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https://github.com/dgrtwo/gleam

Creating interactive visualizations with Python
https://github.com/dgrtwo/gleam

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Creating interactive visualizations with Python

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README 

        
gleam

=====



Gleam lets you build interactive web visualizations of data using Python: no knowledge of HTML or JS necessary! You can choose a number of inputs your users can control, then use any Python graphing library to create plots based on those inputs. Gleam puts it all together creates a web interface that lets anyone play with your data in real time. Now it's easier than ever to help others understand and interpret your data. Gleam was inspired by the [Shiny](http://www.rstudio.com/shiny/) package in R.



[See here for a live demo](http://gleam-demo.herokuapp.com)! (You can find the code for the demo in [examples/baseball.py](examples/baseball.py)).



Example

---------



Let's make an interactive visualization of a scatter plot. Here we'll have three inputs that can be controlled by the user:



* The plot's title

* What variable goes on the y axis

* Whether we add a smoothing curve to the data



Start by importing a few packages. Gleam uses the [wtforms](http://wtforms.readthedocs.org/en/latest/) package to provide form inputs. You can use any Python graphing package you want with Gleam, such as [matplotlib](http://matplotlib.org/), but we recommend the intuitive [ggplot](https://github.com/yhat/ggplot/).



    from wtforms import fields

    from ggplot import *



Then import what you'll need from the gleam package:



    from gleam import Page, panels



### Inputs



An `Inputs` panel lets you specify the inputs that the user can control. Here, we add a string input for the title, a multiple choice select field for the Y-axis variable, and a checkbox for the smoother:



    class ScatterInput(panels.Inputs):

        title = fields.StringField(label="Title of plot:")

        yvar = fields.SelectField(label="Y axis",

                                  choices=[("beef", "Beef"),

                                           ("pork", "Pork")])

        smoother = fields.BooleanField(label="Smoothing Curve")



### Output



The output is where your actual plotting goes. It comes in the form of a `plot` method, which takes an argument `inputs` containing the inputs from above. Here we use ggplot to make the plot, taking the arguments into consideration. (You could use *any* Python graphing packages in this function- the sky's the limit, as long as it creates a plot).



    class ScatterPlot(panels.Plot):

        name = "Scatter"



        def plot(self, inputs):

            p = ggplot(meat, aes(x='date', y=inputs.yvar))

            if inputs.smoother:

                p = p + stat_smooth(color="blue")

            p = p + geom_point() + ggtitle(inputs.title)

            return p



### Tying it together in a Page



Constructing an HTML page to allow this control is as simple as combining the input and the output:



    class ScatterPage(Page):

        input = ScatterInput()

        output = ScatterPlot()



### Run the app



You can then run the app with:



     ScatterPage.run()



By default, it will create a local server hosted at [http://127.0.0.1:5000/](http://127.0.0.1:5000/): you can visit it there to see your cool visualization, which will look something like this:



![plot](http://i.imgur.com/aiEmxbw.png?1)



Try changing the title, the Y axis selector, or try checking the box: you'll see the plot react in real time.



You can add more inputs to the ScatterInput class, and then use them to further customize the plot in the ScatterPlot class.



*Enjoy!*