Did I find the right examples for you? yes no

All Samples(46863)  |  Call(2)  |  Derive(0)  |  Import(46861)
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/p/y/Pylon-0.4.4/examples/pyreto/rlopf.py   Pylon(Download)
import sys
import logging
import numpy
import scipy.io
import pylab
 
# Define a 24-hour load profile with hourly values.
p1h = numpy([0.52, 0.54, 0.52, 0.50, 0.52, 0.57, 0.60, 0.71, 0.89, 0.85, 0.88,
             0.94, 0.90, 0.88, 0.88, 0.82, 0.80, 0.78, 0.76, 0.68, 0.68, 0.68,
             0.65, 0.58])

src/p/y/pylon-HEAD/examples/pyreto/rlopf.py   pylon(Download)
import sys
import logging
import numpy
import scipy.io
import pylab
 
# Define a 24-hour load profile with hourly values.
p1h = numpy([0.52, 0.54, 0.52, 0.50, 0.52, 0.57, 0.60, 0.71, 0.89, 0.85, 0.88,
             0.94, 0.90, 0.88, 0.88, 0.82, 0.80, 0.78, 0.76, 0.68, 0.68, 0.68,
             0.65, 0.58])

src/p/y/PyAstronomy-HEAD/src/funcFit/TutorialExampleSanity.py   PyAstronomy(Download)
  def sanity_simultaneousFit(self):
    from PyAstronomy import funcFit as fuf
    import numpy
    import matplotlib.pylab as mpl
 
  def sanity_MCMCPriorExample(self):
    from PyAstronomy import funcFit as fuf
    import numpy as np
    import matplotlib.pylab as plt
    import pymc
  def sanity_autoMCMCExample1(self):
    from PyAstronomy import funcFit as fuf
    import numpy as np
    import matplotlib.pylab as plt
 
  def sanity_autoMCMCExample2(self):
    from PyAstronomy import funcFit as fuf
    import numpy as np
    import matplotlib.pylab as plt
 
  def sanity_2dCircularFit(self):
    import numpy as np
    import matplotlib.pylab as plt
    from PyAstronomy import funcFit as fuf
 

src/s/t/statsmodels-0.5.0/statsmodels/sandbox/examples/example_crossval.py   statsmodels(Download)
 
import numpy as np
 
from statsmodels.sandbox.tools import cross_val
 
    from statsmodels.iolib.table import (SimpleTable, default_txt_fmt,
                            default_latex_fmt, default_html_fmt)
    import numpy as np
 
    data = load()

src/s/t/statsmodels-HEAD/statsmodels/sandbox/examples/example_crossval.py   statsmodels(Download)
 
import numpy as np
 
from statsmodels.sandbox.tools import cross_val
 
    from statsmodels.iolib.table import (SimpleTable, default_txt_fmt,
                            default_latex_fmt, default_html_fmt)
    import numpy as np
 
    data = load()

src/m/a/matplotlib-1.3.1/examples/axes_grid/inset_locator_demo2.py   matplotlib(Download)
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
 
import numpy as np
 
def get_demo_image():
    from matplotlib.cbook import get_sample_data
    import numpy as np

src/m/a/matplotlib-HEAD/examples/axes_grid/inset_locator_demo2.py   matplotlib(Download)
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
 
import numpy as np
 
def get_demo_image():
    from matplotlib.cbook import get_sample_data
    import numpy as np

src/m/a/matplotlib-ancient-HEAD/examples/axes_grid/inset_locator_demo2.py   matplotlib-ancient(Download)
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
 
import numpy as np
 
def get_demo_image():
    from matplotlib.cbook import get_sample_data
    import numpy as np

src/b/o/bokeh-0.4.4/examples/plotting/server/server_source_upload.py   bokeh(Download)
import numpy as np
from bokeh.plotting import *
from bokeh.objects import ServerDataSource 
import pandas as pd
output_server("remotedata")
server = session().config
import numpy as np

src/a/l/algopy-0.5.1/documentation/sphinx/examples/hessian_of_potential_function.py   algopy(Download)
#Loading the required packages
import scipy as sp
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy import linalg, optimize, constants
 
import numpy
import algopy
import time

  1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9  Next