5 48.7448-87.6486 149.8-61.3 105.2-72.2 6.0 7-57.3 57.3 46.5 223.0
65 SI6(94.1 to 104.2) 2545.1 48.7445-87.6486 160.6-65.4 103.4-78.6 3.8 5-68.1 68.1 49.0 238.8
66 SI6(122.3 to 127.6) 2573.3 48.7439-87.6493 159.0-70.0 79.9-79.5 6.3 7-69.7 69.7 41.7 245.2
67 SI6(180.6 to 188.8) 2631.6 48.7423-87.6500 105.6-56.6 75.3-53.3 4.6 8-33.9 33.9 16.2 215.6
68 SI6(208.4 to 219.9) 2659.4 48.7415-87.6483 125.1-46.8 100.9-52.0 5.8 5-32.6 32.6 30.7 198.3
69 SI6(245.4 to 252.7) 2696.4 48.7405-87.6476 133.1-47.3 108.3-55.3 4.9 7-35.9 35.9 37.5 196.6
70 SI6(268.4 to 289.5) 2719.4 48.7399-87.6467 135.0-60.4 93.1-66.3 2.7 8-48.8 48.8 36.1 217.8
71 SI6(289.8 to 301.1) 2740.8 48.7393-87.6464 133.7-54.0 101.7-61.0 5.0 8-42.0 42.0 37.1 206.7
72 SI7 (0.0 to 3.9) 2798.0 48.7406-87.6682 143.6-37.9 128.6-48.4 2.4 8-29.4 29.4 46.7 175.2
73 SI7 (3.9 to 5.4) 2801.9 48.7405-87.6684 149.4-41.7 132.8-53.6 9.2 7-34.1 34.1 52.4 177.4
74 SI7 (9.6 to 18.0) 2807.6 48.7403-87.6684 141.2-46.6 120.1-55.6 6.9 7-36.1 36.1 45.3 189.2
75 SI5b(3.2 to 30.7) 2843.2 48.7408-87.6765 136.3-50.0 111.9-57.1 4.0 6-37.7 37.7 40.9 196.2
76 SI5b(57.2 to 63.3) 2897.2 48.7402-87.6838 115.1-41.3 97.9-42.5 5.6 6-24.6 24.6 23.3 193.7
77 SI5b(64.6 to 70.4) 2904.6 48.7401-87.6841 121.4-47.9 99.2-50.4 6.1 5-31.1 31.1 28.6 198.0
78 SI5b(72.6 to 79.0) 2912.6 48.7399-87.6845 118.8-47.7 97.0-49.4 3.5 6-30.3 30.3 26.6 198.8
79 SI5b(115.0 to 120.2) 2955.0 48.7388-87.6855 137.6-48.3 114.7-56.0 2.3 9-36.5 36.5 42.0 193.2
80 SI5b(132.4 to 171.9) 2972.4 48.7385-87.6864 133.6-55.5 103.5-60.9 4.6 7-41.9 41.9 38.1 205.5
81 SI5b(183.4 to 188.7) 3023.4 48.7376-87.6934 142.4-52.3 116.0-61.0 3.5 5-42.1 42.1 45.9 198.8
82 SI5b(197.5 to 216.7) 3037.5 48.7373-87.6935 142.5-63.7 98.7-70.0 4.2 6-53.9 53.9 41.8 221.0
83 SI5a(0.0 to 9.0) 3115.0 48.7363-87.6962 133.7-45.7 112.8-52.5 3.9 12-33.1 33.1 38.7 190.9
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The .csv of these data can be downloaded here: [SimpsonIsland_OslerData.csv](https://github.com/Swanson-Hysell/2014_Swanson-Hysell-et-al_Osler/tree/master/2014_Osler_Data)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data_file='../2014_Osler_Data/SimpsonIsland_OslerData.csv'\n", "SimpsonIsland_Strat = np.genfromtxt(data_file,delimiter=\",\", skip_header=1)\n", "\n", "strat_height=SimpsonIsland_Strat[:,1]\n", "Dec_TC=SimpsonIsland_Strat[:,6]\n", "Inc_TC=SimpsonIsland_Strat[:,7]\n", "A95=SimpsonIsland_Strat[:,8]\n", "plat=SimpsonIsland_Strat[:,10]\n", "abs_plat=SimpsonIsland_Strat[:,11]\n", "VGP_lat=SimpsonIsland_Strat[:,12]\n", "VGP_long=SimpsonIsland_Strat[:,13]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Comparison between data from the lower 1/3, middle 1/3 and upper 1/3 of Simpson Island stratigraphy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's consider the data by stratigraphically grouping flow data from the lower third (0 to 1041 meters), middle third (1041 to 2083 meters) and the upper third (2083 to 3124 meters) of the stratigraphy. The Fisher means for these groups are calculated with the resulting dictionaries from the fisher_mean pmag.py fuction being printed below and with the data being displayed on equal area plots with the calculated Fisher means also being plotted." ] }, { "cell_type": "code", "collapsed": false, "input": [ "SI_Directions=[]\n", "SI_Poles=[]\n", "\n", "for n in range(0,84):\n", " Dec,Inc=Dec_TC[n],Inc_TC[n] \n", " SI_Directions.append([Dec,Inc,1.])\n", " Plong,Plat=VGP_long[n],VGP_lat[n]\n", " SI_Poles.append([Plong,Plat,1.])\n", " \n", "SI_LowerThird_Directions=SI_Directions[0:30]\n", "SI_LowerThird_Poles=SI_Poles[0:30]\n", "SI_MiddleThird_Directions=SI_Directions[30:50]\n", "SI_MiddleThird_Poles=SI_Poles[30:50]\n", "SI_UpperThird_Directions=SI_Directions[50:84]\n", "SI_UpperThird_Poles=SI_Poles[50:84]\n", "\n", "#calculate and display the Fisher means for each subset of the directions\n", "lower_third_mean=pmag.fisher_mean(SI_LowerThird_Directions)\n", "middle_third_mean=pmag.fisher_mean(SI_MiddleThird_Directions)\n", "upper_third_mean=pmag.fisher_mean(SI_UpperThird_Directions)\n", "\n", "print 'The Fisher mean parameters for SI_LowerThird_Directions are: '\n", "print 'Dec = ' + str(lower_third_mean['dec']) + ' Inc = ' + str(lower_third_mean['inc'])\n", "print 'alpha95 = ' + str(lower_third_mean['alpha95']) + ' k= ' + str(lower_third_mean['k'])\n", "print ''\n", "print 'The Fisher mean parameters for SI_MiddleThird_Directions are: '\n", "print 'Dec = ' + str(middle_third_mean['dec']) + ' Inc = ' + str(middle_third_mean['inc'])\n", "print 'alpha95 = ' + str(middle_third_mean['alpha95']) + ' k= ' + str(middle_third_mean['k'])\n", "print ''\n", "print 'The Fisher mean parameters for SI_UpperThird_Directions are: '\n", "print 'Dec = ' + str(upper_third_mean['dec']) + ' Inc = ' + str(upper_third_mean['inc'])\n", "print 'alpha95 = ' + str(upper_third_mean['alpha95']) + ' k= ' + str(upper_third_mean['k'])\n", "\n", "#plot the direction of each flow mean on an equal area plot\n", "fignum = 1\n", "pylab.figure(num=fignum,figsize=(10,10),dpi=160)\n", "pmagplotlib.plotNET(fignum)\n", "IPmag.iplotDI(SI_LowerThird_Directions,color='r')\n", "IPmag.iplotDI(SI_MiddleThird_Directions,color='y')\n", "IPmag.iplotDI(SI_UpperThird_Directions,color='b')\n", "title('Directional data from Simpson Island lava flows:')\n", "\n", "#plot the group means and a95 ellipses on same equal area plot\n", "IPmag.iplotDImean(lower_third_mean['dec'],lower_third_mean['inc'],lower_third_mean[\"alpha95\"],\n", " color='r',marker='s',label='lower third')\n", "IPmag.iplotDImean(middle_third_mean['dec'],middle_third_mean['inc'],middle_third_mean[\"alpha95\"],\n", " color='y',marker='s',label='middle third')\n", "IPmag.iplotDImean(upper_third_mean['dec'],upper_third_mean['inc'],upper_third_mean[\"alpha95\"],\n", " color='b',marker='s',label='upper third')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The Fisher mean parameters for SI_LowerThird_Directions are: \n", "Dec = 99.3387210946 Inc = -68.0428839022\n", "alpha95 = 3.358896873 k= 62.2034940674\n", "\n", "The Fisher mean parameters for SI_MiddleThird_Directions are: \n", "Dec = 105.906122722 Inc = -64.89830144\n", "alpha95 = 5.47951200533 k= 36.4544611363\n", "\n", "The Fisher mean parameters for SI_UpperThird_Directions are: \n", "Dec = 107.624499973 Inc = -61.552568869\n", "alpha95 = 3.51420656821 k= 50.020994936\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ISEhQHK+0tDQIIWBoaIjHjx9j2rRpyM7Ofuv+lSk/PXv2xPz583H58mXI5XJs2rRJ6eNS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"text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Test for a common mean between paleomagnetic data from the lower, middle and upper thirds of the stratigraphy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below will compare the subsets of data using these two statistical tests:\n", "\n", "\n", "1. The $V_w$ test of Watson (1983) slightly modified from the implementation of PmagPy (Tauxe, 2013). The test is comprised of calculating the $V_w$ statistic for the two data sets and then determining the critical value of $V_w$ through a Monte Carlo simulation. For the simulation, two Fisher-distributed data sets with a common mean are simulated using their precisions ($k_1$ and $k_2$) and the number of points ($N_1$ and $N_2$) of the datasets being evaluated. Then the $V_w$ statistic that comes from comparison between the two simulated data sets is calculated. A large number of simulations are done (default is 1000; it can be set as the NumSims parameter of the function) in order to determine the $V_w$ values that would result by chance through sampling distributions with the same direction. The critical value of $V_w$ is at the 95% level of confidence (i.e. if 1000 $V_w$ values are simulated, it is the 950th one). This implementation of the test also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.\n", "2. The bootstrap fold test of Tauxe (developed by Tauxe et al., 1991 and implemented per Tauxe (2010)). This approach determines the cumulative distributions of the Cartesian coordinates of bootstrapped means and compares the cumulative distributions to see if the confidence intervals overlap. If they do all overlap, then the two means cannot be distinguished at the 95% level of confidence and they pass the bootstrap test for a common mean. Otherwise, if the two sets of directions are distinct in the X, Y or Z component the two means can be distinguished at the 95% confidence level. \n", "\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Common mean tests between SI_LowerThird_Directions and SI_MiddleThird_Directions:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "IPmag.iWatsonV(SI_LowerThird_Directions,SI_MiddleThird_Directions)\n", "IPmag.iBootstrap(SI_LowerThird_Directions,SI_MiddleThird_Directions)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results of Watson V test: \n", "\n", "Watson's V: 2.6\n", "Critical value of V: 6.2\n", "\"Pass\": Since V is less than Vcrit, the null hypothesis\n", "that the two populations are drawn from distributions\n", "that share a common mean direction can not be rejected.\n", "\n", "M&M1990 classification:\n", "\n", "Angle between data set means: 4.1\n", "Critical angle for M&M1990: 6.3\n", "The McFadden and McElhinny (1990) classification for\n", "this test is: 'B'\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "===============\n", "\n", "Here are the results of the bootstrap test for a common mean\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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A/fDDF6NEab5FzCHvqKRFQktLSyk/P5+mTp1KSqWS8vPzKT8/n6qqqnhFhvMpEhoXF0dh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\nv38fv/32G2QyGaK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GjRun8fon7fbo0QNjx47FyJEj0aFDB0ybNg3FxcVQqVRaZU+dOhXj\nx4/nzh894Msl1YwGDRqoPa4c5cJ7zGqGYMWZOKLCe0wOR4HwHpPDUSDcMDkcBcINk8NRINwwORwF\nwg2Tw1Eg/w+rweBa/kPqUwAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Common mean tests between SI_MiddleThird_Directions and SI_UpperThird_Directions:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "IPmag.iWatsonV(SI_MiddleThird_Directions,SI_UpperThird_Directions)\n", "IPmag.iBootstrap(SI_MiddleThird_Directions,SI_UpperThird_Directions)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results of Watson V test: \n", "\n", "Watson's V: 1.8\n", "Critical value of V: 6.2\n", "\"Pass\": Since V is less than Vcrit, the null hypothesis\n", "that the two populations are drawn from distributions\n", "that share a common mean direction can not be rejected.\n", "\n", "M&M1990 classification:\n", "\n", "Angle between data set means: 3.4\n", "Critical angle for M&M1990: 6.4\n", "The McFadden and McElhinny (1990) classification for\n", "this test is: 'B'\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "===============\n", "\n", "Here are the results of the bootstrap test for a common mean\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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BQVCpVBg0aBDGjRuHILHSfjdESkvh/VNXWZwyIkLapquhDfRonRLZsmULNm/ejISEBNja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\nIJ6bSflodUq1Wo3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GW1tbzJ8/H8XFxVCpVFrLfvfddzFjxgw+0KMDPiViYLRo0UIzsspR\nJrymNDC01WIc5cBrSg5HYfCaksNRGNwpORyFwZ2Sw1EY3Ck5HIXBnZLDURj/D3s2GIw32rb5AAAA\nAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Common mean tests between SI_LowerThird_Directions and SI_UpperThird_Directions:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "IPmag.iWatsonV(SI_LowerThird_Directions,SI_UpperThird_Directions)\n", "IPmag.iBootstrap(SI_LowerThird_Directions,SI_UpperThird_Directions)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results of Watson V test: \n", "\n", "Watson's V: 14.5\n", "Critical value of V: 6.0\n", "\"Fail\": Since V is greater than Vcrit, the two means can\n", "be distinguished at the 95% confidence level.\n", "\n", "M&M1990 classification:\n", "\n", "Angle between data set means: 7.4\n", "Critical angle for M&M1990: 4.8\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "===============\n", "\n", "Here are the results of the bootstrap test for a common mean\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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LubwYIscES5EXNSpK0PzSVZBj3ldJi8bm5eVRcnIyTZ48mVJSUig5OZmSk5OpqKhIo0h3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\nOSWDITP+H9u9wkDRR7PUAAAAAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Common mean test on directions results summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above results from common mean statistical tests show that the directions from the lower third cannot be distinguished as being obtained from a distribution distinct from that of the middle third nor can the directions from the upper third be distinguished from those of the middle third. In contrast, directions from the lower third of the stratigraphy can be distinguished from those of upper third at the 95% confidence level." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Common mean tests between SI_LowerThird_Poles and SI_UpperThird_Poles:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For completeness, let's also conduct tests for a common mean between the virtual geomagnetic poles (VGPs) from the lower third and upper third of the stratigraphy in addition to the tests on the site mean directions in directional space (declination, inclination)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "IPmag.iWatsonV(SI_LowerThird_Poles,SI_UpperThird_Poles)\n", "IPmag.iBootstrap(SI_LowerThird_Poles,SI_UpperThird_Poles)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results of Watson V test: \n", "\n", "Watson's V: 13.6\n", "Critical value of V: 6.1\n", "\"Fail\": Since V is greater than Vcrit, the two means can\n", "be distinguished at the 95% confidence level.\n", "\n", "M&M1990 classification:\n", "\n", "Angle between data set means: 10.0\n", "Critical angle for M&M1990: 6.7\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "===============\n", "\n", "Here are the results of the bootstrap test for a common mean\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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TUSA6OhqDBw/GDz/8wJtA0fPLL2zq2rGj0Ep45exZ4OWXgW7dhFZifmh0yk6d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\nysiIDNkpZWREhuyUMjIiQ3Z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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As when the site declinations and inclinations are considered, the virtual geomagnetic poles from the lower third and upper third of the stratigraphy can be distibuguished at the 95% confidence level according to these tests." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Plotting the data stratigraphically" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the flow means calculated for each portion of the stratigraphy, the paleolatitude and 95% confidence bounds on paleolatitude can be calculated. The stratigraphic bounds (y-axis error bars) and paleolatitude bounds (x-axis error bars) are displayed for points plotted in the middle of the stratigraphic bin and at the mean paleolatitude." ] }, { "cell_type": "code", "collapsed": false, "input": [ "SI_LowerThird_plat=numpy.abs(IPmag.lat_from_i(lower_third_mean['inc']))\n", "SI_LowerThird_plat_max=numpy.abs(IPmag.lat_from_i(lower_third_mean['inc']-\n", " lower_third_mean['alpha95']))\n", "SI_LowerThird_plat_min=numpy.abs(IPmag.lat_from_i(lower_third_mean['inc']+\n", " lower_third_mean['alpha95']))\n", "\n", "SI_MiddleThird_plat=numpy.abs(IPmag.lat_from_i(middle_third_mean['inc']))\n", "SI_MiddleThird_plat_max=numpy.abs(IPmag.lat_from_i(middle_third_mean['inc']-\n", " middle_third_mean['alpha95']))\n", "SI_MiddleThird_plat_min=numpy.abs(IPmag.lat_from_i(middle_third_mean['inc']+\n", " middle_third_mean['alpha95']))\n", "\n", "SI_UpperThird_plat=numpy.abs(IPmag.lat_from_i(upper_third_mean['inc']))\n", "SI_UpperThird_plat_max=numpy.abs(IPmag.lat_from_i(upper_third_mean['inc']-\n", " upper_third_mean['alpha95']))\n", "SI_UpperThird_plat_min=numpy.abs(IPmag.lat_from_i(upper_third_mean['inc']+\n", " upper_third_mean['alpha95']))\n", "\n", "SI_LowerThird_meanstrat=521\n", "SI_MiddleThird_meanstrat=521+1041\n", "SI_UpperThird_meanstrat=521+1041+1041\n", "\n", "figure()\n", "errorbar(SI_LowerThird_plat, SI_LowerThird_meanstrat, yerr=[[521],[521]],\n", " xerr=[[SI_LowerThird_plat-SI_LowerThird_plat_min],\n", " [SI_LowerThird_plat_max-SI_LowerThird_plat]], \n", " fmt='ro',label=\"lower third (N=30)\")\n", "errorbar(SI_MiddleThird_plat, SI_MiddleThird_meanstrat, yerr=[[521],[521]],\n", " xerr=[[SI_MiddleThird_plat-SI_MiddleThird_plat_min],\n", " [SI_MiddleThird_plat_max-SI_MiddleThird_plat]],\n", " fmt='yo',label=\"middle third (N=20)\")\n", "errorbar(SI_UpperThird_plat, SI_UpperThird_meanstrat, yerr=[[521],[521]],\n", " xerr=[[SI_UpperThird_plat-SI_UpperThird_plat_min],\n", " [SI_UpperThird_plat_max-SI_UpperThird_plat]],\n", " fmt='bo',label=\"upper third (N=34)\")\n", "xlabel('paleolatitude (degrees)')\n", "ylabel('stratigraphic thickness (meters)')\n", "legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "fig = matplotlib.pyplot.gcf()\n", "fig.set_size_inches(5,10)\n", "pylab.xlim([20,70])\n", "pylab.ylim([0,3200])\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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4feONN6pGjRravn17kfVfiKsAAMBkx4875HAUHFiK+oH222/Jxjy+vtGlunxRevfuraZN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\n", "text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Comparing larger and smaller stratigraphic subsets of the Simpson Island data" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Comparing data from the lowermost 500 meters to data from the uppermost 500 meters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The analysis above demonstrates that the directions from the lower third of the stratigraphy (0 to 1041 meters; 30 flows) and the upper third of the stratigraphy (2083 to 3124 meters; 34 flows) are statistically distinct and thus indicate statistically significant motion to lower latitudes. While it is preferable to have a high total number of flows for such comparisons in order to average out secular variation, let's conduct this same analysis, but for a more restricted range from the bottom 500 meters (17 flows) and top 500 meters (17 flows) of the stratigraphy. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "SI_Lower500m_Directions=SI_Directions[0:17]\n", "SI_Lower500m_Poles=SI_Poles[0:17]\n", " \n", "SI_Upper500m_Directions=SI_Directions[67:84]\n", "SI_Upper500m_Poles=SI_Poles[67:84]\n", " \n", "Lower500m_mean=pmag.fisher_mean(SI_Lower500m_Directions)\n", "Upper500m_mean=pmag.fisher_mean(SI_Upper500m_Directions)\n", "\n", "print 'The Fisher mean parameters for SI_Lower500m_Directions are: '\n", "print 'Dec = ' + str(Lower500m_mean['dec']) + ' Inc = ' + str(Lower500m_mean['inc'])\n", "print 'alpha95 = ' + str(Lower500m_mean['alpha95']) + ' k= ' + str(Lower500m_mean['k'])\n", "print ''\n", "print 'The Fisher mean parameters for SI_Upper500m_Directions are: '\n", "print 'Dec = ' + str(Upper500m_mean['dec']) + ' Inc = ' + str(Upper500m_mean['inc'])\n", "print 'alpha95 = ' + str(Upper500m_mean['alpha95']) + ' k= ' + str(Upper500m_mean['k'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The Fisher mean parameters for SI_Lower500m_Directions are: \n", "Dec = 89.5592116942 Inc = -69.0370984662\n", "alpha95 = 2.72130375778 k= 172.790689067\n", "\n", "The Fisher mean parameters for SI_Upper500m_Directions are: \n", "Dec = 106.91559555 Inc = -56.3717140613\n", "alpha95 = 4.61609695128 k= 60.6867572134\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "IPmag.iWatsonV(SI_Lower500m_Directions,SI_Upper500m_Directions)\n", "IPmag.iBootstrap(SI_Lower500m_Directions,SI_Upper500m_Directions)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results of Watson V test: \n", "\n", "Watson's V: 50.4\n", "Critical value of V: 6.3\n", "\"Fail\": Since V is greater than Vcrit, the two means can\n", "be distinguished at the 95% confidence level.\n", "\n", "M&M1990 classification:\n", "\n", "Angle between data set means: 14.8\n", "Critical angle for M&M1990: 5.2\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "===============\n", "\n", "Here are the results of the bootstrap test for a common mean\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results of this comparison between the lower 500 meters and upper 500 meters show that they are distinct populations at the 95% confidence level. The difference between the populations is more dramatic in both the Watson V and bootstrap test from the tests on the lower third and upper third subsets that were conducted above. This increase in difference with tighter stratigraphic groups from lower and higher in the sequence is the expected result if the sequence is a record of progressive paleogeographic change." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Comparison between data from the lower 1/2 and upper 1/2 of Simpson Island stratigraphy" ] }, { "cell_type": "code", "collapsed": false, "input": [ "SI_Directions=[]\n", "SI_Poles=[]\n", "\n", "for n in range(0,84):\n", " Dec,Inc=Dec_TC[n],Inc_TC[n] \n", " SI_Directions.append([Dec,Inc,1.])\n", " Plong,Plat=VGP_long[n],VGP_lat[n]\n", " SI_Poles.append([Plong,Plat,1.])\n", " \n", "SI_LowerHalf_Directions=SI_Directions[0:41]\n", "SI_LowerHalf_Poles=SI_Poles[0:41]\n", "SI_UpperHalf_Directions=SI_Directions[41:84]\n", "SI_UpperHalf_Poles=SI_Poles[41:84]\n", " \n", "LowerHalf_mean=pmag.fisher_mean(SI_LowerHalf_Directions)\n", "UpperHalf_mean=pmag.fisher_mean(SI_UpperHalf_Directions)\n", "\n", "print 'The fisher mean parameters for SI_LowerHalf_Directions are: ' \n", "print 'Dec = ' + str(LowerHalf_mean['dec']) + ' Inc = ' + str(LowerHalf_mean['inc'])\n", "print 'alpha95 = ' + str(LowerHalf_mean['alpha95']) + ' k= ' + str(LowerHalf_mean['k'])\n", "print ''\n", "print 'The fisher mean parameters for SI_UpperHalf_Directions are: '\n", "print 'Dec = ' + str(UpperHalf_mean['dec']) + ' Inc = ' + str(UpperHalf_mean['inc'])\n", "print 'alpha95 = ' + str(UpperHalf_mean['alpha95']) + ' k= ' + str(UpperHalf_mean['k'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The fisher mean parameters for SI_LowerHalf_Directions are: \n", "Dec = 98.3683994117 Inc = -66.1920879279\n", "alpha95 = 3.31197629718 k= 46.4016899603\n", "\n", "The fisher mean parameters for SI_UpperHalf_Directions are: \n", "Dec = 109.926576191 Inc = -63.0929234842\n", "alpha95 = 3.05246279053 k= 51.8737553101\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "IPmag.iWatsonV(SI_LowerHalf_Directions,SI_UpperHalf_Directions)\n", "IPmag.iBootstrap(SI_LowerHalf_Directions,SI_UpperHalf_Directions)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results of Watson V test: \n", "\n", "Watson's V: 10.4\n", "Critical value of V: 6.1\n", "\"Fail\": Since V is greater than Vcrit, the two means can\n", "be distinguished at the 95% confidence level.\n", "\n", "M&M1990 classification:\n", "\n", "Angle between data set means: 5.8\n", "Critical angle for M&M1990: 4.5\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "===============\n", "\n", "Here are the results of the bootstrap test for a common mean\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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DOkM2mBEKxFRM5vffc/mP1JDUyaJNvfn53PPgo0B6hnww76hgDcYdcmPHApMm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\nETIYCsOMkMFQGGaEDIbC/D9XkJhajyETugAAAABJRU5ErkJggg==\n", 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"text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ " Calculating and plotting mean pole positions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below calculates the Fisher mean parameters for poles of the stratigraphically grouped virtual geomagnetic poles (VGPs) and then plots them on a \"view from space\" globe. Note that while where the textual output says \"dec\" it actually refers to pole longitude and where it says \"inc\" it actually refers to pole latitude since the pmag.fisher_mean function is being conducted on VGPs." ] }, { "cell_type": "code", "collapsed": false, "input": [ "LowerThird_MeanPole=pmag.fisher_mean(SI_LowerThird_Poles)\n", "MiddleThird_MeanPole=pmag.fisher_mean(SI_MiddleThird_Poles)\n", "UpperThird_MeanPole=pmag.fisher_mean(SI_UpperThird_Poles)\n", "\n", "print 'The Fisher mean parameters for the LowerThird mean pole are: '\n", "print 'Plong = ' + str(LowerThird_MeanPole['dec']) + ' Plat = ' + str(LowerThird_MeanPole['inc'])\n", "print 'A95 = ' + str(LowerThird_MeanPole['alpha95']) + ' k= ' + str(LowerThird_MeanPole['k'])\n", "print ''\n", "print 'The Fisher mean parameters for the MiddleThird mean pole are: '\n", "print 'Plong = ' + str(MiddleThird_MeanPole['dec']) + ' Plat = ' + str(MiddleThird_MeanPole['inc'])\n", "print 'A95 = ' + str(MiddleThird_MeanPole['alpha95']) + ' k= ' + str(MiddleThird_MeanPole['k'])\n", "print ''\n", "print 'The Fisher mean parameters for the UpperThird mean pole are: '\n", "print 'Plong = ' + str(UpperThird_MeanPole['dec']) + ' Plat = ' + str(UpperThird_MeanPole['inc'])\n", "print 'A95 = ' + str(UpperThird_MeanPole['alpha95']) + ' k= ' + str(UpperThird_MeanPole['k'])\n", "\n", "#setup the figure and map\n", "figure(figsize=(8, 8))\n", "m = Basemap(projection='ortho',lat_0=35,lon_0=200,resolution='l')\n", "# draw coastlines, country boundaries, fill continents.\n", "m.drawcoastlines(linewidth=0.25)\n", "#map.drawcountries(linewidth=0.25)\n", "m.fillcontinents(color='coral',lake_color='white')\n", "m.drawmapboundary(fill_color='white')\n", "m.drawmeridians(np.arange(0,360,30))\n", "m.drawparallels(np.arange(-90,90,30))\n", "\n", "#plot the mean poles\n", "IPmag.poleplot(m,LowerThird_MeanPole['dec'],LowerThird_MeanPole['inc'],\n", " LowerThird_MeanPole['alpha95'],color='r',label='Osler Group Lower Reversed Pole')\n", "\n", "IPmag.poleplot(m,MiddleThird_MeanPole['dec'],MiddleThird_MeanPole['inc'],\n", " MiddleThird_MeanPole['alpha95'],color='y',label='Osler Group Middle Reversed Pole')\n", "\n", "IPmag.poleplot(m,UpperThird_MeanPole['dec'],UpperThird_MeanPole['inc'],\n", " UpperThird_MeanPole['alpha95'],color='b',label='Osler Group Upper Reversed Pole')\n", "\n", "#show the plot\n", "legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The Fisher mean parameters for the LowerThird mean pole are: \n", "Plong = 218.637832609 Plat = 40.937318541\n", "A95 = 4.76006232039 k= 31.4671112908\n", "\n", "The Fisher mean parameters for the MiddleThird mean pole are: \n", "Plong = 211.261736125 Plat = 42.7378078852\n", "A95 = 8.1783359746 k= 16.9030328287\n", "\n", "The Fisher mean parameters for the UpperThird mean pole are: \n", "Plong = 205.410227513 Plat = 41.5645761829\n", "A95 = 4.80394668574 k= 27.2260167637\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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yc+dO3N3dtWUCAwNRqVQ0adJEe5yXl1emd3XcuHGMHj2aBQsWYGVlRceOHcss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fTqfh9u33SE7eSN26K3F2Lv13U6VSUatWLXQ63WPylWVBqXSnXr0VZGSc5Nq1YaSm7qZWrS+R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2zZMvT19Tl37hza2tqMGDECmUyGllateJvTiPXr15OYmCh1jArp1asXjRo1YsmSJVJHEQRB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\nK1ZAKpXi7bffxqFDh+jDISGEkCaa6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"text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that in the print out for the mean pole calculations above 'dec' is really the longitude of the pole (Plong) and 'inc' is actually the latitude of the pole (Plat).\n", "\n", "|Pole ID |Plong|Plat|A95| N|\n", "|---------|-----|----|---|--|\n", "|SI_Lower |218.6|40.9|4.8|30|\n", "|SI_Middle|211.3|42.7|8.2|20|\n", "|SI_Upper |205.4|41.6|4.8|34|\n" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Comparison between Halls (1974) data and Simpson Island VGPs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Halls (1974) developed paleomagnetic data from the Osler Volcanic Group in the Nipigon Strait region (bibtex citation below; also see [http://earthref.org/MAGIC/9518](http://earthref.org/MAGIC/9518)). For the purposes of the previously contributed MagIC database entries, site locations were determined using georeferenced versions of the maps provided in Halls (1974). These data allow for the site means of Halls (1974) to be used to calculate VGPs for purposes of directional statistical tests and for combining into a mean paleopole. The Halls (1974) data are predominantly from flows with reversed magnetization below an angular unconformity on Puff Island that separates the reversedly magnetized flows from younger flows of normal polarity (only a few of which are preserved before the sequence is submerged beneath Lake Superior). The analysis below compares paleomagnetic data from this study with the reversed flows from the Halls (1974) data. The result from this analysis is that the Halls (1974) data is statistically distinct from the lower third of the Simpson Island stratigraphy, but is statistically indistinguishable from the upper third of the Simpson Island stratigraphy. This result fits with stratigraphic considerations that place the sites studied by Halls towards the top of the stratigraphy studied herein at Simpson Island." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Bibtex citation info for Halls (1974)\n", "@article{Halls1974a,\n", "\tAuthor = {Halls, H.C.},\n", "\tJournal = {Canadian Journal of Earth Science},\n", "\tPages = {1200-1207},\n", "\tTitle = {A paleomagnetic reversal in the {O}sler {V}olcanic {G}roup, northern {L}ake {S}uperior},\n", "\tVolume = {11},\n", "\tYear = {1974}}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below imports the directional data from Halls (1974) and than uses this data to calculate virtual geomagnetic poles for each site location." ] }, { "cell_type": "code", "collapsed": false, "input": [ "Halls1974_Osler_Data=pandas.read_csv('../2014_Osler_Data/Halls1974_data.csv',sep=',')\n", "Halls1974_Osler_Data " ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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StudySite_IDAFstep_Oedec_tcinc_tckappansite_latsite_long
0 Halls1974a 1 200 289.8 43.6 517 5 48.63-88.11
1 Halls1974a 2 150 285.7 42.0 243 5 48.64-88.09
2 Halls1974a 3 100 307.4 37.9 2485 5 48.64-88.07
3 Halls1974a 4 150 302.8 29.9 331 5 48.66-88.04
4 Halls1974a 5 300 294.7 42.5 130 5 48.66-88.04
5 Halls1974a 6 100 106.1-44.2 70 5 48.66-88.05
6 Halls1974a 7 250 109.8-62.4 351 6 48.68-88.02
7 Halls1974a 8 400 140.7-64.8 679 5 48.68-88.03
8 Halls1974a 9 150 110.0-56.6 321 3 48.68-88.06
9 Halls1974a 10 150 128.2-42.0 59 5 48.66-88.07
10 Halls1974a 11 400 88.3-59.1 456 3 48.66-88.07
11 Halls1974a 12 250 118.6-48.9 240 5 48.66-88.08
12 Halls1974a 13 250 128.1-38.0 886 3 48.65-88.09
13 Halls1974a 14 200 108.2-55.8 121 6 48.65-88.08
14 Halls1974a 15 300 118.5-46.0 402 6 48.65-88.10
15 Halls1974a 16 150 106.0-54.2 125 6 48.64-88.10
16 Halls1974a 17 300 93.7-54.1 362 4 48.63-88.11
17 Halls1974a 18 200 116.0-53.0 224 4 48.61-88.16
18 Halls1974a 19 200 131.7-64.9 871 4 48.61-88.17
19 Halls1974a 20 250 119.9-55.8 108 4 48.59-88.19
20 Halls1974a 21 300 94.3-65.5 84 5 48.64-88.12
21 Halls1974a 22 200 91.8-58.2 94 5 48.64-88.12
22 Halls1974a 23 300 114.3-64.0 69 4 48.62-88.16
23 Halls1974a 24 400 116.8-40.4 77 3 48.68-88.06
24 Halls1974a 25 150 126.3-58.7 166 3 48.69-88.05
25 Halls1974a 26 300 112.0-75.1 3634 3 48.71-88.07
26 Halls1974a 27 400 118.6-70.3 729 3 48.67-88.10
27 Halls1974a 28 150 132.6-54.1 1039 3 48.68-88.10
28 Halls1974a 29 250 129.1-54.5 1149 3 48.68-88.10
29 Halls1974a 30 300 80.5-80.4 267 5 48.59-88.22
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30 rows \u00d7 9 columns

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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ " Study Site_ID AFstep_Oe dec_tc inc_tc kappa n site_lat \\\n", "0 Halls1974a 1 200 289.8 43.6 517 5 48.63 \n", "1 Halls1974a 2 150 285.7 42.0 243 5 48.64 \n", "2 Halls1974a 3 100 307.4 37.9 2485 5 48.64 \n", "3 Halls1974a 4 150 302.8 29.9 331 5 48.66 \n", "4 Halls1974a 5 300 294.7 42.5 130 5 48.66 \n", "5 Halls1974a 6 100 106.1 -44.2 70 5 48.66 \n", "6 Halls1974a 7 250 109.8 -62.4 351 6 48.68 \n", "7 Halls1974a 8 400 140.7 -64.8 679 5 48.68 \n", "8 Halls1974a 9 150 110.0 -56.6 321 3 48.68 \n", "9 Halls1974a 10 150 128.2 -42.0 59 5 48.66 \n", "10 Halls1974a 11 400 88.3 -59.1 456 3 48.66 \n", "11 Halls1974a 12 250 118.6 -48.9 240 5 48.66 \n", "12 Halls1974a 13 250 128.1 -38.0 886 3 48.65 \n", "13 Halls1974a 14 200 108.2 -55.8 121 6 48.65 \n", "14 Halls1974a 15 300 118.5 -46.0 402 6 48.65 \n", "15 Halls1974a 16 150 106.0 -54.2 125 6 48.64 \n", "16 Halls1974a 17 300 93.7 -54.1 362 4 48.63 \n", "17 Halls1974a 18 200 116.0 -53.0 224 4 48.61 \n", "18 Halls1974a 19 200 131.7 -64.9 871 4 48.61 \n", "19 Halls1974a 20 250 119.9 -55.8 108 4 48.59 \n", "20 Halls1974a 21 300 94.3 -65.5 84 5 48.64 \n", "21 Halls1974a 22 200 91.8 -58.2 94 5 48.64 \n", "22 Halls1974a 23 300 114.3 -64.0 69 4 48.62 \n", "23 Halls1974a 24 400 116.8 -40.4 77 3 48.68 \n", "24 Halls1974a 25 150 126.3 -58.7 166 3 48.69 \n", "25 Halls1974a 26 300 112.0 -75.1 3634 3 48.71 \n", "26 Halls1974a 27 400 118.6 -70.3 729 3 48.67 \n", "27 Halls1974a 28 150 132.6 -54.1 1039 3 48.68 \n", "28 Halls1974a 29 250 129.1 -54.5 1149 3 48.68 \n", "29 Halls1974a 30 300 80.5 -80.4 267 5 48.59 \n", "\n", " site_long \n", "0 -88.11 \n", "1 -88.09 \n", "2 -88.07 \n", "3 -88.04 \n", "4 -88.04 \n", "5 -88.05 \n", "6 -88.02 \n", "7 -88.03 \n", "8 -88.06 \n", "9 -88.07 \n", "10 -88.07 \n", "11 -88.08 \n", "12 -88.09 \n", "13 -88.08 \n", "14 -88.10 \n", "15 -88.10 \n", "16 -88.11 \n", "17 -88.16 \n", "18 -88.17 \n", "19 -88.19 \n", "20 -88.12 \n", "21 -88.12 \n", "22 -88.16 \n", "23 -88.06 \n", "24 -88.05 \n", "25 -88.07 \n", "26 -88.10 \n", "27 -88.10 \n", "28 -88.10 \n", "29 -88.22 \n", "\n", "[30 rows x 9 columns]" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate VGPs using the Halls (1974) data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "IPmag.VGP_calc(Halls1974_Osler_Data)\n", "Halls1974_Osler_Data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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StudySite_IDAFstep_Oedec_tcinc_tckappansite_latsite_longpaleolatitudepole_latpole_longpole_lat_revpole_long_rev
0 Halls1974a 1 200 289.8 43.6 517 5 48.63-88.11 25.461154 31.651781 185.568167-31.651781 5.568167
1 Halls1974a 2 150 285.7 42.0 243 5 48.64-88.09 24.237370 28.110360 187.509415-28.110360 7.509415
2 Halls1974a 3 100 307.4 37.9 2485 5 48.64-88.07 21.267947 40.260801 167.888919-40.260801 347.888919
3 Halls1974a 4 150 302.8 29.9 331 5 48.66-88.04 16.040618 33.459149 167.496979-33.459149 347.496979
4 Halls1974a 5 300 294.7 42.5 130 5 48.66-88.04 24.615622 34.309281 181.259648-34.309281 1.259648
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5 rows \u00d7 14 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ " Study Site_ID AFstep_Oe dec_tc inc_tc kappa n site_lat \\\n", "0 Halls1974a 1 200 289.8 43.6 517 5 48.63 \n", "1 Halls1974a 2 150 285.7 42.0 243 5 48.64 \n", "2 Halls1974a 3 100 307.4 37.9 2485 5 48.64 \n", "3 Halls1974a 4 150 302.8 29.9 331 5 48.66 \n", "4 Halls1974a 5 300 294.7 42.5 130 5 48.66 \n", "\n", " site_long paleolatitude pole_lat pole_long pole_lat_rev \\\n", "0 -88.11 25.461154 31.651781 185.568167 -31.651781 \n", "1 -88.09 24.237370 28.110360 187.509415 -28.110360 \n", "2 -88.07 21.267947 40.260801 167.888919 -40.260801 \n", "3 -88.04 16.040618 33.459149 167.496979 -33.459149 \n", "4 -88.04 24.615622 34.309281 181.259648 -34.309281 \n", "\n", " pole_long_rev \n", "0 5.568167 \n", "1 7.509415 \n", "2 347.888919 \n", "3 347.496979 \n", "4 1.259648 \n", "\n", "[5 rows x 14 columns]" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split the dataframe into one for the normally magnetized sites and one for the reversed polarity sites" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Halls1974_Osler_Data_N = Halls1974_Osler_Data[:5]\n", "Halls1974_Osler_Data_R = Halls1974_Osler_Data[5:]\n", "Halls1974_Osler_Data_R=Halls1974_Osler_Data_R.reset_index()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Develop a list of unit vectors of the reversed Halls (1974) VGPs in order to conduct common mean tests with the Simpson Island data using pmag.py functions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "Halls1974_Osler_R_VGPs=[]\n", "Halls1974_Osler_R_Plong=[]\n", "Halls1974_Osler_R_Plat=[]\n", "for n in range(0,len(Halls1974_Osler_Data_R)): \n", " Plong,Plat=Halls1974_Osler_Data_R['pole_long_rev'][n],Halls1974_Osler_Data_R['pole_lat_rev'][n]\n", " Halls1974_Osler_R_Plong.append(Plong)\n", " Halls1974_Osler_R_Plat.append(Plat)\n", " Halls1974_Osler_R_VGPs.append([Plong,Plat,1.])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Common mean tests between Halls (1974) data and the data from the lower third of the Simpson Island stratigraphy" ] }, { "cell_type": "code", "collapsed": false, "input": [ "IPmag.iWatsonV(Halls1974_Osler_R_VGPs,SI_LowerThird_Poles)\n", "IPmag.iBootstrap(Halls1974_Osler_R_VGPs,SI_LowerThird_Poles)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results of Watson V test: \n", "\n", "Watson's V: 31.0\n", "Critical value of V: 6.3\n", "\"Fail\": Since V is greater than Vcrit, the two means can\n", "be distinguished at the 95% confidence level.\n", "\n", "M&M1990 classification:\n", "\n", "Angle between data set means: 16.7\n", "Critical angle for M&M1990: 7.5\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "===============\n", "\n", "Here are the results of the bootstrap test for a common mean\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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V1SgtLUVaWpo2YkbumKLHsyjaSY9nMvgs09nZmerq6uj27ds0ePBgWrFiBS1ZskSQVfMV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\nkABmeAyGBPw/wBcsr0RmMZgAAAAASUVORK5CYII=\n", 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"text": [ "" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A comparison between VGPs calculated from Halls, 1974 data and data from the lower third of the Simpson Island stratigraphy fails Watson's V and bootstrap tests for a common mean indicating that the populations of directions do not share a common mean at the 95% confidence level." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Common mean tests between Halls (1974) data and the data from the upper third of the Simpson Island stratigraphy" ] }, { "cell_type": "code", "collapsed": false, "input": [ "IPmag.iWatsonV(Halls1974_Osler_R_VGPs,SI_UpperThird_Poles)\n", "IPmag.iBootstrap(Halls1974_Osler_R_VGPs,SI_UpperThird_Poles)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results of Watson V test: \n", "\n", "Watson's V: 5.4\n", "Critical value of V: 6.5\n", "\"Pass\": Since V is less than Vcrit, the null hypothesis\n", "that the two populations are drawn from distributions\n", "that share a common mean direction can not be rejected.\n", "\n", "M&M1990 classification:\n", "\n", "Angle between data set means: 7.0\n", "Critical angle for M&M1990: 7.7\n", "The McFadden and McElhinny (1990) classification for\n", "this test is: 'B'\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "===============\n", "\n", "Here are the results of the bootstrap test for a common mean\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SyJQpCmrimBS9RllSUoKcnJwan+fk5KCoqEhRUfWC8nL276RJkg7bv5+Fg23T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\ngxslh2NmcKPkcMyM/wc3ubVNearhrAAAAABJRU5ErkJggg==\n", 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YOnUqTp48if733Dvi4+PRq1cvbNy4Uey+0IKUTQNjxzJPmrfekiAoKYkFP01P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\nARmKIY0dWFMyAAAAAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A comparison between VGPs calculated from Halls, 1974 data (see [http://earthref.org/MAGIC/9518](http://earthref.org/MAGIC/9518)) and data from the upper third of the Simpson Island stratigraphy passes Watson's V and bootstrap tests for a common mean." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Combining the data from the upper third of the Simpson Island stratigraphy and the Halls (1974) reversed polarity data into a single pole for the upper portion of the reversed Osler Group stratigraphy" ] }, { "cell_type": "code", "collapsed": false, "input": [ "upperR_Osler_VGPs=Halls1974_Osler_R_VGPs+SI_UpperThird_Poles\n", "\n", "upperR_Osler_meanpole=pmag.fisher_mean(upperR_Osler_VGPs)\n", "\n", "print 'The fisher mean parameters for the pole calculated from upper reversed'\n", "print 'Osler Group VGPs (SI_UpperThird_Poles+Halls, 1974 reversed data) are: '\n", "print 'Plong = ' + str(upperR_Osler_meanpole['dec']) + ' Plat = ' + str(upperR_Osler_meanpole['inc'])\n", "print 'A95 = ' + str(upperR_Osler_meanpole['alpha95']) + ' k= ' + str(upperR_Osler_meanpole['k']) + ' N= ' + str(upperR_Osler_meanpole['n'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The fisher mean parameters for the pole calculated from upper reversed\n", "Osler Group VGPs (SI_UpperThird_Poles+Halls, 1974 reversed data) are: \n", "Plong = 201.646829641 Plat = 42.5371761039\n", "A95 = 3.72282691107 k= 25.6773898601 N= 59\n" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "|Pole ID |Plong|Plat|A95|N | approximate age|\n", "|-------------------------------|-----|----|---|--|-----------------------|\n", "|upper Osler Group reversed pole|201.6|42.5|3.7|59|1105\u00b12 Ma |\n", "|(this work and Halls, 1974) | | | | |(Davis and Green, 1997)| " ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Paleogeographic work outside of this IPython notebook using the software package GPlates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For details on working with paleomagnetic data in GPlates check out [this tutorial](https://docs.google.com/document/pub?id=1gdgHIaC5WpjLaRZu1JwqotV8zOrgnfu_fZ3aNwQZNmU).\n", "\n", "The steps necessary to develop the reconstruction using these Osler Volcanic Group poles are detailed below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(1) Develop .rot file that rotates Laurentia such that the Osler poles are at geographic north. In the text for the .rot file below the Osler_lowerthird pole is assigned to 1110 Ma, the Osler_middlethird pole is assigned to 1107.5 and the Osler_upperthird pole is assigned to 1105 Ma.\n", "\n", "The .rot file has the format:\n", "\n", "|moving plate ID number|age in millions of years|latitude of Euler pole|longitude of Euler pole|angle|fixed plate|comment|\n", "|----------------------|------------------------|----------------------|-----------------------|-----|-----------|-------|\n", "\n", "In this .rot file, the fixed reference frame is 000 and 001 (which could be split into a relative and absolute reference frame that seperates out TPW), while the plate ID for Laurentia is 199." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "001 0.0 0.0 0.0 0.0 000 ! \\\\\n", "001 3900.0 0.0 0.0 0.0 000 ! \\\\\n", "199 0.0 0.0 0.0 0.0 001 ! \\\\\n", "199 1105.0 0.0 115.4 48.4 001 !Put Osler-upperthird at N for Laurentia \\\\\n", "199 1107.5 0.0 121.3 47.3 001 !Put Osler-middlethird at N for Laurentia \\\\\n", "199 1110.0 0.0 128.7 49.0 001 !Put Osler-lowerthird at N for Laurentia \\\\" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(2) Open the following feature collections in GPlates: 199-Laurentia nogrid.dat, OslerPoles.gpml and Osler.rot where 199-Laurentia nogrid.dat is the outline of Laurentia in the late Proterozoic and OslerPoles.gpml contains the three calculated Osler paleomagnetic poles. An animation from 1110 to 1105 Ma yields the following reconstruction shown here with a time slice for Osler_lowerthird (red), Osler_middlethird (yellow) and Osler_upperthird (blue)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import SVG\n", "SVG(\"Paleogeo.svg\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "svg": [ "\n", "\n", "\t\n", "\t\n", "\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\n", "\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\n", "\t\t\n", "\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\n", "\t\t\n", "\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\n", "\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\t\n", "\t\t\n", "\t\t\n", "\t\t\t\n", "\t\t\n", "\t\t\n", "\t\t\t\n", "\t\t\n", "\t\t\n", "\t\t\t\n", "\t\t\n", "\t\t\n", "\t\t\n", "\t\t\n", "\t\n", "\n", "" ], "text": [ "" ] } ], "prompt_number": 23 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Plate rate estimate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This portion of the analysis calculates plate motion rates and estimates the uncertainty associated with these rates using Monte Carlo simulations that utilize both the uncertainty on the position and age of two paleomagnetic poles. For this analysis we are using these two poles:\n", "1. Pole 1: Osler Volcanic Group (early magmatic stage). The pole is the combined mean of the upper portion of the reversed stratigraphy that uses data from this study and Halls (1974). The date of 1105 $\\pm$ 2 Ma is used to constrain the age of the pole since it comes from an extrusive unit in close stratigraphic proximity to the units from which the paleomagnetic data were obtained.\n", "2. Pole 2: North Shore Volcanic Group (main magmatic stage). The North Shore Volcanic Group spans more than 10 million years and subsets of the data need to be considered for a pole to provide precise constraints. The pole used here is calculated from data developed by Tauxe and Kodama (2009) and is the mean of 47 sites from between the 40th Ave icelandite and the Palisade rhyolite within the southwest limb of the North Shore Volcanic Group. The 40th Ave icelandite has a U-Pb date of 1098.4 $\\pm$ 1.9 Ma and the Palisade rhyolite has a U-Pb date of 1096.6 $\\pm$ 1.7 Ma." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#import a couple special functions from scipy\n", "from scipy import special\n", "from scipy import stats" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Calculating the NSVG pole" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Tauxe_NSVG_Data=pandas.read_csv('../2014_Osler_Data/Tauxe2009a_data.csv',sep=',')\n", "#show first 5 rows\n", "Tauxe_NSVG_Data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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site_IDdec_tcinc_tca95nsite_latsite_longpole_latpole_longrem_typesequenceunit
0 ns002 283.5 38.7 6.6 4 47.7341-90.4292 24.9 185.2 hem nneu ngha
1 ns003 286.9 47.9 3.8 5 47.7371-90.4114 32.0 188.9 mixed nneu ngha
2 ns004 290.0 47.8 5.6 5 47.7328-90.4363 34.0 186.7 mag nneu ngha
3 ns005 299.8 37.6 5.3 4 47.7254-90.4430 35.3 172.4 mag nneu ngt
4 ns006 294.8 38.8 3.0 5 47.7161-90.4904 32.5 177.0 mag nsl NaN
\n", "

5 rows \u00d7 12 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ " site_ID dec_tc inc_tc a95 n site_lat site_long pole_lat pole_long \\\n", "0 ns002 283.5 38.7 6.6 4 47.7341 -90.4292 24.9 185.2 \n", "1 ns003 286.9 47.9 3.8 5 47.7371 -90.4114 32.0 188.9 \n", "2 ns004 290.0 47.8 5.6 5 47.7328 -90.4363 34.0 186.7 \n", "3 ns005 299.8 37.6 5.3 4 47.7254 -90.4430 35.3 172.4 \n", "4 ns006 294.8 38.8 3.0 5 47.7161 -90.4904 32.5 177.0 \n", "\n", " rem_type sequence unit \n", "0 hem nneu ngha \n", "1 mixed nneu ngha \n", "2 mag nneu ngha \n", "3 mag nneu ngt \n", "4 mag nsl NaN \n", "\n", "[5 rows x 12 columns]" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The North Shore Volcanic Group (NSVG) is comprised of two main limbs with distinct stratigraphy and radiometric age control. The southwest limb of the NSVG was particularly well-sampled by Tauxe and Kodama (2009) and those sites can be bracketed with age control from the 40th Ave icelandite (Davis and Green, 1997; 1098.4 \u00b1 1.9 Ma) and the Palisade rhyolite (Davis and Green, 1997; 1096.6 \u00b1 1.7 Ma)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "NSVG_nswu=Tauxe_NSVG_Data.ix[Tauxe_NSVG_Data['sequence'] == 'nswu']\n", "NSVG_nswu.reset_index(inplace=True)\n", "\n", "NSVG_nswu_VGPs=[]\n", "NSVG_nswu_Plong=[]\n", "NSVG_nswu_Plat=[]\n", "for n in range(0,len(NSVG_nswu)): \n", " Plong,Plat=NSVG_nswu['pole_long'][n],NSVG_nswu['pole_lat'][n]\n", " NSVG_nswu_Plong.append(Plong)\n", " NSVG_nswu_Plat.append(Plat)\n", " NSVG_nswu_VGPs.append([Plong,Plat,1.])\n", "NSVG_nswu_mean=pmag.fisher_mean(NSVG_nswu_VGPs)\n", "print 'Here are the details for the calculated pole from the SW limb of the NSVG'\n", "print 'Pole longitude is: ' + str(round(NSVG_nswu_mean['dec'],1))\n", "print 'Pole latitude is: ' + str(round(NSVG_nswu_mean['inc'],1))\n", "print 'Pole kappa is: ' + str(round(NSVG_nswu_mean['k'],1))\n", "print 'Pole A95 is: ' + str(round(NSVG_nswu_mean['alpha95'],1))\n", "print 'Pole N is: ' + str(int(NSVG_nswu_mean['n']))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Here are the details for the calculated pole from the SW limb of the NSVG\n", "Pole longitude is: 182.1\n", "Pole latitude is: 35.8\n", "Pole kappa is: 45.7\n", "Pole A95 is: 3.1\n", "Pole N is: 47\n" ] } ], "prompt_number": 26 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Input pole data and simulation sample size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code box below is where the input parameters for the Monte Carlo rate simulation are entered. These parameters are:\n", "- `samplesize` determines the number of random pole pairs that will be made through Monte Carlo simulations of each pole and its age to determine rate.\n", "- Paleomagnetic pole longitude (`plong`) and latitude (`plat`) for each pole\n", "- The A$_{95}$ confidence ellipse (`A95`), the Fisher precision parameter (`kappa`), and number of virtual geomagnetic poles used to calculate the pole mean (`N`).\n", "- The age assigned to the pole and the 1$\\sigma$ uncertainty on that age (`age_error`)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "samplesize=100000\n", "#set at 100,000 this code will take a long time to run\n", "\n", "#parameters for pole 1 (OVG pole calculated above)\n", "pole1_plong=201.6\n", "pole1_plat=42.5\n", "pole1_A95=3.7\n", "pole1_kappa=25.7\n", "pole1_N=59\n", "pole1_age=1105\n", "#1 sigma age uncertainty \n", "pole1_age_error=1\n", "\n", "#parameters for pole 2 (NSVG pole calculated above)\n", "pole2_plong=182.1\n", "pole2_plat=35.8\n", "pole2_A95=3.1\n", "pole2_kappa=45.7\n", "pole2_N=47\n", "#taking the average of the 40th Ave and Palisade\n", "pole2_age=1097.5\n", "#2sigma uncertainty min age on Palisade is 1094.9\n", "#2sigma uncertainty min age on 40th Ave is 1100.3\n", "#a 2sigma of 2.7 on the pole2_age approx spans this range\n", "#giving a 1 sigma age uncertainty of\n", "pole2_age_error=1.35\n", "\n", "#the longitude, latitude of Duluth, MN as a reference location\n", "Duluth=[267.9, 46.8]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "pole1=(pole1_plong, pole1_plat)\n", "pole1_paleolat=90-pmag.angle(pole1,Duluth)\n", "pole2=(pole2_plong, pole2_plat)\n", "pole2_paleolat=90-pmag.angle(pole2,Duluth)\n", "print \"The paleolatitude for Duluth resulting from pole 1 is:\" + str(pole1_paleolat)\n", "print \"The paleolatitude for Duluth resulting from pole 2 is:\" + str(pole2_paleolat)\n", "rate=((pole1_paleolat-pole2_paleolat)*111*100000)/((pole1_age-pole2_age)*1000000)\n", "print \"The rate of paleolatitudinal change implied by the poles pairs in cm/yr is:\" + str(rate)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The paleolatitude for Duluth resulting from pole 1 is:[ 44.05491798]\n", "The paleolatitude for Duluth resulting from pole 2 is:[ 27.84482051]\n", "The rate of paleolatitudinal change implied by the poles pairs in cm/yr is:[ 23.99094426]\n" ] } ], "prompt_number": 28 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Sampling the age distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Weighted means calculated from geochronological data are typically assumed to have uncertainity that follows a Gaussian distribution. Using this assumption, we can use the numpy function `random` to randomly sample a normal distribution. This approach is straightforward for a paleomagnetic pole where we are assigning a single age (e.g. `pole1` in this case). It is less straightforward when using two dates that stratigraphically bracket flows from which the paleomagnetic data was obtained without an a priori assumption of the distribution of time between those two dated units. In this analysis, we approximate the age error distribution for `pole2` by taking the mean of the dated units that bracket the data as the mean age and then specifying an age uncertainty wherein 2$\\sigma$ reaches out to approximately reach the maximum and minimum dates implied by the 2$\\sigma$ uncertainties on the older and younger dates respectively. This approach specifies a distribution that is centered on the mean of the two dates while also including the possibility that the actual age of the pole could come from a distribution that spans out to include the older and younger possibilities of each dated unit." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pole1_MCages=np.random.normal(pole1_age,pole1_age_error,samplesize)\n", "pole2_MCages=np.random.normal(pole2_age,pole2_age_error,samplesize)\n", "\n", "plt.hist(pole1_MCages,100,histtype='stepfilled',color='darkred',label='Pole 1 ages')\n", "plt.hist(pole2_MCages,100,histtype='stepfilled',color='darkblue',label='Pole 2 ages')\n", "plt.xlabel('Age (Ma)')\n", "plt.ylabel('n')\n", "plt.legend(loc=3)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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CEUKoqquTE0OA6+cWrz0t8l6VuiQFRQghhE1IQRFCCGETUlCEEELYhFyUF6r45JNPePLJ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\n8f/6l9pR6qXGea5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"text": [ "" ] } ], "prompt_number": 29 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Sampling pole distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To determine the uncertainty on the pole position and associated paleolatitude, we can take the approach of random sampling from the Fisher distribution with the precision of the VGPs used to develop the pole. To do this in a Monte Carlo fashion, we can grab random samples utilizing the fshdev function from pmag.py which takes random samples from a distribution with a given Fisher precision parameter (kappa) centered on (Dec=0, Inc=90). We can then define a tilt_direction to be the longitude of the pole mean and a tilt_amount to be the conjugate of the latitude of the pole mean and then use the dotilt function of pmag.py to rotate the polar-centered distribution to be centered about the pole mean. Doing this for the same number of VGPs used to calculate the actual pole allows us to then calculate a mean pole. This can then be done repeated (as dictated by the sample size number) to get a population of mean poles that can result from sampling the distribution." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pole1_MCpoles=[]\n", "pole1_MCpole_lat=[]\n", "pole1_MCpole_long=[]\n", "pole1_MCpaleolat=[]\n", "for n in range(samplesize):\n", " vgp_samples=[]\n", " for vgp in range(pole1_N):\n", " #pmag.dev returns a direction from a fisher distribution with specified kappa\n", " direction_atN=pmag.fshdev(pole1_kappa)\n", " #this direction is centered at latitude of 90\u00ba and needs to be rotated\n", " #to be centered on the mean pole position\n", " tilt_direction=pole1_plong\n", " tilt_amount=90-pole1_plat\n", " direction=pmag.dotilt(direction_atN[0],direction_atN[1],tilt_direction,tilt_amount)\n", " vgp_samples.append([direction[0],direction[1],1.])\n", " mean=pmag.fisher_mean(vgp_samples)\n", " mean_pole_position=(mean['dec'],mean['inc'])\n", " pole1_MCpoles.append([mean['dec'],mean['inc'],1.])\n", " pole1_MCpole_lat.append(mean['inc'])\n", " pole1_MCpole_long.append(mean['dec'])\n", " paleolat=90-pmag.angle(mean_pole_position,Duluth)\n", " pole1_MCpaleolat.append(paleolat[0])\n", "\n", "pole2_MCpoles=[]\n", "pole2_MCpole_lat=[]\n", "pole2_MCpole_long=[]\n", "pole2_MCpaleolat=[]\n", "for n in range(samplesize):\n", " vgp_samples=[]\n", " for vgp in range(pole2_N):\n", " #pmag.dev returns a direction from a fisher distribution with specified kappa\n", " direction_atN=pmag.fshdev(pole2_kappa)\n", " #this direction is centered at latitude of 90\u00ba and needs to be rotated\n", " #to be centered on the mean pole position\n", " tilt_direction=pole2_plong\n", " tilt_amount=90-pole2_plat\n", " direction=pmag.dotilt(direction_atN[0],direction_atN[1],tilt_direction,tilt_amount)\n", " vgp_samples.append([direction[0],direction[1],1.])\n", " mean=pmag.fisher_mean(vgp_samples)\n", " mean_pole_position=(mean['dec'],mean['inc'])\n", " pole2_MCpoles.append([mean['dec'],mean['inc'],1.])\n", " pole2_MCpole_lat.append(mean['inc'])\n", " pole2_MCpole_long.append(mean['dec'])\n", " paleolat=90-pmag.angle(mean_pole_position,Duluth)\n", " pole2_MCpaleolat.append(paleolat[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(8, 8))\n", "m = Basemap(projection='ortho',lat_0=35,lon_0=200,resolution='c',area_thresh=50000)\n", "m.drawcoastlines(linewidth=0.25)\n", "m.fillcontinents(color='bisque',lake_color='white',zorder=1)\n", "m.drawmapboundary(fill_color='white')\n", "m.drawmeridians(np.arange(0,360,30))\n", "m.drawparallels(np.arange(-90,90,30))\n", "\n", "IPmag.vgpplot(m,pole1_MCpole_long,pole1_MCpole_lat,color='b')\n", "IPmag.vgpplot(m,pole2_MCpole_long,pole2_MCpole_lat,color='g')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Fr2dLTI0N2Lr/NK0c7RjRt+Mzz6uouJQ7GXkUlVWBihpnLobTrk1rOrk3fSB3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\nXbhwAe7u7hgyZAhu376NZcuW4dixY8jKyoKNjY1S/j2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"text": [ "" ] } ], "prompt_number": 31 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Calculate and graphically display the confidence interval on the minimum rate estimate implied by the Monte Carlo sampled pairs of sampled ages and paleolatitudes from the two poles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pairs of sampled ages and poles can be used to calculate rates (with the total number of rates being set by `samplesize` in the input box above). The rate calculated above of 24.0 cm/year can now be given confidence bounds by taking 2.5 percentile and 97.5 percentile of the Monte Carlo simulated rates. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "#calculating the change in paleolatitude between the Monte Carlo pairs\n", "pole1_pole2_Delta_degrees=[]\n", "pole1_pole2_Delta_kilometers=[]\n", "pole1_pole2_Delta_myr=[]\n", "pole1_pole2_degrees_per_myr=[]\n", "pole1_pole2_cm_per_yr=[]\n", "\n", "for n in range(samplesize):\n", " Delta_degrees=pole1_MCpaleolat[n]-pole2_MCpaleolat[n]\n", " Delta_Myr=pole1_MCages[n]-pole2_MCages[n]\n", " pole1_pole2_Delta_degrees.append(Delta_degrees)\n", " degrees_per_myr=Delta_degrees/Delta_Myr\n", " cm_per_yr=((Delta_degrees*111)*100000)/(Delta_Myr*1000000)\n", " pole1_pole2_degrees_per_myr.append(degrees_per_myr)\n", " pole1_pole2_cm_per_yr.append(cm_per_yr)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "twopointfive_percentile=stats.scoreatpercentile(pole1_pole2_cm_per_yr,2.5)\n", "fifty_percentile=stats.scoreatpercentile(pole1_pole2_cm_per_yr,50)\n", "ninetysevenpointfive_percentile=stats.scoreatpercentile(pole1_pole2_cm_per_yr,97.5)\n", "print \"2.5th percentile is: \" + str(twopointfive_percentile)\n", "print \"50th percentile is: \" + str(fifty_percentile)\n", "print \"97.5th percentile is: \" + str(ninetysevenpointfive_percentile)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2.5th percentile is: 15.1860289306\n", "50th percentile is: 23.9833375899\n", "97.5th percentile is: 44.0185724854\n" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see here that the 50th percentile from these analysis is the same as the mean rate previously calculated (24.0 cm/yr). The 2.5th and 97.5th percentile give a 95% confidence range of 15.2 to 44.4 cm/yr. The code below generates a plot where 5,000 of the 100,000 (that's what was set as `samplesize` when code was executed) pole pairs and rates are shown (in A) and a histogram of all the rates is plotted along with the above percentiles being marked. This plot is Figure 4 of the manuscript." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plotnumber=5000\n", "figure(num=None, figsize=(14, 4))\n", "\n", "plt.subplot(1, 2, 1)\n", "for n in range(plotnumber):\n", " plt.plot([pole1_MCpaleolat[n],pole2_MCpaleolat[n]],\n", " [pole1_MCages[n],pole2_MCages[n]],'k-',linewidth=0.05,alpha=0.3)\n", "plt.scatter(pole1_MCpaleolat[:plotnumber],pole1_MCages[:plotnumber],color='b',s=3)\n", "plt.scatter(pole1_paleolat,pole1_age,color='lightblue',s=100, edgecolor='w', zorder=10000)\n", "plt.scatter(pole2_MCpaleolat[:plotnumber],pole2_MCages[:plotnumber],color='g',s=3)\n", "plt.scatter(pole2_paleolat,pole2_age,color='lightgreen',s=100, edgecolor='w', zorder=10000)\n", "plt.plot([pole1_paleolat,pole2_paleolat],[pole1_age,pole2_age],'w-',linewidth=2)\n", "plt.gca().invert_yaxis()\n", "plt.xlabel('paleolatitude at Duluth, MN (degrees)',size=14)\n", "plt.ylabel('time (Ma)',size=14)\n", "plt.text(pole1_paleolat,1099,'OVG', color='b',ha ='center',size=14)\n", "plt.text(pole2_paleolat,1104.5,'NSVG', color='g',ha ='center',size=14)\n", "plt.text(20.5,1092.5,'A', color='k',ha ='left',size=28)\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.hist(pole1_pole2_cm_per_yr,bins=600)\n", "plt.vlines(twopointfive_percentile,0,4500,'r', linestyles='dashed')\n", "plt.text(twopointfive_percentile-0.5,4000,'15 cm/yr', color='r',ha ='right',size=12)\n", "plt.vlines(fifty_percentile,0,4500,'r', linestyles='solid')\n", "plt.text(fifty_percentile+0.5,4000,'24 cm/yr', color='r',ha ='left',size=12)\n", "plt.vlines(ninetysevenpointfive_percentile,0,4500,'r', linestyles='dashed')\n", "plt.text(ninetysevenpointfive_percentile+0.5,4000,'44 cm/yr', color='r',ha ='left',size=12)\n", "plt.text(1.5,3930,'B', color='k',ha ='left',size=28)\n", "plt.ylabel('n',size=14)\n", "plt.xlabel('latitudinal drift rate (cm/yr)',size=14)\n", "plt.xlim([0,60])\n", "plt.ylim([0,4500])\n", "plt.savefig('../2014_Osler_Manuscript_Files/2014_Osler_Figures/MonteCarlo.pdf',\n", " bbox_inches='tight')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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CqjbKhkMhhDhnxo8fz1//+tdqGVbXrl1rtehkZGSwZs0awAieMjMzre26dOlCXl4e7du3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"text": [ "" ] } ], "prompt_number": 46 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Latitudinal change from early in the early magmatic stage to the main magmatic stage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the pole from the lower third of the Simpson Island stratigraphy and the pole calculated above from the NSVG to estimate the magnitude of paleolatitudinal change that occured during the early magmatic stage, the latent magmatic stage and into the main magmatic stage. This paleolatitudinal difference is discussed in the manuscript text." ] }, { "cell_type": "code", "collapsed": false, "input": [ "lower_Osler_pole=(218.6,40.9)\n", "NSVG_nswu_pole=(182.1,35.8)\n", "lower_Osler_pole_paleolat=90-pmag.angle(lower_Osler_pole,Duluth)\n", "NSVG_nswu_paleolat=90-pmag.angle(NSVG_nswu_pole,Duluth)\n", "difference=lower_Osler_pole_paleolat-NSVG_nswu_paleolat\n", "print 'The difference in paleolatitude resulting from these poles is: ' + str(difference) \n", "print 'from ' + str(lower_Osler_pole_paleolat) + ' to ' + str(NSVG_nswu_paleolat) + ' using Duluth, MN as a reference location'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The difference in paleolatitude resulting from these poles is: [ 26.7121489]\n", "from [ 54.5569694] to [ 27.84482051] using Duluth, MN as a reference location\n" ] } ], "prompt_number": 35 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Lava flow thickness data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the section of the text entitled \"Osler Volcanic Group lithologies\" the median flow thickness is reported from the subset of flows within measured sections where there is sufficient exposure of the flow base, interior and top to determine thickness. In the text it is reported that the median flow thickness is 4.9 meters with a first quartile thickness of 2.0 meters and third quartile thickness of 9.8 meters. The flow thickness data used in this analysis is presented and analyzed below. Note that not every flow within the measured stratigraphic sections was sampled for paleomagnetic analysis and not every flow on Simpson Island was measured in a stratigraphic section." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#flow thicknesses where bottom and top are exposed without cover in between\n", "#or with minimal cover and assumption of continuity\n", "SI1_flowthickness = array([1.2, 1.1+0.7,0.2+2.9+0.3,5.1+5.4+0.6+3.4,0.6+1+.3,0.9,0.5,2.2,0.8,0.5,0.3+1.3+0.6+1.6,0.4+2.3,0.6,0.8+1.0,1.6+0.3,1.0+0.6+2.4,0.2+4.4+0.8+0.6,3.3+1.6,0.7,4+4.7+5.7+1.7+1.8,0.9,0.2+0.5,0.2+0.8+1.3,0.2+0.9,1.7,0.6,0.2+1.5,1.2+0.4+1.3,1.3+1.5,1.3,2.4,0.3+1.7,0.6+0.3,0.3+0.5+.3+1.3,0.5+1.1,0.1+0.3+1.3,1.5,0.1+1.9+1.7+1.2])\n", "SI2_flowthickness = array([0.6+2.8,5.7+2+3.6,1.3+0.7+1.1+1.1,6.2+1,1.8+3.3+11.4])\n", "SI3_flowthickness = array([0.6+0.8+1+.8,1+4.9+1.1,0.3+1.6+4.5+1.2,0.2+1.5+7.2+0.9,0.5+1.7+9.3,1.4+2.9+0.6,3.1+3+0.9,3.9+1.4+.3])\n", "SI4_flowthickness = array([0.2+1.2+0.9,0.3+1.5+0.4+3.2,14+6.5,0.7+1+3.6,8.2+2.4+4.6,3.1+1.4+1.4,1.6,2.8+2.1,0.7+5.6+2.4+0.6,5.6+0.7,2.2+0.2+0.3,0.4+2.4+1.9+1,4.2+0.7])\n", "SI5_flowthickness = array([6.6+9.8+7.4+1.4+2.3,10.2+0.8,5.6+2.8+1.4+2.2,0.2+2.8+2.4+0.7,0.6,0.2+0.5,0.2+2.2,1.7+1.7,1.6+0.6,0.2+3.8+1.4+1,7.6+20.4+6.3+4.2+1,2.8+1.3+1.2,10.2+9])\n", "SI6_flowthickness = array([13.1+3.3,13.5+3.7,6.5+0.6,6.1+2.1+0.9,0.9+6.6+4.0,7.3+5.6+1.3+7.4,0.5+9.6+3.4+0.5,2.8+4.3,0.3+7.4+3.9])\n", "SI7_flowthickness = array([0.4+0.9+1.2+1.4,0.3+1.2,0.5+2.5+5+1.9])\n", "SI8_flowthickness = array([1.4+1.9+0.6,0.5+1+8.8+4+1.1,5+15.8+2.8+2.1,5+4.2,8.4+8.1+5,1.8+11.8+1+2.2+1.4])\n", "SI9_flowthickness = array([5.0+4.2,14.0+1+2.1,2.8+2.1,3.6+12.3+3.6+0.7,4.2+2.3,1.3+1.4+2.2,0.8+1.8+9.8+4.2+0.8,0.2+2.0+0.6+2.3,0.3+0.4+3.3,7.5])\n", " \n", "SIall_flowthickness = concatenate((SI1_flowthickness, SI2_flowthickness, \n", " SI3_flowthickness, SI4_flowthickness, \n", " SI5_flowthickness, SI6_flowthickness,\n", " SI7_flowthickness, SI8_flowthickness, \n", " SI9_flowthickness))\n", "print \"The number of flows for which thickness estimates could be obtained is:\"\n", "print len(SIall_flowthickness)\n", "print \"The mean flow thickness is:\"\n", "print np.mean(SIall_flowthickness)\n", "print \"The median flow thickness is:\"\n", "print np.median(SIall_flowthickness)\n", "\n", "print \"The first quartile thickness is:\"\n", "print np.percentile(SIall_flowthickness,25)\n", "print \"The third quartile thickness is:\"\n", "print np.percentile(SIall_flowthickness,75)\n", "\n", "hist(SIall_flowthickness, 40, facecolor='green')\n", "title('Histogram of Osler flow thicknesses within measured sections')\n", "ylabel('number of flows')\n", "xlabel('flow thickness (meters)')\n", "plt.show()\n", "data=(SI1_flowthickness,SI2_flowthickness,SI3_flowthickness,\n", " SI4_flowthickness,SI5_flowthickness, SI6_flowthickness,\n", " SI7_flowthickness,SI8_flowthickness,SI9_flowthickness,SIall_flowthickness)\n", "\n", "figure(figsize=(12,4))\n", "title('Boxplot of flow thicknesses grouped by stratigraphic section')\n", "boxplot(data)\n", "labels = ('SI1', 'SI2', 'SI3', 'SI4', 'SI5', 'SI6', \n", " 'SI7', 'SI8', 'SI9', 'all sections')\n", "xticks(range(1,11),labels)\n", "xlabel('stratigraphic section')\n", "ylabel('flow thickness (meters)')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The number of flows for which thickness estimates could be obtained is:\n", "105\n", "The mean flow thickness is:\n", "7.16666666667\n", "The median flow thickness is:\n", "4.9\n", "The first quartile thickness is:\n", "2.0\n", "The third quartile thickness is:\n", "9.8\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ux44dDcIxL41Gg2nTpqF69eoYOHBgsdd3UFAQAgMDDf4pSk1NxYQJEwDktPOGDRuQmJgI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\nzp2LmJgYfPvtt5g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nTyMhIQH9+/dHt27dIIQwOFxkZKTm/yqVCiqVyuC/JSIiIiJ6klqthlqtNuix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\niHRQCAuU6vfv34dKpUJsbCwAYMqUKej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"text": [ "" ] } ], "prompt_number": 36 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "The IPmag.py library of functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the functions used in the analysis above come from the imported IPmag.py library that was developed for this data analysis project relying heavily on the pmag.py functions developed by Lisa Tauxe. The code that is within IPmag.py is shown below so that the PDF output of this IPython notebook can document more of the tools used in the analysis without the reader needing to look into the .py files themselves. This library utilizes functions from the pmag.py and pmagplotlib.py libraries of PmagPy version 2.206." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load IPmag.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "import pmag, pmagplotlib\n", "import pylab\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "def iflip(D): #function simplified from PmagPy pmag.flip function\n", " \"\"\"\n", " This function returns the antipode (flips) of the unit vectors in D (dec,inc,length).\n", " \"\"\"\n", " Dflip=[]\n", " for rec in D:\n", " d,i=(rec[0]-180.)%360.,-rec[1]\n", " Dflip.append([d,i,1.])\n", " return Dflip\n", " \n", "def iBootstrap(Data1,Data2,NumSims=1000):\n", " \"\"\"\n", " Conduct a bootstrap test (Tauxe, 2010) for a common mean on two declination,\n", " inclination data sets\n", " \n", " This function modifies code from PmagPy for use calculating and plotting \n", " bootstrap statistics. Three plots are generated (one for x, one for y and\n", " one for z). If the 95 percent confidence bounds for each component overlap\n", " each other, the two directions are not significantly different.\n", "\n", " Parameters\n", " ----------\n", "\n", " Data1 : a list of directional data [dec,inc]\n", " Data2 : a list of directional data [dec,inc]\n", " NumSims : number of bootstrap samples (default is 1000)\n", " \"\"\" \n", " counter=0\n", " BDI1=pmag.di_boot(Data1)\n", " BDI2=pmag.di_boot(Data2)\n", " print \"\"\n", " print \"===============\"\n", " print \"\"\n", " print \"Here are the results of the bootstrap test for a common mean\"\n", " CDF={'X':1,'Y':2,'Z':3}\n", " pylab.figure(CDF['X'],figsize=(3,3),dpi=160)\n", " pylab.figure(CDF['Y'],figsize=(3,3),dpi=160)\n", " pylab.figure(CDF['Z'],figsize=(3,3),dpi=160)\n", " pmagplotlib.plotCOM(CDF,BDI1,BDI2,[\"\",\"\"])\n", " \n", "def iWatsonV(Data1,Data2,NumSims=5000):\n", " \"\"\"\n", " Conduct a Watson V test for a common mean on two declination, inclination data sets\n", " \n", " This function calculates Watson's V statistic from input files through Monte Carlo\n", " simulation in order to test whether two populations of directional data could have\n", " been drawn from a common mean. The critical angle between the two sample mean\n", " directions and the corresponding McFadden and McElhinny (1990) classification is printed.\n", "\n", "\n", " Parameters\n", " ----------\n", "\n", " Data1 : a list of directional data [dec,inc]\n", " Data2 : a list of directional data [dec,inc]\n", " NumSims : number of Monte Carlo simulations (default is 5000)\n", " \"\"\" \n", " pars_1=pmag.fisher_mean(Data1)\n", " pars_2=pmag.fisher_mean(Data2)\n", "\n", " cart_1=pmag.dir2cart([pars_1[\"dec\"],pars_1[\"inc\"],pars_1[\"r\"]])\n", " cart_2=pmag.dir2cart([pars_2['dec'],pars_2['inc'],pars_2[\"r\"]])\n", " Sw=pars_1['k']*pars_1['r']+pars_2['k']*pars_2['r'] # k1*r1+k2*r2\n", " xhat_1=pars_1['k']*cart_1[0]+pars_2['k']*cart_2[0] # k1*x1+k2*x2\n", " xhat_2=pars_1['k']*cart_1[1]+pars_2['k']*cart_2[1] # k1*y1+k2*y2\n", " xhat_3=pars_1['k']*cart_1[2]+pars_2['k']*cart_2[2] # k1*z1+k2*z2\n", " Rw=np.sqrt(xhat_1**2+xhat_2**2+xhat_3**2)\n", " V=2*(Sw-Rw)\n", " # keep weighted sum for later when determining the \"critical angle\" \n", " # let's save it as Sr (notation of McFadden and McElhinny, 1990)\n", " Sr=Sw \n", " \n", " # do monte carlo simulation of datasets with same kappas as data, \n", " # but a common mean\n", " counter=0\n", " Vp=[] # set of Vs from simulations\n", " for k in range(NumSims): \n", " \n", " # get a set of N1 fisher distributed vectors with k1,\n", " # calculate fisher stats\n", " Dirp=[]\n", " for i in range(pars_1[\"n\"]):\n", " Dirp.append(pmag.fshdev(pars_1[\"k\"]))\n", " pars_p1=pmag.fisher_mean(Dirp)\n", " # get a set of N2 fisher distributed vectors with k2, \n", " # calculate fisher stats\n", " Dirp=[]\n", " for i in range(pars_2[\"n\"]):\n", " Dirp.append(pmag.fshdev(pars_2[\"k\"]))\n", " pars_p2=pmag.fisher_mean(Dirp)\n", " # get the V for these\n", " Vk=pmag.vfunc(pars_p1,pars_p2)\n", " Vp.append(Vk)\n", "\n", " # sort the Vs, get Vcrit (95th percentile one)\n", "\n", " Vp.sort()\n", " k=int(.95*NumSims)\n", " Vcrit=Vp[k]\n", "\n", " # equation 18 of McFadden and McElhinny, 1990 calculates the critical\n", " # value of R (Rwc)\n", "\n", " Rwc=Sr-(Vcrit/2)\n", "\n", " # following equation 19 of McFadden and McElhinny (1990) the critical\n", " # angle is calculated. If the observed angle (also calculated below)\n", " # between the data set means exceeds the critical angle the hypothesis \n", " # of a common mean direction may be rejected at the 95% confidence\n", " # level. The critical angle is simply a different way to present \n", " # Watson's V parameter so it makes sense to use the Watson V parameter\n", " # in comparison with the critical value of V for considering the test\n", " # results. What calculating the critical angle allows for is the \n", " # classification of McFadden and McElhinny (1990) to be made\n", " # for data sets that are consistent with sharing a common mean.\n", "\n", " k1=pars_1['k']\n", " k2=pars_2['k']\n", " R1=pars_1['r']\n", " R2=pars_2['r']\n", " critical_angle=np.degrees(np.arccos(((Rwc**2)-((k1*R1)**2)\n", " -((k2*R2)**2))/\n", " (2*k1*R1*k2*R2)))\n", " D1=(pars_1['dec'],pars_1['inc'])\n", " D2=(pars_2['dec'],pars_2['inc'])\n", " angle=pmag.angle(D1,D2)\n", "\n", " print \"Results of Watson V test: \"\n", " print \"\" \n", " print \"Watson's V: \" '%.1f' %(V)\n", " print \"Critical value of V: \" '%.1f' %(Vcrit)\n", "\n", " if VVcrit:\n", " print '\"Fail\": Since V is greater than Vcrit, the two means can'\n", " print 'be distinguished at the 95% confidence level.'\n", " print \"\" \n", " print \"M&M1990 classification:\"\n", " print \"\" \n", " print \"Angle between data set means: \" '%.1f'%(angle)\n", " print \"Critical angle for M&M1990: \" '%.1f'%(critical_angle)\n", " \n", " if V>Vcrit:\n", " print \"\"\n", " elif V= 0: \n", " X_down.append(XY[0])\n", " Y_down.append(XY[1])\n", " else:\n", " X_up.append(XY[0])\n", " Y_up.append(XY[1])\n", "\n", " if len(X_up)>0:\n", " pylab.scatter(X_up,Y_up,facecolors='none', edgecolors=color)\n", "\n", " if len(X_down)>0: \n", " pylab.scatter(X_down,Y_down,facecolors=color, edgecolors=color)\n", "\n", "def iplotDImean(Dec,Inc,a95,color='k',marker='o',label=''):\n", " \"\"\"\n", " Plot a mean declination, inclination with alpha_95 ellipse on an equal area plot.\n", "\n", " Before this function is called a plot needs to be initialized with code that looks \n", " something like:\n", " >fignum = 1\n", " >pylab.figure(num=fignum,figsize=(10,10),dpi=160)\n", " >pmagplotlib.plotNET(fignum)\n", "\n", " Parameters\n", " ----------\n", "\n", " Dec : declination of mean being plotted\n", " Inc : inclination of mean being plotted\n", " a95 : a95 confidence ellipse of mean being plotted\n", " color : the default color is black. Other colors can be chosen (e.g. 'r')\n", " marker : the default is a circle. Other symbols can be chose (e.g. 's')\n", " label : the default is no label. Labels can be assigned\n", " \"\"\"\n", " DI_dimap=pmag.dimap(Dec,Inc)\n", " pylab.plot(DI_dimap[0],DI_dimap[1],color=color,marker=marker,label=label)\n", " Xcirc,Ycirc=[],[]\n", " Da95,Ia95=pmag.circ(Dec,Inc,a95)\n", " pylab.legend(loc=2)\n", " for k in range(len(Da95)):\n", " XY=pmag.dimap(Da95[k],Ia95[k])\n", " Xcirc.append(XY[0])\n", " Ycirc.append(XY[1])\n", " pylab.plot(Xcirc,Ycirc,color)\n", " \n", "def shoot(lon, lat, azimuth, maxdist=None):\n", " \"\"\"\n", " This function enables A95 error ellipses to be drawn in basemap around paleomagnetic\n", " poles in conjunction with equi\n", " (from: http://www.geophysique.be/2011/02/20/matplotlib-basemap-tutorial-09-drawing-circles/)\n", " \"\"\"\n", " glat1 = lat * np.pi / 180.\n", " glon1 = lon * np.pi / 180.\n", " s = maxdist / 1.852\n", " faz = azimuth * np.pi / 180.\n", " \n", " EPS= 0.00000000005\n", " if ((np.abs(np.cos(glat1)) EPS):\n", " \n", " sy = np.sin(y)\n", " cy = np.cos(y)\n", " cz = np.cos(b + y)\n", " e = 2. * cz * cz - 1.\n", " c = y\n", " x = e * cy\n", " y = e + e - 1.\n", " y = (((sy * sy * 4. - 3.) * y * cz * d / 6. + x) *\n", " d / 4. - cz) * sy * d + tu\n", " \n", " b = cu * cy * cf - su * sy\n", " c = r * np.sqrt(sa * sa + b * b)\n", " d = su * cy + cu * sy * cf\n", " glat2 = (np.arctan2(d, c) + np.pi) % (2*np.pi) - np.pi\n", " c = cu * cy - su * sy * cf\n", " x = np.arctan2(sy * sf, c)\n", " c = ((-3. * c2a + 4.) * f + 4.) * c2a * f / 16.\n", " d = ((e * cy * c + cz) * sy * c + y) * sa\n", " glon2 = ((glon1 + x - (1. - c) * d * f + np.pi) % (2*np.pi)) - np.pi \n", " \n", " baz = (np.arctan2(sa, b) + np.pi) % (2 * np.pi)\n", " \n", " glon2 *= 180./np.pi\n", " glat2 *= 180./np.pi\n", " baz *= 180./np.pi\n", " \n", " return (glon2, glat2, baz)\n", "\n", "def equi(m, centerlon, centerlat, radius, color):\n", " \"\"\"\n", " This function enables A95 error ellipses to be drawn in basemap around paleomagnetic poles\n", " in conjunction with shoot\n", " (from: http://www.geophysique.be/2011/02/20/matplotlib-basemap-tutorial-09-drawing-circles/).\n", " \"\"\"\n", " glon1 = centerlon\n", " glat1 = centerlat\n", " X = []\n", " Y = []\n", " for azimuth in range(0, 360):\n", " glon2, glat2, baz = shoot(glon1, glat1, azimuth, radius)\n", " X.append(glon2)\n", " Y.append(glat2)\n", " X.append(X[0])\n", " Y.append(Y[0])\n", " \n", " X,Y = m(X,Y)\n", " plt.plot(X,Y,color)\n", "\n", "def poleplot(mapname,plong,plat,A95,label='',color='k',marker='o'):\n", " \"\"\"\n", " This function plots a paleomagnetic pole and A95 error ellipse on whatever \n", " current map projection has been set using the basemap plotting library.\n", "\n", " Parameters\n", " -----------\n", " mapname : the name of the current map that has been developed using basemap\n", " plong : the longitude of the paleomagnetic pole being plotted (in degrees E)\n", " plat : the latitude of the paleomagnetic pole being plotted (in degrees)\n", " A95 : the A_95 confidence ellipse of the paleomagnetic pole (in degrees)\n", " label : a string that is the label for the paleomagnetic pole being plotted\n", " color : the color desired for the symbol and its A95 ellipse (default is 'k' aka black)\n", " marker : the marker shape desired for the pole mean symbol (default is 'o' aka a circle)\n", " \"\"\"\n", " centerlon, centerlat = mapname(plong,plat)\n", " A95_km=A95*111.32\n", " mapname.scatter(centerlon,centerlat,20,marker=marker,color=color,label=label,zorder=101)\n", " equi(mapname, plong, plat, A95_km,color)\n", "\n", "def vgpplot(mapname,plong,plat,label='',color='k',marker='o'):\n", " \"\"\"\n", " This function plots a paleomagnetic pole on whatever current map projection\n", " has been set using the basemap plotting library.\n", "\n", " Parameters\n", " -----------\n", " mapname : the name of the current map that has been developed using basemap\n", " plong : the longitude of the paleomagnetic pole being plotted (in degrees E)\n", " plat : the latitude of the paleomagnetic pole being plotted (in degrees)\n", " color : the color desired for the symbol and its A95 ellipse (default is 'k' aka black)\n", " marker : the marker shape desired for the pole mean symbol (default is 'o' aka a circle)\n", " \"\"\"\n", " centerlon, centerlat = mapname(plong,plat)\n", " mapname.scatter(centerlon,centerlat,20,marker=marker,color=color,label=label,zorder=100)\n", "\n", "def VGP_calc(dataframe):\n", " \"\"\"\n", " This function calculates paleomagnetic poles from directional data within a pandas.DataFrame\n", "\n", " Parameters\n", " ----------- \n", " dataframe : the name of the pandas.DataFrame containing the data\n", " dataframe['site_lat'] : the latitude of the site\n", " dataframe['site_long'] : the longitude of the site\n", " dataframe['inc_tc'] : the tilt-corrected inclination \n", " dataframe['dec_tc'] : the tilt-corrected declination\n", " \"\"\"\n", " #calculate the paleolatitude/colatitude\n", " dataframe['paleolatitude']=np.degrees(np.arctan(0.5*np.tan(np.radians(dataframe['inc_tc']))))\n", " dataframe['colatitude']=90-dataframe['paleolatitude']\n", " #calculate the latitude of the pole\n", " dataframe['pole_lat']=np.degrees(np.arcsin(np.sin(np.radians(dataframe['site_lat']))*\n", " np.cos(np.radians(dataframe['colatitude']))+\n", " np.cos(np.radians(dataframe['site_lat']))*\n", " np.sin(np.radians(dataframe['colatitude']))*\n", " np.cos(np.radians(dataframe['dec_tc']))))\n", " #calculate the longitudinal difference between the pole and the site (beta)\n", " dataframe['beta']=np.degrees(np.arcsin((np.sin(np.radians(dataframe['colatitude']))*\n", " np.sin(np.radians(dataframe['dec_tc'])))/\n", " (np.cos(np.radians(dataframe['pole_lat'])))))\n", " #generate a boolean array (mask) to use to distinguish between the two possibilities for pole longitude\n", " #and then calculate pole longitude using the site location and calculated beta\n", " mask = np.cos(np.radians(dataframe['colatitude']))>np.sin(np.radians(dataframe['site_lat']))*np.sin(np.radians(dataframe['pole_lat']))\n", " dataframe['pole_long']=np.where(mask,(dataframe['site_long']+dataframe['beta'])%360.,(dataframe['site_long']+180-dataframe['beta'])%360.)\n", " #calculate the antipode of the poles\n", " dataframe['pole_lat_rev']=-dataframe['pole_lat']\n", " dataframe['pole_long_rev']=(dataframe['pole_long']-180.)%360. \n", " #the 'colatitude' and 'beta' columns were created for the purposes of the pole calculations\n", " #but aren't of further use and are deleted\n", " del dataframe['colatitude']\n", " del dataframe['beta']" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }