def plot_figure(style='default'):
"""Setup and plot the demonstration figure with a given style.
"""
plt.style.use("default") # reset
plt.style.use(style)
# Use a dedicated RandomState instance to draw the same "random" values
# across the different figures.
prng = np.random.RandomState(96917002)
# Tweak the figure size to be better suited for a row of numerous plots:
# double the width and halve the height. NB: use relative changes because
# some styles may have a figure size different from the default one.
figures, axs = zip(*[plt.subplots() for _ in range(6)])
plot_scatter(axs[0], prng)
plot_image_and_patch(axs[1], prng)
plot_bar_graphs(axs[2], prng)
plot_colored_circles(axs[3], prng)
plot_colored_sinusoidal_lines(axs[4])
plot_histograms(axs[5], prng)
if style == 'cyberpunk':
for ax in axs:
mplcyberpunk.make_lines_glow(ax)
for fig in figures:
fig.tight_layout()
return figures
def test_plotting_working_individual_functions():
plt.style.use("cyberpunk")
values = {c: [random.randint(0, 10) for _ in range(7)] for c in 'ABCDEF'}
df = pd.DataFrame(values)
df.plot(marker='o')
mplcyberpunk.make_lines_glow()
mplcyberpunk.add_underglow()
plt.savefig("test_individual.png")
def freq_analysis(input_text: str) -> None:
character_freq = [input_text.count(i) for i in alphabet]
table = dict(
zip(alphabet,
character_freq)) # словарь{буква_алфавита: частота_в_шифротексте}
print(table)
table1 = list(table.items())
table1.sort(key=lambda i: i[1],
reverse=True) # словарь в список с сортировкой по value
table1 = [x[0] for x in table1]
# словарь {буква_в_частотном алфавите : буква_алфавита_с_макс_частотой }
new_table = dict(zip(
table1, freq_alphabet)) # словарь {шифр_символ: настоящий_символ}
print(new_table)
table2 = list(table.items())
table2.sort(key=lambda i: i[1], reverse=True)
table2 = [x[1] for x in table2]
plt.bar(table.keys(), table.values(), width=0.7)
plt.show()
plt.bar(new_table.keys(), table2, width=0.7)
mplcyberpunk.make_lines_glow()
mplcyberpunk.add_underglow()
plt.show()
# output_text = [(i, char) for i in len(text) for char in text ]
'''
Теперь надо заменить буквы в input.txt по словарю new_table:
если буква = ключу словаря, то заменяем ее на значение словаря
'''
new_table = list(new_table.items())
print(new_table)
new_text = ''
for i in text:
for j in new_table:
if i in j and i == j[0]:
i = j[1]
new_text += i
break
with open('output.txt', mode='w') as file:
file.write(new_text)
def test_linestyle_attributes_copied():
plt.style.use("cyberpunk")
values = {c: [random.randint(0, 10) for _ in range(7)] for c in 'A'}
df = pd.DataFrame(values)
marker = 'x'
linestyle = '--'
df.plot(marker=marker, linestyle=linestyle)
n_glow_lines = 10
mplcyberpunk.make_lines_glow(n_glow_lines=n_glow_lines)
# check number of lines
lines = plt.gca().lines
assert len(lines) == 1 + n_glow_lines
# check line styling attributes
for line in lines:
assert line.get_marker() == marker
assert line.get_linestyle() == linestyle
def plot_figure(style_label="", n_columns=6):
"""Setup and plot the demonstration figure with a given style.
"""
# Use a dedicated RandomState instance to draw the same "random" values
# across the different figures.
prng = np.random.RandomState(96917002)
# Tweak the figure size to be better suited for a row of numerous plots:
# double the width and halve the height. NB: use relative changes because
# some styles may have a figure size different from the default one.
n_rows = math.ceil(6/n_columns)
(fig_width, fig_height) = plt.rcParams['figure.figsize']
fig_size = [fig_width * n_columns, fig_height * n_rows]
fig, axs = plt.subplots(ncols=n_columns,
nrows=n_rows,
num=style_label,
figsize=fig_size,
squeeze=False)
axs = [item for sublist in axs for item in sublist]
#axs[0].set_ylabel(style_label)
plot_scatter(axs[0], prng)
plot_image_and_patch(axs[1], prng)
plot_bar_graphs(axs[2], prng)
plot_colored_circles(axs[3], prng)
plot_colored_sinusoidal_lines(axs[4])
plot_histograms(axs[5], prng)
if style_label == 'cyberpunk':
for ax in axs:
mplcyberpunk.make_lines_glow(ax)
fig.tight_layout()
return fig
seconds.append(a_timedelta.total_seconds())
# Compound Concentrations
compound_conc = [
'200 uM', '100 uM', '50 uM', '25 uM', '12.5 uM', '6.25 uM', '3.125 uM',
'1.5625 uM', '0.78125 uM', '0.3906 uM', '0.1953 uM', '+Control (Protein)'
]
# Colors for Each Line
line_colors = [
'#03045e', '#023e8a', '#0077b6', '#0096c7', '#00b4d8', '#48cae4',
'#90e0ef', '#ade8f4', '#caf0f8', '#D6EAF8', '#EBF5FB', 'yellow'
]
# Plot DataFrame
plt.style.use("cyberpunk")
for i in df_mean.columns:
# plt.plot(seconds, df_4[i], marker='o', label=compound_conc[i-1], color=line_colors[i-1])
plt.plot(seconds,
df_mean[i],
marker='o',
label=compound_conc[i - 1],
color=line_colors[i - 1])
plt.plot(seconds, df_5[1], marker='o', label='-Control (No Prot)', color='red')
plt.legend()
plt.title('Compound in 5% DMSO')
plt.xlabel('Time (s)', fontsize=12)
plt.ylabel('Absorbance (405 nm)', fontsize=12)
mplcyberpunk.make_lines_glow()
plt.show()
def PlotResults(self, Normalized=False):
# Plot the data on three separate curves for S(t), I(t) and R(t)
if Normalized == True:
N = self.N
else:
N = 1.0
fig = plt.figure(figsize=(7, 9))
try:
fig.suptitle('Model vs. Data - ' + self.Data.country + ' - ' +
self.Data.state)
except:
fig.suptitle('Model vs. Data - ' + self.Data.country)
ax = fig.add_subplot(211, axisbelow=True)
#ax.plot(self.t, self.S/N, 'blue', alpha=0.5, lw=2, label='Susceptible')
#ax.plot(self.t, self.E/N, 'yellow', alpha=0.5, lw=2, label='Exposed')
#ax.plot(self.t, self.I/N, 'red', alpha=0.5, lw=2, label='Infected')
#ax.plot(self.t, self.Q/N, 'orange', alpha=0.5, lw=2, label='Quarantined')
#ax.plot(self.t, self.P/N, 'teal', alpha=0.5, lw=2, label='Insusceptible')
#ax.plot(self.t, self.Total/N, 'brown', alpha=0.5, lw=2, label='Total')
ax.plot(self.t, self.C / N, label='Confirmed (Model)')
ax.plot(self.t, self.D / N, label='Dead (Model)')
try:
ax.scatter(self.Data.Days,
self.Data.C / N,
s=5,
label='Confirmed (Data)')
ax.scatter(self.Data.Days,
self.Data.D / N,
s=5,
label='Death (Data)')
if self.Data.country != 'Canada':
ax.plot(self.t, self.R / N, label='Recovered (Model)')
ax.scatter(self.Data.Days,
self.Data.R / N,
s=5,
label='Recovered (Data)')
except:
pass
#ax.set_xlabel('Time (days)')
ax.set_ylabel('Number (log)')
#ax.set_ylim(0,1.2)
ax.yaxis.set_tick_params(length=0)
ax.xaxis.set_tick_params(length=0)
#ax.grid(b=True, which='major', c='w', lw=2, ls='-')
# legend = ax.legend()
# legend.get_frame().set_alpha(0.5)
for spine in ('top', 'right', 'bottom', 'left'):
ax.spines[spine].set_visible(False)
ax.set_yscale('log')
############################
ax2 = fig.add_subplot(212, axisbelow=True)
ax2.plot(self.t, self.C / N, label='Confirmed (Model)')
ax2.plot(self.t, self.D / N, label='Dead (Model)')
try:
ax2.scatter(self.Data.Days,
self.Data.C / N,
s=5,
label='Confirmed (Data)')
ax2.scatter(self.Data.Days,
self.Data.D / N,
s=5,
label='Death (Data)')
if self.Data.country != 'Canada':
ax2.plot(self.t, self.R / N, label='Recovered (Model)')
ax2.scatter(self.Data.Days,
self.Data.R / N,
s=5,
label='Recovered (Data)')
except:
pass
ax2.set_xlabel('Time (days)')
ax2.set_ylabel('Number')
ax2.yaxis.set_tick_params(length=0)
ax2.xaxis.set_tick_params(length=0)
legend = ax2.legend()
legend.get_frame().set_alpha(0.5)
for spine in ('top', 'right', 'bottom', 'left'):
ax2.spines[spine].set_visible(False)
##############
mplcyberpunk.make_lines_glow(ax)
mplcyberpunk.make_lines_glow(ax2)
plt.show()
def plot_tariff(tariff: pd.DataFrame,
timeFrom_series: str,
timeTo_series: str,
value_series: str,
saveTo: str = None) -> pd.DataFrame:
plt.style.use("cyberpunk")
tariff['diff'] = -tariff[value_series].diff(periods=1)
fig, ax = plt.subplots()
# Create step functions
timeFrom = tariff[timeFrom_series].repeat(2).values[:-1]
value = tariff[value_series].repeat(2).values[1:]
x, pos = split_array(timeFrom, value, 0, True)
_, neg = split_array(timeFrom, value, 0, False)
ax.plot(x, pos, color='c')
ax.plot(x, neg, color='r')
# Set x axis labels
freq = '3H'
t = pd.date_range(tariff[timeFrom_series].min().ceil(freq),
tariff[timeFrom_series].max(),
freq=freq)
ax.set_ylabel('Tariff (p/kWh)')
ax.set_xticks(t)
ax.set_xticklabels(
[time.time().strftime("%I %p").lstrip('0') for time in t], rotation=90)
def label(label_series: pd.Series, text_offset: Tuple[float, float]):
# Wrapper for annotate method
xlim = [mpl.dates.num2date(l) for l in ax.get_xlim()]
xaxis_range = (xlim[1] - xlim[0]).total_seconds() / 60 / 60
strftime = "%I %p" if label_series[timeTo_series].strftime(
"%M") == "00" else "%I:%M %p"
x_shift_hrs = text_offset[0] * xaxis_range
ax.annotate(
label_series[timeTo_series].strftime(strftime).lstrip('0'),
xy=(label_series[timeTo_series],
label_series[value_series] + label_series['diff'] / 2),
xytext=(label_series[timeTo_series] +
pd.Timedelta(f'{x_shift_hrs} hours'),
(label_series[value_series] + label_series['diff'] / 2) *
text_offset[1]),
arrowprops={'arrowstyle': '->'})
# table start and end times of large jumps in price
for big_step in tariff[tariff['diff'] > 10].iterrows():
big_step = big_step[1]
label(
big_step,
(-0.1 if big_step[timeTo_series].strftime("%M") == "00" else -0.12,
1.1))
for big_step in tariff[tariff['diff'] < -10].iterrows():
big_step = big_step[1]
label(big_step, (0.04, 1.1))
mplcyberpunk.make_lines_glow()
mplcyberpunk.add_underglow()
if saveTo is not None:
plt.savefig(saveTo, bbox_inches='tight')
else:
return ax
