adjust ROI for new Flir camera setup

lib/imaging.py

@@ -11,11 +11,15 @@ import toml
 import warnings
 import PIL.Image
 import lib.plotdefs as pd
+from lib.plotdefs import S
 import lib.dataio as io
 
 H = 6.626070040e-34
 C = 2.99792458e+8
 
+ROI = [932, 729, 155, 125] # [x, y, w, h]
+ROI_s = (S[ROI[1]:ROI[1] + ROI[3]], S[ROI[0]:ROI[0] + ROI[2]])
+
 DEF_DX = 3.45 # pixel size for Flir Grasshopper [um]
 
 class ImagingData:
@@ -219,12 +223,13 @@ class FluorescenceData(ImagingData):
 
 def compute_mot_number(image, QE, gain, exposure_time, solid_angle,
         detuning, intensity_parameter, k=None):
-    H, W = image.shape
-    K_h = 3.0
-    slice_h = pd.S[int(H / K_h) : int((K_h - 1) * H / K_h)]
-    K_w = 3.0
-    slice_w = pd.S[int(W / K_w) : int((K_w - 1) * W / K_w)]
-    im = image[slice_h, slice_w]
+    # H, W = image.shape
+    # K_h = 3.0
+    # slice_h = S[int(H / K_h) : int((K_h - 1) * H / K_h)]
+    # K_w = 3.0
+    # slice_w = S[int(W / K_w) : int((K_w - 1) * W / K_w)]
+    # im = image[slice_h, slice_w]
+    im = image[ROI_s]
     if k is not None:
         pd.Plotter().imshow(image).savefig(f"image_{k}.png").close()
         pd.Plotter().imshow(im).savefig(f"img_{k}.png").close()
@@ -244,7 +249,7 @@ def compute_mot_number(image, QE, gain, exposure_time, solid_angle,
     N = photon_rate / scatter_rate(detuning, intensity_parameter)
     return N
 
-def lls_fit_gaussian(A, dA, k=None):
+def lls_fit_gaussian(A, dA, k=None): # deprecated
     """
     Assumes uniform, square dA.
     """
@@ -274,16 +279,17 @@ def lmfit_gaussian(data, dA, k=None):
     """
     Assumes uniform, square dA.
     """
-    i0, j0 = [k // 2 for k in data.shape]
+    D = data[ROI_s]
+    i0, j0 = [k // 2 for k in D.shape]
     x0 = np.sqrt(dA) * j0
-    x = np.sqrt(dA) * np.arange(data.shape[1])
+    x = np.sqrt(dA) * np.arange(D.shape[1])
     y0 = np.sqrt(dA) * i0
-    y = np.sqrt(dA) * np.arange(data.shape[0])
+    y = np.sqrt(dA) * np.arange(D.shape[0])
     X, Y = np.meshgrid(x, y)
     
-    downsample = 10
-    sampler = pd.S[::downsample]
-    _data = data[sampler, sampler]
+    downsample = 1
+    sampler = S[::downsample]
+    _D = D[sampler, sampler]
     _X = X[sampler, sampler]
     _Y = Y[sampler, sampler]
 
@@ -298,7 +304,7 @@ def lmfit_gaussian(data, dA, k=None):
 
     def residual(params: lmfit.Parameters):
         m = model(params)
-        return ((_data - m)**2).flatten()
+        return ((_D - m)**2).flatten()
 
     params = lmfit.Parameters()
     params.add("A", value=data.max())