def spei_pearson(precip_monthly_values,
temp_monthly_values,
month_scale,
latitude,
valid_min,
valid_max,
data_start_year,
data_end_year,
calibration_start_year,
calibration_end_year):
# compute the PET values using Thornthwaite's equation
pet_monthly_values = thornthwaite(temp_monthly_values, latitude, data_start_year, np.nan)
# offset we add to the (P - PE values) in order to bring all values into the positive range
p_minus_pe_offset = 1000.0
# get the difference of P - PE, assign the values into the temperature array (in order to reuse the memory)
p_minus_pe = (precip_monthly_values - pet_monthly_values) + p_minus_pe_offset
# perform the SPEI computation (fit to the Pearson distribution) and assign the values into the dataset
return distribution_fitter.fit_to_pearson(p_minus_pe,
month_scale,
valid_min,
valid_max,
data_start_year,
data_end_year,
calibration_start_year,
calibration_end_year)
def spi_pearson(precip_monthly_values,
month_scale,
valid_min,
valid_max,
data_start_year,
data_end_year,
calibration_start_year,
calibration_end_year):
# perform the SPI computation (fit to the Pearson distribution) and assign the values into the dataset
return distribution_fitter.fit_to_pearson(precip_monthly_values,
month_scale,
valid_min,
valid_max,
data_start_year,
data_end_year,
calibration_start_year,
calibration_end_year)
def spei_spi_pearson_pet(precip_monthly_values,
temp_monthly_values,
month_scale,
latitude,
valid_min,
valid_max,
data_start_year,
data_end_year,
calibration_start_year,
calibration_end_year):
'''
This function computes PET, SPI and SPEI fitted to the Pearson Type III distribution.
:param precip_monthly_values: an array of monthly total precipitation values, of the same size
and shape as the input temperature array
:param temp_monthly_values: an array of monthly average temperature values, of the same size
and shape as the input precipitation array
:param month_scale: the number of months over which the values should be scaled before computing the index
:param latitude: the latitude, in degrees, of the location
:param valid_min: valid minimum of the resulting SPI and SPEI values
:param valid_max: valid maximum of the resulting SPI and SPEI values
:param data_start_year: the initial year of the input datasets (assumes that the two inputs cover the same period)
:param data_end_year: the final year of the input datasets (assumes that the two inputs cover the same period)
:param calibration_start_year: the initial year of the calibration period
:param calibration_end_year: the final year of the calibration period
:return: an array of SPEI values, of the same size and shape as the input temperature and precipitation arrays
:return: an array of SPI values, of the same size and shape as the input temperature and precipitation arrays
:return: an array of PET values, of the same size and shape as the input temperature and precipitation arrays
'''
# compute the SPI fitted to the Pearson Type III distribution
spi = distribution_fitter.fit_to_pearson(precip_monthly_values,
month_scale,
valid_min,
valid_max,
data_start_year,
data_end_year,
calibration_start_year,
calibration_end_year)
# compute the PET values using Thornthwaite's equation
pet = thornthwaite(temp_monthly_values, latitude, data_start_year, np.nan)
# offset we add to the (P - PE values) in order to bring all values into the positive range
p_minus_pe_offset = 1000.0
# get the difference of P - PE, assign the values into the temperature array (in order to reuse the memory)
p_minus_pe = (precip_monthly_values - pet) + p_minus_pe_offset
# perform the SPEI computation (fit to the Pearson distribution) and assign the values into the dataset
spei = distribution_fitter.fit_to_pearson(p_minus_pe,
month_scale,
valid_min,
valid_max,
data_start_year,
data_end_year,
calibration_start_year,
calibration_end_year)
return spei, spi, pet
