diff --git a/README.md b/README.md
index 68388206..36cf64c2 100644
--- a/README.md
+++ b/README.md
@@ -7,26 +7,19 @@
 
 
 > [!CAUTION]  
-> **NOT READY FOR USE: In active development in version alpha. Beta release scheduled by the end of 2025.**
+> **NOT READY FOR USE: In active development in version alpha. Beta release scheduled soon.**
 
-Cala is a neural endoscope image processing tool designed for neuroscience research, with a focus on long-term massive
+cala is a neural endoscope image processing tool designed for neuroscience research, with a focus on long-term massive
 recordings. It features a no-code approach through configuration files, making it accessible to researchers of all
 programming backgrounds.
 
+Docs: Available on [Read the Docs](https://cala.readthedocs.io/en/latest/)
+
 ## Requirements
 
 - Python 3.11, 3.12, or 3.13
 - Dependencies are handled through [pdm](https://pdm-project.org/en/latest/)
 
-## Installation
-
-## Architecture
-
-Schematics of the architecture can be
-found [here](https://lucid.app/documents/embedded/808097f9-bf66-4ea8-9df0-e957e6bd0931).
-
-1. **API Reference**: Available on [Read the Docs](https://cala.readthedocs.io/en/latest/)
-
 ## Contact
 
 For questions or support, please reach out to Raymond Chang
diff --git a/pdm.lock b/pdm.lock
index 67c41550..e5508f11 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -5,7 +5,7 @@
 groups = ["default", "dev", "docs", "gui", "tests"]
 strategy = []
 lock_version = "4.5.0"
-content_hash = "sha256:b791ad4b9d500b62299dfc14d6b10c6b75e16de030f5c33a1234c6b6c7372f5d"
+content_hash = "sha256:e1aa8ded938ca21abc9f6c6a291eb0092dc5cb5f758eab9b76280d5709fdff1d"
 
 [[metadata.targets]]
 requires_python = ">=3.11"
@@ -16,8 +16,8 @@ version = "1.0.0"
 requires_python = ">=3.10"
 summary = "A light, configurable Sphinx theme"
 files = [
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 [[package]]
@@ -29,8 +29,8 @@ dependencies = [
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 ]
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 [[package]]
@@ -45,8 +45,8 @@ dependencies = [
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 ]
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 [[package]]
@@ -55,8 +55,8 @@ version = "0.1.4"
 requires_python = ">=3.6"
 summary = "Disable App Nap on macOS >= 10.9"
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 [[package]]
@@ -65,8 +65,8 @@ version = "3.0.0"
 requires_python = ">=3.8"
 summary = "Annotate AST trees with source code positions"
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 [[package]]
@@ -75,8 +75,8 @@ version = "25.3.0"
 requires_python = ">=3.8"
 summary = "Classes Without Boilerplate"
 files = [
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 [[package]]
@@ -91,7 +91,7 @@ dependencies = [
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@@ -139,8 +139,8 @@ dependencies = [
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 files = [
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 [[package]]
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 requires_python = ">=3.7"
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 [[package]]
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 [[package]]
@@ -716,8 +716,8 @@ dependencies = [
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 [[package]]
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 [[package]]
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 [[package]]
@@ -755,8 +755,8 @@ dependencies = [
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 [[package]]
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 [[package]]
@@ -785,8 +785,8 @@ dependencies = [
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 [[package]]
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 [[package]]
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 [[package]]
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 [[package]]
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 [[package]]
@@ -1039,8 +1039,8 @@ version = "2.6.12"
 requires_python = ">=3.9"
 summary = "File identification library for Python"
 files = [
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 [[package]]
@@ -1049,8 +1049,8 @@ version = "3.10"
 requires_python = ">=3.6"
 summary = "Internationalized Domain Names in Applications (IDNA)"
 files = [
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 [[package]]
@@ -1063,8 +1063,8 @@ dependencies = [
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 [[package]]
@@ -1073,8 +1073,8 @@ version = "1.4.1"
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 [[package]]
@@ -1087,8 +1087,8 @@ dependencies = [
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 [[package]]
@@ -1097,8 +1097,8 @@ version = "2.1.0"
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 [[package]]
@@ -1122,8 +1122,8 @@ dependencies = [
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 [[package]]
@@ -1145,8 +1145,8 @@ dependencies = [
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 files = [
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 [[package]]
@@ -1158,8 +1158,8 @@ dependencies = [
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 [[package]]
@@ -1171,8 +1171,8 @@ dependencies = [
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 files = [
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 [[package]]
@@ -1184,8 +1184,8 @@ dependencies = [
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 ]
 files = [
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 [[package]]
@@ -1197,8 +1197,8 @@ dependencies = [
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 files = [
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 [[package]]
@@ -1210,8 +1210,8 @@ dependencies = [
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 files = [
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 [[package]]
@@ -1220,8 +1220,8 @@ version = "0.9.0"
 requires_python = ">=3.7"
 summary = "Low-level, pure Python DBus protocol wrapper."
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 [[package]]
@@ -1233,8 +1233,8 @@ dependencies = [
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 files = [
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 [[package]]
@@ -1243,8 +1243,8 @@ version = "1.5.1"
 requires_python = ">=3.9"
 summary = "Lightweight pipelining with Python functions"
 files = [
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 [[package]]
@@ -1261,8 +1261,8 @@ dependencies = [
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 files = [
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 [[package]]
@@ -1274,8 +1274,8 @@ dependencies = [
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 files = [
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 [[package]]
@@ -1294,8 +1294,8 @@ dependencies = [
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 files = [
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 [[package]]
@@ -1312,8 +1312,8 @@ dependencies = [
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 [[package]]
@@ -1327,8 +1327,8 @@ dependencies = [
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 [[package]]
@@ -1347,8 +1347,8 @@ dependencies = [
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 [[package]]
@@ -1357,65 +1357,65 @@ version = "1.4.8"
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 [[package]]
@@ -1721,8 +1721,8 @@ version = "8.4.0"
 requires_python = ">=3.7"
 summary = "Simple yet flexible natural sorting in Python."
 files = [
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 [[package]]
@@ -1737,8 +1737,8 @@ dependencies = [
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 [[package]]
@@ -1753,8 +1753,8 @@ dependencies = [
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 [[package]]
@@ -1763,8 +1763,8 @@ version = "1.6.0"
 requires_python = ">=3.5"
 summary = "Patch asyncio to allow nested event loops"
 files = [
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 [[package]]
@@ -1773,8 +1773,8 @@ version = "3.4.2"
 requires_python = ">=3.10"
 summary = "Python package for creating and manipulating graphs and networks"
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 [[package]]
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 summary = "Python binding to Ammonia HTML sanitizer Rust crate"
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 [[package]]
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 [[package]]
 name = "noob"
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 requires_python = ">=3.11"
-git = "https://github.com/miniscope/noob.git"
-revision = "30a8286a561527456956951ead1c29245511ca90"
-summary = "Default template for PDM package"
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 dependencies = [
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 [[package]]
@@ -3104,8 +3145,8 @@ version = "3.0.1"
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 summary = "This package provides 32 stemmers for 30 languages generated from Snowball algorithms."
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@@ -3114,8 +3155,8 @@ version = "2.7"
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 [[package]]
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 [[package]]
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 [[package]]
@@ -3201,8 +3242,8 @@ dependencies = [
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 [[package]]
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 files = [
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 [[package]]
@@ -3221,8 +3262,8 @@ version = "2.0.0"
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 files = [
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 [[package]]
@@ -3231,8 +3272,8 @@ version = "2.1.0"
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 files = [
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 [[package]]
@@ -3241,8 +3282,8 @@ version = "1.0.1"
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 [[package]]
@@ -3255,8 +3296,8 @@ dependencies = [
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 [[package]]
@@ -3265,8 +3306,8 @@ version = "2.0.0"
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 [[package]]
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 [[package]]
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 [[package]]
@@ -3375,8 +3416,8 @@ dependencies = [
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 [[package]]
@@ -3428,8 +3469,8 @@ dependencies = [
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 [[package]]
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 [[package]]
@@ -3451,8 +3492,8 @@ dependencies = [
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 [[package]]
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 [[package]]
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 [[package]]
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+    { file = "yappi-1.6.10-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3736ea6458edbabd96918d88e2963594823e4ab4c58d62a52ef81f6b5839ec19" },
+    { file = "yappi-1.6.10-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f27bbc3311a3662231cff395d38061683fac5c538f3bab6796ff05511d2cce43" },
+    { file = "yappi-1.6.10-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:354cf94d659302b421b13c03487f2f1bce969b97b85fba88afb11f2ef83c35f3" },
+    { file = "yappi-1.6.10-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1d82839835ae2c291b88fb56d82f80c88c00d76df29f3c1ed050db73b553bef0" },
+    { file = "yappi-1.6.10-cp313-cp313-win32.whl", hash = "sha256:fc84074575afcc5a2a712e132c0b51541b7434b3099be99f573964ef3b6064a8" },
+    { file = "yappi-1.6.10-cp313-cp313-win_amd64.whl", hash = "sha256:334b31dfefae02bc28b7cd50953aaaae3292e40c15efb613792e4a587281a161" },
+    { file = "yappi-1.6.10.tar.gz", hash = "sha256:463b822727658937bd95a7d80ca9758605b8cd0014e004e9e520ec9cb4db0c92" },
 ]
 
 [[package]]
@@ -3826,8 +3867,8 @@ dependencies = [
     "typing-extensions>=4.9",
 ]
 files = [
-    {file = "zarr-3.0.8-py3-none-any.whl", hash = "sha256:7f81e7aec086437d98882aa432209107114bd7f3a9f4958b2af9c6b5928a70a7"},
-    {file = "zarr-3.0.8.tar.gz", hash = "sha256:88505d095af899a88ae8ac4db02f4650ef0801d2ff6f65b6d1f0a45dcf760a6d"},
+    { file = "zarr-3.0.8-py3-none-any.whl", hash = "sha256:7f81e7aec086437d98882aa432209107114bd7f3a9f4958b2af9c6b5928a70a7" },
+    { file = "zarr-3.0.8.tar.gz", hash = "sha256:88505d095af899a88ae8ac4db02f4650ef0801d2ff6f65b6d1f0a45dcf760a6d" },
 ]
 
 [[package]]
@@ -3836,6 +3877,6 @@ version = "3.22.0"
 requires_python = ">=3.9"
 summary = "Backport of pathlib-compatible object wrapper for zip files"
 files = [
-    {file = "zipp-3.22.0-py3-none-any.whl", hash = "sha256:fe208f65f2aca48b81f9e6fd8cf7b8b32c26375266b009b413d45306b6148343"},
-    {file = "zipp-3.22.0.tar.gz", hash = "sha256:dd2f28c3ce4bc67507bfd3781d21b7bb2be31103b51a4553ad7d90b84e57ace5"},
+    { file = "zipp-3.22.0-py3-none-any.whl", hash = "sha256:fe208f65f2aca48b81f9e6fd8cf7b8b32c26375266b009b413d45306b6148343" },
+    { file = "zipp-3.22.0.tar.gz", hash = "sha256:dd2f28c3ce4bc67507bfd3781d21b7bb2be31103b51a4553ad7d90b84e57ace5" },
 ]
diff --git a/pyproject.toml b/pyproject.toml
index 16db0a33..a419deca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,8 +34,9 @@ dependencies = [
     "pyyaml>=6.0.2",
     "typer>=0.15.3",
     "xarray-validate>=0.0.2",
-    "noob @ git+https://github.com/miniscope/noob.git",
+    "noob>=1000.0.1",
     "natsort>=8.4.0",
+    "pympler>=1.1",
 ]
 keywords = [
     "pipeline",
diff --git a/scripts/benchmarking.py b/scripts/benchmarking.py
index b3a3e9f3..5f7f96d6 100644
--- a/scripts/benchmarking.py
+++ b/scripts/benchmarking.py
@@ -1,4 +1,5 @@
 from datetime import datetime
+from pathlib import Path
 
 import cv2
 import matplotlib
@@ -8,7 +9,15 @@
 from noob import SynchronousRunner, Tube
 
 from cala.nodes.io import stream
-from cala.nodes.prep import Anchor, blur, butter, package_frame, remove_mean
+from cala.nodes.prep import (
+    Anchor,
+    blur,
+    butter,
+    package_frame,
+    remove_mean,
+    GlowRemover,
+)
+from cala.nodes.prep.motion import _prepare
 from cala.testing.util import total_gradient_magnitude
 
 sns.set_style("whitegrid")
@@ -17,23 +26,7 @@
 matplotlib.rc("font", **font)
 
 
-VIDEOS = [
-    "minian/msCam1.avi",
-    "minian/msCam2.avi",
-    "minian/msCam3.avi",
-    "minian/msCam4.avi",
-    "minian/msCam5.avi",
-    "minian/msCam6.avi",
-    "minian/msCam7.avi",
-    "minian/msCam8.avi",
-    "minian/msCam9.avi",
-    "minian/msCam10.avi",
-    # "long_recording/0.avi",
-    # "long_recording/1.avi",
-    # "long_recording/2.avi",
-    # "long_recording/3.avi",
-    # "long_recording/4.avi",
-]
+VIDEOS = [f"long_recording/{i}.avi" for i in range(20)]  # minian/msCam{i}
 
 
 def preprocess(arr, idx):
@@ -43,11 +36,11 @@ def preprocess(arr, idx):
     return remove_mean(frame, orient="both")
 
 
-def test_encode():
+def test_write_raw_movie():
     gen = stream(VIDEOS)
 
-    fourcc = cv2.VideoWriter_fourcc(*"FFV1")
-    out = cv2.VideoWriter("encode_test.avi", fourcc, 60.0, (600, 600))
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter("120fps.avi", fourcc, 120.0, (600, 600))
 
     for arr in gen:
         frame_bgr = cv2.cvtColor(arr.astype(np.uint8), cv2.COLOR_GRAY2BGR)
@@ -56,15 +49,15 @@ def test_encode():
     out.release()
 
 
-def test_motion_movie():
+def test_write_stable_movie():
     """
-    For testing how well the motion correction performs with real movie
-
+    For testing how well motion correction performs with real movie
+    See for yourself
     """
     gen = stream(VIDEOS)
 
     fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-    out = cv2.VideoWriter("motion_test.avi", fourcc, 60.0, (600, 1200))
+    out = cv2.VideoWriter("motion_test.avi", fourcc, 60.0, (752, 960))
 
     stab = Anchor()
 
@@ -79,11 +72,80 @@ def test_motion_movie():
     out.release()
 
 
+def test_write_signal_movie():
+    """
+    For testing how well the entire preprocessing line performs with real movie
+    See for yourself
+    """
+    gen = stream(VIDEOS)
+
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter("signal_test.avi", fourcc, 60.0, (752, 960))
+
+    stab = Anchor()
+    bgrm = GlowRemover()
+
+    for idx, arr in enumerate(gen):
+        frame = preprocess(arr, idx)
+        matched = stab.stabilize(frame)
+        signal = bgrm.process(matched)
+        combined = np.concat([matched.array.values, signal.array.values], axis=0)
+
+        frame_bgr = cv2.cvtColor(combined.astype(np.uint8), cv2.COLOR_GRAY2BGR)
+        out.write(frame_bgr)
+
+    out.release()
+
+
+def test_write_all_movies():
+    gen = stream(VIDEOS)
+
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter("4x_long2.mp4", fourcc, 30.0, (2000, 350))
+    stab = Anchor()
+    bgrm = GlowRemover()
+    max_signal = 0
+    for idx, arr in enumerate(gen):
+        if idx % 4 == 0:
+            frame = package_frame(arr[200:550, 50:-50], idx)
+            blurred = blur(frame, method="median", kwargs={"ksize": 3})
+            flat = butter(blurred, {})
+            delined = remove_mean(flat, orient="both")
+            matched = stab.stabilize(delined)
+            denoised = blur(matched, method="gaussian", kwargs={"ksize": (9, 9), "sigmaX": 0})
+            signal = bgrm.process(denoised)
+            # top = np.concat([frame.array.values, flat.array.values], axis=1)
+            # bottom = np.concat([matched.array.values, signal.array.values], axis=1)
+            # combined = np.concatenate((top, bottom), axis=0)
+            combined = np.concat(
+                [
+                    frame.array.values,
+                    flat.array.values,
+                    matched.array.values,
+                    signal.array.values * 3,
+                ],
+                axis=1,
+            )
+            max_signal = max(np.max(signal.array.values), max_signal)
+            frame_bgr = cv2.cvtColor(combined.astype(np.uint8), cv2.COLOR_GRAY2BGR)
+            out.write(frame_bgr)
+
+    out.release()
+    print(f"{max_signal = }")
+
+
 def test_motion_crisp_pics():
     """
-    For generating a mean summary frame across raw vs. motion-corrected video.
-    The motion-corrected video should have a much crisper summary picture.
+    quantify how crisp is a summary image before and after registration.
+    This can be done by computing the norm of the gradient field of the image at all pixels.
+    Intuitively, a dataset with non-registered motion will have a blurred mean image,
+    resulting in a lower value for the total gradient field norm.
+
+    Currently not working properly because of the remaining optical artifacts. (vertical bands)
+    These artifacts move with camera, which becomes clearer if the image is NOT motion-corrected.
 
+    References:
+        [1] NormCorre, https://www.sciencedirect.com/science/article/pii/S0165027017302753
     """
 
     gen = stream(VIDEOS)
@@ -115,8 +177,23 @@ def test_motion_crisp_pics():
 
 def test_motion_mean_corr():
     """
-    For testing how well the motion correction performs with real movie
+    To evaluate the results of the motion correction algorithm across the different frames,
+    we use a metric that is based on the similarity (pixel-wise, Pearson's correlation coefficient r)
+    between the mean image across time and each individual frame.
+    Intuitively, an increase in the correlation coefficient for a given frame
+    indicates a better alignment with the mean.
+
+    Similar metrics have been used before for assessing the quality of registration algorithms
+    in the context of fluorescence microscopy (Lee et al., 2014).
+    To account for border effects during registration,
+    a number of pixels around each boundary (e.g., equal to the maximum shift in each direction over time)
+    is removed prior to computing the correlation coefficients.
+
+    References:
+        [1] NormCorre, https://www.sciencedirect.com/science/article/pii/S0165027017302753
     """
+    figure_dir = Path("figures/motion")
+
     gen = stream(VIDEOS)
 
     stab = Anchor()
@@ -126,6 +203,15 @@ def test_motion_mean_corr():
     for idx, arr in enumerate(gen):
         frame = preprocess(arr, idx)
         matched = stab.stabilize(frame)
+        if idx == 500:
+            curr = _prepare(frame.array, stab.dog_kwargs, stab.gauss_kwargs).values
+            cv2.imwrite(
+                figure_dir / "anchors.png",
+                np.concatenate([curr, stab._local.values, stab._global.values], axis=1),
+            )
+            cv2.imwrite(figure_dir / "curr.png", curr)
+            cv2.imwrite(figure_dir / "local_anchor.png", stab._local.values)
+            cv2.imwrite(figure_dir / "global_anchor.png", stab._global.values)
         raws.append(frame.array.values)
         stabs.append(matched.array.values)
 
@@ -138,20 +224,24 @@ def test_motion_mean_corr():
     raw_cms = [np.corrcoef(r[20:-20, 20:-20].flatten(), raw_mean.flatten())[0, 1] for r in raws]
     stab_cms = [np.corrcoef(s[20:-20, 20:-20].flatten(), stab_mean.flatten())[0, 1] for s in stabs]
 
-    fig, ax = plt.subplots(figsize=(24, 10))
+    fig, ax = plt.subplots(figsize=(12, 5))
     plt.plot(raw_cms)
     plt.plot(stab_cms)
     plt.legend(["raw", "stabilized"], loc="upper right")
-    plt.title("Mean Correlation")
-    plt.xlabel("frame")
-    plt.ylabel("correlation")
+    plt.xlabel("Frame Index")
+    plt.ylabel("Correlation")
     plt.tight_layout()
-    plt.savefig("mc.png")
+
+    figure_dir.mkdir(parents=True, exist_ok=True)
+    plt.savefig(figure_dir / "mean_corr.png")
 
     assert False
 
 
-def test_with_movie():
+def test_whole_pipeline():
+    """
+    Run the entire pipeline with Minian data
+    """
     tube = Tube.from_specification("with-minian")
     runner = SynchronousRunner(tube=tube)
     processed_vid = runner.run()
@@ -164,32 +254,42 @@ def test_with_movie():
     out.release()
 
 
-def test_processing_speed():
-    tube = Tube.from_specification("with-minian")
-    runner = SynchronousRunner(tube=tube)
-    gen = runner.iter()
+def test_speed_per_frame():
+    """
+    Test how long it takes for the entire cala to process each frame.
+    Export a plot showing time taken.
+    """
+    gen = stream(VIDEOS)
+    tube = Tube.from_specification("cala-unraveled", {"cell_size": 10})
+    runner = SynchronousRunner(tube)
     frame_speed = []
-    i = 0
-    while True:
+
+    for idx, arr in enumerate(gen):
         try:
             start = datetime.now()
-            next(gen)
+            runner.process(frame=arr, epoch=idx)
             duration = datetime.now() - start
             frame_speed.append(round(duration.total_seconds(), 2))
-            i += 1
-        except RuntimeError:
+            if idx % 100 == 0:
+                print(f"{idx} frames processed")
+            if idx == 100:
+                break
+        except RuntimeError as e:
+            print(e)
             break
     fig, ax = plt.subplots(figsize=(20, 4))
-    ax.set_yscale("log")
-    plt.plot(frame_speed)
-    plt.xlabel("frame", fontsize=20)
-    plt.ylabel("time taken (s)", fontsize=20)
+    sns.histplot(np.array(frame_speed) / 2)
+    plt.ylabel("percent", fontsize=20)
+    plt.xlabel("time taken (s)", fontsize=20)
     plt.tight_layout()
     plt.savefig("frame_speed.png")
 
 
-def test_deglow():
-
+def test_deglow_write_image():
+    """
+    Write a deglowed frame by accumulating the frames that are preprocessed until motion correction,
+    grab the last frame and subtract the global minimum pixel value through time.
+    """
     gen = stream(VIDEOS[:3])
 
     stab = Anchor()
@@ -202,3 +302,64 @@ def test_deglow():
 
     deglowed = stabs[-1] - np.min(stabs, axis=0)
     plt.imsave("deglowed.png", deglowed, cmap="gray")
+
+
+def test_preprocessing_write_image():
+    """
+    A temporary function for hotswapping preprocessing steps
+    and plot results / diffs fast.
+    """
+    images = {}
+    gen = stream(VIDEOS)
+
+    stab = Anchor()
+    eraser = GlowRemover()
+    raws = []
+    stabs = []
+
+    for idx, arr in enumerate(gen):
+        frame = preprocess(arr, idx)
+        matched = stab.stabilize(frame)
+        signal = eraser.process(matched)
+        if idx == 500:
+            break
+    arr = next(gen)
+    frame = preprocess(arr, idx)
+    matched = stab.stabilize(frame)
+    signal = eraser.process(matched)
+
+    frame = package_frame(arr, 0)
+    hot_pixel = blur(frame, method="median", kwargs={"ksize": 3})
+    buttered = butter(hot_pixel, {})
+    delined = remove_mean(buttered, "both")
+    images["pre"] = buttered.array.values
+    images["post"] = delined.array.values
+    images["diff"] = buttered.array.values - delined.array.values
+    # crop = slice(None, int(arr.shape[0] / 3)), slice(int(arr.shape[1] * 3 / 4), None)
+    # for k, v in images.items():
+    #     images[k] = circle_points(
+    #         np.clip(v, 0, None).astype(np.uint8),
+    #         zip(*np.where(diff > 50)[::-1]),
+    #         radius=20,
+    #         color=(0, 0, 255),
+    #         thickness=2,
+    #     )[crop]
+    images["diff"] = images["diff"] / images["diff"].max() * 255
+    all = np.concatenate([image for image in images.values()], axis=1)
+    figure_dir = Path("figures/deline")
+    figure_dir.mkdir(parents=True, exist_ok=True)
+    cv2.imwrite(figure_dir / "all.png", all)
+    for k in images:
+        cv2.imwrite(figure_dir / f"{k}.png", images[k])
+
+
+def test_recursive():
+    gen = stream(VIDEOS[:3])
+    tube = Tube.from_specification("cala-demo", {"cell_size": 8})
+    runner = SynchronousRunner(tube)
+    for idx, arr in enumerate(gen):
+        res = runner.process(frame=arr, index=idx)
+
+
+if __name__ == "__main__":
+    test_write_all_movies()
diff --git a/scripts/memory_usage.py b/scripts/memory_usage.py
index f2048219..3e49099b 100644
--- a/scripts/memory_usage.py
+++ b/scripts/memory_usage.py
@@ -4,31 +4,62 @@
 from matplotlib import pyplot as plt
 from noob import Tube, SynchronousRunner
 
+from cala.nodes.io import stream
+
+VIDEOS = [f"long_recording/{i}.avi" for i in range(30)]
+
 
 def main():
     process = psutil.Process(os.getpid())
-    tube = Tube.from_specification("test-memory")
-    runner = SynchronousRunner(tube=tube)
-    gen = runner.iter()
+    gen = stream(VIDEOS)
+    tube = Tube.from_specification("cala-unraveled", {"cell_size": 10})
+    runner = SynchronousRunner(tube)
     ram_use_frame = []
-    i = 0
-    while True:
+    for idx, arr in enumerate(gen):
         try:
-            next(gen)
+            runner.process(frame=arr, epoch=idx)
             ram_used = process.memory_info().rss / (1024 * 1024)  # in MB
             ram_use_frame.append(round(ram_used, 2))
-            i += 1
-            if i % 100 == 0:
-                print(f"{i} frames processed")
+            if idx % 100 == 0:
+                print(f"{idx} frames processed")
         except RuntimeError as e:
             print(e)
             break
-    fig, ax = plt.subplots(figsize=(40, 8))
+    fig, ax = plt.subplots(figsize=(12, 5))
     plt.plot(ram_use_frame)
     plt.xlabel("frame")
     plt.ylabel("memory used (MB)")
     plt.tight_layout()
-    plt.savefig("ram_use.svg", format="svg")
+    plt.savefig("ram_use.png", format="png")
+    print(f"{idx} frames complete!")
+
+
+# def main():
+#     from pympler.classtracker import ClassTracker
+#
+#     tracker = ClassTracker()
+#     tracker.track_class(Asset)
+#     gen = stream(VIDEOS)
+#     tube = Tube.from_specification("cala-unraveled", {"cell_size": 10})
+#     runner = SynchronousRunner(tube)
+#     for idx, arr in enumerate(gen):
+#         try:
+#             tracker.create_snapshot()
+#             runner.process(frame=arr, epoch=idx)
+#             tracker.create_snapshot()
+#             tracker.stats.print_summary()
+#             next(gen)
+#             if idx % 100 == 0:
+#                 print(f"{idx} frames processed")
+#         except RuntimeError as e:
+#             print(e)
+#             break
+#     fig, ax = plt.subplots(figsize=(12, 5))
+#     plt.xlabel("frame")
+#     plt.ylabel("memory used (MB)")
+#     plt.tight_layout()
+#     plt.savefig("ram_use.png", format="png")
+#     print(f"{idx} frames complete!")
 
 
 if __name__ == "__main__":
diff --git a/scripts/yappi_profile.py b/scripts/yappi_profile.py
index f7b44da3..0d86ab9d 100644
--- a/scripts/yappi_profile.py
+++ b/scripts/yappi_profile.py
@@ -5,7 +5,7 @@
 try:
     yappi.set_clock_type("WALL")
     yappi.start()
-    main(gui=True, spec="cala-odl")
+    main(gui=True, spec="cala-unraveled")
     yappi.stop()
 finally:
     stat = yappi.get_func_stats()
diff --git a/src/cala/arrays/__init__.py b/src/cala/arrays/__init__.py
index 3fb2a561..78a4eb9c 100644
--- a/src/cala/arrays/__init__.py
+++ b/src/cala/arrays/__init__.py
@@ -1,25 +1,8 @@
 from .axis import AXIS  # noqa: I001
-from .assets import (
-    Buffer,
-    CompStats,
-    Footprint,
-    Footprints,
-    Frame,
-    Movie,
-    Overlaps,
-    PixStats,
-    PopSnap,
-    Trace,
-    Traces,
-)
+from .containers import Buffer, Footprint, Frame, Movie, PopSnap, Trace
 
 __all__ = [
     "AXIS",
-    "Traces",
-    "Footprints",
-    "PixStats",
-    "CompStats",
-    "Overlaps",
     "Buffer",
     "Trace",
     "Footprint",
diff --git a/src/cala/arrays/assets.py b/src/cala/arrays/containers.py
similarity index 50%
rename from src/cala/arrays/assets.py
rename to src/cala/arrays/containers.py
index f8ed9101..c3df05fe 100644
--- a/src/cala/arrays/assets.py
+++ b/src/cala/arrays/containers.py
@@ -1,6 +1,5 @@
 import contextlib
 import shutil
-from copy import deepcopy
 from pathlib import Path
 from typing import ClassVar, Self, TypeVar
 
@@ -17,7 +16,7 @@
 AssetType = TypeVar("AssetType", xr.DataArray, Path, None)
 
 
-class Asset(BaseModel):
+class ArrayContainer(BaseModel):
     array_: AssetType = None
     sparsify: ClassVar[bool] = False
     zarr_path: Path | None = None
@@ -54,8 +53,11 @@ def reset(self) -> None:
             except FileNotFoundError:
                 contextlib.suppress(FileNotFoundError)
 
-    def __eq__(self, other: "Asset") -> bool:
-        return self.array.equals(other.array)
+    def __eq__(self, other: "ArrayContainer") -> bool:
+        if isinstance(other, ArrayContainer):
+            return self.array.equals(other.array)
+        else:
+            return False
 
     @classmethod
     def entity(cls) -> Schema:
@@ -95,25 +97,7 @@ def load_zarr(self, isel_filter: dict = None, sel_filter: dict = None) -> xr.Dat
         )
 
 
-class Footprint(Asset):
-    xr_schema: ClassVar[Schema] = Schema(
-        name="footprint",
-        dims=(Dims.width.value, Dims.height.value),
-        dtype=float,
-        checks=[is_non_negative, has_no_nan],
-    )
-
-
-class Trace(Asset):
-    xr_schema: ClassVar[Schema] = Schema(
-        name="trace",
-        dims=(Dims.frame.value,),
-        dtype=float,
-        checks=[is_non_negative],
-    )
-
-
-class Frame(Asset):
+class Frame(ArrayContainer):
     xr_schema: ClassVar[Schema] = Schema(
         name="frame",
         dims=(Dims.width.value, Dims.height.value),
@@ -122,181 +106,7 @@ class Frame(Asset):
     )
 
 
-class Footprints(Asset):
-    xr_schema: ClassVar[Schema] = Bundle(
-        name="footprint-group",
-        member=Footprint.entity(),
-        group_by=Dims.component,
-        checks=[is_non_negative, has_no_nan],
-        allow_extra_coords=False,
-    )
-
-    sparsify = True
-
-
-class Traces(Asset):
-    peek_size: int = None
-    """How many epochs to return when called."""
-    flush_interval: int | None = None
-    """How many epochs to wait until next flush"""
-
-    _deprecated: list[str] = PrivateAttr(default_factory=list)
-    """
-    Deprecated, or replaced component idx.
-    Since zarr does not support efficiently removing rows and columns,
-    there's no easy way to remove a column when a component has been 
-    removed or replaced. Instead, we "mask" it with this "deprecated"
-    flag.
-
-    When arrays are called, these are filtered out. When new epochs are
-    added, these are added in with nan values. 
-    """
-
-    xr_schema: ClassVar[Schema] = Bundle(
-        name="trace-group",
-        member=Trace.entity(),
-        group_by=Dims.component,
-        checks=[is_non_negative],
-        allow_extra_coords=False,
-    )
-
-    @model_validator(mode="after")
-    def flush_conditions(self) -> Self:
-        assert (self.flush_interval and self.zarr_path) or (
-            not self.flush_interval and not self.zarr_path
-        ), "zarr_path and flush_interval should either be both provided or neither."
-        if self.flush_interval:
-            assert self.flush_interval > self.peek_size, (
-                f"flush_interval must be larger than peek_size. "
-                f"Provided: {self.flush_interval = }, {self.peek_size = }"
-            )
-        return self
-
-    @property
-    def sizes(self) -> dict[str, int]:
-        if self.zarr_path:
-            total_size = {}
-            for key, val in self.array_.sizes.items():
-                if key == AXIS.frame_dim:
-                    total_size[key] = val + self.load_zarr().sizes[key]
-                else:
-                    total_size[key] = val
-            return total_size
-        else:
-            return self.array_.sizes
-
-    @property
-    def array(self) -> xr.DataArray:
-        return (
-            self.array_.isel({AXIS.frame_dim: slice(-self.peek_size, None)})
-            if self.array_ is not None
-            else self.array_
-        )
-
-    @array.setter
-    def array(self, array: xr.DataArray) -> None:
-        """
-        In case zarr_path is defined, if array is larger than peek_size,
-        the epochs older than -peek_size gets flushed to zarr array.
-
-        """
-        if self.validate_schema:
-            array.validate.against_schema(self._entity.model)
-        if self.zarr_path:
-            self.array_ = array.isel({AXIS.frame_dim: slice(-self.peek_size, None)})
-            array.isel({AXIS.frame_dim: slice(None, -self.peek_size)}).to_zarr(
-                self.zarr_path, mode="w"
-            )
-        else:
-            self.array_ = array
-
-    def append(self, array: xr.DataArray, dim: str) -> None:
-        """
-        Since we cannot simply append to zarr array in memory using xarray syntax,
-        we provide a convenience method for appending to zarr array and in-memory array
-        in a streamlined manner.
-
-        Incoming arrays have to be 2-dimensional.
-
-        """
-
-        if dim == AXIS.frame_dim:
-            self.array_ = xr.concat([self.array_, array], dim=AXIS.frame_dim)
-
-            if self.zarr_path and self.array_.sizes[AXIS.frame_dim] > self.flush_interval:
-                self._flush_zarr()
-
-        elif dim == AXIS.component_dim:
-            if self.zarr_path:
-                n_in_memory = self.array_.sizes[AXIS.frame_dim]
-                self.array_ = xr.concat(
-                    [self.array_, array.isel({AXIS.frame_dim: slice(-n_in_memory, None)})],
-                    dim=dim,
-                )
-                array.isel({AXIS.frame_dim: slice(None, -n_in_memory)}).to_zarr(
-                    self.zarr_path, append_dim=dim
-                )
-            else:
-                self.array_ = xr.concat([self.array_, array], dim=dim, combine_attrs="drop")
-
-    def _flush_zarr(self) -> None:
-        """
-        Flushes traces older than peek_size to zarr array.
-        Needs to append nans to deprecated components, since they get deleted
-        in in-memory array, but persist in zarr array.
-
-
-        Could do this much more elegantly by pre-allocating .array_
-        """
-        raw_zarr = self.load_zarr()
-        to_flush = self.array_.isel({AXIS.frame_dim: slice(None, -self.peek_size)})
-        if self._deprecated:
-            zarr_ids = raw_zarr[AXIS.id_coord].values
-            zarr_detects = raw_zarr[AXIS.detect_coord].values
-            intact_mask = ~np.isin(zarr_ids, self._deprecated)
-            n_flush = to_flush.sizes[AXIS.frame_dim]
-            prealloc = xr.DataArray(
-                np.full((raw_zarr.sizes[AXIS.component_dim], n_flush), np.nan),
-                dims=[AXIS.component_dim, AXIS.frame_dim],
-                coords={
-                    AXIS.id_coord: (AXIS.component_dim, zarr_ids),
-                    AXIS.detect_coord: (AXIS.component_dim, zarr_detects),
-                },
-            ).assign_coords(to_flush[AXIS.frame_dim].coords)
-            prealloc.loc[intact_mask] = to_flush
-            prealloc.to_zarr(self.zarr_path, append_dim=AXIS.frame_dim)
-        else:
-            to_flush.to_zarr(self.zarr_path, append_dim=AXIS.frame_dim)
-        self.array_ = self.array_.isel({AXIS.frame_dim: slice(-self.peek_size, None)})
-
-    def keep(self, intact_mask: np.ndarray) -> None:
-        if self.zarr_path:
-            self._deprecated.extend(self.array_[AXIS.id_coord].values[~intact_mask])
-        self.array_ = self.array_[intact_mask]
-
-    @classmethod
-    def from_array(cls, array: xr.DataArray) -> "Traces":
-        """
-        This is only really used for typing / auto-validation purposes,
-        so we don't really have to worry about specifying the parameters.
-
-        """
-        new_cls = cls(peek_size=array.sizes[AXIS.frame_dim])
-        new_cls.array = array
-        return new_cls
-
-    def full_array(self, isel_filter: dict = None, sel_filter: dict = None) -> xr.DataArray:
-        if self.zarr_path:
-            raw_zarr = self.load_zarr(isel_filter, sel_filter)
-            zarr_ids = raw_zarr[AXIS.id_coord].values
-            intact_mask = ~np.isin(zarr_ids, self._deprecated)
-
-            return xr.concat([raw_zarr[intact_mask], self.array_], dim=AXIS.frame_dim).compute()
-        else:
-            return self.array_.isel(isel_filter).sel(sel_filter)
-
-
-class Movie(Asset):
+class Movie(ArrayContainer):
     xr_schema: ClassVar[Schema] = Bundle(
         name="movie",
         member=Frame.entity(),
@@ -306,59 +116,7 @@ class Movie(Asset):
     )
 
 
-class PopSnap(Asset):
-    """
-    A snapshot of a population trait.
-
-    Mainly used for Traces that only has one frame.
-    """
-
-    xr_schema: ClassVar[Schema] = Schema(
-        name="pop-snap",
-        dims=(Dims.component.value,),
-        dtype=float,
-        coords=[Coords.frame.value, Coords.timestamp.value],
-        checks=[is_non_negative, has_no_nan],
-    )
-
-
-comp_dims = (Dims.component.value, deepcopy(Dims.component.value))
-comp_dims[1].name = AXIS.duplicate(comp_dims[1].name)
-for coord in comp_dims[1].coords:
-    coord.name = AXIS.duplicate(coord.name)
-
-
-class CompStats(Asset):
-    xr_schema: ClassVar[Schema] = Schema(
-        name="comp-stat",
-        dims=comp_dims,
-        dtype=float,
-        checks=[is_non_negative, has_no_nan],
-        allow_extra_coords=False,
-    )
-
-
-class PixStats(Asset):
-    xr_schema: ClassVar[Schema] = Schema(
-        name="pix-stat",
-        dims=(Dims.width.value, Dims.height.value, Dims.component.value),
-        dtype=float,
-        checks=[is_non_negative, has_no_nan],
-        allow_extra_coords=False,
-    )
-
-
-class Overlaps(Asset):
-    xr_schema: ClassVar[Schema] = Schema(
-        name="overlap",
-        dims=comp_dims,
-        dtype=bool,
-        checks=[has_no_nan],
-        allow_extra_coords=False,
-    )
-
-
-class Buffer(Asset):
+class Buffer(ArrayContainer):
     """
     Implements a bip buffer to avoid expensive copying that occurs with
     numpy concat, append, and stack.
@@ -440,7 +198,7 @@ def from_array(cls, array: xr.DataArray, size: int) -> Self:
         return buffer
 
 
-class Energy(Asset):
+class Energy(ArrayContainer):
     xr_schema: ClassVar[Schema] = Schema(
         name="energy",
         dims=(Dims.width.value, Dims.height.value),
@@ -462,3 +220,37 @@ def update_std(self, arr: xr.DataArray) -> xr.DataArray:
             self._mean += eta * (arr.values - self._mean)
             self._sq_mean += eta * (np.square(arr.values) - self._sq_mean)
         return xr.DataArray(np.sqrt(self._sq_mean - np.square(self._mean)), dims=arr.dims)
+
+
+class PopSnap(ArrayContainer):
+    """
+    A snapshot of a population trait.
+
+    Mainly used for Traces that only has one frame.
+    """
+
+    xr_schema: ClassVar[Schema] = Schema(
+        name="pop-snap",
+        dims=(Dims.component.value,),
+        dtype=float,
+        coords=[Coords.frame.value, Coords.timestamp.value],
+        checks=[is_non_negative, has_no_nan],
+    )
+
+
+class Footprint(ArrayContainer):
+    xr_schema: ClassVar[Schema] = Schema(
+        name="footprint",
+        dims=(Dims.width.value, Dims.height.value),
+        dtype=float,
+        checks=[is_non_negative, has_no_nan],
+    )
+
+
+class Trace(ArrayContainer):
+    xr_schema: ClassVar[Schema] = Schema(
+        name="trace",
+        dims=(Dims.frame.value,),
+        dtype=float,
+        checks=[is_non_negative],
+    )
diff --git a/src/cala/arrays/models.py b/src/cala/arrays/models.py
new file mode 100644
index 00000000..dca15ebe
--- /dev/null
+++ b/src/cala/arrays/models.py
@@ -0,0 +1,543 @@
+from abc import ABC, abstractmethod
+from copy import deepcopy
+from typing import ClassVar, Self, Mapping, Hashable, Any, TypedDict
+
+import numpy as np
+import xarray as xr
+from pydantic import PrivateAttr, model_validator
+from scipy.sparse import vstack, csc_matrix
+from sparse import COO
+from xarray import Coordinates
+
+from cala.arrays import AXIS
+from cala.arrays.containers import ArrayContainer, Footprint, Trace
+from cala.arrays.validate import Dims, Schema, is_non_negative, has_no_nan, Bundle
+from cala.util import concatenate_coordinates
+
+
+class ModelMixin(ABC):
+    @abstractmethod
+    def commit_extension(self, new, inplace: bool = False) -> None | xr.DataArray:
+        """
+        Commit new components into the model.
+        Currently, the non-inplace option returns updated xr.DataArray,
+        instead of returning the model wrapping the xr.DataArray.
+        This behavior will be fixed in the future
+        when I figure out how to copy over the attributes properly.
+        """
+
+    @abstractmethod
+    def apply_fit(self, new, inplace: bool = False) -> None | xr.DataArray:
+        """
+        Apply the new calculated fit into the model.
+        Currently, the non-inplace option returns updated xr.DataArray,
+        instead of returning the model wrapping the xr.DataArray.
+        This behavior will be fixed in the future
+        when I figure out how to copy over the attributes properly.
+        """
+
+    @abstractmethod
+    def deprecate(
+        self, mask: np.ndarray[Any, np.dtype[np.bool]], inplace: bool = False
+    ) -> None | xr.DataArray:
+        """
+        Deprecate components based on a *mask*.
+        Currently, the non-inplace option returns updated xr.DataArray,
+        instead of returning the model wrapping the xr.DataArray.
+        This behavior will be fixed in the future
+        when I figure out how to copy over the attributes properly.
+        """
+
+
+class Footprints(ArrayContainer, ModelMixin):
+    xr_schema: ClassVar[Schema] = Bundle(
+        name="footprint-group",
+        member=Footprint.entity(),
+        group_by=Dims.component,
+        checks=[is_non_negative, has_no_nan],
+        allow_extra_coords=False,
+    )
+
+    sparsify = True
+
+    def commit_extension(self, new: "Footprints", inplace: bool = False) -> None | xr.DataArray:
+        data = vstack(
+            [self.array.data.reshape((self.array.sizes[AXIS.component_dim], -1)).tocsr(), new]
+        )
+        coords = concatenate_coordinates(
+            self.array[AXIS.component_dim].coords, new.array[AXIS.component_dim].coords
+        )
+        extended = xr.DataArray(
+            COO.from_scipy_sparse(data).reshape((data.shape[0], *self.array.shape[1:])),
+            dims=self.array.dims,
+            coords={k: (AXIS.component_dim, v) for k, v in coords.items()},
+        )
+        extended = extended.assign_coords(self.array[AXIS.width_coord].coords)
+        extended = extended.assign_coords(self.array[AXIS.height_coord].coords)
+
+        if inplace:
+            self.array = extended
+            return None
+        else:
+            return extended
+
+    def apply_fit(self, new: csc_matrix, inplace: bool = False) -> None | xr.DataArray:
+        """
+        Not much to do to apply_fit, since the fit is directly applied
+        during the fit_one optimization.
+        """
+        fitted = xr.DataArray(
+            COO.from_scipy_sparse(new.T).reshape(self.array.shape),
+            dims=self.array.dims,
+            coords=self.array.coords,
+        )
+        if inplace:
+            self.array = fitted
+            return None
+        else:
+            return fitted
+
+    def deprecate(
+        self, mask: np.ndarray[Any, np.dtype[np.bool]], inplace: bool = False
+    ) -> None | xr.DataArray:
+        keep_mask = ~mask
+        val = self.array.data.reshape((self.array.sizes[AXIS.component_dim], -1)).tocsr()[keep_mask]
+        coords = self.array[AXIS.component_dim][keep_mask].coords
+        reduced = xr.DataArray(
+            COO.from_scipy_sparse(val).reshape((val.shape[0], *self.array.shape[1:])),
+            dims=self.array.dims,
+            coords=coords,
+        )
+        reduced = reduced.assign_coords(self.array[AXIS.width_coord].coords)
+        reduced = reduced.assign_coords(self.array[AXIS.height_coord].coords)
+
+        if inplace:
+            self.array = reduced
+            return None
+        else:
+            return reduced
+
+
+class Traces(ArrayContainer, ModelMixin):
+    peek_size: int = None
+    """How many epochs to return when called."""
+    flush_interval: int | None = None
+    """How many epochs to wait until next flush"""
+
+    _deprecated: list[str] = PrivateAttr(default_factory=list)
+    """
+    Deprecated, or replaced component idx.
+    Since zarr does not support efficiently removing rows and columns,
+    there's no easy way to remove a column when a component has been 
+    removed or replaced. Instead, we "mask" it with this "deprecated"
+    flag.
+
+    When arrays are called, these are filtered out. When new epochs are
+    added, these are added in with nan values. 
+    """
+
+    xr_schema: ClassVar[Schema] = Bundle(
+        name="trace-group",
+        member=Trace.entity(),
+        group_by=Dims.component,
+        checks=[is_non_negative],
+        allow_extra_coords=False,
+    )
+
+    @model_validator(mode="after")
+    def flush_conditions(self) -> Self:
+        assert (self.flush_interval and self.zarr_path) or (
+            not self.flush_interval and not self.zarr_path
+        ), "zarr_path and flush_interval should either be both provided or neither."
+        if self.flush_interval:
+            assert self.flush_interval > self.peek_size, (
+                f"flush_interval must be larger than peek_size. "
+                f"Provided: {self.flush_interval = }, {self.peek_size = }"
+            )
+        return self
+
+    @property
+    def sizes(self) -> Mapping[Hashable, int]:
+        if self.zarr_path:
+            total_size = {}
+            for key, val in self.array_.sizes.items():
+                if key == AXIS.frame_dim:
+                    total_size[key] = val + self.load_zarr().sizes[key]
+                else:
+                    total_size[key] = val
+            return total_size
+        else:
+            return self.array_.sizes
+
+    @property
+    def array(self) -> xr.DataArray:
+        return (
+            self.array_.isel({AXIS.frame_dim: slice(-self.peek_size, None)})
+            if self.array_ is not None
+            else self.array_
+        )
+
+    @array.setter
+    def array(self, array: xr.DataArray) -> None:
+        """
+        In case zarr_path is defined, if array is larger than peek_size,
+        the epochs older than -peek_size gets flushed to zarr array.
+
+        """
+        if self.validate_schema:
+            array.validate.against_schema(self._entity.model)
+        if self.zarr_path:
+            self.array_ = array.isel({AXIS.frame_dim: slice(-self.peek_size, None)})
+            array.isel({AXIS.frame_dim: slice(None, -self.peek_size)}).to_zarr(
+                self.zarr_path, mode="w"
+            )
+        else:
+            self.array_ = array
+
+    def commit_extension(self, new: xr.DataArray, inplace: bool = False) -> "None | Traces":
+        """
+        Because of the Zarr implementation,
+        new trace object would either need to:
+        1. copy the entire Zarr array file, (deepcopy-style) or
+        2. simply copy the Zarr array filepath (shallow copy).
+
+        Option 1 is likely too heavy of an implementation and
+        I do not see a lot of use cases for it.
+        Option 2 implements a very inefficient method,
+        where we copy over the entire self to a new Traces instance,
+        and update that new instance's _deprecated and array_ attrs.
+
+        TODO: We're unnecessarily copying array_ twice. Must be made more efficient.
+        """
+        extended = self if inplace else deepcopy(self)
+
+        if extended.zarr_path:
+            n_in_memory = extended.array_.sizes[AXIS.frame_dim]
+            extended.array_ = xr.concat(
+                [extended.array_, new.isel({AXIS.frame_dim: slice(-n_in_memory, None)})],
+                dim=AXIS.component_dim,
+            )
+            new.isel({AXIS.frame_dim: slice(None, -n_in_memory)}).to_zarr(
+                extended.zarr_path, append_dim=AXIS.component_dim
+            )
+        else:
+            extended.array_ = xr.concat(
+                [extended.array_, new], dim=AXIS.component_dim, combine_attrs="drop"
+            )
+
+        return None if inplace else extended
+
+    def apply_fit(self, new: xr.DataArray, inplace: bool = False) -> "None | Traces":
+        """
+        Because of the Zarr implementation,
+        new trace object would either need to:
+        1. copy the entire Zarr array file, (deepcopy-style) or
+        2. simply copy the Zarr array filepath (shallow copy).
+
+        Option 1 is likely too heavy of an implementation and
+        I do not see a lot of use cases for it.
+        Option 2 implements a very inefficient method,
+        where we copy over the entire self to a new Traces instance,
+        and update that new instance's _deprecated and array_ attrs.
+
+        TODO: We're unnecessarily copying array_ twice. Must be made more efficient.
+        """
+        fitted = self if inplace else deepcopy(self)
+
+        fitted.array_ = xr.concat([fitted.array_, new], dim=AXIS.frame_dim)
+
+        if fitted.zarr_path and fitted.array_.sizes[AXIS.frame_dim] > fitted.flush_interval:
+            fitted._flush_zarr()
+
+        return None if inplace else fitted
+
+    def append(self, array: xr.DataArray, dim: str) -> None:
+        """
+        ! DEPRECATION WARNING: Slated to be replaced by
+        ! :meth:`.Traces.commit_extension` and :math:`.Traces.apply_fit`.
+        """
+
+        if dim == AXIS.frame_dim:
+            self.array_ = xr.concat([self.array_, array], dim=AXIS.frame_dim)
+
+            if self.zarr_path and self.array_.sizes[AXIS.frame_dim] > self.flush_interval:
+                self._flush_zarr()
+
+        elif dim == AXIS.component_dim:
+            if self.zarr_path:
+                n_in_memory = self.array_.sizes[AXIS.frame_dim]
+                self.array_ = xr.concat(
+                    [self.array_, array.isel({AXIS.frame_dim: slice(-n_in_memory, None)})],
+                    dim=dim,
+                )
+                array.isel({AXIS.frame_dim: slice(None, -n_in_memory)}).to_zarr(
+                    self.zarr_path, append_dim=dim
+                )
+            else:
+                self.array_ = xr.concat([self.array_, array], dim=dim, combine_attrs="drop")
+
+    def deprecate(
+        self, mask: np.ndarray[Any, np.dtype[np.bool]], inplace: bool = False
+    ) -> "None | Traces":
+        """
+        Because of the Zarr implementation,
+        new trace object would either need to:
+        1. copy the entire Zarr array file, (deepcopy-style) or
+        2. simply copy the Zarr array filepath (shallow copy).
+
+        Option 1 is likely too heavy of an implementation and
+        I do not see a lot of use cases for it.
+        Option 2 implements a very inefficient method,
+        where we copy over the entire self to a new Traces instance,
+        and update that new instance's _deprecated and array_ attrs.
+
+        TODO: We're unnecessarily copying array_ twice. Must be made more efficient.
+        """
+        reduced = self if inplace else deepcopy(self)
+
+        if reduced.zarr_path:
+            reduced._deprecated.extend(reduced.array_[AXIS.id_coord].values[mask])
+        reduced.array_ = reduced.array_[~mask]
+
+        return None if inplace else reduced
+
+    def full_array(self, isel_filter: dict = None, sel_filter: dict = None) -> xr.DataArray:
+        if self.zarr_path:
+            raw_zarr = self.load_zarr(isel_filter, sel_filter)
+            zarr_ids = raw_zarr[AXIS.id_coord].values
+            intact_mask = ~np.isin(zarr_ids, self._deprecated)
+
+            return xr.concat([raw_zarr[intact_mask], self.array_], dim=AXIS.frame_dim).compute()
+        else:
+            return self.array_.isel(isel_filter).sel(sel_filter)
+
+    def _flush_zarr(self) -> None:
+        """
+        Flushes traces older than peek_size to zarr array.
+        Needs to append nans to deprecated components, since they get deleted
+        in in-memory array, but persist in zarr array.
+
+
+        Could do this much more elegantly by pre-allocating .array_
+        """
+        raw_zarr = self.load_zarr()
+        to_flush = self.array_.isel({AXIS.frame_dim: slice(None, -self.peek_size)})
+        if self._deprecated:
+            zarr_ids = raw_zarr[AXIS.id_coord].values
+            zarr_detects = raw_zarr[AXIS.detect_coord].values
+            intact_mask = ~np.isin(zarr_ids, self._deprecated)
+            n_flush = to_flush.sizes[AXIS.frame_dim]
+            prealloc = xr.DataArray(
+                np.full((raw_zarr.sizes[AXIS.component_dim], n_flush), np.nan),
+                dims=[AXIS.component_dim, AXIS.frame_dim],
+                coords={
+                    AXIS.id_coord: (AXIS.component_dim, zarr_ids),
+                    AXIS.detect_coord: (AXIS.component_dim, zarr_detects),
+                },
+            ).assign_coords(to_flush[AXIS.frame_dim].coords)
+            prealloc.loc[intact_mask] = to_flush
+            prealloc.to_zarr(self.zarr_path, append_dim=AXIS.frame_dim)
+        else:
+            to_flush.to_zarr(self.zarr_path, append_dim=AXIS.frame_dim)
+        self.array_ = self.array_.isel({AXIS.frame_dim: slice(-self.peek_size, None)})
+
+    @classmethod
+    def from_array(cls, array: xr.DataArray) -> "Traces":
+        """
+        This is only really used for typing / auto-validation purposes,
+        so we don't really have to worry about specifying the parameters.
+        TODO: not sure what i meant by above... But this function seems replaceable.
+        """
+        new_cls = cls(peek_size=array.sizes[AXIS.frame_dim])
+        new_cls.array = array
+        return new_cls
+
+
+class PixStats(ArrayContainer, ModelMixin):
+    xr_schema: ClassVar[Schema] = Schema(
+        name="pix-stat",
+        dims=(Dims.width.value, Dims.height.value, Dims.component.value),
+        dtype=float,
+        checks=[is_non_negative, has_no_nan],
+        allow_extra_coords=False,
+    )
+
+    def commit_extension(self, new: xr.DataArray, inplace: bool = False) -> None | xr.DataArray:
+        extended = xr.concat([self.array, new], dim=AXIS.component_dim)
+        if inplace:
+            self.array = extended
+            return None
+        else:
+            return extended
+
+    def apply_fit(self, new: xr.DataArray, inplace: bool = False) -> None | xr.DataArray:
+        """
+        Not much to do to apply_fit, since the fit is directly applied
+        during the fit_one optimization.
+        """
+        CY = new.reshape(self.array.shape)
+        fitted = xr.DataArray(CY, dims=self.array.dims, coords=self.array.coords)
+        if inplace:
+            self.array = fitted
+            return None
+        else:
+            return fitted
+
+    def deprecate(
+        self, mask: np.ndarray[Any, np.dtype[np.bool]], inplace: bool = False
+    ) -> None | xr.DataArray:
+        reduced = self.array[~mask]
+        if inplace:
+            self.array = reduced
+            return None
+        else:
+            return reduced
+
+
+comp_dims = (Dims.component.value, deepcopy(Dims.component.value))
+comp_dims[1].name = AXIS.duplicate(comp_dims[1].name)
+for coord in comp_dims[1].coords:
+    coord.name = AXIS.duplicate(coord.name)
+
+
+class MatrixExtension(TypedDict):
+    tr_block: xr.DataArray | tuple[np.ndarray, np.ndarray]
+    bl_block: xr.DataArray | tuple[np.ndarray, np.ndarray]
+    tps_block: xr.DataArray | tuple[np.ndarray, np.ndarray]
+    ext_coords: Coordinates
+
+
+class CompStats(ArrayContainer, ModelMixin):
+    xr_schema: ClassVar[Schema] = Schema(
+        name="comp-stat",
+        dims=comp_dims,
+        dtype=float,
+        checks=[is_non_negative, has_no_nan],
+        allow_extra_coords=False,
+    )
+
+    def apply_fit(self, new: xr.DataArray, inplace: bool = False) -> None | xr.DataArray:
+        epoch = new[AXIS.frame_coord].item()
+        scale = epoch / (epoch + 1)
+        fitted = (scale * self.array + (1 - scale) * new).reset_coords(
+            [AXIS.timestamp_coord, AXIS.frame_coord], drop=True
+        )
+        if inplace:
+            self.array = fitted
+            return None
+        else:
+            return fitted
+
+    def commit_extension(self, new: MatrixExtension, inplace: bool = False) -> None | xr.DataArray:
+        # Top block: [leading principal submatrix, top right block]
+        top_block = xr.concat([self.array, new["tr_block"]], dim=AXIS.component_dim)
+        # Bottom block: [bottom left block, trailing principal submatrix]
+        bottom_block = xr.concat([new["bl_block"], new["tps_block"]], dim=AXIS.component_dim)
+        # Combine blocks
+        extended = xr.concat([top_block, bottom_block], dim=AXIS.duplicate(AXIS.component_dim))
+        if inplace:
+            self.array = extended
+            return None
+        else:
+            return extended
+
+    def deprecate(
+        self, mask: np.ndarray[Any, np.dtype[np.bool]], inplace: bool = False
+    ) -> None | xr.DataArray:
+        keep_mask = ~mask
+        reduced = self.array[keep_mask].T[keep_mask]
+        if inplace:
+            self.array = reduced
+            return None
+        else:
+            return reduced
+
+
+def overlap_format(array: COO, V_comp: xr.DataArray, a_new_comp: Coordinates) -> xr.DataArray:
+    prim_coords = concatenate_coordinates(V_comp.coords, a_new_comp)
+    seco_coords = concatenate_coordinates(
+        V_comp.rename(AXIS.component_rename).coords,
+        a_new_comp.to_dataset().rename(AXIS.component_rename).coords,
+    )
+    return xr.DataArray(
+        array,
+        dims=(AXIS.component_dim, AXIS.duplicate(AXIS.component_dim)),
+        coords={k: (AXIS.component_dim, v) for k, v in prim_coords.items()},
+    ).assign_coords({k: (AXIS.duplicate(AXIS.component_dim), v) for k, v in seco_coords.items()})
+
+
+def assemble_sparse_bool(
+    top_left: tuple[np.ndarray, np.ndarray],
+    top_right: tuple[np.ndarray, np.ndarray],
+    bottom_left: tuple[np.ndarray, np.ndarray],
+    bottom_right: tuple[np.ndarray, np.ndarray],
+    init_shape: tuple[int, int],
+    attach_shape: tuple[int, int],
+) -> COO:
+    """
+    Assemble a sparse boolean array with four coordinates in the format of
+    scipy.sparse.sp_matrix.nonzero()
+
+    """
+    x_coords = top_left[0]
+    y_coords = top_left[1]
+    x_coords = np.concatenate([x_coords, top_right[0]])
+    y_coords = np.concatenate([y_coords, top_right[1] + init_shape[1]])
+    x_coords = np.concatenate([x_coords, bottom_left[0] + init_shape[0]])
+    y_coords = np.concatenate([y_coords, bottom_left[1]])
+    x_coords = np.concatenate([x_coords, bottom_right[0] + init_shape[0]])
+    y_coords = np.concatenate([y_coords, bottom_right[1] + init_shape[1]])
+
+    final_shape = tuple(x1 + x2 for x1, x2 in zip(init_shape, attach_shape))
+    return COO(coords=(x_coords, y_coords), shape=final_shape, data=1)
+
+
+class Overlaps(ArrayContainer, ModelMixin):
+    xr_schema: ClassVar[Schema] = Schema(
+        name="overlap",
+        dims=comp_dims,
+        dtype=bool,
+        checks=[has_no_nan],
+        allow_extra_coords=False,
+    )
+
+    def apply_fit(self, new: xr.DataArray, inplace: bool = False) -> None | xr.DataArray:
+        """
+        Overlaps update from new footprints fits is not implemented
+        """
+        raise NotImplementedError("Overlaps update from new footprint fits is not implemented")
+
+    def commit_extension(self, new: MatrixExtension, inplace: bool = False) -> None | xr.DataArray:
+        extended = assemble_sparse_bool(
+            self.array.data.tocsr().nonzero(),
+            new["tr_block"].nonzero(),
+            new["bl_block"].nonzero(),
+            new["tps_block"].nonzero(),
+            self.array.data.tocsr().shape,
+            new["tps_block"].shape,
+        )
+        extended = overlap_format(extended, self.array[AXIS.component_dim], new["ext_coords"])
+        if inplace:
+            self.array = extended
+            return None
+        else:
+            return extended
+
+    def deprecate(
+        self, mask: np.ndarray[Any, np.dtype[np.bool]], inplace: bool = False
+    ) -> None | xr.DataArray:
+        keep_mask = ~mask
+        reduced = self.array.data.tocsr()[keep_mask].T[keep_mask]
+        reduced = xr.DataArray(
+            COO.from_scipy_sparse(reduced),
+            dims=self.array.dims,
+            coords=self.array[AXIS.component_dim][keep_mask].coords,
+        )
+        reduced = reduced.assign_coords(
+            self.array[AXIS.component_dim][keep_mask].rename(AXIS.component_rename).coords
+        )
+        if inplace:
+            self.array = reduced
+            return None
+        else:
+            return reduced
diff --git a/src/cala/gui/spec.yaml b/src/cala/gui/spec.yaml
index 4167c527..4ae00db0 100644
--- a/src/cala/gui/spec.yaml
+++ b/src/cala/gui/spec.yaml
@@ -28,5 +28,4 @@ nodes:
   component_count:
     type: cala.gui.components.component_counter
     depends:
-      - index: counter.idx
-      - traces: arrays.traces
\ No newline at end of file
+      - traces: fit_trace.new_fit
\ No newline at end of file
diff --git a/src/cala/nodes/io.py b/src/cala/nodes/io.py
index a4ed2bd0..81679596 100644
--- a/src/cala/nodes/io.py
+++ b/src/cala/nodes/io.py
@@ -9,7 +9,7 @@
 from numpy.typing import NDArray
 from skimage import io
 
-from cala.arrays.assets import Asset
+from cala.arrays.containers import ArrayContainer
 from cala.config import config
 
 
@@ -119,7 +119,9 @@ def stream(
         raise ValueError(f"Unsupported file format: {suffix}")
 
 
-def save_asset(asset: Asset, target_epoch: int, curr_epoch: int, path: str | Path) -> Asset:
+def save_asset(
+    asset: ArrayContainer, target_epoch: int, curr_epoch: int, path: str | Path
+) -> ArrayContainer:
     if target_epoch == curr_epoch:
         zarr_dir = config.user_dir
         try:
diff --git a/src/cala/nodes/omf/component_stats.py b/src/cala/nodes/omf/component_stats.py
index a662922c..ac49763c 100644
--- a/src/cala/nodes/omf/component_stats.py
+++ b/src/cala/nodes/omf/component_stats.py
@@ -1,10 +1,13 @@
 import numpy as np
 import xarray as xr
 
-from cala.arrays import AXIS, CompStats, Frame, PopSnap, Traces
+from cala.arrays import AXIS, Frame, PopSnap
+from cala.arrays.models import CompStats, Traces
 
 
-def ingest_frame(component_stats: CompStats, frame: Frame, new_traces: PopSnap) -> CompStats:
+def ingest_frame(
+    component_stats: CompStats, frame: Frame, new_traces: PopSnap, trace_threshold: float = 0.0
+) -> CompStats:
     """
     Update component statistics using current frame and component.
 
@@ -34,6 +37,7 @@ def ingest_frame(component_stats: CompStats, frame: Frame, new_traces: PopSnap)
 
     # New frame traces
     c_t = new_traces.array
+    c_t[c_t < trace_threshold] = 0
 
     # Update component-wise statistics M_t
     # M_t = ((t-1)/t)M_{t-1} + (1/t)c_t c_t^T
diff --git a/src/cala/nodes/omf/footprints.py b/src/cala/nodes/omf/footprints.py
index aba015c8..1ea2889c 100644
--- a/src/cala/nodes/omf/footprints.py
+++ b/src/cala/nodes/omf/footprints.py
@@ -7,14 +7,16 @@
 from scipy.sparse import csc_matrix, vstack
 from sparse import COO
 
-from cala.arrays import AXIS, CompStats, Footprints, PixStats
+from cala.arrays import AXIS
+from cala.arrays.models import CompStats, Footprints, PixStats
 from cala.logging import init_logger
 from cala.util import concatenate_coordinates
 
 
 class Footprinter(BaseModel):
     max_iter: int
-    ratio_lb: float = 0.15
+    ratio_lb: float
+    """low bound for footprint value. values lower than this is floored to zero."""
 
     _logger = init_logger(__name__)
 
diff --git a/src/cala/nodes/omf/overlap.py b/src/cala/nodes/omf/overlap.py
index 971eb9cf..b891d100 100644
--- a/src/cala/nodes/omf/overlap.py
+++ b/src/cala/nodes/omf/overlap.py
@@ -1,9 +1,8 @@
 import numpy as np
-import xarray as xr
-from sparse import COO
 
-from cala.arrays import AXIS, Footprints, Overlaps
-from cala.util import concatenate_coordinates, sp_matmul, stack_sparse
+from cala.arrays import AXIS
+from cala.arrays.models import Footprints, Overlaps, assemble_sparse_bool, overlap_format
+from cala.util import sp_matmul, stack_sparse
 
 
 def initialize(overlaps: Overlaps, footprints: Footprints) -> Overlaps:
@@ -69,58 +68,6 @@ def ingest_component(
         V.nonzero(), v_topright, v_bottleft, v_botright.nonzero(), V.shape, v_botright.shape
     )
 
-    overlaps.array = overlap_format(updated_overlaps, V_side, a_new)
+    overlaps.array = overlap_format(updated_overlaps, V_side, a_new.coords)
 
     return overlaps
-
-
-def overlap_format(array: COO, V_comp: xr.DataArray, a_new_comp: xr.DataArray) -> xr.DataArray:
-    prim_coords = concatenate_coordinates(V_comp.coords, a_new_comp.coords)
-    seco_coords = concatenate_coordinates(
-        V_comp.rename(AXIS.component_rename).coords, a_new_comp.rename(AXIS.component_rename).coords
-    )
-    return xr.DataArray(
-        array,
-        dims=(AXIS.component_dim, AXIS.duplicate(AXIS.component_dim)),
-        coords={k: (AXIS.component_dim, v) for k, v in prim_coords.items()},
-    ).assign_coords({k: (AXIS.duplicate(AXIS.component_dim), v) for k, v in seco_coords.items()})
-
-
-def assemble_sparse_bool(
-    top_left: tuple[np.ndarray, np.ndarray],
-    top_right: tuple[np.ndarray, np.ndarray],
-    bottom_left: tuple[np.ndarray, np.ndarray],
-    bottom_right: tuple[np.ndarray, np.ndarray],
-    init_shape: tuple[int, int],
-    attach_shape: tuple[int, int],
-) -> COO:
-    """
-    Assemble a sparse boolean array with four coordinates in the format of
-    scipy.sparse.sp_matrix.nonzero()
-
-    """
-    x_coords = top_left[0]
-    y_coords = top_left[1]
-    x_coords = np.concatenate([x_coords, top_right[0]])
-    y_coords = np.concatenate([y_coords, top_right[1] + init_shape[1]])
-    x_coords = np.concatenate([x_coords, bottom_left[0] + init_shape[0]])
-    y_coords = np.concatenate([y_coords, bottom_left[1]])
-    x_coords = np.concatenate([x_coords, bottom_right[0] + init_shape[0]])
-    y_coords = np.concatenate([y_coords, bottom_right[1] + init_shape[1]])
-
-    final_shape = tuple(x1 + x2 for x1, x2 in zip(init_shape, attach_shape))
-    return COO(coords=(x_coords, y_coords), shape=final_shape, data=1)
-
-
-def assemble_square(
-    top_left: np.ndarray, top_right: np.ndarray, bottom_left: np.ndarray, bottom_right: np.ndarray
-) -> np.ndarray:
-    """
-    Assemble four 2D arrays into a single 2D array, with one in each corner.
-
-    """
-    top_block = np.hstack([top_left, top_right])
-    bottom_block = np.hstack([bottom_left, bottom_right])
-
-    # Finally combine top and bottom blocks
-    return np.vstack([top_block, bottom_block])
diff --git a/src/cala/nodes/omf/pixel_stats.py b/src/cala/nodes/omf/pixel_stats.py
index 8670c722..060f19f5 100644
--- a/src/cala/nodes/omf/pixel_stats.py
+++ b/src/cala/nodes/omf/pixel_stats.py
@@ -5,11 +5,17 @@
 from noob import Name
 from scipy.sparse import csr_matrix
 
-from cala.arrays import AXIS, Buffer, Footprints, Frame, Movie, PixStats, PopSnap, Traces
+from cala.arrays import AXIS, Buffer, Frame, Movie, PopSnap
+from cala.arrays.models import Footprints, PixStats, Traces
 
 
 def ingest_frame(
-    pixel_stats: PixStats, frame: Frame, new_traces: PopSnap, footprints: Footprints
+    pixel_stats: PixStats,
+    frame: Frame,
+    new_traces: PopSnap,
+    footprints: Footprints,
+    noise_threshold: float = 1.0,
+    trace_threshold: float = 0.0,
 ) -> PixStats:
     """
     Update pixel statistics using current frame and component.
@@ -49,19 +55,21 @@ def ingest_frame(
     # W_t = ((t-1)/t)W_{t-1} + (1/t)y_t c_t^T
     # We only access the footprint area, so we can drastically reduce the calc
     y_flat = y_t.data.flatten()
+    # y_flat[y_flat < y_flat.mean() + y_flat.std() * noise_threshold] = 0
     n_components = A.sizes[AXIS.component_dim]
     A_sparse = A.data.reshape((n_components, -1)).tocsr()
     W_flat = W.data.reshape((n_components, -1)) * prev_scale
     mask_coords = A_sparse.nonzero()
 
     for i in range(n_components):
-        idx = np.where(mask_coords[0] == i)[0]
-        coords = mask_coords[1][idx]
-        data = y_flat[coords] * c_t.values[i] * new_scale
-        # target_masked = csr_matrix((data, coords), shape=y_flat.shape)
-        target_masked = np.zeros(y_flat.shape)
-        target_masked[coords] = data
-        W_flat[i] += target_masked
+        if c_t.values[i] > trace_threshold:
+            idx = np.where(mask_coords[0] == i)[0]
+            coords = mask_coords[1][idx]
+            data = y_flat[coords] * c_t.values[i] * new_scale
+            # target_masked = csr_matrix((data, coords), shape=y_flat.shape)
+            target_masked = np.zeros(y_flat.shape)
+            target_masked[coords] = data
+            W_flat[i] += target_masked
 
     CY = W_flat.reshape(W.shape)
 
diff --git a/src/cala/nodes/omf/residual.py b/src/cala/nodes/omf/residual.py
index 9e4710f8..de1c2562 100644
--- a/src/cala/nodes/omf/residual.py
+++ b/src/cala/nodes/omf/residual.py
@@ -6,7 +6,8 @@
 from pydantic import BaseModel, PrivateAttr
 from scipy.sparse import csr_matrix
 
-from cala.arrays import AXIS, Buffer, Footprints, Frame, Traces
+from cala.arrays import AXIS, Buffer, Frame
+from cala.arrays.models import Footprints, Traces
 
 
 class Residuer(BaseModel):
diff --git a/src/cala/arrays/mixins.py b/src/cala/nodes/omf/test_prune.py
similarity index 100%
rename from src/cala/arrays/mixins.py
rename to src/cala/nodes/omf/test_prune.py
diff --git a/src/cala/nodes/omf/traces.py b/src/cala/nodes/omf/traces.py
index 73a95b4d..07c8d892 100644
--- a/src/cala/nodes/omf/traces.py
+++ b/src/cala/nodes/omf/traces.py
@@ -7,7 +7,8 @@
 from pydantic import BaseModel
 from scipy.sparse.csgraph import connected_components
 
-from cala.arrays import AXIS, Footprints, Frame, Overlaps, PopSnap, Traces
+from cala.arrays import AXIS, Frame, PopSnap
+from cala.arrays.models import Footprints, Overlaps, Traces
 from cala.logging import init_logger
 from cala.util import norm, stack_sparse
 
@@ -21,7 +22,7 @@ class Tracer(BaseModel):
     @process_method
     def ingest_frame(
         self, traces: Traces, footprints: Footprints, frame: Frame, overlaps: Overlaps
-    ) -> A[PopSnap, Name("latest_trace")]:
+    ) -> tuple[A[Traces, Name("traces")], A[PopSnap, Name("new_fit")]]:
         """
         Update temporal traces using current spatial footprints and frame data.
 
@@ -51,7 +52,7 @@ def ingest_frame(
                 components i and j overlap, and 0 otherwise.
         """
         if footprints.array is None:
-            return PopSnap()
+            return traces, PopSnap()
 
         # Prepare inputs for the update algorithm
         A = stack_sparse(footprints.array, AXIS.component_dim).tocsr().T
@@ -82,7 +83,7 @@ def ingest_frame(
         else:
             traces.append(updated_traces, dim=AXIS.frame_dim)
 
-        return PopSnap.from_array(updated_traces)
+        return traces, PopSnap.from_array(updated_traces)
 
 
 def _update_traces(
@@ -157,8 +158,8 @@ def ingest_component(traces: Traces, new_traces: Traces) -> Traces:
 
     merged_ids = c_new.attrs.get("replaces")
     if merged_ids:
-        intact_mask = ~np.isin(traces.array[AXIS.id_coord].values, merged_ids)
-        traces.keep(intact_mask)
+        mask = np.isin(traces.array[AXIS.id_coord].values, merged_ids)
+        traces.deprecate(mask, inplace=True)
 
     c_pad = _pad_history(c_new, total_frames, np.nan) if total_frames > new_n_frames else c_new
 
diff --git a/src/cala/nodes/prep/downsample.py b/src/cala/nodes/prep/downsample.py
index 222855b7..fdc69f38 100644
--- a/src/cala/nodes/prep/downsample.py
+++ b/src/cala/nodes/prep/downsample.py
@@ -7,23 +7,14 @@
 from cala.nodes.prep import package_frame
 
 
-def downsample(
-    frames: list[Frame], x_range: tuple[int, int], y_range: tuple[int, int], t_downsample: int = 1
-) -> A[Frame, Name("frame")]:
+def downsample(frames: list[Frame]) -> A[Frame, Name("frame")]:
     """
     Downsampling in time and cropping in space. Must be followed by gather node, and
     t_downsample has to be same as gather's parameter n value.
 
     :param frames:
-    :param x_range:
-    :param y_range:
-    :param t_downsample:
-    :return:
     """
-    arrays = []
-    for frame in frames:
-        arrays.append(frame.array[x_range[0] : x_range[1], y_range[0] : y_range[1]])
-
+    arrays = [f.array for f in frames]
     return package_frame(
-        np.mean(arrays, axis=0), arrays[-1][AXIS.frame_coord].item() // t_downsample
+        np.mean(arrays, axis=0), arrays[-1][AXIS.frame_coord].item() // len(arrays)
     )
diff --git a/src/cala/nodes/prep/glow_removal.py b/src/cala/nodes/prep/glow_removal.py
index 7eb6a97a..dd860b31 100644
--- a/src/cala/nodes/prep/glow_removal.py
+++ b/src/cala/nodes/prep/glow_removal.py
@@ -2,15 +2,15 @@
 
 import numpy as np
 import xarray as xr
-from noob import Name
+from noob import Name, process_method
 
 from cala.arrays import Frame
 
 
 class GlowRemover:
     base_brightness_: np.ndarray = None
-    _learn_count: int = 0
 
+    @process_method
     def process(self, frame: Frame) -> A[Frame, Name("frame")]:
         frame = frame.array
 
@@ -18,7 +18,6 @@ def process(self, frame: Frame) -> A[Frame, Name("frame")]:
             self.base_brightness_ = frame.values
 
         self.base_brightness_ = np.minimum(frame.values, self.base_brightness_)
-        self._learn_count += 1
 
         shifted = (frame - self.base_brightness_).values
 
diff --git a/src/cala/nodes/prep/motion.py b/src/cala/nodes/prep/motion.py
index 7642ad50..ec2a3df5 100644
--- a/src/cala/nodes/prep/motion.py
+++ b/src/cala/nodes/prep/motion.py
@@ -11,7 +11,6 @@
 from skimage.filters import difference_of_gaussians
 
 from cala.arrays import AXIS, Frame
-from cala.testing.util import shift_by
 
 
 class Shift(BaseModel):
@@ -75,7 +74,7 @@ def stabilize(self, frame: Frame) -> A[Frame, Name("frame")]:
            as the anchor for the next frame
         """
         arr = frame.array
-        prepped = prepare(arr, dog_kwargs=self.dog_kwargs, gauss_kwargs=self.gauss_kwargs)
+        prepped = _prepare(arr, dog_kwargs=self.dog_kwargs, gauss_kwargs=self.gauss_kwargs)
         if not self._has_prereqs:
             self._init(prepped)
             return frame
@@ -114,7 +113,7 @@ def _get_ready_for_next(self, prepped: xr.DataArray) -> None:
         self._global = (self._global * curr_idx + self._local) / (curr_idx + 1)
 
 
-def prepare(image: xr.DataArray, dog_kwargs: dict, gauss_kwargs: dict) -> xr.DataArray:
+def _prepare(image: xr.DataArray, dog_kwargs: dict, gauss_kwargs: dict) -> xr.DataArray:
     tmp = difference_of_gaussians(image, **dog_kwargs)
     tmp = cv2.normalize(tmp, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)
     result = cv2.GaussianBlur(tmp.astype(float), **gauss_kwargs)
@@ -128,41 +127,53 @@ def match_template(
     if image.dtype != np.float32:
         image = image.astype(np.float32)
 
-    h_i, w_i = template.shape
-    ms_h = max_shift_h
-    ms_w = max_shift_w
+    height, width = template.shape
+    max_shift_rows = max_shift_h
+    max_shift_cols = max_shift_w
 
-    templ_crop = template[max_shift_h : h_i - max_shift_h, max_shift_w : w_i - max_shift_w].astype(
-        np.float32
-    )
+    templ_crop = template[
+        max_shift_rows : height - max_shift_rows, max_shift_cols : width - max_shift_cols
+    ].astype(np.float32)
 
     res = cv2.matchTemplate(image, templ_crop, cv2.TM_CCORR_NORMED)
-    top_left = cv2.minMaxLoc(res)[3]
-
-    sh_y, sh_x = top_left
-
-    if (0 < top_left[1] < 2 * ms_h - 1) & (0 < top_left[0] < 2 * ms_w - 1):
-        # if max is internal, check for subpixel shift using gaussian
-        # peak registration
-        log_xm1_y = np.log(res[sh_x - 1, sh_y])
-        log_xp1_y = np.log(res[sh_x + 1, sh_y])
-        log_x_ym1 = np.log(res[sh_x, sh_y - 1])
-        log_x_yp1 = np.log(res[sh_x, sh_y + 1])
-        four_log_xy = 4 * np.log(res[sh_x, sh_y])
-
-        sh_x_n = -(
-            sh_x - ms_h + ((log_xm1_y - log_xp1_y) / (2 * log_xm1_y - four_log_xy + 2 * log_xp1_y))
+    peak_col, peak_row = cv2.minMaxLoc(res)[3]
+
+    if (0 < peak_row < 2 * max_shift_rows - 1) & (0 < peak_col < 2 * max_shift_cols - 1):
+        # if max is internal, check for subpixel shift using gaussian peak registration
+
+        # Near its maximum, a correlation peak often looks approximately like a 2D Gaussian bump
+        # $R(\Delta)\approx A\exp(-\frac{(\Delta-\Delta_0)^2}{2\sigma^2})$
+        # Taking a log turns it into quadratic:
+        # $\log{R(\Delta)}\approx \log{A}-\frac{(\Delta-\Delta_0)^2}{2\sigma^2}$
+        # So if you look at $\log{R}$ at the peak and its immediate neighbors,
+        # you can fit a 1D quadratic in each axis and
+        # estimate the subpixel offset of the true maximum.
+
+        # For a 1D function sampled at -1, 0, +1:
+        # Let $a = \log{R(-1)}, b = \log{R(0)}, c = \log{R(+1)}$
+        # Then the subpixel offset relative to 0 for the parabola's maximum is:
+        # $\delta = \frac{a-c}{2(a-2b+c)}
+        log_up = np.log(res[peak_row - 1, peak_col])
+        log_down = np.log(res[peak_row + 1, peak_col])
+        log_left = np.log(res[peak_row, peak_col - 1])
+        log_right = np.log(res[peak_row, peak_col + 1])
+        log_center = 4 * np.log(res[peak_row, peak_col])
+
+        row_shift = -(
+            peak_row  # integer shift
+            - max_shift_rows  # because of crop
+            + ((log_up - log_down) / (2 * log_up - log_center + 2 * log_down))  # subpixel shift
         )
-        sh_y_n = -(
-            sh_y - ms_w + ((log_x_ym1 - log_x_yp1) / (2 * log_x_ym1 - four_log_xy + 2 * log_x_yp1))
+        col_shift = -(
+            peak_col
+            - max_shift_cols
+            + ((log_left - log_right) / (2 * log_left - log_center + 2 * log_right))
         )
     else:
-        sh_x_n = -(sh_x - ms_h)
-        sh_y_n = -(sh_y - ms_w)
+        row_shift = -(peak_row - max_shift_rows)
+        col_shift = -(peak_col - max_shift_cols)
 
-    shift = np.array([sh_x_n, sh_y_n])
-
-    return shift
+    return np.array([row_shift, col_shift])
 
 
 def apply_shift(image: xr.DataArray, shift: Shift) -> xr.DataArray:
@@ -176,14 +187,3 @@ def apply_shift(image: xr.DataArray, shift: Shift) -> xr.DataArray:
         borderMode=cv2.BORDER_REPLICATE,
     )
     return xr.DataArray(shifted_frame, dims=image.dims, coords=image.coords)
-
-
-def check_shift_validity(
-    source: xr.DataArray, target: xr.DataArray, shift: np.ndarray, threshold: float, func: Callable
-) -> bool:
-    expected = np.array([5, 5])
-    tester = shift_by(source.values, *expected)
-    total = func(tester, target.values)
-    result = total - shift
-    error = np.linalg.norm(result - expected)
-    return error < threshold
diff --git a/src/cala/nodes/segment/__init__.py b/src/cala/nodes/segment/__init__.py
index b91a89be..25fc75f4 100644
--- a/src/cala/nodes/segment/__init__.py
+++ b/src/cala/nodes/segment/__init__.py
@@ -1,5 +1,5 @@
 from .catalog import Cataloger
-from .persist import update_assets
+from .extend import update_assets
 from .slice_nmf import SliceNMF
 
 __all__ = ["SliceNMF", "Cataloger", "update_assets"]
diff --git a/src/cala/nodes/segment/catalog.py b/src/cala/nodes/segment/catalog.py
index c28745b8..1e7c2c31 100644
--- a/src/cala/nodes/segment/catalog.py
+++ b/src/cala/nodes/segment/catalog.py
@@ -14,7 +14,9 @@
 from skimage.measure import label
 from xarray import Coordinates
 
-from cala.arrays import AXIS, Footprint, Footprints, Trace, Traces
+from cala.arrays import AXIS, Footprint, Trace
+from cala.arrays.models import Footprints, Traces
+from cala.nodes.segment.quality_control import morphology_filter
 from cala.util import combine_attr_replaces, concat_components, create_id, rank1nmf
 
 
@@ -227,9 +229,9 @@ def _quality_control(
     ) -> tuple[list[xr.DataArray], list[xr.DataArray]]:
         """
         Filters resulting footprints and traces based on quality thresholds
-
         """
-        mask = [np.sum(fp.data > self.val_threshold) > self.cnt_threshold for fp in footprints]
+
+        mask = morphology_filter(footprints, self.val_threshold, self.cnt_threshold)
         footprints = list(compress(footprints, mask))
         traces = list(compress(traces, mask))
 
@@ -324,13 +326,13 @@ def _gather_discrete(
     footprints = []
     traces = []
 
-    if discrete_idx.size > 0:
-        fps, trs = _register(
-            shapes=fps.isel({AXIS.component_dim: discrete_idx}),
-            tracks=trs.isel({AXIS.component_dim: discrete_idx}),
+    for idx in discrete_idx:
+        reg_fps, reg_trs = _register(
+            shapes=fps.isel({AXIS.component_dim: idx}),
+            tracks=trs.isel({AXIS.component_dim: idx}),
         )
-        footprints.append(fps)
-        traces.append(trs)
+        footprints.append(reg_fps)
+        traces.append(reg_trs)
 
     return footprints, traces
 
diff --git a/src/cala/nodes/segment/cleanup.py b/src/cala/nodes/segment/cleanup.py
deleted file mode 100644
index f424c042..00000000
--- a/src/cala/nodes/segment/cleanup.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from typing import Annotated as A
-
-import numpy as np
-from noob import Name
-
-from cala.arrays import AXIS, Buffer, CompStats, Footprints, Overlaps, PixStats, Traces
-
-
-def deprecate_components(
-    footprints: Footprints,
-    traces: Traces,
-    pix_stats: PixStats,
-    comp_stats: CompStats,
-    overlaps: Overlaps,
-    remove_ids: list[str],
-) -> tuple[
-    A[Footprints, Name("footprints")],
-    A[Traces, Name("traces")],
-    A[PixStats, Name("pix_stats")],
-    A[CompStats, Name("comp_stats")],
-    A[Overlaps, Name("overlaps")],
-]:
-    """
-    Deprecate a list of components from all arrays involved in omf.
-
-    """
-    keep_mask = ~np.isin(traces.array[AXIS.id_coord].values, remove_ids)
-
-    traces.keep(keep_mask)
-    # the line below compiles numba. gotta do it like in footprints.ingest_component
-    # but then i need to redundantly convert COO -> csr -> COO -> csr -> COO
-    footprints.array = footprints.array[keep_mask]
-    pix_stats.array = pix_stats.array[keep_mask]
-    comp_stats.array = comp_stats.array[keep_mask].T[keep_mask]
-    overlaps.array = overlaps.array[keep_mask].T[keep_mask]
-
-    return footprints, traces, pix_stats, comp_stats, overlaps
-
-
-def find_inactive() -> list[str]:
-    """
-    Deprecate inactive components
-    Component is deemed inactive if its own brightness contribution across
-    all of its footprint is below threshold.
-
-    1. has been some time since discovery
-    2. within its own footprint, its brightness contribution is lower than
-        some % of the minimum of the total brightness contributions from all components?
-        - but what if the component is completely occluded sometimes?
-    """
-
-
-def clear_overestimates(
-    footprints: Footprints, residuals: Buffer, nmf_error: float
-) -> A[Footprints, Name("footprints")]:
-    """
-    Remove all sections of the footprints that cause negative residuals.
-
-    This occurs by:
-    1. find "significant" negative residual spots that is more than a noise level, and thus
-    cannot be clipped to zero. !!!! (only of the latest frame, and then go back to trace update..?)
-    2. all footprint values at these spots go to zero.
-    """
-    if residuals.array is None:
-        return footprints
-    R_min = residuals.array.isel({AXIS.frame_dim: -1}).reset_coords(
-        [AXIS.frame_coord, AXIS.timestamp_coord], drop=True
-    )
-    tuned_fp = footprints.array.where(R_min > -nmf_error, 0, drop=False)
-
-    return tuned_fp
diff --git a/src/cala/nodes/segment/persist.py b/src/cala/nodes/segment/extend.py
similarity index 94%
rename from src/cala/nodes/segment/persist.py
rename to src/cala/nodes/segment/extend.py
index 5613733d..44d08401 100644
--- a/src/cala/nodes/segment/persist.py
+++ b/src/cala/nodes/segment/extend.py
@@ -2,7 +2,8 @@
 
 from noob import Name
 
-from cala.arrays import CompStats, Footprints, Movie, Overlaps, PixStats, Traces
+from cala.arrays import Movie
+from cala.arrays.models import CompStats, Footprints, Overlaps, PixStats, Traces
 from cala.nodes.omf.component_stats import ingest_component as update_component_stats
 from cala.nodes.omf.footprints import ingest_component as update_footprints
 from cala.nodes.omf.overlap import ingest_component as update_overlap
diff --git a/src/cala/nodes/segment/prune.py b/src/cala/nodes/segment/prune.py
new file mode 100644
index 00000000..52619629
--- /dev/null
+++ b/src/cala/nodes/segment/prune.py
@@ -0,0 +1,62 @@
+from typing import Annotated as A
+from typing import Any
+
+import numpy as np
+from noob import Name
+
+from cala.arrays import AXIS
+from cala.arrays.models import CompStats, Footprints, Overlaps, PixStats, Traces
+from cala.nodes.segment.quality_control import morphology_filter
+
+
+def deprecate(
+    footprints: Footprints,
+    traces: Traces,
+    pix_stats: PixStats,
+    comp_stats: CompStats,
+    overlaps: Overlaps,
+    mask: np.ndarray[Any, np.dtype[np.bool]] | None = None,
+) -> tuple[
+    A[Footprints, Name("footprints")],
+    A[Traces, Name("traces")],
+    A[PixStats, Name("pix_stats")],
+    A[CompStats, Name("comp_stats")],
+    A[Overlaps, Name("overlaps")],
+]:
+    """
+    Deprecate a set of components from all models.
+    """
+    # ! Refactor the below two lines to a separate node
+    if footprints.array is None:
+        return footprints, traces, pix_stats, comp_stats, overlaps
+    fps = [
+        fp
+        for fp in footprints.array.data.reshape(
+            (footprints.array.sizes[AXIS.component_dim], -1)
+        ).tocsc()
+    ]
+    passed = morphology_filter(footprints=fps, value_threshold=0.8, count_threshold=20)
+    failed = ~np.array(passed)
+
+    if any(failed):
+        traces.deprecate(failed, inplace=True)
+        footprints.deprecate(failed, inplace=True)
+        pix_stats.deprecate(failed, inplace=True)
+        comp_stats.deprecate(failed, inplace=True)
+        overlaps.deprecate(failed, inplace=True)
+
+    return footprints, traces, pix_stats, comp_stats, overlaps
+
+
+def find_inactive() -> list[str]:
+    """
+    Deprecate inactive components
+    Component is deemed inactive if its own brightness contribution across
+    all of its footprint is below threshold.
+
+    1. has been some time since discovery
+    2. within its own footprint, its brightness contribution is lower than
+        some % of the minimum of the total brightness contributions from all components?
+        - but what if the component is completely occluded sometimes?
+    """
+    raise NotImplementedError
diff --git a/src/cala/nodes/segment/quality_control.py b/src/cala/nodes/segment/quality_control.py
new file mode 100644
index 00000000..29ed3653
--- /dev/null
+++ b/src/cala/nodes/segment/quality_control.py
@@ -0,0 +1,39 @@
+import numpy as np
+import xarray as xr
+from sparse import COO
+
+
+def morphology_filter(
+    footprints: list[xr.DataArray], value_threshold: float, count_threshold: int
+) -> list[bool]:
+    if isinstance(footprints[0].data, COO):
+        footprints = [fp.data for fp in footprints]
+    return [_sig_pixel_count(fp, value_threshold, count_threshold) for fp in footprints]
+
+
+def _sig_pixel_count(footprint: xr.DataArray, value_threshold: float, count_threshold: int) -> bool:
+    """
+    True if the number of pixels with "significant" values (compared to max)
+    is above threshold
+    """
+    return (
+        np.sum(footprint.data / footprint.data.max() > value_threshold) > count_threshold
+        if footprint.data.size > 0
+        else False
+    )
+
+
+def separate_by_filter(
+    asset: xr.DataArray, value_threshold: float, count_threshold: int
+) -> tuple[list[xr.DataArray], list[xr.DataArray]]:
+    A = asset.as_numpy().transpose("component", ...)
+
+    accepted_A = []
+    rejected_A = []
+    for fp in A:
+        if _sig_pixel_count(fp, value_threshold, count_threshold):
+            accepted_A.append(fp)
+        else:
+            rejected_A.append(fp)
+
+    return accepted_A, rejected_A
diff --git a/src/cala/nodes/segment/slice_nmf.py b/src/cala/nodes/segment/slice_nmf.py
index e4a6d6e4..065e634c 100644
--- a/src/cala/nodes/segment/slice_nmf.py
+++ b/src/cala/nodes/segment/slice_nmf.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 import xarray as xr
-from noob import Name
+from noob import Name, process_method
 from noob.node import Node
 from pydantic import Field
 
@@ -27,12 +27,13 @@ class SliceNMF(Node):
 
     _logger = init_logger(__name__)
 
+    @process_method
     def process(
-        self, residuals: Buffer, energy: xr.DataArray, detect_radius: int
+        self, residuals: Buffer, energy: xr.DataArray, cell_size: int
     ) -> tuple[A[list[Footprint], Name("new_fps")], A[list[Trace], Name("new_trs")]]:
 
         if residuals.array.sizes[AXIS.frame_dim] < self.min_frames:
-            return [], []
+            return None, None
 
         fps = []
         trs = []
@@ -41,19 +42,15 @@ def process(
 
         while np.max(energy) >= self.detect_thresh:
             # Find and analyze neighborhood of maximum variance
-            slice_ = self._get_max_energy_slice(
-                arr=res, energy_landscape=energy, radius=detect_radius
-            )
+            slice_ = self._get_max_energy_slice(arr=res, energy_landscape=energy, radius=cell_size)
 
             a_new, c_new = self._local_nmf(
                 slice_=slice_,
                 spatial_sizes={k: v for k, v in res.sizes.items() if k in AXIS.spatial_dims},
             )
 
-            l1_norm = np.sum(slice_.values)
-            l1_error = self.error_ / l1_norm
-            l0_norm = np.prod(slice_.shape).astype(float)
-            l0_error = self.error_ / l0_norm
+            l1_error = self.error_ / np.sum(slice_.values)
+            l0_error = self.error_ / np.prod(slice_.shape).astype(float)
 
             energy.loc[{ax: slice_.coords[ax] for ax in AXIS.spatial_dims}] = 0
 
@@ -64,13 +61,10 @@ def process(
             else:
                 res.loc[{ax: slice_.coords[ax] for ax in AXIS.spatial_dims}] = l0_error
 
-        return fps, trs
+        return (fps, trs) if fps else (None, None)
 
     def _get_max_energy_slice(
-        self,
-        arr: xr.DataArray,
-        energy_landscape: xr.DataArray,
-        radius: int,
+        self, arr: xr.DataArray, energy_landscape: xr.DataArray, radius: int
     ) -> xr.DataArray:
         """Find neighborhood around point of maximum variance."""
         # Find maximum point
@@ -93,9 +87,7 @@ def _get_max_energy_slice(
         return neighborhood
 
     def _local_nmf(
-        self,
-        slice_: xr.DataArray,
-        spatial_sizes: Mapping[Hashable, int],
+        self, slice_: xr.DataArray, spatial_sizes: Mapping[Hashable, int]
     ) -> tuple[xr.DataArray, xr.DataArray]:
         """Perform local rank-1 Non-negative Matrix Factorization.
 
@@ -141,7 +133,7 @@ def _local_nmf(
         )
 
         # normalize against the original video (as in whatever the residual used at the time)
-        factor = slice_.data.max() / c_new.data.max()
+        factor = a_new.max()
         a_new = a_new / factor
         c_new = c_new * factor
 
diff --git a/src/cala/plot.py b/src/cala/plot.py
new file mode 100644
index 00000000..29bd4781
--- /dev/null
+++ b/src/cala/plot.py
@@ -0,0 +1,92 @@
+import copy
+from collections import Counter, defaultdict
+from pathlib import Path
+from typing import Literal, Generator
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+
+
+def erode_to_average(
+    array: np.ndarray,
+    average: Literal["mean", "quantile"] = "quantile",
+    low: float = None,
+    high: float = None,
+    mask: np.ndarray = None,
+    q: float = None,
+) -> np.ndarray:
+    """
+    return an array where only the mean values (+_ width / 2) are present.
+    if mask, only include masked area in the mean calculation / result
+    """
+    if average == "mean":
+        center = array[array > 0].mean() if mask is not None else array.mean()
+
+    elif average == "quantile":
+        center = (
+            np.quantile(array[array > 0], q).astype(float)
+            if mask is not None
+            else np.quantile(array, q).astype(float)
+        )
+    else:
+        raise NotImplementedError()
+
+    arr = copy.deepcopy(array)
+    if low:
+        arr[arr < (center - low / 2.0)] = 0
+    if high:
+        arr[arr > (center + high / 2.0)] = 0
+    return arr
+
+
+def mark_boundary(image: np.ndarray, kernel_size: int, kernel: np.ndarray = None) -> np.ndarray:
+    kernel = np.ones((kernel_size, kernel_size), np.uint8) if kernel is None else kernel
+    return cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel)
+
+
+def plot_frames(
+    frames: np.ndarray, path: str | Path, index: np.ndarray, prefix: str = None, scale: bool = False
+) -> None:
+    for frame, i in zip(frames, index):
+        amp = max(abs(frame.max()), abs(frame.min())) if scale else None
+        filename = index if not prefix else prefix + str(i) + ".png"
+        vmin = -amp if amp is not None else None
+        plt.imsave(path / filename, frame, cmap="RdBu", vmin=vmin, vmax=amp)
+
+
+def lineplot_minmax(frames: xr.DataArray, calc_dims: tuple[str, ...], path: Path | str) -> None:
+    plt.plot(frames.max(calc_dims))
+    plt.plot(frames.min(calc_dims))
+    plt.savefig(path)
+    plt.close()
+
+
+def twin_naming(detected_on: np.ndarray) -> Generator[str, None, None]:
+    """
+    naming scheme that separates cells detected at the same frame with "_{duplicate_idx}" suffix.
+    """
+    counts = Counter(detected_on)
+    seen = defaultdict(int)
+
+    for v in detected_on:
+        seen[v] += 1
+        base = str(v)
+
+        if counts[v] > 1 and seen[v] > 1:
+            yield f"{base}_{seen[v]}"
+        else:
+            yield base
+
+
+def footprint_rims(footprints: list[xr.DataArray]) -> np.ndarray:
+    boundary = []
+    for fp in footprints:
+        eroded = erode_to_average(fp.values, "quantile", low=0.1, mask=fp.values > 0, q=0.8)
+        border = mark_boundary(eroded, kernel_size=3)
+        boundary.append(border > (border.max() / 3))
+
+    all_bounds = np.max(boundary, axis=0)
+
+    return all_bounds
diff --git a/src/cala/testing/toy.py b/src/cala/testing/toy.py
index b0467eab..b0e2997d 100644
--- a/src/cala/testing/toy.py
+++ b/src/cala/testing/toy.py
@@ -7,7 +7,8 @@
 from pydantic import BaseModel, ConfigDict, PrivateAttr, field_validator, model_validator
 from skimage.morphology import disk
 
-from cala.arrays import AXIS, Footprints, Frame, Movie, Traces
+from cala.arrays import AXIS, Frame, Movie
+from cala.arrays.models import Footprints, Traces
 
 
 class FrameDims(BaseModel):
diff --git a/src/cala/testing/util.py b/src/cala/testing/util.py
index 13736ffd..ed9b424e 100644
--- a/src/cala/testing/util.py
+++ b/src/cala/testing/util.py
@@ -1,5 +1,7 @@
+from collections.abc import Callable, Iterable
+from copy import deepcopy
 from itertools import compress
-from typing import TypeVar
+from typing import Any, TypeVar
 
 import cv2
 import numpy as np
@@ -133,3 +135,23 @@ def total_gradient_magnitude(image: np.ndarray) -> float:
     c_I = np.linalg.norm(grad_magnitude_map, ord="fro")
 
     return c_I
+
+
+def circle_points(
+    img: np.ndarray, locs: Iterable[tuple[int, int]], grayscale: bool = True, **kwargs: Any
+) -> np.ndarray:
+    color = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) if grayscale else deepcopy(img)
+    for loc in locs:
+        color = cv2.circle(color, loc, **kwargs)
+    return color
+
+
+def check_shift_validity(
+    source: xr.DataArray, target: xr.DataArray, shift: np.ndarray, threshold: float, func: Callable
+) -> bool:
+    expected = np.array([5, 5])
+    tester = shift_by(source.values, *expected)
+    total = func(tester, target.values)
+    result = total - shift
+    error = np.linalg.norm(result - expected)
+    return error < threshold
diff --git a/src/cala/util.py b/src/cala/util.py
index bad5fa98..9cf8f1c0 100644
--- a/src/cala/util.py
+++ b/src/cala/util.py
@@ -1,10 +1,12 @@
 from collections.abc import Callable, Sequence
 from pathlib import Path
 from shutil import rmtree
+from typing import Any
 from uuid import uuid4
 
 import numpy as np
 import xarray as xr
+from noob.event import MetaSignal
 from numpydantic.ndarray import NDArray
 from sparse import COO
 from xarray import Coordinates
@@ -120,3 +122,15 @@ def concatenate_coordinates(left: Coordinates, right: Coordinates) -> dict:
 
     combined = {k: np.concatenate([ll[k], rr[k]]) for k in ll}
     return combined
+
+
+def none_to_noevent(value: Any = None) -> Any:
+    """
+    A temporary WRONG implementation of capturing NoEvent from TubeNodes.
+    Needs to be fixed at the noob level: https://github.com/miniscope/noob/issues/162
+
+    """
+    if value is None:
+        return MetaSignal.NoEvent
+    else:
+        return value
diff --git a/tests/data/pipelines/downsample.yaml b/tests/data/pipelines/downsample.yaml
new file mode 100644
index 00000000..69f267a8
--- /dev/null
+++ b/tests/data/pipelines/downsample.yaml
@@ -0,0 +1,23 @@
+noob_id: cala-downsample
+noob_model: noob.tube.TubeSpecification
+noob_version: 0.1.1.dev231+g30a8286
+
+input:
+  frame:
+    scope: process
+    type: cala.assets.Frame
+
+nodes:
+  gather:
+    type: gather
+    params:
+      n: 4
+    depends:
+      - input.frame
+  downsample:
+    type: cala.nodes.prep.downsample
+    depends:
+      - frames: gather.value
+  return:
+    type: return
+    depends: downsample.frame
diff --git a/tests/data/pipelines/segment.yaml b/tests/data/pipelines/expand_base.yaml
similarity index 60%
rename from tests/data/pipelines/segment.yaml
rename to tests/data/pipelines/expand_base.yaml
index 859855a9..9b03dfa5 100644
--- a/tests/data/pipelines/segment.yaml
+++ b/tests/data/pipelines/expand_base.yaml
@@ -1,65 +1,65 @@
-noob_id: segment-loop
+noob_id: cala-expand
 noob_model: noob.tube.TubeSpecification
 noob_version: 0.1.1.dev209+g1cafe61
+
 input:
-  residual:
-    scope: process
-    type: np.ndarray
+  cell_size:
+    scope: tube
+    type: int
   energy:
     scope: process
-    type: np.ndarray
-  radius:
-    scope: process
-    type: int
-  footprints:
+    type: cala.arrays.Frame
+  residual:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.Residual
   traces:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.Traces
+  footprints:
+    scope: process
+    type: cala.arrays.Footprints
   frame_sum:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.PixStats
   trace_sum:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.CompStats
   overlaps:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.Overlaps
   buffer:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.Buffer
+
 nodes:
   nmf:
     type: cala.nodes.segment.SliceNMF
     params:
-      min_frames: 10
-      detect_thresh: 1.0
-      reprod_tol: 0.001
+      min_frames: 100
+      detect_thresh: 3.0
+      reprod_tol: 0.005
     depends:
       - residuals: input.residual
       - energy: input.energy
-      - detect_radius: input.radius
+      - radius: input.cell_size
   catalog:
     type: cala.nodes.segment.Cataloger
     params:
-      age_limit: 100
+      age_limit: 500
       shape_smooth_kwargs:
-        ksize:
-          - 1
-          - 1
+        ksize: [ 1, 1 ]
         sigmaX: 0
       trace_smooth_kwargs:
         sigma: 2
       merge_threshold: 0.95
-      val_threshold: 0.5
-      cnt_threshold: 10
+      val_threshold: 0.8
+      cnt_threshold: 25
     depends:
       - new_fps: nmf.new_fps
       - new_trs: nmf.new_trs
       - existing_fp: input.footprints
       - existing_tr: input.traces
-  detect_update:
+  expand_update:
     type: cala.nodes.segment.update_assets
     depends:
       - new_footprints: catalog.new_footprints
@@ -70,3 +70,11 @@ nodes:
       - component_stats: input.trace_sum
       - overlaps: input.overlaps
       - buffer: input.buffer
+  return:
+    type: return
+    depends:
+      - traces: expand_update.traces
+      - footprints: expand_update.footprints
+      - frame_sum: expand_update.frame_sum
+      - trace_sum: expand_update.trace_sum
+      - overlaps: expand_update.overlaps
diff --git a/tests/data/pipelines/expand_mp.yaml b/tests/data/pipelines/expand_mp.yaml
new file mode 100644
index 00000000..4d73fae9
--- /dev/null
+++ b/tests/data/pipelines/expand_mp.yaml
@@ -0,0 +1,64 @@
+noob_id: cala-segment
+
+input:
+  cell_size:
+    scope: tube
+    type: int
+  residual_frame:
+    scope: process
+    type: cala.arrays.Frame
+  energy:
+    scope: process
+    type: cala.arrays.Frame
+  footprints:
+    scope: process
+    type: cala.arrays.Footprints
+  traces:
+    scope: process
+    type: cala.arrays.Traces
+
+assets:
+  residuals:
+    type: cala.arrays.Buffer
+    params:
+      size: 100
+    scope: runner
+
+nodes:
+  extend_residuals:
+    type: cala.nodes.segment.extend_residuals
+    depends:
+      - residuals: assets.residuals
+      - frame: input.residual_frame
+  nmf:
+    type: cala.nodes.segment.SliceNMF
+    params:
+      min_frames: 100
+      detect_thresh: 3.0
+      reprod_tol: 0.005
+    depends:
+      - residuals: extend_residuals.movie
+      - energy: input.energy
+      - detect_radius: input.cell_size
+  catalog:
+    type: cala.nodes.segment.Cataloger
+    params:
+      age_limit: 100
+      shape_smooth_kwargs:
+        ksize: [ 7, 7 ]
+        sigmaX: 0
+      trace_smooth_kwargs:
+        sigma: 2
+      merge_threshold: 0.95
+      val_threshold: 0.8
+      cnt_threshold: 20
+    depends:
+      - new_fps: nmf.new_fps
+      - new_trs: nmf.new_trs
+      - existing_fp: input.footprints
+      - existing_tr: input.traces
+  return:
+    type: return
+    depends:
+      - new_traces: catalog.new_traces
+      - new_footprints: catalog.new_footprints
\ No newline at end of file
diff --git a/tests/data/pipelines/ingest.yaml b/tests/data/pipelines/fit_base.yaml
similarity index 59%
rename from tests/data/pipelines/ingest.yaml
rename to tests/data/pipelines/fit_base.yaml
index 25fb9dc7..1d3d6872 100644
--- a/tests/data/pipelines/ingest.yaml
+++ b/tests/data/pipelines/fit_base.yaml
@@ -1,32 +1,31 @@
-noob_id: ingest-loop
+noob_id: cala-fit
 noob_model: noob.tube.TubeSpecification
 noob_version: 0.1.1.dev209+g1cafe61
 input:
   frame:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.Frame
   traces:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.Traces
   footprints:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.Footprints
   overlaps:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.Overlaps
   pix_stats:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.PixStats
   comp_stats:
     scope: process
-    type: np.ndarray
+    type: cala.arrays.CompStats
   residuals:
     scope: process
-    type: np.ndarray
-
+    type: cala.arrays.Residuals
 
 nodes:
-  trace_frame:
+  fit_trace:
     type: cala.nodes.omf.traces.Tracer
     params:
       tol: 0.001
@@ -36,36 +35,41 @@ nodes:
       - footprints: input.footprints
       - frame: input.frame
       - overlaps: input.overlaps
-  pix_frame:
+  fit_frame_summary:
     type: cala.nodes.omf.pixel_stats.ingest_frame
     depends:
       - pixel_stats: input.pix_stats
       - frame: input.frame
-      - new_traces: trace_frame.latest_trace
+      - new_traces: fit_trace.latest_trace
       - footprints: input.footprints
-  comp_frame:
+  fit_trace_summary:
     type: cala.nodes.omf.component_stats.ingest_frame
     depends:
       - component_stats: input.comp_stats
       - frame: input.frame
-      - new_traces: trace_frame.latest_trace
-  footprints_frame:
+      - new_traces: fit_trace.latest_trace
+  fit_footprints:
     type: cala.nodes.omf.footprints.Footprinter
     params:
       max_iter: 5
       ratio_lb: 0.10
     depends:
       - footprints: input.footprints
-      - pixel_stats: pix_frame.value
-      - component_stats: comp_frame.value
-  residual:
+      - pixel_stats: fit_frame_summary.value
+      - component_stats: fit_trace_summary.value
+  fit_residual:
     type: cala.nodes.omf.residual.Residuer
     depends:
       - frame: input.frame
-      - footprints: footprints_frame.footprints
+      - footprints: fit_footprints.footprints
       - traces: input.traces
       - residuals: input.residuals
   return:
     type: return
     depends:
-      residual.std
\ No newline at end of file
+      - energy: fit_residual.std
+      - residual: fit_residual.movie
+      - traces: fit_trace.traces
+      - footprints: fit_footprints.footprints
+      - frame_sum: fit_frame_summary.value
+      - trace_sum: fit_trace_summary.value
\ No newline at end of file
diff --git a/tests/data/pipelines/fit_mp.yaml b/tests/data/pipelines/fit_mp.yaml
new file mode 100644
index 00000000..e591d6ff
--- /dev/null
+++ b/tests/data/pipelines/fit_mp.yaml
@@ -0,0 +1,81 @@
+noob_id: cala-fit-multiprocess
+
+input:
+  frame:
+    scope: process
+    type: cala.arrays.Frame
+  footprints:
+    scope: process
+    type: cala.arrays.Footprints
+  traces:
+    scope: process
+    type: cala.arrays.Traces
+  comp_stats:
+    scope: process
+    type: cala.arrays.CompStats
+  overlaps:
+    scope: process
+    type: cala.arrays.Overlaps
+  pstat_new:
+    scope: process
+    type: cala.arrays.PixStats
+
+assets:
+  pix_stats:
+    type: cala.arrays.PixStats
+    scope: runner
+    depends: fit_pix.pixel_stats
+
+nodes:
+  fit_trace:
+    type: cala.nodes.omf.traces.Tracer
+    params:
+      tol: 0.001
+      max_iter: 100
+    depends:
+      - frame: input.frame
+      - traces: input.traces
+      - footprints: input.footprints
+      - overlaps: input.overlaps
+  extend_pix:
+    type: cala.nodes.omg.pixel_stats.ingest_component
+    depends:
+      - pixel_stats: assets.pix_stats
+      - new_component: input.pix_stats
+  fit_pix:
+    type: cala.nodes.omf.pixel_stats.ingest_frame
+    depends:
+      - frame: input.frame
+      - curr_trace: fit_trace.latest_trace
+      - footprints: input.footprints
+      - pixel_stats: extend_pix.pstat_new
+  fit_comp:
+    type: cala.nodes.omf.component_stats.ingest_frame
+    depends:
+      - frame: input.frame
+      - curr_trace: fit_trace.latest_trace
+      - component_stats: input.comp_stats
+  fit_footprints:
+    type: cala.nodes.omf.footprints.Footprinter
+    params:
+      tol: 0.0001
+      max_iter: 5
+      ratio_lb: 0.10
+    depends:
+      - footprints: input.footprints
+      - pixel_stats: fit_pix.value
+      - component_stats: fit_comp.value
+  residual:
+    type: cala.nodes.omf.residual.Residuer
+    depends:
+      - frame: input.frame
+      - traces: fit_trace.latest_trace
+      - footprints: fit_footprints.footprints
+  return:
+    type: return
+    depends:
+      - curr_trace: fit_trace.latest_trace
+      - footprints: fit_footprints.footprints
+      - component_stats: fit_comp.value
+      - curr_residual: residual.frame
+      - energy: residual.std
diff --git a/tests/data/pipelines/minian.yaml b/tests/data/pipelines/full_minian.yaml
similarity index 99%
rename from tests/data/pipelines/minian.yaml
rename to tests/data/pipelines/full_minian.yaml
index d0dd113b..cfb8b07e 100644
--- a/tests/data/pipelines/minian.yaml
+++ b/tests/data/pipelines/full_minian.yaml
@@ -1,4 +1,4 @@
-noob_id: with-minian
+noob_id: test-with-minian
 noob_model: noob.tube.TubeSpecification
 noob_version: 0.1.1.dev118+g64d81b7
 
diff --git a/tests/data/pipelines/full_mp.yaml b/tests/data/pipelines/full_mp.yaml
new file mode 100644
index 00000000..0a4e48c1
--- /dev/null
+++ b/tests/data/pipelines/full_mp.yaml
@@ -0,0 +1,113 @@
+noob_id: cala-basic
+noob_model: noob.tube.TubeSpecification
+noob_version: 0.1.1.dev231+g30a8286
+
+input:
+  cell_size:
+    scope: tube
+    type: int
+  frame:
+    scope: process
+    type: numpy.ndarray
+  index:
+    scope: process
+    type: int
+
+assets:
+  traces:
+    type: cala.arrays.Traces
+    scope: runner
+    params:
+      zarr_path: traces/
+      peek_size: 100
+      flush_interval: 1000
+    depends: fit_assets.traces
+  footprints:
+    type: cala.arrays.Footprints
+    params:
+      sparsify: true
+    scope: runner
+    depends: fit_assets.footprints
+  comp_stats:
+    type: cala.arrays.CompStats
+    scope: runner
+    depends: fit_assets.comp_stats
+  overlaps:
+    type: cala.arrays.Overlaps
+    scope: runner
+    depends: extend.overlaps
+  residuals:
+    type: cala.arrays.Buffer
+    params:
+      size: 100
+    scope: runner
+  buffer:
+    type: cala.arrays.Buffer
+    params:
+      size: 100
+    scope: runner
+    depends: fill_buffer.buffer
+
+nodes:
+  frame:
+    type: cala.nodes.prep.package_frame
+    depends:
+      - frame: input.frame
+      - index: input.index
+  preprocess:
+    type: tube
+    params:
+      tube: cala-preprocess
+    depends:
+      - frame: frame.value
+  pr_noevent:
+    type: cala.util.none_to_noevent
+    depends:
+      - preprocess.value
+
+  fit:
+    type: tube
+    params:
+      tube: cala-fit
+    depends:
+      - frame: pr_noevent.value
+      - traces: assets.traces
+      - footprints: assets.footprints
+      - comp_stats: assets.comp_stats
+      - overlaps: assets.overlaps
+      - pstat_new: assets.pstat_new
+
+  fit_assets:
+    type: cala.nodes.omf.fit_assets
+    depends:
+      - traces: fit.latest_trace
+      - footprints: fit.footprints
+      - comp_stats: fit.comp_stats
+  segment:
+    type: tube
+    params:
+      tube: cala-segment
+    depends:
+      - cell_size: input.cell_size
+      - traces: fit_assets.traces
+      - footprints: fit_assets.footprints
+      - residual_frame: fit.curr_residual
+      - energy: fit.energy
+  fill_buffer:
+    type: cala.nodes.omf.pixel_stats.fill_buffer
+    depends:
+      - buffer: assets.buffer
+      - frame: pr_noevent.value
+  extend: # passthrough if no new estimator from last iteration.
+    type: cala.nodes.segment.extend_assets
+    depends:
+      - new_traces: segment.new_traces
+      - new_footprints: segment.new_footprints
+      - traces: fit_assets.traces
+      - footprints: fit_assets.footprints
+      - trace_summary: fit_assets.comp_stats
+      - overlaps: assets.overlaps
+  
+  return:
+    type: return
+    depends: pr_noevent.value
diff --git a/tests/data/pipelines/full_recursive.yaml b/tests/data/pipelines/full_recursive.yaml
new file mode 100644
index 00000000..9558b9f7
--- /dev/null
+++ b/tests/data/pipelines/full_recursive.yaml
@@ -0,0 +1,100 @@
+noob_id: cala-long-recording
+noob_model: noob.tube.TubeSpecification
+noob_version: 0.1.1.dev118+g64d81b7
+
+input:
+  cell_size:
+    scope: tube
+    type: int
+  frame:
+    scope: process
+    type: numpy.ndarray
+  epoch:
+    scope: process
+    type: int
+
+assets:
+  footprints:
+    type: cala.arrays.Footprints
+    params:
+      sparsify: True
+    scope: runner
+    depends: expand.footprints
+  traces:
+    type: cala.arrays.Traces
+    scope: runner
+    params:
+      zarr_path: traces/
+      peek_size: 100
+      flush_interval: 1000
+    depends: expand.traces
+  pix_stats:
+    type: cala.arrays.PixStats
+    scope: runner
+    depends: expand.frame_sum
+  comp_stats:
+    type: cala.arrays.CompStats
+    scope: runner
+    depends: expand.trace_sum
+  overlaps:
+    type: cala.arrays.Overlaps
+    scope: runner
+    depends: expand.overlaps
+  residuals:
+    type: cala.arrays.Buffer
+    params:
+      size: 100
+    scope: runner
+    depends: fit.residuals
+  buffer:
+    scope: runner
+    params:
+      size: 100
+    type: cala.arrays.Buffer
+    depends: buffer.buffer
+
+nodes:
+  frame:
+    type: cala.nodes.prep.package_frame
+    depends:
+      - frame: input.frame
+      - index: input.epoch
+
+  preprocess:
+    type: tube
+    params:
+      tube: cala-preprocess
+    depends:
+      - frame: frame.value
+
+  fit:
+    type: tube
+    params:
+      tube: cala-fit
+    depends:
+      - frame: preprocess.value
+      - traces: assets.traces
+      - overlaps: assets.overlaps
+      - footprints: assets.footprints
+      - pix_stats: assets.pix_stats
+      - comp_stats: assets.comp_stats
+      - residuals: assets.residuals
+  buffer:
+    type: cala.nodes.omf.pixel_stats.fill_buffer
+    depends:
+      - buffer: assets.buffer
+      - frame: preprocess.value
+
+  expand:
+    type: tube
+    params:
+      tube: cala-expand
+    depends:
+      - energy: fit.energy
+      - residual: fit.residual
+      - traces: fit.traces
+      - footprints: fit.footprints
+      - buffer: buffer.buffer
+      - frame_sum: fit.frame_sum
+      - trace_sum: fit.trace_sum
+      - overlaps: assets.overlaps
diff --git a/tests/data/pipelines/odl.yaml b/tests/data/pipelines/full_sim.yaml
similarity index 75%
rename from tests/data/pipelines/odl.yaml
rename to tests/data/pipelines/full_sim.yaml
index 2f3333aa..8877e0b8 100644
--- a/tests/data/pipelines/odl.yaml
+++ b/tests/data/pipelines/full_sim.yaml
@@ -2,6 +2,17 @@ noob_id: cala-odl
 noob_model: noob.tube.TubeSpecification
 noob_version: 0.1.1.dev118+g64d81b7
 
+input:
+  source:
+    type: cala.arrays.Frame
+    scope: process
+  epoch:
+    type: int
+    scope: process
+  radius:
+    type: int
+    scope: tube
+
 assets:
   buffer:
     type: cala.arrays.Buffer
@@ -15,11 +26,11 @@ assets:
     scope: runner
   traces:
     type: cala.arrays.Traces
+    scope: runner
     params:
       zarr_path: traces/
       peek_size: 100
       flush_interval: 200
-    scope: runner
   pix_stats:
     type: cala.arrays.PixStats
     scope: runner
@@ -36,40 +47,25 @@ assets:
     scope: runner
 
 nodes:
-  source:
-    type: cala.testing.SingleCellSource
-  counter:
-    type: cala.nodes.prep.counter
   frame:
     type: cala.nodes.prep.package_frame
     depends:
-      - frame: source.frame
-      - index: counter.idx
+      - frame: input.source
+      - index: input.epoch
 
-  #PREPROCESS BEGINS
-  glow:
-    type: cala.nodes.prep.GlowRemover
-    depends:
-      - frame: frame.value
-  radius:
-    type: cala.nodes.prep.SizeEst
+  prep:
+    type: tube
     params:
-      hardset_radius: 5
+      tube: cala-prep
     depends:
-      - frame: glow.frame
-  cache:
-    type: cala.nodes.omf.pixel_stats.fill_buffer
-    depends:
-      - buffer: assets.buffer
-      - frame: glow.frame
-  #PREPROCESS ENDS
+      - frame: frame.value
 
   ingest:
     type: tube
     params:
-      tube: ingest-loop
+      tube: cala-ingest
     depends:
-      - frame: glow.frame
+      - frame: prep.value
       - traces: assets.traces
       - footprints: assets.footprints
       - overlaps: assets.overlaps
@@ -77,14 +73,19 @@ nodes:
       - comp_stats: assets.comp_stats
       - residuals: assets.residuals
 
+  cache:
+    type: cala.nodes.omf.pixel_stats.fill_buffer
+    depends:
+      - buffer: assets.buffer
+      - frame: prep.value
   segment:
     type: tube
     params:
-      tube: segment-loop
+      tube: cala-segment
     depends:
       - residual: assets.residuals
       - energy: ingest.value
-      - radius: radius.radius
+      - radius: input.radius
       - footprints: assets.footprints
       - traces: assets.traces
       - frame_sum: assets.pix_stats
@@ -95,5 +96,4 @@ nodes:
   return:
     type: return
     depends:
-      - raw: frame.value
-      - prep: glow.frame
\ No newline at end of file
+      - prep: prep.value
\ No newline at end of file
diff --git a/tests/data/pipelines/full_unravel.yaml b/tests/data/pipelines/full_unravel.yaml
new file mode 100644
index 00000000..1f031052
--- /dev/null
+++ b/tests/data/pipelines/full_unravel.yaml
@@ -0,0 +1,171 @@
+noob_id: cala-unraveled
+noob_model: noob.tube.TubeSpecification
+noob_version: 0.1.1.dev118+g64d81b7
+
+input:
+  cell_size:
+    scope: tube
+    type: int
+  frame:
+    scope: process
+    type: numpy.ndarray
+  epoch:
+    scope: process
+    type: int
+
+assets:
+  traces:
+    type: cala.arrays.models.Traces
+    scope: runner
+    params:
+      zarr_path: traces/
+      peek_size: 100
+      flush_interval: 1000
+    depends: expand_update.traces
+  footprints:
+    type: cala.arrays.models.Footprints
+    scope: runner
+    params:
+      sparsify: True
+    depends: expand_update.footprints
+  pix_stats:
+    type: cala.arrays.models.PixStats
+    scope: runner
+    depends: expand_update.pixel_stats
+  comp_stats:
+    type: cala.arrays.models.CompStats
+    scope: runner
+    depends: expand_update.component_stats
+  overlaps:
+    type: cala.arrays.models.Overlaps
+    scope: runner
+    depends: expand_update.overlaps
+  residuals:
+    type: cala.arrays.Buffer
+    scope: runner
+    params:
+      size: 100
+    depends: fit_residual.movie
+  buffer:
+    scope: runner
+    params:
+      size: 100
+    type: cala.arrays.Buffer
+    depends: buffer.buffer
+
+nodes:
+  frame:
+    type: cala.nodes.prep.package_frame
+    depends:
+      - frame: input.frame
+      - index: input.epoch
+
+  preprocess:
+    type: tube
+    params:
+      tube: cala-preprocess
+    depends:
+      - frame: frame.value
+
+  buffer:
+    type: cala.nodes.omf.pixel_stats.fill_buffer
+    depends:
+      - buffer: assets.buffer
+      - frame: preprocess.value
+
+  # FIT cannot be TubeNode yet :(
+  fit_trace:
+    type: cala.nodes.omf.traces.Tracer
+    params:
+      tol: 0.001
+      max_iter: 100
+    depends:
+      - traces: assets.traces
+      - footprints: assets.footprints
+      - frame: preprocess.value
+      - overlaps: assets.overlaps
+  fit_frame_summary:
+    type: cala.nodes.omf.pixel_stats.ingest_frame
+    params:
+      noise_threshold: 0
+      trace_threshold: 8
+    depends:
+      - pixel_stats: assets.pix_stats
+      - frame: preprocess.value
+      - new_traces: fit_trace.new_fit
+      - footprints: assets.footprints
+  fit_trace_summary:
+    type: cala.nodes.omf.component_stats.ingest_frame
+    params:
+      trace_threshold: 8
+    depends:
+      - component_stats: assets.comp_stats
+      - frame: preprocess.value
+      - new_traces: fit_trace.new_fit
+  fit_footprint:
+    type: cala.nodes.omf.footprints.Footprinter
+    params:
+      max_iter: 5
+      ratio_lb: 0.10
+    depends:
+      - footprints: assets.footprints
+      - pixel_stats: fit_frame_summary.value
+      - component_stats: fit_trace_summary.value
+  fit_residual:
+    type: cala.nodes.omf.residual.Residuer
+    depends:
+      - frame: preprocess.value
+      - footprints: fit_footprint.footprints
+      - traces: fit_trace.traces
+      - residuals: assets.residuals
+
+
+  prune:
+    type: cala.nodes.segment.prune.deprecate
+    depends:
+      - traces: fit_trace.traces
+      - footprints: fit_footprint.footprints
+      - pix_stats: fit_frame_summary.value
+      - comp_stats: fit_trace_summary.value
+      - overlaps: assets.overlaps
+
+
+  # EXPAND cannot be TubeNode yet :(
+  nmf:
+    type: cala.nodes.segment.SliceNMF
+    params:
+      min_frames: 100
+      detect_thresh: 3.0
+      reprod_tol: 0.005
+    depends:
+      - residuals: fit_residual.movie
+      - energy: fit_residual.std
+      - cell_size: input.cell_size
+  catalog:
+    type: cala.nodes.segment.Cataloger
+    params:
+      age_limit: 500
+      shape_smooth_kwargs:
+        ksize: [ 1, 1 ]
+        sigmaX: 0
+      trace_smooth_kwargs:
+        sigma: 2
+      merge_threshold: 0.95
+      val_threshold: 0.8
+      cnt_threshold: 25
+    depends:
+      - new_fps: nmf.new_fps
+      - new_trs: nmf.new_trs
+      - existing_fp: prune.footprints
+      - existing_tr: prune.traces
+  expand_update:
+    type: cala.nodes.segment.update_assets
+    depends:
+      - new_footprints: catalog.new_footprints
+      - new_traces: catalog.new_traces
+      - footprints: prune.footprints
+      - traces: prune.traces
+      - pixel_stats: prune.pix_stats
+      - component_stats: prune.comp_stats
+      - overlaps: prune.overlaps
+      - buffer: buffer.buffer
\ No newline at end of file
diff --git a/tests/data/pipelines/long_recording.yaml b/tests/data/pipelines/long_recording.yaml
deleted file mode 100644
index 7b2ba130..00000000
--- a/tests/data/pipelines/long_recording.yaml
+++ /dev/null
@@ -1,212 +0,0 @@
-noob_id: long-recording
-noob_model: noob.tube.TubeSpecification
-noob_version: 0.1.1.dev118+g64d81b7
-
-assets:
-  buffer:
-    type: cala.arrays.Buffer
-    params:
-      size: 100
-    scope: runner
-  footprints:
-    type: cala.arrays.Footprints
-    params:
-      sparsify: True
-    scope: runner
-  traces:
-    type: cala.arrays.Traces
-    scope: runner
-    params:
-      zarr_path: traces/
-      peek_size: 100
-      flush_interval: 1000
-  pix_stats:
-    type: cala.arrays.PixStats
-    scope: runner
-  comp_stats:
-    type: cala.arrays.CompStats
-    scope: runner
-  overlaps:
-    type: cala.arrays.Overlaps
-    scope: runner
-  residuals:
-    type: cala.arrays.Buffer
-    params:
-      size: 100
-    scope: runner
-
-
-nodes:
-  source:
-    type: cala.nodes.io.stream
-    params:
-      subdir: long_recording
-      extension: .avi
-  counter:
-    type: cala.nodes.prep.counter
-  frame:
-    type: cala.nodes.prep.package_frame
-    depends:
-      - frame: source.value
-      - index: counter.idx
-
-  #PREPROCESS BEGINS
-  hotpix:
-    type: cala.nodes.prep.blur
-    params:
-      method: median
-      kwargs:
-        ksize: 3
-    depends:
-      - frame: frame.value
-  flatten:
-    type: cala.nodes.prep.butter
-    params:
-      kwargs:
-        cutoff_frequency_ratio: 0.010
-    depends:
-      - frame: hotpix.frame
-  lines:
-    type: cala.nodes.prep.remove_mean
-    params:
-      orient: both
-    depends:
-      - frame: flatten.frame
-  motion:
-    type: cala.nodes.prep.Anchor
-    depends:
-      - frame: lines.frame
-  glow:
-    type: cala.nodes.prep.GlowRemover
-    depends:
-      - frame: motion.frame
-  gather:
-    type: gather
-    params:
-      n: 4
-    depends:
-      - glow.frame
-  downsample: # 20fps -> 5fps
-    type: cala.nodes.prep.downsample
-    params:
-      x_range: [ 250, 350 ]
-      y_range: [ 250, 350 ]
-      t_downsample: 4
-    depends:
-      - frames: gather.value
-  size_est:
-    type: cala.nodes.prep.SizeEst
-    params:
-      hardset_radius: 8
-    depends:
-      - frame: downsample.frame
-  cache:
-    type: cala.nodes.omf.pixel_stats.fill_buffer
-    depends:
-      - buffer: assets.buffer
-      - frame: downsample.frame
-  #PREPROCESS ENDS
-
-  # FRAME UPDATE BEGINS
-  trace_frame:
-    type: cala.nodes.omf.traces.Tracer
-    params:
-      tol: 0.001
-      max_iter: 100
-    depends:
-      - traces: assets.traces
-      - footprints: assets.footprints
-      - frame: downsample.frame
-      - overlaps: assets.overlaps
-  pix_frame:
-    type: cala.nodes.omf.pixel_stats.ingest_frame
-    depends:
-      - pixel_stats: assets.pix_stats
-      - frame: downsample.frame
-      - new_traces: trace_frame.latest_trace
-      - footprints: assets.footprints
-  comp_frame:
-    type: cala.nodes.omf.component_stats.ingest_frame
-    depends:
-      - component_stats: assets.comp_stats
-      - frame: downsample.frame
-      - new_traces: trace_frame.latest_trace
-  footprints_frame:
-    type: cala.nodes.omf.footprints.Footprinter
-    params:
-      bep: 0
-      tol: 0.0001
-      max_iter: 5
-      ratio_lb: 0.10
-    depends:
-      - footprints: assets.footprints
-      - pixel_stats: pix_frame.value
-      - component_stats: comp_frame.value
-
-  residual:
-    type: cala.nodes.omf.residual.Residuer
-    depends:
-      - frame: downsample.frame
-      - footprints: footprints_frame.footprints
-      - traces: assets.traces
-      - residuals: assets.residuals
-  # FRAME UPDATE ENDS
-
-  # DETECT BEGINS
-  nmf:
-    type: cala.nodes.segment.SliceNMF
-    params:
-      min_frames: 100
-      detect_thresh: 8.0
-      reprod_tol: 0.005
-    depends:
-      - residuals: residual.movie
-      - energy: residual.std
-      - detect_radius: size_est.radius
-  catalog:
-    type: cala.nodes.segment.Cataloger
-    params:
-      age_limit: 100
-      shape_smooth_kwargs:
-        ksize: [ 5, 5 ]
-        sigmaX: 0
-      trace_smooth_kwargs:
-        sigma: 2
-      merge_threshold: 0.9
-      val_threshold: 0.5
-      cnt_threshold: 5
-    depends:
-      - new_fps: nmf.new_fps
-      - new_trs: nmf.new_trs
-      - existing_fp: assets.footprints
-      - existing_tr: assets.traces
-  detect_update:
-    type: cala.nodes.segment.update_assets
-    depends:
-      - new_footprints: catalog.new_footprints
-      - new_traces: catalog.new_traces
-      - footprints: assets.footprints
-      - traces: assets.traces
-      - pixel_stats: assets.pix_stats
-      - component_stats: assets.comp_stats
-      - overlaps: assets.overlaps
-      - buffer: assets.buffer
-  #  DETECT ENDS
-
-  save_trace:
-    type: cala.nodes.io.save_asset
-    params:
-      target_epoch: 855999 #  formula: 1000 * n_chunks - 1
-      path: traces_fin/
-    depends:
-      - asset: assets.traces
-      - curr_epoch: counter.idx
-
-  save_shape:
-    type: cala.nodes.io.save_asset
-    params:
-      target_epoch: 855999 #  formula: 1000 * n_chunks - 1
-      path: footprints_fin/
-    depends:
-      - asset: assets.footprints
-      - curr_epoch: counter.idx
diff --git a/tests/data/pipelines/persist.yaml b/tests/data/pipelines/persist.yaml
new file mode 100644
index 00000000..bd2a8b48
--- /dev/null
+++ b/tests/data/pipelines/persist.yaml
@@ -0,0 +1,36 @@
+noob_id: cala-persist
+noob_model: noob.tube.TubeSpecification
+noob_version: 0.1.2.dev297+g57d347b
+
+input:
+  save_epoch:
+    type: int
+    scope: tube
+  epoch:
+    type: int
+    scope: process
+  footprints:
+    type: cala.arrays.Footprints
+    scope: process
+  traces:
+    type: cala.arrays.Traces
+    scope: process
+
+nodes:
+  save_trace:
+    type: cala.nodes.io.save_asset
+    params:
+      target_epoch: input.save_epoch  # 855999 (1000 * n_chunks - 1)
+      path: traces_fin/
+    depends:
+      - asset: input.traces
+      - curr_epoch: input.epoch
+
+  save_shape:
+    type: cala.nodes.io.save_asset
+    params:
+      target_epoch: input.save_epoch  # 855999 (1000 * n_chunks - 1)
+      path: footprints_fin/
+    depends:
+      - asset: input.footprints
+      - curr_epoch: input.epoch
diff --git a/tests/data/pipelines/prep.yaml b/tests/data/pipelines/prep_base.yaml
similarity index 51%
rename from tests/data/pipelines/prep.yaml
rename to tests/data/pipelines/prep_base.yaml
index d846b61c..fcbede22 100644
--- a/tests/data/pipelines/prep.yaml
+++ b/tests/data/pipelines/prep_base.yaml
@@ -1,30 +1,13 @@
-noob_id: cala-prep
+noob_id: cala-preprocess
 noob_model: noob.tube.TubeSpecification
-noob_version: 0.1.1.dev118+g64d81b7
+noob_version: 0.1.1.dev231+g30a8286
 
-nodes:
-  source:
-    type: cala.nodes.io.stream
-    params:
-      files:
-        - cala/msCam1.avi
-        - cala/msCam2.avi
-        - cala/msCam3.avi
-        - cala/msCam4.avi
-        - cala/msCam5.avi
-        - cala/msCam6.avi
-        - cala/msCam7.avi
-        - cala/msCam8.avi
-        - cala/msCam9.avi
-        - cala/msCam10.avi
-  counter:
-    type: cala.nodes.prep.counter
+input:
   frame:
-    type: cala.nodes.prep.package_frame
-    depends:
-      - frame: source.value
-      - index: counter.idx
+    scope: process
+    type: cala.assets.Frame
 
+nodes:
   hotpix:
     type: cala.nodes.prep.blur
     params:
@@ -32,12 +15,12 @@ nodes:
       kwargs:
         ksize: 3
     depends:
-      - frame: frame.value
+      - frame: input.frame
   flatten:
     type: cala.nodes.prep.butter
     params:
       kwargs:
-        cutoff_frequency_ratio: 0.010
+        cutoff_frequency_ratio: 0.01
     depends:
       - frame: hotpix.frame
   lines:
@@ -50,12 +33,23 @@ nodes:
     type: cala.nodes.prep.Anchor
     depends:
       - frame: lines.frame
+  #  downsample:
+  #    type: tube
+  #    params:
+  #      tube: cala-downsample
+  #    depends:
+  #      - frame: motion.frame
+  #  ds_noevent:
+  #    type: cala.util.none_to_noevent
+  #    depends:
+  #      - downsample.value
   denoise:
     type: cala.nodes.prep.blur
     params:
-      method: median
+      method: gaussian
       kwargs:
-        ksize: 7
+        ksize: [ 9, 9 ]
+        sigmaX: 0
     depends:
       - frame: motion.frame
   glow:
@@ -64,4 +58,4 @@ nodes:
       - frame: denoise.frame
   return:
     type: return
-    depends: glow.frame
\ No newline at end of file
+    depends: glow.frame
diff --git a/tests/data/pipelines/prep_sim.yaml b/tests/data/pipelines/prep_sim.yaml
new file mode 100644
index 00000000..e9db3644
--- /dev/null
+++ b/tests/data/pipelines/prep_sim.yaml
@@ -0,0 +1,17 @@
+noob_id: cala-prep
+noob_model: noob.tube.TubeSpecification
+noob_version: 0.1.2.dev297+g57d347b
+
+input:
+  frame:
+    type: cala.arrays.Frame
+    scope: process
+
+nodes:
+  glow:
+    type: cala.nodes.prep.GlowRemover
+    depends:
+      - frame: input.frame
+  return:
+    type: return
+    depends: glow.frame
diff --git a/tests/fixtures/config.py b/tests/fixtures/config.py
index 47c9361f..45fb2c60 100644
--- a/tests/fixtures/config.py
+++ b/tests/fixtures/config.py
@@ -7,7 +7,7 @@
 import tomli_w
 import yaml
 from _pytest.monkeypatch import MonkeyPatch
-from noob.yaml import ConfigYAMLMixin, YamlDumper
+from noob.yaml import ConfigYAMLMixin, YamlRepresenter
 
 
 @pytest.fixture()
@@ -129,7 +129,7 @@ def set_local_yaml(tmp_cwd: Path) -> Callable[[dict[str, Any]], Path]:
 
     def _set_local_yaml(config: dict[str, Any]) -> Path:
         with open(yaml_path, "w") as yfile:
-            yaml.dump(config, yfile, Dumper=YamlDumper)
+            yaml.dump(config, yfile, Dumper=YamlRepresenter)
         return yaml_path
 
     return _set_local_yaml
diff --git a/tests/test_assets.py b/tests/test_assets.py
index 101fd6f0..ae844d70 100644
--- a/tests/test_assets.py
+++ b/tests/test_assets.py
@@ -127,7 +127,7 @@ def test_flush_after_deprecated(four_connected_cells, tmp_path, flush_interval)
 
     merged_ids = zarr_traces.array[AXIS.id_coord].values[0]
     intact_mask = ~np.isin(zarr_traces.array[AXIS.id_coord].values, merged_ids)
-    zarr_traces.keep(intact_mask)
+    zarr_traces.deprecate_except(intact_mask)
     zarr_traces.append(traces[intact_mask], dim=AXIS.frame_dim)
 
     assert zarr_traces.full_array().equals(xr.concat([traces] * 2, dim=AXIS.frame_dim)[intact_mask])
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
index 5cc2e50b..c6dc4db4 100644
--- a/tests/test_pipeline.py
+++ b/tests/test_pipeline.py
@@ -1,3 +1,4 @@
+import cv2
 import numpy as np
 import pytest
 import xarray as xr
@@ -5,6 +6,9 @@
 from noob.node import Node, NodeSpecification
 
 from cala.arrays import AXIS
+from cala.nodes.io import stream
+from cala.nodes.segment.quality_control import separate_by_filter
+from cala.plot import footprint_rims
 
 
 @pytest.fixture(
@@ -25,9 +29,8 @@ def source(request):
 
 
 @pytest.fixture(scope="module")
-def tube(source, tmp_path_factory):
-    tube = Tube.from_specification("cala-odl")
-    tube.nodes["source"] = source
+def tube(tmp_path_factory):
+    tube = Tube.from_specification("cala-odl", {"radius": 10})
     # tube.cube.arrays["traces"].params["zarr_path"] = tmp_path_factory.mktemp("traces")
 
     return tube
@@ -114,13 +117,41 @@ def test_reconstructed_movie(results):
 
     """
     raise NotImplementedError()
-    # elif src_name in ["SingleCellSource", "TwoCellsSource", "SeparateSource"]:
-    # expected = xr.concat(preprocessed_frames, dim=AXIS.frames_dim)
-    # result = (fps.array @ trs.array).transpose(*expected.dims)
-    #
-    # xr.testing.assert_allclose(expected, result.as_numpy(), atol=1e-5, rtol=1e-5)
-    #
-    # elif src_name == "SplitOffSource":
-    # expected = xr.concat(preprocessed_frames, dim=AXIS.frames_dim)
-    # result = (fps.array @ trs.array).transpose(*expected.dims)
-    # raise NotImplementedError("Deprecation not implemented")
+
+
+VIDEOS = [f"long_recording/{i}.avi" for i in range(1)]
+
+
+def test_recursive():
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter("circles_x2.mp4", fourcc, 30.0, (1200, 600))
+
+    gen = stream(VIDEOS)
+    tube = Tube.from_specification("cala-unraveled", {"cell_size": 10})
+    runner = SynchronousRunner(tube)
+    max_frame = np.zeros((600, 600))
+    A = runner.tube.state.assets["footprints"]
+
+    def event_cb(event) -> None:
+        nonlocal max_frame
+        nonlocal A
+        if event["node_id"] == "preprocess":
+            max_frame = np.maximum(max_frame, event["value"].array.values)
+            frame_bgr = cv2.cvtColor(
+                event["value"].array.values.astype(np.uint8) * 2, cv2.COLOR_GRAY2BGR
+            )
+            max_bgr = cv2.cvtColor(max_frame.astype(np.uint8), cv2.COLOR_GRAY2BGR)
+
+            if A.obj.array is not None:
+                accepted, rejected = separate_by_filter(A.obj.array, 0.8, 24)
+                circles = footprint_rims(accepted)
+                frame_bgr[:, :, 0][circles] = 255
+                max_bgr[:, :, 0][circles] = 255
+
+            out.write(np.concatenate([frame_bgr, max_bgr], axis=1))
+
+    runner.add_callback(event_cb)
+    for idx, arr in enumerate(gen):
+        runner.process(frame=arr, epoch=idx)
+
+    out.release()
diff --git a/tests/test_segment/test_catalog.py b/tests/test_segment/test_catalog.py
index 9250bd0d..d1acaabd 100644
--- a/tests/test_segment/test_catalog.py
+++ b/tests/test_segment/test_catalog.py
@@ -179,9 +179,7 @@ def test_absorb_component(slice_nmf, cataloger, single_cell):
     """
     buff = Buffer(size=100)
     buff.array = single_cell.make_movie().array
-    new_component = slice_nmf.process(
-        buff, energy=buff.array.std(dim=AXIS.frame_dim), detect_radius=10
-    )
+    new_component = slice_nmf.process(buff, energy=buff.array.std(dim=AXIS.frame_dim), cell_size=10)
 
     A = single_cell.footprints.array
 
diff --git a/tests/test_segment/test_slice_nmf.py b/tests/test_segment/test_slice_nmf.py
index 558468a7..150bdc15 100644
--- a/tests/test_segment/test_slice_nmf.py
+++ b/tests/test_segment/test_slice_nmf.py
@@ -25,7 +25,7 @@ def test_process(slice_nmf, single_cell):
     new_component = slice_nmf.process(
         residuals=Buffer.from_array(single_cell.make_movie().array, size=100),
         energy=single_cell.make_movie().array.std(dim=AXIS.frame_dim),
-        detect_radius=single_cell.cell_radii[0] * 2,
+        cell_size=single_cell.cell_radii[0] * 2,
     )
     if new_component:
         new_fp, new_tr = new_component
@@ -47,7 +47,7 @@ def test_chunks(single_cell):
     fpts, trcs = nmf.process(
         residuals=Buffer.from_array(single_cell.make_movie().array, size=100),
         energy=single_cell.make_movie().array.std(dim=AXIS.frame_dim),
-        detect_radius=10,
+        cell_size=10,
     )
     if not fpts or not trcs:
         raise AssertionError("Failed to segment a new component")