Merge pull request #95 from ucl-cssb/isntallation_update

Fixed installation issue in documentation

Author: Fontanapink

README.rst

@@ -62,6 +62,8 @@ The repository is organized into the following main directories:
 Installation
 ------------
 
+.. install-begin
+
 Prerequisites
 ^^^^^^^^^^^^^
 
@@ -157,6 +159,7 @@ Compilers
 """""""""
 A g++ compiler is required for the PyMC3 package.
 
+.. install-end
 
 Usage
 -----

docs/source/installation.rst

@@ -5,47 +5,6 @@ Installation
 ============
 
 
-Stable release
---------------
-
-To install MIMIC, run this command in your terminal:
-
-.. code-block:: console
-
-    $ pip install mimic
-
-This is the preferred method to install MIMIC, as it will always install the most recent stable release.
-
-If you don't have `pip`_ installed, this `Python installation guide`_ can guide
-you through the process.
-
-.. _pip: https://pip.pypa.io
-.. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
-
-
-From sources
-------------
-
-The sources for MIMIC can be downloaded from the `Github repo`_.
-
-You can either clone the public repository:
-
-.. code-block:: console
-
-    $ git clone git://github.com/Fontanapink/mimic
-
-Or download the `tarball`_:
-
-.. code-block:: console
-
-    $ curl -OJL https://github.com/Fontanapink/mimic/tarball/master
-
-Once you have a copy of the source, you can install it with:
-
-.. code-block:: console
-
-    $ python setup.py install
-
-
-.. _Github repo: https://github.com/Fontanapink/mimic
-.. _tarball: https://github.com/Fontanapink/mimic/tarball/master
+.. include:: ../../README.rst
+   :start-after: install-begin
+   :end-before: install-end

mimic/model_infer/infer_VAR_bayes.py

@@ -279,7 +279,7 @@ def _run_inference_large(self, **kwargs) -> None:
             c2 = pm.InverseGamma("c2", 2, 8)
             tau = pm.HalfCauchy("tau", beta=tau0)
             lam = pm.HalfCauchy("lam", beta=1, shape=(ndim, ndim))
-            A = pm.Normal('A', mu=A_prior_mu, sigma=tau * lam * \
+            A = pm.Normal('A', mu=A_prior_mu, sigma=tau * lam *
                           at.sqrt(c2 / (c2 + tau**2 * lam**2)), shape=(ndim, ndim))
 
             # If noise covariance is provided, use it as a prior
@@ -438,14 +438,14 @@ def _run_inference_large_xs(self, **kwargs) -> None:
             c2_A = pm.InverseGamma("c2_A", 2, 1)
             tau_A = pm.HalfCauchy("tau_A", beta=tau0_A)
             lam_A = pm.HalfCauchy("lam_A", beta=1, shape=(nX, nX))
-            Ah = pm.Normal('Ah', mu=A_prior_mu, sigma=tau_A * lam_A * \
+            Ah = pm.Normal('Ah', mu=A_prior_mu, sigma=tau_A * lam_A *
                            at.sqrt(c2_A / (c2_A + tau_A**2 * lam_A**2)), shape=(nX, nX))
 
             tau0_B = (DB0 / (DB - DB0)) * 0.1 / np.sqrt(N)
             c2_B = pm.InverseGamma("c2_B", 2, 1)
             tau_B = pm.HalfCauchy("tau_B", beta=tau0_B)
             lam_B = pm.HalfCauchy("lam_B", beta=1, shape=(nS, nX))
-            Bh = pm.Normal('Bh', mu=0, sigma=tau_B * lam_B * \
+            Bh = pm.Normal('Bh', mu=0, sigma=tau_B * lam_B *
                            at.sqrt(c2_B / (c2_B + tau_B**2 * lam_B**2)), shape=(nS, nX))
 
             if noise_cov_prior is not None: