def callbackselect_state(attr, old, new):
try:
div_state_pop.text = """<br>Total population of """+select_state.value+": "+f"{state_population_dict[select_state.value]:,d}"
except:
div_state_pop.text = """<br>Total population of """ + select_state.value + ": Not Available"
try:
#plot2.vbar(x=demography_age, top=list(demography_data.loc[demography_data['Location'] == select_state.value].values[0][1:-2]), width=0.7)
source_bar.data={
'x': demography_age,
'y': list(demography_data.loc[demography_data['Location'] == select_state.value].values[0][1:-2])
}
glyph = VBar(x='x', top='y', bottom=0, width=.8, fill_color="#6599ed")
plot2.add_glyph(source_bar, glyph)
except:
#plot2.vbar(x=demography_age, top=[0,0,0,0,0,0], width=0.7)
source_bar.data = {
'x': demography_age,
'y': [0,0,0,0,0,0]
}
glyph = VBar(x='x', top='y', bottom=0, width=.8, fill_color="#6599ed")
plot2.add_glyph(source_bar, glyph)
df = gp.get_group(state_name_dict[select_state.value])
date = df['date']
date = pd.to_datetime(date, format='%Y%m%d')
positive = df['positive']
negative = df['negative']
recovered = df['recovered']
death = df['death']
positive_increase = df['positiveIncrease']
death_increase = df['deathIncrease']
source1.data={
'x': date,
'y1': positive,
# 'y2' : negative,
'y3': recovered,
'y4': death,
'y5' : positive_increase,
'y6' : death_increase
}
plot.axis.visible = False
toolStat1 = "pan,box_zoom,wheel_zoom,reset,save"
plotStat1 = figure(title="Information content VS performance", sizing_mode='stretch_both', tools = toolStat1, output_backend="webgl", width=650, height=650, toolbar_location="above")
plotStat1.xaxis.axis_label = "Information content"
plotStat1.yaxis.axis_label = "Performance"
#Convert dataframe to datasource
linksource = ColumnDataSource(link)
source = ColumnDataSource(df)
#Draw nodes and lines for go visualiser and statistics plots
lines = MultiLine(xs='x', ys='y', line_alpha=0.2, name='edge', line_width=2.5, line_color='darksalmon')
edges_r = plot.add_glyph(source_or_glyph=linksource, glyph=lines)
if args.models == 2:
lines2 = MultiLine(xs='x', ys='y', line_alpha=0.1, name='edge', line_width=2.5, line_color='grey')
edges_2 = plot.add_glyph(source_or_glyph=linksource, glyph=lines2)
edges_2.visible = False
node1 = VBar(x="X", top="Top", bottom="Y", width=0.95, fill_color={'field': 'Score1', 'transform': color_mapper}, name='node1', line_width=0.1, line_color='black', fill_alpha=1)
node1_unselected = VBar(x="X", top="Top", bottom="Y", width=0.95, fill_color={'field': 'Score1', 'transform': color_mapper}, name='node1', line_width=0.1, line_color='black', fill_alpha=0.05)
nodes1 = plot.add_glyph(source_or_glyph=source, glyph=node1, nonselection_glyph=node1_unselected)
node2 = VBar(x="X", top="Y", bottom="Bottom", width=0.95, fill_color={'field': 'Score2', 'transform': color_mapper}, name='node2', line_width=0.1, line_color='black', fill_alpha=1)
node2_unselected = VBar(x="X", top="Y", bottom="Bottom", width=0.95, fill_color={'field': 'Score2', 'transform': color_mapper}, name='node2', line_width=0.1, line_color='black', fill_alpha=0.05)
nodes2 =plot.add_glyph(source_or_glyph=source, glyph = node2, nonselection_glyph=node2_unselected)
color_mapper_diff = LinearColorMapper(palette=cc.coolwarm, low=df['Difference'].min(), high=df['Difference'].max())
color_bar_diff = ColorBar(color_mapper=color_mapper_diff, ticker=BasicTicker(), title='Difference',
label_standoff=12, location=(0,0))
node3 = VBar(x="X", top="Top", bottom="Bottom", width=0.95, fill_color={'field': 'Difference', 'transform': color_mapper_diff}, name='node3', line_width=0.1, line_color='black', fill_alpha=1)
node3_unselected = VBar(x="X", top="Top", bottom="Bottom", width=0.95, fill_color={'field': 'Difference', 'transform': color_mapper_diff}, name='node3', line_width=0.1, line_color='black', fill_alpha=0.05)
nodes3 = plot.add_glyph(source_or_glyph=source, glyph = node3, nonselection_glyph= node3_unselected)
nodes3.visible = False
taptool = TapTool(renderers=[nodes1, nodes2])
else:
node1 = VBar(x="X", top="Top", bottom="Bottom", width=0.95, fill_color={'field': 'Score1', 'transform': color_mapper}, name='node1', line_width=0.1, line_color='black', fill_alpha=1)
def draw_population_map(*,
population_file,
water_file,
islands_file,
districts_file,
locations,
width,
height=None,
label_font_size,
add_legend=False,
legend_font_size,
legend_padding,
legend_spacing,
kind,
**kwargs):
kinds = 'mosaic', 'stacked bar', 'bar'
if kind not in kinds:
raise ValueError(f'Incorrect type parameter (Must be one of {kinds})')
data_ = pd.read_csv(population_file, index_col=0)
totals = data_.sum(axis=0)
palette = Category10_10[:len(data_)]
data_['colors'] = palette
groups = [g for g in data_.index]
water = gpd.read_file(water_file)
water = get_xy(multipolygons_to_polygons(water))
water_src = GeoJSONDataSource(geojson=water.to_json())
islands = gpd.read_file(islands_file)
islands = get_xy(multipolygons_to_polygons(islands))
islands_src = GeoJSONDataSource(geojson=islands.to_json())
districts = gpd.read_file(districts_file)
districts = get_xy(districts)
districts_src = GeoJSONDataSource(geojson=districts.to_json())
fig = figure(**kwargs)
fig.grid.grid_line_color = None
fig.patches(
xs='x',
ys='y',
source=water_src,
fill_color='#59d0ff',
fill_alpha=0.8,
line_color=None,
line_width=0,
)
fig.patches(
xs='x',
ys='y',
source=islands_src,
fill_color='white',
line_color=None,
line_width=0,
)
fig.patches(
xs='x',
ys='y',
source=districts_src,
fill_color=None,
line_color='black',
line_width=0,
)
if kind.lower() == 'mosaic':
for col, loc in locations.items():
width = width * np.sqrt(totals[col] / totals.max())
mosaic_data = get_mosaic(
data_,
data_col=col,
x=loc.x,
y=loc.y,
height=width * np.cos(loc.y * 0.01745),
width=width,
)
fig.patches(**mosaic_data)
if kind.lower() == 'stacked bar':
for col, loc in locations.items():
loc_height = height * totals[col] / totals.max()
stack_data = get_stacked_bar(
data_,
data_col=col,
x=loc.x,
y=loc.y,
height=loc_height,
width=width,
)
fig.patches(**stack_data)
if kind.lower() == 'bar':
for col, loc in locations.items():
loc_height = height * totals[col] / totals.max()
source = ColumnDataSource(
get_bar(
data_,
data_col=col,
x=loc.x,
y=loc.y,
height=loc_height,
width=width,
))
glyph = VBar(
x='x',
width=width,
top='top',
bottom='bottom',
fill_color='color',
)
fig.add_glyph(source, glyph)
labels = LabelSet(
x='x',
y='top',
text='values',
text_align='center',
text_font_size=label_font_size,
source=source,
)
fig.add_layout(labels)
if add_legend:
for group, color in zip(groups, palette):
fig.circle(x=[], y=[], size=12, fill_color=color, legend=group)
fig.legend.location = 'bottom_left'
fig.legend.background_fill_alpha = 1
fig.legend.border_line_color = 'black'
fig.legend.label_text_font_size = legend_font_size
fig.legend.spacing = legend_spacing
fig.legend.padding = legend_padding
fig.title.text_font_size = '30px'
return fig
N = 9
x = np.linspace(-2, 2, N)
y = x**2
source = ColumnDataSource(dict(
x=x,
top=y,
))
plot = Plot(title=None,
width=300,
height=300,
min_border=0,
toolbar_location=None)
glyph = VBar(x="x", top="top", bottom=0, width=0.5, fill_color="#b3de69")
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis()
plot.add_layout(yaxis, 'left')
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
curdoc().add_root(plot)
show(plot)
def bokehDrawArray(dataFrame, query, figureArray, **kwargs):
"""
Wrapper bokeh draw array of figures
:param dataFrame: - input data frame
:param query: - query
:param figureArray: - figure array
:param kwargs:
:return:
variable list:
* pAll
* handle
* source
* plotArray
See example test:
RootInteractive/InteractiveDrawing/bokeh/test_bokehDrawSA.py
"""
options = {
'line': -1,
'size': 2,
'tools': 'pan,box_zoom, wheel_zoom,box_select,lasso_select,reset,save',
'tooltips': [],
'y_axis_type': 'auto',
'x_axis_type': 'auto',
'plot_width': 600,
'plot_height': 400,
'errX': '',
'errY': '',
'commonX': 0,
'commonY': 0,
'ncols': -1,
'layout': '',
'widgetLayout': '',
'palette': Spectral6,
"marker": "square",
"markers": bokehMarkers,
"color": "#000000",
"colors": 'Category10',
"colorZvar": '',
"filter": '',
'doDraw': 0
}
options.update(kwargs)
dfQuery = dataFrame.query(query)
if hasattr(dataFrame, 'metaData'):
dfQuery.metaData = dataFrame.metaData
logging.info(dfQuery.metaData)
# Check/resp. load derived variables
i: int
for i, variables in enumerate(figureArray):
if len(variables) > 1 and variables[0] is not "table":
lengthX = len(variables[0])
lengthY = len(variables[1])
length = max(len(variables[0]), len(variables[1]))
if len(variables) > 2:
optionLocal = options.copy()
optionLocal.update(variables[2])
else:
optionLocal = options
for j in range(0, length):
dfQuery, varNameX = pandaGetOrMakeColumn(
dfQuery, variables[0][j % lengthX])
dfQuery, varNameY = pandaGetOrMakeColumn(
dfQuery, variables[1][j % lengthY])
if ('errY'
in optionLocal.keys()) & (optionLocal['errY'] != ''):
seriesErrY = dfQuery.eval(optionLocal['errY'])
if varNameY + '_lower' not in dfQuery.columns:
seriesLower = dfQuery[varNameY] - seriesErrY
dfQuery[varNameY + '_lower'] = seriesLower
if varNameY + '_upper' not in dfQuery.columns:
seriesUpper = dfQuery[varNameY] + seriesErrY
dfQuery[varNameY + '_upper'] = seriesUpper
if ('errX'
in optionLocal.keys()) & (optionLocal['errX'] != ''):
seriesErrX = dfQuery.eval(optionLocal['errX'])
if varNameX + '_lower' not in dfQuery.columns:
seriesLower = dfQuery[varNameX] - seriesErrX
dfQuery[varNameX + '_lower'] = seriesLower
if varNameX + '_upper' not in dfQuery.columns:
seriesUpper = dfQuery[varNameX] + seriesErrX
dfQuery[varNameX + '_upper'] = seriesUpper
try:
source = ColumnDataSource(dfQuery)
except:
logging.error("Invalid source:", source)
# define default options
plotArray = []
colorAll = all_palettes[options['colors']]
if isinstance(figureArray[-1], dict):
options.update(figureArray[-1])
for i, variables in enumerate(figureArray):
logging.info(i, variables)
if isinstance(variables, dict): continue
if variables[0] == 'table':
TOptions = {'include': '', 'exclude': ''}
if len(variables) > 1:
TOptions.update(variables[1])
plotArray.append(
makeBokehDataTable(dfQuery, source, TOptions['include'],
TOptions['exclude']))
continue
xAxisTitle = ""
yAxisTitle = ""
# zAxisTitle = ""
plotTitle = ""
for varY in variables[1]:
if hasattr(dfQuery, "meta"):
yAxisTitle += dfQuery.meta.metaData.get(
varY + ".AxisTitle", varY)
else:
yAxisTitle += varY
yAxisTitle += ','
for varX in variables[0]:
if hasattr(dfQuery, "meta"):
xAxisTitle += dfQuery.meta.metaData.get(
varX + ".AxisTitle", varX)
else:
xAxisTitle += varX
xAxisTitle += ','
xAxisTitle = xAxisTitle[:-1]
yAxisTitle = yAxisTitle[:-1]
plotTitle += yAxisTitle + " vs " + xAxisTitle
figureI = figure(plot_width=options['plot_width'],
plot_height=options['plot_height'],
title=plotTitle,
tools=options['tools'],
tooltips=options['tooltips'],
x_axis_type=options['x_axis_type'],
y_axis_type=options['y_axis_type'])
figureI.xaxis.axis_label = xAxisTitle
figureI.yaxis.axis_label = yAxisTitle
# graphArray=drawGraphArray(df, variables)
lengthX = len(variables[0])
lengthY = len(variables[1])
length = max(len(variables[0]), len(variables[1]))
color_bar = None
mapperC = None
for i in range(0, length):
dfQuery, varNameY = pandaGetOrMakeColumn(dfQuery,
variables[1][i % lengthY])
dummy, varNameX = pandaGetOrMakeColumn(dfQuery,
variables[0][i % lengthX])
optionLocal = copy.copy(options)
optionLocal['color'] = colorAll[max(length, 4)][i]
optionLocal['marker'] = optionLocal['markers'][i]
if len(variables) > 2:
logging.info("Option", variables[2])
optionLocal.update(variables[2])
varX = variables[0][i % lengthX]
varY = variables[1][i % lengthY]
if (len(optionLocal["colorZvar"]) > 0):
logging.info(optionLocal["colorZvar"])
varColor = optionLocal["colorZvar"]
mapperC = linear_cmap(field_name=varColor,
palette=options['palette'],
low=min(dfQuery[varColor]),
high=max(dfQuery[varColor]))
optionLocal["color"] = mapperC
color_bar = ColorBar(color_mapper=mapperC['transform'],
width=8,
location=(0, 0),
title=varColor)
# zAxisTitle +=varColor + ","
# view = CDSView(source=source, filters=[GroupFilter(column_name=optionLocal['filter'], group=True)])
figureI.scatter(x=varNameX,
y=varNameY,
fill_alpha=1,
source=source,
size=optionLocal['size'],
color=optionLocal["color"],
marker=optionLocal["marker"],
legend_label=varY + " vs " + varX)
if ('errX' in optionLocal.keys()) & (optionLocal['errX'] != ''):
errors = HBar(y=varNameY,
left=varNameX + "_lower",
right=varNameX + "_upper",
line_color=optionLocal["color"])
figureI.add_glyph(source, errors)
if ('errY' in optionLocal.keys()) & (optionLocal['errY'] != ''):
errors = VBar(x=varNameX,
bottom=varNameY + "_lower",
top=varNameY + "_upper",
line_color=optionLocal["color"])
figureI.add_glyph(source, errors)
# errors = Band(base=varNameX, lower=varNameY+"_lower", upper=varNameY+"_upper",source=source)
# figureI.add_layout(errors)
if color_bar != None:
figureI.add_layout(color_bar, 'right')
figureI.legend.click_policy = "hide"
# zAxisTitle=zAxisTitle[:-1]
# if(len(zAxisTitle)>0):
# plotTitle += " Color:" + zAxisTitle
# figureI.title = plotTitle
plotArray.append(figureI)
if isinstance(options['layout'], list):
pAll = processBokehLayoutArray(options['layout'], plotArray)
layoutList = [pAll]
else:
if len(options['layout']) > 0: # make figure according layout
x, layoutList, optionsLayout = processBokehLayout(
options["layout"], plotArray)
pAll = gridplotRow(layoutList, **optionsLayout)
if options['doDraw'] > 0:
show(pAll)
return pAll, source, layoutList, dfQuery
#source = ColumnDataSource(demography_data.loc[demography_data['Location'] == state])
source_bar = ColumnDataSource(data={
'x' : demography_age,
'y' : list(demography_data.loc[demography_data['Location'] == state].values[0][1:-2])
})
plot2=figure(
title='Agewise distribution of the state',
x_axis_label = 'Age of the Population',
y_axis_label = 'Percentage of population',
x_range=FactorRange(factors=source_bar.data['x']),
plot_width=480, plot_height=400,
y_range=(0, 1)
)
#plot2.vbar(x=demography_age, top=list(demography_data.loc[demography_data['Location'] == state].values[0][1:-2]), width=0.7)
glyph = VBar(x='x', top='y', bottom=0, width=.8, fill_color="#6599ed")
plot2.add_glyph(source_bar, glyph)
print(list(demography_data.loc[demography_data['Location'] == state].values[0][1:-2]))
div_head = Div(text="""<h2>US vs states covid-19 spread plots</h2>""", width=500, height=50)
div_explain = Div(text="""<h3>US plots(thinner lines and smaller dots) are normalized according to the population of the compared state</h3>""", width=300, height=50)
div_us_pop = Div(text="""<br><br><br>Total population of US: """+f"{total_us_population:,d}", width=300, height=50)
div_state_pop = Div(text="""<br>Total population of """+state+" : "+f"{state_population_dict[state]:,d}", width=300, height=50)
checkbox_group = CheckboxGroup(labels=["positive", "recovered", "death", "positive increase", "death increase"], active=[0,1,2,3,4])
select_state = Select(title="Select State", value="Minnesota", options=states_fullname) #list(list(gp.groups.keys())))
def callbackselect_state(attr, old, new):
try:
div_state_pop.text = """<br>Total population of """+select_state.value+": "+f"{state_population_dict[select_state.value]:,d}"
fill_alpha=0.8,
line_color=None)
pltMap.add_glyph(source,
pltMapMarker,
selection_glyph=pltMapMarkerSelected,
nonselection_glyph=pltMapMarker)
pltHist = figure(x_range=Range1d(start=1, end=12),
tools=int_tools,
plot_width=800,
plot_height=200)
pltHist.xaxis.ticker = df['tx_date'].dt.month.unique()
pltHistMarker = VBar(x="x",
top="top",
bottom="bottom",
fill_color="navy",
width=1,
fill_alpha=0.8,
line_color=None)
pltHistMarkerSelected = VBar(x="x",
top="top",
bottom="bottom",
fill_color="orange",
width=1,
fill_alpha=0.8,
line_color=None)
pltHist.add_glyph(sourceHist,
pltHistMarker,
selection_glyph=pltHistMarkerSelected,
nonselection_glyph=pltHistMarker)