nenupy.beamlet.sdata

SData

SData is a class designed for embedding dynamic spectrum data sets. Many methods from the nenupy library return SData objects such as:

select()
beamform()
time_profile()
azel_transit()
radec_transit()
radec_tracking()

It therefore provides a unified way of accessing, comparing and eventually plotting dynamic spectrum data sets throughout every nenupy manipulations.

SData are unit sensitive objets, therefore in addition it is worth importing astropy.time and astropy.units modules:

>>> from nenupy.beamlet import SData
>>> import numpy as np
>>> from astropy.time import Time, TimeDelta
>>> import astropy.units as u

Operations

All basic mathematical operations are enabled between SData instances (addition, subtraction, multiplication and division).

In the following example, s1 and s2 are two identical SData instances with every data set to 1.. They define data taken at the same times, frequencies and polarizations (time, freq and polar respectively):

>>> dts = TimeDelta(np.arange(3), format='sec')
>>> s1 = SData(
        data=np.ones((3, 5, 1)),
        time=Time('2020-04-01 12:00:00') + dts,
        freq=np.ones(5) * u.MHz,
        polar=['NE']
    )
>>> s2 = SData(
        data=np.ones((3, 5, 1)),
        time=Time('2020-04-01 12:00:00') + dts,
        freq=np.ones(5) * u.MHz,
        polar=['NE']
    )

Addition:

>>> s_add = s1 + s2
>>> s_add.amp
array([[2., 2., 2., 2., 2.],
   [2., 2., 2., 2., 2.],
   [2., 2., 2., 2., 2.]])

Subtraction:

>>> s_sub = s1 - s2
>>> s_add.amp
array([[0., 0., 0., 0., 0.],
   [0., 0., 0., 0., 0.],
   [0., 0., 0., 0., 0.]])

Multiplication:

>>> s_mul = s1 * s2
>>> s_add.amp
array([[1., 1., 1., 1., 1.],
   [1., 1., 1., 1., 1.],
   [1., 1., 1., 1., 1.]])

Division:

>>> s_div = s1 / s2
>>> s_add.amp
array([[1., 1., 1., 1., 1.],
   [1., 1., 1., 1., 1.],
   [1., 1., 1., 1., 1.]])

Concatenation

Besides mathematical operations, SData objects may also be concatenated along the time or frequency axis for building up dataset purposes.

In the following example, s1 is the main instance, s2 has similar times but different frequencies and s3 has similar frequencies but different times than s1.

>>> dts = TimeDelta(np.arange(2), format='sec')
>>> s1 = SData(
        data=np.ones((2, 3, 1)),
        time=Time('2020-04-01 12:00:00') + dts,
        freq=np.arange(3) * u.MHz,
        polar=['NE']
    )
>>> s2 = SData(
        data=np.ones((2, 3, 1)),
        time=Time('2020-04-01 12:00:00') + dts,
        freq=(10+np.arange(3)) * u.MHz,
        polar=['NE']
    )
>>> s2 = SData(
        data=np.ones((2, 3, 1)),
        time=Time('2020-04-01 13:00:00') + dts,
        freq=np.arange(3) * u.MHz,
        polar=['NE']
    )

Concatenation in frequency:

>>> s_freq_concat = s1 & s2
>>> s_freq_concat.shape
(2, 6, 1)

Concatenation in time:

>>> s_time_concat = s1 | s3
>>> s_time_concat.shape
(4, 3, 1)

class nenupy.beamlet.sdata.SData(data, time, freq, polar)[source]

Bases: object

A class to handle dynamic spectrum data.

Parameters:

data (ndarray) – Dynamic spectrum data (time, freq, polar)
freq (Quantity) – Times
time (Time) – Frequencies
polar (str, list or ndarray) – Polarizations

property amp

Linear amplitude of the data

Getter:: Data in linear scaling
Type:: ndarray

property background

property data

property datetime

Return datetime dates

Getter:: Times in datetime format
Type:: ndarray

property db

Convert the amplitude in decibels

Getter:: Data in decibels
Type:: ndarray

property fbackground

property freq

property jd

Return JD dates

Getter:: Times in Julian days
Type:: ndarray

property medianFProfile: Added in version 1.1.0.

property medianTProfile: Added in version 1.1.0.

property mjd

Return MJD dates

Getter:: Times in Modified Julian Days
Type:: ndarray

plot(fig=None, ax=None, polarization=None, figname=None, db=True, **kwargs)[source]: kwargs keys: cmap, title, cblabel, figsize, altaza, vmin, vmax

property polar: Returns a list of polarizations

property shape

property time