UnDySPuTeD Time-Frequency Data

Reading a spectra file

Spectra

>>> from nenupy.io import Spectra
>>> sp = Spectra(filename="my_file_0.spectra")

info()

>>> sp.info()
filename: my_file_0.spectra
time_min: 2023-05-27T08:39:02.0000050
time_max: 2023-05-27T08:59:34.2445748
dt: 20.97152 ms
frequency_min: 19.921875 MHz
frequency_max: 57.421875 MHz
df: 3.0517578125 kHz
Available beam indices: ['0']

Pipeline configuration

Predefined pipeline steps

pipeline TFPipeline TFTask

>>> sp.pipeline.info()
Pipeline configuration:
    0 - Correct bandpass
    (1 - Remove subband channels)
    (2 - Rebin in time)
    (3 - Rebin in frequency)
    4 - Compute Stokes parameters

>>> sp.pipeline_parameters.info()
channels: 64
dt: 0.02097152 s
df: 3051.7578125 Hz
tmin: 2023-05-27T08:39:02.0000050
tmax: 2023-05-27T08:59:34.2445748
fmin: 19.921875 MHz
fmax: 57.421875 MHz
beam: 0
dispersion_measure: None
rotation_measure: None
rebin_dt: None
rebin_df: None
remove_channels: None
dreambeam_skycoord: None
dreambeam_dt: None
dreambeam_parallactic: True
stokes: I
ignore_volume_warning: False

Pipeline parameter modification

>>> from astropy.time import Time
>>> import astropy.units as u

>>> sp.pipeline.parameters["tmin"] = "2023-05-27T08:40:00"
>>> sp.pipeline.parameters["tmax"] = Time("2023-05-27 08:42:00", format="iso")
>>> sp.pipeline.parameters["fmin"] = 50
>>> sp.pipeline.parameters["fmax"] = 55*u.MHz

Managing pipeline tasks

remove()

>>> sp.pipeline.remove(2)
>>> sp.pipeline.info()
Pipeline configuration:
    0 - Correct bandpass
    (1 - Remove subband channels)
    (2 - Rebin in frequency)
    3 - Compute Stokes parameters

insert() append()

>>> from nenupy.io.tf import TFTask
>>> sp.pipeline.insert(TFTask.time_rebin(), 1)
>>> sp.pipeline.info()
Pipeline configuration:
    0 - Correct bandpass
    (1 - Rebin in time)
    (2 - Remove subband channels)
    (3 - Rebin in frequency)
    4 - Compute Stokes parameters

Adding custom steps

>>> from nenupy.io.tf import TFTask
>>>
>>> custom_task = TFTask(
        name="my task - multiply the data by n_channels",
        func=lambda data, channels: data*channels,
        args_to_update=["channels"]
    )
>>> sp.pipeline.insert(custom_task, 3)
>>> sp.pipeline.info()
Pipeline configuration:
    0 - Correct bandpass
    (1 - Rebin in time)
    (2 - Remove subband channels)
    3 - my task - multiply the data by n_channels
    (4 - Rebin in frequency)
    5 - Compute Stokes parameters

Warning

This is a warning.

Getting the data

get()

>>> data = sp.get(stokes="I")

>>> data = sp.get(stokes="I", tmin="2023-05-27T08:41:30")

Note

There is a hardcoded size limit to the data output (i.e. after rebinning and all other pipeline operations) fixed at 2 GB, to prevent memory issues. Users willing to bypass this limit may explicitely ask for it using the ignore_volume_warning properties of pipeline(). This property can easily be updated directly by the get() method:

>>> sp.get(
        tmin="2023-05-27T08:40:00", tmax="2023-05-27T18:00:00",
        ignore_volume_warning=True
    )

Saving the data

>>> sp.get(
        file_name="/my/path/filename.hdf5"
        stokes="I",
        tmin="2023-05-27T08:41:30"
    )