Python h5py.Datatype方法代碼示例

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import Datatype [as 別名]
def _write_odim(src, dest):
    """ Writes Odim Attributes.

    Parameters
    ----------
    src : dict
        Attributes to write
    dest : handle
        h5py-group handle
    """
    for key, value in src.items():
        if key in dest.attrs:
            continue
        if isinstance(value, str):
            tid = h5py.h5t.C_S1.copy()
            tid.set_size(len(value) + 1)
            H5T_C_S1_NEW = h5py.Datatype(tid)
            dest.attrs.create(key, value, dtype=H5T_C_S1_NEW)
        else:
            dest.attrs[key] = value 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import Datatype [as 別名]
def _fetchAllChildren(self):
        """ Fetches all sub groups and variables that this group contains.
        """
        assert self._h5Group is not None, "dataset undefined (file not opened?)"
        assert self.canFetchChildren(), "canFetchChildren must be True"

        childItems = []

        for childName, h5Child in self._h5Group.items():
            if isinstance(h5Child, h5py.Group):
                childItems.append(H5pyGroupRti(h5Child, nodeName=childName,
                                               fileName=self.fileName))
            elif isinstance(h5Child, h5py.Dataset):
                if len(h5Child.shape) == 0:
                    childItems.append(H5pyScalarRti(h5Child, nodeName=childName,
                                                    fileName=self.fileName))
                else:
                    childItems.append(H5pyDatasetRti(h5Child, nodeName=childName,
                                                     fileName=self.fileName))

            elif isinstance(h5Child, h5py.Datatype):
                #logger.debug("Ignored DataType item: {}".format(childName))
                pass
            else:
                logger.warn("Ignored {}. It has an unexpected HDF-5 type: {}"
                            .format(childName, type(h5Child)))

        return childItems 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import Datatype [as 別名]
def write_dataframe_with_index_columns(path, df, values_dtype, datetime_func):
    """
    保存dataframe列表，分成三部分保存，時間，列表頭，數據
    因為時間軸可能與默認的不一樣，所以使用這種方法在一個文件中保存一張表
    :param path:
    :param df:
    :param values_dtype:
    :param columns_dtype:
    :return:
    """
    f = h5py.File(path, 'w')

    f.create_dataset('values', data=df.as_matrix(), compression="gzip", compression_opts=6, dtype=values_dtype)

    index = df.index.map(datetime_func)
    f.create_dataset('index', data=index, compression="gzip", compression_opts=6, dtype=np.int64)

    # # 隻能存定長byte類型的，變長的會導致工具打開出錯
    columns = list(df.columns.map(lambda x: np.string_(x)))
    # f.create_dataset('columns', data=columns, dtype=columns_dtype)

    tid = h5py.h5t.C_S1.copy()
    tid.set_size(32)
    H5T_C_S1_32 = h5py.Datatype(tid)
    f.create_dataset('columns', data=columns, compression="gzip", compression_opts=6, dtype=H5T_C_S1_32)

    f.close()

    return 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import Datatype [as 別名]
def _write_odim_dataspace(src, dest):
    """ Writes Odim Dataspaces.

    Parameters
    ----------
    src : dict
        Moments to write
    dest : handle
        h5py-group handle
    """
    keys = [key for key in src if key in ODIM_NAMES]
    data_list = ["data{}".format(i + 1) for i in range(len(keys))]
    data_idx = np.argsort(data_list)
    for idx in data_idx:
        value = src[keys[idx]]
        h5_data = dest.create_group(data_list[idx])
        enc = value.encoding

        # p. 21 ff
        h5_what = h5_data.create_group("what")
        try:
            undetect = float(value._Undetect)
        except AttributeError:
            undetect = np.finfo(np.float).max
        what = {
            "quantity": value.name,
            "gain": float(enc["scale_factor"]),
            "offset": float(enc["add_offset"]),
            "nodata": float(enc["_FillValue"]),
            "undetect": undetect,
        }
        _write_odim(what, h5_what)

        # moments handling
        val = value.sortby("azimuth").values
        fillval = enc["_FillValue"] * enc["scale_factor"]
        fillval += enc["add_offset"]
        val[np.isnan(val)] = fillval
        val = (val - enc["add_offset"]) / enc["scale_factor"]
        val = np.rint(val).astype(enc["dtype"])
        ds = h5_data.create_dataset(
            "data",
            data=val,
            compression="gzip",
            compression_opts=6,
            fillvalue=enc["_FillValue"],
        )
        if enc["dtype"] == "uint8":
            image = "IMAGE"
            version = "1.2"
            tid1 = h5py.h5t.C_S1.copy()
            tid1.set_size(len(image) + 1)
            H5T_C_S1_IMG = h5py.Datatype(tid1)
            tid2 = h5py.h5t.C_S1.copy()
            tid2.set_size(len(version) + 1)
            H5T_C_S1_VER = h5py.Datatype(tid2)
            ds.attrs.create("CLASS", image, dtype=H5T_C_S1_IMG)
            ds.attrs.create("IMAGE_VERSION", version, dtype=H5T_C_S1_VER)