Generating a speckle tracking dataset#
You can simulate one-dimensional speckle tracking scan either using Python interface or Terminal.
Python interface#
Experimental parameters#
Before performing the simulation, you need to choose the experimental
parameters. You can do it with pyrost.simulation.STParams. The
st_sim library has built-in default parameters, which can be accessed
with pyrost.simulation.STParams.import_default().
Note
Full list of experimental parameters is written in Simulation parameters. All the spatial parameters are assumed to be in microns.
>>> import pyrost.simulation as st_sim
>>> params = st_sim.STParams.import_default()
>>> params = params.replace(bar_size=0.7, bar_sigma=0.12, bar_atn=0.18,
>>> bulk_atn=0.2, p0=5e4, th_s=8e-5, n_frames=100,
>>> offset=2.0, step_size=0.1, defocus=150, alpha=0.05,
>>> ab_cnt=0.7, bar_rnd=0.8)
Performing the simulation#
Now you’re able to generate the simulated data. It takes time to calculate the
wavefronts, pyrost.simulation.STSim will post its status during the process. You can
either generate a stack of frames or a ptychograph. pyrost.simulation.STConverter
generates all the data attributes necessary for the speckle tracking reconstruction. Also,
it provides an interface to save the generated attributes to a CXI file with
pyrost.simulation.STConverter.save() method.
>>> sim_obj = st_sim.STSim(params)
>>> ptych = sim_obj.ptychograph()
>>> st_conv = st_sim.STConverter(sim_obj, ptych)
>>> st_conv.save('sim.cxi', mode='overwrite')
>>> fig, ax = plt.subplots(figsize=(14, 6))
>>> ax.imshow(ptych[:, 0, 500:1480])
>>> ax.set_title('Ptychograph', fontsize=20)
>>> ax.tick_params(labelsize=15)
>>> plt.show()
Or you can save the simulated data and generate a pyrost.STData data container with
pyrost.simulation.STConverter() method.
Note
pyrost.simulation.STConverter() returns a pyrost.STData container
without any attributes already loaded, use pyrost.STData.load() to load the data
from the file.
>>> sim_obj = st_sim.STSim(params)
>>> ptych = sim_obj.ptychograph()
>>> st_conv = st_sim.STConverter(sim_obj, ptych)
>>> data = st_conv.export_data('sim.cxi')
>>> data = data.load()
Command-line interface#
You can perform the whole simulation procedure with one command python -m pyrost.simulation. To see all available arguments
just type python -m pyrost.simulation --help.
$ python -m pyrost.simulation --help
usage: __main__.py [-h] [-f INI_FILE] [--defocus DEFOCUS]
[--det_dist DET_DIST] [--step_size STEP_SIZE]
[--step_rnd STEP_RND] [--n_frames N_FRAMES]
[--detx_size DETX_SIZE] [--dety_size DETY_SIZE] [--p0 P0]
[--wl WL] [--th_s TH_S] [--ap_x AP_X] [--ap_y AP_Y]
[--focus FOCUS] [--alpha ALPHA] [--ab_cnt AB_CNT]
[--bar_size BAR_SIZE] [--bar_sigma BAR_SIGMA]
[--bar_atn BAR_ATN] [--bulk_atn BULK_ATN]
[--bar_rnd BAR_RND] [--offset OFFSET] [-p]
out_path
Run Speckle Tracking simulation
positional arguments:
out_path Output folder path
optional arguments:
-h, --help show this help message and exit
-f INI_FILE, --ini_file INI_FILE
Path to an INI file to fetch all of the simulation
parameters (default: None)
--defocus DEFOCUS Lens defocus distance, [um] (default: 100.0)
--det_dist DET_DIST Distance between the barcode and the detector [um]
(default: 2000000.0)
--step_size STEP_SIZE
Scan step size [um] (default: 0.1)
--step_rnd STEP_RND Random deviation of sample translations [0.0 - 1.0]
(default: 0.2)
--n_frames N_FRAMES Number of frames (default: 300)
--detx_size DETX_SIZE
horizontal axis frames size in pixels (default: 2000)
--dety_size DETY_SIZE
vertical axis frames size in pixels (default: 1000)
--p0 P0 Source beam flux [cnt / s] (default: 200000.0)
--wl WL Wavelength [um] (default: 7.29e-05)
--th_s TH_S Source rocking curve width [rad] (default: 0.0002)
--ap_x AP_X Lens size along the x axis [um] (default: 40.0)
--ap_y AP_Y Lens size along the y axis [um] (default: 2.0)
--focus FOCUS Focal distance [um] (default: 1500.0)
--alpha ALPHA Third order aberrations [rad/mrad^3] (default: -0.05)
--ab_cnt AB_CNT Lens' aberrations center point [0.0 - 1.0] (default:
0.5)
--bar_size BAR_SIZE Average bar size [um] (default: 0.5)
--bar_sigma BAR_SIGMA
Bar haziness width [um] (default: 0.12)
--bar_atn BAR_ATN Bar attenuation (default: 0.15)
--bulk_atn BULK_ATN Bulk attenuation (default: 0.15)
--bar_rnd BAR_RND Bar random deviation (default: 0.9)
--offset OFFSET Sample's offset at the beginning and the end of the
scan [um] (default: 0.0)
-p, --ptych Generate ptychograph data (default: False)
$ python -m pyrost.simulation sim.cxi --bar_size 0.7 --bar_sigma 0.12 \
--bar_atn 0.18 --bulk_atn 0.2 --p0 5e4 --th_s 8e-5 --n_frames 200 --offset 2 \
--step_size 0.1 --defocus 150 --alpha 0.05 --ab_cnt 0.7 --bar_rnd 0.8 -p
The simulation results have been saved to sim.cxi
As you can see below, the simulated Speckle Tracking scan was saved to a CXI file.
$ h5ls -r sim.cxi
/ Group
/entry Group
/entry/data Group
/entry/data/data Dataset {200/Inf, 1, 985}
/entry/instrument Group
/entry/instrument/detector Group
/entry/instrument/detector/distance Dataset {SCALAR}
/entry/instrument/detector/x_pixel_size Dataset {SCALAR}
/entry/instrument/detector/y_pixel_size Dataset {SCALAR}
/entry/instrument/source Group
/entry/instrument/source/wavelength Dataset {SCALAR}
/speckle_tracking Group
/speckle_tracking/basis_vectors Dataset {200/Inf, 2, 3}
/speckle_tracking/defocus_x Dataset {SCALAR}
/speckle_tracking/defocus_y Dataset {SCALAR}
/speckle_tracking/mask Dataset {200/Inf, 1, 985}
/speckle_tracking/pixel_translations Dataset {200/Inf, 2}
/speckle_tracking/translations Dataset {200/Inf, 3}
/speckle_tracking/whitefield Dataset {1, 985}