Model.py 文件源码

def time_crop(f_init_date, f_final_date, delta, f_time_array, data_array, multiple=False):
    """
    Crop the data_array between f_init_date and f_final_date.

    :param f_init_date: Float. Initial date
    :param f_final_date: Float. Final date
    :param delta: a datetime instance to step in dates
    :param f_time_array: Float array. All dates of data_array
    :param data_array: The data to be cropped. Its shape must be of the form (time, ...)
    :param multiple: False, just one data_array. True a list of data_arrays.
    :return: Cropped data and according datetime list.
    """
    i_start = np.where(np.array(f_time_array) >= f_init_date)[0][0]
    i_end = np.where(np.array(f_time_array) <= f_final_date + 23 / 24.)[0][-1]

    # TODO: Refactor this while. You can transform delta and operate only on f_dates and then convert the entire list.
    d_date = num2date(f_init_date).replace(minute=0)
    d_Time = []
    f_Time = []
    while f_init_date <= f_final_date + 23 / 24.:
        d_Time.append(d_date)
        f_Time.append(date2num(d_date))
        d_date = d_date + delta
        f_init_date = date2num(d_date)

    if multiple:
        all_cropped_data = []
        for data in data_array:
            new_shape = [len(d_Time)]
            new_shape.extend(list(data.shape[1:]))
            new_shape = tuple(new_shape)
            cropped_data = np.empty(new_shape)
            cropped_data.fill(np.nan)
            # TODO: Use find_nearest
            cropped_data[np.in1d(f_Time, f_time_array[i_start:i_end + 1])] = data[i_start:i_end + 1]
            all_cropped_data.append(cropped_data)
        return all_cropped_data, d_Time
    else:
        new_shape = [len(d_Time)]
        new_shape.extend(list(data_array.shape[1:]))
        new_shape = tuple(new_shape)
        cropped_data = np.empty(new_shape)
        cropped_data.fill(np.nan)
        cropped_data[np.in1d(f_Time, f_time_array[i_start:i_end + 1])] = data_array[i_start:i_end + 1]
        return cropped_data, d_Time