vice.yields.ccsne.table ======================= Look up the mass yield of a given element as a function of stellar mass as reported by a given study. **Signature**: vice.yields.ccsne.table(element, study = "LC18", MoverH = 0, rotation = 0, wind = True, isotopic = False) .. versionadded:: 1.2.0 Parameters ---------- element : ``str`` [case-insensitive] The symbol of the element to look up the yield table for. study : ``str`` [case-insensitive] [default : "LC18"] A keyword denoting which study to pull the table from Keywords and their Associated Studies: - "LC18": Limongi & Chieffi (2018) [1]_ - "S16/W18": Sukhbold et al. (2016) [2]_ (W18 explosion engine) - "S16/W18F": Sukhbold et al. (2016) (W18 engine, forced explosions) - "S16/N20": Sukhbold et al. (2016) (N20 explosion engine) - "CL13": Chieffi & Limongi (2013) [3]_ - "NKT13": Nomoto, Kobayashi & Tominaga (2013) [4]_ - "CL04": Chieffi & Limongi (2004) [5]_ - "WW95": Woosley & Weaver (1995) [6]_ MoverH : real number [default : 0] The total metallicity [M/H] = :math:`\log_{10}(Z/Z_\odot)` of the exploding stars. There are only a handful of metallicities recognized by each study. Keywords and their Associated Metallicities: - "LC18": [M/H] = -3, -2, -1, 0 - "S16/\*": [M/H] = 0 - "CL13": [M/H] = 0 - "NKT13": [M/H] = -inf, -1.15, -0.54, -0.24, 0.15, 0.55 - "CL04": [M/H] = -inf, -4, -2, -1, -0.37, 0.15 - "WW95": [M/H] = -inf, -4, -2, -1, 0 rotation : real number [default : 0] The rotational velocity of the exploding stars in km/s. There are only a handful of rotational velocities recognized by each study. Keywords and their Associated Rotational Velocities: - "LC18": v = 0, 150, 300 - "S16/\*": v = 0 - "CL13": v = 0, 300 - "NKT13": v = 0 - "CL04": v = 0 - "WW95": v = 0 wind : bool [default : ``True``] If True, the stellar wind contribution to the yield will be included in the reported table. If False, the table will include only the supernova explosion yields. .. note:: Wind and explosive yields are only separated for the Limongi & Chieffi (2018) and Sukhbold et al. (2016) studies. Wind yields are not separable from explosive yields for other studies supported by this function. isotopic : ``bool`` [default : ``False``] If ``True``, the full-breakdown of isotopic mass yields is returned. If ``False``, only the total mass yield of the given element is returned. Returns ------- yields : ``ccsn_yield_table`` [VICE ``dataframe`` derived class] A dataframe designed to hold a CCSN yield table. It can be indexed via stellar mass in :math:`M_\odot` or the isotopes of the requested element (if ``isotopic == True``). Raises ------ * ValueError - The element is not built into VICE - The study is not built into VICE * LookupError - The study did not report yields for the requested element - The study did not report yields at the specified metallicity - The study did not report yields at the specified rotational velocity. * ScienceWarning - ``wind = False`` and ``study`` is anything other than LC18 or S16. These are the only studies for which wind yields were reported separate from explosive yields. Notes ----- The tables returned by this function will include stable isotopes *only*. See the notes in the ``vice.yields.ccsne`` docstring for details on the studies to which VICE applies a treatment of radioactive isotopes in its built-in tables. Example Code ------------ >>> import vice >>> example = vice.yields.ccsne.table('o') >>> example vice.dataframe{ 13.0 -----------> 0.247071034 15.0 -----------> 0.585730308 20.0 -----------> 1.256452301 25.0 -----------> 2.4764558329999997 30.0 -----------> 0.073968147 40.0 -----------> 0.087475695 60.0 -----------> 0.149385561 80.0 -----------> 0.24224373600000002 120.0 ----------> 0.368598602 } >>> example.masses (13.0, 15.0, 20.0, 25.0, 30.0, 40.0, 60.0, 80.0, 120.0) >>> example[20.0] 1.256452301 >>> [example[i] for i in example.masses] [0.2470691117, 0.5857306186, 1.256464291, 2.476488843, 0.073968147, 0.087475695, 0.149385561, 0.24224373600000002, 0.368598602] >>> vice.yields.ccsne.table('o', isotopic = True) vice.dataframe{ 13 -------------> {'o16': 0.24337, 'o17': 5.5634e-05, 'o18': 0.0036454} 15 -------------> {'o16': 0.58234, 'o17': 5.5608e-05, 'o18': 0.0033347} 20 -------------> {'o16': 1.2501, 'o17': 4.6201e-05, 'o18': 0.0063061} 25 -------------> {'o16': 2.4724, 'o17': 4.7633e-05, 'o18': 0.0040082} 30 -------------> {'o16': 0.073782, 'o17': 4.0707e-05, 'o18': 0.00014544} 40 -------------> {'o16': 0.087253, 'o17': 4.1705e-05, 'o18': 0.00018099} 60 -------------> {'o16': 0.1491, 'o17': 5.3011e-05, 'o18': 0.00023255} 80 -------------> {'o16': 0.24192, 'o17': 6.1316e-05, 'o18': 0.00026242} 120 ------------> {'o16': 0.36819, 'o17': 7.9192e-05, 'o18': 0.00032941} } >>> example[20] vice.dataframe{ o16 ------------> 1.250112 o17 ------------> 4.6201e-05 o18 ------------> 0.0063060899999999994 } >>> example[20]['o16'] 1.250112 >>> example['o16'] vice.dataframe{ 13.0 -----------> 0.2433681 15.0 -----------> 0.5823402999999999 20.0 -----------> 1.250112 25.0 -----------> 2.472433 30.0 -----------> 0.073782 40.0 -----------> 0.087253 60.0 -----------> 0.1491 80.0 -----------> 0.24192 120.0 ----------> 0.36819 } .. [1] Limongi & Chieffi (2018), ApJS, 237, 13 .. [2] Sukhbold et al. (2016), ApJ, 821, 38 .. [3] Chieffi & Limongi (2013), ApJ, 764, 21 .. [4] Nomoto, Kobayashi & Tominaga (2013), ARA&A, 51, 457 .. [5] Chieffi & Limongi (2004), ApJ, 608, 405 .. [6] Woosley & Weaver (1995), ApJ, 101, 181