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Merge pull request #3834 from ivanov/nbconvert-better-tests...
Merge pull request #3834 from ivanov/nbconvert-better-tests This PR fixes a few issues with nbconvert tests The code for testing 'ipython nbconvert' prior to this PR did not work as intended, and simply swallowed errors when pandoc wasn't installed, for example. This PR adds a new get_output_error_code utility for easier checking of error (looking at return code as opposed to the contents of stdout for the word 'error'). This new machinery is leveraged when calling nbconvert during tests.

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extractoutput.py
101 lines | 4.1 KiB | text/x-python | PythonLexer
"""Module containing a transformer that extracts all of the outputs from the
notebook file. The extracted outputs are returned in the 'resources' dictionary.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2013, the IPython Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
import base64
import sys
import os
from IPython.utils.traitlets import Unicode
from .base import Transformer
from IPython.utils import py3compat
#-----------------------------------------------------------------------------
# Classes
#-----------------------------------------------------------------------------
class ExtractOutputTransformer(Transformer):
"""
Extracts all of the outputs from the notebook file. The extracted
outputs are returned in the 'resources' dictionary.
"""
figure_filename_template = Unicode(
"{unique_key}_{cell_index}_{index}.{extension}", config=True)
def transform_cell(self, cell, resources, cell_index):
"""
Apply a transformation on each cell,
Parameters
----------
cell : NotebookNode cell
Notebook cell being processed
resources : dictionary
Additional resources used in the conversion process. Allows
transformers to pass variables into the Jinja engine.
cell_index : int
Index of the cell being processed (see base.py)
"""
#Get the unique key from the resource dict if it exists. If it does not
#exist, use 'figure' as the default. Also, get files directory if it
#has been specified
unique_key = resources.get('unique_key', 'figure')
output_files_dir = resources.get('output_files_dir', None)
#Make sure outputs key exists
if not 'outputs' in resources:
resources['outputs'] = {}
#Loop through all of the outputs in the cell
for index, out in enumerate(cell.get('outputs', [])):
#Get the output in data formats that the template is interested in.
for out_type in self.display_data_priority:
if out.hasattr(out_type):
data = out[out_type]
#Binary files are base64-encoded, SVG is already XML
if out_type in ('png', 'jpg', 'jpeg', 'pdf'):
# data is b64-encoded as text (str, unicode)
# decodestring only accepts bytes
data = py3compat.cast_bytes(data)
data = base64.decodestring(data)
elif sys.platform == 'win32':
data = data.replace('\n', '\r\n').encode("UTF-8")
else:
data = data.encode("UTF-8")
#Build a figure name
filename = self.figure_filename_template.format(
unique_key=unique_key,
cell_index=cell_index,
index=index,
extension=out_type)
#On the cell, make the figure available via
# cell.outputs[i].svg_filename ... etc (svg in example)
# Where
# cell.outputs[i].svg contains the data
if output_files_dir is not None:
filename = os.path.join(output_files_dir, filename)
out[out_type + '_filename'] = filename
#In the resources, make the figure available via
# resources['outputs']['filename'] = data
resources['outputs'][filename] = data
return cell, resources