def plot_trajectory(cls):
# see: https://aatishb.com/covidtrends/
df = cls.data
sel_countries = chart_trajectory_countries
aff = df[df["entity"].isin(sel_countries)
& (df["confirmed"] > chart_min_pop)]
traj = aff[["entity", "date", "confirmed"]].copy()
traj["increase"] = aff["confirmed"] - UnifiedDataModel.date_shifted_by(
aff, "confirmed", days(7)).fillna(0)
fig, axs = pk.new_regular()
more_cyclers(axs)
sel_countries_sorted = list(
sorted(sel_countries,
key=lambda c: traj[traj["entity"] == c]["increase"].max(),
reverse=True))
for c in sel_countries_sorted:
cdata = traj[traj["entity"] == c]
xydata = cdata[["confirmed", "increase"
]].sort_values(by="confirmed").to_numpy()
p = axs.plot(*xydata.T, label=c)
axs.scatter(*xydata[-1].T, label="_", c=pk.get_object_facecolor(p))
axs.set_xscale("log")
axs.set_yscale("log")
pk.set_grid(axs)
axs.legend()
axs.set_xlabel("Total confirmed cases")
axs.set_ylabel("New confirmed cases / 7 day period")
axs.xaxis.set_major_formatter(mticker.EngFormatter())
axs.yaxis.set_major_formatter(mticker.EngFormatter())
axs.annotate("Last data: " + str(traj["date"].max()),
xy=(0.5, 0),
xycoords="figure fraction",
ha="center",
va="bottom")
pk.finalize(fig, f"trajectory.png")
def draw_box_plot():
# Prepare data for box plots
df_box = clean_df.copy()
df_box.reset_index(inplace=True)
df_box['year'] = [d.year for d in df_box.date]
df_box['month'] = [d.strftime('%b') for d in df_box.date]
# Draw box plots (using Seaborn)
# Create a figure with 2 axes
fig, (ax1, ax2) = plt.subplots(ncols=2,
figsize=(10, 8),
constrained_layout=True,
dpi=200)
# First Axes
sns.boxplot(data=df_box, ax=ax1, x='year', y='value')
ax1.yaxis.set_major_formatter(
ticker.EngFormatter()) #This line could be better
ax1.set(ylim=(20000, 200000))
ax1.set_xlabel('Year')
ax1.set_ylabel('Page Views')
ax1.set_title('Year-wise Box Plot(Trend)')
# Second Axes
sns.boxplot(data=df_box, ax=ax2, x='month', y='value')
ax2.yaxis.set_major_formatter(ticker.EngFormatter())
ax2.set(ylim=(20000, 200000))
ax2.set_xlabel('Month')
ax2.set_ylabel('Page Views')
ax2.set_title('Month-wise Box Plot(Seasonality)')
# Save image and return fig
fig.savefig('box_plot.png')
return fig
def plot_Omega_and_Delta(self, ax, plot_title: bool = True):
"""
Plots Omega and Delta as a function of time.
Includes overlap with GHZ state if `self.solve_result` is not None (self.solve has been called).
:return:
"""
ax.xaxis.set_major_formatter(ticker.EngFormatter('s'))
plt.xlabel('Time')
ax.plot(self.t_list, [self.Omega(t) for t in self.t_list],
label=r"$\Omega{}$",
lw=3,
alpha=0.8)
ax.plot(self.t_list, [self.Delta(t) for t in self.t_list],
label=r"$\Delta{}$",
lw=3,
alpha=0.8)
ax.yaxis.set_major_formatter(ticker.EngFormatter('Hz'))
ax.locator_params(nbins=4, axis='y')
ax.locator_params(nbins=5, axis='x')
ax.legend()
if plot_title:
ax.set_title("Control parameters")
delta = (self.t_list.max() - self.t_list.min()) * 0.01
ax.set_xlim((self.t_list.min() - delta, self.t_list.max() + delta))
def sci_labels(ax,
decimals=2,
x=True,
y=True,
z=False,
unit='',
y_unit: str = None,
rotation=30):
if decimals < 2:
print('Warning, pyplot may round labels')
formatter = tck.EngFormatter(places=decimals,
sep=u"\N{THIN SPACE}",
unit=unit)
if x:
ax.xaxis.set_major_formatter(formatter)
plt.xticks(rotation=rotation)
if z:
# 3D plot
ax.zaxis.set_major_formatter(formatter)
if y:
plt.yticks(rotation=rotation)
if y_unit is not None:
formatter = tck.EngFormatter(places=decimals,
sep=u"\N{THIN SPACE}",
unit=y_unit)
ax.yaxis.set_major_formatter(formatter)
def config_axis(ax=None,
x_lim=None,
y_lim=None,
X_0=None,
Y_0=None,
grd=True,
minorgrd=False,
mult_x=0.2,
mult_y=0.2,
Eng=True):
if (X_0 != None):
ax.xaxis.set_major_locator(ticker.MultipleLocator(mult_x * X_0))
ax.xaxis.set_minor_locator(ticker.MultipleLocator((mult_x / 5) * X_0))
if (Eng):
ax.xaxis.set_major_formatter(ticker.EngFormatter())
ax.yaxis.set_major_formatter(ticker.EngFormatter())
if (Y_0 != None):
ax.yaxis.set_major_locator(ticker.MultipleLocator(mult_y * Y_0))
ax.yaxis.set_minor_locator(ticker.MultipleLocator(mult_y / 5 * Y_0))
if (grd == True):
ax.grid(b=True, which='major', axis='both')
else:
ax.grid(b=False, which='major', axis='both')
if (minorgrd == True):
ax.grid(b=True, which='minor', axis='both')
else:
ax.grid(b=False, which='minor', axis='both')
if (x_lim != None):
ax.set_xlim(x_lim)
if (y_lim != None):
ax.set_ylim(y_lim)
return ax
def plot_bode(outfile):
plt.clf()
printall(ns.cmd("alter c6 100u"))
#printall(ns.cmd("alter v1 3"))
#printall(ns.cmd("alter v2 3"))
gains = [500**(1 / n) for n in range(3, 0, -1)]
impedances = [1000, 10 * 1000, 100 * 1000, 1000 * 1000, 10 * 1000 * 1000]
currents_mA = [0] + list(my_logspace(0.0001, 10, 5 if DRAFT_MODE else 16))
_, plots = plt.subplots(
len(gains),
len(impedances) + 1,
gridspec_kw={'width_ratios': [3] * len(impedances) + [1]})
fmt_Hz = ticker.EngFormatter(unit='Hz')
fmt_dB = ticker.EngFormatter(unit='dB')
ohmfmt = ticker.EngFormatter(unit="Ω")
for y, gain in enumerate(gains):
for x, impedance_ohms in enumerate(impedances):
update_amplifier(impedance_ohms, gain)
plot_single_bode_family(plots[y][x], currents_mA)
plots[y][x].set_ylim(10 * math.log10(gain / 500) - 55,
10 * math.log10(gain / 500) + 5)
plots[y][x].xaxis.set_major_formatter(fmt_Hz)
plots[y][x].yaxis.set_major_formatter(fmt_dB)
if x == 0:
plots[y][x].text(0,
0.5,
'gain = %.1f×\n\n\n\n' % gain,
fontsize=10,
horizontalalignment='right',
verticalalignment='center',
transform=plots[y][x].transAxes,
rotation='vertical')
if y == 0:
plots[y][x].text(0.5,
1.0,
'impedance = %s\n' %
ohmfmt.format_data(impedance_ohms),
fontsize=10,
horizontalalignment='center',
verticalalignment='baseline',
transform=plots[y][x].transAxes)
if y == len(gains) - 1:
plots[y][x].set_xlabel("Frequency")
for p in plots:
p[-1].axis(False)
fmt = ticker.EngFormatter(unit="A")
make_legend([fmt.format_data(c / 1000) for c in currents_mA], plots[0][-1])
plt.gcf().set_size_inches(20, 11)
if outfile is not None:
plt.savefig(outfile)
def plot_detuning_energy_levels(s_qs: StaticQubitSystem,
crossings: np.ndarray,
ax: Axes,
highlighted_indices: Sequence[int] = (-1, )):
if s_qs.Omega_zero_energies is None:
s_qs.get_energies()
crossings_range = crossings.max() - crossings.min()
xlims = crossings_range * -0.1, crossings.max() * 1.1
s_qs = StaticQubitSystem(s_qs.N,
s_qs.V,
s_qs.geometry,
Omega=0,
Delta=np.linspace(xlims[0], xlims[1], 20))
s_qs.get_energies()
g = states_quimb.get_ground_states(1)[0]
for i, state in enumerate(s_qs.states):
is_highlight_state = any(state is s_qs.states[i]
for i in highlighted_indices)
is_ground_state = all((_state == g).all() for _state in state)
color = 'g' if is_ground_state else 'r' if is_highlight_state else 'grey'
linewidth = 5 if is_ground_state or is_highlight_state else 1
z_order = 2 if is_ground_state or is_highlight_state else 1
energies = s_qs.Omega_zero_energies[:, i]
ax.plot(s_qs.Delta,
energies,
color=color,
alpha=0.6,
lw=linewidth,
zorder=z_order,
label=states_quimb.get_label_from_state(state),
picker=3)
def on_pick(event):
line = event.artist
print(f'Clicked on: {line.get_label()}')
plt.gcf().canvas.mpl_connect('pick_event', on_pick)
ax.grid()
scaled_xaxis_ticker = ticker.EngFormatter(unit="Hz")
scaled_yaxis_ticker = ticker.EngFormatter(unit="Hz")
ax.xaxis.set_major_formatter(scaled_xaxis_ticker)
ax.yaxis.set_major_formatter(scaled_yaxis_ticker)
ax.locator_params(nbins=4)
# plt.title(rf"Energy spectrum with $N = {self.N}$, $V = {self.V:0.2e}$, $\Omega = {self.Omega:0.2e}$")
_m, _s = f"{s_qs.V:0.2e}".split('e')
V_text = rf"{_m:s} \times 10^{{{int(_s):d}}}"
plt.title(rf"Energy spectrum with $N = {s_qs.N}$, $V = {V_text:s}$ Hz")
plt.xlabel(r"Detuning $\Delta$")
plt.ylabel("Eigenenergy")
plt.xlim(xlims)
plt.tight_layout()
def _add_std_figure_formatting(self, x_unit, y_unit):
# pylint: disable=no-self-use
x_formatter = ticker.EngFormatter(unit=x_unit)
y_formatter = ticker.EngFormatter(unit=y_unit)
x_formatter.ENG_PREFIXES[-6] = 'u'
y_formatter.ENG_PREFIXES[-6] = 'u'
ax = plt.gca()
ax.xaxis.set_major_formatter(x_formatter)
ax.yaxis.set_major_formatter(y_formatter)
plt.grid('on', which='both')
def test_engformatter_usetex_useMathText():
fig, ax = plt.subplots()
ax.plot([0, 500, 1000], [0, 500, 1000])
ax.set_xticks([0, 500, 1000])
for formatter in (mticker.EngFormatter(usetex=True),
mticker.EngFormatter(useMathText=True)):
ax.xaxis.set_major_formatter(formatter)
fig.canvas.draw()
x_tick_label_text = [labl.get_text() for labl in ax.get_xticklabels()]
# Checking if the dollar `$` signs have been inserted around numbers
# in tick labels.
assert x_tick_label_text == ['$0$', '$500$', '$1$ k']
def plot_trendlines(mx, ax=None, unit=None, **prop):
ax = ax or plt.gca()
color = prop.pop('color', None)
xs, ys = mx[:, 0], mx[:, 1]
yerr = (ys - mx[:, 2], mx[:, 3] - ys)
ax.errorbar(xs, ys, yerr=yerr, fmt='-o', color=color)
fmt = ticker.EngFormatter(unit=unit or "")
ax.yaxis.set_major_formatter(fmt)
ax.xaxis.set_major_formatter(ticker.EngFormatter())
def kreise_plot(entity_parent,
short,
*,
field="confirmed",
maxn: Optional[int] = 10,
stack=False,
order_total: Union[bool, int] = False):
fig, axs = pk.new_regular()
more_cyclers(axs)
pv = pivoted(entity_parent, field, maxn, order_total)
plot_dataframe(axs,
pv,
stacked=stack,
kind="bar" if stack else "line")
axs.set_ylabel(field)
axs.annotate("Last data update: " + str(pv.last_modified),
xy=(0.5, 0),
xycoords="figure fraction",
ha="center",
va="bottom")
set_dateaxis(axs)
pk.set_grid(axs)
axs.set_ylim(0, )
axs.yaxis.set_major_formatter(mticker.EngFormatter())
pk.finalize(fig, f"local_{field}_{short}.png")
def plot_basis_state_populations_3d(e_qs: EvolvingQubitSystem):
if e_qs.solve_result is None:
e_qs.solve()
states = get_states(e_qs.N)
times = []
heights = []
xs = []
for i, _solve_result_state in enumerate(e_qs.solve_result.states):
plt.ylim(0, 1)
plt.grid(axis='y')
solve_result_state_populations = np.abs(_solve_result_state.data.toarray().flatten()) ** 2
for _x, state in enumerate(states):
state_product_basis_index = get_product_basis_states_index(state)
basis_state_population = solve_result_state_populations[state_product_basis_index]
times.append(e_qs.solve_result.times[i])
xs.append(_x)
heights.append(basis_state_population)
fig = plt.figure(figsize=(15, 8))
ax = fig.add_subplot(111, projection="3d")
dt = e_qs.solve_result.times[1] - e_qs.solve_result.times[0]
ax.bar3d(xs, times, z=0, dx=0.8, dy=dt, dz=heights)
labels = [get_label_from_state(state) for state in states]
x = np.arange(len(labels))
plt.xticks(x, labels)
ax.yaxis.set_major_formatter(ticker.EngFormatter('s'))
plt.ylabel('Time')
plt.show()
def plot_top(df, column, top_n=10):
'''
Plot % of top count values in a column in a Spark dataframe
Parameters:
df (DataFrame): Spark dataframe to plot
column (str): Column name to plot
top_n (int): Quantity of top values
'''
column_top = df.groupBy(column).count().sort(
f.desc('count')).limit(top_n).toPandas()
column_top[column] = column_top[column].astype(str) # safely display dates
plt.figure(figsize=(5, 3))
ax = seaborn.barplot(y=column, x='count', data=column_top)
ax.set_xlim(0, df.count())
ax.xaxis.set_major_formatter(
ticker.EngFormatter()) # https://stackoverflow.com/a/53749220
for p in ax.patches: # https://link.medium.com/BNlbiicbjeb
h = p.get_height()
w = p.get_width()
ax.text(x=w + 3, y=p.get_y() + h / 2, s=f'{w:.0f}', va='center')
plt.show()
def plotFourierData(freq, mag, axis=None, color='b', alpha=1.0):
ax = axis
if axis is None:
fig = plt.figure(1337)
fig.clf()
fig.canvas.set_window_title("Non-Intrusive Load Monitoring - Frequency Spectrum - Felix Maass")
ax = fig.add_subplot(111)
ax.plot(freq, mag, color=color, alpha=alpha)
ax.set_title("Frequency Spectrum")
ax.set_xlabel('Frequency [Hz]')
ax.get_xaxis().set_major_formatter(ticker.EngFormatter())
ax.get_xaxis().set_minor_formatter(ticker.NullFormatter())
ax.set_ylabel('Magnitude (log10)')
ax.set_yscale('log')
#######
if axis is None:
fig.tight_layout()
plt.show()
def plotIVCurve(chunk, axis=None):
ax = axis
if axis is None:
fig = plt.figure(314)
fig.clf()
fig.canvas.set_window_title("Non-Intrusive Load Monitoring - Current-Voltage Characteristics - Felix Maass")
ax = fig.add_subplot(111)
ax.set_title("IV-Curve")
ax.set_xlabel('Voltage [V]')
ax.set_ylabel('Amperage [A]')
ax.get_xaxis().set_major_formatter(ticker.EngFormatter())
ax.get_xaxis().set_minor_formatter(ticker.NullFormatter())
if np.max(np.abs(chunk['i'])) < 0.3:
ax.set_ylim([-500,500])
ax.set_autoscaley_on(False)
else:
ax.set_autoscaley_on(True)
ax.plot(chunk['u'], chunk['i'], color='b')
#######
if axis is None:
fig.tight_layout()
plt.show()
def plotPowerCurve(chunk, axis=None):
ax = axis
if axis is None:
fig = plt.figure(271)
fig.clf()
fig.canvas.set_window_title("Non-Intrusive Load Monitoring - Power Characteristics - Felix Maass")
ax = fig.add_subplot(111)
ax.set_title("Power")
ax.set_xlabel('Time [ms]')
ax.set_ylabel('Power [VA / W / VAr]')
ax.get_xaxis().set_major_formatter(ticker.EngFormatter())
ax.get_xaxis().set_minor_formatter(ticker.NullFormatter())
apparentPower = chunk['u'] * chunk['i']
phaseShift = calculatePhaseShift(chunk)
realPower = apparentPower * cos(phaseShift)
reactivePower = apparentPower * sin(phaseShift)
ax.plot(chunk['t'], apparentPower, color='r', label='Apparent')
ax.plot(chunk['t'], realPower, color='g', label='Real')
ax.plot(chunk['t'], reactivePower, color='b', label='Reactive')
ax.legend()
#######
if axis is None:
fig.tight_layout()
plt.show()
def plot_subword_growth():
examples = load_gold_standard()
unique_subwords = set()
n_unique_subwords = list()
for word in examples:
unique_subwords = unique_subwords.union(word)
n_unique_subwords.append(len(unique_subwords))
fig, ax = plt.subplots(1, 1, figsize=(5, 4))
sns.lineplot(range(len(n_unique_subwords)),
n_unique_subwords,
palette='dark') # , c='b')
plt.xlabel("Number of words")
plt.ylabel("Unique subwords")
ax.xaxis.set_major_formatter(ticker.EngFormatter())
ax.yaxis.set_major_formatter(ticker.EngFormatter())
plt.show()
def gen_graph(self):
self.log.append(2,'Generating {} graph'.format(self.name))
dates = mdates.date2num(self.x)
plt.figure(figsize=(20,5))
plt.plot_date(dates,self.y,'r')
plt.title(self.name)
option = sys.argv[1:]
option = option[0]
if option == '-d':
loc = mdates.HourLocator(interval=1)
fmt = mdates.DateFormatter('%H:%M')
label = 'Time [h:m]'
elif option == '-m':
loc = mdates.DayLocator(interval=1)
fmt = mdates.DateFormatter('%m.%d')
label = 'Day [m.d]'
elif option == '-w':
loc = mdates.DayLocator(interval=1)
fmt = mdates.DateFormatter('%d')
label = 'Day [m.d]'
plt.gca().xaxis.set_major_formatter(fmt)
plt.gca().xaxis.set_major_locator(loc)
plt.ticklabel_format(axis='y',style='plain')
plt.gca().yaxis.set_major_formatter(ticker.EngFormatter(unit=self.units))
plt.grid()
plt.xlabel(label)
if self.units == '':
plt.ylabel('[amount]')
plt.gca().yaxis.set_major_locator(ticker.MaxNLocator(integer=True))
else:
plt.ylabel('['+self.units+']')
plt.savefig(self.ID+'.jpg')
plt.close()
def line_plot_day_by_day(data):
data_by_time = data.groupby(
["ObservationDate",
"Country/Region"])["Confirmed", "Deaths", "Recovered",
"Active_cases"].sum().reset_index(drop=True)
plt.figure(figsize=(14, 9))
ax = sns.lineplot(x=data_by_time.index,
y='Confirmed',
label='Confirmed',
data=data_by_time)
sns.lineplot(x=data_by_time.index,
y='Active_cases',
label='Active_Cases',
data=data_by_time)
sns.lineplot(x=data_by_time.index,
y='Deaths',
label='Deaths',
data=data_by_time)
sns.lineplot(x=data_by_time.index,
y='Recovered',
label='Recovered',
data=data_by_time)
ax.yaxis.set_major_formatter(ticker.EngFormatter())
plt.title('Plotted day-by-day')
plt.legend()
def most_attacked_countries():
df2 = df.select(col('TGTCOUNTRY').alias('TargetCountry'), col('NUMWEAPONSDELIVERED').alias('NumWeapons')) \
.dropna() \
.groupBy(col('TargetCountry')) \
.agg(sum('NumWeapons').alias('TotalWeaponsDropped')) \
.filter(col('TotalWeaponsDropped') > 0).filter(col('TargetCountry') != 'UNKNOWN') \
.orderBy(col('TotalWeaponsDropped').desc())
pandf = df2.toPandas()
fig, ax = plt.subplots(figsize=(10, 9))
ax.bar(pandf['TargetCountry'],
pandf['TotalWeaponsDropped'],
color='royalblue')
ax.legend()
#avoid scientific notation on Y-axis
#ax.ticklabel_format(style='plain', axis='y')
ax.yaxis.set_major_formatter(mpt.EngFormatter())
plt.xticks(rotation=10)
plt.xlabel("Attacked Countries")
plt.ylabel("Number of Bombs dropped")
plt.title("Vietnam War Most Affected Countries")
plt.savefig(output_dir + "most_attacked_countries.png")
plt.close(fig)
def most_common_takeoff():
df2 = df.select(col('TAKEOFFLOCATION').alias('Takeoff')) \
.dropna() \
.groupBy(col('Takeoff')) \
.agg(count(lit(1)).alias('NumMissions')) \
.filter(col('NumMissions') > 26000).filter(~(col('Takeoff').rlike('^WESTPAC.*$'))) \
.orderBy(col('NumMissions').desc())
pandf = df2.toPandas()
fig, ax = plt.subplots(figsize=(16, 9))
ax.bar(pandf['Takeoff'], pandf['NumMissions'], color='royalblue')
ax.legend()
#avoid scientific notation on Y-axis
#ax.ticklabel_format(style='plain', axis='y')
ax.yaxis.set_major_formatter(mpt.EngFormatter())
plt.xticks(rotation=20)
plt.xlabel("Take-off location")
plt.ylabel("Number of Missions")
plt.title("Vietnam War Most Common Take-off Locations")
plt.savefig(output_dir + "most_common_takeoff.png")
plt.close(fig)
def make_thd_plot(thdplot, sinplot, voltages):
thdplot.set_ylabel("thd (dB)")
thdplot.set_xlabel("control current (mA)")
thdplot.set_title("total harmonic distortion")
sinplot.set_xticks([], [])
#sinplot.set_yticks([],[])
#sinplot.set_title("sine shape at cc = 1mA")
#for voltage in np.arange(3,18, 1):
for voltage in voltages:
printall(ns.cmd("alter v1 %f" % voltage))
printall(ns.cmd("alter v2 %f" % voltage))
y = []
x = my_logspace(0.01, 1, 8)
for current in x:
printall(ns.cmd("alter i1 %fm" % current))
y.append(calc_thd(300))
printall(ns.cmd("alter i1 40u"))
_, signal, time = simulate_waveform(300,
n_periods=1,
steps_per_period=64)
thdplot.plot(x / 1000, np.log10(y) * 10, label="+/- %fV" % voltage)
sinplot.plot(time, signal)
thdplot.set_xscale('log')
fmt_mA = ticker.EngFormatter(unit='A')
fmt_dB = ticker.FormatStrFormatter('%.0f dB')
thdplot.xaxis.set_major_formatter(fmt_mA)
thdplot.yaxis.set_major_formatter(fmt_dB)
def test_formatting(self, unit, input, expected):
"""
Test the formatting of EngFormatter with some inputs, against
instances with and without units. Cases focus on when no SI
prefix is present, for values in [1, 1000).
"""
fmt = mticker.EngFormatter(unit)
assert fmt(input) == expected
def barcompare(benches, *args, **kw):
"""barcompare (benchmarks, counters?, **keywords)
Compares benchmark results as a bar-chart.
Arguments:
- benches : Sequence of Benches.
- counters : Counters to display.
Keyword arguments:
- title : Figure title.
- est : Regression estimator.
- props : cycler.Cycler or other iterable yielding prop dicts for each counter.
"""
benches = list(benches)
counters = _as_seq(_pos(args, 0, 'time'))
estf = kw.get('est', est.default)
props = _counter_props(counters, kw.get('props'))
w = int(ceil(sqrt(len(counters))))
h = int(ceil(len(counters) / w))
fs = plt.rcParams['figure.figsize']
fig, axes = plt.subplots(nrows=h,
ncols=w,
squeeze=False,
sharey='row',
figsize=(w * fs[0],
max(1,
len(benches) * 0.1) * h * fs[1]))
axes = [ax for row in axes for ax in row]
for ax in axes[len(counters):]:
ax.remove()
names = [b.name for b in benches]
ypos = arange(len(benches) - 1, -1, -1)
for ctr, ax in zip(counters, axes):
es = [bench.estimate(ctr, estf) for bench in benches]
xerr = ([e.b - e.b_min for e in es], [e.b_max - e.b for e in es])
ax.barh(ypos, [e.b for e in es],
xerr=xerr,
alpha=0.3,
edgecolor=props[ctr]['color'],
**props[ctr])
ax.set_yticks(ypos)
ax.set_yticklabels(names)
unit = benches[0].unit(ctr) or ""
ax.xaxis.set_major_formatter(ticker.EngFormatter(unit=unit))
ax.set_xlabel(ctr)
_fig_set_title(fig, kw.get('title'), estf)
def labelsGeneric(graph, paramsList):
graph.yaxis.set_major_formatter(
ticker.EngFormatter()
) #English formatter allows for the graph ticks to be displayed as 10K instead of 10000
graph.set_title(paramsList[0]) #Set the graph title
plt.xlabel(paramsList[3]) #Label the x axis
plt.ylabel(paramsList[4]) #Label the y axis
plt.ylim(int(paramsList[5]), int(
paramsList[6])) #Set the lower limit and upper limit of the yaxis
def test_EngFormatter_formatting():
"""
Create two instances of EngFormatter with default parameters, with and
without a unit string ('s' for seconds). Test the formatting in some cases,
especially the case when no SI prefix is present, for values in [1, 1000).
Should not raise exceptions.
"""
unitless = mticker.EngFormatter()
assert unitless(0.1) == u'100 m'
assert unitless(1) == u'1'
assert unitless(999.9) == u'999.9'
assert unitless(1001) == u'1.001 k'
with_unit = mticker.EngFormatter(unit=u's')
assert with_unit(0.1) == u'100 ms'
assert with_unit(1) == u'1 s'
assert with_unit(999.9) == u'999.9 s'
assert with_unit(1001) == u'1.001 ks'
def bar_plot_province(data):
get_actual_values(data)
data = data.groupby(["Province/State"
])["Confirmed", "Active_cases", "Recovered",
"Deaths"].sum().reset_index().sort_values(
"Confirmed",
ascending=False).reset_index(drop=True)
for each in columns:
plt.figure(figsize=(14, 9))
ax = sns.barplot(x=each, y='Province/State', data=data)
ax.xaxis.set_major_formatter(ticker.EngFormatter())
plt.title(str(each) + ' cases by time in each Province')
def plot_gas_usage(es: Elasticsearch):
body = {
'aggs': {
'half_hour': {
'aggs': {
'used_gas': {
'avg': {
'field': 'gasUsed.num'
}
}
},
'date_histogram': {
'field': 'timestamp',
'interval': '30m',
'min_doc_count': 0
}
}
},
'query': {
'bool': {
'filter': [{
'range': {
'timestamp': {
'gte': 'now/d-7d',
'lte': 'now/d-1d'
}
}
}]
}
},
'size': 0
}
response = es.search(index='ethereum', body=body)
data = response['aggregations']['half_hour']['buckets']
x = [datetime.fromtimestamp(bucket['key'] / 1000) for bucket in data]
y = [bucket['used_gas']['value'] for bucket in data]
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(15, 6)
ax.yaxis.set_major_formatter(mtick.EngFormatter())
ax.set_xlabel('Date')
ax.set_ylabel('Gas Usage')
ax.plot(x, y)
fig.autofmt_xdate()
os.makedirs('plots', exist_ok=True)
fig.savefig('plots/gas_usage_{}.png'.format(datetime.now().isoformat()),
bbox_inches='tight')
plt.show()
def labelsMultiple(graph, paramsList):
graph.yaxis.set_major_formatter(
ticker.EngFormatter()
) #English formatter allows for the graph ticks to be displayed as 10K instead of 10000
graph.set_title(paramsList[0]) #Set the graph title
plt.xlabel(paramsList[1]) #Label the x axis
plt.ylabel(paramsList[2]) #Label the y axis
xLimL, xLimH = limitCheck(int(paramsList[3]), int(
paramsList[4])) #Check for limits that might be entered wrongly
yLimL, yLimH = limitCheck(int(paramsList[5]), int(
paramsList[6])) #Check for limits that might be entered wrongly
plt.xlim(xLimL, xLimH) #Set the lower limit and upper limit of the xaxis
plt.ylim(yLimL, yLimH) #Set the lower limit and upper limit of the yaxis
def plot_ax(
ax,
file_lists,
legend_pattern=".*",
xlabel=None,
ylabel=None,
title=None,
xlim=None,
xkey='env_step',
ykey='rew',
smooth_radius=0,
shaded_std=True,
legend_outside=False,
):
def legend_fn(x):
# return os.path.split(os.path.join(
# args.root_dir, x))[0].replace('/', '_') + " (10)"
return re.search(legend_pattern, x).group(0)
legneds = map(legend_fn, file_lists)
# sort filelist according to legends
file_lists = [f for _, f in sorted(zip(legneds, file_lists))]
legneds = list(map(legend_fn, file_lists))
for index, csv_file in enumerate(file_lists):
csv_dict = csv2numpy(csv_file)
x, y = csv_dict[xkey], csv_dict[ykey]
y = smooth(y, radius=smooth_radius)
color = COLORS[index % len(COLORS)]
ax.plot(x, y, color=color)
if shaded_std and ykey + ':shaded' in csv_dict:
y_shaded = smooth(csv_dict[ykey + ':shaded'], radius=smooth_radius)
ax.fill_between(x,
y - y_shaded,
y + y_shaded,
color=color,
alpha=.2)
ax.legend(legneds,
loc=2 if legend_outside else None,
bbox_to_anchor=(1, 1) if legend_outside else None)
ax.xaxis.set_major_formatter(mticker.EngFormatter())
if xlim is not None:
ax.set_xlim(xmin=0, xmax=xlim)
# add title
ax.set_title(title)
# add labels
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)