def categorical_horizontal_bar_numbers(dataset,
filename: str,
fig_dim=(10, 5),
title="",
x_label="",
y_label="",
data_type="date",
title_fontsize=35):
fmt = EngFormatter(places=0)
data_options = ["date", "decision", "label"]
if data_type not in data_options:
raise ValueError("Invalid date_type. Expected one of: %s" %
data_options)
fig, ax = plt.subplots(figsize=fig_dim)
width = 0.75 # the width of the bars
ind = np.arange(len(dataset.values())) # the x locations for the groups
ax.barh(ind, dataset.values(), width, color="#004561")
plt.rcParams["font.weight"] = "bold"
ax.set_yticks(ind + width / 2)
ax.set_yticklabels(dataset.keys(), minor=False)
plt.grid(False)
plt.title(title, fontsize=title_fontsize)
plt.xlabel(x_label)
plt.ylabel(y_label)
ax.xaxis.set_major_formatter(fmt)
for i, v in enumerate(dataset.values()):
if data_type == data_options[0]:
ax.text(v + 5000000,
i,
s=fmt.format_eng(v),
color='#1e343f',
va='center')
elif data_type == data_options[1]:
ax.text(v + 2000000,
i,
s=fmt.format_eng(v),
color='#1e343f',
va='center',
fontweight='bold')
plt.tight_layout()
elif data_type == data_options[2]:
ax.text(v + 15,
i,
s=fmt.format_eng(v),
color='#1e343f',
va='center',
fontweight='bold')
png_file = filename + ".png"
path_to_png = add_path_to_plot_images_str(png_file)
plt.savefig(path_to_png)
# plt.savefig(path_to_png, format='png', bbox_inches='tight')
plt.tight_layout()
plt.show()
def plot(all_data, key_to_plot, directory=".", eng_fmt=False):
list_of_n = sorted(all_data.keys())
samples = len(next(iter(all_data.values())))
data = [[all_data[n][i][key_to_plot] for i in range(samples)]
for n in list_of_n]
data_mean = list(map(mean, data))
data_min = list(map(min, data))
data_max = list(map(max, data))
data_dev = list(map(pstdev, data))
ax = plt.gca()
ax2 = ax.twinx()
ax.plot(list_of_n, data_mean, label="mean")
ax.plot(list_of_n, data_min, label="min/max", color='#808080')
ax.plot(list_of_n, data_max, color='#808080')
ax2.plot(list_of_n, data_dev, label="stdev", color='#d62728')
ax.fill_between(list_of_n, data_min, data_max, color='#539caf', alpha=0.4)
# x axis
ax.set_xlabel('n')
formatter = EngFormatter(sep="")
ax.xaxis.set_major_formatter(formatter)
# plt.xticks(np.arange(min(list_of_n), max(list_of_n)+1, 10000))
# first y axis
ax.set_ylabel(key_to_plot)
if eng_fmt:
ax.yaxis.set_major_formatter(formatter)
ax.yaxis.set_major_locator(MaxNLocator(nbins='auto', integer=True))
# second y axis
ax1_range = max(data_max) - min(data_min)
dev_middle = min(data_dev) + max(data_dev) / 2
ax2.set_ylim([dev_middle - ax1_range / 2, dev_middle + ax1_range / 2])
no_negative = lambda x, pos=None: "" if x < 0 else (formatter.format_eng(
x) if eng_fmt else "%.2f" % x)
ax2.yaxis.set_major_formatter(FuncFormatter(no_negative))
ax.legend(loc=2)
ax2.legend(loc=1)
plt.savefig(os.path.join(directory, 'plot_{}.pdf'.format(key_to_plot)))
plt.clf()
cmap_purples = mpl.cm.get_cmap('Purples')
cmap_greens = mpl.cm.get_cmap('Greens')
normalize = mpl.colors.Normalize(vmin=0, vmax=3)
beta_colors = [cmap_blues(normalize(t)) for t in range(1, 4)]
gamma_colors = [cmap_purples(normalize(t)) for t in range(1, 4)]
qs_colors = [cmap_greens(normalize(t)) for t in range(1, 4)]
# Plot beta3 only if available
df_beta3 = fit_df[fit_df['inflection_point4'] != 0]
# df_beta3 = df_beta3.reset_index(drop=True)
x12 = list(fit_df.index)
x3 = list(df_beta3.index)
x_labels_bottom = list(fit_df['Country/Region'])
x_labels_top = [engfmt.format_eng(x) for x in list(fit_df['confirmed'])]
mpl.rcParams.update(defaultPlotStyle)
fig = plt.figure()
fig.set_size_inches(6.5, 8.5, forward=True)
fig.subplots_adjust(hspace=0.1, wspace=0.4)
gs0 = gridspec.GridSpec(ncols=1, nrows=1, figure=fig, width_ratios=[1])
gs00 = gridspec.GridSpecFromSubplotSpec(nrows=4,
ncols=1,
subplot_spec=gs0[0])
ax1 = fig.add_subplot(gs00[0, 0])
ax2 = fig.add_subplot(gs00[1, 0])
ax3 = fig.add_subplot(gs00[2, 0])
ax4 = fig.add_subplot(gs00[3, 0])
# |-class matplotlib.ticker.PercentFormatter(xmax=100, decimals=None, symbol='%', is_latex=False)
xaxis = ax.get_xaxis()
pfx = PercentFormatter(
xmax=10, # 百分比的计算分母：输出值计算方式：x / xmax * 100. 比如千分比，使用10
decimals=2, # 小数点维数：精度
symbol='‰$a^2$', # 符号
is_latex=True) # 符号支持Latex语法
xaxis.set_major_formatter(pfx)
# 2.EngFormatter
# |-class matplotlib.ticker.EngFormatter(unit='', places=None, sep=' ')[source]
yaxis = ax.get_yaxis()
pfy = EngFormatter(
unit='Hz', # 单位（后缀）
places=1, # 小数位数
sep='*') # 前缀（常用符号见上面说明与文档说明）
print(pfy.format_eng(0.1)) # 100m
yaxis.set_major_formatter(pfy)
ax2 = figure.add_axes([0.1, 0.55, 0.8, 0.4])
# 3.FuncFormatter
# class matplotlib.ticker.FuncFormatter(func)
xaxis = ax2.get_xaxis()
def my_x_formatter_func(x, pos):
if x and pos:
print(type(x), x, type(pos), pos)
return 'L:%5.2f(%d)' % (x, pos)
else:
return "o"
async def plot_csv(path,
total_confirmed,
total_recovered,
total_deaths,
logarithmic=False,
is_us=False,
is_daily=False):
timeline, confirmed, recovered, deaths, active = await make_courbe(
total_confirmed, total_recovered, total_deaths, is_us=is_us)
alpha = .2
fig, ax = plt.subplots()
ax.spines['bottom'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
if logarithmic:
plt.yscale('log')
ax.xaxis.set_major_locator(MultipleLocator(7))
ax.plot(timeline, deaths, "-", color="#e62712")
ax.plot(timeline, confirmed, "-", color="orange")
if not is_us:
ax.plot(timeline, active, "-", color="yellow", alpha=0.5)
ax.plot(timeline, recovered, "-", color="lightgreen")
leg = plt.legend(["Deaths", "Confirmed", "Active", "Recovered"],
facecolor='0.1',
loc="upper left")
else:
leg = plt.legend(["Deaths", "Confirmed"],
facecolor='0.1',
loc="upper left")
for text in leg.get_texts():
text.set_color("white")
ticks = [i for i in range(len(timeline)) if i % 60 == 0]
plt.xticks(ticks, ha="center")
ax.yaxis.grid(True)
plt.ylabel("Data")
plt.xlabel("Timeline (mm/dd/yy)")
ax.xaxis.label.set_color('white')
ax.yaxis.label.set_color('white')
ax.tick_params(axis='x', colors='white')
ax.tick_params(axis='y', colors='white')
fig.autofmt_xdate()
ax.fmt_xdata = mdates.DateFormatter("%M/%d/%y")
if not logarithmic:
ax.set_ylim(ymin=1)
locs, _ = plt.yticks()
if logarithmic:
locs = list(map(lambda x: x * 100, locs[:-1]))
formatter = EngFormatter(locs)
plt.yticks(locs,
[formatter.format_eng(int(iter_loc)) for iter_loc in locs])
plt.savefig(path, transparent=True)
plt.close('all')
async def plot_bar_daily(path, confirmed, recovered, deaths):
timeline, confirmed, recovered, deaths = make_daily_courbe(
confirmed, recovered, deaths)
ticks = [i for i in range(len(timeline)) if i % 60 == 0]
legs = []
ax1 = plt.subplot(3, 1, 1)
ax1.xaxis.label.set_color('white')
ax1.yaxis.label.set_color('white')
ax1.tick_params(axis='x', colors='white')
ax1.tick_params(axis='y', colors='white')
ax1.spines['bottom'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.axes.get_xaxis().set_visible(False)
plt.xticks(ticks, ha="center")
labels = ["Confirmed"]
handles = [plt.Rectangle((0, 0), 1, 1, color="orange") for label in labels]
legs.append(
plt.legend(handles,
labels,
facecolor='0.1',
loc="upper left",
prop={"size": 8}))
ax1.bar(timeline, confirmed, color="orange")
locs, _ = plt.yticks()
formatter = EngFormatter(locs)
plt.yticks(locs,
[formatter.format_eng(int(iter_loc)) for iter_loc in locs])
ax2 = plt.subplot(3, 1, 2)
ax2.xaxis.label.set_color('white')
ax2.yaxis.label.set_color('white')
ax2.tick_params(axis='x', colors='white')
ax2.tick_params(axis='y', colors='white')
plt.xticks(ticks, ha="center")
labels = ["Recovered"]
handles = [
plt.Rectangle((0, 0), 1, 1, color="lightgreen") for label in labels
]
legs.append(
plt.legend(handles,
labels,
facecolor='0.1',
loc="upper left",
prop={"size": 8}))
ax2.spines['bottom'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax2.axes.get_xaxis().set_visible(False)
ax2.bar(timeline, recovered, color="lightgreen")
locs, _ = plt.yticks()
formatter = EngFormatter(locs)
plt.yticks(locs,
[formatter.format_eng(int(iter_loc)) for iter_loc in locs])
ax3 = plt.subplot(3, 1, 3)
ax3.xaxis.label.set_color('white')
ax3.yaxis.label.set_color('white')
ax3.tick_params(axis='x', colors='white')
ax3.tick_params(axis='y', colors='white')
plt.xticks(ticks, ha="center")
labels = ["Deaths"]
handles = [
plt.Rectangle((0, 0), 1, 1, color="#e62712") for label in labels
]
legs.append(
plt.legend(handles,
labels,
facecolor='0.1',
loc="upper left",
prop={"size": 8}))
ax3.spines['bottom'].set_visible(False)
ax3.spines['top'].set_visible(False)
ax3.spines['right'].set_visible(False)
ax3.spines['left'].set_visible(False)
ax3.bar(timeline, deaths, color="#e62712")
locs, _ = plt.yticks()
formatter = EngFormatter(locs)
plt.yticks(locs,
[formatter.format_eng(int(iter_loc)) for iter_loc in locs])
plt.xlabel("Timeline (mm/dd/yy)")
for leg in legs:
for text in leg.get_texts():
text.set_color("white")
plt.savefig(path, transparent=True)
plt.close('all')
