import matplotlib.pyplot as plt
import pandas as pd
import matplotlib as mpl
from os import path
from itertools import product
from lib.categorical import violinplot
# Style
palette = ['#80e050', '#755575']
volume_path = path.abspath('data/volumes.csv')
df = pd.read_csv(volume_path)
df.loc[df['Processing'] == 'Unprocessed', 'Template'] = ''
ax = violinplot(
x="Processing",
y='Volume Change Factor',
data=df.loc[df['Processing'] != 'Unprocessed'],
hue="Template",
saturation=1,
split=True,
inner='quartile',
palette=palette,
scale='area',
dodge=False,
#inner_linewidth=1.0,
linewidth=mpl.rcParams['grid.linewidth'],
linecolor='w',
)
import matplotlib as mpl
from os import path
from itertools import product
from lib.categorical import violinplot
# Style
palette = ['#80e050', '#755575']
data_path = path.abspath('data/functional_significance.csv')
df = pd.read_csv(data_path)
#df = df.loc[~df['Subject'].isin([4003,4006,4013])]
df.loc[df['Processing'] == 'Unprocessed', 'Template'] = ''
#df = df.loc[df['Contrast']=='CBV']
ax = violinplot(
x="Processing",
y='Mean Significance',
data=df,
hue="Template",
saturation=1,
split=True,
inner='quartile',
palette=palette,
scale='area',
dodge=False,
inner_linewidth=1.0,
linewidth=mpl.rcParams['grid.linewidth'],
linecolor='w',
#bw=0.2,
)
palette = ['#80e050', '#755575']
v_path = 'data/volume.csv'
v = pd.read_csv(path.abspath(v_path))
df_path = 'data/smoothness.csv'
df = pd.read_csv(path.abspath(df_path))
df = df[df['Processing'] != 'Unprocessed']
df = df[df['Template'] != 'Unprocessed']
#df.loc[df['Processing']=='Legacy','Smoothness Conservation Factor'] = df.loc[df['Processing']=='Legacy','Smoothness Conservation Factor']/10
#df[r'$\mathsf{log_{10}(Smoothness\,Change\,Factor)}$'] = np.log10(df['Smoothness Conservation Factor'])
ax = violinplot(
x="Processing",
#y=r'$\mathsf{log_{10}(Smoothness\,Change\,Factor)}$',
#y='Smoothness Conservation Factor',
y='Smoothness Conservation Factor',
#y='Smoothness',
data=df,
hue="Template",
saturation=1,
split=True,
inner='quartile',
palette=palette,
scale='area',
dodge=False,
inner_linewidth=1.0,
linewidth=mpl.rcParams['grid.linewidth'],
linecolor='w',
)
from os import path
from itertools import product
from lib.categorical import violinplot
# Style
palette = ['#ffb66d', '#009093']
data_path = path.abspath('data/functional_t.csv')
df = pd.read_csv(data_path)
df = df.loc[df['Processing'] != 'Unprocessed']
df = df.loc[((df['Processing'] == 'Legacy') & (df['Template'] == 'Legacy')) |
((df['Processing'] == 'Generic') & (df['Template'] == 'Generic'))]
df.loc[df['Processing'] == 'Unprocessed', 'Template'] = ''
ax = violinplot(
x="Processing",
y='Mean DR t',
data=df,
hue="Contrast",
saturation=1,
split=True,
inner='quartile',
palette=palette,
scale='area',
dodge=False,
inner_linewidth=1.0,
linewidth=mpl.rcParams['grid.linewidth'],
linecolor='w',
)
from os import path
import matplotlib as mpl
import pandas as pd
from lib.categorical import violinplot
volume_path = path.abspath('data/volume.csv')
df = pd.read_csv(volume_path)
df = df.loc[df['Processing'] != 'Unprocessed']
df = df.loc[((df['Processing'] == 'Masked') | (df['Processing'] == 'Generic'))]
df.loc[df['Processing'] == 'Unprocessed'] = ''
ax = violinplot(
x='Contrast',
y='Abs(1 - Vcf)',
data=df,
hue="Processing",
saturation=1,
split=True,
inner='quartile',
palette='muted',
scale='area',
dodge=False,
inner_linewidth=1.0,
linewidth=mpl.rcParams['grid.linewidth'],
linecolor='w',
)