Repository: frank1010111/pywaterflood (language: python)

Issue:
capture bottomhole pressure variation
Bottomhole pressure variation at each producer can also affect its production rate. Yousef et al (2006) have a formulation of that contribution to production, which is
<img src="https://render.githubusercontent.com/render/math?math=J\left[p_{wf}(t_0)e^{-(t-t_0)/\tau} %2b \frac{e^{-t/\tau}}{\tau}\int_{t_0}^t e^{-\xi/\tau} p(\xi)d\xi\right]">

Kaviani et al (2012) suggest that a shift to production can be performed in segmented capacitance modeling with the equation
<img src="https://render.githubusercontent.com/render/math?math=q_{BHP,j} = \sum_{k=1}^{K} v_{kj} \left[ p_{i} (t_{s-1})\quad e^{-(t_s-t_{s-1})/\tau_{kj}} - p_{k}(t_s) \quad %2b p'_{kj}(t_s)\right]">

They then suggest it can be further simplified through assuming very large τ values between producers, which gets to the equation

<img src="https://render.githubusercontent.com/render/math?math=q_{BHP,j} = \sum_{k=1}^{K} v_{kj} \left[ p_j (T_{s-1}) - p_k(T_s) \right]">

The fitting parameters here are elements of `v`. This could either be solved simultaneously with the primary production rates and producer-injector connectivities, or as a pre-processing step.


Discussion / hints:

Fix patch:
diff --git a/.flake8 b/.flake8
index 9969dcf..397aa4b 100644
--- a/.flake8
+++ b/.flake8
@@ -1,3 +1,16 @@
 [flake8]
-max-line-length = 88
-ignore = E203, W503
\ No newline at end of file
+max-line-length = 99
+ignore = E203, W503
+exclude =
+    .git,
+    __pycache__,
+    docs/_build,
+    docs/conf.py,
+    build,
+    dist,
+    .tox
+per-file-ignores =
+    # imported but unused
+    __init__.py: F401
+    # testing don't need docs
+    testing/*.py: D
diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
new file mode 100644
index 0000000..e6b2291
--- /dev/null
+++ b/.git-blame-ignore-revs
@@ -0,0 +1,2 @@
+# pre-commit hooks updated
+3e52578d0ff96c0ef27ed77f84c0c8d02acebb19
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 91e27b3..7d305fe 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,4 +1,3 @@
-include: "/.py?$"
 repos:
   - repo: https://github.com/psf/black
     rev: 22.3.0
@@ -59,6 +58,7 @@ repos:
     rev: v2.1.0
     hooks:
       - id: codespell
+        args: ["--skip=docs/example.ipynb"]
   - repo: https://github.com/shellcheck-py/shellcheck-py
     rev: v0.8.0.4
     hooks:
diff --git a/.readthedocs.yml b/.readthedocs.yml
index da799bf..572d87c 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -6,4 +6,4 @@ sphinx:
 python:
   version: 3.8
   install:
-  - requirements: docs/requirements.txt
\ No newline at end of file
+    - requirements: docs/requirements.txt
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 207ac1a..32c4a5a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,7 +5,9 @@
 ## v0.1.1 (3/11/2021)
 
 - The great renaming to `pywaterflood`
+
 ### Features
+
 - multiwell productivity index added
 
 ## v0.1.0 (23/08/2021)
@@ -14,4 +16,4 @@
 
 ## v0.0.1 (15/01/2020)
 
-- First release of `pyCRM`!
\ No newline at end of file
+- First release of `pyCRM`!
diff --git a/CONDUCT.md b/CONDUCT.md
index e205209..feb6b32 100644
--- a/CONDUCT.md
+++ b/CONDUCT.md
@@ -2,42 +2,71 @@
 
 ## Our Pledge
 
-In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, gender identity and expression, level of
+experience, nationality, personal appearance, race, religion, or sexual identity
+and orientation.
 
 ## Our Standards
 
-Examples of behavior that contributes to creating a positive environment include:
+Examples of behavior that contributes to creating a positive environment
+include:
 
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
+- Using welcoming and inclusive language
+- Being respectful of differing viewpoints and experiences
+- Gracefully accepting constructive criticism
+- Focusing on what is best for the community
+- Showing empathy towards other community members
 
 Examples of unacceptable behavior by participants include:
 
-* The use of sexualized language or imagery and unwelcome sexual attention or advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a professional setting
+- The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+- Trolling, insulting/derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+- Other conduct which could reasonably be considered inappropriate in a
+  professional setting
 
 ## Our Responsibilities
 
-Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
 
-Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
+Project maintainers have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, or to ban temporarily or permanently any
+contributor for other behaviors that they deem inappropriate, threatening,
+offensive, or harmful.
 
 ## Scope
 
-This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
 
 ## Enforcement
 
-Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team. The project team will review and
+investigate all complaints, and will respond in a way that it deems appropriate
+to the circumstances. The project team is obligated to maintain confidentiality
+with regard to the reporter of an incident. Further details of specific
+enforcement policies may be posted separately.
 
-Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
 
 ## Attribution
 
-This Code of Conduct is adapted from the [Contributor Covenant homepage](http://contributor-covenant.org/version/1/4), version 1.4.
+This Code of Conduct is adapted from the
+[Contributor Covenant homepage](http://contributor-covenant.org/version/1/4),
+version 1.4.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index cef0d4a..277e3f3 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -9,9 +9,9 @@ helps, and credit will always be given.
 
 If you are reporting a bug, please include:
 
-* Your operating system name and version.
-* Any details about your local setup that might be helpful in troubleshooting.
-* Detailed steps to reproduce the bug.
+- Your operating system name and version.
+- Any details about your local setup that might be helpful in troubleshooting.
+- Detailed steps to reproduce the bug.
 
 ### Fix Bugs
 
@@ -26,17 +26,17 @@ and "help wanted" is open to whoever wants to implement it.
 ### Write Documentation
 
 You can never have enough documentation! Please feel free to contribute to any
-part of the documentation, such as the official docs, docstrings, or even
-on the web in blog posts, articles, and such.
+part of the documentation, such as the official docs, docstrings, or even on the
+web in blog posts, articles, and such.
 
 ### Submit Feedback
 
 If you are proposing a feature:
 
-* Explain in detail how it would work.
-* Keep the scope as narrow as possible, to make it easier to implement.
-* Remember that this is a volunteer-driven project, and that contributions
-  are welcome :)
+- Explain in detail how it would work.
+- Keep the scope as narrow as possible, to make it easier to implement.
+- Remember that this is a volunteer-driven project, and that contributions are
+  welcome :)
 
 ## Get Started!
 
@@ -45,17 +45,19 @@ Ready to contribute? Here's how to set up `pywaterflood` for local development.
 1. Download a copy of `pywaterflood` locally.
 2. Install `pywaterflood` using `pip`:
 
-    ```console
-    $ pip install .
-    ```
+   ```console
+   $ pip install .
+   ```
 
-3. Use `git` (or similar) to create a branch for local development and make your changes:
+3. Use `git` (or similar) to create a branch for local development and make your
+   changes:
 
-    ```console
-    $ git checkout -b name-of-your-bugfix-or-feature
-    ```
+   ```console
+   $ git checkout -b name-of-your-bugfix-or-feature
+   ```
 
-4. When you're done making changes, check that your changes conform to any code formatting requirements and pass any tests.
+4. When you're done making changes, check that your changes conform to any code
+   formatting requirements and pass any tests.
 
 5. Commit your changes and open a pull request.
 
@@ -65,9 +67,10 @@ Before you submit a pull request, check that it meets these guidelines:
 
 1. The pull request should include additional tests if appropriate.
 2. If the pull request adds functionality, the docs should be updated.
-3. The pull request should work for all currently supported operating systems and versions of Python.
+3. The pull request should work for all currently supported operating systems
+   and versions of Python.
 
 ## Code of Conduct
 
-Please note that the `pywaterflood` project is released with a
-Code of Conduct. By contributing to this project you agree to abide by its terms.
+Please note that the `pywaterflood` project is released with a Code of Conduct.
+By contributing to this project you agree to abide by its terms.
diff --git a/README.md b/README.md
index 86aee68..cef4c07 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,5 @@
 # `pywaterflood`: Waterflood Connectivity Analysis
+
 [![PyPI version](https://badge.fury.io/py/pywaterflood.svg)](https://badge.fury.io/py/pywaterflood)
 [![Documentation Status](https://readthedocs.org/projects/pywaterflood/badge/?version=latest)](https://pywaterflood.readthedocs.io/en/latest/?badge=latest)
 [![License](https://img.shields.io/badge/License-BSD_2--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)
@@ -7,24 +8,41 @@
 [![PyPI - Downloads](https://img.shields.io/pypi/dm/pywaterflood)](https://pypi.org/project/pywaterflood/)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/frank1010111/pywaterflood/master?labpath=docs%2Fexample.ipynb)
 
-
-
-`pywaterflood` provides tools for capacitance resistance modeling, a physics-inspired model for estimating waterflood performance. It estimates the connectivities and time decays between injectors and producers.
+`pywaterflood` provides tools for capacitance resistance modeling, a
+physics-inspired model for estimating waterflood performance. It estimates the
+connectivities and time decays between injectors and producers.
 
 ## Overview
 
-A literature review has been written by Holanda, Gildin, Jensen, Lake and Kabir, entitled "A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting." [They](https://doi.org/10.3390/en11123368) describe CRM as the following:
-> The Capacitance Resistance Model (CRM) is a fast way for modeling and simulating gas and waterflooding recovery processes, making it a useful tool for improving flood management in real-time. CRM is an input-output and material balance-based model, and requires only injection and production history, which are the most readily available data gathered throughout the production life of a reservoir.  
-
-There are several CRM versions (see Holanda et al., 2018). Through passing different parameters when creating the CRM instance, you can choose between CRMIP, where a unique time constant is used for each injector-producer pair, and CRMP, where a unique time constant is used for each producer. CRMIP is more reliable given sufficient data. With CRMP, you can reduce the number of unknowns, which is useful if available production data is limited.
+A literature review has been written by Holanda, Gildin, Jensen, Lake and Kabir,
+entitled "A State-of-the-Art Literature Review on Capacitance Resistance Models
+for Reservoir Characterization and Performance Forecasting."
+[They](https://doi.org/10.3390/en11123368) describe CRM as the following:
+
+> The Capacitance Resistance Model (CRM) is a fast way for modeling and
+> simulating gas and waterflooding recovery processes, making it a useful tool
+> for improving flood management in real-time. CRM is an input-output and
+> material balance-based model, and requires only injection and production
+> history, which are the most readily available data gathered throughout the
+> production life of a reservoir.
+
+There are several CRM versions (see Holanda et al., 2018). Through passing
+different parameters when creating the CRM instance, you can choose between
+CRMIP, where a unique time constant is used for each injector-producer pair, and
+CRMP, where a unique time constant is used for each producer. CRMIP is more
+reliable given sufficient data. With CRMP, you can reduce the number of
+unknowns, which is useful if available production data is limited.
 
 ## Getting started
+
 You can install this package from PyPI with the line
+
 ```
 pip install pywaterflood
 ```
 
 ### A simple example
+
     import pandas as pd
     from pywaterflood import CRM
 
diff --git a/docs/Makefile b/docs/Makefile
old mode 100755
new mode 100644
diff --git a/docs/changelog.md b/docs/changelog.md
old mode 100755
new mode 100644
index 8261b35..d9e79ba
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -1,2 +1,3 @@
 ```{include} ../CHANGELOG.md
-```
\ No newline at end of file
+
+```
diff --git a/docs/conduct.md b/docs/conduct.md
old mode 100755
new mode 100644
index 0568705..10a5499
--- a/docs/conduct.md
+++ b/docs/conduct.md
@@ -1,2 +1,3 @@
 ```{include} ../CONDUCT.md
-```
\ No newline at end of file
+
+```
diff --git a/docs/conf.py b/docs/conf.py
index ae811e3..510a4f1 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -4,11 +4,12 @@
 # list see the documentation:
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 
-# -- Project information -----------------------------------------------------
+from __future__ import annotations
 
-project = u"pywaterflood"
-copyright = u"2021, Frank Male"
-author = u"Frank Male"
+# -- Project information -----------------------------------------------------
+project = "pywaterflood"
+copyright = "2021, Frank Male"
+author = "Frank Male"
 
 # -- General configuration ---------------------------------------------------
 
@@ -40,5 +41,7 @@
 
 # Autodoc
 autodoc_default_options = {
-    "members": True, "undoc-members": True,     "private-members": False
-}
\ No newline at end of file
+    "members": True,
+    "undoc-members": True,
+    "private-members": False,
+}
diff --git a/docs/contributing.md b/docs/contributing.md
old mode 100755
new mode 100644
index 435d357..66c1f98
--- a/docs/contributing.md
+++ b/docs/contributing.md
@@ -1,2 +1,3 @@
 ```{include} ../CONTRIBUTING.md
-```
\ No newline at end of file
+
+```
diff --git a/docs/example.ipynb b/docs/example.ipynb
index 00da795..caf0863 100644
--- a/docs/example.ipynb
+++ b/docs/example.ipynb
@@ -35,9 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "gh_url = (\n",
-    "    \"https://raw.githubusercontent.com/frank1010111/pywaterflood/master/testing/data/\"\n",
-    ")\n",
+    "gh_url = \"https://raw.githubusercontent.com/frank1010111/pywaterflood/master/testing/data/\"\n",
     "prod = pd.read_csv(gh_url + \"production.csv\", header=None)\n",
     "inj = pd.read_csv(gh_url + \"injection.csv\", header=None)\n",
     "time = pd.read_csv(gh_url + \"time.csv\", header=None)\n",
@@ -54,7 +52,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x576 with 4 Axes>"
       ]
@@ -174,7 +172,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/docs/index.md b/docs/index.md
index 32a5fd3..fd43f80 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,4 +1,5 @@
 ```{include} ../README.md
+
 ```
 
 ```{toctree}
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 594c0e4..6be6bdd 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,10 +1,10 @@
-sphinx==4.2.0
-sphinx-autoapi==1.8.4
-sphinx-rtd-theme==1.0
-myst-nb==0.13.1
-pandas==1.2.4
 matplotlib==3.3.4
+myst-nb==0.13.1
 numba==0.53.1
 numpy==1.20.1
+pandas==1.2.4
+pywaterflood
 scipy==1.6.2
-pywaterflood
\ No newline at end of file
+sphinx==4.2.0
+sphinx-autoapi==1.8.4
+sphinx-rtd-theme==1.0
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..838955f
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3448 @@
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+[package.dependencies]
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+[package.extras]
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+[package.dependencies]
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+zipp = [
+    {file = "zipp-3.8.0-py3-none-any.whl", hash = "sha256:c4f6e5bbf48e74f7a38e7cc5b0480ff42b0ae5178957d564d18932525d5cf099"},
+    {file = "zipp-3.8.0.tar.gz", hash = "sha256:56bf8aadb83c24db6c4b577e13de374ccfb67da2078beba1d037c17980bf43ad"},
+]
diff --git a/pyproject.toml b/pyproject.toml
index 77ca6b2..be8aead 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,91 @@
+[tool.poetry]
+name = "pywaterflood"
+version = "0.1.1"
+description = "Physics-inspired waterflood performance modeling"
+authors = ["Frank Male <frmale@utexas.edu>"]
+license = "BSD"
+readme = "README.md"
+repository = "https://github.com/frank1010111/pywaterflood"
+documentation = "https://pywaterflood.readthedocs.io"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: BSD License",
+    "Operating System :: OS Independent",
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Science/Research",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Hydrology",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.7,<3.11"
+numpy = ">=1.10"
+scipy = ">=1.4"
+pandas = ">=1.0"
+openpyxl = ">=3.0.9"
+joblib = ">=1.1.0"
+numba = ">=0.54"
+# optional
+jupyterlab = {version = ">=3.2", optional = true}
+seaborn = {version = ">=0.10", optional = true}
+sphinx = {version = ">=4.2.0", optional = true}
+sphinx-autoapi = {version = ">=1.8.4", optional = true}
+sphinx-rtd-theme = {version = ">=1.0", optional = true}
+myst-nb = {version = ">=0.13.1", optional = true}
+
+[tool.poetry.dev-dependencies]
+black = ">=22"
+flake8 = ">=4.0.1"
+flake8-bugbear = ">=21.11.29"
+flake8-builtins = ">=1.5.3"
+flake8-comprehensions = ">=3.7.0"
+flake8-string-format = ">=0.3.0"
+mypy = ">=0.920"
+pre-commit = ">=2.0"
+pytest = ">=6.2.5"
+pydocstyle = ">=6.1.1"
+pytest-cov = ">=3.0.0"
+tox = ">=3.24.4"
+coverage = {version = ">=6.3.3", extras = ["toml"]}
+tox-gh-actions = ">=2.9.1"
+
+[tool.poetry.extras]
+docs = [
+    "sphinx",
+    "sphinx-autoapi",
+    "sphinx-rtd-theme",
+    "myst-nb",
+]
+devwork = [
+    "jupyterlab",
+    "seaborn",
+]
+
+[tool.poetry.urls]
+"Bug Tracker" = "https://github.com/frank1010111/pywaterflood/issues"
+
 [build-system]
-requires = [
-	 "setuptools>=42",
-	 "wheel"
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.coverage.paths]
+source = ["pywaterflood", "*/site-packages"]
+
+[tool.coverage.run]
+branch = true
+source = ["pywaterflood"]
+
+[tool.coverage.report]
+show_missing = true
+
+[tool.pytest.ini_options]
+minversion = "6.0"
+markers = [
+    "slow: mark test as slow."
 ]
-build-backend = "setuptools.build_meta"
\ No newline at end of file
+
+[tool.isort]
+profile = "black"
+
+[tool.black]
+line-length = 99
\ No newline at end of file
diff --git a/pywaterflood/__init__.py b/pywaterflood/__init__.py
index ae254d7..673eeb4 100644
--- a/pywaterflood/__init__.py
+++ b/pywaterflood/__init__.py
@@ -1,6 +1,27 @@
-"pywaterflood"
-from importlib.metadata import version
+"""pywaterflood: A package for explaining and predicting waterflood performance.
 
-__version__ = version("pywaterflood")
-from .crm import CRM
-from .multiwellproductivity import calc_gains_homogeneous, translate_locations
+Provides
+--------
+CRM : a class for performing capacitance resistance modeling
+calc_gains_homogeneous : a function using the multiwell productivity index
+translate_location : a function for moving things from the real world
+  into dimensionless-land
+"""
+from __future__ import annotations
+
+import sys
+
+from pywaterflood.crm import CRM, CrmCompensated
+from pywaterflood.multiwellproductivity import (  # noqa: F401
+    calc_gains_homogeneous,
+    translate_locations,
+)
+
+pyversion = sys.version_info
+if pyversion.major == 3 and pyversion.minor > 7:
+    from importlib.metadata import version
+
+    __version__ = version("pywaterflood")
+del sys, pyversion
+
+__all__ = ["CRM", "CrmCompensated", "calc_gains_homogeneous", "translate_locations"]
diff --git a/pywaterflood/crm.py b/pywaterflood/crm.py
index 9878297..623f1f0 100644
--- a/pywaterflood/crm.py
+++ b/pywaterflood/crm.py
@@ -1,33 +1,54 @@
+"""Analyze waterfloods with capacitance-resistance models. # noqa: D401,D400
+
+Classes
+-------
+CRM : standard capacitance resistance modeling
+CrmCompensated : including pressure
+
+Methods
+-------
+q_primary : primary production
+q_CRM_perpair : production due to injection (injector-producer pairs)
+q_CRM_perproducer : production due to injection (one producer, many injectors)
+q_bhp : production from changing bottomhole pressures of producers
+"""
+from __future__ import annotations
+
+import pickle
+from typing import Any, Optional, Tuple, Union
+
 import numpy as np
-from numpy import ndarray
-from numba import njit
 import pandas as pd
-import pickle
-from scipy import optimize
-from typing import Optional, Tuple, Union
 from joblib import Parallel, delayed
+from numba import njit
+from numpy import ndarray
+from scipy import optimize
 
 
 @njit
 def q_primary(
     production: ndarray, time: ndarray, gain_producer: ndarray, tau_producer: ndarray
 ) -> ndarray:
-    """Calculates primary production contribution using Arps equation with b=0
+    """Calculate primary production contribution.
+
+    Uses Arps equation with b=0
+    .. math::
+        q_{p}(t) = q_i e^{-bt}
 
     Args
     ----------
     production : ndarray
         Production, size: Number of time steps
-    time : ndarray 
+    time : ndarray
         Producing times to forecast, size: Number of time steps
     gain_producer : ndarray
         Arps q_i factor
-    tau_producer : ndarray 
+    tau_producer : ndarray
         Arps time constant
 
     Returns
     ----------
-    q_hat : ndarray 
+    q_hat : ndarray
         Calculated production, size: Number of time steps
     """
     time_decay = np.exp(-time / tau_producer)
@@ -36,11 +57,11 @@ def q_primary(
 
 
 @njit
-def q_CRM_perpair(
-    injection: ndarray, time: ndarray, gains: ndarray, taus: ndarray
-) -> ndarray:
-    """Calculates per injector-producer pair production for all injectors on one producer
-    using CRM model
+def q_CRM_perpair(injection: ndarray, time: ndarray, gains: ndarray, taus: ndarray) -> ndarray:
+    """Calculate per injector-producer pair production.
+
+    Runs for influences of each injector on one producer, assuming
+    individual `gain`s and `tau`s for each pair
 
     Args
     ----------
@@ -66,9 +87,7 @@ def q_CRM_perpair(
 
     # Compute convolved injection rates
     for j in range(injection.shape[1]):
-        conv_injected[0, j] += (1 - np.exp((time[0] - time[1]) / taus[j])) * injection[
-            0, j
-        ]
+        conv_injected[0, j] += (1 - np.exp((time[0] - time[1]) / taus[j])) * injection[0, j]
         for k in range(1, n):
             for m in range(1, k + 1):
                 time_decay = (1 - np.exp((time[m - 1] - time[m]) / taus[j])) * np.exp(
@@ -84,20 +103,19 @@ def q_CRM_perpair(
 
 
 @njit
-def q_CRM_perproducer(
-    injection: ndarray, time: ndarray, gain: ndarray, tau: float
-) -> ndarray:
-    """Calculates per injector-producer pair production for all injectors on one producer
-    using simplified CRMp model that assumes a single tau for each producer
+def q_CRM_perproducer(injection: ndarray, time: ndarray, gain: ndarray, tau: float) -> ndarray:
+    """Calculate per injector-producer pair production (simplified tank).
+
+    Uses simplified CRMp model that assumes a single tau for each producer
 
     Args
     ----------
     injection : ndarray
-        Production, size: Number of time steps
+        injected fluid in reservoir volumes, size: Number of time steps
     time : ndarray
         Producing times to forecast, size: Number of time steps
     gains : ndarray
-        Connectivities between each injector and the producer, 
+        Connectivities between each injector and the producer
         size: Number of injectors
     tau : float
         Time constants all injectors and the producer
@@ -111,16 +129,52 @@ def q_CRM_perproducer(
     return q_CRM_perpair(injection, time, gain, tau2)
 
 
-def random_weights(
-    n_i: int, n_j: int, axis: int = 0, seed: Optional[int] = None
-) -> ndarray:
-    """Generates random weights for producer-injector gains
-    
+@njit
+def _pressure_diff(pressure_local: ndarray, pressure: ndarray) -> ndarray:
+    """Pressure differences from local to each producer each timestep."""
+    n_t, n_p = pressure.shape
+    pressure_diff = np.zeros((n_p, n_t))
+    for j in range(n_p):
+        for t in range(1, n_t):
+            pressure_diff[j, t] = pressure_local[t - 1] - pressure[t, j]
+    return pressure_diff
+
+
+def q_bhp(pressure_local: ndarray, pressure: ndarray, v_matrix: ndarray) -> ndarray:
+    r"""Calculate the production effect from bottom-hole pressure variation.
+
+    This looks like
+    .. math::
+        q_{BHP,j}(t_i) = \sum_{k} v_{kj}\left[ p_j(t_{i-1}) - p_k(t_i) \right]
+
+    Args
+    ----
+    pressure_local : ndarray
+        pressure for the well in question, shape: n_time
+    pressure : ndarray
+        bottomhole pressure, shape: n_time, n_producers
+    v_matrix : ndarray
+        connectivity between one producer and all producers, shape: n_producers
+
+    Returns
+    -------
+    q : ndarray
+        production from changing BHP
+        shape: n_time
+    """
+    pressure_diff = _pressure_diff(pressure_local, pressure)
+    q = np.einsum("j,jt->t", v_matrix, pressure_diff)
+    return q
+
+
+def random_weights(n_i: int, n_j: int, axis: int = 0, seed: Optional[int] = None) -> ndarray:
+    """Generate random weights for producer-injector gains.
+
     Args
     ----
     n_i : int
     n_j : int
-    axis : int, default is 0 
+    axis : int, default is 0
     seed : int, default is None
 
     Returns
@@ -135,7 +189,7 @@ def random_weights(
 
 
 class CRM:
-    """A Capacitance Resistance Model history matcher
+    """A Capacitance Resistance Model history matcher.
 
     CRM uses a physics-inspired mass balance approach to explain production for \
         waterfloods. It treants each injector-producer well pair as a system \
@@ -146,7 +200,7 @@ class CRM:
     ----------
     primary : bool
         Whether to model primary production (strongly recommended)
-    tau_selection : str 
+    tau_selection : str
         How many tau values to select
             - If 'per-pair', fit tau for each producer-injector pair
             - If 'per-producer', fit tau for each producer (CRMp model)
@@ -177,6 +231,7 @@ def __init__(
         tau_selection: str = "per-pair",
         constraints: str = "positive",
     ):
+        """Initialize CRM with appropriate settings."""
         if type(primary) != bool:
             raise TypeError("primary must be a boolean")
         self.primary = primary
@@ -210,7 +265,7 @@ def fit(
         random: bool = False,
         **kwargs,
     ):
-        """Build a CRM model from the production and injection data (production, injection)
+        """Build a CRM model from the production and injection data.
 
         Args
         ----------
@@ -224,30 +279,22 @@ def fit(
             relative time for each rate measurement, starting from 0,
             shape: (n_time)
         initial_guess : ndarray
-            initial guesses for gains, taus, primary production
-            contribution, of 
+            initial guesses for gains, taus, primary production contribution
             shape: (len(guess), n_producers)
         num_cores (int): number of cores to run fitting procedure on, defaults to 1
         random : bool
             whether to randomly initialize the gains
-        **kwargs: 
+        **kwargs:
             keyword arguments to pass to scipy.optimize fitting routine
 
         Returns
         ----------
         self: trained model
         """
+        _validate_inputs(production, injection, time)
         self.production = production
         self.injection = injection
         self.time = time
-        if production.shape[0] != injection.shape[0]:
-            raise ValueError(
-                "production and injection do not have the same number of time steps"
-            )
-        if production.shape[0] != time.shape[0]:
-            raise ValueError(
-                "production and time do not have the same number of timesteps"
-            )
 
         if not initial_guess:
             initial_guess = self._get_initial_guess(random=random)
@@ -258,8 +305,7 @@ def fit_well(production, x0):
             # residual is an L2 norm
             def residual(x, production):
                 return sum(
-                    (production - self._calculate_qhat(x, production, injection, time))
-                    ** 2
+                    (production - self._calculate_qhat(x, production, injection, time)) ** 2
                 )
 
             result = optimize.minimize(
@@ -272,18 +318,15 @@ def residual(x, production):
             )
             return result
 
-        production_perwell = [x for x in self.production.T]
         if num_cores == 1:
-            results = map(fit_well, production_perwell, initial_guess)
+            results = map(fit_well, self.production.T, initial_guess)
         else:
             results = Parallel(n_jobs=num_cores)(
-                delayed(fit_well)(p) for p, x0 in zip(production_perwell, initial_guess)
+                delayed(fit_well)(p, x0) for p, x0 in zip(self.production.T, initial_guess)
             )
 
         opts_perwell = [self._split_opts(r["x"]) for r in results]
-        gains_perwell, tau_perwell, gains_producer, tau_producer = map(
-            list, zip(*opts_perwell)
-        )
+        gains_perwell, tau_perwell, gains_producer, tau_producer = map(list, zip(*opts_perwell))
 
         self.gains = np.vstack(gains_perwell)
         self.tau = np.vstack(tau_perwell)
@@ -291,7 +334,7 @@ def residual(x, production):
         self.tau_producer = np.array(tau_producer)
         return self
 
-    def predict(self, injection=None, time=None, connections={}):
+    def predict(self, injection=None, time=None, connections=None):
         """Predict production for a trained model.
 
         If the injection and time are not provided, this will use the training values
@@ -311,18 +354,16 @@ def predict(self, injection=None, time=None, connections={}):
         q_hat :ndarray
             The predicted values, shape (n_time, n_producers)
         """
-        gains = connections["gains"] if "gains" in connections else self.gains
-        tau = connections["tau"] if "tau" in connections else self.tau
-        gains_producer = (
-            connections["gains_producer"]
-            if "gains_producer" in connections
-            else self.gains_producer
-        )
-        tau_producer = (
-            connections["tau_producer"]
-            if "tau_producer" in connections
-            else self.tau_producer
-        )
+        if connections is not None:
+            gains = connections.get("gains", self.gains)
+            tau = connections.get("tau", self.tau)
+            gains_producer = connections.get("gains_producer", self.gains_producer)
+            tau_producer = connections.get("tau_producer", self.tau_producer)
+        else:
+            gains = self.gains
+            tau = self.tau
+            gains_producer = self.gains_producer
+            tau_producer = self.tau_producer
         production = self.production
         n_producers = production.shape[1]
 
@@ -337,14 +378,23 @@ def predict(self, injection=None, time=None, connections={}):
 
         q_hat = np.zeros((len(time), n_producers))
         for i in range(n_producers):
-            q_hat[:, i] += q_primary(
-                production[:, i], time, gains_producer[i], tau_producer[i]
-            )
+            q_hat[:, i] += q_primary(production[:, i], time, gains_producer[i], tau_producer[i])
             q_hat[:, i] += self.q_CRM(injection, time, gains[i, :], tau[i])
         return q_hat
 
     def set_rates(self, production=None, injection=None, time=None):
-        """Sets production and injection rates and time"""
+        """Set production and injection rates and time array.
+
+        Args
+        -----
+        production : ndarray
+            production rates with shape (n_time, n_producers)
+        injection : ndarray
+            injection rates with shape (n_time, n_injectors)
+        time : ndarray
+            timesteps with shape n_time
+        """
+        _validate_inputs(production, injection, time)
         if production is not None:
             self.production = production
         if injection is not None:
@@ -352,10 +402,22 @@ def set_rates(self, production=None, injection=None, time=None):
         if time is not None:
             self.time = time
 
-    def set_connections(
-        self, gains=None, tau=None, gains_producer=None, tau_producer=None
-    ):
-        """Sets waterflood properties"""
+    def set_connections(self, gains=None, tau=None, gains_producer=None, tau_producer=None):
+        """Set waterflood properties.
+
+        Args
+        -----
+        gains : ndarray
+            connectivity between injector and producer
+            shape: n_gains, n_producers
+        tau : ndarray
+            time-constant for injection to be felt by production
+            shape: either n_producers or (n_gains, n_producers)
+        gains_producer : ndarray
+            gain on primary production, shape: n_producers
+        tau_producer : ndarray
+            Arps time constant for primary production, shape: n_producers
+        """
         if gains is not None:
             self.gains = gains
         if tau is not None:
@@ -376,7 +438,7 @@ def residual(self, production=None, injection=None, time=None):
         production : ndarray
             The production rates observed, shape: (n_timesteps, n_producers)
         injection : ndarray
-            The injection rates to input to the system, 
+            The injection rates to input to the system,
             shape: (n_timesteps, n_injectors)
         time : ndarray
             The timesteps to predict
@@ -392,8 +454,8 @@ def residual(self, production=None, injection=None, time=None):
         return production - q_hat
 
     def to_excel(self, fname: str):
-        """Write trained model to an Excel file
-        
+        """Write trained model to an Excel file.
+
         Args
         ----
         fname : str
@@ -414,8 +476,8 @@ def to_excel(self, fname: str):
             ).to_excel(f, sheet_name="Primary production")
 
     def to_pickle(self, fname: str):
-        """Write trained model to a pickle file
-        
+        """Write trained model to a pickle file.
+
         Args
         -----
         fname : str
@@ -424,8 +486,8 @@ def to_pickle(self, fname: str):
         with open(fname, "wb") as f:
             pickle.dump(self, f)
 
-    def _get_initial_guess(self, tau_selection: str = "", random=False):
-        """Creates the initial guesses for the CRM model parameters
+    def _get_initial_guess(self, tau_selection: Optional[str] = None, random=False):
+        """Create initial guesses for the CRM model parameters.
 
         :meta private:
 
@@ -433,20 +495,21 @@ def _get_initial_guess(self, tau_selection: str = "", random=False):
         ----------
         tau_selection : str, one of 'per-pair' or 'per-producer'
             sets whether to use CRM (per-pair) or CRMp model
-
+        random : bool
+            whether initial gains are randomly (true) or proportionally assigned
         Returns
         ----------
         x0 : ndarray
-            Initial primary production gain, time constant and waterflood gains 
+            Initial primary production gain, time constant and waterflood gains
             and time constants, as one long 1-d array
         """
-        if tau_selection:
+        if tau_selection is not None:
             self.tau_selection = tau_selection
 
         n_inj = self.injection.shape[1]
         n_prod = self.production.shape[1]
         d_t = self.time[1] - self.time[0]
-        n_gains, n_tau, n_primary = self._opt_numbers()
+        n_gains, n_tau, n_primary = self._opt_numbers()[:3]
 
         axis = 1 if (self.constraints == "sum-to-one injector") else 0
         if random:
@@ -476,19 +539,12 @@ def _get_initial_guess(self, tau_selection: str = "", random=False):
                 for i in range(n_prod)
             ]
         else:
-            x0 = [
-                np.concatenate([gains_guess1[i, :], tau_guess1[i, :]])
-                for i in range(n_prod)
-            ]
+            x0 = [np.concatenate([gains_guess1[i, :], tau_guess1[i, :]]) for i in range(n_prod)]
         return x0
 
     def _opt_numbers(self) -> Tuple[int, int, int]:
-        """
-        returns the number of gains, taus, and primary production parameters to fit
-        """
-        n_inj = self.injection.shape[1]
-        # n_prod = self.production.shape[1]
-        n_gains = n_inj
+        """Return the number of gains, taus, and primary production parameters to fit."""
+        n_gains = self.injection.shape[1]
         if self.tau_selection == "per-pair":
             n_tau = n_gains
         else:
@@ -500,19 +556,12 @@ def _opt_numbers(self) -> Tuple[int, int, int]:
         return n_gains, n_tau, n_primary
 
     def _get_bounds(self, constraints: str = "") -> Tuple[Tuple, Union[Tuple, dict]]:
-        """Create bounds for the model from initialized constraints
-        """
+        """Create bounds for the model from initialized constraints."""
         if constraints:
             self.constraints = constraints
 
         n_inj = self.injection.shape[1]
-        if self.tau_selection == "per-pair":
-            n = n_inj * 2
-        else:
-            n = n_inj + 1
-
-        if self.primary:
-            n = n + 2
+        n = sum(self._opt_numbers())
 
         if self.constraints == "positive":
             bounds = ((0, np.inf),) * n
@@ -545,7 +594,6 @@ def _calculate_qhat(
         injection: np.ndarray,
         time: np.ndarray,
     ):
-        ":meta private:"
         gains, tau, gain_producer, tau_producer = self._split_opts(x)
         if self.primary:
             q_hat = q_primary(production, time, gain_producer, tau_producer)
@@ -578,3 +626,222 @@ def _split_opts(self, x: np.ndarray):
         if tau_producer < 1e-10:
             tau_producer = 1e-10
         return gains, tau, gain_producer, tau_producer
+
+
+class CrmCompensated(CRM):
+    """Bottom-hole pressure compensated CRM."""
+
+    def fit(
+        self,
+        production: ndarray,
+        pressure: ndarray,
+        injection: ndarray,
+        time: ndarray,
+        initial_guess: ndarray = None,
+        num_cores: int = 1,
+        random: bool = False,
+        **kwargs,
+    ):
+        """Fit a CRM model from the production, pressure, and injection data.
+
+        Args
+        ----------
+        production : ndarray
+            production rates for each time period,
+            shape: (n_time, n_producers)
+        pressure : ndarray
+            average pressure for each producer for each time period,
+            shape: (n_time, n_producers)
+        injection : ndarray
+            injection rates for each time period,
+            shape: (n_time, n_injectors)
+        time : ndarray
+            relative time for each rate measurement, starting from 0,
+            shape: (n_time)
+        initial_guess : ndarray
+            initial guesses for gains, taus, primary production
+            contribution
+            shape: (len(guess), n_producers)
+        num_cores (int): number of cores to run fitting procedure on, defaults to 1
+        random : bool
+            whether to randomly initialize the gains
+        **kwargs:
+            keyword arguments to pass to scipy.optimize fitting routine
+
+        Returns
+        ----------
+        self: trained model
+        """
+        _validate_inputs(production, injection, time, pressure)
+        self.production = production
+        self.injection = injection
+        self.time = time
+        self.pressure = pressure
+
+        if not initial_guess:
+            initial_guess = self._get_initial_guess(random=random)
+        bounds, constraints = self._get_bounds()
+
+        def fit_well(production, pressure_local, x0):
+            # residual is an L2 norm
+            def residual(x, production):
+                return sum(
+                    (
+                        production
+                        - self._calculate_qhat(
+                            x, production, injection, time, pressure_local, pressure
+                        )
+                    )
+                    ** 2
+                )
+
+            result = optimize.minimize(
+                residual,
+                x0,
+                bounds=bounds,
+                constraints=constraints,
+                args=(production,),
+                **kwargs,
+            )
+            return result
+
+        if num_cores == 1:
+            results = map(fit_well, self.production.T, pressure.T, initial_guess)
+        else:
+            results = Parallel(n_jobs=num_cores)(
+                delayed(fit_well)(prod, pressure, x0)
+                for prod, pressure, x0 in zip(self.production.T, pressure.T, initial_guess)
+            )
+
+        opts_perwell = [self._split_opts(r["x"]) for r in results]
+        gains_perwell, tau_perwell, gains_producer, tau_producer, gain_pressure = map(
+            list, zip(*opts_perwell)
+        )
+
+        self.gains = np.vstack(gains_perwell)
+        self.tau = np.vstack(tau_perwell)
+        self.gains_producer = np.array(gains_producer)
+        self.tau_producer = np.array(tau_producer)
+        self.gain_pressure = np.vstack(gain_pressure)
+        return self
+
+    def _calculate_qhat(  # TODO: start here
+        self,
+        x: np.ndarray,
+        production: np.ndarray,
+        injection: np.ndarray,
+        time: np.ndarray,
+        pressure_local: np.ndarray,
+        pressure: np.ndarray,
+    ):
+        gains, tau, gain_producer, tau_producer, gain_pressure = self._split_opts(x)
+        if self.primary:
+            q_hat = q_primary(production, time, gain_producer, tau_producer)
+        else:
+            q_hat = np.zeros(len(time))
+
+        q_hat += self.q_CRM(injection, time, gains, tau)
+        q_hat += q_bhp(pressure_local, pressure, gain_pressure)
+        return q_hat
+
+    def _opt_numbers(self) -> Tuple[int, int, int, int]:
+        n_gain, n_tau, n_primary = super()._opt_numbers()
+        return n_gain, n_tau, n_primary, self.production.shape[1]
+
+    def _split_opts(self, x: np.ndarray) -> Tuple[ndarray, ndarray, Any, Any, ndarray]:
+        n_gains, n_tau, n_primary = self._opt_numbers()[:3]
+        n_connectivity = n_gains + n_tau
+
+        gains = x[:n_gains]
+        tau = x[n_gains:n_connectivity]
+        if self.primary:
+            gain_producer = x[n_connectivity:][0]
+            tau_producer = x[n_connectivity:][1]
+        else:
+            gain_producer = 0
+            tau_producer = 1
+        gain_pressure = x[n_connectivity + n_primary :]
+
+        # boundary setting
+        if self.tau_selection == "per-pair":
+            tau[tau < 1e-10] = 1e-10
+        elif tau < 1e-10:
+            tau = 1e-10
+        if tau_producer < 1e-10:
+            tau_producer = 1e-10
+        return gains, tau, gain_producer, tau_producer, gain_pressure
+
+    def _get_initial_guess(self, tau_selection: Optional[str] = None, random=False):
+        """Make the initial guesses for the CRM model parameters.
+
+        :meta private:
+
+        Args
+        ----------
+        tau_selection : str, one of 'per-pair' or 'per-producer'
+            sets whether to use CRM (per-pair) or CRMp model
+
+        Returns
+        ----------
+        x0 : ndarray
+            Initial primary production gain, time constant and waterflood gains
+            and time constants, as one long 1-d array
+        """
+        guess = super()._get_initial_guess(tau_selection=tau_selection, random=random)
+        _, _, _, n_pressure = self._opt_numbers()
+        pressure_guess = np.ones(n_pressure)
+        guess = [np.concatenate([guess[i], pressure_guess]) for i in range(len(guess))]
+        return guess
+
+
+def _validate_inputs(
+    production: Optional[ndarray] = None,
+    injection: Optional[ndarray] = None,
+    time: Optional[ndarray] = None,
+    pressure: Optional[ndarray] = None,
+) -> None:
+    """Validate shapes and values of inputs.
+
+    Args
+    ----
+    production : ndarray, optional
+    injection : ndarray, optional
+    time : ndarray, optional
+    pressure : ndarray, optional
+
+    Raises
+    ------
+    ValueError if timesteps don't match or production and pressure don't match
+    """
+    inputs = {
+        "production": production,
+        "injection": injection,
+        "time": time,
+        "pressure": pressure,
+    }
+    inputs = {key: val for key, val in inputs.items() if val is not None}
+    # Shapes
+    test_prod_inj_timesteps = production is not None and injection is not None
+    if test_prod_inj_timesteps and (production.shape[0] != injection.shape[0]):
+        raise ValueError("production and injection do not have the same number of timesteps")
+    if time is not None:
+        for timeseries in inputs:
+            if inputs[timeseries].shape[0] != time.shape[0]:
+                raise ValueError(f"{timeseries} and time do not have the same number of timesteps")
+    if production is not None:
+        if (injection is not None) and (production.shape[0] != injection.shape[0]):
+            raise ValueError("production and injection do not have the same number of timesteps")
+        if (pressure is not None) and (production.shape != pressure.shape):
+            raise ValueError("production and pressure are not of the same shape")
+    if (
+        (injection is not None)
+        and (pressure is not None)
+        and (injection.shape[0] != pressure.shape[0])
+    ):
+        raise ValueError("injection and pressure do not have the same number of timesteps")
+    # Values
+    for timeseries in inputs:
+        if np.any(np.isnan(inputs[timeseries])):
+            raise ValueError(f"{timeseries} cannot have NaNs")
+        if np.any(inputs[timeseries] < 0.0):
+            raise ValueError(f"{timeseries} cannot be negative")
diff --git a/pywaterflood/multiwellproductivity.py b/pywaterflood/multiwellproductivity.py
index d2468f0..5d6a670 100644
--- a/pywaterflood/multiwellproductivity.py
+++ b/pywaterflood/multiwellproductivity.py
@@ -1,23 +1,25 @@
+"""Interwell connectivity through geometric considerations."""
+
+from __future__ import annotations
+
 import numpy as np
-from numpy import ndarray
 import pandas as pd
 import scipy.linalg as sl
 from numba import njit
+from numpy import ndarray
 
 idx = pd.IndexSlice
 
 
-def calc_gains_homogeneous(
-    locations: pd.DataFrame, x_e: float, y_e: float
-) -> pd.DataFrame:
-    """Calculate gains from injectors to producers using multiwell productivity index
+def calc_gains_homogeneous(locations: pd.DataFrame, x_e: float, y_e: float) -> pd.DataFrame:
+    """Calculate gains from injectors to producers using multiwell productivity index.
 
     Args
     ----------
     locations : pd.DataFrame
         columns include:
-        `X` : x-location for well,
-        `Y` : y-location for well, 
+        `X` : x-location for well
+        `Y` : y-location for well
         `Type`: ("Producer" or "Injector")
     x_e : float
         scaling in x-direction (unit size)
@@ -36,30 +38,30 @@ def calc_gains_homogeneous(
 
     References
     ----------
-    Kaviani, D. and Valkó, P.P., 2010. Inferring interwell connectivity using multiwell \
-    productivity index (MPI). Journal of Petroleum Science and Engineering, 73(1-2), \
-    pp.48-58.
+    Kaviani, D. and Valkó, P.P., 2010. Inferring interwell connectivity using \
+    multiwell productivity index (MPI). Journal of Petroleum Science and Engineering, \
+    73(1-2), p.48-58.
     """
     locations = locations.copy()
     locations[["X", "Y"]] /= x_e
     y_D = y_e / x_e
     A_prod = calc_influence_matrix(locations, y_D, "prod").astype(float)
     A_conn = calc_influence_matrix(locations, y_D, "conn").astype(float)
-    A_prod_inv = np.linalg.inv(A_prod.values)
+    A_prod_inv = sl.inv(A_prod.values)
     term1 = A_prod_inv / np.sum(A_prod_inv)
     term2 = np.ones_like(A_prod_inv) @ A_prod_inv @ A_conn.values - 1
     term3 = A_prod_inv @ A_conn.values
     Lambda = term1 @ term2 - term3
-    connectivity_df = pd.DataFrame(
-        Lambda, index=A_prod.index, columns=A_conn.columns
-    ).rename_axis(index="Producers", columns="Injectors")
+    connectivity_df = pd.DataFrame(Lambda, index=A_prod.index, columns=A_conn.columns).rename_axis(
+        index="Producers", columns="Injectors"
+    )
     return connectivity_df
 
 
 def translate_locations(
     locations: pd.DataFrame, x_col: str, y_col: str, type_col: str
 ) -> pd.DataFrame:
-    """Translate locations  to prepare for building connectivity matrix
+    """Translate locations  to prepare for building connectivity matrix.
 
     Moves the lower left edge of the reservoir to (0, 0), and sets up the matrix columns
     to work with `calc_gains_homogeneous`
@@ -90,7 +92,7 @@ def translate_locations(
 def calc_influence_matrix(
     locations: pd.DataFrame, y_D: float, matrix_type: str = "conn", m_max: int = 300
 ) -> pd.DataFrame:
-    """Calculate influence matrix A
+    """Calculate influence matrix A.
 
     Args
     ----------
@@ -127,22 +129,20 @@ def calc_influence_matrix(
 
 
 @njit
-def calc_A_ij(
-    x_i: float, y_i: float, x_j: float, y_j: float, y_D: float, m: ndarray
-) -> float:
-    """Calculate element in the influence matrix
+def calc_A_ij(x_i: float, y_i: float, x_j: float, y_j: float, y_D: float, m: ndarray) -> float:
+    r"""Calculate element in the influence matrix.
 
-    .. math:: 
-        A_{ij} = 2 \\pi y_D (\\frac13 - \\frac{y_i}{y_D} + 
-            \\frac{y_i^2 + y_j^2}{2 y_D^2}) 
-            + \\sum_{m=1}^\\infty \\frac{t_m}m \\cos(m\\pi \\tilde x_i) 
-            \\cos(m \\pi \\tilde x_j)
+    .. math::
+        A_{ij} = 2 \pi y_D (\frac13 - \frac{y_i}{y_D} +
+            \frac{y_i^2 + y_j^2}{2 y_D^2})
+            + \sum_{m=1}^\infty \frac{t_m}m \cos(m\pi \tilde x_i)
+            \cos(m \pi \tilde x_j)
     where
 
     .. math::
-        t_m = \\frac{\\cosh\\left(m\\pi (y_D - |\\tilde y_i - \\tilde y_j|)\\right) 
-        + \\cosh\\left(m\\pi (y_D - \\tilde y_i - \\tilde y_j\\right)}
-        {\\sinh\\left(m\\pi y_D \\right)}
+        t_m = \frac{\cosh\left(m\pi (y_D - |\tilde y_i - \tilde y_j|)\right)
+        + \cosh\left(m\pi (y_D - \tilde y_i - \tilde y_j\right)}
+        {\sinh\left(m\pi y_D \right)}
 
     Args
     ----
@@ -163,9 +163,7 @@ def calc_A_ij(
     -------
     A_ij : float
     """
-    first_term = (
-        2 * np.pi * y_D * (1 / 3.0 - y_i / y_D + (y_i ** 2 + y_j ** 2) / (2 * y_D ** 2))
-    )
+    first_term = 2 * np.pi * y_D * (1 / 3.0 - y_i / y_D + (y_i**2 + y_j**2) / (2 * y_D**2))
     return first_term + calc_summed_term(x_i, y_i, x_j, y_j, y_D, m)
 
 
@@ -173,18 +171,18 @@ def calc_A_ij(
 def calc_summed_term(
     x_i: float, y_i: float, x_j: float, y_j: float, y_D: float, m: ndarray
 ) -> float:
-    """Calculate summed term using Valkó 2000 equations A4-7
+    r"""Calculate summed term using Valkó 2000 equations A4-7.
 
     .. math::
-        \\sum_{m=1}^\\infty \\frac{t_m}m \\cos(m\\pi \\tilde x_i) 
-        \\cos(m \\pi \\tilde x_j)
+        \sum_{m=1}^\infty \frac{t_m}m \cos(m\pi \tilde x_i)
+        \cos(m \pi \tilde x_j)
     where
-    
+
     .. math::
-        t_m = \\frac{\\cosh\\left(m\\pi (y_D - |\\tilde y_i - \\tilde y_j|)\\right) 
-        + \\cosh\\left(m\\pi (y_D - \\tilde y_i - \\tilde y_j\\right)}
-        {\\sinh\\left(m\\pi y_D \\right)}
-    
+        t_m = \frac{\cosh\left(m\pi (y_D - |\tilde y_i - \tilde y_j|)\right)
+        + \cosh\left(m\pi (y_D - \tilde y_i - \tilde y_j\right)}
+        {\sinh\left(m\pi y_D \right)}
+
     Args
     ----
     x_i : float
@@ -205,16 +203,13 @@ def calc_summed_term(
     summed_term : float
     """
     tm = (
-        np.cosh(m * np.pi * (y_D - np.abs(y_i - y_j)))
-        + np.cosh(m * np.pi * (y_D - y_i - y_j))
+        np.cosh(m * np.pi * (y_D - np.abs(y_i - y_j))) + np.cosh(m * np.pi * (y_D - y_i - y_j))
     ) / np.sinh(m * np.pi * y_D)
 
     S1 = 2 * np.sum(tm / m * np.cos(m * np.pi * x_i) * np.cos(m * np.pi * x_j))
     tN = tm[-1]
     S2 = -tN / 2 * np.log(
         (1 - np.cos(np.pi * (x_i + x_j))) ** 2 + np.sin(np.pi * (x_i + x_j)) ** 2
-    ) - tN / 2 * np.log(
-        (1 - np.cos(np.pi * (x_i - x_j))) ** 2 + np.sin(np.pi * (x_i - x_j)) ** 2
-    )
+    ) - tN / 2 * np.log((1 - np.cos(np.pi * (x_i - x_j))) ** 2 + np.sin(np.pi * (x_i - x_j)) ** 2)
     S3 = -2 * tN * np.sum(1 / m * np.cos(m * np.pi * x_i) * np.cos(m * np.pi * x_j))
     return S1 + S2 + S3
diff --git a/requirements.yml b/requirements.yml
index 63a7395..9cef2c3 100644
--- a/requirements.yml
+++ b/requirements.yml
@@ -1,13 +1,13 @@
 channels:
-    - defaults
-    - conda-forge
+  - defaults
+  - conda-forge
 dependencies:
-    - python>=3.8
-    - pip
-    - numpy
-    - scipy
-    - pandas
-    - openpyxl
-    - pytest
-    - joblib
-    - numba
+  - python>=3.8
+  - pip
+  - numpy
+  - scipy
+  - pandas
+  - openpyxl
+  - pytest
+  - joblib
+  - numba
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 083c5c9..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,33 +0,0 @@
-[metadata]
-name = pywaterflood
-version = 0.1.1
-author = Frank Male
-author_email = frmale@utexas.edu
-description = Physics-inspired waterflood performance modeling
-long_description = file: README.md
-long_description_content_type = text/markdown
-url = https://github.com/frank1010111/pywaterflood
-project_urls =
-    Bug Tracker = https://github.com/frank1010111/pywaterflood/issues
-classifiers =
-    Programming Language :: Python :: 3
-    License :: OSI Approved :: BSD License
-    Operating System :: OS Independent
-    Development Status :: 4 - Beta
-    Intended Audience :: Science/Research
-    Topic :: Scientific/Engineering
-    Topic :: Scientific/Engineering :: Hydrology
-license_files = LICENSE
-license=MIT
-
-[options]
-package_dir = 
-packages = pywaterflood
-python_requires = >=3.6
-install_requires = 
-    numpy
-    scipy
-    pandas
-    openpyxl
-    joblib
-    numba
\ No newline at end of file
diff --git a/tox.ini b/tox.ini
new file mode 100644
index 0000000..aeb2f64
--- /dev/null
+++ b/tox.ini
@@ -0,0 +1,23 @@
+[tox]
+isolated_build = True
+skip_missing_interpreters = True
+envlist = linter,py37,py38,py39,py310
+
+[gh-actions]
+python =
+    3.7: py37
+    3.8: py38
+    3.9: py39
+    3.10: py310
+
+[gh-actions:env]
+PLATFORM =
+    ubuntu-latest: linux
+    macos-latest: macos
+    windows-latest: windows
+
+[testenv]
+whitelist_externals = poetry
+commands =
+    poetry install
+    poetry run pytest --cov=./ --cov-append --cov-report=xml


Test patch:
diff --git a/.github/workflows/publish-to-test-pypi.yml b/.github/workflows/publish-to-test-pypi.yml
index 2f7f467..cdf1473 100644
--- a/.github/workflows/publish-to-test-pypi.yml
+++ b/.github/workflows/publish-to-test-pypi.yml
@@ -1,30 +1,33 @@
 name: Publish pywaterflood builds to PyPI and TestPyPI
 
-on: push
+on:
+  push:
+    branches:
+      - master
 
 jobs:
   build-and-publish:
     name: Build and publish pywaterflood to PyPI and TestPyPI
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@master
-    - name: set up python
-      uses: actions/setup-python@v2
-      with:
-        python-version: 3.8
-    - name: Install pypa/build
-      run: python -m pip install build --user
-    - name: build wheel and source tarball
-      run: python -m build --sdist --wheel --outdir dist/
-    - name: Publish distribution to Test PyPI
-      uses: pypa/gh-action-pypi-publish@master
-      with:
-        user: __token__
-        password: ${{ secrets.TEST_PYPI_API_TOKEN }}
-        repository_url: https://test.pypi.org/legacy/
-    - name: Publish distribution to PyPI
-      if: startsWith(github.ref, 'refs/tags')
-      uses: pypa/gh-action-pypi-publish@master
-      with:
-        user: __token__
-        password: ${{ secrets.PYPI_API_TOKEN }}
\ No newline at end of file
+      - uses: actions/checkout@master
+      - name: set up python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+      - name: Install pypa/build
+        run: python -m pip install build --user
+      - name: build wheel and source tarball
+        run: python -m build --sdist --wheel --outdir dist/
+      - name: Publish distribution to Test PyPI
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          user: __token__
+          password: ${{ secrets.TEST_PYPI_API_TOKEN }}
+          repository_url: https://test.pypi.org/legacy/
+      - name: Publish distribution to PyPI
+        if: startsWith(github.ref, 'refs/tags')
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
new file mode 100644
index 0000000..4138580
--- /dev/null
+++ b/.github/workflows/tests.yml
@@ -0,0 +1,53 @@
+name: tests
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+
+jobs:
+  linting:
+    name: Linting
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2.3.4
+
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v2.2.2
+        with:
+          python-version: 3.9
+
+      - name: Linting
+        run: |
+          pip install pre-commit
+          pre-commit run --all-files
+
+  tests:
+    name: ${{ matrix.os }} / ${{ matrix.python-version }}
+    runs-on: ${{ matrix.os }}-latest
+    strategy:
+      matrix:
+        os: [Ubuntu, MacOS, windows]
+        python-version: [3.7, 3.8, 3.9, "3.10"]
+      fail-fast: false
+    steps:
+      - uses: actions/checkout@v2.3.4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2.2.2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install poetry
+        uses: abatilo/actions-poetry@v2.0.0
+        with:
+          version: 1.0.5
+      - name: Install dependencies
+        run: poetry install
+      - name: Test with tox
+        run: poetry run tox
+      - name: "Upload coverage to Codecov"
+        uses: codecov/codecov-action@v2
+        with:
+          fail_ci_if_error: true
+          token: ${{ secrets.CODECOV_TOKEN }} # not required for public repos
diff --git a/pytest.ini b/pytest.ini
deleted file mode 100644
index 9cce55c..0000000
--- a/pytest.ini
+++ /dev/null
@@ -1,3 +0,0 @@
-[pytest]
-markers =
-    slow: mark test as slow.
\ No newline at end of file
diff --git a/testing/conftest.py b/testing/conftest.py
new file mode 100644
index 0000000..dff0f45
--- /dev/null
+++ b/testing/conftest.py
@@ -0,0 +1,3 @@
+from __future__ import annotations
+
+collect_ignore = ["regenerate_results.py"]
diff --git a/testing/data/prediction_noprimary_per-pair.csv b/testing/data/prediction_noprimary_per-pair.csv
new file mode 100644
index 0000000..d7ca02d
--- /dev/null
+++ b/testing/data/prediction_noprimary_per-pair.csv
@@ -0,0 +1,298 @@
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diff --git a/testing/data/prediction.csv b/testing/data/prediction_noprimary_per-producer.csv
similarity index 99%
rename from testing/data/prediction.csv
rename to testing/data/prediction_noprimary_per-producer.csv
index bac0b62..ec9a4be 100644
--- a/testing/data/prediction.csv
+++ b/testing/data/prediction_noprimary_per-producer.csv
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diff --git a/testing/data/prediction_primary_per-pair.csv b/testing/data/prediction_primary_per-pair.csv
new file mode 100644
index 0000000..d7ca02d
--- /dev/null
+++ b/testing/data/prediction_primary_per-pair.csv
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diff --git a/testing/data/prediction_noprimary.csv b/testing/data/prediction_primary_per-producer.csv
similarity index 99%
rename from testing/data/prediction_noprimary.csv
rename to testing/data/prediction_primary_per-producer.csv
index bac0b62..ec9a4be 100644
--- a/testing/data/prediction_noprimary.csv
+++ b/testing/data/prediction_primary_per-producer.csv
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diff --git a/testing/regenerate_results.py b/testing/regenerate_results.py
new file mode 100644
index 0000000..d170caf
--- /dev/null
+++ b/testing/regenerate_results.py
@@ -0,0 +1,67 @@
+"""Regenerate results.
+
+This is for regenerating the results that are tested against during test_predict. Every once in
+a while it makes sense to regenerate it, but be careful.
+
+functions
+--------
+test_predict
+
+use
+----
+run `python testing/regenerate_results.py` from the project directory
+"""
+from __future__ import annotations
+
+import numpy as np
+
+from pywaterflood import CRM
+
+# from pywaterflood.crm import CrmCompensated, q_bhp
+
+data_dir = "testing/data/"
+
+
+def reservoir_simulation_data():
+    injection = np.genfromtxt(data_dir + "injection.csv", delimiter=",")
+    production = np.genfromtxt(data_dir + "production.csv", delimiter=",")
+    time = np.genfromtxt(data_dir + "time.csv", delimiter=",")
+    return injection, production, time
+
+
+def _trained_model(*args, **kwargs):
+    crm = CRM(*args, **kwargs)
+    crm.injection, crm.production, crm.time = reservoir_simulation_data()
+    crm.gains = np.genfromtxt(data_dir + "gains.csv", delimiter=",")
+    if crm.tau_selection == "per-pair":
+        crm.tau = np.genfromtxt(data_dir + "taus_per-pair.csv", delimiter=",")
+    else:
+        crm.tau = np.genfromtxt(data_dir + "taus.csv", delimiter=",")
+    crm.gains_producer = np.genfromtxt(data_dir + "gain_producer.csv", delimiter=",")
+    crm.tau_producer = np.genfromtxt(data_dir + "tau_producer.csv", delimiter=",")
+    return crm
+
+
+def test_predict(primary, tau_selection):
+    """Predictions for test cases."""
+    crm = _trained_model(primary=primary, tau_selection=tau_selection)
+
+    prediction = crm.predict()
+    return prediction
+
+
+if __name__ == "__main__":
+    print("Regenerating prediction files")
+    primary = (True, False)
+    tau_selection = ("per-pair", "per-producer")
+    for p in primary:
+        for t in tau_selection:
+            prediction = test_predict(p, t)
+            if p:
+                primary_str = "primary"
+            else:
+                primary_str = "noprimary"
+            np.savetxt(
+                f"{data_dir}prediction_{primary_str}_{t}.csv", prediction, delimiter=","
+            )
+    print("Finished")
diff --git a/testing/test_CRM.py b/testing/test_CRM.py
index bcfad8c..e0e7798 100644
--- a/testing/test_CRM.py
+++ b/testing/test_CRM.py
@@ -1,13 +1,12 @@
 from __future__ import annotations
 
-import sys
 from itertools import product
 
 import numpy as np
 import pytest
 
-sys.path.append("../pyCRM/")
 from pywaterflood import CRM
+from pywaterflood.crm import CrmCompensated, q_bhp
 
 primary = (True, False)
 tau_selection = ("per-pair", "per-producer")
@@ -43,6 +42,34 @@ def _trained_model(*args, **kwargs):
     return _trained_model
 
 
+def test_q_bhp_nonan(reservoir_simulation_data):
+    "q_BHP functions"
+    production = reservoir_simulation_data[1]
+    _, nprod = production.shape
+    pressure = production.copy()
+    rng = np.random.default_rng(42)
+    producer_gains = rng.normal(0, 1, nprod)
+    q = q_bhp(pressure[:, 0], pressure, producer_gains)
+    assert not np.isnan(q).any()
+
+
+def test_q_bhp():
+    n_time = 5
+    n_prod = 2
+    pressure = np.ones((n_time, n_prod))
+    producer_gains = np.random.rand(n_prod)
+    q = q_bhp(pressure[:, 0], pressure, producer_gains)
+    assert np.allclose(q, 0), "no pressure change -> no prod"
+    pressure[-1] = 0
+    producer_gains = np.ones(n_prod)
+    q = q_bhp(pressure[:, 0], pressure, producer_gains)
+    assert np.allclose(q[-1], 2), "pressure drop -> increase production"
+    pressure[-1] = 2
+    producer_gains = np.ones(n_prod)
+    q = q_bhp(pressure[:, 0], pressure, producer_gains)
+    assert np.allclose(q[-1], -2), "pressure increase -> drop production"
+
+
 @pytest.mark.parametrize("primary,tau_selection,constraints", test_args)
 class TestInstantiate:
     def test_init(self, primary, tau_selection, constraints):
@@ -68,20 +95,20 @@ def test_predict(
 
         prediction1 = crm.predict()
         prediction2 = crm.predict(injection, time)
-        prediction1 == pytest.approx(prediction2)
+        assert prediction1 == pytest.approx(prediction2, abs=1.0)
 
         if primary:
-            assert prediction1 == pytest.approx(
-                np.genfromtxt(data_dir + "prediction.csv", delimiter=","),
-                abs=5.0,
-                rel=1e-2,
-            )
+            primary_str = "primary"
         else:
-            assert prediction1 == pytest.approx(
-                np.genfromtxt(data_dir + "prediction_noprimary.csv", delimiter=","),
-                abs=5.0,
-                rel=1e-2,
-            )
+            primary_str = "noprimary"
+
+        assert prediction1 == pytest.approx(
+            np.genfromtxt(
+                f"{data_dir}prediction_{primary_str}_{tau_selection}.csv", delimiter=","
+            ),
+            abs=5.0,
+            rel=1e-2,
+        )
 
     def test_predict_fails(
         self, reservoir_simulation_data, trained_model, primary, tau_selection
@@ -94,9 +121,41 @@ def test_predict_fails(
             crm.predict(injection, time[:-1])
 
 
-# @pytest.mark.skip()
 @pytest.mark.parametrize("primary,tau_selection,constraints", test_args)
 class TestFit:
+    def test_validate_timeseries(
+        self, reservoir_simulation_data, primary, tau_selection, constraints
+    ):
+        injection, production, time = reservoir_simulation_data
+        crm = CRM(primary, tau_selection, constraints)
+        with pytest.raises(ValueError):
+            crm.set_rates(production[:-5], injection, time)
+        with pytest.raises(ValueError):
+            crm.set_rates(production, injection[:-5], time)
+        with pytest.raises(ValueError):
+            crm.set_rates(production, injection, time[:-5])
+        prod_bad = production.copy()
+        prod_bad[4, 0] = -1
+        with pytest.raises(ValueError):
+            crm.set_rates(prod_bad, injection, time)
+        prod_bad[4, 0] = np.nan
+        with pytest.raises(ValueError):
+            crm.set_rates(prod_bad, injection, time)
+        inj_bad = injection.copy()
+        inj_bad[2, 2] = -1
+        with pytest.raises(ValueError):
+            crm.set_rates(production, inj_bad, time)
+        inj_bad[2, 2] = np.nan
+        with pytest.raises(ValueError):
+            crm.set_rates(production, inj_bad, time)
+        time_bad = time.copy()
+        time_bad[0] = -1
+        with pytest.raises(ValueError):
+            crm.set_rates(time=time_bad)
+        time_bad[0] = np.nan
+        with pytest.raises(ValueError):
+            crm.set_rates(time=time_bad)
+
     def test_fit_fails(
         self, reservoir_simulation_data, primary, tau_selection, constraints
     ):
@@ -112,11 +171,7 @@ def test_fit_fails(
         with pytest.raises(ValueError):
             crm.fit(production, injection[:-1], time)
         with pytest.raises(ValueError):
-            crm.fit(
-                production,
-                injection,
-                time[:-1],
-            )
+            crm.fit(production, injection, time[:-1])
 
     @pytest.mark.skip
     def test_fit_serial(
@@ -124,14 +179,26 @@ def test_fit_serial(
     ):
         injection, production, time = reservoir_simulation_data
         crm = CRM(primary, tau_selection, constraints)
-        crm.fit(production, injection, time, num_cores=1)
+        crm.fit(
+            production,
+            injection,
+            time,
+            num_cores=1,
+            options={"maxiter": 3},
+        )
 
     def test_fit_parallel(
         self, reservoir_simulation_data, primary, tau_selection, constraints
     ):
         injection, production, time = reservoir_simulation_data
         crm = CRM(primary, tau_selection, constraints)
-        crm.fit(production, injection, time, num_cores=4)
+        crm.fit(
+            production,
+            injection,
+            time,
+            num_cores=4,
+            options={"maxiter": 3},
+        )
 
     @pytest.mark.slow
     def test_fit_initial_guess(
@@ -148,7 +215,7 @@ def test_fit_initial_guess(
                 time,
                 random=random,
                 initial_guess=x0,
-                options={"maxiter": 100},
+                options={"maxiter": 3},
             )
 
 
@@ -174,3 +241,10 @@ def test_to_pickle_fails(self, trained_model, primary):
         crm = trained_model(primary)
         with pytest.raises(TypeError):
             crm.to_pickle()
+
+
+@pytest.mark.parametrize("primary,tau_selection,constraints", test_args)
+class TestBhp:
+    def test_init_bhp(self, primary, tau_selection, constraints):
+        crm = CrmCompensated(primary, tau_selection, constraints)
+        assert crm.primary == primary
diff --git a/testing/test_mpi.py b/testing/test_mpi.py
index 223eb4c..224217f 100644
--- a/testing/test_mpi.py
+++ b/testing/test_mpi.py
@@ -1,12 +1,12 @@
-import pytest
-from itertools import product
+from __future__ import annotations
+
 import numpy as np
-from pywaterflood.multiwellproductivity import (
-    calc_gains_homogeneous,
-    translate_locations,
+import pandas as pd
+import pytest
+
+from pywaterflood.multiwellproductivity import (  # calc_gains_homogeneous,; translate_locations,
     calc_A_ij,
 )
-import pandas as pd
 
 raw_locs = pd.DataFrame()