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pyfact

TravisBuildStatus

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

install with

$ pip install git+https://github.com/maxnoe/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. :

from fact import run2dt

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

Submodules

plotting

Utils for plotting data into a FACT camera view. Based on matplotlib. The function factcamera is added to pyplot and matplotlib Axes. So you can do

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

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

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

Or you can start an interactive Viewer which lets you click through the events and save the images:

from fact.plotting import Viewer
from numpy.random import poisson

# pseudo data:
data = poisson(30, (10, 1440))

# call the Viewer with data and a label for the colorbar:
Viewer(data, "label")

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

from fact.plotting import get_pixel_coords

pixel_x, pixel_y = get_pixel_coords()

slowdata

If you like to look at some of the so called "Slow Data" of FACT, you can request the data from a MongoDB. In order to access this db, you currently need to tunnel the database port to your machine, e.g. like this.

ssh -f -L 37017:localhost:27017 [email protected] -N

In case you'd like to open this tunnel always, when you log into your machine, you might want to put something like this into your .bashrc:

check_tunnel (){
    ps aux | grep "ssh -f -L 37017:localhost:27017" | grep fact >/dev/null
}
if ! check_tunnel ; then
    ssh -f -L 37017:localhost:27017 [email protected] -N
fi

Once you've opened that tunnel, try e.g. this...

from fact.slowdata import connect

db = connect()
db.<tab>

For easily correlating slowdata samples by their timestamps, try this:


import matplotlib.pyplot as plt
from fact.slowdata import connect
from fact.slowdata.correlation import CorrelationByInterpolation

db = connect()
magic_weather_sample = db.MAGIC_WEATHER_DATA.from_until(16400, 16403)
fact_camera_temp_sample = db.FSC_CONTROL_TEMPERATURE.from_until(16401, 16403)
coincidence_time_window = 5 # in seconds
correlation_generator = CorrelationByInterpolation(magic_weather_sample, 
                                                  fact_camera_temp_sample, coincidence_time_window)

(interpolated_magic_weather_sample , 
   interpolated_fact_camera_temp_sample) = correlation_generator.correlate()

# The interpolated samples are ensured to have the same "Time" field now.
# assume, we wanted to know if the temperature measured at MAGIC is correlated to 
# the temperature in the SiPM sensor area (using sensor 4, because we can)
plt.hist2d(interpolated_magic_weather_sample["T"], 
           interpolated_fact_camera_temp_sample["T_sens"][:,4])
plt.show()

For a comple example for requesting data and making a plot, please see examples/slowdata_db.py.

dim

Some functions and classes to work with the fact dim servers