pyFAI.gui package

The package pyFAI.gui contains mainly the code of the graphical user interface widgets. This code is not intended to be used by users of pyFAI as a library hence it makes little sense to document it all here. Only documentation on the Jupyter tools are exposed.

pyFAI.gui.cli_calibration module

pyFAI-calib

A tool for determining the geometry of a detector using a reference sample.

class pyFAI.gui.cli_calibration.AbstractCalibration(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, wavelength=None, calibrant=None)

Bases: object

Everything that is common to Calibration and Recalibration

PARAMETERS = ['dist', 'poni1', 'poni2', 'rot1', 'rot2', 'rot3', 'wavelength']

PTS_PER_DEG = 0.3

UNITS = {'dist': 'meter', 'poni1': 'meter', 'poni2': 'meter', 'rot1': 'radian', 'rot2': 'radian', 'rot3': 'radian', 'wavelength': 'meter'}

VALID_URL = ['', 'file', 'hdf5', 'nxs', 'h5']

__init__(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, wavelength=None, calibrant=None)

Constructor of AbstractCalibration

Parameters:

dataFiles – list of filenames containing data images
darkFiles – list of filenames containing dark current images
flatFiles – list of filenames containing flat images
pixelSize – size of the pixel in meter as 2 tuple
splineFile – file containing the distortion of the taper
detector – Detector name or instance
wavelength – radiation wavelength in meter
calibrant – pyFAI.calibrant.Calibrant instance

analyse_options(options=None, args=None)

Analyzes options and arguments

Returns:: option,arguments

chiplot(rings=None)

plot delta_2theta/2theta = f(chi) and fit the curve.

Parameters:: rings – list of rings to consider

configure_parser(version='calibration from pyFAI version 0.21.3: 03/02/2022', usage='pyFAI-calib [options] input_image.edf', description=None, epilog=None): Common configuration for parsers

extract_cpt(method='massif', pts_per_deg=1.0)

Performs an automatic keypoint extraction: Can be used in recalib or in calib after a first calibration has been performed.

Parameters:

method – method for keypoint extraction
pts_per_deg – number of control points per azimuthal degree (increase for better precision)

get_pixelSize(ans): convert a comma separated sting into pixel size

initgeoRef(): Tries to initialise the GeometryRefinement (dist, poni, rot) Returns a dictionary of key value pairs

postProcess(): Common part: shows the result of the azimuthal integration in 1D and 2D

preprocess(): Common part: do dark, flat correction thresholding, … and read missing data from keyboard if needed

prompt()

prompt for commands to guide the calibration process

Returns:: True when the user is happy with what he has, False to request another refinement

read_dSpacingFile(verbose=True): Read the name of the calibrant / file with d-spacing

read_pixelsSize(): Read the pixel size from prompt if not available

read_wavelength(): Read the wavelength

refine(): Contains the common geometry refinement part

reset_geometry(how='center', refine=False)

Reset the geometry: no tilt in all cases

Parameters:

how – multiple options * center: set the PONI at the center of the detector * ring: center the poni at the middle of the inner-most ring * best: try both option and keeps the best (this option is not available)
refine – launch the refinement (argument not used)

set_data(data)

call-back function for the peak-picker

Parameters:: data – list of point with ring index
Returns:: associated azimuthal integrator

validate_calibration(): Validate the calibration and calculate the offset in the diffraction image

validate_center(slices=36)

Validate the position of the center by cross-correlating two spectra 180 deg appart. Output values are in micron.

Designed for orthogonal setup with centered beam…

Parameters:: slices – number of slices on which perform

win_error = 'We are under windows with a 32 bit version of python, matplotlib is not able to display too many images without crashing, this is why the window showing the diffraction image is closed'

class pyFAI.gui.cli_calibration.Calibration(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, gaussianWidth=None, wavelength=None, calibrant=None)

Bases: AbstractCalibration

class doing the calibration of frames

__init__(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, gaussianWidth=None, wavelength=None, calibrant=None)

Constructor for calibration:

Parameters:

dataFiles – list of filenames containing data images
darkFiles – list of filenames containing dark current images
flatFiles – list of filenames containing flat images
pixelSize – size of the pixel in meter as 2 tuple
splineFile – file containing the distortion of the taper
detector – Detector name or instance
wavelength – radiation wavelength in meter
calibrant – pyFAI.calibrant.Calibrant instance

gui_peakPicker()

parse(): parse options from command line

preprocess(): do dark, flat correction thresholding, …

refine(): Contains the geometry refinement part specific to Calibration Sets up the initial guess when starting pyFAI-calib

class pyFAI.gui.cli_calibration.CheckCalib(poni=None, img=None, unit='2th_deg')

Bases: object

__init__(poni=None, img=None, unit='2th_deg')

get_1dsize()

integrate()

parse()

rebuild(): Rebuild the diffraction image and measures the offset with the reference :return: offset

show(): Show the image with the the errors

property size1d

smooth_mask(hwhm=5): smooth out around the mask to avoid aligning on the mask

class pyFAI.gui.cli_calibration.MultiCalib(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None)

Bases: object

__init__(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None)

get_pixelSize(ans): convert a comma separated sting into pixel size

parse(exe=None, description=None, epilog=None): parse options from command line :param exe: name of the program (MX-calibrate) :param description: Description of the program

process()

read_dSpacingFile(): Read the name of the calibrant or the file with d-spacing

read_pixelsSize(): Read the pixel size from prompt if not available

read_wavelength(): Read the wavelength

regression()

class pyFAI.gui.cli_calibration.Recalibration(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, wavelength=None, calibrant=None)

Bases: AbstractCalibration

class doing the re-calibration of frames

__init__(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, wavelength=None, calibrant=None)

Constructor for Recalibration:

Parameters:

dataFiles – list of filenames containing data images
darkFiles – list of filenames containing dark current images
flatFiles – list of filenames containing flat images
pixelSize – size of the pixel in meter as 2 tuple
splineFile – file containing the distortion of the taper
detector – Detector name or instance
wavelength – radiation wavelength in meter
calibrant – pyFAI.calibrant.Calibrant instance

parse(): parse options from command line

preprocess(): do dark, flat correction thresholding, …

read_dSpacingFile(): Read the name of the file with d-spacing

refine(): Contains the geometry refinement part specific to Recalibration

pyFAI.gui.cli_calibration.calib(img, calibrant, detector, basename='from_ipython', reconstruct=False, dist=0.1, gaussian=None, interactive=True)

Procedural interfact for calibration

Parameters:

img – 2d array representing the calibration image
calibrant – Instance of Calibrant, set-up with wavelength
detector – Detector instance containing the mask
basename – output file base
recontruct – perform image reconstruction of masked pixel ?
dist – initial distance
gaussian – width of the gaussian used for difference of gaussian in the “massif” peak-picking algorithm
interactive – set to False for testing

Returns:

AzimuthalIntegrator instance

pyFAI.gui.cli_calibration.get_detector(detector, datafiles=None)

Detector factory taking into account the binning knowing the datafiles

Parameters:

detector – string or detector or other junk
datafiles – can be a list of images to be opened and their shape used

Returns:

pyFAI.detector.Detector instance

Raises:

RuntimeError – If no detector found

pyFAI.gui.jupyter module

pyFAI.gui.matplotlib module

This module inits matplotlib and setups the backend to use.

It MUST be imported prior to any other import of matplotlib.

It provides the matplotlib FigureCanvasQTAgg class corresponding to the used backend.

pyFAI.gui.peak_picker module

Semi-graphical tool for peak-picking and extracting visually control points from an image with Debye-Scherer rings

class pyFAI.gui.peak_picker.PeakPicker(data, reconst=False, mask=None, pointfile=None, calibrant=None, wavelength=None, detector=None, method='massif')

Bases: object

This class is in charge of peak picking, i.e. find bragg spots in the image Two methods can be used : massif or blob

VALID_METHODS = ['massif', 'blob', 'watershed']

__init__(data, reconst=False, mask=None, pointfile=None, calibrant=None, wavelength=None, detector=None, method='massif')

Parameters:

data – input image as numpy array
reconst – shall masked part or negative values be reconstructed (wipe out problems with pilatus gaps)
mask – area in which keypoints will not be considered as valid
pointfile –

property append_mode

closeGUI()

contour(data, cmap='autumn', linewidths=2, linestyles='dashed')

Overlay a contour-plot

Parameters:: data – 2darray with the 2theta values in radians…

display_points(minIndex=0, reset=False): display all points and their ring annotations :param minIndex: ring index to start with :param reset: remove all point before re-displaying them

finish(filename=None, callback=None)

Ask the ring number for the given points

Parameters:

filename – file with the point coordinates saved
callback –

Returns:

list of control points

gui(log=False, maximize=False, pick=True, widget_klass=None)

Display the GUI

Parameters:

log – show z in log scale
maximize – set to true to maximize window
pick – activate pixel picking
widget_klass – provide the MplCalibWidget (either Qt or Jupyter) to instanciate the widget

Returns:

None

help = ['Please select rings on the diffraction image. In parenthesis, some modified shortcuts for single button mouse (Apple):', ' * Right-click (click+n): try an auto find for a ring', ' * Right-click + Ctrl (click+b): create new group with one point', ' * Right-click + Shift (click+v): add one point to current group', ' * Right-click + m (click+m): find more points for current group', ' * Center-click or (click+d): erase current group', ' * Center-click + 1 or (click+1): erase closest point from current group']

init(method, sync=True): Unified initializer

load(filename): load a filename and plot data on the screen (if GUI)

massif_contour(data)

Overlays a mask over a diffraction image

Parameters:: data – mask to be overlaid

on_minus_pts_clicked(*args): callback function

on_plus_pts_clicked(*args): callback function

onclick_append_1_point(yx, ring=None)

Right-click + Shift (click+v): add one point to current group

Parameters:: xy – 2tuple of coordinates

onclick_append_more_points(yx, ring)

Right-click + m (click+m): find more points for current group

onclick_erase_1_point(yx, ring)

Center-click + 1 or (click+1): erase closest point from current group

onclick_erase_grp(yx, ring)

Center-click or (click+d): erase current group

onclick_new_grp(yx, ring)

new_grp Right-click (click+n): try an auto find for a ring

onclick_option(*args): callback function

onclick_refine(*args): callback function

onclick_single_point(yx, ring)

Right-click + Ctrl (click+b): create new group with one single point

peaks_from_area(**kwargs)

Return the list of peaks within an area

Parameters:

mask – 2d array with mask.
Imin – minimum of intensity above the background to keep the point
keep – maximum number of points to keep
method – enforce the use of detection using “massif” or “blob” or “watershed”
ring – ring number to which assign the points
dmin – minimum distance between two peaks (in pixels)
seed – good starting points.

Returns:

list of peaks [y,x], [y,x], …]

remove_grp(lbl)

remove a group of points

Parameters:: lbl – label of the group of points

reset(): Reset control point and graph (if needed)

sync_init()

pyFAI.gui.peak_picker.preprocess_image(data, log=False, clip=0.001)

Preforms the pre-processing of the image

Parameters:

data – the input image
log – set to apply logarithmic intensity scale
clip – discard pixel fraction which are too weak/intense

Returns:

scaled image, bounds