def load_scalar_dataset(h_node): _, base_type, data = get_type_and_data(h_node) if (base_type == b'int'): data = int(data) return (data)
def load_pickled_data(h_node): py_type, data = get_type_and_data(h_node) try: import cPickle as pickle except ModuleNotFoundError: import pickle return pickle.loads(data[0])
def load_list_dataset(h_node): _, _, data = get_type_and_data(h_node) str_type = h_node.attrs.get('str_type', None) if str_type == b'str': return (np.array(data, copy=False, dtype=str).tolist()) else: return (data.tolist())
def load_astropy_time_dataset(h_node): py_type, data = get_type_and_data(h_node) if six.PY3: fmt = h_node.attrs["format"][0].decode('ascii') scale = h_node.attrs["scale"][0].decode('ascii') else: fmt = h_node.attrs["format"][0] scale = h_node.attrs["scale"][0] q = Time(data, format=fmt, scale=scale) return q
def load_ndarray_masked_dataset(h_node): _, _, data = get_type_and_data(h_node) dtype = h_node.attrs['np_dtype'] try: mask_path = h_node.name + "_mask" h_root = h_node.parent mask = h_root.get(mask_path)[:] except (ValueError, IndexError): mask = h_root.get(mask_path) data = np.ma.array(data, mask=mask, dtype=dtype) return data
def load_list_dataset(h_node): py_type, data = get_type_and_data(h_node) py3_str_type = get_py3_string_type(h_node) if py3_str_type == b"<class 'bytes'>": # Yuck. Convert numpy._bytes -> str -> bytes return [bytes(str(item, 'utf8'), 'utf8') for item in data] if py3_str_type == b"<class 'str'>": return [str(item, 'utf8') for item in data] else: return list(data)
def load_astropy_table(h_node): py_type, _, data = get_type_and_data(h_node) metadata = dict(h_node.attrs.items()) metadata.pop('type') metadata.pop('base_type') metadata.pop('colnames') colnames = [cn.decode('ascii') for cn in h_node.attrs["colnames"]] t = py_type(data, names=colnames, meta=metadata) return t
def load_python_dtype_dataset(h_node): py_type, data = get_type_and_data(h_node) subtype = h_node.attrs["python_subdtype"] type_dict = { "<type 'int'>": int, "<type 'float'>": float, "<type 'long'>": long, "<type 'bool'>": bool, "<type 'complex'>": complex } tcast = type_dict.get(subtype) return tcast(data)
def load_ndarray_masked_dataset(h_node): py_type, data = get_type_and_data(h_node) try: mask_path = h_node.name + "_mask" h_root = h_node.parent mask = h_root.get(mask_path)[:] except IndexError: mask = h_root.get(mask_path) except ValueError: mask = h_root.get(mask_path) data = np.ma.array(data, mask=mask) return data
def load_python_dtype_dataset(h_node): py_type, data = get_type_and_data(h_node) subtype = h_node.attrs["python_subdtype"] type_dict = { b"<class 'int'>": int, b"<class 'float'>": float, b"<class 'bool'>": bool, b"<class 'complex'>": complex } tcast = type_dict.get(subtype) return tcast(data)
def load_astropy_table(h_node): py_type, data = get_type_and_data(h_node) metadata = dict(h_node.attrs.items()) metadata.pop('type') metadata.pop('colnames') if six.PY3: colnames = [cn.decode('ascii') for cn in h_node.attrs["colnames"]] else: colnames = h_node.attrs["colnames"] t = Table(data, names=colnames, meta=metadata) return t
def load_astropy_constant_dataset(h_node): py_type, data = get_type_and_data(h_node) unit = h_node.attrs["unit"][0] abbrev = h_node.attrs["abbrev"][0] name = h_node.attrs["name"][0] ref = h_node.attrs["reference"][0] unc = h_node.attrs["uncertainty"][0] system = None if "system" in h_node.attrs.keys(): system = h_node.attrs["system"][0] c = Constant(abbrev, name, data, unit, unc, ref, system) return c
def load_sparse_matrix_data(h_node): py_type, data = get_type_and_data(h_node) h_root = h_node.parent indices = h_root.get('indices')[:] indptr = h_root.get('indptr')[:] shape = h_root.get('shape')[:] if py_type == b'csc_matrix_data': smat = sparse.csc_matrix((data, indices, indptr), dtype=data.dtype, shape=shape) elif py_type == b'csr_matrix_data': smat = sparse.csr_matrix((data, indices, indptr), dtype=data.dtype, shape=shape) elif py_type == b'bsr_matrix_data': smat = sparse.bsr_matrix((data, indices, indptr), dtype=data.dtype, shape=shape) return smat
def load_astropy_angle_dataset(h_node): py_type, data = get_type_and_data(h_node) unit = h_node.attrs["unit"][0] q = Angle(data, unit) return q
def load_unicode_dataset(h_node): py_type, data = get_type_and_data(h_node) return unicode(data[0])
def load_astropy_quantity_dataset(h_node): py_type, data = get_type_and_data(h_node) unit = h_node.attrs["unit"][0] q = Quantity(data, unit) return q
def load_astropy_quantity_dataset(h_node): py_type, _, data = get_type_and_data(h_node) unit = h_node.attrs["unit"] q = py_type(data, unit, copy=False) return q
def load_astropy_time_dataset(h_node): py_type, _, data = get_type_and_data(h_node) fmt = h_node.attrs["format"].decode('ascii') scale = h_node.attrs["scale"].decode('ascii') q = py_type(data, format=fmt, scale=scale) return q
def load_ndarray_dataset(h_node): py_type, data = get_type_and_data(h_node) return np.array(data, copy=False)
def load_pickled_data(h_node): _, _, data = get_type_and_data(h_node) return pickle.loads(data)
def load_string_dataset(h_node): py_type, data = get_type_and_data(h_node) return str(data[0])
def load_astropy_skycoord_dataset(h_node): py_type, _, data = get_type_and_data(h_node) lon_unit = h_node.attrs["lon_unit"] lat_unit = h_node.attrs["lat_unit"] q = py_type(data[..., 0], data[..., 1], unit=(lon_unit, lat_unit)) return q
def load_tuple_dataset(h_node): py_type, data = get_type_and_data(h_node) return tuple(data)
def load_np_scalar_dataset(h_node): _, _, data = get_type_and_data(h_node) return data
def load_np_dtype_dataset(h_node): _, _, data = get_type_and_data(h_node) data = np.dtype(data) return data
def load_astropy_skycoord_dataset(h_node): py_type, data = get_type_and_data(h_node) lon_unit = h_node.attrs["lon_unit"][0] lat_unit = h_node.attrs["lat_unit"][0] q = SkyCoord(data[:, 0], data[:, 1], unit=(lon_unit, lat_unit)) return q
def load_np_dtype_dataset(h_node): py_type, data = get_type_and_data(h_node) data = np.dtype(data[0]) return data
def load_bytes_dataset(h_node): py_type, data = get_type_and_data(h_node) return bytes(data[0])
def load_set_dataset(h_node): py_type, data = get_type_and_data(h_node) return set(data)
def load_np_scalar_dataset(h_node): py_type, data = get_type_and_data(h_node) subtype = h_node.attrs["np_dtype"] data = np.array([data], dtype=subtype)[0] return data
def load_astropy_skycoord_dataset(h_node): py_type, data = get_type_and_data(h_node) lon_unit = h_node.attrs["lon_unit"][0] lat_unit = h_node.attrs["lat_unit"][0] q = SkyCoord(data[:,0], data[:, 1], unit=(lon_unit, lat_unit)) return q
def load_ndarray_dataset(h_node): _, _, data = get_type_and_data(h_node) dtype = h_node.attrs['np_dtype'] return np.array(data, copy=False, dtype=dtype)
def load_pickled_data(h_node): py_type, data = get_type_and_data(h_node) import dill as pickle return pickle.loads(data[0])