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train | Widget._comm_changed | Called when the comm is changed. | ipywidgets/widgets/widget.py | def _comm_changed(self, change):
"""Called when the comm is changed."""
if change['new'] is None:
return
self._model_id = self.model_id
self.comm.on_msg(self._handle_msg)
Widget.widgets[self.model_id] = self | def _comm_changed(self, change):
"""Called when the comm is changed."""
if change['new'] is None:
return
self._model_id = self.model_id
self.comm.on_msg(self._handle_msg)
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train | Widget.close | Close method.
Closes the underlying comm.
When the comm is closed, all of the widget views are automatically
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"""Close method.
Closes the underlying comm.
When the comm is closed, all of the widget views are automatically
removed from the front-end."""
if self.comm is not None:
Widget.widgets.pop(self.model_id, None)
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"""Close method.
Closes the underlying comm.
When the comm is closed, all of the widget views are automatically
removed from the front-end."""
if self.comm is not None:
Widget.widgets.pop(self.model_id, None)
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train | Widget.send_state | Sends the widget state, or a piece of it, to the front-end, if it exists.
Parameters
----------
key : unicode, or iterable (optional)
A single property's name or iterable of property names to sync with the front-end. | ipywidgets/widgets/widget.py | def send_state(self, key=None):
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Parameters
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key : unicode, or iterable (optional)
A single property's name or iterable of property names to sync with the front-end.
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key : unicode, or iterable (optional)
A single property's name or iterable of property names to sync with the front-end.
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train | Widget.get_state | Gets the widget state, or a piece of it.
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key : unicode or iterable (optional)
A single property's name or iterable of property names to get.
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state : dict of states
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A single property's name or iterable of property names to get.
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key : unicode or iterable (optional)
A single property's name or iterable of property names to get.
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train | Widget.set_state | Called when a state is received from the front-end. | ipywidgets/widgets/widget.py | def set_state(self, sync_data):
"""Called when a state is received from the front-end."""
# The order of these context managers is important. Properties must
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train | Widget.on_msg | (Un)Register a custom msg receive callback.
Parameters
----------
callback: callable
callback will be passed three arguments when a message arrives::
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remove: bool
True if the callback should be unregistered. | ipywidgets/widgets/widget.py | def on_msg(self, callback, remove=False):
"""(Un)Register a custom msg receive callback.
Parameters
----------
callback: callable
callback will be passed three arguments when a message arrives::
callback(widget, content, buffers)
remove: bool
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remove: bool
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train | Widget.on_displayed | (Un)Register a widget displayed callback.
Parameters
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callback: method handler
Must have a signature of::
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kwargs from display are passed through without modification.
remove: bool
True if the call... | ipywidgets/widgets/widget.py | def on_displayed(self, callback, remove=False):
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Parameters
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Must have a signature of::
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kwargs from display are passed through without modi... | def on_displayed(self, callback, remove=False):
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train | Widget.add_traits | Dynamically add trait attributes to the Widget. | ipywidgets/widgets/widget.py | def add_traits(self, **traits):
"""Dynamically add trait attributes to the Widget."""
super(Widget, self).add_traits(**traits)
for name, trait in traits.items():
if trait.get_metadata('sync'):
self.keys.append(name)
self.send_state(name) | def add_traits(self, **traits):
"""Dynamically add trait attributes to the Widget."""
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train | Widget.notify_change | Called when a property has changed. | ipywidgets/widgets/widget.py | def notify_change(self, change):
"""Called when a property has changed."""
# Send the state to the frontend before the user-registered callbacks
# are called.
name = change['name']
if self.comm is not None and self.comm.kernel is not None:
# Make sure this isn't infor... | def notify_change(self, change):
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name = change['name']
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train | Widget.hold_sync | Hold syncing any state until the outermost context manager exits | ipywidgets/widgets/widget.py | def hold_sync(self):
"""Hold syncing any state until the outermost context manager exits"""
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yield
else:
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self._holding_sync = True
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finally:
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train | Widget._should_send_property | Check the property lock (property_lock) | ipywidgets/widgets/widget.py | def _should_send_property(self, key, value):
"""Check the property lock (property_lock)"""
to_json = self.trait_metadata(key, 'to_json', self._trait_to_json)
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# model_state, buffer_paths, buffers
split_value = _remove_buffers({ key: to_json(v... | def _should_send_property(self, key, value):
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train | Widget._handle_msg | Called when a msg is received from the front-end | ipywidgets/widgets/widget.py | def _handle_msg(self, msg):
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train | Widget._ipython_display_ | Called when `IPython.display.display` is called on the widget. | ipywidgets/widgets/widget.py | def _ipython_display_(self, **kwargs):
"""Called when `IPython.display.display` is called on the widget."""
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train | Widget._send | Sends a message to the model in the front-end. | ipywidgets/widgets/widget.py | def _send(self, msg, buffers=None):
"""Sends a message to the model in the front-end."""
if self.comm is not None and self.comm.kernel is not None:
self.comm.send(data=msg, buffers=buffers) | def _send(self, msg, buffers=None):
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train | _int_doc | Add int docstring template to class init. | ipywidgets/widgets/widget_int.py | def _int_doc(cls):
"""Add int docstring template to class init."""
def __init__(self, value=None, **kwargs):
if value is not None:
kwargs['value'] = value
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__init__.__doc__ = _int_doc_t
cls.__init__ = __init__
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"""Add int docstring template to class init."""
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train | _bounded_int_doc | Add bounded int docstring template to class init. | ipywidgets/widgets/widget_int.py | def _bounded_int_doc(cls):
"""Add bounded int docstring template to class init."""
def __init__(self, value=None, min=None, max=None, step=None, **kwargs):
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Instantiating a JavaScript Date object with a string assumes that the
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assumes that it's in local time:
>>> cdate = new Date('2015-05-12')
Mon May 11 2015 20:00:00 GMT-0400 (Eastern... | ipywidgets/widgets/trait_types.py | def datetime_to_json(pydt, manager):
"""Serialize a Python datetime object to json.
Instantiating a JavaScript Date object with a string assumes that the
string is a UTC string, while instantiating it with constructor arguments
assumes that it's in local time:
>>> cdate = new Date('2015-05-12')
... | def datetime_to_json(pydt, manager):
"""Serialize a Python datetime object to json.
Instantiating a JavaScript Date object with a string assumes that the
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train | datetime_from_json | Deserialize a Python datetime object from json. | ipywidgets/widgets/trait_types.py | def datetime_from_json(js, manager):
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train | date_to_json | Serialize a Python date object.
Attributes of this dictionary are to be passed to the JavaScript Date
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Attributes of this dictionary are to be passed to the JavaScript Date
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train | _find_widget_refs_by_state | Find references to other widgets in a widget's state | ipywidgets/embed.py | def _find_widget_refs_by_state(widget, state):
"""Find references to other widgets in a widget's state"""
# Copy keys to allow changes to state during iteration:
keys = tuple(state.keys())
for key in keys:
value = getattr(widget, key)
# Trivial case: Direct references to other widgets:
... | def _find_widget_refs_by_state(widget, state):
"""Find references to other widgets in a widget's state"""
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keys = tuple(state.keys())
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value = getattr(widget, key)
# Trivial case: Direct references to other widgets:
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train | _get_recursive_state | Gets the embed state of a widget, and all other widgets it refers to as well | ipywidgets/embed.py | def _get_recursive_state(widget, store=None, drop_defaults=False):
"""Gets the embed state of a widget, and all other widgets it refers to as well"""
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store = dict()
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train | add_resolved_links | Adds the state of any link models between two models in store | ipywidgets/embed.py | def add_resolved_links(store, drop_defaults):
"""Adds the state of any link models between two models in store"""
for widget_id, widget in Widget.widgets.items(): # go over all widgets
if isinstance(widget, Link) and widget_id not in store:
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"""Adds the state of any link models between two models in store"""
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train | dependency_state | Get the state of all widgets specified, and their dependencies.
This uses a simple dependency finder, including:
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- any widget in a list/tuple attribute in the state of an included widget
- any widget in a dict attribute in the state... | ipywidgets/embed.py | def dependency_state(widgets, drop_defaults=True):
"""Get the state of all widgets specified, and their dependencies.
This uses a simple dependency finder, including:
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- any widget in a list/tuple attribute in the state of an included... | def dependency_state(widgets, drop_defaults=True):
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train | embed_data | Gets data for embedding.
Use this to get the raw data for embedding if you have special
formatting needs.
Parameters
----------
{views_attribute}
drop_defaults: boolean
Whether to drop default values from the widget states.
state: dict or None (default)
The state to include... | ipywidgets/embed.py | def embed_data(views, drop_defaults=True, state=None):
"""Gets data for embedding.
Use this to get the raw data for embedding if you have special
formatting needs.
Parameters
----------
{views_attribute}
drop_defaults: boolean
Whether to drop default values from the widget states.
... | def embed_data(views, drop_defaults=True, state=None):
"""Gets data for embedding.
Use this to get the raw data for embedding if you have special
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Parameters
----------
{views_attribute}
drop_defaults: boolean
Whether to drop default values from the widget states.
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train | embed_snippet | Return a snippet that can be embedded in an HTML file.
Parameters
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{embed_kwargs}
Returns
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A unicode string with an HTML snippet containing several `<script>` tags. | ipywidgets/embed.py | def embed_snippet(views,
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state=None,
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embed_url=None,
requirejs=True,
cors=True
):
"""Return a snippet that can be embedded in an HTML file.
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cors=True
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train | embed_minimal_html | Write a minimal HTML file with widget views embedded.
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template: Template in which to embed the widget state.
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"""Write a minimal HTML file with widget views embedded.
Parameters
----------
fp: filename or file-like object
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title: title of the html page.
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train | _make_options | Standardize the options tuple format.
The returned tuple should be in the format (('label', value), ('label', value), ...).
The input can be
* an iterable of (label, value) pairs
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The returned tuple should be in the format (('label', value), ('label', value), ...).
The input can be
* an iterable of (label, value) pairs
* an iterable of values, and labels will be generated
"""
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train | findvalue | A function that uses the compare function to return a value from the list. | ipywidgets/widgets/widget_selection.py | def findvalue(array, value, compare = lambda x, y: x == y):
"A function that uses the compare function to return a value from the list."
try:
return next(x for x in array if compare(x, value))
except StopIteration:
raise ValueError('%r not in array'%value) | def findvalue(array, value, compare = lambda x, y: x == y):
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train | _Selection._propagate_options | Set the values and labels, and select the first option if we aren't initializing | ipywidgets/widgets/widget_selection.py | def _propagate_options(self, change):
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options = self._options_full
self.set_trait('_options_labels', tuple(i[0] for i in options))
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if self._initi... | def _propagate_options(self, change):
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train | _Selection._propagate_index | Propagate changes in index to the value and label properties | ipywidgets/widgets/widget_selection.py | def _propagate_index(self, change):
"Propagate changes in index to the value and label properties"
label = self._options_labels[change.new] if change.new is not None else None
value = self._options_values[change.new] if change.new is not None else None
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train | _MultipleSelection._validate_index | Check the range of each proposed index. | ipywidgets/widgets/widget_selection.py | def _validate_index(self, proposal):
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if all(0 <= i < len(self._options_labels) for i in proposal.value):
return proposal.value
else:
raise TraitError('Invalid selection: index out of bounds') | def _validate_index(self, proposal):
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train | _MultipleSelection._validate_value | Replace all values with the actual objects in the options list | ipywidgets/widgets/widget_selection.py | def _validate_value(self, proposal):
"Replace all values with the actual objects in the options list"
try:
return tuple(findvalue(self._options_values, i, self.equals) for i in proposal.value)
except ValueError:
raise TraitError('Invalid selection: value not found') | def _validate_value(self, proposal):
"Replace all values with the actual objects in the options list"
try:
return tuple(findvalue(self._options_values, i, self.equals) for i in proposal.value)
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train | DOMWidget.add_class | Adds a class to the top level element of the widget.
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Adds a class to the top level element of the widget.
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train | DOMWidget.remove_class | Removes a class from the top level element of the widget.
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"""
Removes a class from the top level element of the widget.
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if className in self._dom_classes:
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Removes a class from the top level element of the widget.
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train | register_comm_target | Register the jupyter.widget comm target | ipywidgets/__init__.py | def register_comm_target(kernel=None):
"""Register the jupyter.widget comm target"""
if kernel is None:
kernel = get_ipython().kernel
kernel.comm_manager.register_target('jupyter.widget', Widget.handle_comm_opened) | def register_comm_target(kernel=None):
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train | doc_subst | Substitute format strings in class or function docstring | ipywidgets/widgets/docutils.py | def doc_subst(snippets):
""" Substitute format strings in class or function docstring """
def decorator(cls):
# Strip the snippets to avoid trailing new lines and whitespace
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train | base_uri | Get absolute path to resource, works for dev and for PyInstaller | webview/util.py | def base_uri(relative_path=''):
""" Get absolute path to resource, works for dev and for PyInstaller """
try:
# PyInstaller creates a temp folder and stores path in _MEIPASS
base_path = sys._MEIPASS
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""" Get absolute path to resource, works for dev and for PyInstaller """
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train | parse_file_type | :param file_type: file type string 'description (*.file_extension1;*.file_extension2)' as required by file filter in create_file_dialog
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:return: (description, file extensions) tuple
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:param string: input string
:return: Unicode string | webview/util.py | def make_unicode(string):
"""
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train | BrowserView._add_app_menu | Create a default Cocoa menu that shows 'Services', 'Hide',
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to some menu items if it's available. | webview/cocoa.py | def _add_app_menu(self):
"""
Create a default Cocoa menu that shows 'Services', 'Hide',
'Hide Others', 'Show All', and 'Quit'. Will append the application name
to some menu items if it's available.
"""
# Set the main menu for the application
mainMenu = AppKit.NSMe... | def _add_app_menu(self):
"""
Create a default Cocoa menu that shows 'Services', 'Hide',
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train | BrowserView._add_view_menu | Create a default View menu that shows 'Enter Full Screen'. | webview/cocoa.py | def _add_view_menu(self):
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Create a default View menu that shows 'Enter Full Screen'.
"""
mainMenu = self.app.mainMenu()
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viewMenu = AppKit.NSMenu.alloc().init()
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train | BrowserView.nscolor_from_hex | Convert given hex color to NSColor.
:hex_string: Hex code of the color as #RGB or #RRGGBB | webview/cocoa.py | def nscolor_from_hex(hex_string):
"""
Convert given hex color to NSColor.
:hex_string: Hex code of the color as #RGB or #RRGGBB
"""
hex_string = hex_string[1:] # Remove leading hash
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Convert given hex color to NSColor.
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hex_string = hex_string[1:] # Remove leading hash
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train | BrowserView.get_instance | Return a BrowserView instance by the :value of its given :attribute,
and None if no match is found. | webview/cocoa.py | def get_instance(attr, value):
"""
Return a BrowserView instance by the :value of its given :attribute,
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"""
for i in list(BrowserView.instances.values()):
try:
if getattr(i, attr) == value:
return i
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"""
Return a BrowserView instance by the :value of its given :attribute,
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if getattr(i, attr) == value:
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train | BrowserView.pyobjc_method_signature | Return a PyObjCMethodSignature object for given signature string.
:param signature_str: A byte string containing the type encoding for the method signature
:return: A method signature object, assignable to attributes like __block_signature__
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"""
Return a PyObjCMethodSignature object for given signature string.
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train | choose_path | Invoke a folder selection dialog here
:return: | examples/flask_app/src/backend/server.py | def choose_path():
"""
Invoke a folder selection dialog here
:return:
"""
dirs = webview.create_file_dialog(webview.FOLDER_DIALOG)
if dirs and len(dirs) > 0:
directory = dirs[0]
if isinstance(directory, bytes):
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response =... | def choose_path():
"""
Invoke a folder selection dialog here
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"""
dirs = webview.create_file_dialog(webview.FOLDER_DIALOG)
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train | set_ie_mode | By default hosted IE control emulates IE7 regardless which version of IE is installed. To fix this, a proper value
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See http://msdn.microsoft.com/en-us/library/ee330730%28v=vs.85%29.aspx#browser_emulation for details on this
behaviour. | webview/win32_shared.py | def set_ie_mode():
"""
By default hosted IE control emulates IE7 regardless which version of IE is installed. To fix this, a proper value
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See http://msdn.microsoft.com/en-us/library/ee330730%28v=vs.85%29.aspx#browser_emulation for details on this
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"""
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"""
By default hosted IE control emulates IE7 regardless which version of IE is installed. To fix this, a proper value
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train | _api_call | Decorator to call a pywebview API, checking for _webview_ready and raisings
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Decorator to call a pywebview API, checking for _webview_ready and raisings
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train | create_window | Create a web view window using a native GUI. The execution blocks after this function is invoked, so other
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:param url: URL to load
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train | create_file_dialog | Create a file dialog
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Create a file dialog
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Create a file dialog
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train | load_html | Load a new content into a previously created WebView window. This function must be invoked after WebView windows is
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train | evaluate_js | Evaluate given JavaScript code and return the result
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:param uid: uid of the target instance
:return: Return value of the evaluated code | webview/__init__.py | def evaluate_js(script, uid='master'):
"""
Evaluate given JavaScript code and return the result
:param script: The JavaScript code to be evaluated
:param uid: uid of the target instance
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"""
escaped_script = 'JSON.stringify(eval("{0}"))'.format(esca... | def evaluate_js(script, uid='master'):
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Evaluate given JavaScript code and return the result
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train | NYUDSegDataLayer.setup | Setup data layer according to parameters:
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- split: train / val / test
- tops: list of tops to output from {color, depth, hha, label}
- randomize: load in random order (default: True)
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train | NYUDSegDataLayer.load_image | Load input image and preprocess for Caffe:
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Load input image and preprocess for Caffe:
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train | NYUDSegDataLayer.load_label | Load label image as 1 x height x width integer array of label indices.
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train | NYUDSegDataLayer.load_depth | Load pre-processed depth for NYUDv2 segmentation set. | torchfcn/ext/fcn.berkeleyvision.org/nyud_layers.py | def load_depth(self, idx):
"""
Load pre-processed depth for NYUDv2 segmentation set.
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Load pre-processed depth for NYUDv2 segmentation set.
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train | NYUDSegDataLayer.load_hha | Load HHA features from Gupta et al. ECCV14.
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"""
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hha = np.array(im, dtype=np.float32)
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Load HHA features from Gupta et al. ECCV14.
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train | SIFTFlowSegDataLayer.load_image | Load input image and preprocess for Caffe:
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train | SIFTFlowSegDataLayer.load_label | Load label image as 1 x height x width integer array of label indices.
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train | get_upsampling_weight | Make a 2D bilinear kernel suitable for upsampling | torchfcn/models/fcn32s.py | def get_upsampling_weight(in_channels, out_channels, kernel_size):
"""Make a 2D bilinear kernel suitable for upsampling"""
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if kernel_size % 2 == 1:
center = factor - 1
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filt = (1 ... | def get_upsampling_weight(in_channels, out_channels, kernel_size):
"""Make a 2D bilinear kernel suitable for upsampling"""
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = np.ogrid[:kernel_size, :kernel_size]
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train | label_accuracy_score | Returns accuracy score evaluation result.
- overall accuracy
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- mean IU
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"""Returns accuracy score evaluation result.
- overall accuracy
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- fwavacc
"""
hist = np.zeros((n_class, n_class))
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- mean accuracy
- mean IU
- fwavacc
"""
hist = np.zeros((n_class, n_class))
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train | transplant | Transfer weights by copying matching parameters, coercing parameters of
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The coercion is useful to convert fully connected layers to their
equivalent convolutional layers, since the weights are the same and only
the shapes are different. In particu... | torchfcn/ext/fcn.berkeleyvision.org/surgery.py | def transplant(new_net, net, suffix=''):
"""
Transfer weights by copying matching parameters, coercing parameters of
incompatible shape, and dropping unmatched parameters.
The coercion is useful to convert fully connected layers to their
equivalent convolutional layers, since the weights are the sa... | def transplant(new_net, net, suffix=''):
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train | interp | Set weights of each layer in layers to bilinear kernels for interpolation. | torchfcn/ext/fcn.berkeleyvision.org/surgery.py | def interp(net, layers):
"""
Set weights of each layer in layers to bilinear kernels for interpolation.
"""
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m, k, h, w = net.params[l][0].data.shape
if m != k and k != 1:
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raise
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"""
Set weights of each layer in layers to bilinear kernels for interpolation.
"""
for l in layers:
m, k, h, w = net.params[l][0].data.shape
if m != k and k != 1:
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raise
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train | expand_score | Transplant an old score layer's parameters, with k < k' classes, into a new
score layer with k classes s.t. the first k' are the old classes. | torchfcn/ext/fcn.berkeleyvision.org/surgery.py | def expand_score(new_net, new_layer, net, layer):
"""
Transplant an old score layer's parameters, with k < k' classes, into a new
score layer with k classes s.t. the first k' are the old classes.
"""
old_cl = net.params[layer][0].num
new_net.params[new_layer][0].data[:old_cl][...] = net.params[l... | def expand_score(new_net, new_layer, net, layer):
"""
Transplant an old score layer's parameters, with k < k' classes, into a new
score layer with k classes s.t. the first k' are the old classes.
"""
old_cl = net.params[layer][0].num
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train | VOCSegDataLayer.setup | Setup data layer according to parameters:
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- split: train / val / test
- mean: tuple of mean values to subtract
- randomize: load in random order (default: True)
- seed: seed for randomization (default: None / current time)
for PASC... | torchfcn/ext/fcn.berkeleyvision.org/voc_layers.py | def setup(self, bottom, top):
"""
Setup data layer according to parameters:
- voc_dir: path to PASCAL VOC year dir
- split: train / val / test
- mean: tuple of mean values to subtract
- randomize: load in random order (default: True)
- seed: seed for randomizatio... | def setup(self, bottom, top):
"""
Setup data layer according to parameters:
- voc_dir: path to PASCAL VOC year dir
- split: train / val / test
- mean: tuple of mean values to subtract
- randomize: load in random order (default: True)
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train | PASCALContextSegDataLayer.setup | Setup data layer according to parameters:
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- context_dir: path to PASCAL-Context annotations
- split: train / val / test
- randomize: load in random order (default: True)
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"""
Setup data layer according to parameters:
- voc_dir: path to PASCAL VOC dir (must contain 2010)
- context_dir: path to PASCAL-Context annotations
- split: train / val / test
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- see... | def setup(self, bottom, top):
"""
Setup data layer according to parameters:
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train | PASCALContextSegDataLayer.load_label | Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
The full 400 labels are translated to the 59 class task labels. | torchfcn/ext/fcn.berkeleyvision.org/pascalcontext_layers.py | def load_label(self, idx):
"""
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
The full 400 labels are translated to the 59 class task labels.
"""
label_400 = scipy.io.loadmat('{}/trainval/{}.... | def load_label(self, idx):
"""
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
The full 400 labels are translated to the 59 class task labels.
"""
label_400 = scipy.io.loadmat('{}/trainval/{}.... | [
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train | voc.load_label | Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss. | torchfcn/ext/fcn.berkeleyvision.org/voc_helper.py | def load_label(self, idx):
"""
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
"""
label = Image.open('{}/SegmentationClass/{}.png'.format(self.dir, idx))
label = np.array(label, dtype=np.uint... | def load_label(self, idx):
"""
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
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label = Image.open('{}/SegmentationClass/{}.png'.format(self.dir, idx))
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train | voc.palette | Transfer the VOC color palette to an output mask for visualization. | torchfcn/ext/fcn.berkeleyvision.org/voc_helper.py | def palette(self, label_im):
'''
Transfer the VOC color palette to an output mask for visualization.
'''
if label_im.ndim == 3:
label_im = label_im[0]
label = Image.fromarray(label_im, mode='P')
label.palette = copy.copy(self.palette)
return label | def palette(self, label_im):
'''
Transfer the VOC color palette to an output mask for visualization.
'''
if label_im.ndim == 3:
label_im = label_im[0]
label = Image.fromarray(label_im, mode='P')
label.palette = copy.copy(self.palette)
return label | [
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train | _shuffled | Deterministically shuffle identically under both py2 + py3. | pre_commit/languages/helpers.py | def _shuffled(seq):
"""Deterministically shuffle identically under both py2 + py3."""
fixed_random = random.Random()
if six.PY2: # pragma: no cover (py2)
fixed_random.seed(FIXED_RANDOM_SEED)
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fixed_random.seed(FIXED_RANDOM_SEED, version=1)
seq = list(... | def _shuffled(seq):
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fixed_random = random.Random()
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fixed_random.seed(FIXED_RANDOM_SEED)
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fixed_random.seed(FIXED_RANDOM_SEED, version=1)
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train | envcontext | In this context, `os.environ` is modified according to `patch`.
`patch` is an iterable of 2-tuples (key, value):
`key`: string
`value`:
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`key`: string
`value`:
- string: `environ[key] == value` inside the context.
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"""In this context, `os.environ` is modified according to `patch`.
`patch` is an iterable of 2-tuples (key, value):
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train | bin_dir | On windows there's a different directory for the virtualenv | pre_commit/languages/python.py | def bin_dir(venv):
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bin_part = 'Scripts' if os.name == 'nt' else 'bin'
return os.path.join(venv, bin_part) | def bin_dir(venv):
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] | 72f98d26e690da11dc2e41861d14c58eb21930cb |
train | install | Install the pre-commit hooks. | pre_commit/commands/install_uninstall.py | def install(
config_file, store,
overwrite=False, hooks=False, hook_type='pre-commit',
skip_on_missing_conf=False,
):
"""Install the pre-commit hooks."""
if cmd_output('git', 'config', 'core.hooksPath', retcode=None)[1].strip():
logger.error(
'Cowardly refusing to ins... | def install(
config_file, store,
overwrite=False, hooks=False, hook_type='pre-commit',
skip_on_missing_conf=False,
):
"""Install the pre-commit hooks."""
if cmd_output('git', 'config', 'core.hooksPath', retcode=None)[1].strip():
logger.error(
'Cowardly refusing to ins... | [
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] | pre-commit/pre-commit | python | https://github.com/pre-commit/pre-commit/blob/72f98d26e690da11dc2e41861d14c58eb21930cb/pre_commit/commands/install_uninstall.py#L69-L126 | [
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train | uninstall | Uninstall the pre-commit hooks. | pre_commit/commands/install_uninstall.py | def uninstall(hook_type='pre-commit'):
"""Uninstall the pre-commit hooks."""
hook_path, legacy_path = _hook_paths(hook_type)
# If our file doesn't exist or it isn't ours, gtfo.
if not os.path.exists(hook_path) or not is_our_script(hook_path):
return 0
os.remove(hook_path)
output.write_... | def uninstall(hook_type='pre-commit'):
"""Uninstall the pre-commit hooks."""
hook_path, legacy_path = _hook_paths(hook_type)
# If our file doesn't exist or it isn't ours, gtfo.
if not os.path.exists(hook_path) or not is_our_script(hook_path):
return 0
os.remove(hook_path)
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train | normalize_cmd | Fixes for the following issues on windows
- https://bugs.python.org/issue8557
- windows does not parse shebangs
This function also makes deep-path shebangs work just fine | pre_commit/parse_shebang.py | def normalize_cmd(cmd):
"""Fixes for the following issues on windows
- https://bugs.python.org/issue8557
- windows does not parse shebangs
This function also makes deep-path shebangs work just fine
"""
# Use PATH to determine the executable
exe = normexe(cmd[0])
# Figure out the sheban... | def normalize_cmd(cmd):
"""Fixes for the following issues on windows
- https://bugs.python.org/issue8557
- windows does not parse shebangs
This function also makes deep-path shebangs work just fine
"""
# Use PATH to determine the executable
exe = normexe(cmd[0])
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train | get_hook_message | Prints a message for running a hook.
This currently supports three approaches:
# Print `start` followed by dots, leaving 6 characters at the end
>>> print_hook_message('start', end_len=6)
start...............................................................
# Print `start` followed by dots with th... | pre_commit/output.py | def get_hook_message(
start,
postfix='',
end_msg=None,
end_len=0,
end_color=None,
use_color=None,
cols=80,
):
"""Prints a message for running a hook.
This currently supports three approaches:
# Print `start` followed by dots, leaving 6 characters at ... | def get_hook_message(
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postfix='',
end_msg=None,
end_len=0,
end_color=None,
use_color=None,
cols=80,
):
"""Prints a message for running a hook.
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train | check_for_cygwin_mismatch | See https://github.com/pre-commit/pre-commit/issues/354 | pre_commit/git.py | def check_for_cygwin_mismatch():
"""See https://github.com/pre-commit/pre-commit/issues/354"""
if sys.platform in ('cygwin', 'win32'): # pragma: no cover (windows)
is_cygwin_python = sys.platform == 'cygwin'
toplevel = cmd_output('git', 'rev-parse', '--show-toplevel')[1]
is_cygwin_git =... | def check_for_cygwin_mismatch():
"""See https://github.com/pre-commit/pre-commit/issues/354"""
if sys.platform in ('cygwin', 'win32'): # pragma: no cover (windows)
is_cygwin_python = sys.platform == 'cygwin'
toplevel = cmd_output('git', 'rev-parse', '--show-toplevel')[1]
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train | xargs | A simplified implementation of xargs.
negate: Make nonzero successful and zero a failure
target_concurrency: Target number of partitions to run concurrently | pre_commit/xargs.py | def xargs(cmd, varargs, **kwargs):
"""A simplified implementation of xargs.
negate: Make nonzero successful and zero a failure
target_concurrency: Target number of partitions to run concurrently
"""
negate = kwargs.pop('negate', False)
target_concurrency = kwargs.pop('target_concurrency', 1)
... | def xargs(cmd, varargs, **kwargs):
"""A simplified implementation of xargs.
negate: Make nonzero successful and zero a failure
target_concurrency: Target number of partitions to run concurrently
"""
negate = kwargs.pop('negate', False)
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train | make_archive | Makes an archive of a repository in the given destdir.
:param text name: Name to give the archive. For instance foo. The file
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:param text repo: Repository to clone.
:param text ref: Tag/SHA/branch to check out.
:param text destdir: Directory to place arc... | pre_commit/make_archives.py | def make_archive(name, repo, ref, destdir):
"""Makes an archive of a repository in the given destdir.
:param text name: Name to give the archive. For instance foo. The file
that is created will be called foo.tar.gz.
:param text repo: Repository to clone.
:param text ref: Tag/SHA/branch to check o... | def make_archive(name, repo, ref, destdir):
"""Makes an archive of a repository in the given destdir.
:param text name: Name to give the archive. For instance foo. The file
that is created will be called foo.tar.gz.
:param text repo: Repository to clone.
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train | _compute_cols | Compute the number of columns to display hook messages. The widest
that will be displayed is in the no files skipped case:
Hook name...(no files to check) Skipped
or in the verbose case
Hook name [hookid]...(no files to check) Skipped | pre_commit/commands/run.py | def _compute_cols(hooks, verbose):
"""Compute the number of columns to display hook messages. The widest
that will be displayed is in the no files skipped case:
Hook name...(no files to check) Skipped
or in the verbose case
Hook name [hookid]...(no files to check) Skipped
"""
if ... | def _compute_cols(hooks, verbose):
"""Compute the number of columns to display hook messages. The widest
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Hook name...(no files to check) Skipped
or in the verbose case
Hook name [hookid]...(no files to check) Skipped
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train | _run_hooks | Actually run the hooks. | pre_commit/commands/run.py | def _run_hooks(config, hooks, args, environ):
"""Actually run the hooks."""
skips = _get_skips(environ)
cols = _compute_cols(hooks, args.verbose)
filenames = _all_filenames(args)
filenames = filter_by_include_exclude(filenames, '', config['exclude'])
classifier = Classifier(filenames)
retval... | def _run_hooks(config, hooks, args, environ):
"""Actually run the hooks."""
skips = _get_skips(environ)
cols = _compute_cols(hooks, args.verbose)
filenames = _all_filenames(args)
filenames = filter_by_include_exclude(filenames, '', config['exclude'])
classifier = Classifier(filenames)
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train | rmtree | On windows, rmtree fails for readonly dirs. | pre_commit/util.py | def rmtree(path):
"""On windows, rmtree fails for readonly dirs."""
def handle_remove_readonly(func, path, exc): # pragma: no cover (windows)
excvalue = exc[1]
if (
func in (os.rmdir, os.remove, os.unlink) and
excvalue.errno == errno.EACCES
):
... | def rmtree(path):
"""On windows, rmtree fails for readonly dirs."""
def handle_remove_readonly(func, path, exc): # pragma: no cover (windows)
excvalue = exc[1]
if (
func in (os.rmdir, os.remove, os.unlink) and
excvalue.errno == errno.EACCES
):
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train | _update_repo | Updates a repository to the tip of `master`. If the repository cannot
be updated because a hook that is configured does not exist in `master`,
this raises a RepositoryCannotBeUpdatedError
Args:
repo_config - A config for a repository | pre_commit/commands/autoupdate.py | def _update_repo(repo_config, store, tags_only):
"""Updates a repository to the tip of `master`. If the repository cannot
be updated because a hook that is configured does not exist in `master`,
this raises a RepositoryCannotBeUpdatedError
Args:
repo_config - A config for a repository
"""
... | def _update_repo(repo_config, store, tags_only):
"""Updates a repository to the tip of `master`. If the repository cannot
be updated because a hook that is configured does not exist in `master`,
this raises a RepositoryCannotBeUpdatedError
Args:
repo_config - A config for a repository
"""
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train | autoupdate | Auto-update the pre-commit config to the latest versions of repos. | pre_commit/commands/autoupdate.py | def autoupdate(config_file, store, tags_only, repos=()):
"""Auto-update the pre-commit config to the latest versions of repos."""
migrate_config(config_file, quiet=True)
retv = 0
output_repos = []
changed = False
input_config = load_config(config_file)
for repo_config in input_config['repo... | def autoupdate(config_file, store, tags_only, repos=()):
"""Auto-update the pre-commit config to the latest versions of repos."""
migrate_config(config_file, quiet=True)
retv = 0
output_repos = []
changed = False
input_config = load_config(config_file)
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train | orig_py_exe | A -mvenv virtualenv made from a -mvirtualenv virtualenv installs
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and invoke `-mvenv` from there.
See:
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- https://github.com/pypa/virtualenv/issues/1095
- https://bugs.pyt... | pre_commit/languages/python_venv.py | def orig_py_exe(exe): # pragma: no cover (platform specific)
"""A -mvenv virtualenv made from a -mvirtualenv virtualenv installs
packages to the incorrect location. Attempt to find the _original_ exe
and invoke `-mvenv` from there.
See:
- https://github.com/pre-commit/pre-commit/issues/755
- ... | def orig_py_exe(exe): # pragma: no cover (platform specific)
"""A -mvenv virtualenv made from a -mvirtualenv virtualenv installs
packages to the incorrect location. Attempt to find the _original_ exe
and invoke `-mvenv` from there.
See:
- https://github.com/pre-commit/pre-commit/issues/755
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train | enable_virtual_terminal_processing | As of Windows 10, the Windows console supports (some) ANSI escape
sequences, but it needs to be enabled using `SetConsoleMode` first.
More info on the escape sequences supported:
https://msdn.microsoft.com/en-us/library/windows/desktop/mt638032(v=vs.85).aspx | pre_commit/color_windows.py | def enable_virtual_terminal_processing():
"""As of Windows 10, the Windows console supports (some) ANSI escape
sequences, but it needs to be enabled using `SetConsoleMode` first.
More info on the escape sequences supported:
https://msdn.microsoft.com/en-us/library/windows/desktop/mt638032(v=vs.85).aspx... | def enable_virtual_terminal_processing():
"""As of Windows 10, the Windows console supports (some) ANSI escape
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More info on the escape sequences supported:
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train | format_color | Format text with color.
Args:
text - Text to be formatted with color if `use_color`
color - The color start string
use_color_setting - Whether or not to color | pre_commit/color.py | def format_color(text, color, use_color_setting):
"""Format text with color.
Args:
text - Text to be formatted with color if `use_color`
color - The color start string
use_color_setting - Whether or not to color
"""
if not use_color_setting:
return text
else:
... | def format_color(text, color, use_color_setting):
"""Format text with color.
Args:
text - Text to be formatted with color if `use_color`
color - The color start string
use_color_setting - Whether or not to color
"""
if not use_color_setting:
return text
else:
... | [
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train | use_color | Choose whether to use color based on the command argument.
Args:
setting - Either `auto`, `always`, or `never` | pre_commit/color.py | def use_color(setting):
"""Choose whether to use color based on the command argument.
Args:
setting - Either `auto`, `always`, or `never`
"""
if setting not in COLOR_CHOICES:
raise InvalidColorSetting(setting)
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"""Choose whether to use color based on the command argument.
Args:
setting - Either `auto`, `always`, or `never`
"""
if setting not in COLOR_CHOICES:
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train | _get_default_directory | Returns the default directory for the Store. This is intentionally
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way to get this information. This is also done so
`Store.get_default_directory` can be mocked in tests and
`_get_default_directory` can be tested. | pre_commit/store.py | def _get_default_directory():
"""Returns the default directory for the Store. This is intentionally
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`_get_default_di... | def _get_default_directory():
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train | Store._shallow_clone | Perform a shallow clone of a repository and its submodules | pre_commit/store.py | def _shallow_clone(self, ref, git_cmd): # pragma: windows no cover
"""Perform a shallow clone of a repository and its submodules """
git_config = 'protocol.version=2'
git_cmd('-c', git_config, 'fetch', 'origin', ref, '--depth=1')
git_cmd('checkout', ref)
git_cmd(
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"""Perform a shallow clone of a repository and its submodules """
git_config = 'protocol.version=2'
git_cmd('-c', git_config, 'fetch', 'origin', ref, '--depth=1')
git_cmd('checkout', ref)
git_cmd(
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train | Store.clone | Clone the given url and checkout the specific ref. | pre_commit/store.py | def clone(self, repo, ref, deps=()):
"""Clone the given url and checkout the specific ref."""
if os.path.isdir(repo):
repo = os.path.abspath(repo)
def clone_strategy(directory):
env = git.no_git_env()
def _git_cmd(*args):
cmd_output('git', *... | def clone(self, repo, ref, deps=()):
"""Clone the given url and checkout the specific ref."""
if os.path.isdir(repo):
repo = os.path.abspath(repo)
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train | compounding | Yield an infinite series of compounding values. Each time the
generator is called, a value is produced by multiplying the previous
value by the compound rate.
EXAMPLE:
>>> sizes = compounding(1., 10., 1.5)
>>> assert next(sizes) == 1.
>>> assert next(sizes) == 1 * 1.5
>>> assert nex... | thinc/rates.py | def compounding(start, stop, compound, t=0.0):
"""Yield an infinite series of compounding values. Each time the
generator is called, a value is produced by multiplying the previous
value by the compound rate.
EXAMPLE:
>>> sizes = compounding(1., 10., 1.5)
>>> assert next(sizes) == 1.
... | def compounding(start, stop, compound, t=0.0):
"""Yield an infinite series of compounding values. Each time the
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value by the compound rate.
EXAMPLE:
>>> sizes = compounding(1., 10., 1.5)
>>> assert next(sizes) == 1.
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train | slanted_triangular | Yield an infinite series of values according to Howard and Ruder's
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train | equal_length | Check that arguments have the same length. | thinc/check.py | def equal_length(*args):
"""Check that arguments have the same length.
"""
for i, arg in enumerate(args):
if not isinstance(arg, Sized):
raise ExpectedTypeError(arg, ["Sized"])
if i >= 1 and len(arg) != len(args[0]):
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"""Check that arguments have the same length.
"""
for i, arg in enumerate(args):
if not isinstance(arg, Sized):
raise ExpectedTypeError(arg, ["Sized"])
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train | equal_axis | Check that elements have the same dimension on specified axis. | thinc/check.py | def equal_axis(*args, **axis):
"""Check that elements have the same dimension on specified axis.
"""
axis = axis.get("axis", -1)
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train | has_shape | Check that a particular argument is an array with a given shape. The
shape may contain string attributes, which will be fetched from arg0 to
the function (usually self). | thinc/check.py | def has_shape(shape):
"""Check that a particular argument is an array with a given shape. The
shape may contain string attributes, which will be fetched from arg0 to
the function (usually self).
"""
def has_shape_inner(arg_id, args, kwargs):
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"""
def has_shape_inner(arg_id, args, kwargs):
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train | Model.define_operators | Bind operators to specified functions for the scope of the context:
Example
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model = Model()
other = Model()
with Model.define_operators({"+": lambda self, other: "plus"}):
print(model + other)
# "plus"
print(model ... | thinc/neural/_classes/model.py | def define_operators(cls, operators):
"""Bind operators to specified functions for the scope of the context:
Example
-------
model = Model()
other = Model()
with Model.define_operators({"+": lambda self, other: "plus"}):
print(model + other)
... | def define_operators(cls, operators):
"""Bind operators to specified functions for the scope of the context:
Example
-------
model = Model()
other = Model()
with Model.define_operators({"+": lambda self, other: "plus"}):
print(model + other)
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train | Model.use_device | Change the device to execute on for the scope of the block. | thinc/neural/_classes/model.py | def use_device(cls, device):
"""Change the device to execute on for the scope of the block."""
if device == cls.ops.device:
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else:
curr_Ops, curr_ops = (cls.Ops, cls.ops)
cls.Ops = get_ops(device)
cls.ops = cls.Ops()
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if device == cls.ops.device:
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curr_Ops, curr_ops = (cls.Ops, cls.ops)
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train | Model.evaluate | x
Must match expected type
Must match expected shape
y
Must match expected type | thinc/neural/_classes/model.py | def evaluate(self, X, y):
"""
x
Must match expected type
Must match expected shape
y
Must match expected type
"""
scores = self.ops.flatten(list(self.pipe(X)))
if not hasattr(y, "shape"):
y = self.ops.flatten(y)
scor... | def evaluate(self, X, y):
"""
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Must match expected shape
y
Must match expected type
"""
scores = self.ops.flatten(list(self.pipe(X)))
if not hasattr(y, "shape"):
y = self.ops.flatten(y)
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train | check_learns_zero_output_rnn | Check we can learn to output a zero vector | examples/wrap_pytorch_rnn.py | def check_learns_zero_output_rnn(model, sgd, X, Y, initial_hidden=None):
"""Check we can learn to output a zero vector"""
outputs, get_dX = model.begin_update(X, initial_hidden)
Yh, h_n = outputs
tupleDy = (Yh - Y, h_n)
dX = get_dX(tupleDy, sgd=sgd)
prev = numpy.abs(Yh.sum())
print(prev)
... | def check_learns_zero_output_rnn(model, sgd, X, Y, initial_hidden=None):
"""Check we can learn to output a zero vector"""
outputs, get_dX = model.begin_update(X, initial_hidden)
Yh, h_n = outputs
tupleDy = (Yh - Y, h_n)
dX = get_dX(tupleDy, sgd=sgd)
prev = numpy.abs(Yh.sum())
print(prev)
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... | 90129be5f0d6c665344245a7c37dbe1b8afceea2 |
train | PyTorchWrapper.begin_update | Return the output of the wrapped PyTorch model for the given input,
along with a callback to handle the backward pass. | thinc/extra/wrappers.py | def begin_update(self, x_data, drop=0.0):
"""Return the output of the wrapped PyTorch model for the given input,
along with a callback to handle the backward pass.
"""
x_var = torch.autograd.Variable(xp2torch(x_data), requires_grad=True)
# Make prediction
y_var = self._m... | def begin_update(self, x_data, drop=0.0):
"""Return the output of the wrapped PyTorch model for the given input,
along with a callback to handle the backward pass.
"""
x_var = torch.autograd.Variable(xp2torch(x_data), requires_grad=True)
# Make prediction
y_var = self._m... | [
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