toupy.io package¶

Submodules¶

toupy.io.dataio module¶

class toupy.io.dataio.LoadData(**params)[source]¶

Bases: PathName, Variables

Load projections from HDF5 file

classmethod load(*args, **params)[source]¶

Load data from h5 file

Parameters

h5name (str) – File name from which data is loaded
**params – Dictionary of additonal parameters
params["autosave"] (bool) – Save the projections once load without asking
params["phaseonly"] (bool) – Load only phase projections. Used when the projections are complex-valued.
params["amponly"] (bool) – Load only amplitude projections. Used when the projections are complex-valued.
params["pixtol"] (float) – Tolerance for alignment, which is also used as a search step
params["alignx"] (bool) – True or False to activate align x using center of mass (default= False, which means align y only)
params["shiftmeth"] (str) – Shift images with fourier method (default). The options are linear -> Shift images with linear interpolation (default); fourier -> Fourier shift or spline -> Shift images with spline interpolation.
params["circle"] (bool) – Use a circular mask to eliminate corners of the tomogram
params["filtertype"] (str) – Filter to use for FBP
params["freqcutoff"] (float) – Frequency cutoff for tomography filter (between 0 and 1)
params["cliplow"] (float) – Minimum value in tomogram
params["cliphigh"] (float) – Maximum value in tomogram
params["correct_bad"] (bool) – If true, it will interpolate bad projections. The numbers of projections to be corrected is given by params[“bad_projs”].
params["bad_projs"] (list of ints) – List of projections to be interpolated. It starts at 0.

Returns

stack_projs (array_like) – Stack of projections
theta (array_like) – Stack of thetas
shiftstack (array_like) – Shifts in vertical (1st dimension) and horizontal (2nd dimension)
datakwargs (dict) – Dictionary with metadata information

classmethod load_olddata(*args, **params)[source]¶

Load old data from h5 file. It should disappear soon.

Parameters

h5name (str) – File name from which data is loaded
**params – Dictionary of additonal parameters
params["autosave"] (bool) – Save the projections once load without asking
params["phaseonly"] (bool) – Load only phase projections. Used when the projections are complex-valued.
params["amponly"] (bool) – Load only amplitude projections. Used when the projections are complex-valued.
params["pixtol"] (float) – Tolerance for alignment, which is also used as a search step
params["alignx"] (bool) – True or False to activate align x using center of mass (default= False, which means align y only)
params["shiftmeth"] (str) – Shift images with fourier method (default). The options are linear -> Shift images with linear interpolation (default); fourier -> Fourier shift or spline -> Shift images with spline interpolation.
params["circle"] (bool) – Use a circular mask to eliminate corners of the tomogram
params["filtertype"] (str) – Filter to use for FBP
params["freqcutoff"] (float) – Frequency cutoff for tomography filter (between 0 and 1)
params["cliplow"] (float) – Minimum value in tomogram
params["cliphigh"] (float) – Maximum value in tomogram
params["correct_bad"] (bool) – If true, it will interpolate bad projections. The numbers of projections to be corrected is given by params[“bad_projs”].
params["bad_projs"] (list of ints) – List of projections to be interpolated. It starts at 0.

Returns

stack_projs (array_like) – Stack of projections
theta (array_like) – Stack of thetas
shiftstack (array_like) – Shifts in vertical (1st dimension) and horizontal (2nd dimension)
datakwargs (dict) – Dictionary with metadata information

classmethod loadmasks(*args, **params)[source]¶

Load masks from previous h5 file

Parameters: h5name (str) – File name from which data is loaded
Returns: masks – Array with the masks
Return type: array_like

classmethod loadshiftstack(*args, **params)[source]¶

Load shitstack from previous h5 file

Parameters: h5name (str) – File name from which data is loaded
Returns: shiftstack – Shifts in vertical (1st dimension) and horizontal (2nd dimension)
Return type: array_like

classmethod loadtheta(*args, **params)[source]¶

Load shitstack from previous h5 file

Parameters: h5name (str) – File name from which data is loaded
Returns: shiftstack – Shifts in vertical (1st dimension) and horizontal (2nd dimension)
Return type: array_like

class toupy.io.dataio.LoadProjections(**params)[source]¶

Bases: PathName, Variables

Load the reconstructed projections from the ptyr files

check_angles()[source]¶: Find the angles of the projections and plot them to be checked Specific to ID16A beamline (ESRF)

check_angles_new()[source]¶: Find the angles of the projections and plot them to be checked Specific to ID16A beamline (ESRF)

static insert_missing(stack_objs, theta, missingnum)[source]¶: Insert missing projections by interpolation of neighbours

classmethod load(**params)[source]¶

Load the reconstructed projections from phase-retrieved files.

Parameters

**params – Container with parameters to load the files.
params["account"] (str) – User experiment number at ESRF.
params["samplename"] (str) – Sample name
params["pathfilename"] (str) – Path to the first projection file.
params["regime"] (str) – Imaging regime. The options are: nearfield, farfield, holoct.
params["showrecons"] (bool) – To show or not the projections once loaded
params["autosave"] (bool) – Save the projections once load without asking
params["phaseonly"] (bool) – Load only phase projections. Used when the projections are complex-valued.
params["amponly"] (bool) – Load only amplitude projections. Used when the projections are complex-valued.
params["border_crop_x"] (int, None) – Amount of pixels to crop at each border in x.
params["border_crop_y"] (int, None) – Amount of pixels to crop at each border in y.
params["checkextraprojs"] (bool) – Check for the projections acquired at and over 180 degrees.
params["missingprojs"] (bool) – Allow to interpolate for missing projections. The numbers of the projections need to be provided in params[“missingnum”].
params["missingnum"] (list of ints) – Numbers of the missing projections to be interpolated.

Returns

stack_objs (array_like) – Array containing the projections
stack_angles (array_like) – Array containing the thetas
pxsize (list of floats) – List containing the pixel size in the vertical and horizontal directions. Typically, the resolution is isotropic and the two values are the same
paramsload (dict) – Parameters of the loading

classmethod loadedf(**params)[source]¶

Load the reconstructed projections from the edf files This is adapted for the phase-contrast imaging generating projections as edf files

Parameters

**params – Container with parameters to load the files.
params["account"] (str) – User experiment number at ESRF.
params["samplename"] (str) – Sample name
params["pathfilename"] (str) – Path to the first projection file.
params["regime"] (str) – Imaging regime. The options are: nearfield, farfield, holoct.
params["showrecons"] (bool) – To show or not the projections once loaded
params["autosave"] (bool) – Save the projections once load without asking

Returns

stack_objs (array_like) – Array containing the projections
stack_angles (array_like) – Array containing the thetas
pxsize (list of floats) – List containing the pixel size in the vertical and horizontal directions. Typically, the resolution is isotropic and the two values are the same
paramsload (dict) – Parameters of the loading

class toupy.io.dataio.LoadTomogram(**params)[source]¶

Bases: LoadData

Load projections from HDF5 file

classmethod load(*args, **params)[source]¶

Load tomographic data from h5 file

Parameters

args[0] (str) – HDF5 file name from which data is loaded

Returns

tomogram (array_like) – Stack of tomographic slices
theta (array_like) – Stack of thetas
shiftstack (array_like) – Shifts in vertical (1st dimension) and horizontal (2nd dimension)
datakwargs (dict) – Dictionary with metadata information

class toupy.io.dataio.PathName(**params)[source]¶

Bases: object

Class to manage file location and paths

datafilewcard()[source]¶: Create file wildcard to search for files

metadatafilewcard()[source]¶: Create file wildcard to search for metafiles

results_datapath(h5name)[source]¶: create path for the h5file in result folder

results_folder()[source]¶: create path for the result folder

search_projections()[source]¶: Search for projection given the filenames

class toupy.io.dataio.SaveData(**params)[source]¶

Bases: PathName, Variables

Save projections to HDF5 file

classmethod save(*args, **params)[source]¶

Save data to HDF5 File

Parameters

*args – positional arguments
args[0] (str) – H5 file name
args[1] (array_like) – Array containing the stack of projections
args[2] (array_like) – Values of theta
args[3] (array_like) – Array containing the shifts for each projection in the stack. If not provided, it will be initialized with zeros
args[4] (array_like or None) – Array containing the projection masks

classmethod saveFSC(*args, **params)[source]¶

Save FSC data to HDF5 file

Parameters

*args – positional arguments
args[0] (str) – H5 file name
args[1] (array_like) – Normalized frequencies
args[2] (array_like) – Value of the threshold for each frequency
args[3] (array_like) – The FSC curve
args[4] (array_like) – The first tomogram
args[5] (array_like) – The second tomogram
args[6] (array_like) – The array of theta values
args[7] (float) – Pixel size

savecheck()[source]¶: Decorator for save data

classmethod savemasks(*args, **params)[source]¶

class toupy.io.dataio.SaveTomogram(**params)[source]¶

Bases: SaveData

Save tomogram to HDF5 file

classmethod convert_to_tiff(*args, **params)[source]¶

Convert the HDF5 file with the tomogram to tiff

Parameters

*args – positional arguments
args[0] (str) – H5 file name
args[1] (array_like) – Array containing the stack of slices (tomogram)
args[2] (array_like) – Values of theta
args[3] (array_like) – Array containing the shifts for each projection in the stack

classmethod save(*args, **params)[source]¶

Parameters

*args – positional arguments
args[0] (str) – H5 file name
args[1] (array_like) – Array containing the stack of slices (tomogram)
args[2] (array_like) – Values of theta
args[3] (array_like) – Array containing the shifts for each projection in the stack

classmethod save_vol_to_h5(*args, **params)[source]¶

savecheck()[source]¶: Decorator for save data

tiff_folderpath(foldername)[source]¶: Create the path to the folder in which the tiff files will be stored.

toupy.io.dataio.remove_extraprojs(stack_projs, theta)[source]¶

Remove extra projections of tomographic scans with projections at 180, 90 and 0 degrees at the end

Parameters

stack_projs (array_like) – Stack of projections with the first index correspoding to the projection number
theta (array_like) – Array of theta values

Returns

stack_projs (array_like) – Stack of projections after the removal
theta (array_like) – Array of theta values after the removal

toupy.io.filesrw module¶

Files read and write

toupy.io.filesrw.convert16bitstiff(tiffimage, low_cutoff, high_cutoff)[source]¶

Convert 16 bits tiff files back to quantitative values.

Parameters

imgpath (array_like) – Image read from 16 bits tiff file.
low_cutoff (float) – Low cutoff of the gray level.
high_cutoff (float) – High cutoff of the gray level.

Returns

tiffimage – Array containing the image with quantitative values.

Return type

array_like

toupy.io.filesrw.convert8bitstiff(filename, low_cutoff, high_cutoff)[source]¶

Convert 8bits tiff files back to quantitative values.

Parameters

imgpath (array_like) – Image read from 8 bits tiff file.
low_cutoff (float) – Low cutoff of the gray level.
high_cutoff (float) – High cutoff of the gray level.

Returns

tiffimage – Array containing the image with quantitative values.

Return type

array_like

toupy.io.filesrw.convertimageto16bits(input_image, low_cutoff, high_cutoff)[source]¶

Convert image gray-level to 16 bits with normalization

Parameters

input_image (array_like) – Input image to be converted.
low_cutoff (float) – Low cutoff of the gray level.
high_cutoff (float) – High cutoff of the gray level.

Returns

tiffimage – Array containing the image at 16 bits.

Return type

array_like

toupy.io.filesrw.convertimageto8bits(input_image, low_cutoff, high_cutoff)[source]¶

Convert image gray-level to 8 bits with normalization.

Parameters

input_image (array_like) – Input image to be converted.
low_cutoff (float) – Low cutoff of the gray level.
high_cutoff (float) – High cutoff of the gray level.

Returns

tiffimage – Array containing the image at 8 bits.

Return type

array_like

toupy.io.filesrw.create_paramsh5(**params)[source]¶

Create parameter file in HDF5 format

Parameters: params (dict) – Dictionary containing the parameters to be saved

toupy.io.filesrw.crop_array(input_array, delcropx, delcropy)[source]¶

Crop borders from 2D arrays

Parameters

input_array (array_like) – Input array to be cropped
delcropx (int) – Number of pixels to be crop from borders in x and y directions
delcropy (int) – Number of pixels to be crop from borders in x and y directions

Returns

cropped_array – Cropped array

Return type

array_like

toupy.io.filesrw.load_paramsh5(**params)[source]¶: Load parameters from HDF5 file of parameters

toupy.io.filesrw.memmap_volfile(filename)[source]¶

Memory map the tomogram from .vol file

Parameters

filename (str) – filename to be read

Returns

tomogram (array_like) – 3D array containing the tomogram
voxelSize (floats) – Voxel size in meters
arrayshape (tuple of floats) – The array shape: (x_size, y_size, z_size)

Examples

>>> volpath = 'volfilename.vol'
>>> tomogram,voxelsize,arrayshape = memmap_volfile(volpath)

Note

The volume info file containing the metadata of the volume should be in the same folder as the volume file.

toupy.io.filesrw.read_cxi(pathfilename, correct_orientation=True)[source]¶

Read reconstruction files .cxi from PyNX

Parameters

pathfilename (str) – Path to file
correct_orientation (bool) – True for correcting the image orientation and False to keep as it is. The default value is True.

Returns

data1 (array_like, complex) – Object image
probe1 (array_like, complex) – Probe images
pixelsize (list of floats) – List with pixelsizes in vertical and horizontal directions
energy (float) – Energy of the incident photons

Examples

>>> imgpath = 'filename.cxi'
>>> objdata, probedata, pixel, energy = read_cxi(imgpath)

toupy.io.filesrw.read_edf(fname)[source]¶

Read EDF files of tomographic datasets

Parameters

fname (str) – Path to file

Returns

projs (array_like) – Array of projections
pixelsize (list of floats) – List with pixelsizes in vertical and horizontal directions
energy (float) – Energy of the incident photons
nvue (int) – Number of projections

toupy.io.filesrw.read_ptyr(pathfilename, correct_orientation=True)[source]¶

Read reconstruction files .ptyr from Ptypy

Parameters

pathfilename (str) – Path to file
correct_orientation (bool, optional) – True for correcting the image orientation and False to keep as it is. The default value is True.

Returns

data1 (array_like, complex) – Object image
probe1 (array_like, complex) – Probe images
pixelsize (list of floats) – List with pixelsizes in vertical and horizontal directions
energy (float) – Energy of the incident photons

Examples

>>> imgpath = 'filename.ptyr'
>>> objdata, probedata, pixel, energy = read_ptyr(imgpath)

toupy.io.filesrw.read_recon(filename, correct_orientation=False)[source]¶

Wrapper for choosing the function to read recon file

Parameters

pathfilename (str) – Path to file
correct_orientation (bool, optional) – True for correcting the image orientation and False to keep as it is. The default value is False.

Returns

data1 (array_like, complex) – Object image
probe1 (array_like, complex) – Probe images
pixelsize (list of floats) – List with pixelsizes in vertical and horizontal directions
energy (float) – Energy of the incident photons

Examples

>>> imgpath = 'filename.ptyr'
>>> objdata, probedata, pixel, energy = read_recon(imgpath)

toupy.io.filesrw.read_theta_raw(pathfilename, detector='Frelon')[source]¶

Auxiliary function to read theta from raw data acquired at ID16A

Parameters: pathfilename (str) – Path to file
Returns: theta – Tomographic angle
Return type: float

Examples

>>> imgpath = 'filename.h5'
>>> theta = read_theta_raw(imgpath)

toupy.io.filesrw.read_theta_recon(reconfile)[source]¶

Auxiliary function to read theta from recon files

Parameters: reconfile (str) – Path to recon file
Returns: theta – Tomographic angle
Return type: float

Examples

>>> imgpath = 'filename.ptyr'
>>> theta = read_theta_recon(imgpath)

toupy.io.filesrw.read_tiff(imgpath)[source]¶

Read tiff files using skimage.io.imread

Parameters: imgpath (str) – Path to tiff file with extension
Returns: imgout – Array containing the image
Return type: array_like

Examples

>>> imgpath = 'image.tiff'
>>> ar = read_tiff(imgpath)
>>> ar.dtype
dtype('uint16')
>>> np.max(ar)
65535

toupy.io.filesrw.read_tiff_info(tiff_info_file)[source]¶

Read info file from tiff slices of the reconstructed tomographic volume

Parameters

tiff_info_file (str) – Info filename

Returns

low_cutoff (float) – Low cutoff of the gray level
high_cutoff (float) – High cutoff of the gray level
pixelsize (float) – Pixelsize in nanometers

Note

The info file here is the file that is save when the volume is exported to Tiff files. It is not the info file saved by the volume reconstruction when saving the file in .vol.

toupy.io.filesrw.read_volfile(filename)[source]¶

Read tomogram from .vol file

Parameters

filename (str) – filename to be read

Returns

tomogram (array_like) – 3D array containing the tomogram
voxelsize (floats) – Voxel size in meters
arrayshape (tuple of floats) – The array shape: (x_size, y_size, z_size)

Examples

>>> volpath = 'volfilename.vol'
>>> tomogram,voxelsize,arrayshape = read_volfile(volpath)

Note

The volume info file containing the metadata of the volume should be in the same folder as the volume file.

toupy.io.filesrw.write_edf(fname, data_array, hd=None)[source]¶

Write EDF files

Parameters

fname (str) – File name
data_array (array_like) – Data to be saved as edf
hd (dict) – Dictionary with header information

toupy.io.filesrw.write_paramsh5(h5filename, **params)[source]¶

Writes params to HDF5 file

Parameters

h5filename (str) – Filename of the params file
params (dict) – Dictionary containing the parameters to be saved

toupy.io.filesrw.write_tiff(input_array, pathfilename, plugin='tifffile')[source]¶

Write tiff files using skimag.io.imsave

Parameters

input_array (array_like) – Input array to be saved
pathfilename (str) – Path and filename to save the file

toupy.io.filesrw.write_tiffmetadata(filename, low_cutoff, high_cutoff, factor, **params)[source]¶

Creates a txt file with the information about the Tiff normalization

Parameters

filename (str) – Filename to save the file.
low_cutoff (float) – Low cutoff value for the tiff normalization.
high_cutoff (float) – High cutoff value for the tiff normalization.
factor (float) – Multiplicative factor in case it is needed.
params (dict) – Dictionary of additional parameters.
params["voxelsize"] (float) – Voxel size.
params["filtertype"] (str) – Filter used in the tomographic reconstruction.
params["freqcutoff"] (float) – Frequency cutoff used in the tomographic reconstruction.
params["bits"] (int) – The tiff type. Options: 8 for 8 bits or 16 for 16 bits.

toupy.io.h5chunk_shape_3D module¶

toupy.io.h5chunk_shape_3D.__all__ = ['binlist', 'numVals', 'perturbShape', 'chunk_shape_3D']¶

toupy.io.h5chunk_shape_3D.binlist(n, width=0)[source]¶

Return list of bits that represent a non-negative integer.

Parameters

n (int) – non-negative integer
width (int) – number of bits in returned zero-filled list (default 0)

toupy.io.h5chunk_shape_3D.chunk_shape_3D(varShape, valSize=4, chunkSize=4096)[source]¶

Return a ‘good shape’ for a 3D variable, assuming balanced 1D/(n-1)D access 1

Parameters

varShape (sequence of ints) – length 3 list of variable dimension sizes
chunkSize (int, optional) – maximum chunksize desired, in bytes (default 4096)
valSize (int, optional) – size of each data value, in bytes (default 4)

Returns

Returns integer chunk lengths of a chunk shape that provides balanced access of 1D subsets and 2D subsets of a netCDF or HDF5 variable var with shape (T, X, Y), where the 1D subsets are of the form var[:,x,y] and the 2D slices are of the form var[t,:,:], typically 1D time series and 2D spatial slices.

Return type

tuple

Notes

‘Good shape’ for chunks means that the number of chunks accessed to read either kind of 1D or 2D subset is approximately equal, and the size of each chunk (uncompressed) is no more than chunkSize, which is often a disk block size. Code fetched from 2 and 3.

References

1: https://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_choosing_shapes
2: https://www.unidata.ucar.edu/blog_content/data/2013/chunk_shape_3D.py
3: https://github.com/HDFGroup/datacontainer/blob/master/lib/chunking.py

toupy.io.h5chunk_shape_3D.numVals(shape)[source]¶

Return number of values in chunk of specified shape, given by a list of dimension lengths.

Parameters: shape (sequence of ints) – list of variable dimension sizes

toupy.io.h5chunk_shape_3D.perturbShape(shape, onbits)[source]¶

Return shape perturbed by adding 1 to elements corresponding to 1 bits in onbits

Parameters

shape (sequence of ints) – list of variable dimension sizes
onbits (int) – non-negative integer less than 2**len(shape)