def update_figure(c_list):
PQ, SE = [], []
for c in c_list:
PQ_fig = px.line(wpgs[c], x='Month', y='Value', color='Year',
facet_row='Type', category_orders={'Type': ['Baht', 'Ton']})
PQ.append(Graph(figure=PQ_fig))
SE_fig = px.bar(csdp[c], x='Month', y='EC', color='Type',
facet_row='Year')
SE.append(Graph(figure=SE_fig))
return PQ, SE
def get_html(self) -> List[ComponentMeta]:
"""Initializes the header dash html
"""
return [
H2(self.content["new-admissions-title"]),
Markdown(self.content["new-admissions-text"]),
Graph(id="new-admissions-graph"),
Table(id="new-admissions-table"),
H2(self.content["admitted-patients-title"]),
Markdown(self.content["admitted-patients-text"]),
Graph(id="admitted-patients-graph"),
Table(id="admitted-patients-table"),
]
def col_histograms(df):
"""
Generate plotly.Dash's Bar chart component. One barchart for each column of the dataframe
:param df: pandas.DataFrame
:return: dash_core_components.Graph
"""
describe_bar_chart = Div(children=[])
for col in df.columns:
t = Histogram(x=df[col], name=col)
l = Layout(title='Histogram for colunm {}'.format(t.name),
autosize=False,
width=500,
height=500,
margin={
'l': 50,
'r': 50,
'b': 100,
't': 100,
'pad': 4
})
fig = Figure(data=[t], layout=l)
g = Graph(figure=fig,className="col-sm-4", config={'staticPlot': True, 'responsive': True})
describe_bar_chart.children.append(g)
return describe_bar_chart
def per_machine_per_device_pie_charts(GPU_STATS, KEEP_ALIVE):
compute_machines = []
for machine_name, machine in GPU_STATS.items():
machine_devices = []
devices = machine["devices"]
for i, d in enumerate(devices):
used = d["used"] // MB_DIVISOR
total = d["total"] // MB_DIVISOR
used_ratio = used / total
used_ratio = [used_ratio] + [1.0 - used_ratio]
hover_text = [f"used:{used:.0f}mb", f"free:{total - used:.0f}mb"]
machine_devices.append(
Div(
[
Graph(
id=f"gpu_stats_{machine_name}{i}",
figure={
"data": [
graph_objs.Pie(
values=used_ratio,
text=hover_text,
name="used ratio",
hole=0.4,
)
],
"layout":
graph_objs.Layout(
title=f"{d['name']} cuda_idx:{d['id']}",
showlegend=False,
hovermode="closest",
),
},
style={
"width": "100%",
},
)
],
className="col",
))
compute_machines.append(
Div(
[
H3(
f"{machine_name}: {KEEP_ALIVE[machine_name]} sec ago",
className="text-monospace",
style={"text-decoration": "underline"},
),
Div([*machine_devices], className="row"),
],
style={
"text-align": "center",
"width": "100%",
},
))
return compute_machines
def layout(symbols):
"""Return the UI layout."""
periods = [('1 day', 0), ('1 week', 1), ('1 month', 2), ('3 months', 3),
('1 year', 4), ('5 years', 5)]
return Div([
H3('Stock prices'),
Div([
Div([_symbol_selector_dropdown(symbols)],
style={
'width': '45%',
'float': 'left',
'display': 'inline-block'
}),
Div([_period_selector_radio(periods)],
style={
'width': '45%',
'float': 'right',
'display': 'inline-block'
})
],
style={
'display': 'inline-block',
'width': '100%'
}),
Graph(id='plot-stock', config={'displayModeBar': False})
])
def makeCard(plot, view, hide=False):
layout = \
{
'title' : f'<b>{plot} of {view}</b>',
'xaxis_showgrid' : False,
'xaxis_zeroline' : False,
'xaxis_visible' : False,
'yaxis_showgrid' : False,
'yaxis_zeroline' : False,
'yaxis_visible' : False,
'paper_bgcolor' : BG_PAPER,
'plot_bgcolor' : BG_PLOT,
}
config = \
{
'displaylogo' : False,
'showTips' : True,
'frameMargins' : 0,
}
style = \
{
'background' : 'blue'
}
wrap = plot.replace('plot', 'wrap')
card = plot.replace('plot', 'card')
mask = plot.replace('plot', 'mask')
load = plot.replace('plot', 'load')
figure = upFigure(traces=[], layout=layout)
return Div(id=wrap,
className='wrap',
children=[
Card(id=card,
className=f'{view}',
children=[
Div(id=mask,
className='mask',
hidden=False,
children=[
Loading(id=load,
className='load',
children=[
Graph(id=plot,
figure=figure,
config=config,
style=style)
]),
])
])
])
def get_altitude_fig(trip: Trip):
conn = Database.get_db()
res = list(map(list, conn.execute("SELECT mileage, altitude FROM position WHERE Timestamp>=? and Timestamp<=?;",
(trip.start_at, trip.end_at)).fetchall()))
start_mileage = res[0][0]
for line in res:
line[0] = line[0] - start_mileage
fig = px.line(res, x=0, y=1)
fig.update_layout(xaxis_title="Distance km", yaxis_title="Altitude m")
conn.close()
return html.Div(Graph(figure=fig))
def plot_listings(listings: DataFrame) -> Graph:
fig = go.Figure(
go.Scattergl(
x=listings["mileage"],
y=listings["price"],
customdata=listings[[
"vin",
"make",
"model",
"style",
"mpg",
"dealer_name",
"distance",
"year",
"price",
"drivetrain",
"engine",
]],
hoverlabel=dict(bgcolor="#F8F5F0"),
hovertemplate=('<b style="color: green;">$%{customdata[8]}</i><br>'
"<i>%{customdata[0]}</i><br>"
'<b style="font-size:16">'
"%{customdata[7]} %{customdata[1]} "
"%{customdata[2]} %{customdata[3]}"
"</b><br>"
"<i>%{customdata[9]} - %{customdata[10]}</b><br>"
"Dealer: %{customdata[5]}<br>"
"<b>About %{customdata[6]:.0f} miles from you.</b>"
"<extra></extra>"),
marker=dict(
color=listings["color_rgb"].fillna("#000000"),
opacity=1 -
0.75 * listings["color_rgb"].transform(pd.isna).astype(int),
size=10,
line=dict(width=0),
),
mode="markers+text",
),
layout=dict(
clickmode="event",
xaxis=dict(title="Mileage, mi", ticks="inside"),
yaxis=dict(title="Price, $", ticks="inside"),
margin=dict(b=0, t=0, l=0, r=0, pad=0),
),
)
fig.update_yaxes(automargin=True)
fig.update_xaxes(automargin=True)
graph = Graph(id=INPID_GRAPH,
config=dict(displayModeBar=False),
figure=fig)
return graph
def get_battery_curve_fig(row: dict, car: Car):
start_date = dash_date_to_datetime(row["start_at"])
stop_at = dash_date_to_datetime(row["stop_at"])
conn = Database.get_db()
res = Database.get_battery_curve(conn, start_date, car.vin)
conn.close()
res.insert(0, {"level": row["start_level"], "date": start_date})
res.append({"level": row["end_level"], "date": stop_at})
battery_curves = []
speed = 0
for x in range(1, len(res)):
start_level = res[x - 1]["level"]
end_level = res[x]["level"]
speed = car.get_charge_speed(start_level, end_level, (res[x]["date"] - res[x - 1]["date"]).total_seconds())
battery_curves.append({"level": start_level, "speed": speed})
battery_curves.append({"level": row["end_level"], "speed": speed})
fig = px.line(battery_curves, x="level", y="speed")
fig.update_layout(xaxis_title="Battery %", yaxis_title="Charging speed in kW")
return html.Div(Graph(figure=fig))
def callback(self, *args, **kwargs) -> List[Any]:
"""Renders the parameter dependent plots and tables
"""
pars = kwargs["pars"]
if kwargs["show_additional_projections"]:
title = self.content["infected-v-revovered-title"]
time_evolution = self._build_frame(**kwargs)
time_evolution_data = plot_dataframe(
time_evolution.drop(columns=self.content["susceptible"]),
max_y_axis=pars.max_y_axis,
)
children = [
H4(title, id="infected-v-revovered-title"),
Graph(figure=time_evolution_data,
id="infected-v-revovered-graph"),
]
if kwargs["show_tables"]:
if kwargs["as_date"]:
time_evolution.index = time_evolution.index.strftime(
"%b, %d")
time_evolution_table_data = (df_to_html_table(
time_evolution, data_only=True, n_mod=7)
if kwargs["show_tables"] else {})
children.append(
Table(time_evolution_table_data,
id="infected-v-revovered-table"))
else:
children = []
return [children]
max=100,
step=1,
value=32,
updatemode='drag'
)],style={"width" : "25%"}),
Div([P('Inventory Target level (units 1 to 100) :')],style={'display': 'inline-block'}),
Div([P(id='inv-target-slider-output')],style={'display': 'inline-block','color':'red','padding':'20px','font-size':'160%'}),
Div([Slider(
id='inv-target-slider',
min=0,
max=100,
step=1,
value=75,
updatemode='drag'
)],style={"width" : "25%"}),
Graph(id='my-graph')
])
#Server logic of DASH app
@app.callback(
[Output('days-slider-output',component_property='children'),
Output('inv-cutoff-slider-output',component_property='children'),
Output('inv-target-slider-output',component_property='children'),
Output('my-graph',component_property='figure')],
[Input('days-slider',component_property='value'),
Input('inv-cutoff-slider',component_property='value'),
Input('inv-target-slider',component_property='value'),
])
def update_graph(days_slider,inv_cutoff_slider,inv_target_slider):
def get_figures(trips: Trips, charging: Tuple[dict]):
global consumption_fig, consumption_df, trips_map, consumption_fig_by_speed, table_fig, info, battery_info, \
battery_table, consumption_graph_by_temp
lats = []
lons = []
names = []
for trip in trips:
for points in trip.positions:
lats = np.append(lats, points.longitude)
lons = np.append(lons, points.latitude)
names = np.append(names, [str(trip.start_at)])
lats = np.append(lats, None)
lons = np.append(lons, None)
names = np.append(names, None)
trips_map = px.line_mapbox(lat=lats, lon=lons, hover_name=names,
mapbox_style="stamen-terrain", zoom=12)
# table
nb_format = Format(precision=2, scheme=Scheme.fixed, symbol=Symbol.yes)
table_fig = dash_table.DataTable(
id='trips-table',
sort_action='native',
# sort_by=[{'column_id': 'start_at', 'direction': 'desc'}],
columns=[{'id': 'start_at', 'name': 'start at', 'type': 'datetime'},
{'id': 'duration', 'name': 'duration', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" min").precision(0)},
{'id': 'speed_average', 'name': 'average speed', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" km/h").precision(0)},
{'id': 'consumption_km', 'name': 'average consumption', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" kWh/100km")},
{'id': 'consumption_fuel_km', 'name': 'average consumption fuel', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" L/100km")},
{'id': 'distance', 'name': 'distance', 'type': 'numeric',
'format': nb_format.symbol_suffix(" km").precision(1)},
{'id': 'mileage', 'name': 'mileage', 'type': 'numeric',
'format': nb_format.symbol_suffix(" km").precision(1)}],
data=[tr.get_info() for tr in trips[::-1]],
page_size=50
)
# consumption_fig
consumption_df = DataFrame.from_records(trips.get_long_trips())
consumption_fig = px.line(consumption_df, x="date", y="consumption", title='Consumption of the car')
consumption_fig.update_layout(yaxis_title="Consumption kWh/100Km")
consumption_fig_by_speed = px.histogram(consumption_df, x="speed", y="consumption_km", histfunc="avg",
title="Consumption by speed")
consumption_fig_by_speed.update_traces(xbins_size=15)
consumption_fig_by_speed.update_layout(bargap=0.05)
consumption_fig_by_speed.add_trace(
go.Scatter(mode="markers", x=consumption_df["speed"], y=consumption_df["consumption_km"],
name="Trips"))
consumption_fig_by_speed.update_layout(xaxis_title="average Speed km/h", yaxis_title="Consumption kWh/100Km")
kw_per_km = float(consumption_df["consumption_km"].mean())
info = "Average consumption: {:.1f} kWh/100km".format(kw_per_km)
# charging
charging_data = DataFrame.from_records(charging)
co2_per_kw = __calculate_co2_per_kw(charging_data)
co2_per_km = co2_per_kw * kw_per_km / 100
try:
charge_speed = 3600 * charging_data["kw"].mean() / \
(charging_data["stop_at"] - charging_data["start_at"]).mean().total_seconds()
except (TypeError, KeyError): # when there is no data yet:
charge_speed = 0
battery_info = dash_table.DataTable(
id='battery_info',
sort_action='native',
columns=[{'id': 'name', 'name': ''},
{'id': 'value', 'name': ''}],
style_header={'display': 'none'},
style_data={'border': '0px'},
data=[{"name": "Average emission:", "value": "{:.1f} g/km".format(co2_per_km)},
{"name": " ", "value:": "{:.1f} g/kWh".format(co2_per_kw)},
{"name": "Average charge speed:", "value": "{:.3f} kW".format(charge_speed)}])
battery_info = html.Div(children=[html.Tr(
[
html.Td('Average emission:', rowSpan=2),
html.Td("{:.1f} g/km".format(co2_per_km)),
]
),
html.Tr(
[
"{:.1f} g/kWh".format(co2_per_kw),
]
),
html.Tr(
[
html.Td("Average charge speed:"),
html.Td("{:.3f} kW".format(charge_speed))
]
)
])
battery_table = dash_table.DataTable(
id='battery-table',
sort_action='native',
sort_by=[{'column_id': 'start_at', 'direction': 'desc'}],
columns=[{'id': 'start_at', 'name': 'start at', 'type': 'datetime'},
{'id': 'stop_at', 'name': 'stop at', 'type': 'datetime'},
{'id': 'start_level', 'name': 'start level', 'type': 'numeric'},
{'id': 'end_level', 'name': 'end level', 'type': 'numeric'},
{'id': 'co2', 'name': 'CO2', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" g/kWh").precision(1)},
{'id': 'kw', 'name': 'consumption', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" kWh").precision(3)}],
data=charging,
)
consumption_by_temp_df = consumption_df[consumption_df["consumption_by_temp"].notnull()]
if len(consumption_by_temp_df) > 0:
consumption_fig_by_temp = px.histogram(consumption_by_temp_df, x="consumption_by_temp", y="consumption_km",
histfunc="avg", title="Consumption by temperature")
consumption_fig_by_temp.update_traces(xbins_size=2)
consumption_fig_by_temp.update_layout(bargap=0.05)
consumption_fig_by_temp.add_trace(
go.Scatter(mode="markers", x=consumption_by_temp_df["consumption_by_temp"],
y=consumption_by_temp_df["consumption_km"], name="Trips"))
consumption_fig_by_temp.update_layout(xaxis_title="average temperature in °C",
yaxis_title="Consumption kWh/100Km")
consumption_graph_by_temp = Graph(figure=consumption_fig_by_temp, id="consumption_fig_by_temp")
else:
consumption_graph_by_temp = Graph(style={'display': 'none'})
def get_figures(trips: Trips, charging: List[dict]):
global consumption_fig, consumption_df, trips_map, consumption_fig_by_speed, table_fig, info, battery_info, \
battery_table, consumption_graph_by_temp
lats = []
lons = []
names = []
for trip in trips:
for points in trip.positions:
lats = np.append(lats, points.latitude)
lons = np.append(lons, points.longitude)
names = np.append(names, [str(trip.start_at)])
lats = np.append(lats, None)
lons = np.append(lons, None)
names = np.append(names, None)
trips_map = px.line_mapbox(lat=lats,
lon=lons,
hover_name=names,
mapbox_style="stamen-terrain",
zoom=12)
# table
nb_format = Format(precision=2, scheme=Scheme.fixed, symbol=Symbol.yes) # pylint: disable=no-member
table_fig = dash_table.DataTable(
id='trips-table',
sort_action='native',
sort_by=[{
'column_id': 'id',
'direction': 'desc'
}],
columns=[{
'id': 'id',
'name': '#',
'type': 'numeric'
}, {
'id': 'start_at',
'name': 'start at',
'type': 'datetime'
}, {
'id':
'duration',
'name':
'duration',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" min").precision(0)
}, {
'id':
'speed_average',
'name':
'average speed',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" km/h").precision(0)
}, {
'id': 'consumption_km',
'name': 'average consumption',
'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" kWh/100km")
}, {
'id': 'consumption_fuel_km',
'name': 'average consumption fuel',
'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" L/100km")
}, {
'id': 'distance',
'name': 'distance',
'type': 'numeric',
'format': nb_format.symbol_suffix(" km").precision(1)
}, {
'id': 'mileage',
'name': 'mileage',
'type': 'numeric',
'format': nb_format
}, {
'id': 'altitude_diff',
'name': 'Altitude diff',
'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" m").precision(0)
}],
style_data_conditional=[{
'if': {
'column_id': ['altitude_diff']
},
'color': 'dodgerblue',
"text-decoration": "underline"
}],
data=trips.get_info(),
page_size=50)
# consumption_fig
consumption_df = DataFrame.from_records(trips.get_long_trips())
consumption_fig = px.histogram(consumption_df,
x="date",
y="consumption_km",
title='Consumption of the car',
histfunc="avg")
consumption_fig.update_layout(yaxis_title="Consumption kWh/100Km")
consumption_fig_by_speed = px.histogram(consumption_df,
x="speed",
y="consumption_km",
histfunc="avg",
title="Consumption by speed")
consumption_fig_by_speed.update_traces(xbins_size=15)
consumption_fig_by_speed.update_layout(bargap=0.05)
consumption_fig_by_speed.add_trace(
go.Scatter(mode="markers",
x=consumption_df["speed"],
y=consumption_df["consumption_km"],
name="Trips"))
consumption_fig_by_speed.update_layout(xaxis_title="average Speed km/h",
yaxis_title="Consumption kWh/100Km")
kw_per_km = float(consumption_df["consumption_km"].mean())
info = "Average consumption: {:.1f} kWh/100km".format(kw_per_km)
# charging
charging_data = DataFrame.from_records(charging)
co2_per_kw = __calculate_co2_per_kw(charging_data)
co2_per_km = co2_per_kw * kw_per_km / 100
try:
charge_speed = 3600 * charging_data["kw"].mean() / \
(charging_data["stop_at"] - charging_data["start_at"]).mean().total_seconds()
price_kw = (charging_data["price"] / charging_data["kw"]).mean()
total_elec = kw_per_km * trips.get_distance() / 100
except (TypeError, KeyError,
ZeroDivisionError): # when there is no data yet:
charge_speed = 0
price_kw = 0
total_elec = 0
SUMMARY_CARDS["Average charge speed"]["text"] = f"{charge_speed:.2f} kW"
SUMMARY_CARDS["Average emission"]["text"] = [
html.P(f"{co2_per_km:.1f} g/km"),
html.P(f"{co2_per_kw:.1f} g/kWh")
]
SUMMARY_CARDS["Electricity consumption"]["text"] = [f"{total_elec:.0f} kWh", html.Br(), \
f"{total_elec * price_kw:.0f} {ElecPrice.currency}"]
SUMMARY_CARDS["Average consumption"][
"text"] = f"{consumption_df['consumption_km'].mean():.1f} kWh/100km"
battery_table = dash_table.DataTable(
id='battery-table',
sort_action='native',
sort_by=[{
'column_id': 'start_at',
'direction': 'desc'
}],
columns=[{
'id': 'start_at',
'name': 'start at',
'type': 'datetime'
}, {
'id': 'stop_at',
'name': 'stop at',
'type': 'datetime'
}, {
'id': 'start_level',
'name': 'start level',
'type': 'numeric'
}, {
'id': 'end_level',
'name': 'end level',
'type': 'numeric'
}, {
'id':
'co2',
'name':
'CO2',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" g/kWh").precision(1)
}, {
'id':
'kw',
'name':
'consumption',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" kWh").precision(2)
}, {
'id':
'price',
'name':
'price',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" " +
ElecPrice.currency).precision(2),
'editable':
True
}],
data=charging,
style_data_conditional=[{
'if': {
'column_id': ['start_level', "end_level"]
},
'color': 'dodgerblue',
"text-decoration": "underline"
}, {
'if': {
'column_id': 'price'
},
'backgroundColor': 'rgb(230, 246, 254)'
}],
)
consumption_by_temp_df = consumption_df[
consumption_df["consumption_by_temp"].notnull()]
if len(consumption_by_temp_df) > 0:
consumption_fig_by_temp = px.histogram(
consumption_by_temp_df,
x="consumption_by_temp",
y="consumption_km",
histfunc="avg",
title="Consumption by temperature")
consumption_fig_by_temp.update_traces(xbins_size=2)
consumption_fig_by_temp.update_layout(bargap=0.05)
consumption_fig_by_temp.add_trace(
go.Scatter(mode="markers",
x=consumption_by_temp_df["consumption_by_temp"],
y=consumption_by_temp_df["consumption_km"],
name="Trips"))
consumption_fig_by_temp.update_layout(
xaxis_title="average temperature in °C",
yaxis_title="Consumption kWh/100Km")
consumption_graph_by_temp = html.Div(
Graph(figure=consumption_fig_by_temp),
id="consumption_graph_by_temp")
else:
consumption_graph_by_temp = html.Div(Graph(style={'display': 'none'}),
id="consumption_graph_by_temp")
return True
def historic(app,
tsa,
routes_pathname_prefix='/tshistory/',
request_pathname_prefix='/',
cachedir=None):
request_pathname_prefix_adv = request_pathname_prefix + routes_pathname_prefix
external_stylesheets = [
'https://codepen.io/chriddyp/pen/bWLwgP.css',
'https://cdn.rawgit.com/plotly/dash-app-stylesheets/2d266c578d2a6e8850ebce48fdb52759b2aef506/stylesheet-oil-and-gas.css'
]
if request_pathname_prefix != '/':
dashboard = dash.Dash(
'tsview',
server=app,
routes_pathname_prefix=routes_pathname_prefix,
requests_pathname_prefix=request_pathname_prefix_adv,
external_stylesheets=external_stylesheets)
else:
dashboard = dash.Dash('tsview',
server=app,
url_base_pathname=routes_pathname_prefix,
external_stylesheets=external_stylesheets)
dashboard.config['suppress_callback_exceptions'] = True
if request_pathname_prefix != '/':
dashboard.config.requests_pathname_prefix = request_pathname_prefix_adv
if cachedir:
cacheconfig = {'CACHE_TYPE': 'filesystem', 'CACHE_DIR': cachedir}
else:
cacheconfig = {'CACHE_TYPE': 'simple'}
cache = Cache(dashboard.server, config=cacheconfig)
cache.init_app(dashboard.server)
dashboard.layout = Div([
Location(id='url', refresh=False),
#Placeholders
Div(Dropdown(id='idate_slider', value=None), style={'display':
'none'}),
Graph(id='ts_snapshot'),
Button(id='submit-button',
n_clicks=0,
children='Show Republications',
style={
'width': '100%',
'background-color': '#F7F7F7',
}),
Graph(id='ts_by_appdate', hoverData={'points': [{
'text': None
}]}),
html.P('Select an insertion date:',
style={
'font-family': 'Helvetica',
"font-size": "100%",
'text-align': 'center',
}),
Div(id='slider-container'),
Div([Br()]),
Div([Br()]),
html.P(
'Place the mouse on the graph above to see the versions '
'of one application date:',
style={
'font-family': 'Helvetica',
"font-size": "100%",
'text-align': 'center',
}),
Graph(id='ts_by_insertdate'),
])
@cache.memoize(timeout=300)
def _history(id_serie, fromdate, todate):
return {
# canonicalize the keys immediately
dt.strftime('%Y-%m-%d %H:%M:%S'): serie
for dt, serie in tsa.history(id_serie,
from_value_date=fromdate,
to_value_date=todate).items()
}
def history(id_serie, fromdate=None, todate=None):
return _history(id_serie, fromdate, todate)
def insertion_dates(id_serie, fromdate=None, todate=None):
return list(history(id_serie, fromdate, todate).keys())
def parse_url(url_string):
if (url_string in (routes_pathname_prefix, request_pathname_prefix_adv)
or url_string is None or len(url_string.strip('/')) == 0):
id_serie = ''
else:
id_serie = url_string.split('/')[-1]
return id_serie
@dashboard.callback(
dash.dependencies.Output('slider-container', 'children'), [
dash.dependencies.Input('url', 'pathname'),
dash.dependencies.Input('submit-button', 'n_clicks')
], [dash.dependencies.State('ts_snapshot', 'relayoutData')])
def adaptable_slider(url_string, n_clicks, graphdata):
if n_clicks == 0:
return Slider(id='idate_slider', value=None)
id_serie = parse_url(url_string)
fromdate, todate = unpack_dates(graphdata)
idates = insertion_dates(id_serie, fromdate, todate)
showlabel = len(idates) < 25
slider = Slider(id='idate_slider',
min=0,
max=len(idates) - 1,
value=len(idates) - 1,
step=None,
marks={
str(idx): elt if showlabel else ''
for idx, elt in enumerate(idates)
})
return slider
@dashboard.callback(dash.dependencies.Output('ts_snapshot', 'figure'),
[dash.dependencies.Input('url', 'pathname')])
def ts_snapshot(url_string):
id_serie = parse_url(url_string)
ts = tsa.get(id_serie)
if id_serie is None or ts is None:
return {'data': [], 'layout': {}}
trace = [
go.Scatter(x=ts.index,
y=ts.values,
name=id_serie,
mode='lines',
line={'color': ('rgb(255, 127, 80)')})
]
layout = go.Layout({
'yaxis': {
'fixedrange': True
},
'showlegend': True,
'title': 'Zoom to select the date interval:'
})
return {'data': trace, 'layout': layout}
@dashboard.callback(dash.dependencies.Output('ts_by_appdate', 'figure'), [
dash.dependencies.Input('idate_slider', 'value'),
dash.dependencies.Input('submit-button', 'n_clicks')
], [
dash.dependencies.State('url', 'pathname'),
dash.dependencies.State('ts_snapshot', 'relayoutData')
])
def ts_by_appdate(idx, n_clicks, url_string, graphdata):
if n_clicks == 0:
return {'data': [], 'layout': {}}
fromdate, todate = unpack_dates(graphdata)
idx = idx if idx else 0
id_serie = parse_url(url_string)
ts_final = tsa.get(id_serie,
from_value_date=fromdate,
to_value_date=todate)
versions = history(id_serie, fromdate, todate)
idates = list(versions.keys())
if idx > len(idates):
# may happen when the input div are not refreshed at the same time
idx = len(idates) - 1
insert_date = idates[idx]
ts_unti_now = versions[insert_date]
traces = []
# all versions
for idate in idates:
ts = versions[idate].loc[fromdate:todate]
# plolty does not plot a line with only one point
mode = 'lines' if len(ts) > 1 else 'markers'
color = COLOR_BEFORE if idate <= insert_date else COLOR_AFTER
traces.append(
go.Scatter(x=ts.index,
y=ts.values,
text=[str(elt) for elt in ts.index],
name=idate,
showlegend=False,
mode=mode,
line={'color': color},
opacity=0.2))
# final snapshot
traces.append(
go.Scatter(x=ts_final.index,
y=ts_final.values,
name='last',
text=[str(elt) for elt in ts_final.index],
mode='lines',
line={'color': COLOR_LAST},
opacity=1))
# ts as of
mode = 'lines' if len(ts_unti_now) > 1 else 'markers'
traces.append(
go.Scatter(
x=ts_unti_now.index,
y=ts_unti_now.values,
text=[str(elt) for elt in ts_unti_now.index],
name=str(pd.to_datetime(insert_date)),
mode=mode,
line={'color': COLOR_CURRENT},
))
tsminvalue = min(ts.values.min() for ts in versions.values()
if len(ts))
tsmaxvalue = max(ts.values.max() for ts in versions.values()
if len(ts))
return {
'data':
traces,
'layout':
go.Layout(
hovermode='closest',
xaxis={'range': [ts_final.index.min(),
ts_final.index.max()]},
yaxis={'range': [tsminvalue, tsmaxvalue]},
title='{} <br>Insertion date : {}'.format(
id_serie, insert_date),
shapes=[{
'type': 'line',
'x0': insert_date,
'y0': tsminvalue,
'x1': insert_date,
'y1': tsmaxvalue,
'line': {
'dash': 'dot',
'color': 'rgb(0, 0, 0)',
'width': 1
}
}])
}
@dashboard.callback(
dash.dependencies.Output('ts_by_insertdate', 'figure'),
[dash.dependencies.Input('ts_by_appdate', 'hoverData')], [
dash.dependencies.State('url', 'pathname'),
dash.dependencies.State('ts_snapshot', 'relayoutData')
])
def ts_by_insertdate(hoverdata, url_string, graphdata):
id_serie = parse_url(url_string)
if id_serie is None:
return {'data': [], 'layout': {}}
date_str = hoverdata['points'][0]['text']
if date_str is None:
return {'data': [go.Scatter()], 'layout': go.Layout()}
fromdate, todate = unpack_dates(graphdata)
versions = history(id_serie, fromdate, todate)
index = []
values = []
lastvalue = None
dt = pd.to_datetime(date_str)
for idate, ts in sorted(versions.items()):
if dt in ts.index:
value = ts[dt]
if value == lastvalue:
continue
index.append(idate)
values.append(value)
lastvalue = value
traces = [
go.Scatter(
x=index,
y=values,
name=date_str,
mode='lines',
line={'color': ('rgb(20, 180, 40)')},
)
]
return {
'data':
traces,
'layout':
go.Layout(
hovermode='closest',
title='Application date : {}'.format(date_str),
)
}
def create_app():
app = Flask(__name__)
dash_app = Dash(__name__,
server=app,
url_base_pathname='/dash/',
external_stylesheets=[{
"href":
"https://fonts.googleapis.com/css2?"
"family=Lato:wght@400;700&display=swap",
"rel":
"stylesheet",
}])
dash_app.layout = Div(children=[
Div(children=[
P(children='📈', className='header-emoji'),
H1(children='Dash-PTS', className='header-title'),
P(
children='''
A Free and Open Source project-tracking systems tool:
''',
className='header-description',
),
P(
children='https://github.com/dunossauro/dash-pts',
className='header-description',
),
],
className='header'),
Div(
children=[
Div(children=[
Div(children='Department', className='menu-title'),
Dropdown(id='department-name',
options=[{
'label': i,
'value': i
} for i in ['Sales', 'R&D', 'Support']],
value='R&D',
className='dropdown')
], ),
Div(children=[
Div(children='Team', className='menu-title'),
Dropdown(
id='team-name',
options=[{
'label': i,
'value': i
} for i in ['Data Science', 'Mobile', 'WEB', 'QA']],
value='Mobile',
className='dropdown')
], ),
],
className='menu',
),
Div(
children=[
Graph(
id='burn-down',
className='card',
config={'displayModeBar': False},
),
Graph(
id='velocity',
className='card',
config={'displayModeBar': False},
)
],
className='wrapper',
)
], )
@app.route('/dash')
def index():
return dash_app.index()
@dash_app.callback([
Output(component_id='burn-down', component_property='figure'),
Output(component_id='velocity', component_property='figure'),
], [
Input(component_id='team-name', component_property='value'),
Input(component_id='department-name', component_property='value'),
])
def generate_graphs(team_name, department_name):
return (burn_down(
sprint='',
initial_data=datetime(2021, 4, 5),
final_data=datetime(2021, 4, 16),
total_points=50,
sprint_data=[50, 45, 41, 37, 39, 39, 39, 35, 27, 13],
),
velocity({
'names': ['Sprint ' + str(x) for x in range(1, 7)],
'commitment': [50, 47, 61, 53, 50, 51],
'completed': [52, 43, 58, 58, 49, 39],
}))
return app
sort_mode='multi',
filter_action='native',
page_size=50,
),
], style={'width': '100%'})
], className='div-content-center', style={'textAlign': 'center'})
]
),
# graph
Loading(
id='download--loading--graph--time-series--number-of-downloaded-scenes-by-date',
type='circle',
color=colors['text'],
children=[
Graph(id='download--graph--graph--time-series--number-of-downloaded-scenes-by-date')
]
),
# map
Loading(
id='download--loading--map--number-of-downloaded-scenes-by-location',
type='circle',
color=colors['text'],
children=[
# leaflet map - my code
Div([
# title
P(
children='Map: Number of Downloaded Scenes by Location (long/lat)',
style={'textAlign': 'left', 'color': colors['text']}
def draw_graph(in_interval, in_channels_selected, in_zoom_in, in_zoom_out, in_reset, in_playstop, in_expand_graphs):
global df, data_shown, playpause, expand_graphs
changed_id = [p['prop_id'] for p in callback_context.triggered][0]
if 'zoom_in' in changed_id:
if data_shown != 200:
data_shown = data_shown - 200
if 'zoom_out' in changed_id:
data_shown = data_shown + 200
if 'reset' in changed_id:
data_shown = 1400
if 'playstop' in changed_id:
playpause = True if playpause == False else False
graphs = []
for index, inlet in enumerate(inlets):
if playpause == True:
samples, timestamps = inlet.pull_chunk(timeout=0.0, max_samples=1024)
utc = [datetime.utcfromtimestamp(timestamp).replace(tzinfo=timezone.utc).astimezone(tz=None).strftime('%H:%M:%S.%f') for index, timestamp in enumerate(timestamps)]
df_aux = pd.DataFrame(samples, columns=cols[index], index=utc)
df[index] = df[index].append(df_aux)
df_to_show = df[index].tail(data_shown)
df_to_show = df_to_show[in_channels_selected]
else:
df_to_show = df[index].tail(data_shown)
df_to_show = df_to_show[in_channels_selected]
channel_qualities = []
for i in in_channels_selected:
if abs(df_to_show[i].tail(200).max() - df_to_show[i].tail(200).min()) < 300:
channel_qualities.append(i + ' - GOOD ' + u'\u2713')
else:
channel_qualities.append(i + ' - BAD ' + u'\u2716')
if in_expand_graphs == False:
if len(df_to_show.columns) % 2 == 0:
fig = make_subplots(rows=int(len(df_to_show.columns) / 2), cols=2, subplot_titles=channel_qualities)
else:
fig = make_subplots(rows=int((len(df_to_show.columns) / 2) + 0.5), cols=2, subplot_titles=channel_qualities)
j = [1, 1, 2, 2, 3]
k = [1, 2, 1, 2, 1]
for index2, column in enumerate(df_to_show.columns):
fig.add_trace(
Scatter({
'x': df_to_show.index2,
'y': df_to_show[column]
}),
row=j[index2],
col=k[index2]
)
heights = [500, 500, 800, 800, 1100]
fig.update_layout(height=heights[len(df_to_show.columns)-1], showlegend=False)
else:
fig = make_subplots(rows=len(df_to_show.columns), cols=1, subplot_titles=channel_qualities)
j = 1
for i in df_to_show.columns:
fig.add_trace(
Scatter({
'x': df_to_show.index,
'y': df_to_show[i]
}),
row=j,
col=1
)
j += 1
heights = [500, 900, 1200, 1500, 1600]
fig.update_layout(height=heights[len(df_to_show.columns)-1], showlegend=False)
graphs.append(
html.Div([
html.H3(inlet.info().name()),
Graph(
id='muse_livestream',
config={
'displaylogo': False,
'modeBarButtonsToRemove': ['pan2d','lasso2d']
},
figure=fig
)
], style={'margin': 'auto', 'text-align': 'center'})
)
return(html.Div(graphs))
html.Div(
id="map-container",
children=[
Graph(
id="choropleth",
# figure=dict(
# # data=[
# # dict(
# # lat=df_lat_lon["Latitude "],
# # lon=df_lat_lon["Longitude"],
# # text=df_lat_lon["Hover"],
# # type="scattermapbox",
# # )
# # ],
# layout=dict(
# mapbox=dict(
# layers=[],
# accesstoken=mapbox_access_token,
# style=mapbox_style,
# center=dict(
# lat=38.72490, lon=-95.61446
# ),
# pitch=0,
# zoom=3.5,
# ),
# autosize=True,
# ),
# ),
),
],
className="six columns"),
html.Div(children=[
def get_html(self) -> List[ComponentMeta]:
"""Initializes the header dash html
"""
today = date.today().strftime(self.content["date-format"])
return [
Container(
className="mt-5",
children=[
H2(self.content["new-admissions-title"]),
Markdown(self.content["new-admissions-text"]),
]
),
Container(fluid=True, children=Graph(id="new_admissions_graph")),
Container([
A(
self.content["download-text"],
id="new_admissions_download",
download="admissions_{}.csv".format(today),
href="",
target="_blank",
className="btn btn-sm btn-info",
),
Div(
children=Button(
"Lock Zoom",
id="new_admissions_lock_zoom",
color="info",
outline=False,
className="btn btn-sm"
),
style={"display": "inline-block", "padding": 10}
),
Div(
className="row justify-content-center",
children=Div(
id="new_admissions_table_container",
className="col-auto",
children=Table(
id="new_admissions_table",
className="table-responsive mx-auto"
),
),
),
]),
Container(
className="mt-5",
children=[
H2(self.content["admitted-patients-title"]),
Markdown(self.content["admitted-patients-text"]),
],
),
Container(fluid=True, children=Graph(id="admitted_patients_graph")),
Container([
A(
self.content["download-text"],
id="admitted_patients_download",
download="census_{}.csv".format(today),
href="",
target="_blank",
className="btn btn-sm btn-info",
),
Div(
children=Button(
"Lock Zoom",
id="admitted_patients_lock_zoom",
color="info",
outline=False,
className="btn btn-sm"
),
style={"display": "inline-block", "padding": 10}
),
Div(
className="row justify-content-center",
children=Div(
id="admitted_patients_table_container",
className="col-auto",
children=Table(
id="admitted_patients_table",
className="table-responsive"
),
),
),
]),
Container(
className="mt-5",
children=[
H2(self.content["SIR-title"]),
Markdown(self.content["SIR-text"]),
],
),
Container(fluid=True, children=Graph(id="SIR_graph")),
Container([
A(
self.content["download-text"],
id="SIR_download",
download="SIR_{}.csv".format(today),
href="",
target="_blank",
className="btn btn-sm btn-info my-4",
),
Div(
children=Button(
"Lock Zoom",
id="SIR_lock_zoom",
color="info",
outline=False,
className="btn btn-sm"
),
style={"display": "inline-block", "padding": 10}
),
Div(
className="row justify-content-center",
children=Div(
id="SIR_table_container",
className="col-auto",
children=Table(
id="SIR_table",
className="table-responsive"
),
),
),
])
]
def update():
if request.endpoint == url_base:
app.index_string = render_template(
'data_page.html'
)
rv = rv_plot.public
curr_user = g.user['atype']
if curr_user == 'admin' or curr_user == 'researcher':
rv = rv_plot.data
app.layout = Div([
Tooltip(
"Set the Y axis to a linear or logarithmic scale.",
target="log-switch",
),
Tooltip(
"Enables data to be separated by order on the spectrograph",
target="dim-switch",
),
Tooltip(
"The name of the file being displayed",
target="click-data",
placement="bottom"
),
Tooltip(
"Selects which orders of the spectrum to render",
target="order-tool",
),
Tooltip(
"Reduces the amount of points rendered to the screen",
target="res-tool",
),
Tooltip(
"Each graph has a tool bar in the upper-right hand corner that appears when you mouse over a graph."
"Use the buttons in the tool bar to zoom in, zoom out, reset the axis, save a screen shot, "
"and more. Click on a point in the Radial Velocity graph to display the Spectrum graph below."
"Hover over the slider and toggle bottoms below the lower graph to further interact with the"
"spectrum data.",
target="graph-tool"
),
Loading([
Div([
Button([
I(
className="fas fa-info-circle",
),
Span(
' Info',
)
],
id='graph-tool',
className='btn btn-warning btn-sm',
),
]),
Div(
id='rv-download-container'
),
Button([
I(
className="fas fa-download",
),
Span(
" Download Radial Velocities",
)
],
id='rv-download',
className='btn btn-primary btn-sm float-right',
),
Download(
id='rv-download-data'
),
DatePickerRange(
id='date-range',
className='pt-1 d-flex justify-content-end w-100',
),
Graph(
id='rv-plot',
className="",
config={
'modeBarButtonsToRemove': ['pan2d', 'lasso2d', 'autoscale']
}
),
], type="default", className='d-flex justify-content-end w-100'),
Div(
id='rv-data',
children=rv[rv_label].to_json(),
className='d-none'
),
Div([
Pre(id='click-data', className=' d-inline'),
]),
Br(),
Br(),
Div([
Loading([
Div([
Div([
Button([
I(
className="fas fa-download",
),
Span(
" Download 1D",
)
],
id='1d-spec-download',
className='btn btn-primary btn-sm',
),
Download(
id='1d-spec-download-data'
),
], className='pr-2'),
Div([
Button([
I(
className="fas fa-download",
),
Span(
" Download 2D",
)],
id='2d-spec-download',
className='btn btn-primary btn-sm',
),
Download(
id='2d-spec-download-data'
),
], className=""),
], className="row justify-content-end d-none", id="spec-download-container"),
Div(
id='spec-data',
children=[],
className='d-none'
),
Graph(
id='spec-plot',
className="pt-0",
config={
'modeBarButtonsToRemove': ['pan2d', 'lasso2d', 'autoscale']
}
),
], type="default", ),
Div([
Span([
'Order Range\n'
], id='order-tool', className='d-none'),
Span([
'Resolution\n'
], id='res-tool', className='d-none')
],
id='slide-label',
className='w-100 text-center'),
Div([
Slider(
min=1,
max=200,
value=100,
marks={
1: {'label': '1:1', 'style': {'color': '#77b0b1'}},
20: {'label': '20:1'},
50: {'label': '50:1'},
100: {'label': '100:1'},
200: {'label': '200:1', 'style': {'color': '#f50'}}
},
id='resolution',
className='col d-none',
),
RangeSlider(
id='spec-range',
className='col d-none',
min=0,
max=85,
step=1,
value=[40, 45],
marks={
0: {'label': '0'},
40: {'label': '40'},
45: {'label': '45'},
85: {'label': '85'}
}
),
], className='row'),
], id='spec-container'),
Br(),
Div([
Div([
daq.ToggleSwitch(
label='lin/log',
id='log-switch',
className='d-inline-block',
labelPosition='bottom'
),
], className='col text-center'),
Div([
daq.ToggleSwitch(
label='1D / 2D',
id='dim-switch',
className='d-inline-block',
labelPosition='bottom'
)
], className='col text-center'),
], className="row")
])
def setup_dash_layout(app: Dash, sk: DivSkeleton) -> dash.Dash:
def create_sliders(
) -> tuple[RangeSlider, RangeSlider, RangeSlider, RangeSlider]:
slider_height = 460
year_slider = RangeSlider(
INPID_YEAR,
min=(mn := etl.ATTRS.index.get_level_values("year").min()),
max=(mx := etl.ATTRS.index.get_level_values("year").max()),
value=[2012, 2018],
marks={y: str(y)
for y in range(mn, mx + 1)},
vertical=True,
verticalHeight=slider_height,
updatemode="mouseup",
**PERSIST_ARGS,
)
mileage_slider = RangeSlider(
INPID_MILEAGE,
min=0,
max=etl.MAX_MILEAGE,
value=[10000, 70000],
marks={
y: f"{y // 1000}k"
for y in range(0, etl.MAX_MILEAGE, 25_000)
},
step=1,
vertical=True,
updatemode="mouseup",
verticalHeight=slider_height,
**PERSIST_ARGS,
)
price_slider = RangeSlider(
INPID_PRICE,
min=0,
max=etl.MAX_PRICE,
value=[10000, 35000],
marks={
int(y): f"{y // 1000}k"
for y in range(0, etl.MAX_PRICE, 10_000)
},
step=1,
vertical=True,
updatemode="mouseup",
verticalHeight=slider_height,
**PERSIST_ARGS,
)
mpg_slider = RangeSlider(
INPID_MPG,
min=etl.ATTRS["mpg"].min(),
max=(mx := etl.ATTRS["mpg"].max()),
value=[20, mx],
marks={int(y): f"{y:.0f}"
for y in range(10,
int(mx) + 1, 10)},
step=1,
vertical=True,
updatemode="mouseup",
verticalHeight=slider_height,
**PERSIST_ARGS,
)
return year_slider, mileage_slider, price_slider, mpg_slider
for name, slider, div_id in zip(
["Year", "Mileage", "Price", "MPG"],
create_sliders(),
[SK_SLIDER_MILEAGE, SK_SLIDER_PRICE, SK_SLIDER_YEAR, SK_SLIDER_MPG],
):
sk.fill(
div_id,
[dbc.Badge(name, color="primary", className="slider"), slider],
)
top_selectors = [
dbc.Alert("Select your location.",
id="alert-loc-picker",
color="primary"),
Dropdown(
id=INPID_ZIPCODE,
placeholder="Zipcode",
clearable=False,
options=opts_from_vals(etl.LATLONG_BY_ZIP.keys()),
**PERSIST_ARGS,
),
Slider(
id=INPID_MAX_DIST,
className="form-control",
min=10,
max=250,
marks={
mark: dict(label=str(mark) + ("mi." if mark == 10 else ""))
for mark in [10, 50, 100, 150, 200, 250]
},
value=50,
**PERSIST_ARGS,
),
dbc.Alert("Limit dealership states.",
id="alert-state-picker",
color="primary"),
Dropdown(
id=INPID_STATE,
# options by callback
multi=True,
**PERSIST_ARGS,
),
Div(
id="plot-info-flex",
children=[
dbc.Button(children="Plot Cars Now!",
id=PLOT_BUTTON,
color="success"),
Div(
id=PLOT_ALERT_BOX,
children=dbc.Alert(id=PLOT_ALERT,
children="",
color="danger"),
hidden=True,
),
dbc.Alert("Plot does not refresh automatically.",
color="light"),
],
style={
"display": "flex",
"flex-direction": "column-reverse"
},
),
]
## car options pickers
top_selectors.extend([])
###
##
# === BOTTOM ROW ===
##
###
sk.fill(SK_TOP_SELECTORS, top_selectors)
mm_picker_menu = [
dbc.Alert(
"Select makes and models you are interested in. "
"Filtered by sliders.",
id=ALERT_MM_PICKER,
color="primary",
),
Dropdown(
id=INPID_MM_PICKER,
# options by callback
multi=True,
placeholder="Select makes",
clearable=False,
**PERSIST_ARGS,
),
]
sk.fill(SK_MM_PICKER, mm_picker_menu)
sk.fill(
SK_LL_INFO,
dbc.Alert(
id="alert-site-info",
children=(
"Used car picker by Evgeny Naumov.",
html.Br(),
"Built on top of Truecar data with Plotly + Dash.",
),
color="light",
),
)
# car type options
button_layout = {
("Transmission", INPID_OPTS_TRANS): scr.TRANSMISSIONS,
("Fuel Type", INPID_OPTS_FUEL): scr.KNOWN_FUEL_TYPES,
("Drivetrain", INPID_OPTS_DRIVETRAIN): scr.KNOWN_DRIVETRAINS,
("Body", INPID_OPTS_BODY): scr.KNOWN_BODIES,
}
car_opt_picker = [
dbc.Alert("Select car attributes.", color="primary"),
Div(
id="car-opts-box",
className=TOGGLE_BUTTON_BOX,
children=[
button
for (inp_name, inp_id), inp_opts in button_layout.items()
for button in ToggleButtonGroup.make_buttons(
label=inp_name,
values=inp_opts,
selectors=dict(input=inp_id),
)
],
),
]
sk.fill(SK_CAR_OPTS_BOX, car_opt_picker)
mmt_refine_menu = [
dbc.Alert("Select models to refine trims.",
id="mmt-alert",
color="secondary"),
Div(id="mmt-card-group"),
]
sk.fill(SK_MMT_MATRIX, mmt_refine_menu)
sk.fill(
SK_CACHE,
[
Interval(IVAL_TRIGGER_LOAD, max_intervals=1, interval=1),
Store(
id=STORE_ALL_CARS,
storage_type="memory",
data=etl.RAW_CLIENT_DATA,
),
Store(id=STORE_FILTERED_CARS, storage_type="session"),
Div(id="devnull"),
],
)
## GRAPH
scatter_graph = html.Div(
id="scatter-box",
children=Graph(id="scatter-price-mileage"),
hidden=True,
)
sk.fill(SK_SCATTER, scatter_graph)
### ALERTS
alert_link = dbc.Alert(
id="output-link",
children="A plot of listings will appear above when executed.",
color="secondary",
)
sk.fill(SK_INFO_A, alert_link)
app.layout = sk["root"]
return app
}, {
'label': 'Maiores de idade',
'value': 'maiores'
}],
value='menores'),
Slider(min=0, max=10, step=1, value=5),
Checklist(options=[{
'label': 'Menores de Idade',
'value': 'menores'
}, {
'label': 'Bebes',
'value': 'bebes'
}, {
'label': 'Maiores de idade',
'value': 'maiores'
}],
value=['bebes']),
Graph(config={'displayModeBar': False},
figure={
'data': [
{
'y': database['maiores'],
'x': database['index'],
'name': 'Maiores'
},
],
})
])
app.run_server(debug=True)
options = []
for k, v in indicators.iteritems():
options.append({'label': k, 'value': k, 'disabled': True})
for i in v:
options.append({'label': i, 'value': i})
world_bank_layout = html.Div([
html.H2('Market Indicators'),
html.H4('Dash Developer Preview App'),
html.Hr(),
Dropdown(id='indicator-dropdown-single',
options=options,
value='GDP growth (annual %)'),
html.Div([
html.Div([
Graph(id='choropleth'),
Graph(id='indicator-over-time'),
],
className="eight columns"),
html.Div([Slider(id='year-slider')],
style={
'marginTop': 25,
'marginBottom': 25
}),
html.Div(id='table',
style={
'height': '850px',
'overflowY': 'scroll'
},
className="four columns"),
],
)
], className='div-content-center'),
P(
id='scene--output-container-date-picker-range',
style={'color': colors['text']}
)
])
], style={'width': '50%'}),
], className='div-content-center'),
# add a loading component during graph loading
Loading(
id='scene--loading--graph--bubble-map--number-of-scenes',
type='circle',
color=colors['text'],
children=[Graph(id='scene--graph--bar-plot--number-of-scenes')]
),
# map
Loading(
id='scene--loading--map--number-of-scenes-by-location',
type='circle',
color=colors['text'],
children=[
# leaflet map - my code
Div([
# title
P(
children='Map: Number of Scenes by Location (long/lat) and Year-Month',
style={'textAlign': 'left', 'color': colors['text']}
),
def get_app(server, path):
df = Model().df
ldesc = Model().ldesc
dash_example = Dash(__name__, server=server, url_base_pathname=path)
label_map = loads(ldesc.to_json(orient="index"))
dash_example.layout = Div(className="dash-div",
children=[
Dropdown(
id='label-dropdown',
options=[{
'label':
label_map[i]["label_description"],
'value':
i
} for i in df['label'].unique()],
value=df['label'].unique()[0]),
Slider(id='filter-slider',
min=0,
max=20,
step=1,
value=10),
Graph(id="bubble-chart")
])
@dash_example.callback(
Output('bubble-chart', 'figure'),
[Input('label-dropdown', 'value'),
Input('filter-slider', 'value')])
def update_figure(label, value):
df = Model().df
ndf = getBubbleData(df, label, value)
#print(ndf.head())
bar = px.bar(ndf,
y="text",
x="count",
color="category",
orientation='h',
barmode='group')
bar.update_layout(autosize=False,
width=960,
height=550,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
hovermode='closest',
font=dict(family="Courier New, monospace",
size=18,
color="white"))
#return fig
return bar
@dash_example.callback(Output('filter-slider', 'value'),
[Input('label-dropdown', 'value')])
def update_slider_min(label):
df = Model().df
ndf = getBubbleData(df, label)
#print(ndf.head())
return ndf["count"].min()
@dash_example.callback(Output('filter-slider', 'min'),
[Input('label-dropdown', 'value')])
def update_slider_min(label):
df = Model().df
ndf = getBubbleData(df, label)
#print(ndf.head())
return ndf["count"].min()
@dash_example.callback(Output('filter-slider', 'max'),
[Input('label-dropdown', 'value')])
def update_slider_max(label):
df = Model().df
ndf = getBubbleData(df, label)
#print(ndf.head())
return ndf["count"].max()
return dash_example
html.Div(id='date_selector_container',
children=[
html.H4(id='text_date_selector'),
DatePickerSingle(id='date_selector',
min_date_allowed=min_date,
max_date_allowed=max_date,
date=max_date),
html.Br(),
],
className="six columns"),
],
className="row"),
html.Div([
html.Div(id="map-container",
children=[
Graph(id="choropleth", ),
],
className="six columns"),
html.Div(children=[
html.H5(id='text_map_description'),
html.Div(id='pie-container',
children=[
Graph(id='piechart'),
html.H5(id='text_pie_description'),
])
],
className="six columns"),
],
className="row"),
])
}],
value=['bebes']),
Dropdown(id='meu_dropdown',
options=[
{
'label': 'Linha',
'value': 'line'
},
{
'label': 'Barra',
'value': 'bar'
},
],
value='line'),
Graph(
id='meu_grafico',
config={'displayModeBar': False},
)
])
@app.callback(Output('meu_grafico', 'figure'), [
Input('meu_check_list', 'value'),
Input('meu_dropdown', 'value'),
])
def my_callback(input_data, graph_type):
grafico = {'data': []}
for x in input_data:
grafico['data'].append(
{
'y': database[x],
'x': database['index'],
def icon(src, href, title):
return A(
[
Img(
src=f'/assets/{src}.png',
className='icon',
),
],
href=href,
title=title,
)
app.layout = Div([
Graph(id='graph'),
Row(
[
Col(
[
Div(
[
'Date Range:',
Button( '1y', id='date-1yr',),
Button( '2y', id='date-2yr',),
Button( '3y', id='date-3yr',),
Button( '4y', id='date-4yr',),
Button( '5y', id='date-5yr',),
Button('All', id='date-all',),
],
className='control-header',
def _create_dash_components(self):
"""Create the graph, slider, figure, etc."""
info = self._slice_info
# Prep low-res slices. The get_thumbnail_size() is a bit like
# a simulation to get the low-res size.
if not self._thumbnail:
thumbnail_size = None
info["thumbnail_size"] = info["size"]
else:
thumbnail_size = self._thumbnail
info["thumbnail_size"] = get_thumbnail_size(
info["size"][:2], thumbnail_size)
thumbnails = [
img_array_to_uri(self._slice(i), thumbnail_size)
for i in range(info["size"][2])
]
# Create the figure object - can be accessed by user via slicer.graph.figure
self._fig = fig = plotly.graph_objects.Figure(data=[])
fig.update_layout(
template=None,
margin={
"l": 0,
"r": 0,
"b": 0,
"t": 0,
"pad": 4
},
dragmode="pan", # good default mode
)
fig.update_xaxes(
showgrid=False,
showticklabels=False,
zeroline=False,
autorange=True,
constrain="range",
)
fig.update_yaxes(
showgrid=False,
scaleanchor="x",
showticklabels=False,
zeroline=False,
autorange="reversed" if self._reverse_y else True,
constrain="range",
)
# Create the graph (graph is a Dash component wrapping a Plotly figure)
self._graph = Graph(
id=self._subid("graph"),
figure=fig,
config={"scrollZoom": True},
)
# Create a slider object that the user can put in the layout (or not).
# Note that the tooltip introduces a measurable performance penalty,
# so maybe we can display it in a different way?
self._slider = Slider(
id=self._subid("slider"),
min=0,
max=info["size"][2] - 1,
step=1,
value=info["size"][2] // 2,
updatemode="drag",
tooltip={
"always_visible": False,
"placement": "left"
},
)
# Create the stores that we need (these must be present in the layout)
# A dict of static info for this slicer
self._info = Store(id=self._subid("info"), data=info)
# A list of low-res slices, or the full-res data (encoded as base64-png)
self._thumbs_data = Store(id=self._subid("thumbs"), data=thumbnails)
# A list of mask slices (encoded as base64-png or null)
self._overlay_data = Store(id=self._subid("overlay"), data=[])
# Slice data provided by the server
self._server_data = Store(id=self._subid("server-data"),
data={
"index": -1,
"slice": None
})
# Store image traces for the slicer.
self._img_traces = Store(id=self._subid("img-traces"), data=[])
# Store indicator traces for the slicer.
self._indicator_traces = Store(id=self._subid("indicator-traces"),
data=[])
# Store user traces for the slider.
self._extra_traces = Store(id=self._subid("extra-traces"), data=[])
# A timer to apply a rate-limit between slider.value and index.data
self._timer = Interval(id=self._subid("timer"),
interval=100,
disabled=True)
# The (public) state of the slicer. This value is rate-limited. Initially null.
self._state = Store(id=self._subid("state", True), data=None)
# Signal to set the position of other slicers with the same scene_id.
self._setpos = Store(id=self._subid("setpos", True), data=None)
self._stores = [
self._info,
self._thumbs_data,
self._overlay_data,
self._server_data,
self._img_traces,
self._indicator_traces,
self._extra_traces,
self._timer,
self._state,
self._setpos,
]
"background": "white",
"border": "#d6d6d6",
"primary": "#1975FA",
},
),
Div(
children=[
P(
id="tab-text",
style={"margin": "20px", "font-size": "13px"},
)
]
+ [
Div(
Graph(
id=x + "-graph"
),
id=x + "-graph-div",
style={"display": "none"}
)
for x in TAB_DICT.keys()
],
style={"border": "1px solid #d6d6d6"},
),
],
className="nine columns",
style={
"padding-left": 20,
"padding-right": 2,
"background-color": "white",
"padding-top": 20,