Moving away from using term 'tutorial'

Author: MichaelClerx

ion-currents-tutorial/README.md renamed to ion-currents/README.md

@@ -1,57 +1,57 @@
 [↩ back to index](../README.md)
 # Estimating parameters of ion current models from whole-cell voltage-clamp data
 
-In this tutorial, we look at the problem of estimating the parameters of an ion current model from whole-cell voltage-clamp data.
+In these notebooks, we look at the problem of estimating the parameters of an ion current model from whole-cell voltage-clamp data.
 
 As we go along, we'll create some classes and utility functions that may be useful in general.
 These are all stored in [library.py](./library.py).
 
 The follow topics are covered:
 
-## [Introduction](introduction.ipynb)
+## [Introduction](introduction.ipynb) - [nbviewer](https://nbviewer.jupyter.org/github/CardiacModelling/fitting-notebooks/blob/main/ion-currents/introduction.ipynb)
 
-This tutorial provides some background on the model we'll use throughout the tutorial.
+This notebook provides some background on the model we'll use in all examples.
 It also introduces a first voltage-protocol (a simplified variant of the "staircase protocol").
 
-## [Basic simulations](basic-simulations.ipynb)
+## [Basic simulations](basic-simulations.ipynb) - [nbviewer](https://nbviewer.jupyter.org/github/CardiacModelling/fitting-notebooks/blob/main/ion-currents/basic-fitting.ipynb)
 
-This tutorial shows how Myokit can be used to simulate patch-clamp experiments.
+This notebook shows how Myokit can be used to simulate patch-clamp experiments.
 It shows you how to create a simulation from a model and protocol stored on disk, and discusses how to change model parameters.
 Finally, it shows how steady-states can be calculated and set as initial conditions.
 
 ## [Basic fitting](basic-fitting.ipynb)
 
-In this tutorial we link Myokit to PINTS.
+In this notebook we link Myokit to PINTS.
 Noise models are discussed and synthetic data is generated, after which an error measure is defined and minimised.
 Inspecting the results, we show how tight solver tolerances are needed for fitting, and how the finite size of our experimental time series can cause a slight bias in the results.
 
 ## Fitting to different voltage protocols
 
 The next four notebooks discuss different voltage protocols, and the simulation methods appropriate to each one.
 
-- [Combining step protocols with sine waves or ramps](more-protocols-1-steps-and-ramps.ipynb)
+- [Combining step protocols with sine waves or ramps](more-protocols-1-steps-and-ramps.ipynb) - [nbviewer](https://nbviewer.jupyter.org/github/CardiacModelling/fitting-notebooks/blob/main/ion-currents/more-protocols-1-steps-and-ramps.ipynb)
 - [Simulating an AP protocol with "data clamp"](more-protocols-2-data-clamp.ipynb)
 - [Analytical solvers for simple step protocols](more-protocols-3-analytic-solvers.ipynb)
 - [Fitting to multiple simple step protocols](more-protocols-4-multiple-protocols.ipynb)
 
 ## [Setting boundaries on model parameters](boundaries.ipynb)
 
-In this tutorial we show how some parameters can cause numerical issues during simulation, and how we can catch and report these errors.
+In this notebook we show how some parameters can cause numerical issues during simulation, and how we can catch and report these errors.
 We then inspect the model equations and use previous estimates of our parameters (or quantities related to the parameters) to define some very wide boundaries, or "prior estimates".
 Finally, we show how we can use this kind of reasoning to define univariate boundaries (one on each parameter), and multivariate boundaries (which restrict the maximum rate coefficients seen during a simulation).
 
 ## [Selecting starting points for an optimisation](starting-points.ipynb)
 
-Continuing from the previous tutorial, this tutorial shows how we can sample from within the (univariate and multivariate) boundaries to select starting points for an optisiation.
-At the end of this tutorial we briefly discuss a "repeated-fits" strategy which allows you to test the reliability of results obtained on real data, where the "true" parameters are not known.
+Continuing from the previous chapters, this notebook shows how we can sample from within the (univariate and multivariate) boundaries to select starting points for an optisiation.
+At the end of this notebook we briefly discuss a "repeated-fits" strategy which allows you to test the reliability of results obtained on real data, where the "true" parameters are not known.
 
 ## [Searching in a transformed space](transformations.ipynb)
 
-This tutorial shows how you can create wrappers around models and boundaries to run optimisations on a transformed parameter space.
+This notebook shows how you can create wrappers around models and boundaries to run optimisations on a transformed parameter space.
 
 ## [Running big fitting experiments](big-fitting.ipynb)
 
-This tutorial focusses on the practical side of fitting.
+This notebook focusses on the practical side of fitting.
 It introduces methods to store simulation results to disk, load and analyse them, and shows a way to "reserve" filenames when multiple processes are running at once.
 It ends with a brief note on multiprocessing.
 

ion-currents-tutorial/basic-fitting.ipynb renamed to ion-currents/basic-fitting.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# First steps: a synthetic data study\n",
     "\n",
-    "In this part of the tutorial, we'll take a [myokit.Simulation](https://myokit.readthedocs.io/api_simulations/Simulation.html) and wrap it in a [pints.ForwardModel](https://pints.readthedocs.io/en/latest/core_classes_and_methods.html#forward-model).\n",
+    "In this notebook, we'll take a [myokit.Simulation](https://myokit.readthedocs.io/api_simulations/Simulation.html) and wrap it in a [pints.ForwardModel](https://pints.readthedocs.io/en/latest/core_classes_and_methods.html#forward-model).\n",
     "We'll then use this ForwardModel to generate some data, add synthetic noise, and set up a [pints.SingleOutputProblem](https://pints.readthedocs.io/en/latest/core_classes_and_methods.html#pints.SingleOutputProblem).\n",
     "Finally, we'll define an [ErrorMeasure](https://pints.readthedocs.io/en/latest/error_measures.html#pints.ErrorMeasure) on this problem and use an [Optimiser](https://pints.readthedocs.io/en/latest/optimisers/index.html) to find that parameters that minimise it.\n",
     "\n",
@@ -141,7 +141,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Real voltage-clamp experiments are affected by various noise and error sources (see the \"real data\" part of the tutorial for details).\n",
+    "Real voltage-clamp experiments are affected by various noise and error sources (see the \"Dealing with real data\" notebooks for details).\n",
     "In this example, we'll keep it simple and only add some Gaussian (i.e. normally distributed) noise:"
    ]
   },
@@ -907,14 +907,14 @@
    "source": [
     "## Summary\n",
     "\n",
-    "In this part of the tutorial we have\n",
+    "In this notebook we have\n",
     "\n",
     "- Set up a PINTS problem and defined an error measure\n",
     "- Used an optimisation to find the parameters that minimise this error\n",
     "- Seen the benefits of using a fine tolerance when running simulations\n",
     "- Seen bias in our estimate of the parameters, caused by our finite sampling of noisy data\n",
     "\n",
-    "In the next tutorial we'll look at some more simulation methods, selecting the appropriate types for different models and protocols."
+    "In the next notebook we'll look at some more simulation methods, selecting the appropriate types for different models and protocols."
    ]
   }
  ],
@@ -934,7 +934,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,