pyMSDtorch.test_data.twoD package

Submodules

pyMSDtorch.test_data.twoD.build_test_data module

pyMSDtorch.test_data.twoD.build_test_data.build_data_mixed_level_sets_2d(n_imgs=1000, n_peaks=3, n_xy=32, mask_radius=1.0, chunk=10)
pyMSDtorch.test_data.twoD.build_test_data.build_data_standard_sets_2d(n_imgs=1000, n_peaks=3, n_xy=64, snr=1.0, mask_radius=2.0, chunk=10)
pyMSDtorch.test_data.twoD.build_test_data.build_data_standard_sets_2d_time(n_imgs=1000, k_time_points=8, n_peaks=3, sigma=0.02, trend=0.01, dxy=0.01, cc=0.6, n_xy=64, normalize=True)

pyMSDtorch.test_data.twoD.diffusion_model module

pyMSDtorch.test_data.twoD.diffusion_model.diffusion_2d(time_steps: int, mean_x: float = 0.0, mean_y: None | float = None, sigma_x: float = 0.01, sigma_y: float = 0.01, cc: float = 0.0) → ndarray

Generates a sequence of displacement steps. If mean_y = None, a random vector will be generated with length mean_x

Parameters:

  • time_steps – The number of displacements to be generated

  • mean_x – The mean displacement in x

  • mean_y – The mean displacement in y. If set to None, a random vector will be generated.

  • sigma_x – The sigma in x

  • sigma_y – The sigma in y

  • cc – The correlation between the two

Returns:

an array with displacements

pyMSDtorch.test_data.twoD.diffusion_model.tst(show=False)

pyMSDtorch.test_data.twoD.noisy_gauss_2d module

class pyMSDtorch.test_data.twoD.noisy_gauss_2d.DataMaker(n_peaks=1, sigma=1.26, n_xy=128, bump=0.0)

Bases: object

A class that can be used to build training data for 2D peak picking classification. The returned data is a stack of 2D images consisting of noisy data, error free data and a ‘mask’.

generate_data_with_normal_noise(n_images, snr=0.5, noise_sigma=4.0, noise_base=100.0, mask_radius=1.0, normalize='linear_scale')

Build a dataset with uniform noise at a set level

Parameters:

  • n_images – The number of

  • snr – Signal to noise ratio

  • noise_sigma – standard deviation of the normal noise (the

` Gaussian sigma) :param noise_base: the base noise level (mean of gauss) :param mask_radius: The mask radius for ground_truth mask building :param normalize: scales noisy array

-if linear_scale, linearly scales to interval [-1,1]

Returns:

ground truth image, mask and noisy images, class images

generate_ground_truth_image_stack(n_images, mask_radius=1.0)

Build a ground truth set of images, and associated binary mask of that image.

Parameters:

  • n_images – Number of images to generate.

  • mask_radius – used to build a binary mask derived from the ground truth image.

Returns:

ground truth images and associated mask.

class pyMSDtorch.test_data.twoD.noisy_gauss_2d.MixedNoiseDataMaker(n_peaks=1, sigma=1.26, n_xy=128, bump=0.0)

Bases: object

A class that can be used to build training data for 2D peak picking classification. The returned data is a stack of 2D images consisting of noisy data, error free data and a class labels. The noise levels change for each peak.

generate_data_with_normal_noise(n_images, snr_brackets=None, noise_sigma=4.0, noise_base=100.0, mask_radius=1.0, normalize='linear_scale')

Build a dataset with uniform noise at a set level

Parameters:

  • n_images – The number of images to generate

  • snr_brackets – Signal to noise ratio

  • noise_sigma – standard deviation of the normal noise (the Gaussian sigma)

  • noise_base – the base noise level (mean of gauss)

  • mask_radius – The mask radius for ground_truth mask building

  • normalize – scales noisy array -if linear_scale, linearly scales to interval [0,1] using range obtained from whole dataset.

Returns:

ground truth image, mask / class, noisy images, scaled noise images

generate_ground_truth_image_stack(n_images, mask_radius=1.0)

Build a ground truth set of images, and associated binary mask of that image.

Parameters:

  • n_images – Number of images to generate.

  • mask_radius – used to build a binary mask derived from the ground truth image.

Returns:

ground truth images stacks and associated mask stacks. These stacks need to be further processed down to get actual images.

set_snr_brackets(snr_brackets=None)

Set the brackets of the peak signal to noise levels for each peak.

Parameters:

snr_brackets – An array of psnr values.

Returns:

void.

pyMSDtorch.test_data.twoD.noisy_gauss_2d.run_tst(n_xy=1000, n_imgs=1)
pyMSDtorch.test_data.twoD.noisy_gauss_2d.tst(show=True, n_xy=1000, n_imgs=10)

Provides a simple check if the peaks are generated Also serves as an indication how to use the above class.

Parameters:

  • show – If True 3 sample images will be shown.

  • n_xy – Dimensions of the box

  • n_imgs – Number of images generated

Returns:

True is test is passed

pyMSDtorch.test_data.twoD.noisy_gauss_2d.tst_mixed(show=True, n_xy=32, n_imgs=10)

Displays data for mixed noise data.

pyMSDtorch.test_data.twoD.noisy_gauss_2d_time module

class pyMSDtorch.test_data.twoD.noisy_gauss_2d_time.DataMaker(n_peaks=1, sigma=0.05, trend=0.0, dxy=0.01, cc=0.5, n_xy=128, bump=4.0)

Bases: object

A class that can be used to build training data for 2D peak picking classification of peaks that diffuse in a 2D plane. The returned data is a stack of 3D images, with two spatial coordinates and one time axis. Returned is noisy data, error free data and a ‘mask’.

generate_data_with_uniform_noise(m_images, k_time_steps, noise_level=1.0, mask_radius=1.0)

Build a dataset with uniform noise at a set level

Parameters:

  • m_images – The number of images

  • k_time_steps – The number of time steps

  • noise_level – The noise level, data drawn from U(0,noise_level)

  • mask_radius – The mask radius for ground_truth mask building

Returns:

ground truth image, mask and noisy images

generate_ground_truth_image_stack(m_images, k_time_points, mask_radius=1.0)

Build a ground truth set of images, and associated binary mask of that image.

Parameters:

  • m_images – Number of images to generate.

  • k_time_points – Number of time points to generate

  • mask_radius – used to build a binary mask derived from the ground truth image.

Returns:

ground truth images and associated mask.

pyMSDtorch.test_data.twoD.noisy_gauss_2d_time.run_tst(n_xy=1000, n_times=10, n_imgs=1)
pyMSDtorch.test_data.twoD.noisy_gauss_2d_time.tst(show=False, n_xy=64, n_times=10, n_img=1)

Provides a simple check if the peaks are generated Also serves as an indication how to use the above class.

Parameters:

  • show – If True the full time series will be shown.

  • n_xy – Spatial dimension of box.

  • n_times – Number of time steps.

  • n_img – Number of movies made.

Returns:

True is test is passed.

pyMSDtorch.test_data.twoD.random_shapes module

class pyMSDtorch.test_data.twoD.random_shapes.Circle(radius=0.25, n_xy=64)

Bases: Shape

class pyMSDtorch.test_data.twoD.random_shapes.Donut(radius=0.5, width=0.25, n_xy=64)

Bases: Shape

class pyMSDtorch.test_data.twoD.random_shapes.Rectangle(height=0.2, width=0.05, n_xy=64)

Bases: Shape

class pyMSDtorch.test_data.twoD.random_shapes.Shape(n_xy=64)

Bases: object

Base class for shapes.

get_random_rotation()

Rotates the canvas in a random fashion

Returns:

return image in random rotation

class pyMSDtorch.test_data.twoD.random_shapes.Triangle(height=0.25, n_xy=64)

Bases: Rectangle

pyMSDtorch.test_data.twoD.random_shapes.build_random_shape_set(n_train, n_test, n_validate, noise_level=0.1, n_xy=64)

Build 3 h5 files containing test data with random shapes. Uses standard filenames: train_shapes_2d.hdf5, etc etc

Parameters:

  • n_train – number of training images

  • n_test – number of test images

  • n_validate – number of validation images

  • noise_level – noise level

  • n_xy – canvas size

Returns:

pyMSDtorch.test_data.twoD.random_shapes.build_random_shape_set_numpy(n_imgs, noise_level=0.1, n_xy=64)

Build a numpy array with random shapes. Returned is a dictionairy with results

Parameters:

  • n_imgs (number of images) –

  • noise_level (noise level) –

  • n_xy (canvas size) –

Return type:

GroundTruth, Noisy, ClassImage,Label

pyMSDtorch.test_data.twoD.random_shapes.get_random_object(noise_level=0.1, n_xy=64)

Build a random shape with fixed uniform noise level

Parameters:

  • noise_level – uniform noise level

  • n_xy – canvas size

Returns:

a random shape

pyMSDtorch.test_data.twoD.random_shapes.random_circle(radius=(0.1, 0.4), n_xy=64)

Build a random circle

Parameters:

  • radius – radius range

  • n_xy – canvas size

Returns:

a random circle

pyMSDtorch.test_data.twoD.random_shapes.random_donut(radius=None, width=None, n_xy=64)

Build a random donut

Parameters:

  • radius – radius range

  • width – fractional width range, relatrive to radius

  • n_xy – canvas size

Returns:

a random donut

pyMSDtorch.test_data.twoD.random_shapes.random_rectangle(width=(0.1, 0.2), height=(0.3, 0.4), n_xy=64)

Build a rectangle of random size

Parameters:

  • width – width range

  • height – height range

  • n_xy – canvas size

Returns:

a random rectangle

pyMSDtorch.test_data.twoD.random_shapes.random_triangle(width=(0.1, 0.35), n_xy=64)

Build a random right equilateral triangle

Parameters:

  • width – width range

  • n_xy – canvas size

Returns:

a random triangle

pyMSDtorch.test_data.twoD.torch_hdf5_loader module

class pyMSDtorch.test_data.twoD.torch_hdf5_loader.Hdf5Dataset2D(filename, x_label, y_label, transform=None, max_size=None)

Bases: Dataset

A pytorch compatible dataset object with input and output based on a single hdf5 file. To be used in tandem with the pyMSDtorch 2d data generators

class pyMSDtorch.test_data.twoD.torch_hdf5_loader.Hdf5Dataset2DClasses(filename, x_label, y_label, transform=None, max_size=None)

Bases: Dataset

A pytorch compatible dataset object with input and output based on a single hdf5 file. To be used in tandem with the noisy_2d scripts

class pyMSDtorch.test_data.twoD.torch_hdf5_loader.Hdf5Dataset2Dtime(filename, x_label, y_label, transform=None, max_size=None, time_point=None)

Bases: Dataset

A pytorch compatible dataset object with input and output based on a single hdf5 file. To be used with the 2D + time simulation code.

pyMSDtorch.test_data.twoD.torch_hdf5_loader.tst()

pyMSDtorch.test_data.twoD.tst module

pyMSDtorch.test_data.twoD.tst.hello_world(a, b, cc)
Parameters:

  • a –

  • b –

  • cc –

Returns:

Module contents