From 7ed6eb6f19c32fe254a26a50600e8a80dd0337da Mon Sep 17 00:00:00 2001 From: Katherine Eaton Date: Thu, 29 Apr 2021 18:02:50 -0400 Subject: [PATCH] test linear regression in stats --- workflow/notebooks/stats.py.ipynb | 1764 +++++++++++++++++------------ workflow/scripts/project_load.sh | 9 + 2 files changed, 1040 insertions(+), 733 deletions(-) diff --git a/workflow/notebooks/stats.py.ipynb b/workflow/notebooks/stats.py.ipynb index 8a9235ae..86cc99a7 100644 --- a/workflow/notebooks/stats.py.ipynb +++ b/workflow/notebooks/stats.py.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "empty-hebrew", + "id": "signed-investigator", "metadata": {}, "source": [ "# Stats Notebook\n", @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "widespread-france", + "id": "coupled-nation", "metadata": {}, "source": [ "---\n", @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "incoming-disposal", + "id": "racial-reconstruction", "metadata": {}, "source": [ "## Modules" @@ -30,8 +30,8 @@ }, { "cell_type": "code", - "execution_count": 67, - "id": "thick-disorder", + "execution_count": 134, + "id": "laughing-atlanta", "metadata": {}, "outputs": [], "source": [ @@ -43,16 +43,22 @@ "import cartopy.crs as ccrs\n", "import scipy\n", "import datetime\n", - "from Bio import AlignIO\n", + "from Bio import AlignIO, Phylo\n", "import seaborn as sns\n", "import subprocess\n", + "import geopy\n", + "from geopy import distance\n", + "from statsmodels.stats.outliers_influence import summary_table\n", + "#import statsmodels.api as smapi\n", + "import statsmodels.formula.api as sm\n", + "import math\n", "\n", "from functions import *" ] }, { "cell_type": "markdown", - "id": "logical-morning", + "id": "dimensional-worst", "metadata": {}, "source": [ "## Paths" @@ -60,8 +66,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "gross-swaziland", + "execution_count": 2, + "id": "described-shipping", "metadata": {}, "outputs": [], "source": [ @@ -69,8 +75,7 @@ " WILDCARDS = snakemake.wildcards\n", " project_dir = os.getcwd()\n", "except NameError:\n", - " WILDCARDS = [\"all\", \"chr\n", - " omosome\", \"full\", \"5\"]\n", + " WILDCARDS = [\"all\", \"chromosome\", \"full\", \"5\"]\n", " project_dir = os.path.dirname(os.path.dirname(os.getcwd()))\n", " \n", "results_dir = os.path.join(project_dir, \"results/\")\n", @@ -83,8 +88,8 @@ }, { "cell_type": "code", - "execution_count": 68, - "id": "compliant-advocate", + "execution_count": 3, + "id": "cloudy-catering", "metadata": {}, "outputs": [], "source": [ @@ -95,6 +100,10 @@ "\n", "# ------------------------------------------\n", "# Alignment\n", + "divtree_path = augur_dir + \"all.divtree.nwk\"\n", + "\n", + "# ------------------------------------------\n", + "# Alignment\n", "constant_sites_path = results_dir + \"snippy_multi/all/chromosome/full/snippy-multi.constant_sites.txt\"\n", "aln_path = iqtree_dir + \"filter-sites/snippy-multi.snps.aln\"\n", "\n", @@ -107,7 +116,7 @@ }, { "cell_type": "markdown", - "id": "balanced-chocolate", + "id": "color-batch", "metadata": {}, "source": [ "## Variables" @@ -115,8 +124,8 @@ }, { "cell_type": "code", - "execution_count": 75, - "id": "serial-merchandise", + "execution_count": 4, + "id": "addressed-liver", "metadata": {}, "outputs": [], "source": [ @@ -124,6 +133,8 @@ "\n", "NO_DATA_CHAR = \"NA\"\n", "\n", + "ALPHA = 0.05\n", + "\n", "# ------------------------------------------\n", "# Time\n", "CURRENT_YEAR = datetime.datetime.utcnow().year\n", @@ -183,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "authorized-allowance", + "id": "pleasant-objective", "metadata": {}, "source": [ "---\n", @@ -192,7 +203,25 @@ }, { "cell_type": "markdown", - "id": "excess-adobe", + "id": "peaceful-conversation", + "metadata": {}, + "source": [ + "## Trees" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "nuclear-hepatitis", + "metadata": {}, + "outputs": [], + "source": [ + "divtree = Phylo.read(divtree_path, \"newick\")" + ] + }, + { + "cell_type": "markdown", + "id": "composite-render", "metadata": {}, "source": [ "## Metadata" @@ -200,8 +229,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "further-dispute", + "execution_count": 6, + "id": "lined-timothy", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +242,7 @@ }, { "cell_type": "markdown", - "id": "exact-windows", + "id": "capable-demographic", "metadata": {}, "source": [ "## Convert Dataframe to Geopandas" @@ -221,8 +250,8 @@ }, { "cell_type": "code", - "execution_count": 78, - "id": "designing-terrain", + "execution_count": 7, + "id": "tutorial-found", "metadata": {}, "outputs": [ { @@ -387,19 +416,19 @@ " -105.608\n", " NA\n", " terminal\n", - " 6.334000e-06\n", + " 4.004600e-06\n", " 100.0\n", - " -29.000\n", + " -29.0000\n", " NA\n", " NA\n", " United States of America\n", " Colorado\n", " 1.ORI\n", " 1.ORI1\n", + " 1.0\n", " 1.00\n", - " 1.00\n", - " 1.00\n", - " 1.00\n", + " 1.0\n", + " 1.0\n", " 39.783730\n", " -100.445882\n", " 38.725178\n", @@ -407,24 +436,82 @@ " 1.0\n", " 1992\n", " 1992\n", - " 0.000072\n", - " 28.00000\n", - " 0.000072\n", + " 0.000073\n", + " 509.00\n", + " 0.000073\n", " #ff0000\n", - " 8352.42496\n", - " 28.00000\n", + " 6330.17150\n", + " 509.00\n", " 1992.0\n", " 0.0\n", " 0.0\n", - " 0.000072\n", - " 6.380000e-06\n", + " 0.000073\n", + " 0.000006\n", " POINT (-105.60772 38.72518)\n", " \n", " \n", - " GCA_009670865.1_ASM967086v1_genomic\n", - " 42012\n", - " 1971.0\n", - " -50\n", + " GCA_009909635.1_ASM990963v1_genomic\n", + " 9_10\n", + " 1923.0\n", + " -98\n", + " Russia\n", + " Rostov Oblast\n", + " 64.6863\n", + " 97.7453\n", + " 47.6222\n", + " 40.7958\n", + " Medievalis\n", + " 2.MED\n", + " 2.MED1\n", + " SAMN13632815\n", + " KEEP: Assembly Modern\n", + " 2\n", + " Europe\n", + " 1923\n", + " 98\n", + " 0\n", + " 47.6222\n", + " 40.7958\n", + " NA\n", + " terminal\n", + " 2.120100e-06\n", + " 100.0\n", + " -98.0000\n", + " NA\n", + " NA\n", + " Russia\n", + " Rostov Oblast\n", + " 2.MED\n", + " 2.MED1\n", + " 1.0\n", + " 1.00\n", + " 1.0\n", + " 1.0\n", + " 64.686314\n", + " 97.745306\n", + " 47.622245\n", + " 40.795794\n", + " 4.0\n", + " 1923\n", + " 1923\n", + " 0.000073\n", + " 339.00\n", + " 0.000073\n", + " #b3f396\n", + " 6261.17070\n", + " 339.00\n", + " 1923.0\n", + " 0.0\n", + " 0.0\n", + " 0.000073\n", + " 0.000010\n", + " POINT (40.79579 47.62225)\n", + " \n", + " \n", + " GCA_009669545.1_ASM966954v1_genomic\n", + " 42126\n", + " 2006.0\n", + " -15\n", " China\n", " Xinjiang\n", " 35.0001\n", @@ -434,223 +521,165 @@ " Antiqua\n", " 0.ANT\n", " 0.ANT1\n", - " SAMN07722853\n", - " KEEP: Assembly Modern Placement\n", + " SAMN07722925\n", + " KEEP: Assembly Modern\n", " 0\n", " Asia\n", - " 1971\n", - " 50\n", + " 2006\n", + " 15\n", " 0\n", " 42.4805\n", " 85.4633\n", " NA\n", " terminal\n", - " 1.078700e-05\n", + " 0.000000e+00\n", " 100.0\n", - " -50.000\n", + " -15.0000\n", " NA\n", " NA\n", " China\n", " Xinjiang\n", " 0.ANT\n", " 0.ANT1\n", + " 1.0\n", " 1.00\n", - " 1.00\n", - " 1.00\n", - " 1.00\n", + " 1.0\n", + " 1.0\n", " 35.000074\n", " 104.999927\n", " 42.480495\n", " 85.463346\n", - " 4.0\n", - " 1971\n", - " 1971\n", - " 0.000053\n", - " 7.00000\n", - " 0.000053\n", - " #4062fa\n", - " 8331.42500\n", - " 7.00000\n", - " 1971.0\n", + " 105.0\n", + " 2006\n", + " 2006\n", + " 0.000054\n", + " 152.00\n", + " 0.000054\n", + " #1996f3\n", + " 6344.17477\n", + " 152.00\n", + " 2006.0\n", " 0.0\n", " 0.0\n", - " 0.000053\n", - " 1.078700e-05\n", + " 0.000054\n", + " 0.000012\n", " POINT (85.46335 42.48050)\n", " \n", " \n", - " GCA_008630435.1_ASM863043v1_genomic\n", - " C-719\n", - " 1996.0\n", - " -25\n", - " Russia\n", - " Karachay-Cherkessia\n", - " 64.6863\n", - " 97.7453\n", - " 43.7368\n", - " 41.7268\n", - " Medievalis\n", - " 2.MED\n", - " 2.MED0\n", - " SAMN12721146\n", - " KEEP: Assembly Modern Placement\n", - " 2\n", - " Europe\n", - " 1996\n", - " 25\n", - " 0\n", - " 43.7368\n", - " 41.7268\n", - " NA\n", - " terminal\n", - " 2.417700e-05\n", - " 100.0\n", - " -25.000\n", - " NA\n", - " NA\n", - " Russia\n", - " Karachay-Cherkessia\n", - " 2.MED\n", - " 2.MED0\n", - " 1.00\n", - " 1.00\n", - " 1.00\n", - " 1.00\n", - " 64.686314\n", - " 97.745306\n", - " 43.736833\n", - " 41.726799\n", - " 1.0\n", - " 1996\n", - " 1996\n", - " 0.000086\n", - " 15.00000\n", - " 0.000086\n", - " #80ffb4\n", - " 8356.42610\n", - " 15.00000\n", - " 1996.0\n", - " 0.0\n", - " 0.0\n", - " 0.000086\n", - " 2.952100e-05\n", - " POINT (41.72680 43.73683)\n", - " \n", - " \n", - " GCA_000169635.1_ASM16963v1_genomic\n", - " MG05-1020\n", + " GCA_009669555.1_ASM966955v1_genomic\n", + " 42123\n", " 2005.0\n", " -16\n", - " Madagascar\n", - " NA\n", - " -18.925\n", - " 46.4416\n", - " NA\n", - " NA\n", - " Orientalis\n", - " 1.ORI\n", - " 1.ORI3\n", - " SAMN02404403\n", - " KEEP: Assembly Modern Morelli 2010 Cui 2013 Ke...\n", - " 1\n", - " Africa\n", + " China\n", + " Xinjiang\n", + " 35.0001\n", + " 105\n", + " 42.4805\n", + " 85.4633\n", + " Antiqua\n", + " 0.ANT\n", + " 0.ANT1\n", + " SAMN07722924\n", + " KEEP: Assembly Modern\n", + " 0\n", + " Asia\n", " 2005\n", " 16\n", " 0\n", - " -18.925\n", - " 46.4416\n", + " 42.4805\n", + " 85.4633\n", " NA\n", " terminal\n", - " 4.927000e-06\n", + " 2.356000e-07\n", " 100.0\n", - " -16.000\n", + " -16.0000\n", " NA\n", " NA\n", - " Madagascar\n", - " Yunnan\n", - " 1.ORI\n", - " 1.ORI3\n", - " 1.00\n", - " 0.21\n", - " 1.00\n", + " China\n", + " Xinjiang\n", + " 0.ANT\n", + " 0.ANT1\n", + " 1.0\n", " 1.00\n", - " -18.924960\n", - " 46.441642\n", - " 25.000000\n", - " 102.000000\n", - " 2.0\n", + " 1.0\n", + " 1.0\n", + " 35.000074\n", + " 104.999927\n", + " 42.480495\n", + " 85.463346\n", + " 105.0\n", " 2005\n", " 2005\n", - " 0.000071\n", - " 26.00000\n", - " 0.000071\n", - " #ff0000\n", - " 8365.42496\n", - " 26.00000\n", + " 0.000055\n", + " 171.00\n", + " 0.000055\n", + " #1996f3\n", + " 6343.17481\n", + " 171.00\n", " 2005.0\n", " 0.0\n", " 0.0\n", - " 0.000071\n", - " 4.950000e-06\n", - " POINT (102.00000 25.00000)\n", + " 0.000055\n", + " 0.000012\n", + " POINT (85.46335 42.48050)\n", " \n", " \n", - " GCA_000170275.1_ASM17027v1_genomic\n", - " F1991016\n", - " 1991.0\n", - " -30\n", + " GCA_009669565.1_ASM966956v1_genomic\n", + " 42118\n", + " 2005.0\n", + " -16\n", " China\n", - " Yunnan\n", + " Xinjiang\n", " 35.0001\n", " 105\n", - " 25\n", - " 102\n", - " Orientalis\n", - " 1.ORI\n", - " 1.ORI2\n", - " SAMN02404399\n", - " KEEP: Assembly Modern Morelli 2010 Cui 2013 Ke...\n", - " 1\n", + " 42.4805\n", + " 85.4633\n", + " Antiqua\n", + " 0.ANT\n", + " 0.ANT1\n", + " SAMN07722923\n", + " KEEP: Assembly Modern\n", + " 0\n", " Asia\n", - " 1991\n", - " 30\n", + " 2005\n", + " 16\n", " 0\n", - " 25\n", - " 102\n", + " 42.4805\n", + " 85.4633\n", " NA\n", " terminal\n", - " 5.396000e-06\n", + " 4.711000e-07\n", " 100.0\n", - " -30.000\n", + " -16.0000\n", " NA\n", " NA\n", " China\n", - " Yunnan\n", - " 1.ORI\n", - " 1.ORI2\n", - " 1.00\n", - " 1.00\n", - " 1.00\n", + " Xinjiang\n", + " 0.ANT\n", + " 0.ANT1\n", + " 1.0\n", " 1.00\n", + " 1.0\n", + " 1.0\n", " 35.000074\n", " 104.999927\n", - " 25.000000\n", - " 102.000000\n", - " 2.0\n", - " 1991\n", - " 1991\n", - " 0.000071\n", - " 25.00000\n", - " 0.000071\n", - " #ff0000\n", - " 8351.42496\n", - " 25.00000\n", - " 1991.0\n", + " 42.480495\n", + " 85.463346\n", + " 105.0\n", + " 2005\n", + " 2005\n", + " 0.000055\n", + " 173.00\n", + " 0.000055\n", + " #1996f3\n", + " 6343.17471\n", + " 173.00\n", + " 2005.0\n", " 0.0\n", " 0.0\n", - " 0.000071\n", - " 5.396000e-06\n", - " POINT (102.00000 25.00000)\n", + " 0.000055\n", + " 0.000012\n", + " POINT (85.46335 42.48050)\n", " \n", " \n", " ...\n", @@ -711,7 +740,7 @@ " ...\n", " \n", " \n", - " NODE22\n", + " NODE595\n", " NA\n", " NA\n", " NA\n", @@ -735,41 +764,41 @@ " NA\n", " NA\n", " internal\n", - " 7.030000e-07\n", - " 0.0\n", - " -697.117\n", - " {138.712,50502.4}\n", - " {-50502.4,-138.712}\n", - " China\n", - " Gansu\n", - " 1.IN\n", - " 1.IN2\n", - " 0.96\n", - " 0.36\n", + " 2.207000e-07\n", + " 13.0\n", + " -147.7590\n", + " {116.143,181.767}\n", + " {-181.767,-116.143}\n", + " Peru\n", + " Cajamarca\n", + " 1.ORI\n", + " 1.ORI1\n", + " 1.0\n", " 1.00\n", - " 0.42\n", - " 35.000074\n", - " 104.999927\n", - " 38.000000\n", - " 102.000000\n", " 1.0\n", + " 1.0\n", + " -6.869970\n", + " -75.045851\n", + " -6.250000\n", + " -78.833333\n", + " 18.0\n", " NA\n", " NA\n", - " 0.000059\n", - " 23.96875\n", - " 0.000059\n", - " #ff6232\n", - " 7684.30810\n", - " 23.96875\n", - " 1324.0\n", - " 49805.0\n", - " 559.0\n", - " 0.000059\n", - " 7.030000e-07\n", - " POINT (102.00000 38.00000)\n", + " 0.000072\n", + " 598.25\n", + " 0.000072\n", + " #ff0000\n", + " 6211.41227\n", + " 598.25\n", + " 1874.0\n", + " 34.0\n", + " 31.0\n", + " 0.000072\n", + " 0.000005\n", + " POINT (-78.83333 -6.25000)\n", " \n", " \n", - " NODE23\n", + " NODE596\n", " NA\n", " NA\n", " NA\n", @@ -793,41 +822,41 @@ " NA\n", " NA\n", " internal\n", - " 7.040000e-07\n", - " 0.0\n", - " -697.117\n", - " {138.712,50502.4}\n", - " {-50502.4,-138.712}\n", - " China\n", - " Yunnan\n", - " 1.IN\n", - " 1.IN3\n", - " 0.96\n", - " 0.83\n", - " 0.97\n", - " 0.42\n", - " 35.000074\n", - " 104.999927\n", - " 25.000000\n", - " 102.000000\n", - " 2.0\n", - " NA\n", - " NA\n", - " 0.000060\n", - " 24.93750\n", - " 0.000060\n", - " #ff6232\n", - " 7684.30810\n", - " 24.93750\n", - " 1324.0\n", - " 49805.0\n", - " 559.0\n", - " 0.000060\n", - " 1.407000e-06\n", - " POINT (102.00000 25.00000)\n", + " 2.356000e-07\n", + " 13.0\n", + " -147.7590\n", + " {116.143,181.767}\n", + " {-181.767,-116.143}\n", + " Peru\n", + " Cajamarca\n", + " 1.ORI\n", + " 1.ORI1\n", + " 1.0\n", + " 1.00\n", + " 1.0\n", + " 1.0\n", + " -6.869970\n", + " -75.045851\n", + " -6.250000\n", + " -78.833333\n", + " 18.0\n", + " NA\n", + " NA\n", + " 0.000072\n", + " 596.75\n", + " 0.000072\n", + " #ff0000\n", + " 6211.41227\n", + " 596.75\n", + " 1874.0\n", + " 34.0\n", + " 31.0\n", + " 0.000072\n", + " 0.000006\n", + " POINT (-78.83333 -6.25000)\n", " \n", " \n", - " NODE24\n", + " NODE597\n", " NA\n", " NA\n", " NA\n", @@ -851,41 +880,41 @@ " NA\n", " NA\n", " internal\n", - " 5.630000e-06\n", - " 0.0\n", - " -689.700\n", - " {129.229,45096.4}\n", - " {-45096.4,-129.229}\n", - " China\n", - " Yunnan\n", + " 2.921000e-07\n", + " 46.0\n", + " -108.6030\n", + " {60.9513,162.9}\n", + " {-162.9,-60.9513}\n", + " Peru\n", + " Cajamarca\n", " 1.ORI\n", - " 1.ORI2\n", - " 0.87\n", - " 0.84\n", - " 0.97\n", - " 0.41\n", - " 35.000074\n", - " 104.999927\n", - " 25.000000\n", - " 102.000000\n", - " 2.0\n", + " 1.ORI1\n", + " 1.0\n", + " 1.00\n", + " 1.0\n", + " 1.0\n", + " -6.869970\n", + " -75.045851\n", + " -6.250000\n", + " -78.833333\n", + " 18.0\n", " NA\n", " NA\n", - " 0.000066\n", - " 25.87500\n", - " 0.000066\n", + " 0.000073\n", + " 597.50\n", + " 0.000073\n", " #ff0000\n", - " 7691.72496\n", - " 25.87500\n", - " 1332.0\n", - " 44407.0\n", - " 560.0\n", - " 0.000066\n", - " 0.000000e+00\n", - " POINT (102.00000 25.00000)\n", + " 6250.56767\n", + " 597.50\n", + " 1913.0\n", + " 54.0\n", + " 48.0\n", + " 0.000073\n", + " 0.000006\n", + " POINT (-78.83333 -6.25000)\n", " \n", " \n", - " NODE25\n", + " NODE598\n", " NA\n", " NA\n", " NA\n", @@ -909,41 +938,41 @@ " NA\n", " NA\n", " internal\n", - " 2.300000e-08\n", - " 0.0\n", - " -689.700\n", - " {129.229,45096.4}\n", - " {-45096.4,-129.229}\n", - " Madagascar\n", - " Yunnan\n", + " 5.010000e-08\n", + " 34.0\n", + " -94.7241\n", + " {39.493,140.814}\n", + " {-140.814,-39.493}\n", + " Peru\n", + " Cajamarca\n", " 1.ORI\n", - " 1.ORI3\n", - " 0.39\n", - " 0.36\n", + " 1.ORI1\n", + " 1.0\n", " 1.00\n", - " 0.36\n", - " -18.924960\n", - " 46.441642\n", - " 25.000000\n", - " 102.000000\n", - " 2.0\n", + " 1.0\n", + " 1.0\n", + " -6.869970\n", + " -75.045851\n", + " -6.250000\n", + " -78.833333\n", + " 18.0\n", " NA\n", " NA\n", - " 0.000066\n", - " 26.75000\n", - " 0.000066\n", + " 0.000072\n", + " 599.75\n", + " 0.000072\n", " #ff0000\n", - " 7691.72496\n", - " 26.75000\n", - " 1332.0\n", - " 44407.0\n", - " 560.0\n", - " 0.000066\n", - " 2.300000e-08\n", - " POINT (102.00000 25.00000)\n", + " 6264.44697\n", + " 599.75\n", + " 1927.0\n", + " 46.0\n", + " 55.0\n", + " 0.000072\n", + " 0.000005\n", + " POINT (-78.83333 -6.25000)\n", " \n", " \n", - " NODE26\n", + " NODE599\n", " NA\n", " NA\n", " NA\n", @@ -967,508 +996,480 @@ " NA\n", " NA\n", " internal\n", - " 2.300000e-08\n", - " 0.0\n", - " -689.700\n", - " {129.229,45096.4}\n", - " {-45096.4,-129.229}\n", - " United States of America\n", - " La Libertad\n", + " 2.248000e-07\n", + " 58.0\n", + " -73.1430\n", + " {23.756,120.156}\n", + " {-120.156,-23.756}\n", + " Peru\n", + " Cajamarca\n", " 1.ORI\n", " 1.ORI1\n", - " 0.37\n", - " 0.36\n", - " 1.00\n", - " 0.98\n", - " 39.783730\n", - " -100.445882\n", - " -8.000000\n", - " -78.500000\n", " 1.0\n", + " 0.99\n", + " 1.0\n", + " 1.0\n", + " -6.869970\n", + " -75.045851\n", + " -6.250000\n", + " -78.833333\n", + " 18.0\n", " NA\n", " NA\n", - " 0.000066\n", - " 27.50000\n", - " 0.000066\n", + " 0.000072\n", + " 600.50\n", + " 0.000072\n", " #ff0000\n", - " 7691.72496\n", - " 27.50000\n", - " 1332.0\n", - " 44407.0\n", - " 560.0\n", - " 0.000066\n", - " 4.600000e-08\n", - " POINT (-78.50000 -8.00000)\n", + " 6286.02807\n", + " 600.50\n", + " 1948.0\n", + " 47.0\n", + " 50.0\n", + " 0.000072\n", + " 0.000006\n", + " POINT (-78.83333 -6.25000)\n", " \n", " \n", "\n", - "

55 rows × 55 columns

\n", + "

1201 rows × 55 columns

\n", "" ], "text/plain": [ - " strain date date_bp \\\n", - "sample \n", - "Reference CO92 1992 -29 \n", - "GCA_009670865.1_ASM967086v1_genomic 42012 1971.0 -50 \n", - "GCA_008630435.1_ASM863043v1_genomic C-719 1996.0 -25 \n", - "GCA_000169635.1_ASM16963v1_genomic MG05-1020 2005.0 -16 \n", - "GCA_000170275.1_ASM17027v1_genomic F1991016 1991.0 -30 \n", - "... ... ... ... \n", - "NODE22 NA NA NA \n", - "NODE23 NA NA NA \n", - "NODE24 NA NA NA \n", - "NODE25 NA NA NA \n", - "NODE26 NA NA NA \n", - "\n", - " country \\\n", - "sample \n", - "Reference United States of America \n", - "GCA_009670865.1_ASM967086v1_genomic China \n", - "GCA_008630435.1_ASM863043v1_genomic Russia \n", - "GCA_000169635.1_ASM16963v1_genomic Madagascar \n", - "GCA_000170275.1_ASM17027v1_genomic China \n", - "... ... \n", - "NODE22 NA \n", - "NODE23 NA \n", - "NODE24 NA \n", - "NODE25 NA \n", - "NODE26 NA \n", + " strain date date_bp \\\n", + "sample \n", + "Reference CO92 1992 -29 \n", + "GCA_009909635.1_ASM990963v1_genomic 9_10 1923.0 -98 \n", + "GCA_009669545.1_ASM966954v1_genomic 42126 2006.0 -15 \n", + "GCA_009669555.1_ASM966955v1_genomic 42123 2005.0 -16 \n", + "GCA_009669565.1_ASM966956v1_genomic 42118 2005.0 -16 \n", + "... ... ... ... \n", + "NODE595 NA NA NA \n", + "NODE596 NA NA NA \n", + "NODE597 NA NA NA \n", + "NODE598 NA NA NA \n", + "NODE599 NA NA NA \n", "\n", - " province country_lat \\\n", - "sample \n", - "Reference Colorado 39.7837 \n", - "GCA_009670865.1_ASM967086v1_genomic Xinjiang 35.0001 \n", - "GCA_008630435.1_ASM863043v1_genomic Karachay-Cherkessia 64.6863 \n", - "GCA_000169635.1_ASM16963v1_genomic NA -18.925 \n", - "GCA_000170275.1_ASM17027v1_genomic Yunnan 35.0001 \n", - "... ... ... \n", - "NODE22 NA NA \n", - "NODE23 NA NA \n", - "NODE24 NA NA \n", - "NODE25 NA NA \n", - "NODE26 NA NA \n", + " country province \\\n", + "sample \n", + "Reference United States of America Colorado \n", + "GCA_009909635.1_ASM990963v1_genomic Russia Rostov Oblast \n", + "GCA_009669545.1_ASM966954v1_genomic China Xinjiang \n", + "GCA_009669555.1_ASM966955v1_genomic China Xinjiang \n", + "GCA_009669565.1_ASM966956v1_genomic China Xinjiang \n", + "... ... ... \n", + "NODE595 NA NA \n", + "NODE596 NA NA \n", + "NODE597 NA NA \n", + "NODE598 NA NA \n", + "NODE599 NA NA \n", "\n", - " country_lon province_lat province_lon \\\n", - "sample \n", - "Reference -100.446 38.7252 -105.608 \n", - "GCA_009670865.1_ASM967086v1_genomic 105 42.4805 85.4633 \n", - "GCA_008630435.1_ASM863043v1_genomic 97.7453 43.7368 41.7268 \n", - "GCA_000169635.1_ASM16963v1_genomic 46.4416 NA NA \n", - "GCA_000170275.1_ASM17027v1_genomic 105 25 102 \n", - "... ... ... ... \n", - "NODE22 NA NA NA \n", - "NODE23 NA NA NA \n", - "NODE24 NA NA NA \n", - "NODE25 NA NA NA \n", - "NODE26 NA NA NA \n", + " country_lat country_lon province_lat \\\n", + "sample \n", + "Reference 39.7837 -100.446 38.7252 \n", + "GCA_009909635.1_ASM990963v1_genomic 64.6863 97.7453 47.6222 \n", + "GCA_009669545.1_ASM966954v1_genomic 35.0001 105 42.4805 \n", + "GCA_009669555.1_ASM966955v1_genomic 35.0001 105 42.4805 \n", + "GCA_009669565.1_ASM966956v1_genomic 35.0001 105 42.4805 \n", + "... ... ... ... \n", + "NODE595 NA NA NA \n", + "NODE596 NA NA NA \n", + "NODE597 NA NA NA \n", + "NODE598 NA NA NA \n", + "NODE599 NA NA NA \n", "\n", - " biovar branch_major branch_minor \\\n", + " province_lon biovar branch_major \\\n", "sample \n", - "Reference Orientalis 1.ORI 1.ORI1 \n", - "GCA_009670865.1_ASM967086v1_genomic Antiqua 0.ANT 0.ANT1 \n", - "GCA_008630435.1_ASM863043v1_genomic Medievalis 2.MED 2.MED0 \n", - "GCA_000169635.1_ASM16963v1_genomic Orientalis 1.ORI 1.ORI3 \n", - "GCA_000170275.1_ASM17027v1_genomic Orientalis 1.ORI 1.ORI2 \n", - "... ... ... ... \n", - "NODE22 NA NA NA \n", - "NODE23 NA NA NA \n", - "NODE24 NA NA NA \n", - "NODE25 NA NA NA \n", - "NODE26 NA NA NA \n", + "Reference -105.608 Orientalis 1.ORI \n", + "GCA_009909635.1_ASM990963v1_genomic 40.7958 Medievalis 2.MED \n", + "GCA_009669545.1_ASM966954v1_genomic 85.4633 Antiqua 0.ANT \n", + "GCA_009669555.1_ASM966955v1_genomic 85.4633 Antiqua 0.ANT \n", + "GCA_009669565.1_ASM966956v1_genomic 85.4633 Antiqua 0.ANT \n", + "... ... ... ... \n", + "NODE595 NA NA NA \n", + "NODE596 NA NA NA \n", + "NODE597 NA NA NA \n", + "NODE598 NA NA NA \n", + "NODE599 NA NA NA \n", "\n", - " biosample_accession \\\n", - "sample \n", - "Reference SAMEA1705942 \n", - "GCA_009670865.1_ASM967086v1_genomic SAMN07722853 \n", - "GCA_008630435.1_ASM863043v1_genomic SAMN12721146 \n", - "GCA_000169635.1_ASM16963v1_genomic SAMN02404403 \n", - "GCA_000170275.1_ASM17027v1_genomic SAMN02404399 \n", - "... ... \n", - "NODE22 NA \n", - "NODE23 NA \n", - "NODE24 NA \n", - "NODE25 NA \n", - "NODE26 NA \n", + " branch_minor biosample_accession \\\n", + "sample \n", + "Reference 1.ORI1 SAMEA1705942 \n", + "GCA_009909635.1_ASM990963v1_genomic 2.MED1 SAMN13632815 \n", + "GCA_009669545.1_ASM966954v1_genomic 0.ANT1 SAMN07722925 \n", + "GCA_009669555.1_ASM966955v1_genomic 0.ANT1 SAMN07722924 \n", + "GCA_009669565.1_ASM966956v1_genomic 0.ANT1 SAMN07722923 \n", + "... ... ... \n", + "NODE595 NA NA \n", + "NODE596 NA NA \n", + "NODE597 NA NA \n", + "NODE598 NA NA \n", + "NODE599 NA NA \n", "\n", - " biosample_comment \\\n", - "sample \n", - "Reference KEEP: Assembly Modern Reference \n", - "GCA_009670865.1_ASM967086v1_genomic KEEP: Assembly Modern Placement \n", - "GCA_008630435.1_ASM863043v1_genomic KEEP: Assembly Modern Placement \n", - "GCA_000169635.1_ASM16963v1_genomic KEEP: Assembly Modern Morelli 2010 Cui 2013 Ke... \n", - "GCA_000170275.1_ASM17027v1_genomic KEEP: Assembly Modern Morelli 2010 Cui 2013 Ke... \n", - "... ... \n", - "NODE22 NA \n", - "NODE23 NA \n", - "NODE24 NA \n", - "NODE25 NA \n", - "NODE26 NA \n", + " biosample_comment \\\n", + "sample \n", + "Reference KEEP: Assembly Modern Reference \n", + "GCA_009909635.1_ASM990963v1_genomic KEEP: Assembly Modern \n", + "GCA_009669545.1_ASM966954v1_genomic KEEP: Assembly Modern \n", + "GCA_009669555.1_ASM966955v1_genomic KEEP: Assembly Modern \n", + "GCA_009669565.1_ASM966956v1_genomic KEEP: Assembly Modern \n", + "... ... \n", + "NODE595 NA \n", + "NODE596 NA \n", + "NODE597 NA \n", + "NODE598 NA \n", + "NODE599 NA \n", "\n", " branch_number continent date_mean \\\n", "sample \n", "Reference 1 North America 1992 \n", - "GCA_009670865.1_ASM967086v1_genomic 0 Asia 1971 \n", - "GCA_008630435.1_ASM863043v1_genomic 2 Europe 1996 \n", - "GCA_000169635.1_ASM16963v1_genomic 1 Africa 2005 \n", - "GCA_000170275.1_ASM17027v1_genomic 1 Asia 1991 \n", + "GCA_009909635.1_ASM990963v1_genomic 2 Europe 1923 \n", + "GCA_009669545.1_ASM966954v1_genomic 0 Asia 2006 \n", + "GCA_009669555.1_ASM966955v1_genomic 0 Asia 2005 \n", + "GCA_009669565.1_ASM966956v1_genomic 0 Asia 2005 \n", "... ... ... ... \n", - "NODE22 NA NA NA \n", - "NODE23 NA NA NA \n", - "NODE24 NA NA NA \n", - "NODE25 NA NA NA \n", - "NODE26 NA NA NA \n", + "NODE595 NA NA NA \n", + "NODE596 NA NA NA \n", + "NODE597 NA NA NA \n", + "NODE598 NA NA NA \n", + "NODE599 NA NA NA \n", "\n", " date_bp_mean date_err lat lon \\\n", "sample \n", "Reference 29 0 38.7252 -105.608 \n", - "GCA_009670865.1_ASM967086v1_genomic 50 0 42.4805 85.4633 \n", - "GCA_008630435.1_ASM863043v1_genomic 25 0 43.7368 41.7268 \n", - "GCA_000169635.1_ASM16963v1_genomic 16 0 -18.925 46.4416 \n", - "GCA_000170275.1_ASM17027v1_genomic 30 0 25 102 \n", + "GCA_009909635.1_ASM990963v1_genomic 98 0 47.6222 40.7958 \n", + "GCA_009669545.1_ASM966954v1_genomic 15 0 42.4805 85.4633 \n", + "GCA_009669555.1_ASM966955v1_genomic 16 0 42.4805 85.4633 \n", + "GCA_009669565.1_ASM966956v1_genomic 16 0 42.4805 85.4633 \n", "... ... ... ... ... \n", - "NODE22 NA NA NA NA \n", - "NODE23 NA NA NA NA \n", - "NODE24 NA NA NA NA \n", - "NODE25 NA NA NA NA \n", - "NODE26 NA NA NA NA \n", + "NODE595 NA NA NA NA \n", + "NODE596 NA NA NA NA \n", + "NODE597 NA NA NA NA \n", + "NODE598 NA NA NA NA \n", + "NODE599 NA NA NA NA \n", "\n", " branch_support_char node_type \\\n", "sample \n", "Reference NA terminal \n", - "GCA_009670865.1_ASM967086v1_genomic NA terminal \n", - "GCA_008630435.1_ASM863043v1_genomic NA terminal \n", - "GCA_000169635.1_ASM16963v1_genomic NA terminal \n", - "GCA_000170275.1_ASM17027v1_genomic NA terminal \n", + "GCA_009909635.1_ASM990963v1_genomic NA terminal \n", + "GCA_009669545.1_ASM966954v1_genomic NA terminal \n", + "GCA_009669555.1_ASM966955v1_genomic NA terminal \n", + "GCA_009669565.1_ASM966956v1_genomic NA terminal \n", "... ... ... \n", - "NODE22 NA internal \n", - "NODE23 NA internal \n", - "NODE24 NA internal \n", - "NODE25 NA internal \n", - "NODE26 NA internal \n", + "NODE595 NA internal \n", + "NODE596 NA internal \n", + "NODE597 NA internal \n", + "NODE598 NA internal \n", + "NODE599 NA internal \n", "\n", " branch_length branch_support \\\n", "sample \n", - "Reference 6.334000e-06 100.0 \n", - "GCA_009670865.1_ASM967086v1_genomic 1.078700e-05 100.0 \n", - "GCA_008630435.1_ASM863043v1_genomic 2.417700e-05 100.0 \n", - "GCA_000169635.1_ASM16963v1_genomic 4.927000e-06 100.0 \n", - "GCA_000170275.1_ASM17027v1_genomic 5.396000e-06 100.0 \n", + "Reference 4.004600e-06 100.0 \n", + "GCA_009909635.1_ASM990963v1_genomic 2.120100e-06 100.0 \n", + "GCA_009669545.1_ASM966954v1_genomic 0.000000e+00 100.0 \n", + "GCA_009669555.1_ASM966955v1_genomic 2.356000e-07 100.0 \n", + "GCA_009669565.1_ASM966956v1_genomic 4.711000e-07 100.0 \n", "... ... ... \n", - "NODE22 7.030000e-07 0.0 \n", - "NODE23 7.040000e-07 0.0 \n", - "NODE24 5.630000e-06 0.0 \n", - "NODE25 2.300000e-08 0.0 \n", - "NODE26 2.300000e-08 0.0 \n", + "NODE595 2.207000e-07 13.0 \n", + "NODE596 2.356000e-07 13.0 \n", + "NODE597 2.921000e-07 46.0 \n", + "NODE598 5.010000e-08 34.0 \n", + "NODE599 2.248000e-07 58.0 \n", "\n", " timetree_date timetree_CI_height \\\n", "sample \n", - "Reference -29.000 NA \n", - "GCA_009670865.1_ASM967086v1_genomic -50.000 NA \n", - "GCA_008630435.1_ASM863043v1_genomic -25.000 NA \n", - "GCA_000169635.1_ASM16963v1_genomic -16.000 NA \n", - "GCA_000170275.1_ASM17027v1_genomic -30.000 NA \n", + "Reference -29.0000 NA \n", + "GCA_009909635.1_ASM990963v1_genomic -98.0000 NA \n", + "GCA_009669545.1_ASM966954v1_genomic -15.0000 NA \n", + "GCA_009669555.1_ASM966955v1_genomic -16.0000 NA \n", + "GCA_009669565.1_ASM966956v1_genomic -16.0000 NA \n", "... ... ... \n", - "NODE22 -697.117 {138.712,50502.4} \n", - "NODE23 -697.117 {138.712,50502.4} \n", - "NODE24 -689.700 {129.229,45096.4} \n", - "NODE25 -689.700 {129.229,45096.4} \n", - "NODE26 -689.700 {129.229,45096.4} \n", + "NODE595 -147.7590 {116.143,181.767} \n", + "NODE596 -147.7590 {116.143,181.767} \n", + "NODE597 -108.6030 {60.9513,162.9} \n", + "NODE598 -94.7241 {39.493,140.814} \n", + "NODE599 -73.1430 {23.756,120.156} \n", "\n", " timetree_CI_date \\\n", "sample \n", "Reference NA \n", - "GCA_009670865.1_ASM967086v1_genomic NA \n", - "GCA_008630435.1_ASM863043v1_genomic NA \n", - "GCA_000169635.1_ASM16963v1_genomic NA \n", - "GCA_000170275.1_ASM17027v1_genomic NA \n", + "GCA_009909635.1_ASM990963v1_genomic NA \n", + "GCA_009669545.1_ASM966954v1_genomic NA \n", + "GCA_009669555.1_ASM966955v1_genomic NA \n", + "GCA_009669565.1_ASM966956v1_genomic NA \n", "... ... \n", - "NODE22 {-50502.4,-138.712} \n", - "NODE23 {-50502.4,-138.712} \n", - "NODE24 {-45096.4,-129.229} \n", - "NODE25 {-45096.4,-129.229} \n", - "NODE26 {-45096.4,-129.229} \n", + "NODE595 {-181.767,-116.143} \n", + "NODE596 {-181.767,-116.143} \n", + "NODE597 {-162.9,-60.9513} \n", + "NODE598 {-140.814,-39.493} \n", + "NODE599 {-120.156,-23.756} \n", "\n", " mugration_country \\\n", "sample \n", "Reference United States of America \n", - "GCA_009670865.1_ASM967086v1_genomic China \n", - "GCA_008630435.1_ASM863043v1_genomic Russia \n", - "GCA_000169635.1_ASM16963v1_genomic Madagascar \n", - "GCA_000170275.1_ASM17027v1_genomic China \n", + "GCA_009909635.1_ASM990963v1_genomic Russia \n", + "GCA_009669545.1_ASM966954v1_genomic China \n", + "GCA_009669555.1_ASM966955v1_genomic China \n", + "GCA_009669565.1_ASM966956v1_genomic China \n", "... ... \n", - "NODE22 China \n", - "NODE23 China \n", - "NODE24 China \n", - "NODE25 Madagascar \n", - "NODE26 United States of America \n", + "NODE595 Peru \n", + "NODE596 Peru \n", + "NODE597 Peru \n", + "NODE598 Peru \n", + "NODE599 Peru \n", "\n", - " mugration_province \\\n", - "sample \n", - "Reference Colorado \n", - "GCA_009670865.1_ASM967086v1_genomic Xinjiang \n", - "GCA_008630435.1_ASM863043v1_genomic Karachay-Cherkessia \n", - "GCA_000169635.1_ASM16963v1_genomic Yunnan \n", - "GCA_000170275.1_ASM17027v1_genomic Yunnan \n", - "... ... \n", - "NODE22 Gansu \n", - "NODE23 Yunnan \n", - "NODE24 Yunnan \n", - "NODE25 Yunnan \n", - "NODE26 La Libertad \n", - "\n", - " mugration_branch_major \\\n", - "sample \n", - "Reference 1.ORI \n", - "GCA_009670865.1_ASM967086v1_genomic 0.ANT \n", - "GCA_008630435.1_ASM863043v1_genomic 2.MED \n", - "GCA_000169635.1_ASM16963v1_genomic 1.ORI \n", - "GCA_000170275.1_ASM17027v1_genomic 1.ORI \n", - "... ... \n", - "NODE22 1.IN \n", - "NODE23 1.IN \n", - "NODE24 1.ORI \n", - "NODE25 1.ORI \n", - "NODE26 1.ORI \n", + " mugration_province mugration_branch_major \\\n", + "sample \n", + "Reference Colorado 1.ORI \n", + "GCA_009909635.1_ASM990963v1_genomic Rostov Oblast 2.MED \n", + "GCA_009669545.1_ASM966954v1_genomic Xinjiang 0.ANT \n", + "GCA_009669555.1_ASM966955v1_genomic Xinjiang 0.ANT \n", + "GCA_009669565.1_ASM966956v1_genomic Xinjiang 0.ANT \n", + "... ... ... \n", + "NODE595 Cajamarca 1.ORI \n", + "NODE596 Cajamarca 1.ORI \n", + "NODE597 Cajamarca 1.ORI \n", + "NODE598 Cajamarca 1.ORI \n", + "NODE599 Cajamarca 1.ORI \n", "\n", " mugration_branch_minor \\\n", "sample \n", "Reference 1.ORI1 \n", - "GCA_009670865.1_ASM967086v1_genomic 0.ANT1 \n", - "GCA_008630435.1_ASM863043v1_genomic 2.MED0 \n", - "GCA_000169635.1_ASM16963v1_genomic 1.ORI3 \n", - "GCA_000170275.1_ASM17027v1_genomic 1.ORI2 \n", + "GCA_009909635.1_ASM990963v1_genomic 2.MED1 \n", + "GCA_009669545.1_ASM966954v1_genomic 0.ANT1 \n", + "GCA_009669555.1_ASM966955v1_genomic 0.ANT1 \n", + "GCA_009669565.1_ASM966956v1_genomic 0.ANT1 \n", "... ... \n", - "NODE22 1.IN2 \n", - "NODE23 1.IN3 \n", - "NODE24 1.ORI2 \n", - "NODE25 1.ORI3 \n", - "NODE26 1.ORI1 \n", + "NODE595 1.ORI1 \n", + "NODE596 1.ORI1 \n", + "NODE597 1.ORI1 \n", + "NODE598 1.ORI1 \n", + "NODE599 1.ORI1 \n", "\n", " mugration_country_confidence \\\n", "sample \n", - "Reference 1.00 \n", - "GCA_009670865.1_ASM967086v1_genomic 1.00 \n", - "GCA_008630435.1_ASM863043v1_genomic 1.00 \n", - "GCA_000169635.1_ASM16963v1_genomic 1.00 \n", - "GCA_000170275.1_ASM17027v1_genomic 1.00 \n", + "Reference 1.0 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.0 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.0 \n", + "GCA_009669555.1_ASM966955v1_genomic 1.0 \n", + "GCA_009669565.1_ASM966956v1_genomic 1.0 \n", "... ... \n", - "NODE22 0.96 \n", - "NODE23 0.96 \n", - "NODE24 0.87 \n", - "NODE25 0.39 \n", - "NODE26 0.37 \n", + "NODE595 1.0 \n", + "NODE596 1.0 \n", + "NODE597 1.0 \n", + "NODE598 1.0 \n", + "NODE599 1.0 \n", "\n", " mugration_province_confidence \\\n", "sample \n", "Reference 1.00 \n", - "GCA_009670865.1_ASM967086v1_genomic 1.00 \n", - "GCA_008630435.1_ASM863043v1_genomic 1.00 \n", - "GCA_000169635.1_ASM16963v1_genomic 0.21 \n", - "GCA_000170275.1_ASM17027v1_genomic 1.00 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.00 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.00 \n", + "GCA_009669555.1_ASM966955v1_genomic 1.00 \n", + "GCA_009669565.1_ASM966956v1_genomic 1.00 \n", "... ... \n", - "NODE22 0.36 \n", - "NODE23 0.83 \n", - "NODE24 0.84 \n", - "NODE25 0.36 \n", - "NODE26 0.36 \n", + "NODE595 1.00 \n", + "NODE596 1.00 \n", + "NODE597 1.00 \n", + "NODE598 1.00 \n", + "NODE599 0.99 \n", "\n", " mugration_branch_major_confidence \\\n", "sample \n", - "Reference 1.00 \n", - "GCA_009670865.1_ASM967086v1_genomic 1.00 \n", - "GCA_008630435.1_ASM863043v1_genomic 1.00 \n", - "GCA_000169635.1_ASM16963v1_genomic 1.00 \n", - "GCA_000170275.1_ASM17027v1_genomic 1.00 \n", + "Reference 1.0 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.0 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.0 \n", + "GCA_009669555.1_ASM966955v1_genomic 1.0 \n", + "GCA_009669565.1_ASM966956v1_genomic 1.0 \n", "... ... \n", - "NODE22 1.00 \n", - "NODE23 0.97 \n", - "NODE24 0.97 \n", - "NODE25 1.00 \n", - "NODE26 1.00 \n", + "NODE595 1.0 \n", + "NODE596 1.0 \n", + "NODE597 1.0 \n", + "NODE598 1.0 \n", + "NODE599 1.0 \n", "\n", " mugration_branch_minor_confidence \\\n", "sample \n", - "Reference 1.00 \n", - "GCA_009670865.1_ASM967086v1_genomic 1.00 \n", - "GCA_008630435.1_ASM863043v1_genomic 1.00 \n", - "GCA_000169635.1_ASM16963v1_genomic 1.00 \n", - "GCA_000170275.1_ASM17027v1_genomic 1.00 \n", + "Reference 1.0 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.0 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.0 \n", + "GCA_009669555.1_ASM966955v1_genomic 1.0 \n", + "GCA_009669565.1_ASM966956v1_genomic 1.0 \n", "... ... \n", - "NODE22 0.42 \n", - "NODE23 0.42 \n", - "NODE24 0.41 \n", - "NODE25 0.36 \n", - "NODE26 0.98 \n", + "NODE595 1.0 \n", + "NODE596 1.0 \n", + "NODE597 1.0 \n", + "NODE598 1.0 \n", + "NODE599 1.0 \n", "\n", " mugration_country_lat \\\n", "sample \n", "Reference 39.783730 \n", - "GCA_009670865.1_ASM967086v1_genomic 35.000074 \n", - "GCA_008630435.1_ASM863043v1_genomic 64.686314 \n", - "GCA_000169635.1_ASM16963v1_genomic -18.924960 \n", - "GCA_000170275.1_ASM17027v1_genomic 35.000074 \n", + "GCA_009909635.1_ASM990963v1_genomic 64.686314 \n", + "GCA_009669545.1_ASM966954v1_genomic 35.000074 \n", + "GCA_009669555.1_ASM966955v1_genomic 35.000074 \n", + "GCA_009669565.1_ASM966956v1_genomic 35.000074 \n", "... ... \n", - "NODE22 35.000074 \n", - "NODE23 35.000074 \n", - "NODE24 35.000074 \n", - "NODE25 -18.924960 \n", - "NODE26 39.783730 \n", + "NODE595 -6.869970 \n", + "NODE596 -6.869970 \n", + "NODE597 -6.869970 \n", + "NODE598 -6.869970 \n", + "NODE599 -6.869970 \n", "\n", " mugration_country_lon \\\n", "sample \n", "Reference -100.445882 \n", - "GCA_009670865.1_ASM967086v1_genomic 104.999927 \n", - "GCA_008630435.1_ASM863043v1_genomic 97.745306 \n", - "GCA_000169635.1_ASM16963v1_genomic 46.441642 \n", - "GCA_000170275.1_ASM17027v1_genomic 104.999927 \n", + "GCA_009909635.1_ASM990963v1_genomic 97.745306 \n", + "GCA_009669545.1_ASM966954v1_genomic 104.999927 \n", + "GCA_009669555.1_ASM966955v1_genomic 104.999927 \n", + "GCA_009669565.1_ASM966956v1_genomic 104.999927 \n", "... ... \n", - "NODE22 104.999927 \n", - "NODE23 104.999927 \n", - "NODE24 104.999927 \n", - "NODE25 46.441642 \n", - "NODE26 -100.445882 \n", + "NODE595 -75.045851 \n", + "NODE596 -75.045851 \n", + "NODE597 -75.045851 \n", + "NODE598 -75.045851 \n", + "NODE599 -75.045851 \n", "\n", " mugration_province_lat \\\n", "sample \n", "Reference 38.725178 \n", - "GCA_009670865.1_ASM967086v1_genomic 42.480495 \n", - "GCA_008630435.1_ASM863043v1_genomic 43.736833 \n", - "GCA_000169635.1_ASM16963v1_genomic 25.000000 \n", - "GCA_000170275.1_ASM17027v1_genomic 25.000000 \n", + "GCA_009909635.1_ASM990963v1_genomic 47.622245 \n", + "GCA_009669545.1_ASM966954v1_genomic 42.480495 \n", + "GCA_009669555.1_ASM966955v1_genomic 42.480495 \n", + "GCA_009669565.1_ASM966956v1_genomic 42.480495 \n", "... ... \n", - "NODE22 38.000000 \n", - "NODE23 25.000000 \n", - "NODE24 25.000000 \n", - "NODE25 25.000000 \n", - "NODE26 -8.000000 \n", + "NODE595 -6.250000 \n", + "NODE596 -6.250000 \n", + "NODE597 -6.250000 \n", + "NODE598 -6.250000 \n", + "NODE599 -6.250000 \n", "\n", " mugration_province_lon geometry_size \\\n", "sample \n", "Reference -105.607716 1.0 \n", - "GCA_009670865.1_ASM967086v1_genomic 85.463346 4.0 \n", - "GCA_008630435.1_ASM863043v1_genomic 41.726799 1.0 \n", - "GCA_000169635.1_ASM16963v1_genomic 102.000000 2.0 \n", - "GCA_000170275.1_ASM17027v1_genomic 102.000000 2.0 \n", + "GCA_009909635.1_ASM990963v1_genomic 40.795794 4.0 \n", + "GCA_009669545.1_ASM966954v1_genomic 85.463346 105.0 \n", + "GCA_009669555.1_ASM966955v1_genomic 85.463346 105.0 \n", + "GCA_009669565.1_ASM966956v1_genomic 85.463346 105.0 \n", "... ... ... \n", - "NODE22 102.000000 1.0 \n", - "NODE23 102.000000 2.0 \n", - "NODE24 102.000000 2.0 \n", - "NODE25 102.000000 2.0 \n", - "NODE26 -78.500000 1.0 \n", + "NODE595 -78.833333 18.0 \n", + "NODE596 -78.833333 18.0 \n", + "NODE597 -78.833333 18.0 \n", + "NODE598 -78.833333 18.0 \n", + "NODE599 -78.833333 18.0 \n", "\n", " date_lower date_upper divtree_coord_x \\\n", "sample \n", - "Reference 1992 1992 0.000072 \n", - "GCA_009670865.1_ASM967086v1_genomic 1971 1971 0.000053 \n", - "GCA_008630435.1_ASM863043v1_genomic 1996 1996 0.000086 \n", - "GCA_000169635.1_ASM16963v1_genomic 2005 2005 0.000071 \n", - "GCA_000170275.1_ASM17027v1_genomic 1991 1991 0.000071 \n", + "Reference 1992 1992 0.000073 \n", + "GCA_009909635.1_ASM990963v1_genomic 1923 1923 0.000073 \n", + "GCA_009669545.1_ASM966954v1_genomic 2006 2006 0.000054 \n", + "GCA_009669555.1_ASM966955v1_genomic 2005 2005 0.000055 \n", + "GCA_009669565.1_ASM966956v1_genomic 2005 2005 0.000055 \n", "... ... ... ... \n", - "NODE22 NA NA 0.000059 \n", - "NODE23 NA NA 0.000060 \n", - "NODE24 NA NA 0.000066 \n", - "NODE25 NA NA 0.000066 \n", - "NODE26 NA NA 0.000066 \n", + "NODE595 NA NA 0.000072 \n", + "NODE596 NA NA 0.000072 \n", + "NODE597 NA NA 0.000073 \n", + "NODE598 NA NA 0.000072 \n", + "NODE599 NA NA 0.000072 \n", "\n", " divtree_coord_y rtt_dist \\\n", "sample \n", - "Reference 28.00000 0.000072 \n", - "GCA_009670865.1_ASM967086v1_genomic 7.00000 0.000053 \n", - "GCA_008630435.1_ASM863043v1_genomic 15.00000 0.000086 \n", - "GCA_000169635.1_ASM16963v1_genomic 26.00000 0.000071 \n", - "GCA_000170275.1_ASM17027v1_genomic 25.00000 0.000071 \n", + "Reference 509.00 0.000073 \n", + "GCA_009909635.1_ASM990963v1_genomic 339.00 0.000073 \n", + "GCA_009669545.1_ASM966954v1_genomic 152.00 0.000054 \n", + "GCA_009669555.1_ASM966955v1_genomic 171.00 0.000055 \n", + "GCA_009669565.1_ASM966956v1_genomic 173.00 0.000055 \n", "... ... ... \n", - "NODE22 23.96875 0.000059 \n", - "NODE23 24.93750 0.000060 \n", - "NODE24 25.87500 0.000066 \n", - "NODE25 26.75000 0.000066 \n", - "NODE26 27.50000 0.000066 \n", + "NODE595 598.25 0.000072 \n", + "NODE596 596.75 0.000072 \n", + "NODE597 597.50 0.000073 \n", + "NODE598 599.75 0.000072 \n", + "NODE599 600.50 0.000072 \n", "\n", " branch_major_color timetree_coord_x \\\n", "sample \n", - "Reference #ff0000 8352.42496 \n", - "GCA_009670865.1_ASM967086v1_genomic #4062fa 8331.42500 \n", - "GCA_008630435.1_ASM863043v1_genomic #80ffb4 8356.42610 \n", - "GCA_000169635.1_ASM16963v1_genomic #ff0000 8365.42496 \n", - "GCA_000170275.1_ASM17027v1_genomic #ff0000 8351.42496 \n", + "Reference #ff0000 6330.17150 \n", + "GCA_009909635.1_ASM990963v1_genomic #b3f396 6261.17070 \n", + "GCA_009669545.1_ASM966954v1_genomic #1996f3 6344.17477 \n", + "GCA_009669555.1_ASM966955v1_genomic #1996f3 6343.17481 \n", + "GCA_009669565.1_ASM966956v1_genomic #1996f3 6343.17471 \n", "... ... ... \n", - "NODE22 #ff6232 7684.30810 \n", - "NODE23 #ff6232 7684.30810 \n", - "NODE24 #ff0000 7691.72496 \n", - "NODE25 #ff0000 7691.72496 \n", - "NODE26 #ff0000 7691.72496 \n", + "NODE595 #ff0000 6211.41227 \n", + "NODE596 #ff0000 6211.41227 \n", + "NODE597 #ff0000 6250.56767 \n", + "NODE598 #ff0000 6264.44697 \n", + "NODE599 #ff0000 6286.02807 \n", "\n", " timetree_coord_y timetree_date_calendar \\\n", "sample \n", - "Reference 28.00000 1992.0 \n", - "GCA_009670865.1_ASM967086v1_genomic 7.00000 1971.0 \n", - "GCA_008630435.1_ASM863043v1_genomic 15.00000 1996.0 \n", - "GCA_000169635.1_ASM16963v1_genomic 26.00000 2005.0 \n", - "GCA_000170275.1_ASM17027v1_genomic 25.00000 1991.0 \n", + "Reference 509.00 1992.0 \n", + "GCA_009909635.1_ASM990963v1_genomic 339.00 1923.0 \n", + "GCA_009669545.1_ASM966954v1_genomic 152.00 2006.0 \n", + "GCA_009669555.1_ASM966955v1_genomic 171.00 2005.0 \n", + "GCA_009669565.1_ASM966956v1_genomic 173.00 2005.0 \n", "... ... ... \n", - "NODE22 23.96875 1324.0 \n", - "NODE23 24.93750 1324.0 \n", - "NODE24 25.87500 1332.0 \n", - "NODE25 26.75000 1332.0 \n", - "NODE26 27.50000 1332.0 \n", + "NODE595 598.25 1874.0 \n", + "NODE596 596.75 1874.0 \n", + "NODE597 597.50 1913.0 \n", + "NODE598 599.75 1927.0 \n", + "NODE599 600.50 1948.0 \n", "\n", " timetree_CI_lower_err \\\n", "sample \n", "Reference 0.0 \n", - "GCA_009670865.1_ASM967086v1_genomic 0.0 \n", - "GCA_008630435.1_ASM863043v1_genomic 0.0 \n", - "GCA_000169635.1_ASM16963v1_genomic 0.0 \n", - "GCA_000170275.1_ASM17027v1_genomic 0.0 \n", + "GCA_009909635.1_ASM990963v1_genomic 0.0 \n", + "GCA_009669545.1_ASM966954v1_genomic 0.0 \n", + "GCA_009669555.1_ASM966955v1_genomic 0.0 \n", + "GCA_009669565.1_ASM966956v1_genomic 0.0 \n", "... ... \n", - "NODE22 49805.0 \n", - "NODE23 49805.0 \n", - "NODE24 44407.0 \n", - "NODE25 44407.0 \n", - "NODE26 44407.0 \n", + "NODE595 34.0 \n", + "NODE596 34.0 \n", + "NODE597 54.0 \n", + "NODE598 46.0 \n", + "NODE599 47.0 \n", "\n", " timetree_CI_upper_err root_rtt_dist \\\n", "sample \n", - "Reference 0.0 0.000072 \n", - "GCA_009670865.1_ASM967086v1_genomic 0.0 0.000053 \n", - "GCA_008630435.1_ASM863043v1_genomic 0.0 0.000086 \n", - "GCA_000169635.1_ASM16963v1_genomic 0.0 0.000071 \n", - "GCA_000170275.1_ASM17027v1_genomic 0.0 0.000071 \n", + "Reference 0.0 0.000073 \n", + "GCA_009909635.1_ASM990963v1_genomic 0.0 0.000073 \n", + "GCA_009669545.1_ASM966954v1_genomic 0.0 0.000054 \n", + "GCA_009669555.1_ASM966955v1_genomic 0.0 0.000055 \n", + "GCA_009669565.1_ASM966956v1_genomic 0.0 0.000055 \n", "... ... ... \n", - "NODE22 559.0 0.000059 \n", - "NODE23 559.0 0.000060 \n", - "NODE24 560.0 0.000066 \n", - "NODE25 560.0 0.000066 \n", - "NODE26 560.0 0.000066 \n", + "NODE595 31.0 0.000072 \n", + "NODE596 31.0 0.000072 \n", + "NODE597 48.0 0.000073 \n", + "NODE598 55.0 0.000072 \n", + "NODE599 50.0 0.000072 \n", "\n", " clade_rtt_dist \\\n", "sample \n", - "Reference 6.380000e-06 \n", - "GCA_009670865.1_ASM967086v1_genomic 1.078700e-05 \n", - "GCA_008630435.1_ASM863043v1_genomic 2.952100e-05 \n", - "GCA_000169635.1_ASM16963v1_genomic 4.950000e-06 \n", - "GCA_000170275.1_ASM17027v1_genomic 5.396000e-06 \n", + "Reference 0.000006 \n", + "GCA_009909635.1_ASM990963v1_genomic 0.000010 \n", + "GCA_009669545.1_ASM966954v1_genomic 0.000012 \n", + "GCA_009669555.1_ASM966955v1_genomic 0.000012 \n", + "GCA_009669565.1_ASM966956v1_genomic 0.000012 \n", "... ... \n", - "NODE22 7.030000e-07 \n", - "NODE23 1.407000e-06 \n", - "NODE24 0.000000e+00 \n", - "NODE25 2.300000e-08 \n", - "NODE26 4.600000e-08 \n", + "NODE595 0.000005 \n", + "NODE596 0.000006 \n", + "NODE597 0.000006 \n", + "NODE598 0.000005 \n", + "NODE599 0.000006 \n", "\n", " geometry \n", "sample \n", "Reference POINT (-105.60772 38.72518) \n", - "GCA_009670865.1_ASM967086v1_genomic POINT (85.46335 42.48050) \n", - "GCA_008630435.1_ASM863043v1_genomic POINT (41.72680 43.73683) \n", - "GCA_000169635.1_ASM16963v1_genomic POINT (102.00000 25.00000) \n", - "GCA_000170275.1_ASM17027v1_genomic POINT (102.00000 25.00000) \n", + "GCA_009909635.1_ASM990963v1_genomic POINT (40.79579 47.62225) \n", + "GCA_009669545.1_ASM966954v1_genomic POINT (85.46335 42.48050) \n", + "GCA_009669555.1_ASM966955v1_genomic POINT (85.46335 42.48050) \n", + "GCA_009669565.1_ASM966956v1_genomic POINT (85.46335 42.48050) \n", "... ... \n", - "NODE22 POINT (102.00000 38.00000) \n", - "NODE23 POINT (102.00000 25.00000) \n", - "NODE24 POINT (102.00000 25.00000) \n", - "NODE25 POINT (102.00000 25.00000) \n", - "NODE26 POINT (-78.50000 -8.00000) \n", + "NODE595 POINT (-78.83333 -6.25000) \n", + "NODE596 POINT (-78.83333 -6.25000) \n", + "NODE597 POINT (-78.83333 -6.25000) \n", + "NODE598 POINT (-78.83333 -6.25000) \n", + "NODE599 POINT (-78.83333 -6.25000) \n", "\n", - "[55 rows x 55 columns]" + "[1201 rows x 55 columns]" ] }, "metadata": {}, @@ -1480,13 +1481,13 @@ "" ] }, - "execution_count": 78, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1520,7 +1521,7 @@ }, { "cell_type": "markdown", - "id": "convenient-plastic", + "id": "rocky-sixth", "metadata": {}, "source": [ "---\n", @@ -1529,7 +1530,7 @@ }, { "cell_type": "markdown", - "id": "residential-offering", + "id": "consistent-tournament", "metadata": {}, "source": [ "## All" @@ -1537,13 +1538,13 @@ }, { "cell_type": "code", - "execution_count": 93, - "id": "floating-ability", + "execution_count": 8, + "id": "cooked-turner", "metadata": {}, "outputs": [ { "data": { - "image/png": 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x4wC48sor+eGHH7jgggv49NNP+fbbb3nnnXf45S9/SWVlZaPlNcZwzTXXkJeXx8svv8zq1auZOHEiXq+XCy+8sMXlExEJOre7YYfygw44DOnZZ5+1xx57rI2OjrZHHnmkfe+99+ocd8ghh9hbb721yXTmzZtnR4wYYZOTk21sbKw9/PDD7c0331xn2E5NXrWHON166621hwAEHrNnzw4cs3jxYnvaaafZbt262cTERDt8+HD7xhtv1Ml/7dq1dsKECbZLly42Li7O9uvXz15zzTW2qqqqyTJXVVXZW2+91aamptqYmBh7wgkn2FWrVtU5piXlk/8Jp/e+iLWhOQzJWKvFzptijPEBhQCFhYX4fL46+7/88ksuvvhinn766TY34zqtoKCAQw89lGXLlnH00Ue7XRwJc+H03hcBKCoqIiMjo+ZphrW2yM3ygJqgRUREXKEALCIi4gINQ+oksrKy0O0GEZHQoQAsItJG1loKCgpYuXIl69evJzMzk5ycHLKysjThizRJAVhEwsK9997L7t27SUpKavPMa8FkrWXBggXk5eUFxrMDeL1ecnNzGTlyZEgF4VB9HTsj3QMOQTW/mo0xxMXF0a9fP/72t7+1ugk5KyuL+++/35EyvvTSS4wZM4bu3btjjGH58uVBS/vFF1+kf//+xMTE0L9/f15++eU6+2u/PrUfV155ZdDKUGPixIn8/ve/b3Tfnj17uOaaazjkkEOIi4tj5MiRLF68+IBpPvDAAxx55JHExcVxxBFH8OSTTwalrOvXr2f8+PHEx8fTvXt3pkyZQllZWZ1j3n77bYYPH05iYiI9evTgnHPOYd26dUHJ32n33nsvf/7zn7n33nvdLkodBQUFDYIv+CeXycvLo6CgwJ2CNSFUX8fOSDXgEHXbbbcxadIk9u/fz7vvvssVV1xBUlISv/nNb9wuGgDFxcUcd9xxnHvuuUyaNClo6S5cuJCf//zn3H777UyYMIGXX36Z8847j/nz5wemmVy8eHGdiTdWr17N6NGjOffcc1ucz7Rp0ygoKGDOnDlNHlNVVcV//vMfXn311Ub3X3755axevZqnnnqK3r178/TTT3Pqqafy+eefk56e3ug5Dz30EFOnTuXRRx/lRz/6EZ9++imTJk2ia9eujB8/vsXlr6+yspJx48bRo0cP5s+fz/bt27n00kux1jJjxgwAvv32W84++2yuu+46nnnmGXbt2sW1117LT3/6U5YtW3bQebe38vJyPvvsM7eLAfjnU1+9ejVbtmxpdP+ePXtYuHAhe/bsoaKiop1L17jy8nK3iyA13B6IHMoPXJqI45BDDrH33XdfnW3HHHOM/elPfxp4/vXXX9uzzjrL9uzZ08bHx9uhQ4fauXPnBvafeOKJDSaVqPHJJ5/Y448/3sbGxlqfz2evuuoqu3fv3oMqa82kG8uWLWuwb+fOnXbSpEm2R48eNjEx0Y4aNcouX7682fTOO+88e/rpp9fZNmbMGHv++ec3ec7VV19tDzvssGYn46jv1ltvtZdeemmzx3z00Ue2Z8+etrKyssG+kpISGxERYV9//fU62wcNGmT/8Ic/NJnmiBEj7A033NCg/Mcdd1ydbY8//rjt16+fjYmJsUcccYR94IEHmi3rG2+8YT0ej92wYUNg23PPPWdjYmLsrl27rLXWvvDCCzYyMrLO9bz66qvWGGPLysqaTb8+NybiSE9Pb3SyFDcfAwYMsBdeeGGzx1x00UW2f//+geepqal2wIABNjU11dWyp6ent9v/XSgIxYk4VAMOcdZaPvzwQ7744gsOP/zwwPa9e/dyxhlncMcddxAbG8sTTzzB+PHjWbNmDZmZmbz00ksMGjSIX//613VqqKtWrWLMmDHcfvvtzJo1i23btjF58mQmT57M7NmzAX/tcM6cOW1qOrPWMm7cOLp168Ybb7xBcnIyDz/8MKeccgpr166lW7dujZ63cOFCrr322jrbxowZ02RTellZGU8//TTXXXdd0O+zvfrqq4wfP77B/NTgn0e7srKS2NjYOtvj4uKYP39+k2mWlpY2es6nn35KeXk5UVFRPProo9x6663MnDmTwYMHs2zZMiZNmkR8fDyXXnppo+kuXLiQo446it69ewe2jRkzhtLSUpYuXcqoUaMYOnQoERERzJ49m4kTJ7J3716eeuopTjvtNKKiolrz0riqZ8+evPnmm24XA/hfDXjNmjVNHnPGGWdw00034fF4KC0tZc2aNYGOWkcccQQxMTENbhU4aezYsWzdurXd8pNmuP0L4GAf+JvP7wDWAfuAb4E/AZ4g5uFaDTg6OtrGx8fbqKgoC9jY2Fj7ySefNHte//797YwZM+qkU78mfckll9hf//rXdbZ9/PHH1uPx2H379llrrZ0xY4Y9+eSTW1TWpmrA7733nk1KSrL79++vs/2www6zDz/8cJPpRUVF2WeeeabOtmeeecZGR0c3evy//vUvGxERUafm1xItqQH37dvXvvrqq03uHzFihD3xxBPthg0bbEVFhX3qqaesMcb27du3yXOmTp1qU1NT7ZIlS2xVVZVdvHix7dmzpwXsxo0brbXWZmRk2GeffbbOebfffrsdMWJEk+lOmjTJjh49usH26OjoOml9+OGHtmfPnjYiIsICdsSIEXWmEW0pN2vAoVZz+/bbb+0ZZ5xhTzrppAaPM844w3777be2qqrKzp8/355xxhk2JSXFJicn25SUFHvGGWfY+fPnt6r1pq1C9XV0WijWgMO5E9bNwG+BycCRwE3AjcBVbhYqWG688UaWL1/Ohx9+yKhRo/jDH/5QZ/We4uJibrrpJvr370+XLl1ISEjgyy+/ZP369c2mu3TpUubMmVNn3dwxY8ZQVVUV6IwzefJk3nvvvTaVf+nSpezdu5eUlJQ6ea1bt45vvvmG9evX19mel5cXOLc1a/fOmjWLsWPH1qn5Nebjjz9ukF/99YNrl+GLL76gqKiIU089tck0n3rqKay1pKenExMTw/Tp07nwwguJiIho8pw//vGPjB07luHDhxMVFcXZZ5/NxIkTAYiIiGDbtm0UFhbyq1/9qk7Z7rjjDr755hvAX4Op2T5gwIAmX7f6r93mzZu5/PLLufTSS1m8eDEffvgh0dHR/OxnP6v5wRnSrrvuOm699daQ67mblZVFbm4uXq+3zvaaXtBZWVl1Ompt376dXbt2sX37dlc6aoXq69gZhXMT9Ajg39ba/1Q/LzDGXAAMbWkC1XM9Nyf1YAvXVt27dw8sq/fiiy/Sp08fhg8fHggIN954I2+//Tb33HNPYOm9n/3sZwdsyqqqquI3v/kNU6ZMabAvMzMzaOWvqqoiLS2NDz74oMG+Ll260KVLlzo9p2uapFNTU9m8eXOd45tau/e7777j3Xff5aWXXjpgeYYOHVonv+nTp7Nhw4Y6awvXbhZ/9dVXGT16NHFxcU2medhhh/Hhhx9SXFzM7t27SUtL4+c//zmHHnpok+fExcXx+OOP8/DDD7NlyxbS0tJ45JFHSExMpHv37mzbtg2ARx99tM7axkAgsD/22GPs27cPINB0nJqayqJFi+ocv2PHDsrLywOv3QMPPEBSUhJ//etfA8c8/fTTZGRksGjRIoYPH95kuUNBqAYMYwwjR45kxowZrFq1iqKiInw+X51xwCtXrmzQS7pGSUkJK1eubPZ9E0yh+jp2RuEcgOcDvzXG9LXWrjXGDAJ+DFzTijQKHSlZkHXt2pWrrrqKG264gWXLlmGM4eOPP2bixIlMmDAB8N8Trv8rOjo6usEyfTVr6vbp08fRMh9zzDFs3ryZyMjIwDKF9TVWhhEjRjB37tw694HfeeedRtfunT17Nj179gwsV9icuLi4Ovl169aN3bt3N/k6/Pvf/+byyy8/YLoA8fHxxMfHs2PHDt5+++06Aa4pUVFRgcU9nn/+ec4880w8Hg+9evUiPT2db7/9losuuqjRcxvrYT1ixAjuvPNONm3aRFpaGuB/3WJiYhgyZAjg/6KvXzuveV6z5rIcHGMM2dnZZGdnN7r/QC1TRUWurwsgLgjnJui7geeAL40x5cAy4H5r7XPuFssZV155JWvWrOHFF18E/MHrpZdeYvny5axYsYILL7ywwZdoVlYWH330ERs2bOD7778H4Oabb2bhwoVceeWVLF++nK+++opXX32Vq676X8v9zJkzOeWUU5otzw8//MDy5cv5/PPPAVizZg3Lly8P1F5PPfVURowYwU9+8hPefvttCgoKWLBgAbfccgtLlixpMt2rr76ad955h7vvvpsvv/ySu+++m3fffZdrrrmmznFVVVXMnj2bSy+9lMjI4P6O3Lp1K4sXL+bMM89s9ri3336bt956i3Xr1jF37lxGjRrFEUccwWWXXRY4ZurUqfziF78IPF+7di1PP/00X331FZ9++innn38+q1evrtP8PW3aNO666y7+8Y9/sHbtWlatWsXs2bObHbd52mmn0b9/fy655BKWLVvGe++9xw033MCkSZNISkoCYNy4cSxevJjbbruNr776is8++4zLLruMQw45hMGDBx/syyUtcKDWpforrUkn4fZN6IN9AOfjr8GeDwwELgG2A5e2Ig3fAR5DCZFhSNb6O9oMGDDAVlZW2nXr1tlRo0bZuLg4m5GRYWfOnGlPPPFEe/XVVweOX7hwoc3JybExMTF1hiF9+umndvTo0TYhIcHGx8fbnJwce+eddwb233rrrfaQQw5ptoyzZ89udGhD7bWEd+/eba+66irbu3dvGxUVZTMyMuxFF11k169f32zaL7zwgj3iiCNsVFSU7devn33xxRcbHPP2229bwK5Zs6bZtJrSXCesxx57rMGwoMb861//stnZ2TY6OtqmpqbaK6+80u7cubPOMZdeeqk98cQTA88///xze/TRR9u4uDiblJRkzz77bPvll182SPuZZ56xRx99tI2OjrZdu3a1J5xwgn3ppZeaLc93331nx40bZ+Pi4my3bt3s5MmTG3SCe+655+zgwYNtfHy87dGjhz3rrLMO6v2r9YBbp3ZHrdqfl9odtcRZodgJy/UCHHTB/cH3ynrbbgG+DGIervSCFneNHz/e3n333W4XI6Tpvd86odQLurMKxQAczveAvUD9G1eVhHezuoSAH//4x1xwwQVuF0M6kJZ01JLOJ5wD8GvAH4wx64F8YDBwHfC4q6WSsHfTTTe5XQTpgA7UUUs6n3AOwFcBtwMPAj2BjcDDwG1uFkpERKQlwjYAW2v34B9ydI27JREREWk93S+VTmvOnDl06dLF7WKISCelACytdqD1epvz9ddfk5iY2CDwzZ8/n+OOO46UlJTAGsj33XdfkEvujA8++IC0tLSanvMiIi2iACytUrNe7yWXXMKKFSu45JJLOO+88xpMg9iY8vJyLrjgAo4//vgG++Lj45k8eTIfffQRX3zxBbfccgu33HILjzzyiBOXEVSvvvoqZ511lms9WSsrKzWTlUgYUgCWVrn//vsZPXo0U6dOpV+/fkydOpVTTjmlyeUCa7vlllvo168f5513XoN9gwcP5oILLmDAgAFkZWVx8cUXM2bMGD7++OMm09uxYwcXXXQRPXr0IC4ujsMPPzywpOIHH3yAMYadO3cGjl++fDnGmAZTdr7yyiv07duX2NhYRo8eTWHh/2YoXbFiBaNGjSIxMZGkpCSGDBnSYCavmgD85JNPkpKSQmlpaZ3955xzTp3ZsF577TWGDBlCbGws2dnZ/PnPf66zWPu9997LwIEDiY+PJyMjg9/97nfs3bs3sL+m6fz1118PtER89913Tb5OIhKaFIClVRYuXMhpp51WZ9uYMWNYsGBBs+fNmzePF154gQceeKBF+SxbtowFCxZw4oknNnnMH//4Rz7//HPefPNNvvjiCx566CG6d+/eovRrlJSUcOedd/LEE0/wySefsHv3bs4///zA/osuugifz8fixYtZunQpv//97+usnZufn8/mzZs55ZRTOPfcc6msrOTVV18N7P/+++95/fXXA9NTvv3221x88cVMmTKFzz//nIcffpg5c+Zw5513Bs7xeDxMnz6d1atX88QTTzBv3rwGQ6NKSkq46667eOyxx8jPz6dnz56tum4RCQFuzwQSyg80E1YDrV2v11prv//+e5uRkWE//PBDa61/Gsvk5ORGj01PT7fR0dHW4/HY2267rdmyjB8/3l522WWN7nv//fctUGet22XLllnArlu3LlAOwP73v/8NHPPFF19YwC5atMhaa21iYqKdM2dOk2W488477U9/+tPA8yuuuMKOHTs28Pz++++32dnZgZmOjj/+eJuXl1cnjaeeesqmpaU1mcf//d//2ZSUlMDzmnIvX768yXOc1hnf+xLeQnEmLNWApdVas14vwKRJk7jwwgs54YQTDpj2xx9/zJIlS/jnP//J/fffz3PPNb22xhVXXMHzzz/P0UcfzU033XTAWnhjIiMjGTr0fytY9uvXjy5duvDFF18A/qXbLr/8ck499VT+8pe/BNbkrfHvf/+bs846K/B80qRJvPPOO2zYsAHwr9g0ceLEwOuzdOlSbrvttjpr/U6aNIlNmzYFlqt7//33GT16NOnp6SQmJvKLX/yC7du3U1xcHMgnOjqanJycVl+viIQOBWBpldas11tj3rx53HPPPURGRhIZGcmvfvUrdu3aRWRkJI8/XnfiskMPPZSBAwcyadIkrr32WqZNm9ZkumPHjuW7777jmmuuYePGjZxyyinccMMNgL8ZF6jTM7m8vLzRdBr78VCzbdq0aeTn5zNu3DjmzZtXp9f35s2b+eyzz+oshzh48GAGDRrEk08+yWeffcaqVauYOHFiYH9VVRV//vOfWb58eeCxatUqvvrqK2JjY/nuu+8444wzOOqoo3jxxRdZunRpoNm+dvnj4uI0faFImAvbiTjEHa1Zr7fGwoUL66xL/O9//5u7776bBQsWNLq2bQ1rbYMOTfX16NGDiRMnMnHiRI4//nhuvPFG7rnnHnr06AHApk2b6Nq1K+DvhFVfRUUFS5YsYdiwYYB/WcWdO3fSr1+/wDF9+/alb9++XHvttVxwwQXMnj2bCRMm8OqrrzJixIgG950vv/xy7rvvPjZs2MCpp55KRkZGYN8xxxzDmjVrmlyHeMmSJVRUVPD3v/898CPi//7v/5p9DUQkPCkAS6tcffXVnHDCCdx9992cffbZ/Pvf/+bdd99l/vz5gWNmzpzJyy+/zHvvvQfAkUceWSeNJUuW4PF4OOqoowLbHnjgATIzMwOBb/78+dxzzz111imu709/+hNDhgxhwIABlJaW8vrrrwfy6tOnDxkZGUybNo077riDr776ir///e8N0oiKiuKqq65i+vTpREVFMXnyZIYPH86wYcPYt28fN954Iz/72c849NBDKSoqYvHixZxzzjmAv/fz2Wef3SDNiy66iBtuuIFHH32UJ598skGZzzzzTDIyMjj33HPxeDysXLmSVatWcccdd3DYYYdRUVHBjBkzGD9+PJ988gn//Oc/m/0/EZEw5fZN6FB+oE5YjTrQer0HWk+4sU5Y06dPtwMGDLBer9cmJSXZwYMH2wcffNBWVlY2mc7tt99ujzzyyMD6t2effXaddVXnz59vBw4caGNjY+3xxx9vX3jhhQadsJKTk+2LL74YWNf35JNPtgUFBdZaa0tLS+35559vMzIybHR0tO3du7edPHmy3bdvn927d6+NjY21a9eubbRsl1xyie3WrVuD9Xittfatt96yI0eODKwJPGzYMPvII48E9t977702LS3NxsXF2TFjxtgnn3yyToey5jqxtZfO+t6X8BWKnbCMtZq9pynGGB/+dYcpLCzE5/PV2f/ll19y8cUX8/TTT9dpspSO76WXXuKWW27h888/b3T/6NGjOfLII5k+fXo7l6x96L0v4aaoqKj27aAMa22Rm+UBNUGLHJSEhATuvvvuBtt/+OEH3nnnHebNm8fMmTNdKJmIhAsFYJGDUH8ykhrHHHMMO3bs4O677+aII45o51KJSDhRABYJovrTXIqINEXjgEVERFygACwiIuICBeB2VjMt4W9/+9sG+373u99hjKkzc1LN8cYYIiMjyczM5IorrmDHjh11zt28eTNXXXUV2dnZxMTEkJGRwfjx4wNjcWvLy8sjIiKCv/zlLwd1DR999BHjx4+nd+/eGGN45ZVXGhwzbdo0+vXrR3x8PF27duXUU09tcslCay1jx45tNK3PPvuM0aNH06VLF1JSUvj1r39dZ2UgEZFwpQDsgoyMDJ5//nn27dsX2LZ//36ee+45MjMzGxx/+umns2nTJgoKCnjsscd47bXX+N3vfhfYX1BQwJAhQ5g3bx5//etfWbVqFW+99RajRo3iyiuvbJDe7NmzuemmmxpMA9lSxcXFDBo0qNlevn379mXmzJmsWrWK+fPnk5WVxWmnnca2bdsaHHv//fc3Oq3ixo0bOfXUU+nTpw+LFi3irbfeIj8/v84PFBGRcKVOWC445phj+Pbbb3nppZe46KKLAP+40oyMDLKzsxscHxMTQ2pqKgA+n4+f//znzJkzJ7C/pub86aefEh8fH9g+YMAAfvnLX9ZJ68MPP2Tfvn3cdtttPPnkk3z00UctWiShtrFjxzJ27Nhmj7nwwgvrPL/33nuZNWsWK1eu5JRTTglsX7FiBffeey+LFy8mLS2tzjmvv/46UVFRPPDAA4FpGR944AEGDx7M119/3eR0jiIi4UA1YJdcdtllgcXjAR5//PEGwbIx3377LW+99VZgTdoffviBt956iyuvvLJO8K3RpUuXOs9nzZrFBRdcQFRUFBdccAGzZs2qs3/OnDlBn+S/rKyMRx55hOTkZAYNGhTYXlJSwgUXXMDMmTMDPzBqKy0tJTo6OhB8wb8IAVBn6ksRkXCkAOySSy65hPnz51NQUMB3333HJ598wsUXX9zosa+//joJCQnExcVx2GGH8fnnn3PzzTcD8PXXX2OtbdFsRLt37+bFF18M5HPxxRfz//7f/2P37t2BY5KTk4M2frWm3LGxsdx3333MnTu3zsIF1157LSNHjmx0PmWAk08+mc2bN/O3v/2NsrIyduzYQW5uLuBfZEFEJJwpALuke/fujBs3jieeeILZs2czbty4Bqvq1Bg1ahTLly9n0aJFXHXVVYwZMyawSEHNVKItqbU+++yzZGdnB2qhRx99NNnZ2Tz//POBYyZMmMCXX37Z1surU+4FCxZw+umnc95557F161bAv5DBvHnzuP/++5s8f8CAATzxxBP8/e9/x+v1kpqaSnZ2Nr169SIiIiIoZRQRcYsCsIt++ctfMmfOHJ544olmm5/j4+Pp06cPOTk5TJ8+ndLSUv785z8DcPjhh2OMCSwg35zHH3+c/Pz8wLq8kZGR5OfnN2iGDpaacg8fPpxZs2YRGRkZyGvevHl88803dOnSJVAWgHPOOYeTTjopkMaFF17I5s2b2bBhA9u3b2fatGls27aNQw891JEyi4i0F3XCctHpp59OWVkZAGPGjGnxebfeeitjx47liiuuoHfv3owZM4YHHniAKVOmNLgPvHPnTrp06cKqVatYsmQJH3zwAd26dauz/4QTTmD16tV1lgd0gq21vu/vf/97Lr/88jr7Bw4cyH333cf48eMbnNurVy/A/yMiNjaW0aNHO1pWERGnKQC7KCIiIlBzbU2T6kknncSAAQPIy8tj5syZPPjgg4wcOZJhw4Zx2223kZOTQ0VFBXPnzuWhhx7iiy++YNasWQwbNqzRHs8jRoxg1qxZ3Hfffbz88stMnTq12WbovXv38vXXXweer1u3juXLl9OtWzcyMzMpLi7mzjvv5KyzziItLY3t27fz4IMPUlRUxLnnngtAampqox2vMjMz69RuZ86cyciRI0lISGDu3LnceOON/OUvf2nQuUxEJNyoCdplSUlJJCUltfq86667jkcffZTCwkIOPfRQPvvsM0aNGsX111/PUUcdxejRo3nvvfd46KGHKCsr4+mnnw4sJF/fOeecw9NPP01ZWRm7du1izZo1zea9ZMkSBg8ezODBgwNlGTx4MH/6058A/4+JL7/8knPOOYe+ffty5plnsm3bNj7++GMGDBjQquv89NNPGT16NAMHDuSRRx7h4YcfZsqUKa1KQ0QkFGk94GZoPWCRxum9L+EmFNcDVg1YRETEBQrAIiIiLlAAFhERcYECsIiIiAsUgEVERFygACwiIuICBWAREREXKACLiEi7Ky0tpaKiwu1iuEoBWERE2o21lj179rB3716qqqrcLo6rNBe0iIi0i6qqKnbv3k1lZaXbRQkJCsBt4PH4GxDKy8tdLolI+6p5z9d8BkQOpKKigj179nT6Wm9t+vS0Qc1qPsuWLXO5JCLtq+Y9n5aW5nJJJBxUVFSwe/duBd96VANug6SkJCZMmMCMGTMAGDx4MFFRUS6XSsQ55eXlLFu2jBkzZjBhwgQSExPdLlJYstZSUFDAypUrWb9+PZmZmeTk5JCVlYUxxu3iBVVN8NXCPw0pALfR1KlTAZg+fbrLJRFpPxMmTAi896V1rLUsWLCAvLw8Fi1aREVFBZGRkRx77LHk5uYycuTIDhOEy8rK2Lt3r4JvE7QcYTMOtBxhbXv27GHTpk1qYpEOzePxkJaWpppvG6xbt47JkydTUlLCBx98ENh+0kkn4fV6mTlzJoceeqh7BQyS4uJi9u/f3+wxiYmJREdHt0t5QnE5QtWAgyQxMVFfSiJyQCtXrqSkpKTRfSUlJaxcuTKsA3DNMCN1Tj0wBWARkXa0fv36ZvcXFR24YubGPeSW5FlZWcmePXs0zKiFFIBFRNpRZmZms/ubu9UF7txDbkmeNcOMdFuz5RwdhmSMOd4Y87QxZqExJr162yXGmB87ma+ISKjKycnB6/U2us/r9ZKTk9Ps+QUFBeTl5VFSUsL27dvZtWsX27dvp6SkhLy8PAoKCoJe5gPl+c0336in80FwLAAbY84B3gb2AYOBmOpdiUCuU/mKiISyrKwscnNz8Xq9pKSkkJycTEpKCl6vl9zcXLKyspo9vyX3kIOtqTyttezevZsVK1Z0mJ7b7cnJJuhbgN9aa580xpxfa/sC4E8O5isiErKMMYwcOZIZM2awatUqioqK8Pl8Lb6HG4x7yK3VVJ41iykUFhbi8Xh077eVnAzARwAfNbJ9N9DFwXxF6uhMkx5IeDDGkJ2dTXZ2dqvPbes95INRk2dTTcwZGRkagnkQnAzAm4A+QEG97T8GvnUwX6l27733snv3bpKSkrjuuuvcLo4ranceqd2EVtPc15EmPTgYeo+En8GDB3PkkUeyZcuWBvtacg/5YOTk5BAXF8eePXsazXPAgAGtvv9rjOnUnz1wcCIOY8xNwKXAL4G5wBnAIcB9wG3W2pmOZBxErZmIIxT5fD42bNhAenq6I81S4aD2pAf1daRJDw6W3iPho3ZLTn5+Ph6Ph+joaJ555hm+++47R3tB79u3j0WLFpGXl8eSJUuorKwkIiKCoUOHkpuby6BBg1pcA/Z4PGzZsoX8/Hw2bNjAIYcc0i4tUp1qIg5r7V+NMcnA+0As/uboUuCecAi+HUl5eTmfffaZ28Vod5GRkaxevbrRmgL4Zy9buHAhe/bs6bQLg2uyhPBQfxhQZGQkXbt2JTk5malTp9KlSxcOO+wwR4JYzYxWgwYNYvr06eTn51NYWEhGRgYDBgygV69erQq+K1asCATyqqqqDjsNZ0s4PhWlMcYL9Mff4/pza+1eRzMMoo5SA+6sBgwYwKBBg3j22WebPOaiiy5i2bJlfP755+1YstCjGnBoc2P6Smste/fupaysrM52Ywwej4eqqqpAs3Nj2xqzbds2pkyZQklJCZ988km7XEeNTlUDrq79RlhrfwCW1NreDaiw1u52Km+pq2fPnrz55ptuF6Pd1dSA16xZ0+QxZ5xxBjfddFOnrQGPHTuWrVu3ul0MOYD2nr6ysrKSvXv3Nvq5sNYGejvXbk6u6eDYVK3YGEN+fn6HnoaztZzshPU88BrwYL3t5wFn4b8nLO0gKiqKY445xu1iuCIxMZFevXo1eQ94xIgRneoDX5+WzwwP7TX0qLy8nH379rXo1kT95uQD3Rf2eDyuDKEKZU4G4GOBxrpVfgDc6WC+Uu26664L9HDtrGomPWiqF/SBJj3o6PQeCQ9ODz0qLS1l3759rRrHu2XLlsDnaseOHYHtNbNjTZ8+nR49egS2V1VVuTKEKpQ5GYBjmkg/Cohra+LGmAL8varre9Bae2Vb0+8INKyk7ZMedHR6j4SHmukrm2rJOdihR1VVVRQXFze4z3sgLWlOzs/PZ9SoUYF7wtZaBgwY4Mh1hCsnA/Bi4NfAVfW2/xZYGoT0fwRE1Hp+FP7hTi8EIW3pQNoy6YFIKKjdkpOSkhJYDKEtLTllZWUUFxcf1AQaLWlObmx2rF69egWuo2vXroFe0J21RcrJAPwH4F1jzCDgveptp+APnKe1NXFr7bbaz40xvwe+AT5saRrVvZybk3oQRRMRCapgtuRUVFRQUlLSpiFoLWlObmx2rKqqqjrDmTZu3NipZ6ZzchzwJ8aYEcCN+Dte7QNWAr+y1n4VzLyMMdHAxcC9tnXjqgqDWQ4REae0pSXHWsv+/fspLS0NynzNLWlObmp2rKqqKnr06MGoUaNITEzs1B0BHV0P2Fq7HLjIyTyq/QT//NJz2iEvEZGwsX//fkpKSoK+VGD95uSaXtA1zckHmqDDWtvply90NAAbYzz454PuSb2lD621jS3UcLB+Bbxprd3YyvMyDrA/Ff+9bBGRsFJRUcHevXsdW6GofnPywc6O1Zk5ORHHcOBZ/D2V6zfsW+p2oGpLPocApwI/be25B5oJpbPdjxCR8GetZd++fezbt8/xvGo3J9eeCUvBt2WcrAH/E/8MWOPwr4zkVFvDZcBW4D8OpS8iEhbKy8spLi5u93V5a8+OJS3nZAA+HPiZtfZrpzKobuK+DHjCWts55xIUkU7LWkt5eTnl5eWUlZWp5hlmnAzAi/Df/3UsAONves4EHncwDxGRkFJWVsb+/fu1mlWYczIAzwD+boxJBVYBdd4p1tqVbc3AWvsODe8vi4h0SM0tkiDhx8kA/GL1v7VrpxZ/wAxaJywRkc6gtLSU4uLiTj90pyNxMgB33iVmRESCoLKykv379+v+bgfl5ExY3zmVtohIR1ZeXh4IvNJxOToRB4Axpj/+jlLRtbdba191Om8RkXBR05NZtd3Ow8mJOLKBl4GB/O/eL/xvPLDuAUu7q/ly83g8RERE4PF4NOGKtLua4UMVFRVUVlZSUVGhoNsJOVkD/gewDv9QoW+BYUAK8HfgBgfzFWlSTdNebcaYQDCu/68CtARLTdAtLS1V07IAzgbgEcDJ1tptxpgqoMpaO98YMxWYDgx2MG+RFrPWNjusoyYQNxWkRZpTVVUVWIlItVypzckAHAHsrf77e6A3sAb4DjjCwXxFgqqqqoqqqqpGg3RN7dkY06DmrADdudRuSq6ZmrHmIdIYJwPwaiAHf/PzIuAmY0wZ8OvqbSJh70C1ZyAQnJv7t+ZRc3zNPgkeay0FBQWsXLmS9evXt2kh+JoAWzMNZEVFhcbnSqs5GYDvAOKr/74FeB34GNgOnO9gviIhpS0T1Td2P7qxv6V51loWLFhAXl4eixYtoqKigsjISI499lhyc3MZOXJk4LiaR03LR2MPBVsJBifHAb9d6+9vgf7GmG7ADqt3r0iL1HzhH0hTgdoYUydYGGMCaVZWVgb+rl3rrv+on0eoqx1Aa/4tLCzkjjvuoKSkhO3btweO3bNnD7fddhvTp0+ne/fuLpZaOiMnhyE9Dlxtrd1Ts81a+4MxJt4YM8Na+0un8hbpbFoaqIOhuR7jQJ3gV78mWfu5tbZB4K/dFF87jZpj6/+oqL32bO1jazPGsGLFCvbs2UN9VVVVFBcXs3r1akaNGqWarbQrJ5ugLwV+D9R/18cBvwAUgEXCUE3HomCsxFM7SDvF4/Gwfv36Zo8pLCzE4/Gow5S0q6AHYGNMEv5JNwyQaIypPegyAjgD2BrsfEVEGlNVVUVmZmazx2RkZGiIkLQ7J2rAO/HPdmWBtY3st8CtDuQrItKAtZYBAwbg9XopKSlpsN/r9TJgwAA1P7cjay3btm2joKCAIUOGuF0c1zgRgEfhr/3OA84Bfqi1rwz4zlq70YF8RUQa1atXL3Jzc8nLy6Nr165UVlYSERGB1+slNzeXXr16qQbsIGstO3fupKioKPAoLS0lOjqanJwcoqKi3C6iK4IegK21HwIYYw4F1qvHs4i4raqqikGDBjF9+nTy8/MpLCwkIyODAQMGhFzwrelsFu7Dnfbu3UthYWEg4BYXF+PxeOjVqxc5OTn4fD769OnTaYMvONsJ60ggA5gPYIy5EpgEfA5caa3d4WDeIiJ1VFVV0aNHD0aNGlUnwIVK8PV4PGzZsoX8/PzARCGh+AOhKfv27WPDhg2BgLtz504AunfvzuGHH05GRgZpaWlER/9vYbzISMcX5AtpTl7934CbAYwxA4F78S/EcHL135c5mLeISKPaMjGKUzweDytWrCAvL48lS5YEmsiHDh1Kbm4ugwYNCrkgXFZWxqZNmwK13O+//x6A5ORkMjIyGD58OOnp6cTFxblc0tDlZAA+FH9tF/z3gl+z1uYaY44B3nAwXxGRsLJlyxby8vIoKSlhx47/NQ6WlJSQl5fH9OnT6dGjh4sl9A8/27x5c6CGu2XLFqqqqoiPj8fn8zFo0CAyMjJISEhwtZzhxMkAXAZ4q/8+FXiy+u8fgCQH8xURCRvGGPLz8xvtoQ3+IJyfn9/uE4VUVVWxbds2NmzYQGFhIZs2baKiooKYmBh8Ph/HH388Pp+PLl26aMnOg+RkAJ4P3GuM+QT/WsA/r97eFyhyMF8RkbARKhOFWGvZsWNHoIa7YcMGSktLiYyMpHfv3gwbNgyfz0ePHj0UcIPEyQA8GXgQ+BlwhbV2Q/X2scBbDuYrIi7oKL1325ubE4Xs2bOnTk/lkpISPB4PqampDBo0CJ/PR69evcJiDvBw5ORiDOuBMxvZfq1TeYpI+wv33rtua8+JQvbt21dnLO6uXbsA6NGjB0cccQQ+n4/evXt36qFB7SmoAdgYk2St3V3zd3PH1hwnIuErHHvvhiKnJgopKyurMzSoZiWorl27kpmZic/nIz09ndjY2GBfUh1qHWmcCeaLYYypBNKstVuNMVX4p51scBhgrbUh36ZhjPEBheC/B+Pz+VwukbRVcXEx+/fvP/CB0iLbtm1jypQplJSU8MknnwS2H3fccXi93pDovRsuarckHOxEIRUVFYGeyoWFhWzduhVrLQkJCfh8PjIyMkhPT2+3nsoHah1JTEysMy7YSUVFRWRkZNQ8zbDWut4XKdhN0Cfzv6knRwU5bREJIaHaezdcHcxEITU9lWvu427atInKykpiY2Px+XwceeSR+Hw+kpOT273jVEtaRzq7oAbgmmko6/8tIh1PqPTedZMTTavNTRRireWHH36o01O5rKyMqKgoevfuzfDhw8nIyCAlJcX1nsotGducnZ3tYgnd51gnLGPM4cDZQBb+puhvgX9ba791Kk8RaT/htsxfMINle3Y82717d6BJecOGDYGeymlpaQwePBifz0fPnj1DqqdyS1tHFIAdYIyZCtwGePCv/WuAHsDdxphca+09TuQrIu0nXJb5C3awdLrjWXFxcZ2OU7t378YYQ48ePejXr19gTuVQnke5pa0jbtfS3Rb0/0FjzCjgDuB24B81iy4YY7oB1wB/McZ8aq39KNh5i0j7CvVl/pwIlsGeNrK0tLROwP3hB383mm7dupGVlRXoqRwTE9Oqcrqppa0jbv84c5sTP6F+CzxmrZ1We6O19gfgT8aYVOAKQAFYJMyF+jJ/wQ6Wweh4VlFREVjEYMOGDYGeyomJifh8PoYMGYLP5yM+Pr51FxtCWto60tk5EYCHAZc0s/8p/jcvtIiEuVBd5s+JXtoH0/GssrKSrVu3Bmq4mzZtoqqqiri4OHw+H/379w/0VO5IWtI60tk5EYB7AQXN7F8HpDqQr4i4KNSW+XOil3ZLmlZ9Ph9btmwJdJzauHEj5eXlREdHk56eznHHHYfP56Nbt24d+h5oqLeOhAInAnAs/pWQmlIOtM/IaxHptJzopd1U02q3bt1IT0+nZ8+efP311+Tn5xMREUFaWlqgSblnz554PJ6Dvp5wFKqtI6HCqW50lxtj9jaxL9GhPEVEApzqpd2rVy+uv/56nnzySXr37k1GRgZJSUlYa0lKSqJfv36kp6eTmpoa0j2V21OotY6ECifeHeuBSS04RkTEUcHqpb1///46PZV37NhB3759iYmJwVpLamoqQ4cOxefzqXYnLRb0AGytzQp2miIiB+Ng70OWl5ezadOmwH3cbdu2AZCcnEx6ejrDhg0jPT2d+Ph4Na3KQVP7iIh0aC25D1lZWRnoOFVUVMTmzZupqqrC6/Xi8/kYOHAgPp+PpKS6i7ypaVXaQgFYRDqF2sHSWsv3339fZxGD8vJyYmJiSE9P58c//jE+n4+uXbt26J7K4i4FYBHp8Ky17Ny5s84iBvv37ycyMpK0tLTA/dsePXq0uaey1r6VllIAFpEOae/evXUWMdi7dy8ej4eePXsycOBA0tPTSUtLC9oiBu25QIN0DArAItIh7Nu3L9BTubCwkF27dgHQvXt3+vTpg8/no3fv3o4sAO/0Ag3SMTkagI0xEcAE4Ej8SxJ+Cbxira1wMl8RCb5Qa1otKysLzKlcVFTE999/D/h7Kvt8PkaMGEF6ejpxcXGOlyXYc05L5+DkesBHAf/GP+3kmurNfYFtxpizrLWrnMpbRIInVJpWKysr2bx5c+A+7pYtW6iqqiI+Ph6fz8egQYPw+XwkJrbvXD9OzDktnYOTNeDHgHxgaK0lCbsCc4BHgBEO5i0iQeBm02pVVRXbtm2rs4hBRUVFoKfy8ccfj8/no0uXLq72VHZizmnpHJwMwIOoFXwBrLU7jDF/ABY7mK+IBEl7Nq1aa9mxY0ednsqlpaVERkbSu3dvhg0bFuipHEpDg5yYc7o9hNothc7IyQC8Bv/KSPn1tvcEvnYwXxEJgvZoWt2zZ0+gl3JRURHFxcV4PB5SU1MDTcq9evUKWk9lJzg157RTQuWWgjgbgHOB6caYacB/q7cNB/4E3GyMCUwpY63d7WA5ROQgONG0um/fvkANt6ioKNBTuUePHvTt2zfQUzkqKqrN5W9PwZpz2mnqrR1anAzAr1f/+3/4e0AD1LQbvVbruQVC9+etSCcVjKbVsrIyNm7cGOipvH37dgC6du1KZmYmPp+P9PR0YmNjg1r29hYua9+qt3ZocTIAj3IwbRFx2ME0rVZUVDToqWytJSEhAZ/PxzHHHEN6ejoJCQnteSntItTXvlVv7dDjWAC21n7oVNoi0j4O1LTao0cPtmzZEriPu3HjRiorK4mNjcXn89GvXz98Ph/Jyckh1XHKSaG6QIN6a4eeoAZgY0wOsNpaW1X9d5OstSuDmbeIBF/9ptX169fTs2dPEhMTWb9+PQsXLqSsrIyoqCh69+7N8OHDycjIICUlpdME3HARrr21O7Jg14CX4594Y2v135b/3fetTfd9RcLEzp072bp1K2VlZVRUVPD1118HeioPHjwYn89Hz549Q7qncmdjjAk8ap4DDBw4kISEhAbN0B6PB6/XS05ODjExMXXOAQJN6TX/auhScAQ7AB8KbKv1t4iEmZKSkjo9lXfv3o0xhh49etC/f3/S09NJTU0Nu57KHUVkZCRRUVFERkYSERGBx+PBGBOoudYOvPUlJSXxxz/+kby8PFJSUqioqCAyMpLExERyc3Pp06dPq1ouagfjyspKKisrqaiooKJCsw23RFADsLX2O2NMpTEmzVr7XTDTFhFnlJaW1ump/MMPPwDQrVs3srKyyMjIIDo6mq+++oq1a9eyf/9+YmNjQ6p3b0djjAkE2IiIiMCkGZGRkU0GyJYso2iMYeTIkcyYMYNVq1ZRVFSEz+cjJyeHrKysVt828Hg8gXxr/yCz1lJRUYG1NlBTrp12zd+dvdXEiU5YuvEjEsIqKirYtGlToIa7detWrLUkJibi8/kYMmQIPp+P+Ph4jRttJzW12qioqECt1inGGLKzs8nOznY0D7WQHJiWIxTp4KqqqtiyZUsg4G7evJnKykri4uLw+Xz0798fn89HUlJSgxqQxo06JyIigtjYWKKjox0NuBK6nArAY4wxu5o7wFr7alszMcakA3cDY4E4YC3wK2vt0ramLRKurLVs3749sC7uxo0bKS8vJzo6mt69ezNy5Eh8Ph/dunVrtslR40adExcXR1xcnHqKd3JOBeAnDrC/zb2gq1dW+gR4H38A3gocBuxsS7oi4cZay+7duwP3cDds2MC+ffuIiIggLS0t0KTcs2fPVtW0NG40uDweDzExMcTExHT6e5/i51QATrXWbnUo7Ro3A4XW2stqbStoTQLGGN8BDkltbaFE2kNxcXGdnsp79uzBGEPPnj3p378/GRkZpKamEhl58B9xjRsNjujoaGJiYoiOjna7KBJinAjA7dUWdRbwtjHmBeBEYAPwoLX20VakUehIyUSCbP/+/YEVg4qKigL3Y1NSUsjOzg4sYlAzhjMYwm2Vn1ARERFBZGQk0dHRREVFqZlZmhTOvaCzgSuAe4E8YBj+1ZdKrbVPtlMZRBxRXl4e6KlcWFjItm3+4fXJycmkp6czbNgw0tPT8Xq9jpYjXFb5cZs6VMnBcCIAPwHscyDd+jzAEmttbvXzZcaYAfiDcksDcMYB9qcCiw+yfCItVllZydatWwP3cTdv3kxVVRVerxefz8fAgQMDPZXbU7is8uMGj8cTaF5uS1O/dF5Bf9fUuyfrpE3A5/W2fQGc09IErLVFze1X05E4xVrL999/H2hSrt1TOT09neOOO46MjAy6du3q+vsw1Ff5aW+xsbEKuhIU4fwO+gQ4ot62voBm4JKQY61l586dbNiwIbBy0P79+4mMjCQtLY2hQ4fi8/no0aNHyDZhhuoqP+0lIiKChIQEBV4JmnB+J90HLDDG5AL/h/8e8K+rHyKu27t3b52eynv37sUYQ69evRg4cCDp6emkpaVpSEoYiI2Nxev1ut4aIR1L2AZga+1iY8wE4C7gT8A64Bpr7TPulkw6q3379tXpqbxz504AunfvTp8+fQI9lTUcJXx4PB7i4+P1fyaOaJcAXD3e1lprNwQzXWvt68DrwUxTpKXKysrqzKlcu6eyz+dj+PDhpKenExcX53JJ5WDExMQQHx+vWq84xrEAbIzxALcA1wMJ1dv2AH8H7rTWds4eHBK2Kisr2bx5cyDgbtmyhaqqKuLj4wMryvh8PhITE90uqrSBMYaEhATVesVxTtaA7wR+Bfwef4cpAxwHTANigT84mLdIm1VVVTXoqVxRUUFMTAzp6ekcf/zx+Hw+unTpolpSBxEREUFiYqLuy0u7cDIAXwpcXm/RhRXGmA3AgygAS4ix1rJjx45AwN2wYQOlpaVERkbSu3dvhg0bhs/no3v37iHbU1kOjjGG2NhYLZAg7crJANwN+LKR7V9W7xNx3Z49ewLDgoqKiiguLsbj8dCrVy9ycnLIyMigV69ena5GVLMAfM2Y345KgVfc5GQAXgFMBqbU2z65ep9IuyspKeGbb74J1HJ37fKvmtmjRw/69u0b6KncWRcT93g8bNmyhfz8fNavX09mZmaHnfUqJiYGr9er1gxxjZMB+CbgP8aYU4GF+BdpGIl/+sczHMxXpFFfffUVzz77LABdu3YlMzMzEHDVU9kffFesWEFeXh5LliwJzPs8dOhQcnNzGTRoUIcIwppQQ0KFY+9Aa+2Hxpi+wJVAP/ydsF7Cv2LRRqfyFWlKeno6Y8eOpVevXiQkJLhdnJCzZcsW8vLyKCkpCay2BP5Wg7y8PKZPn06PHj1cLGHbREREEBcXF9QVo0TawslhSJn41+tt0NnKGJNprW1+pW+RIKtZPm///v1uFyXkGGPIz89vdNlB8Afh/Px8Ro0aFXb3hD0eD16vV4FXQo6TNz/WAQ1+LhtjUqr3iUiI8Hg8rF/f/G/iwsLCsLtfGhMTQ5cuXRR8JSQ5eRPE4L/vW18CoCqISAipqqoiMzOz2WMyMjLC6h5wbGws8fHxbhcj7FhrKSgoYOXKlYGOeDk5OWRlZamneJAFPQAbY+6t/tMCtxtjardpRQDHAsuDna+IHDxrLQMGDMDr9TbaDF3TfB8uzc9er1cd6w6CtZYFCxaQl5fHokWLqKioIDIykmOPPZbc3FxGjhwJoAAdJE7UgAdX/2uAgUBZrX1l+Icg3eNAviLSBr169SI3N5e8vDy6du0a6AXt9XrJzc0Ni6FIGlrUNgUFBYGOeNu3bw9sr+mI989//pP169c3G6AVhFsu6AHYWjsKwBgzG7jaWrs72HmISPBVVVUxaNAgpk+fTn5+PoWFhWRkZITFOOCoqCi8Xq+GFrXRypUrm+2I991333HXXXc1GaBnzpzJoYce2l7FDXtODkO6zKm0RcQZVVVV9OjRg1GjRtWZCStUg68xhvj4eHWyCpLmOuJ169btgD3lV65cqQDcCvq5KCINWGuprKx0uxjN0sIJwddcR7ykpCS2bNnS7PlFRUXBLlKHphslIhJ24uLiSE5OVvANspycHLxeb6P7ysvL6devX7Pn+3w+J4rVYSkAi0jYiIqKokuXLni9XnX2cUBWVha5ubl4vV5SUlJITk4mJSUFr9fLFVdcwdChQ5sM0F6vl5ycnHYucXhzciasE4AF1tqKetsjgZHW2o+cyltEOhaPx0N8fDzR0dFuF6VDM8YwcuRIZsyYwapVqygqKsLn8wWGGQGBnvIpKSmBXtA1PeVrjpGWMU6N6zPGVAJp1tqt9banAFuttSHfdmSM8QGF4J8FSM0r4a+4uFhTUYaZ2NhY1XhDiLWWdevWNRqgQ/n/qKioiIyMjJqnGdZa129YuzETVgpQ7GC+ItIBREZGEh8fr6FFIcYYQ3Z2NtnZ2W4XJew5MRPWS9V/WmCOMaa01u4IIAdYEOx8RaRj0NAi6Syc+Gm5q/pfA+wB9tXaVwb8F3jUgXxFJMxpJivpTJyYCesyAGNMAXCPtVbNzSLSLGMMCQkJ6mQlnYqTM2H9GcAY0wM4An+T9Fpr7Tan8hSR8KMJNaSzcqydxxjjNcY8DmwCPgI+BjYaY2YZYxofSCYinUpUVJQm1JBOy8kbLfcBJwLjgS7Vj7Ort/3dwXxFJAzExsaSlJQU0kNXRJzkZP/+c4CfWWs/qLXtDWPMPuD/gCsczFtEQlhCQoJ6OUun52QA9gKNzdy9tXqfiHQyHo+HhIQEoqKi3C6KiOucbIJeCPzZGBNbs8EYEwfcWr1PRDqRmvu9Cr4ifk7WgK8G3gKKjDEr8PeCPhrYD4xxMF8RCTFer5e4uDi3iyESUpwchrTaGHM4cDHQD//EHM8Dz1hr9zV7soh0CB6Ph8TERE0nKdIIRz8V1YFWs16JdEIxMTHEx8erl7NIExwNwMaYw4BrgCPxN0F/AfzDWvuNk/mKiHs0q5VIyzg5EccY4HNgGLASWA0cC+QbY0Y7la+IuCcqKoouXboo+Iq0gJM14L8A91lrf197ozHmL8DdwFwH8xaRdhYXF4fXqxGGIi3l5DCkI4FZjWx/HOjvYL4i0o6MMSQlJSn4irSSkwF4G/5hR/UdjX8yDhEJc5GRkXTp0kVje0UOgpNN0I8CjxhjsoEF+Dth/Ri4Gc0FLRL2QrmXs7WWgoICVq5cyfr168nMzCQnJ4esrKyQLG840GsafE4G4NuBPcD1wF3V2zYC04DpDuYrIg6Lj48nNjb2wAe6wFrLggULyMvLY9GiRVRUVBAZGcmxxx5Lbm4uI0eOVMBoJb2mznCsCdr63Wet9QHJQLK11met/QfQ26l8RcQ5Nfd7QzX4AhQUFJCXl0dJSQnbt29n165dbN++nZKSEvLy8igoKHC7iGFHr6kznLwHHGCt3WOt3WOMSTXGzAC+bo98RSR4IiIiwmIu55UrV1JSUtLovpKSElauXNnOJQp/ek2dEfQAbIzpYox5xhizzRiz0RgzxRjjMcbcBnwLDAd+Gex8RcQ5NQspREREuF2UA1q/fn2z+4uKitqpJB2HXlNnOHEPOA84AXgCOB24r/rfWGCstfZDB/IUEYfExsYSHx/vdjFaLDMzs9n9Pp+vnUrSceg1dYYTTdDjgMustTcAZ+FfhGGttfZkBV+R8BIfHx9WwRcgJyenyTHJXq+XnJycdi5R+NNr6gwnAnBv/FNQYq39Fv/yg485kI+IOKTmfm8od7ZqSlZWFrm5uXi9XlJSUkhOTiYlJQWv10tubi5ZWVluFzHs6DV1hrHWBjdBYyqBVGvtturne4Aca+26oGbUDowxPqAQoLCwUM0sHUBxcTH79+93uxghLTo6moSEhLAeVmKtZd26daxatYqioiJ8Pp/GrLZRuL+mRUVFZGRk1DzNsNa6fuPaiXvABphjjCmtfh4L/NMYU1z7IGvtTx3IW0TawOv1EhcX53Yx2swYQ3Z2NtnZ2W4XpcPQaxp8TgTgJ+o9f9qBPEQkiLSEoEj7C3oAttZeFuw0RcQ5ERERJCYmhsUQI5GOxMmpKEUkxEVFRZGYmBgW9/BEOhoFYJFOSuv3ysHQogzBowAs0snofq8cLC3KEFztMhe0iISGmvG9Cr5yMLQoQ3ApAIt0EnFxcWEzn7OEJi3KEFxqghbp4DweD4mJiURG6uMubaNFGYJLn0iRDiw6Opr4+Hg8HjV2SdtpUYbg0qdSpIOKi4sjMTFRwVeCRosyBFfYfjKNMdOMMbbeY7Pb5RIJBQkJCRpiJEGnRRmCK9yboPOBU2s9r3SrICKhQPd7xUnGGEaOHMmMGTPCdlGGUBLun9IKa+1B13qrVztqTurBpi3S3jrCKkYS+rQoQ/CEewA+3BizESgFFgG51WsQt1ShM8USaV8dZRUjkc4kbO8B4w+4vwDGAJPw11YXGGNSXC2VSDsyxpCYmKjgKxKGwrYGbK19s9bTVcaYhcA3wKXAvS1MJuMA+1OBxQdRPBHH6X6vSHjrMJ9ca22xMWYVcHgrzml21LjupUmoioyM1BAjkTDXYT69xpgY4Ehgk9tlEXFSTEwMSUlJCr4iYS5sa8DGmHuA14D1QE/gFiAJeMLNcok4KT4+ntjYWLeLISJBELYBGPABzwHdgW3Af4Hh1trvXC2ViAM8Hg8JCQlERUW5XRQRCZKwDcDW2vPdLoNIe9D9XpGOKWwDsEhnEBsbi9frVYdAkQ5IAVgkBBljiI+PJyYmxu2iiIhDFIBFQozG94p0DvqEi4SQqKgoEhMT1eQs0gkoAIuEiLi4OC0hKNKJKACLuMwYQ0JCAtHR0W4XRUTakQKwiIsiIiJITEwkIiLC7aKISDtTABZxSUxMDPHx8brfK9JJKQCLuEDr94qIArBIO9N8ziICCsAi7SohIUGTa4gI0IGWIxQJdZrZSkRqUwAWaQdqdhaR+tQELeKwxMREjfEVkQYUgEUcYowhKSlJczqLSKP0zSDiAE2wISIHogAsEmTR0dEkJCRogg0RaZYCsEgQaUEFEWkpBWCRINEYXxFpDQVgkTbyeDwkJiaqs5WItIq+MUTaIDIyksTERDweDakXkdZRABY5SFrNSETaQgFY5CBoNSMRaSsFYJFWMMaQmJhIVFSU20URkTCnACzSQppcQ0SCSQFYpAU0uYaIBJsCsMgB6H6viDhBAVikCcYYEhIStJKRiDhCAVikEbrfKyJOUwAWqScqKorExETd7xURRykAi9SixRREpL0oAIvgv98bHx+vxRREpN0oAEunp8UURMQN+saRTi0qKoqEhAQtpiAi7U4BWDqt2NhY4uPj3S6GiHRSCsDS6eh+r4iEAgVg6VQiIyOJi4tTk7OIuE4BWDoV1XpFJFSoGiAiIuICBWAREREXKACLiIi4QAFYRETEBQrAIiIiLlAAFhERcYECsIiIiAsUgEVERFygACwiIuICBWAREREXKACLiIi4QAFYRETEBQrAIiIiLlAAFhERcYECsIiIiAsUgEVERFygACwiIuICBWAREREXKACLiIi4oMMEYGPMVGOMNcbc73ZZREREDqRDBGBjzI+AXwMr3S6LiIhIS0S6XYC2MsYkAM8Ak4BbWnmu7wCHpB5suURERJoT9gEYeAD4j7X2XWNMqwIwUOhEgURERA4krAOwMeZ84BjgR26XRUREpDXCNgAbYzKAfwCnWWv3H2QyGQfYnwosPsi0RUREmhS2ARgYAvQElhpjarZFACcYYyYDMdbayuYSsNYWNbe/VroiIiJBFc4B+D1gYL1ts4EvgbsPFHxFRETcFLYB2Fq7B1hde5sxphjYbq1d3fhZIhIKrLUUFBSwcuVK1q9fT2ZmJjk5OWRlZanlSTqNsA3AIhKerLUsWLCAvLw8Fi1aREVFBZGRkRx77LHk5uYycuRIBWHpFDrERBw1rLUnWWuvcbscItK0goIC8vLyKCkpYfv27ezatYvt27dTUlJCXl4eBQUFbhdRpF10qAAsIqFv5cqVlJSUNLqvpKSElSs1oZ10DgrAItKu1q9f3+z+oqJmByeIdBgKwCLSLqy1rF+/noSEBIqKivj+++/Jzs4mLi6uznE+34FmiBXpGBSARcRxNR2vrrjiCnbs2MH333/Pl19+yXfffUdaWlogCHu9XnJyclwurUj7UAAWEcfV7nj18MMPM3XqVOLi4jDGsGHDBg455BC8Xi+5ublkZWW5XVyRdqFhSCLiuNodr9auXcusWbOYOnUqVVVVbNiwgR/96EecdNJJGgcsnYpqwCLiuPodr9auXUtubi6fffYZ+/btA+DQQw9V8JVORQFYRByXmZnZ6PYffviBgoICunXr1s4lEnGfArCIOC4nJwev19voPnW8ks5KAVhEHJeVlUVubi5er5eUlBSSk5NJSUlRxyvp1NQJS0QcZ4xh5MiRzJgxg1WrVlFUVITP59MCDNKpKQCLSLswxpCdnU12drbbRREJCWqCFhERcYECsIiIiAsUgEVERFygACwiIuICBWAREREXKACLiIi4QAFYRETEBQrAIiIiLtBEHM2LqPlj06ZNbpZDRETaoN53eERTx7UnY611uwwhyxgzFFjsdjlERCSofmStXeJ2IdQELSIi4gLVgJthjIkBBlY/3QZUulicYEjlfzX6HwGbXSxLsOiawoOuKTx09GsaB2wFVllrS90rkp/uATej+j/I9WaKYKm34sxma22RW2UJFl1TeNA1hYdOcE0rQ+ma1AQtIiLiAgVgERERFygAi4iIuEABWERExAUKwCIiIi5QABYREXGBArCIiIgLNBGHiIiIC1QDFhERcYECsIiIiAsUgEVERFygACwiIuICBWAREREXKACLiIi4QAFYRETEBQrAIiIiLlAAFhERcYECsIiIiAsUgMOQMeYEY8xrxpiNxhhrjPlJvf29jDFzqveXGGPeMsYcXu+YGGPMDGPM98aYYmPMq8YYX71juhpjnjLG7Kp+PGWM6RKK12SM6VZ9PWuq9683xkw3xiSH6zXVO9YYY95sIp2wuyZjzAhjzLzq995OY8wHxpi4cL0mY0xqdRk3V1/TZ8aYn9U7pl2uyRgz1Riz2Bizxxiz1RjzijHmiHrHGGPMtOpr2lf9+g+od0zIfEcE45pMCH5HKACHp3hgBTC5/g5jjAFeAbKBs4HBwHfAu8aY+FqH3g9MAM4HfgwkAK8bYyJqHfMscDRwevXjaOCpYF5ILW29pt7VjxuAgcDE6jLPqpdcOF1TbdcATU3cHlbXZIwZAbwFvAMMA34EzASqaiUXVtdUXbYjgLPwv/9eAv5ljBlc65j2uqYTgQeA4cBoIBJ4p155bwKuw3/NPwI2A3ONMYm1jrmf0PmOCMY1hd53hLVWjzB+4P9S/kmt532rtw2otS0C2A5cXv08GSgDfl7rmN5AJTCm+vmR1ekcW+uY4dXbjgi1a2oinXOBUiAynK8JGAQUAqmNpBN21wT8F7i9mXTD8Zr2ApfUS2s78KsQuKYe1fmcUP3cAJuAm2sdEwPsBH5T/TzUvyNafU1NpOPqd4RqwB1PTPW/+2s2WGsr8X+Yfly9aQgQhb8GUnPMRmA1MLJ60whgl7V2Ua1j/gvsqnVMe2nJNTUmGdhtra2ofh5212SM8QLPAZOttZsbSSesrskY0xM4FthqjFlgjNlijPnQGFP7/zGsrqnafODn1c2cHmPM+dXnflC9381rqmli/aH630Px/5ir/fkvBT6sVZZQ/444mGtqKh3XviMUgDueL/E3kd1VfS8j2hjze/xvzrTqY1KBMmvtjnrnbqneV3PM1kbS31rrmPbSkmuqwxiTAvwReLjW5nC8pvuABdbafzeRTrhdU3b1v9OAR/E38X0GvFfrvmq4XRPAz/E3i27HX6N6GJhgrf2mer8r11TdhH4vMN9au7pWWcD/ea+t/uc/JL8j2nBN9dNx/TtCAbiDsdaWA+fgbzr7ASgBTgLexN981BxD3fuMjd1zrH+M41p7TcaYJOA/wOfAn+sn10gWIXlNxpizgJPx3/9tNrlGtoXkNfG/75yHrbWzrbXLrLXXAmuAX9ZOrpEsQvWaAO4AugKnAkPxB4gXjDEDayfXSBZOX9NMIAe4oJF99fNtSVlC4TuizdcUKt8RCsAdkLV2qbX2aKALkGatPR1IAdZVH7IZiDbGdK13ak/+9wtyM9CrkeR70PBXpuNacE0AVHe4eAv/PbkJ1V+gNcLtmk4GDgN2GmMqjDE1zWQvGmM+qP473K5pU/W/n9c79Qsgs/rvsLomY8xh+Dv+/NJa+561doW19s/AEuDK6mTa/ZqMMTPwdwobZa0tqrWr5lZG/Rpd/c9/yH1HtPGaatIIme8IBeAOzFq7y1q7rbppbyhQ04y5FCjH35sQAGNMGnAUsKB600Ig2RgzrNYxx+K/Z1JzTLtr5ppqftW+g//+3FnW2v31Tg+3a/oL/l/6R9d6AFwLXFb9d7hdUwGwEX+P4dr64m/qhfC7Jm/1v1X1Tqnkf9+x7XZN1cNxZgI/BU621q6rd8g6/IGm9uc/Gn9P45qyhNR3RJCuKfS+I4Ldq0sP5x/4hwMcXf2w+L+QjwYyq/efi7+ZrGboRAHwYr00HsLfs/YU/EMr3gOWAxG1jnkT//CM4dWPlcBroXhNQCL+3rUr8dcaU2s9wvKamkizTi/dcLwm/E3qu4CfAX2A24F9wGHheE34Oyt9BXyEf1jVYcD1+APyGe19TcCD+Hv/nljvcxBX65ibq4+ZgD+oPov/h1FirWNC5jsiGNdEKH5HOJGoHs4+qr8MbCOPOdX7p1R/cMrw1ypuB6LrpRELzMDfaaQEeA3IqHdMN+BpYHf142mgSyheUzPnWyArHK+piTQbC8Bhd03A76uPK8Zfs/hxOF8TcDjwIv5mymL8X+D1hyW1yzU18zmYWOsYg78j3Cb8Pbw/BI6ql07IfEcE45qa+X927TvCVGcoIiIi7Uj3gEVERFygACwiIuICBWAREREXKACLiIi4QAFYRETEBQrAIiIiLlAAFhERcYECsIiIiAsUgEVERFygACwiIuICBWAREREXKACLiIi4QAFYRETEBQrAIiIiLlAAFulAjDFzjDG2+lFujNlijJlrjPmlMabFn3djzERjzE4HiyrS6SkAi3Q8bwFpQBYwFngf+AfwujEm0sVyiUgtCsAiHU+ptXaztXaDtfYza20ecDb+YDwRwBhznTFmlTGm2BhTaIx50BiTUL3vJGA2kFyrNj2tel+0MeavxpgN1ecuqj5eRFpJAVikE7DWzgNWAD+t3lQFTAGOAi4FTgb+Wr1vAXANsBt/TToNuKd632zgOOB8IAd4AXjLGHO44xch0sEYa63bZRCRIDHGzAG6WGt/0si+54Eca23/RvadCzxkre1e/XwicL+1tkutYw4DvgJ81tqNtba/C3xqrc0N6sWIdHC6HyTSeRjAAhhjRgG5QH8gCf93QawxJt5aW9zE+cdUp7HWGFN7ewyw3alCi3RUCsAinceRwDpjzCHAG8A/gT8CPwA/BmYBUc2c7wEqgSHV/9a2N+ilFengFIBFOgFjzMnAQOA+YCj+z/711tqq6v3n1TulDIiot21Z9bae1tqPnS2xSMenACzS8cQYY1LxB8tewOnAVOB14En8gTgSuMoY8xr+TlW/rZdGAZBgjDkFf+etEmvtWmPMM8CTxpjr8Qfk7vg7cK2y1r7h+JWJdCDqBS3S8ZwObMIfRN8CRuHv8Xy2tbbSWrscuA64GVgNXIQ/QAdYaxfgb6L+F7ANuKl612X4g/jfgTXAq8CxQKGTFyTSEakXtIiIiAtUAxYREXGBArCIiIgLFIBFRERcoAAsIiLiAgVgERERFygAi4iIuEABWERExAUKwCIiIi5QABYREXGBArCIiIgLFIBFRERcoAAsIiLiAgVgERERFygAi4iIuEABWERExAUKwCIiIi5QABYREXGBArCIiIgLFIBFRERcoAAsIiLiAgVgERERFygAi4iIuOD/A2ltNO3ArFsIAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -1579,8 +1580,7 @@ "r2 = r_value * r_value \n", "y_intercept = CURRENT_YEAR - (0 - intercept)/slope\n", "p_sig = \"\"\n", - "p_thresh = 0.05 \n", - "if p_value < p_thresh:\n", + "if p_value < ALPHA:\n", " p_sig = \"*\"\n", "\n", "sub_per_year = SEQ_LEN * slope\n", @@ -1665,7 +1665,7 @@ }, { "cell_type": "markdown", - "id": "adult-trace", + "id": "prerequisite-handling", "metadata": {}, "source": [ "## Clades" @@ -1673,31 +1673,21 @@ }, { "cell_type": "code", - "execution_count": 94, - "id": "adjacent-frost", + "execution_count": 162, + "id": "violent-setting", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Branch: 0.PRE\n", - "Branch: 0.ANT4\n", - "Branch: 0.PE\n", - "Branch: 0.ANT\n", "Branch: 1.PRE\n", - "Branch: 1.ANT\n", - "Branch: 1.IN\n", - "Branch: 1.ORI\n", - "Branch: 2.ANT\n", - "Branch: 2.MED\n", - "Branch: 3.ANT\n", - "Branch: 4.ANT\n" + "5.852696673114088e-08 6.456200500617077e-08 7.059704328120065e-08\n" ] }, { "data": { - 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\n", 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\n", 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" ] @@ -1731,13 +1721,16 @@ "i_col = 0\n", "\n", "for label in BRANCH_LIST:\n", + " if label != \"1.PRE\": continue\n", " print(\"Branch:\", label)\n", " \n", " # ----------------------------------------\n", " # Setup axis\n", " ax = axes[i_row][i_col]\n", "\n", - " df = metadata_gdf[metadata_gdf[\"branch_minor\"].isin(BRANCH_LIST[label])] \n", + " df = copy.copy(metadata_gdf[metadata_gdf[\"branch_minor\"].isin(BRANCH_LIST[label])])\n", + " x = list(df[reg_x])\n", + " y = list(df[reg_y])\n", " # If no records for this branch, continue\n", " if len(df) < 2: \n", " # Update axis\n", @@ -1747,19 +1740,82 @@ " else:\n", " i_col += 1 \n", " continue\n", + "\n", + " \"\"\"\n", + " # -----------------------\n", + " # Detect Outliers\n", + " x = list(df[reg_x])\n", + " y = list(df[reg_y])\n", + " fit = smapi.OLS(x,y).fit()\n", + " test = fit.outlier_test()\n", + " outliers = ((x[i],y[i]) for i,t in enumerate(test) if t[2] < 0.05)\n", + " for o in outliers:\n", + " ox = o[0]\n", + " oy = o[1]\n", + " # Find the matching sample\n", + " sample = df[(df[reg_x] == ox) & (df[reg_y] == oy)].index[0]\n", + " df.drop(index=sample, inplace=True)\n", + " \"\"\"\n", + " \n", " # -----------------------\n", " # Regression\n", - " slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(list(df[reg_x]),list(df[reg_y]))\n", - " r2 = r_value * r_value \n", + " #slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x,y)\n", + " linear_reg = scipy.stats.linregress(x,y)\n", + " r_value = linear_reg.rvalue\n", + " slope = linear_reg.slope\n", + " intercept = linear_reg.intercept\n", + " p_value = linear_reg.pvalue\n", + " std_err = linear_reg.stderr\n", + " \n", + " r2 = r_value**2 \n", " y_intercept = CURRENT_YEAR - (0 - intercept)/slope\n", " p_sig = \"\"\n", - " p_thresh = 0.05 \n", - " if p_value < p_thresh:\n", + " if p_value < ALPHA:\n", " p_sig = \"*\"\n", - "\n", + " \n", " sub_per_year = SEQ_LEN * slope\n", " year_per_sub = 1 / sub_per_year \n", " y_intercept_calendar = CURRENT_YEAR - y_intercept\n", + " \n", + " print(slope - std_err, slope, slope + std_err)\n", + "\n", + " \"\"\"\n", + " # -----------------------\n", + " # Resampling\n", + " reg_df = data_df = pd.DataFrame({'x': x, 'y': y})\n", + " ols_model = sm.ols(formula = 'y ~ x', data=reg_df)\n", + " results = ols_model.fit()\n", + " y_pred = results.predict(reg_df['x'])\n", + " resids = results.resid\n", + " \n", + " boot_slopes = []\n", + " boot_interc = []\n", + " boot_df = pd.DataFrame({'x': [], 'y': []})\n", + " n_boots = 100\n", + " for _ in range(n_boots):\n", + " # create a sampling of the residuals with replacement\n", + " boot_resids = np.random.choice(resids, len(x), replace=True)\n", + " y_temp = [y_pred_i + resid_i for y_pred_i, resid_i in zip(y_pred, boot_resids)]\n", + "\n", + " sample_df = pd.DataFrame({'x': x, 'y': y_temp})\n", + " # Fit a linear regression\n", + " ols_model_temp = sm.ols(formula = 'y ~ x', data=sample_df)\n", + " results_temp = ols_model_temp.fit()\n", + "\n", + " # get coefficients\n", + " boot_interc.append(results_temp.params[0])\n", + " boot_slopes.append(results_temp.params[1])\n", + "\n", + " # plot a greyed out line\n", + " y_pred_temp = ols_model_temp.fit().predict(sample_df['x'])\n", + " row = pd.DataFrame({'x': x, 'y': y_pred_temp})\n", + " boot_df = boot_df.append(row) \n", + " \n", + " \n", + " #sns.displot(boot_slopes)\n", + " #sns.displot(boot_interc) \n", + " #ax.plot(boot_df['x'], boot_df['y'], color='grey', alpha=0.25) \n", + " \"\"\" \n", "\n", " sns.regplot(\n", " ax=ax,\n", @@ -1780,12 +1836,11 @@ " )\n", " )\n", "\n", - " \n", " ax.errorbar(\n", " data=df, \n", - " x=\"date_mean\",\n", - " y=\"rtt_dist\", \n", - " xerr=\"date_err\",\n", + " x=x, \n", + " y=y, \n", + " xerr=list(df[reg_err]), \n", " yerr=None,\n", " ls='none',\n", " c = df[\"branch_major_color\"][0], \n", @@ -1798,14 +1853,14 @@ " sns.scatterplot(\n", " ax=ax,\n", " data=df, \n", - " x=\"date_mean\",\n", + " x=x, \n", + " y=y, \n", " s=10,\n", - " y=\"rtt_dist\", \n", " c = df[\"branch_major_color\"], \n", " ec = \"black\",\n", " alpha=0.75,\n", " zorder=2,\n", - " ) \n", + " )\n", " \n", " # Set xlimits\n", " xlim = ax.get_xlim()\n", @@ -1855,7 +1910,7 @@ }, { "cell_type": "markdown", - "id": "turkish-paragraph", + "id": "genetic-upgrade", "metadata": {}, "source": [ "---\n", @@ -1864,19 +1919,262 @@ }, { "cell_type": "markdown", - "id": "aboriginal-caution", + "id": "communist-european", "metadata": {}, "source": [ - "## All" + "## Calculate" ] }, { "cell_type": "code", "execution_count": null, - "id": "reported-victim", + "id": "sufficient-aerospace", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "ibd_dict = {}\n", + "\n", + "for label in BRANCH_LIST:\n", + " print(\"Label:\", label) \n", + " ibd_dict[label] = {\"geo_dist\" : [], \"genetic_dist\" : []}\n", + " df = metadata_gdf[metadata_gdf[\"branch_minor\"].isin(BRANCH_LIST[label])] \n", + " \n", + " i_compare = 0\n", + " i = 0\n", + " for sample1 in df.index:\n", + " geom1 = df[\"geometry\"][sample1]\n", + " coord1 = (geom1.y, geom1.x) \n", + " for sample2 in df.index[i_compare:]:\n", + " if sample1 == sample2: continue\n", + " geom2 = df[\"geometry\"][sample2]\n", + " coord2 = (geom2.y, geom2.x)\n", + " geo_dist = geopy.distance.great_circle(coord1, coord2).km\n", + " genetic_dist = divtree.distance(sample1, sample2)\n", + "\n", + " ibd_dict[label][\"geo_dist\"].append(geo_dist)\n", + " ibd_dict[label][\"genetic_dist\"].append(genetic_dist)\n", + " i_compare += 1" + ] + }, + { + "cell_type": "markdown", + "id": "informal-program", + "metadata": {}, + "source": [ + "# All" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "statistical-rainbow", + "metadata": {}, + "outputs": [], + "source": [ + "# ---------------------------------------\n", + "# PLOT SETUP\n", + "TARGET_RES = [480, 480]\n", + "DPI=200\n", + "FIGSIZE=[TARGET_RES[0] / DPI, TARGET_RES[1] / DPI]\n", + "FONTSIZE=5\n", + "plt.rc('font', size=FONTSIZE)\n", + "\n", + "geo_dist_all = []\n", + "genetic_dist_all = []\n", + "\n", + "fig, ax = plt.subplots(1,figsize=FIGSIZE, dpi=DPI)\n", + "\n", + "for branch in ibd_dict:\n", + " geo_dist_all = geo_dist_all + ibd_dict[branch][\"geo_dist\"]\n", + " genetic_dist_all = genetic_dist_all + ibd_dict[branch][\"genetic_dist\"]\n", + "\n", + "sns.regplot(\n", + " ax=ax,\n", + " x=geo_dist_all,\n", + " y=genetic_dist_all,\n", + " ci=95,\n", + " scatter_kws={\"s\": 0},\n", + " color=\"grey\",\n", + " line_kws={\"linewidth\":0.5},\n", + " label=(\n", + " \" R²: {}\".format(round(r2,2))\n", + " + \"\\n p: {:.2e}{}\".format(p_value, p_sig) \n", + " )\n", + ")\n", + " \n", + "sns.scatterplot(\n", + " ax=ax,\n", + " x=geo_dist_all,\n", + " y=genetic_dist_all,\n", + " s=1,\n", + " ec = \"white\", \n", + " color=\"black\",\n", + " alpha=0.75,\n", + " zorder=2,\n", + ")\n", + "\n", + "# Format and position legend\n", + "legend = ax.legend(\n", + " borderpad=0.8, \n", + " handletextpad=-2, \n", + " edgecolor=\"black\", \n", + " #bbox_to_anchor=(0.5, -0.30), \n", + " #loc='center',\n", + " fontsize=FONTSIZE,\n", + ")\n", + "frame = legend.get_frame().set_linewidth(0.5)\n", + "\n", + "ax.set_ylabel(\"Genetic Distance (subs / site)\")\n", + "ax.set_xlabel(\"Great Circle Distance (km)\")\n", + "ax.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0)) \n", + "\n", + "out_path = os.path.join(out_dir, \"ibd_all\")\n", + "plt.savefig(out_path + \".png\", bbox_inches=\"tight\")\n", + "plt.savefig(out_path + \".svg\", bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "id": "biological-remainder", + "metadata": {}, + "source": [ + "## Clades" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cognitive-hospital", + "metadata": {}, + "outputs": [], + "source": [ + "# ---------------------------------------\n", + "# PLOT SETUP\n", + "TARGET_RES = [1280, 1280]\n", + "DPI=200\n", + "FIGSIZE=[TARGET_RES[0] / DPI, TARGET_RES[1] / DPI]\n", + "FONTSIZE=5\n", + "plt.rc('font', size=FONTSIZE)\n", + "\n", + "nrow=3\n", + "ncol=4\n", + "\n", + "fig, axes = plt.subplots(nrow,ncol,figsize=FIGSIZE, dpi=DPI)\n", + "fig.subplots_adjust(wspace=0.35, hspace=0.75)\n", + "\n", + "\n", + "i_row = 0\n", + "i_col = 0\n", + "\n", + "for label in BRANCH_LIST:\n", + " # ----------------------------------------\n", + " # Setup axis\n", + " ax = axes[i_row][i_col]\n", + "\n", + " df = metadata_gdf[metadata_gdf[\"branch_minor\"].isin(BRANCH_LIST[label])] \n", + " \n", + " if len(df) < 3:\n", + " # Update axis\n", + " if i_col == ncol - 1:\n", + " i_col = 0\n", + " i_row += 1\n", + " else:\n", + " i_col += 1 \n", + " continue\n", + " \n", + " # -----------------------\n", + " # Regression\n", + " slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(ibd_dict[label][\"geo_dist\"],ibd_dict[label][\"genetic_dist\"])\n", + " r2 = r_value * r_value \n", + " y_intercept = CURRENT_YEAR - (0 - intercept)/slope\n", + " p_sig = \"\" \n", + " if p_value < ALPHA:\n", + " p_sig = \"*\"\n", + " \n", + " mut_per_year = SEQ_LEN * slope\n", + " year_per_mut = 1 / mut_per_year\n", + " \n", + " reg_dict = {\n", + " \"slope\": slope,\n", + " \"x_intercept\" : intercept,\n", + " \"y_intercept\" : y_intercept,\n", + " \"p_value\" : p_value,\n", + " \"p_sig\" : p_sig,\n", + " \"year_per_mut\" : year_per_mut\n", + " }\n", + " \n", + " sns.regplot(\n", + " ax=ax,\n", + " x=ibd_dict[label][\"geo_dist\"],\n", + " y=ibd_dict[label][\"genetic_dist\"],\n", + " ci=95,\n", + " scatter_kws={\"s\": 0},\n", + " color=\"grey\",\n", + " line_kws={\"linewidth\":0.5},\n", + " label=(\n", + " \" R²: {}\".format(round(r2,2))\n", + " + \"\\n p: {:.2e}{}\".format(p_value, p_sig) \n", + " )\n", + " )\n", + "\n", + " sns.scatterplot(\n", + " ax=ax,\n", + " x=ibd_dict[label][\"geo_dist\"],\n", + " y=ibd_dict[label][\"genetic_dist\"],\n", + " s=10,\n", + " ec = \"black\",\n", + " color = df[\"branch_major_color\"][0], \n", + " alpha=0.75,\n", + " zorder=2,\n", + " )\n", + " \n", + " # Format and position legend\n", + " legend = ax.legend(\n", + " borderpad=0.8, \n", + " handletextpad=-2, \n", + " edgecolor=\"black\", \n", + " bbox_to_anchor=(0.5, -0.40), \n", + " loc='center',\n", + " fontsize=FONTSIZE * 0.80,\n", + " )\n", + " frame = legend.get_frame().set_linewidth(0.5) \n", + " \n", + " # Set xlimits\n", + " xlim = ax.get_xlim()\n", + " if xlim[1] > 20000:\n", + " xbuff = 5000 \n", + " elif xlim[1] > 2000:\n", + " xbuff = 500\n", + " elif xlim[1] > 200:\n", + " xbuff = 50\n", + " ax.set_xlim(0-xbuff, xlim[1] + xbuff)\n", + " \n", + " # Format axis\n", + " ax.set_title(label)\n", + "\n", + " if i_col == 0:\n", + " ax.set_ylabel(\"Genetic Distance (subs / site)\")\n", + " else:\n", + " ax.set_ylabel(\"\")\n", + " ax.set_xlabel(\"Great Circle Distance (km)\")\n", + " ax.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0)) \n", + " \n", + " for spine in ax.spines:\n", + " ax.spines[spine].set_linewidth(0.5) \n", + " \n", + "\n", + " # Update axis\n", + " if i_col == ncol - 1:\n", + " i_col = 0\n", + " i_row += 1\n", + " else:\n", + " i_col += 1\n", + "\n", + " \n", + "out_path = os.path.join(out_dir, \"ibd_clades\")\n", + "plt.savefig(out_path + \".png\", bbox_inches=\"tight\")\n", + "plt.savefig(out_path + \".svg\", bbox_inches=\"tight\")" + ] } ], "metadata": { diff --git a/workflow/scripts/project_load.sh b/workflow/scripts/project_load.sh index ea10e9b4..272c2589 100755 --- a/workflow/scripts/project_load.sh +++ b/workflow/scripts/project_load.sh @@ -4,6 +4,10 @@ RESULTS_DIR=$1 BACKUP_DIR=$2 MODE=$3 +# Credits: @Dave Dopson +# https://stackoverflow.com/questions/59895/how-can-i-get-the-source-directory-of-a-bash-script-from-within-the-script-itsel +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + # All directories EXCLUDE_DIR=( beast @@ -15,8 +19,13 @@ then exit 1; fi +echo -e "Cleaning directory ${RESULTS_DIR} before loading..." +echo +#${SCRIPT_DIR}/project_clean.sh ${RESULTS_DIR} + echo -e "\nLoading project ${BACKUP_DIR} to ${RESULTS_DIR}:" echo + mkdir -p ${RESULTS_DIR} for dir in `ls -d $BACKUP_DIR/*`;