python libs for all vis things

In [6]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib






    




WARNING: pylab import has clobbered these variables: ['show', 'figure']
`%matplotlib` prevents importing * from pylab and numpy

In [7]:

    

t = arange(0.0, 1.0, 0.01)

y1 = sin(2*pi*t)
y2 = sin(2*2*pi*t)

import pandas as pd

df = pd.DataFrame({'t': t, 'y1': y1, 'y2': y2})
df.head(10)


    

    
Out[7]:






  

    

      

      
t
      
y1
      
y2
    
  
  

    

      
0
      
0.00
      
0.000000
      
0.000000
    
    

      
1
      
0.01
      
0.062791
      
0.125333
    
    

      
2
      
0.02
      
0.125333
      
0.248690
    
    

      
3
      
0.03
      
0.187381
      
0.368125
    
    

      
4
      
0.04
      
0.248690
      
0.481754
    
    

      
5
      
0.05
      
0.309017
      
0.587785
    
    

      
6
      
0.06
      
0.368125
      
0.684547
    
    

      
7
      
0.07
      
0.425779
      
0.770513
    
    

      
8
      
0.08
      
0.481754
      
0.844328
    
    

      
9
      
0.09
      
0.535827
      
0.904827
    
  

In [8]:

    

from bokeh.plotting import figure, output_notebook, show

# output inline with notebook
output_notebook()

# create a new plot with a title and axis labels
p = figure(title="simple sine example", x_axis_label='t', y_axis_label='sin(2*pi*t)')

# add a line renderer with legend and line thickness
p.line(t, sin(2*pi*t), legend="Temp.", line_width=2)

# show the results
show(p)


    

    







    
    

        
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<bokeh.io._CommsHandle at 0x10c90a1d0>

In [11]:

    

# this uses Bokeh for plotting + ipywidgets for widgets
# translation: no Bokeh server required

from ipywidgets import interact
import numpy as np

from bokeh.io import push_notebook
from bokeh.plotting import figure, show, output_notebook

x = np.linspace(0, 2*np.pi, 2000)
y = np.sin(x)

output_notebook()

p = figure(title="simple curvy example", plot_height=300, plot_width=600, y_range=(-5,5))
r = p.line(x, y, color="#2222aa", line_width=3)

def update(f, w=1, A=1, phi=0):
    if   f == "sin": func = np.sin
    elif f == "cos": func = np.cos
    elif f == "tan": func = np.tan
    r.data_source.data['y'] = A * func(w * x + phi)
    push_notebook() # only updates *last* shown object
    
interact(update, f=["sin", "cos", "tan"], w=(0,100), A=(1,5), phi=(0, 20, 0.1))
show(p)


    

    







    








    
Out[11]:





<bokeh.io._CommsHandle at 0x1029c0c10>

In [12]:

    

from bokeh.plotting import figure, output_file, show

# prepare some data
x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
y0 = [i**2 for i in x]
y1 = [10**i for i in x]
y2 = [10**(i**2) for i in x]

output_notebook()

# create a new plot
p = figure(
   tools="pan,box_zoom,reset,save",
   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)


    

    







    
    

        
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<bokeh.io._CommsHandle at 0x10bd1fc50>