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base4sistemas/satcfe | satcfe/clientesathub.py | ClienteSATHub.atualizar_software_sat | def atualizar_software_sat(self):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.atualizar_software_sat`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
resp = self._http_post('atualizarsoftwaresat')
conteudo = resp.json()
return RespostaSAT.atualizar_software_sat(conteudo.get('retorno')) | python | def atualizar_software_sat(self):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.atualizar_software_sat`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
resp = self._http_post('atualizarsoftwaresat')
conteudo = resp.json()
return RespostaSAT.atualizar_software_sat(conteudo.get('retorno')) | [
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base4sistemas/satcfe | satcfe/clientesathub.py | ClienteSATHub.extrair_logs | def extrair_logs(self):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.extrair_logs`.
:return: Uma resposta SAT especializada em ``ExtrairLogs``.
:rtype: satcfe.resposta.extrairlogs.RespostaExtrairLogs
"""
resp = self._http_post('extrairlogs')
conteudo = resp.json()
return RespostaExtrairLogs.analisar(conteudo.get('retorno')) | python | def extrair_logs(self):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.extrair_logs`.
:return: Uma resposta SAT especializada em ``ExtrairLogs``.
:rtype: satcfe.resposta.extrairlogs.RespostaExtrairLogs
"""
resp = self._http_post('extrairlogs')
conteudo = resp.json()
return RespostaExtrairLogs.analisar(conteudo.get('retorno')) | [
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base4sistemas/satcfe | satcfe/clientesathub.py | ClienteSATHub.bloquear_sat | def bloquear_sat(self):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.bloquear_sat`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
resp = self._http_post('bloquearsat')
conteudo = resp.json()
return RespostaSAT.bloquear_sat(conteudo.get('retorno')) | python | def bloquear_sat(self):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.bloquear_sat`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
resp = self._http_post('bloquearsat')
conteudo = resp.json()
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base4sistemas/satcfe | satcfe/clientesathub.py | ClienteSATHub.desbloquear_sat | def desbloquear_sat(self):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.desbloquear_sat`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
resp = self._http_post('desbloquearsat')
conteudo = resp.json()
return RespostaSAT.desbloquear_sat(conteudo.get('retorno')) | python | def desbloquear_sat(self):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.desbloquear_sat`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
resp = self._http_post('desbloquearsat')
conteudo = resp.json()
return RespostaSAT.desbloquear_sat(conteudo.get('retorno')) | [
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base4sistemas/satcfe | satcfe/clientesathub.py | ClienteSATHub.trocar_codigo_de_ativacao | def trocar_codigo_de_ativacao(self, novo_codigo_ativacao,
opcao=constantes.CODIGO_ATIVACAO_REGULAR,
codigo_emergencia=None):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.trocar_codigo_de_ativacao`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
resp = self._http_post('trocarcodigodeativacao',
novo_codigo_ativacao=novo_codigo_ativacao,
opcao=opcao,
codigo_emergencia=codigo_emergencia)
conteudo = resp.json()
return RespostaSAT.trocar_codigo_de_ativacao(conteudo.get('retorno')) | python | def trocar_codigo_de_ativacao(self, novo_codigo_ativacao,
opcao=constantes.CODIGO_ATIVACAO_REGULAR,
codigo_emergencia=None):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.trocar_codigo_de_ativacao`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
resp = self._http_post('trocarcodigodeativacao',
novo_codigo_ativacao=novo_codigo_ativacao,
opcao=opcao,
codigo_emergencia=codigo_emergencia)
conteudo = resp.json()
return RespostaSAT.trocar_codigo_de_ativacao(conteudo.get('retorno')) | [
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nion-software/nionswift | nion/typeshed/API_1_0.py | Graphic.bounds | def bounds(self) -> typing.Tuple[typing.Tuple[float, float], typing.Tuple[float, float]]:
"""Return the bounds property in relative coordinates.
Bounds is a tuple ((top, left), (height, width))"""
... | python | def bounds(self) -> typing.Tuple[typing.Tuple[float, float], typing.Tuple[float, float]]:
"""Return the bounds property in relative coordinates.
Bounds is a tuple ((top, left), (height, width))"""
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nion-software/nionswift | nion/typeshed/API_1_0.py | Graphic.end | def end(self, value: typing.Union[float, typing.Tuple[float, float]]) -> None:
"""Set the end property in relative coordinates.
End may be a float when graphic is an Interval or a tuple (y, x) when graphic is a Line."""
... | python | def end(self, value: typing.Union[float, typing.Tuple[float, float]]) -> None:
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End may be a float when graphic is an Interval or a tuple (y, x) when graphic is a Line."""
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nion-software/nionswift | nion/typeshed/API_1_0.py | Graphic.start | def start(self, value: typing.Union[float, typing.Tuple[float, float]]) -> None:
"""Set the end property in relative coordinates.
End may be a float when graphic is an Interval or a tuple (y, x) when graphic is a Line."""
... | python | def start(self, value: typing.Union[float, typing.Tuple[float, float]]) -> None:
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End may be a float when graphic is an Interval or a tuple (y, x) when graphic is a Line."""
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nion-software/nionswift | nion/typeshed/API_1_0.py | Graphic.vector | def vector(self) -> typing.Tuple[typing.Tuple[float, float], typing.Tuple[float, float]]:
"""Return the vector property in relative coordinates.
Vector will be a tuple of tuples ((y_start, x_start), (y_end, x_end))."""
... | python | def vector(self) -> typing.Tuple[typing.Tuple[float, float], typing.Tuple[float, float]]:
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nion-software/nionswift | nion/typeshed/API_1_0.py | Library.get_data_item_for_hardware_source | def get_data_item_for_hardware_source(self, hardware_source, channel_id: str=None, processor_id: str=None, create_if_needed: bool=False, large_format: bool=False) -> DataItem:
"""Get the data item associated with hardware source and (optional) channel id and processor_id. Optionally create if missing.
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:param channel_id: The (optional) channel id.
:param processor_id: The (optional) processor id for the channel.
:param create_if_needed: Whether to create a new data item if none is found.
:return: The associated data item. May be None.
.. versionadded:: 1.0
Status: Provisional
Scriptable: Yes
"""
... | python | def get_data_item_for_hardware_source(self, hardware_source, channel_id: str=None, processor_id: str=None, create_if_needed: bool=False, large_format: bool=False) -> DataItem:
"""Get the data item associated with hardware source and (optional) channel id and processor_id. Optionally create if missing.
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:param channel_id: The (optional) channel id.
:param processor_id: The (optional) processor id for the channel.
:param create_if_needed: Whether to create a new data item if none is found.
:return: The associated data item. May be None.
.. versionadded:: 1.0
Status: Provisional
Scriptable: Yes
"""
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nion-software/nionswift | nion/typeshed/API_1_0.py | Library.get_data_item_for_reference_key | def get_data_item_for_reference_key(self, data_item_reference_key: str=None, create_if_needed: bool=False, large_format: bool=False) -> DataItem:
"""Get the data item associated with data item reference key. Optionally create if missing.
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:return: The associated data item. May be None.
.. versionadded:: 1.0
Status: Provisional
Scriptable: Yes
"""
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:param create_if_needed: Whether to create a new data item if none is found.
:return: The associated data item. May be None.
.. versionadded:: 1.0
Status: Provisional
Scriptable: Yes
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nion-software/nionswift | nion/typeshed/API_1_0.py | DocumentWindow.show_get_string_message_box | def show_get_string_message_box(self, caption: str, text: str, accepted_fn, rejected_fn=None, accepted_text: str=None, rejected_text: str=None) -> None:
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.. versionadded:: 1.0
Scriptable: No
"""
... | python | def show_get_string_message_box(self, caption: str, text: str, accepted_fn, rejected_fn=None, accepted_text: str=None, rejected_text: str=None) -> None:
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Caption describes the user prompt. Text is the initial/default string.
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Rejected function can be a function taking no arguments, called if the user clicks Cancel.
.. versionadded:: 1.0
Scriptable: No
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nion-software/nionswift | nion/typeshed/API_1_0.py | API.create_calibration | def create_calibration(self, offset: float=None, scale: float=None, units: str=None) -> Calibration.Calibration:
"""Create a calibration object with offset, scale, and units.
:param offset: The offset of the calibration.
:param scale: The scale of the calibration.
:param units: The units of the calibration as a string.
:return: The calibration object.
.. versionadded:: 1.0
Scriptable: Yes
Calibrated units and uncalibrated units have the following relationship:
:samp:`calibrated_value = offset + value * scale`
"""
... | python | def create_calibration(self, offset: float=None, scale: float=None, units: str=None) -> Calibration.Calibration:
"""Create a calibration object with offset, scale, and units.
:param offset: The offset of the calibration.
:param scale: The scale of the calibration.
:param units: The units of the calibration as a string.
:return: The calibration object.
.. versionadded:: 1.0
Scriptable: Yes
Calibrated units and uncalibrated units have the following relationship:
:samp:`calibrated_value = offset + value * scale`
"""
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nion-software/nionswift | nion/typeshed/API_1_0.py | API.create_data_and_metadata | def create_data_and_metadata(self, data: numpy.ndarray, intensity_calibration: Calibration.Calibration=None, dimensional_calibrations: typing.List[Calibration.Calibration]=None, metadata: dict=None, timestamp: str=None, data_descriptor: DataAndMetadata.DataDescriptor=None) -> DataAndMetadata.DataAndMetadata:
"""Create a data_and_metadata object from data.
:param data: an ndarray of data.
:param intensity_calibration: An optional calibration object.
:param dimensional_calibrations: An optional list of calibration objects.
:param metadata: A dict of metadata.
:param timestamp: A datetime object.
:param data_descriptor: A data descriptor describing the dimensions.
.. versionadded:: 1.0
Scriptable: Yes
"""
... | python | def create_data_and_metadata(self, data: numpy.ndarray, intensity_calibration: Calibration.Calibration=None, dimensional_calibrations: typing.List[Calibration.Calibration]=None, metadata: dict=None, timestamp: str=None, data_descriptor: DataAndMetadata.DataDescriptor=None) -> DataAndMetadata.DataAndMetadata:
"""Create a data_and_metadata object from data.
:param data: an ndarray of data.
:param intensity_calibration: An optional calibration object.
:param dimensional_calibrations: An optional list of calibration objects.
:param metadata: A dict of metadata.
:param timestamp: A datetime object.
:param data_descriptor: A data descriptor describing the dimensions.
.. versionadded:: 1.0
Scriptable: Yes
"""
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nion-software/nionswift | nion/typeshed/API_1_0.py | API.create_data_and_metadata_from_data | def create_data_and_metadata_from_data(self, data: numpy.ndarray, intensity_calibration: Calibration.Calibration=None, dimensional_calibrations: typing.List[Calibration.Calibration]=None, metadata: dict=None, timestamp: str=None) -> DataAndMetadata.DataAndMetadata:
"""Create a data_and_metadata object from data.
.. versionadded:: 1.0
.. deprecated:: 1.1
Use :py:meth:`~nion.swift.Facade.DataItem.create_data_and_metadata` instead.
Scriptable: No
"""
... | python | def create_data_and_metadata_from_data(self, data: numpy.ndarray, intensity_calibration: Calibration.Calibration=None, dimensional_calibrations: typing.List[Calibration.Calibration]=None, metadata: dict=None, timestamp: str=None) -> DataAndMetadata.DataAndMetadata:
"""Create a data_and_metadata object from data.
.. versionadded:: 1.0
.. deprecated:: 1.1
Use :py:meth:`~nion.swift.Facade.DataItem.create_data_and_metadata` instead.
Scriptable: No
"""
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nion-software/nionswift | nion/typeshed/API_1_0.py | API.create_data_descriptor | def create_data_descriptor(self, is_sequence: bool, collection_dimension_count: int, datum_dimension_count: int) -> DataAndMetadata.DataDescriptor:
"""Create a data descriptor.
:param is_sequence: whether the descriptor describes a sequence of data.
:param collection_dimension_count: the number of collection dimensions represented by the descriptor.
:param datum_dimension_count: the number of datum dimensions represented by the descriptor.
.. versionadded:: 1.0
Scriptable: Yes
"""
... | python | def create_data_descriptor(self, is_sequence: bool, collection_dimension_count: int, datum_dimension_count: int) -> DataAndMetadata.DataDescriptor:
"""Create a data descriptor.
:param is_sequence: whether the descriptor describes a sequence of data.
:param collection_dimension_count: the number of collection dimensions represented by the descriptor.
:param datum_dimension_count: the number of datum dimensions represented by the descriptor.
.. versionadded:: 1.0
Scriptable: Yes
"""
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nion-software/nionswift | nion/swift/Inspector.py | make_calibration_row_widget | def make_calibration_row_widget(ui, calibration_observable, label: str=None):
"""Called when an item (calibration_observable) is inserted into the list widget. Returns a widget."""
calibration_row = ui.create_row_widget()
row_label = ui.create_label_widget(label, properties={"width": 60})
row_label.widget_id = "label"
offset_field = ui.create_line_edit_widget(properties={"width": 60})
offset_field.widget_id = "offset"
scale_field = ui.create_line_edit_widget(properties={"width": 60})
scale_field.widget_id = "scale"
units_field = ui.create_line_edit_widget(properties={"width": 60})
units_field.widget_id = "units"
float_point_4_converter = Converter.FloatToStringConverter(format="{0:.4f}")
offset_field.bind_text(Binding.PropertyBinding(calibration_observable, "offset", converter=float_point_4_converter))
scale_field.bind_text(Binding.PropertyBinding(calibration_observable, "scale", converter=float_point_4_converter))
units_field.bind_text(Binding.PropertyBinding(calibration_observable, "units"))
# notice the binding of calibration_index below.
calibration_row.add(row_label)
calibration_row.add_spacing(12)
calibration_row.add(offset_field)
calibration_row.add_spacing(12)
calibration_row.add(scale_field)
calibration_row.add_spacing(12)
calibration_row.add(units_field)
calibration_row.add_stretch()
return calibration_row | python | def make_calibration_row_widget(ui, calibration_observable, label: str=None):
"""Called when an item (calibration_observable) is inserted into the list widget. Returns a widget."""
calibration_row = ui.create_row_widget()
row_label = ui.create_label_widget(label, properties={"width": 60})
row_label.widget_id = "label"
offset_field = ui.create_line_edit_widget(properties={"width": 60})
offset_field.widget_id = "offset"
scale_field = ui.create_line_edit_widget(properties={"width": 60})
scale_field.widget_id = "scale"
units_field = ui.create_line_edit_widget(properties={"width": 60})
units_field.widget_id = "units"
float_point_4_converter = Converter.FloatToStringConverter(format="{0:.4f}")
offset_field.bind_text(Binding.PropertyBinding(calibration_observable, "offset", converter=float_point_4_converter))
scale_field.bind_text(Binding.PropertyBinding(calibration_observable, "scale", converter=float_point_4_converter))
units_field.bind_text(Binding.PropertyBinding(calibration_observable, "units"))
# notice the binding of calibration_index below.
calibration_row.add(row_label)
calibration_row.add_spacing(12)
calibration_row.add(offset_field)
calibration_row.add_spacing(12)
calibration_row.add(scale_field)
calibration_row.add_spacing(12)
calibration_row.add(units_field)
calibration_row.add_stretch()
return calibration_row | [
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nion-software/nionswift | nion/swift/Inspector.py | InspectorSection.add_widget_to_content | def add_widget_to_content(self, widget):
"""Subclasses should call this to add content in the section's top level column."""
self.__section_content_column.add_spacing(4)
self.__section_content_column.add(widget) | python | def add_widget_to_content(self, widget):
"""Subclasses should call this to add content in the section's top level column."""
self.__section_content_column.add_spacing(4)
self.__section_content_column.add(widget) | [
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nion-software/nionswift | nion/swift/Inspector.py | CalibrationsInspectorSection.__create_list_item_widget | def __create_list_item_widget(self, ui, calibration_observable):
"""Called when an item (calibration_observable) is inserted into the list widget. Returns a widget."""
calibration_row = make_calibration_row_widget(ui, calibration_observable)
column = ui.create_column_widget()
column.add_spacing(4)
column.add(calibration_row)
return column | python | def __create_list_item_widget(self, ui, calibration_observable):
"""Called when an item (calibration_observable) is inserted into the list widget. Returns a widget."""
calibration_row = make_calibration_row_widget(ui, calibration_observable)
column = ui.create_column_widget()
column.add_spacing(4)
column.add(calibration_row)
return column | [
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nion-software/nionswift | nion/swift/HistogramPanel.py | AdornmentsCanvasItem._repaint | def _repaint(self, drawing_context):
"""Repaint the canvas item. This will occur on a thread."""
# canvas size
canvas_width = self.canvas_size[1]
canvas_height = self.canvas_size[0]
left = self.display_limits[0]
right = self.display_limits[1]
# draw left display limit
if left > 0.0:
with drawing_context.saver():
drawing_context.begin_path()
drawing_context.move_to(left * canvas_width, 1)
drawing_context.line_to(left * canvas_width, canvas_height-1)
drawing_context.line_width = 2
drawing_context.stroke_style = "#000"
drawing_context.stroke()
# draw right display limit
if right < 1.0:
with drawing_context.saver():
drawing_context.begin_path()
drawing_context.move_to(right * canvas_width, 1)
drawing_context.line_to(right * canvas_width, canvas_height-1)
drawing_context.line_width = 2
drawing_context.stroke_style = "#FFF"
drawing_context.stroke()
# draw border
with drawing_context.saver():
drawing_context.begin_path()
drawing_context.move_to(0,canvas_height)
drawing_context.line_to(canvas_width,canvas_height)
drawing_context.line_width = 1
drawing_context.stroke_style = "#444"
drawing_context.stroke() | python | def _repaint(self, drawing_context):
"""Repaint the canvas item. This will occur on a thread."""
# canvas size
canvas_width = self.canvas_size[1]
canvas_height = self.canvas_size[0]
left = self.display_limits[0]
right = self.display_limits[1]
# draw left display limit
if left > 0.0:
with drawing_context.saver():
drawing_context.begin_path()
drawing_context.move_to(left * canvas_width, 1)
drawing_context.line_to(left * canvas_width, canvas_height-1)
drawing_context.line_width = 2
drawing_context.stroke_style = "#000"
drawing_context.stroke()
# draw right display limit
if right < 1.0:
with drawing_context.saver():
drawing_context.begin_path()
drawing_context.move_to(right * canvas_width, 1)
drawing_context.line_to(right * canvas_width, canvas_height-1)
drawing_context.line_width = 2
drawing_context.stroke_style = "#FFF"
drawing_context.stroke()
# draw border
with drawing_context.saver():
drawing_context.begin_path()
drawing_context.move_to(0,canvas_height)
drawing_context.line_to(canvas_width,canvas_height)
drawing_context.line_width = 1
drawing_context.stroke_style = "#444"
drawing_context.stroke() | [
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nion-software/nionswift | nion/swift/HistogramPanel.py | SimpleLineGraphCanvasItem._repaint | def _repaint(self, drawing_context):
"""Repaint the canvas item. This will occur on a thread."""
# canvas size
canvas_width = self.canvas_size[1]
canvas_height = self.canvas_size[0]
# draw background
if self.background_color:
with drawing_context.saver():
drawing_context.begin_path()
drawing_context.move_to(0,0)
drawing_context.line_to(canvas_width,0)
drawing_context.line_to(canvas_width,canvas_height)
drawing_context.line_to(0,canvas_height)
drawing_context.close_path()
drawing_context.fill_style = self.background_color
drawing_context.fill()
# draw the data, if any
if (self.data is not None and len(self.data) > 0):
# draw the histogram itself
with drawing_context.saver():
drawing_context.begin_path()
binned_data = Image.rebin_1d(self.data, int(canvas_width), self.__retained_rebin_1d) if int(canvas_width) != self.data.shape[0] else self.data
for i in range(canvas_width):
drawing_context.move_to(i, canvas_height)
drawing_context.line_to(i, canvas_height * (1 - binned_data[i]))
drawing_context.line_width = 1
drawing_context.stroke_style = "#444"
drawing_context.stroke() | python | def _repaint(self, drawing_context):
"""Repaint the canvas item. This will occur on a thread."""
# canvas size
canvas_width = self.canvas_size[1]
canvas_height = self.canvas_size[0]
# draw background
if self.background_color:
with drawing_context.saver():
drawing_context.begin_path()
drawing_context.move_to(0,0)
drawing_context.line_to(canvas_width,0)
drawing_context.line_to(canvas_width,canvas_height)
drawing_context.line_to(0,canvas_height)
drawing_context.close_path()
drawing_context.fill_style = self.background_color
drawing_context.fill()
# draw the data, if any
if (self.data is not None and len(self.data) > 0):
# draw the histogram itself
with drawing_context.saver():
drawing_context.begin_path()
binned_data = Image.rebin_1d(self.data, int(canvas_width), self.__retained_rebin_1d) if int(canvas_width) != self.data.shape[0] else self.data
for i in range(canvas_width):
drawing_context.move_to(i, canvas_height)
drawing_context.line_to(i, canvas_height * (1 - binned_data[i]))
drawing_context.line_width = 1
drawing_context.stroke_style = "#444"
drawing_context.stroke() | [
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nion-software/nionswift | nion/swift/HistogramPanel.py | ColorMapCanvasItem.color_map_data | def color_map_data(self, data: numpy.ndarray) -> None:
"""Set the data and mark the canvas item for updating.
Data should be an ndarray of shape (256, 3) with type uint8
"""
self.__color_map_data = data
self.update() | python | def color_map_data(self, data: numpy.ndarray) -> None:
"""Set the data and mark the canvas item for updating.
Data should be an ndarray of shape (256, 3) with type uint8
"""
self.__color_map_data = data
self.update() | [
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nion-software/nionswift | nion/swift/HistogramPanel.py | ColorMapCanvasItem._repaint | def _repaint(self, drawing_context: DrawingContext.DrawingContext):
"""Repaint the canvas item. This will occur on a thread."""
# canvas size
canvas_width = self.canvas_size.width
canvas_height = self.canvas_size.height
with drawing_context.saver():
if self.__color_map_data is not None:
rgba_image = numpy.empty((4,) + self.__color_map_data.shape[:-1], dtype=numpy.uint32)
Image.get_rgb_view(rgba_image)[:] = self.__color_map_data[numpy.newaxis, :, :] # scalar data assigned to each component of rgb view
Image.get_alpha_view(rgba_image)[:] = 255
drawing_context.draw_image(rgba_image, 0, 0, canvas_width, canvas_height) | python | def _repaint(self, drawing_context: DrawingContext.DrawingContext):
"""Repaint the canvas item. This will occur on a thread."""
# canvas size
canvas_width = self.canvas_size.width
canvas_height = self.canvas_size.height
with drawing_context.saver():
if self.__color_map_data is not None:
rgba_image = numpy.empty((4,) + self.__color_map_data.shape[:-1], dtype=numpy.uint32)
Image.get_rgb_view(rgba_image)[:] = self.__color_map_data[numpy.newaxis, :, :] # scalar data assigned to each component of rgb view
Image.get_alpha_view(rgba_image)[:] = 255
drawing_context.draw_image(rgba_image, 0, 0, canvas_width, canvas_height) | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.get_params | def get_params(self):
"Parameters used to initialize the class"
import inspect
a = inspect.getargspec(self.__init__)[0]
out = dict()
for key in a[1:]:
value = getattr(self, "_%s" % key, None)
out[key] = value
return out | python | def get_params(self):
"Parameters used to initialize the class"
import inspect
a = inspect.getargspec(self.__init__)[0]
out = dict()
for key in a[1:]:
value = getattr(self, "_%s" % key, None)
out[key] = value
return out | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.signature | def signature(self):
"Instance file name"
kw = self.get_params()
keys = sorted(kw.keys())
l = []
for k in keys:
n = k[0] + k[-1]
v = kw[k]
if k == 'function_set':
v = "_".join([x.__name__[0] +
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str(x.nargs) for x in kw[k]])
elif k == 'population_class':
v = kw[k].__name__
else:
v = str(v)
l.append('{0}_{1}'.format(n, v))
return '-'.join(l) | python | def signature(self):
"Instance file name"
kw = self.get_params()
keys = sorted(kw.keys())
l = []
for k in keys:
n = k[0] + k[-1]
v = kw[k]
if k == 'function_set':
v = "_".join([x.__name__[0] +
x.__name__[-1] +
str(x.nargs) for x in kw[k]])
elif k == 'population_class':
v = kw[k].__name__
else:
v = str(v)
l.append('{0}_{1}'.format(n, v))
return '-'.join(l) | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.population | def population(self):
"Class containing the population and all the individuals generated"
try:
return self._p
except AttributeError:
self._p = self._population_class(base=self,
tournament_size=self._tournament_size,
classifier=self.classifier,
labels=self._labels,
es_extra_test=self.es_extra_test,
popsize=self._popsize,
random_generations=self._random_generations,
negative_selection=self._negative_selection)
return self._p | python | def population(self):
"Class containing the population and all the individuals generated"
try:
return self._p
except AttributeError:
self._p = self._population_class(base=self,
tournament_size=self._tournament_size,
classifier=self.classifier,
labels=self._labels,
es_extra_test=self.es_extra_test,
popsize=self._popsize,
random_generations=self._random_generations,
negative_selection=self._negative_selection)
return self._p | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.random_leaf | def random_leaf(self):
"Returns a random variable with the associated weight"
for i in range(self._number_tries_feasible_ind):
var = np.random.randint(self.nvar)
v = self._random_leaf(var)
if v is None:
continue
return v
raise RuntimeError("Could not find a suitable random leaf") | python | def random_leaf(self):
"Returns a random variable with the associated weight"
for i in range(self._number_tries_feasible_ind):
var = np.random.randint(self.nvar)
v = self._random_leaf(var)
if v is None:
continue
return v
raise RuntimeError("Could not find a suitable random leaf") | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.random_offspring | def random_offspring(self):
"Returns an offspring with the associated weight(s)"
function_set = self.function_set
function_selection = self._function_selection_ins
function_selection.density = self.population.density
function_selection.unfeasible_functions.clear()
for i in range(self._number_tries_feasible_ind):
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func_index = function_selection.random_function()
func = function_set[func_index]
args = self.get_args(func)
if args is None:
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args = [self.population.population[x].position for x in args]
f = self._random_offspring(func, args)
if f is None:
function_selection.unfeasible_functions.add(func_index)
continue
function_selection[func_index] = f.fitness
return f
raise RuntimeError("Could not find a suitable random offpsring") | python | def random_offspring(self):
"Returns an offspring with the associated weight(s)"
function_set = self.function_set
function_selection = self._function_selection_ins
function_selection.density = self.population.density
function_selection.unfeasible_functions.clear()
for i in range(self._number_tries_feasible_ind):
if self._function_selection:
func_index = function_selection.tournament()
else:
func_index = function_selection.random_function()
func = function_set[func_index]
args = self.get_args(func)
if args is None:
continue
args = [self.population.population[x].position for x in args]
f = self._random_offspring(func, args)
if f is None:
function_selection.unfeasible_functions.add(func_index)
continue
function_selection[func_index] = f.fitness
return f
raise RuntimeError("Could not find a suitable random offpsring") | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.stopping_criteria | def stopping_criteria(self):
"Test whether the stopping criteria has been achieved."
if self.stopping_criteria_tl():
return True
if self.generations < np.inf:
inds = self.popsize * self.generations
flag = inds <= len(self.population.hist)
else:
flag = False
if flag:
return True
est = self.population.estopping
if self._tr_fraction < 1:
if est is not None and est.fitness_vs == 0:
return True
esr = self._early_stopping_rounds
if self._tr_fraction < 1 and esr is not None and est is not None:
position = self.population.estopping.position
if position < self.init_popsize:
position = self.init_popsize
return (len(self.population.hist) +
self._unfeasible_counter -
position) > esr
return flag | python | def stopping_criteria(self):
"Test whether the stopping criteria has been achieved."
if self.stopping_criteria_tl():
return True
if self.generations < np.inf:
inds = self.popsize * self.generations
flag = inds <= len(self.population.hist)
else:
flag = False
if flag:
return True
est = self.population.estopping
if self._tr_fraction < 1:
if est is not None and est.fitness_vs == 0:
return True
esr = self._early_stopping_rounds
if self._tr_fraction < 1 and esr is not None and est is not None:
position = self.population.estopping.position
if position < self.init_popsize:
position = self.init_popsize
return (len(self.population.hist) +
self._unfeasible_counter -
position) > esr
return flag | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.nclasses | def nclasses(self, v):
"Number of classes of v, also sets the labes"
if not self.classifier:
return 0
if isinstance(v, list):
self._labels = np.arange(len(v))
return
if not isinstance(v, np.ndarray):
v = tonparray(v)
self._labels = np.unique(v)
return self._labels.shape[0] | python | def nclasses(self, v):
"Number of classes of v, also sets the labes"
if not self.classifier:
return 0
if isinstance(v, list):
self._labels = np.arange(len(v))
return
if not isinstance(v, np.ndarray):
v = tonparray(v)
self._labels = np.unique(v)
return self._labels.shape[0] | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.fit | def fit(self, X, y, test_set=None):
"""Evolutive process"""
self._init_time = time.time()
self.X = X
if self._popsize == "nvar":
self._popsize = self.nvar + len(self._input_functions)
if isinstance(test_set, str) and test_set == 'shuffle':
test_set = self.shuffle_tr2ts()
nclasses = self.nclasses(y)
if self.classifier and self._multiple_outputs:
pass
elif nclasses > 2:
assert False
self._multiclass = True
return self.multiclass(X, y, test_set=test_set)
self.y = y
if test_set is not None:
self.Xtest = test_set
for _ in range(self._number_tries_feasible_ind):
self._logger.info("Starting evolution")
try:
self.create_population()
if self.stopping_criteria_tl():
break
except RuntimeError as err:
self._logger.info("Done evolution (RuntimeError (%s), hist: %s)" % (err, len(self.population.hist)))
return self
self._logger.info("Population created (hist: %s)" % len(self.population.hist))
if len(self.population.hist) >= self._tournament_size:
break
if len(self.population.hist) == 0:
raise RuntimeError("Could not find a suitable individual")
if len(self.population.hist) < self._tournament_size:
self._logger.info("Done evolution (hist: %s)" % len(self.population.hist))
return self
if self._remove_raw_inputs:
for x in range(self.nvar):
self._X[x] = None
while not self.stopping_criteria():
try:
a = self.random_offspring()
except RuntimeError as err:
self._logger.info("Done evolution (RuntimeError (%s), hist: %s)" % (err, len(self.population.hist)))
return self
self.replace(a)
self._logger.info("Done evolution (hist: %s)" % len(self.population.hist))
return self | python | def fit(self, X, y, test_set=None):
"""Evolutive process"""
self._init_time = time.time()
self.X = X
if self._popsize == "nvar":
self._popsize = self.nvar + len(self._input_functions)
if isinstance(test_set, str) and test_set == 'shuffle':
test_set = self.shuffle_tr2ts()
nclasses = self.nclasses(y)
if self.classifier and self._multiple_outputs:
pass
elif nclasses > 2:
assert False
self._multiclass = True
return self.multiclass(X, y, test_set=test_set)
self.y = y
if test_set is not None:
self.Xtest = test_set
for _ in range(self._number_tries_feasible_ind):
self._logger.info("Starting evolution")
try:
self.create_population()
if self.stopping_criteria_tl():
break
except RuntimeError as err:
self._logger.info("Done evolution (RuntimeError (%s), hist: %s)" % (err, len(self.population.hist)))
return self
self._logger.info("Population created (hist: %s)" % len(self.population.hist))
if len(self.population.hist) >= self._tournament_size:
break
if len(self.population.hist) == 0:
raise RuntimeError("Could not find a suitable individual")
if len(self.population.hist) < self._tournament_size:
self._logger.info("Done evolution (hist: %s)" % len(self.population.hist))
return self
if self._remove_raw_inputs:
for x in range(self.nvar):
self._X[x] = None
while not self.stopping_criteria():
try:
a = self.random_offspring()
except RuntimeError as err:
self._logger.info("Done evolution (RuntimeError (%s), hist: %s)" % (err, len(self.population.hist)))
return self
self.replace(a)
self._logger.info("Done evolution (hist: %s)" % len(self.population.hist))
return self | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.decision_function | def decision_function(self, v=None, X=None):
"Decision function i.e. the raw data of the prediction"
m = self.model(v=v)
return m.decision_function(X) | python | def decision_function(self, v=None, X=None):
"Decision function i.e. the raw data of the prediction"
m = self.model(v=v)
return m.decision_function(X) | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.predict | def predict(self, v=None, X=None):
"""In classification this returns the classes, in
regression it is equivalent to the decision function"""
if X is None:
X = v
v = None
m = self.model(v=v)
return m.predict(X) | python | def predict(self, v=None, X=None):
"""In classification this returns the classes, in
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X = v
v = None
m = self.model(v=v)
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marrow/WebCore | web/server/gevent_.py | serve | def serve(application, host='127.0.0.1', port=8080):
"""Gevent-based WSGI-HTTP server."""
# Instantiate the server with a host/port configuration and our application.
WSGIServer((host, int(port)), application).serve_forever() | python | def serve(application, host='127.0.0.1', port=8080):
"""Gevent-based WSGI-HTTP server."""
# Instantiate the server with a host/port configuration and our application.
WSGIServer((host, int(port)), application).serve_forever() | [
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bitlabstudio/django-subscribe | subscribe/templatetags/subscribe_tags.py | get_subscribers | def get_subscribers(obj):
"""
Returns the subscribers for a given object.
:param obj: Any object.
"""
ctype = ContentType.objects.get_for_model(obj)
return Subscription.objects.filter(content_type=ctype, object_id=obj.pk) | python | def get_subscribers(obj):
"""
Returns the subscribers for a given object.
:param obj: Any object.
"""
ctype = ContentType.objects.get_for_model(obj)
return Subscription.objects.filter(content_type=ctype, object_id=obj.pk) | [
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bitlabstudio/django-subscribe | subscribe/templatetags/subscribe_tags.py | is_subscribed | def is_subscribed(user, obj):
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Returns ``True`` if the user is subscribed to the given object.
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:param obj: Any object.
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if not user.is_authenticated():
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return True | python | def is_subscribed(user, obj):
"""
Returns ``True`` if the user is subscribed to the given object.
:param user: A ``User`` instance.
:param obj: Any object.
"""
if not user.is_authenticated():
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return True | [
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] | train | https://github.com/bitlabstudio/django-subscribe/blob/313de63fb4acda172e88b65c3327c793f98e8aa9/subscribe/templatetags/subscribe_tags.py#L35-L54 |
marrow/WebCore | web/core/context.py | Context._promote | def _promote(self, name, instantiate=True):
"""Create a new subclass of Context which incorporates instance attributes and new descriptors.
This promotes an instance and its instance attributes up to being a class with class attributes, then
returns an instance of that class.
"""
metaclass = type(self.__class__)
contents = self.__dict__.copy()
cls = metaclass(str(name), (self.__class__, ), contents)
if instantiate:
return cls()
return cls | python | def _promote(self, name, instantiate=True):
"""Create a new subclass of Context which incorporates instance attributes and new descriptors.
This promotes an instance and its instance attributes up to being a class with class attributes, then
returns an instance of that class.
"""
metaclass = type(self.__class__)
contents = self.__dict__.copy()
cls = metaclass(str(name), (self.__class__, ), contents)
if instantiate:
return cls()
return cls | [
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NeuroML/pyNeuroML | pyneuroml/tune/NeuroMLController.py | run_individual | def run_individual(sim_var,
reference,
neuroml_file,
nml_doc,
still_included,
generate_dir,
target,
sim_time,
dt,
simulator,
cleanup = True,
show=False):
"""
Run an individual simulation.
The candidate data has been flattened into the sim_var dict. The
sim_var dict contains parameter:value key value pairs, which are
applied to the model before it is simulated.
"""
for var_name in sim_var.keys():
individual_var_names = var_name.split('+')
for individual_var_name in individual_var_names:
words = individual_var_name.split('/')
type, id1 = words[0].split(':')
if ':' in words[1]:
variable, id2 = words[1].split(':')
else:
variable = words[1]
id2 = None
units = words[2]
value = sim_var[var_name]
pyneuroml.pynml.print_comment_v(' Changing value of %s (%s) in %s (%s) to: %s %s'%(variable, id2, type, id1, value, units))
if type == 'channel':
channel = nml_doc.get_by_id(id1)
if channel:
print("Setting channel %s"%(channel))
if variable == 'vShift':
channel.v_shift = '%s %s'%(value, units)
else:
pyneuroml.pynml.print_comment_v('Could not find channel with id %s from expression: %s'%(id1, individual_var_name))
exit()
elif type == 'cell':
cell = None
for c in nml_doc.cells:
if c.id == id1:
cell = c
if variable == 'channelDensity':
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities + cell.biophysical_properties.membrane_properties.channel_density_v_shifts:
if cd.id == id2:
chanDens = cd
chanDens.cond_density = '%s %s'%(value, units)
elif variable == 'vShift_channelDensity':
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_density_v_shifts:
if cd.id == id2:
chanDens = cd
chanDens.v_shift = '%s %s'%(value, units)
elif variable == 'channelDensityNernst':
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_density_nernsts:
if cd.id == id2:
chanDens = cd
chanDens.cond_density = '%s %s'%(value, units)
elif variable == 'erev_id': # change all values of erev in channelDensity elements with only this id
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities + cell.biophysical_properties.membrane_properties.channel_density_v_shifts:
if cd.id == id2:
chanDens = cd
chanDens.erev = '%s %s'%(value, units)
elif variable == 'erev_ion': # change all values of erev in channelDensity elements with this ion
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities + cell.biophysical_properties.membrane_properties.channel_density_v_shifts:
if cd.ion == id2:
chanDens = cd
chanDens.erev = '%s %s'%(value, units)
elif variable == 'specificCapacitance':
specCap = None
for sc in cell.biophysical_properties.membrane_properties.specific_capacitances:
if (sc.segment_groups == None and id2 == 'all') or sc.segment_groups == id2 :
specCap = sc
specCap.value = '%s %s'%(value, units)
elif variable == 'resistivity':
resistivity = None
for rs in cell.biophysical_properties.intracellular_properties.resistivities:
if (rs.segment_groups == None and id2 == 'all') or rs.segment_groups == id2 :
resistivity = rs
resistivity.value = '%s %s'%(value, units)
else:
pyneuroml.pynml.print_comment_v('Unknown variable (%s) in variable expression: %s'%(variable, individual_var_name))
exit()
elif type == 'izhikevich2007Cell':
izhcell = None
for c in nml_doc.izhikevich2007_cells:
if c.id == id1:
izhcell = c
izhcell.__setattr__(variable, '%s %s'%(value, units))
else:
pyneuroml.pynml.print_comment_v('Unknown type (%s) in variable expression: %s'%(type, individual_var_name))
new_neuroml_file = '%s/%s'%(generate_dir,os.path.basename(neuroml_file))
if new_neuroml_file == neuroml_file:
pyneuroml.pynml.print_comment_v('Cannot use a directory for generating into (%s) which is the same location of the NeuroML file (%s)!'% \
(neuroml_file, generate_dir))
pyneuroml.pynml.write_neuroml2_file(nml_doc, new_neuroml_file)
for include in still_included:
inc_loc = '%s/%s'%(os.path.dirname(os.path.abspath(neuroml_file)),include)
pyneuroml.pynml.print_comment_v("Copying non included file %s to %s (%s) beside %s"%(inc_loc, generate_dir,os.path.abspath(generate_dir), new_neuroml_file))
shutil.copy(inc_loc, generate_dir)
from pyneuroml.tune.NeuroMLSimulation import NeuroMLSimulation
sim = NeuroMLSimulation(reference,
neuroml_file = new_neuroml_file,
target = target,
sim_time = sim_time,
dt = dt,
simulator = simulator,
generate_dir = generate_dir,
cleanup = cleanup,
nml_doc = nml_doc)
sim.go()
if show:
sim.show()
return sim.t, sim.volts | python | def run_individual(sim_var,
reference,
neuroml_file,
nml_doc,
still_included,
generate_dir,
target,
sim_time,
dt,
simulator,
cleanup = True,
show=False):
"""
Run an individual simulation.
The candidate data has been flattened into the sim_var dict. The
sim_var dict contains parameter:value key value pairs, which are
applied to the model before it is simulated.
"""
for var_name in sim_var.keys():
individual_var_names = var_name.split('+')
for individual_var_name in individual_var_names:
words = individual_var_name.split('/')
type, id1 = words[0].split(':')
if ':' in words[1]:
variable, id2 = words[1].split(':')
else:
variable = words[1]
id2 = None
units = words[2]
value = sim_var[var_name]
pyneuroml.pynml.print_comment_v(' Changing value of %s (%s) in %s (%s) to: %s %s'%(variable, id2, type, id1, value, units))
if type == 'channel':
channel = nml_doc.get_by_id(id1)
if channel:
print("Setting channel %s"%(channel))
if variable == 'vShift':
channel.v_shift = '%s %s'%(value, units)
else:
pyneuroml.pynml.print_comment_v('Could not find channel with id %s from expression: %s'%(id1, individual_var_name))
exit()
elif type == 'cell':
cell = None
for c in nml_doc.cells:
if c.id == id1:
cell = c
if variable == 'channelDensity':
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities + cell.biophysical_properties.membrane_properties.channel_density_v_shifts:
if cd.id == id2:
chanDens = cd
chanDens.cond_density = '%s %s'%(value, units)
elif variable == 'vShift_channelDensity':
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_density_v_shifts:
if cd.id == id2:
chanDens = cd
chanDens.v_shift = '%s %s'%(value, units)
elif variable == 'channelDensityNernst':
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_density_nernsts:
if cd.id == id2:
chanDens = cd
chanDens.cond_density = '%s %s'%(value, units)
elif variable == 'erev_id': # change all values of erev in channelDensity elements with only this id
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities + cell.biophysical_properties.membrane_properties.channel_density_v_shifts:
if cd.id == id2:
chanDens = cd
chanDens.erev = '%s %s'%(value, units)
elif variable == 'erev_ion': # change all values of erev in channelDensity elements with this ion
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities + cell.biophysical_properties.membrane_properties.channel_density_v_shifts:
if cd.ion == id2:
chanDens = cd
chanDens.erev = '%s %s'%(value, units)
elif variable == 'specificCapacitance':
specCap = None
for sc in cell.biophysical_properties.membrane_properties.specific_capacitances:
if (sc.segment_groups == None and id2 == 'all') or sc.segment_groups == id2 :
specCap = sc
specCap.value = '%s %s'%(value, units)
elif variable == 'resistivity':
resistivity = None
for rs in cell.biophysical_properties.intracellular_properties.resistivities:
if (rs.segment_groups == None and id2 == 'all') or rs.segment_groups == id2 :
resistivity = rs
resistivity.value = '%s %s'%(value, units)
else:
pyneuroml.pynml.print_comment_v('Unknown variable (%s) in variable expression: %s'%(variable, individual_var_name))
exit()
elif type == 'izhikevich2007Cell':
izhcell = None
for c in nml_doc.izhikevich2007_cells:
if c.id == id1:
izhcell = c
izhcell.__setattr__(variable, '%s %s'%(value, units))
else:
pyneuroml.pynml.print_comment_v('Unknown type (%s) in variable expression: %s'%(type, individual_var_name))
new_neuroml_file = '%s/%s'%(generate_dir,os.path.basename(neuroml_file))
if new_neuroml_file == neuroml_file:
pyneuroml.pynml.print_comment_v('Cannot use a directory for generating into (%s) which is the same location of the NeuroML file (%s)!'% \
(neuroml_file, generate_dir))
pyneuroml.pynml.write_neuroml2_file(nml_doc, new_neuroml_file)
for include in still_included:
inc_loc = '%s/%s'%(os.path.dirname(os.path.abspath(neuroml_file)),include)
pyneuroml.pynml.print_comment_v("Copying non included file %s to %s (%s) beside %s"%(inc_loc, generate_dir,os.path.abspath(generate_dir), new_neuroml_file))
shutil.copy(inc_loc, generate_dir)
from pyneuroml.tune.NeuroMLSimulation import NeuroMLSimulation
sim = NeuroMLSimulation(reference,
neuroml_file = new_neuroml_file,
target = target,
sim_time = sim_time,
dt = dt,
simulator = simulator,
generate_dir = generate_dir,
cleanup = cleanup,
nml_doc = nml_doc)
sim.go()
if show:
sim.show()
return sim.t, sim.volts | [
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The candidate data has been flattened into the sim_var dict. The
sim_var dict contains parameter:value key value pairs, which are
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NeuroML/pyNeuroML | pyneuroml/tune/NeuroMLController.py | NeuroMLController.run | def run(self,candidates,parameters):
"""
Run simulation for each candidate
This run method will loop through each candidate and run the simulation
corresponding to its parameter values. It will populate an array called
traces with the resulting voltage traces for the simulation and return it.
"""
traces = []
start_time = time.time()
if self.num_parallel_evaluations == 1:
for candidate_i in range(len(candidates)):
candidate = candidates[candidate_i]
sim_var = dict(zip(parameters,candidate))
pyneuroml.pynml.print_comment_v('\n\n - RUN %i (%i/%i); variables: %s\n'%(self.count,candidate_i+1,len(candidates),sim_var))
self.count+=1
t,v = self.run_individual(sim_var)
traces.append([t,v])
else:
import pp
ppservers = ()
job_server = pp.Server(self.num_parallel_evaluations, ppservers=ppservers)
pyneuroml.pynml.print_comment_v('Running %i candidates across %i local processes'%(len(candidates),job_server.get_ncpus()))
jobs = []
for candidate_i in range(len(candidates)):
candidate = candidates[candidate_i]
sim_var = dict(zip(parameters,candidate))
pyneuroml.pynml.print_comment_v('\n\n - PARALLEL RUN %i (%i/%i of curr candidates); variables: %s\n'%(self.count,candidate_i+1,len(candidates),sim_var))
self.count+=1
cand_dir = self.generate_dir+"/CANDIDATE_%s"%candidate_i
if not os.path.exists(cand_dir):
os.mkdir(cand_dir)
vars = (sim_var,
self.ref,
self.neuroml_file,
self.nml_doc,
self.still_included,
cand_dir,
self.target,
self.sim_time,
self.dt,
self.simulator)
job = job_server.submit(run_individual, vars, (), ("pyneuroml.pynml",'pyneuroml.tune.NeuroMLSimulation','shutil','neuroml'))
jobs.append(job)
for job_i in range(len(jobs)):
job = jobs[job_i]
pyneuroml.pynml.print_comment_v("Checking parallel job %i/%i; set running so far: %i"%(job_i,len(jobs),self.count))
t,v = job()
traces.append([t,v])
#pyneuroml.pynml.print_comment_v("Obtained: %s"%result)
####job_server.print_stats()
job_server.destroy()
print("-------------------------------------------")
end_time = time.time()
tot = (end_time-start_time)
pyneuroml.pynml.print_comment_v('Ran %i candidates in %s seconds (~%ss per job)'%(len(candidates),tot,tot/len(candidates)))
return traces | python | def run(self,candidates,parameters):
"""
Run simulation for each candidate
This run method will loop through each candidate and run the simulation
corresponding to its parameter values. It will populate an array called
traces with the resulting voltage traces for the simulation and return it.
"""
traces = []
start_time = time.time()
if self.num_parallel_evaluations == 1:
for candidate_i in range(len(candidates)):
candidate = candidates[candidate_i]
sim_var = dict(zip(parameters,candidate))
pyneuroml.pynml.print_comment_v('\n\n - RUN %i (%i/%i); variables: %s\n'%(self.count,candidate_i+1,len(candidates),sim_var))
self.count+=1
t,v = self.run_individual(sim_var)
traces.append([t,v])
else:
import pp
ppservers = ()
job_server = pp.Server(self.num_parallel_evaluations, ppservers=ppservers)
pyneuroml.pynml.print_comment_v('Running %i candidates across %i local processes'%(len(candidates),job_server.get_ncpus()))
jobs = []
for candidate_i in range(len(candidates)):
candidate = candidates[candidate_i]
sim_var = dict(zip(parameters,candidate))
pyneuroml.pynml.print_comment_v('\n\n - PARALLEL RUN %i (%i/%i of curr candidates); variables: %s\n'%(self.count,candidate_i+1,len(candidates),sim_var))
self.count+=1
cand_dir = self.generate_dir+"/CANDIDATE_%s"%candidate_i
if not os.path.exists(cand_dir):
os.mkdir(cand_dir)
vars = (sim_var,
self.ref,
self.neuroml_file,
self.nml_doc,
self.still_included,
cand_dir,
self.target,
self.sim_time,
self.dt,
self.simulator)
job = job_server.submit(run_individual, vars, (), ("pyneuroml.pynml",'pyneuroml.tune.NeuroMLSimulation','shutil','neuroml'))
jobs.append(job)
for job_i in range(len(jobs)):
job = jobs[job_i]
pyneuroml.pynml.print_comment_v("Checking parallel job %i/%i; set running so far: %i"%(job_i,len(jobs),self.count))
t,v = job()
traces.append([t,v])
#pyneuroml.pynml.print_comment_v("Obtained: %s"%result)
####job_server.print_stats()
job_server.destroy()
print("-------------------------------------------")
end_time = time.time()
tot = (end_time-start_time)
pyneuroml.pynml.print_comment_v('Ran %i candidates in %s seconds (~%ss per job)'%(len(candidates),tot,tot/len(candidates)))
return traces | [
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This run method will loop through each candidate and run the simulation
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marrow/WebCore | web/ext/local.py | ThreadLocalExtension.prepare | def prepare(self, context):
"""Executed prior to processing a request."""
if __debug__:
log.debug("Assigning thread local request context.")
self.local.context = context | python | def prepare(self, context):
"""Executed prior to processing a request."""
if __debug__:
log.debug("Assigning thread local request context.")
self.local.context = context | [
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marrow/WebCore | web/core/view.py | WebViews.register | def register(self, kind, handler):
"""Register a handler for a given type, class, interface, or abstract base class.
View registration should happen within the `start` callback of an extension. For example, to register the
previous `json` view example:
class JSONExtension:
def start(self, context):
context.view.register(tuple, json)
The approach of explicitly referencing a view handler isn't very easy to override without also replacing the
extension originally adding it, however there is another approach. Using named handlers registered as discrete
plugins (via the `entry_point` argument in `setup.py`) allows the extension to easily ask "what's my handler?"
class JSONExtension:
def start(self, context):
context.view.register(
tuple,
context.view.json
)
Otherwise unknown attributes of the view registry will attempt to look up a handler plugin by that name.
"""
if __debug__: # In production this logging is completely skipped, regardless of logging level.
if py3 and not pypy: # Where possible, we shorten things to just the cannonical name.
log.debug("Registering view handler.", extra=dict(type=name(kind), handler=name(handler)))
else: # Canonical name lookup is not entirely reliable on some combinations.
log.debug("Registering view handler.", extra=dict(type=repr(kind), handler=repr(handler)))
# Add the handler to the pool of candidates. This adds to a list instead of replacing the "dictionary item".
self._map.add(kind, handler)
return handler | python | def register(self, kind, handler):
"""Register a handler for a given type, class, interface, or abstract base class.
View registration should happen within the `start` callback of an extension. For example, to register the
previous `json` view example:
class JSONExtension:
def start(self, context):
context.view.register(tuple, json)
The approach of explicitly referencing a view handler isn't very easy to override without also replacing the
extension originally adding it, however there is another approach. Using named handlers registered as discrete
plugins (via the `entry_point` argument in `setup.py`) allows the extension to easily ask "what's my handler?"
class JSONExtension:
def start(self, context):
context.view.register(
tuple,
context.view.json
)
Otherwise unknown attributes of the view registry will attempt to look up a handler plugin by that name.
"""
if __debug__: # In production this logging is completely skipped, regardless of logging level.
if py3 and not pypy: # Where possible, we shorten things to just the cannonical name.
log.debug("Registering view handler.", extra=dict(type=name(kind), handler=name(handler)))
else: # Canonical name lookup is not entirely reliable on some combinations.
log.debug("Registering view handler.", extra=dict(type=repr(kind), handler=repr(handler)))
# Add the handler to the pool of candidates. This adds to a list instead of replacing the "dictionary item".
self._map.add(kind, handler)
return handler | [
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marrow/WebCore | web/app/static.py | static | def static(base, mapping=None, far=('js', 'css', 'gif', 'jpg', 'jpeg', 'png', 'ttf', 'woff')):
"""Serve files from disk.
This utility endpoint factory is meant primarily for use in development environments; in production environments
it is better (more efficient, secure, etc.) to serve your static content using a front end load balancer such as
Nginx.
The first argument, `base`, represents the base path to serve files from. Paths below the attachment point for
the generated endpoint will combine this base path with the remaining path elements to determine the file to
serve.
The second argument is an optional dictionary mapping filename extensions to template engines, for cooperation
with the TemplateExtension. (See: https://github.com/marrow/template) The result of attempting to serve a
mapped path is a 2-tuple of `("{mapping}:{path}", dict())`. For example, to render all `.html` files as Mako
templates, you would attach something like the following:
class Root:
page = static('/path/to/static/pages', dict(html='mako'))
By default the "usual culprits" are served with far-futures cache expiry headers. If you wish to change the
extensions searched just assign a new `far` iterable. To disable, assign any falsy value.
"""
base = abspath(base)
@staticmethod
def static_handler(context, *parts, **kw):
path = normpath(pathjoin(base, *parts))
if __debug__:
log.debug("Attempting to serve static file.", extra=dict(
request = id(context),
base = base,
path = path
))
if not path.startswith(base): # Ensure we only serve files from the allowed path.
raise HTTPForbidden("Cowardly refusing to violate base path policy." if __debug__ else None)
if not exists(path): # Do the right thing if the file doesn't actually exist.
raise HTTPNotFound()
if not isfile(path): # Only serve normal files; no UNIX domain sockets, FIFOs, etc., etc.
raise HTTPForbidden("Cowardly refusing to open a non-file." if __debug__ else None)
if far and path.rpartition('.')[2] in far:
context.response.cache_expires = 60*60*24*365
if mapping: # Handle the mapping of filename extensions to 2-tuples. 'Cause why not?
_, _, extension = basename(path).partition('.')
if extension in mapping:
return mapping[extension] + ':' + path, dict()
return open(path, 'rb')
return static_handler | python | def static(base, mapping=None, far=('js', 'css', 'gif', 'jpg', 'jpeg', 'png', 'ttf', 'woff')):
"""Serve files from disk.
This utility endpoint factory is meant primarily for use in development environments; in production environments
it is better (more efficient, secure, etc.) to serve your static content using a front end load balancer such as
Nginx.
The first argument, `base`, represents the base path to serve files from. Paths below the attachment point for
the generated endpoint will combine this base path with the remaining path elements to determine the file to
serve.
The second argument is an optional dictionary mapping filename extensions to template engines, for cooperation
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mapped path is a 2-tuple of `("{mapping}:{path}", dict())`. For example, to render all `.html` files as Mako
templates, you would attach something like the following:
class Root:
page = static('/path/to/static/pages', dict(html='mako'))
By default the "usual culprits" are served with far-futures cache expiry headers. If you wish to change the
extensions searched just assign a new `far` iterable. To disable, assign any falsy value.
"""
base = abspath(base)
@staticmethod
def static_handler(context, *parts, **kw):
path = normpath(pathjoin(base, *parts))
if __debug__:
log.debug("Attempting to serve static file.", extra=dict(
request = id(context),
base = base,
path = path
))
if not path.startswith(base): # Ensure we only serve files from the allowed path.
raise HTTPForbidden("Cowardly refusing to violate base path policy." if __debug__ else None)
if not exists(path): # Do the right thing if the file doesn't actually exist.
raise HTTPNotFound()
if not isfile(path): # Only serve normal files; no UNIX domain sockets, FIFOs, etc., etc.
raise HTTPForbidden("Cowardly refusing to open a non-file." if __debug__ else None)
if far and path.rpartition('.')[2] in far:
context.response.cache_expires = 60*60*24*365
if mapping: # Handle the mapping of filename extensions to 2-tuples. 'Cause why not?
_, _, extension = basename(path).partition('.')
if extension in mapping:
return mapping[extension] + ':' + path, dict()
return open(path, 'rb')
return static_handler | [
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marrow/WebCore | web/server/diesel_.py | serve | def serve(application, host='127.0.0.1', port=8080):
"""Diesel-based (greenlet) WSGI-HTTP server.
As a minor note, this is crazy. Diesel includes Flask, too.
"""
# Instantiate the server with a host/port configuration and our application.
WSGIApplication(application, port=int(port), iface=host).run() | python | def serve(application, host='127.0.0.1', port=8080):
"""Diesel-based (greenlet) WSGI-HTTP server.
As a minor note, this is crazy. Diesel includes Flask, too.
"""
# Instantiate the server with a host/port configuration and our application.
WSGIApplication(application, port=int(port), iface=host).run() | [
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NeuroML/pyNeuroML | pyneuroml/plot/PlotSpikes.py | process_args | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(description="A script for plotting files containing spike time data")
parser.add_argument('spiketimeFiles',
type=str,
metavar='<spiketime file>',
help='List of text file containing spike times',
nargs='+')
parser.add_argument('-format',
type=str,
metavar='<format>',
default=DEFAULTS['format'],
help='How the spiketimes are represented on each line of file:\n'+\
'id_t: id of cell, space(s)/tab(s), time of spike (default);\n'+\
't_id: time of spike, space(s)/tab(s), id of cell;\n'+\
'sonata: SONATA format HDF5 file containing spike times')
parser.add_argument('-rates',
action='store_true',
default=DEFAULTS['rates'],
help='Show a plot of rates')
parser.add_argument('-showPlotsAlready',
action='store_true',
default=DEFAULTS['show_plots_already'],
help='Show plots once generated')
parser.add_argument('-saveSpikePlotTo',
type=str,
metavar='<spiketime plot filename>',
default=DEFAULTS['save_spike_plot_to'],
help='Name of file in which to save spiketime plot')
parser.add_argument('-rateWindow',
type=int,
metavar='<rate window>',
default=DEFAULTS['rate_window'],
help='Window for rate calculation in ms')
parser.add_argument('-rateBins',
type=int,
metavar='<rate bins>',
default=DEFAULTS['rate_bins'],
help='Number of bins for rate histogram')
return parser.parse_args() | python | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(description="A script for plotting files containing spike time data")
parser.add_argument('spiketimeFiles',
type=str,
metavar='<spiketime file>',
help='List of text file containing spike times',
nargs='+')
parser.add_argument('-format',
type=str,
metavar='<format>',
default=DEFAULTS['format'],
help='How the spiketimes are represented on each line of file:\n'+\
'id_t: id of cell, space(s)/tab(s), time of spike (default);\n'+\
't_id: time of spike, space(s)/tab(s), id of cell;\n'+\
'sonata: SONATA format HDF5 file containing spike times')
parser.add_argument('-rates',
action='store_true',
default=DEFAULTS['rates'],
help='Show a plot of rates')
parser.add_argument('-showPlotsAlready',
action='store_true',
default=DEFAULTS['show_plots_already'],
help='Show plots once generated')
parser.add_argument('-saveSpikePlotTo',
type=str,
metavar='<spiketime plot filename>',
default=DEFAULTS['save_spike_plot_to'],
help='Name of file in which to save spiketime plot')
parser.add_argument('-rateWindow',
type=int,
metavar='<rate window>',
default=DEFAULTS['rate_window'],
help='Window for rate calculation in ms')
parser.add_argument('-rateBins',
type=int,
metavar='<rate bins>',
default=DEFAULTS['rate_bins'],
help='Number of bins for rate histogram')
return parser.parse_args() | [
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marrow/WebCore | web/server/stdlib.py | simple | def simple(application, host='127.0.0.1', port=8080):
"""Python-standard WSGI-HTTP server for testing purposes.
The additional work performed here is to match the default startup output of "waitress".
This is not a production quality interface and will be have badly under load.
"""
# Try to be handy as many terminals allow clicking links.
print("serving on http://{0}:{1}".format(host, port))
# Bind and launch the server; this is a blocking operation.
make_server(host, int(port), application).serve_forever() | python | def simple(application, host='127.0.0.1', port=8080):
"""Python-standard WSGI-HTTP server for testing purposes.
The additional work performed here is to match the default startup output of "waitress".
This is not a production quality interface and will be have badly under load.
"""
# Try to be handy as many terminals allow clicking links.
print("serving on http://{0}:{1}".format(host, port))
# Bind and launch the server; this is a blocking operation.
make_server(host, int(port), application).serve_forever() | [
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marrow/WebCore | web/server/stdlib.py | iiscgi | def iiscgi(application):
"""A specialized version of the reference WSGI-CGI server to adapt to Microsoft IIS quirks.
This is not a production quality interface and will behave badly under load.
"""
try:
from wsgiref.handlers import IISCGIHandler
except ImportError:
print("Python 3.2 or newer is required.")
if not __debug__:
warnings.warn("Interactive debugging and other persistence-based processes will not work.")
IISCGIHandler().run(application) | python | def iiscgi(application):
"""A specialized version of the reference WSGI-CGI server to adapt to Microsoft IIS quirks.
This is not a production quality interface and will behave badly under load.
"""
try:
from wsgiref.handlers import IISCGIHandler
except ImportError:
print("Python 3.2 or newer is required.")
if not __debug__:
warnings.warn("Interactive debugging and other persistence-based processes will not work.")
IISCGIHandler().run(application) | [
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marrow/WebCore | web/server/fcgi.py | serve | def serve(application, host='127.0.0.1', port=8080, socket=None, **options):
"""Basic FastCGI support via flup.
This web server has many, many options. Please see the Flup project documentation for details.
"""
# Allow either on-disk socket (recommended) or TCP/IP socket use.
if not socket:
bindAddress = (host, int(port))
else:
bindAddress = socket
# Bind and start the blocking web server interface.
WSGIServer(application, bindAddress=bindAddress, **options).run() | python | def serve(application, host='127.0.0.1', port=8080, socket=None, **options):
"""Basic FastCGI support via flup.
This web server has many, many options. Please see the Flup project documentation for details.
"""
# Allow either on-disk socket (recommended) or TCP/IP socket use.
if not socket:
bindAddress = (host, int(port))
else:
bindAddress = socket
# Bind and start the blocking web server interface.
WSGIServer(application, bindAddress=bindAddress, **options).run() | [
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bitlabstudio/django-subscribe | subscribe/forms.py | SubscriptionCreateForm._get_method_kwargs | def _get_method_kwargs(self):
"""
Helper method. Returns kwargs needed to filter the correct object.
Can also be used to create the correct object.
"""
method_kwargs = {
'user': self.user,
'content_type': self.ctype,
'object_id': self.content_object.pk,
}
return method_kwargs | python | def _get_method_kwargs(self):
"""
Helper method. Returns kwargs needed to filter the correct object.
Can also be used to create the correct object.
"""
method_kwargs = {
'user': self.user,
'content_type': self.ctype,
'object_id': self.content_object.pk,
}
return method_kwargs | [
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bitlabstudio/django-subscribe | subscribe/forms.py | SubscriptionCreateForm.save | def save(self, *args, **kwargs):
"""Adds a subscription for the given user to the given object."""
method_kwargs = self._get_method_kwargs()
try:
subscription = Subscription.objects.get(**method_kwargs)
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subscription = Subscription.objects.create(**method_kwargs)
return subscription | python | def save(self, *args, **kwargs):
"""Adds a subscription for the given user to the given object."""
method_kwargs = self._get_method_kwargs()
try:
subscription = Subscription.objects.get(**method_kwargs)
except Subscription.DoesNotExist:
subscription = Subscription.objects.create(**method_kwargs)
return subscription | [
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marrow/WebCore | web/ext/base.py | BaseExtension.prepare | def prepare(self, context):
"""Add the usual suspects to the context.
This adds `request`, `response`, and `path` to the `RequestContext` instance.
"""
if __debug__:
log.debug("Preparing request context.", extra=dict(request=id(context)))
# Bridge in WebOb `Request` and `Response` objects.
# Extensions shouldn't rely on these, using `environ` where possible instead.
context.request = Request(context.environ)
context.response = Response(request=context.request)
# Record the initial path representing the point where a front-end web server bridged to us.
context.environ['web.base'] = context.request.script_name
# Track the remaining (unprocessed) path elements.
context.request.remainder = context.request.path_info.split('/')
if context.request.remainder and not context.request.remainder[0]:
del context.request.remainder[0]
# Track the "breadcrumb list" of dispatch through distinct controllers.
context.path = Bread() | python | def prepare(self, context):
"""Add the usual suspects to the context.
This adds `request`, `response`, and `path` to the `RequestContext` instance.
"""
if __debug__:
log.debug("Preparing request context.", extra=dict(request=id(context)))
# Bridge in WebOb `Request` and `Response` objects.
# Extensions shouldn't rely on these, using `environ` where possible instead.
context.request = Request(context.environ)
context.response = Response(request=context.request)
# Record the initial path representing the point where a front-end web server bridged to us.
context.environ['web.base'] = context.request.script_name
# Track the remaining (unprocessed) path elements.
context.request.remainder = context.request.path_info.split('/')
if context.request.remainder and not context.request.remainder[0]:
del context.request.remainder[0]
# Track the "breadcrumb list" of dispatch through distinct controllers.
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marrow/WebCore | web/ext/base.py | BaseExtension.dispatch | def dispatch(self, context, consumed, handler, is_endpoint):
"""Called as dispatch descends into a tier.
The base extension uses this to maintain the "current url".
"""
request = context.request
if __debug__:
log.debug("Handling dispatch event.", extra=dict(
request = id(context),
consumed = consumed,
handler = safe_name(handler),
endpoint = is_endpoint
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# The leading path element (leading slash) requires special treatment.
if not consumed and context.request.path_info_peek() == '':
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nConsumed = 0
if consumed:
# Migrate path elements consumed from the `PATH_INFO` to `SCRIPT_NAME` WSGI environment variables.
if not isinstance(consumed, (list, tuple)):
consumed = consumed.split('/')
for element in consumed:
if element == context.request.path_info_peek():
context.request.path_info_pop()
nConsumed += 1
else:
break
# Update the breadcrumb list.
context.path.append(Crumb(handler, Path(request.script_name)))
if consumed: # Lastly, update the remaining path element list.
request.remainder = request.remainder[nConsumed:] | python | def dispatch(self, context, consumed, handler, is_endpoint):
"""Called as dispatch descends into a tier.
The base extension uses this to maintain the "current url".
"""
request = context.request
if __debug__:
log.debug("Handling dispatch event.", extra=dict(
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handler = safe_name(handler),
endpoint = is_endpoint
))
# The leading path element (leading slash) requires special treatment.
if not consumed and context.request.path_info_peek() == '':
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nConsumed += 1
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break
# Update the breadcrumb list.
context.path.append(Crumb(handler, Path(request.script_name)))
if consumed: # Lastly, update the remaining path element list.
request.remainder = request.remainder[nConsumed:] | [
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marrow/WebCore | web/ext/base.py | BaseExtension.render_none | def render_none(self, context, result):
"""Render empty responses."""
context.response.body = b''
del context.response.content_length
return True | python | def render_none(self, context, result):
"""Render empty responses."""
context.response.body = b''
del context.response.content_length
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marrow/WebCore | web/ext/base.py | BaseExtension.render_binary | def render_binary(self, context, result):
"""Return binary responses unmodified."""
context.response.app_iter = iter((result, )) # This wraps the binary string in a WSGI body iterable.
return True | python | def render_binary(self, context, result):
"""Return binary responses unmodified."""
context.response.app_iter = iter((result, )) # This wraps the binary string in a WSGI body iterable.
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marrow/WebCore | web/ext/base.py | BaseExtension.render_file | def render_file(self, context, result):
"""Perform appropriate metadata wrangling for returned open file handles."""
if __debug__:
log.debug("Processing file-like object.", extra=dict(request=id(context), result=repr(result)))
response = context.response
response.conditional_response = True
modified = mktime(gmtime(getmtime(result.name)))
response.last_modified = datetime.fromtimestamp(modified)
ct, ce = guess_type(result.name)
if not ct: ct = 'application/octet-stream'
response.content_type, response.content_encoding = ct, ce
response.etag = unicode(modified)
result.seek(0, 2) # Seek to the end of the file.
response.content_length = result.tell()
result.seek(0) # Seek back to the start of the file.
response.body_file = result
return True | python | def render_file(self, context, result):
"""Perform appropriate metadata wrangling for returned open file handles."""
if __debug__:
log.debug("Processing file-like object.", extra=dict(request=id(context), result=repr(result)))
response = context.response
response.conditional_response = True
modified = mktime(gmtime(getmtime(result.name)))
response.last_modified = datetime.fromtimestamp(modified)
ct, ce = guess_type(result.name)
if not ct: ct = 'application/octet-stream'
response.content_type, response.content_encoding = ct, ce
response.etag = unicode(modified)
result.seek(0, 2) # Seek to the end of the file.
response.content_length = result.tell()
result.seek(0) # Seek back to the start of the file.
response.body_file = result
return True | [
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marrow/WebCore | web/ext/base.py | BaseExtension.render_generator | def render_generator(self, context, result):
"""Attempt to serve generator responses through stream encoding.
This allows for direct use of cinje template functions, which are generators, as returned views.
"""
context.response.encoding = 'utf8'
context.response.app_iter = (
(i.encode('utf8') if isinstance(i, unicode) else i) # Stream encode unicode chunks.
for i in result if i is not None # Skip None values.
)
return True | python | def render_generator(self, context, result):
"""Attempt to serve generator responses through stream encoding.
This allows for direct use of cinje template functions, which are generators, as returned views.
"""
context.response.encoding = 'utf8'
context.response.app_iter = (
(i.encode('utf8') if isinstance(i, unicode) else i) # Stream encode unicode chunks.
for i in result if i is not None # Skip None values.
)
return True | [
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marrow/WebCore | web/server/cherrypy_.py | serve | def serve(application, host='127.0.0.1', port=8080):
"""CherryPy-based WSGI-HTTP server."""
# Instantiate the server with our configuration and application.
server = CherryPyWSGIServer((host, int(port)), application, server_name=host)
# Try to be handy as many terminals allow clicking links.
print("serving on http://{0}:{1}".format(host, port))
# Bind and launch the server; this is a blocking operation.
try:
server.start()
except KeyboardInterrupt:
server.stop() | python | def serve(application, host='127.0.0.1', port=8080):
"""CherryPy-based WSGI-HTTP server."""
# Instantiate the server with our configuration and application.
server = CherryPyWSGIServer((host, int(port)), application, server_name=host)
# Try to be handy as many terminals allow clicking links.
print("serving on http://{0}:{1}".format(host, port))
# Bind and launch the server; this is a blocking operation.
try:
server.start()
except KeyboardInterrupt:
server.stop() | [
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broadinstitute/xtermcolor | xtermcolor/ColorMap.py | TerminalColorMap.colorize | def colorize(self, string, rgb=None, ansi=None, bg=None, ansi_bg=None):
'''Returns the colored string'''
if not isinstance(string, str):
string = str(string)
if rgb is None and ansi is None:
raise TerminalColorMapException(
'colorize: must specify one named parameter: rgb or ansi')
if rgb is not None and ansi is not None:
raise TerminalColorMapException(
'colorize: must specify only one named parameter: rgb or ansi')
if bg is not None and ansi_bg is not None:
raise TerminalColorMapException(
'colorize: must specify only one named parameter: bg or ansi_bg')
if rgb is not None:
(closestAnsi, closestRgb) = self.convert(rgb)
elif ansi is not None:
(closestAnsi, closestRgb) = (ansi, self.colors[ansi])
if bg is None and ansi_bg is None:
return "\033[38;5;{ansiCode:d}m{string:s}\033[0m".format(ansiCode=closestAnsi, string=string)
if bg is not None:
(closestBgAnsi, unused) = self.convert(bg)
elif ansi_bg is not None:
(closestBgAnsi, unused) = (ansi_bg, self.colors[ansi_bg])
return "\033[38;5;{ansiCode:d}m\033[48;5;{bf:d}m{string:s}\033[0m".format(ansiCode=closestAnsi, bf=closestBgAnsi, string=string) | python | def colorize(self, string, rgb=None, ansi=None, bg=None, ansi_bg=None):
'''Returns the colored string'''
if not isinstance(string, str):
string = str(string)
if rgb is None and ansi is None:
raise TerminalColorMapException(
'colorize: must specify one named parameter: rgb or ansi')
if rgb is not None and ansi is not None:
raise TerminalColorMapException(
'colorize: must specify only one named parameter: rgb or ansi')
if bg is not None and ansi_bg is not None:
raise TerminalColorMapException(
'colorize: must specify only one named parameter: bg or ansi_bg')
if rgb is not None:
(closestAnsi, closestRgb) = self.convert(rgb)
elif ansi is not None:
(closestAnsi, closestRgb) = (ansi, self.colors[ansi])
if bg is None and ansi_bg is None:
return "\033[38;5;{ansiCode:d}m{string:s}\033[0m".format(ansiCode=closestAnsi, string=string)
if bg is not None:
(closestBgAnsi, unused) = self.convert(bg)
elif ansi_bg is not None:
(closestBgAnsi, unused) = (ansi_bg, self.colors[ansi_bg])
return "\033[38;5;{ansiCode:d}m\033[48;5;{bf:d}m{string:s}\033[0m".format(ansiCode=closestAnsi, bf=closestBgAnsi, string=string) | [
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marrow/WebCore | web/ext/serialize.py | SerializationExtension.render_serialization | def render_serialization(self, context, result):
"""Render serialized responses."""
resp = context.response
serial = context.serialize
match = context.request.accept.best_match(serial.types, default_match=self.default)
result = serial[match](result)
if isinstance(result, str):
result = result.decode('utf-8')
resp.charset = 'utf-8'
resp.content_type = match
resp.text = result
return True | python | def render_serialization(self, context, result):
"""Render serialized responses."""
resp = context.response
serial = context.serialize
match = context.request.accept.best_match(serial.types, default_match=self.default)
result = serial[match](result)
if isinstance(result, str):
result = result.decode('utf-8')
resp.charset = 'utf-8'
resp.content_type = match
resp.text = result
return True | [
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marrow/WebCore | web/server/eventlet_.py | serve | def serve(application, host='127.0.0.1', port=8080):
"""Eventlet-based WSGI-HTTP server.
For a more fully-featured Eventlet-capable interface, see also [Spawning](http://pypi.python.org/pypi/Spawning/).
"""
# Instantiate the server with a bound port and with our application.
server(listen(host, int(port)), application) | python | def serve(application, host='127.0.0.1', port=8080):
"""Eventlet-based WSGI-HTTP server.
For a more fully-featured Eventlet-capable interface, see also [Spawning](http://pypi.python.org/pypi/Spawning/).
"""
# Instantiate the server with a bound port and with our application.
server(listen(host, int(port)), application) | [
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NeuroML/pyNeuroML | examples/component_evaluation.py | main | def main(args=None):
"""Main"""
vs = [(v-100)*0.001 for v in range(200)]
for f in ['IM.channel.nml','Kd.channel.nml']:
nml_doc = pynml.read_neuroml2_file(f)
for ct in nml_doc.ComponentType:
ys = []
for v in vs:
req_variables = {'v':'%sV'%v,'vShift':'10mV'}
vals = pynml.evaluate_component(ct,req_variables=req_variables)
print vals
if 'x' in vals:
ys.append(vals['x'])
if 't' in vals:
ys.append(vals['t'])
if 'r' in vals:
ys.append(vals['r'])
ax = pynml.generate_plot([vs],[ys],
"Some traces from %s in %s"%(ct.name,f),
show_plot_already=False )
print vals
plt.show() | python | def main(args=None):
"""Main"""
vs = [(v-100)*0.001 for v in range(200)]
for f in ['IM.channel.nml','Kd.channel.nml']:
nml_doc = pynml.read_neuroml2_file(f)
for ct in nml_doc.ComponentType:
ys = []
for v in vs:
req_variables = {'v':'%sV'%v,'vShift':'10mV'}
vals = pynml.evaluate_component(ct,req_variables=req_variables)
print vals
if 'x' in vals:
ys.append(vals['x'])
if 't' in vals:
ys.append(vals['t'])
if 'r' in vals:
ys.append(vals['r'])
ax = pynml.generate_plot([vs],[ys],
"Some traces from %s in %s"%(ct.name,f),
show_plot_already=False )
print vals
plt.show() | [
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NeuroML/pyNeuroML | pyneuroml/analysis/NML2ChannelAnalysis.py | process_args | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(
description=("A script which can be run to generate a LEMS "
"file to analyse the behaviour of channels in "
"NeuroML 2"))
parser.add_argument('channelFiles',
type=str,
nargs='+',
metavar='<NeuroML 2 Channel file>',
help="Name of the NeuroML 2 file(s)")
parser.add_argument('-v',
action='store_true',
default=DEFAULTS['v'],
help="Verbose output")
parser.add_argument('-minV',
type=int,
metavar='<min v>',
default=DEFAULTS['minV'],
help="Minimum voltage to test (integer, mV), default: %smV"%DEFAULTS['minV'])
parser.add_argument('-maxV',
type=int,
metavar='<max v>',
default=DEFAULTS['maxV'],
help="Maximum voltage to test (integer, mV), default: %smV"%DEFAULTS['maxV'])
parser.add_argument('-temperature',
type=float,
metavar='<temperature>',
default=DEFAULTS['temperature'],
help="Temperature (float, celsius), default: %sdegC"%DEFAULTS['temperature'])
parser.add_argument('-duration',
type=float,
metavar='<duration>',
default=DEFAULTS['duration'],
help="Duration of simulation in ms, default: %sms"%DEFAULTS['duration'])
parser.add_argument('-clampDelay',
type=float,
metavar='<clamp delay>',
default=DEFAULTS['clampDelay'],
help="Delay before voltage clamp is activated in ms, default: %sms"%DEFAULTS['clampDelay'])
parser.add_argument('-clampDuration',
type=float,
metavar='<clamp duration>',
default=DEFAULTS['clampDuration'],
help="Duration of voltage clamp in ms, default: %sms"%DEFAULTS['clampDuration'])
parser.add_argument('-clampBaseVoltage',
type=float,
metavar='<clamp base voltage>',
default=DEFAULTS['clampBaseVoltage'],
help="Clamp base (starting/finishing) voltage in mV, default: %smV"%DEFAULTS['clampBaseVoltage'])
parser.add_argument('-stepTargetVoltage',
type=float,
metavar='<step target voltage>',
default=DEFAULTS['stepTargetVoltage'],
help=("Voltage in mV through which to step voltage clamps, default: %smV"%DEFAULTS['stepTargetVoltage']))
parser.add_argument('-erev',
type=float,
metavar='<reversal potential>',
default=DEFAULTS['erev'],
help="Reversal potential of channel for currents, default: %smV"%DEFAULTS['erev'])
parser.add_argument('-scaleDt',
type=float,
metavar='<scale dt in generated LEMS>',
default=DEFAULTS['scaleDt'],
help="Scale dt in generated LEMS, default: %s"%DEFAULTS['scaleDt'])
parser.add_argument('-caConc',
type=float,
metavar='<Ca2+ concentration>',
default=DEFAULTS['caConc'],
help=("Internal concentration of Ca2+ (float, "
"concentration in mM), default: %smM"%DEFAULTS['caConc']))
parser.add_argument('-datSuffix',
type=str,
metavar='<dat suffix>',
default=DEFAULTS['datSuffix'],
help="String to add to dat file names (before .dat)")
parser.add_argument('-norun',
action='store_true',
default=DEFAULTS['norun'],
help=("If used, just generate the LEMS file, "
"don't run it"))
parser.add_argument('-nogui',
action='store_true',
default=DEFAULTS['nogui'],
help=("Supress plotting of variables and only save "
"data to file"))
parser.add_argument('-html',
action='store_true',
default=DEFAULTS['html'],
help=("Generate a HTML page featuring the plots for the "
"channel"))
parser.add_argument('-md',
action='store_true',
default=DEFAULTS['md'],
help=("Generate a (GitHub flavoured) Markdown page featuring the plots for the "
"channel"))
parser.add_argument('-ivCurve',
action='store_true',
default=DEFAULTS['ivCurve'],
help=("Save currents through voltage clamp at each "
"level & plot current vs voltage for ion "
"channel"))
return parser.parse_args() | python | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(
description=("A script which can be run to generate a LEMS "
"file to analyse the behaviour of channels in "
"NeuroML 2"))
parser.add_argument('channelFiles',
type=str,
nargs='+',
metavar='<NeuroML 2 Channel file>',
help="Name of the NeuroML 2 file(s)")
parser.add_argument('-v',
action='store_true',
default=DEFAULTS['v'],
help="Verbose output")
parser.add_argument('-minV',
type=int,
metavar='<min v>',
default=DEFAULTS['minV'],
help="Minimum voltage to test (integer, mV), default: %smV"%DEFAULTS['minV'])
parser.add_argument('-maxV',
type=int,
metavar='<max v>',
default=DEFAULTS['maxV'],
help="Maximum voltage to test (integer, mV), default: %smV"%DEFAULTS['maxV'])
parser.add_argument('-temperature',
type=float,
metavar='<temperature>',
default=DEFAULTS['temperature'],
help="Temperature (float, celsius), default: %sdegC"%DEFAULTS['temperature'])
parser.add_argument('-duration',
type=float,
metavar='<duration>',
default=DEFAULTS['duration'],
help="Duration of simulation in ms, default: %sms"%DEFAULTS['duration'])
parser.add_argument('-clampDelay',
type=float,
metavar='<clamp delay>',
default=DEFAULTS['clampDelay'],
help="Delay before voltage clamp is activated in ms, default: %sms"%DEFAULTS['clampDelay'])
parser.add_argument('-clampDuration',
type=float,
metavar='<clamp duration>',
default=DEFAULTS['clampDuration'],
help="Duration of voltage clamp in ms, default: %sms"%DEFAULTS['clampDuration'])
parser.add_argument('-clampBaseVoltage',
type=float,
metavar='<clamp base voltage>',
default=DEFAULTS['clampBaseVoltage'],
help="Clamp base (starting/finishing) voltage in mV, default: %smV"%DEFAULTS['clampBaseVoltage'])
parser.add_argument('-stepTargetVoltage',
type=float,
metavar='<step target voltage>',
default=DEFAULTS['stepTargetVoltage'],
help=("Voltage in mV through which to step voltage clamps, default: %smV"%DEFAULTS['stepTargetVoltage']))
parser.add_argument('-erev',
type=float,
metavar='<reversal potential>',
default=DEFAULTS['erev'],
help="Reversal potential of channel for currents, default: %smV"%DEFAULTS['erev'])
parser.add_argument('-scaleDt',
type=float,
metavar='<scale dt in generated LEMS>',
default=DEFAULTS['scaleDt'],
help="Scale dt in generated LEMS, default: %s"%DEFAULTS['scaleDt'])
parser.add_argument('-caConc',
type=float,
metavar='<Ca2+ concentration>',
default=DEFAULTS['caConc'],
help=("Internal concentration of Ca2+ (float, "
"concentration in mM), default: %smM"%DEFAULTS['caConc']))
parser.add_argument('-datSuffix',
type=str,
metavar='<dat suffix>',
default=DEFAULTS['datSuffix'],
help="String to add to dat file names (before .dat)")
parser.add_argument('-norun',
action='store_true',
default=DEFAULTS['norun'],
help=("If used, just generate the LEMS file, "
"don't run it"))
parser.add_argument('-nogui',
action='store_true',
default=DEFAULTS['nogui'],
help=("Supress plotting of variables and only save "
"data to file"))
parser.add_argument('-html',
action='store_true',
default=DEFAULTS['html'],
help=("Generate a HTML page featuring the plots for the "
"channel"))
parser.add_argument('-md',
action='store_true',
default=DEFAULTS['md'],
help=("Generate a (GitHub flavoured) Markdown page featuring the plots for the "
"channel"))
parser.add_argument('-ivCurve',
action='store_true',
default=DEFAULTS['ivCurve'],
help=("Save currents through voltage clamp at each "
"level & plot current vs voltage for ion "
"channel"))
return parser.parse_args() | [
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] | train | https://github.com/NeuroML/pyNeuroML/blob/aeba2e3040b360bb26556f643cccbfb3dac3b8fb/pyneuroml/analysis/NML2ChannelAnalysis.py#L58-L185 |
NeuroML/pyNeuroML | pyneuroml/analysis/NML2ChannelAnalysis.py | plot_iv_curve | def plot_iv_curve(a, hold_v, i, *plt_args, **plt_kwargs):
"""A single IV curve"""
grid = plt_kwargs.pop('grid',True)
same_fig = plt_kwargs.pop('same_fig',False)
if not len(plt_args):
plt_args = ('ko-',)
if 'label' not in plt_kwargs:
plt_kwargs['label'] = 'Current'
if not same_fig:
make_iv_curve_fig(a, grid=grid)
if type(i) is dict:
i = [i[v] for v in hold_v]
plt.plot([v*1e3 for v in hold_v], [ii*1e12 for ii in i], *plt_args, **plt_kwargs)
plt.legend(loc=2) | python | def plot_iv_curve(a, hold_v, i, *plt_args, **plt_kwargs):
"""A single IV curve"""
grid = plt_kwargs.pop('grid',True)
same_fig = plt_kwargs.pop('same_fig',False)
if not len(plt_args):
plt_args = ('ko-',)
if 'label' not in plt_kwargs:
plt_kwargs['label'] = 'Current'
if not same_fig:
make_iv_curve_fig(a, grid=grid)
if type(i) is dict:
i = [i[v] for v in hold_v]
plt.plot([v*1e3 for v in hold_v], [ii*1e12 for ii in i], *plt_args, **plt_kwargs)
plt.legend(loc=2) | [
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marrow/WebCore | example/stream.py | root | def root(context):
"""Multipart AJAX request example.
See: http://test.getify.com/mpAjax/description.html
"""
response = context.response
parts = []
for i in range(12):
for j in range(12):
parts.append(executor.submit(mul, i, j))
def stream(parts, timeout=None):
try:
for future in as_completed(parts, timeout):
mime, result = future.result()
result = result.encode('utf8')
yield "!!!!!!=_NextPart_{num}\nContent-Type: {mime}\nContent-Length: {length}\n\n".format(
num = randint(100000000, 999999999),
mime = mime,
length = len(result)
).encode('utf8') + result
except TimeoutError:
for future in parts:
future.cancel()
response.content_length = None
response.app_iter = stream(parts, 0.2)
return response | python | def root(context):
"""Multipart AJAX request example.
See: http://test.getify.com/mpAjax/description.html
"""
response = context.response
parts = []
for i in range(12):
for j in range(12):
parts.append(executor.submit(mul, i, j))
def stream(parts, timeout=None):
try:
for future in as_completed(parts, timeout):
mime, result = future.result()
result = result.encode('utf8')
yield "!!!!!!=_NextPart_{num}\nContent-Type: {mime}\nContent-Length: {length}\n\n".format(
num = randint(100000000, 999999999),
mime = mime,
length = len(result)
).encode('utf8') + result
except TimeoutError:
for future in parts:
future.cancel()
response.content_length = None
response.app_iter = stream(parts, 0.2)
return response | [
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See: http://test.getify.com/mpAjax/description.html | [
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contraslash/django-crud-generator | django_crud_generator/django_crud_generator.py | render_template_with_args_in_file | def render_template_with_args_in_file(file, template_file_name, **kwargs):
"""
Get a file and render the content of the template_file_name with kwargs in a file
:param file: A File Stream to write
:param template_file_name: path to route with template name
:param **kwargs: Args to be rendered in template
"""
template_file_content = "".join(
codecs.open(
template_file_name,
encoding='UTF-8'
).readlines()
)
template_rendered = string.Template(template_file_content).safe_substitute(**kwargs)
file.write(template_rendered) | python | def render_template_with_args_in_file(file, template_file_name, **kwargs):
"""
Get a file and render the content of the template_file_name with kwargs in a file
:param file: A File Stream to write
:param template_file_name: path to route with template name
:param **kwargs: Args to be rendered in template
"""
template_file_content = "".join(
codecs.open(
template_file_name,
encoding='UTF-8'
).readlines()
)
template_rendered = string.Template(template_file_content).safe_substitute(**kwargs)
file.write(template_rendered) | [
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:param template_file_name: path to route with template name
:param **kwargs: Args to be rendered in template | [
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contraslash/django-crud-generator | django_crud_generator/django_crud_generator.py | create_or_open | def create_or_open(file_name, initial_template_file_name, args):
"""
Creates a file or open the file with file_name name
:param file_name: String with a filename
:param initial_template_file_name: String with path to initial template
:param args: from console to determine path to save the files
"""
file = None
if not os.path.isfile(
os.path.join(
args['django_application_folder'],
file_name
)
):
# If file_name does not exists, create
file = codecs.open(
os.path.join(
args['django_application_folder'],
file_name
),
'w+',
encoding='UTF-8'
)
print("Creating {}".format(file_name))
if initial_template_file_name:
render_template_with_args_in_file(file, initial_template_file_name, **{})
else:
# If file exists, just load the file
file = codecs.open(
os.path.join(
args['django_application_folder'],
file_name
),
'a+',
encoding='UTF-8'
)
return file | python | def create_or_open(file_name, initial_template_file_name, args):
"""
Creates a file or open the file with file_name name
:param file_name: String with a filename
:param initial_template_file_name: String with path to initial template
:param args: from console to determine path to save the files
"""
file = None
if not os.path.isfile(
os.path.join(
args['django_application_folder'],
file_name
)
):
# If file_name does not exists, create
file = codecs.open(
os.path.join(
args['django_application_folder'],
file_name
),
'w+',
encoding='UTF-8'
)
print("Creating {}".format(file_name))
if initial_template_file_name:
render_template_with_args_in_file(file, initial_template_file_name, **{})
else:
# If file exists, just load the file
file = codecs.open(
os.path.join(
args['django_application_folder'],
file_name
),
'a+',
encoding='UTF-8'
)
return file | [
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:param args: from console to determine path to save the files | [
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] | train | https://github.com/contraslash/django-crud-generator/blob/9080a227291a36cf0d93b49d20601e55a616da1e/django_crud_generator/django_crud_generator.py#L37-L74 |
contraslash/django-crud-generator | django_crud_generator/django_crud_generator.py | generic_insert_module | def generic_insert_module(module_name, args, **kwargs):
"""
In general we have a initial template and then insert new data, so we dont repeat the schema for each module
:param module_name: String with module name
:paran **kwargs: Args to be rendered in template
"""
file = create_or_open(
'{}.py'.format(module_name),
os.path.join(
BASE_TEMPLATES_DIR,
'{}_initial.py.tmpl'.format(module_name)
),
args
)
render_template_with_args_in_file(
file,
os.path.join(
BASE_TEMPLATES_DIR,
'{}.py.tmpl'.format(module_name)
),
**kwargs
)
file.close() | python | def generic_insert_module(module_name, args, **kwargs):
"""
In general we have a initial template and then insert new data, so we dont repeat the schema for each module
:param module_name: String with module name
:paran **kwargs: Args to be rendered in template
"""
file = create_or_open(
'{}.py'.format(module_name),
os.path.join(
BASE_TEMPLATES_DIR,
'{}_initial.py.tmpl'.format(module_name)
),
args
)
render_template_with_args_in_file(
file,
os.path.join(
BASE_TEMPLATES_DIR,
'{}.py.tmpl'.format(module_name)
),
**kwargs
)
file.close() | [
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contraslash/django-crud-generator | django_crud_generator/django_crud_generator.py | sanity_check | def sanity_check(args):
"""
Verify if the work folder is a django app.
A valid django app always must have a models.py file
:return: None
"""
if not os.path.isfile(
os.path.join(
args['django_application_folder'],
'models.py'
)
):
print("django_application_folder is not a Django application folder")
sys.exit(1) | python | def sanity_check(args):
"""
Verify if the work folder is a django app.
A valid django app always must have a models.py file
:return: None
"""
if not os.path.isfile(
os.path.join(
args['django_application_folder'],
'models.py'
)
):
print("django_application_folder is not a Django application folder")
sys.exit(1) | [
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contraslash/django-crud-generator | django_crud_generator/django_crud_generator.py | generic_insert_with_folder | def generic_insert_with_folder(folder_name, file_name, template_name, args):
"""
In general if we need to put a file on a folder, we use this method
"""
# First we make sure views are a package instead a file
if not os.path.isdir(
os.path.join(
args['django_application_folder'],
folder_name
)
):
os.mkdir(os.path.join(args['django_application_folder'], folder_name))
codecs.open(
os.path.join(
args['django_application_folder'],
folder_name,
'__init__.py'
),
'w+'
)
view_file = create_or_open(
os.path.join(
folder_name,
'{}.py'.format(file_name)
),
'',
args
)
# Load content from template
render_template_with_args_in_file(
view_file,
os.path.join(
BASE_TEMPLATES_DIR,
template_name
),
model_name=args['model_name'],
model_prefix=args['model_prefix'],
model_name_lower=args['model_name'].lower(),
application_name=args['django_application_folder'].split("/")[-1]
)
view_file.close() | python | def generic_insert_with_folder(folder_name, file_name, template_name, args):
"""
In general if we need to put a file on a folder, we use this method
"""
# First we make sure views are a package instead a file
if not os.path.isdir(
os.path.join(
args['django_application_folder'],
folder_name
)
):
os.mkdir(os.path.join(args['django_application_folder'], folder_name))
codecs.open(
os.path.join(
args['django_application_folder'],
folder_name,
'__init__.py'
),
'w+'
)
view_file = create_or_open(
os.path.join(
folder_name,
'{}.py'.format(file_name)
),
'',
args
)
# Load content from template
render_template_with_args_in_file(
view_file,
os.path.join(
BASE_TEMPLATES_DIR,
template_name
),
model_name=args['model_name'],
model_prefix=args['model_prefix'],
model_name_lower=args['model_name'].lower(),
application_name=args['django_application_folder'].split("/")[-1]
)
view_file.close() | [
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marrow/WebCore | web/server/waitress_.py | serve | def serve(application, host='127.0.0.1', port=8080, threads=4, **kw):
"""The recommended development HTTP server.
Note that this server performs additional buffering and will not honour chunked encoding breaks.
"""
# Bind and start the server; this is a blocking process.
serve_(application, host=host, port=int(port), threads=int(threads), **kw) | python | def serve(application, host='127.0.0.1', port=8080, threads=4, **kw):
"""The recommended development HTTP server.
Note that this server performs additional buffering and will not honour chunked encoding breaks.
"""
# Bind and start the server; this is a blocking process.
serve_(application, host=host, port=int(port), threads=int(threads), **kw) | [
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NeuroML/pyNeuroML | pyneuroml/tune/NeuroMLSimulation.py | NeuroMLSimulation.show | def show(self):
"""
Plot the result of the simulation once it's been intialized
"""
from matplotlib import pyplot as plt
if self.already_run:
for ref in self.volts.keys():
plt.plot(self.t, self.volts[ref], label=ref)
plt.title("Simulation voltage vs time")
plt.legend()
plt.xlabel("Time [ms]")
plt.ylabel("Voltage [mV]")
else:
pynml.print_comment("First you have to 'go()' the simulation.", True)
plt.show() | python | def show(self):
"""
Plot the result of the simulation once it's been intialized
"""
from matplotlib import pyplot as plt
if self.already_run:
for ref in self.volts.keys():
plt.plot(self.t, self.volts[ref], label=ref)
plt.title("Simulation voltage vs time")
plt.legend()
plt.xlabel("Time [ms]")
plt.ylabel("Voltage [mV]")
else:
pynml.print_comment("First you have to 'go()' the simulation.", True)
plt.show() | [
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marrow/WebCore | example/annotation.py | Root.mul | def mul(self, a: int = None, b: int = None) -> 'json':
"""Multiply two values together and return the result via JSON.
Python 3 function annotations are used to ensure that the arguments are integers. This requires the
functionality of `web.ext.annotation:AnnotationExtension`.
There are several ways to execute this method:
* POST http://localhost:8080/mul
* GET http://localhost:8080/mul?a=27&b=42
* GET http://localhost:8080/mul/27/42
The latter relies on the fact we can't descend past a callable method so the remaining path elements are
used as positional arguments, whereas the others rely on keyword argument assignment from a form-encoded
request body or query string arguments. (Security note: any data in the request body takes presidence over
query string arguments!)
You can easily test these on the command line using cURL:
curl http://localhost:8080/mul/27/42 # HTTP GET
curl -d a=27 -d b=42 http://localhost:8080/mul # HTTP POST
"""
if not a or not b:
return dict(message="Pass arguments a and b to multiply them together!")
return dict(answer=a * b) | python | def mul(self, a: int = None, b: int = None) -> 'json':
"""Multiply two values together and return the result via JSON.
Python 3 function annotations are used to ensure that the arguments are integers. This requires the
functionality of `web.ext.annotation:AnnotationExtension`.
There are several ways to execute this method:
* POST http://localhost:8080/mul
* GET http://localhost:8080/mul?a=27&b=42
* GET http://localhost:8080/mul/27/42
The latter relies on the fact we can't descend past a callable method so the remaining path elements are
used as positional arguments, whereas the others rely on keyword argument assignment from a form-encoded
request body or query string arguments. (Security note: any data in the request body takes presidence over
query string arguments!)
You can easily test these on the command line using cURL:
curl http://localhost:8080/mul/27/42 # HTTP GET
curl -d a=27 -d b=42 http://localhost:8080/mul # HTTP POST
"""
if not a or not b:
return dict(message="Pass arguments a and b to multiply them together!")
return dict(answer=a * b) | [
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curl http://localhost:8080/mul/27/42 # HTTP GET
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broadinstitute/xtermcolor | xtermcolor/__init__.py | colorize | def colorize(string, rgb=None, ansi=None, bg=None, ansi_bg=None, fd=1):
'''Returns the colored string to print on the terminal.
This function detects the terminal type and if it is supported and the
output is not going to a pipe or a file, then it will return the colored
string, otherwise it will return the string without modifications.
string = the string to print. Only accepts strings, unicode strings must
be encoded in advance.
rgb = Rgb color for the text; for example 0xFF0000 is red.
ansi = Ansi for the text
bg = Rgb color for the background
ansi_bg= Ansi color for the background
fd = The file descriptor that will be used by print, by default is the
stdout
'''
#Reinitializes if fd used is different
if colorize.fd != fd:
colorize.init = False
colorize.fd = fd
#Checks if it is on a terminal, and if the terminal is recognized
if not colorize.init:
colorize.init = True
colorize.is_term = isatty(fd)
if 'TERM' in environ:
if environ['TERM'].startswith('xterm'):
colorize.cmap = XTermColorMap()
elif environ['TERM'] == 'vt100':
colorize.cmap = VT100ColorMap()
else:
colorize.is_term = False
else:
colorize.is_term = False
if colorize.is_term:
string = colorize.cmap.colorize(string, rgb, ansi, bg, ansi_bg)
return string | python | def colorize(string, rgb=None, ansi=None, bg=None, ansi_bg=None, fd=1):
'''Returns the colored string to print on the terminal.
This function detects the terminal type and if it is supported and the
output is not going to a pipe or a file, then it will return the colored
string, otherwise it will return the string without modifications.
string = the string to print. Only accepts strings, unicode strings must
be encoded in advance.
rgb = Rgb color for the text; for example 0xFF0000 is red.
ansi = Ansi for the text
bg = Rgb color for the background
ansi_bg= Ansi color for the background
fd = The file descriptor that will be used by print, by default is the
stdout
'''
#Reinitializes if fd used is different
if colorize.fd != fd:
colorize.init = False
colorize.fd = fd
#Checks if it is on a terminal, and if the terminal is recognized
if not colorize.init:
colorize.init = True
colorize.is_term = isatty(fd)
if 'TERM' in environ:
if environ['TERM'].startswith('xterm'):
colorize.cmap = XTermColorMap()
elif environ['TERM'] == 'vt100':
colorize.cmap = VT100ColorMap()
else:
colorize.is_term = False
else:
colorize.is_term = False
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marrow/WebCore | web/ext/annotation.py | AnnotationExtension.mutate | def mutate(self, context, handler, args, kw):
"""Inspect and potentially mutate the given handler's arguments.
The args list and kw dictionary may be freely modified, though invalid arguments to the handler will fail.
"""
def cast(arg, val):
if arg not in annotations:
return
cast = annotations[key]
try:
val = cast(val)
except (ValueError, TypeError) as e:
parts = list(e.args)
parts[0] = parts[0] + " processing argument '{}'".format(arg)
e.args = tuple(parts)
raise
return val
annotations = getattr(handler.__func__ if hasattr(handler, '__func__') else handler, '__annotations__', None)
if not annotations:
return
argspec = getfullargspec(handler)
arglist = list(argspec.args)
if ismethod(handler):
del arglist[0]
for i, value in enumerate(list(args)):
key = arglist[i]
if key in annotations:
args[i] = cast(key, value)
# Convert keyword arguments
for key, value in list(items(kw)):
if key in annotations:
kw[key] = cast(key, value) | python | def mutate(self, context, handler, args, kw):
"""Inspect and potentially mutate the given handler's arguments.
The args list and kw dictionary may be freely modified, though invalid arguments to the handler will fail.
"""
def cast(arg, val):
if arg not in annotations:
return
cast = annotations[key]
try:
val = cast(val)
except (ValueError, TypeError) as e:
parts = list(e.args)
parts[0] = parts[0] + " processing argument '{}'".format(arg)
e.args = tuple(parts)
raise
return val
annotations = getattr(handler.__func__ if hasattr(handler, '__func__') else handler, '__annotations__', None)
if not annotations:
return
argspec = getfullargspec(handler)
arglist = list(argspec.args)
if ismethod(handler):
del arglist[0]
for i, value in enumerate(list(args)):
key = arglist[i]
if key in annotations:
args[i] = cast(key, value)
# Convert keyword arguments
for key, value in list(items(kw)):
if key in annotations:
kw[key] = cast(key, value) | [
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marrow/WebCore | web/ext/annotation.py | AnnotationExtension.transform | def transform(self, context, handler, result):
"""Transform the value returned by the controller endpoint.
This extension transforms returned values if the endpoint has a return type annotation.
"""
handler = handler.__func__ if hasattr(handler, '__func__') else handler
annotation = getattr(handler, '__annotations__', {}).get('return', None)
if annotation:
return (annotation, result)
return result | python | def transform(self, context, handler, result):
"""Transform the value returned by the controller endpoint.
This extension transforms returned values if the endpoint has a return type annotation.
"""
handler = handler.__func__ if hasattr(handler, '__func__') else handler
annotation = getattr(handler, '__annotations__', {}).get('return', None)
if annotation:
return (annotation, result)
return result | [
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NeuroML/pyNeuroML | pyneuroml/tune/NeuroMLTuner.py | process_args | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(
description=("A script which can be run to tune a NeuroML 2 model against a number of target properties. Work in progress!"))
parser.add_argument('prefix',
type=str,
metavar='<prefix>',
help="Prefix for optimisation run")
parser.add_argument('neuromlFile',
type=str,
metavar='<neuromlFile>',
help="NeuroML2 file containing model")
parser.add_argument('target',
type=str,
metavar='<target>',
help="Target in NeuroML2 model")
parser.add_argument('parameters',
type=str,
metavar='<parameters>',
help="List of parameter to adjust")
parser.add_argument('maxConstraints',
type=str,
metavar='<max_constraints>',
help="Max values for parameters")
parser.add_argument('minConstraints',
type=str,
metavar='<min_constraints>',
help="Min values for parameters")
parser.add_argument('targetData',
type=str,
metavar='<targetData>',
help="List of name/value pairs for properties extracted from data to judge fitness against")
parser.add_argument('weights',
type=str,
metavar='<weights>',
help="Weights to assign to each target name/value pair")
parser.add_argument('-simTime',
type=float,
metavar='<simTime>',
default=DEFAULTS['simTime'],
help="Simulation duration")
parser.add_argument('-dt',
type=float,
metavar='<dt>',
default=DEFAULTS['dt'],
help="Simulation timestep")
parser.add_argument('-analysisStartTime',
type=float,
metavar='<analysisStartTime>',
default=DEFAULTS['analysisStartTime'],
help="Analysis start time")
parser.add_argument('-populationSize',
type=int,
metavar='<populationSize>',
default=DEFAULTS['populationSize'],
help="Population size")
parser.add_argument('-maxEvaluations',
type=int,
metavar='<maxEvaluations>',
default=DEFAULTS['maxEvaluations'],
help="Maximum evaluations")
parser.add_argument('-numSelected',
type=int,
metavar='<numSelected>',
default=DEFAULTS['numSelected'],
help="Number selected")
parser.add_argument('-numOffspring',
type=int,
metavar='<numOffspring>',
default=DEFAULTS['numOffspring'],
help="Number offspring")
parser.add_argument('-mutationRate',
type=float,
metavar='<mutationRate>',
default=DEFAULTS['mutationRate'],
help="Mutation rate")
parser.add_argument('-numElites',
type=int,
metavar='<numElites>',
default=DEFAULTS['numElites'],
help="Number of elites")
parser.add_argument('-numParallelEvaluations',
type=int,
metavar='<numParallelEvaluations>',
default=DEFAULTS['numParallelEvaluations'],
help="Number of evaluations to run in parallel")
parser.add_argument('-seed',
type=int,
metavar='<seed>',
default=DEFAULTS['seed'],
help="Seed for optimiser")
parser.add_argument('-simulator',
type=str,
metavar='<simulator>',
default=DEFAULTS['simulator'],
help="Simulator to run")
parser.add_argument('-knownTargetValues',
type=str,
metavar='<knownTargetValues>',
help="List of name/value pairs which represent the known values of the target parameters")
parser.add_argument('-nogui',
action='store_true',
default=DEFAULTS['nogui'],
help="Should GUI elements be supressed?")
parser.add_argument('-showPlotAlready',
action='store_true',
default=DEFAULTS['showPlotAlready'],
help="Should generated plots be suppressed until show() called?")
parser.add_argument('-verbose',
action='store_true',
default=DEFAULTS['verbose'],
help="Verbose mode")
parser.add_argument('-dryRun',
action='store_true',
default=DEFAULTS['dryRun'],
help="Dry run; just print setup information")
parser.add_argument('-extraReportInfo',
type=str,
metavar='<extraReportInfo>',
default=DEFAULTS['extraReportInfo'],
help='Extra tag/value pairs can be put into the report.json: -extraReportInfo=["tag":"value"]')
parser.add_argument('-cleanup',
action='store_true',
default=DEFAULTS['cleanup'],
help="Should (some) generated files, e.g. *.dat, be deleted as optimisation progresses?")
return parser.parse_args() | python | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(
description=("A script which can be run to tune a NeuroML 2 model against a number of target properties. Work in progress!"))
parser.add_argument('prefix',
type=str,
metavar='<prefix>',
help="Prefix for optimisation run")
parser.add_argument('neuromlFile',
type=str,
metavar='<neuromlFile>',
help="NeuroML2 file containing model")
parser.add_argument('target',
type=str,
metavar='<target>',
help="Target in NeuroML2 model")
parser.add_argument('parameters',
type=str,
metavar='<parameters>',
help="List of parameter to adjust")
parser.add_argument('maxConstraints',
type=str,
metavar='<max_constraints>',
help="Max values for parameters")
parser.add_argument('minConstraints',
type=str,
metavar='<min_constraints>',
help="Min values for parameters")
parser.add_argument('targetData',
type=str,
metavar='<targetData>',
help="List of name/value pairs for properties extracted from data to judge fitness against")
parser.add_argument('weights',
type=str,
metavar='<weights>',
help="Weights to assign to each target name/value pair")
parser.add_argument('-simTime',
type=float,
metavar='<simTime>',
default=DEFAULTS['simTime'],
help="Simulation duration")
parser.add_argument('-dt',
type=float,
metavar='<dt>',
default=DEFAULTS['dt'],
help="Simulation timestep")
parser.add_argument('-analysisStartTime',
type=float,
metavar='<analysisStartTime>',
default=DEFAULTS['analysisStartTime'],
help="Analysis start time")
parser.add_argument('-populationSize',
type=int,
metavar='<populationSize>',
default=DEFAULTS['populationSize'],
help="Population size")
parser.add_argument('-maxEvaluations',
type=int,
metavar='<maxEvaluations>',
default=DEFAULTS['maxEvaluations'],
help="Maximum evaluations")
parser.add_argument('-numSelected',
type=int,
metavar='<numSelected>',
default=DEFAULTS['numSelected'],
help="Number selected")
parser.add_argument('-numOffspring',
type=int,
metavar='<numOffspring>',
default=DEFAULTS['numOffspring'],
help="Number offspring")
parser.add_argument('-mutationRate',
type=float,
metavar='<mutationRate>',
default=DEFAULTS['mutationRate'],
help="Mutation rate")
parser.add_argument('-numElites',
type=int,
metavar='<numElites>',
default=DEFAULTS['numElites'],
help="Number of elites")
parser.add_argument('-numParallelEvaluations',
type=int,
metavar='<numParallelEvaluations>',
default=DEFAULTS['numParallelEvaluations'],
help="Number of evaluations to run in parallel")
parser.add_argument('-seed',
type=int,
metavar='<seed>',
default=DEFAULTS['seed'],
help="Seed for optimiser")
parser.add_argument('-simulator',
type=str,
metavar='<simulator>',
default=DEFAULTS['simulator'],
help="Simulator to run")
parser.add_argument('-knownTargetValues',
type=str,
metavar='<knownTargetValues>',
help="List of name/value pairs which represent the known values of the target parameters")
parser.add_argument('-nogui',
action='store_true',
default=DEFAULTS['nogui'],
help="Should GUI elements be supressed?")
parser.add_argument('-showPlotAlready',
action='store_true',
default=DEFAULTS['showPlotAlready'],
help="Should generated plots be suppressed until show() called?")
parser.add_argument('-verbose',
action='store_true',
default=DEFAULTS['verbose'],
help="Verbose mode")
parser.add_argument('-dryRun',
action='store_true',
default=DEFAULTS['dryRun'],
help="Dry run; just print setup information")
parser.add_argument('-extraReportInfo',
type=str,
metavar='<extraReportInfo>',
default=DEFAULTS['extraReportInfo'],
help='Extra tag/value pairs can be put into the report.json: -extraReportInfo=["tag":"value"]')
parser.add_argument('-cleanup',
action='store_true',
default=DEFAULTS['cleanup'],
help="Should (some) generated files, e.g. *.dat, be deleted as optimisation progresses?")
return parser.parse_args() | [
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NeuroML/pyNeuroML | pyneuroml/povray/MakeMovie.py | process_args | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(description="A file for overlaying POVRay files generated from NeuroML by NeuroML1ToPOVRay.py with cell activity (e.g. as generated from a neuroConstruct simulation)")
parser.add_argument('prefix',
type=str,
metavar='<network prefix>',
help='Prefix for files in PovRay, e.g. use PREFIX is files are PREFIX.pov, PREFIX_net.inc, etc.')
parser.add_argument('-activity',
action='store_true',
default=False,
help="If this is specified, overlay network activity (not tested!!)")
parser.add_argument('-maxV',
type=float,
metavar='<maxV>',
default=50.0,
help='Max voltage for colour scale in mV')
parser.add_argument('-minV',
type=float,
metavar='<minV>',
default=-90.0,
help='Min voltage for colour scale in mV')
parser.add_argument('-startTime',
type=float,
metavar='<startTime>',
default=0,
help='Time in ms at which to start overlaying the simulation activity')
parser.add_argument('-endTime',
type=float,
metavar='<endTime>',
default=100,
help='End time of simulation activity in ms')
parser.add_argument('-title',
type=str,
metavar='<title>',
default='Movie generated from neuroConstruct simulation',
help='Title for movie')
parser.add_argument('-left',
type=str,
metavar='<left info>',
default='',
help='Text on left')
parser.add_argument('-frames',
type=int,
metavar='<frames>',
default=100,
help='Number of frames')
parser.add_argument('-name',
type=str,
metavar='<Movie name>',
default='output',
help='Movie name')
return parser.parse_args() | python | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(description="A file for overlaying POVRay files generated from NeuroML by NeuroML1ToPOVRay.py with cell activity (e.g. as generated from a neuroConstruct simulation)")
parser.add_argument('prefix',
type=str,
metavar='<network prefix>',
help='Prefix for files in PovRay, e.g. use PREFIX is files are PREFIX.pov, PREFIX_net.inc, etc.')
parser.add_argument('-activity',
action='store_true',
default=False,
help="If this is specified, overlay network activity (not tested!!)")
parser.add_argument('-maxV',
type=float,
metavar='<maxV>',
default=50.0,
help='Max voltage for colour scale in mV')
parser.add_argument('-minV',
type=float,
metavar='<minV>',
default=-90.0,
help='Min voltage for colour scale in mV')
parser.add_argument('-startTime',
type=float,
metavar='<startTime>',
default=0,
help='Time in ms at which to start overlaying the simulation activity')
parser.add_argument('-endTime',
type=float,
metavar='<endTime>',
default=100,
help='End time of simulation activity in ms')
parser.add_argument('-title',
type=str,
metavar='<title>',
default='Movie generated from neuroConstruct simulation',
help='Title for movie')
parser.add_argument('-left',
type=str,
metavar='<left info>',
default='',
help='Text on left')
parser.add_argument('-frames',
type=int,
metavar='<frames>',
default=100,
help='Number of frames')
parser.add_argument('-name',
type=str,
metavar='<Movie name>',
default='output',
help='Movie name')
return parser.parse_args() | [
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marrow/WebCore | web/server/tornado_.py | serve | def serve(application, host='127.0.0.1', port=8080, **options):
"""Tornado's HTTPServer.
This is a high quality asynchronous server with many options. For details, please visit:
http://www.tornadoweb.org/en/stable/httpserver.html#http-server
"""
# Wrap our our WSGI application (potentially stack) in a Tornado adapter.
container = tornado.wsgi.WSGIContainer(application)
# Spin up a Tornado HTTP server using this container.
http_server = tornado.httpserver.HTTPServer(container, **options)
http_server.listen(int(port), host)
# Start and block on the Tornado IO loop.
tornado.ioloop.IOLoop.instance().start() | python | def serve(application, host='127.0.0.1', port=8080, **options):
"""Tornado's HTTPServer.
This is a high quality asynchronous server with many options. For details, please visit:
http://www.tornadoweb.org/en/stable/httpserver.html#http-server
"""
# Wrap our our WSGI application (potentially stack) in a Tornado adapter.
container = tornado.wsgi.WSGIContainer(application)
# Spin up a Tornado HTTP server using this container.
http_server = tornado.httpserver.HTTPServer(container, **options)
http_server.listen(int(port), host)
# Start and block on the Tornado IO loop.
tornado.ioloop.IOLoop.instance().start() | [
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NeuroML/pyNeuroML | pyneuroml/pynml.py | parse_arguments | def parse_arguments():
"""Parse command line arguments"""
import argparse
parser = argparse.ArgumentParser(
description=('pyNeuroML v%s: Python utilities for NeuroML2' % __version__
+ "\n libNeuroML v%s"%(neuroml.__version__)
+ "\n jNeuroML v%s"%JNEUROML_VERSION),
usage=('pynml [-h|--help] [<shared options>] '
'<one of the mutually-exclusive options>'),
formatter_class=argparse.RawTextHelpFormatter
)
shared_options = parser.add_argument_group(
title='Shared options',
description=('These options can be added to any of the '
'mutually-exclusive options')
)
shared_options.add_argument(
'-verbose',
action='store_true',
default=DEFAULTS['v'],
help='Verbose output'
)
shared_options.add_argument(
'-java_max_memory',
metavar='MAX',
default=DEFAULTS['default_java_max_memory'],
help=('Java memory for jNeuroML, e.g. 400M, 2G (used in\n'
'-Xmx argument to java)')
)
shared_options.add_argument(
'-nogui',
action='store_true',
default=DEFAULTS['nogui'],
help=('Suppress GUI,\n'
'i.e. show no plots, just save results')
)
shared_options.add_argument(
'lems_file',
type=str,
metavar='<LEMS/NeuroML 2 file>',
help='LEMS/NeuroML 2 file to process'
)
mut_exc_opts_grp = parser.add_argument_group(
title='Mutually-exclusive options',
description='Only one of these options can be selected'
)
mut_exc_opts = mut_exc_opts_grp.add_mutually_exclusive_group(required=False)
mut_exc_opts.add_argument(
'-sedml',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert\n'
'simulation settings (duration, dt, what to save)\n'
'to SED-ML format')
)
mut_exc_opts.add_argument(
'-neuron',
nargs=argparse.REMAINDER,
help=('(Via jNeuroML) Load a LEMS file, and convert it to\n'
'NEURON format.\n'
'The full format of the \'-neuron\' option is:\n'
'-neuron [-nogui] [-run] [-outputdir dir] <LEMS file>\n'
' -nogui\n'
' do not generate gtaphical elements in NEURON,\n'
' just run, save data, and quit\n'
' -run\n'
' compile NMODL files and run the main NEURON\n'
' hoc file (Linux only currently)\n'
' -outputdir <dir>\n'
' generate NEURON files in directory <dir>\n'
' <LEMS file>\n'
' the LEMS file to use')
)
mut_exc_opts.add_argument(
'-svg',
action='store_true',
help=('(Via jNeuroML) Convert NeuroML2 file (network & cells)\n'
'to SVG format view of 3D structure')
)
mut_exc_opts.add_argument(
'-png',
action='store_true',
help=('(Via jNeuroML) Convert NeuroML2 file (network & cells)\n'
'to PNG format view of 3D structure')
)
mut_exc_opts.add_argument(
'-dlems',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to dLEMS format, a distilled form of LEMS in JSON')
)
mut_exc_opts.add_argument(
'-vertex',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to VERTEX format')
)
mut_exc_opts.add_argument(
'-xpp',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to XPPAUT format')
)
mut_exc_opts.add_argument(
'-dnsim',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to DNsim format')
)
mut_exc_opts.add_argument(
'-brian',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to Brian format')
)
mut_exc_opts.add_argument(
'-sbml',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to SBML format')
)
mut_exc_opts.add_argument(
'-matlab',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to MATLAB format')
)
mut_exc_opts.add_argument(
'-cvode',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to C format using CVODE package')
)
mut_exc_opts.add_argument(
'-nineml',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to NineML format')
)
mut_exc_opts.add_argument(
'-spineml',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to SpineML format')
)
mut_exc_opts.add_argument(
'-sbml-import',
metavar=('<SBML file>', 'duration', 'dt'),
nargs=3,
help=('(Via jNeuroML) Load a SBML file, and convert it\n'
'toLEMS format using values for duration & dt\n'
'in ms (ignoring SBML units)')
)
mut_exc_opts.add_argument(
'-sbml-import-units',
metavar=('<SBML file>', 'duration', 'dt'),
nargs=3,
help=('(Via jNeuroML) Load a SBML file, and convert it\n'
'to LEMS format using values for duration & dt\n'
'in ms (attempt to extract SBML units; ensure units\n'
'are valid in the SBML!)')
)
mut_exc_opts.add_argument(
'-vhdl',
metavar=('neuronid', '<LEMS file>'),
nargs=2,
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to VHDL format')
)
mut_exc_opts.add_argument(
'-graph',
metavar=('level'),
nargs=1,
help=('Load a NeuroML file, and convert it to a graph using\n'
'GraphViz. Detail is set by level (1, 2, etc.)')
)
mut_exc_opts.add_argument(
'-matrix',
metavar=('level'),
nargs=1,
help=('Load a NeuroML file, and convert it to a matrix displaying\n'
'connectivity. Detail is set by level (1, 2, etc.)')
)
mut_exc_opts.add_argument(
'-validate',
action='store_true',
help=('(Via jNeuroML) Validate NeuroML2 file(s) against the\n'
'latest Schema')
)
mut_exc_opts.add_argument(
'-validatev1',
action='store_true',
help=('(Via jNeuroML) Validate NeuroML file(s) against the\n'
'v1.8.1 Schema')
)
return parser.parse_args() | python | def parse_arguments():
"""Parse command line arguments"""
import argparse
parser = argparse.ArgumentParser(
description=('pyNeuroML v%s: Python utilities for NeuroML2' % __version__
+ "\n libNeuroML v%s"%(neuroml.__version__)
+ "\n jNeuroML v%s"%JNEUROML_VERSION),
usage=('pynml [-h|--help] [<shared options>] '
'<one of the mutually-exclusive options>'),
formatter_class=argparse.RawTextHelpFormatter
)
shared_options = parser.add_argument_group(
title='Shared options',
description=('These options can be added to any of the '
'mutually-exclusive options')
)
shared_options.add_argument(
'-verbose',
action='store_true',
default=DEFAULTS['v'],
help='Verbose output'
)
shared_options.add_argument(
'-java_max_memory',
metavar='MAX',
default=DEFAULTS['default_java_max_memory'],
help=('Java memory for jNeuroML, e.g. 400M, 2G (used in\n'
'-Xmx argument to java)')
)
shared_options.add_argument(
'-nogui',
action='store_true',
default=DEFAULTS['nogui'],
help=('Suppress GUI,\n'
'i.e. show no plots, just save results')
)
shared_options.add_argument(
'lems_file',
type=str,
metavar='<LEMS/NeuroML 2 file>',
help='LEMS/NeuroML 2 file to process'
)
mut_exc_opts_grp = parser.add_argument_group(
title='Mutually-exclusive options',
description='Only one of these options can be selected'
)
mut_exc_opts = mut_exc_opts_grp.add_mutually_exclusive_group(required=False)
mut_exc_opts.add_argument(
'-sedml',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert\n'
'simulation settings (duration, dt, what to save)\n'
'to SED-ML format')
)
mut_exc_opts.add_argument(
'-neuron',
nargs=argparse.REMAINDER,
help=('(Via jNeuroML) Load a LEMS file, and convert it to\n'
'NEURON format.\n'
'The full format of the \'-neuron\' option is:\n'
'-neuron [-nogui] [-run] [-outputdir dir] <LEMS file>\n'
' -nogui\n'
' do not generate gtaphical elements in NEURON,\n'
' just run, save data, and quit\n'
' -run\n'
' compile NMODL files and run the main NEURON\n'
' hoc file (Linux only currently)\n'
' -outputdir <dir>\n'
' generate NEURON files in directory <dir>\n'
' <LEMS file>\n'
' the LEMS file to use')
)
mut_exc_opts.add_argument(
'-svg',
action='store_true',
help=('(Via jNeuroML) Convert NeuroML2 file (network & cells)\n'
'to SVG format view of 3D structure')
)
mut_exc_opts.add_argument(
'-png',
action='store_true',
help=('(Via jNeuroML) Convert NeuroML2 file (network & cells)\n'
'to PNG format view of 3D structure')
)
mut_exc_opts.add_argument(
'-dlems',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to dLEMS format, a distilled form of LEMS in JSON')
)
mut_exc_opts.add_argument(
'-vertex',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to VERTEX format')
)
mut_exc_opts.add_argument(
'-xpp',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to XPPAUT format')
)
mut_exc_opts.add_argument(
'-dnsim',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to DNsim format')
)
mut_exc_opts.add_argument(
'-brian',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to Brian format')
)
mut_exc_opts.add_argument(
'-sbml',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to SBML format')
)
mut_exc_opts.add_argument(
'-matlab',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to MATLAB format')
)
mut_exc_opts.add_argument(
'-cvode',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to C format using CVODE package')
)
mut_exc_opts.add_argument(
'-nineml',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to NineML format')
)
mut_exc_opts.add_argument(
'-spineml',
action='store_true',
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to SpineML format')
)
mut_exc_opts.add_argument(
'-sbml-import',
metavar=('<SBML file>', 'duration', 'dt'),
nargs=3,
help=('(Via jNeuroML) Load a SBML file, and convert it\n'
'toLEMS format using values for duration & dt\n'
'in ms (ignoring SBML units)')
)
mut_exc_opts.add_argument(
'-sbml-import-units',
metavar=('<SBML file>', 'duration', 'dt'),
nargs=3,
help=('(Via jNeuroML) Load a SBML file, and convert it\n'
'to LEMS format using values for duration & dt\n'
'in ms (attempt to extract SBML units; ensure units\n'
'are valid in the SBML!)')
)
mut_exc_opts.add_argument(
'-vhdl',
metavar=('neuronid', '<LEMS file>'),
nargs=2,
help=('(Via jNeuroML) Load a LEMS file, and convert it\n'
'to VHDL format')
)
mut_exc_opts.add_argument(
'-graph',
metavar=('level'),
nargs=1,
help=('Load a NeuroML file, and convert it to a graph using\n'
'GraphViz. Detail is set by level (1, 2, etc.)')
)
mut_exc_opts.add_argument(
'-matrix',
metavar=('level'),
nargs=1,
help=('Load a NeuroML file, and convert it to a matrix displaying\n'
'connectivity. Detail is set by level (1, 2, etc.)')
)
mut_exc_opts.add_argument(
'-validate',
action='store_true',
help=('(Via jNeuroML) Validate NeuroML2 file(s) against the\n'
'latest Schema')
)
mut_exc_opts.add_argument(
'-validatev1',
action='store_true',
help=('(Via jNeuroML) Validate NeuroML file(s) against the\n'
'v1.8.1 Schema')
)
return parser.parse_args() | [
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] | train | https://github.com/NeuroML/pyNeuroML/blob/aeba2e3040b360bb26556f643cccbfb3dac3b8fb/pyneuroml/pynml.py#L43-L245 |
NeuroML/pyNeuroML | pyneuroml/pynml.py | quick_summary | def quick_summary(nml2_doc):
'''
Or better just use nml2_doc.summary(show_includes=False)
'''
info = 'Contents of NeuroML 2 document: %s\n'%nml2_doc.id
membs = inspect.getmembers(nml2_doc)
for memb in membs:
if isinstance(memb[1], list) and len(memb[1])>0 \
and not memb[0].endswith('_'):
info+=' %s:\n ['%memb[0]
for entry in memb[1]:
extra = '???'
extra = entry.name if hasattr(entry,'name') else extra
extra = entry.href if hasattr(entry,'href') else extra
extra = entry.id if hasattr(entry,'id') else extra
info+=" %s (%s),"%(entry, extra)
info+=']\n'
return info | python | def quick_summary(nml2_doc):
'''
Or better just use nml2_doc.summary(show_includes=False)
'''
info = 'Contents of NeuroML 2 document: %s\n'%nml2_doc.id
membs = inspect.getmembers(nml2_doc)
for memb in membs:
if isinstance(memb[1], list) and len(memb[1])>0 \
and not memb[0].endswith('_'):
info+=' %s:\n ['%memb[0]
for entry in memb[1]:
extra = '???'
extra = entry.name if hasattr(entry,'name') else extra
extra = entry.href if hasattr(entry,'href') else extra
extra = entry.id if hasattr(entry,'id') else extra
info+=" %s (%s),"%(entry, extra)
info+=']\n'
return info | [
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NeuroML/pyNeuroML | pyneuroml/pynml.py | execute_command_in_dir | def execute_command_in_dir(command, directory, verbose=DEFAULTS['v'],
prefix="Output: ", env=None):
"""Execute a command in specific working directory"""
if os.name == 'nt':
directory = os.path.normpath(directory)
print_comment("Executing: (%s) in directory: %s" % (command, directory),
verbose)
if env is not None:
print_comment("Extra env variables %s" % (env), verbose)
try:
if os.name == 'nt':
return_string = subprocess.check_output(command,
cwd=directory,
shell=True,
env=env,
close_fds=False)
else:
return_string = subprocess.check_output(command,
cwd=directory,
shell=True,
stderr=subprocess.STDOUT,
env=env,
close_fds=True)
return_string = return_string.decode("utf-8") # For Python 3
print_comment('Command completed. Output: \n %s%s' % \
(prefix,return_string.replace('\n','\n '+prefix)),
verbose)
return return_string
except AttributeError:
# For python 2.6...
print_comment_v('Assuming Python 2.6...')
return_string = subprocess.Popen(command,
cwd=directory,
shell=True,
stdout=subprocess.PIPE).communicate()[0]
return return_string
except subprocess.CalledProcessError as e:
print_comment_v('*** Problem running command: \n %s'%e)
print_comment_v('%s%s'%(prefix,e.output.decode().replace('\n','\n'+prefix)))
return None
except:
print_comment_v('*** Unknown problem running command: %s'%e)
return None
print_comment("Finished execution", verbose) | python | def execute_command_in_dir(command, directory, verbose=DEFAULTS['v'],
prefix="Output: ", env=None):
"""Execute a command in specific working directory"""
if os.name == 'nt':
directory = os.path.normpath(directory)
print_comment("Executing: (%s) in directory: %s" % (command, directory),
verbose)
if env is not None:
print_comment("Extra env variables %s" % (env), verbose)
try:
if os.name == 'nt':
return_string = subprocess.check_output(command,
cwd=directory,
shell=True,
env=env,
close_fds=False)
else:
return_string = subprocess.check_output(command,
cwd=directory,
shell=True,
stderr=subprocess.STDOUT,
env=env,
close_fds=True)
return_string = return_string.decode("utf-8") # For Python 3
print_comment('Command completed. Output: \n %s%s' % \
(prefix,return_string.replace('\n','\n '+prefix)),
verbose)
return return_string
except AttributeError:
# For python 2.6...
print_comment_v('Assuming Python 2.6...')
return_string = subprocess.Popen(command,
cwd=directory,
shell=True,
stdout=subprocess.PIPE).communicate()[0]
return return_string
except subprocess.CalledProcessError as e:
print_comment_v('*** Problem running command: \n %s'%e)
print_comment_v('%s%s'%(prefix,e.output.decode().replace('\n','\n'+prefix)))
return None
except:
print_comment_v('*** Unknown problem running command: %s'%e)
return None
print_comment("Finished execution", verbose) | [
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NeuroML/pyNeuroML | pyneuroml/pynml.py | evaluate_component | def evaluate_component(comp_type, req_variables={}, parameter_values={}):
print_comment('Evaluating %s with req:%s; params:%s'%(comp_type.name,req_variables,parameter_values))
exec_str = ''
return_vals = {}
from math import exp
for p in parameter_values:
exec_str+='%s = %s\n'%(p, get_value_in_si(parameter_values[p]))
for r in req_variables:
exec_str+='%s = %s\n'%(r, get_value_in_si(req_variables[r]))
for c in comp_type.Constant:
exec_str+='%s = %s\n'%(c.name, get_value_in_si(c.value))
for d in comp_type.Dynamics:
for dv in d.DerivedVariable:
exec_str+='%s = %s\n'%(dv.name, dv.value)
exec_str+='return_vals["%s"] = %s\n'%(dv.name, dv.name)
for cdv in d.ConditionalDerivedVariable:
for case in cdv.Case:
if case.condition:
cond = case.condition.replace('.neq.','!=').replace('.eq.','==').replace('.gt.','<').replace('.lt.','<')
exec_str+='if ( %s ): %s = %s \n'%(cond, cdv.name, case.value)
else:
exec_str+='else: %s = %s \n'%(cdv.name, case.value)
exec_str+='\n'
exec_str+='return_vals["%s"] = %s\n'%(cdv.name, cdv.name)
'''print_comment_v(exec_str)'''
exec(exec_str)
return return_vals | python | def evaluate_component(comp_type, req_variables={}, parameter_values={}):
print_comment('Evaluating %s with req:%s; params:%s'%(comp_type.name,req_variables,parameter_values))
exec_str = ''
return_vals = {}
from math import exp
for p in parameter_values:
exec_str+='%s = %s\n'%(p, get_value_in_si(parameter_values[p]))
for r in req_variables:
exec_str+='%s = %s\n'%(r, get_value_in_si(req_variables[r]))
for c in comp_type.Constant:
exec_str+='%s = %s\n'%(c.name, get_value_in_si(c.value))
for d in comp_type.Dynamics:
for dv in d.DerivedVariable:
exec_str+='%s = %s\n'%(dv.name, dv.value)
exec_str+='return_vals["%s"] = %s\n'%(dv.name, dv.name)
for cdv in d.ConditionalDerivedVariable:
for case in cdv.Case:
if case.condition:
cond = case.condition.replace('.neq.','!=').replace('.eq.','==').replace('.gt.','<').replace('.lt.','<')
exec_str+='if ( %s ): %s = %s \n'%(cond, cdv.name, case.value)
else:
exec_str+='else: %s = %s \n'%(cdv.name, case.value)
exec_str+='\n'
exec_str+='return_vals["%s"] = %s\n'%(cdv.name, cdv.name)
'''print_comment_v(exec_str)'''
exec(exec_str)
return return_vals | [
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marrow/WebCore | web/ext/analytics.py | AnalyticsExtension.after | def after(self, context, exc=None):
"""Executed after dispatch has returned and the response populated, prior to anything being sent to the client."""
duration = context._duration = round((time.time() - context._start_time) * 1000) # Convert to ms.
delta = unicode(duration)
# Default response augmentation.
if self.header:
context.response.headers[self.header] = delta
if self.log:
self.log("Response generated in " + delta + " seconds.", extra=dict(
duration = duration,
request = id(context)
)) | python | def after(self, context, exc=None):
"""Executed after dispatch has returned and the response populated, prior to anything being sent to the client."""
duration = context._duration = round((time.time() - context._start_time) * 1000) # Convert to ms.
delta = unicode(duration)
# Default response augmentation.
if self.header:
context.response.headers[self.header] = delta
if self.log:
self.log("Response generated in " + delta + " seconds.", extra=dict(
duration = duration,
request = id(context)
)) | [
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marrow/WebCore | web/ext/args.py | ArgumentExtension._process_flat_kwargs | def _process_flat_kwargs(source, kwargs):
"""Apply a flat namespace transformation to recreate (in some respects) a rich structure.
This applies several transformations, which may be nested:
`foo` (singular): define a simple value named `foo`
`foo` (repeated): define a simple value for placement in an array named `foo`
`foo[]`: define a simple value for placement in an array, even if there is only one
`foo.<id>`: define a simple value to place in the `foo` array at the identified index
By nesting, you may define deeper, more complex structures:
`foo.bar`: define a value for the named element `bar` of the `foo` dictionary
`foo.<id>.bar`: define a `bar` dictionary element on the array element marked by that ID
References to `<id>` represent numeric "attributes", which makes the parent reference be treated as an array,
not a dictionary. Exact indexes might not be able to be preserved if there are voids; Python lists are not
sparse.
No validation of values is performed.
"""
ordered_arrays = []
# Process arguments one at a time and apply them to the kwargs passed in.
for name, value in source.items():
container = kwargs
if '.' in name:
parts = name.split('.')
name = name.rpartition('.')[2]
for target, following in zip(parts[:-1], parts[1:]):
if following.isnumeric(): # Prepare any use of numeric IDs.
container.setdefault(target, [{}])
if container[target] not in ordered_arrays:
ordered_arrays.append(container[target])
container = container[target][0]
continue
container = container.setdefault(target, {})
if name.endswith('[]'): # `foo[]` or `foo.bar[]` etc.
name = name[:-2]
container.setdefault(name, [])
container[name].append(value)
continue
if name.isnumeric() and container is not kwargs: # trailing identifiers, `foo.<id>`
container[int(name)] = value
continue
if name in container:
if not isinstance(container[name], list):
container[name] = [container[name]]
container[name].append(value)
continue
container[name] = value
for container in ordered_arrays:
elements = container[0]
del container[:]
container.extend(value for name, value in sorted(elements.items())) | python | def _process_flat_kwargs(source, kwargs):
"""Apply a flat namespace transformation to recreate (in some respects) a rich structure.
This applies several transformations, which may be nested:
`foo` (singular): define a simple value named `foo`
`foo` (repeated): define a simple value for placement in an array named `foo`
`foo[]`: define a simple value for placement in an array, even if there is only one
`foo.<id>`: define a simple value to place in the `foo` array at the identified index
By nesting, you may define deeper, more complex structures:
`foo.bar`: define a value for the named element `bar` of the `foo` dictionary
`foo.<id>.bar`: define a `bar` dictionary element on the array element marked by that ID
References to `<id>` represent numeric "attributes", which makes the parent reference be treated as an array,
not a dictionary. Exact indexes might not be able to be preserved if there are voids; Python lists are not
sparse.
No validation of values is performed.
"""
ordered_arrays = []
# Process arguments one at a time and apply them to the kwargs passed in.
for name, value in source.items():
container = kwargs
if '.' in name:
parts = name.split('.')
name = name.rpartition('.')[2]
for target, following in zip(parts[:-1], parts[1:]):
if following.isnumeric(): # Prepare any use of numeric IDs.
container.setdefault(target, [{}])
if container[target] not in ordered_arrays:
ordered_arrays.append(container[target])
container = container[target][0]
continue
container = container.setdefault(target, {})
if name.endswith('[]'): # `foo[]` or `foo.bar[]` etc.
name = name[:-2]
container.setdefault(name, [])
container[name].append(value)
continue
if name.isnumeric() and container is not kwargs: # trailing identifiers, `foo.<id>`
container[int(name)] = value
continue
if name in container:
if not isinstance(container[name], list):
container[name] = [container[name]]
container[name].append(value)
continue
container[name] = value
for container in ordered_arrays:
elements = container[0]
del container[:]
container.extend(value for name, value in sorted(elements.items())) | [
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NeuroML/pyNeuroML | pyneuroml/povray/NeuroML2ToPOVRay.py | process_args | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(description="A file for converting NeuroML v2 files into POVRay files for 3D rendering")
parser.add_argument('neuroml_file', type=str, metavar='<NeuroML file>',
help='NeuroML (version 2 beta 3+) file to be converted to PovRay format (XML or HDF5 format)')
parser.add_argument('-split',
action='store_true',
default=False,
help="If this is specified, generate separate pov files for cells & network. Default is false")
parser.add_argument('-background',
type=str,
metavar='<background colour>',
default=_WHITE,
help='Colour of background, e.g. <0,0,0,0.55>')
parser.add_argument('-movie',
action='store_true',
default=False,
help="If this is specified, generate a ini file for generating a sequence of frames for a movie of the 3D structure")
parser.add_argument('-inputs',
action='store_true',
default=False,
help="If this is specified, show the locations of (synaptic, current clamp, etc.) inputs into the cells of the network")
parser.add_argument('-conns',
action='store_true',
default=False,
help="If this is specified, show the connections present in the network with lines")
parser.add_argument('-conn_points',
action='store_true',
default=False,
help="If this is specified, show the end points of the connections present in the network")
parser.add_argument('-v',
action='store_true',
default=False,
help="Verbose output")
parser.add_argument('-frames',
type=int,
metavar='<frames>',
default=36,
help='Number of frames in movie')
parser.add_argument('-posx',
type=float,
metavar='<position offset x>',
default=0,
help='Offset position in x dir (0 is centre, 1 is top)')
parser.add_argument('-posy',
type=float,
metavar='<position offset y>',
default=0,
help='Offset position in y dir (0 is centre, 1 is top)')
parser.add_argument('-posz',
type=float,
metavar='<position offset z>',
default=0,
help='Offset position in z dir (0 is centre, 1 is top)')
parser.add_argument('-viewx',
type=float,
metavar='<view offset x>',
default=0,
help='Offset viewing point in x dir (0 is centre, 1 is top)')
parser.add_argument('-viewy',
type=float,
metavar='<view offset y>',
default=0,
help='Offset viewing point in y dir (0 is centre, 1 is top)')
parser.add_argument('-viewz',
type=float,
metavar='<view offset z>',
default=0,
help='Offset viewing point in z dir (0 is centre, 1 is top)')
parser.add_argument('-scalex',
type=float,
metavar='<scale position x>',
default=1,
help='Scale position from network in x dir')
parser.add_argument('-scaley',
type=float,
metavar='<scale position y>',
default=1.5,
help='Scale position from network in y dir')
parser.add_argument('-scalez',
type=float,
metavar='<scale position z>',
default=1,
help='Scale position from network in z dir')
parser.add_argument('-mindiam',
type=float,
metavar='<minimum diameter dendrites/axons>',
default=0,
help='Minimum diameter for dendrites/axons (to improve visualisations)')
parser.add_argument('-plane',
action='store_true',
default=False,
help="If this is specified, add a 2D plane below cell/network")
parser.add_argument('-segids',
action='store_true',
default=False,
help="Show segment ids")
return parser.parse_args() | python | def process_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser(description="A file for converting NeuroML v2 files into POVRay files for 3D rendering")
parser.add_argument('neuroml_file', type=str, metavar='<NeuroML file>',
help='NeuroML (version 2 beta 3+) file to be converted to PovRay format (XML or HDF5 format)')
parser.add_argument('-split',
action='store_true',
default=False,
help="If this is specified, generate separate pov files for cells & network. Default is false")
parser.add_argument('-background',
type=str,
metavar='<background colour>',
default=_WHITE,
help='Colour of background, e.g. <0,0,0,0.55>')
parser.add_argument('-movie',
action='store_true',
default=False,
help="If this is specified, generate a ini file for generating a sequence of frames for a movie of the 3D structure")
parser.add_argument('-inputs',
action='store_true',
default=False,
help="If this is specified, show the locations of (synaptic, current clamp, etc.) inputs into the cells of the network")
parser.add_argument('-conns',
action='store_true',
default=False,
help="If this is specified, show the connections present in the network with lines")
parser.add_argument('-conn_points',
action='store_true',
default=False,
help="If this is specified, show the end points of the connections present in the network")
parser.add_argument('-v',
action='store_true',
default=False,
help="Verbose output")
parser.add_argument('-frames',
type=int,
metavar='<frames>',
default=36,
help='Number of frames in movie')
parser.add_argument('-posx',
type=float,
metavar='<position offset x>',
default=0,
help='Offset position in x dir (0 is centre, 1 is top)')
parser.add_argument('-posy',
type=float,
metavar='<position offset y>',
default=0,
help='Offset position in y dir (0 is centre, 1 is top)')
parser.add_argument('-posz',
type=float,
metavar='<position offset z>',
default=0,
help='Offset position in z dir (0 is centre, 1 is top)')
parser.add_argument('-viewx',
type=float,
metavar='<view offset x>',
default=0,
help='Offset viewing point in x dir (0 is centre, 1 is top)')
parser.add_argument('-viewy',
type=float,
metavar='<view offset y>',
default=0,
help='Offset viewing point in y dir (0 is centre, 1 is top)')
parser.add_argument('-viewz',
type=float,
metavar='<view offset z>',
default=0,
help='Offset viewing point in z dir (0 is centre, 1 is top)')
parser.add_argument('-scalex',
type=float,
metavar='<scale position x>',
default=1,
help='Scale position from network in x dir')
parser.add_argument('-scaley',
type=float,
metavar='<scale position y>',
default=1.5,
help='Scale position from network in y dir')
parser.add_argument('-scalez',
type=float,
metavar='<scale position z>',
default=1,
help='Scale position from network in z dir')
parser.add_argument('-mindiam',
type=float,
metavar='<minimum diameter dendrites/axons>',
default=0,
help='Minimum diameter for dendrites/axons (to improve visualisations)')
parser.add_argument('-plane',
action='store_true',
default=False,
help="If this is specified, add a 2D plane below cell/network")
parser.add_argument('-segids',
action='store_true',
default=False,
help="Show segment ids")
return parser.parse_args() | [
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marrow/WebCore | web/core/application.py | Application._configure | def _configure(self, config):
"""Prepare the incoming configuration and ensure certain expected values are present.
For example, this ensures BaseExtension is included in the extension list, and populates the logging config.
"""
config = config or dict()
# We really need this to be there.
if 'extensions' not in config: config['extensions'] = list()
if not any(isinstance(ext, BaseExtension) for ext in config['extensions']):
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config['extensions'].insert(0, BaseExtension())
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arguments.ContextArgsExtension(),
arguments.RemainderArgsExtension(),
arguments.QueryStringArgsExtension(),
arguments.FormEncodedKwargsExtension(),
arguments.JSONKwargsExtension(),
])
config['extensions'].append(self) # Allow the application object itself to register callbacks.
try:
addLoggingLevel('trace', logging.DEBUG - 5)
except AttributeError:
pass
# Tests are skipped on these as we have no particular need to test Python's own logging mechanism.
level = config.get('logging', {}).get('level', None)
if level: # pragma: no cover
logging.basicConfig(level=getattr(logging, level.upper()))
elif 'logging' in config: # pragma: no cover
logging.config.dictConfig(config['logging'])
return config | python | def _configure(self, config):
"""Prepare the incoming configuration and ensure certain expected values are present.
For example, this ensures BaseExtension is included in the extension list, and populates the logging config.
"""
config = config or dict()
# We really need this to be there.
if 'extensions' not in config: config['extensions'] = list()
if not any(isinstance(ext, BaseExtension) for ext in config['extensions']):
# Always make sure the BaseExtension is present since request/response objects are handy.
config['extensions'].insert(0, BaseExtension())
if not any(isinstance(ext, arguments.ArgumentExtension) for ext in config['extensions']):
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config['extensions'].extend([
arguments.ValidateArgumentsExtension(),
arguments.ContextArgsExtension(),
arguments.RemainderArgsExtension(),
arguments.QueryStringArgsExtension(),
arguments.FormEncodedKwargsExtension(),
arguments.JSONKwargsExtension(),
])
config['extensions'].append(self) # Allow the application object itself to register callbacks.
try:
addLoggingLevel('trace', logging.DEBUG - 5)
except AttributeError:
pass
# Tests are skipped on these as we have no particular need to test Python's own logging mechanism.
level = config.get('logging', {}).get('level', None)
if level: # pragma: no cover
logging.basicConfig(level=getattr(logging, level.upper()))
elif 'logging' in config: # pragma: no cover
logging.config.dictConfig(config['logging'])
return config | [
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marrow/WebCore | web/core/application.py | Application.serve | def serve(self, service='auto', **options): # pragma: no cover
"""Initiate a web server service to serve this application.
You can always use the Application instance as a bare WSGI application, of course. This method is provided as
a convienence.
Pass in the name of the service you wish to use, and any additional configuration options appropriate for that
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service = load(service, 'web.server') # We don't bother with a full registry for these one-time lookups.
try:
service(self, **options)
except KeyboardInterrupt: # We catch this as SIG_TERM or ^C are basically the only ways to stop most servers.
pass
# Notify extensions that the service has returned and we are exiting.
for ext in self.__context.extension.signal.stop: ext(self.__context) | python | def serve(self, service='auto', **options): # pragma: no cover
"""Initiate a web server service to serve this application.
You can always use the Application instance as a bare WSGI application, of course. This method is provided as
a convienence.
Pass in the name of the service you wish to use, and any additional configuration options appropriate for that
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service = load(service, 'web.server') # We don't bother with a full registry for these one-time lookups.
try:
service(self, **options)
except KeyboardInterrupt: # We catch this as SIG_TERM or ^C are basically the only ways to stop most servers.
pass
# Notify extensions that the service has returned and we are exiting.
for ext in self.__context.extension.signal.stop: ext(self.__context) | [
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marrow/WebCore | web/core/application.py | Application.application | def application(self, environ, start_response):
"""Process a single WSGI request/response cycle.
This is the WSGI handler for WebCore. Depending on the presence of extensions providing WSGI middleware,
the `__call__` attribute of the Application instance will either become this, or become the outermost
middleware callable.
Most apps won't utilize middleware, the extension interface is preferred for most operations in WebCore.
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"""
context = environ['wc.context'] = self.RequestContext(environ=environ)
signals = context.extension.signal
# Announce the start of a request cycle. This executes `prepare` and `before` callbacks in the correct order.
for ext in signals.pre: ext(context)
# Identify the endpoint for this request.
is_endpoint, handler = context.dispatch(context, context.root, context.environ['PATH_INFO'])
if is_endpoint:
try:
result = self._execute_endpoint(context, handler, signals) # Process the endpoint.
except Exception as e:
log.exception("Caught exception attempting to execute the endpoint.")
result = HTTPInternalServerError(str(e) if __debug__ else "Please see the logs.")
if 'debugger' in context.extension.feature:
context.response = result
for ext in signals.after: ext(context) # Allow signals to clean up early.
raise
else: # If no endpoint could be resolved, that's a 404.
result = HTTPNotFound("Dispatch failed." if __debug__ else None)
if __debug__:
log.debug("Result prepared, identifying view handler.", extra=dict(
request = id(context),
result = safe_name(type(result))
))
# Identify a view capable of handling this result.
for view in context.view(result):
if view(context, result): break
else:
# We've run off the bottom of the list of possible views.
raise TypeError("No view could be found to handle: " + repr(type(result)))
if __debug__:
log.debug("View identified, populating response.", extra=dict(
request = id(context),
view = repr(view),
))
for ext in signals.after: ext(context)
def capture_done(response):
for chunk in response:
yield chunk
for ext in signals.done: ext(context)
# This is really long due to the fact we don't want to capture the response too early.
# We need anything up to this point to be able to simply replace `context.response` if needed.
return capture_done(context.response.conditional_response_app(environ, start_response)) | python | def application(self, environ, start_response):
"""Process a single WSGI request/response cycle.
This is the WSGI handler for WebCore. Depending on the presence of extensions providing WSGI middleware,
the `__call__` attribute of the Application instance will either become this, or become the outermost
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Most apps won't utilize middleware, the extension interface is preferred for most operations in WebCore.
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"""
context = environ['wc.context'] = self.RequestContext(environ=environ)
signals = context.extension.signal
# Announce the start of a request cycle. This executes `prepare` and `before` callbacks in the correct order.
for ext in signals.pre: ext(context)
# Identify the endpoint for this request.
is_endpoint, handler = context.dispatch(context, context.root, context.environ['PATH_INFO'])
if is_endpoint:
try:
result = self._execute_endpoint(context, handler, signals) # Process the endpoint.
except Exception as e:
log.exception("Caught exception attempting to execute the endpoint.")
result = HTTPInternalServerError(str(e) if __debug__ else "Please see the logs.")
if 'debugger' in context.extension.feature:
context.response = result
for ext in signals.after: ext(context) # Allow signals to clean up early.
raise
else: # If no endpoint could be resolved, that's a 404.
result = HTTPNotFound("Dispatch failed." if __debug__ else None)
if __debug__:
log.debug("Result prepared, identifying view handler.", extra=dict(
request = id(context),
result = safe_name(type(result))
))
# Identify a view capable of handling this result.
for view in context.view(result):
if view(context, result): break
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# We've run off the bottom of the list of possible views.
raise TypeError("No view could be found to handle: " + repr(type(result)))
if __debug__:
log.debug("View identified, populating response.", extra=dict(
request = id(context),
view = repr(view),
))
for ext in signals.after: ext(context)
def capture_done(response):
for chunk in response:
yield chunk
for ext in signals.done: ext(context)
# This is really long due to the fact we don't want to capture the response too early.
# We need anything up to this point to be able to simply replace `context.response` if needed.
return capture_done(context.response.conditional_response_app(environ, start_response)) | [
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sanger-pathogens/pymummer | pymummer/alignment.py | Alignment._swap | def _swap(self):
'''Swaps the alignment so that the reference becomes the query and vice-versa. Swaps their names, coordinates etc. The frame is not changed'''
self.ref_start, self.qry_start = self.qry_start, self.ref_start
self.ref_end, self.qry_end = self.qry_end, self.ref_end
self.hit_length_ref, self.hit_length_qry = self.hit_length_qry, self.hit_length_ref
self.ref_length, self.qry_length = self.qry_length, self.ref_length
self.ref_name, self.qry_name = self.qry_name, self.ref_name | python | def _swap(self):
'''Swaps the alignment so that the reference becomes the query and vice-versa. Swaps their names, coordinates etc. The frame is not changed'''
self.ref_start, self.qry_start = self.qry_start, self.ref_start
self.ref_end, self.qry_end = self.qry_end, self.ref_end
self.hit_length_ref, self.hit_length_qry = self.hit_length_qry, self.hit_length_ref
self.ref_length, self.qry_length = self.qry_length, self.ref_length
self.ref_name, self.qry_name = self.qry_name, self.ref_name | [
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sanger-pathogens/pymummer | pymummer/alignment.py | Alignment.qry_coords | def qry_coords(self):
'''Returns a pyfastaq.intervals.Interval object of the start and end coordinates in the query sequence'''
return pyfastaq.intervals.Interval(min(self.qry_start, self.qry_end), max(self.qry_start, self.qry_end)) | python | def qry_coords(self):
'''Returns a pyfastaq.intervals.Interval object of the start and end coordinates in the query sequence'''
return pyfastaq.intervals.Interval(min(self.qry_start, self.qry_end), max(self.qry_start, self.qry_end)) | [
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sanger-pathogens/pymummer | pymummer/alignment.py | Alignment.ref_coords | def ref_coords(self):
'''Returns a pyfastaq.intervals.Interval object of the start and end coordinates in the reference sequence'''
return pyfastaq.intervals.Interval(min(self.ref_start, self.ref_end), max(self.ref_start, self.ref_end)) | python | def ref_coords(self):
'''Returns a pyfastaq.intervals.Interval object of the start and end coordinates in the reference sequence'''
return pyfastaq.intervals.Interval(min(self.ref_start, self.ref_end), max(self.ref_start, self.ref_end)) | [
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sanger-pathogens/pymummer | pymummer/alignment.py | Alignment.on_same_strand | def on_same_strand(self):
'''Returns true iff the direction of the alignment is the same in the reference and the query'''
return (self.ref_start < self.ref_end) == (self.qry_start < self.qry_end) | python | def on_same_strand(self):
'''Returns true iff the direction of the alignment is the same in the reference and the query'''
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sanger-pathogens/pymummer | pymummer/alignment.py | Alignment.is_self_hit | def is_self_hit(self):
'''Returns true iff the alignment is of a sequence to itself: names and all coordinates are the same and 100 percent identity'''
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sanger-pathogens/pymummer | pymummer/alignment.py | Alignment.reverse_query | def reverse_query(self):
'''Changes the coordinates as if the query sequence has been reverse complemented'''
self.qry_start = self.qry_length - self.qry_start - 1
self.qry_end = self.qry_length - self.qry_end - 1 | python | def reverse_query(self):
'''Changes the coordinates as if the query sequence has been reverse complemented'''
self.qry_start = self.qry_length - self.qry_start - 1
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] | train | https://github.com/sanger-pathogens/pymummer/blob/fd97bccfbae62719a7247473d73dd6733d4fa903/pymummer/alignment.py#L84-L87 |
sanger-pathogens/pymummer | pymummer/alignment.py | Alignment.reverse_reference | def reverse_reference(self):
'''Changes the coordinates as if the reference sequence has been reverse complemented'''
self.ref_start = self.ref_length - self.ref_start - 1
self.ref_end = self.ref_length - self.ref_end - 1 | python | def reverse_reference(self):
'''Changes the coordinates as if the reference sequence has been reverse complemented'''
self.ref_start = self.ref_length - self.ref_start - 1
self.ref_end = self.ref_length - self.ref_end - 1 | [
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sanger-pathogens/pymummer | pymummer/alignment.py | Alignment.to_msp_crunch | def to_msp_crunch(self):
'''Returns the alignment as a line in MSPcrunch format. The columns are space-separated and are:
1. score
2. percent identity
3. match start in the query sequence
4. match end in the query sequence
5. query sequence name
6. subject sequence start
7. subject sequence end
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aln_score = int(self.percent_identity * 0.005 * (self.hit_length_ref + self.hit_length_qry))
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self.ref_name
]) | python | def to_msp_crunch(self):
'''Returns the alignment as a line in MSPcrunch format. The columns are space-separated and are:
1. score
2. percent identity
3. match start in the query sequence
4. match end in the query sequence
5. query sequence name
6. subject sequence start
7. subject sequence end
8. subject sequence name'''
# we don't know the alignment score. Estimate it. This approximates 1 for a match.
aln_score = int(self.percent_identity * 0.005 * (self.hit_length_ref + self.hit_length_qry))
return ' '.join(str(x) for x in [
aln_score,
'{0:.2f}'.format(self.percent_identity),
self.qry_start + 1,
self.qry_end + 1,
self.qry_name,
self.ref_start + 1,
self.ref_end + 1,
self.ref_name
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sanger-pathogens/pymummer | pymummer/alignment.py | Alignment.qry_coords_from_ref_coord | def qry_coords_from_ref_coord(self, ref_coord, variant_list):
'''Given a reference position and a list of variants ([variant.Variant]),
works out the position in the query sequence, accounting for indels.
Returns a tuple: (position, True|False), where second element is whether
or not the ref_coord lies in an indel. If it is, then
returns the corresponding start position
of the indel in the query'''
if self.ref_coords().distance_to_point(ref_coord) > 0:
raise Error('Cannot get query coord in qry_coords_from_ref_coord because given ref_coord ' + str(ref_coord) + ' does not lie in nucmer alignment:\n' + str(self))
indel_variant_indexes = []
for i in range(len(variant_list)):
if variant_list[i].var_type not in {variant.INS, variant.DEL}:
continue
if not self.intersects_variant(variant_list[i]):
continue
if variant_list[i].ref_start <= ref_coord <= variant_list[i].ref_end:
return variant_list[i].qry_start, True
elif variant_list[i].ref_start < ref_coord:
indel_variant_indexes.append(i)
distance = ref_coord - min(self.ref_start, self.ref_end)
for i in indel_variant_indexes:
if variant_list[i].var_type == variant.INS:
distance += len(variant_list[i].qry_base)
else:
assert variant_list[i].var_type == variant.DEL
distance -= len(variant_list[i].ref_base)
if self.on_same_strand():
return min(self.qry_start, self.qry_end) + distance, False
else:
return max(self.qry_start, self.qry_end) - distance, False | python | def qry_coords_from_ref_coord(self, ref_coord, variant_list):
'''Given a reference position and a list of variants ([variant.Variant]),
works out the position in the query sequence, accounting for indels.
Returns a tuple: (position, True|False), where second element is whether
or not the ref_coord lies in an indel. If it is, then
returns the corresponding start position
of the indel in the query'''
if self.ref_coords().distance_to_point(ref_coord) > 0:
raise Error('Cannot get query coord in qry_coords_from_ref_coord because given ref_coord ' + str(ref_coord) + ' does not lie in nucmer alignment:\n' + str(self))
indel_variant_indexes = []
for i in range(len(variant_list)):
if variant_list[i].var_type not in {variant.INS, variant.DEL}:
continue
if not self.intersects_variant(variant_list[i]):
continue
if variant_list[i].ref_start <= ref_coord <= variant_list[i].ref_end:
return variant_list[i].qry_start, True
elif variant_list[i].ref_start < ref_coord:
indel_variant_indexes.append(i)
distance = ref_coord - min(self.ref_start, self.ref_end)
for i in indel_variant_indexes:
if variant_list[i].var_type == variant.INS:
distance += len(variant_list[i].qry_base)
else:
assert variant_list[i].var_type == variant.DEL
distance -= len(variant_list[i].ref_base)
if self.on_same_strand():
return min(self.qry_start, self.qry_end) + distance, False
else:
return max(self.qry_start, self.qry_end) - distance, False | [
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sanger-pathogens/pymummer | pymummer/nucmer.py | Runner._nucmer_command | def _nucmer_command(self, ref, qry, outprefix):
'''Construct the nucmer command'''
if self.use_promer:
command = 'promer'
else:
command = 'nucmer'
command += ' -p ' + outprefix
if self.breaklen is not None:
command += ' -b ' + str(self.breaklen)
if self.diagdiff is not None and not self.use_promer:
command += ' -D ' + str(self.diagdiff)
if self.diagfactor:
command += ' -d ' + str(self.diagfactor)
if self.maxgap:
command += ' -g ' + str(self.maxgap)
if self.maxmatch:
command += ' --maxmatch'
if self.mincluster is not None:
command += ' -c ' + str(self.mincluster)
if not self.simplify and not self.use_promer:
command += ' --nosimplify'
return command + ' ' + ref + ' ' + qry | python | def _nucmer_command(self, ref, qry, outprefix):
'''Construct the nucmer command'''
if self.use_promer:
command = 'promer'
else:
command = 'nucmer'
command += ' -p ' + outprefix
if self.breaklen is not None:
command += ' -b ' + str(self.breaklen)
if self.diagdiff is not None and not self.use_promer:
command += ' -D ' + str(self.diagdiff)
if self.diagfactor:
command += ' -d ' + str(self.diagfactor)
if self.maxgap:
command += ' -g ' + str(self.maxgap)
if self.maxmatch:
command += ' --maxmatch'
if self.mincluster is not None:
command += ' -c ' + str(self.mincluster)
if not self.simplify and not self.use_promer:
command += ' --nosimplify'
return command + ' ' + ref + ' ' + qry | [
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sanger-pathogens/pymummer | pymummer/nucmer.py | Runner._delta_filter_command | def _delta_filter_command(self, infile, outfile):
'''Construct delta-filter command'''
command = 'delta-filter'
if self.min_id is not None:
command += ' -i ' + str(self.min_id)
if self.min_length is not None:
command += ' -l ' + str(self.min_length)
return command + ' ' + infile + ' > ' + outfile | python | def _delta_filter_command(self, infile, outfile):
'''Construct delta-filter command'''
command = 'delta-filter'
if self.min_id is not None:
command += ' -i ' + str(self.min_id)
if self.min_length is not None:
command += ' -l ' + str(self.min_length)
return command + ' ' + infile + ' > ' + outfile | [
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sanger-pathogens/pymummer | pymummer/nucmer.py | Runner._show_coords_command | def _show_coords_command(self, infile, outfile):
'''Construct show-coords command'''
command = 'show-coords -dTlro'
if not self.coords_header:
command += ' -H'
return command + ' ' + infile + ' > ' + outfile | python | def _show_coords_command(self, infile, outfile):
'''Construct show-coords command'''
command = 'show-coords -dTlro'
if not self.coords_header:
command += ' -H'
return command + ' ' + infile + ' > ' + outfile | [
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sanger-pathogens/pymummer | pymummer/nucmer.py | Runner._write_script | def _write_script(self, script_name, ref, qry, outfile):
'''Write commands into a bash script'''
f = pyfastaq.utils.open_file_write(script_name)
print(self._nucmer_command(ref, qry, 'p'), file=f)
print(self._delta_filter_command('p.delta', 'p.delta.filter'), file=f)
print(self._show_coords_command('p.delta.filter', outfile), file=f)
if self.show_snps:
print(self._show_snps_command('p.delta.filter', outfile + '.snps'), file=f)
pyfastaq.utils.close(f) | python | def _write_script(self, script_name, ref, qry, outfile):
'''Write commands into a bash script'''
f = pyfastaq.utils.open_file_write(script_name)
print(self._nucmer_command(ref, qry, 'p'), file=f)
print(self._delta_filter_command('p.delta', 'p.delta.filter'), file=f)
print(self._show_coords_command('p.delta.filter', outfile), file=f)
if self.show_snps:
print(self._show_snps_command('p.delta.filter', outfile + '.snps'), file=f)
pyfastaq.utils.close(f) | [
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")"
] | Write commands into a bash script | [
"Write",
"commands",
"into",
"a",
"bash",
"script"
] | train | https://github.com/sanger-pathogens/pymummer/blob/fd97bccfbae62719a7247473d73dd6733d4fa903/pymummer/nucmer.py#L118-L126 |
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