def print_data(self, fp):
fp.write("\\startdata\n")
for i, data in enumerate(self.data):
if self.data_labels[i] != '':
if self.data_label_types == "cutin":
fp.write("\\cutinhead{%s}\n" % self.data_labels[i])
else:
fp.write("\\sidehead{%s}\n" % self.data_labels[i])
rows = []
print 'data=', data
for j in range(numpy.shape(data[0])[0]):
rows.append([])
for k in range(len(data)):
sf = self.sigfigs[i][k]
if len(numpy.shape(data[k])) == 1:
if type(data[k][j]) in float_types:
if numpy.isnan(data[k][j]):
rows[-1].append('\\ldots')
else:
rows[-1].append(
sigfig.round_sig(data[k][j], sf))
else:
rows[-1].append(str(data[k][j]))
else:
print data[k]
if numpy.isnan(data[k][j, 0]):
val = "\\ldots"
else:
val = sigfig.round_sig_error(data[k][j, 0],
data[k][j, 1],
sf,
paren=True)
rows[-1].append(val)
for row in rows:
fp.write(" & ".join(row))
fp.write("\\\\\n")
fp.write("\\enddata\n")
def print_data(self,fp):
fp.write("\\startdata\n")
for i,data in enumerate(self.data):
if self.data_labels[i] != '':
if self.data_label_types == "cutin":
fp.write("\\cutinhead{%s}\n" % self.data_labels[i])
else:
fp.write("\\sidehead{%s}\n" % self.data_labels[i])
rows = []
for j in range(numpy.shape(data[0])[0]):
rows.append([])
for k in range(len(data)):
sf = self.sigfigs[i][k]
if len(numpy.shape(data[k])) == 1:
if type(data[k][j]) in float_types:
if numpy.isnan(data[k][j]):
rows[-1].append('\\ldots')
else:
rows[-1].append(sigfig.round_sig(data[k][j], sf))
else:
rows[-1].append(str(data[k][j]))
else:
print data[k]
print 'El cosos de error', [j,0]
# if numpy.isnan(data[k][j,0]):
if numpy.isnan(data[k][j][0]):
val = "\\ldots"
else:
val = sigfig.round_sig_error(data[k][j][0],data[k][j][1],sf,
paren=True)
rows[-1].append(val)
for row in rows:
fp.write(" & ".join(row))
fp.write("\\\\\n")
fp.write("\\enddata\n")
def add_row(self, row, sigfig, errorup=None, errordown=None, percent=False):
if sigfig is None:
temp = np.array(column).reshape((1, len(temp)))
else:
temp = []
for i, v in enumerate(row):
if (errordown is not None) and (errorup is not None):
rounded = sf.round_sig_error2(v, errorup[i], errordown[i], sigfig)
string = "$%s ^{+%s}_{-%s}" % rounded
elif (errordown is None) and (errorup is not None):
rounded = sf.round_sig_error(v, errorup[i], sigfig)
string = "$%s \\pm %s" % rounded
elif (errorup is None):
rounded = sf.round_sig(v, sigfig)
string = "$%s" % rounded
if percent:
string += "\\%$"
else: string += "$"
temp.append(string)
temp = np.array(temp).reshape((1, len(temp)))
if self.array is None:
self.array = deepcopy(temp)
else:
self.array = np.vstack((self.array, temp))
for file_ in allfiles:
a = np.loadtxt(file_, skiprows = 1) #takes normalized and phased files from Matt's matlab script output
t = a[:,0]
y = a[:,1]
temp = temps[temp_counter]
temp_counter += 1
popt, pcov = scipy.optimize.curve_fit(monoexp, t, y, params, bounds = input_file.mono_bnds) #least squares refinement of parameters against data
a1, T1, b = popt
params = np.array([a1, T1, b]) #sets next temperature guess based on prev. temp refinement
perr = np.sqrt(np.diag(pcov)) #converts covariance to esd
a1_err, T1_err, b_err = perr
if input_file.err_append == True:
try:
a1_str = sigfig.round_sig_error(a1, a1_err, 1, paren = True)
except:
a1_str = '{0} +- {1}'.format(a1, a1_err)
try:
T1_str = sigfig.round_sig_error(T1, T1_err, 1, paren = True)
except:
T1_str = '{0} +- {1}'.format(T1, T1_err)
try:
b_str = sigfig.round_sig_error(b, b_err, 1, paren = True)
except:
b_str = '{0} +- {1}'.format(b, b_err)
data.append((a1_str, T1_str, b_str)) #appends the esd to the end of the value to which it refers
else:
data.append((a1, T1, b))
plt.close(fig5)
# Now, let's do some triangle plots and output the parameters of interest.
# Cepheid parameters:
if corner is not None:
tp1 = c.triangle_plot(['M', 'betaP', 'betaVI', 'betaOH'])
tp1.savefig('Ceph_triangle.pdf')
else:
print "Warning: corner is not installed, so no triangle plots. To install:"
print "pip install corner"
# Now we output some tables.
fout = open('results_table.txt', 'w')
fout.write("Cepheids\n")
fout.write("--------\n")
fout.write('M: %s +/- %s\n' % sigfig.round_sig_error(M, e_M, 2))
fout.write('betaP: %s +/- %s\n' % sigfig.round_sig_error(betaP, e_betaP, 2))
fout.write('betaOH: %s +/- %s\n' %
sigfig.round_sig_error(betaOH, e_betaOH, 2))
hosts = []
headers = ['Host', 'DM', 'betaVI']
headers += ['eps%d' % i for i in range(cfg.model.NGauss)]
cols = [[], []]
ecols = [[], []]
for i in range(cfg.model.NGauss):
cols.append([])
ecols.append([])
if cfg.model.use_MW:
hosts.append('MW')
arr = array(arr).T
labs = ['$a_%d$' % j for j in range(a_s.shape[1])]
labs = labs + ['$\epsilon_{sn}$', '$H_0$']
#tp1 = c.triangle_plot(['M_sn:%d' % i, 'beta_st:%d' % i, 'gamma_st:%d' % i,
# 'eps_sn:%d' % i, 'H0'])
tp1 = corner.corner(arr, labels=labs, truths=median(arr, axis=0))
tp1.savefig('SN_triangle_%s.pdf' % cfg.data.sn_filt[i])
plt.close(tp1)
# Now we output some tables.
fout = open('results_table.txt', 'w')
fout.write("SNe\n")
fout.write("---\n")
fout.write('v_pec: %s +/- %s\n' %
sigfig.round_sig_error(pec_vel, e_pec_vel, 2))
headers = ['filter']
headers = headers + ["a[%d]" % i for i in range(Nbasis)]
headers = headers + ["eps"]
if len(cfg.data.sn_filt) == 1:
cols = [[a[i]] for i in range(Nbasis)] + [[eps_sn]]
errors = [[e_a[i]] for i in range(Nbasis)] + [[e_eps_sn]]
else:
cols = [a[i, :] for i in range(Nbasis)] + [eps_sn]
errors = [e_a[i, :] for i in range(Nbasis)] + [e_eps_sn]
labels = cfg.data.sn_filt
print cols
lines = sigfig.format_table(cols=cols,
errors=errors,
n=2,
labels=labels,
the_R = Rlambs[:, i]
the_sig = evar[:, i]
covmat = cov([the_a, the_b, the_c, the_R])
med_a = median(the_a)
med_b = median(the_b)
med_c = median(the_c)
med_R = median(the_R)
e_a = sqrt(covmat[0, 0])
e_b = sqrt(covmat[1, 1])
e_c = sqrt(covmat[2, 2])
e_R = sqrt(covmat[3, 3])
sig = sqrt(median(the_sig))
l = ['%s(%s)' % (data.filters[i], "-".join(cf.Data.color))]
l += list(sigfig.round_sig_error(med_a, e_a, 2))
l += list(sigfig.round_sig_error(med_b, e_b, 2))
l += list(sigfig.round_sig_error(med_c, e_c, 2))
l += list(sigfig.round_sig_error(med_R, e_R, 2))
l += [sigfig.round_sig(sig, 2)]
outcols.append(l)
outcols2 = [[sigfig.round_sig(covmat[i,j],2) for i in range(3)] \
for j in range(3)]
format = ""
for j in range(len(outcols[0])):
maxlen = max([len(outcols[i][j]) for i in range(len(outcols))])
format += " %%%ds" % maxlen
print >> f, "H0 = %.3f +\- %.3f" % (H0, eH0)
the_a = a[:,f2,:]
elif f2 == -1:
# something - B
the_a = -a[:,f1,:]
else:
the_a = a[:,f2,:] - a[:,f1,:]
med_a = median(the_a, axis=0)
covmat = cov(the_a.T)
e_a = sqrt(diag(covmat))
varc = (evar[f1] + evar[f2])/2
this_col = ['%s-%s' % (cols[0],cols[1])]
for i in range(med_a.shape[0]):
this_col += list(sigfig.round_sig_error(med_a[i], e_a[i], 2))
this_col += [sigfig.round_sig(sqrt(varc),2)]
outcols.append(this_col)
format = ""
for j in range(len(outcols[0])):
maxlen = max([len(outcols[i][j]) for i in range(len(outcols))])
format += " %%%ds" % maxlen
labs = ['#clr']
for i in range(med_a.shape[0]):
labs += ['a[%d]' % i,'+/-']
labs += ['sig']
labs = tuple(labs)