diff --git a/poetry.lock b/poetry.lock
index 6bf0fab..184faeb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,32 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "anndata"
+version = "0.10.9"
+description = "Annotated data."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "anndata-0.10.9-py3-none-any.whl", hash = "sha256:e16c259bfd06c0f9ba7fdc9c2208828f5ef5f174356596c33206bb3f015b053a"},
+    {file = "anndata-0.10.9.tar.gz", hash = "sha256:fe36f2f3f8040ffed866c4932253407f594cfe34d420fe5b7854986cb6bb7178"},
+]
+
+[package.dependencies]
+array-api-compat = ">1.4,<1.5 || >1.5"
+h5py = ">=3.1"
+natsort = "*"
+numpy = ">=1.23"
+packaging = ">=20.0"
+pandas = ">=1.4,<2.1.0rc0 || >2.1.0rc0,<2.1.2 || >2.1.2"
+scipy = ">1.8"
+
+[package.extras]
+cu11 = ["cupy-cuda11x"]
+cu12 = ["cupy-cuda12x"]
+dev = ["pytest-xdist", "setuptools-scm", "towncrier (>=24.8.0)"]
+doc = ["awkward (>=2.0.7)", "ipython", "myst-parser", "nbsphinx", "readthedocs-sphinx-search", "scanpydoc[theme,typehints] (>=0.13.6)", "sphinx (>=4.4)", "sphinx-autodoc-typehints (>=2.2.0)", "sphinx-book-theme (>=1.1.0)", "sphinx-copybutton", "sphinx-design (>=0.5.0)", "sphinx-issues", "sphinx-toolbox", "sphinxext-opengraph", "zarr"]
+gpu = ["cupy"]
+test = ["awkward (>=2.3)", "boltons", "dask[array,distributed] (>=2022.09.2,<2024.8.0)", "httpx", "joblib", "loompy (>=3.0.5)", "matplotlib", "openpyxl", "pyarrow", "pytest (>=8.2)", "pytest-cov (>=2.10)", "pytest-memray", "pytest-mock", "scanpy", "scikit-learn", "zarr (<3.0.0a0)"]
 
 [[package]]
 name = "appnope"
@@ -11,6 +39,25 @@ files = [
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
+[[package]]
+name = "array-api-compat"
+version = "1.9.1"
+description = "A wrapper around NumPy and other array libraries to make them compatible with the Array API standard"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "array_api_compat-1.9.1-py3-none-any.whl", hash = "sha256:41a2703a662832d21619359ddddc5c0449876871f6c01e108c335f2a9432df94"},
+    {file = "array_api_compat-1.9.1.tar.gz", hash = "sha256:17bab828c93c79a5bb8b867145b71fcb889686607c5672b060aef437e0359ea8"},
+]
+
+[package.extras]
+cupy = ["cupy"]
+dask = ["dask"]
+jax = ["jax"]
+numpy = ["numpy"]
+pytorch = ["pytorch"]
+sparse = ["sparse (>=0.15.1)"]
+
 [[package]]
 name = "asttokens"
 version = "2.4.1"
@@ -29,94 +76,80 @@ six = ">=1.12.0"
 astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
 test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
-[[package]]
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-version = "1.4.1"
-description = "Atomic file writes."
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-files = [
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-]
-
-[[package]]
-name = "attrs"
-version = "23.2.0"
-description = "Classes Without Boilerplate"
-optional = false
-python-versions = ">=3.7"
-files = [
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-
-[package.extras]
-cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[tests]", "pre-commit"]
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-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
-
 [[package]]
 name = "cffi"
-version = "1.16.0"
+version = "1.17.1"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -152,126 +185,146 @@ test = ["pytest"]
 
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 [package.extras]
@@ -294,33 +347,37 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
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 ]
 
 [package.extras]
@@ -424,6 +489,44 @@ ufo = ["fs (>=2.2.0,<3)"]
 unicode = ["unicodedata2 (>=15.1.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
+[[package]]
+name = "h5py"
+version = "3.12.1"
+description = "Read and write HDF5 files from Python"
+optional = false
+python-versions = ">=3.9"
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+]
+
+[package.dependencies]
+numpy = ">=1.19.3"
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -437,13 +540,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.29.4"
+version = "6.29.5"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -470,13 +573,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.24.0"
+version = "8.29.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -494,7 +597,7 @@ typing-extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 [package.extras]
 all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
-doc = ["docrepr", "exceptiongroup", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "stack-data", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "intersphinx-registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing-extensions"]
 kernel = ["ipykernel"]
 matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
@@ -502,37 +605,37 @@ nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
+test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
 name = "jedi"
-version = "0.19.1"
+version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
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-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
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-version = "3.1.3"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
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 [package.dependencies]
@@ -543,24 +646,24 @@ i18n = ["Babel (>=2.7)"]
 
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+version = "1.4.2"
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 [package.dependencies]
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 [package.extras]
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 [[package]]
 name = "jupyter-core"
@@ -596,391 +699,244 @@ test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"
 
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 ]
 
 [package.dependencies]
@@ -988,12 +944,15 @@ contourpy = ">=1.0.1"
 cycler = ">=0.10"
 fonttools = ">=4.22.0"
 kiwisolver = ">=1.3.1"
-numpy = ">=1.21"
+numpy = ">=1.23"
 packaging = ">=20.0"
 pillow = ">=8"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
+[package.extras]
+dev = ["meson-python (>=0.13.1)", "numpy (>=1.25)", "pybind11 (>=2.6)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.7"
@@ -1010,67 +969,75 @@ traitlets = "*"
 
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 name = "msgpack"
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 description = "MessagePack serializer"
 optional = false
 python-versions = ">=3.8"
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 numpy = ">=1.9.0"
 
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+
 [[package]]
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 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=8.1)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
@@ -1330,19 +1359,19 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "4.2.1"
+version = "4.3.6"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
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-type = ["mypy (>=1.8)"]
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+type = ["mypy (>=1.11.2)"]
 
 [[package]]
 name = "pluggy"
@@ -1361,13 +1390,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.43"
+version = "3.0.48"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1375,31 +1404,33 @@ wcwidth = "*"
 
 [[package]]
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+version = "6.1.0"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
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 ]
 
 [package.extras]
-test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+dev = ["black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "wheel"]
+test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
 name = "ptyprocess"
@@ -1414,29 +1445,18 @@ files = [
 
 [[package]]
 name = "pure-eval"
-version = "0.2.2"
+version = "0.2.3"
 description = "Safely evaluate AST nodes without side effects"
 optional = false
 python-versions = "*"
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+    {file = "scipy-1.14.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b99722ea48b7ea25e8e015e8341ae74624f72e5f21fc2abd45f3a93266de4c5d"},
+    {file = "scipy-1.14.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5149e3fd2d686e42144a093b206aef01932a0059c2a33ddfa67f5f035bdfe13e"},
+    {file = "scipy-1.14.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e4f5a7c49323533f9103d4dacf4e4f07078f360743dec7f7596949149efeec06"},
+    {file = "scipy-1.14.1-cp313-cp313-win_amd64.whl", hash = "sha256:baff393942b550823bfce952bb62270ee17504d02a1801d7fd0719534dfb9c84"},
+    {file = "scipy-1.14.1.tar.gz", hash = "sha256:5a275584e726026a5699459aa72f828a610821006228e841b94275c4a7c08417"},
 ]
 
 [package.dependencies]
-numpy = ">=1.22.4,<2.3"
+numpy = ">=1.23.5,<2.3"
 
 [package.extras]
-dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
-doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
-test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "seaborn"
-version = "0.11.2"
-description = "seaborn: statistical data visualization"
+version = "0.13.2"
+description = "Statistical data visualization"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "seaborn-0.11.2-py3-none-any.whl", hash = "sha256:85a6baa9b55f81a0623abddc4a26b334653ff4c6b18c418361de19dbba0ef283"},
-    {file = "seaborn-0.11.2.tar.gz", hash = "sha256:cf45e9286d40826864be0e3c066f98536982baf701a7caa386511792d61ff4f6"},
+    {file = "seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987"},
+    {file = "seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7"},
 ]
 
 [package.dependencies]
-matplotlib = ">=2.2"
-numpy = ">=1.15"
-pandas = ">=0.23"
-scipy = ">=1.0"
+matplotlib = ">=3.4,<3.6.1 || >3.6.1"
+numpy = ">=1.20,<1.24.0 || >1.24.0"
+pandas = ">=1.2"
+
+[package.extras]
+dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
+docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
+stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
 [[package]]
 name = "setuptools"
@@ -1814,20 +1874,6 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
-[[package]]
-name = "svgutils"
-version = "0.3.4"
-description = "Python SVG editor"
-optional = false
-python-versions = "*"
-files = [
-    {file = "svgutils-0.3.4-py3-none-any.whl", hash = "sha256:4d08524a08126553c1a9bf2639616cf31290adea6fd235a3eb67d77c748abc00"},
-    {file = "svgutils-0.3.4.tar.gz", hash = "sha256:9ef48f44cb1d460a7747dd02694200fda25eb9faf6dea392118def2695e0e053"},
-]
-
-[package.dependencies]
-lxml = "*"
-
 [[package]]
 name = "tabulate"
 version = "0.9.0"
@@ -1847,20 +1893,21 @@ name = "tensordata"
 version = "0.0.8"
 description = "A common repository for tensor structured datasets."
 optional = false
-python-versions = "^3.9"
+python-versions = "^3.10"
 files = []
 develop = false
 
 [package.dependencies]
-numpy = "^1.23"
-pandas = "^1.5.0"
-xarray = "^2022.3.0"
+anndata = "^0.10.7"
+numpy = "^1.26"
+pandas = ">=1.0"
+xarray = ">=2023"
 
 [package.source]
 type = "git"
 url = "https://github.com/meyer-lab/tensordata.git"
 reference = "main"
-resolved_reference = "ecd9f83853885ea0c4c786029abf0eb5987fdbab"
+resolved_reference = "52afd83fd269e45fb63f3e900806f8c5e5d39251"
 
 [[package]]
 name = "tensorly"
@@ -1904,13 +1951,13 @@ all = ["h5py (>=2.1)", "lmdb (>=0.92)", "matplotlib", "python-prctl", "scikit-le
 
 [[package]]
 name = "termcolor"
-version = "2.4.0"
+version = "2.5.0"
 description = "ANSI color formatting for output in terminal"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "termcolor-2.4.0-py3-none-any.whl", hash = "sha256:9297c0df9c99445c2412e832e882a7884038a25617c60cea2ad69488d4040d63"},
-    {file = "termcolor-2.4.0.tar.gz", hash = "sha256:aab9e56047c8ac41ed798fa36d892a37aca6b3e9159f3e0c24bc64a9b3ac7b7a"},
+    {file = "termcolor-2.5.0-py3-none-any.whl", hash = "sha256:37b17b5fc1e604945c2642c872a3764b5d547a48009871aea3edd3afa180afb8"},
+    {file = "termcolor-2.5.0.tar.gz", hash = "sha256:998d8d27da6d48442e8e1f016119076b690d962507531df4890fcd2db2ef8a6f"},
 ]
 
 [package.extras]
@@ -1927,46 +1974,35 @@ files = [
     {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"},
 ]
 
-[[package]]
-name = "toml"
-version = "0.10.2"
-description = "Python Library for Tom's Obvious, Minimal Language"
-optional = false
-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-files = [
-    {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
-    {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
-]
-
 [[package]]
 name = "tornado"
-version = "6.4"
+version = "6.4.1"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">=3.8"
 files = [
-    {file = "tornado-6.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0"},
-    {file = "tornado-6.4-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263"},
-    {file = "tornado-6.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e"},
-    {file = "tornado-6.4-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579"},
-    {file = "tornado-6.4-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212"},
-    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"},
-    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78"},
-    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f"},
-    {file = "tornado-6.4-cp38-abi3-win32.whl", hash = "sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052"},
-    {file = "tornado-6.4-cp38-abi3-win_amd64.whl", hash = "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63"},
-    {file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"},
+    {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:163b0aafc8e23d8cdc3c9dfb24c5368af84a81e3364745ccb4427669bf84aec8"},
+    {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:6d5ce3437e18a2b66fbadb183c1d3364fb03f2be71299e7d10dbeeb69f4b2a14"},
+    {file = "tornado-6.4.1-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2e20b9113cd7293f164dc46fffb13535266e713cdb87bd2d15ddb336e96cfc4"},
+    {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8ae50a504a740365267b2a8d1a90c9fbc86b780a39170feca9bcc1787ff80842"},
+    {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:613bf4ddf5c7a95509218b149b555621497a6cc0d46ac341b30bd9ec19eac7f3"},
+    {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:25486eb223babe3eed4b8aecbac33b37e3dd6d776bc730ca14e1bf93888b979f"},
+    {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:454db8a7ecfcf2ff6042dde58404164d969b6f5d58b926da15e6b23817950fc4"},
+    {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a02a08cc7a9314b006f653ce40483b9b3c12cda222d6a46d4ac63bb6c9057698"},
+    {file = "tornado-6.4.1-cp38-abi3-win32.whl", hash = "sha256:d9a566c40b89757c9aa8e6f032bcdb8ca8795d7c1a9762910c722b1635c9de4d"},
+    {file = "tornado-6.4.1-cp38-abi3-win_amd64.whl", hash = "sha256:b24b8982ed444378d7f21d563f4180a2de31ced9d8d84443907a0a64da2072e7"},
+    {file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"},
 ]
 
 [[package]]
 name = "tqdm"
-version = "4.66.2"
+version = "4.67.0"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
-    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
+    {file = "tqdm-4.67.0-py3-none-any.whl", hash = "sha256:0cd8af9d56911acab92182e88d763100d4788bdf421d251616040cc4d44863be"},
+    {file = "tqdm-4.67.0.tar.gz", hash = "sha256:fe5a6f95e6fe0b9755e9469b77b9c3cf850048224ecaa8293d7d2d31f97d869a"},
 ]
 
 [package.dependencies]
@@ -1974,6 +2010,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
 dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
@@ -1995,13 +2032,24 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "typing-extensions"
-version = "4.11.0"
+version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
-    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2024.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
+    {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
 ]
 
 [[package]]
@@ -2017,29 +2065,28 @@ files = [
 
 [[package]]
 name = "xarray"
-version = "2022.12.0"
+version = "2023.12.0"
 description = "N-D labeled arrays and datasets in Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "xarray-2022.12.0-py3-none-any.whl", hash = "sha256:eaf3e4c0b62faebf7965f272ce76bc2fc1c9d93c2b966a390e929ef082a796dd"},
-    {file = "xarray-2022.12.0.tar.gz", hash = "sha256:083d08e552a7647c7ece136dfa3a4b6a1379256beb55bbed8b8ddf05f1e14ec7"},
+    {file = "xarray-2023.12.0-py3-none-any.whl", hash = "sha256:3c22b6824681762b6c3fcad86dfd18960a617bccbc7f456ce21b43a20e455fb9"},
+    {file = "xarray-2023.12.0.tar.gz", hash = "sha256:4565dbc890de47e278346c44d6b33bb07d3427383e077a7ca8ab6606196fd433"},
 ]
 
 [package.dependencies]
-numpy = ">=1.20"
+numpy = ">=1.22"
 packaging = ">=21.3"
-pandas = ">=1.3"
+pandas = ">=1.4"
 
 [package.extras]
-accel = ["bottleneck", "flox", "numbagg", "scipy"]
-complete = ["bottleneck", "cfgrib", "cftime", "dask[complete]", "flox", "fsspec", "h5netcdf", "matplotlib", "nc-time-axis", "netCDF4", "numbagg", "pooch", "pydap", "rasterio", "scipy", "seaborn", "zarr"]
-docs = ["bottleneck", "cfgrib", "cftime", "dask[complete]", "flox", "fsspec", "h5netcdf", "ipykernel", "ipython", "jupyter-client", "matplotlib", "nbsphinx", "nc-time-axis", "netCDF4", "numbagg", "pooch", "pydap", "rasterio", "scanpydoc", "scipy", "seaborn", "sphinx-autosummary-accessors", "sphinx-rtd-theme", "zarr"]
-io = ["cfgrib", "cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "rasterio", "scipy", "zarr"]
+accel = ["bottleneck", "flox", "numbagg", "opt-einsum", "scipy"]
+complete = ["xarray[accel,io,parallel,viz]"]
+io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "scipy", "zarr"]
 parallel = ["dask[complete]"]
 viz = ["matplotlib", "nc-time-axis", "seaborn"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.11,<3.13"
-content-hash = "5c4e0bfd29d204b600e87f8a89ce33722f09be7135dbf7b79feda74c373a333f"
+content-hash = "2d02b803c08a292a83b0cc27ebc461fdf06c38b22be6430128658ebb34be7d16"
diff --git a/pyproject.toml b/pyproject.toml
index b8c7194..d85628e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,13 +7,12 @@ license = "MIT"
 
 [tool.poetry.dependencies]
 python = ">=3.11,<3.13"
-numpy = "^1.24"
+numpy = "^1.26"
 tensorly = "^0.8.1"
 matplotlib = "^3.5.0"
 tqdm = "^4.65"
-seaborn = "^0.11.2"
-svgutils = "^0.3.4"
-xarray = "^2022.6.0"
+seaborn = "^0.13"
+xarray = "^2023"
 ipykernel = "^6.16.0"
 tensorpack = "^0.11"
 scikit-learn = "^1.1.3"
@@ -22,7 +21,7 @@ openpyxl = "^3.1.2"
 setuptools = "^69.5.1"
 
 [tool.poetry.dev-dependencies]
-pytest = "^6.2"
+pytest = "^8.3"
 pytest-cov = "^3.0.0"
 tensordata = {git = "https://github.com/meyer-lab/tensordata.git", branch = "main"}
 
diff --git a/timpute/common.py b/timpute/common.py
index ae879ce..a393a25 100644
--- a/timpute/common.py
+++ b/timpute/common.py
@@ -5,7 +5,6 @@
 from string import ascii_lowercase
 import matplotlib
 from matplotlib import gridspec, pyplot as plt
-import svgutils.transform as st
 from matplotlib.lines import Line2D
 
 
@@ -56,16 +55,3 @@ def subplotLabel(axs):
     """ Place subplot labels on figure. """
     for ii, ax in enumerate(axs):
         ax.text(-0.2, 1.2, ascii_lowercase[ii], transform=ax.transAxes, fontsize=16, fontweight="bold", va="top")
-
-
-def overlayCartoon(figFile, cartoonFile, x, y, scalee=1, scale_x=1, scale_y=1):
-    """ Add cartoon to a figure file. """
-
-    # Overlay Figure cartoons
-    template = st.fromfile(figFile)
-    cartoon = st.fromfile(cartoonFile).getroot()
-
-    cartoon.moveto(x, y, scale_x=scalee * scale_x, scale_y=scalee * scale_y)
-
-    template.append(cartoon)
-    template.save(figFile)
\ No newline at end of file
diff --git a/timpute/method_ALS.py b/timpute/method_ALS.py
index fa1eec0..466eddf 100644
--- a/timpute/method_ALS.py
+++ b/timpute/method_ALS.py
@@ -1,136 +1,23 @@
 import numpy as np
-import warnings
 
 import tensorly as tl
 from tensorly.cp_tensor import (
     cp_to_tensor,
     CPTensor,
-    cp_norm,
-    cp_normalize,
-    validate_cp_rank,
 )
 from tensorly.tenalg.core_tenalg.mttkrp import unfolding_dot_khatri_rao
 from .initialization import initialize_fac
 
-# Authors: Jean Kossaifi <jean.kossaifi+tensors@gmail.com>
-#          Chris Swierczewski <csw@amazon.com>
-#          Sam Schneider <samjohnschneider@gmail.com>
-#          Aaron Meurer <asmeurer@gmail.com>
-
-# License: BSD 3 clause
-
-
-def sparsify_tensor(tensor, card):
-    """Zeros out all elements in the `tensor` except `card` elements with maximum absolute values.
-
-    Parameters
-    ----------
-    tensor : ndarray
-    card : int
-        Desired number of non-zero elements in the `tensor`
-
-    Returns
-    -------
-    ndarray of shape tensor.shape
-    """
-    if card >= np.prod(tensor.shape):
-        return tensor
-    bound = tl.sort(tl.abs(tensor), axis=None)[-card]
-
-    return tl.where(
-        tl.abs(tensor) < bound, tl.zeros(tensor.shape, **tl.context(tensor)), tensor
-    )
-
-
-def error_calc(tensor, norm_tensor, weights, factors, sparsity, mask, mttkrp=None):
-    r"""Perform the error calculation. Different forms are used here depending upon
-    the available information. If `mttkrp=None` or masking is being performed, then the
-    full tensor must be constructed. Otherwise, the mttkrp is used to reduce the calculation cost.
-
-    Parameters
-    ----------
-    tensor : tensor
-    norm_tensor : float
-        The l2 norm of tensor.
-    weights : tensor
-        The current CP weights
-    factors : tensor
-        The current CP factors
-    sparsity : float or int
-        Whether we allow for a sparse component
-    mask : bool
-        Whether masking is being performed.
-    mttkrp : tensor or None
-        The mttkrp product, if available.
-
-    Returns
-    -------
-    unnorml_rec_error : float
-        The unnormalized reconstruction error.
-    tensor : tensor
-        The tensor, in case it has been updated by masking.
-    norm_tensor: float
-        The tensor norm, in case it has been updated by masking.
-    """
-
-    # If we have to update the mask we already have to build the full tensor
-    if (mask is not None) or (mttkrp is None):
-        low_rank_component = cp_to_tensor((weights, factors))
-
-        # Update the tensor based on the mask
-        if mask is not None:
-            tensor = tensor * mask + low_rank_component * (1 - mask)
-            norm_tensor = tl.norm(tensor, 2)
-
-        if sparsity:
-            sparse_component = sparsify_tensor(tensor - low_rank_component, sparsity)
-        else:
-            sparse_component = 0.0
-
-        unnorml_rec_error = tl.norm(tensor - low_rank_component - sparse_component, 2)
-    else:
-        if sparsity:
-            low_rank_component = cp_to_tensor((weights, factors))
-            sparse_component = sparsify_tensor(tensor - low_rank_component, sparsity)
-
-            unnorml_rec_error = tl.norm(
-                tensor - low_rank_component - sparse_component, 2
-            )
-        else:
-            # ||tensor - rec||^2 = ||tensor||^2 + ||rec||^2 - 2*<tensor, rec>
-            factors_norm = cp_norm((weights, factors))
-
-            # mttkrp and factor for the last mode. This is equivalent to the
-            # inner product <tensor, factorization>
-            iprod = tl.sum(tl.sum(mttkrp * tl.conj(factors[-1]), axis=0))
-            unnorml_rec_error = tl.sqrt(
-                tl.abs(norm_tensor**2 + factors_norm**2 - 2 * iprod)
-            )
-
-    return unnorml_rec_error, tensor, norm_tensor
-
 
 def perform_ALS(
     tensor,
     rank,
     n_iter_max=100,
     init=None,
-    svd="svd",
-    normalize_factors=False,
-    orthogonalise=False,
     tol=1e-6,
-    random_state=None,
-    verbose=0,
-    return_errors=False,
-    sparsity=None,
-    l2_reg=0,
     mask=None,
-    cvg_criterion="abs_rec_error",
-    fixed_modes=None,
-    svd_mask_repeats=5,
-    linesearch=False,
     callback=None,
-)  -> tl.cp_tensor.CPTensor:
+) -> tl.cp_tensor.CPTensor:
     """CANDECOMP/PARAFAC decomposition via alternating least squares (ALS)
     Computes a rank-`rank` decomposition of `tensor` [1]_ such that:
 
@@ -147,38 +34,15 @@ def perform_ALS(
         Type of factor matrix initialization.
         If a CPTensor is passed, this is directly used for initalization.
         See `initialize_factors`.
-    svd : str, default is 'truncated_svd'
-        function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
-    normalize_factors : if True, aggregate the weights of each factor in a 1D-tensor
-        of shape (rank, ), which will contain the norms of the factors
     tol : float, optional
         (Default: 1e-6) Relative reconstruction error tolerance. The
         algorithm is considered to have found the global minimum when the
         reconstruction error is less than `tol`.
-    random_state : {None, int, np.random.RandomState}
-    verbose : int, optional
-        Level of verbosity
-    return_errors : bool, optional
-        Activate return of iteration errors
     mask : ndarray
         array of booleans with the same shape as ``tensor`` should be 0 where
         the values are missing and 1 everywhere else. Note:  if tensor is
         sparse, then mask should also be sparse with a fill value of 1 (or
         True). Allows for missing values [2]_
-    cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
-       Stopping criterion for ALS, works if `tol` is not None.
-       If 'rec_error',  ALS stops at current iteration if ``(previous rec_error - current rec_error) < tol``.
-       If 'abs_rec_error', ALS terminates when `|previous rec_error - current rec_error| < tol`.
-    sparsity : float or int
-        If `sparsity` is not None, we approximate tensor as a sum of low_rank_component and sparse_component, where low_rank_component = cp_to_tensor((weights, factors)). `sparsity` denotes desired fraction or number of non-zero elements in the sparse_component of the `tensor`.
-    fixed_modes : list, default is None
-        A list of modes for which the initial value is not modified.
-        The last mode cannot be fixed due to error computation.
-    svd_mask_repeats: int
-        If using a tensor with masked values, this initializes using SVD multiple times to
-        remove the effect of these missing values on the initialization.
-    linesearch : bool, default is False
-        Whether to perform line search as proposed by Bro [3].
 
     Returns
     -------
@@ -189,7 +53,6 @@ def perform_ALS(
           * weights of the (normalized) factors otherwise
 
         * factors : List of factors of the CP decomposition element `i` is of shape ``(tensor.shape[i], rank)``
-        * sparse_component : nD array of shape tensor.shape. Returns only if `sparsity` is not None.
 
     errors : list
         A list of reconstruction errors at each iteration of the algorithms.
@@ -203,204 +66,57 @@ def perform_ALS(
     .. [3] R. Bro, "Multi-Way Analysis in the Food Industry: Models, Algorithms, and
            Applications", PhD., University of Amsterdam, 1998
     """
-    
     tensor = np.nan_to_num(tensor)
 
-    rank = validate_cp_rank(tl.shape(tensor), rank=rank)
-
-    if return_errors:
-        DeprecationWarning(
-            "return_errors argument will be removed in the next version of TensorLy. Please use a callback function instead."
-        )
-
-    if orthogonalise and not isinstance(orthogonalise, int):
-        orthogonalise = n_iter_max
-
-    if linesearch:
-        acc_pow = 2.0  # Extrapolate to the iteration^(1/acc_pow) ahead
-        acc_fail = 0  # How many times acceleration have failed
-        max_fail = 4  # Increase acc_pow with one after max_fail failure
+    if init is None:
+        init = initialize_fac(tensor, rank, method="random")
 
-    
-    if init is None: init = initialize_fac(tensor,rank,method='random')
-
-    
     weights, factors = init
 
     rec_errors = []
     norm_tensor = tl.norm(tensor, 2)
-    if l2_reg:
-        Id = tl.eye(rank, **tl.context(tensor)) * l2_reg
-    else:
-        Id = 0
-
-    if fixed_modes is None:
-        fixed_modes = []
-
-    if fixed_modes == list(range(tl.ndim(tensor))):  # Check If all modes are fixed
-        cp_tensor = CPTensor(
-            (weights, factors)
-        )  # No need to run optimization algorithm, just return the initialization
-        return cp_tensor
-
-    if tl.ndim(tensor) - 1 in fixed_modes:
-        warnings.warn(
-            "You asked for fixing the last mode, which is not supported.\n The last mode will not be fixed. Consider using tl.moveaxis()"
-        )
-        fixed_modes.remove(tl.ndim(tensor) - 1)
-    modes_list = [mode for mode in range(tl.ndim(tensor)) if mode not in fixed_modes]
-
-    if sparsity:
-        sparse_component = tl.zeros_like(tensor)
-        if isinstance(sparsity, float):
-            sparsity = int(sparsity * np.prod(tensor.shape))
-        else:
-            sparsity = int(sparsity)
 
     for iteration in range(n_iter_max):
-        if orthogonalise and iteration <= orthogonalise:
-            factors = [
-                tl.qr(f)[0] if min(tl.shape(f)) >= rank else f
-                for i, f in enumerate(factors)
-            ]
-
-        if linesearch and iteration % 2 == 0:
-            factors_last = [tl.copy(f) for f in factors]
-            weights_last = tl.copy(weights)
-
-        if verbose > 1:
-            print("Starting iteration", iteration + 1)
-        for mode in modes_list:
-            if verbose > 1:
-                print("Mode", mode, "of", tl.ndim(tensor))
-
-            pseudo_inverse = tl.tensor(np.ones((rank, rank)), **tl.context(tensor))
+        for mode in range(np.ndim(tensor)):
+            pseudo_inverse = np.ones((rank, rank))
             for i, factor in enumerate(factors):
                 if i != mode:
-                    pseudo_inverse = pseudo_inverse * tl.dot(
-                        tl.conj(tl.transpose(factor)), factor
-                    )
-            pseudo_inverse += Id
+                    pseudo_inverse = pseudo_inverse * np.dot(factor.T, factor)
             pseudo_inverse = (
-                tl.reshape(weights, (-1, 1))
+                np.reshape(weights, (-1, 1))
                 * pseudo_inverse
-                * tl.reshape(weights, (1, -1))
+                * np.reshape(weights, (1, -1))
             )
             mttkrp = unfolding_dot_khatri_rao(tensor, (weights, factors), mode)
 
-            factor = tl.transpose(
-                tl.solve(tl.conj(tl.transpose(pseudo_inverse)), tl.transpose(mttkrp))
-            )
+            factor = np.linalg.solve(pseudo_inverse.T, mttkrp.T).T
             factors[mode] = factor
-            if normalize_factors and mode != modes_list[-1]:
-                weights, factors = cp_normalize((weights, factors))
-
-        # Will we be performing a line search iteration
-        if linesearch and iteration % 2 == 0 and iteration > 5:
-            line_iter = True
-        else:
-            line_iter = False
-
-        # Calculate the current unnormalized error if we need it
-        if (tol or return_errors) and not line_iter:
-            unnorml_rec_error, tensor, norm_tensor = error_calc(
-                tensor, norm_tensor, weights, factors, sparsity, mask, mttkrp
-            )
-        else:
-            if mask is not None:
-                tensor = tensor * mask + tl.cp_to_tensor(
-                    (weights, factors), mask=1 - mask
-                )
 
-        # Start line search if requested.
-        if line_iter:
-            jump = iteration ** (1.0 / acc_pow)
-
-            new_weights = weights_last + (weights - weights_last) * jump
-            new_factors = [
-                factors_last[ii] + (factors[ii] - factors_last[ii]) * jump
-                for ii in range(tl.ndim(tensor))
-            ]
-
-            new_rec_error, new_tensor, new_norm_tensor = error_calc(
-                tensor, norm_tensor, new_weights, new_factors, sparsity, mask
-            )
-
-            if (new_rec_error / new_norm_tensor) < rec_errors[-1]:
-                factors, weights = new_factors, new_weights
-                tensor, norm_tensor = new_tensor, new_norm_tensor
-                unnorml_rec_error = new_rec_error
-                acc_fail = 0
-
-                if verbose:
-                    print(f"Accepted line search jump of {jump}.")
-            else:
-                unnorml_rec_error, tensor, norm_tensor = error_calc(
-                    tensor, norm_tensor, weights, factors, sparsity, mask, mttkrp
-                )
-                acc_fail += 1
-
-                if verbose:
-                    print(f"Line search failed for jump of {jump}.")
+        # Calculate the current unnormalized error
+        low_rank_component = cp_to_tensor((weights, factors))
 
-                if acc_fail == max_fail:
-                    acc_pow += 1.0
-                    acc_fail = 0
+        # Update the tensor based on the mask
+        if mask is not None:
+            tensor = tensor * mask + low_rank_component * (1 - mask)
+            norm_tensor = tl.norm(tensor, 2)
 
-                    if verbose:
-                        print("Reducing acceleration.")
+        unnorml_rec_error = tl.norm(tensor - low_rank_component, 2)
 
-        if (tol or return_errors) and not line_iter:
+        if tol:
             rec_error = unnorml_rec_error / norm_tensor
             rec_errors.append(rec_error)
 
         if callback is not None:
             cp_tensor = CPTensor((weights, factors))
-
-            if sparsity:
-                sparse_component = sparsify_tensor(
-                    tensor - cp_to_tensor((weights, factors)), sparsity
-                )
-                retVal = callback((cp_tensor, sparse_component), rec_error)
-            else:
-                retVal = callback(cp_tensor)
+            callback(cp_tensor)
 
         if tol is not None:
             if iteration >= 1:
                 rec_error_decrease = rec_errors[-2] - rec_errors[-1]
 
-                if verbose:
-                    print(
-                        f"iteration {iteration}, reconstruction error: {rec_error}, decrease = {rec_error_decrease}, unnormalized = {unnorml_rec_error}"
-                    )
-
-                if cvg_criterion == "abs_rec_error":
-                    stop_flag = abs(rec_error_decrease) < tol
-                elif cvg_criterion == "rec_error":
-                    stop_flag = rec_error_decrease < tol
-                else:
-                    raise TypeError("Unknown convergence criterion")
-
-                if stop_flag:
-                    if verbose:
-                        print(f"PARAFAC converged after {iteration} iterations")
+                if abs(rec_error_decrease) < tol:
                     break
 
-            else:
-                if verbose:
-                    print(f"reconstruction error={rec_errors[-1]}")
-        if normalize_factors:
-            weights, factors = cp_normalize((weights, factors))
-
     cp_tensor = CPTensor((weights, factors))
 
-    if sparsity:
-        sparse_component = sparsify_tensor(
-            tensor - cp_to_tensor((weights, factors)), sparsity
-        )
-        cp_tensor = (cp_tensor, sparse_component)
-
-    if return_errors:
-        return cp_tensor, rec_errors
-    else:
-        return cp_tensor
\ No newline at end of file
+    return cp_tensor