LED Calibration¶

Why calibrate?¶

Physical LED arrays are manufactured with small tolerances — actual LED positions deviate from their nominal values by tens of microns. These errors translate into incorrect illumination angles, which degrade reconstruction quality. The IlluminationCalibration class fits a geometric model to correct LED positions before reconstruction.

Getting the calibration object¶

IlluminationCalibration is returned automatically by easyInitialize in FPM mode:

import PtyLab
from PtyLab import Engines

data, recon, params, monitor, engine, calib = PtyLab.easyInitialize(
    "fpm_data.hdf5",
    engine=Engines.mqNewton,
    operationMode="FPM",
)

Running calibration¶

calib.fit_mode = "Translation"
calib.runCalibration()

After runCalibration(), the corrected LED positions are propagated into the ExperimentalData and Reconstruction objects automatically.

Calibration modes¶

Mode	Description
`"Translation"`	Fits a global (dx, dy) offset to all LED positions

Note

The "Translation" mode corrects for a uniform shift of the entire LED array (e.g. from physical misalignment). For more complex distortions, more advanced calibration may be needed.

When to skip calibration¶

If you are working with synthetic (simulated) data or have independently verified your LED positions, you can skip calibration:

# Skip calibration, go straight to reconstruction
recon.initializeObjectProbe()
engine.numIterations = 50
for loop, posLoop in engine.reconstruct():
    pass

Inspecting corrected positions¶

After calibration, the corrected encoder values are stored on the Reconstruction object:

import matplotlib.pyplot as plt

plt.figure()
plt.scatter(*data.encoder.T, label="Original", alpha=0.5)
plt.scatter(*recon.encoder_corrected.T, label="Corrected", alpha=0.5)
plt.legend()
plt.axis("equal")
plt.show()