(dtype, ctype) = flags.dtype()
elsize = np.dtype(dtype).itemsize
- ptr = m['data']
- dataptr = int(ptr)
- length = (int(m['dataend']) - dataptr) // elsize
- start = (int(m['datastart']) - dataptr) // elsize
+ shape = size.to_numpy()
+ steps = np.asarray([int(m['step']['p'][i]) for i in range(len(shape))], dtype=np.int64)
- if length == 0:
+ ptr = m['data']
+ # either we are default-constructed or sizes are zero
+ if int(ptr) == 0 or np.prod(shape * steps) == 0:
self.mat = np.array([])
self.view = self.mat
return
+ # we don't want to show excess brackets
+ if flags.channels() != 1:
+ shape = np.append(shape, flags.channels())
+ steps = np.append(steps, elsize)
+
+ # get the length of contiguous array from data to the last element of the matrix
+ length = 1 + np.sum((shape - 1) * steps) // elsize
+
if dtype != np.float16:
+ # read all elements into self.mat
ctype = gdb.lookup_type(ctype)
ptr = ptr.cast(ctype.array(length - 1).pointer()).dereference()
self.mat = np.array([ptr[i] for i in range(length)], dtype=dtype)
else:
+ # read as uint16_t and then reinterpret the bytes as float16
u16 = gdb.lookup_type('uint16_t')
ptr = ptr.cast(u16.array(length - 1).pointer()).dereference()
self.mat = np.array([ptr[i] for i in range(length)], dtype=np.uint16)
self.mat = self.mat.view(np.float16)
- steps = np.asarray([int(m['step']['p'][i]) for i in range(size.dims())], dtype=np.int64)
- self.view = np.lib.stride_tricks.as_strided(self.mat[start:], shape=size.to_numpy(), strides=steps)
+ # numpy will do the heavy lifting of strided access
+ self.view = np.lib.stride_tricks.as_strided(self.mat, shape=shape, strides=steps)
def __iter__(self):
return iter({'data': stri(self.view)}.items())