First final version of the talk

talks/premiere.org

@@ -10,7 +10,7 @@
 #+Subtitle: Reading [KM3NeT] ROOT files without ROOT
 #+Author: Zineb Aly (CPPM), Tamas Gal (ECAP) and Johannes Schumann (ECAP)
 #+Email: zaly@km3et.de, tgal@km3net.de, jschumann@km3net.de
-#+REVEAL_TALK_URL: https://indico.cern.ch/event/878692/
+#+REVEAL_TALK_URL: https://indico.cern.ch/event/871318/contributions/3740124
 
 * Export Options                                         :noexport:
 ** Default
@@ -35,8 +35,7 @@
 #+BEGIN_SRC bash :results silent :async t
 python3 -m venv ~/tmp/km3io-premiere-venv
 . ~/tmp/km3io-premiere-venv/bin/activate
-# pip install km3io==0.8.1
-pip install -e ~/Dev/km3io
+pip install km3io==0.8.2
 #+END_SRC
 
 #+BEGIN_SRC elisp
@@ -49,19 +48,29 @@ pip install -e ~/Dev/km3io
 * km3io
 - [[https://git.km3net.de/km3py/km3io][km3io]]: a tiny Python package with minimal dependencies to read KM3NeT ROOT files
 - *Goal*: provide a **standalone**, **independent** access to KM3NeT data
-- Uses the [[https://github.com/scikit-hep/uproot][uproot]] library to access ROOT data
+- Uses the [[https://github.com/scikit-hep/uproot][uproot]] Python library to access ROOT data
 - Maximum performance due to [[https://www.numpy.org][numpy]] and [[http://numba.pydata.org][numba]]
-- Data are read lazily:
-  - only loaded when directly accessed
-  - cut masks on huge datasets without loading them
+- 100% test coverage
+- Automated testing for Python 3.5, 3.6, 3.7 and 3.8
 
-** uproot
+
+** Data in ~km3io~
+- data are read lazily using the [[https://github.com/scikit-hep/awkward-array][awkwardarray]] Python library
+- only loaded when directly accessed
+- apply cut masks on huge datasets without loading them (wholemeal, database-like workflow)
+- compatible with [[https://pandas.pydata.org][pandas]]
+
+* uproot
+:PROPERTIES:
+:reveal_background: linear-gradient(to left, #910830, #521623)
+:END:
 - ROOT I/O (read/write) in pure Python and Numpy
 - Created by SciKit-HEP ([[https://scikit-hep.org][https://scikit-hep.org]])
 
   "The Scikit-HEP project is a community-driven and community-oriented project
   with the aim of providing Particle Physics at large with an ecosystem for
   data analysis in Python. The project started in Autumn 2016 and is in full swing."
+- Highly recommended if you live in the Python world
 
 #+REVEAL: split
 
@@ -70,42 +79,58 @@ pip install -e ~/Dev/km3io
 - Very helpful developers (*Jim Pivarski*, one of the main authors helped a lot to
   parse KM3NeT ROOT files and we also contributed to uproot)
 - The rate of reading data into arrays with ~uproot~ is shown to be faster than
-  C++ ROOT or ~root_numpy~
+  C++ ROOT, ~PyROOT~ or ~root_numpy~
 - *It's fast!!!*
 
 *** uproot rate / ROOT rate
+:PROPERTIES:
+:reveal_background: linear-gradient(to left, #910830, #521623)
+:END:
 
 [[file:images/uproot_vs_root.png]]
 
 Source: https://github.com/scikit-hep/uproot/blob/master/README.rst
 
 *** uproot rate / ~root_numpy~ rate
+:PROPERTIES:
+:reveal_background: linear-gradient(to left, #910830, #521623)
+:END:
 
 [[file:images/uproot_vs_root_numpy.png]]
 
 Source: https://github.com/scikit-hep/uproot/blob/master/README.rst
 
 ** awkward arrays?
+:PROPERTIES:
+:reveal_background: linear-gradient(to left, #910830, #521623)
+:END:
 - "Manipulate arrays of complex data structures as easily as Numpy."
 - Variable-length lists (jagged/ragged), deeply nested (record structure),
   different data types in the same list, etc.
 - https://github.com/scikit-hep/awkward-array
 - A recommended talk (by Jim himself) on this topic in the HEP context:
   https://www.youtube.com/watch?v=2NxWpU7NArk
-- ~awkward v1.0~ being rewritten in C++ with focus on ~numba~
+- ~awkward v1.0~ being rewritten in C++
 
-** Installation
+* Installation of ~km3io~
 - Dependencies:
   - Python 3.5+
   - uproot (a small Python package, installed automatically via ~pip~)
   - no binaries!
 - *No ROOT, Jpp or aanet* required to read ROOT files
-- Releases are published on the official Python package repository:
-  - ~pip install km3io~
+- Releases are published on the official Python package repository
+
+** ~pip install km3io~
+:PROPERTIES:
+:reveal_background: linear-gradient(to left, #ff12a8, #27aae3)
+:END:
 ** Why is it so cool?
 - Runs on Linux, macOS, Windows, as long as Python 3.5+ is installed
 - Every data is a ~numpy~ array or ~awkward~ array (~numpy~ compatible array of complex data structures)
-* Accessing Online (DAQ) Data
+* Online (DAQ) Data
+:PROPERTIES:
+:reveal_background: linear-gradient(to bottom, #27aae3, #000000)
+:END:
 ** km3io supports the following DAQ datatypes
 - ~JDAQEvent~ (the event dataformat)
   - header information
@@ -138,7 +163,7 @@ print(f.timeslices)
 #+RESULTS:
 : Number of events: 115038
 : Number of summaryslices: 182668
-: Available timeslice streams: L1, SN
+: Available timeslice streams: SN, L1
 
 *** Investigating timeslice frames
 
@@ -158,10 +183,10 @@ print(a_timeslice.frames[806451572].pmt[:42])
 #+END_SRC
 
 #+RESULTS:
-: [ 4 19  3 35 29 21  1 22  6  6 29 21 29 26  3 27 11  4 27 29 13 23  4 28
-:  21 24  3 10 25 23 28 25  9  6 14  3 10 25 11 31 10  2]
-: [27 27 14 14 18 22 13 13 30 30 12 10 27 13  7  7 15 15 27 11 23 23 12 12
-:  18 22 29 29 21  8  1  7  9  9  6  6 23 23 25 26 10 10]
+: [33 16 22 24  7 27  4 31 31 15  5 26 30 24  7 26 26 26 27 15  7  3 63 28
+:  26 30 25 24 20  7 23  6 22 22 26 15 29 25 24 22 23 21]
+: [ 0  9  4 12 13  2 10 10  8  9 27 27 28 18 27  2  6 15  4  2  2 12 27  3
+:  15 10 23 14 19  9  9 24 24  6  7 20  7 20 27 22 24 25]
 
 *** Checking the number of UDP packets in summary slices
 
@@ -189,6 +214,9 @@ print(km3io.daq.get_number_udp_packets(sumslice.dq_status))
 #+end_example
 
 * Offline (MC/reco) Data
+:PROPERTIES:
+:reveal_background: linear-gradient(to bottom, #e3b1e3, #000000)
+:END:
 ** Reading offline files (aka aanet-ROOT files)
 - Events
   - header information
@@ -207,7 +235,7 @@ print(f)
 #+END_SRC
 
 #+RESULTS:
-: <km3io.offline.OfflineReader object at 0x1155bde50>
+: <km3io.offline.OfflineReader object at 0x10b267f50>
 
 ** Investigating events and tracks
 #+BEGIN_SRC python :results output replace :session km3io :exports both
@@ -318,6 +346,11 @@ mask = f.mc_tracks.E.counts > 0
 print(f.mc_tracks.E[mask, 0])
 #+END_SRC
 
+#+RESULTS:
+: Number of events: 12236
+: [11  2  3 ... 10  1  4]
+: [17.72 73.213 10884.78 1694.332 1221.061 22945.123 11019.418 ...]
+
 * ORCA DU4 RBR Analysis Example
 ** A tiny function to extract track attributes from a list of files
 
@@ -366,8 +399,7 @@ ax.legend(); ax.grid();
 
 * Command line tool(s)
 - We are working on some counter parts to the Jpp tools
-  - ~KPrintTree -f FILENAME~
-  - similar to ~JPrintTree~
+  - ~KPrintTree -f FILENAME~ (similar to ~JPrintTree~)
   - more to come (feel free to request or contribute)
 
 * Thanks