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NumPy
=====

Provides
  1. An array object of arbitrary homogeneous items
  2. Fast mathematical operations over arrays
  3. Linear Algebra, Fourier Transforms, Random Number Generation

How to use the documentation
----------------------------(more...)

src/m/a/matplotlib-1.3.1/examples/animation/old_animation/histogram_tkagg.py   matplotlib(Download)
"""
This example shows how to use a path patch to draw a bunch of
rectangles for an animated histogram
"""
import numpy as np

src/m/a/matplotlib-1.3.1/examples/animation/old_animation/gtk_timeout.py   matplotlib(Download)
import gobject
import numpy as np
import matplotlib
matplotlib.use('GTKAgg')
 

src/m/a/matplotlib-1.3.1/examples/animation/old_animation/dynamic_image_wxagg2.py   matplotlib(Download)
 
from matplotlib import rcParams
import numpy as npy
 
import matplotlib.cm as cm

src/m/a/matplotlib-1.3.1/examples/animation/old_animation/animation_blit_wx.py   matplotlib(Download)
import sys
import pylab as p
import numpy as npy
import time
 

src/m/a/matplotlib-1.3.1/examples/animation/old_animation/animation_blit_tk.py   matplotlib(Download)
import sys
import matplotlib.pyplot as plt
import numpy as npy
import time
 

src/m/a/matplotlib-1.3.1/examples/animation/old_animation/animation_blit_qt4.py   matplotlib(Download)
ITERS = 1000
 
import numpy as np
import time
 

src/m/a/matplotlib-1.3.1/examples/animation/old_animation/animation_blit_gtk2.py   matplotlib(Download)
matplotlib.use('GTKAgg')
 
import numpy as np
import matplotlib.pyplot as plt
 

src/m/a/matplotlib-1.3.1/examples/animation/old_animation/animation_blit_gtk.py   matplotlib(Download)
matplotlib.use('GTKAgg')
 
import numpy as np
import matplotlib.pyplot as plt
 

src/m/a/matplotlib-1.3.1/examples/animation/old_animation/animate_decay_tk_blit.py   matplotlib(Download)
from __future__ import print_function
import time, sys
import numpy as np
import matplotlib.pyplot as plt
 

src/s/t/statsmodels-0.5.0/statsmodels/sandbox/tsa/example_arma.py   statsmodels(Download)
'''
 
import numpy as np
from numpy.testing import assert_array_almost_equal
 

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