def plot_ints(tup, wls, factors=None, symlog=True,
norm=False, is_wavelength=True, ax=None, **kwargs):
if ax is None:
ax = plt.gca()
wl, t, d = tup.wl, tup.t, tup.data
lines = []
plotted_vals = []
for i in wls:
dat = dv.spec_int(tup, i, is_wavelength)
if norm is True:
dat = np.sign(dat[np.argmax(abs(dat))]) * dat / abs(dat).max()
elif norm is False:
pass
else:
dat = dat / dat[dv.fi(t, norm)]
plotted_vals.append(dat)
idx1, idx2 = dv.fi(wl, i)
label = 'From {0: .1f} - {1: .1f} {2}'.format(wl[idx1], wl[idx2], freq_unit)
lines += ax.plot(t, dat, label=label, **kwargs)
lbl_trans(ax)
ax.set_xlim(-.5, )
if symlog:
ax.set_xscale('symlog')
ax.axvline(1, c='k', lw=0.5, zorder=1.9)
symticks(ax)
ax.axhline(0, color='k', lw=0.5, zorder=1.9)
ax.legend(loc='best', ncol=1)
return lines
def mean_spec(wl, t, p, t_range, ax=None, pos=(0.1, 0.1),
markers = ['o', '^']):
if ax is None:
ax = plt.gca()
if not isinstance(p, list):
p = [p]
if not isinstance(t_range, list):
t_range = [t_range]
l = []
for j, (x, y) in enumerate(t_range):
for i, d in enumerate(p):
t0, t1 = dv.fi(t, x), dv.fi(t, y)
pd = np.mean(d[t0:t1, :], 0)
lw = 2 if i == 0 else 1
l+= ax.plot(wl, pd, color='C%d'%j, marker=markers[i], lw=lw,
mec='none', ms=3)
ax.text(pos[0], pos[1]+j*0.07,'%.1f - %.1f ps'%(t[t0], t[t1]),
color='C%d'%j,
transform=ax.transAxes)
lbl_spec(ax)
if len(t_range) == 1:
print(len(p))
ax.set_title('mean signal from {0:.1f} to {1:.1f} ps'.format(t[t0], t[t1]))
return l
def make_angle_plot2(wl, t, para, senk, t_range):
p = para
s = senk
t0, t1 = dv.fi(t, t_range[0]), dv.fi(t, t_range[1])
pd = p[t0:t1, :].mean(0)
sd = s[t0:t1, :].mean(0)
ax = plt.subplot(111)
ax.plot(wl, pd)
ax.plot(wl, sd)
ax.plot([], [], 's-', color='k')
ax.axhline(0, c='k', zorder=1.9)
ax.invert_xaxis()
#ax.xaxis.tick_top()
ax.set_ylabel(sig_label)
ax.xaxis.set_label_position('top')
ax.text(0.05, 0.05, 'Signal average\nfor %.1f...%.1f ps'%t_range,
transform=ax.transAxes)
ax.legend(['parallel', 'perpendicular', 'angle'], columnspacing=0.3, ncol=3, frameon=0)
#horizontalalignment='center')
ax2 = plt.twinx(ax)
d = pd/sd
ang = np.arccos(np.sqrt((2*d-1)/(d+2)))/np.pi*180
ax2.plot(wl, ang, 's-', color='k')
for i in np.arange(10, 90, 10):
ax2.axhline(i, c='gray', linestyle='-.', zorder=1.8, lw=.5, alpha=0.5)
ax2.set_ylim(0, 90)
ax2.set_ylabel('angle / degrees')
def mean_spec(wl, t, p, t_range, ax=None, pos=(0.1, 0.1), markers=['o', '^']):
if ax is None:
ax = plt.gca()
if not isinstance(p, list):
p = [p]
if not isinstance(t_range, list):
t_range = [t_range]
l = []
for j, (x, y) in enumerate(t_range):
for i, d in enumerate(p):
t0, t1 = dv.fi(t, x), dv.fi(t, y)
pd = np.mean(d[t0:t1, :], 0)
lw = 2 if i == 0 else 1
l += ax.plot(wl,
pd,
color='C%d' % j,
marker=markers[i],
lw=lw,
mec='none',
ms=3)
ax.text(pos[0],
pos[1] + j * 0.07,
'%.1f - %.1f ps' % (t[t0], t[t1]),
color='C%d' % j,
transform=ax.transAxes)
lbl_spec(ax)
if len(t_range) == 1:
print(len(p))
ax.set_title('mean signal from {0:.1f} to {1:.1f} ps'.format(
t[t0], t[t1]))
return l
def fit_semiconductor(t, data, sav_n=11, sav_deg=4, mode='sav', tr=0.4):
from scipy.signal import savgol_filter
from scipy.ndimage import gaussian_filter1d
from scipy.optimize import leastsq
ger = data[..., -1].sum(2).squeeze()
plt.subplot(121)
plt.title('Germanium sum')
plt.plot(t, ger[:, 0])
plt.plot(t, ger[:, 1])
if mode =='sav':
plt.plot(t, savgol_filter(ger[:, 0], sav_n, sav_deg, 0))
plt.plot(t, savgol_filter(ger[:, 1], sav_n, sav_deg, 0))
plt.xlim(-1, 3)
plt.subplot(122)
plt.title('First dervitate')
if mode == 'sav':
derv0 = savgol_filter(ger[:, 0], sav_n, sav_deg, 1)
derv1 = savgol_filter(ger[:, 1], sav_n, sav_deg, 1)
elif mode == 'gauss':
derv0 = gaussian_filter1d(ger[:, 0], sav_n, order=1)
derv1 = gaussian_filter1d(ger[:, 1], sav_n, order=1)
plt.plot(t , derv0)
plt.plot(t , derv1)
plt.xlim(-.8, .8)
plt.ylim(0, 700)
plt.minorticks_on()
plt.grid(1)
def gaussian(p, ch, res=True):
i, j = dv.fi(t, -tr), dv.fi(t, tr)
w = p[0]
A = p[1]
x0 = p[2]
fit = A*np.exp(-(t[i:j]-x0)**2/(2*w**2))
if res:
return fit-ch[i:j]
else:
return fit
x0 = leastsq(gaussian, [.2, max(derv0), 0], derv0)
plt.plot(t[dv.fi(t, -tr):dv.fi(t, tr)], gaussian(x0[0], 0, 0), '--k', )
plt.text(0.05, 0.9, 'x$_0$ = %.2f\nFWHM = %.2f\nA = %.1f\n'%(x0[0][2],2.35*x0[0][0], x0[0][1]),
transform=plt.gca().transAxes, va='top')
x0 = leastsq(gaussian, [.2, max(derv1), 0], derv1)
plt.plot(t[dv.fi(t, -tr):dv.fi(t, tr)], gaussian(x0[0], 1, 0), '--b', )
plt.xlim(-.8, .8)
plt.minorticks_on()
plt.grid(0)
plt.tight_layout()
plt.text(0.5, 0.9, 'x$_0$ = %.2f\nFWHM = %.2f\nA = %.1f\n'%(x0[0][2],2.35*x0[0][0], x0[0][1]),
transform=plt.gca().transAxes, va='top')
def gaussian(p, ch, res=True):
i, j = dv.fi(t, -tr), dv.fi(t, tr)
w = p[0]
A = p[1]
x0 = p[2]
fit = A * np.exp(-(t[i:j] - x0)**2 / (2 * w**2))
if res:
return fit - ch[i:j]
else:
return fit
def gaussian(p, ch, res=True):
i, j = dv.fi(t, -tr), dv.fi(t, tr)
w = p[0]
A = p[1]
x0 = p[2]
fit = A*np.exp(-(t[i:j]-x0)**2/(2*w**2))
if res:
return fit-ch[i:j]
else:
return fit
def make_angle_plot(wl, t, para, senk, t_range):
p = para
s = senk
t0, t1 = dv.fi(t, t_range[0]), dv.fi(t, t_range[1])
pd = p[t0:t1, :].mean(0)
sd = s[t0:t1, :].mean(0)
ax = plt.subplot(211)
ax.plot(wl, pd)
ax.plot(wl, sd)
ax.axhline(0, c='k')
ax.legend(['Parallel', 'Perpendicular'],
columnspacing=0.3,
ncol=2,
frameon=0)
ax.xaxis.tick_top()
ax.set_ylabel(sig_label)
ax.xaxis.set_label_position('top')
ax.text(0.05,
0.1,
'Signal average\nfor %.1f...%.0f ps' % t_range,
transform=ax.transAxes)
#horizontalalignment='center')
ax2 = plt.subplot(212, sharex=ax)
d = pd / sd
ang = np.arccos(np.sqrt((2 * d - 1) / (d + 2))) / np.pi * 180
ax2.plot(wl, ang, 'o-')
ax2.set_ylim(0, 90)
ax2.set_ylabel('Angle / Degrees')
ax3 = plt.twinx()
ax3.plot(wl, ang, lw=0)
ax2.invert_xaxis()
f = lambda x: "%.1f" % (to_dichro(float(x) / 180. * np.pi))
ax2.set_ylim(0, 90)
def to_angle(d):
return np.arccos(np.sqrt((2 * d - 1) / (d + 2))) / np.pi * 180
def to_dichro(x):
return (1 + 2 * np.cos(x)**2) / (2 - np.cos(x)**2)
n_ticks = ax2.yaxis.get_ticklocs()
ratio_ticks = np.array([0.5, 0.7, 1., 1.5, 2., 2.5, 3.])
ax3.yaxis.set_ticks(to_angle(ratio_ticks))
ax3.yaxis.set_ticklabels([i for i in ratio_ticks])
ax3.set_ylabel('$A_\\parallel / A_\\perp$')
ax2.set_title('Angle calculated from dichroic ratio', fontsize='x-small')
plt.tight_layout(rect=[0, 0, 1, 1], h_pad=0)
return ax, ax2, ax3
def plot_trans(tup,
wls,
symlog=True,
norm=False,
marker=None,
ax=None,
**kwargs):
if ax is None:
ax = plt.gca()
wl, t, d = tup.wl, tup.t, tup.data
ulim = -np.inf
llim = np.inf
plotted_vals = []
l = []
for i in wls:
idx = dv.fi(wl, i)
dat = d[:, idx]
if norm is True:
dat = np.sign(dat[np.argmax(abs(dat))]) * dat / abs(dat).max()
elif norm is False:
pass
else:
dat = dat / dat[dv.fi(t, norm)]
plotted_vals.append(dat)
l.extend(
ax.plot(t,
dat,
label='%.1f %s' % (wl[idx], freq_unit),
marker=marker,
**kwargs))
ulim = np.percentile(plotted_vals, 99.) + 0.5
llim = np.percentile(plotted_vals, 1.) - 0.5
ax.set_xlabel(time_label)
ax.set_ylabel(sig_label)
#plt.ylim(llim, ulim)
if symlog:
ax.set_xscale('symlog', linthreshx=1)
ax.axvline(1, c='k', lw=0.5, zorder=1.9)
symticks(ax)
ax.axhline(0, color='k', lw=0.5, zorder=1.9)
ax.set_xlim(-.5, )
ax.legend(loc='best', ncol=2, title='Wavelength')
return l
def make_angle_plot(wl, t, para, senk, t_range):
p = para
s = senk
t0, t1 = dv.fi(t, t_range[0]), dv.fi(t, t_range[1])
pd = p[t0:t1, :].mean(0)
sd = s[t0:t1, :].mean(0)
ax = plt.subplot(211)
ax.plot(wl, pd)
ax.plot(wl, sd)
ax.axhline(0, c='k')
ax.legend(['Parallel', 'Perpendicular'], columnspacing=0.3, ncol=2, frameon=0)
ax.xaxis.tick_top()
ax.set_ylabel(sig_label)
ax.xaxis.set_label_position('top')
ax.text(0.05, 0.1, 'Signal average\nfor %.1f...%.0f ps'%t_range,
transform=ax.transAxes)
#horizontalalignment='center')
ax2 = plt.subplot(212, sharex=ax)
d = pd/sd
ang = np.arccos(np.sqrt((2*d-1)/(d+2)))/np.pi*180
ax2.plot(wl, ang, 'o-')
ax2.set_ylim(0, 90)
ax2.set_ylabel('Angle / Degrees')
ax3 = plt.twinx()
ax3.plot(wl, ang, lw=0)
ax2.invert_xaxis()
f = lambda x: "%.1f"%(to_dichro(float(x)/180.*np.pi))
ax2.set_ylim(0, 90)
def to_angle(d):
return np.arccos(np.sqrt((2*d-1)/(d+2)))/np.pi*180
def to_dichro(x):
return (1+2*np.cos(x)**2)/(2-np.cos(x)**2)
n_ticks = ax2.yaxis.get_ticklocs()
ratio_ticks = np.array([0.5, 0.7, 1., 1.5, 2., 2.5, 3.])
ax3.yaxis.set_ticks(to_angle(ratio_ticks))
ax3.yaxis.set_ticklabels([i for i in ratio_ticks])
ax3.set_ylabel('$A_\\parallel / A_\\perp$')
ax2.set_title('Angle calculated from dichroic ratio', fontsize='x-small')
plt.tight_layout(rect=[0, 0, 1, 1], h_pad=0)
return ax, ax2, ax3
def nice_lft_map(tup, taus, coefs, show_sums=False, **kwargs):
cmap = kwargs.pop('cmap', 'seismic')
plt.figure(1, figsize=(6, 4))
ax = plt.subplot(111)
#norm = SymLogNorm(linthresh=0.3)
norm = kwargs.pop('norm', MidPointNorm(0))
m = np.abs(coefs[:, :]).max()
c = ax.pcolormesh(tup.wl,
taus[:],
coefs[:, :],
cmap=cmap,
vmin=-m,
vmax=m,
norm=norm,
**kwargs)
cb = plt.colorbar(c, pad=0.01)
cb.set_label('Amplitude')
ax.set_yscale('log')
plt.autoscale(1, 'both', 'tight')
#ax.set_ylim(None, 60)
plt.minorticks_on()
ax.set_xlabel(freq_label)
ax.set_ylabel('Decay constant [ps]')
if inv_freq:
ax.invert_xaxis()
divider = make_axes_locatable(ax)
if show_sums:
axt = divider.append_axes("left", size=.5, sharey=ax, pad=0.05)
pos = np.where(coefs > 0, coefs, 0).sum(1)
neg = np.where(coefs < 0, coefs, 0).sum(1)
axt.plot(pos[:len(taus)], taus, 'r', label='pos.')
axt.plot(-neg[:len(taus)], taus, 'b', label='neg.')
axt.plot(abs(coefs).sum(1)[:len(taus)], taus, 'k', label='abs.')
axt.legend(frameon=False, loc='best')
axt.invert_xaxis()
#axt.plot(out[0].T[:, wi(1513):].sum(1), taus)
#axt.plot(3*out[0].T[:, :wi(1513)].sum(1), taus)
#plt.autoscale(1, 'y', 'tight')
axt.set_ylabel('Decay constant [ps]')
axt.xaxis.set_minor_locator(plt.NullLocator())
axt.xaxis.set_major_locator(plt.MaxNLocator(3))
ax.tick_params(labelleft=0)
else:
ax.set_ylabel('Decay constant [ps]')
if 0:
axt = divider.append_axes("top", size=1, sharex=ax, pad=0.1)
axt.plot(tup.wl, out[0].T[:dv.fi(taus, 0.2), :].sum(0))
axt.plot(tup.wl, out[0].T[dv.fi(taus, 0.3):dv.fi(taus, 1), :].sum(0))
axt.plot(tup.wl, out[0].T[dv.fi(taus, 1):dv.fi(taus, 5), :].sum(0))
axt.plot(tup.wl, out[0].T[dv.fi(taus, 5):dv.fi(taus, 10), :].sum(0))
axt.xaxis.tick_top()
axt.axhline(0, c='k', zorder=1.9)
plt.autoscale(1, 'both', 'tight')
def spec(self, t_list, norm=False, ax=None, n_average=0, **kwargs):
"""
Plot spectra at given times.
Parameters
----------
t_list : list or ndarray
List of the times where the spectra are plotted.
norm : bool
If true, each spectral will be normalized.
ax : plt.Axis or None.
Axis where the spectra are plotted. If none, the current axis will
be used.
n_average : int
For noisy data it may be preferred to average multiple spectra
together. This function plots the average of `n_average` spectra
around the specific time-points.
Returns
-------
list of `Lines2D`
List containing the Line2D objects belonging to the spectra.
"""
if ax is None:
ax = plt.gca()
is_nm = self.freq_unit == 'nm'
if is_nm:
ph.vis_mode()
else:
ph.ir_mode()
ds = self.dataset
x = ds.wavelengths if is_nm else ds.wavenumbers
li = []
for i in t_list:
idx = dv.fi(ds.t, i)
if n_average > 0:
dat = uniform_filter(ds, (2 * n_average + 1, 1)).data[idx, :]
elif n_average == 0:
dat = ds.data[idx, :]
else:
raise ValueError('n_average must be an Integer >= 0.')
if norm:
dat = dat / abs(dat).max()
li += ax.plot(x,
dat,
label=ph.time_formatter(ds.t[idx], ph.time_unit),
**kwargs)
self.lbl_spec(ax)
if not is_nm:
ax.set_xlim(x.max(), x.min())
return li
def spec(self, t_list, norm=False, ax=None, n_average=0, **kwargs):
"""
Plot spectra at given times.
Parameters
----------
t_list : list or ndarray
List of the times where the spectra are plotted.
norm : bool
If true, each spectral will be normalized.
ax : plt.Axis or None.
Axis where the spectra are plotted. If none, the current axis will
be used.
n_average : int
For noisy data it may be preferred to average multiple spectra
together. This function plots the average of `n_average` spectra
around the specific time-points.
Returns
-------
list of `Lines2D`
List containing the Line2D objects belonging to the spectra.
"""
if ax is None:
ax = plt.gca()
is_nm = self.freq_unit == "nm"
if is_nm:
ph.vis_mode()
else:
ph.ir_mode()
ds = self.dataset
x = ds.wavelengths if is_nm else ds.wavenumbers
li = []
for i in t_list:
idx = dv.fi(ds.t, i)
if n_average > 0:
dat = uniform_filter(ds, (2 * n_average + 1, 1)).data[idx, :]
elif n_average == 0:
dat = ds.data[idx, :]
else:
raise ValueError("n_average must be an Integer >= 0.")
if norm:
dat = dat / abs(dat).max()
li += ax.plot(
x, dat, label=ph.time_formatter(ds.t[idx], ph.time_unit), **kwargs
)
self.lbl_spec(ax)
if not is_nm:
ax.set_xlim(x.max(), x.min())
return li
def plot_ints(tup,
wls,
factors=None,
symlog=True,
norm=False,
is_wavelength=True,
ax=None,
**kwargs):
if ax is None:
ax = plt.gca()
wl, t, d = tup.wl, tup.t, tup.data
lines = []
plotted_vals = []
for i in wls:
dat = dv.spec_int(tup, i, is_wavelength)
if norm is True:
dat = np.sign(dat[np.argmax(abs(dat))]) * dat / abs(dat).max()
elif norm is False:
pass
else:
dat = dat / dat[dv.fi(t, norm)]
plotted_vals.append(dat)
idx1, idx2 = dv.fi(wl, i)
label = 'From {0: .1f} - {1: .1f} {2}'.format(wl[idx1], wl[idx2],
freq_unit)
lines += ax.plot(t, dat, label=label, **kwargs)
lbl_trans(ax)
ax.set_xlim(-.5, )
if symlog:
ax.set_xscale('symlog')
ax.axvline(1, c='k', lw=0.5, zorder=1.9)
symticks(ax)
ax.axhline(0, color='k', lw=0.5, zorder=1.9)
ax.legend(loc='best', ncol=1)
return lines
def nice_lft_map(tup, taus, coefs, show_sums=False, **kwargs):
cmap = kwargs.pop('cmap', 'seismic')
plt.figure(1, figsize=(6, 4))
ax = plt.subplot(111)
#norm = SymLogNorm(linthresh=0.3)
norm = kwargs.pop('norm', MidPointNorm(0))
m = np.abs(coefs[:, :]).max()
c = ax.pcolormesh(tup.wl, taus[:], coefs[:, :], cmap=cmap, vmin=-m, vmax=m, norm=norm, **kwargs)
cb = plt.colorbar(c, pad=0.01)
cb.set_label('Amplitude')
ax.set_yscale('log')
plt.autoscale(1, 'both', 'tight')
#ax.set_ylim(None, 60)
plt.minorticks_on()
ax.set_xlabel(freq_label)
ax.set_ylabel('Decay constant [ps]')
if inv_freq:
ax.invert_xaxis()
divider = make_axes_locatable(ax)
if show_sums:
axt = divider.append_axes("left", size=.5, sharey=ax,
pad=0.05)
pos = np.where(coefs>0, coefs, 0).sum(1)
neg = np.where(coefs<0, coefs, 0).sum(1)
axt.plot(pos[:len(taus)], taus, 'r', label='pos.')
axt.plot(-neg[:len(taus)], taus, 'b', label='neg.')
axt.plot(abs(coefs).sum(1)[:len(taus)], taus, 'k', label='abs.')
axt.legend(frameon=False, loc='best')
axt.invert_xaxis()
#axt.plot(out[0].T[:, wi(1513):].sum(1), taus)
#axt.plot(3*out[0].T[:, :wi(1513)].sum(1), taus)
#plt.autoscale(1, 'y', 'tight')
axt.set_ylabel('Decay constant [ps]')
axt.xaxis.set_minor_locator(plt.NullLocator())
axt.xaxis.set_major_locator(plt.MaxNLocator(3))
ax.tick_params(labelleft=0)
else:
ax.set_ylabel('Decay constant [ps]')
if 0:
axt = divider.append_axes("top", size=1, sharex=ax,
pad=0.1)
axt.plot(tup.wl, out[0].T[:dv.fi(taus, 0.2), :].sum(0))
axt.plot(tup.wl, out[0].T[dv.fi(taus, 0.3):dv.fi(taus, 1), :].sum(0))
axt.plot(tup.wl, out[0].T[dv.fi(taus, 1):dv.fi(taus, 5), :].sum(0))
axt.plot(tup.wl, out[0].T[dv.fi(taus, 5):dv.fi(taus, 10), :].sum(0))
axt.xaxis.tick_top()
axt.axhline(0, c='k', zorder=1.9)
plt.autoscale(1, 'both', 'tight')
def plot_trans(tup, wls, symlog=True, norm=False, marker=None, ax=None,
**kwargs):
if ax is None:
ax = plt.gca()
wl, t, d = tup.wl, tup.t, tup.data
ulim = -np.inf
llim = np.inf
plotted_vals = []
l = []
for i in wls:
idx = dv.fi(wl, i)
dat = d[:, idx]
if norm is True:
dat = np.sign(dat[np.argmax(abs(dat))])* dat / abs(dat).max()
elif norm is False:
pass
else:
dat = dat / dat[dv.fi(t, norm)]
plotted_vals.append(dat)
l.extend(ax.plot(t, dat, label='%.1f %s'%(wl[idx], freq_unit), marker=marker, **kwargs))
ulim = np.percentile(plotted_vals, 99.) + 0.5
llim = np.percentile(plotted_vals, 1.) - 0.5
ax.set_xlabel(time_label)
ax.set_ylabel(sig_label)
#plt.ylim(llim, ulim)
if symlog:
ax.set_xscale('symlog', linthreshx=1)
ax.axvline(1, c='k', lw=0.5, zorder=1.9)
symticks(ax)
ax.axhline(0, color='k', lw=0.5, zorder=1.9)
ax.set_xlim(-.5,)
ax.legend(loc='best', ncol=2, title='Wavelength')
return l
def make_angle_plot2(wl, t, para, senk, t_range):
p = para
s = senk
t0, t1 = dv.fi(t, t_range[0]), dv.fi(t, t_range[1])
pd = p[t0:t1, :].mean(0)
sd = s[t0:t1, :].mean(0)
ax = plt.subplot(111)
ax.plot(wl, pd)
ax.plot(wl, sd)
ax.plot([], [], 's-', color='k')
ax.axhline(0, c='k', zorder=1.9)
ax.invert_xaxis()
#ax.xaxis.tick_top()
ax.set_ylabel(sig_label)
ax.xaxis.set_label_position('top')
ax.text(0.05,
0.05,
'Signal average\nfor %.1f...%.1f ps' % t_range,
transform=ax.transAxes)
ax.legend(['parallel', 'perpendicular', 'angle'],
columnspacing=0.3,
ncol=3,
frameon=0)
#horizontalalignment='center')
ax2 = plt.twinx(ax)
d = pd / sd
ang = np.arccos(np.sqrt((2 * d - 1) / (d + 2))) / np.pi * 180
ax2.plot(wl, ang, 's-', color='k')
for i in np.arange(10, 90, 10):
ax2.axhline(i, c='gray', linestyle='-.', zorder=1.8, lw=.5, alpha=0.5)
ax2.set_ylim(0, 90)
ax2.set_ylabel('angle / degrees')
def plot_spec(tup, t_list, ax=None, norm=False, **kwargs):
if ax is None:
ax = plt.gca()
wl, t, d = tup.wl, tup.t, tup.data
li = []
for i in t_list:
idx = dv.fi(t, i)
dat = d[idx, :]
if norm:
dat = dat / abs(dat).max()
li += ax.plot(wl, dat, label=time_formatter(t[idx], time_unit), **kwargs)
#ulim = np.percentile(plotted_vals, 98.) + 0.1
#llim = np.percentile(plotted_vals, 2.) - 0.1
ax.set_xlabel(freq_label)
ax.set_ylabel(sig_label)
ax.autoscale(1, 'x', 1)
ax.axhline(0, color='k', lw=0.5, zorder=1.9)
ax.legend(loc='best', ncol=2, title='Delay time')
return li
def trans_anisotropy(self, wls, symlog=True, ax=None, freq_unit='auto'):
"""
Plots the anisotropy over time for given frequencies.
Parameters
----------
wls : list of floats
Which frequencies are plotted.
symlog : bool
Use symlog scale
ax : plt.Axes or None
Matplotlib Axes, if `None`, defaults to `plt.gca()`.
Returns
-------
: list of Line2D
List with the line objects.
"""
if ax is None:
ax = ax.gca()
ds = self.pol_ds
tmp = self.freq_unit if freq_unit == 'auto' else freq_unit
is_nm = tmp == 'nm'
x = ds.wavelengths if is_nm else ds.wavenumbers
if is_nm:
ph.vis_mode()
else:
ph.ir_mode()
l = []
for i in wls:
idx = dv.fi(x, i)
pa, pe = ds.para.data[:, idx], ds.perp.data[:, idx]
aniso = (pa - pe) / (2 * pe + pa)
l += ax.plot(ds.para.t,
aniso,
label=ph.time_formatter(ds.t[idx], ph.time_unit))
ph.lbl_trans(use_symlog=symlog)
if symlog:
ax.set_xscale('symlog')
ax.set_xlim(-1, )
return l
def trans_anisotropy(self, wls, symlog=True, ax=None, freq_unit="auto"):
"""
Plots the anisotropy over time for given frequencies.
Parameters
----------
wls : list of floats
Which frequencies are plotted.
symlog : bool
Use symlog scale
ax : plt.Axes or None
Matplotlib Axes, if `None`, defaults to `plt.gca()`.
Returns
-------
: list of Line2D
List with the line objects.
"""
if ax is None:
ax = ax.gca()
ds = self.pol_ds
tmp = self.freq_unit if freq_unit == "auto" else freq_unit
is_nm = tmp == "nm"
x = ds.wavelengths if is_nm else ds.wavenumbers
if is_nm:
ph.vis_mode()
else:
ph.ir_mode()
l = []
for i in wls:
idx = dv.fi(x, i)
pa, pe = ds.para.data[:, idx], ds.perp.data[:, idx]
aniso = (pa - pe) / (2 * pe + pa)
l += ax.plot(
ds.para.t, aniso, label=ph.time_formatter(ds.t[idx], ph.time_unit)
)
ph.lbl_trans(use_symlog=symlog)
if symlog:
ax.set_xscale("symlog")
ax.set_xlim(-1)
return l
def plot_spec(tup, t_list, ax=None, norm=False, **kwargs):
if ax is None:
ax = plt.gca()
wl, t, d = tup.wl, tup.t, tup.data
li = []
for i in t_list:
idx = dv.fi(t, i)
dat = d[idx, :]
if norm:
dat = dat/abs(dat).max()
li+= ax.plot(wl, dat, label=time_formatter(t[idx], time_unit),
**kwargs)
#ulim = np.percentile(plotted_vals, 98.) + 0.1
#llim = np.percentile(plotted_vals, 2.) - 0.1
ax.set_xlabel(freq_label)
ax.set_ylabel(sig_label)
ax.autoscale(1, 'x', 1)
ax.axhline(0, color='k', lw=0.5, zorder=1.9)
ax.legend(loc='best', ncol=2, title='Delay time')
return li
def plot_svd_components(tup, n=4, from_t=None):
wl, t, d = tup.wl, tup.t, tup.data
if from_t:
idx = dv.fi(t, from_t)
t = t[idx:]
d = d[idx:, :]
u, s, v = np.linalg.svd(d)
ax1: plt.Axes = plt.subplot(311)
ax1.set_xlim(-1, t.max())
lbl_trans()
plt.minorticks_off()
ax1.set_xscale('symlog')
ax2 = plt.subplot(312)
lbl_spec()
plt.ylabel('')
for i in range(n):
ax1.plot(t, u.T[i], label=str(i))
ax2.plot(wl, v[i])
ax1.legend()
plt.subplot(313)
plot_singular_values(d)
plt.tight_layout()
def plot_svd_components(tup, n=4, from_t = None):
wl, t, d = tup.wl, tup.t, tup.data
if from_t:
idx = dv.fi(t, from_t)
t = t[idx:]
d = d[idx:, :]
u, s, v = np.linalg.svd(d)
ax1: plt.Axes = plt.subplot(311)
ax1.set_xlim(-1, t.max())
lbl_trans()
plt.minorticks_off()
ax1.set_xscale('symlog')
ax2 = plt.subplot(312)
lbl_spec()
plt.ylabel('')
for i in range(n):
ax1.plot(t,u.T[i], label=str(i) )
ax2.plot(wl,v[i] )
ax1.legend()
plt.subplot(313)
plot_singular_values(d)
plt.tight_layout()
def data_preparation(wl,
t,
d,
wiener=3,
trunc_back=0.05,
trunc_scans=0,
start_det0_is_para=True,
do_scan_correction=True,
do_iso_correction=True,
plot=1,
n=10):
d = d.copy()
#d[..., 0, :]= shift_linear_part(d[..., 0, :])
#d[..., 1, :] = shift_linear_part(d[..., 1, :], 1, t)
if wiener == 'svd':
for i in range(d.shape[-1]):
d[:, :, 0, i] = dv.svd_filter(d[:, :, 0, i], 3)
d[:, :, 1, i] = dv.svd_filter(d[:, :, 1, i], 3)
elif wiener > 1:
d = sig.wiener(d, (wiener, 3, 1, 1))
elif wiener < 0:
d = nd.uniform_filter1d(d, -wiener, 0, mode='nearest')
#d, back0 = back_correction(d, use_robust=1)
#back1 = back0
if do_scan_correction:
d = scan_correction(d, dv.fi(t, 0.5))
import astropy.stats as stats
def fi(x, ax=0):
return stats.sigma_clip(x, sigma=trunc_back, iters=3, axis=ax).mean(ax)
back0 = fi(d[:n, ..., 0, :], ax=0)
back1 = fi(d[:n, ..., 1, :], ax=0)
back = 0.5 * (back0 + back1).mean(-1)
d[..., 0, :] -= back.reshape(1, 32, -1)
d[..., 1, :] -= back.reshape(1, 32, -1)
if do_scan_correction:
d = scan_correction(d, dv.fi(t, 0))
# gr -> vert -> parallel zum 0. scan
#fi = lambda x, ax=-1: np.median(x, ax)
fi = lambda x, ax=- \
1: stats.sigma_clip(x, sigma=trunc_scans, iters=2, axis=ax).mean(ax)
if start_det0_is_para:
para_0 = fi(d[..., 0, ::2])
senk_0 = fi(d[..., 0, 1::2])
para_1 = fi(d[..., 1, 1::2])
senk_1 = fi(d[..., 1, 0::2])
else:
para_0 = fi(d[..., 0, 1::2])
senk_0 = fi(d[..., 0, 0::2])
para_1 = fi(d[..., 1, 0::2])
senk_1 = fi(d[..., 1, 1::2])
iso_0 = (para_0 + 2 * senk_0) / 3.
iso_1 = (para_1 + 2 * senk_1) / 3.
if do_iso_correction:
iso_factor = calc_fac(iso_0, iso_1, dv.fi(t, 1))
else:
iso_factor = 1
senk = 0.5 * senk_0 + 0.5 * (iso_factor * senk_1)
senk -= senk[:10, :].mean(0)
para = 0.5 * para_0 + 0.5 * (iso_factor * para_1)
para -= para[:10, :].mean(0)
iso = (2 * senk + para) / 3
if plot:
import matplotlib.pyplot as plt
from skultrafast.plot_helpers import lbl_spec, mean_spec
plt.figure(figsize=(12, 4))
plt.subplot(121)
plt.plot(wl, iso[:10, :].mean(0))
plt.plot(wl, back0)
plt.plot(wl, back1)
lbl_spec()
plt.legend(['iso_rest', 'back0', 'back1'])
plt.subplot(122)
mean_spec(wl, t, [iso_0, iso_factor * iso_1], (1, 100))
mean_spec(wl, t, [para, senk], (1, 100), color='r')
plt.legend(['iso_0', 'iso_1 * %.2f' % iso_factor])
return iso, para, senk
def shift_linear_part(p, steps, t):
"Shift the linear part of the array by steps"
lp = dv.fi(t, -1), dv.fi(t, 3)
p = p.copy()
p[lp[0] + steps:lp[1], ...] = p[lp[0]:lp[1] - steps, ...]
return p
def fit_semiconductor(t, data, sav_n=11, sav_deg=4, mode='sav', tr=0.4):
from scipy.signal import savgol_filter
from scipy.ndimage import gaussian_filter1d
from scipy.optimize import leastsq
ger = data[..., -1].sum(2).squeeze()
plt.subplot(121)
plt.title('Germanium sum')
plt.plot(t, ger[:, 0])
plt.plot(t, ger[:, 1])
if mode == 'sav':
plt.plot(t, savgol_filter(ger[:, 0], sav_n, sav_deg, 0))
plt.plot(t, savgol_filter(ger[:, 1], sav_n, sav_deg, 0))
plt.xlim(-1, 3)
plt.subplot(122)
plt.title('First dervitate')
if mode == 'sav':
derv0 = savgol_filter(ger[:, 0], sav_n, sav_deg, 1)
derv1 = savgol_filter(ger[:, 1], sav_n, sav_deg, 1)
elif mode == 'gauss':
derv0 = gaussian_filter1d(ger[:, 0], sav_n, order=1)
derv1 = gaussian_filter1d(ger[:, 1], sav_n, order=1)
plt.plot(t, derv0)
plt.plot(t, derv1)
plt.xlim(-.8, .8)
plt.ylim(0, 700)
plt.minorticks_on()
plt.grid(1)
def gaussian(p, ch, res=True):
i, j = dv.fi(t, -tr), dv.fi(t, tr)
w = p[0]
A = p[1]
x0 = p[2]
fit = A * np.exp(-(t[i:j] - x0)**2 / (2 * w**2))
if res:
return fit - ch[i:j]
else:
return fit
x0 = leastsq(gaussian, [.2, max(derv0), 0], derv0)
plt.plot(
t[dv.fi(t, -tr):dv.fi(t, tr)],
gaussian(x0[0], 0, 0),
'--k',
)
plt.text(0.05,
0.9,
'x$_0$ = %.2f\nFWHM = %.2f\nA = %.1f\n' %
(x0[0][2], 2.35 * x0[0][0], x0[0][1]),
transform=plt.gca().transAxes,
va='top')
x0 = leastsq(gaussian, [.2, max(derv1), 0], derv1)
plt.plot(
t[dv.fi(t, -tr):dv.fi(t, tr)],
gaussian(x0[0], 1, 0),
'--b',
)
plt.xlim(-.8, .8)
plt.minorticks_on()
plt.grid(0)
plt.tight_layout()
plt.text(0.5,
0.9,
'x$_0$ = %.2f\nFWHM = %.2f\nA = %.1f\n' %
(x0[0][2], 2.35 * x0[0][0], x0[0][1]),
transform=plt.gca().transAxes,
va='top')
def mean_tup(tup, time):
wl, t, d = tup.wl, tup.t, tup.data
new_dat = tup.data / tup.data[dv.fi(t, time), :]
return dv.tup(wl, t, new_dat)
def shift_linear_part(p, steps, t):
"Shift the linear part of the array by steps"
lp = dv.fi(t, -1), dv.fi(t, 3)
p = p.copy()
p[lp[0]+steps:lp[1], ...] = p[lp[0]:lp[1]-steps, ...]
return p
def plot_diff(tup, t0, t_list, **kwargs):
diff = tup.data - tup.data[dv.fi(tup.t, t0), :]
plot_spec(dv.tup(tup.wl, tup.t, diff), t_list, **kwargs)
def data_preparation(wl, t, d, wiener=3, trunc_back=0.05, trunc_scans=0, start_det0_is_para=True,
do_scan_correction=True, do_iso_correction=True, plot=1, n=10):
d = d.copy()
#d[..., 0, :]= shift_linear_part(d[..., 0, :])
#d[..., 1, :] = shift_linear_part(d[..., 1, :], 1, t)
if wiener == 'svd':
for i in range(d.shape[-1]):
d[:, :, 0, i] = dv.svd_filter(d[:, :, 0, i], 3)
d[:, :, 1, i] = dv.svd_filter(d[:, :, 1, i], 3)
elif wiener > 1:
d = sig.wiener(d, (wiener, 3, 1, 1))
elif wiener < 0:
d = nd.uniform_filter1d(d, -wiener, 0, mode='nearest')
#d, back0 = back_correction(d, use_robust=1)
#back1 = back0
if do_scan_correction:
d = scan_correction(d, dv.fi(t, 0.5))
import astropy.stats as stats
def fi(x, ax=0): return stats.sigma_clip(
x, sigma=trunc_back, iters=3, axis=ax).mean(ax)
back0 = fi(d[:n, ..., 0, :], ax=0)
back1 = fi(d[:n, ..., 1, :], ax=0)
back = 0.5*(back0+back1).mean(-1)
d[..., 0, :] -= back.reshape(1, 32, -1)
d[..., 1, :] -= back.reshape(1, 32, -1)
if do_scan_correction:
d = scan_correction(d, dv.fi(t, 0))
# gr -> vert -> parallel zum 0. scan
#fi = lambda x, ax=-1: np.median(x, ax)
fi = lambda x, ax=- \
1: stats.sigma_clip(x, sigma=trunc_scans, iters=2, axis=ax).mean(ax)
if start_det0_is_para:
para_0 = fi(d[..., 0, ::2])
senk_0 = fi(d[..., 0, 1::2])
para_1 = fi(d[..., 1, 1::2])
senk_1 = fi(d[..., 1, 0::2])
else:
para_0 = fi(d[..., 0, 1::2])
senk_0 = fi(d[..., 0, 0::2])
para_1 = fi(d[..., 1, 0::2])
senk_1 = fi(d[..., 1, 1::2])
iso_0 = (para_0 + 2*senk_0) / 3.
iso_1 = (para_1 + 2*senk_1) / 3.
if do_iso_correction:
iso_factor = calc_fac(iso_0, iso_1, dv.fi(t, 1))
else:
iso_factor = 1
senk = 0.5*senk_0 + 0.5 * (iso_factor*senk_1)
senk -= senk[:10, :].mean(0)
para = 0.5*para_0 + 0.5 * (iso_factor*para_1)
para -= para[:10, :].mean(0)
iso = (2*senk + para)/3
if plot:
import matplotlib.pyplot as plt
from skultrafast.plot_helpers import lbl_spec, mean_spec
plt.figure(figsize=(12, 4))
plt.subplot(121)
plt.plot(wl, iso[:10, :].mean(0))
plt.plot(wl, back0)
plt.plot(wl, back1)
lbl_spec()
plt.legend(['iso_rest', 'back0', 'back1'])
plt.subplot(122)
mean_spec(wl, t, [iso_0, iso_factor*iso_1], (1, 100))
mean_spec(wl, t, [para, senk], (1, 100), color='r')
plt.legend(['iso_0', 'iso_1 * %.2f' % iso_factor])
return iso, para, senk
def plot_diff(tup, t0, t_list, **kwargs):
diff = tup.data - tup.data[dv.fi(tup.t, t0), :]
plot_spec(dv.tup(tup.wl, tup.t, diff), t_list, **kwargs)
def mean_tup(tup, time):
wl, t, d = tup.wl, tup.t, tup.data
new_dat = tup.data / tup.data[dv.fi(t, time), :]
return dv.tup(wl, t, new_dat)
def t_idx(self, t: Union[float, Iterable[float]]) -> int:
"""Return nearest idx to nearest time value"""
return dv.fi(self.t, t)
def pump_idx(self, wn: Union[float, Iterable[float]]) -> int:
"""Return nearest idx to nearest pump_wn value"""
return dv.fi(self.pump_wn, wn)
def trans(self, wls, symlog=True, norm=False, ax=None, freq_unit="auto", **kwargs):
"""
Plot the nearest transients for given frequencies.
Parameters
----------
wls : list or ndarray
Spectral positions, should be given in the same unit as
`self.freq_unit`.
symlog : bool
Determines if the x-scale is symlog.
norm : bool or float
If `False`, no normalization is used. If `True`, each transient
is divided by the maximum absolute value. If `norm` is a float,
all transient are normalized by their signal at the time `norm`.
ax : plt.Axes or None
Takes a matplotlib axes. If none, it uses `plt.gca()` to get the
current axes. The lines are plotted in this axis.
freq_unit : 'auto', 'cm' or 'nm'
How to interpret the given frequencies. If 'auto' it defaults to
the plotters freq_unit.
All other kwargs are forwarded to the plot function.
Returns
-------
list of Line2D
List containing the plotted lines.
"""
if ax is None:
ax = plt.gca()
tmp = self.freq_unit if freq_unit is "auto" else freq_unit
is_nm = tmp == "nm"
if is_nm:
ph.vis_mode()
else:
ph.ir_mode()
ds = self.dataset
x = ds.wavelengths if is_nm else ds.wavenumbers
wl, t, d = ds.wl, ds.t, ds.data
l, plotted_vals = [], []
for i in wls:
idx = dv.fi(x, i)
dat = d[:, idx]
if norm is True:
dat = np.sign(dat[np.argmax(abs(dat))]) * dat / abs(dat).max()
elif norm is False:
pass
else:
dat = dat / dat[dv.fi(t, norm)]
plotted_vals.append(dat)
l.extend(
ax.plot(t, dat, label="%.1f %s" % (x[idx], ph.freq_unit), **kwargs)
)
if symlog:
ax.set_xscale("symlog", linthreshx=1.0)
ph.lbl_trans(ax=ax, use_symlog=symlog)
ax.legend(loc="best", ncol=3)
ax.set_xlim(right=t.max())
ax.yaxis.set_tick_params(which="minor", left=True)
return l
def trans(self,
wls,
symlog=True,
norm=False,
ax=None,
freq_unit='auto',
**kwargs):
"""
Plot the nearest transients for given frequencies.
Parameters
----------
wls : list or ndarray
Spectral positions, should be given in the same unit as
`self.freq_unit`.
symlog : bool
Determines if the x-scale is symlog.
norm : bool or float
If `False`, no normalization is used. If `True`, each transient
is divided by the maximum absolute value. If `norm` is a float,
all transient are normalized by their signal at the time `norm`.
ax : plt.Axes or None
Takes a matplotlib axes. If none, it uses `plt.gca()` to get the
current axes. The lines are plotted in this axis.
freq_unit : 'auto', 'cm' or 'nm'
How to interpret the given frequencies. If 'auto' it defaults to
the plotters freq_unit.
All other kwargs are forwarded to the plot function.
Returns
-------
list of Line2D
List containing the plotted lines.
"""
if ax is None:
ax = plt.gca()
tmp = self.freq_unit if freq_unit is 'auto' else freq_unit
is_nm = tmp == 'nm'
if is_nm:
ph.vis_mode()
else:
ph.ir_mode()
ds = self.dataset
x = ds.wavelengths if is_nm else ds.wavenumbers
wl, t, d = ds.wl, ds.t, ds.data
l, plotted_vals = [], []
for i in wls:
idx = dv.fi(x, i)
dat = d[:, idx]
if norm is True:
dat = np.sign(dat[np.argmax(abs(dat))]) * dat / abs(dat).max()
elif norm is False:
pass
else:
dat = dat / dat[dv.fi(t, norm)]
plotted_vals.append(dat)
l.extend(
ax.plot(t,
dat,
label='%.1f %s' % (x[idx], ph.freq_unit),
**kwargs))
if symlog:
ax.set_xscale('symlog', linthreshx=1.)
ph.lbl_trans(ax=ax, use_symlog=symlog)
ax.legend(loc='best', ncol=3)
ax.set_xlim(right=t.max())
ax.yaxis.set_tick_params(which='minor', left=True)
return l
