I am a simple object that lets you access device specifications as my attributes.

Method	g 0	Property: Returns an instance of `AttrGetter`, creating it if necessary, for shorthand access to attributes with zero-value defaults.
Method	g	Undocumented
Method	enumerable	Property: Returns an instance of `Enumerable`, creating it if necessary, for obtaining the unique values of a vector if it is considered enumerable.
Method	__getstate__	Neither properties nor their underlying objects are pickled. The unpickled version can just re-create them.
Method	__setstate__	Undocumented
Method	add	Sets an attribute with name to the specified value.
Method	setdefault	If no attribute with name is present, assigns it the specified value. Returns the value that the attribute ultimately has.
Method	get	No summary
Method	ge	Returns `True` if there is no active (value = 1) entry with the specified name in the 'disabled' dict.
Method	gp	Returns a list of the named entries of the sub-dict of the 'params' dict for the specified setup ID.
Method	gperator	If there is a named entry of the sub-dict of the 'params' dict for the specified setup ID, yields the value of that entry in a single iteration. If not, doesn't iterate at all.
Method	Xmax	Returns the maximum value of the goal points of vector name for all setup IDs listed as subsequent arguments.
Method	Xrange	Returns a 1-D Numpy array of sorted unique values of the goal points of vector name for setup ID.
Method	XYmax	No summary
Method	_remove	Undocumented
Method	_name2k	Returns the column index for parameter name in my 2-D Numpy goals array for the specified setup ID.
Method	_subset	Returns a subset of my 2-D Numpy goals array for the specified setup ID with rows selected such that the values in the column for parameter name are closest to value.
Method	_getX	Returns my 2-D Numpy goals array for the specified setup ID or, if there is a single keyword in kw referencing an enumerable column vector in that array, a subset thereof.

@property
def g 0(self):

Property: Returns an instance of AttrGetter, creating it if necessary, for shorthand access to attributes with zero-value defaults.

@g.setter
def g(self, value):

Undocumented

@property
def enumerable(self):

Property: Returns an instance of Enumerable, creating it if necessary, for obtaining the unique values of a vector if it is considered enumerable.

def __getstate__(self):

Neither properties nor their underlying objects are pickled. The unpickled version can just re-create them.

def __setstate__(self, state):

Undocumented

def add(self, name, value):

Sets an attribute with name to the specified value.

def setdefault(self, name, value):

If no attribute with name is present, assigns it the specified value. Returns the value that the attribute ultimately has.

Same behavior as running the setdefault on a dictionary for one of its entries.

def _remove(self, name):

Undocumented

def get(self, *names, **kw):

Returns the named entry of the named entry ... of the named dict, with the names in top-first order. Returns a 0 entry if it doesn't exist, or the value of default if that keyword is set to something.

An empty top-level dict is returned if only that is requested (no entry keys specified), even if it doesn't exist.

def ge(self, name):

Returns True if there is no active (value = 1) entry with the specified name in the 'disabled' dict.

def gp(self, ID, *names, **kw):

Returns a list of the named entries of the sub-dict of the 'params' dict for the specified setup ID.

The list item for any entry not defined will have a zero value, unless the default keyword is set, in which case that will be used as the default for any missing values.

If you supply just one name, then a single value will be returned instead of a list.

def gperator(self, ID, name):

If there is a named entry of the sub-dict of the 'params' dict for the specified setup ID, yields the value of that entry in a single iteration. If not, doesn't iterate at all.

Use this to selectively execute code using parameter value if it's been defined.

def _name2k(self, ID, name):

Returns the column index for parameter name in my 2-D Numpy goals array for the specified setup ID.

def _subset(self, ID, name, value):

Returns a subset of my 2-D Numpy goals array for the specified setup ID with rows selected such that the values in the column for parameter name are closest to value.

The column vector in the area must be an enumerable vector. (Independent, too, although this is not checked.) See IV_Manager to understand what it means for a vector to be "enumerable."

The enumerated values are examined and the closest one to value is used for the row selection criterion. If there are enumerated values for the column for the parameter specified with name that exactly equal value, then of course an array with exactly those values in the column will be returned. Otherwise, the array will have the rows with the closest enumerated value for that parameter.

def _getX(self, ID, kw):

Returns my 2-D Numpy goals array for the specified setup ID or, if there is a single keyword in kw referencing an enumerable column vector in that array, a subset thereof.

def Xmax(self, name, *IDs, **kw):

Returns the maximum value of the goal points of vector name for all setup IDs listed as subsequent arguments.

With a keyword, you can specify the name and desired value of an enumerable vector. Then, only the goal points having the value closest to that will be considered.

def Xrange(self, ID, name, **kw):

Returns a 1-D Numpy array of sorted unique values of the goal points of vector name for setup ID.

If there are no goal points for this setup or it has a callable goal, returns None.

With a keyword, you can specify the name and desired value of an enumerable vector. Then, only the goal points having the value closest to that will be considered.

def XYmax(self, ID, xName, *args, **kw):

Returns a list with the maximum value of the goal points of vector xName for setup ID followed by the value for the corresponding goal point of each vector named as an additional argument.

If there are no goal points for this setup or it has a callable goal, returns None.

With a keyword, you can specify the name and desired value of an enumerable vector. Then, only the goal points having the value closest to that will be considered.

API Documentation for pingspice, generated by pydoctor at 2021-09-18 08:41:11.

pingspice.analysis.param.Specs(object) class documentation

`pingspice.analysis.param.Specs(object)` class documentation