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plot_original_models.py
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plot_original_models.py
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import matplotlib
matplotlib.use('Agg')
import numpy
import sys
from scipy.stats import rv_discrete
import os.path
import numpy as np
import matplotlib.pyplot as plt
rho_in='5e-9'
adaptive_theta='0'
selection='False'
def set_box_color(bp, color):
plt.setp(bp['boxes'], color=color)
plt.setp(bp['whiskers'], color=color, linestyle='-')
plt.setp(bp['caps'], color=color)
plt.setp(bp['medians'], color=color)
plt.setp(bp['fliers'], color=color, markersize=3)
#################
def isFloat(value):
try:
float(value)
return True
except:
return False
#####################
def initialize_model_dict(summary_statistics,models):
for model in models:
summary_statistics[model]={}
summary_statistics[model]['H12']=[]
summary_statistics[model]['H2H1']=[]
summary_statistics[model]['S']=[]
summary_statistics[model]['Pi']=[]
summary_statistics[model]['avg_dist']=[]
summary_statistics[model]['TajD']=[]
return summary_statistics
summary_statistics={} # store the summary statistics for all the models here. Key = [m_NA][m_NE][H12,S,Pi,avg_dist]
models=['constNe10e6', 'constNe2.7e6', 'dadi1', 'dadi2', 'admixture_mode_Garud2015', 'admixture_bot_mode_Garud2015', 'admixture_mode', 'admixture_bot_mode', 'admixture_posterior', 'admixture_posterior_Harris', 'Arguello' ]
summary_statistics=initialize_model_dict(summary_statistics,models)
##########
for model in models:
if model !='DGRP':
# H12
inFile=open(os.path.expanduser('~/Jensen_response/analysis/%s_neutrality_locusLen100000_snps.txt' %model),'r')
# iterate through the file
# check if there are 17 lines
# store H12 for this parameter configuration in a dictionary
for line in inFile:
items=line.split('\t')
if len(items)==17:
H12=float(items[8])
H2H1=float(items[9])
summary_statistics[model]['H12'].append(H12)
summary_statistics[model]['H2H1'].append(H2H1)
inFile.close()
# Pi, S, TajD
inFile=open(os.path.expanduser('~/Jensen_response/analysis/%s_neutrality_locusLen11000_Pi_S_TajD_perBp.txt' %model),'r')
for line in inFile:
items=line.strip('\n').split('\t')
if len(items)==4:
if isFloat(items[0]):
S=float(items[0])
Pi=float(items[1])
avg_dist=float(items[2])
TajD=float(items[3])
summary_statistics[model]['S'].append(S)
summary_statistics[model]['Pi'].append(Pi)
summary_statistics[model]['avg_dist'].append(avg_dist)
summary_statistics[model]['TajD'].append(TajD)
inFile.close()
########
# add in the new models, which have a different file name format
EUrs=[700000]
NAms=[1110000, 15984500]
for EUr in EUrs:
for NAm in NAms:
model='%s_%s' %(NAm, EUr)
summary_statistics=initialize_model_dict(summary_statistics,[model])
models.append(model)
inFile=open(os.path.expanduser('~/Jensen_response/analysis/Admixture_mode_fixedPopSize_NAm%s_EUr%s_rho_5e-9_theta_0_selection_False_MS_snps.txt') %(NAm, EUr),'r')
# iterate through the file
# check if there are 17 lines
# store H12 for this parameter configuration in a dictionary
for line in inFile:
items=line.split('\t')
if len(items)==17:
H12=float(items[8])
H2H1=float(items[9])
summary_statistics[model]['H12'].append(H12)
summary_statistics[model]['H2H1'].append(H2H1)
inFile.close()
# Pi, S, TajD
inFile=open(os.path.expanduser('~/Jensen_response/analysis/Admixture_mode_fixedPopSize_NAm%s_EUr%s_rho_5e-9_theta_0_selection_False_MS_Pi_S_TajD_perBp.txt' %(NAm, EUr)),'r')
for line in inFile:
items=line.strip('\n').split('\t')
if len(items)==4:
if isFloat(items[0]):
S=float(items[0])
Pi=float(items[1])
avg_dist=float(items[2])
TajD=float(items[3])
summary_statistics[model]['S'].append(S)
summary_statistics[model]['Pi'].append(Pi)
summary_statistics[model]['avg_dist'].append(avg_dist)
summary_statistics[model]['TajD'].append(TajD)
inFile.close()
############################
# load statistics for data #
############################
# mean_H12 in the data:
data_means={}
data_means['H12']=0.01767854
data_means['H2H1']=0.8301393
data_means['TajD']=0.3835
data_means['S']=0.0578
data_means['Pi']=0.0118
# data medians
data_medians={}
data_medians['H12']=0.01545779
data_medians['H2H1']=0.8651685
data_medians['TajD']=0.3835
data_medians['S']=0.0578
data_medians['Pi']=0.0118
# data distribution of S, Pi, and TajD computed from short introns of the DGRP and data distribution of H12 computed from genome-wide DGRP data.
DGRP_S=[]
DGRP_Pi=[]
DGRP_H12=[]
for chrom in ['2R','2L','3R','3L']:
inFile=open(os.path.expanduser('~/Jensen_response/analysis/H12_scan/clusters_082712_400_50_2_2_0_chr%s_withRho_threshold.txt' %(chrom)),'r')
for line in inFile:
items=line.strip().split('\t')
H12=float(items[27])
DGRP_H12.append(H12)
inFile.close()
inFile=open(os.path.expanduser('~/Jensen_response/analysis/S_and_Pi/Chr%s_SNPdensity_perShortIntron.txt' %(chrom)),'r')
for line in inFile:
items=line.strip().split('\t')
S=float(items[2])
Pi=float(items[3])
DGRP_S.append(S)
DGRP_Pi.append(Pi)
inFile.close()
DGRP_S=np.asarray(DGRP_S)
DGRP_Pi=np.asarray(DGRP_Pi)
DGRP_H12=np.asarray(DGRP_H12)
print DGRP_Pi
# read in peaks
inFile=open(os.path.expanduser('~/Jensen_response/analysis/H12_scan/peaks_082712_400_50_2_2_0_pan_509_top50.txt'),'r')
peaks=[]
for line in inFile:
items=line.strip().split('\t')
H12=float(items[27])
peaks.append(H12)
######################################
# make boxplots of the distributions #
######################################
colors=['#fed976','#feb24c', '#fd8d3c','#fc4e2a', '#e31a1c', '#b10026', '#bdd7e7','#6baed6', '#08519c','#8c510a','pink','#d9ef8b', '#66bd63']
matplotlib.rcParams.update({'font.size': 9})
matplotlib.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 12
fig_size[1] = 9
plot_no=1
for summary_statistic in ['Pi', 'S', 'H12']:
plt.subplot(2, 3, plot_no)
if summary_statistic=='H12':
bp = plt.boxplot(DGRP_H12, positions=[1], sym='.',patch_artist=True, labels=['DGRP'], widths=0.5)
set_box_color(bp, 'black')
col_i=0
for model in models:
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+2], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor(colors[col_i])
set_box_color(bp, colors[col_i])
col_i+=1
# dummy boxplot to get the colors to work for last one
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+10], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor('white')
set_box_color(bp, 'white')
plt.xlim(0,len(models)+2)
plt.plot( [1]*len(peaks),peaks, 'r.')
elif summary_statistic == 'Pi':
bp = plt.boxplot(DGRP_Pi, positions=[1], sym='.',patch_artist=True, labels=['DGRP'], widths=0.5)
set_box_color(bp, 'black')
col_i=0
for model in models:
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+2], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor(colors[col_i])
set_box_color(bp, colors[col_i])
col_i+=1
# dummy boxplot to get the colors to work for last one
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+10], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor('white')
set_box_color(bp, 'white')
plt.xlim(0,len(models)+2)
elif summary_statistic == 'S':
bp = plt.boxplot(DGRP_S, positions=[1], sym='.',patch_artist=True, labels=['DGRP'], widths=0.5)
set_box_color(bp, 'black')
col_i=0
for model in models:
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+2], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor(colors[col_i])
set_box_color(bp, colors[col_i])
col_i+=1
# dummy boxplot to get the colors to work for last one
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+10], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor('white')
set_box_color(bp, 'white')
plt.xlim(0,len(models)+2)
plt.axhline(y=data_medians[summary_statistic],linestyle='dashed',color='black', label='Median value in data')
if summary_statistic=='H12':
plt.ylim(0,1)
plt.ylabel('H12 (401 SNP windows)')
elif summary_statistic=='Pi':
plt.ylim(0,0.03)
plt.ylabel('Pi/bp')
elif summary_statistic=='S':
plt.ylim(0,0.1)
plt.ylabel('S/bp')
plot_no+=1
##########################
# Plot LD #
##########################
# load the data
LD_dict={}
for model in models:
LD_dict[model]={}
LD_dict[model]['position']=[]
LD_dict[model]['R2']=[]
if model =='1110000_700000':
inFile=open(os.path.expanduser('~/Jensen_response/analysis/Admixture_mode_fixedPopSize_NAm1110000_EUr700000_rho_5e-9_theta_0_selection_False_MS_LD_0.05_0.95_averaged.txt'))
elif model == '15984500_700000':
inFile=open(os.path.expanduser('~/Jensen_response/analysis/Admixture_mode_fixedPopSize_NAm15984500_EUr700000_rho_5e-9_theta_0_selection_False_MS_LD_0.05_0.95_averaged.txt'))
else:
inFile=open(os.path.expanduser('~/Jensen_response/analysis/%s_neutrality_locusLen11000_LD_0.05_0.95_averaged.txt' %model),'r')
for line in inFile:
items=line.strip().split('\t')
position=items[0]
R2=items[1]
LD_dict[model]['position'].append(int(position))
LD_dict[model]['R2'].append(float(R2))
# load LD estimated directly from the genome
LD_dict['DGRP']={}
LD_dict['DGRP']['position']=[]
LD_dict['DGRP']['R2']=[]
inFile=open(os.path.expanduser('~/Jensen_response/analysis/DGRP_LD/alldata_averaged_sorted'),'r')
for line in inFile:
items=line.strip('\n').split('\t')
position=items[0]
R2=items[1]
LD_dict['DGRP']['position'].append(int(position))
LD_dict['DGRP']['R2'].append(float(R2))
#colors=['green','blue', 'red','yellow', 'purple', 'orange', 'brown','pink', 'turquoise','magenta', 'lightgray','orchid','y']
# plot LD
for xlim_range in [100,10000]:
plt.subplot(2, 3, plot_no)
i=0
for model in models:
plt.semilogy(LD_dict[model]['position'], LD_dict[model]['R2'], label=model, color=colors[i])
plt.xlim(0,xlim_range)
if xlim_range == 100:
plt.ylim(0.1,1.05)
else:
plt.ylim(0.005,1.05)
plt.xlabel('Distance between SNPs (bps)')
plt.ylabel('log($R^2$)')
i +=1
plt.semilogy(LD_dict['DGRP']['position'], LD_dict['DGRP']['R2'], linestyle='dashed',color='black', label='DGRP')
plot_no +=1
print(plot_no)
plt.subplot(2, 3, plot_no)
# put some text in plot number 6
plt.text(0.045, 1.0, "DGRP data", rotation=90.)
plt.text(0.113, 1.0, "Constant $Ne$=$10^6$", rotation=90.)
plt.text(0.18, 1.0, "Constant $Ne$=$2.7*10^6$", rotation=90.)
plt.text(0.248, 1.0, "Severe, short bottleneck", rotation=90.)
plt.text(0.315, 1.0, "Shallow, long bottleneck", rotation=90.)
plt.text(0.383, 1.0, "Admixture (mode), Garud $\it{et al.}$ 2015 implementation", rotation=90.)
plt.text(0.45, 1.0, "Admixture + bottleneck (mode), Garud $\it{et al.}$ 2015 implementation", rotation=90.)
plt.text(0.518, 1.0, "Admixture (mode), Duchen $\it{et al.}$ 2013", rotation=90.)
plt.text(0.585, 1.0, "Admixture + bottleneck (mode), Duchen $\it{et al.}$ 2013", rotation=90.)
plt.text(0.653, 1.0, "Admixture (posterior distribution), Duchen $\it{et al.}$ 2013", rotation=90.)
plt.text(0.72, 1.0, "Admixture (posterior distribution), Harris $\it{et al.}$ 2018 implementation", rotation=90.)
plt.text(0.788, 1.0, "Arguello $\it{et al.}$ 2019", rotation=90.)
plt.text(0.856, 1.0, "Admixture, Eur=$7*10^5$, Nac=$1.11*10^6$", rotation=90.)
plt.text(0.925, 1.0, "Admixture, Eur=$7*10^5$, Nac=$15.9*10^6$", rotation=90.)
plt.axis('off')
plt.tight_layout()
plt.savefig(os.path.expanduser('~/Jensen_response/analysis/original_models_LD.png'), dpi=600)
plt.close()
#########
#########
#################################################
# make a plot of pi, s, LD #
#################################################
colors=['#fed976','#feb24c', '#fd8d3c','#fc4e2a', '#e31a1c', '#b10026', '#bdd7e7','#6baed6', '#08519c','#8c510a','pink','#d9ef8b', '#66bd63']
matplotlib.rcParams.update({'font.size': 9})
matplotlib.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 6
fig_size[1] = 6
plot_no=1
for summary_statistic in ['Pi', 'S']:
plt.subplot(2,2, plot_no)
col_i=0
for model in models:
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+1], sym='.',patch_artist=True, widths=0.5, labels=[''])
set_box_color(bp, colors[col_i])
col_i+=1
# dummy boxplot to get the colors to work for last one
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+10], sym='.',patch_artist=True, widths=0.5, labels=[''])
set_box_color(bp, 'white')
plt.xlim(0,len(models)+2)
plt.axhline(y=data_medians[summary_statistic],linestyle='dashed',color='black', label='Median value in data')
if summary_statistic=='Pi':
plt.ylim(0,0.03)
plt.ylabel('Pi/bp')
elif summary_statistic=='S':
plt.ylim(0,0.1)
plt.ylabel('S/bp')
plot_no+=1
##########################
# Plot LD #
##########################
# load the data
LD_dict={}
for model in models:
LD_dict[model]={}
LD_dict[model]['position']=[]
LD_dict[model]['R2']=[]
if model =='1110000_700000':
inFile=open(os.path.expanduser('~/Jensen_response/analysis/Admixture_mode_fixedPopSize_NAm1110000_EUr700000_rho_5e-9_theta_0_selection_False_MS_LD_0.05_0.95_averaged.txt'))
elif model == '15984500_700000':
inFile=open(os.path.expanduser('~/Jensen_response/analysis/Admixture_mode_fixedPopSize_NAm15984500_EUr700000_rho_5e-9_theta_0_selection_False_MS_LD_0.05_0.95_averaged.txt'))
else:
inFile=open(os.path.expanduser('~/Jensen_response/analysis/%s_neutrality_locusLen11000_LD_0.05_0.95_averaged.txt' %model),'r')
for line in inFile:
items=line.strip().split('\t')
position=items[0]
R2=items[1]
LD_dict[model]['position'].append(int(position))
LD_dict[model]['R2'].append(float(R2))
# load LD estimated directly from the genome
LD_dict['DGRP']={}
LD_dict['DGRP']['position']=[]
LD_dict['DGRP']['R2']=[]
inFile=open(os.path.expanduser('~/Jensen_response/analysis/DGRP_LD/alldata_averaged_sorted'),'r')
for line in inFile:
items=line.strip('\n').split('\t')
position=items[0]
R2=items[1]
LD_dict['DGRP']['position'].append(int(position))
LD_dict['DGRP']['R2'].append(float(R2))
#colors=['green','blue', 'red','yellow', 'purple', 'orange', 'brown','pink', 'turquoise','magenta', 'lightgray','orchid','y']
# plot LD
for xlim_range in [100,10000]:
plt.subplot(2, 2, plot_no)
i=0
for model in models:
plt.semilogy(LD_dict[model]['position'], LD_dict[model]['R2'], label=model, color=colors[i])
plt.xlim(0,xlim_range)
if xlim_range == 100:
plt.ylim(0.1,1.05)
else:
plt.ylim(0.005,1.05)
plt.xlabel('Distance between SNPs (bps)')
plt.ylabel('log($R^2$)')
i +=1
plt.semilogy(LD_dict['DGRP']['position'], LD_dict['DGRP']['R2'], linestyle='dashed',color='black', label='DGRP')
plot_no +=1
#################
# print labels #
#################
'''
print(plot_no)
plt.subplot(2,2,8)
# put some text in plot number 6
plt.text(0.045, 1.0, "DGRP data", rotation=90.)
plt.text(0.113, 1.0, "Constant $Ne$=$10^6$", rotation=90.)
plt.text(0.18, 1.0, "Constant $Ne$=$2.7*10^6$", rotation=90.)
plt.text(0.248, 1.0, "Severe, short bottleneck", rotation=90.)
plt.text(0.315, 1.0, "Shallow, long bottleneck", rotation=90.)
plt.text(0.383, 1.0, "Admixture (mode), Garud $\it{et al.}$ 2015 implementation", rotation=90.)
plt.text(0.45, 1.0, "Admixture + bottleneck (mode), Garud $\it{et al.}$ 2015 implementation", rotation=90.)
plt.text(0.518, 1.0, "Admixture (mode), Duchen $\it{et al.}$ 2013", rotation=90.)
plt.text(0.585, 1.0, "Admixture + bottleneck (mode), Duchen $\it{et al.}$ 2013", rotation=90.)
plt.text(0.653, 1.0, "Admixture (posterior distribution), Duchen $\it{et al.}$ 2013", rotation=90.)
plt.text(0.72, 1.0, "Admixture (posterior distribution), Harris $\it{et al.}$ 2018 implementation", rotation=90.)
plt.text(0.788, 1.0, "Arguello $\it{et al.}$ 2019", rotation=90.)
plt.text(0.856, 1.0, "Admixture, Eur=$7*10^5$, Nac=$1.11*10^6$", rotation=90.)
plt.text(0.925, 1.0, "Admixture, Eur=$7*10^5$, Nac=$15.9*10^6$", rotation=90.)
'''
#plt.axis('off')
plt.tight_layout()
plt.savefig(os.path.expanduser('~/Jensen_response/analysis/original_models_LD_2.png'), dpi=600)
plt.close()
####################
# Plot H12 #
####################
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 5
fig_size[1] = 9
plot_no=1
# the diff. between H12_1 and H12_2 is that the second one has a zoomed in y-axis
for summary_statistic in ['H12_1','H12_2']:
plt.subplot(3,1, plot_no)
if summary_statistic=='H12_1':
summary_statistic='H12'
bp = plt.boxplot(DGRP_H12, positions=[1], sym='.',patch_artist=True, labels=['DGRP'], widths=0.5)
set_box_color(bp, 'black')
col_i=0
for model in models:
bp = plt.boxplot(summary_statistics[model]['H12'], positions=[col_i+2], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor(colors[col_i])
set_box_color(bp, colors[col_i])
col_i+=1
# dummy boxplot to get the colors to work for last one
bp = plt.boxplot(summary_statistics[model]['H12'], positions=[col_i+10], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor('white')
set_box_color(bp, 'white')
plt.xlim(0,len(models)+2)
plt.plot( [1]*len(peaks),peaks, 'r.')
plt.ylim(0,1)
plt.ylabel('H12 (401 SNP windows)')
elif summary_statistic=='H12_2':
summary_statistic='H12'
bp = plt.boxplot(DGRP_H12, positions=[1], sym='.',patch_artist=True, labels=['DGRP'], widths=0.5)
set_box_color(bp, 'black')
col_i=0
for model in models:
bp = plt.boxplot(summary_statistics[model]['H12'], positions=[col_i+2], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor(colors[col_i])
set_box_color(bp, colors[col_i])
col_i+=1
# dummy boxplot to get the colors to work for last one
bp = plt.boxplot(summary_statistics[model]['H12'], positions=[col_i+10], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor('white')
set_box_color(bp, 'white')
plt.xlim(0,len(models)+2)
plt.plot( [1]*len(peaks),peaks, 'r.')
plt.ylim(0,0.1)
plt.ylabel('H12 (401 SNP windows)')
plt.axhline(y=data_medians['H12'],linestyle='dashed',color='black', label='Median value in data')
plt.axhline(y=min(peaks),color='red', label='Lowest H12 peak value')
plot_no +=1
plt.subplot(3,1,3)
# put some text in plot number 3
plt.text(-0.03, 1.0, "DGRP data")
plt.text(-0.03, 0.92, "Constant $Ne$=$10^6$")
plt.text(-0.03, 0.84, "Constant $Ne$=$2.7*10^6$")
plt.text(-0.03, 0.76, "Severe, short bottleneck")
plt.text(-0.03, 0.68, "Shallow, long bottleneck")
plt.text(-0.03, 0.60, "Admixture (mode), Garud $\it{et al.}$ 2015 implementation")
plt.text(-0.03, 0.52, "Admixture + bottleneck (mode), Garud $\it{et al.}$ 2015 implementation")
plt.text(-0.03, 0.44, "Admixture (mode), Duchen $\it{et al.}$ 2013")
plt.text(-0.03, 0.36, "Admixture + bottleneck (mode), Duchen $\it{et al.}$ 2013")
plt.text(-0.03, 0.28, "Admixture (posterior distribution), Duchen $\it{et al.}$ 2013")
plt.text(-0.03, 0.20, "Admixture (posterior distribution), Harris $\it{et al.}$ 2018 implementation")
plt.text(-0.03, 0.12, "Arguello $\it{et al.}$ 2019")
plt.text(-0.03, 0.04, "Admixture, Eur=$7*10^5$, Nac=$1.11*10^6$")
plt.text(-0.03, -0.04, "Admixture, Eur=$7*10^5$, Nac=$15.9*10^6$")
plt.axis('off')
plt.tight_layout()
plt.savefig(os.path.expanduser('~/Jensen_response/analysis/H12_plots.png'), dpi=600)
plt.close()
################
################
import matplotlib.gridspec as gridspec
gs = gridspec.GridSpec(4, 3)
gs.update(wspace=0.5, hspace=0.5)
#ax1 = plt.subplot(gs[0, :])
#ax2 = plt.subplot(gs[1, :-1])
#ax3 = plt.subplot(gs[1:, -1])
#ax4 = plt.subplot(gs[-1, 0])
#ax5 = plt.subplot(gs[-1, -2])
ax1 = plt.subplot(gs[0:2,0])
ax1.text(0.5, 0.5, str((2, 3, 1)),
fontsize=18, ha='center')
ax2 = plt.subplot(gs[0:2,1])
ax3 = plt.subplot(gs[0:2,2])
ax4 = plt.subplot(gs[2:4,0])
ax5 = plt.subplot(gs[2:4,1])
ax6 = plt.subplot(gs[2:3,2])
ax7 = plt.subplot(gs[3:4,2])
col_i=0
summary_statistic='Pi'
for model in models:
bp = ax1.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+1], sym='.',patch_artist=True, widths=0.5, labels=[''])
set_box_color(bp, colors[col_i])
col_i+=1
# dummy boxplot to get the colors to work for last one
bp = ax1.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+10], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor('white')
set_box_color(bp, 'white')
plt.savefig(os.path.expanduser('~/Jensen_response/analysis/tmpFig.png'), dpi=600)
###############
###############
#######################################################################
# supplement: plot zoom in of H12 where y-axis ranges from 0 to 0.1 #
#######################################################################
matplotlib.rcParams.update({'font.size': 9})
matplotlib.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 12
fig_size[1] = 9
plt.subplot(2,1, 1)
for summary_statistic in ['H12']:
bp = plt.boxplot(DGRP_H12, positions=[1], sym='.',patch_artist=True, labels=['DGRP'], widths=0.5)
set_box_color(bp, 'black')
col_i=0
for model in models:
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+2], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor(colors[col_i])
set_box_color(bp, colors[col_i])
col_i+=1
# dummy boxplot to get the colors to work for last one
bp = plt.boxplot(summary_statistics[model][summary_statistic], positions=[col_i+10], sym='.',patch_artist=True, widths=0.5, labels=[''])
#patch.set_facecolor('white')
set_box_color(bp, 'white')
plt.xlim(0,len(models)+2)
plt.plot( [1]*len(peaks),peaks, 'r.')
plt.axhline(y=data_medians[summary_statistic],linestyle='dashed',color='black', label='Median value in data')
plt.ylim(0,.1)
plt.ylabel('H12 (401 SNP windows)')
plt.subplot(2, 1, 2)
# put some text in plot number 2
plt.text(0.059, 1.1, "DGRP data", rotation=90.)
plt.text(0.1256154, 1.1, "Constant $Ne$=$10^6$", rotation=90.)
plt.text(0.1922308, 1.1, "Constant $Ne$=$2.7*10^6$", rotation=90.)
plt.text(0.2588461, 1.1, "Severe, short bottleneck", rotation=90.)
plt.text(0.3254615, 1.1, "Shallow, long bottleneck", rotation=90.)
plt.text(0.3920769, 1.1, "Admixture (mode), Garud $\it{et al.}$ 2015 implementation", rotation=90.)
plt.text(0.4586923, 1.1, "Admixture + bottleneck (mode), Garud $\it{et al.}$ 2015 implementation", rotation=90.)
plt.text(0.5253077, 1.1, "Admixture (mode), Duchen $\it{et al.}$ 2013", rotation=90.)
plt.text(0.591923, 1.1, "Admixture + bottleneck (mode), Duchen $\it{et al.}$ 2013", rotation=90.)
plt.text(0.6585384, 1.1, "Admixture (posterior distribution), Duchen $\it{et al.}$ 2013", rotation=90.)
plt.text(0.7251538, 1.1, "Admixture (posterior distribution), Harris $\it{et al.}$ 2018 implementation", rotation=90.)
plt.text(0.7917692, 1.1, "Arguello $\it{et al.}$ 2019", rotation=90.)
plt.text(0.8583846, 1.1, "Admixture, Eur=$7*10^5$, Nac=$1.11*10^6$", rotation=90.)
plt.text(0.925, 1.1, "Admixture, Eur=$7*10^5$, Nac=$15.9*10^6$", rotation=90.)
plt.axis('off')
plt.savefig(os.path.expanduser('~/Jensen_response/analysis/supplement_H12_zoomIn.png'), dpi=600)
plt.close()
print('done with zoom')
########################
# #
## Plot simulations ##
# #
########################
print 'Experiment 1: fixed population sizes'
#####################################################
# experiment 1:
# fixed population sizes for Europe and North America
######################################################
# load the data
summary_statistics={} # store the summary statistics for all the models here. Key = [m_NA][m_NE][H12,S,Pi,avg_dist]
NAms=[2500, 61659, 1110000, 15984500, 28000000]
EUrs=[16982, 67608, 700000, 2000000, 9550000]
for NAm in NAms:
summary_statistics[NAm]={}
LD_dict[NAm]={}
for EUr in EUrs:
summary_statistics[NAm][EUr]={}
summary_statistics[NAm][EUr]['H12']=[]
summary_statistics[NAm][EUr]['H2H1']=[]
summary_statistics[NAm][EUr]['S']=[]
summary_statistics[NAm][EUr]['Pi']=[]
summary_statistics[NAm][EUr]['avg_dist']=[]
summary_statistics[NAm][EUr]['TajD']=[]
LD_dict[NAm][EUr]={}
LD_dict[NAm][EUr]['position']=[]
LD_dict[NAm][EUr]['R2']=[]
for NAm in NAms:
for EUr in EUrs:
# H12
inFile=open(os.path.expanduser('~/Jensen_response/analysis/Admixture_mode_fixedPopSize_NAm%s_EUr%s_rho_%s_theta_%s_selection_%s_MS_snps.txt') %(NAm, EUr,rho_in, adaptive_theta, selection),'r')
# iterate through the file
# check if there are 17 lines
# store H12 for this parameter configuration in a dictionary
for line in inFile:
items=line.split('\t')
if len(items)==17:
H12=float(items[8])
H2H1=float(items[9])
summary_statistics[NAm][EUr]['H12'].append(H12)
summary_statistics[NAm][EUr]['H2H1'].append(H2H1)
inFile.close()
# Pi, S, TajD
inFile=open(os.path.expanduser('~/Jensen_response/analysis/Admixture_mode_fixedPopSize_NAm%s_EUr%s_rho_%s_theta_%s_selection_%s_MS_Pi_S_TajD_perBp.txt') %(NAm, EUr,rho_in, adaptive_theta, selection),'r')
for line in inFile:
items=line.strip('\n').split('\t')
if len(items)==4:
if isFloat(items[0]) and isFloat(items[1]) and isFloat(items[2]) and isFloat(items[3]):
S=float(items[0])
Pi=float(items[1])
avg_dist=float(items[2])
TajD=float(items[3])
summary_statistics[NAm][EUr]['S'].append(S)
summary_statistics[NAm][EUr]['Pi'].append(Pi)
summary_statistics[NAm][EUr]['avg_dist'].append(avg_dist)
summary_statistics[NAm][EUr]['TajD'].append(TajD)
inFile.close()
# LD
inFile=open(os.path.expanduser('~/Jensen_response/analysis/Admixture_mode_fixedPopSize_NAm%s_EUr%s_rho_5e-9_theta_0_selection_False_MS_LD_0.05_0.95_averaged.txt' %(NAm, EUr)),'r')
for line in inFile:
items=line.strip().split('\t')
position=items[0]
R2=items[1]
LD_dict[NAm][EUr]['position'].append(int(position))
LD_dict[NAm][EUr]['R2'].append(float(R2))
print('data is loaded...')
#########
# plot: #
#########
colors=['#fed976','#feb24c', '#fd8d3c','#fc4e2a', '#e31a1c', '#b10026', '#bdd7e7','#6baed6', '#08519c','#8c510a','pink','#d9ef8b', '#66bd63']
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 12
fig_size[1] = 5
supplement_plot_no=3
for EUr in EUrs:
plot_no=1
for summary_statistic in ['Pi', 'S', 'H12']:
plt.subplot(2, 3, plot_no)
if summary_statistic=='H12':
bp = plt.boxplot(DGRP_H12, positions=[1], sym='.',patch_artist=True, labels=['DGRP'], widths=0.5)
set_box_color(bp, 'black')
plt.plot( [1]*len(peaks),peaks, 'r.')
col_i=0
for NAm in NAms:
bp = plt.boxplot(summary_statistics[NAm][EUr][summary_statistic], positions=[col_i+2], sym='.',patch_artist=True, widths=0.5, labels=[''])
set_box_color(bp, colors[col_i])
col_i+=1
plt.labels=['1','1','1','61659' ,'1110000','15984500']
#xtickNames = plt.setp(ax1, xticklabels=)
plt.setp(['1','1','1','61659' ,'1110000','15984500'])
else:
col_i=0
for NAm in NAms:
bp = plt.boxplot(summary_statistics[NAm][EUr][summary_statistic], positions=[col_i+1], sym='.',patch_artist=True, widths=0.5, labels=[''])
set_box_color(bp, colors[col_i])
col_i+=1
# dummy boxplot to get the colors to work for last one
bp = plt.boxplot(summary_statistics[NAm][EUr][summary_statistic], positions=[col_i+10], sym='.',patch_artist=True, widths=0.5, labels=[''])
set_box_color(bp, 'white')
plt.xlim(0,len(NAms)+2)
# plot data median
plt.axhline(y=data_medians[summary_statistic],linestyle='dashed',color='black', label='Median value in data')
if summary_statistic=='H12':
plt.ylim(0,0.3)
plt.ylabel('H12 (401 SNP windows)')
elif summary_statistic=='Pi':
plt.ylim(0,0.03)
plt.ylabel('Pi/bp')
plt.title('European $Ne$ = %s' %EUr)
elif summary_statistic=='S':
plt.ylim(0,0.1)
plt.ylabel('S/bp')
plot_no +=1
##########################
# Plot LD #
##########################
for xlim_range in [100,10000]:
plt.subplot(2, 3, plot_no)
i=0
for NAm in NAms:
plt.semilogy(LD_dict[NAm][EUr]['position'], LD_dict[NAm][EUr]['R2'], color=colors[i])
plt.xlim(0,xlim_range)
if xlim_range == 100:
plt.ylim(0.1,1.05)
else:
plt.ylim(0.005,1.05)
i +=1
plt.xlabel('Distance between SNPs (bps)')
plt.ylabel('log($R^2$)')
plt.semilogy(LD_dict['DGRP']['position'], LD_dict['DGRP']['R2'], linestyle='dashed',color='black', label='DGRP')
plot_no +=1
plt.subplot(2, 3, plot_no)
# put some text in plot number 6
plt.text(0.128, 1.05, "DGRP data", rotation=90.)
plt.text(0.2704, 1.05, "2,500", rotation=90.)
plt.text(0.4128, 1.05, "61,659", rotation=90.)
plt.text(0.5552, 1.05, "1,110,000", rotation=90.)
plt.text(0.6976, 1.05, "15,984,500", rotation=90.)
plt.text(0.84, 1.05, "28,000,000", rotation=90.)
plt.text(0.3, 0.6, "North American $Ne$")
plt.axis('off')
plt.tight_layout()
plt.savefig(os.path.expanduser('~/Jensen_response/analysis/Fig_S%s.png' %supplement_plot_no), dpi=600)
supplement_plot_no +=1
plt.close()
######################################
# experiment 2: Varying growth rates #
######################################
print 'Experiment 2: Varying growth rates'
# load the data
summary_statistics={} # store the summary statistics for all the models here. Key = [m_NA][m_NE][H12,S,Pi,avg_dist]
NAm_ancs=[2500, 61659]
NAm_curs=[1110000, 15984500, 28000000]
EUr_ancs=[16982, 67608]
EUr_curs=[700000, 2000000, 9550000]
for NAm_anc in NAm_ancs:
summary_statistics[NAm_anc]={}
LD_dict[NAm_anc]={}
for NAm_cur in NAm_curs:
summary_statistics[NAm_anc][NAm_cur]={}
LD_dict[NAm_anc][NAm_cur]={}
for EUr_anc in EUr_ancs:
summary_statistics[NAm_anc][NAm_cur][EUr_anc]={}