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classification_plots.py
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classification_plots.py
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from __future__ import print_function
def classification_plot(myinput, outDir):
import pandas as pd
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from matplotlib import gridspec
import seaborn as sns
import glob
import os
###---Start---###
# find files
myfiles = glob.glob(myinput)
print('Found', len(myfiles), 'file(s)')
# load in data from files
count = 0
for m in myfiles:
res = []
tmp = open(m, 'r').read().split('\n')
for t in tmp[1:-1]:
vals = t.split('\t')
res.append([vals[1],
float(vals[2]),
float(vals[3]),
float(vals[4]),
float(vals[5])])
if count == 0:
df = pd.DataFrame.from_records(res)
else:
df2 = pd.DataFrame.from_records(res)
df = pd.concat([df, df2], ignore_index=True)
count += 1
# get counts
ncomp = len(df)
nmot = len(df.loc[df[0] == "True"])
print('Found', nmot, 'head motion-related components in a total of', ncomp, 'components.')
# add dummy components if needed, this is just for making the plots look nice
tmp = df.loc[df[0] == "True"]
if len(tmp) < 3:
df3 = pd.DataFrame.from_records([["True", 1., 1., 0., 0.],
["True", 1., 1., 0., 0.],
["True", 1., 1., 0., 0.]])
df = pd.concat([df, df3], ignore_index=True)
tmp = df.loc[df[0] == "False"]
if len(tmp) < 3:
df3 = pd.DataFrame.from_records([["False", 0., 0., 0., 0.],
["False", 0., 0., 0., 0.],
["False", 0., 0., 0., 0.]])
df = pd.concat([df, df3], ignore_index=True)
# rename columns
df = df.rename(index=str, columns={0: 'Motion',
1: 'RP',
2: 'Edge',
3: 'Freq',
4: 'CSF'})
# Make pretty figure
# styling
sns.set_style('white')
colortrue = "#FFBF17"
colorfalse = "#69A00A"
# create figure
fig = plt.figure(figsize=[12, 4])
# define grids
gs = gridspec.GridSpec(4, 7, wspace=1)
gs00 = gridspec.GridSpecFromSubplotSpec(4, 4, subplot_spec=gs[:, 0:3])
gs01 = gridspec.GridSpecFromSubplotSpec(4, 1, subplot_spec=gs[:, 3:5])
gs02 = gridspec.GridSpecFromSubplotSpec(4, 1, subplot_spec=gs[:, 5:7])
# define subplots
# Edge/RP
ax1 = fig.add_subplot(gs00[1:4, 0:3])
# distribution edge (ax1 top)
ax1t = fig.add_subplot(gs00[0, 0:3])
# distribution RP (ax1 right)
ax1r = fig.add_subplot(gs00[1:4, 3])
# Freq
ax2 = fig.add_subplot(gs01[1:4, :])
# CSF
ax3 = fig.add_subplot(gs02[1:4, :])
# plot Freq
sns.boxplot(x="Motion",
y="Freq",
data=df,
ax=ax2,
palette=[colortrue, colorfalse],
order=['True', 'False'])
ax2.hlines(0.35, -1, 2, zorder=0, linestyles='dotted', linewidth=0.5)
ax2.set_ylim([0, 1])
ax2.set_xlabel('Classification', fontsize=14, labelpad=10)
ax2.set_ylabel('High-Frequency Content', fontsize=14)
ax2.set_xticklabels(['Motion', 'Other'])
ax2.tick_params(axis='both', labelsize=12)
sns.despine(ax=ax2)
# plot CSF
sns.boxplot(x="Motion",
y="CSF",
data=df,
ax=ax3,
palette=[colortrue, colorfalse],
order=['True', 'False'])
ax3.hlines(0.1, -1, 2, zorder=0, linestyles='dotted', linewidth=0.5)
ax3.set_ylim([0, 1])
ax3.set_xlabel('Classification', fontsize=14, labelpad=10)
ax3.set_ylabel('CSF Fraction', fontsize=14)
ax3.set_xticklabels(['Motion', 'Other'])
ax3.tick_params(axis='both', labelsize=12)
sns.despine(ax=ax3)
# plot Edge/RP relationship
# obtain projection line
hyp = np.array([-19.9751070082159, 9.95127547670627, 24.8333160239175])
a = -hyp[1] / hyp[2]
xx = np.linspace(0, 1)
yy = a * xx - hyp[0] / hyp[2]
# plot scatter and line
if len(df) > 100:
sizemarker = 6
else:
sizemarker = 10
ax1.scatter(x="RP",
y="Edge",
data=df.loc[df['Motion'] == "False"],
color=colorfalse,
s=sizemarker)
# plot true ones on top to see how much the go over the border
# this gives an indication for how many were selected using the
# two other features
ax1.scatter(x="RP",
y="Edge",
data=df.loc[df['Motion'] == "True"],
color=colortrue,
s=sizemarker)
# add decision boundary
ax1.plot(xx, yy, '.', color="k", markersize=1)
# styling
ax1.set_ylim([0, 1])
ax1.set_xlim([0, 1])
ax1.set_xlabel('Maximum RP Correlation', fontsize=14, labelpad=10)
ax1.set_ylabel('Edge Fraction', fontsize=14)
ax1.set_xticks(np.arange(0, 1.2, 0.2))
ax1.set_yticks(np.arange(0, 1.2, 0.2))
ax1.tick_params(axis='both', labelsize=12)
# plot distributions
# RP
sns.distplot(df.loc[df['Motion'] == "True", "RP"],
ax=ax1t,
color=colortrue,
hist_kws={'alpha': 0.2})
sns.distplot(df.loc[df['Motion'] == "False", "RP"],
ax=ax1t,
color=colorfalse,
hist_kws={'alpha': 0.2})
ax1t.set_xlim([0, 1])
# Edge
sns.distplot(df.loc[df['Motion'] == "True", "Edge"],
ax=ax1r,
vertical=True,
color=colortrue,
hist_kws={'alpha': 0.2})
sns.distplot(df.loc[df['Motion'] == "False", "Edge"],
ax=ax1r,
vertical=True,
color=colorfalse,
hist_kws={'alpha': 0.2})
ax1r.set_ylim([0, 1])
# cosmetics
for myax in [ax1t, ax1r]:
myax.set_xticks([])
myax.set_yticks([])
myax.set_xlabel('')
myax.set_ylabel('')
myax.spines['right'].set_visible(False)
myax.spines['top'].set_visible(False)
myax.spines['bottom'].set_visible(False)
myax.spines['left'].set_visible(False)
# bring tickmarks back
for myax in fig.get_axes():
myax.tick_params(which="major", direction='in', length=3)
# add figure title
plt.suptitle('Component Assessment', fontsize=20)
# outtakes
plt.savefig(os.path.join(outDir, 'ICA_AROMA_component_assessment.pdf'),
bbox_inches='tight')
return
# allow use of module on its own
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description="""Plot component classification overview
similar to plot in the main AROMA paper""")
# Required options
reqoptions = parser.add_argument_group('Required arguments')
reqoptions.add_argument('-i', '-in',
dest='myinput',
required=True,
help="""Input query or filename.
Use quotes when specifying a query""")
optoptions = parser.add_argument_group('Optional arguments')
optoptions.add_argument('-outdir',
dest='outDir',
required=False,
default='.',
help="""Specification of directory
where figure will be saved""")
optoptions.add_argument('-type',
dest='plottype',
required=False,
default='assessment',
help="""Specification of the type of plot you want.
Currently this is a placeholder option for
potential other plots that might be added
in the future.""")
# parse arguments
args = parser.parse_args()
if args.plottype == 'assessment':
classification_plot(args.myinput, args.outDir)