def write_Zs_TeX_2(data, location): ''' Write fourquark Zs in a human readable output format, e.g. for TeX. Does the conversion to the 3x3 Delta S = 1 basis. ''' def results_matrix(a33, a33error): return [['{0} +/-{1}'.format(process(a33[i][j]), process(a33error[i][j])) for j in range(3)] for i in range(3)] def matrix_string(m): return '\n'.join([' '.join(row) for row in m]) def new(Zs): # Delta S = 2 --> Delta S = 1. convert = np.array([[1, 1, -0.5], [1, 1, -0.5], [-2, -2, 1]]) return convert*Zs[:3,:3] def new2(Zs): # Scale error bars. convert = np.array([[1, 1, 0.5], [1, 1, 0.5], [2, 2, 1]]) return convert*Zs[:3,:3] with open(location, 'w') as f: # (g, g) - scheme f.write('(g, g) - scheme\n') for d in data: f.write('am = {0}\nmu = {1:.4f}\n--------------\n'.format(d.m, mu(d.ap, d.a))) f.write(matrix_string(results_matrix(new(d.fourquark_Zs), new2(d.fourquark_sigmaJK)))) f.write('\n\n') # (g, q) - scheme f.write('(g, q) - scheme\n') for d in data: f.write('am = {0}\nmu = {1:.4f}\n--------------\n'.format(d.m, mu(d.ap, d.a))) f.write(matrix_string(results_matrix(new(d.fourquark_Zs_q), new2(d.fourquark_sigmaJK_q)))) f.write('\n\n') # (q, g) - scheme f.write('(q, g) - scheme\n') for d in data: f.write('am = {0}\nmu = {1:.4f}\n--------------\n'.format(d.m, mu(d.ap, d.a))) f.write(matrix_string(results_matrix(new(d.fourquark_Zs_qg), new2(d.fourquark_sigmaJK_qg)))) f.write('\n\n') # (q, q) - scheme f.write('(q, q) - scheme\n') for d in data: f.write('am = {0}\nmu = {1:.4f}\n--------------\n'.format(d.m, mu(d.ap, d.a))) f.write(matrix_string(results_matrix(new(d.fourquark_Zs_qq), new2(d.fourquark_sigmaJK_qq)))) f.write('\n\n')
def line_out(d): foo = mu(d.apSq) + d.step_scale.real.reshape(25).tolist() bar = map(process, foo) return ' '.join(bar) for d in data: print line_out(d)
def populate_kinematic_variables(self): '''Requires that L, T, and a are defined.''' self.ap = dw.ap(self.p, self.tw, self.L, self.T) self.ap2 = dw.ap(self.p2, self.tw, self.L, self.T) self.aq = dw.aq(self.ap, self.ap2) # ap - ap2 self.apSq = dw.inner(self.ap) # (ap)^2 self.mu = dw.mu(self.ap, self.a)
def mma_defs(d): # Construct MMA assignments. s = 'ss[{0}, {1}, {2}] = {{{3}, {4}}};\n'\ 'ssJK[{0}, {1}, {2}] = {{{3}, {5}}};\n'.format( d.m, to_list(d.p), d.tw, mu(d.ap, d.a), to_matrix(d.step_scale), to_matrix(d.step_scale_sigma)) return s
def write_stepscale_TeX(data, location): '''Write step-scaling functions in a human readable output format.''' def results_matrix(a55, a55error): return [['{0} +/-{1}'.format(process(a55[i][j]), process(a55error[i][j])) for j in range(5)] for i in range(5)] def results_matrix_33(a33, a33error): return [['{0} +/-{1}'.format(process(a33[i][j]), process(a33error[i][j])) for j in range(3)] for i in range(3)] def matrix_string(m): return '\n'.join([' '.join(row) for row in m]) with open(location, 'w') as f: # (g, g) - scheme f.write('(g, g) - scheme\n') for d in data: f.write('am = {0}\nmu = {1:.4f}\n--------------\n'.format(d.m, mu(d.ap, d.a))) f.write(matrix_string(results_matrix(d.step_scale, d.step_scale_sigma))) f.write('\n\n') # (g, q) - scheme f.write('(g, q) - scheme\n') for d in data: f.write('am = {0}\nmu = {1:.4f}\n--------------\n'.format(d.m, mu(d.ap, d.a))) f.write(matrix_string(results_matrix(d.step_scale_q, d.step_scale_sigma_q))) f.write('\n\n') # (q, g) - scheme f.write('(q, g) - scheme\n') for d in data: f.write('am = {0}\nmu = {1:.4f}\n--------------\n'.format(d.m, mu(d.ap, d.a))) f.write(matrix_string(results_matrix_33(d.step_scale_qg, d.step_scale_sigma_qg))) f.write('\n\n') # (q, q) - scheme f.write('(q, q) - scheme\n') for d in data: f.write('am = {0}\nmu = {1:.4f}\n--------------\n'.format(d.m, mu(d.ap, d.a))) f.write(matrix_string(results_matrix_33(d.step_scale_qq, d.step_scale_sigma_qq))) f.write('\n\n')
def line_out(d): dada = [mu(d.ap, d.a)] +\ (d.step_scale*npr.chiral_mask).reshape(25).tolist() tmp = map(process, dada) return ' '.join(tmp)