Typo corrected

README.md

@@ -6,9 +6,7 @@
 [![Documentation Status](https://readthedocs.org/projects/easyvvuq/badge/?version=latest)](https://easyvvuq.readthedocs.io/)
 [![Coverage Status](https://coveralls.io/repos/github/UCL-CCS/EasyVVUQ/badge.svg?branch=dev&service=github)](https://coveralls.io/github/UCL-CCS/EasyVVUQ?branch=dev)
 [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/3796/badge)](https://bestpractices.coreinfrastructure.org/projects/3796)
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/UCL-CCS/EasyVVUQ/dev?filepath=tutorials)
-
-The aim of EasyVVUQ is to facilitate verification, validation and 
+[![Binder]EasyVVUQ aims to facilitate verification, validation and 
 uncertainty quantification (VVUQ) for a wide variety of
 simulations. While very convenient for simple cases, EasyVVUQ is particularly well suited in situations where the simulations are computationally expensive, 
 heterogeneous computing resources are necessary, the sampling space is very large or book-keeping is prohibitively
@@ -21,11 +19,10 @@ Here are some examples of questions EasyVVUQ can answer about your code:
 
 It also lets you construct surrogate models that are cheaper to evaluate than the complete simulation.
 
-The high-level overview of the library is avalable at our [readthedocs](https://easyvvuq.readthedocs.io/en/dev/).
+The high-level overview of the library is available at our [readthedocs](https://easyvvuq.readthedocs.io/en/dev/).
 
 ## Getting Started
-
-For the quick start with EasyVVUQ we reccommend to check our basic interactive tutorial available [here](https://mybinder.org/v2/gh/UCL-CCS/EasyVVUQ/a6852d6c5ba36f15579e601d7a8d074505f31084?filepath=tutorials%2Fbasic_tutorial.ipynb).
+For a quick start with EasyVVUQ we recommend checking our basic interactive tutorial available [here](https://mybinder.org/v2/gh/UCL-CCS/EasyVVUQ/a6852d6c5ba36f15579e601d7a8d074505f31084?filepath=tutorials%2Fbasic_tutorial.ipynb).
 
 
 ## Functionality

docs/source/old/basic_tutorial.rst

@@ -54,13 +54,13 @@ The `gauss.template` is a template input file, in JSON format ::
 The values for each key are tags (signified by the ``$`` delimiter) which will
 be substituted by EasyVVUQ with values to sample the parameter space.
 In the following tutorial, the template will be used to generate files called
-`in_file.json` that will be the input to each run of `gauss.py`.
+`in_file.json` will be the input to each run of `gauss.py`.
 
 Uncertainty Quantification Workflow
 -----------------------------------
 
-In this dummy workflow we will use the *gauss* application to produce values
-from normal distributions centred on 3 different means `mu`), using 5 repeat
+In this dummy workflow, we will use the *gauss* application to produce values
+from normal distributions centered on 3 different means `mu`), using 5 repeat
 ('replica') runs for each one.
 The output will be collected for each run and bootstrap statistics calculated
 for each set of runs.
@@ -75,7 +75,7 @@ Sections 1 to 9 contain the core EasyVVUQ workflow, section 0 sets up
 convenience variables related to the application.
 
 .. note:: In this tutorial application execution is handled locally and by
-          EasyVVUQ functions. In real world applications (especially for HPC
+          EasyVVUQ functions. In real-world applications (especially for HPC
           applications the run step is beyond the scope of EasyVVUQ.
 
 To run the workflow execute the following command ::
@@ -88,7 +88,7 @@ If this works you should see 15 lines that look something like:
 
 where `<run-location>` is the directory in which you ran the script and
 `EasyVVUQ_Campaign_zxe7_cb2` is an example of the unique directory that
-EasyVVUQ created to hold all of the files created relating to a campaign.
+EasyVVUQ has created to hold all of the files created relating to a campaign.
 
 Followed by a results table that looks like:
 
@@ -107,7 +107,7 @@ The statistics represent the variation across the 5 replica runs executed for
 each of the 3 'mu' values sampled.
 
 Below we go through each section of the workflow, explaining each step and the
-EasyVVUQ elements used to perform them.
+EasyVVUQ elements are used to perform them.
 
 Section 0: Application Setup
 -----------------------------------
@@ -140,7 +140,7 @@ Consequently, the first step of an EasyVVUQ workflow is to create a
     my_campaign = uq.Campaign(name='gauss', work_dir=".")
 
 The reason for having a name is that in some cases it may be necessary to
-combine the output of multiple *Campaigns* in a single analysis and having a
+combine the output of multiple *Campaigns* in a single analysis and have a
 name allows the data from each to be identified easily.
 
 Section 2: Define Parameter Space
@@ -193,15 +193,15 @@ The only two parameters which could (somewhat) sensibly be sampled are 'mu'
 Nonetheless we need to provide a range for 'num_steps'.
 Notice that the keys in the parameter description match the tags in the template.
 
-.. note:: The names of parameters here does not need to match the input of the
-          application directly. In the next section we will see how *Decoder*
+.. note:: The name of parameters here does not need to match the input of the
+          application directly. In the next section, we will see how *Decoder*
           elements map the parameter space to the application inputs.
 
 
 Section 3: Wrap Application
 ---------------------------
 
-In order for an application to be used in an EasyVVUQ workflow two processes
+For an application to be used in an EasyVVUQ workflow two processes
 have to be accounted for:
 
 1. the parameters being sampled need to be converted into a format that
@@ -226,7 +226,7 @@ We create the encoder using the following code::
 .. note:: The tags in the template here use the default $ delimiter.
           Different delimiters can be specified using the `delimiter` keyword.
 
-The output of *gauss* is a CSV format files, so we use a *Decoder* called *SimpleCSV*.
+The output of *gauss* is a CSV format file, so we use a *Decoder* called *SimpleCSV*.
 This requires us to specify the file to be read, the location of the header (line 0)
 and the columns to keep in the data for analysis::
 
@@ -265,9 +265,9 @@ In this example we simply pick 'mu' values from a uniform distribution between
 
     my_campaign.set_sampler(my_sampler)
 
-Real world examples are likely to use more complicated algorithms (such as
+Real-world examples are likely to use more complicated algorithms (such as
 quasi-Monte Carlo or stochastic collocation) but the way of specifying
-parameters to vary remains the same.
+parameters to vary remain the same.
 
 Section 5: Get Run Parameters
 -----------------------------
@@ -280,7 +280,7 @@ We draw samples the number of samples we want from the *Sampler*::
                              replicas=5)
 
 Here we have chosen to have 5 replicas (repeats) of each sample drawn.
-At this stage all that happens is the parameter sets are added to the
+At this stage, all that happens is the parameter sets are added to the
 *CampaignDB*, no input files have been generated.
 
 Section 6: Create Input Directories
@@ -306,7 +306,7 @@ command we specified in Step 0::
 Section 8: Collate Output
 -------------------------
 
-The collection of simulation output simply handled by the *Campaign*::
+The collection of simulation output is simply handled by the *Campaign*::
 
     my_campaign.collate()
 

docs/source/old/cooling_coffee_cup.rst

@@ -3,18 +3,18 @@
 A Cooling Coffee Cup with Polynomial Chaos Expansion
 ====================================================
 
-In this tutorial we will perform a Polynomial Chaos Expansion for a model of a cooling coffee cup.
+In this tutorial, we will perform a Polynomial Chaos Expansion for a model of a cooling coffee cup.
 The model uses Newton's law of cooling to evolve the temperature, :math:`T`, over time (:math:`t`) in an environment at :math:`T_{env}`:
 
 .. math::
     \frac{dT(t)}{dt} = -\kappa (T(t) -T_{env})
 
 The constant :math:`\kappa` characterizes the rate at which the coffee cup transfers heat to the environment.
-In this example we will analyze this model using the polynomial chaos expansion (PCE) UQ algorithm.
+In this example, we will analyze this model using the polynomial chaos expansion (PCE) UQ algorithm.
 e will use a constant initial temperature :math:`T_0 = 95 ^\circ\text{C}`, and vary :math:`\kappa` and :math:`T_{env}` using a uniform distribution in the ranges :math:`0.025-0.075` and :math:`15-25` respectively.
 
 Below we provide a commented script that shows how the Campaign is built up and then employed.
-We also provide an outline of how each element is setup.
+We also provide an outline of how each element is set up.
 
 EasyVVUQ Script Overview
 ------------------------
@@ -31,7 +31,7 @@ To run the script execute the following command
 Import necessary libraries
 --------------------------
 
-For this example we import both easyvvuq and chaospy (for the distributions). EasyVVUQ will be referred to as 'uq' in the code. ::
+For this example, we import both easyvvuq and chaospy (for the distributions). EasyVVUQ will be referred to as 'uq' in the code. ::
 
     import easyvvuq as uq
     import chaospy as cp
@@ -73,7 +73,7 @@ In this example the GenericEncoder and SimpleCSV, both included in the core Easy
 
 GenericEncoder performs simple text substitution into a supplied template, using a specified delimiter to identify where parameters should be placed.
 The template is shown below (\$ is used as the delimiter).
-The template substitution approach is likely to suit most simple applications but in practice many large applications have more complex requirements, for example the multiple input files or the creation of a directory hierarchy.
+The template substitution approach is likely to suit most simple applications but in practice, many large applications have more complex requirements, for example the multiple input files or the creation of a directory hierarchy.
 In such cases, users may write their own encoders by extending the BaseEncoder class. ::
 
     {
@@ -93,7 +93,7 @@ As can be inferred from its name SimpleCSV reads CVS files produced by the cooli
 
 The Sampler
 -----------
-The user specified which parameters will vary and their corresponding distributions. In this case the kappa and t\_env parameters are varied, both according to a uniform distribution: ::
+The user specified which parameters will vary and their corresponding distributions. In this case, the kappa and t\_env parameters are varied, both according to a uniform distribution: ::
 
     vary = {
         "kappa": cp.Uniform(0.025, 0.075),

docs/source/old/custom_encoder.rst

@@ -54,7 +54,7 @@ A custom decoder can be created in a very similar manner to the encoder: ::
 
 The two methods that must be implemented here are sim_complete(),
 which returns True if the simulation has completed (this is handled by
-the decoder because it is an application specific issue), and
+the decoder because it is an application-specific issue), and
 parse_sim_output(), which returns a dictionary containing the desired
 output, distilled from the simulation output files. This dictionary
 has to follow the following list of restrictions:

docs/source/old/dask_tutorial.rst

@@ -3,10 +3,10 @@
 A Cooling Coffee Cup - Using Dask Jobqueue to Run on Clusters
 =============================================================
 
-In this tutorial we expand the previous :doc:`example
+In this tutorial, we expand the previous :doc:`example
 <cooling\_coffee\_cup>` and move our computations to computing
 clusters. In order to run it you will need access to one. And if you
-have access to one you most likely don't need explaining what they are
+have access to one you most likely don't need to explain what they are
 or how they fit in the work you do. So we will skip that part. We will
 also skip the parts that are the same as in the previous tutorial. We
 only outline the parts that will be different from when you ran it on
@@ -16,8 +16,8 @@ your laptop. Luckily there aren't that many differences.
 Import necessary libraries
 --------------------------
 
-In addition we need to import the relevant Dask classes that will let us
-set-up our cluster. Here we assume a SLURM cluster, however, other
+In addition, we need to import the relevant Dask classes that will let us
+set up our cluster. Here we assume a SLURM cluster, however, other
 options (PBS and so on) are possible. Please refer to Dask JobQueue
 `documentation <https://jobqueue.dask.org/en/latest/>`_. ::
 
@@ -28,7 +28,7 @@ Create a new Campaign
 ---------------------
 
 As in the :doc:`Basic Tutorial <basic\_tutorial>`, we start by creating the
-campaign, the only difference is that we instantiate the CampaignDask class
+the campaign, the only difference is that we instantiate the CampaignDask class
 instead of Campaign ::
 
     my_campaign = uq.CampaignDask(name='coffee_pce')
@@ -39,13 +39,13 @@ Initialize Cluster
 Provided that you have access to a computing cluster you can now run
 your UQ workflow on it. You will need to know some technical details
 about the compute nodes of your cluster. Most importantly you need to
-know how many CPU cores does this node have and how much RAM. This
-information is used to figure out the amount of resources we will
+know how many CPU cores this node has and how much RAM. This
+information is used to figure out the number of resources we will
 need, namely, how many nodes to reserve.
 
 Here we describe a single node of an example cluster. Please note that
 you don't need to specify the resources you need for your run as
-such. Only the resources available on a single node. Unless the
+such. Only the resources are available on a single node. Unless the
 resources the job needs are fewer than the node provides. For example,
 if the node has 48 cores and 64 gigabytes of memory ::
 
@@ -60,7 +60,7 @@ for example, we can use ::
 
      cluster.scale(96)
 
-At this stage you can print the batch file that will be used to submit the
+At this stage, you can print the batch file that will be used to submit the
 worker processes. ::
 
     print(cluster.job_script())
@@ -85,7 +85,7 @@ before. ::
     my_campaign.populate_runs_dir()
     my_campaign.apply_for_each_run_dir(uq.actions.ExecuteLocal("python3 cooling_model.py cooling_in.json"), client)
 
-At this stage the computation will block until the requested resources are
+At this stage, the computation will block until the requested resources are
 allocated and all the computations are completed.
 
 
@@ -103,7 +103,7 @@ something like this: ::
     salloc --partition=interactivequeue
 
 The system will then try to allocate resources for you to run the
-interactive job and this might take a couple of moments. After that an
+interactive job and this might take a couple of moments. After that, an
 interactive mode prompt will appear. Commands that you execute there
 will be run on compute nodes. You would then execute the script
 normally, e.g. :: 

docs/source/old/fusion_tutorial.rst

@@ -80,7 +80,7 @@ and
 .. math::
    mtanh(x, b_{slope}) = \frac{(1 + x \cdot b_{slope}) exp(x) - exp(-x)}{exp(x) + exp(-x)}
 
-A typical density profile used in these simulation is shown below:
+A typical density profile used in this simulation is shown below:
 
 .. figure:: ../images/ne.svg
 
@@ -92,7 +92,7 @@ The source is given by
 where :math:`\alpha` is chosen so that :math:`\int\; S(\rho,t) dV =
 Qe_{tot}`, the total heating power.
 
-In this example we will analyze this model using the polynomial chaos
+In this example, we will analyze this model using the polynomial chaos
 expansion (PCE) UQ algorithm.  The parameters that can be varied are:
 
 ==================    =======    =======   =========
@@ -128,13 +128,13 @@ though we will restrict the variation to
 for this analysis.
 
 Below we provide a commented script that shows how the Campaign is built up and then employed.
-We also provide an outline of how each element is setup.
+We also provide an outline of how each element is set up.
 
 EasyVVUQ Script Overview
 ------------------------
 
-We illustrate the intended workflow using the following basic example
-script, a python implementation of the reduced fusion workflow model. 
+We illustrate the intended workflow using the following basic example script, 
+a python implementation of the reduced fusion workflow model. 
 
 The input files for this tutorial are
 
@@ -162,7 +162,7 @@ To run the script execute the following command
 Import necessary libraries
 --------------------------
 
-For this example we import both easyvvuq and chaospy (for the
+For this example, we import both easyvvuq and chaospy (for the
 distributions). EasyVVUQ will be referred to as 'uq' in the code. ::
 
     import easyvvuq as uq
@@ -212,7 +212,7 @@ App Creation
 ------------
 
 In this example the GenericEncoder and SimpleCSV, both included in the
-core EasyVVUQ library, were used as the encoder/decoder pair for this
+core EasyVVUQ library were used as the encoder/decoder pair for this
 application. ::
 
     encoder = uq.encoders.GenericEncoder(
@@ -227,8 +227,8 @@ GenericEncoder performs simple text substitution into a supplied
 template, using a specified delimiter to identify where parameters
 should be placed.  The template is shown below (\$ is used as the
 delimiter).  The template substitution approach is likely to suit most
-simple applications but in practice many large applications have more
-complex requirements, for example the multiple input files or the
+simple applications but in practice, many large applications have more
+complex requirements, for example, the multiple input files or the
 creation of a directory hierarchy.  In such cases, users may write
 their own encoders by extending the BaseEncoder class. ::
 
@@ -289,7 +289,7 @@ Calling the campaign's draw\_samples() method will cause the specified
 number of samples to be added as runs to the campaign database,
 awaiting encoding and execution. If no arguments are passed to
 draw\_samples() then all samples will be drawn, unless the sampler is
-not finite. In this case PCESampler is finite (produces a finite
+not finite. In this case, PCESampler is finite (produces a finite
 number of samples) and we elect to draw all of them at once: ::
 
     my_campaign.draw_samples()
@@ -334,16 +334,16 @@ The output of this is dependent on the type of analysis element. ::
 Typical results
 ---------------
 
-The above workflow calculates the distribution of temeperatures as the
-uncertain parameters are varied.  A typical results is shown below.
+The above workflow calculates the distribution of temperatures as the
+uncertain parameters are varied.  A typical result is shown below.
 
 .. figure:: ../images/Te.svg
 
 Here the mean temperature, the mean plus and minus one sigma, the 10
 and 90 percentiles as well as the complete range are shown as a
 function of :math:`\rho`.
 
-The sensitivity of the results to the varying paramaters can be found
+The sensitivity of the results to the varying parameters can be found
 from the Sobol first
 
 .. figure:: ../images/sobols_first.svg
@@ -383,7 +383,7 @@ in easyvvuq_fusion_dask_tutorial.py and are basically:
   - or using SLURM, here configured to use
 
     - p.tok.openmp.2h QOS
-    - send a mail at completion of the SLURM job(s)
+    - send a mail at the completion of the SLURM job(s)
     - use the p.tok.openmp partition ("queue")
     - 8 cores per job
     - 8 processes per job
@@ -446,7 +446,7 @@ References
 .. [MTANH] |_| See 
 
 - E. Stefanikova, M. Peterka, P. Bohm, P. Bilkova, M. Aftanas, M. Sos, J. Urban, M. Hron and R. Panek:
-  Fitting of the Thomson scatteringdensity and temperature profiles on the COMPASS tokamak.
+  Fitting of the Thomson scattering density and temperature profiles on the COMPASS tokamak.
   Presented at 21st Topical Conference on High-Temperature Plasma Diagnostics
   (HTPD 2016) in Madison, Wisconsin, USA and published in
   Review of Scientific Instruments 87, 11E536 (2016); https://doi.org/10.1063/1.4961554.