Add files via upload

new_pipeline/config.json

@@ -23,6 +23,10 @@
     "lower_freqency_bandpass": 5.0, // Hz
     "upper_freqency_bandpass": 14.0, // Hz
     "heartbeat_filtfilt_chuck_size": 10,
+    // Gauss smear 
+    "gauss_smear_spatial_width": 8,
+    "gauss_smear_temporal_width": 0.1,
+    "gauss_smear_use_matlab_mask": false,
     // LED Ramp on
     "skip_frames_in_the_beginning": 100, // Frames
     // PyTorch

new_pipeline/stage_4_process.py

@@ -19,6 +19,7 @@ from functions.align_refref import align_refref
 from functions.perform_donor_volume_rotation import perform_donor_volume_rotation
 from functions.perform_donor_volume_translation import perform_donor_volume_translation
 from functions.bandpass import bandpass
+from functions.gauss_smear_individual import gauss_smear_individual
 
 import matplotlib.pyplot as plt
 
@@ -600,6 +601,84 @@ def process_trial(
     data[donor_index, ...] += mean_values_donor
     mylogger.info("-==- Done -==-")
 
+    mylogger.info("Divide by mean over time")
+    data /= data[:, config["skip_frames_in_the_beginning"] :, ...].nanmean(
+        dim=1,
+        keepdim=True,
+    )
+    data = data.nan_to_num(nan=0.0)
+    mylogger.info("-==- Done -==-")
+
+    mylogger.info("Preparation for regression -- Gauss smear")
+    spatial_width = float(config["gauss_smear_spatial_width"])
+
+    if config["binning_enable"] and config["binning_before_alignment"]:
+        spatial_width /= float(config["binning_kernel_size"])
+
+    mylogger.info(
+        f"Mask -- "
+        f"spatial width: {spatial_width}, "
+        f"temporal width: {float(config['gauss_smear_temporal_width'])}, "
+        f"use matlab mode: {bool(config['gauss_smear_use_matlab_mask'])} "
+    )
+
+    input_mask = mask_positve.type(dtype=dtype).clone()
+
+    filtered_mask: torch.Tensor
+    filtered_mask, _ = gauss_smear_individual(
+        input=input_mask,
+        spatial_width=spatial_width,
+        temporal_width=float(config["gauss_smear_temporal_width"]),
+        use_matlab_mask=bool(config["gauss_smear_use_matlab_mask"]),
+        epsilon=float(torch.finfo(input_mask.dtype).eps),
+    )
+
+    mylogger.info("creating a copy of the data")
+    data_filtered = data.clone().movedim(1, -1)
+    if device != torch.device("cpu"):
+        free_mem = cuda_total_memory - max(
+            [torch.cuda.memory_reserved(device), torch.cuda.memory_allocated(device)]
+        )
+        mylogger.info(
+            f"CUDA memory after reserving RAM for data_filtered: {free_mem//1024} MByte"
+        )
+
+    overwrite_fft_gauss: None | torch.Tensor = None
+    for i in range(0, data_filtered.shape[0]):
+        mylogger.info(
+            f"{config['required_order'][i]} -- "
+            f"spatial width: {spatial_width}, "
+            f"temporal width: {float(config['gauss_smear_temporal_width'])}, "
+            f"use matlab mode: {bool(config['gauss_smear_use_matlab_mask'])} "
+        )
+        data_filtered[i, ...] *= input_mask.unsqueeze(-1)
+        data_filtered[i, ...], overwrite_fft_gauss = gauss_smear_individual(
+            input=data_filtered[i, ...],
+            spatial_width=spatial_width,
+            temporal_width=float(config["gauss_smear_temporal_width"]),
+            overwrite_fft_gauss=overwrite_fft_gauss,
+            use_matlab_mask=bool(config["gauss_smear_use_matlab_mask"]),
+            epsilon=float(torch.finfo(input_mask.dtype).eps),
+        )
+
+        data_filtered[i, ...] /= filtered_mask + 1e-20
+        data_filtered[i, ...] += 1.0 - input_mask.unsqueeze(-1)
+
+    del filtered_mask
+    del overwrite_fft_gauss
+    del input_mask
+    mylogger.info("data_filtered is populated")
+
+    if device != torch.device("cpu"):
+        free_mem = cuda_total_memory - max(
+            [torch.cuda.memory_reserved(device), torch.cuda.memory_allocated(device)]
+        )
+        mylogger.info(
+            f"CUDA memory after data_filtered is populated: {free_mem//1024} MByte"
+        )
+
+    mylogger.info("-==- Done -==-")
+
     exit()
 
     return