Heatmap Animation in Python/v3

How to make an animated heatmap in Python.

Note: this page is part of the documentation for version 3 of Plotly.py, which is not the most recent version.
See our Version 4 Migration Guide for information about how to upgrade.

New to Plotly?¶

Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!

Version Check¶

Note: Animations are available in version 1.12.10+ Run pip install plotly --upgrade to update your Plotly version.

In [1]:

import plotly
plotly.__version__

Out[1]:

'1.12.12'

Make the Grid¶

Our goal is to generate the contours plots of the bivariate normal distributions of mean vector (0,0), standard deviation vector (1,1), and correlation, $\rho$ , varying from (−1, 1). Since we are making an online animation, we must create our grid first and upload it.

In [2]:

import plotly.plotly as py
from plotly.grid_objs import Grid, Column

import time
import numpy as np
from scipy.stats import multivariate_normal as Nd

colorscale = [
    [0.0, 'rgb(25, 23, 10)'],
    [0.05, 'rgb(69, 48, 44)'],
    [0.1, 'rgb(114, 52, 47)'],
    [0.15, 'rgb(155, 58, 49)'],
    [0.2, 'rgb(194, 70, 51)'],
    [0.25, 'rgb(227, 91, 53)'],
    [0.3, 'rgb(250, 120, 56)'],
    [0.35, 'rgb(255, 152, 60)'],
    [0.4, 'rgb(255, 188, 65)'],
    [0.45, 'rgb(236, 220, 72)'],
    [0.5, 'rgb(202, 243, 80)'],
    [0.55, 'rgb(164, 252, 93)'],
    [0.6, 'rgb(123, 245, 119)'],
    [0.65, 'rgb(93, 225, 162)'],
    [0.7, 'rgb(84, 196, 212)'],
    [0.75, 'rgb(99, 168, 238)'],
    [0.8, 'rgb(139, 146, 233)'],
    [0.85, 'rgb(190, 139, 216)'],
    [0.9, 'rgb(231, 152, 213)'],
    [0.95, 'rgb(241, 180, 226)'],
    [1.0, 'rgb(206, 221, 250)']
]

# returns V=(X,Y)~N(m, Sigma)
def bivariate_N(m=[0., 0.], stdev=[1.0, 1.0], rho=0):
    cov = rho*stdev[0] * stdev[1] # covariance(X,Y)
    Sigma = np.array([[stdev[0]**2, cov], [cov, stdev[1]**2]]) # covariance  matrix 
    return Nd(mean=m, cov=Sigma) # joint distribution of (X,Y), of mean  vector, m, and cov matrix, Sigma

# returns the pdf of the bivariate normal distribution
def pdf_bivariate_N(m, stdev, V):
    X = np.linspace(m[0] - 3*stdev[0], m[0] + 3*stdev[0], 100)
    Y = np.linspace(m[1] - 3*stdev[1], m[1] + 3*stdev[1], 100)
    x, y = np.meshgrid(X, Y)
    pos = np.empty(x.shape + (2, ))
    pos[:, :, 0] = x; pos[:, :, 1] = y
    z = V.pdf(pos)
    return X, Y, z

correls=[-0.95, -0.85, -0.75, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.75, 0.85, 0.95]

m=[0., 0.]
stdev=[1., 1.]
V=bivariate_N()
x, y=pdf_bivariate_N(m, stdev,  V)[:2]
my_columns=[Column(x, 'x'), Column(y, 'y')]
zvmax=[]
for k, rho in enumerate(correls):
    V = bivariate_N(rho = rho)
    z = pdf_bivariate_N(m, stdev, V)[2]
    zvmax.append(np.max(z))
    my_columns.append(Column(z, 'z{}'.format(k + 1)))
grid = Grid(my_columns)
py.grid_ops.upload(grid, 'norm-bivariate1'+str(time.time()), auto_open=False)

Out[2]:

u'https://plotly.com/~AdamKulidjian/2189/'

Make the Figure¶

Make the Figure which references columns from the grid we made. The Figure takes Data, Layout and Frames.

In [3]:

data=[dict(type='heatmap',
           xsrc=grid.get_column_reference('x'),
           ysrc=grid.get_column_reference('y'),
           zsrc=grid.get_column_reference('z1'),
           zmin=0,
           zmax=zvmax[6],
           zsmooth='best',
           colorscale=colorscale,
           colorbar=dict(thickness=20, ticklen=4))]

title='Contour plot for bivariate normal distribution'+\
'<br> N(m=[0,0], sigma=[1,1], rho in (-1, 1))'

layout = dict(title=title,
              autosize=False,
              height=600,
              width=600,
              hovermode='closest',
              xaxis=dict(range=[-3, 3], autorange=False),
              yaxis=dict(range=[-3, 3], autorange=False),
              showlegend=False,
              updatemenus=[dict(type='buttons', showactive=False,
                                y=1, x=-0.05, xanchor='right',
                                yanchor='top', pad=dict(t=0, r=10),
                                buttons=[dict(label='Play',
                                              method='animate',
                                              args=[None,
                                                    dict(frame=dict(duration=100,
                                                                    redraw=True),
                                                    transition=dict(duration=0),
                                                    fromcurrent=True,
                                                    mode='immediate')])])])

frames=[dict(data=[dict(zsrc=grid.get_column_reference('z{}'.format(k + 1)),
                        zmax=zvmax[k])],
                        traces=[0],
                        name='frame{}'.format(k),
                        ) for k in range(len(correls))]


fig=dict(data=data, layout=layout, frames=frames)
py.icreate_animations(fig, filename='animheatmap'+str(time.time()))

Out[3]:

Reference¶

For additional information and attributes for creating heatmaps in Plotly see: https://plotly.com/python/reference/#heatmap. For more documentation on creating animations with Plotly, see https://plotly.com/python/#animations.