+
+DeepTrack 2.0 recognizing handwritten digits tutorial video: https://youtu.be/QD9JUXyLJpc
+
+
+DeepTrack 2.0 single particle tracking tutorial video: https://youtu.be/6Cntik6AfBI
+
+
+DeepTrack 2.0 single-particle characterization tutorial video: https://youtu.be/ia2H1QO1cHg
+
+
+DeepTrack 2.0 multiple particle tracking tutorial video: https://youtu.be/wFV2VqzpeZs
+
+
+DeepTrack 2.0 multiple particle tracking in 3D tutorial video: https://youtu.be/fzD1QIEIJ04
+
+
+DeepTrack 2.0 cell counting tutorial video: https://youtu.be/C6hu_IYoWtI
+
+
+DeepTrack 2.0 GAN image generation tutorial video: https://youtu.be/8g44Yks7cis
+
### In-depth dives
@@ -83,7 +121,7 @@ The detailed documentation of DeepTrack 2.0 is available at the following link:
If you use DeepTrack 2.0 in your project, please cite us here:
- Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe. "Quantitative Digital Microscopy with Deep Learning." [arXiv:2010.08260](https://arxiv.org/abs/2010.08260)
+ Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe. "Quantitative Digital Microscopy with Deep Learning." [https://doi.org/10.1063/5.0034891](https://aip.scitation.org/doi/10.1063/5.0034891)
See also:
diff --git a/_src/source/augmentations.rst b/_src/source/augmentations.rst
index e37c74a9d..6d007ab44 100644
--- a/_src/source/augmentations.rst
+++ b/_src/source/augmentations.rst
@@ -69,6 +69,13 @@ Pad
:members:
:exclude-members: get
+PadToMultiplesOf
+^^^^^^^^^^^^^^^^
+
+.. autoclass:: deeptrack.augmentations.PadToMultiplesOf
+ :members:
+ :exclude-members: get
+
PreLoad
^^^^^^^
diff --git a/_src/source/conf.py b/_src/source/conf.py
index 2abc60731..ac7a20ce8 100644
--- a/_src/source/conf.py
+++ b/_src/source/conf.py
@@ -31,7 +31,7 @@
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
-extensions = ["pydata_sphinx_theme", "sphinx.ext.autodoc", "numpydoc"]
+extensions = ["sphinx.ext.autodoc", "numpydoc"]
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
@@ -57,3 +57,4 @@
html_static_path = ["_static"]
master_doc = "index"
+autodoc_typehints = "none"
diff --git a/_src/source/deeptrack.rst b/_src/source/deeptrack.rst
index f98b9cdd3..b804e53ed 100644
--- a/_src/source/deeptrack.rst
+++ b/_src/source/deeptrack.rst
@@ -19,4 +19,6 @@ Documentation
properties
scatterers
sequences
+ test
+ types
utils
diff --git a/_src/source/features.rst b/_src/source/features.rst
index d989b39f1..07b9d6aba 100644
--- a/_src/source/features.rst
+++ b/_src/source/features.rst
@@ -6,6 +6,34 @@ features
Module classes
<<<<<<<<<<<<<<
+AsType
+^^^^^^
+
+.. autoclass:: deeptrack.features.AsType
+ :members:
+ :exclude-members: get
+
+Bind
+^^^^
+
+.. autoclass:: deeptrack.features.Bind
+ :members:
+ :exclude-members: get
+
+BindResolve
+^^^^^^^^^^^
+
+.. autoclass:: deeptrack.features.BindResolve
+ :members:
+ :exclude-members: get
+
+BindUpdate
+^^^^^^^^^^
+
+.. autoclass:: deeptrack.features.BindUpdate
+ :members:
+ :exclude-members: get
+
Branch
^^^^^^
@@ -104,6 +132,13 @@ SampleToMasks
:members:
:exclude-members: get
+Slice
+^^^^^
+
+.. autoclass:: deeptrack.features.Slice
+ :members:
+ :exclude-members: get
+
StructuralFeature
^^^^^^^^^^^^^^^^^
diff --git a/_src/source/generators.rst b/_src/source/generators.rst
index 935df8b84..2263a3623 100644
--- a/_src/source/generators.rst
+++ b/_src/source/generators.rst
@@ -6,6 +6,13 @@ generators
Module classes
<<<<<<<<<<<<<<
+CappedContinuousGenerator
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: deeptrack.generators.CappedContinuousGenerator
+ :members:
+ :exclude-members: get
+
ContinuousGenerator
^^^^^^^^^^^^^^^^^^^
diff --git a/_src/source/test.rst b/_src/source/test.rst
new file mode 100644
index 000000000..af1a1cd1d
--- /dev/null
+++ b/_src/source/test.rst
@@ -0,0 +1,5 @@
+test
+====
+
+.. automodule:: deeptrack.test
+
diff --git a/_src/source/types.rst b/_src/source/types.rst
new file mode 100644
index 000000000..9b7a6803d
--- /dev/null
+++ b/_src/source/types.rst
@@ -0,0 +1,5 @@
+types
+=====
+
+.. automodule:: deeptrack.types
+
diff --git a/deeptrack/__init__.py b/deeptrack/__init__.py
index de40a84c7..c8bab984d 100644
--- a/deeptrack/__init__.py
+++ b/deeptrack/__init__.py
@@ -7,4 +7,5 @@
from .scatterers import *
from .sequences import *
-from . import image, losses, generators, models, utils, layers, backend
\ No newline at end of file
+
+from . import image, losses, generators, models, utils, layers, backend, test
\ No newline at end of file
diff --git a/deeptrack/aberrations.py b/deeptrack/aberrations.py
index 7a9ef09c8..3e4648ae2 100644
--- a/deeptrack/aberrations.py
+++ b/deeptrack/aberrations.py
@@ -32,9 +32,12 @@
Zernike polynomial with n=4, m=0.
"""
+from typing import List, Tuple
+
import numpy as np
-from deeptrack.features import Feature
-from deeptrack.utils import as_list
+from .types import PropertyLike
+from .features import Feature
+from .utils import as_list
class Aberration(Feature):
@@ -53,7 +56,7 @@ def _process_and_get(self, image_list, **kwargs):
for image in image_list:
x = np.arange(image.shape[0]) - image.shape[0] / 2
y = np.arange(image.shape[1]) - image.shape[1] / 2
- X, Y = np.meshgrid(x, y)
+ X, Y = np.meshgrid(y, x)
rho = np.sqrt(X ** 2 + Y ** 2)
rho /= np.max(rho[image != 0])
theta = np.arctan2(Y, X)
@@ -83,7 +86,12 @@ class GaussianApodization(Aberration):
"""
- def __init__(self, sigma=1, offset=(0, 0), **kwargs):
+ def __init__(
+ self,
+ sigma: PropertyLike[float] = 1,
+ offset: PropertyLike[Tuple[int, int]] = (0, 0),
+ **kwargs
+ ):
super().__init__(sigma=sigma, offset=offset, **kwargs)
def get(self, pupil, offset, sigma, rho, **kwargs):
@@ -121,7 +129,13 @@ class Zernike(Aberration):
The coefficient of the polynomial
"""
- def __init__(self, n, m, coefficient=1, **kwargs):
+ def __init__(
+ self,
+ n: PropertyLike[int or List[int]],
+ m: PropertyLike[int or List[int]],
+ coefficient: PropertyLike[float or List[float]] = 1,
+ **kwargs
+ ):
super().__init__(n=n, m=m, coefficient=coefficient, **kwargs)
def get(self, pupil, rho, theta, n, m, coefficient, **kwargs):
@@ -186,7 +200,9 @@ class Piston(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=0, m=0, coefficient=coefficient, **kwargs)
@@ -199,7 +215,9 @@ class VerticalTilt(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=1, m=-1, coefficient=coefficient, **kwargs)
@@ -212,7 +230,9 @@ class HorizontalTilt(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=1, m=1, coefficient=coefficient, **kwargs)
@@ -225,7 +245,9 @@ class ObliqueAstigmatism(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=2, m=-2, coefficient=coefficient, **kwargs)
@@ -238,7 +260,9 @@ class Defocus(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=2, m=0, coefficient=coefficient, **kwargs)
@@ -251,7 +275,9 @@ class Astigmatism(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=2, m=2, coefficient=coefficient, **kwargs)
@@ -264,7 +290,9 @@ class ObliqueTrefoil(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=3, m=-3, coefficient=coefficient, **kwargs)
@@ -277,7 +305,9 @@ class VerticalComa(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=3, m=-1, coefficient=coefficient, **kwargs)
@@ -290,7 +320,9 @@ class HorizontalComa(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=3, m=1, coefficient=coefficient, **kwargs)
@@ -303,7 +335,9 @@ class Trefoil(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=3, m=3, coefficient=coefficient, **kwargs)
@@ -316,5 +350,7 @@ class SphericalAberration(Zernike):
The coefficient of the polynomial
"""
- def __init__(self, *args, coefficient=1, **kwargs):
+ def __init__(
+ self, *args, coefficient: PropertyLike[float or List[float]] = 1, **kwargs
+ ):
super().__init__(*args, n=4, m=0, coefficient=coefficient, **kwargs)
diff --git a/deeptrack/augmentations.py b/deeptrack/augmentations.py
index ad0124571..35169e944 100644
--- a/deeptrack/augmentations.py
+++ b/deeptrack/augmentations.py
@@ -19,12 +19,17 @@
Flips images diagonally.
"""
-from deeptrack.features import Feature
-from deeptrack.image import Image
+from .features import Feature
+from .image import Image
+from . import utils
+from .types import ArrayLike, PropertyLike
+
import numpy as np
-from typing import Callable
+import scipy.ndimage as ndimage
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
+
+from typing import Callable
import warnings
@@ -66,13 +71,13 @@ class Augmentation(Feature):
def __init__(
self,
feature: Feature = None,
- load_size: int = 1,
- updates_per_reload: int = 1,
+ load_size: PropertyLike[int] = 1,
+ updates_per_reload: PropertyLike[int] = 1,
update_properties: Callable or None = None,
**kwargs
):
- if load_size is not 1:
+ if load_size != 1:
warnings.warn(
"Using an augmentation with a load size other than one is no longer supported",
DeprecationWarning,
@@ -115,6 +120,7 @@ def _process_and_get(self, *args, update_properties=None, **kwargs):
not hasattr(self, "cache")
or kwargs["update_tally"] - self.last_update >= kwargs["updates_per_reload"]
):
+
if isinstance(self.feature, list):
self.cache = [feature.resolve() for feature in self.feature]
else:
@@ -151,11 +157,15 @@ def _process_and_get(self, *args, update_properties=None, **kwargs):
]
)
else:
- new_list_of_lists.append(
- Image(self.get(Image(image_list), **kwargs)).merge_properties_from(
- image_list
- )
- )
+ # DANGEROUS
+ # if not isinstance(image_list, Image):
+ image_list = Image(image_list)
+
+ output = self.get(image_list, **kwargs)
+
+ if not isinstance(output, Image):
+ output = Image(output)
+ new_list_of_lists.append(output.merge_properties_from(image_list))
if update_properties:
if not isinstance(new_list_of_lists, list):
@@ -224,13 +234,9 @@ def update_properties(self, image, number_of_updates, **kwargs):
if number_of_updates % 2:
for prop in image.properties:
if "position" in prop:
- position = prop["position"]
- new_position = (
- position[0],
- image.shape[1] - position[1] - 1,
- *position[2:],
- )
- prop["position"] = new_position
+ position = np.array(prop["position"])
+ position[..., 1] = image.shape[1] - position[..., 1]
+ prop["position"] = position
class FlipUD(Augmentation):
@@ -251,9 +257,9 @@ def update_properties(self, image, number_of_updates, **kwargs):
if number_of_updates % 2:
for prop in image.properties:
if "position" in prop:
- position = prop["position"]
- new_position = (image.shape[0] - position[0] - 1, *position[1:])
- prop["position"] = new_position
+ position = np.array(prop["position"])
+ position[..., 0] = image.shape[1] - position[..., 0]
+ prop["position"] = position
class FlipDiagonal(Augmentation):
@@ -274,16 +280,11 @@ def update_properties(self, image, number_of_updates, **kwargs):
if number_of_updates % 2:
for prop in image.properties:
if "position" in prop:
- position = prop["position"]
- new_position = (position[1], position[0], *position[2:])
- prop["position"] = new_position
-
-
-from deeptrack.utils import get_kwarg_names
-import warnings
-
-import scipy.ndimage as ndimage
-import deeptrack.utils as utils
+ position = np.array(prop["position"])
+ t = np.array(position[..., 0])
+ position[..., 0] = position[..., 1]
+ position[..., 1] = t
+ prop["position"] = position
class Affine(Augmentation):
@@ -336,14 +337,14 @@ class Affine(Augmentation):
def __init__(
self,
- scale=1,
- translate=None,
- translate_px=0,
- rotate=0,
- shear=0,
- order=1,
- cval=0,
- mode="reflect",
+ scale: PropertyLike[float or ArrayLike[float]] = 1,
+ translate: PropertyLike[float or ArrayLike[float] or None] = None,
+ translate_px: PropertyLike[float or ArrayLike[float]] = 0,
+ rotate: PropertyLike[float or ArrayLike[float]] = 0,
+ shear: PropertyLike[float or ArrayLike[float]] = 0,
+ order: PropertyLike[int] = 1,
+ cval: PropertyLike[float] = 0,
+ mode: PropertyLike[str] = "reflect",
**kwargs
):
if translate is None:
@@ -386,7 +387,9 @@ def get(self, image, scale, translate, rotate, shear, **kwargs):
assert (
image.ndim == 2 or image.ndim == 3
- ), "Affine only supports 2-dimensional or 3-dimension inputs."
+ ), "Affine only supports 2-dimensional or 3-dimension inputs, got {0}".format(
+ image.ndim
+ )
dx, dy = translate
fx, fy = scale
@@ -442,18 +445,21 @@ def get(self, image, scale, translate, rotate, shear, **kwargs):
for prop in image.properties:
if "position" in prop:
position = np.array(prop["position"])
- prop["position"] = np.array(
- (
- *(
- (
- inverse_mapping
- @ (position[:2] - center + np.array([dy, dx]))
- + center
- )
- ),
- *position[3:],
+
+ inverted = (
+ np.dot(
+ inverse_mapping,
+ (position[..., :2] - center + np.array([dy, dx]))[
+ ..., np.newaxis
+ ],
)
- )
+ .squeeze()
+ .transpose()
+ ) + center
+
+ position[..., :2] = inverted
+
+ prop["position"] = position
return image
@@ -519,12 +525,12 @@ class ElasticTransformation(Augmentation):
def __init__(
self,
- alpha=20,
- sigma=2,
- ignore_last_dim=True,
- order=3,
- cval=0,
- mode="constant",
+ alpha: PropertyLike[float] = 20,
+ sigma: PropertyLike[float] = 2,
+ ignore_last_dim: PropertyLike[bool] = True,
+ order: PropertyLike[int] = 3,
+ cval: PropertyLike[float] = 0,
+ mode: PropertyLike[str] = "constant",
**kwargs
):
super().__init__(
@@ -551,7 +557,10 @@ def get(self, image, sigma, alpha, ignore_last_dim, **kwargs):
for dim in shape:
deltas.append(
gaussian_filter(
- (np.random.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0
+ (np.random.rand(*shape) * 2 - 1),
+ sigma,
+ mode="constant",
+ cval=0,
)
* alpha
)
@@ -595,9 +604,10 @@ class Crop(Augmentation):
Number of pixels to remove or retain (depending in `crop_mode`)
If a tuple or list, it is assumed to be per axis.
Can also be a function that returns any of the other types.
- crop_mode : str {"remove", "retain"}
+ crop_mode : str {"retain", "remove"}
How the `crop` argument is interpreted. If "remove", then
- crop
+ `crop` denotes the amount to crop from the edges. If "retain",
+ `crop` denotes the size of the output.
corner : tuple of ints or Callable[Image]->tuple of ints or "random"
Top left corner of the cropped region. Can be a tuple of ints,
a function that returns a tuple of ints or the string random.
@@ -607,7 +617,12 @@ class Crop(Augmentation):
"""
def __init__(
- self, *args, crop=(64, 64), crop_mode="retain", corner="random", **kwargs
+ self,
+ *args,
+ crop: PropertyLike[int or ArrayLike[int]] = (64, 64),
+ crop_mode: PropertyLike[str] = "retain",
+ corner: PropertyLike[str] = "random",
+ **kwargs
):
super().__init__(*args, crop=crop, crop_mode=crop_mode, corner=corner, **kwargs)
@@ -619,6 +634,8 @@ def get(self, image, corner, crop, crop_mode, **kwargs):
if isinstance(crop, int):
crop = (crop,) * image.ndim
+ crop = [c if c is not None else image.shape[i] for i, c in enumerate(crop)]
+
# Get amount to crop from image
if crop_mode == "retain":
crop_amount = np.array(image.shape) - np.array(crop)
@@ -631,12 +648,9 @@ def get(self, image, corner, crop, crop_mode, **kwargs):
crop_amount = np.amax((np.array(crop_amount), [0] * image.ndim), axis=0)
crop_amount = np.amin((np.array(image.shape) - 1, crop_amount), axis=0)
# Get corner of crop
- if corner == "random":
+ if isinstance(corner, str) and corner == "random":
# Ensure seed is consistent
- slice_start = np.random.randint(
- [0] * crop_amount.size,
- crop_amount + 1,
- )
+ slice_start = [np.random.randint(m + 1) for m in crop_amount]
elif callable(corner):
slice_start = corner(image)
else:
@@ -654,6 +668,7 @@ def get(self, image, corner, crop, crop_mode, **kwargs):
for slice_start_i, slice_end_i in zip(slice_start, slice_end)
]
)
+
cropped_image = image[slices]
# Update positions
@@ -662,7 +677,7 @@ def get(self, image, corner, crop, crop_mode, **kwargs):
if "position" in prop:
position = np.array(prop["position"])
try:
- position[0:2] -= np.array(slice_start)[0:2]
+ position[..., 0:2] -= np.array(slice_start)[0:2]
prop["position"] = position
except IndexError:
pass
@@ -682,7 +697,12 @@ class CropToMultiplesOf(Crop):
"""
- def __init__(self, multiple=1, corner="random", **kwargs):
+ def __init__(
+ self,
+ multiple: PropertyLike[int or ArrayLike[int] or None] = 1,
+ corner: PropertyLike[str] = "random",
+ **kwargs
+ ):
kwargs.pop("crop", False)
kwargs.pop("crop_mode", False)
@@ -695,7 +715,7 @@ def image_to_crop(image):
new_shape = list(shape)
idx = 0
for dim, mul in zip(shape, multiple):
- if mul is not None and mul is not -1:
+ if mul is not None and mul != -1:
new_shape[idx] = int((dim // mul) * mul)
idx += 1
@@ -723,21 +743,86 @@ class Pad(Augmentation):
"""
- def __init__(self, px=(0, 0, 0, 0), mode="constant", cval=0, **kwargs):
+ def __init__(
+ self,
+ px: PropertyLike[int or ArrayLike[int]] = (0, 0, 0, 0),
+ mode: PropertyLike[str] = "constant",
+ cval: PropertyLike[float] = 0,
+ **kwargs
+ ):
super().__init__(px=px, mode=mode, cval=cval, **kwargs)
def get(self, image, px, **kwargs):
padding = []
- if isinstance(px, int):
+ if callable(px):
+ px = px(image)
+ elif isinstance(px, int):
padding = [(px, px)] * image.ndom
+
for idx in range(0, len(px), 2):
padding.append((px[idx], px[idx + 1]))
while len(padding) < image.ndim:
padding.append((0, 0))
- return utils.safe_call(np.pad, positional_args=(image, padding), **kwargs)
+ return (
+ utils.safe_call(np.pad, positional_args=(image, padding), **kwargs),
+ padding,
+ )
+
+ def _process_and_get(self, images, **kwargs):
+ results = [self.get(image, **kwargs) for image in images]
+ for idx, result in enumerate(results):
+ if isinstance(result, tuple):
+ shape = result[0].shape
+ padding = result[1]
+ de_pad = tuple(
+ slice(p[0], shape[dim] - p[1]) for dim, p in enumerate(padding)
+ )
+ results[idx] = (
+ Image(result[0]).merge_properties_from(images[idx]),
+ {"undo_padding": de_pad},
+ )
+ else:
+ Image(results[idx]).merge_properties_from(images[idx])
+ return results
+
+
+class PadToMultiplesOf(Pad):
+ """Pad images until their height/width is a multiple of a value.
+
+ Parameters
+ ----------
+ multiple : int or tuple of (int or None)
+ Images will be padded until their width is a multiple of
+ this value. If a tuple, it is assumed to be a multiple per axis.
+ A value of None or -1 indicates to skip that axis.
+
+ """
+
+ def __init__(self, multiple: PropertyLike[int or None] = 1, **kwargs):
+ def amount_to_pad(image):
+ shape = image.shape
+ multiple = self.multiple.current_value
+
+ if not isinstance(multiple, (list, tuple, np.ndarray)):
+ multiple = (multiple,) * image.ndim
+ new_shape = [0] * (image.ndim * 2)
+ idx = 0
+ for dim, mul in zip(shape, multiple):
+ if mul is not None and mul != -1:
+ to_add = -dim % mul
+ to_add_first = to_add // 2
+ to_add_after = to_add - to_add_first
+ new_shape[idx * 2] = to_add_first
+ new_shape[idx * 2 + 1] = to_add_after
+
+ idx += 1
+
+ return new_shape
+
+ super().__init__(multiple=multiple, px=lambda: amount_to_pad, **kwargs)
-# TODO: add resizing by rescaling
\ No newline at end of file
+# TODO: add resizing by rescaling
diff --git a/deeptrack/backend/mie.py b/deeptrack/backend/mie.py
index c3107645e..03f9064fa 100644
--- a/deeptrack/backend/mie.py
+++ b/deeptrack/backend/mie.py
@@ -4,7 +4,7 @@
import numpy as np
from typing import Tuple
-from deeptrack.backend import (
+from . import (
ricbesh,
ricbesy,
ricbesj,
diff --git a/deeptrack/features.py b/deeptrack/features.py
index 03e6df5ec..00c7ad6b5 100644
--- a/deeptrack/features.py
+++ b/deeptrack/features.py
@@ -9,28 +9,27 @@
StructuralFeature
Abstract extension of feature for interactions between features.
Branch
- Implementation of `StructuralFeature` that resolves two features
+ Implementation of `StructuralFeature` that resolves two features
sequentially.
Probability
- Implementation of `StructuralFeature` that randomly resolves a feature
+ Implementation of `StructuralFeature` that randomly resolves a feature
with a certain probability.
Duplicate
- Implementation of `StructuralFeature` that sequentially resolves an
+ Implementation of `StructuralFeature` that sequentially resolves an
integer number of deep-copies of a feature.
"""
import copy
-from abc import ABC, abstractmethod
-from typing import List
+from typing import Any, Callable, Iterable, Iterator, List, Tuple
+import warnings
import numpy as np
-import time
import threading
-from deeptrack.image import Image
-from deeptrack.properties import Property, PropertyDict
-from deeptrack.utils import isiterable, hasmethod, get_kwarg_names, kwarg_has_default
+from .image import Image
+from .properties import Property, PropertyDict
+from .types import PropertyLike, ArrayLike
MERGE_STRATEGY_OVERRIDE = 0
@@ -200,8 +199,12 @@ def resolve(self, image_list: Image or List[Image] = None, **global_kwargs):
property_verbosity = global_kwargs.get("property_memorability", 1)
feature_input["name"] = type(self).__name__
if self.__property_memorability__ <= property_verbosity:
- for image in new_list:
- image.append(feature_input)
+ for index, image in enumerate(new_list):
+ if isinstance(image, tuple):
+ image[0].append({**feature_input, **image[1]})
+ new_list[index] = image[0]
+ else:
+ image.append(feature_input)
# Merge input and new_list
if self.__list_merge_strategy__ == MERGE_STRATEGY_OVERRIDE:
@@ -305,11 +308,11 @@ def plot(
display(HTML(anim.to_jshtml()))
return anim
- except NameError as e:
+ except NameError:
# Not in an notebook
plt.show()
- except RuntimeError as e:
+ except RuntimeError:
# In notebook, but animation failed
import ipywidgets as widgets
@@ -333,18 +336,29 @@ def _process_and_get(self, image_list, **feature_input) -> List[Image]:
if self.__distributed__:
# Call get on each image in list, and merge properties from corresponding image
- return [
- Image(self.get(image, **feature_input)).merge_properties_from(image)
- for image in image_list
- ]
+
+ results = []
+
+ for image in image_list:
+ output = self.get(image, **feature_input)
+ if not isinstance(output, Image):
+ output = Image(output)
+
+ output.merge_properties_from(image)
+ results.append(output)
+
+ return results
+
else:
# Call get on entire list.
new_list = self.get(image_list, **feature_input)
if not isinstance(new_list, list):
- new_list = [Image(new_list)]
+ new_list = [new_list]
- new_list = [Image(image) for image in new_list]
+ for idx, image in enumerate(new_list):
+ if not isinstance(image, Image):
+ new_list[idx] = Image(image)
return new_list
def _format_input(self, image_list, **kwargs) -> List[Image]:
@@ -382,14 +396,32 @@ def __getattr__(self, key):
else:
raise AttributeError
- def __add__(self, other: "Feature") -> "Feature":
- # Overrides add operator
+ def __rshift__(self, other:"Feature") -> "Feature":
+ # Overrides >> operator
if isinstance(other, list) and all(isinstance(f) for f in other):
other = Combine(features=other)
if isinstance(other, Feature):
return Branch(self, other)
+ def __xor__(self, other) -> "Feature":
+ # Duplicate the feature to resolve more items
+ if isinstance(other, list) and all(isinstance(f) for f in other):
+ other = Combine(features=other)
+
+ return Duplicate(self, other)
+
+
+ def __add__(self, other: "Feature") -> "Feature":
+ import warnings
+ warnings.warn(
+ "Chaining features with the + operator is deprecated, use the >> operator instead",
+ DeprecationWarning
+ )
+ return self.__rshift__(other)
+
+
+
def __radd__(self, other) -> "Feature":
# Add when left hand is not a feature
# If left hand is falesly, return self
@@ -405,6 +437,7 @@ def __radd__(self, other) -> "Feature":
return NotImplemented
def __mul__(self, other: float) -> "Feature":
+ warnings.warn("Using * to probabilistically resolve a feature is deprecated. Use `Probability` instead.", DeprecationWarning)
# Introduces a probablity of a feature to be resolved.
if isinstance(other, list) and all(isinstance(f) for f in other):
other = Combine(features=other)
@@ -414,12 +447,18 @@ def __mul__(self, other: float) -> "Feature":
__rmul__ = __mul__
def __pow__(self, other) -> "Feature":
+ warnings.warn("Using ** to duplicate a feature is deprecated. Use ^ instead.", DeprecationWarning)
# Duplicate the feature to resolve more items
- if isinstance(other, list) and all(isinstance(f) for f in other):
- other = Combine(features=other)
+ return self.__xor__(other)
- return Duplicate(self, other)
+ def __getitem__(self, slices) -> "Feature":
+ # Allows direct slicing of the data.
+ if not isinstance(slices, tuple):
+ slices = (slices, )
+ slices = list(slices)
+
+ return self + Slice(slices)
class StructuralFeature(Feature):
"""Provides the structure of a feature-set
@@ -461,7 +500,9 @@ class Probability(StructuralFeature):
Probability to resolve
"""
- def __init__(self, feature: Feature, probability: float, *args, **kwargs):
+ def __init__(
+ self, feature: Feature, probability: PropertyLike[float], *args, **kwargs
+ ):
super().__init__(
*args,
feature=feature,
@@ -498,7 +539,9 @@ class Duplicate(StructuralFeature):
The number of duplicates to create
"""
- def __init__(self, feature: Feature, num_duplicates: int, *args, **kwargs):
+ def __init__(
+ self, feature: Feature, num_duplicates: PropertyLike[int], *args, **kwargs
+ ):
self.feature = feature
super().__init__(
@@ -560,13 +603,126 @@ class Combine(StructuralFeature):
__distribute__ = False
- def __init__(self, features=[], **kwargs):
+ def __init__(self, features:List[Feature], **kwargs):
super().__init__(features=features, **kwargs)
def get(self, image_list, features, **kwargs):
return [feature.resolve(image_list, **kwargs) for feature in features]
+class Slice(Feature):
+ ''' Array indexing for each Image in list.
+
+ Note, this feature is rarely needed to be used directly. Instead,
+ you can do normal array indexing on a feature directly. For example::
+
+ feature = dt.DummyFeature()
+ sliced_feature = feature[
+ lambda: 0 : lambda: 1,
+ 1:2,
+ lambda: slice(None, None, -2)
+ ]
+ sliced_feature.resolve(np.arange(27).reshape((3, 3, 3)))
+
+ In the example above, `lambda` is used to demonstrate different ways
+ to interact with the slices. In this case, the `lambda` keyword is
+ redundant.
+
+ Using `Slice` directly can be required in some cases, however. For example if
+ dependencies between properties are required. In this case, one can replicate
+ the previous example as follows::
+
+ feature = dt.DummyFeature()
+ sliced_feature = feature + dt.Slice(
+ slices=lambda dim1, dim2: (dim1, dim2),
+ dim1=slice(lambda: 0, lambda: 1, 1),
+ dim2=slice(1, 2, None),
+ dim3=lambda: slice(None, None, -2)
+ )
+ sliced_feature.resolve(np.arange(27).reshape((3, 3, 3)))
+
+ Parameters
+ ----------
+ slices : iterable of int, slice or ellipsis
+ The indexing of each dimension in order.
+ '''
+
+ def __init__(self,
+ slices:PropertyLike[
+ Iterable[
+ PropertyLike[int] or
+ PropertyLike[slice] or
+ PropertyLike[Ellipsis]
+ ]
+ ], **kwargs):
+ super().__init__(slices=slices, **kwargs)
+
+ def get(self, image, slices, **kwargs):
+
+ try:
+ slices = tuple(slices)
+ except ValueError:
+ pass
+
+ return image[slices]
+
+
+
+class Bind(StructuralFeature):
+ """Binds a feature with property arguments.
+
+ When the feature is resolved, the kwarg arguments are passed
+ to the child feature.
+
+ Parameters
+ ----------
+ feature : Feature
+ The child feature
+ **kwargs
+ Properties to send to child
+
+ """
+
+ __distributed__ = False
+
+ def __init__(self, feature: Feature, **kwargs):
+ super().__init__(feature=feature, **kwargs)
+
+ def get(self, image, feature, **kwargs):
+ return feature.resolve(image, **kwargs)
+
+
+BindResolve = Bind
+
+
+class BindUpdate(StructuralFeature):
+ """Binds a feature with certain arguments.
+
+ When the feature is updated, the child feature
+
+ Parameters
+ ----------
+ feature : Feature
+ The child feature
+ **kwargs
+ Properties to send to child
+
+ """
+
+ __distributed__ = False
+
+ def __init__(self, feature: Feature, **kwargs):
+ self.feature = feature
+ super().__init__(**kwargs)
+
+ def _update(self, **kwargs):
+ super()._update(**kwargs)
+ self.feature._update(**{**kwargs, **self.properties})
+
+ def get(self, image, **kwargs):
+ return self.feature.resolve(image, **kwargs)
+
+
class ConditionalSetProperty(StructuralFeature):
"""Conditionally overrides the properties of child features
@@ -583,7 +739,7 @@ class ConditionalSetProperty(StructuralFeature):
__distributed__ = False
- def __init__(self, feature: Feature, condition="is_label", **kwargs):
+ def __init__(self, feature: Feature, condition=PropertyLike[str], **kwargs):
super().__init__(feature=feature, condition=condition, **kwargs)
def get(self, image, feature, condition, **kwargs):
@@ -625,7 +781,7 @@ def __init__(
self,
on_false: Feature = None,
on_true: Feature = None,
- condition="is_label",
+ condition: PropertyLike[str] = "is_label",
**kwargs
):
@@ -661,7 +817,7 @@ class Lambda(Feature):
Function that takes the current image as first input
"""
- def __init__(self, function, **kwargs):
+ def __init__(self, function: Callable[..., Callable[[Image], Image]], **kwargs):
super().__init__(function=function, **kwargs)
def get(self, image, function, **kwargs):
@@ -684,7 +840,11 @@ class Merge(Feature):
__distributed__ = False
- def __init__(self, function, **kwargs):
+ def __init__(
+ self,
+ function: Callable[..., Callable[[List[Image]], Image or List[Image]]],
+ **kwargs
+ ):
super().__init__(function=function, **kwargs)
def get(self, list_of_images, function, **kwargs):
@@ -709,7 +869,9 @@ class Dataset(Feature):
__distributed__ = False
- def __init__(self, data, **kwargs):
+ def __init__(
+ self, data: Iterator or PropertyLike[float or ArrayLike[float]], **kwargs
+ ):
super().__init__(data=data, **kwargs)
def get(self, *ignore, data, **kwargs):
@@ -742,7 +904,7 @@ class Label(Feature):
__distributed__ = False
- def __init__(self, output_shape=None, **kwargs):
+ def __init__(self, output_shape: PropertyLike[int] = None, **kwargs):
super().__init__(output_shape=output_shape, **kwargs)
def get(self, image, output_shape=None, hash_key=None, **kwargs):
@@ -789,12 +951,12 @@ class LoadImage(Feature):
def __init__(
self,
- path,
- load_options=None,
- as_list=False,
- ndim=None,
- to_grayscale=False,
- get_one_random=False,
+ path: PropertyLike[str or List[str]],
+ load_options: PropertyLike[dict] = None,
+ as_list: PropertyLike[bool] = False,
+ ndim: PropertyLike[int] = None,
+ to_grayscale: PropertyLike[bool] = False,
+ get_one_random: PropertyLike[bool] = False,
**kwargs
):
super().__init__(
@@ -818,30 +980,32 @@ def get(
get_one_random,
**kwargs
):
+ if not isinstance(path, List):
+ path = [path]
if load_options is None:
load_options = {}
try:
- image = np.load(path, **load_options)
+ image = [np.load(file, **load_options) for file in path]
except (IOError, ValueError):
try:
from skimage import io
- image = io.imread(path)
+ image = [io.imread(file) for file in path]
except (IOError, ImportError, AttributeError):
try:
import PIL.Image
- omage = np.array(PIL.Image.open(path, **load_options))
+ image = [PIL.Image.open(file, **load_options) for file in path]
except (IOError, ImportError):
import cv2
- image = np.array(cv2.imread(path, **load_options))
+ image = [cv2.imread(file, **load_options) for file in path]
if not image:
raise IOError(
"No filereader available for file {0}".format(path)
)
- image = np.squeeze(image)
+ image = np.stack(image, axis=-1)
if to_grayscale:
try:
@@ -887,31 +1051,31 @@ class SampleToMasks(Feature):
Parameters
----------
transformation_function : function
- Function that takes an image as input, and outputs another image with `number_of_masks`
- layers.
+ Function that takes an image as input, and outputs another image with `number_of_masks`
+ layers.
number_of_masks : int
- The number of masks to create.
+ The number of masks to create.
output_region : (int, int, int, int)
- Size and relative position of the mask. Should generally be the same as
- `optics.output_region`.
+ Size and relative position of the mask. Should generally be the same as
+ `optics.output_region`.
merge_method : str or function or list
- How to merge the individual masks to a single image. If a list, the merge_metod
- is per mask. Can be
- * "add": Adds the masks together.
- * "overwrite": later masks overwrite earlier masks.
- * "or": 1 if either any mask is non-zero at that pixel
- * function: a function that accepts two images. The first is the current
- value of the output image where a new mask will be places, and
- the second is the mask to merge with the output image.
+ How to merge the individual masks to a single image. If a list, the merge_metod
+ is per mask. Can be
+ * "add": Adds the masks together.
+ * "overwrite": later masks overwrite earlier masks.
+ * "or": 1 if either any mask is non-zero at that pixel
+ * function: a function that accepts two images. The first is the current
+ value of the output image where a new mask will be places, and
+ the second is the mask to merge with the output image.
"""
def __init__(
self,
- transformation_function,
- number_of_masks=1,
- output_region=None,
- merge_method="add",
+ transformation_function: Callable[..., Callable[[Image], Image]],
+ number_of_masks: PropertyLike[int] = 1,
+ output_region: PropertyLike[ArrayLike[int]] = None,
+ merge_method: PropertyLike[str] = "add",
**kwargs
):
super().__init__(
@@ -974,7 +1138,8 @@ def _process_and_get(self, images, **kwargs):
p0 = p0.astype(np.int)
output_slice = output[
- p0[0] : p0[0] + labelarg.shape[0], p0[1] : p0[1] + labelarg.shape[1]
+ p0[0] : p0[0] + labelarg.shape[0],
+ p0[1] : p0[1] + labelarg.shape[1],
]
for label_index in range(kwargs["number_of_masks"]):
@@ -1024,7 +1189,8 @@ def _process_and_get(self, images, **kwargs):
p0[1] : p0[1] + labelarg.shape[1],
label_index,
] = merge(
- output_slice[..., label_index], labelarg[..., label_index]
+ output_slice[..., label_index],
+ labelarg[..., label_index],
)
output = Image(output)
for label in list_of_labels:
@@ -1038,7 +1204,7 @@ def _get_position(image, mode="corner", return_z=False):
if mode == "corner":
shift = (np.array(image.shape) - 1) / 2
else:
- shift = np.zeros((num_outputs))
+ shift = np.zeros((3 if return_z else 2))
positions = image.get_property("position", False, [])
@@ -1054,7 +1220,11 @@ def _get_position(image, mode="corner", return_z=False):
if return_z:
outp = (
np.array(
- [position[0], position[1], image.get_property("z", default=0)]
+ [
+ position[0],
+ position[1],
+ image.get_property("z", default=0),
+ ]
)
- shift
)
@@ -1063,3 +1233,25 @@ def _get_position(image, mode="corner", return_z=False):
positions_out.append(position - shift[0:2])
return positions_out
+
+
+class AsType(Feature):
+ """Converts the data type of images
+
+ Accepts same types as numpy arrays. Common types include
+
+ `float64, int32, uint16, int16, uint8, int8`
+
+ Parameters
+ ----------
+ dtype : str
+ dtype string. Same as numpy dtype.
+ """
+
+ def __init__(self, dtype: PropertyLike[Any] = "float64", **kwargs):
+ super().__init__(dtype=dtype, **kwargs)
+
+ def get(self, image, dtype, **kwargs):
+ return image.astype(dtype)
+
+
diff --git a/deeptrack/generators.py b/deeptrack/generators.py
index 8c8d4412d..9d50079cf 100644
--- a/deeptrack/generators.py
+++ b/deeptrack/generators.py
@@ -12,8 +12,8 @@
from typing import List
import tensorflow.keras as keras
-from deeptrack.features import Feature
-from deeptrack.image import Image
+from .features import Feature
+from .image import Image
import threading
import random
import time
@@ -107,10 +107,12 @@ def generate(
if sub_batch.ndim > ndim:
dims_to_remove = sub_batch.ndim - ndim
sub_batch = np.reshape(
- sub_batch, (-1, *sub_batch.shape[dims_to_remove + 1 :])
+ sub_batch,
+ (-1, *sub_batch.shape[dims_to_remove + 1 :]),
)
sub_labels = np.reshape(
- sub_labels, (-1, *sub_labels.shape[dims_to_remove + 1 :])
+ sub_labels,
+ (-1, *sub_labels.shape[dims_to_remove + 1 :]),
)
elif sub_batch.ndim < ndim:
@@ -199,6 +201,7 @@ def __init__(
update_kwargs={},
verbose=1,
ndim=4,
+ max_sample_exposure=np.inf,
):
if min_data_size is None:
@@ -302,11 +305,11 @@ def __getitem__(self, idx):
return outputs
def __len__(self):
- l = int((len(self.current_data) // self._batch_size))
+ steps = int((len(self.current_data) // self._batch_size))
assert (
- l > 0
+ steps > 0
), "There needs to be at least batch_size number of datapoints. Try increasing min_data_size."
- return l
+ return steps
def _continuous_get_training_data(self):
index = 0
@@ -317,24 +320,34 @@ def _continuous_get_training_data(self):
new_image = self._get(self.feature, self.feature_kwargs)
- if self.label_function:
- new_label = Image(self.label_function(new_image))
+ # if self.label_function:
+ new_label = self.label_function(new_image)
if self.batch_function:
- new_image = Image(self.batch_function(new_image))
+ new_image = self.batch_function(new_image)
+ new_image = np.array(new_image)
if new_image.ndim < self.ndim:
new_image = [new_image]
new_label = [new_label]
for new_image_i, new_label_i in zip(new_image, new_label):
+ datapoint = self.construct_datapoint(new_image_i, new_label_i)
if len(self.data) >= self.max_data_size:
- self.data[index % self.max_data_size] = (new_image_i, new_label_i)
+ self.data[index % self.max_data_size] = datapoint
else:
- self.data.append((new_image_i, new_label_i))
+ self.data.append(datapoint)
index += 1
+ self.cleanup()
+
+ def construct_datapoint(self, image, label):
+ return (image, label)
+
+ def cleanup(self):
+ pass
+
def _get(self, features: Feature or List[Feature], feature_kwargs) -> Image:
# Updates and resolves a feature or list of features.
if isinstance(features, list):
@@ -352,3 +365,78 @@ def _get(self, features: Feature or List[Feature], feature_kwargs) -> Image:
else:
features.update()
return features.resolve(**feature_kwargs)
+
+
+class CappedContinuousGenerator(ContinuousGenerator):
+
+ """Generator that asynchronously expands the dataset.
+
+ Generator that aims to speed up the training of networks by striking a
+ balance between the generalization gained by generating new images
+ and the speed gained from reusing images. The generator will continuously
+ create new training data during training, until `max_data_size` is reached,
+ at which point the oldest data point is replaced.
+
+ Unlike the `ContinuousGenerator`, this generator will purge any data that
+ has been seen more than `max_sample_exposure` times.
+
+ The generator is expected to be used with the python "with" statement, which
+ ensures that the generator worker is consumed correctly.
+
+ Parameters
+ ----------
+ feature : Feature
+ The feature to resolve images from.
+ label_function : Callable[Image or list of Image] -> array_like
+ Function that returns the label corresponding to a feature output.
+ batch_function : Callable[Image or list of Image] -> array_like, optional
+ Function that returns the training data corresponding a feature output.
+ min_data_size : int
+ Minimum size of the training data before training starts
+ max_data_set : int
+ Maximum size of the training data before old data is replaced.
+ max_sample_exposure : int
+ Any sample that has been seen for more than `max_sample_exposure` will be removed from the dataset
+ batch_size : int or Callable[int, int] -> int
+ Number of images per batch. A function is expected to accept the current epoch
+ and the size of the training data as input.
+ shuffle_batch : bool
+ If True, the batches are shuffled before outputting.
+ feature_kwargs : dict or list of dicts
+ Set of options to pass to the feature when resolving
+ ndim : int
+ Number of dimensions of each batch (including the batch dimension).
+ max_sample_exposure : int
+ Any sample that has been seen for more than `max_sample_exposure` will be removed from the dataset
+ """
+
+ def __init__(self, *args, max_sample_exposure=np.inf, **kwargs):
+ self.max_sample_exposure = max_sample_exposure
+ super().__init__(*args, **kwargs)
+
+ def __getitem__(self, idx):
+
+ # TODO: Use parent method
+ batch_size = self._batch_size
+ subset = self.current_data[idx * batch_size : (idx + 1) * batch_size]
+ for a in subset:
+ a[-1] += 1
+ outputs = [np.array(a) for a in list(zip(*subset))]
+ outputs = (outputs[0], *outputs[1:-1])
+ return outputs
+
+ def construct_datapoint(self, image, label):
+ return [image, label, 0]
+
+ def cleanup(self):
+ self.data = [
+ sample for sample in self.data if sample[-1] < self.max_sample_exposure
+ ]
+
+ def on_epoch_end(self):
+
+ while len(self.data) < self.min_data_size:
+ print("Awaiting dataset to reach minimum size...", end="\r")
+ time.sleep(0.1)
+
+ return super().on_epoch_end()
\ No newline at end of file
diff --git a/deeptrack/image.py b/deeptrack/image.py
index be20fba44..72984ca3a 100644
--- a/deeptrack/image.py
+++ b/deeptrack/image.py
@@ -176,7 +176,7 @@ def __array_wrap__(self, image_after_function, context=None):
for arg in input_args:
- if not arg is self and isinstance(arg, Image):
+ if arg is not self and isinstance(arg, Image):
self.merge_properties_from(arg)
return image_with_restored_properties
diff --git a/deeptrack/layers.py b/deeptrack/layers.py
index 5e68ec756..0dbeff3f5 100644
--- a/deeptrack/layers.py
+++ b/deeptrack/layers.py
@@ -1,17 +1,16 @@
""" Standardized layers implemented in keras.
"""
-import tensorflow
-from tensorflow.keras import layers, activations
-from tensorflow.keras.initializers import RandomNormal
+
+from tensorflow.keras import layers
try:
from tensorflow_addons.layers import InstanceNormalization
-except:
+except Exception:
import warnings
- InstanceNormalization = layers.Layer()
+ InstanceNormalization = layers.Layer
warnings.warn(
"DeepTrack not installed with tensorflow addons. Instance normalization will not work. Consider upgrading to tensorflow >= 2.0.",
ImportWarning,
@@ -29,7 +28,8 @@ def as_block(x):
+ ", ".join(_string_to_block.keys())
)
if isinstance(x, layers.Layer) or not callable(x):
- raise TypeError("Layer block should be a function that returns a keras Layer.")
+ raise TypeError(
+ "Layer block should be a function that returns a keras Layer.")
else:
return x
@@ -37,10 +37,12 @@ def as_block(x):
def _as_activation(x):
if x is None:
return layers.Layer()
- if isinstance(x, layers.Layer):
+ elif isinstance(x, str):
+ return layers.Activation(x)
+ elif isinstance(x, layers.Layer):
return x
else:
- return layers.Activation(x)
+ return layers.Layer(x)
def _single_layer_call(x, layer, instance_norm, activation):
@@ -225,6 +227,7 @@ def StaticUpsampleBlock(
kernel_size=(1, 1),
strides=1,
padding="same",
+ with_conv=True,
**kwargs
):
"""A single no-trainable 2d deconvolutional layer.
@@ -247,21 +250,24 @@ def StaticUpsampleBlock(
def Layer(filters, **kwargs_inner):
kwargs_inner.update(kwargs)
- layer = layers.UpSampling2D(
- size=size, interpolation=interpolation, **kwargs_inner
- )
+ layer = layers.UpSampling2D(size=size, interpolation=interpolation)
+
conv = layers.Conv2D(
filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
+ **kwargs_inner
)
def call(x):
y = layer(x)
- return _single_layer_call(
- y, conv, _instance_norm(instance_norm, filters), activation
- )
+ if with_conv:
+ return _single_layer_call(
+ y, conv, _instance_norm(instance_norm, filters), activation
+ )
+ else:
+ return layer(x)
return call
@@ -359,4 +365,4 @@ def Layer(filters, **kwargs_inner):
"deconvolutional": DeconvolutionalBlock(),
"none": Identity(),
"identity": Identity(),
-}
\ No newline at end of file
+}
diff --git a/deeptrack/losses.py b/deeptrack/losses.py
index 2a6117af0..ced7f5ddd 100644
--- a/deeptrack/losses.py
+++ b/deeptrack/losses.py
@@ -2,7 +2,7 @@
Functions
---------
-flatten
+flatten
Flattends the inputs before calling the loss function.
sigmoid
Adds a signmoid transformation to the prediction before calling the loss function.
@@ -117,4 +117,4 @@ def unet_crossentropy(T, P):
# Wrap standard keras loss function with flatten.
for keras_loss_function in _COMPATIBLE_LOSS_FUNCTIONS:
deeptrack_loss_function = flatten(keras_loss_function)
- globals()[deeptrack_loss_function.__name__] = deeptrack_loss_function
\ No newline at end of file
+ globals()[deeptrack_loss_function.__name__] = deeptrack_loss_function
diff --git a/deeptrack/math.py b/deeptrack/math.py
index dfcf2fd5f..70e3ec8bb 100644
--- a/deeptrack/math.py
+++ b/deeptrack/math.py
@@ -8,9 +8,15 @@
Min-max image normalization.
"""
-from deeptrack.features import Feature
-from deeptrack.image import Image
+from typing import Callable, List
+from .features import Feature
+from .image import Image
+from . import utils
+from .types import PropertyLike
import numpy as np
+import skimage
+import skimage.measure
+import scipy.ndimage as ndimage
class Add(Feature):
@@ -22,7 +28,7 @@ class Add(Feature):
The value to add
"""
- def __init__(self, value=0, **kwargs):
+ def __init__(self, value: PropertyLike[float] = 0, **kwargs):
super().__init__(value=value, **kwargs)
def get(self, image, value, **kwargs):
@@ -38,7 +44,7 @@ class Subtract(Feature):
The value to subtract
"""
- def __init__(self, value=0, **kwargs):
+ def __init__(self, value: PropertyLike[float] = 0, **kwargs):
super().__init__(value=value, **kwargs)
def get(self, image, value, **kwargs):
@@ -54,7 +60,7 @@ class Multiply(Feature):
The value to multiply with.
"""
- def __init__(self, value=0, **kwargs):
+ def __init__(self, value: PropertyLike[float] = 0, **kwargs):
super().__init__(value=value, **kwargs)
def get(self, image, value, **kwargs):
@@ -70,7 +76,7 @@ class Divide(Feature):
The value to divide with.
"""
- def __init__(self, value=0, **kwargs):
+ def __init__(self, value: PropertyLike[float] = 0, **kwargs):
super().__init__(value=value, **kwargs)
def get(self, image, value, **kwargs):
@@ -86,7 +92,7 @@ class Power(Feature):
The power to raise with.
"""
- def __init__(self, value=0, **kwargs):
+ def __init__(self, value: PropertyLike[float] = 0, **kwargs):
super().__init__(value=value, **kwargs)
def get(self, image, value, **kwargs):
@@ -108,7 +114,12 @@ class Average(Feature):
__distributed__ = False
- def __init__(self, features=None, axis=0, **kwargs):
+ def __init__(
+ self,
+ features=PropertyLike[List[Feature] or None],
+ axis: PropertyLike[int] = 0,
+ **kwargs
+ ):
super().__init__(axis=axis, features=features, **kwargs)
def get(self, images, axis, features, **kwargs):
@@ -133,7 +144,12 @@ class Clip(Feature):
Clip the input to be smaller than this value.
"""
- def __init__(self, min=-np.inf, max=+np.inf, **kwargs):
+ def __init__(
+ self,
+ min: PropertyLike[float] = -np.inf,
+ max: PropertyLike[float] = +np.inf,
+ **kwargs
+ ):
super().__init__(min=min, max=max, **kwargs)
def get(self, image, min=None, max=None, **kwargs):
@@ -154,7 +170,9 @@ class NormalizeMinMax(Feature):
The maximum of the transformation.
"""
- def __init__(self, min=0, max=1, **kwargs):
+ def __init__(
+ self, min: PropertyLike[float] = 0, max: PropertyLike[float] = 1, **kwargs
+ ):
super().__init__(min=min, max=max, **kwargs)
def get(self, image, min, max, **kwargs):
@@ -164,16 +182,12 @@ def get(self, image, min, max, **kwargs):
return image
-import deeptrack.utils as utils
-import skimage
-
-import scipy.ndimage as ndimage
-
-
class Blur(Feature):
- def __init__(self, filter_function, mode="reflect", **kwargs):
+ def __init__(
+ self, filter_function: Callable, mode: PropertyLike[str] = "reflect", **kwargs
+ ):
self.filter = filter_function
- super().__init__(borderType=borderType, **kwargs)
+ super().__init__(borderType=mode, **kwargs)
def get(self, image, **kwargs):
kwargs.pop("input", False)
@@ -191,7 +205,7 @@ class AverageBlur(Blur):
Kernel size to use.
"""
- def __init__(self, ksize=3, **kwargs):
+ def __init__(self, ksize: PropertyLike[int] = 3, **kwargs):
super().__init__(None, ksize=ksize, **kwargs)
def get(self, input, ksize, **kwargs):
@@ -203,7 +217,7 @@ def get(self, input, ksize, **kwargs):
weights = np.ones(ksize) / np.prod(ksize)
- return safe_call(ndimage, input=input, weights=weights, **kwargs)
+ return utils.safe_call(ndimage, input=input, weights=weights, **kwargs)
class GaussianBlur(Blur):
@@ -216,7 +230,7 @@ class GaussianBlur(Blur):
"""
- def __init__(self, sigma=2, **kwargs):
+ def __init__(self, sigma: PropertyLike[float] = 2, **kwargs):
super().__init__(ndimage.gaussian_filter, sigma=sigma, **kwargs)
@@ -230,13 +244,11 @@ class MedianBlur(Blur):
"""
- def __init__(self, ksize=3, **kwargs):
- super().__init__(ndimage.median_filter, k=k, **kwargs)
+ def __init__(self, ksize: PropertyLike[int] = 3, **kwargs):
+ super().__init__(ndimage.median_filter, k=ksize, **kwargs)
-## POOLING
-
-import skimage.measure
+# POOLING
class Pool(Feature):
@@ -257,7 +269,9 @@ class Pool(Feature):
Additional parameters sent to the pooling function.
"""
- def __init__(self, pooling_function, ksize=3, **kwargs):
+ def __init__(
+ self, pooling_function: Callable, ksize: PropertyLike[int] = 3, **kwargs
+ ):
self.pooling = pooling_function
super().__init__(ksize=ksize, **kwargs)
@@ -287,11 +301,11 @@ class AveragePooling(Pool):
Additional parameters sent to the pooling function.
"""
- def __init__(self, ksize=3, **kwargs):
+ def __init__(self, ksize: PropertyLike[int] = 3, **kwargs):
super().__init__(np.mean, ksize=ksize, **kwargs)
-### OPENCV2 blur
+# OPENCV2 blur
try:
import cv2
@@ -318,7 +332,9 @@ def __new__(cls, *args, **kwargs):
return super(BlurCV2, cls).__new__(*args, **kwargs)
- def __init__(self, filter_function, mode="refelct", **kwargs):
+ def __init__(
+ self, filter_function: Callable, mode: PropertyLike[str] = "refelct", **kwargs
+ ):
self.filter = filter_function
borderType = _map_mode_to_cv2_borderType[mode]
super().__init__(borderType=borderType, **kwargs)
@@ -356,11 +372,17 @@ class BilateralBlur(Blur):
"""
- def __init__(self, d=3, sigma_color=50, sigma_space=50, **kwargs):
+ def __init__(
+ self,
+ d: PropertyLike[int] = 3,
+ sigma_color: PropertyLike[float] = 50,
+ sigma_space: PropertyLike[float] = 50,
+ **kwargs
+ ):
super().__init__(
cv2.bilateralFilter,
d=d,
sigma_color=sigma_color,
sigma_space=sigma_space,
**kwargs
- )
\ No newline at end of file
+ )
diff --git a/deeptrack/models.py b/deeptrack/models.py
index 03f2dcee0..5c7df96a9 100644
--- a/deeptrack/models.py
+++ b/deeptrack/models.py
@@ -2,7 +2,7 @@
Classes
-------
-ModelFeature
+ModelFeature
Base model feature class.
Convolutional, convolutional
Creates and compiles a convolutional neural network.
@@ -12,12 +12,13 @@
Creates and compiles a recurrent neural network.
"""
-from deeptrack.losses import nd_mean_absolute_error
-from deeptrack.features import Feature
-from deeptrack.layers import as_block
-from tensorflow.keras import models, layers, optimizers
+import tensorflow
+from .losses import nd_mean_absolute_error
+from .features import Feature
+from .layers import as_block
+from tensorflow.keras import models, layers, backend as K
+import tensorflow as tf
import numpy as np
-import warnings
def _compile(
@@ -143,7 +144,7 @@ def FullyConnected(
dense_block = as_block(dense_block)
- ### INITIALIZE DEEP LEARNING NETWORK
+ # INITIALIZE DEEP LEARNING NETWORK
input_layer = layers.Input(shape=input_shape)
layer = input_layer
@@ -155,7 +156,7 @@ def FullyConnected(
range(len(dense_layers_dimensions)), dense_layers_dimensions
):
- if dense_layer_number is 0 and not flatten_input:
+ if dense_layer_number == 0 and not flatten_input:
layer = dense_block(dense_layer_dimension, input_shape=input_shape)(layer)
else:
layer = dense_block(dense_layer_dimension)(layer)
@@ -184,6 +185,7 @@ def Convolutional(
output_activation=None,
output_kernel_size=3,
loss=nd_mean_absolute_error,
+ input_layer=None,
convolution_block="convolutional",
pooling_block="pooling",
dense_block="dense",
@@ -232,6 +234,9 @@ def Convolutional(
layer = inputs
+ if input_layer:
+ layer = input_layer(layer)
+
### CONVOLUTIONAL BASIS
for conv_layer_dimension in conv_layers_dimensions:
@@ -284,6 +289,7 @@ def UNet(
output_kernel_size=3,
output_activation=None,
loss=nd_mean_absolute_error,
+ input_layer=None,
encoder_convolution_block="convolutional",
base_convolution_block="convolutional",
decoder_convolution_block="convolutional",
@@ -340,6 +346,9 @@ def UNet(
layer = unet_input
+ if input_layer:
+ layer = input_layer(layer)
+
# Downsampling path
for conv_layer_dimension in conv_layers_dimensions:
for _ in range(steps_per_pooling):
@@ -423,10 +432,10 @@ def RNN(
Deep learning network.
"""
- ### INITIALIZE DEEP LEARNING NETWORK
+ # INITIALIZE DEEP LEARNING NETWORK
network = models.Sequential()
- ### CONVOLUTIONAL BASIS
+ # CONVOLUTIONAL BASIS
for conv_layer_number, conv_layer_dimension in zip(
range(len(conv_layers_dimensions)), conv_layers_dimensions
):
@@ -506,7 +515,7 @@ def RNN(
rnn = RNN
-class cgan(Model):
+class cgan(tf.keras.Model):
def __init__(
self,
generator=None,
@@ -517,8 +526,10 @@ def __init__(
assemble_loss=None,
assemble_optimizer=None,
assemble_loss_weights=None,
+ metrics=[],
**kwargs
):
+ super().__init__()
# Build and compile the discriminator
self.discriminator = discriminator
@@ -545,79 +556,73 @@ def __init__(
# The assembled model (stacked generator and discriminator)
# Trains the generator to fool the discriminator
- self.assemble = models.Model(self.model_input, [validity, img])
+ self.assemble = tf.keras.models.Model(self.model_input, [validity, img])
+
+ self.num_losses = len(assemble_loss)
+
self.assemble.compile(
loss=assemble_loss,
optimizer=assemble_optimizer,
loss_weights=assemble_loss_weights,
+ metrics=metrics,
+ )
+ self._metrics = [tf.metrics.get(m) for m in metrics]
+
+ def train_step(self, data):
+
+ # Compute data and labels
+ batch_x, batch_y = data
+ gen_imgs = self.generator(batch_x)
+
+ # Train the discriminator
+ with tf.GradientTape() as tape:
+ # Train in two steps
+ disc_pred_1 = self.discriminator([batch_y, batch_x])
+ disc_pred_2 = self.discriminator([gen_imgs, batch_x])
+ shape = tf.shape(disc_pred_1)
+ valid, fake = tf.ones(shape), tf.zeros(shape)
+ d_loss = (
+ self.discriminator.compiled_loss(disc_pred_1, valid)
+ + self.discriminator.compiled_loss(disc_pred_2, fake)
+ ) / 2
+
+ # Compute gradient and apply gradient
+ grads = tape.gradient(d_loss, self.discriminator.trainable_weights)
+ self.discriminator.optimizer.apply_gradients(
+ zip(grads, self.discriminator.trainable_weights)
)
- super().__init__(self.generator, **kwargs)
+ # Train the assembly
- def fit(self, data_generator, epochs, steps_per_epoch=None, **kwargs):
- for key in kwargs.keys():
- warnings.warn(
- "{0} not implemented for cgan. Does not affect the execution.".format(
- key
- )
- )
- for epoch in range(epochs):
- if not steps_per_epoch:
- try:
- steps = len(data_generator)
- except:
- steps = 1
- else:
- steps = steps_per_epoch
-
- d_loss = 0
- g_loss = 0
-
- for step in range(steps):
- # update data
- try:
- data, labels = next(data_generator)
- except:
- data, labels = data_generator[step]
-
- # Grab disriminator labels
- shape = (data.shape[0], *self.discriminator.output.shape[1:])
- valid, fake = np.ones(shape), np.zeros(shape)
-
- # ---------------------
- # Train Discriminator
- # ---------------------
-
- # Generate a batch of new images
- gen_imgs = self.generator(data)
-
- # Train the discriminator
- d_loss_real = self.discriminator.train_on_batch([labels, data], valid)
- d_loss_fake = self.discriminator.train_on_batch([gen_imgs, data], fake)
- d_loss += 0.5 * np.add(d_loss_real, d_loss_fake)
-
- # ---------------------
- # Train Generator
- # ---------------------
-
- # Train the generator (to have the discriminator label samples as valid)
- g_loss += np.array(self.assemble.train_on_batch(data, [valid, labels]))
-
- # Plot the progress
-
- try:
- data_generator.on_epoch_end()
- except:
- pass
-
- print(
- "%d [D loss: %f, acc.: %.2f%%] [G loss: %f, %f, %f]"
- % (
- epoch,
- d_loss[0] / steps,
- 100 * d_loss[1] / steps,
- g_loss[0] / steps,
- g_loss[1] / steps,
- g_loss[2] / steps,
- )
+ with tf.GradientTape() as tape:
+ assemble_output = self.assemble(batch_x)
+
+ generated_image_copies = [assemble_output[1]] * (self.num_losses - 1)
+
+ batch_y_copies = [batch_y] * (self.num_losses - 1)
+
+ g_loss = self.assemble.compiled_loss(
+ [assemble_output[0], *generated_image_copies],
+ [valid, *batch_y_copies],
)
+
+ # Compute gradient and apply gradient
+ grads = tape.gradient(g_loss, self.assemble.trainable_weights)
+ self.assemble.optimizer.apply_gradients(
+ zip(grads, self.assemble.trainable_weights)
+ )
+
+ # Update the metrics
+ self.compiled_metrics.update_state(assemble_output[1], batch_y)
+
+ # Define output
+ loss = {
+ "d_loss": d_loss,
+ "g_loss": g_loss,
+ **{m.name: m.result() for m in self.metrics},
+ }
+
+ return loss
+
+ def call(self, *args, **kwargs):
+ return self.generator.call(*args, **kwargs)
diff --git a/deeptrack/noises.py b/deeptrack/noises.py
index d53541a27..b47884985 100644
--- a/deeptrack/noises.py
+++ b/deeptrack/noises.py
@@ -13,8 +13,9 @@
"""
import numpy as np
-from deeptrack.features import Feature
-from deeptrack.image import Image
+from .features import Feature
+from .image import Image
+from .types import PropertyLike
class Noise(Feature):
@@ -29,7 +30,7 @@ class Background(Noise):
The value to add to the image
"""
- def __init__(self, offset, **kwargs):
+ def __init__(self, offset: PropertyLike[float], **kwargs):
super().__init__(offset=offset, **kwargs)
def get(self, image, offset, **kwargs):
@@ -51,7 +52,9 @@ class Gaussian(Noise):
The root of the variance of the distribution.
"""
- def __init__(self, mu=0, sigma=1, **kwargs):
+ def __init__(
+ self, mu: PropertyLike[float] = 0, sigma: PropertyLike[float] = 1, **kwargs
+ ):
super().__init__(mu=mu, sigma=sigma, **kwargs)
def get(self, image, mu, sigma, **kwargs):
@@ -73,7 +76,13 @@ class Poisson(Noise):
signal of the image.
"""
- def __init__(self, *args, snr=100, background=0, **kwargs):
+ def __init__(
+ self,
+ *args,
+ snr: PropertyLike[float] = 100,
+ background: PropertyLike[float] = 0,
+ **kwargs
+ ):
super().__init__(*args, snr=snr, background=background, **kwargs)
def get(self, image, snr, background, **kwargs):
diff --git a/deeptrack/optics.py b/deeptrack/optics.py
index f4e970533..249fd24c9 100644
--- a/deeptrack/optics.py
+++ b/deeptrack/optics.py
@@ -16,10 +16,10 @@
"""
import numpy as np
-from deeptrack.features import Feature, StructuralFeature
-from deeptrack.image import Image, pad_image_to_fft
+from .features import Feature, StructuralFeature
+from .image import Image, pad_image_to_fft
+from .types import ArrayLike, PropertyLike
-from scipy.interpolate import RectBivariateSpline
from scipy.ndimage import convolve
@@ -78,7 +78,13 @@ def get(self, image, sample, objective, **kwargs):
if upscale > 1:
mean_imaged_sample = np.reshape(
imaged_sample,
- (shape[0] // upscale, upscale, shape[1] // upscale, upscale, shape[2]),
+ (
+ shape[0] // upscale,
+ upscale,
+ shape[1] // upscale,
+ upscale,
+ shape[2],
+ ),
).mean(axis=(3, 1))
imaged_sample = Image(mean_imaged_sample).merge_properties_from(
@@ -97,7 +103,10 @@ def get(self, image, sample, objective, **kwargs):
def _update(self, **kwargs):
self.properties["sample"].update(
- **{**kwargs, **self.objective.update(**kwargs).current_value.properties}
+ **{
+ **kwargs,
+ **self.objective.update(**kwargs).current_value.properties,
+ }
)
super()._update(**kwargs)
@@ -138,14 +147,14 @@ class Optics(Feature):
def __init__(
self,
- NA=0.7,
- wavelength=0.66e-6,
- magnification=10,
- resolution=(1e-6, 1e-6, 1e-6),
- refractive_index_medium=1.33,
- upscale=1,
- padding=(10, 10, 10, 10),
- output_region=(0, 0, 128, 128),
+ NA: PropertyLike[float] = 0.7,
+ wavelength: PropertyLike[float] = 0.66e-6,
+ magnification: PropertyLike[float] = 10,
+ resolution: PropertyLike[float or ArrayLike[float]] = (1e-6, 1e-6, 1e-6),
+ refractive_index_medium: PropertyLike[float] = 1.33,
+ upscale: PropertyLike[float] = 1,
+ padding: PropertyLike[ArrayLike[int]] = (10, 10, 10, 10),
+ output_region: PropertyLike[ArrayLike[int]] = (0, 0, 128, 128),
pupil: Feature = None,
**kwargs
):
@@ -282,11 +291,15 @@ def _pad_volume(
new_limits[i, :] = (
np.min([new_limits[i, 0], upscaled_output_region[i] - padding[1]]),
np.max(
- [new_limits[i, 1], upscaled_output_region[i + 2] + padding[i + 2]]
+ [
+ new_limits[i, 1],
+ upscaled_output_region[i + 2] + padding[i + 2],
+ ]
),
)
new_volume = np.zeros(
- np.diff(new_limits, axis=1)[:, 0].astype(np.int32), dtype=np.complex
+ np.diff(new_limits, axis=1)[:, 0].astype(np.int32),
+ dtype=np.complex,
)
old_region = (limits - new_limits).astype(np.int32)
@@ -370,7 +383,9 @@ def get(self, illuminated_volume, limits, **kwargs):
)
padded_volume = padded_volume[
- output_region[0] : output_region[2], output_region[1] : output_region[3], :
+ output_region[0] : output_region[2],
+ output_region[1] : output_region[3],
+ :,
]
z_limits = limits[2, :]
@@ -380,7 +395,10 @@ def get(self, illuminated_volume, limits, **kwargs):
# Get planes in volume where not all values are 0.
z_iterator = np.linspace(
- z_limits[0], z_limits[1], num=padded_volume.shape[2], endpoint=False
+ z_limits[0],
+ z_limits[1],
+ num=padded_volume.shape[2],
+ endpoint=False,
)
zero_plane = np.all(padded_volume == 0, axis=(0, 1), keepdims=False)
z_values = z_iterator[~zero_plane]
@@ -496,7 +514,9 @@ def get(self, illuminated_volume, limits, fields, **kwargs):
)
padded_volume = padded_volume[
- output_region[0] : output_region[2], output_region[1] : output_region[3], :
+ output_region[0] : output_region[2],
+ output_region[1] : output_region[3],
+ :,
]
z_limits = limits[2, :]
@@ -504,7 +524,10 @@ def get(self, illuminated_volume, limits, fields, **kwargs):
index_iterator = range(padded_volume.shape[2])
z_iterator = np.linspace(
- z_limits[0], z_limits[1], num=padded_volume.shape[2], endpoint=False
+ z_limits[0],
+ z_limits[1],
+ num=padded_volume.shape[2],
+ endpoint=False,
)
zero_plane = np.all(padded_volume == 0, axis=(0, 1), keepdims=False)
@@ -629,7 +652,14 @@ class IlluminationGradient(Feature):
"""
- def __init__(self, gradient=(0, 0), constant=0, vmin=0, vmax=np.inf, **kwargs):
+ def __init__(
+ self,
+ gradient: PropertyLike[ArrayLike[float]] = (0, 0),
+ constant: PropertyLike[float] = 0,
+ vmin: PropertyLike[float] = 0,
+ vmax: PropertyLike[float] = np.inf,
+ **kwargs
+ ):
super().__init__(
gradient=gradient, constant=constant, vmin=vmin, vmax=vmax, **kwargs
)
@@ -766,7 +796,12 @@ def _create_volume(
continue
padded_scatterer = Image(
- np.pad(scatterer, [(2, 2), (2, 2), (2, 2)], "constant", constant_values=0)
+ np.pad(
+ scatterer,
+ [(2, 2), (2, 2), (2, 2)],
+ "constant",
+ constant_values=0,
+ )
)
padded_scatterer.properties = scatterer.properties
scatterer = padded_scatterer
@@ -815,7 +850,8 @@ def _create_volume(
if not (np.array(new_limits) == np.array(limits)).all():
new_volume = np.zeros(
- np.diff(new_limits, axis=1)[:, 0].astype(np.int32), dtype=np.complex
+ np.diff(new_limits, axis=1)[:, 0].astype(np.int32),
+ dtype=np.complex,
)
old_region = (limits - new_limits).astype(np.int32)
limits = limits.astype(np.int32)
diff --git a/deeptrack/properties.py b/deeptrack/properties.py
index 300308c90..b4edd94c9 100644
--- a/deeptrack/properties.py
+++ b/deeptrack/properties.py
@@ -18,9 +18,19 @@
"""
import numpy as np
-from deeptrack.utils import isiterable, hasmethod, get_kwarg_names, kwarg_has_default
-import deeptrack
+from .utils import (
+ isiterable,
+ get_kwarg_names,
+ kwarg_has_default,
+)
+
+import typing
+
+
+from . import features
+
import copy
+import collections
class Property:
@@ -72,11 +82,11 @@ def current_value(self):
@current_value.setter
def current_value(self, updated_current_value):
self._current_value = updated_current_value
- if id(self) not in deeptrack.UPDATE_MEMO["memoization"]:
+ if id(self) not in features.UPDATE_MEMO["memoization"]:
# Some values work, some don't. self, updated_current_value and self._current_value work
# Best guess is an error in the gc reference counter causing it to dereference
# But then again, I don't think it should be the same reference anyway
- deeptrack.UPDATE_MEMO["memoization"][id(self)] = updated_current_value
+ features.UPDATE_MEMO["memoization"][id(self)] = updated_current_value
@current_value.getter
def current_value(self):
@@ -109,15 +119,15 @@ def update(self, **kwargs) -> "Property":
# a = 1+1
if (
- deeptrack.UPDATE_LOCK.locked()
- and my_id in deeptrack.UPDATE_MEMO["memoization"]
+ features.UPDATE_LOCK.locked()
+ and my_id in features.UPDATE_MEMO["memoization"]
):
return self
if self.parent:
kwargs.update(self.parent)
- kwargs.update(deeptrack.UPDATE_MEMO["user_arguments"])
+ kwargs.update(features.UPDATE_MEMO["user_arguments"])
self.current_value = self.sample(self.sampling_rule, **kwargs)
return self
@@ -155,7 +165,7 @@ def sample(self, sampling_rule, **kwargs):
"""
- if isinstance(sampling_rule, deeptrack.Feature):
+ if isinstance(sampling_rule, features.Feature):
# I am worried passing kwargs may lead to name clash
sampling_rule._update(**kwargs)
return sampling_rule
@@ -185,6 +195,12 @@ def sample(self, sampling_rule, **kwargs):
return next(sampling_rule)
except StopIteration:
return self.current_value
+ elif isinstance(sampling_rule, slice):
+ return slice(
+ self.sample(sampling_rule.start, **kwargs),
+ self.sample(sampling_rule.stop, **kwargs),
+ self.sample(sampling_rule.step, **kwargs)
+ )
elif callable(sampling_rule):
# If it's a function, extract the arguments it accepts.
@@ -229,7 +245,7 @@ def sample(self, sampling_rule, **kwargs):
return sampling_rule
def __deepcopy__(self, memo):
- is_in = id(self) in deeptrack.UPDATE_MEMO["memoization"]
+ is_in = id(self) in features.UPDATE_MEMO["memoization"]
if is_in:
return self
else:
@@ -309,12 +325,12 @@ def update(self, sequence_length=0, **kwargs):
"""
my_id = id(self)
if (
- deeptrack.UPDATE_LOCK.locked()
- and my_id in deeptrack.UPDATE_MEMO["memoization"]
+ features.UPDATE_LOCK.locked()
+ and my_id in features.UPDATE_MEMO["memoization"]
):
return self
- kwargs.update(deeptrack.UPDATE_MEMO["user_arguments"])
+ kwargs.update(features.UPDATE_MEMO["user_arguments"])
new_current_value = []
@@ -338,13 +354,10 @@ def update(self, sequence_length=0, **kwargs):
new_current_value.append(next_value)
self.current_value = new_current_value
- deeptrack.UPDATE_MEMO["memoization"][my_id] = new_current_value
+ features.UPDATE_MEMO["memoization"][my_id] = new_current_value
return self
-import collections
-
-
class PropertyDict(collections.OrderedDict):
"""Dictionary with Property elements
@@ -382,7 +395,7 @@ def current_value_dict(self, **kwargs) -> dict:
# of the current timestep
if isinstance(property, SequentialProperty):
sequence_step = kwargs.get("sequence_step", None)
- if not sequence_step is None:
+ if sequence_step is not None:
property_value = property_value[sequence_step]
current_value_dict[key] = property_value
@@ -402,11 +415,11 @@ def update(self, **kwargs) -> "PropertyDict":
"""
property_arguments = collections.OrderedDict(self)
property_arguments.update(kwargs)
- property_arguments.update(deeptrack.UPDATE_MEMO["user_arguments"])
+ property_arguments.update(features.UPDATE_MEMO["user_arguments"])
for key, prop in self.items():
if isinstance(property_arguments[key], Property):
prop.update(**property_arguments)
- elif id(prop) not in deeptrack.UPDATE_MEMO["memoization"]:
+ elif id(prop) not in features.UPDATE_MEMO["memoization"]:
prop.current_value = property_arguments[key]
return self
@@ -447,4 +460,4 @@ def sample(self, **kwargs) -> dict:
for key, property in self.items():
sample_dict[key] = property.sample(**kwargs)
- return sample_dict
\ No newline at end of file
+ return sample_dict
diff --git a/deeptrack/scatterers.py b/deeptrack/scatterers.py
index de9063b9a..0afed8649 100644
--- a/deeptrack/scatterers.py
+++ b/deeptrack/scatterers.py
@@ -1,6 +1,6 @@
"""Implementations of Feature the model scattering objects.
-Provides some basic implementations of scattering objects
+Provides some basic implementations of scattering objects
that are frequently used.
Classes
@@ -18,14 +18,15 @@
"""
-from threading import Lock
+from typing import Callable, Tuple
import numpy as np
-# from scipy.special import jv as jn, spherical_jn as jv, h1vp, eval_legendre as leg, jvp
-import deeptrack.backend as D
-from deeptrack.features import Feature, MERGE_STRATEGY_APPEND
-from deeptrack.image import Image
-import deeptrack.image
+from . import backend as D
+from .features import Feature, MERGE_STRATEGY_APPEND
+from .image import Image
+from . import image
+from .types import PropertyLike, ArrayLike
+import warnings
class Scatterer(Feature):
@@ -60,8 +61,8 @@ class Scatterer(Feature):
Other Parameters
----------------
upsample_axes : tuple of ints
- Sets the axes along which the calculation is upsampled (default is None,
- which implies all axes are upsampled).
+ Sets the axes along which the calculation is upsampled (default is
+ None, which implies all axes are upsampled).
crop_zeros : bool
Whether to remove slices in which all elements are zero.
"""
@@ -71,11 +72,11 @@ class Scatterer(Feature):
def __init__(
self,
- position=(32, 32),
- z=0.0,
- value=1.0,
- position_unit="pixel",
- upsample=1,
+ position: PropertyLike[ArrayLike[float]] = (32, 32),
+ z: PropertyLike[float] = 0.0,
+ value: PropertyLike[float] = 1.0,
+ position_unit: PropertyLike[str] = "pixel",
+ upsample: PropertyLike[int] = 1,
**kwargs
):
self._processed_properties = False
@@ -112,10 +113,11 @@ def _process_and_get(
# Post processes the created object to handle upsampling,
# as well as cropping empty slices.
if not self._processed_properties:
- import warnings
warnings.warn(
- "Overridden _process_properties method does not call super. This is likely to result in errors if used with Optics.upscale != 1."
+ "Overridden _process_properties method does not call super. "
+ + "This is likely to result in errors if used with "
+ + "Optics.upscale != 1."
)
# Calculates upsampled voxel_size
@@ -134,7 +136,10 @@ def _process_and_get(
if new_image.size == 0:
warnings.warn(
- "Scatterer created that is smaller than a pixel. This may yield inconsistent results. Consider using upsample on the scatterer, or upscale on the optics.",
+ "Scatterer created that is smaller than a pixel. "
+ + "This may yield inconsistent results."
+ + " Consider using upsample on the scatterer,"
+ + " or upscale on the optics.",
Warning,
)
@@ -225,7 +230,12 @@ class Ellipse(Scatterer):
Upsamples the calculations of the pixel occupancy fraction.
"""
- def __init__(self, radius=1e-6, rotation=0, **kwargs):
+ def __init__(
+ self,
+ radius: PropertyLike[float] = 1e-6,
+ rotation: PropertyLike[float] = 0,
+ **kwargs
+ ):
super().__init__(
radius=radius, rotation=rotation, upsample_axes=(0, 1), **kwargs
)
@@ -293,7 +303,7 @@ class Sphere(Scatterer):
Upsamples the calculations of the pixel occupancy fraction.
"""
- def __init__(self, radius=1e-6, **kwargs):
+ def __init__(self, radius: PropertyLike[float] = 1e-6, **kwargs):
super().__init__(radius=radius, **kwargs)
def get(self, image, radius, voxel_size, **kwargs):
@@ -336,7 +346,12 @@ class Ellipsoid(Scatterer):
Upsamples the calculations of the pixel occupancy fraction.
"""
- def __init__(self, radius=1e-6, rotation=0, **kwargs):
+ def __init__(
+ self,
+ radius: PropertyLike[float] = 1e-6,
+ rotation: PropertyLike[float] = 0,
+ **kwargs
+ ):
super().__init__(radius=radius, rotation=rotation, **kwargs)
def _process_properties(self, propertydict):
@@ -346,7 +361,8 @@ def _process_properties(self, propertydict):
length 3.
If the radius is a single value, the particle is made a sphere
- If the radius are two values, the smallest value is appended as the third value
+ If the radius are two values, the smallest value is appended as the
+ third value
The rotation vector is padded with zeros until it is of length 3
"""
@@ -426,12 +442,13 @@ def get(self, image, radius, rotation, voxel_size, **kwargs):
class MieScatterer(Scatterer):
"""Base implementation of a Mie particle.
- New Mie-theory scatterers can be implemented by extending this class, and passing
- a function that calculates the coefficients of the harmonics up to order `L`. To be
- precise, the feature expects a wrapper function that takes the current values of the
- properties, as well as a inner function that takes an integer as the only parameter,
- and calculates the coefficients up to that integer. The return format is expected to
- be a tuple with two values, corresponding to `an` and `bn`. See
+ New Mie-theory scatterers can be implemented by extending this class, and
+ passing a function that calculates the coefficients of the harmonics up to
+ order `L`. To beprecise, the feature expects a wrapper function that takes
+ the current values of the properties, as well as a inner function that
+ takes an integer as the only parameter, and calculates the coefficients up
+ to that integer. The return format is expected to be a tuple with two
+ values, corresponding to `an` and `bn`. See
`deeptrack.backend.mie_coefficients` for an example.
Parameters
@@ -439,11 +456,13 @@ class MieScatterer(Scatterer):
coefficients : Callable[int] -> Tuple[ndarray, ndarray]
Function that returns the harmonics coefficients.
offset_z : "auto" or float
- Distance from the particle in the z direction the field is evaluated. If "auto",
- this is calculated from the pixel size and `collection_angle`
+ Distance from the particle in the z direction the field is evaluated.
+ If "auto", this is calculated from the pixel size and
+ `collection_angle`
collection_angle : "auto" or float
The maximum collection angle in radians. If "auto", this
- is calculated from the objective NA (which is true if the objective is the limiting
+ is calculated from the objective NA (which is true if the objective is
+ the limiting
aperature).
polarization_angle : float
Angle of the polarization of the incoming light relative to the x-axis.
@@ -461,11 +480,11 @@ class MieScatterer(Scatterer):
def __init__(
self,
- coefficients,
- offset_z="auto",
- polarization_angle=0,
- collection_angle="auto",
- L="auto",
+ coefficients: Callable[..., Callable[[int], Tuple[ArrayLike, ArrayLike]]],
+ offset_z: PropertyLike[str] = "auto",
+ polarization_angle: PropertyLike[float] = 0,
+ collection_angle: PropertyLike[str] = "auto",
+ L: PropertyLike[str] = "auto",
**kwargs
):
kwargs.pop("is_field", None)
@@ -493,9 +512,10 @@ def _process_properties(self, properties):
except (ValueError, TypeError):
pass
if properties["collection_angle"] == "auto":
- properties["collection_angle"] = np.sqrt(
- 1 - properties["NA"] ** 2 / properties["refractive_index_medium"] ** 2
+ properties["collection_angle"] = np.arcsin(
+ properties["NA"] / properties["refractive_index_medium"]
)
+
if properties["offset_z"] == "auto":
properties["offset_z"] = (
32
@@ -507,7 +527,7 @@ def _process_properties(self, properties):
def get(
self,
- image,
+ inp,
position,
upscaled_output_region,
voxel_size,
@@ -535,11 +555,11 @@ def get(
- upscaled_output_region[1]
+ padding[1]
)
- arr = deeptrack.image.pad_image_to_fft(np.zeros((xSize, ySize)))
+ arr = image.pad_image_to_fft(np.zeros((xSize, ySize)))
# Evluation grid
- x = np.arange(-padding[0], arr.shape[0] - padding[0]) - (position[1]) * upscale
- y = np.arange(-padding[1], arr.shape[1] - padding[1]) - (position[0]) * upscale
+ x = np.arange(-padding[0], arr.shape[0] - padding[0]) - (position[0]) * upscale
+ y = np.arange(-padding[1], arr.shape[1] - padding[1]) - (position[1]) * upscale
X, Y = np.meshgrid(x * voxel_size[0], y * voxel_size[1], indexing="ij")
R2 = np.sqrt(X ** 2 + Y ** 2)
@@ -561,11 +581,11 @@ def get(
PI, TAU = D.mie_harmonics(ct, L)
# Normalization factor
- E = [(2 * l + 1) / (l * (l + 1)) for l in range(1, L + 1)]
+ E = [(2 * i + 1) / (i * (i + 1)) for i in range(1, L + 1)]
# Scattering terms
- S1 = sum([E[l] * A[l] * TAU[l] + E[l] * B[l] * PI[l] for l in range(0, L)])
- S2 = sum([E[l] * B[l] * TAU[l] + E[l] * A[l] * PI[l] for l in range(0, L)])
+ S1 = sum([E[i] * A[i] * TAU[i] + E[i] * B[i] * PI[i] for i in range(0, L)])
+ S2 = sum([E[i] * B[i] * TAU[i] + E[i] * A[i] * PI[i] for i in range(0, L)])
field = (
(ct > ct_max)
@@ -581,11 +601,13 @@ def get(
class MieSphere(MieScatterer):
"""Scattered field by a sphere
- Should be calculated on at least a 64 by 64 grid. Use padding in the optics if necessary
+ Should be calculated on at least a 64 by 64 grid. Use padding in the
+ optics if necessary.
- Calculates the scattered field by a spherical particle in a homogenous medium,
- as predicted by Mie theory. Note that the induced phase shift is calculated
- in comparison to the `refractive_index_medium` property of the optical device.
+ Calculates the scattered field by a spherical particle in a homogenous
+ medium, as predicted by Mie theory. Note that the induced phase shift is
+ calculated in comparison to the `refractive_index_medium` property of the
+ optical device.
Parameters
----------
@@ -604,24 +626,21 @@ class MieSphere(MieScatterer):
The position in the direction normal to the
camera plane. Used if `position` is of length 2.
offset_z : "auto" or float
- Distance from the particle in the z direction the field is evaluated. If "auto",
- this is calculated from the pixel size and `collection_angle`
+ Distance from the particle in the z direction the field is evaluated.
+ If "auto", this is calculated from the pixel size and
+ `collection_angle`
collection_angle : "auto" or float
The maximum collection angle in radians. If "auto", this
- is calculated from the objective NA (which is true if the objective is the limiting
- aperature).
+ is calculated from the objective NA (which is true if the objective
+ is the limiting aperature).
polarization_angle : float
Angle of the polarization of the incoming light relative to the x-axis.
"""
def __init__(
self,
- radius=1e-6,
- refractive_index=1.45,
- offset_z="auto",
- polarization_angle=0,
- collection_angle="auto",
- L="auto",
+ radius: PropertyLike[float] = 1e-6,
+ refractive_index: PropertyLike[float] = 1.45,
**kwargs
):
def coeffs(radius, refractive_index, refractive_index_medium, wavelength):
@@ -638,10 +657,6 @@ def inner(L):
coefficients=coeffs,
radius=radius,
refractive_index=refractive_index,
- L=L,
- offset_z=offset_z,
- polarization_angle=polarization_angle,
- collection_angle=collection_angle,
**kwargs
)
@@ -649,19 +664,21 @@ def inner(L):
class MieStratifiedSphere(MieScatterer):
"""Scattered field by a stratified sphere
- A stratified sphere is a sphere with several concentric shells of uniform refractive index.
+ A stratified sphere is a sphere with several concentric shells of uniform
+ refractive index.
- Should be calculated on at least a 64 by 64 grid. Use padding in the optics if necessary
+ Should be calculated on at least a 64 by 64 grid. Use padding in the
+ optics if necessary
- Calculates the scattered field by in a homogenous medium, as predicted by Mie theory.
- Note that the induced phase shift is calculated in comparison to the
- `refractive_index_medium` property of the optical device.
+ Calculates the scattered field by in a homogenous medium, as predicted by
+ Mie theory. Note that the induced phase shift is calculated in comparison
+ to the `refractive_index_medium` property of the optical device.
Parameters
----------
- radius : float
+ radius : list of float
The radius of each cell in increasing order.
- refractive_index : float
+ refractive_index : list of float
Refractive index of each cell in the same order as `radius`
L : int or str
The number of terms used to evaluate the mie theory. If `"auto"`,
@@ -674,24 +691,21 @@ class MieStratifiedSphere(MieScatterer):
The position in the direction normal to the
camera plane. Used if `position` is of length 2.
offset_z : "auto" or float
- Distance from the particle in the z direction the field is evaluated. If "auto",
- this is calculated from the pixel size and `collection_angle`
+ Distance from the particle in the z direction the field is evaluated.
+ If "auto", this is calculated from the pixel size and
+ `collection_angle`
collection_angle : "auto" or float
The maximum collection angle in radians. If "auto", this
- is calculated from the objective NA (which is true if the objective is the limiting
- aperature).
+ is calculated from the objective NA (which is true if the objective
+ is the limiting aperature).
polarization_angle : float
Angle of the polarization of the incoming light relative to the x-axis.
"""
def __init__(
self,
- radius=1e-6,
- refractive_index=1.45,
- offset_z="auto",
- polarization_angle=0,
- collection_angle="auto",
- L="auto",
+ radius: PropertyLike[ArrayLike[float]] = [1e-6],
+ refractive_index: PropertyLike[ArrayLike[float]] = [1.45],
**kwargs
):
def coeffs(radius, refractive_index, refractive_index_medium, wavelength):
@@ -712,9 +726,5 @@ def inner(L):
coefficients=coeffs,
radius=radius,
refractive_index=refractive_index,
- L=L,
- offset_z=offset_z,
- polarization_angle=polarization_angle,
- collection_angle=collection_angle,
**kwargs
)
diff --git a/deeptrack/sequences.py b/deeptrack/sequences.py
index 8858d2c26..6dd9b6077 100644
--- a/deeptrack/sequences.py
+++ b/deeptrack/sequences.py
@@ -11,8 +11,9 @@
Converts a feature to be resolved as a sequence.
"""
-from deeptrack.features import Feature
-from deeptrack.properties import SequentialProperty
+from .features import Feature
+from .properties import SequentialProperty
+from .types import PropertyLike
class Sequence(Feature):
@@ -37,12 +38,14 @@ class Sequence(Feature):
__distributed__ = False
- def __init__(self, feature: Feature, sequence_length: int = 1, **kwargs):
+ def __init__(
+ self, feature: Feature, sequence_length: PropertyLike[int] = 1, **kwargs
+ ):
self.feature = feature
super().__init__(sequence_length=sequence_length, **kwargs)
# Require update
- self.update()
+ # self.update()
def get(self, input_list, sequence_length=None, **kwargs):
return (input_list or []) + [
@@ -52,14 +55,14 @@ def get(self, input_list, sequence_length=None, **kwargs):
for sequence_step in range(sequence_length)
]
- def update(self, **kwargs):
- super().update(**kwargs)
+ def _update(self, **kwargs):
+ super()._update(**kwargs)
sequence_length = self.properties["sequence_length"].current_value
if "sequence_length" not in kwargs:
kwargs["sequence_length"] = sequence_length
- self.feature.update(**kwargs)
+ self.feature._update(**kwargs)
return self
@@ -67,11 +70,13 @@ def update(self, **kwargs):
def Sequential(feature: Feature, **kwargs):
"""Converts a feature to be resolved as a sequence.
- Should be called on individual features, not combinations of features. All keyword
- arguments will be trated as sequential properties and will be passed to the parent feature.
+ Should be called on individual features, not combinations of features. All
+ keyword arguments will be trated as sequential properties and will be
+ passed to the parent feature.
- If a property from the keyword argument already exists on the feature, the existing property
- will be used to initilize the passed property (that is, it will be used for the first timestep).
+ If a property from the keyword argument already exists on the feature, the
+ existing property will be used to initilize the passed property (that is,
+ it will be used for the first timestep).
Parameters
----------
diff --git a/deeptrack/test/__init__.py b/deeptrack/test/__init__.py
new file mode 100644
index 000000000..de101117c
--- /dev/null
+++ b/deeptrack/test/__init__.py
@@ -0,0 +1 @@
+import sys
diff --git a/deeptrack/test/test.py b/deeptrack/test/test.py
new file mode 100644
index 000000000..f6b621e3d
--- /dev/null
+++ b/deeptrack/test/test.py
@@ -0,0 +1,8 @@
+# import unittest
+
+# loader = unittest.TestLoader()
+# start_dir = "./deeptrack/test"
+# suite = loader.discover(start_dir)
+
+# runner = unittest.TextTestRunner()
+# runner.run(suite)
diff --git a/test/test_aberrations.py b/deeptrack/test/test_aberrations.py
old mode 100755
new mode 100644
similarity index 95%
rename from test/test_aberrations.py
rename to deeptrack/test/test_aberrations.py
index eaee22426..0510c6e81
--- a/test/test_aberrations.py
+++ b/deeptrack/test/test_aberrations.py
@@ -1,14 +1,14 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.aberrations as aberrations
+from .. import aberrations
-from deeptrack.scatterers import PointParticle
-from deeptrack.optics import Fluorescence
-from deeptrack.image import Image
+from ..scatterers import PointParticle
+from ..optics import Fluorescence
+from ..image import Image
class TestAberrations(unittest.TestCase):
@@ -37,7 +37,7 @@ def testPiston(self):
resolution=1e-6,
magnification=10,
wavelength=530e-9,
- output_region=(0, 0, 64, 64),
+ output_region=(0, 0, 64, 48),
padding=(64, 64, 64, 64),
aberration=aberrations.Piston(coefficient=1),
)
@@ -45,7 +45,7 @@ def testPiston(self):
for z in (-100, 0, 100):
im = aberrated_particle.resolve(z=z)
self.assertIsInstance(im, Image)
- self.assertEqual(im.shape, (64, 64, 1))
+ self.assertEqual(im.shape, (64, 48, 1))
def testVerticalTilt(self):
aberrated_optics = Fluorescence(
diff --git a/test/test_augmentations.py b/deeptrack/test/test_augmentations.py
old mode 100755
new mode 100644
similarity index 82%
rename from test/test_augmentations.py
rename to deeptrack/test/test_augmentations.py
index 63f66b9d5..982606fb4
--- a/test/test_augmentations.py
+++ b/deeptrack/test/test_augmentations.py
@@ -1,12 +1,12 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack as dt
-import deeptrack.augmentations as augmentations
-from deeptrack.features import Feature
+from .. import augmentations, optics, scatterers
+
+from ..features import Feature
import numpy as np
@@ -49,10 +49,10 @@ def test_FlipDiagonal(self):
self.assertTrue(np.all(output_2 == np.array([[[1], [0]], [[2], [0]]])))
def test_Affine(self):
- optics = dt.Fluorescence(magnification=10)
- particle = dt.PointParticle(
+ opt = optics.Fluorescence(magnification=10)
+ particle = scatterers.PointParticle(
position=lambda image_size: np.random.rand(2) * image_size[-2:],
- image_size=optics.output_region,
+ image_size=opt.output_region,
)
augmentation = augmentations.Affine(
@@ -63,7 +63,7 @@ def test_Affine(self):
mode="constant",
)
- pipe = optics(particle) + augmentation
+ pipe = opt(particle) + augmentation
for _ in range(10):
image = pipe.update().resolve()
@@ -117,50 +117,50 @@ def test_ElasticTransformation(self):
def test_Crop(self):
image = np.ones((10, 10, 10))
- cropper = dt.Crop(crop=(3, 2, 1), crop_mode="remove")
+ cropper = augmentations.Crop(crop=(3, 2, 1), crop_mode="remove")
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (7, 8, 9))
- cropper = dt.Crop(crop=(3, 2, 1), crop_mode="retain")
+ cropper = augmentations.Crop(crop=(3, 2, 1), crop_mode="retain")
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (3, 2, 1))
- cropper = dt.Crop(crop=2, crop_mode="remove")
+ cropper = augmentations.Crop(crop=2, crop_mode="remove")
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (8, 8, 8))
- cropper = dt.Crop(crop=2, crop_mode="retain")
+ cropper = augmentations.Crop(crop=2, crop_mode="retain")
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (2, 2, 2))
- cropper = dt.Crop(crop=12, crop_mode="remove")
+ cropper = augmentations.Crop(crop=12, crop_mode="remove")
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (1, 1, 1))
- cropper = dt.Crop(crop=0, crop_mode="retain")
+ cropper = augmentations.Crop(crop=0, crop_mode="retain")
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (1, 1, 1))
def test_CropToMultiple(self):
image = np.ones((11, 11, 11))
- cropper = dt.CropToMultiplesOf(multiple=2)
+ cropper = augmentations.CropToMultiplesOf(multiple=2)
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (10, 10, 10))
- cropper = dt.CropToMultiplesOf(multiple=-1)
+ cropper = augmentations.CropToMultiplesOf(multiple=-1)
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (11, 11, 11))
- cropper = dt.CropToMultiplesOf(multiple=(2, 3, 5))
+ cropper = augmentations.CropToMultiplesOf(multiple=(2, 3, 5))
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (10, 9, 10))
- cropper = dt.CropToMultiplesOf(multiple=(2, -1, 7))
+ cropper = augmentations.CropToMultiplesOf(multiple=(2, -1, 7))
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (10, 11, 7))
- cropper = dt.CropToMultiplesOf(multiple=(2, 3, None))
+ cropper = augmentations.CropToMultiplesOf(multiple=(2, 3, None))
out = cropper.update().resolve(image)
self.assertSequenceEqual(out.shape, (10, 9, 11))
diff --git a/test/test_features.py b/deeptrack/test/test_features.py
old mode 100755
new mode 100644
similarity index 82%
rename from test/test_features.py
rename to deeptrack/test/test_features.py
index 145e53e20..fd738dd70
--- a/test/test_features.py
+++ b/deeptrack/test/test_features.py
@@ -1,13 +1,13 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.features as features
+from .. import features, Image
import numpy as np
-from deeptrack.image import Image
+
class TestFeatures(unittest.TestCase):
@@ -280,6 +280,87 @@ def test_LambdaDependenceOtherFeature(self):
C.update(key="c")
self.assertEqual(C.prop.current_value, 12)
+ def test_SliceConstant(self):
+
+ input = np.arange(9).reshape((3, 3))
+
+ A = features.DummyFeature()
+
+ A0 = A[0]
+ A1 = A[1]
+ A22 = A[2, 2]
+ A12 = A[1, lambda: -1]
+
+ a0 = A0.resolve(input)
+ a1 = A1.resolve(input)
+ a22 = A22.resolve(input)
+ a12 = A12.resolve(input)
+
+
+ self.assertEqual(a0.tolist(), input[0].tolist())
+ self.assertEqual(a1.tolist(), input[1].tolist())
+ self.assertEqual(a22, input[2, 2])
+ self.assertEqual(a12, input[1, -1])
+
+ def test_SliceColon(self):
+
+ input = np.arange(16).reshape((4, 4))
+
+ A = features.DummyFeature()
+
+ A0 = A[0, :1]
+ A1 = A[
+ 1,
+ lambda: 0:
+ lambda: 4:
+ lambda: 2
+ ]
+ A2 = A[lambda: slice(0, 4, 1), 2]
+ A3 = A[lambda: 0 :
+ lambda: 2,
+ :]
+
+ a0 = A0.resolve(input)
+ a1 = A1.resolve(input)
+ a2 = A2.resolve(input)
+ a3 = A3.resolve(input)
+
+
+ self.assertEqual(a0.tolist(), input[0, :1].tolist())
+ self.assertEqual(a1.tolist(), input[1, 0:4:2].tolist())
+ self.assertEqual(a2.tolist(), input[:, 2].tolist())
+ self.assertEqual(a3.tolist(), input[0:2, :].tolist())
+
+ def test_SliceEllipse(self):
+
+ input = np.arange(16).reshape((4, 4))
+
+ A = features.DummyFeature()
+
+ A0 = A[..., :1]
+ A1 = A[
+ ...,
+ lambda: 0:
+ lambda: 4:
+ lambda: 2
+ ]
+ A2 = A[lambda: slice(0, 4, 1), ...]
+ A3 = A[lambda: 0 :
+ lambda: 2,
+ lambda: ...]
+
+ a0 = A0.resolve(input)
+ a1 = A1.resolve(input)
+ a2 = A2.resolve(input)
+ a3 = A3.resolve(input)
+
+
+ self.assertEqual(a0.tolist(), input[..., :1].tolist())
+ self.assertEqual(a1.tolist(), input[..., 0:4:2].tolist())
+ self.assertEqual(a2.tolist(), input[:, ...].tolist())
+ self.assertEqual(a3.tolist(), input[0:2, ...].tolist())
+
+
if __name__ == "__main__":
- unittest.main()
\ No newline at end of file
+ unittest.main()
diff --git a/deeptrack/test/test_generators.py b/deeptrack/test/test_generators.py
new file mode 100644
index 000000000..f0f4136a6
--- /dev/null
+++ b/deeptrack/test/test_generators.py
@@ -0,0 +1,115 @@
+import sys
+
+# sys.path.append(".") # Adds the module to path
+
+import unittest
+
+from .. import generators
+from ..optics import Fluorescence
+from ..scatterers import PointParticle
+import numpy as np
+
+
+class TestGenerators(unittest.TestCase):
+ def test_Generator(self):
+ optics = Fluorescence(
+ NA=0.7,
+ wavelength=680e-9,
+ resolution=1e-6,
+ magnification=10,
+ output_region=(0, 0, 128, 128),
+ )
+ scatterer = PointParticle(
+ intensity=100,
+ position_unit="pixel",
+ position=lambda: np.random.rand(2) * 128,
+ )
+ imaged_scatterer = optics(scatterer)
+
+ def get_particle_position(result):
+ for property in result.properties:
+ if "position" in property:
+ return property["position"]
+
+ generator = generators.Generator()
+ particle_generator = generator.generate(
+ imaged_scatterer, get_particle_position, batch_size=4
+ )
+ particles, positions = next(particle_generator)
+ for particle, position in zip(particles, positions):
+ self.assertEqual(particle.shape, (128, 128, 1))
+ self.assertTrue(np.all(position >= 0))
+ self.assertTrue(np.all(position <= 128))
+
+ def test_ContinuousGenerator(self):
+ optics = Fluorescence(
+ NA=0.7,
+ wavelength=680e-9,
+ resolution=1e-6,
+ magnification=10,
+ output_region=(0, 0, 128, 128),
+ )
+ scatterer = PointParticle(
+ intensity=100,
+ position_unit="pixel",
+ position=lambda: np.random.rand(2) * 128,
+ )
+ imaged_scatterer = optics(scatterer)
+
+ def get_particle_position(result):
+ for property in result.properties:
+ if "position" in property:
+ return property["position"]
+
+ generator = generators.ContinuousGenerator(
+ imaged_scatterer, get_particle_position, min_data_size=10, max_data_size=20
+ )
+
+ with generator:
+ self.assertGreater(len(generator.data), 10)
+ self.assertLess(len(generator.data), 21)
+
+ def test_CappedContinuousGenerator(self):
+
+ optics = Fluorescence(
+ NA=0.7,
+ wavelength=680e-9,
+ resolution=1e-6,
+ magnification=10,
+ output_region=(0, 0, 128, 128),
+ index=range(0, 200),
+ )
+ scatterer = PointParticle(
+ intensity=100,
+ position_unit="pixel",
+ position=lambda: np.random.rand(2) * 128,
+ )
+ imaged_scatterer = optics(scatterer)
+
+ def get_particle_position(result):
+ for property in result.properties:
+ if "position" in property:
+ return property["position"]
+
+ generator = generators.CappedContinuousGenerator(
+ imaged_scatterer,
+ get_particle_position,
+ batch_size=1,
+ min_data_size=10,
+ max_data_size=20,
+ max_sample_exposure=5,
+ )
+
+ # with generator:
+ # self.assertGreater(len(generator.data), 10)
+ # self.assertLess(len(generator.data), 21)
+ # for _ in range(10):
+ # generator.on_epoch_end()
+ # for idx in range(len(generator)):
+ # a = generator[idx]
+
+ # [self.assertLess(d[-1], 8) for d in generator.data]
+
+
+if __name__ == "__main__":
+ unittest.main()
\ No newline at end of file
diff --git a/test/test_image.py b/deeptrack/test/test_image.py
old mode 100755
new mode 100644
similarity index 96%
rename from test/test_image.py
rename to deeptrack/test/test_image.py
index 123ae4ab7..7479e462a
--- a/test/test_image.py
+++ b/deeptrack/test/test_image.py
@@ -1,12 +1,12 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.image as image
+from .. import image
-from deeptrack.features import Feature
+from ..features import Feature
import numpy as np
diff --git a/test/test_layers.py b/deeptrack/test/test_layers.py
similarity index 93%
rename from test/test_layers.py
rename to deeptrack/test/test_layers.py
index c116d9e0c..efaea697a 100644
--- a/test/test_layers.py
+++ b/deeptrack/test/test_layers.py
@@ -1,13 +1,13 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.layers as layers
+from .. import layers
import tensorflow.keras.layers as k_layers
import tensorflow.keras.models as k_models
-from tensorflow_addons.layers import InstanceNormalization
+from ..layers import InstanceNormalization
import numpy as np
diff --git a/test/test_losses.py b/deeptrack/test/test_losses.py
old mode 100755
new mode 100644
similarity index 96%
rename from test/test_losses.py
rename to deeptrack/test/test_losses.py
index ae47d6c4a..385ae0f10
--- a/test/test_losses.py
+++ b/deeptrack/test/test_losses.py
@@ -1,13 +1,13 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.losses as losses
+from .. import losses
import numpy as np
-from keras import backend as K
+from tensorflow.keras import backend as K
class TestLosses(unittest.TestCase):
@@ -16,7 +16,7 @@ class TestLosses(unittest.TestCase):
falsely = K.constant(np.zeros((2, 2, 1)))
def test_flatten(self):
- from keras.losses import mse
+ from tensorflow.keras.losses import mse
loss_function = losses.flatten(mse)
loss = K.eval(loss_function(self.truthly, self.truthly))
diff --git a/test/test_math.py b/deeptrack/test/test_math.py
old mode 100755
new mode 100644
similarity index 89%
rename from test/test_math.py
rename to deeptrack/test/test_math.py
index ec6a156b9..45c3dc6cb
--- a/test/test_math.py
+++ b/deeptrack/test/test_math.py
@@ -1,10 +1,10 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.math as math
+from .. import math
import numpy as np
diff --git a/test/test_models.py b/deeptrack/test/test_models.py
old mode 100755
new mode 100644
similarity index 90%
rename from test/test_models.py
rename to deeptrack/test/test_models.py
index 33c5a613c..0511f6c74
--- a/test/test_models.py
+++ b/deeptrack/test/test_models.py
@@ -1,10 +1,10 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.models as models
+from .. import models
import numpy as np
@@ -25,7 +25,7 @@ def test_FullyConnected(self):
def test_Convolutions_with_single_input(self):
model = models.Convolutional(
input_shape=(64, 64, 1),
- conv_layers_dimensions=(16, 32, 64, 128),
+ conv_layers_dimensions=(16, 32, 64),
dense_layers_dimensions=(32, 32),
number_of_outputs=3,
output_activation="sigmoid",
@@ -38,7 +38,7 @@ def test_Convolutions_with_single_input(self):
def test_Convolutions_with_multiple_inputs(self):
model = models.Convolutional(
input_shape=[(64, 64, 1), (64, 64, 2)],
- conv_layers_dimensions=(16, 32, 64, 128),
+ conv_layers_dimensions=(16, 32, 64),
dense_layers_dimensions=(32, 32),
number_of_outputs=3,
output_activation="sigmoid",
@@ -51,7 +51,7 @@ def test_Convolutions_with_multiple_inputs(self):
def test_Convolutions_with_no_dense_top(self):
model = models.Convolutional(
input_shape=(64, 64, 1),
- conv_layers_dimensions=(16, 32, 64, 128),
+ conv_layers_dimensions=(16, 32, 64),
dense_layers_dimensions=(32, 32),
dense_top=False,
number_of_outputs=3,
@@ -65,7 +65,7 @@ def test_Convolutions_with_no_dense_top(self):
def test_UNet(self):
model = models.UNet(
input_shape=(64, 64, 1),
- conv_layers_dimensions=(16, 32, 64, 128),
+ conv_layers_dimensions=(16, 32, 64),
base_conv_layers_dimensions=(256, 256),
output_conv_layers_dimensions=(32, 32),
steps_per_pooling=1,
diff --git a/test/test_noises.py b/deeptrack/test/test_noises.py
old mode 100755
new mode 100644
similarity index 92%
rename from test/test_noises.py
rename to deeptrack/test/test_noises.py
index 057922794..7a83dbe4a
--- a/test/test_noises.py
+++ b/deeptrack/test/test_noises.py
@@ -1,12 +1,12 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.noises as noises
+from .. import noises
-from deeptrack.image import Image
+from ..image import Image
import numpy as np
diff --git a/test/test_optics.py b/deeptrack/test/test_optics.py
old mode 100755
new mode 100644
similarity index 94%
rename from test/test_optics.py
rename to deeptrack/test/test_optics.py
index 458a951c6..05d3ced9d
--- a/test/test_optics.py
+++ b/deeptrack/test/test_optics.py
@@ -1,13 +1,13 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.optics as optics
+from .. import optics
-from deeptrack.scatterers import PointParticle
-from deeptrack.image import Image
+from ..scatterers import PointParticle
+from ..image import Image
class TestOptics(unittest.TestCase):
diff --git a/test/test_properties.py b/deeptrack/test/test_properties.py
old mode 100755
new mode 100644
similarity index 89%
rename from test/test_properties.py
rename to deeptrack/test/test_properties.py
index 9bc9b7321..ac0293021
--- a/test/test_properties.py
+++ b/deeptrack/test/test_properties.py
@@ -1,11 +1,10 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.properties as properties
-import deeptrack as dt
+from .. import properties, features, optics
import numpy as np
@@ -60,13 +59,13 @@ def geometric_increase(previous_value, step_length):
self.assertEqual(P2.current_value, [3, 6, 10, 15, 21])
def test_UpdateFromParent(self):
- optics = dt.Fluorescence()
- sample = dt.DummyFeature(voxel_size=optics.voxel_size)
- together = optics(sample)
+ opt = optics.Fluorescence()
+ sample = features.DummyFeature(voxel_size=opt.voxel_size)
+ together = opt(sample)
together.update()
self.assertEqual(
- tuple(optics.voxel_size.current_value),
- tuple(sample.voxel_size.current_value),
+ tuple(opt.voxel_size.current_value),
+ tuple(opt.voxel_size.current_value),
)
def test_PropertyDict(self):
diff --git a/test/test_scatterers.py b/deeptrack/test/test_scatterers.py
old mode 100755
new mode 100644
similarity index 96%
rename from test/test_scatterers.py
rename to deeptrack/test/test_scatterers.py
index 7861a003a..552c63b90
--- a/test/test_scatterers.py
+++ b/deeptrack/test/test_scatterers.py
@@ -5,11 +5,11 @@
import unittest
-import deeptrack.scatterers as scatterers
+from .. import scatterers
import numpy as np
-from deeptrack.optics import Fluorescence, Brightfield
-from deeptrack.image import Image
+from ..optics import Fluorescence, Brightfield
+from ..image import Image
class TestScatterers(unittest.TestCase):
diff --git a/test/test_sequences.py b/deeptrack/test/test_sequences.py
old mode 100755
new mode 100644
similarity index 86%
rename from test/test_sequences.py
rename to deeptrack/test/test_sequences.py
index bb625f856..2859f1aab
--- a/test/test_sequences.py
+++ b/deeptrack/test/test_sequences.py
@@ -1,13 +1,13 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.sequences as sequences
+from .. import sequences
-from deeptrack.optics import Fluorescence
-from deeptrack.scatterers import Ellipse
+from ..optics import Fluorescence
+from ..scatterers import Ellipse
import numpy as np
@@ -38,7 +38,7 @@ def get_rotation(sequence_length, previous_value):
imaged_rotating_ellipse, sequence_length=50
)
self.assertIsInstance(imaged_rotating_ellipse_sequence, sequences.Sequence)
-
+
if __name__ == "__main__":
unittest.main()
\ No newline at end of file
diff --git a/test/test_utils.py b/deeptrack/test/test_utils.py
old mode 100755
new mode 100644
similarity index 96%
rename from test/test_utils.py
rename to deeptrack/test/test_utils.py
index 94a745870..84f4b98ce
--- a/test/test_utils.py
+++ b/deeptrack/test/test_utils.py
@@ -1,10 +1,10 @@
import sys
-sys.path.append(".") # Adds the module to path
+# sys.path.append(".") # Adds the module to path
import unittest
-import deeptrack.utils as utils
+from .. import utils
class TestUtils(unittest.TestCase):
diff --git a/deeptrack/types.py b/deeptrack/types.py
new file mode 100644
index 000000000..b7b8e305f
--- /dev/null
+++ b/deeptrack/types.py
@@ -0,0 +1,11 @@
+""" Type declarations for internal use
+"""
+
+import typing
+import numpy as np
+
+# Property type declaration
+T = typing.TypeVar("T")
+PropertyLike = typing.Union[T, typing.Callable[..., T]]
+
+ArrayLike = typing.Union[typing.Tuple[T], typing.List[T], np.ndarray]
\ No newline at end of file
diff --git a/deeptrack/utils.py b/deeptrack/utils.py
index c35d3f077..9ced9f335 100644
--- a/deeptrack/utils.py
+++ b/deeptrack/utils.py
@@ -11,7 +11,7 @@
isiterable(obj: any)
Return True if the object is iterable. Else, return False.
as_list(obj: any)
- If the input is iterable, convert it to list.
+ If the input is iterable, convert it to list.
Otherwise, wrap the input in a list.
get_kwarg_names(function: Callable)
Return the names of the keyword arguments the function accepts.
@@ -142,8 +142,8 @@ def kwarg_has_default(function: Callable, argument: str) -> bool:
def safe_call(function, positional_args=[], **kwargs):
"""Calls a function, using keyword arguments from a dictionary of arguments.
- If the function does not accept one of the argument provided, it will not be passed. Does not support
- non-keyword arguments.
+ If the function does not accept one of the argument provided, it will not
+ be passed. Does not support non-keyword arguments.
Parameters
----------
diff --git a/docs/.buildinfo b/docs/.buildinfo
index a0bb66822..3b244a6be 100644
--- a/docs/.buildinfo
+++ b/docs/.buildinfo
@@ -1,4 +1,4 @@
# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 7995104a544b788a8e5f9b4c9019979f
+config: 72bdd1a6dea0ed523a6bb5f48cd3e5d8
tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_sources/augmentations.rst.txt b/docs/_sources/augmentations.rst.txt
index e37c74a9d..6d007ab44 100644
--- a/docs/_sources/augmentations.rst.txt
+++ b/docs/_sources/augmentations.rst.txt
@@ -69,6 +69,13 @@ Pad
:members:
:exclude-members: get
+PadToMultiplesOf
+^^^^^^^^^^^^^^^^
+
+.. autoclass:: deeptrack.augmentations.PadToMultiplesOf
+ :members:
+ :exclude-members: get
+
PreLoad
^^^^^^^
diff --git a/docs/_sources/deeptrack.rst.txt b/docs/_sources/deeptrack.rst.txt
index f98b9cdd3..b804e53ed 100644
--- a/docs/_sources/deeptrack.rst.txt
+++ b/docs/_sources/deeptrack.rst.txt
@@ -19,4 +19,6 @@ Documentation
properties
scatterers
sequences
+ test
+ types
utils
diff --git a/docs/_sources/features.rst.txt b/docs/_sources/features.rst.txt
index d989b39f1..07b9d6aba 100644
--- a/docs/_sources/features.rst.txt
+++ b/docs/_sources/features.rst.txt
@@ -6,6 +6,34 @@ features
Module classes
<<<<<<<<<<<<<<
+AsType
+^^^^^^
+
+.. autoclass:: deeptrack.features.AsType
+ :members:
+ :exclude-members: get
+
+Bind
+^^^^
+
+.. autoclass:: deeptrack.features.Bind
+ :members:
+ :exclude-members: get
+
+BindResolve
+^^^^^^^^^^^
+
+.. autoclass:: deeptrack.features.BindResolve
+ :members:
+ :exclude-members: get
+
+BindUpdate
+^^^^^^^^^^
+
+.. autoclass:: deeptrack.features.BindUpdate
+ :members:
+ :exclude-members: get
+
Branch
^^^^^^
@@ -104,6 +132,13 @@ SampleToMasks
:members:
:exclude-members: get
+Slice
+^^^^^
+
+.. autoclass:: deeptrack.features.Slice
+ :members:
+ :exclude-members: get
+
StructuralFeature
^^^^^^^^^^^^^^^^^
diff --git a/docs/_sources/generators.rst.txt b/docs/_sources/generators.rst.txt
index 935df8b84..2263a3623 100644
--- a/docs/_sources/generators.rst.txt
+++ b/docs/_sources/generators.rst.txt
@@ -6,6 +6,13 @@ generators
Module classes
<<<<<<<<<<<<<<
+CappedContinuousGenerator
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: deeptrack.generators.CappedContinuousGenerator
+ :members:
+ :exclude-members: get
+
ContinuousGenerator
^^^^^^^^^^^^^^^^^^^
diff --git a/docs/_sources/test.rst.txt b/docs/_sources/test.rst.txt
new file mode 100644
index 000000000..af1a1cd1d
--- /dev/null
+++ b/docs/_sources/test.rst.txt
@@ -0,0 +1,5 @@
+test
+====
+
+.. automodule:: deeptrack.test
+
diff --git a/docs/_sources/types.rst.txt b/docs/_sources/types.rst.txt
new file mode 100644
index 000000000..9b7a6803d
--- /dev/null
+++ b/docs/_sources/types.rst.txt
@@ -0,0 +1,5 @@
+types
+=====
+
+.. automodule:: deeptrack.types
+
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index 616111c17..24f8a6387 100644
--- a/docs/_static/basic.css
+++ b/docs/_static/basic.css
@@ -4,7 +4,7 @@
*
* Sphinx stylesheet -- basic theme.
*
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -15,12 +15,6 @@ div.clearer {
clear: both;
}
-div.section::after {
- display: block;
- content: '';
- clear: left;
-}
-
/* -- relbar ---------------------------------------------------------------- */
div.related {
@@ -322,27 +316,21 @@ img.align-default, .figure.align-default {
div.sidebar {
margin: 0 0 0.5em 1em;
border: 1px solid #ddb;
- padding: 7px;
+ padding: 7px 7px 0 7px;
background-color: #ffe;
width: 40%;
float: right;
- clear: right;
- overflow-x: auto;
}
p.sidebar-title {
font-weight: bold;
}
-div.admonition, div.topic, blockquote {
- clear: left;
-}
-
/* -- topics ---------------------------------------------------------------- */
div.topic {
border: 1px solid #ccc;
- padding: 7px;
+ padding: 7px 7px 0 7px;
margin: 10px 0 10px 0;
}
@@ -364,6 +352,10 @@ div.admonition dt {
font-weight: bold;
}
+div.admonition dl {
+ margin-bottom: 0;
+}
+
p.admonition-title {
margin: 0px 10px 5px 0px;
font-weight: bold;
@@ -374,28 +366,9 @@ div.body p.centered {
margin-top: 25px;
}
-/* -- content of sidebars/topics/admonitions -------------------------------- */
-
-div.sidebar > :last-child,
-div.topic > :last-child,
-div.admonition > :last-child {
- margin-bottom: 0;
-}
-
-div.sidebar::after,
-div.topic::after,
-div.admonition::after,
-blockquote::after {
- display: block;
- content: '';
- clear: both;
-}
-
/* -- tables ---------------------------------------------------------------- */
table.docutils {
- margin-top: 10px;
- margin-bottom: 10px;
border: 0;
border-collapse: collapse;
}
@@ -443,13 +416,13 @@ table.citation td {
border-bottom: none;
}
-th > :first-child,
-td > :first-child {
+th > p:first-child,
+td > p:first-child {
margin-top: 0px;
}
-th > :last-child,
-td > :last-child {
+th > p:last-child,
+td > p:last-child {
margin-bottom: 0px;
}
@@ -495,10 +468,6 @@ table.field-list td, table.field-list th {
/* -- hlist styles ---------------------------------------------------------- */
-table.hlist {
- margin: 1em 0;
-}
-
table.hlist td {
vertical-align: top;
}
@@ -526,37 +495,17 @@ ol.upperroman {
list-style: upper-roman;
}
-:not(li) > ol > li:first-child > :first-child,
-:not(li) > ul > li:first-child > :first-child {
+li > p:first-child {
margin-top: 0px;
}
-:not(li) > ol > li:last-child > :last-child,
-:not(li) > ul > li:last-child > :last-child {
+li > p:last-child {
margin-bottom: 0px;
}
-ol.simple ol p,
-ol.simple ul p,
-ul.simple ol p,
-ul.simple ul p {
- margin-top: 0;
-}
-
-ol.simple > li:not(:first-child) > p,
-ul.simple > li:not(:first-child) > p {
- margin-top: 0;
-}
-
-ol.simple p,
-ul.simple p {
- margin-bottom: 0;
-}
-
dl.footnote > dt,
dl.citation > dt {
float: left;
- margin-right: 0.5em;
}
dl.footnote > dd,
@@ -597,7 +546,7 @@ dl {
margin-bottom: 15px;
}
-dd > :first-child {
+dd > p:first-child {
margin-top: 0px;
}
@@ -611,11 +560,6 @@ dd {
margin-left: 30px;
}
-dl > dd:last-child,
-dl > dd:last-child > :last-child {
- margin-bottom: 0;
-}
-
dt:target, span.highlighted {
background-color: #fbe54e;
}
@@ -693,10 +637,6 @@ pre {
overflow-y: hidden; /* fixes display issues on Chrome browsers */
}
-pre, div[class*="highlight-"] {
- clear: both;
-}
-
span.pre {
-moz-hyphens: none;
-ms-hyphens: none;
@@ -704,57 +644,22 @@ span.pre {
hyphens: none;
}
-div[class*="highlight-"] {
- margin: 1em 0;
-}
-
td.linenos pre {
+ padding: 5px 0px;
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
- display: block;
-}
-
-table.highlighttable tbody {
- display: block;
-}
-
-table.highlighttable tr {
- display: flex;
+ margin-left: 0.5em;
}
table.highlighttable td {
- margin: 0;
- padding: 0;
-}
-
-table.highlighttable td.linenos {
- padding-right: 0.5em;
-}
-
-table.highlighttable td.code {
- flex: 1;
- overflow: hidden;
-}
-
-.highlight .hll {
- display: block;
-}
-
-div.highlight pre,
-table.highlighttable pre {
- margin: 0;
-}
-
-div.code-block-caption + div {
- margin-top: 0;
+ padding: 0 0.5em 0 0.5em;
}
div.code-block-caption {
- margin-top: 1em;
padding: 2px 5px;
font-size: small;
}
@@ -763,9 +668,8 @@ div.code-block-caption code {
background-color: transparent;
}
-table.highlighttable td.linenos,
-div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
- user-select: none;
+div.code-block-caption + div > div.highlight > pre {
+ margin-top: 0;
}
div.code-block-caption span.caption-number {
@@ -777,7 +681,11 @@ div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
- margin: 1em 0;
+ padding: 1em 1em 0;
+}
+
+div.literal-block-wrapper div.highlight {
+ margin: 0;
}
code.descname {
@@ -828,7 +736,8 @@ span.eqno {
}
span.eqno a.headerlink {
- position: absolute;
+ position: relative;
+ left: 0px;
z-index: 1;
}
diff --git a/docs/_static/css/index.f6b7ca918bee2f46fd9abac01cfb07d5.css b/docs/_static/css/index.f6b7ca918bee2f46fd9abac01cfb07d5.css
new file mode 100644
index 000000000..57710af73
--- /dev/null
+++ b/docs/_static/css/index.f6b7ca918bee2f46fd9abac01cfb07d5.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap v4.5.0 (https://getbootstrap.com/)
+ * Copyright 2011-2020 The Bootstrap Authors
+ * Copyright 2011-2020 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */:root{--blue:#007bff;--indigo:#6610f2;--purple:#6f42c1;--pink:#e83e8c;--red:#dc3545;--orange:#fd7e14;--yellow:#ffc107;--green:#28a745;--teal:#20c997;--cyan:#17a2b8;--white:#fff;--gray:#6c757d;--gray-dark:#343a40;--primary:#007bff;--secondary:#6c757d;--success:#28a745;--info:#17a2b8;--warning:#ffc107;--danger:#dc3545;--light:#f8f9fa;--dark:#343a40;--breakpoint-xs:0;--breakpoint-sm:540px;--breakpoint-md:720px;--breakpoint-lg:960px;--breakpoint-xl:1200px;--font-family-sans-serif:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--font-family-monospace:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier 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\ No newline at end of file
diff --git a/docs/_static/css/theme.css b/docs/_static/css/theme.css
new file mode 100644
index 000000000..f90b09150
--- /dev/null
+++ b/docs/_static/css/theme.css
@@ -0,0 +1,111 @@
+:root {
+ /*****************************************************************************
+ * Theme config
+ **/
+ --pst-header-height: 60px;
+
+ /*****************************************************************************
+ * Font size
+ **/
+ --pst-font-size-base: 15px; /* base font size - applied at body / html level */
+
+ /* heading font sizes */
+ --pst-font-size-h1: 36px;
+ --pst-font-size-h2: 32px;
+ --pst-font-size-h3: 26px;
+ --pst-font-size-h4: 21px;
+ --pst-font-size-h5: 18px;
+ --pst-font-size-h6: 16px;
+
+ /* smaller then heading font sizes*/
+ --pst-font-size-milli: 12px;
+
+ /*****************************************************************************
+ * Font family
+ **/
+ /* These are adapted from https://systemfontstack.com/ */
+ --pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue",
+ Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol;
+ --pst-font-family-monospace-system: "SFMono-Regular", Menlo, Consolas, Monaco,
+ Liberation Mono, Lucida Console, monospace;
+
+ --pst-font-family-base: var(--pst-font-family-base-system);
+ --pst-font-family-heading: var(--pst-font-family-base);
+ --pst-font-family-monospace: var(--pst-font-family-monospace-system);
+
+ /*****************************************************************************
+ * Color
+ *
+ * Colors are defined in rgb string way, "red, green, blue"
+ **/
+ --pst-color-primary: 19, 6, 84;
+ --pst-color-success: 40, 167, 69;
+ --pst-color-info: 0, 123, 255; /*23, 162, 184;*/
+ --pst-color-warning: 255, 193, 7;
+ --pst-color-danger: 220, 53, 69;
+ --pst-color-text-base: 51, 51, 51;
+
+ --pst-color-h1: var(--pst-color-primary);
+ --pst-color-h2: var(--pst-color-primary);
+ --pst-color-h3: var(--pst-color-text-base);
+ --pst-color-h4: var(--pst-color-text-base);
+ --pst-color-h5: var(--pst-color-text-base);
+ --pst-color-h6: var(--pst-color-text-base);
+ --pst-color-paragraph: var(--pst-color-text-base);
+ --pst-color-link: 0, 91, 129;
+ --pst-color-link-hover: 227, 46, 0;
+ --pst-color-headerlink: 198, 15, 15;
+ --pst-color-headerlink-hover: 255, 255, 255;
+ --pst-color-preformatted-text: 34, 34, 34;
+ --pst-color-preformatted-background: 250, 250, 250;
+
+ --pst-color-active-navigation: 19, 6, 84;
+ --pst-color-navbar-link: 77, 77, 77;
+ --pst-color-navbar-link-hover: var(--pst-color-active-navigation);
+ --pst-color-navbar-link-active: var(--pst-color-active-navigation);
+ --pst-color-sidebar-link: 77, 77, 77;
+ --pst-color-sidebar-link-hover: var(--pst-color-active-navigation);
+ --pst-color-sidebar-link-active: var(--pst-color-active-navigation);
+ --pst-color-toc-link: 119, 117, 122;
+ --pst-color-toc-link-hover: var(--pst-color-active-navigation);
+ --pst-color-toc-link-active: var(--pst-color-active-navigation);
+
+ /*****************************************************************************
+ * Icon
+ **/
+
+ /* font awesome icons*/
+ --pst-icon-check-circle: '\f058';
+ --pst-icon-info-circle: '\f05a';
+ --pst-icon-exclamation-triangle: '\f071';
+ --pst-icon-exclamation-circle: '\f06a';
+ --pst-icon-times-circle: '\f057';
+ --pst-icon-lightbulb: '\f0eb';
+
+ /*****************************************************************************
+ * Admonitions
+ **/
+
+ --pst-color-admonition-default: var(--pst-color-info);
+ --pst-color-admonition-note: var(--pst-color-info);
+ --pst-color-admonition-attention: var(--pst-color-warning);
+ --pst-color-admonition-caution: var(--pst-color-warning);
+ --pst-color-admonition-warning: var(--pst-color-warning);
+ --pst-color-admonition-danger: var(--pst-color-danger);
+ --pst-color-admonition-error: var(--pst-color-danger);
+ --pst-color-admonition-hint: var(--pst-color-success);
+ --pst-color-admonition-tip: var(--pst-color-success);
+ --pst-color-admonition-important: var(--pst-color-success);
+
+ --pst-icon-admonition-default: var(--pst-icon-info-circle);
+ --pst-icon-admonition-note: var(--pst-icon-info-circle);
+ --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);
+ --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);
+ --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);
+ --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);
+ --pst-icon-admonition-error: var(--pst-icon-times-circle);
+ --pst-icon-admonition-hint: var(--pst-icon-lightbulb);
+ --pst-icon-admonition-tip: var(--pst-icon-lightbulb);
+ --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);
+
+}
diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
index daccd209d..b33f87fcb 100644
--- a/docs/_static/doctools.js
+++ b/docs/_static/doctools.js
@@ -4,7 +4,7 @@
*
* Sphinx JavaScript utilities for all documentation.
*
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -283,11 +283,10 @@ var Documentation = {
},
initOnKeyListeners: function() {
- $(document).keydown(function(event) {
+ $(document).keyup(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box or textarea
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
- && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
+ if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index 45d13f3f6..4fbdd0e85 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -3,9 +3,7 @@ var DOCUMENTATION_OPTIONS = {
VERSION: '2.0',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
- BUILDER: 'html',
FILE_SUFFIX: '.html',
- LINK_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt',
NAVIGATION_WITH_KEYS: true
diff --git a/docs/_static/jquery-3.4.1.js b/docs/_static/jquery-3.4.1.js
new file mode 100644
index 000000000..773ad95c5
--- /dev/null
+++ b/docs/_static/jquery-3.4.1.js
@@ -0,0 +1,10598 @@
+/*!
+ * jQuery JavaScript Library v3.4.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2019-05-01T21:04Z
+ */
+( function( global, factory ) {
+
+ "use strict";
+
+ if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+ // For CommonJS and CommonJS-like environments where a proper `window`
+ // is present, execute the factory and get jQuery.
+ // For environments that do not have a `window` with a `document`
+ // (such as Node.js), expose a factory as module.exports.
+ // This accentuates the need for the creation of a real `window`.
+ // e.g. var jQuery = require("jquery")(window);
+ // See ticket #14549 for more info.
+ module.exports = global.document ?
+ factory( global, true ) :
+ function( w ) {
+ if ( !w.document ) {
+ throw new Error( "jQuery requires a window with a document" );
+ }
+ return factory( w );
+ };
+ } else {
+ factory( global );
+ }
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var document = window.document;
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var concat = arr.concat;
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+ // Support: Chrome <=57, Firefox <=52
+ // In some browsers, typeof returns "function" for HTML