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reproduction_effort/functions/adjust_factor.py

@@ -9,6 +9,7 @@ def adjust_factor(
     upper_frequency_heartbeat: float,
     sample_frequency: float,
     mask: torch.Tensor,
+    power_factors: None | list[float],
 ) -> tuple[float, float]:
 
     number_of_active_pixel: torch.Tensor = mask.type(dtype=torch.float32).sum()
@@ -23,6 +24,7 @@ def adjust_factor(
     signal_acceptor_offset = signal_acceptor.mean()
     signal_donor_offset = signal_donor.mean()
 
+    if power_factors is None:
         signal_acceptor = signal_acceptor - signal_acceptor_offset
         signal_donor = signal_donor - signal_donor_offset
 
@@ -42,7 +44,8 @@ def adjust_factor(
         signal_donor_fft: torch.Tensor = torch.fft.rfft(signal_donor, n=nfft)
 
         frequency_axis: torch.Tensor = (
-        torch.fft.rfftfreq(nfft, device=signal_acceptor_fft.device) * sample_frequency
+            torch.fft.rfftfreq(nfft, device=signal_acceptor_fft.device)
+            * sample_frequency
         )
 
         signal_acceptor_power: torch.Tensor = torch.abs(signal_acceptor_fft) ** 2
@@ -67,16 +70,21 @@ def adjust_factor(
         signal_acceptor_power = signal_acceptor_power[idx]
         signal_donor_power = signal_donor_power[idx]
 
-    acceptor_range = signal_acceptor_power.max() - signal_acceptor_power.min()
+        acceptor_range: float = float(
+            signal_acceptor_power.max() - signal_acceptor_power.min()
+        )
 
-    donor_range = signal_donor_power.max() - signal_donor_power.min()
+        donor_range: float = float(signal_donor_power.max() - signal_donor_power.min())
+    else:
+        donor_range = float(power_factors[0])
+        acceptor_range = float(power_factors[1])
 
     acceptor_correction_factor: float = float(
         0.5
         * (
             1
-            + (signal_acceptor_offset * torch.sqrt(donor_range))
+            + (signal_acceptor_offset * math.sqrt(donor_range))
-            / (signal_donor_offset * torch.sqrt(acceptor_range))
+            / (signal_donor_offset * math.sqrt(acceptor_range))
         )
     )
 

reproduction_effort/functions/preprocessing.py

@@ -25,6 +25,7 @@ def preprocessing(
     upper_frequency_heartbeat: float,
     sample_frequency: float,
     dtype: torch.dtype = torch.float32,
+    power_factors: None | list[float] = None,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
 
     mask: torch.Tensor = make_mask(
@@ -44,20 +45,24 @@ def preprocessing(
         )
 
     # Interpolate in-between images
+    if power_factors is None:
         interpolate_along_time(camera_sequence)
 
     camera_sequence_filtered: list[torch.Tensor] = []
     for id in range(0, len(camera_sequence)):
         camera_sequence_filtered.append(camera_sequence[id].clone())
 
+    if power_factors is None:
         idx_volume: int = cameras.index("volume")
-    heart_rate: float = heart_beat_frequency(
+        heart_rate: None | float = heart_beat_frequency(
             input=camera_sequence_filtered[idx_volume],
             lower_frequency_heartbeat=lower_frequency_heartbeat,
             upper_frequency_heartbeat=upper_frequency_heartbeat,
             sample_frequency=sample_frequency,
             mask=mask,
         )
+    else:
+        heart_rate = None
 
     camera_sequence_filtered = gauss_smear(
         camera_sequence_filtered,
@@ -88,8 +93,12 @@ def preprocessing(
         )
         results.append(output)
 
+    if heart_rate is not None:
         lower_frequency_heartbeat_selection: float = heart_rate - 3
         upper_frequency_heartbeat_selection: float = heart_rate + 3
+    else:
+        lower_frequency_heartbeat_selection = 0
+        upper_frequency_heartbeat_selection = 0
 
     donor_correction_factor, acceptor_correction_factor = adjust_factor(
         input_acceptor=results[0],
@@ -98,6 +107,7 @@ def preprocessing(
         upper_frequency_heartbeat=upper_frequency_heartbeat_selection,
         sample_frequency=sample_frequency,
         mask=mask,
+        power_factors=power_factors,
     )
 
     results[0] = acceptor_correction_factor * (