mirror of
https://github.com/slsdetectorgroup/aare.git
synced 2025-12-19 19:41:25 +01:00
abae2674a9
- Added function to read calibration file - Multi threaded pedestal subtraction and application of the calibration
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
import pytest
|
|
import numpy as np
|
|
from aare import apply_calibration
|
|
|
|
def test_apply_calibration_small_data():
|
|
# The raw data consists of 10 4x5 images
|
|
raw = np.zeros((10, 4, 5), dtype=np.uint16)
|
|
|
|
# We need a pedestal for each gain, so 3
|
|
pedestal = np.zeros((3, 4, 5), dtype=np.float32)
|
|
|
|
# And the same for calibration
|
|
calibration = np.ones((3, 4, 5), dtype=np.float32)
|
|
|
|
# Set the known values, probing one pixel in each gain
|
|
raw[0, 0, 0] = 100 #ADC value of 100, gain 0
|
|
pedestal[0, 0, 0] = 10
|
|
calibration[0, 0, 0] = 43.7
|
|
|
|
raw[2, 3, 3] = (1<<14) + 1000 #ADC value of 1000, gain 1
|
|
pedestal[1, 3, 3] = 500
|
|
calibration[1, 3, 3] = 2.0
|
|
|
|
raw[1,1,4] = (3<<14) + 857 #ADC value of 857, gain 2
|
|
pedestal[2,1,4] = 100
|
|
calibration[2,1,4] = 3.0
|
|
|
|
|
|
|
|
data = apply_calibration(raw, pd = pedestal, cal = calibration)
|
|
|
|
|
|
# The formula that is applied is:
|
|
# calibrated = (raw - pedestal) / calibration
|
|
assert data.shape == (10, 4, 5)
|
|
assert data[0, 0, 0] == (100 - 10) / 43.7
|
|
assert data[2, 3, 3] == (1000 - 500) / 2.0
|
|
assert data[1, 1, 4] == (857 - 100) / 3.0
|
|
|
|
# Other pixels should be zero
|
|
assert data[2,2,2] == 0
|
|
assert data[0,1,1] == 0
|
|
assert data[1,3,0] == 0
|