我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用numpy.recfromtxt()。
def test_recfromtxt(self): # data = TextIO('A,B\n0,1\n2,3') kwargs = dict(delimiter=",", missing_values="N/A", names=True) test = np.recfromtxt(data, **kwargs) control = np.array([(0, 1), (2, 3)], dtype=[('A', np.int), ('B', np.int)]) self.assertTrue(isinstance(test, np.recarray)) assert_equal(test, control) # data = TextIO('A,B\n0,1\n2,N/A') test = np.recfromtxt(data, dtype=None, usemask=True, **kwargs) control = ma.array([(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[('A', np.int), ('B', np.int)]) assert_equal(test, control) assert_equal(test.mask, control.mask) assert_equal(test.A, [0, 2])
def fromPostSamp(self, burn=None, skipHeader=12): ''' This method uses lalinference samples. If not burn value is given then the entire posterior sample is used. If the burn option is supplied then the initial part of the chain (upto iteration number = burn) is ignored. Output is a list whose first two elements are the probability that the primary and secondary object is a NS respectively. The third element gives the remnant mass outside the black hole in access of the threshold mass supplied. ''' data = np.recfromtxt(self.inputFile, names=True, skip_header=skipHeader) burnin = 0 if burn: burnin = burn mc = data['mc'][burnin:] massRatio = data['q'][burnin:] self.chi = data['a1'][burnin:] self.eta = massRatio/((1 + massRatio)**2) self.mPrimary = (massRatio**(-0.6)) * mc * (1. + massRatio)**0.2 self.mSecondary = (massRatio**0.4) * mc * (1. + massRatio)**0.2 NS_prob_2 = np.sum(self.mSecondary < self.max_ns_g_mass)*100./len(self.mSecondary) # RE: Max NS mass was hardcoded as 3.0. Should be gotten from class variable NS_prob_1 = np.sum(self.mPrimary < self.max_ns_g_mass)*100./len(self.mPrimary) return [NS_prob_1, NS_prob_2, self.computeRemMass()]
def load_data(file_to_read): """Load X_train/y_train/X_val/y_val/X_infer for further processing (e.g. make input queue of tensorflow). Args: file_to_read: Returns: X_train/y_train/X_val/y_val/X_infer. """ data = np.recfromtxt(file_to_read) data = np.asarray(data) return data
def __new__(cls, *args,**kwargs): # if no argument is given, create zero sized recarray if len(args) == 0: args = (0,) elif type(args[0]) is int: # create empty recarray d = numpy.recarray(args[0], dtype = SWCFile.swcformat) else: # create from file or filename d = numpy.recfromtxt(args[0], dtype = SWCFile.swcformat) return d.view(SWCFile)
def ingest_zeta_values(self): t_values = np.arange(2000, 42000, 2000) names = ['atomic_number', 'ion_charge'] names += [str(i) for i in t_values] zeta = np.recfromtxt( self.data_fn, usecols=xrange(1, 23), names=names) zeta_df = ( pd.DataFrame.from_records(zeta).set_index( ['atomic_number', 'ion_charge']).T ) data = list() for i, s in zeta_df.iterrows(): T = Temperature.as_unique(self.session, value=int(i)) if T.id is None: self.session.flush() for (atomic_number, ion_charge), rate in s.iteritems(): data.append( Zeta( atomic_number=atomic_number, ion_charge=ion_charge, data_source=self.data_source, temp=T, zeta=rate ) )
def test_recfromtxt(self): with temppath(suffix='.txt') as path: path = Path(path) with path.open('w') as f: f.write(u'A,B\n0,1\n2,3') kwargs = dict(delimiter=",", missing_values="N/A", names=True) test = np.recfromtxt(path, **kwargs) control = np.array([(0, 1), (2, 3)], dtype=[('A', np.int), ('B', np.int)]) self.assertTrue(isinstance(test, np.recarray)) assert_equal(test, control)
