TODO:
$ conda install ipywidgets wxpython networkx seaborn matplotlib
$ jupyter nbextension enable --py widgetsnbextension --sys-prefix
$ conda install -c bokeh bokehControl every detail of your graphics programmatically.
Have a look at more examples: http://matplotlib.org/gallery.html
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#ipython magic command
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
matplotlib.rcParams.update({'font.size': 18, 'font.family': 'arial'})
x = np.linspace(0.1, 10, 100)
y1 = np.sin(x)
y2 = np.sin(1/x)
#y2 = np.exp(x)
fig, ax = plt.subplots(figsize=(13,4))
#axes[0].plot(x, y1, x, y2)
ax.plot(x, y1, label="y = sin(x)")
ax.plot(x, y2, label=r"$sin(\frac{1}{x})$", color="#05cd90", lw=2, ls='dotted', marker='o', markersize=4)
ax.set_xlabel('time')
ax.set_ylabel('harmonics')
ax.set_ylim([- 1.5, 1.5])
#ax.set_yscale("log")
ax.set_title("Line plots")
ax.legend(loc=2)
yticks = [0, 1]
plt.annotate('wow!', xy=(8, 1), xytext=(7, -1), arrowprops=dict(facecolor='black', shrink=0.05))
ax.set_yticks(yticks)
ax.set_yticklabels([r"rest $\alpha$", r"disturbed $\delta$"], fontsize=18)
#fig.savefig("filename.png")
#fig.savefig("filename.png", dpi=100)
plt.show()
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%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
fig, axes = plt.subplots(2, 2, figsize=(10,8))
matplotlib.rcParams.update({'font.size': 10, 'font.family': 'arial'})
x = 10*np.random.random(100)
y = 10*np.random.random(100)
def f(x,y): return np.sin(x)**2+np.sin(y)**2
axes[0][0].scatter(x, y, c=f(x,y), cmap=plt.cm.Blues)
axes[0][0].set_title("scatter plot")
x1 = np.linspace(-10,10,100)
y1 = np.zeros(100)+5.0
axes[0][1].bar(x1, f(x1,y1), align="center", width=0.5, alpha=0.5)
axes[0][1].set_title("bar plot")
x = np.linspace(-10,10,100)
y = np.linspace(-10,10,100)
X,Y = np.meshgrid(x, y)
Z = f(X,Y)
axes[1][0].pcolor(x, y, Z, cmap=plt.cm.Blues, vmin=np.abs(Z).min(), vmax=np.abs(Z).max())
#axes[1][1].pcolor(x, y, Z, cmap=plt.cm.RdBu, vmin=np.abs(Z).min(), vmax=np.abs(Z).max())
axes[1][0].set_title("colour map (heatmap)")
axes[1][1].contour(Z.T, cmap=plt.cm.Blues, vmin=np.abs(Z).min(), vmax=np.abs(Z).max(), extent=[-10, 10, -10, 10])
axes[1][1].set_title("contour plot")
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Matplotlib was the first big library for visualization in Python, but after its creator tragically died more and more users migrated to Seaborn. Seaborn offers a much easier entry point to the detriment of high customization. But, it looks great! Here is a violin plot example. Check other examples in the galery.
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import seaborn as sns
sns.set(style="whitegrid", palette="pastel", color_codes=True)
# Load the example tips dataset
tips = sns.load_dataset("tips")
# Draw a nested violinplot and split the violins for easier comparison
sns.violinplot(x="day", y="total_bill", hue="sex", data=tips, split=True,
inner="quart", palette={"Male": "b", "Female": "y"})
sns.despine(left=True)
Bokeh is using D3, one of the most complex and performant web visualization libraries. This makes it easy to add web interaction, and also makes very nice publication quality graphics.
jupyter nbextension enable --py widgetsnbextension --sys-prefix
In [1]:
from ipywidgets import interact
import numpy as np
from bokeh.io import push_notebook, show, output_notebook
from bokeh.plotting import figure
output_notebook()
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x = np.linspace(0, 2*np.pi, 2000)
y = np.sin(x)
p = figure(title="simple line example", plot_height=300, plot_width=600, y_range=(-5,5))
r = p.line(x, y, color="#2222aa", line_width=3)
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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()
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show(p, notebook_handle=True)
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In [5]:
from IPython.display import display
from ipywidgets import *
interact(update, f=["sin", "cos", "tan"], w=(0,100), A=(1,5), phi=(0, 20, 0.1))
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In [6]:
from IPython.display import display
from ipywidgets import *
w = IntSlider()
display(w)
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def f(x):
return x
interact(f, x=10);
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%matplotlib inline
import networkx as nx
#net = nx.erdos_renyi_graph(100,0.12)
net = nx.barabasi_albert_graph(100, 1)
nx.write_gml(net,"mynetwork.gml")
import matplotlib.pyplot as plt
#nx.draw(net)
nx.draw(net,pos=nx.spring_layout(net, scale = 5000, iterations = 10))
#nx.draw_random(net)
#nx.draw_circular(net)
#nx.draw_spectral(net, dim = 500, scale = 20)
#plt.show()
## Generate a picture and load it with IPython
#plt.savefig("test.png")
#from IPython.display import Image
#Image(filename='test.png')
#from IPython.core.display import HTML
#HTML('<img src="files/test.png" width=100 height=20/>')
Example of network interactivity in IPython:
http://nbviewer.ipython.org/github/kzuberi/ipython-cytoscape.js/blob/master/demo.ipynb
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import pandas as pd
nodes = pd.DataFrame([['ipcy', 'IPython-Cytoscape.js', 50, 'green', 'square'],
['cy', 'Cytoscape.js', 50, 'blue', 'circle'],
['ip', 'IPython', 50, 'red', 'hexagon']],
columns=['id', 'name', 'weight', 'color', 'shape'])
edges = pd.DataFrame([['ipcy', 'ip', 20, 'gray' ],
['ipcy', 'cy', 20, 'gray']],
columns=['source', 'target', 'strength', 'color'])
%load_ext cyto
network = cyto.Network(nodes, edges)
network
#network.save_state()
#nw2 = cyto.Network(nodes, edges, layout='grid')
#nw2
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%gui wx
"""
A Simple wx example to test IPython's event loop integration.
To run this do:
In [5]: %gui wx # or start IPython with '--gui wx'
In [6]: %run gui-wx.py
Ref: Modified from wxPython source code wxPython/samples/simple/simple.py
"""
import wx
class MyFrame(wx.Frame):
"""
This is MyFrame. It just shows a few controls on a wxPanel,
and has a simple menu.
"""
def __init__(self, parent, title):
wx.Frame.__init__(self, parent, -1, title,
pos=(150, 150), size=(350, 200))
# Create the menubar
menuBar = wx.MenuBar()
# and a menu
menu = wx.Menu()
# add an item to the menu, using \tKeyName automatically
# creates an accelerator, the third param is some help text
# that will show up in the statusbar
menu.Append(wx.ID_EXIT, "E&xit\tAlt-X", "Exit this simple sample")
# bind the menu event to an event handler
self.Bind(wx.EVT_MENU, self.OnTimeToClose, id=wx.ID_EXIT)
# and put the menu on the menubar
menuBar.Append(menu, "&File")
self.SetMenuBar(menuBar)
self.CreateStatusBar()
# Now create the Panel to put the other controls on.
panel = wx.Panel(self)
# and a few controls
text = wx.StaticText(panel, -1, "Hello World!")
text.SetFont(wx.Font(14, wx.SWISS, wx.NORMAL, wx.BOLD))
text.SetSize(text.GetBestSize())
btn = wx.Button(panel, -1, "Close")
funbtn = wx.Button(panel, -1, "Just for fun...")
# bind the button events to handlers
self.Bind(wx.EVT_BUTTON, self.OnTimeToClose, btn)
self.Bind(wx.EVT_BUTTON, self.OnFunButton, funbtn)
# Use a sizer to layout the controls, stacked vertically and with
# a 10 pixel border around each
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(text, 0, wx.ALL, 10)
sizer.Add(btn, 0, wx.ALL, 10)
sizer.Add(funbtn, 0, wx.ALL, 10)
panel.SetSizer(sizer)
panel.Layout()
def OnTimeToClose(self, evt):
"""Event handler for the button click."""
print("See ya later!")
self.Close()
def OnFunButton(self, evt):
"""Event handler for the button click."""
print("Having fun yet?")
class MyApp(wx.App):
def OnInit(self):
frame = MyFrame(None, "Simple wxPython App")
self.SetTopWindow(frame)
print("Print statements go to this stdout window by default.")
frame.Show(True)
return True
app = None
app = wx.GetApp()
if app is None:
app = MyApp(redirect=False, clearSigInt=False)
else:
frame = MyFrame(None, "Simple wxPython App")
app.SetTopWindow(frame)
print("Print statements go to this stdout window by default.")
frame.Show(True)
try:
from IPython.lib.inputhook import enable_wx
enable_wx(app)
except ImportError:
print("error")
app.MainLoop()
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from ipywidgets import widgets
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%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
def plot(amplitude, color):
fig, ax = plt.subplots(figsize=(4, 3),
subplot_kw={'axisbelow':True})
ax.grid(color='w', linewidth=2, linestyle='solid')
x = np.linspace(0, 10, 1000)
ax.plot(x, amplitude * np.sin(x), color=color,
lw=5, alpha=0.4)
ax.set_xlim(0, 10)
ax.set_ylim(-1.1, 1.1)
return fig
from ipywidgets import interact, FloatSlider, RadioButtons
interact(plot,
amplitude=FloatSlider(min=0.1, max=1.0, step=0.1),
color=RadioButtons(options=['blue', 'green', 'red']))
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from ipywidgets import *
IntSlider()
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widgets.Select(
description='OS:',
options=['Linux', 'Windows', 'OSX'],
)
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import ipywidgets as widgets
from IPython.display import display
name = widgets.Text(description='Name:', padding=4)
#name.layout.padding = 4
color = widgets.Dropdown(description='Color:', options=['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'])
#color.layout.padding = 4
page1 = widgets.Box(children=[name, color])
#page1.layout.padding = 4
age = widgets.IntSlider(description='Age:', min=0, max=120, value=50)
#age.layout.padding = 4
gender = widgets.RadioButtons(description='Gender:', options=['male', 'female'])
#gender.layout.padding = 4
page2 = widgets.Box(children=[age, gender])
#page2.layout.padding = 4
tabs = widgets.Tab(children=[page1, page2])
display(tabs)
tabs.set_title(0, 'Name')
tabs.set_title(1, 'Details')
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from ipywidgets import interact
%matplotlib inline
import matplotlib.pyplot as plt
import networkx as nx
# wrap a few graph generation functions so they have the same signature
def random_lobster(n, m, k, p):
return nx.random_lobster(n, p, p / m)
def powerlaw_cluster(n, m, k, p):
return nx.powerlaw_cluster_graph(n, m, p)
def erdos_renyi(n, m, k, p):
return nx.erdos_renyi_graph(n, p)
def newman_watts_strogatz(n, m, k, p):
return nx.newman_watts_strogatz_graph(n, k, p)
def plot_random_graph(n, m, k, p, generator):
g = generator(n, m, k, p)
nx.draw(g)
plt.show()
interact(plot_random_graph, n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),
generator={'lobster': random_lobster,
'power law': powerlaw_cluster,
'Newman-Watts-Strogatz': newman_watts_strogatz,
u'Erdős-Rényi': erdos_renyi,
});
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