tomotok.tools package

4. tomotok.tools package#

class tomotok.tools.Diagnostic#

Bases: object

Base class for diagnostics. Should be subclassed for specific diagnostics.

Methods

load_data(shot[, tvec])

Loads diagnostic data from database specified by source.

load_data(shot: int, tvec: ArrayLike | None = None) → Signals#

Loads diagnostic data from database specified by source.

Parameters:

shotint: Shot number for which the diagnostic data is loaded.
tvecArrayLike, optional: specifies timeslices to be loaded, if not provided, all available timeslices are loaded
sourcestr, optional: specifies source for loading diagnostic data

Returns:

Signals: Loaded diagnostics signal stored in Signals container

class tomotok.tools.Tokamak(divertor_area_r: ArrayLike | None = None, divertor_area_z: ArrayLike | None = None)#

Bases: object

A Template class that should be subclassed for specific tokamaks.

Currently only defines a template method for loading magnetic field data for subclasses and stores divertor area.

Attributes:

divertor_area_rarray-like, optional: radial coordinates of divertor area boundary polygon
divertor_area_zarray-like, optional: vertical coordinates of divertor area boundary polygon

Methods

load_magnetic_field(shot[, tvec])

Loads magnetic field data from database for given shot and time slices.

load_magnetic_field(shot: int, tvec: ArrayLike = None) → Magnetics#

Loads magnetic field data from database for given shot and time slices.

Specific implementation should be provided in subclass.

Parameters:

shotint: Shot number for which the magnetic field is loaded.
tvecarray-like, optional: specifies vector of time slices to be loaded, if not provided, all available time slices are loaded

Returns:

Magnetics: Psi normalized stored in Magnetics dataclass container

4.1. Subpackages#

4.1.1. tomotok.tools.io package

4.2. Submodules#

4.3. tomotok.tools.checkers module#

Contains classes and functions for user checks of selected inputs for tomographic inversion

4.3.1. Examples#

Checking anisotropic derivative matrix

>>> from tomotok.geometry import RegularGrid
>>> from tomotok.regularisations import anisotropic_derivative_matrix
>>> 
>>> grid = RegularGrid(50, 100, (0.5, 1), (-0.5, 0.5))
>>> checker = AnisotropicDerivativeChecker(grid)
>>> dmat_par = anisotropic_derivative_matrix(grid, flux, 'parallel')
>>> dmat_per = anisotropic_derivative_matrix(grid, flux, 'perpendicular')
>>> checker(flux, dmat_par, dmat_per)

class tomotok.tools.checkers.AnisotropicDerivativeChecker(grid: RegularGrid, flux: ndarray, dmat1: spmatrix, dmat2: spmatrix, contour_colors='k', contour_levels=10, dmat_cmap='RdBu')#

Bases: object

Computes anisotropic matrix for a time slice from provided magnetic flux and plots its components. Supports interactive selection of matrix element using mouse.

Attributes:

figmatplotlib.figure: contains color mesh and countour plot of magnetic surfaces
fig2matplotlib.figure: contains two subplots with parallel and perpendicular parts of derivative matrix

Methods

`onclick`(event)	Gets coordinate from the click on the main flux plot
`update`(x, y)	Updates plots for current pixel coordinate

update(x, y)#: Updates plots for current pixel coordinate

onclick(event)#: Gets coordinate from the click on the main flux plot

4.4. tomotok.tools.containers module#

class tomotok.tools.containers.Signals(time_axis: ndarray, channel_numbers: ndarray, values: ndarray)#

Bases: object

Container for signals.

time_axis: ndarray#

channel_numbers: ndarray#

values: ndarray#

class tomotok.tools.containers.Magnetics(time_axis: ndarray, vertical: ndarray, radial: ndarray, flux: ndarray)#

Bases: object

Container for magnetics data.

Methods

interpolate(grid[, tvec, unique])

Interpolates given magnetic field to provided grid using rectangular bivariate spline.

time_axis: ndarray#

vertical: ndarray#

radial: ndarray#

flux: ndarray#

interpolate(grid: RegularGrid, tvec: ArrayLike | None = None, unique: bool = False) → Magnetics#

Interpolates given magnetic field to provided grid using rectangular bivariate spline.

If tvec is provided, only given time slices are interpolated. If unique is set to True, only unique time slices specified in tvec are interpolated.

Parameters:

gridRegularGrid
tvecarray-like, optional
uniquebool, optional: If False, the same time slice can be interpolated multiple times to provide field for each value in tvec. If True, only unique time slices specified in tvec are interpolated. Default value is False.

Returns:

Magnetics

class tomotok.tools.containers.Reconstruction(time_axis: ndarray, grid: Grid, values: ndarray)#

Bases: object

Container for reconstruction results.

time_axis: ndarray#

grid: Grid#

values: ndarray#

class tomotok.tools.containers.Sightlines(start_points: ndarray, end_points: ndarray)#

Bases: object

Container for sight line coordinates.

Attributes:

start_pointsnumpy.ndarray: Array with sight line start points coordinates, with shape (…, 3). Last dimension contains Cartesian coordinates.
end_pointsnumpy.ndarray: Array with sight line end points coordinates, with shape (…, 3). Last dimension contains Cartesian coordinates.

Methods

`coords_r`([number])	Returns given number of radial coordinates along the sight lines.
`coords_z`([number])	Returns given number of vertical coordinates along the sight lines.
`cylindrical_coords`([number])	Returns given number of cylindrical coordinates along the sight lines.

start_points: ndarray#

end_points: ndarray#

coords_r(number: int = 3) → ndarray#

Returns given number of radial coordinates along the sight lines.

Parameters:

numberint, optional: Number of points to interpolate along the sight line. Default is 3 which is minimum not to overlook tangential sight lines.

coords_z(number: int = 3) → ndarray#

Returns given number of vertical coordinates along the sight lines.

Parameters:

numberint, optional: Number of points to interpolate along the sight line. Default is 3 which is minimum not to overlook tangential sight lines.

cylindrical_coords(number: int = 3) → tuple[ndarray, ndarray]#

Returns given number of cylindrical coordinates along the sight lines.

Parameters:

numberint, optional: Number of points to interpolate along the sight line. Default is 3 which is minimum not to overlook tangential sight lines.

4.5. tomotok.tools.divertor module#

tomotok.tools.divertor.get_divertor_channels(grid: RegularGrid, divertor_area_r: ArrayLike, divertor_area_z: ArrayLike, gmat: ndarray | sparray, method: str = 'any') → NDArray[bool]#

Finds channels of geometry matrix that have contributions in the divertor area.

Uses dot product of geometry matrix with divertor area mask matrix obtained from divertor_area_mask().

Parameters:

gridRegularGrid
divertor_area_rArrayLike: radial coordinates of divertor area boundary polygon
divertor_area_zArrayLike: vertical coordinates of divertor area boundary polygon
gmatnumpy.ndarray or scipy.sparse.sparray: geometry matrix with shape (# channels, # nodes)
methodstr, optional: Method for defining divertor area mask. Supported values are ‘any’ and ‘centers’. Default value is ‘any’.

Returns:

numpy.ndarray of bool: mask array with True on channels interfering with predefined divertor area

4.6. tomotok.tools.hdf module#

Handles saving and loading of sparse matrices to/from HDF files.

Currently supported formats:

scipy.sparse.csc_array
scipy.sparse.csr_array
scipy.sparse.dia_array

tomotok.tools.hdf.sparse_to_hdf(matrix: csc_array | csr_array | dia_array, group: Group)#: Saves scipy.sparse matrix of formats (csc, csr, dia) into hdf file group.

tomotok.tools.hdf.hdf_to_sparse(group: Group) → spmatrix#: Loads scipy.sparse matrix of formats (csc, csr, dia) from hdf file group.

tomotok.tools.hdf.dia_to_hdf(matrix: dia_array, group: Group)#: Saves dia matrix to hdf group.

tomotok.tools.hdf.hdf_to_dia(group: Group) → dia_array#: Loads dia matrix from hdf group.

tomotok.tools.hdf.cs_to_hdf(matrix: csc_array | csr_array, group: Group)#: Saves compressed sparse matrix to hdf group.

tomotok.tools.hdf.hdf_to_cs(group: Group) → csc_array | csr_array#: Loads compressed sparse matrix from hdf group.

4.7. tomotok.tools.phantoms module#

Contains utility functions for creation of emissivity phantoms.

The phantoms are based on a gaussian profile as a function of magnetic flux coordinates. A function for creation of a simple flux map with elliptical shape is also provided. Additionally, a function for creation of a polar phase matrix is provided, which can be used to create magnetic island-like structures in the emissivity phantom.

tomotok.tools.phantoms.regular_elliptical_flux(grid: RegularGrid, span: float | tuple[float, float] = 1.5) → ndarray#: Creates a matrix of artificial flux surfaces with elliptical shape based on provided grid.

tomotok.tools.phantoms.elliptical_flux(radial_num: int, vertical_num: int, span: float | tuple[float, float] = 1.5) → ndarray#

Creates a matrix of artificial flux surfaces with elliptical shape.

Minimum values at the border are determined by the span parameter.

Parameters:

radial_numint: number of nodes along radial axis
vertical_numint: number of nodes along vertical axis
spanfloat or tuple of two floats, optional: flux value at the center of grid edge if tuple, first value is for radial axis, second for vertical

Returns:

numpy.ndarray: Matrix with generated fluxes

tomotok.tools.phantoms.gaussian_on_flux(flux: ndarray, amplitude: float = 1, center: float = 0, width: float = 0.1, limit: float = 1, limit_width: float = 0.2, limit_power: int = 2) → ndarray#

Creates gaussian artificial emissivity profile by transforming provided flux values

\[f = a \mathrm{e}^{-(f - c)^2 / w }\]

The limit width parameter allows to create a smooth transition from gaussian profile to zero at the limit value of flux. This is done by multiplying the gaussian profile with a polynomial function that goes from 1 to 0 in the range of limit - limit_width to limit.

Parameters:

fluxnp.ndarray: Flux values for transformation, any shape is supported
amplitudefloat, optional: maximum of gaussian profile,
centerfloat, optional: center of gaussian profile allows hollow profile generation when mapped on psi
widthfloat, optional: width of gaussian profile
limitfloat, optional: flux value where emissivity is forced to reach zero if flux > limit emissivity is set to zero
limit_widthfloat, optional: width of transition from gaussian profile to zero at limit value of flux
limit_powerfloat, optional: power of polynomial transition from gaussian profile to zero at limit value of flux

Returns:

numpy.ndarray: Transformed values of x with same dimensions

tomotok.tools.phantoms.polar_phase_matrix(grid: RegularGrid, num: int = 3, shift: float = 0.0, center: tuple[float, float] | None = None) → ndarray#

Creates a matrix with sine phase in poloidal direction relative to provided center.

The intended use is to modify an emissivity pattern by multiplying it with the returned matrix. This could be used to create island-like radiating structures in the phantom. The phase is applied in the positive angle direction (counterclockwise). If center is not provided, the grid centre is used.

Parameters:

gridRegularGrid: grid for which the phase matrix is generated
numint, optional: number of periods per one rotation
shiftfloat, optional: initial phase shift in degrees
centertuple of two floats, optional: center of polar phase, if None, grid centre is used

tomotok.tools.phantoms.iso_psi(nx: int, ny: int, span: float = 1.5) → ndarray#

Creates matrix of artificial isotropic psi profile with border values for each axis equal to span.

Deprecated since version 2.0: Use elliptical_flux() instead.

Parameters:

nxint: Number of pixels on x axis
nyint: Number of pixels on y axis
spanfloat, optional: Value of result on the center of border

Returns:

numpy.ndarray: Matrix with generated profile

tomotok.tools.phantoms.gauss(x: ndarray, w: float = 0.1, lim: float = 1, amp: float = 1, cen: float = 0.0) → ndarray#

Creates anisotropic gaussian artificial emissivity by 1D transform of x

\[f = amp \left( \mathrm{e}^{-(x-cen)^2 / w } - \mathrm{e}^{-(lim-cen)^2 / w)} \right)\]

Deprecated since version 2.0: Use gaussian_on_flux() instead.

Can be used on np.ndarray. Lim should be greater than cen.

Parameters:

xfloat, array, np.ndarray: Contains values to be transformed, usually psi
wfloat, optional: width of gaussian profile
limfloat, optional: minimal value of x where transform gives zero if x > lim emissivity is set to zero
ampfloat, optional: amplitude of gaussian profile,
cenfloat, optional: center of gaussian profile allows hollow profile generation when mapped on psi

Returns:

numpy.ndarray: Transformed values of x with same dimensions

tomotok.tools.phantoms.gauss_iso(nx: int, ny: int, span: float = 1.2, w: float = 0.1, lim: float = 1, amp: float = 1, cen: float = 0.0) → ndarray#: Creates isotropic gaussian distribution. See references for iso_psi and gauss

Deprecated since version 2.0: Use gaussian_on_flux() instead.

tomotok.tools.phantoms.polar_phase(x: ndarray, num: int = 3, shift: float = 0) → ndarray#

Applies sine phase in radial angle direction to given profile x.

Assumes equal dimension of grid elements in both axes.

Deprecated since version 2.0: Use polar_phase_matrix() instead.

Parameters:

xnumpy.ndarray: Profile for application of polar phase.
numint, optional: number of periods per one rotation
shiftfloat, optional: initial phase shift

Returns:

numpy.ndarray: Matrix with applied polar phase

tomotok.tools.phantoms.islands(psi: ndarray, w: float = 0.01, lim: float = 1, amp: float = 1, cen: float = 0.4, num: int = 3, shift: float = 0.0) → ndarray#

Creates island like phantom from given psi profile. See references for gauss and polar_phase.

Deprecated since version 2.0: Use gaussian_on_flux() and polar_phase_matrix() instead

tomotok.tools package

Contents

4. tomotok.tools package#

4.1. Subpackages#

4.2. Submodules#

4.3. tomotok.tools.checkers module#

4.3.1. Examples#

4.4. tomotok.tools.containers module#

4.5. tomotok.tools.divertor module#

4.6. tomotok.tools.hdf module#

4.7. tomotok.tools.phantoms module#