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In the past few years, the python (scientific) ecosystem has seen intense development of solutions aimed at bringing interactive data visualisation in the browser, through a set of libraries which basically interface with powerful JavaScript visualisations libraries such as D3.js, leaflet.js etc.
In this demo we will see:
import warnings
warnings.filterwarnings('ignore', category=DeprecationWarning)
import os, sys
import pandas as pd
import numpy as np
from numpy import ma
import matplotlib.pyplot as plt
%matplotlib inline
MPLd3 is developed by Jake VanderPlas
MPLD3 provides a simple API for exporting matplotlib graphics to HTML code which can be used within the browser, within standard web pages, blogs, or tools such as the IPython notebook.
import mpld3
from mpld3 import plugins
mpld3.enable_notebook()
xdf = pd.DataFrame(np.random.normal(size=(100)), columns=['A'])
f, ax= plt.subplots(figsize=(8,4));
ax.plot(xdf.index, xdf['A'], lw=2, color='b');
ax.set_xlim(40,80)
ax.grid(color='0.8')
plugins to the figure¶f, ax= plt.subplots(figsize=(8,4));
plt.plot(xdf.index, xdf['A'], lw=2, color='b');
ax.set_xlim(40,80)
ax.grid(color='0.8')
plugins.connect(f, plugins.MousePosition(fontsize=16))
mpld3.save_html(f, './mpld3_figure.html')
some more examples from the mpld3 gallery
bokeh is a library developed by continuum analytics
from their website:
Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
To know more about bokeh:
if you use the anaconda python distribution, all the bokeh examples are saved in
${anaconda}/Examples/bokeh
prepare the data: simple sine
x = np.linspace(0, 10, 1000)
y = np.sin(x)
from bokeh.plotting import figure, output_file, output_notebook, show
output_notebook()
# create a new plot with a title and axis labels
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')
# add a line renderer with legend and line thickness
p.line(x, y, legend="sin(x)", line_width=2)
# show the results
show(p)
<Bokeh Notebook handle for In[20]>
!open sin.html
Or you can output to an HTML file
specify mode = "cdn" so it loads the javascript from the bokeh Content Delivery Network instead of the local copy
output_file("sin.html", title="sin(x)", mode="cdn")
# create a new plot with a title and axis labels
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')
# add a line renderer with legend and line thickness
p.line(x, y, legend="sin(x)", line_width=2)
# show the results
show(p)
<Bokeh Notebook handle for In[22]>
You can control the toolbar: here I want pan, box_zoom, rest, but not save etc
x = np.array([0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0])
y0 = x**2
y1 = 10**x
y2 = 10**(x**2)
# output to static HTML file
output_file("log_lines.html")
# create a new plot
p = figure(
tools="pan,box_zoom,reset",
y_axis_type="log", y_range=[0.001, 10**11], title="log axis example",
x_axis_label='sections', y_axis_label='particles'
)
# add some renderers
p.line(x, x, legend="y=x")
p.circle(x, x, legend="y=x", fill_color="white", size=8)
p.line(x, y0, legend="y=x^2", line_width=3)
p.line(x, y1, legend="y=10^x", line_color="red")
p.circle(x, y1, legend="y=10^x", fill_color="red", line_color="red", size=6)
p.line(x, y2, legend="y=10^x^2", line_color="orange", line_dash="4 4")
# show the results
show(p)