+- DTGS171 **[Creating Custom Scatterers](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/1-getting-started/DTGS171_custom_scatterers.ipynb)**
Creating custom scatterers of arbitrary shapes.
-- DTGS171B **[Creating Custom Scatterers: Bacteria](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/1-getting-started/DTGS171B_custom_scatterers_bacteria.ipynb)**
+- DTGS172 **[Creating Custom Scatterers: Bacteria](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/1-getting-started/DTGS172_custom_scatterers_bacteria.ipynb)**
Creating custom scatterers in the shape of bacteria.
@@ -175,55 +175,53 @@ This section provides a list of advanced topic tutorials. The primary focus of t
- DTAT301 **[deeptrack.features](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT301_features.ipynb)**
-- DTAT306 **[deeptrack.properties](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT306_properties.ipynb)**
+- DTAT311 **[deeptrack.properties](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT311_properties.ipynb)**
-- DTAT311 **[deeptrack.image](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT311_image.ipynb)**
+- DTATo10 **[deeptrack.scatterers](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo10_scatterers.ipynb)**
-- DTAT321 **[deeptrack.scatterers](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT321_scatterers.ipynb)**
+- DTATo20 **[deeptrack.optics](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo20_optics.ipynb)**
-- DTAT323 **[deeptrack.optics](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT323_optics.ipynb)**
+- DTATo30 **[deeptrack.holography](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo30_holography.ipynb)**
-- DTAT324 **[deeptrack.holography](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT324_holography.ipynb)**
+- DTATo40 **[deeptrack.aberrations](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo40_aberrations.ipynb)**
-- DTAT325 **[deeptrack.aberrations](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT325_aberrations.ipynb)**
+- DTATo50 **[deeptrack.noises](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo50_noises.ipynb)**
-- DTAT327 **[deeptrack.noises](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT327_noises.ipynb)**
+- DTATo60 **[deeptrack.augmentations](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo60_augmentations.ipynb)**
-- DTAT329 **[deeptrack.augmentations](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT329_augmentations.ipynb)**
+- DTAT331 **[deeptrack.sequences](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT331_sequences.ipynb)**
-- DTAT341 **[deeptrack.sequences](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT341_sequences.ipynb)**
+- DTATo90 **[deeptrack.math](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo90_math.ipynb)**
-- DTAT381 **[deeptrack.math](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT381_math.ipynb)**
+- DTAT351 **[deeptrack.utils](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT351_utils.ipynb)**
-- DTAT383 **[deeptrack.utils](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT383_utils.ipynb)**
+- DTAT353 **[deeptrack.statistics](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT353_statistics.ipynb)**
-- DTAT385 **[deeptrack.statistics](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT385_statistics.ipynb)**
+- DTAT355 **[deeptrack.types](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT_387_types.ipynb)**
-- DTAT387 **[deeptrack.types](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT_387_types.ipynb)**
+- DTAT357 **[deeptrack.elementwise](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT357_elementwise.ipynb)**
-- DTAT389 **[deeptrack.elementwise](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT389_elementwise.ipynb)**
+- DTAT361 **[deeptrack.sources.base](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT361_sources.base.ipynb)**
-- DTAT391A **[deeptrack.sources.base](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT391A_sources.base.ipynb)**
+- DTAT362 **[deeptrack.sources.folder](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT362_sources.folder.ipynb)**
-- DTAT391B **[deeptrack.sources.folder](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT391B_sources.folder.ipynb)**
+- DTAT366 **[deeptrack.pytorch.data](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT366_pytorch.features.ipynb)**
-- DTAT393A **[deeptrack.pytorch.data](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT393A_pytorch.features.ipynb)**
+- DTAT367 **[deeptrack.pytorch.features](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT367_pytorch.data.ipynb)**
-- DTAT393B **[deeptrack.pytorch.features](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT393B_pytorch.data.ipynb)**
+- DTAT369 **[deeptrack.extras.radialcenter](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT369_extras.radialcenter.ipynb)**
-- DTAT395 **[deeptrack.extras.radialcenter](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT395_extras.radialcenter.ipynb)**
-
-- DTAT399A **[deeptrack.backend.core](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399A_backend.core.ipynb)**
+- DTAT391 **[deeptrack.backend.core](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT391_backend.core.ipynb)**
-- DTAT399B **[deeptrack.backend.pint_definition](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399B_backend.pint_definition.ipynb)**
+- DTAT392 **[deeptrack.backend.pint_definition](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT392_backend.pint_definition.ipynb)**
-- DTAT399C **[deeptrack.backend.units](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399C_backend.units.ipynb)**
+- DTAT393 **[deeptrack.backend.units](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT393_backend.units.ipynb)**
-- DTAT399D **[deeptrack.backend.polynomials](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399D_backend.polynomials.ipynb)**
+- DTAT394 **[deeptrack.backend.polynomials](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT394_backend.polynomials.ipynb)**
-- DTAT399E **[deeptrack.backend.mie](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399E_backend.mie.ipynb)**
+- DTAT395 **[deeptrack.backend.mie](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT395_backend.mie.ipynb)**
-- DTAT399F **[deeptrack.backend._config](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399F_backend._config.ipynb)**
+- DTAT396 **[deeptrack.backend._config](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT396_backend._config.ipynb)**
# Developer Tutorials
diff --git a/README.md b/README.md
index 57cf9032..e8dc9aa4 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@
- 
+ 
@@ -32,16 +32,16 @@
License
-DeepTrack2 is a modular Python library for generating, manipulating, and analyzing image data pipelines for machine learning and experimental imaging.
+DeepTrack2 is a modular Python library for generating, manipulating, and analyzing image data pipelines for machine learning and digital microscopy.
-TensorFlow Compatibility Notice:
-DeepTrack2 version 2.0 and subsequent do not support TensorFlow. If you need TensorFlow support, please install the legacy version 1.7.
+> TensorFlow Compatibility Notice:
+> DeepTrack2 version 2.0 and subsequent do not support TensorFlow. If you need TensorFlow support, please install the legacy version 1.7.
-The following quick start guide is intended for complete beginners to understand how to use DeepTrack2, from installation to training your first model. Let's get started!
+The following quick-start guide is intended for complete beginners to understand how to use DeepTrack2, from installation to training your first model. Let's get started!
# Installation
-DeepTrack2 requires at least python 3.9.
+DeepTrack2 requires at least python 3.10.
To install DeepTrack2, open a terminal or command prompt and run:
```bash
@@ -55,7 +55,7 @@ This will automatically install the required dependencies.
# Getting Started
-Here you find a series of notebooks that give you an overview of the core features of DeepTrack2 and how to use them:
+Here you find a series of notebooks providing an overview of the core features of DeepTrack2 and how to use them:
- DTGS101 **[Introduction to DeepTrack2](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/1-getting-started/DTGS101_intro.ipynb)**
@@ -97,11 +97,11 @@ Here you find a series of notebooks that give you an overview of the core featur
Using PyTorch gradients to fit a Gaussian generated by a DeepTrack2 pipeline.
-- DTGS171A **[Creating Custom Scatterers](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/1-getting-started/DTGS171A_custom_scatterers.ipynb)**
+- DTGS171 **[Creating Custom Scatterers](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/1-getting-started/DTGS171_custom_scatterers.ipynb)**
Creating custom scatterers of arbitrary shapes.
-- DTGS171B **[Creating Custom Scatterers: Bacteria](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/1-getting-started/DTGS171B_custom_scatterers_bacteria.ipynb)**
+- DTGS172 **[Simulating Bacteria](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/1-getting-started/DTGS172_custom_scatterers_bacteria.ipynb)**
Creating custom scatterers in the shape of bacteria.
@@ -218,59 +218,61 @@ Specific examples for graph-neural-network-based particle linking and trace char
# Advanced Tutorials
-This section provides a list of advanced topic tutorials. The primary focus of these tutorials is to demonstrate the functionalities of individual modules and how they work in relative isolation, helping to provide a better understanding of them and their roles in DeepTrack2.
+This section provides a list of advanced tutorials. The primary focus of these tutorials is to demonstrate the functionalities of individual modules and how they work in relative isolation, helping to provide a better understanding of them and their roles in DeepTrack2.
- DTAT301 **[deeptrack.features](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT301_features.ipynb)**
-- DTAT306 **[deeptrack.properties](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT306_properties.ipynb)**
+- DTAT311 **[deeptrack.properties](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT311_properties.ipynb)**
-- DTAT311 **[deeptrack.image](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT311_image.ipynb)**
+- DTAT321 **[deeptrack.properties](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT321_wrappers.ipynb)**
-- DTAT321 **[deeptrack.scatterers](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT321_scatterers.ipynb)**
+- DTAT331 **[deeptrack.sequences](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT331_sequences.ipynb)**
-- DTAT323 **[deeptrack.optics](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT323_optics.ipynb)**
+- DTAT351 **[deeptrack.utils](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT351_utils.ipynb)**
-- DTAT324 **[deeptrack.holography](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT324_holography.ipynb)**
+- DTAT353 **[deeptrack.statistics](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT353_statistics.ipynb)**
-- DTAT325 **[deeptrack.aberrations](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT325_aberrations.ipynb)**
+- DTAT355 **[deeptrack.types](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT_387_types.ipynb)**
-- DTAT327 **[deeptrack.noises](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT327_noises.ipynb)**
+- DTAT357 **[deeptrack.elementwise](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT357_elementwise.ipynb)**
-- DTAT329 **[deeptrack.augmentations](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT329_augmentations.ipynb)**
+- DTAT361 **[deeptrack.sources.base](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT361_sources.base.ipynb)**
-- DTAT341 **[deeptrack.sequences](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT341_sequences.ipynb)**
+- DTAT362 **[deeptrack.sources.folder](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT362_sources.folder.ipynb)**
-- DTAT381 **[deeptrack.math](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT381_math.ipynb)**
+- DTAT366 **[deeptrack.pytorch.data](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT366_pytorch.features.ipynb)**
-- DTAT383 **[deeptrack.utils](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT383_utils.ipynb)**
+- DTAT367 **[deeptrack.pytorch.features](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT367_pytorch.data.ipynb)**
-- DTAT385 **[deeptrack.statistics](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT385_statistics.ipynb)**
+- DTAT369 **[deeptrack.extras.radialcenter](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT369_extras.radialcenter.ipynb)**
-- DTAT387 **[deeptrack.types](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT_387_types.ipynb)**
+- DTAT391 **[deeptrack.backend.core](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT391_backend.core.ipynb)**
+
+- DTAT392 **[deeptrack.backend.pint_definition](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT392_backend.pint_definition.ipynb)**
+
+- DTAT393 **[deeptrack.backend.units](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT393_backend.units.ipynb)**
+
+- DTAT394 **[deeptrack.backend.polynomials](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT394_backend.polynomials.ipynb)**
+
+- DTAT395 **[deeptrack.backend.mie](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT395_backend.mie.ipynb)**
+
+- DTAT396 **[deeptrack.backend._config](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT396_backend._config.ipynb)**
-- DTAT389 **[deeptrack.elementwise](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT389_elementwise.ipynb)**
+## Advanced Tutorials for Optical Microscopy
-- DTAT391A **[deeptrack.sources.base](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT391A_sources.base.ipynb)**
+- DTATo10 **[deeptrack.optical.scatterers](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo10_scatterers.ipynb)**
-- DTAT391B **[deeptrack.sources.folder](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT391B_sources.folder.ipynb)**
+- DTATo20 **[deeptrack.optical.optics](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo20_optics.ipynb)**
-- DTAT393A **[deeptrack.pytorch.data](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT393A_pytorch.features.ipynb)**
+- DTATo30 **[deeptrack.optical.holography](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo30_holography.ipynb)**
-- DTAT393B **[deeptrack.pytorch.features](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT393B_pytorch.data.ipynb)**
+- DTATo40 **[deeptrack.optical.aberrations](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo40_aberrations.ipynb)**
-- DTAT395 **[deeptrack.extras.radialcenter](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT395_extras.radialcenter.ipynb)**
+- DTATo50 **[deeptrack.optical.noises](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo50_noises.ipynb)**
-- DTAT399A **[deeptrack.backend.core](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399A_backend.core.ipynb)**
-
-- DTAT399B **[deeptrack.backend.pint_definition](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399B_backend.pint_definition.ipynb)**
-
-- DTAT399C **[deeptrack.backend.units](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399C_backend.units.ipynb)**
-
-- DTAT399D **[deeptrack.backend.polynomials](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399D_backend.polynomials.ipynb)**
-
-- DTAT399E **[deeptrack.backend.mie](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399E_backend.mie.ipynb)**
-
-- DTAT399F **[deeptrack.backend._config](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT399F_backend._config.ipynb)**
+- DTATo60 **[deeptrack.optical.augmentations](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo60_augmentations.ipynb)**
+
+- DTATo90 **[deeptrack.optical.math](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTATo90_math.ipynb)**
# Developer Tutorials
diff --git a/deeptrack/backend/mie.py b/deeptrack/backend/mie.py
index 368704da..6d92b4cc 100644
--- a/deeptrack/backend/mie.py
+++ b/deeptrack/backend/mie.py
@@ -1,7 +1,7 @@
"""Mie scattering calculations.
-This module provides functions to perform Mie scattering calculations,
-including computation of spherical harmonics coefficients and related
+This module provides functions to perform Mie scattering calculations,
+including computation of spherical harmonics coefficients and related
operations.
Module Structure
@@ -32,9 +32,6 @@
"""
-#TODO ***??*** revise class docstring
-#TODO ***??*** revise DTAT399E
-
from __future__ import annotations
import array_api_compat as apc
@@ -141,7 +138,6 @@ def _zeros(shape, dtype, reference=None):
return xp.zeros(shape, **kwargs)
-#TODO ***??*** revise coefficients - torch, docstring, unit test
def coefficients(
m: float | complex,
a: float,
@@ -150,7 +146,7 @@ def coefficients(
"""Calculate the Mie scattering coefficients for a spherical particle.
These coefficients are used in the computation of the scattering
- and absorption of light by the particle. The terms up to (and including)
+ and absorption of light by the particle. The terms up to (and including)
order L are calculated using Riccati-Bessel polynomials.
Parameters
@@ -192,30 +188,21 @@ def coefficients(
xix = ricbesh(l, a)
dxix = dricbesh(l, a)
- A.append(
- (m * Smx * dSx - Sx * dSmx)
- /
- (m * Smx * dxix - xix * dSmx)
- )
- B.append(
- (Smx * dSx - m * Sx * dSmx)
- /
- (Smx * dxix - m * xix * dSmx)
- )
+ A.append((m * Smx * dSx - Sx * dSmx) / (m * Smx * dxix - xix * dSmx))
+ B.append((Smx * dSx - m * Sx * dSmx) / (Smx * dxix - m * xix * dSmx))
return xp.stack(A), xp.stack(B)
-#TODO ***??*** revise stratified_coefficients - torch, docstring, unit test
def stratified_coefficients(
m: list[complex],
a: list[float],
L: int,
) -> tuple[np.ndarray, np.ndarray]:
- """Calculate the Mie scattering coefficients for stratified spherical
+ """Calculate the Mie scattering coefficients for stratified spherical
particles.
- This function calculates the terms up to (and including) order L using
+ This function calculates the terms up to (and including) order L using
Riccati-Bessel polynomials.
Parameters
@@ -267,10 +254,7 @@ def stratified_coefficients(
if (p - q == 0) or (p - q == 1):
if i % 2 == 0:
- if (
- j < 2 * n_layers - 1
- and (j == 0 or j % 2 == 1)
- ):
+ if j < 2 * n_layers - 1 and (j == 0 or j % 2 == 1):
A_ij = dricbesj(n + 1, m[p] * a[q])
elif j % 2 == 0:
A_ij = dricbesy(n + 1, m[p] * a[q])
@@ -282,10 +266,7 @@ def stratified_coefficients(
else:
C_ij = A_ij
else:
- if (
- j < 2 * n_layers - 1
- and (j == 0 or j % 2 == 1)
- ):
+ if j < 2 * n_layers - 1 and (j == 0 or j % 2 == 1):
C_ij = ricbesj(n + 1, m[p] * a[q])
elif j % 2 == 0:
C_ij = ricbesy(n + 1, m[p] * a[q])
@@ -317,7 +298,6 @@ def stratified_coefficients(
return xp.stack(an), xp.stack(bn)
-#TODO ***??*** revise harmonics - torch, docstring, unit test
def harmonics(
x: NDArray,
L: int,
@@ -329,17 +309,17 @@ def harmonics(
Parameters
----------
x : np.ndarray
- An array representing the cosine of the polar angle (theta) for each
- evaluation point relative to the scattering particle's center
- (the origin).
- The polar angle is the angle between the z-axis (aligned with the
- direction of wave propagation) and the vector from the particle's
+ An array representing the cosine of the polar angle (theta) for each
+ evaluation point relative to the scattering particle's center
+ (the origin).
+ The polar angle is the angle between the z-axis (aligned with the
+ direction of wave propagation) and the vector from the particle's
center to the evaluation point.
-
- Values in `x` should lie in the range [-1, 1], where `x = 1`
+
+ Values in `x` should lie in the range [-1, 1], where `x = 1`
corresponds to theta = 0° (point directly forward along the z-axis),
- `x = -1` corresponds to theta = 180° (point directly backward along the
- z-axis), and `x = 0` corresponds to theta = 90° (point perpendicular to
+ `x = -1` corresponds to theta = 180° (point directly backward along the
+ z-axis), and `x = 0` corresponds to theta = 90° (point perpendicular to
the z-axis).
L : int
@@ -348,7 +328,7 @@ def harmonics(
Returns
-------
Tuple[np.ndarray, np.ndarray]
- A tuple containing arrays of harmonics PI and TAU of
+ A tuple containing arrays of harmonics PI and TAU of
shape (L, *x.shape).
"""
@@ -375,8 +355,7 @@ def harmonics(
for i in range(3, L + 1):
PI.append(
- (2 * i - 1) / (i - 1) * x * PI[i - 2]
- - i / (i - 1) * PI[i - 3]
+ (2 * i - 1) / (i - 1) * x * PI[i - 2] - i / (i - 1) * PI[i - 3]
)
TAU.append(i * x * PI[i - 1] - (i + 1) * PI[i - 2])
diff --git a/deeptrack/backend/polynomials.py b/deeptrack/backend/polynomials.py
index 8bf9e959..96edfcd1 100644
--- a/deeptrack/backend/polynomials.py
+++ b/deeptrack/backend/polynomials.py
@@ -1,7 +1,7 @@
"""Bessel and Riccati-Bessel polynomials.
-This module defines a set of functions for computing Bessel and Riccati-Bessel
-polynomials and their derivatives. It expands the corresponding capabilities of
+This module defines a set of functions for computing Bessel and Riccati-Bessel
+polynomials and their derivatives. It expands the corresponding capabilities of
`scipy`.
Module Structure
@@ -18,11 +18,8 @@
- `dricbesy`: First derivative of the Riccati-Bessel polynomial of the 2nd kind.
- `ricbesh`: Riccati-Bessel polynomial of the 3rd kind.
- `dricbesh`: First derivative of the Riccati-Bessel polynomial of the 3rd kind.
-
-"""
-#TODO ***??*** revise class docstring
-#TODO ***??*** revise DTAT399D
+"""
from __future__ import annotations
@@ -82,9 +79,8 @@ def _dricbesj_array_api(l: int | float, x, namespace=None):
if l == 0:
return xp.cos(x)
- return (
- _ricbesj_array_api(l - 1, x, xp)
- - l / x * _ricbesj_array_api(l, x, xp)
+ return _ricbesj_array_api(l - 1, x, xp) - l / x * _ricbesj_array_api(
+ l, x, xp
)
@@ -115,9 +111,8 @@ def _dricbesy_array_api(l: int | float, x, namespace=None):
if l == 0:
return -xp.sin(x)
- return (
- _ricbesy_array_api(l - 1, x, xp)
- - l / x * _ricbesy_array_api(l, x, xp)
+ return _ricbesy_array_api(l - 1, x, xp) - l / x * _ricbesy_array_api(
+ l, x, xp
)
@@ -135,7 +130,6 @@ def _dricbesh_array_api(l: int | float, x, namespace=None):
return _dricbesj_array_api(l, x, xp) - 1j * _dricbesy_array_api(l, x, xp)
-#TODO ***??*** revise besselj - torch, docstring, unit test
def besselj(
l: int | float,
x: int | float | NDArray,
@@ -159,11 +153,10 @@ def besselj(
return jv(l, x)
-#TODO ***??*** revise dbesselj - torch, docstring, unit test
def dbesselj(
- l: int | float,
- x: int | float | NDArray,
- ) -> float | NDArray:
+ l: int | float,
+ x: int | float | NDArray,
+) -> float | NDArray:
"""The first derivative of the Bessel polynomial of the 1st kind.
Parameters
@@ -183,11 +176,10 @@ def dbesselj(
return 0.5 * (besselj(l - 1, x) - besselj(l + 1, x))
-#TODO ***??*** revise bessely - torch, docstring, unit test
def bessely(
- l: int | float,
- x: int | float | NDArray,
- ) -> float | NDArray:
+ l: int | float,
+ x: int | float | NDArray,
+) -> float | NDArray:
"""The Bessel polynomial of the 2nd kind.
Parameters
@@ -207,11 +199,10 @@ def bessely(
return yv(l, x)
-#TODO ***??*** revise dbessely - torch, docstring, unit test
def dbessely(
- l: int | float,
- x: int | float | NDArray,
- ) -> float | NDArray:
+ l: int | float,
+ x: int | float | NDArray,
+) -> float | NDArray:
"""The first derivative of the Bessel polynomial of the 2nd kind.
Parameters
@@ -231,11 +222,10 @@ def dbessely(
return 0.5 * (bessely(l - 1, x) - bessely(l + 1, x))
-#TODO ***??*** revise ricbesj - torch, docstring, unit test
def ricbesj(
- l: int | float,
- x: int | float | NDArray,
- ) -> float | NDArray:
+ l: int | float,
+ x: int | float | NDArray,
+) -> float | NDArray:
"""The Riccati-Bessel polynomial of the 1st kind.
Parameters
@@ -259,11 +249,10 @@ def ricbesj(
return np.sqrt(np.pi * x / 2) * besselj(l + 0.5, x)
-#TODO ***??*** revise dricbesj - torch, docstring, unit test
def dricbesj(
- l: int | float,
- x: int | float | NDArray,
- ) -> float | NDArray:
+ l: int | float,
+ x: int | float | NDArray,
+) -> float | NDArray:
"""The first derivative of the Riccati-Bessel polynomial of the 1st kind.
Parameters
@@ -289,11 +278,10 @@ def dricbesj(
) * dbesselj(l + 0.5, x)
-#TODO ***??*** revise ricbesy - torch, docstring, unit test
def ricbesy(
- l: int | float,
- x: int | float | NDArray,
- ) -> float | NDArray:
+ l: int | float,
+ x: int | float | NDArray,
+) -> float | NDArray:
"""The Riccati-Bessel polynomial of the 2nd kind.
Parameters
@@ -317,11 +305,10 @@ def ricbesy(
return -np.sqrt(np.pi * x / 2) * bessely(l + 0.5, x)
-#TODO ***??*** revise dricbesy - torch, docstring, unit test
def dricbesy(
- l: int | float,
- x: int | float | NDArray,
- ) -> float | NDArray:
+ l: int | float,
+ x: int | float | NDArray,
+) -> float | NDArray:
"""The first derivative of the Riccati-Bessel polynomial of the 2nd kind.
Parameters
@@ -347,11 +334,10 @@ def dricbesy(
) * dbessely(l + 0.5, x)
-#TODO ***??*** revise ricbesh - torch, docstring, unit test
def ricbesh(
- l: int | float,
- x: int | float | NDArray,
- ) -> float | NDArray:
+ l: int | float,
+ x: int | float | NDArray,
+) -> float | NDArray:
"""The Riccati-Bessel polynomial of the 3rd kind.
Parameters
@@ -375,11 +361,10 @@ def ricbesh(
return np.sqrt(np.pi * x / 2) * h1vp(l + 0.5, x, False)
-#TODO ***??*** revise dricbesh - torch, docstring, unit test
def dricbesh(
- l: int | float,
- x: int | float | NDArray,
- ) -> float | NDArray:
+ l: int | float,
+ x: int | float | NDArray,
+) -> float | NDArray:
"""The first derivative of the Riccati-Bessel polynomial of the 3rd kind.
Parameters
diff --git a/deeptrack/features.py b/deeptrack/features.py
index 2a3c1c60..5adc75b8 100644
--- a/deeptrack/features.py
+++ b/deeptrack/features.py
@@ -1421,11 +1421,11 @@ def batch(
components = list(zip(*normalized))
# Stack each component along a new leading batch axis.
- with config.with_backend(self._backend):
+ with config.with_backend(self._backend):
return tuple(
- xp.stack([xp.asarray(x) for x in component])
- for component in components
- )
+ xp.stack([xp.asarray(x) for x in component])
+ for component in components
+ )
def _action(
self: Feature,
@@ -7659,7 +7659,9 @@ def get(
image = []
for img in raw:
if img.ndim == 3 and img.shape[-1] >= 3:
- image.append(cv2.cvtColor(img, cv2.COLOR_BGR2RGB).copy())
+ image.append(
+ cv2.cvtColor(img, cv2.COLOR_BGR2RGB).copy()
+ )
else:
image.append(np.asarray(img).copy())
@@ -8532,7 +8534,7 @@ def get(
NumPy array or a PyTorch tensor.
"""
-
+
if TORCH_AVAILABLE and isinstance(inputs, torch.Tensor):
if axes is None:
axes = tuple(range(inputs.ndim - 1, -1, -1))
diff --git a/deeptrack/optical/holography.py b/deeptrack/optical/holography.py
index 73672766..26eb1608 100644
--- a/deeptrack/optical/holography.py
+++ b/deeptrack/optical/holography.py
@@ -1,4 +1,4 @@
-"""Core features for manipulating optical fields using Fourier transforms and
+"""Core features for manipulating optical fields using Fourier transforms and
propagation matrices.
This module includes operations to simulate optical field propagation and
@@ -10,18 +10,18 @@
------------
- **Optical Field Processing**
- Provides Fourier transforms, rescaling, and wavefront propagation for
+ Provides Fourier transforms, rescaling, and wavefront propagation for
complex-valued optical fields, handling both real and imaginary components.
- **Fourier Optics and Wave Propagation**
- Implements Fourier transforms with optional padding for accurate
- frequency-domain analysis and propagation matrices to simulate free-space
+ Implements Fourier transforms with optional padding for accurate
+ frequency-domain analysis and propagation matrices to simulate free-space
wavefront propagation with spatial and frequency domain shifts.
- **Phase & Amplitude Manipulation**
- Enables scaling, normalization, and modulation of phase and amplitude to
+ Enables scaling, normalization, and modulation of phase and amplitude to
preserve intensity distribution and enhance wavefront reconstruction.
Module Structure
@@ -29,20 +29,20 @@
Classes:
- `Rescale`:
-
+
Rescales an optical field by subtracting the real part of the
field before multiplication.
- `FourierTransform`:
-
+
Creates matrices for propagating an optical field.
- `InverseFourierTransform`:
-
+
Creates matrices for propagating an optical field.
- `FourierTransformTransformation`:
-
+
Applies a power of the forward or inverse
propagation matrix to an optical field.
@@ -70,7 +70,7 @@ def get_propagation_matrix(
>>> import numpy as np
Define a random optical field:
->>> field = np.random.rand(128, 128, 2)
+>>> field = np.random.rand(128, 128, 2)
Rescale the field and compute the Fourier transform:
>>> rescale_op = dt.holography.Rescale(0.5)
@@ -84,9 +84,6 @@ def get_propagation_matrix(
"""
-#TODO ***??*** revise class docstring
-#TODO ***??*** revise DTAT324
-
from __future__ import annotations
from typing import Any
@@ -99,19 +96,18 @@ def get_propagation_matrix(
from deeptrack import Feature
-#TODO ***??*** revise get_propagation_matrix - torch, typing, docstring, unit test
def get_propagation_matrix(
shape: tuple[int, int],
to_z: float,
pixel_size: float | tuple[float, float],
wavelength: float,
dx: float = 0,
- dy: float = 0
+ dy: float = 0,
) -> np.ndarray:
"""Computes the propagation matrix for simulating the propagation of an
optical field.
- The propagation matrix is used to model wavefront propagation in free space
+ The propagation matrix is used to model wavefront propagation in free space
based on the angular spectrum method.
Parameters
@@ -136,7 +132,7 @@ def get_propagation_matrix(
Notes
-----
- - Uses `np.fft.fftshift` to shift the zero-frequency component to the
+ - Uses `np.fft.fftshift` to shift the zero-frequency component to the
center.
- Computed based on the wave equation in Fourier space.
@@ -178,9 +174,8 @@ def get_propagation_matrix(
)
-#TODO ***??*** revise Rescale - torch, typing, docstring, unit test
class Rescale(Feature):
- """Rescales an optical field by modifying its real and imaginary
+ """Rescales an optical field by modifying its real and imaginary
components.
The transformation is applied as:
@@ -210,9 +205,9 @@ def __init__(self, rescale=1, **kwargs):
super().__init__(rescale=rescale, **kwargs)
def get(
- self: Rescale,
- image: np.ndarray,
- rescale: float,
+ self: Rescale,
+ image: np.ndarray,
+ rescale: float,
**kwargs: Any,
) -> np.ndarray:
"""Rescales the image by subtracting the real part of the field before
@@ -241,12 +236,11 @@ def get(
return image
-#TODO ***??*** revise FourierTransform - torch, typing, docstring, unit test
class FourierTransform(Feature):
- """Computes the Fourier transform of an optical field with optional
+ """Computes the Fourier transform of an optical field with optional
symmetric padding.
- The Fourier transform converts a spatial-domain optical field into
+ The Fourier transform converts a spatial-domain optical field into
the frequency domain.
Parameters
@@ -269,7 +263,7 @@ class FourierTransform(Feature):
- Uses `np.fft.fft2` for fast computation.
- Pads the image symmetrically to avoid edge artifacts.
- Returns a complex-valued result.
-
+
"""
def __init__(self, **kwargs):
@@ -280,7 +274,7 @@ def get(
image: np.ndarray,
padding: int = 32,
**kwargs: Any,
- ) -> np.ndarray:
+ ) -> np.ndarray:
"""Computes the Fourier transform of the image.
Parameters
@@ -295,22 +289,19 @@ def get(
-------
np.ndarray
The Fourier transform of the image.
-
+
"""
im = np.copy(image[..., 0] + 1j * image[..., 1])
im = np.pad(
- im,
- ((padding, padding), (padding, padding)),
- mode="symmetric"
- )
+ im, ((padding, padding), (padding, padding)), mode="symmetric"
+ )
f1 = np.fft.fft2(im)
return f1
-#TODO ***??*** revise InverseFourierTransform - torch, typing, docstring, unit test
class InverseFourierTransform(Feature):
- """Applies a power of the forward or inverse propagation matrix to an
+ """Applies a power of the forward or inverse propagation matrix to an
optical field.
This operation simulates multiple propagation steps in Fourier optics.
@@ -385,9 +376,8 @@ def get(
return imnew
-#TODO ***??*** revise FourierTransformTransformation - torch, typing, docstring, unit test
class FourierTransformTransformation(Feature):
- """Applies a power of the forward or inverse propagation matrix to an
+ """Applies a power of the forward or inverse propagation matrix to an
optical field.
Parameters
@@ -449,7 +439,7 @@ def get(
inverse.
**kwargs: Any
Additional keyword arguments.
-
+
Returns
-------
np.ndarray
@@ -460,5 +450,5 @@ def get(
if i < 0:
image *= Tzinv ** np.abs(i)
else:
- image *= Tz ** i
+ image *= Tz**i
return image
diff --git a/deeptrack/optical/optics.py b/deeptrack/optical/optics.py
index 89b50cd9..702a1eb5 100644
--- a/deeptrack/optical/optics.py
+++ b/deeptrack/optical/optics.py
@@ -1219,6 +1219,10 @@ def _pad_volume(
new_limits[i, 1], output_region[i + 2] + padding[i + 2]
)
+ # Ensure the volume has at least one z-slice.
+ if new_limits[2, 1] <= new_limits[2, 0]:
+ new_limits[2, 1] = new_limits[2, 0] + 1
+
shape = new_limits[:, 1] - new_limits[:, 0]
if TORCH_AVAILABLE and isinstance(shape, torch.Tensor):
shape = shape.to(dtype=torch.int)
@@ -1231,6 +1235,10 @@ def _pad_volume(
dtype=volume.dtype,
device=volume.device,
)
+ # Preserve an autograd connection if the input volume has an empty z-axis
+ # but we expanded it to one z-slice.
+ if volume.shape[2] == 0 and new_volume.shape[2] > 0:
+ new_volume = new_volume + volume.sum() * 0
else:
new_volume = np.zeros(
shape.tolist(),
@@ -2077,7 +2085,7 @@ def get(
for i, z in zip(index_iterator, z_iterator):
light_in = light_in * pupil_step
- if zero_plane[i]:
+ if zero_plane[i] and padded_volume.shape[2] > 1:
continue
ri_slice = volume[:, :, i]
@@ -2109,7 +2117,7 @@ def get(
field = xp.sum(xp.stack(field_arrays, axis=0), axis=0)
- light_in_focus += field[..., 0]
+ light_in_focus = light_in_focus + field[..., 0]
shifted_pupil = xp.fft.fftshift(pupils[-1])
light_in_focus = light_in_focus * shifted_pupil
# Mask to remove light outside the pupil.
@@ -2204,6 +2212,8 @@ def __init__(
self: ISCAT,
illumination_angle: float = np.pi,
amp_factor: float = 1,
+ input_polarization: float | str | None = "circular",
+ output_polarization: float | None = None,
**kwargs: Any,
) -> None:
"""Initializes the ISCAT class.
@@ -2215,16 +2225,37 @@ def __init__(
amp_factor: float
Amplitude factor of the illuminating field relative to the
reference field.
+ input_polarization: float | str | None
+ Forwarded to the scatterer (e.g. `MieSphere`), which is where the
+ polarization projection is actually computed. Default `None`
+ (isotropic illumination, no preferred axis), the standard ISCAT
+ configuration. Override here to use a fixed linear polarization
+ instead.
+ output_polarization: float | None
+ Forwarded to the scatterer. Default `None` (no analyzer), the
+ standard ISCAT configuration. Override here for a fixed linear
+ analyzer angle instead.
**kwargs: Any
Additional parameters for the Brightfield class.
+ Notes
+ -----
+ `input_polarization`/`output_polarization` parametrize the
+ scattered-field calculation, which happens on the scatterer (e.g.
+ `MieSphere`), not here. Setting them on `ISCAT` only supplies the
+ value that gets forwarded to the scatterer, it does not
+ relocate the computation. `MieScatterer`'s own standalone default
+ remains `0`/`0` (linear). `ISCAT`'s default of `"circular"`/`None` is
+ what applies whenever a scatterer is imaged through `ISCAT`.
+
+
"""
super().__init__(
illumination_angle=illumination_angle,
amp_factor=amp_factor,
- input_polarization="circular",
- output_polarization="circular",
+ input_polarization=input_polarization,
+ output_polarization=output_polarization,
phase_shift_correction=True,
**kwargs,
)
diff --git a/deeptrack/optical/scatterers.py b/deeptrack/optical/scatterers.py
index aa471842..940454df 100644
--- a/deeptrack/optical/scatterers.py
+++ b/deeptrack/optical/scatterers.py
@@ -1176,18 +1176,21 @@ class MieScatterer(FieldScatterer):
Maximum collection angle in radians. If `"auto"`, this is computed
from the objective NA (assuming the objective is the limiting
aperture).
- input_polarization: float | Quantity | str
+ input_polarization: float | Quantity | str | None
Polarization angle of the incident illumination in radians. If a float
(or `Quantity`), it specifies the orientation of a linear polarizer
- before the sample. If set to `"circular"`, circular polarization is
- approximated by assigning equal weights to the two orthogonal
- scattering components. `None` is not supported in coherent mode. Use
- `Incoherent` to model unpolarized illumination.
- output_polarization: float | Quantity
+ before the sample. `None` represents genuinely unpolarized
+ illumination with no preferred axis. `"circular"` represents
+ physically circularly polarized illumination (e.g. a standard ISCAT
+ QWP+PBS illumination path); it gives the same phi-independent
+ coefficients as `None` for intensity purposes here, but is the more
+ physically accurate term when the illumination genuinely is
+ circularly polarized.
+ output_polarization: float | Quantity | None
Angle of a polarization analyzer placed after the sample, in radians.
If a float (or `Quantity`), the detected field is projected onto the
- corresponding linear polarization direction. `None` is not supported in
- coherent mode. Use `Incoherent` to model detection without analyzer.
+ corresponding linear polarization direction. `None` represents
+ detection with no analyzer (the standard, recommended way).
L: int | str
Number of terms used to evaluate the Mie series. If `"auto"`,
the number of terms is determined automatically.
@@ -1246,8 +1249,8 @@ class MieScatterer(FieldScatterer):
def __init__(
self: MieScatterer,
coefficients: callable,
- input_polarization: float | Quantity | str = 0,
- output_polarization: float | Quantity = 0,
+ input_polarization: float | Quantity | str | None = 0,
+ output_polarization: float | Quantity | None = 0,
offset_z: str | float = "auto",
collection_angle: str | float = "auto",
L: str | int = "auto",
@@ -1279,16 +1282,17 @@ def __init__(
input_polarization: float | Quantity | str
Polarization angle of the incident illumination in radians. If a
float (or `Quantity`), it specifies the orientation of a linear
- polarizer before the sample. If set to `"circular"`, circular
- polarization is approximated by assigning equal weights to the two
- orthogonal scattering components. `None` is not supported in
- coherent mode. Use `Incoherent` to model unpolarized illumination.
- output_polarization: float | Quantity
+ polarizer before the sample. If set to `"circular"`, circular
+ polarization is approximated by assigning equal weights to the two
+ orthogonal scattering components. If `None`, unpolarized is
+ obtained by assigning equal weights to the two orthogonal
+ scattering components.
+ output_polarization: float | Quantity | None
Angle of a polarization analyzer placed after the sample, in
radians. If a float (or `Quantity`), the detected field is
- projected onto the corresponding linear polarization direction.
- `None` is not supported in coherent mode. Use `Incoherent` to
- model detection without analyzer.
+ projected onto the corresponding linear polarization direction. If
+ `None`, unpolarized is obtained by assigning equal weights to the
+ two orthogonal scattering components.
offset_z: "auto" | float
Distance from the particle in the z direction where the field is
evaluated. If `"auto"`, this is calculated from the pixel size and
@@ -1429,24 +1433,6 @@ def _process_properties(
properties = super()._process_properties(properties)
- # --- polarization validation ---
- inp = properties.get("input_polarization", None)
- out = properties.get("output_polarization", None)
-
- if inp is None:
- raise ValueError(
- "input_polarization must be specified for coherent "
- "scattering. Use the Incoherent feature to model unpolarized "
- "illumination."
- )
-
- if out is None:
- raise ValueError(
- "output_polarization=None (no analyzer) is not supported in "
- "coherent mode. Use the Incoherent feature to model detection "
- "without analyzer."
- )
-
if properties["L"] == "auto":
try:
radius_for_l = properties["radius"]
@@ -1733,12 +1719,12 @@ def _polarization_coefficients(
input_polarization: float | int | str | Quantity
The polarization state of the incident illumination. Can be a float
representing the angle of linear polarization, the string
- "circular" for circular polarization, or a Quantity with angle
- units.
+ "circular" for circular polarization, a Quantity with angle
+ units, or `None` for unpolarized.
output_polarization: float | int | Quantity
The angle of the polarization analyzer for detection. Can be a
- float representing the angle of linear polarization, or a Quantity
- with angle units.
+ float representing the angle of linear polarization, a Quantity
+ with angle units, or `None` for unpolarized.
Returns
-------
@@ -1752,13 +1738,13 @@ def _polarization_coefficients(
if isinstance(input_polarization, Quantity):
input_polarization = input_polarization.to("rad").magnitude
- if isinstance(input_polarization, str):
- if input_polarization != "circular":
+ if input_polarization is None or isinstance(input_polarization, str):
+ if input_polarization not in (None, "circular"):
raise TypeError(
f"Unsupported input_polarization: {input_polarization}"
)
- S1_coef = 1 / np.sqrt(2)
- S2_coef = 1j / np.sqrt(2)
+ S1_coef = 0.5
+ S2_coef = 0.5
else:
input_polarization = _asarray(
input_polarization,
@@ -1770,16 +1756,12 @@ def _polarization_coefficients(
if isinstance(output_polarization, Quantity):
output_polarization = output_polarization.to("rad").magnitude
- output_polarization = _asarray(
- output_polarization,
- dtype=xp.float64,
- )
- S1_coef = S1_coef * xp.sin(phi + output_polarization)
- S2_coef = (
- S2_coef
- * xp.cos(phi + output_polarization)
- * illumination_cos_theta
- )
+ if output_polarization is None or isinstance(output_polarization, str):
+ pass # no analyzer — leave S1_coef/S2_coef unmodified
+ else:
+ output_polarization = _asarray(output_polarization, dtype=xp.float64)
+ S1_coef = S1_coef * xp.sin(phi + output_polarization)
+ S2_coef = S2_coef * xp.cos(phi + output_polarization) * illumination_cos_theta
return S1_coef, S2_coef
@@ -1989,8 +1971,8 @@ def _solve_geometric(
refractive_index_medium: float,
L: int,
collection_angle: float,
- input_polarization: float | int | str | Quantity,
- output_polarization: float | int | Quantity,
+ input_polarization: float | int | str | Quantity | None,
+ output_polarization: float | int | Quantity | None,
coefficients: Any,
offset_z: float,
z: float,
@@ -2149,13 +2131,22 @@ def _solve_geometric(
L, illumination_angle_field, coefficients
)
- arr[pupil_mask] = (
+ scattered_values = (
-1j
/ (k * R3_field)
* xp.exp(1j * k * R3_field)
* (S2 * S2_coef + S1 * S1_coef)
) / amp_factor
+ if TORCH_AVAILABLE and torch.is_tensor(arr):
+ flat_values = torch.zeros(
+ arr.numel(), dtype=arr.dtype, device=arr.device
+ )
+ flat_values[pupil_mask.reshape(-1)] = scattered_values
+ arr = arr + flat_values.reshape(arr.shape)
+ else:
+ arr[pupil_mask] = scattered_values
+
# For phase shift correction (a multiplication of the field
# by exp(1j * k * z)).
if phase_shift_correction:
@@ -2213,8 +2204,8 @@ def _solve_hybrid(
refractive_index_medium: float,
L: int,
collection_angle: float,
- input_polarization: float | int | str | Quantity,
- output_polarization: float | int | Quantity,
+ input_polarization: float | int | str | Quantity | None,
+ output_polarization: float | int | Quantity | None,
coefficients: Any,
offset_z: float,
z: float,
@@ -2365,7 +2356,16 @@ def _solve_hybrid(
)
S1, S2 = self._mie_scattering(L, illum_valid, coefficients)
- arr[pupil_mask] = (S2 * S2_coef + S1 * S1_coef) / amp_factor
+ scattered_values = (S2 * S2_coef + S1 * S1_coef) / amp_factor
+
+ if TORCH_AVAILABLE and torch.is_tensor(arr):
+ flat_values = torch.zeros(
+ arr.numel(), dtype=arr.dtype, device=arr.device
+ )
+ flat_values[pupil_mask.reshape(-1)] = scattered_values
+ arr = arr + flat_values.reshape(arr.shape)
+ else:
+ arr[pupil_mask] = scattered_values
# For phase shift correction (a multiplication of the field
# by exp(1j * k * z)).
diff --git a/tests/test_optics.py b/tests/test_optics.py
index 230a5a18..ba5045ff 100644
--- a/tests/test_optics.py
+++ b/tests/test_optics.py
@@ -5,7 +5,7 @@
from contextlib import contextmanager
from deeptrack.optical import optics
-from deeptrack.optical.scatterers import PointParticle, Sphere
+from deeptrack.optical.scatterers import PointParticle, Sphere, MieSphere
from deeptrack import units_registry as u
from deeptrack.backend import TORCH_AVAILABLE, xp
@@ -195,6 +195,157 @@ def test_ISCAT(self):
self.assertEqual(output_image.shape, (64, 64, 1))
self.assertEqual(microscope.amp_factor(), 1)
+ def _rot90_asymmetry_ratio(self, image: np.ndarray) -> float:
+ """Returns max|img - rot90(img)| / std(img), a scale-independent
+ measure of how far an image is from being rotationally symmetric.
+ """
+ img = np.asarray(image).squeeze()
+ std = img.std()
+ if std == 0:
+ return 0.0
+ return float(np.abs(img - np.rot90(img)).max() / std)
+
+ def test_ISCAT_MieSphere_no_analyzer_is_symmetric(self):
+
+ common_kwargs = dict(
+ NA=0.7,
+ wavelength=660e-9,
+ resolution=1e-6,
+ magnification=10,
+ refractive_index_medium=1.33,
+ output_region=(0, 0, 64, 64),
+ padding=(32, 32, 32, 32),
+ )
+ scatterer_common = dict(
+ position=(32, 32, 2),
+ position_unit="pixel",
+ radius=2e-6,
+ refractive_index=1.42,
+ phase_shift_correction=True,
+ L=10,
+ )
+
+ microscope_no_analyzer = optics.ISCAT(**common_kwargs)
+
+ microscope_fixed_linear = optics.ISCAT(
+ input_polarization=0.0,
+ output_polarization=0.0,
+ **common_kwargs,
+ )
+
+ no_analyzer = MieSphere(
+ **scatterer_common,
+ )
+ fixed_linear = MieSphere(
+ **scatterer_common,
+ )
+
+ with self._suppress_expected_optics_warnings():
+ img_no_analyzer = microscope_no_analyzer(no_analyzer).resolve()
+ img_fixed_linear = microscope_fixed_linear(
+ fixed_linear
+ ).resolve()
+
+ self.assertIsInstance(img_no_analyzer, self.array_type)
+ self.assertEqual(img_no_analyzer.shape, (64, 64, 1))
+
+ ratio_no_analyzer = self._rot90_asymmetry_ratio(img_no_analyzer)
+ ratio_fixed_linear = self._rot90_asymmetry_ratio(img_fixed_linear)
+
+ self.assertLess(ratio_no_analyzer, 3.0)
+ self.assertLess(ratio_no_analyzer, ratio_fixed_linear / 3.0)
+
+ def test_Holography_MieSphere_no_analyzer_is_symmetric(self):
+ common_kwargs = dict(
+ NA=0.7,
+ wavelength=660e-9,
+ resolution=1e-6,
+ magnification=10,
+ refractive_index_medium=1.33,
+ output_region=(0, 0, 64, 64),
+ padding=(32, 32, 32, 32),
+ return_field=True,
+ )
+ scatterer_common = dict(
+ position=(32, 32, 2),
+ position_unit="pixel",
+ radius=2e-6,
+ refractive_index=1.42,
+ L=10,
+ )
+
+ microscope = optics.Holography(**common_kwargs)
+
+ no_analyzer = MieSphere(
+ input_polarization=None,
+ output_polarization=None,
+ **scatterer_common,
+ )
+ fixed_linear = MieSphere(
+ input_polarization=0.0,
+ output_polarization=0.0,
+ **scatterer_common,
+ )
+
+ with self._suppress_expected_optics_warnings():
+ field_no_analyzer = np.asarray(
+ microscope(no_analyzer).resolve()
+ ).squeeze()
+ field_fixed_linear = np.asarray(
+ microscope(fixed_linear).resolve()
+ ).squeeze()
+
+ self.assertTrue(np.iscomplexobj(field_no_analyzer))
+
+ for component in ("real", "imag"):
+ ratio_no_analyzer = self._rot90_asymmetry_ratio(
+ getattr(field_no_analyzer, component)
+ )
+ ratio_fixed_linear = self._rot90_asymmetry_ratio(
+ getattr(field_fixed_linear, component)
+ )
+ self.assertLess(ratio_no_analyzer, 3.0)
+ self.assertLessEqual(ratio_no_analyzer, ratio_fixed_linear)
+
+ def test_ISCAT_Holography_MieSphere_no_analyzer_equivalence(self):
+
+ common = dict(
+ NA=0.7,
+ wavelength=660e-9,
+ resolution=1e-6,
+ magnification=10,
+ refractive_index_medium=1.33,
+ output_region=(0, 0, 64, 64),
+ padding=(32, 32, 32, 32),
+ )
+ scatterer_kwargs = dict(
+ position=(32, 32, 2),
+ position_unit="pixel",
+ radius=2e-6,
+ refractive_index=1.42,
+ input_polarization=None,
+ output_polarization=None,
+ L=10,
+ )
+
+ holography = optics.Holography(
+ phase_shift_correction=True,
+ illumination_angle=np.pi,
+ return_field=True,
+ **common,
+ )
+ iscat = optics.ISCAT(
+ return_field=True,
+ **common,
+ )
+
+ with self._suppress_expected_optics_warnings():
+ img_holo = holography(MieSphere(**scatterer_kwargs)).resolve()
+ img_iscat = iscat(MieSphere(**scatterer_kwargs)).resolve()
+
+ err = float(xp.mean(xp.abs(img_holo - img_iscat)))
+ self.assertLess(err, 1e-10)
+
def test_Darkfield(self):
microscope = optics.Darkfield(
NA=0.7,
@@ -326,4 +477,4 @@ class TestOptics_PyTorch(TestOptics_NumPy):
if __name__ == "__main__":
- unittest.main()
+ unittest.main()
\ No newline at end of file
diff --git a/tests/test_scatterers.py b/tests/test_scatterers.py
index adcbfd16..afc9fb78 100644
--- a/tests/test_scatterers.py
+++ b/tests/test_scatterers.py
@@ -12,6 +12,8 @@
from deeptrack.optical import scatterers
from tests import BackendTestBase
+from packaging.version import parse as parse_version
+
if TORCH_AVAILABLE:
import torch
@@ -270,31 +272,6 @@ def test_MieSphere(self):
)
)
- def test_MieSphere_rejects_none_polarizations(self):
- with self.assertRaises(ValueError):
- scatterers.MieSphere(
- radius=0.5e-6,
- refractive_index=1.45,
- wavelength=680e-9,
- refractive_index_medium=1.33,
- NA=0.7,
- output_region=(0, 0, 32, 32),
- input_polarization=None,
- output_polarization=0.0,
- ).resolve()
-
- with self.assertRaises(ValueError):
- scatterers.MieSphere(
- radius=0.5e-6,
- refractive_index=1.45,
- wavelength=680e-9,
- refractive_index_medium=1.33,
- NA=0.7,
- output_region=(0, 0, 32, 32),
- input_polarization=0.0,
- output_polarization=None,
- ).resolve()
-
def test_MieSphere_auto_parameters(self):
scatterer = scatterers.MieSphere(
radius=0.5e-6,
@@ -400,31 +377,6 @@ def test_MieStratifiedSphere(self):
)
)
- def test_MieStratifiedSphere_rejects_none_polarizations(self):
- with self.assertRaises(ValueError):
- scatterers.MieStratifiedSphere(
- radius=(0.5e-6, 1.0e-6),
- refractive_index=(1.45, 1.52),
- wavelength=680e-9,
- refractive_index_medium=1.33,
- NA=0.7,
- output_region=(0, 0, 32, 32),
- input_polarization=None,
- output_polarization=0.0,
- ).resolve()
-
- with self.assertRaises(ValueError):
- scatterers.MieStratifiedSphere(
- radius=(0.5e-6, 1.0e-6),
- refractive_index=(1.45, 1.52),
- wavelength=680e-9,
- refractive_index_medium=1.33,
- NA=0.7,
- output_region=(0, 0, 32, 32),
- input_polarization=0.0,
- output_polarization=None,
- ).resolve()
-
def test_MieStratifiedSphere_auto_parameters(self):
scatterer = scatterers.MieStratifiedSphere(
radius=(0.5e-6, 1.0e-6),
@@ -657,6 +609,11 @@ def test_mie_sphere_resolves_with_torch_autodiff(self):
self.assertGreater(abs(float(radius.grad)), 0)
self.assertGreater(abs(float(refractive_index.grad)), 0)
+ @unittest.skipIf(
+ not TORCH_AVAILABLE
+ or parse_version(torch.__version__) < parse_version("2.9"),
+ "Autograd through Mie scatterer requires torch >= 2.9"
+ )
def test_mie_sphere_brightfield_sums_multiple_torch_fields(self):
radius_1 = torch.tensor(
0.45e-6,
@@ -717,6 +674,11 @@ def test_mie_sphere_brightfield_sums_multiple_torch_fields(self):
self.assertGreater(abs(float(radius_1.grad)), 0)
self.assertGreater(abs(float(radius_2.grad)), 0)
+ @unittest.skipIf(
+ not TORCH_AVAILABLE
+ or parse_version(torch.__version__) < parse_version("2.9"),
+ "Autograd through Mie scatterer requires torch >= 2.9"
+ )
def test_mie_sphere_brightfield_autodiff_learnable_parameters(self):
cases = [
("x", 14.25, "sample"),
diff --git a/tutorials/1-getting-started/DTGS101_intro.ipynb b/tutorials/1-getting-started/DTGS101_intro.ipynb
index fd20c3db..80eefb93 100644
--- a/tutorials/1-getting-started/DTGS101_intro.ipynb
+++ b/tutorials/1-getting-started/DTGS101_intro.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Introduction to DeepTrack2\n",
+ "# DTGS101. Introduction to DeepTrack2\n",
"\n",
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
]
@@ -58,11 +58,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Features and properties\n",
+ "### 1.1. Features and properties\n",
"\n",
- "In DeepTrack2, these features are classes implementing the class `Feature` (see also [DTAT301_features](../3-advanced-topics/DTAT301_features.ipynb)). The way a feature updates an image is determined by the values passed to the class constructor. These inputs are converted to **properties** (see also [DTAT306_properties](../3-advanced-topics/DTAT306_properties.ipynb)). For example, a property could be the position of a particle. \n",
+ "In DeepTrack2, these features are classes implementing the class `Feature` (see also [DTAT301_features](../3-advanced-topics/DTAT301_features.ipynb)). The way a feature updates an image is determined by the values passed to the class constructor. These inputs are converted to **properties** (see also [DTAT311_properties](../3-advanced-topics/DTAT311_properties.ipynb)). For example, a property could be the position of a particle. \n",
"\n",
- "For example, the code below creates a feature: a point particle implemented by the class `PointParticle` with properties `position=(0, 0)`, and `intensity=100`. We use `dt.units.pixel` to denote the unit of the property. Most of the time (such as here) this can be omitted."
+ "For example, the code below creates a feature: a point particle implemented by the class `PointParticle` with properties `position=(0, 0)` and `intensity=100`. We use `dt.units.pixel` to denote the unit of the property. Most of the time this can be omitted."
]
},
{
@@ -80,7 +80,7 @@
"source": [
"particle = dt.PointParticle(\n",
" position=(0, 0) * dt.units.pixel,\n",
- " intensity=100\n",
+ " intensity=100,\n",
")"
]
},
@@ -108,7 +108,7 @@
"\n",
"particle = dt.PointParticle(\n",
" position=lambda: np.random.rand(2) * IMAGE_SIZE * dt.units.pixel,\n",
- " intensity=100\n",
+ " intensity=100,\n",
")"
]
},
@@ -118,7 +118,7 @@
"source": [
"A point particle on its own does not make much sense when thinking of actual experiments. In order to retrieve an image, you also need to define the optical device through which it is viewed.\n",
"\n",
- "In DeepTrack2, **optical devices** are features that convolve the input image with a pupil function. Here, you will use a fluorescence microscope, which is implemented by the feature `Fluorescence` (see also [optics_example](../3-advanced-topics/DTAT323_optics.ipynb))."
+ "In DeepTrack2, **optical devices** are features that convolve the input image with a pupil function. Here, you will use a fluorescence microscope, which is implemented by the feature `Fluorescence` (see also [DTATo20_optics](../3-advanced-topics/DTATo20_optics.ipynb))."
]
},
{
@@ -139,7 +139,7 @@
" wavelength=680 * dt.units.nm,\n",
" magnification=10,\n",
" resolution=1 * dt.units.micrometer,\n",
- " output_region=(0, 0, IMAGE_SIZE, IMAGE_SIZE)\n",
+ " output_region=(0, 0, IMAGE_SIZE, IMAGE_SIZE),\n",
")"
]
},
@@ -180,7 +180,7 @@
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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",
"text/plain": [
""
]
@@ -35,66 +35,61 @@
"metadata": {},
"outputs": [],
"source": [
- "import numpy as np\n",
"import deeptrack as dt\n",
+ "import numpy as np\n",
"from matplotlib import pyplot as plt\n",
+ "from matplotlib.animation import FuncAnimation\n",
"from IPython.display import HTML\n",
- "from matplotlib.animation import FuncAnimation"
+ "\n",
+ "\n",
+ "IMAGE_SIZE = 150 # Pixels"
]
},
{
"cell_type": "markdown",
- "id": "8f97cea5",
+ "id": "d8a5d2b5",
"metadata": {},
"source": [
- "## Brightfield\n",
- "\n",
- "In `DeepTrack2`, the brightfield microscope model simulates illumination using a single wavelength of light. This is equivalent to a brightfield microscope operating with a monochromatic light source.\n",
+ "## 1. Fluorescence\n",
+ "Fluorescence images in `DeepTrack2` are simulated with discrete volumes that act as light sources emitting light.\n",
"\n",
- "In experimental setups however, a brightfield microscope typically uses a broad spectrum of wavelengths (white light).\n",
+ "Simulating fluorescence images is easy and straightforward to implement. \n",
"\n",
- "To achieve a more realistic simulation of white light, we will need to simulate multiple brightfield images (sampling different parts of the visible light spectrum) and then averaging the contributions of these images. \n",
- "\n",
- "We start by generating a spectrum of wavelengths and a particle scatterer object. "
+ "Start by defining the particle ..."
]
},
{
"cell_type": "code",
"execution_count": 3,
- "id": "d60379dd",
+ "id": "79a4ebad",
"metadata": {},
"outputs": [],
"source": [
- "IMAGE_SIZE = 150\n",
- "\n",
- "# Generate a spectrum of wavelengths to sample from, 5 will be enough.\n",
- "wavelengths = np.linspace(450e-9, 700e-9, 5)\n",
- "\n",
"particle = dt.Sphere(\n",
" position=(IMAGE_SIZE//2, IMAGE_SIZE//2, 2) * dt.units.pixel,\n",
" radius= 0.3e-6,\n",
" position_unit=\"pixel\",\n",
- " refractive_index=1.42,\n",
+ " intensity=1,\n",
")"
]
},
{
"cell_type": "markdown",
- "id": "9828759b",
+ "id": "69714921",
"metadata": {},
"source": [
- "Image the particle by sampling the spectrum."
+ "... define the microscope and image the particle."
]
},
{
"cell_type": "code",
"execution_count": 4,
- "id": "a2debfcf",
+ "id": "dd8108a9",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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",
"text/plain": [
"