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analysis/direction_reconstruction + analysis/event_utils + analysis/find_mpv + analysis/landau + analysis/process_events + analysis/process_traces + analysis/reconstructions + analysis/signal_calibration + analysis/time_deltas diff --git a/_sources/analysis/calibration.rst.txt b/_sources/analysis/calibration.rst.txt new file mode 100644 index 00000000..e53aa80c --- /dev/null +++ b/_sources/analysis/calibration.rst.txt @@ -0,0 +1,8 @@ +.. include:: ../subst.inc + +Data timing and signal calibration +================================== + +.. automodule:: sapphire.analysis.calibration + :members: + :undoc-members: diff --git a/_sources/analysis/coincidence_queries.rst.txt b/_sources/analysis/coincidence_queries.rst.txt new file mode 100644 index 00000000..96f1c4b3 --- /dev/null +++ b/_sources/analysis/coincidence_queries.rst.txt @@ -0,0 +1,8 @@ +.. include:: ../subst.inc + +Query coincidences from a datafile +================================== + +.. automodule:: sapphire.analysis.coincidence_queries + :members: + :undoc-members: diff --git a/_sources/analysis/coincidences.rst.txt b/_sources/analysis/coincidences.rst.txt new file mode 100644 index 00000000..2d64bdb9 --- /dev/null +++ b/_sources/analysis/coincidences.rst.txt @@ -0,0 +1,8 @@ +.. include:: ../subst.inc + +Search for coincidences between |hisparc| stations +================================================== + +.. automodule:: sapphire.analysis.coincidences + :members: + :undoc-members: diff --git a/_sources/analysis/core_reconstruction.rst.txt b/_sources/analysis/core_reconstruction.rst.txt new file mode 100644 index 00000000..ef7dc52e --- /dev/null +++ b/_sources/analysis/core_reconstruction.rst.txt @@ -0,0 +1,6 @@ +Reconstruction of EAS core and size +=================================== + +.. automodule:: sapphire.analysis.core_reconstruction + :members: + :undoc-members: diff --git a/_sources/analysis/direction_reconstruction.rst.txt b/_sources/analysis/direction_reconstruction.rst.txt new file mode 100644 index 00000000..b22c2ee3 --- /dev/null +++ b/_sources/analysis/direction_reconstruction.rst.txt @@ -0,0 +1,6 @@ +Reconstruction of EAS direction +=============================== + +.. automodule:: sapphire.analysis.direction_reconstruction + :members: + :undoc-members: diff --git a/_sources/analysis/event_utils.rst.txt b/_sources/analysis/event_utils.rst.txt new file mode 100644 index 00000000..802a4727 --- /dev/null +++ b/_sources/analysis/event_utils.rst.txt @@ -0,0 +1,8 @@ +.. include:: ../subst.inc + +Get signal and timing values from events +======================================== + +.. automodule:: sapphire.analysis.event_utils + :members: + :undoc-members: diff --git a/_sources/analysis/find_mpv.rst.txt b/_sources/analysis/find_mpv.rst.txt new file mode 100644 index 00000000..aaa7195d --- /dev/null +++ b/_sources/analysis/find_mpv.rst.txt @@ -0,0 +1,6 @@ +MPV fit +======= + +.. automodule:: sapphire.analysis.find_mpv + :members: + :undoc-members: diff --git a/_sources/analysis/landau.rst.txt b/_sources/analysis/landau.rst.txt new file mode 100644 index 00000000..bddf0b87 --- /dev/null +++ b/_sources/analysis/landau.rst.txt @@ -0,0 +1,6 @@ +Landau distribution +=================== + +.. automodule:: sapphire.analysis.landau + :members: + :undoc-members: diff --git a/_sources/analysis/process_events.rst.txt b/_sources/analysis/process_events.rst.txt new file mode 100644 index 00000000..b8fbbcd9 --- /dev/null +++ b/_sources/analysis/process_events.rst.txt @@ -0,0 +1,6 @@ +Processing of HiSPARC events +============================ + +.. automodule:: sapphire.analysis.process_events + :members: + :undoc-members: diff --git a/_sources/analysis/process_traces.rst.txt b/_sources/analysis/process_traces.rst.txt new file mode 100644 index 00000000..7716404e --- /dev/null +++ b/_sources/analysis/process_traces.rst.txt @@ -0,0 +1,6 @@ +Processing of HiSPARC traces +============================ + +.. automodule:: sapphire.analysis.process_traces + :members: + :undoc-members: diff --git a/_sources/analysis/reconstructions.rst.txt b/_sources/analysis/reconstructions.rst.txt new file mode 100644 index 00000000..5a770ce2 --- /dev/null +++ b/_sources/analysis/reconstructions.rst.txt @@ -0,0 +1,6 @@ +Reconstructions +=============== + +.. automodule:: sapphire.analysis.reconstructions + :members: + :undoc-members: diff --git a/_sources/analysis/signal_calibration.rst.txt b/_sources/analysis/signal_calibration.rst.txt new file mode 100644 index 00000000..075e0e81 --- /dev/null +++ b/_sources/analysis/signal_calibration.rst.txt @@ -0,0 +1,8 @@ +.. include:: ../subst.inc + +Detector signal calibration +=========================== + +.. automodule:: sapphire.analysis.signal_calibration + :members: + :undoc-members: diff --git a/_sources/analysis/time_deltas.rst.txt b/_sources/analysis/time_deltas.rst.txt new file mode 100644 index 00000000..498f6e61 --- /dev/null +++ b/_sources/analysis/time_deltas.rst.txt @@ -0,0 +1,6 @@ +Time deltas +=========== + +.. automodule:: sapphire.analysis.time_deltas + :members: + :undoc-members: diff --git a/_sources/api.rst.txt b/_sources/api.rst.txt new file mode 100644 index 00000000..9e4796a3 --- /dev/null +++ b/_sources/api.rst.txt @@ -0,0 +1,6 @@ +HiSPARC API +=========== + +.. automodule:: sapphire.api + :members: + :undoc-members: diff --git a/_sources/clusters.rst.txt b/_sources/clusters.rst.txt new file mode 100644 index 00000000..93be4697 --- /dev/null +++ b/_sources/clusters.rst.txt @@ -0,0 +1,6 @@ +HiSPARC clusters +================ + +.. automodule:: sapphire.clusters + :members: + :undoc-members: diff --git a/_sources/configuration.rst.txt b/_sources/configuration.rst.txt new file mode 100644 index 00000000..df7631ea --- /dev/null +++ b/_sources/configuration.rst.txt @@ -0,0 +1,20 @@ +.. include:: subst.inc + +Configuration +************* + +Various modules (:mod:`sapphire.api`, :mod:`sapphire.esd`, and :mod:`sapphire.publicdb`) +connect to the |hisparc| Public Database to download data, metadata, configurations, etc. +Normally you will want to use the official |hisparc| Public Database, which is hosted +at `https://data.hisparc.nl`. However, during development, tests or other occasions you +may want to use a different server, for example when running a local version of the +publicdb. In that case you can overwrite the base url used. This overwrite is done +using an environment variable: ``PUBLICDB_BASE``. This can be easily set when starting +your Python environment, for example:: + + $ PUBLICDB_BASE=http://localhost:8000 python + +Or while already running Python:: + + from os import environ + environ['PUBLICDB_BASE'] = 'http://localhost:8000' diff --git a/_sources/corsika.rst.txt b/_sources/corsika.rst.txt new file mode 100644 index 00000000..7e7b370c --- /dev/null +++ b/_sources/corsika.rst.txt @@ -0,0 +1,44 @@ +.. include:: subst.inc + +CORSIKA simulations +=================== + +`CORSIKA `_ is an Air Shower +Simulation Program. To be used for detailed simulation of +extensive air showers initiated by high-energy cosmic-ray particles. + +This module makes it easy to work with CORSIKA output, it was +original written by Javier Gonzalez and adapted for `HiSPARC +`_ by Arne de Laat. + +This documentation sometimes refers to the CORSIKA users manual, this +users manual can be found here `CORSIKA User's Guide +`_ + +In addition to reading CORSIKA output, functionally is provided to +easily submit many CORSIKA jobs to the Nikhef batch facility +(Stoomboot). + + +CORSIKA Module +-------------- + +.. automodule:: sapphire.corsika + :members: + + +Contents +-------- + +.. toctree:: + :hidden: + + corsika/blocks + corsika/corsika_queries + corsika/generate_corsika_overview + corsika/particles + corsika/qsub_corsika + corsika/qsub_store_corsika_data + corsika/reader + corsika/store_corsika_data + corsika/units diff --git a/_sources/corsika/blocks.rst.txt b/_sources/corsika/blocks.rst.txt new file mode 100644 index 00000000..7058e074 --- /dev/null +++ b/_sources/corsika/blocks.rst.txt @@ -0,0 +1,6 @@ +CORSIKA Blocks +============== + +.. automodule:: sapphire.corsika.blocks + :members: + :undoc-members: diff --git a/_sources/corsika/corsika_queries.rst.txt b/_sources/corsika/corsika_queries.rst.txt new file mode 100644 index 00000000..c4b164a4 --- /dev/null +++ b/_sources/corsika/corsika_queries.rst.txt @@ -0,0 +1,6 @@ +Query CORSIKA overview for simulations +====================================== + +.. automodule:: sapphire.corsika.corsika_queries + :members: + :undoc-members: diff --git a/_sources/corsika/generate_corsika_overview.rst.txt b/_sources/corsika/generate_corsika_overview.rst.txt new file mode 100644 index 00000000..b9c32b72 --- /dev/null +++ b/_sources/corsika/generate_corsika_overview.rst.txt @@ -0,0 +1,6 @@ +Create an overview of CORSIKA simulations +========================================= + +.. automodule:: sapphire.corsika.generate_corsika_overview + :members: + :undoc-members: diff --git a/_sources/corsika/particles.rst.txt b/_sources/corsika/particles.rst.txt new file mode 100644 index 00000000..2c08d96a --- /dev/null +++ b/_sources/corsika/particles.rst.txt @@ -0,0 +1,6 @@ +CORSIKA Particles +================= + +.. automodule:: sapphire.corsika.particles + :members: + :undoc-members: diff --git a/_sources/corsika/qsub_corsika.rst.txt b/_sources/corsika/qsub_corsika.rst.txt new file mode 100644 index 00000000..8a5906ca --- /dev/null +++ b/_sources/corsika/qsub_corsika.rst.txt @@ -0,0 +1,6 @@ +CORSIKA on Stoomboot +==================== + +.. automodule:: sapphire.corsika.qsub_corsika + :members: + :undoc-members: diff --git a/_sources/corsika/qsub_store_corsika_data.rst.txt b/_sources/corsika/qsub_store_corsika_data.rst.txt new file mode 100644 index 00000000..04bd9e61 --- /dev/null +++ b/_sources/corsika/qsub_store_corsika_data.rst.txt @@ -0,0 +1,6 @@ +Convert CORSIKA simulations on Stoomboot +======================================== + +.. automodule:: sapphire.corsika.qsub_store_corsika_data + :members: + :undoc-members: diff --git a/_sources/corsika/reader.rst.txt b/_sources/corsika/reader.rst.txt new file mode 100644 index 00000000..12fcee50 --- /dev/null +++ b/_sources/corsika/reader.rst.txt @@ -0,0 +1,6 @@ +CORSIKA Reader +============== + +.. automodule:: sapphire.corsika.reader + :members: + :undoc-members: diff --git a/_sources/corsika/store_corsika_data.rst.txt b/_sources/corsika/store_corsika_data.rst.txt new file mode 100644 index 00000000..fe78073e --- /dev/null +++ b/_sources/corsika/store_corsika_data.rst.txt @@ -0,0 +1,6 @@ +Store CORSIKA data as HDF5 +========================== + +.. automodule:: sapphire.corsika.store_corsika_data + :members: + :undoc-members: diff --git a/_sources/corsika/units.rst.txt b/_sources/corsika/units.rst.txt new file mode 100644 index 00000000..9d96aef6 --- /dev/null +++ b/_sources/corsika/units.rst.txt @@ -0,0 +1,6 @@ +CORSIKA Units +============= + +.. automodule:: sapphire.corsika.units + :members: + :undoc-members: diff --git a/_sources/data.rst.txt b/_sources/data.rst.txt new file mode 100644 index 00000000..bd9b0a41 --- /dev/null +++ b/_sources/data.rst.txt @@ -0,0 +1,12 @@ +Updating and extending local data +================================= + +.. automodule:: sapphire.data + :members: + :undoc-members: + +.. toctree:: + :hidden: + + data/extend_local_data + data/update_local_data diff --git a/_sources/data/extend_local_data.rst.txt b/_sources/data/extend_local_data.rst.txt new file mode 100644 index 00000000..2ef28768 --- /dev/null +++ b/_sources/data/extend_local_data.rst.txt @@ -0,0 +1,6 @@ +Download additional local data +============================== + +.. automodule:: sapphire.data.extend_local_data + :members: + :undoc-members: diff --git a/_sources/data/update_local_data.rst.txt b/_sources/data/update_local_data.rst.txt new file mode 100644 index 00000000..bcb1211f --- /dev/null +++ b/_sources/data/update_local_data.rst.txt @@ -0,0 +1,6 @@ +Update default local data with latest data from Publicdb +======================================================== + +.. automodule:: sapphire.data.update_local_data + :members: + :undoc-members: diff --git a/_sources/esd.rst.txt b/_sources/esd.rst.txt new file mode 100644 index 00000000..67a148d2 --- /dev/null +++ b/_sources/esd.rst.txt @@ -0,0 +1,6 @@ +Event Summary Data +================== + +.. automodule:: sapphire.esd + :members: + :undoc-members: diff --git a/_sources/examples.rst.txt b/_sources/examples.rst.txt new file mode 100644 index 00000000..ab08ad4a --- /dev/null +++ b/_sources/examples.rst.txt @@ -0,0 +1,133 @@ +Example scripts +=============== + +In this chapter we'll discuss some scripts showing common examples of how to download and analyse data. We'll start with a short discussion of the general layout of such scripts and how to run them interactively. Then, we'll continue with examples for some common tasks. + + +First, some tips and tricks +--------------------------- + +The ``not in globals()`` trick +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A simple script downloading data for a single day can look like: + +.. literalinclude:: scripts/simple-download.py + :caption: simple-download.py + +This script can be run from the command line like this:: + + $ python + +Every time you run the script, the old data will be removed and new data will be downloaded. Except that the data is probably unchanged. What a waste of time and bandwidth! If you do *not* run this from the command line you can run into trouble. For example, the second time you run the script from an IPython console, you can run into an exception: + +.. code-block:: python + :emphasize-lines: 9 + + >>> %run simple-download.py + 100%|################################################################|Time: 5.22 + >>> %run simple-download.py + Traceback (most recent call last): + File "/Users/david/work/HiSPARC/software/sapphire/doc/scripts/simple-download.py", line 10, in + data = tables.open_file(DATAFILE, 'w') + File "/Users/david/anaconda/lib/python2.7/site-packages/tables/file.py", line 315, in open_file + "close it before reopening in write mode." % filename) + ValueError: The file 'data.h5' is already opened. Please close it before reopening in write mode. + +The second time the script is run, the file is already opened. The script, however, does not know that. You can close the file at the end of your script, but then you don't have access to the file from the interactive console. It is very useful to run the script, keep the file open, and be able to inspect the file and any variables created by the script. The console has access to all variables declared by the script, but by default the script is not allowed to know about any variables defined in the console. In other words, the script *cannot* know about the opened file. This is fixed by running the script with the ``-i`` option:: + + >>> %run -i simple-download.py + +The script still does not check for opened files. To do this, one can use the ``globals()`` function. This will return a dictionary containing all variables in the global scope. If ``data`` is defined, then ``'data'`` will be present in that dictionary. We can check for that: + +.. literalinclude:: scripts/simple-download-with-globals.py + :caption: simple-download-with-globals.py + :emphasize-lines: 10-12 + +Now, the script can be rerun multiple times: + +.. code-block:: pycon + + >>> %run -i simple-download-with-globals.py + 100%|################################################################|Time: 5.29 + >>> %run -i simple-download-with-globals.py + +The second time the script is run, it does nothing. This *is* useful, because we can include stuff that needs to be rerun *after* the ``if``-statement. + + +The ``not in data`` trick +^^^^^^^^^^^^^^^^^^^^^^^^^ + +Every time we run the previous script from the command-line, or start a new IPython console, the script still overwrites the data file with 'new' data. We need something more! In addition to checking if the file is already opened, the script needs to check if the data is already present in the file. If it is, the script should do nothing. It is important to open the file in *append* mode, not in *write* mode. If you do the latter, the file is overwritten the moment you open it. The script becomes: + +.. literalinclude:: scripts/simple-download-with-checks.py + :caption: simple-download-with-checks.py + :emphasize-lines: 13-14 + + +The ``if __name__ == '__main__'`` trick +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Finally, most scripts of any decent size include a weird-looking line:: + + if __name__ == '__main__': + do_something() + +This is very useful, in fact! A small script invariably grows to a larger size. Most probably, you'll write a few functions that are useful elsewhere. If they are, you can import them from a new script. That, however, causes problems if you did not include the lines above. For example, take this script: + +.. literalinclude:: scripts/is_useful.py + :caption: is_useful.py + +This script defines a function to calculate the squares of numbers. We use it to print the squared of the numbers 1, 2, and 3. Now, we want to print the square of 16:: + + >>> from is_useful import square + 1 2 3 + 1 4 9 + >>> print(square(16)) + 256 + +What happened? When we import a module, the code *inside* that module is run as well. We don't want that. When we *run* a script, the special variable ``__name__`` is equal to ``'__main__'``. When we *import* a script, the variable is equal to the name of the module, and *not* equal to ``'__main__'``. So, we can check for that: + +.. literalinclude:: scripts/is_useful_and_importable.py + :caption: is_useful_and_importable.py + +If we run the script stand-alone, we get the expected result:: + + >>> %run is_useful_and_importable.py + 1 2 3 + 1 4 9 + +But we can now also import the script without any unintended side effects:: + + >>> from is_useful_and_importable import square + >>> print(square(16)) + 256 + +It is good practice to always include the ``__name__ == '__main__'`` check. + + +Common tasks +------------ + +Download event summary data for a few stations +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. literalinclude:: scripts/download_esd_events.py + + +Download coincidences for a few stations +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. literalinclude:: scripts/download_esd_coincidences.py + + +Download and reconstruct directions for coincidences +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. literalinclude:: scripts/download_and_reconstruct_coincidences.py + + +Plot zenith angle distribution +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. literalinclude:: scripts/plot_zenith_distribution.py diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..eec21d09 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,31 @@ +Welcome to SAPPHiRE's documentation! +==================================== + +SAPPHiRE is a Simulation and Analysis Program Package for HiSPARC Research +and Education. This is your first stop if you want to download HiSPARC +station data, perform a simulation or start analyzing (simulated) data. + +While SAPPHiRE currently is very capable, the documentation is still being +heavily developed. Please check the `GitHub repository +`_ to view the activity log or browse +or download the code. + +Contents: + +.. toctree:: + :maxdepth: 2 + :includehidden: + + installation + tutorial + examples + configuration + sapphire + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/_sources/installation.rst.txt b/_sources/installation.rst.txt new file mode 100644 index 00000000..3112ccd4 --- /dev/null +++ b/_sources/installation.rst.txt @@ -0,0 +1,210 @@ +.. include:: subst.inc + +Installation +************ + +Python is a very versatile language. As such, it can be either installed with +lots and lots of packages to create an environment for e.g. data analysis or +game development, or it can be installed for a very specific task, taking up +much less space. To install a complete environment without all the hassle, you +can install a *Python distribution* which includes Python and a long list +of packages. This is the recommended approach, certainly if you're new to +Python. If you really want, you can install a much smaller set of packages for +which we'll provide minimal instructions only. + +Before we'll go on describing how to install |sapphire| itself, we will first +install the prerequisites. + + +Installing the prerequisites +============================ + +Python distributions +-------------------- + +|sapphire| supports both Python 2.7 and newer versions of Python, e.g. 3.5. +If you don't mind to install a large package, use a distribution. This mostly +includes everything you'll ever need. One such distribution is `Anaconda +`_. It includes many scientific python +packages, as well as graphical environments for doing your work. Anaconda has +packages available for the three major platforms: Windows, OS X and Linux. + +Anaconda includes a graphical programming environment (`Spyder +`_) which makes it easy to write analysis +scripts while creating plots, testing small snippets of code, or inspecting +your variables. + + +Minimal installation (miniconda) +-------------------------------- + +In theory, installing a Python package should be very easy. To install +|sapphire|, the Python package called ``sapphire`` would be retrieved from the +internet. It would have its dependencies listed and ``pip`` would pull them in +and all should be well. In fact, |sapphire| *does* have its dependencies +listed and Pip *will* pull them in. It is only then, that things start go +wrong. Whether you'll experience difficulties depends on the operating system +you're using and any previously installed software. But don't worry, we've got +you covered. + +Miniconda is the package manager used by the Anaconda distribution. +Installation instructions can be found at +https://conda.pydata.org/docs/install/quick.html. After installation, install +the following package:: + + $ conda install --channel conda-forge hdf5 + +After this is should be possible to simply install |sapphire| using Pip (see below). + + +Minimal installation (OS-specific) +---------------------------------- + +If you want to use a package manager specifically for your OS, follow the +(minimal) instructions below for your operating system of choice. + + +Mac OS X +^^^^^^^^ + +If you're using Mac OS X, the easiest way to install open source software +(like Python, the program language we're using) including lots and lots of +great packages, is done using `Homebrew +`_. Please follow the installation +instructions (really easy) and when done, type the following into a +terminal:: + + $ brew install python + +This will install Python and Pip. + +As of this writing, several of the dependencies listed by |sapphire| do not +have their own dependencies listed in a way that Pip (or other tools, for that +matter) know how to handle. To install matplotlib with its requirements, you +can type:: + + $ brew install homebrew/python/matplotlib + +Warning: this will install gcc and start a very long compilation process. Alternatively, with the introduction of *wheels* in Python, you can simply try to install packages like numpy, scipy and matplotlib using Pip:: + + $ pip install numpy scipy matplotlib + +If you're lucky, using a wheel will not need a compiler. We haven't tried this +ourselves for quite a while. YMMV. + + +Debian and derivatives (like Ubuntu) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Debian has a first-class package manager which other distributions and +operating systems have a hard time competing with. In my personal opinion, +anyway. Lots of Python packages can be installed using ``apt-get``, but some +of them might be outdated, depending on the age of your distribution. Use +``apt-get`` for Python packages at your own discretion (read: risk). Pip +handles Python packages very well, so you'll probably need some packages to +install using Pip. + +As of this writing, several of the dependencies listed by |sapphire| do not +have their own dependencies listed in a way that Pip (or other tools, for +that matter) know how to handle. Furthermore, matplotlib needs to be +installed all by itself, and its dependencies must be installed before it. + +You can try to install packages using ``apt-get``, for example:: + + $ sudo apt-get install python-matplotlib + +To figure out what you need, look at the prerequisites for |sapphire|, listed +in the setup.py file in the code repository. You can also try to install +|sapphire| using pip, and waiting for the installation to fail on some +prerequisites. If you then need development libraries, ``apt-get`` can help you +with that:: + + $ sudo apt-get build-dep python-numpy + +will install everything you need to build numpy from source. + + +Installing |sapphire| +===================== + +The |sapphire| package has been uploaded to `PyPI +`_ so that ``pip`` knows where to find it. + +There are now several scenarios for installing |sapphire|: let ``pip`` +install the release or development version or download the source code +and use setup.py to install it. + + +Just let me get to work! +------------------------ + +These scenarios do not involve fetching the code. They will just +install |sapphire|, so that you can get to work. To get the latest +stable release version, simply issue:: + + $ pip install hisparc-sapphire + +Done. Now get to work. + +If you like living on the edge with a possibly broken version of +|sapphire|, get the latest development version:: + + $ pip install https://github.com/hisparc/sapphire/zipball/master + + +Let me see the code! +-------------------- + +If you're interested in the development of |sapphire|, you can either go to the +`GitHub page `_ or install the version +control system (we use `Git `_) yourself. For that, please +see the `GitHub Help pages `_. + +To download the code and install |sapphire|, first go to +https://github.com/hisparc/sapphire/. Then, click on the *Clone in Desktop*, +which will prompt you to download the GitHub Desktop app (recommended). + +.. image:: images/github-zipball.png + +Open a terminal and navigate to the top-level directory containing the code. +Then issue:: + + $ python setup.py develop + +This takes care of installing the missing dependencies and |sapphire| itself. +Whenever you update the code using the GitHub Desktop application, the new code +is immediately available. + + +Checking that |sapphire| is installed correctly +=============================================== + +First off, the following is not an exhaustive check. But it will tell you +if |sapphire| is, in fact, installed on your system and that Python knows +how to find it. + +.. note:: When you run this check from *inside* the top-level |sapphire| + code directory, it will always return successfully. The reason + for this is that Python also checks the current working + directory for packages. *So, run this check from e.g. your home + directory.* + +Start a Python session. You can use a launcher of some type (e.g. the one +from Python(x,y)), or open a terminal and type:: + + $ python + +Or, if you prefer, `IPython `_:: + + $ ipython + +In fact, we recommend using IPython for interactive use. Then, try to import +|sapphire|:: + + >>> import sapphire + +If this returns without an error message, all is well and |sapphire| should be +correctly installed. As an additional assurance the |sapphire| code tests can +be executed. To run the tests simply call the function to run the tests:: + + >>> sapphire.run_tests() diff --git a/_sources/kascade.rst.txt b/_sources/kascade.rst.txt new file mode 100644 index 00000000..330851f6 --- /dev/null +++ b/_sources/kascade.rst.txt @@ -0,0 +1,6 @@ +KASCADE data +============ + +.. automodule:: sapphire.kascade + :members: + :undoc-members: diff --git a/_sources/publicdb.rst.txt b/_sources/publicdb.rst.txt new file mode 100644 index 00000000..c336aa35 --- /dev/null +++ b/_sources/publicdb.rst.txt @@ -0,0 +1,6 @@ +Public data access +================== + +.. automodule:: sapphire.publicdb + :members: + :undoc-members: diff --git a/_sources/qsub.rst.txt b/_sources/qsub.rst.txt new file mode 100644 index 00000000..a1240717 --- /dev/null +++ b/_sources/qsub.rst.txt @@ -0,0 +1,6 @@ +Nikhef Stoomboot facilities +=========================== + +.. automodule:: sapphire.qsub + :members: + :undoc-members: diff --git a/_sources/sapphire.rst.txt b/_sources/sapphire.rst.txt new file mode 100644 index 00000000..670652ae --- /dev/null +++ b/_sources/sapphire.rst.txt @@ -0,0 +1,23 @@ +SAPPHiRE Reference +================== + +.. automodule:: sapphire + +.. toctree:: + :hidden: + + analysis + api + clusters + corsika + data + esd + kascade + publicdb + qsub + simulations + storage + tests + time_util + transformations + utils diff --git a/_sources/simulations.rst.txt b/_sources/simulations.rst.txt new file mode 100644 index 00000000..2ac0db16 --- /dev/null +++ b/_sources/simulations.rst.txt @@ -0,0 +1,30 @@ +Simulation of the detection of EAS events +========================================= + +Simulations are built up from several components. First the cluster needs to +be defined. This can be a set of real stations or imaginary detectors. Then +systematic detector and station errors are generated. Next a generator for +shower properties is used to determine the properties of each shower (energy +and direction). Then for each detector the shower particles that hit it are +selected. For each of these the actual detected signal strength and arrival +times (detector effects) are determined. Then the trigger conditions are +checked to see if the station triggered. If it did an event will be stored for +that station. This includes a GPS timestamp and the detector observables +(particle count and arrival time of first particle). Finally for each shower a +'coincidence' will be stored, even if it was not detected by any station. The +coincidence stores the parameters of the shower. + + +.. automodule:: sapphire.simulations + :members: + :undoc-members: + +.. toctree:: + :hidden: + + simulations/base + simulations/detector + simulations/groundparticles + simulations/ldf + simulations/showerfront + simulations/gammas diff --git a/_sources/simulations/base.rst.txt b/_sources/simulations/base.rst.txt new file mode 100644 index 00000000..bddce254 --- /dev/null +++ b/_sources/simulations/base.rst.txt @@ -0,0 +1,6 @@ +Simulation base classes +======================= + +.. automodule:: sapphire.simulations.base + :members: + :undoc-members: diff --git a/_sources/simulations/detector.rst.txt b/_sources/simulations/detector.rst.txt new file mode 100644 index 00000000..411c533f --- /dev/null +++ b/_sources/simulations/detector.rst.txt @@ -0,0 +1,6 @@ +Detector simulations +==================== + +.. automodule:: sapphire.simulations.detector + :members: + :undoc-members: diff --git a/_sources/simulations/gammas.rst.txt b/_sources/simulations/gammas.rst.txt new file mode 100644 index 00000000..3e1361b3 --- /dev/null +++ b/_sources/simulations/gammas.rst.txt @@ -0,0 +1,6 @@ +Simulation of detector response due to gammas +==================================================================== + +.. automodule:: sapphire.simulations.gammas + :members: + :undoc-members: diff --git a/_sources/simulations/groundparticles.rst.txt b/_sources/simulations/groundparticles.rst.txt new file mode 100644 index 00000000..0c3c9778 --- /dev/null +++ b/_sources/simulations/groundparticles.rst.txt @@ -0,0 +1,6 @@ +Simulations based on Monte Carlo EAS ground particle data +========================================================= + +.. automodule:: sapphire.simulations.groundparticles + :members: + :undoc-members: diff --git a/_sources/simulations/ldf.rst.txt b/_sources/simulations/ldf.rst.txt new file mode 100644 index 00000000..0e397eb6 --- /dev/null +++ b/_sources/simulations/ldf.rst.txt @@ -0,0 +1,6 @@ +Simulations based on theoretical models of EAS lateral distributions +==================================================================== + +.. automodule:: sapphire.simulations.ldf + :members: + :undoc-members: diff --git a/_sources/simulations/showerfront.rst.txt b/_sources/simulations/showerfront.rst.txt new file mode 100644 index 00000000..f8c5ac43 --- /dev/null +++ b/_sources/simulations/showerfront.rst.txt @@ -0,0 +1,6 @@ +Simulations based on time structure of EAS shower fronts +======================================================== + +.. automodule:: sapphire.simulations.showerfront + :members: + :undoc-members: diff --git a/_sources/storage.rst.txt b/_sources/storage.rst.txt new file mode 100644 index 00000000..57690494 --- /dev/null +++ b/_sources/storage.rst.txt @@ -0,0 +1,7 @@ +Low-level storage constructs +============================ + +.. automodule:: sapphire.storage + :members: + :undoc-members: + :imported-members: diff --git a/_sources/tests.rst.txt b/_sources/tests.rst.txt new file mode 100644 index 00000000..f9ca9497 --- /dev/null +++ b/_sources/tests.rst.txt @@ -0,0 +1,6 @@ +Test installation +================= + +.. automodule:: sapphire.tests + :members: + :undoc-members: diff --git a/_sources/time_util.rst.txt b/_sources/time_util.rst.txt new file mode 100644 index 00000000..98d176e1 --- /dev/null +++ b/_sources/time_util.rst.txt @@ -0,0 +1,6 @@ +GPS date/time utility functions +=============================== + +.. automodule:: sapphire.time_util + :members: + :undoc-members: diff --git a/_sources/transformations.rst.txt b/_sources/transformations.rst.txt new file mode 100644 index 00000000..31c6fcaf --- /dev/null +++ b/_sources/transformations.rst.txt @@ -0,0 +1,16 @@ +Coordinate and time transformations +=================================== + +.. automodule:: sapphire.transformations + :members: + :undoc-members: + +.. toctree:: + :hidden: + + transformations/angles + transformations/axes + transformations/base + transformations/celestial + transformations/clock + transformations/geographic diff --git a/_sources/transformations/angles.rst.txt b/_sources/transformations/angles.rst.txt new file mode 100644 index 00000000..3f6d288d --- /dev/null +++ b/_sources/transformations/angles.rst.txt @@ -0,0 +1,6 @@ +Conversion between different angle notations +============================================ + +.. automodule:: sapphire.transformations.angles + :members: + :undoc-members: diff --git a/_sources/transformations/axes.rst.txt b/_sources/transformations/axes.rst.txt new file mode 100644 index 00000000..ea901db5 --- /dev/null +++ b/_sources/transformations/axes.rst.txt @@ -0,0 +1,6 @@ +Conversion between axes coordinate systems +========================================== + +.. automodule:: sapphire.transformations.axes + :members: + :undoc-members: diff --git a/_sources/transformations/base.rst.txt b/_sources/transformations/base.rst.txt new file mode 100644 index 00000000..3e9a98d7 --- /dev/null +++ b/_sources/transformations/base.rst.txt @@ -0,0 +1,6 @@ +Conversion between different bases +================================== + +.. automodule:: sapphire.transformations.base + :members: + :undoc-members: diff --git a/_sources/transformations/celestial.rst.txt b/_sources/transformations/celestial.rst.txt new file mode 100644 index 00000000..292fb01b --- /dev/null +++ b/_sources/transformations/celestial.rst.txt @@ -0,0 +1,6 @@ +Transformations between Celestial coordinate systems +==================================================== + +.. automodule:: sapphire.transformations.celestial + :members: + :undoc-members: diff --git a/_sources/transformations/clock.rst.txt b/_sources/transformations/clock.rst.txt new file mode 100644 index 00000000..e33888f3 --- /dev/null +++ b/_sources/transformations/clock.rst.txt @@ -0,0 +1,6 @@ +Time conversion functions +========================= + +.. automodule:: sapphire.transformations.clock + :members: + :undoc-members: diff --git a/_sources/transformations/geographic.rst.txt b/_sources/transformations/geographic.rst.txt new file mode 100644 index 00000000..d8acda69 --- /dev/null +++ b/_sources/transformations/geographic.rst.txt @@ -0,0 +1,6 @@ +Transformations between geographic coordinate systems +===================================================== + +.. automodule:: sapphire.transformations.geographic + :members: + :undoc-members: diff --git a/_sources/tutorial.rst.txt b/_sources/tutorial.rst.txt new file mode 100644 index 00000000..5566f69b --- /dev/null +++ b/_sources/tutorial.rst.txt @@ -0,0 +1,842 @@ +.. include:: subst.inc + +Tutorial +======== + +|sapphire| simplifies data access, simulations and analysis for the `HiSPARC +`_ experiment. In this tutorial, we'll try to give +you a feeling for the things you can do with |sapphire|. How can you +download data? How can you analyze this data? How can you produce +pulseheight histograms? How can you calculate the direction of cosmic +rays? This tutorial will only give you an overview of what's possible +with |sapphire|. For details on all available classes and methods, please +see the :doc:`sapphire`. + +.. note:: + We'll require you to know basic Python. If you're unfamiliar with Python, + you can look at the official `Python tutorial + `_, the book `Think Python + `_, or `Getting started with + Python for science `_. + + +First steps +----------- + +In a few examples, we will plot the data which we have produced. We make use +of pylab, which is included with matplotlib. If you have Anaconda installed, +you can use the Spyder environment. You can write a script in the main window, +or type short commands in the command prompt in the bottom right. The first +thing you have to do is import everything from pylab. That makes it +ridiculously easy to plot things:: + + >>> from pylab import * + +You can also start an IPython terminal with pylab using:: + + $ ipython --pylab + +In that case, you don't need to import pylab. + +Whenever you see something like:: + + >>> print('Hello, world') + Hello, world + +it means we're typing in Python code. The ``>>>`` is the Python *prompt*. +It tells you that the Python interpreter is waiting for you to give it +some instructions. In the above example, we've typed in ``print ('Hello, +world')``. The interpreter subsequently executed the code and printed +*Hello, world* on the screen. + +The first thing we'll have to do to start using |sapphire| is to *import* +the |sapphire| module:: + + >>> import sapphire + +There will be no output if everything is successful. It is easy to get +some help from inside the Python terminal. Just say:: + + >>> help(sapphire) + +and you'll be presented with a basic help screen. Instead of +``sapphire``, you can throw in modules, packages, functions, classes, +objects, etc. Everything in Python has *some* help text associated with +it. Not all of it is very helpful to a newcomer, hence this tutorial. +All help text is also available in the :doc:`sapphire`. + + +Downloading and accessing |hisparc| data +---------------------------------------- + +The |sapphire| package comprises multiple modules and packages. To access data +from the public database, we'll have to import the :mod:`sapphire.esd` module. +This module gives us access to the *event summary data*, which is the raw +|hisparc| data with preliminary analysis already included. It is simple to +quickly start playing with data:: + + >>> from sapphire import esd + >>> data = esd.quick_download(102) + 100%|####################################|Time: 5.91 + >>> print(data) + data8.h5 (File) '' + Last modif.: 'Thu Oct 22 16:02:05 2015' + Object Tree: + / (RootGroup) '' + /s102 (Group) '' + /s102/events (Table(46382,)) '' + +First, we import the :mod:`sapphire.esd` module. Then, we download data from +station 102 using the :func:`sapphire.esd.quick_download` function. This +function downloads yesterday's data, and creates a datafile on the fly. More on +that later. + +If we want to exercise more control, we need to do some things manually. First, +we need the ``tables`` module to actually store the data. `PyTables +`_ is based on the open HDF5 data format, which is used by `many +(research) institutes `_. For +example, it is used by the KNMI and by NASA. To specify the date and time for +which to download the data, we need the ``datetime`` module. Thus, we have:: + + >>> import tables + >>> import datetime + >>> from sapphire import esd + +Creating an empty data file, with the name ``mydata.h5``, is done easily:: + + >>> data = tables.open_file('mydata.h5', 'w') + +The ``'w'`` means *write*, which creates a file for writing (and reading). +Mind that this will create an empty file. If there already was a file +with that name, it will be overwritten! Alternatively, you can say +``'a'``, which means *append*, thus adding to an existing file without +overwriting its contents. Finally, you can specify ``'r'`` for +*read-only*. + + +Downloading data +^^^^^^^^^^^^^^^^ + +To download data, we have to specify the date/time *range*. If we want to +download data from the December 1, 2012 all through December 2, 2012, +we can specify this by typing:: + + >>> start = datetime.datetime(2012, 12, 1) + >>> end = datetime.datetime(2012, 12, 3) + +Mind that if we do not specify the hour of day, it is taken to be 00:00 +hours. Thus, there is no data included for December 3. Alternatively, +we can download data from a two hour interval on October 17 by specifying +the hour of day:: + + >>> start = datetime.datetime(2012, 10, 17, 19) + >>> end = datetime.datetime(2012, 10, 17, 21) + +which is from 19:00 to 21:00 hours. It is important to realize that we use a +GPS clock, which is equal to UTC (up to some leap seconds). So, if we download +data for a station in the Netherlands, we have just said from 20:00 to 22:00 +local time (when it's not summer time). You can specify the time up to the +second. + +We have not actually done anything yet. We have just stored our time +window in two arbitrarily-named variables, ``start`` and ``end``. To +download data from station 501 and store it in a group with name ``/s501``, +we can use the :func:`sapphire.esd.download_data` function. Since we've +imported ``esd`` from ``sapphire``, we can drop the ``sapphire`` prefix:: + + >>> esd.download_data(data, '/s501', 501, start, end) + 100%|####################################|Time: 0:00:16 + +It will show a progressbar to indicate the progress of the download. + +As you can see in the reference documentation of +:func:`sapphire.esd.download_data`, available by either clicking on +the link or typing in ``help(esd.download_data)`` in the +interpreter, the function takes six arguments: *file, group, station_number, +start, end* and *type*. The last one has the default argument +*'events'*, and may be omitted, as we have done here. In our example, we +have opened a file, ``mydata.h5``, and have stored the file *handler* in +the variable ``data``. So, we passed ``data`` to the function. The group +name is ``/s501``. Group names in PyTables are just like folders in a +directory hierarchy. So, we might have specified +``/path/to/my/hisparc/data/files/for_station/s501``. It is important to +note that this has absolutely nothing to do with *files*. Whatever path +you specify, it is all contained *inside* your data file. Since this is +just a small data file, we have opted for a simple structure. At the +moment, just one group named ``s501`` at the root of the hierarchy. Group +names must start with a letter, hence the ``s`` for station. + +The *station_number* is simply the station number. Here, we've chosen to +download data for station 501, located at Nikhef. The *start* and *end* +parameters specify the date/time range. + +Finally, *type* selects whether to download event or weather data should +be downloaded. We've selected the default, which is *events*. We can also +download the weather data by changing the type to ``'weather'``:: + + >>> esd.download_data(data, '/s501', 501, start, end, type='weather') + 100%|####################################|Time: 0:00:10 + +To access the raw data that includes the original detector traces the +:func:`sapphire.publicdb.download_data` function can be used instead. However, +downloading data will take much longer that way. If only some traces need to be +accessed, the :mod:`sapphire.api` is a better choice. We'll use that module +later in the tutorial. + + +Looking around +^^^^^^^^^^^^^^ + +If you want to know what groups and tables are contained within the data +file, just ``print`` it:: + + >>> print(data) + mydata.h5 (File) '' + Last modif.: 'Sat Dec 29 14:50:55 2012' + Object Tree: + / (RootGroup) '' + /s501 (Group) 'Data group' + /s501/events (Table(137600,)) 'HiSPARC coincidences table' + /s501/weather (Table(51513,)) 'HiSPARC weather data' + +The *object tree* gives an overview of all groups and tables. As you can +see, the ``/s501`` group contains two tables, ``events`` and ``weather``. +The events table contains the data from the |hisparc| scintillators, while +the weather table contains data from the (optional) weather station. + +To directly access any object in the hierarchy, you can make use of the +``data.root`` object, which points to the root group. Then, just specify +the remaining path, with dots instead of slashes. For example, to access +the events table:: + + >>> print(data.root.s501.events) + /s501/events (Table(137600,)) 'HiSPARC coincidences table' + +If you want, you can also access the object using its name as a string, by calling ``get_node`` on the file handler:: + + >>> print(data.get_node('/s501/events')) + /s501/events (Table(137600,)) 'HiSPARC coincidences table' + +Of course, we'd like to get some more information. You can drop the print +statement, and just access the object directly. PyTables is set up such +that it will give more detailed information whenever you specify the +object directly:: + + >>> data.root.s501.events + /s501/events (Table(137600,)) '' + description := { + "event_id": UInt32Col(shape=(), dflt=0, pos=0), + "timestamp": Time32Col(shape=(), dflt=0, pos=1), + "nanoseconds": UInt32Col(shape=(), dflt=0, pos=2), + "ext_timestamp": UInt64Col(shape=(), dflt=0, pos=3), + "pulseheights": Int16Col(shape=(4,), dflt=0, pos=4), + "integrals": Int32Col(shape=(4,), dflt=0, pos=5), + "n1": Float32Col(shape=(), dflt=0.0, pos=6), + "n2": Float32Col(shape=(), dflt=0.0, pos=7), + "n3": Float32Col(shape=(), dflt=0.0, pos=8), + "n4": Float32Col(shape=(), dflt=0.0, pos=9), + "t1": Float32Col(shape=(), dflt=0.0, pos=10), + "t2": Float32Col(shape=(), dflt=0.0, pos=11), + "t3": Float32Col(shape=(), dflt=0.0, pos=12), + "t4": Float32Col(shape=(), dflt=0.0, pos=13), + "t_trigger": Float32Col(shape=(), dflt=0.0, pos=14)} + byteorder := 'little' + chunkshape := (819,) + +There you go! But what does it all *mean*? Well, if you want to get to +the bottom of it, read the `PyTables documentation +`_. We'll give a quick +overview here. + +First, this table contains 137600 rows. In total, there are fourteen +columns: ``event_id``, ``timestamp``, ``nanoseconds``, ``ext_timestamp``, +``pulseheights``, ``integrals``, ``n1``-``n4``, ``t1``-``t4`` and +``t_trigger``. + +Each event has a unique [#event_id]_ identifier, ``event_id``. Each +event has a Unix timestamp in GPS time, *not* UTC. A `Unix timestamp +`_ is the number of seconds that +have passed since January 1, 1970. The sub-second part of the timestamp +is given in ``nanoseconds``. The ``ext_timestamp`` is the full +timestamp in nanoseconds. Since there cannot exist another event with the same +timestamp, this field in combination with the station number uniquely +identifies the event. The ``pulseheights`` and ``integrals`` fields are +values derived from the PMT traces by the HiSPARC DAQ. The ``n#`` +columns are derived from the ``integrals`` and the ``t#`` and +``t_trigger`` fields are obtained after analyzing the event traces on +the server. For some fields there are four values, one for each detector. +If a station only has two detectors, the values for the missing two +detectors are -1. If the baseline of the trace could not be determined +all these values are -999. + +We'll get to work with this data in a moment. First, we'll take a look at +the weather table:: + + >>> data.root.s501.weather + /s501/weather (Table(51513,)) 'HiSPARC weather data' + description := { + "event_id": UInt32Col(shape=(), dflt=0, pos=0), + "timestamp": Time32Col(shape=(), dflt=0, pos=1), + "temp_inside": Float32Col(shape=(), dflt=0.0, pos=2), + "temp_outside": Float32Col(shape=(), dflt=0.0, pos=3), + "humidity_inside": Int16Col(shape=(), dflt=0, pos=4), + "humidity_outside": Int16Col(shape=(), dflt=0, pos=5), + "barometer": Float32Col(shape=(), dflt=0.0, pos=6), + "wind_dir": Int16Col(shape=(), dflt=0, pos=7), + "wind_speed": Int16Col(shape=(), dflt=0, pos=8), + "solar_rad": Int16Col(shape=(), dflt=0, pos=9), + "uv": Int16Col(shape=(), dflt=0, pos=10), + "evapotranspiration": Float32Col(shape=(), dflt=0.0, pos=11), + "rain_rate": Float32Col(shape=(), dflt=0.0, pos=12), + "heat_index": Int16Col(shape=(), dflt=0, pos=13), + "dew_point": Float32Col(shape=(), dflt=0.0, pos=14), + "wind_chill": Float32Col(shape=(), dflt=0.0, pos=15)} + byteorder := 'little' + chunkshape := (1310,) + +We'll let these column names speak for themselves. + + +Accessing the data +^^^^^^^^^^^^^^^^^^ + +We can access the data in several ways. We can address the complete +table, or just one or several rows from it. We can read out a single +column, or select data based on a query. Before we do any of that, we'll +save us some typing:: + + >>> events = data.root.s501.events + +Now, we have stored a short-hand reference to the events table. Let's get +the first event:: + + >>> events[0] + (0L, 1445385601, 613271528L, 1445385601613271528L, [3, 266, 400, 372], + [0, 3090, 5695, 5621], 0.0, 1.03120, 1.55130, 1.58820, + -999.0, 1002.5, 1000.0, 1022.5, 1030.0, -999.0, -999.0) + +That's the first event! It is not, however, immediately clear what +numbers correspond to which columns. They are in order, however, so you +could find out. It is often easier to specify the column you're +interested in:: + + >>> events[0]['pulseheights'] + array([ 3, 266, 400, 372], dtype=int16) + +Which gives us the pulseheights of the first event. You can recognize the same +numbers in the full event data above. The pulseheights are 16-bit integers +(that's the ``dtype=int16``) and are determined after digitizing the events +using an analog-digital converter (ADC). Each unit corresponds to about -0.57 +mV. You can tell that all but the first detector had relatively large +pulseheights, and that they were registered as a significant *peak* in the +signal. + +If you're interested in the pulseheights of *all* events, the fastest way +to do it is to make use of the ``Table.col`` method of the table:: + + >> events.col('pulseheights') + array([[ 3, 266, 400, 372], + [ 4, 277, 887, 7], + [ 72, 256, 6, 330], + ..., + [162, 78, 4, 299], + [ 10, 6, 618, 446], + [593, 621, 343, 13]], dtype=int16) + +It is also possible to select the data based on a query. For example, to +select all events between timestamps 1354320000 and 1354323600 (a one-hour +time span):: + + >>> t0 = 1354320000 + >>> t1 = t0 + 3600 + >>> sel_events = events.read_where('(t0 <= timestamp) & (timestamp < t1)') + >>> len(sel_events) + 2836 + +Thus, we have selected 2836 events. The variable ``sel_events`` no longer +points to a table. We can no longer make use of the ``Table.col`` +method, but we *can* access all pulseheights in the following way:: + + >>> sel_events['pulseheights'] + array([[173, 3, 407, 3], + [ 1, 2, 313, 756], + [211, 2, 2, 268], + ..., + [328, 556, 255, 15], + [ 3, 325, 309, 3], + [ 2, 2, 271, 381]], dtype=int16) + +This notation is possible for arrays, but not for tables. So, for tables, +use the ``Table.col`` method. For arrays, use this special notation. + + +The :mod:`sapphire.time_util` module +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +|sapphire| includes a handy module: :mod:`sapphire.time_util`. This saves you +from the hassle of converting timestamps and confusing local time and GPS (or +UTC) time. It is important to realize that the |hisparc| station uses a GPS +clock, and thus saves all timestamps in GPS time, which is certainly not local +time! You can look up `GPS time +`_, but +suffice it to say that it is *almost* equal to UTC time. The difference is the +leap seconds introduced after 1980. In January 2013, GPS time was ahead of UTC +by 16 seconds. Since July 2015, it is ahead by 17 seconds. We will not refer +to UTC or local time, but instead always refer to GPS time! + +While you tell |sapphire| to download data using year, month, day, hour, +minutes, seconds notation, the events table contains timestamps. It is +often hard to convert between the two. For example, the correct +conversion between a GPS date/time and a GPS timestamp is given by:: + + >>> calendar.timegm(datetime.datetime(2012, 12, 1, 0).utctimetuple()) + 1354320000 + +Which is, at best, cumbersome. It is easy, however, to screw up and +inadvertently convert to local time. For your benefit, we have included +the :class:`sapphire.time_util.GPSTime` class. You instantiate the class +by giving it a GPS time to work with. It can either be in date/time +notation, or as a timestamp. For example, the exact same result is +obtained by these two last lines of code:: + + >>> import sapphire.time_util + >>> sapphire.time_util.GPSTime(2012, 12, 1) + >>> sapphire.time_util.GPSTime(1354320000) + +If you store the instance, you can then call several methods to convert +the date/time to whatever you require:: + + >>> gpstime = sapphire.time_util.GPSTime(2012, 12, 1) + >>> gpstime.datetime() + datetime.datetime(2012, 12, 1, 0, 0) + >>> gpstime.description() + 'Sat Dec 1 00:00:00 2012' + >>> gpstime.gpstimestamp() + 1354320000 + +Or, indeed:: + + >>> gpstime = sapphire.time_util.GPSTime(1354320000) + >>> gpstime.datetime() + datetime.datetime(2012, 12, 1, 0, 0) + >>> gpstime.description() + 'Sat Dec 1 00:00:00 2012' + >>> gpstime.gpstimestamp() + 1354320000 + +It is now easy to select events occurring between 12:00 and 13:00 hours GPS +time on December 2, 2012:: + + >>> t0 = sapphire.time_util.GPSTime(2012, 12, 2, 12).gpstimestamp() + >>> t1 = sapphire.time_util.GPSTime(2012, 12, 2, 13).gpstimestamp() + >>> t0, t1 + (1354449600, 1354453200) + >>> sel_events = events.read_where('(t0 <= timestamp) & (timestamp < t1)') + >>> len(sel_events) + 2817 + +And when did the tenth event occur? Well:: + + >>> t = sel_events[9]['timestamp'] + >>> t + 1354449620 + >>> sapphire.time_util.GPSTime(t).description() + 'Sun Dec 2 12:00:20 2012' + +To shorten the typing somewhat, remember that in Python you can import +classes from modules into your own namespace:: + + >>> from sapphire.time_util import GPSTime + >>> GPSTime(t).description() + 'Sun Dec 2 12:00:20 2012' + + +Plotting data +^^^^^^^^^^^^^ + +Now that we can access the data, we want to visualize it. Plotting data +is a great way to do that. And of course, the venerable histogram is +still very useful to condense thousands of events into one display. Pylab +contains an easy function to do just that: ``hist``. Let's try to +recreate a few graphs as seen on the |hisparc| `data display +`_:: + + >>> ph = events.col('pulseheights') + >>> hist(ph) + ([array([114794, 17565, 3062, 1009, 502, 285, 170, 86, + 72, 55]), array([112813, 19028, 3339, 1246, 540, 295, 163, 100, + 66, 10]), array([109162, 21833, 4246, 1345, 579, 290, 113, 32, + 0, 0]), array([109996, 21283, 4028, 1285, 581, 251, 133, 43, + 0, 0])], array([ 1.00000000e+00, 3.91100000e+02, 7.81200000e+02, + 1.17130000e+03, 1.56140000e+03, 1.95150000e+03, + 2.34160000e+03, 2.73170000e+03, 3.12180000e+03, + 3.51190000e+03, 3.90200000e+03]), ) + +.. image:: images/tutorial-hist-simple.png + :width: 500px + +This will not do. Firstly, data from the four detectors is pictured as +four side-by-side colored bars. Secondly, the number of bins is very low; +it is only ten. Thirdly, the data range continues up to very high values +with hardly any events. + +To fix this, we'll make use of several arguments that can be passed to the +``hist`` function. We'll also make use of some NumPy (`documentation +`_) functionality. For Pylab +documentation, see the `Matplotlib site `_. +Try this:: + + >>> bins = linspace(0, 2000, 101) + >>> hist(ph, bins, histtype='step', log=True) + >>> xlabel("Pulseheight [ADC]") + >>> ylabel("Counts") + >>> title("Pulseheight histogram (log scale)") + +The ``linspace`` function returns an array with range from 0 to 2000 with a +total number of 101 values. The first value is 0, the last is 2000. These are +the *edges* of the bins. So, 101 values means exactly 100 bins between 0 and +2000. The ``hist`` function will then plot a *stepped* histogram with a log +scale. Finally, we add some labels and a title. This is the result: + +.. image:: images/tutorial-hist-better.png + :width: 500px + +In this plot, the gamma and charged particle part of the spectrum are easy +to distinguish. The cut at 123 ADC is due to the trigger. The bump at +approximately 300 ADC is the so-called MIP (minimum ionizing particle) +peak. For an explanation of this plot and the features of the pulseheight +histogram, see `David's thesis +`_, +page 44–45, and 49–51. + + +Obtaining coincidences +---------------------- + +If you work with |hisparc| data, invariably you'll be interested in +*coincidences* between |hisparc| stations. That is, are there showers +which have been observed by multiple stations? To find out, we'll make +use of the :mod:`sapphire.analysis.coincidences` module. + + +Performing the search +^^^^^^^^^^^^^^^^^^^^^ + +Consider the following script, which you can hopefully understand by now +(note that the prompt (``>>>``) is absent, since this is a *script*:: + + import datetime + + import tables + + from sapphire import download_data, CoincidencesESD + + + STATIONS = [501, 503, 506] + START = datetime.datetime(2013, 1, 1) + END = datetime.datetime(2013, 1, 2) + + + if __name__ == '__main__': + station_groups = ['/s%d' % u for u in STATIONS] + + data = tables.open_file('data.h5', 'w') + for station, group in zip(STATIONS, station_groups): + download_data(data, group, station, START, END) + +At this point, we have downloaded data for three stations. Note that we +used the :mod:`sapphire.esd` for downloading. Thus, we have no traces +and the download is quick. Let's see what the datafile now contains:: + + >>> print(data) + data.h5 (File) '' + Last modif.: 'Mon Jan 14 17:34:39 2013' + Object Tree: + / (RootGroup) '' + /s501 (Group) 'Data group' + /s501/events (Table(70643,)) '' + /s503 (Group) 'Data group' + /s503/events (Table(34937,)) '' + /s506 (Group) 'Data group' + /s506/events (Table(68365,)) '' + +It contains three groups, one for each station. To search for +coincidences between these stations, we first initialize the +:class:`sapphire.analysis.coincidences.CoincidencesESD` class like so:: + + >>> coincidences = CoincidencesESD(data, '/coincidences', station_groups) + +From the documentation (click on the class above the example to go to the +documentation) it is clear that we have to specify the datafile +(``data``), the destination group (``/coincidences``) and the groups +containing the station data (``station_groups``). Once that's done, there +is an easy way to search for coincidences, process the events making up +the coincidences, and store them in the destination group:: + + >>> coincidences.search_and_store_coincidences(station_numbers=STATIONS) + 100%|######################################|Time: 0:00:02 + 100%|######################################|Time: 0:00:00 + +If you want to tweak the process using non-default parameters, see the +module documentation (:mod:`sapphire.analysis.coincidences`). For now, +let us turn to the results:: + + >>> print(data) + data.h5 (File) '' + Last modif.: 'Mon Jan 14 17:46:23 2013' + Object Tree: + / (RootGroup) '' + /coincidences (Group) u'' + /coincidences/c_index (VLArray(2184,)) '' + /coincidences/coincidences (Table(2184,)) '' + /coincidences/s_index (VLArray(3,)) '' + /s501 (Group) '' + /s501/events (Table(70643,)) '' + /s503 (Group) '' + /s503/events (Table(34937,)) '' + /s506 (Group) '' + /s506/events (Table(65935,)) '' + +The new addition is the ``/coincidences`` group. It contains three +tables, which are ``c_index``, ``coincidences`` and ``s_index``. +Information about the coincidences is stored in the ``coincidences`` +table. Let's look at the columns: + +=============== =========== +column description +=============== =========== +id an index number identifying the coincidence +timestamp the unix timestamp +nanoseconds the nanosecond part of the timestamp +ext_timestamp the timestamp in nanoseconds +N the number of stations taking part in the coincidence +x *compatibility reasons* +y *compatibility reasons* +azimuth *compatibility reasons* +zenith *compatibility reasons* +size *compatibility reasons* +energy *compatibility reasons* +s501 flag to indicate if the first station is in coincidence +s503 as previous but for second station +s506 as previous but for third station +=============== =========== + +The columns included for compatibility reasons are used by the event +simulation code. In that case, the ``x``, ``y`` columns give the +position in cartesian coordinates. Furthermore, the ``size`` and +``energy`` give the so-called *shower size*, and ``zenith`` and +``azimuth`` contain the direction of the (simulated) shower. These are +not known for certain when working with |hisparc| data, but are included +nonetheless. These columns are all set to 0.0. + +The ``c_index`` array is used as an index to look up the tables and +individual events making up a coincidence. The second coincidence is +accessed by:: + + >>> data.root.coincidences.coincidences[1] + (2L, 1356998460, 730384055L, 1356998460730384055L, 2, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, True, False, True) + +Remember, the indexes are zero-based. The coincidence id is also 2:: + + >>> data.root.coincidences.coincidences[2]['id'] + 2 + +and the number of stations participating is 2:: + + >>> data.root.coincidences.coincidences[2]['N'] + 2 + +To lookup the indexes of the events taking part in this coincidence, +access the ``c_index`` array using the same id:: + + >>> data.root.coincidences.c_index[2] + array([[ 0, 40], + [ 2, 57]], dtype=uint32) + +That is, event id 40 from station 0, event id 57 from station 2 are part +of this coincidence. The event observables are still stored in their +original location and can be accessed using the ids. To find the +location of the station group the ``s_index`` contains the paths to the +station groups.:: + + >>> data.root.coincidences.s_index[0] + '/s501' + >>> data.get_node('/s501', 'events')[40] + (40L, 1356998460, 730384055L, 1356998460730384055L, [2, 227, 301, 2], + [0, 1657, 3173, 0], 0.0, 0.5117999911308289, 0.9417999982833862, + 0.0, -999.0, 12.5, 57.5, -999.0, 62.5) + + +Downloading coincidences +^^^^^^^^^^^^^^^^^^^^^^^^ + +Just like events or weather data for a single station the ESD provides +coincidence data. This means that you can directly download coincidences from +the server. In most cases this can save a lot of disc space, because only +events in a coincidence will be stored. + +Downloading coincidences is very similar to downloading data. First import the +required packages, open a PyTables file, and specify the date/time *range*. +With coincidences two additional options are available, first you can select +which stations you want to consider. This can be *all* stations in the +network, by not specifying any. It can be a subset of stations by listing +those stations (upto 30 stations), or a cluster of stations by giving the name +of the cluster. + +So first import the packages:: + + >>> import datetime + >>> import tables + >>> from sapphire import esd + +Set the date/time range:: + + >>> start = datetime.datetime(2012, 12, 1) + >>> end = datetime.datetime(2012, 12, 3) + +Open (in this case create) a datafile:: + + >>> data = tables.open_file('data_coincidences.h5', 'w') + +Then download the coincidences. Here we specify the cluster 'Enschede' to +select all stations in that cluster. By setting *n* to 3 we require that a +coincidence has at least 3 events. + + >>> esd.download_coincidences(data, cluster='Enschede', + ... start=start, end=end, n=3) + +If you now look in the data file it will contain event tables for all stations +with at least one event in a coincidence, and a coincidence table for the +coincidence data. Just like when coincidences were searched manually:: + + >>> print(data) + Object Tree: + / (RootGroup) '' + /coincidences (Group) '' + /coincidences/c_index (VLArray(776,)) '' + /coincidences/coincidences (Table(776,)) '' + /coincidences/s_index (VLArray(124,)) '' + /hisparc (Group) '' + /hisparc/cluster_enschede (Group) '' + /hisparc/cluster_enschede/station_7001 (Group) '' + /hisparc/cluster_enschede/station_7001/events (Table(776,)) '' + /hisparc/cluster_enschede/station_7002 (Group) '' + /hisparc/cluster_enschede/station_7002/events (Table(776,)) '' + /hisparc/cluster_enschede/station_7003 (Group) '' + /hisparc/cluster_enschede/station_7003/events (Table(776,)) '' + + +.. rubric:: Footnotes + +.. [#event_id] + + Unique in this table. When data is downloaded for analysis and + combined with other data into one table, the ``event_id`` will be + different. To uniquely define an event, use a station number / + ``ext_timestamp`` combination. + + +Reconstructions +--------------- + +Reconstructing ESD Events +^^^^^^^^^^^^^^^^^^^^^^^^^ + +Assume ``data_s501.h5`` contains events downloaded from the ESD for station +501:: + + >>> import tables + >>> from sapphire import ReconstructESDEvents + >>> data = tables.open_file('data_s501.h5', 'a') + >>> station_path = '/s501' # location of the event table in datafile + >>> rec = ReconstructESDEvents(data, station_path, 501, verbose=True) + Constructed object from public database. + +The parameter ``verbose=True`` forces the reconstruction class to be +verbose about the metadata about station layout of timing offsets that is +being used. Here, a ``HiSPARCStations`` object is created which retrieves +metadata from the public database. + +Now let's reconstruct the events and store the results in the datafile:: + + >>> rec.reconstruct_and_store() + Reading detector offsets from public database. + 100%|#################################################|Time: 0:00:31 + 100%|#################################################|Time: 0:00:21 + +The reconstruction class notifies us that detector timing offsets are being +retrieved from the public database. First directions are reconstructed and +subsequently core positions are reconstructed. + +The results are stored in the datafile in the `reconstructions` group:: + + >>> print(data.root.s501.reconstructions) + /s501/reconstructions (Table(29569,)) '' + + >>> data.root.s501.reconstructions.col('zenith') + array([ nan, nan, nan, ..., nan, nan, 0.66482645], dtype=float32) + +The `nan` values in the zenith angle array are values that cannot be +reconstructed:: + + >>> from numpy import isnan + >>> theta = data.root.s501.reconstructions.col('zenith') + >>> theta = theta.compress(~isnan(theta)) + array([ 0.59542936, 0.74284476, 0.38334498, ..., 0.41376889, + 0.07834953, 0.66482645], dtype=float32) + + +Reconstructing simulated events +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Assume the datafile (`data`) contains simulated events, generated by +:class:`sapphire.simulations.groundparticles.GroundParticlesSimulation`. +This class stores the metadata of the simulated station (cluster) in the +hdf5 file, which can be read by the reconstruction class:: + + >>> rec = ReconstructESDEvents(data, station_path, 501, verbose=True) + Read cluster HiSPARCStations([501]) from datafile. + +The class has read the station (cluster object) from the datafile. This +cluster object contains both the detector locations as well as the +detector timing offsets:: + + >>> rec.reconstruct_and_store() + Read detector offsets from cluster object. + 100%|#################################################|Time: 0:00:31 + 100%|#################################################|Time: 0:00:21 + +Note that the detector (timing) offset from the datafile are used, +and not the offsets from the public database. + + +Reconstructing ESD coinicdences +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Coincidences can be reconstructed analogous to ESD Events:: + + >>> import tables + >>> from sapphire import ReconstructESDCoincidences + >>> data = tables.open_file('data_s501_s502_s503.h5', 'a') + >>> rec = ReconstructESDEvents(data, verbose=True) + Constructed cluster HiSPARCStations([501, 502, 503]) from public + database. + +The constructed cluster object is created from the stations in the +datafile. + +Reconstruct:: + + >>> rec.reconstruct_and_store() + Using timing offsets from public database. + 100%|#################################################|Time: 0:00:31 + 100%|#################################################|Time: 0:00:21 + +Reconstructing simulated coincidences +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The ``ReconstructESDCoincidences`` class can reconstruct simulated +coincidences, using the cluster objects stored by the simulation class +in the datafile analogous to ``ReconstructESDEvents``. diff --git a/_sources/utils.rst.txt b/_sources/utils.rst.txt new file mode 100644 index 00000000..c1eab3ae --- /dev/null +++ b/_sources/utils.rst.txt @@ -0,0 +1,6 @@ +Common utility functions +======================== + +.. automodule:: sapphire.utils + :members: + :undoc-members: diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 00000000..7ebbd6d0 --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,914 @@ +/* + * Sphinx stylesheet -- basic theme. + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; 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+ } +} \ No newline at end of file diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 00000000..0398ebb9 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,149 @@ +/* + * Base JavaScript utilities for all Sphinx HTML documentation. + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 00000000..7f51004a --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '2.0.0', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; 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// [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/minus.png b/_static/minus.png new file mode 100644 index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d GIT binary patch literal 90 zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK literal 0 HcmV?d00001 diff --git a/_static/nature.css b/_static/nature.css new file mode 100644 index 00000000..e26d936f --- /dev/null +++ b/_static/nature.css @@ -0,0 +1,245 @@ +/* + * Sphinx stylesheet -- nature theme. + */ + +@import url("basic.css"); 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+ background-color: #BED4EB; + font-weight: normal; + color: #212224; + margin: 30px 0px 10px 0px; + padding: 5px 0 5px 10px; + text-shadow: 0px 1px 0 white +} + +div.body h1 { border-top: 20px solid white; margin-top: 0; font-size: 200%; } +div.body h2 { font-size: 150%; background-color: #C8D5E3; } +div.body h3 { font-size: 120%; background-color: #D8DEE3; } +div.body h4 { font-size: 110%; background-color: #D8DEE3; } +div.body h5 { font-size: 100%; background-color: #D8DEE3; } +div.body h6 { font-size: 100%; background-color: #D8DEE3; } + +a.headerlink { + color: #c60f0f; + font-size: 0.8em; + padding: 0 4px 0 4px; + text-decoration: none; +} + +a.headerlink:hover { + background-color: #c60f0f; + color: white; +} + +div.body p, div.body dd, div.body li { + line-height: 1.5em; +} + +div.admonition p.admonition-title + p { + display: inline; +} + +div.note { + background-color: #eee; + border: 1px solid #ccc; +} + +div.seealso { + background-color: #ffc; + border: 1px solid #ff6; +} + +nav.contents, +aside.topic, +div.topic { + background-color: #eee; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename, kind] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename, kind] = item; + + let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + SearchResultKind.title, + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + SearchResultKind.object, + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + SearchResultKind.text, + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? 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' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/analysis.html b/analysis.html new file mode 100644 index 00000000..80bcea4f --- /dev/null +++ b/analysis.html @@ -0,0 +1,163 @@ + + + + + + + + Data analysis — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Data analysis

+

Perform data analysis tasks on HiSPARC data

+

This package contains modules for performing various analysis tasks. Most of +the modules are used for automatic processing and reconstuction of traces, +events and coincidences and calculation of timing offsets.

+

The reconstruction classes from the reconstructions +module are often used interactively. Shower reconstructions, both from ESD- +and simulated data are performed by the classes from +direction_reconstruction and +core_reconstruction to reconstruct events. But users +only need to deal with reconstructions.

+
+

Modules in this package:

+
+
calibration

determine timing calibration values

+
+
coincidence_queries

filter coincidences analysed with +CoincidencesESD

+
+
coincidences

search for coincidences between HiSPARC stations

+
+
core_reconstruction

shower core and shower size reconstruction

+
+
direction_reconstruction

EAS direction reconstruction

+
+
event_utils

get data from processed events

+
+
find_mpv

find the MIP peak MPV in pulseintegral data

+
+
landau

Landau distribution

+
+
process_events

process HiSPARC events

+
+
process_traces

process HiSPARC traces

+
+
reconstructions

perform shower reconstructions

+
+
signal_calibration

determine signal calibration values

+
+
time_deltas

determine time deltas for station pairs

+
+
+
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/calibration.html b/analysis/calibration.html new file mode 100644 index 00000000..02192975 --- /dev/null +++ b/analysis/calibration.html @@ -0,0 +1,366 @@ + + + + + + + + Data timing and signal calibration — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Data timing and signal calibration

+

Determine calibration values for data

+

This module can be used to determine calibration values from data.

+

Determine timing offsets for detectors and stations to correct arrival times. +Determine the PMT response curve to correct the detected number of MIPs.

+
+
+sapphire.analysis.calibration.determine_detector_timing_offsets(events, station=None)
+

Determine the timing offsets between station detectors.

+
+
Parameters:
+
    +

  • events – events table of processed events.

    • +

  • station – sapphire.clusters.Station object, to determine +number of detectors and relative altitudes.

    • +
+
+
Returns:
+

list of detector offsets.

+
+
+
+ +
+
+sapphire.analysis.calibration.determine_detector_timing_offset(dt, dz=0)
+

Determine the timing offset between station detectors.

+
+
Parameters:
+
    +

  • dt – array of time differences between detectors (t - t_ref).

    • +

  • dz – height difference between the detectors (z - z_ref).

    • +
+
+
Returns:
+

mean of a gaussian fit to the data corrected for height, and +the error of the mean.

+
+
+
+ +
+
+class sapphire.analysis.calibration.DetermineStationTimingOffsets(stations=None, data=None, progress=False, force_stale=False, time_deltas_group='/coincidences/time_deltas')
+

Determine the timing offsets between stations

+

Initialize the class

+
+
Parameters:
+
    +

  • stations – list of stations for which to determine offsets.

    • +

  • data – the PyTables datafile with timedelta tables.

    • +

  • progress – if True show progressbar when determining offsets.

    • +

  • force_stale – if true: do not get network information from API.

    • +

  • time_deltas_group – path to the time deltas group.

    • +
+
+
+
+
+MAX_DISTANCE = 1000
+
+ +
+
+MIN_LEN_DT = 200
+
+ +
+
+read_dt(station, ref_station, start, end)
+

Read timedeltas from HDF5 file

+
+ +
+
+determine_first_and_last_date(date, station, ref_station)
+

Determine first and last date to include in determination of +station offset around date

+
+
Parameters:
+
    +

  • date – date around which the bounds are to be determined.

    • +

  • station – station number.

    • +

  • ref_station – reference station number.

    • +
+
+
Returns:
+

start and end date bounds.

+
+
+
+ +
+
+determine_station_timing_offset(date, station, ref_station)
+

Determine the timing offset between a station pair at certain date

+
+
Parameters:
+
    +

  • date – date for which to determine offset as datetime.date.

    • +

  • station – station number.

    • +

  • ref_station – reference station number.

    • +
+
+
Returns:
+

station offset and error.

+
+
+
+ +
+
+determine_station_timing_offsets(station, ref_station, start=None, end=None)
+

Determine the timing offsets between a station pair

+
+
Parameters:
+
    +

  • station – station number.

    • +

  • ref_station – reference station number.

    • +

  • start – datetime.date object.

    • +

  • end – datetime.date object.

    • +
+
+
Returns:
+

list of station offsets as tuple (timestamp, offset, error).

+
+
+
+ +
+
+determine_station_timing_offsets_for_date(date)
+

Determine the timing offsets between a station pair

+
+
Parameters:
+

date – date for which to determine offsets as datetime.date.

+
+
Returns:
+

list of station offsets as tuple +(station, ref_station, offset, error).

+
+
+
+ +
+
+get_station_pairs_within_max_distance(date=None)
+

Iterator that yields stations pairs that are close to each other

+
+ +
+ +
+
+sapphire.analysis.calibration.determine_station_timing_offset(dt, dz=0)
+

Determine the timing offset between stations.

+
+
Parameters:
+
    +

  • dt – a list of time differences between stations (t - t_ref).

    • +

  • dz – height difference between the stations (z - z_ref).

    • +
+
+
Returns:
+

mean of a gaussian fit to the data corrected for height, and +the error of the mean.

+
+
+
+ +
+
+sapphire.analysis.calibration.fit_timing_offset(dt, bins)
+

Fit the time difference distribution.

+
+
Parameters:
+
    +

  • dt – a list of time differences between stations (t - t_ref).

    • +

  • bins – bins edges to use for the histogram.

    • +
+
+
Returns:
+

mean of a gaussian fit to the data and the error of the mean.

+
+
+
+ +
+
+sapphire.analysis.calibration.determine_best_reference(filters)
+

Find which detector has most events in common with the others

+
+
Parameters:
+

filters – list of filters for each detector, selecting rows +where that detector has data.

+
+
Returns:
+

index for the detector that has most rows in common with +the other detectors.

+
+
+
+ +
+
+sapphire.analysis.calibration.datetime_range(start, end, step=1)
+

Generator that splits a date range in (almost) equal intervals

+

The yielded interval lengths are integer days +Spreads remaining days over first intervals

+
+
Parameters:
+
    +

  • start – date instance

    • +

  • end – date instance

    • +

  • step – the integer number of days in each interval

    • +
+
+
Returns:
+

a tuple of datetime instances for each interval

+
+
+
+ +
+
+sapphire.analysis.calibration.pairwise(iterable)
+

s -> (s0, s1), (s1, s2), (s2, s3), …

+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/coincidence_queries.html b/analysis/coincidence_queries.html new file mode 100644 index 00000000..242f0542 --- /dev/null +++ b/analysis/coincidence_queries.html @@ -0,0 +1,387 @@ + + + + + + + + Query coincidences from a datafile — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Query coincidences from a datafile

+
+
+class sapphire.analysis.coincidence_queries.CoincidenceQuery(data, coincidence_group='/coincidences')
+

Perform queries on an ESD file where coincidences have been analysed.

+

Functions in this class build and perform queries to easily filter +coincidences based on station participation and station +organization. This assumes coincidences have been analysed using +CoincidencesESD.

+

First initiate the class so it can get the correct tables, then get +coincidences using one of the functions (all_coincidences, any, all, +at_least), then get the events for these coincidences using other +functions (all_events, events_from_[stations, subcluster, cluster]).

+

An exception will occur when you include a station in a query that +does not occur in the datafile.

+

Example usage:

+
>>> from sapphire import CoincidenceQuery
+>>> cq = CoincidenceQuery('2013_8_1.h5')
+>>> coincidences = cq.all([501, 502, 503, 504], iterator=True)
+>>> events = cq.all_events(coincidences)
+>>> specific_events = cq.events_from_stations(coincidences,
+...                                          [501, 502, 503, 504])
+>>> cq.finish()
+
+
+

Setup variables to point to the tables

+
+
Parameters:
+
    +

  • data – either a PyTables file or path to a HDF5 file.

    • +

  • coincidence_group – path to the coincidences group.

    • +
+
+
+
+
+finish()
+

Clean-up after using

+

Do not use if you opened the file yourself and still intent to +use it.

+
+ +
+
+all_coincidences(iterator=False)
+

Get all coincidences

+
+
Returns:
+

all coincidences.

+
+
+
+ +
+
+any(stations, start=None, stop=None, iterator=False)
+

Filter for coincidences that contain any of the given stations

+
+
Parameters:
+

stations – list of stations from which any need to be in +a coincidence.

+
+
Returns:
+

coincidences matching the query.

+
+
+
+ +
+
+all(stations, start=None, stop=None, iterator=False)
+

Filter for coincidences that contain all of the given stations

+
+
Parameters:
+

stations – list of stations which all need to be in a +coincidence.

+
+
Returns:
+

coincidences matching the query.

+
+
+
+ +
+
+at_least(stations, n, start=None, stop=None, iterator=False)
+

Filter coincidences to contain at least n of the given stations

+
+
Parameters:
+
    +

  • stations – list of stations from which any at least n of +any combinations need to be in a coincidence.

    • +

  • n – minimum number of given stations to be in a coincidence.

    • +
+
+
Returns:
+

coincidences matching the query.

+
+
+
+ +
+
+timerange(start, stop, iterator=False)
+

Query based on timestamps

+
+
Parameters:
+
    +

  • start – timestamp from which to look for coincidences.

    • +

  • stop – end timestamp for coincidences.

    • +
+
+
Returns:
+

coincidences within the specified timerange.

+
+
+
+ +
+
+perform_query(query, iterator=False)
+

Perform a query on the coincidences table

+
+
Parameters:
+

query – a valid PyTables query string for the coincidences +table.

+
+
Returns:
+

coincidences matching the query.

+
+
+
+ +
+
+all_events(coincidences, n=0)
+

Get all events for the given coincidences.

+
+
Parameters:
+
    +

  • coincidences – list of coincidence rows.

    • +

  • n – minimum number of events per coincidence.

    • +
+
+
Returns:
+

list of events for each coincidence.

+
+
+
+ +
+
+all_reconstructions(coincidences, n=0)
+

Get all reconstructed events for the given coincidences.

+
+
Parameters:
+
    +

  • coincidences – list of coincidence rows.

    • +

  • n – minimum number of events per coincidence.

    • +
+
+
Returns:
+

list of reconstructed events for each coincidence.

+
+
+
+ +
+
+minimum_events_for_coincidence(coincidences_events, n=2)
+

Filter coincidences to only include those with at least n events.

+
+
Parameters:
+
    +

  • coincidences_events – list of events for each coincidence.

    • +

  • n – minimum number of events per coincidence.

    • +
+
+
+
+ +
+
+events_from_stations(coincidences, stations, n=2)
+

Only get events for specific stations for coincidences.

+
+
Parameters:
+
    +

  • coincidences – list of coincidence rows.

    • +

  • stations – list of station numbers to filter events for.

    • +
+
+
Returns:
+

list of filtered events for each coincidence.

+
+
+
+ +
+
+reconstructions_from_stations(coincidences, stations, n=2)
+

Only get reconstructions for specific stations for coincidences.

+
+
Parameters:
+
    +

  • coincidences – list of coincidence rows.

    • +

  • stations – list of station numbers to filter events for.

    • +
+
+
Returns:
+

list of filtered reconstructed events for each coincidence.

+
+
+
+ +
+
+events_in_subcluster(coincidences, subcluster, n=2)
+

Filter to only events from stations in a specific subcluster

+
+
Parameters:
+
    +

  • coincidences – list of events for each coincidence.

    • +

  • subcluster – subcluster number to filter events for.

    • +
+
+
Returns:
+

tuples of station numbers and events for matched events.

+
+
+
+ +
+
+events_in_cluster(coincidences, cluster, n=2)
+

Filter to only events from stations in a specific subcluster

+
+
Parameters:
+
    +

  • coincidences – list of events for each coincidence.

    • +

  • cluster – cluster number to filter events for.

    • +
+
+
Returns:
+

tuples of station numbers and events for matched events.

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/coincidences.html b/analysis/coincidences.html new file mode 100644 index 00000000..1f706326 --- /dev/null +++ b/analysis/coincidences.html @@ -0,0 +1,485 @@ + + + + + + + + Search for coincidences between HiSPARC stations — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Search for coincidences between HiSPARC stations

+

Search for coincidences between HiSPARC stations

+

This module can be used to search for coincidences between several +HiSPARC stations. To skip this and directly download coincidences +use download_coincidences(), this is slightly +less flexible because you can not choose the coincidence window.

+

For regular usage, download events from the ESD and use the +CoincidencesESD class. Example usage:

+
import datetime
+
+import tables
+
+from sapphire import CoincidencesESD, download_data
+
+STATIONS = [501, 503, 506]
+START = datetime.datetime(2013, 1, 1)
+END = datetime.datetime(2013, 1, 2)
+
+
+if __name__ == '__main__':
+    station_groups = ['/s%d' % u for u in STATIONS]
+
+    data = tables.open_file('data.h5', 'w')
+    for station, group in zip(STATIONS, station_groups):
+        download_data(data, group, station, START, END)
+
+    coin = CoincidencesESD(data, '/coincidences', station_groups)
+    coin.search_and_store_coincidences()
+
+
+
+
+class sapphire.analysis.coincidences.Coincidences(data, coincidence_group, station_groups, overwrite=False, progress=True)
+

Search for and store coincidences between HiSPARC stations.

+
+

Note

+

For better compatibility with other modules, such as +reconstructions, it is recommended to use the +subclass CoincidencesESD instead. This is an old class with a +different way to store the results. It can be used, however, on raw +data downloaded from the public database.

+
+

Suppose you want to search for coincidences between stations 501 and +503. First, download the data for these stations (with, or without +traces, depending on your intentions). Suppose you stored the data in +the ‘/s501’ and ‘/s503’ groups in the ‘data.h5’ file. Then:

+
>>> groups = ['/s501', '/s503']
+>>> with Coincidences('data.h5', '/coincidences', groups) as coin:
+...     coin.search_and_store_coincidences()
+
+
+

If you want a more manual method, replace the last line the separate +methods which it calls:

+
>>> with Coincidences('data.h5', '/coincidences', groups) as coin:
+...     coin.search_coincidences(window=50000)
+...     coin.process_events()
+...     coin.store_coincidences()
+
+
+

You can then provide different parameters to the individual methods. +See the corresponding docstrings.

+

Once the coincidences are stored, there will be a coincidences table in +the group. This table has multiple columns used for storing simulation +inputs like shower direction and energy. At this point, they contain no +information. They are only useful if the original event tables were created +by simulations, instead of real detector data. The useful columns are:

+
+
    +

  • id: the index of this coincidence. This index is identical for +the coincidence and c_index tables.

    • +

  • timestamp: the timestamp of the event in seconds

    • +

  • nanoseconds: the subsecond part of the timestamp in nanoseconds

    • +

  • ext_timestamp: the timestamp of the event in nanoseconds (equal +to timestamp * 1000000000 + nanoseconds)

    • +

  • N: the number of stations participating in this coincidence

    • +

  • s0, s1, …: whether the first (0), second (1) or other +stations participated in the coincidence.

    • +
+
+

The coincidences group furthermore contains the table c_index to track +down the individual events making up the coincidence. The c_index table +gives indexes for the individual events inside the original event tables.

+

If you have obtained a particular coincidence from the coincidences +table, the id is the index into all these tables. For example, looking +up the source events making up the 40th coincidence:

+
>>> group = data.root.coincidences
+>>> idx = 40
+>>> group.coincidences[idx]
+
+
+

can be done in the following way (each row in the c_index table is a +(station index, event index) pair):

+
>>> for event_idx in group.c_index[idx]:
+...     event = group.observables[event_idx]
+
+
+

The event is one of the source events, processed to determine particle +arrival times from the raw traces (if available). It has a +station_id attribute which is an index into station_groups.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – either a PyTables file or path to a HDF5 file.

    • +

  • coincidence_group – the destination group. If None the results +can not be stored, but coincidences can still be searched for.

    • +

  • station_groups – a list of groups containing the station data.

    • +

  • overwrite – if True overwrite a previous coincidences group.

    • +

  • progress – if True show a progressbar while storing coincidences.

    • +
+
+
+
+
+search_and_store_coincidences(window=10000)
+

Search, process and store coincidences.

+

This is a semi-automatic method to search for coincidences, +process the events making up the coincidences and then store the +results in the coincidences group.

+

If you want to make use of non-default parameters like time +shifts or overwriting previously processed events, please call +the individual methods. See the class docstring.

+
+ +
+
+search_coincidences(window=10000, shifts=None, limit=None)
+

Search for coincidences.

+

Search all data in the station_groups for coincidences, and store +rudimentary coincidence data in the coincidences group. This data +might be useful, but is very basic. You can call the +store_coincidences() method to store the coincidences in an +easier format in the coincidences group.

+

If you want to process the preliminary results: they are stored in +_src_c_index and _src_timestamps. The former is a list of +coincidences, which each consist of a list with indexes into the +timestamps array as a pointer to the events making up the +coincidence. The latter is a list of tuples. Each tuple consists +of a timestamp followed by an index into the stations list which +designates the detector station which measured the event, and +finally an index into that station’s event table.

+
+
Parameters:
+
    +

  • window – the coincidence time window in nanoseconds. All events +with delta t’s smaller than this window will be considered a +coincidence.

    • +

  • shifts – optionally shift a station’s data in time. This +can be useful if a station has a misconfigured GPS clock. +Expects a list of shifts, one for each station, in seconds. +Use ‘None’ for no shift.

    • +

  • limit – optionally limit the search for this number of +events.

    • +
+
+
+
+ +
+
+process_events(overwrite=None)
+

Process events using process_events

+

Events making up the coincidences are processed to obtain +observables like number of particles and particle arrival times.

+
+
Parameters:
+

overwrite – if True, overwrite the events tables in the +station groups.

+
+
+
+ +
+
+store_coincidences()
+

Store the previously found coincidences.

+

After you have searched for coincidences, you can store the +more user-friendly results in the coincidences group using this +method.

+
+ +
+ +
+
+class sapphire.analysis.coincidences.CoincidencesESD(data, coincidence_group, station_groups, overwrite=False, progress=True)
+

Store coincidences specifically using the ESD

+

This is a subclass of Coincidences. This subclass stores the paths +to the station_groups that where used to look for coincidences in a +lookup-table. The c_index stores the station and event id for each event in +the coincidence, allowing you to find the original event row.

+

Suppose you want to search for coincidences between stations 501 and 503. +First, download the data for these stations from the ESD. Suppose you +stored the data in the ‘/s501’ and ‘/s503’ groups in the file ‘data.h5’. +Then:

+
>>> groups = ['/s501', '/s503']
+>>> with CoincidencesESD('data.h5', '/coincidences', groups) as coin:
+...     coin.search_and_store_coincidences(station_numbers=[501, 503])
+
+
+

If you want a more manual method, replace the last line with, for example:

+
>>> with CoincidencesESD('data.h5', '/coincidences', groups) as coin:
+...     coin.search_coincidences(5000, shifts=[None, 10], limit=100)
+...     coin.store_coincidences(station_numbers=[501, 503])
+
+
+

You can then provide different parameters to the individual methods. +See the corresponding docstrings for more details.

+

Once the coincidences are stored, there will be a coincidences table in +the group. This table has multiple columns used for storing simulation +inputs like shower direction and energy. At this point, they contain no +information. They are only used if the event and coincidence tables were +created by simulations, instead of real detector data. The useful columns +are:

+
+
    +

  • id: the index of this coincidence. This index is identical for +the coincidence and c_index tables.

    • +

  • timestamp: the timestamp of the event in seconds

    • +

  • nanoseconds: the subsecond part of the timestamp in nanoseconds

    • +

  • ext_timestamp: the timestamp of the event in nanoseconds (equal +to timestamp * 1000000000 + nanoseconds)

    • +

  • N: the number of stations participating in this coincidence

    • +

  • s501, s503, …: for each station indicate whether it +participated in the coincidence.

    • +
+
+

The coincidences group furthermore contains the tables s_index and +c_index to track down the individual events making up the coincidence. +The s_index table contains a row for each station, pointing to the +station’s event tables. The c_index table gives indexes for the +individual events inside those tables.

+

If you have obtained a particular coincidence from the coincidences +table, the id is the index into all these tables. For example, looking +up the source events making up the 40th coincidence:

+
>>> group = data.root.coincidences
+>>> idx = 40
+>>> group.coincidences[idx]
+
+
+

can be done in the following way (each row in the c_index table is a +(station index, event index) pair):

+
>>> for station_idx, event_idx in group.c_index[idx]:
+...     station_path = group.s_index[station_idx]
+...     event_group = data.get_node(station_path, 'events')
+...     event = event_group[event_idx]
+
+
+

The event is one of the events in the coincidence.

+

Coincidences stored by this class can easily be searched by using +a CoincidenceQuery object.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – either a PyTables file or path to a HDF5 file.

    • +

  • coincidence_group – the destination group. If None the results +can not be stored, but coincidences can still be searched for.

    • +

  • station_groups – a list of groups containing the station data.

    • +

  • overwrite – if True overwrite a previous coincidences group.

    • +

  • progress – if True show a progressbar while storing coincidences.

    • +
+
+
+
+
+search_and_store_coincidences(window=10000, station_numbers=None)
+

Search and store coincidences.

+

This is a semi-automatic method to search for coincidences +and then store the results in the coincidences group.

+
+ +
+
+search_coincidences(window=10000, shifts=None, limit=None)
+

Search for coincidences.

+

Search all data in the station_groups for coincidences, and store +rudimentary coincidence data in attributes. This data might be useful, +but is very basic. You can call the store_coincidences() method +to store the coincidences in an easier format in the coincidences +group.

+

If you want to process the preliminary results: they are stored in the +attributes _src_c_index and _src_timestamps. The +former is a list of coincidences, which each consist of a list with +indexes into the timestamps array as a pointer to the events making up +the coincidence. The latter is a list of tuples. Each tuple consists +of a timestamp followed by an index into the stations list which +designates the detector station which measured the event, and finally +an index into that station’s event table.

+
+
Parameters:
+
    +

  • window – the coincidence time window. All events with delta +t’s smaller than this window will be considered a coincidence.

    • +

  • shifts – optionally shift a station’s data in time. This +can be useful if a station has a misconfigured GPS clock. +Expects a list of shifts, one for each station.

    • +

  • limit – optionally limit the search for this number of +events.

    • +
+
+
+
+ +
+
+store_coincidences(station_numbers=None)
+

Store the previously found coincidences.

+

After having searched for coincidences, you can store the more +user-friendly results in the coincidences group using this +method. It also created a c_index and s_index table to +find the source events.

+
+
Parameters:
+

station_numbers – optional list of station_numbers. +If given these will be used to attach correct numbers to the +station column names in the coincidences table. Otherwise +they will simply be numbered by id. This list must be the +same length as the station_groups.

+
+
+
+ +
+ +
+
+sapphire.analysis.coincidences.get_events(data, stations, coincidence, timestamps, get_raw_traces=False)
+

Get event data of a coincidence

+

Return a list of events making up a coincidence.

+
+
Parameters:
+
    +

  • data – the PyTables data file

    • +

  • stations – a list of HiSPARC event tables (normally from +different stations, hence the name)

    • +

  • coincidence – a coincidence, as returned by +search_coincidences().

    • +

  • timestamps – the list of timestamps, as returned by +search_coincidences().

    • +

  • get_raw_traces – boolean. If true, return the raw ADC +values instead of the baseline corrected traces.

    • +
+
+
Returns:
+

a list of tuples. Each tuple consists of (station, event, +traces), where event is the event row from PyTables and traces is +a list of the uncompressed traces.

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/core_reconstruction.html b/analysis/core_reconstruction.html new file mode 100644 index 00000000..8b6ec5bb --- /dev/null +++ b/analysis/core_reconstruction.html @@ -0,0 +1,494 @@ + + + + + + + + Reconstruction of EAS core and size — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Reconstruction of EAS core and size

+

Core reconstruction

+

This module contains two classes that can be used to reconstruct +HiSPARC events and coincidences. The classes know how to extract the +relevant information from the station and event or cluster and +coincidence. Various algorithms which do the reconstruction are also +defined here. The algorithms require positions and particle densties to +do the reconstruction.

+

Each algorithm has a reconstruct_common() +method which always requires particle denisties, x, and y positions +and optionally z positions and previous reconstruction results. The +data is then prepared for the algorithm and passed to +the reconstruct() method which returns the +reconstructed x and y coordinates.

+
+
+class sapphire.analysis.core_reconstruction.EventCoreReconstruction(station)
+

Reconstruct core for station events

+

This class is aware of ‘events’ and ‘stations’. Initialize this class +with a ‘station’ and you can reconstruct events using +reconstruct_event().

+
+
Parameters:
+

station – sapphire.clusters.Station object.

+
+
+
+
+reconstruct_event(event, detector_ids=None, initial=None)
+

Reconstruct a single event

+
+
Parameters:
+
    +

  • event – an event (e.g. from an events table), or any +dictionary-like object containing the keys necessary for +reconstructing the direction of a shower (e.g. number of mips).

    • +

  • detector_ids – list of the detectors to use for +reconstruction. The detector ids are 0-based, unlike the +column names in the esd data.

    • +

  • initial – dictionary with already reconstructed shower +parameters.

    • +
+
+
Returns:
+

(x, y) core position in m.

+
+
+
+ +
+
+reconstruct_events(events, detector_ids=None, progress=True, initials=None)
+

Reconstruct events

+
+
Parameters:
+
    +

  • events – the events table for the station from an ESD data +file.

    • +

  • detector_ids – detectors which use for the reconstructions.

    • +

  • progress – if True show a progress bar while reconstructing.

    • +

  • initials – list of dictionaries with already reconstructed shower +parameters.

    • +
+
+
Returns:
+

(x, y) core positions in m.

+
+
+
+ +
+ +
+
+class sapphire.analysis.core_reconstruction.CoincidenceCoreReconstruction(cluster)
+

Reconstruct core for coincidences

+

This class is aware of ‘coincidences’ and ‘clusters’. Initialize +this class with a ‘cluster’ and you can reconstruct a coincidence +using reconstruct_coincidence().

+
+
Parameters:
+

cluster – sapphire.clusters.BaseCluster object.

+
+
+
+
+reconstruct_coincidence(coincidence, station_numbers=None, initial=None)
+

Reconstruct a single coincidence

+
+
Parameters:
+
    +

  • coincidence – a coincidence list consisting of +multiple (station_number, event) tuples

    • +

  • station_numbers – list of station numbers, to only use +events from those stations.

    • +

  • initial – dictionary with already reconstructed shower +parameters.

    • +
+
+
Returns:
+

(x, y) core position in m.

+
+
+
+ +
+
+reconstruct_coincidences(coincidences, station_numbers=None, progress=True, initials=None)
+

Reconstruct all coincidences

+
+
Parameters:
+
    +

  • coincidences – a list of coincidences, each consisting of +multiple (station_number, event) tuples.

    • +

  • station_numbers – list of station numbers, to only use +events from those stations.

    • +

  • progress – if True show a progress bar while reconstructing.

    • +

  • initials – list of dictionaries with already reconstructed shower +parameters.

    • +
+
+
Returns:
+

(x, y) core positions in m.

+
+
+
+ +
+ +
+
+class sapphire.analysis.core_reconstruction.CoincidenceCoreReconstructionDetectors(cluster)
+

Reconstruct core for coincidences using each detector

+

Instead of using the average station particle density this class +uses the particle density in each detector for the reconstruction.

+
+
+reconstruct_coincidence(coincidence, station_numbers=None, initial=None)
+

Reconstruct a single coincidence

+
+
Parameters:
+
    +

  • coincidence – a coincidence list consisting of +multiple (station_number, event) tuples

    • +

  • station_numbers – list of station numbers, to only use +events from those stations.

    • +

  • initial – dictionary with already reconstructed shower +parameters.

    • +
+
+
Returns:
+

(x, y) core position in m.

+
+
+
+ +
+ +
+
+class sapphire.analysis.core_reconstruction.BaseCoreAlgorithm
+

No actual core reconstruction algorithm

+

Simply returns (nan, nan) as core.

+
+
+classmethod reconstruct_common(p, x, y, z=None, initial=None)
+

Reconstruct core position

+
+
Parameters:
+
    +

  • p – detector particle density in m^-2.

    • +

  • x,y – positions of detectors in m.

    • +

  • z – height of detectors in m.

    • +

  • initial – dictionary containing values from previous +reconstructions.

    • +
+
+
Returns:
+

reconstructed core position.

+
+
+
+ +
+
+static reconstruct()
+

Reconstruct core position

+
+
Returns:
+

reconstructed core position.

+
+
+
+ +
+ +
+
+class sapphire.analysis.core_reconstruction.CenterMassAlgorithm
+

Simple core estimator

+

Estimates the core by center of mass of the measurements.

+
+
+classmethod reconstruct_common(p, x, y, z=None, initial=None)
+

Reconstruct core position

+
+
Parameters:
+
    +

  • p – detector particle density in m^-2.

    • +

  • x,y – positions of detectors in m.

    • +

  • z – height of detectors is ignored.

    • +

  • initial – dictionary containing values from previous +reconstructions.

    • +
+
+
Returns:
+

reconstructed core position.

+
+
+
+ +
+
+static reconstruct(p, x, y)
+

Calculate center of mass

+
+
Parameters:
+
    +

  • p – detector particle density in m^-2.

    • +

  • x,y – positions of detectors in m.

    • +
+
+
Returns:
+

reconstructed core position.

+
+
+
+ +
+ +
+
+class sapphire.analysis.core_reconstruction.AverageIntersectionAlgorithm
+

Core estimator

+

To the densities in 3 stations correspond 2 possible cores. The line +through these points is quite stable for the lateral distribution function. +To each combination of 3 stations out of a set of at least 4 +stations hit corresponds a line. To each combinations of 2 lines out of +the set of lines corresponds a point of intersection (if the 2 lines are +not collinear). Taking the cloud of intersection points close to the core +estimated by the center of mass, and averaging the positions in this cloud +results in an estimation for the core.

+
+
+classmethod reconstruct_common(p, x, y, z=None, initial=None)
+

Reconstruct core

+
+
Parameters:
+
    +

  • p – detector particle density in m^-2.

    • +

  • x,y – positions of detectors in m.

    • +

  • z – height of detectors is ignored.

    • +

  • initial – dictionary containing values from previous +reconstructions.

    • +
+
+
Returns:
+

reconstructed core position.

+
+
+
+ +
+
+static select_newlist(newx, newy, xpointlist, ypointlist, distance)
+

Select intersection points in square around the mean of old list.

+
+ +
+ +
+
+class sapphire.analysis.core_reconstruction.EllipsLdfAlgorithm
+

Simple core estimator

+

Estimates the core by center of mass of the measurements.

+
+
+classmethod reconstruct_common(p, x, y, z=None, initial=None)
+

Reconstruct core position

+
+
Parameters:
+
    +

  • p – detector particle density in m^-2.

    • +

  • x,y – positions of detectors in m.

    • +

  • z – height of detectors is ignored.

    • +

  • initial – dictionary containing values from previous +reconstructions: zenith and azimuth.

    • +
+
+
Returns:
+

reconstructed core position.

+
+
+
+ +
+
+classmethod reconstruct(p, x, y, theta, phi)
+

Reconstruct the number of electrons that fits best.

+
+
Parameters:
+
    +

  • p – detector particle density in m^-2.

    • +

  • x,y – positions of detectors in m.

    • +

  • theta,phi – zenith and azimuth angle in rad.

    • +
+
+
Returns:
+

reconstructed core position, chi square, and shower size.

+
+
+
+ +
+
+static selectbest(p, x, y, xstart, ystart, factorbest, chi2best, gridsize, theta, phi)
+

selects the best core position in grid around (xstart, ystart).

+
+
Parameters:
+
    +

  • p – detector particle density in m^-2.

    • +

  • x,y – positions of detectors in m.

    • +

  • xcmass,ycmass – start position of core in m.

    • +
+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/direction_reconstruction.html b/analysis/direction_reconstruction.html new file mode 100644 index 00000000..25ab8596 --- /dev/null +++ b/analysis/direction_reconstruction.html @@ -0,0 +1,1014 @@ + + + + + + + + Reconstruction of EAS direction — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Reconstruction of EAS direction

+

Direction reconstruction

+

This module contains two classes that can be used to reconstruct +HiSPARC events and coincidences. The classes know how to extract the +relevant information from the station and event or cluster and +coincidence. Various algorithms which do the reconstruction are also +defined here. The algorithms require positions and arrival times to +do the reconstruction.

+

Each algorithm has a reconstruct_common() +method which always requires arrival times, x, and y positions and +optionally z positions and previous reconstruction results. The data +is then prepared for the algorithm and passed to +the reconstruct() method which returns the +reconstructed theta and phi coordinates.

+
+
+class sapphire.analysis.direction_reconstruction.EventDirectionReconstruction(station)
+

Reconstruct direction for station events

+

This class is aware of ‘events’ and ‘stations’. Initialize this class +with a ‘station’ and you can reconstruct events using +reconstruct_event(). To use other algorithms overwrite the +direct and fit attributes.

+
+
Parameters:
+

station – sapphire.clusters.Station object.

+
+
+
+
+reconstruct_event(event, detector_ids=None, offsets=[0.0, 0.0, 0.0, 0.0], initial=None)
+

Reconstruct a single event

+
+
Parameters:
+
    +

  • event – an event (e.g. from an events table), or any +dictionary-like object containing the keys necessary for +reconstructing the direction of a shower (e.g. arrival times).

    • +

  • detector_ids – list of the detectors to use for +reconstruction. The detector ids are 0-based, unlike the +column names in the esd data.

    • +

  • offsets – time offsets for each detector or a +Station object.

    • +

  • initial – dictionary with already fitted shower parameters.

    • +
+
+
Returns:
+

theta, phi, and detector ids.

+
+
+
+ +
+
+reconstruct_events(events, detector_ids=None, offsets=[0.0, 0.0, 0.0, 0.0], progress=True, initials=None)
+

Reconstruct events

+
+
Parameters:
+
    +

  • events – the events table for the station from an ESD data file.

    • +

  • detector_ids – detectors to use for the reconstructions.

    • +

  • offsets – time offsets for each detector or a +Station object.

    • +

  • progress – if True show a progress bar while reconstructing.

    • +

  • initials – list of dictionaries with already reconstructed shower +parameters.

    • +
+
+
Returns:
+

list of theta, phi, and detector ids.

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.CoincidenceDirectionReconstruction(cluster)
+

Reconstruct direction for coincidences

+

This class is aware of ‘coincidences’ and ‘clusters’. Initialize +this class with a ‘cluster’ and you can reconstruct a coincidence +using reconstruct_coincidence(). To use other algorithms +overwrite the direct,``fit``, and curved attributes.

+
+
Parameters:
+

cluster – BaseCluster object.

+
+
+
+
+reconstruct_coincidence(coincidence_events, station_numbers=None, offsets=None, initial=None)
+

Reconstruct a single coincidence

+
+
Parameters:
+
    +

  • coincidence_events – a coincidence list consisting of three +or more (station_number, event) tuples.

    • +

  • station_numbers – list of station numbers, to only use +events from those stations.

    • +

  • offsets – a dictionary of either lists of detector timing +offsets or Station objects for each station.

    • +

  • initial – dictionary with already fitted shower parameters.

    • +
+
+
Returns:
+

list of theta, phi, and station numbers.

+
+
+
+ +
+
+reconstruct_coincidences(coincidences, station_numbers=None, offsets=None, progress=True, initials=None)
+

Reconstruct all coincidences

+
+
Parameters:
+
    +

  • coincidences – a list of coincidence events, each consisting +of three or more (station_number, event) tuples.

    • +

  • station_numbers – list of station numbers, to only use +events from those stations.

    • +

  • offsets – dictionary with detector offsets for each station. +These detector offsets should be relative to one +detector from a specific station.

    • +

  • progress – if True show a progress bar while reconstructing.

    • +

  • initials – list of dictionaries with already reconstructed shower +parameters.

    • +
+
+
Returns:
+

list of theta, phi, and station numbers.

+
+
+
+ +
+
+get_station_offsets(coincidence_events, station_numbers, offsets, ts0)
+
+ +
+
+determine_best_offsets(station_numbers, midnight_ts, offsets)
+

Determine best combined station and detector offsets

+

Check which station is best used as reference. Allow offsets via +other stations, intermediate stations are used if it reduces the +offset error.

+
+
Parameters:
+
    +

  • station_numbers – list of stations in the coincidence or also +other stations can are allow to be the +reference station.

    • +

  • midnight_ts – timestamp of midnight before the coincidence.

    • +

  • offsets – a dictionary of Station objects +for each station.

    • +
+
+
Returns:
+

combined detector and station offsets for given station, +relative to the reference station.

+
+
+
+ +
+
+determine_best_reference(error_matrix, station_numbers)
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.CoincidenceDirectionReconstructionDetectors(cluster)
+

Reconstruct direction for coincidences using each detector

+

Instead of only the first arrival time per station this class +uses the arrival time in each detector for the reconstruction.

+
+
+reconstruct_coincidence(coincidence_events, station_numbers=None, offsets=None, initial=None)
+

Reconstruct a single coincidence

+
+
Parameters:
+
    +

  • coincidence_events – a coincidence list consisting of one +or more (station_number, event) tuples.

    • +

  • station_numbers – list of station numbers, to only use +events from those stations.

    • +

  • offsets – dictionary with detector offsets for each station. +These detector offsets should be relative to one +detector from a specific station.

    • +

  • initial – dictionary with already fitted shower parameters.

    • +
+
+
Returns:
+

list of theta, phi, and station numbers.

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.BaseDirectionAlgorithm
+

No actual direction reconstruction algorithm

+

Simply returns (nan, nan) as direction.

+
+
+classmethod reconstruct_common(t, x, y, z=None, initial=None)
+

Reconstruct shower angles

+
+
Parameters:
+
    +

  • t – detector arrival time in ns.

    • +

  • x,y – positions of detectors in m.

    • +

  • z – height of detectors in m.

    • +

  • initial – dictionary containing values from previous +reconstructions.

    • +
+
+
Returns:
+

reconstructed theta and phi angles.

+
+
+
+ +
+
+static reconstruct()
+

Reconstruct shower angles

+
+
Returns:
+

reconstructed theta and phi angles.

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.DirectAlgorithm
+

Reconstruct angles using direct analytical formula.

+

This implements the equations derived in Fokkema2012 sec 4.2. +(DOI: 10.3990/1.9789036534383)

+

This algorithm assumes each detector is at the same altitude.

+

Note! The detectors are 0-based.

+
+
+classmethod reconstruct_common(t, x, y, z=None, initial=None)
+

Reconstruct angles from 3 detections

+

This function converts the coordinates to be suitable for the +algorithm.

+
+
Parameters:
+
    +

  • t – arrival times in detector 0, 1 and 2 in ns.

    • +

  • x,y – positions of detector 0, 1 and 2 in m.

    • +

  • z – height of detectors 0, 1 and 2 is ignored.

    • +

  • initial – dictionary containing values from previous +reconstructions is ignored.

    • +
+
+
+
+ +
+
+classmethod reconstruct(dt1, dt2, r1, r2, phi1, phi2)
+

Reconstruct angles from 3 detections

+
+
Parameters:
+
    +

  • dt# – arrival times in detector 1 and 2 relative to +detector 0 in ns (!).

    • +

  • r#,phi# – position of detector 1 and 2 relative to +detector 0 in m and radians.

    • +
+
+
Returns:
+

theta as given by Fokkema2012 eq 4.14, +phi as given by Fokkema2012 eq 4.13.

+
+
+
+ +
+
+classmethod rel_theta1_errorsq(theta, phi, phi1, phi2, r1=10, r2=10)
+

Fokkema2012, eq 4.23

+
+ +
+
+classmethod rel_theta2_errorsq(theta, phi, phi1, phi2, r1=10, r2=10)
+

Fokkema2012, eq 4.23

+
+ +
+
+static rel_phi_errorsq(theta, phi, phi1, phi2, r1=10, r2=10)
+

Fokkema2012, eq 4.22

+
+ +
+
+classmethod dphi_dt0(theta, phi, phi1, phi2, r1=10, r2=10)
+

Fokkema2012, eq 4.19

+
+ +
+
+static dphi_dt1(theta, phi, phi1, phi2, r1=10, r2=10)
+

Fokkema2012, eq 4.20

+
+ +
+
+static dphi_dt2(theta, phi, phi1, phi2, r1=10, r2=10)
+

Fokkema2012, eq 4.21

+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.DirectAlgorithmCartesian
+

Reconstruct angles using direct analytical formula.

+

This implements the equations derived in Montanus2014. +“Direction reconstruction of cosmic air showers with +detectorstations at different altitudes”

+

Here the 2D version is used, assuming each detector is at the same +altitude.

+
+
+classmethod reconstruct_common(t, x, y, z=None, initial=None)
+

Reconstruct angles from 3 detections

+

This function converts the coordinates to be suitable for the +algorithm.

+
+
Parameters:
+
    +

  • t – arrival times in detector 0, 1 and 2 in ns.

    • +

  • x,y – positions of detector 0, 1 and 2 in m.

    • +

  • z – height of detectors 0, 1 and 2 is ignored.

    • +

  • initial – dictionary containing values from previous +reconstructions is ignored.

    • +
+
+
+
+ +
+
+static reconstruct(dt1, dt2, dx1, dx2, dy1, dy2)
+

Reconstruct angles from 3 detections

+
+
Parameters:
+
    +

  • dt# – arrival times in detector 1 and 2 relative to +detector 0 in ns.

    • +

  • dx#,dy# – position of detector 1 and 2 relative to +detector 0 in m.

    • +
+
+
Returns:
+

theta as given by Montanus2014 eq 27, +phi as given by Montanus2014 eq 26.

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.DirectAlgorithmCartesian3D
+

Reconstruct angles using direct analytical formula.

+

This implements the equations derived in Montanus2014. +“Direction reconstruction of cosmic air showers with +detectorstations at different altitudes”

+

Here the 3D version is used, assuming each detector is at the same +altitude.

+
+
+classmethod reconstruct_common(t, x, y, z=None, initial=None)
+

Reconstruct angles from 3 detections

+

This function converts the coordinates to be suitable for the +algorithm.

+
+
Parameters:
+
    +

  • t – arrival times in detector 0, 1 and 2 in ns.

    • +

  • x,y,z – positions of detector 0, 1 and 2 in m.

    • +

  • initial – dictionary containing values from previous +reconstructions is ignored.

    • +
+
+
+
+ +
+
+static reconstruct(dt1, dt2, dx1, dx2, dy1, dy2, dz1=0, dz2=0)
+

Reconstruct angles from 3 detections

+
+
Parameters:
+
    +

  • dt# – arrival times in detector 1 and 2 relative to +detector 0 in ns.

    • +

  • dx#,dy#,dz# – position of detector 1 and 2 relative to +detector 0 in m.

    • +
+
+
Returns:
+

theta as given by Montanus2014 eq 24, +phi as given by Montanus2014 eq 22.

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.SphereAlgorithm
+

Reconstruct the direction in equatorial coordinates

+

Note: currently incompatible with the other algorithms!

+

This class uses a different coordinate systems than the other +algorithms. The location input is in ECEF coordinates and a +timestamp is required to connect the direction to the equatorial +coordinates.

+
+
+classmethod reconstruct_equatorial(t, x, y, z, timestamp)
+

Reconstructs the source in the Equatorial Coordinate System.

+
+
Parameters:
+
    +

  • t – An array with three arrival times in ns.

    • +

  • x,y,z – arrays with the ECEF locations of the +three detectors / stations in meters.

    • +

  • timestamp – The UTC timestamp of the coincidence in s.

    • +
+
+
Returns:
+

declination and right ascension of the source. The +apparent location of the cosmic ray source in the +Equatorial Coordinate System.

+
+
+
+ +
+
+static interaction_curve(x, y, z, t, t_int)
+

Calculates the curve of possible primary interactions

+

This uses the arrival times in three detectors. The algorithm is +based on location calculations used for LORAN, DECCA, RACAL, GPS +as described by N.G. Schultheiss 2012

+
+
Parameters:
+
    +

  • x,y,z – arrays with the orthogonal coordinates of the three +detection points in m.

    • +

  • t – arrival times of the detectors in ns.

    • +

  • t_int – interaction time in ns.

    • +
+
+
Returns:
+

parameters x_int, y_int, z_int

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.FitAlgorithm3D
+
+
+classmethod reconstruct_common(t, x, y, z=None, initial=None)
+

Reconstruct angles from 3 or more detections

+

This function converts the arguments to be suitable for the +algorithm.

+
+
Parameters:
+
    +

  • t – arrival times of the detectors in ns.

    • +

  • x,y,z – positions of the detectors in m. The height +for all detectors will be set to 0 if not given.

    • +

  • initial – dictionary containing values from previous +reconstructions is ignored.

    • +
+
+
+
+ +
+
+classmethod reconstruct(t, x, y, z)
+

Reconstruct angles for many detections

+
+
Parameters:
+
    +

  • t# – arrival times in the detectors in ns.

    • +

  • x#,y#,z# – position of the detectors in m.

    • +
+
+
Returns:
+

theta as given by Montanus2014 eq 21, +phi as given by Montanus2014 eq 22.

+
+
+
+ +
+
+static constraint_normal_vector(n)
+

This should be equal to zero

+
+ +
+
+static best_fit(n_xyz, dt, dx, dy, dz)
+

The function to be minimized to find the direction

+
+
Parameters:
+
    +

  • n_xyz – list containing the unit vector.

    • +

  • dt – list of relative arrival times in the detectors in ns.

    • +

  • dx,dy,dz – list of relative detector positions in m.

    • +
+
+
Returns:
+

least sum of squares as in Montanus2014, eq 36

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.RegressionAlgorithm
+

Reconstruct angles using an analytical regression formula.

+

This implements the equations as for ISVHECRI (Montanus 2014). +“Direction reconstruction of cosmic air showers with +three or more detectorstations in a horizontal (for the +moment) plane”

+
+
+classmethod reconstruct_common(t, x, y, z=None, initial=None)
+

Reconstruct angles from 3 or more detections

+

This function converts the arguments to be suitable for the +algorithm.

+
+
Parameters:
+
    +

  • t – arrival times of the detectors in ns.

    • +

  • x,y,z – positions of the detectors in m. The height +is ignored.

    • +

  • initial – dictionary containing values from previous +reconstructions is ignored.

    • +
+
+
+
+ +
+
+classmethod reconstruct(t, x, y)
+

Reconstruct angles for many detections

+
+
Parameters:
+
    +

  • t – arrival times in the detectors in ns.

    • +

  • x,y – positions of the detectors in m.

    • +
+
+
Returns:
+

theta as derived by Montanus2014, +phi as derived by Montanus2014.

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.RegressionAlgorithm3D
+

Reconstruct angles by iteratively applying a regression formula.

+

This implements the equations as recently derived (Montanus 2014). +“Direction reconstruction of cosmic air showers with +three or more detectorstations at arbitrary altitudes”

+
+
+MAX_ITERATIONS = 1000
+
+ +
+
+classmethod reconstruct_common(t, x, y, z=None, initial=None)
+

Reconstruct angles from 3 or more detections

+

This function converts the arguments to be suitable for the +algorithm.

+
+
Parameters:
+
    +

  • t – arrival times of the detectors in ns.

    • +

  • x,y,z – positions of the detectors in m. The height +for all detectors will be set to 0 if not given.

    • +

  • initial – dictionary containing values from previous +reconstructions is ignored.

    • +
+
+
+
+ +
+
+classmethod reconstruct(t, x, y, z)
+

Reconstruct angles for many detections

+
+
Parameters:
+
    +

  • t – arrival times in the detectors in ns.

    • +

  • x,y,z – positions of the detectors in m.

    • +
+
+
Returns:
+

theta as derived by Montanus2014, +phi as derived by Montanus2014.

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.CurvedMixin
+

Provide methods to estimate the time delay due to front curvature

+

Given a core location, detector position, and shower angle the radial core +distance can be determined, which can be used to determine the expected +time delay.

+
+
+time_delay(x, y, core_x, core_y, theta, phi)
+
+ +
+
+classmethod radial_core_distance(x, y, core_x, core_y, theta, phi)
+

Determine the radial core distance

+
+
Parameters:
+
    +

  • x,y,z – positions of the detectors in m.

    • +

  • core_x,core_y – core position at z = 0 in m.

    • +

  • theta,phi – reconstructed shower direction.

    • +
+
+
Returns:
+

radial core distance in m.

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.CurvedRegressionAlgorithm
+

Reconstruct angles taking the shower front curvature into account.

+

Take the shower front curvature into account. Assumes knowledge about the +shower core position.

+
+
+MAX_ITERATIONS = 1000
+
+ +
+
+reconstruct_common(t, x, y, z=None, initial=None)
+

Reconstruct angles from 3 or more detections

+

This function converts the arguments to be suitable for the +algorithm.

+
+
Parameters:
+
    +

  • t – arrival times of the detectors in ns.

    • +

  • x,y,z – positions of the detectors in m. The height +is ignored.

    • +

  • initial – dictionary containing values from previous +reconstructions, including core position.

    • +
+
+
+
+ +
+
+reconstruct(t, x, y, core_x, core_y)
+

Reconstruct angles for many detections

+
+
Parameters:
+
    +

  • t – arrival times in the detectors in ns.

    • +

  • x,y – positions of the detectors in m.

    • +

  • core_x,core_y – core position at z = 0 in m.

    • +
+
+
Returns:
+

theta as derived by Montanus2014, +phi as derived by Montanus2014.

+
+
+
+ +
+ +
+
+class sapphire.analysis.direction_reconstruction.CurvedRegressionAlgorithm3D
+

Reconstruct angles accounting for front curvature and detector altitudes

+

Take the shower front curvature and different detector heights into +account. Assumes knowledge about the shower core position.

+
+
+MAX_ITERATIONS = 1000
+
+ +
+
+reconstruct_common(t, x, y, z=None, initial=None)
+

Reconstruct angles from 3 or more detections

+

This function converts the arguments to be suitable for the +algorithm.

+
+
Parameters:
+
    +

  • t – arrival times of the detectors in ns.

    • +

  • x,y,z – positions of the detectors in m. The height +for all detectors will be set to 0 if not given.

    • +

  • initial – dictionary containing values from previous +reconstructions, including core position.

    • +
+
+
+
+ +
+
+reconstruct(t, x, y, z, core_x, core_y)
+

Reconstruct angles for many detections

+
+
Parameters:
+
    +

  • t – arrival times in the detectors in ns.

    • +

  • x,y,z – positions of the detectors in m.

    • +

  • core_x,core_y – core position at z = 0 in m.

    • +
+
+
Returns:
+

theta as derived by Montanus2014, +phi as derived by Montanus2014.

+
+
+
+ +
+ +
+
+sapphire.analysis.direction_reconstruction.logic_checks(t, x, y, z)
+

Check for impossible reconstructions

+

Criteria:

+
    +

  • No two detectors are at the same position.

    • +

  • Time difference between two detections should be less than distance / c.

    • +

  • All detectors on a line is bad.

    • +
+

To fix:

+
    +

  • Time difference can still be to large in cases where a different +distance becomes relevant.

    • +
+
+
Parameters:
+
    +

  • t – arrival times in the detectors in ns.

    • +

  • x,y,z – positions of the detectors in m.

    • +
+
+
Returns:
+

True if the checks pass, False otherwise.

+
+
+
+ +
+
+sapphire.analysis.direction_reconstruction.warning_only_three()
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/event_utils.html b/analysis/event_utils.html new file mode 100644 index 00000000..16a5635c --- /dev/null +++ b/analysis/event_utils.html @@ -0,0 +1,300 @@ + + + + + + + + Get signal and timing values from events — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Get signal and timing values from events

+

Get data from HiSPARC events

+

This module contains functions to derive data from HiSPARC events. +Common tasks for data reconstruction are getting the particle density +and shower arrival time in detectors or a station. These functions are +aware of processed events (i.e. reconstructed number of MIPs, arrival +times and trigger time) and stations.

+
+
+sapphire.analysis.event_utils.station_density(event, detector_ids=None, station=None)
+

Get particle density in a station

+

Detectors with error values will be ignored.

+
+
Parameters:
+
    +

  • event – Processed event row.

    • +

  • detector_ids – list of detectors ids to consider. If None, the +detectors in the station object will be used.

    • +

  • station – sapphire.clusters.Station object.

    • +
+
+
Returns:
+

average density over the chosen detectors.

+
+
+
+ +
+
+sapphire.analysis.event_utils.detector_densities(event, detector_ids=None, station=None)
+

Get particle density in station detectors

+
+
Parameters:
+
    +

  • event – Processed event row.

    • +

  • detector_ids – list of detectors ids for which to get particle +densities.

    • +

  • station – sapphire.clusters.Station object.

    • +
+
+
Returns:
+

density in each chosen detector.

+
+
+
+ +
+
+sapphire.analysis.event_utils.detector_density(event, detector_id, station=None)
+

Get particle density in station detector

+
+
Parameters:
+
    +

  • event – Processed event row.

    • +

  • detector_id – detector id for which to get particle density.

    • +

  • station – sapphire.clusters.Station object, used to +determine the detector size.

    • +
+
+
Returns:
+

density in the chosen detector.

+
+
+
+ +
+
+sapphire.analysis.event_utils.station_arrival_time(event, reference_ext_timestamp, detector_ids=None, offsets=[0.0, 0.0, 0.0, 0.0], station=None)
+

Get station arrival time, i.e. first detector hit

+

Arrival time of first detector hit in the station. The returned time +is relative to reference_ext_timestamp, because floats do not have +enough precision to represent large timestamps in nanoseconds.

+
+
Parameters:
+
    +

  • event – Processed event row.

    • +

  • reference_ext_timestamp – reference extended timestamp (in ns). +The returned station arrival time will be relative to this timestamp. +Often best to use the timestamp of the first event in a coincidence.

    • +

  • detector_ids – list of detectors ids for which to consider.

    • +

  • offsets – list of detector time offsets.

    • +

  • station – sapphire.clusters.Station object, used to +determine the number of detectors.

    • +
+
+
Returns:
+

shower arrival time of the station relative to the +reference timestamp.

+
+
+
+ +
+
+sapphire.analysis.event_utils.relative_detector_arrival_times(event, reference_ext_timestamp, detector_ids=None, offsets=[0.0, 0.0, 0.0, 0.0], station=None)
+

Get relative arrival times for all detectors

+
+
Parameters:
+
    +

  • event – Processed event row.

    • +

  • reference_ext_timestamp – reference extended timestamp (in ns). +The returned station arrival time will be relative to this timestamp. +Often best to use the timestamp of the first event in a coincidence.

    • +

  • detector_ids – list of detectors ids for which to get arrival times.

    • +

  • offsets – list of detector time offsets.

    • +

  • station – sapphire.clusters.Station object, used to +determine the number of detectors.

    • +
+
+
Returns:
+

list of shower arrival times relative to the given reference.

+
+
+
+ +
+
+sapphire.analysis.event_utils.detector_arrival_times(event, detector_ids=None, offsets=[0.0, 0.0, 0.0, 0.0], station=None)
+

Get corrected arrival times for all detectors

+
+
Parameters:
+
    +

  • event – Processed event row.

    • +

  • detector_ids – list of detectors ids for which to get arrival times.

    • +

  • offsets – list of detector time offsets.

    • +

  • station – sapphire.clusters.Station object, used to +determine the number of detectors.

    • +
+
+
Returns:
+

list of shower arrival times relative to the start of the trace.

+
+
+
+ +
+
+sapphire.analysis.event_utils.detector_arrival_time(event, detector_id, offsets=[0.0, 0.0, 0.0, 0.0])
+

Get corrected arrival time for a detector

+
+
Parameters:
+
    +

  • event – Event row.

    • +

  • detector_id – detector id for which to get arrival times.

    • +

  • offsets – list of detector time offsets.

    • +
+
+
Returns:
+

arrival time corrected by the offset.

+
+
+
+ +
+
+sapphire.analysis.event_utils.get_detector_ids(station=None, event=None)
+

Determine the detector ids based on the station object or event data

+

Returns a list of detectors that should be present.

+

Note: Event based determination might not work for simulated data, +since it currently does not simulate pulseheights.

+
+
Parameters:
+
+
+
Returns:
+

list of detector_ids.

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/find_mpv.html b/analysis/find_mpv.html new file mode 100644 index 00000000..e770b390 --- /dev/null +++ b/analysis/find_mpv.html @@ -0,0 +1,232 @@ + + + + + + + + MPV fit — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

MPV fit

+

Find the most probable value in a HiSPARC spectrum.

+
+
FindMostProbableValueInSpectrum

find the most probable value in a HiSPARC spectrum

+
+
+
+
+class sapphire.analysis.find_mpv.FindMostProbableValueInSpectrum(n, bins)
+

Find the most probable value (MPV) in a HiSPARC spectrum.

+

This is a fast algorithm to find the MPV value in a HiSPARC spectrum. +The MPV value indicates the position of the minimum-ionizing particles +(MIP) peak. The algorithm makes some assumptions about the shape of +the spectrum:

+
+
    +

  • the spectrum includes the gamma peak (left-most part of +spectrum) which has more counts per bin than the MIP peak.

    • +

  • ignoring the gamma peak, the MIP peak can be bracketed on the +left by the numerically largest bin-to-bin increase in the +number of counts.

    • +

  • the MIP peak can be approximated by a normal distribution.

    • +
+
+

Public methods:

+
+
find_mpv()

Find the most probable value

+
+
find_first_guess_mpv()

Make a first guess of the most probable value

+
+
fit_mpv()

Based on a first guess, fit the MIP peak to obtain the MPV

+
+
+

Initialize the class instance

+
+
Parameters:
+

n,bins – histogram counts and bins, as obtained using +numpy.histogram.

+
+
+
+
+find_mpv()
+

Find the most probable value.

+

First perform a first guess, then use that value to fit the MIP +peak.

+
+
Return mpv:
+

best guess of the most probable value.

+
+
Return boolean is_fitted:
+

indicates if the fit was successful.

+
+
+
+ +
+
+find_first_guess_mpv()
+

First guess of most probable value.

+

The algorithm is fast and simple. The following steps are +performed:

+
+
    +

  • From the left: find the greatest value and cut off all data +to the left of that maximum. We now assume the first +datapoint to be the maximum of the gamma peak.

    • +

  • From the right: find the location of the greatest decrease +from bin to bin. We assume that this value is where the MIP +peak dips before joining the gamma peak.

    • +

  • Find the maximum to the right of this value. We assume +this to be the approximate location of the MIP peak.

    • +
+
+
+
Return mpv:
+

first guess of the most probable value.

+
+
+
+ +
+
+fit_mpv(first_guess, width_factor=0.4)
+

Fit a normal distribution to the MIP peak to obtain the MPV.

+

A normal distribution is fitted to the spectrum in a restricted +domain around the first guess value. The width of the domain can +be adjusted by the width_factor parameter.

+
+
Parameters:
+
    +

  • first_guess – approximate location of the most probable +value.

    • +

  • width_factor – float in the range [0., 1.] to indicate the +width of the fit domain. The domain is given by +[(1. - width_factor) * first_guess, (1. + width_factor) * +first_guess].

    • +
+
+
Return mpv:
+

mpv value obtained from the fit.

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/landau.html b/analysis/landau.html new file mode 100644 index 00000000..2c561a29 --- /dev/null +++ b/analysis/landau.html @@ -0,0 +1,324 @@ + + + + + + + + Landau distribution — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Landau distribution

+

Landau distribution function

+

This module computes the Landau distribution, which governs the +fluctuations in energy loss of particles travelling through a +relatively thin layer of matter.

+

Currently, this module only contains functions to calculate the exact +function using two integral representations of the defining complex +integral. This should be extended by approximations when the need for +doing serious work arises.

+

References are made to Fokkema2012, DOI: 10.3990/1.9789036534383.

+
+
+sapphire.analysis.landau.pdf_kernel(y, sf)
+
+ +
+
+sapphire.analysis.landau.pdf_kernel2(u, lf)
+

The Landau kernel

+

Fokkema2012, eq 2.13.

+
+ +
+
+class sapphire.analysis.landau.Scintillator
+
+
+thickness = 0.02
+
+ +
+
+xi = 0.172018
+
+ +
+
+epsilon = 3.10756e-11
+
+ +
+
+delta = 2.97663
+
+ +
+
+Euler = 0.577215665
+
+ +
+
+mev_scale = 1
+
+ +
+
+gauss_scale = 1
+
+ +
+
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29.72972973,   29.92992993,   30.13013013,   30.33033033,          30.53053053,   30.73073073,   30.93093093,   31.13113113,          31.33133133,   31.53153153,   31.73173173,   31.93193193,          32.13213213,   32.33233233,   32.53253253,   32.73273273,          32.93293293,   33.13313313,   33.33333333,   33.53353353,          33.73373373,   33.93393393,   34.13413413,   34.33433433,          34.53453453,   34.73473473,   34.93493493,   35.13513514,          35.33533534,   35.53553554,   35.73573574,   35.93593594,          36.13613614,   36.33633634,   36.53653654,   36.73673674,          36.93693694,   37.13713714,   37.33733734,   37.53753754,          37.73773774,   37.93793794,   38.13813814,   38.33833834,          38.53853854,   38.73873874,   38.93893894,   39.13913914,          39.33933934,   39.53953954,   39.73973974,   39.93993994,          40.14014014,   40.34034034,   40.54054054,   40.74074074,          40.94094094,   41.14114114,   41.34134134,   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76.97697698,   77.17717718,   77.37737738,   77.57757758,          77.77777778,   77.97797798,   78.17817818,   78.37837838,          78.57857858,   78.77877878,   78.97897898,   79.17917918,          79.37937938,   79.57957958,   79.77977978,   79.97997998,          80.18018018,   80.38038038,   80.58058058,   80.78078078,          80.98098098,   81.18118118,   81.38138138,   81.58158158,          81.78178178,   81.98198198,   82.18218218,   82.38238238,          82.58258258,   82.78278278,   82.98298298,   83.18318318,          83.38338338,   83.58358358,   83.78378378,   83.98398398,          84.18418418,   84.38438438,   84.58458458,   84.78478478,          84.98498498,   85.18518519,   85.38538539,   85.58558559,          85.78578579,   85.98598599,   86.18618619,   86.38638639,          86.58658659,   86.78678679,   86.98698699,   87.18718719,          87.38738739,   87.58758759,   87.78778779,   87.98798799,          88.18818819,   88.38838839,   88.58858859,   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+
+ +
+
+pdf_values = None
+
+ +
+
+pdf_domain = array([ -4.9049049 ,  -4.7047047 ,  -4.5045045 ,  -4.3043043 ,         -4.1041041 ,  -3.9039039 ,  -3.7037037 ,  -3.5035035 ,         -3.3033033 ,  -3.1031031 ,  -2.9029029 ,  -2.7027027 ,         -2.5025025 ,  -2.3023023 ,  -2.1021021 ,  -1.9019019 ,         -1.7017017 ,  -1.5015015 ,  -1.3013013 ,  -1.1011011 ,         -0.9009009 ,  -0.7007007 ,  -0.5005005 ,  -0.3003003 ,         -0.1001001 ,   0.1001001 ,   0.3003003 ,   0.5005005 ,          0.7007007 ,   0.9009009 ,   1.1011011 ,   1.3013013 ,          1.5015015 ,   1.7017017 ,   1.9019019 ,   2.1021021 ,          2.3023023 ,   2.5025025 ,   2.7027027 ,   2.9029029 ,          3.1031031 ,   3.3033033 ,   3.5035035 ,   3.7037037 ,          3.9039039 ,   4.1041041 ,   4.3043043 ,   4.5045045 ,          4.7047047 ,   4.9049049 ,   5.10510511,   5.30530531,          5.50550551,   5.70570571,   5.90590591,   6.10610611,          6.30630631,   6.50650651,   6.70670671,   6.90690691,          7.10710711,   7.30730731,   7.50750751,   7.70770771,          7.90790791,   8.10810811,   8.30830831,   8.50850851,          8.70870871,   8.90890891,   9.10910911,   9.30930931,          9.50950951,   9.70970971,   9.90990991,  10.11011011,         10.31031031,  10.51051051,  10.71071071,  10.91091091,         11.11111111,  11.31131131,  11.51151151,  11.71171171,         11.91191191,  12.11211211,  12.31231231,  12.51251251,         12.71271271,  12.91291291,  13.11311311,  13.31331331,         13.51351351,  13.71371371,  13.91391391,  14.11411411,         14.31431431,  14.51451451,  14.71471471,  14.91491491,         15.11511512,  15.31531532,  15.51551552,  15.71571572,         15.91591592,  16.11611612,  16.31631632,  16.51651652,         16.71671672,  16.91691692,  17.11711712,  17.31731732,         17.51751752,  17.71771772,  17.91791792,  18.11811812,         18.31831832,  18.51851852,  18.71871872,  18.91891892,         19.11911912,  19.31931932,  19.51951952,  19.71971972,         19.91991992,  20.12012012,  20.32032032,  20.52052052,         20.72072072,  20.92092092,  21.12112112,  21.32132132,         21.52152152,  21.72172172,  21.92192192,  22.12212212,         22.32232232,  22.52252252,  22.72272272,  22.92292292,         23.12312312,  23.32332332,  23.52352352,  23.72372372,         23.92392392,  24.12412412,  24.32432432,  24.52452452,         24.72472472,  24.92492492,  25.12512513,  25.32532533,         25.52552553,  25.72572573,  25.92592593,  26.12612613,         26.32632633,  26.52652653,  26.72672673,  26.92692693,         27.12712713,  27.32732733,  27.52752753,  27.72772773,         27.92792793,  28.12812813,  28.32832833,  28.52852853,         28.72872873,  28.92892893,  29.12912913,  29.32932933,         29.52952953,  29.72972973,  29.92992993,  30.13013013,         30.33033033,  30.53053053,  30.73073073,  30.93093093,         31.13113113,  31.33133133,  31.53153153,  31.73173173,         31.93193193,  32.13213213,  32.33233233,  32.53253253,         32.73273273,  32.93293293,  33.13313313,  33.33333333,         33.53353353,  33.73373373,  33.93393393,  34.13413413,         34.33433433,  34.53453453,  34.73473473,  34.93493493,         35.13513514,  35.33533534,  35.53553554,  35.73573574,         35.93593594,  36.13613614,  36.33633634,  36.53653654,         36.73673674,  36.93693694,  37.13713714,  37.33733734,         37.53753754,  37.73773774,  37.93793794,  38.13813814,         38.33833834,  38.53853854,  38.73873874,  38.93893894,         39.13913914,  39.33933934,  39.53953954,  39.73973974,         39.93993994,  40.14014014,  40.34034034,  40.54054054,         40.74074074,  40.94094094,  41.14114114,  41.34134134,         41.54154154,  41.74174174,  41.94194194,  42.14214214,         42.34234234,  42.54254254,  42.74274274,  42.94294294,         43.14314314,  43.34334334,  43.54354354,  43.74374374,         43.94394394,  44.14414414,  44.34434434,  44.54454454,         44.74474474,  44.94494494,  45.14514515,  45.34534535,         45.54554555,  45.74574575,  45.94594595,  46.14614615,         46.34634635,  46.54654655,  46.74674675,  46.94694695,         47.14714715,  47.34734735,  47.54754755,  47.74774775,         47.94794795,  48.14814815,  48.34834835,  48.54854855,         48.74874875,  48.94894895,  49.14914915,  49.34934935,         49.54954955,  49.74974975,  49.94994995,  50.15015015,         50.35035035,  50.55055055,  50.75075075,  50.95095095,         51.15115115,  51.35135135,  51.55155155,  51.75175175,         51.95195195,  52.15215215,  52.35235235,  52.55255255,         52.75275275,  52.95295295,  53.15315315,  53.35335335,         53.55355355,  53.75375375,  53.95395395,  54.15415415,         54.35435435,  54.55455455,  54.75475475,  54.95495495,         55.15515516,  55.35535536,  55.55555556,  55.75575576,         55.95595596,  56.15615616,  56.35635636,  56.55655656,         56.75675676,  56.95695696,  57.15715716,  57.35735736,         57.55755756,  57.75775776,  57.95795796,  58.15815816,         58.35835836,  58.55855856,  58.75875876,  58.95895896,         59.15915916,  59.35935936,  59.55955956,  59.75975976,         59.95995996,  60.16016016,  60.36036036,  60.56056056,         60.76076076,  60.96096096,  61.16116116,  61.36136136,         61.56156156,  61.76176176,  61.96196196,  62.16216216,         62.36236236,  62.56256256,  62.76276276,  62.96296296,         63.16316316,  63.36336336,  63.56356356,  63.76376376,         63.96396396,  64.16416416,  64.36436436,  64.56456456,         64.76476476,  64.96496496,  65.16516517,  65.36536537,         65.56556557,  65.76576577,  65.96596597,  66.16616617,         66.36636637,  66.56656657,  66.76676677,  66.96696697,         67.16716717,  67.36736737,  67.56756757,  67.76776777,         67.96796797,  68.16816817,  68.36836837,  68.56856857,         68.76876877,  68.96896897,  69.16916917,  69.36936937,         69.56956957,  69.76976977,  69.96996997,  70.17017017,         70.37037037,  70.57057057,  70.77077077,  70.97097097,         71.17117117,  71.37137137,  71.57157157,  71.77177177,         71.97197197,  72.17217217,  72.37237237,  72.57257257,         72.77277277,  72.97297297,  73.17317317,  73.37337337,         73.57357357,  73.77377377,  73.97397397,  74.17417417,         74.37437437,  74.57457457,  74.77477477,  74.97497497,         75.17517518,  75.37537538,  75.57557558,  75.77577578,         75.97597598,  76.17617618,  76.37637638,  76.57657658,         76.77677678,  76.97697698,  77.17717718,  77.37737738,         77.57757758,  77.77777778,  77.97797798,  78.17817818,         78.37837838,  78.57857858,  78.77877878,  78.97897898,         79.17917918,  79.37937938,  79.57957958,  79.77977978,         79.97997998,  80.18018018,  80.38038038,  80.58058058,         80.78078078,  80.98098098,  81.18118118,  81.38138138,         81.58158158,  81.78178178,  81.98198198,  82.18218218,         82.38238238,  82.58258258,  82.78278278,  82.98298298,         83.18318318,  83.38338338,  83.58358358,  83.78378378,         83.98398398,  84.18418418,  84.38438438,  84.58458458,         84.78478478,  84.98498498,  85.18518519,  85.38538539,         85.58558559,  85.78578579,  85.98598599,  86.18618619,         86.38638639,  86.58658659,  86.78678679,  86.98698699,         87.18718719,  87.38738739,  87.58758759,  87.78778779,         87.98798799,  88.18818819,  88.38838839,  88.58858859,         88.78878879,  88.98898899,  89.18918919,  89.38938939,         89.58958959,  89.78978979,  89.98998999,  90.19019019,         90.39039039,  90.59059059,  90.79079079,  90.99099099,         91.19119119,  91.39139139,  91.59159159,  91.79179179,         91.99199199,  92.19219219,  92.39239239,  92.59259259,         92.79279279,  92.99299299,  93.19319319,  93.39339339,         93.59359359,  93.79379379,  93.99399399,  94.19419419,         94.39439439,  94.59459459,  94.79479479,  94.99499499,         95.1951952 ,  95.3953954 ,  95.5955956 ,  95.7957958 ,         95.995996  ,  96.1961962 ,  96.3963964 ,  96.5965966 ,         96.7967968 ,  96.996997  ,  97.1971972 ,  97.3973974 ,         97.5975976 ,  97.7977978 ,  97.997998  ,  98.1981982 ,         98.3983984 ,  98.5985986 ,  98.7987988 ,  98.998999  ,         99.1991992 ,  99.3993994 ,  99.5995996 ,  99.7997998 ,        100.        ])
+
+ +
+
+landau_pdf(energy_loss)
+

The Landau energy loss distribution function

+

Fokkema2012, eq 2.9, where lf is eq 2.10.

+
+
Parameters:
+

energy_loss – Energy loss in the scintillator.

+
+
Returns:
+

energy loss probability.

+
+
+
+ +
+
+lf(energy_loss)
+

Calculate the lambda parameter

+

Fokkema2012, eq 2.10. +With additional shift by delta.

+
+
Parameters:
+

energy_loss – Energy loss in the scintillator.

+
+
Returns:
+

lambda parameter.

+
+
+
+ +
+
+pdf(lf)
+

The Landau probability density function

+

Fokkema2012, eq 2.13.

+
+
Parameters:
+

lf – lambda parameter.

+
+
Returns:
+

probability.

+
+
+
+ +
+
+conv_landau_for_x(x, count_scale=1, mev_scale=None, gauss_scale=None)
+

Landau convolved with Gaussian

+

Fokkema2012, eq 5.4.

+
+
Parameters:
+
    +

  • x – energy loss(es) for which to get the probability.

    • +

  • count_scale – total number of counts.

    • +

  • mev_scale – number of MeV per unit of x.

    • +

  • gauss_scale – width of the normal distribution.

    • +
+
+
Returns:
+

probability.

+
+
+
+ +
+
+conv_landau(x, count_scale=1, mev_scale=None, gauss_scale=None)
+

Bare-bones convoluted landau function

+

This thing is fragile. Use with great care! First and foremost, +x must be symmetrical around zero. Second, x must contain most of +the Landau function (including a significant part of the tail). +If not, the results cannot be trusted!

+

Better use conv_landau_for_x, which better handles this.

+
+ +
+
+residuals(p, xdata, ydata, a, b)
+
+ +
+
+constrained_residuals(p, xdata, ydata, a, b)
+
+ +
+ +
+
+sapphire.analysis.landau.discrete_convolution(f, g, t)
+

Discrete convolution

+
+
Parameters:
+
    +

  • f,g – two functions that take one argument (t).

    • +

  • t – values for which the functions will be evaluated, and the along +which the convolution will be performed.

    • +
+
+
Returns:
+

convolution of the two functions.

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/process_events.html b/analysis/process_events.html new file mode 100644 index 00000000..e4326105 --- /dev/null +++ b/analysis/process_events.html @@ -0,0 +1,696 @@ + + + + + + + + Processing of HiSPARC events — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Processing of HiSPARC events

+

Process HiSPARC events

+

This module can be used analyse data to get observables like arrival +times and particle count in each detector for each event.

+

Example usage:

+
import datetime
+
+import tables
+
+from sapphire.publicdb import download_data
+from sapphire import ProcessEvents
+
+STATIONS = [501, 503, 506]
+START = datetime.datetime(2013, 1, 1)
+END = datetime.datetime(2013, 1, 2)
+
+
+if __name__ == '__main__':
+    station_groups = ['/s%d' % u for u in STATIONS]
+
+    with tables.open_file('data.h5', 'w') as data:
+        for station, group in zip(STATIONS, station_groups):
+            download_data(data, group, station, START, END, True)
+            proc = ProcessEvents(data, group)
+            proc.process_and_store_results()
+
+
+
+
+sapphire.analysis.process_events.ADC_THRESHOLD = 20
+

Threshold for arrival times, relative to the baseline

+
+ +
+
+sapphire.analysis.process_events.TRIGGER_2 = (2, 0, False, 0)
+

Default trigger for 2-detector station +2 low and no high, no external

+
+ +
+
+sapphire.analysis.process_events.TRIGGER_4 = (3, 2, True, 0)
+

Default trigger for 4-detector station +3 low or 2 high, no external

+
+ +
+
+class sapphire.analysis.process_events.ProcessEvents(data, group, source=None, progress=True)
+

Process HiSPARC events to obtain several observables.

+

This class can be used to process a set of HiSPARC events and adds a +few observables like particle arrival time and number of particles in +the detector to a copy of the event table.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+
+processed_events_description = {'baseline': Int16Col(shape=(np.int64(4),), dflt=np.int16(-1), pos=6), 'data_reduction': BoolCol(shape=(), dflt=np.False_, pos=4), 'event_id': UInt32Col(shape=(), dflt=np.uint32(0), pos=0), 'event_rate': Float32Col(shape=(), dflt=np.float32(0.0), pos=12), 'ext_timestamp': UInt64Col(shape=(), dflt=np.uint64(0), pos=3), 'integrals': Int32Col(shape=(np.int64(4),), dflt=np.int32(-1), pos=10), 'n1': Float32Col(shape=(), dflt=np.float32(-1.0), pos=17), 'n2': Float32Col(shape=(), dflt=np.float32(-1.0), pos=18), 'n3': Float32Col(shape=(), dflt=np.float32(-1.0), pos=19), 'n4': Float32Col(shape=(), dflt=np.float32(-1.0), pos=20), 'n_peaks': Int16Col(shape=(np.int64(4),), dflt=np.int16(-1), pos=8), 'nanoseconds': UInt32Col(shape=(), dflt=np.uint32(0), pos=2), 'pulseheights': Int16Col(shape=(np.int64(4),), dflt=np.int16(-1), pos=9), 'std_dev': Int16Col(shape=(np.int64(4),), dflt=np.int16(-1), pos=7), 't1': Float32Col(shape=(), dflt=np.float32(-1.0), pos=13), 't2': Float32Col(shape=(), dflt=np.float32(-1.0), pos=14), 't3': Float32Col(shape=(), dflt=np.float32(-1.0), pos=15), 't4': Float32Col(shape=(), dflt=np.float32(-1.0), pos=16), 't_trigger': Float32Col(shape=(), dflt=np.float32(-1.0), pos=21), 'timestamp': Time32Col(shape=(), dflt=np.int32(0), pos=1), 'traces': Int32Col(shape=(np.int64(4),), dflt=np.int32(-1), pos=11), 'trigger_pattern': UInt32Col(shape=(), dflt=np.uint32(0), pos=5)}
+
+ +
+
+process_and_store_results(destination=None, overwrite=False, limit=None)
+

Process events and store the results.

+
+
Parameters:
+
    +

  • destination – name of the table where the results will be +written. The default, None, corresponds to ‘events’.

    • +

  • overwrite – if True, overwrite previously obtained results.

    • +

  • limit – the maximum number of events that will be stored. +The default, None, corresponds to no limit.

    • +
+
+
+
+ +
+
+get_traces_for_event(event)
+

Return the traces from an event.

+
+
Parameters:
+

event – a row from the events table.

+
+
Returns:
+

the traces: an array of pulseheight values.

+
+
+
+ +
+
+get_traces_for_event_index(idx)
+

Return the traces from event #idx.

+
+
Parameters:
+

idx – the index number of the event.

+
+
Returns:
+

the traces: an array of pulseheight values.

+
+
+
+ +
+
+process_traces()
+

Process traces to yield pulse timing information.

+
+ +
+
+static first_above_threshold(trace, threshold)
+

Find the first element in the list equal or above threshold

+

If no element matches the condition -999 will be returned.

+
+
Parameters:
+
    +

  • trace – iterable trace.

    • +

  • threshold – value the trace has to be greater or equal to.

    • +
+
+
Returns:
+

index in trace where a value is greater or equal to +threshold.

+
+
+
+ +
+ +
+
+class sapphire.analysis.process_events.ProcessIndexedEvents(data, group, indexes, source=None, progress=True)
+

Process a subset of events using an index.

+

This is a subclass of ProcessEvents. Using an index, this +class will only process a subset of events, thus saving time. For +example, this class can only process events making up a coincidence.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • indexes – a list of indexes into the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+
+process_traces()
+

Process traces to yield pulse timing information.

+

This method makes use of the indexes to build a list of events.

+
+ +
+
+get_traces_for_indexed_event_index(idx)
+
+ +
+ +
+
+class sapphire.analysis.process_events.ProcessEventsWithLINT(data, group, source=None, progress=True)
+

Process events using LInear INTerpolation for arrival times.

+

This is a subclass of ProcessEvents. Use a linear +interpolation method to determine the arrival times of particles.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+ +
+
+class sapphire.analysis.process_events.ProcessIndexedEventsWithLINT(data, group, indexes, source=None, progress=True)
+

Process a subset of events using LInear INTerpolation.

+

This is a subclass of ProcessIndexedEvents and +ProcessEventsWithLINT.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • indexes – a list of indexes into the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+ +
+
+class sapphire.analysis.process_events.ProcessEventsWithoutTraces(data, group, source=None, progress=True)
+

Process events without traces

+

This is a subclass of ProcessEvents. Processing events +without considering traces will invalidate the arrival time +information. However, for some analyses it is not necessary to obtain +this information. Ignoring the traces will then greatly decrease +processing time and data size.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+ +
+
+class sapphire.analysis.process_events.ProcessIndexedEventsWithoutTraces(data, group, indexes, source=None, progress=True)
+

Process a subset of events without traces

+

This is a subclass of ProcessIndexedEvents and +ProcessEventsWithoutTraces. Processing events without +considering traces will invalidate the arrival time information. +However, for some analyses it is not necessary to obtain this +information. Ignoring the traces will then greatly decrease +processing time and data size.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • indexes – a list of indexes into the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+ +
+
+class sapphire.analysis.process_events.ProcessEventsWithTriggerOffset(data, group, source=None, progress=True, station=None)
+

Process events and reconstruct trigger time from traces

+

The trigger times are stored in the columnt_trigger, they are +relative to the start of traces, just like the t# columns.

+

If no trigger can be found, possibly due to the data filter, +a value of -999 will be entered.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +

  • station – station number of station to which the data belongs.

    • +
+
+
+
+ +
+
+class sapphire.analysis.process_events.ProcessEventsFromSource(source_file, dest_file, source_group, dest_group, progress=False)
+

Process HiSPARC events from a different source.

+

This class is a subclass of ProcessEvents. The difference is that in +this class, the source and destination are assumed to be different +files. This also means that the source is untouched (no renaming of +original event tables) and the destination is assumed to be empty.

+

Initialize the class.

+
+
Parameters:
+
    +

  • source_file,dest_file – PyTables source and destination files.

    • +

  • source_group,dest_group – the pathname of the source and +destination group.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+ +
+
+class sapphire.analysis.process_events.ProcessEventsFromSourceWithTriggerOffset(source_file, dest_file, source_group, dest_group, station=None, progress=False)
+

Process events from a different source and find trigger.

+

This is a subclass of ProcessEventsFromSource and +ProcessEventsWithTriggerOffset. Processing events and +finding the trigger time in the traces. And storing the results in a +different file than the source.

+

Initialize the class.

+
+
Parameters:
+
    +

  • source_file,dest_file – PyTables source and destination files.

    • +

  • source_group,dest_group – the pathname of the source and +destination group.

    • +

  • station – station number of station to which the data belongs.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+ +
+
+class sapphire.analysis.process_events.ProcessDataTable(data, group, source=None, progress=True)
+

Process HiSPARC abstract data table to clean the data.

+

Abstract data is a PyTables table containing a timestamp for each row. +Weather and singles data are examples of such tables. This class can be +used to process a set of abstract HiSPARC data, to remove duplicates and +sort the data by timestamp to store it in to a copy of the table.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+
+table_name = 'abstract_data'
+
+ +
+
+process_and_store_results(destination=None, overwrite=False, limit=None)
+

Process table and store the results.

+
+
Parameters:
+
    +

  • destination – name of the table where the results will be +written. The default, None, corresponds to the value stored +in self.table_name.

    • +

  • overwrite – if True, overwrite previously obtained results.

    • +

  • limit – the maximum number of records that will be stored. +The default, None, corresponds to no limit.

    • +
+
+
+
+ +
+ +
+
+class sapphire.analysis.process_events.ProcessDataTableFromSource(source_file, dest_file, source_group, dest_group, progress=False)
+

Process HiSPARC abstract data table from a different source.

+

This class is a subclass of ProcessDataTable. The difference is that in +this class, the source and destination are assumed to be different +files. This also means that the source is untouched (no renaming of +original event tables) and the destination is assumed to be empty.

+

Initialize the class.

+
+
Parameters:
+
    +

  • source_file,dest_file – PyTables source and destination files.

    • +

  • source_group,dest_group – the pathname of the source and +destination group.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+ +
+
+class sapphire.analysis.process_events.ProcessWeather(data, group, source=None, progress=True)
+

Process HiSPARC weather to clean the data.

+

This class can be used to process a set of HiSPARC weather, to +remove duplicates and sort the data by timestamp to store it in to a +copy of the weather table.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+
+table_name = 'weather'
+
+ +
+ +
+
+class sapphire.analysis.process_events.ProcessWeatherFromSource(source_file, dest_file, source_group, dest_group, progress=False)
+

Process HiSPARC weather from a different source.

+

This class behaves like a subclass of ProcessWeather because of a common +ancestor (ProcessDataTable). The difference between this class +and ProcessWeather is that in this class, the source and destination are +assumed to be different files. This also means that the source is +untouched (no renaming of original event tables) and the destination is +assumed to be empty.

+

Initialize the class.

+
+
Parameters:
+
    +

  • source_file,dest_file – PyTables source and destination files.

    • +

  • source_group,dest_group – the pathname of the source and +destination group.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+
+table_name = 'weather'
+
+ +
+ +
+
+class sapphire.analysis.process_events.ProcessSingles(data, group, source=None, progress=True)
+

Process HiSPARC singles data to clean the data.

+

This class can be used to process a set of HiSPARC singles data, to +remove duplicates and sort the data by timestamp to store it in to a +copy of the singles data table.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • group – the group containing the station data. In normal +cases, this is simply the group containing the events table.

    • +

  • source – the name of the events table. Default: None, +meaning the default name ‘events’.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+
+table_name = 'singles'
+
+ +
+ +
+
+class sapphire.analysis.process_events.ProcessSinglesFromSource(source_file, dest_file, source_group, dest_group, progress=False)
+

Process HiSPARC singles data from a different source.

+

This class behaves like a subclass of ProcessSingles because of a common +ancestor (ProcessDataTable). The difference between this class +and ProcessSingles is that in this class, the source and destination are +assumed to be different files. This also means that the source is +untouched (no renaming of original event tables) and the destination is +assumed to be empty.

+

Initialize the class.

+
+
Parameters:
+
    +

  • source_file,dest_file – PyTables source and destination files.

    • +

  • source_group,dest_group – the pathname of the source and +destination group.

    • +

  • progress – if True show a progressbar while copying and +processing events.

    • +
+
+
+
+
+table_name = 'singles'
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/process_traces.html b/analysis/process_traces.html new file mode 100644 index 00000000..1a4d17d3 --- /dev/null +++ b/analysis/process_traces.html @@ -0,0 +1,463 @@ + + + + + + + + Processing of HiSPARC traces — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Processing of HiSPARC traces

+

Process HiSPARC traces

+

This module can be used analyse (raw) traces. It implements the same +algorithms as are implemented in the HiSPARC DAQ.

+

The MeanFilter is meant to mimic the filter in the HiSPARC DAQ. +It is reproduced here to make it easy to read the algorithm.

+
+
+sapphire.analysis.process_traces.PRE_TRIGGER = 400
+

Default pre trigger window, i.e. 1000 ns

+
+ +
+
+sapphire.analysis.process_traces.TRIGGER = 600
+

Default trigger window, i.e. 1500 ns

+
+ +
+
+sapphire.analysis.process_traces.POST_TRIGGER = 1400
+

Default post trigger window, i.e. 3500 ns

+
+ +
+
+sapphire.analysis.process_traces.ADC_FILTER_THRESHOLD = 10
+

Default Mean filter threshold of -6 mV

+
+ +
+
+sapphire.analysis.process_traces.ADC_BASELINE_THRESHOLD = 17
+

Default Baseline threshold of -10 mV

+
+ +
+
+sapphire.analysis.process_traces.ADC_LOW_THRESHOLD = 253
+

Default low ADC threshold for HiSPARC II

+
+ +
+
+sapphire.analysis.process_traces.ADC_HIGH_THRESHOLD = 323
+

Default high ADC threshold for HiSPARC II

+
+ +
+
+sapphire.analysis.process_traces.ADC_LOW_THRESHOLD_III = 82
+

Default low ADC threshold for HiSPARC III

+
+ +
+
+sapphire.analysis.process_traces.ADC_HIGH_THRESHOLD_III = 150
+

Default high ADC threshold for HiSPARC III

+
+ +
+
+sapphire.analysis.process_traces.DATA_REDUCTION_PADDING = 26
+

Padding to allow later baseline determination

+
+ +
+
+class sapphire.analysis.process_traces.TraceObservables(traces, threshold=17, padding=26)
+

Reconstruct trace observables

+

If one wants to reconstruct trace observables from existing data some +caveats apply. If the station applied the Mean Filter the trace values +will no longer match the raw values used to determine the observables +on the station. Additionally, if data reduction was active the trace +may be missing samples without a significant signal, this complicates +the determination of the baseline. Moreover, data reduction uses the +ADC_BASELINE_THRESHOLD to determine what signals to keep, so tiny pulses +may be removed making it impossible to reconstruct tiny pulseheights.

+

The (default) value of ADC_BASELINE_THRESHOLD is different for the +HiSPARC DAQ prior to v4 and also for PySPARC. Those use 25 ADC as +threshold.

+

Each returned list contains at least 4 elements, if there are less than +4 traces the list is padded with the code for missing detectors: -1.

+

Initialize the class

+
+
Parameters:
+
    +

  • traces – a NumPy array of traces, ordered such that the first +element is the first sample of each trace.

    • +

  • threshold – value of the threshold to use, in ADC counts.

    • +

  • padding – number of samples which should be useable to determine +the baseline.

    • +
+
+
+
+
+property baselines
+

Mean value of the first part of the trace

+

This does not perfectly match the implementation in the DAQ which +is more complicated, this does provide the correct value in most +cases, or is off by 1 in most other.

+

Usually this value is either around 200 or 30, depending on the used +version of the DAQ.

+
+
Returns:
+

the baseline in ADC count.

+
+
+
+ +
+
+property std_dev
+

Standard deviation of the first part of the trace

+
+
Returns:
+

the standard deviation in milli ADC count.

+
+
+
+ +
+
+property pulseheights
+

Maximum peak to baseline value in trace

+
+
Returns:
+

the pulseheights in ADC count.

+
+
+
+ +
+
+property integrals
+

Integral of trace for all values over threshold

+

The threshold is defined by ADC_BASELINE_THRESHOLD

+
+
Returns:
+

the pulse integral in ADC count * sample.

+
+
+
+ +
+
+property n_peaks
+

Number of peaks in the trace

+

The peak threshold is defined by ADC_LOW_THRESHOLD

+
+
Returns:
+

the pulse integral in ADC count * sample.

+
+
+
+ +
+ +
+
+class sapphire.analysis.process_traces.MeanFilter(use_threshold=True, threshold=10)
+

Filter raw traces

+

This class replicates the behavior of the Mean_Filter.vi in the HiSPARC +DAQ. A sawtooth-like pattern may be visible in traces due to ADC +misalignment and the synchronization pulse (?). This filter removes such +small oscillations but keeps significant pulses.

+
+

Warning

+

This is a destructive algorithm which removes important information +from the data.

+
+

Verified, by eye, with the LabView VI and Bachelor thesis Oostenbrugge2014.

+

Initialize the class

+
+
Parameters:
+
    +

  • use_threshold – use a threshold when filtering traces.

    • +

  • threshold – value of the threshold to use.

    • +
+
+
+
+
+filter_traces(raw_traces)
+

Apply the mean filter to multiple traces

+
+
Parameters:
+

raw_traces – list of raw event traces.

+
+
Returns:
+

filtered traces.

+
+
+
+ +
+
+filter_trace(raw_trace)
+

Apply the mean filter to a single trace

+

First separate the even and odd ADC traces, filter each separately. +Then recombine them and pass the entire trace through the filter.

+
+
Parameters:
+

raw_trace – raw event trace.

+
+
Returns:
+

filtered trace.

+
+
+
+ +
+
+mean_filter_with_threshold(trace)
+

The mean filter in case use_threshold is True

+
+ +
+
+mean_filter_without_threshold(trace)
+

The mean filter in case use_threshold is False

+
+ +
+ +
+
+class sapphire.analysis.process_traces.DataReduction(threshold=17, padding=26)
+

Data reduce traces

+

This class replicates the behavior also implemented in the HiSPARC DAQ. +The threshold and padding values used by the DAQ may be slightly +different, but these should closely match the defaults.

+

The default padding of 25 samples matches the number of samples used by +the TraceObservables to determine the baseline.

+

Initialize the class

+
+
Parameters:
+
    +

  • threshold – value of the threshold to use, in ADC counts.

    • +

  • padding – number of samples to keep around the determined cuts.

    • +
+
+
+
+
+reduce_traces(traces, baselines=None, return_offset=False)
+

Apply data reduction to the given traces

+
+
Parameters:
+
    +

  • traces – a NumPy array of traces, ordered such that the first +element is the first sample of each trace.

    • +

  • baselines – list of baselines for the traces, if None the +baselines will be determined using TraceObservables.

    • +

  • return_offset – if True the left cut will also be returned.

    • +
+
+
Returns:
+

data reduced traces, including the left cut if return_offset +is True.

+
+
+
+ +
+
+determine_cuts(traces, baselines)
+

Determine the left and right cuts for an event

+

Note that this does not include the padding.

+
+
Parameters:
+
    +

  • traces – a NumPy array of traces, ordered such that the first +element is the first sample of each trace.

    • +

  • baselines – list of baselines for the traces.

    • +
+
+
Returns:
+

indices into traces where the first signals from left and +right cross the threshold.

+
+
+
+ +
+
+add_padding(left, right, length=None)
+

Add padding around the cuts to allow later baseline determination

+

The right value may be larger than the length of the traces if length +is not given. This is only an issues if the actual value is of +interest, because:

+
>>> [1, 2, 3][:6]
+[1, 2, 3]
+
+
+
+
Parameters:
+
    +

  • left,right – the left and right cuts from determine_cuts().

    • +

  • length – optionally the length of the list can be given to +prevent the right cut to be larger than the maximum +length of the trace, this is not required.

    • +
+
+
Returns:
+

indices into traces where to cut the trace.

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/reconstructions.html b/analysis/reconstructions.html new file mode 100644 index 00000000..e8402f9e --- /dev/null +++ b/analysis/reconstructions.html @@ -0,0 +1,500 @@ + + + + + + + + Reconstructions — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Reconstructions

+

Reconstruct HiSPARC events and coincidences

+

This module contains classes that can be used to reconstruct +HiSPARC events and coincidences. These classes can be used to automate +the tasks of reconstructing directions and/or cores.

+

The classes can reconstruct measured data from the ESD as well as +simulated data from sapphire.simulations.

+

The classes read data stored in HDF5 files and extract station metadata +(cluster and detector layout, station and detector offsets) from +various sources:

+ +
+

Reconstructed data is stored in HDF5 files.

+
+
+
+class sapphire.analysis.reconstructions.ReconstructESDEvents(data, station_group, station, overwrite=False, progress=True, verbose=False, destination='reconstructions', force_fresh=False, force_stale=False)
+

Reconstruct events from single stations

+

Example usage:

+
>>> import tables
+>>> from sapphire import ReconstructESDEvents
+
+>>> data = tables.open_file('2014_1_1.h5', 'a')
+>>> station_path = '/hisparc/cluster_amsterdam/station_506'
+>>> rec = ReconstructESDEvents(data, station_path, 506, overwrite=True)
+>>> rec.reconstruct_and_store()
+
+
+

To visualize the results:

+
>>> import matplotlib.pyplot as plt
+>>> plt.polar([p for p in rec.phi if not isnan(p)],
+...           [t for t in rec.theta if not isnan(t)], 'ko', alpha=0.2)
+
+
+

or:

+
>>> plt.polar(rec.reconstructions.col('azimuth'),
+...           rec.reconstructions.col('zenith'), 'ko', alpha=0.2)
+
+
+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile.

    • +

  • station_group – the group containing the event table, +the results will also be stored in this group.

    • +

  • station – either a station number or +sapphire.clusters.Station object. If it is a number the +positions and offsets will be retrieved from the public database +or retrieved from the datafile when stored by a simulation. +Otherwise the offsets will be determined with the available data.

    • +

  • overwrite – if True overwrite existing reconstruction table.

    • +

  • progress – if True show a progressbar while reconstructing.

    • +

  • verbose – if True be verbose about station metadata usage.

    • +

  • destination – alternative name for reconstruction table.

    • +
+
+
+
+
+reconstruct_and_store(detector_ids=None)
+

Shorthand function to reconstruct event and store the results

+
+ +
+
+reconstruct_directions(detector_ids=None)
+

Reconstruct direction for all events

+
+
Parameters:
+

detector_ids – list of detector ids to use for reconstructions.

+
+
+
+ +
+
+reconstruct_cores(detector_ids=None)
+

Reconstruct core for all events

+
+
Parameters:
+

detector_ids – list of detector ids to use for reconstructions.

+
+
+
+ +
+
+prepare_output()
+

Prepare output table

+
+ +
+
+get_detector_offsets()
+

Get or determine detector offsets

+

Try to extract the offsets from the provided cluster object. +If those are not available use the sapphire.api.Station +object for the station number. +Else determine the offsets from the event table.

+
    +

  • if a cluster object is provided:

    +
      +

    • use offsets from that object if available in the object

      • +

    • else determine the offsets from the events in datafile with the +provided cluster object.

      • +
    +
    • +

  • if a station number is provided:

    +
      +

    • if a cluster object is stored in the datafile use offsets from +that object if available.

      • +

    • else get offsets from api.Station object.

      • +
    +
    • +
+
+ +
+
+store_offsets()
+

Store the determined offset in a table.

+
+ +
+
+store_reconstructions()
+

Loop over list of reconstructed data and store results

+

Unsuccessful reconstructions are also stored but with the NumPy +NaN as reconstructed value.

+
+ +
+ +
+
+class sapphire.analysis.reconstructions.ReconstructESDEventsFromSource(source_data, dest_data, source_group, dest_group, station, overwrite=False, progress=True, verbose=False, destination='reconstructions', force_fresh=False, force_stale=False)
+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile.

    • +

  • station_group – the group containing the event table, +the results will also be stored in this group.

    • +

  • station – either a station number or +sapphire.clusters.Station object. If number the +positions and offsets are retrieved from the public database. +Otherwise the offsets will be determined with the available data.

    • +

  • overwrite – if True overwrite existing reconstruction table.

    • +

  • progress – if True show a progressbar while reconstructing.

    • +

  • verbose – if True be verbose about station metadata usage.

    • +

  • destination – alternative name for reconstruction table.

    • +
+
+
+
+
+prepare_output()
+

Prepare output table

+
+ +
+ +
+
+class sapphire.analysis.reconstructions.ReconstructSimulatedEvents(data, station_group, station, overwrite=False, progress=True, verbose=False, destination='reconstructions', force_fresh=False, force_stale=False)
+

Reconstruct simulated events from single stations

+

Simulated events use simulated meta-data (e.g. timing offsets) +which are stored as a BaseCluster object +The object is stored as an node attribute of ‘/coincidences’ in the +HDF5 file. This class will try to read that object and use it’s meta-data +in reconstructions.

+

The station number must match the station number in the stored object.

+

Example usage:

+
>>> import tables
+>>> from sapphire import ReconstructESDEvents
+
+>>> data = tables.open_file('simulation.h5', 'a')
+>>> station_path = '/cluster_simulations/station_506'
+>>> rec = ReconstructESDEvents(data, station_path, 506, overwrite=True)
+>>> rec.reconstruct_and_store()
+
+
+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile.

    • +

  • station_group – the group containing the event table, +the results will also be stored in this group.

    • +

  • station – either a station number or +sapphire.clusters.Station object. If it is a number the +positions and offsets will be retrieved from the public database +or retrieved from the datafile when stored by a simulation. +Otherwise the offsets will be determined with the available data.

    • +

  • overwrite – if True overwrite existing reconstruction table.

    • +

  • progress – if True show a progressbar while reconstructing.

    • +

  • verbose – if True be verbose about station metadata usage.

    • +

  • destination – alternative name for reconstruction table.

    • +
+
+
+
+ +
+
+class sapphire.analysis.reconstructions.ReconstructESDCoincidences(data, coincidences_group='/coincidences', overwrite=False, progress=True, verbose=False, destination='reconstructions', cluster=None, force_fresh=False, force_stale=False)
+

Reconstruct coincidences, e.g. event between multiple stations

+

Example usage:

+
>>> import tables
+>>> from sapphire import ReconstructESDCoincidences
+
+>>> data = tables.open_file('2014_1_1.h5', 'a')
+>>> rec = ReconstructESDCoincidences(data, overwrite=True)
+>>> rec.reconstruct_and_store()
+
+
+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile.

    • +

  • coincidences_group – the destination group.

    • +

  • overwrite – if True overwrite existing reconstruction table.

    • +

  • progress – if True show a progressbar while reconstructing.

    • +

  • verbose – if True be verbose about station metadata usage.

    • +

  • destination – alternative name for reconstruction table.

    • +

  • cluster – a Cluster object to use for the reconstructions.

    • +
+
+
+
+
+reconstruct_and_store(station_numbers=None)
+

Shorthand function to reconstruct coincidences and store results

+
+ +
+
+reconstruct_directions(station_numbers=None)
+

Reconstruct direction for all events

+
+
Parameters:
+

station_numbers – list of stations to use for reconstructions.

+
+
+
+ +
+
+reconstruct_cores(station_numbers=None)
+

Reconstruct core for all events

+
+
Parameters:
+

station_numbers – list of stations to use for reconstructions.

+
+
+
+ +
+
+prepare_output()
+

Prepare output table

+
+ +
+
+get_station_timing_offsets()
+

Construct a dict of Station objects

+

Try to extract offsets from provided cluster objects into a dictionary, +to be used by the reconstructions. +If the cluster is not available create a Station +object for each station in the cluster.

+
+ +
+
+store_reconstructions()
+

Loop over list of reconstructed data and store results

+

Unsuccessful reconstructions are also stored but with the NumPy +NaN as reconstructed value.

+
+ +
+ +
+
+class sapphire.analysis.reconstructions.ReconstructESDCoincidencesFromSource(source_data, dest_data, source_group, dest_group, overwrite=False, progress=True, verbose=False, destination='reconstructions', cluster=None, force_fresh=False, force_stale=False)
+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile.

    • +

  • station_group – the group containing the event table, +the results will also be stored in this group.

    • +

  • station – either a station number or +sapphire.clusters.Station object. If number the +positions and offsets are retrieved from the public database. +Otherwise the offsets will be determined with the available data.

    • +

  • overwrite – if True overwrite existing reconstruction table.

    • +

  • progress – if True show a progressbar while reconstructing.

    • +

  • verbose – if True be verbose about station metadata usage.

    • +

  • destination – alternative name for reconstruction table.

    • +
+
+
+
+
+prepare_output()
+

Prepare output table

+
+ +
+ +
+
+class sapphire.analysis.reconstructions.ReconstructSimulatedCoincidences(data, coincidences_group='/coincidences', overwrite=False, progress=True, verbose=False, destination='reconstructions', cluster=None, force_fresh=False, force_stale=False)
+

Reconstruct simulated coincidences.

+

Simulated coincidences use simulated meta-data (e.g. timing offsets) +which are stored as a BaseCluster object +The object is stored as an node attribute of ‘/coincidences’ in the +HDF5 file. This class will try to read that object and use it’s meta-data +in reconstructions.

+

Example usage:

+
>>> import tables
+>>> from sapphire import ReconstructSimulatedCoincidences
+
+>>> data = tables.open_file('simulated.h5', 'a')
+>>> rec = ReconstructSimulatedCoincidences(data, overwrite=True)
+>>> rec.reconstruct_and_store()
+
+
+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile.

    • +

  • coincidences_group – the destination group.

    • +

  • overwrite – if True overwrite existing reconstruction table.

    • +

  • progress – if True show a progressbar while reconstructing.

    • +

  • verbose – if True be verbose about station metadata usage.

    • +

  • destination – alternative name for reconstruction table.

    • +

  • cluster – a Cluster object to use for the reconstructions.

    • +
+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/signal_calibration.html b/analysis/signal_calibration.html new file mode 100644 index 00000000..17762b26 --- /dev/null +++ b/analysis/signal_calibration.html @@ -0,0 +1,257 @@ + + + + + + + + Detector signal calibration — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Detector signal calibration

+

Determine signal calibration values for data

+

This module can be used to determine signal calibration values from data.

+

Determine the PMT response curve to correct the detected number of MIPs.

+
+
+sapphire.analysis.signal_calibration.linear(x, slope, intercept)
+

Evaluate a linear function at point x

+
+
Parameters:
+
    +

  • x – x coordinate at which to evaluate the function.

    • +

  • slope – slope of the line.

    • +

  • intercept – y-intercept at x = 0.

    • +
+
+
Returns:
+

the y value at x, given the slope and intercept.

+
+
+
+ +
+
+sapphire.analysis.signal_calibration.inverse_linear(y, slope, intercept)
+

Evaluate the inverse linear function for value y

+
+
Parameters:
+
    +

  • y – value y.

    • +

  • slope – slope of the line.

    • +

  • intercept – y-intercept at x = 0.

    • +
+
+
Returns:
+

the x coordinate for value y, given the slope and intercept.

+
+
+
+ +
+
+sapphire.analysis.signal_calibration.log_linear(x, slope, intercept)
+

Evaluate a linear function in log-log space at point x

+
+
Parameters:
+
    +

  • x – x coordinate at which to evaluate the function.

    • +

  • slope – slope of the line.

    • +

  • intercept – log10 y-intercept at x = 1 (log10 1 = 0).

    • +
+
+
Returns:
+

the y value at x, given the slope and intercept.

+
+
+
+ +
+
+sapphire.analysis.signal_calibration.linear_intersection(slope_1, intercept_1, slope_2, intercept_2)
+

The x coordinate at which the two linear lines intersect

+
+
Parameters:
+
    +

  • slope1 – slope of the first line.

    • +

  • intercept1 – y-intercept at x = 0 of the first line.

    • +

  • slope2 – slope of the second line.

    • +

  • intercept2 – y-intercept at x = 0 of the second line.

    • +
+
+
Returns:
+

x coordinate of the intersection.

+
+
+
+ +
+
+sapphire.analysis.signal_calibration.linear_circle_linear(x, radius, slope_low, intercept_low, slope_high, intercept_high)
+

Two linear lines connected by a circle segment

+
+
Parameters:
+
    +

  • x – x coordinate at which to evaluate the function.

    • +

  • radius – radius of the cricle connecting the lines.

    • +

  • slope_low – the slope of the lower (low x) linear line.

    • +

  • intercept_low – the y-intercept of the lower (low x) linear line.

    • +

  • slope_high – the slope of the upper (high x) linear line.

    • +

  • intercept_high – the y-intercept of the upper (high x) linear line.

    • +
+
+
Returns:
+

the y value at x.

+
+
+

The center of the circle is given by (center_x, center_y)

+
+ +
+
+sapphire.analysis.signal_calibration.xy_circle_linear(x, radius, slope_high, intercept_high)
+

An x=y and a linear line connected by a circle segment

+
+
Parameters:
+
    +

  • x – x coordinate at which to evaluate the function.

    • +

  • radius – radius of the cricle connecting the lines.

    • +

  • slope_high – the slope of the upper (high x) linear line.

    • +

  • intercept_high – the y-intercept of the upper (high x) linear line.

    • +
+
+
Returns:
+

the y value at x.

+
+
+
+ +
+
+sapphire.analysis.signal_calibration.loglog_xy_circle_linear(x, radius, slope_high, intercept_high)
+

As xy_circle_linear but in log-log space

+
+ +
+
+sapphire.analysis.signal_calibration.loglog_linear_circle_linear(x, radius, slope_low, intercept_low, slope_high, intercept_high)
+

As linear_circle_linear but in log-log space

+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/analysis/time_deltas.html b/analysis/time_deltas.html new file mode 100644 index 00000000..67319434 --- /dev/null +++ b/analysis/time_deltas.html @@ -0,0 +1,218 @@ + + + + + + + + Time deltas — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Time deltas

+

Determine time differences between coincident events

+

Determine time delta between coincidence events from station pairs.

+

Example usage:

+
import datetime
+
+import tables
+
+from sapphire import download_coincidences
+from sapphire import ProcessTimeDeltas
+
+START = datetime.datetime(2015, 2, 1)
+END = datetime.datetime(2015, 2, 5)
+
+if __name__ == '__main__':
+    with tables.open_file('data.h5', 'w') as data:
+        download_coincidences(data, start=START, end=END)
+        td = ProcessTimeDeltas(data)
+        td.determine_and_store_time_deltas()
+
+
+
+
+class sapphire.analysis.time_deltas.ProcessTimeDeltas(data, coincidence_group='/coincidences', progress=True, destination='time_deltas')
+

Process HiSPARC event coincidences to obtain time deltas.

+

Use this to determine arrival time differences between station pairs which +have coincident events.

+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile.

    • +

  • coincidence_group – path to the coincidences group.

    • +

  • progress – if True show a progressbar while determining and +storing offsets.

    • +

  • destination – group name for the time_deltas, as subgroup of +the coincidence_group.

    • +
+
+
+
+
+determine_and_store_time_deltas()
+

Find station pairs, determine time deltas, and store the results.

+
+ +
+
+determine_and_store_time_deltas_for_pairs()
+

Determine time deltas for all pairs and store the results.

+
+ +
+
+find_station_pairs()
+

Find all unique station pairs which are in a coincidence together

+

Assumes the stations in the s_index are sorted by station number.

+
+ +
+
+get_detector_offsets()
+

Retrieve the API detector_timing_offset method for all pairs

+

The detector_timing_offset methods accept a single timestamp as +argument, and return the detector offsets for this timestamp.

+
+ +
+
+determine_time_deltas_for_pair(ref_station, station)
+

Determine the arrival time differences between two stations.

+
+
Parameters:
+

ref_station,station – station numbers.

+
+
Returns:
+

extended timestamp of the first event and time difference, +t - t_ref. Not corrected for altitude differences.

+
+
+
+ +
+
+store_time_deltas(ext_timestamps, time_deltas, pair)
+

Store determined dt values

+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/api.html b/api.html new file mode 100644 index 00000000..0c41e679 --- /dev/null +++ b/api.html @@ -0,0 +1,877 @@ + + + + + + + + HiSPARC API — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

HiSPARC API

+

Access the HiSPARC public database API.

+

This provides easy classes and functions to access the HiSPARC +publicdb API. This takes care of the url retrieval and conversion +from JSON to Python dictionaries.

+

Example usage:

+
>>> from sapphire import Station
+>>> stations = [5, 3102, 504, 7101, 8008, 13005]
+>>> clusters = [Station(station).cluster for station in stations]
+>>> for station, cluster in zip(stations, clusters):
+...     print('Station %d is in cluster %s.' % (station, cluster))
+Station 5 is in cluster Amsterdam.
+Station 3102 is in cluster Leiden.
+Station 504 is in cluster Amsterdam.
+Station 7101 is in cluster Enschede.
+Station 8008 is in cluster Eindhoven.
+Station 13005 is in cluster Bristol.
+
+
+
+
+sapphire.api.get_api_base()
+
+ +
+
+sapphire.api.get_src_base()
+
+ +
+
+class sapphire.api.API(force_fresh=False, force_stale=False)
+

Base API class

+

This provided the methods to retrieve data from the API. The results +are converted from JSON data to Python objects (dict/list/etc). +Support is also provided for the retrieval of Source TSV data, which +is returned as NumPy arrays.

+

Initialize API class

+
+
Parameters:
+

force_fresh,force_stale – if either of these is set to True the +data must either loaded from server or from local data. Be default +fresh data is preferred, but falls back to local data.

+
+
+
+
+urls = {'clusters': 'clusters/', 'clusters_in_country': 'countries/{country_number}/', 'configuration': 'station/{station_number}/config/{year}/{month}/{day}/', 'countries': 'countries/', 'event_trace': 'station/{station_number}/trace/{ext_timestamp}/', 'has_data': 'station/{station_number}/data/{year}/{month}/{day}/', 'has_weather': 'station/{station_number}/weather/{year}/{month}/{day}/', 'number_of_events': 'station/{station_number}/num_events/{year}/{month}/{day}/{hour}/', 'station_info': 'station/{station_number}/', 'stations': 'stations/', 'stations_in_subcluster': 'subclusters/{subcluster_number}/', 'stations_with_data': 'stations/data/{year}/{month}/{day}/', 'stations_with_weather': 'stations/weather/{year}/{month}/{day}/', 'subclusters': 'subclusters/', 'subclusters_in_cluster': 'clusters/{cluster_number}/'}
+
+ +
+
+src_urls = {'azimuth': 'azimuth/{station_number}/{year}/{month}/{day}/', 'barometer': 'barometer/{station_number}/{year}/{month}/{day}/', 'coincidencenumber': 'coincidencenumber/{year}/{month}/{day}/', 'coincidencetime': 'coincidencetime/{year}/{month}/{day}/', 'current': 'current/{station_number}/', 'detector_timing_offsets': 'detector_timing_offsets/{station_number}/', 'electronics': 'electronics/{station_number}/', 'eventtime': 'eventtime/{station_number}/{year}/{month}/{day}/', 'gps': 'gps/{station_number}/', 'integral': 'pulseintegral/{station_number}/{year}/{month}/{day}/', 'layout': 'layout/{station_number}/', 'pulseheight': 'pulseheight/{station_number}/{year}/{month}/{day}/', 'station_timing_offsets': 'station_timing_offsets/{station_1}/{station_2}/', 'temperature': 'temperature/{station_number}/{year}/{month}/{day}/', 'trigger': 'trigger/{station_number}/', 'voltage': 'voltage/{station_number}/', 'zenith': 'zenith/{station_number}/{year}/{month}/{day}/'}
+
+ +
+
+static check_connection()
+

Open the API man page URL to test the connection

+
+
Returns:
+

boolean indicating the internet status

+
+
+
+ +
+
+static validate_partial_date(year='', month='', day='', hour='')
+
+ +
+ +
+
+class sapphire.api.Network(force_fresh=False, force_stale=False)
+

Get info about the network (countries/clusters/subclusters/stations)

+

Initialize API class

+
+
Parameters:
+

force_fresh,force_stale – if either of these is set to True the +data must either loaded from server or from local data. Be default +fresh data is preferred, but falls back to local data.

+
+
+
+
+countries()
+

Get a list of countries

+
+
Returns:
+

all countries in the region

+
+
+
+ +
+
+country_numbers()
+

Same as countries but only returns a list of country numbers

+
+ +
+
+clusters(country=None)
+

Get a list of clusters

+
+
Parameters:
+

country – the number of the country for which to get all +clusters.

+
+
Returns:
+

all clusters in the region

+
+
+
+ +
+
+cluster_numbers(country=None)
+

Same as clusters but only returns a list of cluster numbers

+
+ +
+
+subclusters(country=None, cluster=None)
+

Get a list of subclusters

+
+
Parameters:
+

country,cluster – the number of the region for which to get +the subclusters it contains, only one or none should +be specified.

+
+
Returns:
+

all subclusters in the region

+
+
+
+ +
+
+subcluster_numbers(country=None, cluster=None)
+

Same as subclusters but only returns a list of subcluster numbers

+
+ +
+
+stations(country=None, cluster=None, subcluster=None)
+

Get a list of stations

+
+
Parameters:
+

country,cluster,subcluster – the number of the region +for which to get all stations, only one or none should +be specified.

+
+
Returns:
+

all stations in the region

+
+
+
+ +
+
+station_numbers(country=None, cluster=None, subcluster=None)
+

Same as stations but only returns a list of station numbers

+
+ +
+
+nested_network()
+

Get a nested list of the full network

+
+ +
+
+stations_with_data(year='', month='', day='')
+

Get a list of stations with data on the specified date

+
+
Parameters:
+

year,month,day – the date for which to check. It is +possible to be less specific.

+
+
Returns:
+

all stations with data.

+
+
+
+ +
+
+stations_with_weather(year='', month='', day='')
+

Get a list of stations with weather data on the specified date

+
+
Parameters:
+

year,month,day – the date for which to check. It is +possible to be less specific.

+
+
Returns:
+

all stations with weather data.

+
+
+
+ +
+
+coincidence_time(year, month, day)
+

Get the coincidences per hour histogram

+
+
Parameters:
+

year,month,day – the date for which to get the histogram.

+
+
Returns:
+

array of bins and counts.

+
+
+
+ +
+
+coincidence_number(year, month, day)
+

Get the number of stations in coincidence histogram

+
+
Parameters:
+

year,month,day – the date for which to get the histogram.

+
+
Returns:
+

array of bins and counts.

+
+
+
+ +
+
+static validate_numbers(country=None, cluster=None, subcluster=None)
+
+ +
+
+uptime(stations, start=None, end=None)
+

Get number of hours for which the given stations have been simultaneously active

+

Using hourly eventrate data the number of hours in which the given +stations all had data is determined. Only hours in which each station +had a reasonable eventrate are counted, to exclude likely bad data.

+
+
Parameters:
+
    +

  • stations – station number or a list of station numbers.

    • +

  • start,end – start, end timestamp.

    • +
+
+
Returns:
+

number of hours with simultaneous data.

+
+
+
+ +
+ +
+
+class sapphire.api.Station(station, force_fresh=False, force_stale=False)
+

Access data about a single station

+

Initialize station

+
+
Parameters:
+
    +

  • station – station number.

    • +

  • force_fresh – set to True to require data to be fresh from the server.

    • +

  • force_stale – set to True to require data to be taken from local +data, not valid for all methods.

    • +
+
+
+
+
+property info
+

Get general station info

+
+ +
+
+country()
+
+ +
+
+cluster()
+
+ +
+
+subcluster()
+
+ +
+
+n_detectors()
+

Get the number of detectors in this station

+
+ +
+
+config(date=None)
+

Get station config

+

Retrieve either the latest, or a config for a specific date.

+
+
Parameters:
+

date – date object for which to get the config

+
+
Returns:
+

the full config for the station

+
+
+
+ +
+
+n_events(year='', month='', day='', hour='')
+

Get number of events

+

Note that it is possible to give only the year to get the total +number of events in that year. If both year and month are given, +the total events in that month are returned.

+
+
Parameters:
+

year,month,day,hour – the date and time for which to +get the number. It is possible to be less specific.

+
+
Returns:
+

the number of events recorded by the station on date.

+
+
+
+ +
+
+has_data(year='', month='', day='')
+

Check for HiSPARC data

+
+
Parameters:
+

year,month,day – the date for which to check. It is +possible to be less specific.

+
+
Returns:
+

boolean, indicating whether the station had air shower +data on the date.

+
+
+
+ +
+
+has_weather(year='', month='', day='')
+

Check for weather data

+
+
Parameters:
+

year,month,day – the date for which to check. It is +possible to be less specific.

+
+
Returns:
+

boolean, indicating whether the station had weather data +on the date.

+
+
+
+ +
+
+event_trace(timestamp, nanoseconds, raw=False)
+

Get the traces for a specific event

+

The exact timestamp and nanoseconds for the event have to be +given.

+
+
Parameters:
+
    +

  • timestamp,nanoseconds – the extended timestamp for which +to get the traces.

    • +

  • raw – get the raw trace, without the subtracted baselines.

    • +
+
+
Returns:
+

an array with the traces for each detector in ADCcounts

+
+
+
+ +
+
+event_time(year='', month='', day='')
+

Get the number of events per hour histogram

+

If no date is given the full event time histogram for this station +will be returned. That means for the entire time this station has +collected data.

+

This data (the full histogram) can made available locally using +the extend_local_data command.

+
+
Parameters:
+

year,month,day – the date for which to get the histogram.

+
+
Returns:
+

array of bins and counts.

+
+
+
+ +
+
+pulse_height(year, month, day)
+

Get the pulseheight histogram

+
+
Parameters:
+

year,month,day – the date for which to get the histogram.

+
+
Returns:
+

array of bins and counts.

+
+
+
+ +
+
+pulse_integral(year, month, day)
+

Get the pulseintegral histogram

+
+
Parameters:
+

year,month,day – the date for which to get the histogram.

+
+
Returns:
+

array of bins and counts.

+
+
+
+ +
+
+azimuth(year, month, day)
+

Get the azimuth histogram

+
+
Parameters:
+

year,month,day – the date for which to get the histogram.

+
+
Returns:
+

array of bins and counts.

+
+
+
+ +
+
+zenith(year, month, day)
+

Get the zenith histogram

+
+
Parameters:
+

year,month,day – the date for which to get the histogram.

+
+
Returns:
+

array of bins and counts.

+
+
+
+ +
+
+barometer(year, month, day)
+

Get the barometer dataset

+
+
Parameters:
+

year,month,day – the date for which to get the dataset.

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+temperature(year, month, day)
+

Get the temperature dataset

+
+
Parameters:
+

year,month,day – the date for which to get the dataset.

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+property electronics
+

Get the electronics version data

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+electronic(timestamp=None)
+

Get electronics version data for specific timestamp

+
+
Parameters:
+

timestamp – timestamp for which the values are valid.

+
+
Returns:
+

list of values for given timestamp.

+
+
+
+ +
+
+property voltages
+

Get the PMT voltage data

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+voltage(timestamp=None)
+

Get PMT voltage data for specific timestamp

+
+
Parameters:
+

timestamp – timestamp for which the values are valid.

+
+
Returns:
+

list of values for given timestamp.

+
+
+
+ +
+
+property currents
+

Get the PMT current data

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+current(timestamp=None)
+

Get PMT current data for specific timestamp

+
+
Parameters:
+

timestamp – timestamp for which the values are valid.

+
+
Returns:
+

list of values for given timestamp.

+
+
+
+ +
+
+property gps_locations
+

Get the GPS location data

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+gps_location(timestamp=None)
+

Get GPS location for specific timestamp

+
+
Parameters:
+

timestamp – optional timestamp or datetime object for which +the values are valid.

+
+
Returns:
+

dictionary with the values for given timestamp.

+
+
+
+ +
+
+property triggers
+

Get the trigger config data

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+trigger(timestamp=None)
+

Get trigger config for specific timestamp

+
+
Parameters:
+

timestamp – timestamp for which the values are valid.

+
+
Returns:
+

thresholds and trigger values for given timestamp.

+
+
+
+ +
+
+property station_layouts
+

Get the station layout data

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+station_layout(timestamp=None)
+

Get station layout data for specific timestamp

+
+
Parameters:
+

timestamp – timestamp for which the values are valid.

+
+
Returns:
+

list of coordinates for given timestamp.

+
+
+
+ +
+
+property detector_timing_offsets
+

Get the detector timing offsets data

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+detector_timing_offset(timestamp=None)
+

Get detector timing offset data for specific timestamp

+
+
Parameters:
+

timestamp – timestamp for which the values are valid.

+
+
Returns:
+

list of values for given timestamp.

+
+
+
+ +
+
+station_timing_offsets(reference_station)
+

Get the station timing offset relative to reference_station

+
+
Parameters:
+

reference_station – reference station

+
+
Returns:
+

array of timestamps and values.

+
+
+
+ +
+
+station_timing_offset(reference_station, timestamp=None)
+

Get station timing offset data for specific timestamp

+
+
Parameters:
+
    +

  • reference_station – reference station

    • +

  • timestamp – timestamp for which the value is valid.

    • +
+
+
Returns:
+

the offset and error for given timestamp.

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/clusters.html b/clusters.html new file mode 100644 index 00000000..d634e47f --- /dev/null +++ b/clusters.html @@ -0,0 +1,785 @@ + + + + + + + + HiSPARC clusters — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

HiSPARC clusters

+

Define HiSPARC detectors, stations and clusters.

+

The BaseCluster defines a HiSPARC cluster consisting of one or +more stations. The Station defines a HiSPARC station, +consisting of one or more Detector objects.

+

To easily create a cluster object for a specific set of real HiSPARC +stations the HiSPARCStations can be used, for example:

+
>>> from sapphire import HiSPARCStations
+>>> cluster = HiSPARCStations([102, 104, 105], force_stale=True)
+
+
+

The use of force_stale forces the use of local data, which +is much faster to load than data from the server.

+

These cluster objects are mainly used by simulations and reconstructions.

+
+
+class sapphire.clusters.Detector(station, position, orientation='UD', detector_timestamps=None)
+

A HiSPARC detector

+

Initialize detector

+
+
Parameters:
+
    +

  • station – station instance this detector is part of.

    • +

  • position – x,y,z position of the center of the detectors +relative to the station center. z is optional. Multiple positions +can be provided by giving lists for each axis.

    • +

  • orientation – orientation of the long side of the detector. +Either the angle in radians, or ‘UD’ or ‘LR’ meaning an +up-down or left-right orientation of the long side of the +detector respectively.

    • +

  • detector_timestamps – list of timestamps, the timestamp at +which each of the layouts became active.

    • +
+
+
+
+
+property detector_size
+
+ +
+
+get_area()
+
+ +
+
+get_xy_coordinates()
+
+ +
+
+get_coordinates()
+
+ +
+
+get_polar_coordinates()
+
+ +
+
+get_cylindrical_coordinates()
+
+ +
+
+get_lla_coordinates()
+

Get detector LLA coordinates (latitude, longitude, altitude)

+

get detector WGS84 coordinates +LLA: Latitude, Longitude, Altitude

+
+
Returns:
+

tuple (latitude, longitude, altitude). +Latitude, longitude in degrees. Altitude in meters.

+
+
+
+ +
+
+get_corners()
+

Get the x, y coordinates of the detector corners

+

The z-coordinate is not returned because all detectors are +assumed to be laying flat.

+
+
Returns:
+

coordinates of detector corners, list of (x, y) tuples.

+
+
+
+ +
+ +
+
+class sapphire.clusters.Station(cluster, station_id, position, angle=None, detectors=None, station_timestamps=None, detector_timestamps=None, number=None)
+

A HiSPARC station

+

Initialize station

+
+
Parameters:
+
    +

  • cluster – cluster this station is a part of

    • +

  • station_id – int (unique identifier)

    • +

  • position – x,y,z position of the station center relative +to the cluster center, z is optional.

    • +

  • angle – angle of rotation of the station in radians

    • +

  • detectors – list of tuples. Each tuple consists of (dx, dy, +orientation) where dx and dy are x and y positions of the +center of the detectors relative to the station center. +Orientation is either ‘UD’ or ‘LR’ meaning an up-down or +left-right orientation of the long side of the detector +respectively.

    • +

  • station_timestamps – list of timestamps, the timestamp at +which each of the positions became active.

    • +

  • detector_timestamps – list of timestamps, the timestamp at +which each of the layouts became active.

    • +

  • number – optional unique identifier for a station this can +be used by the cluster to find a specific station and makes +it easier to link to a real station. If not given it will be +equal to the station_id.

    • +
+
+
+
+
+property detectors
+
+ +
+
+get_area(detector_ids=None)
+

Get the total area covered by the detectors

+
+
Parameters:
+

detector_ids – list of detectors for which to get the total area.

+
+
Returns:
+

total area of the detectors in m^2.

+
+
+
+ +
+
+get_xy_coordinates()
+

Same as get_coordinates but without the z and alpha

+
+ +
+
+get_xyalpha_coordinates()
+

Same as get_coordinates but without the z

+
+ +
+
+get_coordinates()
+

Calculate coordinates of a station

+
+
Returns:
+

x, y, z, alpha; coordinates and rotation of station +relative to absolute coordinate system

+
+
+
+ +
+
+get_polar_alpha_coordinates()
+
+ +
+
+get_cylindrical_alpha_coordinates()
+
+ +
+
+get_lla_coordinates()
+

Get station LLA coordinates (latitude, longitude, altitude)

+

LLA: Latitude, Longitude, Altitude

+
+
Returns:
+

tuple (latitude, longitude, altitude). +Latitude, longitude in degrees. Altitude in meters.

+
+
+
+ +
+
+calc_r_and_phi_for_detectors(d0, d1)
+
+ +
+
+calc_rphiz_for_detectors(d0, d1)
+

Calculate angle and distance between detectors

+
+
Parameters:
+

d0,d1 – detector ids to find the vector between.

+
+
Returns:
+

r,phi,z pointing from d0 to d1.

+
+
+
+ +
+
+calc_xy_center_of_mass_coordinates()
+
+ +
+
+calc_center_of_mass_coordinates()
+

Calculate center of mass coordinates of detectors in station

+
+
Returns:
+

x, y, z; coordinates of station center relative to +absolute coordinate system

+
+
+
+ +
+ +
+
+class sapphire.clusters.BaseCluster(position=(0, 0, 0), angle=0, lla=(52.35592417, 4.95114402, 56.10234594))
+

Base class for HiSPARC clusters

+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+
+set_timestamp(timestamp)
+

Set the timestamp to set the active station and detector locations

+
+
Parameters:
+

timestamp – timestamp in seconds.

+
+
+
+ +
+
+set_center_off_mass_at_origin()
+

Set the cluster center of mass to (0, 0, 0)

+
+ +
+
+property stations
+
+ +
+
+get_station(number)
+

Get a station by its number

+
+ +
+
+get_xy_coordinates()
+

Same as get_coordinates but without the z and alpha

+
+ +
+
+get_xyalpha_coordinates()
+

Like get_coordinates, but without z

+
+ +
+
+get_coordinates()
+

Get cluster coordinates (x, y, z, alpha).

+

The coordinates should be interpreted as follows: first, the +cluster is rotated over angle alpha, around its original center. +Then, the cluster is translated to (x, y, z).

+
+ +
+
+get_polar_alpha_coordinates()
+

Like get_cylindrical_coordinates but without z.

+
+ +
+
+get_cylindrical_alpha_coordinates()
+

Get cluster coordinates (r, phi, z, alpha).

+

The coordinates should be interpreted as follows: first, the +cluster is rotated over angle alpha, around its original center. +Then, the cluster is translated to (r, phi, z).

+
+ +
+
+get_lla_coordinates()
+

Get cluster LLA coordinates (latitude, longitude, altitude)

+

get cluster WGS84 coordinates +LLA: Latitude, Longitude, Altitude

+
+
Returns:
+

tuple (latitude, longitude, altitude). +Latitude, longitude in degrees. Altitude in meters.

+
+
+
+ +
+
+set_coordinates(x, y, z, alpha)
+

Set cluster coordinates (x, y, z, alpha).

+

The coordinates should be interpreted as follows: first, the +cluster is rotated over angle alpha, around its original center. +Then, the cluster is translated to (x, y, z).

+
+ +
+
+set_cylindrical_coordinates(r, phi, z, alpha)
+

Set cluster coordinates (r, phi, z, alpha).

+

The coordinates should be interpreted as follows: first, the +cluster is rotated over angle alpha, around its original center. +Than, the cluster is translated to (r, phi).

+
+ +
+
+calc_rphiz_for_stations(s0, s1)
+

Calculate distance between and direction of two stations

+

The calculated result is between the center of mass coordinates +of the two stations.

+
+
Parameters:
+

s0,s1 – The station ids for the two stations.

+
+
Returns:
+

r, phi, z; the distance between and direction of the two +given stations.

+
+
+
+ +
+
+calc_xy_center_of_mass_coordinates()
+

Like calc_center_of_mass_coordinates, but without z

+
+ +
+
+calc_center_of_mass_coordinates()
+

Calculate center of mass coordinates of all detectors in cluster

+
+
Returns:
+

x, y, z; coordinates of cluster center relative to +absolute coordinate system

+
+
+
+ +
+
+calc_distance_between_stations(s1, s2)
+

Calculate distance between two stations

+
+
Parameters:
+

s1,s2 – station numbers.

+
+
Returns:
+

distance between stations.

+
+
+
+ +
+
+calc_horizontal_distance_between_stations(s1, s2)
+

Calculate 2D distance between two HiSPARC stations. Ignores altitude

+

The 2D plane is the East-North plane defined by the ENU axes at the +reference location for this cluster.

+
+
Parameters:
+

s1,s2 – station numbers.

+
+
Returns:
+

distance between stations.

+
+
+
+ +
+ +
+
+class sapphire.clusters.CompassStations(position=(0, 0, 0), angle=0, lla=(52.35592417, 4.95114402, 56.10234594))
+

Add detectors to stations using compass coordinates

+

Compass coordinates consist of r, alpha, z, beta. These define +the location and orientation of detectors. For more information +see Coordinate systems and units in HiSPARC.

+

This is meant for data from the Publicdb Database API, which +uses that coordinate system.

+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+ +
+
+class sapphire.clusters.SimpleCluster(size=250)
+

Define a simple cluster containing four stations

+
+
Parameters:
+

size – This value is the distance between the three outer stations.

+
+
+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+ +
+
+class sapphire.clusters.SingleStation
+

Define a cluster containing a single station

+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+ +
+
+class sapphire.clusters.SingleDetectorStation
+

Define a cluster containing a single 1-detector station

+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+ +
+
+class sapphire.clusters.SingleTwoDetectorStation
+

Define a cluster containing a single 2 detector station

+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+ +
+
+class sapphire.clusters.SingleDiamondStation
+

Define a cluster containing a single diamond shaped station

+

Detectors 1, 3 and 4 are in the usual position for a 4 detector +layout, detector 2 is moved out of the center and positioned to +create a second equilateral triangle with detectors 1, 2 and 4.

+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+ +
+
+class sapphire.clusters.HiSPARCStations(stations, skip_missing=False, force_fresh=False, force_stale=False)
+

A cluster containing any real station from the HiSPARC network

+

The gps position and number of detectors are taken from the API. +The detector positions are retrieved if available, otherwise +default values are used!

+
+
Parameters:
+
    +

  • stations – A list of station numbers to include. The +coordinates are retrieved from the Public Database API. +The first station is placed at the origin of the cluster.

    • +

  • skip_missing – Set to True to skip stations which have missing +location data, otherwise an exception will be raised. Stations +with missing location data will be excluded. Does not apply +to missing detector positions.

    • +
+
+
+

Example:

+
>>> cluster = HiSPARCStations([7001, 7002, 7003])
+
+
+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+ +
+
+class sapphire.clusters.ScienceParkCluster(stations=None, skip_missing=False, force_fresh=False, force_stale=False)
+

A cluster containing stations from the Science Park subcluster

+
+
Parameters:
+

stations – A list of station numbers to include. Only stations +from the Science Park subcluster (5xx) are supported. By default 507 +is excluded.

+
+
+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+ +
+
+class sapphire.clusters.HiSPARCNetwork(force_fresh=False, force_stale=False)
+

A cluster containing all station from the HiSPARC network

+

Override this function to build your cluster

+
+
Parameters:
+
    +

  • position – x,y,z position for the center of the cluster.

    • +

  • angle – rotation of the cluster in the x,y-plane.

    • +

  • lla – Reference WGS84 location of the cluster origin. +Defaults to (old) GPS location of station 501 (Nikhef).

    • +
+
+
+
+ +
+
+sapphire.clusters.flatten_cluster(cluster)
+

Set the altitudes for all detectors in a cluster object to z=0

+

Modify the given cluster by setting the z coordinates of the stations +and detectors to 0.

+
+
Parameters:
+

cluster – BaseCluster object.

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/configuration.html b/configuration.html new file mode 100644 index 00000000..211bec0a --- /dev/null +++ b/configuration.html @@ -0,0 +1,123 @@ + + + + + + + + Configuration — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Configuration

+

Various modules (sapphire.api, sapphire.esd, and sapphire.publicdb) +connect to the HiSPARC Public Database to download data, metadata, configurations, etc. +Normally you will want to use the official HiSPARC Public Database, which is hosted +at https://data.hisparc.nl. However, during development, tests or other occasions you +may want to use a different server, for example when running a local version of the +publicdb. In that case you can overwrite the base url used. This overwrite is done +using an environment variable: PUBLICDB_BASE. This can be easily set when starting +your Python environment, for example:

+
$ PUBLICDB_BASE=http://localhost:8000 python
+
+
+

Or while already running Python:

+
from os import environ
+environ['PUBLICDB_BASE'] = 'http://localhost:8000'
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika.html b/corsika.html new file mode 100644 index 00000000..2ff21551 --- /dev/null +++ b/corsika.html @@ -0,0 +1,160 @@ + + + + + + + + CORSIKA simulations — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

CORSIKA simulations

+

CORSIKA is an Air Shower +Simulation Program. To be used for detailed simulation of +extensive air showers initiated by high-energy cosmic-ray particles.

+

This module makes it easy to work with CORSIKA output, it was +original written by Javier Gonzalez and adapted for HiSPARC by Arne de Laat.

+

This documentation sometimes refers to the CORSIKA users manual, this +users manual can be found here CORSIKA User’s Guide

+

In addition to reading CORSIKA output, functionally is provided to +easily submit many CORSIKA jobs to the Nikhef batch facility +(Stoomboot).

+
+

CORSIKA Module

+

Process CORSIKA simulation data.

+

This package contains modules for working with CORSIKA simulations:

+
+
blocks

classes for the block and subblocks in CORSIKA data

+
+
corsika_queries

select CORSIKA showers from a corsika overview

+
+
generate_corsika_overview

generate an overview table of available CORSIKA simulations

+
+
particles

convert CORSIKA particle codes to common names

+
+
qsub_corsika

submit CORSIKA simulations to the Nikhef Stoomboot facility

+
+
qsub_store_corsika_data

submit store CORSIKA jobs to the Nikhef Stoomboot facility

+
+
reader

read CORSIKA data files into Python

+
+
store_corsika_data

convert CORSIKA data files to HDF5 files

+
+
units

convert values in units used by CORSIKA to HiSPARC units

+
+
+
+
+

Contents

+
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika/blocks.html b/corsika/blocks.html new file mode 100644 index 00000000..30d13a24 --- /dev/null +++ b/corsika/blocks.html @@ -0,0 +1,330 @@ + + + + + + + + CORSIKA Blocks — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

CORSIKA Blocks

+

Classes corresponding to CORSIKA blocks and sub-blocks

+

The classes in this module correspond one-to-one with the sub-blocks +(and blocks) as specified in the CORSIKA users manual.

+

Author: Javier Gonzalez

+
+
+sapphire.corsika.blocks.jit(func)
+
+ +
+
+class sapphire.corsika.blocks.Format
+

The binary format information of the file.

+

As specified in the CORSIKA user manual, Section 10.2.1.

+
+ +
+
+class sapphire.corsika.blocks.RunHeader(subblock)
+

The run header sub-block

+

As specified in the CORSIKA user manual, Table 7.

+
+
+thickness_to_height(thickness)
+

“Calculate height (m) for given thickness (gramms/cm**2)

+

As specified in the CORSIKA user manual, Appendix D.

+
+ +
+
+height_to_thickness(height)
+

Thickness (gramms/cm**2) of atmosphere given a height (m)

+

As specified in the CORSIKA user manual, Appendix D.

+
+ +
+ +
+
+class sapphire.corsika.blocks.EventHeader(subblock)
+

The event header sub-block

+

As specified in the CORSIKA user manual, Table 8.

+
+
+property hadron_model_low
+
+ +
+
+property hadron_model_high
+
+ +
+
+property computer
+
+ +
+ +
+
+class sapphire.corsika.blocks.RunEnd(subblock)
+

The run end sub-block

+

As specified in the CORSIKA user manual, Table 14.

+
+ +
+
+class sapphire.corsika.blocks.EventEnd(subblock)
+

The event end sub-block

+

As specified in the CORSIKA user manual, Table 13.

+
+ +
+
+sapphire.corsika.blocks.particle_data(subblock)
+

Get particle data.

+

As specified in the CORSIKA user manual, Table 10. High-performing +version of the ParticleData class, but without all the easy-to-use +attribute access.

+

Transformations are needed for the x, y and p_z values from CORSIKA. +CORSIKA coordinate conventions are mentioned in Figure 1 and Table 10.

+
+
Returns:
+

tuple with p_x, p_y, p_z, x, y, t, id, r, hadron_generation, +observation_level, phi data.

+
+
+
+ +
+
+sapphire.corsika.blocks.particle_data_thin(subblock)
+

Get thinned particle data.

+

Similar to particle_data(), but includes particle weight.

+
+
Returns:
+

tuple with p_x, p_y, p_z, x, y, t, id, r, hadron_generation, +observation_level, phi, weight data.

+
+
+
+ +
+
+class sapphire.corsika.blocks.ParticleData(subblock)
+

The particle data sub-block

+

As specified in the CORSIKA user manual, Table 10.

+
+
+property is_detectable
+

Get True or False if the particle is detectable

+

Note: gamma particles are currently not included.

+
+ +
+
+property particle
+
+ +
+
+property is_particle
+
+ +
+
+property is_nucleus
+
+ +
+
+property is_cherenkov
+
+ +
+
+property atomic_number
+
+ +
+
+property atom
+
+ +
+ +
+
+class sapphire.corsika.blocks.CherenkovData(subblock)
+

The cherenkov photon sub-block

+

As specified in CORSIKA user manual, Table 11.

+

The number of CherenkovData records in a sub-block depends on +compilation options.

+
+ +
+
+class sapphire.corsika.blocks.FormatThin
+

The format information of the thinned file

+

As specified in CORSIKA user manual, Section 10.2.2.

+
+ +
+
+class sapphire.corsika.blocks.ParticleDataThin(subblock)
+

The thinned particle data sub-block

+

As specified in the CORSIKA user manual, Table 10.

+
+ +
+
+class sapphire.corsika.blocks.CherenkovDataThin(subblock)
+

The thinned cherenkov photon sub-block

+

As specified in CORSIKA user manual, Table 11.

+

The number of CherenkovData records in a sub-block depends on +compilation options.

+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika/corsika_queries.html b/corsika/corsika_queries.html new file mode 100644 index 00000000..4a1219dc --- /dev/null +++ b/corsika/corsika_queries.html @@ -0,0 +1,344 @@ + + + + + + + + Query CORSIKA overview for simulations — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Query CORSIKA overview for simulations

+
+
+class sapphire.corsika.corsika_queries.CorsikaQuery(data, simulations_group='/simulations')
+

Setup variables to point to the tables

+
+
Parameters:
+
    +

  • data – either a PyTables file or path to a HDF5 file

    • +

  • simulations_group – path to the simulations group

    • +
+
+
+
+
+finish()
+

Clean-up after using

+

Do not use if you opened the file yourself and still intent to +use it.

+
+ +
+
+all_simulations(iterator=False)
+

Get all simulations

+
+
Returns:
+

all simulations.

+
+
+
+ +
+
+seeds(simulations, iterator=False)
+

Get combined seeds for set of simulations

+
+
Returns:
+

combined seed1 and seed2.

+
+
+
+ +
+
+get_info(seeds)
+

Get info for a simulation

+
+
Parameters:
+

seeds – combined string with seed1 and seed2.

+
+
Returns:
+

row matching the seeds.

+
+
+
+ +
+
+property all_energies
+

All available energies

+
+
Returns:
+

set of available simulation energies in log10(eV).

+
+
+
+ +
+
+property all_particles
+

All available particles

+
+
Returns:
+

set of available simulation particles.

+
+
+
+ +
+
+property all_azimuths
+

All available azimuths

+
+
Returns:
+

set of available simulation azimuths.

+
+
+
+ +
+
+property all_zeniths
+

All available zeniths

+
+
Returns:
+

set of available simulation zeniths.

+
+
+
+ +
+
+simulations(particle='proton', energy=None, zenith=None, azimuth=None, iterator=False)
+

Set of available energies given the requirements

+
+
Parameters:
+
    +

  • particle – primary particle must be this kind, name of particle. +Defaults to proton.

    • +

  • energy – primary energy must be this value, in log10(eV).

    • +

  • zenith – shower zenith must be this value, in degrees.

    • +

  • azimuth – shower azimuth must be this value, in degrees.

    • +
+
+
Returns:
+

simulations matching the query.

+
+
+
+ +
+
+available_parameters(parameter, *args, **kwargs)
+

Get set of available values of type parameter for a subset

+
+
Parameters:
+

parameter – name of the parameter for which the available +values are returned.

+
+
Param:
+

use the arguments available to simulations() to filter +the simulations.

+
+
Returns:
+

set of available values.

+
+
+
+ +
+
+filter(key, value)
+

Filter to be in a range

+
+
Parameters:
+
    +

  • key – variable to filter.

    • +

  • value – value to match.

    • +
+
+
Returns:
+

query.

+
+
+
+ +
+
+float_filter(key, value)
+

Filter float values

+

Take into account that the values are likely not a perfect match.

+
+
Parameters:
+
    +

  • key – variable to filter.

    • +

  • value – value to match.

    • +
+
+
Returns:
+

query.

+
+
+
+ +
+
+range_filter(key, min_value=None, max_value=None)
+

Filter to be in a range

+
+
Parameters:
+
    +

  • key – variable to filter.

    • +

  • min_value,max_value – limits on the value.

    • +
+
+
Returns:
+

query.

+
+
+
+ +
+
+perform_query(query, iterator=False)
+

Perform a query on the simulations table

+
+
Parameters:
+

query – a valid PyTables query string for the simulations table.

+
+
Returns:
+

simulations matching the query.

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika/generate_corsika_overview.html b/corsika/generate_corsika_overview.html new file mode 100644 index 00000000..ae7ea0ae --- /dev/null +++ b/corsika/generate_corsika_overview.html @@ -0,0 +1,225 @@ + + + + + + + + Create an overview of CORSIKA simulations — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Create an overview of CORSIKA simulations

+

Generate an overview table of the CORSIKA simulations

+

This script will look for all completed and converted CORSIKA +simulations in the given data path. Information about each +simulation is collected and then summarized in a new h5 file as an +overview.

+

The given source path should contain subdirectories named after the +seeds used for the simulation in the format {seed1}_{seed2}, +e.g. 821280921_182096636. These in turn should contain converted +CORSIKA simulation results called corsika.h5.

+
+
+sapphire.corsika.generate_corsika_overview.write_row(table, seeds, header, end)
+

Write the information of one simulation into a row

+
+
Parameters:
+
    +

  • table – the table where the new data should be appended.

    • +

  • seeds – the unique id consisting of the two seeds.

    • +

  • header,end – the event header and end for the simulation.

    • +
+
+
+
+ +
+
+sapphire.corsika.generate_corsika_overview.read_seeds(simulations_table, source, seeds)
+

Read the header and end of a simulation and write to overview.

+
+
Parameters:
+
    +

  • simulations_table – PyTables table in which the simulation +overview is stored.

    • +

  • source – directory containing the CORSIKA simulations.

    • +

  • seeds – directory name of a simulation, format: ‘{seed1}_{seed2}’.

    • +
+
+
+
+ +
+
+sapphire.corsika.generate_corsika_overview.get_simulations(source, simulations, overview, progress=False)
+

Get the information of the simulations and create a table.

+
+ +
+
+sapphire.corsika.generate_corsika_overview.prepare_output(n)
+

Create a temporary file in which to store the overview

+
+
Parameters:
+

n – the number of simulations, i.e. expected number of rows.

+
+
Returns:
+

path to the temporary file and a PyTables handler for the file.

+
+
+
+ +
+
+sapphire.corsika.generate_corsika_overview.create_tempfile_path()
+
+ +
+
+sapphire.corsika.generate_corsika_overview.move_tempfile_to_destination(tmp_path, destination)
+
+ +
+
+sapphire.corsika.generate_corsika_overview.all_seeds(source)
+

Get set of all seeds in the corsika data directory

+
+ +
+
+sapphire.corsika.generate_corsika_overview.generate_corsika_overview(source, destination, progress=False)
+

Create an overview of CORSIKA simulations for use by CorsikaQuery

+
+
Parameters:
+
    +

  • source – directory containing the CORSIKA simulations.

    • +

  • destination – path of the HDF5 output file.

    • +

  • progress – if True show a progressbar while processing simulations.

    • +
+
+
+
+ +
+
+sapphire.corsika.generate_corsika_overview.main()
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika/particles.html b/corsika/particles.html new file mode 100644 index 00000000..862c127c --- /dev/null +++ b/corsika/particles.html @@ -0,0 +1,171 @@ + + + + + + + + CORSIKA Particles — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

CORSIKA Particles

+

Use this for particle identification for particles in CORSIKA

+

Particle codes as specified in CORSIKA user manual, Table 4.

+

This relates to the Description in Particles as:

+
particle id x 1000 + hadron generation x 10 + no. of observation level
+
+
+

So to find e+/e-:

+
from sapphire.corsika import particles
+
+particle.id = 2
+if particles.name(particle.id) in ['positron', 'electron']:
+    pass
+
+particle.id = 1206
+if particle.id > 200:
+    print('atom: %s' % particles.ATOMIC_NUMBER[particle.id % 100])
+
+
+
+
+sapphire.corsika.particles.name(particle_id)
+

Get the name for a CORSIKA particle code

+
+
Parameters:
+

particle_id – code for the particle

+
+
Returns:
+

name of the particle. In case of atoms the weight is added +to the name.

+
+
+
+ +
+
+sapphire.corsika.particles.particle_id(name)
+

Get the CORSIKA particle code for a particle name

+
+
Parameters:
+

name – name of the particle/atom, for atoms the mass +(neutrons + protons) can be appended to the name.

+
+
Returns:
+

CORSIKA code for the particle. +For atoms the code is: A x 100 + Z

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika/qsub_corsika.html b/corsika/qsub_corsika.html new file mode 100644 index 00000000..291e05e8 --- /dev/null +++ b/corsika/qsub_corsika.html @@ -0,0 +1,306 @@ + + + + + + + + CORSIKA on Stoomboot — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

CORSIKA on Stoomboot

+

Run CORSIKA simulations on Stoomboot

+

In order to quickly get a good sample of simulated showers we use the +Nikhef computer cluster Stoomboot to run multiple jobs simultaneously. +For this purpose a script has been written that will make this easy. +The qsub_corsika script can submit as many +jobs as you want with the parameters that you desire. It automatically +ensures that a unique combination of seeds for the random number +sequences are used for each simulation.

+

To run this file correctly do it in the correct env:

+
$ source activate corsika
+
+
+

The syntax for calling the script can be seen by calling its help:

+
$ qsub_corsika --help
+
+
+

For example, running 100 showers with proton primaries of 1e16 eV +coming in at 22.5 degrees zenith and 90 degrees azimuth on the +standard Stoomboot queue with the default CORSIKA configuration:

+
$ qsub_corsika 100 16 proton 22.5 -q generic -a 90
+
+
+
+
+class sapphire.corsika.qsub_corsika.CorsikaBatch(energy=16, particle='proton', zenith=22.5, azimuth=180, queue='generic', corsika='corsika74000Linux_QGSII_gheisha')
+

Run many simultaneous CORSIKA simulations using Stoomboot

+

Stoomboot is the Nikhef computer cluster.

+
+
Parameters:
+
    +

  • energy – the energy of the primary particle in log10(E[eV]), +so an energy of 16 (10**16 eV) corresponds to 10**7 GeV, +integer values and values ending in .5 are allowed.

    • +

  • particle – name of primary particle, e.g. proton, gamma or iron

    • +

  • zenith – zenith angle of the primary particle (in degrees), +common choices: 0, 7.5, 15, 22.5, 30, 37.5, 45 and 52.5.

    • +

  • azimuth – azimuth angle of the primary particle (in degrees), +common choices: 0, 45, 90, 135, 180, 225, 270 and 315.

    • +

  • queue – choose a queue to submit the job to: +express - max 10 minutes, max 2 jobs +short - max 4 hours, max 1000 jobs +generic - max 24 hours, max 500 jobs +long - max 96 hours (default 48 hours), max 500 jobs

    • +

  • corsika – name of the compiled CORSIKA executable to use: +corsika74000Linux_EPOS_gheisha +corsika74000Linux_QGSII_gheisha +corsika74000Linux_QGSJET_gheisha +corsika74000Linux_SIBYLL_gheisha

    • +
+
+
+
+
+corsika_energy(energy)
+

Convert energy to format for CORSIKA input

+

Convert from log10(E/eV) to E[GeV] and split value to multiplier +and power.

+
+
Parameters:
+

energy – primary particle energy as log10(E/eV).

+
+
Returns:
+

separate multiplier and power

+
+
+
+ +
+
+run()
+
+ +
+
+prepare_env()
+

Setup CORSIKA environment

+
+ +
+
+submit_job()
+

Submit job to Stoomboot

+
+ +
+
+taken_seeds()
+

Get list of seeds already used

+
+ +
+
+generate_random_seeds(taken)
+

Get unused combination of two seeds for CORSIKA

+
+
Parameters:
+

taken – List of seed combinations already taken +each is formatted like this: ‘seed1_seed2’

+
+
+
+ +
+
+make_rundir()
+

Make the run directory

+
+ +
+
+goto_rundir()
+

Move into the run directory

+
+ +
+
+get_rundir()
+

Get run directory path

+
+ +
+
+create_input()
+

Make CORSIKA steering file

+
+ +
+
+create_script()
+

Make Stoomboot script file

+
+ +
+
+copy_config()
+

Copy the CORSIKA config file to the output directory

+

This way we can always check how CORSIKA was configured for each +run.

+
+ +
+ +
+
+sapphire.corsika.qsub_corsika.multiple_jobs(n, energy, particle, zenith, azimuth, queue, corsika, progress=True)
+

Use this to submit multiple jobs to Stoomboot

+
+
Parameters:
+
    +

  • n – Number of jobs to submit

    • +

  • energy – log10(E[eV]) energy of primary particle

    • +

  • particle – Particle kind (as string, see +particles for possibilities)

    • +

  • zenith – Zenith angle in degrees of the primary particle

    • +

  • azimuth – Azimuth angle in degrees of the primary particle

    • +

  • queue – Stoomboot queue to submit to

    • +

  • corsika – Name of the CORSIKA executable to use

    • +

  • progress – if True print an overview of the chosen parameters and +show a progressbar of the job submission progress.

    • +
+
+
+
+ +
+
+sapphire.corsika.qsub_corsika.main()
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika/qsub_store_corsika_data.html b/corsika/qsub_store_corsika_data.html new file mode 100644 index 00000000..89c4b42f --- /dev/null +++ b/corsika/qsub_store_corsika_data.html @@ -0,0 +1,197 @@ + + + + + + + + Convert CORSIKA simulations on Stoomboot — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Convert CORSIKA simulations on Stoomboot

+

Convert CORSIKA stored showers to HDF5 on Stoomboot

+

Automatically submits Stoomboot jobs to convert corsika data. The +script store_corsika_data can be used to convert a DAT000000 +CORSIKA file to a HDF5 file. This script checks our data folder for +new or unconverted simulations and creates Stoomboot jobs to perform +the conversion.

+

This job is run as a cron job to ensure the simulations remain up to +date.

+
+
+sapphire.corsika.qsub_store_corsika_data.all_seeds()
+

Get set of all seeds in the corsika data directory

+
+ +
+
+sapphire.corsika.qsub_store_corsika_data.seeds_processed()
+

Get the seeds of simulations for which the h5 is already created

+
+ +
+
+sapphire.corsika.qsub_store_corsika_data.seeds_in_queue()
+

Get set of seeds already queued to be processed

+
+ +
+
+sapphire.corsika.qsub_store_corsika_data.write_queued_seeds(seeds)
+

Write queued seeds to file

+
+ +
+
+sapphire.corsika.qsub_store_corsika_data.append_queued_seeds(seeds)
+

Add seed to the queued seeds file

+
+ +
+
+sapphire.corsika.qsub_store_corsika_data.get_seeds_todo()
+

Get seeds to be processed

+
+ +
+
+sapphire.corsika.qsub_store_corsika_data.filter_large_seeds(seeds_todo)
+

Exclude seeds for data files that are to large

+
+ +
+
+sapphire.corsika.qsub_store_corsika_data.store_command(seed)
+

Write queued seeds to file

+
+ +
+
+sapphire.corsika.qsub_store_corsika_data.run(queue)
+

Get list of seeds to process, then submit jobs to process them

+
+ +
+
+sapphire.corsika.qsub_store_corsika_data.main()
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika/reader.html b/corsika/reader.html new file mode 100644 index 00000000..317ba2d7 --- /dev/null +++ b/corsika/reader.html @@ -0,0 +1,345 @@ + + + + + + + + CORSIKA Reader — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

CORSIKA Reader

+

Read CORSIKA data files.

+

This provides functionality to read CORSIKA output +files with Python. It provides the following main +classes:

+
    +

  • CorsikaFile: The file class provides a +generator over all events in the file.

    • +

  • CorsikaEvent: The event class that +provides a generator over all particles at ground.

    • +
+

and the following classes that correspond to the sub-blocks defined in +the CORSIKA manual:

+ +

Additionally version for thinned showers are available:

+ +
+

Issues

+

This module does not handle platform dependent issues such as byte +ordering (endianness) and field size. This was the result of an +afternoon hack and has only been tested with files generated using +32 bit CORSIKA files on a linux system compiled with gfortran.

+
    +

  • Field Size: According to the CORSIKA user manual section 10.2 +all quantities are written as single precision real numbers +independently of 32-bit or 64-bit, so each field in the file +should be 4 bytes long.

    • +

  • Endianness: There is no check for byte ordering. It can be added +using Python’s struct module.

    • +

  • Special Particles: This module currently ignores all special +(book-keeping) particles like for muon additional information and +history.

    • +
+
+
+

More Info

+

For short information on fortran unformatted binary files, take a look +at http://paulbourke.net/dataformats/reading/

+

For detailed information on the CORSIKA format, check the ‘Outputs’ +chapter in the CORSIKA user manual. In particular, check the ‘Normal +Particle Output’ section.

+
+
+

Authors

+ +
+
+
+class sapphire.corsika.reader.CorsikaEvent(raw_file, header_index, end_index)
+

CorsikaEvent constructor

+

The user never calls this. The CorsikaFile does.

+
+
Parameters:
+
    +

  • raw_file – CorsikaFile object.

    • +

  • header_index – index where the event header starts.

    • +

  • end_index – index where the event end starts.

    • +
+
+
+
+
+get_header()
+

Get the Event Header

+
+
Returns:
+

an instance of EventHeader

+
+
+
+ +
+
+get_end()
+

Get the Event end sub-block

+
+
Returns:
+

an instance of EventEnd

+
+
+
+ +
+
+get_particles()
+

Generator over particles in the event.

+
+

Note

+

This generator filters out additional muon information and +all particles at observation levels other than 1.

+
+

Use like this:

+
for particle in event.get_particles():
+    pass
+
+
+
+
Yield:
+

each particle in the event

+
+
+
+ +
+ +
+
+class sapphire.corsika.reader.CorsikaFile(filename)
+

CORSIKA output file handler

+

This class will probide an interface for CORSIKA output files. +Allowing you go get at the events and particles in the file. +This class is meant for unthinned simulations.

+

CorsikaFile constructor

+
+
Param:
+

the filemame of the CORSIKA data file

+
+
+
+
+finish()
+

Close the opened CORSIKA data file

+
+ +
+
+check()
+

Check DAT file format

+

Some basic sanity checks.

+

Fortran unformatted files are written in ‘blocks’. Each block +has a header and end. They both contain the same +information: the number of bytes in the block.

+

This function only checks if there is an integer number of +blocks in the file and if the header and end are equal.

+

Here would be the place to dynamically check for endianness and +field size.

+
+ +
+
+get_sub_blocks()
+

Get the sub-blocks in the file.

+

Normally one would not need this function but it is here because +I have used it.

+
+ +
+
+get_header()
+

Get the Run header

+
+
Returns:
+

an instance of RunHeader

+
+
+
+ +
+
+get_end()
+

Get the Run end

+
+
Returns:
+

an instance of RunEnd

+
+
+
+ +
+
+get_events()
+

Generator over the Events in the file

+

This method is a generator over the events in the file. +Use it like this:

+
for event in my_file.get_events():
+    pass
+
+
+
+ +
+ +
+
+class sapphire.corsika.reader.CorsikaFileThin(filename)
+

CORSIKA thinned output file handler

+

Same as the unthinned output handler, but with support for +the different format, particles also have the weight property. +This class is meant for thinned simulations.

+

CorsikaFileThin constructor

+

It takes a filename as argument

+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika/store_corsika_data.html b/corsika/store_corsika_data.html new file mode 100644 index 00000000..1420cd19 --- /dev/null +++ b/corsika/store_corsika_data.html @@ -0,0 +1,201 @@ + + + + + + + + Store CORSIKA data as HDF5 — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Store CORSIKA data as HDF5

+

Store CORSIKA simulation data in HDF5 file

+

This module reads the CORSIKA binary ground particles file and stores +each particle individually in a HDF5 file, using PyTables. This file +can then be used as input for the detector simulation.

+

The syntax and options for calling this script can be seen with:

+
$ store_corsika_data --help
+
+
+

For example to convert a CORSIKA file in the current directory called +DAT000000 to a HDF5 called corsika.h5 with a progress bar run:

+
$ store_corsika_data --progress DAT000000 corsika.h5
+
+
+
+
+sapphire.corsika.store_corsika_data.save_particle(row, particle)
+

Write the information of a particle into a row

+
+ +
+
+sapphire.corsika.store_corsika_data.save_thinned_particle(row, p)
+

Write the information of a thinned particle into a row

+
+ +
+
+sapphire.corsika.store_corsika_data.store_and_sort_corsika_data(source, destination, overwrite=False, progress=False, thin=False)
+

First convert the data to HDF5 and create a sorted version

+
+ +
+
+sapphire.corsika.store_corsika_data.store_corsika_data(source, destination, table_name='groundparticles', progress=False, thin=False)
+

Store particles from a CORSIKA simulation in a HDF5 file

+
+
Parameters:
+
    +

  • source – CorsikaFile instance of the source DAT file.

    • +

  • destination – PyTables file instance of the destination file.

    • +

  • table_name – table name in which particles are stored.

    • +

  • progress – if True show progressbar when saving particles.

    • +

  • thin – if True assume the data contains thinned particles.

    • +
+
+
+
+ +
+
+sapphire.corsika.store_corsika_data.create_index(hdf_data, table_name='groundparticles', progress=False)
+

Create a completely sorted index for the x column

+

This can speed up queries to select data based on the x column.

+
+ +
+
+sapphire.corsika.store_corsika_data.copy_and_sort_node(hdf_temp, hdf_data, table_name='groundparticles', progress=False)
+

Sort the data in the tables by the x column

+

This speeds up queries to select data based on the x column.

+
+ +
+
+sapphire.corsika.store_corsika_data.create_tempfile_path(temp_dir=None)
+

Create a temporary file, close it, and return the path

+
+ +
+
+sapphire.corsika.store_corsika_data.main()
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/corsika/units.html b/corsika/units.html new file mode 100644 index 00000000..b9379c18 --- /dev/null +++ b/corsika/units.html @@ -0,0 +1,154 @@ + + + + + + + + CORSIKA Units — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

CORSIKA Units

+

Defines units in terms of HiSPARC standard units

+

You should use the units defined in this file whenever you +have a dimensional quantity in your code. For example:

+

Do:

+
>>> distance = 1.5 * km
+
+
+

Don’t:

+
>>> distance = 1.5  # don't forget this is in km!
+
+
+

The conversion factors defined in this file convert your data into +HiSPARC base units, so that all dimensional quantities in the code are +in a single system of units! You can also use the conversions defined +here to, for example, display data with the unit of your choice. For +example:

+
>>> print("distance = %f mm" %  distance / mm)
+1500000 mm
+
+
+

The base units are:

+
    +

  • meter (meter)

    • +

  • nanosecond (nanosecond)

    • +

  • electron Volt (eV)

    • +

  • positron charge (eplus)

    • +

  • degree Kelvin (kelvin)

    • +

  • the amount of substance (mole)

    • +

  • luminous intensity (candela)

    • +

  • radian (radian)

    • +

  • steradian (steradian)

    • +
+

The SI numerical value of the positron charge is defined here, +as it is needed for conversion factor: positron charge = eSI (coulomb)

+

This is a slightly modified version of the units definitions +written by the Geant4 collaboration.

+

Authors:

+
    +

  • M Maire

    • +

  • S Giani

    • +

  • T Paul (modifications for Auger)

    • +

  • Arne de Laat <adelaat@nikhef.nl>

    • +
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/data.html b/data.html new file mode 100644 index 00000000..efc6b83f --- /dev/null +++ b/data.html @@ -0,0 +1,119 @@ + + + + + + + + Updating and extending local data — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Updating and extending local data

+

Update or extend local data with latest data from public database

+

This package contains modules for updating local data:

+
+
extend_local_data

add additional data

+
+
update_local_data

bring already included data up to date

+
+
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/data/extend_local_data.html b/data/extend_local_data.html new file mode 100644 index 00000000..bffee1f5 --- /dev/null +++ b/data/extend_local_data.html @@ -0,0 +1,148 @@ + + + + + + + + Download additional local data — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Download additional local data

+

Add more local JSON and TSV data

+

Add additional local data, to be used by api if internet is +unavailable. The use of local data can also be forced to skip calls to the +server or prevented to require fresh data from the server.

+

This data is not included by default because then the SAPPHiRE package would +become to large. By running this script the data is added after installation. +This script downloads approximately 100 MB of data.

+

This can be run by simply running the installed script from the command line:

+
$ extend_local_data
+
+
+

To make the script show information about what it will do add the help flag:

+
$ extend_local_data --help
+
+
+
+
+sapphire.data.extend_local_data.update_additional_local_tsv(progress=True)
+

Get location tsv data for all stations

+
+ +
+
+sapphire.data.extend_local_data.main()
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/data/update_local_data.html b/data/update_local_data.html new file mode 100644 index 00000000..d90389d3 --- /dev/null +++ b/data/update_local_data.html @@ -0,0 +1,191 @@ + + + + + + + + Update default local data with latest data from Publicdb — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Update default local data with latest data from Publicdb

+

Update local JSON and TSV data

+

This script updates the local copies of the JSON and TSV data from the Public +Database API. If internet is unavailable the api uses these +files. The use of local data can also be forced to skip calls to the server or +prevented to require fresh data from the server.

+

Not all available data is included by default because then the SAPPHiRE package +would become to large. It is possible to add those files after installation.

+

This can be run by simply running the installed script from the command line:

+
$ update_local_data
+
+
+

To make the script show information about what it will do add the help flag:

+
$ update_local_data --help
+
+
+
+
+sapphire.data.update_local_data.update_local_json(progress=True)
+

Get cluster organisation and basic station JSON data

+
+ +
+
+sapphire.data.update_local_data.update_local_tsv(progress=True)
+

Get configuration and calibration TSV data for all stations

+
+ +
+
+sapphire.data.update_local_data.update_toplevel_json(data_type)
+
+ +
+
+sapphire.data.update_local_data.update_sublevel_json(arg_type, data_type, progress=True)
+
+ +
+
+sapphire.data.update_local_data.update_sublevel_tsv(data_type, station_numbers, progress=True)
+
+ +
+
+sapphire.data.update_local_data.update_subsublevel_tsv(data_type, station_numbers, network, progress=True)
+
+ +
+
+sapphire.data.update_local_data.get_and_store_json(url)
+
+ +
+
+sapphire.data.update_local_data.get_and_store_tsv(url)
+
+ +
+
+sapphire.data.update_local_data.main()
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/esd.html b/esd.html new file mode 100644 index 00000000..997a8f86 --- /dev/null +++ b/esd.html @@ -0,0 +1,459 @@ + + + + + + + + Event Summary Data — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Event Summary Data

+

Fetch events and other data from the event summary data (ESD).

+

This module enables you to access the event summary data.

+

If you are in a real hurry and know what you’re doing (and took the +time to read this far), you can call the quick_download() +function like this:

+
>>> from sapphire import quick_download
+>>> data = quick_download(501)
+
+
+

For regular use, look up download_data().

+
+
+sapphire.esd.get_base_url()
+
+ +
+
+sapphire.esd.get_events_url()
+
+ +
+
+sapphire.esd.get_weather_url()
+
+ +
+
+sapphire.esd.get_singles_url()
+
+ +
+
+sapphire.esd.get_lightning_url()
+
+ +
+
+sapphire.esd.get_coincidences_url()
+
+ +
+
+sapphire.esd.quick_download(station_number, date=None)
+

Quickly download some data

+
+
Parameters:
+
    +

  • station_number – The HiSPARC station number.

    • +

  • date – the date for which to get data (datetime.datetime +instance), passed unchanged to the download_data() as +:param start:.

    • +
+
+
Returns:
+

handle to an open PyTables file.

+
+
+

Everything is handled by this function, including file creation. +Expect no frills: you just get yesterday’s data.

+

Example usage:

+
>>> import sapphire.esd
+>>> data = sapphire.esd.quick_download(501)
+>>> print(data)
+data1.h5 (File) u''
+Last modif.: 'Mon Jun  9 22:03:50 2014'
+Object Tree:
+/ (RootGroup) u''
+/s501 (Group) u''
+/s501/events (Table(58898,)) ''
+
+
+
+ +
+
+sapphire.esd.load_data(file, group, tsv_file, type='events')
+

Load downloaded event summary data into PyTables file.

+

If you’ve previously downloaded event summary data from the HiSPARC Public +Database in TSV format, you can load them into a PyTables +file using this method. The result is equal to directly downloading data +using download_data().

+
+
Parameters:
+
    +

  • file – the PyTables datafile handler.

    • +

  • group – the PyTables destination group, which need not exist.

    • +

  • tsv_file – path to the tsv file downloaded from the HiSPARC Public Database.

    • +

  • type – the datatype to load, either ‘events’, ‘weather’, ‘singles’, or ‘lightning’.

    • +
+
+
+

Example:

+
>>> import tables
+>>> import sapphire.esd
+>>> data = tables.open_file('data.h5', 'w')
+>>> sapphire.esd.load_data(data, '/s501', 'events-s501-20130910.tsv')
+
+
+
+ +
+
+sapphire.esd.download_data(file, group, station_number, start=None, end=None, type='events', progress=True)
+

Download event summary data

+
+
Parameters:
+
    +

  • file – the PyTables datafile handler.

    • +

  • group – the PyTables destination group, which need not exist.

    • +

  • station_number – The HiSPARC station number for which to get data.

    • +

  • start – a datetime instance defining the start of the search interval.

    • +

  • end – a datetime instance defining the end of the search interval.

    • +

  • type – the datatype to download, either ‘events’, ‘weather’, or ‘singles’.

    • +

  • progress – if True show a progressbar while downloading.

    • +
+
+
+

If group is None, use ‘/s<station_number>’ as a default.

+

The start and stop parameters may both be None. In that case, +yesterday’s data is downloaded. If only end is None, a single day’s +worth of data is downloaded, starting at the datetime specified with +start.

+

Example:

+
>>> import tables
+>>> import datetime
+>>> import sapphire.esd
+>>> data = tables.open_file('data.h5', 'w')
+>>> sapphire.esd.download_data(data, '/s501', 501,
+...     datetime.datetime(2013, 9, 1), datetime.datetime(2013, 9, 2))
+
+
+
+ +
+
+sapphire.esd.download_lightning(file, group, lightning_type=4, start=None, end=None, progress=True)
+

Download KNMI lightning data

+
+
Parameters:
+
    +

  • file – the PyTables datafile handler.

    • +

  • group – the PyTables destination group, which need not exist.

    • +

  • lightning_type – type of lightning, see list below for the possible type codes.

    • +

  • start – a datetime instance defining the start of the search interval.

    • +

  • end – a datetime instance defining the end of the search interval.

    • +

  • progress – if True show a progressbar while downloading.

    • +
+
+
+

Lightning types:

+
+

0 - Single-point +1 - Cloud-cloud +2 - Cloud-cloud mid +3 - Cloud-cloud end +4 - Cloud-ground (default) +5 - Cloud-ground return

+
+
+ +
+
+sapphire.esd.load_coincidences(file, tsv_file, group='')
+

Load downloaded event summary data into PyTables file.

+

If you’ve previously downloaded coincidence data from the HiSPARC Public +Database in TSV format, you can load them into a PyTables +file using this method. The result is equal to directly downloading data +using download_coincidences().

+
+
Parameters:
+
    +

  • file – the PyTables datafile handler.

    • +

  • tsv_file – path to the tsv file downloaded from the HiSPARC +Public Database containing coincidences.

    • +

  • group – the PyTables destination group, which need not exist.

    • +
+
+
+

Example:

+
>>> import tables
+>>> import sapphire.esd
+>>> data = tables.open_file('coincidences.h5', 'w')
+>>> sapphire.esd.load_coincidences(data, 'coincidences-20151130.tsv')
+
+
+
+ +
+
+sapphire.esd.download_coincidences(file, group='', cluster=None, stations=None, start=None, end=None, n=2, progress=True)
+

Download event summary data coincidences

+
+
Parameters:
+
    +

  • file – PyTables datafile handler.

    • +

  • group – path of destination group, which need not exist yet.

    • +

  • cluster – HiSPARC cluster name for which to get data.

    • +

  • stations – a list of HiSPARC station numbers for which to get data.

    • +

  • start – a datetime instance defining the start of the search interval.

    • +

  • end – a datetime instance defining the end of the search interval.

    • +

  • n – the minimum number of events in the coincidence.

    • +
+
+
+

The start and end parameters may both be None. In that case, +yesterday’s data is downloaded. If only end is None, a single day’s +worth of data is downloaded, starting at the datetime specified with +start.

+

Optionally either a cluster or stations can be defined to limit the +results to include only events from those stations.

+

Example:

+
>>> import tables
+>>> import datetime
+>>> import sapphire.esd
+>>> data = tables.open_file('data_coincidences.h5', 'w')
+>>> sapphire.esd.download_coincidences(data, cluster='Aarhus',
+...     start=datetime.datetime(2013, 9, 1),
+...     end=datetime.datetime(2013, 9, 2), n=3)
+
+
+
+ +
+
+class sapphire.esd.ReadLineAndStoreEventClass(table)
+

Store lines of event data from the ESD

+

Use this contextmanager to store events from a TSV file into a PyTables +table.

+
+
Parameters:
+

table – a PyTables Table object in which to store the data.

+
+
+
+
+store_line(line)
+

Store a single line

+
+
Parameters:
+

line – the line to store as a tuple of strings (one element per column).

+
+
Returns:
+

timestamp of the stored event, or 0 if the given line was a +comment line starting with a ‘#’.

+
+
+
+ +
+ +
+
+class sapphire.esd.ReadLineAndStoreWeatherClass(table)
+

Store lines of weather data from the ESD

+
+
+store_line(line)
+

Store a single line

+
+
Parameters:
+

line – the line to store as a tuple of strings (one element per column).

+
+
Returns:
+

timestamp of the stored event, or 0 if the given line was a +comment line starting with a ‘#’.

+
+
+
+ +
+ +
+
+class sapphire.esd.ReadLineAndStoreSinglesClass(table)
+

Store lines of singles data from the ESD

+
+
+store_line(line)
+

Store a single line

+
+
Parameters:
+

line – the line to store as a tuple of strings (one element per column).

+
+
Returns:
+

timestamp of the stored event, or 0 if the given line was a +comment line starting with a ‘#’.

+
+
+
+ +
+ +
+
+class sapphire.esd.ReadLineAndStoreLightningClass(table)
+

Store lines of lightning data from the ESD

+
+
+store_line(line)
+

Store a single line

+
+
Parameters:
+

line – the line to store as a tuple of strings (one element per column).

+
+
Returns:
+

timestamp of the stored event, or 0 if the given line was a +comment line starting with a ‘#’.

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/examples.html b/examples.html new file mode 100644 index 00000000..9dc0dbfc --- /dev/null +++ b/examples.html @@ -0,0 +1,389 @@ + + + + + + + + Example scripts — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Example scripts

+

In this chapter we’ll discuss some scripts showing common examples of how to download and analyse data. We’ll start with a short discussion of the general layout of such scripts and how to run them interactively. Then, we’ll continue with examples for some common tasks.

+
+

First, some tips and tricks

+
+

The not in globals() trick

+

A simple script downloading data for a single day can look like:

+
+
simple-download.py
+
import datetime
+
+import tables
+
+from sapphire import esd
+
+DATAFILE = 'data.h5'
+START = datetime.datetime(2016, 1, 1)
+END = datetime.datetime(2016, 1, 2)
+
+
+data = tables.open_file(DATAFILE, 'w')
+esd.download_data(data, '/s501', 501, START, END)
+
+
+
+

This script can be run from the command line like this:

+
$ python <name_of_script.py>
+
+
+

Every time you run the script, the old data will be removed and new data will be downloaded. Except that the data is probably unchanged. What a waste of time and bandwidth! If you do not run this from the command line you can run into trouble. For example, the second time you run the script from an IPython console, you can run into an exception:

+
>>> %run simple-download.py
+100%|################################################################|Time: 5.22
+>>> %run simple-download.py
+Traceback (most recent call last):
+  File "/Users/david/work/HiSPARC/software/sapphire/doc/scripts/simple-download.py", line 10, in <module>
+    data = tables.open_file(DATAFILE, 'w')
+  File "/Users/david/anaconda/lib/python2.7/site-packages/tables/file.py", line 315, in open_file
+    "close it before reopening in write mode." % filename)
+ValueError: The file 'data.h5' is already opened.  Please close it before reopening in write mode.
+
+
+

The second time the script is run, the file is already opened. The script, however, does not know that. You can close the file at the end of your script, but then you don’t have access to the file from the interactive console. It is very useful to run the script, keep the file open, and be able to inspect the file and any variables created by the script. The console has access to all variables declared by the script, but by default the script is not allowed to know about any variables defined in the console. In other words, the script cannot know about the opened file. This is fixed by running the script with the -i option:

+
>>> %run -i simple-download.py
+
+
+

The script still does not check for opened files. To do this, one can use the globals() function. This will return a dictionary containing all variables in the global scope. If data is defined, then 'data' will be present in that dictionary. We can check for that:

+
+
simple-download-with-globals.py
+
import datetime
+
+import tables
+
+from sapphire import esd
+
+DATAFILE = 'data.h5'
+START = datetime.datetime(2016, 1, 1)
+END = datetime.datetime(2016, 1, 2)
+
+
+if 'data' not in globals():
+    data = tables.open_file(DATAFILE, 'w')
+    esd.download_data(data, '/s501', 501, START, END)
+
+
+
+

Now, the script can be rerun multiple times:

+
>>> %run -i simple-download-with-globals.py
+100%|################################################################|Time: 5.29
+>>> %run -i simple-download-with-globals.py
+
+
+

The second time the script is run, it does nothing. This is useful, because we can include stuff that needs to be rerun after the if-statement.

+
+
+

The not in data trick

+

Every time we run the previous script from the command-line, or start a new IPython console, the script still overwrites the data file with ‘new’ data. We need something more! In addition to checking if the file is already opened, the script needs to check if the data is already present in the file. If it is, the script should do nothing. It is important to open the file in append mode, not in write mode. If you do the latter, the file is overwritten the moment you open it. The script becomes:

+
+
simple-download-with-checks.py
+
import datetime
+
+import tables
+
+from sapphire import esd
+
+DATAFILE = 'data.h5'
+START = datetime.datetime(2016, 1, 1)
+END = datetime.datetime(2016, 1, 2)
+
+
+if 'data' not in globals():
+    data = tables.open_file(DATAFILE, 'a')
+
+if '/s501' not in data:
+    esd.download_data(data, '/s501', 501, START, END)
+
+
+
+
+
+

The if __name__ == '__main__' trick

+

Finally, most scripts of any decent size include a weird-looking line:

+
if __name__ == '__main__':
+   do_something()
+
+
+

This is very useful, in fact! A small script invariably grows to a larger size. Most probably, you’ll write a few functions that are useful elsewhere. If they are, you can import them from a new script. That, however, causes problems if you did not include the lines above. For example, take this script:

+
+
is_useful.py
+
def square(x):
+    return x**2
+
+
+print(1, 2, 3)
+print(square(1), square(2), square(3))
+
+
+
+

This script defines a function to calculate the squares of numbers. We use it to print the squared of the numbers 1, 2, and 3. Now, we want to print the square of 16:

+
>>> from is_useful import square
+1 2 3
+1 4 9
+>>> print(square(16))
+256
+
+
+

What happened? When we import a module, the code inside that module is run as well. We don’t want that. When we run a script, the special variable __name__ is equal to '__main__'. When we import a script, the variable is equal to the name of the module, and not equal to '__main__'. So, we can check for that:

+
+
is_useful_and_importable.py
+
def square(x):
+    return x**2
+
+
+if __name__ == '__main__':
+    print(1, 2, 3)
+    print(square(1), square(2), square(3))
+
+
+
+

If we run the script stand-alone, we get the expected result:

+
>>> %run is_useful_and_importable.py
+1 2 3
+1 4 9
+
+
+

But we can now also import the script without any unintended side effects:

+
>>> from is_useful_and_importable import square
+>>> print(square(16))
+256
+
+
+

It is good practice to always include the __name__ == '__main__' check.

+
+
+
+

Common tasks

+
+

Download event summary data for a few stations

+
import datetime
+
+import tables
+
+from sapphire import esd
+
+DATAFILE = 'data.h5'
+STATIONS = [501, 503, 506]
+START = datetime.datetime(2016, 1, 1)
+END = datetime.datetime(2016, 1, 2)
+
+
+if __name__ == '__main__':
+    if 'data' not in globals():
+        data = tables.open_file(DATAFILE, 'a')
+
+    for station in STATIONS:
+        group = '/s%d' % station
+        if group not in data:
+            esd.download_data(data, group, station, START, END)
+
+
+
+
+

Download coincidences for a few stations

+
import datetime
+
+import tables
+
+from sapphire import esd
+
+DATAFILE = 'data.h5'
+STATIONS = [501, 503, 506]
+START = datetime.datetime(2016, 1, 1)
+END = datetime.datetime(2016, 1, 2)
+
+
+if __name__ == '__main__':
+    if 'data' not in globals():
+        data = tables.open_file(DATAFILE, 'a')
+
+    if '/coincidences' not in data:
+        esd.download_coincidences(data, stations=STATIONS, start=START, end=END)
+
+
+
+
+

Download and reconstruct directions for coincidences

+
import datetime
+
+import tables
+
+from sapphire import esd
+from sapphire.analysis import reconstructions
+
+DATAFILE = 'data.h5'
+STATIONS = [501, 503, 506]
+START = datetime.datetime(2016, 1, 1)
+END = datetime.datetime(2016, 1, 2)
+
+
+if __name__ == '__main__':
+    if 'data' not in globals():
+        data = tables.open_file(DATAFILE, 'a')
+
+    if '/coincidences' not in data:
+        esd.download_coincidences(data, stations=STATIONS, start=START, end=END)
+
+    if '/reconstructions' not in data:
+        rec = reconstructions.ReconstructESDCoincidences(data)
+        rec.reconstruct_and_store()
+
+
+
+
+

Plot zenith angle distribution

+
import tables
+
+import pylab as plt
+
+
+def plot_zenith_distribution(data):
+    events = data.root.coincidences.reconstructions
+
+    # get all zenith values
+    zenith = events.col('zenith')
+
+    # remove all NaNs.
+    zenith = zenith.compress(-isnan(zenith))
+
+    plt.hist(degrees(zenith), bins=linspace(0, 90, 51), histtype='step')
+    plt.xlabel('zenith [deg]')
+    plt.ylabel('count')
+
+
+if __name__ == '__main__':
+    if 'data' not in globals():
+        data = tables.open_file('data.h5')
+
+    plot_zenith_distribution(data)
+
+
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..de2c0500 --- /dev/null +++ b/genindex.html @@ -0,0 +1,2353 @@ + + + + + + + Index — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + +
+
+
+
+ + +

Index

+ +
+ A + | B + | C + | D + | E + | F + | G + | H + | I + | J + | K + | L + | M + | N + | P + | Q + | R + | S + | T + | U + | V + | W + | X + | Y + | Z + +
+

A

+ + + +
+ +

B

+ + + +
+ +

C

+ + + +
+ +

D

+ + + +
+ +

E

+ + + +
+ +

F

+ + + +
+ +

G

+ + + +
+ +

H

+ + + +
+ +

I

+ + + +
+ +

J

+ + + +
+ +

K

+ + + +
+ +

L

+ + + +
+ +

M

+ + + +
+ +

N

+ + + +
+ +

P

+ + + +
+ +

Q

+ + +
+ +

R

+ + + +
+ +

S

+ + + +
    +
  • + sapphire + +
    • +
  • + sapphire.analysis + +
    • +
  • + sapphire.analysis.calibration + +
    • +
  • + sapphire.analysis.coincidence_queries + +
    • +
  • + sapphire.analysis.coincidences + +
    • +
  • + sapphire.analysis.core_reconstruction + +
    • +
  • + sapphire.analysis.direction_reconstruction + +
    • +
  • + sapphire.analysis.event_utils + +
    • +
  • + sapphire.analysis.find_mpv + +
    • +
  • + sapphire.analysis.landau + +
    • +
  • + sapphire.analysis.process_events + +
    • +
  • + sapphire.analysis.process_traces + +
    • +
  • + sapphire.analysis.reconstructions + +
    • +
  • + sapphire.analysis.signal_calibration + +
    • +
  • + sapphire.analysis.time_deltas + +
    • +
  • + sapphire.api + +
    • +
  • + sapphire.clusters + +
    • +
  • + sapphire.corsika + +
    • +
  • + sapphire.corsika.blocks + +
    • +
  • + sapphire.corsika.corsika_queries + +
    • +
  • + sapphire.corsika.generate_corsika_overview + +
    • +
  • + sapphire.corsika.particles + +
    • +
  • + sapphire.corsika.qsub_corsika + +
    • +
  • + sapphire.corsika.qsub_store_corsika_data + +
    • +
  • + sapphire.corsika.reader + +
    • +
  • + sapphire.corsika.store_corsika_data + +
    • +
  • + sapphire.corsika.units + +
    • +
  • + sapphire.data + +
    • +
  • + sapphire.data.extend_local_data + +
    • +
  • + sapphire.data.update_local_data + +
    • +
  • + sapphire.esd + +
    • +
  • + sapphire.kascade + +
    • +
  • + sapphire.publicdb + +
    • +
  • + sapphire.qsub + +
    • +
  • + sapphire.simulations + +
    • +
  • + sapphire.simulations.base + +
    • +
  • + sapphire.simulations.detector + +
    • +
  • + sapphire.simulations.gammas + +
    • +
  • + sapphire.simulations.groundparticles + +
    • +
  • + sapphire.simulations.ldf + +
    • +
  • + sapphire.simulations.showerfront + +
    • +
  • + sapphire.storage + +
    • +
  • + sapphire.tests + +
    • +
  • + sapphire.time_util + +
    • +
  • + sapphire.transformations + +
    • +
  • + sapphire.transformations.angles + +
    • +
  • + sapphire.transformations.axes + +
    • +
  • + sapphire.transformations.base + +
    • +
  • + sapphire.transformations.celestial + +
    • +
  • + sapphire.transformations.clock + +
    • +
  • + sapphire.transformations.geographic + +
    • +
+ +

T

+ + + +
+ +

U

+ + + +
+ +

V

+ + + +
+ +

W

+ + + +
+ +

X

+ + + +
+ +

Y

+ + +
+ +

Z

+ + + +
+ + + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..22594de8 --- /dev/null +++ b/index.html @@ -0,0 +1,159 @@ + + + + + + + + Welcome to SAPPHiRE’s documentation! — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + +
+
+
+
+ +
+

Welcome to SAPPHiRE’s documentation!

+

SAPPHiRE is a Simulation and Analysis Program Package for HiSPARC Research +and Education. This is your first stop if you want to download HiSPARC +station data, perform a simulation or start analyzing (simulated) data.

+

While SAPPHiRE currently is very capable, the documentation is still being +heavily developed. Please check the GitHub repository to view the activity log or browse +or download the code.

+

Contents:

+
+ +
+
+
+

Indices and tables

+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/installation.html b/installation.html new file mode 100644 index 00000000..ffa008be --- /dev/null +++ b/installation.html @@ -0,0 +1,307 @@ + + + + + + + + Installation — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Installation

+

Python is a very versatile language. As such, it can be either installed with +lots and lots of packages to create an environment for e.g. data analysis or +game development, or it can be installed for a very specific task, taking up +much less space. To install a complete environment without all the hassle, you +can install a Python distribution which includes Python and a long list +of packages. This is the recommended approach, certainly if you’re new to +Python. If you really want, you can install a much smaller set of packages for +which we’ll provide minimal instructions only.

+

Before we’ll go on describing how to install SAPPHiRE itself, we will first +install the prerequisites.

+
+

Installing the prerequisites

+
+

Python distributions

+

SAPPHiRE supports both Python 2.7 and newer versions of Python, e.g. 3.5. +If you don’t mind to install a large package, use a distribution. This mostly +includes everything you’ll ever need. One such distribution is Anaconda. It includes many scientific python +packages, as well as graphical environments for doing your work. Anaconda has +packages available for the three major platforms: Windows, OS X and Linux.

+

Anaconda includes a graphical programming environment (Spyder) which makes it easy to write analysis +scripts while creating plots, testing small snippets of code, or inspecting +your variables.

+
+
+

Minimal installation (miniconda)

+

In theory, installing a Python package should be very easy. To install +SAPPHiRE, the Python package called sapphire would be retrieved from the +internet. It would have its dependencies listed and pip would pull them in +and all should be well. In fact, SAPPHiRE does have its dependencies +listed and Pip will pull them in. It is only then, that things start go +wrong. Whether you’ll experience difficulties depends on the operating system +you’re using and any previously installed software. But don’t worry, we’ve got +you covered.

+

Miniconda is the package manager used by the Anaconda distribution. +Installation instructions can be found at +https://conda.pydata.org/docs/install/quick.html. After installation, install +the following package:

+
$ conda install --channel conda-forge hdf5
+
+
+

After this is should be possible to simply install SAPPHiRE using Pip (see below).

+
+
+

Minimal installation (OS-specific)

+

If you want to use a package manager specifically for your OS, follow the +(minimal) instructions below for your operating system of choice.

+
+

Mac OS X

+

If you’re using Mac OS X, the easiest way to install open source software +(like Python, the program language we’re using) including lots and lots of +great packages, is done using Homebrew. Please follow the installation +instructions (really easy) and when done, type the following into a +terminal:

+
$ brew install python
+
+
+

This will install Python and Pip.

+

As of this writing, several of the dependencies listed by SAPPHiRE do not +have their own dependencies listed in a way that Pip (or other tools, for that +matter) know how to handle. To install matplotlib with its requirements, you +can type:

+
$ brew install homebrew/python/matplotlib
+
+
+

Warning: this will install gcc and start a very long compilation process. Alternatively, with the introduction of wheels in Python, you can simply try to install packages like numpy, scipy and matplotlib using Pip:

+
$ pip install numpy scipy matplotlib
+
+
+

If you’re lucky, using a wheel will not need a compiler. We haven’t tried this +ourselves for quite a while. YMMV.

+
+
+

Debian and derivatives (like Ubuntu)

+

Debian has a first-class package manager which other distributions and +operating systems have a hard time competing with. In my personal opinion, +anyway. Lots of Python packages can be installed using apt-get, but some +of them might be outdated, depending on the age of your distribution. Use +apt-get for Python packages at your own discretion (read: risk). Pip +handles Python packages very well, so you’ll probably need some packages to +install using Pip.

+

As of this writing, several of the dependencies listed by SAPPHiRE do not +have their own dependencies listed in a way that Pip (or other tools, for +that matter) know how to handle. Furthermore, matplotlib needs to be +installed all by itself, and its dependencies must be installed before it.

+

You can try to install packages using apt-get, for example:

+
$ sudo apt-get install python-matplotlib
+
+
+

To figure out what you need, look at the prerequisites for SAPPHiRE, listed +in the setup.py file in the code repository. You can also try to install +SAPPHiRE using pip, and waiting for the installation to fail on some +prerequisites. If you then need development libraries, apt-get can help you +with that:

+
$ sudo apt-get build-dep python-numpy
+
+
+

will install everything you need to build numpy from source.

+
+
+
+
+

Installing SAPPHiRE

+

The SAPPHiRE package has been uploaded to PyPI so that pip knows where to find it.

+

There are now several scenarios for installing SAPPHiRE: let pip +install the release or development version or download the source code +and use setup.py to install it.

+
+

Just let me get to work!

+

These scenarios do not involve fetching the code. They will just +install SAPPHiRE, so that you can get to work. To get the latest +stable release version, simply issue:

+
$ pip install hisparc-sapphire
+
+
+

Done. Now get to work.

+

If you like living on the edge with a possibly broken version of +SAPPHiRE, get the latest development version:

+
$ pip install https://github.com/hisparc/sapphire/zipball/master
+
+
+
+
+

Let me see the code!

+

If you’re interested in the development of SAPPHiRE, you can either go to the +GitHub page or install the version +control system (we use Git) yourself. For that, please +see the GitHub Help pages.

+

To download the code and install SAPPHiRE, first go to +https://github.com/hisparc/sapphire/. Then, click on the Clone in Desktop, +which will prompt you to download the GitHub Desktop app (recommended).

+_images/github-zipball.png +

Open a terminal and navigate to the top-level directory containing the code. +Then issue:

+
$ python setup.py develop
+
+
+

This takes care of installing the missing dependencies and SAPPHiRE itself. +Whenever you update the code using the GitHub Desktop application, the new code +is immediately available.

+
+
+
+

Checking that SAPPHiRE is installed correctly

+

First off, the following is not an exhaustive check. But it will tell you +if SAPPHiRE is, in fact, installed on your system and that Python knows +how to find it.

+
+

Note

+

When you run this check from inside the top-level SAPPHiRE +code directory, it will always return successfully. The reason +for this is that Python also checks the current working +directory for packages. So, run this check from e.g. your home +directory.

+
+

Start a Python session. You can use a launcher of some type (e.g. the one +from Python(x,y)), or open a terminal and type:

+
$ python
+
+
+

Or, if you prefer, IPython:

+
$ ipython
+
+
+

In fact, we recommend using IPython for interactive use. Then, try to import +SAPPHiRE:

+
>>> import sapphire
+
+
+

If this returns without an error message, all is well and SAPPHiRE should be +correctly installed. As an additional assurance the SAPPHiRE code tests can +be executed. To run the tests simply call the function to run the tests:

+
>>> sapphire.run_tests()
+
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/kascade.html b/kascade.html new file mode 100644 index 00000000..047b3bfb --- /dev/null +++ b/kascade.html @@ -0,0 +1,199 @@ + + + + + + + + KASCADE data — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

KASCADE data

+

Read and store KASCADE data.

+

Read data files provided by the KASCADE collaboration and store them +in a format compatible with HiSPARC data.

+

This module contains the following class:

+
+
StoreKascadeData

Read and store KASCADE data files.

+
+
KascadeCoincidences

Find HiSPARC and KASCADE events that belong together.

+
+
+
+
+class sapphire.kascade.StoreKascadeData(data, kascade_filename, kascade_path='/kascade', hisparc_path=None, force=False, progress=True)
+

Initialize the class.

+
+
Parameters:
+
    +

  • data – the PyTables datafile

    • +

  • hisparc_path – path to the group containing HiSPARC station data.

    • +

  • kascade_path – path of group where KASCADE data will be stored.

    • +

  • kascade_filename – filename of the KASCADE data source.

    • +

  • force – overwrite existing KASCADE group if it already exists.

    • +

  • progress – if True progress info will be shown.

    • +
+
+
+
+
+read_and_store_data()
+

Read and store KASCADE data matching HiSPARC data

+

This function looks at the HiSPARC event data in the specified +datafile and then processes and adds KASCADE data surrounding +those events, for later coincidence processing.

+
+ +
+ +
+
+class sapphire.kascade.KascadeCoincidences(data, hisparc_group, kascade_group, overwrite=False, ignore_existing=False)
+
+
+search_coincidences(timeshift=0, dtlimit=None, limit=None)
+

Search for coincidences

+

This function does the actual searching of coincidences. It uses +a timeshift to shift the HiSPARC data (we know that these employ +GPS time, so not taking UTC leap seconds into account). The +shift will also compensate for delays in the experimental setup.

+

The coincidences are stored as an instance variable +(self.coincidences). Final storage can be done by calling +store_coincidences().

+
+
Parameters:
+
    +

  • timeshift – the amount of time the HiSPARC data are shifted (in +seconds). Default: 0.

    • +

  • dtlimit – limit on the time difference between hisparc and +kascade events in seconds. If this limit is exceeded, +coincidences are not stored. Default: None.

    • +

  • limit – limit on the number of KASCADE events investigated.

    • +
+
+
+
+ +
+
+store_coincidences()
+
+ +
+ +
+ + +
+
+
+
+ +
+
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z8=a#S7HXrN^kuf1o_M>au;Df;pQjyYr3bd&N~mKC0Wsr&O{D4qlOD9jRol7)QV%BG zyNmn4w404v8L!l+&f-Re=mk>bYTzEQUNXaVVH#^L$|Tx|_LMMtasvoXMS)AnO%?lE zbcfcR7k#Ydbr@zQR=>>7Q?ad@HEqbeepSibY7uV7G#|Q|ZT%XJ9`g1P)E*_czbC!Z zg7pBUfQsZKgc-3quBsDh)8x(P^!XPN3$}c`sXX5NN*<=sMC+3@n@4V&bu)EUAE(hJ z8mC7#AMf|P7NBrm#S#*~0IFP7VvMZ=B|4JfJPC6RQo;<2^p+x7BCUj1=93Ep(E8a1 z<$UC#@2Poigbu=z(2ZQ~OH$fL^)f>AGC(7Q!#accEAzqQE=k*l^lnKW&I?c01+Oq*HvfTKwBudN#mI1soPinA0197z^lHBS^xk5 literal 0 HcmV?d00001 diff --git a/publicdb.html b/publicdb.html new file mode 100644 index 00000000..37656aca --- /dev/null +++ b/publicdb.html @@ -0,0 +1,214 @@ + + + + + + + + Public data access — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Public data access

+

Fetch raw events and other data from the public database

+

This module enables you to access the public database and even the raw +event data. This is intended for specialized use only. For most uses, it is +faster and more convenient to access the event summary data (ESD) using +esd.

+
+
+sapphire.publicdb.get_publicdb_xmlrpc_url()
+
+ +
+
+sapphire.publicdb.download_data(file, group, station_id, start, end, get_blobs=False)
+

Download raw data from the datastore

+

This function downloads data from the datastore, using the XML-RPC API +exposed by the public database.

+
+
Parameters:
+
    +

  • file – The PyTables datafile handler

    • +

  • group – The PyTables destination group, which need not exist

    • +

  • station_id – The HiSPARC station number for which to get events

    • +

  • start – a datetime instance defining the start of the search +interval

    • +

  • end – a datetime instance defining the end of the search +interval

    • +

  • get_blobs – boolean, select whether binary data like traces +should be fetched

    • +
+
+
+

Example:

+
>>> import tables
+>>> import datetime
+>>> import sapphire.publicdb
+>>> data = tables.open_file('data.h5', 'w')
+>>> start = datetime.datetime(2010, 9, 1)
+>>> end = datetime.datetime(2010, 9, 2)
+>>> sapphire.publicdb.download_data(data, '/s501', 501, start, end)
+INFO:sapphire.publicdb:2010-09-01 00:00:00 None
+INFO:sapphire.publicdb:Getting server data URL (2010-09-01 00:00:00)
+INFO:sapphire.publicdb:Downloading data...
+INFO:sapphire.publicdb:Storing data...
+INFO:sapphire.publicdb:Done.
+
+
+
+ +
+
+sapphire.publicdb.datetimerange(start, stop)
+

Generator for datetime ranges

+

This is a very specific generator for datetime ranges. Based on a +start and stop value, it generates one day intervals. On the first +and subsequent full days, it only returns a start value. On the last +day, it returns a start and stop value. See example below.

+
+
Parameters:
+
    +

  • start – a datetime instance

    • +

  • stop – a datetime instance

    • +
+
+
+

Example:

+
>>> for x in datetimerange(datetime.datetime(2010, 1, 1, 11), datetime.datetime(2010, 1, 1, 13)):
+...     x
+...
+(datetime.datetime(2010, 1, 1, 11, 0), datetime.datetime(2010, 1, 1, 13, 0))
+
+>>> for x in datetimerange(datetime.datetime(2010, 1, 1, 11), datetime.datetime(2010, 1, 2)):
+...     x
+...
+(datetime.datetime(2010, 1, 1, 11, 0), None)
+
+>>> for x in datetimerange(datetime.datetime(2010, 1, 1, 11), datetime.datetime(2010, 1, 2, 13)):
+...     x
+...
+(datetime.datetime(2010, 1, 1, 11, 0), None)
+(datetime.datetime(2010, 1, 2, 0, 0), datetime.datetime(2010, 1, 2, 13, 0))
+
+>>> for x in datetimerange(datetime.datetime(2010, 1, 1, 11), datetime.datetime(2010, 1, 5, 13)):
+...     x
+...
+(datetime.datetime(2010, 1, 1, 11, 0), None)
+(datetime.datetime(2010, 1, 2, 0, 0), None)
+(datetime.datetime(2010, 1, 3, 0, 0), None)
+(datetime.datetime(2010, 1, 4, 0, 0), None)
+(datetime.datetime(2010, 1, 5, 0, 0), datetime.datetime(2010, 1, 5, 13, 0))
+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/py-modindex.html b/py-modindex.html new file mode 100644 index 00000000..1c9e1627 --- /dev/null +++ b/py-modindex.html @@ -0,0 +1,355 @@ + + + + + + + Python Module Index — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + + +
+
+
+
+ + +

Python Module Index

+ +
+ s +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
+ s
+ sapphire +
    + sapphire.analysis +
    + sapphire.analysis.calibration +
    + sapphire.analysis.coincidence_queries +
    + sapphire.analysis.coincidences +
    + sapphire.analysis.core_reconstruction +
    + sapphire.analysis.direction_reconstruction +
    + sapphire.analysis.event_utils +
    + sapphire.analysis.find_mpv +
    + sapphire.analysis.landau +
    + sapphire.analysis.process_events +
    + sapphire.analysis.process_traces +
    + sapphire.analysis.reconstructions +
    + sapphire.analysis.signal_calibration +
    + sapphire.analysis.time_deltas +
    + sapphire.api +
    + sapphire.clusters +
    + sapphire.corsika +
    + sapphire.corsika.blocks +
    + sapphire.corsika.corsika_queries +
    + sapphire.corsika.generate_corsika_overview +
    + sapphire.corsika.particles +
    + sapphire.corsika.qsub_corsika +
    + sapphire.corsika.qsub_store_corsika_data +
    + sapphire.corsika.reader +
    + sapphire.corsika.store_corsika_data +
    + sapphire.corsika.units +
    + sapphire.data +
    + sapphire.data.extend_local_data +
    + sapphire.data.update_local_data +
    + sapphire.esd +
    + sapphire.kascade +
    + sapphire.publicdb +
    + sapphire.qsub +
    + sapphire.simulations +
    + sapphire.simulations.base +
    + sapphire.simulations.detector +
    + sapphire.simulations.gammas +
    + sapphire.simulations.groundparticles +
    + sapphire.simulations.ldf +
    + sapphire.simulations.showerfront +
    + sapphire.storage +
    + sapphire.tests +
    + sapphire.time_util +
    + sapphire.transformations +
    + sapphire.transformations.angles +
    + sapphire.transformations.axes +
    + sapphire.transformations.base +
    + sapphire.transformations.celestial +
    + sapphire.transformations.clock +
    + sapphire.transformations.geographic +
    + sapphire.utils +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/qsub.html b/qsub.html new file mode 100644 index 00000000..f62d5cb2 --- /dev/null +++ b/qsub.html @@ -0,0 +1,189 @@ + + + + + + + + Nikhef Stoomboot facilities — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Nikhef Stoomboot facilities

+

Access the Nikhef Stoomboot cluster.

+
+

Note

+

This module is only for use at Nikhef. The Stoomboot cluster is only +accessible for Nikhef users.

+
+

Easy to use functions to make use of the Nikhef Stoomboot facilities. +This checks the available slots on the requested queue, creates the +scripts to submit, submits the jobs, and cleans up afterwards.

+

Example usage:

+
>>> from sapphire import qsub
+>>> qsub.check_queue('long')
+340
+>>> qsub.submit_job('touch /data/hisparc/test', 'job_1', 'express')
+
+
+
+
+sapphire.qsub.check_queue(queue)
+

Check for available job slots on the selected queue for current user

+

Maximum numbers from: +qstat -Q -f | grep -e Queue: -e max_user_queuable -e max_queuable. +Note that some queues also have global maximum number of jobs.

+
+
Parameters:
+

queue – queue name for which to check current number of job +slots in use.

+
+
Returns:
+

number of available slots.

+
+
+
+ +
+
+sapphire.qsub.submit_job(script, name, queue, extra='')
+

Submit a job to Stoomboot

+
+
Parameters:
+
    +

  • script – contents of the script to run.

    • +

  • name – name for the job.

    • +

  • queue – name of the queue to run the job on.

    • +

  • extra – optional extra arguments for the qsub command.

    • +
+
+
+
+ +
+
+sapphire.qsub.create_script(script, name)
+

Create script as temp file to be run on Stoomboot

+
+ +
+
+sapphire.qsub.delete_script(script_path)
+

Delete script after submitting to Stoomboot

+
+
Parameters:
+

script_path – path to the script to be removed

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/sapphire.html b/sapphire.html new file mode 100644 index 00000000..3c2b885b --- /dev/null +++ b/sapphire.html @@ -0,0 +1,147 @@ + + + + + + + + SAPPHiRE Reference — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

SAPPHiRE Reference

+

Simulation and Analysis Program Package for HiSPARC Research

+

SAPPHiRE simplifies data access, simulations and analysis for the HiSPARC experiment. It was born out of a combination of the +old framework and the collection of simulation and analysis scripts developed +by David Fokkema for his PhD thesis work. Development is ongoing, while Arne +de Laat is working on his PhD research.

+

The following packages and modules are included:

+
+
analysis

package containing analysis-related modules

+
+
api

publicdb api interface

+
+
clusters

definitions for HiSPARC detectors, stations and clusters

+
+
corsika

package containing CORSIKA simulation related modules

+
+
data

scripts for updating local data

+
+
esd

event summary data access

+
+
kascade

work on KASCADE data

+
+
publicdb

public data access

+
+
qsub

submit jobs to Stoomboot

+
+
simulations

package containing simulation-related modules

+
+
storage

storage-related definitions

+
+
tests

code tests

+
+
time_util

GPS date/time utility functions

+
+
transformations

transformations between different systems

+
+
utils

commonly used functions such as a progressbar

+
+
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/search.html b/search.html new file mode 100644 index 00000000..d3789e58 --- /dev/null +++ b/search.html @@ -0,0 +1,102 @@ + + + + + + + Search — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+
+ +

Search

+ + + + +

+ Searching for multiple words only shows matches that contain + all words. +

+ + +
+ + + +
+ + +
+ + +
+
+
+
+ +
+
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(sapphire.corsika.qsub_corsika.corsikabatch method)": [[22, "sapphire.corsika.qsub_corsika.CorsikaBatch.create_input", false]], "create_script() (in module sapphire.qsub)": [[36, "sapphire.qsub.create_script", false]], "create_script() (sapphire.corsika.qsub_corsika.corsikabatch method)": [[22, "sapphire.corsika.qsub_corsika.CorsikaBatch.create_script", false]], "create_tempfile_path() (in module sapphire.corsika.generate_corsika_overview)": [[20, "sapphire.corsika.generate_corsika_overview.create_tempfile_path", false]], "create_tempfile_path() (in module sapphire.corsika.store_corsika_data)": [[25, "sapphire.corsika.store_corsika_data.create_tempfile_path", false]], "current() (sapphire.api.station method)": [[14, "sapphire.api.Station.current", false]], "currents (sapphire.api.station property)": [[14, "sapphire.api.Station.currents", false]], "curvedmixin (class in sapphire.analysis.direction_reconstruction)": [[5, "sapphire.analysis.direction_reconstruction.CurvedMixin", false]], "curvedregressionalgorithm (class in sapphire.analysis.direction_reconstruction)": [[5, "sapphire.analysis.direction_reconstruction.CurvedRegressionAlgorithm", false]], "curvedregressionalgorithm3d (class in sapphire.analysis.direction_reconstruction)": [[5, "sapphire.analysis.direction_reconstruction.CurvedRegressionAlgorithm3D", false]], "cylindrical_to_cartesian() (in module sapphire.transformations.axes)": [[50, "sapphire.transformations.axes.cylindrical_to_cartesian", false]], "data (sapphire.simulations.groundparticles.multiplegroundparticlessimulation attribute)": [[42, "sapphire.simulations.groundparticles.MultipleGroundParticlesSimulation.DATA", false]], "data_reduction_padding (in module sapphire.analysis.process_traces)": [[10, "sapphire.analysis.process_traces.DATA_REDUCTION_PADDING", false]], "datareduction (class in sapphire.analysis.process_traces)": [[10, "sapphire.analysis.process_traces.DataReduction", false]], "date_to_juliandate() (in module 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"decimal_to_time() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.decimal_to_time", false]], "degrees_to_hours() (in module sapphire.transformations.angles)": [[49, "sapphire.transformations.angles.degrees_to_hours", false]], "delay_at_r() (sapphire.simulations.showerfront.conefront method)": [[44, "sapphire.simulations.showerfront.ConeFront.delay_at_r", false]], "delay_at_r() (sapphire.simulations.showerfront.corsikastationfront method)": [[44, "sapphire.simulations.showerfront.CorsikaStationFront.delay_at_r", false]], "delay_at_r() (sapphire.simulations.showerfront.flatfront method)": [[44, "sapphire.simulations.showerfront.FlatFront.delay_at_r", false]], "delete_script() (in module sapphire.qsub)": [[36, "sapphire.qsub.delete_script", false]], "delta (sapphire.analysis.landau.scintillator attribute)": [[8, "sapphire.analysis.landau.Scintillator.delta", false]], "description() (sapphire.time_util.gpstime method)": [[47, "sapphire.time_util.GPSTime.description", false]], "detector (class in sapphire.clusters)": [[15, "sapphire.clusters.Detector", false]], "detector_arrival_time() (in module sapphire.analysis.event_utils)": [[6, "sapphire.analysis.event_utils.detector_arrival_time", false]], "detector_arrival_times() (in module sapphire.analysis.event_utils)": [[6, "sapphire.analysis.event_utils.detector_arrival_times", false]], "detector_densities() (in module sapphire.analysis.event_utils)": [[6, "sapphire.analysis.event_utils.detector_densities", false]], "detector_density() (in module sapphire.analysis.event_utils)": [[6, "sapphire.analysis.event_utils.detector_density", false]], "detector_size (sapphire.clusters.detector property)": [[15, "sapphire.clusters.Detector.detector_size", false]], "detector_timing_offset() (sapphire.api.station method)": [[14, "sapphire.api.Station.detector_timing_offset", false]], "detector_timing_offsets (sapphire.api.station property)": [[14, "sapphire.api.Station.detector_timing_offsets", false]], "detectorboundarysimulation (class in sapphire.simulations.groundparticles)": [[42, "sapphire.simulations.groundparticles.DetectorBoundarySimulation", false]], "detectors (sapphire.clusters.station property)": [[15, "sapphire.clusters.Station.detectors", false]], "determine_and_store_time_deltas() (sapphire.analysis.time_deltas.processtimedeltas method)": [[13, "sapphire.analysis.time_deltas.ProcessTimeDeltas.determine_and_store_time_deltas", false]], "determine_and_store_time_deltas_for_pairs() (sapphire.analysis.time_deltas.processtimedeltas method)": [[13, "sapphire.analysis.time_deltas.ProcessTimeDeltas.determine_and_store_time_deltas_for_pairs", false]], "determine_best_offsets() (sapphire.analysis.direction_reconstruction.coincidencedirectionreconstruction method)": [[5, "sapphire.analysis.direction_reconstruction.CoincidenceDirectionReconstruction.determine_best_offsets", false]], "determine_best_reference() (in module sapphire.analysis.calibration)": [[1, "sapphire.analysis.calibration.determine_best_reference", false]], "determine_best_reference() (sapphire.analysis.direction_reconstruction.coincidencedirectionreconstruction method)": [[5, "sapphire.analysis.direction_reconstruction.CoincidenceDirectionReconstruction.determine_best_reference", false]], "determine_cuts() (sapphire.analysis.process_traces.datareduction method)": [[10, "sapphire.analysis.process_traces.DataReduction.determine_cuts", false]], "determine_detector_timing_offset() (in module sapphire.analysis.calibration)": [[1, "sapphire.analysis.calibration.determine_detector_timing_offset", false]], "determine_detector_timing_offsets() (in module sapphire.analysis.calibration)": [[1, "sapphire.analysis.calibration.determine_detector_timing_offsets", false]], "determine_first_and_last_date() (sapphire.analysis.calibration.determinestationtimingoffsets method)": [[1, "sapphire.analysis.calibration.DetermineStationTimingOffsets.determine_first_and_last_date", false]], "determine_station_timing_offset() (in module sapphire.analysis.calibration)": [[1, "sapphire.analysis.calibration.determine_station_timing_offset", false]], "determine_station_timing_offset() (sapphire.analysis.calibration.determinestationtimingoffsets method)": [[1, "sapphire.analysis.calibration.DetermineStationTimingOffsets.determine_station_timing_offset", false]], "determine_station_timing_offsets() (sapphire.analysis.calibration.determinestationtimingoffsets method)": [[1, "sapphire.analysis.calibration.DetermineStationTimingOffsets.determine_station_timing_offsets", false]], "determine_station_timing_offsets_for_date() (sapphire.analysis.calibration.determinestationtimingoffsets method)": [[1, "sapphire.analysis.calibration.DetermineStationTimingOffsets.determine_station_timing_offsets_for_date", false]], "determine_time_deltas_for_pair() (sapphire.analysis.time_deltas.processtimedeltas method)": [[13, "sapphire.analysis.time_deltas.ProcessTimeDeltas.determine_time_deltas_for_pair", false]], "determinestationtimingoffsets (class in sapphire.analysis.calibration)": [[1, "sapphire.analysis.calibration.DetermineStationTimingOffsets", false]], "directalgorithm (class in sapphire.analysis.direction_reconstruction)": [[5, "sapphire.analysis.direction_reconstruction.DirectAlgorithm", false]], "directalgorithmcartesian (class in sapphire.analysis.direction_reconstruction)": [[5, "sapphire.analysis.direction_reconstruction.DirectAlgorithmCartesian", false]], "directalgorithmcartesian3d (class in sapphire.analysis.direction_reconstruction)": [[5, "sapphire.analysis.direction_reconstruction.DirectAlgorithmCartesian3D", false]], "discrete_convolution() (in module sapphire.analysis.landau)": [[8, "sapphire.analysis.landau.discrete_convolution", false]], "distance_between() (in module sapphire.utils)": [[56, "sapphire.utils.distance_between", false]], "download_coincidences() (in module sapphire.esd)": [[30, "sapphire.esd.download_coincidences", false]], "download_data() (in module sapphire.esd)": [[30, "sapphire.esd.download_data", false]], "download_data() (in module sapphire.publicdb)": [[35, "sapphire.publicdb.download_data", false]], "download_lightning() (in module sapphire.esd)": [[30, "sapphire.esd.download_lightning", false]], "dphi_dt0() (sapphire.analysis.direction_reconstruction.directalgorithm class method)": [[5, "sapphire.analysis.direction_reconstruction.DirectAlgorithm.dphi_dt0", false]], "dphi_dt1() (sapphire.analysis.direction_reconstruction.directalgorithm static method)": [[5, "sapphire.analysis.direction_reconstruction.DirectAlgorithm.dphi_dt1", false]], "dphi_dt2() (sapphire.analysis.direction_reconstruction.directalgorithm static method)": [[5, "sapphire.analysis.direction_reconstruction.DirectAlgorithm.dphi_dt2", false]], "e (sapphire.transformations.geographic.wgs84datum attribute)": [[54, "sapphire.transformations.geographic.WGS84Datum.e", false]], "ecef_to_enu() (sapphire.transformations.geographic.fromwgs84toenutransformation method)": [[54, "sapphire.transformations.geographic.FromWGS84ToENUTransformation.ecef_to_enu", false]], "ecef_to_lla() (sapphire.transformations.geographic.fromwgs84toenutransformation method)": [[54, "sapphire.transformations.geographic.FromWGS84ToENUTransformation.ecef_to_lla", false]], "electronic() (sapphire.api.station method)": [[14, "sapphire.api.Station.electronic", false]], "electronics (sapphire.api.station property)": [[14, "sapphire.api.Station.electronics", false]], "ellipsldf (class in sapphire.simulations.ldf)": [[43, "sapphire.simulations.ldf.EllipsLdf", false]], "ellipsldfalgorithm (class in sapphire.analysis.core_reconstruction)": [[4, "sapphire.analysis.core_reconstruction.EllipsLdfAlgorithm", false]], "ellipsldfsimulation (class in sapphire.simulations.ldf)": 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"eventcorereconstruction (class in sapphire.analysis.core_reconstruction)": [[4, "sapphire.analysis.core_reconstruction.EventCoreReconstruction", false]], "eventdirectionreconstruction (class in sapphire.analysis.direction_reconstruction)": [[5, "sapphire.analysis.direction_reconstruction.EventDirectionReconstruction", false]], "eventend (class in sapphire.corsika.blocks)": [[18, "sapphire.corsika.blocks.EventEnd", false]], "eventheader (class in sapphire.corsika.blocks)": [[18, "sapphire.corsika.blocks.EventHeader", false]], "eventobservables (class in sapphire.storage)": [[45, "sapphire.storage.EventObservables", false]], "events_from_stations() (sapphire.analysis.coincidence_queries.coincidencequery method)": [[2, "sapphire.analysis.coincidence_queries.CoincidenceQuery.events_from_stations", false]], "events_in_cluster() (sapphire.analysis.coincidence_queries.coincidencequery method)": [[2, "sapphire.analysis.coincidence_queries.CoincidenceQuery.events_in_cluster", false]], "events_in_subcluster() (sapphire.analysis.coincidence_queries.coincidencequery method)": [[2, "sapphire.analysis.coincidence_queries.CoincidenceQuery.events_in_subcluster", false]], "f (sapphire.transformations.geographic.wgs84datum attribute)": [[54, "sapphire.transformations.geographic.WGS84Datum.f", false]], "filter() (sapphire.corsika.corsika_queries.corsikaquery method)": [[19, "sapphire.corsika.corsika_queries.CorsikaQuery.filter", false]], "filter_large_seeds() (in module sapphire.corsika.qsub_store_corsika_data)": [[23, "sapphire.corsika.qsub_store_corsika_data.filter_large_seeds", false]], "filter_trace() (sapphire.analysis.process_traces.meanfilter method)": [[10, "sapphire.analysis.process_traces.MeanFilter.filter_trace", false]], "filter_traces() (sapphire.analysis.process_traces.meanfilter method)": [[10, "sapphire.analysis.process_traces.MeanFilter.filter_traces", false]], "find_first_guess_mpv() (sapphire.analysis.find_mpv.findmostprobablevalueinspectrum method)": [[7, "sapphire.analysis.find_mpv.FindMostProbableValueInSpectrum.find_first_guess_mpv", false]], "find_mpv() (sapphire.analysis.find_mpv.findmostprobablevalueinspectrum method)": [[7, "sapphire.analysis.find_mpv.FindMostProbableValueInSpectrum.find_mpv", false]], "find_station_pairs() (sapphire.analysis.time_deltas.processtimedeltas method)": [[13, "sapphire.analysis.time_deltas.ProcessTimeDeltas.find_station_pairs", false]], "findmostprobablevalueinspectrum (class in sapphire.analysis.find_mpv)": [[7, "sapphire.analysis.find_mpv.FindMostProbableValueInSpectrum", false]], "finish() (sapphire.analysis.coincidence_queries.coincidencequery method)": [[2, "sapphire.analysis.coincidence_queries.CoincidenceQuery.finish", false]], "finish() (sapphire.corsika.corsika_queries.corsikaquery method)": [[19, "sapphire.corsika.corsika_queries.CorsikaQuery.finish", false]], "finish() (sapphire.corsika.reader.corsikafile method)": [[24, "sapphire.corsika.reader.CorsikaFile.finish", false]], "finish() (sapphire.simulations.groundparticles.groundparticlessimulation method)": [[42, "sapphire.simulations.groundparticles.GroundParticlesSimulation.finish", false]], "finish() (sapphire.simulations.groundparticles.multiplegroundparticlessimulation method)": [[42, "sapphire.simulations.groundparticles.MultipleGroundParticlesSimulation.finish", false]], "first_above_threshold() (sapphire.analysis.process_events.processevents static method)": [[9, "sapphire.analysis.process_events.ProcessEvents.first_above_threshold", false]], "fit_mpv() (sapphire.analysis.find_mpv.findmostprobablevalueinspectrum method)": [[7, "sapphire.analysis.find_mpv.FindMostProbableValueInSpectrum.fit_mpv", false]], "fit_timing_offset() (in module sapphire.analysis.calibration)": [[1, "sapphire.analysis.calibration.fit_timing_offset", false]], "fitalgorithm3d (class in sapphire.analysis.direction_reconstruction)": [[5, "sapphire.analysis.direction_reconstruction.FitAlgorithm3D", false]], "fixedcoredistancesimulation (class in sapphire.simulations.groundparticles)": [[42, "sapphire.simulations.groundparticles.FixedCoreDistanceSimulation", false]], "flatfront (class in sapphire.simulations.showerfront)": [[44, "sapphire.simulations.showerfront.FlatFront", false]], "flatfrontsimulation (class in sapphire.simulations.showerfront)": [[44, "sapphire.simulations.showerfront.FlatFrontSimulation", false]], "flatfrontsimulation2d (class in sapphire.simulations.showerfront)": [[44, "sapphire.simulations.showerfront.FlatFrontSimulation2D", false]], "flatfrontsimulation2dwithouterrors (class in sapphire.simulations.showerfront)": [[44, "sapphire.simulations.showerfront.FlatFrontSimulation2DWithoutErrors", false]], "flatfrontsimulationwithouterrors (class in sapphire.simulations.showerfront)": [[44, "sapphire.simulations.showerfront.FlatFrontSimulationWithoutErrors", false]], "flatten_cluster() (in module sapphire.clusters)": [[15, "sapphire.clusters.flatten_cluster", false]], "float_filter() (sapphire.corsika.corsika_queries.corsikaquery method)": [[19, "sapphire.corsika.corsika_queries.CorsikaQuery.float_filter", false]], "floor_in_base() (in module sapphire.utils)": [[56, "sapphire.utils.floor_in_base", false]], "format (class in sapphire.corsika.blocks)": [[18, "sapphire.corsika.blocks.Format", false]], "formatthin (class in sapphire.corsika.blocks)": [[18, "sapphire.corsika.blocks.FormatThin", false]], "fromwgs84toenutransformation (class in sapphire.transformations.geographic)": [[54, "sapphire.transformations.geographic.FromWGS84ToENUTransformation", false]], "front_shape() (sapphire.simulations.showerfront.conefront method)": [[44, "sapphire.simulations.showerfront.ConeFront.front_shape", false]], "front_shape() (sapphire.simulations.showerfront.corsikastationfront method)": [[44, "sapphire.simulations.showerfront.CorsikaStationFront.front_shape", false]], "front_shape() (sapphire.simulations.showerfront.flatfront method)": [[44, "sapphire.simulations.showerfront.FlatFront.front_shape", false]], "full_domain (sapphire.analysis.landau.scintillator attribute)": [[8, "sapphire.analysis.landau.Scintillator.full_domain", false]], "gauss() (in module sapphire.utils)": [[56, "sapphire.utils.gauss", false]], "gauss_scale (sapphire.analysis.landau.scintillator attribute)": [[8, "sapphire.analysis.landau.Scintillator.gauss_scale", false]], "generate_attenuated_zenith() (sapphire.simulations.detector.hisparcsimulation class method)": [[40, "sapphire.simulations.detector.HiSPARCSimulation.generate_attenuated_zenith", false]], "generate_azimuth() (sapphire.simulations.detector.hisparcsimulation class method)": [[40, "sapphire.simulations.detector.HiSPARCSimulation.generate_azimuth", false]], "generate_core_position() (sapphire.simulations.detector.hisparcsimulation class method)": [[40, "sapphire.simulations.detector.HiSPARCSimulation.generate_core_position", false]], "generate_core_position() (sapphire.simulations.groundparticles.fixedcoredistancesimulation class method)": [[42, "sapphire.simulations.groundparticles.FixedCoreDistanceSimulation.generate_core_position", false]], "generate_corsika_overview() (in module sapphire.corsika.generate_corsika_overview)": [[20, "sapphire.corsika.generate_corsika_overview.generate_corsika_overview", false]], "generate_energy() (sapphire.simulations.detector.hisparcsimulation class method)": [[40, "sapphire.simulations.detector.HiSPARCSimulation.generate_energy", false]], "generate_random_seeds() (sapphire.corsika.qsub_corsika.corsikabatch method)": [[22, "sapphire.corsika.qsub_corsika.CorsikaBatch.generate_random_seeds", false]], "generate_shower_parameters() (sapphire.simulations.base.basesimulation method)": [[39, "sapphire.simulations.base.BaseSimulation.generate_shower_parameters", false]], "generate_shower_parameters() (sapphire.simulations.groundparticles.groundparticlessimulation method)": [[42, "sapphire.simulations.groundparticles.GroundParticlesSimulation.generate_shower_parameters", false]], "generate_shower_parameters() (sapphire.simulations.groundparticles.multiplegroundparticlessimulation method)": [[42, "sapphire.simulations.groundparticles.MultipleGroundParticlesSimulation.generate_shower_parameters", false]], "generate_shower_parameters() (sapphire.simulations.ldf.baseldfsimulation method)": [[43, "sapphire.simulations.ldf.BaseLdfSimulation.generate_shower_parameters", false]], "generate_shower_parameters() (sapphire.simulations.ldf.ellipsldfsimulation method)": [[43, "sapphire.simulations.ldf.EllipsLdfSimulation.generate_shower_parameters", false]], "generate_shower_parameters() (sapphire.simulations.showerfront.conefrontsimulation method)": [[44, "sapphire.simulations.showerfront.ConeFrontSimulation.generate_shower_parameters", false]], "generate_shower_parameters() (sapphire.simulations.showerfront.flatfrontsimulation method)": [[44, "sapphire.simulations.showerfront.FlatFrontSimulation.generate_shower_parameters", false]], "generate_zenith() (sapphire.simulations.detector.hisparcsimulation class method)": [[40, "sapphire.simulations.detector.HiSPARCSimulation.generate_zenith", false]], "geode (sapphire.transformations.geographic.fromwgs84toenutransformation attribute)": [[54, "sapphire.transformations.geographic.FromWGS84ToENUTransformation.geode", false]], "get_active_index() (in module sapphire.utils)": [[56, "sapphire.utils.get_active_index", false]], "get_and_store_json() (in module sapphire.data.update_local_data)": [[29, "sapphire.data.update_local_data.get_and_store_json", false]], "get_and_store_tsv() (in module sapphire.data.update_local_data)": [[29, "sapphire.data.update_local_data.get_and_store_tsv", false]], "get_api_base() (in module sapphire.api)": [[14, "sapphire.api.get_api_base", false]], "get_area() (sapphire.clusters.detector method)": [[15, "sapphire.clusters.Detector.get_area", false]], "get_area() (sapphire.clusters.station method)": [[15, "sapphire.clusters.Station.get_area", false]], "get_arrival_time() (sapphire.simulations.showerfront.conefrontsimulation method)": [[44, "sapphire.simulations.showerfront.ConeFrontSimulation.get_arrival_time", false]], "get_arrival_time() (sapphire.simulations.showerfront.flatfrontsimulation method)": [[44, "sapphire.simulations.showerfront.FlatFrontSimulation.get_arrival_time", false]], "get_arrival_time() (sapphire.simulations.showerfront.flatfrontsimulation2d method)": [[44, "sapphire.simulations.showerfront.FlatFrontSimulation2D.get_arrival_time", false]], "get_base_url() (in module sapphire.esd)": [[30, "sapphire.esd.get_base_url", false]], "get_coincidences_url() (in module sapphire.esd)": [[30, "sapphire.esd.get_coincidences_url", false]], "get_coordinates() (sapphire.clusters.basecluster method)": [[15, "sapphire.clusters.BaseCluster.get_coordinates", false]], "get_coordinates() (sapphire.clusters.detector method)": [[15, "sapphire.clusters.Detector.get_coordinates", false]], "get_coordinates() (sapphire.clusters.station method)": [[15, "sapphire.clusters.Station.get_coordinates", false]], "get_corners() (sapphire.clusters.detector method)": [[15, "sapphire.clusters.Detector.get_corners", false]], "get_cylindrical_alpha_coordinates() (sapphire.clusters.basecluster method)": [[15, "sapphire.clusters.BaseCluster.get_cylindrical_alpha_coordinates", false]], "get_cylindrical_alpha_coordinates() (sapphire.clusters.station method)": [[15, "sapphire.clusters.Station.get_cylindrical_alpha_coordinates", false]], "get_cylindrical_coordinates() (sapphire.clusters.detector method)": [[15, "sapphire.clusters.Detector.get_cylindrical_coordinates", false]], "get_detector_ids() (in module sapphire.analysis.event_utils)": [[6, "sapphire.analysis.event_utils.get_detector_ids", false]], "get_detector_offsets() (sapphire.analysis.reconstructions.reconstructesdevents method)": [[11, "sapphire.analysis.reconstructions.ReconstructESDEvents.get_detector_offsets", false]], "get_detector_offsets() (sapphire.analysis.time_deltas.processtimedeltas method)": [[13, "sapphire.analysis.time_deltas.ProcessTimeDeltas.get_detector_offsets", false]], "get_end() (sapphire.corsika.reader.corsikaevent method)": [[24, "sapphire.corsika.reader.CorsikaEvent.get_end", false]], "get_end() (sapphire.corsika.reader.corsikafile method)": [[24, "sapphire.corsika.reader.CorsikaFile.get_end", false]], "get_events() (in module sapphire.analysis.coincidences)": [[3, "sapphire.analysis.coincidences.get_events", false]], "get_events() (sapphire.corsika.reader.corsikafile method)": [[24, "sapphire.corsika.reader.CorsikaFile.get_events", false]], "get_events_url() (in module sapphire.esd)": [[30, "sapphire.esd.get_events_url", false]], "get_header() (sapphire.corsika.reader.corsikaevent method)": [[24, "sapphire.corsika.reader.CorsikaEvent.get_header", false]], "get_header() (sapphire.corsika.reader.corsikafile method)": [[24, "sapphire.corsika.reader.CorsikaFile.get_header", false]], "get_info() (sapphire.corsika.corsika_queries.corsikaquery method)": [[19, "sapphire.corsika.corsika_queries.CorsikaQuery.get_info", false]], "get_lightning_url() (in module sapphire.esd)": [[30, "sapphire.esd.get_lightning_url", false]], "get_line_boundary_eqs() (sapphire.simulations.groundparticles.detectorboundarysimulation method)": [[42, "sapphire.simulations.groundparticles.DetectorBoundarySimulation.get_line_boundary_eqs", false]], "get_lla_coordinates() (sapphire.clusters.basecluster method)": [[15, "sapphire.clusters.BaseCluster.get_lla_coordinates", false]], "get_lla_coordinates() (sapphire.clusters.detector method)": [[15, "sapphire.clusters.Detector.get_lla_coordinates", false]], "get_lla_coordinates() (sapphire.clusters.station method)": [[15, "sapphire.clusters.Station.get_lla_coordinates", false]], "get_num_particles_in_detector() (sapphire.simulations.ldf.baseldfsimulation method)": [[43, "sapphire.simulations.ldf.BaseLdfSimulation.get_num_particles_in_detector", false]], "get_num_particles_in_detector() (sapphire.simulations.ldf.ellipsldfsimulation method)": [[43, "sapphire.simulations.ldf.EllipsLdfSimulation.get_num_particles_in_detector", false]], "get_particles() (sapphire.corsika.reader.corsikaevent method)": [[24, "sapphire.corsika.reader.CorsikaEvent.get_particles", false]], "get_particles_in_detector() (sapphire.simulations.groundparticles.detectorboundarysimulation method)": [[42, "sapphire.simulations.groundparticles.DetectorBoundarySimulation.get_particles_in_detector", false]], "get_particles_in_detector() (sapphire.simulations.groundparticles.groundparticlesgammasimulation method)": [[42, "sapphire.simulations.groundparticles.GroundParticlesGammaSimulation.get_particles_in_detector", false]], "get_particles_in_detector() (sapphire.simulations.groundparticles.groundparticlessimulation method)": [[42, "sapphire.simulations.groundparticles.GroundParticlesSimulation.get_particles_in_detector", false]], "get_polar_alpha_coordinates() (sapphire.clusters.basecluster method)": [[15, "sapphire.clusters.BaseCluster.get_polar_alpha_coordinates", false]], "get_polar_alpha_coordinates() (sapphire.clusters.station method)": [[15, "sapphire.clusters.Station.get_polar_alpha_coordinates", false]], "get_polar_coordinates() (sapphire.clusters.detector method)": [[15, "sapphire.clusters.Detector.get_polar_coordinates", false]], "get_publicdb_base() (in module sapphire.utils)": [[56, "sapphire.utils.get_publicdb_base", false]], "get_publicdb_xmlrpc_url() (in module sapphire.publicdb)": [[35, "sapphire.publicdb.get_publicdb_xmlrpc_url", false]], "get_rundir() (sapphire.corsika.qsub_corsika.corsikabatch method)": [[22, "sapphire.corsika.qsub_corsika.CorsikaBatch.get_rundir", false]], "get_seeds_todo() (in module sapphire.corsika.qsub_store_corsika_data)": [[23, "sapphire.corsika.qsub_store_corsika_data.get_seeds_todo", false]], "get_simulations() (in module sapphire.corsika.generate_corsika_overview)": [[20, "sapphire.corsika.generate_corsika_overview.get_simulations", false]], "get_singles_url() (in module sapphire.esd)": [[30, "sapphire.esd.get_singles_url", false]], "get_src_base() (in module sapphire.api)": [[14, "sapphire.api.get_src_base", false]], "get_station() (sapphire.clusters.basecluster method)": [[15, "sapphire.clusters.BaseCluster.get_station", false]], "get_station_offsets() (sapphire.analysis.direction_reconstruction.coincidencedirectionreconstruction method)": [[5, "sapphire.analysis.direction_reconstruction.CoincidenceDirectionReconstruction.get_station_offsets", false]], "get_station_pairs_within_max_distance() (sapphire.analysis.calibration.determinestationtimingoffsets method)": [[1, "sapphire.analysis.calibration.DetermineStationTimingOffsets.get_station_pairs_within_max_distance", false]], "get_station_timing_offsets() (sapphire.analysis.reconstructions.reconstructesdcoincidences method)": [[11, "sapphire.analysis.reconstructions.ReconstructESDCoincidences.get_station_timing_offsets", false]], "get_sub_blocks() (sapphire.corsika.reader.corsikafile method)": [[24, "sapphire.corsika.reader.CorsikaFile.get_sub_blocks", false]], "get_traces_for_event() (sapphire.analysis.process_events.processevents method)": [[9, "sapphire.analysis.process_events.ProcessEvents.get_traces_for_event", false]], "get_traces_for_event_index() (sapphire.analysis.process_events.processevents method)": [[9, "sapphire.analysis.process_events.ProcessEvents.get_traces_for_event_index", false]], "get_traces_for_indexed_event_index() (sapphire.analysis.process_events.processindexedevents method)": [[9, "sapphire.analysis.process_events.ProcessIndexedEvents.get_traces_for_indexed_event_index", false]], "get_weather_url() (in module sapphire.esd)": [[30, "sapphire.esd.get_weather_url", false]], "get_xy_coordinates() (sapphire.clusters.basecluster method)": [[15, "sapphire.clusters.BaseCluster.get_xy_coordinates", false]], "get_xy_coordinates() (sapphire.clusters.detector method)": [[15, "sapphire.clusters.Detector.get_xy_coordinates", false]], "get_xy_coordinates() (sapphire.clusters.station method)": [[15, "sapphire.clusters.Station.get_xy_coordinates", false]], "get_xyalpha_coordinates() (sapphire.clusters.basecluster method)": [[15, "sapphire.clusters.BaseCluster.get_xyalpha_coordinates", false]], "get_xyalpha_coordinates() (sapphire.clusters.station method)": [[15, "sapphire.clusters.Station.get_xyalpha_coordinates", false]], "gmst_to_lst() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.gmst_to_lst", false]], "gmst_to_utc() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.gmst_to_utc", false]], "goto_rundir() (sapphire.corsika.qsub_corsika.corsikabatch method)": [[22, "sapphire.corsika.qsub_corsika.CorsikaBatch.goto_rundir", false]], "gps_from_string() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.gps_from_string", false]], "gps_location() (sapphire.api.station method)": [[14, "sapphire.api.Station.gps_location", false]], "gps_locations (sapphire.api.station property)": [[14, "sapphire.api.Station.gps_locations", false]], "gps_to_datetime() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.gps_to_datetime", false]], "gps_to_lst() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.gps_to_lst", false]], "gps_to_utc() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.gps_to_utc", false]], "gpstime (class in sapphire.time_util)": [[47, "sapphire.time_util.GPSTime", false]], "gpstimestamp() (sapphire.time_util.gpstime method)": [[47, "sapphire.time_util.GPSTime.gpstimestamp", false]], "groundparticlesgammasimulation (class in sapphire.simulations.groundparticles)": [[42, "sapphire.simulations.groundparticles.GroundParticlesGammaSimulation", false]], "groundparticlessimulation (class in sapphire.simulations.groundparticles)": [[42, "sapphire.simulations.groundparticles.GroundParticlesSimulation", false]], "groundparticlessimulationwithouterrors (class in sapphire.simulations.groundparticles)": [[42, "sapphire.simulations.groundparticles.GroundParticlesSimulationWithoutErrors", false]], "ha_to_ra() (in module sapphire.transformations.celestial)": [[52, "sapphire.transformations.celestial.ha_to_ra", false]], "hadron_model_high (sapphire.corsika.blocks.eventheader property)": [[18, "sapphire.corsika.blocks.EventHeader.hadron_model_high", false]], "hadron_model_low (sapphire.corsika.blocks.eventheader property)": [[18, "sapphire.corsika.blocks.EventHeader.hadron_model_low", false]], "has_data() (sapphire.api.station method)": [[14, "sapphire.api.Station.has_data", false]], "has_weather() (sapphire.api.station method)": [[14, "sapphire.api.Station.has_weather", false]], "height_to_thickness() (sapphire.corsika.blocks.runheader method)": [[18, "sapphire.corsika.blocks.RunHeader.height_to_thickness", false]], "hisparcnetwork (class in sapphire.clusters)": [[15, "sapphire.clusters.HiSPARCNetwork", false]], "hisparcsimulation (class in sapphire.simulations.detector)": [[40, "sapphire.simulations.detector.HiSPARCSimulation", false]], "hisparcstations (class in sapphire.clusters)": [[15, "sapphire.clusters.HiSPARCStations", false]], "horizontal_to_equatorial() (in module sapphire.transformations.celestial)": [[52, "sapphire.transformations.celestial.horizontal_to_equatorial", false]], "horizontal_to_equatorial_astropy() (in module sapphire.transformations.celestial)": [[52, "sapphire.transformations.celestial.horizontal_to_equatorial_astropy", false]], "horizontal_to_hadec() (in module sapphire.transformations.celestial)": [[52, "sapphire.transformations.celestial.horizontal_to_hadec", false]], "horizontal_to_zenithazimuth() (in module sapphire.transformations.celestial)": [[52, "sapphire.transformations.celestial.horizontal_to_zenithazimuth", false]], "hours_to_degrees() (in module sapphire.transformations.angles)": [[49, "sapphire.transformations.angles.hours_to_degrees", false]], "hours_to_radians() (in module sapphire.transformations.angles)": [[49, "sapphire.transformations.angles.hours_to_radians", false]], "id (sapphire.storage.coincidence attribute)": [[45, "sapphire.storage.Coincidence.id", false]], "id (sapphire.storage.eventobservables attribute)": [[45, "sapphire.storage.EventObservables.id", false]], "id (sapphire.storage.reconstructedevent attribute)": [[45, "sapphire.storage.ReconstructedEvent.id", false]], "info (sapphire.api.station property)": [[14, "sapphire.api.Station.info", false]], "integrals (sapphire.analysis.process_traces.traceobservables property)": [[10, "sapphire.analysis.process_traces.TraceObservables.integrals", false]], "interaction_curve() (sapphire.analysis.direction_reconstruction.spherealgorithm static method)": [[5, "sapphire.analysis.direction_reconstruction.SphereAlgorithm.interaction_curve", false]], "inverse_linear() (in module sapphire.analysis.signal_calibration)": [[12, "sapphire.analysis.signal_calibration.inverse_linear", false]], "inverse_zenith_probability() (sapphire.simulations.detector.hisparcsimulation class method)": [[40, "sapphire.simulations.detector.HiSPARCSimulation.inverse_zenith_probability", false]], "is_cherenkov (sapphire.corsika.blocks.particledata property)": [[18, "sapphire.corsika.blocks.ParticleData.is_cherenkov", false]], "is_detectable (sapphire.corsika.blocks.particledata property)": [[18, "sapphire.corsika.blocks.ParticleData.is_detectable", false]], "is_nucleus (sapphire.corsika.blocks.particledata property)": [[18, "sapphire.corsika.blocks.ParticleData.is_nucleus", false]], "is_particle (sapphire.corsika.blocks.particledata property)": [[18, "sapphire.corsika.blocks.ParticleData.is_particle", false]], "jit() (in module sapphire.corsika.blocks)": [[18, "sapphire.corsika.blocks.jit", false]], "juliandate_to_gmst() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.juliandate_to_gmst", false]], "juliandate_to_modifiedjd() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.juliandate_to_modifiedjd", false]], "juliandate_to_utc() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.juliandate_to_utc", false]], "kascadecoincidences (class in sapphire.kascade)": [[34, "sapphire.kascade.KascadeCoincidences", false]], "kascadeevent (class in sapphire.storage)": [[45, "sapphire.storage.KascadeEvent", false]], "kascadeldf (class in sapphire.simulations.ldf)": [[43, "sapphire.simulations.ldf.KascadeLdf", false]], "kascadeldfsimulation (class in sapphire.simulations.ldf)": [[43, "sapphire.simulations.ldf.KascadeLdfSimulation", false]], "kascadeldfsimulationwithouterrors (class in sapphire.simulations.ldf)": [[43, "sapphire.simulations.ldf.KascadeLdfSimulationWithoutErrors", false]], "landau_pdf() (sapphire.analysis.landau.scintillator method)": [[8, "sapphire.analysis.landau.Scintillator.landau_pdf", false]], "ldf_value() (sapphire.simulations.ldf.ellipsldf method)": [[43, "sapphire.simulations.ldf.EllipsLdf.ldf_value", false]], "ldf_value() (sapphire.simulations.ldf.kascadeldf method)": [[43, "sapphire.simulations.ldf.KascadeLdf.ldf_value", false]], "ldf_value() (sapphire.simulations.ldf.nkgldf method)": [[43, "sapphire.simulations.ldf.NkgLdf.ldf_value", false]], "leap_seconds (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.LEAP_SECONDS", false]], "lf() (sapphire.analysis.landau.scintillator method)": [[8, "sapphire.analysis.landau.Scintillator.lf", false]], "linear() (in module sapphire.analysis.signal_calibration)": [[12, "sapphire.analysis.signal_calibration.linear", false]], "linear_circle_linear() (in module sapphire.analysis.signal_calibration)": [[12, "sapphire.analysis.signal_calibration.linear_circle_linear", false]], "linear_intersection() (in module sapphire.analysis.signal_calibration)": [[12, "sapphire.analysis.signal_calibration.linear_intersection", false]], "lla_to_ecef() (sapphire.transformations.geographic.fromwgs84toenutransformation method)": [[54, "sapphire.transformations.geographic.FromWGS84ToENUTransformation.lla_to_ecef", false]], "lla_to_enu() (sapphire.transformations.geographic.fromwgs84toenutransformation method)": [[54, "sapphire.transformations.geographic.FromWGS84ToENUTransformation.lla_to_enu", false]], "load_coincidences() (in module sapphire.esd)": [[30, "sapphire.esd.load_coincidences", false]], "load_data() (in module sapphire.esd)": [[30, "sapphire.esd.load_data", false]], "log_linear() (in module sapphire.analysis.signal_calibration)": [[12, "sapphire.analysis.signal_calibration.log_linear", false]], "logic_checks() (in module sapphire.analysis.direction_reconstruction)": [[5, "sapphire.analysis.direction_reconstruction.logic_checks", false]], "loglog_linear_circle_linear() (in module sapphire.analysis.signal_calibration)": [[12, "sapphire.analysis.signal_calibration.loglog_linear_circle_linear", false]], "loglog_xy_circle_linear() (in module sapphire.analysis.signal_calibration)": [[12, "sapphire.analysis.signal_calibration.loglog_xy_circle_linear", false]], "lst_to_gmst() (in module sapphire.transformations.clock)": [[53, "sapphire.transformations.clock.lst_to_gmst", false]], "main() (in module sapphire.corsika.generate_corsika_overview)": [[20, "sapphire.corsika.generate_corsika_overview.main", false]], "main() (in module sapphire.corsika.qsub_corsika)": [[22, "sapphire.corsika.qsub_corsika.main", false]], "main() (in module sapphire.corsika.qsub_store_corsika_data)": [[23, "sapphire.corsika.qsub_store_corsika_data.main", false]], "main() (in module sapphire.corsika.store_corsika_data)": [[25, "sapphire.corsika.store_corsika_data.main", false]], "main() (in module sapphire.data.extend_local_data)": [[28, "sapphire.data.extend_local_data.main", false]], "main() (in module sapphire.data.update_local_data)": [[29, "sapphire.data.update_local_data.main", false]], "make_relative() (in module sapphire.utils)": [[56, "sapphire.utils.make_relative", false]], "make_rundir() (sapphire.corsika.qsub_corsika.corsikabatch method)": [[22, "sapphire.corsika.qsub_corsika.CorsikaBatch.make_rundir", false]], "max_distance (sapphire.analysis.calibration.determinestationtimingoffsets attribute)": [[1, "sapphire.analysis.calibration.DetermineStationTimingOffsets.MAX_DISTANCE", false]], "max_energy_deposit_in_mips() (in module sapphire.simulations.gammas)": [[41, "sapphire.simulations.gammas.max_energy_deposit_in_mips", false]], "max_iterations (sapphire.analysis.direction_reconstruction.curvedregressionalgorithm attribute)": [[5, "sapphire.analysis.direction_reconstruction.CurvedRegressionAlgorithm.MAX_ITERATIONS", false]], "max_iterations (sapphire.analysis.direction_reconstruction.curvedregressionalgorithm3d attribute)": [[5, "sapphire.analysis.direction_reconstruction.CurvedRegressionAlgorithm3D.MAX_ITERATIONS", false]], "max_iterations (sapphire.analysis.direction_reconstruction.regressionalgorithm3d attribute)": [[5, "sapphire.analysis.direction_reconstruction.RegressionAlgorithm3D.MAX_ITERATIONS", false]], "mean_filter_with_threshold() (sapphire.analysis.process_traces.meanfilter method)": [[10, "sapphire.analysis.process_traces.MeanFilter.mean_filter_with_threshold", false]], "mean_filter_without_threshold() (sapphire.analysis.process_traces.meanfilter method)": [[10, "sapphire.analysis.process_traces.MeanFilter.mean_filter_without_threshold", false]], "meanfilter (class in sapphire.analysis.process_traces)": [[10, "sapphire.analysis.process_traces.MeanFilter", false]], "memoize() (in module 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false], [3, "module-sapphire.analysis.coincidences", false], [4, "module-sapphire.analysis.core_reconstruction", false], [5, "module-sapphire.analysis.direction_reconstruction", false], [6, "module-sapphire.analysis.event_utils", false], [7, "module-sapphire.analysis.find_mpv", false], [8, "module-sapphire.analysis.landau", false], [9, "module-sapphire.analysis.process_events", false], [10, "module-sapphire.analysis.process_traces", false], [11, "module-sapphire.analysis.reconstructions", false], [12, "module-sapphire.analysis.signal_calibration", false], [13, "module-sapphire.analysis.time_deltas", false], [14, "module-sapphire.api", false], [15, "module-sapphire.clusters", false], [17, "module-sapphire.corsika", false], [18, "module-sapphire.corsika.blocks", false], [19, "module-sapphire.corsika.corsika_queries", false], [20, "module-sapphire.corsika.generate_corsika_overview", false], [21, "module-sapphire.corsika.particles", false], [22, "module-sapphire.corsika.qsub_corsika", 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[[11, "sapphire.analysis.reconstructions.ReconstructESDEvents.store_reconstructions", false]], "store_station_observables() (sapphire.simulations.base.basesimulation method)": [[39, "sapphire.simulations.base.BaseSimulation.store_station_observables", false]], "store_time_deltas() (sapphire.analysis.time_deltas.processtimedeltas method)": [[13, "sapphire.analysis.time_deltas.ProcessTimeDeltas.store_time_deltas", false]], "storekascadedata (class in sapphire.kascade)": [[34, "sapphire.kascade.StoreKascadeData", false]], "subcluster() (sapphire.api.station method)": [[14, "sapphire.api.Station.subcluster", false]], "subcluster_numbers() (sapphire.api.network method)": [[14, "sapphire.api.Network.subcluster_numbers", false]], "subclusters() (sapphire.api.network method)": [[14, "sapphire.api.Network.subclusters", false]], "submit_job() (in module sapphire.qsub)": [[36, "sapphire.qsub.submit_job", false]], "submit_job() (sapphire.corsika.qsub_corsika.corsikabatch method)": [[22, 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16, 19, 22, 24, 26, 30, 31, 32, 33, 35, 45, 47, 55], "your": [3, 15, 16, 26, 31, 32, 33, 52, 53, 55], "yourself": [2, 19, 33], "ypointlist": 4, "ystart": 4, "z": [1, 4, 5, 15, 21, 42, 50, 54, 56], "z_int": 5, "z_ref": 1, "zen": 52, "zenaz_coordin": 52, "zenith": [4, 11, 14, 19, 22, 40, 42, 43, 44, 45, 52, 55, 56], "zenith1": 56, "zenith2": 56, "zenithazimuth": 52, "zenithazimuth_to_equatori": 52, "zenithazimuth_to_equatorial_astropi": 52, "zenithazimuth_to_horizont": 52, "zero": [5, 8, 47, 55], "zip": [3, 9, 14, 55], "zipbal": 33}, "titles": ["Data analysis", "Data timing and signal calibration", "Query coincidences from a datafile", "Search for coincidences between HiSPARC stations", "Reconstruction of EAS core and size", "Reconstruction of EAS direction", "Get signal and timing values from events", "MPV fit", "Landau distribution", "Processing of HiSPARC events", "Processing of HiSPARC traces", "Reconstructions", "Detector signal calibration", "Time deltas", "HiSPARC API", "HiSPARC clusters", "Configuration", "CORSIKA simulations", "CORSIKA Blocks", "Query CORSIKA overview for simulations", "Create an overview of CORSIKA simulations", "CORSIKA Particles", "CORSIKA on Stoomboot", "Convert CORSIKA simulations on Stoomboot", "CORSIKA Reader", "Store CORSIKA data as HDF5", "CORSIKA Units", "Updating and extending local data", "Download additional local data", "Update default local data with latest data from Publicdb", "Event Summary Data", "Example scripts", "Welcome to SAPPHiRE\u2019s documentation!", "Installation", "KASCADE data", "Public data access", "Nikhef Stoomboot facilities", "SAPPHiRE Reference", "Simulation of the detection of EAS events", "Simulation base classes", "Detector simulations", "Simulation of detector response due to gammas", "Simulations based on Monte Carlo EAS ground particle data", "Simulations based on theoretical models of EAS lateral distributions", "Simulations based on time structure of EAS shower fronts", "Low-level storage constructs", "Test installation", "GPS date/time utility functions", "Coordinate and time transformations", "Conversion between different angle notations", "Conversion between axes coordinate systems", "Conversion between different bases", "Transformations between Celestial coordinate systems", "Time conversion functions", "Transformations between geographic coordinate systems", "Tutorial", "Common utility functions"], "titleterms": {"": 32, "The": [31, 55], "__main__": 31, "__name__": 31, "access": [35, 55], "addit": 28, "an": 20, "analysi": 0, "angl": [31, 49], "api": 14, "around": 55, "author": 24, "ax": 50, "base": [39, 42, 43, 44, 51], "between": [3, 49, 50, 51, 52, 54], "block": 18, "calibr": [1, 12], "carlo": 42, "celesti": 52, "check": 33, "class": 39, "cluster": 15, "code": 33, "coincid": [2, 3, 31, 55], "coinicd": 55, "common": [31, 56], "configur": 16, "construct": 45, "content": 17, "convers": [49, 50, 51, 53], "convert": 23, "coordin": [48, 50, 52, 54], "core": 4, "correctli": 33, "corsika": [17, 18, 19, 20, 21, 22, 23, 24, 25, 26], "creat": 20, "data": [0, 1, 25, 27, 28, 29, 30, 31, 34, 35, 42, 55], "datafil": 2, "date": 47, "debian": 33, "default": 29, "delta": 13, "deriv": 33, "detect": 38, "detector": [12, 40, 41], "differ": [49, 51], "direct": [5, 31], "distribut": [8, 31, 33, 43], "document": 32, "download": [28, 31, 55], "due": 41, "ea": [4, 5, 38, 42, 43, 44], "esd": 55, "event": [6, 9, 30, 31, 38, 55], "exampl": 31, "extend": 27, "facil": 36, "few": 31, "first": [31, 55], "fit": 7, "from": [2, 6, 29], "front": 44, "function": [47, 53, 56], "gamma": 41, "geograph": 54, "get": [6, 33], "global": 31, "gp": 47, "ground": 42, "hdf5": 25, "hisparc": [3, 9, 10, 14, 15, 55], "i": 33, "indic": 32, "info": 24, "instal": [33, 46], "issu": 24, "just": 33, "kascad": 34, "landau": 8, "later": 43, "latest": 29, "let": 33, "level": 45, "like": 33, "local": [27, 28, 29], "look": 55, "low": 45, "mac": 33, "me": 33, "miniconda": 33, "minim": 33, "model": 43, "modul": [0, 17, 55], "mont": 42, "more": 24, "mpv": 7, "nikhef": 36, "notat": 49, "o": 33, "obtain": 55, "overview": [19, 20], "packag": 0, "particl": [21, 42], "perform": 55, "plot": [31, 55], "prerequisit": 33, "process": [9, 10], "public": 35, "publicdb": 29, "python": 33, "queri": [2, 19], "reader": 24, "reconstruct": [4, 5, 11, 31, 55], "refer": 37, "respons": 41, "sapphir": [32, 33, 37, 55], "script": 31, "search": [3, 55], "see": 33, "shower": 44, "signal": [1, 6, 12], "simul": [17, 19, 20, 23, 38, 39, 40, 41, 42, 43, 44, 55], "size": 4, "some": 31, "specif": 33, "station": [3, 31], "step": 55, "stoomboot": [22, 23, 36], "storag": 45, "store": 25, "structur": 44, "summari": [30, 31], "system": [50, 52, 54], "tabl": 32, "task": 31, "test": 46, "theoret": 43, "thi": 0, "time": [1, 6, 13, 44, 47, 48, 53], "time_util": 55, "tip": 31, "trace": 10, "transform": [48, 52, 54], "trick": 31, "tutori": 55, "ubuntu": 33, "unit": 26, "updat": [27, 29], "util": [47, 56], "valu": 6, "welcom": 32, "work": 33, "x": 33, "zenith": 31}}) \ No newline at end of file diff --git a/simulations.html b/simulations.html new file mode 100644 index 00000000..84195ef7 --- /dev/null +++ b/simulations.html @@ -0,0 +1,140 @@ + + + + + + + + Simulation of the detection of EAS events — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Simulation of the detection of EAS events

+

Simulations are built up from several components. First the cluster needs to +be defined. This can be a set of real stations or imaginary detectors. Then +systematic detector and station errors are generated. Next a generator for +shower properties is used to determine the properties of each shower (energy +and direction). Then for each detector the shower particles that hit it are +selected. For each of these the actual detected signal strength and arrival +times (detector effects) are determined. Then the trigger conditions are +checked to see if the station triggered. If it did an event will be stored for +that station. This includes a GPS timestamp and the detector observables +(particle count and arrival time of first particle). Finally for each shower a +‘coincidence’ will be stored, even if it was not detected by any station. The +coincidence stores the parameters of the shower.

+

Perform common simulation tasks.

+

This package contains modules for performing simulations:

+
+
base

base simulations class that provides the framework for simulations

+
+
detector

simulate the response of the HiSPARC detectors

+
+
groundparticles

perform simulations using CORSIKA ground particles as input

+
+
ldf

perform simulations using lateral distribution functions for particle +densities

+
+
showerfront

simple simulations of a shower front

+
+
gammas

simulation of detector response due to gammas

+
+
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/simulations/base.html b/simulations/base.html new file mode 100644 index 00000000..a065d8a8 --- /dev/null +++ b/simulations/base.html @@ -0,0 +1,298 @@ + + + + + + + + Simulation base classes — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Simulation base classes

+

Perform simulations of air showers on a cluster of stations

+

This base class can be subclassed to provide various kinds of +simulations. These simulations will inherit the base functionality from +this class, including the creation of event and coincidence tables to +store the results, which will look similar to regular HiSPARC data, such +that the same reconstruction analysis can be applied to both.

+

Example usage:

+
>>> import tables
+
+>>> from sapphire.simulations.base import BaseSimulation
+>>> from sapphire import ScienceParkCluster
+
+>>> data = tables.open_file('/tmp/test_base_simulation.h5', 'w')
+>>> cluster = ScienceParkCluster()
+
+>>> sim = BaseSimulation(cluster, data, '/simulations/this_run', 10)
+>>> sim.run()
+
+
+
+
+class sapphire.simulations.base.BaseSimulation(cluster, data, output_path='/', n=1, seed=None, progress=True)
+

Base class for simulations.

+
+
Parameters:
+
    +

  • cluster – BaseCluster instance.

    • +

  • data – writeable PyTables file handle.

    • +

  • output_path – path (as string) to the PyTables group (need not +exist) in which the result tables will be created.

    • +

  • n – number of simulations to perform.

    • +

  • seed – seed for the pseudo-random number generators.

    • +

  • progress – if True show a progressbar while simulating.

    • +
+
+
+
+
+run()
+

Run the simulations.

+
+ +
+
+generate_shower_parameters()
+

Generate shower parameters like core position, energy, etc.

+
+ +
+
+simulate_events_for_shower(shower_parameters)
+

Simulate station events for a single shower

+
+ +
+
+simulate_station_response(station, shower_parameters)
+

Simulate station response to a shower.

+
+ +
+
+simulate_all_detectors(detectors, shower_parameters)
+

Simulate response of all detectors in a station.

+
+
Parameters:
+
    +

  • detectors – list of detectors

    • +

  • shower_parameters – parameters of the shower

    • +
+
+
+
+ +
+
+simulate_detector_response(detector, shower_parameters)
+

Simulate detector response to a shower.

+
+
Parameters:
+
    +

  • detector – Detector instance

    • +

  • shower_parameters – shower parameters

    • +
+
+
Returns:
+

dictionary with keys ‘n’ (number of particles in +detector) and ‘t’ (time of arrival of first detected particle).

+
+
+
+ +
+
+simulate_trigger(detector_observables)
+

Simulate a trigger response.

+
+ +
+
+simulate_gps(station_observables, shower_parameters, station)
+

Simulate gps timestamp.

+
+ +
+
+process_detector_observables(detector_observables)
+

Process detector observables for a station.

+

The list of detector observables is converted into a dictionary +containing the familiar observables like pulseheights, n1, n2, +…, t1, t2, …, integrals, etc.

+
+
Parameters:
+

detector_observables – list of observables of the detectors +making up a station.

+
+
Returns:
+

dictionary containing the familiar station observables +like n1, n2, n3, etc.

+
+
+
+ +
+
+store_station_observables(station_id, station_observables)
+

Store station observables.

+
+
Parameters:
+
    +

  • station_id – the id of the station in self.cluster

    • +

  • station_observables – A dictionary containing the +variables to be stored for this event.

    • +
+
+
Returns:
+

The index (row number) of the newly added event.

+
+
+
+ +
+
+store_coincidence(shower_id, shower_parameters, station_events)
+

Store coincidence.

+

Store the information to find events of different stations +belonging to the same simulated shower in the coincidences +tables.

+
+
Parameters:
+
    +

  • shower_id – The shower number for the coincidence id.

    • +

  • shower_parameters – A dictionary with the parameters of +the simulated shower.

    • +

  • station_events – A list of tuples containing the +station_id and event_index referring to the events that +participated in the coincidence.

    • +
+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/simulations/detector.html b/simulations/detector.html new file mode 100644 index 00000000..e895df5e --- /dev/null +++ b/simulations/detector.html @@ -0,0 +1,501 @@ + + + + + + + + Detector simulations — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Detector simulations

+

Common HiSPARC station response simulations

+

These are some common simulations for HiSPARC detectors.

+
+
+class sapphire.simulations.detector.HiSPARCSimulation(*args, **kwargs)
+
+
+simulate_and_store_offsets()
+

Simulate and store station and detector offsets

+
+ +
+
+classmethod simulate_detector_offsets(n_detectors)
+

Get multiple detector offsets

+
+
Parameters:
+

n_detectors – number of offsets to return.

+
+
Returns:
+

list of detector timing offsets in ns.

+
+
+
+ +
+
+classmethod simulate_detector_offset()
+

Simulate time offsets between detectors in one station

+

This offset should be fixed for each detector for a simulation run.

+
+
Returns:
+

detector timing offset in ns.

+
+
+
+ +
+
+classmethod simulate_station_offset()
+

Simulate time offsets between different stations

+

This offset should be fixed for each station for a simulation run. +The actual distribution is not yet very clear. We assume it is +gaussian for convenience. Then the stddev is about 16 ns.

+
+
Returns:
+

station timing offset in ns.

+
+
+
+ +
+
+classmethod simulate_gps_uncertainty()
+

Simulate uncertainty from GPS receiver

+
+ +
+
+classmethod simulate_adc_sampling(t)
+

Simulate ADC time binning due to the sampling frequency

+
+
Parameters:
+

t – time to be binned.

+
+
Returns:
+

time ceiled in 2.5 ns base.

+
+
+
+ +
+
+classmethod simulate_signal_transport_time(n=1)
+

Simulate transport times of scintillation light to the PMT

+

Generates random transit times within a given distribution and +adds it to the times the particles passed the detector.

+

Distribution based on Fokkema2012 sec 4.2, figure 4.3

+

Be careful when editing this function, be sure to check both +the single and vectorized part.

+
+
Parameters:
+

n – number of times to simulate

+
+
Returns:
+

list of signal transport times

+
+
+
+ +
+
+classmethod simulate_detector_mips(n, theta)
+

Simulate the detector signal for particles

+

Simulation of convoluted distribution of electron and +muon energy losses with the scintillator response

+

The detector response (energy loss and detector efficiency) is derived +in Montanus2014.

+

The energy loss is taken from the Bethe-Bloch equation. The detector +response is statistically modelled. The effect of particle angle of +incidence is accounted for. The resulting probability +distribution is used below.

+

The statistics can be simulated by taking a random number y between +0 and 1, and convert it to a signal s in MIP using the probability +distribution.

+
+
Montanus2014: J.C.M. Montanus, The Landau distribution,

Internal note (Nikhef), 22 may 2014

+
+
+

Be careful when editing this function, be sure to check both +the single and vectorized part.

+
+
Parameters:
+
    +

  • n – number of particles.

    • +

  • theta – angle of incidence of the particles. Either a single +value valid for all particles, or an array with an angle +for each particle.

    • +
+
+
Returns:
+

signal strength in number of mips.

+
+
+
+ +
+
+classmethod generate_core_position(r_max)
+

Generate a random core position within a circle

+

DF: This is the fastest implementation I could come up with. I +timed several permutations of numpy / math, and tried a Monte +Carlo method in which I pick x and y in a square (fast) and then +determine if they fall inside a circle or not (surprisingly +slow, because of an if-statement, and despite some optimizations +suggested by HM).

+
+
Parameters:
+

r – Maximum core distance, in meters.

+
+
Returns:
+

Random x, y position in the disc with radius r_max.

+
+
+
+ +
+
+classmethod generate_zenith(min_value=0, max_value=1.0471975511965976)
+

Generate a random zenith

+

Generate a random zenith for a uniform distribution on a sphere. +For a random position on a sphere the zenith should not be chosen +from a uniform [0, pi/2] distribution.

+

Source: http://mathworld.wolfram.com/SpherePointPicking.html

+

This function does not account for attenuation due to the extra path +length, nor for the reduced effective surface of the detectors due to +the angle. CORSIKA simulated showers already contain the atmospheric +attenuation and precise positions for each particle.

+
+
Parameters:
+

min,max – minimum and maximum zenith angles, in radians.

+
+
Returns:
+

random zenith position on a sphere, in radians.

+
+
+
+ +
+
+classmethod generate_attenuated_zenith()
+

Generate a random zenith

+

Pick from the expected zenith distribution.

+

There is a difference between expected shower zeniths detected on the +ground and at the top of the atmosphere. This distribution takes the +attenuation of air showers due to the extra path length in the +atmosphere into account.

+
+
Returns:
+

random zenith angle, in radians.

+
+
+
+ +
+
+classmethod inverse_zenith_probability(p)
+

Inverse cumulative probability distribution for zenith

+

Derived from Schultheiss “The acceptancy of the HiSPARC Network”, +(internal note), eq 2.4 from Rossi.

+
+
Parameters:
+

p – probability value between 0 and 1.

+
+
Returns:
+

zenith with corresponding cumulative probability, in radians.

+
+
+
+ +
+
+classmethod generate_azimuth()
+

Generate a random azimuth

+

Showers from each azimuth have equal probability

+
+
Returns:
+

shower azimuth angle, in radians.

+
+
+
+ +
+
+classmethod generate_energy(e_min=100000000000000.0, e_max=1e+21, alpha=-2.75)
+

Generate a random shower energy

+

Source: http://mathworld.wolfram.com/RandomNumber.html

+

Simple approximation of the cosmic-ray energy spectrum. Showers +with higher energy occur less often, following a power law.

+
+
Parameters:
+
    +

  • e_min,e_max – Energy bounds for the distribution (in eV).

    • +

  • alpha – Steepness of the power law distribution.

    • +
+
+
Returns:
+

primary particle energy, in eV.

+
+
+
+ +
+ +
+
+class sapphire.simulations.detector.ErrorlessSimulation(*args, **kwargs)
+
+
+classmethod simulate_detector_offsets(n_detectors)
+

Get multiple detector offsets

+
+
Parameters:
+

n_detectors – number of offsets to return.

+
+
Returns:
+

list of detector timing offsets in ns.

+
+
+
+ +
+
+classmethod simulate_detector_offset()
+

Simulate time offsets between detectors in one station

+

This offset should be fixed for each detector for a simulation run.

+
+
Returns:
+

detector timing offset in ns.

+
+
+
+ +
+
+classmethod simulate_station_offset()
+

Simulate time offsets between different stations

+

This offset should be fixed for each station for a simulation run. +The actual distribution is not yet very clear. We assume it is +gaussian for convenience. Then the stddev is about 16 ns.

+
+
Returns:
+

station timing offset in ns.

+
+
+
+ +
+
+classmethod simulate_gps_uncertainty()
+

Simulate uncertainty from GPS receiver

+
+ +
+
+classmethod simulate_adc_sampling(t)
+

Simulate ADC time binning due to the sampling frequency

+
+
Parameters:
+

t – time to be binned.

+
+
Returns:
+

time ceiled in 2.5 ns base.

+
+
+
+ +
+
+classmethod simulate_signal_transport_time(n=1)
+

Simulate transport times of scintillation light to the PMT

+

Generates random transit times within a given distribution and +adds it to the times the particles passed the detector.

+

Distribution based on Fokkema2012 sec 4.2, figure 4.3

+

Be careful when editing this function, be sure to check both +the single and vectorized part.

+
+
Parameters:
+

n – number of times to simulate

+
+
Returns:
+

list of signal transport times

+
+
+
+ +
+
+classmethod simulate_detector_mips(n, theta)
+

Simulate the detector signal for particles

+

Simulation of convoluted distribution of electron and +muon energy losses with the scintillator response

+

The detector response (energy loss and detector efficiency) is derived +in Montanus2014.

+

The energy loss is taken from the Bethe-Bloch equation. The detector +response is statistically modelled. The effect of particle angle of +incidence is accounted for. The resulting probability +distribution is used below.

+

The statistics can be simulated by taking a random number y between +0 and 1, and convert it to a signal s in MIP using the probability +distribution.

+
+
Montanus2014: J.C.M. Montanus, The Landau distribution,

Internal note (Nikhef), 22 may 2014

+
+
+

Be careful when editing this function, be sure to check both +the single and vectorized part.

+
+
Parameters:
+
    +

  • n – number of particles.

    • +

  • theta – angle of incidence of the particles. Either a single +value valid for all particles, or an array with an angle +for each particle.

    • +
+
+
Returns:
+

signal strength in number of mips.

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/simulations/gammas.html b/simulations/gammas.html new file mode 100644 index 00000000..0f1db8ad --- /dev/null +++ b/simulations/gammas.html @@ -0,0 +1,250 @@ + + + + + + + + Simulation of detector response due to gammas — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Simulation of detector response due to gammas

+

Gammas

+

This module implements simulation of interactions of gamma photons with +a scintillator. Used in groundparticle simulations.

+
+
+sapphire.simulations.gammas.compton_edge(gamma_energy)
+

Calculate Compton edge for a given photon energy

+

W.R. Leo (1987) p.54

+
+
Parameters:
+

gamma_energy – photon energy [MeV].

+
+
Returns:
+

compton edge [MeV].

+
+
+
+ +
+
+sapphire.simulations.gammas.compton_energy_transfer(gamma_energy)
+

Calculate the energy transfer from photon to electron

+

From the differential cross section the cumulative distribution +is calculated. From this distribution a random energy transfer +(within kinematic bounds) is returned.

+
+
Parameters:
+

gamma_energy – photon energy [MeV].

+
+
Returns:
+

transferred energy [MeV].

+
+
+
+ +
+
+sapphire.simulations.gammas.energy_transfer_cross_section(gamma_energy, recoil_energy)
+

Differential cross section dsigma/dT

+

Differential cross section for energy transfer from gamma +to scattered electron in compton scattering.

+

W.R. Leo (1987) p 54

+
+
Parameters:
+
    +

  • gamma_energy – photon energy [MeV].

    • +

  • recoil_energy – electron recoil energy [MeV].

    • +
+
+
+
+ +
+
+sapphire.simulations.gammas.max_energy_deposit_in_mips(depth, scintillator_depth)
+

Maximum energy transfer from electron to scintillator

+

Determine maximum energy transfer based on remaining scinitillator +depth.

+

Assumes scintillator depth is projected onto the direction +of the incident particle (divided by cos(theta)).

+
+
Parameters:
+
    +

  • depth – depth at which the electron is produced [cm].

    • +

  • scintillator_depth – total depth of the scintillator [cm].

    • +
+
+
+
+ +
+
+sapphire.simulations.gammas.simulate_detector_mips_gammas(p, theta)
+

Simulate detection of gammas

+
+
Parameters:
+
    +

  • p – the momenta of the gammas as array, in eV.

    • +

  • theta – angles of incidence of the gammas as array, in radians.

    • +
+
+
Returns:
+

the simulated detector signal (in mips).

+
+
+
+ +
+
+sapphire.simulations.gammas.pair_mean_free_path(gamma_energy)
+

Mean free path pair production

+

NIST XCOM database: https://www.nist.gov/pml/data/xcom/ +compound: C9H10 +pair production (total attenuation)

+

table generated by @tomkooij/lio-project/photons/nist.py

+
+
Parameters:
+

gamma_energy – photon energy [MeV].

+
+
Returns:
+

mean free path [cm].

+
+
+
+ +
+
+sapphire.simulations.gammas.compton_mean_free_path(gamma_energy)
+

Mean free path compton scattering

+

NIST XCOM database: https://www.nist.gov/pml/data/xcom/ +compound: C9H10 +compton scattering (incoherent scattering)

+

table generated by @tomkooij/lio-project/photons/nist.py

+
+
Parameters:
+

gamma_energy – photon energy [MeV].

+
+
Returns:
+

mean free path [cm].

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/simulations/groundparticles.html b/simulations/groundparticles.html new file mode 100644 index 00000000..a2deaff3 --- /dev/null +++ b/simulations/groundparticles.html @@ -0,0 +1,587 @@ + + + + + + + + Simulations based on Monte Carlo EAS ground particle data — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Simulations based on Monte Carlo EAS ground particle data

+

Perform simulations of CORSIKA air showers on a cluster of stations

+

This simulation uses a HDF5 file created from a CORSIKA simulation with +the store_corsika_data script. The shower is ‘thrown’ on the cluster +with random core positions and azimuth angles.

+

Example usage:

+
>>> import tables
+>>> from sapphire import GroundParticlesSimulation, ScienceParkCluster
+>>> data = tables.open_file('/tmp/test_groundparticle_simulation.h5', 'w')
+>>> cluster = ScienceParkCluster()
+>>> sim = GroundParticlesSimulation('corsika.h5', 500, cluster, data,
+...                                 '/', 10)
+>>> sim.run()
+
+
+
+
+class sapphire.simulations.groundparticles.GroundParticlesSimulation(corsikafile_path, max_core_distance, *args, **kwargs)
+

Simulation initialization

+
+
Parameters:
+
    +

  • corsikafile_path – path to the corsika.h5 file containing +the groundparticles.

    • +

  • max_core_distance – maximum distance of shower core to +center of cluster.

    • +
+
+
+
+
+finish()
+

Clean-up after simulation

+
+ +
+
+generate_shower_parameters()
+

Generate shower parameters like core position, energy, etc.

+

For this groundparticles simulation, only the shower core position +and rotation angle of the shower are generated. Do not +interpret these parameters as the position of the cluster, or the +rotation of the cluster! Interpret them as shower parameters.

+
+
Returns:
+

dictionary with shower parameters: core_pos +(x, y-tuple) and azimuth.

+
+
+
+ +
+
+simulate_detector_response(detector, shower_parameters)
+

Simulate detector response to a shower.

+

Checks if leptons have passed a detector. If so, it returns the number +of leptons in the detector and the arrival time of the first lepton +passing the detector.

+
+
Parameters:
+
    +

  • detector – Detector for which +the observables will be determined.

    • +

  • shower_parameters – dictionary with the shower parameters.

    • +
+
+
+
+ +
+
+simulate_detector_mips_for_particles(particles)
+

Simulate the detector signal for particles

+
+
Parameters:
+

particles – particle rows with the p_[x, y, z] +components of the particle momenta.

+
+
+
+ +
+
+simulate_trigger(detector_observables)
+

Simulate a trigger response.

+

This implements the trigger as used on HiSPARC stations: +- 4-detector station: at least two high or three low signals. +- 2-detector station: at least 2 low signals.

+
+
Parameters:
+

detector_observables – list of dictionaries, each containing +the observables of one detector.

+
+
Returns:
+

True if the station triggers, False otherwise.

+
+
+
+ +
+
+simulate_gps(station_observables, shower_parameters, station)
+

Simulate gps timestamp.

+
+
Parameters:
+
    +

  • station_observables – dictionary containing the observables +of the station.

    • +

  • shower_parameters – dictionary with the shower parameters.

    • +

  • station – sapphire.clusters.Station for which +to simulate the gps timestamp.

    • +
+
+
Returns:
+

station_observables updated with gps timestamp and +trigger time.

+
+
+
+ +
+
+get_particles_in_detector(detector, shower_parameters)
+

Get particles that hit a detector.

+

Particle ids 2, 3, 5, 6 are electrons and muons, +id 4 is no longer used (were neutrino’s).

+

The detector is approximated by a square with a surface of 0.5 +square meter which is not correctly rotated. In fact, during +the simulation, the rotation of the detector is undefined. This +is faster than a more thorough implementation.

+

The CORSIKA simulation azimuth is used for the projection because the +cluster is rotated such that from the perspective of the rotated +detectors the CORSIKA showers come from the desired azimuth. In the +simulation frame the CORSIKA shower azimuth remains unchanged.

+
+
Parameters:
+
    +

  • detector – Detector for which +to get particles.

    • +

  • shower_parameters – dictionary with the shower parameters.

    • +
+
+
+
+ +
+ +
+
+class sapphire.simulations.groundparticles.GroundParticlesGammaSimulation(corsikafile_path, max_core_distance, *args, **kwargs)
+

Simulation which includes signals from gamma particles in the shower

+

Simulation initialization

+
+
Parameters:
+
    +

  • corsikafile_path – path to the corsika.h5 file containing +the groundparticles.

    • +

  • max_core_distance – maximum distance of shower core to +center of cluster.

    • +
+
+
+
+
+simulate_detector_response(detector, shower_parameters)
+

Simulate detector response to a shower.

+

Checks if particles have passed a detector. If so, it returns the +number of particles in the detector and the arrival time of the first +particle passing the detector.

+
+
Parameters:
+
    +

  • detector – Detector for which +the observables will be determined.

    • +

  • shower_parameters – dictionary with the shower parameters.

    • +
+
+
+
+ +
+
+get_particles_in_detector(detector, shower_parameters)
+

Get particles that hit a detector.

+

Particle ids 2, 3, 5, 6 are electrons and muons, +id 4 is no longer used (were neutrino’s).

+

The detector is approximated by a square with a surface of 0.5 +square meter which is not correctly rotated. In fact, during +the simulation, the rotation of the detector is undefined. This +is faster than a more thorough implementation.

+

Detector height is ignored!

+
+
Parameters:
+
    +

  • detector – Detector for which +to get particles.

    • +

  • shower_parameters – dictionary with the shower parameters.

    • +
+
+
+
+ +
+
+simulate_detector_mips_for_gammas(particles)
+

Simulate the detector signal for gammas

+
+
Parameters:
+

particles – particle rows with the p_[x, y, z] +components of the particle momenta.

+
+
+
+ +
+ +
+
+class sapphire.simulations.groundparticles.DetectorBoundarySimulation(corsikafile_path, max_core_distance, *args, **kwargs)
+

More accurately simulate the detection area of the detectors.

+

Take the orientation of the detectors into account and use the +exact detector boundaries. This requires a slightly more complex +query which is a bit slower.

+

Simulation initialization

+
+
Parameters:
+
    +

  • corsikafile_path – path to the corsika.h5 file containing +the groundparticles.

    • +

  • max_core_distance – maximum distance of shower core to +center of cluster.

    • +
+
+
+
+
+get_particles_in_detector(detector, shower_parameters)
+

Simulate the detector detection area accurately.

+

First particles are filtered to see which fall inside a +non-rotated square box around the detector (i.e. sides of 1.2m). +For the remaining particles a more accurate query is used to see +which actually hit the detector. The advantage of using the +square is that column indexes can be used, which may speed up +queries.

+
+
Parameters:
+
    +

  • detector – Detector for which +to get particles.

    • +

  • shower_parameters – dictionary with the shower parameters.

    • +
+
+
+
+ +
+
+get_line_boundary_eqs(p0, p1, p2)
+

Get line equations using three points

+

Given three points, this function computes the equations for two +parallel lines going through these points. The first and second +point are on the same line, whereas the third point is taken to +be on a line which runs parallel to the first. The return value +is an equation and two boundaries which can be used to test if a +point is between the two lines.

+
+
Parameters:
+
    +

  • p0,p1 – (x, y) tuples on the same line.

    • +

  • p2 – (x, y) tuple on the parallel line.

    • +
+
+
Returns:
+

value1, equation, value2, such that points satisfying +value1 < equation < value2 are between the parallel lines.

+
+
+

Example:

+
>>> get_line_boundary_eqs((0, 0), (1, 1), (0, 2))
+(0.0, 'y - 1.000000 * x', 2.0)
+
+
+
+ +
+ +
+
+class sapphire.simulations.groundparticles.ParticleCounterSimulation(corsikafile_path, max_core_distance, *args, **kwargs)
+

Do not simulate mips, just count the number of particles.

+

Simulation initialization

+
+
Parameters:
+
    +

  • corsikafile_path – path to the corsika.h5 file containing +the groundparticles.

    • +

  • max_core_distance – maximum distance of shower core to +center of cluster.

    • +
+
+
+
+
+simulate_detector_mips(n, theta)
+

A mip for a mip, count number of particles in a detector.

+
+ +
+ +
+
+class sapphire.simulations.groundparticles.FixedCoreDistanceSimulation(corsikafile_path, max_core_distance, *args, **kwargs)
+

Shower core at a fixed core distance (from cluster origin).

+
+
Parameters:
+

core_distance – distance of shower core to center of cluster.

+
+
+

Simulation initialization

+
+
Parameters:
+
    +

  • corsikafile_path – path to the corsika.h5 file containing +the groundparticles.

    • +

  • max_core_distance – maximum distance of shower core to +center of cluster.

    • +
+
+
+
+
+classmethod generate_core_position(r_max)
+

Generate a random core position on a circle

+
+
Parameters:
+

r_max – Fixed core distance, in meters.

+
+
Returns:
+

Random x, y position on the circle with radius r_max.

+
+
+
+ +
+ +
+
+class sapphire.simulations.groundparticles.GroundParticlesSimulationWithoutErrors(corsikafile_path, max_core_distance, *args, **kwargs)
+

This simulation does not simulate errors/uncertainties

+

This results in perfect timing (first particle through detector) +and particle counting for the detectors.

+

Simulation initialization

+
+
Parameters:
+
    +

  • corsikafile_path – path to the corsika.h5 file containing +the groundparticles.

    • +

  • max_core_distance – maximum distance of shower core to +center of cluster.

    • +
+
+
+
+ +
+
+class sapphire.simulations.groundparticles.MultipleGroundParticlesSimulation(corsikaoverview_path, max_core_distance, min_energy, max_energy, *args, **kwargs)
+

Use multiple CORSIKA simulated air showers in one run.

+

Simulations will be selected from the set of available showers. +Each time an energy and zenith angle is generated a shower is selected +from the CORSIKA overview. Each shower is reused multiple times to +take advantage of caching, and to reduce IO stress.

+
+

Warning

+

This simulation loads a new shower often it is therefore more I/O +intensive than GroundParticlesSimulation. Do not run many +of these simulations simultaneously!

+
+

Simulation initialization

+
+
Parameters:
+
    +

  • corsikaoverview_path – path to the corsika_overview.h5 file +containing the available simulations.

    • +

  • max_core_distance – maximum distance of shower core to +center of cluster.

    • +

  • min_energy,max_energy – upper and lower shower energy limits, +in eV.

    • +
+
+
+
+
+DATA = '/data/hisparc/corsika/data/{seeds}/corsika.h5'
+
+ +
+
+finish()
+

Clean-up after simulation

+
+ +
+
+generate_shower_parameters()
+

Generate shower parameters like core position, energy, etc.

+

For this groundparticles simulation, only the shower core position +and rotation angle of the shower are generated. Do not +interpret these parameters as the position of the cluster, or the +rotation of the cluster! Interpret them as shower parameters.

+
+
Returns:
+

dictionary with shower parameters: core_pos +(x, y-tuple) and azimuth.

+
+
+
+ +
+
+select_simulation()
+

Generate parameters for selecting a CORSIKA simulation

+
+
Returns:
+

simulation row from a CORSIKA Simulations table.

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/simulations/ldf.html b/simulations/ldf.html new file mode 100644 index 00000000..f22cd8f6 --- /dev/null +++ b/simulations/ldf.html @@ -0,0 +1,593 @@ + + + + + + + + Simulations based on theoretical models of EAS lateral distributions — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Simulations based on theoretical models of EAS lateral distributions

+

Lateral distribution functions that can be used for simulating particle +densities and for fitting to data.

+

Example usage:

+
>>> import tables
+
+>>> from sapphire import NkgLdfSimulation, ScienceParkCluster
+
+>>> data = tables.open_file('/tmp/test_ldf_simulation.h5', 'w')
+>>> cluster = ScienceParkCluster()
+
+>>> sim = NkgLdfSimulation(max_core_distance=400, min_energy=1e15,
+...                        max_energy=1e21, cluster=cluster,
+...                        datafile=data, n=200)
+>>> sim.run()
+
+
+
+
+class sapphire.simulations.ldf.BaseLdfSimulation(max_core_distance, min_energy, max_energy, *args, **kwargs)
+

Simulation initialization

+
+
Parameters:
+
    +

  • max_core_distance – maximum distance of shower core to +center of cluster (in meters).

    • +

  • min_energy,max_energy – Minimum and maximum energy of the +shower (in eV).

    • +
+
+
+
+
+generate_shower_parameters()
+

Generate shower parameters, i.e. core position

+

For the simple LDF only the core position is relevant. It +assumes the shower to come from the Zenith.

+
+
Returns:
+

dictionary with shower parameters: core_pos +(x, y-tuple).

+
+
+
+ +
+
+simulate_detector_response(detector, shower_parameters)
+

Simulate detector response to a shower

+

Get the mips in a detector from the LDF.

+
+
Parameters:
+
    +

  • detector – Detector for which +the observables will be determined.

    • +

  • shower_parameters – dictionary with the shower parameters.

    • +
+
+
+
+ +
+
+get_num_particles_in_detector(detector, shower_parameters)
+

Get the number of particles in a detector

+
+
Parameters:
+
    +

  • detector – Detector for which +the number of particles will be determined.

    • +

  • shower_parameters – dictionary with the shower parameters.

    • +
+
+
+
+ +
+
+static simulate_particles_for_density(p)
+

Get number of particles in detector given a particle density

+
+
Parameters:
+

p – particle density in number per detector area.

+
+
Returns:
+

random number from Poisson distribution.

+
+
+
+ +
+ +
+
+class sapphire.simulations.ldf.BaseLdfSimulationWithoutErrors(max_core_distance, min_energy, max_energy, *args, **kwargs)
+

This simulation does not simulate errors/uncertainties

+

This should result in perfect particle counting for the detectors.

+

Simulation initialization

+
+
Parameters:
+
    +

  • max_core_distance – maximum distance of shower core to +center of cluster (in meters).

    • +

  • min_energy,max_energy – Minimum and maximum energy of the +shower (in eV).

    • +
+
+
+
+
+static simulate_particles_for_density(p)
+

Exact number

+
+ +
+ +
+
+class sapphire.simulations.ldf.NkgLdfSimulation(*args, **kwargs)
+

Same as the BaseLdfSimulation but uses the NkgLdf as LDF

+

Simulation initialization

+
+
Parameters:
+
    +

  • max_core_distance – maximum distance of shower core to +center of cluster (in meters).

    • +

  • min_energy,max_energy – Minimum and maximum energy of the +shower (in eV).

    • +
+
+
+
+ +
+
+class sapphire.simulations.ldf.NkgLdfSimulationWithoutErrors(*args, **kwargs)
+

Same as the NkgLdfSimulation but without error simulation

+

Simulation initialization

+
+
Parameters:
+
    +

  • max_core_distance – maximum distance of shower core to +center of cluster (in meters).

    • +

  • min_energy,max_energy – Minimum and maximum energy of the +shower (in eV).

    • +
+
+
+
+ +
+
+class sapphire.simulations.ldf.KascadeLdfSimulation(*args, **kwargs)
+

Same as the BaseLdfSimulation but uses the KascadeLdf as LDF

+

Simulation initialization

+
+
Parameters:
+
    +

  • max_core_distance – maximum distance of shower core to +center of cluster (in meters).

    • +

  • min_energy,max_energy – Minimum and maximum energy of the +shower (in eV).

    • +
+
+
+
+ +
+
+class sapphire.simulations.ldf.KascadeLdfSimulationWithoutErrors(*args, **kwargs)
+

Same as the KascadeLdfSimulation but without error simulation

+

Simulation initialization

+
+
Parameters:
+
    +

  • max_core_distance – maximum distance of shower core to +center of cluster (in meters).

    • +

  • min_energy,max_energy – Minimum and maximum energy of the +shower (in eV).

    • +
+
+
+
+ +
+
+class sapphire.simulations.ldf.EllipsLdfSimulation(*args, **kwargs)
+

Same as BaseLdfSimulation but uses the EllipsLdF as LDF

+

Simulation initialization

+
+
Parameters:
+
    +

  • max_core_distance – maximum distance of shower core to +center of cluster (in meters).

    • +

  • min_energy,max_energy – Minimum and maximum energy of the +shower (in eV).

    • +
+
+
+
+
+generate_shower_parameters()
+

Generate shower parameters, i.e. core position

+

For the elliptic LDF both the core position and the zenith angle +are relevant.

+
+
Returns:
+

dictionary with shower parameters: core_pos +(x, y-tuple).

+
+
+
+ +
+
+get_num_particles_in_detector(detector, shower_parameters)
+

Get the number of particles in a detector

+
+
Parameters:
+
    +

  • detector – Detector for which +the number of particles will be determined.

    • +

  • shower_parameters – dictionary with the shower parameters.

    • +
+
+
+
+ +
+ +
+
+class sapphire.simulations.ldf.BaseLdf
+

Base LDF class

+

No particles! Always returns a particle density of 0.

+
+
+calculate_ldf_value(r, n_electrons=None, s=None)
+
+ +
+
+calculate_core_distance(x, y, x0, y0, theta, phi)
+

Calculate core distance

+

The core distance is the distance of the detector to the shower core, +measured on the shower front. For derivations, see logbook.

+
+
Parameters:
+
    +

  • x,y – detector position in m.

    • +

  • x0,y0 – shower core position in m.

    • +

  • theta,phi – shower axis direction in radians.

    • +
+
+
Returns:
+

distance from detector to the shower core in shower +front plane in m.

+
+
+
+ +
+ +
+
+class sapphire.simulations.ldf.NkgLdf(n_electrons=None, s=None)
+

The Nishimura-Kamata-Greisen function

+

NKG LDF setup

+
+
Parameters:
+
    +

  • n_electrons – Shower size (number of electrons).

    • +

  • s – Shower age parameter.

    • +
+
+
+
+
+calculate_ldf_value(r, n_electrons=None, s=None)
+

Calculate the LDF value

+
+
Parameters:
+
    +

  • r – core distance in m.

    • +

  • n_electrons – number of electrons in the shower.

    • +

  • s – shower age parameter.

    • +
+
+
Returns:
+

particle density in m ** -2.

+
+
+
+ +
+
+ldf_value(r, n_electrons, s)
+

Calculate the LDF value

+

Given a core distance, shower size, and shower age. +As given in Fokkema2012 eq 7.2.

+
+
Parameters:
+
    +

  • r – core distance in m.

    • +

  • n_electrons – number of electrons in the shower.

    • +

  • s – shower age parameter.

    • +
+
+
Returns:
+

particle density in m ** -2.

+
+
+
+ +
+ +
+
+class sapphire.simulations.ldf.KascadeLdf(n_electrons=None, s=None)
+

The KASCADE modified NKG function

+

NKG LDF setup

+
+
Parameters:
+
    +

  • n_electrons – Shower size (number of electrons).

    • +

  • s – Shower age parameter.

    • +
+
+
+
+
+ldf_value(r, n_electrons, s)
+

Calculate the LDF value

+

Given a core distance, shower size, and shower age. +As given in Fokkema2012 eq 7.4.

+
+
Parameters:
+
    +

  • r – core distance in m.

    • +

  • n_electrons – number of electrons in the shower.

    • +

  • s – shower shape parameter.

    • +
+
+
Returns:
+

particle density in m ** -2.

+
+
+
+ +
+ +
+
+class sapphire.simulations.ldf.EllipsLdf(n_electrons=None, zenith=None, azimuth=None, s1=None, s2=None)
+

The NKG function modified for leptons and azimuthal asymmetry

+

NKG LDF setup

+
+
Parameters:
+
    +

  • n_electrons – Shower size (number of electrons).

    • +

  • s – Shower age parameter.

    • +
+
+
+
+
+calculate_ldf_value(r, phi, n_electrons=None, zenith=None, azimuth=None)
+

Calculate the LDF value for a given core distance and polar angle

+
+
Parameters:
+
    +

  • r – core distance in m.

    • +

  • phi – polar angle in rad.

    • +

  • n_electrons – number of electrons in the shower.

    • +
+
+
Returns:
+

particle density in m ** -2.

+
+
+
+ +
+
+ldf_value(r, phi, n_electrons, zenith, azimuth, s1, s2)
+

Calculate the LDF value

+

Given a core distance, core polar angle, zenith angle, azimuth angle, +shower size and three shape parameters (r0, s1, s2) . +As given by Montanus, paper to follow.

+
+

Warning

+

The value 11.24 in the expression: muoncorr is only valid +for: s1 = -.5, s2 = - 2.6 and r0 = 30.

+
+
+
Parameters:
+
    +

  • r – core distance in m.

    • +

  • phi – polar angle in rad.

    • +

  • n_electrons – number of electrons in the shower.

    • +

  • zenith – zenith angle in rad.

    • +

  • azimuth – azimuth angle in rad.

    • +

  • s1 – shower shape parameter.

    • +

  • s2 – shower shape parameter.

    • +
+
+
Returns:
+

particle density in m ** -2.

+
+
+
+ +
+
+calculate_core_distance_and_angle(x, y, x0, y0)
+

Calculate core distance

+

The core distance is the distance of the detector to the shower core, +measured in the horizontal observation plane.

+
+
Parameters:
+
    +

  • x,y – detector position in m.

    • +

  • x0,y0 – shower core position in m.

    • +
+
+
Returns:
+

distance and polar angle from detector to the shower core in +horizontal observation plane in m resp. rad.

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/simulations/showerfront.html b/simulations/showerfront.html new file mode 100644 index 00000000..6bda289d --- /dev/null +++ b/simulations/showerfront.html @@ -0,0 +1,358 @@ + + + + + + + + Simulations based on time structure of EAS shower fronts — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Simulations based on time structure of EAS shower fronts

+

Perform simple simulations for timing

+

Throw a simulated shower front on a cluster from various angles. +Simulate just the arrival times.

+

Example usage:

+
>>> import tables
+
+>>> from sapphire import FlatFrontSimulation, ScienceParkCluster
+
+>>> data = tables.open_file('/tmp/test_showerfront_simulation.h5', 'w')
+>>> cluster = ScienceParkCluster()
+
+>>> sim = FlatFrontSimulation(cluster, data, '/', 200)
+>>> sim.run()
+
+
+
+
+class sapphire.simulations.showerfront.FlatFrontSimulation(*args, **kwargs)
+
+
+generate_shower_parameters()
+

Generate shower parameters, i.e. azimuth and zenith angles.

+

For this groundparticles simulation, only the shower core position +and rotation angle of the shower are generated. Do not +interpret these parameters as the position of the cluster, or the +rotation of the cluster! Interpret them as shower parameters.

+
+
Returns:
+

dictionary with shower parameters: core_pos +(x, y-tuple) and azimuth.

+
+
+
+ +
+
+simulate_detector_response(detector, shower_parameters)
+

Simulate detector response to a shower.

+

Return the arrival time of shower front passing the center of +the detector.

+
+ +
+
+get_arrival_time(detector, shower_parameters)
+

Calculate arrival time

+

Assumes a flat shower front and core position to be +the center of the cluster.

+

Equation based on Fokkema2012 sec 4.2, eq 4.9. +With additions to account for altitude. +(DOI: 10.3990/1.9789036534383) +The directional vector c * dt should be negative, +not apparent in Fokkema2012 fig 4.4.

+
+
Returns:
+

Shower front arrival time in ns.

+
+
+
+ +
+
+simulate_gps(station_observables, shower_parameters, station)
+

Simulate gps timestamp

+

Ensure that all detector arrival times are positive.

+
+ +
+ +
+
+class sapphire.simulations.showerfront.FlatFrontSimulationWithoutErrors(*args, **kwargs)
+

This simulation does not simulate errors/uncertainties

+

This should result in perfect timing for the detectors.

+
+ +
+
+class sapphire.simulations.showerfront.FlatFrontSimulation2D(*args, **kwargs)
+

This simulation ignores detector altitudes.

+
+
+get_arrival_time(detector, shower_parameters)
+

Calculate arrival time

+

Ignore detector altitudes

+

Equation based on Fokkema2012 sec 4.2, eq 4.9. +(DOI: 10.3990/1.9789036534383)

+
+ +
+ +
+
+class sapphire.simulations.showerfront.FlatFrontSimulation2DWithoutErrors(*args, **kwargs)
+

Ignore altitude of detectors and do not simulate errors.

+
+ +
+
+class sapphire.simulations.showerfront.ConeFrontSimulation(max_core_distance, *args, **kwargs)
+

This simulation uses a cone shaped shower front.

+

The opening angle of the cone is given in the init

+

Example usage:

+
>>> import tables
+
+>>> from sapphire import ConeFrontSimulation, ScienceParkCluster
+
+>>> data = tables.open_file('/tmp/test_showerfront_simulation.h5', 'w')
+>>> cluster = ScienceParkCluster()
+
+>>> sim = ConeFrontSimulation(100, cluster, data, '/', 200)
+>>> sim.run()
+
+
+

Calculate arrival time

+
+
Parameters:
+

cone_angle – half of the opening angle of the cone.

+
+
+
+
+generate_shower_parameters()
+

Generate shower parameters

+

For this cone-shaped showerfront, the core position, the azimuth +and zenith angle of the shower are generated.

+
+
Returns:
+

dictionary with shower parameters: core_pos +(x, y-tuple), azimuth and zenith.

+
+
+
+ +
+
+get_arrival_time(detector, shower_parameters)
+

Calculate arrival time

+
+ +
+ +
+
+class sapphire.simulations.showerfront.FlatFront
+

Simple flat shower front

+
+
+delay_at_r(r)
+
+ +
+
+front_shape(r)
+
+ +
+ +
+
+class sapphire.simulations.showerfront.ConeFront
+

Simple cone shaped shower front

+
+
+delay_at_r(r)
+
+ +
+
+front_shape(r)
+

Delay of the showerfront relative to flat as function of distance

+
+
Parameters:
+

r – distance to the shower core in shower frame.

+
+
Returns:
+

delay time of shower front.

+
+
+
+ +
+ +
+
+class sapphire.simulations.showerfront.CorsikaStationFront
+

Shower front shape derived from CORSIKA simulations on a station.

+

A set of CORSIKA generated showers were used to determine the median +detected arrival time in a 4-detector station as a function of core +distance.

+

At large core distances the detection probability decreases and the +arrival time becomes less accurate.

+

Currently only support for energies between 1e15 and 1e17 eV.

+
+
+delay_at_r(r, energy=1e+16, particle='proton')
+
+ +
+
+front_shape(r, energy, particle='proton')
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/storage.html b/storage.html new file mode 100644 index 00000000..d8fbf018 --- /dev/null +++ b/storage.html @@ -0,0 +1,359 @@ + + + + + + + + Low-level storage constructs — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Low-level storage constructs

+

PyTables table descriptions for data storage

+

This module contains the table descriptions used by the detector +simulation to store intermediate and final data in a HDF5 file.

+
+
+class sapphire.storage.EventObservables
+

Store information about the observables of an event.

+

The observables are described for each station independently. So, for each +event (with a unique id), there is a table row for each station +(with a unique station_id), such that only the (id, station_id) +combinations are unique in the table.

+
+
+id
+

a unique identifier for the simulated event (only unique in this table)

+
+ +
+
+station_id
+

station identifier, such that you can do:

+
>>> station = cluster.stations[station_id]
+
+
+
+ +
+
+r, phi, x, y
+

coordinates of the station. Depending on the simulation, this might be +constant throughout the simulation, or it might change event by event.

+
+ +
+
+alpha
+

rotation of the station around its center

+
+ +
+
+N
+

number of detectors with at least one particle

+
+ +
+
+columns = {'N': UInt8Col(shape=(), dflt=np.uint8(0), pos=None), 'alpha': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'ext_timestamp': UInt64Col(shape=(), dflt=np.uint64(0), pos=None), 'id': UInt32Col(shape=(), dflt=np.uint32(0), pos=None), 'n1': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'n2': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'n3': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'n4': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'nanoseconds': UInt32Col(shape=(), dflt=np.uint32(0), pos=None), 'phi': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'r': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'station_id': UInt8Col(shape=(), dflt=np.uint8(0), pos=None), 't1': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 't2': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 't3': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 't4': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'timestamp': Time32Col(shape=(), dflt=np.int32(0), pos=None), 'x': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'y': Float32Col(shape=(), dflt=np.float32(0.0), pos=None)}
+
+ +
+ +
+
+class sapphire.storage.Coincidence
+

Store information about a coincidence of stations within a cluster.

+

An extensive air shower can trigger multiple stations, resulting in a set +of events which are from the same shower. This is called a coincidence.

+

This table assigns an id to a coincidence and provides some +additional information. The events making up the coincidence can be looked +up using the c_index table. Let coincidence be a row from +this table, then you can do:

+
>>> coincidence_id = coincidence['id']
+>>> event_ids = c_index[coincidence_id]
+>>> coincidence_event_list = [events[u] for u in event_ids]
+
+
+

Note that all events included in the coincidence are not required to +actually have measured particles. For example, simulations include all +events from the same shower in the coincidence, regardless of observed +particles. On the other hand, experimental datasets only include stations +which have triggered, but may include events which have not actually +measured the same shower, but simply measured other particles at the same +time, by chance.

+

Simulations may set the x, y, zenith, +azimuth, size, and energy attributes to simulation +parameters, like core position and shower parameters.

+
+
+id
+

a unique identifier for the coincidence (only unique in this table)

+
+ +
+
+N
+

the number of triggered stations

+
+ +
+
+x
+

The x coordinate of the shower core in a simulation.

+
+ +
+
+y
+

The y coordinate of the shower core in a simulation.

+
+ +
+
+zenith
+

The zenith direction of the (simulated) shower.

+
+ +
+
+azimuth
+

The azimuth direction of the (simulated) shower.

+
+ +
+
+size
+

The size (number of leptons) of the (simulated) shower.

+
+ +
+
+energy
+

The primary particle energy of the (simulated) shower.

+
+ +
+
+columns = {'N': UInt8Col(shape=(), dflt=np.uint8(0), pos=4), 'azimuth': Float32Col(shape=(), dflt=np.float32(0.0), pos=8), 'energy': Float32Col(shape=(), dflt=np.float32(0.0), pos=10), 'ext_timestamp': UInt64Col(shape=(), dflt=np.uint64(0), pos=3), 'id': UInt32Col(shape=(), dflt=np.uint32(0), pos=0), 'nanoseconds': UInt32Col(shape=(), dflt=np.uint32(0), pos=2), 'size': Float32Col(shape=(), dflt=np.float32(0.0), pos=9), 'timestamp': Time32Col(shape=(), dflt=np.int32(0), pos=1), 'x': Float32Col(shape=(), dflt=np.float32(0.0), pos=5), 'y': Float32Col(shape=(), dflt=np.float32(0.0), pos=6), 'zenith': Float32Col(shape=(), dflt=np.float32(0.0), pos=7)}
+
+ +
+ +
+
+class sapphire.storage.TimeDelta
+

Store time differences

+
+
+columns = {'delta': Float64Col(shape=(), dflt=np.float64(0.0), pos=3), 'ext_timestamp': UInt64Col(shape=(), dflt=np.uint64(0), pos=0), 'nanoseconds': UInt32Col(shape=(), dflt=np.uint32(0), pos=2), 'timestamp': UInt32Col(shape=(), dflt=np.uint32(0), pos=1)}
+
+ +
+ +
+
+class sapphire.storage.ReconstructedCoincidence
+

Store information about reconstructed coincidences

+
+
+columns = {'azimuth': Float32Col(shape=(), dflt=np.float32(0.0), pos=7), 'energy': Float32Col(shape=(), dflt=np.float32(0.0), pos=9), 'error_azimuth': Float32Col(shape=(), dflt=np.float32(0.0), pos=13), 'error_energy': Float32Col(shape=(), dflt=np.float32(0.0), pos=15), 'error_size': Float32Col(shape=(), dflt=np.float32(0.0), pos=14), 'error_x': Float32Col(shape=(), dflt=np.float32(0.0), pos=10), 'error_y': Float32Col(shape=(), dflt=np.float32(0.0), pos=11), 'error_zenith': Float32Col(shape=(), dflt=np.float32(0.0), pos=12), 'ext_timestamp': UInt64Col(shape=(), dflt=np.uint64(0), pos=2), 'id': UInt32Col(shape=(), dflt=np.uint32(0), pos=1), 'min_n': Float32Col(shape=(), dflt=np.float32(0.0), pos=3), 'reference_azimuth': Float32Col(shape=(), dflt=np.float32(0.0), pos=19), 'reference_energy': Float32Col(shape=(), dflt=np.float32(0.0), pos=21), 'reference_size': Float32Col(shape=(), dflt=np.float32(0.0), pos=20), 'reference_x': Float32Col(shape=(), dflt=np.float32(0.0), pos=16), 'reference_y': Float32Col(shape=(), dflt=np.float32(0.0), pos=17), 'reference_zenith': Float32Col(shape=(), dflt=np.float32(0.0), pos=18), 'size': Float32Col(shape=(), dflt=np.float32(0.0), pos=8), 'x': Float32Col(shape=(), dflt=np.float32(0.0), pos=4), 'y': Float32Col(shape=(), dflt=np.float32(0.0), pos=5), 'zenith': Float32Col(shape=(), dflt=np.float32(0.0), pos=6)}
+
+ +
+ +
+
+class sapphire.storage.ReconstructedEvent
+

Store information about reconstructed events

+
+
+id
+

Index referring to the id of the event that was reconstructed.

+
+ +
+
+d1,d2,d3,d4
+

Booleans indicating which detectors participated in the +reconstruction.

+
+ +
+
+columns = {'azimuth': Float32Col(shape=(), dflt=np.float32(0.0), pos=7), 'd1': BoolCol(shape=(), dflt=np.False_, pos=22), 'd2': BoolCol(shape=(), dflt=np.False_, pos=23), 'd3': BoolCol(shape=(), dflt=np.False_, pos=24), 'd4': BoolCol(shape=(), dflt=np.False_, pos=25), 'energy': Float32Col(shape=(), dflt=np.float32(0.0), pos=9), 'error_azimuth': Float32Col(shape=(), dflt=np.float32(0.0), pos=13), 'error_energy': Float32Col(shape=(), dflt=np.float32(0.0), pos=15), 'error_size': Float32Col(shape=(), dflt=np.float32(0.0), pos=14), 'error_x': Float32Col(shape=(), dflt=np.float32(0.0), pos=10), 'error_y': Float32Col(shape=(), dflt=np.float32(0.0), pos=11), 'error_zenith': Float32Col(shape=(), dflt=np.float32(0.0), pos=12), 'ext_timestamp': UInt64Col(shape=(), dflt=np.uint64(0), pos=2), 'id': UInt32Col(shape=(), dflt=np.uint32(0), pos=1), 'min_n': Float32Col(shape=(), dflt=np.float32(0.0), pos=3), 'reference_azimuth': Float32Col(shape=(), dflt=np.float32(0.0), pos=19), 'reference_energy': Float32Col(shape=(), dflt=np.float32(0.0), pos=21), 'reference_size': Float32Col(shape=(), dflt=np.float32(0.0), pos=20), 'reference_x': Float32Col(shape=(), dflt=np.float32(0.0), pos=16), 'reference_y': Float32Col(shape=(), dflt=np.float32(0.0), pos=17), 'reference_zenith': Float32Col(shape=(), dflt=np.float32(0.0), pos=18), 'size': Float32Col(shape=(), dflt=np.float32(0.0), pos=8), 'x': Float32Col(shape=(), dflt=np.float32(0.0), pos=4), 'y': Float32Col(shape=(), dflt=np.float32(0.0), pos=5), 'zenith': Float32Col(shape=(), dflt=np.float32(0.0), pos=6)}
+
+ +
+ +
+
+class sapphire.storage.KascadeEvent
+

Store events from KASCADE

+
+
+columns = {'Num_e': Float64Col(shape=(), dflt=np.float64(0.0), pos=9), 'Num_mu': Float64Col(shape=(), dflt=np.float64(0.0), pos=10), 'P200': Float64Col(shape=(), dflt=np.float64(0.0), pos=13), 'T200': Float64Col(shape=(), dflt=np.float64(0.0), pos=14), 'azimuth': Float64Col(shape=(), dflt=np.float64(0.0), pos=8), 'core_pos': Float64Col(shape=(np.int64(2),), dflt=np.float64(0.0), pos=6), 'dens_e': Float64Col(shape=(np.int64(4),), dflt=np.float64(0.0), pos=11), 'dens_mu': Float64Col(shape=(np.int64(4),), dflt=np.float64(0.0), pos=12), 'energy': Float64Col(shape=(), dflt=np.float64(0.0), pos=5), 'event_id': Int64Col(shape=(), dflt=np.int64(0), pos=1), 'ext_timestamp': UInt64Col(shape=(), dflt=np.uint64(0), pos=4), 'nanoseconds': UInt32Col(shape=(), dflt=np.uint32(0), pos=3), 'run_id': Int32Col(shape=(), dflt=np.int32(0), pos=0), 'timestamp': Time32Col(shape=(), dflt=np.int32(0), pos=2), 'zenith': Float64Col(shape=(), dflt=np.float64(0.0), pos=7)}
+
+ +
+ +
+
+class sapphire.storage.ReconstructedKascadeEvent
+

Store information about reconstructed events

+
+
+columns = {'alpha': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'id': UInt32Col(shape=(), dflt=np.uint32(0), pos=None), 'k_Num_e': Float64Col(shape=(), dflt=np.float64(0.0), pos=None), 'k_Num_mu': Float64Col(shape=(), dflt=np.float64(0.0), pos=None), 'k_P200': Float64Col(shape=(), dflt=np.float64(0.0), pos=None), 'k_T200': Float64Col(shape=(), dflt=np.float64(0.0), pos=None), 'k_core_pos': Float64Col(shape=(np.int64(2),), dflt=np.float64(0.0), pos=None), 'k_dens_e': Float64Col(shape=(np.int64(4),), dflt=np.float64(0.0), pos=None), 'k_dens_mu': Float64Col(shape=(np.int64(4),), dflt=np.float64(0.0), pos=None), 'k_energy': Float64Col(shape=(), dflt=np.float64(0.0), pos=None), 'min_n134': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'n1': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'n2': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'n3': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'n4': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'phi': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'r': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'reconstructed_phi': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'reconstructed_theta': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'reference_phi': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'reference_theta': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 'station_id': UInt8Col(shape=(), dflt=np.uint8(0), pos=None), 't1': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 't2': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 't3': Float32Col(shape=(), dflt=np.float32(0.0), pos=None), 't4': Float32Col(shape=(), dflt=np.float32(0.0), pos=None)}
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/tests.html b/tests.html new file mode 100644 index 00000000..8618dfef --- /dev/null +++ b/tests.html @@ -0,0 +1,134 @@ + + + + + + + + Test installation — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Test installation

+

Perform code tests.

+

This package contains all tests which verify the proper working of all +SAPPHiRE code. These tests can also be used to verify if SAPPHiRE was +installed correctly. Simply call the run_tests() function.

+
+
+sapphire.tests.run_tests()
+

Collect and run all SAPPHiRE tests

+
+
Returns:
+

unittest.TextTestResult object containing the test results.

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/time_util.html b/time_util.html new file mode 100644 index 00000000..fcc1f8fd --- /dev/null +++ b/time_util.html @@ -0,0 +1,183 @@ + + + + + + + + GPS date/time utility functions — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

GPS date/time utility functions

+

Convert UTC/GPS/local time.

+

The class in this module is intended to save you from typing trainwrecks +just to convert GPS timestamps to GPS date/time and vice versa. +Trainwrecks invite typos and thus many are easily confused about +UTC/GPS/local time. No more!

+
+
+class sapphire.time_util.GPSTime(*args)
+

Date/time utility class.

+

Initialize the class

+

The arguments to the __init__ method can take different forms. +First, it can take year, month, day, hour, minutes, seconds +arguments. The year, month and day are mandatory, while hour, +minutes, seconds default to zero.

+

If the argument is a single number, this is interpreted as a +Unix-like timestamp, in GPS time.

+

Examples specifying the exact same time:

+
>>> GPSTime(2012, 12, 1)
+>>> GPSTime(1354320000)
+
+
+
+
+gpstimestamp()
+

Return the GPS date/time as a timestamp.

+

Example:

+
>>> gpstime = GPSTime(2012, 12, 1)
+>>> gpstime.gpstimestamp()
+1354320000
+
+
+
+ +
+
+description()
+

Return the GPS date/time as a string.

+

Example:

+
>>> gpstime = GPSTime(1354320000)
+>>> gpstime.description()
+'Sat Dec  1 00:00:00 2012'
+
+
+
+ +
+
+datetime()
+

Return the GPS date/time as a datetime.datetime instance.

+

Example:

+
>>> gpstime = GPSTime(1354320000)
+>>> gpstime.datetime()
+datetime.datetime(2012, 12, 1, 0, 0)
+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/transformations.html b/transformations.html new file mode 100644 index 00000000..1880783d --- /dev/null +++ b/transformations.html @@ -0,0 +1,128 @@ + + + + + + + + Coordinate and time transformations — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Coordinate and time transformations

+

Convert coordinate and time systems.

+

Easy transformations between different systems.

+
+
angles

conversion between different angle notations

+
+
axes

rotation matrices and conversion between coordinate systems

+
+
base

conversion between decimal and sexagesimal

+
+
celestial

conversion between celestial coordinate systems, contains both +legacy and new astropy functions

+
+
clock

conversion between different time keeping systems

+
+
geographic

geographic coordinate transformations (e.g. WGS84 to ENU)

+
+
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/transformations/angles.html b/transformations/angles.html new file mode 100644 index 00000000..2900e97d --- /dev/null +++ b/transformations/angles.html @@ -0,0 +1,183 @@ + + + + + + + + Conversion between different angle notations — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Conversion between different angle notations

+

Perform various angle related transformations

+

Transform between different notations for angles: +Degrees, radians and hours.

+
+
+sapphire.transformations.angles.hours_to_degrees(angle)
+

Converts decimal hours to degrees

+
+
Parameters:
+

hours – angle in decimal hours

+
+
Returns:
+

angle in degrees

+
+
+
+ +
+
+sapphire.transformations.angles.hours_to_radians(angle)
+

Converts decimal hours to radians

+
+
Parameters:
+

hours – angle in decimal hours

+
+
Returns:
+

angle in radians

+
+
+
+ +
+
+sapphire.transformations.angles.degrees_to_hours(angle)
+

Converts degrees to decimal hours

+
+
Parameters:
+

angle – angle in degrees

+
+
Returns:
+

angle in decimal hours

+
+
+
+ +
+
+sapphire.transformations.angles.radians_to_hours(angle)
+

Converts degrees to decimal hours

+
+
Parameters:
+

angle – angle in degrees

+
+
Returns:
+

angle in decimal hours

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/transformations/axes.html b/transformations/axes.html new file mode 100644 index 00000000..ef0abc83 --- /dev/null +++ b/transformations/axes.html @@ -0,0 +1,301 @@ + + + + + + + + Conversion between axes coordinate systems — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Conversion between axes coordinate systems

+

Perform various axes related transformations

+
    +

  • Transformation between Cartesian, polar, cylindrical, spherical and compass +coordinate systems.

    • +

  • Create a rotation matrix for rotations around a certain axis.

    • +
+

Cartesian coordinates: x, y, z axes.

+

Spherical coordinates: +- r: length of vector. +- theta: angle of the vector to the z-axis. +- phi: angle of vector to the x-axis in x,y-plane, rotating counterclockwise.

+

Cylindrical and polar coordinates: +- r: length of vector in x,y-plane. +- phi: angle of vector to the x-axis in x,y-plane, rotating counterclockwise. +- z: height above x,y-plane.

+

Compass coordinates: +- r: length of vector in x,y-plane. +- alpha: angle of vector to the y-axis in x,y-plane, rotating clockwise. +- z: height above x,y-plane.

+
+
+sapphire.transformations.axes.cartesian_to_spherical(x, y, z)
+

Converts Cartesian coordinates into spherical coordinates

+
+
Parameters:
+

x,y,z – Cartesian coordinates.

+
+
Returns:
+

tuple of spherical coordinates (r, theta, phi), +with theta and phi in radians.

+
+
+
+ +
+
+sapphire.transformations.axes.cartesian_to_cylindrical(x, y, z)
+

Converts Cartesian coordinates into cylindrical coordinates

+
+
Parameters:
+

x,y,z – Cartesian coordinates.

+
+
Returns:
+

tuple of cylindrical coordinates (r, phi, z), with +phi in radians.

+
+
+
+ +
+
+sapphire.transformations.axes.cartesian_to_polar(x, y)
+

Converts Cartesian coordinates into polar coordinates

+
+
Parameters:
+

x,y – Cartesian coordinates.

+
+
Returns:
+

tuple of polar coordinates (r, phi), with phi in radians.

+
+
+
+ +
+
+sapphire.transformations.axes.cartesian_to_compass(x, y, z)
+

Converts Cartesian coordinates into compass coordinates

+
+
Parameters:
+

x,y,z – Cartesian coordinates.

+
+
Returns:
+

tuple of compass coordinates (r, alpha, z), +with alpha in degrees.

+
+
+
+ +
+
+sapphire.transformations.axes.spherical_to_cartesian(r, theta, phi)
+

Convert spherical coordinates into Cartesian coordinates

+
+
Parameters:
+

r,theta,phi – spherical coordinates, with theta and phi in radians.

+
+
Returns:
+

tuple of Cartesian coordinates (x, y, z).

+
+
+
+ +
+
+sapphire.transformations.axes.cylindrical_to_cartesian(r, phi, z)
+

Convert cylindrical coordinates into Cartesian coordinates

+
+
Parameters:
+

r,phi,z – cylindrical coordinates, with phi in radians.

+
+
Returns:
+

tuple of Cartesian coordinates (x, y, z).

+
+
+
+ +
+
+sapphire.transformations.axes.polar_to_cartesian(r, phi)
+

Convert polar coordinates into Cartesian coordinates

+
+
Parameters:
+

r,phi – polar coordinates, with phi in radians.

+
+
Returns:
+

tuple of Cartesian coordinates (x, y).

+
+
+
+ +
+
+sapphire.transformations.axes.compass_to_cartesian(r, alpha, z)
+

Converts compass coordinates into Cartesian coordinates

+
+
Parameters:
+

r,alpha,z – compass coordinates, with alpha in degrees.

+
+
Returns:
+

tuple of Cartesian coordinates (x, y, z).

+
+
+
+ +
+
+sapphire.transformations.axes.rotate_cartesian(x, y, z, angle, axis='z')
+

Rotate Cartesian coordinates

+
+
Parameters:
+
    +

  • x,y,z – Cartesian coordinates.

    • +

  • angle – amount of rotation in radians.

    • +

  • axis – the axis to rotate around, either 'x', 'y', 'z', +or a (x,y,z) tuple specifying the axis to rotate about.

    • +
+
+
Returns:
+

tuple of Cartesian coordinates (x, y, z).

+
+
+
+ +
+
+sapphire.transformations.axes.rotation_matrix(angle, axis='z')
+

Generate a rotation matrix around an axis

+
+
Parameters:
+
    +

  • angle – amount of rotation in radians.

    • +

  • axis – the axis to rotate around, either 'x', 'y', 'z', +or a (x,y,z) tuple specifying the axis to rotate about.

    • +
+
+
Returns:
+

unitary rotation matrix.

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/transformations/base.html b/transformations/base.html new file mode 100644 index 00000000..eaccb551 --- /dev/null +++ b/transformations/base.html @@ -0,0 +1,160 @@ + + + + + + + + Conversion between different bases — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Conversion between different bases

+

Perform base conversions

+

Currently supports conversion between base 10 (decimal) and +base 60 (sexagesimal).

+
+
+sapphire.transformations.base.decimal_to_sexagesimal(decimal)
+

Convert decimal hours or degrees to sexagesimal.

+
+
Parameters:
+

decimal – decimal number to be converted to sexagismal.

+
+
Returns:
+

tuple of either (hours, minutes, seconds) or +(degrees, arcminutes, arcseconds).

+
+
+
+ +
+
+sapphire.transformations.base.sexagesimal_to_decimal(hd, minutes, seconds)
+

Convert sexagesimal hours or degrees to decimal.

+

Warning! Ensure each part has the correct sign. +e.g. -111d36m12s should be entered as (-111, -36, -12).

+
+
Parameters:
+
    +

  • hd – hours or degrees.

    • +

  • minutes – minutes or arcminutes.

    • +

  • seconds – seconds or arcseconds.

    • +
+
+
Returns:
+

decimal hours or degrees.

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/transformations/celestial.html b/transformations/celestial.html new file mode 100644 index 00000000..a21662f6 --- /dev/null +++ b/transformations/celestial.html @@ -0,0 +1,368 @@ + + + + + + + + Transformations between Celestial coordinate systems — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Transformations between Celestial coordinate systems

+

Perform various Celestial coordinate transformations

+

This module performs transformations between different +Celestial coordinate systems.

+

Legacy transformations (all those not marked astropy): +Formulae from: Duffett-Smith1990 +‘Astronomy with your personal computer’ +ISBN 0-521-38995-X

+

New transformations have been added with _astropy added to function name +They are very exact, in the order of arcsec. +Ethan van Woerkom is the author of the new transformations; contact him +for further information.

+
+
+sapphire.transformations.celestial.zenithazimuth_to_equatorial(latitude, longitude, timestamp, zenith, azimuth)
+

Convert Zenith Azimuth to Equatorial coordinates (J2000.0)

+
+
Parameters:
+
    +

  • latitude,longitude – Position of the observer on Earth in degrees. +North and east positive.

    • +

  • timestamp – GPS timestamp of the observation in seconds.

    • +

  • zenith – zenith is the angle relative to the Zenith in radians.

    • +

  • azimuth – azimuth angle of the observation in radians.

    • +
+
+
Returns:
+

Right ascension (ra) and Declination (dec) in radians.

+
+
+

From Duffett-Smith1990, 1500 EQHOR and 1600 HRANG

+
+ +
+
+sapphire.transformations.celestial.zenithazimuth_to_horizontal(zenith, azimuth)
+

Convert from Zenith Azimuth to Horizontal coordinates

+
+
Parameters:
+
    +

  • zenith – Zenith in radians

    • +

  • azimuth – Azimuth in radians

    • +
+
+
Return altitude, alt_azimuth:
+

Alt, Az in radians

+
+
+

Zenith Azimuth is the coordinate system used by HiSPARC. Zenith is +the angle between the zenith and the direction. Azimuth is the angle +in the horizontal plane, from East to North (ENWS).

+

Horizontal is the coordinate system as described in +Duffett-Smith1990 p38. Altitude is the angle above the horizon and +Azimuth the angle in the horizontal plane, from North to East (NESW).

+
+ +
+
+sapphire.transformations.celestial.horizontal_to_zenithazimuth(altitude, alt_azimuth)
+

Inverse of zenithazimuth_to_horizontal is the same transformation

+
+ +
+
+sapphire.transformations.celestial.horizontal_to_equatorial(latitude, lst, altitude, alt_azimuth)
+

Convert Horizontal to Equatorial coordinates (J2000.0)

+
+
Parameters:
+
    +

  • latitude – Position of the observer on Earth in degrees. +North positive.

    • +

  • lst – Local Siderial Time observer at the time of observation +in decimal hours.

    • +

  • altitude – altitude is the angle above the horizon in radians.

    • +

  • alt_azimuth – Azimuth angle in horizontal plane in radians.

    • +
+
+
Returns:
+

Right ascension (ra) and Declination (dec) in radians.

+
+
+

Warning: Inexact transformation; astropy functions preferred.

+

From Duffett-Smith1990, 1500 EQHOR and 1600 HRANG

+
+ +
+
+sapphire.transformations.celestial.horizontal_to_hadec(latitude, altitude, alt_azimuth)
+

Convert Horizontal to Hour Angle and Declination

+
+
Parameters:
+
    +

  • latitude – Position of the observer on Earth in degrees. +North positive.

    • +

  • altitude – altitude is the angle above the horizon in radians.

    • +

  • alt_azimuth – Azimuth angle in horizontal plane in radians.

    • +
+
+
Returns:
+

Hour angle (ha) and Declination (dec) in radians.

+
+
+

Warning: Inexact transformation; astropy functions preferred.

+

From Duffett-Smith1990, 1500 EQHOR and 1600 HRANG

+
+ +
+
+sapphire.transformations.celestial.ha_to_ra(ha, lst)
+

Convert Hour angle to right ascension

+
+
Parameters:
+
    +

  • ha – Hour angle in radians.

    • +

  • lst – Local Siderial Time observer at the time of observation +in decimal hours.

    • +
+
+
Returns:
+

Right ascension (ra) in radians.

+
+
+
+ +
+
+sapphire.transformations.celestial.equatorial_to_zenithazimuth(latitude, longitude, timestamp, right_ascension, declination)
+

Convert Equatorial (J2000.0) to Zenith Azimuth coordinates

+
+
Parameters:
+
    +

  • latitude,longitude – Position of the observer on Earth in degrees. +North and east positive.

    • +

  • timestamp – GPS timestamp of the observation in seconds.

    • +

  • right_ascension – right_ascension of the observation in radians.

    • +

  • declination – declination of the observation in radians.

    • +
+
+
Returns:
+

zenith and azimuth in radians.

+
+
+

This function was renamed from equatorial_to_horizontal to +equatorial_to_zenithazimuth in order to make it operate as the name does.

+

From Duffett-Smith1990, 1500 EQHOR and 1600 HRANG

+
+ +
+
+sapphire.transformations.celestial.zenithazimuth_to_equatorial_astropy(latitude, longitude, utc_timestamp, zenaz_coordinates)
+
+
Converts iterables of tuples of zenithazimuth

to equatorial coordinates

+
+
+
+
Parameters:
+
    +

  • latitude – Latitude in decimal degrees

    • +

  • longitude – Longitude in decimal degrees

    • +

  • utc_timestamp – Unix UTC timestamp integer

    • +

  • zenaz_coordinates – np.array of tuples (zen, az) in radians

    • +
+
+
Returns:
+

np.array of tuples (ra, dec) in radians

+
+
+

For increased speed using array input is recommended.

+
+ +
+
+sapphire.transformations.celestial.equatorial_to_zenithazimuth_astropy(latitude, longitude, utc_timestamp, equatorial_coordinates)
+
+
Converts iterables of tuples of equatorial

to zenithazimuth coordinates

+
+
+
+
Parameters:
+
    +

  • latitude – Latitude in decimal degrees

    • +

  • longitude – Longitude in decimal degrees

    • +

  • utc_timestamp – Unix UTC timestamp integer

    • +

  • equatorial_coordinates – np.array of tuples (ra, dec) in radians

    • +
+
+
Returns:
+

np.array of tuples (zen, az) in radians

+
+
+

For increased speed using array input is recommended.

+
+ +
+
+sapphire.transformations.celestial.equatorial_to_horizontal_astropy(latitude, longitude, utc_timestamp, equatorial_coordinates)
+
+
Converts iterables of tuples of equatorial coordinates

to horizontal coordinates

+
+
+
+
Parameters:
+
    +

  • latitude – Latitude in decimal degrees

    • +

  • longitude – Longitude in decimal degrees

    • +

  • utc_timestamp – Unix UTC timestamp integer

    • +

  • equatorial_coordinates – np.array of tuples (ra, dec) in radians

    • +
+
+
Returns:
+

np.array of tuples (az, alt) in radians

+
+
+

For increased speed using array input is recommended.

+
+ +
+
+sapphire.transformations.celestial.horizontal_to_equatorial_astropy(latitude, longitude, utc_timestamp, horizontal_coordinates)
+
+
Converts iterables of tuples of

horizontal coordinates to equatorial coordinates

+
+
+
+
Parameters:
+
    +

  • latitude – Latitude in decimal degrees

    • +

  • longitude – Longitude in decimal degrees

    • +

  • utc_timestamp – Unix UTC timestamp integer

    • +

  • horizontal_coordinates – np.array of tuples (az, alt) in radians

    • +
+
+
Returns:
+

np.array of tuples (ra, dec) in radians

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/transformations/clock.html b/transformations/clock.html new file mode 100644 index 00000000..d0a0c798 --- /dev/null +++ b/transformations/clock.html @@ -0,0 +1,508 @@ + + + + + + + + Time conversion functions — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Time conversion functions

+

Time transformations

+

This handles all the wibbly wobbly timey wimey stuff. +Such as easy conversions between different time systems. +Supported systems: GPS, UTC, GMST, LST, JD and MJD.

+

Formulae from:

+

IETF list of leap seconds +‘Leap seconds announced by the IERS’ +https://www.ietf.org/timezones/data/leap-seconds.list

+

Duffett-Smith1990 +‘Astronomy with your personal computer’ +ISBN 0-521-38995-X

+

USNO +‘Computing Greenwich Apparent Sidereal Time’ +http://aa.usno.navy.mil/faq/docs/GAST.php

+

Adrian Price-Whelan +apwlib.convert +https://github.com/adrn/apwlib

+
+
+sapphire.transformations.clock.LEAP_SECONDS = (('January 1, 2017', 18), ('July 1, 2015', 17), ('July 1, 2012', 16), ('January 1, 2009', 15), ('January 1, 2006', 14), ('January 1, 1999', 13), ('July 1, 1997', 12), ('January 1, 1996', 11), ('July 1, 1994', 10), ('July 1, 1993', 9), ('July 1, 1992', 8), ('January 1, 1991', 7), ('January 1, 1990', 6), ('January 1, 1988', 5), ('July 1, 1985', 4), ('July 1, 1983', 3), ('July 1, 1982', 2), ('July 1, 1981', 1))
+

Dates of leap second introductions.

+
+ +
+
+sapphire.transformations.clock.time_to_decimal(time)
+

Converts a time or datetime object into decimal time

+
+
Parameters:
+

time – datetime.time or datetime.datetime object.

+
+
Returns:
+

decimal number representing the input time.

+
+
+
+ +
+
+sapphire.transformations.clock.decimal_to_time(hours)
+

Converts decimal time to a time object

+
+
Parameters:
+

hours – datetime.time or datetime.datetime object.

+
+
Returns:
+

decimal number representing the input time.

+
+
+
+ +
+
+sapphire.transformations.clock.date_to_juliandate(year, month, day)
+

Convert year, month, and day to a Julian Date

+

Julian Date is the number of days since noon on January 1, 4713 B.C. +So the returned date will end in .5 because the date refers to midnight.

+
+
Parameters:
+
    +

  • year – a Gregorian year (B.C. years are negative).

    • +

  • month – a Gregorian month (1-12).

    • +

  • day – a Gregorian day (1-31).

    • +
+
+
Returns:
+

the Julian Date for the given year, month, and day.

+
+
+
+ +
+
+sapphire.transformations.clock.datetime_to_juliandate(dt)
+

Convert a datetime object in UTC to a Julian Date

+
+
Parameters:
+

dt – datetime object.

+
+
Returns:
+

The Julian Date for the given datetime object.

+
+
+
+ +
+
+sapphire.transformations.clock.juliandate_to_modifiedjd(juliandate)
+

Convert a Julian Date to a Modified Julian Date

+
+
Parameters:
+

juliandate – a Julian Date.

+
+
Returns:
+

the Modified Julian Date.

+
+
+
+ +
+
+sapphire.transformations.clock.modifiedjd_to_juliandate(modifiedjd)
+

Convert a Modified Julian Date to Julian Date

+
+
Parameters:
+

modifiedjf – a Modified Julian Date.

+
+
Returns:
+

Julian Date.

+
+
+
+ +
+
+sapphire.transformations.clock.datetime_to_modifiedjd(dt)
+

Convert a datetime object in UTC to a Modified Julian Date

+
+
Parameters:
+

dt – datetime object.

+
+
Returns:
+

the Modified Julian Date.

+
+
+
+ +
+
+sapphire.transformations.clock.juliandate_to_gmst(juliandate)
+

Convert a Julian Date to Greenwich Mean Sidereal Time

+
+
Parameters:
+

juliandate – Julian Date.

+
+
Returns:
+

decimal hours in GMST.

+
+
+
+ +
+
+sapphire.transformations.clock.utc_to_gmst(dt)
+

Convert a datetime object in UTC time to Greenwich Mean Sidereal Time

+
+
Parameters:
+

dt – datetime object in UTC time.

+
+
Returns:
+

decimal hours in GMST.

+
+
+
+ +
+
+sapphire.transformations.clock.gmst_to_utc(dt)
+

Convert datetime object in Greenwich Mean Sidereal Time to UTC

+

Note: this requires a datetime object, not just the decimal hours.

+
+
Parameters:
+

dt – datetime object in GMST time.

+
+
Returns:
+

datetime object in UTC.

+
+
+
+ +
+
+sapphire.transformations.clock.juliandate_to_utc(juliandate)
+

Convert Julian Date to datetime object in UTC

+
+
Parameters:
+

juliandate – a Julian Date.

+
+
Returns:
+

datetime object in UTC time.

+
+
+
+ +
+
+sapphire.transformations.clock.modifiedjd_to_utc(modifiedjd)
+

Convert a Modified Julian Date to datetime object in UTC

+
+
Parameters:
+

juliandate – a Modified Julian Date.

+
+
Returns:
+

datetime object in UTC time.

+
+
+
+ +
+
+sapphire.transformations.clock.gmst_to_lst(hours, longitude)
+

Convert Greenwich Mean Sidereal Time to Local Sidereal Time

+
+
Parameters:
+
    +

  • hours – decimal hours in GMST.

    • +

  • longitude – location in degrees, east positive.

    • +
+
+
Returns:
+

decimal hours in LST.

+
+
+
+ +
+
+sapphire.transformations.clock.lst_to_gmst(hours, longitude)
+

Convert Local Sidereal Time to Greenwich Mean Sidereal Time

+
+
Parameters:
+
    +

  • hours – decimal hours in LST.

    • +

  • longitude – location in degrees, east positive.

    • +
+
+
Returns:
+

decimal hours in GMST.

+
+
+
+ +
+
+sapphire.transformations.clock.utc_to_lst(dt, longitude)
+

Convert UTC to Local Sidereal Time

+
+
Parameters:
+
    +

  • dt – datetime object in UTC.

    • +

  • longitude – location in degrees, east positive.

    • +
+
+
Returns:
+

decimal hours in LST.

+
+
+
+ +
+
+sapphire.transformations.clock.gps_to_utc(timestamp)
+

Convert GPS time to UTC

+
+
Parameters:
+

timestamp – GPS timestamp in seconds.

+
+
Returns:
+

UTC timestamp in seconds.

+
+
+
+ +
+
+sapphire.transformations.clock.utc_to_gps(timestamp)
+

Convert UTC to GPS time

+
+
Parameters:
+

timestamp – UTC timestamp in seconds.

+
+
Returns:
+

GPS timestamp in seconds.

+
+
+
+ +
+
+sapphire.transformations.clock.utc_from_string(date)
+

Convert a date string to UTC

+
+
Parameters:
+

date – date string.

+
+
Returns:
+

UTC timestamp in seconds.

+
+
+
+ +
+
+sapphire.transformations.clock.gps_from_string(date)
+

Convert a date string to GPS time

+
+
Parameters:
+

date – date string.

+
+
Returns:
+

GPS timestamp in seconds.

+
+
+
+ +
+
+sapphire.transformations.clock.gps_to_lst(timestamp, longitude)
+

Convert a GPS timestamp to lst

+
+
Parameters:
+
    +

  • timestamp – GPS timestamp in seconds.

    • +

  • longitude – location in degrees, east positive.

    • +
+
+
Returns:
+

decimal hours in LST.

+
+
+
+ +
+
+sapphire.transformations.clock.gps_to_datetime(timestamp)
+

Convert a GPS timestamp to datetime object

+
+
Parameters:
+

timestamp – GPS timestamp in seconds.

+
+
Returns:
+

datetime object.

+
+
+
+ +
+
+sapphire.transformations.clock.datetime_to_gps(dt)
+

Convert a GPS datetime object to a timestamp

+
+
Parameters:
+

dt – GPS datetime object.

+
+
Returns:
+

GPS timestamp in seconds.

+
+
+
+ +
+
+sapphire.transformations.clock.process_time(time)
+

Convert timestamp or datetime to timestamp

+
+
Parameters:
+

time – GPS datetime object or GPS timestamp.

+
+
Returns:
+

GPS timestamp.

+
+
+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/transformations/geographic.html b/transformations/geographic.html new file mode 100644 index 00000000..fe5eea6e --- /dev/null +++ b/transformations/geographic.html @@ -0,0 +1,288 @@ + + + + + + + + Transformations between geographic coordinate systems — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Transformations between geographic coordinate systems

+

Perform various coordinate transformations

+

This module performs various coordinate transformations, based on some +well-known formulas.

+
+
+class sapphire.transformations.geographic.WGS84Datum
+

Definition of the WGS84 datum

+

These definitions are taken from +https://en.wikipedia.org/wiki/Geodetic_system, believing that enough +editors have gone over them to make sure they are correct.

+
+
+a = 6378137.0
+
+ +
+
+f = 0.0033528106647474805
+
+ +
+
+b = 6356752.314245179
+
+ +
+
+e = 0.08181919084262149
+
+ +
+
+eprime = 0.08209443794969568
+
+ +
+ +
+
+class sapphire.transformations.geographic.FromWGS84ToENUTransformation(ref_llacoordinates)
+

Convert between various geographic coordinate systems

+

This class converts coordinates between LLA, ENU, and ECEF.

+

Initialize the transformation object.

+
+
Parameters:
+

ref_llacoordinates – reference latitude, longitude, and altitude +coordinates. These are used as origin for ENU coordinates.

+
+
+
+
+geode = <sapphire.transformations.geographic.WGS84Datum object>
+
+ +
+
+transform(coordinates)
+

Transform WGS84 coordinates to ENU coordinates

+
+ +
+
+lla_to_enu(coordinates)
+

Transform WGS84 coordinates to ENU coordinates

+
+ +
+
+enu_to_lla(coordinates)
+

Transform WGS84 coordinates to ENU coordinates

+
+ +
+
+lla_to_ecef(coordinates)
+

Convert from LLA coordinates to ECEF coordinates

+

LLA: Latitude, Longitude, Altitude +ECEF: Earth-Centered, Earth-Fixed

+

The conversion formulas are taken from +https://en.wikipedia.org/wiki/Geographic_coordinate_conversion#From_geodetic_to_ECEF_coordinates +but slightly reworked.

+

Mind that the input is expected to be in degrees, as is standard in +coordinate notation.

+
+
Parameters:
+

coordinates – tuple of latitude, longitude (both in degrees) +and altitude (in meters).

+
+
Returns:
+

ECEF coordinates (in meters).

+
+
+
+ +
+
+ecef_to_lla(coordinates)
+

Convert from ECEF coordinates to LLA coordinates

+

ECEF: Earth-Centered, Earth-Fixed +LLA: Latitude, Longitude, Altitude

+

The conversion formulas are taken from +https://gist.github.com/klucar/1536054

+
+
Parameters:
+

coordinates – tuple of X, Y, and Z (in meters).

+
+
Returns:
+

latitude, longitude (in degrees) and altitude (in meters).

+
+
+
+ +
+
+ecef_to_enu(coordinates)
+

Convert from ECEF coordinates to ENU coordinates

+

ECEF: Earth-Centered, Earth-Fixed +ENU: East, North, Up

+

The conversion formulas are taken from +https://en.wikipedia.org/wiki/Geodetic_system#From_ECEF_to_ENU

+
+
Parameters:
+

coordinates – a tuple containing the ECEF coordinates (in meters) +of the point to transform

+
+
Returns:
+

east, north, and up (in meters).

+
+
+
+ +
+
+enu_to_ecef(coordinates)
+

Convert from ENU coordinates to ECEF coordinates

+

ENU: East, North, Up +ECEF: Earth-Centered, Earth-Fixed

+
+
Parameters:
+

coordinates – a tuple containing the ENU coordinates (in meters).

+
+
Returns:
+

ECEF coordinates (in meters).

+
+
+
+ +
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/tutorial.html b/tutorial.html new file mode 100644 index 00000000..d31272b9 --- /dev/null +++ b/tutorial.html @@ -0,0 +1,951 @@ + + + + + + + + Tutorial — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Tutorial

+

SAPPHiRE simplifies data access, simulations and analysis for the HiSPARC experiment. In this tutorial, we’ll try to give +you a feeling for the things you can do with SAPPHiRE. How can you +download data? How can you analyze this data? How can you produce +pulseheight histograms? How can you calculate the direction of cosmic +rays? This tutorial will only give you an overview of what’s possible +with SAPPHiRE. For details on all available classes and methods, please +see the SAPPHiRE Reference.

+
+

Note

+

We’ll require you to know basic Python. If you’re unfamiliar with Python, +you can look at the official Python tutorial, the book Think Python, or Getting started with +Python for science.

+
+
+

First steps

+

In a few examples, we will plot the data which we have produced. We make use +of pylab, which is included with matplotlib. If you have Anaconda installed, +you can use the Spyder environment. You can write a script in the main window, +or type short commands in the command prompt in the bottom right. The first +thing you have to do is import everything from pylab. That makes it +ridiculously easy to plot things:

+
>>> from pylab import *
+
+
+

You can also start an IPython terminal with pylab using:

+
$ ipython --pylab
+
+
+

In that case, you don’t need to import pylab.

+

Whenever you see something like:

+
>>> print('Hello, world')
+Hello, world
+
+
+

it means we’re typing in Python code. The >>> is the Python prompt. +It tells you that the Python interpreter is waiting for you to give it +some instructions. In the above example, we’ve typed in print ('Hello, +world'). The interpreter subsequently executed the code and printed +Hello, world on the screen.

+

The first thing we’ll have to do to start using SAPPHiRE is to import +the SAPPHiRE module:

+
>>> import sapphire
+
+
+

There will be no output if everything is successful. It is easy to get +some help from inside the Python terminal. Just say:

+
>>> help(sapphire)
+
+
+

and you’ll be presented with a basic help screen. Instead of +sapphire, you can throw in modules, packages, functions, classes, +objects, etc. Everything in Python has some help text associated with +it. Not all of it is very helpful to a newcomer, hence this tutorial. +All help text is also available in the SAPPHiRE Reference.

+
+
+

Downloading and accessing HiSPARC data

+

The SAPPHiRE package comprises multiple modules and packages. To access data +from the public database, we’ll have to import the sapphire.esd module. +This module gives us access to the event summary data, which is the raw +HiSPARC data with preliminary analysis already included. It is simple to +quickly start playing with data:

+
>>> from sapphire import esd
+>>> data = esd.quick_download(102)
+100%|####################################|Time: 5.91
+>>> print(data)
+data8.h5 (File) ''
+Last modif.: 'Thu Oct 22 16:02:05 2015'
+Object Tree:
+/ (RootGroup) ''
+/s102 (Group) ''
+/s102/events (Table(46382,)) ''
+
+
+

First, we import the sapphire.esd module. Then, we download data from +station 102 using the sapphire.esd.quick_download() function. This +function downloads yesterday’s data, and creates a datafile on the fly. More on +that later.

+

If we want to exercise more control, we need to do some things manually. First, +we need the tables module to actually store the data. PyTables is based on the open HDF5 data format, which is used by many +(research) institutes. For +example, it is used by the KNMI and by NASA. To specify the date and time for +which to download the data, we need the datetime module. Thus, we have:

+
>>> import tables
+>>> import datetime
+>>> from sapphire import esd
+
+
+

Creating an empty data file, with the name mydata.h5, is done easily:

+
>>> data = tables.open_file('mydata.h5', 'w')
+
+
+

The 'w' means write, which creates a file for writing (and reading). +Mind that this will create an empty file. If there already was a file +with that name, it will be overwritten! Alternatively, you can say +'a', which means append, thus adding to an existing file without +overwriting its contents. Finally, you can specify 'r' for +read-only.

+
+

Downloading data

+

To download data, we have to specify the date/time range. If we want to +download data from the December 1, 2012 all through December 2, 2012, +we can specify this by typing:

+
>>> start = datetime.datetime(2012, 12, 1)
+>>> end = datetime.datetime(2012, 12, 3)
+
+
+

Mind that if we do not specify the hour of day, it is taken to be 00:00 +hours. Thus, there is no data included for December 3. Alternatively, +we can download data from a two hour interval on October 17 by specifying +the hour of day:

+
>>> start = datetime.datetime(2012, 10, 17, 19)
+>>> end = datetime.datetime(2012, 10, 17, 21)
+
+
+

which is from 19:00 to 21:00 hours. It is important to realize that we use a +GPS clock, which is equal to UTC (up to some leap seconds). So, if we download +data for a station in the Netherlands, we have just said from 20:00 to 22:00 +local time (when it’s not summer time). You can specify the time up to the +second.

+

We have not actually done anything yet. We have just stored our time +window in two arbitrarily-named variables, start and end. To +download data from station 501 and store it in a group with name /s501, +we can use the sapphire.esd.download_data() function. Since we’ve +imported esd from sapphire, we can drop the sapphire prefix:

+
>>> esd.download_data(data, '/s501', 501, start, end)
+100%|####################################|Time: 0:00:16
+
+
+

It will show a progressbar to indicate the progress of the download.

+

As you can see in the reference documentation of +sapphire.esd.download_data(), available by either clicking on +the link or typing in help(esd.download_data) in the +interpreter, the function takes six arguments: file, group, station_number, +start, end and type. The last one has the default argument +‘events’, and may be omitted, as we have done here. In our example, we +have opened a file, mydata.h5, and have stored the file handler in +the variable data. So, we passed data to the function. The group +name is /s501. Group names in PyTables are just like folders in a +directory hierarchy. So, we might have specified +/path/to/my/hisparc/data/files/for_station/s501. It is important to +note that this has absolutely nothing to do with files. Whatever path +you specify, it is all contained inside your data file. Since this is +just a small data file, we have opted for a simple structure. At the +moment, just one group named s501 at the root of the hierarchy. Group +names must start with a letter, hence the s for station.

+

The station_number is simply the station number. Here, we’ve chosen to +download data for station 501, located at Nikhef. The start and end +parameters specify the date/time range.

+

Finally, type selects whether to download event or weather data should +be downloaded. We’ve selected the default, which is events. We can also +download the weather data by changing the type to 'weather':

+
>>> esd.download_data(data, '/s501', 501, start, end, type='weather')
+100%|####################################|Time: 0:00:10
+
+
+

To access the raw data that includes the original detector traces the +sapphire.publicdb.download_data() function can be used instead. However, +downloading data will take much longer that way. If only some traces need to be +accessed, the sapphire.api is a better choice. We’ll use that module +later in the tutorial.

+
+
+

Looking around

+

If you want to know what groups and tables are contained within the data +file, just print it:

+
>>> print(data)
+mydata.h5 (File) ''
+Last modif.: 'Sat Dec 29 14:50:55 2012'
+Object Tree:
+/ (RootGroup) ''
+/s501 (Group) 'Data group'
+/s501/events (Table(137600,)) 'HiSPARC coincidences table'
+/s501/weather (Table(51513,)) 'HiSPARC weather data'
+
+
+

The object tree gives an overview of all groups and tables. As you can +see, the /s501 group contains two tables, events and weather. +The events table contains the data from the HiSPARC scintillators, while +the weather table contains data from the (optional) weather station.

+

To directly access any object in the hierarchy, you can make use of the +data.root object, which points to the root group. Then, just specify +the remaining path, with dots instead of slashes. For example, to access +the events table:

+
>>> print(data.root.s501.events)
+/s501/events (Table(137600,)) 'HiSPARC coincidences table'
+
+
+

If you want, you can also access the object using its name as a string, by calling get_node on the file handler:

+
>>> print(data.get_node('/s501/events'))
+/s501/events (Table(137600,)) 'HiSPARC coincidences table'
+
+
+

Of course, we’d like to get some more information. You can drop the print +statement, and just access the object directly. PyTables is set up such +that it will give more detailed information whenever you specify the +object directly:

+
>>> data.root.s501.events
+/s501/events (Table(137600,)) ''
+  description := {
+  "event_id": UInt32Col(shape=(), dflt=0, pos=0),
+  "timestamp": Time32Col(shape=(), dflt=0, pos=1),
+  "nanoseconds": UInt32Col(shape=(), dflt=0, pos=2),
+  "ext_timestamp": UInt64Col(shape=(), dflt=0, pos=3),
+  "pulseheights": Int16Col(shape=(4,), dflt=0, pos=4),
+  "integrals": Int32Col(shape=(4,), dflt=0, pos=5),
+  "n1": Float32Col(shape=(), dflt=0.0, pos=6),
+  "n2": Float32Col(shape=(), dflt=0.0, pos=7),
+  "n3": Float32Col(shape=(), dflt=0.0, pos=8),
+  "n4": Float32Col(shape=(), dflt=0.0, pos=9),
+  "t1": Float32Col(shape=(), dflt=0.0, pos=10),
+  "t2": Float32Col(shape=(), dflt=0.0, pos=11),
+  "t3": Float32Col(shape=(), dflt=0.0, pos=12),
+  "t4": Float32Col(shape=(), dflt=0.0, pos=13),
+  "t_trigger": Float32Col(shape=(), dflt=0.0, pos=14)}
+  byteorder := 'little'
+  chunkshape := (819,)
+
+
+

There you go! But what does it all mean? Well, if you want to get to +the bottom of it, read the PyTables documentation. We’ll give a quick +overview here.

+

First, this table contains 137600 rows. In total, there are fourteen +columns: event_id, timestamp, nanoseconds, ext_timestamp, +pulseheights, integrals, n1-n4, t1-t4 and +t_trigger.

+

Each event has a unique [1] identifier, event_id. Each +event has a Unix timestamp in GPS time, not UTC. A Unix timestamp is the number of seconds that +have passed since January 1, 1970. The sub-second part of the timestamp +is given in nanoseconds. The ext_timestamp is the full +timestamp in nanoseconds. Since there cannot exist another event with the same +timestamp, this field in combination with the station number uniquely +identifies the event. The pulseheights and integrals fields are +values derived from the PMT traces by the HiSPARC DAQ. The n# +columns are derived from the integrals and the t# and +t_trigger fields are obtained after analyzing the event traces on +the server. For some fields there are four values, one for each detector. +If a station only has two detectors, the values for the missing two +detectors are -1. If the baseline of the trace could not be determined +all these values are -999.

+

We’ll get to work with this data in a moment. First, we’ll take a look at +the weather table:

+
>>> data.root.s501.weather
+/s501/weather (Table(51513,)) 'HiSPARC weather data'
+  description := {
+  "event_id": UInt32Col(shape=(), dflt=0, pos=0),
+  "timestamp": Time32Col(shape=(), dflt=0, pos=1),
+  "temp_inside": Float32Col(shape=(), dflt=0.0, pos=2),
+  "temp_outside": Float32Col(shape=(), dflt=0.0, pos=3),
+  "humidity_inside": Int16Col(shape=(), dflt=0, pos=4),
+  "humidity_outside": Int16Col(shape=(), dflt=0, pos=5),
+  "barometer": Float32Col(shape=(), dflt=0.0, pos=6),
+  "wind_dir": Int16Col(shape=(), dflt=0, pos=7),
+  "wind_speed": Int16Col(shape=(), dflt=0, pos=8),
+  "solar_rad": Int16Col(shape=(), dflt=0, pos=9),
+  "uv": Int16Col(shape=(), dflt=0, pos=10),
+  "evapotranspiration": Float32Col(shape=(), dflt=0.0, pos=11),
+  "rain_rate": Float32Col(shape=(), dflt=0.0, pos=12),
+  "heat_index": Int16Col(shape=(), dflt=0, pos=13),
+  "dew_point": Float32Col(shape=(), dflt=0.0, pos=14),
+  "wind_chill": Float32Col(shape=(), dflt=0.0, pos=15)}
+  byteorder := 'little'
+  chunkshape := (1310,)
+
+
+

We’ll let these column names speak for themselves.

+
+
+

Accessing the data

+

We can access the data in several ways. We can address the complete +table, or just one or several rows from it. We can read out a single +column, or select data based on a query. Before we do any of that, we’ll +save us some typing:

+
>>> events = data.root.s501.events
+
+
+

Now, we have stored a short-hand reference to the events table. Let’s get +the first event:

+
>>> events[0]
+(0L, 1445385601, 613271528L, 1445385601613271528L, [3, 266, 400, 372],
+ [0, 3090, 5695, 5621], 0.0, 1.03120, 1.55130, 1.58820,
+ -999.0, 1002.5, 1000.0, 1022.5, 1030.0, -999.0, -999.0)
+
+
+

That’s the first event! It is not, however, immediately clear what +numbers correspond to which columns. They are in order, however, so you +could find out. It is often easier to specify the column you’re +interested in:

+
>>> events[0]['pulseheights']
+array([  3, 266, 400, 372], dtype=int16)
+
+
+

Which gives us the pulseheights of the first event. You can recognize the same +numbers in the full event data above. The pulseheights are 16-bit integers +(that’s the dtype=int16) and are determined after digitizing the events +using an analog-digital converter (ADC). Each unit corresponds to about -0.57 +mV. You can tell that all but the first detector had relatively large +pulseheights, and that they were registered as a significant peak in the +signal.

+

If you’re interested in the pulseheights of all events, the fastest way +to do it is to make use of the Table.col method of the table:

+
>> events.col('pulseheights')
+array([[  3, 266, 400, 372],
+       [  4, 277, 887,   7],
+       [ 72, 256,   6, 330],
+       ...,
+       [162,  78,   4, 299],
+       [ 10,   6, 618, 446],
+       [593, 621, 343,  13]], dtype=int16)
+
+
+

It is also possible to select the data based on a query. For example, to +select all events between timestamps 1354320000 and 1354323600 (a one-hour +time span):

+
>>> t0 = 1354320000
+>>> t1 = t0 + 3600
+>>> sel_events = events.read_where('(t0 <= timestamp) & (timestamp < t1)')
+>>> len(sel_events)
+2836
+
+
+

Thus, we have selected 2836 events. The variable sel_events no longer +points to a table. We can no longer make use of the Table.col +method, but we can access all pulseheights in the following way:

+
>>> sel_events['pulseheights']
+array([[173,   3, 407,   3],
+       [  1,   2, 313, 756],
+       [211,   2,   2, 268],
+       ...,
+       [328, 556, 255,  15],
+       [  3, 325, 309,   3],
+       [  2,   2, 271, 381]], dtype=int16)
+
+
+

This notation is possible for arrays, but not for tables. So, for tables, +use the Table.col method. For arrays, use this special notation.

+
+
+

The sapphire.time_util module

+

SAPPHiRE includes a handy module: sapphire.time_util. This saves you +from the hassle of converting timestamps and confusing local time and GPS (or +UTC) time. It is important to realize that the HiSPARC station uses a GPS +clock, and thus saves all timestamps in GPS time, which is certainly not local +time! You can look up GPS time, but +suffice it to say that it is almost equal to UTC time. The difference is the +leap seconds introduced after 1980. In January 2013, GPS time was ahead of UTC +by 16 seconds. Since July 2015, it is ahead by 17 seconds. We will not refer +to UTC or local time, but instead always refer to GPS time!

+

While you tell SAPPHiRE to download data using year, month, day, hour, +minutes, seconds notation, the events table contains timestamps. It is +often hard to convert between the two. For example, the correct +conversion between a GPS date/time and a GPS timestamp is given by:

+
>>> calendar.timegm(datetime.datetime(2012, 12, 1, 0).utctimetuple())
+1354320000
+
+
+

Which is, at best, cumbersome. It is easy, however, to screw up and +inadvertently convert to local time. For your benefit, we have included +the sapphire.time_util.GPSTime class. You instantiate the class +by giving it a GPS time to work with. It can either be in date/time +notation, or as a timestamp. For example, the exact same result is +obtained by these two last lines of code:

+
>>> import sapphire.time_util
+>>> sapphire.time_util.GPSTime(2012, 12, 1)
+>>> sapphire.time_util.GPSTime(1354320000)
+
+
+

If you store the instance, you can then call several methods to convert +the date/time to whatever you require:

+
>>> gpstime = sapphire.time_util.GPSTime(2012, 12, 1)
+>>> gpstime.datetime()
+datetime.datetime(2012, 12, 1, 0, 0)
+>>> gpstime.description()
+'Sat Dec  1 00:00:00 2012'
+>>> gpstime.gpstimestamp()
+1354320000
+
+
+

Or, indeed:

+
>>> gpstime = sapphire.time_util.GPSTime(1354320000)
+>>> gpstime.datetime()
+datetime.datetime(2012, 12, 1, 0, 0)
+>>> gpstime.description()
+'Sat Dec  1 00:00:00 2012'
+>>> gpstime.gpstimestamp()
+1354320000
+
+
+

It is now easy to select events occurring between 12:00 and 13:00 hours GPS +time on December 2, 2012:

+
>>> t0 = sapphire.time_util.GPSTime(2012, 12, 2, 12).gpstimestamp()
+>>> t1 = sapphire.time_util.GPSTime(2012, 12, 2, 13).gpstimestamp()
+>>> t0, t1
+(1354449600, 1354453200)
+>>> sel_events = events.read_where('(t0 <= timestamp) & (timestamp < t1)')
+>>> len(sel_events)
+2817
+
+
+

And when did the tenth event occur? Well:

+
>>> t = sel_events[9]['timestamp']
+>>> t
+1354449620
+>>> sapphire.time_util.GPSTime(t).description()
+'Sun Dec  2 12:00:20 2012'
+
+
+

To shorten the typing somewhat, remember that in Python you can import +classes from modules into your own namespace:

+
>>> from sapphire.time_util import GPSTime
+>>> GPSTime(t).description()
+'Sun Dec  2 12:00:20 2012'
+
+
+
+
+

Plotting data

+

Now that we can access the data, we want to visualize it. Plotting data +is a great way to do that. And of course, the venerable histogram is +still very useful to condense thousands of events into one display. Pylab +contains an easy function to do just that: hist. Let’s try to +recreate a few graphs as seen on the HiSPARC data display:

+
>>> ph = events.col('pulseheights')
+>>> hist(ph)
+([array([114794,  17565,   3062,   1009,    502,    285,    170,     86,
+           72,     55]), array([112813,  19028,   3339,   1246,    540,    295,    163,    100,
+           66,     10]), array([109162,  21833,   4246,   1345,    579,    290,    113,     32,
+            0,      0]), array([109996,  21283,   4028,   1285,    581,    251,    133,     43,
+            0,      0])], array([  1.00000000e+00,   3.91100000e+02,   7.81200000e+02,
+         1.17130000e+03,   1.56140000e+03,   1.95150000e+03,
+         2.34160000e+03,   2.73170000e+03,   3.12180000e+03,
+         3.51190000e+03,   3.90200000e+03]), <a list of 4 Lists of Patches objects>)
+
+
+_images/tutorial-hist-simple.png + +

This will not do. Firstly, data from the four detectors is pictured as +four side-by-side colored bars. Secondly, the number of bins is very low; +it is only ten. Thirdly, the data range continues up to very high values +with hardly any events.

+

To fix this, we’ll make use of several arguments that can be passed to the +hist function. We’ll also make use of some NumPy (documentation) functionality. For Pylab +documentation, see the Matplotlib site. +Try this:

+
>>> bins = linspace(0, 2000, 101)
+>>> hist(ph, bins, histtype='step', log=True)
+>>> xlabel("Pulseheight [ADC]")
+>>> ylabel("Counts")
+>>> title("Pulseheight histogram (log scale)")
+
+
+

The linspace function returns an array with range from 0 to 2000 with a +total number of 101 values. The first value is 0, the last is 2000. These are +the edges of the bins. So, 101 values means exactly 100 bins between 0 and +2000. The hist function will then plot a stepped histogram with a log +scale. Finally, we add some labels and a title. This is the result:

+_images/tutorial-hist-better.png + +

In this plot, the gamma and charged particle part of the spectrum are easy +to distinguish. The cut at 123 ADC is due to the trigger. The bump at +approximately 300 ADC is the so-called MIP (minimum ionizing particle) +peak. For an explanation of this plot and the features of the pulseheight +histogram, see David’s thesis, +page 44–45, and 49–51.

+
+
+
+

Obtaining coincidences

+

If you work with HiSPARC data, invariably you’ll be interested in +coincidences between HiSPARC stations. That is, are there showers +which have been observed by multiple stations? To find out, we’ll make +use of the sapphire.analysis.coincidences module.

+ +
+

Downloading coincidences

+

Just like events or weather data for a single station the ESD provides +coincidence data. This means that you can directly download coincidences from +the server. In most cases this can save a lot of disc space, because only +events in a coincidence will be stored.

+

Downloading coincidences is very similar to downloading data. First import the +required packages, open a PyTables file, and specify the date/time range. +With coincidences two additional options are available, first you can select +which stations you want to consider. This can be all stations in the +network, by not specifying any. It can be a subset of stations by listing +those stations (upto 30 stations), or a cluster of stations by giving the name +of the cluster.

+

So first import the packages:

+
>>> import datetime
+>>> import tables
+>>> from sapphire import esd
+
+
+

Set the date/time range:

+
>>> start = datetime.datetime(2012, 12, 1)
+>>> end = datetime.datetime(2012, 12, 3)
+
+
+

Open (in this case create) a datafile:

+
>>> data = tables.open_file('data_coincidences.h5', 'w')
+
+
+

Then download the coincidences. Here we specify the cluster ‘Enschede’ to +select all stations in that cluster. By setting n to 3 we require that a +coincidence has at least 3 events.

+
>>> esd.download_coincidences(data, cluster='Enschede',
+...                           start=start, end=end, n=3)
+
+
+

If you now look in the data file it will contain event tables for all stations +with at least one event in a coincidence, and a coincidence table for the +coincidence data. Just like when coincidences were searched manually:

+
>>> print(data)
+Object Tree:
+/ (RootGroup) ''
+/coincidences (Group) ''
+/coincidences/c_index (VLArray(776,)) ''
+/coincidences/coincidences (Table(776,)) ''
+/coincidences/s_index (VLArray(124,)) ''
+/hisparc (Group) ''
+/hisparc/cluster_enschede (Group) ''
+/hisparc/cluster_enschede/station_7001 (Group) ''
+/hisparc/cluster_enschede/station_7001/events (Table(776,)) ''
+/hisparc/cluster_enschede/station_7002 (Group) ''
+/hisparc/cluster_enschede/station_7002/events (Table(776,)) ''
+/hisparc/cluster_enschede/station_7003 (Group) ''
+/hisparc/cluster_enschede/station_7003/events (Table(776,)) ''
+
+
+

Footnotes

+ +
+
+
+

Reconstructions

+
+

Reconstructing ESD Events

+

Assume data_s501.h5 contains events downloaded from the ESD for station +501:

+
>>> import tables
+>>> from sapphire import ReconstructESDEvents
+>>> data = tables.open_file('data_s501.h5', 'a')
+>>> station_path = '/s501'  # location of the event table in datafile
+>>> rec = ReconstructESDEvents(data, station_path, 501, verbose=True)
+Constructed object <Station, id: 0, number: 501, cluster:
+HiSPARCStations([501])> from public database.
+
+
+

The parameter verbose=True forces the reconstruction class to be +verbose about the metadata about station layout of timing offsets that is +being used. Here, a HiSPARCStations object is created which retrieves +metadata from the public database.

+

Now let’s reconstruct the events and store the results in the datafile:

+
>>> rec.reconstruct_and_store()
+Reading detector offsets from public database.
+100%|#################################################|Time: 0:00:31
+100%|#################################################|Time: 0:00:21
+
+
+

The reconstruction class notifies us that detector timing offsets are being +retrieved from the public database. First directions are reconstructed and +subsequently core positions are reconstructed.

+

The results are stored in the datafile in the reconstructions group:

+
>>> print(data.root.s501.reconstructions)
+/s501/reconstructions (Table(29569,)) ''
+
+>>> data.root.s501.reconstructions.col('zenith')
+array([ nan,  nan,  nan, ...,  nan,  nan, 0.66482645], dtype=float32)
+
+
+

The nan values in the zenith angle array are values that cannot be +reconstructed:

+
>>> from numpy import isnan
+>>> theta = data.root.s501.reconstructions.col('zenith')
+>>> theta = theta.compress(~isnan(theta))
+array([ 0.59542936,  0.74284476,  0.38334498, ...,  0.41376889,
+    0.07834953,  0.66482645], dtype=float32)
+
+
+
+
+

Reconstructing simulated events

+

Assume the datafile (data) contains simulated events, generated by +sapphire.simulations.groundparticles.GroundParticlesSimulation. +This class stores the metadata of the simulated station (cluster) in the +hdf5 file, which can be read by the reconstruction class:

+
>>> rec = ReconstructESDEvents(data, station_path, 501, verbose=True)
+Read cluster HiSPARCStations([501]) from datafile.
+
+
+

The class has read the station (cluster object) from the datafile. This +cluster object contains both the detector locations as well as the +detector timing offsets:

+
>>> rec.reconstruct_and_store()
+Read detector offsets from cluster object.
+100%|#################################################|Time: 0:00:31
+100%|#################################################|Time: 0:00:21
+
+
+

Note that the detector (timing) offset from the datafile are used, +and not the offsets from the public database.

+
+
+

Reconstructing ESD coinicdences

+

Coincidences can be reconstructed analogous to ESD Events:

+
>>> import tables
+>>> from sapphire import ReconstructESDCoincidences
+>>> data = tables.open_file('data_s501_s502_s503.h5', 'a')
+>>> rec = ReconstructESDEvents(data, verbose=True)
+Constructed cluster HiSPARCStations([501, 502, 503]) from public
+database.
+
+
+

The constructed cluster object is created from the stations in the +datafile.

+

Reconstruct:

+
>>> rec.reconstruct_and_store()
+Using timing offsets from public database.
+100%|#################################################|Time: 0:00:31
+100%|#################################################|Time: 0:00:21
+
+
+
+
+

Reconstructing simulated coincidences

+

The ReconstructESDCoincidences class can reconstruct simulated +coincidences, using the cluster objects stored by the simulation class +in the datafile analogous to ReconstructESDEvents.

+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/utils.html b/utils.html new file mode 100644 index 00000000..87dbf926 --- /dev/null +++ b/utils.html @@ -0,0 +1,298 @@ + + + + + + + + Common utility functions — SAPPHiRE 2.0.0 documentation + + + + + + + + + + + + + +
+
+
+
+ +
+

Common utility functions

+

Utilities

+

The module contains some commonly functions and classes.

+
+
+sapphire.utils.ERR = [-1, -999]
+

Error values used to indicate missing or bad data. +Code -999 is used if the reconstruction of a quantity failed. +Code -1 is used if that detector/sensor is not present.

+
+ +
+
+sapphire.utils.c = 0.299792458
+

Speed of light in vacuum in m / ns.

+
+ +
+
+sapphire.utils.get_publicdb_base()
+

Get the HiSPARC Public Database base URL

+

This can be configured by setting the PUBLICDB_BASE environment +variable to the desired URL.

+
+ +
+
+sapphire.utils.pbar(iterable, length=None, show=True, **kwargs)
+

Get a new progressbar with our default widgets

+
+
Parameters:
+
    +

  • iterable – the iterable over which will be looped.

    • +

  • length – in case iterable is a generator, this should be its +expected length.

    • +

  • show – boolean, if False simply return the iterable.

    • +
+
+
Returns:
+

a new iterable which iterates over the same elements as +the input, but shows a progressbar if possible.

+
+
+
+ +
+
+sapphire.utils.ceil_in_base(value, base)
+

Get nearest multiple of base above the value

+
+ +
+
+sapphire.utils.floor_in_base(value, base)
+

Get nearest multiple of base below the value

+
+ +
+
+sapphire.utils.round_in_base(value, base)
+

Get nearest multiple of base to the value

+
+ +
+
+sapphire.utils.closest_in_list(value, items)
+

Get nearest item from a list of items to the value

+
+ +
+
+sapphire.utils.get_active_index(values, value)
+

Get the index where the value fits.

+
+
Parameters:
+
    +

  • values – sorted list of values (e.g. list of timestamps).

    • +

  • value – value for which to find the position (e.g. a timestamp).

    • +
+
+
Returns:
+

index into the values list.

+
+
+
+ +
+
+sapphire.utils.gauss(x, n, mu, sigma)
+

Gaussian distribution

+

To be used for fitting where the integral is not 1.

+
+ +
+
+sapphire.utils.norm_angle(angle)
+

Normalize an angle to the range [-pi, pi)

+

We use the range from -pi upto but not including pi to represent +angles.

+
+ +
+
+sapphire.utils.angle_between(zenith1, azimuth1, zenith2, azimuth2)
+

Calculate the angle between two (zenith, azimuth) coordinates

+

Using the haversine formula, +from: https://www.movable-type.co.uk/scripts/latlong.html

+
+
Parameters:
+
    +

  • zenith# – Zenith parts of the coordinates, in radians (0, pi/2).

    • +

  • azimuth# – Azimuth parts of the coordinates, in radians (-pi, pi).

    • +
+
+
Returns:
+

Angle between the two coordinates.

+
+
+
+ +
+
+sapphire.utils.vector_length(x, y, z=0)
+

Length of a vector given by (x, y, z) coordinates

+
+
Parameters:
+

x,y,z – vector components.

+
+
Returns:
+

length of vector.

+
+
+
+ +
+
+sapphire.utils.distance_between(x1, y1, x2, y2)
+

Calculate the distance between two (x, y) coordinates

+
+
Parameters:
+
    +

  • x# – x parts of the coordinates.

    • +

  • y# – y parts of the coordinates.

    • +
+
+
Returns:
+

distance between the two coordinates.

+
+
+
+ +
+
+sapphire.utils.make_relative(x)
+

Make first element the origin and make rest relative to it.

+
+ +
+
+sapphire.utils.which(program)
+

Check if a command line program is available

+

An Exception is raised if the program is not available.

+
+
Parameters:
+

program – name or program to check for, e.g. ‘wget’.

+
+
+
+ +
+
+sapphire.utils.memoize(method)
+

Memoisation cache decorator

+

Source: https://stackoverflow.com/a/29954160/1033535

+
+ +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file