def get_response_content(fs):
M, R = get_input_matrices(fs)
# create the R table string and scripts
headers = [
't', 'mi.true.mut', 'mi.true.mutsel', 'mi.analog.mut',
'mi.analog.mutsel'
]
npoints = 100
t_low = 0.0
t_high = 5.0
t_incr = (t_high - t_low) / (npoints - 1)
t_values = [t_low + t_incr * i for i in range(npoints)]
# get the data for the R table
arr = []
for t in t_values:
mi_mut = ctmcmi.get_mutual_information(M, t)
mi_mutsel = ctmcmi.get_mutual_information(R, t)
mi_analog_mut = ctmcmi.get_ll_ratio_wrong(M, t)
mi_analog_mutsel = ctmcmi.get_ll_ratio_wrong(R, t)
row = [t, mi_mut, mi_mutsel, mi_analog_mut, mi_analog_mutsel]
arr.append(row)
# get the R table
table_string = RUtil.get_table_string(arr, headers)
# get the R script
script = get_ggplot()
# create the R plot image
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
M, R = get_input_matrices(fs)
# create the R table string and scripts
headers = [
't',
'mi.true.mut',
'mi.true.mutsel',
'mi.analog.mut',
'mi.analog.mutsel']
npoints = 100
t_low = 0.0
t_high = 5.0
t_incr = (t_high - t_low) / (npoints - 1)
t_values = [t_low + t_incr*i for i in range(npoints)]
# get the data for the R table
arr = []
for t in t_values:
mi_mut = ctmcmi.get_mutual_information(M, t)
mi_mutsel = ctmcmi.get_mutual_information(R, t)
mi_analog_mut = ctmcmi.get_ll_ratio_wrong(M, t)
mi_analog_mutsel = ctmcmi.get_ll_ratio_wrong(R, t)
row = [t, mi_mut, mi_mutsel, mi_analog_mut, mi_analog_mutsel]
arr.append(row)
# get the R table
table_string = RUtil.get_table_string(arr, headers)
# get the R script
script = get_ggplot()
# create the R plot image
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
M, R = get_input_matrices(fs)
M_v = mrate.R_to_distn(M)
R_v = mrate.R_to_distn(R)
t = fs.t
mi_mut = ctmcmi.get_mutual_information(M, t)
mi_bal = ctmcmi.get_mutual_information(R, t)
fi_mut = divtime.get_fisher_information(M, t)
fi_bal = divtime.get_fisher_information(R, t)
if fs.info_mut:
information_sign = np.sign(mi_mut - mi_bal)
elif fs.info_fis:
information_sign = np.sign(fi_mut - fi_bal)
out = StringIO()
print >> out, '<html>'
print >> out, '<body>'
print >> out
print >> out, '<pre>'
print >> out, 'Explicitly computed answer',
print >> out, '(not a heuristic but may be numerically imprecise):'
if information_sign == 1:
print >> out, '* pure mutation',
print >> out, 'is more informative'
elif information_sign == -1:
print >> out, '* the balance of mutation and selection',
print >> out, 'is more informative'
else:
print >> out, ' the information contents of the two processes',
print >> out, 'are numerically indistinguishable'
print >> out
print >> out
if fs.info_mut:
print >> out, 'Mutual information properties',
print >> out, 'at very small and very large times:'
print >> out
print >> out, get_mi_asymptotics(M, R)
print >> out
print >> out
print >> out, 'Heuristics without regard to time or to the selected',
print >> out, 'information variant (Fisher vs. mutual information):'
print >> out
print >> out, get_heuristics(M, R)
print >> out
print >> out
print >> out, 'Input summary:'
print >> out
print >> out, 'mutation rate matrix:'
print >> out, M
print >> out
print >> out, 'mutation process stationary distribution:'
print >> out, M_v
print >> out
print >> out, 'mutation-selection balance rate matrix:'
print >> out, R
print >> out
print >> out, 'mutation-selection balance stationary distribution:'
print >> out, R_v
print >> out
print >> out, 'mutation process expected rate:'
print >> out, mrate.Q_to_expected_rate(M)
print >> out
print >> out, 'mutation-selection balance expected rate:'
print >> out, mrate.Q_to_expected_rate(R)
print >> out
print >> out
print >> out, 'The following information calculations',
print >> out, 'depend on t = %s:' % t
print >> out
print >> out, 'log(ratio(E(L))) for pure mutation:'
print >> out, ctmcmi.get_ll_ratio_wrong(M, t)
print >> out
print >> out, 'log(ratio(E(L))) for mut-sel balance:'
print >> out, ctmcmi.get_ll_ratio_wrong(R, t)
print >> out
print >> out, 'mutual information for pure mutation:'
print >> out, mi_mut
print >> out
print >> out, 'mutual information for mut-sel balance:'
print >> out, mi_bal
print >> out
print >> out, 'pinsker lower bound mi for pure mutation:'
print >> out, ctmcmi.get_pinsker_lower_bound_mi(M, t)
print >> out
print >> out, 'pinsker lower bound mi for mut-sel balance:'
print >> out, ctmcmi.get_pinsker_lower_bound_mi(R, t)
print >> out
print >> out, 'row based pinsker lower bound mi for pure mutation:'
print >> out, ctmcmi.get_row_based_plb_mi(M, t)
print >> out
print >> out, 'row based pinsker lower bound mi for mut-sel balance:'
print >> out, ctmcmi.get_row_based_plb_mi(R, t)
print >> out
print >> out, 'row based hellinger lower bound mi for pure mutation:'
print >> out, ctmcmi.get_row_based_hellinger_lb_mi(M, t)
print >> out
print >> out, 'row based hellinger lower bound mi for mut-sel balance:'
print >> out, ctmcmi.get_row_based_hellinger_lb_mi(R, t)
print >> out
print >> out, 'Fisher information for pure mutation:'
print >> out, fi_mut
print >> out
print >> out, 'Fisher information for mut-sel balance:'
print >> out, fi_bal
print >> out
print >> out, '</pre>'
#
# create the summaries
summaries = (RateMatrixSummary(M), RateMatrixSummary(R))
print >> out, get_html_table(summaries)
print >> out
print >> out, '<html>'
print >> out, '<body>'
return out.getvalue()
def get_response_content(fs):
M, R = get_input_matrices(fs)
M_v = mrate.R_to_distn(M)
R_v = mrate.R_to_distn(R)
t = fs.t
mi_mut = ctmcmi.get_mutual_information(M, t)
mi_bal = ctmcmi.get_mutual_information(R, t)
fi_mut = divtime.get_fisher_information(M, t)
fi_bal = divtime.get_fisher_information(R, t)
if fs.info_mut:
information_sign = np.sign(mi_mut - mi_bal)
elif fs.info_fis:
information_sign = np.sign(fi_mut - fi_bal)
out = StringIO()
print >> out, '<html>'
print >> out, '<body>'
print >> out
print >> out, '<pre>'
print >> out, 'Explicitly computed answer',
print >> out, '(not a heuristic but may be numerically imprecise):'
if information_sign == 1:
print >> out, '* pure mutation',
print >> out, 'is more informative'
elif information_sign == -1:
print >> out, '* the balance of mutation and selection',
print >> out, 'is more informative'
else:
print >> out, ' the information contents of the two processes',
print >> out, 'are numerically indistinguishable'
print >> out
print >> out
if fs.info_mut:
print >> out, 'Mutual information properties',
print >> out, 'at very small and very large times:'
print >> out
print >> out, get_mi_asymptotics(M, R)
print >> out
print >> out
print >> out, 'Heuristics without regard to time or to the selected',
print >> out, 'information variant (Fisher vs. mutual information):'
print >> out
print >> out, get_heuristics(M, R)
print >> out
print >> out
print >> out, 'Input summary:'
print >> out
print >> out, 'mutation rate matrix:'
print >> out, M
print >> out
print >> out, 'mutation process stationary distribution:'
print >> out, M_v
print >> out
print >> out, 'mutation-selection balance rate matrix:'
print >> out, R
print >> out
print >> out, 'mutation-selection balance stationary distribution:'
print >> out, R_v
print >> out
print >> out, 'mutation process expected rate:'
print >> out, mrate.Q_to_expected_rate(M)
print >> out
print >> out, 'mutation-selection balance expected rate:'
print >> out, mrate.Q_to_expected_rate(R)
print >> out
print >> out
print >> out, 'The following information calculations',
print >> out, 'depend on t = %s:' % t
print >> out
print >> out, 'log(ratio(E(L))) for pure mutation:'
print >> out, ctmcmi.get_ll_ratio_wrong(M, t)
print >> out
print >> out, 'log(ratio(E(L))) for mut-sel balance:'
print >> out, ctmcmi.get_ll_ratio_wrong(R, t)
print >> out
print >> out, 'mutual information for pure mutation:'
print >> out, mi_mut
print >> out
print >> out, 'mutual information for mut-sel balance:'
print >> out, mi_bal
print >> out
print >> out, 'pinsker lower bound mi for pure mutation:'
print >> out, ctmcmi.get_pinsker_lower_bound_mi(M, t)
print >> out
print >> out, 'pinsker lower bound mi for mut-sel balance:'
print >> out, ctmcmi.get_pinsker_lower_bound_mi(R, t)
print >> out
print >> out, 'row based pinsker lower bound mi for pure mutation:'
print >> out, ctmcmi.get_row_based_plb_mi(M, t)
print >> out
print >> out, 'row based pinsker lower bound mi for mut-sel balance:'
print >> out, ctmcmi.get_row_based_plb_mi(R, t)
print >> out
print >> out, 'row based hellinger lower bound mi for pure mutation:'
print >> out, ctmcmi.get_row_based_hellinger_lb_mi(M, t)
print >> out
print >> out, 'row based hellinger lower bound mi for mut-sel balance:'
print >> out, ctmcmi.get_row_based_hellinger_lb_mi(R, t)
print >> out
print >> out, 'Fisher information for pure mutation:'
print >> out, fi_mut
print >> out
print >> out, 'Fisher information for mut-sel balance:'
print >> out, fi_bal
print >> out
print >> out, '</pre>'
#
# create the summaries
summaries = (RateMatrixSummary(M), RateMatrixSummary(R))
print >> out, get_html_table(summaries)
print >> out
print >> out, '<html>'
print >> out, '<body>'
return out.getvalue()