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 test_axis_limits_unchanged_by_underglow():
plt.style.use("cyberpunk")
values = {c: [random.randint(20, 30) for _ in range(7)] for c in 'ABCDEF'}
df = pd.DataFrame(values)
df.plot(marker='o')
ax = plt.gca()
xlims, ylims = ax.get_xlim(), ax.get_ylim()
mplcyberpunk.add_underglow(ax)
assert xlims == ax.get_xlim()
assert ylims == ax.get_ylim()
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 plot(kernel: Kernel, params: dict = None, ax=None, xrange: Tuple[float, float] = (-10, 10.)):
"""
Plot the kernel's shape.
:param kernel: The kernel function
:param params: A dictionary containing the kernel parameters
:param ax: An optional matplotlib axes
:param xrange The tuple pair lower and upper values over which the kernel should be evaluated.
:return:
"""
if params is None:
params = initialise(kernel)
cols = get_colours()
if ax is None:
fig, ax = plt.subplots()
X = jnp.linspace(xrange[0], xrange[1], num=200).reshape(-1, 1)
x1 = jnp.array([[0.0]])
K = gpjax.kernels.cross_covariance(kernel, X, x1, params)
ax.plot(X, K.T, color=cols['base'])
mplcyberpunk.add_underglow(ax=ax)
# 数据 x = np.arange(-7, 7, 0.1) y1 = np.sin(x) y2 = np.sin(x) + x y3 = np.sin(x) * x y4 = np.sin(x) / x plt.plot(x, y1) plt.plot(x, y2) plt.plot(x, y3) plt.plot(x, y4) # 线条发光 mplcyberpunk.make_lines_glow() # 面积图 mplcyberpunk.add_underglow() plt.show() # time t = np.arange(0, 6.4, 0.1) # frequency f = 1 amplitudes = np.arange(-10, 11, 1) # amplitude A = [x * np.cos(f * t) for x in amplitudes] # 设置颜色样式，cool、spring、winter、tab20、coolwarm colormap_sect = np.linspace(0, 1, len(amplitudes)) colors = [cm.cool(x) for x in colormap_sect]
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
def plow():
mplcyberpunk.add_glow_effects()
mplcyberpunk.add_underglow()