doc edits; trivial, plus mostly mcmc

cobaya/collection.py

@@ -182,7 +182,7 @@ def __getitem__(self, *args):
             check_end(args[0].stop, self._n)
             return self.data.iloc[args[0]]
         else:
-            raise ValueError("Index type not recognised: use column names or slices.")
+            raise ValueError("Index type not recognized: use column names or slices.")
 
     # Statistical computations
     def mean(self, first=None, last=None, derived=False):

cobaya/likelihood.py

@@ -326,7 +326,7 @@ def logps(self, input_params, _derived=None, cached=True):
         """
         Computes observables and returns the (log) likelihoods *separately*.
         It takes a list of **input** parameter values, in the same order as they appear
-        in the `OrderedDictionary` of the :class:LikelihoodCollection.
+        in the `OrderedDictionary` of the :class:`LikelihoodCollection`.
         To compute the derived parameters, it takes an optional keyword `_derived` as an
         empty list, which is then populated with the derived parameter values.
         """
@@ -363,7 +363,7 @@ def logp(self, input_params, _derived=None, cached=True):
         """
         Computes observables and returns the (log) likelihood.
         It takes a list of **sampled** parameter values, in the same order as they appear
-        in the `OrderedDictionary` of the :class:LikelihoodCollection.
+        in the `OrderedDictionary` of the :class:`LikelihoodCollection`.
         To compute the derived parameters, it takes an optional keyword `_derived` as an
         empty list, which is then populated with the derived parameter values.
         """

cobaya/likelihoods/planck_2015_lensing/planck_2015_lensing.bibtex

@@ -1,4 +1,4 @@
-Lensing likelihood of Planck's 2015 data release based on temperature+polarisation map-based lensing reconstruction \cite{Ade:2015zua}.
+Lensing likelihood of Planck's 2015 data release based on temperature+polarization map-based lensing reconstruction \cite{Ade:2015zua}.
 
 
 @article{Ade:2015zua,

cobaya/likelihoods/planck_2015_lensing_cmblikes/planck_2015_lensing_cmblikes.bibtex

@@ -1,4 +1,4 @@
-Lensing likelihood of Planck's 2015 data release based on temperature+polarisation map-based lensing reconstruction \cite{Ade:2015zua}.
+Lensing likelihood of Planck's 2015 data release based on temperature+polarization map-based lensing reconstruction \cite{Ade:2015zua}.
 
 
 @article{Ade:2015zua,

cobaya/likelihoods/sn_jla/sn_jla.yaml

@@ -1,5 +1,5 @@
 # Settings for JLA supernova sample (joint SNLS/SDSS SN Ia data)
-# (For the marginalised version, use 'sn_jla_lite')
+# (For the marginalized version, use 'sn_jla_lite')
 
 likelihood:
   sn_jla:

cobaya/likelihoods/sn_jla_lite/sn_jla_lite.bibtex

@@ -1,4 +1,4 @@
-Likelihood (marginalised over nuisance parameters) of the JLA type Ia supernova sample \cite{Betoule:2014frx}, based on observations obtained by the SDSS-II and SNLS collaborations.
+Likelihood (marginalized over nuisance parameters) of the JLA type Ia supernova sample \cite{Betoule:2014frx}, based on observations obtained by the SDSS-II and SNLS collaborations.
 
 
 @article{Betoule:2014frx,

cobaya/likelihoods/sn_jla_lite/sn_jla_lite.yaml

@@ -1,6 +1,6 @@
 # Settings for JLA supernova sample (joint SNLS/SDSS SN Ia data)
-# Marginalised version (useful e.g. for importance sampling)
-# NB: different chi2 normalisation from the non-normalised version
+# Marginalized version (useful e.g. for importance sampling)
+# NB: different chi2 normalization from the non-normalized version
 
 likelihood:
   sn_jla_lite:

cobaya/model.py

@@ -1,7 +1,7 @@
 """
 .. module:: model
 
-:Synopsis: Wrapper for models: parametrisation+prior+likelihood+theory
+:Synopsis: Wrapper for models: parameterization+prior+likelihood+theory
 :Author: Jesus Torrado
 
 """

cobaya/prior.py

@@ -163,9 +163,9 @@
 
 The one-dimensional priors defined within the ``params`` block are automatically
 normalized, so any sampler that computes the evidence will produce the right results as
-long as no external priors have been defined, whose normalisation is unknown.
+long as no external priors have been defined, whose normalization is unknown.
 
-To get the prior normalisation if using external functions as priors, you can substitute
+To get the prior normalization if using external functions as priors, you can substitute
 your likelihood by the :doc:`dummy unit likelihood <likelihood_one>`, and make an initial
 run with :doc:`PolyChord <sampler_polychord>` to get the prior volume
 (see section :ref:`polychord_bayes_ratios`).
@@ -196,7 +196,7 @@
 :ref:`the end of the example <example_advanced_rtheta>`.
 Let us discuss the general case here.
 
-To enble this, **cobaya** creates a `re-parametrisation` layer between the `sampled`
+To enble this, **cobaya** creates a `re-parameterization` layer between the `sampled`
 parameters, and the `input` parameters of the likelihood. E.g. if we want to **sample**
 from the logarithm of an **input** parameter of the likelihood, we would do:
 
@@ -231,7 +231,7 @@
 .. note::
 
    **Dynamical derived** parameters can also be functions of yet-undefined parameters.
-   In that case, those parameters will be automatically requested to the likelihood (or
+   In that case, those parameters will be automatically requested from the likelihood (or
    theory code) that understands them.
 
 .. note::

cobaya/sampler.py

@@ -4,10 +4,10 @@
 :Synopsis: Prototype sampler class and sampler loader
 :Author: Jesus Torrado
 
-cobaya includes by default an
-:doc:`advanced Monte Carlo Markov Chain (MCMC) sampler <sampler_mcmc>`
-(a direct translation from `CosmoMC <https://cosmologist.info/cosmomc/>`_ and a dummy
-:doc:`evaluate <sampler_evaluate>` sampler, that simply evaluates the posterior at a given
+cobaya includes by default a
+:doc:`Monte Carlo Markov Chain (MCMC) sampler <sampler_mcmc>`
+(a direct translation from `CosmoMC <https://cosmologist.info/cosmomc/>`_) and a dummy
+:doc:`evaluate <sampler_evaluate>` sampler that simply evaluates the posterior at a given
 (or sampled) reference point. It also includes an interface to the
 :doc:`PolyChord sampler <sampler_polychord>` (needs to be installed separately).
 

cobaya/samplers/minimize/minimize.bibtex

@@ -1 +1 @@
-The posterior has been maximised using \href{https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html}{\texttt{scipy.optimize.minimize}}
+The posterior has been maximized using \href{https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html}{\texttt{scipy.optimize.minimize}}

cobaya/samplers/minimize/minimize.yaml

@@ -5,7 +5,7 @@ sampler:
     # Minimizes the full posterior (False) or just the likelihood (True)
     ignore_prior: False
     # Tolerance for termination. Default: 0.001
-    # --> expected >0.01 deviation, reasonable value for Chi^2 minimisation
+    # --> expected >0.01 deviation, reasonable value for Chi^2 minimization
     tol: 1e-3
     # Maximum number of iterations. Default: practically infinite
     maxiter: 1e10

cobaya/theories/_cosmo/_cosmo.py

@@ -137,7 +137,7 @@ def get_fsigma8(self, z):
 
     def get_auto_covmat(self, params_info, likes_info):
         """
-        Tries to get an automatic covariance matrix for the current model and data.
+        Tries to get match to a database of existing covariance matrix files for the current model and data.
 
         ``params_info`` should contain preferably the slow parameters only.
         """

cobaya/theories/camb/camb.py

@@ -47,8 +47,8 @@
 ^^^^^^^^^^^^^^
 
 **cobaya** calls CAMB using its Python interface, which requires that you compile CAMB
-using the GNU gfortran compiler version 4.9 or later. To check if you fulfil that
-requisite, type ``gfortran --version`` in the shell, and the first line should look like
+using intel's ifort compiler or the GNU gfortran compiler version 4.9 or later. To check if you have the latter,
+type ``gfortran --version`` in the shell, and the first line should look like
 
 .. code::
 
@@ -57,6 +57,9 @@
 Check that ``[gfortran's version]`` is at least 4.9. If you get an error instead, you need
 to install gfortran (contact your local IT service).
 
+CAMB comes with binaries pre-built for Windows, so if you don't need to modify the CAMB source code, no Fortran compiler is
+needed.
+
 
 Automatic installation
 ^^^^^^^^^^^^^^^^^^^^^^
@@ -325,7 +328,7 @@ def set(self, params_values_dict, i_state):
                     setattr(cambparams, attr, value)
                 else:
                     self.log.error("Some of the attributes to be set manually were not "
-                                   "recognised: %s=%s", attr, value)
+                                   "recognized: %s=%s", attr, value)
                     raise HandledException
             return cambparams
         except CAMBParamRangeError:

cobaya/theories/classy/classy.py

@@ -20,7 +20,7 @@
 
 .. note::
 
-   CLASS is renamed ``classy`` for most purposes within cobaya, due to CLASS' name being
+   CLASS is renamed ``classy`` for most purposes within cobaya, due to CLASS's name being
    a python keyword.
 
 Usage
@@ -59,7 +59,7 @@
 If you are planning to modify CLASS or use an already modified version,
 you should not use the automatic installation script. Use the method below instead.
 
-CLASS' python interface utilizes the ``cython`` compiler. If typing ``cython`` in the
+CLASS's python interface utilizes the ``cython`` compiler. If typing ``cython`` in the
 shell produces an error, install it with ``pip install cython --user``.
 
 .. note::
@@ -93,7 +93,7 @@
 
 If you modify CLASS and add new variables, you don't need to let cobaya know, but make
 sure that the variables you create are exposed in the Python
-interface (contact CLASS' developers if you need help with that).
+interface (contact CLASS's developers if you need help with that).
 
 """
 # Python 2/3 compatibility
@@ -380,7 +380,7 @@ def get_derived_all(self, derived_requested=True):
                 [p for p,v in requested_and_extra.items() if v is None]))
         # Separate the parameters before returning
         # Remember: self.output_params is in sampler nomenclature,
-        # but self.derived_extra is in CLASS'
+        # but self.derived_extra is in CLASS
         derived = {
             p:requested_and_extra[self.translate_param(p)] for p in self.output_params}
         derived_extra = {p:requested_and_extra[p] for p in self.derived_extra}

docs/cosmo_external_likelihood.rst

@@ -166,7 +166,7 @@ And now we can e.g. plot a slice of the log likelihood along different :math:`A_
    - If the likelihood evaluates to ``-inf`` (but the prior is finite) it probably means that the theory code is failing; to display the error information of the theory code, add to it the ``stop_at_error: True`` option, as shown in the example input above.
 
 
-Before we start sampling, it is a good idea to characterise the speed of your likelihood, so that the sampler can behave more efficiently. To do that, set ``timing: True`` in the input before initialising your model (as we did above), evaluate the likelihood a couple of times (as we did for the log-likelihood plot above), and *close* the model as ``model.close()``. This will print the evaluation time (in seconds) of the theory code and the likelihoods. Now, redefine the likelihood in the input to add the speed, which is the inverse of the evaluation time in seconds, e.g. if that was :math:`2\,\mathrm{ms}`:
+Before we start sampling, it is a good idea to characterize the speed of your likelihood, so that the sampler can behave more efficiently. To do that, set ``timing: True`` in the input before initialising your model (as we did above), evaluate the likelihood a couple of times (as we did for the log-likelihood plot above), and *close* the model as ``model.close()``. This will print the evaluation time (in seconds) of the theory code and the likelihoods. Now, redefine the likelihood in the input to add the speed, which is the inverse of the evaluation time in seconds, e.g. if that was :math:`2\,\mathrm{ms}`:
 
 .. code:: python
 

docs/cosmo_model.rst

@@ -7,7 +7,7 @@ You can use it to test your modifications, to evaluate the cosmological observab
 
 Models are created from the same input as the :func:`~run.run` function: a dictionary containing the same blocks, except for the ``sampler`` block (it can be included, but will be ignored).
 
-So let us create a simple one using the input-generator [LINK!!!!!]: Planck 2015 polarised CMB and lensing with CLASS as a theory code. Let us copy the ``python`` version (you can also copy the ``yaml`` version and load it with :func:`~yaml.yaml_load`).
+So let us create a simple one using the :doc:`input generator <cosmo_basic_runs>`: Planck 2015 polarized CMB and lensing with CLASS as a theory code. Let us copy the ``python`` version (you can also copy the ``yaml`` version and load it with :func:`~yaml.yaml_load`).
 
 .. literalinclude:: ./src_examples/cosmo_model/1.py
    :language: python
@@ -17,7 +17,7 @@ Now let's build a model (we will need the path to your modules' installation):
 .. literalinclude:: ./src_examples/cosmo_model/2.py
    :language: python
 
-To get (log)probabilities and derived parameters for particular parameter values, we can use the different methods of the :class:`model.Model` (see below), to which we pass a dictionary of **sampled** parameter values.
+To get (log) probabilities and derived parameters for particular parameter values, we can use the different methods of the :class:`model.Model` (see below), to which we pass a dictionary of **sampled** parameter values.
 
 .. note::
 

docs/cosmo_theories_likes.rst

@@ -6,15 +6,15 @@ Models in Cosmology are usually split in two: :math:`\mathcal{M}=\mathcal{T}+\ma
 * :math:`\mathcal{T}`, the *theoretical* model, is used to compute observable quantities :math:`\mathcal{O}`
 * :math:`\mathcal{E}`, the *experimental* model, accounts for instrumental errors, foregrounds... when comparing the theoretical observable with some data :math:`\mathcal{D}`.
 
-In practice the theoretical model is encapsulated in a **theory code** (:doc:`CLASS <theory_class>`, :doc:`CAMB <theory_camb>`...) and the experimental model in a **likelihood**, which gives the probability of the data being a realisation of the given observable in the context of the experiment:
+In practice the theoretical model is encapsulated in a **theory code** (:doc:`CLASS <theory_class>`, :doc:`CAMB <theory_camb>`...) and the experimental model in a **likelihood**, which gives the probability of the data being a realization of the given observable in the context of the experiment:
 
 .. math::
 
    \mathcal{L}\left[\mathcal{D}\,|\,\mathcal{M}\right] =
    \mathcal{L}\left[\mathcal{D}\,|\,\mathcal{O},\mathcal{E}\right]
 
 
-Each iteration of a sampler reproduces that model along the following steps:
+Each iteration of a sampler reproduces the model using the following steps:
 
 #. A new set of theory+experimental parameters is proposed by the sampler.
 #. The theory parameters are passed to the theory code, which computes one or more observables.

docs/installation.rst

@@ -176,6 +176,8 @@ The recommended way is to get a `GitHub <https://github.com>`_ user and `fork th
    $ git clone https://[email protected]/YOUR_USERNAME/cobaya.git
    $ pip install --editable cobaya[test] --upgrade
 
+(add the --user option if you don't have write access to the default pip installation location).
+
 Alternatively, you can clone from the official **cobaya** repo (but this way you won't be able to upload your changes!).
 
 .. code:: bash

docs/likelihood_gaussian.rst

@@ -1,7 +1,7 @@
 ``gaussian`` likelihood
 =======================
 
-A simple (multi-modal if required) Gaussian likelihood, aimed at testing. The pdf is normalized to 1 when integrated over an infinite domain, regardless of the number of modes.
+A simple (multi-modal if required) Gaussian mixture likelihood, aimed at testing. The pdf is normalized to 1 when integrated over an infinite domain, regardless of the number of modes.
 
 Usage
 -----