Repository: pints-team/pints

Issue:
Transform/Transformation
Before we release a new version of PINTS, we need to sort out the naming of transformations, which are sometimes called "transforms".

E.g. MCMCController and OptimisationController have an argument `transform`. This is confusing as the object you pass in is a `Transformation`.

The method `optimise()` similarly takes a `transform` where it should be a `transformation`.

The method `mcmc_sample` doesn't have a transformation argument at all

The nested sampler controller doesn't take a transformation argument

Discussion:

Patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 205a2dbc..7d09e0cf 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,19 +16,24 @@ All notable changes to this project will be documented in this file.
 - [#1165](https://github.com/pints-team/pints/pull/1165) A new optional argument `transform` was added to both `OptimisationController` and `MCMCController` to transform parameters during optimisation and sampling.
 - [#1112](https://github.com/pints-team/pints/pull/1112) A new `NoUTurnMCMC` sampler (NUTS) was added, along with a `DualAveragingAdaption` class to adaptively tune related Hamiltonian Monte Carlo methods.
 - [#1025](https://github.com/pints-team/pints/pull/1025) Added a stochastic logistic growth problem for use with ABC.
+
 ### Changed
+- [#1375](https://github.com/pints-team/pints/pull/1375) Changed all arguments called `transform` to `transformation` for consistency.
+- [#1365](https://github.com/pints-team/pints/pull/1365) Dropped support for Python 2.7. PINTS now requires Python 3.5 or higher.
+- [#1360](https://github.com/pints-team/pints/pull/1360) The `ParallelEvaluator` will now set a different (pre-determined) random seed for each task, ensuring tasks can use randomness, but results can be reproduced from run to run.
+- [#1357](https://github.com/pints-team/pints/pull/1357) Parallel evaluations using multiprocessing now restrict the number of threads used by Numpy and others to 1 (by default).
+- [#1355](https://github.com/pints-team/pints/pull/1355) When called with `parallel=True` the method `pints.evaluate()` will now limit the number of workers it uses to the number of tasks it needs to process.
 - [#1250](https://github.com/pints-team/pints/pull/1250) The returned values from `SingleChainMCMC.tell()` and `MultiChainMCMC.tell()` have been extended from current position `x` to `x, fx, accepted`, where `fx` is the current log likelihood and `accepted` is a bool indicating whether tell performed an acceptance step in this call.
 - [#1195](https://github.com/pints-team/pints/pull/1195) The installation instructions have been updated to reflect that PINTS in now pip-installable.
 - [#1191](https://github.com/pints-team/pints/pull/1191) Warnings are now emitted using `warnings.warn` rather than `logging.getLogger(..).warning`. This makes them show up like other warnings, and allows them to be suppressed with [filterwarnings](https://docs.python.org/3/library/warnings.html#warnings.filterwarnings).
 - [#1112](https://github.com/pints-team/pints/pull/1112) The `pints.Logger` can now deal with `None` being logged in place of a proper value.
-- [#1355](https://github.com/pints-team/pints/pull/1355) When called with `parallel=True` the method `pints.evaluate()` will now limit the number of workers it uses to the number of tasks it needs to process.
-- [#1357](https://github.com/pints-team/pints/pull/1357) Parallel evaluations using multiprocessing now restrict the number of threads used by Numpy and others to 1 (by default).
-- [#1360](https://github.com/pints-team/pints/pull/1360) The `ParallelEvaluator` will now set a different (pre-determined) random seed for each task, ensuring tasks can use randomness, but results can be reproduced from run to run.
-- [#1365](https://github.com/pints-team/pints/pull/1365) Dropped support for Python 2.7. PINTS now requires Python 3.5 or higher.
+
 ### Deprecated
 - [#1201](https://github.com/pints-team/pints/pull/1201) The method `pints.rhat_all_params` was accidentally removed in 0.3.0, but is now back in deprecated form.
+
 ### Removed
 - [#1250](https://github.com/pints-team/pints/pull/1250) The methods `SingleChainMCMC.current_log_pdf()` and `MultiChainMCMC.current_log_pdf()` have been removed.
+
 ### Fixed
 - [#1264](https://github.com/pints-team/pints/pull/1264) Fixed a bug relating to how NUTS handles nans when values outside the range of the priors are proposed.
 - [#1257](https://github.com/pints-team/pints/pull/1257) Fixed a bug in `GaussianLogPrior`, which meant the distribution could be instantiated with a non-positive standard deviation.
diff --git a/examples/README.md b/examples/README.md
index db8dcdd7..dcd063b8 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -23,9 +23,8 @@ relevant code.
 
 ## Digging deeper
 - [Full control with the ask-and-tell interface](./optimisation/ask-and-tell.ipynb)
-- [Optimisation in a transformed parameter space - using a Transform object](./optimisation/transformed-parameters.ipynb)
-- [Optimisation in a transformed parameter space - writing a model wrapper](./optimisation/transformed-parameters-model-wrapper.ipynb)
-- [Sampling in a transformed parameter space - using a Transform object](./sampling/transformed-parameters.ipynb)
+- [Optimisation in a transformed parameter space](./optimisation/transformed-parameters.ipynb)
+- [Sampling in a transformed parameter space](./sampling/transformed-parameters.ipynb)
 - [Sampling in a transformed parameter space - with or without Jacobian adjustment](./sampling/transformation-with-and-without-jacobian.ipynb)
 
 
diff --git a/examples/optimisation/transformed-parameters-model-wrapper.ipynb b/examples/optimisation/transformed-parameters-model-wrapper.ipynb
deleted file mode 100644
index 73cf32cb..00000000
--- a/examples/optimisation/transformed-parameters-model-wrapper.ipynb
+++ /dev/null
@@ -1,207 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Optimisation in a transformed parameter space - using a model wrapper"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This example shows you how to run an optimisation in a transformed parameter space by writing a wrapper around a [pints.ForwardModel](https://pints.readthedocs.io/en/latest/core_classes_and_methods.html#pints.ForwardModel).\n",
-    "(For an introduction to PINTS optimisation, see: [Optimisation: first example](./first-example.ipynb).)\n",
-    "\n",
-    "Pints provides a transformation interface through [pints.Transformation](https://pints.readthedocs.io/en/latest/transformations.html#pints.Transformation).\n",
-    "We show how to use `pints.Transformation` for an [optimisation example](./transformed-parameters.ipynb) and for a [sampling example](../sampling/transformed-parameters.ipynb).\n",
-    "We strongly recommend using PINTS' transformation interface as it handles all the subtle corrections such as Jacobian adjustment for PDFs, chain rule for calculating sensitivity, etc. that one might easily miss when writing their own transformation wrapper."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we will create a toy model, in this case a [logistic model](https://pints.readthedocs.io/en/latest/toy/logistic_model.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pints\n",
-    "import pints.toy as toy\n",
-    "\n",
-    "model = toy.LogisticModel()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The parameter vector for the toy logistic model is $\\theta_\\text{org} = [r, K]$, where $r$ is a growth rate and $K$ is called the carrying capacity.\n",
-    "Because these parameters have very different magnitudes, we will create a wrapper that instead uses the parameter vector $\\theta = [r, \\log(K)]$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class TransformedModel(pints.ForwardModel):\n",
-    "    def __init__(self, model):\n",
-    "        self._model = model\n",
-    "    \n",
-    "    def n_parameters(self):\n",
-    "        return self._model.n_parameters()\n",
-    "    \n",
-    "    def simulate(self, parameters, times):\n",
-    "        transformed_parameters = [parameters[0], np.exp(parameters[1])]\n",
-    "        return self._model.simulate(transformed_parameters,times)\n",
-    "\n",
-    "transformed_model = TransformedModel(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just like the untransformed model, we can use this transformed variant with several PINTS routines.\n",
-    "\n",
-    "For example, we can fit it to a synthetic data set using CMA-ES:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Minimising error measure\n",
-      "Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES)\n",
-      "Running in sequential mode.\n",
-      "Population size: 6\n",
-      "Iter. Eval. Best      Time m:s\n",
-      "0     6      3.44e+07   0:00.0\n",
-      "1     12     3.4e+07    0:00.0\n",
-      "2     18     3.34e+07   0:00.0\n",
-      "3     24     3.14e+07   0:00.0\n",
-      "20    126    643723.5   0:00.1\n",
-      "40    246    188157.2   0:00.1\n",
-      "60    366    101666.8   0:00.1\n",
-      "80    486    101437.4   0:00.2\n",
-      "100   606    101437.1   0:00.2\n",
-      "120   726    101437.1   0:00.3\n",
-      "140   846    101437.1   0:00.3\n",
-      "160   966    101437.1   0:00.4\n",
-      "180   1086   101437.1   0:00.4\n",
-      "200   1206   101437.1   0:00.5\n",
-      "220   1326   101437.1   0:00.5\n",
-      "240   1446   101437.1   0:00.6\n",
-      "260   1566   101437.1   0:00.6\n",
-      "280   1686   101437.1   0:00.6\n",
-      "300   1806   101437.1   0:00.7\n",
-      "320   1926   101437.1   0:00.7\n",
-      "340   2046   101437.1   0:00.8\n",
-      "342   2052   101437.1   0:00.8\n",
-      "Halting: No significant change for 200 iterations.\n",
-      "Score at true solution: \n",
-      "101503.5654159602\n",
-      "Found solution:          True parameters:\n",
-      " 1.50147206230228803e-02     1.49999999999999994e-02\n",
-      " 5.99920303031473079e+00     6.00000000000000000e+00\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create some toy data\n",
-    "real_parameters = [0.015, 6]\n",
-    "times = np.linspace(0, 1000, 1000)\n",
-    "values = transformed_model.simulate(real_parameters, times)\n",
-    "\n",
-    "# Add noise\n",
-    "values += np.random.normal(0, 10, values.shape)\n",
-    "\n",
-    "# Create an object with links to the model and time series\n",
-    "problem = pints.SingleOutputProblem(transformed_model, times, values)\n",
-    "\n",
-    "# Select a score function\n",
-    "score = pints.SumOfSquaresError(problem)\n",
-    "\n",
-    "# Select some boundaries\n",
-    "boundaries = pints.RectangularBoundaries([0, -6.0], [0.03, 20.0])\n",
-    "\n",
-    "# Perform an optimisation with boundaries and hints\n",
-    "x0 = 0.01,5.0\n",
-    "sigma0 = [0.01, 2.0]\n",
-    "found_parameters, found_value = pints.optimise(\n",
-    "    score,\n",
-    "    x0,\n",
-    "    sigma0,\n",
-    "    boundaries,\n",
-    "    method=pints.CMAES,\n",
-    ")\n",
-    "\n",
-    "# Show score of true solution\n",
-    "print('Score at true solution: ')\n",
-    "print(score(real_parameters))\n",
-    "\n",
-    "# Compare parameters with original\n",
-    "print('Found solution:          True parameters:' )\n",
-    "for k, x in enumerate(found_parameters):\n",
-    "    print(pints.strfloat(x) + '    ' + pints.strfloat(real_parameters[k]))\n",
-    "\n",
-    "# Show quality of fit\n",
-    "plt.figure()\n",
-    "plt.xlabel('Time')\n",
-    "plt.ylabel('Value')\n",
-    "plt.plot(times, values, label='Nosiy data')\n",
-    "plt.plot(times, problem.evaluate(found_parameters), label='Fit')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/optimisation/transformed-parameters.ipynb b/examples/optimisation/transformed-parameters.ipynb
index 4e2bb155..2dc8b3cb 100644
--- a/examples/optimisation/transformed-parameters.ipynb
+++ b/examples/optimisation/transformed-parameters.ipynb
@@ -117,17 +117,17 @@
       "120   123    2.58e+07   0:00.0\n",
       "140   143    2.58e+07   0:00.0\n",
       "160   167    2.58e+07   0:00.0\n",
-      "180   192    2.58e+07   0:00.1\n",
-      "200   217    2.58e+07   0:00.1\n",
-      "220   242    2.58e+07   0:00.1\n",
-      "240   269    2.58e+07   0:00.1\n",
-      "260   295    2.58e+07   0:00.1\n",
-      "280   322    2.58e+07   0:00.1\n",
-      "300   349    2.58e+07   0:00.1\n",
-      "320   375    2.58e+07   0:00.1\n",
-      "340   402    2.58e+07   0:00.1\n",
-      "360   429    2.58e+07   0:00.1\n",
-      "371   442    2.58e+07   0:00.1\n",
+      "180   192    2.58e+07   0:00.0\n",
+      "200   217    2.58e+07   0:00.0\n",
+      "220   242    2.58e+07   0:00.0\n",
+      "240   269    2.58e+07   0:00.0\n",
+      "260   295    2.58e+07   0:00.0\n",
+      "280   322    2.58e+07   0:00.0\n",
+      "300   349    2.58e+07   0:00.0\n",
+      "320   375    2.58e+07   0:00.0\n",
+      "340   402    2.58e+07   0:00.0\n",
+      "360   429    2.58e+07   0:00.0\n",
+      "371   442    2.58e+07   0:00.0\n",
       "Halting: No significant change for 200 iterations.\n",
       "Score at true solution: \n",
       "87997.66499248528\n",
@@ -138,7 +138,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -155,7 +155,7 @@
     "    x0,\n",
     "    sigma0,\n",
     "    method=pints.NelderMead,\n",
-    "    transform=None,\n",
+    "    transformation=None,\n",
     ")\n",
     "\n",
     "# Show score of true solution\n",
@@ -200,7 +200,7 @@
     "transform_K = pints.LogTransformation(n_parameters=1)\n",
     "\n",
     "# The full transformation: [r, K] -> [r, log(K)]\n",
-    "transform = pints.ComposedTransformation(transform_r, transform_K)"
+    "transformation = pints.ComposedTransformation(transform_r, transform_K)"
    ]
   },
   {
@@ -241,10 +241,10 @@
       "240   248    87740.34   0:00.0\n",
       "260   272    87740.34   0:00.0\n",
       "280   297    87740.34   0:00.0\n",
-      "300   323    87740.34   0:00.1\n",
-      "320   350    87740.34   0:00.1\n",
-      "340   377    87740.34   0:00.1\n",
-      "360   403    87740.34   0:00.1\n",
+      "300   323    87740.34   0:00.0\n",
+      "320   350    87740.34   0:00.0\n",
+      "340   377    87740.34   0:00.0\n",
+      "360   403    87740.34   0:00.0\n",
       "380   430    87740.34   0:00.1\n",
       "400   457    87740.34   0:00.1\n",
       "420   483    87740.34   0:00.1\n",
@@ -262,7 +262,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -279,7 +279,7 @@
     "    x0,\n",
     "    sigma0,\n",
     "    method=pints.NelderMead,\n",
-    "    transform=transform,  # Pass the transformation to the optimiser\n",
+    "    transformation=transformation,  # Pass the transformation to the optimiser\n",
     ")\n",
     "\n",
     "# Show score of true solution\n",
@@ -319,7 +319,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/examples/sampling/transformation-with-and-without-jacobian.ipynb b/examples/sampling/transformation-with-and-without-jacobian.ipynb
index 8664e9b3..174f7feb 100644
--- a/examples/sampling/transformation-with-and-without-jacobian.ipynb
+++ b/examples/sampling/transformation-with-and-without-jacobian.ipynb
@@ -118,7 +118,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -165,7 +165,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "transform = pints.LogitTransformation(1)"
+    "transformation = pints.LogitTransformation(1)"
    ]
   },
   {
@@ -205,10 +205,10 @@
     "        # Then we call the log-pdf in the model space x\n",
     "        return self._log_pdf(x)\n",
     "\n",
-    "naive_trans_log_pdf = NaiveTransformedLogPDF(log_pdf, transform)\n",
+    "naive_trans_log_pdf = NaiveTransformedLogPDF(log_pdf, transformation)\n",
     "\n",
     "# Transform the initial position to search space\n",
-    "y0 = [transform.to_search(x) for x in x0]"
+    "y0 = [transformation.to_search(x) for x in x0]"
    ]
   },
   {
@@ -272,7 +272,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -306,7 +306,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -321,7 +321,7 @@
     "# Transform the samples from y back to x\n",
     "naive_chains_in_x = np.zeros(naive_chains_in_y[0].shape)\n",
     "for i, y in enumerate(naive_chains_in_y[0]):\n",
-    "    naive_chains_in_x[i, :] = transform.to_model(y)\n",
+    "    naive_chains_in_x[i, :] = transformation.to_model(y)\n",
     "\n",
     "plt.hist(naive_chains_in_x, bins=15, density=True, label='samples')\n",
     "x = np.linspace(0, 1, 100)\n",
@@ -354,7 +354,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pints_trans_log_pdf = pints.TransformedLogPDF(log_pdf, transform)"
+    "pints_trans_log_pdf = pints.TransformedLogPDF(log_pdf, transformation)"
    ]
   },
   {
@@ -411,7 +411,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -423,7 +423,7 @@
     },
     {
      "data": {
-      "image/png": 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uhU9udgpCy2vg6o+sIJQSQQL0D17Pw9m9YMk4Z8LBHTZoLJm4udD8ENAa2AKgqvMBG0liimfPVnj3UljwIZzzIFz4ks1bVAq9EzofrnzXGS8ypANsXO51JBMnbopCUFW35nvORhWbg7djPQztAqtnOM0SZ95TthbAKWuOvwCu+wL2bIO3OzmL+pgyz82F5oUi0gPwi0gj4A7gh9jGMmXO5t9hxEWw7U/o/j406vC3TUrtIjbJrE463DDR+d290wV6jIajT/M6lYkhN0XhduABnO6oo3DWPng0lqFMGbPhNxjWFbJ3wbVjoG5rrxOVCtEugjFTq3GkMFzs3K4cecCibsqGIpuPVHWXqj6gqieranrk/p54hDNlwLrF8E5nCGfD9eOtICSqanWdVe1qNobR3Z2Za02Z5Kb3UbqIfCIiP4rIzzm3eIQzCe6vBTDsAhAfXDceajcp+j2m9KpU0znTq90U3u8Jiz/3OpGJATfNRyOBfwELIIGXbjLx9efPTnfG1MrOfyQ1GsY9QsI0zySSCtXhms/g3cvgw+vhsiHQ5GKvU5koclMUslTVxiUY99YthuHdnIJw/TionuZ1IhNN5Q+BXp84heHjm8CX4vRUMmWCmy6pA0TkLRHpLiKX5NxinswkpqylMLyrs/bBtWOsIJRV5arA1R/CEc3hw+vg14leJzJR4qYoXA80BzoBF0Zu9rXA/N3G5U4vIwSuHetJk5GJo/JVndXwap8A7/eC5VO8TmSiwE3z0Umq2izmSUxi27bW6cse2uf0MqrZyOtEJh4qVINen8HQC2D01c7ZYZ10r1OZEnBzpjBTRE6IeRKTuHZtcvqv79rkfHM87HivE5l4qnioc42h8mHOFCbrFnudyJSAm6JwOs56Cksj3VEXuOmSKiJvi8h6EVlYwOtX5+ni+oOInHSw4U0psHcHjLwMNq2E7qPgqJZeJzJeqHK40yspUN75grB5ldeJTDG5KQqdgEbAufzvesKFLt43NPLegqwEzlLVE3FGSA92sU9TmoSynYuMa+fB5e9A/TO9TmS8VD3NKQzBPc4Zw86NXicyxeDmmsJAVe2V9wkRGQH0KmB7AFR1moikFfJ63vmTZgJ1XGQxpYUq7w+4jCsDU+mXfROjhwLYuICkd9jx0ON9p8PBqKucZVVtPYaE4uZMYb9hqCLiB1pFOceNQIHj5kWkt4hkiEhGVpbN7V4qTH2cKwNTeTF4MaND53idxpQm9U6BS9+CzDnOOIZwyOtE5iAUWBRE5D4R2Q6cKCLbIrftwHogauPbReRsnKLw74K2UdXBkXmX0mvVqhWtjzbFNe9d+PZJPgiexfPBy7xOY0qjE7rC+U/C0nEwoZ/XacxBKLAoqOrjqloFeFpVq0ZuVVS1hqreF40PF5ETgbeAbqpqDZCJYOU0GPsPaHA29wdvBGw9BFOANrfAqX1h9mCY9YbXaYxLbpqPvhCRSgAi0lNEnhORo0v6wSJSD/gE6KWqv5Z0fyYONixzBikd2hAuH0rQ1SUpk9Q6PgLHdnbOFn79yus0xgU3ReE1YFeky+i9wO/A8KLeJCKjgBnAsSKSKSI3isitInJrZJP+QA3gVRGZLyIZxTsEExe7NsF7l4PP71xIrFDN60QmEfj8zip7tZvAR9c7y3uaUs3NV72gqqqIdANeVNUhInJtUW9S1e5FvH4TcJPLnMZLoSB8eC1szXSmrzjUluhOBtGcZfZwejOz1mNOj6Sbp0KlGlHbt4kuN2cK20XkPqAnMC7S+8hWWk8mX/3HuZZw4YtOzxJjDtJf1HBWbNu+zvmCEcr2OpIpgJszhSuBHsCNqvpX5FrA07GNZWKhON/8LvdP5emUwQwJns+jow+B0TYWwRRTnVbOF4vPboWJD0Dnp7xOZA6gyKKgqn8Bz+V5vBoX1xRM4mshvzEw8DbTQ014LNjD6zimLGjeHdYthBmvwOFNoeU1Xicy+bhZjnN7nnEKe0QkJCJb4xHOeKcmW3kt9QXWaXX6Zt9BCL/XkUxZ0eFhaHA2jLsb/pjrdRqTT5FFITI2IWecQnngUmBQ7KMZr/gJ8XLKy1RjB7dm38kWqngdyZQl/gBcOgQq14YPrrU5kkoZNxea96OqnwE2r0EZ9q/AB5zqX8z92TeyWNO8jmPKoko14IrhsGMdfHyjTYVRihR5TSHf0ps+IB3QmCUyubxYeP4832xuDYxlRLADn4Rt1lMTQ0e1hM7PwNg7YMpj0P5BrxMZ3PU+yjtNdhBYBXSLSRrjqbqyjqdT3mB+uCGPBgudBNeY6Gh1LWTOhu+egaNPhWM6eJ0o6bnpfXR9PIIYb6WSzaCUl1CEvtl3sM+Goph4Of9p+ONH+KQ33Dodqh7pdaKkVmBREJF7VfUpEXmZvzcXKbAJeFdVl8cyoImP+wMjOdG3kpv33UWm2ky0Jo5SK8Llw2BwO/joRmfUvN/m1fJKYReaf4n8mQHMzXf7EdiOM6GdSXDn+2ZxXeAr3gqez9dhW3TdeKBWY7jwBVj9A0z5r9dpklqB5VhVx0b+HFbQNiKyMxahTPwcRRZPprzJ/HBDngwWOl2VMbF14hWw6juY/jw0aAcNzvI6UVIqrPloLIX0MlLVrqpqk6QnMD8hXkgdhKDcnt2XbJsK23it0xOweqZzfaHPDzZxngcKaz56BngWWAnsBt6M3HYAC2MfzcTa7YFPOdn3K//Jvp41WtvrOMZAaiW47G3YvQk+/z9Q6/0eb4WtvPatqn4LtFDVK1V1bOTWAzg9fhFNLJwsS7jd/ykfh07n87D9Ok0pcngzZ3GeX7+EOW95nSbpuBnRXEtEGuQ8EJH6gHVPSWBV2MXzqa+yRg+jf7b1ODalUJtbodG5zrTt65d4nSapuGlEvhOYKiIrIo/TgFtilsjE3EMpwzicTVyePYCdVPA6jkkSBztCvyYXM7HcDP585Uou3vfI3655rXqiSzTjmQg3E+JNABoB/4jcjlXViUW9T0TeFpH1InLA6w/ieElElonIzyLS8mDDm4N3vm8Wl/q/Y1DoIuZpI6/jGFOgDRxCv+ybaepbxT8DH3kdJ2m4nRCvEXAscBJwpYi4mQR9KNCpkNfPj+y3EdAbZy1oE0OHsZnHUoYwP9yAl4MXeR3HmCJ9HU5ndLAdt/rHki7WjBQPbtZTGAC8HLmdDTwFdC3qfao6DWfUc0G6AcPVMROoJiJHuEptikF5KmUw5dnHXdm3EbTupyZBPBrsRabW4rmU16jIHq/jlHluzhQuA9oDf0XmQToJKBeFzz4KWJPncWbkub8Rkd4ikiEiGVlZWVH46ORzpX8q7fw/8XiwOyvU5pYxiWMnFbg7+1bqyAbuC7zndZwyz01R2K2qYSAoIlWB9UCDIt7jhhzguQN2SlbVwaqarqrptWpZx6eDdRRZ/CfwLj+ETmBEqKPXcYw5aBl6HENC59Mr8A1tfQu8jlOmuSkKGSJSDWfgWs68R7Oj8NmZQN08j+sAa6OwX7Mf5cmUwQjKvcFb0INfV8mYUuGZ4BUsDx/BkylvUpldXscps9z0PrpNVbeo6utAR+DaKE2nPQa4JtIL6RRgq6r+GYX9mjx6+r/hdP8i/hu82mY/NQltL6nck30rR7CRBwIjvY5TZh3U1UZVXeV2WxEZBbQDaopIJjAAnEn6IwVmPNAZWAbsAmwUVZQdRRb9AqOYFmrGqJCtoGoS3zxtxODQBfQJjIXlk6Gh/b2Otph1QVHVQqfcVFUF/i9Wn2+Ux1PeQlDuy76JA1/CMSbxvBC8lHN9GTQc8w+4bQaUq+x1pDLFGpjLqEt933GmfwFPBq/iD5uVxJQhe0nl3uzesHUNTH7U6zhljquiICJ+ETlSROrl3GIdzBRfLbbwYMoI5oQbW28jUybN1WOh9c0w6w1nqm0TNW4Gr90OrAO+BsZFbl/EOJcpgYdThlKBffw7u7f1NjJlV/sBcEhd+LwvZNugtmhx8z9GznxHTVS1WeR2YqyDmeLp6Mugs382LwYvsUFqpmwrV9lZwnPjbzD9Oa/TlBluisIaYGusg5iSq8RuHk4Zyi/hugwO2QySJgkc0x6aXQHfPQdZS71OUya46X20Amfq7HHA3pwnVdVKcylzT+ADDmcz/5f9D5vbyCSP8x6DZV/D2H/AdePBZ02mJeHmp7ca53pCKlAlz82UIifKcq71f8WIUAebEtskl8q1oOOjsHoGzBvudZqEV+TXSVV9OB5BTPH5CfF4yluspxpPB6/0Oo4x8deiJ/w0Gr7uD8d2hsqHeZ0oYbnpfVRLRJ4WkfEiMjnnFo9wxp1r/V/RxPc7D2Vfyw4qeh3HmPgTgQueh327nCU8TbG5aT4aCSwB6gMPA6uAOTHMZA5CbTZxV+BDpoROYkL4ZK/jGOOdWo2h7T/g5/dh5Xdep0lYbopCDVUdAmSr6reqegNwSoxzGZf+k/IuKYQYELwOm8rCJL0z7oZq9WDc3RDc53WahOSmKGRH/vxTRLqISAucaa6Nx073LeBC/0wGBbuxWmt7HccY76VWhPOfhg1LYeYgr9MkJDdFYaCIHALcDdwDvAXcGdNUpkipZPNI4B1WhA/njdAFXscxpvQ4thMcdwF8+xRsWVP09mY/btZT+EJVt6rqQlU9W1VbqeqYeIQzBbvJP54Gvr94KHgte0n1Oo4xpUunx0EVvnrA6yQJp8AuqSJyr6o+JSIvc4BlMlX1jpgmMwU6nI30DXzGxFA608IneR3HmNKnWj3n+sKUgbBiKjRo53GgxFHYmcIvkT8zcJbhzH8zHnkgZSQ+wjwa7Ol1FGNKr9Nuh+ppMP5eCGUXublxFFgUVHVs5M9hqjoM+BT4JM/jIolIJxFZKiLLRKTfAV6vJyJTRGSeiPwsIp2LeyDJ4hTfYi70z+T10IVkqg3QMaZAKeWh0xPORefZg71OkzDcDF5LF5EFwM/AQhH5SURauXifHxgEnA+cAHQXkRPybfYf4ANVbQFcBbx6sAeQTPyEGBAYRqbW5LVgV6/jGFP6Ne4Ex3SEqU/A9nVep0kIbnofvQ3cpqppqno0zhKa77h4X2tgmaquUNV9wGigW75tFKgauX8IsNZd7OTUwz+J431reDS7p11cNsYNETj/ScjeDZMf8TpNQnBTFLarau7wQFWdDmx38b6jcKbdzpEZeS6vh4CeIpIJjAdud7HfpHQIO7gr8BE/hE5goo1cNsa9Gg3hlFth3khYO9/rNKVegUVBRFqKSEtgtoi8ISLtROQsEXkVmOpi3wcaXpu/F1N3YKiq1gE6AyNE5G+ZRKS3iGSISEZWVpaLjy57/hn4mKrs5JHgNdjIZWMO0pn/goo1YEI/p6uqKVBhs6Q+m+/xgDz33fxUM4G6eR7X4e/NQzcCnQBUdYaIlAdqAuvzbqSqg4HBAOnp6Un3G20of9DL/zWjQ+ewRG15bGMOWvlDoP2DzpoLiz6Bppd6najUKrAoqOrZJdz3HKCRiNQH/sC5kNwj3zargfbAUBE5HigPJOepQCEeDLzLLsrzbPByr6MYk7ha9ILZb8HXA5zptVMqeJ2oVHLT++gQEXkup/lGRJ6NTHtRKFUNAn2BiThjHj5Q1UUi8oiI5HSduRu4WUR+AkYB16nauV1e7Xzzaef/iReDF7Mp95q8Meag+fzOSOeta+CHV7xOU2q5WbPxbWAhcEXkcS+c3keXFPVGVR2PcwE573P989xfDLR1GzbZ+Alxf2AkK8O1GR46z+s4xiS++mc48yJNfx5aXgNVbCLJ/Nz0PmqoqgMiXUtXRFZiaxDrYAau8E+lse8Pngh2J9vWXDYmOjo8DKG9MPUxr5OUSm6Kwm4ROT3ngYi0BXbHLpIBYO927gp8yOzwsdYF1ZhoqnkMpN8IPw6HdYu9TlPquCkKfYBBIrJKRFYBrwC3xjSVgekvUEu28d/sq7EuqMZE2Vn/htQqzprOZj9ups6er6onAScCJ6pqC1X9KfbRktjWTJjxCp+HTuMnPcbrNMaUPZVqwJn3wLKvYbktOZ9XkQ3VIvIY8JSqbok8rg7craq2OnasTHkMNMzTwSu9TmJM2dXmFpjzpnO2UL8dafd/GdXdr3qiS1T3Fy9umo/OzykIAKq6GWf0sYmFdYtg/nvQujeZWsvrNMb7NWQXAAAW6klEQVSUXYFycM6D8NcCWPCh12lKDTdFwS8i5XIeiEgFoFwh25uS+OZhKFfVWSDEGBNbTS+Dw0+EyQNJxdZcAHfjFN4FJonIOzjTW9wAuFpPwRykVdPht4nQ4SGoeKjXaYwp1dL6jYvKfk73deHd1Mfp5f+aISFrBHFzofkp4L/A8UAT4NHIcyaaVJ22zapHQRvr3GVMvEwPN2NaqBl9A59RlZ1ex/Gcm+YjVPVLVb1HVe9W1YmxDpWUFn8Of8yFs++3OVmMibMng92pLju4NTDW6yieczP30SkiMkdEdojIPhEJici2eIRLGqEgTB4ItY6Dk7p7ncaYpLNI0/g8dBrX+ydQi81ex/GUmzOFV3DWPfgNqADcBLwcy1BJ56f3YONvcM5/nEm7jDFx91zwMgKEuD3wmddRPOW2+WgZ4FfVkKq+A5R0Wm2TI3uPs37sUa2cibqMMZ74XQ/ng1A7uvsnU1eSdz1nN0Vhl4ikAvNF5CkRuROoFONcySNjCGz7A9r3d9aTNcZ45qXgxYTwcWfgY6+jeMZNUegF+HHWRtiJs5qaLVsUDXu3w3fPQv2zoEE7r9MYk/TWcShDQ+dxke97jpXVXsfxhJsuqb+r6m5V3aaqD6vqXZHmJFNSM16FXRuh/YCitzXGxMVrwa7soAJ3B5JzlHOBg9dEZAEFr8W8F1gOPG6T4xXTrk0w4xXnOkKdVl6nMcZEbKUybwY7c3fKRzQLrmCBJtfyMYWdKVwAXFjA7Sqc5TOHFrZzEekkIktFZJmI9CtgmytEZLGILBKR94pxDIlpxiuwdxu0u8/rJMaYfN4JdWKzVk7Ks4UCzxRU9fci3rtcRFoW9KKI+IFBQEcgE5gjImMiS3DmbNMIuA9oq6qbReSwg0qfqHZugJmvQ5NL4PCmXqcxxuSzg4q8HryQ+1JG0Sq4lLl6rNeR4sZVl9SCqGphjeGtgWWRJTz3AaOBbvm2uRkYFJl5FVVdX5I8CWP68xDcbWcJxpRiw0MdydKqSXe2UKKiUISjgDV5HmdGnsurMdBYRL4XkZki0imGeUqH7X/BnLfgxCuhVmOv0xhjCrCb8rwa7MZp/sWc6lvkdZy4iWVROFCn+/wXrgNAI6Adzqjpt0Sk2t92JNJbRDJEJCMrKyvqQePqu+cglA1n3et1EmNMEd4LtedPPZS7Ah9ScL+bssXN3EdtReRrEflVRFaIyEoRWeFi35k4Yxpy1AHWHmCbz1U1W1VXAktxisR+VHWwqqaranqtWgm88My2tTB3KDTvAYcmV48GYxLRXlIZFOzGyb5faetb6HWcuHBzpjAEeA44HTgZSI/8WZQ5QCMRqR8ZEX0VMCbfNp8RmTJDRGriNCe5KTiJafoLoCFbQMeYBPJBqB1r9VD+GfiYZDhbcFMUtkamzl6vqhtzbkW9SVWDOKOgJwK/AB+o6iIReUREukY2mwhsFJHFwBTgX272nZByzhJO6g6H1vc6jTHGpX2k8GoSnS0UNngtp7vpFBF5GvgEZ9AaAKr6Y1E7V9XxwPh8z/XPc1+BuyK3ss3OEoxJWB+E2nFb4HP+GfiY7/c15cCXTMuGwpbjfDbf4/Q89xU4J/pxyig7SzAmoeWcLQxMeYe2voV8H27mdaSYKWzwWk5bfwNV3a+dX0TsKunBsLMEYxJeztnCnWX8bMHNNYWPDvBcco3mKIntf0XOEq6yswRjEljO2UK671dO9S0u+g0JqrBrCscBTYBDROSSPC9VBcrHOliZ8cPLEM6G08v+ZRNjyroPQ2dxe+BT7vB/yoxwE6/jxERhZwrH4kyKV439J8NriTM9hSnKzg2Q8TY0uxxqNPQ6jTGmhPaSyuDgBZzqX0y6LPE6TkwUdk3hc+BzETlVVWfEMVPZMeMVyN4NZ9zjdRJjTJSMDLWnT2AMdwQ+5Zrssjd/WWHNR/eq6lNADxHpnv91Vb0jpskS3a5NMPtNaHKRzXFkTBmyh3K8FexMv5TRnBRcxk96jNeRoqqw5qNfIn9mAHMPcDOFmfUG7NsBZ/7L6yTGmCgbEerIZq3M7YFPvY4SdYU1H42N3P0uf5dUc2Bp/cYBUInd/FDuJWaFW9H7+VXAKi9jGWOibCcVeDvYibtTPuK44GqWaD2vI0WNmy6pQ0VkuYiMFpHbRKTsjtqIkh7+SRwiuxgUzL98hDGmrBgWOpcdWp4+gfxTuiW2IouCqp4JHA+8DFQHxonIplgHS1Tl2MfNgfF8F2pa5toajTH/s43KvBvqyAW+GRwtf3kdJ2rcTJ19OnA38ADQBfgC+L8Y50pYl/mncZhs4dWQnSUYU9YNCZ5PkAC3+McWvXGCcNN89C1wETAYaKeqt6nqqNjGSkx+QtziH8u88DHMCJ/gdRxjTIxlUY0PQmdxmX8atSkbDShuikIN4BHgVGCCiHwjIo/GNlZiusA3g3q+rMi1hLI5L4oxZn9vhC7Ah3JzYJzXUaLCzTWFLTgL36wE/gQaAmfGOFfiUaVPYCxLw3WYFG7hdRpjTJxk6mF8Hj6NHv7JVGO713FKzM01heU402hXB14HjlXVs2IdLOH89hXH+dbwevBCNKZLXxtjSps3ghdSUfZyjf9rr6OUWGHrKeRopKrhmCdJdNNfIFNrMjZ8qtdJjDFx9qvWZVKoBdcGJjI41IU9lPM6UrEV+JVWRP4jIocWVBBE5BwRuaCwnYtIJxFZKiLLRKRfIdtdJiIqIukFbVOqrZkNq3/I7YlgjEk+rwcvpIZs5wr/VK+jlEhh/4MtAMaKyB7gRyALZ8rsRkBz4BvgsYLeLCJ+YBDQEcgE5ojIGFVdnG+7KsAdwKwSHIe3pr8AFaozes/ZXicxxnhkjh7L3HAjbvaPZ2Sog9dxiq3AMwVV/VxV2wK3AosAP7ANeBdorap3qmpWIftuDSxT1RWqug8YDRyo8/6jwFPAnmIeg7eylsLScdC6N7ttmQljkpjwevBC6vqy6OKb6XWYYnPT++g3VR2qqo+r6guqOlFVd7vY91HAmjyPMyPP5RKRFkBdVf2isB2JSG8RyRCRjKyswuqQB75/CQIVoPUtXicxxnjsm3BLfgsfxa2BL0DV6zjFEstuMgfqqJ/7UxIRH/A8zmjpQqnqYFVNV9X0WrVqRTFiCW37E35+H1r0hEo1vE5jjPGY4mNwqAsn+H6HFVO8jlMssSwKmUDdPI/rAGvzPK4CNAWmisgq4BRgTEJdbJ79BmgITrVZP4wxjs9DbVmn1ZyleBNQLLvKzAEaiUh94A/gKqBHzouquhWomfNYRKYC96hqRgwz7SdnquviqMRuZpR7g2nhk+n71GKg7C7kbYxxbx8pDA124t/LR8NfC+DwxJpYurAuqV/luX/Qa86pahDoC0zEWbDnA1VdJCKPiEjX4oQtTa70T6Wq7OKtYBevoxhjSpmRoXMgpRL88IrXUQ5aYc1HeRvvLy/OzlV1vKo2VtWGqvrfyHP9VfVvE5Crart4niWUhJ8QNwS+ZFb4OObb9NjGmHy2URlaXgMLP4Ktf3gd56AUVhQS89J5HHT2zaKObOBNO0swxhTklD5OD6RZr3ud5KAUdk2hgYiMwelFlHM/l6omfBNQ8TizIS4PH2ET3xljClb9aGhyEcwd6qzVXr6q14lcKawo5B1o9kysgySKNrKEE30ruT/7Rpv4zhhTuFP7wsKPYd6IhOmlWGBRUNVvc+6LSK3Ic6Vs5Fj83RgYzyatzMehM7yOYowp7Y5qCfVOc5qQWt8C/tI/N1phvY9ERAaIyAZgCfCriGSJSP/4xStdjpa/6OD7kXdDHdhLqtdxjDGJ4NTbYMtqWFLoxA2lRmHtH/8ETgdOVtUaqlodaAO0FZE745KulLneP4Fs/IwInut1FGNMoji2M1RPg5mvep3ElcKKwjVAd1VdmfOEqq4AekZeSypV2cEV/m8ZGz6NLKp5HccYkyh8fmjTB9bMgszS3+u+sKKQoqob8j8Zua6QErtIpVN3/xQqyl6GBM/3OooxJtG0uBrKHQIzBnmdpEiFFYV9xXytzAkQ5NrARL4PNeEXPdrrOMaYRFOuCrS6BhZ/DlvWFL29hworCieJyDYR2R65bct5DCTWZB4l1Mk3hyNlE2+HOnkdxRiTqHKm15/zprc5ilDYIjt+Va2qqlUit6p5HidV89H1gQmsCtdmsg1WM8YUV7W6cPwFMHcY7NvpdZoCFdYltbyI/FNEXoksclP6O9jGwImynFa+3xgWOtcGqxljSqZNH9izxVmHpZQq7H+5YUA6zlrNnYFn45KolLk+MIHtWoEPQ2d5HcUYk+jqnQJHnASz3ii1K7MVVhROUNWeqvoGcBmQdEN4a7GZLr6ZfBQ6kx1U9DqOMSbRiThnC1lLSu3KbIUVheycO5G1EZJOz8A3BAgzNHSe11GMMWVF00ug0mEws3TOnuqm91FOj6MT8/RG2havgF5JJZur/ZOYHG7O73q413GMMWVFoByk3wC/TYSNy71O8zdueh/l9DgK5Lnvag5YEekkIktFZJmI9DvA63eJyGIR+VlEJolIqRkEcIFvBjVlG+9YN1RjTLSl3wC+FJhd+rqnxqw7jYj4gUHA+cAJQHcROSHfZvOAdFU9EfgIeCpWeQ7WNYGvWBY+ku/DTb2OYowpa6rUdtZamD8S9u7wOs1+YtnHsjWwTFVXqOo+YDT7r9GAqk5R1V2RhzOBOjHM41pzWUZz3wqGhc7FWWPIGGOirPUtsHcb/Dza6yT7iWVROArIO547M/JcQW4EvoxhHteuDUxku1bgE1szwRgTK3XS4cgWThNSKeqeGsuicKCv2Ac8chHpiTMm4ukCXu8tIhkikpGVFdt1fmqyNbcb6k4qxPSzjDFJTARa93a6p66c5nWaXLEsCplA3TyP6wBr828kIh2AB4Cuqrr3QDtS1cGqmq6q6bVq1YpJ2Bzd/ZNIlRDDQ7ZmgjEmxppcAhVrwOzBXifJFcuiMAdoJCL1RSQVuAoYk3cDEWkBvIFTENbHMIsrAYJcHZjEt6ETWalHeB3HGFPWpZSHltfC0vHO6mylQMzmM1LVoIj0BSYCfuBtVV0kIo8AGao6Bqe5qDLwoYgArFbVrrHKVJRzfRkcLpu5P3SjVxGMMWVEWr9xrrY7gvpML6e88cwDPBW8qsDtVj3RJVrRChXTSe5UdTwwPt9z/fPc7xDLzz9YvfzfsCZci6nh5l5HMcYkiT+pwTfhVlzpn8ILwUvZ5/EaZjbtZ8Qxksmp/sWMDLUnbD8WY0wcjQh1pIZsp7NvltdR7H+/HD3937BXU3g/1M7rKMaYJPN9uAnLw0fQK/C111GsKABUYjeX+r/ji3AbNuNqBg9jjIkaxcfIUAda+X6jiazyNIsVBeAi//dUkd28G+zodRRjTJL6KHQGuzWVnn5vzxasKKD09H/NwnAa8/QYr8MYY5LUNirzeeg0LvJ/T1W8W64z6YtCuizleN8aRoQ6YvMcGWO8NCJ0LhVkH5f6vRvhnPRF4erAJLZpRcaETvU6ijEmyS3SNOaHG3K1fxIFzAoUc0ldFKqzjc6+WXwcOoPdlPc6jjHG8G6oA8f41tJGlnjy+UldFC7zT6OcBHkv1N7rKMYYA8AXoVPYqhW5OvCNJ5+fvEUhHOZq/yRmhY/jNy0VyzgYYwx7KMfHoTPp5JtNDbbG/fOTtyisnEqabx0jg3aWYIwpXUaG2pMqIS73fxv3z07eopDxNhu1ChPCrb1OYowx+1muRzEzfDw9/JMQwnH97OQsCtv+hCXj+TB0lueTTxljzIGMDLanni+LM30L4vq5yVkU5r0LGmJU6ByvkxhjzAFNCLdmg1alu39yXD83+YpCOAQ/Dof6Z/G7Hu51GmOMOaBsAnwUOpMOvrnUYnPcPjf5isLyKbB1NbS6zuskxhhTqPdDZxOQcFwvOCdfUZj7DlSsCcdd4HUSY4wp1Eo9gh9CJ9DdPwXC8bngHNOiICKdRGSpiCwTkX4HeL2ciLwfeX2WiKTFMg/b/4KlX0LzHhBIjelHGWNMNIwKnUNdXxasmBKXz4tZURARPzAIOB84AeguIifk2+xGYLOqHgM8DzwZqzxA7gVmWl4b048xxphomRg+mU1aGeYOjcvnxfJMoTWwTFVXqOo+YDTQLd823YBhkfsfAe1FJDZTlYbD8OMwSDsDatoU2caYxLCPFD4KnQVLx8P2dTH/vFgWhaOANXkeZ0aeO+A2qhoEtgI1YpJmxRTYYheYjTGJZ3TobAgHYf7ImH9WIIb7PtA3/vxzwbrZBhHpDfSOPNwhIkuLmakmD1++oZjvTVQ1ATvmss+OuQz7nch/lg/fXRPuLu4xH+1mo1gWhUygbp7HdYC1BWyTKSIB4BBgU/4dqepgYHBJA4lIhqqml3Q/icSOOTnYMSeHeBxzLJuP5gCNRKS+iKQCVwFj8m0zBsi56nsZMFlVvVlZwhhjTOzOFFQ1KCJ9gYmAH3hbVReJyCNAhqqOAYYAI0RkGc4ZwlWxymOMMaZosWw+QlXHA+PzPdc/z/09wOWxzJBPiZugEpAdc3KwY04OMT9msdYaY4wxOZJvmgtjjDEFKpNFodRNrxEHLo75LhFZLCI/i8gkEXHVPa00K+qY82x3mYioiCR8TxU3xywiV0R+14tE5L14Z4w2F3+364nIFBGZF/n73dmLnNEiIm+LyHoRWVjA6yIiL0V+Hj+LSMuoBlDVMnXDuai9HGgApAI/ASfk2+Y24PXI/auA973OHYdjPhuoGLnfJxmOObJdFWAaMBNI9zp3HH7PjYB5QPXI48O8zh2HYx4M9IncPwFY5XXuEh7zmUBLYGEBr3cGvsQZunAKMCuan18WzxRK1/Qa8VHkMavqFFXdFXk4E2fcSCJz83sGeBR4CtgTz3Ax4uaYbwYGqepmAFVdH+eM0ebmmBWoGrl/CH8fD5VQVHUaBxivlUc3YLg6ZgLVROSIaH1+WSwKpWt6jfhwc8x53YjzTSORFXnMItICqKuqX8QzWAy5+T03BhqLyPciMlNEOsUtXWy4OeaHgJ4ikonT2/H2+ETzzMH+ez8oMe2S6pGoTa+RQFwfj4j0BNKBs2KaKPYKPWYR8eHMvHtdvALFgZvfcwCnCakdztngdyLSVFW3xDhbrLg55u7AUFV9VkROxRn71FRV47viffzE9P+vsnimcDDTa1DY9BoJxM0xIyIdgAeArqq6N07ZYqWoY64CNAWmisgqnLbXMQl+sdnt3+3PVTVbVVcCS3GKRKJyc8w3Ah8AqOoMoDzOvEhllat/78VVFotCMk6vUeQxR5pS3sApCInezgxFHLOqblXVmqqapqppONdRuqpqhjdxo8LN3+3PcDoVICI1cZqTVsQ1ZXS5OebVQHsAETkepyhkxTVlfI0Bron0QjoF2Kqqf0Zr52Wu+UiTcHoNl8f8NFAZ+DByTX21qnb1LHQJuTzmMsXlMU8EzhWRxUAI+JeqbvQudcm4POa7gTdF5E6cZpTrEvlLnoiMwmn+qxm5TjIASAFQ1ddxrpt0BpYBu4Dro/r5CfyzM8YYE2VlsfnIGGNMMVlRMMYYk8uKgjHGmFxWFIwxxuSyomCMMSaXFQVjjDG5rCgYY4zJZUXBmBKKzOXfMXJ/oIi85HUmY4qrzI1oNsYDA4BHROQwoAWQsCPFjbERzcZEgYh8izONSDtV3e51HmOKy5qPjCkhEWkGHAHstYJgEp0VBWNKILLi1Uic1bB2ish5HkcypkSsKBhTTCJSEfgEuFtVf8FZ+vMhT0MZU0J2TcEYY0wuO1MwxhiTy4qCMcaYXFYUjDHG5LKiYIwxJpcVBWOMMbmsKBhjjMllRcEYY0wuKwrGGGNy/T97X5zBi47GAQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -443,7 +443,7 @@
     "# Transform the samples from y back to x\n",
     "pints_chains_in_x = np.zeros(pints_chains_in_y[0].shape)\n",
     "for i, y in enumerate(pints_chains_in_y[0]):\n",
-    "    pints_chains_in_x[i, :] = transform.to_model(y)\n",
+    "    pints_chains_in_x[i, :] = transformation.to_model(y)\n",
     "\n",
     "plt.hist(pints_chains_in_x, bins=15, density=True, label='samples')\n",
     "x = np.linspace(0, 1, 100)\n",
@@ -487,7 +487,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/examples/sampling/transformed-parameters.ipynb b/examples/sampling/transformed-parameters.ipynb
index 70c8e2be..1e3fad6b 100644
--- a/examples/sampling/transformed-parameters.ipynb
+++ b/examples/sampling/transformed-parameters.ipynb
@@ -128,7 +128,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 648x648 with 9 Axes>"
       ]
@@ -188,9 +188,9 @@
      "text": [
       "param    mean    std.    2.5%    25%     50%     75%     97.5%    rhat    ess     ess per sec.\n",
       "-------  ------  ------  ------  ------  ------  ------  -------  ------  ------  --------------\n",
-      "r        0.01    0.00    0.01    0.01    0.02    0.02    0.02     1.24    325.81  155.11\n",
-      "k        506.52  29.30   497.38  498.93  499.28  499.68  619.66   1.24    319.67  152.18\n",
-      "sigma    26.15   50.61   8.88    9.25    9.42    9.62    190.10   1.57    291.89  138.95\n"
+      "r        0.01    0.00    0.01    0.01    0.02    0.02    0.02     1.24    325.81  135.76\n",
+      "k        506.52  29.30   497.38  498.93  499.28  499.68  619.66   1.24    319.67  133.20\n",
+      "sigma    26.15   50.61   8.88    9.25    9.42    9.62    190.10   1.57    291.89  121.62\n"
      ]
     }
    ],
@@ -221,7 +221,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 648x648 with 9 Axes>"
       ]
@@ -234,12 +234,12 @@
    ],
    "source": [
     "# Create parameter transformation\n",
-    "transform = pints.LogTransformation(n_parameters=len(xs[0]))\n",
+    "transformation = pints.LogTransformation(n_parameters=len(xs[0]))\n",
     "\n",
     "# Create mcmc routine with four chains\n",
     "mcmc = pints.MCMCController(log_posterior, 3, xs,\n",
     "                            method=pints.HaarioBardenetACMC,\n",
-    "                            transform=transform)\n",
+    "                            transformation=transformation)\n",
     "\n",
     "# Add stopping criterion\n",
     "mcmc.set_max_iterations(4000)\n",
@@ -286,9 +286,9 @@
      "text": [
       "param    mean    std.    2.5%    25%     50%     75%     97.5%    rhat    ess     ess per sec.\n",
       "-------  ------  ------  ------  ------  ------  ------  -------  ------  ------  --------------\n",
-      "r        0.02    0.00    0.01    0.02    0.02    0.02    0.02     1.01    452.24  185.81\n",
-      "k        499.25  0.43    498.40  498.97  499.24  499.51  500.11   1.01    427.07  175.47\n",
-      "sigma    9.40    0.20    9.01    9.26    9.39    9.52    9.83     1.01    445.93  183.22\n"
+      "r        0.02    0.00    0.01    0.02    0.02    0.02    0.02     1.01    452.24  165.68\n",
+      "k        499.25  0.43    498.40  498.97  499.24  499.51  500.11   1.01    427.07  156.46\n",
+      "sigma    9.40    0.20    9.01    9.26    9.39    9.52    9.83     1.01    445.93  163.37\n"
      ]
     }
    ],
@@ -321,7 +321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/pints/_mcmc/__init__.py b/pints/_mcmc/__init__.py
index 3229dbd8..62071cab 100644
--- a/pints/_mcmc/__init__.py
+++ b/pints/_mcmc/__init__.py
@@ -288,7 +288,7 @@ class MCMCController(object):
         Can be specified as a ``(d, d)`` matrix (where ``d`` is the dimension
         of the parameter space) or as a ``(d, )`` vector, in which case
         ``diag(sigma0)`` will be used.
-    transform : pints.Transformation
+    transformation : pints.Transformation
         An optional :class:`pints.Transformation` to allow the sampler to work
         in a transformed parameter space. If used, points shown or returned to
         the user will first be detransformed back to the original space.
@@ -298,7 +298,7 @@ class MCMCController(object):
     """
 
     def __init__(
-            self, log_pdf, chains, x0, sigma0=None, transform=None,
+            self, log_pdf, chains, x0, sigma0=None, transformation=None,
             method=None):
 
         # Check function
@@ -308,12 +308,12 @@ class MCMCController(object):
         # Apply a transformation (if given). From this point onward the MCMC
         # sampler will see only the transformed search space and will know
         # nothing about the model parameter space.
-        if transform is not None:
+        if transformation is not None:
             # Convert log pdf
-            log_pdf = transform.convert_log_pdf(log_pdf)
+            log_pdf = transformation.convert_log_pdf(log_pdf)
 
             # Convert initial positions
-            x0 = [transform.to_search(x) for x in x0]
+            x0 = [transformation.to_search(x) for x in x0]
 
             # Convert sigma0, if provided
             if sigma0 is not None:
@@ -329,12 +329,13 @@ class MCMCController(object):
                     raise ValueError(
                         'sigma0 must be either a (d, d) matrix or a (d, ) '
                         'vector, where d is the number of parameters.')
-                sigma0 = transform.convert_covariance_matrix(sigma0, x0[0])
+                sigma0 = transformation.convert_covariance_matrix(sigma0,
+                                                                  x0[0])
 
-        # Store transform for later detransformation: if using a transform, any
-        # parameters logged to the filesystem or printed to screen should be
-        # detransformed first!
-        self._transform = transform
+        # Store transformation for later detransformation: if using a
+        # transformation, any parameters logged to the filesystem or printed to
+        # screen should be detransformed first!
+        self._transformation = transformation
 
         # Store function
         self._log_pdf = log_pdf
@@ -624,7 +625,7 @@ class MCMCController(object):
 
         # Pre-allocate arrays for chain storage
         # Note: we store the inverse transformed (to model space) parameters
-        # only if transform is provided.
+        #       only if transformation is provided.
         if self._chains_in_memory:
             # Store full chains
             samples = np.zeros(
@@ -690,10 +691,10 @@ class MCMCController(object):
                         # Unpack reply into position, evaluation, and status
                         y, fy, accepted = reply
 
-                        # Inverse transform to model space if transform is
+                        # Inverse transform to model space if transformation is
                         # provided
-                        if self._transform:
-                            y_store = self._transform.to_model(y)
+                        if self._transformation:
+                            y_store = self._transformation.to_model(y)
                         else:
                             y_store = y
 
@@ -749,11 +750,12 @@ class MCMCController(object):
                     # Unpack reply into positions, evaluations, and status
                     ys, fys, accepted = reply
 
-                    # Inverse transform to model space if transform is provided
-                    if self._transform:
+                    # Inverse transform to model space if transformation is
+                    # provided
+                    if self._transformation:
                         ys_store = np.zeros(ys.shape)
                         for i, y in enumerate(ys):
-                            ys_store[i] = self._transform.to_model(y)
+                            ys_store[i] = self._transformation.to_model(y)
                     else:
                         ys_store = ys
 
@@ -1061,17 +1063,19 @@ class MCMCController(object):
 class MCMCSampling(MCMCController):
     """ Deprecated alias for :class:`MCMCController`. """
 
-    def __init__(self, log_pdf, chains, x0, sigma0=None, method=None):
+    def __init__(self, log_pdf, chains, x0, sigma0=None, transformation=None,
+                 method=None):
         # Deprecated on 2019-02-06
         import warnings
         warnings.warn(
             'The class `pints.MCMCSampling` is deprecated.'
             ' Please use `pints.MCMCController` instead.')
         super(MCMCSampling, self).__init__(log_pdf, chains, x0, sigma0,
-                                           method=method)
+                                           transformation, method=method)
 
 
-def mcmc_sample(log_pdf, chains, x0, sigma0=None, method=None):
+def mcmc_sample(log_pdf, chains, x0, sigma0=None, transformation=None,
+                method=None):
     """
     Sample from a :class:`pints.LogPDF` using a Markov Chain Monte Carlo
     (MCMC) method.
@@ -1094,9 +1098,13 @@ def mcmc_sample(log_pdf, chains, x0, sigma0=None, method=None):
         Can be specified as a ``(d, d)`` matrix (where ``d`` is the dimension
         of the parameterspace) or as a ``(d, )`` vector, in which case
         ``diag(sigma0)`` will be used.
+    transformation : pints.Transformation
+        An optional :class:`pints.Transformation` to allow the sampler to work
+        in a transformed parameter space. If used, points shown or returned to
+        the user will first be detransformed back to the original space.
     method : class
         The class of :class:`MCMCSampler` to use. If no method is specified,
         :class:`HaarioBardenetACMC` is used.
     """
     return MCMCController(    # pragma: no cover
-        log_pdf, chains, x0, sigma0, method=method).run()
+        log_pdf, chains, x0, sigma0, transformation, method=method).run()
diff --git a/pints/_optimisers/__init__.py b/pints/_optimisers/__init__.py
index 62df6a93..9358ed24 100644
--- a/pints/_optimisers/__init__.py
+++ b/pints/_optimisers/__init__.py
@@ -299,7 +299,7 @@ class OptimisationController(object):
         this information.
     boundaries
         An optional set of boundaries on the parameter space.
-    transform
+    transformation
         An optional :class:`pints.Transformation` to allow the optimiser to
         search in a transformed parameter space. If used, points shown or
         returned to the user will first be detransformed back to the original
@@ -310,8 +310,8 @@ class OptimisationController(object):
     """
 
     def __init__(
-            self, function, x0, sigma0=None, boundaries=None, transform=None,
-            method=None):
+            self, function, x0, sigma0=None, boundaries=None,
+            transformation=None, method=None):
 
         # Convert x0 to vector
         # This converts e.g. (1, 7) shapes to (7, ), giving users a bit more
@@ -331,26 +331,26 @@ class OptimisationController(object):
         # Apply a transformation (if given). From this point onward the
         # optimiser will see only the transformed search space and will know
         # nothing about the model parameter space.
-        if transform is not None:
+        if transformation is not None:
             # Convert error measure or log pdf
             if self._minimising:
-                function = transform.convert_error_measure(function)
+                function = transformation.convert_error_measure(function)
             else:
-                function = transform.convert_log_pdf(function)
+                function = transformation.convert_log_pdf(function)
 
             # Convert initial position
-            x0 = transform.to_search(x0)
+            x0 = transformation.to_search(x0)
 
             # Convert sigma0, if provided
             if sigma0 is not None:
-                sigma0 = transform.convert_standard_deviation(sigma0, x0)
+                sigma0 = transformation.convert_standard_deviation(sigma0, x0)
             if boundaries:
-                boundaries = transform.convert_boundaries(boundaries)
+                boundaries = transformation.convert_boundaries(boundaries)
 
-        # Store transform for later detransformation: if using a transform, any
-        # parameters logged to the filesystem or printed to screen should be
-        # detransformed first!
-        self._transform = transform
+        # Store transformation for later detransformation: if using a
+        # transformation, any parameters logged to the filesystem or printed to
+        # screen should be detransformed first!
+        self._transformation = transformation
 
         # Store function
         if self._minimising:
@@ -636,8 +636,8 @@ class OptimisationController(object):
             print('Current best position:')
 
             # Inverse transform search parameters
-            if self._transform:
-                xbest = self._transform.to_model(self._optimiser.xbest())
+            if self._transformation:
+                xbest = self._transformation.to_model(self._optimiser.xbest())
             else:
                 xbest = self._optimiser.xbest()
 
@@ -662,8 +662,8 @@ class OptimisationController(object):
         self._iterations = iteration
 
         # Inverse transform search parameters
-        if self._transform:
-            xbest = self._transform.to_model(self._optimiser.xbest())
+        if self._transformation:
+            xbest = self._transformation.to_model(self._optimiser.xbest())
         else:
             xbest = self._optimiser.xbest()
 
@@ -803,20 +803,19 @@ class Optimisation(OptimisationController):
     """ Deprecated alias for :class:`OptimisationController`. """
 
     def __init__(
-            self, function, x0, sigma0=None, boundaries=None, transform=None,
-            method=None):
+            self, function, x0, sigma0=None, boundaries=None,
+            transformation=None, method=None):
         # Deprecated on 2019-02-12
         import warnings
         warnings.warn(
             'The class `pints.Optimisation` is deprecated.'
             ' Please use `pints.OptimisationController` instead.')
         super(Optimisation, self).__init__(
-            function, x0, sigma0=None, boundaries=None, transform=None,
-            method=None)
+            function, x0, sigma0, boundaries, transformation, method=method)
 
 
 def optimise(
-        function, x0, sigma0=None, boundaries=None, transform=None,
+        function, x0, sigma0=None, boundaries=None, transformation=None,
         method=None):
     """
     Finds the parameter values that minimise an :class:`ErrorMeasure` or
@@ -839,7 +838,7 @@ def optimise(
         this information.
     boundaries
         An optional set of boundaries on the parameter space.
-    transform
+    transformation
         An optional :class:`pints.Transformation` to allow the optimiser to
         search in a transformed parameter space. If used, points shown or
         returned to the user will first be detransformed back to the original
@@ -856,7 +855,7 @@ def optimise(
         The corresponding score.
     """
     return OptimisationController(
-        function, x0, sigma0, boundaries, transform, method).run()
+        function, x0, sigma0, boundaries, transformation, method).run()
 
 
 class TriangleWaveTransform(object):
diff --git a/pints/_transformation.py b/pints/_transformation.py
index 7925a6ac..7c352aba 100644
--- a/pints/_transformation.py
+++ b/pints/_transformation.py
@@ -277,51 +277,54 @@ class Transformation(object):
 class ComposedTransformation(Transformation):
     r"""
     N-dimensional :class:`Transformation` composed of one or more other
-    :math:`N_i`-dimensional ``Transformations``, such that
-    :math:`\sum _i N_i = N`. The evaluation and transformation of the composed
-    transformations assume the input transformations are all independent from
-    each other.
+    :math:`N_i`-dimensional sub-transformations, so that
+    :math:`\sum _i N_i = N`.
 
-    For example, a composed transform::
+    The dimensionality of the individual transformations does not have to be
+    the same, i.e. :math:`N_i\neq N_j` is allowed.
 
-        t = pints.ComposedTransformation(transform1, transform2, transform3)
+    For example, a composed transformation::
 
-    where ``transform1``, ``transform2``, and ``transform3`` each have
-    dimension 1, 2 and 1, will have dimension 4.
+        t = pints.ComposedTransformation(
+            transformation_1, transformation_2, transformation_3)
 
-    The dimensionality of the individual priors does not have to be the same,
-    i.e. :math:`N_i\neq N_j` is allowed.
+    where ``transformation_1``, ``transformation_2``, and ``transformation_3``
+    have dimension 1, 2 and 1 respectively, will have dimension N=4.
 
-    The input parameters of the :class:`ComposedTransformation` have to be
-    ordered in the same way as the individual tranforms for the parameter
-    vector. In the above example the transform may be performed by
-    ``t.to_search(p)``, where::
+    The evaluation and transformation of the composed transformations assume
+    that the input transformations are all independent from each other.
 
-        p = [parameter1_transform1,
-             parameter1_transform2,
-             parameter2_transform2,
-             parameter1_transform3]
+    The input parameters of the :class:`ComposedTransformation` are ordered in
+    the same way as the individual tranforms for the parameter vector. In the
+    above example the transformation may be performed by ``t.to_search(p)``,
+    where::
+
+        p = [parameter_1_for_transformation_1,
+             parameter_1_for_transformation_2,
+             parameter_2_for_transformation_2,
+             parameter_1_for_transformation_3]
 
     Extends :class:`Transformation`.
     """
-    def __init__(self, *transforms):
-        # Check if sub-transforms given
-        if len(transforms) < 1:
-            raise ValueError('Must have at least one sub-transform.')
+    def __init__(self, *transformations):
+        # Check if sub-transformations given
+        if len(transformations) < 1:
+            raise ValueError('Must have at least one sub-transformation.')
 
-        # Check if proper transform, count dimension
+        # Check if proper Transformation, count dimension
         self._n_parameters = 0
         self._elementwise = True
-        for transform in transforms:
-            if not isinstance(transform, pints.Transformation):
-                raise ValueError('All sub-transforms must extend '
-                                 'pints.Transformation.')
-            self._n_parameters += transform.n_parameters()
-            # If any transform is not elementwise, then it is not elementwise
-            self._elementwise &= transform.elementwise()
+        for transformation in transformations:
+            if not isinstance(transformation, pints.Transformation):
+                raise ValueError('All entries in `transformations` must extend'
+                                 ' pints.Transformation.')
+            self._n_parameters += transformation.n_parameters()
+            # If any transformation is not elementwise, then it is not
+            # elementwise
+            self._elementwise &= transformation.elementwise()
 
         # Store
-        self._transforms = transforms
+        self._transformations = transformations
 
         # Use elementwise or not
         if self._elementwise:
@@ -347,10 +350,10 @@ class ComposedTransformation(Transformation):
         matrix_shape = (self.n_parameters(), self.n_parameters())
         lo = hi = 0
         output_S1 = np.zeros((self.n_parameters(),) + matrix_shape)
-        for transform in self._transforms:
+        for transformation in self._transformations:
             lo = hi
-            hi += transform.n_parameters()
-            _, jac_S1 = transform.jacobian_S1(q[lo:hi])
+            hi += transformation.n_parameters()
+            _, jac_S1 = transformation.jacobian_S1(q[lo:hi])
             for i, jac_S1_i in enumerate(jac_S1):
                 # Due to the composed transformation are independent, we can
                 # pack the derivative of the Jacobian matrices J_i with zeros:
@@ -377,10 +380,10 @@ class ComposedTransformation(Transformation):
         q = pints.vector(q)
         output = np.zeros(q.shape)
         lo = hi = 0
-        for transform in self._transforms:
+        for transformation in self._transformations:
             lo = hi
-            hi += transform.n_parameters()
-            output[lo:hi] = np.asarray(transform.to_model(q[lo:hi]))
+            hi += transformation.n_parameters()
+            output[lo:hi] = np.asarray(transformation.to_model(q[lo:hi]))
         return output
 
     def to_search(self, p):
@@ -388,10 +391,10 @@ class ComposedTransformation(Transformation):
         p = pints.vector(p)
         output = np.zeros(p.shape)
         lo = hi = 0
-        for transform in self._transforms:
+        for transformation in self._transformations:
             lo = hi
-            hi += transform.n_parameters()
-            output[lo:hi] = np.asarray(transform.to_search(p[lo:hi]))
+            hi += transformation.n_parameters()
+            output[lo:hi] = np.asarray(transformation.to_search(p[lo:hi]))
         return output
 
     def n_parameters(self):
@@ -405,10 +408,10 @@ class ComposedTransformation(Transformation):
         q = pints.vector(q)
         diag = np.zeros(q.shape)
         lo = hi = 0
-        for transform in self._transforms:
+        for transformation in self._transformations:
             lo = hi
-            hi += transform.n_parameters()
-            diag[lo:hi] = np.diagonal(transform.jacobian(q[lo:hi]))
+            hi += transformation.n_parameters()
+            diag[lo:hi] = np.diagonal(transformation.jacobian(q[lo:hi]))
         output = np.diag(diag)
         return output
 
@@ -420,10 +423,10 @@ class ComposedTransformation(Transformation):
         q = pints.vector(q)
         output = 0
         lo = hi = 0
-        for transform in self._transforms:
+        for transformation in self._transformations:
             lo = hi
-            hi += transform.n_parameters()
-            output += transform.log_jacobian_det(q[lo:hi])
+            hi += transformation.n_parameters()
+            output += transformation.log_jacobian_det(q[lo:hi])
         return output
 
     def _elementwise_log_jacobian_det_S1(self, q):
@@ -435,10 +438,10 @@ class ComposedTransformation(Transformation):
         output = 0
         output_S1 = np.zeros(q.shape)
         lo = hi = 0
-        for transform in self._transforms:
+        for transformation in self._transformations:
             lo = hi
-            hi += transform.n_parameters()
-            j, j_S1 = transform.log_jacobian_det_S1(q[lo:hi])
+            hi += transformation.n_parameters()
+            j, j_S1 = transformation.log_jacobian_det_S1(q[lo:hi])
             output += j
             output_S1[lo:hi] = np.asarray(j_S1)
         return output, output_S1
@@ -448,12 +451,12 @@ class ComposedTransformation(Transformation):
         General implementation of :meth:`Transformation.jacobian()`.
         """
         q = pints.vector(q)
-        lo, hi = 0, self._transforms[0].n_parameters()
-        output = self._transforms[0].jacobian(q[lo:hi])
-        for transform in self._transforms[1:]:
+        lo, hi = 0, self._transformations[0].n_parameters()
+        output = self._transformations[0].jacobian(q[lo:hi])
+        for transformation in self._transformations[1:]:
             lo = hi
-            hi += transform.n_parameters()
-            jaco = transform.jacobian(q[lo:hi])
+            hi += transformation.n_parameters()
+            jaco = transformation.jacobian(q[lo:hi])
             # Due to the composed transformation are independent, we can stack
             # the Jacobian matrices J_i and J_{i+1} as
             #
@@ -742,16 +745,16 @@ class RectangularBoundariesTransformation(Transformation):
         \frac{d}{dq} \log(|J(q)|) = 2 \times \exp(-q) \times
             \text{logit}^{-1}(q) - 1.
 
-    For example, to create a transform with :math:`p_1 \in [0, 4)`,
+    For example, to create a transformation with :math:`p_1 \in [0, 4)`,
     :math:`p_2 \in [1, 5)`, and :math:`p_3 \in [2, 6)` use either::
 
-        transform = pints.RectangularBoundariesTransformation([0, 1, 2],
-                                                              [4, 5, 6])
+        transformation = pints.RectangularBoundariesTransformation([0, 1, 2],
+                                                                   [4, 5, 6])
 
     or::
 
         boundaries = pints.RectangularBoundaries([0, 1, 2], [4, 5, 6])
-        transform = pints.RectangularBoundariesTransformation(boundaries)
+        transformation = pints.RectangularBoundariesTransformation(boundaries)
 
     Extends :class:`Transformation`.
     """
@@ -889,17 +892,17 @@ class TransformedBoundaries(pints.Boundaries):
     ----------
     boundaries
         A :class:`pints.Boundaries`.
-    transform
+    transformation
         A :class:`pints.Transformation`.
     """
-    def __init__(self, boundaries, transform):
+    def __init__(self, boundaries, transformation):
         self._boundaries = boundaries
-        self._transform = transform
+        self._transform = transformation
         self._n_parameters = self._boundaries.n_parameters()
 
         if self._transform.n_parameters() != self._n_parameters:
             raise ValueError('Number of parameters for boundaries and '
-                             'transform must match.')
+                             'transformation must match.')
 
     def check(self, q):
         """ See :meth:`Boundaries.check()`. """
@@ -943,16 +946,16 @@ class TransformedErrorMeasure(pints.ErrorMeasure):
     ----------
     error
         A :class:`pints.ErrorMeasure`.
-    transform
+    transformation
         A :class:`pints.Transformation`.
     """
-    def __init__(self, error, transform):
+    def __init__(self, error, transformation):
         self._error = error
-        self._transform = transform
+        self._transform = transformation
         self._n_parameters = self._error.n_parameters()
         if self._transform.n_parameters() != self._n_parameters:
-            raise ValueError('Number of parameters for error and transform '
-                             'must match.')
+            raise ValueError('Number of parameters for error and '
+                             'transformation must match.')
 
     def __call__(self, q):
         # Get parameters in the model space
@@ -1042,16 +1045,16 @@ class TransformedLogPDF(pints.LogPDF):
     ----------
     log_pdf
         A :class:`pints.LogPDF`.
-    transform
+    transformation
         A :class:`pints.Transformation`.
     """
-    def __init__(self, log_pdf, transform):
+    def __init__(self, log_pdf, transformation):
         self._log_pdf = log_pdf
-        self._transform = transform
+        self._transform = transformation
         self._n_parameters = self._log_pdf.n_parameters()
         if self._transform.n_parameters() != self._n_parameters:
-            raise ValueError('Number of parameters for log_pdf and transform '
-                             'must match.')
+            raise ValueError('Number of parameters for log_pdf and '
+                             'transformation must match.')
 
     def __call__(self, q):
         # Get parameters in the model space
@@ -1110,11 +1113,11 @@ class TransformedLogPrior(TransformedLogPDF, pints.LogPrior):
     ----------
     log_prior
         A :class:`pints.LogPrior`.
-    transform
+    transformation
         A :class:`pints.Transformation`.
     """
-    def __init__(self, log_prior, transform):
-        super(TransformedLogPrior, self).__init__(log_prior, transform)
+    def __init__(self, log_prior, transformation):
+        super(TransformedLogPrior, self).__init__(log_prior, transformation)
 
     def sample(self, n):
         """


Test patch:
diff --git a/pints/tests/test_log_pdfs.py b/pints/tests/test_log_pdfs.py
old mode 100644
new mode 100755
diff --git a/pints/tests/test_mcmc_controller.py b/pints/tests/test_mcmc_controller.py
index f61ef4af..ceba9f04 100755
--- a/pints/tests/test_mcmc_controller.py
+++ b/pints/tests/test_mcmc_controller.py
@@ -152,12 +152,12 @@ class TestMCMCController(unittest.TestCase):
         # Test transformation
         logt = pints.LogTransformation(n_parameters)
         mcmc = pints.MCMCController(self.log_posterior, n_chains, xs,
-                                    transform=logt)
+                                    transformation=logt)
         mcmc.set_max_iterations(n_iterations)
         mcmc.set_log_to_screen(False)
         chains = mcmc.run()
         # Test chains inverse transformed
-        # log-transform of the parameter in [0.01, 0.02] will always be
+        # log-transformation of the parameter in [0.01, 0.02] will always be
         # negative values, so checking it larger than zero make sure it's
         # transformed back to the model space.
         self.assertTrue(np.all(chains > 0))
@@ -166,25 +166,25 @@ class TestMCMCController(unittest.TestCase):
         self.assertEqual(chains.shape[2], n_parameters)
         sigma0 = [0.005, 100, 0.5 * self.noise]
         pints.MCMCController(self.log_posterior, n_chains, xs, sigma0,
-                             transform=logt)
+                             transformation=logt)
         sigma0 = np.diag([0.005, 100, 0.5 * self.noise])
         pints.MCMCController(self.log_posterior, n_chains, xs, sigma0,
-                             transform=logt)
+                             transformation=logt)
         sigma0 = [0.005, 100, 0.5 * self.noise, 10]
         self.assertRaises(
             ValueError,
             pints.MCMCController, self.log_posterior, n_chains, xs, sigma0,
-            transform=logt)
+            transformation=logt)
         sigma0 = np.diag([0.005, 100, 0.5 * self.noise, 10])
         self.assertRaises(
             ValueError,
             pints.MCMCController, self.log_posterior, n_chains, xs, sigma0,
-            transform=logt)
+            transformation=logt)
         sigma0 = np.arange(16).reshape(2, 2, 2, 2)
         self.assertRaises(
             ValueError,
             pints.MCMCController, self.log_posterior, n_chains, xs, sigma0,
-            transform=logt)
+            transformation=logt)
 
         # Test multi-chain with single-chain mcmc
 
@@ -331,7 +331,7 @@ class TestMCMCController(unittest.TestCase):
         # Test transformation
         logt = pints.LogTransformation(n_parameters)
         mcmc = pints.MCMCController(self.log_posterior, n_chains, xs,
-                                    method=meth, transform=logt)
+                                    method=meth, transformation=logt)
         self.assertEqual(len(mcmc.samplers()), 1)
         mcmc.set_max_iterations(n_iterations)
         mcmc.set_log_to_screen(False)
@@ -340,26 +340,26 @@ class TestMCMCController(unittest.TestCase):
         self.assertEqual(chains.shape[1], n_iterations)
         self.assertEqual(chains.shape[2], n_parameters)
         # Test chains inverse transformed
-        # log-transform of the parameter in [0.01, 0.02] will always be
+        # log-transformation of the parameter in [0.01, 0.02] will always be
         # negative values, so checking it larger than zero make sure it's
         # transformed back to the model space.
         self.assertTrue(np.all(chains > 0))
         sigma0 = [0.005, 100, 0.5 * self.noise]
         pints.MCMCController(self.log_posterior, n_chains, xs, sigma0,
-                             method=meth, transform=logt)
+                             method=meth, transformation=logt)
         sigma0 = np.diag([0.005, 100, 0.5 * self.noise])
         pints.MCMCController(self.log_posterior, n_chains, xs, sigma0,
-                             method=meth, transform=logt)
+                             method=meth, transformation=logt)
         sigma0 = [0.005, 100, 0.5 * self.noise, 10]
         self.assertRaises(
             ValueError,
             pints.MCMCController, self.log_posterior, n_chains, xs, sigma0,
-            method=meth, transform=logt)
+            method=meth, transformation=logt)
         sigma0 = np.diag([0.005, 100, 0.5 * self.noise, 10])
         self.assertRaises(
             ValueError,
             pints.MCMCController, self.log_posterior, n_chains, xs, sigma0,
-            method=meth, transform=logt)
+            method=meth, transformation=logt)
 
     def test_stopping(self):
         # Test different stopping criteria.
@@ -843,7 +843,7 @@ class TestMCMCControllerLogging(unittest.TestCase):
         # Test transformation
         logt = pints.LogTransformation(len(self.xs[0]))
         mcmc = pints.MCMCController(self.log_posterior, self.nchains, self.xs,
-                                    transform=logt)
+                                    transformation=logt)
         mcmc.set_initial_phase_iterations(5)
         mcmc.set_max_iterations(20)
         mcmc.set_log_to_screen(True)
@@ -889,9 +889,9 @@ class TestMCMCControllerLogging(unittest.TestCase):
                 self.assertTrue(np.all(chains1 == chains2))
 
                 # Test files contain inverse transformed samples
-                # log-transform of the parameter in [0.01, 0.02] will always
-                # be negative values, so checking it larger than zero make sure
-                # it's transformed back to the model space.
+                # log-transformation of the parameter in [0.01, 0.02] will
+                # always be negative values, so checking it larger than zero
+                # make sure it's transformed back to the model space.
                 self.assertTrue(np.all(chains2 > 0))
 
             text = c.text()
@@ -963,7 +963,7 @@ class TestMCMCControllerLogging(unittest.TestCase):
         # Test transformation
         logt = pints.LogTransformation(len(self.xs[0]))
         mcmc = pints.MCMCController(self.log_posterior, self.nchains, self.xs,
-                                    transform=logt)
+                                    transformation=logt)
         mcmc.set_initial_phase_iterations(5)
         mcmc.set_max_iterations(20)
         mcmc.set_log_to_screen(True)
@@ -1014,9 +1014,9 @@ class TestMCMCControllerLogging(unittest.TestCase):
                     chains2.shape, (self.nchains, 20, len(self.xs)))
 
                 # Test files contain inverse transformed samples
-                # log-transform of the parameter in [0.01, 0.02] will always
-                # be negative values, so checking it larger than zero make sure
-                # it's transformed back to the model space.
+                # log-transformation of the parameter in [0.01, 0.02] will
+                # always be negative values, so checking it larger than zero
+                # make sure it's transformed back to the model space.
                 self.assertTrue(np.all(chains2 > 0))
 
             text = c.text()
diff --git a/pints/tests/test_opt_optimisation_controller.py b/pints/tests/test_opt_optimisation_controller.py
index 1b2166d1..37ec7fde 100755
--- a/pints/tests/test_opt_optimisation_controller.py
+++ b/pints/tests/test_opt_optimisation_controller.py
@@ -59,7 +59,7 @@ class TestOptimisationController(unittest.TestCase):
         b = pints.RectangularBoundaries([-1, -1], [1, 1])
         s = 0.1
         t = pints.RectangularBoundariesTransformation(b)
-        pints.OptimisationController(r, x0, boundaries=b, transform=t,
+        pints.OptimisationController(r, x0, boundaries=b, transformation=t,
                                      method=method)
         opt = pints.OptimisationController(r, x0, s, b, t, method)
         opt.set_log_to_screen(False)