def show_roc_curve_bokeh(fpr,
tpr,
rocAuc,
classes,
name,
path,
show_plt=False):
output_file("roc.html")
from bokeh.models import Legend, LegendItem
p = figure(plot_width=400, plot_height=400)
# add a line renderer
r = p.multi_line([fpr["micro"], fpr["macro"], [0, 1]],
[tpr["micro"], tpr["macro"], [0, 1]],
color=["chocolate", "darkslateblue", "black"],
alpha=[0.6, 0.6, 0.3],
line_width=2)
legend = Legend(items=[
LegendItem(label='micro-average ROC curve (area = {0:0.3f})'
''.format(rocAuc["micro"]),
renderers=[r],
index=0),
LegendItem(label='macro-average ROC curve (area = {0:0.2f})'
''.format(rocAuc["macro"]),
renderers=[r],
index=1),
])
p.add_layout(legend)
p.legend.location = "bottom_right"
show(p)
def make_plot(cnv_name, test_name, data, d_min, d_max):
plot = figure(title=test_name,
plot_width=700,
plot_height=500,
min_border=0)
glyph1 = VArea(x="x1",
y1="y01",
y2="y1",
fill_color="LightCoral",
fill_alpha=0.6)
glyph2 = VArea(x="x2",
y1="y02",
y2="y2",
fill_color="LightBlue",
fill_alpha=0.6)
plot.add_glyph(data, glyph1)
plot.add_glyph(data, glyph2)
li1 = LegendItem(label='0 (Absent)', renderers=[plot.renderers[0]])
li2 = LegendItem(label='1 (Present)', renderers=[plot.renderers[1]])
legend1 = Legend(items=[li1, li2], location='top_right')
plot.add_layout(legend1)
plot.legend.title = cnv_name
return plot
def get_map_plot(source, source_elog, source_radius_circle):
#define maps, options
map_options = GMapOptions(lat=51.4416, lng=5.4697, map_type="terrain", zoom=12)
plot = GMapPlot(x_range=Range1d(), y_range=Range1d(), map_options=map_options)
# use your api key below
plot.api_key = get_api_key()
########################################################################
# Define glyphs
########################################################################
# triangle glyphs on the map
triangle_event = Triangle(x="lon", y="lat", size=12, fill_color="red", fill_alpha=0.5, line_color=None, name="events")
glyph_triangle = plot.add_glyph(source, triangle_event, nonselection_glyph=triangle_event)
# circle glyphs on the map
circle_elog = Circle(x="lon_elog", y="lat_elog", size=12, fill_color="color",
fill_alpha=0.8, line_color=None, name="elog_locations")
glyph_circle = plot.add_glyph(source_elog, circle_elog, nonselection_glyph=circle_elog, )
circle_radius = Circle(x="lon_radius", y="lat_radius", radius= "rad_radius", fill_alpha=0.3, line_color='black')
glyph_circle_radius = plot.add_glyph(source_radius_circle, circle_radius)
########################################################################
# Other misc tools: hovers, taps, etc
########################################################################
# tools to include on the visualization
plot.add_tools(PanTool(), WheelZoomTool(),
ResetTool(), TapTool())
# Hover tool for triangles
triangle_hover = HoverTool(renderers=[glyph_triangle],
tooltips=OrderedDict([
("Location", "@city"),
("Date", "@dates"),
("Problem", "@issue")
]))
plot.add_tools(triangle_hover)
# Hover tool for circles
circle_hover = HoverTool(renderers=[glyph_circle],
tooltips=OrderedDict([
("Place", "@place_elog"),
("Usage", '@value_elog'),
("Classification", '@classes')
]))
plot.add_tools(circle_hover)
# Add legend
legend = Legend(items=[
LegendItem(label="elog_locations" , renderers=[glyph_circle]),
LegendItem(label="events" , renderers=[glyph_triangle])
], orientation="vertical", click_policy="hide")
plot.add_layout(legend, "center")
return glyph_circle, plot
def test_legend_item_with_field_label_raises_error_if_field_not_in_cds():
legend_item = LegendItem()
gr_1 = GlyphRenderer(data_source=ColumnDataSource())
legend_item.label = field('label')
legend_item.renderers = [gr_1]
with mock.patch('bokeh.core.validation.check.log') as mock_logger:
check_integrity([legend_item])
assert mock_logger.error.call_count == 1
def test__find_legend_item():
legend = Legend(items=[
LegendItem(label=dict(value="foo")),
LegendItem(label=dict(field="bar"))
])
assert bpl._find_legend_item(dict(value="baz"), legend) is None
assert bpl._find_legend_item(dict(value="foo"), legend) is legend.items[0]
assert bpl._find_legend_item(dict(field="bar"), legend) is legend.items[1]
def test_can_add_multiple_glyph_renderers_to_legend_item() -> None:
legend_item = LegendItem()
gr_1 = GlyphRenderer()
gr_2 = GlyphRenderer()
legend_item.renderers = [gr_1, gr_2]
with mock.patch('bokeh.core.validation.check.log') as mock_logger:
check_integrity([legend_item])
assert mock_logger.error.call_count == 0
def test_can_add_multiple_glyph_renderers_to_legend_item() -> None:
legend_item = LegendItem()
gr_1 = GlyphRenderer(data_source=ColumnDataSource())
gr_2 = GlyphRenderer(data_source=ColumnDataSource())
legend_item.renderers = [gr_1, gr_2]
with mock.patch('bokeh.core.validation.check.log') as mock_logger:
issues = check_integrity([legend_item])
process_validation_issues(issues)
assert mock_logger.error.call_count == 0
def test_legend_item_with_value_label_and_different_data_sources_does_not_raise_a_validation_error() -> None:
legend_item = LegendItem()
gr_1 = GlyphRenderer(data_source=ColumnDataSource())
gr_2 = GlyphRenderer(data_source=ColumnDataSource())
legend_item.label = value('label')
legend_item.renderers = [gr_1, gr_2]
with mock.patch('bokeh.core.validation.check.log') as mock_logger:
check_integrity([legend_item])
assert mock_logger.error.call_count == 0
def render_scatter(
itmdt: Intermediate, plot_width: int, plot_height: int, palette: Sequence[str]
) -> Figure:
"""
Render scatter plot with a regression line and possible most influencial points
"""
# pylint: disable=too-many-locals
df = itmdt["data"]
xcol, ycol, *maybe_label = df.columns
tooltips = [(xcol, f"@{{{xcol}}}"), (ycol, f"@{{{ycol}}}")]
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
toolbar_location=None,
title=Title(text="Scatter Plot & Regression Line", align="center"),
tools=[],
x_axis_label=xcol,
y_axis_label=ycol,
)
# Scatter
scatter = fig.scatter(x=df.columns[0], y=df.columns[1], source=df)
if maybe_label:
assert len(maybe_label) == 1
mapper = CategoricalColorMapper(factors=["=", "+", "-"], palette=palette)
scatter.glyph.fill_color = {"field": maybe_label[0], "transform": mapper}
scatter.glyph.line_color = {"field": maybe_label[0], "transform": mapper}
# Regression line
coeff_a, coeff_b = itmdt["coeffs"]
line_x = np.asarray([df.iloc[:, 0].min(), df.iloc[:, 0].max()])
line_y = coeff_a * line_x + coeff_b
fig.line(x=line_x, y=line_y, line_width=3)
# Not adding the tooltips before because we only want to apply tooltip to the scatter
hover = HoverTool(tooltips=tooltips, renderers=[scatter])
fig.add_tools(hover)
# Add legends
if maybe_label:
nidx = df.index[df[maybe_label[0]] == "-"][0]
pidx = df.index[df[maybe_label[0]] == "+"][0]
legend = Legend(
items=[
LegendItem(label="Most Influential (-)", renderers=[scatter], index=nidx),
LegendItem(label="Most Influential (+)", renderers=[scatter], index=pidx),
],
margin=0,
padding=0,
)
fig.add_layout(legend, place="right")
return fig
def get(self, request):
fieldValue = "respirationRate"
userName = ""
#Checks if the current uesr has been authed
if request.user.is_authenticated:
#Getting the current users username
userName = request.user.username
results = databaseUserQuery(fieldValue, userName)
results = databaseQuerryParser(results,fieldValue)
print("\t\tRESULTS: " + str(results))
startValues = results[0]
endValues = results[1]
xVals = []
for i in range(1,len(startValues)+1):
xVals.append(i)
#xVals = range(len(startValues))
source = ColumnDataSource(data=dict(
x = xVals,
y1 = startValues,
y2 = endValues
))
p = figure( sizing_mode = "stretch_width", plot_height = 350)
r = p.multi_line([xVals,xVals],[startValues,endValues], color=["firebrick", "navy"], alpha=[0.8, 0.3], line_width=4)
p.background_fill_color = None
p.border_fill_color = None
p.title = 'Change in Respiration Rate'
p.yaxis.axis_label = "RespirationRate(Breaths/m)"
p.xaxis.axis_label = "Session"
legend = Legend(items=[
LegendItem(label="Start Values", renderers=[r], index=0),
LegendItem(label="End Values", renderers=[r], index=1),
])
p.add_layout(legend)
p.legend.location = "top_left"
p.legend.title_text_font = 'Arial'
p.legend.title_text_font_size = '20pt'
script, div = components(p)
return pageUserAuth(request,'Patient',"patient_progress.html", {'script' : script , 'div': div} )
def test_legend_item_with_field_label_and_different_data_sources_raises_a_validation_error(
):
legend_item = LegendItem()
gr_1 = GlyphRenderer(data_source=ColumnDataSource(data={'label': [1]}))
gr_2 = GlyphRenderer(data_source=ColumnDataSource(data={'label': [1]}))
legend_item.label = field('label')
legend_item.renderers = [gr_1, gr_2]
with mock.patch('bokeh.core.validation.check.log') as mock_logger:
check_integrity([legend_item])
assert mock_logger.error.call_count == 1
def gen_bokeh_plot():
# retrieve current active symbol and data source
data_source = DATA_STORE['data_source']
title_source = DATA_STORE['title_source']
# calculate mean absolute deviation (MAD)
title = Title(text='Predicted vs Actual Market Open Price')
plot = Plot(title=title,
plot_width=1200,
plot_height=800,
h_symmetry=False,
v_symmetry=False,
min_border=0,
toolbar_location='right')
val_glyph = Line(x='date',
y='val_open',
line_color='black',
line_width=1.5)
pred_glyph = Line(x='date',
y='pred_open',
line_color='blue',
line_width=1.5,
line_dash='dashed')
plot.add_glyph(data_source, val_glyph) # renderer 0
plot.add_glyph(data_source, pred_glyph) # renderer 1
text_glyph = Text(x='x', y='y', text='text')
plot.add_glyph(title_source, text_glyph)
# Format plot
li1 = LegendItem(label='actual', renderers=[plot.renderers[0]])
li2 = LegendItem(label='predicted', renderers=[plot.renderers[1]])
legend1 = Legend(items=[li1, li2], location='top_right')
plot.add_layout(legend1)
xaxis = DatetimeAxis()
xaxis.formatter = DatetimeTickFormatter(years=['%Y'],
months=['%m/%Y'],
days=['%d/%m/%Y'])
xaxis.major_label_orientation = np.pi / 4
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis()
plot.add_layout(yaxis, 'left')
return plot
def display_season_overview(src, seasonNum, yrange, xrange): # creating and displaying visualizations
figure_season_overview = figure(title="Season Overview : "+str(seasonNum), tools="hover, save",\
y_range=yrange, x_range=list(xrange), plot_width=1200, plot_height=500)
figure_season_overview.xaxis.major_label_orientation = 45 # Configuring
figure_season_overview.yaxis.axis_label = 'Stadium Cities' # figure
figure_season_overview.xaxis.axis_label = 'Dates' # settings
rect = Rect(x="dates", y="cities", width=0.8, height=0.8, fill_alpha=0.8, fill_color="type_color")
rect_render = figure_season_overview.add_glyph(src, rect)
legend = Legend(items=[ LegendItem(label=field("label"), renderers=[rect_render]) ])
figure_season_overview.add_layout(legend, 'left')
figure_season_overview.legend.background_fill_color = "grey"
figure_season_overview.legend.background_fill_alpha = 0.1
figure_season_overview.legend.border_line_color = "black"
figure_season_overview.select_one(HoverTool).tooltips = [
("Date", "@dates"),
("Team1", "@team1"), # Configuring
("Team2", "@team2"), # Hover
("Venue", "@venues"), # Tool
("City", "@cities"),
("Winner", "@winners"),
("Man of the match","@player_of_match")
]
return figure_season_overview # displaying generated visualization
def pvmodule_detail(request, pvmodule_id):
pvmod = get_object_or_404(PVModule, pk=pvmodule_id)
fieldnames = PVModule._meta.get_fields()
pvmod_dict = {k.name: getattr(pvmod, k.name) for k in fieldnames}
for k in ['IXO', 'IXXO', 'C4', 'C5', 'C6', 'C7']:
if pvmod_dict[k] is None:
pvmod_dict[k] = 0.
celltemps = np.linspace(0, 100, 5)
effirrad, celltemp = np.meshgrid(np.linspace(0.1, 1, 10), celltemps)
results = sapm(effirrad, celltemp, pvmod_dict)
eff = results['p_mp'] / effirrad / pvmod.Area * 100 / 1000
fig = figure(
x_axis_label='effective irradiance, Ee [suns]',
y_axis_label='efficiency [%]',
title=pvmod.Name,
plot_width=800, plot_height=600, sizing_mode='scale_width'
)
r = fig.multi_line(
effirrad.tolist(), eff.tolist(), color=cmap, line_width=4)
legend = Legend(items=[
LegendItem(label='{:d} [C]'.format(int(ct)), renderers=[r], index=n)
for n, ct in enumerate(celltemps)])
fig.add_layout(legend)
plot_script, plot_div = components(fig)
return render(
request, 'pvmodule_detail.html', {
'path': request.path, 'pvmod': pvmod, 'plot_script': plot_script,
'plot_div': plot_div, 'pvmod_dict': pvmod_dict})
def test_label_already_exists(self) -> None:
legend = Legend(items=[LegendItem(label=dict(value="foo"))])
renderer = GlyphRenderer()
bpl._handle_legend_label("foo", legend, renderer)
assert len(legend.items) == 1
assert legend.items[0].label == dict(value="foo")
assert legend.items[0].renderers == [renderer]
def __populate_scrubber(cls, scrubber, scrubberdf, scrubbersource,
columnmap):
categorycol = columnmap['scrubber_category']
resultcol = columnmap['result']
colorcol = columnmap['scrubber_color']
timecol = columnmap['timeline_start_ts']
results = scrubberdf[resultcol].unique()
scrubber.y_range.update(factors=results)
categories = scrubberdf[categorycol].unique()
scrubber_legends_items = []
for category in categories:
view = CDSView(
source=scrubbersource,
filters=[GroupFilter(column_name=categorycol, group=category)])
renderer = scrubber.circle(x=timecol,
y=resultcol,
size=12,
source=scrubbersource,
view=view,
color=colorcol)
scrubber_legends_items.append(
LegendItem(label=category, renderers=[renderer]))
legend = Legend(items=scrubber_legends_items)
legend.click_policy = 'hide'
scrubber.add_layout(legend, 'left')
return scrubber
def ave_azav_fig(ave_azav, peak_bin, bin_start, bin_end):
"""Generate the azav fig for html file"""
x_vals = np.arange(len(ave_azav))
fig = figure(
title=
f'Average Azimuthal Binned Array: Center - {peak_bin}, min/max - {bin_start}/{bin_end}',
x_axis_label='Bins',
y_axis_label='Intensity',
)
peak_line = Span(location=peak_bin,
dimension='height',
line_color='green',
line_width=2)
lower_line = Span(location=bin_start,
dimension='height',
line_color='black')
upper_line = Span(location=bin_end, dimension='height', line_color='black')
ave_azav_curve = fig.scatter(x_vals, ave_azav)
fig.renderers.extend([peak_line, lower_line, upper_line])
azav_legend = Legend(items=[
LegendItem(label='Azimuthal Average', renderers=[ave_azav_curve])
])
fig.add_layout(azav_legend)
return fig
def test_legend_powered_by_source(output_file_url, selenium, screenshot):
plot = Plot(height=HEIGHT,
width=WIDTH,
x_range=Range1d(0, 4),
y_range=Range1d(0, 4),
toolbar_location=None)
source = ColumnDataSource(
dict(
x=[1, 2, 3],
y=[1, 2, 3],
color=['red', 'green', 'blue'],
label=['Color Red', 'Color Green', 'Color Blue'],
))
circle = Circle(x='x', y='y', fill_color='color', size=20)
circle_renderer = plot.add_glyph(source, circle)
plot.add_layout(
Legend(items=[
LegendItem(label=field('label'), renderers=[circle_renderer])
]))
# Save the plot and start the test
save(plot)
selenium.get(output_file_url)
assert has_no_console_errors(selenium)
# Take screenshot
assert screenshot.is_valid()
def test_LegendItem() -> None:
item = LegendItem()
assert item.index is None
assert item.label is None
assert item.name is None
assert item.renderers == []
assert item.visible is True
def make_pie_plot(src):
p = figure(plot_width=300,
plot_height=800,
title="Pie Chart",
toolbar_location=None,
tools="hover",
tooltips="@word_types: @counts",
x_range=(-0.5, 1.0))
r = p.wedge(
x=0,
y=1,
radius=0.4,
start_angle=cumsum('angle', include_zero=True),
end_angle=cumsum('angle'),
line_color="white",
fill_color='color',
# legend='word_types', source=src)
source=src)
legend = Legend(
items=[LegendItem(label=dict(field="word_types"), renderers=[r])],
location=(0, 30))
p.add_layout(legend, 'below')
p.axis.axis_label = None
p.axis.visible = False
p.grid.grid_line_color = None
return p
def make_plot_diff(src, country_to_plot):
p = figure(
title=f"{country_to_plot}: Number of Cases Per Day Increase",
#y_axis_type="log",
x_axis_type='datetime',
plot_height=600,
plot_width=950)
p.title.text_font = "helvetica"
p.title.text_font_size = "20px"
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
palette = Category20[7] # brewer['PuBu'][9]#gray(21)
colors = [palette[0], palette[6], palette[4]]
cases = ['Confirmed', 'Deaths', 'Recovered']
diffs = ['ConfirmedDiff', 'DeathsDiff', 'RecoveredDiff']
items = []
for i, case in enumerate(diffs):
c = p.line('Date',
case,
line_color=colors[i],
line_width=2.5,
source=src)
point = p.circle('Date',
case,
line_color=colors[i],
fill_color=colors[i],
fill_alpha=0,
size=7,
source=src)
hover = HoverTool(
tooltips=[("Date", "@Date{%F}"), (f"{case}", "$y")],
formatters={
'@Date':
'datetime' # use 'datetime' formatter for '@date' field
},
renderers=[c])
items.append(LegendItem(label=cases[i], renderers=[point, c]))
p.add_tools(hover)
legend = Legend(items=items,
location='top_left') #, location=(0, -30))
p.add_layout(legend, 'center')
p.legend.location = "top_left"
p.legend.click_policy = "hide"
p.legend.background_fill_alpha = 0
p.legend.border_line_alpha = 0
p.legend.glyph_height = 20
p.legend.glyph_width = 20
p.legend.label_text_font_size = '20pt'
p.xaxis.ticker.desired_num_ticks = 25
p.xaxis.axis_label = 'Date'
#p.yaxis.axis_label = 'Counts/100k inhabitants'
p.yaxis.axis_label = 'Per day increase'
return p
def _update_legends(self, space, attr, old, new):
if len(self._plot[space].legend.items) == 3:
self._plot[space].legend.items.pop()
r = self._plot[space].select(name="sel")
pvalue = self._pvalue_rec[space].data["pv"]
if len(r) and len(pvalue):
self._plot[space].legend.items.append(LegendItem(label="p-value = "+format(pvalue[0],'.4f'), \
renderers=[r[0]]))
def compare_plot(data):
"""
functions for creating the WAIC compare plot function. Creates data for WAIC here using the
dictionary of models provided. Plot doesn't need to be dynamically redrawn with interactive
functions so latency is not really an issue. Which is why its easier just to create the WAIC
data here. Plot only redrawn each time a new model is added.
"""
model_data = {}
for key, value in data.items():
model_data[key] = value.model_arviz_data
comp = az.compare(
model_data,
ic='waic',
scale='log',
)
comp.replace([np.inf, -np.inf], np.nan)
if comp.isnull().values.any():
# if null values present then - in the cases ive seen - it means the model is using data
# with missing values. Therefore notify the user that this feature is not available.
return pn.widgets.StaticText(name='', value='Data contains missing values so can\'t compute WAIC')
elif comp.shape[0] < 2:
# for some reason this plot creates an error tha will stop the whole app from loading
# if only one model is plotted. Therefore notify the user that a second configuration
# is required before this feature is enabled.
return pn.widgets.StaticText(name='', value='Add another configuration to compare models')
else:
kwg = dict(height=450, width=650,toolbar_location='right')
plot = az.plot_compare(
comp,
backend='bokeh',
show=False,
backend_kwargs=kwg,
order_by_rank=True,
)
# plot does not generate a legend automatically so create one manually here by capturing the
# plot features and giving them labels
li1 = LegendItem(label='WAIC', renderers=[plot.renderers[2]])
li2 = LegendItem(label='Stadard Error', renderers=[plot.renderers[3]])
li3 = LegendItem(label='In-Sample Deviance', renderers=[plot.renderers[4]])
legend = Legend(items=[li1, li2, li3])
legend.location = (10,-10)
plot.add_layout(legend, place='right')
plot.width = 800
return plot
def when_changing_county_2(attr, old, new):
state_name = select_state_2.value
county_name = select_county_2.value
set_legend1_2(legend1, county_name, state_name, q4, q5)
set_legend1_2(legend1_log, county_name, state_name, q4_log, q5_log)
set_legend2_2(legend2, county_name, state_name, s2, s3)
set_legend2_2(legend2_log, county_name, state_name, s2_log, s3_log)
legend4.items[0] = LegendItem(label=county_name + " Cases/" + state_name + " Cases", renderers=[t2])
legend4.items[1] = LegendItem(label=county_name + " Deaths/" + state_name + " Deaths", renderers=[t3])
state = us_counties.groupby("state").get_group(state_name)
res = state.groupby("county")
county = res.get_group(county_name)
source1.data = county
def test_field_string(self) -> None:
legend = Legend(items=[LegendItem(label=dict(field="foo"))])
renderer = GlyphRenderer(data_source=ColumnDataSource(data=dict(foo=[], bar=[])))
bpl._handle_legend_deprecated("foo", legend, renderer)
assert len(legend.items) == 1
assert all("field" in item.label for item in legend.items)
bpl._handle_legend_deprecated("bar", legend, renderer)
assert len(legend.items) == 2
assert all("field" in item.label for item in legend.items)
def get_detailed_loss(loss, title):
loss, rec_l, kld_l = loss
fig = figure(title=title)
indices = list(range(1, len(loss) + 1))
x = [indices, indices, indices]
y = [loss, rec_l, kld_l]
colors = ["black", "red", "green"]
r = fig.multi_line(x, y, color=colors)
legends = [LegendItem(label="Rec + KLD", renderers=[r], index=0)]
legends.append(LegendItem(label="Reconstr", renderers=[r], index=1))
legends.append(LegendItem(label="KLD", renderers=[r], index=2))
legend = Legend(items=legends)
fig.add_layout(legend)
fig.xaxis.axis_label = 'Epoch'
fig.yaxis.axis_label = 'Loss'
return fig
def test_value_dict(self) -> None:
legend = Legend(items=[LegendItem(label=dict(value="foo"))])
renderer = GlyphRenderer(data_source=ColumnDataSource())
bpl._handle_legend_deprecated(dict(value="foo"), legend, renderer)
assert len(legend.items) == 1
assert all("value" in item.label for item in legend.items)
bpl._handle_legend_deprecated(dict(value="bar"), legend, renderer)
assert len(legend.items) == 2
assert all("value" in item.label for item in legend.items)
def test_label_not_already_exists(self) -> None:
legend = Legend(items=[LegendItem(label=dict(field="foo"))])
renderer = GlyphRenderer()
bpl._handle_legend_field("bar", legend, renderer)
assert len(legend.items) == 2
assert legend.items[0].label == dict(field="foo")
assert legend.items[0].renderers == []
assert legend.items[1].label == dict(field="bar")
assert legend.items[1].renderers == [renderer]
def plot_cost_surface(sample_size=None, fig_kwargs=None):
"""
using a, b, c as parameters of quadratic
and m, k as slope, intercept of line
"""
p = _init_surface_plot(sample_size, fig_kwargs)
source, mapper = _compute_cost_surface(sample_size)
p.image("log",
x="x",
y="y",
dw="dw",
dh="dh",
color_mapper=mapper,
source=source,
name="img",
level="image")
items = []
r = p.plus([b_star], [m_star],
line_color="#000000",
line_width=0.8,
fill_color="#ffffff",
size=10)
items.append(LegendItem(renderers=[r], label="True best parameters"))
if sample_size is not None:
_x = x[:sample_size]
_y = y[:sample_size]
m_hat = ((_y.sum() * _x.sum() / sample_size - (_y * _x).sum()) /
((_x.sum())**2 / sample_size - (_x**2).sum()))
k_hat = _y.sum() / sample_size - m_hat * _x.sum() / sample_size
r = p.plus([k_hat], [m_hat],
line_color="#000000",
line_width=1.5,
fill_color="#000000",
size=10)
items.append(LegendItem(renderers=[r], label="Best fit on sample"))
p.add_layout(Legend(items=items), "right")
p.add_tools(
HoverTool(renderers=p.select("img"),
tooltips=[("Slope", "$y"), ("Intercept", "$x"),
("Expected Error", "@img")]))
return p
def when_changing_county(attr, old, new):
state_name = select_state.value
county_name = select_county.value
set_legend1_1(legend1, county_name, state_name, q2, q3)
set_legend1_1(legend1_log, county_name, state_name, q2_log, q3_log)
set_legend2_1(legend2, county_name, state_name, s0, s1)
set_legend2_1(legend2_log, county_name, state_name, s0_log, s1_log)
legend3.items[0] = LegendItem(label=county_name + " Cases/" + state_name + " Cases", renderers=[t0])
legend3.items[1] = LegendItem(label=county_name + " Deaths/" + state_name + " Deaths", renderers=[t1])
state = us_counties.groupby("state").get_group(state_name)
res = state.groupby("county")
county = res.get_group(county_name)
source.data = county
source2.data = res.get_group(" All Counties")
