def test__check_compatible_scale_and_ranges_compat_numeric() -> None:
plot = Plot(x_scale=LinearScale(), x_range=Range1d())
check = plot._check_compatible_scale_and_ranges()
assert check == []
plot = Plot(y_scale=LogScale(), y_range=DataRange1d())
check = plot._check_compatible_scale_and_ranges()
assert check == []
def test__check_compatible_scale_and_ranges_incompat_numeric_scale_and_factor_range(
) -> None:
plot = Plot(x_scale=LinearScale(), x_range=FactorRange())
check = plot._check_compatible_scale_and_ranges()
assert check != []
def create_bk_fig(x=None, xlab=None, x_min=None, x_max=None,
ylab=None, fh=None, fw=None,
title=None, pw=None, ph=None, x_axis_type="linear",
y_axis_type="linear", x_name=None, y_name=None, **kwargs):
""" Generates a bokeh figure
Parameters
----------
x :obj:`DataArray`
Contains x-axis data
xlab : :obj:`str`
X-axis label
x_min : :obj:`float`
Min x value
x_max : :obj:`float`
Max x value
ylab : :obj:`str`
Y-axis label
fh: :obj:`int`
True height of figure without legends, axes titles etc
fw: :obj:`int`
True width of figure without legends, axes etc
title: :obj:`str`
Title of plot
pw: :obj:`int`
Plot width including legends, axes etc
ph: :obj:`int`
Plot height including legends, axes etc
x_axis_type: :obj:`str`
Type of x-axis can be linear, log, or datetime
y_axis_type: :obj:`str`
Can be linear, log or datetime
x_name: :obj:`str`
Name of the column used for the x-axis. Mostly used to form tooltips
y_name: :obj:`str`
Name of the column used for the y-axis. Also used for tooltips
add_grid: :obj:`bool`
Whether or not to add grid
add_title: :obj:`bool`
Whether or not to add title to plot
add_xaxis: :obj:`bool`
Whether or not to add x-axis and tick marks
add_yaxis: :obj:`bool`
Add y-axis or not
fix_plotsize: :obj:`bool`
Enforce certain dimensions on plot. This is useful for ensuring a plot
is not obscure by axes and other things. If activated, plot's
dimensions will not be responsive. It utilises fw and fh.
Returns
-------
p : :obj:`Plot`
A bokeh Plot object
"""
add_grid = kwargs.pop("add_grid", False)
add_title = kwargs.pop("add_title", True)
add_xaxis = kwargs.pop("add_xaxis", False)
add_yaxis = kwargs.pop("add_yaxis", False)
fix_plotsize = kwargs.pop("fix_plotsize", True)
# addition plot specs
pl_specs = kwargs.pop("pl_specs", {})
# additional axis specs
ax_specs = kwargs.pop("ax_specs", {})
# ticker specs
ti_specs = kwargs.pop("ti_specs", {})
plot_specs = dict(background="white", border_fill_alpha=0.1,
border_fill_color="white", min_border=3,
name="plot", outline_line_dash="solid",
outline_line_width=2, outline_line_color="#017afe",
outline_line_alpha=0.4, output_backend="canvas",
sizing_mode="stretch_width", title_location="above",
toolbar_location="above")
plot_specs.update(pl_specs)
axis_specs = dict(minor_tick_line_alpha=0, axis_label_text_align="center",
axis_label_text_font="monospace",
axis_label_text_font_size="10px",
axis_label_text_font_style="normal",
major_label_orientation="horizontal")
axis_specs.update(ax_specs)
tick_specs = dict(desired_num_ticks=5)
tick_specs.update(ti_specs)
# Define frame width and height
# This is the actual size of the plot without the titles et al
if fix_plotsize and not(fh or fw):
fw = int(0.98 * pw)
fh = int(0.93 * ph)
# define the axes ranges
x_range = DataRange1d(name="p_x_range", only_visible=True)
y_range = DataRange1d(name="p_y_range", only_visible=True)
if x_min is not None and x_max is not None and x_name.lower() in ["channel", "frequency"]:
x_range = Range1d(name="p_x_range", start=x_min, end=x_max)
y_range.only_visible = False
# define items to add on the plot
p_htool = HoverTool(tooltips=[(x_name, "$x"),
(y_name, "$y")],
name="p_htool", point_policy="snap_to_data")
if x_name.lower() == "time":
p_htool.tooltips[0] = (x_name, "$x{%d-%m-%Y %H:%M}")
p_htool.formatters = {"$x": "datetime"}
p_toolbar = Toolbar(name="p_toolbar",
tools=[p_htool, BoxSelectTool(), BoxZoomTool(),
# EditTool(), # BoxEditTool(), # RangeTool(),
LassoSelectTool(), PanTool(), ResetTool(),
SaveTool(), UndoTool(), WheelZoomTool()])
p_ticker = BasicTicker(name="p_ticker", **tick_specs)
# select the axis scales for x and y
if x_axis_type == "linear":
x_scale = LinearScale(name="p_x_scale")
# define the axes and tickers
p_x_axis = LinearAxis(axis_label=xlab, name="p_x_axis",
ticker=p_ticker, **axis_specs)
elif x_axis_type == "datetime":
x_scale = LinearScale(name="p_x_scale")
# define the axes and tickers
p_x_axis = DatetimeAxis(axis_label=xlab, name="p_x_axis",
ticker=p_ticker, **axis_specs)
elif x_axis_type == "log":
x_scale = LogScale(name="p_x_scale")
p_x_axis = LogAxis(axis_label=xlab, name="p_x_axis",
ticker=p_ticker, **axis_specs)
if y_axis_type == "linear":
y_scale = LinearScale(name="p_y_scale")
# define the axes and tickers
p_y_axis = LinearAxis(axis_label=ylab, name="p_y_axis",
ticker=p_ticker, **axis_specs)
elif x_axis_type == "datetime":
y_scale = LinearScale(name="p_y_scale")
# define the axes and tickers
p_y_axis = DatetimeAxis(axis_label=xlab, name="p_y_axis",
ticker=p_ticker, **axis_specs)
elif y_axis_type == "log":
y_scale = LogScale(name="p_y_scale")
# define the axes and tickers
p_y_axis = LogAxis(axis_label=ylab, name="p_y_axis",
ticker=p_ticker, **axis_specs)
# Create the plot object
p = Plot(plot_width=pw, plot_height=ph, frame_height=fh, frame_width=fw,
toolbar=p_toolbar, x_range=x_range, x_scale=x_scale,
y_range=y_range, y_scale=y_scale, **plot_specs)
if add_title:
p_title = Title(align="center", name="p_title", text=title,
text_font_size="24px",
text_font="monospace", text_font_style="bold",)
p.add_layout(p_title, "above")
if add_xaxis:
p.add_layout(p_x_axis, "below")
if add_yaxis:
p.add_layout(p_y_axis, "left")
if add_grid:
p_x_grid = Grid(dimension=0, ticker=p_ticker)
p_y_grid = Grid(dimension=1, ticker=p_ticker)
p.add_layout(p_x_grid)
p.add_layout(p_y_grid)
return p
from bokeh.io import show
from bokeh.models import LinearAxis, LinearScale, Range1d
from bokeh.plotting import figure
source = {
"t": [0, 1, 2, 3, 4],
"v": [1, 10, 100, 1000, 10000],
}
f = figure(y_axis_type="log")
f.yaxis.axis_label = "Log"
f.yaxis.axis_label_text_color = "blue"
f.extra_y_ranges = {"linear": Range1d(-1000, 20000)}
f.extra_y_scales = {"linear": LinearScale()}
ax = LinearAxis(y_range_name="linear",
axis_label="Linear",
axis_label_text_color="red")
f.add_layout(ax, "left")
f.line(x="t", y="v", line_width=2, source=source, color="blue")
f.circle(x="t", y="v", size=5, line_width=2, source=source, color="blue")
f.line(x="t",
y="v",
line_width=2,
source=source,
y_range_name="linear",
color="red")
