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output.py
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output.py
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import numpy as np
import pyNPR as npr
from domain_wall import inner, ap, mu
#np.set_printoptions(precision=4, suppress=True)
process = lambda x: '{0: .5f}'.format(x)
def write_Zs_gnu(data, location):
'''Write fourquark Zs in simple format to be used by, e.g., gnuplot'''
def line_out(d):
dada = [d.apSq] + d.fourquark_Zs.real.reshape(25).tolist()\
+ d.fourquark_sigmaJK.reshape(25).tolist()
tmp = map(process, dada)
return ' '.join(tmp)
with open(location, 'w') as f:
for d in data:
f.write(line_out(d) + '\n')
def write_stepscale_gnu(data, location):
'''Write step-scaling result in a format usable by gnuplot.'''
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)
with open(location, 'w') as f:
for d in data:
f.write(line_out(d) + '\n')
def write_Zs_TeX(data, location):
'''Write fourquark Zs in a human readable output format, e.g. for TeX.'''
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}\nap = {1}, tw = {2}\n--------------\n'.format(d.m, d.p, d.tw))
f.write(matrix_string(results_matrix(d.fourquark_Zs,
d.fourquark_sigmaJK)))
f.write('\n\n')
# (g, q) - scheme
f.write('(g, q) - scheme\n')
for d in data:
f.write('am = {0}\nap = {1}, tw = {2}\n--------------\n'.format(d.m, d.p, d.tw))
f.write(matrix_string(results_matrix(d.fourquark_Zs_q,
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}\nap = {1}, tw = {2}\n--------------\n'.format(d.m, d.p, d.tw))
f.write(matrix_string(results_matrix_33(d.fourquark_Zs_qg,
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}\nap = {1}, tw = {2}\n--------------\n'.format(d.m, d.p, d.tw))
f.write(matrix_string(results_matrix_33(d.fourquark_Zs_qq,
d.fourquark_sigmaJK_qq)))
f.write('\n\n')
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 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 write_Zs_mma(data, location):
'''Write Z matrices as Mathematica expressions.'''
pass
def to_matrix(m): # Numpy array to MMA matrix.
result = ['{']
for i in range(5):
result.append('{')
for j in range(4):
result.append(process(m[i][j]) + ',')
result.append(process(m[i][4]) + '}')
result.append(', ')
result = result[:-1] # Remove last comma.
result.append('}')
return ''.join(result)
def to_list(p): # Form MMA list from python tuple.
s = ','.join(map(str, p))
return '{' + s + '}'
def mma_defs(d): # Construct MMA assignments.
s = 'fourquarkZ[{0},{1},{2}] = {3};\n'\
'fourquarkZJK[{0},{1},{2}] = {4};\n'.format(d.m, to_list(d.p), d.tw,
to_matrix(d.fourquark_Zs),
to_matrix(d.fourquark_sigmaJK))
return s
with open(location, 'w') as f:
for d in data:
f.write(mma_defs(d))
def write_stepscale_mma(data, location):
'''Write step-scaling functions as Mathematica expressions.'''
pass
def to_matrix(m): # Numpy array to MMA matrix.
result = ['{']
for i in range(5):
result.append('{')
for j in range(4):
result.append(process(m[i][j]) + ',')
result.append(process(m[i][4]) + '}')
result.append(', ')
result = result[:-1] # Remove last comma.
result.append('}')
return ''.join(result)
def to_list(p): # Form MMA list from python tuple.
s = ','.join(map(str, p))
return '{' + s + '}'
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
with open(location, 'w') as f:
for d in data:
f.write(mma_defs(d))
def print_step_scaling(data):
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)