Grid1d#

class scqubits.core.discretization.Grid1d(min_val, max_val, pt_count)[source]#

Data structure and methods for setting up discretized 1d coordinate grid, generating corresponding derivative matrices.

Parameters:

min_val (float) – minimum value of the discretized variable
max_val (float) – maximum value of the discretized variable
pt_count (int) – number of grid points

Return type:

SerializableType

Methods

`Grid1d.__init__`(min_val, max_val, pt_count)
`Grid1d.broadcast`(event, **kwargs)	Request a broadcast from CENTRAL_DISPATCH reporting event.
`Grid1d.create_from_file`(filename)	Read initdata and spectral data from file, and use those to create a new SpectrumData object.
`Grid1d.deserialize`(io_data)	Take the given IOData and return an instance of the described class, initialized with the data stored in io_data.
`Grid1d.filewrite`(filename)	Convenience method bound to the class.
`Grid1d.first_derivative_matrix`([prefactor, ...])	Generate sparse matrix for first derivative of the form \(\partial_{x_i}\).
`Grid1d.get_initdata`()	Returns dict appropriate for creating/initializing a new Grid1d object.
`Grid1d.grid_spacing`()	rtype: `float`
`Grid1d.make_linspace`()	Returns a numpy array of the grid points :rtype: ndarray
`Grid1d.receive`(event, sender, **kwargs)	Receive a message from CENTRAL_DISPATCH and initiate action on it.
`Grid1d.second_derivative_matrix`([prefactor, ...])	Generate sparse matrix for second derivative of the form \(\partial^2_{x_i}\).
`Grid1d.serialize`()	Convert the content of the current class instance into IOData format.

Attributes

`max_val`	Descriptor class for properties that are to be monitored for changes.
`min_val`	Descriptor class for properties that are to be monitored for changes.
`pt_count`	Descriptor class for properties that are to be monitored for changes.

broadcast(event, **kwargs)#

Request a broadcast from CENTRAL_DISPATCH reporting event.

Parameters:

event (str) – event name from EVENTS
**kwargs –

Return type:

None

classmethod create_from_file(filename)#

Read initdata and spectral data from file, and use those to create a new SpectrumData object.

Returns:: new SpectrumData object, initialized with data read from file
Return type:: SpectrumData
Parameters:: filename (str) –

classmethod deserialize(io_data)#

Take the given IOData and return an instance of the described class, initialized with the data stored in io_data.

Return type:: TypeVar(SerializableType, bound= Serializable)
Parameters:: io_data (IOData) –

filewrite(filename)#

Convenience method bound to the class. Simply accesses the write function.

Return type:: None
Parameters:: filename (str) –

first_derivative_matrix(prefactor=1.0, periodic=False)[source]#

Generate sparse matrix for first derivative of the form \(\partial_{x_i}\). Uses STENCIL setting to construct the matrix with a multi-point stencil.

Parameters:

prefactor (Union[float, complex]) – prefactor of the derivative matrix (default value: 1.0)
periodic (bool) – set to True if variable is a periodic variable

Return type:

csc_matrix

Returns:

sparse matrix in dia format

get_initdata()[source]#

Returns dict appropriate for creating/initializing a new Grid1d object. :rtype: dict

Return type:: Dict[str, Any]

grid_spacing()[source]#

Return type:: float
Returns:: spacing between neighboring grid points

make_linspace()[source]#

Returns a numpy array of the grid points :rtype: ndarray

Return type:: ndarray

receive(event, sender, **kwargs)#

Receive a message from CENTRAL_DISPATCH and initiate action on it.

Parameters:

event (str) – event name from EVENTS
sender (DispatchClient) – original sender reporting the event
**kwargs –

Return type:

None

second_derivative_matrix(prefactor=1.0, periodic=False)[source]#

Generate sparse matrix for second derivative of the form \(\partial^2_{x_i}\). Uses STENCIL setting to construct the matrix with a multi-point stencil.

Parameters:

prefactor (Union[float, complex]) – optional prefactor of the derivative matrix (default value = 1.0)
periodic (bool) – set to True if variable is a periodic variable (default value = False)

Return type:

csc_matrix

Returns:

sparse matrix in dia format

serialize()#

Convert the content of the current class instance into IOData format.

Return type:: IOData