Scan Data Definition (p.scans.scan_00.data)#

PtyScan#

PtyScan(Param)#: default = Param({})

PtyScan.name(str)#: default = 'PtyScan'

PtyScan.dfile(str)#

File path where prepared data will be saved in the ptyd format.

default = None

PtyScan.chunk_format(str)#

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

PtyScan.save(str)#

Saving mode

Mode to use to save data to file.

None: No saving
'merge': attemts to merge data in single chunk [not implemented]
'append': appends each chunk in master *.ptyd file
'link': appends external links in master *.ptyd file and stores chunks separately

in the path given by the link. Links file paths are relative to master file.

default = None

PtyScan.auto_center(bool)#

Determine if center in data is calculated automatically

False, no automatic centering
None, only if center is None
True, it will be enforced

default = None

PtyScan.load_parallel(str)#

Determines what will be loaded in parallel

Choose from None, 'data', 'common', 'all'

default = 'data'

PtyScan.rebin(int)#

Rebinning factor

Rebinning factor for the raw data frames. 'None' or 1 both mean no binning

default = None (>1, <32)

PtyScan.orientation(int, tuple, list)#

Data frame orientation

Choose

None or 0: correct orientation
1: invert columns (numpy.flip_lr)
2: invert rows (numpy.flip_ud)
3: invert columns, invert rows
4: transpose (numpy.transpose)
4+i: tranpose + other operations from above

Alternatively, a 3-tuple of booleans may be provided (do_transpose, do_flipud, do_fliplr)

default = None

PtyScan.min_frames(int)#

Minimum number of frames loaded by each node

default = 1 (>1)

PtyScan.positions_theory(ndarray)#

Theoretical positions for this scan

If provided, experimental positions from PtyScan subclass will be ignored. If data preparation is called from Ptycho instance, the calculated positions from the ptypy.core.xy.from_pars() dict will be inserted here

default = None

PtyScan.num_frames(int)#

Maximum number of frames to be prepared

If positions_theory are provided, num_frames will be ovverriden with the number of positions available

default = None

PtyScan.label(str)#

The scan label

Unique string identifying the scan

default = None

PtyScan.experimentID(str)#

Name of the experiment

If None, a default value will be provided by the recipe. unused

default = None

PtyScan.version(float)#

TODO: Explain this and decide if it is a user parameter.

default = 0.1

PtyScan.shape(int, tuple)#

Shape of the region of interest cropped from the raw data.

Cropping dimension of the diffraction frame Can be None, (dimx, dimy), or dim. In the latter case shape will be (dim, dim).

default = 256

PtyScan.center(list, tuple, str)#

Center (pixel) of the optical axes in raw data

If None, this parameter will be set by auto_center or elsewhere

default = 'fftshift'

PtyScan.psize(float, tuple)#

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

PtyScan.distance(float)#

Sample to detector distance

In meters.

default = 7.19 (>0.0)

PtyScan.energy(float)#

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

PtyScan.add_poisson_noise(bool)#

Decides whether the scan should have poisson noise or not

default = False

PtydScan#

PtydScan(Param)#: default = Param({})

PtydScan.name(str)#: default = 'PtydScan'

PtydScan.dfile(str)#

Prepared data file path

If source is None or 'file', data will be loaded from this file and processing as well as saving is deactivated. If source is the path to a file, data will be saved to this file.

default = None

PtydScan.chunk_format(str)#

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

PtydScan.save(str)#

Saving mode

Mode to use to save data to file.

None: No saving
'merge': attemts to merge data in single chunk [not implemented]
'append': appends each chunk in master *.ptyd file
'link': appends external links in master *.ptyd file and stores chunks separately

in the path given by the link. Links file paths are relative to master file.

default = None

PtydScan.auto_center(bool)#

Determine if center in data is calculated automatically

False, no automatic centering
None, only if center is None
True, it will be enforced

default = None

PtydScan.load_parallel(str)#

Determines what will be loaded in parallel

Choose from None, 'data', 'common', 'all'

default = 'data'

PtydScan.rebin(int)#

Rebinning factor

Rebinning factor for the raw data frames. 'None' or 1 both mean no binning

default = None (>1, <32)

PtydScan.orientation(int, tuple, list)#

Data frame orientation

Choose

None or 0: correct orientation
1: invert columns (numpy.flip_lr)
2: invert rows (numpy.flip_ud)
3: invert columns, invert rows
4: transpose (numpy.transpose)
4+i: tranpose + other operations from above

Alternatively, a 3-tuple of booleans may be provided (do_transpose, do_flipud, do_fliplr)

default = None

PtydScan.min_frames(int)#

Minimum number of frames loaded by each node

default = 1 (>1)

PtydScan.positions_theory(ndarray)#

Theoretical positions for this scan

default = None

PtydScan.num_frames(int)#

Maximum number of frames to be prepared

If positions_theory are provided, num_frames will be ovverriden with the number of positions available

default = None

PtydScan.label(str)#

The scan label

Unique string identifying the scan

default = None

PtydScan.experimentID(str)#

Name of the experiment

If None, a default value will be provided by the recipe. unused

default = None

PtydScan.version(float)#

TODO: Explain this and decide if it is a user parameter.

default = 0.1

PtydScan.shape(int, tuple)#

Shape of the region of interest cropped from the raw data.

Cropping dimension of the diffraction frame Can be None, (dimx, dimy), or dim. In the latter case shape will be (dim, dim).

default = 256

PtydScan.center(list, tuple, str)#

Center (pixel) of the optical axes in raw data

If None, this parameter will be set by auto_center or elsewhere

default = 'fftshift'

PtydScan.psize(float, tuple)#

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

PtydScan.distance(float)#

Sample to detector distance

In meters.

default = 7.19 (>0.0)

PtydScan.energy(float)#

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

PtydScan.add_poisson_noise(bool)#

Decides whether the scan should have poisson noise or not

default = False

PtydScan.source(str, NoneType)#

Alternate source file path if data is meant to be reprocessed.

None for input shall be deprecated in future

default = 'file'

MoonFlowerScan#

MoonFlowerScan(Param)#: default = Param({})

MoonFlowerScan.name(str)#: default = 'MoonFlowerScan'

MoonFlowerScan.dfile(str)#

File path where prepared data will be saved in the ptyd format.

default = None

MoonFlowerScan.chunk_format(str)#

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

MoonFlowerScan.save(str)#

Saving mode

Mode to use to save data to file.

None: No saving
'merge': attemts to merge data in single chunk [not implemented]
'append': appends each chunk in master *.ptyd file
'link': appends external links in master *.ptyd file and stores chunks separately

in the path given by the link. Links file paths are relative to master file.

default = None

MoonFlowerScan.auto_center(bool)#

Determine if center in data is calculated automatically

False, no automatic centering
None, only if center is None
True, it will be enforced

default = None

MoonFlowerScan.load_parallel(str)#

Determines what will be loaded in parallel

Choose from None, 'data', 'common', 'all'

default = 'data'

MoonFlowerScan.rebin(int)#

Rebinning factor

Rebinning factor for the raw data frames. 'None' or 1 both mean no binning

default = None (>1, <32)

MoonFlowerScan.orientation(int, tuple, list)#

Data frame orientation

Choose

None or 0: correct orientation
1: invert columns (numpy.flip_lr)
2: invert rows (numpy.flip_ud)
3: invert columns, invert rows
4: transpose (numpy.transpose)
4+i: tranpose + other operations from above

Alternatively, a 3-tuple of booleans may be provided (do_transpose, do_flipud, do_fliplr)

default = None

MoonFlowerScan.min_frames(int)#

Minimum number of frames loaded by each node

default = 1 (>1)

MoonFlowerScan.positions_theory(ndarray)#

Theoretical positions for this scan

default = None

MoonFlowerScan.num_frames(int)#

Number of frames to simulate

default = 100

MoonFlowerScan.label(str)#

The scan label

Unique string identifying the scan

default = None

MoonFlowerScan.experimentID(str)#

Name of the experiment

If None, a default value will be provided by the recipe. unused

default = None

MoonFlowerScan.version(float)#

TODO: Explain this and decide if it is a user parameter.

default = 0.1

MoonFlowerScan.shape(int, tuple)#

Shape of the region of interest cropped from the raw data.

Cropping dimension of the diffraction frame Can be None, (dimx, dimy), or dim. In the latter case shape will be (dim, dim).

default = 128

MoonFlowerScan.center(list, tuple, str)#

Center (pixel) of the optical axes in raw data

If None, this parameter will be set by auto_center or elsewhere

default = 'fftshift'

MoonFlowerScan.psize(float, tuple)#

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

MoonFlowerScan.distance(float)#

Sample to detector distance

In meters.

default = 7.19 (>0.0)

MoonFlowerScan.energy(float)#

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

MoonFlowerScan.add_poisson_noise(bool)#

Decides whether the scan should have poisson noise or not

default = True

MoonFlowerScan.density(float)#

Position distance in fraction of illumination frame

default = 0.2

MoonFlowerScan.model(str)#

The scan pattern

default = 'round'

MoonFlowerScan.photons(float)#

Total number of photons for Poisson noise

default = 100000000.0

MoonFlowerScan.psf(float)#

Point spread function of the detector

default = 0.0

MoonFlowerScan.block_wait_count(int)#

Signals a WAIT to the model after this many blocks.

default = 0

QuickScan#

QuickScan(Param)#: default = Param({})

QuickScan.name(str)#: default = 'MoonFlowerScan'

QuickScan.dfile(str)#

File path where prepared data will be saved in the ptyd format.

default = None

QuickScan.chunk_format(str)#

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

QuickScan.save(str)#

Saving mode

Mode to use to save data to file.

None: No saving
'merge': attemts to merge data in single chunk [not implemented]
'append': appends each chunk in master *.ptyd file
'link': appends external links in master *.ptyd file and stores chunks separately

in the path given by the link. Links file paths are relative to master file.

default = None

QuickScan.auto_center(bool)#

Determine if center in data is calculated automatically

False, no automatic centering
None, only if center is None
True, it will be enforced

default = None

QuickScan.load_parallel(str)#

Determines what will be loaded in parallel

Choose from None, 'data', 'common', 'all'

default = 'data'

QuickScan.rebin(int)#

Rebinning factor

Rebinning factor for the raw data frames. 'None' or 1 both mean no binning

default = None (>1, <32)

QuickScan.orientation(int, tuple, list)#

Data frame orientation

Choose

None or 0: correct orientation
1: invert columns (numpy.flip_lr)
2: invert rows (numpy.flip_ud)
3: invert columns, invert rows
4: transpose (numpy.transpose)
4+i: tranpose + other operations from above

Alternatively, a 3-tuple of booleans may be provided (do_transpose, do_flipud, do_fliplr)

default = None

QuickScan.min_frames(int)#

Minimum number of frames loaded by each node

default = 1 (>1)

QuickScan.positions_theory(ndarray)#

Theoretical positions for this scan

default = None

QuickScan.num_frames(int)#

Number of frames to simulate

default = 100

QuickScan.label(str)#

The scan label

Unique string identifying the scan

default = None

QuickScan.experimentID(str)#

Name of the experiment

If None, a default value will be provided by the recipe. unused

default = None

QuickScan.version(float)#

TODO: Explain this and decide if it is a user parameter.

default = 0.1

QuickScan.shape(int, tuple)#

Shape of the region of interest cropped from the raw data.

Cropping dimension of the diffraction frame Can be None, (dimx, dimy), or dim. In the latter case shape will be (dim, dim).

default = 64

QuickScan.center(list, tuple, str)#

Center (pixel) of the optical axes in raw data

If None, this parameter will be set by auto_center or elsewhere

default = 'fftshift'

QuickScan.psize(float, tuple)#

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

QuickScan.distance(float)#

Sample to detector distance

In meters.

default = 7.19 (>0.0)

QuickScan.energy(float)#

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

QuickScan.add_poisson_noise(bool)#

Decides whether the scan should have poisson noise or not

default = False

QuickScan.density(float)#

Position distance in fraction of illumination frame

default = 0.05

SimScan#

SimScan(Param)#: default = Param({})

SimScan.illumination(Param, str)#

Illumination parameters

default = Param({})

SimScan.illumination.aperture(Param)#

Beam aperture parameters

default = Param({})

SimScan.illumination.aperture.rotate(float)#

Rotate aperture by this value

default = 0.0

SimScan.illumination.aperture.central_stop(float)#

size of central stop as a fraction of aperture.size

If not None: places a central beam stop in aperture. The value given here is the fraction of the beam stop compared to size

default = None (>0.0, <1.0)

SimScan.illumination.aperture.diffuser(tuple)#

Noise in the transparen part of the aperture

Can be either: - None : no noise - 2-tuple : noise in phase (amplitude (rms), minimum feature size) - 4-tuple : noise in phase & modulus (rms, mfs, rms_mod, mfs_mod)

default = None

SimScan.illumination.aperture.edge(float)#

Edge width of aperture (in pixels!)

default = 2.0

SimScan.illumination.aperture.form(NoneType, str)#

One of None, ‘rect’ or ‘circ’

One of: - None : no aperture, this may be useful for nearfield - 'rect' : rectangular aperture - 'circ' : circular aperture

default = 'circ'

SimScan.illumination.aperture.offset(float, tuple, list)#

Offset between center of aperture and optical axes

May also be a tuple (vertical,horizontal) for size in case of an asymmetric offset

default = 0.0

SimScan.illumination.aperture.size(float, tuple, list)#

Aperture width or diameter

May also be a tuple (vertical,horizontal) in case of an asymmetric aperture

default = None (>0.0)

SimScan.illumination.diversity(Param, NoneType)#

Probe mode(s) diversity parameters

Can be None i.e. no diversity

default = Param({})

SimScan.illumination.diversity.noise(tuple, list)#

Noise in each non-primary mode of the illumination.

Can be either: - None : no noise - 2-tuple : noise in phase (amplitude (rms), minimum feature size) - 4-tuple : noise in phase & modulus (rms, mfs, rms_mod, mfs_mod)

default = (0.5, 1.0)

SimScan.illumination.diversity.power(tuple, float, list)#

Power of modes relative to main mode (zero-layer)

default = 0.1 (>0.0, <1.0)

SimScan.illumination.diversity.shift(float)#

Lateral shift of modes relative to main mode

[not implemented]

default = None

SimScan.illumination.model(str, ndarray)#

Type of illumination model

One of: - None : model initialitziation defaults to flat array filled with the specified number of photons - 'recon' : load model from previous reconstruction, see recon Parameters - 'stxm' : Estimate model from autocorrelation of mean diffraction data - <resource> : one of ptypys internal image resource strings - <template> : one of the templates inillumination module

In script, you may pass a numpy.ndarray here directly as the model. It is considered as incoming wavefront and will be propagated according to propagation with an optional aperture applied before.

default = None

SimScan.illumination.photons(int, float, NoneType)#

Number of photons in the incident illumination

A value specified here will take precedence over calculated statistics from the loaded data.

default = None (>0)

SimScan.illumination.propagation(Param)#

Parameters for propagation after aperture plane

Propagation to focus takes precedence to parallel propagation if foccused is not None

default = Param({})

SimScan.illumination.propagation.antialiasing(float)#

Antialiasing factor

Antialiasing factor used when generating the probe. (numbers larger than 2 or 3 are memory hungry) [Untested]

default = 1

SimScan.illumination.propagation.focussed(NoneType, float)#

Propagation distance from aperture to focus

If None or 0 : No focus propagation

default = None

SimScan.illumination.propagation.parallel(NoneType, float)#

Parallel propagation distance

If None or 0 : No parallel propagation

default = None

SimScan.illumination.propagation.spot_size(NoneType, float)#

Focal spot diameter

If not None, this parameter is used to generate the appropriate aperture size instead of size

default = None (>0.0)

SimScan.illumination.recon(Param)#

Parameters to load from previous reconstruction

default = Param({})

SimScan.illumination.recon.label(NoneType, str)#

Scan label of diffraction that is to be used for probe estimate

If None, own scan label is used

default = None

SimScan.illumination.recon.rfile(str)#

Path to a .ptyr compatible file

default = '\\*.ptyr'

SimScan.sample(Param, str)#: default = Param({})

SimScan.sample.model(str, ndarray)#

Type of initial object model

One of: - None : model initialitziation defaults to flat array filled fill - 'recon' : load model from STXM analysis of diffraction data - 'stxm' : Estimate model from autocorrelation of mean diffraction data - <resource> : one of ptypys internal model resource strings - <template> : one of the templates in sample module In script, you may pass a numpy.array here directly as the model. This array will be processed according to process in order to simulate a sample from e.g. a thickness profile.

default = None

SimScan.sample.fill(float, complex)#

Default fill value

default = 1

SimScan.sample.recon(Param)#

Parameters to load from previous reconstruction

default = Param({})

SimScan.sample.recon.rfile(str)#

Path to a .ptyr compatible file

default = '\\*.ptyr'

SimScan.sample.stxm(Param)#

STXM analysis parameters

default = Param({})

SimScan.sample.stxm.label(str)#

Scan label of diffraction that is to be used for probe estimate

None, own scan label is used

default = None

SimScan.sample.process(Param, NoneType)#

Model processing parameters

Can be None, i.e. no processing

default = Param({})

SimScan.sample.process.offset(tuple, list)#

Offset between center of object array and scan pattern

default = (0, 0) (>0.0)

SimScan.sample.process.zoom(list, tuple, float)#

Zoom value for object simulation.

If None, leave the array untouched. Otherwise the modeled or loaded image will be resized using zoom().

default = None (>0.0)

SimScan.sample.process.formula(str)#

Chemical formula

A Formula compatible with a cxro database query,e.g. 'Au' or 'NaCl' or 'H2O'

default = None

SimScan.sample.process.density(float)#

Density in [g/ccm]

Only used if formula is not None

default = 1

SimScan.sample.process.thickness(float)#

Maximum thickness of sample

If None, the absolute values of loaded source array will be used

default = 1e-06

SimScan.sample.process.ref_index(list, tuple)#

Assigned refractive index, tuple of format (real, complex)

If None, treat source array as projection of refractive index a+bj for (a, b). If a refractive index is provided the array’s absolute value will be used to scale the refractive index.

default = (0.5, 0.0) (>0.0)

SimScan.sample.process.smoothing(int)#

Smoothing scale

Smooth the projection with gaussian kernel of width given by smoothing_mfs

default = 2 (>0)

SimScan.sample.diversity(Param)#

Probe mode(s) diversity parameters

Can be None i.e. no diversity

default = Param({})

SimScan.sample.diversity.noise(tuple)#

Noise in the generated modes of the illumination

Can be either: - None : no noise - 2-tuple : noise in phase (amplitude (rms), minimum feature size) - 4-tuple : noise in phase & modulus (rms, mfs, rms_mod, mfs_mod)

default = None

SimScan.sample.diversity.power(tuple, float)#

Power of modes relative to main mode (zero-layer)

default = 0.1

SimScan.sample.diversity.shift(float)#

Lateral shift of modes relative to main mode

[not implemented]

default = None

SimScan.xy(Param)#: default = Param({})

SimScan.xy.override(ndarray)#: default = None

SimScan.xy.model(str)#

None, ‘round’, ‘raster’, ‘spiral’ or array-like

default = None

SimScan.xy.extent(float, tuple)#: default = 1.5e-05

SimScan.xy.spacing(float)#

Step size (grid spacing)

default = 1.5e-06

SimScan.xy.steps(int)#: default = 10

SimScan.xy.offset(float)#: default = 0.0

SimScan.xy.jitter(float)#: default = None

SimScan.xy.count(int)#: default = None

SimScan.name(str)#: default = 'SimScan'

SimScan.dfile(str)#

File path where prepared data will be saved in the ptyd format.

default = None

SimScan.chunk_format(str)#

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

SimScan.save(str)#

Saving mode

Mode to use to save data to file.

None: No saving
'merge': attemts to merge data in single chunk [not implemented]
'append': appends each chunk in master *.ptyd file
'link': appends external links in master *.ptyd file and stores chunks separately

in the path given by the link. Links file paths are relative to master file.

default = None

SimScan.auto_center(bool)#

Determine if center in data is calculated automatically

False, no automatic centering
None, only if center is None
True, it will be enforced

default = None

SimScan.load_parallel(str)#

Determines what will be loaded in parallel

Choose from None, 'data', 'common', 'all'

default = 'data'

SimScan.rebin(int)#

Rebinning factor

Rebinning factor for the raw data frames. 'None' or 1 both mean no binning

default = None (>1, <32)

SimScan.orientation(int, tuple, list)#

Data frame orientation

Choose

None or 0: correct orientation
1: invert columns (numpy.flip_lr)
2: invert rows (numpy.flip_ud)
3: invert columns, invert rows
4: transpose (numpy.transpose)
4+i: tranpose + other operations from above

Alternatively, a 3-tuple of booleans may be provided (do_transpose, do_flipud, do_fliplr)

default = None

SimScan.min_frames(int)#

Minimum number of frames loaded by each node

default = 1 (>1)

SimScan.positions_theory(ndarray)#

Theoretical positions for this scan

default = None

SimScan.num_frames(int)#

Maximum number of frames to be prepared

If positions_theory are provided, num_frames will be ovverriden with the number of positions available

default = None

SimScan.label(str)#

The scan label

Unique string identifying the scan

default = None

SimScan.experimentID(str)#

Name of the experiment

If None, a default value will be provided by the recipe. unused

default = None

SimScan.version(float)#

TODO: Explain this and decide if it is a user parameter.

default = 0.1

SimScan.shape(int, tuple)#

Shape of the region of interest cropped from the raw data.

Cropping dimension of the diffraction frame Can be None, (dimx, dimy), or dim. In the latter case shape will be (dim, dim).

default = 256

SimScan.center(list, tuple, str)#

Center (pixel) of the optical axes in raw data

If None, this parameter will be set by auto_center or elsewhere

default = 'fftshift'

SimScan.psize(float, tuple)#

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

SimScan.distance(float)#

Sample to detector distance

In meters.

default = 7.19 (>0.0)

SimScan.energy(float)#

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

SimScan.add_poisson_noise(bool)#

Decides whether the scan should have poisson noise or not

default = False

SimScan.pos_noise(float)#

Uniformly distributed noise in xy experimental positions

default = 1e-10

SimScan.pos_scale(float, list)#

Amplifier for noise.

Will be extended to match number of positions. Maybe used to only put nois on individual points

default = 0.0

SimScan.pos_drift(float, list)#

Drift or offset paramter

Noise independent drift. Will be extended like pos_scale.

default = 0.0

SimScan.detector(str, Param)#: default = Param({})

SimScan.frame_size(float, tuple)#

Final frame size when saving

If None, no cropping/padding happens.

default = None

SimScan.psf(float, tuple, ndarray)#

Parameters for gaussian convolution or convolution kernel after propagation

Use it for simulating partial coherence.

default = None

SimScan.verbose_level(int)#

Verbose level when simulating

default = 1

SimScan.plot(bool)#: default = True

SimScan.propagation(str)#

farfield or nearfield

default = 'farfield'