def sp1_style( ax ):
wf.ax_limits( sp1_lims )
pl.minorticks_on()
wf.ax_labels( sp1_ax_labs )
# wf.major_ticks( ax, sp1_maj_loc )
# pl.legend( sp1_entries, loc=sp1_leg_loc ) # locate legend x,y from bottom left
ax.yaxis.set_ticklabels([]) # y tick labels off
def run_setup():
mpl.rcParams.update(mpl.rcParamsDefault)
mpl.rcParams['font.size'] = 26.
mpl.rcParams['font.family'] = 'serif'
#mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['font.serif'] = [ 'Times New Roman',
'Times','Palatino',
'Charter', 'serif']
mpl.rcParams['font.sans-serif'] = ['Helvetica']
mpl.rcParams['axes.labelsize'] = 24
mpl.rcParams['xtick.labelsize'] = 22.
mpl.rcParams['ytick.labelsize'] = 22.
mpl.rcParams['xtick.major.size']= 10.
mpl.rcParams['xtick.minor.size']= 8.
mpl.rcParams['ytick.major.size']= 10.
mpl.rcParams['ytick.minor.size']= 8.
mpl.rc('axes',**{'labelweight':'normal', 'linewidth':1})
mpl.rc('axes',**{'labelweight':'normal', 'linewidth':1})
mpl.rc('ytick',**{'major.pad':5, 'color':'k'})
mpl.rc('xtick',**{'major.pad':5, 'color':'k'})
#legend parameters too
params = {'legend.fontsize': 24,
'legend.numpoints':1,
'legend.handletextpad':1
}
plt.rcParams.update(params)
plt.minorticks_on()
def plothistogram():
#plotting the histograms
py.figure(2,(6.,3.5))
ax1 = py.subplot(2,1,1)
py.plot(xgauss,num,'k',linewidth = 2,drawstyle = 'steps-pre')
py.plot(xgauss,ygauss,'r--',linewidth = 2)
py.setp(ax1.get_xticklabels(),visible = False)
py.ylim(0,40)
py.yticks([0.0,20,40])
py.xlim(-1.5,1.5)
py.ylabel('N')
py.xlabel('R$_{VI}$')
py.minorticks_on()
ax2 = py.subplot(2,1,2)
py.plot(xgauss,resid,'k',linewidth = 2,drawstyle = 'steps-pre')
py.plot(xgauss,zeroline,'k--',linewidth = 1)
py.yticks([-10,0,10,20])
py.xlim(-2,2)
py.xlabel('R$_{VI}$')
py.ylabel('$\Delta$N')
py.minorticks_on()
py.subplots_adjust(hspace = 0)
py.show()
def test_decomp():
nmax = 10.
kmax = 10.
dx = 0.1 * 2.**(-nmax)
x = np.arange(-10., 10., dx)
f = (2. / np.pi) * np.arctan(0.5 * x)
nvals = np.arange(-nmax, nmax)
kvals = np.arange(-kmax, kmax)
Cnk = wavelet_decomp(x, f, nvals, kvals)
fig, ax = plt.subplots()
im = ax.matshow(Cnk,
origin='lower',
vmin=-1,
vmax=1.5,
extent=(-nmax, nmax, -kmax, kmax))
ax.xaxis.set_ticks_position('bottom')
plt.minorticks_on()
ax.grid(b=True, which='major', color='k', linestyle='-')
ax.grid(b=True, which='minor', color='Grey', linestyle='--', dashes=(2, 5))
ax.set_ylabel(r"$k$", fontsize=18)
ax.set_xlabel(r"$n$", fontsize=18)
plt.colorbar(im)
plt.savefig('Cnk_matrix.pdf')
#plt.show()
plt.clf()
fr = wavelet_recov(Cnk, nvals, kvals, x)
plt.plot(x, f, label='Original')
plt.plot(x, fr, label='Recovered')
plt.legend(loc='lower right')
plt.savefig('recovered_function.pdf')
def view(self, show=True):
""" View generated beam """
if self.generated_beam_data is None:
raise RuntimeError("Beam pattern not generated yet. Run generate() first.")
plt.figure(figsize=(8,4))
plt.subplot(121)
tmin, tmax = self.generated_beam_theta[0], self.generated_beam_theta[-1]
pmin, pmax = self.generated_beam_phi[0], self.generated_beam_phi[-1]
plt.imshow(10**(self.generated_beam_data / 10), extent=(tmin, tmax, pmin, pmax), aspect='auto')
plt.xlabel("Theta [deg]")
plt.ylabel("Phi [deg]")
#plt.colorbar(orientation='horizontal')
plt.subplot(122)
beam_slice = self.generated_beam_data[self.generated_beam_data.shape[0]/2]
print self.generated_beam_phi.shape, beam_slice.shape
plt.plot(self.generated_beam_theta, beam_slice, c='#333333')
plt.xlabel("Theta [deg]")
plt.ylabel("Normalized gain [dB]")
plt.xlim(-91, 91)
plt.ylim(-30, 3)
plt.minorticks_on()
plt.tight_layout()
if show:
plt.show()
def wavelengthScalePlot(out_dir, base_name, order):
pl.figure('wavelength scale', facecolor='white')
pl.cla()
pl.title('wavelength scale, ' + base_name + ', order ' + str(order.orderNum), fontsize=14)
pl.xlabel('column (pixels)')
pl.ylabel('wavelength ($\AA$)')
pl.minorticks_on()
pl.grid(True)
pl.xlim(0, 1023)
pl.plot(order.gratingEqWaveScale, "k-", mfc='none', ms=3.0, linewidth=1,
label='grating equation')
if order.waveScale is not None:
pl.plot(order.waveScale, "b-", mfc='none', ms=3.0, linewidth=1,
label='sky lines')
pl.legend(loc='best', prop={'size': 8})
fn = constructFileName(out_dir, base_name, order.orderNum, 'wavelength_scale.png')
pl.savefig(fn)
pl.close()
return
def plot_coordinate(index, name, negate=False, label=None):
ax = pl.subplot(3, 1, index)
if negate:
plot_primary_leg(name, mult=-1.0)
plot_opposite_leg(name, mult=-1.0)
else:
plot_primary_leg(name)
plot_opposite_leg(name)
# TODO this next line isn't so great of an idea:
if label == None:
label = name.replace('_', ' ')
pl.ylabel('%s (N-m)' % label)
pl.legend()
if toeoff_time != None:
duration = cycle_end - cycle_start
# 'pgc' is percent gait cycle
if toeoff_time > primary_footstrike:
toeoff_pgc = percent_duration_single(
toeoff_time, primary_footstrike,
duration + primary_footstrike)
else:
chunk1 = cycle_end - primary_footstrike
chunk2 = toeoff_time - cycle_start
toeoff_pgc = (chunk1 + chunk2) / duration * 100.0
pl.plot(toeoff_pgc * np.array([1, 1]),
ax.get_ylim(),
c=(0.5, 0.5, 0.5))
pl.xticks([0.0, 25.0, 50.0, 75.0, 100.0])
pl.minorticks_on()
pl.grid(b=True, which='major', color='gray', linestyle='--')
pl.grid(b=True, which='minor', color='gray', linestyle='--')
def plot_ACF(y_avg, n):
n_lags = len(y_avg) // 2
y_avg = differencing(y_avg, n)
results_acf = acf(y_avg, nlags=n_lags - 1)
plt.subplot(2, 1, 1)
plt.plot(range(n_lags), results_acf, '-*')
plt.minorticks_on()
plt.xlabel("lag")
plt.axhline(y=0, c='green', ls='--')
plt.axhline(y=0.2, c='orange', ls='--')
plt.axhline(y=-0.2, c='orange', ls='--')
plt.grid(True, which='both')
plt.ylabel("value of ACF")
plt.title("ACF on Fourier residual")
results_pacf = pacf(y_avg, nlags=n_lags - 1, method='ols')
plt.subplot(2, 1, 2)
plt.plot(range(n_lags), results_pacf, '-*')
plt.minorticks_on()
plt.xlabel("lag")
plt.axhline(y=0, c='green', ls='--')
plt.axhline(y=0.2, c='orange', ls='--')
plt.axhline(y=-0.2, c='orange', ls='--')
plt.grid(True, which='both')
plt.ylabel("value of PACF")
plt.title("PACF on Fourier residual")
plt.suptitle('ACF and PACF plots with differencing = {0}'.format(n))
plt.show()
def style_plot( convolved = True ):
pl.xlim( x_min, x_max )
pl.xlabel( x_str )
pl.ylabel( y_str )
pl.minorticks_on()
if convolved:
pl.xlim( x_min_conv, x_max_conv )
def wavelengthScalePlot(out_dir, base_name, order):
pl.figure('wavelength scale', facecolor='white')
pl.cla()
pl.title('wavelength scale, ' + base_name + ', order ' +
str(order.flatOrder.orderNum), fontsize=14)
pl.xlabel('column (pixels)')
pl.ylabel('wavelength ($\AA$)')
pl.minorticks_on()
pl.grid(True)
pl.xlim(0, 1023)
pl.plot(order.flatOrder.gratingEqWaveScale, "k-", mfc='none', ms=3.0, linewidth=1,
label='grating equation')
if order.waveScale is not None:
pl.plot(order.waveScale, "b-", mfc='none', ms=3.0, linewidth=1,
label='sky lines')
pl.legend(loc='best', prop={'size': 8})
fn = constructFileName(out_dir, base_name, order.flatOrder.orderNum, 'wavelength_scale.png')
pl.savefig(fn)
pl.close()
return
def specrect_plot(outpath, base_name, order_num, before, after):
pl.figure('spectral rectify', facecolor='white')
pl.cla()
pl.title('spectral rectify, ' + base_name + ', order ' + str(order_num),
fontsize=14)
pl.xlabel('column (pixels)')
pl.ylabel('intensity (counts)')
pl.minorticks_on()
pl.grid(True)
pl.xlim(0, 1023)
pl.plot(before[10, :],
"k-",
mfc='none',
ms=3.0,
linewidth=1,
label='before')
pl.plot(after[10, :], "b-", mfc='none', ms=3.0, linewidth=1, label='after')
pl.legend(loc='best', prop={'size': 8})
fn = constructFileName(outpath, base_name, order_num, 'specrect.png')
pl.savefig(fn)
pl.close()
return
def plot_s11_logmag(filename, c0='#333333', label=None, ls='solid'):
""" Plot S11 log mag """
colnames, data = read_s2p(filename)
plt.plot(data[:, 0], data[:, 1], c=c0, label=label, ls=ls)
plt.xlabel(colnames[0])
plt.ylabel(colnames[1])
plt.minorticks_on()
def test_decomp():
nmax = 10.; kmax = 10.
dx = 0.1*2.**(-nmax)
x = np.arange(-10.,10.,dx)
f = (2./np.pi)*np.arctan(0.5*x)
nvals = np.arange(-nmax,nmax)
kvals = np.arange(-kmax,kmax)
Cnk = wavelet_decomp(x,f,nvals,kvals)
fig, ax = plt.subplots()
im = ax.matshow(Cnk,origin='lower',vmin=-1,vmax=1.5,extent=(-nmax,nmax,-kmax,kmax))
ax.xaxis.set_ticks_position('bottom')
plt.minorticks_on()
ax.grid(b=True, which='major', color='k', linestyle='-')
ax.grid(b=True, which='minor', color='Grey', linestyle='--',dashes=(2,5))
ax.set_ylabel(r"$k$",fontsize=18)
ax.set_xlabel(r"$n$",fontsize=18)
plt.colorbar(im)
plt.savefig('Cnk_matrix.pdf')
#plt.show()
plt.clf()
fr = wavelet_recov(Cnk,nvals,kvals,x)
plt.plot(x,f,label='Original')
plt.plot(x,fr,label='Recovered')
plt.legend(loc='lower right')
plt.savefig('recovered_function.pdf')
def plot_s21_logmag(filename, c0='#333333', label=None, atten=0):
""" Plot S11 log mag """
colnames, data = read_s2p(filename)
plt.plot(data[:, 0], data[:, 3] + atten, c=c0, label=label)
plt.xlabel(colnames[0])
plt.ylabel(colnames[1])
plt.minorticks_on()
def tracePlot(outpath, base_name, order_num, raw, fit, mask):
pl.figure("Trace Plot", figsize=(6, 5), facecolor='white')
pl.title('trace, ' + base_name + ", order " + str(order_num), fontsize=14)
pl.xlabel('column (pixels)')
pl.ylabel('row (pixels)')
# yrange = offraw.max() - offraw.min()
x = np.arange(raw.shape[0])
pl.plot(x[mask], raw[mask], "ko", mfc="none", ms=1.0, linewidth=1, label="derived")
pl.plot(x, fit, "k-", mfc="none", ms=1.0, linewidth=1, label="fit")
pl.plot(x[np.logical_not(mask)], raw[np.logical_not(mask)],
"ro", mfc="red", mec="red", ms=2.0, linewidth=2, label="ignored")
rms = np.sqrt(np.mean(np.square(raw - fit)))
pl.annotate('RMS residual = ' + "{:.3f}".format(rms), (0.3, 0.8), xycoords="figure fraction")
pl.minorticks_on()
pl.grid(True)
pl.legend(loc='best', prop={'size': 8})
fn = constructFileName(outpath, base_name, order_num, 'trace.png')
pl.savefig(fn)
pl.close()
log_fn(fn)
return
def single_plot_paras(fontsize=15, i_test=0, numpoints=1):
"""parameters for a single plot"""
if i_test == 1:
x = numpy.linspace(0, 1, 30)
y = numpy.sin(x)
pylab.figure()
pylab.plot(x, y, '*')
pylab.legend([
'test',
])
pylab.xlabel('x label')
pylab.ylabel('y label')
pylab.title('test plot')
pylab.rcParams['legend.numpoints'] = numpoints
pylab.rcParams.update({'font.size': fontsize})
pylab.grid('on')
pylab.minorticks_on()
pylab.tick_params(which='major',
labelsize=fontsize,
width=2,
length=10,
color='black')
pylab.tick_params(which='minor', width=1, length=5)
pylab.tight_layout()
if i_test == 1:
pylab.show()
def view_thetaphi(self, show=True):
""" View generated beam, in theta-phi coordinates """
self._check_generated()
plt.figure(figsize=(8,4))
plt.subplot(121)
tmin, tmax = self.generated_beam_theta[0], self.generated_beam_theta[-1]
pmin, pmax = self.generated_beam_phi[0], self.generated_beam_phi[-1]
plt.imshow(10**(self.generated_beam_data / 10), extent=(tmin, tmax, pmin, pmax), aspect='auto')
plt.xlabel("Theta [deg]")
plt.ylabel("Phi [deg]")
#plt.colorbar(orientation='horizontal')
plt.subplot(122)
beam_slice = self.generated_beam_data[self.generated_beam_data.shape[0]/2]
print self.generated_beam_phi.shape, beam_slice.shape
plt.plot(self.generated_beam_theta, beam_slice, c='#333333')
plt.xlabel("Theta [deg]")
plt.ylabel("Normalized gain [dB]")
plt.xlim(-91, 91)
plt.ylim(-30, 3)
plt.minorticks_on()
plt.tight_layout()
if show:
plt.show()
def initiatePlotting(self):
self.LINES = []
self.line, = plot([0], [0], marker=".")
grid(b=True, which='major', color='0', linestyle='-', alpha=0.3)
grid(b=True, which='minor', color='0', linestyle='-', alpha=0.05)
minorticks_on()
ion()
def my_plot(l1,l2,ll1,ll2,li1,li2,xx,n):
fig = plt.figure(num=None, figsize=(8, 8), dpi=80, facecolor='w', edgecolor='k')
## plt.subplot(1,2,1)
## plt.plot(l1)
## plt.plot(l2)
## plt.grid(1,which='both')
## plt.minorticks_on()
## plt.axvline(x=l2_index,c='g',ls='--')
## plt.axvline(x=l1_index,c='r',ls='--')
## plt.axvline(x=l1_index_rise,c='b',ls='--')
##
## plt.subplot(1,2,2)
plt.plot(xx,ll1,'.-')
plt.plot(xx,ll2,'.-')
xx = np.linspace(1,n,n)
plt.plot(xx,li1,'*-')
plt.plot(xx,li2,'*-')
plt.grid(1,which='both')
plt.minorticks_on()
plt.suptitle('file name : {0}, {1}, time : {2}, ratio : {3} / {4}'.format(read_name,tt1,l2_index,len(ll1),n))
plt.savefig('D:/OneDrive - Tata Insights and Quants, A division of Tata Industries/Confidential/Projects/Steel/LD2 BDS/prelim_analysis/plots/second iteration/extracted_true/{0}_{1}_time_{2}.png'.format(read_name,tt1,l2_index))
plt.close()
## plt.show()
##
pass
def prettifyPlot(xlabel,ylabel): plt.xlabel(xlabel,fontsize=30) plt.ylabel(ylabel,fontsize=30) plt.tick_params(axis='both',labelsize=20) plt.minorticks_on() plt.tick_params(which='both', width=2,direction='in') #this has to be a separate line because width can't be set when using which='minor' plt.tick_params(which='major', length=8, direction='in') #If you want tick marks on the opposite side also, add right=True plt.tick_params(which='minor', length=4)
def sp1_style( ax ):
pl.xlim( sp1_x_lim )
pl.ylim( sp1_y_lim )
pl.minorticks_on()
ax.xaxis.set_major_locator( mpl.ticker.MultipleLocator( sp1_maj_loc[0] ) )
ax.yaxis.set_major_locator( mpl.ticker.MultipleLocator( sp1_maj_loc[1] ) )
ax.xaxis.set_ticklabels( sp1_x_tick_labs ) # x tick labels
mpl.rc( 'legend', numpoints=1, handletextpad=0.5, borderpad=1 )
def spectrumPlot2(outpath,
base_name,
title,
order_num,
y_units,
cont1,
cont2,
wave,
wave_note='unknown'):
"""
Borrow from the code in NSDRP: products.py: to compare flux and noise
"""
if const.upgrade:
endPix = 2048 - 40
else:
endPix = 1024 - 20
pl.figure(title, facecolor='white')
pl.clf()
pl.title(title + ', ' + base_name + ", order " + str(order_num),
fontsize=12)
pl.xlabel('wavelength ($\AA$) (' + wave_note + ')')
if len(y_units) > 0:
pl.ylabel(title + '(' + y_units + ')')
else:
pl.ylabel(title)
pl.grid(True)
pl.plot(wave[:endPix],
cont1[:endPix],
"k-",
wave[:endPix],
cont2[:endPix],
"r-",
mfc="none",
ms=3.0,
linewidth=1)
ymin, ymax = pl.ylim()
pl.plot(wave,
cont1,
"k-",
wave,
cont2,
"r-",
mfc="none",
ms=3.0,
linewidth=1)
pl.ylim(ymin, ymax)
pl.minorticks_on()
# axes = pl.gca()
#axes.set_ylim(0, 300)
fn = constructFileName(outpath, base_name, order_num, title + '.png')
savePreviewPlot(fn)
log_fn(fn)
return
def draw_results(self, T, X):
pylab.close()
pylab.plot(X, T, color='r')
pylab.grid(which='major', color='silver', linestyle="-", linewidth=1)
pylab.minorticks_on()
pylab.title('Tube temperature')
pylab.ylabel('T, K', rotation=0)
pylab.xlabel('Len, cm')
pylab.show()
def show_grid():
minorticks_on()
grid(which='major', linestyle='-')
grid(which='minor', linestyle='--')
xticks(fontsize=12, fontweight='bold', family='Times New Roman')
yticks(fontsize=12,
fontweight='bold',
family='Times New Roman',
rotation=90)
def plot_gplane(d, title='', cbar_label="DM [pc cm$^{-3}$]"):
plt.imshow(d.T[::-1, ::-1], extent=(-180, 180, -90, 90), cmap='magma')
plt.ylim(-60, 60)
cb = plt.colorbar()
cb.set_label(cbar_label)
plt.title(title)
plt.xticks([-180, -120, -60, 0, 60, 120, 180])
plt.yticks([-60, -30, 0, 30, 60])
plt.ylabel("gb [deg]")
plt.minorticks_on()
def axes_format( subplot_number ):
ax = pl.gca()
ax.set_xlabel( x_axis_label )
ax.set_ylabel( y_axis_label )
ax.xaxis.major.formatter._useMathText = True
ax.yaxis.major.formatter._useMathText = True
majorLocator = mpl.ticker.MultipleLocator( major_locators[ subplot_number - 1 ] )
ax.xaxis.set_major_locator( majorLocator )
ax.yaxis.set_major_locator( majorLocator )
pl.minorticks_on() # minor ticks on
def result_plot(history):
# list all data in history
print(history.history.keys())
# summarize history for loss
plt.plot(history.history['loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.minorticks_on()
plt.grid(1, 'both')
plt.show()
def plot_antenna_array(antpos):
""" Plot an antenna array (2D X-Y positions). """
antpos = np.array(antpos)
plt.plot(antpos[:,0], antpos[:, 1], 'o', c='#333333')
plt.xlabel('x-pos [m]')
plt.ylabel('y-pos [m]')
#plt.xlim(np.min(antpos[:, 0]) - 2, np.max(antpos[:, 0]) + 2)
#plt.ylim(np.min(antpos[:, 1]) - 2, np.max(antpos[:, 1]) + 2)
plt.minorticks_on()
plt.savefig("figures/antenna-positions.pdf")
plt.show()
def plot_antenna_array(antpos):
""" Plot an antenna array (2D X-Y positions). """
antpos = np.array(antpos)
plt.plot(antpos[:, 0], antpos[:, 1], 'o', c='#333333')
plt.xlabel('x-pos [m]')
plt.ylabel('y-pos [m]')
#plt.xlim(np.min(antpos[:, 0]) - 2, np.max(antpos[:, 0]) + 2)
#plt.ylim(np.min(antpos[:, 1]) - 2, np.max(antpos[:, 1]) + 2)
plt.minorticks_on()
plt.savefig("figures/antenna-positions.pdf")
plt.show()
def format_axes( subplot_number ):
ax = pl.gca()
ax.set_xlabel( x0_lab )
ax.set_ylabel( y0_lab )
ax.xaxis.major.formatter._useMathText = True
ax.yaxis.major.formatter._useMathText = True
majorLocator = mpl.ticker.MultipleLocator( major_locators[ subplot_number ] )
ax.xaxis.set_major_locator( majorLocator )
ax.yaxis.set_major_locator( majorLocator )
pl.minorticks_on() # minor ticks on
pl.xlim( x_lims[ subplot_number ] ) #define chart limits
pl.ylim( y_lims[ subplot_number ] )
def plot_multiple_1d( data, col_0, col_n, color='', style='', shift='' ):
columns = data.shape[1]
x = data[ :, 0 ]
if len( shift ) == 2 :
shift_value = shift[ 0 ]
shift_center = shift[ 1 ]
x = x + ( shift_center - shift_value )
for i in range( col_0, col_n ):
pl.plot( x, data[ :, i ], color = color, marker = style )
pl.minorticks_on()
pl.show()
def plotabsxsec(axs1,axs2,freq,pres,absspec,
abswhich=None,doboth=1,
meandelta=0, facdiff=0,
newfig=True,save=False,
title='Spectroscopy HITRAN vs. Toolbox: ',
casename='H-vs-TB',
outdir='~/projects/MicrowavePropagationToolbox/study/Validation/basics/figures/'):
'''
'''
sps1,sps2 = prep2to1plot()
col=['b','g','r','c','m','y','k']
if abswhich is None:
abswhich=N.arange(axs1.shape[1])
for i in abswhich:
if ((axs1[0,i,:,:]+axs2[0,i,:,:]).max()!=0.):
p.figure()
ax1=p.subplot(sps1)
ax2=p.subplot(sps2,sharex=ax1)
for j in N.arange(axs2.shape[-1]):
if meandelta:
ax2.plot(freq*1e-9,(axs2[0,i,:,j]-axs1[0,i,:,j])/(axs2[0,i,:,j]+axs1[0,i,:,j])*2e2,
col[j%N.size(col)]+'-',linewidth=2)
elif facdiff:
# ax2.semilogy(freq*1e-9,N.maximum(axs1[0,i,:,j]/axs2[0,i,:,j],axs2[0,i,:,j]/axs1[0,i,:,j])*1e2-1e2,
# col[j%N.size(col)]+'-',linewidth=2)
ax2.semilogy(freq*1e-9,N.maximum(axs1[0,i,:,axs2.shape[-1]-j-1]/axs2[0,i,:,axs2.shape[-1]-j-1],axs2[0,i,:,axs2.shape[-1]-j-1]/axs1[0,i,:,axs2.shape[-1]-j-1])-1.,
col[(axs2.shape[-1]-j-1)%N.size(col)]+'-',linewidth=2)
else:
ax2.plot(freq*1e-9,axs2[0,i,:,j]/axs1[0,i,:,j]*1e2-1e2,
col[j%N.size(col)]+'-',linewidth=2)
ax1.semilogy(freq*1e-9,axs2[0,i,:,j],
col[j%N.size(col)]+'-',linewidth=2,label='%.1ePa' %pres[j])
if doboth:
ax1.semilogy(freq*1e-9,axs1[0,i,:,j],col[j%N.size(col)]+'--',linewidth=2)
ax1.set_xlabel('Frequency [GHz]')
ax1.set_ylabel('absolute cross section [m2]')
if facdiff:
ax2.set_ylabel('factor difference [-]')
else:
ax2.set_ylabel('relative difference [%]')
ax1.legend(loc=0)
p.minorticks_on()
p.title('%s %s' %(title,absspec[i]))
p.tight_layout()
if save:
p.savefig('%sAbsXsec_%s_%s_xsec-log_drel-lin.png'
%(os.path.expanduser(outdir),casename,absspec[i]))
else:
print('No abs xs !=0 (no lines? vmr=0?) for %s' %absspec[i])
p.show()
return ax1,ax2
def specrect_plot2(outpath,
base_name,
order_num,
before,
after,
eta=None,
arc=None):
if const.upgrade:
endPix = 2048
else:
endPix = 1024
pl.figure('spectral rectify', facecolor='white')
pl.cla()
pl.title('spectral rectify, ' + base_name + ', order ' + str(order_num),
fontsize=14)
pl.xlabel('column (pixels)')
pl.ylabel('intensity (counts)')
pl.minorticks_on()
pl.grid(True)
pl.xlim(0, endPix - 1)
pl.plot(before[10, :],
"k-",
mfc='none',
ms=3.0,
linewidth=1,
label='before',
alpha=0.5)
pl.plot(after[10, :],
"b-",
mfc='none',
ms=3.0,
linewidth=0.7,
label='after',
alpha=0.5)
pl.legend(loc='best', prop={'size': 8})
pl.minorticks_on()
fn = constructFileName(outpath, base_name, order_num, 'specrectplot.png')
pl.savefig(fn)
#pl.show()
pl.close()
return
def make_plot(self):
'''Plot the mass detection limits.'''
# Plot BTsettl curve
plt.plot(self.projsep, self.Spiegmasslim, 'b-', lw=2,
label='Spiegel & Burrows')
plt.plot(self.projsep, self.BTmasslim, 'r-', lw=2,
label=' BT-Settl')
plt.xlabel('Projected Separation (AU)')
plt.ylabel(' Planet Mass (M$_{\mathrm{Jup}})$')
plt.minorticks_on()
plt.legend(loc='upper right')
plt.savefig('plots/'+self.title+'.png')
plt.show()
def plot_lombscargle(self):
from scipy.signal import lombscargle
n = 2000
f = np.linspace(2. * np.pi / 20, 2. * np.pi / 0.1, n)
pgram = lombscargle(self.t, self.m, f)
period = 1. / (f / (2. * np.pi))
plt.plot(period, np.sqrt(4 * (pgram / n)), 'r')
plt.xlabel('period [days]')
plt.ylabel('FAP')
plt.xticks(np.arange(20))
plt.xlim(0.1, 20)
plt.minorticks_on()
plt.grid()
def plot_raw(y, time):
n = 60
plt.subplot(2, 1, 1)
plt.plot(time, y, '.-')
plt.plot(time, pd.rolling_mean(y, n), '.-', color='purple')
plt.minorticks_on()
plt.legend(['raw', 'MA 60'])
plt.grid(b=True, which='both')
plt.subplot(2, 1, 2)
plt.plot(time, np.log(y), '.-', color='black')
plt.plot(time, pd.rolling_mean(np.log(y), n), '.-', color='green')
plt.minorticks_on()
plt.legend(['log', 'MA 60'])
plt.grid(b=True, which='both')
plt.show()
def plot_monthly(y_avg, time_avg):
n = 6
plt.subplot(2, 1, 1)
plt.plot(time_avg, y_avg, '.-')
plt.plot(time_avg, pd.rolling_mean(y_avg, n), '.-', color='purple')
plt.minorticks_on()
plt.legend(['raw monthly', 'MA 6'])
plt.grid(b=True, which='both')
plt.subplot(2, 1, 2)
plt.plot(time_avg, np.log(y_avg), '.-', color='black')
plt.plot(time_avg, pd.rolling_mean(np.log(y_avg), n), '.-', color='green')
plt.minorticks_on()
plt.legend(['log monthly', 'MA 6'])
plt.grid(b=True, which='both')
plt.show()
def plot_psd(self, show=False, save=False):
from psd import ppsd
t, m = self.t, self.m - np.mean(self.m)
n = len(t)
t_padded = np.zeros(8 * n)
t_padded[:n] = t
t_padded[n:] = np.linspace(max(t), 8 * max(t), 7 * n)
m_padded = np.zeros(8 * n)
m_padded[:n] = m
filename = config.datapath + '/psd/%s.psd' % self.starid
try:
px, f = np.loadtxt(filename, unpack=True)
except IOError:
# perform a power spectrum analysis
px, f = ppsd(t_padded, m_padded, lower=1. / 20, upper=1. / 0.1)
np.savetxt(filename, np.column_stack((px, f)))
px = np.sqrt(px)
i = np.argmax(px)
period1 = 1. / f[i]
t_window = np.linspace(0, 8 * max(t), 8 * max(t) * 24)
m_window = np.zeros(len(t_window))
for tt in t:
i = np.argmin(abs(t_window - tt))
m_window[i] = max(abs(m)) / 2
p_win = abs(np.fft.rfft(m_window))
p_win /= p_win.size
f_win = np.fft.fftfreq(t_window.size, 1.0 / 24)[:p_win.size]
plt.axvline(x=period1, color='green', alpha=0.5)
plt.plot(1. / f[1:], px[1:] * 1000.0, 'r')
plt.plot(1. / f_win[1:], p_win[1:] * 1000.0, 'k')
plt.axhline(np.std(px) * 5000.0, linestyle='--', color='b')
plt.xticks(np.arange(20))
plt.xlim(0.1, 20)
plt.xlabel('period [days]')
plt.ylabel('semi-amplitude [mmag]')
plt.minorticks_on()
plt.grid()
if show: plt.show()
elif save:
plt.savefig(config.plotpath + '%s_psd.pdf' % self.starid)
def multiSpectrumPlot(outpath, base_name, order, y_units, cont, sky, noise,
wave):
if const.upgrade:
endPix = 2048 - 40
else:
endPix = 1024 - 20
title = 'spectrum'
pl.figure(title, facecolor='white')
pl.clf()
pl.title(title + ', ' + base_name + ", order " + str(order), fontsize=12)
pl.xlabel('Wavelength ($\AA$)')
pl.ylabel(title + '(' + y_units + ')')
pl.grid(True)
pl.plot(wave[:endPix],
cont[:endPix],
"k-",
mfc="none",
ms=3.0,
linewidth=1,
label='object')
pl.plot(wave[:endPix],
sky[:endPix],
"b-",
mfc="none",
ms=3.0,
linewidth=1,
label='sky')
pl.plot(wave[:endPix],
noise[:endPix],
"r-",
mfc="none",
ms=3.0,
linewidth=1,
label='noise (1 sigma)')
pl.legend(loc='best', prop={'size': 8})
pl.minorticks_on()
fn = constructFileName(outpath, base_name, order, 'spectra.png')
savePreviewPlot(fn)
log_fn(fn)
pl.close()
return
def plot_lightcurve(self, show=False, save=False):
"""
plot the lightcurve for a given star
"""
mean = np.mean(self.m)
std = np.std(self.m)
plt.hlines(mean, min(self.t), max(self.t), linestyle='--')
plt.ylim(mean + std * 3, mean - std * 3)
plt.xlim(min(self.t), max(self.t))
plt.grid()
plt.title(self.starid + ' = star #' + str(self['tab']))
plt.xlabel('days')
plt.ylabel('mag')
plt.scatter(self.t, self.m, edgecolor='none')
plt.plot(self.t, self.m, 'gray')
plt.minorticks_on()
def plotSpline(self, x, y):
"""
Plot the spline interpolation of the given x and y values.
"""
# compute fit
yFit = []
for v in x:
yFit.append(self.interpolate(v))
# plot results
plt.plot (x, y, 'bo', label='Original')
plt.plot (x, yFit, 'r-', label='Spline fit')
plt.minorticks_on()
plt.legend()
plt.xlabel('x')
plt.ylabel('y')
plt.show()
def twoDimNoiseOrderPlot(outpath,
base_name,
title,
base_filename,
order_num,
data,
x_scale,
wave_note='unknown'):
"""
Produces a generic 2-d image plot.
Arguments:
output: Directory path of root products directory.
base_name: Base name of object frame.
title: Title of plot, e.g. rectified order image.
base_filename:
order_num:
data:
x_scale:
"""
pl.figure('2d order image', facecolor='white', figsize=(8, 5))
pl.cla()
pl.title(title + ', ' + base_name + ", order " + str(order_num),
fontsize=14)
pl.xlabel('wavelength($\AA$) (' + wave_note + ')', fontsize=12)
pl.ylabel('row (pixel)', fontsize=12)
pl.imshow(exposure.equalize_hist(data),
origin='lower',
extent=[x_scale[0], x_scale[-1], 0, data.shape[0]],
aspect='auto')
# pl.colorbar()
# pl.set_cmap('jet')
pl.set_cmap('gray')
# pl.set_cmap('Blues_r')
pl.minorticks_on()
fn = constructFileName(outpath, base_name, order_num, base_filename)
savePreviewPlot(fn)
log_fn(fn)
pl.close()
return
def plot_s2p(filename,
save_fig=True,
show_fig=True,
s11=True,
c0='#cc0000',
c1='#333333'):
""" Plot S2P file """
colnames, data = read_s2p(filename)
if len(colnames) != 9:
print "Unknown S2P format!"
else:
#plt.figure("S2P", figsize=(8,8))
plt.suptitle(filename)
if s11:
plt.subplot(2, 1, 1)
else:
plt.subplot(2, 2, 1)
plt.plot(data[:, 0], data[:, 1], c=c0)
plt.xlabel(colnames[0])
plt.ylabel(colnames[1])
plt.minorticks_on()
if s11:
plt.subplot(2, 1, 2)
else:
plt.subplot(2, 2, 3)
plt.plot(data[:, 0], data[:, 2], c=c1)
plt.xlabel(colnames[0])
plt.ylabel(colnames[2])
plt.minorticks_on()
if not s11:
plt.subplot(2, 2, 2)
plt.plot(data[:, 0], data[:, 3], c=c0)
plt.xlabel(colnames[0])
plt.ylabel(colnames[1])
plt.minorticks_on()
plt.subplot(2, 2, 4)
plt.plot(data[:, 0], data[:, 4], c=c1)
plt.xlabel(colnames[0])
plt.ylabel(colnames[2])
plt.minorticks_on()
if save_fig:
plt.savefig(filename + '.pdf')
if show_fig:
plt.show()
else:
pass
def tracePlot(outpath, base_name, order_num, raw, fit, mask):
pl.figure("Trace Plot", figsize=(6, 5), facecolor='white')
pl.title('trace, ' + base_name + ", order " + str(order_num), fontsize=14)
pl.xlabel('column (pixels)')
pl.ylabel('row (pixels)')
# yrange = offraw.max() - offraw.min()
x = np.arange(raw.shape[0])
pl.plot(x[mask],
raw[mask],
"ko",
mfc="none",
ms=1.0,
linewidth=1,
label="derived")
pl.plot(x, fit, "k-", mfc="none", ms=1.0, linewidth=1, label="fit")
pl.plot(x[np.logical_not(mask)],
raw[np.logical_not(mask)],
"ro",
mfc="red",
mec="red",
ms=2.0,
linewidth=2,
label="ignored")
rms = np.sqrt(np.mean(np.square(raw - fit)))
pl.annotate('RMS residual = ' + "{:.3f}".format(rms), (0.3, 0.8),
xycoords="figure fraction")
pl.minorticks_on()
pl.grid(True)
pl.legend(loc='best', prop={'size': 8})
fn = constructFileName(outpath, base_name, order_num, 'trace.png')
savePreviewPlot(fn)
pl.close()
log_fn(fn)
return
def plot_toa_reflectance(self, roi=[400], hold="off", show=True):
xdata = scipy.linspace(0, self.num_bands, self.num_bands)
for i in range(len(roi)):
pylab.errorbar(
self.bands[self.sensor_name], self.reflectance[roi[i], :], yerr=self.reflectance_std[roi[i], :]
)
# pylab.scatter(self.bands[self.sensor_name], self.reflectance[roi[i], :])
pylab.minorticks_on()
pylab.xlabel("Wavelength (nm)")
pylab.ylabel("TOA Reflectance (sr^{-1})")
pylab.ylim([0, 1])
pylab.title(self.sensor_name)
if hold == "off" and show:
pylab.show()
if show:
pylab.show()
def plot_setticks(x=True, y=True):
pl.minorticks_on()
ax = pl.gca()
if x:
ax.xaxis.set_major_locator(pl.AutoLocator())
x_major = ax.xaxis.get_majorticklocs()
dx_minor = (x_major[-1] - x_major[0]) / (len(x_major) - 1) / 5.0
ax.xaxis.set_minor_locator(pl.MultipleLocator(dx_minor))
else:
pl.minorticks_off()
if y:
ax.yaxis.set_major_locator(pl.AutoLocator())
y_major = ax.yaxis.get_majorticklocs()
dy_minor = (y_major[-1] - y_major[0]) / (len(y_major) - 1) / 5.0
ax.yaxis.set_minor_locator(pl.MultipleLocator(dy_minor))
else:
pl.minorticks_off()
def timestructure_dataset(ds, calname="p_filt_value_dc"):
pulse_timing.choose_laser_dataset(ds, "not_laser")
cal = ds.calibration[calname]
# energy = ds.p_energy[ds.cuts.good()]
# cmap = plt.get_cmap()
# cmap = [cmap(i/float(len(cal.elements))) for i in xrange(len(cal.elements))]
for line_name in ["CoKAlpha", "FeKAlpha", "MnKAlpha"]:
try:
plt.figure()
low,high = mass.energy_calibration.STANDARD_FEATURES[line_name]*np.array([0.995, 1.005])
use = np.logical_and(np.logical_and(ds.cuts.good(), ds.p_energy>low), ds.p_energy<high)
plt.plot(ds.p_timestamp[use], ds.p_energy[use],'.')
plt.xlabel("frame timestamp (s)")
plt.ylabel("p_energy")
pfit = np.polyfit(ds.p_timestamp[use], ds.p_energy[use],1)
plt.title("chan %d, %s, not_laser pulses selected\nslope= %0.2f eV/hr"%(ds.channum, line_name, pfit[0]*3600))
plt.minorticks_on()
plt.grid("on")
plt.grid("on", which="minor")
except:
pass
def plot_toa_reflectance(self, roi=[2000], hold='off', show=True):
xdata = scipy.linspace(0, self.num_bands, self.num_bands)
for i in range(len(roi)):
#pylab.errorbar(self.bands[self.sensor_name], self.reflectance[roi[i], :],
# yerr=self.reflectance_std[roi[i], :])
xdata = self.bands[self.sensor_name]
for j in range(0, len(self.bands[self.sensor_name])):
xdata[j] = str(xdata[j]).strip('abcdefghijklmnopqrstuvwxyz')
pylab.plot(xdata, self.reflectance[roi[i], :], 'o')
pylab.minorticks_on()
pylab.xlabel('Wavelength (nm)')
pylab.ylabel('TOA Reflectance (sr^{-1})')
pylab.ylim([0, 1])
pylab.title(self.sensor_name)
if hold == 'off' and show:
pylab.show()
if show:
pylab.show()
f5 = np.loadtxt(five_filename)
#f6 = np.loadtxt(six_filename)
#f7 = np.loadtxt(seven_filename)
#f8 = np.loadtxt(eight_filename)
pl.figure()
pl.plot(f1[:,1],f1[:,3],'r-',label='L1',lw=1.5)
#pl.plot(f2[:,1],f2[:,3],'b:',label='L2',lw=2)
#pl.plot(f3[:,1],f3[:,3],'g--',label='L3',lw=2)
#pl.plot(f4[:,1],f4[:,3],'m-.',label='L4',lw=2)
pl.ylim([0,9])
pl.xlim([210,70])
#pl.title('rms source comparison')
pl.xlabel('$\\nu_{\\rm obs}\; \\rm{[MHz]}$')
pl.ylabel('$<\delta T_{\\rm b}>^{1/2}\; \\rm{[mK]}$')
pl.minorticks_on()
leg = pl.legend(loc='upper left',prop={'size':11})
leg.draw_frame(False)
pl.savefig('./eps/dTrms_244_rsd_lc_sources.eps')
pl.savefig('./png/dTrms_244_rsd_lc_sources.png')
pl.clf()
pl.plot(f1[:,1],f1[:,3],'r-',lw=1.5,label='L1')
#pl.plot(f2[:,1],f2[:,7],'b:',lw=2,label='L2')
#pl.plot(f3[:,1],f3[:,7],'g--',lw=2,label='L3')
#pl.plot(f4[:,1],f4[:,7],'m-.',lw=2,label='L4')
#pl.plot(f1[:,1],f1[:,7],'r',label='no LMACHs')
#pl.plot(f2[:,1],f2[:,7],'b',label='supp LMACHs')
#pl.plot(f3[:,1],f3[:,7],'g',label='psupp LMACHs')
#pl.plot(f4[:,1],f4[:,7],'m',label='gsupp LMACHs')
pl.ylim([0,5])
def eels_style( ax ):
pl.xlim( eels_x )
pl.ylim( eels_y )
pl.minorticks_on()
ax.xaxis.set_major_locator( mpl.ticker.MultipleLocator( eels_x_maj_loc ) )
ax.yaxis.set_ticklabels([]) # y tick labels off
mpl.rcParams['ytick.major.size']= 15.
mpl.rcParams['ytick.minor.size']= 10.
#mpl.rcParams['figure.autolayout']= True
#fontsize=26
#mpl.rc('axes', titlesize=fontsize)
#mpl.rc('axes', labelsize=fontsize)
#mpl.rc('xtick', labelsize=fontsize)
#mpl.rc('ytick', labelsize=fontsize)
#mpl.rc('font', size=fontsize, family='serif', serif='Utopia',
# style='normal', variant='normal',
# stretch='normal', weight='normal')
#mpl.rc('font',**{'family':'serif','serif':[ 'Times New Roman', 'Times', 'serif'],
# 'sans-serif':['Helvetica'], 'size':19,
# 'weight':'normal'})
mpl.rc('axes',**{'labelweight':'normal', 'linewidth':1})
mpl.rc('axes',**{'labelweight':'normal', 'linewidth':1})
mpl.rc('ytick',**{'major.pad':5, 'color':'k'})
mpl.rc('xtick',**{'major.pad':5, 'color':'k'})
params = {'legend.fontsize': 24,
'legend.linewidth': 1,
'legend.numpoints':1,
'legend.handletextpad':1
}
plt.rcParams.update(params)
plt.minorticks_on()
"""Plots a histogram showing positional residuals between IPHAS and UCAC4."""
import pylab as p
from astropy.table import Table
t = Table.read('iphas-x-ucac4.fits')
p.figure()
p.hist(t['Separation']*3600., range=[0.0, 0.5], bins=50, lw=1.0, color='#dddddd')
p.minorticks_on()
p.xlim([0,0.5])
p.xlabel('Astrometric residuals against UCAC4 [arcsec]', fontsize=9)
p.ylabel('Stars', fontsize=9)
p.tight_layout(pad=0.5)
p.savefig('astrometry.pdf')
import xray, numpy, pylab
# put materials and thickness in microns in m dictionary
m={}
m['Air']=2e5
m['Kapton']=25*8
m['Al']=20
m['Water']=120
m['Fe']=0.4
e_pulse = {"XOSpolycapillary_1":3.2,"XOSpolycapillary_2":4.125}
pylab.figure()
for optic_name, optic in xray.optics.iteritems():
energies = numpy.linspace(optic.energies[0], optic.energies[-1],1000)
flux = xray.source_optic_materials_absorber(e_pulse[optic_name], optic_name, m, energies=energies, absorber_length_um=2)
pylab.plot(energies,flux,label="%s, %d mJ"%(optic_name, e_pulse[optic_name]))
pylab.xlabel('energy (eV)')
pylab.ylabel('flux (arb)')
pylab.legend()
pylab.xlim(3000,15000)
pylab.title(repr(m))
pylab.minorticks_on()
pylab.grid()
def rthetaz_pop():
# Creates a 1.e3 x 1.e3 x 1.e3 array of random numbers
randa=scipy.random.random_sample(size=(10000,3))
# IAU Galactic constants
R0 = 8500.0
V0 = 220.0
# R is the radius, phi is the azimuth
# Populate the cylinder
phi = -math.pi + 2*math.pi*randa[:,0]
R = 25000.*randa[:,1]
z = -100. + 200*randa[:,2]
# Transform the the (R, phi, z) into cartesian. To be consistent with l,b standards:
# coordinates (X,Y,Z), centred on the Galactic Centre. +Y should point towards the Sun
# +X should point in the direction of rotation, and
# +Z should point above the plane (positive b, North Galactic Pole).
# Defined this way, phi is zero at the Sun-Centre line and increases in the direction of Galactic rotation.
x = R * np.sin(phi)
y = R * np.cos(phi)
z = z
print "Min x: %.2f Max x: %.2f" % (min(x),max(x))
print "Min y: %.2f Max y: %.2f" % (min(y),max(y))
print "Min z: %.2f Max z: %.2f" % (min(z),max(z))
# Make a spiral of the model used for velocity field
X1 = np.arange(-10000, 10000, 100)
Y1 = np.arange(-10000, 10000, 100)
X1, Y1 = np.meshgrid(X1, Y1)
R1 =np.sqrt(X1**2 + Y1**2)
Z1 = 1/np.tan(math.pi/2) * R1
# To Transform to the Galactic positions Y is along the Sun-GC line increasing away from the GC
yprime=R0-1.0*y # Shift to the position of the Sun
d = np.sqrt(yprime**2 + x**2 + z**2)
lat = np.degrees(np.arcsin(z/d))
lon = np.degrees(np.arctan2(x,yprime))
for i in range(len(R)):
if R[i] >= R0 * abs(np.sin(np.radians(lon[i]))) -10. and R[i] <= R0 * abs(np.sin(np.radians(lon[i]))) +10.0:
print " %.2f %.2f %.2f %.2f %.2f" % (R[i], lon[i], d[i], x[i],y[i])
print lon[i],np.degrees(phi[i])
# Get the LSR velocity
# NOTE phi is in radians whereas lon, and lat are in degrees
vel = vlsr(R,z,d,lon,lat,phi) # Standard Galactic rotation
#vel = vnoncirc(R,z,d,lon,lat,phi)
#vel = vel2(R,z,d,lon,lat,phi) # Burton & Liszt (1978) tilted disk
# Plot the distribution of points on a 3d plot
fig = pylab.figure(2)
pylab.clf()
ax = Axes3D(fig)
ax.scatter(x,y,z,marker='o',s=40, c=vel)
surf = ax.plot_surface(X1, Y1, Z1,linewidth=0, antialiased=False)
ax.set_xlabel('X (pc)')
ax.set_ylabel('Y (pc)')
ax.set_zlabel('Z (pc)')
# Plot the x, y disk
pylab.figure(1)
pylab.clf()
CS=pylab.scatter(x,y,s=20,c=vel)
pylab.clim(-250,250)
pylab.set_cmap('jet')
CB=pylab.colorbar(CS)
pylab.xlim(-25000,25000)
pylab.ylim(-25000,25000)
#pylab.contour(x,y,xi)
pylab.xlabel("X (pc)")
pylab.minorticks_on()
pylab.ylabel("Y (pc)")
pylab.savefig("sim_xyv.pdf")
# Plot the l,b,v clouds
pylab.figure(2)
pylab.clf()
CS=pylab.scatter(lon,lat,s=40,c=vel)
pylab.clim(-250,250)
pylab.set_cmap('jet')
CB=pylab.colorbar(CS)
pylab.xlim(180,-180)
pylab.ylim(-5,5)
pylab.xlabel("GALACTIC Longitude (deg)")
pylab.ylabel("GALACTIC Latitude (deg)")
pylab.savefig("sim_lb.pdf")
return lon,lat,vel
def conc_style( ax ):
pl.ylim( conc_y )
pl.minorticks_on()
ax.xaxis.set_major_locator( mpl.ticker.MultipleLocator( conc_x_maj_loc ) )
ax.yaxis.set_major_locator( mpl.ticker.MultipleLocator( conc_y_maj_loc ) )
ax.xaxis.set_ticklabels( x_tick_labs ) # x tick labels
waves.append(ww)
specs.append(spec)
corspecs.append(spec / np.interp(ww, w16, t16))
snrs.append(np.median(spec/espec))
especs.append(espec)
corespecs.append(espec / np.interp(ww, w16, t16))
py.close('all')
tlines = []
py.ioff()
for ii in range(len(waves)):
losnr = signal.medfilt(corspecs[ii]/corespecs[ii], 5)
py.figure(1+ii)
py.plot(waves[ii], corspecs[ii])
py.title('%s, <S/N> ~ %i' % (objs[ii], snrs[ii]))
py.minorticks_on()
py.xlabel('Wavelength [Ang]')
py.ylabel('Flux (F_Lambda)')
ymax = corspecs[ii].max()
if (losnr>10).any():
ymax = corspecs[ii][losnr>10].max()*1.1
py.ylim(0, ymax)
py.figure(1+ii+len(waves))
py.plot(waves[ii], losnr)
if py.ylim()[1]>snrs[ii]*3: py.ylim(0, snrs[ii]*3)
py.title('%s, <S/N> ~ %i' % (objs[ii], snrs[ii]))
py.minorticks_on()
py.xlabel('Wavelength [Ang]')
py.ylabel('S/N')
tlines.append("%s\t%1.1f\t%i" % (objs[ii], exptimes[ii], snrs[ii]))
def sp0_style( ax ):
pl.xlim( sp0_x )
pl.ylim( sp0_y )
pl.minorticks_on()
ax.xaxis.set_major_locator( mpl.ticker.MultipleLocator( sp0_maj_loc[0] ) )
ax.yaxis.set_major_locator( mpl.ticker.MultipleLocator( sp0_maj_loc[1] ) )
