Update Graph/Plot with Fixed Interval of Time

Update graph/plot with fixed interval of time

As an example you can run the following locally:

library(shiny)

runApp(list(
  ui = pageWithSidebar(    

  headerPanel("Hello Shiny!"),

  sidebarPanel(
    sliderInput("obs", 
                "Number of observations:", 
                min = 1,
                max = 1000, 
                value = 500)
  ),

  mainPanel(
    plotOutput("distPlot")
  )
),
  server =function(input, output, session) {
    autoInvalidate <- reactiveTimer(5000, session)
    output$distPlot <- renderPlot({
      autoInvalidate()
      # generate an rnorm distribution and plot it
      dist <- rnorm(input$obs)
      hist(dist)
    })

  }
))

A different normal sample will be generated every 5 seconds

Updating Graph on Interval on React

You could just add a setInterval in your useEffect to grab the data and update them again. Don't forgot to clear the interval on return:

useEffect(() => {
   const interval = setInterval(() => asyncFetch(), 5000)
   return () => clearInterval(interval)
}, []}

This example triggers every 5000ms, change the value according to your needs.

How do I plot in real-time in a while loop using matplotlib?

Here's the working version of the code in question (requires at least version Matplotlib 1.1.0 from 2011-11-14):

import numpy as np
import matplotlib.pyplot as plt

plt.axis([0, 10, 0, 1])

for i in range(10):
    y = np.random.random()
    plt.scatter(i, y)
    plt.pause(0.05)

plt.show()

Note the call to plt.pause(0.05), which both draws the new data and runs the GUI's event loop (allowing for mouse interaction).

Google Visualization Line Chart with fixed interval

In addition to sorting your data, it sounds like you want to use the timeofday type of value, documented minimally at: https://google-developers.appspot.com/chart/interactive/docs/reference?hl=en#DataTable_getValue

You should be able to use values like this:

data.addColumn('timeofday', 'Time');
data.addColumn('number', 'Leads');
data.addRow([[0, 0, 0, 1377011402000],5]);     
data.addRow([[0, 0, 0, 1376967661000],12]);

How to plot multiple daily time series, aligned at specified trigger times?

Assuming the index has already been converted to_datetime, create an IntervalArray from -2H to +8H of the index:

dl, dr = -2, 8
left = df.index + pd.Timedelta(f'{dl}H')
right = df.index + pd.Timedelta(f'{dr}H')

df['interval'] = pd.arrays.IntervalArray.from_arrays(left, right)

Then for each ANNOUNCEMENT, plot the window from interval.left to interval.right:

• Set the x-axis as seconds since ANNOUNCEMENT
• Set the labels as hours since ANNOUNCEMENT

fig, ax = plt.subplots()
for ann in df.loc[df['msg_type'] == 'ANNOUNCEMENT'].itertuples():
    window = df.loc[ann.interval.left:ann.interval.right] # extract interval.left to interval.right
    window.index -= ann.Index                             # compute time since announcement
    window.index = window.index.total_seconds()           # convert to seconds since announcement

    window.plot(ax=ax, y='value', label=ann.Index.date())
    deltas = np.arange(dl, dr + 1)
    ax.set(xticks=deltas * 3600, xticklabels=deltas)      # set tick labels to hours since announcement

ax.legend()

Here is the output with a smaller window -1H to +2H just so we can see the small sample data more clearly (full code below):

Full code:

import io
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

s = '''
date,value,msg_type
2022-03-15 08:15:10+00:00,122,None
2022-03-15 08:25:10+00:00,125,None
2022-03-15 08:30:10+00:00,126,None
2022-03-15 08:30:26.542134+00:00,127,ANNOUNCEMENT
2022-03-15 08:35:10+00:00,128,None
2022-03-15 08:40:10+00:00,122,None
2022-03-15 08:45:09+00:00,127,None
2022-03-15 08:50:09+00:00,133,None
2022-03-15 08:55:09+00:00,134,None
2022-03-16 09:30:09+00:00,132,None
2022-03-16 09:30:13.234425+00:00,135,ANNOUNCEMENT
2022-03-16 09:35:09+00:00,130,None
2022-03-16 09:40:09+00:00,134,None
2022-03-16 09:45:09+00:00,135,None
2022-03-16 09:50:09+00:00,134,None
'''
df = pd.read_csv(io.StringIO(s), index_col=0, parse_dates=['date'])

# create intervals from -1H to +2H of the index
dl, dr = -1, 2
left = df.index + pd.Timedelta(f'{dl}H')
right = df.index + pd.Timedelta(f'{dr}H')
df['interval'] = pd.arrays.IntervalArray.from_arrays(left, right)

# plot each announcement's interval.left to interval.right
fig, ax = plt.subplots()
for ann in df.loc[df['msg_type'] == 'ANNOUNCEMENT')].itertuples():
    window = df.loc[ann.interval.left:ann.interval.right] # extract interval.left to interval.right
    window.index -= ann.Index                             # compute time since announcement
    window.index = window.index.total_seconds()           # convert to seconds since announcement

    window.plot(ax=ax, y='value', label=ann.Index.date())
    deltas = np.arange(dl, dr + 1)
    ax.set(xticks=deltas * 3600, xticklabels=deltas)      # set tick labels to hours since announcement

ax.grid()
ax.legend()

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