def main():
x = np.arange(0.0, 30, 0.1)
y1 = 0.1 * x * np.sin(x)
y2 = 0.001 * x**3 - 0.03 * x**2 + 0.12 * x
fig = plt.figure(figsize=(10, 4))
ax11 = fig.add_subplot(121)
ax12 = ax11.twinx()
ax11.plot(x, y1, color='blue', label='plot 1')
ax12.plot(x, y2, color='red', label='plot 2')
ax11.hlines(y=0, xmin=0, xmax=30)
ax11.set_title('Original')
ax11.set_ylabel('Plot 1', color='blue')
ax12.set_ylabel('Plot 2', color='red')
ax11.set_xlim(0.0, 30.0)
ax21 = fig.add_subplot(122)
ax22 = ax21.twinx()
ax21.plot(x, y1, color='blue', label='plot 1')
ax22.plot(x, y2, color='red', label='plot 2')
ax21.hlines(y=0, xmin=0, xmax=30)
ax21.set_title('Using mpl_axes_aligner.align')
ax21.set_ylabel('Plot 1', color='blue')
ax22.set_ylabel('Plot 2', color='red')
ax21.set_xlim(0.0, 30.0)
align.yaxes(ax21, 0, ax22, 0, 0.5)
plt.tight_layout()
plt.savefig("intro_plt.png")
def plot_option_pnl(self):
"""
Plot holding period return
Returns
-------
"""
fig, ax1 = plt.subplots(figsize=(12, 6))
ax1.plot(self.trading_days, self.option_return, 'r-^', label='Option')
ax1.axhline(0, color='k', linestyle=':')
ax2 = ax1.twinx()
ax2.plot(self.trading_days,
self.underlying_return,
'b-o',
label='Underlying')
ax1.legend(loc="upper left")
ax2.legend(loc="upper right")
ax1.spines['top'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax1.set_xlabel("Date")
ax1.set_ylabel("Option P&L")
ax2.set_ylabel("Underlying P&L")
ax1.yaxis.set_major_formatter(mtick.PercentFormatter())
ax2.yaxis.set_major_formatter(mtick.PercentFormatter())
# Align y-axes
org1 = 0.0 # Origin of first axis
org2 = 0.0 # Origin of second axis
pos = 0.5 # Position the two origins are aligned
align.yaxes(ax1, org1, ax2, org2, pos)
plt.tight_layout()
plt.title('Holding Period Return')
def main3():
x = np.arange(0.0, 30, 0.1)
y1 = 0.1 * x * np.sin(x)
y2 = 0.001 * x**3 - 0.03 * x**2 + 0.12 * x
fig = plt.figure(figsize=(6.8, 2.55), dpi=100)
ax11 = fig.add_subplot(121)
ax12 = ax11.twinx()
ax11.plot(x, y1, color='blue', label='plot 1')
ax12.plot(x, y2, color='red', label='plot 2')
ax11.hlines(y=0, xmin=0, xmax=30, linewidth=1)
ax11.set_title('align.yaxes(ax1, 0, ax2, 0, 0.2)')
ax11.set_ylabel('Plot 1', color='blue')
ax12.set_ylabel('Plot 2', color='red')
ax11.set_xlim(0.0, 30.0)
ax21 = fig.add_subplot(122)
ax22 = ax21.twinx()
ax21.plot(x, y1, color='blue', label='plot 1')
ax22.plot(x, y2, color='red', label='plot 2')
ax21.hlines(y=0, xmin=0, xmax=30, linewidth=1)
ax21.set_title('align.yaxes(ax1, 0, ax2, 0, 0.8)')
ax21.set_ylabel('Plot 1', color='blue')
ax22.set_ylabel('Plot 2', color='red')
ax21.set_xlim(0.0, 30.0)
align.yaxes(ax11, 0, ax12, 0, 0.2)
align.yaxes(ax21, 0, ax22, 0, 0.8)
plt.tight_layout()
plt.savefig("align_plt3.png")
def test_yaxes_pos_ValueError(pos):
fig.clear()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
org1 = 0.0
org2 = 0.0
with pytest.raises(ValueError):
align.yaxes(ax1, org1, ax2, org2, pos)
def test_yaxes_outrange_ValueError():
fig.clear()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.set_ylim(-1.0, 0.0)
ax2.set_ylim(0.0, 1.0)
org1 = 0.5
org2 = 1.1
with pytest.raises(ValueError):
align.yaxes(ax1, org1, ax2, org2)
def test_yaxes_simple2():
fig.clear()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.set_ylim(-1.0, 0.0)
ax2.set_ylim(0.0, 1.0)
org1 = 0.0
org2 = 0.0
align.yaxes(ax1, org1, ax2, org2)
assert ax1.get_ylim() == (-1.0, 1.0)
assert ax2.get_ylim() == (-1.0, 1.0)
def add_fieldmaps_to_axes(lattice, axes, bounds=None, sections='All',
fields=['cavity', 'solenoid'],
magnets = ['quadrupole', 'dipole', 'beam_position_monitor', 'screen'],
include_labels=True, verbose=False):
"""
Adds fieldmaps to an axes.
"""
max_scale = 0
fmaps = load_elements(lattice, bounds=bounds, sections=sections, verbose=verbose, types=fields)
ax1 = axes
ax1rhs = ax1.twinx()
ax = [ax1, ax1rhs]
ylabel = {'cavity': '$E_z$ (MV/m)', 'solenoid':'$B_z$ (T)'}
color = {'cavity': 'green', 'solenoid':'blue'}
for i, section in enumerate(fields):
a = ax[i]
for name, data in fmaps[section].items():
label = f'{section}_{name}'
c = color[section]
# if section == 'cavity':# and not section == 'solenoid':
if section == fields[0]:
max_scale = max(abs(data[:,1])) if max(abs(data[:,1])) > max_scale else max_scale
a.plot(*data.T, label=label, color=c)
a.set_ylabel(ylabel[section])
ax1.set_xlabel('$z$ (m)')
if len(fields) < 1:
for a in ax:
a.set_yticks([])
max_scale = 1 if max_scale == 0 else max_scale
a = ax[0]
fmaps = load_elements(lattice, bounds=bounds, sections=sections, verbose=verbose, types=magnets, scale=max_scale)
for section in magnets:
if len(fmaps[section].items()) > 0:
section_scale = max_scale #/ max([max(abs(d[:,1])) for d in fmaps[section].values()])
for name, (data, c) in fmaps[section].items():
# c = color[section]
data[:,1] = data[:,1] * section_scale
a.fill(*data.T, color=c)
data = np.array([[0,0], [100,0]])
ax[0].plot(*data.T, color='black')
if bounds:
ax1.set_xlim(bounds[0], bounds[1])
align.yaxes(ax[0], 0, ax[1], 0, 0.5)
def test_yaxes_axes_TypeError():
fig.clear()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax3 = ax1.get_yaxis()
org1 = 0.0
org2 = 0.0
pos = 0.5
with pytest.raises(TypeError):
align.yaxes(ax3, org1, ax2, org2, pos)
with pytest.raises(TypeError):
align.yaxes(ax1, org1, ax3, org2, pos)
def plot_study(curr_date, prev_date, uk_active_cases, prev_uk_active_cases):
prev_data_end = prev_date + timedelta(days=1)
diff = uk_active_cases[cases].sub(prev_uk_active_cases[cases],
fill_value=0)
diff[prev_data_end:] = nan
curr_est = uk_active_cases[cases].iloc[-1]
month_ago = curr_date - timedelta(days=30)
start = min(month_ago, diff.index.min() - timedelta(days=7))
data_colour = '#113377'
diff_colour = '#015c00'
curr_colour = 'red'
fig, ax = plt.subplots(figsize=(14, 4))
fig.set_facecolor('white')
data_to_plot = uk_active_cases[cases][start:]
data_to_plot.plot(ax=ax, grid=True, color=data_colour)
ax.set_title(f'ZOE COVID Symptom Study as of {curr_date:%d %b %Y}')
ax.xaxis.grid(True, which='minor')
ax.tick_params(axis='y', labelcolor=data_colour)
ax.yaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))
ax.set_ylabel('Predicted # of active cases', color=data_colour)
ax.axhline(y=curr_est, color=curr_colour, zorder=-100)
trans = transforms.blended_transform_factory(
ax.get_yticklabels()[0].get_transform(), ax.transData)
ax.text(1,
curr_est + 1000,
"{:,.0f}".format(curr_est),
color=curr_colour,
transform=trans,
ha="right",
va="bottom",
zorder=1000)
ax.patch.set_visible(False)
diff_ax = ax.twinx()
diff_ax.set_zorder(-1)
diff_ax.tick_params(axis='y', labelcolor=diff_colour)
diff_ax.set_ylabel(f'Change since {prev_date:%d %b %Y}', color=diff_colour)
diff_to_plot = diff.loc[start:].reindex(data_to_plot.index)
_ = diff_to_plot.plot(ax=diff_ax,
drawstyle='steps',
color=diff_colour,
zorder=-1)
align.yaxes(ax, curr_est, diff_ax, 0, 0.3)
def test_yaxes_inverted():
fig.clear()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.set_ylim(1.0, -1.0)
ax2.set_ylim(1.5, 0.0)
org1 = 0.0
org2 = 0.0
pos = 0.5
align.yaxes(ax1, org1, ax2, org2, pos)
lim1 = list(ax1.get_ylim())
lim2 = list(ax2.get_ylim())
assert lim1[0] > lim1[1]
assert lim2[0] > lim2[1]
def demo(pet, pwe, tth, spe_pre, leng, wave, cid, mode, full=False):
penum = len(tth)
print('PEnum is {}'.format(penum))
pf0 = np.zeros(leng)
pf1 = np.zeros(leng)
if mode == 'Weight':
tru_pet = tth['RiseTime']
t, c = np.unique(tru_pet, return_counts=True)
pf0[t] = c
pf1[pet] = pwe
xlabel = '$PEnum/\mathrm{1}$'
distd = '(W/ns,P/1)'
distl = 'pdist'
Q = penum
q = np.sum(pwe)
edist = np.abs(Q - q) * scipy.stats.poisson.pmf(Q, Q)
elif mode == 'Charge':
t = tth['RiseTime']
w = tth[mode]
t = np.unique(t)
c = np.array([np.sum(w[tth['RiseTime'] == i]) for i in t])
pf0[t] = c / spe_pre['spe'].sum()
pf1[pet] = pwe / spe_pre['spe'].sum()
xlabel = '$Charge/\mathrm{mV}\cdot\mathrm{ns}$'
distd = '(W/ns,C/mV*ns)'
distl = 'cdiff'
edist = pwe.sum() - c.sum()
print('truth RiseTime = {}, Weight = {}'.format(t, c))
wave0 = np.convolve(spe_pre['spe'], pf0, 'full')[:leng]
print('truth Resi-norm = {}'.format(np.linalg.norm(wave - wave0)))
print('RiseTime = {}, Weight = {}'.format(pet, pwe))
wdist = scipy.stats.wasserstein_distance(t,
pet,
u_weights=c,
v_weights=pwe)
print('wdist = {},'.format(wdist) + distl + ' = {}'.format(edist))
wave1 = np.convolve(spe_pre['spe'], pf1, 'full')[:leng]
print('Resi-norm = {}'.format(np.linalg.norm(wave - wave1)))
fig = plt.figure()
fig.tight_layout()
ax = fig.add_subplot(111)
ax.grid()
ax2 = ax.twinx()
ax.plot(wave, c='b', label='origin wave')
ax.plot(wave0, c='k', label='truth wave')
ax.plot(wave1, c='C1', label='recon wave')
ax.set_xlabel('$Time/\mathrm{ns}$')
ax.set_ylabel('$Voltage/\mathrm{mV}$')
ax.hlines(spe_pre['thres'], 0, 1029, color='c', label='threshold')
ax2.set_ylabel(xlabel)
fig.suptitle(
'eid={},cid={},'.format(tth['EventID'][0], tth['ChannelID'][0]) +
distd + '-dist={:.2f},{:.2f}'.format(wdist, edist))
ax2.vlines(t, 0, c, color='g', label='truth ' + mode)
ax2.vlines(pet, -pwe, 0, color='y', label='recon ' + mode)
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
align.yaxes(ax, 0, ax2, 0)
ax2.legend(lines + lines2, labels + labels2)
if full:
ax.set_xlim(max(t.min() - 50, 0), min(t.max() + 150, leng))
fig.savefig('img/demoe{}c{}.png'.format(tth['EventID'][0],
tth['ChannelID'][0]),
bbox_inches='tight')
fig.clf()
plt.close(fig)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(spe_pre['spe'], c='b')
ax.grid()
ax.set_xlabel('$Time/\mathrm{ns}$')
ax.set_ylabel('$Voltage/\mathrm{mV}$')
fig.savefig('img/spe{}.png'.format(cid), bbox_inches='tight')
fig.clf()
return
# set format of y-axis ticks
ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))
# twin object for two different y-axis on the sample plot
ax2 = ax.twinx()
# make a plot with different y-axis using second axis object
ax2.plot(case_per_100k.Date,
case_per_100k["Case_per_100K_7MA"],
color="blue",
marker="",
label="Daily cases per 100,000, 7 day moving average")
ax2.set_ylabel("Cases per 100,000 Population\n7 Day Moving Average",
color="black",
fontsize=28)
# align secondary y-axis with primary y-axis
align.yaxes(ax, 0, ax2, 0)
# set fontsize of ticks
ax2.tick_params(axis='y', labelsize=24)
# remove top line
ax.spines['top'].set_visible(False)
# add legend
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2,
labels + labels2,
loc="upper left",
prop=dict(size=18))
# add data labels
ylabel='% ospedalizzati',
xlabel='Data')
ax1.legend(loc=3)
ax2 = ax1.twinx() # secondo axes che condivide lo stesso asse
ax2.bar(df_a.index,
df_a['terapia_intensiva'].values,
label='terapia intensiva',
color=c2,
alpha=0.25)
ax2.set_ylim(0, 5000)
ax2.legend(loc=4)
# Adjust the plotting range of two y axes
org1 = 0.0 # Origin of first axis
org2 = 0.0 # Origin of second axis
pos = 0.1 # Position the two origins are aligned
align.yaxes(ax1, org1, ax2, org2, pos)
plt.savefig(folder + 'plot_incrementi_pct2.jpg')
plotlist.append('plot_incrementi_pct2.jpg')
# grafico crescita parametri
x = df_a.index.values
y1 = df_a[[
'ricoverati_con_sintomi', 'terapia_intensiva', 'isolamento_domiciliare'
]].values
y2 = df_a[[
'ricoverati_con_sintomi_incr', 'terapia_intensiva_incr',
'isolamento_domiciliare_incr'
]].values
lab = ['ricoverati con sintomi', 'terapia intensiva', 'isolamento domiciliare']
fig, ax = plt.subplots(2, 1, figsize=(14, 12))
ax1 = plt.subplot(211)