def XPlineplot(selectedPlayer):
"""Create individual player XP line plot.
"""
plotdata = activityToPointsMapper(selectedPlayer).sum(axis=1).reset_index()
plotdata.columns = ['semana', 'XP']
plotdata['semana'] = plotdata['semana'].apply(pd.to_datetime)
XPlineplot = alt.Chart(
plotdata,
width=697,
height=400,
title='XP semanal - {}'.format(selectedPlayer)).mark_area(
point={
'color': '#744a98',
'size': 70
},
line={
'color': '#744a98'
},
color=alt.Gradient(gradient='linear',
stops=[
alt.GradientStop(color='#23ac76', offset=0),
alt.GradientStop(color='#23ac7640',
offset=1)
],
x1=1.5,
x2=1.25,
y1=1,
y2=0.1)).encode(
alt.X('semana:T',
axis=alt.Axis(values=list(
plotdata['semana'].array),
format='%d/%m',
grid=False)),
alt.Y('XP:Q'))
return XPlineplot
def get_line(data_dict, sampling_freq, **kwargs):
source = pd.DataFrame(list(data_dict))
source.ts = source.ts.astype("datetime64[ms]")
source = source.set_index("ts").resample(sampling_freq).mean().reset_index().bfill()
base_chart = (
alt.Chart(source)
.mark_area(
line={"color": "darkgreen"},
color=alt.Gradient(
gradient="linear",
stops=[
alt.GradientStop(color="white", offset=0),
alt.GradientStop(color="darkgreen", offset=1),
],
x1=1,
x2=1,
y1=1,
y2=0,
),
)
.encode(
x=alt.X("ts:T"),
y=alt.Y(
"price:Q",
scale=alt.Scale(domain=[source.price.min(), source.price.max()]),
),
)
)
return base_chart
def return_chart(self, start, end):
source = self.calculate_log_return(start, end)
chart = (
alt.Chart(source)
.mark_area(
line={"color": "darkblue"},
color=alt.Gradient(
gradient="linear",
stops=[
alt.GradientStop(color="white", offset=0),
alt.GradientStop(color="darkblue", offset=1),
],
x1=1,
x2=1,
y1=1,
y2=0,
),
)
.encode(
x=alt.X("Date:T", axis=alt.Axis(format="%Y/%m")),
y=alt.Y("Cum Return:Q", title="Cumulative Return"),
)
)
return chart
def type_gradient(df, type, color):
return alt.Chart(df).transform_filter(
f'datum.symbol==="{type}"').mark_area(
line={
'color': color
},
color=alt.Gradient(
gradient='linear',
stops=[
alt.GradientStop(color='white', offset=0),
alt.GradientStop(color=color, offset=1)
],
x1=1,
x2=1,
y1=1,
y2=0)).encode(x=alt.X('date:T',
axis=alt.Axis(title='Weeks',
titleFontSize=18,
titlePadding=20,
titleColor='gray')),
y=alt.Y('number:Q',
axis=alt.Axis(title='Number of Cases',
titleFontSize=18,
titlePadding=20,
titleColor='gray')))
def Charts(df,clm,C):
chart=alt.Chart(df[df.Legend==clm]).mark_area(line={'color':C},
color=alt.Gradient( gradient='linear',
stops=[alt.GradientStop(color='white', offset=.01),
alt.GradientStop(color=C, offset=1)] ,
x1=1,
x2=1,
y1=1,
y2=0)
).encode(
x='Days',
y='% of Population:Q',
tooltip='% of Population',
opacity=alt.value(.5)).properties(
height=300,
width=730).interactive()
return chart
def alt_ploting(df):
df.reset_index(inplace=True)
df['Dates'] = df.Dates.dt.date
col_names = df.columns
# print(col_names)
c = alt.Chart(df).mark_area(line={
'color': 'darkgreen'
},
color=alt.Gradient(
gradient='linear',
stops=[
alt.GradientStop(color='white',
offset=0),
alt.GradientStop(color='darkred',
offset=1)
],
x1=1,
x2=1,
y1=1,
y2=0)).encode(alt.X('Dates:T'),
alt.Y('LR_Prediction:Q'))
st.altair_chart(c, use_container_width=True)
def returns_chart(self, start=None, end=None):
returns = self.check_returns(returns)
returns_chart = (
alt.Chart(returns)
.mark_area(
line={"color": "darkgreen"},
color=alt.Gradient(
gradient="linear",
stops=[
alt.GradientStop(color="white", offset=0),
alt.GradientStop(color="darkgreen", offset=1),
],
x1=1,
x2=1,
y1=1,
y2=0,
),
)
.encode(
x=alt.X("Date:T", axis=alt.Axis(format="%Y/%m/%d")),
y=alt.Y("Cum Return:Q", title="Cumulative Return"),
)
)
return returns_chart
"""
Area Chart with Gradient
------------------------
This example shows how to make an area chart with a gradient fill.
For more information about gradient options see the Vega-Lite `Gradient documentation <https://vega.github.io/vega-lite/docs/types.html#gradient>`_.
"""
# category: area charts
import altair as alt
from vega_datasets import data
source = data.stocks()
alt.Chart(source).transform_filter(
'datum.symbol==="GOOG"'
).mark_area(
line={'color':'darkgreen'},
color=alt.Gradient(
gradient='linear',
stops=[alt.GradientStop(color='white', offset=0),
alt.GradientStop(color='darkgreen', offset=1)],
x1=1,
x2=1,
y1=1,
y2=0
)
).encode(
alt.X('date:T'),
alt.Y('price:Q')
)
def altairEdgePercentageHistoram(df):
"""
Construct an altair area plot for dodo price edge histogram.
Parameters
----------
df : pandas.core.frame.DataFrame
pandas dataframe containing the stored historical price data for dodo and
chainlink for a given currency pair.
Returns
-------
chart : altair.vegalite.v4.api.LayerChart
altair composite chart containing histogram data of price advantage percentage.
"""
source = pd.DataFrame()
currentEdge = df.dodoPriceEdgePercentage.values[-1]
#define vertical line data for zero and for current (most recent) edge value
verticals = pd.DataFrame([{"zero": 0, "currentEdge": currentEdge}])
yy = df.dodoPriceEdgePercentage
x = np.linspace(np.min(yy), np.max(yy), 200)
#use scipy stats module to build kde histogram function, rather than dealing with bins:
y = stats.gaussian_kde(yy)(x)
#get the symmetric x limits based on max mag data value:
maxmag = np.max(np.abs(yy))
xlims = (-maxmag, maxmag)
source['percentEdge'] = x
source['kdeWeight'] = y
#build positive edge histogram (green):
histPos = alt.Chart(source).transform_filter(
alt.datum.percentEdge >= 0).mark_area(
line={
'color': 'darkgreen'
},
color=alt.Gradient(gradient='linear',
stops=[
alt.GradientStop(color='lightGreen',
offset=0),
alt.GradientStop(color='darkGreen',
offset=1)
],
x1=1,
x2=1,
y1=1,
y2=0)).encode(
alt.X('percentEdge:Q',
scale=alt.Scale(domain=xlims)),
alt.Y('kdeWeight:Q')).interactive()
#build negative edge histogram (red)
histNeg = alt.Chart(source).transform_filter(
alt.datum.percentEdge < 0).mark_area(
line={
'color': '#8b0000'
},
color=alt.Gradient(
gradient='linear',
stops=[
alt.GradientStop(color='#E6676B', offset=0),
alt.GradientStop(color='#8b0000', offset=1)
],
x1=1,
x2=1,
y1=1,
y2=0)).encode(
alt.X('percentEdge:Q',
axis=alt.Axis(title="DODO % Price Edge",
titleFontWeight=500,
titleFontSize=20),
scale=alt.Scale(domain=xlims)),
alt.Y('kdeWeight:Q',
axis=alt.Axis(title="historical distribution",
labels=False,
titleFontWeight=500,
titleFontSize=20))).interactive()
#add vertical line at zero for visual reference
zeroRule = alt.Chart(verticals).mark_rule(color="white").encode(
alt.X("zero:Q", axis=alt.Axis(title='')))
#add vertical line at current edge value in yellow for visual reference
currentEdgeRule = alt.Chart(verticals).mark_rule(color="yellow").encode(
alt.X("currentEdge:Q", axis=alt.Axis(title=''))).interactive()
#construct chart as composite of components charts and lines
chart = (zeroRule + currentEdgeRule + histNeg + histPos).interactive()
return chart
y=alt.Y("Aminoacid", sort="-x"))
st.altair_chart(p)
########################
# Predictor results
########################
# Area Chart for predictor
df_predictor = pd.DataFrame({
"Scores":
data[selected_protein]["predictors"][selected_predictor]['scores']
})
df_predictor['Pos'] = list(range(1, df_predictor.shape[0] + 1))
st.subheader("Results form {} predictor".format(selected_predictor))
p = alt.Chart(df_predictor).mark_area(
line={
"color": 'darkblue'
},
color=alt.Gradient(gradient="linear",
stops=[
alt.GradientStop(color='white', offset=0),
alt.GradientStop(color='darkblue', offset=1)
],
x1=1,
x2=1,
y1=1,
y2=0)).encode(alt.X("Pos", title="Position"),
alt.Y("Scores")).properties(width=600,
height=180)
st.altair_chart(p)
def bv_areaPlot(data, engine, xlabel, ylabel1, ylabel2):
data = data.copy()
data.rename(columns={'plotY':ylabel1, 'plotX1':ylabel2}, inplace=True)
if engine == 'Static':
fig, axes = plt.subplots(figsize=(9,6))
_index = data.index.tolist()
axes.fill_between(_index, data[ylabel1].values)
axes.legend([ylabel1], loc=0)
axes_r = axes.twinx()
axes_r.fill_between(_index, data[ylabel2].values, color='orange')
axes_r.legend([ylabel2], loc=0)
axes.set_xlabel(xlabel, fontsize = 15)
axes.set_ylabel(ylabel1, fontsize = 15)
axes_r.set_ylabel(ylabel2, fontsize = 15)
axes.grid(b=True, which='major', color='k', linewidth=0.25)
plt.close()
return pn.pane.Matplotlib(fig, tight=True)
elif engine == 'Interactive':
data=data.dropna()
# Selection Brush
brush = alt.selection(type='interval', encodings=['x'], name='isel')
# Base Plot
base = alt.Chart(data.reset_index())
base = base.encode(x = alt.X('{0}:T'.format(data.index.name), title=''),
tooltip = ylabel1)
base = base.properties(width = 580, height = 275)
# Upper Plot
upper1 = base.mark_area(line={'color':'#3d84ba'},
color=alt.Gradient(
gradient='linear',
stops=[alt.GradientStop(color='white', offset=0),
alt.GradientStop(color='#3d84ba', offset=1)],
x1=1, x2=1,
y1=1, y2=0
))
upper1 = upper1.encode(x = alt.X('{0}:T'.format(data.index.name), scale=alt.Scale(domain=brush), title=''),
y = alt.Y('{0}:Q'.format(ylabel1), scale=alt.Scale(zero=False), axis=alt.Axis(format='~s')))
upper2 = base.mark_area(line={'color':'#f57542'},
color=alt.Gradient(
gradient='linear',
stops=[alt.GradientStop(color='white', offset=0),
alt.GradientStop(color='#f57542', offset=1)],
x1=1, x2=1,
y1=1, y2=0
))
upper2 = upper2.encode(x = alt.X('{0}:T'.format(data.index.name), scale=alt.Scale(domain=brush), title=''),
y = alt.Y('{0}:Q'.format(ylabel2), scale=alt.Scale(zero=False), axis=alt.Axis(format='~s')))
# Lower Plot
lower = base.mark_area(line={'color':'darkgray'},
color=alt.Gradient(
gradient='linear',
stops=[alt.GradientStop(color='white', offset=0),
alt.GradientStop(color='darkgray', offset=1)],
x1=1, x2=1,
y1=1, y2=0
))
lower = lower.encode(y=alt.Y('{0}:Q'.format(ylabel1), title='', axis=None))
lower = lower.properties(height=20)
lower = lower.add_selection(brush)
lower.encoding.x.title = 'Interval Selection'
# Base Statistics1
base_stat1 = upper1.transform_filter(brush)
base_stat1 = base_stat1.transform_aggregate(Mean1='mean({0})'.format(ylabel1),
StdDev1='stdev({0})'.format(ylabel1),
Var1='variance({0})'.format(ylabel1))
label_stat1 = base_stat1.transform_calculate(stat_label1="'Mean = ' + format(datum.Mean1, '~s') + \
'; Standard Deviation = ' + format(datum.StdDev1, '~s') +\
'; Variance = ' + format(datum.Var1, '~s')")
label_stat1 = label_stat1.mark_text(align='left', baseline='bottom', color='#3d84ba')
label_stat1 = label_stat1.encode(x=alt.value(0.0), y=alt.value(12.0), text=alt.Text('stat_label1:N'))
# Base Statistics2
base_stat2 = upper2.transform_filter(brush)
base_stat2 = base_stat2.transform_aggregate(Mean2='mean({0})'.format(ylabel2),
StdDev2='stdev({0})'.format(ylabel2),
Var2='variance({0})'.format(ylabel2))
label_stat2 = base_stat2.transform_calculate(stat_label1="'Mean = ' + format(datum.Mean2, '~s') + \
'; Standard Deviation = ' + format(datum.StdDev2, '~s') +\
'; Variance = ' + format(datum.Var2, '~s')")
label_stat2 = label_stat2.mark_text(align='left', baseline='bottom', color='#f57542')
label_stat2 = label_stat2.encode(x=alt.value(0.0), y=alt.value(25.0), text=alt.Text('stat_label1:N'))
upper1 = upper1 + label_stat1
upper2 = upper2 + label_stat2
upper = (upper1+upper2).resolve_scale(y='independent')
## Y LABEL 1
# Values
_ymean_uu1 = data[ylabel1].max()
_ymean1 = data[ylabel1].mean()
# Inspired from :- https://stats.stackexchange.com/a/350278
_maxvar_in_slice1 = ((data[ylabel1].max()-data[ylabel1].min())/2)**2
_ystd_uu1 = np.sqrt(_maxvar_in_slice1)
_ystd1 = data[ylabel1].std()
_yvar_uu1 = _maxvar_in_slice1
_yvar1 = data[ylabel1].var()
# Stat Bar Base
stats_barbase1 = base_stat1.mark_bar(color='#3d84ba')
stats_barbase1 = stats_barbase1.properties(width = 188, height = 20)
# Mean Bar
mean_bar1 = stats_barbase1.encode(x=alt.X('Mean1:Q', title='',
scale=alt.Scale(domain=[-_ymean_uu1,_ymean_uu1]),
axis=alt.Axis(format='~s')), y=alt.value(10.5))
totmean_line1 = alt.Chart(pd.DataFrame({'x': [_ymean1]}))
totmean_line1 = totmean_line1.mark_rule(color='red', size=5)
totmean_line1 = totmean_line1.encode(x='x')
mean_bar1 += totmean_line1
# Standard Deviation Bar
std_bar1 = stats_barbase1.encode(x=alt.X('StdDev1:Q', title='',
scale=alt.Scale(domain=[-_ystd_uu1,_ystd_uu1]),
axis=alt.Axis(format='~s')), y=alt.value(10.5))
totstd_line1 = alt.Chart(pd.DataFrame({'x': [_ystd1]}))
totstd_line1 = totstd_line1.mark_rule(color='red', size=5)
totstd_line1 = totstd_line1.encode(x='x')
std_bar1 += totstd_line1
# Variance Bar
var_bar1 = stats_barbase1.encode(x=alt.X('Var1:Q', title='',
scale=alt.Scale(domain=[-_yvar_uu1,_yvar_uu1]),
axis=alt.Axis(format='~s')), y=alt.value(10.5))
totvar_line1 = alt.Chart(pd.DataFrame({'x': [_yvar1]}))
totvar_line1 = totvar_line1.mark_rule(color='red', size=5)
totvar_line1 = totvar_line1.encode(x='x')
var_bar1 += totvar_line1
## Y LABEL 2
# Values
_ymean_uu2 = data[ylabel2].max()
_ymean2 = data[ylabel2].mean()
# Inspired from :- https://stats.stackexchange.com/a/350278
_maxvar_in_slice2 = ((data[ylabel2].max()-data[ylabel2].min())/2)**2
_ystd_uu2 = np.sqrt(_maxvar_in_slice2)
_ystd2 = data[ylabel2].std()
_yvar_uu2 = _maxvar_in_slice2
_yvar2 = data[ylabel2].var()
# Stat Bar Base
stats_barbase2 = base_stat2.mark_bar(color='#f57542')
stats_barbase2 = stats_barbase2.properties(width = 188, height = 20)
# Mean Bar
mean_bar2 = stats_barbase2.encode(x=alt.X('Mean2:Q', title='Mean',
scale=alt.Scale(domain=[-_ymean_uu2,_ymean_uu2]),
axis=alt.Axis(format='~s')), y=alt.value(10.5))
totmean_line2 = alt.Chart(pd.DataFrame({'x': [_ymean2]}))
totmean_line2 = totmean_line2.mark_rule(color='red', size=5)
totmean_line2 = totmean_line2.encode(x='x')
mean_bar2 += totmean_line2
# Standard Deviation Bar
std_bar2 = stats_barbase2.encode(x=alt.X('StdDev2:Q', title='Std',
scale=alt.Scale(domain=[-_ystd_uu2,_ystd_uu2]),
axis=alt.Axis(format='~s')), y=alt.value(10.5))
totstd_line2 = alt.Chart(pd.DataFrame({'x': [_ystd2]}))
totstd_line2 = totstd_line2.mark_rule(color='red', size=5)
totstd_line2 = totstd_line2.encode(x='x')
std_bar2 += totstd_line2
# Variance Bar
var_bar2 = stats_barbase2.encode(x=alt.X('Var2:Q', title='Var',
scale=alt.Scale(domain=[-_yvar_uu2,_yvar_uu2]),
axis=alt.Axis(format='~s')), y=alt.value(10.5))
totvar_line2 = alt.Chart(pd.DataFrame({'x': [_yvar2]}))
totvar_line2 = totvar_line2.mark_rule(color='red', size=5)
totvar_line2 = totvar_line2.encode(x='x')
var_bar2 += totvar_line2
# Concatenated
# p = alt.vconcat(upper+label_stat, mean_bar|std_bar|var_bar, lower).configure_concat(spacing=2)
p = alt.vconcat(upper, mean_bar1|std_bar1|var_bar1, mean_bar2|std_bar2|var_bar2, lower).configure_concat(spacing=2)
p = p.configure_axisLeft(labelColor = '#3d84ba', titleColor = '#3d84ba')
p = p.configure_axisRight(labelColor = '#f57542', titleColor = '#f57542')
return p
def uv_candlestickPlot(data, engine, xlabel, ylabel):
data = data.copy()
if engine == 'Static':
fig, _ = mpf.plot(data,
type='candlestick',
figsize=(9, 6),
volume=True,
style='charles',
returnfig=True)
plt.close()
return pn.pane.Matplotlib(fig, tight=True)
elif engine == 'Interactive':
data.columns = [k.lower() for k in data.columns]
# Selection Brush
brush = alt.selection(type='interval', encodings=['x'])
open_close_color = alt.condition("datum.open <= datum.close",
alt.value("#06982d"),
alt.value("#ae1325"))
base = alt.Chart(data.reset_index())
base = base.encode(alt.X('{0}:T'.format(data.index.name),
title='',
axis=None,
scale=alt.Scale(domain=brush)),
color=open_close_color,
tooltip=['open', 'high', 'low', 'close'])
# Rule
rule = base.mark_rule()
rule = rule.encode(
alt.Y('low:Q', title='Price', scale=alt.Scale(zero=False)),
alt.Y2('high:Q'))
# Bars
bar = base.mark_bar()
bar = bar.encode(alt.Y('open:Q'), alt.Y2('close:Q'))
# Candlestick Chart
candlestick_chart = rule + bar
candlestick_chart = candlestick_chart.properties(width=600, height=280)
candlestick_chart = candlestick_chart.transform_filter(brush)
# Volume Chart
volume_chart = alt.Chart(data.reset_index())
volume_chart = volume_chart.mark_bar()
volume_chart = volume_chart.encode(alt.X(
'{0}:T'.format(data.index.name),
title='',
scale=alt.Scale(domain=brush)),
alt.Y('volume:Q',
axis=alt.Axis(format='~s')),
color=open_close_color)
volume_chart = volume_chart.transform_filter(brush)
volume_chart = volume_chart.properties(width=600, height=60)
# Selection Chart
lower = alt.Chart(data.reset_index())
lower = lower.mark_area(line={'color': 'darkgray'},
color=alt.Gradient(
gradient='linear',
stops=[
alt.GradientStop(color='white',
offset=0),
alt.GradientStop(color='darkgray',
offset=1)
],
x1=1,
x2=1,
y1=1,
y2=0))
lower = lower.encode(x=alt.X('{0}:T'.format(data.index.name)),
y=alt.Y('{0}:Q'.format('close'), title=''))
lower = lower.properties(height=20, width=600)
lower = lower.add_selection(brush)
lower.encoding.x.title = 'Interval Selection'
# Concatenated Chart
p = alt.vconcat(candlestick_chart, volume_chart,
lower).configure_concat(spacing=0)
return p
def uv_expSmoothPlot(data, engine, xlabel, ylabel):
# Data Prep
data = data.copy()
data.rename(columns={'plotX1': ylabel}, inplace=True)
_config_expspan = TSMAD_CONFIGS['plotting.uv.exp_span']
if isinstance(_config_expspan, float):
exp_span = max([int(_config_expspan * data.shape[0]), 3])
elif isinstance(_config_expspan, int):
exp_span = _config_expspan
exp_colname = 'Moving Average ({0})'.format(exp_span)
data[exp_colname] = data[ylabel].ewm(span=exp_span).mean()
if engine == 'Static':
fig = data[[ylabel, exp_colname]].plot(figsize=(9, 6),
legend=True).figure
plt.xlabel(xlabel, fontsize=15)
plt.ylabel(ylabel, fontsize=15)
plt.grid(b=True, which='major', color='k', linewidth=0.25)
plt.grid(b=True, which='minor', color='k', linewidth=0.125)
plt.close()
return pn.pane.Matplotlib(fig, tight=True)
elif engine == 'Interactive':
# Selection Brush
brush = alt.selection(type='interval', encodings=['x'], name='isel')
# Base Plot
base = alt.Chart(data.reset_index()).mark_line()
base = base.encode(x=alt.X('{0}:T'.format(data.index.name), title=''),
tooltip=ylabel)
base = base.properties(width=620, height=360)
# Upper Plot
x_ecoder = x = alt.X('{0}:T'.format(data.index.name),
scale=alt.Scale(domain=brush),
title='')
upper = base.encode(x=x_ecoder,
y=alt.Y('{0}:Q'.format(ylabel),
scale=alt.Scale(zero=False),
axis=alt.Axis(format='~s')),
tooltip=[ylabel])
# Exponential Smoothing Line
exp_line = upper.encode(x=alt.X('{0}:T'.format(data.index.name),
scale=alt.Scale(domain=brush),
title=''),
color=alt.ColorValue('red'),
y=alt.Y('{0}:Q'.format(exp_colname),
scale=alt.Scale(zero=False),
axis=alt.Axis(format='~s')))
# Lower Plot
lower = base.mark_area(line={'color': 'darkgray'},
color=alt.Gradient(
gradient='linear',
stops=[
alt.GradientStop(color='white',
offset=0),
alt.GradientStop(color='darkgray',
offset=1)
],
x1=1,
x2=1,
y1=1,
y2=0))
lower = lower.encode(
y=alt.Y('{0}:Q'.format(ylabel), title='', axis=None))
lower = lower.properties(height=20)
lower = lower.add_selection(brush)
lower.encoding.x.title = 'Interval Selection'
# Concatenated
p = alt.vconcat(upper + exp_line, lower).configure_concat(spacing=2)
return p
def uv_linePlot(data, engine, xlabel, ylabel):
data = data.copy()
data.rename(columns={'plotX1': ylabel}, inplace=True)
if engine == 'Static':
fig = data[ylabel].plot(figsize=(9, 6), legend=True).figure
plt.xlabel(xlabel, fontsize=15)
plt.ylabel(ylabel, fontsize=15)
plt.grid(b=True, which='major', color='k', linewidth=0.25)
plt.grid(b=True, which='minor', color='k', linewidth=0.125)
plt.close()
return pn.pane.Matplotlib(fig, tight=True)
elif engine == 'Interactive':
# Selection Brush
brush = alt.selection(type='interval', encodings=['x'], name='isel')
# Base Plot
base = alt.Chart(data.dropna().reset_index()).mark_line()
base = base.encode(x=alt.X('{0}:T'.format(data.index.name), title=''),
tooltip=ylabel)
base = base.properties(width=612, height=300)
# Upper Plot
upper = base.encode(x=alt.X('{0}:T'.format(data.index.name),
scale=alt.Scale(domain=brush),
title=''),
y=alt.Y('{0}:Q'.format(ylabel),
scale=alt.Scale(zero=False),
axis=alt.Axis(format='~s')))
# Lower Plot
lower = base.mark_area(line={'color': 'darkgray'},
color=alt.Gradient(
gradient='linear',
stops=[
alt.GradientStop(color='white',
offset=0),
alt.GradientStop(color='darkgray',
offset=1)
],
x1=1,
x2=1,
y1=1,
y2=0))
lower = lower.encode(
y=alt.Y('{0}:Q'.format(ylabel), title='', axis=None))
lower = lower.properties(height=20)
lower = lower.add_selection(brush)
lower.encoding.x.title = 'Interval Selection'
# Base Statistics
base_stat = upper.transform_filter(brush)
base_stat = base_stat.transform_aggregate(
Mean='mean({0})'.format(ylabel),
StdDev='stdev({0})'.format(ylabel),
Var='variance({0})'.format(ylabel))
# Label Statistics
label_stat = base_stat.transform_calculate(
stat_label="'Mean = ' + format(datum.Mean, '~s') + \
'; Standard Deviation = ' + format(datum.StdDev, '~s') +\
'; Variance = ' + format(datum.Var, '~s')")
label_stat = label_stat.mark_text(align='left', baseline='bottom')
label_stat = label_stat.encode(x=alt.value(0.0),
y=alt.value(12.0),
text=alt.Text('stat_label:N'))
# Values
_ymean_uu = data[ylabel].max()
_ymean = data[ylabel].mean()
# Inspired from :- https://stats.stackexchange.com/a/350278
_maxvar_in_slice = ((data[ylabel].max() - data[ylabel].min()) / 2)**2
_ystd_uu = np.sqrt(_maxvar_in_slice)
_ystd = data[ylabel].std()
_yvar_uu = _maxvar_in_slice
_yvar = data[ylabel].var()
# Stat Bar Base
stats_barbase = base_stat.mark_bar()
stats_barbase = stats_barbase.properties(width=200, height=20)
# Mean Bar
mean_bar = stats_barbase.encode(x=alt.X(
'Mean:Q',
title='Mean',
scale=alt.Scale(domain=[-_ymean_uu, _ymean_uu]),
axis=alt.Axis(format='~s')),
y=alt.value(10.5))
totmean_line = alt.Chart(pd.DataFrame({'x': [_ymean]}))
totmean_line = totmean_line.mark_rule(color='red', size=5)
totmean_line = totmean_line.encode(x='x')
mean_bar += totmean_line
# Standard Deviation Bar
std_bar = stats_barbase.encode(x=alt.X(
'StdDev:Q',
title='Std',
scale=alt.Scale(domain=[-_ystd_uu, _ystd_uu]),
axis=alt.Axis(format='~s')),
y=alt.value(10.5))
totstd_line = alt.Chart(pd.DataFrame({'x': [_ystd]}))
totstd_line = totstd_line.mark_rule(color='red', size=5)
totstd_line = totstd_line.encode(x='x')
std_bar += totstd_line
# Variance Bar
var_bar = stats_barbase.encode(x=alt.X(
'Var:Q',
title='Var',
scale=alt.Scale(domain=[-_yvar_uu, _yvar_uu]),
axis=alt.Axis(format='~s')),
y=alt.value(10.5))
totvar_line = alt.Chart(pd.DataFrame({'x': [_yvar]}))
totvar_line = totvar_line.mark_rule(color='red', size=5)
totvar_line = totvar_line.encode(x='x')
var_bar += totvar_line
# Concatenated
p = alt.vconcat(upper + label_stat, mean_bar | std_bar | var_bar,
lower).configure_concat(spacing=2)
return p