def check_positive_data_length(
tdms_operator: nptdms.TdmsFile) -> either.Either:
"""Checks whether the data length is positive"""
max_length = tdms_helpers.get_maximum_array_size(tdms_operator)
if max_length <= 0:
return either.Left(ErrorCode.DATALENGTH_NONPOSITIVE)
return either.Right(tdms_operator)
def test_correct_array_size_works_for_example(tmpdir):
meta = source.MetaData(
chunk_size=6,
recurrence_size=3, # <- 2 would be correct
recurrence_distance=3,
consistency_sample_size=10,
)
example_source = source.SourceFile.read_from_path(
tdms_path="tests/assets/example_file.tdms",
meta=meta,
)
res = tdms_helpers.get_maximum_array_size(example_source.tdms_operator)
assert res == 19
def prepare_data_correction(
source_file: source.SourceFile, ) -> List[Tuple[int, int]]:
"""Prepares all parameters needed for data correction process.
Arguments:
source_file: Container object for the tdms file with params
Returns:
List of Chunk Indices that point to valid data slices.
"""
maximum_size = tdms_helpers.get_maximum_array_size(
source_file.tdms_operator)
index_ranges = calculate_index_ranges_to_preserve(
source_file.meta.chunk_size,
source_file.meta.recurrence_size,
maximum_size,
)
return index_ranges
def calculate_drop_indices(
source_file: source.SourceFile, ) -> List[Tuple[int, int]]:
"""Calculates index positions of duplicates in file and returns them in the form of tuples
(offset, length)
This function is the counter part of
fix.calculate_index_ranges_to_preserve and is only used in error handling.
Arguments:
source_file: Container object for the tdms file with params
Returns:
List of Chunk Indices that point to invalid data slices.
"""
len_data = tdms_helpers.get_maximum_array_size(source_file.tdms_operator)
chunk_size = source_file.meta.chunk_size
recurrence_size = source_file.meta.recurrence_size
offsets = np.arange(chunk_size, len_data, chunk_size + recurrence_size)
lengths = [recurrence_size] * (len(offsets) - 1)
lengths.append(min(recurrence_size, len_data - offsets[-1]))
return list(zip(offsets, lengths))