Repository: pybamm-team/liionpack (language: python)

Issue:
Remove reference to external variables 

[Bug]: Inconsistent results when using Ray manager
### liionpack Version

0.3.2

### Python Version

3.9.12

### Describe the bug

I get inconsistent results and sometimes errors when using the Ray manager. I don't have these issues when using the Casadi manager. See the example below for more information.

### Steps to Reproduce

This example works fine when using the Casadi manager.

```python
import liionpack as lp
import pybamm
import numpy as np

def main():

    # Define parameters
    Np = 16
    Ns = 2
    Iapp = 20
    nproc = 8
    manager = 'casadi'

    # Generate the netlist
    netlist = lp.setup_circuit(Np=Np, Ns=Ns)

    # Define additional output variables
    output_variables = [
        'Volume-averaged cell temperature [K]']

    # Define a cycling experiment using PyBaMM
    experiment = pybamm.Experiment([
        f'Charge at {Iapp} A for 30 minutes',
        'Rest for 15 minutes',
        f'Discharge at {Iapp} A for 30 minutes',
        'Rest for 30 minutes'],
        period='10 seconds')

    # Define the PyBaMM parameters
    parameter_values = pybamm.ParameterValues("Chen2020")
    inputs = {"Total heat transfer coefficient [W.m-2.K-1]": np.ones(Np * Ns) * 10}

    # Solve the pack
    output = lp.solve(netlist=netlist,
                      sim_func=lp.thermal_simulation,
                      parameter_values=parameter_values,
                      experiment=experiment,
                      output_variables=output_variables,
                      initial_soc=0.5,
                      inputs=inputs,
                      nproc=nproc,
                      manager=manager)

    # Plot the pack and individual cell results
    lp.plot_pack(output)
    lp.plot_cells(output)
    lp.show_plots()

if __name__ == '__main__':
    main()
```

Here are the plots generated from the above example.

<img width="1118" alt="casadi" src="https://user-images.githubusercontent.com/6828967/178777349-b4c35915-7137-48a1-a5f9-f4815ab6e89e.png">

I get the following plots using the Ray manager for the same example.

<img width="1117" alt="ray" src="https://user-images.githubusercontent.com/6828967/178777675-cd0f674f-5a91-41f8-b8eb-7c766dae645a.png">

Sometimes I get the error shown below when using the Ray manager and I eventually have to abort the simulation.

```
Stepping simulation:  52%|█████████████████████████████████████████▏                                      | 325/631 [00:10<00:09, 32.88it/s](RayActor pid=94841) psetup failed: .../casadi/interfaces/sundials/cvodes_interface.cpp:650: 'jacF' calculation failed
(RayActor pid=94841) psetup failed: .../casadi/interfaces/sundials/cvodes_interface.cpp:650: 'jacF' calculation failed
(RayActor pid=94841) psetup failed: .../casadi/interfaces/sundials/cvodes_interface.cpp:650: 'jacF' calculation failed
(RayActor pid=94841) psetup failed: .../casadi/interfaces/sundials/cvodes_interface.cpp:650: 'jacF' calculation failed
(RayActor pid=94841) psetup failed: .../casadi/interfaces/sundials/cvodes_interface.cpp:650: 'jacF' calculation failed
```

### Expected behaviour

Simulation results when using the Casadi or Ray manager should be the same.

### Relevant log output

_No response_

### Additional context

_No response_


Discussion / hints:

Hi @wigging thanks for raising this issue. I don't have a lot of time to look at this right now but will make it high priority to sort out

Fix patch:
diff --git a/codecov.yml b/.github/codecov.yml
similarity index 95%
rename from codecov.yml
rename to .github/codecov.yml
index 4a812f36..920ba9e4 100644
--- a/codecov.yml
+++ b/.github/codecov.yml
@@ -1,5 +1,5 @@
 codecov:
-  branch: main
+  branch: develop
 
 coverage:
   precision: 2
diff --git a/.github/workflows/codecov.yml b/.github/workflows/codecov.yml
deleted file mode 100644
index fe04acc3..00000000
--- a/.github/workflows/codecov.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-name: CodeCov
-on: [push, pull_request]
-jobs:
-  run:
-    runs-on: ubuntu-latest
-    env:
-      OS: ubuntu-latest
-      PYTHON: '3.9'
-    steps:
-    - uses: checkout@v2
-      with:
-        fetch-depth: ‘2’
-
-    - name: Setup Python
-      uses: actions/setup-python@master
-      with:
-        python-version: 3.7
-    - name: Generate Report
-      run: |
-        pip install coverage
-        coverage run -m unittest
-    - name: Upload Coverage to Codecov
-      uses: codecov/codecov-action@v1
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 21f37f59..84f1711e 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -48,7 +48,7 @@ jobs:
 
       - name: Publish on PyPI
         if: github.event.inputs.target == 'pypi'
-        uses: pypa/gh-action-pypi-publish@master
+        uses: pypa/gh-action-pypi-publish@release/v1
         with:
           user: __token__
           password: ${{ secrets.PYPI_TOKEN }}
@@ -56,7 +56,7 @@ jobs:
 
       - name: Publish on TestPyPI
         if: github.event.inputs.target == 'testpypi'
-        uses: pypa/gh-action-pypi-publish@master
+        uses: pypa/gh-action-pypi-publish@release/v1
         with:
           user: __token__
           password: ${{ secrets.TESTPYPI_TOKEN }}
diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml
index e71ae761..72b571cd 100644
--- a/.github/workflows/run_benchmarks_over_history.yml
+++ b/.github/workflows/run_benchmarks_over_history.yml
@@ -14,7 +14,7 @@ on:
         default: 'be96186a91400ed7943378d68229ca65d2177c9c'
       commit_end:
         description: 'Identifier of commit at which to end'
-        default: 'main'
+        default: 'develop'
       ncommits:
         description: 'Number of commits to benchmark between commit_start and commit_end'
         default: '100'
@@ -29,12 +29,13 @@ jobs:
           python-version: 3.8
       - name: Install tox and asv
         run: pip install -U pip tox asv
-      - name: Fetch main branch
+      - name: Fetch develop branch
         # Not required when worklow trigerred
-        # on main, but useful when
+        # on develop, but useful when
         # experimenting/developing on another branch.
+        if: github.ref != 'refs/heads/develop'
         run: |
-          git fetch origin main:main
+          git fetch origin develop:develop
       - name: Run benchmarks
         run: |
           asv machine --machine "GitHubRunner"
@@ -79,9 +80,7 @@ jobs:
           git commit -am "Add new results"
           git push
       - name: Publish results
-        run:
-          |
-            asv publish
-            git fetch origin gh-pages:gh-pages
-            asv gh-pages
-      
\ No newline at end of file
+        run: |
+          asv publish
+          git fetch origin gh-pages:gh-pages
+          asv gh-pages
diff --git a/.gitignore b/.gitignore
index 41e58c3a..49141330 100644
--- a/.gitignore
+++ b/.gitignore
@@ -108,3 +108,6 @@ venv.bak/
 site/
 examples/csv-results/
 examples/npy-results/
+
+# Benchmarks
+results/
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index b2305575..00000000
--- a/.travis.yml
+++ /dev/null
@@ -1,27 +0,0 @@
-sudo: false
-
-language: python
-
-python:
-  - "3.9"
-
-before_install:
-  - export DISPLAY=:99.0
-  - sh -e /etc/init.d/xvfb start
-  - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-  - bash miniconda.sh -b -p $HOME/miniconda
-  - export PATH="$HOME/miniconda/bin:$PATH"
-
-install:
-  - conda install --yes python=$TRAVIS_PYTHON_VERSION --file requirements/conda_requirements.txt
-  - pip install -r requirements/conda_requirements.txt
-  - pip install -r requirements/test_requirements.txt
-  - python setup.py install
-
-script: pytest --pep8 --cov=./
-
-notifications:
-  email: false
-
-after_success:
-  - codecov
diff --git a/CHANGELOG.md b/CHANGELOG.md
index d838a7d4..1776fcd1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,26 @@
 # [Unreleased](https://github.com/pybamm-team/liionpack/)
 
+# [v0.3.3](https://github.com/pybamm-team/PyBaMM/tree/v0.3.3) - 2023-01-05
+## Bug fixes
+
+-   Update the codecov.yaml with develop branch ([#180](https://github.com/pybamm-team/liionpack/pull/180))
+-   Update conda environment to pin minimum versions of PyBaMM and Python ([#169](https://github.com/pybamm-team/liionpack/pull/169))
+-   Fix benchmarking again ([#187](https://github.com/pybamm-team/liionpack/pull/187))
+-   Fix inconsistent results with Ray manager ([#189](https://github.com/pybamm-team/liionpack/pull/189))
+-   Deal with removal of external variables ([#192](https://github.com/pybamm-team/liionpack/pull/192))
+-   Update ParameterValues syntax ([#194](https://github.com/pybamm-team/liionpack/pull/194))
+-   Fix experiment after change in PyBaMM operating conditions ([#196](https://github.com/pybamm-team/liionpack/pull/196))
+-   Fix more external variable references ([#207](https://github.com/pybamm-team/liionpack/pull/207))
+
+## Breaking changes
+
+## Features
+
+-   Update the contributing docs about branches ([#179](https://github.com/pybamm-team/liionpack/pull/179))
+-   Update push to pypi following master sunset ([#193](https://github.com/pybamm-team/liionpack/pull/193))
+-   Change default branch on github to develop to fix benchmarking issues ([#184](https://github.com/pybamm-team/liionpack/pull/184))
+-   Migrate to hatch packaging ([#182](https://github.com/pybamm-team/liionpack/pull/182))
+
 # [v0.3.2](https://github.com/pybamm-team/PyBaMM/tree/v0.3.2) - 2022-07-01
 
 ## Bug fixes
@@ -40,4 +61,4 @@ Please note that liionpack and PyBaMM are both still under active development, a
 - Run pack simulations using PyBaMM simulations and experiments
 - Lumped thermal model with configurable heat transfer coefficients
 - Implement cell variablity with input parameters
-- Run large simulations with parallel processing
\ No newline at end of file
+- Run large simulations with parallel processing
diff --git a/MANIFEST.in b/MANIFEST.in
deleted file mode 100644
index 48b4a671..00000000
--- a/MANIFEST.in
+++ /dev/null
@@ -1,1 +0,0 @@
-include liionpack/circuits/*
diff --git a/README.md b/README.md
index 69b05ea8..4f5e3991 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,25 @@ To install `liionpack` using `pip`, run the following command:
 pip install liionpack
 ```
 
+### Conda
+
+The following terminal commands are for setting up a conda development environment for liionpack. This requires the [Anaconda](https://www.anaconda.com) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html) Python distribution. This environment installs liionpack in editable mode which is useful for development of the liionpack source code. General users should install liionpack with pip.
+
+```bash
+# Create a conda environment named lipack
+cd liionpack
+conda env create --file environment.yml
+
+# Activate the environment
+conda activate lipack
+
+# Exit the environment
+conda deactivate
+
+# Delete the environment
+conda env remove --name lipack
+```
+
 ### LaTeX
 
 In order to use the `draw_circuit` functionality a version of Latex must be installed on your machine. We use an underlying Python package `Lcapy` for making the drawing and direct you to its installation instructions [here](https://lcapy.readthedocs.io/en/latest/install.html) for operating system specifics.
diff --git a/asv.conf.json b/asv.conf.json
index 23d439fa..6af179a4 100644
--- a/asv.conf.json
+++ b/asv.conf.json
@@ -23,14 +23,14 @@
     //
     // "install_command": ["in-dir={env_dir} python -mpip install {wheel_file}"],
     // "uninstall_command": ["return-code=any python -mpip uninstall -y {project}"],
-    // "build_command": [
-    //     "python setup.py build",
-    //     "PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}"
-    // ],
+    "build_command": [
+        "python -m pip install hatch",
+        "PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}"
+    ],
 
     // List of branches to benchmark. If not provided, defaults to "master"
     // (for git) or "default" (for mercurial).
-    "branches": ["main"], // for git
+    "branches": ["develop"], // for git
     // "branches": ["default"],    // for mercurial
 
     // The DVCS being used.  If not set, it will be automatically
diff --git a/docs/contributing.md b/docs/contributing.md
index 1cddde43..419a3592 100644
--- a/docs/contributing.md
+++ b/docs/contributing.md
@@ -36,6 +36,7 @@ You now have everything you need to start making changes!
 11. If you added a major new feature, perhaps it should be showcased in an [example notebook](#example-notebooks).
 12. When you feel your code is finished, or at least warrants serious discussion, run the [pre-commit checks](#pre-commit-checks) and then create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [liionpack's GitHub page](https://github.com/pybamm-team/liionpack).
 13. Once a PR has been created, it will be reviewed by any member of the community. Changes might be suggested which you can make by simply adding new commits to the branch. When everything's finished, someone with the right GitHub permissions will merge your changes into liionpack main repository.
+14. The default branch is the `develop` branch and this is linked to the PyBaMM `develop` branch. We endeavour to make concurrent releases so that no breaking changes are introduced between the packages and at this point `develop` is merged into the `main` branch and this is pushed to PyPi.
 
 
 
diff --git a/docs/examples/09 Terminal locations.ipynb b/docs/examples/09 Terminal locations.ipynb
index b91eba8f..f0b4bf74 100644
--- a/docs/examples/09 Terminal locations.ipynb
+++ b/docs/examples/09 Terminal locations.ipynb
@@ -175,16 +175,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Stepping simulation: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 73.11it/s]\n",
-      "Stepping simulation: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 76.29it/s]\n",
-      "Stepping simulation: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 75.96it/s]\n",
       "Stepping simulation: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 78.86it/s]\n",
-      "Stepping simulation: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 82.33it/s]\n"
+      "Stepping simulation: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 82.57it/s]\n",
+      "Stepping simulation: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 80.67it/s]\n",
+      "Stepping simulation: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 80.67it/s]\n",
+      "Stepping simulation: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 82.57it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 1 Axes>"
       ]
@@ -200,8 +200,7 @@
     "with plt.rc_context(lp.lp_context()):\n",
     "    fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
     "    for i, netlist in enumerate(netlists):\n",
-    "        chemistry = pybamm.parameter_sets.Chen2020\n",
-    "        param = pybamm.ParameterValues(chemistry=chemistry)\n",
+    "        param = pybamm.ParameterValues(\"Chen2020\")\n",
     "        experiment = pybamm.Experiment(\n",
     "            [\n",
     "                f\"Discharge at 1 A for 1 minutes\",\n",
diff --git a/environment.yml b/environment.yml
index b3b7c308..68ab606b 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,38 +1,35 @@
-name: liionpack
+# Use this config file to create a conda development environment. This
+# environment installs liionpack in editable mode which is useful for
+# development of the liionpack source code. General users should install
+# liionpack via pip.
+
+# Create a conda environment named lipack
+# $ cd liionpack
+# $ conda env create --file environment.yml
+
+# Activate the environment
+# $ conda activate lipack
+
+# Exit the environment
+# $ conda deactivate
+
+# Delete the environment
+# $ conda env remove --name lipack
+
+name: lipack
+
 dependencies:
-  - python
-  - pip
+  - python=3.9.*
   - numpy
   - scipy
   - matplotlib
   - pandas
-  - plotly
-  - openpyxl
-  - tqdm
-  - networkx
-  - jupyter
-  - sympy
   - ipython
-  - wrapt
+  - jupyter
   - sympy
-  - redis
-  - pyyaml
-  - protobuf >=3.8.0,<4.0.0
-  - imageio
-  - grpcio
-  - future
-  - flatbuffers
-  - filelock
-  - deprecated
-  - click
-  - autograd
-  - absl-py
-  - hiredis
+  - flake8
+  - pip
   - pip:
-    - pybamm
-    - lcapy
-    - scikit-spatial
-    - ray
-    - mkdocstrings-python-legacy
-    - mkdocs-material
-    - mkdocs-jupyter
\ No newline at end of file
+    - pybamm>=22.6
+    - ipdb
+    - -e .
diff --git a/liionpack/__init__.py b/liionpack/__init__.py
index 70ab858a..7b571672 100644
--- a/liionpack/__init__.py
+++ b/liionpack/__init__.py
@@ -44,4 +44,4 @@
 from .solvers import GenericActor
 from .solvers import RayActor
 
-__version__ = "0.3.2"
+__version__ = "0.3.3"
diff --git a/liionpack/protocols.py b/liionpack/protocols.py
index 00468c18..fe7bfbe9 100644
--- a/liionpack/protocols.py
+++ b/liionpack/protocols.py
@@ -27,21 +27,21 @@ def generate_protocol_from_experiment(experiment, flatten=True):
         dt = op["period"]
         if t % dt != 0:
             raise ValueError("Time must be an integer multiple of the period")
-        I, typ = op["electric"]
-        if typ != "A":
+        typ = op["type"]
+        if typ not in ["current"]:
             raise ValueError("Only constant current operations are supported")
-        if I.__class__ is str:
-            # drive cycle
-            dc_data = op["dc_data"]
-            proto.extend(dc_data[:, 1].tolist())
-        elif I.__class__ is np.ndarray:
-            # drive cycle old
-            proto.extend(I[:, 1].tolist())
         else:
-            proto.extend([I] * int(t / dt))
-            if i == 0:
-                # Include initial state when not a drive cycle for first op
-                proto = [proto[0]] + proto
+            if typ == "current":
+                if "Current input [A]" in op.keys():
+                    I = op["Current input [A]"]
+                    proto.extend([I] * int(t / dt))
+                    if i == 0:
+                        # Include initial state when not drive cycle, first op
+                        proto = [proto[0]] + proto
+                elif "dc_data" in op.keys():
+                    dc_data = op["dc_data"]
+                    proto.extend(dc_data[:, 1].tolist())
+
         if flatten:
             protocol.extend(proto)
         else:
diff --git a/liionpack/sim_utils.py b/liionpack/sim_utils.py
index 9ee1e8a8..c5b0eccd 100644
--- a/liionpack/sim_utils.py
+++ b/liionpack/sim_utils.py
@@ -28,39 +28,7 @@ def get_initial_stoichiometries(initial_soc, parameter_values):
 
     param = pybamm.LithiumIonParameters()
     esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)
-
-    V_min = parameter_values.evaluate(param.voltage_low_cut_dimensional)
-    V_max = parameter_values.evaluate(param.voltage_high_cut_dimensional)
-    try:
-        C_n = parameter_values.evaluate(param.n.cap_init)
-        C_p = parameter_values.evaluate(param.p.cap_init)
-        n_Li = parameter_values.evaluate(param.n_Li_particles_init)
-    except ValueError:
-        # The initial concentration is dependent on an input
-        lp.logger.warning(
-            "Initial concentrations are dependent on an input, "
-            + "please also supply initial concentrations as inputs"
-        )
-        return None, None
-
-    # Solve the model and check outputs
-    sol = esoh_solver.solve(
-        inputs={
-            "V_min": V_min,
-            "V_max": V_max,
-            "C_n": C_n,
-            "C_p": C_p,
-            "n_Li": n_Li,
-        },
-    )
-
-    x_0 = sol["x_0"].data[0]
-    y_0 = sol["y_0"].data[0]
-    C = sol["C"].data[0]
-    x = x_0 + np.asarray(initial_soc) * C / C_n
-    y = y_0 - np.asarray(initial_soc) * C / C_p
-
-    return x, y
+    return esoh_solver.get_initial_stoichiometries(initial_soc)
 
 
 def update_init_conc(param, SoC=None, update=True):
diff --git a/liionpack/solver_utils.py b/liionpack/solver_utils.py
index d9474965..cdf0bebf 100644
--- a/liionpack/solver_utils.py
+++ b/liionpack/solver_utils.py
@@ -317,9 +317,12 @@ def _create_casadi_objects(inputs, sim, dt, Nspm, nproc, variable_names, mapped)
     )
     if mapped:
         integrator = integrator.map(Nspm, "thread", nproc)
-
+    # Get the input parameter order
+    ip_order = inputs[0].keys()
     # Variables function for parallel evaluation
-    casadi_objs = sim.built_model.export_casadi_objects(variable_names=variable_names)
+    casadi_objs = sim.built_model.export_casadi_objects(
+        variable_names=variable_names, input_parameter_order=ip_order
+    )
     variables = casadi_objs["variables"]
     t, x, z, p = (
         casadi_objs["t"],
@@ -337,7 +340,9 @@ def _create_casadi_objects(inputs, sim, dt, Nspm, nproc, variable_names, mapped)
     event_vars = [v for v in all_vars if "Event" in v]
     if len(event_vars) > 0:
         # Variables function for parallel evaluation
-        casadi_objs = sim.built_model.export_casadi_objects(variable_names=event_vars)
+        casadi_objs = sim.built_model.export_casadi_objects(
+            variable_names=variable_names, input_parameter_order=ip_order
+        )
         events = casadi_objs["variables"]
         t, x, z, p = (
             casadi_objs["t"],
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..4b2fdcf4
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,57 @@
+[build-system]
+requires = [
+    "hatchling",
+]
+build-backend = "hatchling.build"
+
+[project]
+name = "liionpack"
+description = "A battery pack simulator for PyBaMM"
+readme = "README.md"
+license = "MIT"
+authors = [
+    { name = "Tom Tranter", email = "t.g.tranter@gmail.com" },
+]
+requires-python = ">=3.7"
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Topic :: Scientific/Engineering",
+]
+dependencies = [
+    "Ipython",
+    "lcapy",
+    "matplotlib",
+    "networkx",
+    "numpy",
+    "openpyxl",
+    "pandas",
+    "plotly",
+    "pybamm>=22.6",
+    "ray",
+    "redis",
+    "scikit-spatial",
+    "scipy",
+    "textwrapper",
+    "tqdm",
+]
+dynamic = [
+    "version",
+]
+
+[project.urls]
+"Bug Tracker" = "https://github.com/pybamm-team/liionpack/issues"
+Changelog = "https://github.com/pybamm-team/liionpack/blob/develop/CHANGELOG.md"
+Documentation = "https://liionpack.readthedocs.io/en/latest/"
+Homepage = "https://github.com/pybamm-team/liionpack"
+
+[tool.hatch.version]
+path = "liionpack/__init__.py"
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index aaae5455..00000000
--- a/requirements.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-numpy
-scipy
-matplotlib
-pybamm
-pandas
-plotly
-openpyxl
-tqdm
-lcapy
-Ipython
-scikit-spatial
-networkx
-textwrapper
-ray
-redis
-protobuf >= 3.8.0, < 4.0.0
diff --git a/setup.py b/setup.py
deleted file mode 100644
index cb6c0334..00000000
--- a/setup.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import os
-import sys
-from distutils.util import convert_path
-
-
-sys.path.append(os.getcwd())
-
-try:
-    from setuptools import setup
-except ImportError:
-    from distutils.core import setup
-
-# Load version
-main_ = {}
-ver_path = convert_path("liionpack/__init__.py")
-with open(ver_path) as f:
-    for line in f:
-        if line.startswith("__version__"):
-            exec(line, main_)
-
-# Load readme for description
-with open("README.md", encoding="utf-8") as f:
-    readme = f.read()
-
-setup(
-    name="liionpack",
-    description="A battery pack simulator for PyBaMM",
-    version=main_["__version__"],
-    long_description=readme,
-    long_description_content_type="text/markdown",
-    classifiers=[
-        "Development Status :: 4 - Beta",
-        "License :: OSI Approved :: MIT License",
-        "Programming Language :: Python",
-        "Topic :: Scientific/Engineering",
-    ],
-    packages=["liionpack"],
-    include_package_data=True,
-    install_requires=[
-        "numpy",
-        "scipy",
-        "matplotlib",
-        "pybamm>=22.6",
-        "pandas",
-        "plotly",
-        "openpyxl",
-        "tqdm",
-        "lcapy",
-        "Ipython",
-        "scikit-spatial",
-        "networkx",
-        "textwrapper",
-        "ray",
-        "redis",
-    ],
-    author="Tom Tranter",
-    author_email="t.g.tranter@gmail.com",
-    url="https://liionpack.readthedocs.io/en/latest/",
-    project_urls={
-        "Documentation": "https://liionpack.readthedocs.io/en/latest/",
-        "Source": "https://github.com/pybamm-team/liionpack",
-        "Tracker": "https://github.com/pybamm-team/liionpack/issues",
-    },
-)


Test patch:
diff --git a/pytest.ini b/pytest.ini
deleted file mode 100644
index 91ca3e49..00000000
--- a/pytest.ini
+++ /dev/null
@@ -1,21 +0,0 @@
-[pytest]
-python_files = *.py
-python_classes = *Test
-python_functions = test_*
-pep8maxlinelength = 85
-pep8ignore =
-    E402  # Module level import not at top of file
-    E226  # Space around arithmetic operators
-
-addopts =
-    --doctest-modules
-    --ignore=setup.py
-	--ignore=._legacy
-
-norecursedirs =
-    .git
-    archive
-    bin
-    build
-    dist
-    locals
diff --git a/tests/unit/test_sim_utils.py b/tests/unit/test_sim_utils.py
index 732f901d..84762ce3 100644
--- a/tests/unit/test_sim_utils.py
+++ b/tests/unit/test_sim_utils.py
@@ -22,18 +22,19 @@ def test_bad_soc(self):
             lp.update_init_conc(self.param, SoC=10.0)
 
     def test_input_logger_warning(self):
-        param = pybamm.ParameterValues(chemistry="Chen2020")
-        neg_conc = 0.0
-        param.update(
-            {
-                "Electrode height [m]": "[input]",
-                "Initial concentration in negative electrode [mol.m-3]": neg_conc,
-            },
-            check_already_exists=False,
-        )
-        lp.update_init_conc(param, SoC=0.5, update=True)
-        a = param["Initial concentration in negative electrode [mol.m-3]"]
-        assert a == neg_conc
+        with self.assertRaises(ValueError):
+            param = pybamm.ParameterValues("Chen2020")
+            neg_conc = 0.0
+            param.update(
+                {
+                    "Electrode height [m]": "[input]",
+                    "Initial concentration in negative electrode [mol.m-3]": neg_conc,
+                },
+                check_already_exists=False,
+            )
+            lp.update_init_conc(param, SoC=0.5, update=True)
+            # a = param["Initial concentration in negative electrode [mol.m-3]"]
+            # assert a == neg_conc
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_simulations.py b/tests/unit/test_simulations.py
index 1dfb7686..c4234bf7 100644
--- a/tests/unit/test_simulations.py
+++ b/tests/unit/test_simulations.py
@@ -15,11 +15,11 @@ def test_thermal_simulation(self):
                   inputs={"Total heat transfer coefficient [W.m-2.K-1]": 1.0})
         assert sim.__class__ == pybamm.Simulation
 
-    def test_thermal_external(self):
-        sim = lp.thermal_external()
-        sim.solve([0, 1800],
-                  external_variables={"Volume-averaged cell temperature": 1.0})
-        assert sim.__class__ == pybamm.Simulation
+    # def test_thermal_external(self):
+    #     sim = lp.thermal_external()
+    #     sim.solve([0, 1800],
+    #               external_variables={"Volume-averaged cell temperature": 1.0})
+    #     assert sim.__class__ == pybamm.Simulation
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_solvers.py b/tests/unit/test_solvers.py
index 3186b744..8ce3b6c2 100644
--- a/tests/unit/test_solvers.py
+++ b/tests/unit/test_solvers.py
@@ -9,7 +9,7 @@ class solversTest(unittest.TestCase):
     def setUpClass(self):
         Np = 21
         Ns = 1
-        Nspm = Np * Ns
+        self.Nspm = Np * Ns
         R_bus = 1e-4
         R_series = 1e-2
         R_int = 5e-2
@@ -21,7 +21,7 @@ def setUpClass(self):
         )
 
         # Heat transfer coefficients
-        self.htc = np.ones(Nspm) * 10
+        self.htc = np.ones(self.Nspm) * 10
         # Cycling experiment
         self.experiment = pybamm.Experiment(
             [
@@ -128,6 +128,23 @@ def test_voltage_limits(self):
         )
         assert True
 
+    # def test_external_variable(self):
+    #     T_non_dim = np.zeros(self.Nspm)  # Ref temperature
+    #     external_variables = {"Volume-averaged cell temperature": T_non_dim}
+    #     output = lp.solve(
+    #         netlist=self.netlist.copy(),
+    #         parameter_values=self.parameter_values,
+    #         sim_func=lp.thermal_external,
+    #         experiment=self.experiment,
+    #         output_variables=["Volume-averaged cell temperature [K]"],
+    #         inputs=None,
+    #         external_variables=external_variables,
+    #         initial_soc=0.5,
+    #         nproc=1,
+    #         manager="casadi",
+    #     )
+    #     assert np.all(output["Volume-averaged cell temperature [K]"] == 298.15)
+
 
 if __name__ == "__main__":
     unittest.main()