diff --git a/README.md b/README.md
index df72d71..08e7bee 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1 @@
-# tensorpack
-A collection of tensor methods from the Meyer lab.
-
-To add it to your Python package, add the following line to `requirements.txt` and remake `venv`:
-```
-git+https://github.com/meyer-lab/tensorpack.git@main
-```
+# tensor-impute
diff --git a/makefile b/makefile
index d5f6405..a45a702 100644
--- a/makefile
+++ b/makefile
@@ -6,7 +6,7 @@ test:
 	poetry run pytest -s -v -x
 
 coverage.xml:
-	poetry run pytest --junitxml=junit.xml --cov=tensorpack --cov-report xml:coverage.xml
+	poetry run pytest --junitxml=junit.xml --cov=timpute --cov-report xml:coverage.xml
 
 clean:
 	rm -rf coverage.xml
diff --git a/poetry.lock b/poetry.lock
index 84b2c34..b9d8fe4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,18 +2,18 @@
 
 [[package]]
 name = "anndata"
-version = "0.10.9"
+version = "0.11.1"
 description = "Annotated data."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
-    {file = "anndata-0.10.9-py3-none-any.whl", hash = "sha256:e16c259bfd06c0f9ba7fdc9c2208828f5ef5f174356596c33206bb3f015b053a"},
-    {file = "anndata-0.10.9.tar.gz", hash = "sha256:fe36f2f3f8040ffed866c4932253407f594cfe34d420fe5b7854986cb6bb7178"},
+    {file = "anndata-0.11.1-py3-none-any.whl", hash = "sha256:2c974e54434548b9c7a415fe9a3082aac76a7233a9b9e785830b9c9161f673ac"},
+    {file = "anndata-0.11.1.tar.gz", hash = "sha256:36bff9a85276fc5f1b9fd01f15aff9aa49408129985f42e0fca4e2c5b7fa909f"},
 ]
 
 [package.dependencies]
 array-api-compat = ">1.4,<1.5 || >1.5"
-h5py = ">=3.1"
+h5py = ">=3.6"
 natsort = "*"
 numpy = ">=1.23"
 packaging = ">=20.0"
@@ -23,10 +23,13 @@ scipy = ">1.8"
 [package.extras]
 cu11 = ["cupy-cuda11x"]
 cu12 = ["cupy-cuda12x"]
+dask = ["dask[array] (>=2022.09.2,<2024.8.dev0 || >=2024.10.dev0)"]
 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"]
+dev-doc = ["towncrier (>=24.8.0)"]
+dev-test = ["pytest-xdist"]
+doc = ["awkward (>=2.0.7)", "ipython", "myst-parser", "nbsphinx", "readthedocs-sphinx-search", "scanpydoc[theme,typehints] (>=0.14.1)", "sphinx (>=7.4.6)", "sphinx-autodoc-typehints (>=2.2.0)", "sphinx-book-theme (>=1.1.0)", "sphinx-copybutton", "sphinx-design (>=0.5.0)", "sphinx-issues", "sphinx-toolbox (>=3.8.0)", "sphinxext-opengraph", "towncrier (>=24.8.0)", "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)"]
+test = ["awkward (>=2.3)", "boltons", "dask[array] (>=2022.09.2,<2024.8.dev0 || >=2024.10.dev0)", "dask[distributed]", "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 = "array-api-compat"
@@ -47,85 +50,6 @@ numpy = ["numpy"]
 pytorch = ["pytorch"]
 sparse = ["sparse (>=0.15.1)"]
 
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-]
-
-[package.dependencies]
-pycparser = "*"
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -299,17 +223,6 @@ files = [
 docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
 tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
-[[package]]
-name = "et-xmlfile"
-version = "2.0.0"
-description = "An implementation of lxml.xmlfile for the standard library"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "et_xmlfile-2.0.0-py3-none-any.whl", hash = "sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa"},
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-]
-
 [[package]]
 name = "fonttools"
 version = "4.55.0"
@@ -432,6 +345,20 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "isort"
+version = "5.13.2"
+description = "A Python utility / library to sort Python imports."
+optional = false
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 [[package]]
 name = "pytest-cov"
-version = "3.0.0"
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+coverage = {version = ">=7.5", extras = ["toml"]}
 pytest = ">=4.6"
 
 [package.extras]
-testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
+testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "python-dateutil"
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-    {file = "pyzmq-26.2.0-pp38-pypy38_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:226af7dcb51fdb0109f0016449b357e182ea0ceb6b47dfb5999d569e5db161d5"},
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-    {file = "pyzmq-26.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:29c7947c594e105cb9e6c466bace8532dc1ca02d498684128b339799f5248277"},
-    {file = "pyzmq-26.2.0-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cdeabcff45d1c219636ee2e54d852262e5c2e085d6cb476d938aee8d921356b3"},
-    {file = "pyzmq-26.2.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:35cffef589bcdc587d06f9149f8d5e9e8859920a071df5a2671de2213bef592a"},
-    {file = "pyzmq-26.2.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:18c8dc3b7468d8b4bdf60ce9d7141897da103c7a4690157b32b60acb45e333e6"},
-    {file = "pyzmq-26.2.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7133d0a1677aec369d67dd78520d3fa96dd7f3dcec99d66c1762870e5ea1a50a"},
-    {file = "pyzmq-26.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:6a96179a24b14fa6428cbfc08641c779a53f8fcec43644030328f44034c7f1f4"},
-    {file = "pyzmq-26.2.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:4f78c88905461a9203eac9faac157a2a0dbba84a0fd09fd29315db27be40af9f"},
-    {file = "pyzmq-26.2.0.tar.gz", hash = "sha256:070672c258581c8e4f640b5159297580a9974b026043bd4ab0470be9ed324f1f"},
-]
-
-[package.dependencies]
-cffi = {version = "*", markers = "implementation_name == \"pypy\""}
-
 [[package]]
 name = "scikit-learn"
 version = "1.5.2"
@@ -1385,22 +1067,6 @@ dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest
 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"
-version = "69.5.1"
-description = "Easily download, build, install, upgrade, and uninstall Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"},
-    {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
-testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -1412,23 +1078,9 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
-[[package]]
-name = "tabulate"
-version = "0.9.0"
-description = "Pretty-print tabular data"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f"},
-    {file = "tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c"},
-]
-
-[package.extras]
-widechars = ["wcwidth"]
-
 [[package]]
 name = "tensordata"
-version = "0.0.8"
+version = "0.0.9"
 description = "A common repository for tensor structured datasets."
 optional = false
 python-versions = "^3.10"
@@ -1436,16 +1088,16 @@ files = []
 develop = false
 
 [package.dependencies]
-anndata = "^0.10.7"
-numpy = "^1.26"
-pandas = ">=1.0"
+anndata = "^0.11"
+numpy = "^2.0"
+pandas = ">=2.0"
 xarray = ">=2023"
 
 [package.source]
 type = "git"
 url = "https://github.com/meyer-lab/tensordata.git"
-reference = "main"
-resolved_reference = "52afd83fd269e45fb63f3e900806f8c5e5d39251"
+reference = "6afaec35c552c3ee91990db74c740728ddf37a55"
+resolved_reference = "6afaec35c552c3ee91990db74c740728ddf37a55"
 
 [[package]]
 name = "tensorly"
@@ -1462,45 +1114,6 @@ files = [
 numpy = "*"
 scipy = "*"
 
-[[package]]
-name = "tensorpack"
-version = "0.11"
-description = "A Neural Network Training Interface on TensorFlow"
-optional = false
-python-versions = "*"
-files = [
-    {file = "tensorpack-0.11-py2.py3-none-any.whl", hash = "sha256:afcdaccf6e8e7d61c98970646d57b1c22372ddd712c462477a90f53e3994c4a1"},
-    {file = "tensorpack-0.11.tar.gz", hash = "sha256:022b610e416e62e3575424cd08e60af27808a5fb6914294615391caf582cbd4f"},
-]
-
-[package.dependencies]
-msgpack = ">=0.5.2"
-msgpack-numpy = ">=0.4.4.2"
-numpy = ">=1.14"
-psutil = ">=5"
-pyzmq = ">=16"
-six = "*"
-tabulate = ">=0.7.7"
-termcolor = ">=1.1"
-tqdm = ">4.29.0"
-
-[package.extras]
-all = ["h5py (>=2.1)", "lmdb (>=0.92)", "matplotlib", "python-prctl", "scikit-learn", "scipy"]
-
-[[package]]
-name = "termcolor"
-version = "2.5.0"
-description = "ANSI color formatting for output in terminal"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "termcolor-2.5.0-py3-none-any.whl", hash = "sha256:37b17b5fc1e604945c2642c872a3764b5d547a48009871aea3edd3afa180afb8"},
-    {file = "termcolor-2.5.0.tar.gz", hash = "sha256:998d8d27da6d48442e8e1f016119076b690d962507531df4890fcd2db2ef8a6f"},
-]
-
-[package.extras]
-tests = ["pytest", "pytest-cov"]
-
 [[package]]
 name = "threadpoolctl"
 version = "3.5.0"
@@ -1557,28 +1170,30 @@ files = [
 
 [[package]]
 name = "xarray"
-version = "2023.12.0"
+version = "2024.11.0"
 description = "N-D labeled arrays and datasets in Python"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
-    {file = "xarray-2023.12.0-py3-none-any.whl", hash = "sha256:3c22b6824681762b6c3fcad86dfd18960a617bccbc7f456ce21b43a20e455fb9"},
-    {file = "xarray-2023.12.0.tar.gz", hash = "sha256:4565dbc890de47e278346c44d6b33bb07d3427383e077a7ca8ab6606196fd433"},
+    {file = "xarray-2024.11.0-py3-none-any.whl", hash = "sha256:6ee94f63ddcbdd0cf3909d1177f78cdac756640279c0e32ae36819a89cdaba37"},
+    {file = "xarray-2024.11.0.tar.gz", hash = "sha256:1ccace44573ddb862e210ad3ec204210654d2c750bec11bbe7d842dfc298591f"},
 ]
 
 [package.dependencies]
-numpy = ">=1.22"
-packaging = ">=21.3"
-pandas = ">=1.4"
+numpy = ">=1.24"
+packaging = ">=23.2"
+pandas = ">=2.1"
 
 [package.extras]
-accel = ["bottleneck", "flox", "numbagg", "opt-einsum", "scipy"]
-complete = ["xarray[accel,io,parallel,viz]"]
+accel = ["bottleneck", "flox", "numba (>=0.54)", "numbagg", "opt_einsum", "scipy"]
+complete = ["xarray[accel,etc,io,parallel,viz]"]
+dev = ["hypothesis", "jinja2", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-env", "pytest-timeout", "pytest-xdist", "ruff", "sphinx", "sphinx_autosummary_accessors", "xarray[complete]"]
+etc = ["sparse"]
 io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "scipy", "zarr"]
 parallel = ["dask[complete]"]
-viz = ["matplotlib", "nc-time-axis", "seaborn"]
+viz = ["cartopy", "matplotlib", "nc-time-axis", "seaborn"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.11,<3.13"
-content-hash = "9f2498ef1ca1aa8a92ebd62db12b17195307eaab559a4780b278093029765e14"
+content-hash = "c0b383aa5e88e52adc1c95f6c9ee6de8b1301a85bc1256903888a312ecfe442d"
diff --git a/pyproject.toml b/pyproject.toml
index cf857ef..a79b400 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,22 +7,20 @@ license = "MIT"
 
 [tool.poetry.dependencies]
 python = ">=3.11,<3.13"
-numpy = "^1.26"
-tensorly = "^0.8.1"
-matplotlib = "^3.5.0"
-tqdm = "^4.65"
+numpy = "^2.1"
+tensorly = "^0.8"
+matplotlib = "^3.9"
+tqdm = "^4.67"
 seaborn = "^0.13"
-xarray = "^2023"
-tensorpack = "^0.11"
+xarray = "^2024"
 scikit-learn = "^1.1.3"
-openpyxl = "^3.1.2"
-setuptools = "^69.5.1"
-pyright = "^1.1.389"
 
 [tool.poetry.dev-dependencies]
 pytest = "^8.3"
-pytest-cov = "^3.0.0"
-tensordata = {git = "https://github.com/meyer-lab/tensordata.git", branch = "main"}
+pytest-cov = "^6.0"
+tensordata = {git = "https://github.com/meyer-lab/tensordata.git", rev = "6afaec35c552c3ee91990db74c740728ddf37a55"}
+pyright = "^1.1.389"
+isort = "^5.13"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 42b67c0..0000000
--- a/setup.py
+++ /dev/null
@@ -1,12 +0,0 @@
-try:
-    from setuptools import setup, find_packages
-except ImportError:
-    from distutils.core import setup, find_packages
-
-setup(name='tensor-impute',
-      version='0.1.0',
-      description='Examining various imputation methods for tensor decomposition.',
-      url='https://github.com/meyer-lab/tensorpack',
-      license='MIT',
-      packages=find_packages(exclude=['doc']),
-      install_requires=['numpy', 'tensorly', 'scikit-learn'])
diff --git a/timpute/__init__.py b/timpute/__init__.py
index 8a583c2..a167b19 100644
--- a/timpute/__init__.py
+++ b/timpute/__init__.py
@@ -1,2 +1,3 @@
+import tensorly as tl
+
 from .decomposition import Decomposition
-import tensorly as tl
\ No newline at end of file
diff --git a/timpute/data/import_hmsData.py b/timpute/data/import_hmsData.py
index 5016f66..dcc2f44 100644
--- a/timpute/data/import_hmsData.py
+++ b/timpute/data/import_hmsData.py
@@ -1,6 +1,7 @@
+import os
+
 import numpy as np
 import pandas as pd
-import os
 import xarray as xa
 
 
diff --git a/timpute/decomposition.py b/timpute/decomposition.py
index a9499a9..c9792d1 100644
--- a/timpute/decomposition.py
+++ b/timpute/decomposition.py
@@ -1,15 +1,14 @@
 import pickle
+from typing import Optional
+
 import numpy as np
 import tensorly as tl
+from tqdm import tqdm
 
-from .tracker import Tracker
+from .impute_helper import calcR2X, chord_drop, entry_drop
 from .initialization import initialize_fac
-from .impute_helper import entry_drop, chord_drop
-from .impute_helper import calcR2X
 from .method_CLS import perform_CLS
-
-from copy import deepcopy
-from tqdm import tqdm
+from .tracker import Tracker
 
 
 class Decomposition:
@@ -43,7 +42,7 @@ def imputation(
         init="random",
         maxiter: int = 50,
         seed=1,
-        callback: Tracker = None,
+        callback: Optional[Tracker] = None,
         printRuntime=False,
     ):
         """
@@ -135,7 +134,7 @@ def imputation(
                     np.random.seed(int(x * seed))
                     CPinit = initialize_fac(missingCube.copy(), rr, init)
                 elif isinstance(init, tl.cp_tensor.CPTensor):
-                    CPinit = deepcopy(init)
+                    CPinit = init.cp_copy()
                 else:
                     raise ValueError(f'Initialization method "{init}" not recognized')
 
@@ -155,7 +154,6 @@ def imputation(
                     missingCube.copy(),
                     rank=rr,
                     n_iter_max=maxiter,
-                    mask=mask,
                     init=CPinit,
                     callback=callback,
                     tol=tol,
diff --git a/timpute/figures/common.py b/timpute/figures/common.py
index 2441d26..7ef5753 100644
--- a/timpute/figures/common.py
+++ b/timpute/figures/common.py
@@ -1,11 +1,13 @@
 """
 This file contains functions that are used in multiple figures.
 """
-import seaborn as sns
 from string import ascii_lowercase
-import matplotlib
-from matplotlib import gridspec, pyplot as plt
 
+import matplotlib
+import seaborn as sns
+from matplotlib import gridspec
+from matplotlib import pyplot as plt
+from matplotlib.lines import Line2D
 
 matplotlib.rcParams["legend.labelspacing"] = 0.2
 matplotlib.rcParams["legend.fontsize"] = 8
@@ -68,4 +70,4 @@ def rgbs(color = 0, transparency = None):
     if transparency is not None:
         return tuple(list(color_rgbs[color]) + [transparency])
     else:
-        return color_rgbs[color]
\ No newline at end of file
+        return color_rgbs[color]
diff --git a/timpute/figures/figure2.py b/timpute/figures/figure2.py
index de938e9..e6a372a 100644
--- a/timpute/figures/figure2.py
+++ b/timpute/figures/figure2.py
@@ -1,8 +1,10 @@
 import math
+
 import numpy as np
+from figures import DATANAMES, METHODNAMES, METHODS, SAVENAMES
+
+from .common import getSetup, rgbs, subplotLabel
 from .figure_helper import loadImputation
-from .common import getSetup, subplotLabel, rgbs
-from figures import METHODS, METHODNAMES, SAVENAMES, DATANAMES
 
 # poetry run python -m timpute.figures.figure2
 
diff --git a/timpute/figures/figure3.py b/timpute/figures/figure3.py
index f0edc26..b998889 100644
--- a/timpute/figures/figure3.py
+++ b/timpute/figures/figure3.py
@@ -1,9 +1,11 @@
 import math
+
 import numpy as np
+from figures import DATANAMES, DROPS, METHODNAMES, METHODS, SAVENAMES
 from matplotlib.lines import Line2D
+
+from .common import getSetup, rgbs, set_boxplot_color, subplotLabel
 from .figure_helper import loadImputation
-from .common import getSetup, subplotLabel, rgbs, set_boxplot_color
-from figures import METHODS, METHODNAMES, SAVENAMES, DATANAMES, DROPS
 
 # poetry run python -m timpute.figures.figure3
 
diff --git a/timpute/figures/figure4.py b/timpute/figures/figure4.py
index b7f424f..3dde14d 100644
--- a/timpute/figures/figure4.py
+++ b/timpute/figures/figure4.py
@@ -1,7 +1,8 @@
 import numpy as np
+from figures import DATANAMES, METHODNAMES, METHODS, SAVENAMES
+
+from .common import getSetup, rgbs, subplotLabel
 from .figure_helper import loadImputation
-from .common import getSetup, subplotLabel, rgbs
-from figures import METHODS, METHODNAMES, SAVENAMES, DATANAMES
 
 # poetry run python -m timpute.figures.figure4
 
diff --git a/timpute/figures/figure5.py b/timpute/figures/figure5.py
index 36fb07f..275f838 100644
--- a/timpute/figures/figure5.py
+++ b/timpute/figures/figure5.py
@@ -1,8 +1,10 @@
 import numpy as np
+from figures import DATANAMES, METHODNAMES, METHODS, SAVENAMES
+
+from .common import getSetup, rgbs, subplotLabel
 from .figure_data import bestComps
 from .figure_helper import loadImputation
-from .common import getSetup, subplotLabel, rgbs
-from figures import METHODS, METHODNAMES, SAVENAMES, DATANAMES
+
 # from matplotlib.legend_handler import HandlerErrorbar
 
 # poetry run python -m timpute.figures.figure5
diff --git a/timpute/figures/figure_data.py b/timpute/figures/figure_data.py
index 1d4ceb1..48328e7 100644
--- a/timpute/figures/figure_data.py
+++ b/timpute/figures/figure_data.py
@@ -1,8 +1,10 @@
 import os
+
 import numpy as np
-from .figure_helper import runImputation, loadImputation
+from figures import DROPS, METHODNAMES, METHODS, SAVENAMES
+
 from ..generateTensor import generateTensor
-from figures import METHODS, METHODNAMES, SAVENAMES, DROPS
+from .figure_helper import loadImputation, runImputation
 
 # poetry run python -m timpute.figures.figure_data  
 
diff --git a/timpute/figures/figure_helper.py b/timpute/figures/figure_helper.py
index 5336817..3db75b2 100644
--- a/timpute/figures/figure_helper.py
+++ b/timpute/figures/figure_helper.py
@@ -1,5 +1,7 @@
 import os
+
 import numpy as np
+
 from ..decomposition import Decomposition
 from ..tracker import Tracker
 
diff --git a/timpute/figures/memUsage.py b/timpute/figures/memUsage.py
index b78c0bc..549dbd4 100644
--- a/timpute/figures/memUsage.py
+++ b/timpute/figures/memUsage.py
@@ -1,11 +1,12 @@
 # import psutil
-import pickle
+import argparse
 import os
-import numpy as np
+import pickle
 import resource
-import argparse
 
-from figures import METHODS, METHODNAMES, SAVENAMES
+import numpy as np
+from figures import METHODNAMES, METHODS, SAVENAMES
+
 from ..decomposition import Decomposition
 from ..generateTensor import generateTensor
 
diff --git a/timpute/figures/supplements.py b/timpute/figures/supplements.py
index d4040fa..3ec7a7e 100644
--- a/timpute/figures/supplements.py
+++ b/timpute/figures/supplements.py
@@ -1,9 +1,11 @@
 import pickle
-from figures import METHODNAMES, SAVENAMES, DATANAMES, DROPS
+
 import numpy as np
 import pandas as pd
-from .common import getSetup, rgbs
+from figures import DATANAMES, DROPS, METHODNAMES, SAVENAMES
+
 from ..decomposition import Decomposition
+from .common import getSetup, rgbs
 
 # poetry run python -m timpute.figures.supplements
 
diff --git a/timpute/generateTensor.py b/timpute/generateTensor.py
index eb65610..58618a6 100644
--- a/timpute/generateTensor.py
+++ b/timpute/generateTensor.py
@@ -1,61 +1,115 @@
+import os
+
 import numpy as np
 import tensorly as tl
 import xarray as xr
-import os
-from tensorly.cp_tensor import CPTensor
-from .impute_helper import entry_drop
-
+from tensordata.alter import data as alter
 from tensordata.atyeo import data as atyeo
 from tensordata.zohar import data as zohar
-from tensordata.alter import data as alter
+from tensorly.cp_tensor import CPTensor
+
 from .data.import_hmsData import hms_tensor
+from .impute_helper import entry_drop
 
-def generateTensor(type=None, r=6, shape=(10,10,10), scale=2, distribution='gamma', par=2, missingness=0.0, noise_scale=50):
+
+def generateTensor(
+    type=None,
+    r: int=6,
+    shape: tuple[int, int, int]=(10, 10, 10),
+    scale: float=2.0,
+    distribution="gamma",
+    par=2,
+    missingness=0.0,
+    noise_scale=50,
+):
     """
     Tensor options: 'known', 'unknown', 'zohar', 'atyeo', 'alter', 'hms', 'coh_receptor', or 'coh response'.
     Defaults to 'known'
     """
-    if type == 'known':
-        temp, _ = createKnownRank(drop_perc=missingness, size=shape, rank=r, distribution=distribution, scale=scale, par=par)
-        return createNoise(temp,noise_scale)
-    elif type == 'unknown':
-        temp = createUnknownRank(drop_perc=missingness, size=shape, distribution=distribution, scale=scale, par=par)
-        return createNoise(temp,noise_scale)
-    elif type == 'zohar': return zohar().to_numpy().copy()
-    elif type == 'atyeo': return atyeo().to_numpy().copy()
-    elif type == 'alter': return alter()['Fc'].to_numpy().copy()
-    elif type == 'hms':   return np.swapaxes(hms_tensor().to_numpy().copy(),0,2)
-    elif type == 'coh_receptor':
+    if type == "known":
+        temp, _ = createKnownRank(
+            drop_perc=missingness,
+            size=shape,
+            rank=r,
+            distribution=distribution,
+            scale=scale,
+            par=par,
+        )
+        return createNoise(temp, noise_scale)
+    elif type == "unknown":
+        temp = createUnknownRank(
+            drop_perc=missingness,
+            size=shape,
+            distribution=distribution,
+            scale=scale,
+            par=par,
+        )
+        return createNoise(temp, noise_scale)
+    elif type == "zohar":
+        return zohar().to_numpy().copy()
+    elif type == "atyeo":
+        return atyeo().to_numpy().copy()
+    elif type == "alter":
+        return alter()["Fc"].to_numpy().copy()
+    elif type == "hms":
+        return np.swapaxes(hms_tensor().to_numpy().copy(), 0, 2)
+    elif type == "coh_receptor":
         receptor = xr.open_dataarray(f"{os.getcwd()}/timpute/data/CoH/CoH_Rec.nc")
         return receptor.to_numpy().copy()
-    elif type == 'coh_response':
-        response = xr.open_dataarray(f"{os.getcwd()}/timpute/data/CoH/CoH_Tensor_DataSet.nc")
+    elif type == "coh_response":
+        response = xr.open_dataarray(
+            f"{os.getcwd()}/timpute/data/CoH/CoH_Tensor_DataSet.nc"
+        )
         return response.to_numpy().copy()
     else:
-        temp, _ = createKnownRank(drop_perc=missingness, size=shape, rank=r, distribution=distribution, scale=scale, par=par)
-        return createNoise(temp,noise_scale)
+        temp, _ = createKnownRank(
+            drop_perc=missingness,
+            size=shape,
+            rank=r,
+            distribution=distribution,
+            scale=scale,
+            par=par,
+        )
+        return createNoise(temp, noise_scale)
+
 
+def createNoise(tensor: np.ndarray, scale: float=1.0):
+    """adds noise in-place"""
+    return np.random.normal(0, scale, tensor.shape) + np.copy(tensor)
 
-def createNoise(tensor,scale=1.0):
-    """ adds noise in-place """
-    noise = np.random.normal(0, scale, tensor.shape)
-    noisyTensor = noise+np.copy(tensor)
-    return noisyTensor
 
-def createUnknownRank(drop_perc=0.0, size=(10, 20, 25), distribution="gamma", scale=1, par=1):
-    if distribution == "gamma": tensor = np.random.gamma(par, scale, size=size)
-    if distribution == "chisquare": tensor = np.random.chisquare(size=size)
-    if distribution == "logistic": tensor = np.random.logistic(size=size)
-    if distribution == "exponential": tensor = np.random.exponential(size=size)
-    if distribution == "uniform": tensor = np.random.uniform(size=size)
-    if distribution == "normal": tensor = np.random.normal(size=size)
+def createUnknownRank(
+    drop_perc=0.0, size=(10, 20, 25), distribution="gamma", scale: float=1.0, par=1
+):
+    if distribution == "gamma":
+        tensor = np.random.gamma(par, scale, size=size)
+    if distribution == "chisquare":
+        tensor = np.random.chisquare(size=size)
+    if distribution == "logistic":
+        tensor = np.random.logistic(size=size)
+    if distribution == "exponential":
+        tensor = np.random.exponential(size=size)
+    if distribution == "uniform":
+        tensor = np.random.uniform(size=size)
+    if distribution == "normal":
+        tensor = np.random.normal(size=size)
 
-    if scale != 1: tensor *= scale
+    if scale != 1:
+        tensor *= scale
 
-    entry_drop(tensor, int(drop_perc*tensor.size), dropany=True)
+    entry_drop(tensor, int(drop_perc * tensor.size), dropany=True)
     return tensor
 
-def createKnownRank(drop_perc=0.0, size=(10,10,10), rank=6, distribution="gamma", scale=1, par=1, noise=True):
+
+def createKnownRank(
+    drop_perc=0.0,
+    size=(10, 10, 10),
+    rank=6,
+    distribution="gamma",
+    scale=1.0,
+    par=1,
+    noise=True,
+):
     r"""
     Creates a random tensor following a set of possible distributions:
     `distribution` = "gamma", "chisquare", "logistic", "exponential", "uniform", "normal"
@@ -65,30 +119,42 @@ def createKnownRank(drop_perc=0.0, size=(10,10,10), rank=6, distribution="gamma"
 
     factors = []
 
-    if type(distribution) == str:
+    if isinstance(distribution, str):
         for i in size:
-            if distribution == "gamma": factors.append(rng.gamma(par, scale, size=(i,rank)))
-            if distribution == "chisquare": factors.append(rng.chisquare(par, size=(i,rank)))
-            if distribution == "logistic": factors.append(rng.logistic(size=(i,rank)))
-            if distribution == "exponential": factors.append(rng.exponential(size=(i,rank)))
-            if distribution == "uniform": factors.append(rng.uniform(size=(i,rank)))
-            if distribution == "normal": factors.append(rng.normal(size=(i,rank)))
-    
+            if distribution == "gamma":
+                factors.append(rng.gamma(par, scale, size=(i, rank)))
+            if distribution == "chisquare":
+                factors.append(rng.chisquare(par, size=(i, rank)))
+            if distribution == "logistic":
+                factors.append(rng.logistic(size=(i, rank)))
+            if distribution == "exponential":
+                factors.append(rng.exponential(size=(i, rank)))
+            if distribution == "uniform":
+                factors.append(rng.uniform(size=(i, rank)))
+            if distribution == "normal":
+                factors.append(rng.normal(size=(i, rank)))
+
     else:
         assert len(distribution) == len(size)
         for i in size:
-            if distribution[i] == "gamma": factors[1] = rng.gamma(par, scale, size=(i,rank))
-            if distribution[i] == "chisquare": factors[1] = rng.chisquare(par, size=(i,rank))
-            if distribution[i] == "logistic": factors[1] = rng.logistic(size=(i,rank))
-            if distribution[i] == "exponential": factors[1] = rng.exponential(size=(i,rank))
-            if distribution[i] == "uniform": factors[1] = rng.uniform(size=(i,rank))
-            if distribution[i] == "normal": factors[1] = rng.normal(size=(i,rank))
-        
+            if distribution[i] == "gamma":
+                factors[1] = rng.gamma(par, scale, size=(i, rank))
+            if distribution[i] == "chisquare":
+                factors[1] = rng.chisquare(par, size=(i, rank))
+            if distribution[i] == "logistic":
+                factors[1] = rng.logistic(size=(i, rank))
+            if distribution[i] == "exponential":
+                factors[1] = rng.exponential(size=(i, rank))
+            if distribution[i] == "uniform":
+                factors[1] = rng.uniform(size=(i, rank))
+            if distribution[i] == "normal":
+                factors[1] = rng.normal(size=(i, rank))
 
-    if scale != 1:
-        for i in factors: i *= scale
+    if scale != 1.0:
+        for i in factors:
+            i *= scale
     temp = tl.cp_to_tensor(CPTensor((None, factors)))
-    if noise: 
-        tensor = np.add(np.random.normal(0.5,0.15, size),temp)
-    entry_drop(tensor, int(drop_perc*tensor.size), dropany=True)
-    return tensor, factors
\ No newline at end of file
+    if noise:
+        tensor = np.add(np.random.normal(0.5, 0.15, size), temp)
+    entry_drop(tensor, int(drop_perc * tensor.size), dropany=True)
+    return tensor, factors
diff --git a/timpute/initialization.py b/timpute/initialization.py
index 0577527..5059df3 100644
--- a/timpute/initialization.py
+++ b/timpute/initialization.py
@@ -1,7 +1,7 @@
 import numpy as np
 import tensorly as tl
-from tensorly.tenalg import svd_interface
 from tensorly.random import random_cp
+from tensorly.tenalg import svd_interface
 
 
 class IterativeSVD(object):
diff --git a/timpute/linesearch.py b/timpute/linesearch.py
new file mode 100644
index 0000000..ce41eba
--- /dev/null
+++ b/timpute/linesearch.py
@@ -0,0 +1,41 @@
+from .impute_helper import calcR2X
+
+
+class Nesterov:
+    def __init__(self, gamma=1.1, gamma_bar=1.03, eta=1.5, beta_i=0.05, beta_i_bar=1.0):
+        self.gamma = gamma
+        self.gamma_bar = gamma_bar
+        self.eta = eta
+        self.beta_i = beta_i
+        self.beta_i_bar = beta_i_bar
+        self.factors_old = None
+
+    def perform(self, factors, tOrig):
+        if self.factors_old is None:
+            self.factors_old = factors
+            self.prev_R2X = calcR2X((None, factors), tOrig)
+
+            return self.factors_old, self.prev_R2X, 1.0
+
+        jump = self.beta_i + 1.0
+
+        factors_ls = [
+            self.factors_old[ii] + (factors[ii] - self.factors_old[ii]) * jump for ii in range(len(factors))
+        ]
+
+        R2X_ls = calcR2X((None, factors_ls), tOrig)
+
+        if R2X_ls > self.prev_R2X:
+            self.factors_old = factors_ls
+            self.prev_R2X = R2X_ls
+
+            self.beta_i = min(self.beta_i_bar, self.gamma * self.beta_i)
+            self.beta_i_bar = max(1.0, self.gamma_bar * self.beta_i_bar)
+        else:
+            self.factors_old = factors
+            self.prev_R2X = calcR2X((None, factors), tOrig)
+
+            self.beta_i_bar = self.beta_i
+            self.beta_i = self.beta_i / self.eta
+
+        return self.factors_old, self.prev_R2X, jump
diff --git a/timpute/method_ALS.py b/timpute/method_ALS.py
index 466eddf..afecae9 100644
--- a/timpute/method_ALS.py
+++ b/timpute/method_ALS.py
@@ -1,21 +1,19 @@
 import numpy as np
-
 import tensorly as tl
-from tensorly.cp_tensor import (
-    cp_to_tensor,
-    CPTensor,
-)
+from tensorly.cp_tensor import cp_normalize, cp_to_tensor
 from tensorly.tenalg.core_tenalg.mttkrp import unfolding_dot_khatri_rao
+from tqdm import tqdm
+
 from .initialization import initialize_fac
+from .linesearch import Nesterov
 
 
 def perform_ALS(
-    tensor,
+    tOrig,
     rank,
-    n_iter_max=100,
+    n_iter_max=50,
     init=None,
     tol=1e-6,
-    mask=None,
     callback=None,
 ) -> tl.cp_tensor.CPTensor:
     """CANDECOMP/PARAFAC decomposition via alternating least squares (ALS)
@@ -38,11 +36,6 @@ def perform_ALS(
         (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`.
-    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]_
 
     Returns
     -------
@@ -66,57 +59,53 @@ 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)
+    tensor = np.nan_to_num(tOrig)
+    mask = np.isfinite(tOrig)
 
     if init is None:
-        init = initialize_fac(tensor, rank, method="random")
+        tFac = initialize_fac(tensor, rank, method="random")
+    else:
+        tFac = init
 
-    weights, factors = init
+    linesrc = Nesterov()
+    fac, R2X, jump = linesrc.perform(tFac.factors, tOrig)
+    tFac.R2X = R2X
+    tFac.factors = fac
 
-    rec_errors = []
-    norm_tensor = tl.norm(tensor, 2)
+    tq = tqdm(range(n_iter_max), disable=False)
+    for _ in tq:
+        # Update the tensor based on the mask
+        low_rank_component = cp_to_tensor(tFac)
+        tensor = tensor * mask + low_rank_component * (1 - mask)
 
-    for iteration in range(n_iter_max):
         for mode in range(np.ndim(tensor)):
             pseudo_inverse = np.ones((rank, rank))
-            for i, factor in enumerate(factors):
+            for i, factor in enumerate(tFac.factors):
                 if i != mode:
                     pseudo_inverse = pseudo_inverse * np.dot(factor.T, factor)
-            pseudo_inverse = (
-                np.reshape(weights, (-1, 1))
-                * pseudo_inverse
-                * np.reshape(weights, (1, -1))
-            )
-            mttkrp = unfolding_dot_khatri_rao(tensor, (weights, factors), mode)
-
-            factor = np.linalg.solve(pseudo_inverse.T, mttkrp.T).T
-            factors[mode] = factor
-
-        # Calculate the current unnormalized error
-        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)
+            mttkrp = unfolding_dot_khatri_rao(tensor, tFac, mode)
 
-        unnorml_rec_error = tl.norm(tensor - low_rank_component, 2)
+            factor = np.linalg.solve(pseudo_inverse.T, mttkrp.T).T
+            tFac.factors[mode] = factor
 
-        if tol:
-            rec_error = unnorml_rec_error / norm_tensor
-            rec_errors.append(rec_error)
+        R2X_last = tFac.R2X
 
-        if callback is not None:
-            cp_tensor = CPTensor((weights, factors))
-            callback(cp_tensor)
+        fac, R2X, jump = linesrc.perform(tFac.factors, tOrig)
+        tFac.R2X = R2X
+        tFac.factors = fac
 
-        if tol is not None:
-            if iteration >= 1:
-                rec_error_decrease = rec_errors[-2] - rec_errors[-1]
+        tq.set_postfix(
+            R2X=tFac.R2X, delta=tFac.R2X - R2X_last, jump=jump, refresh=False
+        )
+        assert tFac.R2X > 0.0
 
-                if abs(rec_error_decrease) < tol:
-                    break
+        if callback:
+            callback(tFac)
 
-    cp_tensor = CPTensor((weights, factors))
+        if tFac.R2X - R2X_last < tol:
+            break
 
-    return cp_tensor
+    tFac_norm = cp_normalize(tFac)
+    tFac_norm.R2X = tFac.R2X
+    return tFac
diff --git a/timpute/method_CLS.py b/timpute/method_CLS.py
index 6cb0f77..f5176cb 100644
--- a/timpute/method_CLS.py
+++ b/timpute/method_CLS.py
@@ -4,17 +4,29 @@
 
 import numpy as np
 import tensorly as tl
-from tensorly.cp_tensor import cp_normalize, cp_flip_sign
+from tensorly.cp_tensor import cp_flip_sign, cp_normalize
 from tensorly.tenalg import khatri_rao
-from .initialization import initialize_fac
-from .impute_helper import calcR2X
+from scipy.linalg import solve as sp_solve
 from tqdm import tqdm
-from sklearn.linear_model import Ridge
+
+from .initialization import initialize_fac
+from .linesearch import Nesterov
+
+tl.set_backend("numpy")
 
 
-tl.set_backend('numpy')
+def ridge_solve_cholesky(X, y, alpha: float):
+    # w = inv(X^t X + alpha*Id) * X.T y
+    A = X.T @ X
+    Xy = X.T @ y
 
-def censored_lstsq(A: np.ndarray, B: np.ndarray, uniqueInfo=None, alpha=None) -> np.ndarray:
+    A.flat[:: X.shape[1] + 1] += alpha
+    return sp_solve(A, Xy, assume_a="pos", overwrite_a=True)
+
+
+def censored_lstsq(
+    A: np.ndarray, B: np.ndarray, uniqueInfo=None, alpha=None
+) -> np.ndarray:
     """Solves least squares problem subject to missing data.
     Note: uses a for loop over the missing patterns of B, leading to a
     slower but more numerically stable algorithm
@@ -27,6 +39,7 @@ def censored_lstsq(A: np.ndarray, B: np.ndarray, uniqueInfo=None, alpha=None) ->
     X (ndarray) : r x n matrix that minimizes norm(M*(AX - B))
     """
     X = np.empty((A.shape[1], B.shape[1]))
+
     # Missingness patterns
     if uniqueInfo is None:
         unique, uIDX = np.unique(np.isfinite(B), axis=1, return_inverse=True)
@@ -35,62 +48,71 @@ def censored_lstsq(A: np.ndarray, B: np.ndarray, uniqueInfo=None, alpha=None) ->
 
     for i in range(unique.shape[1]):
         uI = uIDX == i
-        uu = np.squeeze(unique[:, i])
+        uu = unique[:, i]
 
         Bx = B[uu, :]
 
         if alpha is None:
-            X[:, uI] = np.linalg.lstsq(A[uu, :], Bx[:, uI], rcond=-1)[0]
+            X[:, uI] = np.linalg.lstsq(A[uu, :], Bx[:, uI], rcond=None)[0]
         else:
-            clf = Ridge(alpha=alpha, fit_intercept=False)
-            clf.fit(A[uu, :], Bx[:, uI])
-            X[:, uI] = clf.coef_.T
+            X[:, uI] = ridge_solve_cholesky(A[uu, :], Bx[:, uI], alpha=alpha)
     return X.T
 
-  
-def perform_CLS(tOrig,
-                rank=6,
-                init=None, 
-                alpha=None,
-                tol=1e-6,
-                n_iter_max=50,
-                progress=False,
-                callback=None,
-                **kwargs
-)  -> tl.cp_tensor.CPTensor:
-    """ Perform CP decomposition. """
-
-    if init==None: tFac = initialize_fac(tOrig, rank)
+
+def perform_CLS(
+    tOrig: np.ndarray,
+    rank: int=6,
+    init=None,
+    alpha=None,
+    tol: float=1e-6,
+    n_iter_max: int=50,
+    progress=False,
+    callback=None,
+) -> tl.cp_tensor.CPTensor:
+    """Perform CP decomposition."""
+
+    if init is None:
+        tFac = initialize_fac(tOrig, rank)
     else:
         tFac = init
-        tFac_last = init
-    
+
     # Pre-unfold
     unfolded = [tl.unfold(tOrig, i) for i in range(tOrig.ndim)]
     R2X_last = -np.inf
-    tFac.R2X = calcR2X(tFac, tOrig)
+
+    linesrc = Nesterov()
+    fac, R2X, jump = linesrc.perform(tFac.factors, tOrig)
+    tFac.factors = fac
 
     # Precalculate the missingness patterns
-    uniqueInfo = [np.unique(np.isfinite(B.T), axis=1, return_inverse=True) for B in unfolded]
+    uniqueInfo = [
+        np.unique(np.isfinite(B.T), axis=1, return_inverse=True) for B in unfolded
+    ]
 
     tq = tqdm(range(n_iter_max), disable=(not progress))
     for _ in tq:
         # Solve on each mode
         for m in range(len(tFac.factors)):
             kr = khatri_rao(tFac.factors, skip_matrix=m)
-            tFac.factors[m] = censored_lstsq(kr, unfolded[m].T, uniqueInfo[m], alpha=alpha)
-        
-        R2X_last = tFac.R2X
-        tFac.R2X = calcR2X(tFac, tOrig)
-        tq.set_postfix(R2X=tFac.R2X, delta=tFac.R2X - R2X_last, refresh=False)
-
-        if callback: callback(tFac)
-        if tFac.R2X - R2X_last < tol:
-            break
+            tFac.factors[m] = censored_lstsq(
+                kr, unfolded[m].T, uniqueInfo[m], alpha=alpha
+            )
 
+        R2X_last = R2X
+        fac, R2X, jump = linesrc.perform(tFac.factors, tOrig)
+        tFac.factors = fac
+
+        tq.set_postfix(
+            R2X=R2X, delta=R2X - R2X_last, jump=jump, refresh=False
+        )
+
+        if callback:
+            callback(tFac)
+        if R2X - R2X_last < tol:
+            break
 
     tFac = cp_normalize(tFac)
     tFac = cp_flip_sign(tFac)
-    tFac.R2X = calcR2X(tFac, tOrig)
+    tFac.R2X = R2X
 
     return tFac
diff --git a/timpute/method_DO.py b/timpute/method_DO.py
index ca6ee51..fb22c97 100644
--- a/timpute/method_DO.py
+++ b/timpute/method_DO.py
@@ -2,12 +2,13 @@
 Tensor decomposition methods
 """
 import numpy as np
-from scipy.optimize import minimize
 import tensorly as tl
+from scipy.optimize import minimize
+from tensorly.cp_tensor import cp_lstsq_grad, cp_normalize
+
+from .generateTensor import generateTensor
 from .initialization import initialize_fac
 from .tracker import Tracker
-from tensorly.cp_tensor import cp_normalize, cp_lstsq_grad
-from .generateTensor import generateTensor
 
 tl.set_backend("numpy")
 
@@ -37,13 +38,12 @@ def __call__(self, x):
 
 
 def perform_DO(
-    tensorOrig:np.ndarray=None,
+    tensorOrig: np.ndarray,
     rank:int=6,
     n_iter_max:int=5_000,
     tol = 1e-6,
     callback=None,
     init=None,
-    **kwargs
 ) -> tl.cp_tensor.CPTensor:
     """Perform CP decomposition."""
     if tensorOrig is None:
diff --git a/timpute/method_PM.py b/timpute/method_PM.py
index 95b062d..0eb1924 100644
--- a/timpute/method_PM.py
+++ b/timpute/method_PM.py
@@ -1,11 +1,11 @@
 import numpy as np
 import tensorly as tl
+from tensorly.cp_tensor import cp_flip_sign, cp_normalize
+from tensorly.tenalg import khatri_rao
 from tqdm import tqdm
 
-from tensorly.tenalg import khatri_rao
 from .impute_helper import calcR2X
 from .initialization import initialize_fac
-from tensorly.cp_tensor import cp_normalize, cp_flip_sign
 
 
 def perform_PM(
@@ -16,7 +16,6 @@ def perform_PM(
     callback=None,
     init=None,
     verbose=None,
-    **kwargs,
 ) -> tl.cp_tensor.CPTensor:
     # function [A, B, C, LFT, M]=PARAFACM(XIJK, Fac, epsilon)
     # % Input
diff --git a/timpute/test/test_cls.py b/timpute/test/test_cls.py
index 380b374..64c7ced 100644
--- a/timpute/test/test_cls.py
+++ b/timpute/test/test_cls.py
@@ -2,13 +2,15 @@
 Unit test file.
 """
 
+import warnings
+
 import numpy as np
 import tensorly as tl
-import warnings
-from ..method_CLS import perform_CLS, censored_lstsq
+from sklearn.linear_model import Ridge
 from tensorly.tenalg import khatri_rao
+
 from ..initialization import initialize_fac
-from sklearn.linear_model import Ridge
+from ..method_CLS import censored_lstsq, perform_CLS
 
 
 def createCube(missing=0.0, size=(10, 20, 25)):
diff --git a/timpute/test/test_do.py b/timpute/test/test_do.py
index ae10dfa..4dfab48 100644
--- a/timpute/test/test_do.py
+++ b/timpute/test/test_do.py
@@ -1,9 +1,11 @@
 import numpy as np
-from ..method_DO import perform_DO
-from ..impute_helper import calcR2X
-from ..generateTensor import generateTensor
-from timpute.tracker import Tracker
+
 from timpute.decomposition import Decomposition
+from timpute.tracker import Tracker
+
+from ..generateTensor import generateTensor
+from ..impute_helper import calcR2X
+from ..method_DO import perform_DO
 
 
 def test_decomp_dopt(plot=False, method=perform_DO):
diff --git a/timpute/test/test_impute.py b/timpute/test/test_impute.py
index 4d0c617..f118f6e 100644
--- a/timpute/test/test_impute.py
+++ b/timpute/test/test_impute.py
@@ -1,6 +1,7 @@
 import numpy as np
 import tensorly as tl
 from tensorly.random import random_cp
+
 from ..decomposition import Decomposition
 from ..generateTensor import generateTensor
 from ..impute_helper import entry_drop
diff --git a/timpute/tracker.py b/timpute/tracker.py
index d2eff72..7441a8a 100644
--- a/timpute/tracker.py
+++ b/timpute/tracker.py
@@ -1,8 +1,11 @@
-import numpy as np
-import time
 import pickle
+import time
+
+import numpy as np
+
 from .impute_helper import calcR2X
 
+
 class Tracker():
     """ Tracks next unfilled entry & runtime, holds tracked name for plotting """
         
@@ -22,7 +25,7 @@ def __init__(self, tOrig = [0], mask=None, track_runtime = True):
         
         self.combined = False
 
-    def __call__(self, tFac, **kwargs):
+    def __call__(self, tFac):
         """ Takes a CP tensor object """
         self.total_error[self.rank][self.rep] = np.append(self.fitted_error[self.rank][self.rep],
                                                           calcR2X(tFac, self.data, True))