29 | {% endif %}
30 | {% endfor %}
31 |
32 | {% elif cell.cell_type == 'markdown' %}
33 | {{ cell.source }}
34 | {% endif %}
35 | {% endfor %}
36 | """
37 |
38 |
39 | class MystContentsManager(LargeFileManager):
40 | def save(self, model, path=''):
41 | # maybe we can use pre_save hooks (https://github.com/jupyter/notebook/blob/master/notebook/services/contents/manager.py#L86)
42 | self.log.debug("Converting into Myst markdown")
43 | return super(MystContentsManager, self).save(model, path)
44 |
45 | def get(self, path, content=True, type=None, format=None):
46 | # This is markdown to model
47 | self.log.debug("Opening Myst markdown")
48 | return super(MystContentsManager, self).get(path, content, type, format)
49 |
--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
1 | from setuptools import setup, find_packages
2 | import os
3 |
4 | with open("README.md") as f:
5 | readme = f.read()
6 |
7 |
8 | def package_files():
9 | paths = []
10 | for (path, _, filenames) in os.walk("extra_files"):
11 | for filename in filenames:
12 | paths.append(os.path.join("..", path, filename))
13 | return paths
14 |
15 |
16 | extra_files = package_files()
17 |
18 | setup(
19 | name="mystify",
20 | version="0.0.1",
21 | description="NYI",
22 | long_description=readme,
23 | long_description_content_type="text/markdown",
24 | author="NYI",
25 | author_email="NYI",
26 | url="https://github.com/machine-learning-apps/mystify",
27 | license="MIT License",
28 | keywords=[
29 | "Jupyter",
30 | "Machine Learning",
31 | "Mystify",
32 | ], # Keywords that define your package best
33 | install_requires=["wheel", ],
34 | packages=["mystify"],
35 | include_package_data=True,
36 | package_data={"": extra_files},
37 | zip_safe=False,
38 | classifiers=[
39 | "Development Status :: 3 - Alpha", # Chose either "3 - Alpha", "4 - Beta" or "5 - Production/Stable" as the current state of your package
40 | "Intended Audience :: Developers", # Define that your audience are developers
41 | "Topic :: Software Development :: Build Tools",
42 | "License :: OSI Approved :: MIT License", # Again, pick a license
43 | "Programming Language :: Python :: 3", # Specify which pyhton versions that you want to support
44 | "Programming Language :: Python :: 3.4",
45 | "Programming Language :: Python :: 3.5",
46 | "Programming Language :: Python :: 3.6",
47 | "Programming Language :: Python :: 3.8",
48 | ],
49 | )
50 |
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | pip-wheel-metadata/
24 | share/python-wheels/
25 | *.egg-info/
26 | .installed.cfg
27 | *.egg
28 | MANIFEST
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
32 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 |
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .nox/
44 | .coverage
45 | .coverage.*
46 | .cache
47 | nosetests.xml
48 | coverage.xml
49 | *.cover
50 | *.py,cover
51 | .hypothesis/
52 | .pytest_cache/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | target/
76 |
77 | # Jupyter Notebook
78 | .ipynb_checkpoints
79 |
80 | # IPython
81 | profile_default/
82 | ipython_config.py
83 |
84 | # pyenv
85 | .python-version
86 |
87 | # pipenv
88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
91 | # install all needed dependencies.
92 | #Pipfile.lock
93 |
94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
95 | __pypackages__/
96 |
97 | # Celery stuff
98 | celerybeat-schedule
99 | celerybeat.pid
100 |
101 | # SageMath parsed files
102 | *.sage.py
103 |
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 |
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 |
117 | # Rope project settings
118 | .ropeproject
119 |
120 | # mkdocs documentation
121 | /site
122 |
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 |
128 | # Pyre type checker
129 |
130 | .pyre/
131 |
132 | .vscode/
133 | .DS_Store
134 |
--------------------------------------------------------------------------------
/.flake8:
--------------------------------------------------------------------------------
1 | # .flake8
2 | #
3 | # DESCRIPTION
4 | # Configuration file for the python linter flake8.
5 | #
6 | # This configuration is based on the generic
7 | # configuration published on GitHub.
8 | #
9 | # AUTHOR
10 | # krnd
11 | #
12 | # VERSION
13 | # 1.0
14 | #
15 | # SEE ALSO
16 | # http://flake8.pycqa.org/en/latest/user/options.html
17 | # http://flake8.pycqa.org/en/latest/user/error-codes.html
18 | # https://pycodestyle.readthedocs.io/en/latest/intro.html#error-codes
19 | # https://gist.github.com/krnd
20 | #
21 |
22 |
23 | [flake8]
24 |
25 | ################### PROGRAM ################################
26 |
27 | # Specify the number of subprocesses that Flake8 will use to run checks in parallel.
28 | jobs = auto
29 |
30 |
31 | ################### OUTPUT #################################
32 |
33 | ########## VERBOSITY ##########
34 |
35 | # Increase the verbosity of Flake8’s output.
36 | verbose = 0
37 | # Decrease the verbosity of Flake8’s output.
38 | quiet = 0
39 |
40 |
41 | ########## FORMATTING ##########
42 |
43 | # Select the formatter used to display errors to the user.
44 | format = default
45 |
46 | # Print the total number of errors.
47 | count = True
48 | # Print the source code generating the error/warning in question.
49 | show-source = True
50 | # Count the number of occurrences of each error/warning code and print a report.
51 | statistics = True
52 |
53 |
54 | ########## TARGETS ##########
55 |
56 | # Redirect all output to the specified file.
57 | output-file = .flake8.log
58 | # Also print output to stdout if output-file has been configured.
59 | tee = True
60 |
61 |
62 | ################### FILE PATTERNS ##########################
63 |
64 | # Provide a comma-separated list of glob patterns to exclude from checks.
65 | exclude =
66 | # git folder
67 | .git,
68 | # python cache
69 | __pycache__,
70 | experiment/*,
71 | # Provide a comma-separate list of glob patterns to include for checks.
72 | filename =
73 | *.py
74 |
75 |
76 | ################### LINTING ################################
77 |
78 | ########## ENVIRONMENT ##########
79 |
80 | # Provide a custom list of builtin functions, objects, names, etc.
81 | builtins =
82 |
83 |
84 | ########## OPTIONS ##########
85 |
86 | # Report all errors, even if it is on the same line as a `# NOQA` comment.
87 | disable-noqa = False
88 |
89 | # Toggle whether pycodestyle should enforce matching the indentation of the opening bracket’s line.
90 | # incluences E131 and E133
91 | hang-closing = True
92 |
93 |
94 | ########## RULES ##########
95 |
96 | # ERROR CODES
97 | #
98 | # E/W - PEP8 errors/warnings (pycodestyle)
99 | # F - linting errors (pyflakes)
100 | # C - McCabe complexity error (mccabe)
101 | #
102 | # W503 - line break before binary operator
103 |
104 | # Specify a list of codes to `.
105 | ignore = E203, E266, E501, W503, F403, F401, E133, E402
106 | max-line-length = 79
107 | max-complexity = 18
108 | # Specify the list of error codes you wish Flake8 to report.
109 | select = B,C,E,F,W,T4,B9
110 |
111 | # Enable off-by-default extensions.
112 | enable-extensions =
113 |
114 |
115 | ########## DOCSTRING ##########
116 |
117 | # Enable PyFlakes syntax checking of doctests in docstrings.
118 | doctests = True
119 |
120 | # Specify which files are checked by PyFlakes for doctest syntax.
121 | include-in-doctest = *.py
122 | # Specify which files are not to be checked by PyFlakes for doctest syntax.
123 | exclude-in-doctest =
--------------------------------------------------------------------------------
/examples/example_notebook.html:
--------------------------------------------------------------------------------
1 | print("fbddddoo")
20 | foo
22 |test of markdown2
26 |import numpy as np
30 | import matplotlib.pyplot as plt
31 |
32 |
33 | N = 5
34 | menMeans = (20, 35, 30, 35, 27)
35 | womenMeans = (25, 32, 34, 20, 25)
36 | menStd = (2, 3, 4, 1, 2)
37 | womenStd = (3, 5, 2, 3, 3)
38 | ind = np.arange(N) # the x locations for the groups
39 | width = 0.35 # the width of the bars: can also be len(x) sequence
40 |
41 | p1 = plt.bar(ind, menMeans, width, yerr=menStd)
42 | p2 = plt.bar(ind, womenMeans, width,
43 | bottom=menMeans, yerr=womenStd)
44 |
45 | plt.ylabel('Scores')
46 | plt.title('Scores by group and gender')
47 | plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
48 | plt.yticks(np.arange(0, 81, 10))
49 | plt.legend((p1[0], p2[0]), ('Men', 'Women'))
50 |
51 | plt.show()
52 | 
--------------------------------------------------------------------------------
/mystify/convert.py:
--------------------------------------------------------------------------------
1 | import json
2 | import yaml
3 | import re
4 | from myst_parser.main import default_parser
5 |
6 |
7 | def to_myst(model: str) -> str:
8 | myst_file = ""
9 | model_dict = json.loads(model)
10 | for key in model_dict:
11 | if key == "cells":
12 | myst_file = _handle_cells(model_dict[key])
13 | elif key == "metadata":
14 | myst_file = _handle_metadata(model_dict[key])
15 | elif key == "nbformat":
16 | myst_file = _handle_nbformat(model_dict[key])
17 | elif key == "nbformat_minor":
18 | myst_file = _handle_nbformat_minor(model_dict[key])
19 | else:
20 | raise ValueError("Attempting to parse unknown type.")
21 |
22 | return myst_file
23 |
24 |
25 | def _handle_cells(cell_nodes: list) -> str:
26 | """
27 | Cells can be nested:
28 | cells (list)
29 | {
30 | "metadata" : {
31 | "kernel_info": {
32 | # if kernel_info is defined, its name field is required.
33 | "name" : "the name of the kernel"
34 | },
35 | "language_info": {
36 | # if language_info is defined, its name field is required.
37 | "name" : "the programming language of the kernel",
38 | "version": "the version of the language",
39 | "codemirror_mode": "The name of the codemirror mode to use [optional]"
40 | }
41 | },
42 | "nbformat": 4,
43 | "nbformat_minor": 0,
44 | "cells" : [
45 | # list of cell dictionaries, see below
46 | ],
47 | }
48 | """
49 | pass
50 |
51 |
52 | def _handle_metadata(metadata_nodes: dict) -> str:
53 | """
54 | metadata (dict)
55 | "metadata" : {
56 | "kernel_info": {
57 | # if kernel_info is defined, its name field is required.
58 | "name" : "the name of the kernel"
59 | },
60 | "language_info": {
61 | # if language_info is defined, its name field is required.
62 | "name" : "the programming language of the kernel",
63 | "version": "the version of the language",
64 | "codemirror_mode": "The name of the codemirror mode to use [optional]"
65 | }
66 | },
67 |
68 | """
69 | pass
70 |
71 |
72 | def _handle_nbformat(nbformat: int) -> str:
73 | """
74 | "nbformat" (int)
75 | """
76 | pass
77 |
78 |
79 | def _handle_nbformat_minor(nbformat_minor: int) -> str:
80 | """
81 | "nbformat_minor" (int)
82 | """
83 | pass
84 |
85 |
86 | def _parse_code_output(cell_section):
87 | out = {}
88 | out["output_type"] = cell_section.info.split(" ")[1]
89 | if out["output_type"] == "stream":
90 | out["text"] = cell_section.content.splitlines(True)
91 | out["name"] = "stdout"
92 | return out
93 |
94 |
95 | def _parse_cell(cell):
96 | cell_meta = yaml.safe_load(cell[0].content)
97 | if "metadata" not in cell_meta:
98 | cell_meta["metadata"] = {}
99 | for cell_section in cell[1:]:
100 | if cell_section.info == "{source}":
101 | cell_meta["source"] = cell_section.content.splitlines(True)
102 | cell_meta["source"][-1] = cell_meta["source"][-1].strip()
103 | if cell_section.info.startswith("{output}"):
104 | out = _parse_code_output(cell_section)
105 | if "outputs" not in cell_meta:
106 | cell_meta["outputs"] = []
107 | cell_meta["outputs"].append(out)
108 | return cell_meta
109 |
110 |
111 | def _split_sections(tokens):
112 | append_to_cell = None
113 | sections = {}
114 | for token in tokens:
115 | if token.type == "fence" and token.info == "{metadata}":
116 | # Metadata yaml block
117 | sections = yaml.safe_load(token.content)
118 | sections["cells"] = []
119 | if append_to_cell is not None:
120 | append_to_cell.append(token)
121 | if token.type == "myst_line_comment" and token.content == "cell":
122 | append_to_cell = []
123 | if token.type == "myst_line_comment" and token.content == "endcell":
124 | sections["cells"].append(_parse_cell(append_to_cell))
125 | append_to_cell = None
126 | return sections
127 |
128 |
129 | def to_model(myst):
130 | md = default_parser("docutils")
131 | tokens = md.parse(myst)
132 | sections = _split_sections(tokens)
133 | return sections
134 |
135 |
136 | """
137 | Some fields, such as code input and text output, are characteristically multi-line strings. When these fields are written to disk, they may be written as a list of strings, which should be joined with '' when reading back into memory. In programmatic APIs for working with notebooks (Python, Javascript), these are always re-joined into the original multi-line string. If you intend to work with notebook files directly, you must allow multi-line string fields to be either a string or list of strings.
138 |
139 | Cell Types
140 | There are a few basic cell types for encapsulating code and text. All cells have the following basic structure:
141 |
142 | {
143 | "cell_type" : "type",
144 | "metadata" : {},
145 | "source" : "single string or [list, of, strings]",
146 | }
147 | Note
148 |
149 | On disk, multi-line strings MAY be split into lists of strings. When read with the nbformat Python API, these multi-line strings will always be a single string.
150 |
151 | Markdown cells
152 | Markdown cells are used for body-text, and contain markdown, as defined in GitHub-flavored markdown, and implemented in marked.
153 |
154 | {
155 | "cell_type" : "markdown",
156 | "metadata" : {},
157 | "source" : "[multi-line *markdown*]",
158 | }
159 | Changed in version nbformat: 4.0
160 |
161 | Heading cells have been removed in favor of simple headings in markdown.
162 |
163 | Code cells
164 | Code cells are the primary content of Jupyter notebooks. They contain source code in the language of the document’s associated kernel, and a list of outputs associated with executing that code. They also have an execution_count, which must be an integer or null.
165 |
166 | {
167 | "cell_type" : "code",
168 | "execution_count": 1, # integer or null
169 | "metadata" : {
170 | "collapsed" : True, # whether the output of the cell is collapsed
171 | "scrolled": False, # any of true, false or "auto"
172 | },
173 | "source" : "[some multi-line code]",
174 | "outputs": [{
175 | # list of output dicts (described below)
176 | "output_type": "stream",
177 | ...
178 | }],
179 | }
180 | Changed in version nbformat: 4.0
181 |
182 | input was renamed to source, for consistency among cell types.
183 |
184 | Changed in version nbformat: 4.0
185 |
186 | prompt_number renamed to execution_count
187 |
188 | Code cell outputs
189 | A code cell can have a variety of outputs (stream data or rich mime-type output). These correspond to messages produced as a result of executing the cell.
190 |
191 | All outputs have an output_type field, which is a string defining what type of output it is.
192 |
193 | stream output
194 | {
195 | "output_type" : "stream",
196 | "name" : "stdout", # or stderr
197 | "text" : "[multiline stream text]",
198 | }
199 | Changed in version nbformat: 4.0
200 |
201 | The stream key was changed to name to match the stream message.
202 |
203 | display_data
204 | Rich display outputs, as created by display_data messages, contain data keyed by mime-type. This is often called a mime-bundle, and shows up in various locations in the notebook format and message spec. The metadata of these messages may be keyed by mime-type as well.
205 |
206 | {
207 | "output_type" : "display_data",
208 | "data" : {
209 | "text/plain" : "[multiline text data]",
210 | "image/png": "[base64-encoded-multiline-png-data]",
211 | "application/json": {
212 | # JSON data is included as-is
213 | "key1": "data",
214 | "key2": ["some", "values"],
215 | "key3": {"more": "data"}
216 | },
217 | "application/vnd.exampleorg.type+json": {
218 | # JSON data, included as-is, when the mime-type key ends in +json
219 | "key1": "data",
220 | "key2": ["some", "values"],
221 | "key3": {"more": "data"}
222 | }
223 | },
224 | "metadata" : {
225 | "image/png": {
226 | "width": 640,
227 | "height": 480,
228 | },
229 | },
230 | }
231 | Changed in version nbformat: 4.0
232 |
233 | application/json output is no longer double-serialized into a string.
234 |
235 | Changed in version nbformat: 4.0
236 |
237 | mime-types are used for keys, instead of a combination of short names (text) and mime-types, and are stored in a data key, rather than the top-level. i.e. output.data['image/png'] instead of output.png.
238 |
239 | execute_result
240 | Results of executing a cell (as created by displayhook in Python) are stored in execute_result outputs. execute_result outputs are identical to display_data, adding only a execution_count field, which must be an integer.
241 |
242 | {
243 | "output_type" : "execute_result",
244 | "execution_count": 42,
245 | "data" : {
246 | "text/plain" : "[multiline text data]",
247 | "image/png": "[base64-encoded-multiline-png-data]",
248 | "application/json": {
249 | # JSON data is included as-is
250 | "json": "data",
251 | },
252 | },
253 | "metadata" : {
254 | "image/png": {
255 | "width": 640,
256 | "height": 480,
257 | },
258 | },
259 | }
260 | Changed in version nbformat: 4.0
261 |
262 | pyout renamed to execute_result
263 |
264 | Changed in version nbformat: 4.0
265 |
266 | prompt_number renamed to execution_count
267 |
268 | error
269 | Failed execution may show a traceback
270 |
271 | {
272 | 'output_type': 'error',
273 | 'ename' : str, # Exception name, as a string
274 | 'evalue' : str, # Exception value, as a string
275 |
276 | # The traceback will contain a list of frames,
277 | # represented each as a string.
278 | 'traceback' : list,
279 | }
280 | Changed in version nbformat: 4.0
281 |
282 | pyerr renamed to error
283 |
284 | Raw NBConvert cells
285 | A raw cell is defined as content that should be included unmodified in nbconvert output. For example, this cell could include raw LaTeX for nbconvert to pdf via latex, or restructured text for use in Sphinx documentation.
286 |
287 | The notebook authoring environment does not render raw cells.
288 |
289 | The only logic in a raw cell is the format metadata field. If defined, it specifies which nbconvert output format is the intended target for the raw cell. When outputting to any other format, the raw cell’s contents will be excluded. In the default case when this value is undefined, a raw cell’s contents will be included in any nbconvert output, regardless of format.
290 |
291 | {
292 | "cell_type" : "raw",
293 | "metadata" : {
294 | # the mime-type of the target nbconvert format.
295 | # nbconvert to formats other than this will exclude this cell.
296 | "format" : "mime/type"
297 | },
298 | "source" : "[some nbformat output text]"
299 | }
300 | Cell attachments
301 | Markdown and raw cells can have a number of attachments, typically inline images that can be referenced in the markdown content of a cell. The attachments dictionary of a cell contains a set of mime-bundles (see display_data) keyed by filename that represents the files attached to the cell.
302 |
303 | Note
304 |
305 | The attachments dictionary is an optional field and can be undefined or empty if the cell does not have any attachments.
306 |
307 | {
308 | "cell_type" : "markdown",
309 | "metadata" : {},
310 | "source" : ["Here is an *inline* image "],
311 | "attachments" : {
312 | "test.png": {
313 | "image/png" : "base64-encoded-png-data"
314 | }
315 | }
316 | }
317 | Backward-compatible changes
318 | The notebook format is an evolving format. When backward-compatible changes are made, the notebook format minor version is incremented. When backward-incompatible changes are made, the major version is incremented.
319 |
320 | As of nbformat 4.x, backward-compatible changes include:
321 |
322 | new fields in any dictionary (notebook, cell, output, metadata, etc.)
323 | new cell types
324 | new output types
325 | New cell or output types will not be rendered in versions that do not recognize them, but they will be preserved.
326 |
327 | Because the nbformat python package used to be less strict about validating notebook files, two features have been backported from nbformat 4.x to nbformat 4.0. These are:
328 |
329 | attachment top-level keys in the Markdown and raw cell types (backported from nbformat 4.1)
330 | Mime-bundle attributes are JSON data if the mime-type key ends in +json (backported from nbformat 4.2)
331 | These backports ensure that any valid nbformat 4.4 file is also a valid nbformat 4.0 file.
332 |
333 | Metadata
334 | Metadata is a place that you can put arbitrary JSONable information about your notebook, cell, or output. Because it is a shared namespace, any custom metadata should use a sufficiently unique namespace, such as metadata.kaylees_md.foo = “bar”.
335 |
336 | Metadata fields officially defined for Jupyter notebooks are listed here:
337 |
338 | Notebook metadata
339 | The following metadata keys are defined at the notebook level:
340 |
341 | Key Value Interpretation
342 | kernelspec dict A kernel specification
343 | authors list of dicts A list of authors of the document
344 | A notebook’s authors is a list of dictionaries containing information about each author of the notebook. Currently, only the name is required. Additional fields may be added.
345 |
346 | nb.metadata.authors = [
347 | {
348 | 'name': 'Fernando Perez',
349 | },
350 | {
351 | 'name': 'Brian Granger',
352 | },
353 | ]
354 | Cell metadata
355 | Official Jupyter metadata, as used by Jupyter frontends should be placed in the metadata.jupyter namespace, for example metadata.jupyter.foo = “bar”.
356 |
357 | The following metadata keys are defined at the cell level:
358 |
359 | Key Value Interpretation
360 | collapsed bool Whether the cell’s output container should be collapsed
361 | scrolled bool or ‘auto’ Whether the cell’s output is scrolled, unscrolled, or autoscrolled
362 | deletable bool If False, prevent deletion of the cell
363 | editable bool If False, prevent editing of the cell (by definition, this also prevents deleting the cell)
364 | format ‘mime/type’ The mime-type of a Raw NBConvert Cell
365 | name str A name for the cell. Should be unique across the notebook. Uniqueness must be verified outside of the json schema.
366 | tags list of str A list of string tags on the cell. Commas are not allowed in a tag
367 | jupyter dict A namespace holding jupyter specific fields. See docs below for more details
368 | execution dict A namespace holding execution specific fields. See docs below for more details
369 | The following metadata keys are defined at the cell level within the jupyter namespace
370 |
371 | Key Value Interpretation
372 | source_hidden bool Whether the cell’s source should be shown
373 | outputs_hidden bool Whether the cell’s outputs should be shown
374 | The following metadata keys are defined at the cell level within the execution namespace. These are lower level fields capturing common kernel message timestamps for better visibility in applications where needed. Most users will not look at these directly.
375 |
376 | Key Value Interpretation
377 | iopub.execute_input ISO 8601 format Indicates the time at which the kernel broadcasts an execute_input message. This represents the time when request for work was received by the kernel.
378 | iopub.status.busy ISO 8601 format Indicates the time at which the iopub channel’s kernel status message is ‘busy’. This represents the time when work was started by the kernel.
379 | shell.execute_reply ISO 8601 format Indicates the time at which the shell channel’s execute_reply status message was created. This represents the time when work was completed by the kernel.
380 | iopub.status.idle ISO 8601 format Indicates the time at which the iopub channel’s kernel status message is ‘idle’. This represents the time when the kernel is ready to accept new work.
381 | Output metadata
382 | The following metadata keys are defined for code cell outputs:
383 |
384 | Key Value Interpretation
385 | isolated bool Whether the output should be isolated into an IFrame
386 | © Copyright 2015, Jupyter Development Team Revision a06f4c84.
387 | """
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