Parameter tree structure¶

Note

This section needs to be reworked to account for the recursive nature for PtyPy’s parameter tree. Please use the examples in the templates folder to craft your own scripts.

io¶

io(Param)¶: default = Param({})

io.interaction¶

io.interaction(Param)¶: default = Param({})

io.interaction.server(Param)¶: default = Param({})

io.interaction.server.active(bool)¶

Activation switch

Set to False for no ZeroMQ interaction server

default = True

io.interaction.server.address(str)¶

The address the server is listening to.

Wenn running ptypy on a remote server, it is the servers network address.

default = 'tcp://127.0.0.1'

io.interaction.server.port(int)¶

The port the server is listening to.

Make sure to pick an unused port with a few unused ports close to it.

default = 5560

io.interaction.server.connections(int)¶

Number of concurrent connections on the server

A range [port : port+connections] of ports adjacent port will be opened on demand for connecting clients.

default = 10

io.interaction.server.poll_timeout(float)¶

Network polling interval

Network polling interval, in milliseconds.

default = 10.0

io.interaction.server.pinginterval(float)¶

Interval to check pings

Interval with which to check pings, in seconds.

default = 2

io.interaction.server.pingtimeout(float)¶

Ping time out

Ping time out: client disconnected after this period, in seconds.

default = 10

io.interaction.client(Param)¶: default = Param({})

io.interaction.client.address(str)¶

The address the server is listening to.

Wenn running ptypy on a remote server, it is the servers network address.

default = 'tcp://127.0.0.1'

io.interaction.client.port(int)¶

The port the server is listening to.

Make sure to pick an unused port with a few unused ports close to it.

default = 5560

io.interaction.client.poll_timeout(float)¶

Network polling interval

Network polling interval, in milliseconds.

default = 100.0

io.interaction.client.pinginterval(float)¶

Interval to check pings

Interval with which to check pings, in seconds.

default = 1

io.interaction.client.connection_timeout(float)¶

Timeout for dead server

Timeout for dead server, in milliseconds.

default = 3600000.0

scandata¶

scandata(Param)¶: default = Param({})

scandata.PtyScan¶

scandata.PtyScan(Param)¶: default = Param({})

scandata.PtyScan.name(str)¶: default = 'PtyScan'

scandata.PtyScan.dfile(str)¶

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

default = None

scandata.PtyScan.chunk_format(str)¶

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

scandata.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

scandata.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

scandata.PtyScan.load_parallel(str)¶

Determines what will be loaded in parallel

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

default = 'data'

scandata.PtyScan.rebin(int)¶

Rebinning factor

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

default = None (>1, <32)

scandata.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

scandata.PtyScan.min_frames(int)¶

Minimum number of frames loaded by each node

default = 1 (>1)

scandata.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

scandata.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

scandata.PtyScan.label(str)¶

The scan label

Unique string identifying the scan

default = None

scandata.PtyScan.experimentID(str)¶

Name of the experiment

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

default = None

scandata.PtyScan.version(float)¶

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

default = 0.1

scandata.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

scandata.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'

scandata.PtyScan.psize(float, tuple)¶

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

scandata.PtyScan.distance(float)¶

Sample to detector distance

In meters.

default = 7.19 (>0.0)

scandata.PtyScan.energy(float)¶

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

scandata.PtyScan.add_poisson_noise(bool)¶

Decides whether the scan should have poisson noise or not

default = False

scandata.PtydScan¶

scandata.PtydScan(Param)¶: default = Param({})

scandata.PtydScan.name(str)¶: default = 'PtydScan'

scandata.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

scandata.PtydScan.chunk_format(str)¶

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

scandata.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

scandata.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

scandata.PtydScan.load_parallel(str)¶

Determines what will be loaded in parallel

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

default = 'data'

scandata.PtydScan.rebin(int)¶

Rebinning factor

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

default = None (>1, <32)

scandata.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

scandata.PtydScan.min_frames(int)¶

Minimum number of frames loaded by each node

default = 1 (>1)

scandata.PtydScan.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

scandata.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

scandata.PtydScan.label(str)¶

The scan label

Unique string identifying the scan

default = None

scandata.PtydScan.experimentID(str)¶

Name of the experiment

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

default = None

scandata.PtydScan.version(float)¶

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

default = 0.1

scandata.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

scandata.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'

scandata.PtydScan.psize(float, tuple)¶

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

scandata.PtydScan.distance(float)¶

Sample to detector distance

In meters.

default = 7.19 (>0.0)

scandata.PtydScan.energy(float)¶

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

scandata.PtydScan.add_poisson_noise(bool)¶

Decides whether the scan should have poisson noise or not

default = False

scandata.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'

scandata.MoonFlowerScan¶

scandata.MoonFlowerScan(Param)¶: default = Param({})

scandata.MoonFlowerScan.name(str)¶: default = 'MoonFlowerScan'

scandata.MoonFlowerScan.dfile(str)¶

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

default = None

scandata.MoonFlowerScan.chunk_format(str)¶

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

scandata.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

scandata.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

scandata.MoonFlowerScan.load_parallel(str)¶

Determines what will be loaded in parallel

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

default = 'data'

scandata.MoonFlowerScan.rebin(int)¶

Rebinning factor

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

default = None (>1, <32)

scandata.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

scandata.MoonFlowerScan.min_frames(int)¶

Minimum number of frames loaded by each node

default = 1 (>1)

scandata.MoonFlowerScan.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

scandata.MoonFlowerScan.num_frames(int)¶

Number of frames to simulate

default = 100

scandata.MoonFlowerScan.label(str)¶

The scan label

Unique string identifying the scan

default = None

scandata.MoonFlowerScan.experimentID(str)¶

Name of the experiment

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

default = None

scandata.MoonFlowerScan.version(float)¶

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

default = 0.1

scandata.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

scandata.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'

scandata.MoonFlowerScan.psize(float, tuple)¶

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

scandata.MoonFlowerScan.distance(float)¶

Sample to detector distance

In meters.

default = 7.19 (>0.0)

scandata.MoonFlowerScan.energy(float)¶

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

scandata.MoonFlowerScan.add_poisson_noise(bool)¶

Decides whether the scan should have poisson noise or not

default = True

scandata.MoonFlowerScan.density(float)¶

Position distance in fraction of illumination frame

default = 0.2

scandata.MoonFlowerScan.model(str)¶

The scan pattern

default = 'round'

scandata.MoonFlowerScan.photons(float)¶

Total number of photons for Poisson noise

default = 100000000.0

scandata.MoonFlowerScan.psf(float)¶

Point spread function of the detector

default = 0.0

scandata.MoonFlowerScan.block_wait_count(int)¶

Signals a WAIT to the model after this many blocks.

default = 0

scandata.QuickScan¶

scandata.QuickScan(Param)¶: default = Param({})

scandata.QuickScan.name(str)¶: default = 'MoonFlowerScan'

scandata.QuickScan.dfile(str)¶

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

default = None

scandata.QuickScan.chunk_format(str)¶

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

scandata.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

scandata.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

scandata.QuickScan.load_parallel(str)¶

Determines what will be loaded in parallel

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

default = 'data'

scandata.QuickScan.rebin(int)¶

Rebinning factor

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

default = None (>1, <32)

scandata.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

scandata.QuickScan.min_frames(int)¶

Minimum number of frames loaded by each node

default = 1 (>1)

scandata.QuickScan.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

scandata.QuickScan.num_frames(int)¶

Number of frames to simulate

default = 100

scandata.QuickScan.label(str)¶

The scan label

Unique string identifying the scan

default = None

scandata.QuickScan.experimentID(str)¶

Name of the experiment

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

default = None

scandata.QuickScan.version(float)¶

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

default = 0.1

scandata.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

scandata.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'

scandata.QuickScan.psize(float, tuple)¶

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

scandata.QuickScan.distance(float)¶

Sample to detector distance

In meters.

default = 7.19 (>0.0)

scandata.QuickScan.energy(float)¶

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

scandata.QuickScan.add_poisson_noise(bool)¶

Decides whether the scan should have poisson noise or not

default = False

scandata.QuickScan.density(float)¶

Position distance in fraction of illumination frame

default = 0.05

scandata.SimScan¶

scandata.SimScan(Param)¶: default = Param({})

scandata.SimScan.illumination(Param, str)¶

Illumination parameters

default = Param({})

scandata.SimScan.illumination.aperture(Param)¶

Beam aperture parameters

default = Param({})

scandata.SimScan.illumination.aperture.rotate(float)¶

Rotate aperture by this value

default = 0.0

scandata.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)

scandata.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

scandata.SimScan.illumination.aperture.edge(float)¶

Edge width of aperture (in pixels!)

default = 2.0

scandata.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'

scandata.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

scandata.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)

scandata.SimScan.illumination.diversity(Param, NoneType)¶

Probe mode(s) diversity parameters

Can be None i.e. no diversity

default = Param({})

scandata.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)

scandata.SimScan.illumination.diversity.power(tuple, float, list)¶

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

default = 0.1 (>0.0, <1.0)

scandata.SimScan.illumination.diversity.shift(float)¶

Lateral shift of modes relative to main mode

[not implemented]

default = None

scandata.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

scandata.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)

scandata.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({})

scandata.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

scandata.SimScan.illumination.propagation.focussed(NoneType, float)¶

Propagation distance from aperture to focus

If None or 0 : No focus propagation

default = None

scandata.SimScan.illumination.propagation.parallel(NoneType, float)¶

Parallel propagation distance

If None or 0 : No parallel propagation

default = None

scandata.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)

scandata.SimScan.illumination.recon(Param)¶

Parameters to load from previous reconstruction

default = Param({})

scandata.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

scandata.SimScan.illumination.recon.rfile(str)¶

Path to a .ptyr compatible file

default = '\\*.ptyr'

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

scandata.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

scandata.SimScan.sample.fill(float, complex)¶

Default fill value

default = 1

scandata.SimScan.sample.recon(Param)¶

Parameters to load from previous reconstruction

default = Param({})

scandata.SimScan.sample.recon.rfile(str)¶

Path to a .ptyr compatible file

default = '\\*.ptyr'

scandata.SimScan.sample.stxm(Param)¶

STXM analysis parameters

default = Param({})

scandata.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

scandata.SimScan.sample.process(Param, NoneType)¶

Model processing parameters

Can be None, i.e. no processing

default = Param({})

scandata.SimScan.sample.process.offset(tuple, list)¶

Offset between center of object array and scan pattern

default = (0, 0) (>0.0)

scandata.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)

scandata.SimScan.sample.process.formula(str)¶

Chemical formula

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

default = None

scandata.SimScan.sample.process.density(float)¶

Density in [g/ccm]

Only used if formula is not None

default = 1

scandata.SimScan.sample.process.thickness(float)¶

Maximum thickness of sample

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

default = 1e-06

scandata.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)

scandata.SimScan.sample.process.smoothing(int)¶

Smoothing scale

Smooth the projection with gaussian kernel of width given by smoothing_mfs

default = 2 (>0)

scandata.SimScan.sample.diversity(Param)¶

Probe mode(s) diversity parameters

Can be None i.e. no diversity

default = Param({})

scandata.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

scandata.SimScan.sample.diversity.power(tuple, float)¶

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

default = 0.1

scandata.SimScan.sample.diversity.shift(float)¶

Lateral shift of modes relative to main mode

[not implemented]

default = None

scandata.SimScan.xy(Param)¶: default = Param({})

scandata.SimScan.xy.override(ndarray)¶: default = None

scandata.SimScan.xy.model(str)¶

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

default = None

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

scandata.SimScan.xy.spacing(float)¶

Step size (grid spacing)

default = 1.5e-06

scandata.SimScan.xy.steps(int)¶: default = 10

scandata.SimScan.xy.offset(float)¶: default = 0.0

scandata.SimScan.xy.jitter(float)¶: default = None

scandata.SimScan.xy.count(int)¶: default = None

scandata.SimScan.name(str)¶: default = 'SimScan'

scandata.SimScan.dfile(str)¶

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

default = None

scandata.SimScan.chunk_format(str)¶

Appendix to saved files if save == ‘link’

default = '.chunk%02d'

scandata.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

scandata.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

scandata.SimScan.load_parallel(str)¶

Determines what will be loaded in parallel

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

default = 'data'

scandata.SimScan.rebin(int)¶

Rebinning factor

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

default = None (>1, <32)

scandata.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

scandata.SimScan.min_frames(int)¶

Minimum number of frames loaded by each node

default = 1 (>1)

scandata.SimScan.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

scandata.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

scandata.SimScan.label(str)¶

The scan label

Unique string identifying the scan

default = None

scandata.SimScan.experimentID(str)¶

Name of the experiment

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

default = None

scandata.SimScan.version(float)¶

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

default = 0.1

scandata.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

scandata.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'

scandata.SimScan.psize(float, tuple)¶

Detector pixel size

Dimensions of the detector pixels (in meters)

default = 0.000172 (>0.0)

scandata.SimScan.distance(float)¶

Sample to detector distance

In meters.

default = 7.19 (>0.0)

scandata.SimScan.energy(float)¶

Photon energy of the incident radiation in keV

default = 7.2 (>0.0)

scandata.SimScan.add_poisson_noise(bool)¶

Decides whether the scan should have poisson noise or not

default = False

scandata.SimScan.pos_noise(float)¶

Uniformly distributed noise in xy experimental positions

default = 1e-10

scandata.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

scandata.SimScan.pos_drift(float, list)¶

Drift or offset paramter

Noise independent drift. Will be extended like pos_scale.

default = 0.0

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

scandata.SimScan.frame_size(float, tuple)¶

Final frame size when saving

If None, no cropping/padding happens.

default = None

scandata.SimScan.psf(float, tuple, ndarray)¶

Parameters for gaussian convolution or convolution kernel after propagation

Use it for simulating partial coherence.

default = None

scandata.SimScan.verbose_level(int)¶

Verbose level when simulating

default = 1

scandata.SimScan.plot(bool)¶: default = True

scandata.SimScan.propagation(str)¶

farfield or nearfield

default = 'farfield'

scan¶

scan(Param)¶: default = Param({})

scan.ScanModel¶

scan.ScanModel(Param)¶: default = Param({})

scan.ScanModel.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.ScanModel.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.ScanModel.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.ScanModel.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.ScanModel.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.ScanModel.illumination(Param, str)¶

Container for probe initialization model

default = Param({})

scan.ScanModel.sample(Param, str)¶

Container for sample initialization model

default = Param({})

scan.ScanModel.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.BlockScanModel¶

scan.BlockScanModel(Param)¶: default = Param({})

scan.BlockScanModel.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.BlockScanModel.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.BlockScanModel.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.BlockScanModel.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.BlockScanModel.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.BlockScanModel.illumination(Param, str)¶

Container for probe initialization model

default = Param({})

scan.BlockScanModel.sample(Param, str)¶

Container for sample initialization model

default = Param({})

scan.BlockScanModel.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.Vanilla¶

scan.Vanilla(Param)¶: default = Param({})

scan.Vanilla.name(str)¶: default = 'Vanilla'

scan.Vanilla.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.Vanilla.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.Vanilla.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.Vanilla.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.Vanilla.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.Vanilla.illumination(Param, str)¶

Container for probe initialization model

default = Param({})

scan.Vanilla.illumination.size(float)¶

Initial probe size

The probe is initialized as a flat circle.

default = None

scan.Vanilla.sample(Param, str)¶

Container for sample initialization model

default = Param({})

scan.Vanilla.sample.fill(float, complex)¶

Initial sample value

The sample is initialized with this value everywhere.

default = 1

scan.Vanilla.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.BlockVanilla¶

scan.BlockVanilla(Param)¶: default = Param({})

scan.BlockVanilla.name(str)¶: default = 'Vanilla'

scan.BlockVanilla.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.BlockVanilla.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.BlockVanilla.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.BlockVanilla.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.BlockVanilla.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.BlockVanilla.illumination(Param, str)¶

Container for probe initialization model

default = Param({})

scan.BlockVanilla.illumination.size(float)¶

Initial probe size

The probe is initialized as a flat circle.

default = None

scan.BlockVanilla.sample(Param, str)¶

Container for sample initialization model

default = Param({})

scan.BlockVanilla.sample.fill(float, complex)¶

Initial sample value

The sample is initialized with this value everywhere.

default = 1

scan.BlockVanilla.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.Full¶

scan.Full(Param)¶: default = Param({})

scan.Full.name(str)¶: default = 'Full'

scan.Full.coherence(Param)¶

Coherence parameters

default = Param({}) (>0.0)

scan.Full.coherence.num_probe_modes(int)¶

Number of probe modes

default = 1 (>0)

scan.Full.coherence.num_object_modes(int)¶

Number of object modes

default = 1 (>0)

scan.Full.coherence.energies(list)¶

?

default = [1.0]

scan.Full.coherence.spectrum(list)¶

Amplitude of relative energy bins if the probe modes have a different energy

default = [1.0] (>0.0)

scan.Full.coherence.object_dispersion(str)¶

Energy dispersive response of the object

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.Full.coherence.probe_dispersion(str)¶

Energy dispersive response of the probe

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.Full.resolution(NoneType, float)¶

Will force the reconstruction to adapt to the given resolution, this might lead to cropping/padding in diffraction space which could reduce performance.

Half-period resolution given in [m]

default = None (>0.0)

scan.Full.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.Full.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.Full.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.Full.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.Full.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.Full.illumination(Param, str)¶

Illumination parameters

default = Param({})

scan.Full.illumination.aperture(Param)¶

Beam aperture parameters

default = Param({})

scan.Full.illumination.aperture.rotate(float)¶

Rotate aperture by this value

default = 0.0

scan.Full.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)

scan.Full.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

scan.Full.illumination.aperture.edge(float)¶

Edge width of aperture (in pixels!)

default = 2.0

scan.Full.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'

scan.Full.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

scan.Full.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)

scan.Full.illumination.diversity(Param, NoneType)¶

Probe mode(s) diversity parameters

Can be None i.e. no diversity

default = Param({})

scan.Full.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)

scan.Full.illumination.diversity.power(tuple, float, list)¶

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

default = 0.1 (>0.0, <1.0)

scan.Full.illumination.diversity.shift(float)¶

Lateral shift of modes relative to main mode

[not implemented]

default = None

scan.Full.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

scan.Full.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)

scan.Full.illumination.propagation(Param)¶

Parameters for propagation after aperture plane

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

default = Param({})

scan.Full.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

scan.Full.illumination.propagation.focussed(NoneType, float)¶

Propagation distance from aperture to focus

If None or 0 : No focus propagation

default = None

scan.Full.illumination.propagation.parallel(NoneType, float)¶

Parallel propagation distance

If None or 0 : No parallel propagation

default = None

scan.Full.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)

scan.Full.illumination.recon(Param)¶

Parameters to load from previous reconstruction

default = Param({})

scan.Full.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

scan.Full.illumination.recon.rfile(str)¶

Path to a .ptyr compatible file

default = '\\*.ptyr'

scan.Full.sample(Param, str)¶: default = Param({})

scan.Full.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

scan.Full.sample.fill(float, complex)¶

Default fill value

default = 1

scan.Full.sample.recon(Param)¶

Parameters to load from previous reconstruction

default = Param({})

scan.Full.sample.recon.rfile(str)¶

Path to a .ptyr compatible file

default = '\\*.ptyr'

scan.Full.sample.stxm(Param)¶

STXM analysis parameters

default = Param({})

scan.Full.sample.stxm.label(str)¶

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

None, own scan label is used

default = None

scan.Full.sample.process(Param, NoneType)¶

Model processing parameters

Can be None, i.e. no processing

default = Param({})

scan.Full.sample.process.offset(tuple, list)¶

Offset between center of object array and scan pattern

default = (0, 0) (>0.0)

scan.Full.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)

scan.Full.sample.process.formula(str)¶

Chemical formula

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

default = None

scan.Full.sample.process.density(float)¶

Density in [g/ccm]

Only used if formula is not None

default = 1

scan.Full.sample.process.thickness(float)¶

Maximum thickness of sample

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

default = 1e-06

scan.Full.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)

scan.Full.sample.process.smoothing(int)¶

Smoothing scale

Smooth the projection with gaussian kernel of width given by smoothing_mfs

default = 2 (>0)

scan.Full.sample.diversity(Param)¶

Probe mode(s) diversity parameters

Can be None i.e. no diversity

default = Param({})

scan.Full.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

scan.Full.sample.diversity.power(tuple, float)¶

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

default = 0.1

scan.Full.sample.diversity.shift(float)¶

Lateral shift of modes relative to main mode

[not implemented]

default = None

scan.Full.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.BlockFull¶

scan.BlockFull(Param)¶: default = Param({})

scan.BlockFull.name(str)¶: default = 'Full'

scan.BlockFull.coherence(Param)¶

Coherence parameters

default = Param({}) (>0.0)

scan.BlockFull.coherence.num_probe_modes(int)¶

Number of probe modes

default = 1 (>0)

scan.BlockFull.coherence.num_object_modes(int)¶

Number of object modes

default = 1 (>0)

scan.BlockFull.coherence.energies(list)¶

?

default = [1.0]

scan.BlockFull.coherence.spectrum(list)¶

Amplitude of relative energy bins if the probe modes have a different energy

default = [1.0] (>0.0)

scan.BlockFull.coherence.object_dispersion(str)¶

Energy dispersive response of the object

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.BlockFull.coherence.probe_dispersion(str)¶

Energy dispersive response of the probe

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.BlockFull.resolution(NoneType, float)¶

Will force the reconstruction to adapt to the given resolution, this might lead to cropping/padding in diffraction space which could reduce performance.

Half-period resolution given in [m]

default = None (>0.0)

scan.BlockFull.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.BlockFull.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.BlockFull.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.BlockFull.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.BlockFull.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.BlockFull.illumination(Param, str)¶

Illumination parameters

default = Param({})

scan.BlockFull.illumination.aperture(Param)¶

Beam aperture parameters

default = Param({})

scan.BlockFull.illumination.aperture.rotate(float)¶

Rotate aperture by this value

default = 0.0

scan.BlockFull.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)

scan.BlockFull.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

scan.BlockFull.illumination.aperture.edge(float)¶

Edge width of aperture (in pixels!)

default = 2.0

scan.BlockFull.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'

scan.BlockFull.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

scan.BlockFull.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)

scan.BlockFull.illumination.diversity(Param, NoneType)¶

Probe mode(s) diversity parameters

Can be None i.e. no diversity

default = Param({})

scan.BlockFull.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)

scan.BlockFull.illumination.diversity.power(tuple, float, list)¶

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

default = 0.1 (>0.0, <1.0)

scan.BlockFull.illumination.diversity.shift(float)¶

Lateral shift of modes relative to main mode

[not implemented]

default = None

scan.BlockFull.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

scan.BlockFull.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)

scan.BlockFull.illumination.propagation(Param)¶

Parameters for propagation after aperture plane

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

default = Param({})

scan.BlockFull.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

scan.BlockFull.illumination.propagation.focussed(NoneType, float)¶

Propagation distance from aperture to focus

If None or 0 : No focus propagation

default = None

scan.BlockFull.illumination.propagation.parallel(NoneType, float)¶

Parallel propagation distance

If None or 0 : No parallel propagation

default = None

scan.BlockFull.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)

scan.BlockFull.illumination.recon(Param)¶

Parameters to load from previous reconstruction

default = Param({})

scan.BlockFull.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

scan.BlockFull.illumination.recon.rfile(str)¶

Path to a .ptyr compatible file

default = '\\*.ptyr'

scan.BlockFull.sample(Param, str)¶: default = Param({})

scan.BlockFull.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

scan.BlockFull.sample.fill(float, complex)¶

Default fill value

default = 1

scan.BlockFull.sample.recon(Param)¶

Parameters to load from previous reconstruction

default = Param({})

scan.BlockFull.sample.recon.rfile(str)¶

Path to a .ptyr compatible file

default = '\\*.ptyr'

scan.BlockFull.sample.stxm(Param)¶

STXM analysis parameters

default = Param({})

scan.BlockFull.sample.stxm.label(str)¶

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

None, own scan label is used

default = None

scan.BlockFull.sample.process(Param, NoneType)¶

Model processing parameters

Can be None, i.e. no processing

default = Param({})

scan.BlockFull.sample.process.offset(tuple, list)¶

Offset between center of object array and scan pattern

default = (0, 0) (>0.0)

scan.BlockFull.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)

scan.BlockFull.sample.process.formula(str)¶

Chemical formula

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

default = None

scan.BlockFull.sample.process.density(float)¶

Density in [g/ccm]

Only used if formula is not None

default = 1

scan.BlockFull.sample.process.thickness(float)¶

Maximum thickness of sample

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

default = 1e-06

scan.BlockFull.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)

scan.BlockFull.sample.process.smoothing(int)¶

Smoothing scale

Smooth the projection with gaussian kernel of width given by smoothing_mfs

default = 2 (>0)

scan.BlockFull.sample.diversity(Param)¶

Probe mode(s) diversity parameters

Can be None i.e. no diversity

default = Param({})

scan.BlockFull.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

scan.BlockFull.sample.diversity.power(tuple, float)¶

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

default = 0.1

scan.BlockFull.sample.diversity.shift(float)¶

Lateral shift of modes relative to main mode

[not implemented]

default = None

scan.BlockFull.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.OPRModel¶

scan.OPRModel(Param)¶: default = Param({})

scan.OPRModel.name(str)¶: default = 'Full'

scan.OPRModel.coherence(Param)¶

Coherence parameters

default = Param({}) (>0.0)

scan.OPRModel.coherence.num_probe_modes(int)¶

Number of probe modes

default = 1 (>0)

scan.OPRModel.coherence.num_object_modes(int)¶

Number of object modes

default = 1 (>0)

scan.OPRModel.coherence.energies(list)¶

?

default = [1.0]

scan.OPRModel.coherence.spectrum(list)¶

Amplitude of relative energy bins if the probe modes have a different energy

default = [1.0] (>0.0)

scan.OPRModel.coherence.object_dispersion(str)¶

Energy dispersive response of the object

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.OPRModel.coherence.probe_dispersion(str)¶

Energy dispersive response of the probe

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.OPRModel.resolution(NoneType, float)¶

Will force the reconstruction to adapt to the given resolution, this might lead to cropping/padding in diffraction space which could reduce performance.

Half-period resolution given in [m]

default = None (>0.0)

scan.OPRModel.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.OPRModel.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.OPRModel.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.OPRModel.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.OPRModel.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.OPRModel.illumination(Param, str)¶

Container for probe initialization model

default = Param({})

scan.OPRModel.sample(Param, str)¶

Container for sample initialization model

default = Param({})

scan.OPRModel.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.BlockOPRModel¶

scan.BlockOPRModel(Param)¶: default = Param({})

scan.BlockOPRModel.name(str)¶: default = 'Full'

scan.BlockOPRModel.coherence(Param)¶

Coherence parameters

default = Param({}) (>0.0)

scan.BlockOPRModel.coherence.num_probe_modes(int)¶

Number of probe modes

default = 1 (>0)

scan.BlockOPRModel.coherence.num_object_modes(int)¶

Number of object modes

default = 1 (>0)

scan.BlockOPRModel.coherence.energies(list)¶

?

default = [1.0]

scan.BlockOPRModel.coherence.spectrum(list)¶

Amplitude of relative energy bins if the probe modes have a different energy

default = [1.0] (>0.0)

scan.BlockOPRModel.coherence.object_dispersion(str)¶

Energy dispersive response of the object

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.BlockOPRModel.coherence.probe_dispersion(str)¶

Energy dispersive response of the probe

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.BlockOPRModel.resolution(NoneType, float)¶

Will force the reconstruction to adapt to the given resolution, this might lead to cropping/padding in diffraction space which could reduce performance.

Half-period resolution given in [m]

default = None (>0.0)

scan.BlockOPRModel.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.BlockOPRModel.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.BlockOPRModel.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.BlockOPRModel.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.BlockOPRModel.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.BlockOPRModel.illumination(Param, str)¶

Container for probe initialization model

default = Param({})

scan.BlockOPRModel.sample(Param, str)¶

Container for sample initialization model

default = Param({})

scan.BlockOPRModel.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.GradFull¶

scan.GradFull(Param)¶: default = Param({})

scan.GradFull.name(str)¶: default = 'Full'

scan.GradFull.coherence(Param)¶

Coherence parameters

default = Param({}) (>0.0)

scan.GradFull.coherence.num_probe_modes(int)¶

Number of probe modes

default = 1 (>0)

scan.GradFull.coherence.num_object_modes(int)¶

Number of object modes

default = 1 (>0)

scan.GradFull.coherence.energies(list)¶

?

default = [1.0]

scan.GradFull.coherence.spectrum(list)¶

Amplitude of relative energy bins if the probe modes have a different energy

default = [1.0] (>0.0)

scan.GradFull.coherence.object_dispersion(str)¶

Energy dispersive response of the object

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.GradFull.coherence.probe_dispersion(str)¶

Energy dispersive response of the probe

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.GradFull.resolution(NoneType, float)¶

Will force the reconstruction to adapt to the given resolution, this might lead to cropping/padding in diffraction space which could reduce performance.

Half-period resolution given in [m]

default = None (>0.0)

scan.GradFull.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.GradFull.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.GradFull.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.GradFull.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.GradFull.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.GradFull.illumination(Param, str)¶

Container for probe initialization model

default = Param({})

scan.GradFull.sample(Param, str)¶

Container for sample initialization model

default = Param({})

scan.GradFull.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.BlockGradFull¶

scan.BlockGradFull(Param)¶: default = Param({})

scan.BlockGradFull.name(str)¶: default = 'Full'

scan.BlockGradFull.coherence(Param)¶

Coherence parameters

default = Param({}) (>0.0)

scan.BlockGradFull.coherence.num_probe_modes(int)¶

Number of probe modes

default = 1 (>0)

scan.BlockGradFull.coherence.num_object_modes(int)¶

Number of object modes

default = 1 (>0)

scan.BlockGradFull.coherence.energies(list)¶

?

default = [1.0]

scan.BlockGradFull.coherence.spectrum(list)¶

Amplitude of relative energy bins if the probe modes have a different energy

default = [1.0] (>0.0)

scan.BlockGradFull.coherence.object_dispersion(str)¶

Energy dispersive response of the object

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.BlockGradFull.coherence.probe_dispersion(str)¶

Energy dispersive response of the probe

One of: - None or 'achromatic': no dispersion - 'linear': linear response model - 'irregular': no assumption [not implemented]

default = None

scan.BlockGradFull.resolution(NoneType, float)¶

Will force the reconstruction to adapt to the given resolution, this might lead to cropping/padding in diffraction space which could reduce performance.

Half-period resolution given in [m]

default = None (>0.0)

scan.BlockGradFull.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.BlockGradFull.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.BlockGradFull.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.BlockGradFull.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.BlockGradFull.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.BlockGradFull.illumination(Param, str)¶

Container for probe initialization model

default = Param({})

scan.BlockGradFull.sample(Param, str)¶

Container for sample initialization model

default = Param({})

scan.BlockGradFull.resample(int, NoneType)¶

Resampling fraction of the image frames w.r.t. diffraction frames

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

scan.Bragg3dModel¶

scan.Bragg3dModel(Param)¶: default = Param({})

scan.Bragg3dModel.illumination(Param, str)¶

Container for probe initialization model

default = Param({})

scan.Bragg3dModel.illumination.aperture(Param)¶

Beam aperture parameters

default = Param({})

scan.Bragg3dModel.illumination.aperture.rotate(float)¶

Rotate aperture by this value

default = 0.0

scan.Bragg3dModel.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)

scan.Bragg3dModel.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

scan.Bragg3dModel.illumination.aperture.edge(float)¶

Edge width of aperture (in pixels!)

default = 2.0

scan.Bragg3dModel.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'

scan.Bragg3dModel.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

scan.Bragg3dModel.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)

scan.Bragg3dModel.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

scan.Bragg3dModel.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)

scan.Bragg3dModel.illumination.propagation(Param)¶

Parameters for propagation after aperture plane

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

default = Param({})

scan.Bragg3dModel.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

scan.Bragg3dModel.illumination.propagation.focussed(NoneType, float)¶

Propagation distance from aperture to focus

If None or 0 : No focus propagation

default = None

scan.Bragg3dModel.illumination.propagation.parallel(NoneType, float)¶

Parallel propagation distance

If None or 0 : No parallel propagation

default = None

scan.Bragg3dModel.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)

scan.Bragg3dModel.illumination.recon(Param)¶

Parameters to load from previous reconstruction

default = Param({})

scan.Bragg3dModel.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

scan.Bragg3dModel.illumination.recon.rfile(str)¶

Path to a .ptyr compatible file

default = '\\*.ptyr'

scan.Bragg3dModel.illumination.size(float)¶

Initial probe size

The probe is initialized as a flat circle.

default = None

scan.Bragg3dModel.name(str)¶: default = 'Bragg3dModel'

scan.Bragg3dModel.tags(list)¶

Comma seperated string tags describing the data input

[deprecated?]

default = ['dummy']

scan.Bragg3dModel.propagation(str)¶

Propagation type

Either “farfield” or “nearfield”

default = 'farfield'

scan.Bragg3dModel.ffttype(str)¶

FFT library

Choose from “numpy”, “scipy” or “fftw”

default = 'scipy'

scan.Bragg3dModel.data(Param)¶

Link to container for data preparation

default = scandata.PtyScan

scan.Bragg3dModel.data.name(str)¶

Name of the PtyScan subclass to use

default = None

scan.Bragg3dModel.sample(Param, str)¶

Container for sample initialization model

default = Param({})

scan.Bragg3dModel.sample.fill(float, complex)¶

Initial sample value

The sample is initialized with this value everywhere.

default = 1

scan.Bragg3dModel.resample(int, NoneType)¶

Diffraction resampling CURRENTLY NOT SUPPORTED FOR BRAGG CASE

A resampling of 2 means that the image frame is to be sampled (in the detector plane) twice as densely as the raw diffraction data.

default = 1

engine¶

engine(Param)¶: default = Param({})

engine.DM¶

engine.DM(Param)¶: default = Param({})

engine.DM.numiter(int)¶

Total number of iterations

For on-the-fly (live) processing, the reconstruction engine will iterate at least this many times after all data has been loaded.

default = 20 (>1)

engine.DM.numiter_contiguous(int)¶

Number of iterations without interruption

The engine will not return control to the caller until this number of iterations is completed (not processing server requests, I/O operations, …).

default = 1 (>1)

engine.DM.probe_support(float, NoneType)¶

Valid probe area as fraction of the probe frame

Defines a circular area centered on the probe frame, in which the probe is allowed to be nonzero.

default = 0.7 (>0.0)

engine.DM.probe_fourier_support(float, NoneType)¶

Valid probe area in frequency domain as fraction of the probe frame

Defines a circular area centered on the probe frame (in frequency domain), in which the probe is allowed to be nonzero.

default = None (>0.0)

engine.DM.record_local_error(bool)¶

If True, save the local map of errors into the runtime dictionary.

default = False

engine.DM.position_refinement(Param, bool)¶

If True refine scan positions

default = Param({})

engine.DM.position_refinement.method(str)¶

Annealing or GridSearch

default = 'Annealing'

engine.DM.position_refinement.start(int)¶

Number of iterations until position refinement starts

If None, position refinement starts at first iteration

default = None

engine.DM.position_refinement.stop(int)¶

Number of iterations after which positon refinement stops

If None, position refinement stops after last iteration

default = None

engine.DM.position_refinement.interval(int)¶

Frequency of position refinement

default = 1

engine.DM.position_refinement.nshifts(int)¶

Number of random shifts calculated in each position refinement step (has to be multiple of 4)

default = 4

engine.DM.position_refinement.amplitude(float)¶

Distance from original position per random shift [m]

default = 1e-06

engine.DM.position_refinement.amplitude_decay(bool)¶

After each interation, multiply amplitude by factor (stop - iteration) / (stop - start)

default = True

engine.DM.position_refinement.max_shift(float)¶

Maximum distance from original position [m]

default = 2e-06

engine.DM.position_refinement.metric(str)¶

Error metric, can choose between “fourier” and “photon”

default = 'fourier'

engine.DM.position_refinement.record(bool)¶

record movement of positions

default = False

engine.DM.probe_update_start(int)¶

Number of iterations before probe update starts

default = 2 (>0)

engine.DM.subpix_start(int)¶

Number of iterations before starting subpixel interpolation

default = 0 (>0)

engine.DM.subpix(str)¶

Subpixel interpolation; ‘fourier’,’linear’ or None for no interpolation

default = 'linear'

engine.DM.update_object_first(bool)¶

If True update object before probe

default = True

engine.DM.overlap_converge_factor(float)¶

Threshold for interruption of the inner overlap loop

The inner overlap loop refines the probe and the object simultaneously. This loop is escaped as soon as the overall change in probe, relative to the first iteration, is less than this value.

default = 0.05 (>0.0)

engine.DM.overlap_max_iterations(int)¶

Maximum of iterations for the overlap constraint inner loop

default = 10 (>1)

engine.DM.probe_inertia(float)¶

Weight of the current probe estimate in the update

default = 1e-09 (>0.0)

engine.DM.object_inertia(float)¶

Weight of the current object in the update

default = 0.0001 (>0.0)

engine.DM.fourier_power_bound(float)¶

If rms error of model vs diffraction data is smaller than this value, Fourier constraint is met

For Poisson-sampled data, the theoretical value for this parameter is 1/4. Set this value higher for noisy data. By default, power bound is calculated using fourier_relax_factor

default = None

engine.DM.fourier_relax_factor(float)¶

A factor used to calculate the Fourier power bound as 0.25 * fourier_relax_factor**2 * maximum power in diffraction data

Set this value higher for noisy data.

default = 0.05 (>0.0)

engine.DM.obj_smooth_std(float)¶

Gaussian smoothing (pixel) of the current object prior to update

If None, smoothing is deactivated. This smoothing can be used to reduce the amplitude of spurious pixels in the outer, least constrained areas of the object.

default = None (>0.0)

engine.DM.clip_object(tuple)¶

Clip object amplitude into this interval

default = None

engine.DM.probe_center_tol(float)¶

Pixel radius around optical axes that the probe mass center must reside in

default = None (>0.0)

engine.DM.compute_log_likelihood(bool)¶

A switch for computing the log-likelihood error (this can impact the performance of the engine)

default = True

engine.DM.alpha(float)¶

Mix parameter between Difference Map (alpha=1.) and Alternating Projections (alpha=0.)

default = 1.0 (>0.0)

engine.DM.name(str)¶: default = 'DM'

engine.RAAR¶

engine.RAAR(Param)¶: default = Param({})

engine.RAAR.numiter(int)¶

Total number of iterations

For on-the-fly (live) processing, the reconstruction engine will iterate at least this many times after all data has been loaded.

default = 20 (>1)

engine.RAAR.numiter_contiguous(int)¶

Number of iterations without interruption

The engine will not return control to the caller until this number of iterations is completed (not processing server requests, I/O operations, …).

default = 1 (>1)

engine.RAAR.probe_support(float, NoneType)¶

Valid probe area as fraction of the probe frame

Defines a circular area centered on the probe frame, in which the probe is allowed to be nonzero.

default = 0.7 (>0.0)

engine.RAAR.probe_fourier_support(float, NoneType)¶

Valid probe area in frequency domain as fraction of the probe frame

Defines a circular area centered on the probe frame (in frequency domain), in which the probe is allowed to be nonzero.

default = None (>0.0)

engine.RAAR.record_local_error(bool)¶

If True, save the local map of errors into the runtime dictionary.

default = False

engine.RAAR.position_refinement(Param, bool)¶

If True refine scan positions

default = Param({})

engine.RAAR.position_refinement.method(str)¶

Annealing or GridSearch

default = 'Annealing'

engine.RAAR.position_refinement.start(int)¶

Number of iterations until position refinement starts

If None, position refinement starts at first iteration

default = None

engine.RAAR.position_refinement.stop(int)¶

Number of iterations after which positon refinement stops

If None, position refinement stops after last iteration

default = None

engine.RAAR.position_refinement.interval(int)¶

Frequency of position refinement

default = 1

engine.RAAR.position_refinement.nshifts(int)¶

Number of random shifts calculated in each position refinement step (has to be multiple of 4)

default = 4

engine.RAAR.position_refinement.amplitude(float)¶

Distance from original position per random shift [m]

default = 1e-06

engine.RAAR.position_refinement.amplitude_decay(bool)¶

After each interation, multiply amplitude by factor (stop - iteration) / (stop - start)

default = True

engine.RAAR.position_refinement.max_shift(float)¶

Maximum distance from original position [m]

default = 2e-06

engine.RAAR.position_refinement.metric(str)¶

Error metric, can choose between “fourier” and “photon”

default = 'fourier'

engine.RAAR.position_refinement.record(bool)¶

record movement of positions

default = False

engine.RAAR.probe_update_start(int)¶

Number of iterations before probe update starts

default = 2 (>0)

engine.RAAR.subpix_start(int)¶

Number of iterations before starting subpixel interpolation

default = 0 (>0)

engine.RAAR.subpix(str)¶

Subpixel interpolation; ‘fourier’,’linear’ or None for no interpolation

default = 'linear'

engine.RAAR.update_object_first(bool)¶

If True update object before probe

default = True

engine.RAAR.overlap_converge_factor(float)¶

Threshold for interruption of the inner overlap loop

The inner overlap loop refines the probe and the object simultaneously. This loop is escaped as soon as the overall change in probe, relative to the first iteration, is less than this value.

default = 0.05 (>0.0)

engine.RAAR.overlap_max_iterations(int)¶

Maximum of iterations for the overlap constraint inner loop

default = 10 (>1)

engine.RAAR.probe_inertia(float)¶

Weight of the current probe estimate in the update

default = 1e-09 (>0.0)

engine.RAAR.object_inertia(float)¶

Weight of the current object in the update

default = 0.0001 (>0.0)

engine.RAAR.fourier_power_bound(float)¶

If rms error of model vs diffraction data is smaller than this value, Fourier constraint is met

For Poisson-sampled data, the theoretical value for this parameter is 1/4. Set this value higher for noisy data. By default, power bound is calculated using fourier_relax_factor

default = None

engine.RAAR.fourier_relax_factor(float)¶

A factor used to calculate the Fourier power bound as 0.25 * fourier_relax_factor**2 * maximum power in diffraction data

Set this value higher for noisy data.

default = 0.05 (>0.0)

engine.RAAR.obj_smooth_std(float)¶

Gaussian smoothing (pixel) of the current object prior to update

If None, smoothing is deactivated. This smoothing can be used to reduce the amplitude of spurious pixels in the outer, least constrained areas of the object.

default = None (>0.0)

engine.RAAR.clip_object(tuple)¶

Clip object amplitude into this interval

default = None

engine.RAAR.probe_center_tol(float)¶

Pixel radius around optical axes that the probe mass center must reside in

default = None (>0.0)

engine.RAAR.compute_log_likelihood(bool)¶

A switch for computing the log-likelihood error (this can impact the performance of the engine)

default = True

engine.RAAR.beta(float)¶

Beta parameter for RAAR algorithm

default = 0.75 (>0.0)

engine.RAAR.name(str)¶: default = 'RAAR'

engine.EPIE¶

engine.EPIE(Param)¶: default = Param({})

engine.EPIE.numiter(int)¶

Total number of iterations

For on-the-fly (live) processing, the reconstruction engine will iterate at least this many times after all data has been loaded.

default = 20 (>1)

engine.EPIE.numiter_contiguous(int)¶

Number of iterations without interruption

The engine will not return control to the caller until this number of iterations is completed (not processing server requests, I/O operations, …).

default = 1 (>1)

engine.EPIE.probe_support(float, NoneType)¶

Valid probe area as fraction of the probe frame

Defines a circular area centered on the probe frame, in which the probe is allowed to be nonzero.

default = 0.7 (>0.0)

engine.EPIE.probe_fourier_support(float, NoneType)¶

Valid probe area in frequency domain as fraction of the probe frame

Defines a circular area centered on the probe frame (in frequency domain), in which the probe is allowed to be nonzero.

default = None (>0.0)

engine.EPIE.record_local_error(bool)¶

If True, save the local map of errors into the runtime dictionary.

default = False

engine.EPIE.position_refinement(Param, bool)¶

If True refine scan positions

default = Param({})

engine.EPIE.position_refinement.method(str)¶

Annealing or GridSearch

default = 'Annealing'

engine.EPIE.position_refinement.start(int)¶

Number of iterations until position refinement starts

If None, position refinement starts at first iteration

default = None

engine.EPIE.position_refinement.stop(int)¶

Number of iterations after which positon refinement stops

If None, position refinement stops after last iteration

default = None

engine.EPIE.position_refinement.interval(int)¶

Frequency of position refinement

default = 1

engine.EPIE.position_refinement.nshifts(int)¶

Number of random shifts calculated in each position refinement step (has to be multiple of 4)

default = 4

engine.EPIE.position_refinement.amplitude(float)¶

Distance from original position per random shift [m]

default = 1e-06

engine.EPIE.position_refinement.amplitude_decay(bool)¶

After each interation, multiply amplitude by factor (stop - iteration) / (stop - start)

default = True

engine.EPIE.position_refinement.max_shift(float)¶

Maximum distance from original position [m]

default = 2e-06

engine.EPIE.position_refinement.metric(str)¶

Error metric, can choose between “fourier” and “photon”

default = 'fourier'

engine.EPIE.position_refinement.record(bool)¶

record movement of positions

default = False

engine.EPIE.probe_update_start(int)¶

Number of iterations before probe update starts

default = 0 (>0)

engine.EPIE.probe_center_tol(float)¶

Pixel radius around optical axes that the probe mass center must reside in

default = None (>0.0)

engine.EPIE.compute_log_likelihood(bool)¶

A switch for computing the log-likelihood error (this can impact the performance of the engine)

default = True

engine.EPIE.alpha(float)¶

Parameter for adjusting the step size of the object update

default = 1.0 (>0.0)

engine.EPIE.beta(float)¶

Parameter for adjusting the step size of the probe update

default = 1.0 (>0.0)

engine.EPIE.object_norm_is_global(bool)¶

Calculate the object norm based on the global object instead of the local object

default = False

engine.EPIE.name(str)¶: default = 'EPIE'

engine.SDR¶

engine.SDR(Param)¶: default = Param({})

engine.SDR.numiter(int)¶

Total number of iterations

For on-the-fly (live) processing, the reconstruction engine will iterate at least this many times after all data has been loaded.

default = 20 (>1)

engine.SDR.numiter_contiguous(int)¶

Number of iterations without interruption

The engine will not return control to the caller until this number of iterations is completed (not processing server requests, I/O operations, …).

default = 1 (>1)

engine.SDR.probe_support(float, NoneType)¶

Valid probe area as fraction of the probe frame

Defines a circular area centered on the probe frame, in which the probe is allowed to be nonzero.

default = 0.7 (>0.0)

engine.SDR.probe_fourier_support(float, NoneType)¶

Valid probe area in frequency domain as fraction of the probe frame

Defines a circular area centered on the probe frame (in frequency domain), in which the probe is allowed to be nonzero.

default = None (>0.0)

engine.SDR.record_local_error(bool)¶

If True, save the local map of errors into the runtime dictionary.

default = False

engine.SDR.position_refinement(Param, bool)¶

If True refine scan positions

default = Param({})

engine.SDR.position_refinement.method(str)¶

Annealing or GridSearch

default = 'Annealing'

engine.SDR.position_refinement.start(int)¶

Number of iterations until position refinement starts

If None, position refinement starts at first iteration

default = None

engine.SDR.position_refinement.stop(int)¶

Number of iterations after which positon refinement stops

If None, position refinement stops after last iteration

default = None

engine.SDR.position_refinement.interval(int)¶

Frequency of position refinement

default = 1

engine.SDR.position_refinement.nshifts(int)¶

Number of random shifts calculated in each position refinement step (has to be multiple of 4)

default = 4

engine.SDR.position_refinement.amplitude(float)¶

Distance from original position per random shift [m]

default = 1e-06

engine.SDR.position_refinement.amplitude_decay(bool)¶

After each interation, multiply amplitude by factor (stop - iteration) / (stop - start)

default = True

engine.SDR.position_refinement.max_shift(float)¶

Maximum distance from original position [m]

default = 2e-06

engine.SDR.position_refinement.metric(str)¶

Error metric, can choose between “fourier” and “photon”

default = 'fourier'

engine.SDR.position_refinement.record(bool)¶

record movement of positions

default = False

engine.SDR.probe_update_start(int)¶

Number of iterations before probe update starts

default = 0 (>0)

engine.SDR.probe_center_tol(float)¶

Pixel radius around optical axes that the probe mass center must reside in

default = None (>0.0)

engine.SDR.compute_log_likelihood(bool)¶

A switch for computing the log-likelihood error (this can impact the performance of the engine)

default = True

engine.SDR.sigma(float)¶

Relaxed Fourier reflection parameter.

default = 1 (>0.0)

engine.SDR.tau(float)¶

Relaxed modulus constraint parameter.

default = 1 (>0.0)

engine.SDR.beta_probe(float)¶

Parameter for adjusting the step size of the probe update

default = 0.1 (>0.0)

engine.SDR.beta_object(float)¶

Parameter for adjusting the step size of the object update

default = 0.9 (>0.0)

engine.SDR.name(str)¶: default = 'SDR'

engine.ML¶

engine.ML(Param)¶: default = Param({})

engine.ML.numiter(int)¶

Total number of iterations

For on-the-fly (live) processing, the reconstruction engine will iterate at least this many times after all data has been loaded.

default = 20 (>1)

engine.ML.numiter_contiguous(int)¶

Number of iterations without interruption

The engine will not return control to the caller until this number of iterations is completed (not processing server requests, I/O operations, …).

default = 1 (>1)

engine.ML.probe_support(float, NoneType)¶

Valid probe area as fraction of the probe frame

Defines a circular area centered on the probe frame, in which the probe is allowed to be nonzero.

default = 0.7 (>0.0)

engine.ML.probe_fourier_support(float, NoneType)¶

Valid probe area in frequency domain as fraction of the probe frame

Defines a circular area centered on the probe frame (in frequency domain), in which the probe is allowed to be nonzero.

default = None (>0.0)

engine.ML.record_local_error(bool)¶

If True, save the local map of errors into the runtime dictionary.

default = False

engine.ML.position_refinement(Param, bool)¶

If True refine scan positions

default = Param({})

engine.ML.position_refinement.method(str)¶

Annealing or GridSearch

default = 'Annealing'

engine.ML.position_refinement.start(int)¶

Number of iterations until position refinement starts

If None, position refinement starts at first iteration

default = None

engine.ML.position_refinement.stop(int)¶

Number of iterations after which positon refinement stops

If None, position refinement stops after last iteration

default = None

engine.ML.position_refinement.interval(int)¶

Frequency of position refinement

default = 1

engine.ML.position_refinement.nshifts(int)¶

Number of random shifts calculated in each position refinement step (has to be multiple of 4)

default = 4

engine.ML.position_refinement.amplitude(float)¶

Distance from original position per random shift [m]

default = 1e-06

engine.ML.position_refinement.amplitude_decay(bool)¶

After each interation, multiply amplitude by factor (stop - iteration) / (stop - start)

default = True

engine.ML.position_refinement.max_shift(float)¶

Maximum distance from original position [m]

default = 2e-06

engine.ML.position_refinement.metric(str)¶

Error metric, can choose between “fourier” and “photon”

default = 'fourier'

engine.ML.position_refinement.record(bool)¶

record movement of positions

default = False

engine.ML.name(str)¶: default = 'ML'

engine.ML.ML_type(str)¶

Likelihood model

One of ‘gaussian’, poisson’ or ‘euclid’.

default = 'gaussian'

engine.ML.floating_intensities(bool)¶

Adaptive diffraction pattern rescaling

If True, allow for adaptative rescaling of the diffraction pattern intensities (to correct for incident beam intensity fluctuations).

default = False

engine.ML.intensity_renormalization(float)¶

Rescales the intensities so they can be interpreted as Poisson counts.

default = 1.0 (>0.0)

engine.ML.reg_del2(bool)¶

Whether to use a Gaussian prior (smoothing) regularizer

default = False

engine.ML.reg_del2_amplitude(float)¶

Amplitude of the Gaussian prior if used

default = 0.01 (>0.0)

engine.ML.smooth_gradient(float)¶

Smoothing preconditioner

Sigma for gaussian filter (turned off if 0.)

default = 0.0

engine.ML.smooth_gradient_decay(float)¶

Decay rate for smoothing preconditioner

Sigma for gaussian filter will reduce exponentially at this rate

default = 0.0

engine.ML.scale_precond(bool)¶

Whether to use the object/probe scaling preconditioner

This parameter can give faster convergence for weakly scattering samples.

default = False

engine.ML.scale_probe_object(float)¶

Relative scale of probe to object

default = 1.0 (>0.0)

engine.ML.probe_update_start(int)¶

Number of iterations before probe update starts

default = 2 (>0)

engine.ML.poly_line_coeffs(str)¶

How many coefficients to be used in the the linesearch

choose between the ‘quadratic’ approximation (default) or ‘all’

default = 'quadratic'

engine.DM_3dBragg¶

engine.DM_3dBragg(Param)¶: default = Param({})

engine.DM_3dBragg.numiter(int)¶

Total number of iterations

For on-the-fly (live) processing, the reconstruction engine will iterate at least this many times after all data has been loaded.

default = 20 (>1)

engine.DM_3dBragg.numiter_contiguous(int)¶

Number of iterations without interruption

The engine will not return control to the caller until this number of iterations is completed (not processing server requests, I/O operations, …).

default = 1 (>1)

engine.DM_3dBragg.probe_support(float, NoneType)¶

Valid probe area as fraction of the probe frame

Defines a circular area centered on the probe frame, in which the probe is allowed to be nonzero.

default = 0.7 (>0.0)

engine.DM_3dBragg.probe_fourier_support(float, NoneType)¶

Valid probe area in frequency domain as fraction of the probe frame

Defines a circular area centered on the probe frame (in frequency domain), in which the probe is allowed to be nonzero.

default = None (>0.0)

engine.DM_3dBragg.record_local_error(bool)¶

If True, save the local map of errors into the runtime dictionary.

default = False

engine.DM_3dBragg.position_refinement(Param, bool)¶

If True refine scan positions

default = Param({})

engine.DM_3dBragg.position_refinement.method(str)¶

Annealing or GridSearch

default = 'Annealing'

engine.DM_3dBragg.position_refinement.start(int)¶

Number of iterations until position refinement starts

If None, position refinement starts at first iteration

default = None

engine.DM_3dBragg.position_refinement.stop(int)¶

Number of iterations after which positon refinement stops

If None, position refinement stops after last iteration

default = None

engine.DM_3dBragg.position_refinement.interval(int)¶

Frequency of position refinement

default = 1

engine.DM_3dBragg.position_refinement.nshifts(int)¶

Number of random shifts calculated in each position refinement step (has to be multiple of 4)

default = 4

engine.DM_3dBragg.position_refinement.amplitude(float)¶

Distance from original position per random shift [m]

default = 1e-06

engine.DM_3dBragg.position_refinement.amplitude_decay(bool)¶

After each interation, multiply amplitude by factor (stop - iteration) / (stop - start)

default = True

engine.DM_3dBragg.position_refinement.max_shift(float)¶

Maximum distance from original position [m]

default = 2e-06

engine.DM_3dBragg.position_refinement.metric(str)¶

Error metric, can choose between “fourier” and “photon”

default = 'fourier'

engine.DM_3dBragg.position_refinement.record(bool)¶

record movement of positions

default = False

engine.DM_3dBragg.probe_update_start(int)¶

Number of iterations before probe update starts

default = 2 (>0)

engine.DM_3dBragg.subpix_start(int)¶

Number of iterations before starting subpixel interpolation

default = 0 (>0)

engine.DM_3dBragg.subpix(str)¶

Subpixel interpolation; ‘fourier’,’linear’ or None for no interpolation

default = 'linear'

engine.DM_3dBragg.update_object_first(bool)¶

If True update object before probe

default = True

engine.DM_3dBragg.overlap_converge_factor(float)¶

Threshold for interruption of the inner overlap loop

The inner overlap loop refines the probe and the object simultaneously. This loop is escaped as soon as the overall change in probe, relative to the first iteration, is less than this value.

default = 0.05 (>0.0)

engine.DM_3dBragg.overlap_max_iterations(int)¶

Maximum of iterations for the overlap constraint inner loop

default = 10 (>1)

engine.DM_3dBragg.probe_inertia(float)¶

Weight of the current probe estimate in the update

default = 1e-09 (>0.0)

engine.DM_3dBragg.object_inertia(float)¶

Weight of the current object in the update

default = 0.0001 (>0.0)

engine.DM_3dBragg.fourier_power_bound(float)¶

If rms error of model vs diffraction data is smaller than this value, Fourier constraint is met

For Poisson-sampled data, the theoretical value for this parameter is 1/4. Set this value higher for noisy data. By default, power bound is calculated using fourier_relax_factor

default = None

engine.DM_3dBragg.fourier_relax_factor(float)¶

A factor used to calculate the Fourier power bound as 0.25 * fourier_relax_factor**2 * maximum power in diffraction data

Set this value higher for noisy data.

default = 0.05 (>0.0)

engine.DM_3dBragg.obj_smooth_std(float)¶

Gaussian smoothing (pixel) of the current object prior to update

If None, smoothing is deactivated. This smoothing can be used to reduce the amplitude of spurious pixels in the outer, least constrained areas of the object.

default = None (>0.0)

engine.DM_3dBragg.clip_object(tuple)¶

Clip object amplitude into this interval

default = None

engine.DM_3dBragg.probe_center_tol(float)¶

Pixel radius around optical axes that the probe mass center must reside in

default = None (>0.0)

engine.DM_3dBragg.compute_log_likelihood(bool)¶

A switch for computing the log-likelihood error (this can impact the performance of the engine)

default = True

engine.DM_3dBragg.alpha(float)¶

Mix parameter between Difference Map (alpha=1.) and Alternating Projections (alpha=0.)

default = 1.0 (>0.0)

engine.DM_3dBragg.name(str)¶: default = 'DM_3dBragg'

engine.DM_3dBragg.sample_support(Param)¶

Sample support settings

default = Param({})

engine.DM_3dBragg.sample_support.type(str)¶

Sample support geometry

Options are ‘thinlayer’ for one-dimensional support as function of z, ‘rod’ for one-dimensional radial support around the z axis.

default = 'thinlayer'

engine.DM_3dBragg.sample_support.size(float)¶

Support thickness or radius

This parameter is ignored when shrink wrapping is used.

default = 2e-07

engine.DM_3dBragg.sample_support.coefficient(float)¶

Scaling of region outside the support

Sample amplitude is multiplied by this value outside the support region

default = 0.1 (>0.0, <1.0)

engine.DM_3dBragg.sample_support.shrinkwrap(Param)¶

Shrink wrap settings. None for no shrink wrap.

default = Param({})

engine.DM_3dBragg.sample_support.shrinkwrap.smooth(float)¶

Shrink wrap smoothing parameter in pixels

Sigma of gaussian with which to smooth object profile before applying shrink wrap. Pass None for no smoothing. Values < .3 make little sense.

default = 1.0 (>0.3)

engine.DM_3dBragg.sample_support.shrinkwrap.cutoff(float)¶

Shrink wrap cutoff parameter

The support is truncated where the object profile has decayed to this value relative to the maximum.

default = 0.5

engine.DM_3dBragg.sample_support.shrinkwrap.monotonic(bool)¶

Require the object profile to be monotonic

If the object profile increases again after the maximum, then the support is cut off. Set the cutoff parameter low to make this the dominating criterion.

default = True

engine.DM_3dBragg.sample_support.shrinkwrap.start(int)¶

Start shrink wrap after this iteration

default = 10

engine.DM_3dBragg.sample_support.shrinkwrap.plot(bool)¶

Pass shrink wrap information to the plot client

Puts shrink wrap information in the runtime dict. The plot client can choose to plot it if it likes.

default = False

ptycho¶

ptycho(Param)¶: default = Param({})

ptycho.verbose_level(str, int)¶

Verbosity level

Verbosity level for information logging. - CRITICAL: Only critical errors - ERROR: All errors - WARNING: Warning - INFO: Process Information - INSPECT: Object Information - DEBUG: Debug

default = 'ERROR'

ptycho.data_type(str)¶

Reconstruction floating number precision

Reconstruction floating number precision ('single' or 'double')

default = 'single'

ptycho.run(str)¶

Reconstruction identifier

Reconstruction run identifier. If None, the run name will be constructed at run time from other information.

default = None

ptycho.frames_per_block(int)¶

Max number of frames per block of data

This parameter determines the size of buffer arrays for GPUs. Reduce this number if you run out of memory on the GPU.

default = 100000 (>1)

ptycho.min_frames_for_recon(int)¶

Minimum number of frames to be loaded before reconstruction can start.

For on-the-fly (live) processing, the first reconstruction engine will wait until this many frames have been loaded.

default = 0

ptycho.dry_run(bool)¶

Dry run switch

Run everything skipping all memory and cpu-heavy steps (and file saving). NOT IMPLEMENTED

default = False

ptycho.ipython_kernel(bool)¶

Start an ipython kernel for debugging

Start an ipython kernel for debugging.

default = False

ptycho.io¶

ptycho.io(Param)¶

Global parameters for I/O

Global parameter container for I/O settings.

default = Param({})

ptycho.io.home(str)¶

Base directory for all I/O

home is the root directory for all input/output operations. All other path parameters that are relative paths will be relative to this directory.

default = './'

ptycho.io.rfile(str)¶

Reconstruction file name (or format string)

Reconstruction file name or format string (constructed against runtime dictionary)

default = 'recons/%(run)s/%(run)s_%(engine)s_%(iterations)04d.ptyr'

ptycho.io.rformat(str)¶

Reconstruction file format

Choose a reconstruction file format for after engine completion. - 'minimal': Bare minimum of information - 'dls': Custom format for Diamond Light Source - 'used_params': Same as minimal but including all used parameters

default = 'minimal'

ptycho.io.interaction(Param)¶

ZeroMQ interactor options

Options for the communications server

default = Param({})

ptycho.io.interaction.active(bool)¶

turns on the interaction

If True the interaction starts, if False all interaction is turned off

default = True

ptycho.io.interaction.server(Param)¶

Link to server parameter tree

default = io.interaction.server

ptycho.io.interaction.client(Param)¶

Link to client parameter tree

default = io.interaction.client

ptycho.io.autosave(Param)¶

Auto-save options

Options for automatic saving during reconstruction.

default = Param({})

ptycho.io.autosave.active(bool)¶

Activation switch

If True the current reconstruction will be saved at regular intervals.

default = True

ptycho.io.autosave.interval(int)¶

Auto-save interval

If >0 the current reconstruction will be saved at regular intervals according to the

default = 10 (>-1)

ptycho.io.autosave.rfile(str)¶

Auto-save file name (or format string)

Auto-save file name or format string (constructed against runtime dictionary)

default = 'dumps/%(run)s/%(run)s_%(engine)s_%(iterations)04d.ptyr'

ptycho.io.autoplot(Param)¶

Plotting client parameters

Container for the plotting.

default = Param({})

ptycho.io.autoplot.active(bool)¶

Activation switch

If True the current reconstruction will be plotted at regular intervals.

default = True

ptycho.io.autoplot.imfile(str)¶

Plot images file name (or format string)

Plot images file name (or format string).

default = 'plots/%(run)s/%(run)s_%(engine)s_%(iterations)04d.png'

ptycho.io.autoplot.interval(int)¶

Number of iterations between plot updates

Requests to the server will happen with this iteration intervals. Note that this will work only if interaction.polling_interval is smaller or equal to this number. If interval =0 plotting is disabled which should be used, when ptypy is run on a cluster.

default = 1 (>-1)

ptycho.io.autoplot.threaded(bool)¶

Live plotting switch

If True, a plotting client will be spawned in a new thread and connected at initialization. If False, the master node will carry out the plotting, pausing the reconstruction. This option should be set to True when ptypy is run on an isolated workstation.

default = True

ptycho.io.autoplot.layout(str)¶

Options for default plotter or template name

Flexible layout for default plotter is not implemented yet. Please choose one of the templates 'default',``’black_and_white’,’nearfield’, ``'minimal' or 'weak'

default = 'default'

ptycho.io.autoplot.dump(bool)¶

Switch to dump plots as image files

Switch to dump plots as image files during reconstruction.

default = False

ptycho.io.autoplot.make_movie(bool)¶

Produce reconstruction movie after the reconstruction.

Switch to request the production of a movie from the dumped plots at the end of the reconstruction.

default = False

ptycho.io.benchmark(str)¶

Produce timings for benchmarking the performance of data loaders and engines

Switch to get timings and save results to a json file in p.io.home Choose 'all' for timing data loading, engine_init, engine_prepare, engine_iterate and engine_finalize

default = None

ptycho.scans¶

ptycho.scans(Param)¶

Container for instances of scan parameters

default = Param({})

ptycho.scans.scan_00(Param)¶

Wildcard: multiple entries with arbitrary names are accepted.

Wildcard entry for list of scans to load. See scan

default = scan.ScanModel

ptycho.engines¶

ptycho.engines(Param)¶

Container for instances of engine parameters

default = Param({})

ptycho.engines.engine_00(Param)¶

Wildcard: multiple entries with arbitrary names are accepted.

Wildcard entry for list of engines to run. See engine

The value of engines.*.name is used to choose among the available engines.

default = engine.DM

Parameter tree structure¶

io¶

io.interaction¶

scandata¶

scandata.PtyScan¶

scandata.PtydScan¶

scandata.MoonFlowerScan¶

scandata.QuickScan¶

scandata.SimScan¶

scan¶

scan.ScanModel¶

scan.BlockScanModel¶

scan.Vanilla¶

scan.BlockVanilla¶

scan.Full¶

scan.BlockFull¶

scan.OPRModel¶

scan.BlockOPRModel¶

scan.GradFull¶

scan.BlockGradFull¶

scan.Bragg3dModel¶

engine¶

engine.DM¶

engine.RAAR¶

engine.EPIE¶

engine.SDR¶

engine.ML¶

engine.DM_3dBragg¶

ptycho¶

ptycho.io¶

ptycho.scans¶

ptycho.engines¶

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