# preprocess the data
subject_data = do_subjects_preproc(jobfile, dataset_dir=dataset_dir)[0]
# construct design matrix
nscans = len(subject_data.func[0])
frametimes = np.linspace(0, (nscans - 1) * tr, nscans)
drift_model = 'Cosine'
hrf_model = 'spm + derivative'
design_matrix = make_design_matrix(frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=hfcut)
# plot and save design matrix
ax = plot_design_matrix(design_matrix)
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')
dmat_outfile = os.path.join(subject_data.output_dir, 'design_matrix.png')
plt.savefig(dmat_outfile, bbox_inches="tight", dpi=200)
# specify contrasts
contrasts = {}
_, matrix, names = check_design_matrix(design_matrix)
contrast_matrix = np.eye(len(names))
for i in range(len(names)):
contrasts[names[i]] = contrast_matrix[i]
# more interesting contrasts"""
contrasts = {'active-rest': contrasts['active'] - contrasts['rest']}
def generate_subject_stats_report(stats_report_filename,
contrasts,
z_maps,
mask,
design_matrices=None,
subject_id=None,
anat=None,
display_mode="z",
cut_coords=None,
threshold=2.3,
cluster_th=15,
start_time=None,
title=None,
user_script_name=None,
progress_logger=None,
shutdown_all_reloaders=True,
**glm_kwargs):
"""Generates a report summarizing the statistical methods and results
Parameters
----------
stats_report_filename: string:
html file to which output (generated html) will be written
contrasts: dict of arrays
contrasts we are interested in; same number of contrasts as zmaps;
same keys
zmaps: dict of image objects or strings (image filenames)
zmaps for contrasts we are interested in; one per contrast id
mask: 'nifti image object'
brain mask for ROI
design_matrix: list of 'DesignMatrix', `numpy.ndarray` objects or of
strings (.png, .npz, etc.) for filenames
design matrices for the experimental conditions
contrasts: dict of arrays
dictionary of contrasts of interest; the keys are the contrast ids,
the values are contrast values (lists)
z_maps: dict of 3D image objects or strings (image filenames)
dict with same keys as 'contrasts'; the values are paths of z-maps
for the respective contrasts
anat: 3D array (optional)
brain image to serve bg unto which activation maps will be plotted
anat_affine: 2D array (optional)
affine data for the anat
threshold: float (optional)
threshold to be applied to activation maps voxel-wise
cluster_th: int (optional)
minimal voxel count for clusteres declared as 'activated'
cmap: cmap object (default viz.cm.cold_hot)
color-map to use in plotting activation maps
start_time: string (optional)
start time for the stats analysis (useful for the generated
report page)
user_script_name: string (optional, default None)
existing filename, path to user script used in doing the analysis
progress_logger: ProgressLogger object (optional)
handle for logging progress
shutdown_all_reloaders: bool (optional, default True)
if True, all pages connected to the stats report page will
be prevented from reloading after the stats report page
has been completely generated
**glm_kwargs:
kwargs used to specify the control parameters used to specify the
experimental paradigm and the GLM
"""
# prepare for stats reporting
if progress_logger is None:
progress_logger = base_reporter.ProgressReport()
output_dir = os.path.dirname(stats_report_filename)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# copy css and js stuff to output dir
base_reporter.copy_web_conf_files(output_dir)
# initialize gallery of design matrices
design_thumbs = base_reporter.ResultsGallery(
loader_filename=os.path.join(output_dir, "design.html"))
# initialize gallery of activation maps
activation_thumbs = base_reporter.ResultsGallery(
loader_filename=os.path.join(output_dir, "activation.html"))
# get caller module handle from stack-frame
if user_script_name is None:
user_script_name = sys.argv[0]
user_source_code = base_reporter.get_module_source_code(user_script_name)
methods = """
GLM and Statistical Inference have been done using the <i>%s</i> script, \
powered by <a href="%s">nistats</a>.""" % (user_script_name,
base_reporter.NISTATS_URL)
# report the control parameters used in the paradigm and analysis
design_params = ""
glm_kwargs["contrasts"] = contrasts
if len(glm_kwargs):
design_params += (
"The following control parameters were used for "
" specifying the experimental paradigm and fitting the "
"GLM:<br/><ul>")
# reshape glm_kwargs['paradigm']
if "paradigm" in glm_kwargs:
paradigm_ = glm_kwargs['paradigm']
paradigm = {'name': paradigm_['name'], 'onset': paradigm_['onset']}
if 'duration' in paradigm_.keys():
paradigm['duration'] = paradigm_['duration']
paradigm['n_conditions'] = len(set(paradigm['name']))
paradigm['n_events'] = len(paradigm['name'])
paradigm['type'] = 'event'
if 'duration' in paradigm.keys() and paradigm['duration'][0] > 0:
paradigm['type'] = 'block'
glm_kwargs['paradigm'] = paradigm
design_params += base_reporter.dict_to_html_ul(glm_kwargs)
if start_time is None:
start_time = base_reporter.pretty_time()
if title is None:
title = "GLM and Statistical Inference"
if not subject_id is None:
title += " for subject %s" % subject_id
level1_html_markup = base_reporter.get_subject_report_stats_html_template(
title=title,
start_time=start_time,
subject_id=subject_id,
# insert source code stub
source_script_name=user_script_name,
source_code=user_source_code,
design_params=design_params,
methods=methods,
threshold=threshold)
with open(stats_report_filename, 'w') as fd:
fd.write(str(level1_html_markup))
fd.close()
progress_logger.log("<b>Level 1 statistics</b><br/><br/>")
# create design matrix thumbs
if not design_matrices is None:
if not hasattr(design_matrices, '__len__'):
design_matrices = [design_matrices]
for design_matrix, j in zip(design_matrices,
range(len(design_matrices))):
# Nistats: design matrices should be strings or pandas dataframes
if isinstance(design_matrix, _basestring):
if not isinstance(design_matrix, pd.DataFrame):
# XXX should be a DataFrame pickle here ?
print(design_matrix)
design_matrix = pd.read_pickle(design_matrix)
else:
raise TypeError("Unsupported design matrix type: %s" %
type(design_matrix))
# plot design_matrix proper
ax = plot_design_matrix(design_matrix)
ax.set_position([.05, .25, .9, .65])
dmat_outfile = os.path.join(output_dir,
'design_matrix_%i.png' % (j + 1))
pl.savefig(dmat_outfile, bbox_inches="tight", dpi=200)
pl.close()
thumb = base_reporter.Thumbnail()
thumb.a = base_reporter.a(href=os.path.basename(dmat_outfile))
thumb.img = base_reporter.img(src=os.path.basename(dmat_outfile),
height="500px")
thumb.description = "Design Matrix"
thumb.description += " %s" % (
j + 1) if len(design_matrices) > 1 else ""
# commit activation thumbnail into gallery
design_thumbs.commit_thumbnails(thumb)
# create activation thumbs
for contrast_id, contrast_val in contrasts.items():
z_map = z_maps[contrast_id]
# load the map
if isinstance(z_map, _basestring):
z_map = nibabel.load(z_map)
# generate level 1 stats table
title = "Level 1 stats for %s contrast" % contrast_id
stats_table = os.path.join(output_dir,
"%s_stats_table.html" % (contrast_id))
generate_level1_stats_table(z_map,
mask,
stats_table,
cluster_th=cluster_th,
z_threshold=threshold,
title=title)
# plot activation proper
# XXX: nilearn's plotting bug's about rotations inf affine, etc.
z_map = reorder_img(z_map, resample="continuous")
if not anat is None:
anat = reorder_img(anat, resample="continuous")
plot_stat_map(z_map,
anat,
threshold=threshold,
display_mode=display_mode,
cut_coords=cut_coords,
black_bg=True)
# store activation plot
z_map_plot = os.path.join(output_dir, "%s_z_map.png" % contrast_id)
pl.savefig(z_map_plot,
dpi=200,
bbox_inches='tight',
facecolor="k",
edgecolor="k")
pl.close()
# create thumbnail for activation
thumbnail = base_reporter.Thumbnail(tooltip="Contrast vector: %s" %
contrast_val)
thumbnail.a = base_reporter.a(href=os.path.basename(stats_table))
thumbnail.img = base_reporter.img(
src=os.path.basename(z_map_plot),
height="150px",
)
thumbnail.description = contrast_id
activation_thumbs.commit_thumbnails(thumbnail)
# we're done, shut down re-loaders
progress_logger.log('<hr/>')
# prevent stats report page from reloading henceforth
progress_logger.finish(stats_report_filename)
# prevent any related page from reloading
if shutdown_all_reloaders:
progress_logger.finish_dir(output_dir)
# return generated html
with open(stats_report_filename, 'r') as fd:
stats_report = fd.read()
fd.close()
return stats_report
def generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
mask,
design_matrices=None,
subject_id=None,
anat=None,
display_mode="z",
cut_coords=None,
threshold=2.3,
cluster_th=15,
start_time=None,
title=None,
user_script_name=None,
progress_logger=None,
shutdown_all_reloaders=True,
**glm_kwargs):
"""Generates a report summarizing the statistical methods and results
Parameters
----------
stats_report_filename: string:
html file to which output (generated html) will be written
contrasts: dict of arrays
contrasts we are interested in; same number of contrasts as zmaps;
same keys
zmaps: dict of image objects or strings (image filenames)
zmaps for contrasts we are interested in; one per contrast id
mask: 'nifti image object'
brain mask for ROI
design_matrix: list of 'DesignMatrix', `numpy.ndarray` objects or of
strings (.png, .npz, etc.) for filenames
design matrices for the experimental conditions
contrasts: dict of arrays
dictionary of contrasts of interest; the keys are the contrast ids,
the values are contrast values (lists)
z_maps: dict of 3D image objects or strings (image filenames)
dict with same keys as 'contrasts'; the values are paths of z-maps
for the respective contrasts
anat: 3D array (optional)
brain image to serve bg unto which activation maps will be plotted
anat_affine: 2D array (optional)
affine data for the anat
threshold: float (optional)
threshold to be applied to activation maps voxel-wise
cluster_th: int (optional)
minimal voxel count for clusteres declared as 'activated'
cmap: cmap object (default viz.cm.cold_hot)
color-map to use in plotting activation maps
start_time: string (optional)
start time for the stats analysis (useful for the generated
report page)
user_script_name: string (optional, default None)
existing filename, path to user script used in doing the analysis
progress_logger: ProgressLogger object (optional)
handle for logging progress
shutdown_all_reloaders: bool (optional, default True)
if True, all pages connected to the stats report page will
be prevented from reloading after the stats report page
has been completely generated
**glm_kwargs:
kwargs used to specify the control parameters used to specify the
experimental paradigm and the GLM
"""
# prepare for stats reporting
if progress_logger is None:
progress_logger = base_reporter.ProgressReport()
output_dir = os.path.dirname(stats_report_filename)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# copy css and js stuff to output dir
base_reporter.copy_web_conf_files(output_dir)
# initialize gallery of design matrices
design_thumbs = base_reporter.ResultsGallery(
loader_filename=os.path.join(output_dir,
"design.html")
)
# initialize gallery of activation maps
activation_thumbs = base_reporter.ResultsGallery(
loader_filename=os.path.join(output_dir,
"activation.html")
)
# get caller module handle from stack-frame
if user_script_name is None:
user_script_name = sys.argv[0]
user_source_code = base_reporter.get_module_source_code(
user_script_name)
methods = """
GLM and Statistical Inference have been done using the <i>%s</i> script, \
powered by <a href="%s">nistats</a>.""" % (user_script_name,
base_reporter.NISTATS_URL)
# report the control parameters used in the paradigm and analysis
design_params = ""
glm_kwargs["contrasts"] = contrasts
if len(glm_kwargs):
design_params += ("The following control parameters were used for "
" specifying the experimental paradigm and fitting the "
"GLM:<br/><ul>")
# reshape glm_kwargs['paradigm']
if "paradigm" in glm_kwargs:
paradigm_ = glm_kwargs['paradigm']
paradigm = {'name' : paradigm_['name'],
'onset' : paradigm_['onset']}
if 'duration' in paradigm_.keys():
paradigm['duration'] = paradigm_['duration']
paradigm['n_conditions'] = len(set(paradigm['name']))
paradigm['n_events'] = len(paradigm['name'])
paradigm['type'] = 'event'
if 'duration' in paradigm.keys() and paradigm['duration'][0] > 0:
paradigm['type'] = 'block'
glm_kwargs['paradigm'] = paradigm
design_params += base_reporter.dict_to_html_ul(glm_kwargs)
if start_time is None:
start_time = base_reporter.pretty_time()
if title is None:
title = "GLM and Statistical Inference"
if not subject_id is None:
title += " for subject %s" % subject_id
level1_html_markup = base_reporter.get_subject_report_stats_html_template(
title=title,
start_time=start_time,
subject_id=subject_id,
# insert source code stub
source_script_name=user_script_name,
source_code=user_source_code,
design_params=design_params,
methods=methods,
threshold=threshold)
with open(stats_report_filename, 'w') as fd:
fd.write(str(level1_html_markup))
fd.close()
progress_logger.log("<b>Level 1 statistics</b><br/><br/>")
# create design matrix thumbs
if not design_matrices is None:
if not hasattr(design_matrices, '__len__'):
design_matrices = [design_matrices]
for design_matrix, j in zip(design_matrices,
range(len(design_matrices))):
# Nistats: design matrices should be strings or pandas dataframes
if isinstance(design_matrix, basestring):
if not isinstance(design_matrix, pd.DataFrame):
# XXX should be a DataFrame pickle here ?
print design_matrix
design_matrix = pd.read_pickle(design_matrix)
else:
raise TypeError(
"Unsupported design matrix type: %s" % type(
design_matrix))
# plot design_matrix proper
ax = plot_design_matrix(design_matrix)
ax.set_position([.05, .25, .9, .65])
dmat_outfile = os.path.join(output_dir,
'design_matrix_%i.png' % (j + 1))
pl.savefig(dmat_outfile, bbox_inches="tight", dpi=200)
pl.close()
thumb = base_reporter.Thumbnail()
thumb.a = base_reporter.a(href=os.path.basename(dmat_outfile))
thumb.img = base_reporter.img(src=os.path.basename(dmat_outfile),
height="500px")
thumb.description = "Design Matrix"
thumb.description += " %s" % (j + 1) if len(
design_matrices) > 1 else ""
# commit activation thumbnail into gallery
design_thumbs.commit_thumbnails(thumb)
# create activation thumbs
for contrast_id, contrast_val in contrasts.iteritems():
z_map = z_maps[contrast_id]
# load the map
if isinstance(z_map, basestring):
z_map = nibabel.load(z_map)
# generate level 1 stats table
title = "Level 1 stats for %s contrast" % contrast_id
stats_table = os.path.join(output_dir, "%s_stats_table.html" % (
contrast_id))
generate_level1_stats_table(
z_map, mask, stats_table, cluster_th=cluster_th,
z_threshold=threshold, title=title)
# plot activation proper
# XXX: nilearn's plotting bug's about rotations inf affine, etc.
z_map = reorder_img(z_map, resample="continuous")
if not anat is None:
anat = reorder_img(anat, resample="continuous")
plot_stat_map(z_map, anat, threshold=threshold,
display_mode=display_mode, cut_coords=cut_coords,
black_bg=True)
# store activation plot
z_map_plot = os.path.join(output_dir,
"%s_z_map.png" % contrast_id)
pl.savefig(z_map_plot, dpi=200, bbox_inches='tight', facecolor="k",
edgecolor="k")
pl.close()
# create thumbnail for activation
thumbnail = base_reporter.Thumbnail(
tooltip="Contrast vector: %s" % contrast_val)
thumbnail.a = base_reporter.a(href=os.path.basename(stats_table))
thumbnail.img = base_reporter.img(src=os.path.basename(z_map_plot),
height="150px",)
thumbnail.description = contrast_id
activation_thumbs.commit_thumbnails(thumbnail)
# we're done, shut down re-loaders
progress_logger.log('<hr/>')
# prevent stats report page from reloading henceforth
progress_logger.finish(stats_report_filename)
# prevent any related page from reloading
if shutdown_all_reloaders:
progress_logger.finish_dir(output_dir)
# return generated html
with open(stats_report_filename, 'r') as fd:
stats_report = fd.read()
fd.close()
return stats_report
fd.close()
# preprocess the data
subject_data = do_subjects_preproc(jobfile, dataset_dir=dataset_dir)[0]
# construct design matrix
nscans = len(subject_data.func[0])
frametimes = np.linspace(0, (nscans - 1) * tr, nscans)
drift_model = 'Cosine'
hrf_model = 'spm + derivative'
design_matrix = make_design_matrix(
frametimes, paradigm, hrf_model=hrf_model, drift_model=drift_model,
period_cut=hfcut)
# plot and save design matrix
ax = plot_design_matrix(design_matrix)
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')
dmat_outfile = os.path.join(subject_data.output_dir, 'design_matrix.png')
plt.savefig(dmat_outfile, bbox_inches="tight", dpi=200)
# specify contrasts
contrasts = {}
_, matrix, names = check_design_matrix(design_matrix)
contrast_matrix = np.eye(len(names))
for i in range(len(names)):
contrasts[names[i]] = contrast_matrix[i]
# more interesting contrasts"""
contrasts = {'active-rest': contrasts['active'] - contrasts['rest']}
