threeML.utils.spectrum.binned_spectrum module¶
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class
threeML.utils.spectrum.binned_spectrum.
BinnedSpectrum
(counts, exposure, ebounds, count_errors=None, sys_errors=None, quality=None, scale_factor=1.0, is_poisson=False, mission=None, instrument=None, tstart=None, tstop=None)[source]¶ Bases:
threeML.utils.histogram.Histogram
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INTERVAL_TYPE
¶
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clone
(new_counts=None, new_count_errors=None, new_exposure=None, new_scale_factor=None)[source]¶ make a new spectrum with new counts and errors and all other parameters the same
- Parameters
new_counts – new counts for the spectrum
new_count_errors – new errors from the spectrum
- Returns
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property
count_errors
¶ count error per channel
- Type
return
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property
counts
¶ counts per channel
- Type
return
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property
exposure
¶ Exposure in seconds
- Returns
exposure
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classmethod
from_pandas
(pandas_dataframe, exposure, scale_factor=1.0, is_poisson=False, mission=None, instrument=None)[source]¶ Build a spectrum from data contained within a pandas data frame.
The required columns are:
‘emin’: low energy bin edge ‘emax’: high energy bin edge ‘counts’: the counts in each bin
Optional column names are:
‘count_errors’: errors on the counts for non-Poisson data ‘sys_errors’: systematic error per channel ‘quality’ list of 3ML quality flags ‘good’, ‘warn’, ‘bad’
- Parameters
pandas_dataframe – data frame containing information to be read into spectrum
exposure – the exposure of the spectrum
scale_factor – the scale factor of the spectrum
is_poisson – if the data are Poisson distributed
mission – (optional) the mission name
instrument – (optional) the instrument name
- Returns
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classmethod
from_time_series
(time_series, use_poly=False, from_model=False, **kwargs)[source]¶ - Parameters
time_series –
use_poly –
- Returns
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property
instrument
¶
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property
is_poisson
¶
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property
mission
¶
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property
n_channel
¶
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property
n_channels
¶
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property
quality
¶
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property
rate_errors
¶ If the spectrum has no Poisson error (POISSER is False in the header), this will return the STAT_ERR column :return: errors on the rates
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property
rates
¶ rates per channel
- Type
return
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property
scale_factor
¶
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property
sys_errors
¶ Systematic errors per channel. This is nonzero only if the SYS_ERR column is present in the input file.
- Returns
the systematic errors stored in the input spectrum
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to_pandas
(use_rate=True)[source]¶ make a pandas table from the spectrum.
- Parameters
use_rate – if the table should use rates or counts
- Returns
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property
total_count
¶ total counts
- Type
return
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property
total_count_error
¶ total count error
- Type
return
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property
total_rate
¶ total rate
- Type
return
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property
total_rate_error
¶ total rate error
- Type
return
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property
tstart
¶
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property
tstop
¶
-
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class
threeML.utils.spectrum.binned_spectrum.
BinnedSpectrumWithDispersion
(counts, exposure, response, count_errors=None, sys_errors=None, quality=None, scale_factor=1.0, is_poisson=False, mission=None, instrument=None, tstart=None, tstop=None)[source]¶ Bases:
threeML.utils.spectrum.binned_spectrum.BinnedSpectrum
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clone
(new_counts=None, new_count_errors=None, new_sys_errors=None, new_exposure=None, new_scale_factor=None)[source]¶ make a new spectrum with new counts and errors and all other parameters the same
- Parameters
new_sys_errors –
new_exposure –
new_scale_factor –
new_counts – new counts for the spectrum
new_count_errors – new errors from the spectrum
- Returns
-
classmethod
from_time_series
(time_series, response=None, use_poly=False, extract=False)[source]¶ - Parameters
time_series –
use_poly –
- Returns
-
property
response
¶
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class
threeML.utils.spectrum.binned_spectrum.
Channel
(start: float, stop: float, swap_if_inverted: bool = False)[source]¶ Bases:
threeML.utils.interval.Interval
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property
channel_width
¶
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property
-
class
threeML.utils.spectrum.binned_spectrum.
ChannelSet
(list_of_intervals=())[source]¶ Bases:
threeML.utils.interval.IntervalSet
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INTERVAL_TYPE
¶
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property
channels_widths
¶
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class
threeML.utils.spectrum.binned_spectrum.
Quality
(quality)[source]¶ Bases:
object
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property
bad
¶
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classmethod
create_all_good
(n_channels)[source]¶ construct a quality object with all good channels :param n_channels: :return:
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property
good
¶
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property
n_elements
¶
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to_ogip
()[source]¶ makes a quality array following the OGIP standards: 0 = good 2 = warn 5 = bad
- Returns
-
property
warn
¶
-
property