nenupy.io.io_tools.ST_Slice

class nenupy.io.io_tools.ST_Slice(time, frequency, value, analog_pointing_times=<Time object: scale='utc' format='jd' value=[]>, digital_pointing_times=<Time object: scale='utc' format='jd' value=[]>, polarization='NW')[source]

Bases: object

Class to handle data sub-set from Statistical data.

Attributes Summary

`time`	Data record times.
`frequency`	Data record frequencies.
`value`	Data values.
`analog_pointing_times`	Analog pointing start times corresponding to this data set.
`digital_pointing_times`	Digital pointing start times corresponding to this data set.

Methods Summary

`plot`([fig_ax])	Plots the data, while automatically taking into account its shape (lightcurve, spectrum or dynamic spectrum).
`rebin`([dt, df, method])	Rebins the data in time and frequency.
`fit_transit`([method, upper_threshold])	Do a fit.
`flatten_frequency`()
`flatten_time`()
`clear_pointing_switch`([flatten_frequency, ...])

Attributes and Methods Documentation

__init__(time, frequency, value, analog_pointing_times=<Time object: scale='utc' format='jd' value=[]>, digital_pointing_times=<Time object: scale='utc' format='jd' value=[]>, polarization='NW')[source]

Methods

`__init__`(time, frequency, value[, ...])
`clean_rfi`([t_sigma, f_sigma])
`clear_pointing_switch`([flatten_frequency, ...])
`fit_transit`([method, upper_threshold])	Do a fit.
`flatten_frequency`()
`flatten_time`()
`median_filter`([filter_size])
`plot`([fig_ax])	Plots the data, while automatically taking into account its shape (lightcurve, spectrum or dynamic spectrum).
`rebin`([dt, df, method])	Rebins the data in time and frequency.

Attributes

`analog_pointing_times`	Analog pointing start times corresponding to this data set.
`digital_pointing_times`	Digital pointing start times corresponding to this data set.
`frequency`	Data record frequencies.
`polarization`	Data record polarization.
`sdata`	_summary_
`time`	Data record times.
`value`	Data values.

property analog_pointing_times

Analog pointing start times corresponding to this data set.

Getter:: Starting times array.
Type:: Time

clear_pointing_switch(flatten_frequency=True, pointing_dt=<TimeDelta object: scale='None' format='sec' value=360.0>, peak_sample_error=2, return_correction=False)[source]: \[p(t) = a \log(t) + bt^2 + ct + d\]

property digital_pointing_times

Digital pointing start times corresponding to this data set.

Getter:: Starting times array.
Type:: Time

fit_transit(method='gaussian', upper_threshold=None, **kwargs)[source]

Do a fit.

kwargs: filter_window (default (200))

property frequency

Data record frequencies.

Getter:: Frequency array.
Type:: Quantity

plot(fig_ax=None, **kwargs)[source]

Plots the data, while automatically taking into account its shape (lightcurve, spectrum or dynamic spectrum).

Several parameters, listed below, can be tuned to adapt the plot to the user requirements:

Data display keywords

Parameters:

decibel (bool) – If set to True, the data will be displayed in a decibel scale (i.e., \({\rm dB} = 10 \log_{10}({\rm data})\)). Default is True.
xmin (Quantity or TimeDatetime) – Minimal x-axis value (could be either time or frequency units depending on the data shape). Default is automatic scaling.
xmax (Quantity or TimeDatetime) – Maximal x-axis value (could be either time or frequency units depending on the data shape). Default is automatic scaling.
ymin (float or Quantity) – Minimal y-axis value (could be either data amplitude or frequency units depending on the data shape). Default is automatic scaling.
ymax (float or Quantity) – Maximal y-axis value (could be either data amplitude or frequency units depending on the data shape). Default is automatic scaling.
vmin (float) – Dynamic spectrum plot only. Minimal data value to display.
vmax (float) – Dynamic spectrum plot only. Maximal data value to display.

Overplot keywords

Parameters:

vlines ([TimeDatetime]) – Temporal plot only. Adds vertical lines at specific times. The expected format is an array of TimeDatetime. Default is [].
analog_pointing (bool) – Temporal plot only. Overplots vertical dot-dashed black lines at analog pointing start times. Default is False.
digital_pointing (bool) – Temporal plot only. Overplots vertical dotted black lines at analog pointing start times. Default is False.
hatched_overlay ((Time, Quantity, ndarray)) – Dynamic spectrum plot only. Produces a hatched overlay on top of the dynamic spectrum. The expected format is (time, frequency, hatched_array) where hatched_array is a boolean ndarray, shaped as (frequency, time), set to True where a hatched cell should be drawn. Default is None.

Plotting layout keywords

Parameters:

figname (str) – Name of the file (absolute or relative path) to save the figure. Default is '' (i.e., only show the figure).
figsize (tuple) – Set the figure size. Default is (15, 7).
title (str) – Set the figure title. Default is ''.
colorbar_label (str) – Dynamic spectrum plot only. Label of the color bar. Default is 'Amp' if decibel=False and 'dB' otherwise.
cmap (str) – Dynamic spectrum plot only. Color map used to represent the data. Default is 'YlGnBu_r'.

Example:

from nenupy.io.bst import BST
from astropy.time import Time, TimeDelta
import astropy.units as u
import numpy as np

# Select BST data
bst = BST("/path/to/BST.fits")
data = bst.get()

# Prepare a boolean array to overlay a hatched pattern
hatch_array = np.zeros((30, 300), dtype=bool)
hatch_array[5:20, 100:200] = True
# Specify time and frequency arrays
time_dts = np.arange(300)*TimeDelta(1, format='sec')
times = Time("2022-01-24T11:01:00") + time_dts
frequencies = np.linspace(47, 52, 30)*u.MHz

# Plot
data.plot(
    hatched_overlay=(
        times,
        frequencies,
        hatch_array
    )
)

property polarization

Data record polarization.

Getter:: Polarization value.
Type:: str

rebin(dt=None, df=None, method='mean')[source]

Rebins the data in time and frequency.

Parameters:

dt (Quantity) – Time bin widths. Default is None (i.e., no rebin in time).
df (Quantity) – Frequency bin widths. Default is None (i.e., no rebin in frequency).
method (str) – Type of method for rebin purpose (either 'mean' or 'median'). Default is 'mean'.

Returns:

Rebinned data.

Return type:

ST_Slice

Example:

from nenupy.io.bst import BST
import astropy.units as u

bst = BST("/path/to/BST.fits")
data = bst.get()
rebin_data = data.rebin(
    dt=3*u.s,
    df=2*u.MHz
)

property sdata

_summary_

Returns:: _description_
Return type:: SData

property time

Data record times.

Getter:: Time array.
Type:: Time

property value

Data values.

Getter:: Values array.
Type:: ndarray