def add_genotype_annotations_to_plot(
self, ax: Axes, points: Dict[str, Tuple[float, float]],
annotations: Dict[str,
List[str]], color_palette: Dict[str,
str]) -> Axes:
locations = list()
for genotype_label, point in points.items():
if genotype_label == self.root_genotype_name:
# There's no point in adding an annotation for the root genotype.
continue
genotype_color: str = color_palette[genotype_label]
genotype_annotations: List[str] = annotations.get(
genotype_label, [])
if not genotype_annotations:
# No annotations for this genome. Don't draw anything.
continue
background_properties = self._get_annotation_label_background_properties(
genotype_color)
label_properties = self._get_annotation_label_font_properties(
genotype_color)
if locations:
x_loc, y_loc = relocate_point(point, locations)
else:
x_loc, y_loc = point
locations.append((x_loc, y_loc))
ax.text(x_loc,
y_loc,
"\n".join(genotype_annotations),
bbox=background_properties,
fontdict=label_properties)
return ax
def plot_sticks(atoms: np.ndarray,
ax: Axes,
bt=(0.2, 2),
nodup=True,
threshold=0.1,
**kwargs) -> None:
"""Plot bonds on given Axes.
Parameters
----------
atoms: array
Array of atom positions (2D or 3D).
ax: matplotlib.axes.Axes
Axes to plot on.
bs: tuple(int, int)
Bond threshold,
e.g. (0.2, 2) means distance of atom between 0.2 and 2 (Å).
nodup: bool
No duplicate: if several bonds are overlayed, only plot one of them.
threshold: float
Threshold of two bonds to regard as duplicate.
**kwargs: dict
Matplotlib plot kwargs.
Default style: {'color': 'tan', 'lw': 2, 'zorder': -1}.
"""
style = {'color': 'tan', 'lw': 2, 'zorder': -1}
style.update(kwargs)
paired = bond_pair(atoms, bt)
if nodup:
paired = rm_dup_bond(paired)
for pair in paired:
ax.plot(pair[:, 0], pair[:, 1], **style)
def plot_balls(atoms: np.ndarray,
ax: Axes,
bt=(0.2, 2),
nodup=True,
threshold=0.1,
**kwargs) -> None:
"""Plot bonds on given Axes.
Parameters
----------
atoms: array
Array of atom positions (2D or 3D).
ax: matplotlib.axes.Axes
Axes to plot on.
bs: tuple(int, int)
Bond threshold,
e.g. (0.2, 2) means distance of atom between 0.2 and 2 (Å).
nodup: bool
No duplicate: if several bonds are overlayed, only plot one of them.
threshold: float
Threshold of two bonds to regard as duplicate.
**kwargs: dict
Matplotlib plot kwargs.
Default style: {'s': 32, 'lw': 2, 'c': 'w', 'edgecolors': 'tan'}.
"""
style = {'s': 32, 'lw': 2, 'c': 'w', 'edgecolors': 'tan'}
style.update(kwargs)
if nodup:
atoms = rm_dup_atom(atoms)
ax.scatter(atoms[:, 0], atoms[:, 1], **style)
def _setup_axes_for_russian_regions_stat(self,
ax: _figure.Axes,
title: str = None,
grid: bool = True,
legend: bool = True,
draw_key_dates: bool = True):
ax.xaxis_date()
if draw_key_dates:
self.key_russian_dates(ax)
if legend:
ax.legend(loc='upper left')
ax.set_ylim(bottom=0)
if grid:
ax.grid(axis='y', color='black', linestyle='dashed', alpha=0.4)
if title:
ax.set_title(title)
def _draw_daily_stats(self, ax: _figure.Axes, df: _pd.DataFrame,
draw_key_dates: bool):
index = df.index
confirmed_daily = df.Confirmed_Change
recovered_daily = df.Recovered_Change
deaths_daily = df.Deaths_Change
self.bar_with_sma_line(ax, confirmed_daily, label="Заболевшие")
self.bar_with_sma_line(ax, recovered_daily, label="Выздоровевшие")
ax.bar(index,
deaths_daily,
label='Смерти',
alpha=0.3,
bottom=recovered_daily)
ax.plot(index,
(recovered_daily + deaths_daily).rolling(window=7).mean(),
label='Смерти-SMA7')
self._setup_axes_for_russian_regions_stat(
ax, "Статистика день ко дню", draw_key_dates=draw_key_dates)
def get_plot_formatting(ax: Axes, series: MediaResource, index_attribute: str,
by: str, scheme: str) -> Axes:
""" Formats the plot aesthetics
Parameters
----------
ax: matplotlib.axes._subplots.AxesSubplot
The plot to format
Returns
----------
ax : matplotlib.axes._subplots.AxesSubplot
"""
color_scheme_label = scheme if scheme in colorschemes else 'graphtv'
color_scheme = colorschemes.get(color_scheme_label)
background_color = color_scheme.background
tick_color = color_scheme.ticks
font_color = color_scheme.font
ax.patch.set_facecolor(background_color)
ax.spines['bottom'].set_color(background_color)
ax.spines['top'].set_color(background_color)
ax.spines['left'].set_color(background_color)
ax.spines['right'].set_color(background_color)
# change the label colors
[i.set_color(tick_color) for i in plt.gca().get_yticklabels()]
[i.set_color(tick_color) for i in plt.gca().get_xticklabels()]
# Change tick size
ax.tick_params(axis='y', which='major', labelsize=22)
ax.tick_params(axis='y', which='minor', labelsize=22)
ax.yaxis.grid(True)
ax.xaxis.grid(False)
# Add series parameters
episode_indicies = [
float(episode[index_attribute]) for season in series.seasons
for episode in season
]
# Set plot bounds
x_min = min(episode_indicies)
x_max = max(episode_indicies)
if by == 'index':
x_max += 1
else:
x_min -= 1 / 12
x_max += 1 / 12
ax.set_xlim((x_min, x_max))
ax.set_ylim(ymax=10)
plt.xlabel(index_attribute, fontsize=16, color=font_color)
plt.ylabel('imdbRating', fontsize=16, color=font_color)
plt.title(series.title, fontsize=24, color=font_color)
return ax
def _draw_stats_bar(self, ax: _figure.Axes, df: _pd.DataFrame):
index = df.index
confirmed = df.Confirmed_Change
recovered = df.Recovered_Change
deaths = df.Deaths_Change
one_day = self.__dates.to_Timedelta(1)
ax.bar(index, confirmed, label='Заболевшие', width=2)
ax.bar(index + one_day, recovered, label='Выздоровевшие', width=2)
ax.bar(index + one_day * 2, deaths, label='Смерти', width=2)
def bar_with_sma_line(self,
ax: _figure.Axes,
values: _pd.Series,
sma_window: int = 7,
label: str = None,
bar_alpha: float = 0.3,
color: str = None):
if label:
ax.plot(values.rolling(window=sma_window).mean(),
label=label + "-SMA" + str(sma_window),
color=color)
else:
ax.plot(values.rolling(window=sma_window).mean(), color=color)
ax.bar(values.index, values, alpha=bar_alpha, color=color)
def key_russian_dates(self, ax: _figure.Axes):
pass
''' Draws key dates from Russia on plot as vertical lines and spans. '''
for (date, length, name) in self.__dates.get_key_russian_dates():
x = None
if length == self.__dates.to_Timedelta(1):
ax.axvline(date, color='Gray', alpha=0.6)
x = date
else:
ax.axvspan(date, date + length, color='Gray', alpha=0.6)
x = date + length / 2
ax.text(s=name,
x=x,
y=ax.get_ylim()[1] * .95,
rotation=90,
ha='center',
va='top',
fontsize='small',
bbox=dict(boxstyle="square,pad=0.6",
fc="white",
ec=(0.7, 0.7, 0.7),
lw=2))
def write(self, x: Axes) -> DPTmpFile:
f: Figure = x.get_figure()
return super()._write_figure(f)
def _draw_subplot(subplot: Subplot, ax: Axes, start: datetime.datetime,
end: datetime.datetime):
data_series = [line.load(start, end) for line in subplot.lines]
for line, data in zip(subplot.lines, data_series):
_draw_series(data, ax, line)
if subplot.ylim:
if np.isfinite(subplot.ylim[0]):
ax.set_ylim(bottom=subplot.ylim[0])
if np.isfinite(subplot.ylim[1]):
ax.set_ylim(top=subplot.ylim[1])
ax.set_ylabel(subplot.ylabel, fontsize=subplot.plot.fontsize(1.2))
# Show log book entries for the logsite of this subplot
# Draw only logs if logsite is a site of the subplot's lines
if subplot.logsite in [l.siteid for l in subplot.lines]:
# Traverse logs and draw them
for logtime, logtype, logtext in subplot.get_logs():
x = np.datetime64(logtime)
ax.axvline(x, linestyle='-', color='r', alpha=0.5, linewidth=3)
ax.text(x,
ax.get_ylim()[0],
logtype,
ha='left',
va='bottom',
fontsize=subplot.plot.fontsize(0.9))
ax.set_xlim(subplot.plot.start, subplot.plot.end)
for xtl in ax.get_xticklabels():
xtl.set_rotation(15)
ax.yaxis.set_major_locator(MaxNLocator(prune='upper'))
ax.tick_params(axis='both',
which='major',
labelsize=subplot.plot.fontsize(1.1))
ax.grid()
ax.legend(loc=0, prop=dict(size=subplot.plot.fontsize(1)))