def plot_abide_stripplots(inputs, figsize=(15, 2), out_file=None,
rating_label='rater_1', dpi=100):
import seaborn as sn
sn.set(style="whitegrid")
mdata = []
pp_cols = []
for X, Y, sitename in inputs:
sitedata, cols = read_dataset(X, Y, rate_label=rating_label,
binarize=False, site_name=sitename)
sitedata['database'] = [sitename] * len(sitedata)
if sitename == 'DS030':
sitedata['site'] = [sitename] * len(sitedata)
mdata.append(sitedata)
pp_cols.append(cols)
mdata = pd.concat(mdata)
pp_cols = pp_cols[0]
for col in mdata.columns.ravel().tolist():
if col.startswith('rater_') and col != rating_label:
del mdata[col]
mdata = mdata.loc[mdata[rating_label].notnull()]
for col in ['size_x', 'size_y', 'size_z', 'spacing_x', 'spacing_y', 'spacing_z']:
del mdata[col]
try:
pp_cols.remove(col)
except ValueError:
pass
zscored = zscore_dataset(mdata, excl_columns=[rating_label])
sites = list(set(mdata.site.values.ravel()))
nsites = len(sites)
# palette = ['dodgerblue', 'darkorange']
palette = ['limegreen', 'tomato']
if len(set(mdata[[rating_label]].values.ravel().tolist())) == 3:
palette = ['tomato', 'gold', 'limegreen']
# pp_cols = pp_cols[:5]
nrows = len(pp_cols)
fig = plt.figure(figsize=(figsize[0], figsize[1] * nrows))
# ncols = 2 * (nsites - 1) + 2
gs = GridSpec(nrows, 4, wspace=0.02)
gs.set_width_ratios([nsites, len(inputs), len(inputs), nsites])
for i, colname in enumerate(pp_cols):
ax_nzs = plt.subplot(gs[i, 0])
axg_nzs = plt.subplot(gs[i, 1])
axg_zsc = plt.subplot(gs[i, 2])
ax_zsc = plt.subplot(gs[i, 3])
# plots
sn.stripplot(x='site', y=colname, data=mdata, hue=rating_label, jitter=0.18, alpha=.6,
split=True, palette=palette, ax=ax_nzs)
sn.stripplot(x='site', y=colname, data=zscored, hue=rating_label, jitter=0.18, alpha=.6,
split=True, palette=palette, ax=ax_zsc)
sn.stripplot(x='database', y=colname, data=mdata, hue=rating_label, jitter=0.18, alpha=.6,
split=True, palette=palette, ax=axg_nzs)
sn.stripplot(x='database', y=colname, data=zscored, hue=rating_label, jitter=0.18,
alpha=.6, split=True, palette=palette, ax=axg_zsc)
ax_nzs.legend_.remove()
ax_zsc.legend_.remove()
axg_nzs.legend_.remove()
axg_zsc.legend_.remove()
if i == nrows - 1:
ax_nzs.set_xticklabels(ax_nzs.xaxis.get_majorticklabels(), rotation=80)
ax_zsc.set_xticklabels(ax_zsc.xaxis.get_majorticklabels(), rotation=80)
axg_nzs.set_xticklabels(axg_nzs.xaxis.get_majorticklabels(), rotation=80)
axg_zsc.set_xticklabels(axg_zsc.xaxis.get_majorticklabels(), rotation=80)
else:
ax_nzs.set_xticklabels([])
ax_zsc.set_xticklabels([])
axg_nzs.set_xticklabels([])
axg_zsc.set_xticklabels([])
ax_nzs.set_xlabel('', visible=False)
ax_zsc.set_xlabel('', visible=False)
ax_zsc.set_ylabel('', visible=False)
ax_zsc.yaxis.tick_right()
axg_nzs.set_yticklabels([])
axg_nzs.set_xlabel('', visible=False)
axg_nzs.set_ylabel('', visible=False)
axg_zsc.set_yticklabels([])
axg_zsc.set_xlabel('', visible=False)
axg_zsc.set_ylabel('', visible=False)
for yt in ax_nzs.yaxis.get_major_ticks()[1:-1]:
yt.label1.set_visible(False)
for yt in axg_nzs.yaxis.get_major_ticks()[1:-1]:
yt.label1.set_visible(False)
for yt in zip(ax_zsc.yaxis.get_majorticklabels(), axg_zsc.yaxis.get_majorticklabels()):
yt[0].set_visible(False)
yt[1].set_visible(False)
if out_file is None:
out_file = 'stripplot.svg'
fname, ext = op.splitext(out_file)
if ext[1:] not in ['pdf', 'svg', 'png']:
ext = '.svg'
out_file = fname + '.svg'
fig.savefig(op.abspath(out_file), format=ext[1:],
bbox_inches='tight', pad_inches=0, dpi=dpi)
return fig
def plot_abide_stripplots(inputs,
figsize=(15, 2),
out_file=None,
rating_label='rater_1',
dpi=100):
import seaborn as sn
from ..classifier.helper import FEATURE_NORM
sn.set(style="whitegrid")
mdata = []
pp_cols = []
for X, Y, sitename in inputs:
sitedata, cols = read_dataset(X,
Y,
rate_label=rating_label,
binarize=False,
site_name=sitename)
sitedata['database'] = [sitename] * len(sitedata)
if sitename == 'DS030':
sitedata['site'] = [sitename] * len(sitedata)
mdata.append(sitedata)
pp_cols.append(cols)
mdata = pd.concat(mdata)
pp_cols = pp_cols[0]
for col in mdata.columns.ravel().tolist():
if col.startswith('rater_') and col != rating_label:
del mdata[col]
mdata = mdata.loc[mdata[rating_label].notnull()]
for col in [
'size_x', 'size_y', 'size_z', 'spacing_x', 'spacing_y', 'spacing_z'
]:
del mdata[col]
try:
pp_cols.remove(col)
except ValueError:
pass
zscored = BatchRobustScaler(by='site',
columns=FEATURE_NORM).fit_transform(mdata)
sites = list(set(mdata.site.values.ravel()))
nsites = len(sites)
# palette = ['dodgerblue', 'darkorange']
palette = ['limegreen', 'tomato']
if len(set(mdata[[rating_label]].values.ravel().tolist())) == 3:
palette = ['tomato', 'gold', 'limegreen']
# pp_cols = pp_cols[:5]
nrows = len(pp_cols)
fig = plt.figure(figsize=(figsize[0], figsize[1] * nrows))
# ncols = 2 * (nsites - 1) + 2
gs = GridSpec(nrows, 4, wspace=0.02)
gs.set_width_ratios([nsites, len(inputs), len(inputs), nsites])
for i, colname in enumerate(pp_cols):
ax_nzs = plt.subplot(gs[i, 0])
axg_nzs = plt.subplot(gs[i, 1])
axg_zsc = plt.subplot(gs[i, 2])
ax_zsc = plt.subplot(gs[i, 3])
# plots
sn.stripplot(x='site',
y=colname,
data=mdata,
hue=rating_label,
jitter=0.18,
alpha=.6,
split=True,
palette=palette,
ax=ax_nzs)
sn.stripplot(x='site',
y=colname,
data=zscored,
hue=rating_label,
jitter=0.18,
alpha=.6,
split=True,
palette=palette,
ax=ax_zsc)
sn.stripplot(x='database',
y=colname,
data=mdata,
hue=rating_label,
jitter=0.18,
alpha=.6,
split=True,
palette=palette,
ax=axg_nzs)
sn.stripplot(x='database',
y=colname,
data=zscored,
hue=rating_label,
jitter=0.18,
alpha=.6,
split=True,
palette=palette,
ax=axg_zsc)
ax_nzs.legend_.remove()
ax_zsc.legend_.remove()
axg_nzs.legend_.remove()
axg_zsc.legend_.remove()
if i == nrows - 1:
ax_nzs.set_xticklabels(ax_nzs.xaxis.get_majorticklabels(),
rotation=80)
ax_zsc.set_xticklabels(ax_zsc.xaxis.get_majorticklabels(),
rotation=80)
axg_nzs.set_xticklabels(axg_nzs.xaxis.get_majorticklabels(),
rotation=80)
axg_zsc.set_xticklabels(axg_zsc.xaxis.get_majorticklabels(),
rotation=80)
else:
ax_nzs.set_xticklabels([])
ax_zsc.set_xticklabels([])
axg_nzs.set_xticklabels([])
axg_zsc.set_xticklabels([])
ax_nzs.set_xlabel('', visible=False)
ax_zsc.set_xlabel('', visible=False)
ax_zsc.set_ylabel('', visible=False)
ax_zsc.yaxis.tick_right()
axg_nzs.set_yticklabels([])
axg_nzs.set_xlabel('', visible=False)
axg_nzs.set_ylabel('', visible=False)
axg_zsc.set_yticklabels([])
axg_zsc.set_xlabel('', visible=False)
axg_zsc.set_ylabel('', visible=False)
for yt in ax_nzs.yaxis.get_major_ticks()[1:-1]:
yt.label1.set_visible(False)
for yt in axg_nzs.yaxis.get_major_ticks()[1:-1]:
yt.label1.set_visible(False)
for yt in zip(ax_zsc.yaxis.get_majorticklabels(),
axg_zsc.yaxis.get_majorticklabels()):
yt[0].set_visible(False)
yt[1].set_visible(False)
if out_file is None:
out_file = 'stripplot.svg'
fname, ext = op.splitext(out_file)
if ext[1:] not in ['pdf', 'svg', 'png']:
ext = '.svg'
out_file = fname + '.svg'
fig.savefig(op.abspath(out_file),
format=ext[1:],
bbox_inches='tight',
pad_inches=0,
dpi=dpi)
return fig
def plot_abide_stripplots(X,
Y,
figsize=(15, 80),
out_file=None,
rating_label='rater_1'):
import seaborn as sn
sn.set(style="whitegrid")
mdata, pp_cols = read_dataset(X, Y, rate_label=rating_label)
for col in mdata.columns.ravel().tolist():
if col.startswith('rater_') and col != rating_label:
del mdata[col]
mdata = mdata.loc[mdata[rating_label].notnull()]
mdata['database'] = ['ABIDE'] * len(mdata.site.values.ravel())
mdata['rate'] = [''] * len(mdata[rating_label])
mdata.loc[mdata[rating_label] == 0, 'rate'] = 'OK'
mdata.loc[mdata[rating_label] == 1, 'rate'] = 'exclude'
for col in [
rating_label, 'size_x', 'size_y', 'size_z', 'spacing_x',
'spacing_y', 'spacing_z'
]:
del mdata[col]
try:
pp_cols.remove(col)
except ValueError:
pass
zscored = zscore_dataset(mdata, excl_columns=['rate'])
sites = list(set(mdata.site.values.ravel()))
nsites = len(sites)
palette = ['limegreen', 'tomato']
# palette = ['dodgerblue', 'darkorange']
nrows = len(pp_cols)
fig = plt.figure(figsize=figsize)
# ncols = 2 * (nsites - 1) + 2
gs = GridSpec(nrows, 4, wspace=0.02)
gs.set_width_ratios([nsites, 1, 1, nsites])
for i, colname in enumerate(pp_cols):
ax_nzs = plt.subplot(gs[i, 0])
axg_nzs = plt.subplot(gs[i, 1])
axg_zsc = plt.subplot(gs[i, 2])
ax_zsc = plt.subplot(gs[i, 3])
# plots
sn.stripplot(x='site',
y=colname,
data=mdata,
hue='rate',
jitter=0.18,
alpha=.4,
split=True,
palette=palette,
ax=ax_nzs)
sn.stripplot(x='site',
y=colname,
data=zscored,
hue='rate',
jitter=0.18,
alpha=.4,
split=True,
palette=palette,
ax=ax_zsc)
sn.stripplot(x='database',
y=colname,
data=mdata,
hue='rate',
jitter=0.18,
alpha=.4,
split=True,
palette=palette,
ax=axg_nzs)
sn.stripplot(x='database',
y=colname,
data=zscored,
hue='rate',
jitter=0.18,
alpha=.4,
split=True,
palette=palette,
ax=axg_zsc)
ax_nzs.legend_.remove()
ax_zsc.legend_.remove()
axg_nzs.legend_.remove()
axg_zsc.legend_.remove()
if i == nrows - 1:
ax_nzs.set_xticklabels(ax_nzs.xaxis.get_majorticklabels(),
rotation=80)
ax_zsc.set_xticklabels(ax_zsc.xaxis.get_majorticklabels(),
rotation=80)
axg_nzs.set_xticklabels(axg_nzs.xaxis.get_majorticklabels(),
rotation=80)
axg_zsc.set_xticklabels(axg_zsc.xaxis.get_majorticklabels(),
rotation=80)
else:
ax_nzs.set_xticklabels([])
ax_zsc.set_xticklabels([])
axg_nzs.set_xticklabels([])
axg_zsc.set_xticklabels([])
ax_nzs.set_xlabel('', visible=False)
ax_zsc.set_xlabel('', visible=False)
ax_zsc.set_ylabel('', visible=False)
ax_zsc.yaxis.tick_right()
axg_nzs.set_yticklabels([])
axg_nzs.set_xlabel('', visible=False)
axg_nzs.set_ylabel('', visible=False)
axg_zsc.set_yticklabels([])
axg_zsc.set_xlabel('', visible=False)
axg_zsc.set_ylabel('', visible=False)
for yt in ax_nzs.yaxis.get_major_ticks()[1:-1]:
yt.label1.set_visible(False)
for yt in axg_nzs.yaxis.get_major_ticks()[1:-1]:
yt.label1.set_visible(False)
for yt in zip(ax_zsc.yaxis.get_majorticklabels(),
axg_zsc.yaxis.get_majorticklabels()):
yt[0].set_visible(False)
yt[1].set_visible(False)
if out_file is None:
out_file = 'stripplot.svg'
fname, ext = op.splitext(out_file)
if ext[1:] not in ['pdf', 'svg', 'png']:
ext = '.svg'
out_file = fname + '.svg'
fig.savefig(op.abspath(out_file),
format=ext[1:],
bbox_inches='tight',
pad_inches=0,
dpi=300)
return fig
