from app import app, html, dcc, dash
# sensor data for table
global sensors
global values
sensors = ["S1", "S2", "S3", "S4", "S5"]
values = [1, 2, 3, 4, 5]
overview = html.Div([
html.H2('Hello World'),
dcc.Dropdown(id='dropdown',
options=[{
'label': i,
'value': i
} for i in ['LA', 'NYC', 'MTL']],
value='LA'),
html.Div(id='display-value'),
html.Table(
# Sensor names
[html.Tr([html.Th("Sensor", ), html.Th("Value")])] +
# Sensor Values
[
html.Tr([html.Td(sensors[i]),
html.Td(values[i])])
for i in range(max(len(sensors), len(values)))
])
])
from app import app, html, dcc, dash
from db import Data, Sensor
import dash_table
import pandas as pd
sensors = html.Div([
html.H2('Sensors'),
html.Div(id='display-sensor'),
dcc.Interval(
id='interval-component',
interval=1 * 1000, # in milliseconds
n_intervals=0),
html.H2('Sensor Values'),
html.Div(id='display-value')
])
def getSensorTable():
sensor_query = Sensor.query
df = pd.read_sql(sensor_query.statement, sensor_query.session.bind)
return dash_table.DataTable(
id='sensor-table',
data=df.to_dict('records'),
columns=[{
"name": i,
"id": i
} for i in df.columns],
style_cell={
'textAlign': 'center',
'min-width': '50px'
from app import app, html, dcc, dash, Data, Sensor
import dash_table
import pandas as pd
import plotly.express as px
#f = px.data.iris()
#ig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")
# If you print fig, you'll see that it's just a regular figure with data and layout
# print(fig)
fig.show()
overview = html.Div([
html.H2('Sensors'),
html.Div(id='display-value'),
dcc.Interval(
id='interval-component',
interval=1 * 1000, # in milliseconds
n_intervals=0)
])
def getSensorTable():
sensor_query = Sensor.query
print(sensor_query)
df = pd.read_sql(sensor_query.statement, sensor_query.session.bind)
return dash_table.DataTable(
id='sensor-table',
data=df.to_dict('records'),
import numpy as np
from app import IBNR, cl, MyPickle, html, dbc, dash_table, Format, Scheme
NAV_STYLE = {
"height": 10,
"padding": "2rem 1rem",
}
CONTENT_STYLE = {
"padding": "5rem 1rem",
}
nav = dbc.Nav(
[
html.H2(children="I.B.N.R.",
style={
"color": "#fff",
"margin-right": "2rem"
}),
dbc.NavItem(dbc.NavLink("Step One ", href="/step_one")),
dbc.NavItem(dbc.NavLink("Step Two ", active=True, href="/step_two")),
],
pills=True,
className="navbar navbar-expand-lg navbar-dark bg-primary fixed-top",
style=NAV_STYLE)
def _get_paid_df_ult_ibnr_M2Qper(param_hist):
df = IBNR.Data_Handler.data
output = pd.DataFrame([])
for i, row in param_hist.iterrows():
product_code_claim = eval(row['product_code_claim'])
from app import app, html, dcc, dash
from db import Data, Sensor
import plotly.graph_objs as go
import dash_table
import pandas as pd
from util import angle_between
from pages.components.orientation import Orientation
from pages.components.card import Card
from pages.components.row import Row
orientation = Orientation()
card = Card()
row = Row()
overview = html.Div([
html.H2('Overview'),
dcc.Interval(
id='interval-component',
interval=1*1000, # in milliseconds
n_intervals=0
),
row.create(children=[
card.create(
title='Orientation',
children=[orientation.create(id='orientation')],
footer_id='orientation_text',
col_sizes={'md': 6, 'xl': 4}
),
card.create(
title='Altitude',
children=[
{'name': 'claim_category', 'id': 'claim_category'},
{'name': 'updated_on', 'id': 'updated_on'},
{'name': 'params', 'id': 'params'}
],
data = None,
row_selectable="multi",
selected_rows =[],
row_deletable=False,
sort_action="native",
page_current= 0,
page_size= 10,
style_header={'textAlign': 'center'},
style_table={'overflowX': 'auto'},
)
table_incremental = html.Div([
html.H2('Incremental Triangle'),
dash_table.DataTable(
id='incremental triangle',
columns=None,
data=None,
export_format="csv",
merge_duplicate_headers=True,
style_data_conditional = None,
style_header={
'fontWeight': 'bold',
'textAlign': 'center'
},
style_table={'overflowX': 'auto'},
)
])
table_cummulative = html.Div([