"""Generate self-contained HTML reports from MNE objects."""
# Authors: Alex Gramfort
# Mainak Jas
# Teon Brooks
#
# License: BSD (3-clause)
import base64
from io import BytesIO
import os
import os.path as op
import fnmatch
import re
import codecs
from shutil import copyfile
import time
from glob import glob
import warnings
import webbrowser
import numpy as np
from . import read_evokeds, read_events, pick_types, read_cov
from .io import read_raw_fif, read_info, _stamp_to_dt
from .utils import (logger, verbose, get_subjects_dir, warn, _import_mlab,
fill_doc, _check_option)
from .viz import plot_events, plot_alignment, plot_cov
from .viz._3d import _plot_mri_contours
from .forward import read_forward_solution
from .epochs import read_epochs
from .minimum_norm import read_inverse_operator
from .parallel import parallel_func, check_n_jobs
from .viz.raw import _data_types
from .externals.tempita import HTMLTemplate, Template
from .externals.h5io import read_hdf5, write_hdf5
VALID_EXTENSIONS = ['raw.fif', 'raw.fif.gz', 'sss.fif', 'sss.fif.gz',
'-eve.fif', '-eve.fif.gz', '-cov.fif', '-cov.fif.gz',
'-trans.fif', '-trans.fif.gz', '-fwd.fif', '-fwd.fif.gz',
'-epo.fif', '-epo.fif.gz', '-inv.fif', '-inv.fif.gz',
'-ave.fif', '-ave.fif.gz', 'T1.mgz']
SECTION_ORDER = ['raw', 'events', 'epochs', 'evoked', 'covariance', 'trans',
'mri', 'forward', 'inverse']
###############################################################################
# PLOTTING FUNCTIONS
def _ndarray_to_fig(img):
"""Convert to MPL figure, adapted from matplotlib.image.imsave."""
figsize = np.array(img.shape[:2][::-1]) / 100.
fig = _figure_agg(dpi=100, figsize=figsize, frameon=False)
fig.figimage(img)
return fig
def _fig_to_img(fig, image_format='png', scale=None, **kwargs):
"""Plot figure and create a binary image."""
# fig can be ndarray, mpl Figure, Mayavi Figure, or callable that produces
# a mpl Figure
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
if isinstance(fig, np.ndarray):
fig = _ndarray_to_fig(fig)
elif callable(fig):
plt.close('all')
fig = fig(**kwargs)
elif not isinstance(fig, Figure):
mlab = None
try:
mlab = _import_mlab()
# on some systems importing Mayavi raises SystemExit (!)
except Exception:
is_mayavi = False
else:
import mayavi
is_mayavi = isinstance(fig, mayavi.core.scene.Scene)
if not is_mayavi:
raise TypeError('Each fig must be a matplotlib Figure, mayavi '
'Scene, or NumPy ndarray, got %s (type %s)'
% (fig, type(fig)))
if fig.scene is not None:
img = mlab.screenshot(figure=fig)
else: # Testing mode
img = np.zeros((2, 2, 3))
mlab.close(fig)
fig = _ndarray_to_fig(img)
output = BytesIO()
if scale is not None:
_scale_mpl_figure(fig, scale)
logger.debug('Saving figure %s with dpi %s'
% (fig.get_size_inches(), fig.get_dpi()))
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore') # incompatible axes
fig.savefig(output, format=image_format, dpi=fig.get_dpi(),
bbox_to_inches='tight')
plt.close(fig)
output = output.getvalue()
return (output.decode('utf-8') if image_format == 'svg' else
base64.b64encode(output).decode('ascii'))
def _scale_mpl_figure(fig, scale):
"""Magic scaling helper.
Keeps font-size and artist sizes constant
0.5 : current font - 4pt
2.0 : current font + 4pt
XXX it's unclear why this works, but good to go for most cases
"""
scale = float(scale)
fig.set_size_inches(fig.get_size_inches() * scale)
fig.set_dpi(fig.get_dpi() * scale)
import matplotlib as mpl
if scale >= 1:
sfactor = scale ** 2
else:
sfactor = -((1. / scale) ** 2)
for text in fig.findobj(mpl.text.Text):
fs = text.get_fontsize()
new_size = fs + sfactor
if new_size <= 0:
raise ValueError('could not rescale matplotlib fonts, consider '
'increasing "scale"')
text.set_fontsize(new_size)
fig.canvas.draw()
def _figs_to_mrislices(sl, n_jobs, **kwargs):
import matplotlib.pyplot as plt
plt.close('all')
use_jobs = min(n_jobs, max(1, len(sl)))
parallel, p_fun, _ = parallel_func(_plot_mri_contours, use_jobs)
outs = parallel(p_fun(slices=s, **kwargs)
for s in np.array_split(sl, use_jobs))
for o in outs[1:]:
outs[0] += o
return outs[0]
def _iterate_trans_views(function, **kwargs):
"""Auxiliary function to iterate over views in trans fig."""
import matplotlib.pyplot as plt
from mayavi import mlab, core
from pyface.api import GUI
fig = function(**kwargs)
gui = GUI()
gui.process_events()
assert isinstance(fig, core.scene.Scene)
views = [(90, 90), (0, 90), (0, -90)]
fig2, axes = plt.subplots(1, len(views))
for view, ax in zip(views, axes):
mlab.view(view[0], view[1])
gui.process_events()
if fig.scene is not None:
im = mlab.screenshot(figure=fig)
else: # Testing mode
im = np.zeros((2, 2, 3))
ax.imshow(im)
ax.axis('off')
mlab.close(fig)
img = _fig_to_img(fig2, image_format='png')
return img
###############################################################################
# TOC FUNCTIONS
def _is_bad_fname(fname):
"""Identify bad file naming patterns and highlight them in the TOC."""
if fname.endswith('(whitened)'):
fname = fname[:-11]
if not fname.endswith(tuple(VALID_EXTENSIONS + ['bem', 'custom'])):
return 'red'
else:
return ''
def _get_fname(fname):
"""Get fname without -#-."""
if '-#-' in fname:
fname = fname.split('-#-')[0]
else:
fname = op.basename(fname)
fname = ' ... %s' % fname
return fname
def _get_toc_property(fname):
"""Assign class names to TOC elements to allow toggling with buttons."""
if fname.endswith(('-eve.fif', '-eve.fif.gz')):
div_klass = 'events'
tooltip = fname
text = op.basename(fname)
elif fname.endswith(('-ave.fif', '-ave.fif.gz')):
div_klass = 'evoked'
tooltip = fname
text = op.basename(fname)
elif fname.endswith(('-cov.fif', '-cov.fif.gz')):
div_klass = 'covariance'
tooltip = fname
text = op.basename(fname)
elif fname.endswith(('raw.fif', 'raw.fif.gz',
'sss.fif', 'sss.fif.gz')):
div_klass = 'raw'
tooltip = fname
text = op.basename(fname)
elif fname.endswith(('-trans.fif', '-trans.fif.gz')):
div_klass = 'trans'
tooltip = fname
text = op.basename(fname)
elif fname.endswith(('-fwd.fif', '-fwd.fif.gz')):
div_klass = 'forward'
tooltip = fname
text = op.basename(fname)
elif fname.endswith(('-inv.fif', '-inv.fif.gz')):
div_klass = 'inverse'
tooltip = fname
text = op.basename(fname)
elif fname.endswith(('-epo.fif', '-epo.fif.gz')):
div_klass = 'epochs'
tooltip = fname
text = op.basename(fname)
elif fname.endswith(('.nii', '.nii.gz', '.mgh', '.mgz')):
div_klass = 'mri'
tooltip = 'MRI'
text = 'MRI'
elif fname.endswith(('bem')):
div_klass = 'mri'
tooltip = 'MRI'
text = 'MRI'
elif fname.endswith('(whitened)'):
div_klass = 'evoked'
tooltip = fname
text = op.basename(fname[:-11]) + '(whitened)'
else:
div_klass = fname.split('-#-')[1]
tooltip = fname.split('-#-')[0]
text = fname.split('-#-')[0]
return div_klass, tooltip, text
def _iterate_files(report, fnames, info, cov, baseline, sfreq, on_error,
image_format):
"""Parallel process in batch mode."""
htmls, report_fnames, report_sectionlabels = [], [], []
def _update_html(html, report_fname, report_sectionlabel):
"""Update the lists above."""
htmls.append(html)
report_fnames.append(report_fname)
report_sectionlabels.append(report_sectionlabel)
for fname in fnames:
logger.info("Rendering : %s"
% op.join('...' + report.data_path[-20:],
fname))
try:
if fname.endswith(('raw.fif', 'raw.fif.gz',
'sss.fif', 'sss.fif.gz')):
html = report._render_raw(fname)
report_fname = fname
report_sectionlabel = 'raw'
elif fname.endswith(('-fwd.fif', '-fwd.fif.gz')):
html = report._render_forward(fname)
report_fname = fname
report_sectionlabel = 'forward'
elif fname.endswith(('-inv.fif', '-inv.fif.gz')):
html = report._render_inverse(fname)
report_fname = fname
report_sectionlabel = 'inverse'
elif fname.endswith(('-ave.fif', '-ave.fif.gz')):
if cov is not None:
html = report._render_whitened_evoked(fname, cov, baseline,
image_format)
report_fname = fname + ' (whitened)'
report_sectionlabel = 'evoked'
_update_html(html, report_fname, report_sectionlabel)
html = report._render_evoked(fname, baseline, image_format)
report_fname = fname
report_sectionlabel = 'evoked'
elif fname.endswith(('-eve.fif', '-eve.fif.gz')):
html = report._render_eve(fname, sfreq, image_format)
report_fname = fname
report_sectionlabel = 'events'
elif fname.endswith(('-epo.fif', '-epo.fif.gz')):
html = report._render_epochs(fname, image_format)
report_fname = fname
report_sectionlabel = 'epochs'
elif (fname.endswith(('-cov.fif', '-cov.fif.gz')) and
report.info_fname is not None):
html = report._render_cov(fname, info, image_format)
report_fname = fname
report_sectionlabel = 'covariance'
elif (fname.endswith(('-trans.fif', '-trans.fif.gz')) and
report.info_fname is not None and report.subjects_dir
is not None and report.subject is not None):
html = report._render_trans(fname, report.data_path, info,
report.subject,
report.subjects_dir)
report_fname = fname
report_sectionlabel = 'trans'
else:
html = None
report_fname = None
report_sectionlabel = None
except Exception as e:
if on_error == 'warn':
warn('Failed to process file %s:\n"%s"' % (fname, e))
elif on_error == 'raise':
raise
html = None
report_fname = None
report_sectionlabel = None
_update_html(html, report_fname, report_sectionlabel)
return htmls, report_fnames, report_sectionlabels
def open_report(fname, **params):
"""Read a saved report or, if it doesn't exist yet, create a new one.
The returned report can be used as a context manager, in which case any
changes to the report are saved when exiting the context block.
Parameters
----------
fname : str
The file containing the report, stored in the HDF5 format. If the file
does not exist yet, a new report is created that will be saved to the
specified file.
**params : kwargs
When creating a new report, any named parameters other than ``fname``
are passed to the ``__init__`` function of the `Report` object. When
reading an existing report, the parameters are checked with the
loaded report and an exception is raised when they don't match.
Returns
-------
report : instance of Report
The report.
"""
if op.exists(fname):
# Check **params with the loaded report
state = read_hdf5(fname, title='mnepython')
for param in params.keys():
if param not in state:
raise ValueError('The loaded report has no attribute %s' %
param)
if params[param] != state[param]:
raise ValueError("Attribute '%s' of loaded report does not "
"match the given parameter." % param)
report = Report()
report.__setstate__(state)
else:
report = Report(**params)
# Keep track of the filename in case the Report object is used as a context
# manager.
report._fname = fname
return report
###############################################################################
# IMAGE FUNCTIONS
def _figure_agg(**kwargs):
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
fig = Figure(**kwargs)
FigureCanvas(fig)
return fig
def _build_image_png(data, cmap='gray'):
"""Build an image encoded in base64."""
import matplotlib.pyplot as plt
figsize = data.shape[::-1]
if figsize[0] == 1:
figsize = tuple(figsize[1:])
data = data[:, :, 0]
fig = _figure_agg(figsize=figsize, dpi=1.0, frameon=False)
cmap = getattr(plt.cm, cmap, plt.cm.gray)
fig.figimage(data, cmap=cmap)
output = BytesIO()
fig.savefig(output, dpi=fig.get_dpi(), format='png')
return base64.b64encode(output.getvalue()).decode('ascii')
def _iterate_sagittal_slices(array, limits=None):
"""Iterate sagittal slices."""
shape = array.shape[0]
for ind in range(shape):
if limits and ind not in limits:
continue
yield ind, array[ind, :, :]
def _iterate_axial_slices(array, limits=None):
"""Iterate axial slices."""
shape = array.shape[1]
for ind in range(shape):
if limits and ind not in limits:
continue
yield ind, array[:, ind, :]
def _iterate_coronal_slices(array, limits=None):
"""Iterate coronal slices."""
shape = array.shape[2]
for ind in range(shape):
if limits and ind not in limits:
continue
yield ind, np.flipud(np.rot90(array[:, :, ind]))
def _iterate_mri_slices(name, ind, global_id, slides_klass, data, cmap):
"""Auxiliary function for parallel processing of mri slices."""
img_klass = 'slideimg-%s' % name
caption = u'Slice %s %s' % (name, ind)
slice_id = '%s-%s-%s' % (name, global_id, ind)
div_klass = 'span12 %s' % slides_klass
img = _build_image_png(data, cmap=cmap)
first = True if ind == 0 else False
html = _build_html_image(img, slice_id, div_klass, img_klass, caption,
first, image_format='png')
return ind, html
###############################################################################
# HTML functions
def _build_html_image(img, id, div_klass, img_klass, caption=None,
show=True, image_format='png'):
"""Build a html image from a slice array."""
html = []
add_style = u'' if show else u'style="display: none"'
html.append(u'
""")
def _build_html_slider(slices_range, slides_klass, slider_id,
start_value=None):
"""Build an html slider for a given slices range and a slices klass."""
if start_value is None:
start_value = slices_range[len(slices_range) // 2]
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore')
out = slider_template.substitute(
slider_id=slider_id, klass=slides_klass,
step=slices_range[1] - slices_range[0],
minvalue=slices_range[0], maxvalue=slices_range[-1],
startvalue=start_value)
return out
###############################################################################
# HTML scan renderer
header_template = Template(u"""
{{include}}
""")
footer_template = HTMLTemplate(u"""
""")
html_template = Template(u"""
""")
def _check_scale(scale):
"""Ensure valid scale value is passed."""
if np.isscalar(scale) and scale <= 0:
raise ValueError('scale must be positive, not %s' % scale)
def _check_image_format(rep, image_format):
"""Ensure fmt is valid."""
if rep is None:
_check_option('image_format', image_format, ['png', 'svg'])
elif image_format is not None:
_check_option('image_format', image_format, ['png', 'svg', None])
else: # rep is not None and image_format is None
image_format = rep.image_format
return image_format
@fill_doc
class Report(object):
r"""Object for rendering HTML.
Parameters
----------
info_fname : str
Name of the file containing the info dictionary.
subjects_dir : str | None
Path to the SUBJECTS_DIR. If None, the path is obtained by using
the environment variable SUBJECTS_DIR.
subject : str | None
Subject name.
title : str
Title of the report.
cov_fname : str
Name of the file containing the noise covariance.
baseline : None or tuple of length 2 (default (None, 0))
The time interval to apply baseline correction for evokeds.
If None do not apply it. If baseline is (a, b)
the interval is between "a (s)" and "b (s)".
If a is None the beginning of the data is used
and if b is None then b is set to the end of the interval.
If baseline is equal to (None, None) all the time
interval is used.
The baseline (a, b) includes both endpoints, i.e. all
timepoints t such that a <= t <= b.
image_format : str
Default image format to use (default is 'png').
SVG uses vector graphics, so fidelity is higher but can increase
file size and browser image rendering time as well.
.. versionadded:: 0.15
raw_psd : bool | dict
If True, include PSD plots for raw files. Can be False (default) to
omit, True to plot, or a dict to pass as ``kwargs`` to
:meth:`mne.io.Raw.plot_psd`.
.. versionadded:: 0.17
%(verbose)s
Notes
-----
See :ref:`tut-report` for an introduction to using ``mne.Report``, and
:ref:`this example ` for an example of customizing the report
with a slider.
.. versionadded:: 0.8.0
"""
def __init__(self, info_fname=None, subjects_dir=None,
subject=None, title=None, cov_fname=None, baseline=None,
image_format='png', raw_psd=False, verbose=None):
self.info_fname = info_fname
self.cov_fname = cov_fname
self.baseline = baseline
self.subjects_dir = get_subjects_dir(subjects_dir, raise_error=False)
self.subject = subject
self.title = title
self.image_format = _check_image_format(None, image_format)
self.verbose = verbose
self.initial_id = 0
self.html = []
self.fnames = [] # List of file names rendered
self.sections = [] # List of sections
self.lang = 'en-us' # language setting for the HTML file
self._sectionlabels = [] # Section labels
self._sectionvars = {} # Section variable names in js
# boolean to specify if sections should be ordered in natural
# order of processing (raw -> events ... -> inverse)
self._sort_sections = False
if not isinstance(raw_psd, bool) and not isinstance(raw_psd, dict):
raise TypeError('raw_psd must be bool or dict, got %s'
% (type(raw_psd),))
self.raw_psd = raw_psd
self._init_render() # Initialize the renderer
def __repr__(self):
"""Print useful info about report."""
s = ' 4:
s += '\n%s' % '\n'.join(fnames[:2])
s += '\n ...\n'
s += '\n'.join(fnames[-2:])
elif len(self.fnames) > 0:
s += '\n%s' % '\n'.join(fnames)
s += '\n>'
return s
def __len__(self):
"""Return the number of items in report."""
return len(self.fnames)
def _get_id(self):
"""Get id of plot."""
self.initial_id += 1
return self.initial_id
def _validate_input(self, items, captions, section, comments=None):
"""Validate input."""
if not isinstance(items, (list, tuple)):
items = [items]
if not isinstance(captions, (list, tuple)):
captions = [captions]
if not isinstance(comments, (list, tuple)):
if comments is None:
comments = [comments] * len(captions)
else:
comments = [comments]
if len(comments) != len(items):
raise ValueError('Comments and report items must have the same '
'length or comments should be None, got %d and %d'
% (len(comments), len(items)))
elif len(captions) != len(items):
raise ValueError('Captions and report items must have the same '
'length, got %d and %d'
% (len(captions), len(items)))
# Book-keeping of section names
if section not in self.sections:
self.sections.append(section)
self._sectionvars[section] = _clean_varnames(section)
return items, captions, comments
def remove(self, caption, section=None):
"""Remove a figure from the report.
The figure to remove is searched for by its caption. When searching by
caption, the section label can be specified as well to narrow down the
search. If multiple figures match the search criteria, the last one
will be removed.
Any empty sections will be removed as well.
Parameters
----------
caption : str
If set, search for the figure by caption.
section : str | None
If set, limit the search to the section with the given label.
Returns
-------
removed_index : int | None
The integer index of the figure that was removed, or ``None`` if no
figure matched the search criteria.
"""
# Construct the search pattern
pattern = r'^%s-#-.*-#-custom$' % caption
# Search for figures matching the search pattern, regardless of
# section
matches = [i for i, fname_ in enumerate(self.fnames)
if re.match(pattern, fname_)]
if section is not None:
# Narrow down the search to the given section
svar = self._sectionvars[section]
matches = [i for i in matches
if self._sectionlabels[i] == svar]
if len(matches) == 0:
return None
# Remove last occurrence
index = max(matches)
# Remove the figure
del self.fnames[index]
del self._sectionlabels[index]
del self.html[index]
# Remove any (now) empty sections.
# We use a list() to copy the _sectionvars dictionary, since we are
# removing elements during the loop.
for section_, sectionlabel_ in list(self._sectionvars.items()):
if sectionlabel_ not in self._sectionlabels:
self.sections.remove(section_)
del self._sectionvars[section_]
return index
def _add_or_replace(self, fname, sectionlabel, html, replace=False):
"""Append a figure to the report, or replace it if it already exists.
Parameters
----------
fname : str
A unique identifier for the figure. If a figure with this
identifier has already been added, it will be replaced.
sectionlabel : str
The section to place the figure in.
html : str
The HTML that contains the figure.
replace : bool
Existing figures are only replaced if this is set to ``True``.
Defaults to ``False``.
"""
assert isinstance(html, str) # otherwise later will break
if replace and fname in self.fnames:
# Find last occurrence of the figure
ind = max([i for i, existing in enumerate(self.fnames)
if existing == fname])
self.fnames[ind] = fname
self._sectionlabels[ind] = sectionlabel
self.html[ind] = html
else:
# Append new record
self.fnames.append(fname)
self._sectionlabels.append(sectionlabel)
self.html.append(html)
def add_figs_to_section(self, figs, captions, section='custom',
scale=None, image_format=None, comments=None,
replace=False):
"""Append custom user-defined figures.
Parameters
----------
figs : matplotlib.figure.Figure | mlab.Figure | array | list
A figure or a list of figures to add to the report. Each figure in
the list can be an instance of :class:`matplotlib.figure.Figure`,
:class:`mayavi.core.api.Scene`, or :class:`numpy.ndarray`.
captions : str | list of str
A caption or a list of captions to the figures.
section : str
Name of the section to place the figure in. If section already
exists, the figures will be appended to the end of the section.
scale : float | None | callable
Scale the images maintaining the aspect ratio.
If None, no scaling is applied. If float, scale will determine
the relative scaling (might not work for scale <= 1 depending on
font sizes). If function, should take a figure object as input
parameter. Defaults to None.
image_format : str | None
The image format to be used for the report, can be 'png' or 'svd'.
None (default) will use the default specified during Report
class construction.
comments : None | str | list of str
A string of text or a list of strings of text to be appended after
the figure.
replace : bool
If ``True``, figures already present that have the same caption
will be replaced. Defaults to ``False``.
"""
figs, captions, comments = self._validate_input(figs, captions,
section, comments)
image_format = _check_image_format(self, image_format)
_check_scale(scale)
for fig, caption, comment in zip(figs, captions, comments):
caption = 'custom plot' if caption == '' else caption
sectionvar = self._sectionvars[section]
global_id = self._get_id()
div_klass = self._sectionvars[section]
img_klass = self._sectionvars[section]
img = _fig_to_img(fig, image_format, scale)
html = image_template.substitute(img=img, id=global_id,
div_klass=div_klass,
img_klass=img_klass,
caption=caption,
show=True,
image_format=image_format,
comment=comment)
self._add_or_replace('%s-#-%s-#-custom' % (caption, sectionvar),
sectionvar, html, replace)
def add_images_to_section(self, fnames, captions, scale=None,
section='custom', comments=None, replace=False):
"""Append custom user-defined images.
Parameters
----------
fnames : str | list of str
A filename or a list of filenames from which images are read.
Images can be PNG, GIF or SVG.
captions : str | list of str
A caption or a list of captions to the images.
scale : float | None
Scale the images maintaining the aspect ratio.
Defaults to None. If None, no scaling will be applied.
section : str
Name of the section. If section already exists, the images
will be appended to the end of the section.
comments : None | str | list of str
A string of text or a list of strings of text to be appended after
the image.
replace : bool
If ``True``, figures already present that have the same caption
will be replaced. Defaults to ``False``.
"""
# Note: using scipy.misc is equivalent because scipy internally
# imports PIL anyway. It's not possible to redirect image output
# to binary string using scipy.misc.
fnames, captions, comments = self._validate_input(fnames, captions,
section, comments)
_check_scale(scale)
for fname, caption, comment in zip(fnames, captions, comments):
caption = 'custom plot' if caption == '' else caption
sectionvar = self._sectionvars[section]
global_id = self._get_id()
div_klass = self._sectionvars[section]
img_klass = self._sectionvars[section]
image_format = os.path.splitext(fname)[1][1:]
image_format = image_format.lower()
_check_option('image_format', image_format, ['png', 'gif', 'svg'])
# Convert image to binary string.
with open(fname, 'rb') as f:
img = base64.b64encode(f.read()).decode('ascii')
html = image_template.substitute(img=img, id=global_id,
image_format=image_format,
div_klass=div_klass,
img_klass=img_klass,
caption=caption,
width=scale,
comment=comment,
show=True)
self._add_or_replace('%s-#-%s-#-custom' % (caption, sectionvar),
sectionvar, html, replace)
def add_htmls_to_section(self, htmls, captions, section='custom',
replace=False):
"""Append htmls to the report.
Parameters
----------
htmls : str | list of str
An html str or a list of html str.
captions : str | list of str
A caption or a list of captions to the htmls.
section : str
Name of the section. If section already exists, the images
will be appended to the end of the section.
replace : bool
If ``True``, figures already present that have the same caption
will be replaced. Defaults to ``False``.
Notes
-----
.. versionadded:: 0.9.0
"""
htmls, captions, _ = self._validate_input(htmls, captions, section)
for html, caption in zip(htmls, captions):
caption = 'custom plot' if caption == '' else caption
sectionvar = self._sectionvars[section]
global_id = self._get_id()
div_klass = self._sectionvars[section]
self._add_or_replace(
'%s-#-%s-#-custom' % (caption, sectionvar), sectionvar,
html_template.substitute(div_klass=div_klass, id=global_id,
caption=caption, html=html), replace)
@fill_doc
def add_bem_to_section(self, subject, caption='BEM', section='bem',
decim=2, n_jobs=1, subjects_dir=None,
replace=False):
"""Render a bem slider html str.
Parameters
----------
subject : str
Subject name.
caption : str
A caption for the bem.
section : str
Name of the section. If section already exists, the bem
will be appended to the end of the section.
decim : int
Use this decimation factor for generating MRI/BEM images
(since it can be time consuming).
%(n_jobs)s
subjects_dir : str | None
Path to the SUBJECTS_DIR. If None, the path is obtained by using
the environment variable SUBJECTS_DIR.
replace : bool
If ``True``, figures already present that have the same caption
will be replaced. Defaults to ``False``.
Notes
-----
.. versionadded:: 0.9.0
"""
caption = 'custom plot' if caption == '' else caption
html = self._render_bem(subject=subject, subjects_dir=subjects_dir,
decim=decim, n_jobs=n_jobs, section=section,
caption=caption)
html, caption, _ = self._validate_input(html, caption, section)
sectionvar = self._sectionvars[section]
# convert list->str
assert isinstance(html, list)
html = u''.join(html)
self._add_or_replace('%s-#-%s-#-custom' % (caption[0], sectionvar),
sectionvar, html)
def add_slider_to_section(self, figs, captions=None, section='custom',
title='Slider', scale=None, image_format=None,
replace=False):
"""Render a slider of figs to the report.
Parameters
----------
figs : list of figures.
Each figure in the list can be an instance of
:class:`matplotlib.figure.Figure`,
:class:`mayavi.core.api.Scene`, or :class:`numpy.ndarray`.
Must have at least 2 elements.
captions : list of str | list of float | None
A list of captions to the figures. If float, a str will be
constructed as `%f s`. If None, it will default to
`Data slice %d`.
section : str
Name of the section. If section already exists, the figures
will be appended to the end of the section.
title : str
The title of the slider.
scale : float | None | callable
Scale the images maintaining the aspect ratio.
If None, no scaling is applied. If float, scale will determine
the relative scaling (might not work for scale <= 1 depending on
font sizes). If function, should take a figure object as input
parameter. Defaults to None.
image_format : str | None
The image format to be used for the report, can be 'png' or 'svd'.
None (default) will use the default specified during Report
class construction.
replace : bool
If ``True``, figures already present that have the same caption
will be replaced. Defaults to ``False``.
Notes
-----
.. versionadded:: 0.10.0
"""
_check_scale(scale)
image_format = _check_image_format(self, image_format)
if isinstance(figs[0], list):
raise NotImplementedError('`add_slider_to_section` '
'can only add one slider at a time.')
if len(figs) < 2:
raise ValueError('figs must be at least length 2, got %s'
% (len(figs),))
figs = [figs]
figs, _, _ = self._validate_input(figs, section, section)
figs = figs[0]
sectionvar = self._sectionvars[section]
global_id = self._get_id()
name = 'slider'
html = []
slides_klass = '%s-%s' % (name, global_id)
div_klass = 'span12 %s' % slides_klass
sl = np.arange(0, len(figs))
slices = []
img_klass = 'slideimg-%s' % name
if captions is None:
captions = ['Data slice %d' % ii for ii in sl]
elif isinstance(captions, (list, tuple, np.ndarray)):
if len(figs) != len(captions):
raise ValueError('Captions must be the same length as the '
'number of slides.')
if isinstance(captions[0], (float, int)):
captions = ['%0.3f s' % caption for caption in captions]
else:
raise TypeError('Captions must be None or an iterable of '
'float, int, str, Got %s' % type(captions))
for ii, (fig, caption) in enumerate(zip(figs, captions)):
img = _fig_to_img(fig, image_format, scale)
slice_id = '%s-%s-%s' % (name, global_id, sl[ii])
first = True if ii == 0 else False
slices.append(_build_html_image(img, slice_id, div_klass,
img_klass, caption, first,
image_format=image_format))
# Render the slider
slider_id = 'select-%s-%s' % (name, global_id)
# Render the slices
image_html = u'\n'.join(slices)
html.append(_build_html_slider(sl, slides_klass, slider_id,
start_value=0))
html = '\n'.join(html)
slider_klass = sectionvar
self._add_or_replace(
'%s-#-%s-#-custom' % (title, sectionvar), sectionvar,
slider_full_template.substitute(id=global_id, title=title,
div_klass=slider_klass,
slider_id=slider_id, html=html,
image_html=image_html))
###########################################################################
# HTML rendering
def _render_one_axis(self, slices_iter, name, global_id, cmap,
n_elements, n_jobs):
"""Render one axis of the array."""
global_id = global_id or name
html = []
html.append(u'
')
slides_klass = '%s-%s' % (name, global_id)
use_jobs = min(n_jobs, max(1, n_elements))
parallel, p_fun, _ = parallel_func(_iterate_mri_slices, use_jobs)
r = parallel(p_fun(name, ind, global_id, slides_klass, data, cmap)
for ind, data in slices_iter)
slices_range, slices = zip(*r)
# Render the slider
slider_id = 'select-%s-%s' % (name, global_id)
html.append(u'' % slider_id)
html.append(u'
')
# Render the slices
html.append(u'\n'.join(slices))
html.append(u'
')
return '\n'.join(html)
###########################################################################
# global rendering functions
@verbose
def _init_render(self, verbose=None):
"""Initialize the renderer."""
inc_fnames = ['/usr/share/javascript/jquery/jquery.min.js', '/usr/share/javascript/jquery-ui/jquery-ui.min.js',
'bootstrap.min.js', 'jquery-ui.min.css',
'bootstrap.min.css']
include = list()
for inc_fname in inc_fnames:
logger.info('Embedding : %s' % inc_fname)
if not os.path.isabs(inc_fname):
inc_fname = op.join(op.dirname(__file__), 'html', inc_fname)
f = open(inc_fname, 'r')
if PY3:
file_content = f.read()
else:
file_content = f.read().decode('UTF-8')
if inc_fname.endswith('.js'):
include.append(u'')
elif inc_fname.endswith('.css'):
include.append(u'')
self.include = ''.join(include)
@verbose
def parse_folder(self, data_path, pattern='*.fif', n_jobs=1, mri_decim=2,
sort_sections=True, on_error='warn', image_format=None,
render_bem=True, verbose=None):
r"""Render all the files in the folder.
Parameters
----------
data_path : str
Path to the folder containing data whose HTML report will be
created.
pattern : str | list of str
Filename pattern(s) to include in the report.
Example: [\*raw.fif, \*ave.fif] will include Raw as well as Evoked
files.
%(n_jobs)s
mri_decim : int
Use this decimation factor for generating MRI/BEM images
(since it can be time consuming).
sort_sections : bool
If True, sort sections in the order: raw -> events -> epochs
-> evoked -> covariance -> trans -> mri -> forward -> inverse.
on_error : str
What to do if a file cannot be rendered. Can be 'ignore',
'warn' (default), or 'raise'.
image_format : str | None
The image format to be used for the report, can be 'png' or 'svd'.
None (default) will use the default specified during Report
class construction.
.. versionadded:: 0.15
render_bem : bool
If True (default), try to render the BEM.
.. versionadded:: 0.16
%(verbose_meth)s
"""
image_format = _check_image_format(self, image_format)
_check_option('on_error', on_error, ['ignore', 'warn', 'raise'])
self._sort = sort_sections
n_jobs = check_n_jobs(n_jobs)
self.data_path = data_path
if self.title is None:
self.title = 'MNE Report for ...%s' % self.data_path[-20:]
if not isinstance(pattern, (list, tuple)):
pattern = [pattern]
# iterate through the possible patterns
fnames = list()
for p in pattern:
fnames.extend(sorted(_recursive_search(self.data_path, p)))
if self.info_fname is not None:
info = read_info(self.info_fname, verbose=False)
sfreq = info['sfreq']
else:
# only warn if relevant
if any(fname.endswith(('-cov.fif', '-cov.fif.gz'))
for fname in fnames):
warn('`info_fname` not provided. Cannot render '
'-cov.fif(.gz) files.')
if any(fname.endswith(('-trans.fif', '-trans.fif.gz'))
for fname in fnames):
warn('`info_fname` not provided. Cannot render '
'-trans.fif(.gz) files.')
info, sfreq = None, None
cov = None
if self.cov_fname is not None:
cov = read_cov(self.cov_fname)
baseline = self.baseline
# render plots in parallel; check that n_jobs <= # of files
logger.info('Iterating over %s potential files (this may take some '
'time)' % len(fnames))
use_jobs = min(n_jobs, max(1, len(fnames)))
parallel, p_fun, _ = parallel_func(_iterate_files, use_jobs)
r = parallel(p_fun(self, fname, info, cov, baseline, sfreq, on_error,
image_format)
for fname in np.array_split(fnames, use_jobs))
htmls, report_fnames, report_sectionlabels = zip(*r)
# combine results from n_jobs discarding plots not rendered
self.html = [html for html in sum(htmls, []) if html is not None]
self.fnames = [fname for fname in sum(report_fnames, []) if
fname is not None]
self._sectionlabels = [slabel for slabel in
sum(report_sectionlabels, [])
if slabel is not None]
# find unique section labels
self.sections = sorted(set(self._sectionlabels))
self._sectionvars = dict(zip(self.sections, self.sections))
# render mri
if render_bem:
if self.subjects_dir is not None and self.subject is not None:
logger.info('Rendering BEM')
self.html.append(self._render_bem(
self.subject, self.subjects_dir, mri_decim, n_jobs))
self.fnames.append('bem')
self._sectionlabels.append('mri')
else:
warn('`subjects_dir` and `subject` not provided. Cannot '
'render MRI and -trans.fif(.gz) files.')
def _get_state_params(self):
"""Obtain all fields that are in the state dictionary of this object.
Returns
-------
non_opt_params : list of str
All parameters that must be present in the state dictionary.
opt_params : list of str
All parameters that are optionally present in the state dictionary.
"""
# Note: self._fname is not part of the state
return (['baseline', 'cov_fname', 'fnames', 'html', 'include',
'image_format', 'info_fname', 'initial_id', 'raw_psd',
'_sectionlabels', 'sections', '_sectionvars',
'_sort_sections', 'subjects_dir', 'subject', 'title',
'verbose'],
['data_path', 'lang', '_sort'])
def __getstate__(self):
"""Get the state of the report as a dictionary."""
state = dict()
non_opt_params, opt_params = self._get_state_params()
for param in non_opt_params:
state[param] = getattr(self, param)
for param in opt_params:
if hasattr(self, param):
state[param] = getattr(self, param)
return state
def __setstate__(self, state):
"""Set the state of the report."""
non_opt_params, opt_params = self._get_state_params()
for param in non_opt_params:
setattr(self, param, state[param])
for param in opt_params:
if param in state:
setattr(self, param, state[param])
return state
def save(self, fname=None, open_browser=True, overwrite=False):
"""Save the report and optionally open it in browser.
Parameters
----------
fname : str | None
File name of the report. If the file name ends in '.h5' or '.hdf5',
the report is saved in HDF5 format, so it can later be loaded again
with :func:`open_report`. If the file name ends in anything else,
the report is rendered to HTML. If ``None``, the report is saved to
'report.html' in the current working directory.
Defaults to ``None``.
open_browser : bool
When saving to HTML, open the rendered HTML file browser after
saving if True. Defaults to True.
overwrite : bool
If True, overwrite report if it already exists. Defaults to False.
Returns
-------
fname : str
The file name to which the report was saved.
"""
if fname is None:
if not hasattr(self, 'data_path'):
self.data_path = os.getcwd()
warn('`data_path` not provided. Using %s instead'
% self.data_path)
fname = op.realpath(op.join(self.data_path, 'report.html'))
else:
fname = op.realpath(fname)
if not overwrite and op.isfile(fname):
msg = ('Report already exists at location %s. '
'Overwrite it (y/[n])? '
% fname)
answer = input(msg)
if answer.lower() == 'y':
overwrite = True
_, ext = op.splitext(fname)
is_hdf5 = ext.lower() in ['.h5', '.hdf5']
if overwrite or not op.isfile(fname):
logger.info('Saving report to location %s' % fname)
if is_hdf5:
write_hdf5(fname, self.__getstate__(), overwrite=overwrite,
title='mnepython')
else:
self._render_toc()
# Annotate the HTML with a TOC and footer.
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore')
html = footer_template.substitute(
date=time.strftime("%B %d, %Y"),
current_year=time.strftime("%Y"))
self.html.append(html)
# Writing to disk may fail. However, we need to make sure that
# the TOC and footer are removed regardless, otherwise they
# will be duplicated when the user attempts to save again.
try:
# Write HTML
with codecs.open(fname, 'w', 'utf-8') as fobj:
fobj.write(_fix_global_ids(u''.join(self.html)))
finally:
self.html.pop(0)
self.html.pop(0)
self.html.pop()
building_doc = os.getenv('_MNE_BUILDING_DOC', '').lower() == 'true'
if open_browser and not is_hdf5 and not building_doc:
webbrowser.open_new_tab('file://' + fname)
self.fname = fname
return fname
def __enter__(self):
"""Do nothing when entering the context block."""
return self
def __exit__(self, type, value, traceback):
"""Save the report when leaving the context block."""
if self._fname is not None:
self.save(self._fname, open_browser=False, overwrite=True)
@verbose
def _render_toc(self, verbose=None):
"""Render the Table of Contents."""
logger.info('Rendering : Table of Contents')
html_toc = u'
'
html_toc += u'
CONTENTS
'
global_id = 1
# Reorder self.sections to reflect natural ordering
if self._sort_sections:
sections = list(set(self.sections) & set(SECTION_ORDER))
custom = [section for section in self.sections if section
not in SECTION_ORDER]
order = [sections.index(section) for section in SECTION_ORDER if
section in sections]
self.sections = np.array(sections)[order].tolist() + custom
# Sort by section
html, fnames, sectionlabels = [], [], []
for section in self.sections:
logger.info('%s' % section)
for sectionlabel, this_html, fname in (zip(self._sectionlabels,
self.html, self.fnames)):
if self._sectionvars[section] == sectionlabel:
html.append(this_html)
fnames.append(fname)
sectionlabels.append(sectionlabel)
logger.info(_get_fname(fname))
color = _is_bad_fname(fname)
div_klass, tooltip, text = _get_toc_property(fname)
# loop through conditions for evoked
if fname.endswith(('-ave.fif', '-ave.fif.gz',
'(whitened)')):
text = os.path.basename(fname)
if fname.endswith('(whitened)'):
fname = fname[:-11]
# XXX: remove redundant read_evokeds
evokeds = read_evokeds(fname, verbose=False)
html_toc += toc_list.substitute(
div_klass=div_klass, id=None, tooltip=fname,
color='#428bca', text=text)
html_toc += u'
'
for ev in evokeds:
html_toc += toc_list.substitute(
div_klass=div_klass, id=global_id,
tooltip=fname, color=color, text=ev.comment)
global_id += 1
html_toc += u'
')
return '\n'.join(html)
def _render_image_png(self, image, cmap='gray', n_jobs=1):
"""Render one slice of mri without bem as a PNG."""
import nibabel as nib
global_id = self._get_id()
if 'mri' not in self.sections:
self.sections.append('mri')
self._sectionvars['mri'] = 'mri'
nim = nib.load(image)
data = nim.get_data()
shape = data.shape
limits = {'sagittal': range(0, shape[0], 2),
'axial': range(0, shape[1], 2),
'coronal': range(0, shape[2], 2)}
name = op.basename(image)
html = u'
\n' % global_id
html += u'
%s
\n' % name
html += self._render_array(data, global_id=global_id,
cmap=cmap, limits=limits, n_jobs=n_jobs)
html += u'
\n'
return html
def _render_raw(self, raw_fname):
"""Render raw (only text)."""
import matplotlib.pyplot as plt
global_id = self._get_id()
raw = read_raw_fif(raw_fname, allow_maxshield='yes')
extra = ' (MaxShield on)' if raw.info.get('maxshield', False) else ''
caption = u'Raw : %s%s' % (raw_fname, extra)
n_eeg = len(pick_types(raw.info, meg=False, eeg=True))
n_grad = len(pick_types(raw.info, meg='grad'))
n_mag = len(pick_types(raw.info, meg='mag'))
pick_eog = pick_types(raw.info, meg=False, eog=True)
if len(pick_eog) > 0:
eog = ', '.join(np.array(raw.info['ch_names'])[pick_eog])
else:
eog = 'Not available'
pick_ecg = pick_types(raw.info, meg=False, ecg=True)
if len(pick_ecg) > 0:
ecg = ', '.join(np.array(raw.info['ch_names'])[pick_ecg])
else:
ecg = 'Not available'
meas_date = raw.info['meas_date']
if meas_date is not None:
meas_date = _stamp_to_dt(meas_date).strftime("%B %d, %Y") + ' GMT'
html = raw_template.substitute(
div_klass='raw', id=global_id, caption=caption, info=raw.info,
meas_date=meas_date, n_eeg=n_eeg, n_grad=n_grad, n_mag=n_mag,
eog=eog, ecg=ecg, tmin=raw._first_time, tmax=raw._last_time)
raw_psd = {} if self.raw_psd is True else self.raw_psd
if isinstance(raw_psd, dict):
from matplotlib.backends.backend_agg import FigureCanvasAgg
n_ax = sum(kind in raw for kind in _data_types)
fig, axes = plt.subplots(n_ax, 1, figsize=(6, 1 + 1.5 * n_ax),
dpi=92)
FigureCanvasAgg(fig)
img = _fig_to_img(raw.plot_psd, self.image_format,
ax=axes, **raw_psd)
new_html = image_template.substitute(
img=img, div_klass='raw', img_klass='raw',
caption='PSD', show=True, image_format=self.image_format)
html += '\n\n' + new_html
return html
def _render_forward(self, fwd_fname):
"""Render forward."""
div_klass = 'forward'
caption = u'Forward: %s' % fwd_fname
fwd = read_forward_solution(fwd_fname)
repr_fwd = re.sub('>', '', re.sub('<', '', repr(fwd)))
global_id = self._get_id()
html = repr_template.substitute(div_klass=div_klass,
id=global_id,
caption=caption,
repr=repr_fwd)
return html
def _render_inverse(self, inv_fname):
"""Render inverse."""
div_klass = 'inverse'
caption = u'Inverse: %s' % inv_fname
inv = read_inverse_operator(inv_fname)
repr_inv = re.sub('>', '', re.sub('<', '', repr(inv)))
global_id = self._get_id()
html = repr_template.substitute(div_klass=div_klass,
id=global_id,
caption=caption,
repr=repr_inv)
return html
def _render_evoked(self, evoked_fname, baseline, image_format):
"""Render evoked."""
logger.debug('Evoked: Reading %s' % evoked_fname)
evokeds = read_evokeds(evoked_fname, baseline=baseline, verbose=False)
html = []
for ei, ev in enumerate(evokeds):
global_id = self._get_id()
kwargs = dict(show=False)
logger.debug('Evoked: Plotting instance %s/%s'
% (ei + 1, len(evokeds)))
img = _fig_to_img(ev.plot, image_format, **kwargs)
caption = u'Evoked : %s (%s)' % (evoked_fname, ev.comment)
html.append(image_template.substitute(
img=img, id=global_id, div_klass='evoked',
img_klass='evoked', caption=caption, show=True,
image_format=image_format))
has_types = []
if len(pick_types(ev.info, meg=False, eeg=True)) > 0:
has_types.append('eeg')
if len(pick_types(ev.info, meg='grad', eeg=False)) > 0:
has_types.append('grad')
if len(pick_types(ev.info, meg='mag', eeg=False)) > 0:
has_types.append('mag')
for ch_type in has_types:
logger.debug(' Topomap type %s' % ch_type)
img = _fig_to_img(ev.plot_topomap, image_format,
ch_type=ch_type, **kwargs)
caption = u'Topomap (ch_type = %s)' % ch_type
html.append(image_template.substitute(
img=img, div_klass='evoked', img_klass='evoked',
caption=caption, show=True, image_format=image_format))
logger.debug('Evoked: done')
return '\n'.join(html)
def _render_eve(self, eve_fname, sfreq, image_format):
"""Render events."""
global_id = self._get_id()
events = read_events(eve_fname)
kwargs = dict(events=events, sfreq=sfreq, show=False)
img = _fig_to_img(plot_events, image_format, **kwargs)
caption = 'Events : ' + eve_fname
html = image_template.substitute(
img=img, id=global_id, div_klass='events', img_klass='events',
caption=caption, show=True, image_format=image_format)
return html
def _render_epochs(self, epo_fname, image_format):
"""Render epochs."""
global_id = self._get_id()
epochs = read_epochs(epo_fname)
kwargs = dict(subject=self.subject, show=False)
img = _fig_to_img(epochs.plot_drop_log, image_format, **kwargs)
caption = 'Epochs : ' + epo_fname
show = True
html = image_template.substitute(
img=img, id=global_id, div_klass='epochs', img_klass='epochs',
caption=caption, show=show, image_format=image_format)
return html
def _render_cov(self, cov_fname, info_fname, image_format, show_svd=True):
"""Render cov."""
global_id = self._get_id()
cov = read_cov(cov_fname)
fig, svd = plot_cov(cov, info_fname, show=False, show_svd=show_svd)
html = []
figs = [fig]
captions = ['Covariance : %s (n_samples: %s)' % (cov_fname, cov.nfree)]
if svd is not None:
figs.append(svd)
captions.append('Singular values of the noise covariance')
for fig, caption in zip(figs, captions):
img = _fig_to_img(fig, image_format)
show = True
html.append(image_template.substitute(
img=img, id=global_id, div_klass='covariance',
img_klass='covariance', caption=caption, show=show,
image_format=image_format))
return '\n'.join(html)
def _render_whitened_evoked(self, evoked_fname, noise_cov, baseline,
image_format):
"""Render whitened evoked."""
evokeds = read_evokeds(evoked_fname, verbose=False)
html = []
for ev in evokeds:
ev = read_evokeds(evoked_fname, ev.comment, baseline=baseline,
verbose=False)
global_id = self._get_id()
kwargs = dict(noise_cov=noise_cov, show=False)
img = _fig_to_img(ev.plot_white, image_format, **kwargs)
caption = u'Whitened evoked : %s (%s)' % (evoked_fname, ev.comment)
show = True
html.append(image_template.substitute(
img=img, id=global_id, div_klass='evoked',
img_klass='evoked', caption=caption, show=show,
image_format=image_format))
return '\n'.join(html)
def _render_trans(self, trans, path, info, subject, subjects_dir):
"""Render trans (only PNG)."""
kwargs = dict(info=info, trans=trans, subject=subject,
subjects_dir=subjects_dir)
try:
img = _iterate_trans_views(function=plot_alignment, **kwargs)
except IOError:
img = _iterate_trans_views(function=plot_alignment,
surfaces=['head'], **kwargs)
if img is not None:
global_id = self._get_id()
html = image_template.substitute(
img=img, id=global_id, div_klass='trans',
img_klass='trans', caption='Trans : ' + trans, width=75,
show=True, image_format='png')
return html
def _render_bem(self, subject, subjects_dir, decim, n_jobs,
section='mri', caption='BEM'):
"""Render mri+bem (only PNG)."""
import nibabel as nib
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
# Get the MRI filename
mri_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz')
if not op.isfile(mri_fname):
warn('MRI file "%s" does not exist' % mri_fname)
# Get the BEM surface filenames
bem_path = op.join(subjects_dir, subject, 'bem')
if not op.isdir(bem_path):
warn('Subject bem directory "%s" does not exist' % bem_path)
return self._render_image_png(mri_fname, cmap='gray',
n_jobs=n_jobs)
surf_fnames = []
for surf_name in ['*inner_skull', '*outer_skull', '*outer_skin']:
surf_fname = glob(op.join(bem_path, surf_name + '.surf'))
if len(surf_fname) > 0:
surf_fnames.append(surf_fname[0])
else:
warn('No surface found for %s.' % surf_name)
continue
if len(surf_fnames) == 0:
warn('No surfaces found at all, rendering empty MRI')
return self._render_image_png(mri_fname, cmap='gray',
n_jobs=n_jobs)
# XXX : find a better way to get max range of slices
nim = nib.load(mri_fname)
data = nim.get_data()
shape = data.shape
del data # free up memory
html = []
global_id = self._get_id()
if section == 'mri' and 'mri' not in self.sections:
self.sections.append('mri')
self._sectionvars['mri'] = 'mri'
name = caption
html += u'
\n' % global_id
html += u'
%s
\n' % name # all other captions are h4
html += self._render_one_bem_axis(mri_fname, surf_fnames, global_id,
shape, 'axial', decim, n_jobs)
html += self._render_one_bem_axis(mri_fname, surf_fnames, global_id,
shape, 'sagittal', decim, n_jobs)
html += self._render_one_bem_axis(mri_fname, surf_fnames, global_id,
shape, 'coronal', decim, n_jobs)
html += u'
\n'
return ''.join(html)
def _clean_varnames(s):
# Remove invalid characters
s = re.sub('[^0-9a-zA-Z_]', '', s)
# add report_ at the beginning so that the javascript class names
# are valid ones
return 'report_' + s
def _recursive_search(path, pattern):
"""Auxiliary function for recursive_search of the directory."""
filtered_files = list()
for dirpath, dirnames, files in os.walk(path):
for f in fnmatch.filter(files, pattern):
# only the following file types are supported
# this ensures equitable distribution of jobs
if f.endswith(tuple(VALID_EXTENSIONS)):
filtered_files.append(op.realpath(op.join(dirpath, f)))
return filtered_files
def _fix_global_ids(html):
"""Fix the global_ids after reordering in _render_toc()."""
html = re.sub(r'id="\d+"', 'id="###"', html)
global_id = 1
while len(re.findall('id="###"', html)) > 0:
html = re.sub('id="###"', 'id="%s"' % global_id, html, count=1)
global_id += 1
return html
###############################################################################
# Scraper for sphinx-gallery
_SCRAPER_TEXT = '''
.. only:: builder_html
.. container:: row
.. rubric:: The `HTML document <{0}>`__ written by :meth:`mne.Report.save`:
.. raw:: html
''' # noqa: E501
# Adapted from fa-file-code
_FA_FILE_CODE = '' # noqa: E501
class _ReportScraper(object):
"""Scrape Report outputs.
Only works properly if conf.py is configured properly and the file
is written to the same directory as the example script.
"""
def __init__(self):
self.app = None
self.files = dict()
def __repr__(self):
return ''
def __call__(self, block, block_vars, gallery_conf):
for report in block_vars['example_globals'].values():
if (isinstance(report, Report) and hasattr(report, 'fname') and
report.fname.endswith('.html') and
gallery_conf['builder_name'] == 'html'):
# Thumbnail
image_path_iterator = block_vars['image_path_iterator']
img_fname = next(image_path_iterator)
img_fname = img_fname.replace('.png', '.svg')
with open(img_fname, 'w') as fid:
fid.write(_FA_FILE_CODE)
# copy HTML file
html_fname = op.basename(report.fname)
out_fname = op.join(
self.app.builder.outdir,
op.relpath(op.dirname(block_vars['target_file']),
self.app.builder.srcdir), html_fname)
self.files[report.fname] = out_fname
# embed links/iframe
data = _SCRAPER_TEXT.format(html_fname)
return data
return ''
def copyfiles(self, *args, **kwargs):
for key, value in self.files.items():
copyfile(key, value)
{{else}}
{{else}}