import plotly

plotly.__version__

import plotly.plotly as py  # (New syntax!) tools to communicate with Plotly's server
import plotly.tools as tls  # (NEW!) useful Python/Plotly tools 

my_creds = tls.get_credentials_file()                  # read credentials
py.sign_in(my_creds['username'], my_creds['api_key'])  # (New syntax!) Plotly sign in

import numpy as np
 
def makeGaussian(A, L, fwhm = 3, center=None):
    """ Make a square gaussian kernel.
 
    A is the amplitude of the curve
    L is the length of a side of the square
    fwhm is full-width-half-maximum, which
    can be thought of as an effective radius.
    """
 
    x = np.arange(0, L, 0.1, float)
    y = x[:,np.newaxis]
    
    if center is None:
        x0 = y0 = L // 2
    else:
        x0 = center[0]
        y0 = center[1]
    
    return A*np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)

A = 10  # choose a maximum amplitude
L = 8   # choose length of square domain

# Get coordinate arrays
z = makeGaussian(A,L)

# Print shape of z
z.shape 

my_surface = dict(z=z,            # z coords, a 2D array
                  type='surface', # N.B. 'surface' plot type
                  )

my_fig = dict(data=[my_surface])  # N.B. value link to 'data' must be a list

my_fig  # print figure dictionary below

py.plot(my_fig, validate=False, filename='test-3d')

py.iplot(my_fig, validate=False, filename='test-3d')

my_scatter3d = dict(x=np.random.rand(50),  # x coords 
                    y=np.random.rand(50),  # y coords 
                    z=np.random.rand(50),  # z coords 
                    type='scatter3d',      # N.B. 'scatter3d' plot type
                    )

my_fig = dict(data=[my_scatter3d])  # N.B. value link to 'data' must be a list

my_fig

py.plot(my_fig, validate=False, filename='test-3d-scatter')

py.iplot(my_fig, validate=False, filename='test-3d-scatter')

# CSS styling within IPython notebook
from IPython.core.display import HTML
def css_styling():
    styles = open("../../python-user-guide/custom.css", "r").read()
    return HTML(styles)
css_styling()