Python numpy.ma 模块，asarray() 实例源码

def _transform1d(plotfunc):
    # shift data and longitudes to map projection region, then compute
    # transformation to map projection coordinates.
    @functools.wraps(plotfunc)
    def with_transform(self,x,y,*args,**kwargs):
        x = np.asarray(x)
        # input coordinates are latitude/longitude, not map projection coords.
        if kwargs.pop('latlon', latlon_default):
            # shift data to map projection region for
            # cylindrical and pseudo-cylindrical projections.
            if self.projection in _cylproj or self.projection in _pseudocyl:
                if x.ndim == 1:
                    x = self.shiftdata(x)
                elif x.ndim == 0:
                    if x > 180:
                        x = x - 360.
            # convert lat/lon coords to map projection coords.
            x, y = self(x,y)
        return plotfunc(self,x,y,*args,**kwargs)
    return with_transform

def _transform1d(plotfunc):
    # shift data and longitudes to map projection region, then compute
    # transformation to map projection coordinates.
    @functools.wraps(plotfunc)
    def with_transform(self,x,y,*args,**kwargs):
        x = np.asarray(x)
        # input coordinates are latitude/longitude, not map projection coords.
        if kwargs.pop('latlon', latlon_default):
            # shift data to map projection region for
            # cylindrical and pseudo-cylindrical projections.
            if self.projection in _cylproj or self.projection in _pseudocyl:
                if x.ndim == 1:
                    x = self.shiftdata(x)
                elif x.ndim == 0:
                    if x > 180:
                        x = x - 360.
            # convert lat/lon coords to map projection coords.
            x, y = self(x,y)
        return plotfunc(self,x,y,*args,**kwargs)
    return with_transform

def detect_contour(img, level):

  #parameter
  mask = None;
  corner_mask = True;
  nchunk = 0;

  #prepare image data
  z = ma.asarray(img, dtype=np.float64); 
  ny, nx = z.shape;
  x, y = np.meshgrid(np.arange(nx), np.arange(ny));

  #find contour
  contour_generator = _contour.QuadContourGenerator(x, y, z.filled(), mask, corner_mask, nchunk)
  vertices = contour_generator.create_contour(level);

  return vertices;

def detect_contour(img, level):

  #parameter
  mask = None;
  corner_mask = True;
  nchunk = 0;

  #prepare image data
  z = ma.asarray(img, dtype=np.float64); 
  ny, nx = z.shape;
  x, y = np.meshgrid(np.arange(nx), np.arange(ny));

  #find contour
  contour_generator = _contour.QuadContourGenerator(x, y, z.filled(), mask, corner_mask, nchunk)
  vertices = contour_generator.create_contour(level);

  return vertices;

def inverse(self, value):
        if not self.scaled():
            raise ValueError("Not invertible until scaled")
        vmin, vmax, midpoint = self.vmin, self.vmax, self.midpoint

        if cbook.iterable(value):
            val = ma.asarray(value)
            val = 2 * (val-0.5)
            val[val>0]  *= abs(vmax - midpoint)
            val[val<0] *= abs(vmin - midpoint)
            val += midpoint
            return val
        else:
            val = 2 * (val - 0.5)
            if val < 0:
                return  val*abs(vmin-midpoint) + midpoint
            else:
                return  val*abs(vmax-midpoint) + midpoint

def detect_contour(img, level):
  """Returns list of vertices of contours at different levels

  Arguments:
    img (array): the image array
    level (number): the level at which to create the contour

  Returns:
    (list of nx2 arrays): list of list of vertices of the different contours

  Note:
    The contour detection is based on matplotlib's QuadContourGenerator
  """
  #parameter
  mask = None;
  corner_mask = True;
  nchunk = 0;

  #prepare image data
  z = ma.asarray(img, dtype=np.float64); 
  ny, nx = z.shape;
  x, y = np.meshgrid(np.arange(nx), np.arange(ny));

  #find contour
  contour_generator = _contour.QuadContourGenerator(x, y, z.filled(), mask, corner_mask, nchunk)
  vertices = contour_generator.create_contour(level);

  return vertices;

def _get_refvals(referent, varname, repeat, multi):
    try:
        from netCDF4 import Dataset as NetCDF
    except:
        pass

    refnc = NetCDF(referent)

    if not multi:
        ref_vals = refnc.variables[varname][:]
    else:
        data = refnc.variables[varname][:]
        new_dims = [repeat] + [x for x in data.shape]
        masked=False
        if (isinstance(data, ma.core.MaskedArray)):
            masked=True

        if not masked:
            ref_vals = np.zeros(new_dims, data.dtype)
        else:
            ref_vals = ma.asarray(np.zeros(new_dims, data.dtype))

        for i in xrange(repeat):
            ref_vals[i,:] = data[:]

            if masked:
                ref_vals.mask[i,:] = data.mask[:]

    return ref_vals