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cfriedline/eckert_2010.ipynb

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eckert_2010.ipynb
{
"cells": [
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "import os, sys\nfrom IPython.display import Image\nimport pandas as pd\nfrom __future__ import division\nimport numpy as np\nimport rpy2\nfrom rpy2 import robjects as ro\nimport pandas.rpy.common as com\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport operator\nimport scipy as sp\nimport traceback\nfrom sklearn import preprocessing\nfrom IPython.parallel import Client\nfrom subprocess import Popen, PIPE\nimport shutil\nfrom IPython.display import FileLink, FileLinks, Image\nimport psutil\nimport multiprocessing\n%matplotlib inline\n\n%load_ext rpy2.ipython\npd.set_option('display.width', 80)\npd.set_option('max.columns', 30)\n\nsns.set_context(\"talk\")",
"execution_count": 1202,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "The rpy2.ipython extension is already loaded. To reload it, use:\n %reload_ext rpy2.ipython\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "%%R\nR.home()",
"execution_count": 2,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "[1] \"/gdc_home4/cfried/R3/lib64/R\"\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "data_ai = pd.read_excel(\"/gdc_home4/cfried/landscape_genetics_data/Genetics_2010/Eckert_Genetics_2010_data.xlsx\")",
"execution_count": 676,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "ai_cols = ['AI_Q1','AI_Q2','AI_Q3','AI_Q4']",
"execution_count": 675,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "data_gt = pd.read_excel(\"/gdc_home4/cfried/landscape_genetics_data/Genetics_2010/Eckert_Genetics_2010_data.xlsx\", \n sheetname=\"genotyping_data\")\n\ndata_loc = pd.read_excel(\"/gdc_home4/cfried/landscape_genetics_data/Genetics_2010/Eckert_Genetics_2010_data.xlsx\",\n sheetname=\"county_locality\")\n\nresults = pd.read_excel(\"/gdc_home4/cfried/landscape_genetics_data/Genetics_2010/Eckert_Genetics_2010_results.xlsx\")",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "trait_name = \"sucrose\"",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "pheno = pd.read_excel(\"/gdc_home4/cfried/landscape_genetics_data/Pinus_taeda_metabolite_data.xlsx\", \n sheetname=\"metabolite_phenotype_data\",\n header=2)\npheno = pheno[['Longitude', 'Latitude','Clone_id',trait_name]]\n\npheno.index = pheno.Clone_id\n#pheno = pheno.drop('Clone_id', axis=1)\npheno[0:5]",
"execution_count": 374,
"outputs": [
{
"execution_count": 374,
"output_type": "execute_result",
"data": {
"text/plain": " Longitude Latitude Clone_id sucrose\nClone_id \n105A -77.05205 35.55349 105A 5.554807\n109B -76.93578 36.39002 109B 5.770389\n112C -77.48749 35.06925 112C 5.611106\n118B -78.29901 36.10041 118B 5.593997\n121C -87.38771 34.14891 121C 5.073928",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Longitude</th>\n <th>Latitude</th>\n <th>Clone_id</th>\n <th>sucrose</th>\n </tr>\n <tr>\n <th>Clone_id</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>105A</th>\n <td>-77.05205</td>\n <td> 35.55349</td>\n <td> 105A</td>\n <td> 5.554807</td>\n </tr>\n <tr>\n <th>109B</th>\n <td>-76.93578</td>\n <td> 36.39002</td>\n <td> 109B</td>\n <td> 5.770389</td>\n </tr>\n <tr>\n <th>112C</th>\n <td>-77.48749</td>\n <td> 35.06925</td>\n <td> 112C</td>\n <td> 5.611106</td>\n </tr>\n <tr>\n <th>118B</th>\n <td>-78.29901</td>\n <td> 36.10041</td>\n <td> 118B</td>\n <td> 5.593997</td>\n </tr>\n <tr>\n <th>121C</th>\n <td>-87.38771</td>\n <td> 34.14891</td>\n <td> 121C</td>\n <td> 5.073928</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "def get_phenotype(row):\n return np.max(pheno[(pheno.Longitude==row.long) & (pheno.Latitude==row.lat)])",
"execution_count": 375,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "data_gt[:5]",
"execution_count": 6,
"outputs": [
{
"execution_count": 6,
"output_type": "execute_result",
"data": {
"text/plain": " county state 0-10037-01-257 0-10040-02-394 0-10044-01-392 \\\n0 CHEROKEE GA G/G A/C G/G \n1 BARTOW GA A/A C/C G/G \n2 SUSSEX VA A/A C/C C/C \n3 KING & QUEEN VA A/A A/A C/C \n4 KING & QUEEN VA A/A A/C C/C \n\n 0-10048-01-60 0-10051-02-166 0-10054-01-402 0-10067-03-111 0-10079-02-168 \\\n0 A/A G/G A/G A/A G/G \n1 A/A G/G A/G A/A A/G \n2 G/G G/G A/G A/A G/G \n3 G/G A/G G/G A/A G/G \n4 ?/? G/G A/G A/A G/G \n\n 0-10112-01-169 0-10113-01-119 0-10116-01-165 0-10151-01-86 0-10162-01-255 \\\n0 A/C A/A A/A A/A A/A \n1 A/A G/G A/A A/A A/A \n2 A/A A/G A/A A/A A/G \n3 A/A A/G A/A A/A A/A \n4 A/A A/A A/A A/A ?/? \n\n ... 4003_02 4033_01 4033_02 4056_01 4056_02 4058_01 4058_02 4093_01 \\\n0 ... 155 131 143 413 413 143 150 307 \n1 ... 155 ? ? 413 437 137 143 307 \n2 ... 155 133 133 413 413 150 152 322 \n3 ... 155 133 152 413 413 146 154 307 \n4 ... 155 ? ? 413 431 143 146 310 \n\n 4093_02 4112_01 4112_02 4137_01 4137_02 4181_01 4181_0 \n0 310 462 462 161 161 365 390 \n1 307 440 448 161 176 365 365 \n2 325 462 462 161 169 378 395 \n3 325 460 462 163 190 390 417 \n4 322 462 462 169 169 395 409 \n\n[5 rows x 3130 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>county</th>\n <th>state</th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>0-10116-01-165</th>\n <th>0-10151-01-86</th>\n <th>0-10162-01-255</th>\n <th>...</th>\n <th>4003_02</th>\n <th>4033_01</th>\n <th>4033_02</th>\n <th>4056_01</th>\n <th>4056_02</th>\n <th>4058_01</th>\n <th>4058_02</th>\n <th>4093_01</th>\n <th>4093_02</th>\n <th>4112_01</th>\n <th>4112_02</th>\n <th>4137_01</th>\n <th>4137_02</th>\n <th>4181_01</th>\n <th>4181_0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> CHEROKEE</td>\n <td> GA</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> 155</td>\n <td> 131</td>\n <td> 143</td>\n <td> 413</td>\n <td> 413</td>\n <td> 143</td>\n <td> 150</td>\n <td> 307</td>\n <td> 310</td>\n <td> 462</td>\n <td> 462</td>\n <td> 161</td>\n <td> 161</td>\n <td> 365</td>\n <td> 390</td>\n </tr>\n <tr>\n <th>1</th>\n <td> BARTOW</td>\n <td> GA</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> 155</td>\n <td> ?</td>\n <td> ?</td>\n <td> 413</td>\n <td> 437</td>\n <td> 137</td>\n <td> 143</td>\n <td> 307</td>\n <td> 307</td>\n <td> 440</td>\n <td> 448</td>\n <td> 161</td>\n <td> 176</td>\n <td> 365</td>\n <td> 365</td>\n </tr>\n <tr>\n <th>2</th>\n <td> SUSSEX</td>\n <td> VA</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> 155</td>\n <td> 133</td>\n <td> 133</td>\n <td> 413</td>\n <td> 413</td>\n <td> 150</td>\n <td> 152</td>\n <td> 322</td>\n <td> 325</td>\n <td> 462</td>\n <td> 462</td>\n <td> 161</td>\n <td> 169</td>\n <td> 378</td>\n <td> 395</td>\n </tr>\n <tr>\n <th>3</th>\n <td> KING &amp; QUEEN</td>\n <td> VA</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> 155</td>\n <td> 133</td>\n <td> 152</td>\n <td> 413</td>\n <td> 413</td>\n <td> 146</td>\n <td> 154</td>\n <td> 307</td>\n <td> 325</td>\n <td> 460</td>\n <td> 462</td>\n <td> 163</td>\n <td> 190</td>\n <td> 390</td>\n <td> 417</td>\n </tr>\n <tr>\n <th>4</th>\n <td> KING &amp; QUEEN</td>\n <td> VA</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> C/C</td>\n <td> ?/?</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> ?/?</td>\n <td>...</td>\n <td> 155</td>\n <td> ?</td>\n <td> ?</td>\n <td> 413</td>\n <td> 431</td>\n <td> 143</td>\n <td> 146</td>\n <td> 310</td>\n <td> 322</td>\n <td> 462</td>\n <td> 462</td>\n <td> 169</td>\n <td> 169</td>\n <td> 395</td>\n <td> 409</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 3130 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "data_loc",
"execution_count": 376,
"outputs": [
{
"execution_count": 376,
"output_type": "execute_result",
"data": {
"text/plain": " county state lat long\n0 CHEROKEE GA 34.24000 -84.47000\n1 BARTOW GA 34.24000 -84.84000\n2 SUSSEX VA 36.92093 -77.28034\n3 KING & QUEEN VA 37.66986 -76.87746\n4 KING & QUEEN VA 37.66986 -76.87746\n5 NEW KENT VA 37.51160 -76.97319\n6 WARREN NC 34.80551 -76.80890\n7 NORTHAMPTON NC 36.39032 -77.42219\n8 COLUMBUS NC 34.33010 -78.70453\n9 WILLIAMSBURG SC 33.66538 -79.81968\n10 ONSLOW NC 34.75963 -77.40977\n11 ONSLOW NC 34.75963 -77.40977\n12 GEORGETOWN SC 33.36318 -79.30539\n13 BEAUFORT NC 35.55349 -77.05205\n14 BERTIE NC 35.99815 -76.94897\n15 CRAVEN NC 35.10917 -77.06917\n16 ONSLOW NC 34.75963 -77.40977\n17 CHATHAM NC 35.72033 -79.17639\n18 BEAUFORT NC 35.55349 -77.05205\n19 DARE NC 35.99784 -76.98390\n20 CRAVEN NC 35.10917 -77.06917\n21 CRAVEN NC 35.10917 -77.06917\n22 CARTERET NC 34.72073 -76.65257\n23 TYRRELL NC 35.91778 -76.24972\n24 CRAVEN NC 35.10917 -77.06917\n25 BEAUFORT NC 35.55349 -77.05205\n26 BEAUFORT NC 35.55349 -77.05205\n27 BEAUFORT NC 35.55349 -77.05205\n28 BEAUFORT NC 35.55349 -77.05205\n29 BEAUFORT NC 35.55349 -77.05205\n.. ... ... ... ...\n592 EDGEFIELD SC 33.78684 -81.92784\n593 WARREN GA 33.40760 -82.66291\n594 BERKELEY SC 33.19661 -80.00666\n595 HALIFAX NC 36.32840 -77.59073\n596 CHATHAM NC 35.72033 -79.17639\n597 MARSHALL AL 34.33572 -86.30198\n598 GEORGETOWN SC 33.36318 -79.30539\n599 COLUMBUS NC 34.33010 -78.70453\n600 GEORGETOWN SC 33.36318 -79.30539\n601 HORRY SC 33.83809 -79.05610\n602 HORRY SC 33.83809 -79.05610\n603 WILLIAMSBURG SC 33.66538 -79.81968\n604 MARION SC 34.18009 -79.39710\n605 ONSLOW NC 34.64551 -77.41295\n606 JONES NC 35.06925 -77.48749\n607 ONSLOW NC 34.64551 -77.41295\n608 WINSTON AL 34.14891 -87.38771\n609 CARTERET NC 34.72073 -76.65257\n610 CRAVEN NC 35.10917 -77.06917\n611 JONES NC 35.06925 -77.48749\n612 CRAVEN NC 35.10917 -77.06917\n613 PIKE AR 34.06626 -93.68926\n614 BERKELEY SC 33.19661 -80.00666\n615 BERKELEY SC 33.19661 -80.00666\n616 BERKELEY SC 33.19661 -80.00666\n617 BERKELEY SC 33.19661 -80.00666\n618 BERKELEY SC 33.19661 -80.00666\n619 TYLER TX 30.77611 -94.42111\n620 UNION LA 32.77361 -92.40417\n621 UNION LA 32.77361 -92.40417\n\n[622 rows x 4 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>county</th>\n <th>state</th>\n <th>lat</th>\n <th>long</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> CHEROKEE</td>\n <td> GA</td>\n <td> 34.24000</td>\n <td>-84.47000</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> BARTOW</td>\n <td> GA</td>\n <td> 34.24000</td>\n <td>-84.84000</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> SUSSEX</td>\n <td> VA</td>\n <td> 36.92093</td>\n <td>-77.28034</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> KING &amp; QUEEN</td>\n <td> VA</td>\n <td> 37.66986</td>\n <td>-76.87746</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> KING &amp; QUEEN</td>\n <td> VA</td>\n <td> 37.66986</td>\n <td>-76.87746</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> NEW KENT</td>\n <td> VA</td>\n <td> 37.51160</td>\n <td>-76.97319</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> WARREN</td>\n <td> NC</td>\n <td> 34.80551</td>\n <td>-76.80890</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> NORTHAMPTON</td>\n <td> NC</td>\n <td> 36.39032</td>\n <td>-77.42219</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> WILLIAMSBURG</td>\n <td> SC</td>\n <td> 33.66538</td>\n <td>-79.81968</td>\n </tr>\n <tr>\n <th>10 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n </tr>\n <tr>\n <th>11 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n </tr>\n <tr>\n <th>12 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n </tr>\n <tr>\n <th>13 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n </tr>\n <tr>\n <th>14 </th>\n <td> BERTIE</td>\n <td> NC</td>\n <td> 35.99815</td>\n <td>-76.94897</td>\n </tr>\n <tr>\n <th>15 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n </tr>\n <tr>\n <th>16 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n </tr>\n <tr>\n <th>17 </th>\n <td> CHATHAM</td>\n <td> NC</td>\n <td> 35.72033</td>\n <td>-79.17639</td>\n </tr>\n <tr>\n <th>18 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n </tr>\n <tr>\n <th>19 </th>\n <td> DARE</td>\n <td> NC</td>\n <td> 35.99784</td>\n <td>-76.98390</td>\n </tr>\n <tr>\n <th>20 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n </tr>\n <tr>\n <th>21 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n </tr>\n <tr>\n <th>22 </th>\n <td> CARTERET</td>\n <td> NC</td>\n <td> 34.72073</td>\n <td>-76.65257</td>\n </tr>\n <tr>\n <th>23 </th>\n <td> TYRRELL</td>\n <td> NC</td>\n <td> 35.91778</td>\n <td>-76.24972</td>\n </tr>\n <tr>\n <th>24 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n </tr>\n <tr>\n <th>25 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n </tr>\n <tr>\n <th>26 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n </tr>\n <tr>\n <th>27 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n </tr>\n <tr>\n <th>28 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n </tr>\n <tr>\n <th>29 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>592</th>\n <td> EDGEFIELD</td>\n <td> SC</td>\n <td> 33.78684</td>\n <td>-81.92784</td>\n </tr>\n <tr>\n <th>593</th>\n <td> WARREN</td>\n <td> GA</td>\n <td> 33.40760</td>\n <td>-82.66291</td>\n </tr>\n <tr>\n <th>594</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n </tr>\n <tr>\n <th>595</th>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 36.32840</td>\n <td>-77.59073</td>\n </tr>\n <tr>\n <th>596</th>\n <td> CHATHAM</td>\n <td> NC</td>\n <td> 35.72033</td>\n <td>-79.17639</td>\n </tr>\n <tr>\n <th>597</th>\n <td> MARSHALL</td>\n <td> AL</td>\n <td> 34.33572</td>\n <td>-86.30198</td>\n </tr>\n <tr>\n <th>598</th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n </tr>\n <tr>\n <th>599</th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n </tr>\n <tr>\n <th>600</th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n </tr>\n <tr>\n <th>601</th>\n <td> HORRY</td>\n <td> SC</td>\n <td> 33.83809</td>\n <td>-79.05610</td>\n </tr>\n <tr>\n <th>602</th>\n <td> HORRY</td>\n <td> SC</td>\n <td> 33.83809</td>\n <td>-79.05610</td>\n </tr>\n <tr>\n <th>603</th>\n <td> WILLIAMSBURG</td>\n <td> SC</td>\n <td> 33.66538</td>\n <td>-79.81968</td>\n </tr>\n <tr>\n <th>604</th>\n <td> MARION</td>\n <td> SC</td>\n <td> 34.18009</td>\n <td>-79.39710</td>\n </tr>\n <tr>\n <th>605</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.64551</td>\n <td>-77.41295</td>\n </tr>\n <tr>\n <th>606</th>\n <td> JONES</td>\n <td> NC</td>\n <td> 35.06925</td>\n <td>-77.48749</td>\n </tr>\n <tr>\n <th>607</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.64551</td>\n <td>-77.41295</td>\n </tr>\n <tr>\n <th>608</th>\n <td> WINSTON</td>\n <td> AL</td>\n <td> 34.14891</td>\n <td>-87.38771</td>\n </tr>\n <tr>\n <th>609</th>\n <td> CARTERET</td>\n <td> NC</td>\n <td> 34.72073</td>\n <td>-76.65257</td>\n </tr>\n <tr>\n <th>610</th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n </tr>\n <tr>\n <th>611</th>\n <td> JONES</td>\n <td> NC</td>\n <td> 35.06925</td>\n <td>-77.48749</td>\n </tr>\n <tr>\n <th>612</th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n </tr>\n <tr>\n <th>613</th>\n <td> PIKE</td>\n <td> AR</td>\n <td> 34.06626</td>\n <td>-93.68926</td>\n </tr>\n <tr>\n <th>614</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n </tr>\n <tr>\n <th>615</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n </tr>\n <tr>\n <th>616</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n </tr>\n <tr>\n <th>617</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n </tr>\n <tr>\n <th>618</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n </tr>\n <tr>\n <th>619</th>\n <td> TYLER</td>\n <td> TX</td>\n <td> 30.77611</td>\n <td>-94.42111</td>\n </tr>\n <tr>\n <th>620</th>\n <td> UNION</td>\n <td> LA</td>\n <td> 32.77361</td>\n <td>-92.40417</td>\n </tr>\n <tr>\n <th>621</th>\n <td> UNION</td>\n <td> LA</td>\n <td> 32.77361</td>\n <td>-92.40417</td>\n </tr>\n </tbody>\n</table>\n<p>622 rows × 4 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
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},
"cell_type": "code",
"source": "results.index = results.locus\nresults = results.drop(\"locus\", axis=1)",
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"collapsed": false,
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},
"cell_type": "code",
"source": "results[0:5]",
"execution_count": 8,
"outputs": [
{
"execution_count": 8,
"output_type": "execute_result",
"data": {
"text/plain": " AI_Q1 AI_Q2 AI_Q3 AI_Q4 AI_Q1_p AI_Q2_p \\\nlocus \n0-10037-01-257 3.090870 1.864283 0.149907 2.461218 0.078733 0.172131 \n0-10040-02-394 0.222454 0.278099 0.022114 0.017258 0.637176 0.597950 \n0-10044-01-392 0.089144 0.223369 0.555988 0.021623 0.765268 0.636485 \n0-10048-01-60 1.414864 0.015237 1.090495 0.137387 0.234251 0.901761 \n0-10051-02-166 0.058918 0.040176 0.027423 0.001222 0.808214 0.841136 \n\n AI_Q3_p AI_Q4_p AI_Q1_q AI_Q2_q AI_Q3_q AI_Q4_q \nlocus \n0-10037-01-257 0.698625 0.116688 0.747412 0.810414 0.921038 0.780214 \n0-10040-02-394 0.881783 0.895482 0.915915 0.913184 0.930757 0.931667 \n0-10044-01-392 0.455881 0.883096 0.924059 0.915847 0.892786 0.930859 \n0-10048-01-60 0.296362 0.710892 0.831868 0.932005 0.860061 0.922234 \n0-10051-02-166 0.868472 0.972119 0.925925 0.927279 0.928358 0.937372 ",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>AI_Q1</th>\n <th>AI_Q2</th>\n <th>AI_Q3</th>\n <th>AI_Q4</th>\n <th>AI_Q1_p</th>\n <th>AI_Q2_p</th>\n <th>AI_Q3_p</th>\n <th>AI_Q4_p</th>\n <th>AI_Q1_q</th>\n <th>AI_Q2_q</th>\n <th>AI_Q3_q</th>\n <th>AI_Q4_q</th>\n </tr>\n <tr>\n <th>locus</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0-10037-01-257</th>\n <td> 3.090870</td>\n <td> 1.864283</td>\n <td> 0.149907</td>\n <td> 2.461218</td>\n <td> 0.078733</td>\n <td> 0.172131</td>\n <td> 0.698625</td>\n <td> 0.116688</td>\n <td> 0.747412</td>\n <td> 0.810414</td>\n <td> 0.921038</td>\n <td> 0.780214</td>\n </tr>\n <tr>\n <th>0-10040-02-394</th>\n <td> 0.222454</td>\n <td> 0.278099</td>\n <td> 0.022114</td>\n <td> 0.017258</td>\n <td> 0.637176</td>\n <td> 0.597950</td>\n <td> 0.881783</td>\n <td> 0.895482</td>\n <td> 0.915915</td>\n <td> 0.913184</td>\n <td> 0.930757</td>\n <td> 0.931667</td>\n </tr>\n <tr>\n <th>0-10044-01-392</th>\n <td> 0.089144</td>\n <td> 0.223369</td>\n <td> 0.555988</td>\n <td> 0.021623</td>\n <td> 0.765268</td>\n <td> 0.636485</td>\n <td> 0.455881</td>\n <td> 0.883096</td>\n <td> 0.924059</td>\n <td> 0.915847</td>\n <td> 0.892786</td>\n <td> 0.930859</td>\n </tr>\n <tr>\n <th>0-10048-01-60</th>\n <td> 1.414864</td>\n <td> 0.015237</td>\n <td> 1.090495</td>\n <td> 0.137387</td>\n <td> 0.234251</td>\n <td> 0.901761</td>\n <td> 0.296362</td>\n <td> 0.710892</td>\n <td> 0.831868</td>\n <td> 0.932005</td>\n <td> 0.860061</td>\n <td> 0.922234</td>\n </tr>\n <tr>\n <th>0-10051-02-166</th>\n <td> 0.058918</td>\n <td> 0.040176</td>\n <td> 0.027423</td>\n <td> 0.001222</td>\n <td> 0.808214</td>\n <td> 0.841136</td>\n <td> 0.868472</td>\n <td> 0.972119</td>\n <td> 0.925925</td>\n <td> 0.927279</td>\n <td> 0.928358</td>\n <td> 0.937372</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
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}
]
},
{
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"cell_type": "code",
"source": "genotypes = data_gt.ix[:,[x for x in data_gt.columns if '-' in x]]",
"execution_count": 370,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "genotypes[:5]",
"execution_count": 371,
"outputs": [
{
"execution_count": 371,
"output_type": "execute_result",
"data": {
"text/plain": " 0-10037-01-257 0-10040-02-394 0-10044-01-392 0-10048-01-60 0-10051-02-166 \\\n0 G/G A/C G/G A/A G/G \n1 A/A C/C G/G A/A G/G \n2 A/A C/C C/C G/G G/G \n3 A/A A/A C/C G/G A/G \n4 A/A A/C C/C ?/? G/G \n\n 0-10054-01-402 0-10067-03-111 0-10079-02-168 0-10112-01-169 0-10113-01-119 \\\n0 A/G A/A G/G A/C A/A \n1 A/G A/A A/G A/A G/G \n2 A/G A/A G/G A/A A/G \n3 G/G A/A G/G A/A A/G \n4 A/G A/A G/G A/A A/A \n\n 0-10116-01-165 0-10151-01-86 0-10162-01-255 0-10207-01-280 0-10210-01-41 \\\n0 A/A A/A A/A A/C ?/? \n1 A/A A/A A/A A/C A/A \n2 A/A A/A A/G A/A A/A \n3 A/A A/A A/A C/C A/A \n4 A/A A/A ?/? A/C ?/? \n\n ... UMN-CL299Contig1-01-46 UMN-CL306Contig1-04-261 \\\n0 ... A/A T/T \n1 ... A/A T/T \n2 ... A/A T/T \n3 ... A/A T/T \n4 ... A/A T/T \n\n UMN-CL307Contig1-04-143 UMN-CL319Contig1-03-131 UMN-CL326Contig1-05-421 \\\n0 C/C A/C A/G \n1 C/C C/C G/G \n2 C/G C/C G/G \n3 C/C C/C G/G \n4 C/G C/C G/G \n\n UMN-CL339Contig1-05-39 UMN-CL34Contig1-03-89 UMN-CL353Contig1-04-64 \\\n0 A/A C/G A/A \n1 A/A G/G A/A \n2 A/A G/G A/A \n3 A/A C/G A/A \n4 A/A C/G A/A \n\n UMN-CL362Contig1-07-133 UMN-CL363Contig1-01-233 UMN-CL379Contig1-12-117 \\\n0 ?/? G/G A/A \n1 A/A G/G A/A \n2 A/A G/G A/A \n3 ?/? A/G A/A \n4 C/C A/G A/A \n\n UMN-CL424Contig1-03-94 UMN-CL54Contig1-07-88 UMN-CL91Contig1-02-246 \\\n0 C/C G/G C/C \n1 A/C A/G C/C \n2 A/C G/G C/C \n3 A/C A/G C/C \n4 A/C G/G A/C \n\n UMN-CL97Contig \n0 A/G \n1 A/A \n2 G/G \n3 G/G \n4 G/G \n\n[5 rows x 3082 columns]",
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},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "genotypes.shape",
"execution_count": 372,
"outputs": [
{
"execution_count": 372,
"output_type": "execute_result",
"data": {
"text/plain": "(622, 3082)"
},
"metadata": {}
}
]
},
{
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},
"cell_type": "code",
"source": "def is_homozygous(gt):\n if len(set([x.strip() for x in gt.split(\"/\")])) == 1:\n return True\n return False\n\ndef get_allele_counts(counts):\n a = {}\n het = 0\n for gt in counts.index:\n for allele in [x.strip() for x in gt.split(\"/\")]:\n if not allele in a:\n a[allele] = 0\n a[allele] += counts[gt]\n if not is_homozygous(gt):\n het += counts[gt]\n return sorted(a.items(), key=lambda x: x[1], reverse=True), het\n\ndef get_correction(n):\n #for finite sample size\n return (2*n)/(2*n-1)\n\ndef get_allele_freqs(locus):\n locus = locus[locus != '?/?']\n locus = locus[locus != 'NA']\n c = locus.value_counts()\n c = c.sort(inplace=False, ascending=False)\n allele_counts = get_allele_counts(c)\n total_alleles = 2.0*sum(c)\n num_individuals = sum(c)\n A = \"\"\n a = \"\"\n P = 0\n Q = 0\n if len(allele_counts[0]) == 2:\n A = allele_counts[0][0][0]\n a = allele_counts[0][1][0]\n P = allele_counts[0][0][1]\n Q = allele_counts[0][1][1]\n else:\n A = allele_counts[0][0][0]\n P = P = allele_counts[0][0][1]\n PQ = allele_counts[-1]\n p = P/total_alleles\n q = Q/total_alleles\n assert p + q == 1.0\n He = 2 * p * q * get_correction(num_individuals)\n Ho = PQ*1.0/num_individuals\n Fis = 1 - (Ho/He)\n #print p, q, He, Ho, Fis\n ret = pd.Series({\"p\":p, \n \"q\":q,\n \"P\":P,\n \"Q\":Q,\n \"He\":He,\n \"Ho\":Ho, \n \"Fis\":Fis,\n \"PQ\": PQ,\n \"total_alleles\":total_alleles,\n \"num_indiv\":num_individuals,\n \"A\":A,\n \"a\":a})\n return ret\n#genotypes.ix[:,0:2].apply(get_allele_freqs)",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "af = genotypes.apply(get_allele_freqs)",
"execution_count": 12,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "genotypes",
"execution_count": 369,
"outputs": [
{
"execution_count": 369,
"output_type": "execute_result",
"data": {
"text/plain": "['G/G', 'G/A', 'A/A']"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "af.ix[:,0:5]",
"execution_count": 13,
"outputs": [
{
"execution_count": 13,
"output_type": "execute_result",
"data": {
"text/plain": " 0-10037-01-257 0-10040-02-394 0-10044-01-392 0-10048-01-60 \\\nA A C C G \nFis -0.03216907 0.02119724 0.09921339 0.1181548 \nHe 0.4090983 0.4999238 0.5004262 0.450996 \nHo 0.4222586 0.4893268 0.4507772 0.3977087 \nP 872 628 581 803 \nPQ 258 298 261 243 \nQ 350 590 577 419 \na G A G A \nnum_indiv 611 609 579 611 \np 0.7135843 0.5155993 0.5017271 0.6571195 \nq 0.2864157 0.4844007 0.4982729 0.3428805 \ntotal_alleles 1222 1218 1158 1222 \n\n 0-10051-02-166 \nA G \nFis 0.03167254 \nHe 0.1261452 \nHo 0.1221498 \nP 1145 \nPQ 75 \nQ 83 \na A \nnum_indiv 614 \np 0.9324104 \nq 0.06758958 \ntotal_alleles 1228 ",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>A</th>\n <td> A</td>\n <td> C</td>\n <td> C</td>\n <td> G</td>\n <td> G</td>\n </tr>\n <tr>\n <th>Fis</th>\n <td>-0.03216907</td>\n <td> 0.02119724</td>\n <td> 0.09921339</td>\n <td> 0.1181548</td>\n <td> 0.03167254</td>\n </tr>\n <tr>\n <th>He</th>\n <td> 0.4090983</td>\n <td> 0.4999238</td>\n <td> 0.5004262</td>\n <td> 0.450996</td>\n <td> 0.1261452</td>\n </tr>\n <tr>\n <th>Ho</th>\n <td> 0.4222586</td>\n <td> 0.4893268</td>\n <td> 0.4507772</td>\n <td> 0.3977087</td>\n <td> 0.1221498</td>\n </tr>\n <tr>\n <th>P</th>\n <td> 872</td>\n <td> 628</td>\n <td> 581</td>\n <td> 803</td>\n <td> 1145</td>\n </tr>\n <tr>\n <th>PQ</th>\n <td> 258</td>\n <td> 298</td>\n <td> 261</td>\n <td> 243</td>\n <td> 75</td>\n </tr>\n <tr>\n <th>Q</th>\n <td> 350</td>\n <td> 590</td>\n <td> 577</td>\n <td> 419</td>\n <td> 83</td>\n </tr>\n <tr>\n <th>a</th>\n <td> G</td>\n <td> A</td>\n <td> G</td>\n <td> A</td>\n <td> A</td>\n </tr>\n <tr>\n <th>num_indiv</th>\n <td> 611</td>\n <td> 609</td>\n <td> 579</td>\n <td> 611</td>\n <td> 614</td>\n </tr>\n <tr>\n <th>p</th>\n <td> 0.7135843</td>\n <td> 0.5155993</td>\n <td> 0.5017271</td>\n <td> 0.6571195</td>\n <td> 0.9324104</td>\n </tr>\n <tr>\n <th>q</th>\n <td> 0.2864157</td>\n <td> 0.4844007</td>\n <td> 0.4982729</td>\n <td> 0.3428805</td>\n <td> 0.06758958</td>\n </tr>\n <tr>\n <th>total_alleles</th>\n <td> 1222</td>\n <td> 1218</td>\n <td> 1158</td>\n <td> 1222</td>\n <td> 1228</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def plot_hist(df, index):\n d = df.ix[index,:]\n plt.hist(d, bins=20)\n plt.title(\"%s %.2f $\\pm$ %.3f [%.2f, %.2f]\" % (index, \n np.mean(d), \n np.std(d),\n np.min(d),\n np.max(d)))\n plt.show()\nplot_hist(af, \"Fis\")",
"execution_count": 1215,
"outputs": [
{
"output_type": "display_data",
"data": {
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KYriTJEkqiOFOkiSpIIY7SZKkghjuJEmSCmK4kyRJKojhTpIkqSCGO0mSpIIY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\nJ0mSVBDDnSRJUkEMd5IkSQX5//ti8Cv/zJkIAAAAAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fde5342d050>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def convert_to_z12(locus):\n freq = af[locus.name]\n trans = {\"%s/%s\" % (freq[\"A\"],freq[\"A\"]): 0,\n \"%s/%s\" % (freq[\"a\"],freq[\"a\"]): 2,\n \"%s/%s\" % (freq[\"A\"],freq[\"a\"]): 1,\n \"%s/%s\" % (freq[\"a\"],freq[\"A\"]): 1,\n \"?/?\":-1}\n return locus.apply(lambda x: trans[x])\nz12 = genotypes.apply(convert_to_z12)",
"execution_count": 15,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def center_and_standardize_value(val, u, var):\n if val == -1:\n return 0.0\n return (val-u)/np.sqrt(var)\n\ndef center_and_standardize(snp):\n maf = af.ix[\"q\",snp.name]\n u = np.mean([x for x in snp if x != -1])\n var = np.sqrt(maf*(1-maf))\n return snp.apply(center_and_standardize_value, args=(u, var))",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "pca_std = z12.apply(center_and_standardize)\npca_std.apply(np.mean)",
"execution_count": 17,
"outputs": [
{
"execution_count": 17,
"output_type": "execute_result",
"data": {
"text/plain": "0-10037-01-257 3.855437e-17\n0-10040-02-394 -1.713527e-17\n0-10044-01-392 1.285146e-16\n0-10048-01-60 4.283819e-17\n0-10051-02-166 1.427940e-17\n0-10054-01-402 -1.427940e-17\n0-10067-03-111 -5.711758e-18\n0-10079-02-168 5.140582e-17\n0-10112-01-169 2.998673e-17\n0-10113-01-119 -8.567637e-17\n0-10116-01-165 -7.139698e-18\n0-10151-01-86 5.140582e-17\n0-10162-01-255 3.569849e-18\n0-10207-01-280 -6.282934e-17\n0-10210-01-41 0.000000e+00\n...\nUMN-CL299Contig1-01-46 1.142352e-17\nUMN-CL306Contig1-04-261 -8.567637e-18\nUMN-CL307Contig1-04-143 3.712643e-17\nUMN-CL319Contig1-03-131 1.999115e-17\nUMN-CL326Contig1-05-421 -4.569407e-17\nUMN-CL339Contig1-05-39 -1.713527e-17\nUMN-CL34Contig1-03-89 -9.709989e-17\nUMN-CL353Contig1-04-64 1.320844e-17\nUMN-CL362Contig1-07-133 3.141467e-17\nUMN-CL363Contig1-01-233 -3.284261e-17\nUMN-CL379Contig1-12-117 -1.142352e-17\nUMN-CL424Contig1-03-94 -5.140582e-17\nUMN-CL54Contig1-07-88 -5.426170e-17\nUMN-CL91Contig1-02-246 7.996462e-17\nUMN-CL97Contig 4.283819e-17\nLength: 3082, dtype: float64"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "r = ro.r",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "prcomp = r('prcomp')\nsummary = r('summary')",
"execution_count": 19,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "prcomp_res = prcomp(pca_std, scale=False, center=False)",
"execution_count": 20,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "print summary(prcomp_res)",
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Importance of components:\n PC1 PC2 PC3 PC4 PC5 PC6 PC7\nStandard deviation 6.07193 4.20751 3.79014 3.20076 3.08532 3.04595 2.96424\nProportion of Variance 0.01789 0.00859 0.00697 0.00497 0.00462 0.00450 0.00426\nCumulative Proportion 0.01789 0.02649 0.03346 0.03843 0.04305 0.04755 0.05182\n PC8 PC9 PC10 PC11 PC12 PC13 PC14\nStandard deviation 2.93988 2.89841 2.88317 2.83414 2.82524 2.80280 2.79039\nProportion of Variance 0.00419 0.00408 0.00403 0.00390 0.00387 0.00381 0.00378\nCumulative Proportion 0.05601 0.06009 0.06412 0.06802 0.07190 0.07571 0.07949\n PC15 PC16 PC17 PC18 PC19 PC20 PC21\nStandard deviation 2.75606 2.74385 2.73991 2.72647 2.71721 2.70782 2.6857\nProportion of Variance 0.00369 0.00365 0.00364 0.00361 0.00358 0.00356 0.0035\nCumulative Proportion 0.08317 0.08683 0.09047 0.09408 0.09766 0.10122 0.1047\n PC22 PC23 PC24 PC25 PC26 PC27 PC28\nStandard deviation 2.67354 2.66834 2.66206 2.65313 2.64125 2.63434 2.62303\nProportion of Variance 0.00347 0.00346 0.00344 0.00342 0.00339 0.00337 0.00334\nCumulative Proportion 0.10819 0.11165 0.11509 0.11850 0.12189 0.12526 0.12860\n PC29 PC30 PC31 PC32 PC33 PC34 PC35\nStandard deviation 2.62150 2.6056 2.60081 2.59788 2.58517 2.58116 2.57338\nProportion of Variance 0.00334 0.0033 0.00328 0.00328 0.00324 0.00323 0.00321\nCumulative Proportion 0.13193 0.1352 0.13851 0.14179 0.14503 0.14826 0.15148\n PC36 PC37 PC38 PC39 PC40 PC41 PC42\nStandard deviation 2.56472 2.56032 2.55498 2.54940 2.54408 2.54019 2.5262\nProportion of Variance 0.00319 0.00318 0.00317 0.00315 0.00314 0.00313 0.0031\nCumulative Proportion 0.15467 0.15785 0.16102 0.16417 0.16731 0.17045 0.1735\n PC43 PC44 PC45 PC46 PC47 PC48 PC49\nStandard deviation 2.52336 2.51833 2.50697 2.50208 2.50140 2.49100 2.4870\nProportion of Variance 0.00309 0.00308 0.00305 0.00304 0.00304 0.00301 0.0030\nCumulative Proportion 0.17663 0.17971 0.18276 0.18580 0.18884 0.19185 0.1948\n PC50 PC51 PC52 PC53 PC54 PC55 PC56\nStandard deviation 2.48189 2.47526 2.47445 2.46884 2.46450 2.46135 2.44698\nProportion of Variance 0.00299 0.00297 0.00297 0.00296 0.00295 0.00294 0.00291\nCumulative Proportion 0.19784 0.20081 0.20379 0.20674 0.20969 0.21263 0.21554\n PC57 PC58 PC59 PC60 PC61 PC62 PC63\nStandard deviation 2.4446 2.43970 2.43748 2.43447 2.42718 2.42174 2.41902\nProportion of Variance 0.0029 0.00289 0.00288 0.00288 0.00286 0.00285 0.00284\nCumulative Proportion 0.2184 0.22133 0.22421 0.22709 0.22995 0.23279 0.23563\n PC64 PC65 PC66 PC67 PC68 PC69 PC70\nStandard deviation 2.41498 2.4037 2.39800 2.39696 2.39276 2.38981 2.38419\nProportion of Variance 0.00283 0.0028 0.00279 0.00279 0.00278 0.00277 0.00276\nCumulative Proportion 0.23846 0.2413 0.24406 0.24685 0.24963 0.25240 0.25516\n PC71 PC72 PC73 PC74 PC75 PC76 PC77\nStandard deviation 2.38277 2.37708 2.37680 2.36945 2.36841 2.36540 2.36127\nProportion of Variance 0.00276 0.00274 0.00274 0.00272 0.00272 0.00272 0.00271\nCumulative Proportion 0.25791 0.26066 0.26340 0.26612 0.26884 0.27156 0.27427\n PC78 PC79 PC80 PC81 PC82 PC83 PC84\nStandard deviation 2.3586 2.35567 2.34959 2.34706 2.34345 2.34086 2.33637\nProportion of Variance 0.0027 0.00269 0.00268 0.00267 0.00267 0.00266 0.00265\nCumulative Proportion 0.2770 0.27966 0.28234 0.28501 0.28768 0.29034 0.29299\n PC85 PC86 PC87 PC88 PC89 PC90 PC91\nStandard deviation 2.32823 2.32634 2.32191 2.3167 2.3134 2.31117 2.30806\nProportion of Variance 0.00263 0.00263 0.00262 0.0026 0.0026 0.00259 0.00259\nCumulative Proportion 0.29562 0.29824 0.30086 0.3035 0.3061 0.30866 0.31124\n PC92 PC93 PC94 PC95 PC96 PC97 PC98\nStandard deviation 2.30513 2.29617 2.29411 2.28684 2.28492 2.28241 2.27992\nProportion of Variance 0.00258 0.00256 0.00255 0.00254 0.00253 0.00253 0.00252\nCumulative Proportion 0.31382 0.31638 0.31893 0.32147 0.32401 0.32653 0.32906\n PC99 PC100 PC101 PC102 PC103 PC104 PC105\nStandard deviation 2.27338 2.27275 2.2698 2.26511 2.26131 2.25865 2.25193\nProportion of Variance 0.00251 0.00251 0.0025 0.00249 0.00248 0.00248 0.00246\nCumulative Proportion 0.33156 0.33407 0.3366 0.33906 0.34154 0.34402 0.34648\n PC106 PC107 PC108 PC109 PC110 PC111 PC112\nStandard deviation 2.24812 2.24520 2.24300 2.24108 2.23781 2.23475 2.23229\nProportion of Variance 0.00245 0.00245 0.00244 0.00244 0.00243 0.00242 0.00242\nCumulative Proportion 0.34893 0.35138 0.35382 0.35626 0.35869 0.36111 0.36353\n PC113 PC114 PC115 PC116 PC117 PC118 PC119\nStandard deviation 2.22785 2.2223 2.2215 2.21866 2.21437 2.21244 2.20828\nProportion of Variance 0.00241 0.0024 0.0024 0.00239 0.00238 0.00238 0.00237\nCumulative Proportion 0.36594 0.3683 0.3707 0.37312 0.37550 0.37788 0.38025\n PC120 PC121 PC122 PC123 PC124 PC125 PC126\nStandard deviation 2.20470 2.20139 2.19967 2.19576 2.19273 2.18979 2.18800\nProportion of Variance 0.00236 0.00235 0.00235 0.00234 0.00233 0.00233 0.00232\nCumulative Proportion 0.38260 0.38496 0.38730 0.38964 0.39198 0.39431 0.39663\n PC127 PC128 PC129 PC130 PC131 PC132 PC133\nStandard deviation 2.18583 2.18415 2.1767 2.1755 2.16959 2.16859 2.16344\nProportion of Variance 0.00232 0.00232 0.0023 0.0023 0.00228 0.00228 0.00227\nCumulative Proportion 0.39895 0.40126 0.4036 0.4059 0.40814 0.41043 0.41270\n PC134 PC135 PC136 PC137 PC138 PC139 PC140\nStandard deviation 2.16009 2.15877 2.15304 2.14991 2.14825 2.14372 2.14069\nProportion of Variance 0.00226 0.00226 0.00225 0.00224 0.00224 0.00223 0.00222\nCumulative Proportion 0.41496 0.41722 0.41947 0.42172 0.42396 0.42619 0.42841\n PC141 PC142 PC143 PC144 PC145 PC146 PC147\nStandard deviation 2.13764 2.13262 2.1310 2.1293 2.1272 2.11931 2.11776\nProportion of Variance 0.00222 0.00221 0.0022 0.0022 0.0022 0.00218 0.00218\nCumulative Proportion 0.43063 0.43284 0.4350 0.4372 0.4394 0.44162 0.44379\n PC148 PC149 PC150 PC151 PC152 PC153 PC154\nStandard deviation 2.11641 2.11504 2.11306 2.10972 2.10262 2.09904 2.09536\nProportion of Variance 0.00217 0.00217 0.00217 0.00216 0.00215 0.00214 0.00213\nCumulative Proportion 0.44597 0.44814 0.45031 0.45247 0.45461 0.45675 0.45888\n PC155 PC156 PC157 PC158 PC159 PC160 PC161\nStandard deviation 2.09299 2.09106 2.08920 2.08685 2.08452 2.0790 2.07592\nProportion of Variance 0.00213 0.00212 0.00212 0.00211 0.00211 0.0021 0.00209\nCumulative Proportion 0.46101 0.46313 0.46525 0.46736 0.46947 0.4716 0.47366\n PC162 PC163 PC164 PC165 PC166 PC167 PC168\nStandard deviation 2.07367 2.07173 2.06784 2.06548 2.06158 2.06041 2.05555\nProportion of Variance 0.00209 0.00208 0.00208 0.00207 0.00206 0.00206 0.00205\nCumulative Proportion 0.47575 0.47783 0.47991 0.48198 0.48404 0.48610 0.48815\n PC169 PC170 PC171 PC172 PC173 PC174 PC175\nStandard deviation 2.05354 2.05144 2.04852 2.04580 2.04421 2.04231 2.04047\nProportion of Variance 0.00205 0.00204 0.00204 0.00203 0.00203 0.00202 0.00202\nCumulative Proportion 0.49020 0.49224 0.49428 0.49631 0.49834 0.50036 0.50238\n PC176 PC177 PC178 PC179 PC180 PC181 PC182\nStandard deviation 2.03752 2.03654 2.0317 2.0304 2.02531 2.02400 2.02031\nProportion of Variance 0.00201 0.00201 0.0020 0.0020 0.00199 0.00199 0.00198\nCumulative Proportion 0.50440 0.50641 0.5084 0.5104 0.51240 0.51439 0.51637\n PC183 PC184 PC185 PC186 PC187 PC188 PC189\nStandard deviation 2.01939 2.01214 2.00975 2.00705 2.00602 2.00300 1.99826\nProportion of Variance 0.00198 0.00197 0.00196 0.00196 0.00195 0.00195 0.00194\nCumulative Proportion 0.51835 0.52032 0.52228 0.52423 0.52619 0.52813 0.53007\n PC190 PC191 PC192 PC193 PC194 PC195 PC196\nStandard deviation 1.99701 1.99326 1.99100 1.98977 1.98761 1.98118 1.9799\nProportion of Variance 0.00194 0.00193 0.00192 0.00192 0.00192 0.00191 0.0019\nCumulative Proportion 0.53201 0.53394 0.53586 0.53778 0.53970 0.54160 0.5435\n PC197 PC198 PC199 PC200 PC201 PC202 PC203\nStandard deviation 1.9782 1.97523 1.97181 1.96896 1.96787 1.96575 1.96473\nProportion of Variance 0.0019 0.00189 0.00189 0.00188 0.00188 0.00188 0.00187\nCumulative Proportion 0.5454 0.54730 0.54919 0.55107 0.55295 0.55482 0.55670\n PC204 PC205 PC206 PC207 PC208 PC209 PC210\nStandard deviation 1.96149 1.95933 1.95472 1.95165 1.95119 1.94478 1.94270\nProportion of Variance 0.00187 0.00186 0.00185 0.00185 0.00185 0.00184 0.00183\nCumulative Proportion 0.55856 0.56043 0.56228 0.56413 0.56598 0.56781 0.56964\n PC211 PC212 PC213 PC214 PC215 PC216 PC217\nStandard deviation 1.93959 1.93909 1.93483 1.93262 1.92923 1.9270 1.9255\nProportion of Variance 0.00183 0.00182 0.00182 0.00181 0.00181 0.0018 0.0018\nCumulative Proportion 0.57147 0.57329 0.57511 0.57692 0.57873 0.5805 0.5823\n PC218 PC219 PC220 PC221 PC222 PC223 PC224\nStandard deviation 1.92204 1.91830 1.91603 1.91451 1.91171 1.90946 1.90506\nProportion of Variance 0.00179 0.00179 0.00178 0.00178 0.00177 0.00177 0.00176\nCumulative Proportion 0.58413 0.58591 0.58769 0.58947 0.59125 0.59302 0.59478\n PC225 PC226 PC227 PC228 PC229 PC230 PC231\nStandard deviation 1.90342 1.90091 1.89775 1.89344 1.89219 1.88924 1.88755\nProportion of Variance 0.00176 0.00175 0.00175 0.00174 0.00174 0.00173 0.00173\nCumulative Proportion 0.59654 0.59829 0.60004 0.60178 0.60351 0.60525 0.60698\n PC232 PC233 PC234 PC235 PC236 PC237 PC238\nStandard deviation 1.88361 1.88316 1.88038 1.87986 1.87657 1.8715 1.8711\nProportion of Variance 0.00172 0.00172 0.00172 0.00172 0.00171 0.0017 0.0017\nCumulative Proportion 0.60870 0.61042 0.61214 0.61385 0.61556 0.6173 0.6190\n PC239 PC240 PC241 PC242 PC243 PC244 PC245\nStandard deviation 1.86705 1.86286 1.86179 1.85717 1.85505 1.85346 1.85246\nProportion of Variance 0.00169 0.00168 0.00168 0.00167 0.00167 0.00167 0.00167\nCumulative Proportion 0.62065 0.62233 0.62402 0.62569 0.62736 0.62903 0.63069\n PC246 PC247 PC248 PC249 PC250 PC251 PC252\nStandard deviation 1.84834 1.84734 1.84310 1.84122 1.83682 1.83593 1.83305\nProportion of Variance 0.00166 0.00166 0.00165 0.00165 0.00164 0.00164 0.00163\nCumulative Proportion 0.63235 0.63401 0.63566 0.63730 0.63894 0.64058 0.64221\n PC253 PC254 PC255 PC256 PC257 PC258 PC259\nStandard deviation 1.83005 1.82754 1.82416 1.82296 1.82092 1.8185 1.8146\nProportion of Variance 0.00163 0.00162 0.00162 0.00161 0.00161 0.0016 0.0016\nCumulative Proportion 0.64383 0.64545 0.64707 0.64868 0.65029 0.6519 0.6535\n PC260 PC261 PC262 PC263 PC264 PC265 PC266\nStandard deviation 1.8130 1.81103 1.80835 1.80698 1.80449 1.80089 1.79898\nProportion of Variance 0.0016 0.00159 0.00159 0.00158 0.00158 0.00157 0.00157\nCumulative Proportion 0.6551 0.65668 0.65827 0.65985 0.66143 0.66301 0.66458\n PC267 PC268 PC269 PC270 PC271 PC272 PC273\nStandard deviation 1.79647 1.79428 1.79295 1.78709 1.78582 1.78422 1.78366\nProportion of Variance 0.00157 0.00156 0.00156 0.00155 0.00155 0.00155 0.00154\nCumulative Proportion 0.66614 0.66771 0.66927 0.67082 0.67236 0.67391 0.67545\n PC274 PC275 PC276 PC277 PC278 PC279 PC280\nStandard deviation 1.77856 1.77821 1.77479 1.77156 1.76977 1.76688 1.76339\nProportion of Variance 0.00154 0.00153 0.00153 0.00152 0.00152 0.00152 0.00151\nCumulative Proportion 0.67699 0.67852 0.68005 0.68158 0.68310 0.68461 0.68612\n PC281 PC282 PC283 PC284 PC285 PC286 PC287\nStandard deviation 1.76163 1.7589 1.7581 1.7571 1.75062 1.74938 1.74840\nProportion of Variance 0.00151 0.0015 0.0015 0.0015 0.00149 0.00149 0.00148\nCumulative Proportion 0.68763 0.6891 0.6906 0.6921 0.69361 0.69510 0.69658\n PC288 PC289 PC290 PC291 PC292 PC293 PC294\nStandard deviation 1.74700 1.74474 1.74364 1.73735 1.73661 1.73448 1.72962\nProportion of Variance 0.00148 0.00148 0.00148 0.00146 0.00146 0.00146 0.00145\nCumulative Proportion 0.69806 0.69954 0.70102 0.70248 0.70395 0.70541 0.70686\n PC295 PC296 PC297 PC298 PC299 PC300 PC301\nStandard deviation 1.72735 1.72641 1.72240 1.72099 1.71936 1.71630 1.71387\nProportion of Variance 0.00145 0.00145 0.00144 0.00144 0.00143 0.00143 0.00143\nCumulative Proportion 0.70831 0.70975 0.71119 0.71263 0.71406 0.71549 0.71692\n PC302 PC303 PC304 PC305 PC306 PC307 PC308\nStandard deviation 1.71190 1.70859 1.70708 1.70306 1.70230 1.7006 1.6983\nProportion of Variance 0.00142 0.00142 0.00141 0.00141 0.00141 0.0014 0.0014\nCumulative Proportion 0.71834 0.71976 0.72117 0.72258 0.72399 0.7254 0.7268\n PC309 PC310 PC311 PC312 PC313 PC314 PC315\nStandard deviation 1.69395 1.69284 1.68963 1.68870 1.68390 1.68123 1.67894\nProportion of Variance 0.00139 0.00139 0.00139 0.00138 0.00138 0.00137 0.00137\nCumulative Proportion 0.72818 0.72957 0.73096 0.73234 0.73372 0.73509 0.73646\n PC316 PC317 PC318 PC319 PC320 PC321 PC322\nStandard deviation 1.67650 1.67284 1.67028 1.66885 1.66680 1.66354 1.66156\nProportion of Variance 0.00136 0.00136 0.00135 0.00135 0.00135 0.00134 0.00134\nCumulative Proportion 0.73782 0.73918 0.74054 0.74189 0.74324 0.74458 0.74592\n PC323 PC324 PC325 PC326 PC327 PC328 PC329\nStandard deviation 1.66040 1.65961 1.65717 1.65438 1.65296 1.64908 1.64713\nProportion of Variance 0.00134 0.00134 0.00133 0.00133 0.00133 0.00132 0.00132\nCumulative Proportion 0.74726 0.74859 0.74993 0.75126 0.75258 0.75390 0.75522\n PC330 PC331 PC332 PC333 PC334 PC335 PC336\nStandard deviation 1.64344 1.64103 1.64053 1.6388 1.6347 1.63295 1.62973\nProportion of Variance 0.00131 0.00131 0.00131 0.0013 0.0013 0.00129 0.00129\nCumulative Proportion 0.75653 0.75784 0.75914 0.7604 0.7617 0.76304 0.76433\n PC337 PC338 PC339 PC340 PC341 PC342 PC343\nStandard deviation 1.62656 1.62576 1.62381 1.62108 1.61565 1.61364 1.61072\nProportion of Variance 0.00128 0.00128 0.00128 0.00128 0.00127 0.00126 0.00126\nCumulative Proportion 0.76561 0.76689 0.76817 0.76945 0.77072 0.77198 0.77324\n PC344 PC345 PC346 PC347 PC348 PC349 PC350\nStandard deviation 1.60914 1.60810 1.60584 1.60275 1.60121 1.59932 1.59750\nProportion of Variance 0.00126 0.00126 0.00125 0.00125 0.00124 0.00124 0.00124\nCumulative Proportion 0.77449 0.77575 0.77700 0.77825 0.77949 0.78073 0.78197\n PC351 PC352 PC353 PC354 PC355 PC356 PC357\nStandard deviation 1.59305 1.59113 1.58959 1.58868 1.58643 1.58416 1.58156\nProportion of Variance 0.00123 0.00123 0.00123 0.00122 0.00122 0.00122 0.00121\nCumulative Proportion 0.78320 0.78443 0.78566 0.78688 0.78811 0.78932 0.79054\n PC358 PC359 PC360 PC361 PC362 PC363 PC364\nStandard deviation 1.57974 1.57693 1.57607 1.5737 1.5707 1.56897 1.56513\nProportion of Variance 0.00121 0.00121 0.00121 0.0012 0.0012 0.00119 0.00119\nCumulative Proportion 0.79175 0.79296 0.79416 0.7954 0.7966 0.79776 0.79894\n PC365 PC366 PC367 PC368 PC369 PC370 PC371\nStandard deviation 1.56265 1.56049 1.55918 1.55815 1.55296 1.55251 1.55033\nProportion of Variance 0.00119 0.00118 0.00118 0.00118 0.00117 0.00117 0.00117\nCumulative Proportion 0.80013 0.80131 0.80249 0.80367 0.80484 0.80601 0.80718\n PC372 PC373 PC374 PC375 PC376 PC377 PC378\nStandard deviation 1.54932 1.54672 1.54415 1.54243 1.53930 1.53740 1.53640\nProportion of Variance 0.00117 0.00116 0.00116 0.00115 0.00115 0.00115 0.00115\nCumulative Proportion 0.80834 0.80950 0.81066 0.81181 0.81296 0.81411 0.81526\n PC379 PC380 PC381 PC382 PC383 PC384 PC385\nStandard deviation 1.53490 1.53240 1.52809 1.52498 1.52316 1.52098 1.51708\nProportion of Variance 0.00114 0.00114 0.00113 0.00113 0.00113 0.00112 0.00112\nCumulative Proportion 0.81640 0.81754 0.81867 0.81980 0.82093 0.82205 0.82317\n PC386 PC387 PC388 PC389 PC390 PC391 PC392\nStandard deviation 1.51562 1.51452 1.51189 1.50970 1.5064 1.5035 1.50154\nProportion of Variance 0.00111 0.00111 0.00111 0.00111 0.0011 0.0011 0.00109\nCumulative Proportion 0.82428 0.82540 0.82651 0.82761 0.8287 0.8298 0.83090\n PC393 PC394 PC395 PC396 PC397 PC398 PC399\nStandard deviation 1.49855 1.49655 1.49413 1.49161 1.49063 1.48747 1.48468\nProportion of Variance 0.00109 0.00109 0.00108 0.00108 0.00108 0.00107 0.00107\nCumulative Proportion 0.83199 0.83308 0.83417 0.83525 0.83632 0.83740 0.83847\n PC400 PC401 PC402 PC403 PC404 PC405 PC406\nStandard deviation 1.48328 1.48237 1.48038 1.47582 1.47461 1.47215 1.47019\nProportion of Variance 0.00107 0.00107 0.00106 0.00106 0.00106 0.00105 0.00105\nCumulative Proportion 0.83953 0.84060 0.84167 0.84272 0.84378 0.84483 0.84588\n PC407 PC408 PC409 PC410 PC411 PC412 PC413\nStandard deviation 1.46635 1.46279 1.46140 1.45998 1.45842 1.45645 1.45505\nProportion of Variance 0.00104 0.00104 0.00104 0.00103 0.00103 0.00103 0.00103\nCumulative Proportion 0.84692 0.84796 0.84900 0.85003 0.85106 0.85209 0.85312\n PC414 PC415 PC416 PC417 PC418 PC419 PC420\nStandard deviation 1.45158 1.45039 1.44737 1.44510 1.44358 1.44116 1.4384\nProportion of Variance 0.00102 0.00102 0.00102 0.00101 0.00101 0.00101 0.0010\nCumulative Proportion 0.85414 0.85516 0.85618 0.85719 0.85821 0.85921 0.8602\n PC421 PC422 PC423 PC424 PC425 PC426 PC427\nStandard deviation 1.4356 1.4332 1.43039 1.42888 1.42843 1.42486 1.42333\nProportion of Variance 0.0010 0.0010 0.00099 0.00099 0.00099 0.00099 0.00098\nCumulative Proportion 0.8612 0.8622 0.86321 0.86420 0.86519 0.86618 0.86716\n PC428 PC429 PC430 PC431 PC432 PC433 PC434\nStandard deviation 1.41960 1.41679 1.41491 1.41379 1.41242 1.40943 1.40694\nProportion of Variance 0.00098 0.00097 0.00097 0.00097 0.00097 0.00096 0.00096\nCumulative Proportion 0.86814 0.86911 0.87008 0.87105 0.87202 0.87299 0.87395\n PC435 PC436 PC437 PC438 PC439 PC440 PC441\nStandard deviation 1.40394 1.40279 1.40006 1.39811 1.39599 1.39309 1.39174\nProportion of Variance 0.00096 0.00096 0.00095 0.00095 0.00095 0.00094 0.00094\nCumulative Proportion 0.87490 0.87586 0.87681 0.87776 0.87870 0.87965 0.88059\n PC442 PC443 PC444 PC445 PC446 PC447 PC448\nStandard deviation 1.38968 1.38532 1.38342 1.38164 1.37657 1.37595 1.37217\nProportion of Variance 0.00094 0.00093 0.00093 0.00093 0.00092 0.00092 0.00091\nCumulative Proportion 0.88152 0.88245 0.88338 0.88431 0.88523 0.88615 0.88706\n PC449 PC450 PC451 PC452 PC453 PC454 PC455\nStandard deviation 1.36971 1.36850 1.3655 1.3628 1.3620 1.3599 1.35618\nProportion of Variance 0.00091 0.00091 0.0009 0.0009 0.0009 0.0009 0.00089\nCumulative Proportion 0.88797 0.88888 0.8898 0.8907 0.8916 0.8925 0.89338\n PC456 PC457 PC458 PC459 PC460 PC461 PC462\nStandard deviation 1.35263 1.35196 1.34972 1.34764 1.34580 1.34332 1.34147\nProportion of Variance 0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087\nCumulative Proportion 0.89427 0.89515 0.89604 0.89692 0.89780 0.89867 0.89955\n PC463 PC464 PC465 PC466 PC467 PC468 PC469\nStandard deviation 1.33750 1.33721 1.33454 1.32991 1.32979 1.32854 1.32668\nProportion of Variance 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00085\nCumulative Proportion 0.90042 0.90128 0.90215 0.90301 0.90386 0.90472 0.90558\n PC470 PC471 PC472 PC473 PC474 PC475 PC476\nStandard deviation 1.32410 1.32119 1.31975 1.31701 1.31490 1.31164 1.30886\nProportion of Variance 0.00085 0.00085 0.00085 0.00084 0.00084 0.00083 0.00083\nCumulative Proportion 0.90643 0.90727 0.90812 0.90896 0.90980 0.91063 0.91147\n PC477 PC478 PC479 PC480 PC481 PC482 PC483\nStandard deviation 1.30854 1.30704 1.30369 1.29922 1.29889 1.29565 1.29523\nProportion of Variance 0.00083 0.00083 0.00082 0.00082 0.00082 0.00081 0.00081\nCumulative Proportion 0.91230 0.91313 0.91395 0.91477 0.91559 0.91640 0.91722\n PC484 PC485 PC486 PC487 PC488 PC489 PC490\nStandard deviation 1.29192 1.29008 1.2877 1.2833 1.27978 1.27882 1.27725\nProportion of Variance 0.00081 0.00081 0.0008 0.0008 0.00079 0.00079 0.00079\nCumulative Proportion 0.91803 0.91884 0.9196 0.9204 0.92123 0.92203 0.92282\n PC491 PC492 PC493 PC494 PC495 PC496 PC497\nStandard deviation 1.27330 1.27133 1.26837 1.26798 1.26374 1.26227 1.25829\nProportion of Variance 0.00079 0.00078 0.00078 0.00078 0.00078 0.00077 0.00077\nCumulative Proportion 0.92361 0.92439 0.92517 0.92595 0.92673 0.92750 0.92827\n PC498 PC499 PC500 PC501 PC502 PC503 PC504\nStandard deviation 1.25653 1.25535 1.25320 1.24898 1.24658 1.24500 1.24375\nProportion of Variance 0.00077 0.00076 0.00076 0.00076 0.00075 0.00075 0.00075\nCumulative Proportion 0.92904 0.92980 0.93056 0.93132 0.93207 0.93283 0.93358\n PC505 PC506 PC507 PC508 PC509 PC510 PC511\nStandard deviation 1.24177 1.23858 1.23782 1.23572 1.23244 1.23058 1.22851\nProportion of Variance 0.00075 0.00074 0.00074 0.00074 0.00074 0.00073 0.00073\nCumulative Proportion 0.93433 0.93507 0.93581 0.93656 0.93729 0.93803 0.93876\n PC512 PC513 PC514 PC515 PC516 PC517 PC518\nStandard deviation 1.22669 1.22394 1.22254 1.21792 1.21618 1.21275 1.21253\nProportion of Variance 0.00073 0.00073 0.00073 0.00072 0.00072 0.00071 0.00071\nCumulative Proportion 0.93949 0.94022 0.94094 0.94166 0.94238 0.94309 0.94381\n PC519 PC520 PC521 PC522 PC523 PC524 PC525\nStandard deviation 1.21019 1.20742 1.20729 1.2028 1.2001 1.1982 1.19533\nProportion of Variance 0.00071 0.00071 0.00071 0.0007 0.0007 0.0007 0.00069\nCumulative Proportion 0.94452 0.94523 0.94593 0.9466 0.9473 0.9480 0.94872\n PC526 PC527 PC528 PC529 PC530 PC531 PC532\nStandard deviation 1.19195 1.18793 1.18662 1.18435 1.18200 1.17824 1.17749\nProportion of Variance 0.00069 0.00068 0.00068 0.00068 0.00068 0.00067 0.00067\nCumulative Proportion 0.94941 0.95010 0.95078 0.95146 0.95214 0.95282 0.95349\n PC533 PC534 PC535 PC536 PC537 PC538 PC539\nStandard deviation 1.17355 1.17108 1.16862 1.16720 1.16460 1.16294 1.15917\nProportion of Variance 0.00067 0.00067 0.00066 0.00066 0.00066 0.00066 0.00065\nCumulative Proportion 0.95416 0.95482 0.95549 0.95615 0.95680 0.95746 0.95811\n PC540 PC541 PC542 PC543 PC544 PC545 PC546\nStandard deviation 1.15770 1.15405 1.15218 1.14854 1.14765 1.14146 1.13728\nProportion of Variance 0.00065 0.00065 0.00064 0.00064 0.00064 0.00063 0.00063\nCumulative Proportion 0.95876 0.95941 0.96005 0.96069 0.96133 0.96197 0.96259\n PC547 PC548 PC549 PC550 PC551 PC552 PC553\nStandard deviation 1.13477 1.13328 1.12947 1.12777 1.12517 1.12139 1.11832\nProportion of Variance 0.00062 0.00062 0.00062 0.00062 0.00061 0.00061 0.00061\nCumulative Proportion 0.96322 0.96384 0.96446 0.96508 0.96569 0.96630 0.96691\n PC554 PC555 PC556 PC557 PC558 PC559 PC560\nStandard deviation 1.11662 1.1139 1.1093 1.10640 1.09994 1.09976 1.09705\nProportion of Variance 0.00061 0.0006 0.0006 0.00059 0.00059 0.00059 0.00058\nCumulative Proportion 0.96752 0.9681 0.9687 0.96931 0.96990 0.97048 0.97107\n PC561 PC562 PC563 PC564 PC565 PC566 PC567\nStandard deviation 1.09580 1.09218 1.09037 1.08494 1.08201 1.08092 1.07642\nProportion of Variance 0.00058 0.00058 0.00058 0.00057 0.00057 0.00057 0.00056\nCumulative Proportion 0.97165 0.97223 0.97281 0.97338 0.97395 0.97451 0.97508\n PC568 PC569 PC570 PC571 PC572 PC573 PC574\nStandard deviation 1.07366 1.06988 1.06893 1.06718 1.06193 1.05932 1.05763\nProportion of Variance 0.00056 0.00056 0.00055 0.00055 0.00055 0.00054 0.00054\nCumulative Proportion 0.97563 0.97619 0.97674 0.97730 0.97784 0.97839 0.97893\n PC575 PC576 PC577 PC578 PC579 PC580 PC581\nStandard deviation 1.05431 1.05353 1.04958 1.04530 1.03981 1.03950 1.03877\nProportion of Variance 0.00054 0.00054 0.00053 0.00053 0.00052 0.00052 0.00052\nCumulative Proportion 0.97947 0.98001 0.98055 0.98108 0.98160 0.98212 0.98265\n PC582 PC583 PC584 PC585 PC586 PC587 PC588\nStandard deviation 1.03475 1.03404 1.02786 1.02553 1.02488 1.0160 1.00982\nProportion of Variance 0.00052 0.00052 0.00051 0.00051 0.00051 0.0005 0.00049\nCumulative Proportion 0.98317 0.98369 0.98420 0.98471 0.98522 0.9857 0.98622\n PC589 PC590 PC591 PC592 PC593 PC594 PC595\nStandard deviation 1.00707 1.00421 1.00113 0.99715 0.99454 0.98658 0.98538\nProportion of Variance 0.00049 0.00049 0.00049 0.00048 0.00048 0.00047 0.00047\nCumulative Proportion 0.98671 0.98720 0.98768 0.98817 0.98865 0.98912 0.98959\n PC596 PC597 PC598 PC599 PC600 PC601 PC602\nStandard deviation 0.97969 0.97736 0.97340 0.97157 0.96478 0.96393 0.95886\nProportion of Variance 0.00047 0.00046 0.00046 0.00046 0.00045 0.00045 0.00045\nCumulative Proportion 0.99006 0.99052 0.99098 0.99144 0.99189 0.99234 0.99279\n PC603 PC604 PC605 PC606 PC607 PC608 PC609\nStandard deviation 0.95223 0.95115 0.94917 0.93894 0.93367 0.92958 0.92447\nProportion of Variance 0.00044 0.00044 0.00044 0.00043 0.00042 0.00042 0.00041\nCumulative Proportion 0.99323 0.99367 0.99410 0.99453 0.99495 0.99537 0.99579\n PC610 PC611 PC612 PC613 PC614 PC615 PC616\nStandard deviation 0.92051 0.91714 0.9106 0.9091 0.89817 0.88785 0.88454\nProportion of Variance 0.00041 0.00041 0.0004 0.0004 0.00039 0.00038 0.00038\nCumulative Proportion 0.99620 0.99661 0.9970 0.9974 0.99780 0.99819 0.99857\n PC617 PC618 PC619 PC620 PC621 PC622\nStandard deviation 0.87264 0.85493 0.7873 0.67124 0.62720 3.017e-15\nProportion of Variance 0.00037 0.00035 0.0003 0.00022 0.00019 0.000e+00\nCumulative Proportion 0.99893 0.99929 0.9996 0.99981 1.00000 1.000e+00\n\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "x = com.convert_robj(prcomp_res.rx2(\"x\"))\nx.index = pca_std.index\nx.ix[0:5,0:10]",
"execution_count": 1213,
"outputs": [
{
"execution_count": 1213,
"output_type": "execute_result",
"data": {
"text/plain": " PC1 PC2 PC3 PC4 PC5 PC6 PC7 \\\n0 -4.227659 -0.930500 1.853226 -2.682931 0.923642 -0.432286 2.962403 \n1 0.888892 -0.179881 -8.694134 -2.872412 -3.320970 1.533524 -1.861266 \n2 -3.319852 2.300243 1.670677 -9.666361 -9.595642 4.668615 -20.648915 \n3 -5.508931 -1.505746 5.299569 5.727700 -4.449550 -0.807838 2.172512 \n4 -6.334801 -0.261818 6.637892 5.268738 0.579049 -0.522321 0.370901 \n5 -3.629128 -0.334265 2.039921 -3.229526 -1.352967 5.201557 -0.460188 \n\n PC8 PC9 PC10 \n0 -1.233186 2.818495 -0.604278 \n1 -1.118133 -3.388559 1.083895 \n2 2.828419 13.892349 6.112814 \n3 5.269906 5.609469 2.110526 \n4 -3.753941 -0.828416 3.331711 \n5 2.400639 -3.360119 0.391390 ",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PC1</th>\n <th>PC2</th>\n <th>PC3</th>\n <th>PC4</th>\n <th>PC5</th>\n <th>PC6</th>\n <th>PC7</th>\n <th>PC8</th>\n <th>PC9</th>\n <th>PC10</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>-4.227659</td>\n <td>-0.930500</td>\n <td> 1.853226</td>\n <td>-2.682931</td>\n <td> 0.923642</td>\n <td>-0.432286</td>\n <td> 2.962403</td>\n <td>-1.233186</td>\n <td> 2.818495</td>\n <td>-0.604278</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 0.888892</td>\n <td>-0.179881</td>\n <td>-8.694134</td>\n <td>-2.872412</td>\n <td>-3.320970</td>\n <td> 1.533524</td>\n <td> -1.861266</td>\n <td>-1.118133</td>\n <td> -3.388559</td>\n <td> 1.083895</td>\n </tr>\n <tr>\n <th>2</th>\n <td>-3.319852</td>\n <td> 2.300243</td>\n <td> 1.670677</td>\n <td>-9.666361</td>\n <td>-9.595642</td>\n <td> 4.668615</td>\n <td>-20.648915</td>\n <td> 2.828419</td>\n <td> 13.892349</td>\n <td> 6.112814</td>\n </tr>\n <tr>\n <th>3</th>\n <td>-5.508931</td>\n <td>-1.505746</td>\n <td> 5.299569</td>\n <td> 5.727700</td>\n <td>-4.449550</td>\n <td>-0.807838</td>\n <td> 2.172512</td>\n <td> 5.269906</td>\n <td> 5.609469</td>\n <td> 2.110526</td>\n </tr>\n <tr>\n <th>4</th>\n <td>-6.334801</td>\n <td>-0.261818</td>\n <td> 6.637892</td>\n <td> 5.268738</td>\n <td> 0.579049</td>\n <td>-0.522321</td>\n <td> 0.370901</td>\n <td>-3.753941</td>\n <td> -0.828416</td>\n <td> 3.331711</td>\n </tr>\n <tr>\n <th>5</th>\n <td>-3.629128</td>\n <td>-0.334265</td>\n <td> 2.039921</td>\n <td>-3.229526</td>\n <td>-1.352967</td>\n <td> 5.201557</td>\n <td> -0.460188</td>\n <td> 2.400639</td>\n <td> -3.360119</td>\n <td> 0.391390</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "plt.scatter(x.PC1, x.PC2)\nplt.show()",
"execution_count": 1214,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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AEO4AAAACQrgDAAAICOEOAAAgIIQ7AACAgBDuAAAAAkK4AwAACAjhDgAAICCEOwAAgICM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\n7zzBZZ9zfaPLOqb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"text/plain": "<matplotlib.figure.Figure at 0x7fde50a44350>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "%%R\nsource(\"tw_calc.R\")\ntest=read.table(\"twtable\", header=F)",
"execution_count": 24,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "TWcalc = r('TWcalc')",
"execution_count": 25,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "tw = TWcalc(com.convert_to_r_matrix(pca_std), 25)",
"execution_count": 26,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "tw_p = com.convert_robj(tw.rx2(2))\ntw_e = com.convert_robj(tw.rx2(1))",
"execution_count": 27,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "tw_num = 0\nfor i, p in enumerate(tw_p):\n print i, p\n if p > 0.05:\n tw_num = i\n break\nprint \"Tracy-Widom test yields %d axes of pop structure\" % tw_num",
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0 8e-09\n1 8e-09\n2 8e-09\n3 8e-09\n4 8e-09\n5 8e-09\n6 8e-09\n7 8e-09\n8 8e-09\n9 8e-09\n10 8e-09\n11 8e-09\n12 8e-09\n13 8e-09\n14 2.2872e-05\n15 0.000220344\n16 0.000177359\n17 0.00206759\n18 0.006211384\n19 0.01992964\n20 0.328982392\nTracy-Widom test yields 20 axes of pop structure\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "y = pd.DataFrame(x)\nfor col in y.columns[0:10]:\n s_cutoff = np.std(y[col])*6\n u = np.mean(y[col])\n cutoff = sorted([u+s_cutoff, u-s_cutoff], reverse=True)\n outliers = y[col][(y[col] > cutoff[0]) | (y[col] < cutoff[1])]\n print col\n print outliers\n y = y.drop(outliers.index)\ny.ix[0:5,0:10]",
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "PC1\nSeries([], name: PC1, dtype: float64)\nPC2\n348 86.725320\n572 41.627337\nName: PC2, dtype: float64\nPC3\nSeries([], name: PC3, dtype: float64)\nPC4\n596 23.223851\nName: PC4, dtype: float64\nPC5\n303 -15.047861\nName: PC5, dtype: float64\nPC6\n9 -32.477463\n271 -25.089614\n296 -25.141168\n603 -22.043115\nName: PC6, dtype: float64\nPC7\n2 -20.648915\n333 -17.148680\n391 -17.088184\nName: PC7, dtype: float64\nPC8\n290 -30.003228\n561 -25.400725\nName: PC8, dtype: float64\nPC9\n578 15.177391\nName: PC9, dtype: float64\nPC10\n606 -17.195924\nName: PC10, dtype: float64\n"
},
{
"execution_count": 29,
"output_type": "execute_result",
"data": {
"text/plain": " PC1 PC2 PC3 PC4 PC5 PC6 PC7 \\\n0 -4.227659 -0.930500 1.853226 -2.682931 0.923642 -0.432286 2.962403 \n1 0.888892 -0.179881 -8.694134 -2.872412 -3.320970 1.533524 -1.861266 \n3 -5.508931 -1.505746 5.299569 5.727700 -4.449550 -0.807838 2.172512 \n4 -6.334801 -0.261818 6.637892 5.268738 0.579049 -0.522321 0.370901 \n5 -3.629128 -0.334265 2.039921 -3.229526 -1.352967 5.201557 -0.460188 \n\n PC8 PC9 PC10 \n0 -1.233186 2.818495 -0.604278 \n1 -1.118133 -3.388559 1.083895 \n3 5.269906 5.609469 2.110526 \n4 -3.753941 -0.828416 3.331711 \n5 2.400639 -3.360119 0.391390 ",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PC1</th>\n <th>PC2</th>\n <th>PC3</th>\n <th>PC4</th>\n <th>PC5</th>\n <th>PC6</th>\n <th>PC7</th>\n <th>PC8</th>\n <th>PC9</th>\n <th>PC10</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>-4.227659</td>\n <td>-0.930500</td>\n <td> 1.853226</td>\n <td>-2.682931</td>\n <td> 0.923642</td>\n <td>-0.432286</td>\n <td> 2.962403</td>\n <td>-1.233186</td>\n <td> 2.818495</td>\n <td>-0.604278</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 0.888892</td>\n <td>-0.179881</td>\n <td>-8.694134</td>\n <td>-2.872412</td>\n <td>-3.320970</td>\n <td> 1.533524</td>\n <td>-1.861266</td>\n <td>-1.118133</td>\n <td>-3.388559</td>\n <td> 1.083895</td>\n </tr>\n <tr>\n <th>3</th>\n <td>-5.508931</td>\n <td>-1.505746</td>\n <td> 5.299569</td>\n <td> 5.727700</td>\n <td>-4.449550</td>\n <td>-0.807838</td>\n <td> 2.172512</td>\n <td> 5.269906</td>\n <td> 5.609469</td>\n <td> 2.110526</td>\n </tr>\n <tr>\n <th>4</th>\n <td>-6.334801</td>\n <td>-0.261818</td>\n <td> 6.637892</td>\n <td> 5.268738</td>\n <td> 0.579049</td>\n <td>-0.522321</td>\n <td> 0.370901</td>\n <td>-3.753941</td>\n <td>-0.828416</td>\n <td> 3.331711</td>\n </tr>\n <tr>\n <th>5</th>\n <td>-3.629128</td>\n <td>-0.334265</td>\n <td> 2.039921</td>\n <td>-3.229526</td>\n <td>-1.352967</td>\n <td> 5.201557</td>\n <td>-0.460188</td>\n <td> 2.400639</td>\n <td>-3.360119</td>\n <td> 0.391390</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
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"cell_type": "code",
"source": "gt_drop = genotypes.ix[y.index,:]",
"execution_count": 30,
"outputs": []
},
{
"metadata": {
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"cell_type": "code",
"source": "gt_drop[0:10]",
"execution_count": 31,
"outputs": [
{
"execution_count": 31,
"output_type": "execute_result",
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"text/plain": " 0-10037-01-257 0-10040-02-394 0-10044-01-392 0-10048-01-60 0-10051-02-166 \\\n0 G/G A/C G/G A/A G/G \n1 A/A C/C G/G A/A G/G \n3 A/A A/A C/C G/G A/G \n4 A/A A/C C/C ?/? G/G \n5 A/G C/C C/G A/A G/G \n6 A/A A/A G/G A/G G/G \n7 A/G A/C C/G G/G A/G \n8 ?/? A/A C/C G/G G/G \n10 A/A A/A C/G G/G G/G \n11 A/A A/C ?/? A/G G/G \n\n 0-10054-01-402 0-10067-03-111 0-10079-02-168 0-10112-01-169 0-10113-01-119 \\\n0 A/G A/A G/G A/C A/A \n1 A/G A/A A/G A/A G/G \n3 G/G A/A G/G A/A A/G \n4 A/G A/A G/G A/A A/A \n5 A/G A/A G/G A/A A/G \n6 A/G A/A G/G A/A A/A \n7 A/G A/A G/G A/A A/G \n8 G/G A/A G/G A/A A/G \n10 A/G A/A G/G A/A A/A \n11 A/G A/A G/G A/A A/G \n\n 0-10116-01-165 0-10151-01-86 0-10162-01-255 0-10207-01-280 0-10210-01-41 \\\n0 A/A A/A A/A A/C ?/? \n1 A/A A/A A/A A/C A/A \n3 A/A A/A A/A C/C A/A \n4 A/A A/A ?/? A/C ?/? \n5 A/A A/A A/A C/C A/A \n6 A/A A/A A/A A/A A/A \n7 A/A A/A A/A A/C A/A \n8 ?/? A/A A/G A/C A/A \n10 A/A A/A A/A A/A A/A \n11 A/A A/A A/A C/C A/A \n\n ... UMN-CL299Contig1-01-46 UMN-CL306Contig1-04-261 \\\n0 ... A/A T/T \n1 ... A/A T/T \n3 ... A/A T/T \n4 ... A/A T/T \n5 ... A/A T/T \n6 ... A/C T/T \n7 ... A/A T/T \n8 ... A/A ?/? \n10 ... A/A T/T \n11 ... A/A T/T \n\n UMN-CL307Contig1-04-143 UMN-CL319Contig1-03-131 UMN-CL326Contig1-05-421 \\\n0 C/C A/C A/G \n1 C/C C/C G/G \n3 C/C C/C G/G \n4 C/G C/C G/G \n5 C/G C/C A/G \n6 C/G A/C G/G \n7 C/C A/C A/G \n8 C/G C/C G/G \n10 C/C C/C A/G \n11 C/C A/C ?/? \n\n UMN-CL339Contig1-05-39 UMN-CL34Contig1-03-89 UMN-CL353Contig1-04-64 \\\n0 A/A C/G A/A \n1 A/A G/G A/A \n3 A/A C/G A/A \n4 A/A C/G A/A \n5 A/A C/G A/A \n6 A/A G/G A/A \n7 A/A C/G A/A \n8 A/A G/G A/A \n10 A/A C/G A/A \n11 A/A G/G A/A \n\n UMN-CL362Contig1-07-133 UMN-CL363Contig1-01-233 UMN-CL379Contig1-12-117 \\\n0 ?/? G/G A/A \n1 A/A G/G A/A \n3 ?/? A/G A/A \n4 C/C A/G A/A \n5 ?/? A/G A/A \n6 C/C G/G A/A \n7 A/C A/G A/A \n8 ?/? G/G A/A \n10 A/C G/G A/A \n11 C/C G/G A/A \n\n UMN-CL424Contig1-03-94 UMN-CL54Contig1-07-88 UMN-CL91Contig1-02-246 \\\n0 C/C G/G C/C \n1 A/C A/G C/C \n3 A/C A/G C/C \n4 A/C G/G A/C \n5 A/C G/G A/C \n6 A/C A/A C/C \n7 C/C A/G C/C \n8 A/C G/G A/C \n10 C/C A/G C/C \n11 C/C A/G C/C \n\n UMN-CL97Contig \n0 A/G \n1 A/A \n3 G/G \n4 G/G \n5 A/G \n6 A/G \n7 A/A \n8 A/A \n10 G/G \n11 A/G \n\n[10 rows x 3082 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>0-10116-01-165</th>\n <th>0-10151-01-86</th>\n <th>0-10162-01-255</th>\n <th>0-10207-01-280</th>\n <th>0-10210-01-41</th>\n <th>...</th>\n <th>UMN-CL299Contig1-01-46</th>\n <th>UMN-CL306Contig1-04-261</th>\n <th>UMN-CL307Contig1-04-143</th>\n <th>UMN-CL319Contig1-03-131</th>\n <th>UMN-CL326Contig1-05-421</th>\n <th>UMN-CL339Contig1-05-39</th>\n <th>UMN-CL34Contig1-03-89</th>\n <th>UMN-CL353Contig1-04-64</th>\n <th>UMN-CL362Contig1-07-133</th>\n <th>UMN-CL363Contig1-01-233</th>\n <th>UMN-CL379Contig1-12-117</th>\n <th>UMN-CL424Contig1-03-94</th>\n <th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> G/G</td>\n <td> A/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> ?/?</td>\n <td>...</td>\n <td> A/A</td>\n <td> T/T</td>\n <td> C/C</td>\n <td> A/C</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> C/G</td>\n <td> A/A</td>\n <td> ?/?</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> A/G</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> T/T</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> A/A</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> T/T</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/G</td>\n <td> A/A</td>\n <td> ?/?</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> G/G</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> A/A</td>\n <td> A/C</td>\n <td> C/C</td>\n <td> ?/?</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> ?/?</td>\n <td> A/C</td>\n <td> ?/?</td>\n <td>...</td>\n <td> A/A</td>\n <td> T/T</td>\n <td> C/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> G/G</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> A/G</td>\n <td> C/C</td>\n <td> C/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> T/T</td>\n <td> C/G</td>\n <td> C/C</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> C/G</td>\n <td> A/A</td>\n <td> ?/?</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/G</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/C</td>\n <td> T/T</td>\n <td> C/G</td>\n <td> A/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/G</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> A/G</td>\n <td> A/C</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> T/T</td>\n <td> C/C</td>\n <td> A/C</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> C/G</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> A/A</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> ?/?</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> ?/?</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> ?/?</td>\n <td> C/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> ?/?</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/A</td>\n </tr>\n <tr>\n <th>10</th>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> T/T</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> C/G</td>\n <td> A/A</td>\n <td> A/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> G/G</td>\n </tr>\n <tr>\n <th>11</th>\n <td> A/A</td>\n <td> A/C</td>\n <td> ?/?</td>\n <td> A/G</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> T/T</td>\n <td> C/C</td>\n <td> A/C</td>\n <td> ?/?</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> A/G</td>\n </tr>\n </tbody>\n</table>\n<p>10 rows × 3082 columns</p>\n</div>"
},
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},
{
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"collapsed": false,
"trusted": false
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"cell_type": "code",
"source": "gt_drop.shape",
"execution_count": 32,
"outputs": [
{
"execution_count": 32,
"output_type": "execute_result",
"data": {
"text/plain": "(607, 3082)"
},
"metadata": {}
}
]
},
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"cell_type": "code",
"source": "z12_drop = gt_drop.apply(convert_to_z12)\nz12_drop[0:10]",
"execution_count": 33,
"outputs": [
{
"execution_count": 33,
"output_type": "execute_result",
"data": {
"text/plain": " 0-10037-01-257 0-10040-02-394 0-10044-01-392 0-10048-01-60 \\\n0 2 1 2 2 \n1 0 0 2 2 \n3 0 2 0 0 \n4 0 1 0 -1 \n5 1 0 1 2 \n6 0 2 2 1 \n7 1 1 1 0 \n8 -1 2 0 0 \n10 0 2 1 0 \n11 0 1 -1 1 \n\n 0-10051-02-166 0-10054-01-402 0-10067-03-111 0-10079-02-168 \\\n0 0 1 0 0 \n1 0 1 0 1 \n3 1 0 0 0 \n4 0 1 0 0 \n5 0 1 0 0 \n6 0 1 0 0 \n7 1 1 0 0 \n8 0 0 0 0 \n10 0 1 0 0 \n11 0 1 0 0 \n\n 0-10112-01-169 0-10113-01-119 0-10116-01-165 0-10151-01-86 \\\n0 1 0 0 0 \n1 0 2 0 0 \n3 0 1 0 0 \n4 0 0 0 0 \n5 0 1 0 0 \n6 0 0 0 0 \n7 0 1 0 0 \n8 0 1 -1 0 \n10 0 0 0 0 \n11 0 1 0 0 \n\n 0-10162-01-255 0-10207-01-280 0-10210-01-41 ... \\\n0 0 1 -1 ... \n1 0 1 0 ... \n3 0 2 0 ... \n4 -1 1 -1 ... \n5 0 2 0 ... \n6 0 0 0 ... \n7 0 1 0 ... \n8 1 1 0 ... \n10 0 0 0 ... \n11 0 2 0 ... \n\n UMN-CL299Contig1-01-46 UMN-CL306Contig1-04-261 UMN-CL307Contig1-04-143 \\\n0 0 0 0 \n1 0 0 0 \n3 0 0 0 \n4 0 0 1 \n5 0 0 1 \n6 1 0 1 \n7 0 0 0 \n8 0 -1 1 \n10 0 0 0 \n11 0 0 0 \n\n UMN-CL319Contig1-03-131 UMN-CL326Contig1-05-421 UMN-CL339Contig1-05-39 \\\n0 1 1 0 \n1 0 0 0 \n3 0 0 0 \n4 0 0 0 \n5 0 1 0 \n6 1 0 0 \n7 1 1 0 \n8 0 0 0 \n10 0 1 0 \n11 1 -1 0 \n\n UMN-CL34Contig1-03-89 UMN-CL353Contig1-04-64 UMN-CL362Contig1-07-133 \\\n0 1 0 -1 \n1 0 0 2 \n3 1 0 -1 \n4 1 0 0 \n5 1 0 -1 \n6 0 0 0 \n7 1 0 1 \n8 0 0 -1 \n10 1 0 1 \n11 0 0 0 \n\n UMN-CL363Contig1-01-233 UMN-CL379Contig1-12-117 UMN-CL424Contig1-03-94 \\\n0 0 0 0 \n1 0 0 1 \n3 1 0 1 \n4 1 0 1 \n5 1 0 1 \n6 0 0 1 \n7 1 0 0 \n8 0 0 1 \n10 0 0 0 \n11 0 0 0 \n\n UMN-CL54Contig1-07-88 UMN-CL91Contig1-02-246 UMN-CL97Contig \n0 0 0 1 \n1 1 0 2 \n3 1 0 0 \n4 0 1 0 \n5 0 1 1 \n6 2 0 1 \n7 1 0 2 \n8 0 1 2 \n10 1 0 0 \n11 1 0 1 \n\n[10 rows x 3082 columns]",
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<th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 2</td>\n <td> 1</td>\n <td> 2</td>\n <td> 2</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td>-1</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td>-1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 0</td>\n <td> 0</td>\n <td> 2</td>\n <td> 2</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 2</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 2</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 2</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 0</td>\n <td> 2</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 2</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td>-1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td>-1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td>-1</td>\n <td> 1</td>\n <td>-1</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 2</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 2</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td>-1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 0</td>\n <td> 2</td>\n <td> 2</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td>...</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 2</td>\n <td> 0</td>\n <td> 1</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 1</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 2</td>\n </tr>\n <tr>\n <th>8 </th>\n <td>-1</td>\n <td> 2</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td>-1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td>-1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td>-1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 2</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 0</td>\n <td> 2</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 0</td>\n <td> 1</td>\n <td>-1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 2</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td>-1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n </tr>\n </tbody>\n</table>\n<p>10 rows × 3082 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "pca_drop_std = z12_drop.apply(center_and_standardize)",
"execution_count": 34,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "pca_drop_std.shape",
"execution_count": 544,
"outputs": [
{
"execution_count": 544,
"output_type": "execute_result",
"data": {
"text/plain": "(607, 3082)"
},
"metadata": {}
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]
},
{
"metadata": {
"collapsed": false,
"trusted": false
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"cell_type": "code",
"source": "pca_drop_std[0:10]",
"execution_count": 35,
"outputs": [
{
"execution_count": 35,
"output_type": "execute_result",
"data": {
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"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>0-10116-01-165</th>\n <th>0-10151-01-86</th>\n <th>0-10162-01-255</th>\n <th>0-10207-01-280</th>\n <th>0-10210-01-41</th>\n <th>...</th>\n <th>UMN-CL299Contig1-01-46</th>\n <th>UMN-CL306Contig1-04-261</th>\n <th>UMN-CL307Contig1-04-143</th>\n <th>UMN-CL319Contig1-03-131</th>\n <th>UMN-CL326Contig1-05-421</th>\n <th>UMN-CL339Contig1-05-39</th>\n <th>UMN-CL34Contig1-03-89</th>\n <th>UMN-CL353Contig1-04-64</th>\n <th>UMN-CL362Contig1-07-133</th>\n <th>UMN-CL363Contig1-01-233</th>\n <th>UMN-CL379Contig1-12-117</th>\n <th>UMN-CL424Contig1-03-94</th>\n <th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 2.107587</td>\n <td> 0.047548</td>\n <td> 1.421714</td>\n <td> 1.890773</td>\n <td>-0.266114</td>\n <td> 0.011785</td>\n <td>-0.179495</td>\n <td>-0.173554</td>\n <td> 1.829378</td>\n <td>-0.950368</td>\n <td>-0.126562</td>\n <td>-0.327354</td>\n <td>-0.210419</td>\n <td> 0.569546</td>\n <td> 0.00000</td>\n <td>...</td>\n <td>-0.141186</td>\n <td>-0.388627</td>\n <td>-0.560743</td>\n <td> 1.165155</td>\n <td> 1.034360</td>\n <td>-0.2251</td>\n <td> 0.740705</td>\n <td>-0.044142</td>\n <td> 0.000000</td>\n <td>-0.277861</td>\n <td>-0.064989</td>\n <td>-0.779494</td>\n <td>-0.904873</td>\n <td>-0.472712</td>\n <td> 0.877175</td>\n </tr>\n <tr>\n <th>1 </th>\n <td>-0.866951</td>\n <td>-1.367010</td>\n <td> 1.421714</td>\n <td> 1.890773</td>\n <td>-0.266114</td>\n <td> 0.011785</td>\n <td>-0.179495</td>\n <td> 2.101516</td>\n <td>-0.243688</td>\n <td> 1.968620</td>\n <td>-0.126562</td>\n <td>-0.327354</td>\n <td>-0.210419</td>\n <td> 0.569546</td>\n <td>-0.22353</td>\n <td>...</td>\n <td>-0.141186</td>\n <td>-0.388627</td>\n <td>-0.560743</td>\n <td>-0.506106</td>\n <td>-0.580452</td>\n <td>-0.2251</td>\n <td>-0.774024</td>\n <td>-0.044142</td>\n <td> 2.041739</td>\n <td>-0.277861</td>\n <td>-0.064989</td>\n <td> 0.729526</td>\n <td> 0.567087</td>\n <td>-0.472712</td>\n <td> 2.434228</td>\n </tr>\n <tr>\n <th>3 </th>\n <td>-0.866951</td>\n <td> 1.462106</td>\n <td>-1.406722</td>\n <td>-1.012134</td>\n <td> 1.729738</td>\n <td>-1.402474</td>\n <td>-0.179495</td>\n <td>-0.173554</td>\n <td>-0.243688</td>\n <td> 0.509126</td>\n <td>-0.126562</td>\n <td>-0.327354</td>\n <td>-0.210419</td>\n <td> 2.042511</td>\n <td>-0.22353</td>\n <td>...</td>\n <td>-0.141186</td>\n <td>-0.388627</td>\n <td>-0.560743</td>\n <td>-0.506106</td>\n <td>-0.580452</td>\n <td>-0.2251</td>\n <td> 0.740705</td>\n <td>-0.044142</td>\n <td> 0.000000</td>\n <td> 1.686782</td>\n <td>-0.064989</td>\n <td> 0.729526</td>\n <td> 0.567087</td>\n <td>-0.472712</td>\n <td>-0.679877</td>\n </tr>\n <tr>\n <th>4 </th>\n <td>-0.866951</td>\n <td> 0.047548</td>\n <td>-1.406722</td>\n <td> 0.000000</td>\n <td>-0.266114</td>\n <td> 0.011785</td>\n <td>-0.179495</td>\n <td>-0.173554</td>\n <td>-0.243688</td>\n <td>-0.950368</td>\n <td>-0.126562</td>\n <td>-0.327354</td>\n <td> 0.000000</td>\n <td> 0.569546</td>\n <td> 0.00000</td>\n <td>...</td>\n <td>-0.141186</td>\n <td>-0.388627</td>\n <td> 1.064324</td>\n <td>-0.506106</td>\n <td>-0.580452</td>\n <td>-0.2251</td>\n <td> 0.740705</td>\n <td>-0.044142</td>\n <td>-0.900924</td>\n <td> 1.686782</td>\n <td>-0.064989</td>\n <td> 0.729526</td>\n <td>-0.904873</td>\n <td> 1.229051</td>\n <td>-0.679877</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 0.620318</td>\n <td>-1.367010</td>\n <td> 0.007496</td>\n <td> 1.890773</td>\n <td>-0.266114</td>\n <td> 0.011785</td>\n <td>-0.179495</td>\n <td>-0.173554</td>\n <td>-0.243688</td>\n <td> 0.509126</td>\n <td>-0.126562</td>\n <td>-0.327354</td>\n <td>-0.210419</td>\n <td> 2.042511</td>\n <td>-0.22353</td>\n <td>...</td>\n <td>-0.141186</td>\n <td>-0.388627</td>\n <td> 1.064324</td>\n <td>-0.506106</td>\n <td> 1.034360</td>\n <td>-0.2251</td>\n <td> 0.740705</td>\n <td>-0.044142</td>\n <td> 0.000000</td>\n <td> 1.686782</td>\n <td>-0.064989</td>\n <td> 0.729526</td>\n <td>-0.904873</td>\n <td> 1.229051</td>\n <td> 0.877175</td>\n </tr>\n <tr>\n <th>6 </th>\n <td>-0.866951</td>\n <td> 1.462106</td>\n <td> 1.421714</td>\n <td> 0.439320</td>\n <td>-0.266114</td>\n <td> 0.011785</td>\n <td>-0.179495</td>\n <td>-0.173554</td>\n <td>-0.243688</td>\n <td>-0.950368</td>\n <td>-0.126562</td>\n <td>-0.327354</td>\n <td>-0.210419</td>\n <td>-0.903418</td>\n <td>-0.22353</td>\n <td>...</td>\n <td> 2.299313</td>\n <td>-0.388627</td>\n <td> 1.064324</td>\n <td> 1.165155</td>\n <td>-0.580452</td>\n <td>-0.2251</td>\n <td>-0.774024</td>\n <td>-0.044142</td>\n <td>-0.900924</td>\n <td>-0.277861</td>\n <td>-0.064989</td>\n <td> 0.729526</td>\n <td> 2.039047</td>\n <td>-0.472712</td>\n <td> 0.877175</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 0.620318</td>\n <td> 0.047548</td>\n <td> 0.007496</td>\n <td>-1.012134</td>\n <td> 1.729738</td>\n <td> 0.011785</td>\n <td>-0.179495</td>\n <td>-0.173554</td>\n <td>-0.243688</td>\n <td> 0.509126</td>\n <td>-0.126562</td>\n <td>-0.327354</td>\n <td>-0.210419</td>\n <td> 0.569546</td>\n <td>-0.22353</td>\n <td>...</td>\n <td>-0.141186</td>\n <td>-0.388627</td>\n <td>-0.560743</td>\n <td> 1.165155</td>\n <td> 1.034360</td>\n <td>-0.2251</td>\n <td> 0.740705</td>\n <td>-0.044142</td>\n <td> 0.570407</td>\n <td> 1.686782</td>\n <td>-0.064989</td>\n <td>-0.779494</td>\n <td> 0.567087</td>\n <td>-0.472712</td>\n <td> 2.434228</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 0.000000</td>\n <td> 1.462106</td>\n <td>-1.406722</td>\n <td>-1.012134</td>\n <td>-0.266114</td>\n <td>-1.402474</td>\n <td>-0.179495</td>\n <td>-0.173554</td>\n <td>-0.243688</td>\n <td> 0.509126</td>\n <td> 0.000000</td>\n <td>-0.327354</td>\n <td> 1.925866</td>\n <td> 0.569546</td>\n <td>-0.22353</td>\n <td>...</td>\n <td>-0.141186</td>\n <td> 0.000000</td>\n <td> 1.064324</td>\n <td>-0.506106</td>\n <td>-0.580452</td>\n <td>-0.2251</td>\n <td>-0.774024</td>\n <td>-0.044142</td>\n <td> 0.000000</td>\n <td>-0.277861</td>\n <td>-0.064989</td>\n <td> 0.729526</td>\n <td>-0.904873</td>\n <td> 1.229051</td>\n <td> 2.434228</td>\n </tr>\n <tr>\n <th>10</th>\n <td>-0.866951</td>\n <td> 1.462106</td>\n <td> 0.007496</td>\n <td>-1.012134</td>\n <td>-0.266114</td>\n <td> 0.011785</td>\n <td>-0.179495</td>\n <td>-0.173554</td>\n <td>-0.243688</td>\n <td>-0.950368</td>\n <td>-0.126562</td>\n <td>-0.327354</td>\n <td>-0.210419</td>\n <td>-0.903418</td>\n <td>-0.22353</td>\n <td>...</td>\n <td>-0.141186</td>\n <td>-0.388627</td>\n <td>-0.560743</td>\n <td>-0.506106</td>\n <td> 1.034360</td>\n <td>-0.2251</td>\n <td> 0.740705</td>\n <td>-0.044142</td>\n <td> 0.570407</td>\n <td>-0.277861</td>\n <td>-0.064989</td>\n <td>-0.779494</td>\n <td> 0.567087</td>\n <td>-0.472712</td>\n <td>-0.679877</td>\n </tr>\n <tr>\n <th>11</th>\n <td>-0.866951</td>\n <td> 0.047548</td>\n <td> 0.000000</td>\n <td> 0.439320</td>\n <td>-0.266114</td>\n <td> 0.011785</td>\n <td>-0.179495</td>\n <td>-0.173554</td>\n <td>-0.243688</td>\n <td> 0.509126</td>\n <td>-0.126562</td>\n <td>-0.327354</td>\n <td>-0.210419</td>\n <td> 2.042511</td>\n <td>-0.22353</td>\n <td>...</td>\n <td>-0.141186</td>\n <td>-0.388627</td>\n <td>-0.560743</td>\n <td> 1.165155</td>\n <td> 0.000000</td>\n <td>-0.2251</td>\n <td>-0.774024</td>\n <td>-0.044142</td>\n <td>-0.900924</td>\n <td>-0.277861</td>\n <td>-0.064989</td>\n <td>-0.779494</td>\n <td> 0.567087</td>\n <td>-0.472712</td>\n <td> 0.877175</td>\n </tr>\n </tbody>\n</table>\n<p>10 rows × 3082 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "pca_drop_std.describe().ix[:,0:5]",
"execution_count": 36,
"outputs": [
{
"execution_count": 36,
"output_type": "execute_result",
"data": {
"text/plain": " 0-10037-01-257 0-10040-02-394 0-10044-01-392 0-10048-01-60 \\\ncount 6.070000e+02 6.070000e+02 6.070000e+02 6.070000e+02 \nmean 5.852906e-17 -1.755872e-17 4.682324e-17 -4.389679e-18 \nstd 9.303810e-01 9.941637e-01 1.009829e+00 1.021606e+00 \nmin -8.669507e-01 -1.367010e+00 -1.406722e+00 -1.012134e+00 \n25% -8.669507e-01 -1.367010e+00 -1.406722e+00 -1.012134e+00 \n50% 0.000000e+00 4.754816e-02 7.495854e-03 4.393196e-01 \n75% 6.203181e-01 4.754816e-02 1.421714e+00 4.393196e-01 \nmax 2.107587e+00 1.462106e+00 1.421714e+00 1.890773e+00 \n\n 0-10051-02-166 \ncount 6.070000e+02 \nmean 8.779358e-18 \nstd 7.036968e-01 \nmin -2.661136e-01 \n25% -2.661136e-01 \n50% -2.661136e-01 \n75% -2.661136e-01 \nmax 3.725590e+00 ",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 6.070000e+02</td>\n <td> 6.070000e+02</td>\n <td> 6.070000e+02</td>\n <td> 6.070000e+02</td>\n <td> 6.070000e+02</td>\n </tr>\n <tr>\n <th>mean</th>\n <td> 5.852906e-17</td>\n <td>-1.755872e-17</td>\n <td> 4.682324e-17</td>\n <td>-4.389679e-18</td>\n <td> 8.779358e-18</td>\n </tr>\n <tr>\n <th>std</th>\n <td> 9.303810e-01</td>\n <td> 9.941637e-01</td>\n <td> 1.009829e+00</td>\n <td> 1.021606e+00</td>\n <td> 7.036968e-01</td>\n </tr>\n <tr>\n <th>min</th>\n <td>-8.669507e-01</td>\n <td>-1.367010e+00</td>\n <td>-1.406722e+00</td>\n <td>-1.012134e+00</td>\n <td>-2.661136e-01</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>-8.669507e-01</td>\n <td>-1.367010e+00</td>\n <td>-1.406722e+00</td>\n <td>-1.012134e+00</td>\n <td>-2.661136e-01</td>\n </tr>\n <tr>\n <th>50%</th>\n <td> 0.000000e+00</td>\n <td> 4.754816e-02</td>\n <td> 7.495854e-03</td>\n <td> 4.393196e-01</td>\n <td>-2.661136e-01</td>\n </tr>\n <tr>\n <th>75%</th>\n <td> 6.203181e-01</td>\n <td> 4.754816e-02</td>\n <td> 1.421714e+00</td>\n <td> 4.393196e-01</td>\n <td>-2.661136e-01</td>\n </tr>\n <tr>\n <th>max</th>\n <td> 2.107587e+00</td>\n <td> 1.462106e+00</td>\n <td> 1.421714e+00</td>\n <td> 1.890773e+00</td>\n <td> 3.725590e+00</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "prcomp_res_drop = prcomp(pca_drop_std, scale=False, center=False)",
"execution_count": 37,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "x_drop = com.convert_robj(prcomp_res_drop.rx2(\"x\"))\nx_drop.index = pca_drop_std.index\nx_drop.ix[0:5,0:5]",
"execution_count": 38,
"outputs": [
{
"execution_count": 38,
"output_type": "execute_result",
"data": {
"text/plain": " PC1 PC2 PC3 PC4 PC5\n0 -4.333631 1.477887 2.473628 -2.610228 3.732781\n1 0.972361 -8.577780 2.513130 8.103148 2.426614\n3 -5.587363 4.702591 -5.712560 1.822264 -5.001879\n4 -6.356667 6.632884 -6.070111 -0.607114 4.199792\n5 -3.653699 2.052517 5.030496 2.775605 6.097295",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PC1</th>\n <th>PC2</th>\n <th>PC3</th>\n <th>PC4</th>\n <th>PC5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>-4.333631</td>\n <td> 1.477887</td>\n <td> 2.473628</td>\n <td>-2.610228</td>\n <td> 3.732781</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 0.972361</td>\n <td>-8.577780</td>\n <td> 2.513130</td>\n <td> 8.103148</td>\n <td> 2.426614</td>\n </tr>\n <tr>\n <th>3</th>\n <td>-5.587363</td>\n <td> 4.702591</td>\n <td>-5.712560</td>\n <td> 1.822264</td>\n <td>-5.001879</td>\n </tr>\n <tr>\n <th>4</th>\n <td>-6.356667</td>\n <td> 6.632884</td>\n <td>-6.070111</td>\n <td>-0.607114</td>\n <td> 4.199792</td>\n </tr>\n <tr>\n <th>5</th>\n <td>-3.653699</td>\n <td> 2.052517</td>\n <td> 5.030496</td>\n <td> 2.775605</td>\n <td> 6.097295</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "plt.scatter(x_drop.PC1, x_drop.PC2)\nplt.show()",
"execution_count": 1211,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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vdZ90Q40DV2HHja/A83dbARefndsOzNwJfZemKWUyw3GcovwTS2dNsXNObVFm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\nz7HxecmcArqBv8CLk7v9Hda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IgIpoC1F9qP2YKJBYVuuV7uP0DF9rrX7g++6I/V+KtRw7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\nQ9bUm23BJKlwDstKmpLBNfW2UH293IsnSZoWw50kSVJBDHe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"text/plain": "<matplotlib.figure.Figure at 0x7fde39d38cd0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "print summary(prcomp_res_drop)",
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Importance of components:\n PC1 PC2 PC3 PC4 PC5 PC6 PC7\nStandard deviation 6.04309 3.80099 3.14407 2.95914 2.89047 2.85444 2.82015\nProportion of Variance 0.01799 0.00712 0.00487 0.00431 0.00412 0.00401 0.00392\nCumulative Proportion 0.01799 0.02511 0.02998 0.03430 0.03841 0.04243 0.04635\n PC8 PC9 PC10 PC11 PC12 PC13 PC14\nStandard deviation 2.80660 2.79209 2.78734 2.77330 2.76292 2.74726 2.73089\nProportion of Variance 0.00388 0.00384 0.00383 0.00379 0.00376 0.00372 0.00367\nCumulative Proportion 0.05023 0.05407 0.05790 0.06169 0.06545 0.06917 0.07284\n PC15 PC16 PC17 PC18 PC19 PC20 PC21\nStandard deviation 2.70648 2.70016 2.69946 2.69158 2.68432 2.68015 2.66814\nProportion of Variance 0.00361 0.00359 0.00359 0.00357 0.00355 0.00354 0.00351\nCumulative Proportion 0.07645 0.08004 0.08364 0.08720 0.09076 0.09429 0.09780\n PC22 PC23 PC24 PC25 PC26 PC27 PC28\nStandard deviation 2.6645 2.64459 2.64158 2.63103 2.62462 2.62051 2.61768\nProportion of Variance 0.0035 0.00345 0.00344 0.00341 0.00339 0.00338 0.00338\nCumulative Proportion 0.1013 0.10475 0.10818 0.11160 0.11499 0.11837 0.12175\n PC29 PC30 PC31 PC32 PC33 PC34 PC35\nStandard deviation 2.60253 2.59829 2.59439 2.59176 2.57763 2.57151 2.56083\nProportion of Variance 0.00334 0.00333 0.00332 0.00331 0.00327 0.00326 0.00323\nCumulative Proportion 0.12509 0.12841 0.13173 0.13504 0.13831 0.14157 0.14480\n PC36 PC37 PC38 PC39 PC40 PC41 PC42\nStandard deviation 2.55704 2.55479 2.5470 2.54427 2.53246 2.52660 2.52451\nProportion of Variance 0.00322 0.00322 0.0032 0.00319 0.00316 0.00315 0.00314\nCumulative Proportion 0.14802 0.15124 0.1544 0.15763 0.16079 0.16393 0.16707\n PC43 PC44 PC45 PC46 PC47 PC48 PC49\nStandard deviation 2.51976 2.51260 2.5085 2.50314 2.49651 2.49382 2.48842\nProportion of Variance 0.00313 0.00311 0.0031 0.00309 0.00307 0.00306 0.00305\nCumulative Proportion 0.17020 0.17331 0.1764 0.17950 0.18257 0.18563 0.18869\n PC50 PC51 PC52 PC53 PC54 PC55 PC56\nStandard deviation 2.48064 2.47479 2.47183 2.47132 2.4662 2.45608 2.45287\nProportion of Variance 0.00303 0.00302 0.00301 0.00301 0.0030 0.00297 0.00296\nCumulative Proportion 0.19172 0.19474 0.19775 0.20076 0.2037 0.20672 0.20969\n PC57 PC58 PC59 PC60 PC61 PC62 PC63\nStandard deviation 2.44503 2.44453 2.44125 2.43053 2.4271 2.42019 2.41750\nProportion of Variance 0.00295 0.00294 0.00294 0.00291 0.0029 0.00289 0.00288\nCumulative Proportion 0.21263 0.21558 0.21852 0.22143 0.2243 0.22721 0.23009\n PC64 PC65 PC66 PC67 PC68 PC69 PC70\nStandard deviation 2.40876 2.40764 2.40425 2.40010 2.39960 2.39375 2.38956\nProportion of Variance 0.00286 0.00286 0.00285 0.00284 0.00284 0.00282 0.00281\nCumulative Proportion 0.23295 0.23581 0.23866 0.24150 0.24433 0.24716 0.24997\n PC71 PC72 PC73 PC74 PC75 PC76 PC77\nStandard deviation 2.38819 2.3848 2.38148 2.37706 2.37440 2.37280 2.36630\nProportion of Variance 0.00281 0.0028 0.00279 0.00278 0.00278 0.00277 0.00276\nCumulative Proportion 0.25278 0.2556 0.25838 0.26116 0.26394 0.26671 0.26947\n PC78 PC79 PC80 PC81 PC82 PC83 PC84\nStandard deviation 2.36313 2.35909 2.34970 2.34693 2.34585 2.3419 2.3403\nProportion of Variance 0.00275 0.00274 0.00272 0.00271 0.00271 0.0027 0.0027\nCumulative Proportion 0.27222 0.27497 0.27769 0.28040 0.28311 0.2858 0.2885\n PC85 PC86 PC87 PC88 PC89 PC90 PC91\nStandard deviation 2.33620 2.33244 2.32253 2.31886 2.31689 2.31579 2.31155\nProportion of Variance 0.00269 0.00268 0.00266 0.00265 0.00264 0.00264 0.00263\nCumulative Proportion 0.29120 0.29388 0.29654 0.29919 0.30183 0.30448 0.30711\n PC92 PC93 PC94 PC95 PC96 PC97 PC98\nStandard deviation 2.30745 2.30453 2.30239 2.29457 2.28945 2.28542 2.28183\nProportion of Variance 0.00262 0.00262 0.00261 0.00259 0.00258 0.00257 0.00257\nCumulative Proportion 0.30973 0.31235 0.31496 0.31756 0.32014 0.32271 0.32528\n PC99 PC100 PC101 PC102 PC103 PC104 PC105\nStandard deviation 2.27754 2.27459 2.27278 2.26827 2.26452 2.26242 2.25961\nProportion of Variance 0.00256 0.00255 0.00255 0.00254 0.00253 0.00252 0.00252\nCumulative Proportion 0.32783 0.33038 0.33293 0.33546 0.33799 0.34051 0.34303\n PC106 PC107 PC108 PC109 PC110 PC111 PC112\nStandard deviation 2.25641 2.2518 2.24685 2.24385 2.24156 2.23910 2.23561\nProportion of Variance 0.00251 0.0025 0.00249 0.00248 0.00248 0.00247 0.00246\nCumulative Proportion 0.34554 0.3480 0.35052 0.35300 0.35548 0.35795 0.36041\n PC113 PC114 PC115 PC116 PC117 PC118 PC119\nStandard deviation 2.23135 2.23061 2.22593 2.22392 2.22052 2.21487 2.21200\nProportion of Variance 0.00245 0.00245 0.00244 0.00244 0.00243 0.00242 0.00241\nCumulative Proportion 0.36287 0.36532 0.36776 0.37020 0.37262 0.37504 0.37745\n PC120 PC121 PC122 PC123 PC124 PC125 PC126\nStandard deviation 2.21099 2.20953 2.2093 2.20295 2.19658 2.19337 2.19028\nProportion of Variance 0.00241 0.00241 0.0024 0.00239 0.00238 0.00237 0.00236\nCumulative Proportion 0.37986 0.38227 0.3847 0.38706 0.38944 0.39181 0.39417\n PC127 PC128 PC129 PC130 PC131 PC132 PC133\nStandard deviation 2.18762 2.18642 2.18367 2.18169 2.17918 2.17778 2.16952\nProportion of Variance 0.00236 0.00236 0.00235 0.00235 0.00234 0.00234 0.00232\nCumulative Proportion 0.39653 0.39889 0.40124 0.40358 0.40592 0.40826 0.41058\n PC134 PC135 PC136 PC137 PC138 PC139 PC140\nStandard deviation 2.16902 2.16301 2.1601 2.1592 2.15393 2.14780 2.14719\nProportion of Variance 0.00232 0.00231 0.0023 0.0023 0.00229 0.00227 0.00227\nCumulative Proportion 0.41290 0.41520 0.4175 0.4198 0.42208 0.42436 0.42663\n PC141 PC142 PC143 PC144 PC145 PC146 PC147\nStandard deviation 2.14447 2.14083 2.13701 2.13421 2.13273 2.12913 2.12508\nProportion of Variance 0.00227 0.00226 0.00225 0.00224 0.00224 0.00223 0.00223\nCumulative Proportion 0.42890 0.43115 0.43340 0.43565 0.43789 0.44012 0.44235\n PC148 PC149 PC150 PC151 PC152 PC153 PC154\nStandard deviation 2.12141 2.11858 2.11686 2.1153 2.11060 2.10851 2.10796\nProportion of Variance 0.00222 0.00221 0.00221 0.0022 0.00219 0.00219 0.00219\nCumulative Proportion 0.44457 0.44678 0.44899 0.4512 0.45338 0.45558 0.45776\n PC155 PC156 PC157 PC158 PC159 PC160 PC161\nStandard deviation 2.10025 2.09946 2.09444 2.09194 2.08932 2.08661 2.08236\nProportion of Variance 0.00217 0.00217 0.00216 0.00216 0.00215 0.00215 0.00214\nCumulative Proportion 0.45994 0.46211 0.46427 0.46643 0.46858 0.47072 0.47286\n PC162 PC163 PC164 PC165 PC166 PC167 PC168\nStandard deviation 2.08083 2.07533 2.07492 2.07159 2.06862 2.06783 2.0664\nProportion of Variance 0.00213 0.00212 0.00212 0.00211 0.00211 0.00211 0.0021\nCumulative Proportion 0.47499 0.47712 0.47924 0.48135 0.48346 0.48557 0.4877\n PC169 PC170 PC171 PC172 PC173 PC174 PC175\nStandard deviation 2.0642 2.05881 2.05646 2.05416 2.05218 2.05056 2.04712\nProportion of Variance 0.0021 0.00209 0.00208 0.00208 0.00208 0.00207 0.00206\nCumulative Proportion 0.4898 0.49186 0.49394 0.49602 0.49810 0.50017 0.50223\n PC176 PC177 PC178 PC179 PC180 PC181 PC182\nStandard deviation 2.04174 2.03732 2.03420 2.03038 2.02984 2.02683 2.02539\nProportion of Variance 0.00205 0.00205 0.00204 0.00203 0.00203 0.00202 0.00202\nCumulative Proportion 0.50429 0.50633 0.50837 0.51040 0.51243 0.51446 0.51648\n PC183 PC184 PC185 PC186 PC187 PC188 PC189\nStandard deviation 2.02122 2.02045 2.01728 2.0151 2.01198 2.00963 2.00284\nProportion of Variance 0.00201 0.00201 0.00201 0.0020 0.00199 0.00199 0.00198\nCumulative Proportion 0.51849 0.52050 0.52251 0.5245 0.52650 0.52849 0.53047\n PC190 PC191 PC192 PC193 PC194 PC195 PC196\nStandard deviation 2.00032 1.99741 1.99696 1.99295 1.99157 1.99130 1.98671\nProportion of Variance 0.00197 0.00197 0.00196 0.00196 0.00195 0.00195 0.00194\nCumulative Proportion 0.53244 0.53441 0.53637 0.53833 0.54028 0.54224 0.54418\n PC197 PC198 PC199 PC200 PC201 PC202 PC203\nStandard deviation 1.98577 1.98203 1.98015 1.97907 1.97338 1.97010 1.96868\nProportion of Variance 0.00194 0.00194 0.00193 0.00193 0.00192 0.00191 0.00191\nCumulative Proportion 0.54613 0.54806 0.54999 0.55192 0.55384 0.55575 0.55766\n PC204 PC205 PC206 PC207 PC208 PC209 PC210\nStandard deviation 1.9658 1.9634 1.9619 1.95990 1.95720 1.95106 1.94759\nProportion of Variance 0.0019 0.0019 0.0019 0.00189 0.00189 0.00188 0.00187\nCumulative Proportion 0.5596 0.5615 0.5634 0.56526 0.56714 0.56902 0.57089\n PC211 PC212 PC213 PC214 PC215 PC216 PC217\nStandard deviation 1.94572 1.94367 1.94134 1.93648 1.93366 1.93053 1.92852\nProportion of Variance 0.00187 0.00186 0.00186 0.00185 0.00184 0.00184 0.00183\nCumulative Proportion 0.57275 0.57462 0.57647 0.57832 0.58016 0.58200 0.58383\n PC218 PC219 PC220 PC221 PC222 PC223 PC224\nStandard deviation 1.92759 1.92570 1.92318 1.92088 1.91846 1.91485 1.9108\nProportion of Variance 0.00183 0.00183 0.00182 0.00182 0.00181 0.00181 0.0018\nCumulative Proportion 0.58566 0.58749 0.58931 0.59113 0.59294 0.59475 0.5966\n PC225 PC226 PC227 PC228 PC229 PC230 PC231\nStandard deviation 1.90837 1.90603 1.90428 1.90091 1.89921 1.89549 1.89199\nProportion of Variance 0.00179 0.00179 0.00179 0.00178 0.00178 0.00177 0.00176\nCumulative Proportion 0.59834 0.60013 0.60192 0.60370 0.60548 0.60725 0.60901\n PC232 PC233 PC234 PC235 PC236 PC237 PC238\nStandard deviation 1.88815 1.88513 1.88295 1.88224 1.88108 1.87493 1.87335\nProportion of Variance 0.00176 0.00175 0.00175 0.00175 0.00174 0.00173 0.00173\nCumulative Proportion 0.61077 0.61252 0.61427 0.61601 0.61776 0.61949 0.62122\n PC239 PC240 PC241 PC242 PC243 PC244 PC245\nStandard deviation 1.87238 1.86869 1.86397 1.86320 1.8599 1.8561 1.8560\nProportion of Variance 0.00173 0.00172 0.00171 0.00171 0.0017 0.0017 0.0017\nCumulative Proportion 0.62294 0.62467 0.62638 0.62809 0.6298 0.6315 0.6332\n PC246 PC247 PC248 PC249 PC250 PC251 PC252\nStandard deviation 1.85075 1.84853 1.84556 1.84385 1.84118 1.83964 1.83662\nProportion of Variance 0.00169 0.00168 0.00168 0.00168 0.00167 0.00167 0.00166\nCumulative Proportion 0.63487 0.63656 0.63824 0.63991 0.64158 0.64325 0.64491\n PC253 PC254 PC255 PC256 PC257 PC258 PC259\nStandard deviation 1.83391 1.83271 1.82941 1.82651 1.82632 1.82232 1.81746\nProportion of Variance 0.00166 0.00165 0.00165 0.00164 0.00164 0.00164 0.00163\nCumulative Proportion 0.64657 0.64822 0.64987 0.65152 0.65316 0.65480 0.65642\n PC260 PC261 PC262 PC263 PC264 PC265 PC266\nStandard deviation 1.81683 1.81562 1.81483 1.80912 1.80868 1.80572 1.8037\nProportion of Variance 0.00163 0.00162 0.00162 0.00161 0.00161 0.00161 0.0016\nCumulative Proportion 0.65805 0.65967 0.66130 0.66291 0.66452 0.66613 0.6677\n PC267 PC268 PC269 PC270 PC271 PC272 PC273\nStandard deviation 1.8001 1.79668 1.79469 1.79272 1.78877 1.78801 1.78647\nProportion of Variance 0.0016 0.00159 0.00159 0.00158 0.00158 0.00158 0.00157\nCumulative Proportion 0.6693 0.67092 0.67251 0.67409 0.67567 0.67724 0.67881\n PC274 PC275 PC276 PC277 PC278 PC279 PC280\nStandard deviation 1.78476 1.78271 1.78022 1.77883 1.77840 1.77103 1.76918\nProportion of Variance 0.00157 0.00157 0.00156 0.00156 0.00156 0.00155 0.00154\nCumulative Proportion 0.68038 0.68195 0.68351 0.68507 0.68663 0.68817 0.68972\n PC281 PC282 PC283 PC284 PC285 PC286 PC287\nStandard deviation 1.76654 1.76346 1.75938 1.75836 1.75675 1.75394 1.75122\nProportion of Variance 0.00154 0.00153 0.00153 0.00152 0.00152 0.00152 0.00151\nCumulative Proportion 0.69125 0.69279 0.69431 0.69583 0.69735 0.69887 0.70038\n PC288 PC289 PC290 PC291 PC292 PC293 PC294\nStandard deviation 1.7475 1.7461 1.7429 1.74141 1.74030 1.73634 1.73557\nProportion of Variance 0.0015 0.0015 0.0015 0.00149 0.00149 0.00149 0.00148\nCumulative Proportion 0.7019 0.7034 0.7049 0.70638 0.70787 0.70936 0.71084\n PC295 PC296 PC297 PC298 PC299 PC300 PC301\nStandard deviation 1.73244 1.72923 1.72456 1.72227 1.71963 1.71878 1.71811\nProportion of Variance 0.00148 0.00147 0.00147 0.00146 0.00146 0.00146 0.00145\nCumulative Proportion 0.71232 0.71379 0.71526 0.71672 0.71818 0.71963 0.72109\n PC302 PC303 PC304 PC305 PC306 PC307 PC308\nStandard deviation 1.71597 1.71284 1.70997 1.70822 1.70648 1.70217 1.70075\nProportion of Variance 0.00145 0.00145 0.00144 0.00144 0.00143 0.00143 0.00143\nCumulative Proportion 0.72254 0.72398 0.72543 0.72686 0.72830 0.72973 0.73115\n PC309 PC310 PC311 PC312 PC313 PC314 PC315\nStandard deviation 1.69463 1.69396 1.69210 1.68959 1.6886 1.6829 1.68099\nProportion of Variance 0.00142 0.00141 0.00141 0.00141 0.0014 0.0014 0.00139\nCumulative Proportion 0.73257 0.73398 0.73539 0.73680 0.7382 0.7396 0.74099\n PC316 PC317 PC318 PC319 PC320 PC321 PC322\nStandard deviation 1.68076 1.67898 1.67703 1.67482 1.67394 1.66997 1.66660\nProportion of Variance 0.00139 0.00139 0.00139 0.00138 0.00138 0.00137 0.00137\nCumulative Proportion 0.74238 0.74377 0.74516 0.74654 0.74792 0.74929 0.75066\n PC323 PC324 PC325 PC326 PC327 PC328 PC329\nStandard deviation 1.66491 1.66404 1.66011 1.65582 1.65487 1.65244 1.65101\nProportion of Variance 0.00137 0.00136 0.00136 0.00135 0.00135 0.00135 0.00134\nCumulative Proportion 0.75203 0.75339 0.75475 0.75610 0.75745 0.75880 0.76014\n PC330 PC331 PC332 PC333 PC334 PC335 PC336\nStandard deviation 1.64931 1.64513 1.64338 1.64137 1.63705 1.63662 1.63303\nProportion of Variance 0.00134 0.00133 0.00133 0.00133 0.00132 0.00132 0.00131\nCumulative Proportion 0.76148 0.76281 0.76414 0.76547 0.76679 0.76811 0.76942\n PC337 PC338 PC339 PC340 PC341 PC342 PC343\nStandard deviation 1.62881 1.62815 1.6252 1.6227 1.61974 1.61816 1.61541\nProportion of Variance 0.00131 0.00131 0.0013 0.0013 0.00129 0.00129 0.00129\nCumulative Proportion 0.77073 0.77204 0.7733 0.7746 0.77593 0.77722 0.77851\n PC344 PC345 PC346 PC347 PC348 PC349 PC350\nStandard deviation 1.61343 1.61160 1.60880 1.60585 1.60514 1.60323 1.60233\nProportion of Variance 0.00128 0.00128 0.00128 0.00127 0.00127 0.00127 0.00127\nCumulative Proportion 0.77979 0.78107 0.78234 0.78361 0.78488 0.78615 0.78742\n PC351 PC352 PC353 PC354 PC355 PC356 PC357\nStandard deviation 1.59783 1.59499 1.59310 1.59049 1.58766 1.58584 1.58329\nProportion of Variance 0.00126 0.00125 0.00125 0.00125 0.00124 0.00124 0.00124\nCumulative Proportion 0.78867 0.78993 0.79118 0.79242 0.79367 0.79490 0.79614\n PC358 PC359 PC360 PC361 PC362 PC363 PC364\nStandard deviation 1.58234 1.57971 1.57615 1.57535 1.57225 1.57039 1.56888\nProportion of Variance 0.00123 0.00123 0.00122 0.00122 0.00122 0.00122 0.00121\nCumulative Proportion 0.79737 0.79860 0.79983 0.80105 0.80227 0.80348 0.80470\n PC365 PC366 PC367 PC368 PC369 PC370 PC371\nStandard deviation 1.56779 1.56478 1.5589 1.5575 1.55653 1.55260 1.55220\nProportion of Variance 0.00121 0.00121 0.0012 0.0012 0.00119 0.00119 0.00119\nCumulative Proportion 0.80591 0.80711 0.8083 0.8095 0.81070 0.81189 0.81307\n PC372 PC373 PC374 PC375 PC376 PC377 PC378\nStandard deviation 1.55044 1.54760 1.54552 1.54140 1.53862 1.53659 1.53557\nProportion of Variance 0.00118 0.00118 0.00118 0.00117 0.00117 0.00116 0.00116\nCumulative Proportion 0.81426 0.81544 0.81662 0.81779 0.81895 0.82012 0.82128\n PC379 PC380 PC381 PC382 PC383 PC384 PC385\nStandard deviation 1.53481 1.53128 1.52725 1.52340 1.52299 1.52121 1.51745\nProportion of Variance 0.00116 0.00116 0.00115 0.00114 0.00114 0.00114 0.00113\nCumulative Proportion 0.82244 0.82359 0.82474 0.82589 0.82703 0.82817 0.82931\n PC386 PC387 PC388 PC389 PC390 PC391 PC392\nStandard deviation 1.51596 1.51205 1.50928 1.50726 1.50505 1.50415 1.50012\nProportion of Variance 0.00113 0.00113 0.00112 0.00112 0.00112 0.00111 0.00111\nCumulative Proportion 0.83044 0.83156 0.83269 0.83381 0.83492 0.83604 0.83715\n PC393 PC394 PC395 PC396 PC397 PC398 PC399\nStandard deviation 1.4971 1.4947 1.4936 1.4922 1.48919 1.48799 1.48646\nProportion of Variance 0.0011 0.0011 0.0011 0.0011 0.00109 0.00109 0.00109\nCumulative Proportion 0.8383 0.8394 0.8405 0.8416 0.84264 0.84373 0.84482\n PC400 PC401 PC402 PC403 PC404 PC405 PC406\nStandard deviation 1.48163 1.48085 1.47793 1.47633 1.47410 1.47180 1.47061\nProportion of Variance 0.00108 0.00108 0.00108 0.00107 0.00107 0.00107 0.00107\nCumulative Proportion 0.84590 0.84698 0.84806 0.84913 0.85020 0.85127 0.85234\n PC407 PC408 PC409 PC410 PC411 PC412 PC413\nStandard deviation 1.47004 1.46675 1.46419 1.46067 1.45954 1.45680 1.45490\nProportion of Variance 0.00106 0.00106 0.00106 0.00105 0.00105 0.00105 0.00104\nCumulative Proportion 0.85340 0.85446 0.85552 0.85657 0.85762 0.85866 0.85971\n PC414 PC415 PC416 PC417 PC418 PC419 PC420\nStandard deviation 1.45231 1.44918 1.44573 1.44403 1.44066 1.43855 1.43566\nProportion of Variance 0.00104 0.00103 0.00103 0.00103 0.00102 0.00102 0.00102\nCumulative Proportion 0.86075 0.86178 0.86281 0.86384 0.86486 0.86588 0.86690\n PC421 PC422 PC423 PC424 PC425 PC426 PC427\nStandard deviation 1.43404 1.43277 1.42965 1.4264 1.4250 1.4227 1.4213\nProportion of Variance 0.00101 0.00101 0.00101 0.0010 0.0010 0.0010 0.0010\nCumulative Proportion 0.86791 0.86892 0.86993 0.8709 0.8719 0.8729 0.8739\n PC428 PC429 PC430 PC431 PC432 PC433 PC434\nStandard deviation 1.41989 1.41639 1.41549 1.40963 1.40812 1.40579 1.40296\nProportion of Variance 0.00099 0.00099 0.00099 0.00098 0.00098 0.00097 0.00097\nCumulative Proportion 0.87492 0.87590 0.87689 0.87787 0.87885 0.87982 0.88079\n PC435 PC436 PC437 PC438 PC439 PC440 PC441\nStandard deviation 1.39977 1.39704 1.39550 1.39262 1.39133 1.38653 1.38374\nProportion of Variance 0.00097 0.00096 0.00096 0.00096 0.00095 0.00095 0.00094\nCumulative Proportion 0.88176 0.88272 0.88368 0.88463 0.88559 0.88653 0.88748\n PC442 PC443 PC444 PC445 PC446 PC447 PC448\nStandard deviation 1.38250 1.37999 1.37863 1.37633 1.37391 1.36981 1.36870\nProportion of Variance 0.00094 0.00094 0.00094 0.00093 0.00093 0.00092 0.00092\nCumulative Proportion 0.88842 0.88936 0.89029 0.89123 0.89216 0.89308 0.89401\n PC449 PC450 PC451 PC452 PC453 PC454 PC455\nStandard deviation 1.36580 1.36477 1.36242 1.35875 1.35843 1.35611 1.3546\nProportion of Variance 0.00092 0.00092 0.00091 0.00091 0.00091 0.00091 0.0009\nCumulative Proportion 0.89493 0.89584 0.89676 0.89767 0.89858 0.89948 0.9004\n PC456 PC457 PC458 PC459 PC460 PC461 PC462\nStandard deviation 1.3525 1.3493 1.3479 1.34514 1.34428 1.33931 1.33785\nProportion of Variance 0.0009 0.0009 0.0009 0.00089 0.00089 0.00088 0.00088\nCumulative Proportion 0.9013 0.9022 0.9031 0.90397 0.90486 0.90575 0.90663\n PC463 PC464 PC465 PC466 PC467 PC468 PC469\nStandard deviation 1.33390 1.33208 1.32916 1.32868 1.32666 1.32519 1.32181\nProportion of Variance 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086\nCumulative Proportion 0.90750 0.90838 0.90925 0.91012 0.91099 0.91185 0.91271\n PC470 PC471 PC472 PC473 PC474 PC475 PC476\nStandard deviation 1.31974 1.31854 1.31379 1.31060 1.30860 1.30594 1.30257\nProportion of Variance 0.00086 0.00086 0.00085 0.00085 0.00084 0.00084 0.00084\nCumulative Proportion 0.91357 0.91443 0.91528 0.91612 0.91697 0.91781 0.91864\n PC477 PC478 PC479 PC480 PC481 PC482 PC483\nStandard deviation 1.29989 1.29780 1.29762 1.29305 1.29025 1.28962 1.28832\nProportion of Variance 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082\nCumulative Proportion 0.91948 0.92031 0.92114 0.92196 0.92278 0.92360 0.92442\n PC484 PC485 PC486 PC487 PC488 PC489 PC490\nStandard deviation 1.28516 1.28262 1.2771 1.2744 1.2731 1.2720 1.26952\nProportion of Variance 0.00081 0.00081 0.0008 0.0008 0.0008 0.0008 0.00079\nCumulative Proportion 0.92523 0.92604 0.9268 0.9276 0.9284 0.9292 0.93004\n PC491 PC492 PC493 PC494 PC495 PC496 PC497\nStandard deviation 1.26779 1.26500 1.26145 1.25954 1.25799 1.25504 1.25413\nProportion of Variance 0.00079 0.00079 0.00078 0.00078 0.00078 0.00078 0.00077\nCumulative Proportion 0.93083 0.93162 0.93240 0.93318 0.93396 0.93474 0.93551\n PC498 PC499 PC500 PC501 PC502 PC503 PC504\nStandard deviation 1.25220 1.24881 1.24809 1.24538 1.24210 1.24022 1.23714\nProportion of Variance 0.00077 0.00077 0.00077 0.00076 0.00076 0.00076 0.00075\nCumulative Proportion 0.93629 0.93705 0.93782 0.93859 0.93935 0.94010 0.94086\n PC505 PC506 PC507 PC508 PC509 PC510 PC511\nStandard deviation 1.23600 1.22994 1.22768 1.22643 1.22418 1.22290 1.22073\nProportion of Variance 0.00075 0.00075 0.00074 0.00074 0.00074 0.00074 0.00073\nCumulative Proportion 0.94161 0.94236 0.94310 0.94384 0.94458 0.94531 0.94605\n PC512 PC513 PC514 PC515 PC516 PC517 PC518\nStandard deviation 1.21941 1.21595 1.21452 1.21172 1.21018 1.20413 1.20268\nProportion of Variance 0.00073 0.00073 0.00073 0.00072 0.00072 0.00071 0.00071\nCumulative Proportion 0.94678 0.94751 0.94824 0.94896 0.94968 0.95040 0.95111\n PC519 PC520 PC521 PC522 PC523 PC524 PC525\nStandard deviation 1.19923 1.19861 1.1955 1.1906 1.1891 1.18578 1.18306\nProportion of Variance 0.00071 0.00071 0.0007 0.0007 0.0007 0.00069 0.00069\nCumulative Proportion 0.95182 0.95253 0.9532 0.9539 0.9546 0.95532 0.95601\n PC526 PC527 PC528 PC529 PC530 PC531 PC532\nStandard deviation 1.17909 1.17776 1.17571 1.17445 1.17280 1.16838 1.16298\nProportion of Variance 0.00069 0.00068 0.00068 0.00068 0.00068 0.00067 0.00067\nCumulative Proportion 0.95669 0.95738 0.95806 0.95874 0.95941 0.96009 0.96075\n PC533 PC534 PC535 PC536 PC537 PC538 PC539\nStandard deviation 1.15885 1.15633 1.15446 1.15006 1.14780 1.14485 1.14063\nProportion of Variance 0.00066 0.00066 0.00066 0.00065 0.00065 0.00065 0.00064\nCumulative Proportion 0.96142 0.96207 0.96273 0.96338 0.96403 0.96468 0.96532\n PC540 PC541 PC542 PC543 PC544 PC545 PC546\nStandard deviation 1.13865 1.13741 1.13405 1.13299 1.12734 1.12346 1.12033\nProportion of Variance 0.00064 0.00064 0.00063 0.00063 0.00063 0.00062 0.00062\nCumulative Proportion 0.96596 0.96659 0.96723 0.96786 0.96849 0.96911 0.96973\n PC547 PC548 PC549 PC550 PC551 PC552 PC553\nStandard deviation 1.11934 1.11738 1.11391 1.11106 1.10892 1.1049 1.1012\nProportion of Variance 0.00062 0.00062 0.00061 0.00061 0.00061 0.0006 0.0006\nCumulative Proportion 0.97034 0.97096 0.97157 0.97218 0.97279 0.9734 0.9740\n PC554 PC555 PC556 PC557 PC558 PC559 PC560\nStandard deviation 1.0999 1.09758 1.09311 1.08947 1.08735 1.08606 1.08186\nProportion of Variance 0.0006 0.00059 0.00059 0.00058 0.00058 0.00058 0.00058\nCumulative Proportion 0.9746 0.97517 0.97576 0.97635 0.97693 0.97751 0.97809\n PC561 PC562 PC563 PC564 PC565 PC566 PC567\nStandard deviation 1.08025 1.07435 1.07238 1.06965 1.06688 1.06236 1.06016\nProportion of Variance 0.00057 0.00057 0.00057 0.00056 0.00056 0.00056 0.00055\nCumulative Proportion 0.97866 0.97923 0.97980 0.98036 0.98092 0.98148 0.98203\n PC568 PC569 PC570 PC571 PC572 PC573 PC574\nStandard deviation 1.05798 1.05346 1.05129 1.04752 1.04500 1.04319 1.03509\nProportion of Variance 0.00055 0.00055 0.00054 0.00054 0.00054 0.00054 0.00053\nCumulative Proportion 0.98258 0.98313 0.98368 0.98422 0.98475 0.98529 0.98582\n PC575 PC576 PC577 PC578 PC579 PC580 PC581\nStandard deviation 1.02906 1.02414 1.02254 1.01782 1.01355 1.0086 1.0061\nProportion of Variance 0.00052 0.00052 0.00052 0.00051 0.00051 0.0005 0.0005\nCumulative Proportion 0.98634 0.98686 0.98737 0.98788 0.98839 0.9889 0.9894\n PC582 PC583 PC584 PC585 PC586 PC587 PC588\nStandard deviation 1.0048 0.99461 0.99397 0.99041 0.98879 0.98543 0.98105\nProportion of Variance 0.0005 0.00049 0.00049 0.00048 0.00048 0.00048 0.00047\nCumulative Proportion 0.9899 0.99037 0.99086 0.99134 0.99183 0.99230 0.99278\n PC589 PC590 PC591 PC592 PC593 PC594 PC595\nStandard deviation 0.97196 0.96737 0.96389 0.95702 0.95335 0.94892 0.94601\nProportion of Variance 0.00047 0.00046 0.00046 0.00045 0.00045 0.00044 0.00044\nCumulative Proportion 0.99324 0.99371 0.99416 0.99461 0.99506 0.99551 0.99595\n PC596 PC597 PC598 PC599 PC600 PC601 PC602\nStandard deviation 0.93930 0.93429 0.93035 0.92419 0.91349 0.9063 0.88897\nProportion of Variance 0.00043 0.00043 0.00043 0.00042 0.00041 0.0004 0.00039\nCumulative Proportion 0.99638 0.99681 0.99724 0.99766 0.99807 0.9985 0.99886\n PC603 PC604 PC605 PC606 PC607\nStandard deviation 0.88636 0.80084 0.68584 0.6382 3.021e-15\nProportion of Variance 0.00039 0.00032 0.00023 0.0002 0.000e+00\nCumulative Proportion 0.99925 0.99957 0.99980 1.0000 1.000e+00\n\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "tw = TWcalc(com.convert_to_r_matrix(pca_drop_std), 25)",
"execution_count": 41,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "tw_p = com.convert_robj(tw.rx2(2))\ntw_e = com.convert_robj(tw.rx2(1))",
"execution_count": 42,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "tw_num = 0\nfor i, p in enumerate(tw_p):\n print i, p\n if p > 0.05:\n tw_num = i\n break\nprint \"Tracy-Widom test yields %d axes of pop structure\" % tw_num",
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0 8e-09\n1 8e-09\n2 8e-09\n3 8e-09\n4 8e-09\n5 8e-09\n6 8e-09\n7 5.9e-08\n8 1.501e-06\n9 1.501e-06\n10 2.8955e-05\n11 0.000177359\n12 0.003013114\n13 0.042180992\n14 0.573774198\nTracy-Widom test yields 14 axes of pop structure\n"
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "hierf_trans = {0:11, 1:12, 2:22, -1:'NA'}",
"execution_count": 44,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "def apply_hierf_trans(series):\n return [hierf_trans[x] if x in hierf_trans else x for x in series]",
"execution_count": 45,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "hierf_df = z12_drop.apply(apply_hierf_trans)",
"execution_count": 542,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "hierf_df.shape",
"execution_count": 545,
"outputs": [
{
"execution_count": 545,
"output_type": "execute_result",
"data": {
"text/plain": "(607, 3083)"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "hierf_df.insert(0, \"countyid\", None)\nhierf_df[0:5]",
"execution_count": 543,
"outputs": [
{
"execution_count": 543,
"output_type": "execute_result",
"data": {
"text/plain": " countyid 0-10037-01-257 0-10040-02-394 0-10044-01-392 0-10048-01-60 \\\n0 None 22 12 22 22 \n1 None 11 11 22 22 \n3 None 11 22 11 11 \n4 None 11 12 11 NA \n5 None 12 11 12 22 \n\n 0-10051-02-166 0-10054-01-402 0-10067-03-111 0-10079-02-168 0-10112-01-169 \\\n0 11 12 11 11 12 \n1 11 12 11 12 11 \n3 12 11 11 11 11 \n4 11 12 11 11 11 \n5 11 12 11 11 11 \n\n 0-10113-01-119 0-10116-01-165 0-10151-01-86 0-10162-01-255 0-10207-01-280 \\\n0 11 11 11 11 12 \n1 22 11 11 11 12 \n3 12 11 11 11 22 \n4 11 11 11 NA 12 \n5 12 11 11 11 22 \n\n ... UMN-CL299Contig1-01-46 UMN-CL306Contig1-04-261 \\\n0 ... 11 11 \n1 ... 11 11 \n3 ... 11 11 \n4 ... 11 11 \n5 ... 11 11 \n\n UMN-CL307Contig1-04-143 UMN-CL319Contig1-03-131 UMN-CL326Contig1-05-421 \\\n0 11 12 12 \n1 11 11 11 \n3 11 11 11 \n4 12 11 11 \n5 12 11 12 \n\n UMN-CL339Contig1-05-39 UMN-CL34Contig1-03-89 UMN-CL353Contig1-04-64 \\\n0 11 12 11 \n1 11 11 11 \n3 11 12 11 \n4 11 12 11 \n5 11 12 11 \n\n UMN-CL362Contig1-07-133 UMN-CL363Contig1-01-233 UMN-CL379Contig1-12-117 \\\n0 NA 11 11 \n1 22 11 11 \n3 NA 12 11 \n4 11 12 11 \n5 NA 12 11 \n\n UMN-CL424Contig1-03-94 UMN-CL54Contig1-07-88 UMN-CL91Contig1-02-246 \\\n0 11 11 11 \n1 12 12 11 \n3 12 12 11 \n4 12 11 12 \n5 12 11 12 \n\n UMN-CL97Contig \n0 12 \n1 22 \n3 11 \n4 11 \n5 12 \n\n[5 rows x 3083 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>countyid</th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>0-10116-01-165</th>\n <th>0-10151-01-86</th>\n <th>0-10162-01-255</th>\n <th>0-10207-01-280</th>\n <th>...</th>\n <th>UMN-CL299Contig1-01-46</th>\n <th>UMN-CL306Contig1-04-261</th>\n <th>UMN-CL307Contig1-04-143</th>\n <th>UMN-CL319Contig1-03-131</th>\n <th>UMN-CL326Contig1-05-421</th>\n <th>UMN-CL339Contig1-05-39</th>\n <th>UMN-CL34Contig1-03-89</th>\n <th>UMN-CL353Contig1-04-64</th>\n <th>UMN-CL362Contig1-07-133</th>\n <th>UMN-CL363Contig1-01-233</th>\n <th>UMN-CL379Contig1-12-117</th>\n <th>UMN-CL424Contig1-03-94</th>\n <th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> None</td>\n <td> 22</td>\n <td> 12</td>\n <td> 22</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n </tr>\n <tr>\n <th>1</th>\n <td> None</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n </tr>\n <tr>\n <th>3</th>\n <td> None</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n </tr>\n <tr>\n <th>4</th>\n <td> None</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n </tr>\n <tr>\n <th>5</th>\n <td> None</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 3083 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf = data_loc.join(hierf_df, how=\"inner\")\nbayenv_df = loc_hierf.copy()",
"execution_count": 549,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "print hierf_df.shape, data_loc.shape, loc_hierf.shape, bayenv_df.shape",
"execution_count": 551,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "(607, 3083) (622, 4) (607, 3087) (607, 3087)\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf['county_state'] = loc_hierf.apply(lambda row: \"%s_%s\" % (row.county, row.state), axis=1)\nusable_counties = set()\ncounty_counts = loc_hierf.county_state.value_counts()\ncounty_counts = county_counts.sort(inplace=False, ascending=False)\nfor c in county_counts.index:\n print c, county_counts[c]\nfor c in county_counts.index:\n if county_counts[c] >=5:\n usable_counties.add(c)\nusable_counties = sorted(list(usable_counties))",
"execution_count": 562,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "TUSCALOOSA_AL 63\nBEAUFORT_NC 36\nCRAVEN_NC 23\nPICKENS_AL 17\nGREENE_AL 17\nCHOCTAW_AL 13\nMARION_FL 13\nONSLOW_NC 13\nBRUNSWICK_NC 11\nLEVY_FL 11\nNEWBERRY_SC 11\nHERTFORD_NC 10\nGEORGETOWN_SC 10\nPRINCE GEORGE_VA 9\nANSON_NC 9\nBRUNSWICK_VA 8\nMECKLENBURG_VA 8\nDINWIDDIE_VA 8\nBERKELEY_SC 7\nWAKE_NC 7\nBERTIE_NC 7\nSUMTER_AL 7\nMcCURTAIN_OK 7\nWILCOX_AL 7\nMARENGO_AL 6\nCLARKE_AL 6\nMARTIN_NC 6\nJASPER_MS 6\nHALIFAX_NC 6\nCOLUMBUS_NC 6\nCHESTERFIELD_VA 5\nSUSSEX_VA 5\nSPOTSYLVANIA_VA 5\nCLEVELAND_AR 5\nHALE_AL 4\nPOLK_TX 4\nLAMAR_AL 4\nHALIFAX_VA 4\nKING & QUEEN_VA 4\nRICHMOND_NC 4\nFLUVANNA_VA 4\nSALUDA_SC 4\nMOORE_NC 4\nFRANKLIN_NC 3\nGATES_NC 3\nCALHOUN_AL 3\nCUMBERLAND_VA 3\nCLARKE_MS 3\nCHOWAN_NC 3\nWINSTON_AL 3\nHOUSTON_TX 3\nTYRRELL_NC 3\nCALHOUN_AR 3\nLUNENBURG_VA 3\nGREENWOOD_SC 3\nJONES_NC 3\nBIBB_AL 3\nJASPER_SC 3\nNEW KENT_VA 3\nWARREN_NC 3\nDURHAM_NC 2\nNORTHAMPTON_NC 2\nFAYETTE_AL 2\nBAMBERG_SC 2\nYORK_SC 2\nBARTOW_GA 2\nMARSHALL_AL 2\nCONECUH_AL 2\nLEE_NC 2\nCHATTOOGA_GA 2\nPASQUOTANK_NC 2\nUNION_SC 2\nROWAN_NC 2\nDORCHESTER_SC 2\nDALLAS_AL 2\nHANCOCK_GA 2\nPICKENS_SC 2\nHORRY_SC 2\nHEARD_GA 2\nMARION_AL 2\nPIKE_AR 2\nUNION_LA 2\nAIKEN_SC 2\nEDGEFIELD_SC 2\nMONTGOMERY_NC 2\nESCAMBIA_AL 2\nTALLAPOOSA_AL 2\nWARREN_GA 2\nMOREHOUSE_LA 2\nCARTERET_NC 2\nWAYNE_TN 1\nMONROE_MS 1\nFAYETTE_TX 1\nKERSHAW_SC 1\nPAMILICO_NC 1\nHOWARD_AR 1\nFLOYD_GA 1\nTISHOMINGO_MS 1\nHARRIS_GA 1\nSCREVEN_GA 1\nCRENSHAW_AL 1\nCHATHAM_NC 1\nDREW_AR 1\nORANGEBURG_SC 1\nLAMAR_GA 1\nHAMPTON_SC 1\nTALIAFERRO_GA 1\nWINN_LA 1\nNATCHITOCHES_LA 1\nPENDER_NC 1\nIREDELL_NC 1\nWAYNE_MS 1\nWALKER_AL 1\nNASH_NC 1\nSTANLY_NC 1\nGRANVILLE_NC 1\nSURRY_NC 1\nLIVINGSTON_LA 1\nMURRAY_GA 1\nTYLER_TX 1\nCAMPBELL_VA 1\nCHILTON_AL 1\nCAROLINE_VA 1\nRANDOLPH_NC 1\nLINCOLN_GA 1\nCHEROKEE_GA 1\nJOHNSTON_NC 1\nCHESTER_SC 1\nPRINCE_VA 1\nCHARLESTON_SC 1\nDARE_NC 1\nFAIRFIELD_SC 1\nMARION_SC 1\nGUILFORD_NC 1\nJASPER_GA 1\nCALDWELL_LA 1\nMERIWETHER_GA 1\nISLE OF WIGHT_VA 1\nWASHINGTON_NC 1\nBARNWELL_SC 1\nELBERT_GA 1\nCHESTERFIELD_SC 1\nDE SOTO_LA 1\nNOTTOWAY_VA 1\nPOLK_NC 1\nTALBOT_GA 1\nHANOVER_VA 1\nPRINCE EDWARD_VA 1\nGREENE_GA 1\nMONROE_GA 1\nGLOUCESTER_VA 1\nCALHOUN_SC 1\nBARROW_GA 1\nPERRY_AL 1\nLENOIR_NC 1\nLAURENS_SC 1\nGASTON_NC 1\nAPPOMATTOX_VA 1\nLAWRENCE_AL 1\nBUCKINGHAM_VA 1\nANDERSON_SC 1\nCHARLES CITY_VA 1\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "county_id = {}\nfor i, county in enumerate(usable_counties):\n county_id[county] = i+1\ncounty_id\n",
"execution_count": 563,
"outputs": [
{
"execution_count": 563,
"output_type": "execute_result",
"data": {
"text/plain": "{u'ANSON_NC': 1,\n u'BEAUFORT_NC': 2,\n u'BERKELEY_SC': 3,\n u'BERTIE_NC': 4,\n u'BRUNSWICK_NC': 5,\n u'BRUNSWICK_VA': 6,\n u'CHESTERFIELD_VA': 7,\n u'CHOCTAW_AL': 8,\n u'CLARKE_AL': 9,\n u'CLEVELAND_AR': 10,\n u'COLUMBUS_NC': 11,\n u'CRAVEN_NC': 12,\n u'DINWIDDIE_VA': 13,\n u'GEORGETOWN_SC': 14,\n u'GREENE_AL': 15,\n u'HALIFAX_NC': 16,\n u'HERTFORD_NC': 17,\n u'JASPER_MS': 18,\n u'LEVY_FL': 19,\n u'MARENGO_AL': 20,\n u'MARION_FL': 21,\n u'MARTIN_NC': 22,\n u'MECKLENBURG_VA': 23,\n u'McCURTAIN_OK': 24,\n u'NEWBERRY_SC': 25,\n u'ONSLOW_NC': 26,\n u'PICKENS_AL': 27,\n u'PRINCE GEORGE_VA': 28,\n u'SPOTSYLVANIA_VA': 29,\n u'SUMTER_AL': 30,\n u'SUSSEX_VA': 31,\n u'TUSCALOOSA_AL': 32,\n u'WAKE_NC': 33,\n u'WILCOX_AL': 34}"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf['usable'] = loc_hierf.apply(lambda row: row.county_state in county_id, axis=1)",
"execution_count": 564,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "drop = loc_hierf[loc_hierf.usable==False]",
"execution_count": 565,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf = loc_hierf.drop(drop.index)",
"execution_count": 566,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf",
"execution_count": 568,
"outputs": [
{
"execution_count": 568,
"output_type": "execute_result",
"data": {
"text/plain": " county state lat long countyid 0-10037-01-257 \\\n8 COLUMBUS NC 34.33010 -78.70453 NaN NA \n10 ONSLOW NC 34.75963 -77.40977 NaN 11 \n11 ONSLOW NC 34.75963 -77.40977 NaN 11 \n12 GEORGETOWN SC 33.36318 -79.30539 NaN NA \n13 BEAUFORT NC 35.55349 -77.05205 NaN 11 \n14 BERTIE NC 35.99815 -76.94897 NaN 12 \n15 CRAVEN NC 35.10917 -77.06917 NaN 12 \n16 ONSLOW NC 34.75963 -77.40977 NaN 11 \n18 BEAUFORT NC 35.55349 -77.05205 NaN 11 \n20 CRAVEN NC 35.10917 -77.06917 NaN 11 \n21 CRAVEN NC 35.10917 -77.06917 NaN 11 \n24 CRAVEN NC 35.10917 -77.06917 NaN 11 \n25 BEAUFORT NC 35.55349 -77.05205 NaN 11 \n26 BEAUFORT NC 35.55349 -77.05205 NaN 11 \n27 BEAUFORT NC 35.55349 -77.05205 NaN 12 \n28 BEAUFORT NC 35.55349 -77.05205 NaN 12 \n29 BEAUFORT NC 35.55349 -77.05205 NaN 22 \n30 BEAUFORT NC 35.55349 -77.05205 NaN 11 \n31 BEAUFORT NC 35.55349 -77.05205 NaN 11 \n32 BEAUFORT NC 35.55349 -77.05205 NaN 22 \n33 BEAUFORT NC 35.55349 -77.05205 NaN NA \n34 BEAUFORT NC 35.55349 -77.05205 NaN 11 \n35 CRAVEN NC 35.10917 -77.06917 NaN 22 \n36 CRAVEN NC 35.10917 -77.06917 NaN 11 \n37 CRAVEN NC 35.10917 -77.06917 NaN 11 \n38 ONSLOW NC 34.75963 -77.40977 NaN 11 \n39 ONSLOW NC 34.75963 -77.40977 NaN 11 \n40 ONSLOW NC 34.75963 -77.40977 NaN 12 \n41 ONSLOW NC 34.75963 -77.40977 NaN 11 \n42 ONSLOW NC 34.75963 -77.40977 NaN NA \n.. ... ... ... ... ... ... \n533 MECKLENBURG VA 36.66800 -78.38900 NaN 11 \n534 MECKLENBURG VA 36.66800 -78.38900 NaN 11 \n544 BRUNSWICK VA 36.75843 -77.85042 NaN 11 \n545 PRINCE GEORGE VA 37.22056 -77.28806 NaN 12 \n546 PRINCE GEORGE VA 37.22056 -77.28806 NaN 11 \n547 PRINCE GEORGE VA 37.22056 -77.28806 NaN 11 \n548 PRINCE GEORGE VA 37.22056 -77.28806 NaN 12 \n549 PRINCE GEORGE VA 37.22056 -77.28806 NaN 11 \n553 SPOTSYLVANIA VA 38.20208 -77.58750 NaN 12 \n555 SPOTSYLVANIA VA 38.20208 -77.58750 NaN 12 \n556 SPOTSYLVANIA VA 38.20208 -77.58750 NaN 12 \n581 JASPER MS 31.97676 -89.27957 NaN 11 \n582 JASPER MS 31.97676 -89.27957 NaN 12 \n583 McCURTAIN OK 33.89639 -94.82917 NaN 12 \n584 McCURTAIN OK 33.89639 -94.82917 NaN 12 \n585 McCURTAIN OK 33.89639 -94.82917 NaN 12 \n594 BERKELEY SC 33.19661 -80.00666 NaN 11 \n595 HALIFAX NC 36.32840 -77.59073 NaN 12 \n598 GEORGETOWN SC 33.36318 -79.30539 NaN 12 \n599 COLUMBUS NC 34.33010 -78.70453 NaN 11 \n600 GEORGETOWN SC 33.36318 -79.30539 NaN 11 \n605 ONSLOW NC 34.64551 -77.41295 NaN 22 \n607 ONSLOW NC 34.64551 -77.41295 NaN 11 \n610 CRAVEN NC 35.10917 -77.06917 NaN 12 \n612 CRAVEN NC 35.10917 -77.06917 NaN 12 \n614 BERKELEY SC 33.19661 -80.00666 NaN 12 \n615 BERKELEY SC 33.19661 -80.00666 NaN 11 \n616 BERKELEY SC 33.19661 -80.00666 NaN 11 \n617 BERKELEY SC 33.19661 -80.00666 NaN 11 \n618 BERKELEY SC 33.19661 -80.00666 NaN 22 \n\n 0-10040-02-394 0-10044-01-392 0-10048-01-60 0-10051-02-166 0-10054-01-402 \\\n8 22 11 11 11 11 \n10 22 12 11 11 12 \n11 12 NA 12 11 12 \n12 12 NA NA 11 12 \n13 NA 12 12 11 22 \n14 12 11 11 11 12 \n15 12 12 12 11 12 \n16 11 11 11 11 22 \n18 11 22 11 12 12 \n20 12 12 22 11 22 \n21 12 11 12 11 12 \n24 22 11 11 11 12 \n25 12 NA 11 11 22 \n26 11 11 11 11 12 \n27 22 22 11 11 22 \n28 11 22 11 11 12 \n29 22 12 11 12 12 \n30 22 11 12 11 12 \n31 22 22 11 11 12 \n32 11 11 11 11 12 \n33 11 12 22 11 22 \n34 12 12 11 11 12 \n35 22 12 12 12 22 \n36 12 12 11 11 12 \n37 11 11 11 11 22 \n38 22 11 11 11 12 \n39 12 12 12 11 12 \n40 12 22 11 11 12 \n41 12 22 22 11 22 \n42 11 22 12 11 12 \n.. ... ... ... ... ... \n533 12 22 11 11 11 \n534 11 22 11 12 12 \n544 11 11 12 11 12 \n545 11 12 12 11 12 \n546 12 22 12 12 12 \n547 22 11 11 12 12 \n548 12 12 11 11 11 \n549 12 11 11 11 12 \n553 12 NA 11 11 12 \n555 12 12 11 12 11 \n556 12 22 12 12 12 \n581 11 12 22 11 22 \n582 12 22 11 11 12 \n583 11 22 12 11 12 \n584 12 22 22 11 22 \n585 12 22 22 11 22 \n594 12 12 11 11 12 \n595 12 22 11 11 22 \n598 12 12 11 11 22 \n599 11 11 11 11 22 \n600 11 22 11 11 12 \n605 12 11 12 11 22 \n607 12 12 12 11 11 \n610 22 12 12 11 22 \n612 12 22 11 11 12 \n614 12 22 11 11 12 \n615 12 11 12 11 12 \n616 12 12 22 11 11 \n617 22 11 11 12 22 \n618 12 12 11 11 11 \n\n 0-10067-03-111 0-10079-02-168 0-10112-01-169 0-10113-01-119 ... \\\n8 11 11 11 12 ... \n10 11 11 11 11 ... \n11 11 11 11 12 ... \n12 11 11 12 12 ... \n13 11 11 11 12 ... \n14 11 11 11 11 ... \n15 11 11 11 22 ... \n16 12 11 12 11 ... \n18 11 11 11 12 ... \n20 11 12 11 11 ... \n21 11 11 11 12 ... \n24 11 11 11 11 ... \n25 11 11 12 12 ... \n26 11 11 11 12 ... \n27 11 11 11 11 ... \n28 11 12 11 11 ... \n29 11 11 11 11 ... \n30 11 11 11 12 ... \n31 11 11 12 22 ... \n32 11 11 11 12 ... \n33 11 11 11 NA ... \n34 11 11 11 12 ... \n35 11 11 11 11 ... \n36 12 12 11 11 ... \n37 11 11 12 11 ... \n38 11 11 11 12 ... \n39 11 12 11 12 ... \n40 11 11 12 11 ... \n41 11 11 11 11 ... \n42 11 11 11 11 ... \n.. ... ... ... ... ... \n533 11 11 11 12 ... \n534 11 11 12 11 ... \n544 11 12 11 11 ... \n545 11 11 11 11 ... \n546 11 11 12 12 ... \n547 11 11 11 12 ... \n548 11 11 11 12 ... \n549 11 11 11 11 ... \n553 11 11 11 12 ... \n555 12 11 12 11 ... \n556 11 11 11 11 ... \n581 11 11 11 11 ... \n582 11 11 11 11 ... \n583 11 11 11 11 ... \n584 11 11 11 11 ... \n585 11 11 11 11 ... \n594 11 11 11 12 ... \n595 11 11 11 12 ... \n598 11 11 11 12 ... \n599 11 11 11 12 ... \n600 11 22 11 22 ... \n605 11 11 11 11 ... \n607 11 11 12 11 ... \n610 12 11 11 11 ... \n612 11 11 11 22 ... \n614 11 11 11 11 ... \n615 11 12 11 11 ... \n616 11 11 11 12 ... \n617 11 11 11 12 ... \n618 11 11 11 11 ... \n\n UMN-CL307Contig1-04-143 UMN-CL319Contig1-03-131 UMN-CL326Contig1-05-421 \\\n8 12 11 11 \n10 11 11 12 \n11 11 12 NA \n12 12 NA 12 \n13 11 11 11 \n14 11 12 11 \n15 11 11 11 \n16 11 12 11 \n18 11 11 11 \n20 12 11 12 \n21 22 12 22 \n24 11 11 11 \n25 12 11 12 \n26 12 22 11 \n27 11 11 11 \n28 12 11 12 \n29 NA 11 11 \n30 11 11 11 \n31 12 12 11 \n32 12 11 22 \n33 NA 11 11 \n34 11 11 12 \n35 11 11 12 \n36 11 11 11 \n37 11 11 11 \n38 11 11 11 \n39 11 12 12 \n40 11 11 12 \n41 11 11 11 \n42 11 11 11 \n.. ... ... ... \n533 11 11 11 \n534 12 12 11 \n544 12 12 NA \n545 12 12 11 \n546 22 11 11 \n547 12 12 11 \n548 12 11 12 \n549 12 11 11 \n553 22 11 11 \n555 12 11 11 \n556 11 11 12 \n581 11 12 12 \n582 11 11 22 \n583 11 11 11 \n584 11 11 11 \n585 11 11 11 \n594 11 11 11 \n595 12 12 11 \n598 12 12 11 \n599 11 12 11 \n600 11 11 11 \n605 11 11 22 \n607 11 11 22 \n610 12 12 22 \n612 12 12 12 \n614 11 11 11 \n615 11 12 11 \n616 12 22 11 \n617 12 11 12 \n618 11 22 NA \n\n UMN-CL339Contig1-05-39 UMN-CL34Contig1-03-89 UMN-CL353Contig1-04-64 \\\n8 11 11 11 \n10 11 12 11 \n11 11 11 11 \n12 11 NA 11 \n13 11 11 11 \n14 12 12 11 \n15 11 12 11 \n16 11 11 11 \n18 12 11 11 \n20 11 12 11 \n21 11 11 11 \n24 11 12 11 \n25 11 12 11 \n26 12 12 11 \n27 11 11 11 \n28 11 NA 11 \n29 11 11 11 \n30 11 11 11 \n31 11 11 11 \n32 11 11 11 \n33 11 11 11 \n34 11 12 11 \n35 11 11 11 \n36 11 11 11 \n37 11 11 11 \n38 11 12 11 \n39 11 11 11 \n40 11 12 11 \n41 11 11 11 \n42 11 NA 11 \n.. ... ... ... \n533 12 11 11 \n534 11 11 11 \n544 11 11 11 \n545 11 12 11 \n546 11 11 11 \n547 11 12 11 \n548 11 12 11 \n549 11 12 11 \n553 11 12 11 \n555 11 11 11 \n556 11 11 11 \n581 11 12 11 \n582 11 12 11 \n583 11 12 11 \n584 11 11 11 \n585 11 11 11 \n594 11 11 11 \n595 11 22 11 \n598 11 11 11 \n599 11 12 11 \n600 11 11 11 \n605 11 12 12 \n607 11 12 11 \n610 11 12 11 \n612 11 11 11 \n614 11 11 11 \n615 12 11 11 \n616 11 22 11 \n617 11 11 11 \n618 11 11 11 \n\n UMN-CL362Contig1-07-133 UMN-CL363Contig1-01-233 UMN-CL379Contig1-12-117 \\\n8 NA 11 11 \n10 12 11 11 \n11 11 11 11 \n12 12 11 11 \n13 12 11 11 \n14 12 11 11 \n15 12 12 11 \n16 12 11 11 \n18 NA 11 11 \n20 11 11 12 \n21 12 12 11 \n24 11 11 11 \n25 12 11 11 \n26 12 11 11 \n27 12 11 11 \n28 12 11 11 \n29 11 11 11 \n30 12 11 11 \n31 11 11 11 \n32 11 11 11 \n33 NA 11 11 \n34 NA 12 11 \n35 12 11 11 \n36 12 11 11 \n37 12 11 11 \n38 12 11 11 \n39 NA NA 11 \n40 11 11 11 \n41 11 12 11 \n42 11 11 11 \n.. ... ... ... \n533 12 11 11 \n534 11 12 11 \n544 11 11 11 \n545 12 11 11 \n546 11 11 11 \n547 12 11 11 \n548 11 11 11 \n549 11 11 11 \n553 12 12 11 \n555 12 11 11 \n556 11 11 11 \n581 11 12 11 \n582 11 11 11 \n583 11 11 11 \n584 11 11 11 \n585 12 11 11 \n594 NA 11 11 \n595 11 11 11 \n598 11 11 11 \n599 11 11 11 \n600 11 12 11 \n605 12 12 11 \n607 12 11 11 \n610 12 NA 11 \n612 11 11 11 \n614 NA 11 11 \n615 12 11 11 \n616 11 11 11 \n617 NA 11 11 \n618 12 NA 11 \n\n UMN-CL424Contig1-03-94 UMN-CL54Contig1-07-88 UMN-CL91Contig1-02-246 \\\n8 12 11 12 \n10 11 12 11 \n11 11 12 11 \n12 11 NA 11 \n13 22 11 12 \n14 11 12 11 \n15 11 12 11 \n16 11 11 11 \n18 12 11 11 \n20 11 12 22 \n21 12 11 11 \n24 12 12 11 \n25 12 11 11 \n26 11 11 11 \n27 12 11 11 \n28 12 12 11 \n29 11 11 11 \n30 11 11 12 \n31 12 11 12 \n32 11 11 12 \n33 NA 12 NA \n34 11 11 11 \n35 12 12 22 \n36 11 11 11 \n37 12 12 11 \n38 12 11 11 \n39 22 11 11 \n40 22 11 12 \n41 11 12 11 \n42 11 12 11 \n.. ... ... ... \n533 11 12 12 \n534 11 22 12 \n544 11 11 11 \n545 12 11 12 \n546 11 11 12 \n547 11 11 11 \n548 11 11 11 \n549 11 11 12 \n553 11 12 11 \n555 11 12 11 \n556 12 11 11 \n581 12 12 12 \n582 11 22 22 \n583 22 12 11 \n584 11 12 11 \n585 12 11 11 \n594 12 11 11 \n595 11 12 12 \n598 11 11 11 \n599 12 12 11 \n600 12 11 12 \n605 12 12 11 \n607 11 12 11 \n610 11 11 22 \n612 11 12 12 \n614 12 11 11 \n615 12 11 11 \n616 11 11 11 \n617 22 12 12 \n618 22 12 11 \n\n UMN-CL97Contig county_state usable \n8 22 COLUMBUS_NC True \n10 11 ONSLOW_NC True \n11 12 ONSLOW_NC True \n12 NA GEORGETOWN_SC True \n13 11 BEAUFORT_NC True \n14 11 BERTIE_NC True \n15 11 CRAVEN_NC True \n16 11 ONSLOW_NC True \n18 11 BEAUFORT_NC True \n20 11 CRAVEN_NC True \n21 11 CRAVEN_NC True \n24 11 CRAVEN_NC True \n25 11 BEAUFORT_NC True \n26 12 BEAUFORT_NC True \n27 12 BEAUFORT_NC True \n28 11 BEAUFORT_NC True \n29 12 BEAUFORT_NC True \n30 11 BEAUFORT_NC True \n31 12 BEAUFORT_NC True \n32 12 BEAUFORT_NC True \n33 12 BEAUFORT_NC True \n34 11 BEAUFORT_NC True \n35 11 CRAVEN_NC True \n36 NA CRAVEN_NC True \n37 11 CRAVEN_NC True \n38 11 ONSLOW_NC True \n39 11 ONSLOW_NC True \n40 11 ONSLOW_NC True \n41 12 ONSLOW_NC True \n42 11 ONSLOW_NC True \n.. ... ... ... \n533 11 MECKLENBURG_VA True \n534 11 MECKLENBURG_VA True \n544 22 BRUNSWICK_VA True \n545 11 PRINCE GEORGE_VA True \n546 12 PRINCE GEORGE_VA True \n547 12 PRINCE GEORGE_VA True \n548 12 PRINCE GEORGE_VA True \n549 12 PRINCE GEORGE_VA True \n553 11 SPOTSYLVANIA_VA True \n555 11 SPOTSYLVANIA_VA True \n556 11 SPOTSYLVANIA_VA True \n581 12 JASPER_MS True \n582 11 JASPER_MS True \n583 22 McCURTAIN_OK True \n584 11 McCURTAIN_OK True \n585 11 McCURTAIN_OK True \n594 11 BERKELEY_SC True \n595 22 HALIFAX_NC True \n598 12 GEORGETOWN_SC True \n599 12 COLUMBUS_NC True \n600 11 GEORGETOWN_SC True \n605 11 ONSLOW_NC True \n607 11 ONSLOW_NC True \n610 12 CRAVEN_NC True \n612 11 CRAVEN_NC True \n614 12 BERKELEY_SC True \n615 12 BERKELEY_SC True \n616 12 BERKELEY_SC True \n617 12 BERKELEY_SC True \n618 11 BERKELEY_SC True \n\n[388 rows x 3089 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>county</th>\n <th>state</th>\n <th>lat</th>\n <th>long</th>\n <th>countyid</th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>...</th>\n <th>UMN-CL307Contig1-04-143</th>\n <th>UMN-CL319Contig1-03-131</th>\n <th>UMN-CL326Contig1-05-421</th>\n <th>UMN-CL339Contig1-05-39</th>\n <th>UMN-CL34Contig1-03-89</th>\n <th>UMN-CL353Contig1-04-64</th>\n <th>UMN-CL362Contig1-07-133</th>\n <th>UMN-CL363Contig1-01-233</th>\n <th>UMN-CL379Contig1-12-117</th>\n <th>UMN-CL424Contig1-03-94</th>\n <th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n <th>county_state</th>\n <th>usable</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>8 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> NaN</td>\n <td> NA</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> COLUMBUS_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>10 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>11 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>12 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> NaN</td>\n <td> NA</td>\n <td> 12</td>\n <td> NA</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> NA</td>\n <td> GEORGETOWN_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>13 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 11</td>\n <td> NA</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>14 </th>\n <td> BERTIE</td>\n <td> NC</td>\n <td> 35.99815</td>\n <td>-76.94897</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> BERTIE_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>15 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>16 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>18 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>20 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>21 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 22</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>24 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>25 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>26 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>27 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 22</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>28 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>29 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 22</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>30 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>31 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 22</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td>...</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>32 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>33 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td>...</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 12</td>\n <td> NA</td>\n <td> 12</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>34 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>35 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> NaN</td>\n <td> 22</td>\n <td> 22</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>36 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>37 </th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>38 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>39 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> NA</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>40 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>41 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 22</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>42 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> NaN</td>\n <td> NA</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>533</th>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 36.66800</td>\n <td>-78.38900</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> MECKLENBURG_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>534</th>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 36.66800</td>\n <td>-78.38900</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> MECKLENBURG_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>544</th>\n <td> BRUNSWICK</td>\n <td> VA</td>\n <td> 36.75843</td>\n <td>-77.85042</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 12</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> BRUNSWICK_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>545</th>\n <td> PRINCE GEORGE</td>\n <td> VA</td>\n <td> 37.22056</td>\n <td>-77.28806</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> PRINCE GEORGE_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>546</th>\n <td> PRINCE GEORGE</td>\n <td> VA</td>\n <td> 37.22056</td>\n <td>-77.28806</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td>...</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> PRINCE GEORGE_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>547</th>\n <td> PRINCE GEORGE</td>\n <td> VA</td>\n <td> 37.22056</td>\n <td>-77.28806</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> PRINCE GEORGE_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>548</th>\n <td> PRINCE GEORGE</td>\n <td> VA</td>\n <td> 37.22056</td>\n <td>-77.28806</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> PRINCE GEORGE_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>549</th>\n <td> PRINCE GEORGE</td>\n <td> VA</td>\n <td> 37.22056</td>\n <td>-77.28806</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> PRINCE GEORGE_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>553</th>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 38.20208</td>\n <td>-77.58750</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> SPOTSYLVANIA_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>555</th>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 38.20208</td>\n <td>-77.58750</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> SPOTSYLVANIA_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>556</th>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 38.20208</td>\n <td>-77.58750</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> SPOTSYLVANIA_VA</td>\n <td> True</td>\n </tr>\n <tr>\n <th>581</th>\n <td> JASPER</td>\n <td> MS</td>\n <td> 31.97676</td>\n <td>-89.27957</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> JASPER_MS</td>\n <td> True</td>\n </tr>\n <tr>\n <th>582</th>\n <td> JASPER</td>\n <td> MS</td>\n <td> 31.97676</td>\n <td>-89.27957</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 22</td>\n <td> 11</td>\n <td> JASPER_MS</td>\n <td> True</td>\n </tr>\n <tr>\n <th>583</th>\n <td> McCURTAIN</td>\n <td> OK</td>\n <td> 33.89639</td>\n <td>-94.82917</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> McCURTAIN_OK</td>\n <td> True</td>\n </tr>\n <tr>\n <th>584</th>\n <td> McCURTAIN</td>\n <td> OK</td>\n <td> 33.89639</td>\n <td>-94.82917</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 22</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> McCURTAIN_OK</td>\n <td> True</td>\n </tr>\n <tr>\n <th>585</th>\n <td> McCURTAIN</td>\n <td> OK</td>\n <td> 33.89639</td>\n <td>-94.82917</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 22</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> McCURTAIN_OK</td>\n <td> True</td>\n </tr>\n <tr>\n <th>594</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> BERKELEY_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>595</th>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 36.32840</td>\n <td>-77.59073</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> HALIFAX_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>598</th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> GEORGETOWN_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>599</th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> COLUMBUS_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>600</th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 22</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> GEORGETOWN_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>605</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.64551</td>\n <td>-77.41295</td>\n <td> NaN</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>607</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.64551</td>\n <td>-77.41295</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>610</th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 22</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>612</th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td>...</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>614</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> NaN</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> BERKELEY_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>615</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> BERKELEY_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>616</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> BERKELEY_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>617</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> NaN</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> BERKELEY_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>618</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> NaN</td>\n <td> 22</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 22</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> BERKELEY_SC</td>\n <td> True</td>\n </tr>\n </tbody>\n</table>\n<p>388 rows × 3089 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf['countyid'] = loc_hierf.apply(lambda row: county_id[row.county_state], axis=1)",
"execution_count": 569,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf[0:10]",
"execution_count": 570,
"outputs": [
{
"execution_count": 570,
"output_type": "execute_result",
"data": {
"text/plain": " county state lat long countyid 0-10037-01-257 \\\n8 COLUMBUS NC 34.33010 -78.70453 11 NA \n10 ONSLOW NC 34.75963 -77.40977 26 11 \n11 ONSLOW NC 34.75963 -77.40977 26 11 \n12 GEORGETOWN SC 33.36318 -79.30539 14 NA \n13 BEAUFORT NC 35.55349 -77.05205 2 11 \n14 BERTIE NC 35.99815 -76.94897 4 12 \n15 CRAVEN NC 35.10917 -77.06917 12 12 \n16 ONSLOW NC 34.75963 -77.40977 26 11 \n18 BEAUFORT NC 35.55349 -77.05205 2 11 \n20 CRAVEN NC 35.10917 -77.06917 12 11 \n\n 0-10040-02-394 0-10044-01-392 0-10048-01-60 0-10051-02-166 0-10054-01-402 \\\n8 22 11 11 11 11 \n10 22 12 11 11 12 \n11 12 NA 12 11 12 \n12 12 NA NA 11 12 \n13 NA 12 12 11 22 \n14 12 11 11 11 12 \n15 12 12 12 11 12 \n16 11 11 11 11 22 \n18 11 22 11 12 12 \n20 12 12 22 11 22 \n\n 0-10067-03-111 0-10079-02-168 0-10112-01-169 0-10113-01-119 ... \\\n8 11 11 11 12 ... \n10 11 11 11 11 ... \n11 11 11 11 12 ... \n12 11 11 12 12 ... \n13 11 11 11 12 ... \n14 11 11 11 11 ... \n15 11 11 11 22 ... \n16 12 11 12 11 ... \n18 11 11 11 12 ... \n20 11 12 11 11 ... \n\n UMN-CL307Contig1-04-143 UMN-CL319Contig1-03-131 UMN-CL326Contig1-05-421 \\\n8 12 11 11 \n10 11 11 12 \n11 11 12 NA \n12 12 NA 12 \n13 11 11 11 \n14 11 12 11 \n15 11 11 11 \n16 11 12 11 \n18 11 11 11 \n20 12 11 12 \n\n UMN-CL339Contig1-05-39 UMN-CL34Contig1-03-89 UMN-CL353Contig1-04-64 \\\n8 11 11 11 \n10 11 12 11 \n11 11 11 11 \n12 11 NA 11 \n13 11 11 11 \n14 12 12 11 \n15 11 12 11 \n16 11 11 11 \n18 12 11 11 \n20 11 12 11 \n\n UMN-CL362Contig1-07-133 UMN-CL363Contig1-01-233 UMN-CL379Contig1-12-117 \\\n8 NA 11 11 \n10 12 11 11 \n11 11 11 11 \n12 12 11 11 \n13 12 11 11 \n14 12 11 11 \n15 12 12 11 \n16 12 11 11 \n18 NA 11 11 \n20 11 11 12 \n\n UMN-CL424Contig1-03-94 UMN-CL54Contig1-07-88 UMN-CL91Contig1-02-246 \\\n8 12 11 12 \n10 11 12 11 \n11 11 12 11 \n12 11 NA 11 \n13 22 11 12 \n14 11 12 11 \n15 11 12 11 \n16 11 11 11 \n18 12 11 11 \n20 11 12 22 \n\n UMN-CL97Contig county_state usable \n8 22 COLUMBUS_NC True \n10 11 ONSLOW_NC True \n11 12 ONSLOW_NC True \n12 NA GEORGETOWN_SC True \n13 11 BEAUFORT_NC True \n14 11 BERTIE_NC True \n15 11 CRAVEN_NC True \n16 11 ONSLOW_NC True \n18 11 BEAUFORT_NC True \n20 11 CRAVEN_NC True \n\n[10 rows x 3089 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>county</th>\n <th>state</th>\n <th>lat</th>\n <th>long</th>\n <th>countyid</th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>...</th>\n <th>UMN-CL307Contig1-04-143</th>\n <th>UMN-CL319Contig1-03-131</th>\n <th>UMN-CL326Contig1-05-421</th>\n <th>UMN-CL339Contig1-05-39</th>\n <th>UMN-CL34Contig1-03-89</th>\n <th>UMN-CL353Contig1-04-64</th>\n <th>UMN-CL362Contig1-07-133</th>\n <th>UMN-CL363Contig1-01-233</th>\n <th>UMN-CL379Contig1-12-117</th>\n <th>UMN-CL424Contig1-03-94</th>\n <th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n <th>county_state</th>\n <th>usable</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>8 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> NA</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> COLUMBUS_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>10</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>11</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>12</th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> NA</td>\n <td> 12</td>\n <td> NA</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> NA</td>\n <td> GEORGETOWN_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>13</th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> 2</td>\n <td> 11</td>\n <td> NA</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>14</th>\n <td> BERTIE</td>\n <td> NC</td>\n <td> 35.99815</td>\n <td>-76.94897</td>\n <td> 4</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> BERTIE_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>15</th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>16</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>18</th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> 2</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>20</th>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n </tbody>\n</table>\n<p>10 rows × 3089 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf.shape",
"execution_count": 571,
"outputs": [
{
"execution_count": 571,
"output_type": "execute_result",
"data": {
"text/plain": "(388, 3089)"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "sorted(loc_hierf.countyid.unique())",
"execution_count": 572,
"outputs": [
{
"execution_count": 572,
"output_type": "execute_result",
"data": {
"text/plain": "[1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 26,\n 27,\n 28,\n 29,\n 30,\n 31,\n 32,\n 33,\n 34]"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf.ix[:,4:-2].to_csv(\"hierf.txt\", sep=\"\\t\", header=True, index=False)",
"execution_count": 573,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "%%R\nlibrary(hierfstat)\ndata = read.table(\"hierf.txt\", header=T, sep=\"\\t\")\ndata = data[order(data$countyid),]\nlevels = data.frame(data$countyid)\nloci = data[,2:ncol(data)]\nbs = basic.stats(data)\nsaveRDS(bs, \"basic_stats.rds\")\nres = varcomp.glob(levels=levels, loci=loci, diploid=T)\nsaveRDS(res, \"hierf.rds\")",
"execution_count": 574,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "%%R\nbs = readRDS(\"basic_stats.rds\")\nres = readRDS(\"hierf.rds\")",
"execution_count": 575,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "res = com.convert_robj(ro.r('res'))",
"execution_count": 576,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bs = com.convert_robj(ro.r('bs'))\nFis = bs['Fis']\nHs = bs['Hs']\npop_freq_temp = bs['pop.freq']\npop_freq = {}\nperloc = bs['perloc']\nn_ind_samp = bs['n.ind.samp']\nHo = bs['Ho']\noverall = bs['overall']\n\nfor df in [Fis, Hs, perloc, n_ind_samp, Ho]:\n df.index = [x[1:].replace(\".\",\"-\") for x in df.index]\n\nfor locus, data in pop_freq_temp.items():\n if len(data) == 2:\n data.index = ['p','q']\n else:\n data.index = ['p']\n pop_freq[locus[1:].replace(\".\", \"-\")] = data\n\nHo = Ho.T\nperloc = perloc.T\nn_ind_samp = n_ind_samp.T\nHs = Hs.T\nFis = Fis.T",
"execution_count": 577,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "Ho",
"execution_count": 578,
"outputs": [
{
"execution_count": 578,
"output_type": "execute_result",
"data": {
"text/plain": " 0-10037-01-257 0-10040-02-394 0-10044-01-392 0-10048-01-60 \\\n1 0.2222 0.1111 0.4444 0.5556 \n2 0.2059 0.4194 0.5714 0.3824 \n3 0.1429 0.8571 0.4286 0.2857 \n4 0.6667 0.7143 0.5000 0.1667 \n5 0.4545 0.7778 0.7273 0.5000 \n6 0.6250 0.6250 0.2500 0.5000 \n7 0.4000 0.4000 0.5000 0.6000 \n8 0.4615 0.7692 0.5833 0.4615 \n9 0.1667 0.6667 0.5000 0.1667 \n10 0.6000 0.7500 0.8000 0.2000 \n11 0.6000 0.3333 0.3333 0.1667 \n12 0.2857 0.4091 0.4500 0.5238 \n13 0.5000 0.3750 0.2500 0.2500 \n14 0.2222 0.5000 0.5556 0.3333 \n15 0.3529 0.5000 0.5333 0.5882 \n16 0.3333 0.5000 0.5000 0.1667 \n17 0.5000 0.8000 0.5000 0.2000 \n18 0.3333 0.1667 0.6000 0.6000 \n19 0.4545 0.6364 0.6364 0.3636 \n20 0.5000 0.6667 0.6667 0.5000 \n21 0.5385 0.6154 0.6154 0.5385 \n22 0.5000 0.5000 0.6667 0.3333 \n23 0.3750 0.3750 0.5000 0.5000 \n24 1.0000 0.5714 0.1429 0.4286 \n25 0.2727 0.5455 0.5000 0.2727 \n26 0.2500 0.5385 0.3636 0.4615 \n27 0.6250 0.5294 0.4375 0.4118 \n28 0.4444 0.7778 0.4444 0.2222 \n29 0.6000 0.8000 0.2500 0.2000 \n30 0.1429 0.5714 0.4286 0.2857 \n31 0.6000 0.4000 0.2000 0.2000 \n32 0.4286 0.3968 0.4426 0.3492 \n33 0.4286 0.4286 0.7143 0.2857 \n34 0.4286 0.4286 0.1667 0.8571 \n\n 0-10051-02-166 0-10054-01-402 0-10067-03-111 0-10079-02-168 \\\n1 0.0000 0.5556 0.0000 0.1111 \n2 0.0606 0.5312 0.0571 0.0278 \n3 0.1429 0.4286 0.0000 0.1429 \n4 0.0000 0.7143 0.0000 0.0000 \n5 0.1000 0.6000 0.0000 0.0000 \n6 0.2500 0.8750 0.0000 0.1250 \n7 0.2000 0.4000 0.0000 0.0000 \n8 0.0769 0.5385 0.1538 0.0769 \n9 0.0000 0.4000 0.0000 0.1667 \n10 0.0000 0.4000 0.2000 0.0000 \n11 0.3333 0.3333 0.0000 0.1667 \n12 0.1364 0.6522 0.0909 0.1364 \n13 0.2500 0.5000 0.0000 0.0000 \n14 0.1000 0.7000 0.0000 0.2000 \n15 0.0000 0.2941 0.1176 0.0000 \n16 0.3333 0.5000 0.1667 0.0000 \n17 0.3000 0.4000 0.2000 0.2000 \n18 0.0000 0.1667 0.0000 0.0000 \n19 0.0909 0.4545 0.0909 0.1818 \n20 0.0000 0.6667 0.1667 0.1667 \n21 0.0769 0.3846 0.3077 0.4615 \n22 0.3333 0.1667 0.1667 0.0000 \n23 0.3750 0.3750 0.0000 0.0000 \n24 0.0000 0.2857 0.0000 0.0000 \n25 0.1818 0.5455 0.0000 0.0000 \n26 0.0769 0.5385 0.1538 0.0769 \n27 0.0625 0.2941 0.0625 0.0588 \n28 0.2222 0.5556 0.1111 0.0000 \n29 0.6000 0.6000 0.2000 0.0000 \n30 0.1429 0.5714 0.0000 0.0000 \n31 0.2000 0.6000 0.0000 0.0000 \n32 0.0476 0.4603 0.0968 0.0806 \n33 0.0000 0.4286 0.0000 0.2857 \n34 0.0000 0.4286 0.1429 0.0000 \n\n 0-10112-01-169 0-10113-01-119 0-10116-01-165 0-10151-01-86 \\\n1 0.1111 0.5556 0.1111 0.1111 \n2 0.2000 0.5152 0.0278 0.0857 \n3 0.0000 0.4286 0.0000 0.2857 \n4 0.2857 0.2857 0.0000 0.1429 \n5 0.1818 0.4545 0.0000 0.0000 \n6 0.0000 0.3750 0.0000 0.1250 \n7 0.4000 0.4000 0.2000 0.0000 \n8 0.0000 0.4615 0.0000 0.0769 \n9 0.3333 0.3333 0.0000 0.0000 \n10 0.0000 0.4000 0.0000 0.0000 \n11 0.0000 0.6667 0.0000 0.0000 \n12 0.0435 0.3636 0.0952 0.2000 \n13 0.0000 0.5000 0.0000 0.0000 \n14 0.2000 0.5000 0.2000 0.1000 \n15 0.1765 0.6471 0.1176 0.2353 \n16 0.3333 0.3333 0.0000 0.0000 \n17 0.2000 0.3000 0.0000 0.2000 \n18 0.0000 0.6000 0.1667 0.0000 \n19 0.1818 0.4545 0.0000 0.0000 \n20 0.0000 0.1667 0.1667 0.1667 \n21 0.1538 0.4615 0.0000 0.3077 \n22 0.1667 0.3333 0.1667 0.1667 \n23 0.2500 0.5000 0.1250 0.1250 \n24 0.0000 0.4286 0.1429 0.1429 \n25 0.0000 0.6364 0.0000 0.1818 \n26 0.2308 0.3077 0.0000 0.1538 \n27 0.0000 0.6471 0.1176 0.0588 \n28 0.2222 0.5556 0.1111 0.3333 \n29 0.2000 0.2000 0.0000 0.2000 \n30 0.0000 0.1429 0.0000 0.1429 \n31 0.2000 0.4000 0.0000 0.2000 \n32 0.0968 0.5714 0.0000 0.1774 \n33 0.1429 0.4286 0.0000 0.0000 \n34 0.0000 0.4286 0.1429 0.2857 \n\n 0-10162-01-255 0-10207-01-280 0-10210-01-41 ... \\\n1 0.1111 0.5556 0.0000 ... \n2 0.0571 0.3333 0.1714 ... \n3 0.0000 0.2857 0.0000 ... \n4 0.1429 0.7143 0.0000 ... \n5 0.0000 0.5455 0.1000 ... \n6 0.0000 0.1250 0.0000 ... \n7 0.0000 0.6000 0.2000 ... \n8 0.0000 0.1538 0.1667 ... \n9 0.1667 0.5000 0.2000 ... \n10 0.2000 0.2000 0.0000 ... \n11 0.1667 0.6667 0.0000 ... \n12 0.0952 0.4545 0.1176 ... \n13 0.1250 0.5000 0.0000 ... \n14 0.0000 0.3000 0.0000 ... \n15 0.1176 0.3529 0.0000 ... \n16 0.3333 0.5000 0.0000 ... \n17 0.4000 0.4000 0.0000 ... \n18 0.1667 0.1667 0.1667 ... \n19 0.1818 0.1818 0.0000 ... \n20 0.0000 0.3333 0.1667 ... \n21 0.0000 0.2308 0.0000 ... \n22 0.1667 0.3333 0.0000 ... \n23 0.1250 0.2500 0.1429 ... \n24 0.1429 0.1667 0.2500 ... \n25 0.3000 0.6364 0.0000 ... \n26 0.2308 0.3846 0.2500 ... \n27 0.0000 0.4375 0.0714 ... \n28 0.1111 0.3333 0.3750 ... \n29 0.6000 0.6000 0.0000 ... \n30 0.0000 0.1429 0.3333 ... \n31 0.2000 0.4000 0.0000 ... \n32 0.0476 0.3810 0.1017 ... \n33 0.4286 0.4286 0.0000 ... \n34 0.0000 0.1429 0.4000 ... \n\n MN-CL299Contig1-01-46 MN-CL306Contig1-04-261 MN-CL307Contig1-04-143 \\\n1 0.0000 0.0000 0.3333 \n2 0.0278 0.0606 0.4062 \n3 0.0000 0.4286 0.2857 \n4 0.1429 0.2857 0.1667 \n5 0.0000 0.0000 0.4545 \n6 0.0000 0.1250 0.3750 \n7 0.0000 0.0000 0.6000 \n8 0.0769 0.1538 0.2308 \n9 0.0000 0.6667 0.3333 \n10 0.0000 0.2500 0.0000 \n11 0.0000 0.2000 0.4000 \n12 0.0000 0.1364 0.2174 \n13 0.0000 0.2500 0.3750 \n14 0.1000 0.5000 0.4000 \n15 0.0000 0.2353 0.1765 \n16 0.0000 0.0000 0.6667 \n17 0.0000 0.2000 0.4444 \n18 0.0000 0.3333 0.1667 \n19 0.0000 0.0909 0.1818 \n20 0.0000 0.0000 0.1667 \n21 0.1538 0.4615 0.2308 \n22 0.0000 0.0000 0.3333 \n23 0.0000 0.0000 0.3750 \n24 0.0000 0.2857 0.0000 \n25 0.1818 0.1818 0.0909 \n26 0.0000 0.1667 0.0769 \n27 0.0588 0.0588 0.2941 \n28 0.0000 0.3333 0.5556 \n29 0.0000 0.2000 0.4000 \n30 0.0000 0.1429 0.2857 \n31 0.0000 0.0000 0.0000 \n32 0.1111 0.2581 0.3175 \n33 0.1429 0.1429 0.8571 \n34 0.0000 0.1429 0.4286 \n\n MN-CL319Contig1-03-131 MN-CL326Contig1-05-421 MN-CL339Contig1-05-39 \\\n1 0.2222 0.2222 0.1111 \n2 0.2647 0.1875 0.2286 \n3 0.1429 0.3333 0.1429 \n4 0.2857 0.5000 0.1429 \n5 0.0000 0.4000 0.0000 \n6 0.2500 0.4286 0.1250 \n7 0.4000 0.2000 0.2000 \n8 0.3846 0.4615 0.3077 \n9 0.1667 0.1667 0.0000 \n10 0.0000 0.0000 0.0000 \n11 0.6667 0.2000 0.0000 \n12 0.2727 0.3043 0.0000 \n13 0.1250 0.0000 0.0000 \n14 0.2222 0.3000 0.1000 \n15 0.1765 0.3529 0.1875 \n16 0.3333 0.3333 0.0000 \n17 0.1000 0.2222 0.1000 \n18 0.3333 0.2000 0.0000 \n19 0.2727 0.1818 0.0909 \n20 0.1667 0.6667 0.1667 \n21 0.3077 0.1538 0.0000 \n22 0.5000 0.5000 0.3333 \n23 0.3750 0.4286 0.1250 \n24 0.1429 0.0000 0.0000 \n25 0.2727 0.2727 0.1818 \n26 0.2308 0.2500 0.0769 \n27 0.2353 0.3333 0.0588 \n28 0.2222 0.2222 0.0000 \n29 0.0000 0.4000 0.0000 \n30 0.0000 0.4286 0.0000 \n31 0.2000 0.0000 0.0000 \n32 0.2857 0.3387 0.1111 \n33 0.5714 0.0000 0.2857 \n34 0.0000 0.1429 0.0000 \n\n MN-CL34Contig1-03-89 MN-CL353Contig1-04-64 MN-CL362Contig1-07-133 \\\n1 0.2857 0.0000 0.2500 \n2 0.3235 0.0278 0.5357 \n3 0.1429 0.0000 0.5000 \n4 0.4286 0.0000 0.3333 \n5 0.5455 0.0000 0.4000 \n6 0.5000 0.0000 0.5000 \n7 0.4000 0.0000 0.0000 \n8 0.3846 0.0000 0.7273 \n9 0.4000 0.0000 0.8000 \n10 0.8000 0.0000 0.5000 \n11 0.3333 0.0000 0.2000 \n12 0.3810 0.0000 0.5909 \n13 0.3750 0.0000 0.3750 \n14 0.2222 0.0000 0.3000 \n15 0.4375 0.0000 0.4286 \n16 0.6667 0.1667 0.0000 \n17 0.2000 0.0000 0.3750 \n18 0.6667 0.0000 0.6000 \n19 0.4545 0.0000 0.3636 \n20 0.5000 0.0000 0.6000 \n21 0.5385 0.0769 0.4000 \n22 0.3333 0.0000 0.2000 \n23 0.2500 0.1250 0.3750 \n24 0.4286 0.0000 0.2857 \n25 0.4545 0.0000 0.6667 \n26 0.5000 0.0769 0.4167 \n27 0.5000 0.0000 0.5294 \n28 0.4444 0.0000 0.4444 \n29 0.4000 0.0000 0.6000 \n30 0.5000 0.0000 0.7143 \n31 0.4000 0.0000 0.2000 \n32 0.3968 0.0000 0.4483 \n33 0.4286 0.0000 0.8571 \n34 0.2857 0.0000 0.6667 \n\n MN-CL363Contig1-01-233 MN-CL379Contig1-12-117 MN-CL424Contig1-03-94 \\\n1 0.3333 0.0000 0.4444 \n2 0.1389 0.0278 0.4706 \n3 0.0000 0.0000 0.5714 \n4 0.0000 0.0000 0.7143 \n5 0.2727 0.0000 0.3636 \n6 0.1250 0.0000 0.5000 \n7 0.2000 0.0000 0.2000 \n8 0.1538 0.0000 0.2308 \n9 0.1667 0.0000 0.3333 \n10 0.0000 0.0000 0.2000 \n11 0.1667 0.0000 0.6667 \n12 0.1364 0.0435 0.4545 \n13 0.0000 0.0000 0.2500 \n14 0.3000 0.0000 0.3000 \n15 0.0588 0.0000 0.2941 \n16 0.0000 0.0000 0.3333 \n17 0.1111 0.0000 0.3000 \n18 0.3333 0.0000 0.3333 \n19 0.0000 0.0000 0.6364 \n20 0.3333 0.0000 0.1667 \n21 0.2308 0.0769 0.6154 \n22 0.3333 0.0000 0.6667 \n23 0.1250 0.0000 0.3750 \n24 0.0000 0.0000 0.4286 \n25 0.0909 0.1818 0.0909 \n26 0.2500 0.0000 0.1538 \n27 0.0000 0.0000 0.2941 \n28 0.3333 0.0000 0.1111 \n29 0.2000 0.0000 0.4000 \n30 0.1429 0.0000 0.5714 \n31 0.2000 0.0000 0.6000 \n32 0.0484 0.0317 0.2698 \n33 0.0000 0.0000 0.2857 \n34 0.1429 0.0000 0.4286 \n\n MN-CL54Contig1-07-88 MN-CL91Contig1-02-246 MN-CL97Contig \n1 0.4444 0.6667 0.4444 \n2 0.3611 0.2353 0.4118 \n3 0.2857 0.1429 0.5714 \n4 0.5714 0.1429 0.1429 \n5 0.7273 0.4545 0.1000 \n6 0.1250 0.2500 0.3750 \n7 0.6000 0.0000 0.0000 \n8 0.3077 0.3077 0.3846 \n9 0.3333 0.1667 0.4000 \n10 0.5000 0.0000 0.2000 \n11 0.3333 0.1667 0.6667 \n12 0.4783 0.2727 0.2000 \n13 0.5000 0.3750 0.3750 \n14 0.2222 0.4000 0.3333 \n15 0.4118 0.1333 0.4118 \n16 0.5000 0.5000 0.0000 \n17 0.8000 0.3000 0.4444 \n18 0.3333 0.3333 0.2000 \n19 0.3636 0.2727 0.5455 \n20 0.5000 0.2000 0.1667 \n21 0.4615 0.2308 0.2308 \n22 0.5000 0.0000 0.3333 \n23 0.5000 0.2857 0.2500 \n24 0.2857 0.0000 0.2857 \n25 0.4545 0.2727 0.3636 \n26 0.6154 0.0769 0.2308 \n27 0.3750 0.0625 0.2667 \n28 0.1111 0.3333 0.6667 \n29 0.4000 0.2000 0.2000 \n30 0.1429 0.2857 0.2857 \n31 0.2000 0.6000 0.4000 \n32 0.3000 0.3492 0.3103 \n33 0.2857 0.5714 0.4286 \n34 0.4286 0.2857 0.1429 \n\n[34 rows x 3082 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>0-10116-01-165</th>\n <th>0-10151-01-86</th>\n <th>0-10162-01-255</th>\n <th>0-10207-01-280</th>\n <th>0-10210-01-41</th>\n <th>...</th>\n <th>MN-CL299Contig1-01-46</th>\n <th>MN-CL306Contig1-04-261</th>\n <th>MN-CL307Contig1-04-143</th>\n <th>MN-CL319Contig1-03-131</th>\n <th>MN-CL326Contig1-05-421</th>\n <th>MN-CL339Contig1-05-39</th>\n <th>MN-CL34Contig1-03-89</th>\n <th>MN-CL353Contig1-04-64</th>\n <th>MN-CL362Contig1-07-133</th>\n <th>MN-CL363Contig1-01-233</th>\n <th>MN-CL379Contig1-12-117</th>\n <th>MN-CL424Contig1-03-94</th>\n <th>MN-CL54Contig1-07-88</th>\n <th>MN-CL91Contig1-02-246</th>\n <th>MN-CL97Contig</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td> 0.2222</td>\n <td> 0.1111</td>\n <td> 0.4444</td>\n <td> 0.5556</td>\n <td> 0.0000</td>\n <td> 0.5556</td>\n <td> 0.0000</td>\n <td> 0.1111</td>\n <td> 0.1111</td>\n <td> 0.5556</td>\n <td> 0.1111</td>\n <td> 0.1111</td>\n <td> 0.1111</td>\n <td> 0.5556</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.2222</td>\n <td> 0.2222</td>\n <td> 0.1111</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.2500</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.4444</td>\n <td> 0.4444</td>\n <td> 0.6667</td>\n <td> 0.4444</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 0.2059</td>\n <td> 0.4194</td>\n <td> 0.5714</td>\n <td> 0.3824</td>\n <td> 0.0606</td>\n <td> 0.5312</td>\n <td> 0.0571</td>\n <td> 0.0278</td>\n <td> 0.2000</td>\n <td> 0.5152</td>\n <td> 0.0278</td>\n <td> 0.0857</td>\n <td> 0.0571</td>\n <td> 0.3333</td>\n <td> 0.1714</td>\n <td>...</td>\n <td> 0.0278</td>\n <td> 0.0606</td>\n <td> 0.4062</td>\n <td> 0.2647</td>\n <td> 0.1875</td>\n <td> 0.2286</td>\n <td> 0.3235</td>\n <td> 0.0278</td>\n <td> 0.5357</td>\n <td> 0.1389</td>\n <td> 0.0278</td>\n <td> 0.4706</td>\n <td> 0.3611</td>\n <td> 0.2353</td>\n <td> 0.4118</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 0.1429</td>\n <td> 0.8571</td>\n <td> 0.4286</td>\n <td> 0.2857</td>\n <td> 0.1429</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.2857</td>\n <td> 0.1429</td>\n <td> 0.3333</td>\n <td> 0.1429</td>\n <td> 0.1429</td>\n <td> 0.0000</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.5714</td>\n <td> 0.2857</td>\n <td> 0.1429</td>\n <td> 0.5714</td>\n </tr>\n <tr>\n <th>4</th>\n <td> 0.6667</td>\n <td> 0.7143</td>\n <td> 0.5000</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.7143</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.1429</td>\n <td> 0.7143</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.1429</td>\n <td> 0.2857</td>\n <td> 0.1667</td>\n <td> 0.2857</td>\n <td> 0.5000</td>\n <td> 0.1429</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.7143</td>\n <td> 0.5714</td>\n <td> 0.1429</td>\n <td> 0.1429</td>\n </tr>\n <tr>\n <th>5</th>\n <td> 0.4545</td>\n <td> 0.7778</td>\n <td> 0.7273</td>\n <td> 0.5000</td>\n <td> 0.1000</td>\n <td> 0.6000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.1818</td>\n <td> 0.4545</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.5455</td>\n <td> 0.1000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.4545</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.5455</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.2727</td>\n <td> 0.0000</td>\n <td> 0.3636</td>\n <td> 0.7273</td>\n <td> 0.4545</td>\n <td> 0.1000</td>\n </tr>\n <tr>\n <th>6</th>\n <td> 0.6250</td>\n <td> 0.6250</td>\n <td> 0.2500</td>\n <td> 0.5000</td>\n <td> 0.2500</td>\n <td> 0.8750</td>\n <td> 0.0000</td>\n <td> 0.1250</td>\n <td> 0.0000</td>\n <td> 0.3750</td>\n <td> 0.0000</td>\n <td> 0.1250</td>\n <td> 0.0000</td>\n <td> 0.1250</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.1250</td>\n <td> 0.3750</td>\n <td> 0.2500</td>\n <td> 0.4286</td>\n <td> 0.1250</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.5000</td>\n <td> 0.1250</td>\n <td> 0.0000</td>\n <td> 0.5000</td>\n <td> 0.1250</td>\n <td> 0.2500</td>\n <td> 0.3750</td>\n </tr>\n <tr>\n <th>7</th>\n <td> 0.4000</td>\n <td> 0.4000</td>\n <td> 0.5000</td>\n <td> 0.6000</td>\n <td> 0.2000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.4000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.6000</td>\n <td> 0.2000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.6000</td>\n <td> 0.4000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.6000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n </tr>\n <tr>\n <th>8</th>\n <td> 0.4615</td>\n <td> 0.7692</td>\n <td> 0.5833</td>\n <td> 0.4615</td>\n <td> 0.0769</td>\n <td> 0.5385</td>\n <td> 0.1538</td>\n <td> 0.0769</td>\n <td> 0.0000</td>\n <td> 0.4615</td>\n <td> 0.0000</td>\n <td> 0.0769</td>\n <td> 0.0000</td>\n <td> 0.1538</td>\n <td> 0.1667</td>\n <td>...</td>\n <td> 0.0769</td>\n <td> 0.1538</td>\n <td> 0.2308</td>\n <td> 0.3846</td>\n <td> 0.4615</td>\n <td> 0.3077</td>\n <td> 0.3846</td>\n <td> 0.0000</td>\n <td> 0.7273</td>\n <td> 0.1538</td>\n <td> 0.0000</td>\n <td> 0.2308</td>\n <td> 0.3077</td>\n <td> 0.3077</td>\n <td> 0.3846</td>\n </tr>\n <tr>\n <th>9</th>\n <td> 0.1667</td>\n <td> 0.6667</td>\n <td> 0.5000</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.5000</td>\n <td> 0.2000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.3333</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.8000</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.1667</td>\n <td> 0.4000</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 0.6000</td>\n <td> 0.7500</td>\n <td> 0.8000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.2500</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.8000</td>\n <td> 0.0000</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 0.6000</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.1667</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.6667</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.4000</td>\n <td> 0.6667</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.3333</td>\n <td> 0.1667</td>\n <td> 0.6667</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 0.2857</td>\n <td> 0.4091</td>\n <td> 0.4500</td>\n <td> 0.5238</td>\n <td> 0.1364</td>\n <td> 0.6522</td>\n <td> 0.0909</td>\n <td> 0.1364</td>\n <td> 0.0435</td>\n <td> 0.3636</td>\n <td> 0.0952</td>\n <td> 0.2000</td>\n <td> 0.0952</td>\n <td> 0.4545</td>\n <td> 0.1176</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.1364</td>\n <td> 0.2174</td>\n <td> 0.2727</td>\n <td> 0.3043</td>\n <td> 0.0000</td>\n <td> 0.3810</td>\n <td> 0.0000</td>\n <td> 0.5909</td>\n <td> 0.1364</td>\n <td> 0.0435</td>\n <td> 0.4545</td>\n <td> 0.4783</td>\n <td> 0.2727</td>\n <td> 0.2000</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 0.5000</td>\n <td> 0.3750</td>\n <td> 0.2500</td>\n <td> 0.2500</td>\n <td> 0.2500</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.1250</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.2500</td>\n <td> 0.3750</td>\n <td> 0.1250</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.3750</td>\n <td> 0.0000</td>\n <td> 0.3750</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2500</td>\n <td> 0.5000</td>\n <td> 0.3750</td>\n <td> 0.3750</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 0.2222</td>\n <td> 0.5000</td>\n <td> 0.5556</td>\n <td> 0.3333</td>\n <td> 0.1000</td>\n <td> 0.7000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.5000</td>\n <td> 0.2000</td>\n <td> 0.1000</td>\n <td> 0.0000</td>\n <td> 0.3000</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.1000</td>\n <td> 0.5000</td>\n <td> 0.4000</td>\n <td> 0.2222</td>\n <td> 0.3000</td>\n <td> 0.1000</td>\n <td> 0.2222</td>\n <td> 0.0000</td>\n <td> 0.3000</td>\n <td> 0.3000</td>\n <td> 0.0000</td>\n <td> 0.3000</td>\n <td> 0.2222</td>\n <td> 0.4000</td>\n <td> 0.3333</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 0.3529</td>\n <td> 0.5000</td>\n <td> 0.5333</td>\n <td> 0.5882</td>\n <td> 0.0000</td>\n <td> 0.2941</td>\n <td> 0.1176</td>\n <td> 0.0000</td>\n <td> 0.1765</td>\n <td> 0.6471</td>\n <td> 0.1176</td>\n <td> 0.2353</td>\n <td> 0.1176</td>\n <td> 0.3529</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.2353</td>\n <td> 0.1765</td>\n <td> 0.1765</td>\n <td> 0.3529</td>\n <td> 0.1875</td>\n <td> 0.4375</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.0588</td>\n <td> 0.0000</td>\n <td> 0.2941</td>\n <td> 0.4118</td>\n <td> 0.1333</td>\n <td> 0.4118</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 0.3333</td>\n <td> 0.5000</td>\n <td> 0.5000</td>\n <td> 0.1667</td>\n <td> 0.3333</td>\n <td> 0.5000</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.5000</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 0.5000</td>\n <td> 0.8000</td>\n <td> 0.5000</td>\n <td> 0.2000</td>\n <td> 0.3000</td>\n <td> 0.4000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.3000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.4000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.4444</td>\n <td> 0.1000</td>\n <td> 0.2222</td>\n <td> 0.1000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.3750</td>\n <td> 0.1111</td>\n <td> 0.0000</td>\n <td> 0.3000</td>\n <td> 0.8000</td>\n <td> 0.3000</td>\n <td> 0.4444</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 0.3333</td>\n <td> 0.1667</td>\n <td> 0.6000</td>\n <td> 0.6000</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.6000</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.1667</td>\n <td> 0.3333</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.0000</td>\n <td> 0.6000</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.2000</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 0.4545</td>\n <td> 0.6364</td>\n <td> 0.6364</td>\n <td> 0.3636</td>\n <td> 0.0909</td>\n <td> 0.4545</td>\n <td> 0.0909</td>\n <td> 0.1818</td>\n <td> 0.1818</td>\n <td> 0.4545</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.1818</td>\n <td> 0.1818</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.0909</td>\n <td> 0.1818</td>\n <td> 0.2727</td>\n <td> 0.1818</td>\n <td> 0.0909</td>\n <td> 0.4545</td>\n <td> 0.0000</td>\n <td> 0.3636</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.6364</td>\n <td> 0.3636</td>\n <td> 0.2727</td>\n <td> 0.5455</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 0.5000</td>\n <td> 0.6667</td>\n <td> 0.6667</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.1667</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td> 0.6667</td>\n <td> 0.1667</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.6000</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.5000</td>\n <td> 0.2000</td>\n <td> 0.1667</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 0.5385</td>\n <td> 0.6154</td>\n <td> 0.6154</td>\n <td> 0.5385</td>\n <td> 0.0769</td>\n <td> 0.3846</td>\n <td> 0.3077</td>\n <td> 0.4615</td>\n <td> 0.1538</td>\n <td> 0.4615</td>\n <td> 0.0000</td>\n <td> 0.3077</td>\n <td> 0.0000</td>\n <td> 0.2308</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.1538</td>\n <td> 0.4615</td>\n <td> 0.2308</td>\n <td> 0.3077</td>\n <td> 0.1538</td>\n <td> 0.0000</td>\n <td> 0.5385</td>\n <td> 0.0769</td>\n <td> 0.4000</td>\n <td> 0.2308</td>\n <td> 0.0769</td>\n <td> 0.6154</td>\n <td> 0.4615</td>\n <td> 0.2308</td>\n <td> 0.2308</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 0.5000</td>\n <td> 0.5000</td>\n <td> 0.6667</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.3333</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td> 0.1667</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.5000</td>\n <td> 0.5000</td>\n <td> 0.3333</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 0.3750</td>\n <td> 0.3750</td>\n <td> 0.5000</td>\n <td> 0.5000</td>\n <td> 0.3750</td>\n <td> 0.3750</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2500</td>\n <td> 0.5000</td>\n <td> 0.1250</td>\n <td> 0.1250</td>\n <td> 0.1250</td>\n <td> 0.2500</td>\n <td> 0.1429</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.3750</td>\n <td> 0.3750</td>\n <td> 0.4286</td>\n <td> 0.1250</td>\n <td> 0.2500</td>\n <td> 0.1250</td>\n <td> 0.3750</td>\n <td> 0.1250</td>\n <td> 0.0000</td>\n <td> 0.3750</td>\n <td> 0.5000</td>\n <td> 0.2857</td>\n <td> 0.2500</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 1.0000</td>\n <td> 0.5714</td>\n <td> 0.1429</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.1429</td>\n <td> 0.1429</td>\n <td> 0.1429</td>\n <td> 0.1667</td>\n <td> 0.2500</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 0.2727</td>\n <td> 0.5455</td>\n <td> 0.5000</td>\n <td> 0.2727</td>\n <td> 0.1818</td>\n <td> 0.5455</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.6364</td>\n <td> 0.0000</td>\n <td> 0.1818</td>\n <td> 0.3000</td>\n <td> 0.6364</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.1818</td>\n <td> 0.1818</td>\n <td> 0.0909</td>\n <td> 0.2727</td>\n <td> 0.2727</td>\n <td> 0.1818</td>\n <td> 0.4545</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.0909</td>\n <td> 0.1818</td>\n <td> 0.0909</td>\n <td> 0.4545</td>\n <td> 0.2727</td>\n <td> 0.3636</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 0.2500</td>\n <td> 0.5385</td>\n <td> 0.3636</td>\n <td> 0.4615</td>\n <td> 0.0769</td>\n <td> 0.5385</td>\n <td> 0.1538</td>\n <td> 0.0769</td>\n <td> 0.2308</td>\n <td> 0.3077</td>\n <td> 0.0000</td>\n <td> 0.1538</td>\n <td> 0.2308</td>\n <td> 0.3846</td>\n <td> 0.2500</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.1667</td>\n <td> 0.0769</td>\n <td> 0.2308</td>\n <td> 0.2500</td>\n <td> 0.0769</td>\n <td> 0.5000</td>\n <td> 0.0769</td>\n <td> 0.4167</td>\n <td> 0.2500</td>\n <td> 0.0000</td>\n <td> 0.1538</td>\n <td> 0.6154</td>\n <td> 0.0769</td>\n <td> 0.2308</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 0.6250</td>\n <td> 0.5294</td>\n <td> 0.4375</td>\n <td> 0.4118</td>\n <td> 0.0625</td>\n <td> 0.2941</td>\n <td> 0.0625</td>\n <td> 0.0588</td>\n <td> 0.0000</td>\n <td> 0.6471</td>\n <td> 0.1176</td>\n <td> 0.0588</td>\n <td> 0.0000</td>\n <td> 0.4375</td>\n <td> 0.0714</td>\n <td>...</td>\n <td> 0.0588</td>\n <td> 0.0588</td>\n <td> 0.2941</td>\n <td> 0.2353</td>\n <td> 0.3333</td>\n <td> 0.0588</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.5294</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2941</td>\n <td> 0.3750</td>\n <td> 0.0625</td>\n <td> 0.2667</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 0.4444</td>\n <td> 0.7778</td>\n <td> 0.4444</td>\n <td> 0.2222</td>\n <td> 0.2222</td>\n <td> 0.5556</td>\n <td> 0.1111</td>\n <td> 0.0000</td>\n <td> 0.2222</td>\n <td> 0.5556</td>\n <td> 0.1111</td>\n <td> 0.3333</td>\n <td> 0.1111</td>\n <td> 0.3333</td>\n <td> 0.3750</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.3333</td>\n <td> 0.5556</td>\n <td> 0.2222</td>\n <td> 0.2222</td>\n <td> 0.0000</td>\n <td> 0.4444</td>\n <td> 0.0000</td>\n <td> 0.4444</td>\n <td> 0.3333</td>\n <td> 0.0000</td>\n <td> 0.1111</td>\n <td> 0.1111</td>\n <td> 0.3333</td>\n <td> 0.6667</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 0.6000</td>\n <td> 0.8000</td>\n <td> 0.2500</td>\n <td> 0.2000</td>\n <td> 0.6000</td>\n <td> 0.6000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.6000</td>\n <td> 0.6000</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.6000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.4000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 0.1429</td>\n <td> 0.5714</td>\n <td> 0.4286</td>\n <td> 0.2857</td>\n <td> 0.1429</td>\n <td> 0.5714</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.3333</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.5000</td>\n <td> 0.0000</td>\n <td> 0.7143</td>\n <td> 0.1429</td>\n <td> 0.0000</td>\n <td> 0.5714</td>\n <td> 0.1429</td>\n <td> 0.2857</td>\n <td> 0.2857</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 0.6000</td>\n <td> 0.4000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.6000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.4000</td>\n <td> 0.0000</td>\n <td> 0.2000</td>\n <td> 0.2000</td>\n <td> 0.0000</td>\n <td> 0.6000</td>\n <td> 0.2000</td>\n <td> 0.6000</td>\n <td> 0.4000</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 0.4286</td>\n <td> 0.3968</td>\n <td> 0.4426</td>\n <td> 0.3492</td>\n <td> 0.0476</td>\n <td> 0.4603</td>\n <td> 0.0968</td>\n <td> 0.0806</td>\n <td> 0.0968</td>\n <td> 0.5714</td>\n <td> 0.0000</td>\n <td> 0.1774</td>\n <td> 0.0476</td>\n <td> 0.3810</td>\n <td> 0.1017</td>\n <td>...</td>\n <td> 0.1111</td>\n <td> 0.2581</td>\n <td> 0.3175</td>\n <td> 0.2857</td>\n <td> 0.3387</td>\n <td> 0.1111</td>\n <td> 0.3968</td>\n <td> 0.0000</td>\n <td> 0.4483</td>\n <td> 0.0484</td>\n <td> 0.0317</td>\n <td> 0.2698</td>\n <td> 0.3000</td>\n <td> 0.3492</td>\n <td> 0.3103</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 0.4286</td>\n <td> 0.4286</td>\n <td> 0.7143</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.1429</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td>...</td>\n <td> 0.1429</td>\n <td> 0.1429</td>\n <td> 0.8571</td>\n <td> 0.5714</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.8571</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.2857</td>\n <td> 0.5714</td>\n <td> 0.4286</td>\n </tr>\n <tr>\n <th>34</th>\n <td> 0.4286</td>\n <td> 0.4286</td>\n <td> 0.1667</td>\n <td> 0.8571</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.1429</td>\n <td> 0.0000</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.1429</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.4000</td>\n <td>...</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.4286</td>\n <td> 0.0000</td>\n <td> 0.1429</td>\n <td> 0.0000</td>\n <td> 0.2857</td>\n <td> 0.0000</td>\n <td> 0.6667</td>\n <td> 0.1429</td>\n <td> 0.0000</td>\n <td> 0.4286</td>\n <td> 0.4286</td>\n <td> 0.2857</td>\n <td> 0.1429</td>\n </tr>\n </tbody>\n</table>\n<p>34 rows × 3082 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "perloc['0-10037-01-257']",
"execution_count": 579,
"outputs": [
{
"execution_count": 579,
"output_type": "execute_result",
"data": {
"text/plain": "Ho 0.4312\nHs 0.4181\nHt 0.4139\nDst -0.0043\nHtp 0.4137\nDstp -0.0044\nFst -0.0103\nFstp -0.0106\nFis -0.0313\nDest -0.0075\nName: 0-10037-01-257, dtype: float64"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loci_fst['0-10037-01-257']",
"execution_count": 580,
"outputs": [
{
"execution_count": 580,
"output_type": "execute_result",
"data": {
"text/plain": "-0.018173713535718613"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "af['0-10037-01-257']",
"execution_count": 581,
"outputs": [
{
"execution_count": 581,
"output_type": "execute_result",
"data": {
"text/plain": "A A\nFis -0.03216907\nHe 0.4090983\nHo 0.4222586\nP 872\nPQ 258\nQ 350\na G\nnum_indiv 611\np 0.7135843\nq 0.2864157\ntotal_alleles 1222\nName: 0-10037-01-257, dtype: object"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_df = res['loc']\nF_df = res['F']\noverall_df = res['overall']",
"execution_count": 582,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "F_df",
"execution_count": 583,
"outputs": [
{
"execution_count": 583,
"output_type": "execute_result",
"data": {
"text/plain": " data.countyid Ind\nTotal 0.009032 0.014992\ndata.countyid 0.000000 0.006014",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>data.countyid</th>\n <th>Ind</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Total</th>\n <td> 0.009032</td>\n <td> 0.014992</td>\n </tr>\n <tr>\n <th>data.countyid</th>\n <td> 0.000000</td>\n <td> 0.006014</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "def compute_fst(series):\n Va = series[0]\n Vt = sum(series)\n return Va/Vt",
"execution_count": 584,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loci_fst = loc_df.apply(compute_fst, axis=1).dropna()\nloci_fst.index = [x[1:].replace(\".\", \"-\") for x in loci_fst.index]",
"execution_count": 585,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "plt.hist(loci_fst, bins=50)\nplt.xlim(-.03, .5)\nplt.title(\"Fst for %d loci ($\\mu=%.3f \\pm %.4f$) [%.3f, %.3f]\" % (len(loci_fst),\n np.mean(loci_fst),\n np.std(loci_fst),\n np.min(loci_fst),\n np.max(loci_fst)))\nplt.show()",
"execution_count": 1210,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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AhjxJkqQGMuRJkiQ1kCFPkiSpgQx5kiRJDWTIkyRJaiBDniRJUgMZ8iRJkhpo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\n5EmSJDWQIU+SJKmBDHmSJEkN9P8DOcM9ZmGg48cAAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fde39e9e810>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "loc_hierf.shape",
"execution_count": 587,
"outputs": [
{
"execution_count": 587,
"output_type": "execute_result",
"data": {
"text/plain": "(388, 3089)"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "trait = loc_hierf.apply(get_phenotype, axis=1)",
"execution_count": 588,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "trait_loc_hierf = trait.join(loc_hierf, how=\"inner\")",
"execution_count": 589,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "trait_complete = trait_loc_hierf.drop(trait_loc_hierf[np.isnan(trait_loc_hierf[trait_name])].index)",
"execution_count": 590,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "trait_complete[:5]",
"execution_count": 591,
"outputs": [
{
"execution_count": 591,
"output_type": "execute_result",
"data": {
"text/plain": " Longitude Latitude Clone_id sucrose county state lat \\\n8 -78.70453 34.33010 153B 5.578561 COLUMBUS NC 34.33010 \n12 -79.30539 33.36318 90C 5.718644 GEORGETOWN SC 33.36318 \n13 -77.05205 35.55349 382A 5.591146 BEAUFORT NC 35.55349 \n15 -77.06917 35.10917 383C 5.492224 CRAVEN NC 35.10917 \n18 -77.05205 35.55349 382A 5.591146 BEAUFORT NC 35.55349 \n\n long countyid 0-10037-01-257 0-10040-02-394 0-10044-01-392 \\\n8 -78.70453 11 NA 22 11 \n12 -79.30539 14 NA 12 NA \n13 -77.05205 2 11 NA 12 \n15 -77.06917 12 12 12 12 \n18 -77.05205 2 11 11 22 \n\n 0-10048-01-60 0-10051-02-166 0-10054-01-402 ... UMN-CL307Contig1-04-143 \\\n8 11 11 11 ... 12 \n12 NA 11 12 ... 12 \n13 12 11 22 ... 11 \n15 12 11 12 ... 11 \n18 11 12 12 ... 11 \n\n UMN-CL319Contig1-03-131 UMN-CL326Contig1-05-421 UMN-CL339Contig1-05-39 \\\n8 11 11 11 \n12 NA 12 11 \n13 11 11 11 \n15 11 11 11 \n18 11 11 12 \n\n UMN-CL34Contig1-03-89 UMN-CL353Contig1-04-64 UMN-CL362Contig1-07-133 \\\n8 11 11 NA \n12 NA 11 12 \n13 11 11 12 \n15 12 11 12 \n18 11 11 NA \n\n UMN-CL363Contig1-01-233 UMN-CL379Contig1-12-117 UMN-CL424Contig1-03-94 \\\n8 11 11 12 \n12 11 11 11 \n13 11 11 22 \n15 12 11 11 \n18 11 11 12 \n\n UMN-CL54Contig1-07-88 UMN-CL91Contig1-02-246 UMN-CL97Contig county_state \\\n8 11 12 22 COLUMBUS_NC \n12 NA 11 NA GEORGETOWN_SC \n13 11 12 11 BEAUFORT_NC \n15 12 11 11 CRAVEN_NC \n18 11 11 11 BEAUFORT_NC \n\n usable \n8 True \n12 True \n13 True \n15 True \n18 True \n\n[5 rows x 3093 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Longitude</th>\n <th>Latitude</th>\n <th>Clone_id</th>\n <th>sucrose</th>\n <th>county</th>\n <th>state</th>\n <th>lat</th>\n <th>long</th>\n <th>countyid</th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>...</th>\n <th>UMN-CL307Contig1-04-143</th>\n <th>UMN-CL319Contig1-03-131</th>\n <th>UMN-CL326Contig1-05-421</th>\n <th>UMN-CL339Contig1-05-39</th>\n <th>UMN-CL34Contig1-03-89</th>\n <th>UMN-CL353Contig1-04-64</th>\n <th>UMN-CL362Contig1-07-133</th>\n <th>UMN-CL363Contig1-01-233</th>\n <th>UMN-CL379Contig1-12-117</th>\n <th>UMN-CL424Contig1-03-94</th>\n <th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n <th>county_state</th>\n <th>usable</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>8 </th>\n <td>-78.70453</td>\n <td> 34.33010</td>\n <td> 153B</td>\n <td> 5.578561</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> NA</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> COLUMBUS_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>12</th>\n <td>-79.30539</td>\n <td> 33.36318</td>\n <td> 90C</td>\n <td> 5.718644</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> NA</td>\n <td> 12</td>\n <td> NA</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> NA</td>\n <td> GEORGETOWN_SC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>13</th>\n <td>-77.05205</td>\n <td> 35.55349</td>\n <td> 382A</td>\n <td> 5.591146</td>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> 2</td>\n <td> 11</td>\n <td> NA</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>15</th>\n <td>-77.06917</td>\n <td> 35.10917</td>\n <td> 383C</td>\n <td> 5.492224</td>\n <td> CRAVEN</td>\n <td> NC</td>\n <td> 35.10917</td>\n <td>-77.06917</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> CRAVEN_NC</td>\n <td> True</td>\n </tr>\n <tr>\n <th>18</th>\n <td>-77.05205</td>\n <td> 35.55349</td>\n <td> 382A</td>\n <td> 5.591146</td>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> 2</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n <td> True</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 3093 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "trait_complete.shape",
"execution_count": 592,
"outputs": [
{
"execution_count": 592,
"output_type": "execute_result",
"data": {
"text/plain": "(330, 3093)"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def convert_to_snpassoc(col):\n if \"-\" in col.name:\n freqs = af[col.name]\n trans = {11: \"%s/%s\" % (freqs[\"A\"], freqs[\"A\"]),\n 12: \"%s/%s\" % (freqs[\"A\"], freqs[\"a\"]),\n 22: \"%s/%s\" % (freqs[\"a\"], freqs[\"a\"]),\n \"NA\":\"NA\"}\n return col.apply(lambda x: trans[x])\n return col\ntrait_snpassoc = trait_complete.apply(convert_to_snpassoc)",
"execution_count": 593,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "pca_cov = x_drop.ix[:,0:14]",
"execution_count": 594,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "trait_snpassoc_pca = trait_snpassoc.join(pca_cov, how=\"inner\")",
"execution_count": 595,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "trait_snpassoc_pca = trait_snpassoc_pca.drop(['county_state',\n 'usable',\n 'Longitude',\n 'Latitude',\n 'Clone_id',\n 'county',\n 'state',\n 'lat',\n 'long',\n 'countyid'], axis=1)",
"execution_count": 596,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "trait_snpassoc_pca[0:10]",
"execution_count": 597,
"outputs": [
{
"execution_count": 597,
"output_type": "execute_result",
"data": {
"text/plain": " sucrose 0-10037-01-257 0-10040-02-394 0-10044-01-392 0-10048-01-60 \\\n8 5.578561 NA A/A C/C G/G \n12 5.718644 NA C/A NA NA \n13 5.591146 A/A NA C/G G/A \n15 5.492224 A/G C/A C/G G/A \n18 5.591146 A/A C/C G/G G/G \n20 5.492224 A/A C/A C/G A/A \n21 5.492224 A/A C/A C/C G/A \n24 5.492224 A/A A/A C/C G/G \n25 5.591146 A/A C/A NA G/G \n26 5.591146 A/A C/C C/C G/G \n\n 0-10051-02-166 0-10054-01-402 0-10067-03-111 0-10079-02-168 0-10112-01-169 \\\n8 G/G G/G A/A G/G A/A \n12 G/G G/A A/A G/G A/C \n13 G/G A/A A/A G/G A/A \n15 G/G G/A A/A G/G A/A \n18 G/A G/A A/A G/G A/A \n20 G/G A/A A/A G/A A/A \n21 G/G G/A A/A G/G A/A \n24 G/G G/A A/A G/G A/A \n25 G/G A/A A/A G/G A/C \n26 G/G G/A A/A G/G A/A \n\n 0-10113-01-119 0-10116-01-165 0-10151-01-86 0-10162-01-255 0-10207-01-280 \\\n8 A/G NA A/A A/G A/C \n12 A/G A/C A/A A/A A/A \n13 A/G A/A A/A A/A C/C \n15 G/G A/C A/T A/A C/C \n18 A/G A/A A/A A/A A/A \n20 A/A A/A A/A A/A A/C \n21 A/G A/A A/A A/A A/C \n24 A/A A/A A/A A/A A/A \n25 A/G A/A A/T A/A C/C \n26 A/G A/A A/A A/A A/A \n\n ... UMN-CL97Contig PC1 PC2 PC3 PC4 PC5 \\\n8 ... A/A -3.639308 1.929420 1.193841 -3.447265 0.971588 \n12 ... NA 2.329722 1.624366 0.335046 -0.417052 -1.013743 \n13 ... G/G -2.708652 3.668354 4.708462 0.815316 5.354841 \n15 ... G/G -3.152409 2.664518 2.302811 -0.078610 0.237071 \n18 ... G/G -3.806482 2.723616 -5.248239 -2.665990 4.428649 \n20 ... G/G -3.674586 5.344520 -3.761804 -0.478603 1.534471 \n21 ... G/G -3.751640 1.047446 2.527706 0.383311 1.258178 \n24 ... G/G -4.243603 2.818623 -0.340036 -2.954661 1.515424 \n25 ... G/G -3.540567 1.402466 5.500744 -3.573384 1.453907 \n26 ... G/A 3.738074 -7.598378 -2.400551 1.107317 2.126793 \n\n PC6 PC7 PC8 PC9 PC10 PC11 PC12 \\\n8 1.904022 -2.019537 4.332102 -2.889919 1.682956 -6.441526 4.149832 \n12 -0.138149 1.068138 0.402887 -0.010373 0.559439 2.445590 0.335334 \n13 5.688271 6.544227 1.869980 -0.408042 2.274323 0.627999 -6.249547 \n15 -3.257392 -2.636748 3.809404 -0.443443 7.257342 -0.466697 1.824831 \n18 -2.327034 -2.104905 5.450938 -3.403810 -0.727319 4.328464 -1.099518 \n20 -8.286508 0.466899 -0.567112 0.364850 -1.035324 -3.491548 -1.579615 \n21 -1.602399 -1.049422 -0.513667 0.813623 -3.173031 3.134579 -1.376190 \n24 -3.745098 -0.131087 -1.287536 1.850114 -4.802101 2.334467 1.679421 \n25 -2.465346 -1.806674 -1.587390 -0.767331 0.265662 -1.329243 -1.248258 \n26 -3.917600 -1.719408 4.265561 -0.418051 -0.352412 0.283295 -1.652070 \n\n PC13 PC14 \n8 -0.223308 -0.780265 \n12 -0.047097 -1.344021 \n13 -8.086907 8.983447 \n15 -0.198020 -1.268526 \n18 0.683061 0.534313 \n20 3.806241 -2.126386 \n21 -3.413124 0.899154 \n24 -2.270278 -0.127229 \n25 0.659197 2.035803 \n26 2.133209 -2.821890 \n\n[10 rows x 3097 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>sucrose</th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>0-10116-01-165</th>\n <th>0-10151-01-86</th>\n <th>0-10162-01-255</th>\n <th>0-10207-01-280</th>\n <th>...</th>\n <th>UMN-CL97Contig</th>\n <th>PC1</th>\n <th>PC2</th>\n <th>PC3</th>\n <th>PC4</th>\n <th>PC5</th>\n <th>PC6</th>\n <th>PC7</th>\n <th>PC8</th>\n <th>PC9</th>\n <th>PC10</th>\n <th>PC11</th>\n <th>PC12</th>\n <th>PC13</th>\n <th>PC14</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>8 </th>\n <td> 5.578561</td>\n <td> NA</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> NA</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/C</td>\n <td>...</td>\n <td> A/A</td>\n <td>-3.639308</td>\n <td> 1.929420</td>\n <td> 1.193841</td>\n <td>-3.447265</td>\n <td> 0.971588</td>\n <td> 1.904022</td>\n <td>-2.019537</td>\n <td> 4.332102</td>\n <td>-2.889919</td>\n <td> 1.682956</td>\n <td>-6.441526</td>\n <td> 4.149832</td>\n <td>-0.223308</td>\n <td>-0.780265</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 5.718644</td>\n <td> NA</td>\n <td> C/A</td>\n <td> NA</td>\n <td> NA</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> NA</td>\n <td> 2.329722</td>\n <td> 1.624366</td>\n <td> 0.335046</td>\n <td>-0.417052</td>\n <td>-1.013743</td>\n <td>-0.138149</td>\n <td> 1.068138</td>\n <td> 0.402887</td>\n <td>-0.010373</td>\n <td> 0.559439</td>\n <td> 2.445590</td>\n <td> 0.335334</td>\n <td>-0.047097</td>\n <td>-1.344021</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 5.591146</td>\n <td> A/A</td>\n <td> NA</td>\n <td> C/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td>...</td>\n <td> G/G</td>\n <td>-2.708652</td>\n <td> 3.668354</td>\n <td> 4.708462</td>\n <td> 0.815316</td>\n <td> 5.354841</td>\n <td> 5.688271</td>\n <td> 6.544227</td>\n <td> 1.869980</td>\n <td>-0.408042</td>\n <td> 2.274323</td>\n <td> 0.627999</td>\n <td>-6.249547</td>\n <td>-8.086907</td>\n <td> 8.983447</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 5.492224</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/T</td>\n <td> A/A</td>\n <td> C/C</td>\n <td>...</td>\n <td> G/G</td>\n <td>-3.152409</td>\n <td> 2.664518</td>\n <td> 2.302811</td>\n <td>-0.078610</td>\n <td> 0.237071</td>\n <td>-3.257392</td>\n <td>-2.636748</td>\n <td> 3.809404</td>\n <td>-0.443443</td>\n <td> 7.257342</td>\n <td>-0.466697</td>\n <td> 1.824831</td>\n <td>-0.198020</td>\n <td>-1.268526</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 5.591146</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> G/G</td>\n <td>-3.806482</td>\n <td> 2.723616</td>\n <td>-5.248239</td>\n <td>-2.665990</td>\n <td> 4.428649</td>\n <td>-2.327034</td>\n <td>-2.104905</td>\n <td> 5.450938</td>\n <td>-3.403810</td>\n <td>-0.727319</td>\n <td> 4.328464</td>\n <td>-1.099518</td>\n <td> 0.683061</td>\n <td> 0.534313</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 5.492224</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/C</td>\n <td>...</td>\n <td> G/G</td>\n <td>-3.674586</td>\n <td> 5.344520</td>\n <td>-3.761804</td>\n <td>-0.478603</td>\n <td> 1.534471</td>\n <td>-8.286508</td>\n <td> 0.466899</td>\n <td>-0.567112</td>\n <td> 0.364850</td>\n <td>-1.035324</td>\n <td>-3.491548</td>\n <td>-1.579615</td>\n <td> 3.806241</td>\n <td>-2.126386</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 5.492224</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/C</td>\n <td>...</td>\n <td> G/G</td>\n <td>-3.751640</td>\n <td> 1.047446</td>\n <td> 2.527706</td>\n <td> 0.383311</td>\n <td> 1.258178</td>\n <td>-1.602399</td>\n <td>-1.049422</td>\n <td>-0.513667</td>\n <td> 0.813623</td>\n <td>-3.173031</td>\n <td> 3.134579</td>\n <td>-1.376190</td>\n <td>-3.413124</td>\n <td> 0.899154</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 5.492224</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> G/G</td>\n <td>-4.243603</td>\n <td> 2.818623</td>\n <td>-0.340036</td>\n <td>-2.954661</td>\n <td> 1.515424</td>\n <td>-3.745098</td>\n <td>-0.131087</td>\n <td>-1.287536</td>\n <td> 1.850114</td>\n <td>-4.802101</td>\n <td> 2.334467</td>\n <td> 1.679421</td>\n <td>-2.270278</td>\n <td>-0.127229</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 5.591146</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> NA</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/T</td>\n <td> A/A</td>\n <td> C/C</td>\n <td>...</td>\n <td> G/G</td>\n <td>-3.540567</td>\n <td> 1.402466</td>\n <td> 5.500744</td>\n <td>-3.573384</td>\n <td> 1.453907</td>\n <td>-2.465346</td>\n <td>-1.806674</td>\n <td>-1.587390</td>\n <td>-0.767331</td>\n <td> 0.265662</td>\n <td>-1.329243</td>\n <td>-1.248258</td>\n <td> 0.659197</td>\n <td> 2.035803</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 5.591146</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> G/A</td>\n <td> 3.738074</td>\n <td>-7.598378</td>\n <td>-2.400551</td>\n <td> 1.107317</td>\n <td> 2.126793</td>\n <td>-3.917600</td>\n <td>-1.719408</td>\n <td> 4.265561</td>\n <td>-0.418051</td>\n <td>-0.352412</td>\n <td> 0.283295</td>\n <td>-1.652070</td>\n <td> 2.133209</td>\n <td>-2.821890</td>\n </tr>\n </tbody>\n</table>\n<p>10 rows × 3097 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "trait_snpassoc.shape",
"execution_count": 598,
"outputs": [
{
"execution_count": 598,
"output_type": "execute_result",
"data": {
"text/plain": "(330, 3093)"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "trait_snpassoc_pca.to_csv(\"snpassoc.txt\",\n header=True,\n index=True,\n sep=\"\\t\")",
"execution_count": 599,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "def write_snpassoc_file(df, input_file, num_pca_axes):\n pheno = df.columns[0:1]\n out_files = []\n for p in pheno:\n with open(\"snpassoc_%s.R\" % p.lower(), \"w\") as o:\n print \"writing %s\" % o.name\n out_files.append(o.name)\n text = '''\nlibrary(SNPassoc)\n\nd = read.table('%s', sep=\"\\\\t\", row.names=1, header=T)\n\n#subtract b/c those are the PCA axes\nsnp_cols = 2:(ncol(d)-%d)\nsnp_data = setupSNP(d, colSNPs=snp_cols, sep=\"/\")\npca_cols = (ncol(d)-%d):ncol(d)\npca_data = d[,pca_cols]\n\nwg = WGassociation(%s~1+pca_data$PC1+pca_data$PC2+pca_data$PC3+pca_data$PC4+\npca_data$PC5+pca_data$PC6+pca_data$PC7+pca_data$PC8+pca_data$PC9+pca_data$PC10+\n+pca_data$PC11+pca_data$PC12+pca_data$PC13+pca_data$PC14, \ndata=snp_data, \nmodel=\"co\", \ngenotypingRate=5)\n\nsaveRDS(wg, \"wg_%s_co.rds\")\nstats = WGstats(wg)\nsaveRDS(stats, \"wgstats_%s.rds\")\n''' % (input_file, \n num_pca_axes,\n num_pca_axes-1,\n p, \n p.lower(), \n p.lower())\n \n o.write(text)\n return out_files",
"execution_count": 600,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "write_snpassoc_file(trait_snpassoc_pca, \"snpassoc.txt\", 14)",
"execution_count": 601,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "writing snpassoc_sucrose.R\n"
},
{
"execution_count": 601,
"output_type": "execute_result",
"data": {
"text/plain": "[u'snpassoc_sucrose.R']"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "##Run this in R\n```R\nsource(\"snpassoc_<trait>.R\")\n```"
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "%%R\nwg_trait_co.rds = readRDS('wg_sucrose_co.rds')\nwgstats_trait.rds = readRDS('wgstats_sucrose.rds')",
"execution_count": 602,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "wgstats_trait = r['wgstats_trait.rds']\nwgstats_trait_labels = r('labels(wg_trait_co.rds)')",
"execution_count": 603,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "wgstats = {trait_name:[wgstats_trait, wgstats_trait_labels.rx2(1)]}\nfor key, datalist in wgstats.items():\n print \"converting %s\" % key\n wgstats[key] = [com.convert_robj(x) for x in datalist]",
"execution_count": 604,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "converting sucrose\n"
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "def get_alleles(data):\n a = set()\n for x in data.index:\n for elem in x.split(\"/\"):\n a.add(elem)\n return list(a) \n\ndef get_allele_freqs_wg(data, AA, Aa, aa):\n total = np.sum(data['n'])*2\n A = data.ix[AA, \"n\"]*2 + data.ix[Aa, \"n\"]\n a = data.ix[aa, \"n\"]*2 + data.ix[Aa, \"n\"]\n return A/total, a/total\n\ndef get_genotypes(data, alleles):\n homos = [\"%s/%s\" % (x,x) for x in alleles]\n Aa = \"%s/%s\" % (alleles[0], alleles[1])\n if Aa not in data.index:\n Aa = Aa[::-1] #reverse it\n AA, aa = homos\n if data.ix[AA, \"n\"] < data.ix[aa, \"n\"]:\n AA, aa = homos[::-1] #reverse it so that major is first\n return AA, Aa, aa\n\ndef get_genotypic_values(data, alleles):\n AA, Aa, aa = get_genotypes(data, alleles)\n G_AA = float(data.ix[AA, 'me'])\n G_aa = float(data.ix[aa, 'me'])\n additive = (G_AA-G_aa)/2\n G_Aa = float(data.ix[Aa, 'me'])\n dominance = G_Aa - ((G_AA+G_aa)/2)\n return additive, dominance, AA, Aa, aa\n \ndef get_alpha(data):\n alleles = get_alleles(data)\n additive, dominance, AA, Aa, aa = get_genotypic_values(data, alleles)\n p, q = get_allele_freqs_wg(data, AA, Aa, aa)\n alpha = additive + (dominance*(q-p))\n return alpha, AA, aa, p, q",
"execution_count": 605,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "alpha_vals = {}\nfor p in wgstats:\n print \"running %s\" % p\n df = pd.DataFrame(index=[\"alpha\", \"p-value\", \"AA\", \"aa\", \"p\", \"q\"])\n alpha_vals[p] = df\n d = wgstats[p][0]\n labels = wgstats[p][1]\n for i, locus in enumerate(d):\n try:\n data = pd.DataFrame(d[locus])\n snp = labels[i]\n genotypes = [g for g in data.index if \"/\" in g]\n data = data.ix[genotypes,:]\n pvalue = data['p-value'].dropna()[0]\n if len(genotypes) == 3:\n alpha, AA, aa, p, q = get_alpha(data)\n df[snp] = [alpha, pvalue, AA, aa, p, q]\n except Exception as e: \n pass",
"execution_count": 606,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "running sucrose\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "alpha_vals[trait_name].ix[:,0:10]",
"execution_count": 607,
"outputs": [
{
"execution_count": 607,
"output_type": "execute_result",
"data": {
"text/plain": " X0.10037.01.257 X0.10040.02.394 X0.10044.01.392 X0.10048.01.60 \\\nalpha -0.004097739 -0.00181165 -0.0002803176 0.0279112 \np-value 0.8362344 0.9471765 0.6266669 0.07377968 \nAA G/G T/T G/G A/A \naa A/A A/A A/A C/C \np 0.7299383 0.6554878 0.9542683 0.8953846 \nq 0.2700617 0.3445122 0.04573171 0.1046154 \n\n X0.10051.02.166 X0.10054.01.402 X0.10079.02.168 X0.10112.01.169 \\\nalpha -0.003872464 0.02172985 0.04191577 -0.003347441 \np-value 0.7658862 0.9580188 0.422769 0.3233936 \nAA A/A G/G A/A A/A \naa G/G A/A G/G G/G \np 0.8611111 0.9832827 0.9542587 0.9557927 \nq 0.1388889 0.01671733 0.04574132 0.04420732 \n\n X0.10113.01.119 X0.10116.01.165 \nalpha 0.003736298 0.02158743 \np-value 0.6388786 0.2011802 \nAA C/C G/G \naa G/G C/C \np 0.781155 0.859375 \nq 0.218845 0.140625 ",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>X0.10037.01.257</th>\n <th>X0.10040.02.394</th>\n <th>X0.10044.01.392</th>\n <th>X0.10048.01.60</th>\n <th>X0.10051.02.166</th>\n <th>X0.10054.01.402</th>\n <th>X0.10079.02.168</th>\n <th>X0.10112.01.169</th>\n <th>X0.10113.01.119</th>\n <th>X0.10116.01.165</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>alpha</th>\n <td>-0.004097739</td>\n <td>-0.00181165</td>\n <td>-0.0002803176</td>\n <td> 0.0279112</td>\n <td>-0.003872464</td>\n <td> 0.02172985</td>\n <td> 0.04191577</td>\n <td>-0.003347441</td>\n <td> 0.003736298</td>\n <td> 0.02158743</td>\n </tr>\n <tr>\n <th>p-value</th>\n <td> 0.8362344</td>\n <td> 0.9471765</td>\n <td> 0.6266669</td>\n <td> 0.07377968</td>\n <td> 0.7658862</td>\n <td> 0.9580188</td>\n <td> 0.422769</td>\n <td> 0.3233936</td>\n <td> 0.6388786</td>\n <td> 0.2011802</td>\n </tr>\n <tr>\n <th>AA</th>\n <td> G/G</td>\n <td> T/T</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n </tr>\n <tr>\n <th>aa</th>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> C/C</td>\n </tr>\n <tr>\n <th>p</th>\n <td> 0.7299383</td>\n <td> 0.6554878</td>\n <td> 0.9542683</td>\n <td> 0.8953846</td>\n <td> 0.8611111</td>\n <td> 0.9832827</td>\n <td> 0.9542587</td>\n <td> 0.9557927</td>\n <td> 0.781155</td>\n <td> 0.859375</td>\n </tr>\n <tr>\n <th>q</th>\n <td> 0.2700617</td>\n <td> 0.3445122</td>\n <td> 0.04573171</td>\n <td> 0.1046154</td>\n <td> 0.1388889</td>\n <td> 0.01671733</td>\n <td> 0.04574132</td>\n <td> 0.04420732</td>\n <td> 0.218845</td>\n <td> 0.140625</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "plt.hist(alpha_vals[trait_name].ix['p-value',:], bins=30)\nplt.title(\"p-values\")\nplt.show()",
"execution_count": 1209,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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k8cN6ZyT5u9ba3kvdZ3ZuHuP5+CR/nGRNVR2a5BVJzmut3Wap+8zOjTqes+rv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\nYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fde39e2cf10>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "plt.hist(alpha_vals[trait_name].ix['alpha',:], bins=30)\nplt.title(\"alpha values $\\mu %.4f \\pm %.4f \\ [%.4f, %.4f]$\" % (np.mean(alpha_vals[trait_name].ix['alpha',:]),\n np.std(alpha_vals[trait_name].ix['alpha',:]),\n np.min(alpha_vals[trait_name].ix['alpha',:]),\n np.max(alpha_vals[trait_name].ix['alpha',:])))\nplt.show()",
"execution_count": 1208,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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BT5IkqXAGPkmSpMIZ+CRJkgpn4JMkSSqcgU+SJKlwBj5JkqTCGfgkSZIKZ+CTJEkqnIFP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\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fde43f8fad0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "trait_snpassoc_pca_county = pd.concat([loc_hierf.countyid, trait_snpassoc_pca], axis=1)\ntrait_snpassoc_pca_county = trait_snpassoc_pca_county.drop(trait_snpassoc_pca_county[np.isnan(trait_snpassoc_pca_county[trait_name])].index)\ntrait_snpassoc_pca_county[0:5]\nsnpassoc_af = trait_snpassoc_pca_county.ix[:,2:-14].apply(get_allele_freqs)",
"execution_count": 610,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "pop_allele_freqs = {}\nfor pop,data in trait_snpassoc_pca_county.groupby(\"countyid\"):\n print \"getting allele freqs for pop % d\" % pop\n pop_allele_freqs[pop] = data.ix[:,2:-14].apply(get_allele_freqs)",
"execution_count": 611,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "getting allele freqs for pop 1\ngetting allele freqs for pop 2\ngetting allele freqs for pop 3\ngetting allele freqs for pop 5\ngetting allele freqs for pop 6\ngetting allele freqs for pop 7\ngetting allele freqs for pop 8\ngetting allele freqs for pop 9\ngetting allele freqs for pop 11\ngetting allele freqs for pop 12\ngetting allele freqs for pop 13\ngetting allele freqs for pop 14\ngetting allele freqs for pop 16\ngetting allele freqs for pop 17\ngetting allele freqs for pop 18\ngetting allele freqs for pop 19\ngetting allele freqs for pop 20\ngetting allele freqs for pop 21\ngetting allele freqs for pop 23\ngetting allele freqs for pop 24\ngetting allele freqs for pop 25\ngetting allele freqs for pop 26\ngetting allele freqs for pop 27\ngetting allele freqs for pop 28\ngetting allele freqs for pop 29\ngetting allele freqs for pop 30\ngetting allele freqs for pop 31\ngetting allele freqs for pop 32\ngetting allele freqs for pop 33\ngetting allele freqs for pop 34\n"
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "def write_gwas_data_file(df, pheno, outdir):\n out = \"%s_gwas_data_file.txt\" % pheno\n out = os.path.join(outdir, out)\n df = df.sort_index()\n df[['A1', 'A2', 'EFF', 'FRQ']].to_csv(out,\n header=True, \n index=True,\n sep=\"\\t\")\n print out\n return out\n\ndef write_freqs_file(df, pheno, pop_freqs, outdir):\n out = \"%s_freqs_file.txt\" % pheno\n out = os.path.join(outdir, out)\n print out\n with open(out, \"w\") as o:\n o.write(\"SNP\\tCLST\\tA1\\tA2\\tFRQ\\n\")\n for pop, data in pop_freqs.items():\n m = data.T.merge(df, how=\"inner\", left_index=True, right_index=True)\n m['population'] = pop\n m.index.name = 'SNP'\n m = m.sort_index()\n o.write(m[['population','A1','A2','p']].to_csv(header=False, \n index=True,\n sep=\"\\t\"))\ndef write_match_pop_file(df, pheno, pop_freqs, pop, outdir):\n out = \"%s_match_pop_file.txt\" % pheno\n out = os.path.join(outdir, out)\n print out\n with open(out, \"w\") as o:\n o.write(\"SNP\\tCLST\\tA1\\tA2\\tFRQ\\n\")\n for key, data in pop_freqs.items():\n if key == pop:\n m = data.T.merge(df, how=\"inner\", left_index=True, right_index=True)\n m['population'] = pop\n m.index.name = 'SNP'\n m = m.sort_index()\n o.write(m[['population','A1','A2','p']].to_csv(header=False, \n index=True,\n sep=\"\\t\"))\n break\n \ndef write_full_dataset_file(df, pheno, pop_freqs, outdir):\n out = \"%s_full_dataset_file.txt\" % pheno\n out = os.path.join(outdir, out)\n print out\n with open(out, \"w\") as o:\n o.write(\"SNP\\tCLST\\tA1\\tA2\\tFRQ\\n\")\n for pop, data in pop_freqs.items():\n m = data.T.merge(df, how=\"inner\", left_index=True, right_index=True)\n m['population'] = pop\n m.index.name = 'SNP'\n m = m.sort_index()\n o.write(m[['population','A1','A2','p']].to_csv(header=False, \n index=True,\n sep=\"\\t\")) \ndef write_env_var_data_file(pheno, pop_freqs, outdir):\n out = \"%s_env_var_data_file.txt\" % pheno\n out = os.path.join(outdir, out)\n print out\n with open(out, \"w\") as o:\n o.write(\"CLST\\tENV\\tREG\\n\")\n pop_id = 0\n for pop in pop_freqs:\n pop_id += 1\n o.write(\"%s\\t%f\\t%d\\n\" % (pop, np.random.randn(), pop_id))",
"execution_count": 616,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "pwd",
"execution_count": 617,
"outputs": [
{
"execution_count": 617,
"output_type": "execute_result",
"data": {
"text/plain": "u'/gdc_home4/cfried/ipython'"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "squat_outdir = \"squat_cfried\" #change for your username\nif not os.path.exists(squat_outdir):\n os.mkdir(squat_outdir)\n\nfor p in alpha_vals:\n full = alpha_vals[p].T\n full.index = [x.replace(\".\", \"-\") for x in full.index]\n full.index = [x[1:] if x.startswith(\"X\") else x for x in full.index]\n full.index.name = \"SNP\"\n full.AA = full.AA.apply(lambda x: x[0])\n full.aa = full.aa.apply(lambda x: x[0])\n full = full.rename(columns={'alpha':'EFF',\n 'AA':'A1',\n 'aa':'A2',\n 'p': 'FRQ'})\n candidates = full[full['p-value']<0.001]\n write_gwas_data_file(candidates, p, squat_outdir)\n write_freqs_file(candidates, p, pop_allele_freqs, squat_outdir)\n write_match_pop_file(full, p, pop_allele_freqs, 2, squat_outdir)\n write_full_dataset_file(full, p, pop_allele_freqs, squat_outdir)\n write_env_var_data_file(p, pop_allele_freqs, squat_outdir)",
"execution_count": 618,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "squat_cfried/sucrose_gwas_data_file.txt\nsquat_cfried/sucrose_freqs_file.txt\nsquat_cfried/sucrose_match_pop_file.txt\nsquat_cfried/sucrose_full_dataset_file.txt\nsquat_cfried/sucrose_env_var_data_file.txt\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "pwd",
"execution_count": 619,
"outputs": [
{
"execution_count": 619,
"output_type": "execute_result",
"data": {
"text/plain": "u'/gdc_home4/cfried/ipython'"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "squat_scripts_dir = \"/gdc_home4/cfried/src/PolygenicAdaptationCode/Scripts\"\n!rm {squat_outdir}/Scripts && ln -s {squat_scripts_dir} {squat_outdir}/Scripts\ndef get_squat_vars(pheno):\n d = {\"gwas.data.file\":\"'%s_gwas_data_file.txt'\" % pheno,\n \"freqs.file\":\"'%s_freqs_file.txt'\" % pheno,\n \"env.var.data.files\":\"list('%s_env_var_data_file.txt')\" % pheno,\n \"match.pop.file\":\"'%s_match_pop_file.txt'\" % pheno,\n \"full.dataset.file\":\"'%s_full_dataset_file.txt'\" % pheno,\n \"path\":\"'%s'\" % pheno,\n \"match.categories\":\"c('MAF')\",\n \"match.bins\":\"list(seq(0,0.5,0.02), c(2), seq(0,1000,100))\",\n \"cov.SNPs.per.cycle\":5000,\n \"cov.cycles\":1,\n \"null.phenos.per.cycle\":1000,\n \"null.cycles\":1,\n \"load.cov.mat\":\"F\",\n \"sim.null\":\"T\",\n \"check.allele.orientation\":\"F\"}\n return ',\\n'.join(\"%s=%s\" % (key,val) for (key,val) in d.items())\n\ndef create_squat_run_file(pheno):\n squat_file = os.path.join(squat_outdir, \"squat_%s.r\" % pheno)\n with open(squat_file, \"w\") as o:\n o.write('system(\"rm -rf %s\")\\n'% pheno)\n o.write(\"source('%s')\\n\" % os.path.join(squat_scripts_dir, \"CreateTraitFile.R\"))\n o.write(\"source('%s')\\n\" % os.path.join(squat_scripts_dir, \"functions.R\"))\n o.write(\"PolygenicAdaptationFunction(%s)\\n\" % get_squat_vars(pheno))\n return squat_file\n\nfor pheno in alpha_vals:\n squat_file = create_squat_run_file(pheno)\n print squat_file\n !cat $squat_file\n print \"\"",
"execution_count": 620,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "squat_cfried/squat_sucrose.r\nsystem(\"rm -rf sucrose\")\nsource('/gdc_home4/cfried/src/PolygenicAdaptationCode/Scripts/CreateTraitFile.R')\nsource('/gdc_home4/cfried/src/PolygenicAdaptationCode/Scripts/functions.R')\nPolygenicAdaptationFunction(sim.null=T,\ncov.SNPs.per.cycle=5000,\nnull.cycles=1,\nmatch.bins=list(seq(0,0.5,0.02), c(2), seq(0,1000,100)),\nload.cov.mat=F,\npath='sucrose',\ncov.cycles=1,\nmatch.pop.file='sucrose_match_pop_file.txt',\nfreqs.file='sucrose_freqs_file.txt',\nenv.var.data.files=list('sucrose_env_var_data_file.txt'),\ngwas.data.file='sucrose_gwas_data_file.txt',\nfull.dataset.file='sucrose_full_dataset_file.txt',\nmatch.categories=c('MAF'),\ncheck.allele.orientation=F,\nnull.phenos.per.cycle=1000)\n\n"
}
]
},
{
"metadata": {
"scrolled": true,
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def run_squat(p):\n print \"running %s\" % p\n output = \"%s/%s\" % (squat_outdir, p)\n if os.path.exists(output):\n !rm -rf {output}\n cmds = [\"setwd('%s')\" % squat_outdir,\n 'source(\"squat_%s.r\")' % (p),\n \"setwd('../')\"]\n for cmd in cmds:\n print cmd\n r(cmd)\n \nrun_squat(trait_name)",
"execution_count": 621,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "running sucrose\nsetwd('squat_cfried')\nsource(\"squat_sucrose.r\")\nsetwd('../')\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "rfiles = !find {squat_outdir} | grep Robj | grep Output | grep {trait_name}\nbc = {}\nfor f in rfiles:\n d = f.split(\"/\")\n if not d[1] in bc:\n bc[d[1]] = []\n bc[d[1]].append(f)\nbc",
"execution_count": 622,
"outputs": [
{
"execution_count": 622,
"output_type": "execute_result",
"data": {
"text/plain": "{'sucrose': ['squat_cfried/sucrose/Output/genetic.values.Robj',\n 'squat_cfried/sucrose/Output/theStats.Robj',\n 'squat_cfried/sucrose/Output/asymptotic.pVals.Robj',\n 'squat_cfried/sucrose/Output/pVals.Robj',\n 'squat_cfried/sucrose/Output/nullStats.Robj']}"
},
"metadata": {}
}
]
},
{
"metadata": {
"scrolled": false,
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "for pheno in bc:\n print pheno\n for obj in bc[pheno]:\n r('load(\"%s\")' % obj)\n print r(\"the.stats\")\n print(\"------------------\")\n print r(\"p.vals\")",
"execution_count": 623,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "sucrose\n$Qx\n[1] 31.34068\n\n$Fst.comp\n[1] 36.00321\n\n$LD.component\n[1] -4.66253\n\n$betas\n$betas[[1]]\n[1] -0.0004502027\n\n\n$pearson.rs\n$pearson.rs[[1]]\n[1] -0.04663192\n\n\n$spearman.rhos\n$spearman.rhos[[1]]\n [,1]\n[1,] -0.07635468\n\n\n$reg.Z\n Env File 1\nRegion 1 -0.38473504\nRegion 2 0.13799305\nRegion 3 -1.56939196\nRegion 4 -0.87265487\nRegion 5 0.18913812\nRegion 6 1.48832317\nRegion 7 -1.39472707\nRegion 8 2.32689339\nRegion 9 0.31990463\nRegion 10 -0.65852595\nRegion 11 -0.54544812\nRegion 12 -0.47055792\nRegion 13 1.05163363\nRegion 14 0.67712369\nRegion 15 0.31234583\nRegion 16 0.16537951\nRegion 17 0.84293436\nRegion 18 -0.87072851\nRegion 19 2.14058635\nRegion 20 0.11556705\nRegion 21 -0.36336549\nRegion 22 0.26142682\nRegion 23 0.06634565\nRegion 24 -2.15547018\nRegion 25 -0.84694448\nRegion 26 -1.30809803\nRegion 27 1.20997437\nRegion 28 0.04058527\nRegion 29 1.91141560\nRegion 30 -0.23137908\n\n$ind.Z\n 1 2 3 5 6 7 \n-0.38473504 0.13799305 -1.56939196 -0.87265487 0.18913812 1.48832317 \n 8 9 11 12 13 14 \n-1.39472707 2.32689339 0.31990463 -0.65852595 -0.54544812 -0.47055792 \n 16 17 18 19 20 21 \n 1.05163363 0.67712369 0.31234583 0.16537951 0.84293436 -0.87072851 \n 23 24 25 26 27 28 \n 2.14058635 0.11556705 -0.36336549 0.26142682 0.06634565 -2.15547018 \n 29 30 31 32 33 34 \n-0.84694448 -1.30809803 1.20997437 0.04058527 1.91141560 -0.23137908 \n\n\n------------------\n$Qx\n[1] 0.275\n\n$Fst.comp\n[1] 0.083\n\n$LD.comp\n[1] 0.797\n\n$betas\n[1] 0.756\n\n$pearson.rs\n[1] 0.798\n\n$spearman.rhos\n[1] 0.75\n\n$reg.Z\n Env File 1\nRegion 1 0.734\nRegion 2 0.946\nRegion 3 0.104\nRegion 4 0.414\nRegion 5 0.850\nRegion 6 0.148\nRegion 7 0.174\nRegion 8 0.022\nRegion 9 0.884\nRegion 10 0.456\nRegion 11 0.640\nRegion 12 0.556\nRegion 13 0.304\nRegion 14 0.510\nRegion 15 0.992\nRegion 16 0.912\nRegion 17 0.410\nRegion 18 0.372\nRegion 19 0.000\nRegion 20 0.994\nRegion 21 0.584\nRegion 22 0.874\nRegion 23 0.960\nRegion 24 0.032\nRegion 25 0.446\nRegion 26 0.176\nRegion 27 0.184\nRegion 28 0.840\nRegion 29 0.026\nRegion 30 0.994\n\n$ind.Z\n 1 2 3 5 6 7 8 9 11 12 13 14 16 \n0.734 0.946 0.104 0.414 0.850 0.148 0.174 0.022 0.884 0.456 0.640 0.556 0.304 \n 17 18 19 20 21 23 24 25 26 27 28 29 30 \n0.510 0.992 0.912 0.410 0.372 0.000 0.994 0.584 0.874 0.960 0.032 0.446 0.176 \n 31 32 33 34 \n0.184 0.840 0.026 0.994 \n\n\n"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#Bayenv\n\n##Setup Bayenv input files"
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bayenv_df['county_state'] = bayenv_df.apply(lambda row: \"%s_%s\" % (row.county, row.state), axis=1)\nbayenv_df = bayenv_df.drop(drop.index)\nbayenv_df['countyid'] = bayenv_df.apply(lambda row: county_id[row.county_state], axis=1)",
"execution_count": 638,
"outputs": []
},
{
"metadata": {
"scrolled": true,
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bayenv_df[:5]",
"execution_count": 640,
"outputs": [
{
"execution_count": 640,
"output_type": "execute_result",
"data": {
"text/plain": " county state lat long countyid 0-10037-01-257 \\\n8 COLUMBUS NC 34.33010 -78.70453 11 NA \n10 ONSLOW NC 34.75963 -77.40977 26 11 \n11 ONSLOW NC 34.75963 -77.40977 26 11 \n12 GEORGETOWN SC 33.36318 -79.30539 14 NA \n13 BEAUFORT NC 35.55349 -77.05205 2 11 \n\n 0-10040-02-394 0-10044-01-392 0-10048-01-60 0-10051-02-166 0-10054-01-402 \\\n8 22 11 11 11 11 \n10 22 12 11 11 12 \n11 12 NA 12 11 12 \n12 12 NA NA 11 12 \n13 NA 12 12 11 22 \n\n 0-10067-03-111 0-10079-02-168 0-10112-01-169 0-10113-01-119 ... \\\n8 11 11 11 12 ... \n10 11 11 11 11 ... \n11 11 11 11 12 ... \n12 11 11 12 12 ... \n13 11 11 11 12 ... \n\n UMN-CL306Contig1-04-261 UMN-CL307Contig1-04-143 UMN-CL319Contig1-03-131 \\\n8 NA 12 11 \n10 11 11 11 \n11 11 11 12 \n12 11 12 NA \n13 12 11 11 \n\n UMN-CL326Contig1-05-421 UMN-CL339Contig1-05-39 UMN-CL34Contig1-03-89 \\\n8 11 11 11 \n10 12 11 12 \n11 NA 11 11 \n12 12 11 NA \n13 11 11 11 \n\n UMN-CL353Contig1-04-64 UMN-CL362Contig1-07-133 UMN-CL363Contig1-01-233 \\\n8 11 NA 11 \n10 11 12 11 \n11 11 11 11 \n12 11 12 11 \n13 11 12 11 \n\n UMN-CL379Contig1-12-117 UMN-CL424Contig1-03-94 UMN-CL54Contig1-07-88 \\\n8 11 12 11 \n10 11 11 12 \n11 11 11 12 \n12 11 11 NA \n13 11 22 11 \n\n UMN-CL91Contig1-02-246 UMN-CL97Contig county_state \n8 12 22 COLUMBUS_NC \n10 11 11 ONSLOW_NC \n11 11 12 ONSLOW_NC \n12 11 NA GEORGETOWN_SC \n13 12 11 BEAUFORT_NC \n\n[5 rows x 3088 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>county</th>\n <th>state</th>\n <th>lat</th>\n <th>long</th>\n <th>countyid</th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>...</th>\n <th>UMN-CL306Contig1-04-261</th>\n <th>UMN-CL307Contig1-04-143</th>\n <th>UMN-CL319Contig1-03-131</th>\n <th>UMN-CL326Contig1-05-421</th>\n <th>UMN-CL339Contig1-05-39</th>\n <th>UMN-CL34Contig1-03-89</th>\n <th>UMN-CL353Contig1-04-64</th>\n <th>UMN-CL362Contig1-07-133</th>\n <th>UMN-CL363Contig1-01-233</th>\n <th>UMN-CL379Contig1-12-117</th>\n <th>UMN-CL424Contig1-03-94</th>\n <th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n <th>county_state</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>8 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> NA</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> COLUMBUS_NC</td>\n </tr>\n <tr>\n <th>10</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n </tr>\n <tr>\n <th>11</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> ONSLOW_NC</td>\n </tr>\n <tr>\n <th>12</th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> NA</td>\n <td> 12</td>\n <td> NA</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> NA</td>\n <td> GEORGETOWN_SC</td>\n </tr>\n <tr>\n <th>13</th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> 2</td>\n <td> 11</td>\n <td> NA</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> BEAUFORT_NC</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 3088 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "bayenv_dir = \"bayenv\"\nsnp_names = [x for x in bayenv_df.columns if \"-\" in x]\npopids = sorted(trait_snpassoc.countyid.unique())\n\nif not os.path.exists(bayenv_dir):\n os.mkdir(bayenv_dir)",
"execution_count": 641,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "def get_bayenv_snp(snp_name, popids):\n P = []\n Q = []\n for popid in popids:\n P.append(pop_allele_freqs[popid].ix[\"P\",name])\n Q.append(pop_allele_freqs[popid].ix[\"Q\",name])\n return P, Q\n\ndef write_bayenv_snp(fh_snp, fh_names, name, P, Q):\n if sum(Q) > 0: #exclude monomorphic loci\n if fh_names:\n fh_names.write(\"%s\\n\" % name)\n P = [str(x) for x in P]\n Q = [str(x) for x in Q]\n fh_snp.write(\"%s\\t\\n\" % \"\\t\".join(Q))\n fh_snp.write(\"%s\\t\\n\" % \"\\t\".join(P))\n\n",
"execution_count": 642,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "with open(\"bayenv.txt\", \"w\") as o:\n with open(\"bayenv_names.txt\", \"w\") as n:\n for name in snp_names:\n P,Q = get_bayenv_snp(name, popids)\n write_bayenv_snp(o, n, name, P, Q)",
"execution_count": 651,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "!cp bayenv.txt {bayenv_dir}",
"execution_count": 652,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "!head bayenv.txt",
"execution_count": 653,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "4\t23\t3\t5\t5\t4\t6\t3\t3\t14\t4\t4\t4\t5\t2\t5\t3\t7\t7\t7\t7\t3\t3\t4\t3\t1\t3\t37\t3\t5\t\r\n14\t45\t11\t17\t11\t6\t20\t9\t7\t28\t12\t14\t8\t15\t10\t17\t9\t19\t9\t7\t15\t3\t5\t14\t7\t13\t7\t89\t11\t9\t\r\n7\t29\t6\t9\t7\t4\t12\t6\t4\t21\t3\t5\t3\t8\t1\t11\t4\t12\t5\t6\t8\t2\t2\t9\t4\t6\t2\t61\t5\t7\t\r\n11\t33\t8\t9\t9\t6\t14\t6\t8\t23\t13\t15\t9\t12\t11\t11\t8\t14\t11\t8\t14\t4\t6\t9\t6\t8\t8\t65\t9\t7\t\r\n8\t28\t5\t10\t6\t4\t9\t5\t4\t17\t6\t9\t4\t4\t3\t11\t6\t10\t8\t1\t9\t1\t3\t8\t1\t5\t1\t59\t7\t3\t\r\n10\t28\t9\t12\t10\t4\t15\t7\t8\t23\t10\t9\t4\t12\t7\t11\t6\t16\t8\t13\t11\t5\t5\t10\t7\t9\t9\t63\t7\t9\t\r\n5\t23\t4\t7\t6\t3\t8\t3\t1\t21\t4\t5\t1\t4\t5\t6\t5\t9\t4\t5\t5\t3\t1\t4\t1\t2\t1\t32\t2\t6\t\r\n13\t45\t10\t13\t10\t7\t18\t9\t11\t21\t12\t13\t11\t16\t5\t16\t7\t17\t12\t9\t17\t3\t7\t14\t9\t12\t9\t94\t12\t8\t\r\n0\t2\t1\t3\t2\t1\t1\t0\t2\t3\t2\t1\t2\t3\t0\t1\t0\t1\t5\t0\t2\t0\t1\t2\t3\t1\t1\t5\t0\t0\t\r\n18\t64\t13\t17\t14\t9\t25\t12\t10\t41\t14\t19\t10\t17\t12\t21\t12\t25\t11\t14\t20\t6\t7\t16\t7\t13\t9\t121\t14\t14\t\r\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "!head bayenv_names.txt",
"execution_count": 647,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0-10037-01-257\r\n0-10040-02-394\r\n0-10044-01-392\r\n0-10048-01-60\r\n0-10051-02-166\r\n0-10054-01-402\r\n0-10067-03-111\r\n0-10079-02-168\r\n0-10112-01-169\r\n0-10113-01-119\r\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "len(popids)",
"execution_count": 658,
"outputs": [
{
"execution_count": 658,
"output_type": "execute_result",
"data": {
"text/plain": "30"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "##Run Bayenv to create variance-covariance matrix\n\n```bash\n cd bayenv && /gdc_home4/cfried/src/bayenv2/bayenv2 -i bayenv.txt -p 30 -k 100000 -r 63479 > matrix.out\n```\n\n* -p number of populations (`len(popids)`)\n* -k mcmc generations\n* -r random seed"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "##Run Bayenv mcmc"
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "data_ai['county_state'] = data_ai.apply(lambda row: \"%s_%s\" % (row.County, row.State), axis=1)",
"execution_count": 677,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bayenv_df_ai = bayenv_df.merge(data_ai, on='county_state')",
"execution_count": 678,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bayenv_df_ai[0:10]",
"execution_count": 679,
"outputs": [
{
"execution_count": 679,
"output_type": "execute_result",
"data": {
"text/plain": " county state lat long countyid 0-10037-01-257 0-10040-02-394 \\\n0 COLUMBUS NC 34.33010 -78.70453 11 NA 22 \n1 COLUMBUS NC 34.33010 -78.70453 11 12 11 \n2 COLUMBUS NC 34.33010 -78.70453 11 12 11 \n3 COLUMBUS NC 34.33010 -78.70453 11 11 12 \n4 COLUMBUS NC 34.33010 -78.70453 11 12 12 \n5 COLUMBUS NC 34.33010 -78.70453 11 11 11 \n6 ONSLOW NC 34.75963 -77.40977 26 11 22 \n7 ONSLOW NC 34.75963 -77.40977 26 11 12 \n8 ONSLOW NC 34.75963 -77.40977 26 11 11 \n9 ONSLOW NC 34.75963 -77.40977 26 11 22 \n\n 0-10044-01-392 0-10048-01-60 0-10051-02-166 0-10054-01-402 0-10067-03-111 \\\n0 11 11 11 11 11 \n1 11 12 11 12 11 \n2 22 11 12 12 11 \n3 12 11 12 11 11 \n4 12 11 11 11 11 \n5 11 11 11 22 11 \n6 12 11 11 12 11 \n7 NA 12 11 12 11 \n8 11 11 11 22 12 \n9 11 11 11 12 11 \n\n 0-10079-02-168 0-10112-01-169 0-10113-01-119 ... \\\n0 11 11 12 ... \n1 11 11 11 ... \n2 11 11 12 ... \n3 12 11 12 ... \n4 11 11 11 ... \n5 11 11 12 ... \n6 11 11 11 ... \n7 11 11 12 ... \n8 11 12 11 ... \n9 11 11 12 ... \n\n UMN-CL353Contig1-04-64 UMN-CL362Contig1-07-133 UMN-CL363Contig1-01-233 \\\n0 11 NA 11 \n1 11 12 11 \n2 11 11 12 \n3 11 11 11 \n4 11 11 11 \n5 11 11 11 \n6 11 12 11 \n7 11 11 11 \n8 11 12 11 \n9 11 12 11 \n\n UMN-CL379Contig1-12-117 UMN-CL424Contig1-03-94 UMN-CL54Contig1-07-88 \\\n0 11 12 11 \n1 11 12 12 \n2 11 11 11 \n3 11 11 11 \n4 11 12 22 \n5 11 12 12 \n6 11 11 12 \n7 11 11 12 \n8 11 11 11 \n9 11 12 11 \n\n UMN-CL91Contig1-02-246 UMN-CL97Contig county_state County State \\\n0 12 22 COLUMBUS_NC COLUMBUS NC \n1 11 12 COLUMBUS_NC COLUMBUS NC \n2 11 11 COLUMBUS_NC COLUMBUS NC \n3 11 12 COLUMBUS_NC COLUMBUS NC \n4 11 12 COLUMBUS_NC COLUMBUS NC \n5 11 12 COLUMBUS_NC COLUMBUS NC \n6 11 11 ONSLOW_NC ONSLOW NC \n7 11 12 ONSLOW_NC ONSLOW NC \n8 11 11 ONSLOW_NC ONSLOW NC \n9 11 11 ONSLOW_NC ONSLOW NC \n\n AI_Q1 AI_Q2 AI_Q3 AI_Q4 \n0 6.737349 1.031164 1.017138 2.443279 \n1 6.737349 1.031164 1.017138 2.443279 \n2 6.737349 1.031164 1.017138 2.443279 \n3 6.737349 1.031164 1.017138 2.443279 \n4 6.737349 1.031164 1.017138 2.443279 \n5 6.737349 1.031164 1.017138 2.443279 \n6 6.094570 1.059794 1.153282 2.515655 \n7 6.094570 1.059794 1.153282 2.515655 \n8 6.094570 1.059794 1.153282 2.515655 \n9 6.094570 1.059794 1.153282 2.515655 \n\n[10 rows x 3094 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>county</th>\n <th>state</th>\n <th>lat</th>\n <th>long</th>\n <th>countyid</th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>...</th>\n <th>UMN-CL353Contig1-04-64</th>\n <th>UMN-CL362Contig1-07-133</th>\n <th>UMN-CL363Contig1-01-233</th>\n <th>UMN-CL379Contig1-12-117</th>\n <th>UMN-CL424Contig1-03-94</th>\n <th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n <th>county_state</th>\n <th>County</th>\n <th>State</th>\n <th>AI_Q1</th>\n <th>AI_Q2</th>\n <th>AI_Q3</th>\n <th>AI_Q4</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> NA</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> NA</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>1</th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>2</th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>3</th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>4</th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 22</td>\n <td> 11</td>\n <td> 12</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>5</th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>6</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>7</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> 11</td>\n <td> 12</td>\n <td> NA</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>8</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 22</td>\n <td> 12</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>9</th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> 11</td>\n <td> 22</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td>...</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 12</td>\n <td> 11</td>\n <td> 11</td>\n <td> 11</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n </tbody>\n</table>\n<p>10 rows × 3094 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bayenv_df_ai.shape",
"execution_count": 673,
"outputs": [
{
"execution_count": 673,
"output_type": "execute_result",
"data": {
"text/plain": "(388, 3094)"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def get_bayenv_env(data):\n E = pd.Series()\n for col in data.columns[:-1]:\n E[col] = data[col].values[0]\n return E\n\nai_cols = [x for x in bayenv_df_ai if 'AI_' in x]\nai_cols.append('countyid')\nbayenv_df_ai_groups = bayenv_df_ai.ix[:,ai_cols].groupby(\"countyid\")\nenv_ai = []\nfor popid in popids:\n env_ai.append(get_bayenv_env(bayenv_df_ai_groups.get_group(popid))) \nenv_ai_df = pd.DataFrame(env_ai).T\nenv_ai_df",
"execution_count": 680,
"outputs": [
{
"execution_count": 680,
"output_type": "execute_result",
"data": {
"text/plain": " 0 1 2 3 4 5 6 \\\nAI_Q1 5.763893 6.241730 6.181043 5.910243 6.139059 5.708096 9.495094 \nAI_Q2 0.889168 1.048126 1.048869 0.999777 0.909382 0.862454 1.175420 \nAI_Q3 0.818064 0.994917 1.042944 1.187256 0.752952 0.748589 0.797054 \nAI_Q4 2.488546 2.433338 2.354930 2.514009 2.650772 2.603981 3.467764 \n\n 7 8 9 10 11 12 13 \\\nAI_Q1 8.009286 6.737349 5.976190 4.915650 6.051620 5.674788 5.531475 \nAI_Q2 1.172265 1.031164 1.052110 0.865492 1.024486 0.920890 0.955357 \nAI_Q3 0.829200 1.017138 1.094621 0.753428 1.129748 0.798284 0.863209 \nAI_Q4 3.155869 2.443279 2.492955 2.493957 2.631683 2.431289 2.364550 \n\n 14 15 16 17 18 19 20 \\\nAI_Q1 8.609783 5.380024 7.684877 5.305689 5.839441 4.561122 6.346381 \nAI_Q2 1.172684 1.061354 1.049691 1.175078 0.899897 1.154652 0.919565 \nAI_Q3 0.779626 1.345191 0.718964 1.292199 0.711595 0.620383 0.777438 \nAI_Q4 3.493251 1.839989 3.077337 2.028077 2.700255 2.997242 2.494796 \n\n 21 22 23 24 25 26 27 \\\nAI_Q1 6.094570 8.852098 5.456879 5.494060 9.031872 5.273974 9.496544 \nAI_Q2 1.059794 1.147775 0.868314 0.888762 1.127415 0.890081 1.186712 \nAI_Q3 1.153282 0.767467 0.781805 0.709162 0.757448 0.777549 0.759514 \nAI_Q4 2.515655 3.414992 2.583106 2.838240 3.381259 2.400160 3.395808 \n\n 28 29 \nAI_Q1 4.820668 9.683140 \nAI_Q2 0.876340 1.140042 \nAI_Q3 0.738630 0.806234 \nAI_Q4 2.248372 3.265560 ",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n <th>13</th>\n <th>14</th>\n <th>15</th>\n <th>16</th>\n <th>17</th>\n <th>18</th>\n <th>19</th>\n <th>20</th>\n <th>21</th>\n <th>22</th>\n <th>23</th>\n <th>24</th>\n <th>25</th>\n <th>26</th>\n <th>27</th>\n <th>28</th>\n <th>29</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>AI_Q1</th>\n <td> 5.763893</td>\n <td> 6.241730</td>\n <td> 6.181043</td>\n <td> 5.910243</td>\n <td> 6.139059</td>\n <td> 5.708096</td>\n <td> 9.495094</td>\n <td> 8.009286</td>\n <td> 6.737349</td>\n <td> 5.976190</td>\n <td> 4.915650</td>\n <td> 6.051620</td>\n <td> 5.674788</td>\n <td> 5.531475</td>\n <td> 8.609783</td>\n <td> 5.380024</td>\n <td> 7.684877</td>\n <td> 5.305689</td>\n <td> 5.839441</td>\n <td> 4.561122</td>\n <td> 6.346381</td>\n <td> 6.094570</td>\n <td> 8.852098</td>\n <td> 5.456879</td>\n <td> 5.494060</td>\n <td> 9.031872</td>\n <td> 5.273974</td>\n <td> 9.496544</td>\n <td> 4.820668</td>\n <td> 9.683140</td>\n </tr>\n <tr>\n <th>AI_Q2</th>\n <td> 0.889168</td>\n <td> 1.048126</td>\n <td> 1.048869</td>\n <td> 0.999777</td>\n <td> 0.909382</td>\n <td> 0.862454</td>\n <td> 1.175420</td>\n <td> 1.172265</td>\n <td> 1.031164</td>\n <td> 1.052110</td>\n <td> 0.865492</td>\n <td> 1.024486</td>\n <td> 0.920890</td>\n <td> 0.955357</td>\n <td> 1.172684</td>\n <td> 1.061354</td>\n <td> 1.049691</td>\n <td> 1.175078</td>\n <td> 0.899897</td>\n <td> 1.154652</td>\n <td> 0.919565</td>\n <td> 1.059794</td>\n <td> 1.147775</td>\n <td> 0.868314</td>\n <td> 0.888762</td>\n <td> 1.127415</td>\n <td> 0.890081</td>\n <td> 1.186712</td>\n <td> 0.876340</td>\n <td> 1.140042</td>\n </tr>\n <tr>\n <th>AI_Q3</th>\n <td> 0.818064</td>\n <td> 0.994917</td>\n <td> 1.042944</td>\n <td> 1.187256</td>\n <td> 0.752952</td>\n <td> 0.748589</td>\n <td> 0.797054</td>\n <td> 0.829200</td>\n <td> 1.017138</td>\n <td> 1.094621</td>\n <td> 0.753428</td>\n <td> 1.129748</td>\n <td> 0.798284</td>\n <td> 0.863209</td>\n <td> 0.779626</td>\n <td> 1.345191</td>\n <td> 0.718964</td>\n <td> 1.292199</td>\n <td> 0.711595</td>\n <td> 0.620383</td>\n <td> 0.777438</td>\n <td> 1.153282</td>\n <td> 0.767467</td>\n <td> 0.781805</td>\n <td> 0.709162</td>\n <td> 0.757448</td>\n <td> 0.777549</td>\n <td> 0.759514</td>\n <td> 0.738630</td>\n <td> 0.806234</td>\n </tr>\n <tr>\n <th>AI_Q4</th>\n <td> 2.488546</td>\n <td> 2.433338</td>\n <td> 2.354930</td>\n <td> 2.514009</td>\n <td> 2.650772</td>\n <td> 2.603981</td>\n <td> 3.467764</td>\n <td> 3.155869</td>\n <td> 2.443279</td>\n <td> 2.492955</td>\n <td> 2.493957</td>\n <td> 2.631683</td>\n <td> 2.431289</td>\n <td> 2.364550</td>\n <td> 3.493251</td>\n <td> 1.839989</td>\n <td> 3.077337</td>\n <td> 2.028077</td>\n <td> 2.700255</td>\n <td> 2.997242</td>\n <td> 2.494796</td>\n <td> 2.515655</td>\n <td> 3.414992</td>\n <td> 2.583106</td>\n <td> 2.838240</td>\n <td> 3.381259</td>\n <td> 2.400160</td>\n <td> 3.395808</td>\n <td> 2.248372</td>\n <td> 3.265560</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "env_ai_df = env_ai_df.apply(preprocessing.scale, axis=1)",
"execution_count": 681,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "env_ai_df",
"execution_count": 682,
"outputs": [
{
"execution_count": 682,
"output_type": "execute_result",
"data": {
"text/plain": " 0 1 2 3 4 5 6 \\\nAI_Q1 -0.516714 -0.199489 -0.239778 -0.419555 -0.267650 -0.553756 1.960343 \nAI_Q2 -1.147633 0.256334 0.262891 -0.170703 -0.969091 -1.383574 1.380630 \nAI_Q3 -0.317329 0.627480 0.884058 1.655020 -0.665182 -0.688492 -0.429575 \nAI_Q4 -0.502653 -0.629859 -0.810520 -0.443984 -0.128866 -0.236677 1.753577 \n\n 7 8 9 10 11 12 13 \\\nAI_Q1 0.973950 0.129541 -0.375775 -1.079842 -0.325698 -0.575869 -0.671011 \nAI_Q2 1.352763 0.106519 0.291520 -1.356744 0.047537 -0.867452 -0.563033 \nAI_Q3 -0.257836 0.746192 1.160134 -0.662638 1.347794 -0.423004 -0.076149 \nAI_Q4 1.034936 -0.606954 -0.492495 -0.490186 -0.172850 -0.634580 -0.788354 \n\n 14 15 16 17 18 19 20 \\\nAI_Q1 1.372606 -0.771555 0.758582 -0.820905 -0.466559 -1.315205 -0.130014 \nAI_Q2 1.356466 0.373167 0.270158 1.377604 -1.052871 1.197202 -0.879159 \nAI_Q3 -0.522680 2.498766 -0.846757 2.215661 -0.886127 -1.373409 -0.534369 \nAI_Q4 1.812301 -1.997003 0.853989 -1.563627 -0.014851 0.669440 -0.488254 \n\n 21 22 23 24 25 26 27 \\\nAI_Q1 -0.297185 1.533472 -0.720533 -0.695850 1.652820 -0.841960 1.961305 \nAI_Q2 0.359386 1.136461 -1.331815 -1.151216 0.956636 -1.139565 1.480357 \nAI_Q3 1.473522 -0.587637 -0.511039 -0.899122 -0.641163 -0.533775 -0.630125 \nAI_Q4 -0.440191 1.631984 -0.284777 0.303081 1.554259 -0.706304 1.587781 \n\n 28 29 \nAI_Q1 -1.142899 2.085182 \nAI_Q2 -1.260931 1.068156 \nAI_Q3 -0.741692 -0.380529 \nAI_Q4 -1.056040 1.287676 ",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n <th>13</th>\n <th>14</th>\n <th>15</th>\n <th>16</th>\n <th>17</th>\n <th>18</th>\n <th>19</th>\n <th>20</th>\n <th>21</th>\n <th>22</th>\n <th>23</th>\n <th>24</th>\n <th>25</th>\n <th>26</th>\n <th>27</th>\n <th>28</th>\n <th>29</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>AI_Q1</th>\n <td>-0.516714</td>\n <td>-0.199489</td>\n <td>-0.239778</td>\n <td>-0.419555</td>\n <td>-0.267650</td>\n <td>-0.553756</td>\n <td> 1.960343</td>\n <td> 0.973950</td>\n <td> 0.129541</td>\n <td>-0.375775</td>\n <td>-1.079842</td>\n <td>-0.325698</td>\n <td>-0.575869</td>\n <td>-0.671011</td>\n <td> 1.372606</td>\n <td>-0.771555</td>\n <td> 0.758582</td>\n <td>-0.820905</td>\n <td>-0.466559</td>\n <td>-1.315205</td>\n <td>-0.130014</td>\n <td>-0.297185</td>\n <td> 1.533472</td>\n <td>-0.720533</td>\n <td>-0.695850</td>\n <td> 1.652820</td>\n <td>-0.841960</td>\n <td> 1.961305</td>\n <td>-1.142899</td>\n <td> 2.085182</td>\n </tr>\n <tr>\n <th>AI_Q2</th>\n <td>-1.147633</td>\n <td> 0.256334</td>\n <td> 0.262891</td>\n <td>-0.170703</td>\n <td>-0.969091</td>\n <td>-1.383574</td>\n <td> 1.380630</td>\n <td> 1.352763</td>\n <td> 0.106519</td>\n <td> 0.291520</td>\n <td>-1.356744</td>\n <td> 0.047537</td>\n <td>-0.867452</td>\n <td>-0.563033</td>\n <td> 1.356466</td>\n <td> 0.373167</td>\n <td> 0.270158</td>\n <td> 1.377604</td>\n <td>-1.052871</td>\n <td> 1.197202</td>\n <td>-0.879159</td>\n <td> 0.359386</td>\n <td> 1.136461</td>\n <td>-1.331815</td>\n <td>-1.151216</td>\n <td> 0.956636</td>\n <td>-1.139565</td>\n <td> 1.480357</td>\n <td>-1.260931</td>\n <td> 1.068156</td>\n </tr>\n <tr>\n <th>AI_Q3</th>\n <td>-0.317329</td>\n <td> 0.627480</td>\n <td> 0.884058</td>\n <td> 1.655020</td>\n <td>-0.665182</td>\n <td>-0.688492</td>\n <td>-0.429575</td>\n <td>-0.257836</td>\n <td> 0.746192</td>\n <td> 1.160134</td>\n <td>-0.662638</td>\n <td> 1.347794</td>\n <td>-0.423004</td>\n <td>-0.076149</td>\n <td>-0.522680</td>\n <td> 2.498766</td>\n <td>-0.846757</td>\n <td> 2.215661</td>\n <td>-0.886127</td>\n <td>-1.373409</td>\n <td>-0.534369</td>\n <td> 1.473522</td>\n <td>-0.587637</td>\n <td>-0.511039</td>\n <td>-0.899122</td>\n <td>-0.641163</td>\n <td>-0.533775</td>\n <td>-0.630125</td>\n <td>-0.741692</td>\n <td>-0.380529</td>\n </tr>\n <tr>\n <th>AI_Q4</th>\n <td>-0.502653</td>\n <td>-0.629859</td>\n <td>-0.810520</td>\n <td>-0.443984</td>\n <td>-0.128866</td>\n <td>-0.236677</td>\n <td> 1.753577</td>\n <td> 1.034936</td>\n <td>-0.606954</td>\n <td>-0.492495</td>\n <td>-0.490186</td>\n <td>-0.172850</td>\n <td>-0.634580</td>\n <td>-0.788354</td>\n <td> 1.812301</td>\n <td>-1.997003</td>\n <td> 0.853989</td>\n <td>-1.563627</td>\n <td>-0.014851</td>\n <td> 0.669440</td>\n <td>-0.488254</td>\n <td>-0.440191</td>\n <td> 1.631984</td>\n <td>-0.284777</td>\n <td> 0.303081</td>\n <td> 1.554259</td>\n <td>-0.706304</td>\n <td> 1.587781</td>\n <td>-1.056040</td>\n <td> 1.287676</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "env_ai_df.apply(np.mean, axis=1)",
"execution_count": 683,
"outputs": [
{
"execution_count": 683,
"output_type": "execute_result",
"data": {
"text/plain": "AI_Q1 -4.440892e-16\nAI_Q2 2.094621e-15\nAI_Q3 5.310567e-16\nAI_Q4 -3.774758e-16\ndtype: float64"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "with open(\"%s/envmatrix.txt\" % bayenv_dir, \"w\") as o:\n for row in env_ai_df.iterrows():\n vals = \"\\t\".join([str(x) for x in row[1].values])\n o.write(\"%s\\t\\n\" % vals)",
"execution_count": 684,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "!tail -n 13 bayenv/matrix.out > bayenv/matrix_last.out",
"execution_count": 685,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def setup_bayenv_cmd(snpfile, name):\n work_dir = \"/gdc_home4/cfried/ipython/bayenv\"\n bayenv = \"/gdc_home4/cfried/src/bayenv2/bayenv2\"\n bayenv_matrix = \"matrix_last.out\"\n bayenv_seed = -47372\n bayenv_pops = 12\n bayenv_runs = 100000\n bayenv_environs = 4\n bayenv_envmatrix = \"envmatrix.txt\"\n bayenv_cmd = \"cd %s/%s && %s -i %s -m %s -e %s -p %d -k %d -n %d -t -c -f -o %s\" % (work_dir, \n name,\n bayenv,\n snpfile,\n bayenv_matrix,\n bayenv_envmatrix,\n bayenv_pops,\n bayenv_runs,\n bayenv_environs,\n snpfile)\n shutil.copy(bayenv_matrix, os.path.join(work_dir, name))\n shutil.copy(bayenv_envmatrix, os.path.join(work_dir, name))\n return bayenv_cmd",
"execution_count": 686,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "cmds = []\nif not os.path.exists(bayenv_dir):\n os.mkdir(bayenv_dir)\n\nfor name in snp_names:\n P,Q = get_bayenv_snp(name,popids)\n if sum(Q) > 0:\n file_dir = os.path.join(bayenv_dir, name)\n \n if os.path.exists(file_dir):\n shutil.rmtree(file_dir)\n \n if not os.path.exists(file_dir):\n os.mkdir(file_dir)\n o = open(os.path.join(file_dir, \"%s.txt\" % name), \"w\")\n write_bayenv_snp(o, None, name, P, Q)\n o.close()\n cmd = setup_bayenv_cmd(os.path.basename(o.name), name)\n cmds.append(cmd)",
"execution_count": 1127,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "print cmds[0]",
"execution_count": 691,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "cd /gdc_home4/cfried/ipython/bayenv/0-10037-01-257 && /gdc_home4/cfried/src/bayenv2/bayenv2 -i 0-10037-01-257.txt -m matrix_last.out -e envmatrix.txt -p 12 -k 100000 -n 4 -t -c -f -o 0-10037-01-257.txt\n"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "rc = Client(profile=\"gdcsrv2\")",
"execution_count": 1004,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "dview = rc[:]\nlview = rc.load_balanced_view()\nlen(lview)",
"execution_count": 1057,
"outputs": [
{
"execution_count": 1057,
"output_type": "execute_result",
"data": {
"text/plain": "40"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "len(dview)",
"execution_count": 1059,
"outputs": [
{
"execution_count": 1059,
"output_type": "execute_result",
"data": {
"text/plain": "40"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def get_hostname():\n import socket\n return socket.gethostname()\ndview['get_hostname'] = get_hostname",
"execution_count": 1044,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "dview.scatter(\"cpu\", range(len(rc)), flatten=True)\ndef run_cmd(cmd):\n import stopwatch\n from subprocess import Popen, PIPE\n import psutil\n import multiprocessing\n t = stopwatch.Timer()\n p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)\n proc = psutil.Process(p.pid)\n proc.set_cpu_affinity([cpu])\n print \"affinity is %s\" % proc.get_cpu_affinity() \n stdout, stderr = p.communicate()\n t.stop()\n return cmd, stdout, stderr, str(t)\n",
"execution_count": 1118,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "dview['run_cmd'] = run_cmd",
"execution_count": 1119,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "%%px\nimport psutil\nimport os\nimport multiprocessing\np = psutil.Process(os.getpid())\np.set_cpu_affinity([cpu])\n#p.set_cpu_affinity(range(multiprocessing.cpu_count()))",
"execution_count": 1120,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "bayenv_jobs = lview.map_async(run_cmd, cmds)\n",
"execution_count": 1128,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bayenv_jobs.progress",
"execution_count": 1135,
"outputs": [
{
"execution_count": 1135,
"output_type": "execute_result",
"data": {
"text/plain": "3082"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bf_files = !find bayenv | grep bf\nbf_data = {}\nfor b in bf_files:\n d = open(b).readlines()\n d = d[-1].strip().split(\"\\t\")[1:]\n if len(d) == 12:\n bf_data[os.path.basename(b).replace(\".txt.bf\",\"\")] = d",
"execution_count": 1145,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bf = pd.DataFrame(bf_data).T.astype(float)\nbf.shape",
"execution_count": 1146,
"outputs": [
{
"execution_count": 1146,
"output_type": "execute_result",
"data": {
"text/plain": "(3073, 12)"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "freq_files = !find bayenv | grep freqs\nfreq_data = {}\nfor f in freq_files:\n freq_data[os.path.basename(f).replace(\".txt.freqs\",\"\")] = open(f).readline().strip().split()",
"execution_count": 1147,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "freq_df = pd.DataFrame(freq_data).T\nfreq_df.shape",
"execution_count": 175,
"outputs": [
{
"execution_count": 175,
"output_type": "execute_result",
"data": {
"text/plain": "(3078, 12)"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "freq_df.to_csv(\"bayenv_freqs.txt\", header=True, index=True, sep=\"\\t\")\nbf.to_csv(\"bayenv_bf.txt\", header=True, index=True, sep=\"\\t\")",
"execution_count": 1148,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "FileLink(\"bayenv_bf.txt\")",
"execution_count": 1149,
"outputs": [
{
"execution_count": 1149,
"output_type": "execute_result",
"data": {
"text/plain": "/gdc_home4/cfried/ipython/bayenv_bf.txt",
"text/html": "<a href='bayenv_bf.txt' target='_blank'>bayenv_bf.txt</a><br>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "FileLink(\"bayenv_freqs.txt\")",
"execution_count": 1150,
"outputs": [
{
"execution_count": 1150,
"output_type": "execute_result",
"data": {
"text/plain": "/gdc_home4/cfried/ipython/bayenv_freqs.txt",
"text/html": "<a href='bayenv_freqs.txt' target='_blank'>bayenv_freqs.txt</a><br>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "plt.scatter(bf.ix[:,1], bf.ix[:,2])\nplt.show()\n\nplt.scatter(bf.ix[:,1], bf.ix[:,0])\nplt.show()",
"execution_count": 1207,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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5Nv2zcpduNJfoUwRn8cbtgi0dzGVbuBMmdwwlDn4Bhr5vKVDygEewXH2ZwKzT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CAAAg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\n9uliL2Yfdxft3w2MC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"text/plain": "<matplotlib.figure.Figure at 0x7fde3a1f8f90>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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2nrrl5zbYbm3IHqzstW/4KwfZzsKi7027/s2+ut7JRQrGtfOXFo2Dwfy6x3d8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\nfO0fFSN4DkwDbgUuLvPPe4C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"text/plain": "<matplotlib.figure.Figure at 0x7fde39acb110>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "##Outliers"
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def get_outliers(df, key, num_std):\n if key == \"bf\":\n key = 0\n elif key == \"rho\":\n key = 1 \n outliers = {} \n ai = 0\n for i in xrange(key, len(df.columns), 3):\n d = df.ix[:,i]\n d_std = np.std(d)\n d_mean = np.mean(d)\n cutoffs = [d_mean + (num_std*d_std), d_mean - (num_std*d_std)]\n env = ai_cols[ai]\n outliers[env] = d[(d >= cutoffs[0]) | (d <= cutoffs[1])]\n ai += 1\n return outliers",
"execution_count": 1152,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "def plot_outliers(df, key, num_std):\n if key == \"bf\":\n key = 0\n elif key == \"rho\":\n key = 1 \n ai = 0\n for i in xrange(key, len(df.columns), 3):\n d = df.ix[:,i]\n d_std = np.std(d)\n d_mean = np.mean(d)\n env = ai_cols[ai]\n ax = plt.gca()\n if key == 0:\n ax.set_yscale('log')\n plt.hist(d, bins=100)\n plt.xlim(np.min(d), d_mean+(num_std*d_std))\n plt.title(\"%s $\\mu = %.4f \\pm %.4f [%.4f, %.4f])$\" % (env,\n d_mean,\n d_std,\n np.min(d),\n np.max(d)))\n plt.show()\n ai += 1",
"execution_count": 1153,
"outputs": []
},
{
"metadata": {
"scrolled": false,
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "plot_outliers(bf, \"bf\", 20)",
"execution_count": 1205,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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VyBAnSZJUIUOcJElShQxxkiRJFTLESZIkVcgQJ0mSVCFDnCRJUoUMcZIkSRUyxEmSJFXI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\nYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fde3c18e7d0>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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iJEmSCmSIkyRJKpAhTpIkqUCGOEmSpAIZ4iRJkgpkiJMkSSqQIU6SJKlAhjhJ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\nqUCGOEmSpAL9fxgya+GAqYF8AAAAAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fde3b6f1110>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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EOACADglxAAAdEuIAADokxAEAdEiIAwDokBAHANAhIQ4AoENCHABAh4Q4AIAOCXEAAB0S\n4gAAOiTEAQB0SIgDAOiQEAcA0CEhDgCgQ0IcAECHhDgAgA4JcQAAHdp3vQuYy9TU1APnmXzv9PT0\n3WtWDADABrNhQ9zjTvulG+aa9vXrPvOnSc5Yw3IAADaUDRvijnjsjz1srmk3/+21u9ayFgCAjcaY\nOACADglxAAAdEuIAADokxAEAdEiIAwDokBAHANAhIQ4AoENCHABAh4Q4AIAOCXEAAB0S4gAAOiTE\nAQB0SIgDAOiQEAcA0CEhDgCgQ0IcAECHhDgAgA4JcQAAHRLiAAA6JMQBAHRIiAMA6JAQBwDQISEO\nAKBDQhwAQIeEOACADglxAAAdEuIAADokxAEAdEiIAwDo0L6TWnFr7TFJzk1yW5IHJ/mFqrpxUtsD\nANhMJtkTt2+SF1bVTye5PMkTJ7gtAIBNZWIhrqquSXJAa+3yJKck+fCktgUAsNlMdExcVX2pqp6c\n5A+TvGKS2wIA2EwWPSautXZmhjFu51TVuTPaT0hyfpJDk9yT5C1V9YHW2huSfKqqrkjy9SQnrGbh\nAACb2aJCXGvtgiQHJbk2yfSM9v2TXJrkFVV1SWvtyCRXtdauTnJxkv/SWrstyZYkL17t4gEANqvF\n9sRdWFU7RuPbZjo1yXRVXZIkVXVja+3jSZ5TVa9Ncvoq1vpthzz4Qd+V5NGTWDeb1taxT1hrW8c+\nYT1sHftk/V0314RFhbiq2jHHpKOSXD/Lxo5bXF3Lc+qPnvSsJM+a5DbYtD653gWw6TkH2QichxvH\n1FwTVvqcuAOT3DnWdteofWI+9RdXfiTJr01yG2w6WzP8T+spSXauayVsVlvjHGT9bY3zsBsrDXG3\nJnnAWNuBGR7wOzE3/9M/35J5uhdhBXbGucX62hnnIOtvZ5yHG95KHzFybe47Nu3oJNescL0AAMxj\nqSFuKntem708yb2ttW1J0lo7JslpST64KtUBADCrBS+nttb2SXJ7hkeL7JfkCa21NyW5uKpe2lp7\nRpILWmuvyTAe7oVVdcMkiwYA2OwWDHFVtSvJAfNMvybJyatZFAAA85voa7cAAJgMIQ4AoENCHABA\nh4Q4AIAOCXEAAB0S4gAAOiTEAQB0SIgDAOiQEAcA0CEhDgCgQ0IcAECHhDgAgA4JcQAAHRLiAAA6\nJMQBAHRIiAMA6JAQBwDQISEOAKBDQhwAQIeEOACADglxAAAdEuIAADokxAEAdEiIAwDokBAHANAh\nIQ4AoENCHABAh4Q4AIAOCXEAAB0S4gAAOiTEAQB0SIgDAOiQEAcA0KF917uApdp1zzdz123/eMjU\n1NRx88z2xenp6TvWrCgAgDXWXYi77eabcvhjTn360U98/tNnm37rzTfl85edd3ySHWtcGgDAmuku\nxCXJlkMOz8GHHbneZQAArBtj4gAAOiTEAQB0SIgDAOiQEAcA0CEhDgCgQ0IcAECHhDgAgA4JcQAA\nHRLiAAA6JMQBAHRIiAMA6JAQBwDQISEOAKBDQhwAQIeEOACADglxAAAdEuIAADokxAEAdEiIAwDo\nkBAHANAhIQ4AoENCHABAh4Q4AIAOCXEAAB0S4gAAOiTEAQB0SIgDAOiQEAcA0CEhDgCgQ0IcAECH\nhDgAgA4JcQAAHRLiAAA6JMQBAHRIiAMA6JAQBwDQISEOAKBDQhwAQIeEOACADglxAAAdEuIAADok\nxAEAdEiIAwDokBAHANAhIQ4AoENCHABAh4Q4AIAOCXEAAB0S4gAAOrTvpFbcWtuS5KIku5I8JMkv\nVdUXJrU9AIDNZJI9cd+f5OKqenaS85OcMcFtAQBsKhPriauqK2f8eHqSD09qWwAAm83EQlyStNYO\nSvLOJH9cVX82yW0BAGwmiw5xrbUzk5yb5JyqOndG+wkZLpcemuSeJG+pqg+01vZPcnGS11XVtatb\nNgDA5raoENdauyDJQUmuTTI9o33/JJcmeUVVXdJaOzLJVa21q5OckuToJG9qrSXJlVX11tUtHwBg\nc1psT9yFVbWjtXb5WPupSaar6pIkqaobW2sfT/KcqnptknetYq0AAIwsKsRV1Y45Jh2V5PqxtuuS\nHLeSolZi171358QTTzzlBS94wRFzzXPmmWd+6aSTTrprLetiw9s69glrbevYJ6yHrWOfrL/r5pqw\n0hsbDkxy51jbXaP2dXHnLd/I9KEnn3vjPYfPOv3Wm2/K/e9//zWuio58cr0LYNNzDrIROA83jqm5\nJqw0xN2a5AFjbQcmuW2F612RLYccnoMPO3LO6eeff/4zt2/f7sHDzLQ1w/+0npJk57pWwma1Nc5B\n1t/WOA+7sdIQd22SV461HZ3kmhWud6Iuuuiir2zfvn3O7kk2tZ2Zp+sa1sDOOAdZfzvjPNzwlvrG\nhqns2a13eZJ7W2vbkqS1dkyS05J8cFWqAwBgVgv2xLXW9klye4ZHi+yX5AmttTdleKXWS1trz0hy\nQWvtNRnGw72wqm6YZNEAAJvdgiGuqnYlOWCe6dckOXk1iwIAYH5LvZwKAMAGIMQBAHRIiAMA6JAQ\nBwDQISEOAKBDQhwAQIeEOACADglxAAAdEuIAADokxAEAdEiIAwDokBAHANAhIQ4AoENCHABAh4Q4\nAIAOCXEAAB0S4gAAOiTEAQB0SIgDAOiQEAcA0CEhDgCgQ0IcAECHhDgAgA4JcQAAHdp3vQtYa7vu\nvTtJjpqamppvti9OT0/fsTYVAQAs3aYLcXfe8o0ce/rZH9pyyOGzTr/15pvy+cvOOz7JjrWtDABg\n8TZdiEuSLYccnoMPO3K9ywAAWDZj4gAAOiTEAQB0SIgDAOiQEAcA0CEhDgCgQ0IcAECHhDgAgA4J\ncQAAHRLiAAA6JMQBAHRIiAMA6JAQBwDQISEOAKBDQhwAQIeEOACADglxAAAdEuIAADokxAEAdEiI\nAwDokBAHANAhIQ4AoENCHABAh4Q4AIAOCXEAAB0S4gAAOiTEAQB0SIgDAOiQEAcA0CEhDgCgQ0Ic\nAECH9l3vAjaaXffenSRHTU1NzTfbF6enp+9Ym4oAAO5LiBtz5y3fyLGnn/2hLYccPuv0W2++KZ+/\n7Lzjk+xY28oAAL5DiJvFlkMOz8GHHTnrND11AMBGIMQtkZ46AGAjEOKWYb6eOgCAteDuVACADglx\nAAAdEuIAADokxAEAdEiIAwDokBAHANAhIQ4AoENCHABAh4Q4AIAOCXEAAB0S4gAAOiTEAQB0SIgD\nAOiQEAcA0CEhDgCgQ0IcAECHhDgAgA4JcQAAHRLiAAA6JMQBAHRIiAMA6JAQBwDQISEOAKBDQhwA\nQIf2ndSKW2v3S/KKJK9O8gNV9Y1JbQsAYLOZZE/c9yS5Msm1E9wGAMCmNLGeuKr6WpKvtdYmtQkA\ngE3LmDgAgA4tuieutXZmknOTnFNV585oPyHJ+UkOTXJPkrdU1QfGFp9ahVoBABhZVIhrrV2Q5KAM\n49umZ7Tvn+TSJK+oqktaa0cmuaq1dnWSg5O8MskPJNneWrusqs5f7R0AANiMFtsTd2FV7WitXT7W\nfmqS6aq6JEmq6sbW2seTPKeqXpvks6tYKwAAI4sKcVW1Y45JRyW5fqztuiTHraSo3m3btu2IJLet\ndx0sydaxT1hrW8c+YT1sHftk/V0314SV3p16YJI7x9ruGrVvWmedddal610Dy/bJ9S6ATc85yEbg\nPNw45ryvYKUh7tYkDxhrOzCbvBfq/PPPf+b27du/sN51sCRbM/xP6ylJdq5rJWxWW+McZP1tjfOw\nGysNcddmuHlhpqOTXLPC9Xbtoosu+sr27dvn7P5kQ9uZebquYQ3sjHOQ9bczzsMNb6nPiZvKnt16\nlye5t7W2LUlaa8ckOS3JB1elOgAAZrVgT1xrbZ8kt2d4tMh+SZ7QWntTkour6qWttWckuaC19poM\n4+FeWFU3TLLojWzXvXcnyVFTU/M+Gu+L09PTd6xNRQDA3mjBEFdVu5IcMM/0a5KcvJpF9ezOW76R\nY08/+0NbDjl81um33nxTPn/ZeccnmeuOXwCABU3s3amb2ZZDDs/Bhx253mUAAHsx704FAOiQEAcA\n0CGXU9fYIm58cNMDALAgIW6NzXfjg5seAIDFEuLWgRsfAICVMiYOAKBDQhwAQIeEOACADglxAAAd\nEuIAADokxAEAdEiIAwDokBAHANAhIQ4AoEPe2LCBLOK9qol3qwIAEeI2lPneq5p4tyoA8B1C3Abj\nvaoAwGIYEwcA0CEhDgCgQ0IcAECHhDgAgA4JcQAAHXJ3akcW8Ry5A0afdy1zerJBn0M3NTX1wCRH\nLTDbhqwdACZBiOvIQs+R+/svfy4PPPiwLHf6Bn8O3VHHnn725zqtHQBWnRDXmfmeI3fbzTfloBVM\n3+g8Qw8AvsOYOACADglxAAAdEuIAADokxAEAdEiIAwDokBAHANAhIQ4AoENCHABAh4Q4AIAOCXEA\nAB0S4gAAOiTEAQB0SIgDAOiQEAcA0CEhDgCgQ0IcAECHhDgAgA4JcQAAHRLiAAA6JMQBAHRIiAMA\n6JAQBwDQISEOAKBDQhwAQIeEOACADglxAAAdEuIAADokxAEAdEiIAwDokBAHANAhIQ4AoENCHABA\nh4Q4AIAOCXEAAB0S4gAAOiTEAQB0SIgDAOjQvutdAHuPqampByY5aoHZvjg9PX3HMpZdaL3raiX7\nDgDLIcSxmo469vSzP7flkMNnnXjrzTfl85edd3ySHUtd9u+//LnVq3IyVrLvALBkQhyrasshh+fg\nw45c9WVvu/mmlZS1Jlay7wCwVMbEAQB0SIgDAOiQEAcA0CEhDgCgQ0IcAECHhDgAgA4JcQAAHRLi\nAAA6JMQBAHRIiAMA6JAQBwDQISEOAKBDQhwAQIeEOACADglxAAAdEuIAADokxAEAdEiIAwDokBAH\nANAhIQ4AoEP7TmrFrbUHJLk4ya4kD0jyoqr6f5PaHgDAZjLJnrjnJ/lsVf1chjB39gS3BQCwqUwy\nxB2TZMfov69OctwEtwUAsKlMekzc7vVPJZme8LYAADaNRY+Ja62dmeTcJOdU1bkz2k9Icn6SQ5Pc\nk+QtVfWBJP8nyfFJ/izJCUmuXL2yAQA2t0WFuNbaBUkOSnJtZvSotdb2T3JpkldU1SWttSOTXNVa\nuzrJB5Nc1Fr7/dHsL1nVygEANrHF9sRdWFU7WmuXj7WfmmS6qi5Jkqq6sbX28STPqarXJvn5VawV\nAICRRYW4qtoxx6Sjklw/1nZd3MTQpV333p0TTzzxlBe84AVHzDXPmWee+aWTTjrprtmmbdu27Ygb\n75l/G9u2bTsiyW3LWXYhc607Sa688soD3vOe9zxqrmUf/vCHP/JpT3tarrjiih+rqu+fbZ6V7PtC\nx3a+da/UQvt+22237Tc1NTV14IEHfnOueSZZ33wWqj1Zv9omYOvY57paz2O/yb73JVmDY7N17LMb\ne/F5c91cE6ampxd/v8GoJ+5jVfWO0c+vS/L4qvqJGfO8OslpVXXa8usFAGA+K7079dYMD/Kd6cDM\n0RsCAMDqWGmIuzbJo8fajk5yzQrXCwDAPJYa4qZGf3a7PMm9rbVtSdJaOybJaRnuTAUAYEIWHBPX\nWtsnye0ZHi2yX4Z3oe5KcnFVvXQU3C5I8tAkdyV5fVVdOtGqAQA2uSXd2AAAwMYw6dduAQAwAUIc\nAECHhDgAgA4t9rVbsFdqrW1N8qUkNTbp5Kq6ee0rYjNprZ2Z5Nwk51TVuaO2hyT5nSSPSfKtJH+U\n5FVVZQAzEzHHebgzw9Mo7pgx69lV9Yk1L5A5CXGQpKqOXu8a2FxaaxckOSjD8zZnBrT3JLmpqp7R\nWntgkj9PcmaSd699lezt5jkPp5OcUVV/sS6FsSgupwKsjwur6owMj3BKkrTWtiR5RpJ3JElV3ZHk\nvUmeuy4Vshnc5zycYWqWNjYQPXGQpLV2cZIfzPCsw3dWlQdWM1FVtWOW5u8bTbtxRtv1GS6twqqb\n4zzc7ezW2tszvE7z0iRvqKp71qYyFkNPHJvdrRnGH729qh6b5JeTvLe19sT1LYtN6sAkd4+13Tlq\nh7X0Bxke6n9Ckh/P0EP8q+tbEuP0xLGpVdU/JnnJjJ8/21r7oyRPT3LFuhXGZnVbkv3H2g4ctcOa\nqapXzfhTBkPCAAABFUlEQVTvm1pr5yd5cZI3rl9VjNMTx6bWWntwa+37xpr3yX17Q2AtXJdk19g5\neXSSa9apHjah1tr+o1dqzuT/ixuQEMdm94Qkn2mtHZEkrbUfSPLUJB9d16rYTKZGf1JVt2e4jPWa\nJGmtPSjJLybZvm7VsVl8+zxMsiXJ/2qtPS0Z/rGboRfuI+tUG3Pw7lQ2vdbaf0jy7zPcUn9XkrdU\n1SXrWxV7s9baPhnuBpxOsl+SXaM/Fyf5lSQXJjl21PZfq+oN61Mpe7MFzsPfS/K2DI8f+VaS30/y\n61X1rfWpltkIcQAAHXI5FQCgQ0IcAECHhDgAgA4JcQAAHRLiAAA6JMQBAHRIiAMA6JAQBwDQISEO\nAKBD/x8NLjhI3Hr2KwAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fde3b99e250>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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"text/plain": "<matplotlib.figure.Figure at 0x7fde3bd7f8d0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "plot_outliers(bf, \"rho\", 6)",
"execution_count": 1206,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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TJEkqjAFOkiSpMAY4SZKkwhjgJEmSCmOAkyRJKowBTpIkqTAGOEmSpMIY4CRJ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\ncHB/aPsQ8HCq/m/vyMwt83I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"text/plain": "<matplotlib.figure.Figure at 0x7fde3a9279d0>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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WKwOcRoIj7EuSFhMDnEaCI+xLkhYTA5xGhk2qkqTFwmFEJEmSKsYrcJIGrkkf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\n9Ue/mlAlSZLUJwY4SZKkijH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"text/plain": "<matplotlib.figure.Figure at 0x7fde3b8be0d0>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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ENW2StHgY4KTRYU2bJC0SBjhphFjTJkmLgzVwkiRJFWOAkyRJqhgDnCRJUsUY4CRJkirG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\n73aw7TFmHoG7Abi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"text/plain": "<matplotlib.figure.Figure at 0x7fde3aaf4390>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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Azdu2snnzZlasWDHAYkmSGhngJO3n0COSVA0GOEnzylkgJGnhGeAkzTdngZCkBWaAkzTv\nbIqVpIXlXaiSJEkVY4CTJEmqGAOcJElSxRjgJEmSKsYAJ0mSVDEGOEmSpIoxwEmSJFWMAU6SJKli\nDHCSJEkVY4CTJEmqGAOcpJb27d3DbbfdxurVq0+o1Woryp9Fgy6XJI0750KV1NLu++7l0mvvZcnS\nEzee9vrLnJhekoaEAU7SrJyYXpKGj02okiRJFWOAkyRJqhgDnCRJUsW01QcuIi4ALgN+KzMvK5cd\nCXwEeBbwEPBF4G2ZOR0RjwAuAV5dHuLbwBsz84fzXH5JkqSxM+cVuIi4EjiVIoRN1626CtiamccC\ny4HTgQvKdb8CvBh4dmYeB3wPuHIeyy1JkjS22mlCvSYzzwfun1kQEUuAs4HLATJzF3A1cG65yfnA\nVZm5u3x+BXBORDxmvgouSZI0ruYMcJnZbLyn48p1d9Utu4OiORUggNvr1n2nfK1ndldMSZIkzeh2\nHLjFwJ6GZbvL5TPrZ66+kZkPRcRP6tbP5UnAY7ssW7cmGh41OBMNj+rQqlWrjr7rwYOXATs72afV\ndp0eZ6591JaJhkcNzkTDowZnouFx1NzeakW3AW4ncGjDssU8/AG9E9jfXBoRjyy3b/cDfKrJ8fvl\nhgG9rg5mXXRpzZo1XHT51xuXbex0nxbbdXycufZRR3xfDA/rYniMal3UWq3oNsDdDuyLiOMy845y\n2fHALeW/vw1MAt8onwewF8g2jz/BYK7A3QCcRREgNTgTWBc9Wbdu3Qlw4saGZeesX7/+1k72abFd\nx8eZax+1ZQLfF8NiAutiWEwwpnXRSYCrlT9k5v0R8VngncDqiHgc8Bbg0nLbDcBbI+LTwA7gHcB1\nmfmTNl/rnvJnEKaY5ZKl+moK66IrGzZsOPy011/WuOzu9evXt/x9NtunxXYdH2eufdSRKXxfDIsp\nrIthMcWY1cWsAa5s+ryfYviQRwOnRsR7gI8DFwLXRMSdwD6KgPYxgMz8cEQ8A7iRIvT9A8XQIpIk\nSerRrAEuM/cBh82yyWtm2fcdFFfeJEmSNI+cSkuSJKliur2JQdIY2rd3D8BkrXbAjVGbp6endw2m\nRJI0ngxwktq2+757Wb5y7bVLli4DYMf2rdx8/RUnAc0G/JYkLRADnKSOLFm6jCOOOmbQxZCksWYf\nOEmSpIoxwEmSJFWMAU6SJKliDHCSJEkVY4CTJEmqGAOcJElSxRjgJEmSKsYAJ0mSVDEGOEmSpIox\nwEmSJFWMU2lJY6DFJPTQh4noB/nakjSqDHDSGGichB76NxH9IF9bkkaVAU4aE4OchH6Qry1Jo8g+\ncJIkSRVjgJMkSaoYA5wkSVLFGOAkSZIqxpsYJA1crVZbBEw2WeVQI5LUhAFO0jCYXL5y7SaHGpGk\n9hjgJA0FhxqRpPbZB06SJKliDHCSJEkVY4CTJEmqGPvASRXT4o7Nju/WbDLJfLO7QCVJQ8gAJ1XP\nAXdsdnu3ZuMk89u2bJr3gkqSFoYBTqqg+bpjs/44O7dv7fl4kqT+sA+cJElSxXgFThoi89W/TZI0\n2gxw0nCZl/5tkqTRZoCThowzEkiS5mIfOEmSpIoxwEmSJFWMAU6SJKliDHCSJEkVY4CTJEmqGAOc\nJElSxRjgJEmSKsZx4CQNpX179wBM1mq1+sXOSiFJGOAkDand993L8pVrr3VWCkk6mAFO0tByVgpJ\nas4+cJIkSRVjgJMkSaoYA5wkSVLFGOAkSZIqxgAnSZJUMQY4SZKkijHASZIkVYzjwEkV12LGgsnB\nlEaS1A8GOKniGmcsANi2ZdMASyRJWmgGOGkENM5YsHP71gGWRpK00OwDJ0mSVDEGOEmSpIrpqQk1\nIl4MXAI8FtgLfDgzPxARRwIfAZ4FPAR8EXhbZk73WF5JkqSx1/UVuIhYBHwB+N3MPB54GfCbEXEW\ncBWwNTOPBZYDpwMXzEN5JQ2RujtgV8z84B2wkrTgerkCdzRwBHADQGZui4hbgOcDZ1N+iGfmroi4\nGlgN/FFvxZU0TLwDVpIGo5c+cHcAtwPnAkTEMcCJwJ8CZOZdDds+q4fXkjSkZu6AnflZfMRRgy6S\nJI28rgNcZu6juKp2SUT8AEhgHbAY2NOw+e5yuSRJknrUdRNqRDwZ+BLwi5n51Yh4AsXVt0cAhzZs\nvhjY2cHhn0RxY0Q/TTQ8anAmGh7HxqpVq46+68GDl1H3/mm2TRXVn1e759T4uxgzEw2PGpyJhkcN\nzkTD46i5vdWKXvrAvQj4UWZ+FSAzfxgRXwJeDOyNiOMy845y2+OBWzo49hQHh8B+uWFAr6uDjV1d\nrFmzhosu/3rjso1zbVNF9efV7jk1/i7G1Ni9L4aYdTE8RrUuaq1W9BLgbgWeGhHPy8wby7tSXw78\nFfAD4J3A6oh4HPAW4NIOjj3BYK7A3QCcRREgNTgTjGldrFu37gQ4cWPDsnPWr19/62zbVFH9ebV7\nTo2/izEzwZi+L4bQBNbFsJhgTOui6wCXmbdGxBuBj0TEoRQp8X8DFwOHAddExJ3APuC6zPxYB4e/\np/wZhClmuWSpvppizOpiw4YNh5/2+ssal929fv3622fbporqz6vdc2r8XYypKcbsfTHEprAuhsUU\nY1YXPQ3km5mfAj7VZNUDwGt6ObYkSZKacyotSZKkiunpCpwkDVKtVlvEwTM/bJ6ent41iPJIUr8Y\n4CRV2eTylWs3zcwEsWP7Vm6+/oqTgJsGWyxJWlgGOEmVNjMThCSNE/vASZIkVYxX4CT13b69ewAm\na7X9Y1Q29mOTJM3CACep73bfdy/LV669dqbv2rYtmwZcIkmqFgOcpIGo77u2c/vWAZdGkqrFACdp\nZDRpmp3h0CKSRooBTtLIaGyaBYcWkTSaDHCSRorDikgaBw4jIkmSVDEGOEmSpIoxwEmSJFWMAU6S\nJKliDHCSJEkVY4CTJEmqGAOcJElSxTgOnKSxV6vVFgGTDYudvUHS0DLASRJMLl+5dtPMDA7O3iBp\n2BngJAlncJBULfaBkyRJqhgDnCRJUsUY4CRJkirGACdJklQxBjhJkqSKMcBJkiRVjAFOkiSpYhwH\nThqQFqP/Nz6XJOkgBjhpcA4Y/R9g25ZNAyyOJKkqDHDSADWO/r9z+9YBlkaSVBX2gZMkSaoYA5wk\nSVLFGOAkSZIqxgAnSZJUMd7EIKkS9u3dAzBZq9XqFzvsiqSxZICTVAm777uX5SvXXuuwK5JkgJNU\nIQ67IkkFA5ykkdai6XXz9PT0rsGUSJJ6Z4CTNNIam153bN/KzddfcRJw02BLJkndM8BJGnmNTa+S\nVHUOIyJJklQxBjhJkqSKMcBJkiRVjAFOkiSpYgxwkiRJFWOAkyRJqhgDnCRJUsUY4CRJkirGACdJ\nklQxBjhJkqSKcSotaR7UarVFwGTD4p4nTG8xEXvj66gD/k4ljQIDnDQ/JpevXLtpvidMb5yIHWDb\nlk09FXTc+TuVNAoMcNI8WagJ0xuPu3P71nl/jXHj71RS1dkHTpIkqWIMcJIkSRVjgJMkSaqYnvrA\nRcRS4GrgFOBBYENm/m5EHAl8BHgW8BDwReBtmTndY3klSZLGXq9X4NYD92Tm0RQh7mURcRxwFbA1\nM48FlgOnAxf0+FpSZdQNVbGi4WfRoMsmSaq+rq/ARcRTgJXAkwEy89+A0yNiCXA25bhKmbkrIq4G\nVgN/1HOJpQpoNlTFfA0tIklSL02oy4F7gTdExHkUTaVXAX8PkJl31W17B0VzqjQ2FmpYEUmSeglw\njwd+CnggM58dEScC3wAuBfY0bLsbWNzBsZ8EPLaHsnVjouFRgzPR8Dj0Vq1adfRdD7a3HbCzk300\nGPV1NSQmGh41OBMNjxqciYbHUXN7qxW9BLgfAdPABwEy858j4ivAS4FDG7ZdTGcfhFNNjtEvNwzo\ndXWwytTFmjVruOjyr7ez3cZO99Fg1NfVkKnM+2IMWBfDY1TrotZqRS8B7k7gEOBwYEfd8huBF0XE\ncZl5R7nseOCWDo49wWCuwN0AnEURIDU4E1SsLtatW3cCnDjnF/66devOWb9+/a2d7KPBqK+rITFB\nxd4XI2wC62JYTDCmddF1gMvMjIhvAu8E3hERExQ3NZwNPLVcvjoiHge8haJptV33lD+DMMUslyzV\nV1NUpC42bNhw+Gmvv6yd7e5ev3797Z3so8Gor6shM0VF3hdjYArrYlhMMWZ10eswIucBJ0fEFPAV\n4O2Z+Q3gQmBJRNwJfAv4XGZ+rMfXkiRJEj0O5JuZU8CZTZb/CHhNL8eWJElSc06lJUmSVDE9XYGT\nxlE5m8Jkw+LG5wepm52h7X0kSWrGACd1bnL5yrWb6mdZ2LZl05w7Nc7O0M4+kiQ1Y4CTutA4y8LO\n7Vs73q/dfSRJamQfOEmSpIoxwEmSJFWMAU6SJKliDHCSJEkVY4CTJEmqGAOcJElSxRjgJEmSKsYA\nJ0mSVDEGOEmSpIoxwEmSJFWMU2lJUoN9e/cATNZqtcZVm6enp3f1v0SSdCADnCQ12H3fvSxfufba\nJUuX7V+2Y/tWbr7+ipOAmwZXMkkqGOAkqYklS5dxxFHHDLoYktSUfeAkSZIqxgAnSZJUMQY4SZKk\nijHASZIkVYwBTpIkqWIMcJIkSRVjgJMkSaoYA5wkSVLFGOAkSZIqxgAnSZJUMQY4SZKkijHASZIk\nVYwBTpIkqWIMcJIkSRVjgJMkSaoYA5wkSVLFGOAkSZIqxgAnSZJUMQY4SZKkijHASZIkVcyjBl0A\nadjVarVFwGTdoslW22q8NflbAdg8PT29axDlkTS6DHDS3CaXr1y7acnSZQBs27JpwMXREDvgb2XH\n9q3cfP0VJwE3DbZYkkaNAU5qw5KlyzjiqGMA2Ll964BLo2FW/7ciSQvFPnCSJEkV4xU4jTX7LEmS\nqsgAp3FnnyVJUuUY4DT27LMkSaoa+8BJkiRVjAFOkiSpYgxwkiRJFWOAkyRJqhgDnCRJUsUY4CRJ\nkirGACdJklQxBjhJkqSKMcBJkiRVjAFOkiSpYgxwkiRJFWOAkyRJqph5mcw+Ih4HfBv4amaujogj\ngY8AzwIeAr4IvC0zp+fj9SRJksbZfF2B+0NgNzAT0K4CtmbmscBy4HTggnl6LUmSpLHWc4CLiFcC\nTweuBWoRcThwNnA5QGbuAq4Gzu31tSRJktRjgIuIxwNXAKt5+OrbMwEy8666Te+gaE6VJElSj3q9\nAveHwAfLsDYT4BYBexq22w0s7vG1JEmSRA83MUTEq4CnAb9ULqqVj/cDhzZsvhjY2cHhnwQ8ttuy\ndWmi4VGDM9HwuGBWrVp19F0PPvx83949nHzyyWesXr366JllJ5988jMWuhwafs3+Ni644ILvnHLK\nKQ/MPG/8e5pZRvn5961vfeuwq6666qC/p8bjtDDR8KjBmWh41OBMNDyOmttbrejlLtTXAscC34kI\ngMeVx3sOsDcijsvMO8ptjwdu6eDYUxwcAvvlhgG9rg624HWxZs0aLrr86/uf777vXqaf8KLL7npw\n2f5lP3rUHo5a6IJo6DX+bezYvpVDDjnkgG0a/57KZRtn/n3IIYdw87bHs2Tpw39fzY4zBz+jhod1\nMTxGtS5qrVZ0HeAy87z65xHxLuBpmfmGiLgWeCewuhxi5C3ApR0cfoLBXIG7ATiLIkBqcCboU12s\nW7fuBDhxY/2yJUuXccRRx+x/vnP71oUsgiqk8W9j3bp156xfv/7WuucH/T3Vb7Nu3boTliw9cWP9\nMZodp4UJ/IwaFhNYF8NigjGti3kZB66JC4FrIuJOYB9wXWZ+rIP97yl/BmGKWS5Zqq+mWOC62LBh\nw+Gnvf6yhXwJjbANGzbcvX79+tvrnh/091S/Tau/t8bjzGEKP6OGxRTWxbCYYszqYt4CXGb+dt2/\nfwS8Zr6OLUmSpIc5lZYkSVLFGOAkSZIqxgAnSZJUMQY4SZKkijHASZIkVcxCDSMiSWNv3949AJO1\n2v6xOCcHVxpJo8QAJ0kLZPd997J85dprZ2Ze2LZl04BLJGlUGOAkaQHVz97grB6S5osBTpK60KR5\nFGwildQnBjhJ6kJj8yjYRCqpfwxwktSlxsntbSKV1C8OIyJJklQxBjhJkqSKMcBJkiRVjAFOkiSp\nYryJQSONskNIAAAO/0lEQVSrVqstovmwDpunp6d39bs8kiTNFwOcRtnk8pVrN9UP87Bj+1Zuvv6K\nk4CbBlcsSZJ6Y4DTSGsc5kGSpFFgHzhJkqSKMcBJkiRVjAFOkiSpYgxwkiRJFWOAkyRJqhgDnCRJ\nUsUY4CRJkirGACdJklQxBjhJkqSKMcBJkiRVjAFOkoZIrVZbVKvVVjT8nPra1772uTfddBOrV68+\noVy2aNBllTQ4zoUqScNlcvnKtZuWLF22f8G2LZu4Y+dRXHT514ETNy5fuZabr7/iJOCmQRVS0mAZ\n4CRpyCxZuowjjjpm//Od27dyeMMySePNAKdKKpuPJpus2jw9Pb2r3+WRJKmfDHCqqoOamXZs32qz\nkiRpLBjgVFmNzUySJI0L70KVJEmqGAOcJElSxRjgJEmSKsYAJ0mSVDEGOEmSpIoxwEmSJFWMAU6S\nJKliDHCSJEkVY4CTJEmqGAOcJElSxRjgJEmSKsa5UCVpgPbt3QMwWavVZhZNDq40kqrCACdJA7T7\nvntZvnLttUuWLgNg25ZNAy6RpCowwEnSgC1ZuowjjjoGgJ3btw64NJKqwD5wkiRJFWOAkyRJqhgD\nnCRJUsXYB04jo527+bzjT5I0CgxwGhnt3M3nHX+SpFFggNNIaeduPu/4kyRVnX3gJEmSKsYAJ0mS\nVDEGOEmSpIrpqQ9cRJwJvBc4AngkcGVm/kFEHAl8BHgW8BDwReBtmTndY3klSZLGXtdX4CLiScDn\ngXdk5vHATwO/ExEvAK4CtmbmscBy4HTggnkoryRJ0tjrpQl1L3BuZn4NIDO/A9wKnAycDVxeLt8F\nXA2c21tRJUmSBD00oWbmvwFfmHkeEccA/x74x3L9XXWb30HRnCpJkqQezcs4cBGxDPgS8Hvloj0N\nm+wGFndwyCcBj52HonViouFRgzPR8HiQVatWHX3Xg30pizSUVq1adTSwc9DlGFMTDY8anImGx1Fz\ne6sVPQe4iFhB0RduXWZeEhHPBQ5t2GwxnX3QTDU5Rr/cMKDX1cFa1sWaNWu46PKv97Eo0nBZs2bN\nxkGXQX5fDJFRrYtaqxW93oW6AvgK8CuZOfNhcjuwLyKOy8w7ymXHA7d0cOgJBnMF7gbgLIoAqcGZ\nYI66WLdu3Qlwol9gGlvr1q07Z/369bcOuhxjagK/L4bFBGNaF10HuIg4DPhfHBjeyMz7I+KzwDuB\n1RHxOOAtwKUdHP6e8mcQppjlkqX6aooWdbFhw4bDT3v9Zf0tjTRENmzYcPf69ev9rBqsKfy+GBZT\njFld9HIF7hzgacDFEXFx3fLrgAuBayLiTmAfcF1mfqyH19IIq9Vqi4DJmeerVq06+kMf+hCLFi0a\nYKkkSRpevdyFeh1FWGvlNd0eW2NncvnKtZuWLF0GwM3btrJ582ZWrFgx4GJJkjSc5uUuVKlXS5Yu\n44ijjhl0MSRJqgQDnBZUY/NoafP09PSuQZRHkqRRYIDTQjugeXTH9q3cfP0VJwE3DbZYkiRVlwFO\nC87mUUmS5pcBTn21b+8egMla7YCxCRubWA/SpCl2zn2kUdXifQRzdE+wS4M0Ogxw6qvd993L8pVr\nr51pUgXYtmVTO7se0BTb5j7SSGr2Pmqze4JdGqQRYYBT3zU2qe7cvrXj/drdRxpV3XZNsEuDNBoe\nMegCSJIkqTMGOEmSpIoxwEmSJFWMAU6SJKliDHAaOvv27uG2225j9erVJ9RqtRW1Wm0FDhsizapu\naJEVdT+LBl0uSQvDu1A1dHbfdy+XXnsvS5aeuPG0118GOGyINJfGoUUcIkQabQY4DaVuhxqRxplD\nhEjjwyZUSZKkijHASZIkVYwBTpIkqWLsAydJI6jFhPdz3s3thPdSNRjgJGkENZvwvs27uZ3wXqoA\nA5wkjahu7+b2blZp+NkHTpIkqWK8Aqe2tOgXA/aNkUZai7504HtfGigDnNp1QL8YsG+MNA6a9aXz\nvS8NngFObbNfjDSefO9Lw8cAp3nTopnVSeglSZpnBjjNp4OaWZ2EXpKk+WeA07xyEnpJkhaew4hI\nkiRVjAFOkiSpYgxwkiRJFWOAkyRJqhhvYpCkMdVilgWH/pEqwAAnSWOq2SwLDv0jVYMBTpLGmEP/\nSNVkgBtDLWZMOGBi6ibb2KwiqWstPneg4bNHUnsMcOPpgBkTWkxMfcA2NqtI6tFBM7W0+OyR1AYD\n3JhqZ3Lq+m1sVpHUq3Y+dyS1x2FEJEmSKsYAJ0mSVDEGOEmSpIoxwEmSJFWMNzFUSIvb8A8rHx+o\nW+Zt+ZL6xiFCpP4zwFXLQbfhb9uyiUVHHMUcQ4JI0kJyiBCpzwxwFdNs1PTDvTVf0oA5RIjUX/aB\nkyRJqhgDnCRJUsUY4CRJkirGPnAjrsXdYQc837d3D8BkrVZruU0zTfZzwntJkvrAADf6mt65Wm/3\nffeyfOXaa2fbppnG/ZzwXpKk/jDAjYFmd652s81cx3bCe0mS+sM+cJIkSRXjFThJUkfa6f/aTR/Z\nFn12nc1BasIAJ0nqSDv9X7vsI3tAn11nc5BaM8BJkjrWTv/XbvrIOqOD1B4D3Cy6nTy+nWaAJts0\nO+5B+0nSuGgxxNGcn8HSODDAza7byePbaQY4YJvG486ynySNhVZDHLXxGSyNvAULcBHxfGAd8ATg\nQeB9mfmJhXq9hdLt5PHtNAM0Ni84Kb0kHajbz2Bp1C1IgIuIQ4GNwH/NzM9ExDHAjRHxj5n5f3s9\n/jDdqdTtLAZdHtvZESSNjPn4vFrIZtZZvms6LWY7x505tk3BastCXYE7E5jOzM8AZOZdEfEV4BeA\n35hr52e+8Oe/OfPvBx/Yeej3Nn/jLXt23/cPdZsMzZ1K3c5i0M2xnR1B0iiZj8+rBW5mbfVds7Pj\ngs5y3B7LqDG1UAFuErijYdntwIp2do5Tf/HUmX/v+OF3+dc7/u7wxm2G6U6lbmcx6PTYzo4gadTM\nx+fVQjazLtR3zTB9h6maFmomhsXA7oZlD5TLJUmS1IOFugK3A3hMw7LFtHnZecumjbc+8hG1RwA8\nsOvHjz7xhONOWb169REz608++eRn7Kj7n9qO7Vs5+eSTz1i9evXR3RY4Ip7yile8gq9+9asvy8wT\nmr0OwP0/3sb0LM+bLWtWvsZjt3OcbrZZqOOO6jbDXr5h22bYyzds2wx7+YZtm/k6brffEa2+a97/\n/vc/0Ph90ctxZ469atWqo+m9eXbcTDQ8jprbW62o9doZs5mIeDmwPjOX1S37DHBrZr573l9QkiRp\njCxUE+rXgL0RsQogIp4DvBz45AK9niRJ0thYkCtwsD+0XQk8kaL/27syc+OCvJgkSdIYWbAAJ0mS\npIWxUE2okiRJWiAGOEmSpIoxwEmSJFWMAU6SJKliDHCSJEkVs1AzMVRCRPwa8EaKIHs38MuZ+Z05\n9rkSuCAzDb/zqN26iIgnAn8IPBc4hGLi57dk5g/7WNyRExHPB9YBTwAeBN6XmZ9ost35wNspfvc/\nBN6amTf2s6yjroO6+C/Amyk+x3cBv5aZ/7ufZR117dZF3fYvAL4JvCEzP9afUo6HDt4Xy4GreHgI\ns3dk5hf7WdZ+GdsQEhGvBC4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"text/plain": "<matplotlib.figure.Figure at 0x7fde3a7ca450>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bf_outliers = get_outliers(bf, \"bf\", 6) \nrho_outliers = get_outliers(bf, \"rho\", 3)",
"execution_count": 1156,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bf_outliers.keys()",
"execution_count": 1163,
"outputs": [
{
"execution_count": 1163,
"output_type": "execute_result",
"data": {
"text/plain": "[u'AI_Q3', u'AI_Q2', u'AI_Q1', u'AI_Q4']"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "rho_outliers.keys()",
"execution_count": 1164,
"outputs": [
{
"execution_count": 1164,
"output_type": "execute_result",
"data": {
"text/plain": "[u'AI_Q3', u'AI_Q2', u'AI_Q1', u'AI_Q4']"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "%%R\nlibrary(VennDiagram)",
"execution_count": 1159,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "def draw_venn(outliers, title):\n keys = sorted(list(outliers.keys()))\n a1 = set(outliers[keys[0]].index)\n a2 = set(outliers[keys[1]].index)\n a3 = set(outliers[keys[2]].index)\n a4 = set(outliers[keys[3]].index)\n area1 = len(a1)\n area2 = len(a2)\n area3 = len(a3) \n area4 = len(a4)\n n12 = len(a1.intersection(a2))\n n13 = len(a1.intersection(a3))\n n14 = len(a1.intersection(a4))\n n23 = len(a2.intersection(a3))\n n24 = len(a2.intersection(a4))\n n34 = len(a3.intersection(a4))\n n123 = len(set.intersection(a1, a2, a3))\n n124 = len(set.intersection(a1, a2, a4))\n n134 = len(set.intersection(a1, a3, a4))\n n234 = len(set.intersection(a2, a3, a4))\n n1234 = len(set.intersection(a1, a2, a3, a4))\n venn = \"venn_%s.png\" % title.replace(\" \", \"_\")\n r(\"library(VennDiagram)\")\n r(\"png('%s')\" % venn)\n r('draw.quad.venn')(area1, \n area2,\n area3,\n area4,\n n12,\n n13,\n n14,\n n23,\n n24,\n n34,\n n123,\n n124,\n n134,\n n234,\n n1234,\n category=keys)\n r('dev.off()')\n return venn",
"execution_count": 1174,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "Image(draw_venn(bf_outliers, \"Bayes factor outliers\"))",
"execution_count": 1175,
"outputs": [
{
"execution_count": 1175,
"output_type": "execute_result",
"data": {
"image/png": 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qXsDcSM+8FyaB5ZbSw2QRSBuZst/U0wp2PO0fZJVS/HIxf/LKTJY8Lh0oNZH7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\noa9FpF56qUp4N4SsTVWlNzI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jAAAAAElFTkSuQmCC\n",
"text/plain": "<IPython.core.display.Image object>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "Image(draw_venn(rho_outliers, \"Rho outliers\"))",
"execution_count": 1176,
"outputs": [
{
"execution_count": 1176,
"output_type": "execute_result",
"data": {
"image/png": 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TABFDgvN/MAvsLcfTCJunQU0RWj0pE0JncX4ysKRd+rKLJQPJqm3aktVCJ/1F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\n",
"text/plain": "<IPython.core.display.Image object>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "code",
"source": "combined_outliers = {}\nfor key in bf_outliers:\n a = bf_outliers[key].index\n b = rho_outliers[key].index\n combined_outliers[key] = pd.Series(index=a.intersection(b))",
"execution_count": 1177,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "Image(draw_venn(combined_outliers, \"combined\"))",
"execution_count": 1204,
"outputs": [
{
"execution_count": 1204,
"output_type": "execute_result",
"data": {
"image/png": 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zWfOodJDURO5btS9gLlGX9yJPYKP8wQwWGaXKS04/x1NMqfhncDErL48zxYLp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\n2Mn5P3AKPpEV/1SDAAAAAElFTkSuQmCC\n",
"text/plain": "<IPython.core.display.Image object>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "boxplot_data = {}\nfor key, val in bf_outliers.items():\n val = val.sort(inplace=False, ascending=False)\n boxplot_data[key] = {val.index[0]: val[0]}",
"execution_count": 1217,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "boxplot_data",
"execution_count": 1218,
"outputs": [
{
"execution_count": 1218,
"output_type": "execute_result",
"data": {
"text/plain": "{u'AI_Q1': {'0-11252-01-353': 182.49000000000001},\n u'AI_Q2': {'2-10309-01-389': 85.611000000000004},\n u'AI_Q3': {'2-10390-01-65': 24.484000000000002},\n u'AI_Q4': {'UMN-6288-01-39': 108.73}}"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "",
"execution_count": 1225,
"outputs": [
{
"execution_count": 1225,
"output_type": "execute_result",
"data": {
"text/plain": " county state lat long countyid 0-10037-01-257 \\\n0 COLUMBUS NC 34.33010 -78.70453 11 NA \n1 COLUMBUS NC 34.33010 -78.70453 11 A/G \n2 COLUMBUS NC 34.33010 -78.70453 11 A/G \n3 COLUMBUS NC 34.33010 -78.70453 11 A/A \n4 COLUMBUS NC 34.33010 -78.70453 11 A/G \n5 COLUMBUS NC 34.33010 -78.70453 11 A/A \n6 ONSLOW NC 34.75963 -77.40977 26 A/A \n7 ONSLOW NC 34.75963 -77.40977 26 A/A \n8 ONSLOW NC 34.75963 -77.40977 26 A/A \n9 ONSLOW NC 34.75963 -77.40977 26 A/A \n10 ONSLOW NC 34.75963 -77.40977 26 A/A \n11 ONSLOW NC 34.75963 -77.40977 26 A/G \n12 ONSLOW NC 34.75963 -77.40977 26 A/A \n13 ONSLOW NC 34.75963 -77.40977 26 NA \n14 ONSLOW NC 34.75963 -77.40977 26 A/A \n15 ONSLOW NC 34.75963 -77.40977 26 A/G \n16 ONSLOW NC 34.64551 -77.41295 26 A/G \n17 ONSLOW NC 34.64551 -77.41295 26 G/G \n18 ONSLOW NC 34.64551 -77.41295 26 A/A \n19 GEORGETOWN SC 33.36318 -79.30539 14 NA \n20 GEORGETOWN SC 33.36318 -79.30539 14 A/G \n21 GEORGETOWN SC 33.36318 -79.30539 14 G/G \n22 GEORGETOWN SC 33.36318 -79.30539 14 A/A \n23 GEORGETOWN SC 33.36318 -79.30539 14 A/A \n24 GEORGETOWN SC 33.36318 -79.30539 14 A/A \n25 GEORGETOWN SC 33.36318 -79.30539 14 A/A \n26 GEORGETOWN SC 33.36318 -79.30539 14 A/A \n27 GEORGETOWN SC 33.36318 -79.30539 14 A/G \n28 GEORGETOWN SC 33.36318 -79.30539 14 A/A \n29 BEAUFORT NC 35.55349 -77.05205 2 A/A \n.. ... ... ... ... ... ... \n358 MECKLENBURG VA 36.66800 -78.38900 23 G/G \n359 MECKLENBURG VA 36.66800 -78.38900 23 A/G \n360 MECKLENBURG VA 36.66800 -78.38900 23 G/G \n361 MECKLENBURG VA 36.66800 -78.38900 23 A/A \n362 MECKLENBURG VA 36.66800 -78.38900 23 A/A \n363 HALIFAX NC 36.32840 -77.59073 16 A/A \n364 HALIFAX NC 36.32840 -77.59073 16 A/A \n365 HALIFAX NC 36.32840 -77.59073 16 A/A \n366 HALIFAX NC 36.32840 -77.59073 16 A/G \n367 HALIFAX NC 36.32840 -77.59073 16 G/G \n368 HALIFAX NC 36.32840 -77.59073 16 A/G \n369 BERKELEY SC 33.19661 -80.00666 3 A/A \n370 BERKELEY SC 33.19661 -80.00666 3 A/A \n371 BERKELEY SC 33.19661 -80.00666 3 A/G \n372 BERKELEY SC 33.19661 -80.00666 3 A/A \n373 BERKELEY SC 33.19661 -80.00666 3 A/A \n374 BERKELEY SC 33.19661 -80.00666 3 A/A \n375 BERKELEY SC 33.19661 -80.00666 3 G/G \n376 WILCOX AL 31.98678 -87.28057 34 A/A \n377 WILCOX AL 31.98678 -87.28057 34 A/A \n378 WILCOX AL 31.98678 -87.28057 34 A/A \n379 WILCOX AL 31.98678 -87.28057 34 G/G \n380 WILCOX AL 31.98678 -87.28057 34 A/G \n381 WILCOX AL 31.98678 -87.28057 34 A/G \n382 WILCOX AL 31.98678 -87.28057 34 A/G \n383 SPOTSYLVANIA VA 38.20208 -77.58750 29 A/A \n384 SPOTSYLVANIA VA 38.20208 -77.58750 29 A/A \n385 SPOTSYLVANIA VA 38.20208 -77.58750 29 A/G \n386 SPOTSYLVANIA VA 38.20208 -77.58750 29 A/G \n387 SPOTSYLVANIA VA 38.20208 -77.58750 29 A/G \n\n 0-10040-02-394 0-10044-01-392 0-10048-01-60 0-10051-02-166 0-10054-01-402 \\\n0 A/A C/C G/G G/G G/G \n1 C/C C/C G/A G/G G/A \n2 C/C G/G G/G G/A G/A \n3 C/A C/G G/G G/A G/G \n4 C/A C/G G/G G/G G/G \n5 C/C C/C G/G G/G A/A \n6 A/A C/G G/G G/G G/A \n7 C/A NA G/A G/G G/A \n8 C/C C/C G/G G/G A/A \n9 A/A C/C G/G G/G G/A \n10 C/A C/G G/A G/G G/A \n11 C/A G/G G/G G/G G/A \n12 C/A G/G A/A G/G A/A \n13 C/C G/G G/A G/G G/A \n14 C/A NA G/G G/G G/G \n15 A/A C/G G/G G/A A/A \n16 C/C C/C G/A G/G G/A \n17 C/A C/C G/A G/G A/A \n18 C/A C/G G/A G/G G/G \n19 C/A NA NA G/G G/A \n20 C/C C/G G/A G/G G/A \n21 C/C C/G G/G G/A A/A \n22 C/C C/G G/G G/G G/A \n23 C/A G/G G/A G/G G/A \n24 C/C C/C A/A G/G G/G \n25 C/A C/C G/G G/G G/A \n26 C/A C/G G/A G/G G/A \n27 C/A C/G G/G G/G A/A \n28 C/C G/G G/G G/G G/A \n29 NA C/G G/A G/G A/A \n.. ... ... ... ... ... \n358 C/A C/C G/G G/G G/A \n359 C/C C/G G/A G/G G/G \n360 A/A C/C G/A G/A G/A \n361 C/A G/G G/G G/G G/G \n362 C/C G/G G/G G/A G/A \n363 A/A NA G/A G/A G/A \n364 A/A C/C G/G G/G G/A \n365 C/A C/G G/G G/A A/A \n366 A/A NA G/G G/G G/A \n367 C/A C/G G/G G/G A/A \n368 C/A G/G G/G G/G A/A \n369 C/A C/C G/A G/G G/G \n370 C/A C/G G/G G/G G/A \n371 C/A G/G G/G G/G G/A \n372 C/A C/C G/A G/G G/A \n373 C/A C/G A/A G/G G/G \n374 A/A C/C G/G G/A A/A \n375 C/A C/G G/G G/G G/G \n376 A/A C/C G/A G/G G/A \n377 C/A G/G G/A G/G A/A \n378 C/A NA G/A G/G A/A \n379 C/C G/G G/G G/G G/G \n380 A/A G/G G/A G/G G/A \n381 C/A C/G G/A G/G G/G \n382 C/C G/G G/A G/G G/A \n383 C/A G/G G/G G/G G/G \n384 A/A G/G G/G G/A G/A \n385 C/A NA G/G G/G G/A \n386 C/A C/G G/G G/A G/G \n387 C/A G/G G/A G/A G/A \n\n 0-10067-03-111 0-10079-02-168 0-10112-01-169 0-10113-01-119 ... \\\n0 A/A G/G A/A A/G ... \n1 A/A G/G A/A A/A ... \n2 A/A G/G A/A A/G ... \n3 A/A G/A A/A A/G ... \n4 A/A G/G A/A A/A ... \n5 A/A G/G A/A A/G ... \n6 A/A G/G A/A A/A ... \n7 A/A G/G A/A A/G ... \n8 A/G G/G A/C A/A ... \n9 A/A G/G A/A A/G ... \n10 A/A G/A A/A A/G ... \n11 A/A G/G A/C A/A ... \n12 A/A G/G A/A A/A ... \n13 A/A G/G A/A A/A ... \n14 A/A G/G A/A A/A ... \n15 A/G G/G A/A A/A ... \n16 A/A G/G A/A A/G ... \n17 A/A G/G A/A A/A ... \n18 A/A G/G A/C A/A ... \n19 A/A G/G A/C A/G ... \n20 A/A G/G A/A A/G ... \n21 A/A G/G A/A A/A ... \n22 A/A G/G A/A A/A ... \n23 A/A G/A A/A A/A ... \n24 A/A G/G A/A A/G ... \n25 A/A G/G A/A A/G ... \n26 A/A G/A A/C A/A ... \n27 A/A G/G A/A A/G ... \n28 A/A A/A A/A G/G ... \n29 A/A G/G A/A A/G ... \n.. ... ... ... ... ... \n358 A/A G/G A/A A/A ... \n359 A/A G/G A/A A/A ... \n360 A/A G/G A/A G/G ... \n361 A/A G/G A/A A/G ... \n362 A/A G/G A/C A/A ... \n363 A/A G/G A/C A/A ... \n364 A/A G/G A/C A/G ... \n365 A/G G/G A/A A/A ... \n366 A/A G/G A/A A/A ... \n367 A/A G/G A/A A/A ... \n368 A/A G/G A/A A/G ... \n369 A/A G/G A/A G/G ... \n370 A/A G/G A/A A/G ... \n371 A/A G/G A/A A/A ... \n372 A/A G/A A/A A/A ... \n373 A/A G/G A/A A/G ... \n374 A/A G/G A/A A/G ... \n375 A/A G/G A/A A/A ... \n376 A/A G/G A/A A/G ... \n377 A/A G/G A/A A/A ... \n378 A/A G/G A/A A/G ... \n379 A/A G/G A/A A/G ... \n380 A/A G/G A/A A/A ... \n381 A/A G/G A/A A/A ... \n382 A/G G/G A/A A/A ... \n383 A/A G/G A/A A/A ... \n384 A/A G/G A/A A/A ... \n385 A/A G/G A/A A/G ... \n386 A/G G/G A/C A/A ... \n387 A/A G/G A/A A/A ... \n\n UMN-CL353Contig1-04-64 UMN-CL362Contig1-07-133 UMN-CL363Contig1-01-233 \\\n0 A/A NA G/G \n1 A/A C/A G/G \n2 A/A C/C G/A \n3 A/A C/C G/G \n4 A/A C/C G/G \n5 A/A C/C G/G \n6 A/A C/A G/G \n7 A/A C/C G/G \n8 A/A C/A G/G \n9 A/A C/A G/G \n10 A/A NA NA \n11 A/A C/C G/G \n12 A/A C/C G/A \n13 A/A C/C G/G \n14 A/A C/C G/G \n15 A/A A/A G/G \n16 A/A C/C G/A \n17 A/G C/A G/A \n18 A/A C/A G/G \n19 A/A C/A G/G \n20 A/A C/A G/A \n21 A/A C/C G/G \n22 A/A C/C G/A \n23 A/A C/C G/G \n24 A/A C/A G/G \n25 A/A C/C G/G \n26 A/A C/C G/G \n27 A/A C/C G/G \n28 A/A C/C G/A \n29 A/A C/A G/G \n.. ... ... ... \n358 A/G A/A G/G \n359 A/A C/A G/G \n360 A/A C/C G/G \n361 A/A C/A G/G \n362 A/A C/C G/A \n363 A/A C/C G/G \n364 A/A C/C G/G \n365 A/G C/C G/G \n366 A/A C/C G/G \n367 A/A C/C G/G \n368 A/A C/C G/G \n369 A/A C/C A/A \n370 A/A NA G/G \n371 A/A NA G/G \n372 A/A C/A G/G \n373 A/A C/C G/G \n374 A/A NA G/G \n375 A/A C/A NA \n376 A/A C/A G/G \n377 A/A NA G/G \n378 A/A C/A G/A \n379 A/A C/A G/G \n380 A/A C/C G/G \n381 A/A C/A G/G \n382 A/A A/A G/G \n383 A/A C/A G/G \n384 A/A C/C G/G \n385 A/A C/A G/A \n386 A/A C/A G/G \n387 A/A C/C G/G \n\n UMN-CL379Contig1-12-117 UMN-CL424Contig1-03-94 UMN-CL54Contig1-07-88 \\\n0 A/A C/A G/G \n1 A/A C/A G/A \n2 A/A C/C G/G \n3 A/A C/C G/G \n4 A/A C/A A/A \n5 A/A C/A G/A \n6 A/A C/C G/A \n7 A/A C/C G/A \n8 A/A C/C G/G \n9 A/A C/A G/G \n10 A/A A/A G/G \n11 A/A A/A G/G \n12 A/A C/C G/A \n13 A/A C/C G/A \n14 A/A C/C G/A \n15 A/A C/C G/A \n16 A/A C/C G/G \n17 A/A C/A G/A \n18 A/A C/C G/A \n19 A/A C/C NA \n20 A/A C/A G/G \n21 A/A C/A G/A \n22 A/A C/C G/G \n23 A/A C/C G/G \n24 A/A C/C G/A \n25 A/A C/C G/G \n26 A/A C/C G/G \n27 A/A C/C G/G \n28 A/A C/A G/G \n29 A/A A/A G/G \n.. ... ... ... \n358 A/A C/A G/G \n359 A/A C/A G/A \n360 A/A A/A G/G \n361 A/A C/C G/A \n362 A/A C/C A/A \n363 A/A C/C G/A \n364 A/A C/A G/G \n365 A/A C/A A/A \n366 A/A C/C G/G \n367 A/A C/C G/A \n368 A/A C/C G/A \n369 A/A C/A G/G \n370 A/A C/A G/G \n371 A/A C/A G/G \n372 A/A C/A G/G \n373 A/A C/C G/G \n374 A/A A/A G/A \n375 A/A A/A G/A \n376 A/A C/C G/A \n377 A/A A/A G/G \n378 A/A C/A G/G \n379 A/A C/A G/A \n380 A/A C/C G/A \n381 A/A C/A G/G \n382 A/A C/C G/G \n383 A/A C/C G/G \n384 A/A C/A A/A \n385 A/A C/C G/A \n386 A/A C/C G/A \n387 A/A C/A G/G \n\n UMN-CL91Contig1-02-246 UMN-CL97Contig county_state County \\\n0 C/A A/A COLUMBUS_NC COLUMBUS \n1 C/C G/A COLUMBUS_NC COLUMBUS \n2 C/C G/G COLUMBUS_NC COLUMBUS \n3 C/C G/A COLUMBUS_NC COLUMBUS \n4 C/C G/A COLUMBUS_NC COLUMBUS \n5 C/C G/A COLUMBUS_NC COLUMBUS \n6 C/C G/G ONSLOW_NC ONSLOW \n7 C/C G/A ONSLOW_NC ONSLOW \n8 C/C G/G ONSLOW_NC ONSLOW \n9 C/C G/G ONSLOW_NC ONSLOW \n10 C/C G/G ONSLOW_NC ONSLOW \n11 C/A G/G ONSLOW_NC ONSLOW \n12 C/C G/A ONSLOW_NC ONSLOW \n13 C/C G/G ONSLOW_NC ONSLOW \n14 C/C G/A ONSLOW_NC ONSLOW \n15 C/C G/G ONSLOW_NC ONSLOW \n16 C/C G/G ONSLOW_NC ONSLOW \n17 C/C G/G ONSLOW_NC ONSLOW \n18 C/C G/G ONSLOW_NC ONSLOW \n19 C/C NA GEORGETOWN_SC GEORGETOWN \n20 C/A G/G GEORGETOWN_SC GEORGETOWN \n21 C/C A/A GEORGETOWN_SC GEORGETOWN \n22 C/C G/A GEORGETOWN_SC GEORGETOWN \n23 C/C G/G GEORGETOWN_SC GEORGETOWN \n24 C/C G/A GEORGETOWN_SC GEORGETOWN \n25 C/A G/G GEORGETOWN_SC GEORGETOWN \n26 C/A G/G GEORGETOWN_SC GEORGETOWN \n27 C/C G/A GEORGETOWN_SC GEORGETOWN \n28 C/A G/G GEORGETOWN_SC GEORGETOWN \n29 C/A G/G BEAUFORT_NC BEAUFORT \n.. ... ... ... ... \n358 C/C G/A MECKLENBURG_VA MECKLENBURG \n359 C/C G/A MECKLENBURG_VA MECKLENBURG \n360 C/C G/G MECKLENBURG_VA MECKLENBURG \n361 C/A G/G MECKLENBURG_VA MECKLENBURG \n362 C/A G/G MECKLENBURG_VA MECKLENBURG \n363 C/A G/G HALIFAX_NC HALIFAX \n364 C/C G/G HALIFAX_NC HALIFAX \n365 C/C G/G HALIFAX_NC HALIFAX \n366 C/A G/G HALIFAX_NC HALIFAX \n367 C/C G/G HALIFAX_NC HALIFAX \n368 C/A A/A HALIFAX_NC HALIFAX \n369 C/C G/G BERKELEY_SC BERKELEY \n370 C/C G/G BERKELEY_SC BERKELEY \n371 C/C G/A BERKELEY_SC BERKELEY \n372 C/C G/A BERKELEY_SC BERKELEY \n373 C/C G/A BERKELEY_SC BERKELEY \n374 C/A G/A BERKELEY_SC BERKELEY \n375 C/C G/G BERKELEY_SC BERKELEY \n376 C/C G/G WILCOX_AL WILCOX \n377 C/C G/G WILCOX_AL WILCOX \n378 C/A G/G WILCOX_AL WILCOX \n379 C/A G/A WILCOX_AL WILCOX \n380 C/C G/G WILCOX_AL WILCOX \n381 C/C G/G WILCOX_AL WILCOX \n382 C/C G/G WILCOX_AL WILCOX \n383 C/C G/A SPOTSYLVANIA_VA SPOTSYLVANIA \n384 C/A G/G SPOTSYLVANIA_VA SPOTSYLVANIA \n385 C/C G/G SPOTSYLVANIA_VA SPOTSYLVANIA \n386 C/C G/G SPOTSYLVANIA_VA SPOTSYLVANIA \n387 C/C G/G SPOTSYLVANIA_VA SPOTSYLVANIA \n\n State AI_Q1 AI_Q2 AI_Q3 AI_Q4 \n0 NC 6.737349 1.031164 1.017138 2.443279 \n1 NC 6.737349 1.031164 1.017138 2.443279 \n2 NC 6.737349 1.031164 1.017138 2.443279 \n3 NC 6.737349 1.031164 1.017138 2.443279 \n4 NC 6.737349 1.031164 1.017138 2.443279 \n5 NC 6.737349 1.031164 1.017138 2.443279 \n6 NC 6.094570 1.059794 1.153282 2.515655 \n7 NC 6.094570 1.059794 1.153282 2.515655 \n8 NC 6.094570 1.059794 1.153282 2.515655 \n9 NC 6.094570 1.059794 1.153282 2.515655 \n10 NC 6.094570 1.059794 1.153282 2.515655 \n11 NC 6.094570 1.059794 1.153282 2.515655 \n12 NC 6.094570 1.059794 1.153282 2.515655 \n13 NC 6.094570 1.059794 1.153282 2.515655 \n14 NC 6.094570 1.059794 1.153282 2.515655 \n15 NC 6.094570 1.059794 1.153282 2.515655 \n16 NC 6.094570 1.059794 1.153282 2.515655 \n17 NC 6.094570 1.059794 1.153282 2.515655 \n18 NC 6.094570 1.059794 1.153282 2.515655 \n19 SC 6.051620 1.024486 1.129748 2.631683 \n20 SC 6.051620 1.024486 1.129748 2.631683 \n21 SC 6.051620 1.024486 1.129748 2.631683 \n22 SC 6.051620 1.024486 1.129748 2.631683 \n23 SC 6.051620 1.024486 1.129748 2.631683 \n24 SC 6.051620 1.024486 1.129748 2.631683 \n25 SC 6.051620 1.024486 1.129748 2.631683 \n26 SC 6.051620 1.024486 1.129748 2.631683 \n27 SC 6.051620 1.024486 1.129748 2.631683 \n28 SC 6.051620 1.024486 1.129748 2.631683 \n29 NC 6.241730 1.048126 0.994917 2.433338 \n.. ... ... ... ... ... \n358 VA 5.839441 0.899897 0.711595 2.700255 \n359 VA 5.839441 0.899897 0.711595 2.700255 \n360 VA 5.839441 0.899897 0.711595 2.700255 \n361 VA 5.839441 0.899897 0.711595 2.700255 \n362 VA 5.839441 0.899897 0.711595 2.700255 \n363 NC 5.674788 0.920890 0.798284 2.431289 \n364 NC 5.674788 0.920890 0.798284 2.431289 \n365 NC 5.674788 0.920890 0.798284 2.431289 \n366 NC 5.674788 0.920890 0.798284 2.431289 \n367 NC 5.674788 0.920890 0.798284 2.431289 \n368 NC 5.674788 0.920890 0.798284 2.431289 \n369 SC 6.181043 1.048869 1.042944 2.354930 \n370 SC 6.181043 1.048869 1.042944 2.354930 \n371 SC 6.181043 1.048869 1.042944 2.354930 \n372 SC 6.181043 1.048869 1.042944 2.354930 \n373 SC 6.181043 1.048869 1.042944 2.354930 \n374 SC 6.181043 1.048869 1.042944 2.354930 \n375 SC 6.181043 1.048869 1.042944 2.354930 \n376 AL 9.683140 1.140042 0.806234 3.265560 \n377 AL 9.683140 1.140042 0.806234 3.265560 \n378 AL 9.683140 1.140042 0.806234 3.265560 \n379 AL 9.683140 1.140042 0.806234 3.265560 \n380 AL 9.683140 1.140042 0.806234 3.265560 \n381 AL 9.683140 1.140042 0.806234 3.265560 \n382 AL 9.683140 1.140042 0.806234 3.265560 \n383 VA 5.494060 0.888762 0.709162 2.838240 \n384 VA 5.494060 0.888762 0.709162 2.838240 \n385 VA 5.494060 0.888762 0.709162 2.838240 \n386 VA 5.494060 0.888762 0.709162 2.838240 \n387 VA 5.494060 0.888762 0.709162 2.838240 \n\n[388 rows x 3094 columns]",
"text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>county</th>\n <th>state</th>\n <th>lat</th>\n <th>long</th>\n <th>countyid</th>\n <th>0-10037-01-257</th>\n <th>0-10040-02-394</th>\n <th>0-10044-01-392</th>\n <th>0-10048-01-60</th>\n <th>0-10051-02-166</th>\n <th>0-10054-01-402</th>\n <th>0-10067-03-111</th>\n <th>0-10079-02-168</th>\n <th>0-10112-01-169</th>\n <th>0-10113-01-119</th>\n <th>...</th>\n <th>UMN-CL353Contig1-04-64</th>\n <th>UMN-CL362Contig1-07-133</th>\n <th>UMN-CL363Contig1-01-233</th>\n <th>UMN-CL379Contig1-12-117</th>\n <th>UMN-CL424Contig1-03-94</th>\n <th>UMN-CL54Contig1-07-88</th>\n <th>UMN-CL91Contig1-02-246</th>\n <th>UMN-CL97Contig</th>\n <th>county_state</th>\n <th>County</th>\n <th>State</th>\n <th>AI_Q1</th>\n <th>AI_Q2</th>\n <th>AI_Q3</th>\n <th>AI_Q4</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> NA</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> NA</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> A/A</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 34.33010</td>\n <td>-78.70453</td>\n <td> 11</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> COLUMBUS_NC</td>\n <td> COLUMBUS</td>\n <td> NC</td>\n <td> 6.737349</td>\n <td> 1.031164</td>\n <td> 1.017138</td>\n <td> 2.443279</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> NA</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>10 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> NA</td>\n <td> NA</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>11 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>12 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>13 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> NA</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>14 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> NA</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>15 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.75963</td>\n <td>-77.40977</td>\n <td> 26</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>16 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.64551</td>\n <td>-77.41295</td>\n <td> 26</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>17 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.64551</td>\n <td>-77.41295</td>\n <td> 26</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>18 </th>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 34.64551</td>\n <td>-77.41295</td>\n <td> 26</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> ONSLOW_NC</td>\n <td> ONSLOW</td>\n <td> NC</td>\n <td> 6.094570</td>\n <td> 1.059794</td>\n <td> 1.153282</td>\n <td> 2.515655</td>\n </tr>\n <tr>\n <th>19 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> NA</td>\n <td> C/A</td>\n <td> NA</td>\n <td> NA</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> NA</td>\n <td> C/C</td>\n <td> NA</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>20 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> C/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>21 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> A/A</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>22 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>23 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>24 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> C/C</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>25 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>26 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/A</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>27 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>28 </th>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 33.36318</td>\n <td>-79.30539</td>\n <td> 14</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> GEORGETOWN_SC</td>\n <td> GEORGETOWN</td>\n <td> SC</td>\n <td> 6.051620</td>\n <td> 1.024486</td>\n <td> 1.129748</td>\n <td> 2.631683</td>\n </tr>\n <tr>\n <th>29 </th>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 35.55349</td>\n <td>-77.05205</td>\n <td> 2</td>\n <td> A/A</td>\n <td> NA</td>\n <td> C/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> BEAUFORT_NC</td>\n <td> BEAUFORT</td>\n <td> NC</td>\n <td> 6.241730</td>\n <td> 1.048126</td>\n <td> 0.994917</td>\n <td> 2.433338</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>358</th>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 36.66800</td>\n <td>-78.38900</td>\n <td> 23</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> MECKLENBURG_VA</td>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 5.839441</td>\n <td> 0.899897</td>\n <td> 0.711595</td>\n <td> 2.700255</td>\n </tr>\n <tr>\n <th>359</th>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 36.66800</td>\n <td>-78.38900</td>\n <td> 23</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> C/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> MECKLENBURG_VA</td>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 5.839441</td>\n <td> 0.899897</td>\n <td> 0.711595</td>\n <td> 2.700255</td>\n </tr>\n <tr>\n <th>360</th>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 36.66800</td>\n <td>-78.38900</td>\n <td> 23</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> MECKLENBURG_VA</td>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 5.839441</td>\n <td> 0.899897</td>\n <td> 0.711595</td>\n <td> 2.700255</td>\n </tr>\n <tr>\n <th>361</th>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 36.66800</td>\n <td>-78.38900</td>\n <td> 23</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> MECKLENBURG_VA</td>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 5.839441</td>\n <td> 0.899897</td>\n <td> 0.711595</td>\n <td> 2.700255</td>\n </tr>\n <tr>\n <th>362</th>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 36.66800</td>\n <td>-78.38900</td>\n <td> 23</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> MECKLENBURG_VA</td>\n <td> MECKLENBURG</td>\n <td> VA</td>\n <td> 5.839441</td>\n <td> 0.899897</td>\n <td> 0.711595</td>\n <td> 2.700255</td>\n </tr>\n <tr>\n <th>363</th>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 36.32840</td>\n <td>-77.59073</td>\n <td> 16</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> NA</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> HALIFAX_NC</td>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 5.674788</td>\n <td> 0.920890</td>\n <td> 0.798284</td>\n <td> 2.431289</td>\n </tr>\n <tr>\n <th>364</th>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 36.32840</td>\n <td>-77.59073</td>\n <td> 16</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> HALIFAX_NC</td>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 5.674788</td>\n <td> 0.920890</td>\n <td> 0.798284</td>\n <td> 2.431289</td>\n </tr>\n <tr>\n <th>365</th>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 36.32840</td>\n <td>-77.59073</td>\n <td> 16</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> HALIFAX_NC</td>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 5.674788</td>\n <td> 0.920890</td>\n <td> 0.798284</td>\n <td> 2.431289</td>\n </tr>\n <tr>\n <th>366</th>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 36.32840</td>\n <td>-77.59073</td>\n <td> 16</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> NA</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> HALIFAX_NC</td>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 5.674788</td>\n <td> 0.920890</td>\n <td> 0.798284</td>\n <td> 2.431289</td>\n </tr>\n <tr>\n <th>367</th>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 36.32840</td>\n <td>-77.59073</td>\n <td> 16</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> HALIFAX_NC</td>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 5.674788</td>\n <td> 0.920890</td>\n <td> 0.798284</td>\n <td> 2.431289</td>\n </tr>\n <tr>\n <th>368</th>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 36.32840</td>\n <td>-77.59073</td>\n <td> 16</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/A</td>\n <td> A/A</td>\n <td> HALIFAX_NC</td>\n <td> HALIFAX</td>\n <td> NC</td>\n <td> 5.674788</td>\n <td> 0.920890</td>\n <td> 0.798284</td>\n <td> 2.431289</td>\n </tr>\n <tr>\n <th>369</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> 3</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> BERKELEY_SC</td>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 6.181043</td>\n <td> 1.048869</td>\n <td> 1.042944</td>\n <td> 2.354930</td>\n </tr>\n <tr>\n <th>370</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> 3</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> NA</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> BERKELEY_SC</td>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 6.181043</td>\n <td> 1.048869</td>\n <td> 1.042944</td>\n <td> 2.354930</td>\n </tr>\n <tr>\n <th>371</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> 3</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> NA</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> BERKELEY_SC</td>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 6.181043</td>\n <td> 1.048869</td>\n <td> 1.042944</td>\n <td> 2.354930</td>\n </tr>\n <tr>\n <th>372</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> 3</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> BERKELEY_SC</td>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 6.181043</td>\n <td> 1.048869</td>\n <td> 1.042944</td>\n <td> 2.354930</td>\n </tr>\n <tr>\n <th>373</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> 3</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> BERKELEY_SC</td>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 6.181043</td>\n <td> 1.048869</td>\n <td> 1.042944</td>\n <td> 2.354930</td>\n </tr>\n <tr>\n <th>374</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> 3</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> NA</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> BERKELEY_SC</td>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 6.181043</td>\n <td> 1.048869</td>\n <td> 1.042944</td>\n <td> 2.354930</td>\n </tr>\n <tr>\n <th>375</th>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 33.19661</td>\n <td>-80.00666</td>\n <td> 3</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> NA</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> BERKELEY_SC</td>\n <td> BERKELEY</td>\n <td> SC</td>\n <td> 6.181043</td>\n <td> 1.048869</td>\n <td> 1.042944</td>\n <td> 2.354930</td>\n </tr>\n <tr>\n <th>376</th>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 31.98678</td>\n <td>-87.28057</td>\n <td> 34</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> WILCOX_AL</td>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 9.683140</td>\n <td> 1.140042</td>\n <td> 0.806234</td>\n <td> 3.265560</td>\n </tr>\n <tr>\n <th>377</th>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 31.98678</td>\n <td>-87.28057</td>\n <td> 34</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> NA</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> WILCOX_AL</td>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 9.683140</td>\n <td> 1.140042</td>\n <td> 0.806234</td>\n <td> 3.265560</td>\n </tr>\n <tr>\n <th>378</th>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 31.98678</td>\n <td>-87.28057</td>\n <td> 34</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> NA</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> WILCOX_AL</td>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 9.683140</td>\n <td> 1.140042</td>\n <td> 0.806234</td>\n <td> 3.265560</td>\n </tr>\n <tr>\n <th>379</th>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 31.98678</td>\n <td>-87.28057</td>\n <td> 34</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> WILCOX_AL</td>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 9.683140</td>\n <td> 1.140042</td>\n <td> 0.806234</td>\n <td> 3.265560</td>\n </tr>\n <tr>\n <th>380</th>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 31.98678</td>\n <td>-87.28057</td>\n <td> 34</td>\n <td> A/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> WILCOX_AL</td>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 9.683140</td>\n <td> 1.140042</td>\n <td> 0.806234</td>\n <td> 3.265560</td>\n </tr>\n <tr>\n <th>381</th>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 31.98678</td>\n <td>-87.28057</td>\n <td> 34</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> WILCOX_AL</td>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 9.683140</td>\n <td> 1.140042</td>\n <td> 0.806234</td>\n <td> 3.265560</td>\n </tr>\n <tr>\n <th>382</th>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 31.98678</td>\n <td>-87.28057</td>\n <td> 34</td>\n <td> A/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> WILCOX_AL</td>\n <td> WILCOX</td>\n <td> AL</td>\n <td> 9.683140</td>\n <td> 1.140042</td>\n <td> 0.806234</td>\n <td> 3.265560</td>\n </tr>\n <tr>\n <th>383</th>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 38.20208</td>\n <td>-77.58750</td>\n <td> 29</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> SPOTSYLVANIA_VA</td>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 5.494060</td>\n <td> 0.888762</td>\n <td> 0.709162</td>\n <td> 2.838240</td>\n </tr>\n <tr>\n <th>384</th>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 38.20208</td>\n <td>-77.58750</td>\n <td> 29</td>\n <td> A/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> SPOTSYLVANIA_VA</td>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 5.494060</td>\n <td> 0.888762</td>\n <td> 0.709162</td>\n <td> 2.838240</td>\n </tr>\n <tr>\n <th>385</th>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 38.20208</td>\n <td>-77.58750</td>\n <td> 29</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> NA</td>\n <td> G/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/G</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> SPOTSYLVANIA_VA</td>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 5.494060</td>\n <td> 0.888762</td>\n <td> 0.709162</td>\n <td> 2.838240</td>\n </tr>\n <tr>\n <th>386</th>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 38.20208</td>\n <td>-77.58750</td>\n <td> 29</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> C/G</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/G</td>\n <td> A/G</td>\n <td> G/G</td>\n <td> A/C</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> SPOTSYLVANIA_VA</td>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 5.494060</td>\n <td> 0.888762</td>\n <td> 0.709162</td>\n <td> 2.838240</td>\n </tr>\n <tr>\n <th>387</th>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 38.20208</td>\n <td>-77.58750</td>\n <td> 29</td>\n <td> A/G</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> G/A</td>\n <td> A/A</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> A/A</td>\n <td>...</td>\n <td> A/A</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> A/A</td>\n <td> C/A</td>\n <td> G/G</td>\n <td> C/C</td>\n <td> G/G</td>\n <td> SPOTSYLVANIA_VA</td>\n <td> SPOTSYLVANIA</td>\n <td> VA</td>\n <td> 5.494060</td>\n <td> 0.888762</td>\n <td> 0.709162</td>\n <td> 2.838240</td>\n </tr>\n </tbody>\n</table>\n<p>388 rows × 3094 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"scrolled": false,
"collapsed": false,
"trusted": false
},
"cell_type": "code",
"source": "bayenv_df_ai_basegt = bayenv_df_ai.apply(convert_to_snpassoc)\nfor env in boxplot_data:\n for snp in boxplot_data[env]:\n vals = {}\n for gt, group in bayenv_df_ai_basegt.groupby(snp):\n if not gt == 'NA':\n vals[gt.replace(\"/\", \"\")] = group[env]\n vals = pd.DataFrame(vals, dtype=float)\n vals.index.name = env\n\n sns.boxplot([vals[x].dropna() for x in vals], \n names=vals.columns)\n plt.title(\"%s/%s (%.4f)\" % (testsnp, vals.index.name, boxplot_data[env][snp]))\n plt.show()\n\n sns.violinplot([vals[x].dropna() for x in vals], \n names=vals.columns)\n plt.title(\"%s/%s (%.4f)\" % (testsnp, vals.index.name, boxplot_data[env][snp]))\n plt.show()",
"execution_count": 1228,
"outputs": [
{
"output_type": "display_data",
"data": {
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