a = None b = 5 c = 5.0 d = [5, 0] e = ndarray([5, 0]) f = 'coso' g = uarray(5.5, 0.1) h = ndarray([3.0]) i = uarray([1, 2], [0.01, 0.002]) j = ufloat(5.5, 0.1) k = uarray([5.5], [0.1]) logSI_OI = ufloat(-1.53, 0.05) print logSI_OI SI_OI = uma_pow(10, logSI_OI) print SI_OI OI_SI = 1 / SI_OI print OI_SI OI_SI2 = uma_pow(10, -logSI_OI) print OI_SI2 # print unum_log10(j), type(unum_log10(j)) # print umath_log10(j), type(umath_log10(j)) # print math_log10(j), type(math_log10(j)) # print num_log10(j), 'este no fona' # print j.nominal_value, nominal_values(j), type(j.nominal_value), type(nominal_values(j)) # print j.std_dev, std_devs(j),type(j.std_dev), type(std_devs(j)) # # print 'g', isinstance(g, (Sequence, np.ndarray))
a = None b = 5 c = 5.0 d = [5,0] e = ndarray([5,0]) f = 'coso' g = uarray(5.5, 0.1) h = ndarray([3.0]) i = uarray([1, 2], [0.01, 0.002]) j = ufloat(5.5, 0.1) k = uarray([5.5], [0.1]) logSI_OI = ufloat(-1.53, 0.05) print logSI_OI SI_OI = uma_pow(10, logSI_OI) print SI_OI OI_SI = 1 / SI_OI print OI_SI OI_SI2 = uma_pow(10, -logSI_OI) print OI_SI2 # print unum_log10(j), type(unum_log10(j)) # print umath_log10(j), type(umath_log10(j)) # print math_log10(j), type(math_log10(j)) # print num_log10(j), 'este no fona'
from uncertainties import ufloat from uncertainties.umath import pow as uma_pow #Sulfur O/H ratio logSI_OI_Gradient = ufloat(-1.53, 0.05) OI_SI = uma_pow(10, -logSI_OI_Gradient) HeII_HI = ufloat(0.105926246317, 1.38777878078e-17) OI_HI = ufloat(0.000129583794561, 1.354535563e-06) SI_HI = ufloat(3.58051262182e-06, 8.61374827027e-08) HeIII_HeII = ufloat(0.000577309209945, 7.63955464343e-05) HeI_HI = HeII_HI + HeIII_HeII print 'HeI_HI', HeI_HI Y_mass_InferenceO = (4 * HeI_HI * (1 - 20 * OI_HI)) / (1 + 4 * HeI_HI) Y_mass_InferenceS = (4 * HeI_HI * (1 - 20 * OI_SI * SI_HI)) / (1 + 4 * HeI_HI) print 'este radio', OI_SI.nominal_value print OI_SI.nominal_value * 3.58051262182e-06, 'comparado con', 0.000129583794561 print 'Via single lines' print (4 * 0.109697250692 * (1 - 20 * 0.000129583794561)) / (1 + 4 * 0.109697250692) print (4 * 0.109697250692 * (1 - 20 * 33.8844156139 * 3.58051262182e-06)) / (1 + 4 * 0.109697250692) print 'Via inference' print Y_mass_InferenceO print Y_mass_InferenceS
