def convert(self, remove_executed=False):
"""
Convert the executed notebook to a restructured text (RST) file.
Parameters
----------
delete_executed : bool, optional
Controls whether to remove the executed notebook or not.
"""
if not path.exists(self._executed_nb_path):
raise IOError(
"Executed notebook file doesn't exist! Expected: {0}".format(
self._executed_nb_path))
if path.exists(self._rst_path) and not self.overwrite:
logger.debug(
"RST version of notebook already exists at {0}. Use "
"overwrite=True or --overwrite (at cmd line) to re-run".format(
self._rst_path))
return self._rst_path
# Initialize the resources dict - see:
# https://github.com/jupyter/nbconvert/blob/master/nbconvert/nbconvertapp.py#L327
resources = {}
resources['config_dir'] = '' # we don't need to specify config
resources['unique_key'] = self.nb_name
# path to store extra files, like plots generated
resources['output_files_dir'] = 'nboutput'
# Exports the notebook to RST
logger.debug('Exporting notebook to RST...')
exporter = RSTExporter()
if self.template_file:
exporter.template_file = self.template_file
output, resources = exporter.from_filename(self._executed_nb_path,
resources=resources)
# Write the output RST file
writer = FilesWriter()
output_file_path = writer.write(output,
resources,
notebook_name=self.nb_name)
if remove_executed: # optionally, clean up the executed notebook file
remove(self._executed_nb_path)
return output_file_path
def convert(self):
"""
Converts the executed notebook to a restructured text (RST) file.
Returns
-------
output_file_path : str``
The path to the converted notebook
"""
# Only convert if executed notebook exists
if not os.path.exists(self.executed_nb_path):
raise IOError(
"Executed notebook file doesn't exist! Expected: {0}".format(
self.executed_nb_path))
# Initialize the resources dict
resources = dict()
resources['unique_key'] = self.nb_name
# path to store extra files, like plots generated
resources['output_files_dir'] = 'nboutput/'
# Exports the notebook to RST
_logger.info("Exporting executed notebook to RST format")
exporter = RSTExporter()
# If a RST template file has been specified use this template
if self.rst_template:
exporter.template_file = self.rst_template
output, resources = exporter.from_filename(self.executed_nb_path,
resources=resources)
# Write the output RST file
writer = FilesWriter()
output_file_path = writer.write(output,
resources,
notebook_name=self.nb_name)
return output_file_path
def convert(self, remove_executed=False):
"""
Convert the executed notebook to a restructured text (RST) file.
Parameters
----------
delete_executed : bool, optional
Controls whether to remove the executed notebook or not.
"""
if not path.exists(self._executed_nb_path):
raise IOError("Executed notebook file doesn't exist! Expected: {0}"
.format(self._executed_nb_path))
if path.exists(self._rst_path) and not self.overwrite:
logger.debug("RST version of notebook already exists at {0}. Use "
"overwrite=True or --overwrite (at cmd line) to re-run"
.format(self._rst_path))
return self._rst_path
# Initialize the resources dict - see:
# https://github.com/jupyter/nbconvert/blob/master/nbconvert/nbconvertapp.py#L327
resources = {}
resources['config_dir'] = '' # we don't need to specify config
resources['unique_key'] = self.nb_name
# path to store extra files, like plots generated
resources['output_files_dir'] = 'nboutput'
# Exports the notebook to RST
logger.debug('Exporting notebook to RST...')
exporter = RSTExporter()
if self.template_file:
exporter.template_file = self.template_file
output, resources = exporter.from_filename(self._executed_nb_path,
resources=resources)
# Write the output RST file
writer = FilesWriter()
output_file_path = writer.write(output, resources,
notebook_name=self.nb_name)
# read the executed notebook, grab the keywords from the header,
# add them in to the RST as filter keywords
with open(self._executed_nb_path) as f:
nb = nbformat.read(f, as_version=IPYTHON_VERSION)
top_cell_text = nb['cells'][0]['source']
match = re.search('## [kK]eywords\s+(.*)', top_cell_text)
if match:
keywords = match.groups()[0].split(',')
keywords = [clean_keyword(k) for k in keywords if k.strip()]
keyword_filters = ['filter{0}'.format(k) for k in keywords]
else:
keyword_filters = []
# Add metatags to top of RST files to get rendered into HTML, used for
# the search and filter functionality in Learn Astropy
meta_tutorials = '.. meta::\n :keywords: {0}\n'
filters = ['filterTutorials'] + keyword_filters
meta_tutorials = meta_tutorials.format(', '.join(filters))
with open(output_file_path, 'r') as f:
rst_text = f.read()
with open(output_file_path, 'w') as f:
rst_text = '{0}\n{1}'.format(meta_tutorials, rst_text)
f.write(rst_text)
if remove_executed: # optionally, clean up the executed notebook file
remove(self._executed_nb_path)
return output_file_path
def convert(self, remove_executed=False):
"""
Convert the executed notebook to a restructured text (RST) file.
Parameters
----------
delete_executed : bool, optional
Controls whether to remove the executed notebook or not.
"""
if not path.exists(self._executed_nb_path):
raise IOError("Executed notebook file doesn't exist! Expected: {0}"
.format(self._executed_nb_path))
if path.exists(self._rst_path) and not self.overwrite:
logger.debug("RST version of notebook already exists at {0}. Use "
"overwrite=True or --overwrite (at cmd line) to re-run"
.format(self._rst_path))
return self._rst_path
# Initialize the resources dict - see:
# https://github.com/jupyter/nbconvert/blob/master/nbconvert/nbconvertapp.py#L327
resources = {}
resources['config_dir'] = '' # we don't need to specify config
resources['unique_key'] = self.nb_name
# path to store extra files, like plots generated
resources['output_files_dir'] = 'nboutput'
# these keywords are used to build the filter keywords
# TODO: add a pre-processor that extracts the keywords from the markdown
# cell in the header and adds them to this list
# NOTE: the split[-4] trick below is brittle in that it will break if
# a notebook is, say, nested two layers deep instead of just one like
# all of our notebooks thus far.
resources['nb_keywords'] = [self.nb_path.split(sep)[-4]]
# Exports the notebook to RST
logger.debug('Exporting notebook to RST...')
exporter = RSTExporter()
if self.template_file:
exporter.template_file = self.template_file
output, resources = exporter.from_filename(self._executed_nb_path,
resources=resources)
# Write the output RST file
writer = FilesWriter()
output_file_path = writer.write(output, resources,
notebook_name=self.nb_name)
# read the executed notebook, grab the keywords from the header,
# add them in to the RST as filter keywords
with open(self._executed_nb_path) as f:
nb = nbformat.read(f, as_version=IPYTHON_VERSION)
top_cell_text = nb['cells'][0]['source']
match = re.search('## [kK]eywords\s+(.*)', top_cell_text)
if match:
keywords = match.groups()[0].split(',')
keywords = [clean_keyword(k) for k in keywords if k.strip()]
keyword_filters = ['filter{0}'.format(k) for k in keywords]
else:
keyword_filters = []
# Add metatags to top of RST files to get rendered into HTML, used for
# the search and filter functionality in Learn Astropy
meta_tutorials = '.. meta::\n :keywords: {0}\n'
filters = ['filterTutorials'] + keyword_filters
meta_tutorials = meta_tutorials.format(', '.join(filters))
with open(output_file_path, 'r') as f:
rst_text = f.read()
with open(output_file_path, 'w') as f:
rst_text = '{0}\n{1}'.format(meta_tutorials, rst_text)
f.write(rst_text)
if remove_executed: # optionally, clean up the executed notebook file
remove(self._executed_nb_path)
return output_file_path
def convert(self, remove_executed=False):
"""
Convert the executed notebook to a restructured text (RST) file or HTML.
Parameters
----------
delete_executed : bool, optional
Controls whether to remove the executed notebook or not.
"""
if not path.exists(self._executed_nb_path):
raise IOError(
"Executed notebook file doesn't exist! Expected: {0}".format(
self._executed_nb_path))
if path.exists(self._output_path) and not self.overwrite:
logger.debug(
"{0} version of notebook already exists at {1}. Use "
"overwrite=True or --overwrite (at cmd line) to re-run".format(
self._output_type, self._output_path))
return self._output_path
# Initialize the resources dict - see:
# https://github.com/jupyter/nbconvert/blob/master/nbconvert/nbconvertapp.py#L327
resources = {}
resources['config_dir'] = '' # we don't need to specify config
resources['unique_key'] = self.nb_name
# path to store extra files, like plots generated
resources['output_files_dir'] = 'nboutput'
if self.base_path is None:
path_to_root = ''
else:
path_to_root = path.relpath(self.base_path,
start=path.split(self.nb_path)[0])
path_to_root += path.sep
resources['path_to_pages_root'] = request.pathname2url(path_to_root)
# Exports the notebook to the output format
logger.debug('Exporting notebook to {}...'.format(self._output_type))
if self._output_type == 'RST':
exporter = RSTExporter()
elif self._output_type == 'HTML':
exporter = HTMLExporter()
else:
raise ValueError('This should be impossible... output_type should '
'have been checked earlier, but it is '
'unrecognized')
if self.template_file:
exporter.template_file = self.template_file
output, resources = exporter.from_filename(self._executed_nb_path,
resources=resources)
# Write the output file
writer = FilesWriter()
output_file_path = writer.write(output,
resources,
notebook_name=self.nb_name)
if self._output_type == 'RST':
self._add_filter_keywords(output_file_path)
if remove_executed: # optionally, clean up the executed notebook file
remove(self._executed_nb_path)
title = ''
try:
with open(self.nb_path) as f:
nb = nbformat.reader.read(f)
title = nb['cells'][0]['source'].split('#')[1].split(
"\n")[0].strip()
except Exception:
print(
'Failed to parse notebook title from first cell, please check notebook.'
)
page_info = dict(output_file_path=output_file_path,
name=self.nb_name.replace("_", ' ').title(),
title=title)
return page_info