try: 
    %matplotlib inline
except:
    %pylab inline

import matplotlib.pyplot as plt

#when you import scipy, you do NOT import all the sub packages
import scipy


scipy.i

scipy.linspace(10,11, num=10)

# you need to import a subpackage to have access
scipy.io



import scipy.io as io
io?

## note about namespaces
#import scipy.linalg
from scipy import linalg

linalg.

from scipy import interpolate
interpolate

from numpy import r_, sin
from scipy.signal import cspline1d, cspline1d_eval

x = r_[0:10] #matlab like range creation 
print x
dx = x[1]-x[0]
newx = r_[-3:13:0.1]  # notice outside the original domain 
y = sin(x)
## find cubic spline coefficients for a 1D signal
cj = cspline1d(y)
## Evaluate spline on a new set of points
###  dx is old sampling space, x0 was old sample origin
newy = cspline1d_eval(cj, newx, dx=dx,x0=x[0])

plt.plot(newx, newy, x, y, 'o')


## example r_
r_[1:2:.1]

import numpy as np
from sklearn import mixture

n_samples = 300

# generate random sample, two components
np.random.seed(0)
C = np.array([[0., -0.7], [3.5, .7]])
X_train = np.r_[np.dot(np.random.randn(n_samples, 2), C),
                np.random.randn(n_samples, 2) + np.array([20, 20])]

clf = mixture.GMM(n_components=2, covariance_type='full')
clf.fit(X_train)

x = np.linspace(-20.0, 30.0)
y = np.linspace(-20.0, 40.0)
X, Y = np.meshgrid(x, y)
XX = np.c_[X.ravel(), Y.ravel()]
Z = np.log(-clf.score_samples(XX)[0])
Z = Z.reshape(X.shape)

CS = plt.contour(X, Y, Z)
CB = plt.colorbar(CS, shrink=0.8, extend='both')
plt.scatter(X_train[:, 0], X_train[:, 1], .8)

plt.axis('tight')

import numpy as np
C = np.array([[0., -0.7], [3.5, .7]])

n_samples = 300
np.dot(np.random.randn(n_samples, 2), C)

np.random.randn(n_samples, 2) + np.array([20, 20])
X_train = np.r_[np.dot(np.random.randn(n_samples, 2), C),
                np.random.randn(n_samples, 2) + np.array([20, 20])]


plt.plot(X_train[:,0], X_train[:,1], 'o')


##Basic scikit-image example

from skimage import data, io, filter
import matplotlib.pyplot as plt

image = data.coins() # or any NumPy array!
edges = filter.sobel(image)
plt.figure(figsize=(10,10))
plt.subplot(121)
io.imshow(image)
plt.subplot(122)
io.imshow(edges)

#statsmodel simple example

#Regression plot and loading datasets from R

http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/regression_plots.html


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.formula.api import ols

prestige = sm.datasets.get_rdataset("Duncan", "car", cache=True).data


## remember pandas, this should look familiar

prestige.head()

prestige_model = ols("prestige ~ income + education", data=prestige).fit()

print prestige_model.summary()

fig, ax = plt.subplots(figsize=(12,8))
fig = sm.graphics.influence_plot(prestige_model, ax=ax, criterion="cooks")


fix, ax = plt.subplots(figsize=(12,14))
fig = sm.graphics.plot_partregress("prestige", "income", ["education"], data=prestige, ax=ax)