split up line in Example.py into three

Example.py

@@ -25,7 +25,9 @@
 import os
 
 from smodels.statistics.pyhfInterface import setBackend
-setBackend("pytorch") # set to one of: numpy (default), pytorch, tensorflow, jax. WARNING: if backend specified is not found, we fall back to numpy!
+# set pyhf backend to one of: numpy (default), pytorch, tensorflow, jax. 
+# WARNING: if backend specified is not found, we fall back to numpy!
+setBackend("pytorch")
 
 def main(inputFile='./inputFiles/slha/lightEWinos.slha', sigmacut=0.05*fb,
          database = 'official'):

docs/manual/source/RunningSModelS.rst

@@ -402,44 +402,44 @@ Below we go step-by-step through this example code:
 * *Define the pyhfbackend to use*. Specify which `pyhfbackend <statistics.html#statistics.pyhfInterface.setBackend>`_ to use. One of: numpy, pytorch, tensorflow, jax.
 
 .. literalinclude:: /examples/Example.py
-   :lines: 27-28
+   :lines: 27-30
 
 * *Set the path to the database URL*. Specify which :ref:`database <databaseStruct>` to use. It can be the path
   to the smodels-database folder, the path to a :ref:`pickle file <databasePickle>` or (starting with v1.1.3) a URL path.
 
 .. literalinclude:: /examples/Example.py
-   :lines: 36-37
+   :lines: 38-39
 
 
 * *Load the BSM particles*. By default SModelS assumes the MSSM particle content. For using SModelS
   with a different particle content, the user must define the new particle content and set modelFile
   to the path of the model file (see **particles:model** in :ref:`Parameter File <parameterFile>`).
 
 .. literalinclude:: /examples/Example.py
-   :lines: 39-41
+   :lines: 41-43
 
 * *Load the model and set the path to the input file*. Load BSM and SM particle content; specify the location of the input file (must be an SLHA or LHE file, see :ref:`Basic Input <BasicInput>`) and update particles in the model.
 
 .. literalinclude:: /examples/Example.py
-   :lines: 43,46,48-49
+   :lines: 45,48,50-51
 
 * *Set main options for* |decomposition|.
   Specify the values of :ref:`sigmacut <minweight>` and :ref:`minmassgap <massComp>`:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 52-53
+   :lines: 54-55
 
 * |Decompose| *model* using the
   `decomposer.decompose <decomposition.html#decomposition.decomposer.decompose>`_ method. The **doCompress** and **doInvisible** options turn the |mass compression| and |invisible compression| on/off.
 
 .. literalinclude:: /examples/Example.py
-   :lines: 57-59
+   :lines: 59-61
 
 * *Access basic information* from decomposition, using the
   `dictionary of SMS <decomposition.html#decomposition.topologyDict.TopologyDict>`_ (the dictionary has as keys the :ref:`canonical name <canonicalName>` for the topology and as values the list of |SMS topologies| for the corresponding canonical name):
 
 .. literalinclude:: /examples/Example.py
-   :lines: 62-66
+   :lines: 64-68
 
 *output:*
 
@@ -449,7 +449,7 @@ Below we go step-by-step through this example code:
 * *Print information about the SMS topologies* from the decomposition:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 69-75
+   :lines: 71-77
 
 *output:*
 
@@ -461,7 +461,7 @@ Below we go step-by-step through this example code:
   Here, all results are used:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 79
+   :lines: 81
 
 Alternatively, the `getExpResults  <experiment.html#experiment.databaseObj.Database.getExpResults>`_ method
 can take as arguments specific results to be loaded and used.
@@ -470,7 +470,7 @@ can take as arguments specific results to be loaded and used.
   Below we show how to count the number of |ULrs| and |EMrs| loaded:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 85-92
+   :lines: 87-94
 
 *output:*
 
@@ -483,13 +483,13 @@ can take as arguments specific results to be loaded and used.
   `theory prediction objects <matching.html#matching.theoryPrediction.TheoryPrediction>`_:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 97
+   :lines: 99
 
 * *Print the results*. For each |expres|, loop over the corresponding |theory predictions|
   and print the relevant information:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 98-107
+   :lines: 100-109
 
 *output:*
 
@@ -500,7 +500,7 @@ can take as arguments specific results to be loaded and used.
   be compared to the |theory prediction| to decide whether a model is excluded or not:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 110
+   :lines: 112
 
 *output:*
 
@@ -513,7 +513,7 @@ can take as arguments specific results to be loaded and used.
   Determine the most constraining result:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 113-119
+   :lines: 115-121
 
 *output:*
 
@@ -524,7 +524,7 @@ can take as arguments specific results to be loaded and used.
   determine if the model has been excluded or not by the selected |express|:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 120-129
+   :lines: 122-131
 
 
 *output:*
@@ -535,17 +535,17 @@ can take as arguments specific results to be loaded and used.
 * *Select analyses*. Using the theory predictions, select a (user-defined) subset of analyses to be combined:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 134-142
+   :lines: 136-144
 
 * *Combine analyses*. Using the selected analyses, combine them under the assumption they are fully uncorrelated:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 149
+   :lines: 151
 
 * *Print the combination*. Print the *r*-values and likelihoods for the combination:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 154-157
+   :lines: 156-159
 
 *output:*
 
@@ -556,7 +556,7 @@ can take as arguments specific results to be loaded and used.
   the :ref:`missing topologies <topCoverage>` and print some basic information:
 
 .. literalinclude:: /examples/Example.py
-   :lines: 162-168
+   :lines: 164-170
 
 
 *output:*

docs/manual/source/examples/Example.py

@@ -25,7 +25,9 @@
 import os
 
 from smodels.statistics.pyhfInterface import setBackend
-setBackend("pytorch") # set to one of: numpy (default), pytorch, tensorflow, jax. WARNING: if backend specified is not found, we fall back to numpy!
+# set pyhf backend to one of: numpy (default), pytorch, tensorflow, jax. 
+# WARNING: if backend specified is not found, we fall back to numpy!
+setBackend("pytorch")
 
 def main(inputFile='./inputFiles/slha/lightEWinos.slha', sigmacut=0.05*fb,
          database = 'official'):