Day 2 document

influxdata

https://www.influxdata.com/

Result Shuffle

based on the rand()

done

Lesson

• step by step updates

Update the domain on one sec based manner

import datetime

import dash
from dash import dcc, html
import plotly
from dash.dependencies import Input, Output
import datetime
import random
import dash_table

import pandas as pd

total_power_df = pd.DataFrame(columns=["Total Power"], index=pd.DatetimeIndex([]))
def random_data_df():
    total_power_df.loc[datetime.datetime.now()] = random.randint(0, 200)
    return total_power_df


# pip install pyorbital
from pyorbital.orbital import Orbital
satellite = Orbital('TERRA')

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div(
    html.Div([
        html.H4('TERRA Satellite Live Feed'),
        html.Div(id='live-update-text'),
        dcc.Graph(id='live-update-graph'),
        dash_table.DataTable(total_power_df.to_dict('records'), [{"name": i, "id": i} for i in total_power_df.columns]),
        dcc.Interval(
            id='interval-component',
            interval=1*1000, # in milliseconds
            n_intervals=0
        )
    ])
)


@app.callback(Output('live-update-text', 'children'),
              Input('interval-component', 'n_intervals'))
def update_metrics(n):
    lon, lat, alt = satellite.get_lonlatalt(datetime.datetime.now())
    random_data_df()
    style = {'padding': '5px', 'fontSize': '16px'}
    return [
        html.Span('Longitude: {0:.2f}'.format(lon), style=style),
        html.Span('Latitude: {0:.2f}'.format(lat), style=style),
        html.Span('Altitude: {0:0.2f}'.format(alt), style=style)
    ]


# Multiple components can update everytime interval gets fired.
@app.callback(Output('live-update-graph', 'figure'),
              Input('interval-component', 'n_intervals'))
def update_graph_live(n):
    satellite = Orbital('TERRA')
    data = {
        'time': [],
        'Latitude': [],
        'Longitude': [],
        'Altitude': []
    }

    # Collect some data
    for i in range(180):
        time = datetime.datetime.now() - datetime.timedelta(seconds=i*20)
        lon, lat, alt = satellite.get_lonlatalt(
            time
        )
        data['Longitude'].append(lon)
        data['Latitude'].append(lat)
        data['Altitude'].append(alt)
        data['time'].append(time)

    # Create the graph with subplots
    fig = plotly.tools.make_subplots(rows=2, cols=1, vertical_spacing=0.2)
    fig['layout']['margin'] = {
        'l': 30, 'r': 10, 'b': 30, 't': 10
    }
    fig['layout']['legend'] = {'x': 0, 'y': 1, 'xanchor': 'left'}

    fig.append_trace({
        'x': data['time'],
        'y': data['Altitude'],
        'name': 'Altitude',
        'mode': 'lines+markers',
        'type': 'scatter'
    }, 1, 1)
    fig.append_trace({
        'x': data['Longitude'],
        'y': data['Latitude'],
        'text': data['time'],
        'name': 'Longitude vs Latitude',
        'mode': 'lines+markers',
        'type': 'scatter'
    }, 2, 1)

    return fig


if __name__ == '__main__':
    app.run_server(debug=True)

While approach

from dash import Dash, dcc, html, Input, Output, dash_table, callback
import dash_html_components as html
import dash_core_components as dcc
import numpy as np
import datetime
import random
from time import time, sleep

import pandas as pd
from dash.dependencies import Input, Output
import time
total_power_df = pd.DataFrame(columns=["Total Power"], index=pd.DatetimeIndex([]))
def random_data_df():
    total_power_df.loc[datetime.datetime.now()] = random.randint(0, 200)
    return total_power_df
t_end = time.time() + 2
while time.time() < t_end:
    sleep(1)
    random_data_df()
    app = Dash(__name__)

    app.layout = html.Div([
        html.H6("Change the value in the text box to see callbacks in action!"),
        dash_table.DataTable(total_power_df.to_dict('records'), [{"name": i, "id": i} for i in total_power_df.columns]),

    ])

if __name__ == '__main__':
    app.run_server(debug=True)
    # do whatever you do

Auto update

import datetime
import datetime
import random
import dash
from dash import dcc, html,dash_table
import plotly
import pandas as pd

total_power_df = pd.DataFrame(columns=["Total Power"], index=pd.DatetimeIndex([]))
from dash.dependencies import Input, Output

# pip install pyorbital
from pyorbital.orbital import Orbital
satellite = Orbital('TERRA')
def random_data_df():
    total_power_df.loc[datetime.datetime.now()] = random.randint(0, 200)
    return total_power_df
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div(
    html.Div([
        html.H4('Total Power'),
        html.Div(id='live-update-text'),
        dcc.Interval(
            id='interval-component',
            interval=1*1000, # in milliseconds
            n_intervals=0
        )
    ])
)


@app.callback(Output('live-update-text', 'children'),
              Input('interval-component', 'n_intervals'))
def update_metrics(n):
    lon, lat, alt = satellite.get_lonlatalt(datetime.datetime.now())
    random_data_df()
    style = {'padding': '5px', 'fontSize': '16px'}
    return [
        total_power_df["Total Power"].iloc[-1]
    ]


# Multiple components can update everytime interval gets fired.
@app.callback(Output('live-update-graph', 'figure'),
              Input('interval-component', 'n_intervals'))
def update_graph_live(n):
    satellite = Orbital('TERRA')
    data = {
        'time': [],
        'Latitude': [],
        'Longitude': [],
        'Altitude': []
    }

    # Collect some data
    for i in range(180):
        time = datetime.datetime.now() - datetime.timedelta(seconds=i*20)
        lon, lat, alt = satellite.get_lonlatalt(
            time
        )
        data['Longitude'].append(lon)
        data['Latitude'].append(lat)
        data['Altitude'].append(alt)
        data['time'].append(time)

    # Create the graph with subplots
    fig = plotly.tools.make_subplots(rows=2, cols=1, vertical_spacing=0.2)
    fig['layout']['margin'] = {
        'l': 30, 'r': 10, 'b': 30, 't': 10
    }
    fig['layout']['legend'] = {'x': 0, 'y': 1, 'xanchor': 'left'}

    fig.append_trace({
        'x': data['time'],
        'y': data['Altitude'],
        'name': 'Altitude',
        'mode': 'lines+markers',
        'type': 'scatter'
    }, 1, 1)
    fig.append_trace({
        'x': data['Longitude'],
        'y': data['Latitude'],
        'text': data['time'],
        'name': 'Longitude vs Latitude',
        'mode': 'lines+markers',
        'type': 'scatter'
    }, 2, 1)




if __name__ == '__main__':
    app.run_server(debug=True)

Second based updated table

Put the shuffled data

• done

Put the data in the table header

• MANUFACTURING SPC DASHBOARD - PROCESS CONTROL AND EXCEPTION REPORTING

what to write here?