Python h5py.check_dtype方法代碼示例

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def test_vlen_enum(self):
        fname = self.mktemp()
        arr1 = [[1],[1,2]]
        dt1 = h5py.special_dtype(vlen=h5py.special_dtype(
            enum=('i', dict(foo=1, bar=2))))

        with h5py.File(fname,'w') as f:
            df1 = f.create_dataset('test', (len(arr1),), dtype=dt1)
            df1[:] = np.array(arr1)

        with h5py.File(fname,'r') as f:
            df2  = f['test']
            dt2  = df2.dtype
            arr2 = [e.tolist() for e in df2[:]]

        self.assertEqual(arr1, arr2)
        self.assertEqual(h5py.check_dtype(enum=h5py.check_dtype(vlen=dt1)),
                         h5py.check_dtype(enum=h5py.check_dtype(vlen=dt2))) 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def _check_valid_netcdf_dtype(self, dtype, stacklevel=3):
        dtype = np.dtype(dtype)

        if dtype == bool:
            description = 'boolean'
        elif dtype == complex:
            description = 'complex'
        elif h5py.check_dtype(enum=dtype) is not None:
            description = 'enum'
        elif h5py.check_dtype(ref=dtype) is not None:
            description = 'reference'
        elif h5py.check_dtype(vlen=dtype) not in {None, unicode, bytes}:
            description = 'non-string variable length'
        else:
            description = None

        if description is not None:
            _invalid_netcdf_feature('{} dtypes'.format(description),
                                    allow=self.invalid_netcdf,
                                    file=self,
                                    stacklevel=stacklevel + 1) 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def _get_group_info(path, grouppath, keys):
    with h5py.File(path, "r") as f:
        grp = f[grouppath]

        if keys is None:
            keys = list(grp.keys())

        nrows = len(grp[keys[0]])

        categoricals = {}
        for key in keys:
            dt = h5py.check_dtype(enum=grp[key].dtype)
            if dt is not None:
                categoricals[key] = sorted(dt, key=dt.__getitem__)

        # Meta is an empty dataframe that serves as a compound "dtype"
        meta = pd.DataFrame(
            {key: np.array([], dtype=grp[key].dtype) for key in keys}, columns=keys
        )

        for key in categoricals:
            meta[key] = pd.Categorical([], categories=categoricals[key], ordered=True)

    return nrows, keys, meta, categoricals 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def test_create(self):
        """ Enum datasets can be created and type correctly round-trips """
        dt = h5py.special_dtype(enum=('i', self.EDICT))
        ds = self.f.create_dataset('x', (100, 100), dtype=dt)
        dt2 = ds.dtype
        dict2 = h5py.check_dtype(enum=dt2)
        self.assertEqual(dict2, self.EDICT) 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def test_compound(self):

        fields = []
        fields.append(('field_1', h5py.special_dtype(vlen=str)))
        fields.append(('field_2', np.int32))
        dt = np.dtype(fields)
        self.f['mytype'] = np.dtype(dt)
        dt_out = self.f['mytype'].dtype.fields['field_1'][0]
        self.assertEqual(h5py.check_dtype(vlen=dt_out), str) 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def get_annotations(path, fields, enum_field):
    data_labels = {}
    for field in fields:
        data_labels[field] = path[field]
    data_dtypes = {}
    if h5py.check_dtype(enum=path.dtype[enum_field]):
        dataset_dtype = h5py.check_dtype(enum=path.dtype[enum_field])
        # data_dtype may lose some dataset dtypes there are duplicates of 'v'
        data_dtypes = {v: k for k, v in dataset_dtype.items()}
    labels_df = pd.DataFrame(data=data_labels)
    return labels_df, data_dtypes 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def get_annotations(path, enum_field):
    data_dtypes = {}
    if h5py.check_dtype(enum=path.dtype[enum_field]):
        data_dtypes = h5py.check_dtype(enum=path.dtype[enum_field])
    return data_dtypes 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def dtype(self):
        dt = self._h5ds.dtype
        if h5py.check_dtype(vlen=dt) is unicode:
            return str
        return dt 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def test_put():
    f = make_hdf5_table('a')

    # append
    df = pd.DataFrame({
        'chrom': ['chr3', 'chr3'],
        'start': [0, 20],
        'end': [20, 40],
        'value': [4.0, 5.0],
    })
    core.put(f['table'], df, lo=5)
    f.flush()
    out = core.get(f['table'])
    assert len(out) == 7

    # insert a categorical column
    s = pd.Series(pd.Categorical(out['chrom'], ordered=True), index=out.index)
    s.name = 'chrom_enum'
    core.put(f['table'], s)
    assert h5py.check_dtype(enum=f['table/chrom_enum'].dtype)
    out = core.get(f['table'])
    assert len(out.columns) == 5
    assert pd.api.types.is_categorical_dtype(out['chrom_enum'].dtype)
    out = core.get(f['table'], convert_enum=False)
    assert len(out.columns) == 5
    assert pd.api.types.is_integer_dtype(out['chrom_enum'].dtype)

    # don't convert categorical to enum
    s.name = 'chrom_string'
    core.put(f['table'], s, store_categories=False)
    out = core.get(f['table'])
    assert len(out.columns) == 6
    assert not pd.api.types.is_categorical_dtype(out['chrom_string'].dtype)

    # scalar input
    core.put(f['table'], {'foo': 42})
    out = core.get(f['table'])
    assert len(out.columns) == 7
    assert (out['foo'] == 42).all() 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import check_dtype [as 別名]
def get_values(
        self, queries: List[Dict[str, Union[str, bool]]], subset: Optional[List[str]] = None
    ) -> Tuple[pd.DataFrame, Dict[str, str]]:
        """
        Parameters
        ----------
        subset
        queries: List[Dict[str, Union[str, bool]]]
            List of queries. Fields actually used are native, name, driver
        """
        import h5py

        units = {}
        entries = self.get_index(subset)
        indexes = entries._h5idx
        with self._read_file() as f:
            ret = pd.DataFrame(index=entries["index"])

            for query in queries:
                dataset_name = "value/" if query["native"] else "contributed_value/"
                dataset_name += self._normalize_hdf5_name(query["name"])
                driver = query["driver"]

                dataset = f[dataset_name]
                if not h5py.check_dtype(vlen=dataset.dtype):
                    data = [dataset[i] for i in indexes]
                else:
                    if driver.lower() == "gradient":
                        data = [np.reshape(dataset[i], (-1, 3)) for i in indexes]
                    elif driver.lower() == "hessian":
                        data = []
                        for i in indexes:
                            n2 = len(dataset[i])
                            n = int(round(np.sqrt(n2)))
                            data.append(np.reshape(dataset[i], (n, n)))
                    else:
                        warnings.warn(
                            f"Variable length data type not understood, returning flat array " f"(driver = {driver}).",
                            RuntimeWarning,
                        )
                        try:
                            data = [np.array(dataset[i]) for i in indexes]
                        except ValueError:
                            data = [dataset[i] for i in indexes]
                column_name = query["name"]
                column_units = self._deserialize_field(dataset.attrs["units"])
                ret[column_name] = data
                units[column_name] = column_units

        return ret, units