pyfact |TravisBuildStatus| |PyPIStatus|

A python package with utils to work with the FACT Imaging Cerenkov Telescope

install with

.. code::

 $ pip install pyfact

This takes automatically care of the dependencies which are installable with pip.

However, if you want to use the GUI Event Viewer you will need to install Tk before you install matplotlib as it depends on the tkagg backend.

functions:

``fact`` includes several functions to convert the times used in fact
data to more standard formats and vice versa.

e.g. :

.. code:: python

    from fact import run2dt

    # convert fact fNight format to python datetime object:
    date = run2dt("20150101")

Submodules
----------

io
~~

To store pandas dataframes in column-oriented storage into hdf5 files,
we created some helpfull wrappers around ``pandas`` and ``h5py``:

.. code:: python

    from fact.io import read_h5py, to_h5py
    import pandas as pd

    df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    to_h5py(df, 'test.hdf5', key='events')

    print(read_h5py('test.hdf5', key='events'))

plotting

Utils for plotting data into a FACT camera view. Based on matplotlib.

.. code:: python

import matplotlib.pyplot as plt
from fact.plotting import camera
from numpy.random import normal

# create some pseudo data with shape (10, 1440):
data = normal(30, 5, (10, 1440))

camera(data[0])
plt.show()

There are also functions to get the camera_geometry from the delivered source file:

.. code:: python

from fact.plotting import get_pixel_coords

pixel_x, pixel_y = get_pixel_coords()

factdb

This module contains peewee Models for our factdata MySQL database. These were automatically created by peewee and provide means to query this database in python without writing raw sql queries.

For example, to get the total number of runs take by FACT you can do:

.. code:: python

from fact.factdb import connect_database, RunInfo

connect_database()  # this uses the credentials module if no config is given

num_runs = RunInfo.select().count()

A few convenience functions are already implemented. To get a pandas.DataFrame containing the observation time per source and runtype, you can do:

.. code:: python

from fact.factdb import connect_database, get_ontime_per_source_and_runtype

connect_database()

num_runs = RunInfo.select().count()
print(get_ontime_by_source_and_runtype())

To download the database and read it to Pandas dataframe without using peewee:

.. code:: python

 from fact import credentials
 import pandas as pd

 factDB = credentials.create_factdb_engine()
 runInfo = pd.read_sql_table(table_name="RunInfo", con=factDB)  

auxservices

Utilities to read in our aux fits files into pandas dataframes.

.. code:: python

from fact.auxservices import MagicWeather
from datetime import date

weather = MagicWeather(auxdir='/fact/aux/')

df = weather.read_date(date(2016, 1, 1))

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