python类float128()的实例源码

def f2(x,returntype=LINALGMAXFLOAT):
    '''
    Second derivative of sigmoid

    (q - p) p q
    '''
    x = np.float128(x)
    logp = -np.logaddexp(ZERO128,-x)
    logq = -np.logaddexp(ZERO128, x)
    p  = np.exp(np.minimum(F128EMAX,logp))
    q  = np.exp(np.minimum(F128EMAX,logq))
    return returntype((q-p)*q*p);

def sample_patch_projective(image, inv_xform_3x3, patch_shape):
  """ return a warped image as a numpy array with dtype float64 of size patch_size.
    if input image is not already of type float64, it will be converted """
  P = skimage.transform.ProjectiveTransform(inv_xform_3x3)
  # skimage clips values to range [0,1] for floating point images.  do scale and unscale here.
  do_scale = False
  og_dtype = image.dtype
  if image.dtype in (np.float32, np.float64, np.float128, np.float16):
    minval = np.nanmin(image)
    maxval = np.nanmax(image)
    # if range is good, no need to rescale
    if minval < 0.0 or maxval > 1.0:
      do_scale = True
      # make a copy of image so as not to corrupt original data
      image = image.copy()
      scale_factor = maxval - minval
      image -= minval
      if scale_factor != 0:
        image /= scale_factor

  # do the warping
  patch = skimage.transform.warp(image, P, output_shape=patch_shape, mode='constant', cval=np.nan)

  # revert to original type if necessary
  if og_dtype != patch.dtype:
    if og_dtype == np.uint8:
      patch = skimage.img_as_ubyte(patch)
    elif og_dtype == np.bool:
      patch = skimage.img_as_bool(patch)
    elif og_dtype == np.uint16:
      patch = skimage.img_as_uint(patch)
    elif og_dtype == np.int16:
      patch = skimage.img_as_int(patch)
    else:
      # just to straight cast, hope for the best
      patch_out = np.zeros(patch.shape, og_dtype)
      np.copyto(patch_out,patch)
      patch = patch_out
  # unscale if necessary
  if do_scale:
    patch *= scale_factor
    patch += minval

  return patch