data_conv

libra_py.data_conv.MATRIX2nparray(data)[source]

Converts both Libra MATRIX ( N, M ) object and CMATRIX ( N, M ) object into a 2D np.array of shape( N, M )

Parameters

data (Libra MATRIX object of dimension N x M) – data to be converted

Returns

2D np.array of shape( N, M )

Return type

2d np.array

libra_py.data_conv.form_block_matrix(mat_a, mat_b, mat_c, mat_d)[source]

This function gets four numpy arrays and concatenate them into a new matrix in a block format. These matrices should have the same shape on each side which they get concatenated.

S = | |

|mat_c mat_d|

Parameters

  • mat_a (2D numpy arrays) – The matrices which will form the block matrix

  • mat_b (2D numpy arrays) – The matrices which will form the block matrix

  • mat_c (2D numpy arrays) – The matrices which will form the block matrix

  • mat_d (2D numpy arrays) – The matrices which will form the block matrix

Returns

The block matrix of the four matrices above.

Return type

block_matrix ( numpy 2D array )

libra_py.data_conv.list2MATRIX(data)[source]

Converts a list of N doubles into a MATRIX(N,1) object

Parameters

data (list of doubles) – data to be converted

Returns

a matrix representation of the data

Return type

MATRIX(N,1)

libra_py.data_conv.make_list(nitems, value)[source]

Creates a list of nitems items, each of which is value

Parameters

  • nitems (int) – the size of the resulting list to create

  • value (any type) – the value of each initialized element of the list

Returns

the list of the added values

Return type

list

libra_py.data_conv.matrix2list(q)[source]

Converts the MATRIX(ndof, 1) or CMATRIX(ndof, 1) to a list of ndof float/complex numbers

Args:

q ( MATRIX(ndof, 1) or CMATRIX(ndof, 1) ): input matrix

Returns:

list : list representation of the matrix

libra_py.data_conv.nparray2CMATRIX(data)[source]

Converts 2D np.array of shape( N, M ) doubles into a CMATRIX( N, M ) object The numpy array should be complex

Parameters

data (2D np.array of dimension N x M) – data to be converted

Returns

a matrix representation of the data

Return type

MATRIX( N, M )

libra_py.data_conv.nparray2MATRIX(data)[source]

Converts 2D np.array of shape( N, M ) doubles into a MATRIX( N, M ) object The numpy array can contain either complex or real values

Parameters

data (2D np.array of dimension N x M) – data to be converted

Returns

a matrix representation of the data

Return type

MATRIX( N, M )

libra_py.data_conv.scale_NAC(X, a, b, scaling_factor)[source]

Rescales only the matrix elements X_ab by a uniform scaling factor: X_ab(original) -> ( X_ab ) x (scaling_factor),

Parameters

  • X (list of lists of CMATRIX(nstates, nstates)) – the original data stored as X[idata][step] - a CMATRIX(nstates, nstates) for the dataset idata and time step step

  • scaling_factor (double or complex) – the rescaling factor

Returns

but transforms X input directly, so changes the original input

Return type

None

libra_py.data_conv.scale_NACs(X, scaling_factor)[source]

Rescales all the off-diagonal matrix elements X_ab by a uniform scaling factor: X_ab(original) -> ( X_ab ) x (scaling_factor), for all a!=b

Parameters

  • X (list of lists of CMATRIX(nstates, nstates)) – the original data stored as X[idata][step] - a CMATRIX(nstates, nstates) for the dataset idata and time step step

  • scaling_factor (double or complex) – the rescaling factor

Returns

but transforms X input directly, so changes the original input

Return type

None

libra_py.data_conv.scissor(X, a, dE)[source]

Shift the diagonal elements of X : X_ii for all i = a, a+1, … by a constant value dE X_ii(original) -> ( X_ii ) + dE, for all i >= a

Parameters

  • X (list of lists of CMATRIX(nstates, nstates)) – the original data stored as X[idata][step] - a CMATRIX(nstates, nstates) for the dataset idata and time step step

  • dE (double or complex) – the shift magnitude

Returns

but transforms X input directly, so changes the original input

Return type

None

libra_py.data_conv.transform_data(X, params)[source]

This is an auxiliary function to transform the original matrices X (e.g. H_vib) according to: X(original) -> ( X + shift1 ) (x) (scale) + shift2,

Here, (x) indicates the element-wise multiplicaiton, and shift1, shift2, and scale are matrices

Parameters

  • X (list of lists of CMATRIX(nstates, nstates)) – the original data stored as X[idata][step] - a CMATRIX(nstates, nstates) for the dataset idata and time step step

  • params (dictionary) –

    parameters controlling the transformation

    • params[“shift1”] ( CMATRIX(nstates,nstates) ): first shift corrections [units of X], [default: zero]

    • params[“shift2”] ( CMATRIX(nstates,nstates) ): second shift corrections [units of X], [default: zero]

    • params[“scale”] ( CMATRIX(nstates,nstates) ): scaling of the X [unitless], [default: 1.0 in all matrix elements]

Returns

but transforms X input directly, so changes the original input

Return type

None

Example

Lets say we have a data set of 2x2 matrices and we want to increase the energy gap by 0.1 units and scale the couplings by a factor of 3. Then, the input is going to be like this:

>>> scl = CMATRIX(2,2);
>>> scl.set(0,0, 1.0+0.0j);  scl.set(0,1, 3.0+0.0j);
>>> scl.set(0,0, 3.0+0.0j);  scl.set(0,1, 1.0+0.0j);
>>> shi = CMATRIX(2,2);
>>> shi.set(0,0, 0.0+0.0j);  shi.set(0,1, 0.0+0.0j);
>>> shi.set(0,0, 0.0+0.0j);  shi.set(0,1, 0.1+0.0j);
>>> transform_data(X, {"shift2":shi, "scale":scl })
libra_py.data_conv.unit_conversion(X, scaling_factor)[source]

Rescales the data X uniformly: X(original) -> ( X ) (x) (scaling_factor),

Here, (x) indicates the element-wise multiplicaiton, and shift1, shift2, and scale are matrices

Parameters

  • X (list of lists of CMATRIX(nstates, nstates)) – the original data stored as X[idata][step] - a CMATRIX(nstates, nstates) for the dataset idata and time step step

  • scaling_factor (double or complex) – the rescaling factor

Returns

but transforms X input directly, so changes the original input

Return type

None

libra_py.data_conv.unpack1(H, i, j, component=2)[source]

Converts a list of CMATRIX or MATRIX objects into a list of doubles. These numbers are the time-series of a specifiend component (real of imaginary) of a specified matrix element (i,j) of each matrix: res[k] = H[k].get(i,j).component

Parameters

  • H (list of CMATRIX(n, m) or MATRIX(n, m)) – time-series of the matrices

  • i (int) – row index of the matrix element of interest

  • j (int) – column index of the matrix element of interest

  • component (int) –

    index selecting real or imaginary component

    • 0: real

    • 1: imaginary

    • 2: the whole thing - use for real matrices [ default ]

Returns

time-series of a given matrix element’s component

Return type

list of doubles

libra_py.data_conv.unpack2(H, i, component=2)[source]

Converts a CMATRIX or MATRIX object into a list of doubles. These numbers are the time-series of a specifiend component (real of imaginary) of a specified matrix column `i` res[k] = H.get(k,i).component

Parameters

  • H (CMATRIX(n, m) or MATRIX(n, m)) – time-series in a form of a matrix

  • i (int) – column index of interest

  • component (int) –

    index selecting real or imaginary component

    • 0: real

    • 1: imaginary

    • 2: the whole thing - use for real matrices

Returns

time-series of a given matrix column’s components

Return type

list of doubles