diff --git a/NOTES.org b/NOTES.org
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-* Install for development
-  #+begin_src sh
-    pip install --no-deps -e .
-  #+end_src
-
-* creating enviroments for development
-
-  create the base runtime enviroment with
-  #+begin_src sh
-    conda env create -n 'env name' -f environment.yml
-  #+end_src
-
-  To add development stuff do
-  #+begin_src sh
-    conda env update -n 'env name' -f environment-dev.yml -f 'optional-recipes'
-  #+end_src
-
-
-* Using pre-commit
-  see https://pre-commit.com/
-
-  pre-commit adds hooks to autocheck
-
-  #+begin_src sh
-    # install
-    conda install -n env-name pre-commit
-
-    # init
-    pre-commit install
-
-    # run on all files
-    pre-commit run --all-files
-
-    # run on staged
-    pre-commit run
-  #+end_src
-
-* Testing
-  #+begin_src sh
-    # run all tests
-    pytest -x -v --runslow
-
-    # omit slow tests
-    pytest -x -v
-
-  #+end_src
diff --git a/README-1.md b/README-1.md
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-# `thermoextrap`: Thermodynamic Extrapolation/Interpolation Library
-This repository contains code used and described in:
-
-Monroe, J. I.; Hatch, H. W.; Mahynski, N. A.; Shell, M. S.; Shen, V. K. Extrapolation and Interpolation Strategies for Efficiently Estimating Structural Observables as a Function of Temperature and Density. J. Chem. Phys. 2020, 153 (14), 144101. https://doi.org/10.1063/5.0014282.
-
-Monroe, J. I.; Krekelberg, W. P.; McDannald, A.; Shen, V. K. Leveraging Uncertainty Estiamtes and Derivative Information in Gaussian Process Regression for Expediated Data Collection in Molecular Simulations. In preparation.
-
-# Overview
-
-If you find this code useful in producing published works, please provide an appropriate citation.
-Note that the second citation is focused on adding features that make use of GPR models based on derivative information produced by the core code base.
-For now, the GPR code, along with more information, may be found under docs/notebooks/gpr.
-In a future release, we expect this to be fully integrated into the code base rather than a standalone module.
-
-Code included here can be used to perform thermodynamic extrapolation and
-interpolation of observables calculated from molecular simulations. This allows
-for more efficient use of simulation data for calculating how observables change
-with simulation conditions, including temperature, density, pressure, chemical
-potential, or force field parameters. Users are highly encourage to work through
-the Jupyter Notebook tutorial (Ideal_Gas_Example.ipynb) presenting examples for
-a variety of different observable functional forms. We only guarantee that this
-code is functional for the test cases we present here or for which it has
-previously been applied Additionally, the code may be in continuous development
-at any time. Use at your own risk and always check to make sure the produced
-results make sense. If bugs are found, please report them. If specific features
-would be helpful just let us know and we will be happy to work with you to come
-up with a solution.
-
-
-# Status
-
-This package is actively used by the author.  Please feel free to create a pull request for wanted features and suggestions!
-
-
-# Installation
-
-`thermoextrap` may be installed with either (recommended)
-```bash
-conda install -c wpk-nist thermoextrap
-```
-or
-```bash
-pip install thermoextrap
-```
-
-If you use pip, then you can include additional dependencies using
-```bash
-pip install thermoextrap[all]
-```
-
-If you install `thermoextrap` with conda, there are additional optional dependencies that take some care for installation.  We recommend installing the following via `pip`, as the versions on the conda/conda-forge channels are often a bit old.
-```bash
-pip install tensorflow tensorflow-probability gpflow
-```
-
-# Contact
-Questions may be addressed to Bill Krekelberg at william.krekelberg@nist.gov or Jacob Monroe at jacob.monroe@uark.edu.
-
-
-# Documentation
-
-Documentation can be found at
-For a deeper dive, look at the [documentation](https://pages.nist.gov/thermo-extrap/)
-
-
-## License
-
-This is free software.  See [LICENSE](LICENSE).
-
-## Related work
-
-This package extensively uses the ``cmomy`` package to handle central comoments.  See [here](https://github.com/usnistgov/cmomy).
-
-
-## Credits
-
-This package was created with
-[Cookiecutter](https://github.com/audreyr/cookiecutter) and the
-[wpk-nist-gov/cookiecutter-pypackage](https://github.com/wpk-nist-gov/cookiecutter-pypackage)
-Project template forked from
-[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage).