cb786bb07f23af7792f05a940a50da08ee11d406,mir_eval/io.py,,load_ragged_time_series,#Any#Any#Any#Any#,482

Before Change

    splitter = re.compile(delimiter)

    // Keep track of whether we create our own file handle
    own_fh = False
    // If the filename input is a string, need to open it
    if isinstance(filename, six.string_types):
        // Remember that we need to close it later
        own_fh = True
        // Open the file for reading
        input_file = open(filename, "r")
    // If the provided has a read attribute, we can use it as a file handle
    elif hasattr(filename, "read"):
        input_file = filename
    // Raise error otherwise
    else:
        raise ValueError("filename must be a string or file handle")
    if header:
        start_row = 1
    else:
        start_row = 0
    for row, line in enumerate(input_file, start_row):
        // Split each line using the supplied delimiter
        data = splitter.split(line.strip())
        try:
            converted_time = float(data[0])
        except (TypeError, ValueError) as exe:
            six.raise_from(ValueError("Couldn"t convert value {} using {} "
                                      "found at {}:{:d}:\n\t{}".format(
                                        data[0], float.__name__,
                                        filename, row, line)), exe)
        times.append(converted_time)

        // cast values to a numpy array. time stamps with no values are cast
        // to an empty array.
        try:
            converted_value = np.array(data[1:], dtype=dtype)
        except (TypeError, ValueError) as exe:
            six.raise_from(ValueError("Couldn"t convert value {} using {} "
                                      "found at {}:{:d}:\n\t{}".format(
                                        data[1:], dtype.__name__,
                                        filename, row, line)), exe)
        values.append(converted_value)

    // Close the file handle if we opened it
    if own_fh:
        input_file.close()

    return np.array(times), values

After Change

        start_row = 1
    else:
        start_row = 0
    with _open(filename, mode="r") as input_file:
        for row, line in enumerate(input_file, start_row):
            // Split each line using the supplied delimiter
            data = splitter.split(line.strip())
            try:
                converted_time = float(data[0])
            except (TypeError, ValueError) as exe:
                six.raise_from(ValueError("Couldn"t convert value {} using {} "
                                          "found at {}:{:d}:\n\t{}".format(
                                            data[0], float.__name__,
                                            filename, row, line)), exe)
            times.append(converted_time)

            // cast values to a numpy array. time stamps with no values are cast
            // to an empty array.
            try:
                converted_value = np.array(data[1:], dtype=dtype)
            except (TypeError, ValueError) as exe:
                six.raise_from(ValueError("Couldn"t convert value {} using {} "
                                          "found at {}:{:d}:\n\t{}".format(
                                            data[1:], dtype.__name__,
                                            filename, row, line)), exe)
            values.append(converted_value)

    return np.array(times), values

Instances