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train | RadioGroup.handleImplicitCheck | Checkboxes are hidden when inside of a RadioGroup as a selection of
the Radio button is an implicit selection of the Checkbox. As such, we have
to manually "check" any checkbox as needed. | gooey/gui/components/widgets/radio_group.py | def handleImplicitCheck(self):
"""
Checkboxes are hidden when inside of a RadioGroup as a selection of
the Radio button is an implicit selection of the Checkbox. As such, we have
to manually "check" any checkbox as needed.
"""
for button, widget in zip(self.radioBut... | def handleImplicitCheck(self):
"""
Checkboxes are hidden when inside of a RadioGroup as a selection of
the Radio button is an implicit selection of the Checkbox. As such, we have
to manually "check" any checkbox as needed.
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train | RadioGroup.createWidgets | Instantiate the Gooey Widgets that are used within the RadioGroup | gooey/gui/components/widgets/radio_group.py | def createWidgets(self):
"""
Instantiate the Gooey Widgets that are used within the RadioGroup
"""
from gooey.gui.components import widgets
return [getattr(widgets, item['type'])(self, item)
for item in getin(self.widgetInfo, ['data', 'widgets'], [])] | def createWidgets(self):
"""
Instantiate the Gooey Widgets that are used within the RadioGroup
"""
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return [getattr(widgets, item['type'])(self, item)
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train | GooeyApplication.onStart | Verify user input and kick off the client's program if valid | gooey/gui/containers/application.py | def onStart(self, *args, **kwarg):
"""
Verify user input and kick off the client's program if valid
"""
with transactUI(self):
config = self.navbar.getActiveConfig()
config.resetErrors()
if config.isValid():
self.clientRunner.ru... | def onStart(self, *args, **kwarg):
"""
Verify user input and kick off the client's program if valid
"""
with transactUI(self):
config = self.navbar.getActiveConfig()
config.resetErrors()
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train | GooeyApplication.onEdit | Return the user to the settings screen for further editing | gooey/gui/containers/application.py | def onEdit(self):
"""Return the user to the settings screen for further editing"""
with transactUI(self):
if self.buildSpec['poll_external_updates']:
self.fetchExternalUpdates()
self.showSettings() | def onEdit(self):
"""Return the user to the settings screen for further editing"""
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if self.buildSpec['poll_external_updates']:
self.fetchExternalUpdates()
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train | GooeyApplication.buildCliString | Collect all of the required information from the config screen and
build a CLI string which can be used to invoke the client program | gooey/gui/containers/application.py | def buildCliString(self):
"""
Collect all of the required information from the config screen and
build a CLI string which can be used to invoke the client program
"""
config = self.navbar.getActiveConfig()
group = self.buildSpec['widgets'][self.navbar.getSelectedGro... | def buildCliString(self):
"""
Collect all of the required information from the config screen and
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train | GooeyApplication.onComplete | Display the appropriate screen based on the success/fail of the
host program | gooey/gui/containers/application.py | def onComplete(self, *args, **kwargs):
"""
Display the appropriate screen based on the success/fail of the
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"""
with transactUI(self):
if self.clientRunner.was_success():
if self.buildSpec.get('return_to_config', False):
... | def onComplete(self, *args, **kwargs):
"""
Display the appropriate screen based on the success/fail of the
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train | GooeyApplication.fetchExternalUpdates | !Experimental!
Calls out to the client code requesting seed values to use in the UI
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"""
!Experimental!
Calls out to the client code requesting seed values to use in the UI
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train | GooeyApplication.buildNavigation | Chooses the appropriate layout navigation component based on user prefs | gooey/gui/containers/application.py | def buildNavigation(self):
"""
Chooses the appropriate layout navigation component based on user prefs
"""
if self.buildSpec['navigation'] == constants.TABBED:
navigation = Tabbar(self, self.buildSpec, self.configs)
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"""
Chooses the appropriate layout navigation component based on user prefs
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navigation = Tabbar(self, self.buildSpec, self.configs)
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train | Console.getFontFace | Choose the best font face available given the user options | gooey/gui/components/console.py | def getFontFace(self):
"""Choose the best font face available given the user options"""
userFace = self.buildSpec['terminal_font_family'] or self.defaultFont.GetFaceName()
return (''
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train | ProcessController._forward_stdout | Reads the stdout of `process` and forwards lines and progress
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'''
Reads the stdout of `process` and forwards lines and progress
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'''
while True:
line = process.stdout.readline()
if not line:
break
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Reads the stdout of `process` and forwards lines and progress
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train | ProcessController._extract_progress | Finds progress information in the text using the
user-supplied regex and calculation instructions | gooey/gui/processor.py | def _extract_progress(self, text):
'''
Finds progress information in the text using the
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# monad-ish dispatch to avoid the if/else soup
find = partial(re.search, string=text.strip().decode(self.encoding))
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Finds progress information in the text using the
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train | ProcessController._calculate_progress | Calculates the final progress value found by the regex | gooey/gui/processor.py | def _calculate_progress(self, match):
'''
Calculates the final progress value found by the regex
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if not self.progress_expr:
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train | ProcessController._eval_progress | Runs the user-supplied progress calculation rule | gooey/gui/processor.py | def _eval_progress(self, match):
'''
Runs the user-supplied progress calculation rule
'''
_locals = {k: safe_float(v) for k, v in match.groupdict().items()}
if "x" not in _locals:
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try:
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'''
Runs the user-supplied progress calculation rule
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train | Sidebar.swapConfigPanels | Hide/show configuration panels based on the currently selected
option in the sidebar | gooey/gui/components/sidebar.py | def swapConfigPanels(self, event):
"""Hide/show configuration panels based on the currently selected
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for id, panel in enumerate(self.configPanels):
panel.Hide()
self.activeSelection = event.Selection
self.configPanels[event.Selection].... | def swapConfigPanels(self, event):
"""Hide/show configuration panels based on the currently selected
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panel.Hide()
self.activeSelection = event.Selection
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train | Footer.updateProgressBar | value, disable_animation=False
:param args:
:param kwargs:
:return: | gooey/gui/components/footer.py | def updateProgressBar(self, *args, **kwargs):
'''
value, disable_animation=False
:param args:
:param kwargs:
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'''
value = kwargs.get('progress')
pb = self.progress_bar
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value = kwargs.get('progress')
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train | Gooey | Decorator for client code's main function.
Serializes argparse data to JSON for use with the Gooey front end | gooey/python_bindings/gooey_decorator.py | def Gooey(f=None,
advanced=True,
language='english',
auto_start=False, # TODO: add this to the docs. Used to be `show_config=True`
target=None,
program_name=None,
program_description=None,
default_size=(610, 530),
use_legacy_titles... | def Gooey(f=None,
advanced=True,
language='english',
auto_start=False, # TODO: add this to the docs. Used to be `show_config=True`
target=None,
program_name=None,
program_description=None,
default_size=(610, 530),
use_legacy_titles... | [
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train | fetchDynamicProperties | Sends a gooey-seed-ui request to the client program it retrieve
dynamically generated defaults with which to seed the UI | gooey/gui/seeder.py | def fetchDynamicProperties(target, encoding):
"""
Sends a gooey-seed-ui request to the client program it retrieve
dynamically generated defaults with which to seed the UI
"""
cmd = '{} {}'.format(target, 'gooey-seed-ui --ignore-gooey')
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, s... | def fetchDynamicProperties(target, encoding):
"""
Sends a gooey-seed-ui request to the client program it retrieve
dynamically generated defaults with which to seed the UI
"""
cmd = '{} {}'.format(target, 'gooey-seed-ui --ignore-gooey')
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train | ConfigPage.makeGroup | Messily builds the (potentially) nested and grouped layout
Note! Mutates `self.reifiedWidgets` in place with the widgets as they're
instantiated! I cannot figure out how to split out the creation of the
widgets from their styling without WxPython violently exploding
TODO: sort ou... | gooey/gui/components/config.py | def makeGroup(self, parent, thissizer, group, *args):
'''
Messily builds the (potentially) nested and grouped layout
Note! Mutates `self.reifiedWidgets` in place with the widgets as they're
instantiated! I cannot figure out how to split out the creation of the
widgets from... | def makeGroup(self, parent, thissizer, group, *args):
'''
Messily builds the (potentially) nested and grouped layout
Note! Mutates `self.reifiedWidgets` in place with the widgets as they're
instantiated! I cannot figure out how to split out the creation of the
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train | ConfigPage.chunkWidgets | chunk the widgets up into groups based on their sizing hints | gooey/gui/components/config.py | def chunkWidgets(self, group):
''' chunk the widgets up into groups based on their sizing hints '''
ui_groups = []
subgroup = []
for index, item in enumerate(group['items']):
if getin(item, ['options', 'full_width'], False):
ui_groups.append(subgroup)
... | def chunkWidgets(self, group):
''' chunk the widgets up into groups based on their sizing hints '''
ui_groups = []
subgroup = []
for index, item in enumerate(group['items']):
if getin(item, ['options', 'full_width'], False):
ui_groups.append(subgroup)
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train | ConfigPage.reifyWidget | Convert a JSON description of a widget into a WxObject | gooey/gui/components/config.py | def reifyWidget(self, parent, item):
''' Convert a JSON description of a widget into a WxObject '''
from gooey.gui.components import widgets
widgetClass = getattr(widgets, item['type'])
return widgetClass(parent, item) | def reifyWidget(self, parent, item):
''' Convert a JSON description of a widget into a WxObject '''
from gooey.gui.components import widgets
widgetClass = getattr(widgets, item['type'])
return widgetClass(parent, item) | [
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train | extract_groups | Recursively extract argument groups and associated actions
from ParserGroup objects | gooey/python_bindings/argparse_to_json.py | def extract_groups(action_group):
'''
Recursively extract argument groups and associated actions
from ParserGroup objects
'''
return {
'name': action_group.title,
'description': action_group.description,
'items': [action for action in action_group._group_actions
... | def extract_groups(action_group):
'''
Recursively extract argument groups and associated actions
from ParserGroup objects
'''
return {
'name': action_group.title,
'description': action_group.description,
'items': [action for action in action_group._group_actions
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train | is_required | _actions possessing the `required` flag and not implicitly optional
through `nargs` being '*' or '?' | gooey/python_bindings/argparse_to_json.py | def is_required(action):
'''
_actions possessing the `required` flag and not implicitly optional
through `nargs` being '*' or '?'
'''
return not isinstance(action, _SubParsersAction) and (
action.required == True and action.nargs not in ['*', '?']) | def is_required(action):
'''
_actions possessing the `required` flag and not implicitly optional
through `nargs` being '*' or '?'
'''
return not isinstance(action, _SubParsersAction) and (
action.required == True and action.nargs not in ['*', '?']) | [
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train | is_standard | actions which are general "store" instructions.
e.g. anything which has an argument style like:
$ script.py -f myfilename.txt | gooey/python_bindings/argparse_to_json.py | def is_standard(action):
""" actions which are general "store" instructions.
e.g. anything which has an argument style like:
$ script.py -f myfilename.txt
"""
boolean_actions = (
_StoreConstAction, _StoreFalseAction,
_StoreTrueAction
)
return (not action.choices
... | def is_standard(action):
""" actions which are general "store" instructions.
e.g. anything which has an argument style like:
$ script.py -f myfilename.txt
"""
boolean_actions = (
_StoreConstAction, _StoreFalseAction,
_StoreTrueAction
)
return (not action.choices
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train | is_flag | _actions which are either storeconst, store_bool, etc.. | gooey/python_bindings/argparse_to_json.py | def is_flag(action):
""" _actions which are either storeconst, store_bool, etc.. """
action_types = [_StoreTrueAction, _StoreFalseAction, _StoreConstAction]
return any(list(map(lambda Action: isinstance(action, Action), action_types))) | def is_flag(action):
""" _actions which are either storeconst, store_bool, etc.. """
action_types = [_StoreTrueAction, _StoreFalseAction, _StoreConstAction]
return any(list(map(lambda Action: isinstance(action, Action), action_types))) | [
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train | counter | Returns
str(option_string * DropDown Value)
e.g.
-vvvvv | gooey/gui/formatters.py | def counter(metatdata, value):
'''
Returns
str(option_string * DropDown Value)
e.g.
-vvvvv
'''
if not str(value).isdigit():
return None
arg = str(metatdata['commands'][0]).replace('-', '')
repeated_args = arg * int(value)
return '-' + repeated_args | def counter(metatdata, value):
'''
Returns
str(option_string * DropDown Value)
e.g.
-vvvvv
'''
if not str(value).isdigit():
return None
arg = str(metatdata['commands'][0]).replace('-', '')
repeated_args = arg * int(value)
return '-' + repeated_args | [
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train | load | Open and return the supplied json file | gooey/gui/lang/i18n.py | def load(language_dir, filename, encoding):
''' Open and return the supplied json file '''
global _DICTIONARY
try:
json_file = filename + '.json'
with io.open(os.path.join(language_dir, json_file), 'r', encoding=encoding) as f:
_DICTIONARY = json.load(f)
except IOError:
raise IOError('... | def load(language_dir, filename, encoding):
''' Open and return the supplied json file '''
global _DICTIONARY
try:
json_file = filename + '.json'
with io.open(os.path.join(language_dir, json_file), 'r', encoding=encoding) as f:
_DICTIONARY = json.load(f)
except IOError:
raise IOError('... | [
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train | getin | returns the value in a nested dict | gooey/util/functional.py | def getin(m, path, default=None):
"""returns the value in a nested dict"""
keynotfound = ':com.gooey-project/not-found'
result = reduce(lambda acc, val: acc.get(val, {keynotfound: None}), path, m)
# falsey values like 0 would incorrectly trigger the default to be returned
# so the keynotfound v... | def getin(m, path, default=None):
"""returns the value in a nested dict"""
keynotfound = ':com.gooey-project/not-found'
result = reduce(lambda acc, val: acc.get(val, {keynotfound: None}), path, m)
# falsey values like 0 would incorrectly trigger the default to be returned
# so the keynotfound v... | [
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train | assoc | Copy-on-write associates a value in a dict | gooey/util/functional.py | def assoc(m, key, val):
"""Copy-on-write associates a value in a dict"""
cpy = deepcopy(m)
cpy[key] = val
return cpy | def assoc(m, key, val):
"""Copy-on-write associates a value in a dict"""
cpy = deepcopy(m)
cpy[key] = val
return cpy | [
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train | associn | Copy-on-write associates a value in a nested dict | gooey/util/functional.py | def associn(m, path, value):
""" Copy-on-write associates a value in a nested dict """
def assoc_recursively(m, path, value):
if not path:
return value
p = path[0]
return assoc(m, p, assoc_recursively(m.get(p,{}), path[1:], value))
return assoc_recursively(m, path,... | def associn(m, path, value):
""" Copy-on-write associates a value in a nested dict """
def assoc_recursively(m, path, value):
if not path:
return value
p = path[0]
return assoc(m, p, assoc_recursively(m.get(p,{}), path[1:], value))
return assoc_recursively(m, path,... | [
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train | merge | Merge all maps left to right | gooey/util/functional.py | def merge(*maps):
"""Merge all maps left to right"""
copies = map(deepcopy, maps)
return reduce(lambda acc, val: acc.update(val) or acc, copies) | def merge(*maps):
"""Merge all maps left to right"""
copies = map(deepcopy, maps)
return reduce(lambda acc, val: acc.update(val) or acc, copies) | [
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train | findfirst | Return first occurrence matching f, otherwise None | gooey/util/functional.py | def findfirst(f, coll):
"""Return first occurrence matching f, otherwise None"""
result = list(dropwhile(f, coll))
return result[0] if result else None | def findfirst(f, coll):
"""Return first occurrence matching f, otherwise None"""
result = list(dropwhile(f, coll))
return result[0] if result else None | [
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train | visualize_activation_with_losses | Generates the `input_tensor` that minimizes the weighted `losses`. This function is intended for advanced
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Args:
input_tensor: An input tensor of shape: `(samples, channels, image_dims...)` if `image_data_format=
channels_first` or `(samples, image_d... | vis/visualization/activation_maximization.py | def visualize_activation_with_losses(input_tensor, losses, wrt_tensor=None,
seed_input=None, input_range=(0, 255),
**optimizer_params):
"""Generates the `input_tensor` that minimizes the weighted `losses`. This function is intended for advanc... | def visualize_activation_with_losses(input_tensor, losses, wrt_tensor=None,
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train | visualize_activation | Generates the model input that maximizes the output of all `filter_indices` in the given `layer_idx`.
Args:
model: The `keras.models.Model` instance. The model input shape must be: `(samples, channels, image_dims...)`
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train | Optimizer._rmsprop | Uses RMSProp to compute step from gradients.
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grads: numpy array of gradients.
cache: numpy array of same shape as `grads` as RMSProp cache
decay_rate: How fast to decay cache
Returns:
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Args:
grads: numpy array of gradients.
cache: numpy array of same shape as `grads` as RMSProp cache
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grads: numpy array of gradients.
cache: numpy array of same shape as `grads` as RMSProp cache
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train | Optimizer._get_seed_input | Creates a random `seed_input` if None. Otherwise:
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"""
desired_shape = (1, ) + K.int_shape(self.input_tensor)[1:]
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train | Optimizer.minimize | Performs gradient descent on the input image with respect to defined losses.
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Args:
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train | order_by_line_nos | Orders the set of `objs` by `line_nos` | docs/md_autogen.py | def order_by_line_nos(objs, line_nos):
"""Orders the set of `objs` by `line_nos`
"""
ordering = sorted(range(len(line_nos)), key=line_nos.__getitem__)
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train | to_md_file | Import a module path and create an api doc from it
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string (str): string with line breaks to write to file.
filename (str): filename without the .md
out_path (str): The output directory | docs/md_autogen.py | def to_md_file(string, filename, out_path="."):
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Args:
string (str): string with line breaks to write to file.
filename (str): filename without the .md
out_path (str): The output directory
"""
md_file = "%s.md" % filename
... | def to_md_file(string, filename, out_path="."):
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string (str): string with line breaks to write to file.
filename (str): filename without the .md
out_path (str): The output directory
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train | MarkdownAPIGenerator.get_src_path | Creates a src path string with line info for use as markdown link. | docs/md_autogen.py | def get_src_path(self, obj, append_base=True):
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"""
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# this can happen with e.g.
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train | MarkdownAPIGenerator.doc2md | Parse docstring (parsed with getdoc) according to Google-style
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Args, Kwargs:
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freeform text
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Args, Kwargs:
argname (type): text
freeform text
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freeform text
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train | MarkdownAPIGenerator.func2md | Takes a function (or method) and documents it.
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clsname (str, optional): class name to prepend to funcname.
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Args:
clsname (str, optional): class name to prepend to funcname.
depth (int, optional): number of ### to append to function name
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depth (int, optional): number of ### to append to function name
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train | MarkdownAPIGenerator.class2md | Takes a class and creates markdown text to document its methods and variables. | docs/md_autogen.py | def class2md(self, cls, depth=2):
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train | MarkdownAPIGenerator.module2md | Takes an imported module object and create a Markdown string containing functions and classes. | docs/md_autogen.py | def module2md(self, module):
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"""
modname = module.__name__
path = self.get_src_path(module, append_base=False)
path = "[{}]({})".format(path, os.path.join(self.github_link, path))
... | def module2md(self, module):
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modname = module.__name__
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path = "[{}]({})".format(path, os.path.join(self.github_link, path))
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train | _find_penultimate_layer | Searches for the nearest penultimate `Conv` or `Pooling` layer.
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model: The `keras.models.Model` instance.
layer_idx: The layer index within `model.layers`.
penultimate_layer_idx: The pre-layer to `layer_idx`. If set to None, the nearest penultimate
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Args:
model: The `keras.models.Model` instance.
layer_idx: The layer index within `model.layers`.
penultimate_layer_idx: The pre-layer to `layer_idx`.... | def _find_penultimate_layer(model, layer_idx, penultimate_layer_idx):
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model: The `keras.models.Model` instance.
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train | visualize_saliency_with_losses | Generates an attention heatmap over the `seed_input` by using positive gradients of `input_tensor`
with respect to weighted `losses`.
This function is intended for advanced use cases where a custom loss is desired. For common use cases,
refer to `visualize_class_saliency` or `visualize_regression_saliency`... | vis/visualization/saliency.py | def visualize_saliency_with_losses(input_tensor, losses, seed_input, wrt_tensor=None, grad_modifier='absolute', keepdims=False):
"""Generates an attention heatmap over the `seed_input` by using positive gradients of `input_tensor`
with respect to weighted `losses`.
This function is intended for advanced us... | def visualize_saliency_with_losses(input_tensor, losses, seed_input, wrt_tensor=None, grad_modifier='absolute', keepdims=False):
"""Generates an attention heatmap over the `seed_input` by using positive gradients of `input_tensor`
with respect to weighted `losses`.
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train | visualize_saliency | Generates an attention heatmap over the `seed_input` for maximizing `filter_indices`
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Args:
model: The `keras.models.Model` instance. The model input shape must be: `(samples, channels, image_dims...)`
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backprop_modifier=None, grad_modifier='absolute', keepdims=False):
"""Generates an attention heatmap over the `seed_input` for maximizing `filter_indices`
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Args:
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backprop_modifier=None, grad_modifier='absolute', keepdims=False):
"""Generates an attention heatmap over the `seed_input` for maximizing `filter_indices`
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train | visualize_cam_with_losses | Generates a gradient based class activation map (CAM) by using positive gradients of `input_tensor`
with respect to weighted `losses`.
For details on grad-CAM, see the paper:
[Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization]
(https://arxiv.org/pdf/... | vis/visualization/saliency.py | def visualize_cam_with_losses(input_tensor, losses, seed_input, penultimate_layer, grad_modifier=None):
"""Generates a gradient based class activation map (CAM) by using positive gradients of `input_tensor`
with respect to weighted `losses`.
For details on grad-CAM, see the paper:
[Grad-CAM: Why did yo... | def visualize_cam_with_losses(input_tensor, losses, seed_input, penultimate_layer, grad_modifier=None):
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train | visualize_cam | Generates a gradient based class activation map (grad-CAM) that maximizes the outputs of
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Args:
model: The `keras.models.Model` instance. The model input shape must be: `(samples, channels, image_dims...)`
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backprop_modifier=None, grad_modifier=None):
"""Generates a gradient based class activation map (grad-CAM) that maximizes the outputs of
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train | modify_model_backprop | Creates a copy of model by modifying all activations to use a custom op to modify the backprop behavior.
Args:
model: The `keras.models.Model` instance.
backprop_modifier: One of `{'guided', 'rectified'}`
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"""Creates a copy of model by modifying all activations to use a custom op to modify the backprop behavior.
Args:
model: The `keras.models.Model` instance.
backprop_modifier: One of `{'guided', 'rectified'}`
Returns:
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train | normalize | Normalizes the `output_tensor` with respect to `input_tensor` dimensions.
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input_tensor: An tensor of shape: `(samples, channels, image_dims...)` if `image_data_format=
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"""Normalizes the `output_tensor` with respect to `input_tensor` dimensions.
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train | TotalVariation.build_loss | r"""Implements the N-dim version of function
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to return total variation for all images in the batch. | vis/regularizers.py | def build_loss(self):
r"""Implements the N-dim version of function
$$TV^{\beta}(x) = \sum_{whc} \left ( \left ( x(h, w+1, c) - x(h, w, c) \right )^{2} +
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to return total variation for all images in the batch.
... | def build_loss(self):
r"""Implements the N-dim version of function
$$TV^{\beta}(x) = \sum_{whc} \left ( \left ( x(h, w+1, c) - x(h, w, c) \right )^{2} +
\left ( x(h+1, w, c) - x(h, w, c) \right )^{2} \right )^{\frac{\beta}{2}}$$
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train | _find_font_file | Utility to find font file. | vis/utils/utils.py | def _find_font_file(query):
"""Utility to find font file.
"""
return list(filter(lambda path: query.lower() in os.path.basename(path).lower(), fontman.findSystemFonts())) | def _find_font_file(query):
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train | add_defaults_to_kwargs | Updates `kwargs` with dict of `defaults`
Args:
defaults: A dictionary of keys and values
**kwargs: The kwargs to update.
Returns:
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"""Updates `kwargs` with dict of `defaults`
Args:
defaults: A dictionary of keys and values
**kwargs: The kwargs to update.
Returns:
The updated kwargs.
"""
defaults = dict(defaults)
defaults.update(kwargs)
return defa... | def add_defaults_to_kwargs(defaults, **kwargs):
"""Updates `kwargs` with dict of `defaults`
Args:
defaults: A dictionary of keys and values
**kwargs: The kwargs to update.
Returns:
The updated kwargs.
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defaults = dict(defaults)
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train | get_identifier | Helper utility to retrieve the callable function associated with a string identifier.
Args:
identifier: The identifier. Could be a string or function.
module_globals: The global objects of the module.
module_name: The module name
Returns:
The callable associated with the identi... | vis/utils/utils.py | def get_identifier(identifier, module_globals, module_name):
"""Helper utility to retrieve the callable function associated with a string identifier.
Args:
identifier: The identifier. Could be a string or function.
module_globals: The global objects of the module.
module_name: The modul... | def get_identifier(identifier, module_globals, module_name):
"""Helper utility to retrieve the callable function associated with a string identifier.
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identifier: The identifier. Could be a string or function.
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train | apply_modifications | Applies modifications to the model layers to create a new Graph. For example, simply changing
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Args:
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"""Applies modifications to the model layers to create a new Graph. For example, simply changing
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train | random_array | Creates a uniformly distributed random array with the given `mean` and `std`.
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shape: The desired shape
mean: The desired mean (Default value = 128)
std: The desired std (Default value = 20)
Returns: Random numpy array of given `shape` uniformly distributed with desired `mean` and... | vis/utils/utils.py | def random_array(shape, mean=128., std=20.):
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Args:
shape: The desired shape
mean: The desired mean (Default value = 128)
std: The desired std (Default value = 20)
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shape: The desired shape
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train | find_layer_idx | Looks up the layer index corresponding to `layer_name` from `model`.
Args:
model: The `keras.models.Model` instance.
layer_name: The name of the layer to lookup.
Returns:
The layer index if found. Raises an exception otherwise. | vis/utils/utils.py | def find_layer_idx(model, layer_name):
"""Looks up the layer index corresponding to `layer_name` from `model`.
Args:
model: The `keras.models.Model` instance.
layer_name: The name of the layer to lookup.
Returns:
The layer index if found. Raises an exception otherwise.
"""
... | def find_layer_idx(model, layer_name):
"""Looks up the layer index corresponding to `layer_name` from `model`.
Args:
model: The `keras.models.Model` instance.
layer_name: The name of the layer to lookup.
Returns:
The layer index if found. Raises an exception otherwise.
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train | deprocess_input | Utility function to scale the `input_array` to `input_range` throwing away high frequency artifacts.
Args:
input_array: An N-dim numpy array.
input_range: Specifies the input range as a `(min, max)` tuple to rescale the `input_array`.
Returns:
The rescaled `input_array`. | vis/utils/utils.py | def deprocess_input(input_array, input_range=(0, 255)):
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Args:
input_array: An N-dim numpy array.
input_range: Specifies the input range as a `(min, max)` tuple to rescale the `input_array`.
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"""Utility function to scale the `input_array` to `input_range` throwing away high frequency artifacts.
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input_array: An N-dim numpy array.
input_range: Specifies the input range as a `(min, max)` tuple to rescale the `input_array`.
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train | stitch_images | Utility function to stitch images together with a `margin`.
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cols: Max number of image cols. New row is created when number of images exceed the column size.
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train | get_img_shape | Returns image shape in a backend agnostic manner.
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train | load_img | Utility function to load an image from disk.
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path: The image file path.
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path: The image file path.
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train | lookup_imagenet_labels | Utility function to return the image net label for the final `dense` layer output index.
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indices: Could be a single value or an array of indices whose labels should be looked up.
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Image net label corresponding to the image category.
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indices: Could be a single value or an array of indices whose labels should be looked up.
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Image net label corresponding to the image category.
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train | draw_text | Draws text over the image. Requires PIL.
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img: The image to use.
text: The text string to overlay.
position: The text (x, y) position. (Default value = (10, 10))
font: The ttf or open type font to use. (Default value = 'FreeSans.ttf')
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img: The image to use.
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train | normalize | Normalizes the numpy array to (min_value, max_value)
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array: The numpy array
min_value: The min value in normalized array (Default value = 0)
max_value: The max value in normalized array (Default value = 1)
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"""Normalizes the numpy array to (min_value, max_value)
Args:
array: The numpy array
min_value: The min value in normalized array (Default value = 0)
max_value: The max value in normalized array (Default value = 1)
Returns:
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array: The numpy array
min_value: The min value in normalized array (Default value = 0)
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train | get_num_filters | Determines the number of filters within the given `layer`.
Args:
layer: The keras layer to use.
Returns:
Total number of filters within `layer`.
For `keras.layers.Dense` layer, this is the total number of outputs. | vis/visualization/__init__.py | def get_num_filters(layer):
"""Determines the number of filters within the given `layer`.
Args:
layer: The keras layer to use.
Returns:
Total number of filters within `layer`.
For `keras.layers.Dense` layer, this is the total number of outputs.
"""
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"""Determines the number of filters within the given `layer`.
Args:
layer: The keras layer to use.
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Total number of filters within `layer`.
For `keras.layers.Dense` layer, this is the total number of outputs.
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alpha: The alpha value of `array1` as overlayed onto `array2`. This value needs to be between [0, 1],
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array1: The first numpy array.
array2: The second numpy array.
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train | _infer_coords_and_dims | All the logic for creating a new DataArray | xarray/core/dataarray.py | def _infer_coords_and_dims(shape, coords, dims):
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dim : str, optional
Name of the dimension on this array along which to split this array
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name : st... | xarray/core/dataarray.py | def to_dataset(self, dim=None, name=None):
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Parameters
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dim : str, optional
Name of the dimension on this array along which to split this array
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train | DataArray._level_coords | Return a mapping of all MultiIndex levels and their corresponding
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"""Return a mapping of all MultiIndex levels and their corresponding
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train | DataArray.indexes | Mapping of pandas.Index objects used for label based indexing | xarray/core/dataarray.py | def indexes(self):
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train | DataArray.reset_coords | Given names of coordinates, reset them to become variables.
Parameters
----------
names : str or list of str, optional
Name(s) of non-index coordinates in this dataset to reset into
variables. By default, all non-index coordinates are reset.
drop : bool, optional... | xarray/core/dataarray.py | def reset_coords(self, names=None, drop=False, inplace=None):
"""Given names of coordinates, reset them to become variables.
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----------
names : str or list of str, optional
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train | DataArray.load | Manually trigger loading of this array's data from disk or a
remote source into memory and return this array.
Normally, it should not be necessary to call this method in user code,
because all xarray functions should either work on deferred data or
load data automatically. However, this... | xarray/core/dataarray.py | def load(self, **kwargs):
"""Manually trigger loading of this array's data from disk or a
remote source into memory and return this array.
Normally, it should not be necessary to call this method in user code,
because all xarray functions should either work on deferred data or
l... | def load(self, **kwargs):
"""Manually trigger loading of this array's data from disk or a
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train | DataArray.persist | Trigger computation in constituent dask arrays
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memory. This is particularly useful when on a distributed machine.
When on a single machine consider using ``.compute()`` instead.
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""" Trigger computation in constituent dask arrays
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When on a single machine consider using ``.compute()`` instead.
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""" Trigger computation in constituent dask arrays
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When on a single machine consider using ``.compute()`` instead.
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train | DataArray.copy | Returns a copy of this array.
If `deep=True`, a deep copy is made of the data array.
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array's dataset is also a variable in this array's dataset.
Use `data` to create a new object with the same structure as
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"""Returns a copy of this array.
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train | DataArray.isel | Return a new DataArray whose dataset is given by integer indexing
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See Also
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"""Return a new DataArray whose dataset is given by integer indexing
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See Also
--------
Dataset.isel
DataArray.sel
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See Also
--------
Dataset.isel
DataArray.sel
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train | DataArray.sel | Return a new DataArray whose dataset is given by selecting
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``isel`` or ``sel``::
da = xr.DataArray([0, 1, 2, 3], dims=['x'])
# DO ... | xarray/core/dataarray.py | def sel(self, indexers=None, method=None, tolerance=None, drop=False,
**indexers_kwargs):
"""Return a new DataArray whose dataset is given by selecting
index labels along the specified dimension(s).
.. warning::
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train | DataArray.isel_points | Return a new DataArray whose dataset is given by pointwise integer
indexing along the specified dimension(s).
See Also
--------
Dataset.isel_points | xarray/core/dataarray.py | def isel_points(self, dim='points', **indexers):
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indexing along the specified dimension(s).
See Also
--------
Dataset.isel_points
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ds = self._to_temp_dataset().isel_points(dim=dim, **indexer... | def isel_points(self, dim='points', **indexers):
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See Also
--------
Dataset.isel_points
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train | DataArray.sel_points | Return a new DataArray whose dataset is given by pointwise selection
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See Also
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Dataset.sel_points | xarray/core/dataarray.py | def sel_points(self, dim='points', method=None, tolerance=None,
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"""Return a new DataArray whose dataset is given by pointwise selection
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See Also
--------
Dataset.sel_points
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See Also
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train | DataArray.reindex | Conform this object onto a new set of indexes, filling in
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Parameters
----------
indexers : dict, optional
Dictionary with keys given by dimension names and values given by
arrays of coordinates tick labels. Any mis-matched coordinate
... | xarray/core/dataarray.py | def reindex(self, indexers=None, method=None, tolerance=None, copy=True,
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"""Conform this object onto a new set of indexes, filling in
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Parameters
----------
indexers : dict, optional
Dictionary with keys giv... | def reindex(self, indexers=None, method=None, tolerance=None, copy=True,
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train | DataArray.interp | Multidimensional interpolation of variables.
coords : dict, optional
Mapping from dimension names to the new coordinates.
new coordinate can be an scalar, array-like or DataArray.
If DataArrays are passed as new coordates, their dimensions are
used for the broadc... | xarray/core/dataarray.py | def interp(self, coords=None, method='linear', assume_sorted=False,
kwargs={}, **coords_kwargs):
""" Multidimensional interpolation of variables.
coords : dict, optional
Mapping from dimension names to the new coordinates.
new coordinate can be an scalar, array-li... | def interp(self, coords=None, method='linear', assume_sorted=False,
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""" Multidimensional interpolation of variables.
coords : dict, optional
Mapping from dimension names to the new coordinates.
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train | DataArray.interp_like | Interpolate this object onto the coordinates of another object,
filling out of range values with NaN.
Parameters
----------
other : Dataset or DataArray
Object with an 'indexes' attribute giving a mapping from dimension
names to an 1d array-like, which provides c... | xarray/core/dataarray.py | def interp_like(self, other, method='linear', assume_sorted=False,
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"""Interpolate this object onto the coordinates of another object,
filling out of range values with NaN.
Parameters
----------
other : Dataset or DataArray
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train | DataArray.rename | Returns a new DataArray with renamed coordinates or a new name.
Parameters
----------
new_name_or_name_dict : str or dict-like, optional
If the argument is dict-like, it it used as a mapping from old
names to new names for coordinates. Otherwise, use the argument
... | xarray/core/dataarray.py | def rename(self, new_name_or_name_dict=None, **names):
"""Returns a new DataArray with renamed coordinates or a new name.
Parameters
----------
new_name_or_name_dict : str or dict-like, optional
If the argument is dict-like, it it used as a mapping from old
names... | def rename(self, new_name_or_name_dict=None, **names):
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new_name_or_name_dict : str or dict-like, optional
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train | DataArray.swap_dims | Returns a new DataArray with swapped dimensions.
Parameters
----------
dims_dict : dict-like
Dictionary whose keys are current dimension names and whose values
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Returns
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"""Returns a new DataArray with swapped dimensions.
Parameters
----------
dims_dict : dict-like
Dictionary whose keys are current dimension names and whose values
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train | DataArray.expand_dims | Return a new object with an additional axis (or axes) inserted at
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If dim is already a scalar coordinate, it will be promoted to a 1D
coordinate consisting of a single value.
Parameters
----------
dim : str, sequence of str,... | xarray/core/dataarray.py | def expand_dims(self, dim=None, axis=None, **dim_kwargs):
"""Return a new object with an additional axis (or axes) inserted at
the corresponding position in the array shape.
If dim is already a scalar coordinate, it will be promoted to a 1D
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If dim is already a scalar coordinate, it will be promoted to a 1D
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train | DataArray.set_index | Set DataArray (multi-)indexes using one or more existing
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Parameters
----------
indexes : {dim: index, ...}
Mapping from names matching dimensions and values given
by (lists of) the names of existing coordinates or variables to set
as new ... | xarray/core/dataarray.py | def set_index(self, indexes=None, append=False, inplace=None,
**indexes_kwargs):
"""Set DataArray (multi-)indexes using one or more existing
coordinates.
Parameters
----------
indexes : {dim: index, ...}
Mapping from names matching dimensions and va... | def set_index(self, indexes=None, append=False, inplace=None,
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train | DataArray.reset_index | Reset the specified index(es) or multi-index level(s).
Parameters
----------
dims_or_levels : str or list
Name(s) of the dimension(s) and/or multi-index level(s) that will
be reset.
drop : bool, optional
If True, remove the specified indexes and/or mu... | xarray/core/dataarray.py | def reset_index(self, dims_or_levels, drop=False, inplace=None):
"""Reset the specified index(es) or multi-index level(s).
Parameters
----------
dims_or_levels : str or list
Name(s) of the dimension(s) and/or multi-index level(s) that will
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drop ... | def reset_index(self, dims_or_levels, drop=False, inplace=None):
"""Reset the specified index(es) or multi-index level(s).
Parameters
----------
dims_or_levels : str or list
Name(s) of the dimension(s) and/or multi-index level(s) that will
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train | DataArray.reorder_levels | Rearrange index levels using input order.
Parameters
----------
dim_order : optional
Mapping from names matching dimensions and values given
by lists representing new level orders. Every given dimension
must have a multi-index.
inplace : bool, optiona... | xarray/core/dataarray.py | def reorder_levels(self, dim_order=None, inplace=None,
**dim_order_kwargs):
"""Rearrange index levels using input order.
Parameters
----------
dim_order : optional
Mapping from names matching dimensions and values given
by lists representin... | def reorder_levels(self, dim_order=None, inplace=None,
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"""Rearrange index levels using input order.
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dim_order : optional
Mapping from names matching dimensions and values given
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train | DataArray.stack | Stack any number of existing dimensions into a single new dimension.
New dimensions will be added at the end, and the corresponding
coordinate variables will be combined into a MultiIndex.
Parameters
----------
dimensions : Mapping of the form new_name=(dim1, dim2, ...)
... | xarray/core/dataarray.py | def stack(self, dimensions=None, **dimensions_kwargs):
"""
Stack any number of existing dimensions into a single new dimension.
New dimensions will be added at the end, and the corresponding
coordinate variables will be combined into a MultiIndex.
Parameters
----------
... | def stack(self, dimensions=None, **dimensions_kwargs):
"""
Stack any number of existing dimensions into a single new dimension.
New dimensions will be added at the end, and the corresponding
coordinate variables will be combined into a MultiIndex.
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----------
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train | DataArray.unstack | Unstack existing dimensions corresponding to MultiIndexes into
multiple new dimensions.
New dimensions will be added at the end.
Parameters
----------
dim : str or sequence of str, optional
Dimension(s) over which to unstack. By default unstacks all
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"""
Unstack existing dimensions corresponding to MultiIndexes into
multiple new dimensions.
New dimensions will be added at the end.
Parameters
----------
dim : str or sequence of str, optional
Dimension(s) over which to ... | def unstack(self, dim=None):
"""
Unstack existing dimensions corresponding to MultiIndexes into
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New dimensions will be added at the end.
Parameters
----------
dim : str or sequence of str, optional
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train | DataArray.transpose | Return a new DataArray object with transposed dimensions.
Parameters
----------
*dims : str, optional
By default, reverse the dimensions. Otherwise, reorder the
dimensions to this order.
Returns
-------
transposed : DataArray
The retu... | xarray/core/dataarray.py | def transpose(self, *dims) -> 'DataArray':
"""Return a new DataArray object with transposed dimensions.
Parameters
----------
*dims : str, optional
By default, reverse the dimensions. Otherwise, reorder the
dimensions to this order.
Returns
-----... | def transpose(self, *dims) -> 'DataArray':
"""Return a new DataArray object with transposed dimensions.
Parameters
----------
*dims : str, optional
By default, reverse the dimensions. Otherwise, reorder the
dimensions to this order.
Returns
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train | DataArray.drop | Drop coordinates or index labels from this DataArray.
Parameters
----------
labels : scalar or list of scalars
Name(s) of coordinate variables or index labels to drop.
dim : str, optional
Dimension along which to drop index labels. By default (if
``di... | xarray/core/dataarray.py | def drop(self, labels, dim=None):
"""Drop coordinates or index labels from this DataArray.
Parameters
----------
labels : scalar or list of scalars
Name(s) of coordinate variables or index labels to drop.
dim : str, optional
Dimension along which to drop ... | def drop(self, labels, dim=None):
"""Drop coordinates or index labels from this DataArray.
Parameters
----------
labels : scalar or list of scalars
Name(s) of coordinate variables or index labels to drop.
dim : str, optional
Dimension along which to drop ... | [
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train | DataArray.dropna | Returns a new array with dropped labels for missing values along
the provided dimension.
Parameters
----------
dim : str
Dimension along which to drop missing values. Dropping along
multiple dimensions simultaneously is not yet supported.
how : {'any', 'a... | xarray/core/dataarray.py | def dropna(self, dim, how='any', thresh=None):
"""Returns a new array with dropped labels for missing values along
the provided dimension.
Parameters
----------
dim : str
Dimension along which to drop missing values. Dropping along
multiple dimensions sim... | def dropna(self, dim, how='any', thresh=None):
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----------
dim : str
Dimension along which to drop missing values. Dropping along
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train | DataArray.fillna | Fill missing values in this object.
This operation follows the normal broadcasting and alignment rules that
xarray uses for binary arithmetic, except the result is aligned to this
object (``join='left'``) instead of aligned to the intersection of
index coordinates (``join='inner'``).
... | xarray/core/dataarray.py | def fillna(self, value):
"""Fill missing values in this object.
This operation follows the normal broadcasting and alignment rules that
xarray uses for binary arithmetic, except the result is aligned to this
object (``join='left'``) instead of aligned to the intersection of
inde... | def fillna(self, value):
"""Fill missing values in this object.
This operation follows the normal broadcasting and alignment rules that
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train | DataArray.ffill | Fill NaN values by propogating values forward
*Requires bottleneck.*
Parameters
----------
dim : str
Specifies the dimension along which to propagate values when
filling.
limit : int, default None
The maximum number of consecutive NaN values ... | xarray/core/dataarray.py | def ffill(self, dim, limit=None):
'''Fill NaN values by propogating values forward
*Requires bottleneck.*
Parameters
----------
dim : str
Specifies the dimension along which to propagate values when
filling.
limit : int, default None
... | def ffill(self, dim, limit=None):
'''Fill NaN values by propogating values forward
*Requires bottleneck.*
Parameters
----------
dim : str
Specifies the dimension along which to propagate values when
filling.
limit : int, default None
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train | DataArray.bfill | Fill NaN values by propogating values backward
*Requires bottleneck.*
Parameters
----------
dim : str
Specifies the dimension along which to propagate values when
filling.
limit : int, default None
The maximum number of consecutive NaN values... | xarray/core/dataarray.py | def bfill(self, dim, limit=None):
'''Fill NaN values by propogating values backward
*Requires bottleneck.*
Parameters
----------
dim : str
Specifies the dimension along which to propagate values when
filling.
limit : int, default None
... | def bfill(self, dim, limit=None):
'''Fill NaN values by propogating values backward
*Requires bottleneck.*
Parameters
----------
dim : str
Specifies the dimension along which to propagate values when
filling.
limit : int, default None
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train | DataArray.reduce | Reduce this array by applying `func` along some dimension(s).
Parameters
----------
func : function
Function which can be called in the form
`f(x, axis=axis, **kwargs)` to return the result of reducing an
np.ndarray over an integer valued axis.
dim : ... | xarray/core/dataarray.py | def reduce(self, func, dim=None, axis=None, keep_attrs=None, **kwargs):
"""Reduce this array by applying `func` along some dimension(s).
Parameters
----------
func : function
Function which can be called in the form
`f(x, axis=axis, **kwargs)` to return the resul... | def reduce(self, func, dim=None, axis=None, keep_attrs=None, **kwargs):
"""Reduce this array by applying `func` along some dimension(s).
Parameters
----------
func : function
Function which can be called in the form
`f(x, axis=axis, **kwargs)` to return the resul... | [
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train | DataArray.to_pandas | Convert this array into a pandas object with the same shape.
The type of the returned object depends on the number of DataArray
dimensions:
* 1D -> `pandas.Series`
* 2D -> `pandas.DataFrame`
* 3D -> `pandas.Panel`
Only works for arrays with 3 or fewer dimensions.
... | xarray/core/dataarray.py | def to_pandas(self):
"""Convert this array into a pandas object with the same shape.
The type of the returned object depends on the number of DataArray
dimensions:
* 1D -> `pandas.Series`
* 2D -> `pandas.DataFrame`
* 3D -> `pandas.Panel`
Only works for arrays w... | def to_pandas(self):
"""Convert this array into a pandas object with the same shape.
The type of the returned object depends on the number of DataArray
dimensions:
* 1D -> `pandas.Series`
* 2D -> `pandas.DataFrame`
* 3D -> `pandas.Panel`
Only works for arrays w... | [
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train | DataArray.to_dataframe | Convert this array and its coordinates into a tidy pandas.DataFrame.
The DataFrame is indexed by the Cartesian product of index coordinates
(in the form of a :py:class:`pandas.MultiIndex`).
Other coordinates are included as columns in the DataFrame. | xarray/core/dataarray.py | def to_dataframe(self, name=None):
"""Convert this array and its coordinates into a tidy pandas.DataFrame.
The DataFrame is indexed by the Cartesian product of index coordinates
(in the form of a :py:class:`pandas.MultiIndex`).
Other coordinates are included as columns in the DataFrame... | def to_dataframe(self, name=None):
"""Convert this array and its coordinates into a tidy pandas.DataFrame.
The DataFrame is indexed by the Cartesian product of index coordinates
(in the form of a :py:class:`pandas.MultiIndex`).
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train | DataArray.to_series | Convert this array into a pandas.Series.
The Series is indexed by the Cartesian product of index coordinates
(in the form of a :py:class:`pandas.MultiIndex`). | xarray/core/dataarray.py | def to_series(self):
"""Convert this array into a pandas.Series.
The Series is indexed by the Cartesian product of index coordinates
(in the form of a :py:class:`pandas.MultiIndex`).
"""
index = self.coords.to_index()
return pd.Series(self.values.reshape(-1), index=index... | def to_series(self):
"""Convert this array into a pandas.Series.
The Series is indexed by the Cartesian product of index coordinates
(in the form of a :py:class:`pandas.MultiIndex`).
"""
index = self.coords.to_index()
return pd.Series(self.values.reshape(-1), index=index... | [
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