Custom tspan, current_C modes, reset_state options

NREL · Nov 13, 2024 · 3e1b370 · 3e1b370
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diff --git a/README.md b/README.md
@@ -72,7 +72,9 @@ If you prefer using the `conda` package manager, you can install `thevenin` from
 conda install -c conda-forge thevenin
 ```
 
-If you run into issues with installation due to the [scikit-sundae](https://github.com/NREL/scikit-sundae) dependency, please submit an issue [here](https://github.com/NREL/scikit-sundae/issues). We also manage our own solver package, but distribute it separately.
+If you run into issues with installation due to the [scikit-sundae](https://github.com/NREL/scikit-sundae) dependency, please submit an issue [here](https://github.com/NREL/scikit-sundae/issues). We also manage this solver package, but distribute it separately since it is not developed in pure Python.
+
+For those interested in setting up a developer and/or editable version of this software please see the directions available in the "Development" section of our [documentation](https://thevenin.readthedocs.io/en/latest/development).
 
 ## Get Started
 The API is organized around three main classes that allow you to construct the model, define an experiment, and interact with the solution. A basic example for a constant-current discharge is given below. To learn more about the model and see more detailed examples check out the [documentation](https://thevenin.readthedocs.io/) on Read the Docs.
@@ -113,7 +115,7 @@ For convenience, we also provide the following for your BibTex:
 The motivation and funding for this project came from the Rapid Operational Validation Initiative (ROVI) sponsored by the Office of Electricity. The focus of ROVI is "to greatly reduce time required for emerging energy storage technologies to go from lab to market by developing new tools that will accelerate the testing and validation process needed to ensure commercial success." If interested, you can read more about ROVI [here](https://www.energy.gov/oe/rapid-operational-validation-initiative-rovi).
 
 ## Contributing
-If you'd like to contribute to this package, please look through the existing [issues](https://github.com/NREL/thevenin/issues). If the bug you've caught or the feature you'd like to add isn't already being worked on, please submit a new issue before getting started. You should also read through the [developer guidelines](https://thevenin.readthedocs.io/development).
+If you'd like to contribute to this package, please look through the existing [issues](https://github.com/NREL/thevenin/issues). If the bug you've caught or the feature you'd like to add isn't already being worked on, please submit a new issue before getting started. You should also read through the [developer guidelines](https://thevenin.readthedocs.io/en/latest/development).
 
 ## Disclaimer
 This work was authored by the National Renewable Energy Laboratory (NREL), operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE). The views expressed in the repository do not necessarily represent the views of the DOE or the U.S. Government.
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diff --git a/docs/jupyter_execute/examples/dict_inputs.ipynb b/docs/jupyter_execute/examples/dict_inputs.ipynb
@@ -177,7 +177,9 @@
    "metadata": {},
    "source": [
     "## Run the Experiment\n",
-    "Experiments are run using either the `run` method, as shown below, or the `run_step` method. The difference between the two is that the `run` method will run all experiment steps with one call. If you would prefer to run the discharge first, perform an analysis, and then run the rest, etc. then you will want to use the `run_step` method. In this case, you should always start with step 0 and then run the following steps in order. When you use `run_step` the models internal state is saved at the end of each step. Therefore, after all steps have been run, you should run the `pre` method to pre-process the model back to its original initial state. All of this is handled automatically in the `run` method.\n",
+    "Experiments are run using either the `run()` method, as shown below, or the `run_step()` method. To run the discharge first, perform an analysis, and then run a rest, etc. then you will want to use the `run_step()` method. Steps should always be run in order because the model's state is always updated at the end of each step in preparation for the next step. At the end of all steps you can manually reset the model state back to a resting condition at `soc0` using the `pre()` method.\n",
+    "\n",
+    "The default behavior of `run()` handles all of this \"complexity\" for you. A single call to `run()` will execute all steps of an experiment in the correct order AND will call `pre()` at the end of all steps. To bypass the call to the pre-processor you can use the optional `reset_state=False` keyword argument. This is important if you need the model to run multiple experiments back-to-back and want the start of each experiment to be consistent with the end of the last experiment. In the case below, we use the default behaior and allow the state to reset since we are only running a single experiment.\n",
     "\n",
     "Regardless of how you run your experiment, the return value will be a solution instance. Solution instances each contain a `vars` attribute which contains a dictionary of the output variables. Keys are generally self descriptive and include units where applicable. To quickly plot any two variables against one another, use the `plot` method with the two keys of interest specified for the `x` and `y` variables of the figure. Below, time (in hours) is plotted against voltage."
    ]
@@ -202,13 +204,6 @@
     "soln = model.run(expr)\n",
     "soln.plot('time_h', 'voltage_V')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

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