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Fig3.py
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Fig3.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 14 09:19:29 2020
@author: grotheer
"""
#import all dependencies
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from scipy.io import loadmat
from matplotlib.lines import Line2D
import pylab as plot
from numpy.matlib import repmat
from itertools import repeat
#load the necessary data
mri0R1=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri0R1ForPy.mat')
mri0Tracts=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri0TractsForPy.mat')
mri0Subj=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri0SubjForPy.mat')
mri0Nodes=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri0NodesForPy.mat')
mri3R1=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri3R1ForPy.mat')
mri3Tracts=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri3TractsForPy.mat')
mri3Subj=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri3SubjForPy.mat')
mri3Nodes=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri3NodesForPy.mat')
mri6R1=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data//mri6R1ForPy.mat')
mri6Tracts=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri6TractsForPy.mat')
mri6Subj=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri6SubjForPy.mat')
mri6Nodes=loadmat('/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Data/mri6NodesForPy.mat')
df0=pd.DataFrame(mri0Subj['mri0_subj'])
df0.columns=['Subj']
df0.insert(1,"tractIdx",mri0Tracts['mri0_tracts'])
df0.insert(2,"Nodes",mri0Nodes['mri0_nodes'])
df0.insert(3,"R1",mri0R1['mri0_R1']*1000)
df3=pd.DataFrame(mri3Subj['mri3_subj'])
df3.columns=['Subj']
df3.insert(1,"tractIdx",mri3Tracts['mri3_tracts'])
df3.insert(2,"Nodes",mri3Nodes['mri3_nodes'])
df3.insert(3,"R1",mri3R1['mri3_R1']*1000)
df6=pd.DataFrame(mri6Subj['mri6_subj'])
df6.columns=['Subj']
df6.insert(1,"tractIdx",mri6Tracts['mri6_tracts'])
df6.insert(2,"Nodes",mri6Nodes['mri6_nodes'])
df6.insert(3,"R1",mri6R1['mri6_R1']*1000)
# set up the bundle names and colors
tracts=['ATR', 'ATR', 'CS', 'CS', 'CC', 'CC',
'CH', 'CH', 'FcMa', 'FcMi','IFOF', 'IFOF',
'ILF', 'ILF', 'SLF', 'SLF','UCI', 'UCI',
'AF', 'AF', 'MLF','MLF', 'VOF', 'VOF', 'pAF', 'pAF']
tractPos = {'CS': (0, 0), 'ATR': (0, 1),'VOF': (1, 3), 'pAF': (1, 4),
'AF': (1, 0), 'UCI': (1, 1),'FcMa': (0,3),'FcMi':(0,4),
'SLF': (2, 0), 'CC': (2, 1),'ILF': (2, 2), 'MLF': (2, 3),'IFOF': (2, 4)}
figsize=(15,10)
fig, axes=plt.subplots(3,5,frameon=False,figsize=figsize)
fig.tight_layout
plt.subplots_adjust(left=0.07,bottom=0.01,right=0.99,top=1,wspace=0.2,hspace=0.2)
sns.despine()
color_list_all=sns.color_palette("tab20")+sns.color_palette("tab20b")
color_list_chosen=color_list_all[0:6]
color_list_chosen.extend(color_list_all[33:34])
color_list_chosen.extend(color_list_all[35:36])
color_list_chosen.extend(color_list_all[6:14])
color_list_chosen.extend(color_list_all[16:20])
color_list_chosen.extend(color_list_all[24:25])
color_list_chosen.extend(color_list_all[27:28])
color_list_chosen.extend(color_list_all[20:21])
color_list_chosen.extend(color_list_all[23:24])
color_list_chosen.extend(color_list_all[36:37])
color_list_chosen.extend(color_list_all[39:40])
#sns.palplot(color_list_chosen)
sns.palplot(color_list_chosen)
x_labels=['Node (pos -> ant)', 'Node (pos -> ant)',
'Node (inf -> sup)','Node (inf -> sup)',
'Node (pos -> ant)', 'Node (pos -> ant)',
'Node (pos -> ant)', 'Node (pos -> ant)',
'Node (lh -> rh)','Node (lh -> rh)',
'Node (pos -> ant)', 'Node (pos -> ant)',
'Node (pos -> ant)', 'Node (pos -> ant)',
'Node (pos -> ant)', 'Node (pos -> ant)',
'Node (pos -> ant)', 'Node (pos -> ant)',
'Node (pos -> ant)', 'Node (pos -> ant)',
'Node (pos -> ant)', 'Node (pos -> ant)',
'Node (inf -> sup)','Node (inf -> sup)',
'Node (inf -> sup)','Node (inf -> sup)']
params={'legend.fontsize':12,'legend.handlelength':1,'legend.labelspacing':0.25, 'legend.columnspacing':0.25, 'legend.handletextpad':0}
plot.rcParams.update(params)
type=['mean']
hems=['LH']
#create the actual figure
for hem in hems:
if hem=='LH':
foi=[0,2,4,8,9,10,12,14,16,18,20,22,24]
else:
foi=[1,3,5,11,13,15,17,19,21,23,25]
for t in type:
for ct in foi:
if ct==8:
col=ct
elif ct==9:
col=col-2
else:
col=ct
if hem=='RH':
col=col-1
colors=sns.color_palette("tab20")
ax=axes[tractPos[tracts[ct]]]
ax.clear()
ax.set_ylim([0.3,0.8])
ax.set_aspect(150)
if t=="mean":
currentTract=df0.query("tractIdx == @ct")
plt=sns.lineplot(x="Nodes", y="R1",
data=currentTract,hue="tractIdx",
estimator='mean',ci=95,n_boot=1000,
palette=[color_list_chosen[col+1]],
legend=False,ax=ax,style=True,dashes=[(2,2)],alpha=0.5)
if ct!=8 and ct!=9:
tr=ct+1
currentTract=df0.query("tractIdx == @tr")
plt=sns.lineplot(x="Nodes", y="R1",
data=currentTract,hue="tractIdx",
estimator='mean',ci=95,n_boot=1000,
palette=[color_list_chosen[col+1]],
legend=False,ax=ax,style=True,dashes=[(2,2)],alpha=0.5)
currentTract=df3.query("tractIdx == @ct")
sns.set(rc={"lines.linewidth": 3})
plt=sns.lineplot(x="Nodes", y="R1",
data=currentTract,hue="tractIdx",
estimator='mean',ci=95,n_boot=1000,
palette=[np.mean([color_list_chosen[col+1],color_list_chosen[col]],axis=0)],
legend=False,ax=ax,style=True,dashes=[(4,4)],alpha=0.5)
if ct!=8 and ct!=9:
tr=ct+1
currentTract=df3.query("tractIdx == @tr")
sns.set(style="ticks",rc={"lines.linewidth": 3})
plt=sns.lineplot(x="Nodes", y="R1",
data=currentTract,hue="tractIdx",
estimator='mean',ci=95,n_boot=1000,
palette=[np.mean([color_list_chosen[col+1],color_list_chosen[col]],axis=0)],
legend=False,ax=ax,style=True,dashes=[(4,4)],alpha=0.5)
currentTract=df6.query("tractIdx == @ct")
sns.set(style="ticks",rc={"lines.linewidth": 3})
plt=sns.lineplot(x="Nodes", y="R1",
data=currentTract,hue="tractIdx",
estimator='mean',ci=95,n_boot=1000,
palette=[color_list_chosen[col]],legend=False,ax=ax,alpha=0.5)
if ct!=8 and ct!=9:
tr=ct+1
currentTract=df6.query("tractIdx == @tr")
sns.set(style="ticks",rc={"lines.linewidth": 3})
plt=sns.lineplot(x="Nodes", y="R1",
data=currentTract,hue="tractIdx",
estimator='mean',ci=95,n_boot=1000,
palette=[color_list_chosen[col]],legend=False,ax=ax,alpha=0.5)
line1=Line2D([],[],color=color_list_chosen[col],linestyle=':')
line2=Line2D([],[],color=color_list_chosen[col],linestyle='dashed')
line3=Line2D([],[],color=color_list_chosen[col],linestyle='-')
ax.legend([line1,line2,line3],['0m','3m','6m'],loc="upper left",bbox_to_anchor=(0.3,0.22),ncol=3,frameon=False,prop={'size':12},fancybox=True,handlelength=1.5)
ax.set_title(tracts[ct],fontsize=25)
ax.set_xlabel(x_labels[ct],fontsize=20)
if ct == 2 or ct==14 or ct==18 or ct==8 or ct==22 :
ax.set_ylabel(r'R1 [s$\mathregular{^{-1}}$]',fontsize=20)
#ax.set_ylabel('R1 [1/s]',fontsize=20)
ax.set_yticks([0.4,0.6,0.8])
ax.set_yticklabels([0.4,0.6,0.8],Fontsize=18)
else:
ax.set_ylabel('',fontsize=14)
ax.set_yticks([0.6,0.8])
ax.set_yticklabels([])
ax.spines['left'].set_visible(False)
ax.set_xticks([1,50,100])
ax.set_xticklabels([1,50,100],Fontsize=18)
ax.tick_params(axis='y',which='both',left=False,right=False)
ax.tick_params(axis='x',which='both',bottom=False,top=False)
axes[0, 2].axis("off")
axes[1, 2].axis("off")
fig.savefig("/biac2/kgs/projects/babybrains/mri/code/babyDWI/python/plotting/figs/linePlot_"+t+'_'+hem+"_masked.png",format='png')
else:
# ax.set_ylim([1,1.8])
# ax.set_aspect(35)
#ax.set_ylim([1,2.8])
sns.set(rc={"lines.linewidth": 2})
currentTract=df0.query("tractIdx == @ct")
plt=sns.lineplot(x="Nodes", y="R1",
data=currentTract,hue="tractIdx",
estimator=None,units='Subj',
palette=[color_list_chosen[col+1]],
legend=False,ax=ax,style=True,dashes=[(2,2)])
currentTract=df3.query("tractIdx == @ct")
plt=sns.lineplot(x="Nodes", y="R1",
data=currentTract,hue="tractIdx",
estimator=None,units='Subj',
palette=[np.mean([color_list_chosen[col+1],color_list_chosen[col]],axis=0)],
legend=False,ax=ax,style=True,dashes=[(4,4)])
currentTract=df6.query("tractIdx == @ct")
plt=sns.lineplot(x="Nodes", y="R1",
data=currentTract,hue="tractIdx",
estimator=None,units='Subj',
palette=[color_list_chosen[col]],legend=False,ax=ax)
line1=Line2D([],[],color=color_list_chosen[col],linestyle=':')
line2=Line2D([],[],color=color_list_chosen[col],linestyle='dashed')
line3=Line2D([],[],color=color_list_chosen[col],linestyle='-')
ax.legend([line1,line2,line3],['0m','3m','6m'],loc="upper left",borderpad=0.01,bbox_to_anchor=(0.25,0.2),ncol=3,framealpha=0.75,frameon=True,edgecolor=[1,1,1],prop={'size':12},fancybox=True,handlelength=1.5)
ax.set_title(tracts[ct],fontsize=20)
ax.set_xlabel(x_labels[ct],fontsize=14)
ax.set_ylabel('R1 [1/s]',fontsize=14)
axes[0, 2].axis("off")
axes[1, 2].axis("off")
if hem=='RH':
axes[1, 3].axis("off")
axes[1, 4].axis("off")
#save the figure
fig.savefig("/biac2/kgs/projects/babybrains/mri/code/babyDWI/babyWmDev/Output/Fig3.png",format='png')