def _generate_activity_count_plot(activity_data: pd.DataFrame,
ax: mpl.axes.Axes, colours: str):
"""
Generate a bar plot of activity counts over time (by type).
Arguments:
activity data - A pandas DataFrame containing the activity data.
ax - A set of matplotlib axes to generate the plot on.
colours - A name of the colour palette to generate the plot with.
"""
# Group the activity data by month and calculate the count of each activity type
data = (activity_data.groupby([activity_data.index.to_period('M'), 'type'
]).size().to_frame('count').reset_index())
# Generate and format the bar plot
sns.barplot(x='start_date_local',
y='count',
hue='type',
data=data,
palette=colours,
ax=ax)
ax.set(title='Activities over time',
ylabel='Number of activities',
xlabel='Month')
ax.set_xticklabels(ax.get_xticklabels(),
rotation=45,
horizontalalignment='right')
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', linewidth=1.0)
ax.yaxis.grid(b=True, which='minor', linewidth=0.5)
ax.set_axisbelow(True)
def _generate_commute_count_plot(commute_data: pd.DataFrame, ax: mpl.axes.Axes,
colours: dict):
"""
Generate a bar plot of number of commutes per month.
Arguments:
commute_data - A pandas DataFrame containing the commute activity data.
ax - A set of matplotlib axes to generate the plot on.
colours - A dictionary of colours to generate the plot with.
"""
# Group the commute data by month
data = commute_data.resample('M').count()
# Generate and format the bar plot
sns.barplot(x=data.index.to_period('M'),
y=data['distance'],
color=colours['commute_count'],
ax=ax)
ax.set(ylabel='Number of commutes', xlabel='Month')
ax.set_xticklabels(ax.get_xticklabels(),
rotation=45,
horizontalalignment='right')
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', linewidth=1.0)
ax.yaxis.grid(b=True, which='minor', linewidth=0.5)
ax.set_axisbelow(True)
def _generate_mean_distance_plot(activity_data: pd.DataFrame,
ax: mpl.axes.Axes, colour_palette: list):
"""
Generate a bar plot of mean activity distance over time (by type).
Arguments:
activity data - A pandas DataFrame containing the activity data.
ax - A set of matplotlib axes to generate the plot on.
colour_palette - The colour palette to generate the plot with.
"""
# Group the activity data by month and calculate the mean distance of each activity type
data = activity_data.groupby([activity_data.index.to_period('Y'),
'type']).mean().reset_index()
# Generate and format the bar plot
sns.barplot(x='start_date_local',
y='distance',
hue='type',
data=data,
palette=colour_palette,
ax=ax)
ax.set(title='Mean activity distance over time',
ylabel='Mean distance (km)',
xlabel='Year')
ax.set_xticklabels(ax.get_xticklabels(),
rotation=45,
horizontalalignment='right')
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', linewidth=1.0)
ax.yaxis.grid(b=True, which='minor', linewidth=0.5)
ax.set_axisbelow(True)
def _generate_commute_days_plot(commute_data: pd.DataFrame, ax: mpl.axes.Axes,
colours: dict):
"""
Generate a line plot of commute days per year.
Arguments:
commute_data - A pandas DataFrame containing the commute activity data.
ax - A set of matplotlib axes to generate the plot on.
colours - A dictionary of colours to generate the plot with.
"""
# Group the commute data by day
data = commute_data.groupby(commute_data.index.to_period('D')).agg(
{'distance': 'mean'})
# Generate and format the line plot
sns.lineplot(x=data.index.year.value_counts().index,
y=data.index.year.value_counts(),
color=colours['commute_days'],
marker='o',
ax=ax)
ax.set(ylabel='Commute days', xlabel='Year')
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', linewidth=1.0)
ax.yaxis.grid(b=True, which='minor', linewidth=0.5)
def text_box(ax: mp.axes.Axes,
text: str,
colors: List[str] = ["#FFFFFF", "#000000"],
fontsize: int = 14,
x: int = 0,
y: int = 0) -> bool:
log = logging.getLogger('text_box')
log.info(" >>")
rv = False
try:
edgecolor = "none"
boxstyle = "square"
if len(colors) >= 3 and colors[2] is not None:
edgecolor = colors[2]
boxstyle = "round,pad=1"
ax.text(x,
y,
text,
ha="left",
va="center",
bbox=dict(boxstyle=boxstyle,
facecolor=colors[0],
edgecolor=edgecolor),
color=colors[1],
fontsize=fontsize)
set_axes_common_properties(ax, no_grid=True)
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
except Exception as ex:
print("text_box failed - {ex}".format(ex=ex))
else:
rv = True
log.info(" <<")
return rv
def pretty_axes(ax: matplotlib.axes.Axes) -> matplotlib.axes.Axes:
"""Better looking matplotlib axes object."""
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().set_visible(False)
ax.grid(axis="y", alpha=0.75)
return ax
def __configPlot2(df: pd.DataFrame, ax: matplotlib.axes.Axes):
"""グラフの描画設定を行う。
"""
ax.get_xaxis().set_major_locator(ticker.MaxNLocator(integer=True))
ax.get_yaxis().set_major_locator(ticker.MaxNLocator(integer=True))
ax.ticklabel_format(style='plain',
axis='both',
useOffset=False,
useMathText=False)
ax.margins(x=0, y=0)
ax.set_xticks(list(df.index))
ax.set_xticklabels(list(df['Gregorian']), rotation=30)
def _generate_commute_distance_plot(commute_data: pd.DataFrame,
ax: mpl.axes.Axes, colours: dict):
"""
Generate a line plot of total and mean commute distance per year.
Arguments:
commute_data - A pandas DataFrame containing the commute activity data.
ax - A set of matplotlib axes to generate the plot on.
colours - A dictionary of colours to generate the plot with.
"""
# Group the commute data by year
data = commute_data.resample('Y').agg({'distance': ['sum', 'mean']})
# Generate and format the total distance line plot
sns.lineplot(x=data.index.year,
y=data['distance', 'sum'],
color=colours['commute_distance_sum'],
marker='o',
ax=ax)
ax.set_xlabel('Year')
ax.set_ylabel('Total commute distance (km)',
color=colours['commute_distance_sum'])
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', linewidth=1.0)
ax.yaxis.grid(b=True, which='minor', linewidth=0.5)
# Generate and format the mean distance line plot
ax_mean = ax.twinx()
sns.lineplot(x=data.index.year,
y=data['distance', 'mean'],
color=colours['commute_distance_mean'],
marker='o',
ax=ax_mean)
ax_mean.set_ylabel('Average commute distance (km)',
color=colours['commute_distance_mean'])
def axis(self, axs: mpl.axes.Axes) -> None:
"""Modify font of axis-labels."""
if not self.axisfont:
return ()
return (axs.get_xaxis().get_label(), axs.get_yaxis().get_label())
def plot_vaccinations_by_time(df: pd.DataFrame,
df_delivered: pd.DataFrame,
ax: mp.axes.Axes,
wich: str = "first") -> ResultValue:
log = logging.getLogger('plot_vaccinations_by_time')
log.info(" >>")
try:
ln_one_color = "#f08814"
ln_two_color = "#92b7e9"
ln_one_label = "Cumulata numero vaccinazioni"
ln_two_label = "Distribuzione giornaliera"
grp_by_time = df.groupby("data_somministrazione").sum()
x = grp_by_time.index.values
y = grp_by_time["prima_dose"]
y_cum_sum = grp_by_time["prima_dose"].cumsum()
set_axes_common_properties(ax, no_grid=False)
ax.get_yaxis().set_major_formatter(
mp.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
remove_tick_lines('x', ax)
remove_tick_lines('y', ax)
ax.set_xticks(x)
ax.set_xticklabels(x, rotation=80)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%d/%m/%y"))
ax.xaxis.set_minor_formatter(mdates.DateFormatter("%d/%m"))
ax.xaxis.set_major_locator(mdates.DayLocator(interval=2))
ax.set_ylabel(ln_one_label, fontsize=14)
ax.set_xlabel("Data", fontsize=14)
ax.set_title("Vaccinazioni nel tempo - prima dose", fontsize=18)
ax.tick_params(axis='y', colors=ln_one_color)
ax.yaxis.label.set_color(ln_one_color)
ax.scatter(x, y_cum_sum, color=ln_one_color, s=30, marker='.')
ln_one = ax.plot(x,
y_cum_sum,
'b-',
linewidth=2,
color=ln_one_color,
label=ln_one_label)
result = plot_delivered_vaccines_quantity(df_delivered, ax)
if result.is_in_error() == True:
log.error(result())
return result
line_three = result()
ax_dec = ax.twinx()
remove_tick_lines('y', ax_dec)
remove_tick_lines('x', ax_dec)
set_axes_common_properties(ax_dec, no_grid=True)
ax_dec.scatter(x, y, color=ln_two_color, s=30, marker='.')
ln_two = ax_dec.plot(x,
y,
'b-',
linewidth=2,
color=ln_two_color,
label=ln_two_label)
ax_dec.set_ylabel(ln_two_label, fontsize=14)
ax_dec.yaxis.label.set_color(ln_two_color)
ax_dec.tick_params(axis='y', colors=ln_two_color)
lns = ln_one + ln_two + line_three
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc='upper left')
except Exception as ex:
log.error("Exception caught - {ex}".format(ex=ex))
return ResultKo(ex)
log.info(" <<")
return ResultOk(True)
