def overview(
workflow,
identifiers,
cut_coords=[None, [0, -4.5, -3.3]],
threshold=2.5,
template="~/ni_data/templates/DSURQEc_40micron_masked.nii.gz",
orientation="portrait",
save_as="",
):
"""Plot the statistical maps per-factor from a 2nd level GLM workflow result directory."""
plt.style.use('samri.conf')
stat_maps_ = [
"~/ni_data/ofM.dr/l2/{0}/{1}/tstat1.nii.gz".format(workflow, i)
for i in identifiers
]
stat_maps_ = [i for i in stat_maps_ if path.isfile(i)]
identifiers = [[i] * len(cut_coords) for i in identifiers]
stat_maps = [[i] * len(cut_coords) for i in stat_maps_]
stat_maps = [item for sublist in stat_maps for item in sublist]
identifiers = [item for sublist in identifiers for item in sublist]
cut_coords = cut_coords * len(stat_maps_)
maps.stat(
stat_maps,
template=template,
threshold=threshold,
interpolation="none",
figure_title=workflow,
subplot_titles=identifiers,
cut_coords=cut_coords,
orientation=orientation,
save_as=save_as,
)
def multipage_plot(results, subjects, page_rows=4, template='/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii', base_cut_coords=[None], save_as="", overlays=[], scale=0.6, threshold=0.1, dim=0.8, figure_title="", ): rcParams['axes.titlepad'] = 200 save_as_base, save_as_ext = path.splitext(save_as) valid_subjects = list(set([i['subject'] for i in results if bool(i['result']) and i['subject'] in subjects])) subjects_paginated = [valid_subjects[i:i+page_rows] for i in range(0, len(valid_subjects), page_rows)] for ix, subjects_page in enumerate(subjects_paginated): results_page = [i for i in results if i['subject'] in subjects_page] my_maps, subplot_titles, cut_coords = multiplot_matrix(results_page, 'result', base_cut_coords=base_cut_coords) save_as_page = save_as_base+str(ix)+save_as_ext maps.stat(my_maps, figure_title=figure_title, template=template, threshold=threshold, cut_coords=cut_coords, overlays=overlays, save_as=save_as_page, scale=scale, subplot_titles=subplot_titles, dim=dim, )
def multipage_plot(results, subjects, page_rows=4, template='~/ni_data/templates/DSURQEc_40micron_masked.nii.gz', base_cut_coords=[None], save_as="", overlays=[], scale=0.6, threshold=0.1, dim=0.8, figure_title="", ): rcParams['axes.titlepad'] = 200 save_as_base, save_as_ext = path.splitext(save_as) valid_subjects = list(set([i['subject'] for i in results if bool(i['result']) and i['subject'] in subjects])) subjects_paginated = [valid_subjects[i:i+page_rows] for i in range(0, len(valid_subjects), page_rows)] for ix, subjects_page in enumerate(subjects_paginated): results_page = [i for i in results if i['subject'] in subjects_page] my_maps, subplot_titles, cut_coords = multiplot_matrix(results_page, 'result', base_cut_coords=base_cut_coords) save_as_page = save_as_base+str(ix)+save_as_ext maps.stat(my_maps, figure_title=figure_title, template=template, threshold=threshold, cut_coords=cut_coords, overlays=overlays, save_as=save_as_page, scale=scale, subplot_titles=subplot_titles, dim=dim, )
def blur_kernel_compare_dr(conditions=["ofM","ofM_aF","ofM_cF1","ofM_cF2","ofM_pF"], parameters=["level2_dgamma","level2_dgamma_blurxy4","level2_dgamma_blurxy5", "level2_dgamma_blurxy6", "level2_dgamma_blurxy7"], threshold=3):
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages('~/DR.pdf')
for condition in conditions:
stat_maps = ["~/ni_data/ofM.dr/GLM/"+parameter+"/_category_multi_"+condition+"/flameo/mapflow/_flameo0/stats/tstat1.nii.gz" for parameter in parameters]
titles = [stat_map[32:-43] for stat_map in stat_maps]
maps.stat(stat_maps, cut_coords=(-49,8,43), threshold=threshold, interpolation="none", template="~/ni_data/templates/hires_QBI_chr.nii.gz", save_as=pp, figure_title=condition, subplot_titles=parameters)
pp.close()
def single_ts_seed_connectivity( template="~/ni_data/templates/DSURQEc_40micron_masked.nii.gz", save_as="" ): # connectivity_img = fc.seed_based_connectivity( # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-5689/ses-ofM/func/sub-5689_ses-ofM_trial-EPI_CBV_chr_longSOA.nii.gz", # # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-5706/ses-ofM_aF/func/sub-5706_ses-ofM_aF_trial-EPI_CBV_chr_longSOA.nii.gz", # # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-5690/ses-ofM/func/sub-5690_ses-ofM_trial-EPI_CBV_chr_longSOA.nii.gz", # # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-4011/ses-ofM_aF/func/sub-4011_ses-ofM_aF_trial-EPI_CBV_jb_long.nii.gz", # "~/ni_data/templates/roi/DSURQEc_dr.nii.gz", # save_as="~/fc.nii.gz" # ) # connectivity_img1 = fc.seed_based_connectivity( # # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-5689/ses-ofM/func/sub-5689_ses-ofM_trial-EPI_CBV_chr_longSOA.nii.gz", # # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-5706/ses-ofM_aF/func/sub-5706_ses-ofM_aF_trial-EPI_CBV_chr_longSOA.nii.gz", # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-5690/ses-ofM/func/sub-5690_ses-ofM_trial-EPI_CBV_chr_longSOA.nii.gz", # # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-4011/ses-ofM_aF/func/sub-4011_ses-ofM_aF_trial-EPI_CBV_jb_long.nii.gz", # "~/ni_data/templates/roi/DSURQEc_dr.nii.gz", # save_as="~/fc.nii.gz" # ) connectivity_img2 = fc.seed_based_connectivity( # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-5689/ses-ofM/func/sub-5689_ses-ofM_trial-EPI_CBV_chr_longSOA.nii.gz", # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-5706/ses-ofM_aF/func/sub-5706_ses-ofM_aF_trial-EPI_CBV_chr_longSOA.nii.gz", "~/ni_data/ofM.dr/preprocessing/as_composite/sub-5690/ses-ofM_aF/func/sub-5690_ses-ofM_aF_trial-EPI_CBV_chr_longSOA.nii.gz", # "~/ni_data/ofM.dr/preprocessing/as_composite/sub-4011/ses-ofM_aF/func/sub-4011_ses-ofM_aF_trial-EPI_CBV_jb_long.nii.gz", "~/ni_data/templates/roi/DSURQEc_dr.nii.gz", save_as="~/fc.nii.gz" ) stat_maps=[connectivity_img2] # stat_maps=[connectivity_img,connectivity_img,"~/fc.nii.gz",connectivity_img1,connectivity_img1, connectivity_img2,] # stat_maps=[connectivity_img,connectivity_img,] maps.stat(stat_maps, template=template, threshold=0.1, orientation="landscape", # cut_coords=[None,], cut_coords=[None,[0,-4.5,-3.3],None,[0,-4.5,-3.3],None,None], # cut_coords=[None,[0,-4.5,-3.3],], overlays=["~/ni_data/templates/roi/DSURQEc_dr.nii.gz",], save_as=save_as, scale=0.8, dim=0.8, )
def single_ts_seed_connectivity( template="/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii", save_as="fcs.pdf" ): connectivity_img2 = fc.seed_based_connectivity( "~/ni_data/ofM.dr/preprocessing/composite/sub-6255/ses-ofM/func/sub-6255_ses-ofM_task-EPI_CBV_chr_longSOA.nii.gz", "~/ni_data/templates/roi/DSURQEc_dr.nii.gz", save_as="~/fc.nii.gz" ) stat_maps=[connectivity_img2,connectivity_img2] maps.stat(stat_maps, template=template, threshold=0.1, shape="landscape", cut_coords=[None,[0,-4.9,-3.3]], overlays=["~/ni_data/templates/roi/DSURQEc_dr.nii.gz",], save_as=save_as, scale=0.6, dim=0.8, )
def single_ts_seed_connectivity( template="/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii", save_as="fcs.pdf" ): connectivity_img2 = fc.seed_based_connectivity( "~/ni_data/ofM.dr/preprocessing/composite/sub-6255/ses-ofM/func/sub-6255_ses-ofM_task-EPI_CBV_chr_longSOA.nii.gz", "~/ni_data/templates/roi/DSURQEc_dr.nii.gz", save_as="~/fc.nii.gz" ) stat_maps=[connectivity_img2,connectivity_img2] maps.stat(stat_maps, template=template, threshold=0.1, shape="landscape", cut_coords=[None,[0,-4.9,-3.3]], overlays=["~/ni_data/templates/roi/DSURQEc_dr.nii.gz",], save_as=save_as, scale=0.6, dim=0.8, )
import samri.plotting.maps as maps
from os import path
stat_map = 'data/l2/generic/acq-EPIlowcov_run-1_tstat.nii.gz'
template = '/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii'
maps.stat(
stat_maps=[stat_map],
template=template,
overlays=['data/acquisition_area_dsurqec.nii.gz'],
annotate=True,
scale=0.2,
show_plot=False,
interpolation=None,
draw_colorbar=True,
black_bg=False,
threshold=1,
dim=0.2,
)
def plot_roi_per_session(
df,
x='Session',
y='Mean t-Statistic',
condition='treatment',
unit='subject',
ci=90,
palette=["#56B4E9", "#E69F00"],
dodge=True,
order=[],
feature_map=True,
roi_left=0.02,
roi_bottom=0.74,
roi_width=0.3,
roi_height=0.2,
roi_anat="/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii",
roi_threshold=None,
cut_coords=None,
samri_style=True,
renames=[],
save_as='',
ax=None,
fig=None,
):
"""Plot a ROI t-values over the session timecourse
"""
if samri_style:
plt.style.use(u'seaborn-darkgrid')
plt.style.use('ggplot')
try:
df = path.abspath(path.expanduser(df))
except AttributeError:
pass
# definitions for the axes
height = rcParams['figure.subplot.top']
bottom = rcParams['figure.subplot.bottom']
left = rcParams['figure.subplot.left']
width = rcParams['figure.subplot.right']
session_coordinates = [left, bottom, width, height]
roi_coordinates = [
left + roi_left, bottom + roi_bottom, roi_width, roi_height
]
if not fig:
fig = plt.figure(1)
if renames:
for key in renames:
for subkey in renames[key]:
df.loc[df[key] == subkey, key] = renames[key][subkey]
if not ax:
ax1 = plt.axes(session_coordinates)
else:
ax1 = ax
ax = sns.pointplot(
x=x,
y=y,
units=unit,
data=df,
hue=condition,
dodge=dodge,
palette=sns.color_palette(palette),
order=order,
ax=ax1,
ci=ci,
)
ax.set_ylabel(y)
if isinstance(feature_map, str):
ax2 = plt.axes(roi_coordinates)
if roi_threshold and cut_coords:
maps.stat(
feature,
cut_coords=cut_coords,
template=roi_anat,
annotate=False,
scale=0.3,
show_plot=False,
interpolation=None,
threshold=roi_threshold,
draw_colorbar=False,
ax=ax2,
)
else:
maps.atlas_label(
feature_map,
scale=0.3,
color="#E69F00",
ax=ax2,
annotate=False,
alpha=0.8,
)
elif feature_map:
try:
features = df['feature'].unique()
except KeyError:
pass
else:
if len(features) > 1:
print(
'WARNING: The features list contains more than one feature. We will highlight the first one in the list. This may be incorrect.'
)
feature = features[0]
ax2 = plt.axes(roi_coordinates)
if path.isfile(feature):
if roi_threshold and cut_coords:
maps.stat(
stat_maps=feature,
cut_coords=cut_coords,
template=roi_anat,
annotate=False,
scale=0.3,
show_plot=False,
interpolation=None,
threshold=roi_threshold,
draw_colorbar=False,
ax=ax2,
)
else:
maps.atlas_label(
feature,
scale=0.3,
color="#E69F00",
ax=ax2,
annotate=False,
alpha=0.8,
)
else:
atlas = df['atlas'].unique()[0]
mapping = df['mapping'].unique()[0]
if isinstance(feature, str):
feature = [feature]
maps.atlas_label(
atlas,
scale=0.3,
color="#E69F00",
ax=ax2,
mapping=mapping,
label_names=feature,
alpha=0.8,
annotate=False,
)
if save_as:
plt.savefig(path.abspath(path.expanduser(save_as)),
bbox_inches='tight')
return fig, ax
renames={
'Session': {
'ofM': 'naïve',
'ofMaF': 'acute',
'ofMcF1': 'chronic (2w)',
'ofMcF2': 'chronic (4w)',
'ofMpF': 'post',
},
},
)
ax2 = plt.axes(roi_coordinates)
stat_map = path.abspath(
path.expanduser(
'~/ni_data/ofM.dr/bids/l2/best_responders/sessionofM/tstat1.nii.gz'))
template = path.abspath(
path.expanduser(
'/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii'))
maps.stat(
stat_maps=[stat_map],
template=template,
cut_coords=[(0, -4.3, -3.3)],
annotate=False,
scale=0.3,
show_plot=False,
interpolation=None,
threshold=4,
draw_colorbar=False,
ax=ax2,
)
def multi(
timecourses,
designs,
stat_maps,
events_dfs,
subplot_titles,
colors=QUALITATIVE_COLORSET,
figure="maps",
quantitative=True,
save_as="",
samri_style=True,
):
if figure == "maps":
maps.stat(stat_maps,
template="~/ni_data/templates/ds_QBI_chr.nii.gz",
threshold=0.1,
interpolation="gaussian",
subplot_titles=subplot_titles)
elif figure == "timecourses":
if len(timecourses) > 1:
ncols = 2
#we use inverse floor division to get the ceiling
max_rows = (len(timecourses) // ncols) + 1
min_rows = len(timecourses) % max_rows
if samri_style:
fig, axes = plt.subplots(figsize=(10 * max_rows, 7 * ncols),
facecolor='#eeeeee',
nrows=max_rows * min_rows,
ncols=ncols)
else:
fig, axes = plt.subplots(facecolor='#eeeeee',
nrows=max_rows * min_rows,
ncols=ncols)
xlabel_positive = [(i * max_rows) - 1 for i in range(1, ncols)]
xlabel_positive.append(len(timecourses) - 1)
max_ylim = [0, 0]
for ix, timecourse in enumerate(timecourses):
timecourse = timecourses[ix]
design = designs[ix]
events_df = events_dfs[ix]
subplot_title = subplot_titles[ix]
col = ix // max_rows
row = ix % max_rows
if col + 1 == ncols:
ax = plt.subplot2grid((max_rows * min_rows, ncols),
(row * max_rows, col),
rowspan=max_rows)
else:
ax = plt.subplot2grid((max_rows * min_rows, ncols),
(row * min_rows, col),
rowspan=min_rows)
for d, o in zip(events_df["duration"], events_df["onset"]):
d = round(d)
o = round(o)
ax.axvspan(o, o + d, facecolor="cyan", alpha=0.15)
ax.plot(timecourse, lw=rcParams['lines.linewidth'] / 4)
ax.plot(design[0])
if not ix in xlabel_positive:
plt.setp(ax.get_xticklabels(), visible=False)
if not quantitative:
ax.yaxis.grid(False)
ax.set_yticks([])
else:
ax.yaxis.set_label_position("right")
ax.tick_params(axis='y', )
ax.set_xlim([0, len(timecourse)])
ax.set_ylabel(subplot_title)
else:
fig, ax = plt.subplots(facecolor='#eeeeee')
timecourse = timecourses[0]
design = designs[0]
events_df = events_dfs[0]
subplot_title = "Arbitrary Units"
for d, o in zip(events_df["duration"], events_df["onset"]):
d = round(d)
o = round(o)
ax.axvspan(o, o + d, facecolor="cyan", alpha=0.15)
ax.plot(timecourse,
lw=rcParams['lines.linewidth'] * 1.5,
color=colors[0],
alpha=1)
for ix, i in enumerate(design):
try:
iteration_color = colors[ix + 1]
except IndexError:
pass
ax.plot(design[ix],
lw=rcParams['lines.linewidth'] * 2,
color=iteration_color,
alpha=1)
if not quantitative:
ax.yaxis.grid(False)
ax.set_yticks([])
else:
ax.yaxis.set_label_position("right")
ax.set_xlim([0, len(timecourse)])
ax.set_ylabel(subplot_title)
ax.set_xlabel("TR[1s]")
else:
print("WARNING: you must specify either 'maps' or 'timecourses'")
if save_as:
save_as = path.abspath(path.expanduser(save_as))
plt.savefig(save_as)
