Exemplo n.º 1
0
    def fromArraysAsImages(self, arrays):
        """Create a Series object from a sequence of numpy ndarrays resident in memory on the driver.

        The arrays will be interpreted as though each represents a single time point - effectively the same
        as if converting Images to a Series, with each array representing a volume image at a particular
        point in time. Thus in the resulting Series, the value of the record with key (0,0,0) will be
        array([arrays[0][0,0,0], arrays[1][0,0,0],... arrays[n][0,0,0]).

        The dimensions of the resulting Series will be *opposite* that of the passed numpy array. Their dtype will not
        be changed.
        """
        # if passed a single array, cast it to a sequence of length 1
        if isinstance(arrays, ndarray):
            arrays = [arrays]

        # check that shapes of passed arrays are consistent
        shape = arrays[0].shape
        dtype = arrays[0].dtype
        for ary in arrays:
            if not ary.shape == shape:
                raise ValueError("Inconsistent array shapes: first array had shape %s, but other array has shape %s" %
                                 (str(shape), str(ary.shape)))
            if not ary.dtype == dtype:
                raise ValueError("Inconsistent array dtypes: first array had dtype %s, but other array has dtype %s" %
                                 (str(dtype), str(ary.dtype)))

        # get indices so that fastest index changes first
        shapeiters = (xrange(n) for n in shape)
        keys = [idx[::-1] for idx in itertools.product(*shapeiters)]

        values = vstack([ary.ravel() for ary in arrays]).T

        dims = Dimensions.fromTuple(shape[::-1])

        return Series(self.sc.parallelize(zip(keys, values), self.minPartitions), dims=dims, dtype=str(dtype))
Exemplo n.º 2
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 def __init__(self, rdd, dims=None, nrecords=None, dtype=None):
     super(Images, self).__init__(rdd, nrecords=nrecords, dtype=dtype)
     if dims and not isinstance(dims, Dimensions):
         try:
             dims = Dimensions.fromTuple(dims)
         except:
             raise TypeError("Images dims parameter must be castable to Dimensions object, got: %s" % str(dims))
     self._dims = dims
Exemplo n.º 3
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 def populateParamsFromFirstRecord(self):
     record = super(Images, self).populateParamsFromFirstRecord()
     self._dims = Dimensions.fromTuple(record[1].shape)
     return record
Exemplo n.º 4
0
 def populateParamsFromFirstRecord(self):
     record = super(SimpleBlocks, self).populateParamsFromFirstRecord()
     self._dims = Dimensions.fromTuple(record[0].origShape)
     return record