def interactive_plot(c4, c46, c6, c9, ts):
from bokeh.layouts import gridplot
from bokeh.plotting import figure, show, output_file
from bokeh.models import Range1d, LinearAxis, LogAxis
x = np.linspace(0, 4 * np.pi, 100)
y = np.sin(x)
TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select"
p1 = figure(title="Legend Example", tools=TOOLS,
plot_width=1200, plot_height=400,
y_axis_type="log", x_axis_type='datetime'
)
p2 = figure(title="Legend Example", tools=TOOLS,
plot_width=1200, plot_height=400,
y_axis_type="linear", x_axis_type='datetime',
x_range=p1.x_range
)
# Setting the second y axis range name and range
p1.extra_y_ranges = {"foo": Range1d(*ts[c9].quantile([.01, .99]))}
# Adding the second axis to the plot.
p1.add_layout(LogAxis(y_range_name="foo"), 'right')
p1.line('time_utc', c4, legend_label=c4, source=ts, color='black')
p1.line('time_utc', c6, legend_label=c6, source=ts, color='blue')
p1.line('time_utc', c9, legend_label=c9, source=ts, color='red',
y_range_name="foo"
)
p2.line('time_utc', c46, legend_label=c4, source=ts, color='black'
)
# p1.circle(x, 2 * y, legend_label="2*sin(x)", color="orange")
# p1.circle(x, 3 * y, legend_label="3*sin(x)", color="green")
#
# p1.legend.title = 'Example Title'
#
# p2 = figure(title="Another Legend Example", tools=TOOLS)
#
# p2.circle(x, y, legend_label="sin(x)")
# p2.line(x, y, legend_label="sin(x)")
#
# p2.line(x, 2 * y, legend_label="2*sin(x)", line_dash=(4, 4),
# line_color="orange", line_width=2)
#
# p2.square(x, 3 * y, legend_label="3*sin(x)", fill_color=None,
# line_color="green")
# p2.line(x, 3 * y, legend_label="3*sin(x)", line_color="green")
output_file("legend.html", title="legend.py example")
show(gridplot([p1, p2], ncols=1, ))
def large_plot():
source = ColumnDataSource(data=dict(x=[0, 1], y=[0, 1]))
xdr = Range1d(start=0, end=1)
xdr.tags.append("foo")
xdr.tags.append("bar")
ydr = Range1d(start=10, end=20)
ydr.tags.append("foo")
ydr.tags.append(11)
plot = Plot(x_range=xdr, y_range=ydr)
ydr2 = Range1d(start=0, end=100)
plot.extra_y_ranges = {"liny": ydr2}
circle = Circle(x="x", y="y", fill_color="red", size=5, line_color="black")
plot.add_glyph(source, circle, name="mycircle")
line = Line(x="x", y="y")
plot.add_glyph(source, line, name="myline")
rect = Rect(x="x", y="y", width=1, height=1, fill_color="green")
plot.add_glyph(source, rect, name="myrect")
plot.add_layout(DatetimeAxis(), 'below')
plot.add_layout(LogAxis(), 'left')
plot.add_layout(LinearAxis(y_range_name="liny"), 'left')
plot.add_layout(Grid(dimension=0), 'left')
plot.add_layout(Grid(dimension=1), 'left')
plot.add_tools(
BoxZoomTool(),
PanTool(),
SaveTool(),
ResetTool(),
ResizeTool(),
WheelZoomTool(),
)
return plot
def test_rect_rendering_with_log_axis(output_file_url, selenium, screenshot):
plot = Plot(plot_height=400,
plot_width=400,
x_range=Range1d(0, 30),
y_range=Range1d(1, 100),
y_axis_type="log")
x = [10, 20]
y = [10, 20]
source = ColumnDataSource(data=dict(x=[(x[0] + x[1]) / 2],
y=[(y[0] + y[1]) / 2],
width=[x[1] - x[0]],
height=[y[1] - y[0]]))
plot.add_glyph(source, Rect(x='x', y='y', width='width', height='height'))
plot.add_layout(LogAxis(), "left")
# Save the plot and start the test
save(plot)
selenium.get(output_file_url)
assert has_no_console_errors(selenium)
screenshot.assert_is_valid()
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
rb_patches_list.append(rb_patches)
# Hide yaxis
if i_ppr != 0:
temp_plot.yaxis.visible = False
temp_plot2.yaxis.visible = False
temp_plot3.yaxis.visible = False
# Hide xaxis
if i_pfd != 2:
temp_plot.xaxis.visible = False
temp_plot2.xaxis.visible = False
temp_plot3.xaxis.visible = False
# Add Row labels on right
if i_ppr == 2:
temp_plot.add_layout(
LogAxis(axis_label=pfd_type,
axis_label_text_font_style='bold',
ticker=raw_ticker), 'right')
temp_plot2.add_layout(
LogAxis(axis_label=pfd_type,
axis_label_text_font_style='bold',
ticker=rel_ticker), 'right')
temp_plot3.add_layout(
LogAxis(axis_label=pfd_type,
axis_label_text_font_style='bold',
ticker=rel_ticker), 'right')
# Add Col labels on top
if i_pfd == 0:
temp_plot.title.text = ppr_type
temp_plot.title.align = 'center'
temp_plot2.title.text = ppr_type
temp_plot2.title.align = 'center'
from bokeh.io import save
from bokeh.models import ColumnDataSource, LogAxis, Plot, Range1d, Rect
plot = Plot(height=400,
width=400,
x_range=Range1d(0, 30),
y_range=Range1d(1, 100))
[x0, x1] = [10, 20]
[y0, y1] = [10, 20]
source = ColumnDataSource(data=dict(
x=[(x0 + x1) / 2], y=[(y0 + y1) / 2], width=[x1 - x0], height=[y1 - y0]))
plot.add_glyph(source, Rect(x='x', y='y', width='width', height='height'))
plot.add_layout(LogAxis(), "left")
save(plot)
def create_plots(state, oil_prod, unemp, lab_force):
target_state = (state)
state_dict = {'tx': 'Texas', 'nd': 'North Dakota', 'wy': 'Wyoming'}
min_date = max(min(unemp.index), min(oil_prod.index))
max_date = min(max(unemp.index), max(oil_prod.index))
cur_sp500 = sp500.loc[min_date:max_date]
source_sp500 = ColumnDataSource(cur_sp500)
cur_oil_price = oil_price.loc[min_date:max_date]
source_oil_price = ColumnDataSource(cur_oil_price)
oil_prod = oil_prod.loc[min_date:max_date]
unemp = unemp.loc[min_date:max_date]
lab_force = lab_force.loc[min_date:max_date]
county_xs = [
counties[code]["lons"] for code in counties
if counties[code]["state"] in target_state
]
county_ys = [
counties[code]["lats"] for code in counties
if counties[code]["state"] in target_state
]
county_names = unemp.columns
county_rates = unemp.loc[cur_date].values[0]
county_prods = oil_prod.loc[cur_date].values[0]
TOOLS = "pan,wheel_zoom,box_zoom,reset,hover,save,tap"
source_maps = ColumnDataSource(data=dict(x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
prod=county_prods))
unemp_color_mapper = LogColorMapper(palette="Viridis256",
low=max(min(county_rates), 0.1),
high=max(county_rates))
fig_unemp = figure(title='%s unemployment rate for %s, %d' %
(state_dict[state], month_dict[cur_month], cur_year),
toolbar_location="left",
tools=TOOLS,
plot_width=500,
plot_height=450)
pyglyph = fig_unemp.patches(
'x',
'y',
source=source_maps,
fill_color={
'field': 'rate',
'transform': unemp_color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5,
# set visual properties for selected glyphs
selection_color="firebrick",
# set visual properties for non-selected glyphs
nonselection_fill_alpha=0.2,
nonselection_fill_color="blue",
nonselection_line_color="firebrick",
nonselection_line_alpha=1.0)
hover_unemp = fig_unemp.select(HoverTool)
hover_unemp.point_policy = "follow_mouse"
hover_unemp.tooltips = [
("Name", "@name"),
("Unemployment rate", "@rate%"),
("(Long, Lat)", "($x, $y)"),
]
unemp_color_bar = ColorBar(color_mapper=unemp_color_mapper,
ticker=LogTicker(),
border_line_color=None,
location=(0, 0))
fig_unemp.add_layout(unemp_color_bar, 'right')
prod_color_mapper = LogColorMapper(palette="BuGn7",
low=max(min(county_prods), 0.1),
high=max(county_prods))
fig_prods = figure(title='%s production data for %s, %d' %
(state_dict[state], month_dict[cur_month], cur_year),
toolbar_location="left",
tools=TOOLS,
plot_width=500,
plot_height=450)
pyglyph_prod = fig_prods.patches(
'x',
'y',
source=source_maps,
fill_color={
'field': 'prod',
'transform': prod_color_mapper
},
fill_alpha=0.7,
line_color="grey",
line_width=0.5,
# set visual properties for selected glyphs
selection_color="firebrick",
# set visual properties for non-selected glyphs
nonselection_fill_alpha=0.2,
nonselection_fill_color="blue",
nonselection_line_color="firebrick",
nonselection_line_alpha=1.0)
hover_prod = fig_prods.select(HoverTool)
hover_prod.point_policy = "follow_mouse"
hover_prod.tooltips = [
("Name", "@name"),
("Production", "@prod bbls"),
("(Long, Lat)", "($x, $y)"),
]
prod_color_bar = ColorBar(color_mapper=prod_color_mapper,
ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
fig_prods.add_layout(prod_color_bar, 'right')
cur_county = county_names[0]
source_oil = ColumnDataSource(
data=dict(date=oil_prod.index, oil_prod=oil_prod[cur_county]))
fig1 = figure(title='Employment vs Oil prices for ' + cur_county,
x_axis_type='datetime',
plot_width=700,
plot_height=400,
toolbar_location='left',
tools=TOOLS)
source_figures = ColumnDataSource(
dict(date=unemp.index,
unemp=unemp[cur_county],
lab_force=lab_force[cur_county]))
fig1.circle('Date',
'WTI',
source=source_oil_price,
legend="Oil Price, $",
selection_color='red',
nonselection_fill_color='grey',
nonselection_fill_alpha=0.2)
fig1.line('Date', 'WTI', source=source_oil_price, legend="Oil Price, $")
fig1.xaxis.axis_label = 'Date'
fig1.yaxis.axis_label = 'Oil Price, $ (month avg.)'
fig1.y_range = Range1d(start=0, end=150)
# Adding second y axis for unemployment
fig1.extra_y_ranges['unemp'] = Range1d(start=0, end=50)
fig1.add_layout(
LinearAxis(y_range_name="unemp", axis_label='Unemployment Rate (%)'),
'right')
# Adding third y axis for labor force
fig1.extra_y_ranges['labforce'] = Range1d(
start=max(100,
min(lab_force[cur_county]) - 1000),
end=max(lab_force[cur_county]) + 1000)
fig1.add_layout(
LogAxis(y_range_name="labforce", axis_label='Labor Force (log)'),
'right')
fig1.circle('date',
'unemp',
source=source_figures,
y_range_name="unemp",
legend="Unemployment rate (%)",
color='orange')
fig1.line('date',
'unemp',
source=source_figures,
y_range_name="unemp",
legend="Unemployment rate (%)",
color='orange')
fig1.circle('date',
'lab_force',
source=source_figures,
y_range_name="labforce",
legend="Labor Force (log)",
color='green')
fig1.line('date',
'lab_force',
source=source_figures,
y_range_name="labforce",
legend="Labor Force (log)",
color='green')
fig1.legend.location = 'top_left'
fig1.legend.label_text_font_size = '8pt'
fig2 = figure(title='Employment vs Oil production for ' + cur_county,
x_axis_type='datetime',
plot_width=700,
plot_height=400,
toolbar_location='left',
tools=TOOLS)
fig2.circle('date',
'oil_prod',
source=source_oil,
legend='Oil production (bbls)',
hover_color='red',
selection_color='red',
nonselection_fill_color='grey',
nonselection_fill_alpha=0.2)
fig2.xaxis.axis_label = 'Date'
fig2.yaxis.axis_label = 'Oil Production (bbls)'
fig2.y_range = Range1d(start=max(0,
min(oil_prod[cur_county]) - 1000),
end=max(oil_prod[cur_county]) + 1000)
# Adding second y axis for unemployment
fig2.extra_y_ranges['unemp'] = Range1d(start=0, end=50)
fig2.add_layout(
LinearAxis(y_range_name="unemp", axis_label='Unemployment Rate (%)'),
'right')
# Adding third y axis for labor force
fig2.extra_y_ranges['labforce'] = Range1d(
start=max(100,
min(lab_force[cur_county]) - 1000),
end=max(lab_force[cur_county] + 1000))
fig2.add_layout(
LogAxis(y_range_name="labforce", axis_label='Labor Force (log)'),
'right')
fig2.circle('date',
'unemp',
source=source_figures,
y_range_name="unemp",
legend="Unemployment rate (%)",
color='orange')
fig2.line('date',
'unemp',
source=source_figures,
y_range_name="unemp",
legend="Unemployment rate (%)",
color='orange')
fig2.circle('date',
'lab_force',
source=source_figures,
y_range_name="labforce",
legend="Labor Force (log)",
color='green')
fig2.line('date',
'lab_force',
source=source_figures,
y_range_name="labforce",
legend="Labor Force (log)",
color='green')
fig2.legend.location = 'top_left'
fig2.legend.label_text_font_size = '8pt'
fig3 = figure(title='Employment vs S&P 500 for ' + cur_county,
x_axis_type='datetime',
plot_width=700,
plot_height=400,
toolbar_location='left',
tools=TOOLS)
fig3.circle('Date',
'SP500',
source=source_sp500,
legend='S&P 500 index',
hover_color='red',
selection_color='red',
nonselection_fill_color='grey',
nonselection_fill_alpha=0.2)
fig3.xaxis.axis_label = 'Date'
fig3.yaxis.axis_label = 'S&P 500 index'
fig3.y_range = Range1d(start=0, end=max(sp500['SP500']))
# Adding second y axis for unemployment
fig3.extra_y_ranges['unemp'] = Range1d(start=0, end=50)
fig3.add_layout(
LinearAxis(y_range_name="unemp", axis_label='Unemployment Rate (%)'),
'right')
# Adding third y axis for labor force
fig3.extra_y_ranges['labforce'] = Range1d(
start=max(100,
min(lab_force[cur_county]) - 1000),
end=max(lab_force[cur_county] + 1000))
fig3.add_layout(
LogAxis(y_range_name="labforce", axis_label='Labor Force (log)'),
'right')
fig3.circle('date',
'unemp',
source=source_figures,
y_range_name="unemp",
legend="Unemployment rate (%)",
color='orange')
fig3.line('date',
'unemp',
source=source_figures,
y_range_name="unemp",
legend="Unemployment rate (%)",
color='orange')
fig3.circle('date',
'lab_force',
source=source_figures,
y_range_name="labforce",
legend="Labor Force (log)",
color='green')
fig3.line('date',
'lab_force',
source=source_figures,
y_range_name="labforce",
legend="Labor Force (log)",
color='green')
fig3.legend.location = 'top_left'
fig3.legend.label_text_font_size = '8pt'
def fig_callback_tap(attr, old, new):
try:
# The index of the selected glyph is : new['1d']['indices'][0]
selections = new['1d']['indices']
patch_name = source_maps.data['name'][selections[0]]
source_figures.data = dict(date=unemp.index,
unemp=unemp[patch_name],
lab_force=lab_force[patch_name])
source_oil.data = dict(date=oil_prod.index,
oil_prod=oil_prod[patch_name])
except:
selections = old['1d']['indices']
patch_name = source_maps.data['name'][selections[0]]
source_figures.data = dict(date=unemp.index,
unemp=unemp[patch_name],
lab_force=lab_force[patch_name])
source_oil.data = dict(date=oil_prod.index,
oil_prod=oil_prod[patch_name])
fig1.title.text = 'Employment vs Oil prices for ' + patch_name
fig2.title.text = 'Employment vs Oil production for ' + patch_name
fig3.title.text = 'Employment vs S&P 500 for ' + patch_name
fig2.y_range.start = max(0, min(oil_prod[patch_name]) - 1000)
fig2.y_range.end = max(oil_prod[patch_name]) + 1000
fig1.extra_y_ranges['labforce'].start = max(
100,
min(lab_force[patch_name]) - 1000)
fig1.extra_y_ranges['labforce'].end = max(lab_force[patch_name] + 1000)
fig2.extra_y_ranges['labforce'].start = max(
100,
min(lab_force[patch_name]) - 1000)
fig2.extra_y_ranges['labforce'].end = max(lab_force[patch_name] + 1000)
fig3.extra_y_ranges['labforce'].start = max(
100,
min(lab_force[patch_name]) - 1000)
fig3.extra_y_ranges['labforce'].end = max(lab_force[patch_name] + 1000)
pyglyph.data_source.on_change('selected', fig_callback_tap)
pyglyph_prod.data_source.on_change('selected', fig_callback_tap)
def update_plot_yr(attr, old, new):
# Assign the value of the slider: new_year
new_year = slider_yr.value
new_month = slider_month.value
new_date = pd.to_datetime({
'year': [new_year],
'month': [new_month],
'day': [1]
})
new_rates = unemp.loc[new_date].values[0]
new_prods = oil_prod.loc[new_date].values[0]
# Set new_data
new_data = dict(x=county_xs,
y=county_ys,
name=county_names,
rate=new_rates,
prod=new_prods)
# Assign new_data to: source.data
source_maps.data = new_data
# Add title to figure: plot.title.text
fig_unemp.title.text = '%s unemployment rate for %s, %d' % (
state_dict[state], month_dict[new_month], new_year)
# Add title to figure: plot.title.text
fig_prods.title.text = '%s production data for %s, %d' % (
state_dict[state], month_dict[new_month], new_year)
# Make a slider object: slider
slider_yr = Slider(title='Year',
start=1990,
end=2016,
step=1,
value=cur_year)
# Attach the callback to the 'value' property of slider
slider_yr.on_change('value', update_plot_yr)
# Make a slider object: slider
slider_month = Slider(title='Month',
start=1,
end=12,
step=1,
value=cur_month)
# Attach the callback to the 'value' property of slider
slider_month.on_change('value', update_plot_yr)
return fig_unemp, fig_prods, fig1, fig2, fig3, slider_yr, slider_month
taptool = fig.select(TapTool)
taptool.callback = OpenURL(url=url)
# figure out what the default axis limits are
ydiff = np.log10(ymax) - np.log10(ymin)
ystart = 10.**(np.log10(ymin) - 0.05 * ydiff)
yend = 10.**(np.log10(ymax) + 0.05 * ydiff)
# jupiter/earth mass ratio
massratio = 317.8
# set up the second axis with the proper scaling
fig.extra_y_ranges = {
"jup": Range1d(start=ystart / massratio, end=yend / massratio)
}
fig.add_layout(LogAxis(y_range_name="jup"), 'right')
# add the first y-axis's label and use our custom log formatting for both axes
fig.yaxis.axis_label = 'Mass (Earth Masses)'
fig.yaxis.formatter = FuncTickFormatter(code=log_axis_labels())
# add the x-axis's label and use our custom log formatting
fig.xaxis.axis_label = 'Period (days)'
fig.xaxis.formatter = FuncTickFormatter(code=log_axis_labels())
# add the second y-axis's label
fig.right[0].axis_label = 'Mass (Jupiter Masses)'
# set up all the legend objects
items = [
LegendItem(label=ii + f' ({counts[methods.index(ii)]})', renderers=[jj])
def plotting(self):
if self.debug:
self.debug_file = open("debug_output.txt", "w")
self.debug_file.write("Initialized plotting subroutine \n")
TOOLS="pan,wheel_zoom,box_zoom,reset,hover,previewsave"
tab_plots = []
self.all_elements = []
self.elements_comparison = []
for filename in self.filenames:
if "ITO" in filename:
tab_plots.append(self.mass_plotting(filename))
continue
data_dict = self.data_generation(filename)
self.data_test(data_dict)
name_check = data_dict["gen_info"]["DATA FILES"]
attr_id = name_check[1][4][:-3] + "_" + name_check[2][2]
self.attribute_ids.append(attr_id)
attr_extra_y_ranges = False
attr_extra_x_ranges = False
local_source_line = []
"""
create plots for each datafile and put them in a tab.
"""
y_axis_units = [x["y_unit"] for x in data_dict["data"]]
x_axis_units = [x["x_unit"] for x in data_dict["data"]]
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log",
title = attr_id, tools = TOOLS)
#figure_obj.axes.major_label_text_font_size("12pt")
#figure_obj.major_label_text_font_size("12pt")
hover = figure_obj.select(dict(type = HoverTool))
hover.tooltips = [
("Element:", "@element"),
("(x, y):", "($x, $y)")]
self.figure_data.append((figure_obj, data_dict))
figure_obj.yaxis.axis_label = y_axis_units[0]
figure_obj.xaxis.axis_label = x_axis_units[0]
if not all(x == y_axis_units[0] for x in y_axis_units):
for unit, dataset in zip(y_axis_units, data_dict["data"]):
if not unit == y_axis_units[0]:
extra_y_ranges_exists = attr_extra_y_ranges
extra_y_ranges_exists = True
if self.debug:
self.debug_file.write("Added extra y-axis for file_id: %s, element: %s | New length %g \n"
%(attr_id, dataset["sample_element"], len(figure_obj.yaxis)))
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(dataset["y"]),
end = np.amax(dataset["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[0] for x in x_axis_units):
for unit, dataset in zip(x_axis_units, data_dict["data"]):
if not unit == x_axis_units[0]:
extra_x_ranges_exists = attr_extra_x_ranges
extra_x_ranges_exists = True
if self.debug:
self.debug_file.write("Added extra x-axis for file_id: %s, element: %s. | New length %g \n"
%(attr_id, dataset["sample_element"], len(figure_obj.yaxis)))
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(dataset["x"]),
end = np.amax(dataset["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
figure_obj.xaxis.axis_label = x_axis_units[0]
colour_list = Spectral11 + RdPu9 + Oranges9
colour_indices = [0, 2, 8, 10, 12, 14, 20, 22, 1, 3, 9, 11, 13, 15]
list_of_elements = []
source_list = []
line_list = []
for dataset, color_index in zip(data_dict["data"], colour_indices):
self.all_elements.append(dataset["sample_element"]) #strip isotope number
color = colour_list[color_index]
source = ColumnDataSource(data = dataset) #Datastructure for source of plotting
self.source_test(source)
list_of_elements.append(dataset["sample_element"])
line_glyph = figure_obj.line("x", "y", source = source,
line_width = 2,
line_color = color,
legend = dataset["sample_element"])
if self.debug:
self.debug_file.write("Create line object on figure %s at %s \n" %(id(figure_obj), id(line_glyph)))
line_list.append(line_glyph)
source_list.append(source)
local_source_line.append([[source, line] for source, line in zip(source_list, line_list)])
self.source_line.append(local_source_line)
#Calculations on the dataset
text_input_rsf = TextInput(value = "default", title = "RSF or SF (at/cm^3): ")
do_integral_button = Button(label = "Calibration integral")
smoothing_button = Button(label = "smth selct elem")
matplot_button = Button(label = "Create matplotlib fig")
text_input_sputter = TextInput(value = "default", title = "Sputter speed: number unit")
text_input_crater_depth = TextInput(value = "default", title = "Depth of crater in: number unit")
radio_group = RadioGroup(labels = list_of_elements, active=0)
text_input_xval_integral = TextInput(value = "0", title = "x-delimiter ")
text_input_dose = TextInput(value = "0", title = "Dose[cm^-2] ")
#Save files for later use
save_flexDPE_button = Button(label = "Save element for FlexPDE")
save_all_flexDPE_button = Button(label = "Save all elements for FlexPDE")
save_textfile_button = Button(label = "Sava Data in textfile")
#Pointers to methods on click / change handlers
radio_group.on_change("active", lambda attr, old, new: None)
matplot_button.on_click(lambda source_list = source_list:
self.matplotlib_export(source_list))
do_integral_button.on_click(lambda
source_list = source_list,
line_list = line_list,
source_line = self.source_line,
figure_data = self.figure_data,
data_dict = data_dict,
radio = radio_group,
x_box = text_input_xval_integral,
dose = text_input_dose,
extra_y_ranges = attr_extra_y_ranges:
self.integrate(data_dict, source_list, line_list, source_line, figure_data, radio, x_box, dose, extra_y_ranges))
smoothing_button.on_click(lambda
source_list = source_list,
radio = radio_group,
data_dict = data_dict,
x_box = text_input_xval_integral:
self.smoothing(source_list, data_dict, radio, x_box) )
save_flexDPE_button.on_click(lambda
source_list = source_list,
attrname = attr_id,
radio = radio_group:
self.write_to_flexPDE(source_list, attrname, radio))
save_all_flexDPE_button.on_click(lambda
source_list = source_list,
attrname = attr_id:
self.write_all_to_flexPDE(source_list, attrname))
save_textfile_button.on_click(lambda
data_dict = data_dict,
source_list = source_list,
attrname = attr_id,
radio = radio_group:
self.write_new_datafile(data_dict, source_list, attrname,radio))
text_input_rsf.on_change("value", lambda attr, old, new,
radio = radio_group,
data_dict = data_dict,
figure = figure_obj,
source_list = source_list,
text_input = text_input_rsf,
line_list = line_list,
which = "rsf":
self.update_data(line_list, data_dict, source_list, figure, radio, text_input, new, which))
text_input_sputter.on_change("value", lambda attr, old, new,
radio = radio_group,
data_dict = data_dict,
figure = figure_obj,
source_list = source_list,
text_input = text_input_sputter,
which = "sputter":
self.update_data(data_dict, source_list, figure, radio, text_input, new, which))
text_input_crater_depth.on_change("value", lambda attr, old, new,
radio = radio_group,
data_dict = data_dict,
source_list = source_list,
figure = figure_obj,
text_input = text_input_crater_depth,
which = "crater_depth":
self.update_data(data_dict, source_list, figure, radio, text_input, new, which))
#Initialization of actual plotting.
tab_plots.append(Panel(child = hplot(figure_obj,
vform(
vform(radio_group, save_flexDPE_button, save_all_flexDPE_button, save_textfile_button, matplot_button),
vform(text_input_rsf, smoothing_button, text_input_sputter, text_input_crater_depth)
),
vform(text_input_xval_integral, text_input_dose, do_integral_button)),
title = attr_id))
"""
Check to see if one or more element exists in the samples and creat a comparison plot for each
of those elements.
"""
for element in self.all_elements:
checkers = list(self.all_elements)
checkers.remove(element)
if element in checkers and not element in self.elements_comparison:
self.elements_comparison.append(element)
"""create plots for each element that is to be compared """
for comparison_element in self.elements_comparison:
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log", title = comparison_element, tools = TOOLS)
#figure_obj.xaxis.major_label_text_font_size("12pt")
#figure_obj.yaxis.major_label_text_font_size("12pt")
y_axis_units = []
x_axis_units = []
comparison_datasets = []
for data_dict_iter in self.column(self.figure_data, 1):
for dataset in data_dict_iter["data"]:
if dataset["sample_element"] == comparison_element:
comparison_datasets.append(dataset)
y_axis_units.append(dataset["y_unit"])
x_axis_units.append(dataset["x_unit"])
figure_obj.xaxis.axis_label = comparison_datasets[-1]["x_unit"]
figure_obj.yaxis.axis_label = comparison_datasets[-1]["y_unit"]
if not all(x == y_axis_units[-1] for x in y_axis_units):
for unit, data in zip(y_axis_units, comparison_datasets):
if not unit == y_axis_units[-1]:
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(data["y"]),
end = np.amax(data["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[-1] for x in x_axis_units):
for unit, data in zip(x_axis_units, comparison_datasets):
if not unit == x_axis_units[-1]:
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(data["x"]),
end = np.amax(data["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
active_sources = []
for data_dict, source_line_nested, attr_id, color_index in zip(self.column(self.figure_data, 1), self.source_line, self.attribute_ids, colour_indices):
for dataset, source_lis_coup, in zip(data_dict["data"], source_line_nested[0]):
source_local = source_lis_coup[0]
active_sources.append(source_local)
self.source_test(source_local)
self.source_dataset_test(source_local, dataset)
if dataset["sample_element"] == comparison_element:
color = colour_list[color_index]
"""
Logic that ensures that plots get put with correspoinding axes.
"""
if dataset["x_unit"] != x_axis_units[-1] or dataset["y_unit"] != y_axis_units[-1]:
if dataset["x_unit"] != x_axis_units[-1] and dataset["y_unit"] != y_axis_units[-1]:
name_check = data_dict["gen_info"]["DATA FILES"]
attr_id = name_check[1][4][:-3] + "_" + name_check[2][2]
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id,
x_range_name = "bar",
y_range_name = "foo")
elif dataset["x_unit"] != x_axis_units[-1]:
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id,
x_range_name = "bar")
else:
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id,
y_range_name = "foo")
else:
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id)
matplot_button = Button(label = "Create matplotlib fig")
save_all_flexDPE_button = Button(label = "Save all elements for FlexPDE")
matplot_button.on_click(lambda source_list = active_sources:
self.matplotlib_export(source_list))
save_all_flexDPE_button.on_click(lambda
source_list = active_sources,
attrname = comparison_element:
self.write_all_to_flexPDE(source_list, attrname))
tab_plots.append(Panel(child = hplot(figure_obj, vform(save_all_flexDPE_button, matplot_button)),
title = comparison_element))
tabs = Tabs(tabs = tab_plots)
#curdoc().add_root(tabs)
session = push_session(curdoc())
session.show()
session.loop_until_closed()
fig1_tx.yaxis.axis_label = 'Oil Price, $ (month avg.)'
fig1_tx.y_range = Range1d(start=0, end=150)
# Adding second y axis for unemployment
fig1_tx.extra_y_ranges['unemp'] = Range1d(start=0, end=50)
fig1_tx.add_layout(
LinearAxis(y_range_name="unemp", axis_label='Unemployment Rate (%)'),
'right')
# Adding third y axis for labor force
fig1_tx.extra_y_ranges['labforce'] = Range1d(
start=max(100,
min(tx_lab_force[cur_tx_county]) - 1000),
end=max(tx_lab_force[cur_tx_county]) + 1000)
fig1_tx.add_layout(
LogAxis(y_range_name="labforce", axis_label='Labor Force (log)'), 'right')
fig1_tx.circle('date',
'unemp',
source=source_tx_figures,
y_range_name="unemp",
legend="Unemployment rate (%)",
color='orange')
fig1_tx.line('date',
'unemp',
source=source_tx_figures,
y_range_name="unemp",
legend="Unemployment rate (%)",
color='orange')
fig1_tx.circle('date',
plot.outline_line_color = c_gray
plot.line(x=[0,L], y=[0,0], line_color = c_gray, line_dash ="4 4") # beam initial position
plot.add_glyph(support_left_source,ImageURL(url="src", x='x', y='y', w= 'w', h= 'h', anchor= "top_center")) # beam supports
plot.add_glyph(support_right_source,ImageURL(url="src", x='x', y='y', w= 'w', h= 'h', anchor= "top_center")) # beam supports
plot.line(x = 'x', y = 'y', source = beam_coordinates_source, color = c_black)
plot.line(x = 'x', y = 'y', source = lfa_coordinates_source, color = c_green, line_dash = "dashed")
arrow_load = Arrow(start=NormalHead(line_color=c_orange, fill_color = c_orange, fill_alpha = 0.5),
end=NormalHead(line_alpha = 0, fill_alpha = 0),
x_start='xs', y_start='ye', x_end='xe', y_end='ys', line_alpha = 0, source=load_arrow_source,line_color=c_white)
plot.add_layout(arrow_load)
# amplitude for every excitation frequency ratio
disp_freq = figure(x_range = [0.04, max_r], y_axis_type="log", y_range = [0.00001,100],
height = 335, width = 280,toolbar_location = None, tools = "")
disp_freq.yaxis.visible = False
disp_freq.add_layout(LogAxis(axis_label='Normalized Deflection W(x)⋅E⋅I/(F⋅L³)'), 'right')
disp_freq.xaxis.axis_label = "Excitation Frequency Ratio"
disp_freq.outline_line_color = c_gray
disp_freq.line(x = 'x', y = 'y', source = amp_coordinates_source)
disp_freq.line(x = 'x', y = 'y', source = freq_coordinates_source, line_dash = "dashed")
# phase angle for every excitation frequency ratio
disp_phase = figure(x_range = [0.04, max_r], y_range = [-pi-0.1, pi+0.1],
height = 335, width = 280,toolbar_location = None, tools = "")
disp_phase.yaxis.axis_label = "Phase Angle [rad]"
disp_phase.yaxis.visible = False
disp_phase.add_layout(LinearAxis(axis_label='Phase Angle [rad]'), 'right')
disp_phase.xaxis.axis_label = "Excitation Frequency Ratio"
disp_phase.outline_line_color = c_gray
disp_phase.line(x = 'x', y = 'y', source = phase_coordinates_source)
disp_phase.line(x = 'x', y = 'y', source = freq2_coordinates_source, line_dash = "dashed")
def plotting(self):
#Tools = [hover, TapTool(), BoxZoomTool(), BoxSelectTool(), PreviewSaveTool(), ResetTool()]
TOOLS = "crosshair,pan,wheel_zoom,box_zoom,reset,hover,previewsave"
tab_plots = []
#output_file("test.html")
self.all_elements = []
self.elements_comparison = []
for attr_id, i in zip(self.attribute_ids,
range(len(self.attribute_ids))):
"""
create plots for each datafile and put them in a tab.
"""
list_of_datasets = getattr(self, attr_id)
y_axis_units = [x["y_unit"] for x in list_of_datasets]
x_axis_units = [x["x_unit"] for x in list_of_datasets]
figure_obj = figure(plot_width=1000,
plot_height=800,
y_axis_type="log",
title=attr_id,
tools=TOOLS)
#figure_obj.axes.major_label_text_font_size("12pt")
#figure_obj.major_label_text_font_size("12pt")
setattr(self, attr_id + "_" + "figure_obj", figure_obj)
figure_obj.yaxis.axis_label = y_axis_units[0]
figure_obj.xaxis.axis_label = x_axis_units[0]
if not all(x == y_axis_units[0] for x in y_axis_units):
for unit, data in zip(y_axis_units, list_of_datasets):
if not unit == y_axis_units[0]:
figure_obj.extra_y_ranges = {
"foo":
Range1d(start=np.amin(data["data"]["y"]),
end=np.amax(data["data"]["y"]))
}
figure_obj.add_layout(
LogAxis(y_range_name="foo", axis_label=unit),
"right")
break
if not all(x == x_axis_units[0] for x in x_axis_units):
for unit, data in zip(x_axis_units, list_of_datasets):
if not unit == x_axis_units[0]:
figure_obj.extra_x_ranges = {
"bar":
Range1d(start=np.amin(data["data"]["x"]),
end=np.amax(data["data"]["x"]))
}
figure_obj.add_layout(
LinearAxis(x_range_name="bar", axis_label=unit),
"above")
break
figure_obj.xaxis.axis_label = list_of_datasets[0]["x_unit"]
colour_list = Spectral11 + RdPu9 + Oranges9
colour_indices = [0, 2, 8, 10, 12, 14, 20, 22, 1, 3, 9, 11, 13, 15]
list_of_elements = []
for dataset, color_index in zip(list_of_datasets, colour_indices):
self.all_elements.append(
dataset["sample element"]) #strip isotope number
color = colour_list[color_index]
source = ColumnDataSource(
data=dataset["data"]
) #Datastructure for source of plotting
setattr(self,
attr_id + "_" + dataset["sample element"] + "_source",
source) #Source element generalized for all plotting
list_of_elements.append(dataset["sample element"])
figure_obj.line(
"x",
"y",
source=getattr(
self,
attr_id + "_" + dataset["sample element"] + "_source"),
line_width=2,
line_color=color,
legend=dataset["sample element"],
name=dataset["sample element"],
)
hover = figure_obj.select_one(HoverTool).tooltips = [
("element", "@element"), ("(x,y)", "($x, $y)")
]
radio_group = RadioGroup(labels=list_of_elements, active=0)
"""
Need to fetch default variables from input file and replace DEFAULT
Block of code produces the layout of buttons and callbacks
"""
#Calculations on the dataset
text_input_rsf = TextInput(value="default",
title="RSF (at/cm^3): ")
do_integral_button = Button(label="Calibration Integral")
smoothing_button = Button(label="Smoothing on selected curve")
text_input_sputter = TextInput(value="default",
title="Sputter speed: float unit")
text_input_crater_depth = TextInput(
value="default", title="Depth of crater in: float")
radio_group.on_change("active", lambda attr, old, new: None)
text_input_xval_integral = TextInput(
value="0", title="x-value for calibration integral ")
text_input_yval_integral = TextInput(
value="0", title="y-value for calibration integral ")
#Save files for later use
save_flexDPE_button = Button(label="Save element for FlexPDE")
save_all_flexDPE_button = Button(
label="Save all elements for FlexPDE")
#Pointers to methods on click / change handlers
do_integral_button.on_click(
lambda identity=self.attribute_ids[i], radio=radio_group, x_box
=text_input_xval_integral, y_box=text_input_yval_integral: self
.integrate(identity, radio, x_box, y_box))
smoothing_button.on_click(lambda identity=self.attribute_ids[
i], radio=radio_group: self.smoothing(identity, radio))
save_flexDPE_button.on_click(lambda identity=self.attribute_ids[
i], radio=radio_group: self.write_to_flexPDE(identity, radio))
save_all_flexDPE_button.on_click(
lambda identity=self.attribute_ids[i], radio=radio_group: self.
write_all_to_flexPDE(identity, radio))
text_input_rsf.on_change(
"value",
lambda attr, old, new, radio=radio_group, identity=self.
attribute_ids[i], text_input=text_input_rsf, which="rsf": self.
update_data(identity, radio, text_input, new, which))
text_input_sputter.on_change(
"value",
lambda attr, old, new, radio=radio_group, identity=self.
attribute_ids[i], text_input=
text_input_sputter, which="sputter": self.update_data(
identity, radio, text_input, new, which))
text_input_crater_depth.on_change(
"value",
lambda attr, old, new, radio=radio_group, identity=self.
attribute_ids[i], text_input=text_input_crater_depth, which=
"crater_depth": self.update_data(identity, radio, text_input,
new, which))
#Initialization of actual plotting.
tab_plots.append(
Panel(child=hplot(
figure_obj,
vform(radio_group, save_flexDPE_button,
save_all_flexDPE_button),
vform(text_input_rsf, smoothing_button, text_input_sputter,
text_input_crater_depth),
vform(text_input_xval_integral, text_input_yval_integral,
do_integral_button)),
title=attr_id))
"""
Check to see if one or more element exists in the samples and creat a comparison plot for each
of those elements.
"""
for element in self.all_elements:
checkers = list(self.all_elements)
checkers.remove(element)
if element in checkers and not element in self.elements_comparison:
self.elements_comparison.append(element)
"""create plots for each element that is to be compared """
for comparison_element in self.elements_comparison:
colour_list = Spectral11 + RdPu9 + Oranges9
colour_indices = [0, 2, 8, 10, 12, 14, 20, 22, 1, 3, 9, 11, 13, 15]
figure_obj = figure(plot_width=1000,
plot_height=800,
y_axis_type="log",
title=comparison_element,
tools=TOOLS)
#figure_obj.xaxis.major_label_text_font_size("12pt")
#figure_obj.yaxis.major_label_text_font_size("12pt")
y_axis_units = []
x_axis_units = []
comparison_datasets = []
for attr_id, color_index in zip(self.attribute_ids,
colour_indices):
list_of_datasets = getattr(self, attr_id)
for dataset in list_of_datasets:
if dataset["sample element"] == comparison_element:
comparison_datasets.append(dataset)
y_axis_units.append(dataset["y_unit"])
x_axis_units.append(dataset["x_unit"])
figure_obj.xaxis.axis_label = comparison_datasets[-1]["x_unit"]
figure_obj.yaxis.axis_label = comparison_datasets[-1]["y_unit"]
if not all(x == y_axis_units[-1] for x in y_axis_units):
for unit, data in zip(y_axis_units, comparison_datasets):
if not unit == y_axis_units[-1]:
figure_obj.extra_y_ranges = {
"foo":
Range1d(start=np.amin(data["data"]["y"]),
end=np.amax(data["data"]["y"]))
}
figure_obj.add_layout(
LogAxis(y_range_name="foo", axis_label=unit),
"right")
break
if not all(x == x_axis_units[-1] for x in x_axis_units):
for unit, data in zip(x_axis_units, comparison_datasets):
if not unit == x_axis_units[-1]:
figure_obj.extra_x_ranges = {
"bar":
Range1d(start=np.amin(data["data"]["x"]),
end=np.amax(data["data"]["x"]))
}
figure_obj.add_layout(
LinearAxis(x_range_name="bar", axis_label=unit),
"above")
break
for attr_id, color_index in zip(self.attribute_ids,
colour_indices):
list_of_datasets = getattr(self, attr_id)
for dataset in list_of_datasets:
if dataset["sample element"] == comparison_element:
color = colour_list[color_index]
"""
Logic that ensures that plots get put with correspoinding axes.
"""
if dataset["x_unit"] != x_axis_units[-1] or dataset[
"y_unit"] != y_axis_units[-1]:
if dataset["x_unit"] != x_axis_units[
-1] and dataset["y_unit"] != y_axis_units[
-1]:
figure_obj.line(
"x",
"y",
source=getattr(
self, attr_id + "_" +
dataset["sample element"] + "_source"),
line_width=2,
line_color=color,
legend=attr_id,
x_range_name="bar",
y_range_name="foo")
elif dataset["x_unit"] != x_axis_units[-1]:
figure_obj.line(
"x",
"y",
source=getattr(
self, attr_id + "_" +
dataset["sample element"] + "_source"),
line_width=2,
line_color=color,
legend=attr_id,
x_range_name="bar")
else:
figure_obj.line(
"x",
"y",
source=getattr(
self, attr_id + "_" +
dataset["sample element"] + "_source"),
line_width=2,
line_color=color,
legend=attr_id,
y_range_name="foo")
else:
figure_obj.line(
"x",
"y",
source=getattr(
self, attr_id + "_" +
dataset["sample element"] + "_source"),
line_width=2,
line_color=color,
legend=attr_id)
tab_plots.append(Panel(child=figure_obj, title=comparison_element))
tabs = Tabs(tabs=tab_plots)
session = push_session(curdoc())
session.show()
session.loop_until_closed()
def make_plot(self):
self.p = figure(
x_axis_label="Date",
x_axis_type="datetime",
y_axis_label="Total Cases and Deaths",
width=900,
)
self.p.extra_y_ranges = {"ratio_axis": Range1d()}
axis = LinearAxis(y_range_name="ratio_axis")
axis.formatter = NumeralTickFormatter(format="0 %")
self.p.add_layout(axis, "right")
colors = Category20_3
self.p.line(
source=self.src,
x="date",
y="cases",
line_width=2,
color=colors[0],
legend_label="Cases",
)
self.p.line(
source=self.src,
x="date",
y="deaths",
line_width=2,
color=colors[1],
legend_label="Deaths",
)
self.p.line(
source=self.src,
x="date",
y="ratio",
line_width=2,
y_range_name="ratio_axis",
color=colors[2],
legend_label="Deaths/Cases",
)
self.p.legend.location = "top_left"
self.logp = figure(
x_axis_label="Date",
x_axis_type="datetime",
y_axis_label="Total Cases and Deaths",
y_axis_type="log",
width=900,
)
self.logp.extra_y_ranges = {"ratio_axis": Range1d()}
logaxis = LogAxis(y_range_name="ratio_axis")
logaxis.formatter = NumeralTickFormatter(format="0 %")
self.logp.add_layout(logaxis, "right")
self.logp.line(
source=self.src,
x="date",
y="cases",
line_width=2,
color=colors[0],
legend_label="Cases",
)
self.logp.line(
source=self.src,
x="date",
y="deaths",
line_width=2,
color=colors[1],
legend_label="Deaths",
)
self.logp.line(
source=self.src,
x="date",
y="ratio",
line_width=2,
y_range_name="ratio_axis",
color=colors[2],
legend_label="Deaths/Cases",
)
self.p.legend.location = "top_left"
def update_scale(attr, old, new):
ticker = LinearAxis()
if new == "Log scale":
ticker = LogAxis()
pairplots['dimdem'].yaxis[0] = ticker