本文整理汇总了Python中stingray.Powerspectrum.rebin方法的典型用法代码示例。如果您正苦于以下问题：Python Powerspectrum.rebin方法的具体用法？Python Powerspectrum.rebin怎么用？Python Powerspectrum.rebin使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类stingray.Powerspectrum的用法示例。
在下文中一共展示了Powerspectrum.rebin方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: rebin_several
# 需要导入模块: from stingray import Powerspectrum [as 别名]
# 或者: from stingray.Powerspectrum import rebin [as 别名]
def rebin_several(self, df):
"""
TODO: Not sure how to write tests for the rebin method!
"""
ps = Powerspectrum(lc=self.lc, norm="Leahy")
bin_ps = ps.rebin(df)
assert np.isclose(bin_ps.freq[0], bin_ps.df, atol=1e-4, rtol=1e-4)示例2: test_rebin_makes_right_attributes
# 需要导入模块: from stingray import Powerspectrum [as 别名]
# 或者: from stingray.Powerspectrum import rebin [as 别名]
def test_rebin_makes_right_attributes(self):
ps = Powerspectrum(lc=self.lc, norm="Leahy")
# replace powers
ps.ps = np.ones_like(ps.ps) * 2.0
rebin_factor = 2.0
bin_ps = ps.rebin(rebin_factor*ps.df)
assert bin_ps.freq is not None
assert bin_ps.ps is not None
assert bin_ps.df == rebin_factor * 1.0 / self.lc.tseg
assert bin_ps.norm.lower() == "leahy"
assert bin_ps.m == 2
assert bin_ps.n == self.lc.time.shape[0]
assert bin_ps.nphots == np.sum(self.lc.counts)示例3: test_ccf
# 需要导入模块: from stingray import Powerspectrum [as 别名]
# 或者: from stingray.Powerspectrum import rebin [as 别名]
def test_ccf(self):
# to make testing faster, fitting is not done.
ref_ps = Powerspectrum(self.ref_lc, norm='abs')
ci_counts_0 = self.ci_counts[0]
ci_times = np.arange(0, self.n_seconds * self.n_seg, self.dt)
ci_lc = Lightcurve(ci_times, ci_counts_0, dt=self.dt)
# rebinning factor used in `rebin_log`
rebin_log_factor = 0.4
acs = AveragedCrossspectrum(lc1=ci_lc, lc2=self.ref_lc,
segment_size=self.n_seconds, norm='leahy',
power_type="absolute")
acs = acs.rebin_log(rebin_log_factor)
# parest, res = fit_crossspectrum(acs, self.model, fitmethod="CG")
acs_result_model = self.model
# using optimal filter
optimal_filter = Optimal1D(acs_result_model)
optimal_filter_freq = optimal_filter(acs.freq)
filtered_acs_power = optimal_filter_freq * np.abs(acs.power)
# rebinning power spectrum
new_df = spec.get_new_df(ref_ps, self.n_bins)
ref_ps_rebinned = ref_ps.rebin(df=new_df)
# parest, res = fit_powerspectrum(ref_ps_rebinned, self.model)
ref_ps_rebinned_result_model = self.model
# calculating rms from power spectrum
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="optimal")
# calculating normalized ccf
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
# calculating ccf error
meta = {'N_SEG': self.n_seg, 'NSECONDS': self.n_seconds, 'DT': self.dt,
'N_BINS': self.n_bins}
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts, ci_counts_0,
acs_result_model,
rebin_log_factor,
meta, ref_ps_rebinned_rms,
filter_type="optimal")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(np.isclose(error_ccf, np.zeros(shape=error_ccf.shape),
atol=0.01))
# using window function
tophat_filter = Window1D(acs_result_model)
tophat_filter_freq = tophat_filter(acs.freq)
filtered_acs_power = tophat_filter_freq * np.abs(acs.power)
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="window")
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts, ci_counts_0,
acs_result_model,
rebin_log_factor,
meta, ref_ps_rebinned_rms,
filter_type="window")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(np.isclose(error_ccf, np.zeros(shape=error_ccf.shape),
atol=0.01))