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)
