Python violinplot Examples

Example #1

File: vc_violin.py Project: TheChymera/RepSeP

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',
)

Example #2

File: ms_violin.py Project: Jimmy2027/mlebe_RepSep

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,
)

Example #3

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',
)

Example #4

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',
)

Example #5

File: vc_violin_absdiff.py Project: Jimmy2027/mlebe_RepSep

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',
)