I managed to set up the tljh and nbgrader but the validation doesn't work in my notebook. As you can see it displays this:
"""Module containing a preprocessor that executes the code cells
and updates outputs"""
# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import base64
from textwrap import dedent
from contextlib import contextmanager
try:
from time import monotonic # Py 3
except ImportError:
from time import time as monotonic # Py 2
try:
from queue import Empty # Py 3
except ImportError:
from Queue import Empty # Py 2
try:
TimeoutError # Py 3
except NameError:
TimeoutError = RuntimeError # Py 2
from traitlets import List, Unicode, Bool, Enum, Any, Type, Dict, Integer, default
from nbformat.v4 import output_from_msg
from .base import Preprocessor
from ..utils.exceptions import ConversionException
class DeadKernelError(RuntimeError):
pass
class CellExecutionComplete(Exception):
"""
Used as a control signal for cell execution across run_cell and
process_message function calls. Raised when all execution requests
are completed and no further messages are expected from the kernel
over zeromq channels.
"""
pass
class CellExecutionError(ConversionException):
"""
Custom exception to propagate exceptions that are raised during
notebook execution to the caller. This is mostly useful when
using nbconvert as a library, since it allows to deal with
failures gracefully.
"""
def _init_(self, traceback):
super(CellExecutionError, self)._init_(traceback)
self.traceback = traceback
def _str_(self):
s = self._unicode_()
if not isinstance(s, str):
s = s.encode('utf8', 'replace')
return s
def _unicode_(self):
return self.traceback
@classmethod
def from_cell_and_msg(cls, cell, msg):
"""Instantiate from a code cell object and a message contents
(message is either execute_reply or error)
"""
tb = '\n'.join(msg.get('traceback', []))
return cls(exec_err_msg.format(cell=cell, traceback=tb,
ename=msg.get('ename', '<Error>'),
evalue=msg.get('evalue', '')
))
exec_err_msg = u"""\
An error occurred while executing the following cell:
------------------
{cell.source}
------------------
{traceback}
{ename}: {evalue}
"""
class ExecutePreprocessor(Preprocessor):
"""
Executes all the cells in a notebook
"""
timeout = Integer(30, allow_none=True,
help=dedent(
"""
The time to wait (in seconds) for output from executions.
If a cell execution takes longer, an exception (TimeoutError
on python 3+, RuntimeError on python 2) is raised.
`None` or `-1` will disable the timeout. If `timeout_func` is set,
it overrides `timeout`.
"""
)
).tag(config=True)
timeout_func = Any(
default_value=None,
allow_none=True,
help=dedent(
"""
A callable which, when given the cell source as input,
returns the time to wait (in seconds) for output from cell
executions. If a cell execution takes longer, an exception
(TimeoutError on python 3+, RuntimeError on python 2) is
raised.
Returning `None` or `-1` will disable the timeout for the cell.
Not setting `timeout_func` will cause the preprocessor to
default to using the `timeout` trait for all cells. The
`timeout_func` trait overrides `timeout` if it is not `None`.
"""
)
).tag(config=True)
interrupt_on_timeout = Bool(False,
help=dedent(
"""
If execution of a cell times out, interrupt the kernel and
continue executing other cells rather than throwing an error and
stopping.
"""
)
).tag(config=True)
startup_timeout = Integer(60,
help=dedent(
"""
The time to wait (in seconds) for the kernel to start.
If kernel startup takes longer, a RuntimeError is
raised.
"""
)
).tag(config=True)
allow_errors = Bool(False,
help=dedent(
"""
If `False` (default), when a cell raises an error the
execution is stopped and a `CellExecutionError`
is raised.
If `True`, execution errors are ignored and the execution
is continued until the end of the notebook. Output from
exceptions is included in the cell output in both cases.
"""
)
).tag(config=True)
force_raise_errors = Bool(False,
help=dedent(
"""
If False (default), errors from executing the notebook can be
allowed with a `raises-exception` tag on a single cell, or the
`allow_errors` configurable option for all cells. An allowed error
will be recorded in notebook output, and execution will continue.
If an error occurs when it is not explicitly allowed, a
`CellExecutionError` will be raised.
If True, `CellExecutionError` will be raised for any error that occurs
while executing the notebook. This overrides both the
`allow_errors` option and the `raises-exception` cell tag.
"""
)
).tag(config=True)
extra_arguments = List(Unicode())
kernel_name = Unicode('',
help=dedent(
"""
Name of kernel to use to execute the cells.
If not set, use the kernel_spec embedded in the notebook.
"""
)
).tag(config=True)
raise_on_iopub_timeout = Bool(False,
help=dedent(
"""
If `False` (default), then the kernel will continue waiting for
iopub messages until it receives a kernel idle message, or until a
timeout occurs, at which point the currently executing cell will be
skipped. If `True`, then an error will be raised after the first
timeout. This option generally does not need to be used, but may be
useful in contexts where there is the possibility of executing
notebooks with memory-consuming infinite loops.
"""
)
).tag(config=True)
store_widget_state = Bool(True,
help=dedent(
"""
If `True` (default), then the state of the Jupyter widgets created
at the kernel will be stored in the metadata of the notebook.
"""
)
).tag(config=True)
iopub_timeout = Integer(4, allow_none=False,
help=dedent(
"""
The time to wait (in seconds) for IOPub output. This generally
doesn't need to be set, but on some slow networks (such as CI
systems) the default timeout might not be long enough to get all
messages.
"""
)
).tag(config=True)
shutdown_kernel = Enum(['graceful', 'immediate'],
default_value='graceful',
help=dedent(
"""
If `graceful` (default), then the kernel is given time to clean
up after executing all cells, e.g., to execute its `atexit` hooks.
If `immediate`, then the kernel is signaled to immediately
terminate.
"""
)
).tag(config=True)
ipython_hist_file = Unicode(
default_value=':memory:',
help="""Path to file to use for SQLite history database for an IPython kernel.
The specific value `:memory:` (including the colon
at both end but not the back ticks), avoids creating a history file. Otherwise, IPython
will create a history file for each kernel.
When running kernels simultaneously (e.g. via multiprocessing) saving history a single
SQLite file can result in database errors, so using `:memory:` is recommended in non-interactive
contexts.
""").tag(config=True)
kernel_manager_class = Type(
config=True,
help='The kernel manager class to use.'
)
@default('kernel_manager_class')
def _kernel_manager_class_default(self):
"""Use a dynamic default to avoid importing jupyter_client at startup"""
try:
from jupyter_client import KernelManager
except ImportError:
raise ImportError("`nbconvert --execute` requires the jupyter_client package: `pip install jupyter_client`")
return KernelManager
_display_id_map = Dict(
help=dedent(
"""
mapping of locations of outputs with a given display_id
tracks cell index and output index within cell.outputs for
each appearance of the display_id
{
'display_id': {
cell_idx: [output_idx,]
}
}
"""))
def start_new_kernel(self, **kwargs):
"""Creates a new kernel manager and kernel client.
Parameters
----------
kwargs :
Any options for `self.kernel_manager_class.start_kernel()`. Because
that defaults to KernelManager, this will likely include options
accepted by `KernelManager.start_kernel()``, which includes `cwd`.
Returns
-------
km : KernelManager
A kernel manager as created by self.kernel_manager_class.
kc : KernelClient
Kernel client as created by the kernel manager `km`.
"""
if not self.kernel_name:
self.kernel_name = self.nb.metadata.get(
'kernelspec', {}).get('name', 'python')
km = self.kernel_manager_class(kernel_name=self.kernel_name,
config=self.config)
if km.ipykernel and self.ipython_hist_file:
self.extra_arguments += ['--HistoryManager.hist_file={}'.format(self.ipython_hist_file)]
km.start_kernel(extra_arguments=self.extra_arguments, **kwargs)
kc = km.client()
kc.start_channels()
try:
kc.wait_for_ready(timeout=self.startup_timeout)
except RuntimeError:
kc.stop_channels()
km.shutdown_kernel()
raise
kc.allow_stdin = False
return km, kc
@contextmanager
def setup_preprocessor(self, nb, resources, km=None, **kwargs):
"""
Context manager for setting up the class to execute a notebook.
The assigns `nb` to `self.nb` where it will be modified in-place. It also creates
and assigns the Kernel Manager (`self.km`) and Kernel Client(`self.kc`).
It is intended to yield to a block that will execute codeself.
When control returns from the yield it stops the client's zmq channels, shuts
down the kernel, and removes the now unused attributes.
Parameters
----------
nb : NotebookNode
Notebook being executed.
resources : dictionary
Additional resources used in the conversion process. For example,
passing ``{'metadata': {'path': run_path}}`` sets the
execution path to ``run_path``.
km : KernerlManager (optional)
Optional kernel manager. If none is provided, a kernel manager will
be created.
Returns
-------
nb : NotebookNode
The executed notebook.
resources : dictionary
Additional resources used in the conversion process.
"""
path = resources.get('metadata', {}).get('path', '') or None
self.nb = nb
# clear display_id map
self._display_id_map = {}
self.widget_state = {}
self.widget_buffers = {}
if km is None:
kwargs["cwd"] = path
self.km, self.kc = self.start_new_kernel(**kwargs)
try:
# Yielding unbound args for more easier understanding and downstream consumption
yield nb, self.km, self.kc
finally:
self.kc.stop_channels()
self.km.shutdown_kernel(now=self.shutdown_kernel == 'immediate')
for attr in ['nb', 'km', 'kc']:
delattr(self, attr)
else:
self.km = km
if not km.has_kernel:
km.start_kernel(extra_arguments=self.extra_arguments, **kwargs)
self.kc = km.client()
self.kc.start_channels()
try:
self.kc.wait_for_ready(timeout=self.startup_timeout)
except RuntimeError:
self.kc.stop_channels()
raise
self.kc.allow_stdin = False
try:
yield nb, self.km, self.kc
finally:
for attr in ['nb', 'km', 'kc']:
delattr(self, attr)
def preprocess(self, nb, resources=None, km=None):
"""
Preprocess notebook executing each code cell.
The input argument `nb` is modified in-place.
Parameters
----------
nb : NotebookNode
Notebook being executed.
resources : dictionary (optional)
Additional resources used in the conversion process. For example,
passing ``{'metadata': {'path': run_path}}`` sets the
execution path to ``run_path``.
km: KernelManager (optional)
Optional kernel manager. If none is provided, a kernel manager will
be created.
Returns
-------
nb : NotebookNode
The executed notebook.
resources : dictionary
Additional resources used in the conversion process.
"""
if not resources:
resources = {}
with self.setup_preprocessor(nb, resources, km=km):
self.log.info("Executing notebook with kernel: %s" % self.kernel_name)
nb, resources = super(ExecutePreprocessor, self).preprocess(nb, resources)
info_msg = self._wait_for_reply(self.kc.kernel_info())
nb.metadata['language_info'] = info_msg['content']['language_info']
self.set_widgets_metadata()
return nb, resources
def set_widgets_metadata(self):
if self.widget_state:
self.nb.metadata.widgets = {
'application/vnd.jupyter.widget-state+json': {
'state': {
model_id: _serialize_widget_state(state)
for model_id, state in self.widget_state.items() if '_model_name' in state
},
'version_major': 2,
'version_minor': 0,
}
}
for key, widget in self.nb.metadata.widgets['application/vnd.jupyter.widget-state+json']['state'].items():
buffers = self.widget_buffers.get(key)
if buffers:
widget['buffers'] = buffers
def preprocess_cell(self, cell, resources, cell_index, store_history=True):
"""
Executes a single code cell. See base.py for details.
To execute all cells see :meth:`preprocess`.
"""
if cell.cell_type != 'code' or not cell.source.strip():
return cell, resources
reply, outputs = self.run_cell(cell, cell_index, store_history)
# Backwards compatibility for processes that wrap run_cell
cell.outputs = outputs
cell_allows_errors = (self.allow_errors or "raises-exception"
in cell.metadata.get("tags", []))
if self.force_raise_errors or not cell_allows_errors:
for out in cell.outputs:
if out.output_type == 'error':
raise CellExecutionError.from_cell_and_msg(cell, out)
if (reply is not None) and reply['content']['status'] == 'error':
raise CellExecutionError.from_cell_and_msg(cell, reply['content'])
return cell, resources
def _update_display_id(self, display_id, msg):
"""Update outputs with a given display_id"""
if display_id not in self._display_id_map:
self.log.debug("display id %r not in %s", display_id, self._display_id_map)
return
if msg['header']['msg_type'] == 'update_display_data':
msg['header']['msg_type'] = 'display_data'
try:
out = output_from_msg(msg)
except ValueError:
self.log.error("unhandled iopub msg: " + msg['msg_type'])
return
for cell_idx, output_indices in self._display_id_map[display_id].items():
cell = self.nb['cells'][cell_idx]
outputs = cell['outputs']
for output_idx in output_indices:
outputs[output_idx]['data'] = out['data']
outputs[output_idx]['metadata'] = out['metadata']
def _poll_for_reply(self, msg_id, cell=None, timeout=None):
try:
# check with timeout if kernel is still alive
msg = self.kc.shell_channel.get_msg(timeout=timeout)
if msg['parent_header'].get('msg_id') == msg_id:
return msg
except Empty:
# received no message, check if kernel is still alive
self._check_alive()
# kernel still alive, wait for a message
def _get_timeout(self, cell):
if self.timeout_func is not None and cell is not None:
timeout = self.timeout_func(cell)
else:
timeout = self.timeout
if not timeout or timeout < 0:
timeout = None
return timeout
def _handle_timeout(self):
self.log.error(
"Timeout waiting for execute reply (%is)." % self.timeout)
if self.interrupt_on_timeout:
self.log.error("Interrupting kernel")
self.km.interrupt_kernel()
else:
raise TimeoutError("Cell execution timed out")
def _check_alive(self):
if not self.kc.is_alive():
self.log.error(
"Kernel died while waiting for execute reply.")
raise DeadKernelError("Kernel died")
def _wait_for_reply(self, msg_id, cell=None):
# wait for finish, with timeout
timeout = self._get_timeout(cell)
cummulative_time = 0
timeout_interval = 5
while True:
try:
msg = self.kc.shell_channel.get_msg(timeout=timeout_interval)
except Empty:
self._check_alive()
cummulative_time += timeout_interval
if timeout and cummulative_time > timeout:
self._handle_timeout()
break
else:
if msg['parent_header'].get('msg_id') == msg_id:
return msg
def _timeout_with_deadline(self, timeout, deadline):
if deadline is not None and deadline - monotonic() < timeout:
timeout = deadline - monotonic()
if timeout < 0:
timeout = 0
return timeout
def _passed_deadline(self, deadline):
if deadline is not None and deadline - monotonic() <= 0:
self._handle_timeout()
return True
return False
def run_cell(self, cell, cell_index=0, store_history=True):
parent_msg_id = self.kc.execute(cell.source,
store_history=store_history, stop_on_error=not self.allow_errors)
self.log.debug("Executing cell:\n%s", cell.source)
exec_timeout = self._get_timeout(cell)
deadline = None
if exec_timeout is not None:
deadline = monotonic() + exec_timeout
cell.outputs = []
self.clear_before_next_output = False
# This loop resolves #659. By polling iopub_channel's and shell_channel's
# output we avoid dropping output and important signals (like idle) from
# iopub_channel. Prior to this change, iopub_channel wasn't polled until
# after exec_reply was obtained from shell_channel, leading to the
# aforementioned dropped data.
# These two variables are used to track what still needs polling:
# more_output=true => continue to poll the iopub_channel
more_output = True
# polling_exec_reply=true => continue to poll the shell_channel
polling_exec_reply = True
while more_output or polling_exec_reply:
if polling_exec_reply:
if self._passed_deadline(deadline):
polling_exec_reply = False
continue
# Avoid exceeding the execution timeout (deadline), but stop
# after at most 1s so we can poll output from iopub_channel.
timeout = self._timeout_with_deadline(1, deadline)
exec_reply = self._poll_for_reply(parent_msg_id, cell, timeout)
if exec_reply is not None:
polling_exec_reply = False
if more_output:
try:
timeout = self.iopub_timeout
if polling_exec_reply:
# Avoid exceeding the execution timeout (deadline) while
# polling for output.
timeout = self._timeout_with_deadline(timeout, deadline)
msg = self.kc.iopub_channel.get_msg(timeout=timeout)
except Empty:
if polling_exec_reply:
# Still waiting for execution to finish so we expect that
# output may not always be produced yet.
continue
if self.raise_on_iopub_timeout:
raise TimeoutError("Timeout waiting for IOPub output")
else:
self.log.warning("Timeout waiting for IOPub output")
more_output = False
continue
if msg['parent_header'].get('msg_id') != parent_msg_id:
# not an output from our execution
continue
try:
# Will raise CellExecutionComplete when completed
self.process_message(msg, cell, cell_index)
except CellExecutionComplete:
more_output = False
# Return cell.outputs still for backwards compatibility
return exec_reply, cell.outputs
def process_message(self, msg, cell, cell_index):
"""
Processes a kernel message, updates cell state, and returns the
resulting output object that was appended to cell.outputs.
The input argument `cell` is modified in-place.
Parameters
----------
msg : dict
The kernel message being processed.
cell : nbformat.NotebookNode
The cell which is currently being processed.
cell_index : int
The position of the cell within the notebook object.
Returns
-------
output : dict
The execution output payload (or None for no output).
Raises
------
CellExecutionComplete
Once a message arrives which indicates computation completeness.
"""
msg_type = msg['msg_type']
self.log.debug("msg_type: %s", msg_type)
content = msg['content']
self.log.debug("content: %s", content)
display_id = content.get('transient', {}).get('display_id', None)
if display_id and msg_type in {'execute_result', 'display_data', 'update_display_data'}:
self._update_display_id(display_id, msg)
# set the prompt number for the input and the output
if 'execution_count' in content:
cell['execution_count'] = content['execution_count']
if msg_type == 'status':
if content['execution_state'] == 'idle':
raise CellExecutionComplete()
elif msg_type == 'clear_output':
self.clear_output(cell.outputs, msg, cell_index)
elif msg_type.startswith('comm'):
self.handle_comm_msg(cell.outputs, msg, cell_index)
# Check for remaining messages we don't process
elif msg_type not in ['execute_input', 'update_display_data']:
# Assign output as our processed "result"
return self.output(cell.outputs, msg, display_id, cell_index)
def output(self, outs, msg, display_id, cell_index):
msg_type = msg['msg_type']
try:
out = output_from_msg(msg)
except ValueError:
self.log.error("unhandled iopub msg: " + msg_type)
return
if self.clear_before_next_output:
self.log.debug('Executing delayed clear_output')
outs[:] = []
self.clear_display_id_mapping(cell_index)
self.clear_before_next_output = False
if display_id:
# record output index in:
# _display_id_map[display_id][cell_idx]
cell_map = self._display_id_map.setdefault(display_id, {})
output_idx_list = cell_map.setdefault(cell_index, [])
output_idx_list.append(len(outs))
outs.append(out)
return out
def clear_output(self, outs, msg, cell_index):
content = msg['content']
if content.get('wait'):
self.log.debug('Wait to clear output')
self.clear_before_next_output = True
else:
self.log.debug('Immediate clear output')
outs[:] = []
self.clear_display_id_mapping(cell_index)
def clear_display_id_mapping(self, cell_index):
for display_id, cell_map in self._display_id_map.items():
if cell_index in cell_map:
cell_map[cell_index] = []
def handle_comm_msg(self, outs, msg, cell_index):
content = msg['content']
data = content['data']
if self.store_widget_state and 'state' in data: # ignore custom msg'es
self.widget_state.setdefault(content['comm_id'], {}).update(data['state'])
if 'buffer_paths' in data and data['buffer_paths']:
self.widget_buffers[content['comm_id']] = _get_buffer_data(msg)
def executenb(nb, cwd=None, km=None, **kwargs):
"""Execute a notebook's code, updating outputs within the notebook object.
This is a convenient wrapper around ExecutePreprocessor. It returns the
modified notebook object.
Parameters
----------
nb : NotebookNode
The notebook object to be executed
cwd : str, optional
If supplied, the kernel will run in this directory
km : KernelManager, optional
If supplied, the specified kernel manager will be used for code execution.
kwargs :
Any other options for ExecutePreprocessor, e.g. timeout, kernel_name
"""
resources = {}
if cwd is not None:
resources['metadata'] = {'path': cwd}
ep = ExecutePreprocessor(**kwargs)
return ep.preprocess(nb, resources, km=km)[0]
def _serialize_widget_state(state):
"""Serialize a widget state, following format in @jupyter-widgets/schema."""
return {
'model_name': state.get('_model_name'),
'model_module': state.get('_model_module'),
'model_module_version': state.get('_model_module_version'),
'state': state,
}
def _get_buffer_data(msg):
encoded_buffers = []
paths = msg['content']['data']['buffer_paths']
buffers = msg['buffers']
for path, buffer in zip(paths, buffers):
encoded_buffers.append({
'data': base64.b64encode(buffer).decode('utf-8'),
'encoding': 'base64',
'path': path
})
return encoded_buffers
I hope that the problem can be understood and would be grateful for any help:)