generated from bartonlab/paper-template
/
figures.py
2931 lines (2349 loc) · 118 KB
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figures.py
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############# PACKAGES #############
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.image as mpimg
from scipy import integrate
import scipy.interpolate as sp_interpolate
import seaborn as sns
from colorsys import hls_to_rgb
from dataclasses import dataclass
import mplot as mp
import re
############# PARAMETERS #############
# GLOBAL VARIABLES -- simulation
NUC = ['-', 'A', 'C', 'G', 'T']
# GitHub
HIV_DIR = 'data/HIV'
SIM_DIR = 'data/simulation'
FIG_DIR = 'figures'
# Standard color scheme
BKCOLOR = '#252525'
C_BEN = '#EB4025'
C_BEN_LT = '#F08F78'
C_NEU = '#969696'
C_NEU_LT = '#E8E8E8'
C_DEL = '#3E8DCF'
C_DEL_LT = '#78B4E7'
C_group = ['#32b166','#e5a11c', '#a48cf4','#ff69b4','#ff8c00','#36ada4','#f0e54b']
# Plot conventions
def cm2inch(x): return float(x)/2.54
SINGLE_COLUMN = cm2inch(8.5)
DOUBLE_COLUMN = cm2inch(17.4)
GOLDR = (1.0 + np.sqrt(5)) / 2.0
TICKLENGTH = 3
TICKPAD = 3
AXWIDTH = 0.4
# paper style
FONTFAMILY = 'Arial'
SIZESUBLABEL = 8
SIZELABEL = 6
SIZETICK = 6
SMALLSIZEDOT = 6.
SIZELINE = 0.6
FIGPROPS = {
'transparent' : True,
#'bbox_inches' : 'tight'
}
DEF_ERRORPROPS = {
'mew' : AXWIDTH,
'markersize' : SMALLSIZEDOT/2,
'fmt' : 'o',
'elinewidth' : SIZELINE/2,
'capthick' : 0,
'capsize' : 0
}
DEF_LABELPROPS = {
'family' : FONTFAMILY,
'size' : SIZELABEL,
'color' : BKCOLOR
}
DEF_SUBLABELPROPS = {
'family' : FONTFAMILY,
'size' : SIZESUBLABEL+1,
'weight' : 'bold',
'ha' : 'center',
'va' : 'center',
'color' : 'k',
'clip_on' : False
}
############# PLOTTING FUNCTIONS #############
def read_file(name):
trait = []
p = open(SIM_DIR+'/jobs/'+name,'r')
maxlen = 0
maxnum = 0
for line in p:
temp = re.findall(r'-?\d+\.?\d*e?-?\d*?', line)
data = [float(item) for item in temp]
trait.append(data)
#print(data)
if len(data) > maxlen:
maxlen = len(data)
maxnum += 1
arr = np.zeros((maxnum, maxlen))*np.nan
for cnt in np.arange(maxnum):
arr[cnt, 0:len(trait[cnt])] = np.array(trait[cnt])
p.close()
for i in range(len(trait)):
for j in range(len(trait[i])):
trait[i][j] = int(trait[i][j])
return trait
def plot_example_mpl(**pdata):
"""
Example evolutionary trajectory for a 50-site system and inferred selection coefficients
and trait coefficients, together with aggregate properties for different levels of sampling..
"""
# unpack passed data
n_gen = pdata['n_gen']
dg = pdata['dg']
pop_size = pdata['N']
xfile = pdata['xfile']
n_ben = pdata['n_ben']
n_neu = pdata['n_neu']
n_del = pdata['n_del']
n_tra = pdata['n_tra']
s_ben = pdata['s_ben']
s_neu = pdata['s_neu']
s_del = pdata['s_del']
s_tra = pdata['s_tra']
r_seed = pdata['r_seed']
np.random.seed(r_seed)
show_fig = pdata['show_fig']
# load and process data files
data = np.loadtxt('%s/jobs/sequences/example-%s.dat' % (SIM_DIR, xfile))
x_index = xfile.split('_')[0]
trait_site = read_file('traitsite/traitsite-%s.dat'%(x_index))
#allele frequency x
x = []
for i in range(0, n_gen, dg):
idx = data.T[0]==i
t_data = data[idx].T[2:].T
t_num = data[idx].T[1].T
t_freq = np.einsum('i,ij->j', t_num, t_data) / float(np.sum(t_num))
x.append(t_freq)
x = np.array(x).T # get allele frequency (binary case)
#trait frequency y
y = []
for t in range(0, n_gen, dg):
idx = data.T[0]==t
t_num = data[idx].T[1].T
t_fre = []
for i in range(len(trait_site)):
t_data_i = t_num*0
for j in range(len(trait_site[i])):
site = trait_site[i][j]
t_data_i += data[idx].T[site+2]
for k in range(len(t_data_i)):
if t_data_i[k] != 0:
t_data_i[k] = 1
t_freq_i = np.einsum('i,i', t_num, t_data_i) / float(np.sum(t_num))
t_fre.append(t_freq_i)
y.append(t_fre)
y = np.array(y).T # get trait frequency
s_true = [s_ben for i in range(n_ben)] + [0 for i in range(n_neu)]
s_true += [s_del for i in range(n_del)] + [s_tra for i in range(n_tra)]
# binary case
s_inf = np.loadtxt('%s/jobs/output/sc-%s.dat' %(SIM_DIR,x_index))
# multiple case
# s_origin = np.loadtxt('%s/jobs/output_multiple/sc-%s.dat' %(SIM_DIR,x_index))
# s_inf = np.zeros(seq_length+len(trait_site))
# for i in range(seq_length):
# s_inf[i] = s_origin[2*i+1] - s_origin[2*i]
# s_inf[-2:] = s_origin[-2:]
cov = np.loadtxt('%s/jobs/covariance/covariance-%s.dat' %(SIM_DIR,x_index))
ds = np.linalg.inv(cov) / pop_size
# PLOT FIGURE
## set up figure grid
w = DOUBLE_COLUMN
goldh = w/2.5
fig = plt.figure(figsize=(w, goldh),dpi=1000)
box_tra1 = dict(left=0.10, right=0.48, bottom=0.55, top=0.98)
box_tra2 = dict(left=0.58, right=0.96, bottom=0.55, top=0.98)
box_coe1 = dict(left=0.10, right=0.48, bottom=0.05, top=0.42)
box_coe2 = dict(left=0.58, right=0.96, bottom=0.05, top=0.42)
gs_tra1 = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_tra1)
gs_tra2 = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_tra2)
gs_coe1 = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_coe1)
gs_coe2 = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_coe2)
ax_tra1 = plt.subplot(gs_tra1[0, 0])
ax_tra2 = plt.subplot(gs_tra2[0, 0])
ax_coe1 = plt.subplot(gs_coe1[0, 0])
ax_coe2 = plt.subplot(gs_coe2[0, 0])
dx = -0.04
dy = -0.02
## a -- all allele trajectories together
pprops = { 'xticks': [0, 200, 400, 600, 800,1000],
'yticks': [0,1],
'yminorticks': [0.25, 0.5, 0.75],
'nudgey': 1.1,
'xlabel': 'Generation',
'ylabel': 'Allele\nfrequency, ' + r'$x$',
'plotprops': {'lw': SIZELINE, 'ls': '-', 'alpha': 1 },
'axoffset': 0.1,
'theme': 'open' }
xdat = [range(0, n_gen, dg) for k in range(n_ben)]
ydat = [k for k in x[:n_ben]]
mp.line(ax=ax_tra1, x=xdat, y=ydat, colors=[C_BEN_LT for k in range(len(x))], **pprops)
xdat = [range(0, n_gen, dg) for k in range(n_del)]
ydat = [k for k in x[n_ben+n_neu:]]
mp.line(ax=ax_tra1, x=xdat, y=ydat, colors=[C_DEL_LT for k in range(len(x))], **pprops)
xdat = [range(0, n_gen, dg) for k in range(n_neu)]
ydat = [k for k in x[n_ben:n_ben+n_neu]]
pprops['plotprops']['alpha'] = 0.4
mp.plot(type='line',ax=ax_tra1, x=xdat, y=ydat, colors = [C_NEU for k in range(len(x))], **pprops)
ax_tra1.text(box_tra1['left']+dx, box_tra1['top']+dy, 'a'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
# b -- all trait trajectories together
pprops = { 'xticks': [0, 200, 400, 600, 800,1000],
'yticks': [0, 1],
'yminorticks': [0.25,0.5, 0.75],
'nudgey': 1.1,
'xlabel': 'Generation',
'ylabel': 'Frequency, ' + r'$x$',
'plotprops': {'lw': SIZELINE, 'ls': '-' , 'alpha': 1},
'axoffset': 0.1,
'theme': 'open' }
traj_legend_x = 220
traj_legend_y = [0.65, 0.40]
traj_legend_t = ['Trait\nfrequency','Individual \nallele frequency']
for k in range(len(traj_legend_y)):
x1 = traj_legend_x-50
x2 = traj_legend_x-10
y1 = traj_legend_y[0] + (0.5-k) * 0.03
y2 = traj_legend_y[1] + (0.5-k) * 0.03
pprops['plotprops']['alpha'] = 1
mp.line(ax=ax_tra2, x=[[x1, x2]], y=[[y1, y1]], colors=[C_group[k]], **pprops)
pprops['plotprops']['alpha'] = 0.4
mp.line(ax=ax_tra2, x=[[x1, x2]], y=[[y2, y2]], colors=[C_group[k]], **pprops)
ax_tra2.text(traj_legend_x, traj_legend_y[k], traj_legend_t[k], ha='left', va='center', **DEF_LABELPROPS)
xdat = [range(0, n_gen, dg)]
for i in range(len(trait_site)):
for j in range(len(trait_site[i])):
site = trait_site[i][j]
ydat = [k for k in x[site:site+1]]
pprops['plotprops']['alpha'] = 0.6
mp.line(ax=ax_tra2, x=xdat, y=ydat, colors=[C_group[i]], **pprops)
if i > 0:
ydat = [k for k in y[i:i+1]]
pprops['plotprops']['alpha'] = 1
mp.line(ax=ax_tra2, x=xdat, y=ydat, colors = [C_group[i]], **pprops)
ydat = [k for k in y[0:1]]
pprops['plotprops']['alpha'] = 1
mp.plot(type='line',ax=ax_tra2, x=xdat, y=ydat, colors = [C_group[0]], **pprops)
ax_tra2.text(box_tra2['left']+dx, box_tra1['top']+dy, 'b'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
## c -- individual beneficial/neutral/deleterious selection coefficients
sprops = { 'lw' : 0, 's' : 9., 'marker' : 'o' }
pprops = { 'xlim': [ -0.3, 4],
'ylim': [-0.05, 0.04],
'yticks': [-0.04, 0, 0.04],
'yminorticks': [-0.03, -0.02, -0.01, 0.01, 0.02, 0.03],
'yticklabels': [-4, 0, 4],
'xticks': [],
'ylabel': 'Inferred selection\ncoefficient, ' + r'$\hat{s}$' + ' (%)',
'theme': 'open',
'hide': ['bottom'] }
n_coe1 = [n_ben, n_neu, n_del]
c_coe1 = [C_BEN, C_NEU, C_DEL]
c_coe1_lt = [C_BEN_LT, C_NEU_LT, C_DEL_LT]
offset = [0, n_ben, n_ben+n_neu]
for k in range(len(n_coe1)):
mp.line(ax=ax_coe1, x=[[k-0.35, k+0.35]], y=[[s_true[offset[k]], s_true[offset[k]]]], \
colors=[BKCOLOR], plotprops=dict(lw=SIZELINE, ls=':'), **pprops)
plotprops = DEF_ERRORPROPS.copy()
plotprops['alpha'] = 1
for i in range(n_coe1[k]):
xdat = [k + np.random.normal(0, 0.08)]
ydat = [s_inf[offset[k]+i]]
yerr = np.sqrt(ds[offset[k]+i][offset[k]+i])
if i==n_coe1[k]-1 and k==len(n_coe1)-1:
mp.plot(type='error', ax=ax_coe1, x=[xdat], y=[ydat], yerr=[yerr], \
edgecolor=[c_coe1[k]], facecolor=[c_coe1_lt[k]], plotprops=plotprops, **pprops)
else:
mp.error(ax=ax_coe1, x=[xdat], y=[ydat], yerr=[yerr], edgecolor=[c_coe1[k]], \
facecolor=[c_coe1_lt[k]], plotprops=plotprops, **pprops)
coef_legend_x = 2.8
coef_legend_d = -0.15
coef_legend_dy = -0.011
coef_legend_y = [0.02, 0.02 + coef_legend_dy, 0.02 + (2*coef_legend_dy)]
coef_legend_t = ['Beneficial', 'Neutral', 'Deleterious']
for k in range(len(coef_legend_y)):
mp.error(ax=ax_coe1, x=[[coef_legend_x+coef_legend_d]], y=[[coef_legend_y[k]]], \
edgecolor=[c_coe1[k]], facecolor=[c_coe1_lt[k]], plotprops=plotprops, **pprops)
ax_coe1.text(coef_legend_x, coef_legend_y[k], coef_legend_t[k], ha='left', va='center', **DEF_LABELPROPS)
yy = 0.02 + 3.5 * coef_legend_dy
mp.line(ax=ax_coe1, x=[[coef_legend_x-0.21, coef_legend_x-0.09]], y=[[yy, yy]], \
colors=[BKCOLOR], plotprops=dict(lw=SIZELINE, ls=':'), **pprops)
ax_coe1.text(coef_legend_x, yy, 'True \ncoefficient', ha='left', va='center', **DEF_LABELPROPS)
ax_coe1.text(box_coe1['left']+dx, box_coe1['top']+0.04, 'c'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
## d -- trait coefficients
sprops = { 'lw' : 0, 's' : 9., 'marker' : 'o' }
pprops = { 'xlim': [-0.3, 6],
'ylim': [ 0,0.16],
'yticks': [ 0,0.08,0.16],
'yminorticks': [0.02,0.04,0.06,0.10,0.12,0.14],
'yticklabels': [ 0, 8, 16],
'xticks': [],
'ylabel': 'Inferred trait\ncoefficient, ' + r'$\hat{s}$' + ' (%)',
'theme': 'open',
'hide': ['bottom'] }
n_coe2 = []
c_coe2 = []
c_coe2_lt = []
offset = n_ben+n_neu+n_del
for i in range(n_tra):
c_coe2.append(C_group[i])
c_coe2_lt.append(C_group[i])
mp.line(ax=ax_coe2, x=[[0.15, 0.85]], y=[[s_true[-1], s_true[-1]]], \
colors=[BKCOLOR], plotprops=dict(lw=SIZELINE, ls=':'), **pprops)
plotprops = DEF_ERRORPROPS.copy()
plotprops['alpha'] = 1
for i in range(n_tra):
xdat = [np.random.normal(0.5, 0.18)]
ydat = [s_inf[offset+i]]
yerr = np.sqrt(ds[offset+i][offset+i])
if i==n_tra-1:
mp.plot(type='error', ax=ax_coe2, x=[xdat], y=[ydat], yerr=[yerr], \
edgecolor=[c_coe2[i]], facecolor=[c_coe2_lt[i]], plotprops=plotprops, **pprops)
else:
mp.error(ax=ax_coe2, x=[xdat], y=[ydat], yerr=[yerr], \
edgecolor=[c_coe2[i]], facecolor=[c_coe2_lt[i]], plotprops=plotprops, **pprops)
coef_legend_d = -0.15 * (6.3 / 4.3)
coef_legend_x = 1.8
coef_legend_dy = -0.11 * (0.16 / 0.9) #-0.02
coef_legend_y = []
coef_legend_t = []
for i in range(len(trait_site)):
coef_legend_y.append(0.135 + i * coef_legend_dy)
coef_legend_t.append('Trait %d'%(i+1))
for k in range(len(coef_legend_y)):
mp.error(ax=ax_coe2, x=[[coef_legend_x+coef_legend_d+0.03]], y=[[coef_legend_y[k]]], \
edgecolor=[c_coe2[k]], facecolor=[c_coe2_lt[k]], plotprops=plotprops, **pprops)
ax_coe2.text(coef_legend_x, coef_legend_y[k], coef_legend_t[k], ha='left', va='center', **DEF_LABELPROPS)
yy = 0.135 +(0.5+n_tra) * coef_legend_dy
mp.line(ax=ax_coe2, x=[[coef_legend_x-0.24, coef_legend_x-0.09]], y=[[yy, yy]], \
colors=[BKCOLOR], plotprops=dict(lw=SIZELINE, ls=':'), **pprops)
ax_coe2.text(coef_legend_x, yy, 'True\ncoefficient', ha='left', va='center', **DEF_LABELPROPS)
ax_coe2.text(box_coe2['left']+dx, box_coe2['top']+0.04, 'd'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
# SAVE FIGURE
if show_fig:
plt.savefig('%s/fig1-sim.pdf' % (FIG_DIR), facecolor = fig.get_facecolor(), edgecolor=None, **FIGPROPS)
print('Figure saved as fig1-sim.pdf')
else:
plt.savefig('%s/sim/%s.pdf' % (FIG_DIR,x_index), facecolor = fig.get_facecolor(), edgecolor=None, **FIGPROPS)
plt.close()
def plot_histogram_sim(**pdata):
"""
histogram of selection coefficients and trait coefficients
"""
# unpack passed data
n_ben = pdata['n_ben']
n_neu = pdata['n_neu']
n_del = pdata['n_del']
n_tra = pdata['n_tra']
s_ben = pdata['s_ben']
s_neu = pdata['s_neu']
s_del = pdata['s_del']
s_tra = pdata['s_tra']
# PLOT FIGURE
## set up figure grid
w = DOUBLE_COLUMN #SLIDE_WIDTH
goldh = w / 1.8
fig = plt.figure(figsize=(w, goldh),dpi=1000)
box_se = dict(left=0.10, right=0.62, bottom=0.65, top=0.95)
box_tra = dict(left=0.69, right=0.92, bottom=0.65, top=0.95)
box_aur1 = dict(left=0.10, right=0.32, bottom=0.07, top=0.50)
box_aur2 = dict(left=0.40, right=0.62, bottom=0.07, top=0.50)
box_erro = dict(left=0.70, right=0.92, bottom=0.07, top=0.50)
gs_se = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_se)
gs_tra = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_tra)
gs_aur1 = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_aur1)
gs_aur2 = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_aur2)
gs_erro = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_erro)
ax_se = plt.subplot(gs_se[0, 0])
ax_tra = plt.subplot(gs_tra[0, 0])
ax_aur1 = plt.subplot(gs_aur1[0, 0])
ax_aur2 = plt.subplot(gs_aur2[0, 0])
ax_erro = plt.subplot(gs_erro[0, 0])
dx = -0.04
dy = 0.03
### plot histogram
df_all = pd.read_csv('%s/mpl_collected_nsdt.csv' % SIM_DIR, memory_map=True)
df = df_all[(df_all.ns==1000) & (df_all.delta_t==1)]
ben_cols = ['sc_%d' % i for i in range(n_ben)]
neu_cols = ['sc_%d' % i for i in range(n_ben, n_ben+n_neu)]
del_cols = ['sc_%d' % i for i in range(n_ben+n_neu, n_ben+n_neu+n_del)]
tra_cols = ['tc_%d' % i for i in range(n_tra)]
colors = [C_BEN, C_NEU, C_DEL]
tags = ['beneficial', 'neutral', 'deleterious','trait']
cols = [ben_cols, neu_cols, del_cols, tra_cols]
s_true_loc = [s_ben, s_neu, s_del,s_tra]
## a -- selection part
dashlineprops = { 'lw' : SIZELINE * 1.5, 'ls' : ':', 'alpha' : 0.5, 'color' : BKCOLOR }
histprops = dict(histtype='bar', lw=SIZELINE/2, rwidth=0.8, ls='solid', alpha=0.6, edgecolor='none')
pprops = { 'xlim' : [ -0.04, 0.04],
'xticks' : [ -0.04, -0.03, -0.02, -0.01, 0., 0.01, 0.02, 0.03, 0.04],
'xticklabels' : [ -4, -3, -2, -1, 0, 1, 2, 3, 4],
'ylim' : [0., 0.10],
'yticks' : [0., 0.05, 0.10],
'xlabel' : 'Inferred selection coefficient, ' + r'$\hat{s}$' + ' (%)',
'ylabel' : 'Frequency',
'bins' : np.arange(-0.04, 0.04, 0.001),
'combine' : True,
'plotprops' : histprops,
'axoffset' : 0.1,
'theme' : 'boxed' }
for i in range(len(tags)-1):
x = [np.array(df[cols[i]]).flatten()]
tprops = dict(ha='center', va='center', family=FONTFAMILY, size=SIZELABEL, clip_on=False)
ax_se.text(s_true_loc[i], 0.106, r'$s_{%s}$' % (tags[i]), color=colors[i], **tprops)
ax_se.axvline(x=s_true_loc[i], **dashlineprops)
if i<len(tags)-2: mp.hist( ax=ax_se, x=x, colors=[colors[i]], **pprops)
else: mp.plot(type='hist', ax=ax_se, x=x, colors=[colors[i]], **pprops)
ax_se.text( box_se['left']+dx, box_se['top']+dy, 'a'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
## b -- trait part
histprops = dict(histtype='bar', lw=SIZELINE/2, rwidth=0.8, ls='solid', alpha=0.6, edgecolor='none')
pprops = { 'xlim' : [ 0, 0.12],
'xticks' : [ 0, 0.04, 0.08, 0.10, 0.12],
'xticklabels' : [ 0, 4, 8, 10, 12],
'ylim' : [0., 0.15],
'yticks' : [0., 0.05, 0.10, 0.15],
'xlabel' : 'Inferred trait coefficient, ' + r'$\hat{s}$' + ' (%)',
'ylabel' : 'Frequency',
'bins' : np.arange(0, 0.12, 0.003),
'plotprops' : histprops,
'axoffset' : 0.1,
'theme' : 'boxed' }
x = [np.array(df[cols[3]]).flatten()]
tprops = dict(ha='center', va='center', family=FONTFAMILY, size=SIZELABEL, clip_on=False)
ax_tra.text(s_true_loc[3], 0.159, r'$s_{%s}$' % (tags[3]), color=C_group[0], **tprops)
ax_tra.axvline(x=s_true_loc[3], **dashlineprops)
mp.plot(type='hist', ax=ax_tra, x=x, colors=[C_group[0]], **pprops)
ax_tra.text( box_tra['left']+dx, box_tra['top']+dy, 'b'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
## c,d -- AUCs for inferring beneficial/deleterious mutations and error for trait part
df = pd.read_csv('%s/mpl_collected_extended.csv' % SIM_DIR, memory_map=True)
ns_vals = [10, 20, 30, 40, 50, 80, 100]
dt_vals = [1, 5, 10, 20, 50]
AUC_matrix_ben = np.zeros((len(dt_vals), len(ns_vals)))
AUC_matrix_del = np.zeros((len(dt_vals), len(ns_vals)))
err_matrix_tra = np.zeros((len(dt_vals), len(ns_vals)))
for i in range(len(dt_vals)):
for j in range(len(ns_vals)):
df_AUC = df[(df.delta_t==dt_vals[i]) & (df.ns==ns_vals[j])]
AUC_matrix_ben[i, j] = np.mean(df_AUC.AUROC_ben)
AUC_matrix_del[i, j] = np.mean(df_AUC.AUROC_del)
err_matrix_tra[i, j] = np.mean(df_AUC.error_tra)
pprops = { 'xlim' : [0, len(dt_vals)],
'xticks' : np.arange(len(dt_vals))+0.5,
'xticklabels' : [int(k) for k in dt_vals],
'ylim' : [0, len(ns_vals)],
'yticks' : np.arange(len(ns_vals))+0.5,
'yticklabels' : [int(k) for k in ns_vals],
'xlabel' : 'Time between samples, '+r'$\Delta t$' + ' (generations)',
'ylabel' : 'Number of sequences per time point, '+r'$n_s$',
'theme' : 'boxed' }
tprops = dict(ha='center', va='center', family=FONTFAMILY, size=SIZELABEL, clip_on=False)
ax_aur1.pcolor(AUC_matrix_ben.T, vmin=0.75, vmax=1.0, cmap='GnBu', alpha=0.75)
for i in range(len(AUC_matrix_ben)):
for j in range(len(AUC_matrix_ben[0])):
tc = 'k'
if AUC_matrix_ben[i,j]>0.96: tc = 'white'
ax_aur1.text(i+0.5, j+0.5, '%.2f' % (AUC_matrix_ben[i,j]), color=tc, **tprops)
mp.plot(type='scatter', ax=ax_aur1, x=[[-1]], y=[[-1]], colors=[BKCOLOR], **pprops)
ax_aur2.pcolor(AUC_matrix_del.T, vmin=0.75, vmax=1.0, cmap='GnBu', alpha=0.75)
for i in range(len(AUC_matrix_del)):
for j in range(len(AUC_matrix_del[0])):
tc = 'k'
if AUC_matrix_del[i,j]>0.96: tc = 'white'
ax_aur2.text(i+0.5, j+0.5, '%.2f' % (AUC_matrix_del[i,j]), color=tc, **tprops)
mp.plot(type='scatter', ax=ax_aur2, x=[[-1]], y=[[-1]], colors=[BKCOLOR], **pprops)
ax_erro.pcolor(err_matrix_tra.T, vmin=0.2, vmax=0.8, cmap='GnBu', alpha=0.75)
for i in range(len(err_matrix_tra)):
for j in range(len(err_matrix_tra[0])):
tc = 'k'
ax_erro.text(i+0.5, j+0.5, '%.2f' % (err_matrix_tra[i,j]), color=tc, **tprops)
mp.plot(type='scatter', ax=ax_erro, x=[[-1]], y=[[-1]], colors=[BKCOLOR], **pprops)
## outside text labels
tprops = dict(color=BKCOLOR, ha='center', va='center', family=FONTFAMILY, size=SIZELABEL,
clip_on=False, transform=fig.transFigure)
ax_aur1.text((box_aur1['right']-box_aur1['left'])/2+box_aur1['left'], \
box_aur1['top']+dy, 'Mean AUROC (beneficial)', **tprops)
ax_aur2.text((box_aur2['right']-box_aur2['left'])/2+box_aur2['left'], \
box_aur2['top']+dy, 'Mean AUROC (deleterious)', **tprops)
ax_erro.text((box_erro['right']-box_erro['left'])/2+box_erro['left'], \
box_erro['top']+dy, 'NRMSE (trait)', **tprops)
ax_aur1.text(box_aur1['left']+dx, box_aur1['top']+dy, 'c'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
ax_aur2.text(box_aur2['left']+dx, box_aur2['top']+dy, 'd'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
ax_erro.text(box_erro['left']+dx, box_erro['top']+dy, 'e'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
# SAVE FIGURE
plt.savefig('%s/sim-his.pdf' % FIG_DIR, facecolor = fig.get_facecolor(), edgecolor=None, **FIGPROPS)
print('Figure saved as sim-his.pdf')
def plot_sc_escape(**pdata):
"""
a. Histogram of selection coefficients for trait sites with group term
b. Histogram of selection coefficients for trait sites without group term
"""
# unpack passed data
tags = pdata['tags']
# get all selection coefficients for trait sites
sc_all = []
sc_all_notrait = []
for tag in tags:
df_epitope = pd.read_csv('%s/group/escape_group-%s.csv'%(HIV_DIR,tag), comment='#', memory_map=True)
df_epitope = df_epitope[(df_epitope.escape == True)]
for i in range(len(df_epitope)):
sc_all_notrait.append(df_epitope.iloc[i].sc_old)
sc_all.append(df_epitope.iloc[i].sc_MPL)
# PLOT FIGURE
## set up figure grid
w = SINGLE_COLUMN
goldh = w / 1.5
fig = plt.figure(figsize=(w, goldh),dpi=1000)
box_old = dict(left=0.15, right=0.92, bottom=0.61, top=0.95)
box_new = dict(left=0.15, right=0.92, bottom=0.14, top=0.48)
gs_old = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_old)
gs_new = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_new)
ax_old = plt.subplot(gs_old[0, 0])
ax_new = plt.subplot(gs_new[0, 0])
dx = -0.10
dy = 0.02
### plot histogram - with and without escape term
histprops = dict(histtype='bar', lw=SIZELINE/2, rwidth=0.8, ls='solid', alpha=0.6, edgecolor='none')
pprops = { 'xlim' : [ -0.05, 0.10],
'xticks' : [ -0.05, 0, 0.05, 0.1],
'xticklabels' : [ ],
'ylim' : [0., 0.30],
'yticks' : [0., 0.10, 0.20, 0.30],
'ylabel' : 'Frequency',
'bins' : np.arange(-0.05, 0.10, 0.002),
'combine' : True,
'plotprops' : histprops,
'axoffset' : 0.1,
'theme' : 'boxed' }
# without escape term
x = [np.array(sc_all_notrait)]
mp.plot(type='hist', ax=ax_old, x=x, colors=[C_group[1]], **pprops)
# with escape term
pprops['xticklabels'] = [ -5, 0, 5, 10]
pprops['xlabel'] = 'Inferred selection coefficient for escape sites, ' + r'$\hat{s}$ ' +'(%)'
x = [np.array(sc_all)]
mp.plot(type='hist', ax=ax_new, x=x, colors=[C_group[0]], **pprops)
# add labels
ax_old.text(box_old['left']+dx, box_old['top']+dy, 'a'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
ax_new.text(box_new['left']+dx, box_new['top']+dy, 'b'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
ax_old.text(0.02, 0.21, 'Without escape trait', **DEF_LABELPROPS)
ax_new.text(0.02, 0.21, 'With escape trait', **DEF_LABELPROPS)
# SAVE FIGURE
plt.savefig('%s/sc_escape.pdf' % FIG_DIR, facecolor = fig.get_facecolor(), edgecolor=None, **FIGPROPS)
print('Figure saved as sc_escape.pdf')
def plot_tc_rec(**pdata):
"""
a. Histogram of trait coefficients with recombination
b. Histogram of trait coefficients without recombination
"""
# unpack passed data
tags = pdata['tags']
# get all trait coefficients
tc_all = []
tc_all_noR = []
for tag in tags:
df_tc = pd.read_csv('%s/group/escape_group-%s.csv'%(HIV_DIR,tag), comment='#', memory_map=True)
df_tc_noR = pd.read_csv('%s/noR/group/escape_group-%s.csv'%(HIV_DIR,tag), comment='#', memory_map=True)
unique_epitopes = df_tc['epitope'].unique()
for epitope in unique_epitopes:
tc_all.append(df_tc[df_tc.epitope == epitope].iloc[0].tc_MPL)
tc_all_noR.append(df_tc_noR[df_tc_noR.epitope == epitope].iloc[0].tc_MPL)
# PLOT FIGURE
## set up figure grid
w = SINGLE_COLUMN
goldh = w / 1.5
fig = plt.figure(figsize=(w, goldh),dpi=1000)
box_old = dict(left=0.15, right=0.92, bottom=0.61, top=0.95)
box_new = dict(left=0.15, right=0.92, bottom=0.14, top=0.48)
gs_old = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_old)
gs_new = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_new)
ax_old = plt.subplot(gs_old[0, 0])
ax_new = plt.subplot(gs_new[0, 0])
dx = -0.10
dy = 0.02
### plot histogram - with and without escape term
histprops = dict(histtype='bar', lw=SIZELINE/2, rwidth=0.8, ls='solid', alpha=0.6, edgecolor='none')
pprops = { 'xlim' : [ -0.05, 0.30],
'xticks' : [ -0.05, 0, 0.05, 0.1 , 0.15, 0.2, 0.25, 0.30],
'xticklabels' : [ ],
'ylim' : [0., 0.20],
'yticks' : [0., 0.10, 0.20],
'ylabel' : 'Frequency',
'bins' : np.arange(-0.05, 0.30, 0.005),
'combine' : True,
'plotprops' : histprops,
'axoffset' : 0.1,
'theme' : 'boxed' }
# without escape term
x = [np.array(tc_all_noR)]
mp.plot(type='hist', ax=ax_old, x=x, colors=[C_group[1]], **pprops)
# with escape term
pprops['xticklabels'] = [ -5, 0, 5, 10, 15, 20, 25, 30]
pprops['xlabel'] = 'Inferred escape coefficient, ' + r'$\hat{s}$ ' +'(%)'
x = [np.array(tc_all)]
mp.plot(type='hist', ax=ax_new, x=x, colors=[C_group[0]], **pprops)
ax_old.text(0.113, 0.14, 'Without recombination', **DEF_LABELPROPS)
ax_new.text(0.113, 0.14, 'With recombination', **DEF_LABELPROPS)
ax_old.text(box_new['left']+dx, box_new['top']+dy, 'b'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
ax_new.text(box_old['left']+dx, box_old['top']+dy, 'a'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
# SAVE FIGURE
plt.savefig('%s/tc_rec.pdf' % FIG_DIR, facecolor = fig.get_facecolor(), edgecolor=None, **FIGPROPS)
print('Figure saved as tc_rec.pdf')
def plot_histogram_fraction_HIV(**pdata):
"""
a. Histogram of selection coefficients and escape coefficients
b. Fraction for escape part
"""
# unpack passed data
tags = pdata['tags']
ppts = pdata['ppts']
# get all escape coefficients
tc = []
for tag in tags:
df_tc = pd.read_csv('%s/group/escape_group-%s.csv'%(HIV_DIR,tag), comment='#', memory_map=True)
unique_epitopes = df_tc['epitope'].unique()
for epitope in unique_epitopes:
tc.append(df_tc[df_tc.epitope == epitope].iloc[0].tc_MPL)
# get all escape contribution
fractions = []
common_times = []
for i in range(len(ppts)):
ppt = ppts[i]
tag_3 = ppt + '-' + str(3)
tag_5 = ppt + '-' + str(5)
t_3, f_3,n_3 = getFitness(tag_3)
t_5, f_5,n_5 = getFitness(tag_5)
common_t = np.intersect1d(t_3, t_5)
fraction = np.zeros(len(common_t))
for i in range(len(common_t)):
index_3 = list(t_3).index(common_t[i])
index_5 = list(t_5).index(common_t[i])
FitAll = (f_3[0,index_3]*n_3[index_3]+f_5[0,index_5]*n_5[index_5])/(n_3[index_3]+n_5[index_5])
FitEscape = (f_3[1,index_3]*n_3[index_3]+f_5[1,index_5]*n_5[index_5])/(n_3[index_3]+n_5[index_5])
if FitAll != 1:
fraction[i] = FitEscape/(FitAll-1)
if FitAll<1 and FitEscape<0:
fraction[i] = 0
if fraction[i] < 0:
fraction[i] = 0
if fraction[i] > 1:
fraction[i] = 1
common_times.append(common_t)
fractions.append(fraction)
max_times = [max(common_times[i]) for i in range(len(common_times))]
max_time = int(max(max_times))
whole_time = np.linspace(0,max_time,max_time+1)
interpolation = lambda a,b: sp_interpolate.interp1d(a,b,kind='linear',fill_value=(0,0), bounds_error=False)
IntFractions = np.zeros((len(common_times),len(whole_time)))
IntNumber = np.zeros((len(common_times),len(whole_time)))
for i in range(len(common_times)):
IntFractions[i] = interpolation(common_times[i], fractions[i])(whole_time)
IntNumber[i] = interpolation(common_times[i], np.ones(len(common_times[i])))(whole_time)
AveFraction = np.zeros(len(whole_time))
for t in range(len(whole_time)):
fraction_t = np.sum(IntFractions[:,t])
number_t = np.sum(IntNumber[:,t])
AveFraction[t] = fraction_t/number_t
# PLOT FIGURE
## set up figure grid
w = SINGLE_COLUMN
goldh = w / 1.1
fig = plt.figure(figsize=(w, goldh),dpi=1000)
box_tc = dict(left=0.15, right=0.92, bottom=0.59, top=0.96)
box_frac = dict(left=0.15, right=0.92, bottom=0.12, top=0.42)
gs_frac = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_frac)
gs_tc = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_tc)
ax_tc = plt.subplot(gs_tc[0, 0])
ax_frac = plt.subplot(gs_frac[0, 0])
dx = -0.10
dy = 0.02
### plot histogram
histprops = dict(histtype='bar', lw=SIZELINE/2, rwidth=0.8, ls='solid', alpha=0.6, edgecolor='none')
pprops = { 'xlim' : [ -0.05, 0.30],
'xticks' : [ -0.05, 0, 0.05, 0.1 , 0.15, 0.2, 0.25, 0.30],
'xticklabels' : [ -5, 0, 5, 10, 15, 20, 25, 30],
'ylim' : [0., 0.30],
'yticks' : [0., 0.15, 0.30],
'xlabel' : 'Inferred escape coefficient, ' + r'$\hat{s}$ ' +'(%)',
'ylabel' : 'Frequency',
'bins' : np.arange(-0.05, 0.30, 0.01),
'combine' : True,
'plotprops' : histprops,
'axoffset' : 0.1,
'theme' : 'boxed' }
x = [np.array(tc)]
mp.plot(type='hist', ax=ax_tc, x=x, colors=[C_group[0]], **pprops)
ax_tc.text(box_tc['left']+dx, box_tc['top']+dy, 'a'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
### plot fraction
pprops = { #'xticks': [ 0, 100, 200, 300, 400, 500, 600, 700],
'xticks': [ 0, np.log(11),np.log(51),np.log(101), np.log(201),np.log(401),np.log(701)],
'xticklabels': [ 0, 10, 50, 100, 200, 400, 700],
'ylim' : [0., 1.01],
'yticks': [0., 1],
'yminorticks': [0.25, 0.5, 0.75],
'yticklabels': [0, 1],
'nudgey': 1.1,
'xlabel': 'Time (days)',
'ylabel': 'Fitness gain fraction \ndue to escape',
'plotprops': {'lw': SIZELINE, 'ls': '-','alpha':0.5},
'axoffset': 0.1,
'theme': 'open',
'combine' : True}
# individual fraction
for i in range(len(common_times)):
max_t_i = int(max(common_times[i]))
time_i = np.linspace(0,max_t_i,max_t_i+1)
time = np.log(time_i+1)
mp.line(ax=ax_frac, x=[time], y=[IntFractions[i][:max_t_i+1]], colors=[C_group[0]], **pprops)
# 0
pprops['plotprops']['ls'] = '--'
mp.line(ax=ax_frac,x=[[0,6.5]], y=[[0,0]],colors=[C_NEU], **pprops)
# average curve
pprops['plotprops']['alpha'] = 1
pprops['plotprops']['lw'] = SIZELINE*1.8
pprops['plotprops']['ls'] = '-'
time = np.log(whole_time+1)
mp.plot(type='line', ax=ax_frac, x=[time], y=[AveFraction],colors=[C_NEU], **pprops)
# legend
traj_legend_x = np.log(300)
traj_legend_y = [1.0, 0.87]
traj_legend_t = ['Individual', 'Average']
x1 = traj_legend_x-0.4
x2 = traj_legend_x-0.1
y1 = traj_legend_y[0] + 0.015
y2 = traj_legend_y[1] + 0.015
pprops['plotprops']['alpha'] = 0.5
pprops['plotprops']['lw'] = SIZELINE
mp.line(ax=ax_frac, x=[[x1, x2]], y=[[y1, y1]], colors=[C_group[0]], **pprops)
pprops['plotprops']['alpha'] = 1
pprops['plotprops']['lw'] = SIZELINE*1.8
mp.line(ax=ax_frac, x=[[x1, x2]], y=[[y2, y2]], colors=[C_NEU], **pprops)
ax_frac.text(traj_legend_x, traj_legend_y[0], traj_legend_t[0], ha='left', va='center', **DEF_LABELPROPS)
ax_frac.text(traj_legend_x, traj_legend_y[1], traj_legend_t[1], ha='left', va='center', **DEF_LABELPROPS)
# label
ax_frac.text(box_frac['left']+dx, box_frac['top']+dy, 'b'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
# SAVE FIGURE
plt.savefig('%s/fig2-HIV.pdf' % FIG_DIR, facecolor = fig.get_facecolor(), edgecolor=None, **FIGPROPS)
print('Figure saved as fig2-HIV.pdf')
def plot_CH470_3(**pdata):
"""
epitope escape mutation frequencies, inferred escape coefficients.
"""
# unpack data
patient = pdata['patient']
region = pdata['region']
epitope = pdata['epitope']
traj_ticks = pdata['traj_ticks']
tag = patient+'-'+region
# process stored data
df_poly = pd.read_csv('%s/group/escape_group-%s.csv' % (HIV_DIR, tag), comment='#', memory_map=True)
times = [int(i.split('_')[-1]) for i in df_poly.columns if 'f_at_' in i]
times.sort()
var_tag = []
traj_poly = []
poly_info = {}
var_tc = []
var_traj = []
var_color = []
for i in range(len(epitope)):
df_esc = df_poly[(df_poly.epitope==epitope[i])]
df_row = df_esc.iloc[0]
epi_nuc = ''.join(epitope[i])
p_tag = epi_nuc[0]+epi_nuc[-1]+str(len(epi_nuc))
p_c = df_esc.iloc[0].tc_MPL
poly_info[p_tag] = p_c
traj_poly.append([df_row['xp_at_%d' % t] for t in times])
traj_indi = []
for df_iter, df_entry in df_esc.iterrows():
traj_indi.append([df_entry['f_at_%d' % t] for t in times])
var_traj.append(traj_indi)
var_c = sns.husl_palette(len(traj_poly))
# Sort by the value of inferred escape coefficients
poly_info_1 = sorted(poly_info.items(),key=lambda x: x[1],reverse=True)
poly_key = list(poly_info.keys())
for i in poly_info_1:
var_tag.append(i[0])
var_tc.append(i[1])
index_i = poly_key.index(i[0])
var_color.append(var_c[index_i])
# PLOT FIGURE
## set up figure grid
w = SINGLE_COLUMN
goldh = w /2
fig = plt.figure(figsize=(w, goldh),dpi=1000)
box_traj = dict(left=0.17, right=0.95, bottom=0.57, top=0.90)
box_coef = dict(left=0.17, right=0.44, bottom=0.15, top=0.38)
box_lend = dict(left=0.55, right=0.95, bottom=0.15, top=0.38)
gs_traj = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_traj)
gs_coef = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_coef)
gs_lend = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_lend)
ax_traj = plt.subplot(gs_traj[0, 0])
ax_coef = plt.subplot(gs_coef[0, 0])
ax_lend = plt.subplot(gs_lend[0, 0])
dx = -0.15
dy = 0.06
## a -- trajectory plot
pprops = { 'xticks': traj_ticks,
'yticks': [0, 1.0],
'ylim': [0, 0.33],
'yminorticks': [0.25, 0.5, 0.75],
'nudgey': 1.1,
'xlabel': 'Time (days)',
'ylabel': 'Variant frequency',
'plotprops': {'lw': SIZELINE, 'ls': '-', 'alpha': 1.0 },
'axoffset': 0.1,
'theme': 'open',
'combine' : True}
for i in range(len(var_tag)):
pprops['plotprops']['alpha'] = 1
xdat = [times]
ydat = [traj_poly[i]]
mp.line(ax=ax_traj, x=xdat, y=ydat, colors=[var_c[i]], **pprops)
pprops['plotprops']['alpha'] = 0.4
for j in range(len(var_traj[i])):
ydat = [var_traj[i][j]]