def test_sesolver():
s0, sx, sy, sz = pauli()
H = (-0.05) * sz - 0. * sx
solver = SESolver(H)
Nt = 1000
dt = 4
psi0 = (basis(2, 0) + basis(2, 1)) / np.sqrt(2)
start_time = time.time()
result = solver.evolve(psi0, dt=dt, Nt=Nt, e_ops=[sx])
print("--- %s seconds ---" % (time.time() - start_time))
start_time = time.time()
times = np.arange(Nt) * dt
r = _ode_solver(H, psi0, dt=dt, Nt=Nt, nout=2, e_ops=[sx])
print("--- %s seconds ---" % (time.time() - start_time))
# test correlation functions
# corr = solver.correlation_3p_1t(psi0=psi0, oplist=[s0, sx, sx],
# dt=0.05, Nt=Nt)
#
# times = np.arange(Nt)
#
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(times, result.observables[:, 0])
ax.plot(r.times, r.observables[:, 0], '--')
# ax.plot(times, corr.real)
plt.show()
def test_etpa():
epp = Biphoton(0, 0.04 / au2ev, Te=10. / au2fs)
p = np.linspace(-4, 4, 256) / au2ev
q = p
epp.set_grid(p, q)
epp.get_jsa()
# epp.plt_jsa()
pump = np.linspace(0.5, 1.5, 100) / au2ev
signal = etpa(pump, mol, epp, [0], [1, 2, 3], [2, 3])
fig, ax = subplots()
ax.plot(pump * au2ev, np.abs(signal)**2)
plt.show()
return
def plot_stats(data,
x_key,
x_label=None,
y_keys=None,
y_labels=None,
start_index=0,
save_filename=None):
"""
Generate time-series plots of stats specified by keys
Args:
data: [dict] containing data to be plotted. len of all values should be equal
data must include x_key as a valid key
x_key: [str] key for the x-axis (time varying component)
x_label: [str] corresponding label for the x_key, if not provided then the key is used
y_keys: [list of str] optional list of keys to plot, each should exist in data.keys()
If nothing is given, data.keys() will be used
y_labels: [list of str] optional list of labels, should be the same length as keys input
If nothing is given, y_keys will be used
start_index: [int] time offset for plotting data - default=0 will plot all data
save_filename: [str] containing the complete output filename.
Returns:
fig: matplotlib figure handle
"""
assert x_key in list(data.keys()), ('x_key=%s must be in data.keys()' %
x_key)
if x_label is None:
x_label = x_key
if y_keys is None:
y_keys = list(data.keys())
if 'epoch' in y_keys:
y_keys.remove('epoch')
if 'batch_step' in y_keys:
y_keys.remove('batch_step')
else:
assert all([y_key in list(data.keys()) for y_key in y_keys])
if y_labels is None:
y_labels = [' '.join(y_key.split('_')) for y_key in y_keys]
else:
assert len(y_labels) == len(y_keys), (
'The number of y_labels must match the number of y_keys')
num_y_keys = len(y_keys)
num_plots_y = int(np.ceil(np.sqrt(num_y_keys)))
num_plots_x = int(np.ceil(np.sqrt(num_y_keys)))
fig, axes = plot.subplots(nrows=num_plots_y,
ncols=num_plots_x,
sharex=False,
sharey=False)
key_idx = 0
for plot_id in np.ndindex((num_plots_y, num_plots_x)):
if key_idx < num_y_keys:
x_dat = data[x_key][start_index:]
y_dat = data[y_keys[key_idx]][start_index:]
if len(x_dat) == len(y_dat):
ax = axes[plot_id]
ax.plot(x_dat, y_dat)
ax.format(yticks=[
np.minimum(0.0, np.min(y_dat)),
np.maximum(0.0, np.max(y_dat))
],
ylabel=y_labels[key_idx])
key_idx += 1
else:
ax = clear_axis(axes[plot_id])
print(
'utils/plot_functions.py: WARNING: x and y for key %s must have same first dimensions but are %g and %g'
% (y_keys[key_idx], len(x_dat), len(y_dat)))
else:
ax = clear_axis(axes[plot_id])
axes.format(xlabel=x_label, suptitle='Stats per Batch')
if save_filename is not None:
fig.savefig(save_filename, transparent=True)
plot.close(fig)
return None
plot.show()
return fig
print(i, d)
# fit curve
popt, _ = scy.curve_fit(objective, d['x'], d['y'])
xx = np.linspace(0, 10, 100)
yy = [objective(j, popt[0], popt[1], popt[2]) for j in xx]
fig = pplt.figure(share=False)
ax = fig.subplot(title='Alternate y twin x')
ax.plot(d)
ax.plot(d)
#
#
ax.set(xlim=(0, 10), ylim=(0, 150))
ax.set(xlabel="U [m/s]", ylabel='$\Delta$P [Pa]')
# Put the legend out of the figure
pplt.legend()
#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
pplt.tight_layout()
# plt.text(0.5, 0.5, 'matplotlib', horizontalalignment='center', verticalalignment='center', transform=ax.transAxes)
pplt.savefig("Darcy_Forchheimer_Fit_pplot.pdf")
pplt.savefig("Darcy_Forchheimer_Fit_pplot.png", dpi=600)
pplt.show()