neuro_dot.Light_Modeling#
Module Contents#
Functions#
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CALC_NN Calculates the Nearest Neigbor value for all measurement pairs. |
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GENERATE_PAD_FROM_GRID Generates info structures "optodes" and "pairs" from a given "grid". |
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MAKEFLATFIELDRECON Generates the flat field reconstruction of a given "A" sensitivity matrix. |
- neuro_dot.Light_Modeling.calc_NN(info_in, dr)#
CALC_NN Calculates the Nearest Neigbor value for all measurement pairs.
- Inputs:
- info_in:
info structure containing data measurement list
- dr:
minimum separation for sources and detectors to be grouped into. (default = 10 mm) NOTE: distances are in millimeters
- Outputs:
- info_out:
info structure containing updated data measurement list with “info.pairs.NN”
- neuro_dot.Light_Modeling.Generate_pad_from_grid(grid, params, info)#
GENERATE_PAD_FROM_GRID Generates info structures “optodes” and “pairs” from a given “grid”.
The input: “grid” must have fields that list the spatial locations of sources and detectors in 3D: spos3, dpos3.
The input: “params” can be used to pass in mod type (default is ‘CW’ but can be the modulation frequency if fd) and wavelength(s) of the data in field ‘lambda’.
- Params:
- dr:
Minimum separation for sources and detectors to be grouped into different neighbors.
- lambda:
Wavelengths of the light. Any number of comma-separated values is allowed. Default: [750,850].
- Mod:
Modulation type or frequency. Can be ‘CW or ‘FD’ or can be the actual modulation frequency (e.g., 0 or 200) in MHz.
- pos2:
Flag which determines NN classification. Defaults to 0, where 3D coordinates are used. If set to 1, 2D coordinates will be used for NN classification.
- CapName:
Name for your pad file.
- neuro_dot.Light_Modeling.makeFlatFieldRecon(A, iA)#
MAKEFLATFIELDRECON Generates the flat field reconstruction of a given “A” sensitivity matrix.
- Inputs:
- A:
“A” sensitivity matrix
- iA:
inverted “A” sensitivity matrix
- Outputs:
- Asens:
flat field reconstruction of a given “A” sensitivity matrix