Analyze-mtt-permutation-test-results.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyze data from mtt permutation tests \n",
"\n",
"June 13, 2019 \n",
"\n",
"I ran 10 mtt datasets that had been subsampled to have equal numbers of community and non-community member tips in the tree. I want to compare the trees and also the parameter estimates."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The rpy2.ipython extension is already loaded. To reload it, use:\n",
" %reload_ext rpy2.ipython\n"
]
}
],
"source": [
"import sys, subprocess, glob, os, shutil, re, importlib, Bio, datetime\n",
"from subprocess import call\n",
"from Bio import SeqIO\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import rpy2\n",
"import seaborn as sns\n",
"%load_ext rpy2.ipython\n",
"\n",
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"from matplotlib import pyplot as plt\n",
"import matplotlib.patheffects as path_effects\n",
"import matplotlib.lines as mlines\n",
"from matplotlib.font_manager import FontProperties\n",
"import matplotlib.colors as clr\n",
"import textwrap as textwrap\n",
"from textwrap import wrap\n",
"\n",
"import numpy as np\n",
"from scipy.special import binom\n",
"\n",
"import imp\n",
"bt = imp.load_source('baltic', '../../baltic/baltic/baltic.py')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"mpl.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
"mpl.rcParams['font.weight']=300\n",
"mpl.rcParams['axes.labelweight']=300"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, in the output directory, rename the files and run tree annotater: \n",
"\n",
"`for f in *; do mv \"$f\" \"${f/mumps-wa-3-clades-mtt-2020-01-21-/}\"; done`\n",
"\n",
"then \n",
"\n",
"`for f in *.typedNode.trees; do /Applications/BEAST\\ 2.6.2/bin/treeannotator -heights 'mean' -burnin 0 $f $f.mcc.tree; done`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summarize the subsampled mtt mcc trees"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def generate_tree_object(tree_path):\n",
"\n",
" # load mtt trees\n",
" tree = bt.loadNexus(tree_path)\n",
" \n",
" # pull out the name of the tree file \n",
" filename = tree_path.replace(\".mcc.tree\",\"-community_member_tree.pdf\")\n",
" \n",
" return(tree,filename)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def return_metadata_dictionaries(tree):\n",
" # parse leaf and node information into a dictionary for plotting\n",
" metadata = {}\n",
" node_types = {}\n",
" node_probs = {}\n",
"\n",
" for k in tree.Objects: ## iterate over objects in tree\n",
" if isinstance(k, bt.leaf) == True or k.branchType=='leaf':\n",
" community_status = k.traits['type']\n",
" vaccine_status = k.name.split(\"|\")[7]\n",
" metadata[k] = {}\n",
" metadata[k][\"community_status\"] = community_status\n",
" metadata[k][\"vaccine_status\"] = vaccine_status\n",
"\n",
" # parse the nodes now and pick out the type trait\n",
" if isinstance(k, bt.node) == True or k.branchType=='node':\n",
" node_community_status = k.traits[\"type\"]\n",
" node_types[k] = {}\n",
" node_types[k][\"node_community_status\"] = node_community_status\n",
" \n",
" # if there is a set of probabilities, assign them properly\n",
" if len(k.traits[\"type.set.prob\"]) > 1: \n",
" prob_node_Marshallese = k.traits[\"type.set.prob\"][0]\n",
" else:\n",
" if node_community_status == \"Marshallese\":\n",
" prob_node_Marshallese = 1.0\n",
" else: \n",
" prob_node_Marshallese = 0.0\n",
" \n",
" node_probs[k] = {}\n",
" node_probs[k] = prob_node_Marshallese\n",
" \n",
" \n",
" return(metadata, node_types, node_probs)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def generate_node_probs_df(node_probs,df,count):\n",
" x = pd.DataFrame.from_dict(node_probs, orient=\"index\")\n",
" x.reset_index(inplace=True)\n",
" x['iteration'] = str(count)\n",
" df = df.append(x)\n",
" return(df) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def plot_mtt_subtrees_together(iterations_data_dict, width, height):\n",
" \n",
" # to add back in grey: \"#808080\",\n",
" #make color dictionary to set colors based on Marshallese descent; also make a labels dictionary for legend\n",
" colors = {'Marshallese':Marshallese_color, 'Not_Marshallese':Not_Marshallese_color} #blue and orange 0f4296, #2C7BB6, #fcd64e, #F78536\n",
" labels = {'Marshallese':'Marshallese', 'Not_Marshallese':'Not Marshallese'}\n",
" node_colors2 = clr.LinearSegmentedColormap.from_list('custom ', [Not_Marshallese_color,Marshallese_color], N=256)\n",
" branchWidth=2.5 ## default branch width\n",
" tipSize = 45 \n",
" \n",
" # to add back in light grey in between color bar\n",
" \n",
" # specify a plotting grid and assign axes\n",
" fig,((ax1),(ax2)) = plt.subplots(1, 2, gridspec_kw={'hspace':0.005, 'wspace':0.2}, figsize=(width, height),facecolor='w')\n",
" \n",
" axes = [ax1,ax2] \n",
" axes_dict = {ax1:\"2bc\",ax2:\"3ac\"}\n",
" \n",
" # set up the grey areas and dashed lines\n",
" for a in axes:\n",
" title = \"subsampled dataset \" + str(axes_dict[a]).replace(\"3ac\",\"2\").replace(\"2bc\",\"1\")\n",
" cumulative_y = 0\n",
" \n",
" # this sets the grey vertical shading \n",
" xDates = ['2016-03-01','2016-05-01','2016-07-01','2016-09-01','2016-11-01','2017-01-01','2017-03-01','2017-05-01','2017-07-01','2017-09-01'] \n",
"# [a.axvspan(bt.decimalDate(xDates[x]),bt.decimalDate(xDates[x])+1/float(4),facecolor='k',edgecolor='none',alpha=0.04) for x in range(0,len(xDates))]\n",
"\n",
" # this sets the vertical dashed lines on the tree\n",
" dates = [2016.33,2016.66,2017,2017.33] \n",
"# [a.axvline(i,ls='--',lw=2,color='grey',zorder=0) for i in dates]\n",
" \n",
" # loop through subtrees \n",
" for i in iterations_data_dict[axes_dict[a]]:\n",
" tree = i['tree']\n",
" metadata = i['metadata']\n",
" node_probs = i['node_probs']\n",
"\n",
" # k objects are tips, nodes, branches\n",
" for k in tree.Objects: ## iterate over objects in tree\n",
" x=k.absoluteTime \n",
" y=k.y + cumulative_y\n",
"\n",
" xp=k.parent.absoluteTime \n",
" if x==None: ## matplotlib won't plot Nones, like root\n",
" x=0.0\n",
" if xp==None:\n",
" xp=x\n",
"\n",
" if k.branchType=='leaf': \n",
" c=colors[metadata[k][\"community_status\"]]\n",
" s=tipSize \n",
" a.scatter(x,y,s=s,facecolor=c,edgecolor='black',zorder=11) ## plot circle for every tip\n",
"\n",
" elif k.branchType=='node': \n",
" c=node_colors2(node_probs[k])\n",
" # this is the vertical lines\n",
" a.plot([x,x],[k.children[-1].y+cumulative_y,k.children[0].y+cumulative_y],lw=branchWidth,color=c,ls='-',zorder=9) #color=node_colors[node_types[k][\"node_community_status\"]]\n",
" \n",
" # horizontal lines\n",
" a.plot([xp,x],[y,y],lw=branchWidth,color=c,ls='-',zorder=9)\n",
" \n",
" cumulative_y+=tree.ySpan+5 ## increment y displacement\n",
"\n",
" # set axis limits, remove border lines \n",
" a.spines['left'].set_visible(False)\n",
" a.spines['right'].set_visible(False)\n",
" a.spines['top'].set_visible(False)\n",
" #a.spines['bottom'].set_visible(False)\n",
"\n",
" a.set_ylim(-1,cumulative_y)\n",
" a.set_xlim(2016.33,2017.66)\n",
" a.tick_params(axis='y',labelsize=0,size=0)\n",
" a.tick_params(axis='x',labelsize=14,size=5, width=2,color='grey')\n",
" a.set_xticks([2016.33,2016.495,2016.66,2016.83,2017,2017.165,2017.33,2017.495,2017.66])\n",
" a.set_xticklabels(['May\\n2016',\"\",'Sep\\n2016','','Jan\\n2017','','May\\n2017','','Sep\\n2017'])\n",
" a.set_yticklabels([])\n",
" a.set_title(title, size=18)\n",
"\n",
" # put in color bar\n",
" # these coordinates are right, bottom, width, length\n",
" ax3 = fig.add_axes([0.0, 0.30, 0.05, 0.25]) \n",
" mpl.colorbar.ColorbarBase(ax3, cmap=node_colors2, ticks=np.linspace(0.0,1.0,5))\n",
" ax3.tick_params(size=2,labelsize=18)\n",
" ax3.set_title('posterior probability\\nnode labelled\\nMarshallese\\n', size=18)\n",
" #ax2.set_label_coords(-0.1,1.02)\n",
"\n",
"# mpl.colorbar.ColorbarBase(ax3, cmap=node_colors2, ticks=np.linspace(0.0,1.0,5))\n",
"# ax3.tick_params(size=2,labelsize=24)\n",
"# ax3.set_title('\\n\\nposterior probability\\ncommunity member\\n', size=18)\n",
" \n",
" # add more space between the subplots\n",
" fig.subplots_adjust(hspace=0.2, wspace=0.2)\n",
" plt.savefig(combined_figure_filename, bbox_inches = 'tight',pad_inches = 0)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run and plot trees"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# read in files and run \n",
"directory = \"/Users/lmoncla/Documents/Mumps/beast/mtt/2020-04-02-hot-chain/nicola-priors-limit-50/subsampled/try2-2020-07-20/\"\n",
"figures_directory = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/individual-PDFs/\"\n",
"combined_figure_filename = figures_directory + \"combined_subsampled_plots_2020-09-15.pdf\"\n",
"Not_Marshallese_color = '#012E74'\n",
"Marshallese_color = '#80AAC9'"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": false
},
"outputs": [
{
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\n",
"text/plain": [
"<Figure size 720x864 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# read in files and run; this will generate multitype tree plots for each iteration and generate a dataframe that \n",
"# can be used to look at and plot the distribution of posterior probabilities that a node is community, for each node \n",
"# in each tree \n",
"df = pd.DataFrame()\n",
"count = 0\n",
"axes_list = []\n",
"iterations_data_dict = {}\n",
"\n",
"for f in glob.glob(directory + \"*.mcc.tree\"):\n",
" \n",
" clade = int(f.split(\"/\")[-1].split(\"-\")[10].replace(\".typedNode.trees.mcc.tree\",\"\"))\n",
" if clade == 69:\n",
" list_location = 2\n",
" elif clade == 10: \n",
" list_location = 1\n",
" else:\n",
" list_location = 0\n",
" \n",
" iteration = f.split(\"/\")[-1].split(\"-\")[8]\n",
" if iteration not in iterations_data_dict:\n",
" iterations_data_dict[iteration] = [\"\",\"\",\"\"]\n",
" \n",
" tree_path = f\n",
" tree,filename = generate_tree_object(tree_path)\n",
" metadata,node_types,node_probs = return_metadata_dictionaries(tree)\n",
" \n",
" df = generate_node_probs_df(node_probs, df, iteration)\n",
" #plot_tree(tree, metadata, node_types, node_probs, filename)\n",
"\n",
" subtree_dict = {\"tree\":tree,\"metadata\":metadata,\"node_types\":node_types,\"node_probs\":node_probs,\n",
" \"filename\":filename}\n",
" iterations_data_dict[iteration][list_location] = subtree_dict\n",
" \n",
" #plot_tree(tree, metadata, node_types, node_probs, filename)\n",
" \n",
"plot_mtt_subtrees_together(iterations_data_dict, 10,12) # width, height"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot the posterior probabilities of internal nodes"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>node</th>\n",
" <th>pp_community</th>\n",
" <th>iteration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td><baltic.node object at 0x7face2cc2908></td>\n",
" <td>0.993154</td>\n",
" <td>3ac</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td><baltic.node object at 0x7face2cc2b38></td>\n",
" <td>0.997436</td>\n",
" <td>3ac</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td><baltic.node object at 0x7face2cc29e8></td>\n",
" <td>1.000000</td>\n",
" <td>3ac</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td><baltic.node object at 0x7face2cc2978></td>\n",
" <td>1.000000</td>\n",
" <td>3ac</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td><baltic.node object at 0x7face2cc2a58></td>\n",
" <td>1.000000</td>\n",
" <td>3ac</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" node pp_community iteration\n",
"0 <baltic.node object at 0x7face2cc2908> 0.993154 3ac\n",
"1 <baltic.node object at 0x7face2cc2b38> 0.997436 3ac\n",
"2 <baltic.node object at 0x7face2cc29e8> 1.000000 3ac\n",
"3 <baltic.node object at 0x7face2cc2978> 1.000000 3ac\n",
"4 <baltic.node object at 0x7face2cc2a58> 1.000000 3ac"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns = ['node','pp_community','iteration']\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# row1 = df[df['iteration'].isin([\"1\",\"2\",\"3\",\"4\",\"5\"])]\n",
"# row1['row'] = '1'\n",
"# row2 = df[~df['iteration'].isin([\"1\",\"2\",\"3\",\"4\",\"5\"])]\n",
"# row2['row'] = '2'\n",
"# row3 = df[df['iteration'].isin([\"11\",\"12\",\"13\",\"14\",\"15\"])]\n",
"# row3['row'] = '3'\n",
"# row4 = df[~df['iteration'].isin([\"11\",\"12\",\"13\",\"14\",\"15\"])]\n",
"# row4['row'] = '4'\n",
"\n",
"# df2 = row1.append(row2)\n",
"# df2 = df2.append(row3)\n",
"# df2 = df2.append(row4)\n",
"# df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def relabel_iterations(iteration):\n",
" if iteration == \"3ac\":\n",
" label = 'subsampled\\ndataset 2'\n",
" elif iteration == \"2bc\":\n",
" label = 'subsampled\\ndataset 1'\n",
" return(label)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>node</th>\n",
" <th>pp_community</th>\n",
" <th>iteration</th>\n",
" <th>iteration2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td><baltic.node object at 0x7face2cc2908></td>\n",
" <td>0.993154</td>\n",
" <td>3ac</td>\n",
" <td>subsampled\\ndataset 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td><baltic.node object at 0x7face2cc2b38></td>\n",
" <td>0.997436</td>\n",
" <td>3ac</td>\n",
" <td>subsampled\\ndataset 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td><baltic.node object at 0x7face2cc29e8></td>\n",
" <td>1.000000</td>\n",
" <td>3ac</td>\n",
" <td>subsampled\\ndataset 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td><baltic.node object at 0x7face2cc2978></td>\n",
" <td>1.000000</td>\n",
" <td>3ac</td>\n",
" <td>subsampled\\ndataset 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td><baltic.node object at 0x7face2cc2a58></td>\n",
" <td>1.000000</td>\n",
" <td>3ac</td>\n",
" <td>subsampled\\ndataset 2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" node pp_community iteration \\\n",
"0 <baltic.node object at 0x7face2cc2908> 0.993154 3ac \n",
"1 <baltic.node object at 0x7face2cc2b38> 0.997436 3ac \n",
"2 <baltic.node object at 0x7face2cc29e8> 1.000000 3ac \n",
"3 <baltic.node object at 0x7face2cc2978> 1.000000 3ac \n",
"4 <baltic.node object at 0x7face2cc2a58> 1.000000 3ac \n",
"\n",
" iteration2 \n",
"0 subsampled\\ndataset 2 \n",
"1 subsampled\\ndataset 2 \n",
"2 subsampled\\ndataset 2 \n",
"3 subsampled\\ndataset 2 \n",
"4 subsampled\\ndataset 2 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['iteration2'] = df['iteration'].apply(relabel_iterations)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 1000 -h 500 -u px -i df,figures_directory # this sets the size of the plot...otherwise, it will go off the page\n",
"require(ggplot2)\n",
"library(ggplot2)\n",
"library(gridExtra)\n",
"library(grid)\n",
"library(RColorBrewer)\n",
"darkcols <- brewer.pal(10, \"Dark2\")\n",
"\n",
"# df$iteration = as.numeric(df$iteration)\n",
"# df$color = as.character(df$iteration)\n",
"# df$node = as.character(df$node)\n",
"\n",
"p <- ggplot(data=df, aes(x=iteration2, y=pp_community, color=iteration)) + \n",
" #facet_wrap(~iteration)+\n",
" geom_jitter(width = 0.3, alpha=0.7)+\n",
" #geom_vline(xintercept=vlines1, size=0.8, linetype='dashed', color = '#808080')+\n",
" labs(x=NULL,y=\"posterior probability node\\nlabelled Marshallese\")+\n",
" theme(plot.title = element_text(size=20, hjust=0.5))+\n",
" scale_colour_manual(values=c(\"#4C9C86\",\"#271B9C\"), guide=FALSE)+\n",
" #scale_x_continuous(limits=c(0.75,5.25), breaks=seq(1,5,1))+\n",
" scale_y_continuous(limits=c(0,1))+\n",
" theme(panel.grid.major=element_line(colour=NA,size=NA))+\n",
" theme(panel.grid.minor=element_line(colour=NA,size=NA))+ \n",
" theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
" theme(axis.line.x=element_line(colour=\"black\"))+\n",
" theme(axis.line.y=element_line(colour=\"black\"))+\n",
" theme(strip.text.x=element_text(size=15))+\n",
" theme(axis.title.y=element_text(size=15, vjust=8))+\n",
" theme(axis.title.x=element_text(size=15))+\n",
" theme(axis.text=element_text(size=15, colour=\"black\"))+\n",
" theme(legend.text=element_text(size=15))+\n",
" theme(legend.title=element_blank())+\n",
" theme(panel.margin=unit(1, \"lines\"))+\n",
" theme(plot.margin=unit(c(1.0,0.5,0.5,1.0),\"cm\"))+\n",
" theme(legend.key.size=unit(0.7, \"cm\"))+\n",
" theme(panel.background=element_rect(fill=NA))+\n",
" theme(legend.key=element_rect(fill=NA))\n",
"\n",
"ggsave(\"node_pp_community-2020-09-15.pdf\", p, width = 7, height = 4, path=figures_directory)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TableGrob (5 x 2) \"arrange\": 6 grobs\n",
" z cells name grob\n",
"1 1 (1-1,2-2) arrange gtable[layout]\n",
"2 2 (2-2,2-2) arrange gtable[layout]\n",
"3 3 (3-3,2-2) arrange gtable[layout]\n",
"4 4 (4-4,2-2) arrange gtable[layout]\n",
"5 5 (5-5,2-2) arrange text[GRID.text.315]\n",
"6 6 (1-5,1-1) arrange text[GRID.text.316]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 1000 -h 500 -u px -i df,directory # this sets the size of the plot...otherwise, it will go off the page\n",
"require(ggplot2)\n",
"library(ggplot2)\n",
"library(gridExtra)\n",
"library(grid)\n",
"library(RColorBrewer)\n",
"darkcols <- brewer.pal(10, \"Dark2\")\n",
"\n",
"df$iteration = as.numeric(df$iteration)\n",
"df$color = as.character(df$iteration)\n",
"df$node = as.character(df$node)\n",
"\n",
"vlines = c(1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,11.5,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5)\n",
"vlines1 = c(1.5,2.5,3.5,4.5)\n",
"vlines2 = c(6.5,7.5,8.5,9.5)\n",
"vlines3 = c(11.5,12.5,13.5,14.5)\n",
"vlines4 = c(16.5,17.5,18.5,19.5)\n",
"\n",
"df1 <- subset(df, iteration <= 5)\n",
"df2 <- subset(df, iteration > 5 & iteration <= 10)\n",
"df3 <- subset(df, iteration > 10 & iteration <= 15)\n",
"df4 <- subset(df, iteration > 15)\n",
"\n",
"p1 <- ggplot(data=df1, aes(x=iteration, y=pp_community, color=color)) + \n",
" geom_jitter(width = 0.3, alpha=0.7)+\n",
" geom_vline(xintercept=vlines1, size=0.8, linetype='dashed', color = '#808080')+\n",
" labs(x=NULL,y=NULL)+\n",
" theme(plot.title = element_text(size=20, hjust=0.5))+\n",
" scale_x_continuous(limits=c(0.75,5.25), breaks=seq(1,5,1))+\n",
" scale_y_continuous(limits=c(0,1))+\n",
" scale_colour_manual(values = (brewer.pal(10, \"Paired\")), guide=FALSE)+\n",
" theme(panel.grid.major=element_line(colour=NA,size=NA))+\n",
" theme(panel.grid.minor=element_line(colour=NA,size=NA))+ \n",
" theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
" theme(axis.line.x=element_line(colour=\"black\"))+\n",
" theme(axis.line.y=element_line(colour=\"black\"))+\n",
" theme(strip.text.x=element_text(size=16))+\n",
" theme(axis.title.y=element_text(size=16, vjust=8))+\n",
" theme(axis.title.x=element_text(size=16))+\n",
" theme(axis.text=element_text(size=16, colour=\"black\"))+\n",
" theme(legend.text=element_text(size=16))+\n",
" theme(legend.title=element_blank())+\n",
" theme(panel.margin=unit(1, \"lines\"))+\n",
" theme(plot.margin=unit(c(0.5,0.5,0.5,0.5),\"cm\"))+\n",
" theme(legend.key.size=unit(0.7, \"cm\"))+\n",
" theme(panel.background=element_rect(fill=NA))+\n",
" theme(legend.key=element_rect(fill=NA))\n",
"\n",
"p2 <- ggplot(data=df2, aes(x=iteration, y=pp_community, color=color)) + \n",
" geom_jitter(width = 0.3, alpha=0.7)+\n",
" geom_vline(xintercept=vlines2, size=0.8, linetype='dashed', color = '#808080')+\n",
" labs(x=NULL,y=NULL)+\n",
" theme(plot.title = element_text(size=20, hjust=0.5))+\n",
" scale_x_continuous(limits=c(5.75,10.25), breaks=seq(6,10,1))+\n",
" scale_y_continuous(limits=c(0,1))+\n",
" scale_colour_manual(values = rev(brewer.pal(10, \"Paired\")), guide=FALSE)+\n",
" #scale_fill_manual(values=c('#292166', '#8FD086'))+\n",
" theme(panel.grid.major=element_line(colour=NA,size=NA))+\n",
" theme(panel.grid.minor=element_line(colour=NA,size=NA))+ \n",
" theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
" theme(axis.line.x=element_line(colour=\"black\"))+\n",
" theme(axis.line.y=element_line(colour=\"black\"))+\n",
" theme(strip.text.x=element_text(size=16))+\n",
" theme(axis.title.y=element_text(size=16, vjust=8))+\n",
" theme(axis.title.x=element_text(size=16))+\n",
" theme(axis.text=element_text(size=16, colour=\"black\"))+\n",
" theme(legend.text=element_text(size=16))+\n",
" theme(legend.title=element_blank())+\n",
" theme(panel.margin=unit(1, \"lines\"))+\n",
" theme(plot.margin=unit(c(0.5,0.5,0.5,0.5),\"cm\"))+\n",
" theme(legend.key.size=unit(0.7, \"cm\"))+\n",
" theme(panel.background=element_rect(fill=NA))+\n",
" theme(legend.key=element_rect(fill=NA))\n",
"\n",
"p3 <- ggplot(data=df3, aes(x=iteration, y=pp_community, color=color)) + \n",
" geom_jitter(width = 0.3, alpha=0.7)+\n",
" geom_vline(xintercept=vlines3, size=0.8, linetype='dashed', color = '#808080')+\n",
" labs(x=NULL,y=NULL)+\n",
" theme(plot.title = element_text(size=20, hjust=0.5))+\n",
" scale_x_continuous(limits=c(10.75,15.25), breaks=seq(11,15,1))+\n",
" scale_y_continuous(limits=c(0,1))+\n",
" scale_colour_manual(values = (brewer.pal(10, \"Paired\")), guide=FALSE)+\n",
" #scale_fill_manual(values=c('#292166', '#8FD086'))+\n",
" theme(panel.grid.major=element_line(colour=NA,size=NA))+\n",
" theme(panel.grid.minor=element_line(colour=NA,size=NA))+ \n",
" theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
" theme(axis.line.x=element_line(colour=\"black\"))+\n",
" theme(axis.line.y=element_line(colour=\"black\"))+\n",
" theme(strip.text.x=element_text(size=16))+\n",
" theme(axis.title.y=element_text(size=16, vjust=8))+\n",
" theme(axis.title.x=element_text(size=16))+\n",
" theme(axis.text=element_text(size=16, colour=\"black\"))+\n",
" theme(legend.text=element_text(size=16))+\n",
" theme(legend.title=element_blank())+\n",
" theme(panel.margin=unit(1, \"lines\"))+\n",
" theme(plot.margin=unit(c(0.5,0.5,0.5,0.5),\"cm\"))+\n",
" theme(legend.key.size=unit(0.7, \"cm\"))+\n",
" theme(panel.background=element_rect(fill=NA))+\n",
" theme(legend.key=element_rect(fill=NA))\n",
"\n",
"p4 <- ggplot(data=df4, aes(x=iteration, y=pp_community, color=color)) + \n",
" geom_jitter(width = 0.3, alpha=0.7)+\n",
" geom_vline(xintercept=vlines4, size=0.8, linetype='dashed', color = '#808080')+\n",
" labs(x=NULL,y=NULL)+\n",
" theme(plot.title = element_text(size=20, hjust=0.5))+\n",
" scale_x_continuous(limits=c(15.75,20.25), breaks=seq(16,20,1))+\n",
" scale_y_continuous(limits=c(0,1))+\n",
" scale_colour_manual(values = rev(brewer.pal(10, \"Paired\")), guide=FALSE)+\n",
" #scale_fill_manual(values=c('#292166', '#8FD086'))+\n",
" theme(panel.grid.major=element_line(colour=NA,size=NA))+\n",
" theme(panel.grid.minor=element_line(colour=NA,size=NA))+ \n",
" theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
" theme(axis.line.x=element_line(colour=\"black\"))+\n",
" theme(axis.line.y=element_line(colour=\"black\"))+\n",
" theme(strip.text.x=element_text(size=16))+\n",
" theme(axis.title.y=element_text(size=16, vjust=8))+\n",
" theme(axis.title.x=element_text(size=16))+\n",
" theme(axis.text=element_text(size=16, colour=\"black\"))+\n",
" theme(legend.text=element_text(size=16))+\n",
" theme(legend.title=element_blank())+\n",
" theme(panel.margin=unit(1, \"lines\"))+\n",
" theme(plot.margin=unit(c(0.5,0.5,0.5,0.5),\"cm\"))+\n",
" theme(legend.key.size=unit(0.7, \"cm\"))+\n",
" theme(panel.background=element_rect(fill=NA))+\n",
" theme(legend.key=element_rect(fill=NA))\n",
"\n",
"\n",
"p = grid.arrange(p1,p2,p3,p4, nrow=4, left=textGrob(\"posterior probability node is\\nMarshallese\", gp=gpar(fontsize=18), rot=90), bottom=textGrob(\"subsampled iteration\", gp=gpar(fontsize=16)))\n",
"ggsave(\"node_pp_community-2020-06-02.pdf\", p, width = 9, height = 9, path=directory)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot and perform statistics on mtt original and subsampled datasets\n",
"\n",
"I generated 10 randomly subsampled datasets with equal numbers of community and non-community tips. I now need to read in all of those log file and plot their migration rates and Ne values. "
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [],
"source": [
"def read_in_log_files(directory):\n",
" # read in log files\n",
" log_files = []\n",
" \n",
" for f in glob.glob(directory + \"*.log\"):\n",
" log_files.append(f)\n",
" \n",
" return(log_files)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [],
"source": [
"def generate_dataframes_from_log_files(log_file,burnin_percent):\n",
" log = pd.read_table(log_file, sep='\\t', header='infer')\n",
"\n",
" # remove burnin; length of dataframe / 10, rounded up\n",
" burnin = round(len(log) * burnin_percent)\n",
" log_burnin_removed = log.iloc[burnin:]\n",
" burnin = round(len(log) * burnin_percent)\n",
" log_burnin_removed = log.iloc[burnin:]\n",
" \n",
" # in this model, Ne and migration rate have scale factors, while jump counts do not, which is why the selected columns differ\n",
" Ne_cols = [col for col in log_burnin_removed.columns if '.popSize' in col][1:3]\n",
" mig_cols = [col for col in log_burnin_removed.columns if '.rateMatrix' in col][1:3]\n",
" mig_cols = [col for col in mig_cols if 'ScaleFactor' not in col]\n",
" jump_cols = [col for col in log_burnin_removed.columns if '.count' in col][0:2]\n",
" jump_cols = [col for col in jump_cols if 'to' in col]\n",
"\n",
" # subset to just Ne, migration rates, or jump counts\n",
" Ne = log_burnin_removed[Ne_cols]\n",
" mig_rates = log_burnin_removed[mig_cols]\n",
" jump_counts = log_burnin_removed[jump_cols]\n",
" \n",
" # rename columns, and melt\n",
" mig_rates.columns = ['marshallese_to_not','not_to_marshallese']\n",
" mig_rates = pd.melt(mig_rates)\n",
"\n",
" jump_counts.columns = ['marshallese_to_not','not_to_marshallese']\n",
" jump_counts = pd.melt(jump_counts)\n",
"\n",
" Ne.columns = ['Marshallese','Not_Marshallese']\n",
" Ne = pd.melt(Ne)\n",
"\n",
" \n",
" return(Ne,mig_rates,jump_counts)"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {},
"outputs": [],
"source": [
"def create_outfile_names(directory,subsampled):\n",
" outfile_directory = directory\n",
" date = (datetime.datetime.now()).strftime('%Y-%m-%d')\n",
" \n",
" if subsampled == True: \n",
" s = 'subsampled-iterations-'\n",
" if subsampled == False:\n",
" s = ''\n",
" \n",
" Ne_outfile_name = \"Ne-mtt-\" + s + date + \".pdf\"\n",
" mig_outfile_name = \"migration-rates-mtt-\" + s + date + \".pdf\"\n",
" jumps_outfile_name = \"jump-counts-\" + s + date + \".pdf\"\n",
" \n",
" return(outfile_directory,Ne_outfile_name,mig_outfile_name,jumps_outfile_name)"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [],
"source": [
"def run_functions(directory, subsampled,burnin_percent):\n",
" \n",
" count = 0\n",
"\n",
" # generate log files list\n",
" log_files = read_in_log_files(directory)\n",
"\n",
" # generate output file names \n",
" outfile_directory,Ne_outfile_name,mig_outfile_name,jumps_outfile_name = create_outfile_names(directory,subsampled)\n",
"\n",
" # generate dataframes and append together\n",
" Ne_all = pd.DataFrame()\n",
" mig_all = pd.DataFrame()\n",
" jumps_all = pd.DataFrame()\n",
"\n",
" for l in log_files:\n",
" count += 1\n",
" Ne,mig_rates,jump_counts = generate_dataframes_from_log_files(l,burnin_percent)\n",
" Ne['iteration'] = str(count)\n",
" mig_rates['iteration'] = str(count)\n",
" jump_counts['iteration'] = str(count)\n",
"\n",
" Ne_all = Ne_all.append(Ne)\n",
" mig_all = mig_all.append(mig_rates)\n",
" jumps_all = jumps_all.append(jump_counts)\n",
"\n",
" return(Ne_all,mig_all,jumps_all,outfile_directory,Ne_outfile_name,mig_outfile_name,jumps_outfile_name)"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/lmoncla/anaconda/envs/LHM-basics/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: read_table is deprecated, use read_csv instead.\n",
" \n"
]
}
],
"source": [
"# run for subsampled datasets\n",
"subsampled = False\n",
"burnin_percent = 0 # I already removed burnin with log combiner\n",
"directory = \"/Users/lmoncla/Documents/Mumps/beast/mtt/2020-04-02-hot-chain/nicola-priors-limit-50/\"\n",
"#directory = \"/Users/lmoncla/Documents/Mumps/beast/mtt/2019-05-22-skygrid-clade1/subsampled2/\"\n",
"#directory = \"/Users/lmoncla/Documents/Mumps/beast/mtt/2019-05-22-skygrid-clade2/subsampled2/\"\n",
"\n",
"Ne,mig,jumps,outfile_directory,Ne_outfile_name,mig_outfile_name,jumps_outfile_name = run_functions(directory, subsampled,burnin_percent)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/lmoncla/anaconda/envs/LHM-basics/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: read_table is deprecated, use read_csv instead.\n",
" \n"
]
}
],
"source": [
"# run for subsampled datasets\n",
"subsampled = True\n",
"burnin_percent = 0\n",
"directory = \"/Users/lmoncla/Documents/Mumps/beast/mtt/2020-04-02-hot-chain/nicola-priors-limit-50/subsampled/try2-2020-07-20/\"\n",
"#directory = \"/Users/lmoncla/Documents/Mumps/beast/mtt/2019-05-22-skygrid-clade1/subsampled2/\"\n",
"#directory = \"/Users/lmoncla/Documents/Mumps/beast/mtt/2019-05-22-skygrid-clade2/subsampled2/\"\n",
"\n",
"Ne_all,mig_all,jumps_all,sub_outfile_directory,Ne_all_outfile_name,mig_all_outfile_name,jumps_all_outfile_name = run_functions(directory, subsampled,burnin_percent)"
]
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>variable</th>\n",
" <th>value</th>\n",
" <th>iteration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>1.023827</td>\n",
" <td>1</td>\n",
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" <th>1</th>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.957913</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.848661</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>1.027214</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.544496</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.480741</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.295675</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>1.118801</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>1.148329</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.223960</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.155063</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>2.473581</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.678045</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>1.989242</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>1.033694</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>1.035065</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.527633</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>1.648921</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>1.079656</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>2.014806</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.265556</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.185572</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.077569</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.012292</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.087815</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.006736</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.190716</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.276872</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>marshallese_to_not</td>\n",
" <td>0.174094</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27294</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>5.391916</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27295</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>6.795037</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27296</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>7.735987</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27297</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>7.719987</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27298</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>8.277838</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27299</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>9.627699</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27300</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>9.726806</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27301</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>6.900155</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27302</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>6.224528</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27303</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>4.606828</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27304</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>4.301047</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27305</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>4.325170</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27306</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>4.288159</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27307</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>7.830364</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27308</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>7.868187</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27309</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>7.581689</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27310</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>7.512418</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27311</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>7.083505</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27312</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>8.820695</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27313</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>5.925972</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27314</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>6.365261</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27315</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>6.372135</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27316</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>6.016981</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27317</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>5.712836</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27318</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>6.300474</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27319</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>9.855665</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27320</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>6.080710</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27321</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>9.120162</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27322</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>9.052299</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27323</th>\n",
" <td>not_to_marshallese</td>\n",
" <td>7.588295</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>35500 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" variable value iteration\n",
"0 marshallese_to_not 1.023827 1\n",
"1 marshallese_to_not 1.071296 1\n",
"2 marshallese_to_not 0.957913 1\n",
"3 marshallese_to_not 0.848661 1\n",
"4 marshallese_to_not 1.027214 1\n",
"5 marshallese_to_not 0.544496 1\n",
"6 marshallese_to_not 0.480741 1\n",
"7 marshallese_to_not 0.295675 1\n",
"8 marshallese_to_not 1.118801 1\n",
"9 marshallese_to_not 1.148329 1\n",
"10 marshallese_to_not 0.223960 1\n",
"11 marshallese_to_not 0.155063 1\n",
"12 marshallese_to_not 2.473581 1\n",
"13 marshallese_to_not 0.678045 1\n",
"14 marshallese_to_not 1.989242 1\n",
"15 marshallese_to_not 1.033694 1\n",
"16 marshallese_to_not 1.035065 1\n",
"17 marshallese_to_not 0.527633 1\n",
"18 marshallese_to_not 1.648921 1\n",
"19 marshallese_to_not 1.079656 1\n",
"20 marshallese_to_not 2.014806 1\n",
"21 marshallese_to_not 0.265556 1\n",
"22 marshallese_to_not 0.185572 1\n",
"23 marshallese_to_not 0.077569 1\n",
"24 marshallese_to_not 0.012292 1\n",
"25 marshallese_to_not 0.087815 1\n",
"26 marshallese_to_not 0.006736 1\n",
"27 marshallese_to_not 0.190716 1\n",
"28 marshallese_to_not 0.276872 1\n",
"29 marshallese_to_not 0.174094 1\n",
"... ... ... ...\n",
"27294 not_to_marshallese 5.391916 2\n",
"27295 not_to_marshallese 6.795037 2\n",
"27296 not_to_marshallese 7.735987 2\n",
"27297 not_to_marshallese 7.719987 2\n",
"27298 not_to_marshallese 8.277838 2\n",
"27299 not_to_marshallese 9.627699 2\n",
"27300 not_to_marshallese 9.726806 2\n",
"27301 not_to_marshallese 6.900155 2\n",
"27302 not_to_marshallese 6.224528 2\n",
"27303 not_to_marshallese 4.606828 2\n",
"27304 not_to_marshallese 4.301047 2\n",
"27305 not_to_marshallese 4.325170 2\n",
"27306 not_to_marshallese 4.288159 2\n",
"27307 not_to_marshallese 7.830364 2\n",
"27308 not_to_marshallese 7.868187 2\n",
"27309 not_to_marshallese 7.581689 2\n",
"27310 not_to_marshallese 7.512418 2\n",
"27311 not_to_marshallese 7.083505 2\n",
"27312 not_to_marshallese 8.820695 2\n",
"27313 not_to_marshallese 5.925972 2\n",
"27314 not_to_marshallese 6.365261 2\n",
"27315 not_to_marshallese 6.372135 2\n",
"27316 not_to_marshallese 6.016981 2\n",
"27317 not_to_marshallese 5.712836 2\n",
"27318 not_to_marshallese 6.300474 2\n",
"27319 not_to_marshallese 9.855665 2\n",
"27320 not_to_marshallese 6.080710 2\n",
"27321 not_to_marshallese 9.120162 2\n",
"27322 not_to_marshallese 9.052299 2\n",
"27323 not_to_marshallese 7.588295 2\n",
"\n",
"[35500 rows x 3 columns]"
]
},
"execution_count": 216,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mig_all"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Try plotting them together now "
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {},
"outputs": [],
"source": [
"Not_Marshallese_color = '#012E74'\n",
"Marshallese_color = '#80AAC9'"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {},
"outputs": [
{
"data": {
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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 1000 -h 500 -u px -i Ne,Ne_all,sub_outfile_directory,Ne_all_outfile_name,Marshallese_color,Not_Marshallese_color # this sets the size of the plot...otherwise, it will go off the page\n",
"require(ggplot2)\n",
"library(ggplot2)\n",
"\n",
"p <- ggplot(data=NULL, aes(x=value, group=iteration, color=variable, fill=variable)) + \n",
" geom_density(data=Ne, alpha=0.60, linetype=\"blank\")+\n",
" geom_density(data=Ne_all, alpha=0.001, linetype=\"dashed\",size=1)+\n",
" #geom_line(data=dat, x=dat$x, y=dat$px, color=\"black\")+\n",
" facet_wrap(~variable, scales='free')+\n",
" labs(x=\"effective population size (Ne)\",y=\"density\")+\n",
" #ggtitle(\"Re estimates\") + \n",
" theme(plot.title = element_text(size=20, hjust=0.5))+\n",
" scale_x_continuous(limits=c(0.5,10))+ \n",
" #scale_y_continuous(limits=c(0,10))+\n",
" scale_color_manual(values=c(Marshallese=Marshallese_color, Not_Marshallese=Not_Marshallese_color,\"#808080\"), labels=c(\"Marshallese\",\"Not Marshallese\",\"prior\"))+\n",
" scale_fill_manual(values=c(Marshallese=Marshallese_color, Not_Marshallese=Not_Marshallese_color,\"#808080\"), labels=c(\"Marshallese\",\"Not Marshallese\",\"prior\"))+\n",
" theme(panel.grid.major=element_line(colour=NA,size=NA))+\n",
" theme(panel.grid.minor=element_line(colour=NA,size=NA))+ \n",
" theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
" theme(axis.line.x=element_line(colour=\"black\"))+\n",
" theme(axis.line.y=element_line(colour=\"black\"))+\n",
" theme(strip.text.x=element_text(size=16))+\n",
" theme(axis.title.y=element_text(size=16, vjust=8))+\n",
" theme(axis.title.x=element_text(size=16, vjust=-8))+\n",
" theme(axis.text=element_text(size=16, colour=\"black\"))+\n",
" theme(axis.text.x=element_text(size=16))+\n",
" theme(legend.text=element_text(size=16))+\n",
" theme(legend.title=element_blank())+\n",
" theme(panel.margin=unit(1, \"lines\"))+\n",
" theme(plot.margin=unit(c(1,1,1,1),\"cm\"))+\n",
" theme(legend.key.size=unit(0.7, \"cm\"))+\n",
" theme(panel.background=element_rect(fill=NA))+\n",
" theme(legend.key=element_rect(fill=NA))\n",
"\n",
"ggsave(Ne_all_outfile_name, p, width = 12, height = 4,path=sub_outfile_directory)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {},
"outputs": [],
"source": [
"outfile_directory = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/individual-PDFs\""
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 1000 -h 500 -u px -i mig,mig_all,outfile_directory,mig_all_outfile_name,Marshallese_color,Not_Marshallese_color # this sets the size of the plot...otherwise, it will go off the page\n",
"require(ggplot2)\n",
"library(ggplot2)\n",
"\n",
"x <- seq(0, 15, length.out=1000)\n",
"dat <- data.frame(value=x, y=dexp(x, rate=1.0))\n",
"dat$iteration = 'prior'\n",
"\n",
"blank_data <- data.frame(x=0, y=c(0, 0.25, 0, 2.5), group=c(\"marshallese_to_not\",\"marshallese_to_not\",\"not_to_marshallese\",\"not_to_marshallese\"),color=\"prior\",fill=\"prior\")\n",
"\n",
"p <- ggplot(data=NULL) + \n",
" geom_density(data=mig, aes(x=value, group=iteration, color=variable, fill=variable), alpha=0.60, linetype=\"blank\")+\n",
" geom_line(data=dat, aes(x=value, y=y, group=iteration, color=\"prior\", fill=\"prior\"), linetype=\"solid\", size=1, alpha=0.8)+\n",
" geom_density(data=mig_all, aes(x=value, group=iteration, color=variable, fill=variable), alpha=0.001, linetype=\"dashed\",size=0.8)+\n",
" geom_blank(data = blank_data, aes(x = x, y = y))+\n",
" facet_wrap(~variable, scales='free_y')+\n",
" labs(x=\"backwards in time migration rate\",y=\"density\")+\n",
" #ggtitle(\"Re estimates\") + \n",
" scale_color_manual(values=c(marshallese_to_not = Not_Marshallese_color,not_to_marshallese=Marshallese_color, prior=\"#808080\"), labels=c(\"Marshallese to Not Marshallese\",\"Not Marshallese to Marshallese\",\"prior\"))+\n",
" scale_fill_manual(values=c(marshallese_to_not = Not_Marshallese_color,not_to_marshallese=Marshallese_color, prior=\"#808080\"), labels=c(\"Marshallese to Not Marshallese\",\"Not Marshallese to Marshallese\",\"prior\"))+\n",
" theme(panel.grid.major=element_line(colour=NA,size=NA))+\n",
" theme(panel.grid.minor=element_line(colour=NA,size=NA))+ \n",
" theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
" theme(axis.line.x=element_line(colour=\"black\"))+\n",
" theme(axis.line.y=element_line(colour=\"black\"))+\n",
" theme(strip.text.x=element_blank())+\n",
" theme(axis.title.y=element_text(size=16, vjust=8))+\n",
" theme(axis.title.x=element_text(size=16, vjust=-8))+\n",
" theme(axis.text=element_text(size=16, colour=\"black\"))+\n",
" theme(axis.text.x=element_text(size=16))+\n",
" theme(legend.text=element_text(size=16))+\n",
" theme(legend.title=element_blank())+\n",
" theme(panel.margin=unit(1, \"lines\"))+\n",
" theme(plot.margin=unit(c(1,1,1,1),\"cm\"))+\n",
" theme(legend.key.size=unit(0.7, \"cm\"))+\n",
" theme(panel.background=element_rect(fill=NA))+\n",
" theme(panel.spacing.x=unit(2.5, \"lines\"))+ # increase space between facets\n",
" theme(legend.key=element_rect(fill=NA)) +\n",
" scale_x_continuous(expand = c(0, 0)) +\n",
" scale_y_continuous(expand = c(0, 0), limits=c(0,1.0))\n",
"\n",
"ggsave(mig_all_outfile_name, p, width = 12, height = 4,path=outfile_directory)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "LHM-basics (python3)",
"language": "python",
"name": "lhm-basics"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
