Reference

LiberTEM-blobfinder is structured into three parts:

  1. A “base” package with numerics functions that work independent of LiberTEM.

  2. A “common” package that uses other “common” aspects of LiberTEM for convenience, but can be used independent of LiberTEM core facilities.

  3. A “udf” package with classes and functions to use this functionality with full LiberTEM integration.

Basic numerics functions

These functions work independent of any LiberTEM infrastructure.

libertem_blobfinder.base.correlation.allocate_crop_bufs(crop_size, n_peaks, dtype, limit=524288)[source]

Allocate buffer for stack of cropped peaks

The size is optimized to fit within limit. An aligned buffer for the FFT back-end is created if possible.

Parameters
  • crop_size (int) – The cropped parts will have size (2 * crop-size, 2 * crop_size)

  • n_peaks (int) – Number of peaks

  • dtype (numpy.dtype) – dtype of the buffer

  • limit (int, optional) – Upper limit, default 1/2 MB to be L3 cache friendly

Returns

crop_bufs – Shape (n, 2*crop_size, 2*crop_size)

Return type

np.ndarray

libertem_blobfinder.base.correlation.do_correlations(template, crop_parts)[source]

Calculate the correlation of the pre-calculated template with a stack of cropped peaks using fast correlation.

Parameters
  • template (numpy.ndarray) – Real Fourier transform of the correlation pattern. The source pattern should have the same size as the cropped parts. Please note that the real Fourier transform (fft.rfft2) of the source pattern has a different shape!

  • crop_parts (numpy.ndarray) – Stack of peaks cropped from the frame.

Returns

corrs – Correlation of the correlation pattern and the peaks.

Return type

numpy.ndarray

libertem_blobfinder.base.correlation.get_buf_count(crop_size, n_peaks, dtype, limit=524288)[source]

Calculate the optimal number of peaks in a stack to fit within the limit.

Parameters
  • crop_size (int) – The cropped parts will have size (2 * crop-size, 2 * crop_size)

  • n_peaks (int) – Number of peaks

  • dtype (numpy.dtype) – dtype of the data for size calculation

  • limit (int, optional) – Upper limit, default 1/2 MB to be L3 cache friendly

Returns

Return type

int

libertem_blobfinder.base.correlation.peak_elevation[source]

Return the slope of the tightest cone around center with height height that touches corrmap between r_min and r_max.

The correlation of two disks – mask and perfect diffraction spot – has the shape of a cone. The function’s return value correlates with the quality of a correlation. Higher slope means a strong peak and no side maxima, while weak signal or side maxima lead to a flatter slope.

Parameters
  • center (numpy.ndarray) – (y, x) coordinates of the center within the corrmap

  • corrmap (numpy.ndarray) – Correlation map

  • height (float) – The height is provided as a parameter since center can be float values from refinement and the height value is conveniently available from the calling function.

  • r_min (float, optional) – Masks out a small local plateau around the peak that would distort and dominate the calculation.

  • r_max (float, optional) – Mask out neighboring peaks if a large area with several legitimate peaks is correlated.

Returns

elevation – Elevation of the tightest cone that fits the correlation map within the given parameter range.

Return type

float

libertem_blobfinder.base.correlation.process_frame_fast(template, crop_size, frame, peaks, out_centers, out_refineds, out_heights, out_elevations, crop_bufs)[source]

Find the parameters of peaks in a diffraction pattern by correlation with a template

This function is designed to be used in an optimized pipeline with a pre-calculated Fourier transform of the match pattern and optional pre-allocated buffers. It is the engine of the libertem_blobfinder.udf.correlation.FastCorrelationUDF for stand-alone use independent of LiberTEM.

libertem_blobfinder.common.correlation.process_frames_fast() offers a more convenient interface for batch processing.

Parameters
  • template (numpy.ndarray) – Real Fourier transform of the correlation pattern. The source pattern should have size (2 * crop_size, 2 * crop_size). Please note that the real Fourier transform (fft.rfft2) of the source pattern has a different shape!

  • crop_size (int) – Half the size of the correlation pattern. Given as a parameter since real Fourier transform changes the size.

  • frame (np.ndarray) – Frame data. Currently, only Real values are supported.

  • peaks (np.ndarray) – List of peaks of shape (n_peaks, 2)

  • out_centers (np.ndarray) – Output buffer for center positions of shape (n_peaks, 2) and integer dtype.

  • out_refineds (np.ndarray) – Output buffer for refined center positions of shape (n_peaks, 2) and float dtype.

  • out_heights (np.ndarray) – Output buffer for peak height in log scaled frame. Shape (n_peaks, ) and float dtype.

  • out_elevations (np.ndarray) – Output buffer for peak elevation in log scaled frame. Shape (n_peaks, ) and float dtype.

  • crop_bufs (np.ndarray) – Aligned buffer for pyfftw. Shape (n, 2 * crop_size, 2 * crop_size) and float dtype. n doesn’t have to match the number of peaks. Instead, it should be chosen for good L3 cache efficiency. allocate_crop_bufs() can be used to allocate this buffer.

Returns

The values are placed in the provided output buffers.

Return type

None

Example

>>> from libertem_blobfinder.common.patterns import RadialGradient
>>> from libertem_blobfinder.base.correlation import allocate_crop_bufs
>>>
>>> frames, indices, peaks = libertem.utils.generate.cbed_frame(radius=4)
>>> pattern = RadialGradient(radius=4)
>>> crop_size = pattern.get_crop_size()
>>> template = pattern.get_template(sig_shape=(2 * crop_size, 2 * crop_size))
>>>
>>> centers = np.zeros((len(frames), len(peaks), 2), dtype=np.uint16)
>>> refineds = np.zeros((len(frames), len(peaks), 2), dtype=np.float32)
>>> heights = np.zeros((len(frames), len(peaks)), dtype=np.float32)
>>> elevations = np.zeros((len(frames), len(peaks)), dtype=np.float32)
>>>
>>> crop_bufs = allocate_crop_bufs(crop_size, len(peaks), frames.dtype)
>>>
>>> for i, f in enumerate(frames):
...     process_frame_fast(
...         template=template, crop_size=crop_size,
...         frame=f, peaks=peaks.astype(np.int32),
...         out_centers=centers[i], out_refineds=refineds[i],
...         out_heights=heights[i], out_elevations=elevations[i],
...         crop_bufs=crop_bufs
...     )
>>> assert np.allclose(refineds[0], peaks, atol=0.1)
libertem_blobfinder.base.correlation.process_frame_full(template, crop_size, frame, peaks, out_centers=None, out_refineds=None, out_heights=None, out_elevations=None, frame_buf=None, buf_count=None)[source]

Find the parameters of peaks in a diffraction pattern by correlation with a template

This function is designed to be used in an optimized pipeline with a pre-calculated Fourier transform of the match pattern and optional pre-allocated buffers. It is the engine of the libertem_blobfinder.udf.correlation.FullFrameCorrelationUDF for stand-alone use independent of LiberTEM.

libertem_blobfinder.common.correlation.process_frames_full() offers a more convenient interface for batch processing.

Parameters
  • template (numpy.ndarray) – Real Fourier transform of the correlation pattern. The source pattern should have size (2 * crop_size, 2 * crop_size). Please note that the real Fourier transform (fft.rfft2) of the source pattern has a different shape!

  • crop_size (int) – Half the size of the correlation pattern. Given as a parameter since real Fourier transform changes the size.

  • frame (np.ndarray) – Frame data. Currently, only real values are supported.

  • peaks (np.ndarray) – List of peaks of shape (n_peaks, 2)

  • out_centers (np.ndarray, optional) – Output buffer for center positions of shape (n_peaks, 2) and integer dtype. Will be allocated if needed.

  • out_refineds (np.ndarray, optional) – Output buffer for refined center positions of shape (n_peaks, 2) and float dtype. Will be allocated if needed.

  • out_heights (np.ndarray, optional) – Output buffer for peak height in log scaled frame. Shape (n_peaks, ) and float dtype. Will be allocated if needed.

  • out_elevations (np.ndarray, optional) – Output buffer for peak elevation in log scaled frame. Shape (n_peaks, ) and float dtype. Will be allocated if needed.

  • frame_buf (np.ndarray) – Aligned buffer for FFT back-end, such as pyfftw. Shape of a frame and float dtype. libertem_blobfinder.base.correlation.zero() can be used.

  • buf_count (int) – Number of peaks to process per outer loop iteration. This allows optimization of L3 cache efficiency.

Returns

The values are placed in the provided output buffers.

Return type

None

Example

>>> from libertem_blobfinder.common.patterns import RadialGradient
>>> from libertem_blobfinder.base.correlation import get_buf_count, zeros
>>>
>>> frames, indices, peaks = libertem.utils.generate.cbed_frame()
>>> pattern = RadialGradient(radius=4)
>>> crop_size = pattern.get_crop_size()
>>> template = pattern.get_template(sig_shape=frames[0].shape)
>>>
>>> centers = np.zeros((len(frames), len(peaks), 2), dtype=np.uint16)
>>> refineds = np.zeros((len(frames), len(peaks), 2), dtype=np.float32)
>>> heights = np.zeros((len(frames), len(peaks)), dtype=np.float32)
>>> elevations = np.zeros((len(frames), len(peaks)), dtype=np.float32)
>>>
>>> frame_buf = zeros(frames[0].shape, dtype=np.float32)
>>> buf_count = get_buf_count(crop_size, len(peaks), frame_buf.dtype)
>>>
>>> for i, f in enumerate(frames):
...     process_frame_full(
...         template=template, crop_size=crop_size,
...         frame=f, peaks=peaks.astype(np.int32),
...         out_centers=centers[i], out_refineds=refineds[i],
...         out_heights=heights[i], out_elevations=elevations[i],
...         frame_buf=frame_buf, buf_count=buf_count
...     )
>>> assert np.allclose(refineds[0], peaks, atol=0.1)

Common classes and functions

These functions and classes depend on other LiberTEM “common” packages, but can be used without the LiberTEM core infrastructure.

class libertem_blobfinder.common.patterns.BackgroundSubtraction(radius, search=None, radius_outer=None)[source]

Solid circular disk surrounded with a balancing negative area

This pattern rejects background and avoids false positives at positions between peaks

__init__(radius, search=None, radius_outer=None)[source]
Parameters
  • radius (float) – Radius of the circular pattern in px

  • search (float, optional) – Range from the center point in px to include in the correlation. max(2*radius, radius_outer) by default. Defining the size of the square correlation pattern.

  • radius_outer (float, optional) – Radius of the negative region in px. 1.5x radius by default.

class libertem_blobfinder.common.patterns.Circular(radius, search=None)[source]

Circular pattern with radius radius.

This pattern is useful for constructing feature vectors using feature_vector().

New in version 0.3.0.

__init__(radius, search=None)[source]
Parameters
  • radius (float) – Radius of the circular pattern in px

  • search (float, optional) – Range from the center point in px to include in the correlation, 2x radius by default. Defining the size of the square correlation pattern.

class libertem_blobfinder.common.patterns.MatchPattern(search)[source]

Abstract base class for correlation patterns.

This class provides an API to provide a template for fast correlation-based peak finding.

__init__(search)[source]
Parameters

search (float) – Range from the center point in px to include in the correlation, defining the size of the square correlation pattern. Will be ceiled to the next int for performing the correlation.

class libertem_blobfinder.common.patterns.RadialGradient(radius, search=None)[source]

Radial gradient from zero in the center to one at radius.

This pattern rejects the influence of internal intensity variations of the CBED disk.

__init__(radius, search=None)[source]
Parameters
  • radius (float) – Radius of the circular pattern in px

  • search (float, optional) – Range from the center point in px to include in the correlation, 2x radius by default. Defining the size of the square correlation pattern.

class libertem_blobfinder.common.patterns.RadialGradientBackgroundSubtraction(radius, search=None, radius_outer=None, delta=1, radial_map=None)[source]

Combination of radial gradient with background subtraction

__init__(radius, search=None, radius_outer=None, delta=1, radial_map=None)[source]

See radial_gradient_background_subtraction() for details.

Parameters
  • radius (float) – Radius of the circular pattern in px

  • search (float, optional) – Range from the center point in px to include in the correlation. max(2*radius, radius_outer) by default Defining the size of the square correlation pattern.

  • radius_outer (float, optional) – Radius of the negative region in px. 1.5x radius by default.

  • delta (float, optional) – Width of the transition region between positive and negative in px

  • radial_map (numpy.ndarray, optional) – Radius value of each pixel in px. This can be used to distort the shape as needed or work in physical coordinates instead of pixels. A suitable map can be generated with libertem.masks.polar_map().

Example

>>> import matplotlib.pyplot as plt
>>> (radius, phi) = libertem.masks.polar_map(
...     centerX=64, centerY=64,
...     imageSizeX=128, imageSizeY=128,
...     stretchY=2., angle=np.pi/4
... )
>>> template = RadialGradientBackgroundSubtraction(
...     radius=30, radial_map=radius)
>>> # This shows an elliptical template that is stretched
>>> # along the 45 ° bottom-left top-right diagonal
>>> plt.imshow(template.get_mask(sig_shape=(128, 128)))
<matplotlib.image.AxesImage object at ...>
>>> plt.show() 
class libertem_blobfinder.common.patterns.UserTemplate(template, search=None)[source]

User-defined template

__init__(template, search=None)[source]
Parameters
  • template (numpy.ndarray) – Correlation template as 2D numpy.ndarray

  • search (float, optional) – Range from the center point in px to include in the correlation. Half diagonal of the template by default. Defining the size of the square correlation pattern.

libertem_blobfinder.common.patterns.feature_vector(imageSizeX, imageSizeY, peaks, match_pattern: libertem_blobfinder.common.patterns.MatchPattern)[source]

This function generates a sparse mask stack to extract a feature vector.

A match template based on the parameters in parameters is placed at each peak position in an individual mask layer. This mask stack can then be used in libertem.udf.masks.ApplyMasksUDF to generate a feature vector for each frame.

Summing up the mask stack along the first axis generates a mask that can be used for virtual darkfield imaging of all peaks together.

Parameters
  • imageSizeX,imageSizeY (int) – Frame size in px

  • peaks (numpy.ndarray) – Peak positions in px as numpy.ndarray of shape (n, 2) with integer type

  • match_pattern (MatchPattern) – Instance of MatchPattern

libertem_blobfinder.common.correlation.get_correlation(sum_result, match_pattern: libertem_blobfinder.common.patterns.MatchPattern)[source]

Calculate the correlation between sum_result and match_pattern.

New in version 0.4.0.dev0.

Parameters
  • sum_result (numpy.ndarray) – 2D result frame as correlation input

  • match_pattern (MatchPattern) – Instance of MatchPattern to correlate sum_result with

libertem_blobfinder.common.correlation.get_peaks(sum_result, match_pattern: libertem_blobfinder.common.patterns.MatchPattern, num_peaks)[source]

Find peaks of the correlation between sum_result and match_pattern.

The result can then be used as input to full_match() to extract grid parameters, run_fastcorrelation() to find the position in each frame or to construct a mask to extract feature vectors with feature_vector().

Parameters
  • sum_result (numpy.ndarray) – 2D result frame as correlation input

  • match_pattern (MatchPattern) – Instance of MatchPattern to correlate sum_result with

  • num_peaks (int) – Number of peaks to find

Example

>>> frame, _, _ = libertem.utils.generate.cbed_frame(radius=4)
>>> pattern = libertem_blobfinder.common.patterns.RadialGradient(radius=4)
>>> peaks = get_peaks(frame[0], pattern, 7)
>>> print(peaks)
[[64 64]
 [64 80]
 [80 80]
 [80 64]
 [48 80]
 [48 64]
 [64 96]]
libertem_blobfinder.common.correlation.process_frames_fast(pattern: libertem_blobfinder.common.patterns.MatchPattern, frames, peaks)[source]

Find the parameters of peaks in a diffraction pattern by correlation with a match pattern.

This method crops regions of interest around the peaks from the frames before correlation, which is usually fastest for a moderate amount of moderately sized peaks per frame.

Note

FastCorrelationUDF is a parallelized, distributed version for large-scale data.

Parameters
  • pattern (MatchPattern) – Pattern to correlate with.

  • frames (np.ndarray) – Frame data. Currently, only Real values are supported.

  • peaks (np.ndarray) – List of peaks of shape (n_peaks, 2)

Returns

  • centers (np.ndarray) – Center positions of shape (n_peaks, 2) and integer dtype.

  • refineds (np.ndarray) – Refined center positions of shape (n_peaks, 2) and float dtype.

  • heights (np.ndarray) – Peak height in log scaled frame. Shape (n_peaks, ) and float dtype.

  • elevations (np.ndarray) – Peak elevation in log scaled frame. Shape (n_peaks, ) and float dtype

Example

>>> frames, indices, peaks = libertem.utils.generate.cbed_frame()
>>> pattern = libertem_blobfinder.common.patterns.RadialGradient(radius=4)
>>> (centers, refineds, heights, elevations) = process_frames_fast(
...     pattern=pattern,
...     frames=frames,
...     peaks=peaks.astype(np.int32),
... )
>>> assert np.allclose(refineds[0], peaks, atol=0.1)
libertem_blobfinder.common.correlation.process_frames_full(pattern: libertem_blobfinder.common.patterns.MatchPattern, frames, peaks)[source]

Find the parameters of peaks in a diffraction pattern by correlation with a match pattern.

This method crops regions of interest around the peaks after correlation, which can be faster for many peaks on smaller frames.

Note

FullFrameCorrelationUDF is a parallelized, distributed version for large-scale data.

Parameters
  • pattern (MatchPattern) – Pattern to correlate with.

  • frame (np.ndarray) – Frame data. Currently, only real values are supported.

  • peaks (np.ndarray) – List of peaks of shape (n_peaks, 2)

Returns

  • centers (np.ndarray) – Center positions of shape (n_peaks, 2) and integer dtype.

  • refineds (np.ndarray) – Refined center positions of shape (n_peaks, 2) and float dtype.

  • heights (np.ndarray) – Peak height in log scaled frame. Shape (n_peaks, ) and float dtype.

  • elevations (np.ndarray) – Peak elevation in log scaled frame. Shape (n_peaks, ) and float dtype

Example

>>> frames, indices, peaks = libertem.utils.generate.cbed_frame(radius=4)
>>> pattern = libertem_blobfinder.common.patterns.RadialGradient(radius=4)
>>> (centers, refineds, heights, elevations) = process_frames_full(
...     pattern=pattern,
...     frames=frames,
...     peaks=peaks.astype(np.int32)
... )
>>> assert np.allclose(refineds[0], peaks, atol=0.1)

User-defined functions

These functions and classes depend on LiberTEM core infrastructure.

Correlation

UDFs and utility functions to find peaks and refine their positions by using correlation.

class libertem_blobfinder.udf.correlation.CorrelationUDF(peaks, *args, **kwargs)[source]

Bases: libertem.udf.base.UDF

Abstract base class for peak correlation implementations

__init__(peaks, *args, **kwargs)[source]
Parameters

peaks (numpy.ndarray) – Numpy array of (y, x) coordinates with peak positions in px to correlate

get_result_buffers()[source]

The common buffers for all correlation methods.

centers:

(y, x) integer positions.

refineds:

(y, x) positions with subpixel refinement.

peak_values:

Peak height in the log scaled frame.

peak_elevations:

Peak quality (result of peak_elevation()).

See source code for details of the buffer declaration.

output_buffers()[source]

This function allows abstraction of the result buffers from the default implementation in get_result_buffers().

Override this function if you wish to redirect the results to different buffers, for example ragged arrays or binned processing.

class libertem_blobfinder.udf.correlation.FastCorrelationUDF(*args, **kwargs)[source]

Bases: libertem_blobfinder.udf.correlation.CorrelationUDF

Fourier-based fast correlation-based refinement of peak positions within a search frame for each peak.

__init__(*args, **kwargs)[source]
Parameters
  • peaks (numpy.ndarray) – Numpy array of (y, x) coordinates with peak positions in px to correlate

  • match_pattern (MatchPattern) – Instance of MatchPattern

class libertem_blobfinder.udf.correlation.FullFrameCorrelationUDF(*args, **kwargs)[source]

Bases: libertem_blobfinder.udf.correlation.CorrelationUDF

Fourier-based correlation-based refinement of peak positions within a search frame for each peak using a single correlation step. This can be faster for correlating a large number of peaks in small frames in comparison to FastCorrelationUDF. However, it is more sensitive to interference from strong peaks next to the peak of interest.

New in version 0.3.0.

__init__(*args, **kwargs)[source]
Parameters
  • peaks (numpy.ndarray) – Numpy array of (y, x) coordinates with peak positions in px to correlate

  • match_pattern (MatchPattern) – Instance of MatchPattern

class libertem_blobfinder.udf.correlation.SparseCorrelationUDF(*args, **kwargs)[source]

Bases: libertem_blobfinder.udf.correlation.CorrelationUDF

Direct correlation using sparse matrices

This method allows to adjust the number of correlation steps independent of the template size.

__init__(*args, **kwargs)[source]
Parameters
  • peaks (numpy.ndarray) – Numpy array of (y, x) coordinates with peak positions in px to correlate

  • match_pattern (MatchPattern) – Instance of MatchPattern

  • steps (int) – The template is correlated with 2 * steps + 1 symmetrically around the peak position in x and y direction. This defines the maximum shift that can be detected. The number of calculations grows with the square of this value, that means keeping this as small as the data allows speeds up the calculation.

get_result_buffers()[source]

This method adds the corr buffer to the result of CorrelationUDF.get_result_buffers(). See source code for the exact buffer declaration.

postprocess()[source]

The correlation results are evaluated during postprocessing since this implementation uses tiled processing where the correlations are incomplete in process_tile().

libertem_blobfinder.udf.correlation.run_blobfinder(ctx, dataset, match_pattern: libertem_blobfinder.common.patterns.MatchPattern, num_peaks, roi=None)[source]

Wrapper function to find peaks in a dataset and refine their position using FastCorrelationUDF

Parameters
Returns

  • sum_result (numpy.ndarray) – Log-scaled sum frame of the dataset/ROI

  • centers, refineds, peak_values, peak_elevations (libertem.common.buffers.BufferWrapper) – See CorrelationUDF.get_result_buffers() for details.

  • peaks (numpy.ndarray) – List of found peaks with (y, x) coordinates

libertem_blobfinder.udf.correlation.run_fastcorrelation(ctx, dataset, peaks, match_pattern: libertem_blobfinder.common.patterns.MatchPattern, roi=None)[source]

Wrapper function to construct and run a FastCorrelationUDF

Parameters
Returns

buffers – See CorrelationUDF.get_result_buffers() for details.

Return type

Dict[libertem.common.buffers.BufferWrapper]

Refinement

UDFs and utility functions to refine grid parameters from peak positions.

class libertem_blobfinder.udf.refinement.AffineMixin(*args, **kwargs)[source]

Bases: libertem_blobfinder.udf.refinement.RefinementMixin

Refinement using affinematch()

__init__(*args, **kwargs)[source]
Parameters
  • matcher (libertem.analysis.gridmatching.Matcher) – Instance of Matcher

  • indices (numpy.ndarray) – List of indices [(h1, k1), (h2, k2), …] of all peaks. The indices can be non-integer and relative to any base vectors, including virtual ones like (1, 0); (0, 1). See documentation of affinematch() for details.

class libertem_blobfinder.udf.refinement.FastmatchMixin(*args, **kwargs)[source]

Bases: libertem_blobfinder.udf.refinement.RefinementMixin

Refinement using fastmatch()

__init__(*args, **kwargs)[source]
Parameters
  • matcher (libertem.analysis.gridmatching.Matcher) – Instance of Matcher

  • start_zero (numpy.ndarray) – Approximate value (y, x) in px for “zero” point (origin, zero order peak)

  • start_a (numpy.ndarray) – Approximate value (y, x) in px for “a” vector.

  • start_b (numpy.ndarray) – Approximate value (y, x) in px for “b” vector.

class libertem_blobfinder.udf.refinement.RefinementMixin[source]

Bases: object

To be combined with a libertem_blobfinder.CorrelationUDF using multiple inheritance.

The mixin must come before the UDF in the inheritance list.

The subclasses implement a postprocess method that calculates a refinement of start_zero, start_a and start_b based on the correlation result and populates the appropriate result buffers with this refinement result.

This allows combining arbitrary implementations of correlation-based matching with arbitrary implementations of the refinement by declaring an ad-hoc class that inherits from one subclass of RefinementMixin and one subclass of CorrelationUDF.

apply_match(index, match)[source]

Override this method to change how a match is saved in the result buffers, for example to support binned processing or ragged result arrays.

get_result_buffers()[source]

This adds zero, a, b, selector, error to the superclass result buffer declaration.

zero, a, b:

Grid refinement parameters for each frame.

selector:

Boolean mask of the peaks that were used in the fit.

error:

Residual of the fit.

See source code for the exact buffer declaration.

libertem_blobfinder.udf.refinement.run_refine(ctx, dataset, zero, a, b, match_pattern: libertem_blobfinder.common.patterns.MatchPattern, matcher: libertem.analysis.gridmatching.Matcher, correlation='fast', match='fast', indices=None, steps=5, roi=None)[source]

Wrapper function to refine the given lattice for each frame by calculating approximate peak positions and refining them for each frame using a combination of libertem_blobfinder.CorrelationUDF and libertem_blobfinder.RefinementMixin.

Changed in version 0.3.0: Support for FullFrameCorrelationUDF through parameter correlation = 'fullframe'

Parameters
  • ctx (libertem.api.Context) – Instance of a LiberTEM Context

  • dataset (libertem.io.dataset.base.DataSet) – Instance of a DataSet

  • zero (numpy.ndarray) – Approximate value for “zero” point (y, x) in px (origin, zero order peak)

  • a (numpy.ndarray) – Approximate value for “a” vector (y, x) in px.

  • b (numpy.ndarray) – Approximate value for “b” vector (y, x) in px.

  • match_pattern (MatchPattern) – Instance of MatchPattern

  • matcher (libertem.analysis.gridmatching.Matcher) – Instance of Matcher to perform the matching

  • correlation ({'fast', 'sparse', 'fullframe'}, optional) – ‘fast’, ‘sparse’ or ‘fullframe’ to select FastCorrelationUDF, SparseCorrelationUDF or FullFrameCorrelationUDF

  • match ({'fast', 'affine'}, optional) – ‘fast’ or ‘affine’ to select FastmatchMixin or AffineMixin

  • indices (numpy.ndarray, optional) – Indices to refine. This is trimmed down to positions within the frame. As a convenience, for the indices parameter this function accepts both shape (n, 2) and (2, n, m) so that numpy.mgrid[h:k, i:j] works directly to specify indices. This saves boilerplate code when using this function. Default: numpy.mgrid[-10:10, -10:10].

  • steps (int, optional) – Only for correlation == ‘sparse’: Correlation steps. See __init__() for details.

  • roi (numpy.ndarray, optional) – ROI for run_udf()

Returns

  • result (Dict[str, BufferWrapper]) – Result buffers of the UDF. See libertem_blobfinder.correlation.CorrelationUDF.get_result_buffers() and RefinementMixin.get_result_buffers() for details on the available buffers.

  • used_indices (numpy.ndarray) – The peak indices that were within the frame.

Examples

>>> dataset = ctx.load(
...     filetype="memory",
...     data=np.zeros(shape=(2, 2, 128, 128), dtype=np.float32)
... )
>>> (result, used_indices) = run_refine(
...     ctx, dataset,
...     zero=(64, 64), a=(1, 0), b=(0, 1),
...     match_pattern=libertem_blobfinder.common.patterns.RadialGradient(radius=4),
...     matcher=grm.Matcher()
... )
>>> result['centers'].data  
array(...)

Utilities

General utility functions.

libertem_blobfinder.udf.utils.visualize_frame(ctx, ds, result, indices, r, y, x, axes, colors=None, stretch=10)[source]

Visualize the refinement of a specific frame in matplotlib axes