05 – CPM Reflection¶

This tutorial demonstrates ptychographic reconstruction in reflection geometry using a two-layer sample measured at 708 nm. It shows how to set up the aPIE engine for datasets acquired at a tilt angle.

What you'll learn:

• How to configure ptychography for reflection geometry
• How to use the tilt angle parameter (theta) in the data
• How to run aPIE for angle-correcting reconstruction

!!! note "Dataset" The dataset (TwoLayer_bin4.hdf5) is downloaded automatically into the example_data/ folder at the project root the first time you run the notebook. If the file already exists it is not re-downloaded.

!!! tip "Prerequisites" Complete 01 – CPM Simulation before this tutorial.

In [9]:

import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
import h5py

import matplotlib import matplotlib.pyplot as plt import numpy as np from pathlib import Path import h5py

In [10]:

# import the PtyLab module
import PtyLab
from PtyLab import ExperimentalData
from PtyLab import Reconstruction
from PtyLab import Monitor
from PtyLab import Params
from PtyLab import Engines

# import the PtyLab module import PtyLab from PtyLab import ExperimentalData from PtyLab import Reconstruction from PtyLab import Monitor from PtyLab import Params from PtyLab import Engines

The dataset (TwoLayer_bin4.hdf5) is downloaded automatically into the example_data/ folder at the project root the first time you run the next cell. If the file already exists it is not re-downloaded.

In [11]:

from PtyLab.io import getExampleDataFolder
import urllib.request

fileName = "TwoLayer_bin4.hdf5"
filePath = getExampleDataFolder() / fileName

if not filePath.exists():
    print(f"Downloading {fileName}...")
    urllib.request.urlretrieve(
        "https://ndownloader.figshare.com/files/28720086",
        filePath,
    )
    print("Download complete.")
else:
    print(f"{fileName} found, skipping download.")

from PtyLab.io import getExampleDataFolder import urllib.request fileName = "TwoLayer_bin4.hdf5" filePath = getExampleDataFolder() / fileName if not filePath.exists(): print(f"Downloading {fileName}...") urllib.request.urlretrieve( "https://ndownloader.figshare.com/files/28720086", filePath, ) print("Download complete.") else: print(f"{fileName} found, skipping download.")

TwoLayer_bin4.hdf5 found, skipping download.

Step 2: Initialize the ExperimentalData class

In [12]:

# Initialize the ExperimentalData using the filePath, and choose the operation Mode (default is 'CPM')
experimentalData = ExperimentalData(filePath, operationMode="CPM")
# show measured ptychogram in log scale. Need to close the window to continue
# experimentalData.showPtychogram()

# Initialize the ExperimentalData using the filePath, and choose the operation Mode (default is 'CPM') experimentalData = ExperimentalData(filePath, operationMode="CPM") # show measured ptychogram in log scale. Need to close the window to continue # experimentalData.showPtychogram()

Found encoder with shape (202, 2)

Step 3: Initialize the Monitor class and set properties

In [13]:

# Initialize the Monitor class
monitor = Monitor()
# Set monitor properties
monitor.figureUpdateFrequency = 5
monitor.objectPlot = "complex"  # complex abs angle
monitor.verboseLevel = "high"  # high: plot two figures, low: plot only one figure
monitor.probeZoom = 1  # control probe plot FoV
monitor.objectZoom = 2  # control object plot FoV
monitor.objectPlotContrast = (
    0.8  # control object plot contrast, 0-1, higher constrast with smaller number
)
monitor.probePlotContrast = (
    0.5  # control probe plot contrast, 0-1, higher constrast with smaller number
)

# Initialize the Monitor class monitor = Monitor() # Set monitor properties monitor.figureUpdateFrequency = 5 monitor.objectPlot = "complex" # complex abs angle monitor.verboseLevel = "high" # high: plot two figures, low: plot only one figure monitor.probeZoom = 1 # control probe plot FoV monitor.objectZoom = 2 # control object plot FoV monitor.objectPlotContrast = ( 0.8 # control object plot contrast, 0-1, higher constrast with smaller number ) monitor.probePlotContrast = ( 0.5 # control probe plot contrast, 0-1, higher constrast with smaller number )

/mnt/home/shantanu/projects/PtyLab.py/PtyLab/Monitor/Monitor.py:191: UserWarning: For diffraction data plot, preferably use an interactive matplotlib backend or set `monitor.verboseLevel = "low"`. 
  warnings.warn(

Step 4: Initialize the Params class and set properties

In [14]:

# Initialize the Params class
params = Params()
# Set params properties
params.gpuSwitch = True
params.positionOrder = "random"  # 'sequential' or 'random'
params.propagator = (
    "Fraunhofer"  # Fraunhofer Fresnel ASP scaledASP polychromeASP scaledPolychromeASP
)
params.probePowerCorrectionSwitch = True
params.comStabilizationSwitch = True
params.fftshiftSwitch = False
params.backgroundModeSwitch = True
params.intensityConstraint = "standard"

# Initialize the Params class params = Params() # Set params properties params.gpuSwitch = True params.positionOrder = "random" # 'sequential' or 'random' params.propagator = ( "Fraunhofer" # Fraunhofer Fresnel ASP scaledASP polychromeASP scaledPolychromeASP ) params.probePowerCorrectionSwitch = True params.comStabilizationSwitch = True params.fftshiftSwitch = False params.backgroundModeSwitch = True params.intensityConstraint = "standard"

INFO:GPU:cupy and CUDA available, switching to GPU

Step 5: Initialize the Reconstruction class and set properties

In [15]:

# Initialize the Reconstruction class
reconstruction = Reconstruction(experimentalData, params)
# reconstruction.No = 2**11

# choose the inital format of the probe and object, and initialize them
reconstruction.initialProbe = "circ"
reconstruction.initialObject = "ones"
reconstruction.initializeObjectProbe()

# Optional: customize initial probe quadratic phase:
# since we know our beam is divergent, we add a diverging wavefront to the initial beam
reconstruction.probe = reconstruction.probe * np.exp(
    1.0j
    * 2
    * np.pi
    / reconstruction.wavelength
    * (reconstruction.Xp**2 + reconstruction.Yp**2)
    / (3 * 6e-3)
)

# Initialize the Reconstruction class reconstruction = Reconstruction(experimentalData, params) # reconstruction.No = 2**11 # choose the inital format of the probe and object, and initialize them reconstruction.initialProbe = "circ" reconstruction.initialObject = "ones" reconstruction.initializeObjectProbe() # Optional: customize initial probe quadratic phase: # since we know our beam is divergent, we add a diverging wavefront to the initial beam reconstruction.probe = reconstruction.probe * np.exp( 1.0j * 2 * np.pi / reconstruction.wavelength * (reconstruction.Xp**2 + reconstruction.Yp**2) / (3 * 6e-3) )

INFO:Reconstruction:Copying attribute wavelength
INFO:Reconstruction:Copying attribute dxd
INFO:Reconstruction:Copying attribute theta
INFO:Reconstruction:Copying attribute spectralDensity
INFO:Reconstruction:Copying attribute entrancePupilDiameter
INFO:Reconstruction:Initial object set to ones
INFO:Reconstruction:Initial probe set to circ

Step 6: Choose the engine, set the properties, and do reconstruction

In [16]:

# Choose mPIE engine
mPIE = Engines.mPIE(reconstruction, experimentalData, params, monitor)
mPIE.numIterations = 300
mPIE.betaProbe = 0.25
mPIE.betaObject = 0.25
mPIE.reconstruct()

# Choose mPIE engine mPIE = Engines.mPIE(reconstruction, experimentalData, params, monitor) mPIE.numIterations = 300 mPIE.betaProbe = 0.25 mPIE.betaObject = 0.25 mPIE.reconstruct()

                                                       
iteration: 295                                         
error: 58.1                                            
estimated linear overlap: 86.1 %                       
estimated area overlap: 77.3 %                         
mPIE:  99%|█████████▊| 296/300 [00:59<00:00,  4.01it/s]

INFO:mPIE:Doing probe com stabilization

mPIE:  99%|█████████▉| 297/300 [00:59<00:00,  4.63it/s]

INFO:mPIE:Doing probe com stabilization

mPIE:  99%|█████████▉| 298/300 [00:59<00:00,  5.20it/s]

INFO:mPIE:Doing probe com stabilization

mPIE: 100%|█████████▉| 299/300 [00:59<00:00,  5.68it/s]

INFO:mPIE:Doing probe com stabilization

mPIE: 100%|██████████| 300/300 [01:00<00:00,  4.99it/s]

INFO:mPIE:switch to cpu

In [17]:

## now save the data
# reconstruction.saveResults(fileName+'_reconstruction.hdf5')

## now save the data # reconstruction.saveResults(fileName+'_reconstruction.hdf5')