Add unit tests for index functions

Implemented tests:

• CountLevelCrossings
• CountOccurrences
• CountJointOccurrencesPrecipitationTemperature
• CountJointOccurrencesTemperature
• DiurnalTemperatureRange
• ExtremeTemperatureRange
• FirstOccurrence (moved to later release)
• InterdayDiurnalTemperatureRange
• LastOccurrence (moved to later release)
• Percentile
• Statistics
• ThresholdedPercentile
• ThresholdedStatistics
• RunningStatistics
• ThresholdedRunningStatistics
• TemperatureSum

The following unit tests needs to be reviewed from a scientific point of view:

• CountLevelCrossings - Gustav
• CountOccurrences - Gustav
• CountJointOccurrencesPrecipitationTemperature
• CountJointOccurrencesTemperature
• DiurnalTemperatureRange - Renate
• ExtremeTemperatureRange - Renate
• InterdayDiurnalTemperatureRange - Renate
• Statistics
• ThresholdedStatistics - Renate
• RunningStatistics (complex output with post-processing included)
• ThresholdedRunningStatistics (complex output with post-processing included)
• TemperatureSum

E.g.,

TEST_COUNT_LEVEL_CROSSINGS_PARAMETERS = [
    (
        {"data": (-1) * np.arange(12).reshape(2, 2, 3), "units": "degree_Celsius"},
        {"data": np.arange(12).reshape(2, 2, 3), "units": "degree_Celsius"},
        {"data": 0, "units": "degree_Celsius", "standard_name": "air_temperature"},
        np.array([[1, 2, 2], [2, 2, 2]]),
    ),  # ordinary np
]
parameter_names = "f_cube_tasmin, f_cube_tasmax, f_first_threshold, expected"

The index function is count_level_crossings, bellow the parameter list with the data we can see the parameter names. Such that,

f_cube_tasmin = {"data": (-1) * np.arange(12).reshape(2, 2, 3), "units": "degree_Celsius"}
f_cube_tasmax = {"data": np.arange(12).reshape(2, 2, 3), "units": "degree_Celsius"}
f_first_threshold = {"data": 0, "units": "degree_Celsius", "standard_name": "air_temperature"}
expected = np.array([[1, 2, 2], [2, 2, 2]])