diff --git a/conftest.py b/conftest.py
index 38d1c181..786f970e 100644
--- a/conftest.py
+++ b/conftest.py
@@ -1 +1,17 @@
-# empty file so that pyetst adds top-level directory
+from typing import Generator
+
+import numpy as np
+import pytest
+
+
+@pytest.fixture(scope="module")
+def rng() -> Generator[np.random.Generator, None, None]:
+    """
+    Create a new Random Number Generator for tests which require randomized data.
+
+    Yields:
+        Generator[np.random.Generator, None, None]: The generator used for creating randomized
+        numbers.
+    """
+    rng: np.random.Generator = np.random.default_rng(12345)
+    yield rng
diff --git a/poetry.lock b/poetry.lock
index 8c8d6807..8829ecc0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,10 +1,9 @@
-# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
 
 [[package]]
 name = "aiofiles"
 version = "22.1.0"
 description = "File support for asyncio."
-category = "dev"
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
@@ -16,7 +15,6 @@ files = [
 name = "aiosqlite"
 version = "0.19.0"
 description = "asyncio bridge to the standard sqlite3 module"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -30,53 +28,51 @@ docs = ["sphinx (==6.1.3)", "sphinx-mdinclude (==0.5.3)"]
 
 [[package]]
 name = "alpineer"
-version = "0.1.7"
+version = "0.1.9"
 description = "Toolbox for Multiplexed Imaging. Contains scripts and little tools which are used throughout ark-analysis, mibi-bin-tools, and toffy."
-category = "main"
 optional = false
 python-versions = ">=3.9,<4.0"
 files = [
-    {file = "alpineer-0.1.7-py3-none-any.whl", hash = "sha256:e266e28505501117a470442f5cd9ece0d68e1b05cdab1a4ff642e98664afc69b"},
-    {file = "alpineer-0.1.7.tar.gz", hash = "sha256:8daec081c956d929f4e0d05874cf65b49e57d748070a214a063a0e901c079ed1"},
+    {file = "alpineer-0.1.9-py3-none-any.whl", hash = "sha256:915447d097a332dfb30fa3c6a9eb08ebcaeb75094934e856750ca0f7869d1555"},
+    {file = "alpineer-0.1.9.tar.gz", hash = "sha256:d1613f7ae2d993d13f8b46261c86e4904033ea2fadcb9a7051110a4157da7f8e"},
 ]
 
 [package.dependencies]
 charset-normalizer = ">=2.1.1,<3.0.0"
 matplotlib = ">=3,<4"
 natsort = ">=8,<9"
-numpy = ">=1.0.0,<2.0.0"
+numpy = "==1.*"
 pillow = ">=9,<10"
-scikit-image = "<1.0.0"
+scikit-image = "==0.*"
 tifffile = "*"
 xarray = "*"
 xmltodict = ">=0.13.0,<0.14.0"
 
 [[package]]
 name = "anyio"
-version = "3.6.2"
+version = "3.7.1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
-category = "dev"
 optional = false
-python-versions = ">=3.6.2"
+python-versions = ">=3.7"
 files = [
-    {file = "anyio-3.6.2-py3-none-any.whl", hash = "sha256:fbbe32bd270d2a2ef3ed1c5d45041250284e31fc0a4df4a5a6071842051a51e3"},
-    {file = "anyio-3.6.2.tar.gz", hash = "sha256:25ea0d673ae30af41a0c442f81cf3b38c7e79fdc7b60335a4c14e05eb0947421"},
+    {file = "anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5"},
+    {file = "anyio-3.7.1.tar.gz", hash = "sha256:44a3c9aba0f5defa43261a8b3efb97891f2bd7d804e0e1f56419befa1adfc780"},
 ]
 
 [package.dependencies]
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
 
 [package.extras]
-doc = ["packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["contextlib2", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (<0.15)", "uvloop (>=0.15)"]
-trio = ["trio (>=0.16,<0.22)"]
+doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
+test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (<0.22)"]
 
 [[package]]
 name = "appnope"
 version = "0.1.3"
 description = "Disable App Nap on macOS >= 10.9"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -88,7 +84,6 @@ files = [
 name = "argon2-cffi"
 version = "21.3.0"
 description = "The secure Argon2 password hashing algorithm."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -108,7 +103,6 @@ tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
 name = "argon2-cffi-bindings"
 version = "21.2.0"
 description = "Low-level CFFI bindings for Argon2"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -146,7 +140,6 @@ tests = ["pytest"]
 name = "arrow"
 version = "1.2.3"
 description = "Better dates & times for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -161,7 +154,6 @@ python-dateutil = ">=2.7.0"
 name = "asttokens"
 version = "2.2.1"
 description = "Annotate AST trees with source code positions"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -179,7 +171,6 @@ test = ["astroid", "pytest"]
 name = "attrs"
 version = "23.1.0"
 description = "Classes Without Boilerplate"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -198,7 +189,6 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte
 name = "babel"
 version = "2.12.1"
 description = "Internationalization utilities"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -210,7 +200,6 @@ files = [
 name = "backcall"
 version = "0.2.0"
 description = "Specifications for callback functions passed in to an API"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -222,7 +211,6 @@ files = [
 name = "beautifulsoup4"
 version = "4.12.2"
 description = "Screen-scraping library"
-category = "dev"
 optional = false
 python-versions = ">=3.6.0"
 files = [
@@ -241,7 +229,6 @@ lxml = ["lxml"]
 name = "black"
 version = "22.12.0"
 description = "The uncompromising code formatter."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -277,7 +264,6 @@ uvloop = ["uvloop (>=0.15.2)"]
 name = "bleach"
 version = "6.0.0"
 description = "An easy safelist-based HTML-sanitizing tool."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -296,7 +282,6 @@ css = ["tinycss2 (>=1.1.0,<1.2)"]
 name = "certifi"
 version = "2023.5.7"
 description = "Python package for providing Mozilla's CA Bundle."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -308,7 +293,6 @@ files = [
 name = "cffi"
 version = "1.15.1"
 description = "Foreign Function Interface for Python calling C code."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -385,7 +369,6 @@ pycparser = "*"
 name = "charset-normalizer"
 version = "2.1.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-category = "main"
 optional = false
 python-versions = ">=3.6.0"
 files = [
@@ -400,7 +383,6 @@ unicode-backport = ["unicodedata2"]
 name = "click"
 version = "8.1.3"
 description = "Composable command line interface toolkit"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -415,7 +397,6 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 name = "colorama"
 version = "0.4.6"
 description = "Cross-platform colored terminal text."
-category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 files = [
@@ -427,7 +408,6 @@ files = [
 name = "comm"
 version = "0.1.3"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -445,84 +425,66 @@ typing = ["mypy (>=0.990)"]
 
 [[package]]
 name = "contourpy"
-version = "1.0.7"
+version = "1.1.0"
 description = "Python library for calculating contours of 2D quadrilateral grids"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 numpy = ">=1.16"
 
 [package.extras]
-bokeh = ["bokeh", "chromedriver", "selenium"]
+bokeh = ["bokeh", "selenium"]
 docs = ["furo", "sphinx-copybutton"]
-mypy = ["contourpy[bokeh]", "docutils-stubs", "mypy (==0.991)", "types-Pillow"]
-test = ["Pillow", "matplotlib", "pytest"]
-test-no-images = ["pytest"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
 [[package]]
 name = "coverage"
 version = "6.5.0"
 description = "Code coverage measurement for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -588,7 +550,6 @@ toml = ["tomli"]
 name = "coveralls"
 version = "3.3.1"
 description = "Show coverage stats online via coveralls.io"
-category = "dev"
 optional = false
 python-versions = ">= 3.5"
 files = [
@@ -597,7 +558,7 @@ files = [
 ]
 
 [package.dependencies]
-coverage = ">=4.1,<6.0.0 || >6.1,<6.1.1 || >6.1.1,<7.0"
+coverage = ">=4.1,<6.0.dev0 || >6.1,<6.1.1 || >6.1.1,<7.0"
 docopt = ">=0.6.1"
 requests = ">=1.0.0"
 
@@ -608,7 +569,6 @@ yaml = ["PyYAML (>=3.10)"]
 name = "cycler"
 version = "0.11.0"
 description = "Composable style cycles"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -618,59 +578,19 @@ files = [
 
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-version = "0.29.34"
+version = "0.29.36"
 description = "The Cython compiler for writing C extensions for the Python language."
-category = "main"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
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-    {file = "Cython-0.29.34.tar.gz", hash = "sha256:1909688f5d7b521a60c396d20bba9e47a1b2d2784bfb085401e1e1e7d29a29a8"},
+    {file = "Cython-0.29.36-py2.py3-none-any.whl", hash = "sha256:95bb13d8be507425d03ebe051f90d4b2a9fdccc64e4f30b35645fdb7542742eb"},
+    {file = "Cython-0.29.36.tar.gz", hash = "sha256:41c0cfd2d754e383c9eeb95effc9aa4ab847d0c9747077ddd7c0dcb68c3bc01f"},
 ]
 
 [[package]]
 name = "debugpy"
 version = "1.6.7"
 description = "An implementation of the Debug Adapter Protocol for Python"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -698,7 +618,6 @@ files = [
 name = "decopatch"
 version = "1.4.10"
 description = "Create decorators easily in python."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -713,7 +632,6 @@ makefun = ">=1.5.0"
 name = "decorator"
 version = "5.1.1"
 description = "Decorators for Humans"
-category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -725,7 +643,6 @@ files = [
 name = "defusedxml"
 version = "0.7.1"
 description = "XML bomb protection for Python stdlib modules"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -737,7 +654,6 @@ files = [
 name = "docopt"
 version = "0.6.2"
 description = "Pythonic argument parser, that will make you smile"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -746,14 +662,13 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.1.1"
+version = "1.1.2"
 description = "Backport of PEP 654 (exception groups)"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.1.1-py3-none-any.whl", hash = "sha256:232c37c63e4f682982c8b6459f33a8981039e5fb8756b2074364e5055c498c9e"},
-    {file = "exceptiongroup-1.1.1.tar.gz", hash = "sha256:d484c3090ba2889ae2928419117447a14daf3c1231d5e30d0aae34f354f01785"},
+    {file = "exceptiongroup-1.1.2-py3-none-any.whl", hash = "sha256:e346e69d186172ca7cf029c8c1d16235aa0e04035e5750b4b95039e65204328f"},
+    {file = "exceptiongroup-1.1.2.tar.gz", hash = "sha256:12c3e887d6485d16943a309616de20ae5582633e0a2eda17f4e10fd61c1e8af5"},
 ]
 
 [package.extras]
@@ -763,7 +678,6 @@ test = ["pytest (>=6)"]
 name = "executing"
 version = "1.2.0"
 description = "Get the currently executing AST node of a frame, and other information"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -776,14 +690,13 @@ tests = ["asttokens", "littleutils", "pytest", "rich"]
 
 [[package]]
 name = "fastjsonschema"
-version = "2.16.3"
+version = "2.17.1"
 description = "Fastest Python implementation of JSON schema"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
-    {file = "fastjsonschema-2.16.3-py3-none-any.whl", hash = "sha256:04fbecc94300436f628517b05741b7ea009506ce8f946d40996567c669318490"},
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+    {file = "fastjsonschema-2.17.1.tar.gz", hash = "sha256:f4eeb8a77cef54861dbf7424ac8ce71306f12cbb086c45131bcba2c6a4f726e3"},
 ]
 
 [package.extras]
@@ -791,14 +704,45 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "fonttools"
-version = "4.39.3"
+version = "4.40.0"
 description = "Tools to manipulate font files"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "fonttools-4.40.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d5adf4ba114f028fc3f5317a221fd8b0f4ef7a2e5524a2b1e0fd891b093791a"},
+    {file = "fonttools-4.40.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:aa83b3f151bc63970f39b2b42a06097c5a22fd7ed9f7ba008e618de4503d3895"},
+    {file = "fonttools-4.40.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:97d95b8301b62bdece1af943b88bcb3680fd385f88346a4a899ee145913b414a"},
+    {file = "fonttools-4.40.0-cp39-cp39-win32.whl", hash = "sha256:1a003608400dd1cca3e089e8c94973c6b51a4fb1ef00ff6d7641617b9242e637"},
+    {file = "fonttools-4.40.0-cp39-cp39-win_amd64.whl", hash = "sha256:7961575221e3da0841c75da53833272c520000d76f7f71274dbf43370f8a1065"},
+    {file = "fonttools-4.40.0-py3-none-any.whl", hash = "sha256:200729d12461e2038700d31f0d49ad5a7b55855dec7525074979a06b46f88505"},
+    {file = "fonttools-4.40.0.tar.gz", hash = "sha256:337b6e83d7ee73c40ea62407f2ce03b07c3459e213b6f332b94a69923b9e1cb9"},
 ]
 
 [package.extras]
@@ -819,7 +763,6 @@ woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 name = "fqdn"
 version = "1.5.1"
 description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
 files = [
@@ -831,7 +774,6 @@ files = [
 name = "idna"
 version = "3.4"
 description = "Internationalized Domain Names in Applications (IDNA)"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -841,14 +783,13 @@ files = [
 
 [[package]]
 name = "imageio"
-version = "2.28.1"
+version = "2.31.1"
 description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "imageio-2.28.1-py3-none-any.whl", hash = "sha256:b9b456146aab459e648cde633b81bf487eb45248948f79c033e55af3bf1e6d70"},
-    {file = "imageio-2.28.1.tar.gz", hash = "sha256:5db5087be5c814ecf7e2c7d30a1a15c97eca97d8c26f31ddc54d767d4a43bce8"},
+    {file = "imageio-2.31.1-py3-none-any.whl", hash = "sha256:4106fb395ef7f8dc0262d6aa1bb03daba818445c381ca8b7d5dfc7a2089b04df"},
+    {file = "imageio-2.31.1.tar.gz", hash = "sha256:f8436a02af02fd63f272dab50f7d623547a38f0e04a4a73e2b02ae1b8b180f27"},
 ]
 
 [package.dependencies]
@@ -873,14 +814,13 @@ tifffile = ["tifffile"]
 
 [[package]]
 name = "importlib-metadata"
-version = "6.6.0"
+version = "6.7.0"
 description = "Read metadata from Python packages"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "importlib_metadata-6.6.0-py3-none-any.whl", hash = "sha256:43dd286a2cd8995d5eaef7fee2066340423b818ed3fd70adf0bad5f1fac53fed"},
-    {file = "importlib_metadata-6.6.0.tar.gz", hash = "sha256:92501cdf9cc66ebd3e612f1b4f0c0765dfa42f0fa38ffb319b6bd84dd675d705"},
+    {file = "importlib_metadata-6.7.0-py3-none-any.whl", hash = "sha256:cb52082e659e97afc5dac71e79de97d8681de3aa07ff18578330904a9d18e5b5"},
+    {file = "importlib_metadata-6.7.0.tar.gz", hash = "sha256:1aaf550d4f73e5d6783e7acb77aec43d49da8017410afae93822cc9cca98c4d4"},
 ]
 
 [package.dependencies]
@@ -889,13 +829,12 @@ zipp = ">=0.5"
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
 [[package]]
 name = "importlib-resources"
 version = "5.12.0"
 description = "Read resources from Python packages"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -914,7 +853,6 @@ testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-chec
 name = "iniconfig"
 version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -924,14 +862,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.23.0"
+version = "6.24.0"
 description = "IPython Kernel for Jupyter"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.23.0-py3-none-any.whl", hash = "sha256:fc886f1dcdc0ec17f277e4d21fd071c857d381adcb04f3f3735d25325ca323c6"},
-    {file = "ipykernel-6.23.0.tar.gz", hash = "sha256:bd6f487d9e2744c84f6e667d46462d7647a4c862e70e08282f05a52b9d4b705f"},
+    {file = "ipykernel-6.24.0-py3-none-any.whl", hash = "sha256:2f5fffc7ad8f1fd5aadb4e171ba9129d9668dbafa374732cf9511ada52d6547f"},
+    {file = "ipykernel-6.24.0.tar.gz", hash = "sha256:29cea0a716b1176d002a61d0b0c851f34536495bc4ef7dd0222c88b41b816123"},
 ]
 
 [package.dependencies]
@@ -940,7 +877,7 @@ comm = ">=0.1.1"
 debugpy = ">=1.6.5"
 ipython = ">=7.23.1"
 jupyter-client = ">=6.1.12"
-jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
@@ -958,14 +895,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 
 [[package]]
 name = "ipython"
-version = "8.13.2"
+version = "8.14.0"
 description = "IPython: Productive Interactive Computing"
-category = "main"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "ipython-8.13.2-py3-none-any.whl", hash = "sha256:ffca270240fbd21b06b2974e14a86494d6d29290184e788275f55e0b55914926"},
-    {file = "ipython-8.13.2.tar.gz", hash = "sha256:7dff3fad32b97f6488e02f87b970f309d082f758d7b7fc252e3b19ee0e432dbb"},
+    {file = "ipython-8.14.0-py3-none-any.whl", hash = "sha256:248aca623f5c99a6635bc3857677b7320b9b8039f99f070ee0d20a5ca5a8e6bf"},
+    {file = "ipython-8.14.0.tar.gz", hash = "sha256:1d197b907b6ba441b692c48cf2a3a2de280dc0ac91a3405b39349a50272ca0a1"},
 ]
 
 [package.dependencies]
@@ -1000,7 +936,6 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pa
 name = "ipython-genutils"
 version = "0.2.0"
 description = "Vestigial utilities from IPython"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1010,14 +945,13 @@ files = [
 
 [[package]]
 name = "ipywidgets"
-version = "8.0.6"
+version = "8.0.7"
 description = "Jupyter interactive widgets"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ipywidgets-8.0.6-py3-none-any.whl", hash = "sha256:a60bf8d2528997e05ac83fd19ea2fbe65f2e79fbe1b2b35779bdfc46c2941dcc"},
-    {file = "ipywidgets-8.0.6.tar.gz", hash = "sha256:de7d779f2045d60de9f6c25f653fdae2dba57898e6a1284494b3ba20b6893bb8"},
+    {file = "ipywidgets-8.0.7-py3-none-any.whl", hash = "sha256:e0aed0c95a1e55b6a123f64305245578bdc09e52965a34941c2b6a578b8c64a0"},
+    {file = "ipywidgets-8.0.7.tar.gz", hash = "sha256:50ace0a8886e9a0d68b980db82f94c25d55d21ff2340ed36f802dd9365e94acf"},
 ]
 
 [package.dependencies]
@@ -1034,7 +968,6 @@ test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
 name = "isoduration"
 version = "20.11.0"
 description = "Operations with ISO 8601 durations"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1049,7 +982,6 @@ arrow = ">=0.15.0"
 name = "isort"
 version = "5.12.0"
 description = "A Python utility / library to sort Python imports."
-category = "dev"
 optional = false
 python-versions = ">=3.8.0"
 files = [
@@ -1067,7 +999,6 @@ requirements-deprecated-finder = ["pip-api", "pipreqs"]
 name = "jedi"
 version = "0.18.2"
 description = "An autocompletion tool for Python that can be used for text editors."
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -1087,7 +1018,6 @@ testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 name = "jinja2"
 version = "3.1.2"
 description = "A very fast and expressive template engine."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1103,26 +1033,24 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.2.0"
+version = "1.3.1"
 description = "Lightweight pipelining with Python functions"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "joblib-1.2.0-py3-none-any.whl", hash = "sha256:091138ed78f800342968c523bdde947e7a305b8594b910a0fea2ab83c3c6d385"},
-    {file = "joblib-1.2.0.tar.gz", hash = "sha256:e1cee4a79e4af22881164f218d4311f60074197fb707e082e803b61f6d137018"},
+    {file = "joblib-1.3.1-py3-none-any.whl", hash = "sha256:89cf0529520e01b3de7ac7b74a8102c90d16d54c64b5dd98cafcd14307fdf915"},
+    {file = "joblib-1.3.1.tar.gz", hash = "sha256:1f937906df65329ba98013dc9692fe22a4c5e4a648112de500508b18a21b41e3"},
 ]
 
 [[package]]
 name = "json5"
-version = "0.9.11"
+version = "0.9.14"
 description = "A Python implementation of the JSON5 data format."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
-    {file = "json5-0.9.11-py2.py3-none-any.whl", hash = "sha256:1aa54b80b5e507dfe31d12b7743a642e2ffa6f70bf73b8e3d7d1d5fba83d99bd"},
-    {file = "json5-0.9.11.tar.gz", hash = "sha256:4f1e196acc55b83985a51318489f345963c7ba84aa37607e49073066c562e99b"},
+    {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
+    {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"},
 ]
 
 [package.extras]
@@ -1130,21 +1058,19 @@ dev = ["hypothesis"]
 
 [[package]]
 name = "jsonpointer"
-version = "2.3"
+version = "2.4"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
-category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 files = [
-    {file = "jsonpointer-2.3-py2.py3-none-any.whl", hash = "sha256:51801e558539b4e9cd268638c078c6c5746c9ac96bc38152d443400e4f3793e9"},
-    {file = "jsonpointer-2.3.tar.gz", hash = "sha256:97cba51526c829282218feb99dab1b1e6bdf8efd1c43dc9d57be093c0d69c99a"},
+    {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
+    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
 ]
 
 [[package]]
 name = "jsonschema"
 version = "4.17.3"
 description = "An implementation of JSON Schema validation for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1170,19 +1096,18 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jupyter-client"
-version = "8.2.0"
+version = "8.3.0"
 description = "Jupyter protocol implementation and client libraries"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.2.0-py3-none-any.whl", hash = "sha256:b18219aa695d39e2ad570533e0d71fb7881d35a873051054a84ee2a17c4b7389"},
-    {file = "jupyter_client-8.2.0.tar.gz", hash = "sha256:9fe233834edd0e6c0aa5f05ca2ab4bdea1842bfd2d8a932878212fc5301ddaf0"},
+    {file = "jupyter_client-8.3.0-py3-none-any.whl", hash = "sha256:7441af0c0672edc5d28035e92ba5e32fadcfa8a4e608a434c228836a89df6158"},
+    {file = "jupyter_client-8.3.0.tar.gz", hash = "sha256:3af69921fe99617be1670399a0b857ad67275eefcfa291e2c81a160b7b650f5f"},
 ]
 
 [package.dependencies]
 importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
-jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 python-dateutil = ">=2.8.2"
 pyzmq = ">=23.0"
 tornado = ">=6.2"
@@ -1196,7 +1121,6 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt
 name = "jupyter-contrib-core"
 version = "0.4.2"
 description = "Common utilities for jupyter-contrib projects."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1217,7 +1141,6 @@ testing-utils = ["mock", "nose"]
 name = "jupyter-contrib-nbextensions"
 version = "0.7.0"
 description = "A collection of Jupyter nbextensions."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1241,14 +1164,13 @@ test = ["mock", "nbformat", "nose", "pip", "requests"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.3.0"
+version = "5.3.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.3.0-py3-none-any.whl", hash = "sha256:d4201af84559bc8c70cead287e1ab94aeef3c512848dde077b7684b54d67730d"},
-    {file = "jupyter_core-5.3.0.tar.gz", hash = "sha256:6db75be0c83edbf1b7c9f91ec266a9a24ef945da630f3120e1a0046dc13713fc"},
+    {file = "jupyter_core-5.3.1-py3-none-any.whl", hash = "sha256:ae9036db959a71ec1cac33081eeb040a79e681f08ab68b0883e9a676c7a90dce"},
+    {file = "jupyter_core-5.3.1.tar.gz", hash = "sha256:5ba5c7938a7f97a6b0481463f7ff0dbac7c15ba48cf46fa4035ca6e838aa1aba"},
 ]
 
 [package.dependencies]
@@ -1264,7 +1186,6 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 name = "jupyter-events"
 version = "0.6.3"
 description = "Jupyter Event System library"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1289,7 +1210,6 @@ test = ["click", "coverage", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=
 name = "jupyter-highlight-selected-word"
 version = "0.2.0"
 description = "Jupyter notebook extension that enables highlighting every instance of the current word in the notebook."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1301,7 +1221,6 @@ files = [
 name = "jupyter-nbextensions-configurator"
 version = "0.6.3"
 description = "jupyter serverextension providing configuration interfaces for nbextensions."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1321,14 +1240,13 @@ test = ["jupyter-contrib-core[testing-utils]", "mock", "nose", "requests", "sele
 
 [[package]]
 name = "jupyter-server"
-version = "2.5.0"
+version = "2.7.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.5.0-py3-none-any.whl", hash = "sha256:e6bc1e9e96d7c55b9ce9699ff6cb9a910581fe7349e27c40389acb67632e24c0"},
-    {file = "jupyter_server-2.5.0.tar.gz", hash = "sha256:9fde612791f716fd34d610cd939704a9639643744751ba66e7ee8fdc9cead07e"},
+    {file = "jupyter_server-2.7.0-py3-none-any.whl", hash = "sha256:6a77912aff643e53fa14bdb2634884b52b784a4be77ce8e93f7283faed0f0849"},
+    {file = "jupyter_server-2.7.0.tar.gz", hash = "sha256:36da0a266d31a41ac335a366c88933c17dfa5bb817a48f5c02c16d303bc9477f"},
 ]
 
 [package.dependencies]
@@ -1336,11 +1254,12 @@ anyio = ">=3.1.0"
 argon2-cffi = "*"
 jinja2 = "*"
 jupyter-client = ">=7.4.4"
-jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
-jupyter-events = ">=0.4.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+jupyter-events = ">=0.6.0"
 jupyter-server-terminals = "*"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
+overrides = "*"
 packaging = "*"
 prometheus-client = "*"
 pywinpty = {version = "*", markers = "os_name == \"nt\""}
@@ -1352,14 +1271,13 @@ traitlets = ">=5.6.0"
 websocket-client = "*"
 
 [package.extras]
-docs = ["docutils (<0.20)", "ipykernel", "jinja2", "jupyter-client", "jupyter-server", "mistune (<1.0.0)", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
-test = ["ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"]
+docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"]
 
 [[package]]
 name = "jupyter-server-fileid"
 version = "0.9.0"
 description = ""
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1379,7 +1297,6 @@ test = ["jupyter-server[test] (>=1.15,<3)", "pytest", "pytest-cov"]
 name = "jupyter-server-terminals"
 version = "0.4.4"
 description = "A Jupyter Server Extension Providing Terminals."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1399,7 +1316,6 @@ test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov",
 name = "jupyter-server-ydoc"
 version = "0.8.0"
 description = "A Jupyter Server Extension Providing Y Documents."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1419,7 +1335,6 @@ test = ["coverage", "jupyter-server[test] (>=2.0.0a0)", "pytest (>=7.0)", "pytes
 name = "jupyter-ydoc"
 version = "0.2.4"
 description = "Document structures for collaborative editing using Ypy"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1437,14 +1352,13 @@ test = ["pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)", "ypy-we
 
 [[package]]
 name = "jupyterlab"
-version = "3.6.3"
+version = "3.6.5"
 description = "JupyterLab computational environment"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyterlab-3.6.3-py3-none-any.whl", hash = "sha256:6aba0caa771697d02fbf409f9767b2fdb4ee32ce935940e3b9a0d5d48d994d0f"},
-    {file = "jupyterlab-3.6.3.tar.gz", hash = "sha256:373e9cfb8a72edd294be14f16662563a220cecf0fb26de7aab1af9a29b689b82"},
+    {file = "jupyterlab-3.6.5-py3-none-any.whl", hash = "sha256:4d13665c2c2f42c753140d88b52ff8722cd5b38629b934f5612bfa5490bcdc65"},
+    {file = "jupyterlab-3.6.5.tar.gz", hash = "sha256:ac0cb19756be1d1e14b2be1f23c603de46e0f0113960fce9888889ca55ae8923"},
 ]
 
 [package.dependencies]
@@ -1453,7 +1367,7 @@ jinja2 = ">=2.1"
 jupyter-core = "*"
 jupyter-server = ">=1.16.0,<3"
 jupyter-server-ydoc = ">=0.8.0,<0.9.0"
-jupyter-ydoc = ">=0.2.3,<0.3.0"
+jupyter-ydoc = ">=0.2.4,<0.3.0"
 jupyterlab-server = ">=2.19,<3.0"
 nbclassic = "*"
 notebook = "<7"
@@ -1468,7 +1382,6 @@ test = ["check-manifest", "coverage", "jupyterlab-server[test]", "pre-commit", "
 name = "jupyterlab-pygments"
 version = "0.2.2"
 description = "Pygments theme using JupyterLab CSS variables"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1478,14 +1391,13 @@ files = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.22.1"
+version = "2.23.0"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyterlab_server-2.22.1-py3-none-any.whl", hash = "sha256:1c8eb55c7cd70a50a51fef42a7b4e26ef2f7fc48728f0290604bd89b1dd156e6"},
-    {file = "jupyterlab_server-2.22.1.tar.gz", hash = "sha256:dfaaf898af84b9d01ae9583b813f378b96ee90c3a66f24c5186ea5d1bbdb2089"},
+    {file = "jupyterlab_server-2.23.0-py3-none-any.whl", hash = "sha256:a5ea2c839336a8ba7c38c8e7b2f24cedf919f0d439f4d2e606d9322013a95788"},
+    {file = "jupyterlab_server-2.23.0.tar.gz", hash = "sha256:83c01aa4ad9451cd61b383e634d939ff713850f4640c0056b2cdb2b6211a74c7"},
 ]
 
 [package.dependencies]
@@ -1499,27 +1411,25 @@ packaging = ">=21.3"
 requests = ">=2.28"
 
 [package.extras]
-docs = ["autodoc-traits", "docutils (<0.20)", "jinja2 (<3.2.0)", "mistune (<3)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi"]
+docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
 openapi = ["openapi-core (>=0.16.1,<0.17.0)", "ruamel-yaml"]
 test = ["hatch", "ipykernel", "jupyterlab-server[openapi]", "openapi-spec-validator (>=0.5.1,<0.6.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "3.0.7"
+version = "3.0.8"
 description = "Jupyter interactive widgets for JupyterLab"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyterlab_widgets-3.0.7-py3-none-any.whl", hash = "sha256:c73f8370338ec19f1bec47254752d6505b03601cbd5a67e6a0b184532f73a459"},
-    {file = "jupyterlab_widgets-3.0.7.tar.gz", hash = "sha256:c3a50ed5bf528a0c7a869096503af54702f86dda1db469aee1c92dc0c01b43ca"},
+    {file = "jupyterlab_widgets-3.0.8-py3-none-any.whl", hash = "sha256:4715912d6ceab839c9db35953c764b3214ebbc9161c809f6e0510168845dfdf5"},
+    {file = "jupyterlab_widgets-3.0.8.tar.gz", hash = "sha256:d428ab97b8d87cc7c54cbf37644d6e0f0e662f23876e05fa460a73ec3257252a"},
 ]
 
 [[package]]
 name = "kiwisolver"
 version = "1.4.4"
 description = "A fast implementation of the Cassowary constraint solver"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1595,118 +1505,137 @@ files = [
 
 [[package]]
 name = "lazy-loader"
-version = "0.2"
+version = "0.3"
 description = "lazy_loader"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "lazy_loader-0.2-py3-none-any.whl", hash = "sha256:c35875f815c340f823ce3271ed645045397213f961b40ad0c0d395c3f5218eeb"},
-    {file = "lazy_loader-0.2.tar.gz", hash = "sha256:0edc7a5175c400acb108f283749951fefdadedeb00adcec6e88b974a9254f18a"},
+    {file = "lazy_loader-0.3-py3-none-any.whl", hash = "sha256:1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554"},
+    {file = "lazy_loader-0.3.tar.gz", hash = "sha256:3b68898e34f5b2a29daaaac172c6555512d0f32074f147e2254e4a6d9d838f37"},
 ]
 
 [package.extras]
-lint = ["pre-commit (>=3.1)"]
-test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+lint = ["pre-commit (>=3.3)"]
+test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
+
+[[package]]
+name = "loguru"
+version = "0.7.0"
+description = "Python logging made (stupidly) simple"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "loguru-0.7.0-py3-none-any.whl", hash = "sha256:b93aa30099fa6860d4727f1b81f8718e965bb96253fa190fab2077aaad6d15d3"},
+    {file = "loguru-0.7.0.tar.gz", hash = "sha256:1612053ced6ae84d7959dd7d5e431a0532642237ec21f7fd83ac73fe539e03e1"},
+]
+
+[package.dependencies]
+colorama = {version = ">=0.3.4", markers = "sys_platform == \"win32\""}
+win32-setctime = {version = ">=1.0.0", markers = "sys_platform == \"win32\""}
+
+[package.extras]
+dev = ["Sphinx (==5.3.0)", "colorama (==0.4.5)", "colorama (==0.4.6)", "freezegun (==1.1.0)", "freezegun (==1.2.2)", "mypy (==v0.910)", "mypy (==v0.971)", "mypy (==v0.990)", "pre-commit (==3.2.1)", "pytest (==6.1.2)", "pytest (==7.2.1)", "pytest-cov (==2.12.1)", "pytest-cov (==4.0.0)", "pytest-mypy-plugins (==1.10.1)", "pytest-mypy-plugins (==1.9.3)", "sphinx-autobuild (==2021.3.14)", "sphinx-rtd-theme (==1.2.0)", "tox (==3.27.1)", "tox (==4.4.6)"]
 
 [[package]]
 name = "lxml"
-version = "4.9.2"
+version = "4.9.3"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
 files = [
-    {file = "lxml-4.9.2-cp27-cp27m-macosx_10_15_x86_64.whl", hash = "sha256:76cf573e5a365e790396a5cc2b909812633409306c6531a6877c59061e42c4f2"},
-    {file = "lxml-4.9.2-cp27-cp27m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b1f42b6921d0e81b1bcb5e395bc091a70f41c4d4e55ba99c6da2b31626c44892"},
-    {file = "lxml-4.9.2-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:9f102706d0ca011de571de32c3247c6476b55bb6bc65a20f682f000b07a4852a"},
-    {file = "lxml-4.9.2-cp27-cp27m-win32.whl", hash = "sha256:8d0b4612b66ff5d62d03bcaa043bb018f74dfea51184e53f067e6fdcba4bd8de"},
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 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
-source = ["Cython (>=0.29.7)"]
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 [[package]]
 name = "makefun"
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 python-versions = "*"
 files = [
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 ]
 
 [[package]]
 name = "matplotlib"
 version = "3.7.1"
 description = "Python plotting package"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1841,7 +1768,6 @@ python-dateutil = ">=2.7"
 name = "matplotlib-inline"
 version = "0.1.6"
 description = "Inline Matplotlib backend for Jupyter"
-category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -1856,7 +1782,6 @@ traitlets = "*"
 name = "mibi-bin-tools"
 version = "0.2.9"
 description = "Source for extracting .bin files from the commercial MIBI."
-category = "main"
 optional = false
 python-versions = ">=3.9"
 files = [
@@ -1897,21 +1822,19 @@ test = ["coveralls[toml]", "pytest", "pytest-cases", "pytest-cov", "pytest-mock"
 
 [[package]]
 name = "mistune"
-version = "2.0.5"
-description = "A sane Markdown parser with useful plugins and renderers"
-category = "dev"
+version = "3.0.1"
+description = "A sane and fast Markdown parser with useful plugins and renderers"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 files = [
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-    {file = "mistune-2.0.5.tar.gz", hash = "sha256:0246113cb2492db875c6be56974a7c893333bf26cd92891c85f63151cee09d34"},
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+    {file = "mistune-3.0.1.tar.gz", hash = "sha256:e912116c13aa0944f9dc530db38eb88f6a77087ab128f49f84a48f4c05ea163c"},
 ]
 
 [[package]]
 name = "mock"
 version = "5.0.2"
 description = "Rolling backport of unittest.mock for all Pythons"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -1928,7 +1851,6 @@ test = ["pytest", "pytest-cov"]
 name = "mypy-extensions"
 version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -1938,14 +1860,13 @@ files = [
 
 [[package]]
 name = "natsort"
-version = "8.3.1"
+version = "8.4.0"
 description = "Simple yet flexible natural sorting in Python."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "natsort-8.4.0.tar.gz", hash = "sha256:45312c4a0e5507593da193dedd04abb1469253b601ecaf63445ad80f0a1ea581"},
 ]
 
 [package.extras]
@@ -1956,7 +1877,6 @@ icu = ["PyICU (>=1.0.0)"]
 name = "nbclassic"
 version = "1.0.0"
 description = "Jupyter Notebook as a Jupyter Server extension."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1990,56 +1910,54 @@ test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-p
 
 [[package]]
 name = "nbclient"
-version = "0.7.4"
+version = "0.8.0"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
-category = "dev"
 optional = false
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+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 nbformat = ">=5.1"
-traitlets = ">=5.3"
+traitlets = ">=5.4"
 
 [package.extras]
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 [[package]]
 name = "nbconvert"
-version = "7.4.0"
+version = "7.6.0"
 description = "Converting Jupyter Notebooks"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 beautifulsoup4 = "*"
-bleach = "*"
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 defusedxml = "*"
 importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
 jupyterlab-pygments = "*"
 markupsafe = ">=2.0"
-mistune = ">=2.0.3,<3"
+mistune = ">=2.0.3,<4"
 nbclient = ">=0.5.0"
-nbformat = ">=5.1"
+nbformat = ">=5.7"
 packaging = "*"
 pandocfilters = ">=1.4.1"
 pygments = ">=2.4.1"
 tinycss2 = "*"
-traitlets = ">=5.0"
+traitlets = ">=5.1"
 
 [package.extras]
 all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
@@ -2052,14 +1970,13 @@ webpdf = ["pyppeteer (>=1,<1.1)"]
 
 [[package]]
 name = "nbformat"
-version = "5.8.0"
+version = "5.9.0"
 description = "The Jupyter Notebook format"
-category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2076,7 +1993,6 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]
 name = "nest-asyncio"
 version = "1.5.6"
 description = "Patch asyncio to allow nested event loops"
-category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -2088,7 +2004,6 @@ files = [
 name = "networkx"
 version = "3.1"
 description = "Python package for creating and manipulating graphs and networks"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -2107,7 +2022,6 @@ test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
 name = "notebook"
 version = "6.5.4"
 description = "A web-based notebook environment for interactive computing"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2142,7 +2056,6 @@ test = ["coverage", "nbval", "pytest", "pytest-cov", "requests", "requests-unixs
 name = "notebook-shim"
 version = "0.2.3"
 description = "A shim layer for notebook traits and config"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2158,47 +2071,53 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
 [[package]]
 name = "numpy"
-version = "1.24.3"
+version = "1.25.0"
 description = "Fundamental package for array computing in Python"
-category = "main"
 optional = false
-python-versions = ">=3.8"
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 version = "23.1"
 description = "Core utilities for Python packages"
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 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2208,39 +2127,36 @@ files = [
 
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2249,17 +2165,37 @@ numpy = [
     {version = ">=1.21.0", markers = "python_version >= \"3.10\""},
     {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
 ]
-python-dateutil = ">=2.8.1"
+python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
-
-[package.extras]
-test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
+tzdata = ">=2022.1"
+
+[package.extras]
+all = ["PyQt5 (>=5.15.1)", "SQLAlchemy (>=1.4.16)", "beautifulsoup4 (>=4.9.3)", "bottleneck (>=1.3.2)", "brotlipy (>=0.7.0)", "fastparquet (>=0.6.3)", "fsspec (>=2021.07.0)", "gcsfs (>=2021.07.0)", "html5lib (>=1.1)", "hypothesis (>=6.34.2)", "jinja2 (>=3.0.0)", "lxml (>=4.6.3)", "matplotlib (>=3.6.1)", "numba (>=0.53.1)", "numexpr (>=2.7.3)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pandas-gbq (>=0.15.0)", "psycopg2 (>=2.8.6)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)", "python-snappy (>=0.6.0)", "pyxlsb (>=1.0.8)", "qtpy (>=2.2.0)", "s3fs (>=2021.08.0)", "scipy (>=1.7.1)", "tables (>=3.6.1)", "tabulate (>=0.8.9)", "xarray (>=0.21.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)", "zstandard (>=0.15.2)"]
+aws = ["s3fs (>=2021.08.0)"]
+clipboard = ["PyQt5 (>=5.15.1)", "qtpy (>=2.2.0)"]
+compression = ["brotlipy (>=0.7.0)", "python-snappy (>=0.6.0)", "zstandard (>=0.15.2)"]
+computation = ["scipy (>=1.7.1)", "xarray (>=0.21.0)"]
+excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pyxlsb (>=1.0.8)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)"]
+feather = ["pyarrow (>=7.0.0)"]
+fss = ["fsspec (>=2021.07.0)"]
+gcp = ["gcsfs (>=2021.07.0)", "pandas-gbq (>=0.15.0)"]
+hdf5 = ["tables (>=3.6.1)"]
+html = ["beautifulsoup4 (>=4.9.3)", "html5lib (>=1.1)", "lxml (>=4.6.3)"]
+mysql = ["SQLAlchemy (>=1.4.16)", "pymysql (>=1.0.2)"]
+output-formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"]
+parquet = ["pyarrow (>=7.0.0)"]
+performance = ["bottleneck (>=1.3.2)", "numba (>=0.53.1)", "numexpr (>=2.7.1)"]
+plot = ["matplotlib (>=3.6.1)"]
+postgresql = ["SQLAlchemy (>=1.4.16)", "psycopg2 (>=2.8.6)"]
+spss = ["pyreadstat (>=1.1.2)"]
+sql-other = ["SQLAlchemy (>=1.4.16)"]
+test = ["hypothesis (>=6.34.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
+xml = ["lxml (>=4.6.3)"]
 
 [[package]]
 name = "pandocfilters"
 version = "1.5.0"
 description = "Utilities for writing pandoc filters in python"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -2271,7 +2207,6 @@ files = [
 name = "parso"
 version = "0.8.3"
 description = "A Python Parser"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2287,7 +2222,6 @@ testing = ["docopt", "pytest (<6.0.0)"]
 name = "pathspec"
 version = "0.11.1"
 description = "Utility library for gitignore style pattern matching of file paths."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2299,7 +2233,6 @@ files = [
 name = "pexpect"
 version = "4.8.0"
 description = "Pexpect allows easy control of interactive console applications."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -2314,7 +2247,6 @@ ptyprocess = ">=0.5"
 name = "pickleshare"
 version = "0.7.5"
 description = "Tiny 'shelve'-like database with concurrency support"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -2326,7 +2258,6 @@ files = [
 name = "pillow"
 version = "9.5.0"
 description = "Python Imaging Library (Fork)"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2404,30 +2335,28 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 
 [[package]]
 name = "platformdirs"
-version = "3.5.0"
+version = "3.8.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
-docs = ["furo (>=2023.3.27)", "proselint (>=0.13)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"]
+docs = ["furo (>=2023.5.20)", "proselint (>=0.13)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)"]
 
 [[package]]
 name = "pluggy"
-version = "1.0.0"
+version = "1.2.0"
 description = "plugin and hook calling mechanisms for python"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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-    {file = "pluggy-1.0.0.tar.gz", hash = "sha256:4224373bacce55f955a878bf9cfa763c1e360858e330072059e10bad68531159"},
+    {file = "pluggy-1.2.0-py3-none-any.whl", hash = "sha256:c2fd55a7d7a3863cba1a013e4e2414658b1d07b6bc57b3919e0c63c9abb99849"},
+    {file = "pluggy-1.2.0.tar.gz", hash = "sha256:d12f0c4b579b15f5e054301bb226ee85eeeba08ffec228092f8defbaa3a4c4b3"},
 ]
 
 [package.extras]
@@ -2436,14 +2365,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "prometheus-client"
-version = "0.16.0"
+version = "0.17.0"
 description = "Python client for the Prometheus monitoring system."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "prometheus_client-0.16.0-py3-none-any.whl", hash = "sha256:0836af6eb2c8f4fed712b2f279f6c0a8bbab29f9f4aa15276b91c7cb0d1616ab"},
-    {file = "prometheus_client-0.16.0.tar.gz", hash = "sha256:a03e35b359f14dd1630898543e2120addfdeacd1a6069c1367ae90fd93ad3f48"},
+    {file = "prometheus_client-0.17.0-py3-none-any.whl", hash = "sha256:a77b708cf083f4d1a3fb3ce5c95b4afa32b9c521ae363354a4a910204ea095ce"},
+    {file = "prometheus_client-0.17.0.tar.gz", hash = "sha256:9c3b26f1535945e85b8934fb374678d263137b78ef85f305b1156c7c881cd11b"},
 ]
 
 [package.extras]
@@ -2451,14 +2379,13 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.38"
+version = "3.0.39"
 description = "Library for building powerful interactive command lines in Python"
-category = "main"
 optional = false
 python-versions = ">=3.7.0"
 files = [
-    {file = "prompt_toolkit-3.0.38-py3-none-any.whl", hash = "sha256:45ea77a2f7c60418850331366c81cf6b5b9cf4c7fd34616f733c5427e6abbb1f"},
-    {file = "prompt_toolkit-3.0.38.tar.gz", hash = "sha256:23ac5d50538a9a38c8bde05fecb47d0b403ecd0662857a86f886f798563d5b9b"},
+    {file = "prompt_toolkit-3.0.39-py3-none-any.whl", hash = "sha256:9dffbe1d8acf91e3de75f3b544e4842382fc06c6babe903ac9acb74dc6e08d88"},
+    {file = "prompt_toolkit-3.0.39.tar.gz", hash = "sha256:04505ade687dc26dc4284b1ad19a83be2f2afe83e7a828ace0c72f3a1df72aac"},
 ]
 
 [package.dependencies]
@@ -2468,7 +2395,6 @@ wcwidth = "*"
 name = "psutil"
 version = "5.9.5"
 description = "Cross-platform lib for process and system monitoring in Python."
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -2495,7 +2421,6 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
 name = "ptyprocess"
 version = "0.7.0"
 description = "Run a subprocess in a pseudo terminal"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -2507,7 +2432,6 @@ files = [
 name = "pure-eval"
 version = "0.2.2"
 description = "Safely evaluate AST nodes without side effects"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -2522,7 +2446,6 @@ tests = ["pytest"]
 name = "py"
 version = "1.11.0"
 description = "library with cross-python path, ini-parsing, io, code, log facilities"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -2534,7 +2457,6 @@ files = [
 name = "pycodestyle"
 version = "2.10.0"
 description = "Python style guide checker"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2546,7 +2468,6 @@ files = [
 name = "pycparser"
 version = "2.21"
 description = "C parser in Python"
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -2558,7 +2479,6 @@ files = [
 name = "pygments"
 version = "2.15.1"
 description = "Pygments is a syntax highlighting package written in Python."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2571,14 +2491,13 @@ plugins = ["importlib-metadata"]
 
 [[package]]
 name = "pyparsing"
-version = "3.0.9"
+version = "3.1.0"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-category = "main"
 optional = false
 python-versions = ">=3.6.8"
 files = [
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 ]
 
 [package.extras]
@@ -2588,7 +2507,6 @@ diagrams = ["jinja2", "railroad-diagrams"]
 name = "pyrsistent"
 version = "0.19.3"
 description = "Persistent/Functional/Immutable data structures"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2623,14 +2541,13 @@ files = [
 
 [[package]]
 name = "pytest"
-version = "7.3.1"
+version = "7.4.0"
 description = "pytest: simple powerful testing with Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -2642,13 +2559,12 @@ pluggy = ">=0.12,<2.0"
 tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
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+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
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@@ -2662,14 +2578,13 @@ makefun = ">=1.9.5"
 
 [[package]]
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-version = "4.0.0"
+version = "4.1.0"
 description = "Pytest plugin for measuring coverage."
-category = "dev"
 optional = false
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+python-versions = ">=3.7"
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@@ -2681,14 +2596,13 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
 
 [[package]]
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-version = "3.10.0"
+version = "3.11.1"
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-category = "dev"
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 [package.dependencies]
@@ -2701,7 +2615,6 @@ dev = ["pre-commit", "pytest-asyncio", "tox"]
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 version = "2.3.1"
 description = "pytest plugin to run pycodestyle"
-category = "dev"
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@@ -2720,7 +2633,6 @@ tests = ["pytest-isort"]
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 description = "Pytest plugin to randomly order tests and control random.seed."
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@@ -2736,7 +2648,6 @@ pytest = "*"
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-category = "main"
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@@ -2751,7 +2662,6 @@ six = ">=1.5"
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@@ -2763,7 +2673,6 @@ files = [
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 description = "World timezone definitions, modern and historical"
-category = "main"
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@@ -2775,7 +2684,6 @@ files = [
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@@ -2813,7 +2721,6 @@ numpy = ">=1.17.3"
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-category = "main"
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 python-versions = ">=3.6"
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 [package.dependencies]
@@ -2991,14 +2895,13 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
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 description = "Python HTTP for Humans."
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 [package.dependencies]
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 version = "0.1.4"
 description = "A pure python RFC3339 validator"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -3030,7 +2932,6 @@ six = "*"
 name = "rfc3986-validator"
 version = "0.1.1"
 description = "Pure python rfc3986 validator"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
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+    {file = "scipy-1.11.1-cp39-cp39-win_amd64.whl", hash = "sha256:be8c962a821957fdde8c4044efdab7a140c13294997a407eaee777acf63cbf0c"},
+    {file = "scipy-1.11.1.tar.gz", hash = "sha256:fb5b492fa035334fd249f0973cc79ecad8b09c604b42a127a677b45a9a3d4289"},
 ]
 
 [package.dependencies]
-numpy = ">=1.18.5,<1.26.0"
+numpy = ">=1.21.6,<1.28.0"
 
 [package.extras]
-dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
-doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
-test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
+test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "seaborn"
 version = "0.12.2"
 description = "Statistical data visualization"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3236,7 +3091,6 @@ stats = ["scipy (>=1.3)", "statsmodels (>=0.10)"]
 name = "send2trash"
 version = "1.8.2"
 description = "Send file to trash natively under Mac OS X, Windows and Linux"
-category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 files = [
@@ -3251,26 +3105,24 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "setuptools"
-version = "67.7.2"
+version = "68.0.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "setuptools-67.7.2-py3-none-any.whl", hash = "sha256:23aaf86b85ca52ceb801d32703f12d77517b2556af839621c641fca11287952b"},
-    {file = "setuptools-67.7.2.tar.gz", hash = "sha256:f104fa03692a2602fa0fec6c6a9e63b6c8a968de13e17c026957dd1f53d80990"},
+    {file = "setuptools-68.0.0-py3-none-any.whl", hash = "sha256:11e52c67415a381d10d6b462ced9cfb97066179f0e871399e006c4ab101fc85f"},
+    {file = "setuptools-68.0.0.tar.gz", hash = "sha256:baf1fdb41c6da4cd2eae722e135500da913332ab3f2f5c7d33af9b492acb5235"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "six"
 version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
@@ -3282,7 +3134,6 @@ files = [
 name = "sniffio"
 version = "1.3.0"
 description = "Sniff out which async library your code is running under"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3294,7 +3145,6 @@ files = [
 name = "soupsieve"
 version = "2.4.1"
 description = "A modern CSS selector implementation for Beautiful Soup."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3306,7 +3156,6 @@ files = [
 name = "stack-data"
 version = "0.6.2"
 description = "Extract data from python stack frames and tracebacks for informative displays"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -3326,7 +3175,6 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 name = "terminado"
 version = "0.17.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3347,7 +3195,6 @@ test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
 name = "threadpoolctl"
 version = "3.1.0"
 description = "threadpoolctl"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -3357,14 +3204,13 @@ files = [
 
 [[package]]
 name = "tifffile"
-version = "2023.4.12"
+version = "2023.7.4"
 description = "Read and write TIFF files"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tifffile-2023.4.12-py3-none-any.whl", hash = "sha256:3161954746fe32c4f4244d0fb2eb0a272f3a3760b78882a42faa83ac5e6e0b74"},
-    {file = "tifffile-2023.4.12.tar.gz", hash = "sha256:2fa99f9890caab919d932a0acaa9d0f5843dc2ef3594e212963932e20713badd"},
+    {file = "tifffile-2023.7.4-py3-none-any.whl", hash = "sha256:f56d20ecce270f2ca5704ade578edfe4bf30158db96f50f5da39c816655311b8"},
+    {file = "tifffile-2023.7.4.tar.gz", hash = "sha256:2f9ed41bb531832b5a32c35e241fcb5a0aa322323485f2a82afb7df78b900534"},
 ]
 
 [package.dependencies]
@@ -3377,7 +3223,6 @@ all = ["defusedxml", "fsspec", "imagecodecs (>=2023.1.23)", "lxml", "matplotlib"
 name = "tinycss2"
 version = "1.2.1"
 description = "A tiny CSS parser"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3396,7 +3241,6 @@ test = ["flake8", "isort", "pytest"]
 name = "tomli"
 version = "2.0.1"
 description = "A lil' TOML parser"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3406,30 +3250,28 @@ files = [
 
 [[package]]
 name = "tornado"
-version = "6.3.1"
+version = "6.3.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
-category = "main"
 optional = false
 python-versions = ">= 3.8"
 files = [
-    {file = "tornado-6.3.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:db181eb3df8738613ff0a26f49e1b394aade05034b01200a63e9662f347d4415"},
-    {file = "tornado-6.3.1-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:b4e7b956f9b5e6f9feb643ea04f07e7c6b49301e03e0023eedb01fa8cf52f579"},
-    {file = "tornado-6.3.1-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9661aa8bc0e9d83d757cd95b6f6d1ece8ca9fd1ccdd34db2de381e25bf818233"},
-    {file = "tornado-6.3.1-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:81c17e0cc396908a5e25dc8e9c5e4936e6dfd544c9290be48bd054c79bcad51e"},
-    {file = "tornado-6.3.1-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a27a1cfa9997923f80bdd962b3aab048ac486ad8cfb2f237964f8ab7f7eb824b"},
-    {file = "tornado-6.3.1-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:d7117f3c7ba5d05813b17a1f04efc8e108a1b811ccfddd9134cc68553c414864"},
-    {file = "tornado-6.3.1-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:ffdce65a281fd708da5a9def3bfb8f364766847fa7ed806821a69094c9629e8a"},
-    {file = "tornado-6.3.1-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:90f569a35a8ec19bde53aa596952071f445da678ec8596af763b9b9ce07605e6"},
-    {file = "tornado-6.3.1-cp38-abi3-win32.whl", hash = "sha256:3455133b9ff262fd0a75630af0a8ee13564f25fb4fd3d9ce239b8a7d3d027bf8"},
-    {file = "tornado-6.3.1-cp38-abi3-win_amd64.whl", hash = "sha256:1285f0691143f7ab97150831455d4db17a267b59649f7bd9700282cba3d5e771"},
-    {file = "tornado-6.3.1.tar.gz", hash = "sha256:5e2f49ad371595957c50e42dd7e5c14d64a6843a3cf27352b69c706d1b5918af"},
+    {file = "tornado-6.3.2-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:c367ab6c0393d71171123ca5515c61ff62fe09024fa6bf299cd1339dc9456829"},
+    {file = "tornado-6.3.2-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:b46a6ab20f5c7c1cb949c72c1994a4585d2eaa0be4853f50a03b5031e964fc7c"},
+    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c2de14066c4a38b4ecbbcd55c5cc4b5340eb04f1c5e81da7451ef555859c833f"},
+    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:05615096845cf50a895026f749195bf0b10b8909f9be672f50b0fe69cba368e4"},
+    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5b17b1cf5f8354efa3d37c6e28fdfd9c1c1e5122f2cb56dac121ac61baa47cbe"},
+    {file = "tornado-6.3.2-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:29e71c847a35f6e10ca3b5c2990a52ce38b233019d8e858b755ea6ce4dcdd19d"},
+    {file = "tornado-6.3.2-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:834ae7540ad3a83199a8da8f9f2d383e3c3d5130a328889e4cc991acc81e87a0"},
+    {file = "tornado-6.3.2-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:6a0848f1aea0d196a7c4f6772197cbe2abc4266f836b0aac76947872cd29b411"},
+    {file = "tornado-6.3.2-cp38-abi3-win32.whl", hash = "sha256:7efcbcc30b7c654eb6a8c9c9da787a851c18f8ccd4a5a3a95b05c7accfa068d2"},
+    {file = "tornado-6.3.2-cp38-abi3-win_amd64.whl", hash = "sha256:0c325e66c8123c606eea33084976c832aa4e766b7dff8aedd7587ea44a604cdf"},
+    {file = "tornado-6.3.2.tar.gz", hash = "sha256:4b927c4f19b71e627b13f3db2324e4ae660527143f9e1f2e2fb404f3a187e2ba"},
 ]
 
 [[package]]
 name = "tqdm"
 version = "4.65.0"
 description = "Fast, Extensible Progress Meter"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3450,7 +3292,6 @@ telegram = ["requests"]
 name = "traitlets"
 version = "5.9.0"
 description = "Traitlets Python configuration system"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3464,41 +3305,49 @@ test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.5.0"
+version = "4.7.1"
 description = "Backported and Experimental Type Hints for Python 3.7+"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "typing_extensions-4.5.0-py3-none-any.whl", hash = "sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4"},
-    {file = "typing_extensions-4.5.0.tar.gz", hash = "sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb"},
+    {file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"},
+    {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2023.3"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
+    {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
 ]
 
 [[package]]
 name = "uri-template"
-version = "1.2.0"
+version = "1.3.0"
 description = "RFC 6570 URI Template Processor"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "uri_template-1.2.0-py3-none-any.whl", hash = "sha256:f1699c77b73b925cf4937eae31ab282a86dc885c333f2e942513f08f691fc7db"},
-    {file = "uri_template-1.2.0.tar.gz", hash = "sha256:934e4d09d108b70eb8a24410af8615294d09d279ce0e7cbcdaef1bd21f932b06"},
+    {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
+    {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
 ]
 
 [package.extras]
-dev = ["flake8 (<4.0.0)", "flake8-annotations", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-noqa", "flake8-requirements", "flake8-type-annotations", "flake8-use-fstring", "mypy", "pep8-naming"]
+dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
 
 [[package]]
 name = "urllib3"
-version = "2.0.2"
+version = "2.0.3"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.0.2-py3-none-any.whl", hash = "sha256:d055c2f9d38dc53c808f6fdc8eab7360b6fdbbde02340ed25cfbcd817c62469e"},
-    {file = "urllib3-2.0.2.tar.gz", hash = "sha256:61717a1095d7e155cdb737ac7bb2f4324a858a1e2e6466f6d03ff630ca68d3cc"},
+    {file = "urllib3-2.0.3-py3-none-any.whl", hash = "sha256:48e7fafa40319d358848e1bc6809b208340fafe2096f1725d05d67443d0483d1"},
+    {file = "urllib3-2.0.3.tar.gz", hash = "sha256:bee28b5e56addb8226c96f7f13ac28cb4c301dd5ea8a6ca179c0b9835e032825"},
 ]
 
 [package.extras]
@@ -3511,7 +3360,6 @@ zstd = ["zstandard (>=0.18.0)"]
 name = "watchdog"
 version = "3.0.0"
 description = "Filesystem events monitoring"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3551,7 +3399,6 @@ watchmedo = ["PyYAML (>=3.10)"]
 name = "wcwidth"
 version = "0.2.6"
 description = "Measures the displayed width of unicode strings in a terminal"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -3563,7 +3410,6 @@ files = [
 name = "webcolors"
 version = "1.13"
 description = "A library for working with the color formats defined by HTML and CSS."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3579,7 +3425,6 @@ tests = ["pytest", "pytest-cov"]
 name = "webencodings"
 version = "0.5.1"
 description = "Character encoding aliases for legacy web content"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3589,14 +3434,13 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.5.1"
+version = "1.6.1"
 description = "WebSocket client for Python with low level API options"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "websocket-client-1.5.1.tar.gz", hash = "sha256:3f09e6d8230892547132177f575a4e3e73cfdf06526e20cc02aa1c3b47184d40"},
-    {file = "websocket_client-1.5.1-py3-none-any.whl", hash = "sha256:cdf5877568b7e83aa7cf2244ab56a3213de587bbe0ce9d8b9600fc77b455d89e"},
+    {file = "websocket-client-1.6.1.tar.gz", hash = "sha256:c951af98631d24f8df89ab1019fc365f2227c0892f12fd150e935607c79dd0dd"},
+    {file = "websocket_client-1.6.1-py3-none-any.whl", hash = "sha256:f1f9f2ad5291f0225a49efad77abf9e700b6fef553900623060dad6e26503b9d"},
 ]
 
 [package.extras]
@@ -3606,26 +3450,38 @@ test = ["websockets"]
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.7"
+version = "4.0.8"
 description = "Jupyter interactive widgets for Jupyter Notebook"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "widgetsnbextension-4.0.7-py3-none-any.whl", hash = "sha256:be3228a73bbab189a16be2d4a3cd89ecbd4e31948bfdc64edac17dcdee3cd99c"},
-    {file = "widgetsnbextension-4.0.7.tar.gz", hash = "sha256:ea67c17a7cd4ae358f8f46c3b304c40698bc0423732e3f273321ee141232c8be"},
+    {file = "widgetsnbextension-4.0.8-py3-none-any.whl", hash = "sha256:2e37f0ce9da11651056280c7efe96f2db052fe8fc269508e3724f5cbd6c93018"},
+    {file = "widgetsnbextension-4.0.8.tar.gz", hash = "sha256:9ec291ba87c2dfad42c3d5b6f68713fa18be1acd7476569516b2431682315c17"},
 ]
 
+[[package]]
+name = "win32-setctime"
+version = "1.1.0"
+description = "A small Python utility to set file creation time on Windows"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"},
+    {file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"},
+]
+
+[package.extras]
+dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
+
 [[package]]
 name = "xarray"
-version = "2023.4.2"
+version = "2023.6.0"
 description = "N-D labeled arrays and datasets in Python"
-category = "main"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "xarray-2023.4.2-py3-none-any.whl", hash = "sha256:1b6d577c1217ad6bf7458426af19ed7a489ab6c41220ca791f55f5df9648173a"},
-    {file = "xarray-2023.4.2.tar.gz", hash = "sha256:958ec588220352343b910cbc05e54e7ab54d4e8c1c3a7783d6bfe7549d0bd0d2"},
+    {file = "xarray-2023.6.0-py3-none-any.whl", hash = "sha256:bdd4c45511ab4e84f4249ea1030336db59b750968f25369d8e132d6d7ead7cc9"},
+    {file = "xarray-2023.6.0.tar.gz", hash = "sha256:267a231ee4efc0341ebbffc6d4ec60e4a66e4849c16e0305c03fcefeca77698c"},
 ]
 
 [package.dependencies]
@@ -3645,7 +3501,6 @@ viz = ["matplotlib", "nc-time-axis", "seaborn"]
 name = "xmltodict"
 version = "0.13.0"
 description = "Makes working with XML feel like you are working with JSON"
-category = "main"
 optional = false
 python-versions = ">=3.4"
 files = [
@@ -3657,7 +3512,6 @@ files = [
 name = "y-py"
 version = "0.5.9"
 description = "Python bindings for the Y-CRDT built from yrs (Rust)"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3733,7 +3587,6 @@ files = [
 name = "ypy-websocket"
 version = "0.8.2"
 description = "WebSocket connector for Ypy"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3753,7 +3606,6 @@ test = ["mypy", "pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)"]
 name = "zipp"
 version = "3.15.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3767,5 +3619,5 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.9"
-content-hash = "80560b3ed1dc640fb5b10966688a1811f351aa968d2094567b2f0d8a62a6770a"
+python-versions = ">=3.9,<3.12"
+content-hash = "2fe07f2c6eb19f355a5dd660202faf5465ebcc36ad7beb2212588b1ec62e1ea1"
diff --git a/pyproject.toml b/pyproject.toml
index 53259f9e..5a15e8ff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,8 +36,8 @@ style = "pep440"
 metadata = false
 
 [tool.poetry.dependencies]
-python = "^3.9"
-alpineer = "0.1.7"
+python = ">=3.9,<3.12"
+alpineer = ">=0.1.9"
 mibi-bin-tools = "0.2.9"
 ipywidgets = "^8"
 numpy = "1.*"
@@ -46,7 +46,8 @@ seaborn = "^0.12"
 scikit-learn = "^1"
 watchdog = "^3"
 tqdm = "^4"
-pandas = "^1"
+scipy = "^1.10.1"
+pandas = "^2"
 
 [tool.poetry.group.test]
 optional = true
@@ -69,6 +70,7 @@ black = "^22.10.0"
 isort = "^5.10.1"
 jupyterlab = "^3.6.1"
 jupyter-contrib-nbextensions = "^0.7.0"
+loguru = "^0.7.0"
 
 ## TYPE CHECKING ##
 
diff --git a/src/toffy/bin_extraction.py b/src/toffy/bin_extraction.py
index 707c560d..3dbd400c 100644
--- a/src/toffy/bin_extraction.py
+++ b/src/toffy/bin_extraction.py
@@ -24,7 +24,6 @@ def extract_missing_fovs(
         extract_intensities (bool): whether to extract intensities from the bin files
         replace (bool): whether to replace pulse images with intensity
     """
-
     # retrieve all fov names from base_dir and extracted fovs from extraction_dir
     fovs = io_utils.remove_file_extensions(io_utils.list_files(bin_file_dir, substrs=".bin"))
     extracted_fovs = io_utils.list_folders(extraction_dir, substrs="fov")
diff --git a/src/toffy/qc_comp.py b/src/toffy/qc_comp.py
index 1bf6b6cb..339b1722 100644
--- a/src/toffy/qc_comp.py
+++ b/src/toffy/qc_comp.py
@@ -1,17 +1,24 @@
 import copy
+import itertools
 import os
 import pathlib
+import re
+from dataclasses import dataclass, field
 from shutil import rmtree
-from typing import List, Optional, Union
+from typing import Dict, List, Optional, Tuple, Union
 
-import matplotlib.pyplot as plt
+import natsort as ns
 import numpy as np
+import numpy.ma as ma
 import pandas as pd
-import seaborn as sns
-import seaborn.objects as so
+import xarray as xr
 from alpineer import image_utils, io_utils, load_utils, misc_utils
+from numpy.ma import MaskedArray
+from pandas.core.groupby import DataFrameGroupBy
 from requests.exceptions import HTTPError
 from scipy.ndimage import gaussian_filter
+from scipy.stats import rankdata
+from tqdm.auto import tqdm
 
 from toffy import settings
 from toffy.mibitracker_utils import MibiRequests, MibiTrackerError
@@ -278,7 +285,7 @@ def compute_qc_metrics(
 
     Args:
         extracted_imgs_path (str):
-            the directory when extracted images are stored
+            the directory where extracted images are stored
         fov_name (str):
             the name of the FOV to extract from `bin_file_path`, needs to correspond with JSON name
         gaussian_blur (bool):
@@ -291,8 +298,7 @@ def compute_qc_metrics(
             path to save csvs of the qc metrics to
 
     Returns:
-        None | Dict[str, pd.DataFrame]:
-            If save_csv is False, returns qc metrics. Otherwise, no return
+        None
     """
 
     # path validation checks
@@ -325,10 +331,6 @@ def compute_qc_metrics_direct(image_data, fov_name, gaussian_blur=False, blur_fa
             set to 0 to use raw inputs without Gaussian blurring
             ignored if `gaussian_blur` set to `False`
 
-    Returns:
-        Dict[str, pd.DataFrame]:
-            Returns qc metrics
-
     """
 
     # there's only 1 FOV and 1 type ('pulse'), so subset on that
@@ -418,129 +420,500 @@ def combine_qc_metrics(qc_metrics_dir):
         metric_df.to_csv(os.path.join(qc_metrics_dir, "combined_%s.csv" % ms), index=False)
 
 
-def visualize_qc_metrics(
-    metric_name: str,
-    qc_metric_dir: Union[str, pathlib.Path],
-    save_dir: Union[str, pathlib.Path],
-    channel_filters: Optional[List[str]] = ["chan_"],
-    axes_font_size: int = 16,
-    wrap: int = 6,
-    dpi: int = 300,
-    return_plot: bool = False,
-) -> Optional[sns.FacetGrid]:
+def format_img_data(img_data):
+    """Formats the image array from load_imgs_from_tree to be same structure as the array returned
+    by extract_bin_files. Works for one FOV data at a time.
+    Args:
+        img_data (str): current image data array as produced by load function
+    Returns:
+         xarray.DataArray: image data array with shape [fov, type, x, y, channel]
+    """
+
+    # add type dimension
+    img_data = img_data.assign_coords(type="pulse")
+    img_data = img_data.expand_dims("type", 1)
+
+    # edit dimension names
+    img_data = img_data.rename({"fovs": "fov", "rows": "x", "cols": "y", "channels": "channel"})
+
+    return img_data
+
+
+def qc_filtering(qc_metrics: List[str]) -> Tuple[List[str], List[str]]:
+    """
+    Filters the QC columns and suffixes based on the user specified QC metrics,
+    then sorts the suffixes w.r.t the columns. Refer to `settings.py` for the
+    Column and Suffixes available.
+
+    Args:
+        qc_metrics (List[str]): A list of QC metrics to use. Options include:
+
+                - `"Non-zero mean intensity"`
+                - `"Total intensity"`
+                - `"99.9% intensity value"`
+
+
+    Returns:
+        Tuple[List[str], List[str]]: Returns the QC Columns and the QC Suffixes
+    """
+    # Filter out unused QC columns and suffixes
+    if qc_metrics is not None:
+        selected_qcs: List[bool] = [qcm in qc_metrics for qcm in settings.QC_COLUMNS]
+        qc_cols = list(itertools.compress(settings.QC_COLUMNS, selected_qcs))
+        qc_suffixes = list(itertools.compress(settings.QC_SUFFIXES, selected_qcs))
+    else:
+        qc_cols: List[str] = settings.QC_COLUMNS
+        qc_suffixes: List[str] = settings.QC_SUFFIXES
+
+    return qc_cols, qc_suffixes
+
+
+def _channel_filtering(
+    df: pd.DataFrame, channel_include: List[str] = None, channel_exclude: List[str] = None
+) -> pd.DataFrame:
     """
-    Visualize the barplot of a specific QC metric.
+    Filters the DataFrame based on the included and excluded channels. In addition
+    the default ignored channels; Au, Fe, Na, Ta, Noodle, are removed.
 
     Args:
-        metric_name (str):
-            The name of the QC metric to plot. Used as the y-axis label. Options include:
-            `"Non-zero mean intensity"`, `"Total intensity"`, `"99.9% intensity value"`.
-        qc_metric_dir (Union[str, pathlib.Path]):
-            The path to the directory containing the `'combined_{qc_metric}.csv'` files
-        save_dir (Optional[Union[str, pathlib.Path]], optional):
-            The name of the directory to save the plot to. Defaults to None.
-        channel_filters (List[str], optional):
-            A list of channels to filter out.
-        axes_font_size (int, optional):
-            The font size of the axes labels. Defaults to 16.
-        wrap (int, optional):
-            The number of plots to display per row. Defaults to 6.
-        dpi (Optional[int], optional):
-            The resolution of the image to use for saving. Defaults to None.
-        return_plot (bool):
-            If `True`, this will return the plot. Defaults to `False`
-
-    Raises:
-        ValueError:
-            When an invalid metric is provided.
-        FileNotFoundError:
-            The QC metric directory `qc_metric_dir` does not exist.
-        FileNotFoundError:
-            The QC metric `combined_csv` file is does not exist in `qc_metric_dir`.
+        df (pd.DataFrame): The DataFrame to filter.
+        channel_include (List[str], optional): A list of channels to include. Defaults to None.
+        channel_exclude (List[str], optional): A list of channels to exclude. Defaults to None.
 
     Returns:
-        Optional[sns.FacetGrid]: Returns the Seaborn FacetGrid catplot of the QC metrics.
+        pd.DataFrame: The filtered DataFrame.
     """
-    # verify the metric provided is valid
-    if metric_name not in settings.QC_COLUMNS:
-        raise ValueError(
-            "Invalid metric %s provided, must be set to 'Non-zero mean intensity', "
-            "'Total intensity', or '99.9%% intensity value'" % metric_name
+
+    if (
+        isinstance(channel_include, list)
+        and isinstance(channel_exclude, list)
+        and not set(channel_exclude).isdisjoint(set(channel_include))
+    ):
+        raise ValueError("You cannot include and exclude the same channel.")
+
+    # Filter out the default ignored channels
+    df = df[~df["channel"].isin(settings.QC_CHANNEL_IGNORE)]
+
+    # Remove the excluded channels
+    # If a channel does not exist, it is ignored
+    if channel_exclude is not None:
+        df: pd.DataFrame = df[~df["channel"].isin(channel_exclude)]
+
+    # Then filter the excluded channels
+    # If a channel does not exist, it is ignored
+    if channel_include is not None:
+        df: pd.DataFrame = df[df["channel"].isin(channel_include)]
+    return df
+
+
+@dataclass
+class QCTMA:
+    """
+    Computes the QC metrics for a given list of TMAs of interest and saves TMA specific QC files
+    in the `qc_tma_metrics_dir` directory.
+
+
+    Args:
+        cohort_path (Union[str,pathlib.Path]): The directory where the extracted images are
+            stored.
+        qc_tma_metrics_dir (Union[str, pathlib.path]): The directory where to save the QC TMA
+            metrics.
+        qc_metrics (List[str]): A list of QC metrics to use. Options include:
+
+                - `"Non-zero mean intensity"`
+                - `"Total intensity"`
+                - `"99.9% intensity value"`
+
+    Attributes:
+        qc_cols (List[str]): A list of the QC columns.
+        qc_suffixes (List[str]): A list of the QC suffixes, ordered w.r.t `qc_cols`.
+        search_term (re.Pattern): The regex pattern to extract n,m from FOV names of the form RnCm.
+        tma_avg_ranks (Dict[str, xr.DataArray]): A dictionary containing the average ranks for each
+            TMA for each QC Metric in `qc_metrics`.
+    """
+
+    qc_metrics: Optional[List[str]]
+    cohort_path: Union[str, pathlib.Path]
+    metrics_dir: Union[str, pathlib.Path]
+
+    # Fields initialized after `__post_init__`
+    search_term: re.Pattern = field(init=False)
+    qc_cols: List[str] = field(init=False)
+    qc_suffixes: List[str] = field(init=False)
+
+    # Set by methods
+    tma_avg_ranks: Dict[str, xr.DataArray] = field(init=False)
+
+    def __post_init__(self):
+        # Input validation: Ensure that the paths exist
+        io_utils.validate_paths([self.cohort_path, self.metrics_dir])
+
+        self.qc_cols, self.qc_suffixes = qc_filtering(qc_metrics=self.qc_metrics)
+
+        # Create regex pattern for searching RnCm
+        self.search_term: re.Pattern = re.compile(r"R\+?(\d+)C\+?(\d+)")
+
+        # Set the tma_avg_ranks to be an empty dictionary
+        self.tma_avg_ranks = {}
+
+    def _get_r_c(self, fov_name: pd.Series) -> pd.Series:
+        """Extracts the row and column value from a FOV's name containing RnCm.
+
+        Args:
+            fov_name (pd.Series): The FOV's name.
+        Returns:
+            pd.Series: Returns `n` and `m` as a series of integers.
+        """
+        r, c = map(int, re.search(self.search_term, fov_name).group(1, 2))
+        return pd.Series([r, c])
+
+    def _create_r_c_tma_matrix(
+        self, group: DataFrameGroupBy, n_cols: int, n_rows: int, qc_col: str
+    ) -> pd.Series:
+        """
+        Ranks all FOVS for a given channel and creates a matrix of size `n_rows` by `n_cols`
+        as the TMA grid.
+
+
+        Args:
+            group (DataFrameGroupBy): Each group consists of an individual channel, and all of it's
+                associated FOVs.
+            n_cols (int): The number of columns in the matrix.
+            n_rows (int): The number of rows in the matrix.
+            qc_col (str): The column to get the the QC data.
+
+        Returns:
+            pd.Series[np.ndarray]: Returns the a series containing the ranked matrix.
+        """
+
+        rc_array: np.ndarray = np.full(shape=(n_cols, n_rows), fill_value=np.nan)
+        rc_array[group["column"] - 1, group["row"] - 1] = group[qc_col].rank()
+
+        return pd.Series([rc_array])
+
+    def compute_qc_tma_metrics(self, tmas: List[str]):
+        """
+        Calculates the QC metrics for a user specified list of TMAs.
+
+        Args:
+            tmas (List[str]): The FOVs with the TMA in the folder name to gather.
+        """
+        with tqdm(
+            total=len(tmas), desc="Computing QC TMA Metrics", unit="TMA", leave=True
+        ) as tma_pbar:
+            for tma in tmas:
+                self._compute_qc_tma_metrics(tma=tma)
+                tma_pbar.set_postfix(TMA=tma)
+                tma_pbar.update(n=1)
+
+    def _compute_qc_tma_metrics(self, tma: str):
+        """
+        Computes the FOV QC metrics for all FOVs in a given TMA.
+        If the QC metrics have already been computed, then
+
+        Args:
+            tma (str): The TMA to compute the QC metrics for.
+        """
+
+        # cannot use `io_utils.list_folders` because it cannot do a partial "exact match"
+        # i.e. if we want to match `tma_1_Rn_Cm` but not `tma_10_Rn_Cm`, `io_utils.list_folders`
+        # will return both for `tma_1`
+        fovs: List[str] = ns.natsorted(
+            seq=(p.name for p in pathlib.Path(self.cohort_path).glob(f"{tma}_*"))
         )
 
-    # verify the path to the QC metric datasets exist
-    if not os.path.exists(qc_metric_dir):
-        raise FileNotFoundError("qc_metric_dir %s does not exist" % qc_metric_dir)
+        # Compute the QC metrics
+        with tqdm(fovs, desc="Computing QC Metrics", unit="FOV", leave=False) as pbar:
+            for fov in pbar:
+                # Gather the qc tma files for the current fov if they exist
+                pre_computed_metrics = filter(
+                    lambda f: "combined" not in f,
+                    io_utils.list_files(
+                        dir_name=self.metrics_dir,
+                        substrs=[f"{fov}_{qc_suffix}.csv" for qc_suffix in self.qc_suffixes],
+                    ),
+                )
 
-    # get the file name of the combined QC metric .csv file to use
-    qc_metric_index = settings.QC_COLUMNS.index(metric_name)
-    qc_metric_suffix = settings.QC_SUFFIXES[qc_metric_index]
-    qc_metric_path = os.path.join(qc_metric_dir, "combined_%s.csv" % qc_metric_suffix)
+                # only compute if any QC files are missing for the current fov
+                if len(list(pre_computed_metrics)) != len(self.qc_cols):
+                    compute_qc_metrics(
+                        extracted_imgs_path=self.cohort_path,
+                        fov_name=fov,
+                        save_csv=self.metrics_dir,
+                    )
+                    pbar.set_postfix(FOV=fov, status="Computing")
+                else:
+                    pbar.set_postfix(FOV=fov, status="Already Computed")
+
+        # Generate the combined metrics for each TMA
+        for qc_suffix in self.qc_suffixes:
+            metric_files: List[str] = ns.natsorted(
+                (
+                    io_utils.list_files(
+                        dir_name=self.metrics_dir,
+                        substrs=[f"{fov}_{qc_suffix}.csv" for fov in fovs],
+                    )
+                )
+            )
+
+            # Define an aggregated metric DataFrame
+            combined_metric_tissue_df: pd.DataFrame = pd.concat(
+                (pd.read_csv(os.path.join(self.metrics_dir, mf)) for mf in metric_files)
+            )
+
+            combined_metric_tissue_df.to_csv(
+                os.path.join(self.metrics_dir, f"{tma}_combined_{qc_suffix}.csv"),
+                index=False,
+            )
 
-    # ensure the user set the right qc_metric_dir
-    if not os.path.exists(qc_metric_path):
-        raise FileNotFoundError(
-            "Could not locate %s, ensure qc_metric_dir is correct" % qc_metric_path
+    def qc_tma_metrics_rank(self, tmas: List[str], channel_exclude: List[str] = None):
+        """
+        Creates the average rank for a given TMA across all FOVs and unexcluded channels.
+        By default the following channels are excluded: Au, Fe, Na, Ta, Noodle.
+
+
+        Args:
+            tmas (List[str]): The FOVs withmetet the TMA in the folder name to gather.
+            channel_exclude (List[str], optional): An optional list of channels to further filter
+                out. Defaults to None.
+        """
+
+        with tqdm(total=len(tmas), desc="Computing QC TMA Metric Ranks", unit="TMA") as pbar:
+            for tma in tmas:
+                self.tma_avg_ranks[tma] = self._compute_qc_tma_metrics_rank(
+                    tma, channel_exclude=channel_exclude
+                )
+                pbar.set_postfix(TMA=tma)
+                pbar.update()
+
+    def _compute_qc_tma_metrics_rank(
+        self,
+        tma: str,
+        channel_exclude: List[str] = None,
+    ) -> xr.DataArray:
+        """
+        Creates the average rank for a given TMA across all FOVs and unexcluded channels.
+        By default the following channels are excluded: Au, Fe, Na, Ta, Noodle.
+
+
+        Args:
+            tma (str): The TMA to compute the average rank for.
+            channel_exclude (List[str], optional): An optional list of channels to further filter
+                out. Defaults to None.
+
+        Returns:
+            xr.DataArray: An xarray DataArray containing the average rank for each channel across
+                a TMA.
+        """
+
+        # Sort the loaded combined csv files based on the filtered `qc_suffixes`
+        combined_metric_tmas: List[str] = ns.natsorted(
+            io_utils.list_files(self.metrics_dir, substrs=f"{tma}_combined"),
+            key=lambda tma_mf: (i for i, qc_s in enumerate(self.qc_suffixes) if qc_s in tma_mf),
         )
 
-    # read in the QC metric data
-    qc_metric_df = pd.read_csv(qc_metric_path)
-
-    # filter out naturally-occurring elements as well as Noodle
-    qc_metric_df = qc_metric_df[~qc_metric_df["channel"].isin(settings.QC_CHANNEL_IGNORE)]
-
-    # filter out any channel in the channel_filters list
-    if channel_filters is not None:
-        qc_metric_df: pd.DataFrame = qc_metric_df[
-            ~qc_metric_df["channel"].str.contains("|".join(channel_filters))
-        ]
-
-    # catplot allows for easy facets on a barplot
-    qc_fg: sns.FacetGrid = sns.catplot(
-        x="fov",
-        y=metric_name,
-        col="channel",
-        col_wrap=wrap,
-        data=qc_metric_df,
-        kind="bar",
-        color="black",
-        sharex=True,
-        sharey=False,
-    )
+        ranked_channels_matrix = []
+        n_cols: int = None
+        n_rows: int = None
 
-    # remove the 'channel =' in each subplot title
-    qc_fg.set_titles(template="{col_name}")
-    qc_fg.figure.supxlabel(t="fov", x=0.5, y=0, ha="center", size=axes_font_size)
-    qc_fg.figure.supylabel(t=f"{metric_name}", x=0, y=0.5, va="center", size=axes_font_size)
-    qc_fg.set(xticks=[])
+        for cmt, qc_col in zip(combined_metric_tmas, self.qc_cols):
+            # Open and filter the default ignored channels, along with the user specified channels
+            cmt_df: pd.DataFrame = _channel_filtering(
+                df=pd.read_csv(os.path.join(self.metrics_dir, cmt)), channel_exclude=channel_exclude
+            )
 
-    # per Erin's visualization remove the default axis title on the y-axis
-    # and instead show 'fov' along x-axis and the metric name along the y-axis (overarching)
-    qc_fg.set_axis_labels(x_var="", y_var="")
-    qc_fg.set_xticklabels([])
+            cmt_df[["column", "row"]] = cmt_df["fov"].apply(self._get_r_c)
 
-    # save the figure always
-    # Return the figure if specified.
-    qc_fg.savefig(os.path.join(save_dir, f"{metric_name}_barplot_stats.png"), dpi=dpi)
+            # Get matrix dimensions
+            n_cols = cmt_df["column"].max()
+            n_rows = cmt_df["row"].max()
 
-    if return_plot:
-        return qc_fg
+            # Rank all FOVs per channel, and then create the heatmap matrix
+            ranked_channel_tmas: pd.DataFrame = cmt_df.groupby(by="channel", sort=True).apply(
+                lambda group: self._create_r_c_tma_matrix(group, n_cols, n_rows, qc_col)
+            )
+            ranked_channel_matrices: np.ndarray = np.array(
+                [c_tma[0] for c_tma in ranked_channel_tmas.values],
+            )
 
+            avg_rank = np.mean(ranked_channel_matrices, axis=0)
+
+            ranked_channels_matrix.append(avg_rank)
+
+        return xr.DataArray(
+            data=np.stack(ranked_channels_matrix),
+            coords=[self.qc_cols, np.arange(n_cols), np.arange(n_rows)],
+            dims=["qc_col", "cols", "rows"],
+        )
+
+
+@dataclass
+class QCControlMetrics:
+    """
+    Computes QC Metrics for a set of control sample FOVs across various runs, and saves the QC
+    files in the `longitudinal_control_metrics_dir`.
 
-def format_img_data(img_data):
-    """Formats the image array from load_imgs_from_tree to be same structure as the array returned
-    by extract_bin_files. Works for one FOV data at a time.
     Args:
-        img_data (str): current image data array as produced by load function
-    Returns:
-         xarray.DataArray: image data array with shape [fov, type, x, y, channel]
+        cohort_path (Union[str,pathlib.Path]): The directory where the extracted images are
+        stored for the control FOVs.
+        longitudinal_control_metrics_dir (Union[str, pathlib.Path]): The directory where to save
+        the QC Control metrics.
+        qc_metrics (List[str]): A list of QC metrics to use. Options include:
+
+                - `"Non-zero mean intensity"`
+                - `"Total intensity"`
+                - `"99.9% intensity value"`
+
+    Attributes:
+        qc_cols (List[str]): A list of the QC columns.
+        qc_suffixes (List[str]): A list of the QC suffixes, ordered w.r.t `qc_cols`.
     """
 
-    # add type dimension
-    img_data = img_data.assign_coords(type="pulse")
-    img_data = img_data.expand_dims("type", 1)
+    qc_metrics: Optional[List[str]]
+    cohort_path: Union[str, pathlib.Path]
+    metrics_dir: Union[str, pathlib.Path]
+
+    # Fields initialized after `__post_init__`
+    qc_cols: List[str] = field(init=False)
+    qc_suffixes: List[str] = field(init=False)
+    longitudinal_control_metrics: Dict[Tuple[str, str], pd.DataFrame] = field(init=False)
+
+    def __post_init__(self):
+        # Input validation: Ensure that the paths exist
+        io_utils.validate_paths([self.cohort_path, self.metrics_dir])
+
+        self.qc_cols, self.qc_suffixes = qc_filtering(qc_metrics=self.qc_metrics)
+
+        self.longitudinal_control_metrics = {}
+
+    def compute_control_qc_metrics(
+        self,
+        control_sample_name: str,
+        fovs: List[str],
+        channel_exclude: List[str] = None,
+        channel_include: List[str] = None,
+    ) -> None:
+        """
+        Computes QC metrics for a set of Control Sample FOVs and saves their QC files in the
+        `longitudinal_control_metrics_dir`. Calculates the following metrics for the specified
+        control samples:
+                - `"Non-zero mean intensity"`
+                - `"Total intensity"`
+                - `"99.9% intensity value"`
+
+        Args:
+            control_sample_name (str): An identifier for naming the control sample.
+            fovs (List[str]): A list of control samples to find QC metrics for.
+            channel_exclude (List[str], optional): A list of channels to exclude. Defaults to None.
+            channel_include (List[str], optional): A list of channels to include. Defaults to None.
+
+
+        Raises:
+            ValueError: Errors if `tissues` is either None, or a list of size 0.
+        """
+        if fovs is None or not isinstance(fovs, list):
+            raise ValueError("The tissues must be specified as a list of strings")
+
+        with tqdm(
+            total=len(fovs),
+            desc=f"Computing QC Longitudinal Control metrics - {control_sample_name}",
+            unit="FOVs",
+        ) as pbar:
+            for fov in ns.natsorted(fovs):
+                # Gather the qc files for the current fov if they exist
+                pre_computed_metrics = filter(
+                    lambda f: "combined" not in f,
+                    io_utils.list_files(
+                        dir_name=self.metrics_dir,
+                        substrs=[f"{fov}_{qc_suffix}.csv" for qc_suffix in self.qc_suffixes],
+                    ),
+                )
 
-    # edit dimension names
-    img_data = img_data.rename({"fovs": "fov", "rows": "x", "cols": "y", "channels": "channel"})
+                if len(list(pre_computed_metrics)) != len(self.qc_cols):
+                    compute_qc_metrics(
+                        extracted_imgs_path=self.cohort_path,
+                        fov_name=fov,
+                        save_csv=self.metrics_dir,
+                    )
+                    pbar.set_postfix(FOV=fov, status="Computing")
+                else:
+                    pbar.set_postfix(FOV=fov, status="Already Computed")
+                pbar.update()
+
+        # Combine metrics for the set of FOVs into a single file per QC metric
+        for qc_col, qc_suffix in zip(self.qc_cols, self.qc_suffixes):
+            metric_files = filter(
+                lambda f: "combined" not in f,
+                io_utils.list_files(
+                    dir_name=self.metrics_dir,
+                    substrs=[f"{fov}_{qc_suffix}.csv" for fov in fovs],
+                ),
+            )
 
-    return img_data
+            # Define an aggregated metric DataFrame, and filter channels
+            combined_lc_df: pd.DataFrame = _channel_filtering(
+                df=pd.concat(
+                    (pd.read_csv(os.path.join(self.metrics_dir, mf)) for mf in metric_files),
+                ),
+                channel_include=channel_include,
+                channel_exclude=channel_exclude,
+            )
+
+            self.longitudinal_control_metrics.update(
+                {(control_sample_name, qc_col): combined_lc_df}
+            )
+
+            combined_lc_df.to_csv(
+                os.path.join(self.metrics_dir, f"{control_sample_name}_combined_{qc_suffix}.csv"),
+                index=False,
+            )
+
+    def transformed_control_effects_data(
+        self, control_sample_name: str, qc_metric: str
+    ) -> pd.DataFrame:
+        """
+        Creates a transformed DataFrame for the Longitudinal Control effects data, normalizing by the mean,
+        then taking the `log2` of each value.
+
+        Args:
+            control_sample_name (str): A control sample to tranform the longitudinal control effects for.
+            qc_metric (str): The metric to transform.
+
+        Returns:
+            pd.DataFrame: The transformed QC Longitudinal Control data.
+        """
+        misc_utils.verify_in_list(user_metric=qc_metric, qc_metrics=self.qc_cols)
+
+        try:
+            df: pd.DataFrame = self.longitudinal_control_metrics[control_sample_name, qc_metric]
+        except KeyError:
+            # A qc file which isn't stored in the longitudinal_control_metrics dictionary, try to load it
+            # in if it exists as a file
+            df: pd.DataFrame = pd.read_csv(
+                os.path.join(
+                    self.metrics_dir,
+                    f"{control_sample_name}_combined_{self.qc_suffixes[self.qc_cols.index(qc_metric)]}.csv",
+                )
+            )
+        except FileNotFoundError as e:
+            raise FileNotFoundError(
+                f"QC Metric Not Found for the Control Sample {control_sample_name}"
+            ) from e
+
+        # Apply a log2 transformation to the mean normalized data.
+        log2_norm_df: pd.DataFrame = df.pivot(
+            index="channel", columns="fov", values=qc_metric
+        ).transform(func=lambda row: np.log2(row / row.mean()), axis=1)
+
+        mean_log2_norm_df: pd.DataFrame = (
+            log2_norm_df.mean(axis=0)
+            .to_frame(name="mean")
+            .transpose()
+            .sort_values(by="mean", axis=1)
+        )
+
+        transformed_df: pd.DataFrame = pd.concat(
+            objs=[log2_norm_df, mean_log2_norm_df]
+        ).sort_values(by="mean", axis=1, inplace=False)
+
+        return transformed_df
diff --git a/src/toffy/qc_metrics_plots.py b/src/toffy/qc_metrics_plots.py
index 058634ca..0cd91afa 100644
--- a/src/toffy/qc_metrics_plots.py
+++ b/src/toffy/qc_metrics_plots.py
@@ -1,87 +1,338 @@
+import os
+import pathlib
+from typing import List, Optional, Union
+
+import matplotlib.gridspec as gridspec
 import matplotlib.pyplot as plt
+import natsort as ns
+import numpy as np
 import pandas as pd
 import seaborn as sns
-from alpineer import io_utils, load_utils
+from matplotlib import cm
+from matplotlib.axes import Axes
+from matplotlib.colors import ListedColormap, Normalize
+from matplotlib.figure import Figure
+from tqdm.auto import tqdm
+
+from toffy import settings
+from toffy.qc_comp import QCTMA, QCControlMetrics
 
-from toffy import qc_comp
 
+def visualize_qc_metrics(
+    metric_name: str,
+    qc_metric_dir: Union[str, pathlib.Path],
+    save_dir: Union[str, pathlib.Path],
+    channel_filters: Optional[List[str]] = ["chan_"],
+    axes_font_size: int = 16,
+    wrap: int = 6,
+    dpi: int = 300,
+    return_plot: bool = False,
+) -> Optional[sns.FacetGrid]:
+    """
+    Visualize the barplot of a specific QC metric.
+
+    Args:
+        metric_name (str):
+            The name of the QC metric to plot. Used as the y-axis label. Options include:
+            `"Non-zero mean intensity"`, `"Total intensity"`, `"99.9% intensity value"`.
+        qc_metric_dir (Union[str, pathlib.Path]):
+            The path to the directory containing the `'combined_{qc_metric}.csv'` files
+        save_dir (Optional[Union[str, pathlib.Path]], optional):
+            The name of the directory to save the plot to. Defaults to None.
+        channel_filters (List[str], optional):
+            A list of channels to filter out.
+        axes_font_size (int, optional):
+            The font size of the axes labels. Defaults to 16.
+        wrap (int, optional):
+            The number of plots to display per row. Defaults to 6.
+        dpi (Optional[int], optional):
+            The resolution of the image to use for saving. Defaults to None.
+        return_plot (bool):
+            If `True`, this will return the plot. Defaults to `False`
 
-def call_violin_swarm_plot(plotting_df, fig_label, figsize=(20, 3), fig_dir=None):
-    """Makes violin plot with swarm dots. Used with make_batch_effect_plot()
+    Raises:
+        ValueError:
+            When an invalid metric is provided.
+        FileNotFoundError:
+            The QC metric directory `qc_metric_dir` does not exist.
+        FileNotFoundError:
+            The QC metric `combined_csv` file is does not exist in `qc_metric_dir`.
 
-    Args: plotting_df (pandas dataframe): "sample", "channel", "tma", "99.9th_percentile"
-          figsize (tuple): (length x width) of figsize
-          fig_dir (str): Dir to save plots.
+    Returns:
+        Optional[sns.FacetGrid]: Returns the Seaborn FacetGrid catplot of the QC metrics.
     """
-    plt.figure(figsize=figsize)
-    ax = sns.violinplot(
-        x="channel",
-        y="99.9th_percentile",
-        data=plotting_df,
-        inner=None,
-        scale="width",
-        color="gray",
-    )
-    ax = sns.swarmplot(
-        x="channel",
-        y="99.9th_percentile",
-        data=plotting_df,
-        edgecolor="black",
-        hue="tma",
-        palette="tab20",
+    # verify the metric provided is valid
+    if metric_name not in settings.QC_COLUMNS:
+        raise ValueError(
+            "Invalid metric %s provided, must be set to 'Non-zero mean intensity', "
+            "'Total intensity', or '99.9%% intensity value'" % metric_name
+        )
+
+    # verify the path to the QC metric datasets exist
+    if not os.path.exists(qc_metric_dir):
+        raise FileNotFoundError("qc_metric_dir %s does not exist" % qc_metric_dir)
+
+    # get the file name of the combined QC metric .csv file to use
+    qc_metric_index = settings.QC_COLUMNS.index(metric_name)
+    qc_metric_suffix = settings.QC_SUFFIXES[qc_metric_index]
+    qc_metric_path = os.path.join(qc_metric_dir, "combined_%s.csv" % qc_metric_suffix)
+
+    # ensure the user set the right qc_metric_dir
+    if not os.path.exists(qc_metric_path):
+        raise FileNotFoundError(
+            "Could not locate %s, ensure qc_metric_dir is correct" % qc_metric_path
+        )
+
+    # read in the QC metric data
+    qc_metric_df = pd.read_csv(qc_metric_path)
+
+    # filter out naturally-occurring elements as well as Noodle
+    qc_metric_df = qc_metric_df[~qc_metric_df["channel"].isin(settings.QC_CHANNEL_IGNORE)]
+
+    # filter out any channel in the channel_filters list
+    if channel_filters is not None:
+        qc_metric_df: pd.DataFrame = qc_metric_df[
+            ~qc_metric_df["channel"].str.contains("|".join(channel_filters))
+        ]
+
+    # catplot allows for easy facets on a barplot
+    qc_fg: sns.FacetGrid = sns.catplot(
+        x="fov",
+        y=metric_name,
+        col="channel",
+        col_wrap=wrap,
+        data=qc_metric_df,
+        kind="bar",
+        color="black",
+        sharex=True,
+        sharey=False,
     )
-    ax.set_title(fig_label)
-    plt.xticks(rotation=45)
-    if fig_dir:
-        plt.savefig(fig_dir + fig_label + "_batch_effects.png", dpi=300)
-    return ax
-
-
-def make_batch_effect_plot(
-    data_dir,
-    normal_tissues,
-    exclude_channels=None,
-    img_sub_folder=None,
-    qc_metric="99.9th_percentile",
-    fig_dir=None,
-):
-    """Makes violin plots based on tissue type. Calls call_violin_swarm_plot.
+
+    # remove the 'channel =' in each subplot title
+    qc_fg.set_titles(template="{col_name}")
+    qc_fg.figure.supxlabel(t="fov", x=0.5, y=0, ha="center", size=axes_font_size)
+    qc_fg.figure.supylabel(t=f"{metric_name}", x=0, y=0.5, va="center", size=axes_font_size)
+    qc_fg.set(xticks=[])
+
+    # per Erin's visualization remove the default axis title on the y-axis
+    # and instead show 'fov' along x-axis and the metric name along the y-axis (overarching)
+    qc_fg.set_axis_labels(x_var="", y_var="")
+    qc_fg.set_xticklabels([])
+
+    # save the figure always
+    # Return the figure if specified.
+    qc_fg.savefig(os.path.join(save_dir, f"{metric_name}_barplot_stats.png"), dpi=dpi)
+
+    if return_plot:
+        return qc_fg
+
+
+def qc_tmas_metrics_plot(
+    qc_tmas: QCTMA,
+    tmas: List[str],
+    save_figure: bool = False,
+    dpi: int = 300,
+) -> None:
+    """
+    Produces the QC TMA metrics plot for a given set of QC metrics applied to a user specified
+    TMA. The figures are saved in `qc_tma_metrics_dir/figures`.
 
     Args:
-        normal_tissues (str): is a list of the tissue type substring to match
-        exclude_channels (str): is a list of channels to not plot
-        img_sub_folder (str): in case theres additional sub folder structure
-        qc_metric (str): Type of qc_metric. Currently only 99.9th percentile.
+        qc_tmas (QCTMA): The class which contains the QC TMA data, filepaths, and methods.
+        QC matrix.
+        tma (str): The TMAs to plot the QC metrics for.
+        save_figure (bool, optional): If `True`, the figure is saved in a subdirectory in the
+        `QCTMA.qc_tma_metrics_dir` directory. Defaults to `False`.
+        dpi (int, optional): Dots per inch, the resolution of the image. Defaults to 300.
+    """
+
+    if save_figure:
+        fig_dir: pathlib.Path = pathlib.Path(qc_tmas.metrics_dir) / "figures"
+        fig_dir.mkdir(parents=True, exist_ok=True)
 
+    with tqdm(total=len(tmas), desc="Plotting QC TMA Metric Ranks", unit="TMAs") as pbar:
+        for tma in tmas:
+            _qc_tma_metrics_plot(qc_tmas, tma, fig_dir=fig_dir, save_figure=save_figure, dpi=dpi)
+            pbar.set_postfix(TMA=tma)
+            pbar.update(n=1)
+
+
+def _qc_tma_metrics_plot(
+    qc_tmas: QCTMA,
+    tma: str,
+    fig_dir: pathlib.Path,
+    save_figure: bool = False,
+    dpi: int = 300,
+) -> None:
     """
-    for i in range(len(normal_tissues)):
-        samples = io_utils.list_folders(dir_name=data_dir, substrs=normal_tissues[i])
-        data = load_utils.load_imgs_from_tree(
-            data_dir=data_dir, img_sub_folder=img_sub_folder, fovs=samples
+    Produces the QC TMA metrics plot for a given set of QC metrics applied to a user specified
+    TMA. The figures are saved in `qc_tma_metrics_dir/figures`.
+
+    Args:
+        qc_tmas (QCTMA): The class which contains the QC TMA data, filepaths, and methods.
+        tma (str): The TMA to plot the metrics for.
+        save_figure (bool, optional): If `True`, the figure is saved in a subdirectory in the
+        `qc_tma_metrics_dir` directory. Defaults to `False`.
+        dpi (int, optional): Dots per inch, the resolution of the image. Defaults to 300.
+    """
+
+    for qc_metric, suffix in zip(qc_tmas.qc_cols, qc_tmas.qc_suffixes):
+        qc_tma_data: np.ndarray = qc_tmas.tma_avg_ranks[tma].loc[qc_metric].values
+
+        # Set up the Figure for multiple axes
+        fig: Figure = plt.figure(dpi=dpi)
+        fig.set_layout_engine(layout="constrained")
+        gs = gridspec.GridSpec(1, 2, figure=fig, wspace=0.05)
+        fig.suptitle(f"{tma} - {qc_metric}")
+
+        # Heatmap
+        ax_heatmap: Axes = fig.add_subplot(gs[0, 0])
+        sns.heatmap(
+            data=qc_tma_data,
+            square=True,
+            ax=ax_heatmap,
+            linewidths=1,
+            linecolor="black",
+            cbar_kws={"shrink": 0.5},
+            annot=True,
+            cmap=sns.color_palette(palette="Blues", as_cmap=True),
+        )
+        # Set ticks
+        ax_heatmap.set_xticks(
+            ticks=ax_heatmap.get_xticks(),
+            labels=[f"{i+1}" for i in range(qc_tma_data.shape[1])],
+            rotation=0,
+        )
+
+        ax_heatmap.set_yticks(
+            ticks=ax_heatmap.get_yticks(),
+            labels=[f"{i+1}" for i in range(qc_tma_data.shape[0])],
+            rotation=0,
         )
-        channels = list(data.channels.values)
-        if exclude_channels:
-            channels = [x for x in channels if x not in exclude_channels]
-
-        # i could add a separate function to produce the plotting_df that is testable
-        plotting_df = pd.DataFrame(columns=["sample", "channel", "tma", "99.9th_percentile"])
-
-        for j in range(len(channels)):
-            qc_metrics_per_channel = []
-
-            for k in range(len(samples)):
-                tma = [x for x in samples[k].split("_") if "TMA" in x][0]
-                qc_metrics_per_channel += [
-                    [
-                        normal_tissues[i],
-                        channels[j],
-                        tma,
-                        qc_comp.compute_99_9_intensity(data.loc[samples[k], :, :, channels[j]]),
-                    ]
-                ]
-
-            plotting_df = plotting_df.append(
-                pd.DataFrame(qc_metrics_per_channel, columns=plotting_df.columns)
+
+        ax_heatmap.set_xlabel("Column")
+        ax_heatmap.set_ylabel("Row")
+        ax_heatmap.set_title("Average Rank")
+
+        # Histogram
+        ax_hist: Axes = fig.add_subplot(gs[0, 1])
+        sns.histplot(qc_tma_data.ravel(), ax=ax_hist, bins=10)
+        ax_hist.set(xlabel="Average Rank", ylabel="Count")
+        ax_hist.set_title("Average Rank Distribution")
+
+        if save_figure:
+            fig.savefig(
+                fname=fig_dir / f"{tma}_{suffix}.png",
+                dpi=dpi,
+                bbox_inches="tight",
             )
+            plt.close(fig)
+
+
+def longitudinal_control_heatmap(
+    qc_control: QCControlMetrics,
+    control_sample_name: str,
+    save_figure: bool = False,
+    dpi: int = 300,
+) -> None:
+    """
+    Generates a heatmap of the QC metrics for the QC Control FOVs.
+
+    Args:
+        qc_control (QCControlMetrics): The class which contains the QC LC data, filepaths
+        , and methods.
+        control_sample_name (List[str]): A list of tissues to plot the QC metrics for.
+        save_figure (bool, optional): If `True`, the figure is saved in a subdirectory in the
+        `longitudinal_control_metrics_dir` directory. Defaults to `False`.
+        dpi (int, optional): Dots per inch, the resolution of the image. Defaults to 300.
+
+    Raises:
+        ValueError: Raised when the input tissues are not a list of strings.
+    """
+    if control_sample_name is None or not isinstance(control_sample_name, str):
+        raise ValueError("The control sample name must be string.")
+    if save_figure:
+        fig_dir: pathlib.Path = pathlib.Path(qc_control.metrics_dir) / "figures"
+        fig_dir.mkdir(parents=True, exist_ok=True)
+
+    for qc_col, qc_suffix in zip(qc_control.qc_cols, qc_control.qc_suffixes):
+        t_df: pd.DataFrame = qc_control.transformed_control_effects_data(
+            control_sample_name=control_sample_name, qc_metric=qc_col
+        )
 
-        call_violin_swarm_plot(plotting_df, fig_label=normal_tissues[i], fig_dir=fig_dir)
+        # Set up the Figure for multiple axes
+        fig: Figure = plt.figure(figsize=(12, 12), dpi=dpi)
+        fig.set_layout_engine(layout="constrained")
+        gs = gridspec.GridSpec(nrows=2, ncols=1, figure=fig, height_ratios=[len(t_df.index) - 1, 1])
+
+        # Colorbar Normalization
+        _norm = Normalize(vmin=-1, vmax=1)
+        _cmap = sns.color_palette("vlag", as_cmap=True)
+
+        fig.suptitle(f"{control_sample_name} - QC: {qc_col}")
+
+        # Heatmap
+        ax_heatmap: Axes = fig.add_subplot(gs[0, 0])
+
+        sns.heatmap(
+            t_df[~t_df.index.isin(["mean"])],
+            ax=ax_heatmap,
+            linewidths=1,
+            linecolor="black",
+            cbar_kws={"shrink": 0.5},
+            annot=True,
+            xticklabels=False,
+            norm=_norm,
+            cmap=_cmap,
+        )
+
+        # cbar title
+        ax_heatmap.collections[0].colorbar.ax.set_title(r"$\log_2(QC)$")
+
+        # Axes labels, and ticks
+        ax_heatmap.set_yticks(
+            ticks=ax_heatmap.get_yticks(),
+            labels=ax_heatmap.get_yticklabels(),
+            rotation=0,
+        )
+        ax_heatmap.set_xlabel(None)
+
+        # Averaged values
+        ax_avg: Axes = fig.add_subplot(gs[1, 0])
+
+        sns.heatmap(
+            data=t_df[t_df.index.isin(["mean"])],
+            ax=ax_avg,
+            linewidths=1,
+            linecolor="black",
+            annot=True,
+            fmt=".2f",
+            cmap=ListedColormap(["white"]),
+            cbar=False,
+        )
+        ax_avg.set_yticks(
+            ticks=ax_avg.get_yticks(),
+            labels=["Mean"],
+            rotation=0,
+        )
+        ax_avg.set_xticks(
+            ticks=ax_avg.get_xticks(),
+            labels=ax_avg.get_xticklabels(),
+            rotation=45,
+            ha="right",
+            rotation_mode="anchor",
+        )
+        ax_heatmap.set_ylabel("Channel")
+        ax_avg.set_xlabel("FOV")
+
+        # Save figure
+        if save_figure:
+            fig.savefig(
+                fname=pathlib.Path(qc_control.metrics_dir)
+                / "figures"
+                / f"{control_sample_name}_heatmap_{qc_suffix}.png",
+                dpi=dpi,
+                bbox_inches="tight",
+            )
+        plt.show()
+        plt.close(fig)
diff --git a/src/toffy/rosetta.py b/src/toffy/rosetta.py
index 0cb7d3a0..6e714a60 100644
--- a/src/toffy/rosetta.py
+++ b/src/toffy/rosetta.py
@@ -1,5 +1,4 @@
 import copy
-import json
 import os
 import random
 import shutil
diff --git a/src/toffy/watcher_callbacks.py b/src/toffy/watcher_callbacks.py
index d288016a..aad6836b 100644
--- a/src/toffy/watcher_callbacks.py
+++ b/src/toffy/watcher_callbacks.py
@@ -1,6 +1,5 @@
 import inspect
 import os
-import platform
 from dataclasses import dataclass, field
 from typing import Iterable
 
@@ -9,7 +8,6 @@
 
 matplotlib.use("Agg")
 
-import matplotlib.pyplot as plt
 import pandas as pd
 import xarray as xr
 from alpineer import misc_utils
@@ -19,8 +17,9 @@
 from toffy.image_stitching import stitch_images
 from toffy.mph_comp import combine_mph_metrics, compute_mph_metrics, visualize_mph
 from toffy.normalize import write_mph_per_mass
-from toffy.qc_comp import combine_qc_metrics, compute_qc_metrics_direct, visualize_qc_metrics
-from toffy.settings import QC_COLUMNS, QC_SUFFIXES
+from toffy.qc_comp import combine_qc_metrics, compute_qc_metrics_direct
+from toffy.qc_metrics_plots import visualize_qc_metrics
+from toffy.settings import QC_COLUMNS
 
 RUN_PREREQUISITES = {
     "plot_qc_metrics": set(["generate_qc"]),
diff --git a/templates/3c_generate_qc_metrics.ipynb b/templates/3c_generate_qc_metrics.ipynb
index 746a7152..b93afd59 100644
--- a/templates/3c_generate_qc_metrics.ipynb
+++ b/templates/3c_generate_qc_metrics.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "78c18a71",
    "metadata": {},
@@ -9,6 +10,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "cf556c06-55eb-4ce1-a040-cb5260379244",
    "metadata": {},
@@ -26,11 +28,12 @@
     "import os\n",
     "import natsort as ns\n",
     "\n",
-    "from toffy import qc_comp\n",
+    "from toffy import qc_comp, qc_metrics_plots\n",
     "from alpineer import io_utils"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e764f21a",
    "metadata": {},
@@ -39,6 +42,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "54dae734",
    "metadata": {},
@@ -62,6 +66,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "2572f3e4",
    "metadata": {},
@@ -89,6 +94,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "58f57192",
    "metadata": {},
@@ -124,6 +130,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "87e82396",
    "metadata": {
@@ -145,6 +152,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "25542275-207a-44d2-b315-358f9759c221",
    "metadata": {},
@@ -205,7 +213,7 @@
    ],
    "source": [
     "# visualize the QC stats\n",
-    "qc_comp.visualize_qc_metrics(\n",
+    "qc_metrics_plots.visualize_qc_metrics(\n",
     "    qc_metric,\n",
     "    qc_out_dir,\n",
     "    axes_font_size=16,\n",
diff --git a/templates/6_tma_batch_qc_metrics.ipynb b/templates/6_tma_batch_qc_metrics.ipynb
new file mode 100644
index 00000000..5a24c7c4
--- /dev/null
+++ b/templates/6_tma_batch_qc_metrics.ipynb
@@ -0,0 +1,545 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Computing TMA and Cohort-wise QC Metrics Notebook"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook requires that you run `5_rename_and_reorganize.ipynb` beforehand. After you have your reorganized data, it may be useful to investigate if there are any widespread issues for TMAs and control tissues.\n",
+    "\n",
+    "The purpose of this notebook is to run QC checks on the following conditions:\n",
+    "- FOVs across a TMA\n",
+    "- Across various Samples for a control Tissue\n",
+    "\n",
+    "There are two parts which can be done in any order, depending on which type of QC effects are of interest to you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from toffy import qc_comp, qc_metrics_plots"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## QC TMA Metrics"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can utilize QC Metrics to validate that are no spatial biases across your TMAs, or if they do exist, identify where they are most prevalent."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0. Prerequisites"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to use the QC TMA Metrics functionality, you need to have run `5_rename_and_reorganize.ipynb` beforehand. In addition, each FOV within the cohort should be suffixed with the Row number and Column number it's associated TMA.\n",
+    "\n",
+    "For example, use Notebook 5 to combine, reorganize and rename the runs to something akin to `MY_COHORT_TMA1_R1C1` and `MY_COHORT_TMA11_R10C11`.\n",
+    "\n",
+    "To make use of the QC TMA Metrics, your cohort should look like something below:\n",
+    "\n",
+    "\n",
+    "```sh\n",
+    "my_cohort_tmas/\n",
+    "├── MY_COHORT_TMA1_R1C1/\n",
+    "├── MY_COHORT_TMA1_R1C2/\n",
+    "├── ...\n",
+    "├── MY_COHORT_TMA2_R1C2/\n",
+    "├── ...\n",
+    "├── MY_COHORT_TMA2_R7C10/\n",
+    "├── ...\n",
+    "└── MY_COHORT_TMA11_R11C10/\n",
+    "```\n",
+    "\n",
+    "It is **necessary** that the Row number and Column number values are the suffixes for each FOV."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Select QC Metrics and TMAs"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the name of the relevant folders:\n",
+    "- `cohort_path`: The path to the cohort containing ready-to-analyze FOVs.\n",
+    "\n",
+    "The following folder will be created for you:\n",
+    "- `qc_tma_metrics_dir`: The path where the QC TMA metrics should be saved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cohort_path = \"D:\\\\Cohorts\\\\20220101_new_cohort\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qc_tma_metrics_dir = os.path.join(cohort_path, \"qc_tma_metrics\")\n",
+    "\n",
+    "if not os.path.exists(qc_tma_metrics_dir):\n",
+    "    os.makedirs(qc_tma_metrics_dir)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set `tmas` to a list of TMAs you wish to compute QC metrics for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the `tmas` variable to be a list of the tmas you want to run the QC on\n",
+    "tmas = [\"MY_COHORT_TMA1\", \"MY_COHORT_TMA1\", \"MY_COHORT_TMA1\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select any combination of the following three QC metrics:\n",
+    "1. `\"Non-zero mean intensity\"`\n",
+    "2. `\"Total intensity\"`\n",
+    "3. `\"99.9% intensity value\"`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qc_metrics = [\"Non-zero mean intensity\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Compute QC TMA Metrics"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize the QCTMA class with the path to your cohort (`cohort_path`), the path to the folder containing the QC TMA metrics (`qc_tma_metrics_dir`) and the QC Metrics of interest themselves (`qc_metrics`).\n",
+    "\n",
+    "Then compute the QC metrics per FOV. FOVs which already have QC metrics files do not get theirs recomputed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qc_tmas = qc_comp.QCTMA(\n",
+    "    qc_metrics=qc_metrics,\n",
+    "    cohort_path=cohort_path,\n",
+    "    metrics_dir=qc_tma_metrics_dir,\n",
+    ")\n",
+    "\n",
+    "qc_tmas.compute_qc_tma_metrics(tmas=tmas)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may want to exclude channels depending on their impact, the `channel_exclude` variable will filter out those channels when creating the ranked QC metrics.\n",
+    "\n",
+    "The following channels will *always* be excluded from the TMA Metrics ranking below:\n",
+    "- Au\n",
+    "- Fe\n",
+    "- Na\n",
+    "- Ta\n",
+    "- Noodle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "channel_exclude = [\"chan_39\", \"chan_45\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Compute the Ranked QC TMA Metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qc_tmas.qc_tma_metrics_rank(tmas=tmas, channel_exclude=channel_exclude)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Plot the QC TMA Metrics\n",
+    "\n",
+    "The following plot below depicts a heatmap of the TMA along with a histogram. \n",
+    "\n",
+    "The TMA QC metrics are processed first by a FOV wise ranking for each channel, then each channel is averaged for each FOV.\n",
+    "\n",
+    "What we are looking for is that any particular region's average rank isn't higher than any other. An issue arises when, say all FOVs in the upper left corner of the TMA are systematically brighter than the others.\n",
+    "\n",
+    "\n",
+    "These plots get saved in a `figures` subfolder within `qc_tma_metrics_dir`."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "    <img src=\"img/nb6_ex_avg_tma_rank.png\" />\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qc_metrics_plots.qc_tmas_metrics_plot(qc_tmas=qc_tmas, tmas=tmas, save_figure=True, dpi=300)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## QC Longitudinal Control Metrics"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second half of this notebook is dedicated to looking at QC metrics for a particular control sample across many runs, these are called *longitudinal control* metrics."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0. Prerequisites"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to make use of the QC Longitudinal Control Metrics, you need to have run `5_rename_and_reorganize.ipynb` beforehand. There is no set naming convention here for each FOV.\n",
+    "\n",
+    "To make use of the LC Metrics, your cohort should consist of one control sample across several runs\n",
+    "\n",
+    "```sh\n",
+    "my_control_sample_runs/\n",
+    "├── MY_CONTROL_SAMPLE_RUN1/\n",
+    "├── MY_CONTROL_SAMPLE_RUN2/\n",
+    "├── ...\n",
+    "├── MY_CONTROL_SAMPLE_RUN5/\n",
+    "├── MY_CONTROL_SAMPLE_RUN6/\n",
+    "└── MY_CONTROL_SAMPLE_RUN7/\n",
+    "```\n",
+    "\n",
+    "Longitudinal Control Metrics can be computed for control sample FOVs across different cohorts. For a given control sample, we will be able to analyze it's run-to-run variance. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Set QC metrics, Control Sample FOVs, Paths, and the Control Sample Name"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the names of the relevant folders:\n",
+    "- `control_path`: The path to the cohort containing ready-to-analyze FOVs.\n",
+    "\n",
+    "The following folder will be created for you:\n",
+    "- `qc_control_metrics_dir`: The path where the QC TMA metrics should be saved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "control_path = \"D:\\\\Cohorts\\\\20220101_new_cohort_controls\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qc_control_metrics_dir = os.path.join(control_path, \"qc_longitudinal_control\")\n",
+    "\n",
+    "if not os.path.exists(qc_control_metrics_dir):\n",
+    "    os.makedirs(qc_control_metrics_dir)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select any combination of the following three QC metrics:\n",
+    "1. `\"Non-zero mean intensity\"`\n",
+    "2. `\"Total intensity\"`\n",
+    "3. `\"99.9% intensity value\"`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qc_metrics = [\"Non-zero mean intensity\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set `control_sample_name` to an informative name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "control_sample_name = \"MY_CONTROL_SAMPLE\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set `fovs` to a list of FOVs you wish to compute the Longitudinal QC Metrics for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fovs = [\"MY_CONTROL_SAMPLE_RUN1\", \"MY_CONTROL_SAMPLE_RUN2\", \"MY_CONTROL_SAMPLE_RUN3\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may want to include, and exclude various channels depending on their impact.\n",
+    "\n",
+    "- `channel_exclude`: A list of channels to filter out for the Longitudinal Control QC Metrics.\n",
+    "- `channel_include`: A list of channels to *only* include for the Longitudinal Control QC Metrics.\n",
+    "\n",
+    "The following channels will always be excluded from the Longitudinal Control QC Metrics ranking below:\n",
+    "- Au\n",
+    "- Fe\n",
+    "- Na\n",
+    "- Ta\n",
+    "- Noodle\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "channel_exclude = [\"chan_39\", \"Biotin\", \"PDL1\", \"chan_45\"]\n",
+    "channel_include = None"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Compute the Longitudinal Control QC metrics for the set of Control Sample FOVs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the QC Control Metrics class\n",
+    "qc_control = qc_comp.QCControlMetrics(\n",
+    "    qc_metrics=qc_metrics,\n",
+    "    cohort_path=control_path,\n",
+    "    metrics_dir=qc_control_metrics_dir,\n",
+    ")\n",
+    "\n",
+    "# Compute the QC metrics for the FOVs provided\n",
+    "qc_control.compute_control_qc_metrics(\n",
+    "    control_sample_name=control_sample_name,\n",
+    "    fovs=fovs,\n",
+    "    channel_exclude=channel_exclude,\n",
+    "    channel_include=channel_include,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Longitudinal Control Heatmap"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following plot below is a heatmap for each Control Sample FOV associated with a particular tissue, in this case it is `\"MY_CONTROL_SAMPLE\"`.\n",
+    "\n",
+    "Given each FOV $i$, it's associated QC metric $qc_i$, we calculate the Longitudinal value $l_i$ with the following:\n",
+    "\n",
+    "$$\n",
+    "l_i = \\log_2\\left(\\frac{qc_i}{\\frac{1}{n}\\sum_{i}^{n} qc_i}\\right)\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "A value of $1$ would be interpreted as be $2$ times greater than the row average, and a value of $-1$ would be $2$ times less than the row average.\n",
+    "\n",
+    "These plots get saved in a `figures` subfolder within `qc_control_effect_metrics_dir`."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "    <img src=\"img/nb6_ex_batch_effect_heatmap.png\" />\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qc_metrics_plots.longitudinal_control_heatmap(\n",
+    "    qc_control=qc_control, control_sample_name=control_sample_name, save_figure=True, dpi=300\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "toffy-r1jqSdeC-py3.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/templates/batch_effects_violin_example.ipynb b/templates/batch_effects_violin_example.ipynb
deleted file mode 100644
index ee7bd648..00000000
--- a/templates/batch_effects_violin_example.ipynb
+++ /dev/null
@@ -1,103 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "043d3cf0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from toffy import qc_metrics_plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b203e00a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# single tissue specified.\n",
-    "qc_metrics_plots.make_batch_effect_plot(data_dir=\"/Users/heskett/control_sample_Examples\",\n",
-    "                 normal_tissues=[\"ln\"], \n",
-    "                 exclude_channels=[\"Au\",\"chan_39\",\"Fe\",\"ChyTr\",\"Noodle\",\"chan_45\",\n",
-    "                                    \"chan_48\",\"chan_115\",\"chan_141\",\"SMA\"],\n",
-    "                 fig_dir=\"/Users/heskett/toffy/\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ccf33b83",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Multiple tissue types. \n",
-    "qc_metrics_plots.make_batch_effect_plot(data_dir=\"/Users/heskett/control_sample_Examples\",\n",
-    "                 normal_tissues=[\"ln_top\",\"ln_bottom\"], \n",
-    "                 exclude_channels=[\"Au\",\"chan_39\",\"Fe\",\"ChyTr\",\"Noodle\",\"chan_45\",\n",
-    "                                    \"chan_48\",\"chan_115\",\"chan_141\",\"SMA\"],\n",
-    "                 fig_dir=\"/Users/heskett/toffy/\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/templates/img/nb6_ex_avg_tma_rank.png b/templates/img/nb6_ex_avg_tma_rank.png
new file mode 100644
index 00000000..a250e378
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diff --git a/templates/img/nb6_ex_batch_effect_heatmap.png b/templates/img/nb6_ex_batch_effect_heatmap.png
new file mode 100644
index 00000000..8b989f63
Binary files /dev/null and b/templates/img/nb6_ex_batch_effect_heatmap.png differ
diff --git a/tests/qc_comp_test.py b/tests/qc_comp_test.py
index bf6724b7..7ad22ea2 100644
--- a/tests/qc_comp_test.py
+++ b/tests/qc_comp_test.py
@@ -1,15 +1,20 @@
+import itertools
 import os
+import pathlib
 import tempfile
+from dataclasses import dataclass
 from pathlib import Path
+from typing import Generator, List
 
 import numpy as np
 import pandas as pd
 import pytest
-from alpineer import io_utils, load_utils, misc_utils
+import xarray as xr
+from alpineer import io_utils, load_utils, test_utils
 from mibi_bin_tools import bin_files
+from tqdm import tqdm
 
 from toffy import qc_comp, settings
-from toffy.mibitracker_utils import MibiRequests
 
 parametrize = pytest.mark.parametrize
 
@@ -214,65 +219,6 @@ def test_combine_qc_metrics(fovs):
             assert list(metric_data[mc]) == qc_metric_vals
 
 
-def test_visualize_qc_metrics():
-    # define the channels to use
-    chans = ["chan0", "chan1", "chan2"]
-
-    # define the fov names to use for each channel
-    fov_batches = [["fov0", "fov1"], ["fov2", "fov3"], ["fov4", "fov5"]]
-
-    # define the supported metrics to iterate over
-    metrics = ["Non-zero mean intensity", "Total intensity", "99.9% intensity value"]
-
-    with tempfile.TemporaryDirectory() as temp_dir:
-        # save sample combined .csv files for each metric
-        for metric in metrics:
-            # define the test melted DataFrame for an arbitrary QC metric
-            sample_qc_metric_data = pd.DataFrame()
-
-            for chan, fovs in zip(chans, fov_batches):
-                chan_data = pd.DataFrame(np.random.rand(len(fovs)), columns=[metric])
-
-                chan_data["fov"] = fovs
-                chan_data["channel"] = chan
-
-                sample_qc_metric_data = pd.concat([sample_qc_metric_data, chan_data])
-
-            # get the file name of the combined QC metric .csv file to use
-            qc_metric_index = settings.QC_COLUMNS.index(metric)
-            qc_metric_suffix = settings.QC_SUFFIXES[qc_metric_index] + ".csv"
-
-            # save the combined data
-            sample_qc_metric_data.to_csv(
-                os.path.join(temp_dir, "combined_%s" % qc_metric_suffix), index=False
-            )
-
-        # pass an invalid metric
-        with pytest.raises(ValueError):
-            qc_comp.visualize_qc_metrics(metric_name="bad_metric", qc_metric_dir="", save_dir="")
-
-        # pass an invalid qc_metric_dir
-        with pytest.raises(FileNotFoundError):
-            qc_comp.visualize_qc_metrics("Non-zero mean intensity", "bad_qc_dir", save_dir="")
-
-        # pass a qc_metric_dir without the combined files
-        os.mkdir(os.path.join(temp_dir, "empty_qc_dir"))
-        with pytest.raises(FileNotFoundError):
-            qc_comp.visualize_qc_metrics(
-                "Non-zero mean intensity", os.path.join(temp_dir, "empty_qc_dir"), save_dir=""
-            )
-
-        # now test the visualization process for each metric
-        for metric in metrics:
-            # test without saving (should raise an error)
-            with pytest.raises(TypeError):
-                qc_comp.visualize_qc_metrics(metric, temp_dir)
-
-            # test with saving
-            qc_comp.visualize_qc_metrics(metric, temp_dir, save_dir=temp_dir)
-            assert os.path.exists(os.path.join(temp_dir, "%s_barplot_stats.png" % metric))
-
-
 def test_format_img_data():
     # define a sample panel, leave panel correctness/incorrectness test for mibi_bin_tools
     panel = pd.DataFrame(
@@ -309,3 +255,434 @@ def test_format_img_data():
         # test for successful reformatting
         load_data = qc_comp.format_img_data(load_data)
         assert load_data.equals(extracted_data)
+
+
+@dataclass
+class QCMetricData:
+    """
+    Contains misc information for a testing set of QC information such as the tma names, fovs,
+    channels, and a DataFrame containing testing data.
+    """
+
+    tma_name: str
+    fov_count: int
+    channel_ignore_count: int
+    tma_n_m: np.ndarray
+    fovs: List[str]
+    channels: List[str]
+    qc_df: pd.DataFrame
+    cohort_path: pathlib.Path
+    qc_metrics_dir: pathlib.Path
+    qc_metrics: List[str]
+
+
+@pytest.fixture(scope="function")
+def qc_tmas(
+    rng: np.random.Generator, tmp_path: pathlib.Path
+) -> Generator[QCMetricData, None, None]:
+    """
+    A Fixture which yields a dataclass used containing the QC dataframe with the three metrics,
+    the tma name, fovs, channels and the regex search term for RnCm.
+
+    Args:
+        rng (np.random.Generator): The random number generator in `conftest.py`.
+        tmp_path (pathlib.Path): The temporary path to the test directory.
+
+    Yields:
+        Generator[QCMetricData, None, None]: The dataclass containing testing data.
+    """
+
+    # Set up the testing data features
+    fov_count: int = 5
+    channel_ignore_count: int = len(settings.QC_CHANNEL_IGNORE)
+    channel_count: int = 3
+    _, chan_names = test_utils.gen_fov_chan_names(num_fovs=fov_count, num_chans=channel_count)
+    chan_names.extend(settings.QC_CHANNEL_IGNORE)
+
+    total_n_m_options: np.ndarray = np.arange(0, 13)
+    tma_name: str = "Project_TMA1"
+
+    tma_n_m: np.ndarray = rng.choice(a=total_n_m_options, size=(fov_count, 2), replace=False)
+    fovs: List[str] = [f"Project_TMA1_R{tma[0]}C{tma[1]}" for tma in tma_n_m]
+
+    # Create the directory containing extracted images for a tma
+    cohort_path: Path = tmp_path / "tma"
+    cohort_path.mkdir(parents=True, exist_ok=True)
+
+    # Create the directory containing the QC metrics data
+    qc_metrics_dir: Path = tmp_path / "qc_metrics"
+    qc_metrics_dir.mkdir(parents=True, exist_ok=True)
+
+    _ = test_utils.create_paired_xarray_fovs(
+        base_dir=cohort_path,
+        fov_names=fovs,
+        channel_names=chan_names,
+        img_shape=(20, 20),
+    )
+
+    qc_df: pd.DataFrame = pd.DataFrame(
+        data={
+            "fov": fovs * (channel_count + channel_ignore_count),
+            "channel": chan_names * fov_count,
+            **{
+                qc_col: rng.random(size=(fov_count * len(chan_names)))
+                for qc_col in settings.QC_COLUMNS
+            },
+            "row": np.tile(A=tma_n_m[:, 0], reps=len(chan_names)),
+            "column": np.tile(A=tma_n_m[:, 1], reps=len(chan_names)),
+        }
+    )
+
+    for qc_col, qc_suffix in zip(settings.QC_COLUMNS, settings.QC_SUFFIXES):
+        qc_df[["fov", "channel", qc_col, "row", "column"]].to_csv(
+            qc_metrics_dir / f"{tma_name}_combined_{qc_suffix}.csv", index=False
+        )
+
+    yield QCMetricData(
+        tma_name=tma_name,
+        fov_count=fov_count,
+        channel_ignore_count=channel_ignore_count,
+        tma_n_m=tma_n_m,
+        fovs=fovs,
+        channels=chan_names,
+        qc_df=qc_df,
+        cohort_path=cohort_path,
+        qc_metrics_dir=qc_metrics_dir,
+        qc_metrics=settings.QC_COLUMNS,
+    )
+
+
+@parametrize(
+    "_qc_metrics, _qc_suffixes",
+    [
+        (settings.QC_COLUMNS[:2], settings.QC_SUFFIXES[:2]),
+        ([settings.QC_COLUMNS[0]], [settings.QC_SUFFIXES[0]]),
+        (settings.QC_COLUMNS, settings.QC_SUFFIXES),
+    ],
+)
+def test_qc_filtering(_qc_metrics, _qc_suffixes):
+    qc_cols, qc_suffixes = qc_comp.qc_filtering(qc_metrics=_qc_metrics)
+
+    # Assert correct number of elements
+    assert len(qc_cols) == len(qc_suffixes)
+    assert len(qc_suffixes) == len(_qc_metrics)
+
+    # Assert correct ordering of elements
+    for qc_col, qc_suffix in zip(qc_cols, qc_suffixes):
+        assert qc_col in _qc_metrics
+        assert qc_suffix in _qc_suffixes
+
+    # Assert uniqueness of elements
+    assert set(qc_cols) == set(_qc_metrics)
+    assert set(qc_suffixes) == set(_qc_suffixes)
+
+
+@parametrize(
+    "_channel_exclude, _channel_include",
+    [
+        (None, None),  # Default, only settings.QC_CHANNEL_IGNORE is removed
+        (["chan0", "chan1"], None),  # Remove chan_0 and chan_1
+        (["chan0"], ["chan1"]),  # Remove chan_0, and only include chan_1 (df with chan_1 only)
+        (
+            None,
+            ["chan0", "chan1"],
+        ),  # Only include chan_0 and chan_1 (df with chan_0 and chan_1 only)
+        pytest.param(
+            ["chan0", "chan1"],
+            ["chan0", "chan_"],
+            marks=pytest.mark.xfail,
+        ),  # Error, both exclude and include channels are the same
+    ],
+)
+def test__channel_filtering(qc_tmas: QCMetricData, _channel_exclude, _channel_include):
+    # Filter out channels that are in the ignore list
+    qc_df = qc_comp._channel_filtering(
+        df=qc_tmas.qc_df, channel_exclude=_channel_exclude, channel_include=_channel_include
+    )
+
+    # Assert that the default excluded channels: Au, Fe, Na, Ta, Noodle are not in the dataframe
+    assert set(qc_df["channel"]).isdisjoint(set(settings.QC_CHANNEL_IGNORE))
+
+    # Assert that the excluded channels are removed from the dataframe
+    assert set(qc_df["channel"]).isdisjoint(set(_channel_exclude) if _channel_exclude else set())
+
+    # Asser that the included channels are included in the dataframe
+    assert set(qc_df["channel"]).issuperset(set(_channel_include) if _channel_include else set())
+
+
+class TestQCTMA:
+    @pytest.fixture(scope="function", autouse=True)
+    def _setup(self, qc_tmas: QCMetricData) -> None:
+        self.qc_tmas_fixture = qc_tmas
+
+        self.qc_tma = qc_comp.QCTMA(
+            qc_metrics=qc_tmas.qc_metrics,
+            cohort_path=qc_tmas.cohort_path,
+            metrics_dir=qc_tmas.qc_metrics_dir,
+        )
+
+    def test__post_init__(self) -> None:
+        assert self.qc_tma.qc_cols == settings.QC_COLUMNS
+        assert self.qc_tma.qc_suffixes == settings.QC_SUFFIXES
+
+        assert self.qc_tma.tma_avg_ranks == {}
+
+    def test__get_r_c(self) -> None:
+        result = pd.DataFrame()
+        result[["R", "C"]]: pd.DataFrame = self.qc_tmas_fixture.qc_df["fov"].apply(
+            lambda row: self.qc_tma._get_r_c(row)
+        )
+
+        # Make sure the lengths are the same
+        assert len(result) == len(self.qc_tmas_fixture.qc_df)
+
+        # Make sure the set of values are the same for Row and Column
+        assert set(result["R"]) == set(self.qc_tmas_fixture.tma_n_m[:, 0])
+        assert set(result["C"]) == set(self.qc_tmas_fixture.tma_n_m[:, 1])
+
+    def test_compute_qc_tma_metrics(self) -> None:
+        self.qc_tma.compute_qc_tma_metrics(tmas=[self.qc_tmas_fixture.tma_name])
+
+        for ms in settings.QC_SUFFIXES:
+            all_metric_files: str = io_utils.list_files(
+                self.qc_tmas_fixture.qc_metrics_dir, substrs=f"{ms}.csv"
+            )
+
+            # Get the combined file
+            combined_metric_file: str = next(filter(lambda mf: "combined" in mf, all_metric_files))
+
+            # Filter out combined files
+            metric_files: List[str] = list(
+                filter(lambda mf: "combined" not in mf, all_metric_files)
+            )
+
+            # Make sure that they all have the `tma_name` as the prefix
+            assert all(mf.startswith(self.qc_tmas_fixture.tma_name) for mf in all_metric_files)
+
+            # Combined metric file df
+            combined_mf_df = pd.read_csv(self.qc_tmas_fixture.qc_metrics_dir / combined_metric_file)
+
+            for mf in metric_files:
+                mf_df: pd.DataFrame = pd.read_csv(self.qc_tmas_fixture.qc_metrics_dir / mf)
+                # Merge the dataframes together, and check that "fov", "channel", and metric val
+                # Assert that all elements in the metric csv exist in the combined metric csv
+                pd.testing.assert_frame_equal(
+                    left=combined_mf_df.merge(mf_df).iloc[:, 0:3], right=mf_df
+                )
+
+    def test__create_r_c_tma_matrix(self) -> None:
+        y_size, x_size = np.max(self.qc_tmas_fixture.tma_n_m, axis=0)
+
+        for qc_col in settings.QC_COLUMNS:
+            r_c_tma_matrix_df = pd.DataFrame()
+
+            r_c_tma_matrix_df[["rc_matrix"]] = self.qc_tmas_fixture.qc_df.groupby(
+                by="channel", sort=True
+            ).apply(lambda group: self.qc_tma._create_r_c_tma_matrix(group, x_size, y_size, qc_col))
+
+            # Assert that the shapes are correct.
+            for rc_matrix in r_c_tma_matrix_df["rc_matrix"]:
+                assert rc_matrix.shape == (x_size, y_size)
+
+    @parametrize(
+        "channel_exclude",
+        [
+            (None),
+            (["chan0"]),
+            (["chan0", "chan1"]),
+        ],
+    )
+    # test_qc_tma_metrics_compute_qc_tma_metrics
+    def test_qc_tma_metrics_rank(self, channel_exclude) -> None:
+        self.qc_tma.qc_tma_metrics_rank(
+            tmas=[self.qc_tmas_fixture.tma_name], channel_exclude=channel_exclude
+        )
+
+        assert self.qc_tma.tma_avg_ranks[self.qc_tmas_fixture.tma_name].shape == (
+            3,
+            *self.qc_tmas_fixture.tma_n_m.max(0).tolist(),
+        )
+
+
+@dataclass
+class BatchEffectMetricData:
+    """
+    Contains misc information for a testing set of QC information such as the tma names, fovs,
+    channels, and a DataFrame containing testing data.
+    """
+
+    qc_metrics: List[str]
+    qc_df: pd.DataFrame
+    fovs: List[str]
+    cohort_img_dir: pathlib.Path
+    cohort_metrics_dir: pathlib.Path
+
+
+@pytest.fixture(scope="function")
+def cohort_data(
+    rng: np.random.Generator, tmp_path: Path
+) -> Generator[BatchEffectMetricData, None, None]:
+    """
+    A fixture for generating cohort fovs, and channels for various tissues.
+
+    Args:
+        rng (np.random.Generator): The random number generator in `conftest.py`.
+        tmp_path (Path): A temporary directory to write files for testing.
+
+    Yields:
+        Generator[BatchEffectMetricData, None, None]: Yields a dataclass containing
+        the testing data: qc_metrics, qc dataframe, tissues, and cohort data_directory.
+    """
+
+    # Set up Directories for the cohort data
+    cohort_dir: Path = tmp_path / "my_cohort"
+    cohort_img_dir: Path = cohort_dir / "images"
+    cohort_metrics_dir: Path = cohort_dir / "metrics"
+
+    for directory in [cohort_dir, cohort_img_dir, cohort_metrics_dir]:
+        directory.mkdir(parents=True, exist_ok=True)
+
+    # Set up the testing data fovs
+    channel_count: int = 5
+    fov_count: int = 3
+
+    fov_names, chan_names = test_utils.gen_fov_chan_names(
+        num_fovs=fov_count, num_chans=channel_count
+    )
+
+    # Add the default channels to ignore
+    chan_names.extend(settings.QC_CHANNEL_IGNORE)
+
+    _ = test_utils.create_paired_xarray_fovs(
+        base_dir=cohort_img_dir, fov_names=fov_names, channel_names=chan_names, img_shape=(20, 20)
+    )
+
+    control_sample_names = ["batch0", "batch1", "batch2", "batch3"]
+
+    qc_df: pd.DataFrame = pd.DataFrame(
+        data={
+            "fov": fov_names * (channel_count + len(settings.QC_CHANNEL_IGNORE)),
+            "channel": chan_names * fov_count,
+            **{
+                qc_col: rng.random(
+                    size=(fov_count * (channel_count + len(settings.QC_CHANNEL_IGNORE)))
+                )
+                for qc_col in settings.QC_COLUMNS
+            },
+        }
+    )
+
+    for control_sample_name, (qc_col, qc_suffix) in itertools.product(
+        control_sample_names, zip(settings.QC_COLUMNS, settings.QC_SUFFIXES)
+    ):
+        qc_df[
+            [
+                "fov",
+                "channel",
+                qc_col,
+            ]
+        ].to_csv(
+            cohort_metrics_dir / f"{control_sample_name}_combined_{qc_suffix}.csv", index=False
+        )
+
+    yield BatchEffectMetricData(
+        qc_metrics=settings.QC_COLUMNS,
+        qc_df=qc_df,
+        fovs=fov_names,
+        cohort_img_dir=cohort_img_dir,
+        cohort_metrics_dir=cohort_metrics_dir,
+    )
+
+
+class TestQCControlMetrics:
+    @pytest.fixture(autouse=True)
+    def _setup(self, cohort_data: BatchEffectMetricData) -> None:
+        self.cohort_data: BatchEffectMetricData = cohort_data
+
+        self.qc_batch_effect = qc_comp.QCControlMetrics(
+            qc_metrics=settings.QC_COLUMNS,
+            cohort_path=self.cohort_data.cohort_img_dir,
+            metrics_dir=self.cohort_data.cohort_metrics_dir,
+        )
+
+    def test__post_init__(self) -> None:
+        assert self.qc_batch_effect.qc_cols == settings.QC_COLUMNS
+        assert self.qc_batch_effect.qc_suffixes == settings.QC_SUFFIXES
+
+        assert self.qc_batch_effect.longitudinal_control_metrics == {}
+
+    @parametrize(
+        "_control_sample_name,_channel_include,_channel_exclude",
+        [
+            ("batch0", None, None),
+            ("batch1", ["chan0"], None),
+            ("batch2", None, ["chan0", "chan1"]),
+            pytest.param("batch3", ["chan0"], ["chan0"], marks=pytest.mark.xfail),
+            pytest.param(None, None, None, marks=pytest.mark.xfail),
+        ],
+    )
+    def test_compute_control_qc_metrics(
+        self,
+        _control_sample_name,
+        _channel_include,
+        _channel_exclude,
+    ) -> None:
+        self.qc_batch_effect.compute_control_qc_metrics(
+            control_sample_name=_control_sample_name,
+            fovs=self.cohort_data.fovs,
+            channel_exclude=_channel_exclude,
+            channel_include=_channel_include,
+        )
+
+        for qc_suffix in settings.QC_SUFFIXES:
+            assert os.path.exists(
+                self.qc_batch_effect.metrics_dir
+                / f"{_control_sample_name}_combined_{qc_suffix}.csv"
+            )
+
+            qc_df = pd.read_csv(
+                self.qc_batch_effect.metrics_dir
+                / f"{_control_sample_name}_combined_{qc_suffix}.csv"
+            )
+            assert set(qc_df["channel"]).isdisjoint(set(settings.QC_CHANNEL_IGNORE))
+
+            # Assert that the excluded channels are removed from the dataframe
+            assert set(qc_df["channel"]).isdisjoint(
+                set(_channel_exclude) if _channel_exclude else set()
+            )
+
+            # Assert that the included channels are included in the dataframe
+            assert set(qc_df["channel"]).issuperset(
+                set(_channel_include) if _channel_include else set()
+            )
+
+    @parametrize(
+        "_control_sample_name,_metric",
+        [
+            ("batch0", settings.QC_COLUMNS[0]),
+            ("batch1", settings.QC_COLUMNS[1]),
+            ("batch2", settings.QC_COLUMNS[2]),
+            (
+                "batch3",
+                settings.QC_COLUMNS[0],
+            ),  # Test reading from a file if the metric has been already computed
+        ],
+    )
+    def test_transformed_control_effects_data(self, _control_sample_name, _metric) -> None:
+        self.qc_batch_effect.compute_control_qc_metrics(
+            control_sample_name=_control_sample_name,
+            fovs=self.cohort_data.fovs,
+            channel_exclude=None,
+            channel_include=None,
+        )
+
+        transformed_df = self.qc_batch_effect.transformed_control_effects_data(
+            control_sample_name=_control_sample_name, qc_metric=_metric
+        )
+        # self.cohort_data.qc_df
+
+        # All FOVs exist
+        assert set(transformed_df.axes[1]) == set(self.cohort_data.qc_df["fov"])
+
+        # Ignored channels are not in the dataframe
+        assert set(transformed_df.axes[0]).isdisjoint(set(settings.QC_CHANNEL_IGNORE))
diff --git a/tests/qc_metrics_plots_test.py b/tests/qc_metrics_plots_test.py
index 0a363107..f7b25f7b 100644
--- a/tests/qc_metrics_plots_test.py
+++ b/tests/qc_metrics_plots_test.py
@@ -1,97 +1,203 @@
+import itertools
 import os
-import tempfile
+from pathlib import Path
+from typing import Generator, List, Optional
 
+import numpy as np
 import pandas as pd
+import pytest
 from alpineer import test_utils
+from traitlets import Callable
 
-from toffy import qc_metrics_plots
+from toffy import qc_metrics_plots, settings
+from toffy.qc_comp import QCTMA, QCControlMetrics
 
+from .qc_comp_test import BatchEffectMetricData, QCMetricData, cohort_data, qc_tmas
 
-def test_call_violin_swarm():
-    # generate fake df
-    plotting_df = pd.DataFrame(columns=["sample", "channel", "tma", "99.9th_percentile"])
-    plotting_df = plotting_df.append(
-        pd.DataFrame([["TMA1_fov1", "chan0", "TMA1", 0.5]], columns=plotting_df.columns)
-    )
 
-    # Test that file is created.
-    with tempfile.TemporaryDirectory() as temp_dir:
-        _ = qc_metrics_plots.call_violin_swarm_plot(
-            plotting_df, fig_label="test123", figsize=(20, 3), fig_dir=temp_dir
-        )
-        assert os.path.exists(temp_dir + "test123" + "_batch_effects.png")
+def test_visualize_qc_metrics(tmp_path: Path):
+    # define the channels to use
+    chans = ["chan0", "chan1", "chan2"]
+
+    # define the fov names to use for each channel
+    fov_batches = [["fov0", "fov1"], ["fov2", "fov3"], ["fov4", "fov5"]]
+
+    # define the supported metrics to iterate over
+    metrics = ["Non-zero mean intensity", "Total intensity", "99.9% intensity value"]
+
+    # save sample combined .csv files for each metric
+    for metric in metrics:
+        # define the test melted DataFrame for an arbitrary QC metric
+        sample_qc_metric_data = pd.DataFrame()
+
+        for chan, fovs in zip(chans, fov_batches):
+            chan_data = pd.DataFrame(np.random.rand(len(fovs)), columns=[metric])
 
-    # Test that no file is created when not passing fig_dir
-    with tempfile.TemporaryDirectory() as temp_dir:
-        file_number_before = len(os.listdir(temp_dir))
-        _ = qc_metrics_plots.call_violin_swarm_plot(
-            plotting_df, fig_label="test123", figsize=(20, 3), fig_dir=None
+            chan_data["fov"] = fovs
+            chan_data["channel"] = chan
+
+            sample_qc_metric_data = pd.concat([sample_qc_metric_data, chan_data])
+
+        # get the file name of the combined QC metric .csv file to use
+        qc_metric_index = settings.QC_COLUMNS.index(metric)
+        qc_metric_suffix = settings.QC_SUFFIXES[qc_metric_index] + ".csv"
+
+        # save the combined data
+        sample_qc_metric_data.to_csv(
+            os.path.join(tmp_path, "combined_%s" % qc_metric_suffix), index=False
         )
-        file_number_after = len(os.listdir(temp_dir))
-        assert file_number_before == file_number_after
-
-
-def test_make_batch_effect_plot():
-    # Testing exclude_channels would require making a separate function in
-    # qc_metrics_plots to generate and return the plotting_df
-    with tempfile.TemporaryDirectory() as top_level_dir:
-        data_dir = os.path.join(top_level_dir, "data_dir")
-        os.makedirs(data_dir)
-        fovs, chans = test_utils.gen_fov_chan_names(num_fovs=3, num_chans=3)
-        chans = ["chan0", "chan1", "chan2"]
-        fovs = ["TMA1_fov1", "TMA1_fov2", "TMA2_fov1", "TMA2_fov2"]
-        filelocs, data_xr = test_utils.create_paired_xarray_fovs(
-            data_dir, fovs, chans, img_shape=(10, 10), fills=True
+
+    # pass an invalid metric
+    with pytest.raises(ValueError):
+        qc_metrics_plots.visualize_qc_metrics(
+            metric_name="bad_metric", qc_metric_dir="", save_dir=""
         )
 
-        # Test no plot is made when not passing fig dir
-        file_number_before = len(os.listdir(data_dir))
-        qc_metrics_plots.make_batch_effect_plot(
-            data_dir=data_dir,
-            normal_tissues=["fov1"],
-            exclude_channels=None,
-            img_sub_folder=None,
-            qc_metric="99.9th_percentile",
-            fig_dir=None,
+    # pass an invalid qc_metric_dir
+    with pytest.raises(FileNotFoundError):
+        qc_metrics_plots.visualize_qc_metrics("Non-zero mean intensity", "bad_qc_dir", save_dir="")
+
+    # pass a qc_metric_dir without the combined files
+    os.mkdir(os.path.join(tmp_path, "empty_qc_dir"))
+    with pytest.raises(FileNotFoundError):
+        qc_metrics_plots.visualize_qc_metrics(
+            "Non-zero mean intensity", os.path.join(tmp_path, "empty_qc_dir"), save_dir=""
         )
-        file_number_after = len(os.listdir(data_dir))
-        assert file_number_before == file_number_after
-
-        # Test plots are made when passing fig dir
-        qc_metrics_plots.make_batch_effect_plot(
-            data_dir=data_dir,
-            normal_tissues=["fov1", "fov2"],
-            exclude_channels=None,
-            img_sub_folder=None,
-            qc_metric="99.9th_percentile",
-            fig_dir=data_dir,
+
+    # now test the visualization process for each metric
+    for metric in metrics:
+        # test without saving (should raise an error)
+        with pytest.raises(TypeError):
+            qc_metrics_plots.visualize_qc_metrics(metric, tmp_path)
+
+        # test with saving
+        qc_metrics_plots.visualize_qc_metrics(metric, tmp_path, save_dir=tmp_path)
+        assert os.path.exists(os.path.join(tmp_path, "%s_barplot_stats.png" % metric))
+
+
+@pytest.fixture(scope="function")
+def qc_tma_data(qc_tmas: QCMetricData) -> Generator[Callable, None, None]:
+    """
+    A fixture which yields a function which creates the QCTMA class,
+    and computes the metrics, and the rank metrics.
+
+    Args:
+        qc_tmas (QCMetricData): The fixture which creates the QCMetricData class.
+
+    Yields:
+        Generator[Callable, None, None]: Yields a function which creates the QCTMA class,
+        and `tmas` and `channel_exclude` are variables which can be passed to the function.
+    """
+
+    def _compute_qc_tmas_metrics(
+        tmas: List[str],
+        channel_exclude: List[str] = None,
+    ) -> QCTMA:
+        qc_tmas_data = QCTMA(
+            qc_metrics=qc_tmas.qc_metrics,
+            cohort_path=qc_tmas.cohort_path,
+            metrics_dir=qc_tmas.qc_metrics_dir,
         )
-        file_number_after = len(os.listdir(data_dir))
-        assert os.path.exists(data_dir + "fov1" + "_batch_effects.png")
-        assert os.path.exists(data_dir + "fov2" + "_batch_effects.png")
-
-    # test img sub folder
-    with tempfile.TemporaryDirectory() as top_level_dir:
-        data_dir = os.path.join(top_level_dir, "data_dir")
-        os.makedirs(data_dir)
-        fovs, chans = test_utils.gen_fov_chan_names(num_fovs=3, num_chans=3)
-        chans = ["chan0", "chan1", "chan2"]
-        fovs = ["TMA1_fov1", "TMA1_fov2", "TMA2_fov1", "TMA2_fov2"]
-        img_sub_folder = "sub_folder"
-        filelocs, data_xr = test_utils.create_paired_xarray_fovs(
-            data_dir,
-            fovs,
-            chans,
-            img_shape=(10, 10),
-            fills=True,
-            sub_dir=img_sub_folder,
+
+        qc_tmas_data.compute_qc_tma_metrics(tmas=tmas)
+        qc_tmas_data.qc_tma_metrics_rank(tmas=tmas, channel_exclude=channel_exclude)
+
+        return qc_tmas_data
+
+    yield _compute_qc_tmas_metrics
+
+
+@pytest.mark.parametrize(
+    "_tmas,_channel_exclude",
+    [
+        (["Project_TMA1"], ["chan0"]),
+        (["Project_TMA1"], ["chan0", "chan1"]),
+    ],
+)
+def test_qc_tmas_metrics_plot(
+    qc_tma_data: QCTMA, _tmas: List[str], _channel_exclude: List[str]
+) -> None:
+    qc_tma: QCTMA = qc_tma_data(tmas=_tmas, channel_exclude=_channel_exclude)
+
+    qc_metrics_plots.qc_tmas_metrics_plot(qc_tmas=qc_tma, tmas=_tmas, dpi=30, save_figure=True)
+
+    total_figures = [
+        f"{tissue}_{qc}.png" for tissue, qc in itertools.product(_tmas, qc_tma.qc_suffixes)
+    ]
+
+    # Assert the existance of the QC TMA metrics figures
+    for fig in total_figures:
+        assert os.path.exists(qc_tma.metrics_dir / "figures" / fig)
+
+
+@pytest.fixture(scope="function")
+def batch_effect_qc_data(cohort_data: BatchEffectMetricData) -> Generator[Callable, None, None]:
+    """
+    Creates the QCControlMetrics class, and computes the metrics, and then filters out unwanted
+        channels.
+
+    Args:
+        cohort_data (BatchEffectMetricData): The Fixture which creates the BatchEffectMetricData
+            class.
+
+    Yields:
+        Generator[Callable, None, None]: A Function which generates the QCControlMetrics object,
+        computes the QC metricss, and filters channels..
+    """
+
+    def _compute_qc_batch_effects(
+        fovs: List[str],
+        channel_include: List[str] = None,
+        channel_exclude: List[str] = None,
+    ) -> QCControlMetrics:
+        qc_batch_effects = QCControlMetrics(
+            qc_metrics=cohort_data.qc_metrics,
+            cohort_path=cohort_data.cohort_img_dir,
+            metrics_dir=cohort_data.cohort_metrics_dir,
         )
-        qc_metrics_plots.make_batch_effect_plot(
-            data_dir=data_dir,
-            normal_tissues=["fov1", "fov2"],
-            exclude_channels=None,
-            img_sub_folder=img_sub_folder,
-            qc_metric="99.9th_percentile",
-            fig_dir=data_dir,
+
+        qc_batch_effects.compute_control_qc_metrics(
+            control_sample_name="Project_Batch1",
+            fovs=fovs,
+            channel_exclude=channel_exclude,
+            channel_include=channel_include,
         )
-        assert os.path.exists(data_dir + "fov1" + "_batch_effects.png")
+        return qc_batch_effects
+
+    yield _compute_qc_batch_effects
+
+
+@pytest.mark.parametrize(
+    "_fovs,_channel_include,_channel_exclude",
+    [
+        (["fov0", "fov1", "fov2"], ["chan0", "chan1"], None),
+        (["fov0"], ["chan0"], ["chan1", "chan2"]),
+    ],
+)
+def test_longitudinal_control_heatmap(
+    batch_effect_qc_data: Callable,
+    _fovs: List[str],
+    _channel_include: Optional[List[str]],
+    _channel_exclude: Optional[List[str]],
+) -> None:
+    _save_figure: bool = True
+
+    qc_control: QCControlMetrics = batch_effect_qc_data(_fovs, _channel_include, _channel_exclude)
+
+    _control_sample_name = "Project_Batch1"
+
+    qc_metrics_plots.longitudinal_control_heatmap(
+        qc_control=qc_control,
+        control_sample_name=_control_sample_name,
+        save_figure=_save_figure,
+        dpi=30,
+    )
+
+    total_figures: List[str] = [
+        f"{_control_sample_name}_heatmap_{qc}.png"
+        for fov, qc in itertools.product(_fovs, qc_control.qc_suffixes)
+    ]
+
+    # Assert the existance of the Longitudinal Control figures
+    for fig in total_figures:
+        assert os.path.exists(qc_control.metrics_dir / "figures" / fig)
diff --git a/tests/reorg_test.py b/tests/reorg_test.py
index 2b1b913a..fe7c653b 100644
--- a/tests/reorg_test.py
+++ b/tests/reorg_test.py
@@ -1,4 +1,3 @@
-import json
 import os
 import tempfile
 
diff --git a/tests/streak_detection_test.py b/tests/streak_detection_test.py
index 0b51ad98..edb74be7 100644
--- a/tests/streak_detection_test.py
+++ b/tests/streak_detection_test.py
@@ -12,7 +12,7 @@
 
 
 @pytest.fixture(scope="function")
-def streak_dataset() -> Callable:
+def streak_dataset(rng: np.random.Generator) -> Callable:
     """A wrapper for creating a StreakData DataClass for testing purposes.
 
     Returns:
@@ -27,9 +27,9 @@ def streak_data_generator(corrected_dir, fov: str, chan: str, shape: Tuple[int,
             corrected_dir=corrected_dir,
             streak_channel=chan,
             streak_mask=np.zeros(shape=shape),
-            streak_df=pd.util.testing.makeDataFrame(),
+            streak_df=pd.DataFrame({"A": rng.random(10), "B": rng.random(10)}),
             filtered_streak_mask=np.zeros(shape=shape),
-            filtered_streak_df=pd.util.testing.makeDataFrame(),
+            filtered_streak_df=pd.DataFrame({"A": rng.random(10), "B": rng.random(10)}),
             boxed_streaks=np.zeros(shape=shape),
             corrected_streak_mask=np.zeros(shape=shape),
         )