implement online cnmf and register_multisession

studio/app/common/dataclass/image.py

@@ -1,4 +1,6 @@
 import gc
+import math
+import os
 from typing import Optional
 
 import imageio
@@ -14,6 +16,7 @@
 from studio.app.common.dataclass.base import BaseData
 from studio.app.common.dataclass.utils import create_images_list
 from studio.app.common.schemas.outputs import PlotMetaData
+from studio.app.const import MAX_IMAGE_DATA_PART_SIZE
 from studio.app.dir_path import DIRPATH
 
 
@@ -47,6 +50,36 @@ def __init__(
             del data
             gc.collect()
 
+    def split_image(self, output_dir: str):
+        image = self.data
+        size = image.nbytes
+        frames = image.shape[0]
+
+        if size > MAX_IMAGE_DATA_PART_SIZE:
+            frames_per_part = math.ceil(
+                frames / math.ceil(size / MAX_IMAGE_DATA_PART_SIZE)
+            )
+        else:
+            frames_per_part = frames // 2
+
+        file_name = self.path[0] if isinstance(self.path, list) else self.path
+        name, ext = os.path.splitext(os.path.basename(file_name))
+        save_paths = []
+
+        _dir = join_filepath([output_dir, "image_split", name])
+        create_directory(_dir)
+
+        for t in np.arange(0, frames, frames_per_part):
+            _path = join_filepath([_dir, f"{name}_{t//frames_per_part}{ext}"])
+            with tifffile.TiffWriter(_path, bigtiff=True) as tif:
+                if t == frames - 1:
+                    tif.write(image[t:])
+                else:
+                    tif.write(image[t : t + frames_per_part])
+            save_paths.append(_path)
+
+        return save_paths
+
     @property
     def data(self):
         if isinstance(self.path, list):

studio/app/const.py

@@ -20,3 +20,5 @@ class FILETYPE:
 NOT_DISPLAY_ARGS_LIST = ["params", "output_dir", "nwbfile", "kwargs"]
 
 DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
+
+MAX_IMAGE_DATA_PART_SIZE = 1_000_000_000  # 1GB

studio/app/optinist/core/nwb/nwb_creater.py

@@ -222,14 +222,19 @@ def fluorescence(cls, nwbfile, function_id, roi_list):
                 roi["table_name"], region=roi["region"]
             )
 
-            roi_resp_series = RoiResponseSeries(
-                name=roi["name"],
-                data=roi["data"],
-                rois=region_roi,
-                unit=roi["unit"],
-                timestamps=roi.get("timestamps"),
-                rate=float(roi.get("rate", 0.0)),
-            )
+            roi_resp_dict = {
+                "name": roi["name"],
+                "data": roi["data"],
+                "rois": region_roi,
+                "unit": roi["unit"],
+                "timestamps": roi.get("timestamps"),
+                "rate": float(roi.get("rate", 0.0)),
+            }
+            if "comments" in roi:
+                roi_resp_dict["comments"] = roi["comments"]
+
+            roi_resp_series = RoiResponseSeries(**roi_resp_dict)
+
             fluo.add_roi_response_series(roi_resp_series)
 
         nwbfile.processing["ophys"].add(fluo)

studio/app/optinist/wrappers/caiman/__init__.py

@@ -1,4 +1,7 @@
 from studio.app.optinist.wrappers.caiman.cnmf import caiman_cnmf
+from studio.app.optinist.wrappers.caiman.cnmf_multisession import (
+    caiman_cnmf_multisession,
+)
 from studio.app.optinist.wrappers.caiman.cnmfe import caiman_cnmfe
 from studio.app.optinist.wrappers.caiman.motion_correction import caiman_mc
 
@@ -16,5 +19,9 @@
             "function": caiman_cnmfe,
             "conda_name": "caiman",
         },
+        "cnmf_multisession": {
+            "function": caiman_cnmf_multisession,
+            "conda_name": "caiman",
+        },
     }
 }

studio/app/optinist/wrappers/caiman/cnmf.py

@@ -69,6 +69,36 @@ def get_roi(A, roi_thr, thr_method, swap_dim, dims):
     return ims
 
 
+def util_get_memmap(images: np.ndarray, file_path: str):
+    """
+    convert np.ndarray to mmap
+    """
+    from caiman.mmapping import prepare_shape
+    from caiman.paths import memmap_frames_filename
+
+    order = "C"
+    dims = images.shape[1:]
+    T = images.shape[0]
+    shape_mov = (np.prod(dims), T)
+
+    dir_path = join_filepath(file_path.split("/")[:-1])
+    basename = file_path.split("/")[-1]
+    fname_tot = memmap_frames_filename(basename, dims, T, order)
+    mmap_path = join_filepath([dir_path, fname_tot])
+
+    mmap_images = np.memmap(
+        mmap_path,
+        mode="w+",
+        dtype=np.float32,
+        shape=prepare_shape(shape_mov),
+        order=order,
+    )
+
+    mmap_images = np.reshape(mmap_images.T, [T] + list(dims), order="F")
+    mmap_images[:] = images[:]
+    return mmap_images, dims, mmap_path
+
+
 def util_recursive_flatten_params(params, result_params: dict, nest_counter=0):
     """
     Recursively flatten node parameters (operation for CaImAn CNMFParams)
@@ -89,9 +119,7 @@ def caiman_cnmf(
 ) -> dict(fluorescence=FluoData, iscell=IscellData):
     from caiman import local_correlations, stop_server
     from caiman.cluster import setup_cluster
-    from caiman.mmapping import prepare_shape
-    from caiman.paths import memmap_frames_filename
-    from caiman.source_extraction.cnmf import cnmf
+    from caiman.source_extraction.cnmf import cnmf, online_cnmf
     from caiman.source_extraction.cnmf.params import CNMFParams
 
     function_id = output_dir.split("/")[-1]
@@ -111,33 +139,14 @@ def caiman_cnmf(
     Ain = reshaped_params.pop("Ain", None)
     do_refit = reshaped_params.pop("do_refit", None)
     roi_thr = reshaped_params.pop("roi_thr", None)
+    use_online = reshaped_params.pop("use_online", False)
 
     file_path = images.path
     if isinstance(file_path, list):
         file_path = file_path[0]
 
     images = images.data
-
-    # np.arrayをmmapへ変換
-    order = "C"
-    dims = images.shape[1:]
-    T = images.shape[0]
-    shape_mov = (np.prod(dims), T)
-
-    dir_path = join_filepath(file_path.split("/")[:-1])
-    basename = file_path.split("/")[-1]
-    fname_tot = memmap_frames_filename(basename, dims, T, order)
-
-    mmap_images = np.memmap(
-        join_filepath([dir_path, fname_tot]),
-        mode="w+",
-        dtype=np.float32,
-        shape=prepare_shape(shape_mov),
-        order=order,
-    )
-
-    mmap_images = np.reshape(mmap_images.T, [T] + list(dims), order="F")
-    mmap_images[:] = images[:]
+    mmap_images, dims, mmap_path = util_get_memmap(images, file_path)
 
     del images
     gc.collect()
@@ -157,11 +166,25 @@ def caiman_cnmf(
         backend="local", n_processes=None, single_thread=True
     )
 
-    cnm = cnmf.CNMF(n_processes=n_processes, dview=dview, Ain=Ain, params=ops)
-    cnm = cnm.fit(mmap_images)
+    if use_online:
+        ops.change_params(
+            {
+                "fnames": [mmap_path],
+                # NOTE: These params uses np.inf as default in CaImAn.
+                # Yaml cannot serialize np.inf, so default value in yaml is None.
+                "max_comp_update_shape": reshaped_params["max_comp_update_shape"]
+                or np.inf,
+                "num_times_comp_updated": reshaped_params["update_num_comps"] or np.inf,
+            }
+        )
+        cnm = online_cnmf.OnACID(dview=dview, Ain=Ain, params=ops)
+        cnm.fit_online()
+    else:
+        cnm = cnmf.CNMF(n_processes=n_processes, dview=dview, Ain=Ain, params=ops)
+        cnm = cnm.fit(mmap_images)
 
-    if do_refit:
-        cnm = cnm.refit(mmap_images, dview=dview)
+        if do_refit:
+            cnm = cnm.refit(mmap_images, dview=dview)
 
     cnm.estimates.evaluate_components(mmap_images, cnm.params, dview=dview)