def plot_happiness(self,
figsize=(600, 300),
colors=['darkgreen', 'darksalmon', 'darkred', 'gold']):
df = self.happiness
df['Dates'] = pd.to_datetime(df['Dates'])
df.index = df['Dates']
p = bkh.figure(x_axis_type='datetime',
plot_width=figsize[0],
plot_height=figsize[1])
p.xaxis[0].formatter = bkm.DatetimeTickFormatter(days=['%d/%m'])
p.xaxis.axis_label = 'Date'
p.yaxis.axis_label = 'Survey Response 0-10 ("Not at all"-"Completely")'
for i, s in enumerate([
'Life satisfaction', 'Feeling worthwhile', 'Happiness',
'Anxiety'
]):
p.line(x=df.index.values,
y=df[s].values,
color=colors[0],
legend_label=s,
line_color=colors[i])
p.legend.location = 'center_right'
bkh.show(p)
def plot_demand_discrepancy_bkh(self,
figsize=(600, 300),
plot_confidence=True):
p = bkh.figure(plot_width=figsize[0],
plot_height=figsize[1],
x_axis_type='datetime')
if plot_confidence:
p.varea(x=self.grid_average.DATE[self.COVID_CUTOFF:],
y1=(self.Y_COVID.flatten() /
(self.Y_COVID_PREDICT_mean.flatten() +
self.Y_COVID_PREDICT_conf.flatten())).flatten(),
y2=(self.Y_COVID.flatten() /
(self.Y_COVID_PREDICT_mean.flatten() -
self.Y_COVID_PREDICT_conf.flatten())).flatten(),
alpha=0.2,
legend_label='Confidence')
p.line(x=self.grid_average.DATE[self.COVID_CUTOFF:],
y=np.ones(len(self.grid_average.DATE[self.COVID_CUTOFF:])),
line_dash='dashed',
color='black')
p.line(x=self.grid_average.DATE[self.COVID_CUTOFF:],
y=self.Y_COVID.flatten() / self.Y_COVID_PREDICT_mean.flatten(),
legend_label='Mean')
p.xaxis.axis_label = 'Date'
p.xaxis[0].formatter = bkm.DatetimeTickFormatter(days=['%d/%m'])
p.yaxis.axis_label = 'Net Demand (True) / Net Demand (Expected)'
#bkh.output_notebook()
bkh.show(p)
def plot_domestic_issues(self,
figsize=(600, 300),
plot_bounds=True,
colors=['black', 'darkturquoise']):
df = self.wellness
df['Date'] = pd.to_datetime(df['Date'])
df.index = df['Date']
p = bkh.figure(x_axis_type='datetime',
plot_width=figsize[0],
plot_height=figsize[1])
p.xaxis[0].formatter = bkm.DatetimeTickFormatter(days=['%d/%m'])
p.xaxis.axis_label = 'Date'
p.yaxis.axis_label = '% of actions delivered above normal levels'
df['avg'] = df.mean(axis=1)
p.line(x=df.index.values,
y=df['avg'].values,
color=colors[0],
legend_label="Mean action on domestic issues")
if plot_bounds:
df['min'] = df.min(axis=1)
df['max'] = df.max(axis=1)
p.varea(x=df.index.values,
y1=df['min'].values,
y2=df['max'].values,
alpha=0.2,
color=colors[1],
legend_label='Max/min bounds')
bkh.show(p)
def new_plot(self, title, units, *, line_width=None, precision=2):
"""
Creates a blank line plot for with timestamps on the x-axis and
a line for each data series on the y-axis.
"""
plot = plotting.figure(title=title, tools=[])
self.active_plot = plot
self.plots.append(plot)
self.colors = list(colors)
self.units = units
self.line_width = line_width or self.line_width
plot.plot_width = self.width
plot.plot_height = self.height
plot.x_range = self.x_range
datetime_tick_formats = {
key: ["%a %b %d %H:%M:%S"]
for key in ("seconds", "minsec", "minutes", "hourmin", "hours", "days")
}
plot.xaxis.formatter = models.DatetimeTickFormatter(**datetime_tick_formats)
# https://bokeh.pydata.org/en/latest/docs/reference/models/formatters.html
# plot.yaxis.formatter = models.NumeralTickFormatter(format="0a")
plot.yaxis.formatter = models.NumeralTickFormatter(format="0,0.00 a")
# With default precision level 2 (decimal places)
# The units_formats = "@y{0,0.00}"
# and the value would look like 1,234,567.89
units_formats = f"@y{{0,0.{'0' * precision}}}"
hover = models.HoverTool(
# mode = vline would be nice to use,
# but then separate hovers block each when lines are too close.
# Would like a single hover box with time once, and a value per line
# perhaps this will help acheive that:
# https://stackoverflow.com/questions/29435200/bokeh-plotting-enable-tooltips-for-only-some-glyphs
mode="mouse", # other optins: vline
line_policy="nearest", # other optins: prev, next, nearest, interp, none
tooltips=[
("Name", "$name"),
("Time", "@x{%a %m/%d %H:%M:%S}"),
(self.units, units_formats),
],
formatters={"x": "datetime", "Time": "datetime", "@x": "datetime"},
)
plot.add_tools(hover)
plot.add_tools(models.BoxZoomTool())
plot.add_tools(models.HelpTool())
plot.add_tools(models.PanTool())
plot.add_tools(models.WheelZoomTool(dimensions="width"))
plot.toolbar.active_scroll = plot.select_one(models.WheelZoomTool)
plot.add_tools(models.WheelZoomTool(dimensions="height"))
plot.add_tools(models.UndoTool())
plot.add_tools(models.RedoTool())
plot.add_tools(models.ResetTool())
plot.add_tools(models.SaveTool())
def create_plot(self, counties):
filename = self.create_filename()
bp.output_file(filename, title=self.title)
p = bp.figure(title=self.title,
x_axis_type="datetime",
width=1200,
height=700)
legend_items = []
for a, b in counties:
if self.dublin == False and a == "Dublin":
continue
temp = b.copy()
temp['StrDate'] = temp['TimeStamp'].apply(
lambda x: x.strftime("%a, %b %d"))
source = bp.ColumnDataSource(temp)
leg = p.line("TimeStamp",
self.metric,
source=source,
line_width=2,
line_color=self.counties[a]['color'],
line_dash=self.counties[a]['dash'])
legend_items.append((a, [leg]))
legend = bm.Legend(items=legend_items,
location='top_right',
orientation='vertical',
border_line_color="black")
p.add_layout(legend, 'right')
p.legend.click_policy = "hide"
p.legend.label_text_font_size = '12px'
p.legend.label_text_font = 'Helvetica'
p.legend.location = "top_left"
p.legend.background_fill_color = 'slategray'
p.legend.background_fill_alpha = 0.5
tools = bm.HoverTool(
tooltips=[("Date", "@StrDate"), (
"County",
"@CountyName"), ("Cases", "@ConfirmedCovidCases{0, 0}")])
p.add_tools(tools)
p.title.text_font = "Helvetica"
p.title.text_font_size = "18px"
p.background_fill_color = 'slategray'
p.background_fill_alpha = 0.5
p.xaxis.formatter = bm.DatetimeTickFormatter(months=["%b, %Y"],
days=["%b, %d"],
hours=["%H:%M"])
bp.show(p)
def make_timeseries_plot() -> plotting.Figure:
"""Make an empty timeseries plot with all my customizations."""
plot = plotting.figure(plot_width=900, plot_height=400, x_axis_type="datetime")
plot.toolbar.active_drag = None
plot.toolbar.active_scroll = None
plot.toolbar.active_tap = None
plot.toolbar.logo = None
plot.toolbar_location = None
plot.xaxis.formatter = models.DatetimeTickFormatter(
hours=["%a %I%p"], days=["%a %I%p"]
)
return plot
def plot_daily_gas_bkh(self,
figsize=(650, 450),
plot_temperature=False,
colors=['black', 'darkturquoise']):
p = bkh.figure(x_axis_type='datetime',
plot_width=figsize[0],
plot_height=figsize[1])
p.line(x=self.energy_average['Date_'],
y=32 * np.ones(len(self.energy_average)),
line_dash='dashed',
line_color=colors[0],
legend_label='Typical domestic use (medium)')
p.line(x=self.energy_average['Date_'],
y=self.energy_average['gas_daily_total'],
line_color=colors[0],
legend_label='Mean of 115,000 UK households')
p.xaxis.axis_label = 'Date'
p.xaxis[0].formatter = bkm.DatetimeTickFormatter(days=['%d/%m'])
p.y_range = bkm.Range1d(15, 70)
p.yaxis.axis_label = 'Gas Consumption (Corrected) (kWh/ALP)'
p.yaxis.axis_label_text_color = colors[0]
if plot_temperature and self.weather_file:
p.extra_y_ranges = {'temperature': bkm.Range1d(start=6, end=24)}
p.line(x=self.energy_average['Date_'],
y=self.energy_average['temperature'],
line_color=colors[1],
legend_label='Temperature',
y_range_name='temperature')
p.add_layout(
bkm.LinearAxis(y_range_name='temperature',
axis_label='Mean Temperature (°C)',
axis_label_text_color=colors[1],
axis_line_color=colors[1],
major_label_text_color=colors[1],
major_tick_line_color=colors[1],
minor_tick_line_color=colors[1]), 'right')
r = self.energy_average[['gas_daily_total', 'temperature'
]].corr(method='pearson').values[1, 0]
p.legend.title = f'R = {r:.3f}'
p.legend.location = 'top_left'
p.legend.background_fill_alpha = 1.0
#bkh.output_notebook()
bkh.show(p)
def create_plot(self):
plot = bokeh_plotting.figure(
width=1000,
height=500,
title='Files per {} minutes'.format(self.interval),
x_axis_label='Time (UTC)',
x_axis_type='datetime',
y_axis_label='Fileprocesslog Count',
)
plot.add_tools(self.hover)
plot.xaxis.formatter = bokeh_models.DatetimeTickFormatter(
minutes=["%H:%M"],
hours=["%H:%M"],
days=["%Y-%m-%d"],
)
plot.xaxis.major_label_orientation = pi / 4
return plot
def create_plot(self, data):
filename = self.create_filename()
bp.output_file(filename, title=self.title)
p = bp.figure(title=self.title,
x_axis_type="datetime",
width=1200,
height=700)
temp = data.copy()
temp['StrDate'] = temp['TimeStamp'].apply(
lambda x: x.strftime("%a, %b %d"))
temp['Base'] = 0
source = bp.ColumnDataSource(temp)
p.varea("TimeStamp",
"Daily",
"Base",
source=source,
fill_color="pink",
fill_alpha=0.5,
legend_label="Daily Cases")
p.line("TimeStamp",
"Average",
source=source,
line_width=3,
line_color="crimson",
legend_label="Average Daily Cases")
tools = bm.HoverTool(tooltips=[("Date", "@StrDate"),
("Daily Cases", "@Daily{0, 0}"),
("Average Cases", "@Average{0, 0}")],
mode="vline")
p.add_tools(tools)
p.legend.location = "top_left"
p.legend.click_policy = "hide"
p.title.text_font = "Helvetica"
p.title.text_font_size = "18px"
p.background_fill_color = 'slategray'
# p.background_fill_alpha = 0.25
p.xaxis.formatter = bm.DatetimeTickFormatter(months=["%b, %Y"],
days=["%b, %d"],
hours=["%H:%M"])
bp.show(p)
def plot_timeline_bkh(self, figsize=(600, 300)):
p = bkh.figure(x_axis_type='datetime',
plot_width=figsize[0],
plot_height=figsize[1])
p.line(x=self.energy['Date'],
y=self.energy['Electricity'],
color='royalblue',
legend_label='Electricity')
p.line(x=self.energy['Date'],
y=self.energy['Gas (corrected)'],
color='orange',
legend_label='Gas (corrected)')
p.yaxis.axis_label = 'kWh'
p.xaxis.axis_label = 'Date'
p.xaxis[0].formatter = bkm.DatetimeTickFormatter(days=['%d/%m'])
#bkh.output_notebook()
bkh.show(p)
def preparePlot():
coinTypes = ["Maple", "Phil", "Kang", "Krueger"]
colors = itertools.cycle(palettes)
pl.output_file("graph.html", title="GoldWatch")
p = pl.figure(title="Preisverlauf verschiedener Goldmünzen (basierend auf den Daten von https://www.exchange-ag.de/)",plot_width=1000, x_axis_type="datetime")
datetime = md.DatetimeTickFormatter(days="%d/%m %H:%M", months="%d/%m %H:%M", hours="%d/%m %H:%M", minutes="%d/%m %H:%M")
p.xaxis.formatter = datetime
p.add_layout(md.Title(text="Zeitpunkt der Datenerfassung", align="center"), "below")
p.add_layout(md.Title(text="Goldpreis in €", align="center"), "left")
tooltip = [
("Münztyp", "$name"),
("Datum", "$x"),
("Wert in €", "$y{int}")
]
formatters = { "@x": "datetime"}
p.add_tools(md.HoverTool(tooltips=tooltip, formatters=formatters))
for coin,color in zip(coinTypes,colors):
data = db_handler.getData(coin)
tmp = ()
x = []
yB = []
yS = []
for idx,elem in enumerate(data):
nTime = time.ctime(elem[0])
x.append(dt.datetime.fromtimestamp(elem[0]))
yB.append(elem[1])
yS.append(elem[2])
# nTime = time.s
print(x,yB)
p.line(x, yB, legend_label=f"{coin} Buy", line_color=color, line_width=2, name=f"{coin} Buy")
p.line(x, yS, legend_label=f"{coin} Sell", line_color=color, line_width=2, line_dash="dashed", name=f"{coin} Sell")
pl.save(p)
def style_figure(fig, legend_loc):
fig.xaxis.formatter = bo_mo.DatetimeTickFormatter(days="%d/%m/%y")
fig.legend.location = legend_loc
fig.legend.click_policy = "hide"
return fig
# Fill with the data so far.
for line in temperature_file:
line = line.strip().split(',')
date, temp = line
date = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
temp = float(temp)
new_line = {'time': [date], 'temp': [temp]}
data.stream(new_line, rollover=100)
# Here, we make the figure and add the line glyph
p = bkp.figure(title='Streaming Data Demo',
plot_width=1600,
plot_height=450,
x_axis_type='datetime')
p.line(x='time', y='temp', source=data)
# Make pretty axes
p.xaxis.formatter = bkm.DatetimeTickFormatter(
hours=["%d %B %Y"],
days=["%d %B %Y"],
months=["%d %B %Y"],
years=["%d %B %Y"],
)
p.xaxis.axis_label = 'Time'
p.yaxis.axis_label = 'Temperature'
# Add the figure, and the periodic callback.
bkp.curdoc().add_root(p)
bkp.curdoc().add_periodic_callback(monitor_file, 50)
def make_power_graph(output_file: str, data: typ.List, window: int) -> None:
"""グラフ作成.
Args:
output_file: 出力ファイル名
data: データ
window: 移動平均のサンプル数
"""
cols: typ.Tuple = ("time", "電力量", "電力", "電流R", "電流T", "MA電力", "MA電流R",
"MA電流T")
datadict: typ.Dict = {}
for col in cols:
datadict[col] = []
has_data: bool = len(data) > 0
for row in data:
datadict["time"].append(row["created_at"])
datadict["電力量"].append(calc_電力量(row))
datadict["電力"].append(row["瞬時電力"])
datadict["電流R"].append(row["瞬時電流_r"] / 10.0)
datadict["電流T"].append(row["瞬時電流_t"] / 10.0)
datadict["MA電力"].append(statistics.mean(datadict["電力"][-window:]))
datadict["MA電流T"].append(statistics.mean(datadict["電流T"][-window:]))
datadict["MA電流R"].append(statistics.mean(datadict["電流R"][-window:]))
source: bp.ColumnDataSource = bp.ColumnDataSource(datadict)
tooltips: typ.List[typ.Tuple[str, str]] = [
("time", "@time{%F %T}"),
("積算電力量", "@{電力量}{0,0.0}"),
("瞬時電力", "@{電力}{0,0}"),
("瞬時電流(R相)", "@{電流R}{0,0.0}"),
("瞬時電流(T相)", "@{電流T}{0,0.0}"),
]
hover_tool: bm.HoverTool = bm.HoverTool(tooltips=tooltips,
formatters={"@time": "datetime"})
bp.output_file(output_file, title="Power consumption")
fig: bp.figure = bp.figure(
title="Power consumption",
x_axis_type="datetime",
x_axis_label="時刻",
y_axis_label="電力量[kWh]",
sizing_mode="stretch_both",
)
fig.add_tools(hover_tool)
fmt: typ.List[str] = ["%H:%M"]
fig.xaxis.formatter = bm.DatetimeTickFormatter(hours=fmt,
hourmin=fmt,
minutes=fmt)
if has_data:
電力量_min: float = min(datadict["電力量"])
電力量_max: float = max(datadict["電力量"])
電力量_5p: float = (電力量_max - 電力量_min) * 0.05
fig.y_range = bm.Range1d(電力量_min - 電力量_5p, 電力量_max + 電力量_5p)
fig.extra_y_ranges["W"] = bm.Range1d(
0,
max(datadict["電力"]) * 1.05 if has_data else 0)
fig.add_layout(bm.LinearAxis(y_range_name="W", axis_label="電力[W]"), "left")
fig.extra_y_ranges["A"] = bm.Range1d(
0,
max(*datadict["電流R"], *datadict["電流T"]) * 1.05 if has_data else 0)
fig.add_layout(bm.LinearAxis(y_range_name="A", axis_label="電流[A]"),
"right")
fig.line("time",
"電力量",
legend_label="積算電力量",
line_color="red",
source=source)
raw_data: typ.List = [
("電力", "W", "瞬時電力", "orange"),
("電流R", "A", "瞬時電流(R相)", "blue"),
("電流T", "A", "瞬時電流(T相)", "green"),
]
for col, range_name, legend_label, color in raw_data:
fig.line(
"time",
col,
y_range_name=range_name,
legend_label=legend_label,
line_color=color,
line_alpha=0.8,
source=source,
).visible = False
ma_data: typ.List = [
("MA電力", "W", "瞬時電力(移動平均)", "orange"),
("MA電流R", "A", "瞬時電流(R相)(移動平均)", "blue"),
("MA電流T", "A", "瞬時電流(T相)(移動平均)", "green"),
]
for col, range_name, legend_label, color in ma_data:
fig.line(
"time",
col,
y_range_name=range_name,
legend_label=legend_label,
line_color=color,
line_width=2,
line_alpha=0.8,
line_dash="dotted",
source=source,
)
fig.legend.click_policy = "hide"
fig.legend.location = "top_left"
bp.save(fig)
def make_temp_graph(output_file: str, temp_data: typ.List, co2_data: typ.List,
bme280_data: typ.List) -> None:
"""グラフ作成.
Args:
output_file: 出力ファイル名
temp_data: 温度データ
co2_data: CO2データ
bme280_data: BME280のデータ
"""
tooltips: typ.List[typ.Tuple[str, str]] = [
("time", "@time{%F %T}"),
]
df: pd.DataFrame
deg_max: int = 0
num_data: int = 0
y_axis_label: str = "温度[℃]"
if len(temp_data) > 0:
num_data += 1
if len(co2_data) > 0:
num_data += 1
if len(bme280_data) > 0:
num_data += 1
if len(temp_data) > 0:
df1: pd.DataFrame = pd.DataFrame(temp_data,
columns=list(temp_data[0].keys()))
df1 = df1.rename(columns={"created_at": "time"})
if num_data > 1:
df1["time"] = df1["time"].apply(
lambda x: x.replace(second=0, microsecond=0))
df1["temp"] /= 1000
df1 = df1[["time", "temp"]].drop_duplicates(subset="time")
df = df1
tooltips.append(("CPU温度", "@temp{0.0}"))
deg_max = int(df["temp"].max()) + 10
if len(co2_data) > 0:
df2: pd.DataFrame = pd.DataFrame(co2_data,
columns=list(co2_data[0].keys()))
df2 = df2.rename(columns={"temp": "temp2", "created_at": "time"})
if num_data > 1:
df2["time"] = df2["time"].apply(
lambda x: x.replace(second=0, microsecond=0))
df2 = df2[["time", "co2", "temp2"]].drop_duplicates(subset="time")
if len(temp_data) > 0:
df = pd.merge(df1, df2, on="time", how="outer").sort_values("time")
else:
df = df2
if len(bme280_data) == 0:
tooltips.append(("気温", "@temp2"))
tooltips.append(("CO₂", "@co2"))
deg_max = max(deg_max, int(df["temp2"].max()) + 10)
if len(bme280_data) > 0:
df3: pd.DataFrame = pd.DataFrame(bme280_data,
columns=list(bme280_data[0].keys()))
df3 = df3.rename(columns={"temp": "temp3", "created_at": "time"})
if num_data > 1:
df3["time"] = df3["time"].apply(
lambda x: x.replace(second=0, microsecond=0))
df3 = df3[["time", "temp3", "pressure",
"humidity"]].drop_duplicates(subset="time")
if num_data > 1:
df = pd.merge(df, df3, on="time", how="outer").sort_values("time")
else:
df = df3
tooltips.append(("気温", "@temp3{0.0}"))
tooltips.append(("湿度", "@humidity{0.0}"))
tooltips.append(("気圧", "@pressure{0,0.0}"))
deg_max = max(deg_max,
int(df["temp3"].max()) + 10,
int(df["humidity"].max()) + 10)
y_axis_label += "/湿度[%]"
source: bp.ColumnDataSource = bp.ColumnDataSource(df)
hover_tool: bm.HoverTool = bm.HoverTool(tooltips=tooltips,
formatters={"@time": "datetime"})
bp.output_file(output_file, title="Temperature")
fig: bp.figure = bp.figure(
title="Temperature",
x_axis_type="datetime",
x_axis_label="時刻",
y_axis_label=y_axis_label,
sizing_mode="stretch_both",
)
fig.add_tools(hover_tool)
fmt: typ.List[str] = ["%H:%M"]
fig.xaxis.formatter = bm.DatetimeTickFormatter(hours=fmt,
hourmin=fmt,
minutes=fmt)
fig.y_range = bm.Range1d(0, deg_max)
if len(temp_data) > 0:
fig.line("time",
"temp",
legend_label="CPU温度",
line_color="red",
source=source)
if len(co2_data) > 0:
if len(bme280_data) == 0:
fig.line("time",
"temp2",
legend_label="気温",
line_color="darkorange",
source=source)
fig.extra_y_ranges["ppm"] = bm.Range1d(
0, max(2000, df["co2"].max() * 1.05))
fig.add_layout(bm.LinearAxis(y_range_name="ppm", axis_label="濃度[ppm]"),
"right")
fig.line("time",
"co2",
legend_label="CO₂",
line_color="green",
y_range_name="ppm",
source=source)
if len(bme280_data) > 0:
fig.line("time",
"temp3",
legend_label="気温",
line_color="darkorange",
source=source)
fig.line("time",
"humidity",
legend_label="湿度",
line_color="blue",
source=source)
fig.extra_y_ranges["pressure"] = bm.Range1d(
min(990, df["pressure"].min()), max(1020, df["pressure"].max()))
fig.add_layout(
bm.LinearAxis(y_range_name="pressure", axis_label="気圧[hPa]"),
"right")
fig.line("time",
"pressure",
legend_label="気圧",
line_color="deeppink",
y_range_name="pressure",
source=source)
fig.legend.click_policy = "hide"
fig.legend.location = "top_left"
bp.save(fig)
def plot_all(self) -> plotting.Figure:
"""Creates plot containing all transations and balance over time.
The plot will contain a line showing the balance over time, with
scatter points to show the absolute value of each transation.
Returns
-------
plotting.Figure
Figure object with all midata transactions plotted.
"""
balance_hover = models.HoverTool(
names=["balance"],
toggleable=False,
tooltips=[
("Date", "@date{%d/%m/%Y}"),
("Balance (£)", "@balance{0,0.00}"),
],
formatters={"@date": "datetime"},
)
amount_hover = models.HoverTool(
names=["abs_amount"],
toggleable=False,
tooltips=[
("Date", "@date{%d/%m/%Y}"),
("Transaction (£)", "@amount{+0,0.00}"),
("Balance (£)", "@balance{+0,0.00}"),
("Merchant / Description", "@description"),
],
formatters={"@date": "datetime"},
)
p = plotting.figure(
title="All transactions",
x_axis_label="Date",
y_axis_label="Amount (£)",
sizing_mode="stretch_both",
tools=[
"pan",
"box_select",
"xwheel_zoom",
"reset",
"save",
balance_hover,
amount_hover,
],
)
p.xaxis[0].formatter = models.DatetimeTickFormatter()
p.y_range = models.DataRange1d(bounds=(0, 10000))
epoch = dt.datetime.utcfromtimestamp(0)
max_date = (dt.datetime.today() - epoch).total_seconds()
min_date = max_date - (2 * 365 * 24 * 60 * 60)
p.x_range = models.DataRange1d(bounds=(min_date * 1000,
max_date * 1000))
p.line(
x="date",
y="balance",
source=self.source,
legend_label="Balance",
name="balance",
line_width=2,
)
p.scatter(
"date",
"abs_amount",
size=5,
color="colour",
name="abs_amount",
source=self.source,
legend_label="Absolute Transaction",
fill_alpha=0.8,
)
p.legend.click_policy = "hide"
return p