本文整理汇总了Python中sherpa.astro.data.DataPHA.ignore方法的典型用法代码示例。如果您正苦于以下问题：Python DataPHA.ignore方法的具体用法？Python DataPHA.ignore怎么用？Python DataPHA.ignore使用的例子？那么, 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sherpa.astro.data.DataPHA的用法示例。
在下文中一共展示了DataPHA.ignore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: test_filter_energy_grid
# 需要导入模块: from sherpa.astro.data import DataPHA [as 别名]
# 或者: from sherpa.astro.data.DataPHA import ignore [as 别名]
class test_filter_energy_grid(SherpaTestCase):
_notice = numpy.ones(46, dtype=bool)
_notice[44:46]=False
_ignore = numpy.zeros(46, dtype=bool)
_ignore[14:33]=True
_emin = numpy.array([
1.46000006e-03, 2.48199999e-01, 3.06600004e-01, 4.67200011e-01,
5.69400012e-01, 6.42400026e-01, 7.00800002e-01, 7.44599998e-01,
7.88399994e-01, 8.17600012e-01, 8.61400008e-01, 8.90600026e-01,
9.49000001e-01, 9.92799997e-01, 1.03659999e+00, 1.09500003e+00,
1.13880002e+00, 1.19719994e+00, 1.28480005e+00, 1.40160000e+00,
1.47459996e+00, 1.60599995e+00, 1.69360006e+00, 1.81040001e+00,
1.89800000e+00, 1.94180000e+00, 2.02940011e+00, 2.08780003e+00,
2.19000006e+00, 2.27760005e+00, 2.39439988e+00, 2.58419991e+00,
2.71560001e+00, 2.86159992e+00, 3.08060002e+00, 3.38720012e+00,
3.56240010e+00, 3.79600000e+00, 4.02960014e+00, 4.24860001e+00,
4.71579981e+00, 5.02239990e+00, 5.37279987e+00, 5.89839983e+00,
6.57000017e+00, 9.86960030e+00], numpy.float)
_emax = numpy.array([
0.2482 , 0.3066 , 0.46720001, 0.56940001, 0.64240003,
0.7008 , 0.7446 , 0.78839999, 0.81760001, 0.86140001,
0.89060003, 0.949 , 0.9928 , 1.03659999, 1.09500003,
1.13880002, 1.19719994, 1.28480005, 1.4016 , 1.47459996,
1.60599995, 1.69360006, 1.81040001, 1.898 , 1.9418 ,
2.02940011, 2.08780003, 2.19000006, 2.27760005, 2.39439988,
2.58419991, 2.71560001, 2.86159992, 3.08060002, 3.38720012,
3.5624001 , 3.796 , 4.02960014, 4.24860001, 4.71579981,
5.0223999 , 5.37279987, 5.89839983, 6.57000017, 9.8696003 ,
14.95040035], numpy.float)
def setUp(self):
self.old_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
self.pha = DataPHA('', numpy.arange(46, dtype=float)+1.,
numpy.zeros(46),
bin_lo = self._emin, bin_hi = self._emax )
self.pha.units="energy"
def tearDown(self):
logger.setLevel(self.old_level)
def test_notice(self):
#clear mask
self.pha.notice()
self.pha.notice(0.0, 6.0)
#self.assertEqual(self._notice, self.pha.mask)
assert (self._notice==numpy.asarray(self.pha.mask)).all()
def test_ignore(self):
#clear mask
self.pha.notice()
self.pha.ignore(0.0, 1.0)
self.pha.ignore(3.0, 15.0)
#self.assertEqual(self._ignore, self.pha.mask)
assert (self._ignore==numpy.asarray(self.pha.mask)).all()示例2: test_filter_wave_grid
# 需要导入模块: from sherpa.astro.data import DataPHA [as 别名]
# 或者: from sherpa.astro.data.DataPHA import ignore [as 别名]
class test_filter_wave_grid(SherpaTestCase):
_notice = np.ones(16384, dtype=bool)
_notice[8465:16384] = False
_ignore = np.zeros(16384, dtype=bool)
_ignore[14064:15984] = True
_emin = np.arange(205.7875, 0.9875, -0.0125)
_emax = _emin + 0.0125
def setUp(self):
self.old_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
self.pha = DataPHA('', np.arange(16384, dtype=float) + 1,
np.zeros(16384),
bin_lo=self._emin,
bin_hi=self._emax)
def tearDown(self):
logger.setLevel(self.old_level)
def test_notice(self):
self.pha.units = 'wavelength'
self.pha.notice()
self.pha.notice(100.0, 225.0)
assert (self._notice == np.asarray(self.pha.mask)).all()
def test_ignore(self):
self.pha.units = 'wavelength'
self.pha.notice()
self.pha.ignore(30.01, 225.0)
self.pha.ignore(0.1, 6.0)
assert (self._ignore == np.asarray(self.pha.mask)).all()示例3: test_filter_energy_grid_reversed
# 需要导入模块: from sherpa.astro.data import DataPHA [as 别名]
# 或者: from sherpa.astro.data.DataPHA import ignore [as 别名]
class test_filter_energy_grid_reversed(SherpaTestCase):
_notice = numpy.zeros(204, dtype=bool)
_notice[0:42]=True
_ignore = numpy.ones(204, dtype=bool)
_ignore[66:70]=False
_ignore[0:17]=False
_emin = numpy.array([
2.39196181, 2.35973215, 2.34076023, 2.30973101, 2.2884388 ,
2.25861454, 2.22371697, 2.20662117, 2.18140674, 2.14317489,
2.12185216, 2.09055495, 2.06256914, 2.04509854, 2.02788448,
2.00133967, 1.97772908, 1.96379483, 1.93868744, 1.91855776,
1.89444292, 1.87936974, 1.85819471, 1.84568763, 1.82923627,
1.78920078, 1.77360916, 1.76206875, 1.74499893, 1.73006463,
1.70084822, 1.6883322 , 1.67772949, 1.65171933, 1.63476169,
1.59687376, 1.5745424 , 1.55736887, 1.54051399, 1.52546024,
1.50043869, 1.48890531, 1.47329199, 1.46072423, 1.44289041,
1.43344045, 1.41616774, 1.40441585, 1.3979584 , 1.38773119,
1.37138033, 1.35170007, 1.33725214, 1.33249414, 1.31839108,
1.30797839, 1.29657102, 1.28310275, 1.26550889, 1.25471842,
1.24513853, 1.23672664, 1.22944438, 1.21509433, 1.21003771,
1.20401597, 1.19705439, 1.18722582, 0.90194935, 0.89519638,
0.88912934, 0.88492262, 0.87837797, 0.87366825, 0.8689999 ,
0.86437255, 0.85693878, 0.84793305, 0.84404182, 0.83580172,
0.82876647, 0.82395256, 0.81865752, 0.81185687, 0.80004948,
0.79450154, 0.78852075, 0.77920061, 0.77340651, 0.76626247,
0.76202762, 0.75783074, 0.75413191, 0.74727529, 0.74321008,
0.73474538, 0.73166627, 0.72687 , 0.71785438, 0.71488959,
0.71068853, 0.70199603, 0.69832331, 0.69387686, 0.68788701,
0.68354762, 0.67847627, 0.67117327, 0.66512167, 0.66175646,
0.65620857, 0.6518243 , 0.64605182, 0.64142239, 0.63754696,
0.63128632, 0.62478495, 0.62006336, 0.61440694, 0.60915887,
0.60591549, 0.60078359, 0.5938406 , 0.59103745, 0.58488411,
0.58124125, 0.57883304, 0.57406437, 0.57023615, 0.56442606,
0.56041539, 0.55701393, 0.55392498, 0.55030966, 0.54346251,
0.53728294, 0.53515989, 0.5291304 , 0.52448714, 0.51990861,
0.51589233, 0.50996011, 0.50509953, 0.49889025, 0.49512967,
0.49003205, 0.48888513, 0.48524383, 0.48164544, 0.47720695,
0.47283325, 0.46916556, 0.46660379, 0.46280268, 0.45925769,
0.45514211, 0.45290345, 0.44987884, 0.44589564, 0.44333643,
0.44099477, 0.43790293, 0.43446559, 0.43088335, 0.42605683,
0.42131537, 0.41826019, 0.41506338, 0.41155648, 0.40895697,
0.40502119, 0.40400422, 0.40164718, 0.39864835, 0.39584854,
0.39389083, 0.39130434, 0.38890362, 0.38526753, 0.38292497,
0.38075879, 0.37891743, 0.37648395, 0.37557775, 0.37347662,
0.37154216, 0.36742872, 0.3641032 , 0.36167556, 0.35983625,
0.35634032, 0.35248783, 0.35085678, 0.34843227, 0.34669766,
0.34418666, 0.33912122, 0.33720407, 0.33505177, 0.33279634,
0.33081138, 0.32847831, 0.32592943, 0.3111549 ], numpy.float)
_emax = numpy.array([
3.06803656, 2.39196181, 2.35973215, 2.34076023, 2.30973101,
2.2884388 , 2.25861454, 2.22371697, 2.20662117, 2.18140674,
2.14317489, 2.12185216, 2.09055495, 2.06256914, 2.04509854,
2.02788448, 2.00133967, 1.97772908, 1.96379483, 1.93868744,
1.91855776, 1.89444292, 1.87936974, 1.85819471, 1.84568763,
1.82923627, 1.78920078, 1.77360916, 1.76206875, 1.74499893,
1.73006463, 1.70084822, 1.6883322 , 1.67772949, 1.65171933,
1.63476169, 1.59687376, 1.5745424 , 1.55736887, 1.54051399,
1.52546024, 1.50043869, 1.48890531, 1.47329199, 1.46072423,
1.44289041, 1.43344045, 1.41616774, 1.40441585, 1.3979584 ,
1.38773119, 1.37138033, 1.35170007, 1.33725214, 1.33249414,
1.31839108, 1.30797839, 1.29657102, 1.28310275, 1.26550889,
1.25471842, 1.24513853, 1.23672664, 1.22944438, 1.21509433,
1.21003771, 1.20401597, 1.19705439, 1.18722582, 0.90194935,
0.89519638, 0.88912934, 0.88492262, 0.87837797, 0.87366825,
0.8689999 , 0.86437255, 0.85693878, 0.84793305, 0.84404182,
0.83580172, 0.82876647, 0.82395256, 0.81865752, 0.81185687,
0.80004948, 0.79450154, 0.78852075, 0.77920061, 0.77340651,
0.76626247, 0.76202762, 0.75783074, 0.75413191, 0.74727529,
0.74321008, 0.73474538, 0.73166627, 0.72687 , 0.71785438,
0.71488959, 0.71068853, 0.70199603, 0.69832331, 0.69387686,
0.68788701, 0.68354762, 0.67847627, 0.67117327, 0.66512167,
0.66175646, 0.65620857, 0.6518243 , 0.64605182, 0.64142239,
0.63754696, 0.63128632, 0.62478495, 0.62006336, 0.61440694,
0.60915887, 0.60591549, 0.60078359, 0.5938406 , 0.59103745,
0.58488411, 0.58124125, 0.57883304, 0.57406437, 0.57023615,
0.56442606, 0.56041539, 0.55701393, 0.55392498, 0.55030966,
0.54346251, 0.53728294, 0.53515989, 0.5291304 , 0.52448714,
0.51990861, 0.51589233, 0.50996011, 0.50509953, 0.49889025,
0.49512967, 0.49003205, 0.48888513, 0.48524383, 0.48164544,
0.47720695, 0.47283325, 0.46916556, 0.46660379, 0.46280268,
0.45925769, 0.45514211, 0.45290345, 0.44987884, 0.44589564,
0.44333643, 0.44099477, 0.43790293, 0.43446559, 0.43088335,
0.42605683, 0.42131537, 0.41826019, 0.41506338, 0.41155648,
0.40895697, 0.40502119, 0.40400422, 0.40164718, 0.39864835,
0.39584854, 0.39389083, 0.39130434, 0.38890362, 0.38526753,
0.38292497, 0.38075879, 0.37891743, 0.37648395, 0.37557775,
0.37347662, 0.37154216, 0.36742872, 0.3641032 , 0.36167556,
0.35983625, 0.35634032, 0.35248783, 0.35085678, 0.34843227,
0.34669766, 0.34418666, 0.33912122, 0.33720407, 0.33505177,
0.33279634, 0.33081138, 0.32847831, 0.32592943], numpy.float)
def setUp(self):
#self.old_level = logger.getEffectiveLevel()
#logger.setLevel(logging.ERROR)
self.pha = DataPHA('', numpy.arange(204, dtype=float)+1.,
#.........这里部分代码省略.........