diff --git a/.gitignore b/.gitignore
index 79a677e..2dff6ac 100644
--- a/.gitignore
+++ b/.gitignore
@@ -167,3 +167,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+*.ipynb
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 9b1d19e..47d3c7a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,14 +1,14 @@
 [[package]]
 name = "alabaster"
-version = "0.7.12"
+version = "0.7.13"
 description = "A configurable sidebar-enabled Sphinx theme"
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 
 [[package]]
 name = "alembic"
-version = "1.8.1"
+version = "1.11.1"
 description = "A database migration tool for SQLAlchemy."
 category = "main"
 optional = false
@@ -19,6 +19,7 @@ importlib-metadata = {version = "*", markers = "python_version < \"3.9\""}
 importlib-resources = {version = "*", markers = "python_version < \"3.9\""}
 Mako = "*"
 SQLAlchemy = ">=1.3.0"
+typing-extensions = ">=4"
 
 [package.extras]
 tz = ["python-dateutil"]
@@ -52,7 +53,7 @@ python-dateutil = ">=2.7.0"
 
 [[package]]
 name = "asttokens"
-version = "2.0.8"
+version = "2.2.1"
 description = "Annotate AST trees with source code positions"
 category = "dev"
 optional = false
@@ -62,7 +63,7 @@ python-versions = "*"
 six = "*"
 
 [package.extras]
-test = ["astroid (<=2.5.3)", "pytest"]
+test = ["astroid", "pytest"]
 
 [[package]]
 name = "atomicwrites"
@@ -81,10 +82,10 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [package.extras]
-dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"]
-docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
-tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
+dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "six", "sphinx", "sphinx-notfound-page", "zope.interface"]
+docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"]
+tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "six", "zope.interface"]
+tests_no_zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "six"]
 
 [[package]]
 name = "autopage"
@@ -107,15 +108,15 @@ pycodestyle = ">=2.9.1"
 toml = "*"
 
 [[package]]
-name = "babel"
-version = "2.10.3"
+name = "Babel"
+version = "2.12.1"
 description = "Internationalization utilities"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
-pytz = ">=2015.7"
+pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
 
 [[package]]
 name = "backcall"
@@ -127,7 +128,7 @@ python-versions = "*"
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.11.1"
+version = "4.12.2"
 description = "Screen-scraping library"
 category = "dev"
 optional = false
@@ -167,7 +168,7 @@ platformdirs = ">=2"
 tomli = ">=0.2.6,<2.0.0"
 typing-extensions = [
     {version = ">=3.10.0.0", markers = "python_version < \"3.10\""},
-    {version = "!=3.10.0.1", markers = "python_version >= \"3.10\""},
+    {version = ">=3.10.0.0,<3.10.0.1 || >3.10.0.1", markers = "python_version >= \"3.10\""},
 ]
 
 [package.extras]
@@ -179,7 +180,7 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "bleach"
-version = "5.0.1"
+version = "6.0.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 category = "dev"
 optional = false
@@ -191,11 +192,10 @@ webencodings = "*"
 
 [package.extras]
 css = ["tinycss2 (>=1.1.0,<1.2)"]
-dev = ["build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "Sphinx (==4.3.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)", "black (==22.3.0)", "mypy (==0.961)"]
 
 [[package]]
 name = "blis"
-version = "0.9.1"
+version = "0.7.9"
 description = "The Blis BLAS-like linear algebra library, as a self-contained C-extension."
 category = "main"
 optional = false
@@ -214,7 +214,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "certifi"
-version = "2022.9.14"
+version = "2023.5.7"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
@@ -241,22 +241,19 @@ python-versions = ">=3.6.1"
 
 [[package]]
 name = "chardet"
-version = "5.0.0"
+version = "5.1.0"
 description = "Universal encoding detector for Python 3"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [[package]]
 name = "charset-normalizer"
-version = "2.1.1"
+version = "3.1.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 category = "main"
 optional = false
-python-versions = ">=3.6.0"
-
-[package.extras]
-unicode_backport = ["unicodedata2"]
+python-versions = ">=3.7.0"
 
 [[package]]
 name = "click"
@@ -271,7 +268,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cliff"
-version = "4.0.0"
+version = "4.3.0"
 description = "Command Line Interface Formulation Framework"
 category = "main"
 optional = false
@@ -287,7 +284,7 @@ stevedore = ">=2.0.1"
 
 [[package]]
 name = "cloudpickle"
-version = "2.2.0"
+version = "2.2.1"
 description = "Extended pickling support for Python objects"
 category = "main"
 optional = false
@@ -295,18 +292,21 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "cmaes"
-version = "0.8.2"
+version = "0.9.1"
 description = "Lightweight Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implementation for Python 3."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 numpy = "*"
 
+[package.extras]
+cmawm = ["scipy"]
+
 [[package]]
 name = "cmd2"
-version = "2.4.2"
+version = "2.4.3"
 description = "cmd2 - quickly build feature-rich and user-friendly interactive command line applications in Python"
 category = "main"
 optional = false
@@ -319,17 +319,17 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\""}
 wcwidth = ">=0.1.7"
 
 [package.extras]
-dev = ["codecov", "doc8", "flake8", "invoke", "mypy (==0.902)", "nox", "pytest (>=4.6)", "pytest-cov", "pytest-mock", "sphinx", "sphinx-rtd-theme", "sphinx-autobuild", "twine (>=1.11)"]
-test = ["codecov", "coverage", "pytest (>=4.6)", "pytest-cov", "pytest-mock", "gnureadline"]
-validate = ["flake8", "mypy (==0.902)", "types-pkg-resources"]
+dev = ["codecov", "doc8", "flake8", "invoke", "mypy", "nox", "pytest (>=4.6)", "pytest-cov", "pytest-mock", "sphinx", "sphinx-autobuild", "sphinx-rtd-theme", "twine (>=1.11)"]
+test = ["codecov", "coverage", "gnureadline", "pytest (>=4.6)", "pytest-cov", "pytest-mock"]
+validate = ["flake8", "mypy", "types-pkg-resources"]
 
 [[package]]
 name = "colorama"
-version = "0.4.5"
+version = "0.4.6"
 description = "Cross-platform colored terminal text."
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 
 [[package]]
 name = "colorlog"
@@ -345,6 +345,22 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 [package.extras]
 development = ["black", "flake8", "mypy", "pytest", "types-colorama"]
 
+[[package]]
+name = "comm"
+version = "0.1.3"
+description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+traitlets = ">=5.3"
+
+[package.extras]
+lint = ["black (>=22.6.0)", "mdformat (>0.7)", "mdformat-gfm (>=0.3.5)", "ruff (>=0.0.156)"]
+test = ["pytest"]
+typing = ["mypy (>=0.990)"]
+
 [[package]]
 name = "commonmark"
 version = "0.9.1"
@@ -358,33 +374,33 @@ test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"]
 
 [[package]]
 name = "confection"
-version = "0.0.1"
+version = "0.1.0"
 description = "The sweetest config system for Python"
 category = "main"
 optional = false
 python-versions = ">=3.6"
 
 [package.dependencies]
-pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<1.10.0"
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<1.11.0"
 srsly = ">=2.4.0,<3.0.0"
 
 [[package]]
 name = "contourpy"
-version = "1.0.5"
+version = "1.1.0"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.dependencies]
 numpy = ">=1.16"
 
 [package.extras]
-test-no-codebase = ["pillow", "matplotlib", "pytest"]
-test-minimal = ["pytest"]
-test = ["isort", "flake8", "pillow", "matplotlib", "pytest"]
-docs = ["sphinx-rtd-theme", "sphinx", "docutils (<0.18)"]
-bokeh = ["selenium", "bokeh"]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx-copybutton"]
+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 = "cookiecutter"
@@ -424,7 +440,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "cymem"
-version = "2.0.6"
+version = "2.0.7"
 description = "Manage calls to calloc/free through Cython"
 category = "main"
 optional = false
@@ -432,7 +448,7 @@ python-versions = "*"
 
 [[package]]
 name = "databricks-cli"
-version = "0.17.3"
+version = "0.17.6"
 description = "A command line interface for Databricks"
 category = "main"
 optional = false
@@ -448,7 +464,7 @@ tabulate = ">=0.7.7"
 
 [[package]]
 name = "debugpy"
-version = "1.6.3"
+version = "1.6.7"
 description = "An implementation of the Debug Adapter Protocol for Python"
 category = "dev"
 optional = false
@@ -480,20 +496,21 @@ python-versions = "*"
 
 [[package]]
 name = "docker"
-version = "5.0.3"
+version = "6.1.3"
 description = "A Python library for the Docker Engine API."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
-pywin32 = {version = "227", markers = "sys_platform == \"win32\""}
-requests = ">=2.14.2,<2.18.0 || >2.18.0"
+packaging = ">=14.0"
+pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""}
+requests = ">=2.26.0"
+urllib3 = ">=1.26.0"
 websocket-client = ">=0.32.0"
 
 [package.extras]
-ssh = ["paramiko (>=2.4.2)"]
-tls = ["pyOpenSSL (>=17.5.0)", "cryptography (>=3.4.7)", "idna (>=2.0.0)"]
+ssh = ["paramiko (>=2.4.3)"]
 
 [[package]]
 name = "docker-pycreds"
@@ -524,42 +541,46 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "executing"
-version = "1.0.0"
+version = "1.2.0"
 description = "Get the currently executing AST node of a frame, and other information"
 category = "dev"
 optional = false
 python-versions = "*"
 
+[package.extras]
+tests = ["asttokens", "littleutils", "pytest", "rich"]
+
 [[package]]
 name = "fastai"
-version = "2.7.9"
+version = "2.7.12"
 description = "fastai simplifies training fast and accurate neural nets using modern best practices"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.dependencies]
-fastcore = ">=1.4.5,<1.6"
+fastcore = ">=1.5.29,<1.6"
 fastdownload = ">=0.0.5,<2"
 fastprogress = ">=0.2.4"
 matplotlib = "*"
 packaging = "*"
 pandas = "*"
 pillow = ">6.0.0"
+pip = "*"
 pyyaml = "*"
 requests = "*"
 scikit-learn = "*"
 scipy = "*"
 spacy = "<4"
-torch = ">=1.7,<1.14"
+torch = ">=1.7,<2.1"
 torchvision = ">=0.8.2"
 
 [package.extras]
-dev = ["ipywidgets", "pytorch-lightning", "pytorch-ignite", "transformers", "sentencepiece", "tensorboard", "pydicom", "catalyst", "flask-compress", "captum (>=0.3)", "flask", "wandb", "kornia", "scikit-image", "neptune-client", "comet-ml", "albumentations", "opencv-python", "pyarrow", "ninja", "timm (>=0.6.2.dev)", "accelerate (>=0.10.0)"]
+dev = ["accelerate (>=0.10.0)", "albumentations", "captum (>=0.3)", "catalyst", "comet-ml", "flask", "flask-compress", "ipywidgets", "kornia", "neptune-client", "ninja", "opencv-python", "pyarrow", "pydicom", "pytorch-ignite", "pytorch-lightning", "scikit-image", "sentencepiece", "tensorboard", "timm (>=0.6.2.dev)", "transformers", "wandb"]
 
 [[package]]
 name = "fastcore"
-version = "1.5.27"
+version = "1.5.29"
 description = "Python supercharged for fastai development"
 category = "main"
 optional = false
@@ -567,9 +588,10 @@ python-versions = ">=3.7"
 
 [package.dependencies]
 packaging = "*"
+pip = "*"
 
 [package.extras]
-dev = ["numpy", "nbdev (>=0.2.39)", "matplotlib", "pillow", "torch", "pandas", "jupyterlab"]
+dev = ["jupyterlab", "matplotlib", "nbdev (>=0.2.39)", "numpy", "pandas", "pillow", "torch"]
 
 [[package]]
 name = "fastdownload"
@@ -585,14 +607,14 @@ fastprogress = "*"
 
 [[package]]
 name = "fastjsonschema"
-version = "2.16.1"
+version = "2.17.1"
 description = "Fastest Python implementation of JSON schema"
 category = "dev"
 optional = false
 python-versions = "*"
 
 [package.extras]
-devel = ["colorama", "jsonschema", "json-spec", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
+devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
 
 [[package]]
 name = "fastprogress"
@@ -604,19 +626,19 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "filelock"
-version = "3.8.0"
+version = "3.12.2"
 description = "A platform independent file lock."
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-docs = ["furo (>=2022.6.21)", "sphinx (>=5.1.1)", "sphinx-autodoc-typehints (>=1.19.1)"]
-testing = ["covdefaults (>=2.2)", "coverage (>=6.4.2)", "pytest (>=7.1.2)", "pytest-cov (>=3)", "pytest-timeout (>=2.1)"]
+docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
 
 [[package]]
-name = "flask"
-version = "2.2.2"
+name = "Flask"
+version = "2.2.5"
 description = "A simple framework for building complex web applications."
 category = "main"
 optional = false
@@ -635,16 +657,16 @@ dotenv = ["python-dotenv"]
 
 [[package]]
 name = "fonttools"
-version = "4.37.2"
+version = "4.40.0"
 description = "Tools to manipulate font files"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.extras]
-all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"]
+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["scipy", "munkres"]
+interpolatable = ["munkres", "scipy"]
 lxml = ["lxml (>=4.0,<5)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
@@ -652,24 +674,24 @@ repacker = ["uharfbuzz (>=0.23.0)"]
 symfont = ["sympy"]
 type1 = ["xattr"]
 ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=14.0.0)"]
-woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]
+unicode = ["unicodedata2 (>=15.0.0)"]
+woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
 [[package]]
 name = "gitdb"
-version = "4.0.9"
+version = "4.0.10"
 description = "Git Object Database"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 smmap = ">=3.0.1,<6"
 
 [[package]]
-name = "gitpython"
-version = "3.1.27"
-description = "GitPython is a python library used to interact with Git repositories"
+name = "GitPython"
+version = "3.1.31"
+description = "GitPython is a Python library used to interact with Git repositories"
 category = "main"
 optional = false
 python-versions = ">=3.7"
@@ -679,14 +701,15 @@ gitdb = ">=4.0.1,<5"
 
 [[package]]
 name = "greenlet"
-version = "1.1.3"
+version = "2.0.2"
 description = "Lightweight in-process concurrent programming"
 category = "main"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*"
 
 [package.extras]
-docs = ["sphinx"]
+docs = ["Sphinx", "docutils (<0.18)"]
+test = ["objgraph", "psutil"]
 
 [[package]]
 name = "gunicorn"
@@ -696,6 +719,9 @@ category = "main"
 optional = false
 python-versions = ">=3.5"
 
+[package.dependencies]
+setuptools = ">=3.0"
+
 [package.extras]
 eventlet = ["eventlet (>=0.24.1)"]
 gevent = ["gevent (>=1.4.0)"]
@@ -704,18 +730,18 @@ tornado = ["tornado (>=0.2)"]
 
 [[package]]
 name = "h5py"
-version = "3.7.0"
+version = "3.9.0"
 description = "Read and write HDF5 files from Python"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.dependencies]
-numpy = ">=1.14.5"
+numpy = ">=1.17.3"
 
 [[package]]
 name = "hdf5storage"
-version = "0.1.18"
+version = "0.1.19"
 description = "Utilities to read/write Python types to/from HDF5 files, including MATLAB v7.3 MAT files."
 category = "main"
 optional = false
@@ -727,7 +753,7 @@ numpy = {version = "*", markers = "python_version >= \"3.4\""}
 
 [[package]]
 name = "identify"
-version = "2.5.5"
+version = "2.5.24"
 description = "File identification library for Python"
 category = "dev"
 optional = false
@@ -744,6 +770,34 @@ category = "main"
 optional = false
 python-versions = ">=3.5"
 
+[[package]]
+name = "imageio"
+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"
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=8.3.2"
+
+[package.extras]
+all-plugins = ["astropy", "av", "imageio-ffmpeg", "psutil", "tifffile"]
+all-plugins-pypy = ["av", "imageio-ffmpeg", "psutil", "tifffile"]
+build = ["wheel"]
+dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
+docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
+ffmpeg = ["imageio-ffmpeg", "psutil"]
+fits = ["astropy"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
+gdal = ["gdal"]
+itk = ["itk"]
+linting = ["black", "flake8"]
+pyav = ["av"]
+test = ["fsspec[github]", "pytest", "pytest-cov"]
+tifffile = ["tifffile"]
+
 [[package]]
 name = "imagesize"
 version = "1.4.1"
@@ -754,7 +808,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "importlib-metadata"
-version = "4.12.0"
+version = "5.2.0"
 description = "Read metadata from Python packages"
 category = "main"
 optional = false
@@ -764,13 +818,13 @@ python-versions = ">=3.7"
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"]
+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)"]
 
 [[package]]
 name = "importlib-resources"
-version = "5.9.0"
+version = "5.12.0"
 description = "Read resources from Python packages"
 category = "main"
 optional = false
@@ -780,44 +834,50 @@ python-versions = ">=3.7"
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
-docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
 [[package]]
 name = "iniconfig"
-version = "1.1.1"
-description = "iniconfig: brain-dead simple config-ini parsing"
+version = "2.0.0"
+description = "brain-dead simple config-ini parsing"
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 
 [[package]]
 name = "ipykernel"
-version = "6.15.3"
+version = "6.24.0"
 description = "IPython Kernel for Jupyter"
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.dependencies]
 appnope = {version = "*", markers = "platform_system == \"Darwin\""}
-debugpy = ">=1.0"
+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"
 matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
 psutil = "*"
-pyzmq = ">=17"
+pyzmq = ">=20"
 tornado = ">=6.1"
-traitlets = ">=5.1.0"
+traitlets = ">=5.4.0"
 
 [package.extras]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest-cov", "pytest-timeout", "pytest (>=6.0)"]
+cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+pyqt5 = ["pyqt5"]
+pyside6 = ["pyside6"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "8.5.0"
+version = "8.12.2"
 description = "IPython: Productive Interactive Computing"
 category = "dev"
 optional = false
@@ -832,15 +892,16 @@ jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
 pickleshare = "*"
-prompt-toolkit = ">3.0.1,<3.1.0"
+prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
+typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
 
 [package.extras]
-all = ["black", "Sphinx (>=1.3)", "ipykernel", "nbconvert", "nbformat", "ipywidgets", "notebook", "ipyparallel", "qtconsole", "pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "numpy (>=1.19)", "pandas", "trio"]
+all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
-doc = ["Sphinx (>=1.3)"]
+doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
@@ -848,7 +909,7 @@ notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
 test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.19)", "pandas", "trio"]
+test_extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
 
 [[package]]
 name = "itsdangerous"
@@ -860,7 +921,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "jedi"
-version = "0.18.1"
+version = "0.18.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 category = "dev"
 optional = false
@@ -870,11 +931,12 @@ python-versions = ">=3.6"
 parso = ">=0.8.0,<0.9.0"
 
 [package.extras]
+docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
 qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
-testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
-name = "jinja2"
+name = "Jinja2"
 version = "3.1.2"
 description = "A very fast and expressive template engine."
 category = "main"
@@ -901,7 +963,7 @@ jinja2 = "*"
 
 [[package]]
 name = "joblib"
-version = "1.2.0"
+version = "1.3.1"
 description = "Lightweight pipelining with Python functions"
 category = "main"
 optional = false
@@ -909,7 +971,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "jsonschema"
-version = "4.16.0"
+version = "4.17.3"
 description = "An implementation of JSON Schema validation for Python"
 category = "dev"
 optional = false
@@ -927,38 +989,39 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jupyter-client"
-version = "7.3.5"
+version = "8.3.0"
 description = "Jupyter protocol implementation and client libraries"
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.dependencies]
-entrypoints = "*"
-jupyter-core = ">=4.9.2"
-nest-asyncio = ">=1.5.4"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
 python-dateutil = ">=2.8.2"
 pyzmq = ">=23.0"
 tornado = ">=6.2"
-traitlets = "*"
+traitlets = ">=5.3"
 
 [package.extras]
-doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"]
-test = ["codecov", "coverage", "ipykernel (>=6.5)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
+docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-core"
-version = "4.11.1"
+version = "5.3.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.dependencies]
-pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
-traitlets = "*"
+platformdirs = ">=2.5"
+pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
+traitlets = ">=5.3"
 
 [package.extras]
+docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
 test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
@@ -1007,6 +1070,18 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 [package.dependencies]
 six = ">=1.4.1"
 
+[[package]]
+name = "lazy_loader"
+version = "0.3"
+description = "lazy_loader"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+lint = ["pre-commit (>=3.3)"]
+test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
+
 [[package]]
 name = "livereload"
 version = "2.6.3"
@@ -1020,22 +1095,8 @@ six = "*"
 tornado = {version = "*", markers = "python_version > \"2.7\""}
 
 [[package]]
-name = "lxml"
-version = "4.9.1"
-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.*"
-
-[package.extras]
-cssselect = ["cssselect (>=0.7)"]
-html5 = ["html5lib"]
-htmlsoup = ["beautifulsoup4"]
-source = ["Cython (>=0.29.7)"]
-
-[[package]]
-name = "mako"
-version = "1.2.2"
+name = "Mako"
+version = "1.2.4"
 description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
 category = "main"
 optional = false
@@ -1045,7 +1106,7 @@ python-versions = ">=3.7"
 MarkupSafe = ">=0.9.2"
 
 [package.extras]
-babel = ["babel"]
+babel = ["Babel"]
 lingua = ["lingua"]
 testing = ["pytest"]
 
@@ -1065,12 +1126,12 @@ code_style = ["pre-commit (==2.6)"]
 compare = ["commonmark (>=0.9.1,<0.10.0)", "markdown (>=3.2.2,<3.3.0)", "mistletoe-ebp (>=0.10.0,<0.11.0)", "mistune (>=0.8.4,<0.9.0)", "panflute (>=1.12,<2.0)"]
 linkify = ["linkify-it-py (>=1.0,<2.0)"]
 plugins = ["mdit-py-plugins"]
-rtd = ["myst-nb (==0.13.0a1)", "pyyaml", "sphinx (>=2,<4)", "sphinx-copybutton", "sphinx-panels (>=0.4.0,<0.5.0)", "sphinx-book-theme"]
+rtd = ["myst-nb (==0.13.0a1)", "pyyaml", "sphinx (>=2,<4)", "sphinx-book-theme", "sphinx-copybutton", "sphinx-panels (>=0.4.0,<0.5.0)"]
 testing = ["coverage", "psutil", "pytest (>=3.6,<4)", "pytest-benchmark (>=3.2,<4.0)", "pytest-cov", "pytest-regressions"]
 
 [[package]]
-name = "markupsafe"
-version = "2.1.1"
+name = "MarkupSafe"
+version = "2.1.3"
 description = "Safely add untrusted strings to HTML/XML markup."
 category = "main"
 optional = false
@@ -1090,7 +1151,7 @@ numpy = "*"
 
 [[package]]
 name = "matplotlib"
-version = "3.6.0"
+version = "3.7.1"
 description = "Python plotting package"
 category = "main"
 optional = false
@@ -1100,11 +1161,12 @@ python-versions = ">=3.8"
 contourpy = ">=1.0.1"
 cycler = ">=0.10"
 fonttools = ">=4.22.0"
+importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.0.1"
-numpy = ">=1.19"
+numpy = ">=1.20"
 packaging = ">=20.0"
 pillow = ">=6.2.0"
-pyparsing = ">=2.2.1"
+pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 setuptools_scm = ">=7"
 
@@ -1137,15 +1199,15 @@ testing = ["coverage", "pytest (>=3.6,<4)", "pytest-cov", "pytest-regressions"]
 
 [[package]]
 name = "mistune"
-version = "2.0.4"
-description = "A sane Markdown parser with useful plugins and renderers"
+version = "3.0.1"
+description = "A sane and fast Markdown parser with useful plugins and renderers"
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 
 [[package]]
 name = "mlflow"
-version = "1.28.0"
+version = "1.30.1"
 description = "MLflow: A Platform for ML Development and Productionization"
 category = "main"
 optional = false
@@ -1156,12 +1218,12 @@ alembic = "<2"
 click = ">=7.0,<9"
 cloudpickle = "<3"
 databricks-cli = ">=0.8.7,<1"
-docker = ">=4.0.0,<6"
+docker = ">=4.0.0,<7"
 entrypoints = "<1"
 Flask = "<3"
 gitpython = ">=2.1.0,<4"
 gunicorn = {version = "<21", markers = "platform_system != \"Windows\""}
-importlib-metadata = ">=3.7.0,<4.7.0 || >4.7.0,<5"
+importlib-metadata = ">=3.7.0,<4.7.0 || >4.7.0,<6"
 numpy = "<2"
 packaging = "<22"
 pandas = "<2"
@@ -1178,13 +1240,27 @@ waitress = {version = "<3", markers = "platform_system == \"Windows\""}
 
 [package.extras]
 aliyun-oss = ["aliyunstoreplugin"]
-extras = ["scikit-learn", "pyarrow", "boto3", "google-cloud-storage (>=1.30.0)", "azureml-core (>=1.2.0)", "pysftp", "kubernetes", "mlserver (>=0.5.3)", "mlserver-mlflow (>=0.5.3)", "virtualenv"]
-pipelines = ["scikit-learn (>=1.0)", "pyarrow (>=7.0)", "shap (>=0.40)", "pandas-profiling (>=3.1)", "ipython (>=7.0)", "markdown (>=3.3)", "Jinja2 (>=3.0)"]
+extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=0.5.3)", "mlserver-mlflow (>=0.5.3)", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "scikit-learn", "virtualenv"]
+pipelines = ["Jinja2 (>=2.11)", "Jinja2 (>=3.0)", "ipython (>=7.0)", "markdown (>=3.3)", "pandas-profiling (>=3.1)", "pyarrow (>=7.0)", "scikit-learn (>=1.0)", "shap (>=0.40)"]
 sqlserver = ["mlflow-dbstore"]
 
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4)"]
+tests = ["pytest (>=4.6)"]
+
 [[package]]
 name = "murmurhash"
-version = "1.0.8"
+version = "1.0.9"
 description = "Cython bindings for MurmurHash"
 category = "main"
 optional = false
@@ -1192,11 +1268,11 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "mypy-extensions"
-version = "0.4.3"
-description = "Experimental type system extensions for programs checked with the mypy typechecker."
+version = "1.0.0"
+description = "Type system extensions for programs checked with the mypy type checker."
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.5"
 
 [[package]]
 name = "myst-parser"
@@ -1217,30 +1293,31 @@ sphinx = ">=3.1,<5"
 [package.extras]
 code_style = ["pre-commit (>=2.12,<3.0)"]
 linkify = ["linkify-it-py (>=1.0,<2.0)"]
-rtd = ["ipython", "sphinx-book-theme (>=0.1.0,<0.2.0)", "sphinx-panels (>=0.5.2,<0.6.0)", "sphinxcontrib-bibtex (>=2.1,<3.0)", "sphinxext-rediraffe (>=0.2,<1.0)", "sphinxcontrib.mermaid (>=0.6.3,<0.7.0)", "sphinxext-opengraph (>=0.4.2,<0.5.0)"]
+rtd = ["ipython", "sphinx-book-theme (>=0.1.0,<0.2.0)", "sphinx-panels (>=0.5.2,<0.6.0)", "sphinxcontrib-bibtex (>=2.1,<3.0)", "sphinxcontrib.mermaid (>=0.6.3,<0.7.0)", "sphinxext-opengraph (>=0.4.2,<0.5.0)", "sphinxext-rediraffe (>=0.2,<1.0)"]
 testing = ["beautifulsoup4", "coverage", "docutils (>=0.17.0,<0.18.0)", "pytest (>=3.6,<4)", "pytest-cov", "pytest-regressions"]
 
 [[package]]
 name = "nbclient"
-version = "0.6.8"
+version = "0.8.0"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 category = "dev"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
 
 [package.dependencies]
-jupyter-client = ">=6.1.5"
-nbformat = ">=5.0"
-nest-asyncio = "*"
-traitlets = ">=5.2.2"
+jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+nbformat = ">=5.1"
+traitlets = ">=5.4"
 
 [package.extras]
-sphinx = ["autodoc-traits", "mock", "moto", "myst-parser", "Sphinx (>=1.7)", "sphinx-book-theme"]
-test = ["black", "check-manifest", "flake8", "ipykernel", "ipython", "ipywidgets", "mypy", "nbconvert", "pip (>=18.1)", "pre-commit", "pytest (>=4.1)", "pytest-asyncio", "pytest-cov (>=2.6.1)", "setuptools (>=60.0)", "testpath", "twine (>=1.11.0)", "xmltodict"]
+dev = ["pre-commit"]
+docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
 name = "nbconvert"
-version = "7.0.0"
+version = "7.6.0"
 description = "Converting Jupyter Notebooks"
 category = "dev"
 optional = false
@@ -1248,52 +1325,52 @@ python-versions = ">=3.7"
 
 [package.dependencies]
 beautifulsoup4 = "*"
-bleach = "*"
+bleach = "!=5.0.0"
 defusedxml = "*"
 importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
 jupyterlab-pygments = "*"
-lxml = "*"
 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 = ["ipykernel", "ipython", "ipywidgets (>=7)", "nbsphinx (>=0.2.12)", "pre-commit", "pyppeteer (>=1,<1.1)", "pyqtwebengine (>=5.15)", "pytest", "pytest-cov", "pytest-dependency", "sphinx-rtd-theme", "sphinx (==5.0.2)", "tornado (>=6.1)"]
-docs = ["ipython", "nbsphinx (>=0.2.12)", "sphinx-rtd-theme", "sphinx (==5.0.2)"]
-qtpdf = ["pyqtwebengine (>=5.15)"]
+all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
+docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
+qtpdf = ["nbconvert[qtpng]"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
-test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pyppeteer (>=1,<1.1)", "pytest", "pytest-cov", "pytest-dependency"]
+test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pytest", "pytest-dependency"]
 webpdf = ["pyppeteer (>=1,<1.1)"]
 
 [[package]]
 name = "nbformat"
-version = "5.5.0"
+version = "5.9.0"
 description = "The Jupyter Notebook format"
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.dependencies]
 fastjsonschema = "*"
 jsonschema = ">=2.6"
-jupyter_core = "*"
+jupyter-core = "*"
 traitlets = ">=5.1"
 
 [package.extras]
-test = ["check-manifest", "testpath", "pytest", "pre-commit", "pep440"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["pep440", "pre-commit", "pytest", "testpath"]
 
 [[package]]
 name = "nbsphinx"
-version = "0.8.9"
+version = "0.8.12"
 description = "Jupyter Notebook Tools for Sphinx"
 category = "dev"
 optional = false
@@ -1309,23 +1386,41 @@ traitlets = ">=5"
 
 [[package]]
 name = "nest-asyncio"
-version = "1.5.5"
+version = "1.5.6"
 description = "Patch asyncio to allow nested event loops"
 category = "dev"
 optional = false
 python-versions = ">=3.5"
 
+[[package]]
+name = "networkx"
+version = "3.1"
+description = "Python package for creating and manipulating graphs and networks"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.extras]
+default = ["matplotlib (>=3.4)", "numpy (>=1.20)", "pandas (>=1.3)", "scipy (>=1.8)"]
+developer = ["mypy (>=1.1)", "pre-commit (>=3.2)"]
+doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.13)", "sphinx (>=6.1)", "sphinx-gallery (>=0.12)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.10)"]
+test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
 [[package]]
 name = "nodeenv"
-version = "1.7.0"
+version = "1.8.0"
 description = "Node.js virtual environment builder"
 category = "dev"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
 
+[package.dependencies]
+setuptools = "*"
+
 [[package]]
 name = "numpy"
-version = "1.23.3"
+version = "1.23.5"
 description = "NumPy is the fundamental package for array computing with Python."
 category = "main"
 optional = false
@@ -1333,7 +1428,7 @@ python-versions = ">=3.8"
 
 [[package]]
 name = "oauthlib"
-version = "3.2.1"
+version = "3.2.2"
 description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
 category = "main"
 optional = false
@@ -1366,14 +1461,14 @@ tqdm = "*"
 
 [package.extras]
 benchmark = ["asv", "virtualenv"]
-checking = ["black", "hacking", "isort", "mypy (==0.790)", "blackdoc"]
+checking = ["black", "blackdoc", "hacking", "isort", "mypy (==0.790)"]
 codecov = ["codecov", "pytest-cov"]
-doctest = ["cma", "matplotlib (>=3.0.0)", "pandas", "plotly (>=4.0.0)", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "mlflow"]
-document = ["Jinja2 (<3.0.0)", "MarkupSafe (<=2.0.1)", "sphinx (<=3.5.4)", "sphinx-rtd-theme (<=1.0.0)", "sphinx-copybutton (<=0.4.0)", "sphinx-gallery (<=0.10.0)", "sphinx-plotly-directive (<=0.1.3)", "pillow", "matplotlib", "scikit-learn (<1.0.0)", "plotly (>=4.0.0)", "pandas", "lightgbm", "torch (==1.8.0)", "torchvision (==0.9.0)", "torchaudio (==0.8.0)", "thop"]
+doctest = ["cma", "matplotlib (>=3.0.0)", "mlflow", "pandas", "plotly (>=4.0.0)", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize"]
+document = ["Jinja2 (<3.0.0)", "MarkupSafe (<=2.0.1)", "lightgbm", "matplotlib", "pandas", "pillow", "plotly (>=4.0.0)", "scikit-learn (<1.0.0)", "sphinx (<=3.5.4)", "sphinx-copybutton (<=0.4.0)", "sphinx-gallery (<=0.10.0)", "sphinx-plotly-directive (<=0.1.3)", "sphinx-rtd-theme (<=1.0.0)", "thop", "torch (==1.8.0)", "torchaudio (==0.8.0)", "torchvision (==0.9.0)"]
 experimental = ["redis"]
-integration = ["chainer (>=5.0.0)", "cma", "lightgbm", "mlflow", "wandb", "mpi4py", "mxnet", "pandas", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "xgboost", "tensorflow", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning (>=1.0.2)", "skorch", "catalyst (>=21.3)", "torchaudio (==0.8.0)", "allennlp (>=2.2.0,<2.7.0)", "fastai", "botorch (>=0.4.0)", "torch (==1.8.0+cpu)", "torchvision (==0.9.0+cpu)", "torch (==1.8.0)", "torchvision (==0.9.0)"]
+integration = ["allennlp (>=2.2.0,<2.7.0)", "botorch (>=0.4.0)", "catalyst (>=21.3)", "chainer (>=5.0.0)", "cma", "fastai", "lightgbm", "mlflow", "mpi4py", "mxnet", "pandas", "pytorch-ignite", "pytorch-lightning (>=1.0.2)", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "skorch", "tensorflow", "tensorflow-datasets", "torch (==1.8.0)", "torch (==1.8.0+cpu)", "torchaudio (==0.8.0)", "torchvision (==0.9.0)", "torchvision (==0.9.0+cpu)", "wandb", "xgboost"]
 optional = ["bokeh (<2.0.0)", "matplotlib (>=3.0.0)", "pandas", "plotly (>=4.0.0)", "redis", "scikit-learn (>=0.24.2,<1.0.0)"]
-testing = ["bokeh (<2.0.0)", "chainer (>=5.0.0)", "cma", "fakeredis", "lightgbm", "matplotlib (>=3.0.0)", "mlflow", "mpi4py", "mxnet", "pandas", "plotly (>=4.0.0)", "pytest", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "xgboost", "tensorflow", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning (>=1.0.2)", "skorch", "catalyst (>=21.3)", "torchaudio (==0.8.0)", "allennlp (>=2.2.0,<2.7.0)", "fastai", "botorch (>=0.4.0)", "torch (==1.8.0+cpu)", "torchvision (==0.9.0+cpu)", "torch (==1.8.0)", "torchvision (==0.9.0)"]
+testing = ["allennlp (>=2.2.0,<2.7.0)", "bokeh (<2.0.0)", "botorch (>=0.4.0)", "catalyst (>=21.3)", "chainer (>=5.0.0)", "cma", "fakeredis", "fastai", "lightgbm", "matplotlib (>=3.0.0)", "mlflow", "mpi4py", "mxnet", "pandas", "plotly (>=4.0.0)", "pytest", "pytorch-ignite", "pytorch-lightning (>=1.0.2)", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "skorch", "tensorflow", "tensorflow-datasets", "torch (==1.8.0)", "torch (==1.8.0+cpu)", "torchaudio (==0.8.0)", "torchvision (==0.9.0)", "torchvision (==0.9.0+cpu)", "xgboost"]
 tests = ["fakeredis", "pytest"]
 
 [[package]]
@@ -1389,7 +1484,7 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
 
 [[package]]
 name = "pandas"
-version = "1.4.4"
+version = "1.5.3"
 description = "Powerful data structures for data analysis, time series, and statistics"
 category = "main"
 optional = false
@@ -1397,10 +1492,9 @@ python-versions = ">=3.8"
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.18.5", markers = "platform_machine != \"aarch64\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
-    {version = ">=1.19.2", markers = "platform_machine == \"aarch64\" and python_version < \"3.10\""},
-    {version = ">=1.20.0", markers = "platform_machine == \"arm64\" and python_version < \"3.10\""},
+    {version = ">=1.20.3", markers = "python_version < \"3.10\""},
     {version = ">=1.21.0", markers = "python_version >= \"3.10\""},
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
 ]
 python-dateutil = ">=2.8.1"
 pytz = ">=2020.1"
@@ -1430,7 +1524,7 @@ testing = ["docopt", "pytest (<6.0.0)"]
 
 [[package]]
 name = "pathspec"
-version = "0.10.1"
+version = "0.11.1"
 description = "Utility library for gitignore style pattern matching of file paths."
 category = "dev"
 optional = false
@@ -1446,25 +1540,26 @@ python-versions = "*"
 
 [[package]]
 name = "pathy"
-version = "0.6.2"
+version = "0.10.2"
 description = "pathlib.Path subclasses for local and cloud bucket storage"
 category = "main"
 optional = false
 python-versions = ">= 3.6"
 
 [package.dependencies]
-smart-open = ">=5.2.1,<6.0.0"
+smart-open = ">=5.2.1,<7.0.0"
 typer = ">=0.3.0,<1.0.0"
 
 [package.extras]
-all = ["google-cloud-storage (>=1.26.0,<2.0.0)", "boto3", "pytest", "pytest-coverage", "mock", "typer-cli"]
+all = ["azure-storage-blob", "boto3", "google-cloud-storage (>=1.26.0,<2.0.0)", "mock", "pytest", "pytest-coverage", "typer-cli"]
+azure = ["azure-storage-blob"]
 gcs = ["google-cloud-storage (>=1.26.0,<2.0.0)"]
 s3 = ["boto3"]
-test = ["pytest", "pytest-coverage", "mock", "typer-cli"]
+test = ["mock", "pytest", "pytest-coverage", "typer-cli"]
 
 [[package]]
 name = "pbr"
-version = "5.10.0"
+version = "5.11.1"
 description = "Python Build Reasonableness"
 category = "main"
 optional = false
@@ -1490,19 +1585,27 @@ optional = false
 python-versions = "*"
 
 [[package]]
-name = "pillow"
-version = "9.2.0"
+name = "Pillow"
+version = "9.5.0"
 description = "Python Imaging Library (Fork)"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
-name = "pkgutil-resolve-name"
+name = "pip"
+version = "23.1.2"
+description = "The PyPA recommended tool for installing Python packages."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "pkgutil_resolve_name"
 version = "1.3.10"
 description = "Resolve a name to an object."
 category = "dev"
@@ -1511,34 +1614,35 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "platformdirs"
-version = "2.5.2"
-description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+version = "3.8.0"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-docs = ["furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx-autodoc-typehints (>=1.12)", "sphinx (>=4)"]
-test = ["appdirs (==1.4.4)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)", "pytest (>=6)"]
+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 = "plotly"
-version = "5.10.0"
+version = "5.15.0"
 description = "An open-source, interactive data visualization library for Python"
 category = "main"
 optional = false
 python-versions = ">=3.6"
 
 [package.dependencies]
+packaging = "*"
 tenacity = ">=6.2.0"
 
 [[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"
 
 [package.extras]
 dev = ["pre-commit", "tox"]
@@ -1546,7 +1650,7 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pre-commit"
-version = "2.20.0"
+version = "2.21.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 category = "dev"
 optional = false
@@ -1557,12 +1661,11 @@ cfgv = ">=2.0.0"
 identify = ">=1.0.0"
 nodeenv = ">=0.11.1"
 pyyaml = ">=5.1"
-toml = "*"
-virtualenv = ">=20.0.8"
+virtualenv = ">=20.10.0"
 
 [[package]]
 name = "preshed"
-version = "3.0.7"
+version = "3.0.8"
 description = "Cython hash table that trusts the keys are pre-hashed"
 category = "main"
 optional = false
@@ -1574,11 +1677,11 @@ murmurhash = ">=0.28.0,<1.1.0"
 
 [[package]]
 name = "prettytable"
-version = "3.4.1"
+version = "3.8.0"
 description = "A simple Python library for easily displaying tabular data in a visually appealing ASCII table format"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.dependencies]
 wcwidth = "*"
@@ -1588,7 +1691,7 @@ tests = ["pytest", "pytest-cov", "pytest-lazy-fixture"]
 
 [[package]]
 name = "prometheus-client"
-version = "0.14.1"
+version = "0.17.0"
 description = "Python client for the Prometheus monitoring system."
 category = "main"
 optional = false
@@ -1599,7 +1702,7 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prometheus-flask-exporter"
-version = "0.20.3"
+version = "0.22.4"
 description = "Prometheus metrics exporter for Flask"
 category = "main"
 optional = false
@@ -1621,22 +1724,22 @@ python-versions = "*"
 six = "*"
 
 [package.extras]
-test = ["pytest (>=2.7.3)", "pytest-cov", "coveralls", "futures", "pytest-benchmark", "mock"]
+test = ["coveralls", "futures", "mock", "pytest (>=2.7.3)", "pytest-benchmark", "pytest-cov"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.31"
+version = "3.0.39"
 description = "Library for building powerful interactive command lines in Python"
 category = "dev"
 optional = false
-python-versions = ">=3.6.2"
+python-versions = ">=3.7.0"
 
 [package.dependencies]
 wcwidth = "*"
 
 [[package]]
 name = "protobuf"
-version = "3.20.2"
+version = "3.20.3"
 description = "Protocol Buffers"
 category = "main"
 optional = false
@@ -1644,14 +1747,14 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "psutil"
-version = "5.9.2"
+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.*"
 
 [package.extras]
-test = ["ipaddress", "mock", "enum34", "pywin32", "wmi"]
+test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
 
 [[package]]
 name = "ptyprocess"
@@ -1682,15 +1785,18 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "pyaml"
-version = "21.10.1"
-description = "PyYAML-based module to produce pretty and readable YAML-serialized data"
+version = "23.7.0"
+description = "PyYAML-based module to produce a bit more pretty and readable YAML-serialized data"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 
 [package.dependencies]
 PyYAML = "*"
 
+[package.extras]
+anchors = ["unidecode"]
+
 [[package]]
 name = "pybtex"
 version = "0.24.0"
@@ -1732,7 +1838,7 @@ pybtex = ">=0.24.0,<0.25.0"
 
 [[package]]
 name = "pycodestyle"
-version = "2.9.1"
+version = "2.10.0"
 description = "Python style guide checker"
 category = "dev"
 optional = false
@@ -1748,54 +1854,54 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "pydantic"
-version = "1.9.2"
+version = "1.10.11"
 description = "Data validation and settings management using python type hints"
 category = "main"
 optional = false
-python-versions = ">=3.6.1"
+python-versions = ">=3.7"
 
 [package.dependencies]
-typing-extensions = ">=3.7.4.3"
+typing-extensions = ">=4.2.0"
 
 [package.extras]
 dotenv = ["python-dotenv (>=0.10.4)"]
 email = ["email-validator (>=1.0.3)"]
 
 [[package]]
-name = "pygments"
-version = "2.13.0"
+name = "Pygments"
+version = "2.15.1"
 description = "Pygments is a syntax highlighting package written in Python."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.extras]
 plugins = ["importlib-metadata"]
 
 [[package]]
-name = "pyjwt"
-version = "2.4.0"
+name = "PyJWT"
+version = "2.7.0"
 description = "JSON Web Token implementation in Python"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.extras]
-crypto = ["cryptography (>=3.3.1)"]
-dev = ["sphinx", "sphinx-rtd-theme", "zope.interface", "cryptography (>=3.3.1)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "mypy", "pre-commit"]
-docs = ["sphinx", "sphinx-rtd-theme", "zope.interface"]
-tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"]
+crypto = ["cryptography (>=3.4.0)"]
+dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
+docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
+tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[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"
 
 [package.extras]
-diagrams = ["railroad-diagrams", "jinja2"]
+diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pyperclip"
@@ -1815,7 +1921,7 @@ python-versions = "*"
 
 [[package]]
 name = "pyrsistent"
-version = "0.18.1"
+version = "0.19.3"
 description = "Persistent/Functional/Immutable data structures"
 category = "dev"
 optional = false
@@ -1855,11 +1961,11 @@ six = ">=1.5"
 
 [[package]]
 name = "python-slugify"
-version = "6.1.2"
+version = "8.0.1"
 description = "A Python slugify application that also handles Unicode"
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = ">=3.7"
 
 [package.dependencies]
 text-unidecode = ">=1.3"
@@ -1869,22 +1975,33 @@ unidecode = ["Unidecode (>=1.1.1)"]
 
 [[package]]
 name = "pytz"
-version = "2022.2.1"
+version = "2022.7.1"
 description = "World timezone definitions, modern and historical"
 category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "PyWavelets"
+version = "1.4.1"
+description = "PyWavelets, wavelet transform module"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+numpy = ">=1.17.3"
+
 [[package]]
 name = "pywin32"
-version = "227"
+version = "306"
 description = "Python for Window Extensions"
 category = "main"
 optional = false
 python-versions = "*"
 
 [[package]]
-name = "pyyaml"
+name = "PyYAML"
 version = "6.0"
 description = "YAML parser and emitter for Python"
 category = "main"
@@ -1893,7 +2010,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "pyzmq"
-version = "24.0.0"
+version = "25.1.0"
 description = "Python bindings for 0MQ"
 category = "dev"
 optional = false
@@ -1901,7 +2018,6 @@ python-versions = ">=3.6"
 
 [package.dependencies]
 cffi = {version = "*", markers = "implementation_name == \"pypy\""}
-py = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "querystring-parser"
@@ -1916,17 +2032,17 @@ six = "*"
 
 [[package]]
 name = "requests"
-version = "2.28.1"
+version = "2.31.0"
 description = "Python HTTP for Humans."
 category = "main"
 optional = false
-python-versions = ">=3.7, <4"
+python-versions = ">=3.7"
 
 [package.dependencies]
 certifi = ">=2017.4.17"
-charset-normalizer = ">=2,<3"
+charset-normalizer = ">=2,<4"
 idna = ">=2.5,<4"
-urllib3 = ">=1.21.1,<1.27"
+urllib3 = ">=1.21.1,<3"
 
 [package.extras]
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
@@ -1948,9 +2064,37 @@ pygments = ">=2.6.0,<3.0.0"
 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<8.0.0)"]
 
+[[package]]
+name = "scikit-image"
+version = "0.21.0"
+description = "Image processing in Python"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+imageio = ">=2.27"
+lazy_loader = ">=0.2"
+networkx = ">=2.8"
+numpy = ">=1.21.1"
+packaging = ">=21"
+pillow = ">=9.0.1"
+PyWavelets = ">=1.1.1"
+scipy = ">=1.8"
+tifffile = ">=2022.8.12"
+
+[package.extras]
+build = ["Cython (>=0.29.32)", "build", "meson-python (>=0.13)", "ninja", "numpy (>=1.21.1)", "packaging (>=21)", "pythran", "setuptools (>=67)", "spin (==0.3)", "wheel"]
+data = ["pooch (>=1.6.0)"]
+default = ["PyWavelets (>=1.1.1)", "imageio (>=2.27)", "lazy_loader (>=0.2)", "networkx (>=2.8)", "numpy (>=1.21.1)", "packaging (>=21)", "pillow (>=9.0.1)", "scipy (>=1.8)", "tifffile (>=2022.8.12)"]
+developer = ["pre-commit", "rtoml"]
+docs = ["dask[array] (>=2022.9.2)", "ipykernel", "ipywidgets", "kaleido", "matplotlib (>=3.5)", "myst-parser", "numpydoc (>=1.5)", "pandas (>=1.5)", "plotly (>=5.10)", "pooch (>=1.6)", "pydata-sphinx-theme (>=0.13)", "pytest-runner", "scikit-learn (>=0.24.0)", "seaborn (>=0.11)", "sphinx (>=5.0)", "sphinx-copybutton", "sphinx-gallery (>=0.11)", "sphinx_design (>=0.3)", "tifffile (>=2022.8.12)"]
+optional = ["SimpleITK", "astropy (>=5.0)", "cloudpickle (>=0.2.1)", "dask[array] (>=2021.1.0)", "matplotlib (>=3.5)", "pooch (>=1.6.0)", "pyamg", "scikit-learn (>=0.24.0)"]
+test = ["asv", "matplotlib (>=3.5)", "pooch (>=1.6.0)", "pytest (>=7.0)", "pytest-cov (>=2.11.0)", "pytest-faulthandler", "pytest-localserver"]
+
 [[package]]
 name = "scikit-learn"
-version = "1.1.2"
+version = "1.1.3"
 description = "A set of python modules for machine learning and data mining"
 category = "main"
 optional = false
@@ -1963,10 +2107,10 @@ scipy = ">=1.3.2"
 threadpoolctl = ">=2.0.0"
 
 [package.extras]
-tests = ["numpydoc (>=1.2.0)", "pyamg (>=4.0.0)", "mypy (>=0.961)", "black (>=22.3.0)", "flake8 (>=3.8.2)", "pytest-cov (>=2.9.0)", "pytest (>=5.0.1)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "matplotlib (>=3.1.2)"]
-examples = ["seaborn (>=0.9.0)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "matplotlib (>=3.1.2)"]
-docs = ["sphinxext-opengraph (>=0.4.2)", "sphinx-prompt (>=1.3.0)", "Pillow (>=7.1.2)", "numpydoc (>=1.2.0)", "sphinx-gallery (>=0.7.0)", "sphinx (>=4.0.1)", "memory-profiler (>=0.57.0)", "seaborn (>=0.9.0)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "matplotlib (>=3.1.2)"]
-benchmark = ["memory-profiler (>=0.57.0)", "pandas (>=1.0.5)", "matplotlib (>=3.1.2)"]
+benchmark = ["matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"]
+docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
+examples = ["matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"]
+tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.2)", "mypy (>=0.961)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pyamg (>=4.0.0)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "scikit-image (>=0.16.2)"]
 
 [[package]]
 name = "scikit-optimize"
@@ -1988,18 +2132,23 @@ plots = ["matplotlib (>=2.0.0)"]
 
 [[package]]
 name = "scipy"
-version = "1.9.1"
-description = "SciPy: Scientific Library for Python"
+version = "1.10.1"
+description = "Fundamental algorithms for scientific computing in Python"
 category = "main"
 optional = false
-python-versions = ">=3.8,<3.12"
+python-versions = "<3.12,>=3.8"
 
 [package.dependencies]
-numpy = ">=1.18.5,<1.25.0"
+numpy = ">=1.19.5,<1.27.0"
+
+[package.extras]
+dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"]
+doc = ["matplotlib (>2)", "numpydoc", "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 = "sentry-sdk"
-version = "1.9.8"
+version = "1.27.1"
 description = "Python client for Sentry (https://sentry.io)"
 category = "main"
 optional = false
@@ -2011,6 +2160,7 @@ urllib3 = {version = ">=1.26.11", markers = "python_version >= \"3.6\""}
 
 [package.extras]
 aiohttp = ["aiohttp (>=3.5)"]
+arq = ["arq (>=0.23)"]
 beam = ["apache-beam (>=2.12)"]
 bottle = ["bottle (>=0.12.13)"]
 celery = ["celery (>=3)"]
@@ -2018,15 +2168,21 @@ chalice = ["chalice (>=1.16.0)"]
 django = ["django (>=1.8)"]
 falcon = ["falcon (>=1.4)"]
 fastapi = ["fastapi (>=0.79.0)"]
-flask = ["flask (>=0.11)", "blinker (>=1.1)"]
+flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
+grpcio = ["grpcio (>=1.21.1)"]
 httpx = ["httpx (>=0.16.0)"]
-pure_eval = ["pure-eval", "executing", "asttokens"]
+huey = ["huey (>=2)"]
+loguru = ["loguru (>=0.5)"]
+opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
+pure_eval = ["asttokens", "executing", "pure-eval"]
+pymongo = ["pymongo (>=3.1)"]
 pyspark = ["pyspark (>=2.4.4)"]
-quart = ["quart (>=0.16.1)", "blinker (>=1.1)"]
+quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
 rq = ["rq (>=0.6)"]
 sanic = ["sanic (>=0.8)"]
 sqlalchemy = ["sqlalchemy (>=1.2)"]
 starlette = ["starlette (>=0.19.1)"]
+starlite = ["starlite (>=1.48)"]
 tornado = ["tornado (>=5)"]
 
 [[package]]
@@ -2040,9 +2196,22 @@ python-versions = ">=3.7"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
+name = "setuptools"
+version = "68.0.0"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[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)", "flake8-2020", "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 = "setuptools-scm"
-version = "7.0.5"
+version = "7.1.0"
 description = "the blessed package to manage your versions by scm tags"
 category = "main"
 optional = false
@@ -2050,7 +2219,8 @@ python-versions = ">=3.7"
 
 [package.dependencies]
 packaging = ">=20.0"
-tomli = ">=1.0.0"
+setuptools = "*"
+tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 typing-extensions = "*"
 
 [package.extras]
@@ -2059,7 +2229,7 @@ toml = ["setuptools (>=42)"]
 
 [[package]]
 name = "shortuuid"
-version = "1.0.9"
+version = "1.0.11"
 description = "A generator library for concise, unambiguous and URL-safe UUIDs."
 category = "main"
 optional = false
@@ -2075,19 +2245,20 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 
 [[package]]
 name = "smart-open"
-version = "5.2.1"
+version = "6.3.0"
 description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)"
 category = "main"
 optional = false
 python-versions = ">=3.6,<4.0"
 
 [package.extras]
-all = ["boto3", "google-cloud-storage", "azure-storage-blob", "azure-common", "azure-core", "requests"]
-azure = ["azure-storage-blob", "azure-common", "azure-core"]
-gcs = ["google-cloud-storage"]
+all = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "paramiko", "requests"]
+azure = ["azure-common", "azure-core", "azure-storage-blob"]
+gcs = ["google-cloud-storage (>=2.6.0)"]
 http = ["requests"]
 s3 = ["boto3"]
-test = ["boto3", "google-cloud-storage", "azure-storage-blob", "azure-common", "azure-core", "requests", "moto[server] (==1.3.14)", "pathlib2", "responses", "paramiko", "parameterizedtestcase", "pytest", "pytest-rerunfailures"]
+ssh = ["paramiko"]
+test = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "moto[server]", "paramiko", "pytest", "pytest-rerunfailures", "requests", "responses"]
 webhdfs = ["requests"]
 
 [[package]]
@@ -2108,15 +2279,15 @@ python-versions = "*"
 
 [[package]]
 name = "soupsieve"
-version = "2.3.2.post1"
+version = "2.4.1"
 description = "A modern CSS selector implementation for Beautiful Soup."
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [[package]]
 name = "spacy"
-version = "3.4.1"
+version = "3.5.4"
 description = "Industrial-strength Natural Language Processing (NLP) in Python"
 category = "main"
 optional = false
@@ -2130,46 +2301,50 @@ langcodes = ">=3.2.0,<4.0.0"
 murmurhash = ">=0.28.0,<1.1.0"
 numpy = ">=1.15.0"
 packaging = ">=20.0"
-pathy = ">=0.3.5"
+pathy = ">=0.10.0"
 preshed = ">=3.0.2,<3.1.0"
-pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<1.10.0"
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<1.11.0"
 requests = ">=2.13.0,<3.0.0"
-spacy-legacy = ">=3.0.9,<3.1.0"
+setuptools = "*"
+smart-open = ">=5.2.1,<7.0.0"
+spacy-legacy = ">=3.0.11,<3.1.0"
 spacy-loggers = ">=1.0.0,<2.0.0"
 srsly = ">=2.4.3,<3.0.0"
-thinc = ">=8.1.0,<8.2.0"
+thinc = ">=8.1.8,<8.2.0"
 tqdm = ">=4.38.0,<5.0.0"
-typer = ">=0.3.0,<0.5.0"
-wasabi = ">=0.9.1,<1.1.0"
+typer = ">=0.3.0,<0.10.0"
+wasabi = ">=0.9.1,<1.2.0"
 
 [package.extras]
 apple = ["thinc-apple-ops (>=0.1.0.dev0,<1.0.0)"]
-cuda = ["cupy (>=5.0.0b4,<11.0.0)"]
-cuda100 = ["cupy-cuda100 (>=5.0.0b4,<11.0.0)"]
-cuda101 = ["cupy-cuda101 (>=5.0.0b4,<11.0.0)"]
-cuda102 = ["cupy-cuda102 (>=5.0.0b4,<11.0.0)"]
-cuda110 = ["cupy-cuda110 (>=5.0.0b4,<11.0.0)"]
-cuda111 = ["cupy-cuda111 (>=5.0.0b4,<11.0.0)"]
-cuda112 = ["cupy-cuda112 (>=5.0.0b4,<11.0.0)"]
-cuda113 = ["cupy-cuda113 (>=5.0.0b4,<11.0.0)"]
-cuda114 = ["cupy-cuda114 (>=5.0.0b4,<11.0.0)"]
-cuda115 = ["cupy-cuda115 (>=5.0.0b4,<11.0.0)"]
-cuda116 = ["cupy-cuda116 (>=5.0.0b4,<11.0.0)"]
-cuda117 = ["cupy-cuda117 (>=5.0.0b4,<11.0.0)"]
-cuda80 = ["cupy-cuda80 (>=5.0.0b4,<11.0.0)"]
-cuda90 = ["cupy-cuda90 (>=5.0.0b4,<11.0.0)"]
-cuda91 = ["cupy-cuda91 (>=5.0.0b4,<11.0.0)"]
-cuda92 = ["cupy-cuda92 (>=5.0.0b4,<11.0.0)"]
-ja = ["sudachipy (>=0.5.2,!=0.6.1)", "sudachidict-core (>=20211220)"]
-ko = ["natto-py (==0.9.0)"]
+cuda = ["cupy (>=5.0.0b4,<13.0.0)"]
+cuda-autodetect = ["cupy-wheel (>=11.0.0,<13.0.0)"]
+cuda100 = ["cupy-cuda100 (>=5.0.0b4,<13.0.0)"]
+cuda101 = ["cupy-cuda101 (>=5.0.0b4,<13.0.0)"]
+cuda102 = ["cupy-cuda102 (>=5.0.0b4,<13.0.0)"]
+cuda110 = ["cupy-cuda110 (>=5.0.0b4,<13.0.0)"]
+cuda111 = ["cupy-cuda111 (>=5.0.0b4,<13.0.0)"]
+cuda112 = ["cupy-cuda112 (>=5.0.0b4,<13.0.0)"]
+cuda113 = ["cupy-cuda113 (>=5.0.0b4,<13.0.0)"]
+cuda114 = ["cupy-cuda114 (>=5.0.0b4,<13.0.0)"]
+cuda115 = ["cupy-cuda115 (>=5.0.0b4,<13.0.0)"]
+cuda116 = ["cupy-cuda116 (>=5.0.0b4,<13.0.0)"]
+cuda117 = ["cupy-cuda117 (>=5.0.0b4,<13.0.0)"]
+cuda11x = ["cupy-cuda11x (>=11.0.0,<13.0.0)"]
+cuda80 = ["cupy-cuda80 (>=5.0.0b4,<13.0.0)"]
+cuda90 = ["cupy-cuda90 (>=5.0.0b4,<13.0.0)"]
+cuda91 = ["cupy-cuda91 (>=5.0.0b4,<13.0.0)"]
+cuda92 = ["cupy-cuda92 (>=5.0.0b4,<13.0.0)"]
+ja = ["sudachidict-core (>=20211220)", "sudachipy (>=0.5.2,!=0.6.1)"]
+ko = ["natto-py (>=0.9.0)"]
 lookups = ["spacy-lookups-data (>=1.0.3,<1.1.0)"]
 ray = ["spacy-ray (>=0.1.0,<1.0.0)"]
 th = ["pythainlp (>=2.0)"]
-transformers = ["spacy-transformers (>=1.1.2,<1.2.0)"]
+transformers = ["spacy-transformers (>=1.1.2,<1.3.0)"]
 
 [[package]]
 name = "spacy-legacy"
-version = "3.0.10"
+version = "3.0.12"
 description = "Legacy registered functions for spaCy backwards compatibility"
 category = "main"
 optional = false
@@ -2177,17 +2352,14 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "spacy-loggers"
-version = "1.0.3"
+version = "1.0.4"
 description = "Logging utilities for SpaCy"
 category = "main"
 optional = false
 python-versions = ">=3.6"
 
-[package.dependencies]
-wasabi = ">=0.8.1,<1.1.0"
-
 [[package]]
-name = "sphinx"
+name = "Sphinx"
 version = "4.5.0"
 description = "Python documentation generator"
 category = "dev"
@@ -2215,8 +2387,8 @@ sphinxcontrib-serializinghtml = ">=1.1.5"
 
 [package.extras]
 docs = ["sphinxcontrib-websupport"]
-lint = ["flake8 (>=3.5.0)", "isort", "mypy (>=0.931)", "docutils-stubs", "types-typed-ast", "types-requests"]
-test = ["pytest", "pytest-cov", "html5lib", "cython", "typed-ast"]
+lint = ["docutils-stubs", "flake8 (>=3.5.0)", "isort", "mypy (>=0.931)", "types-requests", "types-typed-ast"]
+test = ["cython", "html5lib", "pytest", "pytest-cov", "typed-ast"]
 
 [[package]]
 name = "sphinx-autobuild"
@@ -2252,7 +2424,7 @@ typer = ">=0.4.0"
 [package.source]
 type = "git"
 url = "https://github.com/rbturnbull/sphinx-click.git"
-reference = "master"
+reference = "HEAD"
 resolved_reference = "ec700fc5864f42cfb71f143f0ec077c89e1102eb"
 
 [[package]]
@@ -2268,33 +2440,34 @@ sphinx = ">=1.8"
 
 [package.extras]
 code_style = ["pre-commit (==2.12.1)"]
-rtd = ["sphinx", "ipython", "sphinx-book-theme"]
+rtd = ["ipython", "sphinx", "sphinx-book-theme"]
 
 [[package]]
 name = "sphinx-rtd-theme"
-version = "1.0.0"
+version = "1.2.2"
 description = "Read the Docs theme for Sphinx"
 category = "dev"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 
 [package.dependencies]
-docutils = "<0.18"
-sphinx = ">=1.6"
+docutils = "<0.19"
+sphinx = ">=1.6,<7"
+sphinxcontrib-jquery = ">=4,<5"
 
 [package.extras]
-dev = ["transifex-client", "sphinxcontrib-httpdomain", "bump2version"]
+dev = ["bump2version", "sphinxcontrib-httpdomain", "transifex-client", "wheel"]
 
 [[package]]
 name = "sphinxcontrib-applehelp"
-version = "1.0.2"
-description = "sphinxcontrib-applehelp is a sphinx extension which outputs Apple help books"
+version = "1.0.4"
+description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
 category = "dev"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
 
 [package.extras]
-lint = ["flake8", "mypy", "docutils-stubs"]
+lint = ["docutils-stubs", "flake8", "mypy"]
 test = ["pytest"]
 
 [[package]]
@@ -2306,20 +2479,31 @@ optional = false
 python-versions = ">=3.5"
 
 [package.extras]
-lint = ["flake8", "mypy", "docutils-stubs"]
+lint = ["docutils-stubs", "flake8", "mypy"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-htmlhelp"
-version = "2.0.0"
+version = "2.0.1"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 
 [package.extras]
-lint = ["flake8", "mypy", "docutils-stubs"]
-test = ["pytest", "html5lib"]
+lint = ["docutils-stubs", "flake8", "mypy"]
+test = ["html5lib", "pytest"]
+
+[[package]]
+name = "sphinxcontrib-jquery"
+version = "4.1"
+description = "Extension to include jQuery on newer Sphinx releases"
+category = "dev"
+optional = false
+python-versions = ">=2.7"
+
+[package.dependencies]
+Sphinx = ">=1.8"
 
 [[package]]
 name = "sphinxcontrib-jsmath"
@@ -2330,7 +2514,7 @@ optional = false
 python-versions = ">=3.5"
 
 [package.extras]
-test = ["pytest", "flake8", "mypy"]
+test = ["flake8", "mypy", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-qthelp"
@@ -2341,7 +2525,7 @@ optional = false
 python-versions = ">=3.5"
 
 [package.extras]
-lint = ["flake8", "mypy", "docutils-stubs"]
+lint = ["docutils-stubs", "flake8", "mypy"]
 test = ["pytest"]
 
 [[package]]
@@ -2353,12 +2537,12 @@ optional = false
 python-versions = ">=3.5"
 
 [package.extras]
-lint = ["flake8", "mypy", "docutils-stubs"]
+lint = ["docutils-stubs", "flake8", "mypy"]
 test = ["pytest"]
 
 [[package]]
-name = "sqlalchemy"
-version = "1.4.41"
+name = "SQLAlchemy"
+version = "1.4.49"
 description = "Database Abstraction Library"
 category = "main"
 optional = false
@@ -2368,37 +2552,42 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
 
 [package.extras]
-aiomysql = ["greenlet (!=0.4.17)", "aiomysql"]
-aiosqlite = ["typing_extensions (!=3.10.0.1)", "greenlet (!=0.4.17)", "aiosqlite"]
+aiomysql = ["aiomysql", "greenlet (!=0.4.17)"]
+aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
 asyncio = ["greenlet (!=0.4.17)"]
-asyncmy = ["greenlet (!=0.4.17)", "asyncmy (>=0.2.3,!=0.2.4)"]
+asyncmy = ["asyncmy (>=0.2.3,!=0.2.4)", "greenlet (!=0.4.17)"]
 mariadb_connector = ["mariadb (>=1.0.1,!=1.1.2)"]
 mssql = ["pyodbc"]
 mssql_pymssql = ["pymssql"]
 mssql_pyodbc = ["pyodbc"]
-mypy = ["sqlalchemy2-stubs", "mypy (>=0.910)"]
-mysql = ["mysqlclient (>=1.4.0,<2)", "mysqlclient (>=1.4.0)"]
+mypy = ["mypy (>=0.910)", "sqlalchemy2-stubs"]
+mysql = ["mysqlclient (>=1.4.0)", "mysqlclient (>=1.4.0,<2)"]
 mysql_connector = ["mysql-connector-python"]
-oracle = ["cx_oracle (>=7,<8)", "cx_oracle (>=7)"]
+oracle = ["cx_oracle (>=7)", "cx_oracle (>=7,<8)"]
 postgresql = ["psycopg2 (>=2.7)"]
-postgresql_asyncpg = ["greenlet (!=0.4.17)", "asyncpg"]
+postgresql_asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
 postgresql_pg8000 = ["pg8000 (>=1.16.6,!=1.29.0)"]
 postgresql_psycopg2binary = ["psycopg2-binary"]
 postgresql_psycopg2cffi = ["psycopg2cffi"]
-pymysql = ["pymysql (<1)", "pymysql"]
-sqlcipher = ["sqlcipher3-binary"]
+pymysql = ["pymysql", "pymysql (<1)"]
+sqlcipher = ["sqlcipher3_binary"]
 
 [[package]]
 name = "sqlparse"
-version = "0.4.2"
+version = "0.4.4"
 description = "A non-validating SQL parser."
 category = "main"
 optional = false
 python-versions = ">=3.5"
 
+[package.extras]
+dev = ["build", "flake8"]
+doc = ["sphinx"]
+test = ["pytest", "pytest-cov"]
+
 [[package]]
 name = "srsly"
-version = "2.4.4"
+version = "2.4.6"
 description = "Modern high-performance serialization utilities for Python"
 category = "main"
 optional = false
@@ -2409,23 +2598,23 @@ catalogue = ">=2.0.3,<2.1.0"
 
 [[package]]
 name = "stack-data"
-version = "0.5.0"
+version = "0.6.2"
 description = "Extract data from python stack frames and tracebacks for informative displays"
 category = "dev"
 optional = false
 python-versions = "*"
 
 [package.dependencies]
-asttokens = "*"
-executing = "*"
+asttokens = ">=2.1.0"
+executing = ">=1.2.0"
 pure-eval = "*"
 
 [package.extras]
-tests = ["cython", "littleutils", "pygments", "typeguard", "pytest"]
+tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "stevedore"
-version = "4.0.0"
+version = "5.1.0"
 description = "Manage dynamic plugins for Python applications"
 category = "main"
 optional = false
@@ -2434,20 +2623,31 @@ python-versions = ">=3.8"
 [package.dependencies]
 pbr = ">=2.0.0,<2.1.0 || >2.1.0"
 
+[[package]]
+name = "sympy"
+version = "1.12"
+description = "Computer algebra system (CAS) in Python"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+mpmath = ">=0.19"
+
 [[package]]
 name = "tabulate"
-version = "0.8.10"
+version = "0.9.0"
 description = "Pretty-print tabular data"
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.7"
 
 [package.extras]
 widechars = ["wcwidth"]
 
 [[package]]
 name = "tenacity"
-version = "8.0.1"
+version = "8.2.2"
 description = "Retry code until it succeeds"
 category = "main"
 optional = false
@@ -2466,26 +2666,29 @@ python-versions = "*"
 
 [[package]]
 name = "thinc"
-version = "8.1.1"
+version = "8.1.10"
 description = "A refreshing functional take on deep learning, compatible with your favorite libraries"
 category = "main"
 optional = false
 python-versions = ">=3.6"
 
 [package.dependencies]
-blis = ">=0.7.8,<0.10.0"
+blis = ">=0.7.8,<0.8.0"
 catalogue = ">=2.0.4,<2.1.0"
 confection = ">=0.0.1,<1.0.0"
 cymem = ">=2.0.2,<2.1.0"
 murmurhash = ">=1.0.2,<1.1.0"
 numpy = ">=1.15.0"
+packaging = ">=20.0"
 preshed = ">=3.0.2,<3.1.0"
-pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<1.10.0"
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<1.11.0"
+setuptools = "*"
 srsly = ">=2.4.0,<3.0.0"
-wasabi = ">=0.8.1,<1.1.0"
+wasabi = ">=0.8.1,<1.2.0"
 
 [package.extras]
 cuda = ["cupy (>=5.0.0b4)"]
+cuda-autodetect = ["cupy-wheel (>=11.0.0)"]
 cuda100 = ["cupy-cuda100 (>=5.0.0b4)"]
 cuda101 = ["cupy-cuda101 (>=5.0.0b4)"]
 cuda102 = ["cupy-cuda102 (>=5.0.0b4)"]
@@ -2495,6 +2698,9 @@ cuda112 = ["cupy-cuda112 (>=5.0.0b4)"]
 cuda113 = ["cupy-cuda113 (>=5.0.0b4)"]
 cuda114 = ["cupy-cuda114 (>=5.0.0b4)"]
 cuda115 = ["cupy-cuda115 (>=5.0.0b4)"]
+cuda116 = ["cupy-cuda116 (>=5.0.0b4)"]
+cuda117 = ["cupy-cuda117 (>=5.0.0b4)"]
+cuda11x = ["cupy-cuda11x (>=11.0.0)"]
 cuda80 = ["cupy-cuda80 (>=5.0.0b4)"]
 cuda90 = ["cupy-cuda90 (>=5.0.0b4)"]
 cuda91 = ["cupy-cuda91 (>=5.0.0b4)"]
@@ -2504,6 +2710,17 @@ mxnet = ["mxnet (>=1.5.1,<1.6.0)"]
 tensorflow = ["tensorflow (>=2.0.0,<2.6.0)"]
 torch = ["torch (>=1.6.0)"]
 
+[[package]]
+name = "thop"
+version = "0.1.1.post2209072238"
+description = "A tool to count the FLOPs of PyTorch model."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+torch = "*"
+
 [[package]]
 name = "threadpoolctl"
 version = "3.1.0"
@@ -2512,20 +2729,34 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "tifffile"
+version = "2023.7.4"
+description = "Read and write TIFF files"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+all = ["defusedxml", "fsspec", "imagecodecs (>=2023.1.23)", "lxml", "matplotlib", "zarr"]
+
 [[package]]
 name = "tinycss2"
-version = "1.1.1"
+version = "1.2.1"
 description = "A tiny CSS parser"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 webencodings = ">=0.4"
 
 [package.extras]
-doc = ["sphinx", "sphinx-rtd-theme"]
-test = ["pytest", "pytest-cov", "pytest-flake8", "pytest-isort", "coverage"]
+doc = ["sphinx", "sphinx_rtd_theme"]
+test = ["flake8", "isort", "pytest"]
 
 [[package]]
 name = "toml"
@@ -2545,18 +2776,25 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "torch"
-version = "1.12.1"
+version = "2.0.1"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 category = "main"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
 
 [package.dependencies]
+filelock = "*"
+jinja2 = "*"
+networkx = "*"
+sympy = "*"
 typing-extensions = "*"
 
+[package.extras]
+opt-einsum = ["opt-einsum (>=3.3)"]
+
 [[package]]
 name = "torchapp"
-version = "0.2.3"
+version = "0.2.6"
 description = "A wrapper for fastai projects to create easy command-line inferfaces and manage hyper-parameter tuning."
 category = "main"
 optional = false
@@ -2569,7 +2807,7 @@ click = "8.0.4"
 cookiecutter = "^2.1.1"
 fastai = "^2.5.3"
 mlflow = "^1.25.1"
-numpy = "^1.22.0"
+numpy = ">=1.22.0,<1.24.0"
 optuna = "^2.10.0"
 pandas = "^1.3.5"
 Pillow = "^9.0.1"
@@ -2578,53 +2816,65 @@ pybtexnbib = "^0.1.1"
 pyjwt = ">=2.4.0"
 PyYAML = "^6.0"
 rich = "^10.16.1"
-scikit-learn = "^1.0.2"
+scikit-learn = ">=1.0.2,<1.2.0"
 scikit-optimize = "^0.9.0"
 scipy = "^1.9.1"
-torch = "^1.12.1"
-torchvision = "^0.13.1"
+torch = ">=1.12.1"
+torchvision = ">=0.13.0"
 typer = "^0.4.0"
 wandb = "^0.12.9"
 
 [package.source]
 type = "git"
 url = "https://github.com/rbturnbull/torchapp.git"
-reference = "master"
-resolved_reference = "5465da49df06f526a684db0065265487e533a06e"
+reference = "diffusion"
+resolved_reference = "b79a5a33f2a11c351af17f8afb1b34c824ba9fc3"
+
+[[package]]
+name = "torchprofile"
+version = "0.0.4"
+description = ""
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+numpy = ">=1.14"
+torch = ">=1.4"
+torchvision = ">=0.4"
 
 [[package]]
 name = "torchvision"
-version = "0.13.1"
+version = "0.15.2"
 description = "image and video datasets and models for torch deep learning"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.dependencies]
 numpy = "*"
 pillow = ">=5.3.0,<8.3.0 || >=8.4.0"
 requests = "*"
-torch = "1.12.1"
-typing-extensions = "*"
+torch = "2.0.1"
 
 [package.extras]
 scipy = ["scipy"]
 
 [[package]]
 name = "tornado"
-version = "6.2"
+version = "6.3.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 category = "dev"
 optional = false
-python-versions = ">= 3.7"
+python-versions = ">= 3.8"
 
 [[package]]
 name = "tqdm"
-version = "4.64.1"
+version = "4.65.0"
 description = "Fast, Extensible Progress Meter"
 category = "main"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+python-versions = ">=3.7"
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
@@ -2637,14 +2887,15 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.4.0"
-description = ""
+version = "5.9.0"
+description = "Traitlets Python configuration system"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-test = ["pre-commit", "pytest"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
 
 [[package]]
 name = "tricubic"
@@ -2658,7 +2909,7 @@ develop = false
 [package.source]
 type = "git"
 url = "https://github.com/rbturnbull/pytricubic.git"
-reference = "master"
+reference = "HEAD"
 resolved_reference = "8fb4a944d2f60a5d50eb48c4e2803cae20b85644"
 
 [[package]]
@@ -2675,12 +2926,12 @@ click = ">=7.1.1,<9.0.0"
 [package.extras]
 all = ["colorama (>=0.4.3,<0.5.0)", "shellingham (>=1.3.0,<2.0.0)"]
 dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
-doc = ["mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "mdx-include (>=1.4.1,<2.0.0)"]
-test = ["shellingham (>=1.3.0,<2.0.0)", "pytest (>=4.4.0,<5.4.0)", "pytest-cov (>=2.10.0,<3.0.0)", "coverage (>=5.2,<6.0)", "pytest-xdist (>=1.32.0,<2.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "mypy (==0.910)", "black (>=22.3.0,<23.0.0)", "isort (>=5.0.6,<6.0.0)"]
+doc = ["mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)"]
+test = ["black (>=22.3.0,<23.0.0)", "coverage (>=5.2,<6.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<5.4.0)", "pytest-cov (>=2.10.0,<3.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<2.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.3.0"
+version = "4.7.1"
 description = "Backported and Experimental Type Hints for Python 3.7+"
 category = "main"
 optional = false
@@ -2688,33 +2939,34 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "urllib3"
-version = "1.26.12"
+version = "2.0.3"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, <4"
+python-versions = ">=3.7"
 
 [package.extras]
-brotli = ["brotlicffi (>=0.8.0)", "brotli (>=1.0.9)", "brotlipy (>=0.6.0)"]
-secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "urllib3-secure-extra", "ipaddress"]
-socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.16.5"
+version = "20.23.1"
 description = "Virtual Python Environment builder"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
-distlib = ">=0.3.5,<1"
-filelock = ">=3.4.1,<4"
-platformdirs = ">=2.4,<3"
+distlib = ">=0.3.6,<1"
+filelock = ">=3.12,<4"
+platformdirs = ">=3.5.1,<4"
 
 [package.extras]
-docs = ["proselint (>=0.13)", "sphinx (>=5.1.1)", "sphinx-argparse (>=0.3.1)", "sphinx-rtd-theme (>=1)", "towncrier (>=21.9)"]
-testing = ["coverage (>=6.2)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=21.3)", "pytest (>=7.0.1)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.2)", "pytest-mock (>=3.6.1)", "pytest-randomly (>=3.10.3)", "pytest-timeout (>=2.1)"]
+docs = ["furo (>=2023.5.20)", "proselint (>=0.13)", "sphinx (>=7.0.1)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.3.1)", "pytest-env (>=0.8.1)", "pytest-freezer (>=0.4.6)", "pytest-mock (>=3.10)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=67.8)", "time-machine (>=2.9)"]
 
 [[package]]
 name = "waitress"
@@ -2726,7 +2978,7 @@ python-versions = ">=3.7.0"
 
 [package.extras]
 docs = ["Sphinx (>=1.8.1)", "docutils", "pylons-sphinx-themes (>=1.0.9)"]
-testing = ["pytest", "pytest-cover", "coverage (>=5.0)"]
+testing = ["coverage (>=5.0)", "pytest", "pytest-cover"]
 
 [[package]]
 name = "wandb"
@@ -2748,6 +3000,7 @@ PyYAML = "*"
 requests = ">=2.0.0,<3"
 sentry-sdk = ">=1.0.0"
 setproctitle = "*"
+setuptools = "*"
 shortuuid = ">=0.5.0"
 six = ">=1.13.0"
 
@@ -2756,23 +3009,26 @@ aws = ["boto3"]
 azure = ["azure-storage-blob"]
 gcp = ["google-cloud-storage"]
 grpc = ["grpcio (>=1.27.2)"]
-kubeflow = ["kubernetes", "minio", "google-cloud-storage", "sh"]
-launch = ["nbconvert", "nbformat", "chardet", "iso8601", "typing-extensions", "boto3", "google-cloud-storage", "kubernetes"]
-media = ["numpy", "moviepy", "pillow", "bokeh", "soundfile", "plotly", "rdkit-pypi"]
+kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
+launch = ["boto3", "chardet", "google-cloud-storage", "iso8601", "kubernetes", "nbconvert", "nbformat", "typing-extensions"]
+media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"]
 models = ["cloudpickle"]
 sweeps = ["sweeps (>=0.1.0)"]
 
 [[package]]
 name = "wasabi"
-version = "0.10.1"
+version = "1.1.2"
 description = "A lightweight console printing and formatting toolkit"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
+
+[package.dependencies]
+colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\" and python_version >= \"3.7\""}
 
 [[package]]
 name = "wcwidth"
-version = "0.2.5"
+version = "0.2.6"
 description = "Measures the displayed width of unicode strings in a terminal"
 category = "main"
 optional = false
@@ -2788,7 +3044,7 @@ python-versions = "*"
 
 [[package]]
 name = "websocket-client"
-version = "1.4.1"
+version = "1.6.1"
 description = "WebSocket client for Python with low level API options"
 category = "main"
 optional = false
@@ -2800,50 +3056,64 @@ optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
 [[package]]
-name = "werkzeug"
-version = "2.2.2"
+name = "Werkzeug"
+version = "2.3.6"
 description = "The comprehensive WSGI web application library."
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 
 [package.dependencies]
 MarkupSafe = ">=2.1.1"
 
 [package.extras]
-watchdog = ["watchdog"]
+watchdog = ["watchdog (>=2.3)"]
 
 [[package]]
 name = "zipp"
-version = "3.8.1"
+version = "3.15.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
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 torchapp = []
-torchvision = []
-tornado = []
-tqdm = []
-traitlets = []
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+    {file = "torchvision-0.15.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:9a192f2aa979438f23c20e883980b23d13268ab9f819498774a6d2eb021802c2"},
+    {file = "torchvision-0.15.2-cp38-cp38-win_amd64.whl", hash = "sha256:c07071bc8d02aa8fcdfe139ab6a1ef57d3b64c9e30e84d12d45c9f4d89fb6536"},
+    {file = "torchvision-0.15.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4790260fcf478a41c7ecc60a6d5200a88159fdd8d756e9f29f0f8c59c4a67a68"},
+    {file = "torchvision-0.15.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:987ab62225b4151a11e53fd06150c5258ced24ac9d7c547e0e4ab6fbca92a5ce"},
+    {file = "torchvision-0.15.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:63df26673e66cba3f17e07c327a8cafa3cce98265dbc3da329f1951d45966838"},
+    {file = "torchvision-0.15.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b85f98d4cc2f72452f6792ab4463a3541bc5678a8cdd3da0e139ba2fe8b56d42"},
+    {file = "torchvision-0.15.2-cp39-cp39-win_amd64.whl", hash = "sha256:07c462524cc1bba5190c16a9d47eac1fca024d60595a310f23c00b4ffff18b30"},
+]
+tornado = [
+    {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"},
+]
+tqdm = [
+    {file = "tqdm-4.65.0-py3-none-any.whl", hash = "sha256:c4f53a17fe37e132815abceec022631be8ffe1b9381c2e6e30aa70edc99e9671"},
+    {file = "tqdm-4.65.0.tar.gz", hash = "sha256:1871fb68a86b8fb3b59ca4cdd3dcccbc7e6d613eeed31f4c332531977b89beb5"},
+]
+traitlets = [
+    {file = "traitlets-5.9.0-py3-none-any.whl", hash = "sha256:9e6ec080259b9a5940c797d58b613b5e31441c2257b87c2e795c5228ae80d2d8"},
+    {file = "traitlets-5.9.0.tar.gz", hash = "sha256:f6cde21a9c68cf756af02035f72d5a723bf607e862e7be33ece505abf4a3bad9"},
+]
 tricubic = []
-typer = []
-typing-extensions = []
-urllib3 = []
-virtualenv = []
+typer = [
+    {file = "typer-0.4.2-py3-none-any.whl", hash = "sha256:023bae00d1baf358a6cc7cea45851639360bb716de687b42b0a4641cd99173f1"},
+    {file = "typer-0.4.2.tar.gz", hash = "sha256:b8261c6c0152dd73478b5ba96ba677e5d6948c715c310f7c91079f311f62ec03"},
+]
+typing-extensions = [
+    {file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"},
+    {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"},
+]
+urllib3 = [
+    {file = "urllib3-2.0.3-py3-none-any.whl", hash = "sha256:48e7fafa40319d358848e1bc6809b208340fafe2096f1725d05d67443d0483d1"},
+    {file = "urllib3-2.0.3.tar.gz", hash = "sha256:bee28b5e56addb8226c96f7f13ac28cb4c301dd5ea8a6ca179c0b9835e032825"},
+]
+virtualenv = [
+    {file = "virtualenv-20.23.1-py3-none-any.whl", hash = "sha256:34da10f14fea9be20e0fd7f04aba9732f84e593dac291b757ce42e3368a39419"},
+    {file = "virtualenv-20.23.1.tar.gz", hash = "sha256:8ff19a38c1021c742148edc4f81cb43d7f8c6816d2ede2ab72af5b84c749ade1"},
+]
 waitress = [
     {file = "waitress-2.1.2-py3-none-any.whl", hash = "sha256:7500c9625927c8ec60f54377d590f67b30c8e70ef4b8894214ac6e4cad233d2a"},
     {file = "waitress-2.1.2.tar.gz", hash = "sha256:780a4082c5fbc0fde6a2fcfe5e26e6efc1e8f425730863c04085769781f51eba"},
 ]
-wandb = []
-wasabi = []
+wandb = [
+    {file = "wandb-0.12.21-py2.py3-none-any.whl", hash = "sha256:150842447d355d90dc7f368b824951a625e5b2d1be355a00e99b11b73728bc1f"},
+    {file = "wandb-0.12.21.tar.gz", hash = "sha256:1975ff88c5024923c3321c93cfefb8d9b871543c0b009f34001bf0f31e444b04"},
+]
+wasabi = [
+    {file = "wasabi-1.1.2-py3-none-any.whl", hash = "sha256:0a3f933c4bf0ed3f93071132c1b87549733256d6c8de6473c5f7ed2e171b5cf9"},
+    {file = "wasabi-1.1.2.tar.gz", hash = "sha256:1aaef3aceaa32edb9c91330d29d3936c0c39fdb965743549c173cb54b16c30b5"},
+]
 wcwidth = [
-    {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
-    {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
+    {file = "wcwidth-0.2.6-py2.py3-none-any.whl", hash = "sha256:795b138f6875577cd91bba52baf9e445cd5118fd32723b460e30a0af30ea230e"},
+    {file = "wcwidth-0.2.6.tar.gz", hash = "sha256:a5220780a404dbe3353789870978e472cfe477761f06ee55077256e509b156d0"},
 ]
 webencodings = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
-websocket-client = []
-werkzeug = []
-zipp = []
+websocket-client = [
+    {file = "websocket-client-1.6.1.tar.gz", hash = "sha256:c951af98631d24f8df89ab1019fc365f2227c0892f12fd150e935607c79dd0dd"},
+    {file = "websocket_client-1.6.1-py3-none-any.whl", hash = "sha256:f1f9f2ad5291f0225a49efad77abf9e700b6fef553900623060dad6e26503b9d"},
+]
+Werkzeug = [
+    {file = "Werkzeug-2.3.6-py3-none-any.whl", hash = "sha256:935539fa1413afbb9195b24880778422ed620c0fc09670945185cce4d91a8890"},
+    {file = "Werkzeug-2.3.6.tar.gz", hash = "sha256:98c774df2f91b05550078891dee5f0eb0cb797a522c757a2452b9cee5b202330"},
+]
+zipp = [
+    {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"},
+    {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"},
+]
diff --git a/pyproject.toml b/pyproject.toml
index 11ce486..657afb1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supercat"
-version = "0.1.0"
+version = "0.2.1"
 description = "A deep learning model for CT scan super-resolution."
 authors = ["MDAP <robert.turnbull@unimelb.edu.au>"]
 license = "Apache Software License 2.0"
@@ -8,7 +8,7 @@ readme = "README.rst"
 homepage = "https://rbturnbull.github.io/supercat/"
 repository = "https://github.com/rbturnbull/supercat"
 documentation = "https://rbturnbull.github.io/supercat/"
-keywords = ["super-resolution", "Computed tomography (CT)", "torchapp", "rock", "sand", "3D"]
+keywords = ["super-resolution", "Computed tomography (CT)", "torchapp", "rock", "sand", "3D", "diffusion"]
 classifiers = [
     "License :: OSI Approved :: Apache Software License",
     "Intended Audience :: Science/Research",
@@ -18,12 +18,12 @@ classifiers = [
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.12"
-torchapp = {git = "https://github.com/rbturnbull/torchapp.git"}
+torchapp = {git = "https://github.com/rbturnbull/torchapp.git", branch="diffusion"}
 mat73 = "^0.59"
 hdf5storage = "^0.1.18"
-tricubic = {git = "https://github.com/rbturnbull/pytricubic.git"}
 plotly = "^5.8.2"
 kaleido = "0.2.1"
+scikit-image = "^0.21.0"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"
@@ -40,6 +40,10 @@ sphinx-copybutton = "^0.4.0"
 black = "^21.10b0"
 sphinx-click = {git = "https://github.com/rbturnbull/sphinx-click.git"}
 
+[tool.poetry.group.dev.dependencies]
+thop = "^0.1.1.post2209072238"
+torchprofile = "^0.0.4"
+
 [tool.pytest.ini_options]
 minversion = "6.0"
 testpaths = [
@@ -51,5 +55,7 @@ requires = ["setuptools","poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry.scripts]
-supercat = "supercat.apps:Supercat.main"
-supercat3d = "supercat.apps:Supercat3d.main"
+supercat-train = "supercat.apps:Supercat.main"
+supercat = "supercat.apps:Supercat.inference_only_main"
+supercat-diffusion-train = "supercat.apps:SupercatDiffusion.main"
+supercat-diffusion = "supercat.apps:SupercatDiffusion.inference_only_main"
\ No newline at end of file
diff --git a/supercat/__init__.py b/supercat/__init__.py
index 8a99a8b..d3c90a9 100644
--- a/supercat/__init__.py
+++ b/supercat/__init__.py
@@ -1 +1 @@
-from .apps import Supercat, Supercat3d
\ No newline at end of file
+from .apps import Supercat, SupercatDiffusion
\ No newline at end of file
diff --git a/supercat/apps.py b/supercat/apps.py
index b6c3859..58ca8d0 100644
--- a/supercat/apps.py
+++ b/supercat/apps.py
@@ -1,86 +1,65 @@
 import re
+import torchapp as ta
+from typing import List
 import torch
-from enum import Enum
+import random
 from pathlib import Path
-from typing import List
-from PIL import Image
-from torch import nn
+from fastai.callback.core import Callback, CancelBatchException
+from fastai.data.block import DataBlock, TransformBlock
+from fastai.data.core import DataLoaders, DisplayedTransform
 import torch.nn.functional as F
+from rich.progress import track
+from fastai.data.transforms import get_image_files
+import torchvision.transforms as T
+from fastai.data.transforms import ToTensor
 from fastcore.transform import Pipeline
-from fastai.data.transforms import get_image_files, FuncSplitter, ToTensor
-from fastai.data.core import DataLoaders
-from fastai.data.block import DataBlock
-from fastai.vision.data import ImageBlock
-from fastai.vision.core import PILImageBW
-from fastai.vision.augment import RandomCrop, Resize
-from fastai.learner import Learner, load_learner
-import torchapp as ta
-from torchapp.util import call_func, add_kwargs, change_typer_to_defaults
-
-from .metrics import psnr, mse
-from .transforms import ImageBlock3D, read3D, write3D, InterpolateTransform
-from .interpolation import interpolate3D, InterpolationMethod
-from .models import ResidualUNet, VideoUnet3d
+from fastai.vision.augment import Resize
+from fastai.data.transforms import FuncSplitter
+from fastai.learner import load_learner
+from PIL import Image
+from functools import partial
+from fastai.vision.data import ImageBlock, TensorImage
+from fastai.vision.core import PILImageBW, TensorImageBW
+from supercat.worley import WorleyNoise, WorleyNoiseTensor
+from supercat.fractal import * # remove this
+
+from supercat.models import ResidualUNet
+from supercat.transforms import ImageBlock3D, RescaleImage, write3D, read3D, InterpolateTransform
+from supercat.enums import DownsampleScale, DownsampleMethod
+from supercat.diffusion import DDPMCallback, DDPMSamplerCallback
+from skimage.transform import resize as skresize
 
 from rich.console import Console
 console = Console()
 
-class DownsampleScale(Enum):
-    X2 = "X2"
-    X4 = "X4"
-
-
-class ClipUnitInterval(nn.Module):
-    def forward(self, input):
-        return F.hardtanh(input, 0.0, 1.0)
-
-
-class DownsampleMethod(Enum):
-    DEFAULT = "default"
-    UNKNOWN = "unknown"
-
-
-def get_y(item):
-    dir_name = re.sub(r"_BI_.*", "_HR", item.parent.name)            
-    return item.parent.parent/dir_name/item.name
-
-def get_y_3d(item):
-    dir_name = re.sub(r"_TRI_.*", "_HR", item.parent.name)            
-    return item.parent.parent/dir_name/item.name
 
 def is_validation_image(item:tuple):
     "Returns True if this image should be part of the validation set i.e. if the parent directory doesn't have the string `_train_` in it."
     return "_train_" not in item.parent.name
 
 
-class Supercat(ta.TorchApp):
-    """
-    A deep learning model for CT scan superresolution.
-    """
-    def __init__(self):
-        super().__init__()
-
-        self.validate_individual = self.copy_method(self.validate_individual)
-        add_kwargs(to_func=self.validate_individual, from_funcs=[self.pretrained_local_path, self.inference_dataloader])
+def get_y(item, pattern=r"_BI_.*"):
+    dir_name = re.sub(pattern, "_HR", item.parent.name)            
+    return item.parent.parent/dir_name/item.name
 
-        # Make copies of methods to use just for the CLI
-        self.validate_individual_cli = self.copy_method(self.validate_individual)
 
-        # Remove params from defaults in methods not used for the cli
-        change_typer_to_defaults(self.validate_individual)
-    
+class Supercat(ta.TorchApp):
     def get_items(self, directory):
-        return get_image_files(directory)
+        if self.dim == 2:
+            return get_image_files(directory)
+        
+        directory = Path(directory)
+        return list(directory.glob("*.mat"))            
 
     def dataloaders(
         self,
-        deeprock:Path = ta.Param(help="The path to the DeepRockSR-2D dataset."), 
+        dim:int = ta.Param(default=2, help="The dimension of the dataset. 2 or 3."),
+        deeprock:Path = ta.Param(help="The path to the DeepRockSR dataset."), 
         downsample_scale:DownsampleScale = ta.Param(DownsampleScale.X4.value, help="Should it use the 2x or 4x downsampled images.", case_sensitive=False),
         downsample_method:DownsampleMethod = ta.Param(DownsampleMethod.UNKNOWN.value, help="Should it use the default method to downsample (bicubic) or a random kernel (UNKNOWN)."),
         batch_size:int = ta.Param(default=10, help="The batch size."),
         force:bool = ta.Param(default=False, help="Whether or not to force the conversion of the bicubic upscaling."),
         max_samples:int = ta.Param(default=None, help="If set, then the number of input samples for training/validation is truncated at this number."),
-        random_crop:int = ta.Param(default=None, help="If set, then randomly crop the images to this resolution during training."),
         include_sand:bool = ta.Param(default=False, help="Including DeepSand-SR dataset."),
     ) -> DataLoaders:
         """
@@ -88,22 +67,26 @@ def dataloaders(
         """
         assert deeprock is not None
 
+        self.dim = dim
         deeprock = Path(deeprock)
-        bicubic = []
+        upscaled = []
         highres = []
         
         # sources = ["shuffled2D"]
-        sources = ["carbonate2D","coal2D","sandstone2D"]
+        sources = [f"carbonate{dim}D",f"coal{dim}D",f"sandstone{dim}D"]
         if include_sand:
-            sources.append("sand2D")
+            sources.append(f"sand{dim}D")
 
         if isinstance(downsample_method, DownsampleMethod):
             downsample_method = downsample_method.value
+
         if isinstance(downsample_scale, DownsampleScale):
             downsample_scale = downsample_scale.value
 
         split_types = ["train","valid"] # There is also "test"
         # split_types = ["train","valid","test"] # hack
+
+        UP = "BI" if dim == 2 else "TRI"
         
         for source in sources:
             for split_type in split_types:
@@ -113,70 +96,108 @@ def dataloaders(
 
                 lowres_dir = deeprock/source/f"{source}_{split_type}_LR_{downsample_method}_{downsample_scale}"
                 
-                # We will save bicubic upscaled images
-                bicubic_dir = deeprock/source/f"{source}_{split_type}_BI_{downsample_method}_{downsample_scale}" 
-                bicubic_dir.mkdir(exist_ok=True)
+                # We will save upscaled images
+                upscale_dir = deeprock/source/f"{source}_{split_type}_{UP}_{downsample_method}_{downsample_scale}" 
+                upscale_dir.mkdir(exist_ok=True)
 
                 for index, highres_path in enumerate(highres_split):
-                    bicubic_path = bicubic_dir/highres_path.name
+                    upscale_path = upscale_dir/highres_path.name
 
-                    if not bicubic_path.exists() or force:
+                    if not upscale_path.exists() or force:
                         components = highres_path.name.split(".")
                         lowres_name = f'{components[0]}{downsample_scale.lower()}.{components[1]}'
                         lowres_path = lowres_dir/lowres_name
-                        print(split_type, highres_path, bicubic_path, lowres_path)
+                        print(split_type, highres_path, upscale_path, lowres_path)
                         
-                        # upscale with bicubic interpolation
-                        print("Upscaling with bicubic")
-                        highres_img = Image.open(highres_path)
-                        lowres_img = Image.open(lowres_path)
-
-                        # Convert to single channel
-                        if lowres_img.mode == "RGB":
-                            lowres_img = lowres_img.getchannel('R')
-                            lowres_img.save(lowres_path)
-                        if highres_img.mode == "RGB":
-                            highres_img = highres_img.getchannel('R')
-                            highres_img.save(highres_path)
-
-                        bicubic_img = lowres_img.resize(highres_img.size,Image.BICUBIC)
-                        if bicubic_img.mode == "RGB":
-                            bicubic_img = bicubic_img.getchannel('R')
-
-                        bicubic_img.save(bicubic_path)
-                    bicubic.append(bicubic_path)
+                        # upscale with upscale interpolation
+                        print("Upscaling")
+                        if dim == 2:
+                            highres_img = Image.open(highres_path)
+                            lowres_img = Image.open(lowres_path)
+
+                            # Convert to single channel
+                            if lowres_img.mode == "RGB":
+                                lowres_img = lowres_img.getchannel('R')
+                                lowres_img.save(lowres_path)
+                            if highres_img.mode == "RGB":
+                                highres_img = highres_img.getchannel('R')
+                                highres_img.save(highres_path)
+
+                            upscale_img = lowres_img.resize(highres_img.size,Image.upscale)
+                            if upscale_img.mode == "RGB":
+                                upscale_img = upscale_img.getchannel('R')
+
+                            upscale_img.save(upscale_path)
+                        else:
+                            components = highres_path.name.split(".")
+                            lowres_name = f'{components[0]}{downsample_scale.lower()}.{components[1]}'
+                            lowres_path = lowres_dir/lowres_name
+                            print(split_type, highres_path, upscale_path, lowres_path)
+                            
+                            # upscale with tricubic interpolation
+                            print("Upscaling with tricubic")
+                            highres_img = read3D(highres_path)
+                            lowres_img = read3D(lowres_path)
+
+                            tricubic_img = skresize(lowres_img, highres_img.shape, order=3)
+                            write3D(upscale_path, tricubic_img)
+
+                    upscaled.append(upscale_path)
 
                     if max_samples and index > max_samples:
                         break
 
-        item_transforms = [RandomCrop(random_crop)] if random_crop else []
+        if len(upscaled) == 0:
+            raise ValueError("No images found.")
+
+        if dim == 2:
+            blocks = (ImageBlock(cls=PILImageBW), ImageBlock(cls=PILImageBW))
+        else:
+            blocks = (ImageBlock3D, ImageBlock3D,)
 
         datablock = DataBlock(
-            blocks=(ImageBlock(cls=PILImageBW), ImageBlock(cls=PILImageBW)),
+            blocks=blocks,
             splitter=FuncSplitter(is_validation_image),
-            get_y=get_y,
-            item_tfms=item_transforms,
+            get_y=get_y if dim == 2 else partial(get_y, pattern=r"_TRI_.*"),
+            batch_tfms=[RescaleImage],
         )
 
         dataloaders = DataLoaders.from_dblock(
             datablock, 
-            source=bicubic,
+            source=upscaled,
             bs=batch_size,
         )
 
         dataloaders.c = 1
 
         return dataloaders
+        
+    def model(self, pretrained:Path=None):
+        if pretrained:
+            learner = load_learner(pretrained)
+            return learner.model
+
+        return ResidualUNet(dim=self.dim, in_channels=1)
+
+    def loss_func(self):
+        """
+        Returns the loss function to use with the model.
+        """
+        return F.smooth_l1_loss
 
     def inference_dataloader(
         self, 
         learner, 
+        dim:int = ta.Param(default=2, help="The dimension of the dataset. 2 or 3."),
         items:List[Path] = None, 
         item_dir: Path = ta.Param(None, help="A directory with images to upscale."), 
-        width:int = ta.Param(500, help="The width of the final image."), 
-        height:int = ta.Param(None, help="The height of the final image."), 
+        width:int = ta.Param(500, help="The width of the final image/volume."), 
+        height:int = ta.Param(None, help="The height of the final image/volume."), 
+        depth:int = ta.Param(None, help="The depth of the final image/volume."), 
         **kwargs
-    ):
+    ):  
+        self.dim = dim
+
         if not items:
             items = []
         if isinstance(items, (Path, str)):
@@ -187,263 +208,97 @@ def inference_dataloader(
         items = [Path(item) for item in items]
         self.items = items
         dataloader = learner.dls.test_dl(items, with_labels=True, **kwargs)
-
+        dataloader.transform = dataloader.transform[:1] # ignore the get_y function
         height = height or width
-        dataloader.after_item = Pipeline( [Resize(height, width), ToTensor] )
+        depth = depth or width
+        
+        interpolation = Resize(height, width) if dim == 2 else InterpolateTransform(width, height, depth)
+        dataloader.after_item = Pipeline( [interpolation, ToTensor] )
 
         return dataloader
 
-    def output_results(self, results, return_images=False, **kwargs):
+    def output_results(
+        self, 
+        results, 
+        return_data:bool=False, 
+        output_dir: Path = ta.Param(None, help="The location of the output directory. If not given then it uses the directory of the item."),
+        suffix:str = ta.Param("", help="The file extension for the output file."),
+        **kwargs
+    ):
         list_to_return = []
+        if output_dir:
+            output_dir = Path(output_dir)
+            output_dir.mkdir(exist_ok=True, parents=True)
+        
         for item, result in zip(self.items, results[0]):
-            extension = item.name[item.name.rfind(".")+1:].lower() 
-            stub = item.name[:-len(extension)]
-            new_name = f"{stub}upscaled.{extension}"
-            new_path = item.parent/new_name
-            pixels = torch.clip(result[0]*255, min=0, max=255)
-            
-            im = Image.fromarray( pixels.cpu().detach().numpy().astype('uint8') )
-            
+            my_suffix = suffix or item.suffix
+            if my_suffix[0] != ".":
+                my_suffix = "." + my_suffix
+
+            new_name = item.with_suffix("").name + f".upscaled{my_suffix}"
+            my_output_dir = output_dir or item.parent
+            new_path = my_output_dir/new_name
+
+            dim = len(result.shape) - 1
+
+            result[0] = result[0] * 0.5 + 0.5
+            if dim == 2:
+                pixels = torch.clip(result[0]*255, min=0, max=255)
+                im = Image.fromarray( pixels.cpu().detach().numpy().astype('uint8') )
+                im.save(new_path)
+            else:
+                write3D(new_path, result[0].cpu().detach().numpy())            
+                            
+            list_to_return.append(result[0] if return_data else new_path)
             console.print(f"Upscaled '{item}' ⮕ '{new_path}'")
-            im.save(new_path)
-            list_to_return.append(im if return_images else new_path)
 
         return list_to_return
+    
 
-    def model(
-        self,
-        initial_features:int = ta.Param(
-            64,
-            tune=True, 
-            tune_min=16,
-            tune_max=256,
-            help="The number of features after the initial CNN layer."
-        ),
-        growth_factor:float = ta.Param(
-            2.0,
-            tune=True, 
-            tune_min=1.0,
-            tune_max=4.0,
-            #log=True,
-            help="The factor to grow the number of convolutional filters each time the model downscales."
-        ),
-    ):
-        return ResidualUNet(in_channels=1, out_channels=1, initial_features=initial_features, growth_factor=growth_factor, dim=2)
-
-    def loss_func(self, mse:bool=True):
-        """
-        Returns the loss function to use with the model.
-
-        By default the L1 loss or the Mean Absolute Error (MAE) is used.
-        See 10.1029/2019WR026052
-        """
-        if mse:
-            return nn.MSELoss()
-        return nn.L1Loss()
-
-    def metrics(self):
-        metrics = super().metrics()
-        metrics.extend([psnr])
-        return metrics
-
-    def monitor(self):
-        return "psnr"
-
-    def validate_individual(self, csv, item_dir: Path = ta.Param(None, help="The dir with the images to upscale."), **kwargs):
-        path = call_func(self.pretrained_local_path, **kwargs)
-        learner = load_learner(path)
-        with open(csv, 'w') as f:
-            items = self.get_items(Path(item_dir).expanduser().resolve())
-            print("image", "loss", "psnr", sep=",", file=f)
-            for item in items:
-                print(item)
-                dataloader = learner.dls.test_dl([item], with_labels=True)
-                values = learner.validate(dl=dataloader)
-                print(item, *values, sep=",", file=f)
-
-
-class Supercat3d(Supercat):
-    def dataloaders(
-        self,
-        deeprock:Path = ta.Param(help="The path to the DeepRockSR-3D dataset."), 
-        downsample_scale:DownsampleScale = ta.Param(DownsampleScale.X4.value, help="Should it use the 2x or 4x downsampled images.", case_sensitive=False),
-        downsample_method:DownsampleMethod = ta.Param(DownsampleMethod.UNKNOWN.value, help="Should it use the default method to downsample (bicubic) or a random kernel (UNKNOWN)."),
-        batch_size:int = ta.Param(default=8, help="The batch size."),
-        force:bool = ta.Param(default=False, help="Whether or not to force the conversion of the tricubic upscaling."),
-        max_samples:int = ta.Param(default=None, help="If set, then the number of input samples for training/validation is truncated at this number."),
-        include_sand:bool = ta.Param(default=False, help="Including DeepSand-SR dataset."),
-    ) -> DataLoaders:
-        """
-        Creates a FastAI DataLoaders object which Supercat uses in training and prediction.
-
-        Args:
-            deeprock (Path): The path to the DeepRockSR-3D dataset.
-            batch_size (int): The number of elements to use in a batch for training and prediction. Defaults to 32.
-        """
-        assert deeprock is not None
-
-        deeprock = Path(deeprock)
-        tricubic = []
-        highres = []
-
-        sources = ["carbonate3D","coal3D","sandstone3D"]
-
-        if include_sand:
-            sources += ["sand3D"]
-
-        if isinstance(downsample_method, DownsampleMethod):
-            downsample_method = downsample_method.value
-        if isinstance(downsample_scale, DownsampleScale):
-            downsample_scale = downsample_scale.value
+class SupercatDiffusion(Supercat):
+    def model(self, pretrained:Path=None):
+        if pretrained:
+            learner = load_learner(pretrained)
+            return learner.model
 
-        split_types = ["train","valid"] # There is also "test"
-        # split_types = ["train","valid","test"] # hack
-        
-        for source in sources:
-            for split_type in split_types:
-                highres_dir = deeprock/source/f"{source}_{split_type}_HR"
-                highres_split = self.get_items(highres_dir)
-                highres.extend( highres_split )
+        return ResidualUNet(dim=self.dim, in_channels=3)
 
-                lowres_dir = deeprock/source/f"{source}_{split_type}_LR_{downsample_method}_{downsample_scale}"
+    def extra_callbacks(self):
+        return [DDPMCallback()]
+    
+    def inference_callbacks(self):
+        return [DDPMSamplerCallback()]        
+
+    # def output_results(
+    #     self, 
+    #     results, 
+    #     output_dir: Path = ta.Param("./outputs", help="The location of the output directory."),
+    #     diffusion_gif:bool=False,        
+    #     diffusion_gif_fps:float=ta.Param(120.0, help="The frames per second to use when generating the gif."),
+    #     **kwargs,
+    # ):
+    #     breakpoint()
+    #     # final_results = [[result[-1] for result in results[0][0]]]
+    #     to_return = super().output_results(results, output_dir=output_dir, **kwargs)
+
+    #     if diffusion_gif:
+    #         assert self.dim == 2
+
+    #         output_dir = Path(output_dir)
+    #         print(f"Saving {len(results[0])} generated images:")
+
+    #         transform = T.ToPILImage()
+    #         output_dir.mkdir(exist_ok=True, parents=True)
+    #         images = []
+    #         for index, image in enumerate(results[0][0]):
+    #             path = output_dir/f"image.{index}.png"
                 
-                # We will save tricubic upscaled images
-                tricubic_dir = deeprock/source/f"{source}_{split_type}_TRI_{downsample_method}_{downsample_scale}" 
-                tricubic_dir.mkdir(exist_ok=True)
-
-                for index, highres_path in enumerate(highres_split):
-                    tricubic_path = tricubic_dir/highres_path.name
-
-                    if not tricubic_path.exists() or force:
-                        components = highres_path.name.split(".")
-                        lowres_name = f'{components[0]}{downsample_scale.lower()}.{components[1]}'
-                        lowres_path = lowres_dir/lowres_name
-                        print(split_type, highres_path, tricubic_path, lowres_path)
-                        
-                        # upscale with tricubic interpolation
-                        print("Upscaling with tricubic")
-                        highres_img = read3D(highres_path)
-                        lowres_img = read3D(lowres_path)
+    #             image = transform(torch.clip(image[0]/2.0 + 0.5, min=0.0, max=1.0))
+    #             images.append(image)
+    #         print(f"\t{path}")
+    #         images[0].save(output_dir/f"image.gif", save_all=True, append_images=images[1:], fps=diffusion_gif_fps)
 
-                        tricubic_img = interpolate3D(lowres_img, new_shape=highres_img.shape, method=InterpolationMethod.TRICUBIC)
-                        write3D(tricubic_path, tricubic_img)
-                    tricubic.append(tricubic_path)
-
-                    if max_samples and index > max_samples:
-                        break
-
-        assert "test" not in split_types
-        datablock = DataBlock(
-            blocks=(ImageBlock3D, ImageBlock3D),
-            splitter=FuncSplitter(is_validation_image),
-            get_y=get_y_3d,
-        )
-
-        dataloaders = DataLoaders.from_dblock(
-            datablock, 
-            source=tricubic,
-            bs=batch_size,
-        )
-
-        dataloaders.c = 1
-
-        return dataloaders    
-
-    def model(
-        self,
-        video_unet:bool = False, 
-        pretrained:bool = True,
-        initial_features:int = ta.Param(
-            64,
-            tune=True, 
-            tune_min=16,
-            tune_max=256,
-            help="The number of features after the initial CNN layer."
-        ),
-        growth_factor:int = ta.Param(
-            2.0,
-            tune=True, 
-            tune_min=1.0,
-            tune_max=4.0,
-            log=True,
-            help="The factor to grow the number of convolutional filters each time the model downscales."
-        ),
-        # more should be added
-    ):
-        if video_unet:
-            return VideoUnet3d(in_channels=1, out_channels=1, pretrained=pretrained)
+    #     return to_return
 
-        return ResidualUNet(in_channels=1, out_channels=1, initial_features=initial_features, growth_factor=growth_factor, dim=3)
-
-    def build_learner_func(self):
-        return Learner
-
-    def learner_kwargs(
-        self,
-        output_dir: Path = ta.Param("./outputs", help="The location of the output directory."),
-        **kwargs,
-    ):
-        output_dir = Path(output_dir)
-        output_dir.mkdir(exist_ok=True, parents=True)
-
-        return dict(
-            loss_func=self.loss_func(),
-            metrics=self.metrics(),
-            path=output_dir,
-        )
-
-    def get_items(self, directory):
-        directory = Path(directory)
-        return list(directory.glob("*.mat"))
-
-    def validate_individual(self, csv, do_nothing:bool=False, item_dir: Path = ta.Param(None, help="The dir with the images to upscale."), **kwargs):
-        path = call_func(self.pretrained_local_path, **kwargs)
-        learner = load_learner(path)
-        if do_nothing:
-            learner.model = DoNothing() 
-                 
-        with open(csv, 'w') as f:
-            items = self.get_items(Path(item_dir).expanduser().resolve())
-            print("image", "loss", "l2", "psnr", sep=",", file=f)
-            for item in items:
-                dataloader = learner.dls.test_dl([item], with_labels=True)
-                values = learner.validate(dl=dataloader)
-                print(item, *values)
-                print(item, *values, sep=",", file=f)
-
-    def inference_dataloader(
-        self, 
-        learner, 
-        items:List[Path] = None, 
-        width:int = ta.Param(100, help="The width of the final volume."), 
-        height:int = ta.Param(None, help="The height of the final volume."), 
-        depth:int = ta.Param(None, help="The depth of the final volume."), 
-        **kwargs
-    ):
-        if not items:
-            items = []
-        if isinstance(items, (Path, str)):
-            items = [items]
-
-        items = [Path(item) for item in items]
-        self.items = items
-        dataloader = learner.dls.test_dl(items, with_labels=True, **kwargs)
-
-        height = height or width
-        depth = depth or width
-        dataloader.after_item = Pipeline( [InterpolateTransform(width, height, depth), ToTensor] )
-        return dataloader
-
-    def output_results(self, results, return_volumes=False, **kwargs):
-        list_to_return = []
-        for item, result in zip(self.items, results[0]):
-            extension = item.name[item.name.rfind(".")+1:].lower() 
-            stub = item.name[:-len(extension)]
-            new_name = f"{stub}upscaled.{extension}"
-            new_path = item.parent/new_name
-            write3D(new_path, result[0].cpu().detach().numpy())            
-                        
-            console.print(f"Upscaled '{item}' ⮕ '{new_path}'")
-            list_to_return.append(result[0] if return_volumes else new_path)
-
-        return list_to_return
+if __name__ == "__main__":
+    SupercatDiffusion.main()
diff --git a/supercat/augmentation.py b/supercat/augmentation.py
new file mode 100644
index 0000000..868739a
--- /dev/null
+++ b/supercat/augmentation.py
@@ -0,0 +1,28 @@
+from fastai.callback.core import Callback
+
+
+# Can this be a transform?
+class DihedralCallback(Callback):
+    def before_batch(self):
+        """
+        x: (batch_size, c, d, h, w)
+        """
+        xb = self.xb[0]
+        yb = self.yb[0]
+
+        k = random.randint(0,7)
+
+        if k in [1,3,4,7]: 
+            xb = xb.flip(-1)
+            yb = yb.flip(-1)
+        
+        if k in [2,4,5,7]:
+            xb = xb.flip(-2)
+            yb = yb.flip(-2)
+
+        if k in [3,5,6,7]: 
+            xb = xb.transpose(-1,-2)
+            yb = yb.transpose(-1,-2)
+
+        self.learn.xb = (xb,)
+        self.learn.yb = (yb,)
diff --git a/supercat/diffusion.py b/supercat/diffusion.py
new file mode 100644
index 0000000..e77a675
--- /dev/null
+++ b/supercat/diffusion.py
@@ -0,0 +1,75 @@
+import torch
+from rich.progress import track
+from fastai.callback.core import Callback, CancelBatchException
+
+
+class DDPMCallback(Callback):
+    """
+    Derived from https://wandb.ai/capecape/train_sd/reports/How-To-Train-a-Conditional-Diffusion-Model-From-Scratch--VmlldzoyNzIzNTQ1#using-fastai-to-train-your-diffusion-model
+    """
+    def __init__(self, n_steps:int=1000, s:float = 0.008):
+        self.n_steps = n_steps
+        self.s = s
+
+        t = torch.arange(self.n_steps)
+        self.alpha_bar = torch.cos((t/self.n_steps+self.s)/(1+self.s) * torch.pi * 0.5)**2
+        self.alpha = self.alpha_bar/torch.cat([torch.ones(1), self.alpha_bar[:-1]])
+        self.beta = 1.0 - self.alpha
+        self.sigma = torch.sqrt(self.beta)
+
+    def before_batch(self):
+        """
+        x: (batch_size, c, d, h, w)
+        """
+        lr = self.xb[0]
+        hr = self.yb[0]
+
+        noise = torch.randn_like(hr)
+
+        batch_size = hr.shape[0]
+        dim = len(hr.shape) - 2
+
+        # lookup noise schedule
+        t = torch.randint(0, self.n_steps, (batch_size,), dtype=torch.long) # select random timesteps
+        if dim == 2:
+            alpha_bar_t = self.alpha_bar[t, None, None, None]
+        else:
+            alpha_bar_t = self.alpha_bar[t, None, None, None, None]
+        alpha_bar_t = alpha_bar_t.to(self.dls.device)
+        
+        # noisify the image
+        xt =  torch.sqrt(alpha_bar_t) * hr + torch.sqrt(1-alpha_bar_t) * noise 
+        
+        # Stack input with low-resolution image (upscaled) and noise level
+        self.learn.xb = (torch.cat([xt, lr, alpha_bar_t.repeat(1,1,*hr.shape[2:])], dim=1),)
+        self.learn.yb = (noise,) # we are trying to predict the noise
+
+
+class DDPMSamplerCallback(DDPMCallback):
+    def before_batch(self):        
+        lr = self.xb[0]
+        
+        # Generate a batch of random noise to start with
+        xt = torch.randn_like(lr)
+        
+        outputs = [xt] 
+        for t in track(reversed(range(self.n_steps)), total=self.n_steps, description="Performing diffusion steps for batch:"):
+            z = torch.randn(xt.shape, device=xt.device) if t > 0 else torch.zeros(xt.shape, device=xt.device)
+            alpha_t = self.alpha[t] # get noise level at current timestep
+            alpha_bar_t = self.alpha_bar[t]
+            sigma_t = self.sigma[t]
+            model_input = torch.cat(
+                [xt, lr, alpha_bar_t.repeat(1,1,*lr.shape[2:]).to(xt.device)], 
+                dim=1,
+            )
+            predicted_noise = self.model(model_input)
+            
+            # predict x_(t-1) in accordance to Algorithm 2 in paper
+            xt = 1/torch.sqrt(alpha_t) * (xt - (1-alpha_t)/torch.sqrt(1-alpha_bar_t) * predicted_noise)  + sigma_t*z 
+            outputs.append(xt)
+
+        # self.learn.pred = (torch.stack(outputs, dim=1),)
+        self.learn.pred = (xt,)
+
+        raise CancelBatchException
+
diff --git a/supercat/enums.py b/supercat/enums.py
new file mode 100644
index 0000000..69e198c
--- /dev/null
+++ b/supercat/enums.py
@@ -0,0 +1,13 @@
+from enum import Enum
+
+
+class DownsampleScale(Enum):
+    X2 = "X2"
+    X4 = "X4"
+
+
+class DownsampleMethod(Enum):
+    DEFAULT = "default"
+    UNKNOWN = "unknown"
+
+
diff --git a/supercat/fractal.py b/supercat/fractal.py
new file mode 100644
index 0000000..4fba32c
--- /dev/null
+++ b/supercat/fractal.py
@@ -0,0 +1,270 @@
+
+from supercat.worley import WorleySR
+import numpy as np
+import torch
+
+
+def interpolant(t):
+    """    
+    Taken from https://github.com/pvigier/perlin-numpy/
+    """
+    return t*t*t*(t*(t*6 - 15) + 10)
+
+
+def shape_for_fractal_noise(shape, res, octaves, lacunarity):
+    shape = list(shape)
+    for i, (s, r) in enumerate(zip(shape, res)):
+        multiple_of = lacunarity**(octaves-1)*r
+        if s % multiple_of != 0:
+            shape[i] += multiple_of - (s % multiple_of)
+    return tuple(shape)
+
+
+def generate_perlin_noise_2d(
+        shape, res, tileable=(False, False), interpolant=interpolant
+):
+    """Generate a 2D numpy array of perlin noise.
+
+    Taken from https://github.com/pvigier/perlin-numpy/
+
+    Args:
+        shape: The shape of the generated array (tuple of two ints).
+            This must be a multple of res.
+        res: The number of periods of noise to generate along each
+            axis (tuple of two ints). Note shape must be a multiple of
+            res.
+        tileable: If the noise should be tileable along each axis
+            (tuple of two bools). Defaults to (False, False).
+        interpolant: The interpolation function, defaults to
+            t*t*t*(t*(t*6 - 15) + 10).
+
+    Returns:
+        A numpy array of shape shape with the generated noise.
+
+    Raises:
+        ValueError: If shape is not a multiple of res.
+    """
+    delta = (res[0] / shape[0], res[1] / shape[1])
+    d = (shape[0] // res[0], shape[1] // res[1])
+    grid = np.mgrid[0:res[0]:delta[0], 0:res[1]:delta[1]]\
+             .transpose(1, 2, 0) % 1
+    # Gradients
+    angles = 2*np.pi*np.random.rand(res[0]+1, res[1]+1)
+    gradients = np.dstack((np.cos(angles), np.sin(angles)))
+    if tileable[0]:
+        gradients[-1,:] = gradients[0,:]
+    if tileable[1]:
+        gradients[:,-1] = gradients[:,0]
+    gradients = gradients.repeat(d[0], 0).repeat(d[1], 1)
+    g00 = gradients[    :-d[0],    :-d[1]]
+    g10 = gradients[d[0]:     ,    :-d[1]]
+    g01 = gradients[    :-d[0],d[1]:     ]
+    g11 = gradients[d[0]:     ,d[1]:     ]
+    # Ramps
+    n00 = np.sum(np.dstack((grid[:,:,0]  , grid[:,:,1]  )) * g00, 2)
+    n10 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1]  )) * g10, 2)
+    n01 = np.sum(np.dstack((grid[:,:,0]  , grid[:,:,1]-1)) * g01, 2)
+    n11 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1]-1)) * g11, 2)
+    # Interpolation
+    t = interpolant(grid)
+    n0 = n00*(1-t[:,:,0]) + t[:,:,0]*n10
+    n1 = n01*(1-t[:,:,0]) + t[:,:,0]*n11
+    return np.sqrt(2)*((1-t[:,:,1])*n0 + t[:,:,1]*n1)
+
+
+def generate_fractal_noise_2d(
+        shape, res, octaves=1, persistence=0.5,
+        lacunarity=2, tileable=(False, False),
+        interpolant=interpolant
+):
+    """Generate a 2D numpy array of fractal noise.
+
+    Adapted from https://github.com/pvigier/perlin-numpy/
+
+    Args:
+        shape: The shape of the generated array (tuple of two ints).
+            This must be a multiple of lacunarity**(octaves-1)*res.
+        res: The number of periods of noise to generate along each
+            axis (tuple of two ints). Note shape must be a multiple of
+            (lacunarity**(octaves-1)*res).
+        octaves: The number of octaves in the noise. Defaults to 1.
+        persistence: The scaling factor between two octaves.
+        lacunarity: The frequency factor between two octaves.
+        tileable: If the noise should be tileable along each axis
+            (tuple of two bools). Defaults to (False, False).
+        interpolant: The, interpolation function, defaults to
+            t*t*t*(t*(t*6 - 15) + 10).
+
+    Returns:
+        A numpy array of fractal noise and of shape shape generated by
+        combining several octaves of perlin noise.
+    """
+
+    # Make shape bigger if necessary
+    fractal_shape = shape_for_fractal_noise(shape, res, octaves, lacunarity)
+
+    noise = np.zeros(fractal_shape)
+    frequency = 1
+    amplitude = 1
+    for _ in range(octaves):
+        noise += amplitude * generate_perlin_noise_2d(
+            fractal_shape, (frequency*res[0], frequency*res[1]), tileable, interpolant
+        )
+        frequency *= lacunarity
+        amplitude *= persistence
+
+    # Crop if necessary
+    if fractal_shape != shape:
+        noise = noise[:shape[0], :shape[1]]
+
+    return noise
+
+
+def generate_perlin_noise_3d(
+        shape, res, tileable=(False, False, False),
+        interpolant=interpolant
+):
+    """Generate a 3D numpy array of perlin noise.
+
+    Taken from https://github.com/pvigier/perlin-numpy/
+
+    Args:
+        shape: The shape of the generated array (tuple of three ints).
+            This must be a multiple of res.
+        res: The number of periods of noise to generate along each
+            axis (tuple of three ints). Note shape must be a multiple
+            of res.
+        tileable: If the noise should be tileable along each axis
+            (tuple of three bools). Defaults to (False, False, False).
+        interpolant: The interpolation function, defaults to
+            t*t*t*(t*(t*6 - 15) + 10).
+
+    Returns:
+        A numpy array of shape shape with the generated noise.
+
+    Raises:
+        ValueError: If shape is not a multiple of res.
+    """
+    delta = (res[0] / shape[0], res[1] / shape[1], res[2] / shape[2])
+    d = (shape[0] // res[0], shape[1] // res[1], shape[2] // res[2])
+    grid = np.mgrid[0:res[0]:delta[0],0:res[1]:delta[1],0:res[2]:delta[2]]
+    grid = np.mgrid[0:res[0]:delta[0],0:res[1]:delta[1],0:res[2]:delta[2]]
+    grid = grid.transpose(1, 2, 3, 0) % 1
+    # Gradients
+    theta = 2*np.pi*np.random.rand(res[0] + 1, res[1] + 1, res[2] + 1)
+    phi = 2*np.pi*np.random.rand(res[0] + 1, res[1] + 1, res[2] + 1)
+    gradients = np.stack(
+        (np.sin(phi)*np.cos(theta), np.sin(phi)*np.sin(theta), np.cos(phi)),
+        axis=3
+    )
+    if tileable[0]:
+        gradients[-1,:,:] = gradients[0,:,:]
+    if tileable[1]:
+        gradients[:,-1,:] = gradients[:,0,:]
+    if tileable[2]:
+        gradients[:,:,-1] = gradients[:,:,0]
+    gradients = gradients.repeat(d[0], 0).repeat(d[1], 1).repeat(d[2], 2)
+    g000 = gradients[    :-d[0],    :-d[1],    :-d[2]]
+    g100 = gradients[d[0]:     ,    :-d[1],    :-d[2]]
+    g010 = gradients[    :-d[0],d[1]:     ,    :-d[2]]
+    g110 = gradients[d[0]:     ,d[1]:     ,    :-d[2]]
+    g001 = gradients[    :-d[0],    :-d[1],d[2]:     ]
+    g101 = gradients[d[0]:     ,    :-d[1],d[2]:     ]
+    g011 = gradients[    :-d[0],d[1]:     ,d[2]:     ]
+    g111 = gradients[d[0]:     ,d[1]:     ,d[2]:     ]
+    # Ramps
+    n000 = np.sum(np.stack((grid[:,:,:,0]  , grid[:,:,:,1]  , grid[:,:,:,2]  ), axis=3) * g000, 3)
+    n100 = np.sum(np.stack((grid[:,:,:,0]-1, grid[:,:,:,1]  , grid[:,:,:,2]  ), axis=3) * g100, 3)
+    n010 = np.sum(np.stack((grid[:,:,:,0]  , grid[:,:,:,1]-1, grid[:,:,:,2]  ), axis=3) * g010, 3)
+    n110 = np.sum(np.stack((grid[:,:,:,0]-1, grid[:,:,:,1]-1, grid[:,:,:,2]  ), axis=3) * g110, 3)
+    n001 = np.sum(np.stack((grid[:,:,:,0]  , grid[:,:,:,1]  , grid[:,:,:,2]-1), axis=3) * g001, 3)
+    n101 = np.sum(np.stack((grid[:,:,:,0]-1, grid[:,:,:,1]  , grid[:,:,:,2]-1), axis=3) * g101, 3)
+    n011 = np.sum(np.stack((grid[:,:,:,0]  , grid[:,:,:,1]-1, grid[:,:,:,2]-1), axis=3) * g011, 3)
+    n111 = np.sum(np.stack((grid[:,:,:,0]-1, grid[:,:,:,1]-1, grid[:,:,:,2]-1), axis=3) * g111, 3)
+    # Interpolation
+    t = interpolant(grid)
+    n00 = n000*(1-t[:,:,:,0]) + t[:,:,:,0]*n100
+    n10 = n010*(1-t[:,:,:,0]) + t[:,:,:,0]*n110
+    n01 = n001*(1-t[:,:,:,0]) + t[:,:,:,0]*n101
+    n11 = n011*(1-t[:,:,:,0]) + t[:,:,:,0]*n111
+    n0 = (1-t[:,:,:,1])*n00 + t[:,:,:,1]*n10
+    n1 = (1-t[:,:,:,1])*n01 + t[:,:,:,1]*n11
+    return ((1-t[:,:,:,2])*n0 + t[:,:,:,2]*n1)
+
+
+def generate_fractal_noise_3d(
+        shape, res, octaves=1, persistence=0.5, lacunarity=2,
+        tileable=(False, False, False), interpolant=interpolant
+):
+    """Generate a 3D numpy array of fractal noise.
+
+    Adapted from https://github.com/pvigier/perlin-numpy/
+
+    Args:
+        shape: The shape of the generated array (tuple of three ints).
+            This must be a multiple of lacunarity**(octaves-1)*res.
+        res: The number of periods of noise to generate along each
+            axis (tuple of three ints). Note shape must be a multiple of
+            (lacunarity**(octaves-1)*res).
+        octaves: The number of octaves in the noise. Defaults to 1.
+        persistence: The scaling factor between two octaves.
+        lacunarity: The frequency factor between two octaves.
+        tileable: If the noise should be tileable along each axis
+            (tuple of three bools). Defaults to (False, False, False).
+        interpolant: The, interpolation function, defaults to
+            t*t*t*(t*(t*6 - 15) + 10).
+
+    Returns:
+        A numpy array of fractal noise and of shape shape generated by
+        combining several octaves of perlin noise.
+    """
+
+    # Make shape bigger if necessary
+    fractal_shape = shape_for_fractal_noise(shape, res, octaves, lacunarity)
+
+    noise = np.zeros(fractal_shape)
+    frequency = 1
+    amplitude = 1
+    for _ in range(octaves):
+        noise += amplitude * generate_perlin_noise_3d(
+            fractal_shape,
+            (frequency*res[0], frequency*res[1], frequency*res[2]),
+            tileable,
+            interpolant
+        )
+        frequency *= lacunarity
+        amplitude *= persistence
+
+    # Crop if necessary
+    if fractal_shape != shape:
+        noise = noise[:shape[0], :shape[1], :shape[2]]
+
+    return noise
+
+
+class FractalNoiseTensor():
+    def __init__(self, shape):
+        self.shape = shape
+        self.dim = len(shape)
+        self.func = generate_fractal_noise_3d if self.dim == 3 else generate_fractal_noise_2d
+
+    def __call__(self, *args, **kwargs):
+        res = np.random.choice([1,2,4] if self.dim == 3 else [1,2,4,8])
+        octaves = np.random.randint(low=1, high=5 if self.dim == 3 else 7)
+        res_tuple = (res,) * self.dim
+
+        x = self.func(self.shape, res=res_tuple, octaves=octaves)
+
+        x = torch.from_numpy(x).float()
+        x = (x - x.min())/(x.max()-x.min())*2.0 - 1.0
+        x = x.unsqueeze(0)
+        return x
+
+
+class FractalSR(WorleySR):
+    def build_generator(self, shape):
+        return FractalNoiseTensor(shape=shape)
+
+
+if __name__ == "__main__":
+    FractalSR.main()
diff --git a/supercat/interpolation.py b/supercat/interpolation.py
deleted file mode 100644
index 89a8972..0000000
--- a/supercat/interpolation.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from enum import Enum
-import tricubic
-import numpy as np
-
-class InterpolationMethod(Enum):
-    TRICUBIC = 0
-    LINEAR = 1
-
-
-def interpolate3D(image:np.ndarray, new_shape, method:InterpolationMethod = InterpolationMethod.TRICUBIC):
-    interpolator = tricubic.tricubic(list(image), list(image.shape))
-    upscaled = np.empty( new_shape )
-    xs = np.linspace(0.0, image.shape[0]-1, num=new_shape[0])
-    ys = np.linspace(0.0, image.shape[1]-1, num=new_shape[1])
-    zs = np.linspace(0.0, image.shape[2]-1, num=new_shape[2])
-    for i, x in enumerate(xs):
-        for j, y in enumerate(ys):
-            for k, z in enumerate(zs):
-                upscaled[i,j,k] = interpolator.ip( [x,y,z] )
-
-    return np.float32(upscaled)
\ No newline at end of file
diff --git a/supercat/models.py b/supercat/models.py
index 244d8b9..8abfc25 100644
--- a/supercat/models.py
+++ b/supercat/models.py
@@ -1,9 +1,7 @@
 import torch
 from torch import nn
 from torch import Tensor
-from torchvision.models import video
-from torchvision.models.video.resnet import VideoResNet
-from fastai.vision.learner import _load_pretrained_weights, _get_first_layer
+import numpy as np
 
 @torch.jit.script
 def autocrop(encoder_layer: torch.Tensor, decoder_layer: torch.Tensor):
@@ -76,19 +74,24 @@ class ResBlock(nn.Module):
         https://towardsdev.com/implement-resnet-with-pytorch-a9fb40a77448 
         https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py
     """
-    def __init__(self, dim:int, in_channels:int, out_channels:int, downsample:bool):
+    def __init__(self, dim:int, in_channels:int, out_channels:int, downsample:bool, kernel_size:int=3):
         super().__init__()
+        
+        # calculate padding so that the output is the same as a kernel size of 1 with zero padding
+        # this is required to be calculated becaues padding="same" doesn't work with a stride
+        padding = (kernel_size - 1)//2 
+        
         if downsample:
-            self.conv1 = Conv(in_channels, out_channels, kernel_size=3, stride=2, padding=1, dim=dim)
+            self.conv1 = Conv(in_channels, out_channels, kernel_size=kernel_size, stride=2, padding=padding, dim=dim)
             self.shortcut = nn.Sequential(
                 Conv(in_channels, out_channels, kernel_size=1, stride=2, dim=dim), 
                 BatchNorm(out_channels, dim=dim)
             )
         else:
-            self.conv1 = Conv(in_channels, out_channels, kernel_size=3, stride=1, padding=1, dim=dim)
+            self.conv1 = Conv(in_channels, out_channels, kernel_size=kernel_size, stride=1, padding=padding, dim=dim)
             self.shortcut = nn.Sequential()
 
-        self.conv2 = Conv(out_channels, out_channels, kernel_size=3, stride=1, padding=1, dim=dim)
+        self.conv2 = Conv(out_channels, out_channels, kernel_size=kernel_size, stride=1, padding=padding, dim=dim)
         self.bn1 = BatchNorm(out_channels, dim=dim)
         self.bn2 = BatchNorm(out_channels, dim=dim)
         self.relu = nn.ReLU(inplace=True)
@@ -108,6 +111,7 @@ def __init__(
         in_channels:int = 1,
         downsample:bool = True,
         growth_factor:float = 2.0,
+        kernel_size:int = 3,
     ):
         super().__init__()
         self.in_channels = in_channels
@@ -115,8 +119,8 @@ def __init__(
         if downsample:
             self.out_channels = int(growth_factor*self.out_channels)
 
-        self.block1 = ResBlock(in_channels=in_channels, out_channels=self.out_channels, downsample=downsample, dim=dim)
-        self.block2 = ResBlock(in_channels=self.out_channels, out_channels=self.out_channels, downsample=False, dim=dim)
+        self.block1 = ResBlock(in_channels=in_channels, out_channels=self.out_channels, downsample=downsample, dim=dim, kernel_size=kernel_size)
+        self.block2 = ResBlock(in_channels=self.out_channels, out_channels=self.out_channels, downsample=False, dim=dim, kernel_size=kernel_size)
 
     def forward(self, x: Tensor) -> Tensor:
         x = self.block1(x)
@@ -130,16 +134,17 @@ def __init__(
         dim:int,
         in_channels:int,
         out_channels:int, 
-        kernel_size:int = 2,
+        resblock_kernel_size:int = 3,
+        upsample_kernel_size:int = 2,
     ):
         super().__init__()
         self.in_channels = in_channels
         self.out_channels = out_channels
 
-        self.upsample = ConvTranspose(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=kernel_size, stride=2, dim=dim)
+        self.upsample = ConvTranspose(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=upsample_kernel_size, stride=2, dim=dim)
 
-        self.block1 = ResBlock(in_channels=self.out_channels, out_channels=self.out_channels, downsample=False, dim=dim)
-        # self.block2 = ResBlock(in_channels=self.out_channels, out_channels=self.out_channels, downsample=False, dim=dim)
+        self.block1 = ResBlock(in_channels=self.out_channels, out_channels=self.out_channels, downsample=False, dim=dim, kernel_size=resblock_kernel_size)
+        # self.block2 = ResBlock(in_channels=self.out_channels, out_channels=self.out_channels, downsample=False, dim=dim, kernel_size=resblock_kernel_size)
 
     def forward(self, x: Tensor, shortcut: Tensor) -> Tensor:
         x = self.upsample(x)
@@ -158,34 +163,58 @@ def __init__(
         in_channels:int = 1,
         initial_features:int = 64,
         growth_factor:float = 2.0,
+        kernel_size:int = 3,
+        stub_kernel_size:int = 7,
+        layers:int = 4,
     ):
         super().__init__()
 
         self.initial_features = initial_features
         self.in_channels = in_channels
+        self.growth_factor = growth_factor
+        self.kernel_size = kernel_size
+        self.stub_kernel_size = stub_kernel_size
+        self.layers = layers
+        self.dim = dim
 
         current_num_features = initial_features
-        self.layer0 = nn.Sequential(
-            Conv(in_channels=in_channels, out_channels=current_num_features, kernel_size=7, stride=2, padding=3, dim=dim),
+        padding = (stub_kernel_size - 1)//2
+        
+        self.stem = nn.Sequential(
+            Conv(in_channels=in_channels, out_channels=current_num_features, kernel_size=stub_kernel_size, stride=2, padding=padding, dim=dim),
             BatchNorm(num_features=current_num_features, dim=dim),
             nn.ReLU(inplace=True),
-            # nn.MaxPool3d(kernel_size=3, stride=2, padding=1),
         )
 
-        self.layer1 = DownBlock( in_channels=current_num_features, downsample=True, dim=dim, growth_factor=growth_factor )
-        self.layer2 = DownBlock( in_channels=self.layer1.out_channels, downsample=True, dim=dim, growth_factor=growth_factor )
-        self.layer3 = DownBlock( in_channels=self.layer2.out_channels, downsample=True, dim=dim, growth_factor=growth_factor )
-        self.layer4 = DownBlock( in_channels=self.layer3.out_channels, downsample=True, dim=dim, growth_factor=growth_factor )
-        self.output_features = self.layer4.out_channels
+        self.downblock_layers = []
+        for _ in range(layers):
+            downblock = DownBlock( 
+                in_channels=current_num_features, 
+                downsample=True, 
+                dim=dim, 
+                growth_factor=growth_factor, 
+                kernel_size=kernel_size 
+            )
+            self.downblock_layers.append(downblock)
+            current_num_features = downblock.out_channels
+        self.downblocks = nn.Sequential(*self.downblock_layers)
+        self.output_features = current_num_features
         
     def forward(self, x: Tensor) -> Tensor:
-        x = self.layer0(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
+        x = self.stem(x)
+        x = self.downblocks(x)
         return x
 
+    def macs(self):
+        return resnetbody_macs(
+            dim=self.dim,
+            growth_factor=self.growth_factor,
+            kernel_size=self.kernel_size,
+            stub_kernel_size=self.stub_kernel_size,
+            initial_features=self.initial_features,
+            downblock_layers=self.layers,
+        )
+
 
 class ResNet(nn.Module):
     def __init__(
@@ -196,13 +225,15 @@ def __init__(
         in_channels:int = 1,
         initial_features:int = 64,
         growth_factor:float = 2.0,
+        layers:int = 4,
     ):
         super().__init__()
         self.body = body if body is not None else ResNetBody(
             dim=dim, 
             in_channels=in_channels, 
             initial_features=initial_features, 
-            growth_factor=growth_factor
+            growth_factor=growth_factor,
+            layers=layers,
         )
         assert in_channels == self.body.in_channels
         assert initial_features == self.body.initial_features
@@ -227,25 +258,35 @@ def __init__(
         initial_features:int = 64,
         out_channels: int = 1,
         growth_factor:float = 2.0,
+        kernel_size:int = 3,
+        downblock_layers:int = 4,
     ):
         super().__init__()
+        self.dim = dim
         self.body = body if body is not None else ResNetBody(
             dim=dim, 
             in_channels=in_channels, 
             initial_features=initial_features, 
             growth_factor=growth_factor,
+            kernel_size=kernel_size,
+            layers=downblock_layers,
         )
         assert in_channels == self.body.in_channels
         assert initial_features == self.body.initial_features
 
-        self.up_block4 = UpBlock(dim=dim, in_channels=self.body.layer4.out_channels, out_channels=self.body.layer4.in_channels)
-        self.up_block3 = UpBlock(dim=dim, in_channels=self.body.layer3.out_channels, out_channels=self.body.layer3.in_channels)
-        self.up_block2 = UpBlock(dim=dim, in_channels=self.body.layer2.out_channels, out_channels=self.body.layer2.in_channels)
-        self.up_block1 = UpBlock(dim=dim, in_channels=self.body.layer1.out_channels, out_channels=self.body.layer1.in_channels)
+        self.upblock_layers = nn.ModuleList()
+        for downblock in reversed(self.body.downblock_layers):
+            upblock = UpBlock(
+                dim=dim, 
+                in_channels=downblock.out_channels, 
+                out_channels=downblock.in_channels, 
+                resblock_kernel_size=kernel_size
+            )
+            self.upblock_layers.append(upblock)
 
-        self.final_upsample_dims = self.up_block1.out_channels//2
+        self.final_upsample_dims = self.upblock_layers[-1].out_channels//2
         self.final_upsample = ConvTranspose(
-            in_channels=self.up_block1.out_channels, 
+            in_channels=self.upblock_layers[-1].out_channels, 
             out_channels=self.final_upsample_dims, 
             kernel_size=2, 
             stride=2,
@@ -263,115 +304,125 @@ def __init__(
     def forward(self, x: Tensor) -> Tensor:
         x = x.float()
         input = x
-        x = encoded_0 = self.body.layer0(x)
-        x = encoded_1 = self.body.layer1(x)
-        x = encoded_2 = self.body.layer2(x)
-        x = encoded_3 = self.body.layer3(x)
-        x = self.body.layer4(x)
-
-        x = self.up_block4(x, encoded_3)
-        x = self.up_block3(x, encoded_2)
-        x = self.up_block2(x, encoded_1)
-        x = self.up_block1(x, encoded_0)
+        encoded_list = []
+        x = self.body.stem(x)
+        for downblock in self.body.downblock_layers:
+            encoded_list.append(x)
+            x = downblock(x)
+
+        for encoded, upblock in zip(reversed(encoded_list), self.upblock_layers):
+            x = upblock(x, encoded)
+
         x = self.final_upsample(x)
         x = torch.cat([input,x], dim=1)
         x = self.final_layer(x)
         # activation?
         return x
 
+    def macs(self) -> float:
+        return residualunet_macs(
+            dim=self.dim,
+            growth_factor=self.body.growth_factor,
+            kernel_size=self.body.kernel_size,
+            stub_kernel_size=self.body.stub_kernel_size,
+            initial_features=self.body.initial_features,
+            downblock_layers=self.body.layers,
+        )
 
-class DoNothing(nn.Module):
-    def __init__(
-        self,
-    ):
-        super().__init__()
 
-        # dummy layer so that there are some parameters
-        self.dummy = nn.Conv3d(
-            in_channels=1, 
-            out_channels=1, 
-            kernel_size=1,
-            stride=1,
+def residualunet_macs(
+    dim:int,
+    growth_factor:float,
+    kernel_size:int,
+    stub_kernel_size:int,
+    initial_features:int,
+    downblock_layers:int,
+) -> float:
+    """
+    M = L + \sum_{i=0}^n D_i + U_i
+    D_0 = \frac{\kappa ^ d}{2^d}  f
+    D_i = \frac{1}{2^{d(i+1)}} g^{2i-1} ( k^d( 3g + 1 ) + 1) f^2 
+    U_i = \frac{f^2}{2^{d i}}  (2^d g^{2i-1} + 2 k^d g^{2i-2}) 
+    U_0 = 2^{d-1} f ^ 2
+    L = \frac{f}{2} + 1
+    """
+    stride = 2
+    U_0 = 2 ** (dim - 1) * initial_features **2
+    L = initial_features/2 + 1
+    body_macs = resnetbody_macs(
+        dim=dim,
+        growth_factor=growth_factor,
+        kernel_size=kernel_size,
+        stub_kernel_size=stub_kernel_size,
+        initial_features=initial_features,
+        downblock_layers=downblock_layers,
+    )
+    M = L + body_macs + U_0
+
+    for i in range(1, downblock_layers+1):
+        U_i = initial_features**2/(stride**(dim * i)) * (
+            2**dim * growth_factor ** (2 * i - 1) + 
+            2 * kernel_size**dim * growth_factor ** ( 2 * i - 2 )
         )
 
-    def forward(self, x: Tensor) -> Tensor:
-        return x
+        M += U_i
 
+    return M
 
-def get_in_out_channels(layer):
-    first_conv = next(next(next(layer.children()).children()).children())
-    return first_conv.in_channels, first_conv.out_channels
 
+def resnetbody_macs(
+    dim:int,
+    growth_factor:float,
+    kernel_size:int,
+    stub_kernel_size:int,
+    initial_features:int,
+    downblock_layers:int,
+) -> float:
+    """
+    M = \sum_{i=0}^n D_i
+    D_0 = \frac{\kappa ^ d}{2^d}  f
+    D_i = \frac{1}{2^{d(i+1)}} g^{2i-1} ( k^d( 3g + 1 ) + 1) f^2 
+    """
+    stride = 2
+    D_0 = stub_kernel_size **dim * initial_features / (stride ** dim) 
+    M = D_0
 
-def update_first_layer(model, n_in=1, pretrained=True):
-    first_layer, parent, name = _get_first_layer(model)
-    assert isinstance(first_layer, (nn.Conv2d, nn.Conv3d)), f'Change of input channels only supported with Conv2d or Conv3d, found {first_layer.__class__.__name__}'
-    assert getattr(first_layer, 'in_channels') == 3, f'Unexpected number of input channels, found {getattr(first_layer, "in_channels")} while expecting 3'
-    params = {attr:getattr(first_layer, attr) for attr in 'out_channels kernel_size stride padding dilation groups padding_mode'.split()}
-    params['bias'] = getattr(first_layer, 'bias') is not None
-    params['in_channels'] = n_in
-    new_layer = type(first_layer)(**params)
-    if pretrained:
-        _load_pretrained_weights(new_layer, first_layer)
-    setattr(parent, name, new_layer)
+    for i in range(1, downblock_layers+1):
+        D_i = initial_features**2 /(stride**(dim * (i+1))) * growth_factor ** (2 * i - 1) * (
+            kernel_size**dim * (3 * growth_factor + 1) + 1
+        )
+        M += D_i
 
+    return M
 
-class VideoUnet3d(nn.Module):
-    def __init__(self, base:VideoResNet = None, in_channels:int = 1, out_channels:int = 1, pretrained:bool = True, **kwargs):
-        super().__init__(**kwargs)
-        base = base or video.r3d_18(pretrained=pretrained)
-        self.base = base
-        self.initial_in_channels = in_channels
-        self.final_out_channels = out_channels
-        
-        self.base_layers = {name:child for name,child in base.named_children()}
-
-        # Edit the first layer as needed
-        if in_channels != 3:
-            update_first_layer(base, in_channels, pretrained=pretrained)
-        first_layer = next(base.stem.children())
-        first_layer.stride = (2,2,2)
-        first_layer.padding = (1,3,3)
-
-        in_channels, out_channels = get_in_out_channels(self.base_layers['layer4'])
-        self.up_block4 = UpBlock(in_channels=out_channels, out_channels=in_channels)
-        in_channels, out_channels = get_in_out_channels(self.base_layers['layer3'])
-        self.up_block3 = UpBlock(in_channels=out_channels, out_channels=in_channels)
-        in_channels, out_channels = get_in_out_channels(self.base_layers['layer2'])
-        self.up_block2 = UpBlock(in_channels=out_channels, out_channels=in_channels)
-        in_channels, out_channels = get_in_out_channels(self.base_layers['layer1'])
-        self.up_block1 = UpBlock(in_channels=out_channels, out_channels=in_channels)
-
-        self.final_upsample_dims = out_channels//2
-        self.final_upsample = nn.ConvTranspose3d(
-            in_channels=out_channels, 
-            out_channels=self.final_upsample_dims, 
-            kernel_size=2, 
-            stride=2
+
+def calc_initial_features_residualunet(
+    macc:int,
+    dim:int,
+    growth_factor:float,
+    kernel_size:int,
+    stub_kernel_size:int,
+    downblock_layers:int,
+) -> int:
+    """
+    """
+    stride = 2    
+    a = 2 ** (dim - 1)
+    for i in range(1, downblock_layers+1):
+        D_i_over_f2 = 1 /(stride**(dim * (i+1))) * growth_factor ** (2 * i - 1) * (
+            kernel_size**dim * (3 * growth_factor + 1) + 1
+        )
+        U_i_over_f2 = 1/(stride**(dim * i)) * (
+            2**dim * growth_factor ** (2 * i - 1) + 
+            2 * kernel_size**dim * growth_factor ** ( 2 * i - 2 )
         )
 
-        self.final_layer = nn.Conv3d(
-            in_channels=self.final_upsample_dims+self.initial_in_channels, 
-            out_channels=self.final_out_channels, 
-            kernel_size=1,
-            stride=1,
-        )        
+        a += D_i_over_f2 + U_i_over_f2
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        input = x
+    b = stub_kernel_size **dim / (stride ** dim) + 0.5
+    c = -macc + 1
 
-        x = encoded_0 = self.base.stem(x)
-        x = encoded_1 = self.base.layer1(x)
-        x = encoded_2 = self.base.layer2(x)
-        x = encoded_3 = self.base.layer3(x)
-        x = self.base.layer4(x)
+    initial_features = (-b + np.sqrt(b**2 - 4*a*c))/(2 * a)
 
-        x = self.up_block4(x, encoded_3)
-        x = self.up_block3(x, encoded_2)
-        x = self.up_block2(x, encoded_1)
-        x = self.up_block1(x, encoded_0)
-        x = self.final_upsample(x)
-        x = torch.cat([input,x], dim=1)
-        x = self.final_layer(x)
-        # activation?
-        return x    
\ No newline at end of file
+    return int(initial_features + 0.5)    
+    
diff --git a/supercat/transforms.py b/supercat/transforms.py
index 5423aed..b109dcd 100644
--- a/supercat/transforms.py
+++ b/supercat/transforms.py
@@ -3,7 +3,9 @@
 import hdf5storage
 import numpy as np
 from pathlib import Path
-from .interpolation import interpolate3D
+from fastai.vision.data import TensorImage
+from skimage.transform import resize as skresize
+
 
 DEEPROCK_HDF5_KEY = "temp"
 
@@ -37,6 +39,13 @@ def __init__(self, width, height, depth):
         self.shape = (width, height, depth)
 
     def encodes(self, data:np.ndarray):
-        data = interpolate3D(data, self.shape)
-        return data
+        return skresize(data, self.shape, order=3)    
+
+
+class RescaleImage(DisplayedTransform):
+    order = 20 #Need to run after IntToFloatTensor
     
+    def encodes(self, item:TensorImage): 
+        return item.float()*2.0 - 1.0
+
+
diff --git a/supercat/visualization.py b/supercat/visualization.py
index f4155b1..ab7c277 100644
--- a/supercat/visualization.py
+++ b/supercat/visualization.py
@@ -151,6 +151,9 @@ def comparison_plot(originals, downscaled_images, upscaled_images, titles, crops
         crop_x = crop[0:2]
         crop_y = (crop[3], crop[2])
 
+        if isinstance(upscaled, (Path, str)):
+            upscaled = Image.open(upscaled)
+
         difference = np.asarray(upscaled).astype(int) - np.asarray(original_im.convert("RGB"))[:,:,0].astype(int)
         # squared_error = np.power(difference.astype(float)/255, 2.0)
 
@@ -252,16 +255,16 @@ def add_volume_face_traces(fig, volume, coloraxis="coloraxis", **kwargs):
 def comparison_plot3D(originals, downscaled_volumes, upscaled_volumes, titles):
     fig = make_subplots(
         rows=len(originals), 
-        cols=4,
+        cols=3,
         subplot_titles=(
             "Original", 
             "Downscaled",
             "Upscaled",
-            "Difference",
+            # "Difference",
         ),
         vertical_spacing = 0.02,
         horizontal_spacing = 0.02,
-        specs=[[{'type':"surface"}, {'type':"surface"}, {'type':"surface"}, {'type':"surface"},]]*len(originals),
+        specs=[[{'type':"surface"}, {'type':"surface"}, {'type':"surface"}, ]]*len(originals), # hack
     )
 
     axis = dict(showgrid=False, showticklabels=False, showaxeslabels=False, title="", showbackground=False)
@@ -276,10 +279,16 @@ def comparison_plot3D(originals, downscaled_volumes, upscaled_volumes, titles):
         downscaled = read3D(downscaled) if isinstance(downscaled, (str, Path)) else downscaled
         upscaled = read3D(upscaled) if isinstance(upscaled, (str, Path)) else upscaled
 
+        # upscaled = (upscaled - upscaled.mean())/upscaled.std()
+        # upscaled = upscaled * downscaled.std() + downscaled.mean()
+        # breakpoint()
+        # upscaled *= 255.0
+        # breakpoint()
+
         add_volume_face_traces(fig, original, row=row+1, col=1)
         add_volume_face_traces(fig, downscaled, row=row+1, col=2)
         add_volume_face_traces(fig, upscaled, row=row+1, col=3)
-        add_volume_face_traces(fig, upscaled-original, row=row+1, col=4, coloraxis="coloraxis2")
+        # add_volume_face_traces(fig, upscaled-original, row=row+1, col=4, coloraxis="coloraxis2")
 
         scenes = {f"scene{row*4+column}":scene for column in range(1,5)}
         fig.update_layout(**scenes)
diff --git a/supercat/worley.py b/supercat/worley.py
new file mode 100644
index 0000000..fba60a8
--- /dev/null
+++ b/supercat/worley.py
@@ -0,0 +1,160 @@
+import torchapp as ta
+import torch
+from pathlib import Path
+from fastai.callback.core import Callback, CancelBatchException
+from fastai.data.block import DataBlock, TransformBlock
+from fastai.data.core import DataLoaders
+import torch.nn.functional as F
+import numpy as np
+from rich.progress import track
+import torchvision.transforms as T
+
+from supercat.models import ResidualUNet
+from supercat.diffusion import DDPMCallback, DDPMSamplerCallback
+
+class ShrinkCallBack(Callback):
+    def __init__(self, factor:int=4, **kwargs):
+        super().__init__(**kwargs)
+        self.factor = factor
+
+    def before_batch(self):
+        hr = self.xb[0]
+        lr_shape = tuple(s//self.factor for s in hr.shape[2:])
+        mode = "bilinear" if len(lr_shape) == 2 else "trilinear"
+        lr = F.interpolate(hr, lr_shape, mode=mode)
+        pseudo_hr = F.interpolate(lr, hr.shape[2:], mode=mode)
+
+        self.learn.xb = (pseudo_hr,)
+        self.learn.yb = (hr,)
+
+
+class WorleyNoise:
+    """
+    Derived from:
+        https://stackoverflow.com/a/65704227
+        https://stackoverflow.com/q/65703414
+    """
+    def __init__(self, shape, density, n:int=0, seed=None):
+        np.random.seed(seed)
+
+        self.density = density
+        self.shape = shape
+        self.dims = len(self.shape)
+        self.coords = [np.arange(s) for s in self.shape]
+        self.points = None
+        self.n = n
+        
+    def __call__(self):
+        self.points = np.random.rand(self.density, self.dims) 
+
+        for i, size in enumerate(self.shape):
+            self.points[:, i] *= size
+
+        axes = list(range(1, self.dims+1))
+        squared_d = sum(
+            np.expand_dims(
+                np.power(self.points[:, i, np.newaxis] - self.coords[i], 2), 
+                axis=axes[:i]+axes[i+1:]
+            )
+            for i in range(self.dims)
+        )
+
+        if self.n == 0:
+            return np.sqrt(squared_d.min(axis=0))
+        elif self.n is None:
+            return np.sqrt(squared_d)
+        return  np.sqrt(np.sort(squared_d, axis=0)[self.n])
+
+
+class WorleyNoiseTensor(WorleyNoise):
+    def __call__(self, *args):
+        x = super().__call__()
+        x = torch.from_numpy(x).float()
+        x = x/x.max()*2.0 - 1.0
+        x = x.unsqueeze(0)
+        
+        return x
+
+
+class WorleySR(ta.TorchApp):
+    def build_generator(self, shape):
+        return WorleyNoiseTensor(shape=shape, density=200)
+    
+    def dataloaders(
+        self,
+        dim:int=2,
+        depth:int=500,
+        width:int=500,
+        height:int=500,
+        batch_size:int=16,
+        item_count:int=1024,
+    ):
+
+        shape = (height, width) if dim == 2 else (depth, height, width)
+        self.shape = shape
+        self.dim = dim
+
+        datablock = DataBlock(
+            blocks=(TransformBlock),
+            get_x=self.build_generator(shape),
+        )
+
+        dataloaders = DataLoaders.from_dblock(
+            datablock,
+            source=range(item_count),
+            bs=batch_size,
+        )
+
+        return dataloaders
+    
+    def extra_callbacks(self, diffusion:bool=True):
+        self.diffusion = diffusion
+        callbacks = [ShrinkCallBack(factor=4)]
+        if self.diffusion:
+            callbacks.append(DDPMCallback())
+        return callbacks
+    
+    def inference_callbacks(self, diffusion:bool=True):
+        callbacks = [ShrinkCallBack(factor=4)]
+        if diffusion:
+            callbacks.append(DDPMSamplerCallback())
+        return callbacks
+    
+    def model(self):
+        return ResidualUNet(dim=self.dim, in_channels=3 if self.diffusion else 1)
+
+    def loss_func(self):
+        """
+        Returns the loss function to use with the model.
+        """
+        return F.smooth_l1_loss
+
+    def inference_dataloader(self, learner, **kwargs):
+        dataloader = learner.dls.test_dl([0], **kwargs) # output single test image
+        return dataloader
+
+    def output_results(
+        self, 
+        results, 
+        output_dir: Path = ta.Param("./outputs", help="The location of the output directory."),
+        fps:float=ta.Param(30.0, help="The frames per second to use when generating the gif."),
+        **kwargs,
+    ):
+        output_dir = Path(output_dir)
+        print(f"Saving {len(results)} generated images:")
+
+        transform = T.ToPILImage()
+        output_dir.mkdir(exist_ok=True, parents=True)
+        images = []
+        for index, image in enumerate(results[0]):
+            path = output_dir/f"image.{index}.jpg"
+            
+            image = transform(torch.clip(image[0]/2.0 + 0.5, min=0.0, max=1.0))
+            image.save(path)
+            images.append(image)
+        print(f"\t{path}")
+        images[0].save(output_dir/f"image.gif", save_all=True, append_images=images[1:], fps=fps)
+
+
+if __name__ == "__main__":
+    WorleySR.main()
diff --git a/tests/expected/TestSupercat/test_cli/train_help.yaml b/tests/expected/TestSupercat/test_cli/train_help.yaml
index 4ce5594..0503c8b 100644
--- a/tests/expected/TestSupercat/test_cli/train_help.yaml
+++ b/tests/expected/TestSupercat/test_cli/train_help.yaml
@@ -18,7 +18,7 @@ output:
       --weight-decay FLOAT            The amount of weight decay. If None then it
                                       uses the default amount of weight decay in
                                       fastai.
-      --mse / --no-mse                [default: mse]
+      --l1-loss / --no-l1-loss        [default: no-l1-loss]
       --deeprock PATH                 The path to the DeepRockSR-2D dataset.
       --downsample-scale [X2|X4]      Should it use the 2x or 4x downsampled images.
                                       [default: X4]
@@ -38,10 +38,22 @@ output:
                                       Including DeepSand-SR dataset.  [default: no-
                                       include-sand]
       --initial-features INTEGER      The number of features after the initial CNN
-                                      layer.  [default: 64]
-      --growth-factor INTEGER         The factor to grow the number of convolutional
+                                      layer. If not set then it is derived from the
+                                      MACC.
+      --growth-factor FLOAT           The factor to grow the number of convolutional
                                       filters each time the model downscales.
                                       [default: 2.0]
+      --kernel-size INTEGER           The size of the kernel in the convolutional
+                                      layers.  [default: 3]
+      --stub-kernel-size INTEGER      The size of the kernel in the initial stub
+                                      convolutional layer.  [default: 7]
+      --downblock-layers INTEGER      The number of layers to downscale (and
+                                      upscale) in the UNet.  [default: 4]
+      --macc INTEGER                  The approximate number of multiply or
+                                      accumulate operations in the model per
+                                      pixel/voxel. Used to set initial_features if
+                                      it is not provided explicitly.  [default:
+                                      10000000]
       --epochs INTEGER                The number of epochs.  [default: 20]
       --freeze-epochs INTEGER         The number of epochs to train when the learner
                                       is frozen and the last layer is trained by
diff --git a/tests/expected/TestSupercat/test_cli/tune_help.yaml b/tests/expected/TestSupercat/test_cli/tune_help.yaml
index 9b7de2d..be4f6ca 100644
--- a/tests/expected/TestSupercat/test_cli/tune_help.yaml
+++ b/tests/expected/TestSupercat/test_cli/tune_help.yaml
@@ -37,7 +37,7 @@ output:
       --weight-decay FLOAT            The amount of weight decay. If None then it
                                       uses the default amount of weight decay in
                                       fastai.
-      --mse / --no-mse                [default: mse]
+      --l1-loss / --no-l1-loss        [default: no-l1-loss]
       --deeprock PATH                 The path to the DeepRockSR-2D dataset.
       --downsample-scale [X2|X4]      Should it use the 2x or 4x downsampled images.
                                       [default: X4]
@@ -57,9 +57,21 @@ output:
                                       Including DeepSand-SR dataset.  [default: no-
                                       include-sand]
       --initial-features INTEGER      The number of features after the initial CNN
-                                      layer.
-      --growth-factor INTEGER         The factor to grow the number of convolutional
+                                      layer. If not set then it is derived from the
+                                      MACC.
+      --growth-factor FLOAT           The factor to grow the number of convolutional
                                       filters each time the model downscales.
+      --kernel-size INTEGER           The size of the kernel in the convolutional
+                                      layers.
+      --stub-kernel-size INTEGER      The size of the kernel in the initial stub
+                                      convolutional layer.
+      --downblock-layers INTEGER      The number of layers to downscale (and
+                                      upscale) in the UNet.
+      --macc INTEGER                  The approximate number of multiply or
+                                      accumulate operations in the model per
+                                      pixel/voxel. Used to set initial_features if
+                                      it is not provided explicitly.  [default:
+                                      10000000]
       --epochs INTEGER                The number of epochs.  [default: 20]
       --freeze-epochs INTEGER         The number of epochs to train when the learner
                                       is frozen and the last layer is trained by
diff --git a/tests/expected/TestSupercat/test_model/model_default.yaml b/tests/expected/TestSupercat/test_model/model_default.yaml
index d1069d6..2d028b6 100644
--- a/tests/expected/TestSupercat/test_model/model_default.yaml
+++ b/tests/expected/TestSupercat/test_model/model_default.yaml
@@ -2,140 +2,144 @@ params: {}
 output: |-
   ResidualUNet(
     (body): ResNetBody(
-      (layer0): Sequential(
-        (0): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
-        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (stem): Sequential(
+        (0): Conv2d(1, 408, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
+        (1): ReLU()
         (2): ReLU(inplace=True)
       )
-      (layer1): DownBlock(
-        (block1): ResBlock(
-          (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
-          (shortcut): Sequential(
-            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))
-            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (downblocks): Sequential(
+        (0): DownBlock(
+          (block1): ResBlock(
+            (conv1): Conv2d(408, 816, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+            (shortcut): Sequential(
+              (0): Conv2d(408, 816, kernel_size=(1, 1), stride=(2, 2))
+              (1): ReLU()
+            )
+            (conv2): Conv2d(816, 816, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+          (block2): ResBlock(
+            (conv1): Conv2d(816, 816, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (shortcut): Sequential()
+            (conv2): Conv2d(816, 816, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
           )
-          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
         )
-        (block2): ResBlock(
-          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (shortcut): Sequential()
-          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
+        (1): DownBlock(
+          (block1): ResBlock(
+            (conv1): Conv2d(816, 1632, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+            (shortcut): Sequential(
+              (0): Conv2d(816, 1632, kernel_size=(1, 1), stride=(2, 2))
+              (1): ReLU()
+            )
+            (conv2): Conv2d(1632, 1632, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+          (block2): ResBlock(
+            (conv1): Conv2d(1632, 1632, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (shortcut): Sequential()
+            (conv2): Conv2d(1632, 1632, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
         )
-      )
-      (layer2): DownBlock(
-        (block1): ResBlock(
-          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
-          (shortcut): Sequential(
-            (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2))
-            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (2): DownBlock(
+          (block1): ResBlock(
+            (conv1): Conv2d(1632, 3264, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+            (shortcut): Sequential(
+              (0): Conv2d(1632, 3264, kernel_size=(1, 1), stride=(2, 2))
+              (1): ReLU()
+            )
+            (conv2): Conv2d(3264, 3264, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+          (block2): ResBlock(
+            (conv1): Conv2d(3264, 3264, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (shortcut): Sequential()
+            (conv2): Conv2d(3264, 3264, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
           )
-          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
         )
-        (block2): ResBlock(
-          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (shortcut): Sequential()
-          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
+        (3): DownBlock(
+          (block1): ResBlock(
+            (conv1): Conv2d(3264, 6528, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+            (shortcut): Sequential(
+              (0): Conv2d(3264, 6528, kernel_size=(1, 1), stride=(2, 2))
+              (1): ReLU()
+            )
+            (conv2): Conv2d(6528, 6528, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+          (block2): ResBlock(
+            (conv1): Conv2d(6528, 6528, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (shortcut): Sequential()
+            (conv2): Conv2d(6528, 6528, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
         )
       )
-      (layer3): DownBlock(
+    )
+    (upblock_layers): ModuleList(
+      (0): UpBlock(
+        (upsample): ConvTranspose2d(6528, 3264, kernel_size=(2, 2), stride=(2, 2))
         (block1): ResBlock(
-          (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
-          (shortcut): Sequential(
-            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))
-            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          )
-          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (conv1): Conv2d(3264, 3264, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (shortcut): Sequential()
+          (conv2): Conv2d(3264, 3264, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (bn1): ReLU()
+          (bn2): ReLU()
           (relu): ReLU(inplace=True)
         )
-        (block2): ResBlock(
-          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+      )
+      (1): UpBlock(
+        (upsample): ConvTranspose2d(3264, 1632, kernel_size=(2, 2), stride=(2, 2))
+        (block1): ResBlock(
+          (conv1): Conv2d(1632, 1632, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
           (shortcut): Sequential()
-          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (conv2): Conv2d(1632, 1632, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (bn1): ReLU()
+          (bn2): ReLU()
           (relu): ReLU(inplace=True)
         )
       )
-      (layer4): DownBlock(
+      (2): UpBlock(
+        (upsample): ConvTranspose2d(1632, 816, kernel_size=(2, 2), stride=(2, 2))
         (block1): ResBlock(
-          (conv1): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
-          (shortcut): Sequential(
-            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2))
-            (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          )
-          (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (conv1): Conv2d(816, 816, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (shortcut): Sequential()
+          (conv2): Conv2d(816, 816, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (bn1): ReLU()
+          (bn2): ReLU()
           (relu): ReLU(inplace=True)
         )
-        (block2): ResBlock(
-          (conv1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+      )
+      (3): UpBlock(
+        (upsample): ConvTranspose2d(816, 408, kernel_size=(2, 2), stride=(2, 2))
+        (block1): ResBlock(
+          (conv1): Conv2d(408, 408, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
           (shortcut): Sequential()
-          (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-          (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (conv2): Conv2d(408, 408, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (bn1): ReLU()
+          (bn2): ReLU()
           (relu): ReLU(inplace=True)
         )
       )
     )
-    (up_block4): UpBlock(
-      (upsample): ConvTranspose2d(1024, 512, kernel_size=(2, 2), stride=(2, 2))
-      (block1): ResBlock(
-        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-        (shortcut): Sequential()
-        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (relu): ReLU(inplace=True)
-      )
-    )
-    (up_block3): UpBlock(
-      (upsample): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2))
-      (block1): ResBlock(
-        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-        (shortcut): Sequential()
-        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (relu): ReLU(inplace=True)
-      )
-    )
-    (up_block2): UpBlock(
-      (upsample): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2))
-      (block1): ResBlock(
-        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-        (shortcut): Sequential()
-        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (relu): ReLU(inplace=True)
-      )
-    )
-    (up_block1): UpBlock(
-      (upsample): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2))
-      (block1): ResBlock(
-        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-        (shortcut): Sequential()
-        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (relu): ReLU(inplace=True)
-      )
-    )
-    (final_upsample): ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2))
-    (final_layer): Conv2d(33, 1, kernel_size=(1, 1), stride=(1, 1))
+    (final_upsample): ConvTranspose2d(408, 204, kernel_size=(2, 2), stride=(2, 2))
+    (final_layer): Conv2d(205, 1, kernel_size=(1, 1), stride=(1, 1))
   )
diff --git a/tests/expected/TestSupercat3d/test_cli/train_help.yaml b/tests/expected/TestSupercat3d/test_cli/train_help.yaml
index 9c74d3b..f8589b2 100644
--- a/tests/expected/TestSupercat3d/test_cli/train_help.yaml
+++ b/tests/expected/TestSupercat3d/test_cli/train_help.yaml
@@ -15,7 +15,7 @@ output:
                                       fp16]
       --output-dir PATH               The location of the output directory.
                                       [default: ./outputs]
-      --mse / --no-mse                [default: mse]
+      --l1-loss / --no-l1-loss        [default: no-l1-loss]
       --deeprock PATH                 The path to the DeepRockSR-3D dataset.
       --downsample-scale [X2|X4]      Should it use the 2x or 4x downsampled images.
                                       [default: X4]
diff --git a/tests/expected/TestSupercat3d/test_cli/tune_help.yaml b/tests/expected/TestSupercat3d/test_cli/tune_help.yaml
index 6181f9d..b2c6943 100644
--- a/tests/expected/TestSupercat3d/test_cli/tune_help.yaml
+++ b/tests/expected/TestSupercat3d/test_cli/tune_help.yaml
@@ -34,7 +34,7 @@ output:
                                       fp16]
       --output-dir PATH               The location of the output directory.
                                       [default: ./outputs]
-      --mse / --no-mse                [default: mse]
+      --l1-loss / --no-l1-loss        [default: no-l1-loss]
       --deeprock PATH                 The path to the DeepRockSR-3D dataset.
       --downsample-scale [X2|X4]      Should it use the 2x or 4x downsampled images.
                                       [default: X4]
diff --git a/tests/expected/TestSupercat3d/test_model/model_default.yaml b/tests/expected/TestSupercat3d/test_model/model_default.yaml
index 7c6f237..91d49dc 100644
--- a/tests/expected/TestSupercat3d/test_model/model_default.yaml
+++ b/tests/expected/TestSupercat3d/test_model/model_default.yaml
@@ -2,140 +2,144 @@ params: {}
 output: |-
   ResidualUNet(
     (body): ResNetBody(
-      (layer0): Sequential(
+      (stem): Sequential(
         (0): Conv3d(1, 64, kernel_size=(7, 7, 7), stride=(2, 2, 2), padding=(3, 3, 3))
-        (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (1): ReLU()
         (2): ReLU(inplace=True)
       )
-      (layer1): DownBlock(
-        (block1): ResBlock(
-          (conv1): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
-          (shortcut): Sequential(
-            (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2))
-            (1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (downblocks): Sequential(
+        (0): DownBlock(
+          (block1): ResBlock(
+            (conv1): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
+            (shortcut): Sequential(
+              (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+              (1): ReLU()
+            )
+            (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+          (block2): ResBlock(
+            (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (shortcut): Sequential()
+            (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
           )
-          (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-          (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
         )
-        (block2): ResBlock(
-          (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-          (shortcut): Sequential()
-          (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-          (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
+        (1): DownBlock(
+          (block1): ResBlock(
+            (conv1): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
+            (shortcut): Sequential(
+              (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+              (1): ReLU()
+            )
+            (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+          (block2): ResBlock(
+            (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (shortcut): Sequential()
+            (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+        )
+        (2): DownBlock(
+          (block1): ResBlock(
+            (conv1): Conv3d(256, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
+            (shortcut): Sequential(
+              (0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+              (1): ReLU()
+            )
+            (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+          (block2): ResBlock(
+            (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (shortcut): Sequential()
+            (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+        )
+        (3): DownBlock(
+          (block1): ResBlock(
+            (conv1): Conv3d(512, 1024, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
+            (shortcut): Sequential(
+              (0): Conv3d(512, 1024, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+              (1): ReLU()
+            )
+            (conv2): Conv3d(1024, 1024, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
+          (block2): ResBlock(
+            (conv1): Conv3d(1024, 1024, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (shortcut): Sequential()
+            (conv2): Conv3d(1024, 1024, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+            (bn1): ReLU()
+            (bn2): ReLU()
+            (relu): ReLU(inplace=True)
+          )
         )
       )
-      (layer2): DownBlock(
+    )
+    (upblock_layers): ModuleList(
+      (0): UpBlock(
+        (upsample): ConvTranspose3d(1024, 512, kernel_size=(2, 2, 2), stride=(2, 2, 2))
         (block1): ResBlock(
-          (conv1): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
-          (shortcut): Sequential(
-            (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2))
-            (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          )
-          (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-          (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+          (shortcut): Sequential()
+          (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+          (bn1): ReLU()
+          (bn2): ReLU()
           (relu): ReLU(inplace=True)
         )
-        (block2): ResBlock(
+      )
+      (1): UpBlock(
+        (upsample): ConvTranspose3d(512, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2))
+        (block1): ResBlock(
           (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
           (shortcut): Sequential()
           (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-          (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (bn1): ReLU()
+          (bn2): ReLU()
           (relu): ReLU(inplace=True)
         )
       )
-      (layer3): DownBlock(
+      (2): UpBlock(
+        (upsample): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))
         (block1): ResBlock(
-          (conv1): Conv3d(256, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
-          (shortcut): Sequential(
-            (0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2))
-            (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          )
-          (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-          (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (block2): ResBlock(
-          (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+          (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
           (shortcut): Sequential()
-          (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-          (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+          (bn1): ReLU()
+          (bn2): ReLU()
           (relu): ReLU(inplace=True)
         )
       )
-      (layer4): DownBlock(
+      (3): UpBlock(
+        (upsample): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))
         (block1): ResBlock(
-          (conv1): Conv3d(512, 1024, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
-          (shortcut): Sequential(
-            (0): Conv3d(512, 1024, kernel_size=(1, 1, 1), stride=(2, 2, 2))
-            (1): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          )
-          (conv2): Conv3d(1024, 1024, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-          (bn1): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (block2): ResBlock(
-          (conv1): Conv3d(1024, 1024, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+          (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
           (shortcut): Sequential()
-          (conv2): Conv3d(1024, 1024, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-          (bn1): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (bn2): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+          (bn1): ReLU()
+          (bn2): ReLU()
           (relu): ReLU(inplace=True)
         )
       )
     )
-    (up_block4): UpBlock(
-      (upsample): ConvTranspose3d(1024, 512, kernel_size=(2, 2, 2), stride=(2, 2, 2))
-      (block1): ResBlock(
-        (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-        (shortcut): Sequential()
-        (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-        (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (relu): ReLU(inplace=True)
-      )
-    )
-    (up_block3): UpBlock(
-      (upsample): ConvTranspose3d(512, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2))
-      (block1): ResBlock(
-        (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-        (shortcut): Sequential()
-        (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-        (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (relu): ReLU(inplace=True)
-      )
-    )
-    (up_block2): UpBlock(
-      (upsample): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))
-      (block1): ResBlock(
-        (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-        (shortcut): Sequential()
-        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-        (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (relu): ReLU(inplace=True)
-      )
-    )
-    (up_block1): UpBlock(
-      (upsample): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))
-      (block1): ResBlock(
-        (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-        (shortcut): Sequential()
-        (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
-        (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        (relu): ReLU(inplace=True)
-      )
-    )
     (final_upsample): ConvTranspose3d(64, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2))
     (final_layer): Conv3d(33, 1, kernel_size=(1, 1, 1), stride=(1, 1, 1))
   )