Dashboards

library(shiny)
library(shinybg)

ui <- fluidPage(
  titlePanel("Old Faithful Geyser Data"),
  sidebarLayout(
    sidebarPanel(
      sliderInput("bins", "Number of bins:", min = 1, max = 50, value = 30)
    ),
    mainPanel(plotOutput("distPlot"))
  )
)

server <- function(input, output) {
  output$distPlot <- renderPlot({
    x <- faithful[, 2]
    bins <- seq(min(x), max(x), length.out = input$bins + 1)
    hist(x, breaks = bins, col = 'darkgray', border = 'white')
  })
}

renderShinyApp(ui = ui, server = server)

from ipywidgets import FloatSlider
from ipywidgets import Button, Layout, jslink, IntText, IntSlider
from ipywidgets import TwoByTwoLayout
import bqplot as bq
import numpy as np


size = 100
np.random.seed(0)

x_data = range(size)
y_data = np.random.randn(size)
y_data_2 = np.random.randn(size)
y_data_3 = np.cumsum(np.random.randn(size) * 100.)

x_ord = bq.OrdinalScale()
y_sc = bq.LinearScale()

bar = bq.Bars(x=np.arange(10), y=np.random.rand(10), scales={'x': x_ord, 'y': y_sc})
ax_x = bq.Axis(scale=x_ord)
ax_y = bq.Axis(scale=y_sc, tick_format='0.2f', orientation='vertical')

fig = bq.Figure(marks=[bar], axes=[ax_x, ax_y], padding_x=0.025, padding_y=0.025,
                layout=Layout(width='auto', height='90%'))
                


max_slider = FloatSlider(min=0, max=10, default_value=2, description="Max: ",
                         layout=Layout(width='auto', height='auto'))
min_slider = FloatSlider(min=-1, max=10, description="Min: ",
                         layout=Layout(width='auto', height='auto'))
app = TwoByTwoLayout(top_left=min_slider,
                     bottom_left=max_slider,
                     bottom_right=fig,
                     align_items="center",
                     height='700px')

jslink((y_sc, 'max'), (max_slider, 'value'))
jslink((y_sc, 'min'), (min_slider, 'value'))
jslink((min_slider, 'max'), (max_slider, 'value'))
jslink((max_slider, 'min'), (min_slider, 'value'))

max_slider.value = 1.5
app

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
import panel as pn

pn.extension()

import matplotlib as mpl
mpl.use('agg')

def mpl_plot(avg, highlight):
    fig = Figure()
    ax = fig.add_subplot()
    avg.plot(ax=ax)
    if len(highlight): highlight.plot(style='o', ax=ax)
    return fig

def find_outliers(variable='Temperature', window=30, sigma=10, view_fn=mpl_plot):
    avg = data[variable].rolling(window=window).mean()
    residual = data[variable] - avg
    std = residual.rolling(window=window).std()
    outliers = (np.abs(residual) > std * sigma)
    return view_fn(avg, avg[outliers])

csv_file = 'https://raw.githubusercontent.com/holoviz/panel/main/examples/assets/occupancy.csv'
data = pd.read_csv(csv_file, parse_dates=['date'], index_col='date')

window = pn.widgets.IntSlider(name='window', value=30, start=1, end=60)
sigma = pn.widgets.IntSlider(name='sigma', value=10, start=0, end=20)

interactive = pn.bind(find_outliers, window=window, sigma=sigma)
first_app = pn.Column(window, sigma, interactive)
first_app