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Perform optimizations on flat ROOT TTrees

Project description

Optimization - A PyRoot Codebase
================================

This tool allows you to take a series of ROOT ntuples, signal &
background, apply a lot of cuts automatically, and figure out the most
optimal selections to maximize significance. It comes packed with a lot
of features

- generator script to create, what we call, a supercuts file containing
all the rules to tell the script what cuts to apply and on which
branches
- cut script which will take your signal, background, and supercuts;
run them all; and output a series of files with the appropriate event
counts for all cuts provided
- optimization script which will take your signal counts and background
counts; run them all; and output a sorted list of optimal cuts
- hash look up script to reverse-engineer the cut for a given hash when
you supply the supercuts file

*Note*: as part of making the script run as fast as possible, I try to
maintain a low memory profile. It will not store (or remember) the cut
used to create a significance value. Instead, we compute a 32-bit hash
which is used to encode the cuts, and a way to "decode" the hash is also
provided.

.. raw:: html

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**Table of Contents** *generated with
`DocToc <https://github.com/thlorenz/doctoc>`__*

- `Major Dependencies <#major-dependencies>`__
- `Quick Start <#quick-start>`__
- `Installing <#installing>`__

- `Using virtual environment <#using-virtual-environment>`__
- `Without using virtual
environment <#without-using-virtual-environment>`__
- `Errors with root-numpy and
TMVA <#errors-with-root-numpy-and-tmva>`__

- `Using <#using>`__

- `Grab some optimization
ntuples <#grab-some-optimization-ntuples>`__
- `Generate a supercuts template <#generate-a-supercuts-template>`__
- `Running the cuts <#running-the-cuts>`__
- `Calculating the significances <#calculating-the-significances>`__
- `Looking up a cut (or two) <#looking-up-a-cut-or-two>`__

- `Profiling Code <#profiling-code>`__
- `Example Script <#example-script>`__
- `Documentation <#documentation>`__
- `Top-Level <#top-level>`__

- `Parameters <#parameters>`__

- `Action:Generate <#actiongenerate>`__

- `Required Parameters <#required-parameters>`__
- `Optional Parameters <#optional-parameters>`__
- `Output <#output>`__

- `Action:Cut <#actioncut>`__

- `Required Parameters <#required-parameters-1>`__
- `Optional Parameters <#optional-parameters-1>`__
- `Output <#output-1>`__

- `Action:Optimize <#actionoptimize>`__

- `Required Parameters <#required-parameters-2>`__
- `Optional Parameters <#optional-parameters-2>`__
- `Output <#output-2>`__

- `Action:Hash <#actionhash>`__

- `Required Parameters <#required-parameters-3>`__
- `Optional Parameters <#optional-parameters-3>`__
- `Output <#output-3>`__

- `Action:Summary <#actionsummary>`__

- `Required Parameters <#required-parameters-4>`__
- `Optional Parameters <#optional-parameters-4>`__
- `Output <#output-4>`__

- `Supercuts File <#supercuts-file>`__

- `Defining a fixed cut <#defining-a-fixed-cut>`__
- `Defining a supercut <#defining-a-supercut>`__
- `Example of a supercuts file <#example-of-a-supercuts-file>`__
- `More Complicated Selections <#more-complicated-selections>`__

- `Authors <#authors>`__

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Major Dependencies
------------------

- `PyROOT <https://root.cern.ch/drupal/content/pyroot>`__ (which
technically requires ROOT)
- `numpy <http://www.numpy.org/>`__
- `root\_numpy <http://rootpy.github.io/root_numpy/>`__

All other dependencies are listed in
`requirements.txt <requirements.txt>`__ and can be installed in one line
with ``pip install -r requirements.txt``.

Quick Start
-----------

tl;dr - copy and paste, and off you go.

Installing
~~~~~~~~~~

Using virtual environment
^^^^^^^^^^^^^^^^^^^^^^^^^

I use
```virtualenvwrapper`` <https://virtualenvwrapper.readthedocs.org/en/latest/>`__
to manage my python dependencies and workspace. I assume you have
``pip``.

.. code:: bash

pip install virtualenvwrapper
echo "source /usr/local/bin/virtualenvwrapper.sh" >> ~/.bash_profile
source ~/.bash_profile
mkvirtualenv optimization
workon optimization
git clone git@github.com:kratsg/Optimization
cd Optimization
pip install numpy
pip install -r requirements.txt
python optimize.py -h

Start a new environment with ``mkvirtualenv NameOfEnv`` and everytime
you open a new shell, you just need to type ``workon NameOfEnv``. Type
``workon`` alone to see a list of environments you've created already.
Read the `virtualenvwrapper
docs <https://virtualenvwrapper.readthedocs.org/en/latest/>`__ for more
information.

Without using virtual environment
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: bash

git clone git@github.com:kratsg/Optimization
cd Optimization
pip install numpy
pip install -r requirements.txt
python optimize.py -h

Errors with root-numpy and TMVA
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

ROOT changed the TMVA version so to make ``root-numpy`` behave nicely,
just run

.. code:: bash

NOTMVA=1 pip install root-numpy

which will install it without TMVA support. Then comment out the
relevant line. ``root-numpy`` also requires ``numpy`` so you'll want to
have that installed beforehand as well.

Using
~~~~~

Grab some optimization ntuples
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

I grab a set of optimization ntuples from
`dropbox <https://www.dropbox.com/sh/s2f5n1oaojnxonn/AABnajIwGGGqMz2RlVO2zjkka?dl=0>`__
and extract them

.. code:: bash

tar -xzvf TA07_MBJ10V1.tar.gz

Generate a supercuts template
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

A straightforward example is simply just

.. code:: bash

python optimize.py generate "Gtt_0L_a/fetch/data-optimizationTree/user.lgagnon:user.lgagnon.370101.Gtt.DAOD_SUSY10.e4049_s2608_r6765_r6282_p2411_tag_10_v1_output_xAOD.root-0.root"

which will create a ``supercuts.json`` file for you to edit so that you
can run the optimizations. As a more advanced example, I only wanted to
generate a file using a subset of the branches in my file as well as
setting some of them to be a fixed cut that I would configure, so I ran

.. code:: bash

python optimize.py generate "Gtt_0L_a/fetch/data-optimizationTree/user.lgagnon:user.lgagnon.370101.Gtt.DAOD_SUSY10.e4049_s2608_r6765_r6282_p2411_tag_10_v1_output_xAOD.root-0.root" --fixedBranches multiplicity_topTag* -o dump.json -b -vv --skipBranches *_jet_rc*

which will write branches that match ``multiplicity_topTag*`` to have a
fixed cut when I eventually run ``optimize`` over it; and will also skip
branches that match ``*_jet_rc*`` so they won't be considered at all for
cuts.

Running the cuts
^^^^^^^^^^^^^^^^

After that, we just specify all of our ROOT files. The script takes
advantage of ``TChain`` and \*nix file handling, it will automatically
handle multiple files specified either as a pattern or just explicitly
writing them out. We will group every output by the DID passed in, so
please try not to deviate from the default sample names or this breaks
the code quite badly.

.. code:: bash

python optimize.py cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_a -b
python optimize.py cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_b -b
python optimize.py cut TA07_MBJ10V1/*_1L/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_1L -b

By default, we use ``TTree::Draw`` in order to calculate the number of
events passing a given cut. We will also attempt to parallelize the
computations as much as possible. In cases where you have a fast
computer and the ntuples are reasonably small (can fit in memory), you
might benefit from using a ``numpy`` boost by adding the ``--numpy``
flag like so

.. code:: bash

python optimize.py cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_a -b --numpy
python optimize.py cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_b -b --numpy
python optimize.py cut TA07_MBJ10V1/*_1L/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_1L -b --numpy

Calculating the significances
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

After that, we just (at a bare minimum) specify the ``signal`` and
``bkgd`` json cut files. The following example takes the ``0L_a`` files
and calculates significances for two different values of luminosity

.. code:: bash

python optimize.py optimize --signal 37* --bkgd 4* --searchDirectory=cuts_0L_a -b --o=significances_0L_a_lumi1 --lumi=1
python optimize.py optimize --signal 37* --bkgd 4* --searchDirectory=cuts_0L_a -b --o=significances_0L_a_lumi2 --lumi=2

and this will automatically combine background and produce a
significances file for each signal DID passed in.

Looking up a cut (or two)
^^^^^^^^^^^^^^^^^^^^^^^^^

When the optimizations have finished running, you'll want to take the
given hash(es) and figure out what cut it corresponds to, you can do
this with

.. code:: bash

python optimize.py hash e31dcf5ba4786d9e8ffa9e642729a6b9 4e16fdc03c171913bc309d57739c7225 8fa0e0ab6bf6a957d545df68dba97a53 --supercuts=supercuts_small.json

which will create ``outputHash/<hash>.json`` files detailing the cuts
involved.

Profiling Code
~~~~~~~~~~~~~~

This is one of those pieces of python code we always want to run as fast
as possible. Optimization should not take long. To figure out those
dead-ends, I use `snakeviz <https://jiffyclub.github.io/snakeviz/>`__.
The ``requirements.txt`` file contains this dependency. To run it, I
first profile the code by running it:

.. code:: bash

python -m cProfile -o profiler.log python optimize.py cut TA06_MBJ05/*_0L/fetch/data-optimizationTree/*.root --supercuts=supercuts.json -o cuts_0L -b --numpy

then I use the ``snakeviz`` script to help me visualize this

.. code:: bash

snakeviz profiler.log

and I'm good to go.

Example Script
~~~~~~~~~~~~~~

See `example\_script.sh <example_script.sh>`__ for an idea how how to
run everything in order to produce a plot of significances.

Documentation
-------------

Top-Level
~~~~~~~~~

.. code:: bash

python optimize.py

or

.. code:: bash

python optimize.py -h

::

usage: optimize.py [-h] [-a] {generate,cut,optimize,hash} ...

Author: Giordon Stark. v.eea1e27

positional arguments:
{generate,cut,optimize,hash}
actions available
generate Write supercuts template
cut Apply the cuts
optimize Calculate significances for a series of computed
cuts
hash Translate hash to cut

optional arguments:
-h, --help show this help message and exit
-a, --allhelp show this help message and all subcommand help
messages and exit

This is the top-level. You have no power here.

Parameters
^^^^^^^^^^

There is only one required position argument: the ``action``. You can
choose from

- `generate <#actiongenerate>`__
- `cut <#actioncut>`__
- `optimize <#actionoptimize>`__
- `hash <#actionhash>`__

We also provide an optional argument ``-a, --allhelp`` which will print
all the help documentation at once instead of just the top-level
``-h, --help``.

Action:Generate
~~~~~~~~~~~~~~~

Generate helps you quickly start. Given the ROOT ntuples, generate a
supercuts.json template.

.. code:: bash

usage: optimize.py generate --signal=signal.root [..] --bkgd=bkgd.root [...] [options]

Required Parameters
^^^^^^^^^^^^^^^^^^^

+------------+----------+----------------------------------------------------------------+
| Variable | Type | Description |
+============+==========+================================================================+
| file | string | path to a root file containing an optimization ntuple to use |
+------------+----------+----------------------------------------------------------------+

Optional Parameters
^^^^^^^^^^^^^^^^^^^

+-------------------+-----------+------------------------------------------------------+
| Variable | Type | Description |
+===================+===========+======================================================+
| -h, --help | bool | display help message |
+-------------------+-----------+------------------------------------------------------+
| -v, --verbose | count | enable more verbose output |
+-------------------+-----------+------------------------------------------------------+
| --debug | bool | enable full-on debugging |
+-------------------+-----------+------------------------------------------------------+
| -b, --batch | bool | enable batch mode for ROOT |
+-------------------+-----------+------------------------------------------------------+
| --tree | string | ttree name in the ntuples |
+-------------------+-----------+------------------------------------------------------+
| --eventWeight | string | event weight branch name |
+-------------------+-----------+------------------------------------------------------+
| --o, --output | string | output json file to store generated supercuts file |
+-------------------+-----------+------------------------------------------------------+
| --fixedBranches | strings | branches that should have a fixed cut |
+-------------------+-----------+------------------------------------------------------+
| --skipBranches | strings | branches that should not have a cut (skip them) |
+-------------------+-----------+------------------------------------------------------+

- ``--globalMinVal`` is just an aesthetic feature to make it easier to
identify the "true" minimum of your ntuples. I often output -99.0 in
case there is (for example) no 4th jet, or I could not calculate some
substructure information, this allows me to automatically chop off
the low end of a branch to get a better calculation of the
percentiles
- ``--fixedBranches`` and ``--skipBranches`` can take a series of
strings or a series of patterns

``bash --fixedBranches multiplicity_jet multiplicity_topTag_loose multiplicity_topTag_tight``

or

``bash --fixedBranches multiplicity_* pt_jet_rc8_1``

which aims to make life easier for all of us.

Output
^^^^^^

This script will generate a supercuts json file. See `Supercuts
File <#supercuts-file>`__ for more information.

Action:Cut
~~~~~~~~~~

Cut helps you by generating the cuts from a supercuts file and applying
them to create an output file of counts. Process ROOT ntuples and apply
cuts.

.. code:: bash

usage: optimize.py cut <file.root> ... [options]

Required Parameters
^^^^^^^^^^^^^^^^^^^

+------------+----------+--------------------------------------------+
| Variable | Type | Description |
+============+==========+============================================+
| files | string | path(s) to root files containing ntuples |
+------------+----------+--------------------------------------------+

Optional Parameters
^^^^^^^^^^^^^^^^^^^

+------------+---------+----------------+------------+
| Variable | Type | Description | Default |
+============+=========+================+============+
| -h, --help | bool | display help | False |
| | | message | |
+------------+---------+----------------+------------+
| -v, | count | enable more | 0 |
| --verbose | | verbose output | |
+------------+---------+----------------+------------+
| --debug | bool | enable full-on | False |
| | | debugging | |
+------------+---------+----------------+------------+
| -b, | bool | enable batch | False |
| --batch | | mode for ROOT | |
+------------+---------+----------------+------------+
| --tree | string | ttree name in | oTree |
| | | the ntuples | |
+------------+---------+----------------+------------+
| --eventWei | string | event weight | event\_wei |
| ght | | branch name | ght |
+------------+---------+----------------+------------+
| --supercut | string | path to json | supercuts. |
| s | | dict of | json |
| | | supercuts for | |
| | | generating | |
| | | cuts | |
+------------+---------+----------------+------------+
| --weightsF | string | .json file |
| ile | | containing |
| | | weights in |
| | | proper |
| | | formatting - |
| | | see |
| | | SampleWeights |
+------------+---------+----------------+------------+
| --o, | directo | output | cuts |
| --output | ry | directory to | |
| | | store json | |
| | | files | |
| | | containing | |
| | | cuts | |
+------------+---------+----------------+------------+
| --numpy | bool | if enabled, |
| | | use ``numpy`` |
| | | and |
| | | ``numexpr`` |
| | | instead of |
| | | ROOT. `See |
| | | this section |
| | | for more |
| | | information. < |
| | | #more-complica |
| | | ted-selections |
| | | >`__ |
+------------+---------+----------------+------------+

Output
^^^^^^

+------------+-----------------+----------------------------------------------------------------+
| Variable | Type | Description |
+============+=================+================================================================+
| hash | 32-bit string | md5 hash of the cut |
+------------+-----------------+----------------------------------------------------------------+
| raw | integer | raw number of events passing cut |
+------------+-----------------+----------------------------------------------------------------+
| weighted | float | apply event weights to events passing cut |
+------------+-----------------+----------------------------------------------------------------+
| scaled | float | apply sample weights and event weights to events passing cut |
+------------+-----------------+----------------------------------------------------------------+

Note that weights are applied in order of prominance and specificity:
weighted events are applying the monte-carlo event weights (from the
generators themselves). Scaled events are with the mc weights applied
but also scaled using the sample weights (the ones that differ from
sample to sample) and this does not include luminosity at this stage.
The calculation of significance includes the luminosity scale factor.

The output is a directory of json files which will look like

.. code:: json

{
...
"09a130622e1e6345b83739b3527eccb1": {
"raw": 90909,
"scaled": 90909.0,
"weighted": 2.503
},
...
}

This code will group your input files by DIDs and will try its best to
do its job to group your sample files.

Action:Optimize
~~~~~~~~~~~~~~~

Optimize helps you find your optimal cuts. Process cuts and determine
significance.

.. code:: bash

usage: optimize.py optimize --signal=signal.root [..] --bkgd=bkgd.root [...] [options]

**Note**: You can specify multiple backgrounds and multiple signals.
Each signal will be run over separately and each background will be
merged and treated as a single background.

Required Parameters
^^^^^^^^^^^^^^^^^^^

+------------+----------+----------------------------------------------------+
| Variable | Type | Description |
+============+==========+====================================================+
| --signal | string | path(s) to json files containing signal cuts |
+------------+----------+----------------------------------------------------+
| --bkgd | string | path(s) to json files containing background cuts |
+------------+----------+----------------------------------------------------+

**Note**: this will search for files under the ``search_directory``
option, default is ``cuts`` to search for files produced by
``optimize.py cut``.

Optional Parameters
^^^^^^^^^^^^^^^^^^^

+------------+---------+----------------+------------+
| Variable | Type | Description | Default |
+============+=========+================+============+
| -h, --help | bool | display help | False |
| | | message | |
+------------+---------+----------------+------------+
| -v, | count | enable more | 0 |
| --verbose | | verbose output | |
+------------+---------+----------------+------------+
| --debug | bool | enable full-on | False |
| | | debugging | |
+------------+---------+----------------+------------+
| -b, | bool | enable batch | False |
| --batch | | mode for ROOT | |
+------------+---------+----------------+------------+
| --searchDi | string | the directory | 'cuts' |
| rectory | | that contains | |
| | | all cut.json | |
| | | files | |
+------------+---------+----------------+------------+
| --bkgdUnce | float | bkgd sigma for | 0.3 |
| rtainty | | calculating | |
| | | sig. | |
+------------+---------+----------------+------------+
| --bkgdStat | float | bkgd | 0.3 |
| Uncertaint | | statistical | |
| y | | uncertainty | |
| | | for | |
| | | significance | |
+------------+---------+----------------+------------+
| --insignif | int | min. number of | 0.5 |
| icance | | events for | |
| | | non-zero sig. | |
+------------+---------+----------------+------------+
| --o, | string | output | significan |
| --output | | directory to | ces |
| | | store | |
| | | significances | |
| | | calculated | |
+------------+---------+----------------+------------+
| --lumi | float | apply the | 1.0 |
| | | luminosity | |
| | | when | |
| | | calculating | |
| | | significances, | |
| | | to avoid | |
| | | having to redo | |
| | | all the cuts | |
+------------+---------+----------------+------------+
| -n, | int | maximum number | 25 |
| --max-num- | | of hashes to | |
| hashes | | dump in the | |
| | | significance | |
| | | files | |
+------------+---------+----------------+------------+

Output
^^^^^^

+----------------+-----------------+--------------------------------------+
| Variable | Type | Description |
+================+=================+======================================+
| hash | 32-bit string | md5 hash of the cut |
+----------------+-----------------+--------------------------------------+
| significance | float | calculated significance of the cut |
+----------------+-----------------+--------------------------------------+
| yield | float | number of events passing the cut |
+----------------+-----------------+--------------------------------------+

The output is a directory of json files which will look like

.. code:: json

[
...
{
"hash": "7595976a84303a003f6a4a7458f12b8d",
"significance_raw": 7.643122000999725,
"significance_scaled": 4.382066929290212,
"significance_weighted": 18.34212454602254,
"yield_raw": { ... },
"yield_scaled": { ... },
"yield_weighted": { ... }
},
...
]

if a significance was calculated successfully or

.. code:: json

[
...
{
"hash": "c911af35708dcdc51380ebbde81c9b1e",
"significance_raw": -3,
"significance_scaled": -1,
"significance_weighted": -3,
"yield_raw": { ... },
"yield_scaled": { ... },
"yield_weighted": { ... }
},
{
"hash": "b383cea24037667ffb6136d670a33468",
"significance_raw": -2,
"significance_scaled": -1,
"significance_weighted": -2,
"yield_raw": { ... },
"yield_scaled": { ... },
"yield_weighted": { ... }
},
{
"hash": "095414bacf1022f2c941cc6164b175a1",
"significance_raw": 9.421795580339449,
"significance_scaled": -2,
"significance_weighted": 20.37611073465684,
"yield_raw": { ... },
"yield_scaled": { ... },
"yield_weighted": { ... }
},
...
]

if the number of events in signal or background did not pass the
``--insignificance`` minimum threshold set. The significance will always
be flagged as a negative number depending on the insignificance
observed. The table below summarizes these cases:

+--------------+-------------------------------------------------------------+
| Sig. Value | What Happened |
+==============+=============================================================+
| -1 | The signal was insignificant |
+--------------+-------------------------------------------------------------+
| -2 | The background was insignificant |
+--------------+-------------------------------------------------------------+
| -3 | There were not enough statistics in the background events |
+--------------+-------------------------------------------------------------+

Note that ``--max-num-hashes`` determines how many hashes you will
actually see in these output files.

Action:Hash
~~~~~~~~~~~

Hash to cut translation. Given a hash from optimization, dump the cuts
associated with it.

.. code:: bash

usage: optimize.py hash <hash> [<hash> ...] [options]

Required Parameters
^^^^^^^^^^^^^^^^^^^

+------------+---------+---------------+
| Variable | Type | Description |
+============+=========+===============+
| hash | string | 32-bit |
| (positiona | | hash(es) to |
| l) | | decode as |
| | | cuts. If |
| | | --use-summary |
| | | is flagged, |
| | | you can pass |
| | | in your |
| | | summary.json |
| | | file instead. |
+------------+---------+---------------+

Optional Parameters
^^^^^^^^^^^^^^^^^^^

+------------+---------+---------------+------------+
| Variable | Type | Description | Default |
+============+=========+===============+============+
| -h, --help | bool | display help | False |
| | | message | |
+------------+---------+---------------+------------+
| -v, | count | enable more | 0 |
| --verbose | | verbose | |
| | | output | |
+------------+---------+---------------+------------+
| --debug | bool | enable | False |
| | | full-on | |
| | | debugging | |
+------------+---------+---------------+------------+
| -b, | bool | enable batch | False |
| --batch | | mode for ROOT | |
+------------+---------+---------------+------------+
| --supercut | string | path to json | supercuts. |
| s | | dict of | json |
| | | supercuts | |
+------------+---------+---------------+------------+
| --o, | directo | output | outputHash |
| --output | ry | directory to | |
| | | store json | |
| | | files | |
| | | containing | |
| | | cuts | |
+------------+---------+---------------+------------+
| --use-summ | bool | if enabled, | False |
| ary | | you can pass | |
| | | in your | |
| | | summary.json | |
| | | file instead | |
| | | of a bunch of | |
| | | hashes | |
+------------+---------+---------------+------------+

Output
^^^^^^

The hash subcommand will create an output directory with multiple json
files, one for each hash, containing details about the cut applied.
Unlike a standard supercuts file, the hash will only output dictionaries
of **4** elements

+---------------------+----------+----------------------------------------------------------------+
| Variable | Type | Description |
+=====================+==========+================================================================+
| branch | string | name of branch that cut was applied on |
+---------------------+----------+----------------------------------------------------------------+
| fixed | bool | whether the cut was from a fixed cut or a supercut |
+---------------------+----------+----------------------------------------------------------------+
| pivot | number | the value which we cut on, see ``signal_direction`` for more |
+---------------------+----------+----------------------------------------------------------------+
| signal\_direction | string | ``? = >`` or ``? = <``, cuts obey the rule ``value ? pivot`` |
+---------------------+----------+----------------------------------------------------------------+

Action:Summary
~~~~~~~~~~~~~~

Optimize results to summary json. Given the finished results of an
optimization, produce a json summarizing it entirely.

.. code:: bash

usage: optimize.py summary --searchDirectory significances/ --massWindows massWindows.txt [options]

Required Parameters
^^^^^^^^^^^^^^^^^^^

+------------+---------+---------------+
| Variable | Type | Description |
+============+=========+===============+
| --searchDi | str | The directory |
| rectory | | containing |
| | | the |
| | | significances |
| | | produced from |
| | | ``optimize.py |
| | | optimize`` |
+------------+---------+---------------+
| --massWind | str | The file |
| ows | | containing |
| | | the mapping |
| | | between DID |
| | | and mass |
+------------+---------+---------------+

Optional Parameters
^^^^^^^^^^^^^^^^^^^

+-----------------+---------+----------------------------------------------+----------------+
| Variable | Type | Description | Default |
+=================+=========+==============================================+================+
| -h, --help | bool | display help message | False |
+-----------------+---------+----------------------------------------------+----------------+
| -v, --verbose | count | enable more verbose output | 0 |
+-----------------+---------+----------------------------------------------+----------------+
| --debug | bool | enable full-on debugging | False |
+-----------------+---------+----------------------------------------------+----------------+
| -b, --batch | bool | enable batch mode for ROOT | False |
+-----------------+---------+----------------------------------------------+----------------+
| --ncores | int | Number of cores to use for parallelization | |
+-----------------+---------+----------------------------------------------+----------------+
| -o, --output | str | Name of output file to use | summary.json |
+-----------------+---------+----------------------------------------------+----------------+
| --stop-masses | int | Allowed stop masses to include in summary | [5000] |
+-----------------+---------+----------------------------------------------+----------------+

Output
^^^^^^

The summary subcommand will create an output json file containing a list
of dictionaries, one for each signal DID used in optimization. It will
contain the following variables in each dictionary:

+----------------+-----------------+-------------------------------+
| Variable | Type | Description |
+================+=================+===============================+
| bkgd | float | Background yield |
+----------------+-----------------+-------------------------------+
| did | str | DID of signal |
+----------------+-----------------+-------------------------------+
| hash | 32-bit string | md5 hash of the optimal cut |
+----------------+-----------------+-------------------------------+
| m\_gluino | int | Mass of Gluino |
+----------------+-----------------+-------------------------------+
| m\_lsp | int | Mass of LSP |
+----------------+-----------------+-------------------------------+
| m\_stop | int | Mass of Stop |
+----------------+-----------------+-------------------------------+
| ratio | float | Ratio of signal/bkgd |
+----------------+-----------------+-------------------------------+
| significance | float | Significance of optimal cut |
+----------------+-----------------+-------------------------------+

This will look something like:

::

[
...
{
"bkgd": 5.656293846714225,
"did": "370102",
"hash": "dc41780c77207a9a5dcf6b97b0cac5ac",
"m_gluino": 900,
"m_lsp": 400,
"m_stop": 5000,
"ratio": 79.33950854202577,
"signal": 448.76757396759103,
"significance": 29.78897424015455
},
...
]

Supercuts File
~~~~~~~~~~~~~~

This is a potentially large `JSON <http://www.json.org/>`__ file that
tells the `optimize <#actionoptimize>`__, `hash <#actionhash>`__, and
`generate <#actiongenerate>`__ commands the rules of your cuts.

- The ``optimize`` command uses it to generate a series of cuts to
apply to your ntuples, then hash these cuts and store them with their
calculated significance.
- The ``hash`` command uses it to recompute the hash and find the cuts
that match up to the hashes you need to decode.
- The ``generate`` command creates this file for you based on your
ntuples to help you get started.

The file will always contain a list of objects (dictionaries) for each
branch that you care about cutting on.

Defining a fixed cut
^^^^^^^^^^^^^^^^^^^^

A fixed cut is a single cut on a single branch. This is like taking a
partial derivative where you fix one thing and vary others. In this
case, we fix a branch defined by a fixed cut.

+--------------+----------+--------------------------------------------------+
| Key | Type | Description |
+==============+==========+==================================================+
| selections | string | the various selections to apply for the cut |
+--------------+----------+--------------------------------------------------+
| pivot | number | the value at which we cut (or *pivot* against) |
+--------------+----------+--------------------------------------------------+

The simplest example is when we want to use a single fixed cut on a
single branch. Your object will look like

.. code:: json

[
...
{
"selections": "multiplicity_jet > {0}",
"pivot": 3,
},
...
]

This says we would like a fixed cut on ``multiplicity_jet`` requiring
that there are more than 3 jets (eg: the rule we obey is ``value > 3``).

Defining a supercut
^^^^^^^^^^^^^^^^^^^

A supercut is our term for an object that generates more than 1 cut on
the defined branch. A fixed cut will generate 1 cut, but a supercut can
generate a boundless number of cuts.

+--------------+----------+---------------------------------------------------------------+
| Key | Type | Description |
+==============+==========+===============================================================+
| selections | string | the various selections to apply for the cut |
+--------------+----------+---------------------------------------------------------------+
| st3 | list | a list of [start, stop, step] values for each set of pivots |
+--------------+----------+---------------------------------------------------------------+

**Note**: the direction in which cuts are generated can be controlled by
running cuts in increasing values (``start < stop``, ``step > 0``) or
decreasing values (``start > stop``, ``step < 0``).

There are two main examples we will provide to show the different cuts
that could be generated.

.. code:: json

[
...
{
"selections": "multiplicity_jet < {0}",
"st3": [
[2, 7, 2]
]
},
...
]

This says we would like a supercut on ``multiplicity_jet`` where the
pivot values are ``2, 4, 6`` obeying the rule that ``value < pivot``.
This supercut will generate 3 cuts:

- ``value < 2``
- ``value < 4``
- ``value < 6``

in that order.

.. code:: json

[
...
{
"selections": "multiplicity_jet > {0}",
"st3": [
[3, 1, -1]
]
},
...
]

This says we would like a supercut on ``multiplicity_jet`` where the
pivot values are ``3, 2`` obeying the rule that ``value > pivot``. This
supercut will generate 2 cuts:

- ``value > 3``
- ``value > 2``

in that order.

Example of a supercuts file
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Here is an example ``supercuts.json`` file

.. code:: json

[
{
"selections": "multiplicity_jet > {0}",
"st3": [
[2, 15, 1]
]
},
{
"selections": "multiplicity_jet_largeR > {0}",
"st3": [
[3, 1, -1]
]
},
{
"selections": "multiplicity_topTag_loose > {0}",
"pivot": [1]
}
]

How do we interpret this? This file tells the code that there are 3
branches to apply cuts on: ``multiplicity_jet``,
``multiplicity_jet_largeR``, and ``multiplicity_topTag_loose``. Each
object ``{...}`` represents a branch. In order:

- This is a supercut. **13** cuts will be generated for
``multiplicity_jet`` starting from ``2`` to ``15`` in increments of
``1``. This means the cut values (``pivot``) used will be
``2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14`` (inclusive start,
exclusive end - adhere to python standards). The ``signal_direction``
specifies where we expect the signal to be. ``>`` means to cut on the
**right** so we only want to keep events with ``value > pivot``.
- This is a supercut.\ **2** cuts will be generated for
``multiplicity_jet_largeR`` starting from ``3`` to ``1`` in
incremenets of ``-1``. This means the cut values (``pivot``) used
will be ``3, 2`` (inclusive start, exclusive end - adhere to python
standards). The ``signal_direction`` specifies where we expect the
signal to be. ``<`` means to cut on the **left** so we only want to
keep events with ``value < pivot``.
- This is a fixed cut. **1** cut will be used for
``multiplicity_topTag_loose`` with a ``pivot = 1`` and
``signal_direction = >``. This means we will only select events with
``value > 1`` always. The ``pivot`` will be fixed. One could also fix
the cut by providing ``start``, ``stop``, ``step`` such that it only
generates 1 cut, but the script will not identify this as a fixed cut
for you when you look up the ``cut`` using `hash <#actionhash>`__.

This supercuts file will generate **26** total cuts (``13*2*1 = 26``).
Each cut will have an associated hash value and an associated
significance which will be recorded to an output file when you run
`optimize <#actionoptimize>`__.

If you wish to provide a fixed cut (the pivot does not change), you
simply need to specify the pivot instead. Taking the example shown
above, you might have something like

.. code:: json

[
{
"selections": "multiplicity_jet >= {0}",
"pivot": [4]
},
{
"selections": "multiplicity_jet_largeR > {0}",
"st3": [
[3, 1, -1]
]
},
{
"selections": "multiplicity_topTag_loose > {0}",
"pivot": [1]
}
]

which tells the code to always apply a cut of ``multiplicity_jet >= 4``
always.

More Complicated Selections
^^^^^^^^^^^^^^^^^^^^^^^^^^^

One can certainly provide more complicated selections involving multiple
pivots and multiple branches. In fact, this makes our optimization
increasingly more flexible and faster than any other code in existence.
If you use ``--numpy``, we use the
`numexpr <https://github.com/pydata/numexpr/>`__ package to provide the
parsing of the more complicated selection strings (they have examples of
what you can do). If you do not use ``--numpy``, we default to use
``ROOT`` and ``TTree::Draw`` to make the cuts, which means a standard
``TCut`` or ``TFormula`` can be used for your selection. Either way, you
still need to specify placeholders for your pivots.

.. code:: json

[
...
{
"selections": "(mass_jets_largeR_1 > {0} & mass_jets_largeR_2 > {0} & mass_jets_largeR_3 > {0}) >= {1}",
"st3": [
[50, 2000, 50],
[0, 4, 1],
]
},
...
]

is an example of a perhaps more complicated selection that can be done.
In this case, we are determining how many of the 3 leading jets pass a
mass cut, but also applying a cut on that count. In this case, the
``{0}`` pivot placeholder refers to the first ``st^3`` option:
``[50, 2000, 50]`` which is to vary the first pivot ``{0}`` from 50 GeV
to 2 TeV in 50 GeV steps. The ``{1}`` pivot placeholder refers to the
second ``st^3`` option: ``[0, 4, 1]`` which is to vary the second pivot
``{1}`` from 0 to 4 in steps of 1. This will allow us to iterate over
all possible values of pivots (the product of
``[50, 2000, 50] X [0, 4, 1]``).

Authors
-------

- `Giordon Stark <https://github.com/kratsg>`__
- `Aviv Cukierman <https://github.com/AvivCukierman>`__

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