`pyFAI` Package¶

pyFAI.__init__.benchmarks(*arg, **kwarg)¶

Run the integrated benchmarks.

See the documentation of pyFAI.benchmark.run_benchmark

pyFAI.__init__.tests(deprecation=False)¶

Runs the test suite of the installed version

Parameters:	deprecation – enable/disables deprecation warning in the tests

`average` Module¶

Utilities, mainly for image treatment

exception pyFAI.average.AlgorithmCreationError¶

Bases: exceptions.RuntimeError

Exception returned if creation of an ImageReductionFilter is not possible

class pyFAI.average.Average¶

Bases: object

Process images to generate an average using different algorithms.

__init__()¶: Constructor

add_algorithm(algorithm)¶

Defines another algorithm which will be computed on the source.

Parameters:	algorithm (ImageReductionFilter) – An averaging algorithm.

get_counter_frames()¶

Returns the number of frames used for the process.

Return type:	int

get_fabio_images()¶

Returns source images as fabio images.

Return type:	list(fabio.fabioimage.FabioImage)

get_image_reduction(algorithm)¶

Returns the result of an algorithm. The process must be already done.

Parameters:	algorithm (ImageReductionFilter) – An averaging algorithm
Return type:	numpy.ndarray

process()¶: Process source images to all defined averaging algorithms defined using defined parameters. To access to the results you have to define a writer (AverageWriter). To follow the process forward you have to define an observer (AverageObserver).

set_correct_flat_from_dark(correct_flat_from_dark)¶

Defines if the dark must be applied on the flat.

Parameters:	correct_flat_from_dark (bool) – If true, the dark is applied.

set_dark(dark_list)¶

Defines images used as dark.

Parameters:	dark_list (list) – List of dark used

set_flat(flat_list)¶

Defines images used as flat.

Parameters:	flat_list (list) – List of dark used

set_images(image_list)¶

Defines the set set of source images to used to process an average.

Parameters:	image_list (list) – List of filename, numpy arrays, fabio images used as source for the computation.

set_monitor_name(monitor_name)¶

Defines the monitor name used to correct images before processing the average. This monitor must be part of the file header, else the image is skipped.

Parameters:	monitor_name (str) – Name of the monitor available on the header file

set_observer(observer)¶

Set an observer to the average process.

Parameters:	observer (AverageObserver) – An observer

set_pixel_filter(threshold, minimum, maximum)¶

Defines the filter applied on each pixels of the images before processing the average.

Parameters:	threshold – what is the upper limit? all pixel > max(1-threshold) are discareded. minimum* – minimum valid value or True maximum – maximum valid value

set_writer(writer)¶

Defines the object write which will be used to store the result.

Parameters:	writer (AverageWriter) – The writer to use.

class pyFAI.average.AverageDarkFilter(filter_name, cut_off, quantiles)¶

Bases: pyFAI.average.ImageStackFilter

Filter based on the algorithm of average_dark

TODO: Must be split according to each filter_name, and removed

__init__(filter_name, cut_off, quantiles)¶

get_parameters()¶: Return a dictionary containing filter parameters

name¶

class pyFAI.average.AverageObserver¶

Bases: object

algorithm_finished(algorithm)¶: Called when an algorithm is finished

algorithm_started(algorithm)¶: Called when an algorithm is started

frame_processed(algorithm, frame_index, frames_count)¶: Called after providing a frame to an algorithm

image_loaded(fabio_image, image_index, images_count)¶: Called when an input image is loaded

process_finished()¶: Called when the full process is finished

process_started()¶: Called when the full processing is started

result_processing(algorithm)¶: Called before the result of an algorithm is computed

class pyFAI.average.AverageWriter¶

Interface for using writer in Average process.

close()¶: Close the writer. Must not be used anymore.

write_header(merged_files, nb_frames, monitor_name)¶

Write the header of the average

Parameters:	merged_files (list) – List of files used to generate this output nb_frames (int) – Number of frames used monitor_name (str) – Name of the monitor used. Can be None.

write_reduction(algorithm, data)¶

Write one reduction

Parameters:	algorithm (ImageReductionFilter) – Algorithm used data (object) – Data of this reduction

class pyFAI.average.ImageAccumulatorFilter¶

Bases: pyFAI.average.ImageReductionFilter

Filter applied in a set of images in which it is possible to reduce data step by step into a single merged image.

add_image(image)¶

Add an image to the filter.

Parameters:	image (numpy.ndarray) – image to add

get_result()¶

Get the result of the filter.

Returns:	result filter
Return type:	numpy.ndarray

init(max_images=None)¶

class pyFAI.average.ImageReductionFilter¶

Bases: object

Generic filter applied in a set of images.

add_image(image)¶

Add an image to the filter.

Parameters:	image (numpy.ndarray) – image to add

get_parameters()¶

Return a dictionary containing filter parameters

Return type:	dict

get_result()¶

Get the result of the filter.

Returns:	result filter

init(max_images=None)¶

Initialize the filter before using it.

Parameters:	max_images (int) – Max images supported by the filter

class pyFAI.average.ImageStackFilter¶

Bases: pyFAI.average.ImageReductionFilter

Filter creating a stack from all images and computing everything at the end.

add_image(image)¶

Add an image to the filter.

Parameters:	image (numpy.ndarray) – image to add

get_result()¶

init(max_images=None)¶

class pyFAI.average.MaxAveraging¶

Bases: pyFAI.average.ImageAccumulatorFilter

name = 'max'¶

class pyFAI.average.MeanAveraging¶

Bases: pyFAI.average.SumAveraging

get_result()¶

name = 'mean'¶

class pyFAI.average.MinAveraging¶

Bases: pyFAI.average.ImageAccumulatorFilter

name = 'min'¶

exception pyFAI.average.MonitorNotFound¶

Bases: exceptions.Exception

Raised when monitor information in not found or is not valid.

class pyFAI.average.MultiFilesAverageWriter(file_name_pattern, file_format, dry_run=False)¶

Bases: pyFAI.average.AverageWriter

Write reductions into multi files. File headers are duplicated.

__init__(file_name_pattern, file_format, dry_run=False)¶

Parameters:	file_name_pattern (str) – File name pattern for the output files. If it contains “{method_name}”, it is updated for each reduction writing with the name of the reduction. file_format (str) – File format used. It is the default extension file. dry_run (bool) – If dry_run, the file is created on memory but not saved on the file system at the end

close()¶: Close the writer. Must not be used anymore.

get_fabio_image(algorithm)¶

Get the constructed fabio image

Return type:	fabio.fabioimage.FabioImage

write_header(merged_files, nb_frames, monitor_name)¶

write_reduction(algorithm, data)¶

class pyFAI.average.SumAveraging¶

Bases: pyFAI.average.ImageAccumulatorFilter

name = 'sum'¶

pyFAI.average.average_dark(lstimg, center_method='mean', cutoff=None, quantiles=(0.5, 0.5))¶

Averages a serie of dark (or flat) images. Centers the result on the mean or the median ... but averages all frames within cutoff*std

Parameters:	lstimg – list of 2D images or a 3D stack center_method (str) – is the center calculated by a “mean”, “median”, “quantile”, “std” cutoff (float or None) – keep all data where (I-center)/std < cutoff quantiles (tuple(float, float) or None) – 2-tuple of floats average out data between the two quantiles
Returns:	2D image averaged

pyFAI.average.average_images(listImages, output=None, threshold=0.1, minimum=None, maximum=None, darks=None, flats=None, filter_='mean', correct_flat_from_dark=False, cutoff=None, quantiles=None, fformat='edf', monitor_key=None)¶

Takes a list of filenames and create an average frame discarding all: saturated pixels.

Parameters:

listImages – list of string representing the filenames
output – name of the optional output file
threshold – what is the upper limit? all pixel > max*(1-threshold) are discareded.
minimum – minimum valid value or True
maximum – maximum valid value
darks – list of dark current images for subtraction
flats – list of flat field images for division
filter – can be “min”, “max”, “median”, “mean”, “sum”, “quantiles” (default=’mean’)
correct_flat_from_dark – shall the flat be re-corrected ?
cutoff – keep all data where (I-center)/std < cutoff
quantiles – 2-tuple containing the lower and upper quantile (0<q<1) to average out.
fformat – file format of the output image, default: edf
str (monitor_key) – Key containing the monitor. Can be none.

Returns:

filename with the data or the data ndarray in case format=None

pyFAI.average.bounding_box(img)¶

Tries to guess the bounding box around a valid massif

Parameters:	img – 2D array like
Returns:	4-typle (d0_min, d1_min, d0_max, d1_max)

pyFAI.average.common_prefix(string_list)¶

Return the common prefix of a list of strings

TODO: move it into utils package

Parameters:	string_list (list(str)) – List of strings
Return type:	str

pyFAI.average.create_algorithm(filter_name, cut_off=None, quantiles=None)¶

Factory to create algorithm according to parameters

Raises AlgorithmCreationError:
Parameters:	cutoff (float or None) – keep all data where (I-center)/std < cutoff quantiles (tuple(float, float) or None) – 2-tuple of floats average out data between the two quantiles
Returns:	An algorithm
Return type:	ImageReductionFilter
	If it is not possible to create the algorithm

pyFAI.average.f¶: alias of SumAveraging

pyFAI.average.get_monitor_value(image, monitor_key)¶

Return the monitor value from an image using an header key.

Raises MonitorNotFound:
Parameters:	image (fabio.fabioimage.FabioImage) – Image containing the header monitor_key (str) – Key containing the monitor
Returns:	returns the monitor else raise an exception
Return type:	float
	when the expected monitor is not found on the header

pyFAI.average.is_algorithm_name_exists(filter_name)¶: Return true if the name is a name of a filter algorithm

pyFAI.average.remove_saturated_pixel(ds, threshold=0.1, minimum=None, maximum=None)¶

Remove saturated fixes from an array inplace.

Parameters:	ds – a dataset as ndarray threshold (float) – what is the upper limit? all pixel > max(1-threshold) are discareded. minimum* (float) – minumum valid value (or True for auto-guess) maximum (float) – maximum valid value
Returns:	the input dataset

`azimuthalIntegrator` Module¶

class pyFAI.azimuthalIntegrator.AzimuthalIntegrator(dist=1, poni1=0, poni2=0, rot1=0, rot2=0, rot3=0, pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None)¶

Bases: pyFAI.geometry.Geometry

This class is an azimuthal integrator based on P. Boesecke’s geometry and histogram algorithm by Manolo S. del Rio and V.A Sole

All geometry calculation are done in the Geometry class

main methods are:

>>> tth, I = ai.integrate1d(data, npt, unit="2th_deg")
>>> q, I, sigma = ai.integrate1d(data, npt, unit="q_nm^-1", error_model="poisson")
>>> regrouped = ai.integrate2d(data, npt_rad, npt_azim, unit="q_nm^-1")[0]

DEFAULT_METHOD = 'splitbbox'¶

__init__(dist=1, poni1=0, poni2=0, rot1=0, rot2=0, rot3=0, pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None)¶

Parameters:

dist (float) – distance sample - detector plan (orthogonal distance, not along the beam), in meter.
poni1 (float) – coordinate of the point of normal incidence along the detector’s first dimension, in meter
poni2 (float) – coordinate of the point of normal incidence along the detector’s second dimension, in meter
rot1 (float) – first rotation from sample ref to detector’s ref, in radians
rot2 (float) – second rotation from sample ref to detector’s ref, in radians
rot3 (float) – third rotation from sample ref to detector’s ref, in radians
pixel1 (float) – Deprecated. Pixel size of the fist dimension of the detector, in meter. If both pixel1 and pixel2 are not None, detector pixel size is overwritten. Prefer defining the detector pixel size on the provided detector object. Prefer defining the detector pixel size on the provided detector object (detector.pixel1 = 5e-6).
pixel2 (float) – Deprecated. Pixel size of the second dimension of the detector, in meter. If both pixel1 and pixel2 are not None, detector pixel size is overwritten. Prefer defining the detector pixel size on the provided detector object (detector.pixel2 = 5e-6).
splineFile (str) – Deprecated. File containing the geometric distortion of the detector. If not None, pixel1 and pixel2 are ignored and detector spline is overwritten. Prefer defining the detector spline manually (detector.splineFile = "file.spline").
detector (str or pyFAI.Detector) – name of the detector or Detector instance. String description is deprecated. Prefer using the result of the detector factory: pyFAI.detector_factory("eiger4m")
wavelength (float) – Wave length used in meter

create_mask(data, mask=None, dummy=None, delta_dummy=None, mode='normal')¶

Combines various masks into another one.

Parameters:	data (ndarray) – input array of data mask (ndarray) – input mask (if none, self.mask is used) dummy (float) – value of dead pixels delta_dumy – precision of dummy pixels mode (str) – can be “normal” or “numpy” (inverted) or “where” applied to the mask
Returns:	the new mask
Return type:	ndarray of bool

This method combine two masks (dynamic mask from data & dummy and mask) to generate a new one with the ‘or’ binary operation. One can adjust the level, with the dummy and the delta_dummy parameter, when you consider the data values needs to be masked out.

This method can work in two different mode:

“normal”: False for valid pixels, True for bad pixels

“numpy”: True for valid pixels, false for others

This method tries to accomodate various types of masks (like valid=0 & masked=-1, ...) and guesses if an input mask needs to be inverted.

dark_correction(data, dark=None)¶

Correct for Dark-current effects. If dark is not defined, correct for a dark set by “set_darkfiles”

Parameters:	data – input ndarray with the image dark – ndarray with dark noise or None
Returns:	2tuple: corrected_data, dark_actually used (or None)

darkcurrent¶

darkfiles¶

empty¶

flat_correction(data, flat=None)¶

Correct for flat field. If flat is not defined, correct for a flat set by “set_flatfiles”

Parameters:	data – input ndarray with the image flat – ndarray with flatfield or None for no correction
Returns:	2tuple: corrected_data, flat_actually used (or None)

flatfield¶

flatfiles¶

get_darkcurrent()¶

get_empty()¶

get_flatfield()¶

inpainting(data, mask, npt_rad=1024, npt_azim=512, unit='r_m', method='splitpixel', poissonian=False, grow_mask=3)¶

Re-invent the values of masked pixels

Parameters:

data – input image as 2d numpy array
mask – masked out pixels array
npt_rad – number of radial points
npt_azim – number of azimuthal points
unit – unit to be used for integration
method – pathway for integration
poissonian – If True, add some poisonian noise to the data to make then more realistic
grow_mask – grow mask in polar coordinated to accomodate pixel splitting algoritm

Returns:

inpainting object which contains the restored image as .data

integrate1d(data, npt, filename=None, correctSolidAngle=True, variance=None, error_model=None, radial_range=None, azimuth_range=None, mask=None, dummy=None, delta_dummy=None, polarization_factor=None, dark=None, flat=None, method='csr', unit=q_nm^-1, safe=True, normalization_factor=1.0, block_size=32, profile=False, all=False, metadata=None)¶

Calculate the azimuthal integrated Saxs curve in q(nm^-1) by default

Multi algorithm implementation (tries to be bullet proof), suitable for SAXS, WAXS, ... and much more

Parameters:	data (ndarray) – 2D array from the Detector/CCD camera npt (int) – number of points in the output pattern filename (str) – output filename in 2/3 column ascii format correctSolidAngle (bool) – correct for solid angle of each pixel if True variance (ndarray) – array containing the variance of the data. If not available, no error propagation is done error_model (str) – When the variance is unknown, an error model can be given: “poisson” (variance = I), “azimuthal” (variance = (I-<I>)^2) radial_range ((float, float), optional) – The lower and upper range of the radial unit. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored. azimuth_range ((float, float), optional) – The lower and upper range of the azimuthal angle in degree. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored. mask (ndarray) – array (same size as image) with 1 for masked pixels, and 0 for valid pixels dummy (float) – value for dead/masked pixels delta_dummy (float) – precision for dummy value polarization_factor (float) – polarization factor between -1 (vertical) and +1 (horizontal). 0 for circular polarization or random, None for no correction, True for using the former correction dark (ndarray) – dark noise image flat (ndarray) – flat field image method (str) – can be “numpy”, “cython”, “BBox” or “splitpixel”, “lut”, “csr”, “nosplit_csr”, “full_csr”, “lut_ocl” and “csr_ocl” if you want to go on GPU. To Specify the device: “csr_ocl_1,2” unit (pyFAI.units.Unit) – Output units, can be “q_nm^-1”, “q_A^-1”, “2th_deg”, “2th_rad”, “r_mm” for now safe (bool) – Do some extra checks to ensure LUT/CSR is still valid. False is faster. normalization_factor (float) – Value of a normalization monitor block_size – size of the block for OpenCL integration (unused?) profile – set to True to enable profiling in OpenCL all (bool) – if true return a dictionary with many more parameters (deprecated, please refer to the documentation of Integrate1dResult). metadata – JSON serializable object containing the metadata, usually a dictionary.
Returns:	q/2th/r bins center positions and regrouped intensity (and error array if variance or variance model provided), uneless all==True.
Return type:	Integrate1dResult, dict

integrate2d(data, npt_rad, npt_azim=360, filename=None, correctSolidAngle=True, variance=None, error_model=None, radial_range=None, azimuth_range=None, mask=None, dummy=None, delta_dummy=None, polarization_factor=None, dark=None, flat=None, method='bbox', unit=q_nm^-1, safe=True, normalization_factor=1.0, all=False, metadata=None)¶

Calculate the azimuthal regrouped 2d image in q(nm^-1)/chi(deg) by default

Multi algorithm implementation (tries to be bullet proof)

Parameters:	data (ndarray) – 2D array from the Detector/CCD camera npt_rad (int) – number of points in the radial direction npt_azim (int) – number of points in the azimuthal direction filename (str) – output image (as edf format) correctSolidAngle (bool) – correct for solid angle of each pixel if True variance (ndarray) – array containing the variance of the data. If not available, no error propagation is done error_model (str) – When the variance is unknown, an error model can be given: “poisson” (variance = I), “azimuthal” (variance = (I-<I>)^2) radial_range ((float, float), optional) – The lower and upper range of the radial unit. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored. azimuth_range ((float, float), optional) – The lower and upper range of the azimuthal angle in degree. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored. mask (ndarray) – array (same size as image) with 1 for masked pixels, and 0 for valid pixels dummy (float) – value for dead/masked pixels delta_dummy (float) – precision for dummy value polarization_factor (float) – polarization factor between -1 (vertical) and +1 (horizontal). 0 for circular polarization or random, None for no correction dark (ndarray) – dark noise image flat (ndarray) – flat field image method (str) – can be “numpy”, “cython”, “BBox” or “splitpixel”, “lut”, “csr; “lut_ocl” and “csr_ocl” if you want to go on GPU. To Specify the device: “csr_ocl_1,2” unit (pyFAI.units.Unit) – Output units, can be “q_nm^-1”, “q_A^-1”, “2th_deg”, “2th_rad”, “r_mm” for now safe (bool) – Do some extra checks to ensure LUT is still valid. False is faster. normalization_factor (float) – Value of a normalization monitor all (bool) – if true, return many more intermediate results as a dict (deprecated, please refer to the documentation of Integrate2dResult). metadata – JSON serializable object containing the metadata, usually a dictionary.
Returns:	azimuthaly regrouped intensity, q/2theta/r pos. and chi pos.
Return type:	Integrate2dResult, dict

integrate_radial(data, npt, npt_rad=100, correctSolidAngle=True, radial_range=None, azimuth_range=None, mask=None, dummy=None, delta_dummy=None, polarization_factor=None, dark=None, flat=None, method='csr', unit=chi_deg, radial_unit=q_nm^-1, normalization_factor=1.0)¶

Calculate the radial integrated profile curve as I = f(chi)

Parameters:	data (ndarray) – 2D array from the Detector/CCD camera npt (int) – number of points in the output pattern npt_rad (int) – number of points in the radial space. Too few points may lead to huge rounding errors. filename (str) – output filename in 2/3 column ascii format correctSolidAngle (bool) – correct for solid angle of each pixel if True radial_range (Tuple(float, float)) – The lower and upper range of the radial unit. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored. Optional. azimuth_range (Tuple(float, float)) – The lower and upper range of the azimuthal angle in degree. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored. Optional. mask (ndarray) – array (same size as image) with 1 for masked pixels, and 0 for valid pixels dummy (float) – value for dead/masked pixels delta_dummy (float) – precision for dummy value polarization_factor (float) – polarization factor between -1 (vertical) and +1 (horizontal). * 0 for circular polarization or random, * None for no correction, * True for using the former correction dark (ndarray) – dark noise image flat (ndarray) – flat field image method (str) – can be “numpy”, “cython”, “BBox” or “splitpixel”, “lut”, “csr”, “nosplit_csr”, “full_csr”, “lut_ocl” and “csr_ocl” if you want to go on GPU. To Specify the device: “csr_ocl_1,2” unit (pyFAI.units.Unit) – Output units, can be “chi_deg” or “chi_rad” radial_unit (pyFAI.units.Unit) – unit used for radial representation, can be “q_nm^-1”, “q_A^-1”, “2th_deg”, “2th_rad”, “r_mm” for now normalization_factor (float) – Value of a normalization monitor
Returns:	chi bins center positions and regrouped intensity
Return type:	Integrate1dResult

medfilt1d(data, npt_rad=1024, npt_azim=512, correctSolidAngle=True, polarization_factor=None, dark=None, flat=None, method='splitpixel', unit=q_nm^-1, percentile=50, mask=None, normalization_factor=1.0, metadata=None)¶

Perform the 2D integration and filter along each row using a median filter

Parameters:

data – input image as numpy array
npt_rad – number of radial points
npt_azim – number of azimuthal points
correctSolidAngle (bool) – correct for solid angle of each pixel if True
polarization_factor (float) – polarization factor between -1 (vertical) and +1 (horizontal). 0 for circular polarization or random, None for no correction, True for using the former correction
dark (ndarray) – dark noise image
flat (ndarray) – flat field image
unit – unit to be used for integration
method – pathway for integration and sort
percentile – which percentile use for cutting out percentil can be a 2-tuple to specify a region to average out
mask – masked out pixels array
normalization_factor (float) – Value of a normalization monitor
metadata (JSON serializable dict) – any other metadata,

Returns:

Integrate1D like result like

reset()¶: Reset azimuthal integrator in addition to other arrays.

save1D(*arg, **kw)¶: decorator that deprecates the use of a function

save2D(*arg, **kw)¶: decorator that deprecates the use of a function

saxs(*arg, **kw)¶: decorator that deprecates the use of a function

separate(data, npt_rad=1024, npt_azim=512, unit='2th_deg', method='splitpixel', percentile=50, mask=None, restore_mask=True)¶

Separate bragg signal from powder/amorphous signal using azimuthal integration, median filering and projected back before subtraction.

Parameters:	data – input image as numpy array npt_rad – number of radial points npt_azim – number of azimuthal points unit – unit to be used for integration method – pathway for integration and sort percentile – which percentile use for cutting out mask – masked out pixels array restore_mask – masked pixels have the same value as input data provided
Returns:	bragg, amorphous

set_darkcurrent(dark)¶

set_darkfiles(files=None, method='mean')¶

Moved to Detector

Parameters:	files (str or list(str) or None) – file(s) used to compute the dark. method (str) – method used to compute the dark, “mean” or “median”

Set the dark current from one or mutliple files, avaraged according to the method provided

set_empty(value)¶

set_flatfield(flat)¶

set_flatfiles(files, method='mean')¶

Moved to Detector

Parameters:	files (str or list(str) or None) – file(s) used to compute the flat-field. method (str) – method used to compute the dark, “mean” or “median”

Set the flat field from one or mutliple files, averaged according to the method provided

setup_CSR(shape, npt, mask=None, pos0_range=None, pos1_range=None, mask_checksum=None, unit=2th_deg, split='bbox')¶

Prepare a look-up-table

Parameters:

shape ((int, int)) – shape of the dataset
npt (int or (int, int)) – number of points in the the output pattern
mask (ndarray) – array with masked pixel (1=masked)
pos0_range ((float, float)) – range in radial dimension
pos1_range ((float, float)) – range in azimuthal dimension
mask_checksum (int (or anything else ...)) – checksum of the mask buffer
unit (pyFAI.units.Unit) – use to propagate the LUT object for further checkings
split – Splitting scheme: valid options are “no”, “bbox”, “full”

This method is called when a look-up table needs to be set-up. The shape parameter, correspond to the shape of the original datatset. It is possible to customize the number of point of the output histogram with the npt parameter which can be either an integer for an 1D integration or a 2-tuple of integer in case of a 2D integration. The LUT will have a different shape: (npt, lut_max_size), the later parameter being calculated during the instanciation of the splitBBoxLUT class.

It is possible to prepare the LUT with a predefine mask. This operation can speedup the computation of the later integrations. Instead of applying the patch on the dataset, it is taken into account during the histogram computation. If provided the mask_checksum prevent the re-calculation of the mask. When the mask changes, its checksum is used to reset (or not) the LUT (which is a very time consuming operation !)

It is also possible to restrain the range of the 1D or 2D pattern with the pos1_range and pos2_range.

The unit parameter is just propagated to the LUT integrator for further checkings: The aim is to prevent an integration to be performed in 2th-space when the LUT was setup in q space.

setup_LUT(shape, npt, mask=None, pos0_range=None, pos1_range=None, mask_checksum=None, unit=2th_deg)¶

Prepare a look-up-table

Parameters:

shape ((int, int)) – shape of the dataset
npt (int or (int, int)) – number of points in the the output pattern
mask (ndarray) – array with masked pixel (1=masked)
pos0_range ((float, float)) – range in radial dimension
pos1_range ((float, float)) – range in azimuthal dimension
mask_checksum (int (or anything else ...)) – checksum of the mask buffer
unit (pyFAI.units.Unit) – use to propagate the LUT object for further checkings

This method is called when a look-up table needs to be set-up. The shape parameter, correspond to the shape of the original datatset. It is possible to customize the number of point of the output histogram with the npt parameter which can be either an integer for an 1D integration or a 2-tuple of integer in case of a 2D integration. The LUT will have a different shape: (npt, lut_max_size), the later parameter being calculated during the instanciation of the splitBBoxLUT class.

It is possible to prepare the LUT with a predefine mask. This operation can speedup the computation of the later integrations. Instead of applying the patch on the dataset, it is taken into account during the histogram computation. If provided the mask_checksum prevent the re-calculation of the mask. When the mask changes, its checksum is used to reset (or not) the LUT (which is a very time consuming operation !)

It is also possible to restrain the range of the 1D or 2D pattern with the pos1_range and pos2_range.

The unit parameter is just propagated to the LUT integrator for further checkings: The aim is to prevent an integration to be performed in 2th-space when the LUT was setup in q space.

sigma_clip(data, npt_rad=1024, npt_azim=512, correctSolidAngle=True, polarization_factor=None, dark=None, flat=None, method='splitpixel', unit=q_nm^-1, thres=3, max_iter=5, mask=None, normalization_factor=1.0, metadata=None)¶

Perform the 2D integration and perform a sigm-clipping iterative filter along each row. see the doc of scipy.stats.sigmaclip for the options.

Parameters:

data – input image as numpy array
npt_rad – number of radial points
npt_azim – number of azimuthal points
correctSolidAngle (bool) – correct for solid angle of each pixel if True
polarization_factor (float) – polarization factor between -1 (vertical) and +1 (horizontal). 0 for circular polarization or random, None for no correction, True for using the former correction
dark (ndarray) – dark noise image
flat (ndarray) – flat field image
unit – unit to be used for integration
method – pathway for integration and sort
thres – cut-off for n*sigma: discard any values with (I-<I>)/sigma > thres. The threshold can be a 2-tuple with sigma_low and sigma_high.
max_iter – maximum number of iterations :param mask: masked out pixels array
normalization_factor (float) – Value of a normalization monitor
metadata (JSON serializable dict) – any other metadata,

Returns:

Integrate1D like result like

xrpd(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd2(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd2_histogram(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd2_numpy(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd2_splitBBox(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd2_splitPixel(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd_CSR_OCL(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd_LUT(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd_LUT_OCL(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd_OpenCL(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd_cython(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd_numpy(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd_splitBBox(*arg, **kw)¶: decorator that deprecates the use of a function

xrpd_splitPixel(*arg, **kw)¶: decorator that deprecates the use of a function

`multi_geometry` Module¶

Module for treating simultaneously multiple detector configuration within a single integration

class pyFAI.multi_geometry.MultiGeometry(ais, unit='2th_deg', radial_range=(0, 180), azimuth_range=(-180, 180), wavelength=None, empty=0.0, chi_disc=180)¶

Bases: object

This is an Azimuthal integrator containing multiple geometries, for example when the detector is on a goniometer arm

__init__(ais, unit='2th_deg', radial_range=(0, 180), azimuth_range=(-180, 180), wavelength=None, empty=0.0, chi_disc=180)¶: Constructor of the multi-geometry integrator :param ais: list of azimuthal integrators :param radial_range: common range for integration :param azimuthal_range: common range for integration :param empty: value for empty pixels :param chi_disc: if 0, set the chi_discontinuity at

integrate1d(lst_data, npt=1800, correctSolidAngle=True, lst_variance=None, error_model=None, polarization_factor=None, normalization_factor=None, all=False, lst_mask=None, lst_flat=None)¶

Perform 1D azimuthal integration

Parameters:	lst_data – list of numpy array npt – number of points int the integration correctSolidAngle – correct for solid angle (all processing are then done in absolute solid angle !) lst_variance (list of ndarray) – list of array containing the variance of the data. If not available, no error propagation is done error_model (str) – When the variance is unknown, an error model can be given: “poisson” (variance = I), “azimuthal” (variance = (I-<I>)^2) polarization_factor – Apply polarization correction ? is None: not applies. Else provide a value from -1 to +1 normalization_factor – normalization monitors value (list of floats) all – return a dict with all information in it (deprecated, please refer to the documentation of Integrate1dResult). lst_mask – numpy.Array or list of numpy.array which mask the lst_data. lst_flat – numpy.Array or list of numpy.array which flat the lst_data.
Returns:	2th/I or a dict with everything depending on “all”
Return type:	Integrate1dResult, dict

integrate2d(lst_data, npt_rad=1800, npt_azim=3600, correctSolidAngle=True, lst_variance=None, error_model=None, polarization_factor=None, normalization_factor=None, all=False, lst_mask=None, lst_flat=None)¶

Performs 2D azimuthal integration of multiples frames, one for each geometry

Parameters:	lst_data – list of numpy array npt – number of points int the integration correctSolidAngle – correct for solid angle (all processing are then done in absolute solid angle !) lst_variance (list of ndarray) – list of array containing the variance of the data. If not available, no error propagation is done error_model (str) – When the variance is unknown, an error model can be given: “poisson” (variance = I), “azimuthal” (variance = (I-<I>)^2) polarization_factor – Apply polarization correction ? is None: not applies. Else provide a value from -1 to +1 normalization_factor – normalization monitors value (list of floats) all – return a dict with all information in it (deprecated, please refer to the documentation of Integrate2dResult). lst_mask – numpy.Array or list of numpy.array which mask the lst_data. lst_flat – numpy.Array or list of numpy.array which flat the lst_data.
Returns:	I/2th/chi or a dict with everything depending on “all”
Return type:	Integrate2dResult, dict

set_wavelength(value)¶: Changes the wavelength of a group of azimuthal integrators

`integrate_widget` Module¶

pyFAI-integrate

A graphical tool for performing azimuthal integration on series of files.

class pyFAI.integrate_widget.AIWidget(input_data=None, output_path=None, output_format=None, slow_dim=None, fast_dim=None, json_file='.azimint.json')¶

Bases: PyQt4.QtGui.QWidget

URL = 'http://pyfai.readthedocs.org/en/latest/man/pyFAI-integrate.html'¶

__init__(input_data=None, output_path=None, output_format=None, slow_dim=None, fast_dim=None, json_file='.azimint.json')¶

assign_unit()¶: assign unit to the corresponding widget

detector_changed()¶

die()¶

dump(filename=None)¶

Dump the status of the current widget to a file in JSON

Parameters:	filename (string) – path where to save the config
Returns:	dict with configuration

get_config()¶

Read the configuration of the plugin and returns it as a dictionary

Returns:	dict with all information.

get_method()¶: Return the method name for azimuthal intgration

help()¶

openCL_changed()¶

platform_changed()¶

proceed()¶

restore(filename='.azimint.json')¶

Restore from JSON file the status of the current widget

Parameters:	filename (str) – path where the config was saved

save_config()¶

select_darkcurrent()¶

select_flatfield()¶

select_maskfile()¶

select_ponifile()¶

select_splinefile()¶

setStackDataObject(stack, stack_name=None)¶

set_config(dico)¶

Setup the widget from its description

Parameters:	dico (dict) – dictionary with description of the widget

set_input_data(stack, stack_name=None)¶

set_ponifile(ponifile=None)¶

set_validators()¶: Set all validators for text entries

`geometry` Module¶

This modules contains only one (large) class in charge of:

calculating the geometry, i.e. the position in the detector space of each pixel of the detector
manages caches to store intermediate results

class pyFAI.geometry.Geometry(dist=1, poni1=0, poni2=0, rot1=0, rot2=0, rot3=0, pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None)¶

Bases: object

This class is an azimuthal integrator based on P. Boesecke’s geometry and histogram algorithm by Manolo S. del Rio and V.A Sole

Detector is assumed to be corrected from “raster orientation” effect. It is not addressed here but rather in the Detector object or at read time. Considering there is no tilt:

Detector fast dimension (dim2) is supposed to be horizontal (dimension X of the image)
Detector slow dimension (dim1) is supposed to be vertical, upwards (dimension Y of the image)
The third dimension is chose such as the referential is orthonormal, so dim3 is along incoming X-ray beam

Demonstration of the equation done using Mathematica:

Axis 1 is allong first dimension of detector (when not tilted), this is the slow dimension of the image array in C or Y
In[5]:= x1={1,0,0}
Out[5]= {1,0,0}
 Axis 2 is allong second dimension of detector (when not tilted), this is the fast dimension of the image in C or X
In[6]:= x2={0,1,0}
Out[6]= {0,1,0}
Axis 3 is along the incident X-Ray beam
In[7]:= x3={0,0,1}
Out[7]= {0,0,1}
In[9]:= id3={x1,x2,x3}
Out[9]= {{1,0,0},{0,1,0},{0,0,1}}
In[10]:= {{1,0,0},{0,1,0},{0,0,1}}
Out[10]= {{1,0,0},{0,1,0},{0,0,1}}
In[11]:= rotM1=RotationMatrix[rot1,x1]
Out[11]= {{1,0,0},{0,Cos[rot1],-Sin[rot1]},{0,Sin[rot1],Cos[rot1]}}
In[12]:= rotM2 =  RotationMatrix[rot2,x2]
Out[12]= {{Cos[rot2],0,Sin[rot2]},{0,1,0},{-Sin[rot2],0,Cos[rot2]}}
In[13]:= rotM3 =  RotationMatrix[rot3,x3]
Out[13]= {{Cos[rot3],-Sin[rot3],0},{Sin[rot3],Cos[rot3],0},{0,0,1}}
Rotations of the detector are applied first Rot around axis 1, then axis 2 and finally around axis 3
In[14]:= R=rotM3.rotM2.rotM1
Out[14]= {{Cos[rot2] Cos[rot3],Cos[rot3] Sin[rot1] Sin[rot2]-Cos[rot1] Sin[rot3],Cos[rot1] Cos[rot3] Sin[rot2]+Sin[rot1] Sin[rot3]},{Cos[rot2] Sin[rot3],Cos[rot1] Cos[rot3]+Sin[rot1] Sin[rot2] Sin[rot3],-Cos[rot3] Sin[rot1]+Cos[rot1] Sin[rot2] Sin[rot3]},{-Sin[rot2],Cos[rot2] Sin[rot1],Cos[rot1] Cos[rot2]}}
In[15]:= CForm[R.x1]

Out[15]//CForm=
List(Cos(rot2)*Cos(rot3),Cos(rot2)*Sin(rot3),-Sin(rot2))
In[16]:= CForm[R.x2]

Out[16]//CForm=
List(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3),Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3),Cos(rot2)*Sin(rot1))
In[17]:= CForm[R.x3]
Out[17]//CForm=
List(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3),-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3),Cos(rot1)*Cos(rot2))
In[18]:= CForm[Det[R]]
Out[18]//CForm=
Power(Cos(rot1),2)*Power(Cos(rot2),2)*Power(Cos(rot3),2) + Power(Cos(rot2),2)*Power(Cos(rot3),2)*Power(Sin(rot1),2) + Power(Cos(rot1),2)*Power(Cos(rot3),2)*Power(Sin(rot2),2) +
   Power(Cos(rot3),2)*Power(Sin(rot1),2)*Power(Sin(rot2),2) + Power(Cos(rot1),2)*Power(Cos(rot2),2)*Power(Sin(rot3),2) + Power(Cos(rot2),2)*Power(Sin(rot1),2)*Power(Sin(rot3),2) +
   Power(Cos(rot1),2)*Power(Sin(rot2),2)*Power(Sin(rot3),2) + Power(Sin(rot1),2)*Power(Sin(rot2),2)*Power(Sin(rot3),2)
In[13]:=
Any pixel on detector plan at coordianate (d1, d2) in meters. Detector is at z=L

In[22]:= P={d1,d2,L}
CForm[R.P]

Out[22]= {d1,d2,L}
Out[23]//CForm=
List(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),
   d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2))
In[24]:= t1 = R.P.x1
CForm[t1]
Out[24]= d1 Cos[rot2] Cos[rot3]+d2 (Cos[rot3] Sin[rot1] Sin[rot2]-Cos[rot1] Sin[rot3])+L (Cos[rot1] Cos[rot3] Sin[rot2]+Sin[rot1] Sin[rot3])
Out[25]//CForm=
d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3))
In[26]:= t2 = R.P.x2
CForm[t2]
Out[26]= d1 Cos[rot2] Sin[rot3]+L (-Cos[rot3] Sin[rot1]+Cos[rot1] Sin[rot2] Sin[rot3])+d2 (Cos[rot1] Cos[rot3]+Sin[rot1] Sin[rot2] Sin[rot3])
Out[27]//CForm=
d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3))
In[28]:= t3=R.P.x3
CForm[t3]
Out[28]= L Cos[rot1] Cos[rot2]+d2 Cos[rot2] Sin[rot1]-d1 Sin[rot2]
Out[29]//CForm=
L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2)
Distance sample to detector point (d1,d2)
(no Mathematica translations)
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 14}, {14, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
BaseStyle->"ImageGraphics",
ImageSize->Magnification[1],
ImageSizeRaw->{14, 14},
PlotRange->{{0, 14}, {0, 14}}]


In[30]:= dist = Norm[R.P]
CForm[dist]
Out[30]= √(Abs[L Cos[rot1] Cos[rot2]+d2 Cos[rot2] Sin[rot1]-d1 Sin[rot2]]^2+Abs[d1 Cos[rot2] Cos[rot3]+d2 (Cos[rot3] Sin[rot1] Sin[rot2]-Cos[rot1] Sin[rot3])+L (Cos[rot1] Cos[rot3] Sin[rot2]+Sin[rot1] Sin[rot3])]^2+Abs[d1 Cos[rot2] Sin[rot3]+L (-Cos[rot3] Sin[rot1]+Cos[rot1] Sin[rot2] Sin[rot3])+d2 (Cos[rot1] Cos[rot3]+Sin[rot1] Sin[rot2] Sin[rot3])]^2)
Out[31]//CForm=
Sqrt(Power(Abs(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2)),2) + Power(Abs(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
       L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3))),2) + Power(Abs(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3))),
     2))
cos(2theta) can be defined as (R.P component along x3) over the distance |R.P|
In[32]:= tthc = ArcCos [-(R.P).x3/Norm[R.P]]
CForm[tthc]

Out[32]= ArcCos[(-L Cos[rot1] Cos[rot2]-d2 Cos[rot2] Sin[rot1]+d1 Sin[rot2])/(√(Abs[L Cos[rot1] Cos[rot2]+d2 Cos[rot2] Sin[rot1]-d1 Sin[rot2]]^2+Abs[d1 Cos[rot2] Cos[rot3]+d2 (Cos[rot3] Sin[rot1] Sin[rot2]-Cos[rot1] Sin[rot3])+L (Cos[rot1] Cos[rot3] Sin[rot2]+Sin[rot1] Sin[rot3])]^2+Abs[d1 Cos[rot2] Sin[rot3]+L (-Cos[rot3] Sin[rot1]+Cos[rot1] Sin[rot2] Sin[rot3])+d2 (Cos[rot1] Cos[rot3]+Sin[rot1] Sin[rot2] Sin[rot3])]^2))]
Out[33]//CForm=
ArcCos((-(L*Cos(rot1)*Cos(rot2)) - d2*Cos(rot2)*Sin(rot1) + d1*Sin(rot2))/
    Sqrt(Power(Abs(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2)),2) + Power(Abs(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
         L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3))),2) + Power(Abs(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) +
         d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3))),2)))



In[41]:= ttht = ArcTan[t3,Sqrt[t1^2 + t2^2]]

CForm[ttht]


Out[41]= ArcTan[L Cos[rot1] Cos[rot2]+d2 Cos[rot2] Sin[rot1]-d1 Sin[rot2],√((d1 Cos[rot2] Cos[rot3]+d2 (Cos[rot3] Sin[rot1] Sin[rot2]-Cos[rot1] Sin[rot3])+L (Cos[rot1] Cos[rot3] Sin[rot2]+Sin[rot1] Sin[rot3]))^2+(d1 Cos[rot2] Sin[rot3]+L (-Cos[rot3] Sin[rot1]+Cos[rot1] Sin[rot2] Sin[rot3])+d2 (Cos[rot1] Cos[rot3]+Sin[rot1] Sin[rot2] Sin[rot3]))^2)]
Out[42]//CForm=
ArcTan(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),
      2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))
Tangeant of angle chi is defined as (R.P component along x1) over (R.P component along x2). Arctan2 should be used in actual calculation
In[36]:= chi =ArcTan[t1  , t2]
CForm[chi]
Out[36]= ArcTan[d1 Cos[rot2] Cos[rot3]+d2 (Cos[rot3] Sin[rot1] Sin[rot2]-Cos[rot1] Sin[rot3])+L (Cos[rot1] Cos[rot3] Sin[rot2]+Sin[rot1] Sin[rot3]),d1 Cos[rot2] Sin[rot3]+L (-Cos[rot3] Sin[rot1]+Cos[rot1] Sin[rot2] Sin[rot3])+d2 (Cos[rot1] Cos[rot3]+Sin[rot1] Sin[rot2] Sin[rot3])]
Out[37]//CForm=
ArcTan(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),
   d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)))
Coodinates of the Point of Normal Incidence

In[38]:= PONI = R.{0,0,L}
CForm[PONI]
Out[38]= {L (Cos[rot1] Cos[rot3] Sin[rot2]+Sin[rot1] Sin[rot3]),L (-Cos[rot3] Sin[rot1]+Cos[rot1] Sin[rot2] Sin[rot3]),L Cos[rot1] Cos[rot2]}
Out[39]//CForm=
List(L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)),L*Cos(rot1)*Cos(rot2))
Derivatives of 2Theta
In[43]:= CForm[D[ttht,d1]]
Out[43]//CForm=
((L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2))*(2*Cos(rot2)*Cos(rot3)*(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
           L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3))) + 2*Cos(rot2)*Sin(rot3)*
         (d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)))))/
    (2.*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
        Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))*
      (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
          L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))
     + (Sin(rot2)*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
        Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))/
    (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),
       2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))
In[44]:= CForm[D[ttht,d2]]

Out[44]//CForm=
((L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2))*(2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3))*
         (d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3))) +
        2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3))*(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)))))/
    (2.*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
        Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))*
      (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
          L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))
     - (Cos(rot2)*Sin(rot1)*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
        Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))/
    (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),
       2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))
In[47]:= CForm[D[ttht,L]]
Out[47]//CForm=
((L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2))*(2*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3))*
         (d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3))) +
        2*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3))*(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)))))/
    (2.*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
        Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))*
      (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
          L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))
     - (Cos(rot1)*Cos(rot2)*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
        Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))/
    (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),
       2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))
In[48]:= CForm[D[ttht,rot1]]
Out[48]//CForm=
((L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2))*(2*(L*(-(Cos(rot3)*Sin(rot1)*Sin(rot2)) + Cos(rot1)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)))*
         (d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3))) +
        2*(d2*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + L*(-(Cos(rot1)*Cos(rot3)) - Sin(rot1)*Sin(rot2)*Sin(rot3)))*
         (d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)))))/
    (2.*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
        Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))*
      (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
          L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))
     - ((d2*Cos(rot1)*Cos(rot2) - L*Cos(rot2)*Sin(rot1))*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
        Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))/
    (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),
       2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))
In[49]:= CForm[D[ttht,rot2]]
Out[49]//CForm=
((L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2))*(2*(L*Cos(rot1)*Cos(rot2)*Cos(rot3) + d2*Cos(rot2)*Cos(rot3)*Sin(rot1) - d1*Cos(rot3)*Sin(rot2))*
         (d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3))) +
        2*(L*Cos(rot1)*Cos(rot2)*Sin(rot3) + d2*Cos(rot2)*Sin(rot1)*Sin(rot3) - d1*Sin(rot2)*Sin(rot3))*
         (d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)))))/
    (2.*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
        Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))*
      (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
          L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))
     - ((-(d1*Cos(rot2)) - L*Cos(rot1)*Sin(rot2) - d2*Sin(rot1)*Sin(rot2))*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
          L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))
     /(Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
        L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))
In[50]:= CForm[D[ttht,rot3]]
Out[50]//CForm=
((L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2))*(2*(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)))*
        (-(d1*Cos(rot2)*Sin(rot3)) + L*(Cos(rot3)*Sin(rot1) - Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(-(Cos(rot1)*Cos(rot3)) - Sin(rot1)*Sin(rot2)*Sin(rot3))) +
       2*(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)))*
        (d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)))))/
   (2.*Sqrt(Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) + L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) +
       Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2))*
     (Power(L*Cos(rot1)*Cos(rot2) + d2*Cos(rot2)*Sin(rot1) - d1*Sin(rot2),2) + Power(d1*Cos(rot2)*Cos(rot3) + d2*(Cos(rot3)*Sin(rot1)*Sin(rot2) - Cos(rot1)*Sin(rot3)) +
         L*(Cos(rot1)*Cos(rot3)*Sin(rot2) + Sin(rot1)*Sin(rot3)),2) + Power(d1*Cos(rot2)*Sin(rot3) + L*(-(Cos(rot3)*Sin(rot1)) + Cos(rot1)*Sin(rot2)*Sin(rot3)) + d2*(Cos(rot1)*Cos(rot3) + Sin(rot1)*Sin(rot2)*Sin(rot3)),2)))

__init__(dist=1, poni1=0, poni2=0, rot1=0, rot2=0, rot3=0, pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None)¶

Parameters:

dist – distance sample - detector plan (orthogonal distance, not along the beam), in meter.
poni1 – coordinate of the point of normal incidence along the detector’s first dimension, in meter
poni2 – coordinate of the point of normal incidence along the detector’s second dimension, in meter
rot1 – first rotation from sample ref to detector’s ref, in radians
rot2 – second rotation from sample ref to detector’s ref, in radians
rot3 – third rotation from sample ref to detector’s ref, in radians
pixel1 (float) – Deprecated. Pixel size of the fist dimension of the detector, in meter. If both pixel1 and pixel2 are not None, detector pixel size is overwritten. Prefer defining the detector pixel size on the provided detector object. Prefer defining the detector pixel size on the provided detector object (detector.pixel1 = 5e-6).
pixel2 (float) – Deprecated. Pixel size of the second dimension of the detector, in meter. If both pixel1 and pixel2 are not None, detector pixel size is overwritten. Prefer defining the detector pixel size on the provided detector object (detector.pixel2 = 5e-6).
splineFile (str) – Deprecated. File containing the geometric distortion of the detector. If not None, pixel1 and pixel2 are ignored and detector spline is overwritten. Prefer defining the detector spline manually (detector.splineFile = "file.spline").
detector (str or pyFAI.Detector) – name of the detector or Detector instance. String description is deprecated. Prefer using the result of the detector factory: pyFAI.detector_factory("eiger4m")
wavelength (float) – Wave length used in meter

array_from_unit(shape=None, typ='center', unit=2th_deg, scale=True)¶

Generate an array of position in different dimentions (R, Q, 2Theta)

Parameters:	shape (ndarray.shape) – shape of the expected array, leave it to None for safety typ (str) – “center”, “corner” or “delta” unit (pyFAI.units.Enum) – can be Q, TTH, R for now scale – set to False for returning the internal representation (S.I. often) which is faster
Returns:	R, Q or 2Theta array depending on unit
Return type:	ndarray

calc_pos_zyx(d0=None, d1=None, d2=None, param=None, corners=False, use_cython=True)¶

Calculate the position of a set of points in space in the sample’s centers referential.

This is usually used for calculating the pixel position in space.

Parameters:	d0 – altitude on the point compared to the detector (i.e. z), may be None d1 – position on the detector along the slow dimention (i.e. y) d2 – position on the detector along the fastest dimention (i.e. x) corners – return positions on the corners (instead of center)
Returns:	3-tuple of nd-array, with dim0=along the beam, dim1=along slowest dimension dim2=along fastest dimension

calc_transmission(t0, shape=None)¶

Defines the absorption correction for a phosphor screen or a scintillator from t0, the normal transmission of the screen.

$Icor = \frac{Iobs(1-t0)}{1-exp(ln(t0)/cos(incidence))} let_t = \frac{1-exp(ln(t0)/cos(incidence))}{1 - t0}$

See reference on: J. Appl. Cryst. (2002). 35, 356–359 G. Wu et al. CCD phosphor

Parameters:	t0 – value of the normal transmission (from 0 to 1) shape – shape of the array
Returns:	actual

calcfrom1d(tth, I, shape=None, mask=None, dim1_unit=2th_deg, correctSolidAngle=True, dummy=0.0, polarization_factor=None, polarization_axis_offset=0, dark=None, flat=None)¶

Computes a 2D image from a 1D integrated profile

Parameters:

tth – 1D array with radial unit, this array needs to be ordered
I – scattering intensity, corresponding intensity
shape – shape of the image (if not defined by the detector)
dim1_unit – unit for the “tth” array
correctSolidAngle –
dummy – value for masked pixels
polarization_factor – set to true to use previously used value
polarization_axis_offset – axis_offset to be send to the polarization method
dark – dark current correction
flat – flatfield corrction

Returns:

2D image reconstructed

center_array(shape=None, unit='2th_deg', scale=True)¶

Generate a 2D array of the given shape with (i,j) (radial angle ) for all elements.

Parameters:	shape (2-tuple of integer) – expected shape unit – string like “2th_deg” or an instance of pyFAI.units.Unit scale – set to False for returning the internal representation (S.I. often) which is faster
Returns:	3d array with shape=(*shape,4,2) the two elements are: - dim3[0]: radial angle 2th, q, r... - dim3[1]: azimuthal angle chi

chi(d1, d2, path='cython')¶

Calculate the chi (azimuthal angle) for the centre of a pixel at coordinate d1,d2 which in the lab ref has coordinate:

X1 = p1*cos(rot2)*cos(rot3) + p2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) - L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3)) X2 = p1*cos(rot2)*sin(rot3) - L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) + p2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3)) X3 = -(L*cos(rot1)*cos(rot2)) + p2*cos(rot2)*sin(rot1) - p1*sin(rot2) hence tan(Chi) = X2 / X1

Parameters:	d1 (float or array of them) – pixel coordinate along the 1st dimention (C convention) d2 (float or array of them) – pixel coordinate along the 2nd dimention (C convention) path – can be “tan” (i.e via numpy) or “cython”
Returns:	chi, the azimuthal angle in rad

chiArray(shape=None)¶

Generate an array of azimuthal angle chi(i,j) for all elements in the detector.

Azimuthal angles are in radians

Nota: Refers to the pixel centers !

Parameters:	shape – the shape of the chi array
Returns:	the chi array as numpy.ndarray

chi_corner(d1, d2)¶

Calculate the chi (azimuthal angle) for the corner of a pixel at coordinate d1,d2 which in the lab ref has coordinate:

X1 = p1*cos(rot2)*cos(rot3) + p2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) - L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3)) X2 = p1*cos(rot2)*sin(rot3) - L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) + p2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3)) X3 = -(L*cos(rot1)*cos(rot2)) + p2*cos(rot2)*sin(rot1) - p1*sin(rot2) hence tan(Chi) = X2 / X1

Parameters:	d1 (float or array of them) – pixel coordinate along the 1st dimention (C convention) d2 (float or array of them) – pixel coordinate along the 2nd dimention (C convention)
Returns:	chi, the azimuthal angle in rad

chia¶: chi array in cache

cornerArray(*arg, **kw)¶: decorator that deprecates the use of a function

cornerQArray(*arg, **kw)¶: decorator that deprecates the use of a function

cornerRArray(*arg, **kw)¶: decorator that deprecates the use of a function

cornerRd2Array(*arg, **kw)¶: decorator that deprecates the use of a function

corner_array(shape=None, unit=None, use_cython=True, scale=True)¶

Generate a 3D array of the given shape with (i,j) (radial angle 2th, azimuthal angle chi ) for all elements.

Parameters:	shape (2-tuple of integer) – expected shape unit – string like “2th_deg” or an instance of pyFAI.units.Unit use_cython – set to False to use the slower Python path (for tests) scale – set to False for returning the internal representation (S.I. often) which is faster
Returns:	3d array with shape=(*shape,4,2) the two elements are: - dim3[0]: radial angle 2th, q, r... - dim3[1]: azimuthal angle chi

correct_SA_spline¶

cosIncidance(d1, d2, path='cython')¶

Calculate the incidence angle (alpha) for current pixels (P). The poni being the point of normal incidence, it’s incidence angle is ${alpha} = 0$ hence $cos({alpha}) = 1$

Parameters:	d1 – 1d or 2d set of points in pixel coord d2 – 1d or 2d set of points in pixel coord
Returns:	cosine of the incidence angle

del_chia()¶

del_dssa()¶

del_qa()¶

del_ra()¶

del_ttha()¶

delta2Theta(shape=None)¶

Generate a 3D array of the given shape with (i,j) with the max distance between the center and any corner in 2 theta

Parameters:	shape – The shape of the detector array: 2-tuple of integer
Returns:	2D-array containing the max delta angle between a pixel center and any corner in 2theta-angle (rad)

deltaChi(shape=None, use_cython=True)¶

Generate a 3D array of the given shape with (i,j) with the max distance between the center and any corner in chi-angle (rad)

Parameters:	shape – The shape of the detector array: 2-tuple of integer
Returns:	2D-array containing the max delta angle between a pixel center and any corner in chi-angle (rad)

deltaQ(shape=None)¶

Generate a 2D array of the given shape with (i,j) with the max distance between the center and any corner in q_vector unit (nm^-1)

Parameters:	shape – The shape of the detector array: 2-tuple of integer
Returns:	array 2D containing the max delta Q between a pixel center and any corner in q_vector unit (nm^-1)

deltaR(shape=None)¶

Generate a 2D array of the given shape with (i,j) with the max distance between the center and any corner in radius unit (mm)

Parameters:	shape – The shape of the detector array: 2-tuple of integer
Returns:	array 2D containing the max delta Q between a pixel center and any corner in q_vector unit (nm^-1)

deltaRd2(shape=None)¶

Generate a 2D array of the given shape with (i,j) with the max distance between the center and any corner in unit: reciprocal spacing squarred (1/nm^2)

Parameters:	shape – The shape of the detector array: 2-tuple of integer
Returns:	array 2D containing the max delta (d*)^2 between a pixel center and any corner in reciprocal spacing squarred (1/nm^2)

delta_array(shape=None, unit='2th_deg', scale=False)¶

Generate a 2D array of the given shape with (i,j) (delta-radial angle) for all elements.

Parameters:

shape (2-tuple of integer) – expected shape
unit – string like “2th_deg” or an instance of pyFAI.units.Unit
scale – set to False for returning the internal representation (S.I. often) which is faster

Returns:

3d array with shape=(*shape,4,2) the two elements are:

dim3[0]: radial angle 2th, q, r...
dim3[1]: azimuthal angle chi

diffSolidAngle(d1, d2)¶

Calculate the solid angle of the current pixels (P) versus the PONI (C)

$dOmega = \frac{Omega(P)}{Omega(C)} = \frac{A \cdot cos(a)}{SP^2} \cdot \frac{SC^2}{A \cdot cos(0)} = \frac{3}{cos(a)} = \frac{SC^3}{SP^3} cos(a) = \frac{SC}{SP}$

Parameters:	d1 – 1d or 2d set of points d2 – 1d or 2d set of points (same size&shape as d1)
Returns:	solid angle correction array

dist¶

dssa¶: solid angle array in cache

getFit2D()¶

Export geometry setup with the geometry of Fit2D

Returns:	dict with parameters compatible with fit2D geometry

getPyFAI()¶

Export geometry setup with the geometry of PyFAI

Returns:	dict with the parameter-set of the PyFAI geometry

getSPD()¶

get the SPD like parameter set: For geometry description see Peter Boesecke J.Appl.Cryst.(2007).40, s423–s427

Basically the main difference with pyFAI is the order of the axis which are flipped

Returns: dictionnary with those parameters: SampleDistance: distance from sample to detector at the PONI (orthogonal projection) Center_1, pixel position of the PONI along fastest axis Center_2: pixel position of the PONI along slowest axis Rot_1: rotation around the fastest axis (x) Rot_2: rotation around the slowest axis (y) Rot_3: rotation around the axis ORTHOGONAL to the detector plan PSize_1: pixel size in meter along the fastest dimention PSize_2: pixel size in meter along the slowst dimention splineFile: name of the file containing the spline BSize_1: pixel binning factor along the fastest dimention BSize_2: pixel binning factor along the slowst dimention WaveLength: wavelength used in meter

get_chia()¶

get_correct_solid_angle_for_spline()¶

get_dist()¶

get_dssa()¶

get_mask()¶

get_maskfile()¶

get_pixel1()¶

get_pixel2()¶

get_poni1()¶

get_poni2()¶

get_qa()¶

get_ra()¶

get_rot1()¶

get_rot2()¶

get_rot3()¶

get_shape(shape=None)¶

Guess what is the best shape ....

Parameters:	shape – force this value (2-tuple of int)
Returns:	2-tuple of int

get_spline()¶

get_splineFile()¶

get_ttha()¶

get_wavelength()¶

load(filename)¶

Load the refined parameters from a file.

Parameters:	filename (string) – name of the file to load

make_headers(type_='list')¶

Create a configuration for the

Parameters:	type – can be “list” or “dict”
Returns:	the header with the proper format

mask¶

maskfile¶

oversampleArray(myarray)¶

pixel1¶

pixel2¶

polarization(shape=None, factor=None, axis_offset=0, with_checksum=False)¶

Calculate the polarization correction accoding to the polarization factor:

If the polarization factor is None,

the correction is not applied (returns 1)
If the polarization factor is 0 (circular polarization),

the correction correspond to (1+(cos2θ)^2)/2
If the polarization factor is 1 (linear horizontal polarization),

there is no correction in the vertical plane and a node at 2th=90, chi=0
If the polarization factor is -1 (linear vertical polarization),

there is no correction in the horizontal plane and a node at 2th=90, chi=90
If the polarization is elliptical, the polarization factor varies between -1 and +1.

The axis_offset parameter allows correction for the misalignement of the polarization plane (or ellipse main axis) and the the detector’s X axis.

Parameters:	factor – (Ih-Iv)/(Ih+Iv): varies between 0 (circular/random polarization) and 1 (where division by 0 could occure at 2th=90, chi=0) axis_offset – Angle between the polarization main axis and detector’s X direction (in radians !!!)
Returns:	2D array with polarization correction array (intensity/polarisation)

poni1¶

poni2¶

positionArray(*arg, **kw)¶: decorator that deprecates the use of a function

position_array(shape=None, corners=False, dtype=<type 'numpy.float64'>, use_cython=True)¶

Generate an array for the pixel position given the shape of the detector.

if corners is False, the coordinates of the center of the pixel is returned in an array of shape: (shape[0], shape[1], 3) where the 3 coordinates are: * z: along incident beam, * y: to the top/sky, * x: towards the center of the ring

If is True, the corner of each pixels are then returned. the output shape is then (shape[0], shape[1], 4, 3)

Parameters:	shape – shape of the array expected corners – set to true to receive a (...,4,3) array of corner positions dtype – output format requested. Double precision is needed for fitting the geometry use_cython ((bool)) – set to false to test the Python path (slower)
Returns:	3D coodinates as nd-array of size (...,3) or (...,3) (default)

Nota: this value is not cached and actually generated on demand (costly)

qArray(shape=None)¶: Generate an array of the given shape with q(i,j) for all elements.

qCornerFunct(*arg, **kw)¶: decorator that deprecates the use of a function

qFunction(d1, d2, param=None, path='cython')¶

Calculates the q value for the center of a given pixel (or set of pixels) in nm-1

q = 4pi/lambda sin( 2theta / 2 )

Parameters:	d1 (scalar or array of scalar) – position(s) in pixel in first dimension (c order) d2 (scalar or array of scalar) – position(s) in pixel in second dimension (c order)
Returns:	q in in nm^(-1)
Return type:	float or array of floats.

qa¶: Q array in cache

rArray(shape=None)¶

Generate an array of the given shape with r(i,j) for all elements; The radius r being in meters.

Parameters:	shape – expected shape of the detector
Returns:	2d array of the given shape with radius in m from beam center on detector.

rCornerFunct(*arg, **kw)¶: decorator that deprecates the use of a function

rFunction(d1, d2, param=None, path='cython')¶

Calculates the radius value for the center of a given pixel (or set of pixels) in m

r = distance to the incident beam

Parameters:	d1 (scalar or array of scalar) – position(s) in pixel in first dimension (c order) d2 (scalar or array of scalar) – position(s) in pixel in second dimension (c order)
Returns:	r in in m
Return type:	float or array of floats.

ra¶: R array in cache

rd2Array(shape=None)¶

Generate an array of the given shape with (d*(i,j))^2 for all pixels.

d*^2 is the reciprocal spacing squared in inverse nm squared

Parameters:	shape – expected shape of the detector
Returns:	2d array of the given shape with reciprocal spacing squared

read(filename)¶

Load the refined parameters from a file.

Parameters:	filename (string) – name of the file to load

reset()¶: reset most arrays that are cached: used when a parameter changes.

rot1¶

rot2¶

rot3¶

save(filename)¶

Save the geometry parameters.

Parameters:	filename (string) – name of the file where to save the parameters

setChiDiscAtPi()¶: Set the position of the discontinuity of the chi axis between -pi and +pi. This is the default behavour

setChiDiscAtZero()¶: Set the position of the discontinuity of the chi axis between 0 and 2pi. By default it is between pi and -pi

setFit2D(directDist, centerX, centerY, tilt=0.0, tiltPlanRotation=0.0, pixelX=None, pixelY=None, splineFile=None)¶

Set the Fit2D-like parameter set: For geometry description see HPR 1996 (14) pp-240

Warning: Fit2D flips automatically images depending on their file-format. By reverse engineering we noticed this behavour for Tiff and Mar345 images (at least). To obtaine correct result you will have to flip images using numpy.flipud.

Parameters:

direct – direct distance from sample to detector along the incident beam (in millimeter as in fit2d)
tilt – tilt in degrees
tiltPlanRotation – Rotation (in degrees) of the tilt plan arround the Z-detector axis * 0deg -> Y does not move, +X goes to Z<0 * 90deg -> X does not move, +Y goes to Z<0 * 180deg -> Y does not move, +X goes to Z>0 * 270deg -> X does not move, +Y goes to Z>0
pixelX,pixelY – as in fit2d they ar given in micron, not in meter
centerY (centerX,) – pixel position of the beam center
splineFile – name of the file containing the spline

setOversampling(*arg, **kw)¶: decorator that deprecates the use of a function

setPyFAI(**kwargs)¶: set the geometry from a pyFAI-like dict

setSPD(SampleDistance, Center_1, Center_2, Rot_1=0, Rot_2=0, Rot_3=0, PSize_1=None, PSize_2=None, splineFile=None, BSize_1=1, BSize_2=1, WaveLength=None)¶

Set the SPD like parameter set: For geometry description see Peter Boesecke J.Appl.Cryst.(2007).40, s423–s427

Basically the main difference with pyFAI is the order of the axis which are flipped

Parameters:

SampleDistance – distance from sample to detector at the PONI (orthogonal projection)
Center_1 – pixel position of the PONI along fastest axis
Center_2 – pixel position of the PONI along slowest axis
Rot_1 – rotation around the fastest axis (x)
Rot_2 – rotation around the slowest axis (y)
Rot_3 – rotation around the axis ORTHOGONAL to the detector plan
PSize_1 – pixel size in meter along the fastest dimention
PSize_2 – pixel size in meter along the slowst dimention
splineFile – name of the file containing the spline
BSize_1 – pixel binning factor along the fastest dimention
BSize_2 – pixel binning factor along the slowst dimention
WaveLength – wavelength used

set_chia(_)¶

set_correct_solid_angle_for_spline(value)¶

set_dist(value)¶

set_dssa(_)¶

set_mask(mask)¶

set_maskfile(maskfile)¶

set_param(param)¶: set the geometry from a 6-tuple with dist, poni1, poni2, rot1, rot2, rot3

set_pixel1(pixel1)¶

set_pixel2(pixel2)¶

set_poni1(value)¶

set_poni2(value)¶

set_qa(_)¶

set_ra(_)¶

set_rot1(value)¶

set_rot2(value)¶

set_rot3(value)¶

set_spline(spline)¶

set_splineFile(splineFile)¶

set_ttha(_)¶

set_wavelength(value)¶

classmethod sload(filename)¶

A static method combining the constructor and the loader from a file

Parameters:	filename (string) – name of the file to load
Returns:	instance of Gerometry of AzimuthalIntegrator set-up with the parameter from the file.

solidAngleArray(shape=None, order=3, absolute=False)¶

Generate an array for the solid angle correction given the shape of the detector.

solid_angle = cos(incidence)^3

Parameters:	shape – shape of the array expected order – should be 3, power of the formula just obove absolute – the absolute solid angle is calculated as:

SA = pix1*pix2/dist^2 * cos(incidence)^3

spline¶

splineFile¶

tth(d1, d2, param=None, path='cython')¶

Calculates the 2theta value for the center of a given pixel (or set of pixels)

Parameters:	d1 (scalar or array of scalar) – position(s) in pixel in first dimension (c order) d2 (scalar or array of scalar) – position(s) in pixel in second dimension (c order) path – can be “cos”, “tan” or “cython”
Returns:	2theta in radians
Return type:	float or array of floats.

tth_corner(*arg, **kw)¶: decorator that deprecates the use of a function

ttha¶: 2theta array in cache

twoThetaArray(shape=None)¶

Generate an array of two-theta(i,j) in radians for each pixel in detector

the 2theta array values are in radians

Parameters:	shape – shape of the detector
Returns:	array of 2theta position in radians

wavelength¶

write(filename)¶

Save the geometry parameters.

Parameters:	filename (string) – name of the file where to save the parameters

class pyFAI.geometry.PolarizationArray¶

Bases: tuple

PolarizationArray(array, checksum)

array¶: Alias for field number 0

checksum¶: Alias for field number 1

class pyFAI.geometry.PolarizationDescription¶

Bases: tuple

PolarizationDescription(polarization_factor, axis_offset)

axis_offset¶: Alias for field number 1

polarization_factor¶: Alias for field number 0

`geometryRefinement` Module¶

Module used to perform the geometric refinement of the model

class pyFAI.geometryRefinement.GeometryRefinement(data=None, dist=1, poni1=None, poni2=None, rot1=0, rot2=0, rot3=0, pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None, calibrant=None)¶

Bases: pyFAI.azimuthalIntegrator.AzimuthalIntegrator

__init__(data=None, dist=1, poni1=None, poni2=None, rot1=0, rot2=0, rot3=0, pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None, calibrant=None)¶

Parameters:

data – ndarray float64 shape = n, 3 col0: pos in dim0 (in pixels) col1: pos in dim1 (in pixels) col2: ring index in calibrant object
dist – guessed sample-detector distance (optional, in m)
poni1 – guessed PONI coordinate along the Y axis (optional, in m)
poni2 – guessed PONI coordinate along the X axis (optional, in m)
rot1 – guessed tilt of the detector around the Y axis (optional, in rad)
rot2 – guessed tilt of the detector around the X axis (optional, in rad)
rot3 – guessed tilt of the detector around the incoming beam axis (optional, in rad)
pixel1 – Pixel size along the vertical direction of the detector (in m), almost mandatory
pixel2 – Pixel size along the horizontal direction of the detector (in m), almost mandatory
splineFile – file describing the detector as 2 cubic splines. Replaces pixel1 & pixel2
detector – name of the detector or Detector instance. Replaces splineFile, pixel1 & pixel2
wavelength – wavelength in m (1.54e-10)
calibrant – instance of pyFAI.calibrant.Calibrant containing the d-Spacing

anneal(maxiter=1000000)¶

calc_2th(rings, wavelength=None)¶

Parameters:	rings – indices of the rings. starts at 0 and self.dSpacing should be long enough !!! wavelength – wavelength in meter

chi2(param=None)¶

chi2_wavelength(param=None)¶

confidence(with_rot=True)¶

Confidence interval obtained from the second derivative of the error function next to its minimum value.

Note the confidence interval increases with the number of points which is “surprizing”

Parameters:	with_rot – if true include rot1 & rot2 in the parameter set.
Returns:	std_dev, confidence

curve_fit(with_rot=True)¶

Refine the geometry and provide confidence interval Use curve_fit from scipy.optimize to not only refine the geometry (unconstrained fit)

Parameters:	with_rot – include rotation intro error measurment
Returns:	std_dev, confidence

dist_max¶

dist_min¶

get_dist_max()¶

get_dist_min()¶

get_poni1_max()¶

get_poni1_min()¶

get_poni2_max()¶

get_poni2_min()¶

get_rot1_max()¶

get_rot1_min()¶

get_rot2_max()¶

get_rot2_min()¶

get_rot3_max()¶

get_rot3_min()¶

get_wavelength_max()¶

get_wavelength_min()¶

guess_poni()¶

PONI can be guessed by the centroid of the ring with lowest 2Theta

It may try to fit an ellipse and sometimes it works

poni1_max¶

poni1_min¶

poni2_max¶

poni2_min¶

refine1()¶

refine2(maxiter=1000000, fix=None)¶

refine2_wavelength(maxiter=1000000, fix=None)¶

residu1(param, d1, d2, rings)¶

residu1_wavelength(param, d1, d2, rings)¶

residu2(param, d1, d2, rings)¶

residu2_wavelength(param, d1, d2, rings)¶

residu2_wavelength_weighted(param, d1, d2, rings, weight)¶

residu2_weighted(param, d1, d2, rings, weight)¶

roca()¶: run roca to optimise the parameter set

rot1_max¶

rot1_min¶

rot2_max¶

rot2_min¶

rot3_max¶

rot3_min¶

set_dist_max(value)¶

set_dist_min(value)¶

set_poni1_max(value)¶

set_poni1_min(value)¶

set_poni2_max(value)¶

set_poni2_min(value)¶

set_rot1_max(value)¶

set_rot1_min(value)¶

set_rot2_max(value)¶

set_rot2_min(value)¶

set_rot3_max(value)¶

set_rot3_min(value)¶

set_tolerance(value=10)¶

Set the tolerance for a refinement of the geometry; in percent of the original value

Parameters:	value – Tolerance as a percentage

set_wavelength_max(value)¶

set_wavelength_min(value)¶

simplex(maxiter=1000000)¶

wavelength_max¶

wavelength_min¶

`goniometer` Module¶

Everything you need to calibrate a detector mounted on a goniometer or any translation table

class pyFAI.goniometer.GeometryTransformation(dist_expr, poni1_expr, poni2_expr, rot1_expr, rot2_expr, rot3_expr, param_names, pos_names=None, constants=None, content=None)¶

Bases: object

This class, once instanciated, behaves like a function (via the __call__ method). It is responsible for taking any input geometry and translate it into a set of parameters compatible with pyFAI, i.e. a tuple with: (dist, poni1, poni2, rot1, rot2, rot3)

This function uses numexpr for formula evaluation.

__init__(dist_expr, poni1_expr, poni2_expr, rot1_expr, rot2_expr, rot3_expr, param_names, pos_names=None, constants=None, content=None)¶

Constructor of the class

Parameters:

dist_expr – formula (as string) providing with the dist
poni1_expr – formula (as string) providing with the poni1
poni2_expr – formula (as string) providing with the poni2
rot1_expr – formula (as string) providing with the rot1
rot2_expr – formula (as string) providing with the rot2
rot3_expr – formula (as string) providing with the rot3
param_names – list of names of the parameters used in the model
pos_names – list of motor names for gonio with >1 degree of freedom
constants – a dictionary with some constants the user may want to use
content – Should be None or the name of the class (may be used in the future to dispatch to multiple derivative classes)

to_dict()¶: Export the instance representation for serialization as a dictionary

pyFAI.goniometer.GeometryTranslation¶: alias of GeometryTransformation

class pyFAI.goniometer.Goniometer(param, trans_function, detector='Detector', wavelength=None, param_names=None, pos_names=None)¶

Bases: object

This class represents the goniometer model. Unlike this name suggests, it may include translation in addition to rotations

__init__(param, trans_function, detector='Detector', wavelength=None, param_names=None, pos_names=None)¶

Constructor of the Goniometer class.

Parameters:

param – vector of parameter to refine for defining the detector position on the goniometer
trans_function – function taking the parameters of the goniometer and the goniometer position and return the 6 parameters [dist, poni1, poni2, rot1, rot2, rot3]
detector – detector mounted on the moving arm
wavelength – the wavelength used for the experiment
param_names – list of names to “label” the param vector.
pos_names – list of names to “label” the position vector of the gonio.

file_version = 'Goniometer calibration v1.0'¶

get_ai(position)¶

Creates an azimuthal integrator from the motor position

Parameters:	position – the goniometer position, a float for a 1 axis goniometer
Returns:	A freshly build AzimuthalIntegrator

get_mg(positions)¶

Creates a MultiGeometry integrator from a list of goniometer positions.

Parameters:	positions – A list of goniometer positions
Returns:	A freshly build multi-geometry

save(filename)¶

Save the goniometer configuration to file

Parameters:	filename – name of the file to save configuration to

classmethod sload(filename)¶

to_dict()¶

Export the goniometer configuration to a dictionary

Returns:	Ordered dictionary

write(filename)¶

Save the goniometer configuration to file

Parameters:	filename – name of the file to save configuration to

class pyFAI.goniometer.GoniometerRefinement(param, pos_function, trans_function, detector='Detector', wavelength=None, param_names=None, pos_names=None, bounds=None)¶

Bases: pyFAI.goniometer.Goniometer

This class allow the translation of a goniometer geometry into a pyFAI geometry using a set of parameter to refine.

__init__(param, pos_function, trans_function, detector='Detector', wavelength=None, param_names=None, pos_names=None, bounds=None)¶

Constructor of the GoniometerRefinement class

Parameters:

param – vector of parameter to refine for defining the detector position on the goniometer
pos_function – a function taking metadata and extracting the goniometer position
trans_function – function taking the parameters of the goniometer and the gonopmeter position and return the 6 parameters [dist, poni1, poni2, rot1, rot2, rot3]
detector – detector mounted on the moving arm
wavelength – the wavelength used for the experiment
param_names – list of names to “label” the param vector.
pos_names – list of names to “label” the position vector of the gonio.
bounds – list of 2-tuple with the lower and upper bound of each function

chi2(param=None)¶: Calculate the average of the square of the error for a given parameter set

new_geometry(label, image=None, metadata=None, control_points=None, calibrant=None, geometry=None)¶

Add a new geometry for calibration

Parameters:	label – usually a string image – 2D numpy array with the Debye scherrer rings metadata – some metadata control_points – an instance of ControlPoints calibrant – the calibrant used for calibrating geometry – poni or AzimuthalIntegrator instance.

refine2(method='slsqp', **options)¶

Geometry refinement tool

See https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.minimize.html

Parameters:	method – name of the minimizer options – options for the minimizer

residu2(param)¶: Actually performs the calulation of the average of the error squared

set_bounds(name, mini=None, maxi=None)¶

Redefines the bounds for the refinement

Parameters:	name – name of the parameter or index in the parameter set mini – minimum value maxi – maximum value

class pyFAI.goniometer.PoniParam¶

Bases: tuple

PoniParam(dist, poni1, poni2, rot1, rot2, rot3)

dist¶: Alias for field number 0

poni1¶: Alias for field number 1

poni2¶: Alias for field number 2

rot1¶: Alias for field number 3

rot2¶: Alias for field number 4

rot3¶: Alias for field number 5

class pyFAI.goniometer.SingleGeometry(label, image=None, metadata=None, pos_function=None, control_points=None, calibrant=None, detector=None, geometry=None)¶

Bases: object

This class represents a single geometry of a detector position on a goniometer arm

__init__(label, image=None, metadata=None, pos_function=None, control_points=None, calibrant=None, detector=None, geometry=None)¶

Constructor of the SingleGeometry class, used for calibrating a multi-geometry setup with a moving detector.

Parameters:

label – name of the geometry, a string or anything unmutable
image – image with Debye-Scherrer rings as 2d numpy array
metadata – anything which contains the goniometer position
pos_function – a function which takes the metadata as input and returns the goniometer arm position
control_points – a pyFAI.control_points.ControlPoints instance (optional parameter)
calibrant – a pyFAI.calibrant.Calibrant instance. Contains the wavelength to be used (optional parameter)
detector – a pyFAI.detectors.Detector instance or something like that Contains the mask to be used (optional parameter)
geometry – an azimuthal integrator or a ponifile (or a dict with the geometry) (optional parameter)

extract_cp(max_rings=None, pts_per_deg=1.0)¶

Performs an automatic keypoint extraction and update the geometry refinement part

Parameters:	max_ring – extract at most N rings from the image pts_per_deg – number of control points per azimuthal degree (increase for better precision)

get_ai()¶

Create a new azimuthal integrator to be used.

Returns:	Azimuthal Integrator instance

get_position()¶: This method is in charge of calculating the motor position from metadata/label/...

`detectors` Module¶

Description of all detectors with a factory to instantiate them

class pyFAI.detectors.ADSC_Q210(pixel1=5.1e-05, pixel2=5.1e-05)¶

Bases: pyFAI.detectors.Detector

ADSC Quantum 210r detector, 2x2 chips

Informations from http://www.adsc-xray.com/products/ccd-detectors/q210r-ccd-detector/

Question: how are the gaps handled ?

MAX_SHAPE = (4096, 4096)¶

__init__(pixel1=5.1e-05, pixel2=5.1e-05)¶

aliases = ['Quantum 210']¶

force_pixel = True¶

class pyFAI.detectors.ADSC_Q270(pixel1=6.48e-05, pixel2=6.48e-05)¶

Bases: pyFAI.detectors.Detector

ADSC Quantum 270r detector, 2x2 chips

Informations from http://www.adsc-xray.com/products/ccd-detectors/q270-ccd-detector/

Question: how are the gaps handled ?

MAX_SHAPE = (4168, 4168)¶

__init__(pixel1=6.48e-05, pixel2=6.48e-05)¶

aliases = ['Quantum 270']¶

force_pixel = True¶

class pyFAI.detectors.ADSC_Q315(pixel1=5.1e-05, pixel2=5.1e-05)¶

Bases: pyFAI.detectors.Detector

ADSC Quantum 315r detector, 3x3 chips

Informations from http://www.adsc-xray.com/products/ccd-detectors/q315r-ccd-detector/

Question: how are the gaps handled ?

MAX_SHAPE = (6144, 6144)¶

__init__(pixel1=5.1e-05, pixel2=5.1e-05)¶

aliases = ['Quantum 315']¶

force_pixel = True¶

class pyFAI.detectors.ADSC_Q4(pixel1=8.2e-05, pixel2=8.2e-05)¶

Bases: pyFAI.detectors.Detector

ADSC Quantum 4r detector, 2x2 chips

Informations from http://proteincrystallography.org/detectors/adsc.php

Question: how are the gaps handled ?

MAX_SHAPE = (2304, 2304)¶

__init__(pixel1=8.2e-05, pixel2=8.2e-05)¶

aliases = ['Quantum 4']¶

force_pixel = True¶

class pyFAI.detectors.Aarhus(pixel1=2.5e-05, pixel2=2.5e-05, radius=0.3)¶

Bases: pyFAI.detectors.Detector

Cylindrical detector made of a bent imaging-plate. Developped at the Danish university of Aarhus r = 1.2m or 0.3m

The image has to be laid-out horizontally

Nota: the detector is bending towards the sample, hence reducing the sample-detector distance. This is why z<0 (or p3<0)

TODO: update cython code for 3d detectors use expand2d instead of outer product with ones

IS_FLAT = False¶

MAX_SHAPE = (1000, 16000)¶

__init__(pixel1=2.5e-05, pixel2=2.5e-05, radius=0.3)¶

calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)¶

Calculate the position of each pixel center in cartesian coordinate and in meter of a couple of coordinates. The half pixel offset is taken into account here !!! Adapted to Nexus detector definition

Parameters:	d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension) d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension) center – retrieve the coordinate of the center of the pixel use_cython – set to False to test Python implementeation
Returns:	position in meter of the center of each pixels.
Return type:	ndarray

d1 and d2 must have the same shape, returned array will have the same shape.

force_pixel = True¶

get_pixel_corners(use_cython=True)¶

Calculate the position of the corner of the pixels

This should be overwritten by class representing non-contiguous detector (Xpad, ...)

Returns:	4D array containing: pixel index (slow dimension) pixel index (fast dimension) corner index (A, B, C or D), triangles or hexagons can be handled the same way vertex position (z,y,x)

class pyFAI.detectors.Apex2(pixel1=0.00012, pixel2=0.00012)¶

Bases: pyFAI.detectors.Detector

BrukerApex2 detector

Actually a derivative from the Fairchild detector with higher binning

DEFAULT_PIXEL1 = 6e-05¶

DEFAULT_PIXEL2 = 6e-05¶

MAX_SHAPE = (1024, 1024)¶

__init__(pixel1=0.00012, pixel2=0.00012)¶: Defaults to 2x2 binning

aliases = ['ApexII', 'Bruker']¶

force_pixel = True¶

class pyFAI.detectors.Basler(pixel=3.75e-06)¶

Bases: pyFAI.detectors.Detector

Basler camera are simple CCD camara over GigaE

MAX_SHAPE = (966, 1296)¶

__init__(pixel=3.75e-06)¶

aliases = ['aca1300']¶

force_pixel = True¶

class pyFAI.detectors.Detector(pixel1=None, pixel2=None, splineFile=None, max_shape=None)¶

Bases: object

Generic class representing a 2D detector

API_VERSION = '1.0'¶

HAVE_TAPER = False¶: If true a spline file is mandatory to correct the geometry

IS_CONTIGUOUS = True¶

IS_FLAT = True¶

__init__(pixel1=None, pixel2=None, splineFile=None, max_shape=None)¶

Parameters:	pixel1 (float) – size of the pixel in meter along the slow dimension (often Y) pixel2 (float) – size of the pixel in meter along the fast dimension (often X) splineFile (str) – path to file containing the geometric correction. max_shape (2-tuple of integrers) – maximum size of the detector

aliases = []¶

binning¶

calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)¶

Calculate the position of each pixel center in cartesian coordinate and in meter of a couple of coordinates. The half pixel offset is taken into account here !!! Adapted to Nexus detector definition

Parameters:	d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension) d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension) center – retrieve the coordinate of the center of the pixel, unless gives one corner use_cython – set to False to test Python implementation
Returns:	position in meter of the center of each pixels.
Return type:	3xndarray, the later being None if IS_FLAT

d1 and d2 must have the same shape, returned array will have the same shape.

pos_z is None for flat detectors

calc_mask()¶

Method calculating the mask for a given detector

Detectors with gaps should overwrite this method with something actually calculating the mask!

Returns:	the mask with valid pixel to 0
Return type:	numpy ndarray of int8 or None

darkcurrent¶

classmethod factory(name, config=None)¶

A kind of factory...

Parameters:	name (str) – name of a detector config (dict or JSON representation of it.) – configuration of the detector
Returns:	an instance of the right detector, set-up if possible
Return type:	pyFAI.detectors.Detector

flatfield¶

force_pixel = False¶

classmethod from_dict(dico)¶

Creates a brand new detector from the description of the detector as a dict

Parameters:	dico – JSON serializable dictionary
Returns:	Detector instance

getFit2D()¶

Helper method to serialize the description of a detector using the Fit2d units

Returns:	representation of the detector easy to serialize
Return type:	dict

getPyFAI()¶

Helper method to serialize the description of a detector using the pyFAI way with everything in S.I units.

Returns:	representation of the detector easy to serialize
Return type:	dict

get_binning()¶

get_darkcurrent()¶

get_darkcurrent_crc()¶

get_flatfield()¶

get_flatfield_crc()¶

get_mask()¶

get_mask_crc()¶

get_maskfile()¶

get_name()¶: Get a meaningful name for detector

get_pixel1()¶

get_pixel2()¶

get_pixel_corners()¶

Calculate the position of the corner of the pixels

This should be overwritten by class representing non-contiguous detector (Xpad, ...)

Precision float32 is ok: precision of 1µm for a detector size of 1m

Returns:	4D array containing: pixel index (slow dimension) pixel index (fast dimension) corner index (A, B, C or D), triangles or hexagons can be handled the same way vertex position (z,y,x)

get_splineFile()¶

guess_binning(data)¶: Guess the binning/mode depending on the image shape :param data: 2-tuple with the shape of the image or the image with a .shape attribute.

mask¶

maskfile¶

name¶: Get a meaningful name for detector

pixel1¶

pixel2¶

registry = {'pilatus1m_cdte': <class 'pyFAI.detectors.PilatusCdTe1M'>, 'imxpads10': <class 'pyFAI.detectors.ImXPadS10'>, 'pilatus1mcdte': <class 'pyFAI.detectors.PilatusCdTe1M'>, 'rayonix_mx225hs': <class 'pyFAI.detectors.RayonixMx225hs'>, 'pilatus_cdte_2m': <class 'pyFAI.detectors.PilatusCdTe2M'>, 'd5': <class 'pyFAI.detectors.Xpad_flat'>, 'eiger_500k': <class 'pyFAI.detectors.Eiger500k'>, 'rayonix_mx170-hs': <class 'pyFAI.detectors.RayonixMx170'>, 'imxpad_s10': <class 'pyFAI.detectors.ImXPadS10'>, 'rayonix_mx340hs': <class 'pyFAI.detectors.RayonixMx340hs'>, 'condor': <class 'pyFAI.detectors.Fairchild'>, 'eiger16m': <class 'pyFAI.detectors.Eiger16M'>, 'pilatus_1m': <class 'pyFAI.detectors.Pilatus1M'>, 'oxd_titan': <class 'pyFAI.detectors.Titan'>, 'perkin_detector': <class 'pyFAI.detectors.Perkin'>, 'pilatus6m': <class 'pyFAI.detectors.Pilatus6M'>, 'titan_2k_x_2k': <class 'pyFAI.detectors.Titan'>, 'hf_262k': <class 'pyFAI.detectors.HF_262k'>, 'thales_electronics': <class 'pyFAI.detectors.Pixium'>, 'rayonixlx255': <class 'pyFAI.detectors.RayonixLx255'>, 'rayonixsx200': <class 'pyFAI.detectors.RayonixSx200'>, 'eiger4m': <class 'pyFAI.detectors.Eiger4M'>, 'rayonix_lx170': <class 'pyFAI.detectors.RayonixLx170'>, 'pilatuscdte300kw': <class 'pyFAI.detectors.PilatusCdTe300kw'>, 'pilatus300k': <class 'pyFAI.detectors.Pilatus300k'>, 'picamv2': <class 'pyFAI.detectors.RaspberryPi8M'>, 'picamv1': <class 'pyFAI.detectors.RaspberryPi5M'>, 'quantum_270': <class 'pyFAI.detectors.ADSC_Q270'>, 'rayonixmx300': <class 'pyFAI.detectors.RayonixMx300'>, 'pilatus300kw_cdte': <class 'pyFAI.detectors.PilatusCdTe300kw'>, 'rayonixlx225hs': <class 'pyFAI.detectors.RayonixLx255'>, 'pilatuscdte1m': <class 'pyFAI.detectors.PilatusCdTe1M'>, 'raspberrypi5m': <class 'pyFAI.detectors.RaspberryPi5M'>, 'pilatus_cdte_300kw': <class 'pyFAI.detectors.PilatusCdTe300kw'>, 'perkin': <class 'pyFAI.detectors.Perkin'>, 'rayonixmx340hs': <class 'pyFAI.detectors.RayonixMx340hs'>, 'rayonix_mx325': <class 'pyFAI.detectors.RayonixMx325'>, 'picam_v2': <class 'pyFAI.detectors.RaspberryPi8M'>, 'adsc_q270': <class 'pyFAI.detectors.ADSC_Q270'>, 'rayonixmx170hs': <class 'pyFAI.detectors.RayonixMx170'>, 'pilatus_6m': <class 'pyFAI.detectors.Pilatus6M'>, 'oxdtitan': <class 'pyFAI.detectors.Titan'>, 'dexela2923': <class 'pyFAI.detectors.Dexela2923'>, 'xpads540flat': <class 'pyFAI.detectors.Xpad_flat'>, 'titan': <class 'pyFAI.detectors.Titan'>, 'aarhus': <class 'pyFAI.detectors.Aarhus'>, 'rayonix_mx425hs': <class 'pyFAI.detectors.RayonixMx425hs'>, 'hf_4m': <class 'pyFAI.detectors.HF_4M'>, 'pilatuscdte2m': <class 'pyFAI.detectors.PilatusCdTe2M'>, 'pilatuscdte300k': <class 'pyFAI.detectors.PilatusCdTe300k'>, 'pilatus_1m_cdte': <class 'pyFAI.detectors.PilatusCdTe1M'>, 'pilatus_300kw': <class 'pyFAI.detectors.Pilatus300kw'>, 'rayonixmx170-hs': <class 'pyFAI.detectors.RayonixMx170'>, 'rayonixmx300hs': <class 'pyFAI.detectors.RayonixMx300hs'>, 'pixium4700detector': <class 'pyFAI.detectors.Pixium'>, 'rayonixlx255-hs': <class 'pyFAI.detectors.RayonixLx255'>, 'agilenttitan': <class 'pyFAI.detectors.Titan'>, 'eiger500k': <class 'pyFAI.detectors.Eiger500k'>, 'rayonixsx165': <class 'pyFAI.detectors.RayonixSx165'>, 'pilatus100k': <class 'pyFAI.detectors.Pilatus100k'>, 'rayonix_lx170-hs': <class 'pyFAI.detectors.RayonixLx170'>, 'perkin_elmer': <class 'pyFAI.detectors.Perkin'>, 'aca1300': <class 'pyFAI.detectors.Basler'>, 'quantum315': <class 'pyFAI.detectors.ADSC_Q315'>, 'quantum4': <class 'pyFAI.detectors.ADSC_Q4'>, 'rayonixsx85hs': <class 'pyFAI.detectors.RayonixSx85hs'>, 'eiger_4m': <class 'pyFAI.detectors.Eiger4M'>, 'imxpads140': <class 'pyFAI.detectors.ImXPadS140'>, 'rayonix_mx170': <class 'pyFAI.detectors.RayonixMx170'>, 'pilatus300kw': <class 'pyFAI.detectors.Pilatus300kw'>, 'hf-130k': <class 'pyFAI.detectors.HF_130K'>, 'pilatus_100k': <class 'pyFAI.detectors.Pilatus100k'>, 'rayonixsx30hs': <class 'pyFAI.detectors.RayonixSx30hs'>, 'mar345': <class 'pyFAI.detectors.Mar345'>, 'perkindetector': <class 'pyFAI.detectors.Perkin'>, 'rayonix_mx425_hs': <class 'pyFAI.detectors.RayonixMx425hs'>, 'rayonix_mx225': <class 'pyFAI.detectors.RayonixMx225'>, 'eiger1m': <class 'pyFAI.detectors.Eiger1M'>, 'mar_345': <class 'pyFAI.detectors.Mar345'>, 'pilatus2m': <class 'pyFAI.detectors.Pilatus2M'>, 'hf_9m': <class 'pyFAI.detectors.HF_9M'>, 'rayonixlx170hs': <class 'pyFAI.detectors.RayonixLx170'>, 'hf-1m': <class 'pyFAI.detectors.HF_1M'>, 'eiger_1m': <class 'pyFAI.detectors.Eiger1M'>, 'rayonix_lx170_hs': <class 'pyFAI.detectors.RayonixLx170'>, 'rayonixmx170': <class 'pyFAI.detectors.RayonixMx170'>, 'pilatus2mcdte': <class 'pyFAI.detectors.PilatusCdTe2M'>, 'mar165': <class 'pyFAI.detectors.RayonixSx165'>, 'pilatus200k': <class 'pyFAI.detectors.Pilatus200k'>, 'pilatus_200k': <class 'pyFAI.detectors.Pilatus200k'>, 'hf_2m': <class 'pyFAI.detectors.HF_2M'>, 'titan2kx2k': <class 'pyFAI.detectors.Titan'>, 'pilatus_cdte_1m': <class 'pyFAI.detectors.PilatusCdTe1M'>, 'hf-9.4m': <class 'pyFAI.detectors.HF_9M'>, 'fairchild_condor_486:90': <class 'pyFAI.detectors.Fairchild'>, 'hf-262k': <class 'pyFAI.detectors.HF_262k'>, 'xpad_flat': <class 'pyFAI.detectors.Xpad_flat'>, 'quantum_315': <class 'pyFAI.detectors.ADSC_Q315'>, 'eiger_16m': <class 'pyFAI.detectors.Eiger16M'>, 'pilatus_300k': <class 'pyFAI.detectors.Pilatus300k'>, 'raspberrypi8m': <class 'pyFAI.detectors.RaspberryPi8M'>, 'pilatus1m': <class 'pyFAI.detectors.Pilatus1M'>, 'xpad_s540_flat': <class 'pyFAI.detectors.Xpad_flat'>, 'pixium_4700_detector': <class 'pyFAI.detectors.Pixium'>, 'basler': <class 'pyFAI.detectors.Basler'>, 'pilatus2m_cdte': <class 'pyFAI.detectors.PilatusCdTe2M'>, 'rayonix_sx200': <class 'pyFAI.detectors.RayonixSx200'>, 'quantum270': <class 'pyFAI.detectors.ADSC_Q270'>, 'dexela_2923': <class 'pyFAI.detectors.Dexela2923'>, 'mar3450': <class 'pyFAI.detectors.Mar345'>, 'pixium': <class 'pyFAI.detectors.Pixium'>, 'fairchild': <class 'pyFAI.detectors.Fairchild'>, 'rayonixmx225hs': <class 'pyFAI.detectors.RayonixMx225hs'>, 'apexii': <class 'pyFAI.detectors.Apex2'>, 'quantum_210': <class 'pyFAI.detectors.ADSC_Q210'>, 'bruker': <class 'pyFAI.detectors.Apex2'>, 'thaleselectronics': <class 'pyFAI.detectors.Pixium'>, 'pilatus300kwcdte': <class 'pyFAI.detectors.PilatusCdTe300kw'>, 'pilatus_2m': <class 'pyFAI.detectors.Pilatus2M'>, 'rayonixmx425hs': <class 'pyFAI.detectors.RayonixMx425hs'>, 'imxpads70': <class 'pyFAI.detectors.ImXPadS70'>, 'pilatus_300k_cdte': <class 'pyFAI.detectors.PilatusCdTe300k'>, 'rayonix_sx30hs': <class 'pyFAI.detectors.RayonixSx30hs'>, 'eiger9m': <class 'pyFAI.detectors.Eiger9M'>, 'agilent_titan': <class 'pyFAI.detectors.Titan'>, 'rayonixlx255hs': <class 'pyFAI.detectors.RayonixLx255'>, 'rayonix133': <class 'pyFAI.detectors.Rayonix133'>, 'rayonix_lx255': <class 'pyFAI.detectors.RayonixLx255'>, 'rayonixlx170-hs': <class 'pyFAI.detectors.RayonixLx170'>, 'perkinelmer': <class 'pyFAI.detectors.Perkin'>, 'rayonix': <class 'pyFAI.detectors.Rayonix'>, 'rayonix_sx165': <class 'pyFAI.detectors.RayonixSx165'>, 'rayonix_mx300hs': <class 'pyFAI.detectors.RayonixMx300hs'>, 'rayonix_lx255-hs': <class 'pyFAI.detectors.RayonixLx255'>, 'adsc_q4': <class 'pyFAI.detectors.ADSC_Q4'>, 'rayonixmx225': <class 'pyFAI.detectors.RayonixMx225'>, 'rayonix_sx30_hs': <class 'pyFAI.detectors.RayonixSx30hs'>, 'apex2': <class 'pyFAI.detectors.Apex2'>, 'hf-2.4m': <class 'pyFAI.detectors.HF_2M'>, 'pilatus_cdte_300k': <class 'pyFAI.detectors.PilatusCdTe300k'>, 'detector': <class 'pyFAI.detectors.Detector'>, 'pilatus_2m_cdte': <class 'pyFAI.detectors.PilatusCdTe2M'>, 'picam_v1': <class 'pyFAI.detectors.RaspberryPi5M'>, 'imxpad_s140': <class 'pyFAI.detectors.ImXPadS140'>, 'fairchildcondor486:90': <class 'pyFAI.detectors.Fairchild'>, 'adsc_q210': <class 'pyFAI.detectors.ADSC_Q210'>, 'pilatus300kcdte': <class 'pyFAI.detectors.PilatusCdTe300k'>, 'rayonixlx170': <class 'pyFAI.detectors.RayonixLx170'>, 'pilatus300k_cdte': <class 'pyFAI.detectors.PilatusCdTe300k'>, 'rayonix_sx85_hs': <class 'pyFAI.detectors.RayonixSx85hs'>, 'rayonix_mx170_hs': <class 'pyFAI.detectors.RayonixMx170'>, 'rayonix_lx_255hs': <class 'pyFAI.detectors.RayonixLx255'>, 'quantum210': <class 'pyFAI.detectors.ADSC_Q210'>, 'hf_1m': <class 'pyFAI.detectors.HF_1M'>, 'rayonix_mx225_hs': <class 'pyFAI.detectors.RayonixMx225hs'>, 'mar133': <class 'pyFAI.detectors.Rayonix133'>, 'frelon': <class 'pyFAI.detectors.FReLoN'>, 'hf_130k': <class 'pyFAI.detectors.HF_130K'>, 'pilatus_300kw_cdte': <class 'pyFAI.detectors.PilatusCdTe300kw'>, 'imxpad_s70': <class 'pyFAI.detectors.ImXPadS70'>, 'adsc_q315': <class 'pyFAI.detectors.ADSC_Q315'>, 'rayonixmx325': <class 'pyFAI.detectors.RayonixMx325'>, 'rayonix_sx85hs': <class 'pyFAI.detectors.RayonixSx85hs'>, 'eiger_9m': <class 'pyFAI.detectors.Eiger9M'>, 'rayonix_mx300_hs': <class 'pyFAI.detectors.RayonixMx300hs'>, 'hf-4m': <class 'pyFAI.detectors.HF_4M'>, 'rayonix_mx300': <class 'pyFAI.detectors.RayonixMx300'>, 'quantum_4': <class 'pyFAI.detectors.ADSC_Q4'>}¶

save(filename)¶

Saves the detector description into a NeXus file, adapted from: http://download.nexusformat.org/sphinx/classes/base_classes/NXdetector.html Main differences:

differentiate pixel center from pixel corner offsets

store all offsets are ndarray according to slow/fast dimension (not x, y)

Parameters:	filename – name of the file on the disc

setFit2D(**kwarg)¶

Twin method of getFit2D: setup a detector instance according to a description

Parameters:	kwarg – dictionary containing pixel1, pixel2 and splineFile

setPyFAI(**kwarg)¶

Twin method of getPyFAI: setup a detector instance according to a description

Parameters:	kwarg – dictionary containing detector, pixel1, pixel2 and splineFile

set_binning(bin_size=(1, 1))¶

Set the “binning” of the detector,

Parameters:	bin_size ((int, int)) – binning as integer or tuple of integers.

set_config(config)¶

Sets the configuration of the detector. This implies:

Orientation: integers
Binning
ROI

The configuration is either a python dictionary or a JSON string or a file containing this JSON configuration

keys in that dictionary are :

“orientation”: integers from 0 to 7
“binning”: integer or 2-tuple of integers. If only one integer is

provided,
“offset”: coordinate (in pixels) of the start of the detector

set_darkcurrent(dark)¶

set_darkfiles(files=None, method='mean')¶

Parameters:	files (str or list(str) or None) – file(s) used to compute the dark. method (str) – method used to compute the dark, “mean” or “median”

Set the dark current from one or mutliple files, avaraged according to the method provided

set_dx(dx=None)¶: set the pixel-wise displacement along X (dim2):

set_dy(dy=None)¶: set the pixel-wise displacement along Y (dim1):

set_flatfield(flat)¶

set_flatfiles(files, method='mean')¶

Parameters:	files (str or list(str) or None) – file(s) used to compute the flat-field. method (str) – method used to compute the dark, “mean” or “median”

Set the flat field from one or mutliple files, averaged according to the method provided

set_mask(mask)¶

set_maskfile(maskfile)¶

set_pixel1(value)¶

set_pixel2(value)¶

set_splineFile(splineFile)¶

splineFile¶

uniform_pixel = True¶

class pyFAI.detectors.DetectorMeta(name, bases, dct)¶

Bases: type

Metaclass used to register all detector classes inheriting from Detector

__init__(name, bases, dct)¶

class pyFAI.detectors.Dexela2923(pixel1=7.5e-05, pixel2=7.5e-05)¶

Bases: pyFAI.detectors.Detector

Dexela CMOS family detector

MAX_SHAPE = (3888, 3072)¶

__init__(pixel1=7.5e-05, pixel2=7.5e-05)¶

aliases = ['Dexela 2923']¶

force_pixel = True¶

class pyFAI.detectors.Eiger(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)¶

Bases: pyFAI.detectors.Detector

Eiger detector: generic description containing mask algorithm

MODULE_GAP = (37, 10)¶

MODULE_SIZE = (514, 1030)¶

__init__(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)¶

calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)¶

Calculate the position of each pixel center in cartesian coordinate and in meter of a couple of coordinates. The half pixel offset is taken into account here !!!

Parameters:	d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension) d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension)
Returns:	p1, p2 position in meter of the center of each pixels.
Return type:	2-tuple of numpy.ndarray

d1 and d2 must have the same shape, returned array will have the same shape.

calc_mask()¶: Returns a generic mask for Pilatus detectors...

force_pixel = True¶

class pyFAI.detectors.Eiger16M(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)¶

Bases: pyFAI.detectors.Eiger

Eiger 16M detector

MAX_SHAPE = (4371, 4150)¶

aliases = ['Eiger 16M']¶

class pyFAI.detectors.Eiger1M(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)¶

Bases: pyFAI.detectors.Eiger

Eiger 1M detector

MAX_SHAPE = (1065, 1030)¶

aliases = ['Eiger 1M']¶

class pyFAI.detectors.Eiger4M(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)¶

Bases: pyFAI.detectors.Eiger

Eiger 4M detector

MAX_SHAPE = (2167, 2070)¶

aliases = ['Eiger 4M']¶

class pyFAI.detectors.Eiger500k(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)¶

Bases: pyFAI.detectors.Eiger

Eiger 1M detector

MAX_SHAPE = (512, 1030)¶

aliases = ['Eiger 500k']¶

class pyFAI.detectors.Eiger9M(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)¶

Bases: pyFAI.detectors.Eiger

Eiger 9M detector

MAX_SHAPE = (3269, 3110)¶

aliases = ['Eiger 9M']¶

class pyFAI.detectors.FReLoN(splineFile=None)¶

Bases: pyFAI.detectors.Detector

FReLoN detector: The spline is mandatory to correct for geometric distortion of the taper

TODO: create automatically a mask that removes pixels out of the “valid reagion”

HAVE_TAPER = True¶

MAX_SHAPE = (2048, 2048)¶

__init__(splineFile=None)¶

calc_mask()¶: Returns a generic mask for Frelon detectors... All pixels which (center) turns to be out of the valid region are by default discarded

class pyFAI.detectors.Fairchild(pixel1=1.5e-05, pixel2=1.5e-05)¶

Bases: pyFAI.detectors.Detector

Fairchild Condor 486:90 detector

MAX_SHAPE = (4096, 4096)¶

__init__(pixel1=1.5e-05, pixel2=1.5e-05)¶

aliases = ['Fairchild', 'Condor', 'Fairchild Condor 486:90']¶

force_pixel = True¶

uniform_pixel = True¶

class pyFAI.detectors.HF_130K(pixel1=0.00015, pixel2=0.00015)¶

Bases: pyFAI.detectors.Detector

ADSC HF-130K 1 module

Informations from http://www.adsc-xray.com/products/pixel-array-detectors/hf-130k/

MAX_SHAPE = (256, 512)¶

__init__(pixel1=0.00015, pixel2=0.00015)¶

aliases = ['HF-130k']¶

force_pixel = True¶

class pyFAI.detectors.HF_1M(pixel1=0.00015, pixel2=0.00015)¶

Bases: pyFAI.detectors.Detector

ADSC HF-1M 2x4 modules

Informations from http://www.adsc-xray.com/products/pixel-array-detectors/hf-1m/

Nota: gaps between modules is not known/described

MAX_SHAPE = (1024, 1024)¶

__init__(pixel1=0.00015, pixel2=0.00015)¶

aliases = ['HF-1M']¶

force_pixel = True¶

class pyFAI.detectors.HF_262k(pixel1=0.00015, pixel2=0.00015)¶

Bases: pyFAI.detectors.Detector

ADSC HF-262k 2 module

Informations from http://www.adsc-xray.com/products/pixel-array-detectors/hf-262k/

Nota: gaps between modules is not known/described

MAX_SHAPE = (512, 512)¶

__init__(pixel1=0.00015, pixel2=0.00015)¶

aliases = ['HF-262k']¶

force_pixel = True¶

class pyFAI.detectors.HF_2M(pixel1=0.00015, pixel2=0.00015)¶

Bases: pyFAI.detectors.Detector

ADSC HF-1M 3x6 modules

Informations from http://www.adsc-xray.com/products/pixel-array-detectors/hf-2.4m/

Nota: gaps between modules is not known/described

MAX_SHAPE = (1536, 1536)¶

__init__(pixel1=0.00015, pixel2=0.00015)¶

aliases = ['HF-2.4M']¶

force_pixel = True¶

class pyFAI.detectors.HF_4M(pixel1=0.00015, pixel2=0.00015)¶

Bases: pyFAI.detectors.Detector

ADSC HF-4M 4x8 modules

Informations from http://www.adsc-xray.com/products/pixel-array-detectors/hf-4m/

MAX_SHAPE = (2048, 2048)¶

__init__(pixel1=0.00015, pixel2=0.00015)¶

aliases = ['HF-4M']¶

force_pixel = True¶

class pyFAI.detectors.HF_9M(pixel1=0.00015, pixel2=0.00015)¶

Bases: pyFAI.detectors.Detector

ADSC HF-130K 1 module

Informations from http://www.adsc-xray.com/products/pixel-array-detectors/hf-9-4m/

MAX_SHAPE = (3072, 3072)¶

__init__(pixel1=0.00015, pixel2=0.00015)¶

aliases = ['HF-9.4M']¶

force_pixel = True¶

class pyFAI.detectors.ImXPadS10(pixel1=0.00013, pixel2=0.00013, max_shape=None, module_size=None)¶

Bases: pyFAI.detectors.Detector

ImXPad detector: ImXPad s10 detector with 1x1modules

BORDER_SIZE_RELATIVE = 2.5¶

MAX_SHAPE = (120, 80)¶

MODULE_SIZE = (120, 80)¶

PIXEL_SIZE = (0.00013, 0.00013)¶

__init__(pixel1=0.00013, pixel2=0.00013, max_shape=None, module_size=None)¶

aliases = ['Imxpad S10']¶

calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)¶

Calculate the position of each pixel center in cartesian coordinate and in meter of a couple of coordinates. The half pixel offset is taken into account here !!!

Parameters:	d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension) d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension)
Returns:	position in meter of the center of each pixels.
Return type:	ndarray

d1 and d2 must have the same shape, returned array will have the same shape.

calc_mask()¶: Calculate the mask

calc_pixels_edges()¶: Calculate the position of the pixel edges

force_pixel = True¶

get_pixel_corners(d1=None, d2=None)¶

Calculate the position of the corner of the pixels

This should be overwritten by class representing non-contiguous detector (Xpad, ...)

Precision float32 is ok: precision of 1µm for a detector size of 1m

Returns:	4D array containing: pixel index (slow dimension) pixel index (fast dimension) corner index (A, B, C or D), triangles or hexagons can be handled the same way vertex position (z,y,x)

uniform_pixel = False¶

class pyFAI.detectors.ImXPadS140(pixel1=0.00013, pixel2=0.00013)¶

Bases: pyFAI.detectors.ImXPadS10

ImXPad detector: ImXPad s140 detector with 2x7modules

BORDER_PIXEL_SIZE_RELATIVE = 2.5¶

MAX_SHAPE = (240, 560)¶

MODULE_SIZE = (120, 80)¶

PIXEL_SIZE = (0.00013, 0.00013)¶

__init__(pixel1=0.00013, pixel2=0.00013)¶

aliases = ['Imxpad S140']¶

force_pixel = True¶

class pyFAI.detectors.ImXPadS70(pixel1=0.00013, pixel2=0.00013)¶

Bases: pyFAI.detectors.ImXPadS10

ImXPad detector: ImXPad s70 detector with 1x7modules

BORDER_SIZE_RELATIVE = 2.5¶

MAX_SHAPE = (120, 560)¶

MODULE_SIZE = (120, 80)¶

PIXEL_EDGES = None¶

PIXEL_SIZE = (0.00013, 0.00013)¶

__init__(pixel1=0.00013, pixel2=0.00013)¶

aliases = ['Imxpad S70']¶

force_pixel = True¶

class pyFAI.detectors.Mar345(pixel1=0.0001, pixel2=0.0001)¶

Bases: pyFAI.detectors.Detector

Mar345 Imaging plate detector

In this detector, pixels are always square The valid image size are 2300, 2000, 1600, 1200, 3450, 3000, 2400, 1800

MAX_SHAPE = (3450, 3450)¶

VALID_SIZE = {2000: 0.00015, 1600: 0.00015, 3000: 0.0001, 2400: 0.0001, 3450: 0.0001, 1200: 0.00015, 2300: 0.00015, 1800: 0.0001}¶

__init__(pixel1=0.0001, pixel2=0.0001)¶

aliases = ['MAR 345', 'Mar3450']¶

calc_mask()¶

force_pixel = True¶

guess_binning(data)¶: Guess the binning/mode depending on the image shape :param data: 2-tuple with the shape of the image or the image with a .shape attribute.

class pyFAI.detectors.NexusDetector(filename=None)¶

Bases: pyFAI.detectors.Detector

Class representing a 2D detector loaded from a NeXus file

__init__(filename=None)¶

getFit2D()¶

Helper method to serialize the description of a detector using the Fit2d units

Returns:	representation of the detector easy to serialize
Return type:	dict

getPyFAI()¶

Helper method to serialize the description of a detector using the pyFAI way with everything in S.I units.

Returns:	representation of the detector easy to serialize
Return type:	dict

load(filename)¶

Loads the detector description from a NeXus file, adapted from: http://download.nexusformat.org/sphinx/classes/base_classes/NXdetector.html

Parameters:	filename – name of the file on the disk

classmethod sload(filename)¶

Instantiate the detector description from a NeXus file, adapted from: http://download.nexusformat.org/sphinx/classes/base_classes/NXdetector.html

Parameters:	filename – name of the file on the disk
Returns:	Detector instance

class pyFAI.detectors.Perkin(pixel1=0.0002, pixel2=0.0002)¶

Bases: pyFAI.detectors.Detector

Perkin detector

DEFAULT_PIXEL1 = 0.0002¶

DEFAULT_PIXEL2 = 0.0002¶

MAX_SHAPE = (4096, 4096)¶

__init__(pixel1=0.0002, pixel2=0.0002)¶

aliases = ['Perkin detector', 'Perkin Elmer']¶

force_pixel = True¶

class pyFAI.detectors.Pilatus(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.Detector

Pilatus detector: generic description containing mask algorithm

Sub-classed by Pilatus1M, Pilatus2M and Pilatus6M

MODULE_GAP = (17, 7)¶

MODULE_SIZE = (195, 487)¶

__init__(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)¶

Calculate the position of each pixel center in cartesian coordinate and in meter of a couple of coordinates. The half pixel offset is taken into account here !!!

Parameters:	d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension) d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension)
Returns:	position in meter of the center of each pixels.
Return type:	ndarray

d1 and d2 must have the same shape, returned array will have the same shape.

calc_mask()¶: Returns a generic mask for Pilatus detectors...

force_pixel = True¶

get_splineFile()¶

set_splineFile(splineFile=None)¶: In this case splinefile is a couple filenames

splineFile¶

class pyFAI.detectors.Pilatus100k(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.Pilatus

Pilatus 100k detector

MAX_SHAPE = (195, 487)¶

aliases = ['Pilatus 100k']¶

class pyFAI.detectors.Pilatus1M(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.Pilatus

Pilatus 1M detector

MAX_SHAPE = (1043, 981)¶

aliases = ['Pilatus 1M']¶

class pyFAI.detectors.Pilatus200k(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.Pilatus

Pilatus 200k detector

MAX_SHAPE = (407, 487)¶

aliases = ['Pilatus 200k']¶

class pyFAI.detectors.Pilatus2M(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.Pilatus

Pilatus 2M detector

MAX_SHAPE = (1679, 1475)¶

aliases = ['Pilatus 2M']¶

class pyFAI.detectors.Pilatus300k(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.Pilatus

Pilatus 300k detector

MAX_SHAPE = (619, 487)¶

aliases = ['Pilatus 300k']¶

class pyFAI.detectors.Pilatus300kw(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.Pilatus

Pilatus 300k-wide detector

MAX_SHAPE = (195, 1475)¶

aliases = ['Pilatus 300kw']¶

class pyFAI.detectors.Pilatus6M(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.Pilatus

Pilatus 6M detector

MAX_SHAPE = (2527, 2463)¶

aliases = ['Pilatus 6M']¶

class pyFAI.detectors.PilatusCdTe(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.Pilatus

Pilatus CdTe detector: Like the Pilatus with an extra 3 pixel in the middle of every module (vertically)

calc_mask()¶: Returns a generic mask for Pilatus detectors...

class pyFAI.detectors.PilatusCdTe1M(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.PilatusCdTe

Pilatus CdTe 1M detector

MAX_SHAPE = (1043, 981)¶

aliases = ['Pilatus CdTe 1M', 'Pilatus 1M CdTe', 'Pilatus1M CdTe', 'Pilatus1MCdTe']¶

class pyFAI.detectors.PilatusCdTe2M(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.PilatusCdTe

Pilatus CdTe 2M detector

MAX_SHAPE = (1679, 1475)¶

aliases = ['Pilatus CdTe 2M', 'Pilatus 2M CdTe', 'Pilatus2M CdTe', 'Pilatus2MCdTe']¶

class pyFAI.detectors.PilatusCdTe300k(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.PilatusCdTe

Pilatus CdTe 300k detector

MAX_SHAPE = (619, 487)¶

aliases = ['Pilatus CdTe 300k', 'Pilatus 300k CdTe', 'Pilatus300k CdTe', 'Pilatus300kCdTe']¶

class pyFAI.detectors.PilatusCdTe300kw(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)¶

Bases: pyFAI.detectors.PilatusCdTe

Pilatus CdTe 300k-wide detector

MAX_SHAPE = (195, 1475)¶

aliases = ['Pilatus CdTe 300kw', 'Pilatus 300kw CdTe', 'Pilatus300kw CdTe', 'Pilatus300kwCdTe']¶

class pyFAI.detectors.Pixium(pixel1=0.000308, pixel2=0.000308)¶

Bases: pyFAI.detectors.Detector

PIXIUM 4700 detector

High energy X ray diffraction using the Pixium 4700 flat panel detector J E Daniels, M Drakopoulos, et al.; Journal of Synchrotron Radiation 16(Pt 4):463-8 · August 2009

DEFAULT_PIXEL1 = 0.000154¶

DEFAULT_PIXEL2 = 0.000154¶

MAX_SHAPE = (1910, 2480)¶

__init__(pixel1=0.000308, pixel2=0.000308)¶: Defaults to 2x2 binning

aliases = ['Pixium 4700 detector', 'Thales Electronics']¶

force_pixel = True¶

class pyFAI.detectors.RaspberryPi5M(pixel1=1.4e-06, pixel2=1.4e-06)¶

Bases: pyFAI.detectors.Detector

5 Mpix detector from Raspberry Pi

MAX_SHAPE = (1944, 2592)¶

__init__(pixel1=1.4e-06, pixel2=1.4e-06)¶

aliases = ['Picam v1']¶

force_pixel = True¶

class pyFAI.detectors.RaspberryPi8M(pixel1=1.12e-06, pixel2=1.12e-06)¶

Bases: pyFAI.detectors.Detector

8 Mpix detector from Raspberry Pi

MAX_SHAPE = (2464, 3280)¶

__init__(pixel1=1.12e-06, pixel2=1.12e-06)¶

aliases = ['Picam v2']¶

force_pixel = True¶

class pyFAI.detectors.Rayonix(pixel1=3.2e-05, pixel2=3.2e-05)¶

Bases: pyFAI.detectors.Detector

BINNED_PIXEL_SIZE = {1: 3.2e-05}¶

MAX_SHAPE = (4096, 4096)¶

__init__(pixel1=3.2e-05, pixel2=3.2e-05)¶

binning¶

force_pixel = True¶

get_binning()¶

guess_binning(data)¶: Guess the binning/mode depending on the image shape :param data: 2-tuple with the shape of the image or the image with a .shape attribute.

set_binning(bin_size=(1, 1))¶

Set the “binning” of the detector,

Parameters:	bin_size (int or (int, int)) – set the binning of the detector

class pyFAI.detectors.Rayonix133(pixel1=6.4e-05, pixel2=6.4e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix 133 2D CCD detector detector also known as mar133

Personnal communication from M. Blum

What should be the default binning factor for those cameras ?

Circular detector

BINNED_PIXEL_SIZE = {8: 0.000256, 1: 3.2e-05, 2: 6.4e-05, 4: 0.000128}¶

MAX_SHAPE = (4096, 4096)¶

__init__(pixel1=6.4e-05, pixel2=6.4e-05)¶

aliases = ['MAR133']¶

calc_mask()¶: Circular mask

force_pixel = True¶

class pyFAI.detectors.RayonixLx170(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix lx170 2d CCD Detector (2x1 CCDs).

Nota: this is the same for lx170hs

BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}¶

MAX_SHAPE = (1920, 3840)¶

__init__(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

aliases = ['Rayonix LX170', 'Rayonix LX170-HS', 'Rayonix LX170 HS', 'RayonixLX170HS']¶

force_pixel = True¶

class pyFAI.detectors.RayonixLx255(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix lx255 2d Detector (3x1 CCDs)

Nota: this detector is also called lx255hs

BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}¶

MAX_SHAPE = (1920, 5760)¶

__init__(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

aliases = ['Rayonix LX255', 'Rayonix LX255-HS', 'Rayonix LX 255HS', 'RayonixLX225HS']¶

class pyFAI.detectors.RayonixMx170(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix mx170 2d CCD Detector (2x2 CCDs).

Nota: this is the same for mx170hs

BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}¶

MAX_SHAPE = (3840, 3840)¶

__init__(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

aliases = ['Rayonix MX170', 'Rayonix MX170-HS', 'RayonixMX170HS', 'Rayonix MX170 HS']¶

class pyFAI.detectors.RayonixMx225(pixel1=7.3242e-05, pixel2=7.3242e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix mx225 2D CCD detector detector

Nota: this is the same definition for mx225he Personnal communication from M. Blum

BINNED_PIXEL_SIZE = {8: 0.000292969, 1: 3.6621e-05, 2: 7.3242e-05, 3: 0.000109971, 4: 0.000146484}¶

MAX_SHAPE = (6144, 6144)¶

__init__(pixel1=7.3242e-05, pixel2=7.3242e-05)¶

aliases = ['Rayonix MX225']¶

force_pixel = True¶

class pyFAI.detectors.RayonixMx225hs(pixel1=7.8125e-05, pixel2=7.8125e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix mx225hs 2D CCD detector detector

Pixel size from a personnal communication from M. Blum

BINNED_PIXEL_SIZE = {1: 3.90625e-05, 2: 7.8125e-05, 3: 0.0001171875, 4: 0.00015625, 5: 0.0001953125, 6: 0.000234375, 8: 0.0003125, 10: 0.000390625}¶

MAX_SHAPE = (5760, 5760)¶

__init__(pixel1=7.8125e-05, pixel2=7.8125e-05)¶

aliases = ['Rayonix MX225HS', 'Rayonix MX225 HS']¶

force_pixel = True¶

class pyFAI.detectors.RayonixMx300(pixel1=7.3242e-05, pixel2=7.3242e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix mx300 2D detector (4x4 CCDs)

Pixel size from a personnal communication from M. Blum

BINNED_PIXEL_SIZE = {8: 0.000292969, 1: 3.6621e-05, 2: 7.3242e-05, 3: 0.000109971, 4: 0.000146484}¶

MAX_SHAPE = (8192, 8192)¶

__init__(pixel1=7.3242e-05, pixel2=7.3242e-05)¶

aliases = ['Rayonix mx300']¶

force_pixel = True¶

class pyFAI.detectors.RayonixMx300hs(pixel1=7.8125e-05, pixel2=7.8125e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix mx300hs 2D detector (4x4 CCDs)

Pixel size from a personnal communication from M. Blum

BINNED_PIXEL_SIZE = {1: 3.90625e-05, 2: 7.8125e-05, 3: 0.0001171875, 4: 0.00015625, 5: 0.0001953125, 6: 0.000234375, 8: 0.0003125, 10: 0.000390625}¶

MAX_SHAPE = (7680, 7680)¶

__init__(pixel1=7.8125e-05, pixel2=7.8125e-05)¶

aliases = ['Rayonix MX300HS', 'Rayonix MX300 HS']¶

force_pixel = True¶

class pyFAI.detectors.RayonixMx325(pixel1=7.9346e-05, pixel2=7.9346e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix mx325 and mx325he 2D detector (4x4 CCD chips)

Pixel size from a personnal communication from M. Blum

BINNED_PIXEL_SIZE = {8: 0.000317383, 1: 3.9673e-05, 2: 7.9346e-05, 3: 0.000119135, 4: 0.000158691}¶

MAX_SHAPE = (8192, 8192)¶

__init__(pixel1=7.9346e-05, pixel2=7.9346e-05)¶

aliases = ['Rayonix MX325']¶

class pyFAI.detectors.RayonixMx340hs(pixel1=8.85417e-05, pixel2=8.85417e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix mx340hs 2D detector (4x4 CCDs)

Pixel size from a personnal communication from M. Blum

BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}¶

MAX_SHAPE = (7680, 7680)¶

__init__(pixel1=8.85417e-05, pixel2=8.85417e-05)¶

aliases = ['Rayonix MX340HS', 'Rayonix MX340HS']¶

force_pixel = True¶

class pyFAI.detectors.RayonixMx425hs(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix mx425hs 2D CCD camera (5x5 CCD chip)

Pixel size from a personnal communication from M. Blum

BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}¶

MAX_SHAPE = (9600, 9600)¶

__init__(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

aliases = ['Rayonix MX425HS', 'Rayonix MX425 HS']¶

class pyFAI.detectors.RayonixSx165(pixel1=3.95e-05, pixel2=3.95e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix sx165 2d Detector also known as MAR165.

Circular detector

BINNED_PIXEL_SIZE = {8: 0.000316, 1: 3.95e-05, 2: 7.9e-05, 3: 0.000118616, 4: 0.000158}¶

MAX_SHAPE = (4096, 4096)¶

__init__(pixel1=3.95e-05, pixel2=3.95e-05)¶

aliases = ['MAR165', 'Rayonix Sx165']¶

calc_mask()¶: Circular mask

force_pixel = True¶

class pyFAI.detectors.RayonixSx200(pixel1=4.8e-05, pixel2=4.8e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix sx200 2d CCD Detector.

Pixel size are personnal communication from M. Blum.

BINNED_PIXEL_SIZE = {8: 0.000384, 1: 4.8e-05, 2: 9.6e-05, 3: 0.000144, 4: 0.000192}¶

MAX_SHAPE = (4096, 4096)¶

__init__(pixel1=4.8e-05, pixel2=4.8e-05)¶

aliases = ['Rayonix sx200']¶

class pyFAI.detectors.RayonixSx30hs(pixel1=1.5625e-05, pixel2=1.5625e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix sx30hs 2D CCD camera (1 CCD chip)

Pixel size from a personnal communication from M. Blum

BINNED_PIXEL_SIZE = {1: 1.5625e-05, 2: 3.125e-05, 3: 4.6875e-05, 4: 6.25e-05, 5: 7.8125e-05, 6: 9.375e-05, 8: 0.000125, 10: 0.00015625}¶

MAX_SHAPE = (1920, 1920)¶

__init__(pixel1=1.5625e-05, pixel2=1.5625e-05)¶

aliases = ['Rayonix SX30HS', 'Rayonix SX30 HS']¶

class pyFAI.detectors.RayonixSx85hs(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

Bases: pyFAI.detectors.Rayonix

Rayonix sx85hs 2D CCD camera (1 CCD chip)

Pixel size from a personnal communication from M. Blum

BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}¶

MAX_SHAPE = (1920, 1920)¶

__init__(pixel1=4.42708e-05, pixel2=4.42708e-05)¶

aliases = ['Rayonix SX85HS', 'Rayonix SX85 HS']¶

class pyFAI.detectors.Titan(pixel1=6e-05, pixel2=6e-05)¶

Bases: pyFAI.detectors.Detector

Titan CCD detector from Agilent. Mask not handled

MAX_SHAPE = (2048, 2048)¶

__init__(pixel1=6e-05, pixel2=6e-05)¶

aliases = ['Titan 2k x 2k', 'OXD Titan', 'Agilent Titan']¶

force_pixel = True¶

uniform_pixel = True¶

class pyFAI.detectors.Xpad_flat(pixel1=0.00013, pixel2=0.00013, max_shape=None, module_size=None)¶

Bases: pyFAI.detectors.ImXPadS10

Xpad detector: generic description for ImXPad detector with 8x7modules

BORDER_PIXEL_SIZE_RELATIVE = 2.5¶

IS_CONTIGUOUS = False¶

MAX_SHAPE = (960, 560)¶

MODULE_GAP = (0.00357, 0)¶

MODULE_SIZE = (120, 80)¶

PIXEL_SIZE = (0.00013, 0.00013)¶

__init__(pixel1=0.00013, pixel2=0.00013, max_shape=None, module_size=None)¶

aliases = ['Xpad S540 flat', 'd5']¶

calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)¶

Calculate the position of each pixel center in cartesian coordinate and in meter of a couple of coordinates. The half pixel offset is taken into account here !!! Adapted to Nexus detector definition

Parameters:	d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension) d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension) center – retrieve the coordinate of the center of the pixel use_cython – set to False to test Numpy implementation
Returns:	position in meter of the center of each pixels.
Return type:	ndarray

d1 and d2 must have the same shape, returned array will have the same shape.

calc_mask()¶: Returns a generic mask for Xpad detectors... discards the first line and raw form all modules: those are 2.5x bigger and often mis - behaving

calc_pixels_edges()¶: Calculate the position of the pixel edges, specific to the S540, d5 detector

force_pixel = True¶

get_pixel_corners()¶

Calculate the position of the corner of the pixels

Returns:	4D array containing: pixel index (slow dimension) pixel index (fast dimension) corner index (A, B, C or D), triangles or hexagons can be handled the same way vertex position (z,y,x)

uniform_pixel = False¶

`spline` Module¶

This is piece of software aims at manipulating spline files describing for geometric corrections of the 2D detectors using cubic-spline.

Mainly used at ESRF with FReLoN CCD camera.

class pyFAI.spline.Spline(filename=None)¶

Bases: object

This class is a python representation of the spline file

Those file represent cubic splines for 2D detector distortions and makes heavy use of fitpack (dierckx in netlib) — A Python-C wrapper to FITPACK (by P. Dierckx). FITPACK is a collection of FORTRAN programs for curve and surface fitting with splines and tensor product splines. See _http://www.cs.kuleuven.ac.be/cwis/research/nalag/research/topics/fitpack.html or _http://www.netlib.org/dierckx/index.html

__init__(filename=None)¶

This is the constructor of the Spline class.

Parameters:	filename (str) – name of the ascii file containing the spline

array2spline(smoothing=1000, timing=False)¶

Calculates the spline coefficients from the displacements matrix using fitpack.

Parameters:	smoothing (float) – the greater the smoothing, the fewer the number of knots remaining timing (bool) – print the profiling of the calculation

bin(binning=None)¶

Performs the binning of a spline (same camera with different binning)

Parameters:	binning – binning factor as integer or 2-tuple of integers
Type:	int or (int, int)

comparison(ref, verbose=False)¶

Compares the current spline distortion with a reference

Parameters:	ref (Spline instance) – another spline file verbose (bool) – print or not pylab plots
Returns:	True or False depending if the splines are the same or not
Return type:	bool

correct(pos)¶

fliplr()¶: Flip the spline :return: new spline object

fliplrud()¶: Flip the spline left-right and up-down :return: new spline object

flipud()¶: Flip the spline up-down :return: new spline object

getPixelSize()¶

Return the size of the pixel from as a 2-tuple of floats expressed in meters.

Returns:	the size of the pixel from a 2D detector
Return type:	2-tuple of floats expressed in meter.

read(filename)¶

read an ascii spline file from file

Parameters:	filename (str) – file containing the cubic spline distortion file

setPixelSize(pixelSize)¶

Sets the size of the pixel from a 2-tuple of floats expressed in meters.

Param:	pixel size in meter

spline2array(timing=False)¶

Calculates the displacement matrix using fitpack bisplev(x, y, tck, dx = 0, dy = 0)

Parameters:	timing (bool) – profile the calculation or not
Returns:	Nothing !
Return type:	float or ndarray

Evaluate a bivariate B-spline and its derivatives. Return a rank-2 array of spline function values (or spline derivative values) at points given by the cross-product of the rank-1 arrays x and y. In special cases, return an array or just a float if either x or y or both are floats.

splineFuncX(x, y, list_of_points=False)¶

Calculates the displacement matrix using fitpack for the X direction on the given grid.

Parameters:	x (ndarray) – points of the grid in the x direction y (ndarray) – points of the grid in the y direction list_of_points – if true, consider the zip(x,y) instead of the of the square array
Returns:	displacement matrix for the X direction
Return type:	ndarray

splineFuncY(x, y, list_of_points=False)¶

calculates the displacement matrix using fitpack for the Y direction

Parameters:	x (ndarray) – points in the x direction y (ndarray) – points in the y direction list_of_points – if true, consider the zip(x,y) instead of the of the square array
Returns:	displacement matrix for the Y direction
Return type:	ndarray

tilt(center=(0.0, 0.0), tiltAngle=0.0, tiltPlanRot=0.0, distanceSampleDetector=1.0, timing=False)¶

The tilt method apply a virtual tilt on the detector, the point of tilt is given by the center

Parameters:	center (2-tuple of floats) – position of the point of tilt, this point will not be moved. tiltAngle (float in the range [-90:+90] degrees) – the value of the tilt in degrees tiltPlanRot (Float in the range [-180:180]) – the rotation of the tilt plan with the Ox axis (0 deg for y axis invariant, 90 deg for x axis invariant) distanceSampleDetector (float) – the distance from sample to detector in meter (along the beam, so distance from sample to center)
Returns:	tilted Spline instance
Return type:	Spline

write(filename)¶

save the cubic spline in an ascii file usable with Fit2D or SPD

Parameters:	filename (str) – name of the file containing the cubic spline distortion file

writeEDF(basename)¶

save the distortion matrices into a couple of files called basename-x.edf and basename-y.edf

Parameters:	basename (str) – base of the name used to save the data

zeros(xmin=0.0, ymin=0.0, xmax=2048.0, ymax=2048.0, pixSize=None)¶

Defines a spline file with no ( zero ) displacement.

Parameters:	xmin (float) – minimum coordinate in x, usually zero xmax (float) – maximum coordinate in x (+1) usually 2048 ymin (float) – minimum coordinate in y, usually zero ymax (float) – maximum coordinate y (+1) usually 2048 pixSize (float) – size of the pixel

zeros_like(other)¶

Defines a spline file with no ( zero ) displacement with the same shape as the other one given.

Parameters:	other (Spline instance) – another Spline instance

pyFAI.spline.main()¶: Some tests ....

`io` Module¶

Module for “high-performance” writing in either 1D with Ascii , or 2D with FabIO or even nD with n varying from 2 to 4 using HDF5

Stand-alone module which tries to offer interface to HDF5 via H5Py and capabilities to write EDF or other formats using fabio.

Can be imported without h5py but then limited to fabio & ascii formats.

TODO:

Add monitor to HDF5

class pyFAI.io.AsciiWriter(filename=None, prefix='fai_', extension='.dat')¶

Bases: pyFAI.io.Writer

Ascii file writer (.xy or .dat)

__init__(filename=None, prefix='fai_', extension='.dat')¶

init(fai_cfg=None, lima_cfg=None)¶: Creates the directory that will host the output file(s)

write(data, index=0)¶

class pyFAI.io.DefaultAiWriter(filename, engine=None)¶

Bases: pyFAI.io.Writer

__init__(filename, engine=None)¶

Constructor of the historical writer of azimuthalIntegrator.

Parameters:	filename – name of the output file ai – integrator, should provide make_headers method.

close()¶

flush()¶

make_headers(hdr='#', has_mask=None, has_dark=None, has_flat=None, polarization_factor=None, normalization_factor=None, metadata=None)¶

Parameters:	hdr (str) – string used as comment in the header has_dark (bool) – save the darks filenames (default: no) has_flat (bool) – save the flat filenames (default: no) polarization_factor (float) – the polarization factor
Returns:	the header
Return type:	str

save1D(filename, dim1, I, error=None, dim1_unit='2th_deg', has_mask=None, has_dark=False, has_flat=False, polarization_factor=None, normalization_factor=None, metadata=None)¶

This method save the result of a 1D integration as ASCII file.

Parameters:

filename (str) – the filename used to save the 1D integration
dim1 (numpy.ndarray) – the x coordinates of the integrated curve
I (numpy.mdarray) – The integrated intensity
error (numpy.ndarray or None) – the error bar for each intensity
dim1_unit (pyFAI.units.Unit) – the unit of the dim1 array
has_dark (bool) – save the darks filenames (default: no)
has_flat (bool) – save the flat filenames (default: no)
polarization_factor (float, None) – the polarization factor
normalization_factor (float, None) – the monitor value
metadata – JSON serializable dictionary containing the metadata

This method save the result of a 1D integration.

save2D(filename, I, dim1, dim2, error=None, dim1_unit='2th_deg', has_dark=False, has_flat=False, polarization_factor=None, normalization_factor=None, metadata=None)¶

This method save the result of a 2D integration.

Parameters:

filename (str) – the filename used to save the 2D histogram
dim1 (numpy.ndarray) – the 1st coordinates of the histogram
dim1 – the 2nd coordinates of the histogram
I (numpy.mdarray) – The integrated intensity
error (numpy.ndarray or None) – the error bar for each intensity
dim1_unit (pyFAI.units.Unit) – the unit of the dim1 array
has_dark (bool) – save the darks filenames (default: no)
has_flat (bool) – save the flat filenames (default: no)
polarization_factor (float, None) – the polarization factor
normalization_factor (float, None) – the monitor value
metadata – JSON serializable dictionary containing the metadata

set_filename(filename)¶: Define the filename while will be used

write(data)¶

Minimalistic method to limit the overhead.

Parameters:	data – array with intensities or tuple (2th,I) or (I,2th,chi) :type data: Integrate1dResult, Integrate2dResult

class pyFAI.io.FabioWriter(filename=None)¶

Bases: pyFAI.io.Writer

Image file writer based on FabIO

TODO !!!

__init__(filename=None)¶

init(fai_cfg=None, lima_cfg=None)¶: Creates the directory that will host the output file(s)

write(data, index=0)¶

class pyFAI.io.HDF5Writer(filename, hpath='data', fast_scan_width=None)¶

Bases: pyFAI.io.Writer

Class allowing to write HDF5 Files.

CONFIG = 'pyFAI'¶

DATASET_NAME = 'data'¶

__init__(filename, hpath='data', fast_scan_width=None)¶

Constructor of an HDF5 writer:

Parameters:	filename – name of the file hpath – name of the group: it will contain data (2-4D dataset), [tth\|q\|r] and pyFAI, group containing the configuration fast_scan_width – set it to define the width of

close()¶

flush(radial=None, azimuthal=None)¶

Update some data like axis units and so on.

Parameters:	radial – position in radial direction azimuthal – position in azimuthal direction

init(fai_cfg=None, lima_cfg=None)¶: Initializes the HDF5 file for writing :param fai_cfg: the configuration of the worker as a dictionary

write(data, index=0)¶: Minimalistic method to limit the overhead. :param data: array with intensities or tuple (2th,I) or (I,2th,chi)

class pyFAI.io.Nexus(filename, mode='r')¶

Bases: object

Writer class to handle Nexus/HDF5 data

Manages:

entry
- pyFAI-subentry
  detector

TODO: make it thread-safe !!!

__init__(filename, mode='r')¶

Constructor

Parameters:	filename – name of the hdf5 file containing the nexus mode – can be r or a

close()¶: close the filename and update all entries

deep_copy(name, obj, where='/', toplevel=None, excluded=None, overwrite=False)¶

perform a deep copy: create a “name” entry in self containing a copy of the object

Parameters:	where – path to the toplevel object (i.e. root) toplevel – firectly the top level Group excluded – list of keys to be excluded overwrite – replace content if already existing

find_detector(all=False)¶

Tries to find a detector within a NeXus file, takes the first compatible detector

Parameters:	all – return all detectors found as a list

classmethod get_attr(dset, name, default=None)¶

Return the attribute of the dataset

Handles the ascii -> unicode issue in python3 #275

Parameters:	dset – a HDF5 dataset (or a group) name – name of the attribute default – default value to be returned
Returns:	attribute value decoded in python3 or default

get_class(grp, class_type='NXcollection')¶

return all sub-groups of the given type within a group

Parameters:	grp – HDF5 group class_type – name of the NeXus class

get_data(grp, class_type='NXdata')¶

return all dataset of the the NeXus class NXdata

Parameters:	grp – HDF5 group class_type – name of the NeXus class

get_entries()¶

retrieves all entry sorted the latest first.

Returns:	list of HDF5 groups

get_entry(name)¶

Retrieves an entry from its name

Parameters:	name – name of the entry to retrieve
Returns:	HDF5 group of NXclass == NXentry

new_class(grp, name, class_type='NXcollection')¶: create a new sub-group with type class_type :param grp: parent group :param name: name of the sub-group :param class_type: NeXus class name :return: subgroup created

new_detector(name='detector', entry='entry', subentry='pyFAI')¶

Create a new entry/pyFAI/Detector

Parameters:	detector – name of the detector entry – name of the entry subentry – all pyFAI description of detectors should be in a pyFAI sub-entry

new_entry(entry='entry', program_name='pyFAI', title='description of experiment', force_time=None, force_name=False)¶

Create a new entry

Parameters:	entry – name of the entry program_name – value of the field as string title – value of the field as string force_time – enforce the start_time (as string!) force_name – force the entry name as such, without numerical suffix.
Returns:	the corresponding HDF5 group

new_instrument(entry='entry', instrument_name='id00')¶: Create an instrument in an entry or create both the entry and the instrument if

class pyFAI.io.Writer(filename=None, extension=None)¶

Bases: object

Abstract class for writers.

CONFIG_ITEMS = ['filename', 'dirname', 'extension', 'subdir', 'hpath']¶

__init__(filename=None, extension=None)¶: Constructor of the class

flush(*arg, **kwarg)¶: To be implemented

init(fai_cfg=None, lima_cfg=None)¶: Creates the directory that will host the output file(s) :param fai_cfg: configuration for worker :param lima_cfg: configuration for acquisition

setJsonConfig(json_config=None)¶: Sets the JSON configuration

write(data)¶: To be implemented

pyFAI.io.from_isotime(text, use_tz=False)¶

Parameters:	text – string representing the time is iso format

pyFAI.io.get_isotime(forceTime=None)¶

Parameters:	forceTime (float) – enforce a given time (current by default)
Returns:	the current time as an ISO8601 string
Return type:	string

pyFAI.io.is_hdf5(filename)¶

Check if a file is actually a HDF5 file

Parameters:	filename – this file has better to exist

`calibration` Module¶

pyFAI-calib

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

class pyFAI.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

HELP = {'reset': 'Reset the geometry to the initial guess (rotation to zero, distance to 0.1m, poni at the center of the image)', 'delete': 'delete a group of points, provide the letter.', 'set': "set the value of a parameter to the given value, i.e 'set wavelength 0.1 nm', units are optional", 'chiplot': 'plot control point radial error as function of azimuthal angle, optional parameters: the rings for which this need to be plotted', 'help': "Try to get the help of a given action, like 'refine?'. Use done when finished. Most command are composed of 'action parameter value' like 'set wavelength 1 A'.", 'weight': 'toggle from weighted to unweighted mode...', 'get': 'print he value of a parameter', 'fix': "fixes the value of a parameter so that its value will not be optimized, i.e. 'fix wavelength'", 'validate2': 'measures the offset of the center as function of azimuthal angle by cross-correlation of 2 plots, 180 deg appart. Option: number of azimuthal sliced, default: 36', 'show': "Just print out the current parameter set. Optional parameters are units for length, rotation and wavelength, i.e. 'show mm deg A'", 'bound': "sets the upper and lower bound of a parameter: 'bound dist 0.1 0.2'", 'free': "frees the parameter so that the value can be optimized, i.e. 'free wavelength'", 'bounds': 'sets the upper and lower bound of all parameters', 'abort': 'quit immediately, discarding any unsaved changes', 'done': 'finishes the processing, performs an integration and quits', 'refine': 'performs a new cycle of refinement', 'integrate': 'perform the azimuthal integration and display results', 'validate': 'plot the offset between the calibrated image and the back-projected image', 'recalib': 'extract a new set of rings and re-perform the calibration. One can specify how many rings to extract and the algorithm to use (blob, massif, watershed) and the nb_pts_per_deg in azimuth', 'assign': 'Change the assignment of a group of points to a rings', 'define': 'Re-define the value for a constant internal parameter of the program like max_iter, nPt_1D, nPt_2D_azim, nPt_2D_rad, integrator_method, error_model. Warning: attribute change may be harmful !'}¶

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

PTS_PER_DEG = 0.3¶

UNITS = {'poni1': 'meter', 'poni2': 'meter', 'rot1': 'radian', 'rot3': 'radian', 'rot2': 'radian', 'wavelength': 'meter', 'dist': '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 0.14.0: 20/07/2017', 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

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

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.calibration.Calibration(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, gaussianWidth=None, wavelength=None, calibrant=None)¶

Bases: pyFAI.calibration.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()¶

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

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.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

size1d¶

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

class pyFAI.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.calibration.Recalibration(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, wavelength=None, calibrant=None)¶

Bases: pyFAI.calibration.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.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.calibration.get_detector(detector, datafiles=None)¶

Detector factory taking into account the binning knowing the datafiles

Raises RuntimeError:
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
	If no detector found

`control_points` Module¶

ControlPoints: a set of control points associated with a calibration image

PointGroup: a group of points

class pyFAI.control_points.ControlPoints(filename=None, calibrant=None, wavelength=None)¶

Bases: object

This class contains a set of control points with (optionally) their ring number hence d-spacing and diffraction 2Theta angle ...

__init__(filename=None, calibrant=None, wavelength=None)¶

append(points, ring=None, annotate=None, plot=None)¶

Append a group of points to a given ring

Parameters:	point – list of points ring – ring number annotate – matplotlib.annotate reference plot – matplotlib.plot reference
Returns:	PointGroup instance

append_2theta_deg(points, angle=None, ring=None)¶

Append a group of points to a given ring

Parameters:	point – list of points angle – 2-theta angle in degrees
Param:	ring: ring number

check()¶: check internal consistency of the class, disabled for now

dSpacing¶

get(ring=None, lbl=None)¶

Retireves the last group of points for a given ring (by default the last)

Parameters:	ring – index of ring to search for lbl – label of the group to retrieve

getList()¶: Retrieve the list of control points suitable for geometry refinement with ring number

getList2theta()¶: Retrieve the list of control points suitable for geometry refinement

getListRing()¶: Retrieve the list of control points suitable for geometry refinement with ring number

getWeightedList(image)¶

Retrieve the list of control points suitable for geometry refinement with ring number and intensities :param image: :return: a (x,4) array with pos0, pos1, ring nr and intensity

#TODO: refine the value of the intensity using 2nd order polynomia

get_dSpacing()¶

get_labels()¶

Retieve the list of labels

Returns:	list of labels as string

get_wavelength()¶

load(filename)¶: load all control points from a file

pop(ring=None, lbl=None)¶

Remove the set of points, either from its code or from a given ring (by default the last)

Parameters:	ring – index of ring of which remove the last group lbl – code of the ring to remove

readRingNrFromKeyboard()¶: Ask the ring number values for the given points

reset()¶: remove all stored values and resets them to default

save(filename)¶: Save a set of control points to a file :param filename: name of the file :return: None

setWavelength_change2th(value=None)¶

setWavelength_changeDs(value=None)¶: This is probably not a good idea, but who knows !

set_dSpacing(lst)¶

set_wavelength(value=None)¶

wavelength¶

class pyFAI.control_points.PointGroup(points=None, ring=None, annotate=None, plot=None, force_label=None)¶

Bases: object

Class contains a group of points ... They all belong to the same Debye-Scherrer ring

__init__(points=None, ring=None, annotate=None, plot=None, force_label=None)¶

Constructor

Parameters:	points – list of points ring – ring number annotate – reference to the matplotlib annotate output plot – reference to the matplotlib plot force_label – allows to enforce the label

code¶: Numerical value for the label: mainly for sorting

classmethod get_label()¶: return the next label

get_ring()¶

label¶

last_label = 0¶

classmethod reset_label()¶: reset intenal counter

ring¶

classmethod set_label(label)¶: update the internal counter if needed

set_ring(value)¶

`peak_picker` Module¶

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

class pyFAI.peak_picker.ControlPoints(filename=None, calibrant=None, wavelength=None)¶

Bases: object

This class contains a set of control points with (optionally) their ring number hence d-spacing and diffraction 2Theta angle ...

__init__(filename=None, calibrant=None, wavelength=None)¶

append(points, ring=None, annotate=None, plot=None)¶

Append a group of points to a given ring

Parameters:	point – list of points ring – ring number annotate – matplotlib.annotate reference plot – matplotlib.plot reference
Returns:	PointGroup instance

append_2theta_deg(points, angle=None, ring=None)¶

Append a group of points to a given ring

Parameters:	point – list of points angle – 2-theta angle in degrees
Param:	ring: ring number

check()¶: check internal consistency of the class, disabled for now

dSpacing¶

get(ring=None, lbl=None)¶

Retireves the last group of points for a given ring (by default the last)

Parameters:	ring – index of ring to search for lbl – label of the group to retrieve

getList()¶: Retrieve the list of control points suitable for geometry refinement with ring number

getList2theta()¶: Retrieve the list of control points suitable for geometry refinement

getListRing()¶: Retrieve the list of control points suitable for geometry refinement with ring number

getWeightedList(image)¶

Retrieve the list of control points suitable for geometry refinement with ring number and intensities :param image: :return: a (x,4) array with pos0, pos1, ring nr and intensity

#TODO: refine the value of the intensity using 2nd order polynomia

get_dSpacing()¶

get_labels()¶

Retieve the list of labels

Returns:	list of labels as string

get_wavelength()¶

load(filename)¶: load all control points from a file

pop(ring=None, lbl=None)¶

Remove the set of points, either from its code or from a given ring (by default the last)

Parameters:	ring – index of ring of which remove the last group lbl – code of the ring to remove

readRingNrFromKeyboard()¶: Ask the ring number values for the given points

reset()¶: remove all stored values and resets them to default

save(filename)¶: Save a set of control points to a file :param filename: name of the file :return: None

setWavelength_change2th(value=None)¶

setWavelength_changeDs(value=None)¶: This is probably not a good idea, but who knows !

set_dSpacing(lst)¶

set_wavelength(value=None)¶

wavelength¶

class pyFAI.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 –

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

gui(log=False, maximize=False, pick=True)¶

Parameters:	log – show z in log scale

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_option_clicked(*args)¶: callback function

on_plus_pts_clicked(*args)¶: callback function

on_refine_clicked(*args)¶: callback function

onclick(event)¶: Called when a mouse is clicked

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()¶

class pyFAI.peak_picker.PointGroup(points=None, ring=None, annotate=None, plot=None, force_label=None)¶

Bases: object

Class contains a group of points ... They all belong to the same Debye-Scherrer ring

__init__(points=None, ring=None, annotate=None, plot=None, force_label=None)¶

Constructor

Parameters:	points – list of points ring – ring number annotate – reference to the matplotlib annotate output plot – reference to the matplotlib plot force_label – allows to enforce the label

code¶: Numerical value for the label: mainly for sorting

classmethod get_label()¶: return the next label

get_ring()¶

label¶

last_label = 0¶

classmethod reset_label()¶: reset intenal counter

ring¶

classmethod set_label(label)¶: update the internal counter if needed

set_ring(value)¶

`massif` Module¶

class pyFAI.massif.Massif(data=None)¶

Bases: object

A massif is defined as an area around a peak, it is used to find neighboring peaks

TARGET_SIZE = 1024¶

__init__(data=None)¶

calculate_massif(x)¶: defines a map of the massif around x and returns the mask

delValleySize()¶

find_peaks(x, nmax=200, annotate=None, massif_contour=None, stdout=<open file '<stdout>', mode 'w' at 0x7fb662eae150>)¶: All in one function that finds a maximum from the given seed (x) then calculates the region extension and extract position of the neighboring peaks. :param x: coordinates of the peak, seed for the calculation :type x: tuple of integer :param nmax: maximum number of peak per region :param annotate: call back method taking number of points + coordinate as input. :param massif_contour: callback to show the contour of a massif with the given index. :param stdout: this is the file where output is written by default. :return: list of peaks

getBinnedData()¶

Returns:	binned data

getBluredData()¶

Returns:	a blurred image

getLabeledMassif(pattern=None)¶

Returns:	an image composed of int with a different value for each massif

getMedianData()¶

Returns:	a spacial median filtered image

getValleySize()¶

initValleySize()¶

nearest_peak(x)¶

Parameters:	x – coordinates of the peak
Returns:	the coordinates of the nearest peak

peaks_from_area(mask, Imin=None, keep=1000, dmin=0.0, seed=None, **kwarg)¶

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 kwarg – ignored parameters dmin – minimum distance to another peak seed – list of good guesses to start with
Returns:	list of peaks [y,x], [y,x], ...]

setValleySize(size)¶

valley_size¶: Defines the minimum distance between two massifs

`blob_detection` Module¶

class pyFAI.blob_detection.BlobDetection(img, cur_sigma=0.25, init_sigma=0.5, dest_sigma=1, scale_per_octave=2, mask=None)¶

Bases: object

Performs a blob detection: http://en.wikipedia.org/wiki/Blob_detection using a Difference of Gaussian + Pyramid of Gaussians

__init__(img, cur_sigma=0.25, init_sigma=0.5, dest_sigma=1, scale_per_octave=2, mask=None)¶

Performs a blob detection: http://en.wikipedia.org/wiki/Blob_detection using a Difference of Gaussian + Pyramid of Gaussians

Parameters:

img – input image
cur_sigma – estimated smoothing of the input image. 0.25 correspond to no interaction between pixels.
init_sigma – start searching at this scale (sigma=0.5: 10% interaction with first neighbor)
dest_sigma – sigma at which the resolution is lowered (change of octave)
scale_per_octave – Number of scale to be performed per octave
mask – mask where pixel are not valid

direction()¶: Perform and plot the two main directions of the peaks, considering their previously calculated scale ,by calculating the Hessian at different sizes as the combination of gaussians and their first and second derivatives

nearest_peak(p, refine=True, Imin=None)¶

Return the nearest peak from a position

Parameters:	p – input position (y,x) 2-tuple of float refine – shall the position be refined on the raw data Imin – minimum of intensity above the background

peaks_from_area(mask, keep=None, refine=True, Imin=None, dmin=0.0, **kwargs)¶

Return the list of peaks within an area

Parameters:	mask – 2d array with mask. refine – shall the position be refined on the raw data Imin – minimum of intensity above the background kwarg – ignored parameters
Returns:	list of peaks [y,x], [y,x], ...]

process(max_octave=None)¶: Perform the keypoint extraction for max_octave cycles or until all octaves have been processed. :param max_octave: number of octave to process

refine_Hessian(kpx, kpy, kps)¶

Refine the keypoint location based on a 3 point derivative, and delete non-coherent keypoints.

Parameters:	kpx – x_pos of keypoint kpy – y_pos of keypoint kps – s_pos of keypoint
Returns:	arrays of corrected coordinates of keypoints, values and locations of keypoints

refine_Hessian_SG(kpx, kpy, kps)¶: Savitzky Golay algorithm to check if a point is really the maximum :param kpx: x_pos of keypoint :param kpy: y_pos of keypoint :param kps: s_pos of keypoint :return: array of corrected keypoints

refinement()¶

show_neighboor()¶

show_stats()¶: Shows a window with the repartition of keypoint in function of scale/intensity

tresh = 0.6¶

pyFAI.blob_detection.image_test()¶

pyFAI.blob_detection.local_max(dogs, mask=None, n_5=True)¶

Parameters:	dogs – 3d array with (sigma,y,x) containing difference of gaussians mask – mask out keypoint next to the mask (or inside the mask) n_5 – look for a larger neighborhood

pyFAI.blob_detection.make_gaussian(im, sigma, xc, yc)¶

`calibrant` Module¶

Calibrant

A module containing classical calibrant and also tools to generate d-spacing.

Interesting formula: http://geoweb3.princeton.edu/research/MineralPhy/xtalgeometry.pdf

pyFAI.calibrant.CALIBRANT_FACTORY = Calibrants available: Ni, CrOx, NaCl, Si_SRM640e, Si_SRM640d, Si_SRM640a, Si_SRM640c, alpha_Al2O3, Cr2O3, AgBh, Si_SRM640, CuO, PBBA, Si_SRM640b, mock, quartz, C14H30O, cristobaltite, Si, LaB6, CeO2, LaB6_SRM660a, LaB6_SRM660b, LaB6_SRM660c, TiO2, ZnO, Al, Au¶: Default calibration factory provided by the library.

class pyFAI.calibrant.Calibrant(filename=None, dSpacing=None, wavelength=None)¶

Bases: object

A calibrant is a reference compound where the d-spacing (interplanar distances) are known. They are expressed in Angstrom (in the file)

__init__(filename=None, dSpacing=None, wavelength=None)¶

append_2th(value)¶

append_dSpacing(value)¶

dSpacing¶

fake_calibration_image(ai, shape=None, Imax=1.0, U=0, V=0, W=0.0001)¶

Generates a fake calibration image from an azimuthal integrator

Parameters:	ai – azimuthal integrator Imax – maximum intensity of rings V, W (U,) – width of the peak from Caglioti’s law (FWHM^2 = Utan(th)^2 + Vtan(th) + W)

get_2th()¶

get_2th_index(angle)¶: return the index in the 2theta angle index

get_dSpacing()¶

get_wavelength()¶

load_file(filename=None)¶

save_dSpacing(filename=None)¶: save the d-spacing to a file

setWavelength_change2th(value=None)¶

setWavelength_changeDs(value=None)¶: This is probably not a good idea, but who knows !

set_dSpacing(lst)¶

set_wavelength(value=None)¶

wavelength¶

class pyFAI.calibrant.CalibrantFactory(basedir=None)¶

Bases: object

Behaves like a dict but is actually a factory:

Each time one retrieves an object it is a new geniune new calibrant (unmodified)

__init__(basedir=None)¶

Constructor

Parameters:	basedir – directory name where to search for the calibrants

get(what, notfound=None)¶

has_key(k)¶

items()¶

keys()¶

values()¶

class pyFAI.calibrant.Cell(a=1, b=1, c=1, alpha=90, beta=90, gamma=90, lattice='triclinic', lattice_type='P')¶

Bases: object

This is a cell object, able to calculate the volume and d-spacing according to formula from:

http://geoweb3.princeton.edu/research/MineralPhy/xtalgeometry.pdf

__init__(a=1, b=1, c=1, alpha=90, beta=90, gamma=90, lattice='triclinic', lattice_type='P')¶

Constructor of the Cell class:

Crystalographic units are Angstrom for distances and degrees for angles !

Parameters:	a,b,c – unit cell length in Angstrom beta, gamma (alpha,) – unit cell angle in degrees lattice – “cubic”, “tetragonal”, “hexagonal”, “rhombohedral”, “orthorhombic”, “monoclinic”, “triclinic” lattice_type – P, I, F, C or R

classmethod cubic(a, lattice_type='P')¶

Factory for cubic lattices

Parameters:	a – unit cell length

d(hkl)¶

Calculate the actual d-spacing for a 3-tuple of integer representing a family of Miller plans

Parameters:	hkl – 3-tuple of integers
Returns:	the inter-planar distance

d_spacing(dmin=1.0)¶

Calculate all d-spacing down to dmin

applies selection rules

Parameters:	dmin – minimum value of spacing requested
Returns:	dict d-spacing as string, list of tuple with Miller indices preceded with the numerical value

classmethod diamond(a)¶

Factory for Diamond type FCC like Si and Ge

Parameters:	a – unit cell length

get_type()¶

classmethod hexagonal(a, c, lattice_type='P')¶

Factory for hexagonal lattices

Parameters:	a – unit cell length c – unit cell length

lattices = ['cubic', 'tetragonal', 'hexagonal', 'rhombohedral', 'orthorhombic', 'monoclinic', 'triclinic']¶

classmethod monoclinic(a, b, c, beta, lattice_type='P')¶

Factory for hexagonal lattices

Parameters:	a – unit cell length b – unit cell length c – unit cell length beta – unit cell angle

classmethod orthorhombic(a, b, c, lattice_type='P')¶

Factory for orthorhombic lattices

Parameters:	a – unit cell length b – unit cell length c – unit cell length

classmethod rhombohedral(a, alpha, lattice_type='P')¶

Factory for hexagonal lattices

Parameters:	a – unit cell length alpha – unit cell angle

save(name, long_name=None, doi=None, dmin=1.0, dest_dir=None)¶

Save informations about the cell in a d-spacing file, usable as Calibrant

Parameters:	name – name of the calibrant doi – reference of the publication used to parametrize the cell dmin – minimal d-spacing dest_dir – name of the directory where to save the result

selection_rules = None¶: contains a list of functions returning True(allowed)/False(forbiden)/None(unknown)

set_type(lattice_type)¶

classmethod tetragonal(a, c, lattice_type='P')¶

Factory for tetragonal lattices

Parameters:	a – unit cell length c – unit cell length

type¶

types = {'I': 'Body centered', 'P': 'Primitive', 'C': 'Side centered', 'R': 'Rhombohedral', 'F': 'Face centered'}¶

volume¶

pyFAI.calibrant.get_calibrant(calibrant_name)¶

Returns a new instance of the calibrant by it’s name.

Parameters:	calibrant_name (str) – Name of the calibrant

pyFAI.calibrant.names()¶

Returns the list of registred calibrant names.

Return type:	str

`distortion` Module¶

class pyFAI.distortion.Distortion(detector='detector', shape=None, resize=False, empty=0, mask=None, method='CSR', device=None, workgroup=8)¶

Bases: object

This class applies a distortion correction on an image.

New version compatible both with CSR and LUT...

__init__(detector='detector', shape=None, resize=False, empty=0, mask=None, method='CSR', device=None, workgroup=8)¶

Parameters:

detector – detector instance or detector name
shape – shape of the output image
resize – allow the output shape to be different from the input shape
empty – value to be given for empty bins
method – “lut” or “csr”, the former is faster
device – Name of the device: None for OpenMP, “cpu” or “gpu” or the id of the OpenCL device a 2-tuple of integer
workgroup – workgroup size for CSR on OpenCL

calc_LUT(use_common=True)¶

Calculate the Look-up table

Returns:	look up table either in CSR or LUT format depending on serl.method

calc_init()¶: Initialize all arrays

calc_pos(use_cython=True)¶

Calculate the pixel boundary position on the regular grid

Returns:	pixel corner positions (in pixel units) on the regular grid
Return type:	ndarray of shape (nrow, ncol, 4, 2)

calc_size(use_cython=True)¶

Calculate the number of pixels falling into every single bin and

Returns:	max of pixel falling into a single bin

Considering the “half-CCD” spline from ID11 which describes a (1025,2048) detector, the physical location of pixels should go from: [-17.48634 : 1027.0543, -22.768829 : 2028.3689] We chose to discard pixels falling outside the [0:1025,0:2048] range with a lose of intensity

correct(image, dummy=None, delta_dummy=None)¶

Correct an image based on the look-up table calculated ...

Parameters:	image – 2D-array with the image dummy – value suggested for bad pixels delta_dummy – precision of the dummy value
Returns:	corrected 2D image

reset(method=None, device=None, workgroup=None, prepare=True)¶

reset the distortion correction and re-calculate the look-up table

Parameters:	method – can be “lut” or “csr”, “lut” looks faster device – can be None, “cpu” or “gpu” or the id as a 2-tuple of integer worgroup – enforce the workgroup size for CSR. prepare – set to false to only reset and not re-initialize

shape_out¶

Calculate/cache the output shape

Returns:	output shape

uncorrect(image, use_cython=False)¶

Take an image which has been corrected and transform it into it’s raw (with loss of information)

Parameters:	image – 2D-array with the image
Returns:	uncorrected 2D image

Nota: to retrieve the input mask on can do:

>>> msk =  dis.uncorrect(numpy.ones(dis._shape_out)) <= 0

class pyFAI.distortion.Quad(buffer)¶

Bases: object

Quad modelisation.

__init__(buffer)¶

calc_area()¶

calc_area_AB(I1, I2)¶

calc_area_BC(J1, J2)¶

calc_area_CD(K1, K2)¶

calc_area_DA(L1, L2)¶

calc_area_old()¶

calc_area_vectorial()¶

get_box(i, j)¶

get_box_size0()¶

get_box_size1()¶

get_idx(i, j)¶

get_offset0()¶

get_offset1()¶

init_slope()¶

integrateAB(start, stop, calc_area)¶

populate_box()¶

reinit(A0, A1, B0, B1, C0, C1, D0, D1)¶

pyFAI.distortion.test()¶

`worker` Module¶

This module contains the Worker class:

A tool able to perform azimuthal integration with: additional saving capabilities like

save as 2/3D structure in a HDF5 File
read from HDF5 files

Aims at being integrated into a plugin like LImA or as model for the GUI

The configuration of this class is mainly done via a dictionary transmitted as a JSON string: Here are the valid keys:

“dist”
“poni1”
“poni2”
“rot1”
“rot3”
“rot2”
“pixel1”
“pixel2”
“splineFile”
“wavelength”
“poni” #path of the file
“chi_discontinuity_at_0”
“do_mask”
“do_dark”
“do_azimuthal_range”
“do_flat”
“do_2D”
“azimuth_range_min”
“azimuth_range_max”
“polarization_factor”
“nbpt_rad”
“do_solid_angle”
“do_radial_range”
“do_poisson”
“delta_dummy”
“nbpt_azim”
“flat_field”
“radial_range_min”
“dark_current”
“do_polarization”
“mask_file”
“detector”
“unit”
“radial_range_max”
“val_dummy”
“do_dummy”
“method”

class pyFAI.worker.DistortionWorker(detector=None, dark=None, flat=None, solidangle=None, polarization=None, mask=None, dummy=None, delta_dummy=None, device=None)¶

Bases: object

Simple worker doing dark, flat, solid angle and polarization correction

__init__(detector=None, dark=None, flat=None, solidangle=None, polarization=None, mask=None, dummy=None, delta_dummy=None, device=None)¶: Constructor of the worker :param dark: array :param flat: array :param solidangle: solid-angle array :param polarization: numpy array with 2D polarization corrections :param device: Used to influance OpenCL behavour: can be “cpu”, “GPU”, “Acc” or even an OpenCL context

process(data, normalization_factor=1.0)¶: Process the data and apply a normalization factor :param data: input data :param normalization: normalization factor :return: processed data

class pyFAI.worker.PixelwiseWorker(dark=None, flat=None, solidangle=None, polarization=None, mask=None, dummy=None, delta_dummy=None, device=None)¶

Bases: object

Simple worker doing dark, flat, solid angle and polarization correction

__init__(dark=None, flat=None, solidangle=None, polarization=None, mask=None, dummy=None, delta_dummy=None, device=None)¶

Constructor of the worker

Parameters:	dark – array flat – array solidangle – solid-angle array polarization – numpy array with 2D polarization corrections device – Used to influance OpenCL behavour: can be “cpu”, “GPU”, “Acc” or even an OpenCL context

process(data, normalization_factor=None)¶: Process the data and apply a normalization factor :param data: input data :param normalization: normalization factor :return: processed data

class pyFAI.worker.Worker(azimuthalIntegrator=None, shapeIn=(2048, 2048), shapeOut=(360, 500), unit='r_mm', dummy=None, delta_dummy=None, azimuthalIntgrator=None)¶

Bases: object

__init__(azimuthalIntegrator=None, shapeIn=(2048, 2048), shapeOut=(360, 500), unit='r_mm', dummy=None, delta_dummy=None, azimuthalIntgrator=None)¶

Parameters:	AzimuthalIntegrator (azimuthalIntgrator) – pyFAI.AzimuthalIntegrator instance AzimuthalIntegrator – pyFAI.AzimuthalIntegrator instance (deprecated) shapeIn – image size in input shapeOut – Integrated size: can be (1,2000) for 1D integration unit – can be “2th_deg, r_mm or q_nm^-1 ...

do_2D()¶

error_model¶

get_config()¶: return configuration as a dictionary

get_error_model()¶

get_json_config()¶: return configuration as a JSON string

get_normalization_factor()¶

get_unit()¶

normalization_factor¶

process(data, normalization_factor=1.0, writer=None, metadata=None)¶

Process a frame #TODO: dark, flat, sa are missing

Parameters:	data – numpy array containing the input image writer – An open writer in which ‘write’ will be called with the result of the integration

reconfig(shape=(2048, 2048), sync=False)¶

This is just to force the integrator to initialize with a given input image shape

Parameters:	shape – shape of the input image sync – return only when synchronized

reset()¶: this is just to force the integrator to initialize

save_config(filename=None)¶

setDarkcurrentFile(imagefile)¶

setExtension(ext)¶: enforce the extension of the processed data file written

setFlatfieldFile(imagefile)¶

setJsonConfig(jsonconfig)¶

setSubdir(path)¶: Set the relative or absolute path for processed data

set_error_model(value)¶

set_normalization_factor(value)¶

set_unit(value)¶

unit¶

warmup(sync=False)¶

Process a dummy image to ensure everything is initialized

Parameters:	sync – wait for processing to be finished

pyFAI.worker.make_ai(config)¶

Create an Azimuthal integrator from the configuration stand alone function !

Parameters:	config – dict with all parameters
Returns:	configured (but uninitialized) AzimuthalIntgrator

`units` Module¶

Manages the different units

Nota for developers: this module is used a singleton to store all units in a unique manner. This explains the number of top-level variables on the one hand and their CAPITALIZATION on the other.

class pyFAI.units.Unit(name, scale=1, label=None, equation=None, center=None, corner=None, delta=None)¶

Bases: object

Represents a unit.

It has at least a name and a scale (in SI-unit)

__init__(name, scale=1, label=None, equation=None, center=None, corner=None, delta=None)¶

Constructor of a unit.

Parameters:

name ((str)) – name of the unit
scale ((float)) – scale of th unit to go to SI
label ((string)) – label for nice representation in matplotlib, can use latex representation
equation ((funct)) – equation to calculate the value from coordinates (x,y,z) in detector space. Parameters of the function are x, y, z, lambda
center ((str)) – name of the fast-path function

get(key)¶

Mimic the dictionary interface

Parameters:	key ((str)) – key wanted
Returns:	self.key

pyFAI.units.eq_2th(x, y, z, wavelength=None)¶

Calculates the 2theta aperture of the cone

Parameters:	x – horizontal position, towards the center of the ring, from sample position y – Vertical position, to the roof, from sample position z – distance from sample along the beam wavelength – in meter

pyFAI.units.eq_q(x, y, z, wavelength)¶

Calculates the modulus of the scattering vector

Parameters:	x – horizontal position, towards the center of the ring, from sample position y – Vertical position, to the roof, from sample position z – distance from sample along the beam wavelength – in meter

pyFAI.units.eq_r(x, y, z=None, wavelength=None)¶

Calculates the radius

Parameters:	x – horizontal position, towards the center of the ring, from sample position y – Vertical position, to the roof, from sample position z – distance from sample along the beam wavelength – in meter

pyFAI.units.register_radial_unit(name, scale=1, label=None, equation=None, center=None, corner=None, delta=None)¶

pyFAI.units.to_unit(obj, type_=None)¶

`utils.init` Module¶

Sub module with miscelaneous tools

Utilities, mainly for image treatment

class pyFAI.utils.__init__.FixedParameters¶

Bases: set

Like a set, made for FixedParameters in geometry refinement

add_or_discard(key, value=True)¶: Add a value to a set if value, else discard it :param key: element to added or discared from set :type value: boolean. If None do nothing ! :return: None

pyFAI.utils.__init__.binning(input_img, binsize, norm=True)¶

Parameters:	input_img – input ndarray binsize – int or 2-tuple representing the size of the binning norm – if False, do average instead of sum
Returns:	binned input ndarray

pyFAI.utils.__init__.calc_checksum(ary, safe=True)¶: Calculate the checksum by default (or returns its buffer location if unsafe)

pyFAI.utils.__init__.center_of_mass(img)¶: Calculate the center of mass of of the array. Like scipy.ndimage.measurements.center_of_mass :param img: 2-D array :return: 2-tuple of float with the center of mass

pyFAI.utils.__init__.convert_CamelCase(name)¶: convert a function name in CamelCase into camel_case

pyFAI.utils.__init__.deg2rad(dd)¶

Convert degrees to radian in the range -pi->pi

Parameters:	dd – angle in degrees

Nota: depending on the platform it could be 0<2pi A branch is cheaper than a trigo operation

pyFAI.utils.__init__.dog(s1, s2, shape=None)¶: 2D difference of gaussian typically 1 to 10 parameters

pyFAI.utils.__init__.dog_filter(input_img, sigma1, sigma2, mode='reflect', cval=0.0)¶

2-dimensional Difference of Gaussian filter implemented with FFT

Parameters:

input_img (array-like) – input_img array to filter
sigma (scalar or sequence of scalars) – standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
mode – {‘reflect’,’constant’,’nearest’,’mirror’, ‘wrap’}, optional The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
cval – scalar, optional Value to fill past edges of input if mode is ‘constant’. Default is 0.0

pyFAI.utils.__init__.expand(input_img, sigma, mode='constant', cval=0.0)¶

Expand array a with its reflection on boundaries

Parameters:	a – 2D array sigma – float or 2-tuple of floats. mode – “constant”, “nearest”, “reflect” or “mirror” cval – filling value used for constant, 0.0 by default

Nota: sigma is the half-width of the kernel. For gaussian convolution it is adviced that it is 4*sigma_of_gaussian

pyFAI.utils.__init__.expand2d(vect, size2, vertical=True)¶

This expands a vector to a 2d-array.

The result is the same as:

if vertical:
    numpy.outer(numpy.ones(size2), vect)
else:
    numpy.outer(vect, numpy.ones(size2))

This is a ninja optimization: replace *1 with a memcopy, saves 50% of time at the ms level.

Parameters:	vect – 1d vector size2 – size of the expanded dimension vertical – if False the vector is expanded to the first dimension. If True, it is expanded to the second dimension.

pyFAI.utils.__init__.expand_args(args)¶

Takes an argv and expand it (under Windows, cmd does not convert *.tif into a list of files. Keeps only valid files (thanks to glob)

Parameters:	args – list of files or wilcards
Returns:	list of actual args

pyFAI.utils.__init__.float_(val)¶: Convert anything to a float ... or None if not applicable

pyFAI.utils.__init__.fully_qualified_name(obj)¶: Return the fully qualified name of an object

pyFAI.utils.__init__.gaussian(M, std)¶

Return a Gaussian window of length M with standard-deviation std.

This differs from the scipy.signal.gaussian implementation as: - The default for sym=False (needed for gaussian filtering without shift) - This implementation is normalized

Parameters:	M – length of the windows (int) std – standatd deviation sigma

The FWHM is 2*numpy.sqrt(2 * numpy.pi)*std

pyFAI.utils.__init__.gaussian_filter(input_img, sigma, mode='reflect', cval=0.0, use_scipy=True)¶

2-dimensional Gaussian filter implemented with FFT

Parameters:

input_img (array-like) – input array to filter
sigma (scalar or sequence of scalars) – standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
mode – {‘reflect’,’constant’,’nearest’,’mirror’, ‘wrap’}, optional The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
cval – scalar, optional Value to fill past edges of input if mode is ‘constant’. Default is 0.0

pyFAI.utils.__init__.get_calibration_dir()¶

get the full path of a calibration directory

Returns:	the full path of the calibrant file

pyFAI.utils.__init__.get_cl_file(filename)¶

get the full path of a openCL file

Returns:	the full path of the openCL source file

pyFAI.utils.__init__.get_ui_file(filename)¶

get the full path of a user-interface file

Returns:	the full path of the ui

pyFAI.utils.__init__.int_(val)¶: Convert anything to an int ... or None if not applicable

pyFAI.utils.__init__.is_far_from_group(pt, lst_pts, d2)¶

Tells if a point is far from a group of points, distance greater than d2 (distance squared)

Parameters:	pt – point of interest lst_pts – list of points d2 – minimum distance squarred
Returns:	True If the point is far from all others.

class pyFAI.utils.__init__.lazy_property(fget)¶

Bases: object

meant to be used for lazy evaluation of an object attribute. property should represent non-mutable data, as it replaces itself.

__init__(fget)¶

pyFAI.utils.__init__.maximum_position(img)¶

Same as scipy.ndimage.measurements.maximum_position: Find the position of the maximum of the values of the array.

Parameters:	img – 2-D image
Returns:	2-tuple of int with the position of the maximum

pyFAI.utils.__init__.measure_offset(img1, img2, method='numpy', withLog=False, withCorr=False)¶: Measure the actual offset between 2 images :param img1: ndarray, first image :param img2: ndarray, second image, same shape as img1 :param withLog: shall we return logs as well ? boolean :return: tuple of floats with the offsets

pyFAI.utils.__init__.readFloatFromKeyboard(text, dictVar)¶

Read float from the keyboard ....

Parameters:	text – string to be displayed dictVar – dict of this type: {1: [set_dist_min],3: [set_dist_min, set_dist_guess, set_dist_max]}

pyFAI.utils.__init__.relabel(label, data, blured, max_size=None)¶

Relabel limits the number of region in the label array. They are ranked relatively to their max(I0)-max(blur(I0)

Parameters:	label – a label array coming out of `scipy.ndimage.measurement.label` data – an array containing the raw data blured – an array containing the blurred data max_size – the max number of label wanted
Returns:	array like label

pyFAI.utils.__init__.roundfft(N)¶

This function returns the integer >=N for which size the Fourier analysis is faster (fron the FFT point of view) Credit: Alessandro Mirone, ESRF, 2012

Parameters:	N – interger on which one would like to do a Fourier transform
Returns:	integer with a better choice

pyFAI.utils.__init__.shift(input_img, shift_val)¶: Shift an array like scipy.ndimage.interpolation.shift(input_img, shift_val, mode=”wrap”, order=0) but faster :param input_img: 2d numpy array :param shift_val: 2-tuple of integers :return: shifted image

pyFAI.utils.__init__.shiftFFT(input_img, shift_val, method='fft')¶

Do shift using FFTs

Shift an array like scipy.ndimage.interpolation.shift(input, shift, mode=”wrap”, order=”infinity”) but faster :param input_img: 2d numpy array :param shift_val: 2-tuple of float :return: shifted image

pyFAI.utils.__init__.str_(val)¶: Convert anything to a string ... but None -> “”

pyFAI.utils.__init__.unBinning(binnedArray, binsize, norm=True)¶

Parameters:	binnedArray – input ndarray binsize – 2-tuple representing the size of the binning norm – if True (default) decrease the intensity by binning factor. If False, it is non-conservative
Returns:	unBinned input ndarray

`utils.bayes` Module¶

Tool for bayesian filtering-out of positive peaks

Bayesian evaluation of background for 1D powder diffraction pattern

Code according to Sivia and David, J. Appl. Cryst. (2001). 34, 318-324 # Version: 0.1 2012/03/28 # Version: 0.2 2016/10/07: OOP implementation

class pyFAI.utils.bayes.BayesianBackground¶

Bases: object

This class estimates the background of a powder diffraction pattern

http://journals.iucr.org/j/issues/2001/03/00/he0278/he0278.pdf

The log likelihood is described in correspond to eq7 of the paper: z = y/sigma^2 * if z<0: a quadratic behaviour is expected * if z>>1 it is likely a bragg peak so the penalty should be small: log(z). * The spline is used to have a quadratic behaviour near 0 and the log one

near the junction

The threshold is taken at 8 as erf is 1 above 6: The points 6, 7 and 8 are used in the spline to ensure a continuous junction with the logarithmic continuation

PREFACTOR = 1¶

__init__()¶

background_image(img, sigma=None, mask=None, npt=10, k=3)¶

classmethod bayes_llk(z)¶

Calculate actually the log-likelihood from a set of weighted error

Re implementation of: (y<=0)*5*y**2 + (y>0)*(y<8)*pyFAI.utils.bayes.background.spline(y) + (y>=8)*(s1+log(abs(y)+1*(y<8))) even slightly faster

:param float[:] z: weighted error :return: log likelihood :rtype: float[:]

classmethod bayes_llk_large(z)¶: used to calculate the log-likelihood of large positive values: logarithmic

classmethod bayes_llk_negative(z)¶: used to calculate the log-likelihood of negative values: quadratic

classmethod bayes_llk_small(z)¶: used to calculate the log-likelihood of small positive values: fitted with spline

classmethod classinit()¶

classmethod func2d_min(values, d0_sparse, d1_sparse, d0_pos, d1_pos, y_obs, w_obs, valid, k)¶

Function to optimize

Parameters:

values – values of the background on spline knots
d0_sparse – positions along slowest axis of the spline knots
d1_pos – positions along fastest axis of the spline knots
d0_pos – positions along slowest axis (all coordinates)
d1_pos – positions along fastest axis (all coordinates)
y_obs – intensities actually measured
w_obs – weights of the experimental points
valid – coordinated of valid pixels
k – order of the spline, usually 3

Returns:

sum of the log-likelihood to be minimized

classmethod func_min(y0, x_obs, y_obs, w_obs, x0, k)¶

Function to optimize

Parameters:	y0 – values of the background x_obs – experimental values y_obs – experimental values w_obs – weights of the experimental points x0 – position of evaluation of the spline k – order of the spline, usually 3
Returns:	sum of the log-likelihood to be minimized

s1 = 2.5435828321944816¶

spline = <scipy.interpolate.fitpack2.InterpolatedUnivariateSpline object at 0x7fb609de4890>¶

classmethod test_bayes_llk()¶: Test plot of log(likelihood) Similar to as figure 3 of Sivia and David, J. Appl. Cryst. (2001). 34, 318-324

`utils.shell` Module¶

Contains the progress-bar in the shell command line

Module containing utilities around shell

class pyFAI.utils.shell.ProgressBar(title, max_value, bar_width)¶

Progress bar in shell mode

__init__(title, max_value, bar_width)¶

Create a progress bar using a title, a maximum value and a graphical size.

The display is done with stdout using carriage return to to hide the previous progress. It is not possible to use stdout for something else whill a progress bar is in use.

The result looks like:

Parameters:	title (str) – Title displayed before the progress bar max_value (float) – The maximum value of the progress bar bar_width (int) – Size of the progressbar in the screen

clear()¶: Remove the progress bar from the display and move the cursor at the beginning of the line using carriage return.

update(value, message='')¶

Update the progrss bar with the progress bar’s current value.

Set the progress bar’s current value, compute the percentage of progress and update the screen with. Carriage return is used first and then the content of the progress bar. The cursor is at the begining of the line.

Parameters:	value (float) – progress bar’s current value message (str) – message displayed after the progress bar

`utils.string` Module¶

Enhanced string formatter

`opencl.init` Module¶

Contains all OpenCL implementation.

`opencl.common` Module¶

pyFAI.opencl.common.mf¶: alias of mem_flags

pyFAI.opencl.common.release_cl_buffers(cl_buffers)¶

Parameters:	cl_buffer (dict(str, pyopencl.Buffer)) – the buffer you want to release

This method release the memory of the buffers store in the dict

pyFAI.opencl.common.allocate_cl_buffers(buffers, device=None, context=None)¶

Parameters:	buffers – the buffers info use to create the pyopencl.Buffer
Returns:	a dict containing the instanciated pyopencl.Buffer
Return type:	dict(str, pyopencl.Buffer)

This method instanciate the pyopencl.Buffer from the buffers description.

pyFAI.opencl.common.measure_workgroup_size(device)¶

Measure the actual size of the workgroup

Parameters:	device – device or context or 2-tuple with indexes
Returns:	the actual measured workgroup size

if device is “all”, returns a dict with all devices with their ids as keys.

pyFAI.opencl.common.kernel_workgroup_size(program, kernel)¶

Extract the compile time maximum workgroup size

Parameters:	program – OpenCL program kernel – kernel or name of the kernel
Returns:	the maximum acceptable workgroup size for the given kernel

`opencl.processing` Module¶

Common OpenCL abstract base classes for different processing

class pyFAI.opencl.processing.BufferDescription¶

Bases: tuple

BufferDescription(name, size, dtype, flags)

dtype¶: Alias for field number 2

flags¶: Alias for field number 3

name¶: Alias for field number 0

size¶: Alias for field number 1

class pyFAI.opencl.processing.EventDescription¶

Bases: tuple

EventDescription(name, event)

event¶: Alias for field number 1

name¶: Alias for field number 0

class pyFAI.opencl.processing.OpenclProcessing(ctx=None, devicetype='all', platformid=None, deviceid=None, block_size=None, profile=False)¶

Bases: object

Abstract class for different types of OpenCL processing.

This class provides: * Generation of the context, queues, profiling mode * Additional function to allocate/free all buffers declared as static attributes of the class * Functions to compile kernels, cache them and clean them * helper functions to clone the object

__init__(ctx=None, devicetype='all', platformid=None, deviceid=None, block_size=None, profile=False)¶

Constructor of the abstract OpenCL processing class

Parameters:

ctx – actual working context, left to None for automatic initialization from device type or platformid/deviceid
devicetype – type of device, can be “CPU”, “GPU”, “ACC” or “ALL”
platformid – integer with the platform_identifier, as given by clinfo
deviceid – Integer with the device identifier, as given by clinfo
block_size – preferred workgroup size, may vary depending on the outpcome of the compilation
profile – switch on profiling to be able to profile at the kernel level, store profiling elements (makes code slightly slower)

allocate_buffers(buffers=None)¶

Allocate OpenCL buffers required for a specific configuration

Parameters:	buffers – a list of BufferDescriptions, leave to None for paramatrized buffers.

Note that an OpenCL context also requires some memory, as well as Event and other OpenCL functionalities which cannot and are not taken into account here. The memory required by a context varies depending on the device. Typical for GTX580 is 65Mb but for a 9300m is ~15Mb In addition, a GPU will always have at least 3-5Mb of memory in use. Unfortunately, OpenCL does NOT have a built-in way to check the actual free memory on a device, only the total memory.

buffers = [BufferDescription(name='output', size=10, dtype=<type 'numpy.float32'>, flags=None)]¶

compile_kernels(kernel_files=None, compile_options=None)¶

Call the OpenCL compiler

Parameters:	kernel_files – list of path to the kernel (by default use the one declared in the class)

free_buffers()¶: free all device.memory allocated on the device

free_kernels()¶: Free all kernels

kernel_files = []¶

log_profile()¶: If we are in profiling mode, prints out all timing for every single OpenCL call

reset_profile()¶: Resets the profiling timers

set_profiling(value=True)¶

Switch On/Off the profiling flag of the command queue to allow debugging

Parameters:	value – set to True to enable profiling, or to False to disable it. Without profiling, the processing is marginally faster

Profiling information can then be retrieved with the ‘log_profile’ method

`opencl.preproc` Module¶

OpenCL implementation of the preproc module

class pyFAI.opencl.preproc.OCL_Preproc(image_size=None, image_dtype=None, image=None, dark=None, flat=None, solidangle=None, polarization=None, absorption=None, mask=None, dummy=None, delta_dummy=None, empty=None, split_result=False, calc_variance=False, poissonian=False, ctx=None, devicetype='all', platformid=None, deviceid=None, block_size=32, profile=False)¶

Bases: pyFAI.opencl.processing.OpenclProcessing

OpenCL class for pre-processing ... mainly for demonstration

__init__(image_size=None, image_dtype=None, image=None, dark=None, flat=None, solidangle=None, polarization=None, absorption=None, mask=None, dummy=None, delta_dummy=None, empty=None, split_result=False, calc_variance=False, poissonian=False, ctx=None, devicetype='all', platformid=None, deviceid=None, block_size=32, profile=False)¶

Parameters:

image_size – (int) number of element of the input image
image_dtype – dtype of the input image
image – retrieve image_size and image_dtype from template
dark – dark current image as numpy array
flat – flat field image as numpy array
solidangle – solid angle image as numpy array
absorption – absorption image as numpy array
mask – array of int8 with 0 where the data are valid
dummy – value of impossible values: dynamic mask
delta_dummy – precision for dummy values
empty – value to be assigned to pixel without contribution (i.e masked)
split_result – return the result a tuple: data, [variance], normalization, so the last dim becomes 2 or 3
calc_variance – report the result as data, variance, normalization
poissonian – assumes poisson law for data and dark,
ctx – actual working context, left to None for automatic initialization from device type or platformid/deviceid
devicetype – type of device, can be “CPU”, “GPU”, “ACC” or “ALL”
platformid – integer with the platform_identifier, as given by clinfo
deviceid – Integer with the device identifier, as given by clinfo
block_size – preferred workgroup size, may vary depending on the outpcome of the compilation
profile – switch on profiling to be able to profile at the kernel level, store profiling elements (makes code slower)

buffers = [BufferDescription(name='output', size=3, dtype=<type 'numpy.float32'>, flags=2), BufferDescription(name='image_raw', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='image', size=1, dtype=<type 'numpy.float32'>, flags=1), BufferDescription(name='variance', size=1, dtype=<type 'numpy.float32'>, flags=1), BufferDescription(name='dark', size=1, dtype=<type 'numpy.float32'>, flags=1), BufferDescription(name='dark_variance', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='flat', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='polarization', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='solidangle', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='absorption', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='mask', size=1, dtype=<type 'numpy.int8'>, flags=4)]¶

compile_kernels(kernel_files=None, compile_options=None)¶

Call the OpenCL compiler

Parameters:	kernel_files – list of path to the kernel (by default use the one declared in the class)

delta_dummy¶

dummy¶

empty¶

kernel_files = ['preprocess.cl']¶

mapping = {<type 'numpy.int16'>: 's16_to_float', <type 'numpy.int32'>: 's32_to_float', <type 'numpy.uint8'>: 'u8_to_float', <type 'numpy.uint16'>: 'u16_to_float', <type 'numpy.uint32'>: 'u32_to_float', <type 'numpy.int8'>: 's8_to_float'}¶

process(image, dark=None, variance=None, dark_variance=None, normalization_factor=1.0)¶

Perform the pixel-wise operation of the array

Parameters:	raw – numpy array with the input image dark – numpy array with the dark-current image variance – numpy array with the variance of input image dark_variance – numpy array with the variance of dark-current image normalization_factor – divide the result by this
Returns:	array with processed data, may be an array of (data,variance,normalization) depending on class initialization

send_buffer(data, dest)¶

Send a numpy array to the device

Parameters:	data – numpy array with data dest – name of the buffer as registered in the class

set_kernel_arguments()¶: Tie arguments of OpenCL kernel-functions to the actual kernels

pyFAI.opencl.preproc.preproc(raw, dark=None, flat=None, solidangle=None, polarization=None, absorption=None, mask=None, dummy=None, delta_dummy=None, normalization_factor=1.0, empty=None, split_result=False, variance=None, dark_variance=None, poissonian=False, dtype=<type 'numpy.float32'>)¶

Common preprocessing step, implemented using OpenCL. May be inefficient

Parameters:

data – raw value, as a numpy array, 1D or 2D
mask – array non null where data should be ignored
dummy – value of invalid data
delta_dummy – precision for invalid data
dark – array containing the value of the dark noise, to be subtracted
flat – Array containing the flatfield image. It is also checked for dummies if relevant.
solidangle – the value of the solid_angle. This processing may be performed during the rebinning instead. left for compatibility
polarization – Correction for polarization of the incident beam
absorption – Correction for absorption in the sensor volume
normalization_factor – final value is divided by this
empty – value to be given for empty pixels
split_result – set to true to separate numerator from denominator and return an array of float2 or float3 (with variance)
variance – provide an estimation of the variance, enforce split_result=True and return an float3 array with variance in second position.
poissonian – set to “True” for assuming the detector is poissonian and variance = raw + dark
dtype – dtype for all processing

All calculation are performed in single precision floating point (32 bits).

NaN are always considered as invalid values

if neither empty nor dummy is provided, empty pixels are 0. Empty pixels are always zero in “split_result” mode

Split result:

When set to False, i.e the default, the pixel-wise operation is: I = (raw - dark)/(flat * solidangle * polarization * absorption) Invalid pixels are set to the dummy or empty value.
When split_ressult is set to True, each result result is a float2 or a float3 (with an additional value for the variance) as such:

I = [(raw - dark), (variance), (flat * solidangle * polarization * absorption)]

Empty pixels will have all their 2 or 3 values to 0 (and not to dummy or empty value)
If poissonian is set to True, the variance is evaluated as (raw + dark)

`opencl.azim_hist` Module¶

class pyFAI.opencl.azim_hist.Integrator1d(filename=None)¶

Bases: object

Attempt to implements ocl_azim using pyopencl

BLOCK_SIZE = 128¶

__init__(filename=None)¶

Parameters:	filename – file in which profiling information are saved

clean(preserve_context=False)¶

Free OpenCL related resources allocated by the library.

clean() is used to reinitiate the library back in a vanilla state. It may be asked to preserve the context created by init or completely clean up OpenCL. Guard/Status flags that are set will be reset.

Parameters:	preserve_context (bool) – preserves or destroys all OpenCL resources

configure(kernel=None)¶

The method configure() allocates the OpenCL resources required and compiled the OpenCL kernels. An active context must exist before a call to configure() and getConfiguration() must have been called at least once. Since the compiled OpenCL kernels carry some information on the integration parameters, a change to any of the parameters of getConfiguration() requires a subsequent call to configure() for them to take effect.

If a configuration exists and configure() is called, the configuration is cleaned up first to avoid OpenCL memory leaks

Parameters:	kernel_path – is the path to the actual kernel

execute(image)¶

Perform a 1D azimuthal integration

execute() may be called only after an OpenCL device is configured and a Tth array has been loaded (at least once) It takes the input image and based on the configuration provided earlier it performs the 1D integration. Notice that if the provided image is bigger than N then only N points will be taked into account, while if the image is smaller than N the result may be catastrophic. set/unset and loadTth methods have a direct impact on the execute() method. All the rest of the methods will require at least a new configuration via configure().

Takes an image, integrate and return the histogram and weights

Parameters:	image – image to be processed as a numpy array
Returns:	tth_out, histogram, bins

TODO: to improve performances, the image should be casted to float32 in an optimal way: currently using numpy machinery but would be better if done in OpenCL

getConfiguration(Nimage, Nbins, useFp64=None)¶

getConfiguration gets the description of the integrations to be performed and keeps an internal copy

Parameters:	Nimage – number of pixel in image Nbins – number of bins in regrouped histogram useFp64 – use double precision. By default the same as init!

get_status()¶: return a dictionnary with the status of the integrator: for compatibilty with former implementation

init(devicetype='GPU', useFp64=True, platformid=None, deviceid=None)¶

Initial configuration: Choose a device and initiate a context. Devicetypes can be GPU, gpu, CPU, cpu, DEF, ACC, ALL. Suggested are GPU,CPU. For each setting to work there must be such an OpenCL device and properly installed. E.g.: If Nvidia driver is installed, GPU will succeed but CPU will fail. The AMD SDK kit (AMD APP) is required for CPU via OpenCL.

Parameters:	devicetype – string in [“cpu”,”gpu”, “all”, “acc”] useFp64 – boolean specifying if double precision will be used platformid – integer devid – integer

loadTth(tth, dtth, tth_min=None, tth_max=None)¶

Load the 2th arrays along with the min and max value.

loadTth maybe be recalled at any time of the execution in order to update the 2th arrays.

loadTth is required and must be called at least once after a configure()

log(**kwarg)¶: log in a file all opencl events

setDummyValue(dummy, delta_dummy)¶

Enables dummy value functionality and uploads the value to the OpenCL device.

Image values that are similar to the dummy value are set to 0.

Parameters:	dummy – value in image of missing values (masked pixels?) delta_dummy – precision for dummy values

setMask(mask)¶

Enables the use of a Mask during integration. The Mask can be updated by recalling setMask at any point.

The Mask must be a PyFAI Mask. Pixels with 0 are masked out. TODO: check and invert!

Parameters:	mask – numpy.ndarray of integer.

setRange(lowerBound, upperBound)¶

Instructs the program to use a user - defined range for 2th values

setRange is optional. By default the integration will use the tth_min and tth_max given by loadTth() as integration range. When setRange is called it sets a new integration range without affecting the 2th array. All values outside that range will then be discarded when interpolating. Currently, if the interval of 2th (2th + -d2th) is not all inside the range specified, it is discarded. The bins of the histogram are RESCALED to the defined range and not the original tth_max - tth_min range.

setRange can be called at any point and as many times required after a valid configuration is created.

Parameters:	lowerBound (float) – lower bound of the integration range upperBound (float) – upper bound of the integration range

setSolidAngle(solidAngle)¶

Enables SolidAngle correction and uploads the suitable array to the OpenCL device.

By default the program will assume no solidangle correction unless setSolidAngle() is called. From then on, all integrations will be corrected via the SolidAngle array.

If the SolidAngle array needs to be changes, one may just call setSolidAngle() again with that array

Parameters:	solidAngle (ndarray) – the solid angle of the given pixel

unsetDummyValue()¶: Disable a dummy value. May be re-enabled at any time by setDummyValue

unsetMask()¶: Disables the use of a Mask from that point. It may be re-enabled at any point via setMask

unsetRange()¶

Disable the use of a user-defined 2th range and revert to tth_min,tth_max range

unsetRange instructs the program to revert to its default integration range. If the method is called when no user-defined range had been previously specified, no action will be performed

unsetSolidAngle()¶

Instructs the program to not perform solidangle correction from now on.

SolidAngle correction may be turned back on at any point

`opencl.azim_lut` Module¶

class pyFAI.opencl.azim_lut.OCL_LUT_Integrator(lut, image_size, checksum=None, empty=None, ctx=None, devicetype='all', platformid=None, deviceid=None, block_size=None, profile=False)¶

Bases: pyFAI.opencl.processing.OpenclProcessing

Class in charge of doing a sparse-matrix multiplication in OpenCL using the LUT representation of the matrix. It also performs the preprocessing using the preproc kernel

BLOCK_SIZE = 16¶

__init__(lut, image_size, checksum=None, empty=None, ctx=None, devicetype='all', platformid=None, deviceid=None, block_size=None, profile=False)¶

Constructor of the OCL_LUT_Integrator class

Parameters:

lut – array of int32 - float32 with shape (nbins, lut_size) with indexes and coefficients
image_size – Expected image size: image.size
checksum – pre-calculated checksum of the LUT to prevent re-calculating it :)
empty – value to be assigned to bins without contribution from any pixel
ctx – actual working context, left to None for automatic initialization from device type or platformid/deviceid
devicetype – type of device, can be “CPU”, “GPU”, “ACC” or “ALL”
platformid – integer with the platform_identifier, as given by clinfo
deviceid – Integer with the device identifier, as given by clinfo
block_size – preferred workgroup size, may vary depending on the outpcome of the compilation
profile – switch on profiling to be able to profile at the kernel level, store profiling elements (makes code slightly slower)

buffers = [BufferDescription(name='output', size=1, dtype=<type 'numpy.float32'>, flags=2), BufferDescription(name='image_raw', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='image', size=1, dtype=<type 'numpy.float32'>, flags=1), BufferDescription(name='variance', size=1, dtype=<type 'numpy.float32'>, flags=1), BufferDescription(name='dark', size=1, dtype=<type 'numpy.float32'>, flags=1), BufferDescription(name='dark_variance', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='flat', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='polarization', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='solidangle', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='absorption', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='mask', size=1, dtype=<type 'numpy.int8'>, flags=4)]¶

compile_kernels(kernel_file=None)¶: Call the OpenCL compiler :param kernel_file: path to the kernel (by default use the one in the resources directory)

integrate(data, dummy=None, delta_dummy=None, dark=None, flat=None, solidangle=None, polarization=None, absorption=None, dark_checksum=None, flat_checksum=None, solidangle_checksum=None, polarization_checksum=None, absorption_checksum=None, preprocess_only=False, safe=True, normalization_factor=1.0)¶

Before performing azimuthal integration, the preprocessing is:

$data = (data - dark) / (flat * solidangle * polarization * absorption)$

Integration is performed using the LUT representation of the look-up table

Parameters:

dark – array of same shape as data for pre-processing
flat – array of same shape as data for pre-processing
solidangle – array of same shape as data for pre-processing
polarization – array of same shape as data for pre-processing
dark_checksum – CRC32 checksum of the given array
flat_checksum – CRC32 checksum of the given array
solidangle_checksum – CRC32 checksum of the given array
polarization_checksum – CRC32 checksum of the given array
safe – if True (default) compares arrays on GPU according to their checksum, unless, use the buffer location is used
preprocess_only – return the dark subtracted; flat field & solidangle & polarization corrected image, else
normalization_factor – divide raw signal by this value

Returns:

averaged data, weighted histogram, unweighted histogram

kernel_files = ['preprocess.cl', 'memset.cl', 'ocl_azim_LUT.cl']¶

mapping = {<type 'numpy.int16'>: 's16_to_float', <type 'numpy.int32'>: 's32_to_float', <type 'numpy.uint8'>: 'u8_to_float', <type 'numpy.uint16'>: 'u16_to_float', <type 'numpy.uint32'>: 'u32_to_float', <type 'numpy.int8'>: 's8_to_float'}¶

send_buffer(data, dest, checksum=None)¶

Send a numpy array to the device, including the cast on the device if possible

Parameters:	data – numpy array with data dest – name of the buffer as registered in the class

set_kernel_arguments()¶

Tie arguments of OpenCL kernel-functions to the actual kernels

set_kernel_arguments() is a private method, called by configure(). It uses the dictionary _cl_kernel_args. Note that by default, since TthRange is disabled, the integration kernels have tth_min_max tied to the tthRange argument slot. When setRange is called it replaces that argument with tthRange low and upper bounds. When unsetRange is called, the argument slot is reset to tth_min_max.

`opencl.azim_csr` Module¶

class pyFAI.opencl.azim_csr.OCL_CSR_Integrator(lut, image_size, checksum=None, empty=None, ctx=None, devicetype='all', platformid=None, deviceid=None, block_size=None, profile=False)¶

Bases: pyFAI.opencl.processing.OpenclProcessing

Class in charge of doing a sparse-matrix multiplication in OpenCL using the CSR representation of the matrix.

It also performs the preprocessing using the preproc kernel

BLOCK_SIZE = 32¶

__init__(lut, image_size, checksum=None, empty=None, ctx=None, devicetype='all', platformid=None, deviceid=None, block_size=None, profile=False)¶

Parameters:

lut – 3-tuple of arrays data: coefficient of the matrix in a 1D vector of float32 - size of nnz indices: Column index position for the data (same size as data) indptr: row pointer indicates the start of a given row. len nbin+1
image_size – Expected image size: image.size
checksum – pre-calculated checksum of the LUT to prevent re-calculating it :)
empty – value to be assigned to bins without contribution from any pixel
ctx – actual working context, left to None for automatic initialization from device type or platformid/deviceid
devicetype – type of device, can be “CPU”, “GPU”, “ACC” or “ALL”
platformid – integer with the platform_identifier, as given by clinfo
deviceid – Integer with the device identifier, as given by clinfo
block_size – preferred workgroup size, may vary depending on the outpcome of the compilation
profile – switch on profiling to be able to profile at the kernel level, store profiling elements (makes code slightly slower)

buffers = [BufferDescription(name='output', size=1, dtype=<type 'numpy.float32'>, flags=2), BufferDescription(name='image_raw', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='image', size=1, dtype=<type 'numpy.float32'>, flags=1), BufferDescription(name='variance', size=1, dtype=<type 'numpy.float32'>, flags=1), BufferDescription(name='dark', size=1, dtype=<type 'numpy.float32'>, flags=1), BufferDescription(name='dark_variance', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='flat', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='polarization', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='solidangle', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='absorption', size=1, dtype=<type 'numpy.float32'>, flags=4), BufferDescription(name='mask', size=1, dtype=<type 'numpy.int8'>, flags=4)]¶

compile_kernels(kernel_file=None)¶: Call the OpenCL compiler :param kernel_file: path to the kernel (by default use the one in the resources directory)

integrate(data, dummy=None, delta_dummy=None, dark=None, flat=None, solidangle=None, polarization=None, absorption=None, dark_checksum=None, flat_checksum=None, solidangle_checksum=None, polarization_checksum=None, absorption_checksum=None, preprocess_only=False, safe=True, normalization_factor=1.0)¶

Before performing azimuthal integration, the preprocessing is:

$data = (data - dark) / (flat * solidangle * polarization)$

Integration is performed using the CSR representation of the look-up table

Parameters:

dark – array of same shape as data for pre-processing
flat – array of same shape as data for pre-processing
solidangle – array of same shape as data for pre-processing
polarization – array of same shape as data for pre-processing
dark_checksum – CRC32 checksum of the given array
flat_checksum – CRC32 checksum of the given array
solidangle_checksum – CRC32 checksum of the given array
polarization_checksum – CRC32 checksum of the given array
safe – if True (default) compares arrays on GPU according to their checksum, unless, use the buffer location is used
preprocess_only – return the dark subtracted; flat field & solidangle & polarization corrected image, else
normalization_factor – divide raw signal by this value

Returns:

averaged data, weighted histogram, unweighted histogram

kernel_files = ['kahan.cl', 'preprocess.cl', 'memset.cl', 'ocl_azim_CSR.cl']¶

mapping = {<type 'numpy.int16'>: 's16_to_float', <type 'numpy.int32'>: 's32_to_float', <type 'numpy.uint8'>: 'u8_to_float', <type 'numpy.uint16'>: 'u16_to_float', <type 'numpy.uint32'>: 'u32_to_float', <type 'numpy.int8'>: 's8_to_float'}¶

send_buffer(data, dest, checksum=None)¶

Send a numpy array to the device, including the cast on the device if possible

Parameters:	data – numpy array with data dest – name of the buffer as registered in the class

set_kernel_arguments()¶: Tie arguments of OpenCL kernel-functions to the actual kernels

`gui.init` Module¶

Set of Qt widgets

`gui.matplotlib` Module¶

Initialize properly Matplotlib

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.

`gui.qt` Module¶

Common wrapper over Python Qt bindings:

If a Qt binding is already loaded, it will use it, otherwise the different Qt bindings are tried in this order: PyQt4, PySide, PyQt5.

The name of the loaded Qt binding is stored in the BINDING variable.

This module provides a flat namespace over Qt bindings by importing all symbols from QtCore and QtGui packages and if available from QtOpenGL and QtSvg packages. For PyQt5, it also imports all symbols from QtWidgets and QtPrintSupport packages.

Example of using silx.gui.qt module:

>>> from silx.gui import qt
>>> app = qt.QApplication([])
>>> widget = qt.QWidget()

For an alternative solution providing a structured namespace, see qtpy which provides the namespace of PyQt5 over PyQt4 and PySide.

pyFAI.gui.qt.BINDING = 'PyQt4'¶: The name of the Qt binding in use: ‘PyQt5’, ‘PyQt4’ or ‘PySide’.

pyFAI.gui.qt.HAS_SVG = True¶: True if Qt provides support for Scalable Vector Graphics (QtSVG).

pyFAI.gui.qt.QtBinding = <module 'PyQt4' from '/usr/lib/python2.7/dist-packages/PyQt4/__init__.pyc'>¶: The Qt binding module in use: PyQt5, PyQt4 or PySide.

pyFAI.gui.qt.bin(QTextStream) → QTextStream¶

pyFAI.gui.qt.hex(QTextStream) → QTextStream¶

pyFAI.gui.qt.oct(QTextStream) → QTextStream¶

pyFAI.gui.qt.supportedImageFormats()¶: Return a set of string of file format extensions supported by the Qt runtime.

`gui.utils` Module¶

Module providing gui util tools

class pyFAI.gui.utils.Event(width, height)¶

Bases: object

Dummy class for dummy things

__init__(width, height)¶

pyFAI.gui.utils.maximize_fig(fig=None)¶: Try to set the figure fullscreen

pyFAI.gui.utils.update_fig(fig=None)¶

Update a matplotlib figure with a Qt4 backend

Parameters:	fig – pylab figure

`ext.init` Module¶

Sub-module with all Cython binary extensions

`ext.bilinear` Module¶

This extension makes a discrete 2D-array appear like a continuous function thanks to bilinear interpolations.

class pyFAI.ext.bilinear.Bilinear¶

Bases: object

Bilinear interpolator for finding max.

Instance attribute defined in pxd file

cp_local_maxi(self, size_t x) → size_t¶

data¶

f_cy(self, x)¶

Function -f((y,x)) where f is a continuous function (y,x) are pixel coordinates pixels outside the image are given an arbitrary high value to help the minimizer

Parameters:	x – 2-tuple of float
Returns:	Interpolated negative signal from the image

(negative for using minimizer to search for peaks)

height¶

local_maxi(self, x)¶

Return the local maximum with sub-pixel refinement.

Sub-pixel refinement: Second order Taylor expansion of the function; first derivative is null

$delta = x-i = -Inverse[Hessian].gradient$

If Hessian is singular or $|delta|>1$ : use a center of mass.

Parameters:	x – 2-tuple of integers w – half with of the window: 1 or 2 are advised
Returns:	2-tuple of float with the nearest local maximum

maxi¶

mini¶

width¶

pyFAI.ext.bilinear.calc_cartesian_positions¶

calc_cartesian_positions(signatures, args, kwargs, defaults)

Calculate the Cartesian position for array of position (d1, d2) with pixel coordinated stored in array pos. This is bilinear interpolation.

Parameters:	d1 – position in dim1 d2 – position in dim2 pos – array with position of pixels corners
Returns:	3-tuple of position.

pyFAI.ext.bilinear.convert_corner_2D_to_4D¶

convert_corner_2D_to_4D(signatures, args, kwargs, defaults)

Convert 2 (or 3) arrays of corner position into a 4D array of pixel corner coordinates

Parameters:	ndim – 2d or 3D output d1 – 2D position in dim1 (shape +1) d2 – 2D position in dim2 (shape +1) d3 – 2D position in dim3 (z) (shape +1)
Returns:	pos 4D array with position of pixels corners

`ext._bispev` Module¶

This extension is a re-implementation of bi-cubic spline evaluation from scipy

Spline evaluation function

Created on Nov 4, 2013

pyFAI.ext._bispev.bisplev(x, y, tck, dx=0, dy=0)¶

Evaluate a bivariate B-spline and its derivatives.

Return a rank-2 array of spline function values (or spline derivative values) at points given by the cross-product of the rank-1 arrays x and y. In special cases, return an array or just a float if either x or y or both are floats. Based on BISPEV from FITPACK.

See bisplrep() to generate the tck representation.

See also splprep(), splrep(), splint(), sproot(), splev(), UnivariateSpline(), BivariateSpline()

[1]	Dierckx P. : An algorithm for surface fitting with spline functions Ima J. Numer. Anal. 1 (1981) 267-283.

[2]	Dierckx P. : An algorithm for surface fitting with spline functions report tw50, Dept. Computer Science,K.U.Leuven, 1980.

[3]	Dierckx P. : Curve and surface fitting with splines, Monographs on Numerical Analysis, Oxford University Press, 1993.

Parameters:	x (ndarray) – Rank-1 arrays specifying the domain over which to evaluate the spline or its derivative. y (ndarray) – Rank-1 arrays specifying the domain over which to evaluate the spline or its derivative. tck (tuple) – A sequence of length 5 returned by bisplrep containing the knot locations, the coefficients, and the degree of the spline: [tx, ty, c, kx, ky]. dx (int) – The orders of the partial derivatives in x. This version does not implement derivatives. dy (int) – The orders of the partial derivatives in y. This version does not implement derivatives.
Return type:	ndarray
Returns:	The B-spline or its derivative evaluated over the set formed by the cross-product of x and y.

`ext._blob` Module¶

Blob detection is used to find peaks in images by performing subsequent blurs

Some Cythonized function for blob detection function

pyFAI.ext._blob.local_max(__Pyx_memviewslice dogs, mask=None, bool n_5=False)¶

Calculate if a point is a maximum in a 3D space: (scale, y, x)

Parameters:	dogs – 3D array of difference of gaussian mask – mask with invalid pixels N_5 – take a neighborhood of 5x5 pixel in plane
Returns:	3d_array with 1 where is_max

`ext._convolution` Module¶

Convolutions in real space are used to blurs images, used in blob-detection algorithm

Implementation of a separable 2D convolution

pyFAI.ext._convolution.gaussian(sigma, width=None)¶

Return a Gaussian window of length “width” with standard-deviation “sigma”.

Parameters:	sigma – standard deviation sigma width – length of the windows (int) By default 8*sigma+1,

Width should be odd.

The FWHM is 2*sqrt(2 * pi)*sigma

pyFAI.ext._convolution.gaussian_filter(img, sigma)¶

Performs a gaussian bluring using a gaussian kernel.

Parameters:	img – input image sigma – width parameter of the gaussian

pyFAI.ext._convolution.horizontal_convolution(__Pyx_memviewslice img, __Pyx_memviewslice filter)¶

Implements a 1D horizontal convolution with a filter. The only implemented mode is “reflect” (default in scipy.ndimage.filter)

Parameters:	img – input image filter – 1D array with the coefficients of the array
Returns:	array of the same shape as image with

pyFAI.ext._convolution.vertical_convolution(__Pyx_memviewslice img, __Pyx_memviewslice filter)¶

Implements a 1D vertical convolution with a filter. The only implemented mode is “reflect” (default in scipy.ndimage.filter)

Parameters:	img – input image filter – 1D array with the coefficients of the array
Returns:	array of the same shape as image with

`ext._distortion` Module¶

Distortion correction are correction are applied by Look-up table (or CSR)

class pyFAI.ext._distortion.Distortion(self, detector='detector', shape=None)¶

Bases: object

This class applies a distortion correction on an image.

It is also able to apply an inversion of the correction.

__init__(self, detector='detector', shape=None)¶

Parameters:	detector – detector instance or detector name

calc_LUT(self)¶

calc_LUT_size(self)¶

Considering the “half-CCD” spline from ID11 which describes a (1025,2048) detector, the physical location of pixels should go from: [-17.48634 : 1027.0543, -22.768829 : 2028.3689] We chose to discard pixels falling outside the [0:1025,0:2048] range with a lose of intensity

We keep self.pos: pos_corners will not be compatible with systems showing non adjacent pixels (like some xpads)

calc_pos(self)¶

correct(self, image)¶

Correct an image based on the look-up table calculated ...

Parameters:	image – 2D-array with the image
Returns:	corrected 2D image

uncorrect(self, image)¶

Take an image which has been corrected and transform it into it’s raw (with loss of information)

Parameters:	image – 2D-array with the image
Returns:	uncorrected 2D image and a mask (pixels in raw image

pyFAI.ext._distortion.calc_CSR(__Pyx_memviewslice pos, shape, bin_size, max_pixel_size, __Pyx_memviewslice mask=None)¶

Calculate the Look-up table as CSR format

Parameters:	pos – 4D position array shape – output shape bin_size – number of input element per output element (as numpy array) max_pixel_size – (2-tuple of int) size of a buffer covering the largest pixel
Returns:	look-up table in CSR format: 3-tuple of array

pyFAI.ext._distortion.calc_LUT(__Pyx_memviewslice pos, shape, bin_size, max_pixel_size, __Pyx_memviewslice mask=None)¶

Parameters:	pos – 4D position array shape – output shape bin_size – number of input element per output element (numpy array) max_pixel_size – (2-tuple of int) size of a buffer covering the largest pixel mask – arry with bad pixels marked as True
Returns:	look-up table

pyFAI.ext._distortion.calc_area(float I1, float I2, float slope, float intercept) → float¶: Calculate the area between I1 and I2 of a line with a given slope & intercept

pyFAI.ext._distortion.calc_openmp(__Pyx_memviewslice pos, shape, max_pixel_size=(8, 8), __Pyx_memviewslice mask=None, format='csr', int bins_per_pixel=8)¶

Calculate the look-up table (or CSR) using OpenMP

Parameters:	pos – 4D position array shape – output shape max_pixel_size – (2-tuple of int) size of a buffer covering the largest pixel format – can be “CSR” or “LUT” bins_per_pixel – average splitting factor (number of pixels per bin)
Returns:	look-up table in CSR/LUT format

pyFAI.ext._distortion.calc_pos¶

calc_pos(signatures, args, kwargs, defaults) Calculate the pixel boundary position on the regular grid

param pixel_corners:

pixel corner coordinate as detector.get_pixel_corner()

param shape: requested output shape. If None, it is calculated

param pixel1, pixel2:

pixel size along row and column coordinates

return: pos, delta1, delta2, shape_out, offset

pyFAI.ext._distortion.calc_size¶

calc_size(signatures, args, kwargs, defaults) Calculate the number of items per output pixel

param pos: 4D array with position in space

param shape: shape of the output array

param mask: input data mask

param offset: 2-tuple of float with the minimal index of

return: number of input element per output elements

pyFAI.ext._distortion.clip(int value, int min_val, int max_val) → int¶: Limits the value to bounds

pyFAI.ext._distortion.correct_CSR(image, shape_in, shape_out, LUT, dummy=None, delta_dummy=None)¶

Correct an image based on the look-up table calculated ...

Parameters:	image – 2D-array with the image shape_in – shape of input image shape_out – shape of output image LUT – Look up table, here a 3-tuple array of ndarray dummy – value for invalid pixels delta_dummy – precision for invalid pixels
Returns:	corrected 2D image

pyFAI.ext._distortion.correct_LUT(image, shape_in, shape_out, __Pyx_memviewslice LUT, dummy=None, delta_dummy=None)¶

Correct an image based on the look-up table calculated ...

Parameters:	image – 2D-array with the image shape_in – shape of input image shape_out – shape of output image LUT – Look up table, here a 2D-array of struct dummy – value for invalid pixels delta_dummy – precision for invalid pixels
Returns:	corrected 2D image

pyFAI.ext._distortion.uncorrect_CSR(image, shape, LUT)¶

Take an image which has been corrected and transform it into it’s raw (with loss of information)

Parameters:	image – 2D-array with the image shape – shape of output image LUT – Look up table, here a 3-tuple of ndarray
Returns:	uncorrected 2D image and a mask (pixels in raw image not existing)

pyFAI.ext._distortion.uncorrect_LUT(image, shape, __Pyx_memviewslice LUT)¶

Take an image which has been corrected and transform it into it’s raw (with loss of information)

Parameters:	image – 2D-array with the image shape – shape of output image LUT – Look up table, here a 2D-array of struct
Returns:	uncorrected 2D image and a mask (pixels in raw image not existing)

`ext._geometry` Module¶

This extension is a fast-implementation for calculating the geometry, i.e. where every pixel of an array stays in space (x,y,z) or its (r, chi) coordinates.

pyFAI.ext._geometry.calc_chi(double L, double rot1, double rot2, double rot3, ndarray pos1, ndarray pos2, ndarray pos3=None)¶

Calculate the chi array (azimuthal angles) using OpenMP

X1 = p1*cos(rot2)*cos(rot3) + p2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) - L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3)) X2 = p1*cos(rot2)*sin(rot3) - L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) + p2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3)) X3 = -(L*cos(rot1)*cos(rot2)) + p2*cos(rot2)*sin(rot1) - p1*sin(rot2) tan(Chi) = X2 / X1

Parameters:	L – distance sample - PONI rot1 – angle1 rot2 – angle2 rot3 – angle3 pos1 – numpy array with distances in meter along dim1 from PONI (Y) pos2 – numpy array with distances in meter along dim2 from PONI (X) pos3 – numpy array with distances in meter along Sample->PONI (Z), positive behind the detector
Returns:	ndarray of double with same shape and size as pos1

pyFAI.ext._geometry.calc_cosa(double L, ndarray pos1, ndarray pos2, ndarray pos3=None)¶

Calculate the cosine of the incidence angle using OpenMP. Used for sensors thickness effect corrections

Parameters:	L – distance sample - PONI pos1 – numpy array with distances in meter along dim1 from PONI (Y) pos2 – numpy array with distances in meter along dim2 from PONI (X) pos3 – numpy array with distances in meter along Sample->PONI (Z), positive behind the detector
Returns:	ndarray of double with same shape and size as pos1

pyFAI.ext._geometry.calc_delta_chi¶

calc_delta_chi(signatures, args, kwargs, defaults) Calculate the delta chi array (azimuthal angles) using OpenMP

param centers: numpy array with chi angles of the center of the pixels

param corners: numpy array with chi angles of the corners of the pixels

return: ndarray of double with same shape and size as centers woth the delta chi per pixel

pyFAI.ext._geometry.calc_pos_zyx(double L, double poni1, double poni2, double rot1, double rot2, double rot3, ndarray pos1, ndarray pos2, ndarray pos3=None)¶

Calculate the 3D coordinates in the sample’s referential

Parameters:

L – distance sample - PONI
poni1 – PONI coordinate along y axis
poni2 – PONI coordinate along x axis
rot1 – angle1
rot2 – angle2
rot3 – angle3
pos1 – numpy array with distances in meter along dim1 from PONI (Y)
pos2 – numpy array with distances in meter along dim2 from PONI (X)
pos3 – numpy array with distances in meter along Sample->PONI (Z), positive behind the detector

Returns:

3-tuple of ndarray of double with same shape and size as pos1

pyFAI.ext._geometry.calc_q(double L, double rot1, double rot2, double rot3, ndarray pos1, ndarray pos2, double wavelength, pos3=None)¶

Calculate the q (scattering vector) array using OpenMP

X1 = p1*cos(rot2)*cos(rot3) + p2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) - L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3)) X2 = p1*cos(rot2)*sin(rot3) - L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) + p2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3)) X3 = -(L*cos(rot1)*cos(rot2)) + p2*cos(rot2)*sin(rot1) - p1*sin(rot2) tan(Chi) = X2 / X1

Parameters:	L – distance sample - PONI rot1 – angle1 rot2 – angle2 rot3 – angle3 pos1 – numpy array with distances in meter along dim1 from PONI (Y) pos2 – numpy array with distances in meter along dim2 from PONI (X) pos3 – numpy array with distances in meter along Sample->PONI (Z), positive behind the detector wavelength – in meter to get q in nm-1
Returns:	ndarray of double with same shape and size as pos1

pyFAI.ext._geometry.calc_r(double L, double rot1, double rot2, double rot3, ndarray pos1, ndarray pos2, ndarray pos3=None)¶

Calculate the radius array (radial direction) in parallel

Parameters:	L – distance sample - PONI rot1 – angle1 rot2 – angle2 rot3 – angle3 pos1 – numpy array with distances in meter along dim1 from PONI (Y) pos2 – numpy array with distances in meter along dim2 from PONI (X) pos3 – numpy array with distances in meter along Sample->PONI (Z), positive behind the detector
Returns:	ndarray of double with same shape and size as pos1

pyFAI.ext._geometry.calc_rad_azim(double L, double poni1, double poni2, double rot1, double rot2, double rot3, ndarray pos1, ndarray pos2, ndarray pos3=None, space='2th', wavelength=None)¶

Calculate the radial & azimutal position for each pixel from pos1, pos2, pos3.

Parameters:	L – distance sample - PONI poni1 – PONI coordinate along y axis poni2 – PONI coordinate along x axis rot1 – angle1 rot2 – angle2 rot3 – angle3 pos1 – numpy array with distances in meter along dim1 from PONI (Y) pos2 – numpy array with distances in meter along dim2 from PONI (X) pos3 – numpy array with distances in meter along Sample->PONI (Z), positive behind the detector space – can be “2th”, “q” or “r” for radial units. Azimuthal units are radians
Returns:	ndarray of double with same shape and size as pos1 + (2,),
Raise:	KeyError when space is bad ! ValueError when wavelength is missing

pyFAI.ext._geometry.calc_tth(double L, double rot1, double rot2, double rot3, ndarray pos1, ndarray pos2, ndarray pos3=None)¶

Calculate the 2theta array (radial angle) in parallel

Parameters:	L – distance sample - PONI rot1 – angle1 rot2 – angle2 rot3 – angle3 pos1 – numpy array with distances in meter along dim1 from PONI (Y) pos2 – numpy array with distances in meter along dim2 from PONI (X) pos3 – numpy array with distances in meter along Sample->PONI (Z), positive behind the detector
Returns:	ndarray of double with same shape and size as pos1

`ext.histogram` Module¶

Re-implementation of the numpy.histogram, optimized for azimuthal integration. Deprecated, will be replaced by silx.math.histogramnd

Re-implementation of numpy histograms using OpenMP

pyFAI.ext.histogram.histogram(ndarray pos, ndarray weights, int bins=100, bin_range=None, pixelSize_in_Pos=None, nthread=None, double empty=0.0, double normalization_factor=1.0)¶

Calculates histogram of pos weighted by weights

Parameters:	pos – 2Theta array weights – array with intensities bins – number of output bins pixelSize_in_Pos – size of a pixels in 2theta: DESACTIVATED nthread – OpenMP is disabled. unused empty – value given to empty bins normalization_factor – divide the result by this value
Returns:	2theta, I, weighted histogram, raw histogram

pyFAI.ext.histogram.histogram2d(ndarray pos0, ndarray pos1, bins, ndarray weights, split=False, nthread=None, double empty=0.0, double normalization_factor=1.0)¶

Calculate 2D histogram of pos0,pos1 weighted by weights

Parameters:	pos0 – 2Theta array pos1 – Chi array weights – array with intensities bins – number of output bins int or 2-tuple of int split – pixel splitting is disabled in histogram nthread – maximum number of thread to use. By default: maximum available. empty – value given to empty bins normalization_factor – divide the result by this value
Returns:	I, edges0, edges1, weighted histogram(2D), unweighted histogram (2D)

One can also limit this with OMP_NUM_THREADS environment variable

`ext.marchingsquares` Module¶

The marchingsquares algorithm is used for calculating an iso-contour curve (displayed on the screen while calibrating) but also to seed the points for the “massif” algoritm during recalib.

Cythonized version of the marching square function for “isocontour” plot

pyFAI.ext.marchingsquares.isocontour(img, isovalue=None, sorted=False)¶

isocontour(img, isovalue=None)

Calculate the iso contours for the given 2D image. If isovalue is not given or None, a value between the min and max of the image is used.

Parameters:	img – 2D array representing the image isovalue – the value for which the iso_contour shall be calculated sorted – perform a sorting of the points to have them contiguous ?

Returns a pointset in which each two subsequent points form a line piece. This van be best visualized using “vv.plot(result, ls=’+’)”.

pyFAI.ext.marchingsquares.marching_squares(__Pyx_memviewslice img, double isovalue, __Pyx_memviewslice cellToEdge, __Pyx_memviewslice edgeToRelativePosX, __Pyx_memviewslice edgeToRelativePosY)¶

pyFAI.ext.marchingsquares.sort_edges(edges)¶: Reorder edges in such a way they become contiguous

`ext.morphology` Module¶

The morphology extension provides a couple of binary morphology operations on images. They are also implemented in scipy.ndimage in the general case, but not as fast.

A few binary morphology operation

pyFAI.ext.morphology.binary_dilation(__Pyx_memviewslice image, float radius=1.0)¶

Return fast binary morphological dilation of an image.

Morphological dilation sets a pixel at (i,j) to the maximum over all pixels in the neighborhood centered at (i,j). Dilation enlarges bright regions and shrinks dark regions.

:param image : ndarray :param radius: float :return: ndiamge

pyFAI.ext.morphology.binary_erosion(__Pyx_memviewslice image, float radius=1.0)¶

Return fast binary morphological erosion of an image.

Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions.

:param image : ndarray :param radius: float :return: ndiamge

`ext.reconstruct` Module¶

Very simple inpainting module for reconstructing the missing part of an image (masked) to be able to use more common algorithms.

Cython module to reconstruct the masked values of an image

pyFAI.ext.reconstruct.reconstruct(ndarray data, ndarray mask=None, dummy=None, delta_dummy=None)¶

reconstruct missing part of an image (tries to be continuous)

Parameters:	data – the input image mask – where data should be reconstructed. dummy – value of the dummy (masked out) data delta_dummy – precision for dummy values
Returns:	reconstructed image.

`ext.relabel` Module¶

Relabel regions, used to flag from largest regions to the smallest

A module to relabel regions

pyFAI.ext.relabel.countThem(ndarray label, ndarray data, ndarray blured)¶

Count

Parameters:

label – 2D array containing labeled zones
data – 2D array containing the raw data
blured – 2D array containing the blured data

Returns:

2D arrays containing:

count pixels in labeled zone: label == index).sum()
max of data in that zone: data[label == index].max()
max of blurred in that zone: blured[label == index].max()
data-blurred where data is max.

`ext.preproc` Module¶

Contains a preprocessing function in charge of the dark-current subtraction, flat-field normalization, ... taking care of masked values and normalization.

pyFAI.ext.preproc.preproc(raw, dark=None, flat=None, solidangle=None, polarization=None, absorption=None, mask=None, dummy=None, delta_dummy=None, float normalization_factor=1.0, empty=None, bool split_result=False, variance=None, dark_variance=None, bool poissonian=False)¶

Common preprocessing step for all

Parameters:

raw – raw value, as a numpy array, 1D or 2D
mask – array non null where data should be ignored
dummy – value of invalid data
delta_dummy – precision for invalid data
dark – array containing the value of the dark noise, to be subtracted
flat – Array containing the flatfield image. It is also checked for dummies if relevant.
solidangle – the value of the solid_angle. This processing may be performed during the rebinning instead. left for compatibility
polarization – Correction for polarization of the incident beam
absorption – Correction for absorption in the sensor volume
normalization_factor – final value is divided by this
empty – value to be given for empty bins
variance – variance of the data
dark_variance – variance of the dark

All calculation are performed in single precision floating point.

NaN are always considered as invalid

if neither empty nor dummy is provided, empty pixels are 0

`ext._tree` Module¶

The tree is used in file hierarchy tree for the diff_map graphical user interface.

class pyFAI.ext._tree.TreeItem¶

Bases: object

TreeItem(str label=None, TreeItem parent=None)

Node of a tree ...

Each node contains:

children: list

parent: TreeItem parent

label: str

order: int

type: str can be “dir”, “file”, “group” or “dataset”

extra: any object

__init__¶: x.__init__(...) initializes x; see help(type(x)) for signature

add_child(self, TreeItem child)¶

children¶: children: list

extra¶: extra: object

first(self) → TreeItem¶

get(self, str label) → TreeItem¶

has_child(self, str label) → bool¶

label¶: label: str

last(self) → TreeItem¶

name¶

next(self) → TreeItem¶

order¶: order: ‘int’

parent¶: parent: pyFAI.ext._tree.TreeItem

previous(self) → TreeItem¶

size¶

sort(self)¶

type¶: type: str

update(self, TreeItem new_root)¶: Add new children in tree

`ext.watershed` Module¶

Peak peaking via inverse watershed for connecting region of high intensity

Inverse watershed for connecting region of high intensity

class pyFAI.ext.watershed.Bilinear¶

Bases: object

Bilinear interpolator for finding max.

Instance attribute defined in pxd file

cp_local_maxi(self, size_t x) → size_t¶

data¶

f_cy(self, x)¶

Function -f((y,x)) where f is a continuous function (y,x) are pixel coordinates pixels outside the image are given an arbitrary high value to help the minimizer

Parameters:	x – 2-tuple of float
Returns:	Interpolated negative signal from the image

(negative for using minimizer to search for peaks)

height¶

local_maxi(self, x)¶

Return the local maximum with sub-pixel refinement.

Sub-pixel refinement: Second order Taylor expansion of the function; first derivative is null

$delta = x-i = -Inverse[Hessian].gradient$

If Hessian is singular or $|delta|>1$ : use a center of mass.

Parameters:	x – 2-tuple of integers w – half with of the window: 1 or 2 are advised
Returns:	2-tuple of float with the nearest local maximum

maxi¶

mini¶

width¶

class pyFAI.ext.watershed.InverseWatershed(self, data, thres=1.0)¶

Bases: object

Idea:

label all peaks
define region around those peaks which raise always to this peak
define the border of such region
search for the pass between two peaks
merge region with high pass between them

NAME = 'Inverse watershed'¶

VERSION = '1.0'¶

__init__(self, data, thres=1.0)¶

Parameters:	data – 2d image as numpy array

init(self)¶

init_borders(self)¶

init_labels(self)¶

init_pass(self)¶

init_regions(self)¶

classmethod load(cls, fname)¶: Load data from a HDF5 file

merge_intense(self, thres=1.0)¶: Merge groups then (pass-mini)/(maxi-mini) >=thres

merge_singleton(self)¶: merge single pixel region

merge_twins(self)¶: Twins are two peak region which are best linked together: A -> B and B -> A

peaks_from_area(self, mask, Imin=None, keep=None, bool refine=True, float dmin=0.0, **kwarg)¶

Parameters:	mask – mask of data points valid Imin – Minimum intensity for a peak keep – Number of points to keep refine – refine sub-pixel position dmin – minimum distance from

save(self, fname)¶: Save all regions into a HDF5 file

class pyFAI.ext.watershed.Region¶

Bases: object

border¶

get_borders(self)¶

get_highest_pass(self)¶

get_index(self)¶

get_maxi(self)¶

get_mini(self)¶

get_neighbors(self)¶

get_pass_to(self)¶

get_size(self)¶

highest_pass¶

index¶

init_values(self, __Pyx_memviewslice flat)¶: Initialize the values : maxi, mini and pass both height and so on :param flat: flat view on the data (intensity) :return: True if there is a problem and the region should be removed

maxi¶

merge(self, Region other)¶: merge 2 regions

mini¶

neighbors¶

pass_to¶

peaks¶

size¶

param pixel_corners:
	pixel corner coordinate as detector.get_pixel_corner()
param shape:	requested output shape. If None, it is calculated
param pixel1, pixel2:
	pixel size along row and column coordinates
return:	pos, delta1, delta2, shape_out, offset

param pos:	4D array with position in space
param shape:	shape of the output array
param mask:	input data mask
param offset:	2-tuple of float with the minimal index of
return:	number of input element per output elements

param centers:	numpy array with chi angles of the center of the pixels
param corners:	numpy array with chi angles of the corners of the pixels
return:	ndarray of double with same shape and size as centers woth the delta chi per pixel

Navigation

pyFAI Package¶

average Module¶

azimuthalIntegrator Module¶

multi_geometry Module¶

integrate_widget Module¶

geometry Module¶

geometryRefinement Module¶

goniometer Module¶

detectors Module¶

spline Module¶

io Module¶

calibration Module¶

control_points Module¶

peak_picker Module¶

massif Module¶

blob_detection Module¶

calibrant Module¶

distortion Module¶

worker Module¶

units Module¶

utils.__init__ Module¶

utils.bayes Module¶

utils.shell Module¶

utils.string Module¶

opencl.__init__ Module¶

opencl.common Module¶

opencl.processing Module¶

opencl.preproc Module¶

opencl.azim_hist Module¶

opencl.azim_lut Module¶

opencl.azim_csr Module¶

gui.__init__ Module¶

gui.matplotlib Module¶

gui.qt Module¶

gui.utils Module¶

ext.__init__ Module¶

ext.bilinear Module¶

ext._bispev Module¶

ext._blob Module¶

ext._convolution Module¶

ext._distortion Module¶

ext._geometry Module¶

ext.histogram Module¶

ext.marchingsquares Module¶

ext.morphology Module¶

ext.reconstruct Module¶

ext.relabel Module¶

ext.preproc Module¶

ext._tree Module¶

ext.watershed Module¶

Table Of Contents

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`pyFAI` Package¶

`average` Module¶

`azimuthalIntegrator` Module¶

`multi_geometry` Module¶

`integrate_widget` Module¶

`geometry` Module¶

`geometryRefinement` Module¶

`goniometer` Module¶

`detectors` Module¶

`spline` Module¶

`io` Module¶

`calibration` Module¶

`control_points` Module¶

`peak_picker` Module¶

`massif` Module¶

`blob_detection` Module¶

`calibrant` Module¶

`distortion` Module¶

`worker` Module¶

`units` Module¶

`utils.init` Module¶

`utils.bayes` Module¶

`utils.shell` Module¶

`utils.string` Module¶

`opencl.init` Module¶

`opencl.common` Module¶

`opencl.processing` Module¶

`opencl.preproc` Module¶

`opencl.azim_hist` Module¶

`opencl.azim_lut` Module¶

`opencl.azim_csr` Module¶

`gui.init` Module¶

`gui.matplotlib` Module¶

`gui.qt` Module¶

`gui.utils` Module¶

`ext.init` Module¶

`ext.bilinear` Module¶

`ext._bispev` Module¶

`ext._blob` Module¶

`ext._convolution` Module¶

`ext._distortion` Module¶

`ext._geometry` Module¶

`ext.histogram` Module¶

`ext.marchingsquares` Module¶

`ext.morphology` Module¶

`ext.reconstruct` Module¶

`ext.relabel` Module¶

`ext.preproc` Module¶

`ext._tree` Module¶

`ext.watershed` Module¶