smlmlp.locs_individual_gaussfit module

smlmlp.locs_individual_gaussfit(crops, X0, Y0, /, ch=None, *, optimizer='lm', estimator='mle', distribution='poisson', channels_pixels_nm=1.0, channels_gains=1.0, channels_QE=1.0, cuda=False, parallel=False, channels_psf_xsigmas_nm=93.8, channels_psf_ysigmas_nm=93.8, channels_psf_thetas_deg=0.0, channels_fit_thetas=False)[source]

Fit each crop independently with an anisotropic 2D Gaussian model.

The function loops through channels, initializes a funclp.Gaussian2D model per event, runs the selected optimizer/estimator combination, and returns localized coordinates with fitted parameters.

Parameters:

  • crops (sequence of array-like) – Sequence of crop stacks, one per channel, shaped (N, Y, X).

  • X0 (array-like) – Detection-aligned 1D vector of x-origin pixel indices.

  • Y0 (array-like) – Detection-aligned 1D vector of y-origin pixel indices.

  • ch (array-like or None, optional) – One-based channel index for each detection. Required when crops has several channels.

  • optimizer (str, optional) – Optimizer key.

  • estimator (str, optional) – Estimator key.

  • distribution (str, optional) – Distribution key used by the estimator.

  • channels_pixels_nm (float or sequence, optional) – Pixel size specification per channel.

  • channels_gains (float or sequence, optional) – Gain value(s) used to convert fitted amplitudes.

  • channels_QE (float or sequence, optional) – Quantum efficiency value(s) used to convert fitted amplitudes.

  • cuda (bool, optional) – Whether to run the fit on GPU.

  • parallel (bool, optional) – Whether to enable CPU parallelization.

  • channels_psf_xsigmas_nm (float or sequence, optional) – Initial/fixed PSF sigma along x for each channel.

  • channels_psf_ysigmas_nm (float or sequence, optional) – Initial/fixed PSF sigma along y for each channel.

  • channels_psf_thetas_deg (float or sequence, optional) – Initial/fixed PSF angle in degrees for each channel.

  • channels_fit_thetas (bool, optional) – Whether to fit the PSF rotation angle.

Returns:

A tuple (mux, muy, info) where:

  • mux is the detection-aligned x localization array in nanometers,

  • muy is the detection-aligned y localization array in nanometers,

  • info is a dictionary with fitted parameter arrays.

info contains:

'amp'

Detection-aligned converted amplitudes.

'offset'

Detection-aligned converted offsets.

'sigmax'

Detection-aligned fitted x sigmas.

'sigmay'

Detection-aligned fitted y sigmas.

Return type:

tuple

Notes

  1. X0 and Y0 are split by ch so each origin vector follows the crop order inside its channel stack.

  2. A local coordinate grid is built from the channel pixel size, and each anisotropic Gaussian is initialized at the crop center.

  3. The optimizer updates local Gaussian parameters, local coordinates are shifted by crop origins, and all outputs are remapped to detection order.

Examples

>>> import numpy as np
>>> crops = [np.random.rand(2, 7, 7).astype(np.float32)]
>>> x0 = np.array([10, 20], dtype=np.float32)
>>> y0 = np.array([30, 40], dtype=np.float32)
>>> mux, muy, info = locs_individual_gaussfit(
...     crops,
...     x0,
...     y0,
...     channels_pixels_nm=[(100.0, 100.0)],
...     channels_psf_xsigmas_nm=[90.0],
...     channels_psf_ysigmas_nm=[90.0],
... )
>>> mux.shape == muy.shape
True
>>> sorted(info)
['amp', 'offset', 'sigmax', 'sigmay']

>>> mux, muy, info = locs_individual_gaussfit(
...     crops,
...     x0,
...     y0,
...     channels_pixels_nm=[(100.0, 120.0)],
...     channels_psf_xsigmas_nm=[80.0],
...     channels_psf_ysigmas_nm=[95.0],
...     channels_psf_thetas_deg=[5.0],
...     channels_fit_thetas=True,
... )
>>> info['sigmax'].ndim
1