def graph_transit_filtre_multiple(df_transit_avec_filtre, date_debut, date_fin,
o_d, nb_jours):
"""
Regroupement de charts altair issues de graph_transit_filtre sur un plusieurs jours
en entre :
cf graph_transit_filtre
nb_jours : integer : nb de jours à concatener
en sortie :
une chart altair concatenee verticalement avec un pour chaque jour
"""
dico_graph = {
'graph' + str(indice): graph_transit_filtre(df_transit_avec_filtre,
str(dates[0]),
str(dates[1]), o_d)
for indice, dates in enumerate(
zip([
str(x)
for x in pd.date_range(date_debut, periods=nb_jours, freq='D')
], [
str(x)
for x in pd.date_range(date_fin, periods=nb_jours, freq='D')
]))
}
liste_graph = [dico_graph[key] for key in dico_graph.keys()]
return alt.VConcatChart(vconcat=(liste_graph))
def graph_trajet_multiple(dico_od, date, o_d, nb_jours):
"""
Regroupement de charts altair issues de graph_trajet sur un plusieurs jours
"""
dico_od_graph=dico_od.loc[(dico_od.date_cam_1.between(pd.to_datetime(date+' 00:00:00'),pd.to_datetime(date+' 23:59:59')+pd.Timedelta(str(nb_jours)+'D'))) & (
dico_od.apply(lambda x : x['o_d']==o_d,axis=1))].copy()
return alt.VConcatChart(vconcat=([graph_trajet(dico_od_graph,date_g.strftime('%Y-%m-%d'), o_d)
for date_g in pd.date_range(date, periods=nb_jours, freq='D')]))
def comp_lapi_gest_multicam(df_passages_immat_ok,donnees_gest, date_d='2019-01-28', date_f='2019-02-11',
liste_num_cam=list(range(1,14))+[15,18,19]):
"""
porposer un graph unique pour toute les cmaraison gest-lapi
en entree : la liste des cameras
"""
return alt.VConcatChart(vconcat=[comp_lapi_gest(df_passages_immat_ok,donnees_gest, camera)
for camera in liste_num_cam])
def facet_wrap(charts, charts_per_row):
rows_of_charts = [
charts[i:i+charts_per_row]
for i in range(0, len(charts), charts_per_row)]
vconcat = [make_hcc(r) for r in rows_of_charts]
vcc = alt.VConcatChart(vconcat=vconcat)\
.configure_axisX(grid=True)\
.configure_axisY(grid=True)
return vcc
def facet_wrap(charts, charts_per_row):
rows_of_charts = [
charts[i:i + charts_per_row]
for i in range(0, len(charts), charts_per_row)
]
vconcat = [make_hcc(r) for r in rows_of_charts]
vcc = alt.VConcatChart(vconcat=vconcat, padding={'left':20, 'top':5}, spacing=40)\
.configure_axisX(grid=True)\
.configure_axisY(grid=True)
return vcc
def push_report(service):
user_email = service.users().getProfile(
userId='me').execute()['emailAddress']
print(user_email)
global params
y_bottom = np.zeros(12)
charts = []
df1 = pd.DataFrame()
for company_name in params:
company = params[company_name]
if company_name in IGNORE_LIST or not create_dump(service, company):
continue
# if company_name in IGNORE_LIST:
# continue
try:
x, y = getData(company_name, company['fname'])
x, y = coerceData(x, y)
except Exception as e:
print(e)
continue
print(company_name, y)
y_bottom = np.add(y_bottom, y)
source = pd.DataFrame({
'month': x,
'spent': y,
'company': [company_name] * 12
})
chart = alt.Chart(source).mark_bar(size=15).encode(
x=alt.X('month', title=''),
y=alt.Y('spent',
title='Amount spent (₹)')).properties(title=company_name, )
charts.append(chart)
source.set_index('month')
if not df1.size:
df1 = source
else:
df1 = df1.append(source)
if not df1.size:
print('No data found for ' + user_email)
return
stackedchart = alt.Chart(df1).mark_bar(size=15).encode(
alt.X('month', title=''),
y=alt.Y('sum(spent)', title='Amount spent (₹)'),
color='company').properties(title='Aggregate Monthly Spending')
charts.insert(0, stackedchart)
repchart = alt.VConcatChart(vconcat=charts)
save(repchart,
os.path.dirname(os.path.abspath(__file__)) + '/data/report.png',
scale_factor=1.5)
with app.app_context():
context = {'amount': np.sum(y_bottom)}
email_content = render_template('report.html', **context)
push_email(email_content, user_email)
def graph_transit_vl_pl_camera_multi_j(synt_nb_veh_cam, date,cam,nb_jour):
"""
Regroupement de charts altair issues de graph_VL_PL_transit_j_cam sur plusieurs jours
en entre :
cf graph_VL_PL_transit_j_cam
nb_jours : integer : nb de jours à concatener
en sortie :
une chart altair concatenee verticalement avec un pour chaque jour
"""
dico_graph={'graph'+str(indice):graph_VL_PL_transit_j_cam(synt_nb_veh_cam, date, cam)
for indice,date in enumerate(zip([str(x) for x in pd.date_range(date, periods=nb_jour, freq='D')]))}
liste_graph=[dico_graph[key] for key in dico_graph.keys()]
return alt.VConcatChart(vconcat=(liste_graph))
def graph_nb_veh_ttjours_ttcam(df) :
"""
Fonction de graph du nb ceh par jour et par pour l'ensembles des cameras
en entree :
df : la df des passages isssue de la bdd
en sortie :
graph_filtre_tps : une chart altair concatenee vertical. en x les jours, en y le nb de veh
"""
nb_passage_j_cam=df.reset_index().set_index('created').groupby('camera_id').resample('D').count().drop('camera_id',axis=1).reset_index()
return alt.VConcatChart(vconcat=([alt.Chart(nb_passage_j_cam.loc[nb_passage_j_cam['camera_id']==camera], title=f'camera {camera}').mark_bar(size=20).encode(
x=alt.X('created',axis=alt.Axis(title='Jour', format='%A %d-%m-%y',labelAngle=45)),
y=alt.Y('immat',axis=alt.Axis(title='Nombre de véhicules'))).properties(width=1000) for camera in
[1,2,3,4,5,6,7,8,9,10,11,12,13,15,18,19]]))
def graph_nb_veh_jour_camera_multi_j(df,date_debut,date_fin,cam,nb_jour, type_v='TV'):
"""
Regroupement de charts altair issues de graph_nb_veh_jour_camera sur plusieurs jours
en entre :
cf graph_nb_veh_jour_camera
nb_jours : integer : nb de jours à concatener
en sortie :
une chart altair concatenee verticalement avec un pour chaque jour
"""
df_index_ok=df.reset_index()
dico_graph={indice:graph_nb_veh_jour_camera(df_index_ok, dates[0], dates[1], cam, type_v)
for indice,dates in enumerate(zip([str(x) for x in pd.date_range(date_debut, periods=nb_jour, freq='D')],
[str(x) for x in pd.date_range(date_fin, periods=nb_jour, freq='D')]))}
liste_graph=[graph for graph in {'liste_graph'+str(i):alt.HConcatChart(hconcat=([dico_graph[key] for key in dico_graph.keys() if key in range(i-7,i)])) for i in [8,15,22]}.values()]
return alt.VConcatChart(vconcat=(liste_graph))
def plot(df, main, new):
print('Combining into thumbnail...')
today = pd.to_datetime(df.iloc[-1].date).strftime('%b %d')
nv = int(df.iloc[-1].new_vaccinated)
v = int(df.iloc[-1].vaccinated)
nd = int(df.iloc[-1].new)
d = int(df.iloc[-1].total)
thumb = alt.VConcatChart(vconcat=[main, new]).configure_view(
strokeWidth=0).properties(
title={
'text': [
'Ontario Doses and Vaccinations',
f'{today}: {v:,d} people vaccinated so far'
],
'subtitle': [
f'Total doses: {d:,}; total vaccinations: {v:,},',
f'New doses: {nd:,}; new vaccinations: {nv:,},',
'@OntVaccine by @__martinallen__'
]
}).configure_title(fontSize=30, color='#66334B')
return thumb
"""
Interval Selection Example
==========================
This is an example of creating a stacked chart for which the domain of the
top chart can be selected by interacting with the bottom chart.
"""
# category: interactive
import altair as alt
from vega_datasets import data
sp500 = data.sp500.url
brush = alt.selection(type='interval', encodings=['x'])
upper = alt.Chart().mark_area().encode(
x=alt.X('date:T', scale={'domain': brush.ref()}),
y='price:Q'
).properties(
width=600,
height=200
)
lower = upper.properties(
selection=brush,
height=60
)
chart = alt.VConcatChart([upper, lower], data=sp500)
def get_interactive_detail_multi_line_chart(
column_dfs: List[Tuple[str, pd.DataFrame]],
metric_name: str,
metric_type: alt.StandardType,
domain_name: str,
domain_type: alt.StandardType,
) -> alt.VConcatChart:
"""
Args:
column_dfs: A list of tuples pairing pandas dataframes with the columns they correspond to
metric_name: The name of the metric as it exists in the pandas dataframe
metric_type: The altair data type for the metric being plotted
domain_name: The name of the domain as it exists in the pandas dataframe
domain_type: The altair data type for the domain being plotted
Returns:
A interactive detail altair multi-line chart
"""
metric_title: str = metric_name.replace("_", " ").title()
domain_title: str = domain_name.title()
title: alt.TitleParams = alt.TitleParams(
f"{metric_title} per {domain_title}",
dy=-30,
)
batch_id: str = "batch_id"
batch_id_title: str = batch_id.replace("_", " ").title().replace("Id", "ID")
batch_id_type: alt.StandardType = AltairDataTypes.NOMINAL.value
column_name: str = "column_name"
column_name_title: str = "Column Name"
column_name_type: alt.StandardType = AltairDataTypes.NOMINAL.value
detail_title_font_size: int = 14
detail_title_font_weight: str = "bold"
line_chart_height: int = 150
detail_line_chart_height: int = 75
point_size: int = 50
unselected_color: alt.value = alt.value("lightgray")
selected_opacity: float = 1.0
unselected_opacity: float = 0.4
tooltip: List[alt.Tooltip] = [
alt.Tooltip(
field=column_name, type=column_name_type, title=column_name_title
),
alt.Tooltip(field=batch_id, type=batch_id_type, title=batch_id_title),
alt.Tooltip(
field=metric_name, type=metric_type, title=metric_title, format=","
),
]
df: pd.DataFrame = pd.DataFrame(
columns=[column_name, domain_name, batch_id, metric_name]
)
for column, column_df in column_dfs:
column_df[column_name] = column
df = pd.concat([df, column_df], axis=0)
columns: List[str] = [" "] + pd.unique(df[column_name]).tolist()
input_dropdown: alt.binding_select = alt.binding_select(
options=columns, name="Select Column: "
)
selection: alt.selection_single = alt.selection_single(
empty="none",
bind=input_dropdown,
fields=[column_name],
)
line: alt.Chart = (
alt.Chart(df)
.mark_line()
.encode(
x=alt.X(
domain_name,
type=domain_type,
axis=alt.Axis(ticks=False, title=None, labels=False),
),
y=alt.Y(metric_name, type=metric_type, title=None),
color=alt.condition(
selection,
alt.Color(
column_name,
type=AltairDataTypes.NOMINAL.value,
scale=alt.Scale(range=ColorPalettes.ORDINAL_7.value),
legend=None,
),
unselected_color,
),
opacity=alt.condition(
selection,
alt.value(selected_opacity),
alt.value(unselected_opacity),
),
tooltip=tooltip,
)
.properties(height=line_chart_height, title=title)
)
points: alt.Chart = (
alt.Chart(df)
.mark_point(size=point_size)
.encode(
x=alt.X(
domain_name,
type=domain_type,
axis=alt.Axis(ticks=False, title=None, labels=False),
),
y=alt.Y(metric_name, type=metric_type, title=None),
color=alt.condition(
selection,
alt.value(Colors.GREEN.value),
unselected_color,
),
opacity=alt.condition(
selection,
alt.value(selected_opacity),
alt.value(unselected_opacity),
),
tooltip=tooltip,
)
.properties(height=line_chart_height, title=title)
)
highlight_line: alt.Chart = (
alt.Chart(df)
.mark_line(strokeWidth=2.5)
.encode(
x=alt.X(
domain_name,
type=domain_type,
axis=alt.Axis(ticks=False, title=None, labels=False),
),
y=alt.Y(metric_name, type=metric_type, title=None),
color=alt.condition(
selection,
alt.Color(
column_name,
type=AltairDataTypes.NOMINAL.value,
scale=alt.Scale(range=ColorPalettes.ORDINAL_7.value),
legend=None,
),
unselected_color,
),
opacity=alt.condition(
selection,
alt.value(selected_opacity),
alt.value(unselected_opacity),
),
tooltip=tooltip,
)
.properties(height=line_chart_height, title=title)
.transform_filter(selection)
)
highlight_points: alt.Chart = (
alt.Chart(df)
.mark_point(size=40)
.encode(
x=alt.X(
domain_name,
type=domain_type,
axis=alt.Axis(ticks=False, title=None, labels=False),
),
y=alt.Y(metric_name, type=metric_type, title=None),
color=alt.condition(
selection,
alt.value(Colors.GREEN.value),
unselected_color,
),
opacity=alt.condition(
selection,
alt.value(selected_opacity),
alt.value(unselected_opacity),
),
tooltip=tooltip,
)
.properties(height=line_chart_height, title=title)
.transform_filter(selection)
)
detail_line: alt.Chart = (
alt.Chart(
df,
)
.mark_line(opacity=selected_opacity)
.encode(
x=alt.X(
domain_name,
type=domain_type,
title=domain_title,
),
y=alt.Y(metric_name, type=metric_type, title=None),
color=alt.Color(
column_name,
type=AltairDataTypes.NOMINAL.value,
scale=alt.Scale(range=ColorPalettes.ORDINAL_7.value),
),
tooltip=tooltip,
)
.properties(height=detail_line_chart_height)
.transform_filter(selection)
)
detail_points: alt.Chart = (
alt.Chart(
df,
)
.mark_point(
size=point_size, color=Colors.GREEN.value, opacity=selected_opacity
)
.encode(
x=alt.X(
domain_name,
type=domain_type,
title=domain_title,
),
y=alt.Y(metric_name, type=metric_type, title=None),
tooltip=tooltip,
)
.properties(height=detail_line_chart_height)
.transform_filter(selection)
)
detail_title_column_names: pd.DataFrame = pd.DataFrame(
{column_name: pd.unique(df[column_name])}
)
detail_title_column_titles: str = "column_title"
detail_title_column_names[
detail_title_column_titles
] = detail_title_column_names[column_name].apply(
lambda x: f"Column ({x}) Selection Detail"
)
detail_title_text: alt.condition = alt.condition(
selection, detail_title_column_titles, alt.value("")
)
detail_title = (
alt.Chart(detail_title_column_names)
.mark_text(
color=Colors.PURPLE.value,
fontSize=detail_title_font_size,
fontWeight=detail_title_font_weight,
)
.encode(text=detail_title_text)
.transform_filter(selection)
.properties(height=10)
)
# special title for combined y-axis across two charts
y_axis_title = alt.TitleParams(
metric_title,
color=Colors.PURPLE.value,
orient="left",
angle=270,
fontSize=14,
dx=70,
dy=-5,
)
return (
alt.VConcatChart(
vconcat=[
line + points + highlight_line + highlight_points,
detail_title,
detail_line + detail_points,
],
)
.properties(title=y_axis_title)
.add_selection(selection)
)
def draw_resid_dashboard(resids_df, target_units, cat_col, tooltip_features):
brush = alt.selection(type='interval', resolve='global')
max_act = resids_df["actual"].max() * 1.1
max_pred = resids_df["pred"].max() * 1.1
max_pred_act = max(max_act, max_pred)
max_abs_resid = resids_df["abs_resid"].max() * 1.1
## Scatterplots
scatter_base = \
alt.Chart().mark_circle(size=70, clip=True).encode(
x=alt.X("actual:Q", title="Actual Value", scale=alt.Scale(domain=(0, max_pred_act))),
color=alt.condition(brush, cat_col, alt.ColorValue('gray'), legend=None),
tooltip=tooltip_features
).add_selection(brush
).properties(
width=610,
height=610
)
# Actual vs Predicted Scatterplot
pred_scatter = \
scatter_base.encode(y=alt.Y("pred:Q", title="Predicted Value", scale=alt.Scale(domain=(0, max_pred_act)))
).properties(title=f"Actuals vs. Predictions, colored by {cat_col}")
identity_rule_df = pd.DataFrame([{
"x": 0,
"y": 0
}, {
"x": max_pred_act,
"y": max_pred_act
}])
identity_rule = alt.Chart(identity_rule_df).mark_line(
color="#000000", clip=True).encode(
x=alt.X("x:Q", scale=alt.Scale(domain=(0, max_pred_act))),
y='y:Q',
)
pred_scatter += identity_rule # Draw the line on top
# Actual vs Residual
resid_scatter = \
scatter_base.encode(y=alt.Y("resid:Q", title="Residual (Predicted minus Actual)", scale=alt.Scale(domain=(-max_abs_resid, max_abs_resid)))
).properties(title=f"Actuals vs. Residuals, colored by {cat_col}")#.interactive()
zero_rule_df = pd.DataFrame([{
"x": 0,
"y": 0
}, {
"x": max_pred_act,
"y": 0
}])
zero_rule = alt.Chart(zero_rule_df).mark_line(color="#000000").encode(
x=alt.X("x:Q", scale=alt.Scale(domain=(0, max_pred_act))),
y='y:Q',
)
resid_scatter += zero_rule # Draw the line on top
scatterplots = pred_scatter | resid_scatter
## Bar Charts
bars_base = \
alt.Chart().mark_bar().encode(
y=alt.Y(f'{cat_col}:N', title=None),
color=alt.Color(f'{cat_col}:N', legend=None)
).properties(
width=350,
height=150
).transform_filter(brush)
vc_bars = bars_base.encode(
x='count({cat_col}):Q',
y=alt.Y(f'{cat_col}:N')).properties(title="Value Counts")
mape_bars = bars_base.encode(
x=alt.X('mean(abs_perc_resid):Q', title="Percent")).properties(
title="Mean Absolute Percent Error (MAPE)")
#Stacked bars
avg_act_bars = bars_base.encode(alt.X('mean(actual):Q', stack=None))
mae_bars = bars_base.encode(
alt.X('mean(abs_resid):Q', title=target_units, stack=None),
color=alt.value("black")).properties(
title="Mean Absolute Error (black) vs. Mean of Target")
avg_act_vs_mae = avg_act_bars + mae_bars
bar_charts = alt.HConcatChart(hconcat=(vc_bars, avg_act_vs_mae,
mape_bars), )
dash = alt.VConcatChart(data=resids_df, vconcat=(scatterplots, bar_charts))
return dash.configure_axis(labelFontSize=13,
titleFontSize=15,
labelFlush=True,
labelLimit=100).configure_title(fontSize=20)
def seasonEvo1415():
premier1415 = pd.read_csv('App/Data/premier1415.csv')
premier1415mod = premier1415.copy()
premier1415mod[
'cumpoints'] = premier1415mod['cumpoints'] - premier1415mod['points']
topteams1415 = [
'Leicester City', 'Chelsea', 'Manchester City', 'Arsenal', 'Hull City',
'Burnley', 'Queens Park Rangers'
]
noLC = [
'Chelsea', 'Manchester City', 'Arsenal', 'Hull City', 'Burnley',
'Queens Park Rangers'
]
LC = ['Leicester City']
premier1415noLC = premier1415[premier1415['team_long_name'].isin(noLC)]
premier1415LC = premier1415[premier1415['team_long_name'].isin(LC)]
noLClast1415 = premier1415noLC[premier1415noLC['stage'] == 38]
LClast1415 = premier1415LC[premier1415LC['stage'] == 38]
LClast1415['position'] = '14th'
noLClast1415['position'] = ['19th', '1st', '2nd', '18th', '3rd', '20th']
a = {'x': [30], 'y': [19], 'textof': ['➟']}
arrow = pd.DataFrame(a)
palette = alt.Scale(domain=topteams1415,
range=[
'#0062cc', 'green', '#6CABDD', '#EF0107',
'#663f3f', '#e1d89f', '#d89216'
])
pointarrow = alt.Chart(arrow).mark_circle(size=100,
color='black').encode(x='x',
y='y')
textarrow = alt.Chart(arrow).mark_text(dx=-20,
dy=0,
angle=240,
fontSize=30).encode(x='x',
y='y',
text='textof')
noLC = [
'Chelsea', 'Manchester City', 'Arsenal', 'Queens Park Rangers',
'Burnley', 'Hull City'
]
LC = ['Leicester City']
nearest = alt.selection(type='single',
nearest=True,
on='mouseover',
fields=['stage'],
init={'stage': 20},
empty='none')
lineLC = alt.Chart(premier1415LC).mark_line(size=5).encode(
x=alt.X('stage', title='Gameweek', axis=alt.Axis(grid=False)),
y=alt.Y('cumpoints:Q', title='Points', axis=alt.Axis(grid=False)),
color=alt.Color('team_long_name:N', title='Team'),
tooltip='team_long_name:N')
line = alt.Chart(premier1415noLC).mark_line(size=2).encode(
x=alt.X('stage', title='Gameweek', axis=alt.Axis(grid=False)),
y=alt.Y('cumpoints:Q', title='Points', axis=alt.Axis(grid=False)),
color=alt.Color('team_long_name:N', title='Team'),
tooltip='team_long_name:N')
selectors = alt.Chart(premier1415).mark_point().encode(
x='stage',
opacity=alt.value(0),
).add_selection(nearest)
labelsLC = alt.Chart(LClast1415).mark_text(
align='left', dx=3, size=14).encode(
alt.X('stage', title='Gameweek', axis=alt.Axis(grid=False)),
alt.Y('cumpoints:Q', title='Points', axis=alt.Axis(grid=False)),
alt.Text('position'),
alt.Color('team_long_name:N', title='Team', scale=palette),
)
labels = alt.Chart(noLClast1415).mark_text(
align='left', dx=3, size=12).encode(
alt.X('stage', title='Gameweek', axis=alt.Axis(grid=False)),
alt.Y('cumpoints:Q', title='Points', axis=alt.Axis(grid=False)),
alt.Text('position'),
alt.Color('team_long_name:N', title='Team', scale=palette),
)
points = line.mark_circle(size=80).encode(
opacity=alt.condition(nearest, alt.value(1), alt.value(0)))
pointsLC = lineLC.mark_circle(size=100).encode(opacity=alt.condition(
nearest, alt.value(1), alt.value(0)), )
rules = alt.Chart(premier1415).mark_rule(color='gray').encode(
x='stage', ).transform_filter(nearest)
tick = alt.Chart(premier1415mod).mark_tick(
color='black',
thickness=2,
strokeDash=[1, 1],
size=40 * 0.9, # controls width of tick.
).encode(
x=alt.X('cumpoints:Q', scale=alt.Scale(domain=[0, 81]),
title='Points'),
y=alt.Y('team_long_name:N',
sort=alt.EncodingSortField("cumpoints",
op="max",
order='descending'),
title='Team'),
).transform_filter(nearest)
hist = alt.Chart(premier1415).mark_bar().encode(
x=alt.X('cumpoints:Q', scale=alt.Scale(domain=[0, 81]),
title='Points'),
y=alt.Y('team_long_name:N',
sort=alt.EncodingSortField("cumpoints",
op="max",
order='descending'),
title='Team'),
color=alt.Color(
'team_long_name:N',
scale=alt.Scale(domain=topteams1415))).transform_filter(nearest)
text = hist.mark_text(
align='left',
baseline='middle',
dx=3, # Nudges text to right so it doesn't appear on top of the bar
fontSize=14).encode(text='cumpoints:Q')
text1415 = alt.Chart({
'values': [{
'x': 26,
'y': 8
}]
}).mark_text(
text=
'With only 8 games left, Leicester City\n was at the bottom of the table',
lineBreak='\n',
align='left',
fontSize=14
# , angle=346
).encode(x='x:Q', y='y:Q')
layer = alt.layer(lineLC, line, labels, labelsLC, selectors, points,
pointsLC, rules, text1415, pointarrow,
textarrow).properties(width=1100, height=300, title='')
layer2 = alt.layer(hist, text, tick).properties(
width=1120, height=300, title='Points at the selected Gameweek')
return alt.VConcatChart(vconcat=(layer, layer2), padding={
"left": 120
}).configure_title(fontSize=16).configure_axis(
labelFontSize=12,
titleFontSize=12).configure_legend(labelFontSize=12,
titleFontSize=12).to_json()
def seasonEvolution():
premier1516 = pd.read_csv('App/Data/premier1516.csv')
topteams1516 = [
'Leicester City', 'Arsenal', 'Tottenham Hotspur', 'Manchester City',
'Chelsea'
]
noLC = ['Arsenal', 'Tottenham Hotspur', 'Manchester City', 'Chelsea']
LC = ['Leicester City']
premier1516noLC = premier1516[premier1516['team_long_name'].isin(noLC)]
premier1516LC = premier1516[premier1516['team_long_name'].isin(LC)]
premier1516mod = premier1516.copy()
premier1516mod[
'cumpoints'] = premier1516mod['cumpoints'] - premier1516mod['points']
noLClast = premier1516noLC[premier1516noLC['stage'] == 38]
LClast = premier1516LC[premier1516LC['stage'] == 38]
LClast['position'] = '1st'
noLClast['position'] = ['10th', '4th', '3rd', '2nd']
noLClast['labely'] = [50, 66, 70, 72]
a = {'x': [13, 23], 'y': [28, 47], 'textof': ['➟', '➟']}
arrow = pd.DataFrame(a)
palette = alt.Scale(
domain=topteams1516,
range=['#0062cc', '#EF0107', 'yellow', '#6CABDD', "green"])
pointarrow = alt.Chart(arrow).mark_circle(size=100,
color='black').encode(x='x',
y='y')
textarrow = alt.Chart(arrow).mark_text(dx=-20, dy=0, angle=45,
fontSize=30).encode(x='x',
y='y',
text='textof')
nearest = alt.selection(type='single',
nearest=True,
on='mouseover',
fields=['stage'],
init={'stage': 20},
empty='none')
lineLC = alt.Chart(premier1516LC).mark_line(size=5).encode(
x=alt.X('stage', title='Gameweek', axis=alt.Axis(grid=False)),
y=alt.Y('cumpoints:Q', title='Points', axis=alt.Axis(grid=False)),
color=alt.Color('team_long_name:N', title='Team'),
tooltip='team_long_name:N')
line = alt.Chart(premier1516noLC).mark_line(size=2).encode(
x=alt.X('stage', title='Gameweek', axis=alt.Axis(grid=False)),
y=alt.Y('cumpoints:Q', title='Points', axis=alt.Axis(grid=False)),
color=alt.Color('team_long_name:N', title='Team'),
tooltip='team_long_name:N')
selectors = alt.Chart(premier1516).mark_point().encode(
x='stage', opacity=alt.value(0)).add_selection(nearest)
points = line.mark_circle(size=80).encode(
opacity=alt.condition(nearest, alt.value(1), alt.value(0)))
labelsLC = alt.Chart(LClast).mark_text(align='left', dx=3, size=14).encode(
alt.X('stage', title='Gameweek', axis=alt.Axis(grid=False)),
alt.Y('cumpoints:Q', title='Points', axis=alt.Axis(grid=False)),
alt.Text('position'),
alt.Color('team_long_name:N', title='Team', scale=palette),
)
labels = alt.Chart(noLClast).mark_text(align='left', dx=3, size=12).encode(
alt.X('stage', title='Gameweek', axis=alt.Axis(grid=False)),
alt.Y('labely:Q', title='Points', axis=alt.Axis(grid=False)),
alt.Text('position'),
alt.Color('team_long_name:N', title='Team', scale=palette),
)
pointsLC = lineLC.mark_circle(size=100).encode(opacity=alt.condition(
nearest, alt.value(1), alt.value(0)), )
rules = alt.Chart(premier1516).mark_rule(color='gray').encode(
x='stage', ).transform_filter(nearest)
hist = alt.Chart(premier1516).mark_bar().encode(
x=alt.X('cumpoints:Q', scale=alt.Scale(domain=[0, 81]),
title='Points'),
y=alt.Y('team_long_name:N',
sort=alt.EncodingSortField("cumpoints",
op="max",
order='descending'),
title='Team'),
color=alt.Color(
'team_long_name:N',
scale=alt.Scale(domain=topteams1516))).transform_filter(nearest)
tick = alt.Chart(premier1516mod).mark_tick(
color='black',
thickness=2,
strokeDash=[1, 1],
size=60 * 0.9, # controls width of tick.
).encode(
x=alt.X('cumpoints:Q', scale=alt.Scale(domain=[0, 81]),
title='Points'),
y=alt.Y('team_long_name:N',
sort=alt.EncodingSortField("cumpoints",
op="max",
order='descending'),
title='Team'),
).transform_filter(nearest)
text = hist.mark_text(
align='left',
baseline='middle',
dx=3, # Nudges text to right so it doesn't appear on top of the bar
fontSize=14).encode(text='cumpoints:Q')
text1516a = alt.Chart({
'values': [{
'x': 8.5,
'y': 44
}]
}).mark_text(
text='Leicester took the lead \n for the first time at Gameweek 13',
lineBreak='\n',
align='left',
fontSize=14
# , angle=346
).encode(x='x:Q', y='y:Q')
text1516b = alt.Chart({
'values': [{
'x': 18,
'y': 62
}]
}).mark_text(
text=
'At Gameweek 23 Leicester City took the lead\n once again and never looked back',
lineBreak='\n',
align='left',
fontSize=14
# , angle=346
).encode(x='x:Q', y='y:Q')
layer = alt.layer(lineLC, line, labelsLC, labels, selectors, points,
pointsLC, rules, text1516a, text1516b, pointarrow,
textarrow).properties(width=1200, height=300, title='')
layer2 = alt.layer(hist, text, tick).properties(
width=1200, height=300, title='Points at the selected Gameweek')
return alt.VConcatChart(vconcat=(layer, layer2), padding={
"left": 100
}).configure_title(fontSize=16).configure_axis(
labelFontSize=12,
titleFontSize=12).configure_legend(labelFontSize=12,
titleFontSize=12).to_json()
#print(enumerate(provs_json)
for index, p in enumerate(provs_json):
name = provs[index]
slug = ids[index]
dates = [d['date'] for d in p]
rate = [(d['deaths_acum']/d['cases_acum'])*100 if d['cases_acum'] != 0 else 0 for d in p]
df = pd.DataFrame({'fecha':dates,'razon':rate})
line = alt.Chart(df).mark_line(color='#009999').encode(x=alt.X('fecha:T', title=''), y=alt.Y('razon:Q', title='Razón de letalidad'))
selectors = alt.Chart(df).mark_point().encode(x='fecha:T', opacity=alt.value(0)).add_selection(nearest)
points = line.mark_point().encode(opacity=alt.condition(nearest, alt.value(1), alt.value(0)))
text = line.mark_text(align='left', dx=5, dy=-5).encode(text=alt.condition(nearest, 'razon:Q', alt.value(' ')))
rules = alt.Chart(df).mark_rule(color='gray').encode(x='fecha:T').transform_filter(nearest)
graph = alt.layer(line, selectors, points, text, rules).properties(width=500, height=200, title=name)
charts[str(slug).zfill(2)] = graph
array_charts = list(charts.values())
def make_hcc(row_of_charts):
hconcat = [chart for chart in row_of_charts]
hcc = alt.HConcatChart(hconcat=hconcat)
return hcc
rows_of_charts = [array_charts[i:i+3] for i in range(0, len(array_charts), 3)]
vconcat = [make_hcc(r) for r in rows_of_charts]
compound_chart = alt.VConcatChart(vconcat=vconcat).configure_axisX(grid=True).configure_axisY(grid=True)
compound_chart.properties(title='Razon de letalidad de los casos por provincia')
compound_chart.show()
#altair_viewer.show(compound_chart)
