def change_limits(self, ax=None, limits=None):
if ax is None:
ax = self.select_subplot()
ax_settings = self.settings['{0:d}.{1:d}'.format(ax.row, ax.col)]
if limits is None:
x_limits = utils.get_input_float('\nPlot limits for ' + \
ax.parameters[0] + \
' (lower upper)?\n> ', num=2)
else:
x_limits = limits[0]
ax.set_xlim(x_limits)
ax_settings['x_limits'] = x_limits
if limits is None:
if len(ax.parameters) == 2:
y_limits = utils.get_input_float('\nPlot limits for ' + \
ax.parameters[1] + \
' (lower upper)?\n> ',
num=2)
else:
y_limits = utils.get_input_float('\nPlot limits for ' + \
'y axis' + \
' (lower upper)?\n> ',
num=2)
elif len(limits) == 2:
y_limits = limits[1]
else:
y_limits = None
if y_limits is not None:
ax.set_ylim(y_limits)
ax_settings['y_limits'] = y_limits
def set_color(self, color=None):
if color is None:
color = utils.get_input_float('\nRGB values for main color?\n' + \
'(3 numbers between 0 and 1 ' + \
'separated by spaces)\n> ',
num=3)
if not np.all([0.<=x<=1. for x in list(color)]):
print 'Values must be between 0 and 1.'
self.set_color()
self.settings['color'] = tuple(color)
def set_color(self, color=None):
if color is None:
color = utils.get_input_float('\nRGB values for main color?\n' + \
'(3 numbers between 0 and 1 ' + \
'separated by spaces)\n> ',
num=3)
if not np.all([0. <= x <= 1. for x in list(color)]):
print 'Values must be between 0 and 1.'
self.set_color()
self.settings['color'] = tuple(color)
def define_new_chain(self):
files = []
while len(files) == 0:
for f in raw_input('\nChain file names?\n> ').split():
new_files = glob.glob(f)
if len(new_files) == 0:
print 'No files matching ' + f + ' found.'
elif not np.array([os.path.isfile(nf) \
for nf in new_files]).all():
print 'Some of the files specified are not valid.'
else:
files += new_files
name = raw_input('Label for chain?\n> ')
burn_in = utils.get_input_float( \
'Burn-in fraction or number of samples?\n> ')[0]
mult_column = utils.get_input_integer( \
'Multiplicity column? (Enter -1 if none.)\n> ')[0]
if mult_column < 0:
mult_column = None
lnlike_column = utils.get_input_integer( \
'Log likelihood column? (Enter -1 if none.)\n> ')[0]
if lnlike_column < 0:
lnlike_column = None
first_par_column = utils.get_input_integer( \
'Column of first chain parameter?\n> ')[0]
m = Menu(options=['File named as chain label + .paramnames',
'A different file',
'Header of chain files'],
exit_str=None,
header='Where are the chain parameter names?')
m.get_choice()
paramname_file = None
params_in_header = False
if m.i_choice == 1:
paramname_file = raw_input('Enter file name:\n> ')
elif m.i_choice == 2:
params_in_header = True
chain_settings = (name, files, burn_in,
mult_column, lnlike_column, first_par_column,
paramname_file, params_in_header)
# check if name is already in history; if so, replace with new
for chain in list(self.history['chains']):
if chain[0] == name:
self.history['chains'].remove(chain)
self.history['chains'].append([1] + list(chain_settings))
return chain_settings
print(
f"\tInsertion Cost={DEFAULT_INS_COST}, Deletion Cost={DEFAULT_DEL_COST}, Match Cost={DEFAULT_MATCH_COST}, Mismatch Cost={DEFAULT_MISMATCH_COST}"
)
use_default = get_input_boolean(
'Do you want to use the above costs? [Y/N]')
#%% Costs
if use_default:
print("* Using default costs *")
ins_cost = DEFAULT_INS_COST
del_cost = DEFAULT_DEL_COST
match_cost = DEFAULT_MATCH_COST
mismatch_cost = DEFAULT_MISMATCH_COST
else:
print("* Defining own costs *")
ins_cost = get_input_float('Please type the insertion penalty/cost')
del_cost = get_input_float('Please type the deletion penalty/cost')
match_cost = get_input_float('Please type the matching penalty/cost')
mismatch_cost = get_input_float(
'Please type the mismatching penalty/cost')
#%% Algorithm Timing
start_time = timer()
V, paths = needleman_wunsch(S1, S2, ins_cost, del_cost, match_cost,
mismatch_cost)
st1, st2, score = reconstruct(S1, S2, V, paths)
combined = combine(V, paths, PATH_CHARACTERS)
end_time = timer()
duration_sec = end_time - start_time
S1 = input('Type the first string (S1)')
S2 = input('Type the second string (S2)')
print("*** Default penalty costs and parameters for f(k) = a + (b * k) ***")
print(f"\ta={DEFAULT_A}, b={DEFAULT_B}, Match Cost={DEFAULT_MATCH_COST}, Mismatch Cost={DEFAULT_MISMATCH_COST}")
use_default = get_input_boolean('Do you want to use the above costs? [Y/N]')
#%% Costs
if use_default:
print("* Using default costs *")
a = DEFAULT_A
b = DEFAULT_B
match_cost = DEFAULT_MATCH_COST
mismatch_cost = DEFAULT_MISMATCH_COST
else:
print("* Defining own costs *")
a = get_input_float('Please type the value for a in f(k) = a + (b * k)')
b = get_input_float('Please type the value for b in f(k) = a + (b * k)')
match_cost = get_input_float('Please type the matching penalty/cost')
mismatch_cost = get_input_float('Please type the mismatching penalty/cost')
#%% Algorithm Timing
start_time = timer()
F, E, G, F_paths, E_paths, G_paths = gotoh(S1, S2, match_cost, mismatch_cost, a, b)
st1, st2, score = reconstruct(S1, S2, F, E, G, F_paths, E_paths, G_paths)
combined_F = combine(F, F_paths, PATH_F_CHARACTERS)
combined_G = combine(G, G_paths, PATH_G_CHARACTERS)
combined_E = combine(E, E_paths, PATH_E_CHARACTERS)
end_time = timer()
duration_sec = end_time - start_time
def define_new_likelihood(self, pdf):
# if chain exists, choose some parameters from there
# can also add new parameters
name = raw_input('Label for likelihood?\n> ')
m = Menu(options=['Flat', 'Gaussian',
'SNe (Gaussian with analytic marginalization)',
'Inverse Gaussian'],
exit_str=None,
header='Choose the form of the likelihood:')
m.get_choice()
form = m.choice
if form[:3] == 'SNe':
form = 'SNe'
lk_dict = {'form': form}
priors = []
if form != 'SNe':
parameters = pdf.choose_parameters(allow_extra_parameters=True)
pdf.add_parameters(parameters)
lk_dict['parameters'] = parameters
if form == 'Flat':
for p in parameters:
if len(pdf.settings['parameters'][p]) != 2:
new_prior = utils.get_input_float( \
'Enter lower and upper limits of the prior on ' + \
p + ':\n> ', num=2)
priors.append(sorted(new_prior))
else:
priors.append(pdf.settings['parameters'][p])
elif form in ['Gaussian', 'Inverse Gaussian']:
means = utils.get_input_float('Enter mean values:\n> ',
num=len(parameters))
variances = utils.get_input_float('Enter variances:\n> ',
num=len(parameters))
for i, p in enumerate(parameters):
if len(pdf.settings['parameters'][p]) != 2:
priors.append([means[i] - 5.*np.sqrt(variances[i]),
means[i] + 5.*np.sqrt(variances[i])])
else:
priors.append(pdf.settings['parameters'][p])
covariance = np.diag(variances)
for i, j in itertools.combinations(range(len(parameters)), 2):
covariance[i, j] = utils.get_input_float( \
'Cov(' + parameters[i] + ', ' + parameters[j] + ')?\n> ')[0]
covariance[j, i] = covariance[i, j]
lk_dict['means'] = means
lk_dict['covariance'] = covariance.tolist()
elif form == 'SNe':
sn_z_mu_file = raw_input('Name of file with mean SN distance ' + \
'modulus vs. z?\n> ')
sn_z, sn_mu = np.loadtxt(sn_z_mu_file, unpack=True)
lk_dict['means'] = list(sn_mu)
sn_cov_file = raw_input('Name of file with SN covariance ' + \
'matrix?\n> ')
sn_cov = np.loadtxt(sn_cov_file)
lk_dict['covariance'] = sn_cov.tolist()
sn_parameters = []
for i, z in enumerate(sn_z):
p_sn_z = 'mu_SN_z' + str(z)
sn_parameters.append(p_sn_z)
priors.append([sn_mu[i] - 5.*np.sqrt(sn_cov[i,i]),
sn_mu[i] + 5.*np.sqrt(sn_cov[i,i])])
pdf.add_parameters(sn_parameters)
lk_dict['parameters'] = sn_parameters
lk_dict['priors'] = priors
new_lk = (name, 1, lk_dict)
# check if name is already in history; if so, replace with new
for lk in list(self.history['likelihoods']):
if lk[0] == name:
self.history['likelihoods'].remove(lk)
self.history['likelihoods'].append(list(new_lk))
return new_lk
