def snow_procedure_filter(observations, quality, dates, proc_params):
"""
Filter for initial stages of the snow procedure
Clear or Water
and Snow
Args:
observations: 2-d ndarray, spectral observations
quality: 1-d ndarray quality information
dates: 1-d ndarray ordinal observation dates
thermal_idx: int value identifying the thermal band in the observations
proc_params: dictionary of processing parameters
Returns:
1-d boolean ndarray
"""
thermal_idx = proc_params.THERMAL_IDX
clear = proc_params.QA_CLEAR
water = proc_params.QA_WATER
snow = proc_params.QA_SNOW
mask = ((mask_value(quality, water) | mask_value(quality, clear))
& filter_thermal_celsius(observations[thermal_idx])
& filter_saturated(observations)) | mask_value(quality, snow)
date_mask = mask_duplicate_values(dates[mask])
mask[mask] = date_mask
return mask
def insufficient_clear_filter(observations, quality, dates, proc_params):
"""
Filter for the initial stages of the insufficient clear procedure.
The main difference being there is an additional exclusion of observations
where the green value is > the median green + 400.
Args:
observations: 2-d ndarray, spectral observations
quality: 1-d ndarray quality information
dates: 1-d ndarray ordinal observation dates
proc_params: dictionary of processing parameters
Returns:
1-d boolean ndarray
"""
green_idx = proc_params.GREEN_IDX
filter_range = proc_params.MEDIAN_GREEN_FILTER
standard_mask = standard_procedure_filter(observations, quality, dates,
proc_params)
green_mask = filter_median_green(observations[:, standard_mask][green_idx],
filter_range)
standard_mask[standard_mask] &= green_mask
date_mask = mask_duplicate_values(dates[standard_mask])
standard_mask[standard_mask] = date_mask
return standard_mask
def standard_procedure_filter(observations, quality, dates, proc_params):
"""
Filter for the initial stages of the standard procedure.
Clear or Water
and Unsaturated
Temperatures are expected to be in celsius
Args:
observations: 2-d ndarray, spectral observations
quality: 1-d ndarray observation quality information
dates: 1-d ndarray ordinal observation dates
proc_params: dictionary of processing parameters
Returns:
1-d boolean ndarray
"""
thermal_idx = proc_params.THERMAL_IDX
clear = proc_params.QA_CLEAR
water = proc_params.QA_WATER
mask = ((mask_value(quality, water) | mask_value(quality, clear))
& filter_thermal_celsius(observations[thermal_idx])
& filter_saturated(observations))
date_mask = mask_duplicate_values(dates[mask])
mask[mask] = date_mask
return mask