def _create_loss_figure(self,
df: pd.DataFrame,
width: int = 1000,
height: int = 300):
colors = itertools.cycle(palette)
df.columns = [str(i) for i in df.columns]
ds = ColumnDataSource(df)
fig = Figure(y_axis_type="log", width=width, height=height)
fig.below[0].formatter.use_scientific = False
for column, color in zip(df.columns, colors):
glyph = fig.line(x='index', y=column, source=ds, color=color)
fig.add_tools(
HoverTool(tooltips=[("epoch", "@index")] +
[(f"loss_{column}", f"@{column}")],
mode='vline',
renderers=[glyph]))
def update_epoch(self: App, event):
epoch = reduce(lambda a, b: a if a[0] < b[0] else b,
[(abs(e - event.x), e)
for e in self.state.epochs])[1]
self.widget_epoch_select.value = str(epoch)
fig.on_event(DoubleTap, partial(update_epoch, self))
return fig
def init_time_series_plot(hover):
"""
Defaults for time series plot
"""
plot = Figure(
plot_height=300,
plot_width=900,
responsive=True,
tools="xpan,xwheel_zoom,xbox_zoom,reset,tap",
x_axis_type='datetime',
min_border_top=0,
min_border_right=10,
min_border_bottom=50,
min_border_left=50,
outline_line_color=None,
)
plot.add_tools(hover)
plot.x_range.follow = "end"
plot.x_range.range_padding = 0
plot.xaxis.axis_label = "Time"
plot.xaxis.major_label_orientation = pi/4
plot.xaxis.formatter = DatetimeTickFormatter(formats=dict(
hours=["%d %B"],
days=["%d %B"],
months=["%d %B"],
years=["%d %B"],
))
return plot
def __plot_control_chart(self, index):
plot_args = self.__conf.plot_args[index]
annotations = self.__conf.annotations[index]
if not annotations:
annotations = PlotAnnotationContainer()
plot = Figure(plot_height=500, plot_width=600,
x_range=FactorRange(factors=self.__factors,
name='x_factors'))
plot.toolbar.logo = None
plot.title.text = 'Control chart'
hover_tool = self.__create_tooltips()
plot.add_tools(hover_tool)
plot.xaxis.major_label_orientation = pi / 4
plot.xaxis.major_label_standoff = 10
if not self.__values['_calc_value'].empty:
if 'color' not in plot_args:
plot_args['color'] = 'navy'
if 'alpha' not in plot_args:
plot_args['alpha'] = 0.5
self.__values['s_fac'] = self.__factors
col_ds = ColumnDataSource(self.__values, name='control_data')
col_ds.js_on_change('data', CustomJS(code="refresh_maps();"))
plot.circle('s_fac', '_calc_value', source=col_ds, name='circle',
**plot_args)
plot.line('s_fac', '_calc_value', source=col_ds, name='line',
**plot_args)
min_anno, max_anno = annotations.calc_min_max_annotation(
self.__values['_calc_value'])
annotations.plot(plot, self.__values['_calc_value'])
anno_range = max_anno - min_anno
if anno_range and not isnan(anno_range):
plot.y_range.start = min_anno - anno_range
plot.y_range.end = max_anno + anno_range
return plot
def createPlot(df, boundaryDF):
p = Figure(plot_height=900, plot_width=PLOT_WIDTH, title="", y_range=[], title_text_font_size=TITLE_FONT_SIZE)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
quad = p.quad(
top="top",
bottom="bottom",
left="left",
right="right",
source=blockSource,
fill_color="grey",
hover_fill_color="firebrick",
fill_alpha=0.05,
hover_alpha=0.3,
line_color=None,
hover_line_color="white",
)
p.multi_line(xs="xs", ys="ys", source=blockSource, color="black", line_width=2, line_alpha=0.4, line_dash="dotted")
p.multi_line(xs="xs", ys="ys", source=source, color="color", line_width="width", line_alpha="line_alpha")
p.add_tools(
HoverTool(
tooltips=[("chromosome", "@chromosome"), ("exon", "@exon"), ("start", "@start"), ("end", "@end")],
renderers=[quad],
)
)
return p
def plot_bar_stack(
p: Figure, source: ColumnDataSource, df: pd.DataFrame
) -> List[GlyphRenderer]:
# Plot bar stack.
measurements = list(df["Measurement"].unique())
n_measurements = len(measurements)
color = list((Category20_20 * 2)[:n_measurements])
if "None" in measurements:
color[measurements.index("None")] = "black"
bar_stack = p.vbar_stack(
**BAR_STACK_KWARGS, stackers=measurements, color=color, source=source,
)
stack_hover = HoverTool(
renderers=bar_stack, tooltips=STACK_HOVER_TOOLTIPS,
)
p.add_tools(stack_hover)
# Customize left y-axis range.
p.y_range.start = 0
max_count = bar_stack[0].data_source.data["total"].max() + 1
p.y_range.end = max_count
# Create a legend.
kwargs = LEGEND_KWARGS.copy()
if len(measurements) == 1:
kwargs["location"] = (0, 250)
legend = Legend(
items=[
(measurement, [bar_stack[i]])
for i, measurement in enumerate(measurements)
],
**kwargs,
)
p.add_layout(legend, "right")
return bar_stack
def init_time_series_plot(hover):
"""
Defaults for time series plot
"""
plot = Figure(
plot_height=400,
plot_width=1000,
sizing_mode="scale_width",
toolbar_location="above",
tools="pan, wheel_zoom, xbox_zoom, reset, tap",
active_scroll='wheel_zoom',
x_axis_type="datetime",
min_border_top=0,
min_border_right=10,
min_border_bottom=50,
min_border_left=50,
outline_line_color=None,
)
plot.add_tools(hover)
plot.x_range.follow = "end"
plot.x_range.range_padding = 0
plot.xaxis.axis_label = "Time"
plot.xaxis.axis_label_text_font_style = "normal"
plot.xaxis.axis_label_text_font_size = "14pt"
plot.yaxis.axis_label_text_font_style = "normal"
plot.yaxis.axis_label_text_font_size = "14pt"
return plot
def plot_df(df: pd.DataFrame, legend: Optional[List[str]] = None, width: int = 1200, height: int = 700,
title: str = 'loss', x_label: str = 'epoch') -> Figure:
colors = itertools.cycle(palette)
df = df.copy()
df.columns = [str(i) for i in df.columns]
if legend is None:
legend = df.columns
df.columns = [BokehPlot._sanitize_column_name(i) for i in df.columns]
ds = ColumnDataSource(df)
fig = Figure(y_axis_type="log", width=width, height=height, title=title, x_axis_label=x_label)
fig.below[0].formatter.use_scientific = False
for column, color, legend in zip(df.columns, colors, legend):
glyph = fig.line(x='index', y=column, source=ds, color=color, legend_label=legend)
# fig.add_tools(HoverTool(tooltips=[(f"{column} - {legend}", f"@{column}")], mode='vline', renderers=[glyph]))
fig.add_tools(HoverTool(tooltips=[(f"{legend}", f"@{column}")], mode='vline', renderers=[glyph]))
fig.legend.location = 'bottom_left'
fig.legend.click_policy = "hide"
fig.legend.label_text_font_size = '12px'
fig.legend.spacing = -4
fig.legend.background_fill_alpha = 0.6
return fig
def plot_prices(df,
template_name='stock',
notebook=False,
x='Date',
y='Adj. Close',
title="Stock prices",
xlabel="Date",
ylabel="Price"):
"""
Bokeh Plot for stock prices with a custom title and meaningful axis labels.
template_name: output file name of plot
notebook: boolean for showing plot on jupyter notebook
"""
source = ColumnDataSource(df)
# create a new plot with a title and axis labels
p = Figure(
title=title,
x_axis_label=xlabel,
y_axis_label=ylabel,
x_axis_type='datetime',
plot_height=500,
plot_width=950,
tools=['pan', 'wheel_zoom'],
toolbar_location='below',
)
# configure so that Bokeh chooses what (if any) scroll tool is active
p.toolbar.active_scroll = "auto"
# add a line renderer with legend and line thickness
p.line(x=x, y=y, line_width=2, legend='Adj Close', source=source)
p.legend.location = "top_left"
p.legend.click_policy = "hide"
hover = HoverTool(
tooltips=[
('Date', '@Date{%F}'),
('Close',
'$@{Adj. Close}{%0.2f}'), # use @{ } for field names with spaces
('Volume', '@Volume{0.00 a}'),
],
formatters={
'Date': 'datetime', # use 'datetime' formatter for 'date' field
'Adj. Close':
'printf', # use 'printf' formatter for 'adj close' field
# use default 'numeral' formatter for other fields
},
# display a tooltip whenever the cursor is vertically in line with a glyph
mode='vline')
p.add_tools(hover)
if notebook:
# show the results
show(p, notebook_handle=True)
push_notebook()
else:
# output the results
output_file('%s.html' % template_name)
save(p)
def buildVendorsTab():
defaultGfxVendors = [
gfxVendors.index('NVIDIA Corporation'),
gfxVendors.index('Advanced Micro Devices, Inc. [AMD/ATI]'),
gfxVendors.index('Intel Corporation')
]
gfxVendorCheckbox = CheckboxGroup(labels=gfxVendors, active=defaultGfxVendors)
source_release = ColumnDataSource(data=dict(x=[], y=[], height=[]))
source_beta = ColumnDataSource(data=dict(x=[], y=[], height=[]))
fig = Figure(title="GFX Vendors",
x_range=[],
y_range=[0, 0],
plot_width=1000, plot_height=650)
hover = HoverTool(tooltips=[
('Users', '@height %')
])
fig.add_tools(hover)
fig.rect(x='x', y='y', height='height', source=source_release,
width=0.4, color='orange', legend='Release')
fig.rect(x='x', y='y', height='height', source=source_beta,
width=0.4, color='blue', legend='Beta')
fig.xaxis.major_label_orientation = np.pi / 3
def update(selected):
vendors = [gfxVendors[i] for i in range(len(gfxVendors)) if i in selected]
releaseUsers = 100 * getUsersForVendors('release', vendors) / gfxTotalReleaseUsers
betaUsers = 100 * getUsersForVendors('beta', vendors) / gfxTotalBetaUsers
fig.x_range.factors = vendors
fig.y_range.end = max([releaseUsers.max(), betaUsers.max()])
source_release.data = dict(
x=[c + ':0.3' for c in vendors],
y=releaseUsers / 2,
height=releaseUsers,
)
source_beta.data = dict(
x=[c + ':0.7' for c in vendors],
y=betaUsers / 2,
height=betaUsers,
)
gfxVendorCheckbox.on_click(update)
update(gfxVendorCheckbox.active)
vendorComparison = HBox(HBox(VBoxForm(*[gfxVendorCheckbox]), width=300), fig, width=1100)
return Panel(child=vendorComparison, title="GFX Vendor Comparison")
def init_xy_plot(hover):
"""
Defaults for xy plots
"""
plot = Figure(tools="pan,wheel_zoom,box_zoom,reset,tap")
plot.add_tools(hover)
return plot
def dt_line_viz(
df: pd.DataFrame,
x: str,
timeunit: str,
yscale: str,
plot_width: int,
plot_height: int,
show_yticks: bool,
miss_pct: Optional[float] = None,
y: Optional[str] = None,
) -> Figure:
"""
Render a line chart
"""
# pylint: disable=too-many-arguments
if miss_pct is not None:
title = f"{x} ({miss_pct}% missing)" if miss_pct > 0 else f"{x}"
tooltips = [(timeunit, "@lbl"), ("Frequency", "@freq"),
("Percent", "@pct%")]
agg = "freq"
else:
title = title = f"{df.columns[1]} of {y} by {x}"
agg = f"{df.columns[1]}"
tooltips = [(timeunit, "@lbl"), (agg, f"@{df.columns[1]}")]
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
toolbar_location=None,
title=title,
tools=[],
y_axis_type=yscale,
x_axis_type="datetime",
)
fig.line(
source=df,
x=x,
y=agg,
line_width=2,
line_alpha=0.8,
color="#7e9ac8",
)
hover = HoverTool(
tooltips=tooltips,
mode="vline",
)
fig.add_tools(hover)
tweak_figure(fig, "line", show_yticks)
if show_yticks and yscale == "linear":
_format_axis(fig, 0, df[agg].max(), "y")
if y:
fig.yaxis.axis_label = f"{df.columns[1]} of {y}"
fig.xaxis.axis_label = x
return Panel(child=fig, title="line chart")
fig.yaxis.axis_label = "Frequency"
return fig
def performance():
plot = Figure(x_axis_type="datetime", tools="save", toolbar_location=None, plot_width=1000, plot_height=300)
l1 = plot.line(x="time", y="power_out", source=source_perfomance, line_color="green", name="local")
plot.add_tools(HoverTool(renderers=[l1]))
plot.select(dict(type=HoverTool)).tooltips = [("Date", "@hover_time"), ("Performance", "@power_out")]
l2 = plot.line(x="time", y="avg_perf", source=source_perfomance, line_color="red", name="global")
plot.x_range = DataRange1d(range_padding=0.0, bounds=None)
plot.title = "Plant Performance"
return plot
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 dt_line_viz(
df: List[pd.DataFrame],
x: str,
timeunit: str,
yscale: str,
plot_width: int,
plot_height: int,
show_yticks: bool,
orig: List[str],
df_labels: List[str],
) -> Figure:
"""
Render a line chart
"""
# pylint: disable=too-many-arguments
tooltips = [(timeunit, "@lbl"), ("Frequency", "@freq"),
("Source", "@orig")]
for i, _ in enumerate(df):
df[i]["orig"] = orig[i]
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
toolbar_location=None,
title=x,
tools=[],
y_axis_type=yscale,
x_axis_type="datetime",
)
for i, _ in enumerate(df):
fig.line(
source=df[i],
x=x,
y="freq",
line_width=2,
line_alpha=0.8,
color=CATEGORY10[i],
)
hover = HoverTool(
tooltips=tooltips,
mode="vline",
)
fig.add_tools(hover)
tweak_figure(fig, "line", show_yticks)
if show_yticks and yscale == "linear":
_format_axis(fig, 0, max([d["freq"].max() for d in df]), "y")
if orig != df_labels:
fig.xaxis.axis_label = f"This variable is only in {','.join(orig)}"
fig.xaxis.axis_label_standoff = 0
fig.yaxis.axis_label = "Frequency"
return fig
def hist_kde_viz(
df: pd.DataFrame,
pts_rng: np.ndarray,
pdf: np.ndarray,
col: str,
yscale: str,
plot_width: int,
plot_height: int,
) -> Panel:
"""
Render histogram with overlayed kde
"""
# pylint: disable=too-many-arguments
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
title=f"{col}",
tools=[],
toolbar_location=None,
y_axis_type=yscale,
)
bottom = 0 if yscale == "linear" or df.empty else df["freq"].min() / 2
hist = fig.quad(
source=df,
left="left",
right="right",
bottom=bottom,
alpha=0.5,
top="freq",
fill_color="#6baed6",
)
hover_hist = HoverTool(
renderers=[hist],
tooltips=[("Bin", "@intervals"), ("Density", "@freq")],
mode="vline",
)
line = fig.line( # pylint: disable=too-many-function-args
pts_rng,
pdf,
line_color="#9467bd",
line_width=2,
alpha=0.5)
hover_dist = HoverTool(renderers=[line],
tooltips=[("x", "@x"), ("y", "@y")])
fig.add_tools(hover_hist)
fig.add_tools(hover_dist)
tweak_figure(fig, "kde")
fig.yaxis.axis_label = "Density"
fig.xaxis.axis_label = col
_format_axis(fig, df.iloc[0]["left"], df.iloc[-1]["right"], "x")
if yscale == "linear":
_format_axis(fig, 0, df["freq"].max(), "y")
return Panel(child=fig, title="KDE plot")
def createPlots():
######################################## BS Pressure Plot ########################################
sourceBuySell, sourceVolume = requestHoseData()
pBuySell = Figure(plot_width=PLOT_WIDTH, plot_height=BUYSELL_PLOT_HEIGHT, name="pltBuySell")
pBuySell.xaxis.ticker = [8.75, 9, 9.5, 10, 10.5, 11, 11.5, 13, 13.5, 14, 14.5, 14.75]
pBuySell.xaxis.major_label_overrides = {
8.5: "8:30", 8.75: "8:45", 9: "9:00", 9.5: "9:30", 10: "10:00",
10.5: "10:30", 11: "11:00", 11.5: "11:30", 13: "13:00",
13.5: "13:30", 14: "14:00", 14.5: "14:30", 14.75: "14:45"}
pBuySell.line(x='index', y='buyPressure', source=sourceBuySell, color='green',
legend_label="Tổng đặt mua", name="glyphSellPressure")
pBuySell.line(x='index', y='sellPressure', source=sourceBuySell, color='red',
legend_label="Tổng đặt bán", name="glyphBuyPressure")
wz = WheelZoomTool(dimensions="width"); pBuySell.add_tools(wz); pBuySell.toolbar.active_scroll = wz
pBuySell.toolbar.logo = None
pBuySell.axis[0].visible = False
pBuySell.legend.location = "top_left"
pBuySell.legend.click_policy = "hide"
pBuySell.legend.background_fill_alpha = 0.0
######################################## Volume Plot ########################################
pVolume = Figure(width=PLOT_WIDTH, height=VOLUME_PLOT_HEIGHT, tools="pan, reset",
name="pltVolume")
pVolume.toolbar.logo = None
wz = WheelZoomTool(dimensions="height"); pVolume.add_tools(wz); pVolume.toolbar.active_scroll = wz
pVolume.vbar(x='index', top='totalValue', width=1 /60, color='blue',
source=sourceVolume, fill_alpha=LIQUIDITY_ALPHA, line_alpha=LIQUIDITY_ALPHA,
name="glyphTotalValue", legend_label="Thanh khoản toàn tt")
pVolume.vbar(x='index', top='nnBuy', width=1/1.2/60, color='green', source=sourceVolume
,name="glyphNNBuy", legend_label="NN mua",)
pVolume.vbar(x='index', top='nnSell', width=1/1.2/60, color='red', source=sourceVolume
,name="glyphNNSell", legend_label="NN bán",)
pVolume.x_range = pBuySell.x_range
pVolume.y_range=Range1d(-10, 45)
pVolume.legend.location = "top_left"
pVolume.legend.click_policy = "hide"
pVolume.legend.background_fill_alpha = 0.0
######################################### OHLC plot #########################################
orders, source, pressure = requestPSData()
plotOhlc = createOhlcPlot(source)
pCandle, divCandle = hookupFigure(plotOhlc) # "divCustomJS" "pltOHLC" "glyphOHLCSegment"
pDebug = Paragraph(text=f"""["num_ps_orders": "{len(orders['index'])}"]\n"""
"""""",
width=DIV_TEXT_WIDTH, height=100, name="pDebug")
################################# Putting all plots together ################################
def activation_function():
pBuySell._document.add_periodic_callback(lambda: updateDoc(pBuySell._document), 500)
print("Document activated !")
return pCandle, pBuySell, pVolume, divCandle , activation_function, sourceBuySell, sourceVolume, pDebug
def hist_viz(
df: pd.DataFrame,
miss_pct: float,
col: str,
yscale: str,
plot_width: int,
plot_height: int,
show_yaxis: bool,
) -> Figure:
"""
Render a histogram
"""
# pylint: disable=too-many-arguments
title = f"{col} ({miss_pct}% missing)" if miss_pct > 0 else f"{col}"
tooltips = [
("Bin", "@intervals"),
("Frequency", "@freq"),
("Percent", "@pct{0.2f}%"),
]
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
toolbar_location=None,
title=title,
tools=[],
y_axis_type=yscale,
)
bottom = 0 if yscale == "linear" or df.empty else df["freq"].min() / 2
fig.quad(
source=df,
left="left",
right="right",
bottom=bottom,
alpha=0.5,
top="freq",
fill_color="#6baed6",
)
hover = HoverTool(
tooltips=tooltips,
mode="vline",
)
fig.add_tools(hover)
tweak_figure(fig, "hist", show_yaxis)
fig.yaxis.axis_label = "Frequency"
if not df.empty:
_format_axis(fig, df.iloc[0]["left"], df.iloc[-1]["right"], "x")
if show_yaxis and yscale == "linear":
_format_axis(fig, 0, df["freq"].max(), "y")
return fig
def buildOSesTab():
osesCheckbox = CheckboxGroup(labels=oses, active=[i for i in range(len(oses))])
source_release = ColumnDataSource(data=dict(x=[], y=[], height=[]))
source_beta = ColumnDataSource(data=dict(x=[], y=[], height=[]))
fig = Figure(title='OS', x_range=[], y_range=[0, 0], plot_width=1000, plot_height=650)
hover = HoverTool(tooltips=[
('Users', '@height %')
])
fig.add_tools(hover)
fig.rect(x='x', y='y', height='height', source=source_release,
width=0.4, color='orange', legend='Release')
fig.rect(x='x', y='y', height='height', source=source_beta,
width=0.4, color='blue', legend='Beta')
fig.xaxis.major_label_orientation = np.pi / 3
def update(selected):
cur_oses = [oses[i] for i in range(len(oses)) if i in selected]
releaseUsers = 100 * getUsersForOses('release', cur_oses) / osTotalReleaseUsers
betaUsers = 100 * getUsersForOses('beta', cur_oses) / osTotalBetaUsers
fig.x_range.factors = cur_oses
fig.y_range.end = max([releaseUsers.max(), betaUsers.max()])
source_release.data = dict(
x=[c + ':0.3' for c in cur_oses],
y=releaseUsers / 2,
height=releaseUsers,
)
source_beta.data = dict(
x=[c + ':0.7' for c in cur_oses],
y=betaUsers / 2,
height=betaUsers,
)
osesCheckbox.on_click(update)
update(osesCheckbox.active)
osesComparison = HBox(HBox(VBoxForm(*[osesCheckbox]), width=300), fig, width=1100)
return Panel(child=osesComparison, title="OS Comparison")
def render_dendrogram(dend: Dict["str", Any], plot_width: int,
plot_height: int) -> Figure:
"""
Render a missing dendrogram.
"""
# list of lists of dcoords and icoords from scipy.dendrogram
xs, ys, cols = dend["icoord"], dend["dcoord"], dend["ivl"]
# if the number of columns is greater than 20, make the plot wider
if len(cols) > 20:
plot_width = 28 * len(cols)
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
toolbar_location=None,
tools="",
title=" ",
)
# round the coordinates to integers, and plot the dendrogram
xs = [[round(coord) for coord in coords] for coords in xs]
ys = [[round(coord, 2) for coord in coords] for coords in ys]
fig.multi_line(xs=xs, ys=ys, line_color="#8073ac")
# extract the horizontal lines for the hover tooltip
h_lns_x = [coords[1:3] for coords in xs]
h_lns_y = [coords[1:3] for coords in ys]
null_mismatch_vals = [coord[0] for coord in h_lns_y]
source = ColumnDataSource(dict(x=h_lns_x, y=h_lns_y, n=null_mismatch_vals))
h_lns = fig.multi_line(xs="x", ys="y", source=source, line_color="#8073ac")
hover_pts = HoverTool(
renderers=[h_lns],
tooltips=[("Average distance", "@n{0.1f}")],
line_policy="interp",
)
fig.add_tools(hover_pts)
# shorten column labels if necessary, and override coordinates with column names
cols = [f"{col[:16]}..." if len(col) > 18 else col for col in cols]
axis_coords = list(range(5, 10 * len(cols) + 1, 10))
axis_overrides = dict(zip(axis_coords, cols))
fig.xaxis.ticker = axis_coords
fig.xaxis.major_label_overrides = axis_overrides
fig.xaxis.major_label_orientation = np.pi / 3
fig.yaxis.axis_label = "Average Distance Between Clusters"
fig.grid.visible = False
fig.frame_width = plot_width
return fig
def cmatrix_plot_format(plot: Figure, format_cols: List, hover_code: str) -> Figure:
plot.toolbar.logo = None
plot.yaxis.axis_label_text_font_size = "16px"
plot.yaxis.axis_label_text_color = '#003566'
plot.xaxis.major_label_text_color = '#003566'
plot.y_range.range_padding = 0
plot.hover.point_policy = "follow_mouse"
plot.hover.attachment = "above"
cust_formatters = {}
for f in format_cols:
code_ref = dashboard_constants.bucket_cust_format.format(field_name=f) if f == 'max_percentile_conf' else \
dashboard_constants.scale_format_code.format(field_name=f)
cust_formatters[f] = CustomJSHover(args=dict(source=plot.renderers[0].data_source), code=code_ref)
custom_hover_format = CustomJS(code=hover_code)
format_dict = {f'@{c}': cust_formatters[c] for c in format_cols}
plot.add_tools(HoverTool(tooltips=None, callback=custom_hover_format, formatters=format_dict))
someargs = dict(yaxis=plot.yaxis[0], xaxis=plot.xaxis[0], legend=plot.legend[0])
fig_resize_callback = CustomJS(args=someargs, code=dashboard_constants.fig_resize_callback_code)
plot.js_on_change('inner_width', fig_resize_callback)
return plot
def render(df: pd.DataFrame) -> Figure:
from bokeh.models import HoverTool
from bokeh.palettes import Category10_10
from bokeh.plotting import ColumnDataSource
p = Figure(plot_width=1200, plot_height=600, x_axis_type='datetime')
names = [x for x in df.columns.tolist() if x not in ('date', 'zeros')]
source = ColumnDataSource(df)
p.varea_stack(stackers=names,
x='date',
color=Category10_10[:len(names)],
legend_label=names,
source=source)
p.line(x='date', y='zeros', line_alpha=0, source=source)
p.legend.location = 'top_left'
p.add_tools(
HoverTool(tooltips=[('date', '@date{%Y-%m-%d}')] + [(x, f'@{x}')
for x in names],
formatters={'@date': 'datetime'},
mode='vline'))
return p
def _add_tools(plot: Figure, on_click: str, nodes_glyph: GlyphRenderer,
renderer: GlyphRenderer, **kw):
"""Add hover and tap tools to the plot.
Args:
plot (Figure): Plot to add these tools to.
on_click (str): JS code to be run when the renderer is clicked.
nodes_glyph (GlyphRenderer): Glyph for the nodes on this plot.
renderer (GlyphRenderer): Renderer that should call on_click code.
"""
on_hover_code = "source.data['last_index'] = cb_data.index.indices[0]"
plot.add_tools(
HoverTool(tooltips=[("Index", "$index"), ("Name", "@name")],
renderers=[nodes_glyph]),
HoverTool(tooltips=None,
callback=CustomJS(args=dict(source=kw['source']),
code=on_hover_code),
renderers=[renderer]),
TapTool(callback=CustomJS(args=kw, code=on_click),
renderers=[renderer]))
def _add_hover_tool(cls, chart_layers: [Layer], plot: Figure) -> None:
"""
Add hover tool to a bokeh plot. Method iterates through chart layers to create a hover tool tooltip for each of
them based on axis of the layer.
:param chart_layers: list(Layer) list of layers to prepare tooltips for.
:param plot: (Figure) bokeh figure to create hover tool for.
:return: None
"""
tooltips = []
for layer in chart_layers:
axis = layer.axis
cls._prepare_tooltip(tooltips=tooltips, axis=axis)
if len(tooltips) > 0:
hover_tool = HoverTool(tooltips=tooltips,
mode="mouse",
point_policy="follow_mouse")
plot.add_tools(hover_tool)
def render_crossfilter(itmdt: Intermediate, plot_width: int,
plot_height: int) -> column:
"""
Render crossfilter scatter plot with a regression line.
"""
# pylint: disable=too-many-locals, too-many-function-args
df = itmdt["data"]
df["__x__"] = df[df.columns[0]]
df["__y__"] = df[df.columns[0]]
source_scatter = ColumnDataSource(df)
source_xy_value = ColumnDataSource({
"x": [df.columns[0]],
"y": [df.columns[0]]
})
var_list = list(df.columns[:-2])
xcol = source_xy_value.data["x"][0]
ycol = source_xy_value.data["y"][0]
tooltips = [("X-Axis: ", "@__x__"), ("Y-Axis: ", "@__y__")]
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
toolbar_location=None,
title=Title(text="Scatter Plot", align="center"),
tools=[],
x_axis_label=xcol,
y_axis_label=ycol,
)
scatter = fig.scatter("__x__", "__y__", source=source_scatter)
hover = HoverTool(tooltips=tooltips, renderers=[scatter])
fig.add_tools(hover)
x_select = Select(title="X-Axis", value=xcol, options=var_list, width=150)
y_select = Select(title="Y-Axis", value=ycol, options=var_list, width=150)
x_select.js_on_change(
"value",
CustomJS(
args=dict(
scatter=source_scatter,
xy_value=source_xy_value,
x_axis=fig.xaxis[0],
),
code="""
let currentSelect = this.value;
let xyValueData = xy_value.data;
let scatterData = scatter.data;
xyValueData['x'][0] = currentSelect;
scatterData['__x__'] = scatterData[currentSelect];
x_axis.axis_label = currentSelect;
scatter.change.emit();
xy_value.change.emit();
""",
),
)
y_select.js_on_change(
"value",
CustomJS(
args=dict(
scatter=source_scatter,
xy_value=source_xy_value,
y_axis=fig.yaxis[0],
),
code="""
let currentSelect = this.value;
let xyValueData = xy_value.data;
let scatterData = scatter.data;
xyValueData['y'][0] = currentSelect;
scatterData['__y__'] = scatterData[currentSelect];
y_axis.axis_label = currentSelect;
scatter.change.emit();
xy_value.change.emit();
""",
),
)
fig = column(row(x_select, y_select, align="center"),
fig,
sizing_mode="stretch_width")
return fig
def buildDevicesTab():
gfxVendorSelect = Select(title='Vendor', options=gfxVendors, value=gfxVendors[0])
gfxDeviceCheckbox = CheckboxGroup()
source_release = ColumnDataSource(data=dict(x=[], y=[], height=[]))
source_beta = ColumnDataSource(data=dict(x=[], y=[], height=[]))
fig = Figure(title="GFX Devices",
x_range=[],
y_range=[0, 0],
plot_width=1000, plot_height=650)
hover = HoverTool(tooltips=[
('Users', '@height %')
])
fig.add_tools(hover)
fig.rect(x='x', y='y', height='height', source=source_release,
width=0.4, color='orange', legend='Release')
fig.rect(x='x', y='y', height='height', source=source_beta,
width=0.4, color='blue', legend='Beta')
fig.xaxis.major_label_orientation = np.pi / 3
def update_view():
vendor = gfxVendorSelect.value
deviceNames = getDeviceNames('release', vendor, True)
gfxDeviceCheckbox.labels = deviceNames
devices = [deviceNames[i] for i in range(len(deviceNames)) if i in gfxDeviceCheckbox.active]
releaseUsers = 100 * getUsersForDevices('release', vendor, devices) / gfxTotalReleaseUsers
betaUsers = 100 * getUsersForDevices('beta', vendor, devices) / gfxTotalBetaUsers
fig.x_range.factors = devices
fig.y_range.end = max([releaseUsers.max(), betaUsers.max()])
source_release.data = dict(
x=[c + ':0.3' for c in devices],
y=releaseUsers / 2,
height=releaseUsers,
)
source_beta.data = dict(
x=[c + ':0.7' for c in devices],
y=betaUsers / 2,
height=betaUsers,
)
def update(attrname, old, new):
gfxDeviceCheckbox.active = [i for i in range(5)]
update_view()
def click(selected):
update_view()
gfxVendorSelect.on_change('value', update)
gfxDeviceCheckbox.on_click(click)
update('value', '', gfxVendorSelect.value)
deviceComparison = HBox(HBox(VBoxForm(*[gfxVendorSelect, gfxDeviceCheckbox]), width=300), fig, width=1100)
return Panel(child=deviceComparison, title="GFX Device Comparison")
class DynamicPlotHandler(Handler):
def __init__(self, network):
self._network = weakref.ref(network)
self.sources = {}
self._last_time_list = None
self._update_complete = False
self._recurring_update = RecurringTask(self.plan_update_data, delay=5)
self._pcb = None # Periodic callback
self._ntcb = None # Next Tick callback
super().__init__()
@property
def network(self):
return self._network()
def organize_data(self):
self._log.debug("Organize Data")
self.s = {}
for point in self.network.trends:
self.s[point.history.name] = (point.history, point.history.units)
self.lst_of_trends = [his[0] for name, his in self.s.items()]
def build_data_sources(self):
sources = {}
self.organize_data()
for each in self.lst_of_trends:
df = pd.DataFrame(each)
df = df.reset_index()
df["name"] = each.name
df["units"] = str(each.units)
df["time_s"] = df["index"].apply(str)
df.states = each.states
try:
df = (
df.fillna(method="ffill")
.fillna(method="bfill")
.replace(["inactive", "active"], [0, 1])
)
except TypeError:
df = df.fillna(method="ffill").fillna(method="bfill")
sources[each.name] = ColumnDataSource(
data=dict(
x=df["index"],
y=df[each.name],
time=df["time_s"],
name=df["name"],
units=df["units"],
)
)
return sources
def build_plot(self):
self._log.debug("Build Plot")
self.stop_update_data()
self.sources = self.build_data_sources()
TOOLS = "pan,box_zoom,wheel_zoom,save,reset"
self.p = Figure(
x_axis_type="datetime",
x_axis_label="Time",
y_axis_label="Numeric Value",
title="BAC0 Trends",
tools=TOOLS,
plot_width=800,
plot_height=600,
toolbar_location="above",
)
self.p.background_fill_color = "#f4f3ef"
self.p.border_fill_color = "#f4f3ef"
self.p.extra_y_ranges = {
"bool": Range1d(start=0, end=1.1),
"enum": Range1d(start=0, end=10),
}
self.p.add_layout(LinearAxis(y_range_name="bool", axis_label="Binary"), "left")
self.p.add_layout(
LinearAxis(y_range_name="enum", axis_label="Enumerated"), "right"
)
hover = HoverTool(
tooltips=[
("name", "@name"),
("value", "@y"),
("units", "@units"),
("time", "@time"),
]
)
self.p.add_tools(hover)
length = len(self.s.keys())
if length <= 10:
if length < 3:
length = 3
color_mapper = dict(zip(self.s.keys(), d3["Category10"][length]))
else:
# This would be a very loaded trend...
color_mapper = dict(zip(self.s.keys(), Spectral6[:length]))
for each in self.lst_of_trends:
if each.states == "binary":
self.p.circle(
"x",
"y",
source=self.sources[each.name],
name=each.name,
color=color_mapper[each.name],
legend=("%s | %s (OFF-ON)" % (each.name, each.description)),
y_range_name="bool",
size=10,
)
elif each.states == "multistates":
self.p.diamond(
"x",
"y",
source=self.sources[each.name],
name=each.name,
color=color_mapper[each.name],
legend=("%s | %s (%s)" % (each.name, each.description, each.units)),
y_range_name="enum",
size=20,
)
else:
self.p.line(
"x",
"y",
source=self.sources[each.name],
name=each.name,
color=color_mapper[each.name],
legend=("%s | %s (%s)" % (each.name, each.description, each.units)),
line_width=2,
)
self.p.legend.location = "bottom_right"
self.p.legend.click_policy = "hide"
self.plots = [self.p]
def update_data(self):
self._log.debug("Update Data")
doc = curdoc()
# self.organize_data()
if self._last_time_list:
if self._last_time_list != self.s.keys():
self._list_have_changed = True
self.stop_update_data()
# doc.add_next_tick_callback(self.modify_document)
self.modify_document(doc)
else:
self._list_have_changed = False
l = []
for each in self.p.renderers:
l.append(each.name)
# for each in self.lst_of_trends:
# df = pd.DataFrame(each)
# df = df.reset_index()
# df['name'] = each.name
# df['units'] = str(each.units)
# df['time_s'] = df['index'].apply(str)
# try:
# df = df.fillna(method='ffill').fillna(
# method='bfill').replace(['inactive', 'active'], [0, 1])
# except TypeError:
# df = df.fillna(method='ffill').fillna(method='bfill')
index = l.index(each.name)
# renderer = self.p.renderers[index]
# new_data = {}
# new_data['name'] = df['name']
# new_data['x'] = df['index']
# new_data['y'] = df[each.name]
# if each.states == 'binary':
# new_data['units'] = [each.units[int(x)] for x in df[each.name]]
# elif each.states == 'multistates':
# new_data['units'] = [
# each.units[int(math.fabs(x-1))] for x in df[each.name]]
# else:
# new_data['units'] = df['units']
# new_data['time'] = df['time_s']
# renderer.data_source.data = new_data
try:
new_data = self.build_data_sources()
for each in self.lst_of_trends:
self.sources[each.name].data = new_data[each.name].data
except KeyError:
self._log.warning(
"Problem updating {} on chart, will try again next time.".format(
each.name
)
)
else:
self._last_time_list = self.s.keys()
# self.start_update_data()
self._update_complete = True
def modify_document(self, doc):
curdoc().clear()
# doc = curdoc()
try:
curdoc().remove_periodic_callback(self._pcb)
except:
pass
doc.clear()
self.build_plot()
layout = gridplot(self.plots, ncols=2)
doc.add_root(layout)
self._pcb = doc.add_periodic_callback(self.update_data, 10000)
return doc
def plan_update_data(self):
doc = curdoc()
if self._update_complete == True:
self._update_complete = False
self._ntcb = doc.add_next_tick_callback(self.update_data)
def stop_update_data(self):
doc = curdoc()
if self._recurring_update.is_running:
self._recurring_update.stop()
while self._recurring_update.is_running:
pass
try:
doc.remove_next_tick_callback(self._ntcb)
except (ValueError, RuntimeError):
pass # Already gone
def start_update_data(self):
if not self._recurring_update.is_running:
try:
self._recurring_update.start()
while not self._recurring_update.is_running:
pass
except RuntimeError:
pass
tt = [
("Source ID", "@src"),
("Epoch (" + catalog[entry]['photometry'][0]['timeunit'] + ")", "@x{1.11}"),
("Magnitude", "@y{1.111}"),
("Error", "@err{1.111}"),
("Band", "@desc")
]
if len(list(filter(None, photoinstru))):
tt += [("Instrument", "@instr")]
hover = HoverTool(tooltips = tt)
p1 = Figure(title='Photometry for ' + eventname, x_axis_label='Time (' + catalog[entry]['photometry'][0]['timeunit'] + ')',
y_axis_label='AB Magnitude', x_range = x_range, tools = tools,
y_range = (0.5 + max([x + y for x, y in list(zip(photoAB, photoerrs))]), -0.5 + min([x - y for x, y in list(zip(photoAB, photoerrs))])))
p1.add_tools(hover)
err_xs = []
err_ys = []
for x, y, yerr in list(zip(phototime, photoAB, photoerrs)):
err_xs.append((x, x))
err_ys.append((y - yerr, y + yerr))
bandset = set(photoband)
bandset = [i for (j, i) in sorted(list(zip(list(map(bandaliasf, bandset)), bandset)))]
for band in bandset:
bandname = bandaliasf(band)
indb = [i for i, j in enumerate(photoband) if j == band]
indt = [i for i, j in enumerate(phototype) if not j]
tcolor = 'black'
falpha = 0.0
else:
tcolor = colors[ci]
falpha = 1.0
glyphs.append(
p1.circle('x',
'y',
source=source,
color=tcolor,
fill_alpha=falpha,
legend=ct,
size=glsize))
hover = HoverTool(tooltips=tt, renderers=glyphs)
p1.add_tools(hover)
p1.legend.location = "bottom_center"
p1.legend.orientation = "horizontal"
p1.legend.label_text_font_size = '7pt'
p1.legend.label_width = 20
p1.legend.label_height = 8
p1.legend.glyph_height = 8
p1.legend.spacing = 0
html = file_html(p1, CDN,
'Supernova locations').replace('width: 90%;',
'width: inherit;')
with open(outdir + modulename + "-locations.html", "w") as f:
f.write(html)
def correlation():
"the scatter plot"
args = flask.request.args
xattr = args.get("x", "atomic_number")
yattr = args.get("y", "covalent_radius_pyykko")
categ = args.get("categ", "name_series")
data = get_data()
properties = get_property_names(data)
categories = get_category_names()
fig = Figure(title="{} vs {}".format(properties[xattr], properties[yattr]),
plot_width=PLOT_WIDTH,
plot_height=PLOT_HEIGHT,
tools="box_zoom,pan,resize,save,reset",
toolbar_location="above",
toolbar_sticky=False,
)
fig.xaxis.axis_label = properties[xattr]
fig.yaxis.axis_label = properties[yattr]
ccm = get_color_mapper(categ, data)
if categ == "None":
legend = None
color_dict = "#1F77B4"
else:
legend = categ
color_dict = {"field": categ, "transform": ccm}
fig.circle(x=xattr, y=yattr, fill_alpha=0.7, size=10,
source=ColumnDataSource(data=data),
fill_color=color_dict,
line_color=color_dict,
legend=legend)
if categ != "None":
fig.legend.location = (0, 0)
fig.legend.plot = None
fig.add_layout(fig.legend[0], "right")
hover = HoverTool(tooltips=HOVER_TOOLTIPS)
fig.add_tools(hover)
script, div = components(fig)
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
html = render_template(
"correlations.html",
plot_script=script,
plot_div=div,
properties=properties,
categories=categories,
xselected=xattr,
yselected=yattr,
catselected=categ,
js_resources=js_resources,
css_resources=css_resources,
)
return encode_utf8(html)
state_drunk=state_drunk,
state_percent=state_percent,
state_percent_sp=state_percent_sp))
patches = fig.patches(xs="borders_x",
ys="borders_y",
source=patch_source,
fill_alpha="alpha",
fill_color="colors",
line_alpha=0,
hover_alpha=.3,
line_width=5)
fig.add_tools(
HoverTool(renderers=[patches],
tooltips=[("State", "@state_names"), ("Total", "@state_totals"),
("Drunk%", "@state_percent{1.11}" + "%"),
("Speeding%", "@state_percent_sp{1.11}" + "%")]))
select = Select(title="Color States By:",
value="None",
options=["None", "Drunk%", "Speeding%"])
def update_color(attrname, old, new):
if select.value == "Drunk%":
patch_source.data["colors"] = drunk_colors
patch_source.data["alpha"] = [.3] * len(state_names)
elif select.value == "Speeding%":
patch_source.data["colors"] = speeding_colors
patch_source.data["alpha"] = [.3] * len(state_names)
else:
x=speeding_accidents['x'],
y=speeding_accidents['y'] ))
drinking_source = ColumnDataSource(dict(
x=drinking_accidents['x'],
y=drinking_accidents['y'] ))
other_source = ColumnDataSource(dict(
x=other_accidents['x'],
y=other_accidents['y'] ))
fig.circle('x', 'y', source=speeding_source, fill_color="red", size=3)
fig.circle('x', 'y', source=drinking_source, fill_color="green", size=3)
fig.circle('x', 'y', source=other_source, fill_color="blue", size=3)
fig.add_tools(HoverTool(renderers=[states], tooltips=[("State", "@state"), ("Total", "@total"), ("Drunk Percent", "@perc_drunk"), ("Speeding Percent", "@perc_speeding")]))
# -------------------------------------------------
from bokeh.io import output_file, show
from bokeh.layouts import column
from bokeh.models.widgets import Select
from bokeh.palettes import Reds9
output_file("final_bokeh.html")
select = Select(title="Option:", value="Default",
options=["Other", "Speeding", "Drunk", "Default"])
border_source.add("white", name="color")
# temporarily ignoring bad types from import
sntypes.append(ctv)
sntypes = sorted(sntypes)
snoffs = [[] for x in range(len(sntypes))]
tt = [
("Type", "@ct")
]
hover = HoverTool(tooltips=tt, line_policy='interp')
p = Figure(x_range=[0., 100.], y_range=[0., 1.],
title='Supernova Host Offsets',
x_axis_label='Offset (kpc)', y_axis_label='CDF',
plot_width=980, plot_height=500,
title_text_font='futura', title_text_font_size='14pt')
p.add_tools(hover)
for si, sntype in enumerate(tq(sntypes)):
for event in meta:
if ('hostoffsetdist' in event and event['hostoffsetdist'] and
is_number(event['hostoffsetdist'][0]['value']) and
'claimedtype' in event and event['claimedtype'] and
sntype in [x['value'] for x in event['claimedtype']]):
snoffs[si].append(float(event['hostoffsetdist'][0]['value']))
snoffs[si] = sorted(snoffs[si])
colors = sns.color_palette("hls", n_colors=sum(
[1 if len(snoffs[i]) >= mincnt else 0 for i, x in
enumerate(snoffs)])).as_hex()
cnt = 0
# coding=utf-8
from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource, TapTool, HoverTool, CustomJS
from bokeh.plotting import Figure
from bokeh.layouts import row
output_file("layout.html")
source = ColumnDataSource(
data=dict(x=[2,3,5], y=[8,5,2]),
name='source'
)
p1 = Figure(plot_width=550, plot_height=550, sizing_mode="fixed", title="With inspect behavior")
p1.circle(x='x', y='y', source=source, size=50)
p1.add_tools(TapTool(behavior='inspect', callback=CustomJS(code="alert('foo');")))
p1.add_tools(HoverTool())
p2 = Figure(plot_width=550, plot_height=550, sizing_mode="fixed", title="With select behavior")
p2.circle(x='x', y='y', source=source, size=50)
p2.add_tools(TapTool(behavior='select', callback=CustomJS(code="alert('foo');")))
p2.add_tools(HoverTool())
layout = row(p1, p2)
show(layout)
def freq_plot(freq_array, date_array, plot_name,
init_key1='tea', init_key2='coffee'):
"""
create frequency plot
:param freq_array: columns: dates, rows: keywords
:param date_array: dates
:param plot_name:
"""
def get_dataset(key1, key2):
return ColumnDataSource(data=dict(dates=date_array,
freq1=freq_array[key2index(key1), :].tolist(),
freq2=freq_array[key2index(key2), :].tolist(),
))
source = get_dataset(init_key1, init_key2)
freq_list = freq_array.tolist()
freq = ColumnDataSource(data=dict(freq_list=freq_list))
callback1 = CustomJS(args=dict(source=source, freq=freq), code="""
var data = source.get('data');
var freq = freq.get('data');
var freq_list = freq['freq_list'];
var option = cb_obj.get('value');
var index = option.split(':')[0];
for (var i = 0; i < freq_list[0].length; i++)
{data['freq1'][i] = freq_list[index][i]
};
source.trigger('change');
""")
callback2 = CustomJS(args=dict(source=source, freq=freq), code="""
var data = source.get('data');
var freq = freq.get('data');
var freq_list = freq['freq_list'];
var option = cb_obj.get('value');
var index = option.split(':')[0];
for (var i = 0; i < freq_list[0].length; i++)
{data['freq2'][i] = freq_list[index][i]
};
source.trigger('change');
""")
# set up plot
plot = Figure(plot_width=1000, tools="pan,reset,"
"resize,save,wheel_zoom",
responsive=True, x_axis_type='datetime')
plot.min_border = 0
colormap = Spectral9
keys = get_keys()
for i in range(len(keys)):
plot.line(x=date_array, y=freq_array[i, :].tolist(),
line_color=colormap[4], line_alpha=0.5,
line_width=1)
plot.line(x='dates', y='freq1',
line_width=4,
line_alpha=1, source=source,
line_color=colormap[0],
legend='keyword 1',
)
plot.line(x='dates', y='freq2',
line_width=4,
line_alpha=1, source=source,
line_color=colormap[1],
legend='keyword 2',
)
plot.xaxis.axis_label = 'Date [Month/Day/Year]'
plot.yaxis.axis_label = 'Variation relative to mean'
plot.legend.location = 'top_right'
# show events
m, d, y, e = get_events()
all_dates = [datetime.datetime(y[i], m[i], d[i]) for i in range(y.shape[0])]
event_source = ColumnDataSource(data=dict(date_x=all_dates,
date_y=[10 for x in all_dates],
event=e,
date=[x.strftime('%m-%d-%Y') for x in all_dates]))
event_points = plot.circle(x='date_x', y='date_y', size=20,
color=colormap[8], source=event_source,
alpha=.5)
hover = HoverTool(renderers=[event_points],
tooltips=[("date", "@date"), ("event", "@event")],
)
plot.add_tools(hover)
# set up widgets
options = [str(x) + ': ' + get_keys()[x] for x in range(len(get_keys()))]
first_key1 = str(key2index(init_key1)) + ': ' + init_key1
first_key2 = str(key2index(init_key2)) + ': ' + init_key2
key1_select = Select(value=first_key1, title='keyword 1',
options=options,
callback=callback1)
key2_select = Select(value=first_key2, title='keyword 2',
options=options,
callback=callback2)
inputs = HBox(key1_select, key2_select, width=400)
layout = VBox(inputs, plot)
# for website
script, html = components(layout)
print (html)
# curdoc().add_root(HBox(inputs, plot))
# save output file
output_file(plot_name + '.html', title=plot_name)
# save js file for website
js_file = open(plot_name + '.js', 'w')
js_file.write(script)
js_file.close()
show(layout)
plot.yaxis.axis_label = "Frequency"
plot.yaxis.axis_line_color = None
plot.yaxis.major_tick_line_color = None
plot.yaxis.minor_tick_line_color = None
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
plot.outline_line_color = None
plot.logo = None
bar_hover = HoverTool(renderers=[freq_bars], tooltips=[("Range", '@left_edge to @right_edge'),
("Frequency", '@hist'), ("Total # Samples", '@samples')])
plot.add_tools(bar_hover)
# Dropdown and interactive UI elements
selectable_years = ["All Years"] + list(map(str, compdata['Year'].unique()))
selectable_events = ["All Events"] + [event.replace(' Score', '') for event in compdata.columns.values.tolist()[5:13]]
select_year = Select(title="Year", value="All Years", options=selectable_years)
select_event = Select(title="Event", value="All Events", options=selectable_events)
# Interactive callbacks
def update_histogram_data(year='All Years', event='All Events'):
# TODO: Properly sanitize input data
event_index_name = EVENT_CONSTANTS[event]['fullname']
event_max_points = EVENT_CONSTANTS[event]['points']
source = ColumnDataSource(data=dict(x=[], y=[], color=[], raw=[], wikidataID=[], counts=[]))
selected, new_x_factors, new_y_factors = get_subset(dictionary_selector.value, dictionary_selector.value)
TOOLS="tap, reset"
hover = HoverTool(names = ["glyphs"],
tooltips=[("Cooccurring", "@x, @y"),
("Counts", "@counts"),
("wikidataID for this item (x-axis)", "@wikidataID")])
fig = Figure(plot_height=700, plot_width=700, title="",
tools=TOOLS, toolbar_location="above",
x_range=new_x_factors[:top_n.value], y_range=new_y_factors[:top_n.value])
fig.add_tools(hover)
update(None, None, None) # initial load of the data
rects = fig.rect(x="x", y="y", source=source, color="color", width=0.95, height=0.95, name="glyphs")
fig.xaxis.major_label_orientation = np.pi/4
fig.yaxis.major_label_orientation = np.pi/4
fig.xgrid.visible = False
fig.ygrid.visible = False
url = "https://www.wikidata.org/wiki/@wikidataID"
taptool = fig.select(type=TapTool)
taptool.callback = OpenURL(url=url)
renderer = fig.select(name="glyphs")[0]
renderer.selection_glyph = renderer.glyph
renderer.nonselection_glyph = renderer.glyph
def __init__(self, fig: Figure):
"""
Constructs a new PassGeneratorPlotter associated to fig.
:param fig: A Bokeh figure
"""
self.fig = fig
self.zones_source = ColumnDataSource(
dict(center_xs=[], center_ys=[], widths=[], heights=[]))
self.fig.rect(
source=self.zones_source,
x="center_xs",
y="center_ys",
width="widths",
height="heights",
fill_alpha=0,
line_color="blue",
line_width=1.2,
legend_label="Pass Generator zones",
)
self.passes_source = ColumnDataSource(
dict(
receiver_xs=[],
receiver_ys=[],
pass_line_xs=[],
pass_line_ys=[],
pass_rating=[],
line_width=[],
))
self.pass_color_mapper = LinearColorMapper(palette=mpl["Plasma"][256],
low=0,
high=1)
color_bar = ColorBar(
color_mapper=self.pass_color_mapper,
width=8,
height=400,
location="bottom_right",
title="Pass rating",
)
self.fig.add_layout(color_bar, "right")
self.fig.multi_line(
source=self.passes_source,
xs="pass_line_xs",
ys="pass_line_ys",
line_width="line_width",
line_color=dict(field="pass_rating",
transform=self.pass_color_mapper),
)
receiver_point_glyph = self.fig.circle_cross(
source=self.passes_source,
x="receiver_xs",
y="receiver_ys",
size=10,
fill_alpha=0.0,
legend_label="Receiver points",
)
pass_rating_tooltip = ("Pass rating", "@pass_rating")
hover_tool = HoverTool(tooltips=[pass_rating_tooltip],
renderers=[receiver_point_glyph])
fig.add_tools(hover_tool)
def periodic_plot(cds, title="Periodic Table", width=PLOT_WIDTH,
height=PLOT_HEIGHT, cmap="viridis",
showfblock=True, long_version=False,
color_mapper=None):
"""
Create the periodic plot
Args:
df : DataFrame
Pandas DataFrame with the data on elements
tile : str
Title to appear above the periodic table
colorby : str
Name of the column containig the colors
width : int
Width of the figure in pixels
height : int
Height of the figure in pixels
cmap : str
Colormap to use, see matplotlib colormaps
long_version : bool
Show the long version of the periodic table with the f block between
the s and d blocks
showfblock : bool
Show the elements from the f block
.. note::
`property` attribute holds the current property to be displayed
"""
fig = Figure(title=title,
x_axis_location="above",
x_range=(0.5, 18.5),
y_range=(10.0, 0.5),
plot_width=width,
plot_height=height,
tools="box_zoom,pan,resize,save,reset",
toolbar_location="above",
toolbar_sticky=False,
)
if color_mapper is None:
color_dict = "#1F77B4"
else:
color_dict = {"field": "value", "transform": color_mapper}
fig.rect("x", "y", 0.9, 0.9, source=cds, fill_alpha=0.6,
fill_color=color_dict, line_color=color_dict)
# adjust the ticks and axis bounds
fig.yaxis.bounds = (1, 7)
fig.axis[1].ticker.num_minor_ticks = 0
fig.axis[0].ticker = FixedTicker(ticks=list(range(1, 19)))
text_props = {
"source": cds,
"angle": 0,
"color": "black",
"text_align": "center",
"text_baseline": "middle"
}
fig.text(x="x", y="y_symbol", text="symbol",
text_font_style="bold", text_font_size="15pt", **text_props)
fig.text(x="x", y="y_anumber", text="atomic_number",
text_font_size="9pt", **text_props)
fig.text(x="x", y="y_name", text="name",
text_font_size="7pt", **text_props)
fig.text(x="x", y="y_prop", text="value_str",
text_font_size="8pt", **text_props)
fig.grid.grid_line_color = None
hover = HoverTool(tooltips=HOVER_TOOLTIPS)
fig.add_tools(hover)
return fig
def plot_carto_single(self, data, frente, palette, path=FILE_OUT,
name_file="", low=0, high=100, show_plot=True):
"""
:param data: df loaded by data_load
:param frente: string, name of "partido" lowercase: diff, mas, cc, creemos, fpv, pan_bol
:param palette: ej: P_GRAD_CC
:param name_file: default:test
:param low: cmap low limit: default: -80
:param high: cmap high limit: defauilt: +80.
:param path: file out
:return: df
"""
da_col = ['HAB','PAIS','MUN','REC','X','Y','LAT','LON','x','y',
'r','r2','GX','GY'
]
cart_init_val = self.CART_SLIDER_INIT # add slider
self.process_data(cart_init_val, data)
if frente == "diff":
low = self.C_BAR_LOW
high = self.C_BAR_HIGH
frente = "d_mas_cc"
f1 = 'mas_o_cc'
f2 = 'ad_mas_cc'
_p = 'mas'
_p1 = 'cc'
da_col.append(frente)
da_col.append(f1)
da_col.append(f2)
da_col.append(_p)
da_col.append(_p1)
if frente == "d_mas_creemos":
low = self.C_BAR_LOW
high = self.C_BAR_HIGH
f1 = 'mas_o_creemos'
f2 = 'ad_mas_creemos'
da_col.append(frente)
da_col.append(f1)
da_col.append(f2)
da_col.append('mas')
da_col.append('creemos')
da_col.append(frente)
cm = linear_cmap(frente, palette=palette, low=low, high=high)
data = data[da_col]
source_master = ColumnDataSource(data)
source_red_map = ColumnDataSource({'gx': [], 'gy': []})
# la, lo = ebu.get_la_lo_bolivia()
# source_bol = ColumnDataSource({'la': la, 'lo': lo})
# source_red_car = ColumnDataSource({'lo': [], 'la': []})
# JS CODE
code_draw_red_map = """
const data = {'gx': [], 'gy': []}
const indices = cb_data.index.indices
for (var i = 0; i < indices.length; i++ ) {
data['gx'].push(source_master.data.GX[indices[i]])
data['gy'].push(source_master.data.GY[indices[i]])
}
source_red_map.data = data
"""
code_slider = """
var data = source.data;
var f = cb_obj.value
var x = data['x']
var y = data['y']
var Y = data['Y']
var X = data['X']
var lat = data['LAT']
var lon = data['LON']
for (var i = 0; i < x.length; i++) {
y[i] = (1-f)*lat[i] + f*Y[i]
x[i] = (1-f)*lon[i] + f*X[i]
}
source.change.emit();
"""
# FIGURES
curr_time = ebu.get_bolivian_time(-3)
pw = self.FIG_WIDTH
callback_red_map = CustomJS(
args={'source_master': source_master,
'source_red_map': source_red_map, },
code=code_draw_red_map)
hover_cart = bokeh.models.HoverTool(
tooltips=self.TOOL_TIP_DIC[frente],
callback=callback_red_map,
# renderers = [red_scat_car]
)
cart_fig = Figure(plot_width=pw, plot_height=pw,
output_backend="webgl", )
cart_fig.background_fill_color = "grey"
cart_fig.background_fill_alpha = .5
cart_fig.scatter('x', 'y', source=source_master, radius='r', color=cm)
cart_fig.add_tools(hover_cart, )
title = "Última actualización: " + curr_time["datetime_val"].strftime(
"%Y-%m-%d %H:%M") + "BOT"
map_fig = Figure(plot_width=pw, plot_height=pw,
x_axis_type='mercator',
y_axis_type='mercator',
output_backend="webgl",
title=title,
)
# cb_fig = bokeh.plotting.Figure(plot_height=pw,plot_width=)
# cb_fig.toolbar.logo = None
# cb_fig.toolbar_location = None
# SCATTER
# noinspection PyUnresolvedReferences
# add tiles
tile_provider = bokeh.tile_providers.get_provider(
bokeh.tile_providers.Vendors.CARTODBPOSITRON)
map_fig.add_tile(tile_provider)
# scatter in map
map_fig.scatter(
'GX', 'GY', source=source_master, size='r2',
color=cm
)
# todo if we wont use map then we nee to delete the source
# cart_fig.line('lo', 'la', source=source_bol, color='black')
# noinspection PyUnusedLocal
red_scat_map = map_fig.circle_cross('gx', 'gy',
source=source_red_map,
fill_color=None,
size=20,
line_color="white",
line_width=4
)
# noinspection PyUnusedLocal
red_scat_map = map_fig.circle_cross('gx', 'gy',
source=source_red_map,
fill_color=None,
size=20,
line_color="red",
line_width=1
)
# red_scat_car = cart_fig.scatter('lo', 'la',
# source=source_red_car, color='green')
# add a hover tool that sets the link data for a hovered circle
# callbacks
# code = code_merged)
# callback_red_car = CustomJS(
# args={'source_master': source_master, 'source_red_car': source_red_car},
# code=code_draw_red_car)
# tools
hover_map = bokeh.models.HoverTool(
tooltips=self.TOOL_TIP_DIC[frente],
# callback=callback_red_car,
# renderers = [red_scat_map]
)
map_fig.add_tools(hover_map, )
# slider
callback_slider = CustomJS(args=dict(source=source_master),
code=code_slider)
slider = Slider(start=0, end=1, value=cart_init_val, step=.02,
title="carto")
slider.js_on_change('value', callback_slider)
# COLOR BAR
ml = {int(i): str(np.abs(i)) for i in np.arange(-80, 81, 20)}
cb = bokeh.models.ColorBar(
color_mapper=cm['transform'],
# width=int(.9 * 450),
width='auto',
location=(0, 0),
# title="DEN (N/km^2)",
# title=(BAR_TITLE),
# margin=0,padding=0,
title_standoff=10,
# ticker=bokeh.models.LogTicker(),
orientation='horizontal',
major_label_overrides=ml
)
cart_fig.add_layout(cb, 'above')
# cb.title_text_align = 'left'
cart_fig.title.text = self.BAR_TITLE_DIC[frente]
cart_fig.title.align = 'center'
# layout = row(column(slider, cart_f),map_f)
layout = bokeh.layouts.gridplot(
[[slider, None], [cart_fig, map_fig]], sizing_mode='scale_width',
merge_tools=False)
layout.max_width = 1400
# layout = bokeh.layouts.column([slider, cart_fig])
cart_fig.x_range.start = self.CXS
cart_fig.x_range.end = self.CXE
cart_fig.y_range.start = self.CYS
cart_fig.y_range.end = self.CYE
_ll = ebu.lola_to_cart(lo=[self.MXS, self.MXE], la=[self.MYS, self.MYE])
map_fig.x_range.start = _ll[0][0]
map_fig.x_range.end = _ll[0][1]
map_fig.y_range.start = _ll[1][0]
map_fig.y_range.end = _ll[1][1]
cart_fig.xaxis.major_tick_line_color = None
# turn off x-axis major ticks
cart_fig.xaxis.minor_tick_line_color = None
# turn off x-axis minor ticks
cart_fig.yaxis.major_tick_line_color = None
# turn off y-axis major ticks
cart_fig.yaxis.minor_tick_line_color = None
cart_fig.xaxis.major_label_text_font_size = '0pt'
# turn off x-axis tick labels
cart_fig.yaxis.major_label_text_font_size = '0pt'
# turn off y-axis tick labels
nam = 'z037_' + frente + '_' + name_file + '.html'
nam_lat = 'z037_' + frente + '_' + 'latest' + '.html'
nam1 = os.path.join(path, nam)
nam2 = os.path.join(os.path.dirname(ebu.DIR), 'docs',
'graficas_htmls',
nam_lat)
# bokeh.plotting.output_file(nam2)
if show_plot:
bokeh.plotting.show(layout)
bokeh.plotting.save(layout, nam1)
bokeh.plotting.save(layout, nam2)
return data
# tools
ebu.TOOL_TIPS1 = [('Inscritos', '@HAB'), ('PAIS', '@PAIS'),
('Municipalidad', '@MUN'), ('Recinto', '@REC'),
('MAS-CC%', '@d_mas_cc{0.0}')
# ('DEN %', '@DEN')
# ('PAIS', '@PAIS'),
]
hover_cart = bokeh.models.HoverTool(
tooltips=ebu.TOOL_TIPS1,
callback=callback_red_map,
# renderers = [red_scat_car]
)
cart_fig.add_tools(hover_cart, )
hover_map = bokeh.models.HoverTool(
tooltips=ebu.TOOL_TIPS1,
# callback=callback_red_car,
# renderers = [red_scat_map]
)
map_fig.add_tools(hover_map, )
# slider
callback_slider = CustomJS(args=dict(source=source_master), code=code_slider)
slider = Slider(start=0, end=1, value=cart_init_val, step=.01, title="carto")
slider.js_on_change('value', callback_slider)
# %%
# rank chart 그리는 부분임
ranksource = ColumnDataSource(
data={
'crop': list(rank.keys()),
'rank': list(rank.values()),
'year': [db.column_names] * len(rank),
'color': Category20[14]
})
p = Figure(plot_width=1000,
plot_height=300,
x_axis_label='Year',
y_axis_label='Rank',
toolbar_location='above',
title='재배면적 상위 10개종목 순위변화')
p.multi_line('year', 'rank', alpha=1, color='color', source=ranksource)
p.add_tools(HoverTool(tooltips=[('Crop', '@crop')]))
# 작물 select 하면 맞춰서 그래프 움직이는 부분
select = Select(title="Crop", value="감자", options=crops)
def update_crop(attr, old, new):
source.data = get_data(str(select.value))
select.on_change('value', update_crop)
seconddiv = Div(text="""<h1>읍면동별 작물 시계열 차트</h1>""", width=1000)
rankwidget = WidgetBox(titlediv, data_table, para, cums_table)
sntypes = sorted(sntypes)
snoffs = [[] for x in range(len(sntypes))]
tt = [("Type", "@ct")]
hover = HoverTool(tooltips=tt, line_policy='interp')
p = Figure(x_range=[0., 100.],
y_range=[0., 1.],
title='Supernova Host Offsets',
x_axis_label='Offset (kpc)',
y_axis_label='CDF',
plot_width=980,
plot_height=500,
title_text_font='futura',
title_text_font_size='14pt')
p.add_tools(hover)
for si, sntype in enumerate(sntypes):
for event in meta:
if 'hostoffkpc' in event and is_number(event['hostoffkpc']) \
and 'claimedtype' in event and event['claimedtype'] and sntype in [x['value'] for x in event['claimedtype']]:
snoffs[si].append(float(event['hostoffkpc']))
snoffs[si] = sorted(snoffs[si])
colors = sns.color_palette("hls",
n_colors=sum([
1 if len(snoffs[i]) >= mincnt else 0
for i, x in enumerate(snoffs)
])).as_hex()
cnt = 0
class DynamicPlotHandler(Handler):
def __init__(self, network):
self._network = weakref.ref(network)
self.sources = {}
self._last_time_list = None
self._update_complete = False
self._recurring_update = RecurringTask(self.plan_update_data, delay=5)
self._pcb = None # Periodic callback
self._ntcb = None # Next Tick callback
super().__init__()
@property
def network(self):
return self._network()
def organize_data(self):
self._log.debug("Organize Data")
self.s = {}
for point in self.network.trends:
self.s[point.history.name] = (point.history, point.history.units)
self.lst_of_trends = [his[0] for name, his in self.s.items()]
def build_data_sources(self):
sources = {}
self.organize_data()
for each in self.lst_of_trends:
df = pd.DataFrame(each)
df = df.reset_index()
df["name"] = each.name
df["units"] = str(each.units)
df["time_s"] = df["index"].apply(str)
df.states = each.states
try:
df = (
df.fillna(method="ffill")
.fillna(method="bfill")
.replace(["inactive", "active"], [0, 1])
)
except TypeError:
df = df.fillna(method="ffill").fillna(method="bfill")
sources[each.name] = ColumnDataSource(
data=dict(
x=df["index"],
y=df[each.name],
time=df["time_s"],
name=df["name"],
units=df["units"],
)
)
return sources
def build_plot(self):
self._log.debug("Build Plot")
self.stop_update_data()
self.sources = self.build_data_sources()
TOOLS = "pan,box_zoom,wheel_zoom,save,reset"
self.p = Figure(
x_axis_type="datetime",
x_axis_label="Time",
y_axis_label="Numeric Value",
title="BAC0 Trends",
tools=TOOLS,
plot_width=800,
plot_height=600,
toolbar_location="above",
)
self.p.background_fill_color = "#f4f3ef"
self.p.border_fill_color = "#f4f3ef"
self.p.extra_y_ranges = {
"bool": Range1d(start=0, end=1.1),
"enum": Range1d(start=0, end=10),
}
self.p.add_layout(LinearAxis(y_range_name="bool", axis_label="Binary"), "left")
self.p.add_layout(
LinearAxis(y_range_name="enum", axis_label="Enumerated"), "right"
)
hover = HoverTool(
tooltips=[
("name", "@name"),
("value", "@y"),
("units", "@units"),
("time", "@time"),
]
)
self.p.add_tools(hover)
self.legends_list = []
length = len(self.s.keys())
if length <= 10:
if length < 3:
length = 3
color_mapper = dict(zip(self.s.keys(), d3["Category10"][length]))
else:
# This would be a very loaded trend...
color_mapper = dict(zip(self.s.keys(), Spectral6[:length]))
for each in self.lst_of_trends:
if each.states == "binary":
self.p.circle(
"x",
"y",
source=self.sources[each.name],
name=each.name,
color=color_mapper[each.name],
legend=("{} | {} (OFF-ON)".format(each.name, each.description)),
y_range_name="bool",
size=10,
)
# self.legends_list.append(
# (("{} | {} (OFF-ON)".format(each.name, each.description)), [c])
# )
elif each.states == "multistates":
self.p.diamond(
"x",
"y",
source=self.sources[each.name],
name=each.name,
color=color_mapper[each.name],
legend=(
"{} | {} ({})".format(each.name, each.description, each.units)
),
y_range_name="enum",
size=20,
)
else:
self.p.line(
"x",
"y",
source=self.sources[each.name],
name=each.name,
color=color_mapper[each.name],
legend=(
"{} | {} ({})".format(each.name, each.description, each.units)
),
line_width=2,
)
self.p.legend.location = "top_left"
# legend = Legend(items=self.legends_list, location=(0, -60))
self.p.legend.click_policy = "hide"
# self.p.add_layout(legend, "right")
self.plots = [self.p]
def update_data(self):
self._log.debug("Update Data")
doc = curdoc()
# self.organize_data()
if self._last_time_list:
if self._last_time_list != self.s.keys():
self._list_have_changed = True
self.stop_update_data()
# doc.add_next_tick_callback(self.modify_document)
self.modify_document(doc)
else:
self._list_have_changed = False
l = []
for each in self.p.renderers:
l.append(each.name)
# for each in self.lst_of_trends:
# df = pd.DataFrame(each)
# df = df.reset_index()
# df['name'] = each.name
# df['units'] = str(each.units)
# df['time_s'] = df['index'].apply(str)
# try:
# df = df.fillna(method='ffill').fillna(
# method='bfill').replace(['inactive', 'active'], [0, 1])
# except TypeError:
# df = df.fillna(method='ffill').fillna(method='bfill')
index = l.index(each.name)
# renderer = self.p.renderers[index]
# new_data = {}
# new_data['name'] = df['name']
# new_data['x'] = df['index']
# new_data['y'] = df[each.name]
# if each.states == 'binary':
# new_data['units'] = [each.units[int(x)] for x in df[each.name]]
# elif each.states == 'multistates':
# new_data['units'] = [
# each.units[int(math.fabs(x-1))] for x in df[each.name]]
# else:
# new_data['units'] = df['units']
# new_data['time'] = df['time_s']
# renderer.data_source.data = new_data
try:
new_data = self.build_data_sources()
for each in self.lst_of_trends:
self.sources[each.name].data = new_data[each.name].data
except KeyError:
self._log.warning(
"Problem updating {} on chart, will try again next time.".format(
each.name
)
)
else:
self._last_time_list = self.s.keys()
# self.start_update_data()
self._update_complete = True
def modify_document(self, doc):
doc.clear()
self.build_plot()
layout = gridplot(self.plots, ncols=2)
doc.add_root(layout)
self._pcb = doc.add_periodic_callback(self.update_data, 10000)
return doc
def plan_update_data(self):
doc = curdoc()
if self._update_complete == True:
self._update_complete = False
self._ntcb = doc.add_next_tick_callback(self.update_data)
def stop_update_data(self):
doc = curdoc()
try:
doc.remove_periodic_callback(self._pcb)
except:
pass
if self._recurring_update.is_running:
self._recurring_update.stop()
while self._recurring_update.is_running:
pass
try:
doc.remove_next_tick_callback(self._ntcb)
except (ValueError, RuntimeError):
pass # Already gone
def start_update_data(self):
if not self._recurring_update.is_running:
try:
self._recurring_update.start()
while not self._recurring_update.is_running:
pass
except RuntimeError:
pass
max_x_range = x_buffer + \
max([x + y for x, y in list(zip(phototime, phototimelowererrs))])
p1 = Figure(title='Average Photometry for Type ' + averagetype + ' SNe', x_axis_label='Time (MJD)',
# responsive = True,
y_axis_label='Absolute Magnitude', tools=tools, plot_width=1000, plot_height=1000,
x_range=(min_x_range, max_x_range),
y_range=(0.5 + max([x + y for x, y in list(zip(photoAB, photoABerrs))]),
-0.5 + min([x - y for x, y in list(zip(photoAB, photoABerrs))])),
title_text_font_size='20pt', webgl=True)
p1.xaxis.axis_label_text_font_size = '16pt'
p1.yaxis.axis_label_text_font_size = '16pt'
p1.xaxis.major_label_text_font_size = '12pt'
p1.yaxis.major_label_text_font_size = '12pt'
p1.add_tools(hover)
xs = []
ys = []
err_xs = []
err_ys = []
for x, y, xlowerr, xupperr, yerr in list(zip(phototime, photoAB, phototimelowererrs, phototimeuppererrs, photoABerrs)):
xs.append(x)
ys.append(y)
err_xs.append((x - xlowerr, x + xupperr))
err_ys.append((y - yerr, y + yerr))
for band in bandset:
bandname = bandaliasf(band)
indb = [i for i, j in enumerate(photoband) if j == band]
borders_y=b["y"],
colors=no_colors,
alpha=alpha,
state_names=state_names,
state_totals=state_totals,
state_drunk=state_drunk,
state_percent=state_percent,
state_percent_sp=state_percent_sp
)
)
patches = fig.patches(xs="borders_x", ys="borders_y", source=patch_source, fill_alpha="alpha",
fill_color="colors", line_alpha=0, hover_alpha=.3, line_width=5)
fig.add_tools(HoverTool(renderers=[patches], tooltips=[("State", "@state_names"),
("Total", "@state_totals"),
("Drunk%", "@state_percent{1.11}" + "%"),
("Speeding%", "@state_percent_sp{1.11}" + "%")]))
select = Select(title="Color States By:", value="None", options=["None", "Drunk%", "Speeding%"])
def update_color(attrname, old, new):
if select.value == "Drunk%":
patch_source.data["colors"] = drunk_colors
patch_source.data["alpha"] = [.3]*len(state_names)
elif select.value == "Speeding%":
patch_source.data["colors"] = speeding_colors
patch_source.data["alpha"] = [.3]*len(state_names)
else:
patch_source.data["colors"] = no_colors
patch_source.data["alpha"] = [0]*len(state_names)
select.on_change('value', update_color)
def build_local_uk_tab():
s3_root = 'https://covid19-bokeh-app.s3.eu-west-2.amazonaws.com'
la_boundaries_gdf = gpd.read_file(
f'{s3_root}/data/_geo_data/la_districts_dec19.zip'
).loc[:, ['lad19cd', 'lad19nm', 'geometry']]
# Importing uk local authority data
la_cases_df = pd.read_csv(f'{s3_root}/data/local_uk.csv')
# Filter for latest date
la_cases_latest_df = la_cases_df.loc[la_cases_df.date ==
la_cases_df.date.max()]
# Import local authority population data
la_pop_df = pd.read_csv(f'{s3_root}/data/local_authority_populations.csv'
).loc[:, ['code', 'population']]
# Remove commas and convert to numeric dtype
la_pop_df['population'] = pd.to_numeric(
(la_pop_df['population'].str.replace(",", "")))
# Merge cases and population datasets
la_cases_latest_df = la_cases_latest_df.merge(la_pop_df,
left_on="area_code",
right_on="code",
how="left")
# Merge geo-boundaries GeoDataFrame with cases, pop dataset
la_cases_gdf = la_boundaries_gdf.merge(la_cases_latest_df,
left_on="lad19cd",
right_on="area_code",
how="left")
# Calculate weekly cases per 100,000
la_cases_gdf['cases_per_pop'] = (
100000 * la_cases_gdf['weekly_cases'] /
la_cases_gdf['population']).fillna(0).astype(int)
# Convert dataset to GeoJSONDataSource to feed geo-plot
geosource = GeoJSONDataSource(geojson=la_cases_gdf.to_json())
# Adding figure and geographical patches from shapefile
local_uk_geo_plot = Figure(title="Weekly Cases per 100,000",
plot_height=500,
plot_width=500,
name="local_uk_geo_plot",
output_backend="webgl")
# Add linear colour mapper for case density
mapper = linear_cmap(field_name='cases_per_pop',
palette=brewer['YlOrRd'][9][:7][::-1],
low=min(la_cases_gdf['cases_per_pop']),
high=max(la_cases_gdf['cases_per_pop']))
# Add local authority patches to figure
geo_patches = local_uk_geo_plot.patches('xs',
'ys',
source=geosource,
alpha=0.8,
color=mapper)
# Adding hover tool and tap tool
hover = HoverTool(
tooltips=[('Local Authority', '@lad19nm'), (
'Daily Cases',
'@new_cases'), ('Population', '@population{0,0}'
), ('Weekly Cases per 100,000', '@cases_per_pop')])
local_uk_geo_plot.add_tools(hover)
local_uk_geo_plot.add_tools(TapTool())
# Adding color bar
color_bar = ColorBar(color_mapper=mapper['transform'], location=(0, 0))
local_uk_geo_plot.add_layout(color_bar, 'right')
# Adding recent trend figure
area_name = "Wandsworth"
cases_trend_df = la_cases_df.loc[la_cases_df.area_name == area_name]
cases_trend_df.date = pd.to_datetime(cases_trend_df.date,
format="%Y-%m-%d")
ninety_days_back = cases_trend_df.date.max() - timedelta(days=90)
cases_trend_df = cases_trend_df.loc[
cases_trend_df.date >= ninety_days_back]
cases_trend_cds = ColumnDataSource(cases_trend_df)
cases_trend_plot = Figure(title=f"New Cases in {area_name}",
plot_height=500,
plot_width=500,
name="cases_trend_plot",
x_axis_type="datetime")
cases_trend_plot.line(x='date',
y='new_cases',
legend="New Cases",
source=cases_trend_cds)
cases_trend_plot.line(x='date',
y='weekly_average',
line_color="orange",
legend="Weekly Average",
source=cases_trend_cds)
cases_trend_hover = HoverTool(tooltips=[('Date', '@date{%F}'),
('Daily Cases', '@new_cases'),
('Weekly Average',
'@weekly_average{int}')],
formatters={"@date": "datetime"})
cases_trend_plot.add_tools(cases_trend_hover)
def callback(attr, old, new):
index = geosource.selected.indices[0]
geojson = json.loads(geosource.geojson)
area_name = geojson['features'][index]['properties']['area_name']
# Adding recent trend figure
cases_trend_df = la_cases_df.loc[la_cases_df.area_name == area_name]
cases_trend_df.date = pd.to_datetime(cases_trend_df.date,
format="%Y-%m-%d")
ninety_days_back = cases_trend_df.date.max() - timedelta(days=90)
cases_trend_df = cases_trend_df.loc[
cases_trend_df.date >= ninety_days_back]
cases_trend_cds.data = cases_trend_df
cases_trend_plot.title.text = f"New Cases in {area_name}"
geosource.selected.on_change('indices', callback)
# Add the plots to the current document
curdoc().add_root(local_uk_geo_plot)
curdoc().add_root(cases_trend_plot)
class AppView(object):
def __init__(self, app_model):
self.model = app_model
self.create_layout()
def create_layout(self):
# create figure
self.x_range = Range1d(start=self.model.map_extent[0],
end=self.model.map_extent[2], bounds=None)
self.y_range = Range1d(start=self.model.map_extent[1],
end=self.model.map_extent[3], bounds=None)
self.fig = Figure(tools='wheel_zoom,pan',
x_range=self.x_range,
lod_threshold=None,
plot_width=self.model.plot_width,
plot_height=self.model.plot_height,
y_range=self.y_range)
self.fig.min_border_top = 0
self.fig.min_border_bottom = 10
self.fig.min_border_left = 0
self.fig.min_border_right = 0
self.fig.axis.visible = False
self.fig.xgrid.grid_line_color = None
self.fig.ygrid.grid_line_color = None
# add tiled basemap
self.tile_source = WMTSTileSource(url=self.model.basemap)
self.tile_renderer = TileRenderer(tile_source=self.tile_source)
self.fig.renderers.append(self.tile_renderer)
# add datashader layer
self.image_source = ImageSource(url=self.model.service_url,
extra_url_vars=self.model.shader_url_vars)
self.image_renderer = DynamicImageRenderer(image_source=self.image_source)
self.fig.renderers.append(self.image_renderer)
# add label layer
self.label_source = WMTSTileSource(url=self.model.labels_url)
self.label_renderer = TileRenderer(tile_source=self.label_source)
self.fig.renderers.append(self.label_renderer)
# Add a hover tool
hover_layer = HoverLayer()
hover_layer.field_name = self.model.field_title
hover_layer.is_categorical = self.model.field in self.model.categorical_fields
self.fig.renderers.append(hover_layer.renderer)
self.fig.add_tools(hover_layer.tool)
self.model.hover_layer = hover_layer
self.model.legend_side_vbox = VBox()
self.model.legend_bottom_vbox = VBox()
# add ui components
controls = []
axes_select = Select.create(name='Axes',
options=self.model.axes)
axes_select.on_change('value', self.on_axes_change)
controls.append(axes_select)
self.field_select = Select.create(name='Field', options=self.model.fields)
self.field_select.on_change('value', self.on_field_change)
controls.append(self.field_select)
self.aggregate_select = Select.create(name='Aggregate',
options=self.model.aggregate_functions)
self.aggregate_select.on_change('value', self.on_aggregate_change)
controls.append(self.aggregate_select)
transfer_select = Select.create(name='Transfer Function',
options=self.model.transfer_functions)
transfer_select.on_change('value', self.on_transfer_function_change)
controls.append(transfer_select)
color_ramp_select = Select.create(name='Color Ramp', options=self.model.color_ramps)
color_ramp_select.on_change('value', self.on_color_ramp_change)
controls.append(color_ramp_select)
spread_size_slider = Slider(title="Spread Size (px)", value=0, start=0,
end=10, step=1)
spread_size_slider.on_change('value', self.on_spread_size_change)
controls.append(spread_size_slider)
hover_size_slider = Slider(title="Hover Size (px)", value=8, start=4,
end=30, step=1)
hover_size_slider.on_change('value', self.on_hover_size_change)
controls.append(hover_size_slider)
controls.append(self.model.legend_side_vbox)
# add map components
basemap_select = Select.create(name='Basemap', value='Imagery',
options=self.model.basemaps)
basemap_select.on_change('value', self.on_basemap_change)
image_opacity_slider = Slider(title="Opacity", value=100, start=0,
end=100, step=1)
image_opacity_slider.on_change('value', self.on_image_opacity_slider_change)
basemap_opacity_slider = Slider(title="Basemap Opacity", value=100, start=0,
end=100, step=1)
basemap_opacity_slider.on_change('value', self.on_basemap_opacity_slider_change)
show_labels_chk = CheckboxGroup(labels=["Show Labels"], active=[0])
show_labels_chk.on_click(self.on_labels_change)
map_controls = [basemap_select, basemap_opacity_slider,
image_opacity_slider, show_labels_chk]
self.controls = VBox(width=200, height=600, children=controls)
self.map_controls = HBox(width=self.fig.plot_width, children=map_controls)
self.map_area = VBox(width=self.fig.plot_width, children=[self.map_controls,
self.fig,
self.model.legend_bottom_vbox])
self.layout = HBox(width=1366, children=[self.controls, self.map_area])
def update_image(self):
self.model.shader_url_vars['cachebust'] = str(uuid.uuid4())
self.image_renderer.image_source = ImageSource(url=self.model.service_url,
extra_url_vars=self.model.shader_url_vars)
def on_field_change(self, attr, old, new):
self.model.field_title = new
self.model.field = self.model.fields[new]
self.model.hover_layer.field_name = new
self.model.hover_layer.is_categorical = self.model.field in self.model.categorical_fields
self.update_image()
if not self.model.field:
self.aggregate_select.options = [dict(name="No Aggregates Available", value="")]
elif self.model.field in self.model.categorical_fields:
self.model.hover_layer.is_categorical = True
self.aggregate_select.options = [dict(name="Categorical", value="count_cat")]
else:
opts = [dict(name=k, value=k) for k in self.model.aggregate_functions.keys()]
self.aggregate_select.options = opts
self.model.hover_layer.is_categorical = False
def on_basemap_change(self, attr, old, new):
self.model.basemap = self.model.basemaps[new]
self.tile_renderer.tile_source = WMTSTileSource(url=self.model.basemap)
def on_hover_size_change(self, attr, old, new):
self.model.hover_layer.size = int(new)
def on_spread_size_change(self, attr, old, new):
self.model.spread_size = int(new)
self.update_image()
def on_axes_change(self, attr, old, new):
self.model.active_axes = self.model.axes[new]
self.update_image()
def on_aggregate_change(self, attr, old, new):
self.model.agg_function_name = new
self.update_image()
def on_transfer_function_change(self, attr, old, new):
self.model.transfer_function = self.model.transfer_functions[new]
self.update_image()
def on_color_ramp_change(self, attr, old, new):
self.model.color_ramp = self.model.color_ramps[new]
self.update_image()
def on_image_opacity_slider_change(self, attr, old, new):
self.image_renderer.alpha = new / 100
def on_basemap_opacity_slider_change(self, attr, old, new):
self.tile_renderer.alpha = new / 100
def on_labels_change(self, new):
self.label_renderer.alpha = 1 if new else 0
