{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Magmatic activity and plate motion during the \u201clatent stage\u201d of Midcontinent Rift development"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Corresponding Author: Nicholas L. Swanson-Hysell (swanson-hysell@berkeley.edu)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook contains data analysis used for the paper entitled *Magmatic activity and plate motion during the latent stage of Midcontinent Rift development* published in the journal GEOLOGY in 2014. The article is availible in the UC eScholarship repository: http://escholarship.org/uc/item/989179cs. The citation information is provided in bibtex format below:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"@article{Swanson-Hysell2014a,\n",
"\tAuthor = {Swanson-Hysell, Nicholas L. and Burgess, Seth D. and Maloof, Adam C. and Bowring, Samuel A.},\n",
"\tDoi = {10.1130/G35271.1},\n",
"\tJournal = {Geology},\n",
"\tTitle = {Magmatic activity and plate motion during the latent stage of {M}idcontinent {R}ift development},\n",
"\tYear = {2014}}"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Preamble materials"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import necessary libraries and start the interactive pylab environment. 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) and the IPmag.py function library that modifies some of these functions for use in the IPython environment (https://github.com/Swanson-Hysell/PmagPy_IPython). 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 the libraries that are included in the Github repository for this work. The IPmag.py function library is included within the Github repository as well as an archive of the exact code that was used in the analysis."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas\n",
"from IPython.core.display import HTML\n",
"import pmag\n",
"import IPmag\n",
"import pmagplotlib"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"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": 3,
"metadata": {},
"source": [
"Import Mamainse Point paleomagnetic data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import the data as a pandas dataframe and display the first 5 rows."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Mamainse_Data_all=pandas.read_csv('../Data/Mamainse_Point_data.csv',sep=',')\n",
"Mamainse_Data_all.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" stratigraphic_section | \n",
" section_meter_level | \n",
" composite_meter_level | \n",
" site_lat | \n",
" site_long | \n",
" n | \n",
" D_geo | \n",
" I_geo | \n",
" D_tilt-cor | \n",
" I_tilt-cor | \n",
" a_95 | \n",
" k | \n",
" fit_used | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" MP101 | \n",
" 0.0 to 15.5 | \n",
" 68 | \n",
" 47.0988 | \n",
" -84.7123 | \n",
" 6 | \n",
" 203.2 | \n",
" -50.3 | \n",
" 131.6 | \n",
" -63.2 | \n",
" 9.2 | \n",
" 54 | \n",
" mag | \n",
"
\n",
" \n",
" 1 | \n",
" MP101 | \n",
" 15.5 to 16.8 | \n",
" 84 | \n",
" 47.0987 | \n",
" -84.7124 | \n",
" 3 | \n",
" 225.0 | \n",
" -47.2 | \n",
" 146.0 | \n",
" -77.2 | \n",
" 13.7 | \n",
" 54 | \n",
" mag | \n",
"
\n",
" \n",
" 2 | \n",
" MP101 | \n",
" 16.8 to 18.7 | \n",
" 85 | \n",
" 47.0986 | \n",
" -84.7124 | \n",
" 5 | \n",
" 226.7 | \n",
" -52.8 | \n",
" 120.3 | \n",
" -77.5 | \n",
" 7.4 | \n",
" 88 | \n",
" mag | \n",
"
\n",
" \n",
" 3 | \n",
" MP101 | \n",
" 20.7 to 21.3 | \n",
" 89 | \n",
" 47.0986 | \n",
" -84.7125 | \n",
" 4 | \n",
" 209.1 | \n",
" -46.8 | \n",
" 141.5 | \n",
" -66.4 | \n",
" 8.4 | \n",
" 121 | \n",
" mag | \n",
"
\n",
" \n",
" 4 | \n",
" MP101 | \n",
" 21.3 to 21.9 | \n",
" 89 | \n",
" 47.0986 | \n",
" -84.7126 | \n",
" 6 | \n",
" 218.3 | \n",
" -52.3 | \n",
" 126.6 | \n",
" -72.7 | \n",
" 4.5 | \n",
" 189 | \n",
" mag | \n",
"
\n",
" \n",
"
\n",
"
"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" stratigraphic_section section_meter_level composite_meter_level site_lat \\\n",
"0 MP101 0.0 to 15.5 68 47.0988 \n",
"1 MP101 15.5 to 16.8 84 47.0987 \n",
"2 MP101 16.8 to 18.7 85 47.0986 \n",
"3 MP101 20.7 to 21.3 89 47.0986 \n",
"4 MP101 21.3 to 21.9 89 47.0986 \n",
"\n",
" site_long n D_geo I_geo D_tilt-cor I_tilt-cor a_95 k fit_used \n",
"0 -84.7123 6 203.2 -50.3 131.6 -63.2 9.2 54 mag \n",
"1 -84.7124 3 225.0 -47.2 146.0 -77.2 13.7 54 mag \n",
"2 -84.7124 5 226.7 -52.8 120.3 -77.5 7.4 88 mag \n",
"3 -84.7125 4 209.1 -46.8 141.5 -66.4 8.4 121 mag \n",
"4 -84.7126 6 218.3 -52.3 126.6 -72.7 4.5 189 mag "
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Reversal tests on directional data from the succession"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a list of unit vectors from the data frame to use for the statistical tests."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MP_DIdata=[]\n",
"for n in range(0,len(Mamainse_Data_all)):\n",
" Dec=Mamainse_Data_all['D_tilt-cor'][n]\n",
" Inc=Mamainse_Data_all['I_tilt-cor'][n]\n",
" MP_DIdata.append([Dec,Inc,1.])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"First reversal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the data from section MP214 which crosses the first reversal in the Mamainse Point stratigraphy and conduct a reversal test between the reversed and normal directions (Watson's V test with simulation and McFadden and McElhinny (1990) classification criteria)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MP214r=MP_DIdata[33:38]\n",
"MP214n=MP_DIdata[38:43]\n",
" \n",
"fignum = 1 #set the figure number here\n",
"pylab.figure(num=fignum,figsize=(8,8),dpi=160) #size and resolution can be changed here\n",
"pmagplotlib.plotNET(fignum)\n",
"IPmag.iplotDI(MP214r,'r')\n",
"IPmag.iplotDI(MP214n,'b')\n",
"\n",
"#the nested iflip function takes the antipode of the MP214r data to conduct the Watson V test for a common mean\n",
"IPmag.iWatsonV(IPmag.iflip(MP214r),MP214n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Results of Watson V test: \n",
"\n",
"Watson's V: 6.8\n",
"Critical value of V: 7.5\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: 9.2\n",
"Critical angle for M&M1990: 9.7\n",
"The McFadden and McElhinny (1990) classification for\n",
"this test is: 'B'\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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Vq1ZV//79tWPHDqWmpqpHjx7y9/c3HQ2ACypbtqw+/fRTValSRbVr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\nRK5CYII=\n",
"text": [
""
]
}
],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Second reversal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the data from the normal polarity zone below and the reversed polarity zone above the second reversal in the Mamainse Point stratigraphy. Conduct a reversal test between the reversed and normal directions (Watson's V test with simulation and McFadden and McElhinny (1990) classification criteria)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MPlowerN=MP_DIdata[38:52]\n",
"MPupperR=MP_DIdata[55:65]\n",
"\n",
"fignum = 2 #set the figure number here\n",
"pylab.figure(num=fignum,figsize=(8,8),dpi=160) #size and resolution can be changed here\n",
"pmagplotlib.plotNET(fignum)\n",
"IPmag.iplotDI(MPlowerN,'b')\n",
"IPmag.iplotDI(MPupperR,'r')\n",
"\n",
"#the nested iflip function takes the antipode of the MPupperR data to conduct the Watson V test for a common mean\n",
"IPmag.iWatsonV(IPmag.iflip(MPupperR),MPlowerN) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Results of Watson V test: \n",
"\n",
"Watson's V: 0.9\n",
"Critical value of V: 6.3\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: 3.2\n",
"Critical angle for M&M1990: 8.6\n",
"The McFadden and McElhinny (1990) classification for\n",
"this test is: 'B'\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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kyBG4urqKjkSkEYMGDUJWVhaGDRuG1NRUfPbZZ5DJZKJjkY7gFAYZtL/++gs+Pj4oV64c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\n",
"text": [
""
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Third reversal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the data from the reversed polarity zone below and first 1000 meters of the normal polarity zone above the third reversal in the Mamainse Point stratigraphy. Conduct a reversal test between the reversed and normal directions (Watson's V test with simulation and McFadden and McElhinny (1990) classification criteria)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MPupperR=MP_DIdata[55:65]\n",
"MPupperN=MP_DIdata[65:92]\n",
"\n",
"fignum = 2 #set the figure number here\n",
"pylab.figure(num=fignum,figsize=(8,8),dpi=160) #size and resolution can be changed here\n",
"pmagplotlib.plotNET(fignum)\n",
"IPmag.iplotDI(MPupperN,'b')\n",
"IPmag.iplotDI(MPupperR,'r')\n",
"\n",
"#the nested iflip function takes the antipode of the MPupperR data to conduct the Watson V test for a common mean\n",
"IPmag.iWatsonV(IPmag.iflip(MPupperR),MPupperN) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Results of Watson V test: \n",
"\n",
"Watson's V: 5.2\n",
"Critical value of V: 6.8\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: 5.7\n",
"Critical angle for M&M1990: 6.5\n",
"The McFadden and McElhinny (1990) classification for\n",
"this test is: 'B'\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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WSsHJk8BffwGnTxf9uqwsoF8/6QmLmjWB7t0LP6L5bCRj1ixgxw5pLURuLrB/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\nAQEAAMwICAAAYEZAAAAAMwICAACY/Q+Wk3LuX3qpJwAAAABJRU5ErkJggg==\n",
"text": [
""
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Calculate Mamainse Point paleomagnetic poles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create an empty dataframe that will be populated with pole means."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pole_means = pandas.DataFrame(columns=['name','pole_lat','pole_long','A_95','N'], index=['lower_R1','lower_R2','lower_N_upper_R','upper_N','upper_N_woMP306'])\n",
"pole_means"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" pole_lat | \n",
" pole_long | \n",
" A_95 | \n",
" N | \n",
"
\n",
" \n",
" \n",
" \n",
" lower_R1 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" lower_R2 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" lower_N_upper_R | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" upper_N | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" upper_N_woMP306 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
" name pole_lat pole_long A_95 N\n",
"lower_R1 NaN NaN NaN NaN NaN\n",
"lower_R2 NaN NaN NaN NaN NaN\n",
"lower_N_upper_R NaN NaN NaN NaN NaN\n",
"upper_N NaN NaN NaN NaN NaN\n",
"upper_N_woMP306 NaN NaN NaN NaN NaN"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the site directional data and site locations within the dataframe calculate the virtual geomagnetic pole positions. Once calculated display the first 5 rows of the now expanded Mamainse_Data_all dataframe."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#calculate the paleolatitude/colatitude\n",
"Mamainse_Data_all['paleolatitude']=np.degrees(np.arctan(0.5*np.tan(np.radians(Mamainse_Data_all['I_tilt-cor']))))\n",
"Mamainse_Data_all['colatitude']=90-Mamainse_Data_all['paleolatitude']\n",
"#calculate the latitude of the pole\n",
"Mamainse_Data_all['pole_lat']=np.degrees(np.arcsin(np.sin(np.radians(Mamainse_Data_all['site_lat']))*\n",
" np.cos(np.radians(Mamainse_Data_all['colatitude']))+\n",
" np.cos(np.radians(Mamainse_Data_all['site_lat']))*\n",
" np.sin(np.radians(Mamainse_Data_all['colatitude']))*\n",
" np.cos(np.radians(Mamainse_Data_all['D_tilt-cor']))))\n",
"#calculate the longitudinal difference between the pole and the site (beta)\n",
"Mamainse_Data_all['beta']=np.degrees(np.arcsin((np.sin(np.radians(Mamainse_Data_all['colatitude']))*\n",
" np.sin(np.radians(Mamainse_Data_all['D_tilt-cor'])))/\n",
" (np.cos(np.radians(Mamainse_Data_all['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(Mamainse_Data_all['colatitude']))>np.sin(np.radians(Mamainse_Data_all['site_lat']))*np.sin(np.radians(Mamainse_Data_all['pole_lat']))\n",
"Mamainse_Data_all['pole_long']=np.where(mask,(Mamainse_Data_all['site_long']+Mamainse_Data_all['beta'])%360.,(Mamainse_Data_all['site_long']+180-Mamainse_Data_all['beta'])%360.)\n",
"#calculate the antipode of the poles\n",
"Mamainse_Data_all['pole_lat_rev']=-Mamainse_Data_all['pole_lat']\n",
"Mamainse_Data_all['pole_long_rev']=(Mamainse_Data_all['pole_long']-180.)%360. \n",
"Mamainse_Data_all.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" stratigraphic_section | \n",
" section_meter_level | \n",
" composite_meter_level | \n",
" site_lat | \n",
" site_long | \n",
" n | \n",
" D_geo | \n",
" I_geo | \n",
" D_tilt-cor | \n",
" I_tilt-cor | \n",
" a_95 | \n",
" k | \n",
" fit_used | \n",
" paleolatitude | \n",
" colatitude | \n",
" pole_lat | \n",
" beta | \n",
" pole_long | \n",
" pole_lat_rev | \n",
" pole_long_rev | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" MP101 | \n",
" 0.0 to 15.5 | \n",
" 68 | \n",
" 47.0988 | \n",
" -84.7123 | \n",
" 6 | \n",
" 203.2 | \n",
" -50.3 | \n",
" 131.6 | \n",
" -63.2 | \n",
" 9.2 | \n",
" 54 | \n",
" mag | \n",
" -44.707215 | \n",
" 134.707215 | \n",
" -56.775933 | \n",
" 75.926276 | \n",
" 19.361424 | \n",
" 56.775933 | \n",
" 199.361424 | \n",
"
\n",
" \n",
" 1 | \n",
" MP101 | \n",
" 15.5 to 16.8 | \n",
" 84 | \n",
" 47.0987 | \n",
" -84.7124 | \n",
" 3 | \n",
" 225.0 | \n",
" -47.2 | \n",
" 146.0 | \n",
" -77.2 | \n",
" 13.7 | \n",
" 54 | \n",
" mag | \n",
" -65.563482 | \n",
" 155.563482 | \n",
" -64.207227 | \n",
" 32.116906 | \n",
" 63.170694 | \n",
" 64.207227 | \n",
" 243.170694 | \n",
"
\n",
" \n",
" 2 | \n",
" MP101 | \n",
" 16.8 to 18.7 | \n",
" 85 | \n",
" 47.0986 | \n",
" -84.7124 | \n",
" 5 | \n",
" 226.7 | \n",
" -52.8 | \n",
" 120.3 | \n",
" -77.5 | \n",
" 7.4 | \n",
" 88 | \n",
" mag | \n",
" -66.088013 | \n",
" 156.088013 | \n",
" -53.985198 | \n",
" 36.525485 | \n",
" 58.762115 | \n",
" 53.985198 | \n",
" 238.762115 | \n",
"
\n",
" \n",
" 3 | \n",
" MP101 | \n",
" 20.7 to 21.3 | \n",
" 89 | \n",
" 47.0986 | \n",
" -84.7125 | \n",
" 4 | \n",
" 209.1 | \n",
" -46.8 | \n",
" 141.5 | \n",
" -66.4 | \n",
" 8.4 | \n",
" 121 | \n",
" mag | \n",
" -48.853737 | \n",
" 138.853737 | \n",
" -64.443167 | \n",
" 71.706913 | \n",
" 23.580587 | \n",
" 64.443167 | \n",
" 203.580587 | \n",
"
\n",
" \n",
" 4 | \n",
" MP101 | \n",
" 21.3 to 21.9 | \n",
" 89 | \n",
" 47.0986 | \n",
" -84.7126 | \n",
" 6 | \n",
" 218.3 | \n",
" -52.3 | \n",
" 126.6 | \n",
" -72.7 | \n",
" 4.5 | \n",
" 189 | \n",
" mag | \n",
" -58.079957 | \n",
" 148.079957 | \n",
" -56.757528 | \n",
" 50.744843 | \n",
" 44.542557 | \n",
" 56.757528 | \n",
" 224.542557 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
" stratigraphic_section section_meter_level composite_meter_level site_lat \\\n",
"0 MP101 0.0 to 15.5 68 47.0988 \n",
"1 MP101 15.5 to 16.8 84 47.0987 \n",
"2 MP101 16.8 to 18.7 85 47.0986 \n",
"3 MP101 20.7 to 21.3 89 47.0986 \n",
"4 MP101 21.3 to 21.9 89 47.0986 \n",
"\n",
" site_long n D_geo I_geo D_tilt-cor I_tilt-cor a_95 k fit_used \\\n",
"0 -84.7123 6 203.2 -50.3 131.6 -63.2 9.2 54 mag \n",
"1 -84.7124 3 225.0 -47.2 146.0 -77.2 13.7 54 mag \n",
"2 -84.7124 5 226.7 -52.8 120.3 -77.5 7.4 88 mag \n",
"3 -84.7125 4 209.1 -46.8 141.5 -66.4 8.4 121 mag \n",
"4 -84.7126 6 218.3 -52.3 126.6 -72.7 4.5 189 mag \n",
"\n",
" paleolatitude colatitude pole_lat beta pole_long pole_lat_rev \\\n",
"0 -44.707215 134.707215 -56.775933 75.926276 19.361424 56.775933 \n",
"1 -65.563482 155.563482 -64.207227 32.116906 63.170694 64.207227 \n",
"2 -66.088013 156.088013 -53.985198 36.525485 58.762115 53.985198 \n",
"3 -48.853737 138.853737 -64.443167 71.706913 23.580587 64.443167 \n",
"4 -58.079957 148.079957 -56.757528 50.744843 44.542557 56.757528 \n",
"\n",
" pole_long_rev \n",
"0 199.361424 \n",
"1 243.170694 \n",
"2 238.762115 \n",
"3 203.580587 \n",
"4 224.542557 "
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that the virtual paleomagnetic poles have been calculated from the directional data, calculate mean paleomagnetic poles as the Fisher means of the VGPs in stratigraphic groupings. After these poles are calculated using the fisher_mean function in the pmag.py library, they are added to the pole_means dataframe."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MPlowerR1_VGPs=[]\n",
"for n in range(0,24): \n",
" Plong,Plat=Mamainse_Data_all['pole_long_rev'][n],Mamainse_Data_all['pole_lat_rev'][n]\n",
" MPlowerR1_VGPs.append([Plong,Plat,1.])\n",
"MPlowerR1_mean=pmag.fisher_mean(MPlowerR1_VGPs)\n",
"\n",
"\n",
"pole_means.loc['lower_R1'] = pandas.Series({'name':'lower R pole 1', \n",
" 'pole_lat':str(round(MPlowerR1_mean['inc'],1)),\n",
" 'pole_long':str(round(MPlowerR1_mean['dec'],1)),\n",
" 'A_95':str(round(MPlowerR1_mean['alpha95'],1)),\n",
" 'N':str(int(MPlowerR1_mean['n']))})\n",
"\n",
"MPlowerR2_VGPs=[]\n",
"for n in range(24,38): \n",
" Plong,Plat=Mamainse_Data_all['pole_long_rev'][n],Mamainse_Data_all['pole_lat_rev'][n]\n",
" MPlowerR2_VGPs.append([Plong,Plat,1.])\n",
"MPlowerR2_mean=pmag.fisher_mean(MPlowerR2_VGPs)\n",
"\n",
"\n",
"pole_means.loc['lower_R2'] = pandas.Series({'name':'lower R pole 2', \n",
" 'pole_lat':str(round(MPlowerR2_mean['inc'],1)),\n",
" 'pole_long':str(round(MPlowerR2_mean['dec'],1)),\n",
" 'A_95':str(round(MPlowerR2_mean['alpha95'],1)),\n",
" 'N':str(int(MPlowerR2_mean['n']))})\n",
"\n",
"MPlower_N_upper_R_VGPs=[]\n",
"for n in range(38,52): \n",
" Plong,Plat=Mamainse_Data_all['pole_long'][n],Mamainse_Data_all['pole_lat'][n]\n",
" MPlower_N_upper_R_VGPs.append([Plong,Plat,1.])\n",
"for n in range(55,65): \n",
" Plong,Plat=Mamainse_Data_all['pole_long_rev'][n],Mamainse_Data_all['pole_lat_rev'][n]\n",
" MPlower_N_upper_R_VGPs.append([Plong,Plat,1.])\n",
"MPlower_N_upper_R_mean=pmag.fisher_mean(MPlower_N_upper_R_VGPs)\n",
"\n",
"pole_means.loc['lower_N_upper_R'] = pandas.Series({'name':'lower N and upper R', \n",
" 'pole_lat':str(round(MPlower_N_upper_R_mean['inc'],1)),\n",
" 'pole_long':str(round(MPlower_N_upper_R_mean['dec'],1)),\n",
" 'A_95':str(round(MPlower_N_upper_R_mean['alpha95'],1)),\n",
" 'N':str(int(MPlower_N_upper_R_mean['n']))})\n",
"\n",
"MPupper_N_VGPs=[]\n",
"for n in range(65,99): \n",
" Plong,Plat=Mamainse_Data_all['pole_long'][n],Mamainse_Data_all['pole_lat'][n]\n",
" MPupper_N_VGPs.append([Plong,Plat,1.])\n",
"MPupper_N_mean=pmag.fisher_mean(MPupper_N_VGPs)\n",
" \n",
"pole_means.loc['upper_N'] = pandas.Series({'name':'upper N', \n",
" 'pole_lat':str(round(MPupper_N_mean['inc'],1)),\n",
" 'pole_long':str(round(MPupper_N_mean['dec'],1)),\n",
" 'A_95':str(round(MPupper_N_mean['alpha95'],1)),\n",
" 'N':str(int(MPupper_N_mean['n']))})\n",
"\n",
"MPupper_N_woMP306_VGPs=[]\n",
"for n in range(65,92): \n",
" Plong,Plat=Mamainse_Data_all['pole_long'][n],Mamainse_Data_all['pole_lat'][n]\n",
" MPupper_N_woMP306_VGPs.append([Plong,Plat,1.])\n",
"MPupper_N_woMP306_mean=pmag.fisher_mean(MPupper_N_woMP306_VGPs)\n",
" \n",
"pole_means.loc['upper_N_woMP306'] = pandas.Series({'name':'upper N (w/o MP306)', \n",
" 'pole_lat':str(round(MPupper_N_woMP306_mean['inc'],1)),\n",
" 'pole_long':str(round(MPupper_N_woMP306_mean['dec'],1)),\n",
" 'A_95':str(round(MPupper_N_woMP306_mean['alpha95'],1)),\n",
" 'N':str(int(MPupper_N_woMP306_mean['n']))})\n",
"\n",
"pole_means"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" pole_lat | \n",
" pole_long | \n",
" A_95 | \n",
" N | \n",
"
\n",
" \n",
" \n",
" \n",
" lower_R1 | \n",
" lower R pole 1 | \n",
" 49.5 | \n",
" 227.0 | \n",
" 5.3 | \n",
" 24 | \n",
"
\n",
" \n",
" lower_R2 | \n",
" lower R pole 2 | \n",
" 37.5 | \n",
" 205.2 | \n",
" 4.5 | \n",
" 14 | \n",
"
\n",
" \n",
" lower_N_upper_R | \n",
" lower N and upper R | \n",
" 36.1 | \n",
" 189.7 | \n",
" 4.9 | \n",
" 24 | \n",
"
\n",
" \n",
" upper_N | \n",
" upper N | \n",
" 31.2 | \n",
" 183.2 | \n",
" 2.5 | \n",
" 34 | \n",
"
\n",
" \n",
" upper_N_woMP306 | \n",
" upper N (w/o MP306) | \n",
" 33.7 | \n",
" 183.1 | \n",
" 2.0 | \n",
" 27 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
" name pole_lat pole_long A_95 N\n",
"lower_R1 lower R pole 1 49.5 227.0 5.3 24\n",
"lower_R2 lower R pole 2 37.5 205.2 4.5 14\n",
"lower_N_upper_R lower N and upper R 36.1 189.7 4.9 24\n",
"upper_N upper N 31.2 183.2 2.5 34\n",
"upper_N_woMP306 upper N (w/o MP306) 33.7 183.1 2.0 27"
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Comparision between Mamainse Point VGPs and other Midcontinent Rift VGPs"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Comparision between Siemens Creek Volcanics VGPs and Mamainse Point lower R pole 1 VGPs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import data from the Siemens Creek Volcanics (Palmer and Halls, 1986) from sites 15 to 24 which are those that the authors argue come from the panel with the most robust tilt-correction. Tests for a common mean are conducted between these data and data from the lower reversed polarity zone of Mamainse Point (the lowermost 600 meters of stratigraphy; flows used for Mamainse Point lower R pole 1)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"SCV_VGP=[]\n",
"SCV_Plong=[]\n",
"SCV_Plat=[]\n",
"\n",
"data_file='../Data/SCV_VGP.txt'\n",
"f=open(data_file,'rU')\n",
"for line in f.readlines():\n",
" rec=line.split()\n",
" Plong,Plat=float(rec[0]),float(rec[1]) \n",
" SCV_VGP.append([Plong,Plat,1.])\n",
" SCV_Plong.append(Plong)\n",
" SCV_Plat.append(Plat) "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Conduct the Watson V and Bootstrap tests for a common mean\n",
"IPmag.iWatsonV(MPlowerR1_VGPs,SCV_VGP)\n",
"IPmag.iBootstrap(MPlowerR1_VGPs,SCV_VGP)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Results of Watson V test: \n",
"\n",
"Watson's V: 5.5\n",
"Critical value of V: 6.6\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: 9.5\n",
"Critical angle for M&M1990: 10.4\n",
"The McFadden and McElhinny (1990) classification for\n",
"this test is: 'C'\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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KKoJQJr/9xn2thBkcUZSWcld6Sq4nC0Y/Ti1atNCt4zRo0AB9+vTBJ598IrswQjwRETwm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\nk16p2mGoLGP3m1r+lStXsGv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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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0kFoJIWdMGo8lS5boPM/KysL48eMFE6Q44uKAJ5uIy8W7v2MHbScJyOf7kCsW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\nZ8+ejcaNGyMlJQUpKSkGfSZ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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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CderUkUMXQgaswW0aG8u3QhT9u8YYcPy4sPWaNYyDVBicyaxduxa9evWCr68v\nGj46dqlSqbBixQrhyhH2SWQk8FgGVvn54w+guBhwcVFYEevHoJGZMGEC+vTpA19fXzg6OoIxRlUF\nCKHs2wd8/rnCSpQWeKLfdbMxaGTS09PLJRYmCJHcvQtcuQL07KmwIjExgI+PwkrYBgb3ZEaPHo2l\nS5ciJSWlXJE3ghDBxo38AJ6jo8KKnD4NeHsrrIRtYDDVg4uLS6XLI2PrYUsBpXqQDkuOmcnL45u9\nv/0GeHgY8YCo9AdaLdC8OV8yCdp9psx4FgYZGemw5LpL69fzTd9du4x8QFRnTp8GgoOBS5ekb/sR\nllp3qaJYyidD2BBbtvA0m4oTFQUMHKi0FjYD5ZMhLIL8fD6BsIjCoMeOAW+8obQWNgPlkyEsguho\nvs9av77CipQewlM0iY1tQflkCItArVY4GLKUlBTu2mrVSmlNbAbKJ2NnWGLMzOXLwHffWUCsEsBT\n8clg7SxxHERh0LtU9iBenTp14OnpiVoyl+sk75LtwhjQpQsvd6KngrF+RHhc3n4baNMGKJNHySKw\nRe/SlStXcOvWLV0+mVI0Gg1atmwJNzc3swQTBMC9xJmZwOTJSmvyCI2mkgJPhDno3ZOZOXNmpTOW\nOnXqYObMmUKVIuyH6Gigb18LCRH680+eKYvSbUqKXiOTmpqKHpWsTbt16ybraV/CttmyBXjtNaW1\neMSRI4CfH+BgcKuSMAG9RqagoADp6ekVPk9PT0deXp5QpQj74NYtXjtt8GClNXnEb78BvXsrrYXN\nodfI9O3bF1999VWFz5cvX46+ffsKVYoQhyXFLa1fD7z+ugVNHE6elM3IWNI4iEavdykrKwuTJk3C\n6dOn0adPHwDAkSNH0KVLF3zzzTeyJhIn75J0WErs0p07fOvj2DHuzKkWUnYmMxNwdQXS02UJAafY\nJQCNGjVCREQEHjx4gL1790KlUmHt2rWor/iRTMIW2LABGDHCDAMjNWfP8iJuiueYsD0MTlTr16+P\n1yxmZ46wFX76CfjyS6W1KMPhw7QfIwiTwwoIwlxu3gSuXQN69VJakzLs3UuR14IQbmQ0Gg3c3d3R\npk0brFy5ssL1rVu3wsPDAx4eHhgzZgwuX74sWiVCYX79FQgKAh6V8VKejAye85NmMkIQbmRmzJiB\nsLAwHDx4EKtXr0ZGRka5666urtBoNIiPj8fAgQOxdOlS0SrZNZYQM7NxIzB6tNJalKF0qSSjm8sS\nxkEuhBobbHPNAAARJ0lEQVSZnJwcAICfnx+cnZ0RGBiImJiYcveULbUSFBSEw4cPi1TJ7lHadbp1\nK3DvngWdjQGAQ4eAF1+UVaTS4yAnQo1MbGws2rVrp3vfvn17nDhxQu/969evLxf1TdgWOTnA9OnA\nDz8AT1jSbmB0NNCvn9Ja2CyWcgwKBw8exJYtW3Ds2LFKry8uY/r9/f0rBG4Sls+vv/LNXosqAnD7\nNo9ZMipzue2jVqulL4HEBJKdnc08PT1179955x0WGRlZ4b74+Hjm5ubGrly5Umk7gtUkZGLkSMY2\nbJCwQSl+L7ZtY2zECPPbEY1C3wEpvntCJ62ley0ajQapqamIiopC9+7dy92TlpaGV199FVu3bkXr\n1q1FqkMoyP37PDRo0CClNXkMBfZj7A3hK+PQ0FCEhIQgICAAU6dOhZOTE8LCwhAWFgYA+PDDD3Hv\n3j28/fbb8PLygg9V7ROKUhuOmzYBAwYAzZopI18v0dGKGBl72vilukt2hhIhMFeu8AwKP/8s8QE8\nczuTlgZ07coDqWTeiban2CVL2uMnbJDiYl4n7d13LeyELwBERABDh1qYq8v2oJ8uIZRdu4AaNYAZ\nM5TWpBJ+/RV45RWltbB5yMgQQtm0CXjnHQtJr1mWP/8E4uPpfIwMkJEhhJGdzU/sW9Tp3lL27eMB\nkXXrKq2JzUNGxs6QK2aGMX66NygIcHKSR6ZJqNVAz56Kiben2CXyLhFC2LcPmDWLZ7Rs0ECQkOp6\nXAoLgebNgXPngJYtpddLBORdIojyfPwx/2stzMCYw4EDPPentRgYK4eMDCE5GRm85OyrryqtiR5+\n+IFnMCdkgYwMITk//mhhSanKUlAAREZasAW0PSwmCpuwDdLTgY8+4ufcLJJ9+3jxbYuLb7BdaCZj\nZ4iMmcnM5N/fKVOASoqPWgYWslSi2CULg7xL0iHSSfH++zxJ+KZNYtqvgKmdyc/nXqWrV4EmTcTp\nZQT2FLtEyyVCEoqLgW+/BY4fV1qTKtizB+jeXXEDY2/QcomQhK1bgXbteBFGi+XHHy1iqWRv0HLJ\nzhAx6/71V2DiRB4MKWtVEVM6c/8+4OICJCUBTZsKVcsY7Gm5REbGzpD6d/X+faBDB152NiBAunaN\nwpTOfPABcOMGsHmzWJ2MxJ6MDO3J2BlSx8ysXcs9SbIbGFMoKuKKnjmjtCY6KHbJwqCZjGWSnQ10\n6sSXSV5eCihg7F/3iAjgq6+A338Xr5MorHgmQxu/RLXIzuapWF5+WSEDYwoffWShWbPsA5rJENVi\n8mQezLxxo4IJqYz56x4byy3h9es8RZ+1YsUzGTIyhMncvQu0bQskJwONGyuoiDFfvKAgPuWaM0ce\nnURBRkYsZGQsB8Z4npjsbD6LURRDX7ykJMDXl3uVLDLnhAlYsZGhPRk7w5yYmYwMvv9y5Ajwn/9I\nppIYGANmzgQWLrRIA0OxSxYGzWSkw5w/iK+/zs+xLV9uIYnBq+rMnj3A3LlAQoJF5pygczIEUYb8\nfGDdOn7M5OxZCzEwVaHVAv/3f8Ann1ikgbE3yMgQVXL1KuDvD7RuzaOrrSK5/8aNQL16wPDhSmtC\ngJZLdocps+6zZ4F//hN47TVg3jyxelWLyjqTng54ePCAKguuq25PyyXa+CUqwBgwbRowZAifDFiV\n93fePGD0aIs2MPYGLZfsjKpiZrRaYPt2YOVKfm4tKckiHTP6CQ/nri8LilHSB8UuWRi0XBIPY8Ab\nbwApKcDUqfz/Fn9AtuwS4vp1npBq716eA9TWsOLlEs1k7BzG+N7L3r3AqVPc41urltJamQhjwNix\nPLmwLRoYK4f2ZOyYzEx+9mXECODyZeCnn6zQwADAp58CDx5wtzVhcdBMxg7JzuYxg6dO8X8TE7nH\n1yr59FNg/Xpe29ri13f2CRkZO+L2bWDbNl4VpFMnXoLIKmcuAA8BB4AtW/hmL5WctVhouWTjFBYC\nP//MN3M7dOAOmEmTgDVrrNjA/PAD0K0b/79GY5UGxp5il4QaGY1GA3d3d7Rp0wYrV66s9J4FCxbA\n1dUVXbt2xcWLF0WqY1fcusU3c/38gBUrgFatgHPngPh4ICTECg3MnTs8kfCAAcCSJcCHH/LPGzVS\nVq9qsmSJ0hrICBOIp6cnO3z4MEtNTWVt27Zl6enp5a7HxMSwXr16sczMTLZt2zYWFBRUaTuC1TRI\ndHS0VchPTWVs5UrGAgIYa9SIsX79GFu1irGSkr/vqc6PUrH+Z2ez6K++Yuwf/2DsqacYGzWKsc2b\nGSss5Ndl+L0Q1XdjVdfJV+g7IMV3T9hMJicnBwDg5+cHZ2dnBAYGIiYmptw9MTExGDlyJBo3bozg\n4GAkJSWJUscs1Gq1xcgvKQFSU3mQ8Zo13Gs7bBjfY/H2BuLigLffBv74A/jtN+Bf/wKeMHOUZe1/\nQQHfZ/HzA1q2hPrLL/k0LCmJ10166y3A0VE2dSxp7K0VYRu/sbGxaNeune59+/btceLECQQFBek+\nO3nyJN566y3d+yZNmiA5ORlubm6i1FKcoiIgL6/y1717fJlz8yZ3L2dmAjk5wKVLvDpjTg6/r3lz\nvr/y/PO8mNrAgUCLFkDXrlbgYGGMJ5G6c4fHGf35J0+xd/UqcOUK96X36sU3kUaO5Pl5//1vpbUm\nzEBR7xJjrMJpQpXCeQSysoA33/z7cCVj/Hf/+HH+/8peWi2fYRQX//0q+76oiNcnevCA31uv3t+v\nunX//n/jxryC6rPPAp078/eNGnGP0IIFwFNPAfXrmz8zkYXwcB6jUPrD0Wp51quUFN7Zli2BZ57h\nL1dXHiTVujXwwgu8o4TtYPaCSw/Z2dnM09NT9/6dd95hkZGR5e5ZsWIF++qrr3TvXV1dK23Lzc2N\nAaAXvegl88vNzc1sWyBsJtOwYUMA3MP03HPPISoqCoseiwrr3r07Zs+ejbFjx2L//v1wd3evtK2r\nV6+KUpMgCMEIXS6FhoYiJCQERUVFmD59OpycnBAWFgYACAkJgY+PD3r37g1vb280btwYW7ZsEakO\nQRAKYBVR2ARBWC+KbiEaOqynVqvRsGFDeHl5wcvLCx999JHRz4qQv3TpUt01FxcXdO7cGV5eXvCp\nZoIkY/oQGxuLbt26wd3dHf7+/iY9K1K+HP3/4osvdD/7Tp06wcHBAdnZ2UbrLlK+HP0vKCjAuHHj\n4OXlhb59+2Lnzp1GPytavkn9N3tXxwwMHdaLjo5mQ4cOrdazouW7uLiwzMxMk2WaIl+r1bKOHTuy\nqKgoxhgrd12O/lclX47+l2X37t2sf//+1XpWhHw5+r927Vo2ZcoUxhhjqampzNXVlWm1WpN1FyHf\nlP4rNpMx5rAegEoT5hj7rCj5xlyTQn5cXBw6d+6MgIAAAICTk5NJuouSX4ro/pdl27ZtCA4Ortaz\nUssvRXT/GzZsiNzcXBQVFeHevXuoW7cuVCqVbP3XJ78UY/uvmJHRd1ivLCqVCseOHYOnpydmz56N\n5ORko58VKb/0Wr9+/TBixAjs2rXLJNnGyt+/fz9UKhX69OmDoUOHYv/+/UY/K1I+IE//S8nPz8f+\n/fvx6quvmvysCPmAPP0PDg5GSUkJnJyc0Lt3b2zdutVk3aWUX9YxY0r/LTrVQ5cuXXDjxg3UrFkT\nmzZtwowZMxAZGWkR8o8ePYrmzZsjKSkJQ4cOhY+PD5o1ayap/IcPH+Ls2bM4ePAg8vPzMWDAAJw/\nf15SGdWRX6dOHVn6X8ru3bvRu3dvPKXQIb3K5MvR/1WrVsHBwQG3b9/GuXPnEBQUhOvXr0sqwxT5\nQ4YMQVpaGlQqlUn9V2wm061bt3JR1xcuXECPHj3K3dOgQQPUrVsXNWvWxKRJkxAbG4vCwkJ4e3sb\nfFakfABo3rw5AMDd3R3Dhg3D7t27JZfv6+uLwYMHo1mzZnB1dYW3tzeOHDli1LOi5Gs0GgDy9L+U\n77//vtxSRa7+65MPyNN/jUaDN954A3Xr1kX37t3RokULXL58Wbb+Vyb/0qVLAEzsv8m7RRJSuvF0\n7dq1Sjee/vzzT91G086dO1lAQIDRz4qUn5eXx+7fv88YY+zu3busffv2LC0tTXL5GRkZrFu3biwv\nL49lZmayNm3asNzcXNn6r0++XP1njJ8cb9y4McvPzzf5WVHy5er/unXr2L/+9S9WUlLCkpOTWevW\nrU3SXZR8U/uvqJFRq9WsXbt2zM3NjS1fvpwxxju2bt06xhhjq1atYh06dGAeHh7srbfeYvHx8VU+\nK5f85ORk5uHhwTw8PFi/fv3Yt99+K0Q+Y4ytWbOGubu7Mz8/P7Z9+3ZZ+69Pvpz937hxIwsODjbq\nWbnkp6SkyNL/7OxsNn36dObl5cUCAwPZnj17qnxWLvmmjj8dxiMIQijWEM9LEIQVQ0aGIAihkJEh\nCEIoZGQIghAKGRmCIIRCRoYgCKGQkbFxfvnlF126gtJXjRo1ysUh2SqhoaEoKChQWg27h87J2Bnr\n16/H9u3bER0drbQqwnn++ecRFxeHp59+WmlV7BqaydgRly9fxtKlSxEeHl7p9f3792P48OHw9PTE\n2LFjAQC3bt3CjBkz4OHhgVmzZuHOnTsAgPHjx2Pu3Lnw8fFB27ZtcebMGUyePBkdOnTA4jI1WOvX\nr48PPvgA7dq1w8yZM3VJn65cuYKJEyfC09MTixYtQm5uLgDA398fS5Ysgbe3N/r27YszZ84A4GkF\nvv76awwYMAABAQGIiIgAwBOL9e/fH6NHj0b79u3xwQcfAABWrFiBW7du4cUXX0T//v2l/2ESxlOt\n88iE1fHXX3+xrl27sh9//LHS63l5eczNzY1dvnyZMcZYVlYWY4yxWbNmsc8++4wxxtjHH3/M5s+f\nzxhjbNy4cWzw4MGssLCQbdy4kdWvX5+p1WpWWFjI3N3dWUZGBmOMMZVKxb766itWXFzMpk2bxr74\n4gvGGGMvv/wy+/7771lRURGbMmUKW7NmDWOMMX9/fzZhwgRWXFzMtmzZwiZMmMAY4wnEZs+ezbRa\nLXvw4AHz8vJihYWFLDo6mtWsWZNdvHiRPXz4kHXs2JHduHGDMSZNYinCfGgmYycsXLgQnTp1wqhR\noyq9vmfPHgQEBKBNmzYAoEtr8L///Q8TJ04EAEyaNEkXbatSqTBy5Eg4OjrC19cXTz31FPr27QtH\nR0d4eXnpcpOoVCqMGzcONWrUwNixY7Fv3z4UFRUhNjYWr732GhwcHDBhwoRyOUneeOMN1KhRAy++\n+CKOHz8OANixYwciIyPRpUsX9O7dGzk5OToZpbOpWrVqoWfPnjh69KiAnyBRXSw6nwwhDWq1Gr/8\n8gtOnz5d5X1Mz/acvs9Ly944OjqWy7Xi6OioS4lhSJ5KparQfqNGjXTtPHz4EACg1Wrx/vvvY9y4\nceXuVavVuvtNkU3IB81kbJysrCxMmDABmzdvRr169fTeFxQUhIMHD+Ly5cu65wDgpZdewqZNm6DV\navHdd99h2LBhJslnjCE8PBwlJSUIDw/H4MGDUbNmTfj4+GDHjh0oLi7Gpk2bMHz48CrbGTNmDDZv\n3oz09HQAfH8pPz+/ymecnZ1x9+5dk/QlpIeMjI2zbt06pKen4+233y7nxv7pp5/K3Ve3bl2sXbsW\ns2bNgoeHB+bMmQMAmDt3LtLS0uDl5YU7d+5g9uzZumfK5nvVV164Xr16uHv3Ljp06ACVSoVJkyYB\nAJYtW4b//e9/8Pb2hpOTE958881Kny9tt1evXhgzZgxGjRqFTp06YcqUKSguLoZKpdIre/LkyRg7\ndixt/CoMubAJoTRo0EDnOSLsE5rJEELRN8sg7AeayRAEIRSayRAEIRQyMgRBCIWMDEEQQiEjQxCE\nUMjIEAQhFDIyBEEI5f8BNmz702cNuVwAAAAASUVORK5CYII=\n",
"text": [
""
]
}
],
"prompt_number": 12
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Comparision between Osler Volcanic Group VGPs and Mamainse Point lower R pole 2 VGPs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import data from the upper portion of the stratigraphy of the Osler Volcanics Group (upper third of the Simpson Island stratigraphy; data of Swanson-Hysell et al., in review). Tests for a common mean are conducted between these data and upper portion of the lower reversed polarity zone at Mamainse Point (flows used for Mamainse Point lower R pole 2)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Osler_upperthird_VGP=[]\n",
"Osler_upperthird_Plong=[]\n",
"Osler_upperthird_Plat=[]\n",
"\n",
"data_file='../Data/SI_upperthird_VGP.txt'\n",
"f=open(data_file,'rU')\n",
"for line in f.readlines():\n",
" rec=line.split()\n",
" Plong,Plat=float(rec[0]),float(rec[1]) \n",
" Osler_upperthird_VGP.append([Plong,Plat,1.])\n",
" Osler_upperthird_Plong.append(Plong)\n",
" Osler_upperthird_Plat.append(Plat)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Conduct the Watson V and Bootstrap tests for a common mean\n",
"IPmag.iWatsonV(MPlowerR2_VGPs,Osler_upperthird_VGP)\n",
"IPmag.iBootstrap(MPlowerR2_VGPs,Osler_upperthird_VGP)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Results of Watson V test: \n",
"\n",
"Watson's V: 2.4\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: 4.0\n",
"Critical angle for M&M1990: 6.5\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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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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lTsCOHdChg8lTUlPh11/ho4800iRxeMwanYdzom7cuMH27dvx9/eXRsfZEUJx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\nVK5cmWXLlqkpRyKR6ACHyL2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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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xKuzYsQMLFizA7du30fTBL7FGo0FaWppZByYSiYnhS22qeFJkaSmQlFTrttyk\nATNayCxcuBDh4eGwo1241Gn3bqDSQmB1odT+Qv/9L2/0bdNGmeOZi/ZdkpbR3qXOnTujefPmSmQh\n9XHiBODpWa+XKtVNGx1tGV3XFaj7WlpGazKrV6/G888/j4EDB8LGxgYAv1xatmyZ7OGIESUlvG9Y\nxTsTAIBOB3h5iU5BRDFayEyaNAmDBg3CwIEDYW1tDcYY7SqgFklJfDPp1q1FJ6mVTqf6zi8iI6OF\nTFZWFk2WVKu4OFUvtQkAf/4J5OcDTk6ikxBRjLbJvPHGG1i8eDHS0tKqbPJGVCA2lq8CpWIHDgBD\nhvBuYdI4GR0nY29vX+Plkan7YUuBxskY0K8fsGIFX0emHpSYozN5Mi8Hp02rdKfKB6LQ3KX/kXVl\nPDWhQqYG9+4B7doBOTlAPXv/lPiuOzkBW7Y81Dat8kJG5fEUpchgPFpPRqWiovhWtCoeXpCaCty6\nRYPwGjtaT8ZS7dsHjBwpOkWt9u0DXnhB1SuCEgXQejKW6vBh4MsvRaeolVYLPLTjMGmEaD0ZS5Sf\nz9eQGTBAdJJaRUcDzz0nOgURjdaTsUTHj/OlNs1sj5Fzjk7F+Jhnn5XvGHKhuUvSMtq7VHkgXvPm\nzeHi4oJmzZrJnasK6l16yMcf84aOxYvleX8JrFrFJ4hv2FDDg9R9YzFk7V26cOECMjIy4OPjU+V+\nnU6HJ598Eo6OjmYdmJjh8GHVrxGp1dZ7cjhpYAy2ycyaNavGGkvz5s0xa9YsWUORWhQWAidP1nsA\nnhIY4+UgrYRHgFoKmcuXL2NADQ2L/fv3V3S0L3lIVBTQpw/f71WlTp/mczbt7UUnIWpgsJApKipC\nVlZWtfuzsrJQUED7GAuj06m+inDgAODrKzoFUQuDhYy3tzeWLl1a7f6QkBB4q/yXvEHT6eq9SNXD\n5JqfEx4ODB0qz3srgeYtSctg79Lt27cxZcoUJCQkYNCgQQCAI0eOoG/fvvjuu+9oIXFj5OhBycsD\nnn4ayMyU5HJJjohZWUCXLsDNm4DBTkiV9y6pPJ6iZO1datu2LXbs2IG7d+/i999/h0ajwerVq9Gq\nVSuzDkjM8McffLl/FbfH7NvHl3ZQeJQDUTGjg/FatWqF1157TYksxJg9e1TfL7xjB/Dyy6JTEDWh\npR7kInWdmzHgySeBI0cAicYoSR2xpATo0AFISQEef1zBA0tM5fEUJcV3r85zl+pKp9PByckJXbt2\nxfLly6uvpin4AAAQ6klEQVQ9vnnzZvTp0wd9+vTBuHHjkJKSIncky5SYyKcRODiITmLQiRM8Xq0F\nDGl0ZC9kZs6cidDQUERGRmLlypXIzs6u8riDgwN0Oh1OnTqF4cOHY7GKh8oLtXkzMG6cpOtYSj1H\nZ88e1W/JbRKauyQtWS+X8vLy4OPjg8TERADAjBkzMHz4cPgbaFfIzs5G3759kZ6eXjUkXS7xCZHB\nwardW4QxPhly1y4+VrBWdD1iMVR/uRQbG4vu3bvrbzs7OyM6Otrg89euXVtl1jd54OJFPq1Zxesm\nnDoFlJcDvXuLTkLUxmjvklIiIyOxadMmHD9+vMbHF1YaIeXj41Nt4maDFhbGu2ysVPNxVbNnDzB2\nLO1KYOm0Wq30WyAxGeXm5jIXFxf97ffee4+Fh4dXe96pU6eYo6Mju3DhQo3vI3NMeUiZ2deXsZ07\npXs/Gfj6MrZ7t4lPtsTPs5GS4rsn6+VSxba2Op0Oly9fxv79++Hh4VHlOenp6Xj11VexefNmdOnS\nRc44lik/ny/MouJx+sXFPKJKm4uIYLL3LgUHByMoKAhDhw7FtGnTYGtri9DQUISGhgIAPv/8c9y6\ndQvvvvsuXF1d4W5JO7Mr4cABvsymDCOtpZqjc/Agb+x98DfF4tHcJWnRYDy5SNWDMnEi3x1t+nTz\n3+shUkV87z0+TnD+fIUPLBOVx1MUbe6mZlL8ppaXA5068WsRGRZnkSriU0/xlfD+8hcFDywjlcdT\nlOq7sImZTpwAbG1VvfpTVBTQvn0dChjS6FAho2a7dgEvvSQ6Ra22bQNefVV0CqJmVMio2d69qp51\nXV4O/Por8PrropMQNaNCRq1u3gQuXwZk7G0zd45OQgLQpg3g5CRNHrWguUvSooZfuZjberhlC/DL\nL8Bvv0mXSWJz5gBNm9Zjt1xqWbUYsq6MRwSLjFT1ADwA2LoV+P130SmI2tHlkhqVlfH2mOHDRScx\nKC0NuHu34V0qEelRIaNG27cDdnZ8RW6VWr0amDwZeIR+g4gRdLmkRj/8wIfRqnRK8/37fI9rAxPm\nCamC/g6pTXo6H4SnwGrc9Z2jExHBB9+puKJlFpq7JC3qXZJLfXtQFi8Grl8HVq6UPtND6htx/Hi+\nM8u0aQofWCEqj6comrukZvX5TS0r4zsRbN8O9OsnT65K6hOxsBDo3NmEHQmkPrCCVB5PUTR3qaHZ\nvx947DFFCpj6Cg8HPDxoRwJiOipk1KK0FPjoI2DePNFJDCovB0JDgTfeEJ2EWBIqZNRi926gdWtV\nTwTatg3IyFB1RKJCVMioxerVvCVVwW7rus7RCQ4GvvoKaNFCnjxqQXOXpEUNv3KpS+vhhQuApyfv\nvlbpTvVnzgBDhgCZmRJsmkAtqxaDGn4bigULgHffVW0BAwCffMKX11TxrixEpagmIxdT/1ofPgxM\nmACcPcvbZFQoNZV3eGVm8u24zUY1GYtBNZmG4IsvgM8+U20BA/B2mHfflaiAIY0OVX5Fio7m7TFv\nvSU6iUFxcXxszLlzopMQS0U1GZH+/nfe0GFtLeTwpszRWbKED9957DHZ46gGzV2SFrXJyMVYu8PP\nPwOLFgGnT/Pl5QQwFvHqVb5pW3q6xHvLqbxNRuXxFEVzl9Sstt/UjAygb1++tOaAAcrmqqS2iIwB\nQUG8w2v5cgUPrAIqj6coWn7TUn38Md8ZUmABY0xICN9TTqsVnYRYOqrJyMXQn8OMDKBHD+DKFb7U\nv0CGIh44wHvVjxwBHBwUPLBKqDyeoqgL2xL95z/Am28KL2AMyc0Fpk4Fli2TqYAhjQ5dLilpxw7g\nxx+B+HjRSQBUn6Nz5QowahTg6wu88oqYTGpAc5ekRZdLcnm4zh0WxlferliQRWUqLpHmzAFmzZJ5\nniZdj1gM6l1Ss8pfpO+/5+Nhdu1SZWPvlStA//68kjVihAIHpELGYlDvktoxBsydy5fTPHiQN/iq\nTGYm32573jyFChjS6FAhI6cXXwSys4HERFUOmc3NBYYN44tQzZ4tOg1pqKh3SWqMAXv28P/38OD9\nwCotYMaOBby9gU8/FZ2GNGRUyEgpIYFfe1RUCz77TNi8JEOKi/lKny4uwK1bfNCdSveQE4bmLklL\n1kJGp9PByckJXbt2xXIDY9Pnz58PBwcH9OvXD+fPn5czjjzKy3nX9GuvAS+8AHh5AUlJolNVU1LC\nG3b/8he+tdOKFfwqjhahqm7RItEJGhgmIxcXF3b48GF2+fJl1q1bN5aVlVXl8ZiYGPb888+znJwc\n9tNPPzF/f/8a30fmmHV35w479M03jM2dy1jfvow5OzO2ciVjt2//7zkCMh86dKjaffn5jIWGMvbs\ns4wNHsyYTve/x5SKWC2XCj7Pms5VBVHxasskihTfPdlqMnl5eQAALy8v2NnZwc/PDzExMVWeExMT\ng7Fjx6Jdu3YIDAxEcnKyXHHMd/cun9AYEAB06gRtSAhfxemrr3iVYNo04W0vWq0WWVnA3r28tjJs\nGNCpE7882riRd3ANGiQml9pQJuXIVlmOjY1F9+7d9bednZ0RHR0Nf39//X0nTpzAW5UWbOrQoQNS\nU1Ph6OgoV6zqysqAmzd5X27ln//+l/cM5eTwbWOzs/k31N8fWL8e+Ne/hF68l5YCp04Bly7xda/i\n4oDISL6jgJsb//nb34CdOyVepoGQOhJ6Rc4YqzbQRyNVK+TSpXwYa3l51Z+yMr7XakEBkJcH3LgB\ntG3L/+R36sT3YO3UCRg8GHjySf7YE0/w+2Re6Fur5e0BRUXA/fu8kdbQf8vK+LCbrl35zrZjx/Lo\nISHAI9ScT9TE7AsuA3Jzc5mLi4v+9nvvvcfCw8OrPGfZsmVs6dKl+tsODg41vpejoyMDQD/0Qz8K\n/zg6OppdFshWk7GxsQHAe5ieeeYZ7N+/Hwsemnnm4eGB2bNnY8KECYiIiICTk1ON73Xx4kW5YhJC\nZCbr5VJwcDCCgoJQUlKCGTNmwNbWFqGhoQCAoKAguLu7w9PTE25ubmjXrh02bdokZxxCiAAWMUGS\nEGK5hDYRGhusp9VqYWNjA1dXV7i6uuKLL74w+bVK5lq8eLH+MXt7e/Tu3Ruurq5wd3dXLBPAe/T6\n9+8PJycn+Pj41Om1SmcSdZ6++eYb/efWq1cvWFlZITc31+R/j4hcos5VUVERJk6cCFdXV3h7e2PX\nrl0mv7YKs1t1zGBssN6hQ4fYqFGj6vVaUbns7e1ZTk6OZFlMzVReXs569uzJ9u/fzxhjVR6X61yZ\nk0nUeaosLCyM+fr61uu1SuYSda5Wr17Npk6dyhhj7PLly8zBwYGVl5fX+d8jrCZjymA9ADWuZWHq\na5XOZcpjcmWKi4tD7969MXToUACAra2tya9VOlMFEeepsp9++gmBgYH1eq1SuSqIOFc2NjbIz89H\nSUkJbt26hRYtWkCj0dT53yOskDE0WK8yjUaD48ePw8XFBbNnz0ZqaqrJrxWRq+KxIUOG4KWXXsLu\n3bsVyxQREQGNRoNBgwZh1KhRiIiIMPm1SmcCxJ2nCoWFhYiIiMCrr75a59cqmQsQd64CAwNRVlYG\nW1tbeHp6YvPmzXX+9wAqX0+mb9++uHr1Kpo2bYoNGzZg5syZCA8PFx2r1lzHjh1Dp06dkJycjFGj\nRsHd3R0dO3aUPdO9e/dw8uRJREZGorCwEMOGDcPZs2dlP259MjVv3lzYeaoQFhYGT09PPKayZThq\nyiXqXK1YsQJWVlbIzMzEmTNn4O/vjytXrtT5fYTVZPr3719l1nVSUhIGPLQ0ZevWrdGiRQs0bdoU\nU6ZMQWxsLIqLi+Hm5mb0tSJyAUCnTp0AAE5OThg9ejTCwsIUyTRw4ECMHDkSHTt2hIODA9zc3HDk\nyBGTXqtkJp1OB0Dcearw888/V7kkkes8mZsLEHeudDodxo8fjxYtWsDDwwOdO3dGSkpK3c+V5K1J\ndVDReHTp0qUaG4+uX7+ub2jatWsXGzp0qMmvFZGroKCA3blzhzHG2M2bN5mzszNLT09XJFN2djbr\n378/KygoYDk5Oaxr164sPz/fpNcqnUnkeWKMj0Zv164dKywsrPNrlc4l8lytWbOG/e1vf2NlZWUs\nNTWVdenSpU7/ngpCCxmtVsu6d+/OHB0dWUhICGOM/8PWrFnDGGNsxYoVrEePHqxPnz7srbfeYqdO\nnar1taJzpaamsj59+rA+ffqwIUOGsHXr1imWiTHGVq1axZycnJiXlxfbsmVLra8VmUn0eVq/fj0L\nDAw06bWic6WlpQk7V7m5uWzGjBnM1dWV+fn5sT179tT6WkNoMB4hRFY0X5cQIisqZAghsqJChhAi\nKypkCCGyokKGECIrKmQIIbKiQqaB27lzp34JgYqfJk2aVJlH1FAFBwejqKhIdIxGj8bJNDJr167F\nli1bcOjQIdFRZPfss88iLi4O7du3Fx2lUaOaTCOSkpKCxYsXY+PGjTU+HhERgTFjxsDFxQUTJkwA\nAGRkZGDmzJno06cP3n//fdy4cQMA8Pbbb2POnDlwd3dHt27dkJiYiHfeeQc9evTAwkpbxbRq1Qqf\nfPIJunfvjlmzZukXYrpw4QImT54MFxcXLFiwAPn5+QAAHx8fLFq0CG5ubvD29kZiYiIAvtTBt99+\ni2HDhmHo0KHYsWMHAL6AmK+vL9544w04Ozvjk08+AQAsW7YMGRkZGDx4MHx9faU/mcR0ko1RJqp2\n//591q9fP/brr7/W+HhBQQFzdHRkKSkpjDHGbj/YDfP9999nX3/9NWOMsX/+85/sww8/ZIwxNnHi\nRDZy5EhWXFzM1q9fz1q1asW0Wi0rLi5mTk5OLDs7mzHGmEajYUuXLmWlpaVs+vTp7JtvvmGMMfby\nyy+zn3/+mZWUlLCpU6eyVatWMcYY8/HxYZMmTWKlpaVs06ZNbNKkSYwxvlDY7NmzWXl5Obt79y5z\ndXVlxcXF7NChQ6xp06bs/Pnz7N69e6xnz57s6tWrjDH5FnsidUM1mUbi73//O3r16oWAgIAaH9+z\nZw+GDh2Krl27AoB+qYG9e/di8uTJAIApU6boZwBrNBqMHTsW1tbWGDhwIB577DF4e3vD2toarq6u\n+vVFNBoNJk6ciCZNmmDChAnYt28fSkpKEBsbi9deew1WVlaYNGlSlXVSxo8fjyZNmmDw4MGIiooC\nAGzfvh3h4eHo27cvPD09kZeXpz9GRW2qWbNmeO6553Ds2DEZziCpL1WvJ0OkodVqsXPnTiQkJNT6\nPGagec7Q/RXb3lhbW1dZ/8Ta2lq/9IWx42k0mmrv37ZtW/373Lt3DwBQXl6Ojz/+GBMnTqzyXK1W\nq39+XY5NlEM1mQbu9u3bmDRpEn788Ue0bNnS4PP8/f0RGRmJlJQU/esA4IUXXsCGDRtQXl6O77//\nHqNHj67T8Rlj2LhxI8rKyrBx40aMHDkSTZs2hbu7O7Zv347S0lJs2LABY8aMqfV9xo0bhx9//BFZ\nWVkAePtSYWFhra+xs7PDzZs365SXSI8KmQZuzZo1yMrKwrvvvlulG3vr1q1VnteiRQusXr0a77//\nPvr06YMPPvgAADBnzhykp6fD1dUVN27cwOzZs/WvqbylsKHthVu2bImbN2+iR48e0Gg0mDJlCgBg\nyZIl2Lt3L9zc3GBra4s333yzxtdXvO/zzz+PcePGISAgAL169cLUqVNRWloKjUZj8NjvvPMOJkyY\nQA2/glEXNpFV69at9T1HpHGimgyRlaFaBmk8qCZDCJEV1WQIIbKiQoYQIisqZAghsqJChhAiKypk\nCCGyokKGECKr/we/SOMLV1VlXAAAAABJRU5ErkJggg==\n",
"text": [
""
]
}
],
"prompt_number": 14
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Comparision between SW limb of the North Shore Volcanic Group VGPs and Mamainse Point upper normal polarity zone VGPs "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import VGPs from the North Shore Volcanic Group (Tauxe and Kodama, 2009) from flows on the SW limb that are between the 40th Ave Icelandite and the Palisade Rhyolite. Tests for a common mean are conducted between these data and data from the upper normal polarity at Mamainse Point (VGPs from the 1000 meters above Great Conglomerate; upper N [w/o MP306] pole)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"NSVG_SWlimb_VGP=[]\n",
"NSVG_SWlimb_Plong=[]\n",
"NSVG_SWlimb_Plat=[]\n",
"\n",
"data_file='../Data/NSVGsubset_VGP.txt' #This file contains data from flows on the SW limb that are between the 40th Ave Icelandite and the Palisade Rhyolite\n",
"f=open(data_file,'rU')\n",
"for line in f.readlines():\n",
" rec=line.split()\n",
" Plong,Plat=float(rec[0]),float(rec[1]) \n",
" NSVG_SWlimb_VGP.append([Plong,Plat,1.])\n",
" NSVG_SWlimb_Plong.append(Plong)\n",
" NSVG_SWlimb_Plat.append(Plat) "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Conduct the Watson V and Bootstrap tests for a common mean\n",
"IPmag.iWatsonV(MPupper_N_woMP306_VGPs,NSVG_SWlimb_VGP)\n",
"IPmag.iBootstrap(MPupper_N_woMP306_VGPs,NSVG_SWlimb_VGP)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Results of Watson V test: \n",
"\n",
"Watson's V: 2.2\n",
"Critical value of V: 6.1\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: 2.2\n",
"Critical angle for M&M1990: 3.7\n",
"The McFadden and McElhinny (1990) classification for\n",
"this test is: 'A'\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": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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LOmfPnTsnZPiKbmAIogSN0aUdO3ZUOaPRlIxHSEBcHLB4sU5DHD0KjBsnkj4E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\nNjY2sLe3h5eXF1QqVa30BdQ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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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EE0SURefMxR9/8HglhSTOZQzYu5fvEmlrWPPskhCYHOxib2+PO3fuiKGFqACd\ne/r8+aei0jvExHB/17at3Eqkh/Zd0o/BlkxAQID2/wUFBUhISMDHtNeo/ERHA2+9JbcKLZs2ARMm\nKGq5DqEQDC7GKz3LVKNGDXh4eKBGjRpi6yqDLa+T0Vlkq1Y8QEgBTYe8PKBxY+DyZf5vGWxgnYw1\nV1HUdTLPeJZSIScnBwUFBcjNzUVubi7tViAnd+4AqamKSVR15AgfHirnYAgCRjiZvXv3YuHChcjI\nyEC1pynOVCoVbty4Ibo4QgenTgHduilmxdu+fTRtTejG4F26aNEihIaGwsnJSQo9xHNUuKdPTAzQ\nvr3kWiri7l0e3fDll3IrkQ/ad0k/BmeXXn31VdSkcFrZqHBqNCqKRyEqgF27eMR1w4ZyK5EPmr7W\nj8GB3+TkZPj6+qJTp06wt7fnJ6lUWL16tSQCn9mz1YHfcuTlAY6OPAL7xRfFtWUEHToAS5cCvr46\nDrDmUVEbQJKB38DAQHTr1g2dOnWCnZ0dGGO0q4CcREfz9TEKcDDXrwN//w306iW3EkLJGHQyqamp\nFCypJDQaoEsXuVUAANavB0aMUMz4M6FQDHaXli5dCgAYOXIk6tatq/1cyils6i6Von17YPlyQObd\nGhgDnJx4aodS22uVh7pLFo2oSaue4ezsXGH3yNj9sIXAlp1MmbiY7GzglVf4NigSL4h8nrNngcBA\nIDHRwCpfG3Ay1hy7JImTUQK27GTKFHnkCLBsGaCA7uvs2UCtWnzQVy824GSsuYqSDPxSPhkFcfIk\n0KOH3CpQUAD88ANAyREJY6B8MpaERgMsWSK3CuzeDbi6GhiLIYinmNxdepZP5ujRo2JpKgd1lwDk\n5gINGvBkVbVqCWrDVFq3BlavBnr3NuJga+5LPMWaqyjEs0f5ZCyFqCgecS2zg7l4kY8/09oYwlgo\nn4zC0cbFKGQ8ZscOYNQoyhtTGopd0o9J+WRq1qwJDw8PVK9eXWxdZbDl7pKWnj2Bjz8G/PzEKd8I\ncnL4nkonT5owHmPNfQkbQNTZpWvXruHu3bvafDLP0Gg0aNy4MVxcXMwyTJhAQQGPvJZ5pe/mzTzD\nBA34Eqagc0xmxowZFbZYatasiRkzZogqiniOixeBZs2Al16STQJjwLZtQFCQbBIIC0Wnk0lJSUHH\nCtIJtG/fXtLVvgSACxcACbcFroiYGCAjgwZ8CdPR6WTy8vKQmppa7vPU1FTk5OSIKop4jvPnAS8v\nWSWsXw+eWejHAAATb0lEQVRMmqSYHVgIC0Knk+nRowdWrFhR7vNVq1ahhwJmOWyFRQsZDxTy9pZN\nQ3o630+JttuqGGuNWxIKnbNLGRkZGD9+PC5cuIBu3boBACIjI9GuXTts3ryZorArQqzFeM1bAAkJ\nsuVU+OorvgXttm2VONkGZpesuYqSBEg+fvwYhw4dgkqlgp+fH2rXrm2Wwcpg807m47myJdEtKQGa\nNwd27qxkxk9rfgKfYs1VlCRAsnbt2hgyZIhZRggzkXHq+vffAXt7nmaTICqDyWEFhITk5fF/ZUpQ\nxRjw9dfAhx/SCl+i8ojuZDQaDVxdXdG8eXOsWbOm3Pc7d+6Eu7s73N3dMWLECFy9elVsSZZDZCT/\nV6b1MSdOALdu8TACgqgsojuZ6dOnIyQkBOHh4Vi3bh3S0tLKfN+0aVNoNBpcvHgR/fr106b7JACc\nOoWF3Y7LZn7dOmDiRMDOTjYJFgHFLulH1Mx4WVlZUKvViIuLAwBMmzYN/fr1g7+/f4XHp6WloV27\ndrh582ZZkbY68Nu7NzBtmizbM8bGcrPXrpkZ+G3No6I2gCypHkwhJiYGrUoFuri5uSEqKkrn8Rs3\nbiwT9W3TPHzIl9n27Su5acaAOXOAf/9b9swShBWgmM0swsPDsWPHDpw5c6bC7xeVWvGkVqvLBW5a\nHaGhfA2/DE/577/z3Fjjx0tumpCZiIgI4bdAYiKSmZnJPDw8tO8//PBDFhoaWu64ixcvMhcXF3bt\n2rUKyxFZpnAIqfO99xj7/nvhyjOB/v0Z27JFoMIs5W9HVIgQz56o3aVn29pqNBqkpKQgLCwMHZ5b\ncHHz5k0MHDgQO3fuRLNmzcSUYzkUFADHjgFvvSW56evXeajU0KGSmyasFNFnl4KDgxEUFIQ+ffpg\nypQpcHBwQEhICEJCQgAAS5YswcOHDzFp0iR4enrCx8dHbEnKJzwcaNMGcHSUPC5m/Xq+n1LNmtLa\ntWQodkk/tO+SkAg1kzJuHODuDkyfLunkzIMHQMuWwB9/8N0hBcEGZpesuYq0uZvSEOJuy88HXn+d\nzyw5O0t6A8+cCTx5wtfHCIY1P4FPseYqShK7REjMzz/z3DHOzpKaffQI+P57ID5eUrOEDUCxS0pj\n927g/fclN/vtt0C/fsCrr0pumrByqLskJOa2mwsLAUdHPsXj4CBIkcaabdYM2LNHhNxY1tyXeIo1\nV1HxK34JEzl/nneTnjoYQJq4mA0beM4YGZPvWTQUu6QfaskIibk/aUuWAFlZPL+CRNy9C7z5Jt9L\nqU0bEQxY88+8DUAtGWtj3z7g7bclNfnFF3wISBQHQxCgloywmPOrffs23+v6wQPJcvnevw+4ufH0\nwY0aiWSEWjIWDbVkrImffgLefVfSZOGLFwPDhonoYAgCtE5GGRQUAN98U8ntACrHgQPA4cN8dS9B\niAm1ZJRAaChfoFJBwnAx4mIyMoAJE4Dt24G6dYUv39ag2CX90JiMkFR2/GHQIB5xXcHuaWIMaSxc\nCCQnS9RwsoExGWuuIsUuKY3K3G2FhcArrwAXLwKNGwtSpD4yMngQpEYDlEpaKB7W/AQ+xZqrSAO/\n1sBvvwGtW1foYISmqIjniRk9WiIHQxAgJyM/wcF8YyMJ+PhjoEoV2TajJGwUml2SkzNngHv3gPfe\nE93UiRM8Nik2VrYttQkbhVoycsEYMH8+T+JSpYrOw4SKi1m3Dpg9m8dfEsJCsUv6oYFfITFlBPCn\nn4CvvgKio/U6GSG4eBHw9eUzSpJvfmDNo6I2ACWtslSePOGLK778UnQHk5PDY5MWLaI9lAh5oO6S\nHGzaBDRtKvrOkAUFfNGdmxsQFCSqKYLQCXWXhMSYrkFhIU/e8tNPQMeOoklhjE9Vp6fzZHu1a4tm\nSj/UXbJoqLtkiezaBTRpIqqDAYCtW3l0dWQk8OKLopoiCL1Qd0lKiop46PMnnxh9SmXiYhITgblz\ngc2bycFIAcUu6Ye6S0JiqGvw/ffAli08DZ1ART7PuXN82c1//gOMHWv8eaJhA90la64ixS4pDX13\nW1YW4OoK7N1rUlfJlBv4+HFgyBCes3fQIKNNiIs1P4FPseYqkpNRGrrutuJioH9/oEULk3dOM/YG\nTkoCunXjW5v4+ZlkQlys+Ql8ijVXkQIkLYWlS/l4zKpVohR//TrQpw8fh1GUgyEIUEtGWCr6Sbt8\nmTcxEhN5SgcBiizNgwdA+/bAvHnApEkmFy8+1vwz/xRrriK1ZJTO3buAWs1Ta1bCwQD642KuX+e9\nsLFjFepgbASKXdIPtWSEpPRPWkEBEBAAuLsDy5cLbkqj4YO8H30EzJrFTSsSa/6ZtwFo4FdpPHug\nGAMCA/ku9rt2CZpb4ZdfeAqaq1eBnTuBvn0FK1ocyMlYNLTiV4kUFPDFdnFxwNmzgjmYc+d4qoZb\nt/j4cf/+QPXqghRNEKJCTkZoAgKAatWAgwcFCXtOTQXWruWv//wHGD+eF08QlgIN/ApBSQnw3Xf8\n/+7uPG/va6+ZVWRuLrBjB99U8u5dntFu0iRyMITlQWMy5vLnn8CnnwIpKTw7lJk6ExOBlSt55HTH\njjyTnYR7vgmPDYzJLFpkvfFLip/C1mg0cHV1RfPmzbFmzZoKj5k3bx6aNm0KLy8vXLlyRUw5wpKY\nCLi4AL16Ad7efLqnEmRnAzExfDDX1xdo1w6oXx+Ij+c7PG7fLrBuQnAWL5ZbgcJhIuLh4cFOnjzJ\nUlJSWMuWLVlqamqZ76Ojo1mXLl1Yeno6++GHH5i/v3+F5YgssxwnTpyo+Iv8fMb27mVswADG6tVj\nbPVqxkpK/ve9Hp15eYxducLYoUOMzZvHi3B2Zqx6dcZcXE6wiRMZ276dsZycsueJUXWd9RMDQFJ7\nktbtqT0pb0+p6yfEsydaSyYrKwsA0L17dzg5OcHX1xfR0dFljomOjsagQYNQv359DB8+HImJiWLJ\nMYmIiAj+n+JivqPAypXAgAF8Z/oVK/jg7vXrwNSp2gUq2dnABXgiPBz4+WfefB49mq/Fa9wYsLcH\n/P356S+8AIwZw8eGc3KAUaMiEBICjBolTYpMbf0kQkp71lw3OewJgWizSzExMWhVagcxNzc3REVF\nwd/fX/vZuXPnMHr0aO17R0dHJCUlwcXFRSxZnMJCIDNT9+vYMSAqiqdkaNIET7r2wuN3AnFn5ndI\nTHVAaiqQ+Q3fjTElhXdtbt4EmmELHL7g3Z1mzfgalkaN+EZqjRuLns6XIBSJrFPYjLFyg0qqyixd\n3bYNG77MwoF/fMBKGH8x9r//l3BbKGFgxSX8s6rVUFLVDoVVaqCwyhsoeqEZClV2KEQ1PMhLw+Za\nC5FTvQYeX3sBuAbU+QVo0IBv9tiwIVCvHv/X0xP47DOeR7eanQdwzLoHOQnCZMzucOkgMzOTeXh4\naN9/+OGHLDQ0tMwxq1evZitWrNC+b9q0aYVlubi4MAD0ohe9JH65uLiY7QtEa8nY29sD4DNMb7zx\nBsLCwrDwuUiyDh06YNasWRgzZgyOHj0KV1fXCsu6fv26WDIJghAZUbtLwcHBCAoKQmFhIaZNmwYH\nBweEhIQAAIKCguDj44OuXbvC29sb9evXx44dO8SUQxCEDFjEYjyCICwXWcMKDC3Wi4iIgL29PTw9\nPeHp6YnPP//c6HOFtHfr1i307NkTrVu3hlqtxg8//CB6/QCguLgYnp6eCAgIEN1eTk4Oxo4dixYt\nWmhnAsW0t2nTJnTu3BleXl6YMWOGIPUD+Kxm+/bt4erqCrVabdK5QtkT637RVz9A+PtFnz2T7hez\nR3XMwNBivRMnTrCAgIBKnSukvXv37rG4uDjGGGOpqamsSZMm7NGjR6LWjzHGvv76azZixAi9xwhl\nb/bs2ezTTz9leXl5rLCwkGVmZopmLz09nTk7O7PHjx+z4uJi5ufnx44cOWK2vZKSEtamTRsWFhbG\nGGNlvhfjftFlT6z7RV/9GBP+ftFnz5T7RbaWjDGL9QBUGDdh7LlC2WvUqBE8PDwAAA4ODmjdujVi\nY2NFswcAt2/fxqFDhzBhwgSjYkfMtRceHo758+ejRo0aqFq1qnbgXgx7NWvWBGMMWVlZyMvLQ25u\nLurVq2e2vdjYWLRt2xZ9+vQBwP9WpmgVyp5Y94sue4A494s+e6bcL7I5GV2L9UqjUqlw5swZeHh4\nYNasWUhKSjL6XCHtleb69euIj4+Hj4+PqPZmzpyJ5cuX44UXjPsTmWPv9u3byM/Px+TJk9GhQwd8\n+eWXyM/PF81ezZo18c0338DZ2RmNGjVCly5dBLmeR48ehUqlQrdu3RAQEICjR48afa6Q9koj5P2i\nz54Y94sue6beL4pO9dCuXTvcunULMTExcHNzw/Tp02W1l52djaFDh2LlypV4UYCtGXXZCw0NRYMG\nDeDp6Slo9Lkue/n5+bh69SoGDhyIiIgIxMfHY9euXaLZS01NxeTJk5GQkICUlBScPXsWBw8eNNte\nfn4+/vjjD+zevRurVq3ClClTkJeXZ3a5lbUn9P2iy55Y94sueybfL0Z13kTAmMV6pSkpKWENGjRg\n+fn5LCMjw6RzzbXHGGNPnjxhffv2ZStXrjRYN3Ps5eXlsXnz5rHXXnuNOTs7s0aNGrFatWqx0aNH\ni1q/Vq1aab87dOgQGzZsmGj2QkND2dChQ7XfrV+/nn388cdm2wsNDWVz5szRvh8yZAg7evSoyVrN\nsfdsbEmM+0WXPbHuF13XkzHT7hdFDPwmJydXOPB0//59VvI0ynnfvn2sT58+Rp8rpL2SkhI2evRo\nNnPmTMnq94yIiAj29ttvi24vICCARUVFseLiYvbBBx+wzZs3i2YvMzOTubi4sPT0dJafn88CAgJY\neHi42fbS0tJY+/btWU5ODktPT2fNmzdn2dnZRp0rpD2x7hd99XuGkPeLPnum3C+yOpmIiAjWqlUr\n5uLiwlatWsUYY2zDhg1sw4YNjDHG1q5dy1q3bs3c3d3Z6NGj2cWLF/WeK5a9yMhIplKpmLu7O/Pw\n8GAeHh7s8OHDotavdBnGzhaYY++vv/5iHTp0YO7u7mz27Nns8ePHotrbsmUL6969O/P29maffvop\nKy4uNtseY7xV5Orqyrp3785+/PFHveeKZU+s+0Vf/UqXIdT9os+eKfcLLcYjCEJUFD3wSxCE5UNO\nhiAIUSEnQxCEqJCTIQhCVMjJEAQhKuRkCIIQFXIyVs6vv/6qTbXw7FWlSpUK42ysjeDgYFHDCgjj\noHUyNsbGjRvx448/4sSJE3JLEZ0mTZogNjYWL7/8stxSbBpqydgQV69exdKlS7Fdx7aUR48exTvv\nvAMPDw+MGTMGAHD37l1Mnz4d7u7umDlzJv755x8AwPvvv485c+bAx8cHLVu2RFxcHCZOnIjWrVtj\nUak9W2vXro0FCxagVatWmDFjBjIzMwEA165dw7hx4+Dh4YGFCxciOzsbAKBWq7F48WJ4e3ujR48e\niIuLA8BTRmzatAl9+/ZFnz59sHfvXgA8MVbv3r0xbNgwuLm5YcGCBQCA1atX4+7du+jZsyd69+4t\n/MUkjMeo9ceExfPkyRPm5eXFdu3aVeH3OTk5zMXFhV29epUxxlhGRgZjjLGZM2ey//73v4wxxv7v\n//5PG8g4duxY5ufnxwoKCtj333/PateuzSIiIlhBQQFzdXVlaWlpjDHGVCoVW7FiBSsqKmJTp05l\nX331FWOMsXfffZf99NNPrLCwkE2ePJmtX7+eMcaYWq1mgYGBrKioiO3YsYMFBgYyxngCrFmzZrGS\nkhL2+PFj5unpyQoKCtiJEydYtWrV2JUrV1h+fj5r06YNu3XrFmOMMWdnZ5aeni7G5SRMgFoyNsJn\nn32GN998E4MHD67w+4MHD6JPnz5o3rw5AKBu3boAgMOHD2PcuHEAgPHjx+PAgQMAeK6YQYMGwc7O\nDp06dULdunXRo0cP2NnZwdPTU5ubRKVSYezYsahSpQrGjBmDI0eOoLCwEDExMRgyZAiqVq2KwMBA\n7N+/X6tl5MiRqFKlCnr27ImzZ88CAPbs2YPQ0FC0a9cOXbt2RVZWltbGs9ZU9erV0blzZ5w+fVqE\nK0hUFlk3dyOkISIiAr/++isuXLig9zimY3hO1+fPsqHZ2dlpndKz9wUFBQZ1McagUqnKlf8sS56d\nnZ02GVJJSQnmz5+PsWPHljk2IiKiTFY9Y20T0kEtGSsnIyMDgYGB2LZtm97ESf7+/ggPD8fVq1e1\n5wHAW2+9ha1bt6KkpATfffcdBgwYYJJ9xhi2b9+O4uJibN++HX5+fqhWrRp8fHywZ88eFBUVYevW\nrXjnnXf0ljNixAhs27YNqampAPj4Um5urt5znJyc8ODBA5P0EsJDTsbK2bBhA1JTUzFp0qQy09i7\nd+8uc1ytWrXwzTffYObMmXB3d8fs2bMBAHPmzMHNmzfh6emJf/75B7NmzdKeU3pLYV3bC7/44ot4\n8OABWrduDZVKhfHjxwMAli1bhsOHD8Pb2xsODg4YNWpUhec/K7dLly4YMWIEBg8ejDfffBOTJ09G\nUVERVCqVTtsTJ07EmDFjaOBXZmgKmxCVOnXqaGeOCNuEWjKEqOhqZRC2A7VkCIIQFWrJEAQhKuRk\nCIIQFXIyBEGICjkZgiBEhZwMQRCiQk6GIAhR+f+CZkOCleWdcwAAAABJRU5ErkJggg==\n",
"text": [
""
]
}
],
"prompt_number": 16
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Stratigraphic plot of VGP paleolatitude"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This code takes the Mamainse Point data and creates a stratigraphic plot of the absolute value of the paleolatitude implied by each virtual geomagnetic pole. This plot was published in the supplementary materials as Figure DR3."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"strat_height_forplot=[]\n",
"abs_paleolatitude_forplot=[]\n",
"inclination_forplot=[]\n",
"\n",
"for n in range(0,52):\n",
" strat_height_forplot.append(Mamainse_Data_all['composite_meter_level'][n])\n",
" abs_paleolatitude_forplot.append(numpy.absolute(Mamainse_Data_all['paleolatitude'][n]))\n",
" inclination_forplot.append(Mamainse_Data_all['I_tilt-cor'][n])\n",
"for n in range(55,99):\n",
" strat_height_forplot.append(Mamainse_Data_all['composite_meter_level'][n])\n",
" abs_paleolatitude_forplot.append(numpy.absolute(Mamainse_Data_all['paleolatitude'][n])) \n",
" inclination_forplot.append(Mamainse_Data_all['I_tilt-cor'][n])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for n in range(0, len(strat_height_forplot)):\n",
" if inclination_forplot[n]>0:\n",
" pylab.plot(abs_paleolatitude_forplot[n],strat_height_forplot[n],'bo')\n",
" #print strat_height[item]\n",
" elif inclination_forplot[n]<0:\n",
" pylab.plot(abs_paleolatitude_forplot[n],strat_height_forplot[n],'ro')\n",
"\n",
"plt.title(\"VGP paleolatitude from Mamainse Point\")\n",
"plt.xlabel(\"VGP paleolatitude\")\n",
"plt.ylabel(\"stratigraphic height\")\n",
"pylab.xlim([0,90])\n",
"fig = matplotlib.pyplot.gcf()\n",
"fig.set_size_inches(3,10)\n",
"savefig('Mamainse_Strat.svg')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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FFwgODjZWtYm6NmONVh48eFAEBASIESNGiIiICPHpp58KIYQoKysTEydOFO7u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\nihHx8fj04EEkNdltmOfigt/xEmsdildMIilZpcK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"text": [
""
]
}
],
"prompt_number": 18
}
],
"metadata": {}
}
]
}