remove need for background capture in fluorescence data processing

lib/imaging.py

@@ -165,15 +165,15 @@ class FluorescenceData(ImagingData):
         _dA = self.config.get("bin_size", 1)**2 \
                 * (DEF_DX**2 if dA is None else dA)
         if printflag: print("[imaging] Compute results")
-        N_bkgd = compute_mot_number(
-            self.arrays["background"].astype(np.float64),
-            0.40,
-            self.config["gain"],
-            self.config["exposure_time"] * 1e-6, # s
-            (0.0127**2 * np.pi) / (4 * np.pi * 0.125**2),
-            50e6 * 2 * np.pi,
-            1.0
-        ) if "background" in self.arrays else 0
+        #N_bkgd = compute_mot_number(
+        #    self.arrays["background"].astype(np.float64),
+        #    0.40,
+        #    self.config["gain"], # dB
+        #    self.config["exposure_time"] * 1e-6, # s
+        #    (0.0127**2 * np.pi) / (4 * np.pi * 0.125**2),
+        #    50e6 * 2 * np.pi,
+        #    1.0
+        #) if "background" in self.arrays.keys() else 0.0
         for label, array in self.arrays.items():
             if label == "background":
                 continue
@@ -181,14 +181,14 @@ class FluorescenceData(ImagingData):
             if array.max() >= (dtype_info.max - dtype_info.bits):
                 print(f"[imaging] WARNING: image '{label}' may contain clipping")
             N = compute_mot_number(
-                array,
+                array.astype(np.float64),
                 0.40,
                 self.config["gain"], # dB
                 self.config["exposure_time"] * 1e-6, # s
                 (0.0127**2 * np.pi) / (4 * np.pi * 0.125**2),
                 50e6 * 2 * np.pi,
                 1.0
-            ) - N_bkgd
+            )
             sx, sy = lls_fit_gaussian(array, _dA)
             self.results = dict() if self.results is None else self.results
             self.results.update({
@@ -203,22 +203,36 @@ class FluorescenceData(ImagingData):
 def compute_mot_number(image, QE, gain, exposure_time, solid_angle,
         detuning, intensity_parameter):
     H, W = image.shape
-    im = image[H//4:3*H//4, W//4:3*W//4]
+    K = 4
+    im = image[H//K:(K - 1)*H//K, W//K:(K - 1)*W//K]
+    im_bkgd = image.copy()
+    im_bkgd[H//K:(K - 1)*H//K, W//K:(K - 1)*W//K] = 0
+    #pd.Plotter().imshow(image).savefig("image.png").close()
     #pd.Plotter().imshow(im).savefig("img.png").close()
+    #pd.Plotter().imshow(im_bkgd).savefig("img_bkgd.png").close()
     electron_rate = im.astype(np.float64).sum()
+    electron_rate_bkgd = im_bkgd.astype(np.float64).sum()
     photon_rate \
             = electron_rate \
             / 10**(gain / 10) \
             / QE \
             / solid_angle \
             / exposure_time
+    photon_rate_bkgd \
+            = electron_rate_bkgd \
+            / 10**(gain / 10) \
+            / QE \
+            / solid_angle \
+            / exposure_time
     Y = 29.1e6 * 2 * np.pi # transition linewidth; s^-1
     l = 399e-9 # transition wavelength; m
     I_sat = (Y * H * C * np.pi) / (3 * l**3)
     scatter_rate = lambda D, I: \
             (Y / 2) * I / (1 + 4 * (D / Y)**2 + I)
     N = photon_rate / scatter_rate(detuning, intensity_parameter)
-    return N
+    N_bkgd = (K - 2)**2 / (4 * K - 4) \
+            * photon_rate_bkgd / scatter_rate(detuning, intensity_parameter)
+    return N - N_bkgd
 
 def lls_fit_gaussian(A, dA):
     """