Python numerix.zeros函数代码示例

 def __init__(self, nmax):
     'buffer up to nmax points'
     self._xa = nx.zeros((nmax,), typecode=nx.Float)
     self._ya = nx.zeros((nmax,), typecode=nx.Float)        
     self._xs = nx.zeros((nmax,), typecode=nx.Float)
     self._ys = nx.zeros((nmax,), typecode=nx.Float)        
     self._ind = 0
     self._nmax = nmax
     self.dataLim = None
     self.callbackd = {}

def movavg(x,n):
    'compute the len(n) moving average of x'
    n = int(n)
    N = len(x)
    assert(N>n)
    y = zeros(N-(n-1),Float)
    for i in range(n):
       y += x[i:N-(n-1)+i]
    y /= float(n)
    return y

def symbols(epsoutfile):
    y = arange(5)/5.0+0.1
    
    g = pyxgraph(xlimits=(-1, 25), ylimits=(0, 1),           
                 xticks=(0, 24, 2), yticks=(0, 1, 1), key=None) 
    
    for i in xrange(25):
        x = zeros(5)+i   
        g.pyxplot((x, y), style="p", pt=i)   # ``pt=i`` can be omitted
                                             # (then the next symbol is choosen
                                             #  automatically)
    g.pyxsave(epsoutfile)

def makeMappingArray(N, data):
    """Create an N-element 1-d lookup table

    data represented by a list of x,y0,y1 mapping correspondences.
    Each element in this list represents how a value between 0 and 1
    (inclusive) represented by x is mapped to a corresponding value
    between 0 and 1 (inclusive). The two values of y are to allow
    for discontinuous mapping functions (say as might be found in a
    sawtooth) where y0 represents the value of y for values of x
    <= to that given, and y1 is the value to be used for x > than
    that given). The list must start with x=0, end with x=1, and
    all values of x must be in increasing order. Values between
    the given mapping points are determined by simple linear interpolation.

    The function returns an array "result" where result[x*(N-1)]
    gives the closest value for values of x between 0 and 1.
    """
    try:
        adata = array(data)
    except:
        raise TypeError("data must be convertable to an array")
    shape = adata.shape
    if len(shape) != 2 and shape[1] != 3:
        raise ValueError("data must be nx3 format")

    x  = adata[:,0]
    y0 = adata[:,1]
    y1 = adata[:,2]

    if x[0] != 0. or x[-1] != 1.0:
        raise ValueError(
           "data mapping points must start with x=0. and end with x=1")
    if sometrue(sort(x)-x):
        raise ValueError(
           "data mapping points must have x in increasing order")
    # begin generation of lookup table
    x = x * (N-1)
    lut = zeros((N,), Float)
    xind = arange(float(N))
    ind = searchsorted(x, xind)[1:-1]

    lut[1:-1] = ( divide(xind[1:-1] - take(x,ind-1),
                         take(x,ind)-take(x,ind-1) )
                  *(take(y0,ind)-take(y1,ind-1)) + take(y1,ind-1))
    lut[0] = y1[0]
    lut[-1] = y0[-1]
    # ensure that the lut is confined to values between 0 and 1 by clipping it
    clip(lut, 0.0, 1.0)
    #lut = where(lut > 1., 1., lut)
    #lut = where(lut < 0., 0., lut)
    return lut

def zero(data, *args):
    if isinstance(data, numerix.ndarray):
        return numerix.zeros(data.shape, data.dtype)
    return numerix.zeros(len(data))