Python h5py.special_dtype方法代碼示例

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def set_predicted_description(self, split, data_key, sentence):
        '''
        Set the predicted sentence tokens in the data_key group,
        creating the group if necessary, or erasing the current value if
        necessary.
        '''

        if self.openmode != "r+":
            # forcefully quit when trying to write to a read-only file
            raise RuntimeError("Dataset is read-only, try again with --h5_writable")

        dataset_key = 'predicted_description'

        try:
            predicted_text = self.dataset[split][data_key].create_dataset(dataset_key, (1,), dtype=h5py.special_dtype(vlen=unicode))
        except RuntimeError:
            # the dataset already exists, erase it and create an empty space
            del self.dataset[split][data_key][dataset_key]
            predicted_text = self.dataset[split][data_key].create_dataset(dataset_key, (1,), dtype=h5py.special_dtype(vlen=unicode))

        predicted_text[0] = " ".join([x for x in sentence]) 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def finish_chunck(self):
        if len(self.text) == 0:
            return

        codec = self.compute_codec()

        filename = "{}_{:03d}{}".format(self.output_filename, self.current_chunk, DataSetType.gt_extension(DataSetType.HDF5))
        self.files.append(filename)
        file = h5py.File(filename, 'w')
        dti32 = h5py.special_dtype(vlen=np.dtype('int32'))
        dtui8 = h5py.special_dtype(vlen=np.dtype('uint8'))
        file.create_dataset('transcripts', (len(self.text),), dtype=dti32, compression='gzip')
        file.create_dataset('images_dims', data=[d.shape for d in self.data], dtype=int)
        file.create_dataset('images', (len(self.text),), dtype=dtui8, compression='gzip')
        file.create_dataset('codec', data=list(map(ord, codec)))
        file['transcripts'][...] = [list(map(codec.index, d)) for d in self.text]
        file['images'][...] = [d.reshape(-1) for d in self.data]
        file.close()

        self.current_chunk += 1
        self.data = []
        self.text = [] 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def _hfd5_from_dataframe(ratings, movies, outputfilename):
    # transform ratings dataframe into a sparse matrix
    m = coo_matrix((ratings['rating'].astype(np.float32),
                   (ratings['movieId'], ratings['userId']))).tocsr()

    with h5py.File(outputfilename, "w") as f:
        # write out the ratings matrix
        g = f.create_group('movie_user_ratings')
        g.create_dataset("data", data=m.data)
        g.create_dataset("indptr", data=m.indptr)
        g.create_dataset("indices", data=m.indices)

        # write out the titles as a numpy array
        titles = np.empty(shape=(movies.movieId.max()+1,), dtype=np.object)
        titles[movies.movieId] = movies.title
        dt = h5py.special_dtype(vlen=str)
        dset = f.create_dataset('movie', (len(titles),), dtype=dt)
        dset[:] = titles 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def _hfd5_from_dataframe(data, outputfilename):
    # create a sparse matrix of all the users/plays
    plays = coo_matrix((data['plays'].astype(np.float32),
                       (data['artist'].cat.codes.copy(),
                        data['user'].cat.codes.copy()))).tocsr()

    with h5py.File(outputfilename, "w") as f:
        g = f.create_group('artist_user_plays')
        g.create_dataset("data", data=plays.data)
        g.create_dataset("indptr", data=plays.indptr)
        g.create_dataset("indices", data=plays.indices)

        dt = h5py.special_dtype(vlen=str)
        artist = list(data['artist'].cat.categories)
        dset = f.create_dataset('artist', (len(artist),), dtype=dt)
        dset[:] = artist

        user = list(data['user'].cat.categories)
        dset = f.create_dataset('user', (len(user),), dtype=dt)
        dset[:] = user 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def _hfd5_from_dataframe(data, outputfilename):
    items = data['mid'].cat.codes.copy()
    users = data['uid'].cat.codes.copy()
    values = np.ones(len(items)).astype(np.float32)

    # create a sparse matrix of all the item/users/likes
    likes = coo_matrix((values, (items, users))).astype(np.float32).tocsr()

    with h5py.File(outputfilename, "w") as f:
        g = f.create_group('item_user_likes')
        g.create_dataset("data", data=likes.data)
        g.create_dataset("indptr", data=likes.indptr)
        g.create_dataset("indices", data=likes.indices)

        dt = h5py.special_dtype(vlen=str)
        item = list(data['mid'].cat.categories)
        dset = f.create_dataset('item', (len(item),), dtype=dt)
        dset[:] = item

        user = list(data['uid'].cat.categories)
        dset = f.create_dataset('user', (len(user),), dtype=dt)
        dset[:] = user 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def _hfd5_from_dataframe(data, track_info, outputfilename):
    # create a sparse matrix of all the users/plays
    plays = coo_matrix((data['plays'].astype(np.float32),
                       (data['track'].cat.codes.copy(),
                        data['user'].cat.codes.copy()))).tocsr()

    with h5py.File(outputfilename, "w") as f:
        g = f.create_group('track_user_plays')
        g.create_dataset("data", data=plays.data)
        g.create_dataset("indptr", data=plays.indptr)
        g.create_dataset("indices", data=plays.indices)

        dt = h5py.special_dtype(vlen=str)
        dset = f.create_dataset('track', track_info.shape, dtype=dt)
        dset[:] = track_info

        user = list(data['user'].cat.categories)
        dset = f.create_dataset('user', (len(user),), dtype=dt)
        dset[:] = user 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def write_data(h5py_file, mode, x_paths, y_paths):
    num_data = len(x_paths)

    uint8_dt = h5py.special_dtype(vlen=np.uint8)
    string_dt = h5py.special_dtype(vlen=str)

    group = h5py_file.create_group(mode)
    h5_name = group.create_dataset('name', shape=(num_data,), dtype=string_dt)
    h5_image = group.create_dataset('image', shape=(num_data,), dtype=uint8_dt)
    h5_label = group.create_dataset('label', shape=(num_data,), dtype=uint8_dt)

    h5_image.attrs['size'] = [256,512,3]
    h5_label.attrs['size'] = [256,512,1]

    for i in range(num_data):
        x_img = cv2.imread(x_paths[i], 1)
        y_img = cv2.imread(y_paths[i], 0)
        x_img = cv2.resize(x_img, None, fx=0.25, fy=0.25, interpolation=cv2.INTER_LINEAR)
        y_img = cv2.resize(y_img, None, fx=0.25, fy=0.25, interpolation=cv2.INTER_NEAREST)

        h5_image[i] = x_img.flatten()
        h5_label[i] = y_img.flatten()
        h5_name[i] = os.path.basename(x_paths[i])

        # break 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def test_int(self):
        dt = h5py.special_dtype(vlen=int)
        ds = self.f.create_dataset('vlen', (4,), dtype=dt)
        ds[0] = np.arange(3)
        ds[1] = np.arange(0)
        ds[2] = [1, 2, 3]
        ds[3] = np.arange(1)
        self.assertArrayEqual(ds[0], np.arange(3))
        self.assertArrayEqual(ds[1], np.arange(0))
        self.assertArrayEqual(ds[2], np.array([1, 2, 3]))
        self.assertArrayEqual(ds[1], np.arange(0))
        ds[0:2] = np.array([np.arange(5), np.arange(4)])
        self.assertArrayEqual(ds[0], np.arange(5))
        self.assertArrayEqual(ds[1], np.arange(4))
        ds[0:2] = np.array([np.arange(3), np.arange(3)])
        self.assertArrayEqual(ds[0], np.arange(3))
        self.assertArrayEqual(ds[1], np.arange(3)) 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def test_convert(self):
        dt = h5py.special_dtype(vlen=int)
        ds = self.f.create_dataset('vlen', (3,), dtype=dt)
        ds[0] = np.array([1.4, 1.2])
        ds[1] = np.array([1.2])
        ds[2] = [1.2, 2, 3]
        self.assertArrayEqual(ds[0], np.array([1, 1]))
        self.assertArrayEqual(ds[1], np.array([1]))
        self.assertArrayEqual(ds[2], np.array([1, 2, 3]))
        ds[0:2] = np.array([[0.1, 1.1, 2.1, 3.1, 4], np.arange(4)])
        self.assertArrayEqual(ds[0], np.arange(5))
        self.assertArrayEqual(ds[1], np.arange(4))
        ds[0:2] = np.array([np.array([0.1, 1.2, 2.2]),
                            np.array([0.2, 1.2, 2.2])])
        self.assertArrayEqual(ds[0], np.arange(3))
        self.assertArrayEqual(ds[1], np.arange(3)) 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def test_compound_vlen_enum(self):
        eidt = h5py.special_dtype(enum=(np.uint8, {'OFF': 0, 'ON': 1}))
        vidt = h5py.special_dtype(vlen=np.uint8)
        def a(items):
            return np.array(items, dtype=np.uint8)

        f = self.f

        dt_vve = np.dtype([
            ('foo', vidt),
            ('bar', vidt),
            ('switch', eidt)])
        vve = f.create_dataset('dt_vve', shape=(2,), dtype=dt_vve)
        data = np.array([(a([1,2,3]), a([1,2]),   1),
                         (a([]),      a([2,4,6]), 0),],
                         dtype=dt_vve)
        vve[:] = data
        actual = vve[:]
        self.assertVlenArrayEqual(data['foo'], actual['foo'])
        self.assertVlenArrayEqual(data['bar'], actual['bar'])
        self.assertArrayEqual(data['switch'], actual['switch']) 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_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 special_dtype [as 別名]
def test_compound_vlen(self):
        vidt = h5py.special_dtype(vlen=np.uint8)
        eidt = h5py.special_dtype(enum=(np.uint8, {'OFF': 0, 'ON': 1}))

        for np_align in (False, True):
            dt = np.dtype([
                ('a', eidt),
                ('foo', vidt),
                ('bar', vidt),
                ('switch', eidt)], align=np_align)
            np_offsets = [dt.fields[i][1] for i in dt.names]

            for logical in (False, True):
                if logical and np_align:
                    # Vlen types have different size in the numpy struct
                    self.assertRaises(TypeError, h5py.h5t.py_create, dt,
                            logical=logical)
                else:
                    ht = h5py.h5t.py_create(dt, logical=logical)
                    offsets = [ht.get_member_offset(i)
                               for i in range(ht.get_nmembers())]
                    if np_align:
                        self.assertEqual(np_offsets, offsets) 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def prepare_hdf5_file(hdf5_file, n_train, n_valid, n_test):
    """Create datasets within a given HDF5 file.

    Parameters
    ----------
    hdf5_file : :class:`h5py.File` instance
        HDF5 file handle to which to write.
    n_train : int
        The number of training set examples.
    n_valid : int
        The number of validation set examples.
    n_test : int
        The number of test set examples.

    """
    n_total = n_train + n_valid + n_test
    splits = create_splits(n_train, n_valid, n_test)
    hdf5_file.attrs['split'] = H5PYDataset.create_split_array(splits)
    vlen_dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))
    hdf5_file.create_dataset('encoded_images', shape=(n_total,),
                             dtype=vlen_dtype)
    hdf5_file.create_dataset('targets', shape=(n_total, 1), dtype=numpy.int16)
    hdf5_file.create_dataset('filenames', shape=(n_total, 1), dtype='S32') 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def write_h5(datasetDict, out_file, metadata=None, ref_file=None, compression=None):

    if os.path.isfile(out_file):
        print('delete exsited file: {}'.format(out_file))
        os.remove(out_file)

    print('create HDF5 file: {} with w mode'.format(out_file))
    dt = h5py.special_dtype(vlen=np.dtype('float64'))

    
    with h5py.File(out_file, 'w') as f:
        for dsName in datasetDict.keys():
            data = datasetDict[dsName]
            ds = f.create_dataset(dsName,
                              data=data,
                              compression=compression)
        
        for key, value in metadata.items():
            f.attrs[key] = str(value)
            #print(key + ': ' +  value)
    print('finished writing to {}'.format(out_file))
        
    return out_file 
    
###################################################################### 

# 需要導入模塊: import h5py [as 別名]
# 或者: from h5py import special_dtype [as 別名]
def write(self, example, filename, image_types=[]):
        '''
        Write an example out to disk.

        status: success, failure or error.failure
        '''
        filename = os.path.join(self.name, filename)
        f = h5f.File(filename, 'w')
        if image_types != []:
            dt = h5f.special_dtype(vlen=bytes)
            for (img_type_str, img_format_str) in image_types:
                f.create_dataset("type_" + img_type_str, data=[img_format_str])
        for key, value in example.items():
            if self.verbose > 0:
                print('H5fDataset writing key: ' + str(key))
            f.create_dataset(key, data=value)
        f.close()