Data Classes

The Base classes defined for Profile, and so on.

Profile class

The Class of Profile

class tatpulsar.data.profile.Profile(counts, cycles=1, error=None)[source]

Profile class

Parameters

counts : array-like

the counts in each phase bin of Profile

cycles : int

the period cycles of input Profile (default is 1). If cycles=2, the phase of profile would be np.linspace(0, 2, size_of_Profile+1)[:-1]

error : array-like

the error of each phase bin, if not given the error will be the poisson error of counts (sqruare root of counts)

Attributes

counts : array-like

The counts in each phase bin of Profile

phase : array-like

The midpoints of phase bins

phase_off : list

The list of phase off interval, the two value are the left and right phase bin of off pulse phases. left_edge = phase_off[0] right_edge = phase_ff[1]

norm(method=0, bkg_range=None)[source]

normalize the profile, and return a normalized Profile object

bkg_range is the background phase range selected to calculated the mean level of background, used in method=0.

Parameters

method: int, optional

The normalization method utilized, optional methods are {0, 1} method = 0 : \(N = (P - P_{min})/(P_{max} - P_{min})\) if background range are selected (bkg_range is not None) \(N = (P - \bar{B})/(P_{max} - \bar{B})\) where \(\bar{B}\) is the mean level in bkg_range method = 1 : \(N = (P-P_{min})/\bar{P}\)

bkg_range: list, optional

The background phase range for background estimation

resample(sample_num=1, kind='poisson')[source]

resampling the profile

Parameters

sample_num : int, optional

number of the resamplings for the profile, the default number is 1

kind : str, optional

The distribution of the profile, default is poisson. (‘poisson’, ‘gaussian’) are refering to the poisson and gauss distribution

Returns

resampled_profile : array or ndarray

if sample_num == 1, return a one dimensional array if sample_num >1 , return a multi-dimensional array

tatpulsar.data.profile.phihist(phi, nbins, **kwargs)[source]

Ensure that the input and output of the histogram are appropriate. The input variables are the pulse phi of events, and the nbins. The counts of each bin are calculated by dividing [0, 1] into number of nbins.

Parameters

phi : array

a set of phase value of events.

nbins : int

the number of bins of profile

Returns

Profile : object

return the object of Profile