The IPywidgets manual is very helpful for the most part, but is lacking some explanation about how one goes about creating complex dashboards. In particular, I am trying to figure our how to:
- Design abstractions to assist in building easily extendable dashboards which contain multiple interdependent widgets, some of which hide/show other widgets.
- Do this in a way which would allow me to get and set the state of all the widgets as a
dictso I could subsequently implement buttons to save and load the configuration of my dashboard to a JSON file.
To make this question more concrete, I designed a minimal example of my current approach, which based on a design pattern I received from @jasongrout on the Jupyter Widgets channel on Gitter; when answering, please demonstrate your design pattern by reimplementing this example in it, ensuring the two criteria above are clearly fulfilled:
from IPython.display import display
import IPython.display as ipd
import ipywidgets as widgets
import matplotlib.pyplot as plt
class ModelUI:
def __init__(self):
self.par = dict()
self.initUI()
def initUI(self):
self.funcPars = widgets.VBox()
self.initLinear()
self.initQuadratic()
self.initFunc()
self.initButton()
self.initOutput()
self.controls = widgets.HBox([
self.func,
self.funcPars,
self.plot
])
self.UI = widgets.VBox([
self.controls,
self.output
])
def initFunc(self):
self.func = widgets.Dropdown(
description="Function:",
options=[
"Linear",
"Quadratic"
])
self.func.observe(self.updateFunc, "value")
self.updateFunc(None)
def initButton(self):
self.plot = widgets.Button(description="Plot")
self.plot.on_click(self.plotFunction)
def updateFunc(self, change):
if self.func.value == "Linear":
self.funcPars.children = self.linPars
self.par['func'] = "Linear"
elif self.func.value == "Quadratic":
self.funcPars.children = self.quadPars
self.par['func'] = "Quadratic"
else:
pass
def initLinear(self):
self.m = widgets.FloatSlider(
description="m",
min=-10, max=10, value=2)
self.k = widgets.FloatSlider(
description="k",
min=-10, max=10, value=1)
self.linPars = [self.m, self.k]
self.m.observe(self.updateLinear, "value")
self.k.observe(self.updateLinear, "value")
self.updateLinear(None)
def updateLinear(self, change):
self.par['m'] = self.m.value
self.par['k'] = self.k.value
def initQuadratic(self):
self.a = widgets.FloatSlider(
description="a",
min=-10, max=10, value=1)
self.b = widgets.FloatSlider(
description="b",
min=-10, max=10, value=2)
self.c = widgets.FloatSlider(
description="c",
min=-10, max=10, value=3)
self.quadPars = [self.a, self.b, self.c]
self.a.observe(self.updateQuadratic, "value")
self.b.observe(self.updateQuadratic, "value")
self.c.observe(self.updateQuadratic, "value")
self.updateQuadratic(None)
def updateQuadratic(self, change):
self.par['a'] = self.a.value
self.par['b'] = self.b.value
self.par['c'] = self.c.value
def initOutput(self):
self.output = widgets.Output()
self.plotFunction(None)
def plotFunction(self, change):
self.function = {
"Linear": lambda x: self.par['m']*x + self.par['k'],
"Quadratic": lambda x: self.par['a']*x**2 + self.par['b']*x + self.par['c']
}
with self.output:
ipd.clear_output()
xvals = [ i/10 for i in range(-100,100)]
yvals = list(map(self.function[self.par['func']], xvals))
plt.plot(xvals,yvals)
plt.show()
def _ipython_display_(self):
display(self.UI)
ModelUI()
