restructured example notebooks and changed internal links between them so they work

Author: ben18785

.error-books

@@ -1,2 +1,2 @@
 # Notebooks that contain deliberate errors
-optimisation-on-a-loglikelihood
+optimisation/maximum-likelihood

.slow-books

@@ -1,14 +1,14 @@
 # Slow notebooks
-automatic-differentiation-using-autograd.ipynb
-sampling-hamiltonian-mcmc.ipynb
-sampling-slice-doubling-mcmc.ipynb
-sampling-slice-overrelaxation-mcmc.ipynb
-sampling-slice-stepout-mcmc.ipynb
-stats-autoregressive-moving-average-errors.ipynb
-toy-model-beeler-reuter-ap.ipynb
-toy-model-fitzhugh-nagumo.ipynb
-toy-model-goodwin-oscillator.ipynb
-toy-model-hes1-michaelis-menten.ipynb
-toy-model-hodgkin-huxley-ik.ipynb
-toy-model-lotka-volterra.ipynb
-toy-model-repressilator.ipynb
+other-functionality/toy/automatic-differentiation-using-autograd.ipynb
+sampling/hamiltonian-mcmc.ipynb
+sampling/slice-doubling-mcmc.ipynb
+sampling/slice-overrelaxation-mcmc.ipynb
+sampling/slice-stepout-mcmc.ipynb
+other-functionality/stats/autoregressive-moving-average-errors.ipynb
+other-functionality/toy/model-beeler-reuter-ap.ipynb
+other-functionality/toy/model-fitzhugh-nagumo.ipynb
+other-functionality/toy/model-goodwin-oscillator.ipynb
+other-functionality/toy/model-hes1-michaelis-menten.ipynb
+other-functionality/toy/model-hodgkin-huxley-ik.ipynb
+other-functionality/toy/model-lotka-volterra.ipynb
+other-functionality/toy/model-repressilator.ipynb

examples/README.md

@@ -8,103 +8,101 @@ relevant code.
 
 
 ## Getting started
-- [Optimisation: First example](./optimisation-first-example.ipynb)
-- [Sampling: First example](./sampling-first-example.ipynb)
-- [Writing a model](./writing-a-model.ipynb)
-- [Writing a custom LogPDF](./writing-a-logpdf.ipynb)
-- [Writing a custom LogPrior](./writing-a-prior.ipynb)
+- [Optimisation: First example](./optimisation/first-example.ipynb)
+- [Sampling: First example](./sampling/first-example.ipynb)
+- [Writing a model](./other-functionality/stats/custom-model.ipynb)
+- [Writing a custom LogPDF](./other-functionality/stats/custom-logpdf.ipynb)
+- [Writing a custom LogPrior](./other-functionality/stats/custom-prior.ipynb)
 
 
 ## Optimisation
 
 ### Particle-based methods
-- [CMA-ES](./optimisation-cmaes.ipynb)
-- [PSO](./optimisation-pso.ipynb)
-- [SNES](./optimisation-snes.ipynb)
-- [XNES](./optimisation-xnes.ipynb)
+- [CMA-ES](./optimisation/cmaes.ipynb)
+- [PSO](./optimisation/pso.ipynb)
+- [SNES](./optimisation/snes.ipynb)
+- [XNES](./optimisation/xnes.ipynb)
 
 ### Local optimisers
-- [Nelder-Mead](./optimisation-nelder-mead.ipynb)
+- [Nelder-Mead](./optimisation/nelder-mead.ipynb)
 
 ### Further optimisation
-- [Ask-and-tell interface](./optimisation-ask-and-tell.ipynb)
-- [Convenience methods fmin() and curve\_fit()](./optimisation-convenience.ipynb)
-- [Multiple objectives](./optimisation-multi-objective.ipynb)
-- [Optimisation on a loglikelihood](./optimisation-on-a-loglikelihood.ipynb)
-- [Transformed parameter space](./optimisation-transformed-parameters.ipynb)
+- [Ask-and-tell interface](./optimisation/ask-and-tell.ipynb)
+- [Convenience methods fmin() and curve\_fit()](./optimisation/convenience.ipynb)
+- [Maximum loglikelihood](./optimisation/maximum-likelihood.ipynb)
+- [Multiple objectives](./optimisation/multi-objective.ipynb)
+- [Transformed parameter space](./optimisation/transformed-parameters.ipynb)
 
 
 ## Sampling
 
 ### MCMC without gradients
-- [Differential Evolution MCMC](./sampling-differential-evolution-mcmc.ipynb)
-- [DREAM MCMC](./sampling-dream-mcmc.ipynb)
-- [Emcee Hammer](./sampling-emcee-hammer.ipynb)
-- [Haario Adaptive Covariance MCMC](./sampling-adaptive-covariance-haario.ipynb)
-- [Haario-Bardenet Adaptive Covariance MCMC](./sampling-adaptive-covariance-haario-bardenet.ipynb)
-- [Metropolis Random Walk MCMC](./sampling-metropolis-mcmc.ipynb)
-- [Population MCMC](./sampling-population-mcmc.ipynb)
-- [Rao-Blackwell Adaptive Covariance MCMC](./sampling-adaptive-covariance-rao-blackwell.ipynb)
-- [Slice Sampling: Stepout MCMC](./sampling-slice-stepout-mcmc.ipynb)
-- [Slice Sampling: Doubling MCMC](./sampling-slice-doubling-mcmc.ipynb)
-- [Slice Sampling: Overrelaxation MCMC](./sampling-slice-overrelaxation-mcmc.ipynb)
+- [Differential Evolution MCMC](./sampling/differential-evolution-mcmc.ipynb)
+- [DREAM MCMC](./sampling/dream-mcmc.ipynb)
+- [Emcee Hammer](./sampling/emcee-hammer.ipynb)
+- [Haario Adaptive Covariance MCMC](./sampling/adaptive-covariance-haario.ipynb)
+- [Haario-Bardenet Adaptive Covariance MCMC](./sampling/adaptive-covariance-haario-bardenet.ipynb)
+- [Metropolis Random Walk MCMC](./sampling/metropolis-mcmc.ipynb)
+- [Population MCMC](./sampling/population-mcmc.ipynb)
+- [Rao-Blackwell Adaptive Covariance MCMC](./sampling/adaptive-covariance-rao-blackwell.ipynb)
+- [Slice Sampling: Doubling MCMC](./sampling/slice-doubling-mcmc.ipynb)
+- [Slice Sampling: Overrelaxation MCMC](./sampling/slice-overrelaxation-mcmc.ipynb)
+- [Slice Sampling: Stepout MCMC](./sampling/slice-stepout-mcmc.ipynb)
 
 ### MCMC with gradients
-- [Hamiltonian MCMC](./sampling-hamiltonian-mcmc.ipynb)
-- [MALA MCMC](./sampling-mala-mcmc.ipynb)
-- [Monomial-Gamma HMC MCMC](./sampling-monomial-gamma-hmc.ipynb)
-- [Relativistic MCMC](./sampling-relativistic-mcmc.ipynb)
+- [Hamiltonian MCMC](./sampling/hamiltonian-mcmc.ipynb)
+- [MALA MCMC](./sampling/mala-mcmc.ipynb)
+- [Monomial-Gamma HMC MCMC](./sampling/monomial-gamma-hmc.ipynb)
+- [Relativistic MCMC](./sampling/relativistic-mcmc.ipynb)
 
 ### Nested sampling
-- [Ellipsoidal nested sampling](./sampling-nested-ellipsoidal-sampling.ipynb)
-- [Rejection nested sampling](./sampling-nested-rejection-sampling.ipynb)
+- [Ellipsoidal nested sampling](./sampling/nested-ellipsoidal-sampling.ipynb)
+- [Rejection nested sampling](./sampling/nested-rejection-sampling.ipynb)
 
 ### Analysing sampling results
-- [Autocorrelation](./plot-mcmc-autocorrelation.ipynb)
-- [Effective sample size](./sampling-effective-sample-size.ipynb)
-- [Pairwise scatterplots](./plot-mcmc-pairwise-scatterplots.ipynb)
-- [Pairwise scatterplots with KDE](./plot-mcmc-pairwise-kde-plots.ipynb)
-- [Predicted time series](./plot-mcmc-predicted-time-series.ipynb)
-- [Trace plots](./plot-mcmc-trace-plots.ipynb)
+- [Autocorrelation](./other-functionality/plotting/mcmc-autocorrelation.ipynb)
+- [Effective sample size](./sampling/effective-sample-size.ipynb)
+- [Evaluating noise models using autocorrelation plots of the residuals](./other-functionality/plotting/residuals-autocorrelation.ipynb)
+- [Pairwise scatterplots](./other-functionality/plotting/mcmc-pairwise-scatterplots.ipynb)
+- [Predicted time series](./other-functionality/plotting/mcmc-predicted-time-series.ipynb)
+- [Trace plots](./other-functionality/plotting/mcmc-trace-plots.ipynb)
 
 
 ## Statistical modelling
-- [Autoregressive moving average errors](./stats-autoregressive-moving-average-errors.ipynb)
-- [Cauchy sampling error](./stats-cauchy-sampling-error.ipynb)
-- [Integrated noise model](./sampling-integrated-gaussian-log-likelihood.ipynb)
-- [Log priors](./stats-log-priors.ipynb)
-- [Student-t noise model](./stats-student-t-sampling-error.ipynb)
+- [Autoregressive moving average errors](./other-functionality/stats/autoregressive-moving-average-errors.ipynb)
+- [Cauchy sampling error](./other-functionality/stats/cauchy-sampling-error.ipynb)
+- [Integrated noise model](./other-functionality/stats/integrated-gaussian-log-likelihood.ipynb)
+- [Log priors](./other-functionality/stats/log-priors.ipynb)
+- [Student-t noise model](./other-functionality/stats/student-t-sampling-error.ipynb)
 
 
 ## Toy problems
 
 ### Models
-- [Beeler-Reuter action potential model](./toy-model-beeler-reuter-ap.ipynb)
-- [Constant model](./toy-model-constant.ipynb)
-- [Fitzhugh-Nagumo model](./toy-model-fitzhugh-nagumo.ipynb)
-- [Goodwin oscillator model](./toy-model-goodwin-oscillator.ipynb)
-- [HES1 Michaelis-Menten model](./toy-model-hes1-michaelis-menten.ipynb)
-- [Hodgkin-Huxley Potassium current model](./toy-model-hodgkin-huxley-ik.ipynb)
-- [Logistic growth model](./toy-model-logistic.ipynb)
-- [Lotka-Volterra predator-prey model](./toy-model-lotka-volterra.ipynb)
-- [Repressilator model](./toy-model-repressilator.ipynb)
-- [Simple Harmonic Oscillator model](./toy-model-simple-harmonic-oscillator.ipynb)
-- [SIR Epidemiology model](./toy-model-sir.ipynb)
-- [Stochastic Degradation model](./toy-model-stochastic-degradation.ipynb)
+- [Beeler-Reuter action potential model](./other-functionality/toy/model-beeler-reuter-ap.ipynb)
+- [Constant model](./other-functionality/toy/model-constant.ipynb)
+- [Fitzhugh-Nagumo model](./other-functionality/toy/model-fitzhugh-nagumo.ipynb)
+- [Goodwin oscillator model](./other-functionality/toy/model-goodwin-oscillator.ipynb)
+- [HES1 Michaelis-Menten model](./other-functionality/toy/model-hes1-michaelis-menten.ipynb)
+- [Hodgkin-Huxley Potassium current model](./other-functionality/toy/model-hodgkin-huxley-ik.ipynb)
+- [Logistic growth model](./other-functionality/toy/model-logistic.ipynb)
+- [Lotka-Volterra predator-prey model](./other-functionality/toy/model-lotka-volterra.ipynb)
+- [Repressilator model](./other-functionality/toy/model-repressilator.ipynb)
+- [Simple Harmonic Oscillator model](./other-functionality/toy/model-simple-harmonic-oscillator.ipynb)
+- [SIR Epidemiology model](./other-functionality/toy/model-sir.ipynb)
+- [Stochastic Degradation model](./other-functionality/toy/model-stochastic-degradation.ipynb)
 
 ### Distributions
-- [Annulus](./toy-distribution-annulus.ipynb)
-- [Cone](./toy-distribution-cone.ipynb)
-- [High dimensional gaussian](./toy-distribution-high-dimensional-gaussian.ipynb)
-- [Multimodal gaussian distribution](./toy-distribution-multimodal-gaussian.ipynb)
-- [Neals Funnel](./toy-distribution-neals-funnel.ipynb)
-- [Rosenbrock function](./toy-distribution-rosenbrock.ipynb)
-- [Simple Egg Box](./toy-distribution-simple-egg-box.ipynb)
-- [Twisted Gaussian Banana](./toy-distribution-twisted-gaussian.ipynb)
+- [Annulus](./other-functionality/toy/distribution-annulus.ipynb)
+- [Cone](./other-functionality/toy/distribution-cone.ipynb)
+- [High dimensional gaussian](./other-functionality/toy/distribution-high-dimensional-gaussian.ipynb)
+- [Multimodal gaussian distribution](./other-functionality/toy/distribution-multimodal-gaussian.ipynb)
+- [Neals Funnel](./other-functionality/toy/distribution-neals-funnel.ipynb)
+- [Rosenbrock function](./other-functionality/toy/distribution-rosenbrock.ipynb)
+- [Simple Egg Box](./other-functionality/toy/distribution-simple-egg-box.ipynb)
+- [Twisted Gaussian Banana](./other-functionality/toy/distribution-twisted-gaussian.ipynb)
 
 
 ## Miscellaneous
-
-- [Automatic differentiation using autograd](./automatic-differentiation-using-autograd.ipynb)
-- [Evaluating noise models using autocorrelation plots of the residuals](./plot-residuals-autocorrelation.ipynb)
-- [The example shown on the landing page](./readme-example.ipynb)
+- [Automatic differentiation using autograd](./other-functionality/toy/automatic-differentiation-using-autograd.ipynb)
+- [The example shown on the landing page](./sampling/readme-example.ipynb)

examples/optimisation-ask-and-tell.ipynb renamed to examples/optimisation/ask-and-tell.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "This example shows you how to run a global optimisation with [CMA-ES](http://pints.readthedocs.io/en/latest/optimisers/cmaes.html).\n",
     "\n",
-    "For a more elaborate example of an optimisation, see: [basic optimisation example](./optimisation-first-example.ipynb)."
+    "For a more elaborate example of an optimisation, see: [basic optimisation example](./first-example.ipynb)."
    ]
   },
   {
@@ -134,14 +134,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
   }
  },
  "nbformat": 4,

examples/optimisation-cmaes.ipynb renamed to examples/optimisation/cmaes.ipynb

@@ -171,14 +171,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
   }
  },
  "nbformat": 4,

examples/optimisation-convenience.ipynb renamed to examples/optimisation/convenience.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "As well as minimising error functions, PINTS optimisation can be used to find the maximum of a loglikelihood (or of any [pints.LogPDF object](https://pints.readthedocs.io/en/latest/log_pdfs.html#pints.LogPDF)).\n",
     "\n",
-    "Following on from the [first example](optimisation-first-example.ipynb), we can define an inference problem using the [logistic model](https://pints.readthedocs.io/en/latest/toy/logistic_model.html#module-pints.toy):"
+    "Following on from the [first example](first-example.ipynb), we can define an inference problem using the [logistic model](https://pints.readthedocs.io/en/latest/toy/logistic_model.html#module-pints.toy):"
    ]
   },
   {
@@ -102,7 +102,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "log_likelihood = pints.GaussianLogLikelihood(problem)"
@@ -381,21 +383,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
   }
  },
  "nbformat": 4,

examples/optimisation-first-example.ipynb renamed to examples/optimisation/first-example.ipynb

@@ -13,7 +13,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To simulate this, we'll load two copies of the [logistic model](/toy-model-logistic.ipynb), and run two simulations with the same parameters (the same system) but different initial conditions (a different experiment)."
+    "To simulate this, we'll load two copies of the [logistic model](../other-functionality/toy/model-logistic.ipynb), and run two simulations with the same parameters (the same system) but different initial conditions (a different experiment)."
    ]
   },
   {
@@ -79,7 +79,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "problem_1 = pints.SingleOutputProblem(model_1, times_1, experiment_1)\n",
@@ -101,7 +103,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "error = pints.SumOfErrors((error_1, error_2))"
@@ -170,14 +174,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
   }
  },
  "nbformat": 4,

examples/optimisation-on-a-loglikelihood.ipynb renamed to examples/optimisation/maximum-likelihood.ipynb

@@ -135,21 +135,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
   }
  },
  "nbformat": 4,

examples/optimisation-multi-objective.ipynb renamed to examples/optimisation/multi-objective.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "This example shows you how to run a global optimisation with [PSO (particle swarm optimisation)](http://pints.readthedocs.io/en/latest/optimisers/pso.html).\n",
     "\n",
-    "For a more elaborate example of an optimisation, see: [basic optimisation example](./optimisation-first-example.ipynb)."
+    "For a more elaborate example of an optimisation, see: [basic optimisation example](./first-example.ipynb)."
    ]
   },
   {
@@ -127,14 +127,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
   }
  },
  "nbformat": 4,