def generate_plots(p_list, fobj, mu_vals, fmt_vals, ax=None):
"""Makes plots. Yeah. Logical follow-on to generate_tabs"""
if ax is None:
plt.figure()
ax = plt.gca()
ax.errorbar(fobj.kevs, fobj.data, yerr=fobj.eps, fmt='ok', label='Data')
for p, mu, fmt in zip(p_list, mu_vals, fmt_vals):
fit_type = p.metadata['fit-type']
fit_kws = p.metadata['fit-kws']
if fit_type == 'simp':
kevs_m = np.linspace(fobj.kevs[0]-0.2, fobj.kevs[-1]+0.2, 100)
fwhms_m = models.width_dump(p, kevs_m, fobj.snr)
elif fit_type == 'full':
fill_kevs = np.linspace(fobj.kevs[0]-0.2, fobj.kevs[-1]+0.2, 5)
kevs_m = np.sort(np.hstack((fobj.kevs, fill_kevs)))
if fit_kws is not None and 'model_kws' in fit_kws:
model_kws = fit_kws['model_kws']
else:
model_kws = {}
fwhms_m = models.width_cont(p, kevs_m, fobj.snr, verbose=False,
**model_kws)
ax.plot(kevs_m, fwhms_m, fmt, label=r'$\mu = {:0.2f}$'.format(mu))
# Prepare composite plot
fplot('Energy (keV)', 'FWHM (arcsec)', axargs='tight')
ax.legend(loc='best')
ax.set_title(fobj.title)
return ax
def generate_plots(p_list, fobj, mu_vals, fmt_vals, ax=None):
"""Makes plots. Yeah. Logical follow-on to generate_tabs"""
if ax is None:
plt.figure()
ax = plt.gca()
ax.errorbar(fobj.kevs, fobj.data, yerr=fobj.eps, fmt='ok', label='Data')
for p, mu, fmt in zip(p_list, mu_vals, fmt_vals):
fit_type = p.metadata['fit-type']
fit_kws = p.metadata['fit-kws']
if fit_type == 'simp':
kevs_m = np.linspace(fobj.kevs[0] - 0.2, fobj.kevs[-1] + 0.2, 100)
fwhms_m = models.width_dump(p, kevs_m, fobj.snr)
elif fit_type == 'full':
fill_kevs = np.linspace(fobj.kevs[0] - 0.2, fobj.kevs[-1] + 0.2, 5)
kevs_m = np.sort(np.hstack((fobj.kevs, fill_kevs)))
if fit_kws is not None and 'model_kws' in fit_kws:
model_kws = fit_kws['model_kws']
else:
model_kws = {}
fwhms_m = models.width_cont(p,
kevs_m,
fobj.snr,
verbose=False,
**model_kws)
ax.plot(kevs_m, fwhms_m, fmt, label=r'$\mu = {:0.2f}$'.format(mu))
# Prepare composite plot
fplot('Energy (keV)', 'FWHM (arcsec)', axargs='tight')
ax.legend(loc='best')
ax.set_title(fobj.title)
return ax
def check_eta2_simp(self, eta2_vals, mus, fmts):
"""Plot chi-squared space for eta2, based on simple model"""
ax = plt.gca()
for mu, fmt in zip(mus, fmts):
b0_vals = []
chisqr_vals = []
for eta2 in eta2_vals:
res = self.fitter_simp(mu, eta2=eta2, eta2_free=False)
b0_vals.append(res.params['B0'])
chisqr_vals.append(res.chisqr)
ax.plot(eta2_vals, chisqr_vals, fmt, lw=2)
fplot(r'$\eta_2$ (-)', r'Best fit $\chi^2$', scales=('log', 'log'))
ax.legend(tuple(r'$\mu = {:0.2f}$'.format(mu) for mu in mus),
loc='best')
return ax
def check_eta2_simp(self, eta2_vals, mus, fmts):
"""Plot chi-squared space for eta2, based on simple model"""
ax = plt.gca()
for mu, fmt in zip(mus, fmts):
b0_vals = []
chisqr_vals = []
for eta2 in eta2_vals:
res = self.fitter_simp(mu, eta2=eta2, eta2_free=False)
b0_vals.append(res.params['B0'])
chisqr_vals.append(res.chisqr)
ax.plot(eta2_vals, chisqr_vals, fmt, lw=2)
fplot(r'$\eta_2$ (-)', r'Best fit $\chi^2$', scales=('log','log'))
ax.legend( tuple(r'$\mu = {:0.2f}$'.format(mu) for mu in mus),
loc='best')
return ax
def check_B0_grid(self, eta2, mus, fmts):
"""Plot chisqr(B0) from grid for fixed eta2, multiple mu values
Verify that we can resolve chisq minimum in B0 grid
Input:
eta2: where in grid shall we slice?
mus: list of mu values to check/plot
fmts: list of linespec strings for plots
Output:
matplotlib.Axis object
"""
ax = plt.gca()
for mu, fmt in zip(mus, fmts):
B0_vals, fwhms = self.tab[mu][eta2]
chisqr_vals = self.fwhm_scan(fwhms)
ax.plot(B0_vals * 1e6, chisqr_vals, fmt)
fplot(r'$B_0$ ($\mu$G)', r'$\chi^2$', axargs='tight')
return ax
def check_eta2_grid(self, mus, fmts):
"""Plot chisqr-eta2 space from (eta2,B0) grids for multiple mu values
JACKSON POLLOCK DOES ASTROPHYSICS
Input:
mus: list of mu values to check/plot
fmts: list of linespec strings for plots
Output:
matplotlib.Axis object
"""
ax = plt.gca()
for mu, fmt in zip(mus, fmts):
eta2s, B0s, fwhms, chisqrs = self.grid_scan(mu)
ax.plot(eta2s, chisqrs, fmt)
ax.set_xscale('log')
ax.set_yscale('log')
ax.legend(tuple(r'$\mu={:0.2f}$'.format(mu) for mu in mus), loc='best')
fplot(r'$\eta_2$', r'$\chi^2$', axargs='tight')
return ax
def check_B0_grid(self, eta2, mus, fmts):
"""Plot chisqr(B0) from grid for fixed eta2, multiple mu values
Verify that we can resolve chisq minimum in B0 grid
Input:
eta2: where in grid shall we slice?
mus: list of mu values to check/plot
fmts: list of linespec strings for plots
Output:
matplotlib.Axis object
"""
ax = plt.gca()
for mu, fmt in zip(mus, fmts):
B0_vals, fwhms = self.tab[mu][eta2]
chisqr_vals = self.fwhm_scan(fwhms)
ax.plot(B0_vals*1e6, chisqr_vals, fmt)
fplot(r'$B_0$ ($\mu$G)', r'$\chi^2$', axargs='tight')
return ax
def check_eta2_grid(self, mus, fmts):
"""Plot chisqr-eta2 space from (eta2,B0) grids for multiple mu values
JACKSON POLLOCK DOES ASTROPHYSICS
Input:
mus: list of mu values to check/plot
fmts: list of linespec strings for plots
Output:
matplotlib.Axis object
"""
ax = plt.gca()
for mu, fmt in zip(mus, fmts):
eta2s, B0s, fwhms, chisqrs = self.grid_scan(mu)
ax.plot(eta2s, chisqrs, fmt)
ax.set_xscale('log')
ax.set_yscale('log')
ax.legend(tuple(r'$\mu={:0.2f}$'.format(mu) for mu in mus), loc='best')
fplot(r'$\eta_2$', r'$\chi^2$', axargs='tight')
return ax
def get_fwhm_fits(rdict, n_reg, labels, fitter, blist_reg=None, **kwargs):
"""Compute and store FWHM fits for all rdict bands
Intended for interactive IPython notebook usage
Input:
rdict, n_reg: initialized region dict, region number
fitter: function passed to bdict_fwhm_fit for profile fitting
blist_reg: list of blacklisted (bad) energy bands, else None
Other **kwargs (cap, sub_bkg, dx, xconst) sent to bdict_fwhm_fit
Output:
region dict w/data populated for each energy band
"""
fig, axes = plt.subplots(1, len(rdict), figsize=(5 * len(rdict), 4))
print '\nRegion: {:02d}\n'.format(n_reg), 10 * '='
for lab, ax in zip(labels, axes):
print '\nEnergy band: {}\n'.format(lab)
want_err = not (blist_reg and lab in blist_reg)
if not want_err:
print 'Blacklisted profile'
bdict = bdict_fwhm_fit(rdict[lab], fitter, want_err=want_err, **kwargs)
print 'FWHM = {}, errors {}'.format(bdict['fwhm'], bdict['errs'])
rdu.plot_fwhm_fit(bdict, ax)
rdict[lab] = bdict
plt.show()
plt.figure(figsize=(8, 6))
for lab in labels:
bdict = rdict[lab]
fwhm = bdict['fwhm']
if np.isfinite(fwhm):
nerr, perr = bdict['errs']
en = float(re.split('-', lab)[0])
plt.errorbar([en], [fwhm], yerr=[[nerr], [perr]], fmt='bs')
fplot('Energy (keV)',
'FWHM (arcsec)',
ax=plt.gca(),
axargs=[0., 7., plt.ylim()[0], plt.ylim()[1]])
plt.show()
return rdict
def plot_smoothing_cut(bdict, ax, show_smth=True):
"""Plot cut/uncut data, smoothing used to determine cut (optional)
Adds axis labels and sets limits
"""
x, y, y_err, c = bdict['data']
wlen, wtype = bdict['wlen'], bdict['wtype']
# Plot data outside, inside fit domain
ax.errorbar(x[:c], y[:c], yerr=y_err[:c], fmt='r.', capsize=0, alpha=0.5,
markersize=3)
ax.errorbar(x[c:], y[c:], yerr=y_err[c:], fmt='k.', capsize=0, alpha=1,
markersize=3)
if show_smth: # Smoothing used to determine fit domain
ax.plot(x, fsmooth.std_smooth(y, wlen, wtype), '-k', alpha = 0.3)
# IF necessary, use axargs='tight' and ax.set_ylim(0., ax.get_ylim()[1])
fplot('Radial position (arcsec.)', 'Intensity (a.u.)', ax=ax,
axargs='tight')
ax.set_ylim(0., ax.get_ylim()[1])
return ax
def get_fwhm_fits(rdict, n_reg, labels, fitter, blist_reg=None, **kwargs):
"""Compute and store FWHM fits for all rdict bands
Intended for interactive IPython notebook usage
Input:
rdict, n_reg: initialized region dict, region number
fitter: function passed to bdict_fwhm_fit for profile fitting
blist_reg: list of blacklisted (bad) energy bands, else None
Other **kwargs (cap, sub_bkg, dx, xconst) sent to bdict_fwhm_fit
Output:
region dict w/data populated for each energy band
"""
fig, axes = plt.subplots(1, len(rdict), figsize=(5*len(rdict),4))
print '\nRegion: {:02d}\n'.format(n_reg), 10*'='
for lab, ax in zip(labels, axes):
print '\nEnergy band: {}\n'.format(lab)
want_err = not(blist_reg and lab in blist_reg)
if not want_err:
print 'Blacklisted profile'
bdict = bdict_fwhm_fit(rdict[lab], fitter, want_err=want_err, **kwargs)
print 'FWHM = {}, errors {}'.format(bdict['fwhm'], bdict['errs'])
rdu.plot_fwhm_fit(bdict, ax)
rdict[lab] = bdict
plt.show()
plt.figure(figsize=(8,6))
for lab in labels:
bdict = rdict[lab]
fwhm = bdict['fwhm']
if np.isfinite(fwhm):
nerr, perr = bdict['errs']
en = float(re.split('-', lab)[0])
plt.errorbar([en], [fwhm], yerr=[[nerr], [perr]], fmt='bs')
fplot('Energy (keV)', 'FWHM (arcsec)', ax=plt.gca(),
axargs=[0., 7., plt.ylim()[0], plt.ylim()[1]])
plt.show()
return rdict
def generate_plots(rspecs, datroot, pltroot, labels,
verbose=False, subplot=False):
"""Iterate through generated dat files and make multi-band plots"""
if subplot:
n = len(labels)
plt.figure(figsize=(4*n,4))
else:
plt.figure(figsize=(6,4))
if verbose:
print 'Generating {} plots...'.format(len(rspecs))
print 'First plot will hang a little longer'
# Make a plot for each region
for i in xrange(len(rspecs)):
# Plot line/points for each label
for flab, j in zip(labels, xrange(n)):
dat_fname = '{0}_{1:02d}_band_{2}.dat'.format(datroot, i+1, flab)
a = np.loadtxt(dat_fname)
if subplot:
plt.subplot(1, n, j+1) # j indexes labels
plt.plot(a[:,0], a[:,1], '-o') # Use default color cycle
plt.title('Region {:g}, band {}'.format(i+1, flab))
fplot('Radial dist. (?)', 'Intensity? (?)')
else:
plt.plot(a[:,0], a[:,1], '-o') # Use default color cycle
# Format overall plot
if not subplot:
fplot('Radial dist. (?)', 'Intensity? (?)')
plt.title('Region %g' % (i+1))
plt.legend(labels, loc='best')
plt.tight_layout()
plt.savefig('{0}_{1:02d}.png'.format(pltroot, i+1), dpi=150)
plt.clf()
if verbose:
print 'Saved: {0}_{1:02d}.png'.format(pltroot, i+1)
def generate_plots(rspecs, datroot, pltroot, labels,
verbose=False, subplot=False):
"""Iterate through generated dat files and make multi-band plots"""
if subplot:
n = len(labels)
plt.figure(figsize=(4*n,4))
else:
plt.figure(figsize=(6,4))
if verbose:
print 'Generating {} plots...'.format(len(rspecs))
print 'First plot will hang a little longer'
# Make a plot for each region
for i in xrange(len(rspecs)):
# Plot line/points for each label
for flab, j in zip(labels, xrange(n)):
dat_fname = '{0}_{1:02d}_band_{2}.dat'.format(datroot, i+1, flab)
a = np.loadtxt(dat_fname)
if subplot:
plt.subplot(1, n, j+1) # j indexes labels
plt.plot(a[:,0], a[:,1], '-o') # Use default color cycle
plt.title('Region {:g}, band {}'.format(i+1, flab))
fplot('Radial dist. (?)', 'Intensity? (?)')
else:
plt.plot(a[:,0], a[:,1], '-o') # Use default color cycle
# Format overall plot
if not subplot:
fplot('Radial dist. (?)', 'Intensity? (?)')
plt.title('Region %g' % (i+1))
plt.legend(labels, loc='best')
plt.tight_layout()
plt.savefig('{0}_{1:02d}.png'.format(pltroot, i+1), dpi=150)
plt.clf()
if verbose:
print 'Saved: {0}_{1:02d}.png'.format(pltroot, i+1)
import numpy as np
import matplotlib.pyplot as plt
from fplot import fplot
a = np.loadtxt('tycho_velocs.txt')
msk = a[:, 1] > 2500
nmsk = np.array(map(lambda x: not (x), msk))
plt.errorbar(a[:, 0][msk], a[:, 1][msk], yerr=a[:, 2][msk], fmt='ob')
plt.errorbar(a[:, 0][nmsk], a[:, 1][nmsk], yerr=a[:, 2][nmsk], fmt='or')
fplot('Azimuthal angle east of north (deg.)',
r'Shock velocity (km s${}^{-1}$), with $D = 2.3$ kpc')
plt.show()
print 'Statistics of data with vs > 2500 km/s'
print 'Mean: {}, median: {}'.format(np.mean(a[:, 1][msk]),
np.median(a[:, 1][msk]))
print 'Min: {}, max: {}'.format(min(a[:, 1][msk]), max(a[:, 1][msk]))
import numpy as np
import matplotlib.pyplot as plt
from fplot import fplot
a = np.loadtxt('tycho_velocs.txt')
msk = a[:,1]>2500
nmsk = np.array(map(lambda x: not(x), msk))
plt.errorbar(a[:,0][msk], a[:,1][msk], yerr=a[:,2][msk], fmt='ob')
plt.errorbar(a[:,0][nmsk], a[:,1][nmsk], yerr=a[:,2][nmsk], fmt='or')
fplot('Azimuthal angle east of north (deg.)', r'Shock velocity (km s${}^{-1}$), with $D = 2.3$ kpc')
plt.show()
print 'Statistics of data with vs > 2500 km/s'
print 'Mean: {}, median: {}'.format(np.mean(a[:,1][msk]), np.median(a[:,1][msk]))
print 'Min: {}, max: {}'.format(min(a[:,1][msk]), max(a[:,1][msk]))