def barplot(
x=None, y=None, hue=None, data=None,
estimator=np.mean, ci=95, size=None, aspect=1,
orient=None, color=None, palette=None, saturation=.75, dodge=True
):
xs, ys = "x", "y"
orient = infer_orient(data[x], data[y], orient)
if orient == "h":
x, y = y, x
xs, ys = ys, xs
estimator = _validate_estimator(estimator, ci)
encodings = _bar_encodings(x, y, xs, ys, hue, dodge, estimator)
chart = alt.Chart(data).mark_bar().encode(**encodings)
if ci:
ci_encodings = _bar_ci_encodings(x, y, xs, ys, hue, dodge, estimator)
ci_layer = alt.Chart().mark_rule().encode(**ci_encodings)
chart.data = alt.Undefined
chart = alt.LayerChart(data=data, layer=[chart, ci_layer])
if hue and dodge:
facet_dir = "column" if orient == "v" else "row"
chart = chart.facet(**{facet_dir: "%s:N" % x})
size_chart(chart, size, aspect)
pal = vega_palette(palette, color, saturation)
return chart.configure_range(category=pal)
def pointplot(
x=None, y=None, hue=None, data=None,
estimator=np.mean, ci=95, join=True, size=None, aspect=1,
orient=None, color=None, palette=None, saturation=.75
):
xs, ys = "x", "y"
orient = infer_orient(data[x], data[y], orient)
if orient == "h":
x, y = y, x
xs, ys = ys, xs
estimator = _validate_estimator(estimator, ci)
encodings = _point_encodings(x, y, xs, ys, hue, estimator)
chart = alt.Chart(data).mark_circle().encode(**encodings)
layers = [chart]
if join:
layers.append(alt.Chart().mark_line().encode(**encodings))
if ci:
ci_encodings = _point_ci_encodings(x, y, xs, ys, hue, estimator)
cfield = hue if hue else "___"
ci_encodings["color"] = alt.Color(field=cfield, type="nominal", legend=None)
layers.append(alt.Chart().mark_rule().encode(**ci_encodings))
if len(layers) > 1:
chart.data = alt.Undefined
chart = alt.LayerChart(data=data, layer=layers)
size_chart(chart, size, aspect)
pal = vega_palette(palette, color, saturation)
return chart.configure_range(category=pal)
def graphPayouts(sim):
#Make a version of this with pops
aggRecord = sim["aggRecord"][['LPayoff', 'HPayoff']].copy()
meltedAgg = aggRecord.assign(round= aggRecord.index).melt(id_vars="round")
display(alt.LayerChart(meltedAgg).encode(
x='round',
y='value:Q',
color='variable:N'
).add_layers(
alt.Chart().mark_point(),
alt.Chart().mark_line()
))
def graphPayoutsJustGraph(sim):
#Make a version of this with pops
aggRecord = sim["aggRecord"][['LPayoff', 'HPayoff']].copy()
meltedAgg = aggRecord.assign(round= aggRecord.index).melt(id_vars="round")
return alt.LayerChart(meltedAgg).encode(
x='round',
y='value:Q',
color=alt.Color('variable:N', legend=alt.Legend(title="Payout", orient="left"))
).add_layers(
alt.Chart().mark_point(),
alt.Chart().mark_line()
).properties(
width=700,
height = 180)
def to_chart(G, pos):
"""Construct a single Altair Chart for
"""
# Build node layer
node_df = to_pandas_nodes(G, pos)
node_layer = alt.Chart(node_df)
# Build edge layer
edge_df = to_pandas_edges(G, pos)
edge_layer = alt.Chart(edge_df)
# Layer chart
chart = alt.LayerChart(layer=(edge_layer, node_layer))
chart = despine(chart)
return chart
def plotStrategyWealthOverTime(sim):
simRecord = sim["balanceRecord"].copy()
simRecord["Strategy"] = sim["finalScores"]["Strategy"]
wideScores = (simRecord
.groupby("Strategy")[simRecord.columns[:-1]]
.mean()
.transpose()
.assign(round= range(101))
.melt(id_vars="round"))
display(alt.LayerChart(wideScores).encode(
x='round',
y='value:Q',
color=alt.Color('Strategy:N', legend=alt.Legend(title="Payout", orient="left"))
).add_layers(
alt.Chart().mark_point(),
alt.Chart().mark_line()
).properties(
width=700,
height = 180))
def daily():
# data
ldn = pd.read_csv('../data/output/ml_general.csv')[::-1][35:]
ldn = ldn.rename(columns={'London': 'new', 'London_7': 'avg'})
# getting date and new cases
today = pd.to_datetime(ldn.iloc[-1].date).strftime('%b %d')
cases = int(ldn.iloc[-1].new)
# avg line
avg = alt.Chart(ldn).mark_line(strokeWidth=2.5,
color='#27516B').encode(x='date:T',
y='avg:Q')
# daily feather
new = alt.Chart(ldn).mark_line(opacity=0.35, strokeDash=[1, 1]).encode(
x='date:T',
y=alt.Y('new:Q', scale=alt.Scale(domain=[0, int(ldn.new.max())])))
# final datum dot
dot = alt.Chart(ldn).mark_circle(size=5, opacity=0.8).encode(
x='date:T',
y=alt.Y('new:Q', scale=alt.Scale(domain=[0, int(ldn.new.max())])))
# combined
d = alt.LayerChart(layer=[new, avg, dot]).properties(
width=375,
height=375
# title = {
# 'text': '15 new cases',
# 'subtitle': 'new cases with 7-day average'
# }
)
return d, today, cases
"""
Multiple Marks
==============
This example demonstrates creating a single chart with multiple markers
representing the same data.
"""
import altair as alt
from vega_datasets import data
stocks = data.stocks()
alt.LayerChart(stocks).encode(x='date:T', y='price:Q',
color='symbol:N').add_layers(
alt.Chart().mark_point(),
alt.Chart().mark_line())
"""
Ranged Dot Plot
-----------------
This example shows a ranged dot plot that uses 'layer' to convey changing life expectancy for the five most populous countries (between 1955 and 2000).
"""
import altair as alt
from vega_datasets import data
chart = alt.LayerChart(data=data.countries.url).transform_filter(
filter={
"field": 'country',
"oneOf": ["China", "India", "United States", "Indonesia", "Brazil"]
}).transform_filter(filter={
'field': 'year',
"oneOf": [1955, 2000]
})
chart += alt.Chart().mark_line(color='#db646f').encode(x='life_expect:Q',
y='country:N',
detail='country:N')
# Add points for life expectancy in 1955 & 2000
chart += alt.Chart().mark_point(size=100, opacity=1, filled=True).encode(
x='life_expect:Q',
y='country:N',
color=alt.Color('year:O',
scale=alt.Scale(domain=['1955', '2000'],
range=['#e6959c',
'#911a24']))).interactive()
def create_scatter(self, name='chart.html'):
"""
:param name: str
name of the output file
"""
case_a = self.case_a
case_b = self.case_b
# add regression layer, True by default
regression_layer = self.chart + self.chart.transform_regression(
case_a, case_b).mark_line(color='black')
# nearest will popup data of the nearest point
nearest = alt.selection(type='single',
nearest=True,
on='mouseover',
fields=['x'],
empty='none')
text = alt.Chart(self.corona_df).mark_text(
align='left',
baseline='middle',
color='black',
dy=-9,
dx=-7,
fontSize=15,
).encode(text='text_popup',
tooltip=[alt.Tooltip('location:N', title='Country')] + [
alt.Tooltip(case_a, type='quantitative', title=case_a),
alt.Tooltip(case_b, type='quantitative', title=case_b)
],
x=f'{case_a}:Q',
y=f'{case_b}:Q').add_selection(nearest)
text_layer = self.chart + text
line = self.chart + alt.Chart(self.corona_df).mark_rule(
color='red', strokeWidth=2).encode(y='medianY:Q') + alt.Chart(
self.corona_df).mark_rule(color='red',
strokeWidth=2).encode(x='medianX:Q')
# add layers to the map
layers = [line, regression_layer, text_layer]
chart = alt.LayerChart(self.corona_df, layers)
chart = chart.add_selection(self.selection).transform_filter(
self.selection
).properties(
width=490,
height=395,
padding=40
# interactive to add interaction to the plot
# Cant work on Android
).interactive()
# save here
dir = 'corona_full/templates/charts/'
location = dir + name
# renderer svg is a bit faster
chart.save(location, embed_options={'renderer': 'svg'})
# update created HTML page
new_style = '''
<script>
const msPerDay = 24 * 60 * 60 * 1000;
const date = new Date('December 31, 2019').getTime()
function toDateTime(x) {
// transform ms to date
let new_date = new Date(x * msPerDay + date);
return new_date.toLocaleDateString();
}
function Init () {
// Launch in start - attach event to the only input
var target = document.getElementsByTagName("input")[0];
if (target.addEventListener) {
target.addEventListener("input", updateTextInput, false);
} else if (target.attachEvent) {
target.attachEvent("oninout", updateTextInput);
} else {
target["oninput"] = updateTextInput;
}
}
function getTexts() {
let g_el = document.getElementsByClassName('mark-text role-mark layer_2_layer_1_marks')[0]
let iso_code = parent.document.getElementsByName('iso_code');
let checked = []
for (let i = 0; i<iso_code.length;i++) {
if (iso_code[i].checked) {checked.push(iso_code[i].value)}
}
var texts = g_el.getElementsByTagName('text');
return [texts, checked]
}
function clearIso() {
a = getTexts()
let texts = a[0]
let checked = a[1]
for (let i=0; i<texts.length; i++) {
if (checked.includes(texts[i].innerHTML)) {
texts[i].style.display = 'block'
}
else {
texts[i].style.display = 'none'
}
}
}
async function updateTextInput(val) {
// Show date of showed data on page
// base day is 31.12.2019
var x = document.getElementsByClassName("vega-bind-name")[0]
val = document.getElementsByTagName("input")[0].value
val = toDateTime(val);
x.innerHTML = val;
//console.log('UpdateTextInput', getTexts()[0][0], getTexts()[0][0].style)
await sleep(1)
clearIso()
}
window.onload = function() {
// Hide iso codes text: needed by default
clearIso()
Init();
// attach some buttons
var divv = document.createElement("div");
// next day
a= createfn('+', plus)
// auto scroll
b = createfn('auto', changeAuto)
// prev day
c = createfn('-', minus)
divv.appendChild(c);
divv.appendChild(b);
divv.appendChild(a);
divv.classList.add('button-div');
document.getElementsByTagName('body')[0].appendChild(divv);
// let inputElement = document.getElementsByTagName("input")[0]
// attach one more event to input, clearIso() on change
// inputElement.addEventListener("input", clearIso, false);
// inputElement.addEventListener("change", clearIso, false);
}
function plus(){
let inputElement = document.getElementsByTagName("input")[0]
inputElement.value++;
inputElement.dispatchEvent(new Event('input'))
// await sleep(.01)
// clearIso();
}
function minus(){
let inputElement = document.getElementsByTagName("input")[0]
inputElement.value--;
inputElement.dispatchEvent(new Event('input'))
}
function sleep(ms) {
return new Promise(
resolve => setTimeout(resolve, ms)
);
}
run = false
async function changeAuto() {
// change showed day
run = !run
var ele = document.getElementsByTagName('input')[0];
if (ele.value === ele.max && run) {
ele.value = 0;
}
do {
plus();
// wait half sec
await sleep(499);
}
while (run);
}
function createfn(sign, operation){
// create a button with a function on click
var element = document.createElement("button");
var para = document.createTextNode(sign);
element.appendChild(para);
element.addEventListener("click", operation, false);
return element
}
</script>
<style>
input {
width: 95%;
}
span {
display: none;
}
span[class] {
display: inline;
}
.vega-bind-name {
visibility: visible;
}
.vega-bind {
text-align: center;
paddin-left: 2%;
padding-right: 3%;
}
.button-div {
display: table;
margin-right: auto;
margin-left: auto;
}
''' # NO ENDING STYLE TAG !!!
with open(location, 'r', encoding='utf-8') as fr:
data = fr.read()
current_day = max(self.corona_df_full['date'])
data = data.replace('<style>', new_style).replace('Date:', current_day)
# write new styles and JS to the sasme document
with open(location, 'w', encoding='utf-8') as fw:
fw.write(data)
def __init__(self):
self.chart = alt.LayerChart()
def regplot(
x, y, data=None, x_estimator=None, x_bins=None, x_ci="ci",
x_range=None, y_range=None, truncate=False,
scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None,
order=1, logistic=False, lowess=False, robust=False, logx=False,
color=None, scatter_kws={}, line_kws={}, ax=None,
palette=None, height=None, aspect=1, color_scale=None
):
if data is None:
data, names = build_dataframe({"x": x, "y": y})
x, y = names["x"], names["y"]
if x_range is None:
x_raw_range = (data[x].min(), data[x].max())
x_pad = 0.05*(x_raw_range[1] - x_raw_range[0])
x_range = (x_raw_range[0] - x_pad, x_raw_range[1] + x_pad)
def plot_regression(data, color):
p = sns.regression._RegressionPlotter(
data[x], data[y], x_estimator=x_estimator, x_bins=x_bins, x_ci=x_ci,
n_boot=n_boot, units=units, ci=ci, truncate=truncate,
order=order, logistic=logistic, lowess=lowess, robust=robust, logx=logx
)
layers = []
grid, yhat, err_bands = p.fit_regression(x_range=x_range)
layers.append(plot(grid, yhat, color=color, **line_kws))
if err_bands is not None:
area = fill_between(grid, *err_bands, color=color)
area.encoding.opacity = alt.value(0.15)
layers.append(area)
return layers
def plot_scatter(data, color):
p = sns.regression._RegressionPlotter(
data[x], data[y], x_estimator=x_estimator, x_bins=x_bins, x_ci=x_ci,
n_boot=n_boot, units=units, ci=ci, truncate=truncate,
order=order, logistic=logistic, lowess=lowess, robust=robust, logx=logx
)
layers = []
if p.x_estimator is None:
layers.append(pscatter(x, y, data=data, color=color, **scatter_kws))
else:
xs, ys, cis = p.estimate_data
if [ci for ci in cis if ci is not None]:
for xval, cci in zip(xs, cis):
ci_df = pd.DataFrame({x: [xval, xval], y: cci})
layers.append(plot(x, y, data=ci_df))
estimate_df = pd.DataFrame({x: xs, y: ys})
layers.append(pscatter(x, y, data=estimate_df, color=color, **scatter_kws))
return layers
if color and color in list(data.columns):
if color_scale is None:
val = data[color].unique()
pal = sns.color_palette(palette)
color_scale = alt.Scale(domain=list(val), range=vega_palette(pal))
else:
val = color_scale.domain
color_map = {}
for i in range(len(color_scale.domain)):
color_map[color_scale.domain[i]] = color_scale.range[i % len(color_scale.range)]
layers = []
if scatter:
for v in data[color].unique():
part = data.loc[data[color] == v]
layers += plot_scatter(part, color_map[v])
if fit_reg:
for v in data[color].unique():
part = data.loc[data[color] == v]
layers += plot_regression(part, color_map[v])
else:
layers = []
if scatter:
layers += plot_scatter(data, color)
if fit_reg:
layers += plot_regression(data, color)
for layer in layers:
if isinstance(layer.mark, six.string_types):
layer.mark = dict(type=layer.mark, clip=True)
else:
layer.mark.clip = True
layer.encoding.x.scale=alt.Scale(domain=x_range, nice=False)
if y_range is not None:
layer.encoding.y.scale=alt.Scale(domain=y_range, nice=False)
layer.config = alt.Undefined
chart = alt.LayerChart(layer=layers)
return chart
def Plots(result, nth, w, h):
# This allows for data greater than 5000 rows to be plotted
alt.data_transformers.disable_max_rows()
x, y17, y17w, y17b, y18, y40, y40b = (result[4], result[5], result[6],
result[7], result[8], result[9],
result[10])
TPDsi, TPDei, Arsi, Arei = (result[14], result[15], result[16], result[17])
wf, watstart, watend, ratio1718 = (result[18], result[11], result[12],
result[13])
temp = list(x[TPDsi:TPDei])
temp = temp - temp[0]
temp = 323 + temp * 10
temp = list(temp[0::nth])
# cut out and keep every nth point
# Raw data Cuts
xr = list(x[0::nth])
y17r = list(y17[0::nth])
y18r = list(y18[0::nth])
y40r = list(y40[0::nth])
# TPDsi:TPDei cuts
xt = (list(x[TPDsi:TPDei]))[0::nth]
y17t = (list(y17[TPDsi:TPDei]))[0::nth]
y17wt = (list(y17w[TPDsi:TPDei]))[0::nth]
y17bt = (list(y17b[TPDsi:TPDei]))[0::nth]
y18t = (list(y18[TPDsi:TPDei]))[0::nth]
# Ar cuts
xa = (list(x[Arsi:Arei]))[0::nth]
y40a = (list(y40[Arsi:Arei]))[0::nth]
y40ba = (list(y40b[Arsi:Arei]))[0::nth]
raw_data = pd.DataFrame({'x': xr, 'y17': y17r, 'y18': y18r, 'y40': y40r})
raw_reshape = pd.melt(raw_data,
id_vars=['x'],
value_vars=['y17', 'y18', 'y40'],
var_name='legend',
value_name='y')
raw_chart = alt.Chart(raw_reshape).mark_line(size=3).encode(
alt.X('x', axis=alt.Axis(tickCount=7, title='Time (min)')),
alt.Y('y', axis=alt.Axis(tickCount=7, title='Intensity (counts)')),
alt.Color('legend', legend=alt.Legend(
orient='top-left'))).configure_axis(grid=False).properties(
width=w, height=h, title='Raw Data from CSV').interactive()
TPD_data = pd.DataFrame({
'x': xt,
'y17': y17t,
'y17 water corrected': y17wt,
'y17 baseline corrected': y17bt,
'y18': y18t
})
TPD_reshape = pd.melt(TPD_data,
id_vars=['x'],
value_vars=[
'y17', 'y17 water corrected',
'y17 baseline corrected', 'y18'
],
var_name='legend',
value_name='y')
TPD_chart = alt.Chart(TPD_reshape).mark_line(size=3).encode(
alt.X('x', axis=alt.Axis(title='Time (min)')),
alt.Y('y', axis=alt.Axis(title='Intensity (counts)')),
alt.Color('legend', legend=alt.Legend(
orient='top-right'))).configure_axis(grid=False).properties(
width=w,
height=h,
title='TPD: y17 Water and Baseline Correction ')
Ar_data = pd.DataFrame({
'x': xa,
'y40': y40a,
'y40 baseline corrected': y40ba
})
Ar_reshape = pd.melt(Ar_data,
id_vars=['x'],
value_vars=['y40', 'y40 baseline corrected'],
var_name='legend',
value_name='y')
Ar_chart = alt.Chart(Ar_reshape).mark_line(size=3).encode(
alt.X('x', axis=alt.Axis(title='Time (min)')),
alt.Y('y', axis=alt.Axis(title='Intensity (counts)')),
alt.Color('legend',
legend=alt.Legend(orient='top-right'))).configure_axis(
grid=False).properties(width=w,
height=h,
title='Ar Pulse')
T_data = pd.DataFrame({'T': temp, 'y': y17bt})
T_chart = alt.Chart(T_data).mark_line(size=3).encode(
alt.X('T', axis=alt.Axis(title='Temp (K)')),
alt.Y('y', axis=alt.Axis(title='Intensity (counts)')),
).configure_axis(grid=False).properties(
width=w, height=h, title='TPD as a Function of Temperature')
if wf == 1:
W_data = pd.DataFrame({'x': x[watstart:watend], 'y': ratio1718})
W_chart = alt.Chart(W_data).mark_line(size=3).encode(
alt.X('x', axis=alt.Axis(title='Time (min)')),
alt.Y('y', axis=alt.Axis(title='Value of y17/y18 Preceeding TPD')),
).configure_axis(grid=False).properties(
width=w, height=h, title='Water Correction Factor')
else:
W_chart = alt.LayerChart()
return (raw_chart, TPD_chart, Ar_chart, T_chart, W_chart)
def create_graph():
# retira o número máximo de linhas para pot com Altair
alt.data_transformers.disable_max_rows()
# faz query no banco de dados
autores = queryDB('author', ['ID_author','author'])
artigos = queryDB('paper', ['ID_paper','paper'])
author_paper = queryDB('author_paper', ['ID_paper','ID_author'])
autores['ID_author'] = autores['ID_author'].astype(str)
artigos['ID_paper'] = artigos['ID_paper'].astype(str)
### renderiza os gráficos
## Grafo 1 - Autores (authors)
print('Preparando grafo dos autores...')
graph = nx.Graph()
# dataframe com colunas: paper e [lista_autores]
group = pd.DataFrame(author_paper.groupby('ID_paper')['ID_author'].apply(list))
# Adicionando "edges"
for j,row in group.iterrows():
i=len(row['ID_author'])
for i in range(len(row['ID_author'])):
for k in range(i,len(row['ID_author'])):
graph.add_edge(row['ID_author'][i], row['ID_author'][k])
pos = nx.spring_layout(graph,k=0.2, iterations=50, weight=0.1, center=(0.5,0.5)) # forces graph layout
# coletando nodes
nodes = to_pandas_nodes(graph,pos)
nodes.reset_index(inplace=True)
nodes.rename(columns={'index':'ID_author'}, inplace=True)
nodes = pd.merge(nodes,autores,on='ID_author') # coletando nome dos autores
nodes = pd.merge(nodes,author_paper, on='ID_author') # coletando ID_paper
# coletando edges
edges = to_pandas_edges(graph,pos)
# Gráfico 1
print('Criando interatividade com o Altair (autores) ...')
selector = alt.selection_single(empty='all',fields=['ID_author']) # iniciando seletor
points = alt.Chart(nodes).add_selection(selector).mark_point(filled=True,size=90).encode(
alt.X('x', axis=alt.Axis(title='')),
alt.Y('y', axis=alt.Axis(title='')),
tooltip='author',
opacity=alt.condition(selector,alt.value(0.95),alt.value(0.4),legend=None),
color=alt.condition(selector, 'ID_author', alt.value('lightgray'), legend=None)
).properties( selection=selector ).transform_filter(selector)
# cria um background para efeitos de transição do seletor
bk = alt.Chart(nodes).mark_point(color='lightgray',filled=True,size=90).encode(
alt.X('x', axis=alt.Axis(title='')),
alt.Y('y', axis=alt.Axis(title='')),
tooltip='author',
opacity=alt.value(0.4),
)
lines = alt.Chart(edges).mark_line(color='salmon').encode(
alt.X('x', axis=alt.Axis(title='')),
alt.Y('y', axis=alt.Axis(title='')),
detail='edge',
opacity=alt.value(0.15)
)
chart = alt.LayerChart(layer=(lines,bk+points)).properties(
height=350,
width=450
).interactive()
## Grafo 2 - Artigos (papers)
print('Preparando grafo dos artigos...')
graph1 = nx.Graph()
group1 = pd.DataFrame(author_paper.groupby('ID_author')['ID_paper'].apply(list))
# Adicionando "edges"
for j,row in group1.iterrows():
i=len(row['ID_paper'])
for i in range(len(row['ID_paper'])):
for k in range(i,len(row['ID_paper'])):
graph1.add_edge(row['ID_paper'][i], row['ID_paper'][k])
pos1 = nx.spring_layout(graph1,k=0.2, iterations=50, weight=0.1, center=(0.5,0.5)) # forces graph layout
# coletando nodes
nodes1 = to_pandas_nodes(graph1, pos1)
nodes1.reset_index(inplace=True)
nodes1.rename(columns={'index':'ID_paper'}, inplace=True)
nodes1 = pd.merge(nodes1,artigos,on='ID_paper') # coletando nome dos papers
nodes1 = pd.merge(nodes1,author_paper,on='ID_paper') # coletando ID_author
# coletando edges
edges1 = to_pandas_edges(graph1,pos1)
# Gráfico 2
print('Criando interatividade com o Altair (artigos)...')
points1 = alt.Chart(nodes1).add_selection(selector).mark_point(filled=True,size=90).encode(
alt.X('x', axis=alt.Axis(title='')),
alt.Y('y', axis=alt.Axis(title='')),
tooltip='paper',
opacity=alt.condition(selector,alt.value(0.95),alt.value(0.4),legend=None),
color=alt.condition(selector, 'ID_author', alt.value('lightgray'), legend=None)
).transform_filter(selector)
# cria um background para efeitos de transição do seletor
bk1 = alt.Chart(nodes1).mark_point(color='lightgray',filled=True,size=90).encode(
alt.X('x', axis=alt.Axis(title='')),
alt.Y('y', axis=alt.Axis(title='')),
tooltip='paper',
opacity=alt.value(0.4),
)
lines1 = alt.Chart(edges1).mark_line(color='lightblue').encode(
alt.X('x', axis=alt.Axis(title='')),
alt.Y('y', axis=alt.Axis(title='')),
detail='edge',
opacity=alt.value(0.2)
)
chart1 = alt.LayerChart(layer=(lines1,bk1 + points1)).properties(
height=350,
width=450
).interactive()
### Concatenando horizontamnete os gráficos 1 e 2
horiz_chart = alt.hconcat(chart, chart1 ).configure_axis( ticks=False,
grid=False,
domain=False,
labels=False).configure_view(
strokeWidth=0
)
return horiz_chart.to_json()
start_date = cases.Date.iloc[0].replace(tzinfo=None)
end_date = cases.Date.iloc[-1].replace(tzinfo=None)
#st.markdown("### Please choose the range of dates")
dates = st.date_input("Please choose the range of dates",
[start_date, end_date], start_date, end_date)
if len(dates) < 2:
start_date = dates[0]
else:
start_date, end_date = dates
cases = cases[(cases.Date >= pd.Timestamp(start_date, tz="Europe/Paris"))
& (cases.Date < pd.Timestamp(end_date + dt.timedelta(days=1),
tz="Europe/Paris"))]
chart = alt.LayerChart()
#st.markdown("### Evolution of Covid-19 cases for the chosen postal codes")
tooltip = [
'Date',
alt.Tooltip('Week_of_year', title='Week of year'), 'Name', 'District',
'Population',
alt.Tooltip('Area', title='Area (km²)', format='0.3'),
alt.Tooltip(plot_y_axis_column_range, title='Range'),
alt.Tooltip('NewDeaths_in_district_in_week',
title='Weekly deaths in district')
] #,alt.Tooltip(plot_y_axis_column_min, title='Min', format='0.3'),
x_axis = alt.X('Date', axis=alt.Axis(labels=True, format='%d %b'))
y_axis = alt.Y(plot_y_axis_column_avg, title=plot_y_axis_title)
legend = "|Area | Color | \n | - | - | \n"
for i, plz_name in enumerate(chosen_postal_areas):
color = colors[i]
def boxplot(
x=None, y=None, hue=None, data=None, size=None, aspect=1,
orient=None, color=None, palette=None, saturation=.75, dodge=True
):
xs, ys = "x", "y"
if data is None:
data = pd.DataFrame({"x": x})
x = "x"
if y:
data["y"] = y
y = "y"
if x is None and y is None:
# Make a box plot for each numeric column
numeric_cols = [c for c in data if data[c].dtype in [np.float32, np.float64]]
col = []
val = []
for c in numeric_cols:
for v in data[c]:
col.append(c)
val.append(v)
data = pd.DataFrame({"column": col, "value": val})
x = "column"
y = "value"
if orient == "h":
x, y = y, x
if y:
orient = infer_orient(data[x], data[y], orient)
elif orient is None:
orient = "h"
if orient == "h":
x, y = y, x
xs, ys = ys, xs
xf = hue if hue and dodge else x
# Main bar
encodings = {
ys: alt.Y(field=y, aggregate="q1", type="quantitative", axis={"title": y}),
"%s2" % ys: alt.Y(field=y, aggregate="q3", type="quantitative"),
"color": alt.Color(field=x ,type="nominal", legend=None),
xs: alt.X(field=xf, type="nominal")
}
if x is None:
del encodings["color"]
del encodings[xs]
if hue:
legend = None if dodge else alt.Undefined
encodings["color"] = alt.Color(field=hue ,type="nominal", legend=legend)
bar_layer = alt.Chart().mark_bar().encode(**encodings)
# Min/max range line
range_encodings = {
ys: alt.Y(field=y, aggregate="min", type="quantitative"),
"%s2" % ys: alt.Y(field=y, aggregate="max", type="quantitative"),
xs: alt.X(field=xf, type="nominal")
}
if x is None:
del range_encodings[xs]
range_layer = alt.Chart().mark_rule().encode(**range_encodings)
# Median line
median_encodings = {
ys: alt.Y(field=y, aggregate="median", type="quantitative"),
xs: alt.X(field=xf, type="nominal")
}
if x is None:
del median_encodings[xs]
median_layer = alt.Chart().mark_tick(size=18, color="black").encode(**median_encodings)
chart = alt.LayerChart(data=data, layer=[range_layer, bar_layer, median_layer])
if hue and dodge:
facet_dir = "column" if orient == "v" else "row"
chart = chart.facet(**{facet_dir: "%s:N" % x})
size_chart(chart, size, aspect)
pal = vega_palette(palette, color, saturation)
return chart.configure_range(category=pal)
