deletions | additions       diff --git a/subsection_Timestamps_and_burst_data__.tex b/subsection_Timestamps_and_burst_data__.tex  index a1fd362..a86333d 100644  --- a/subsection_Timestamps_and_burst_data__.tex  +++ b/subsection_Timestamps_and_burst_data__.tex 
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times and indexes, while all the other fields are automatically  computed. Bursts methods allow to recompute indexes relative to a different photon  selection or recompute start and stop times relative to a new timestamps array.  Addiational, Additional  methods perform fusion of nearby bursts or intersection of two set of bursts (functionality used by the dual-channel burst search).  In conclusion, Bursts efficiently implements all the common operations performed   with burst data, providing and easy-to-use interface and high reliability due to   its 100 \% unit test coverage. well tested algorithms.  Leveraging Bursts methods, users can implement new types of analysis without wasting time implementing (and debugging) standard manipulation routines. \begin{itemize}  \item ease of use: \subsubsection{Python details}  Bursts objects store the start and stop times and indexes in a numpy array.  The other  fields are accessed by name (e.g. start, stop, counts, etc...)  \item performances: element access computed on-fly using class properties, so they are always  up to date even if start  and iteration stop are modified. Iteration over bursts  is faster than numpy arrays or Pandas Dataframes  \item pretty printing in the notebook (displayed as HTML table)  \item added functionality: methods relatively fast, with performances similar  to transform burst data to be relative to different timestamps array  \end{itemize} iterating through numpy rows.