x_dim_name = 'lon'
y_dim_name = 'lat'
# create the variable name from the indicator, distribution, and month scale
variable_name = 'spi_gamma_{}'.format(str(month_scale).zfill(2))
# make a basic set of variable attributes
variable_attributes = {'valid_min' : valid_min,
'valid_max' : valid_max,
'long_name' : 'SPI (Gamma), {}-month scale'.format(str(month_scale))}
# initialize the output NetCDF with the same dimensionality, coordinates, etc. as the input precipitation NetCDF
output_dataset = netcdf_utils.initialize_dataset(output_file_base + variable_name + '.nc',
precip_dataset,
x_dim_name,
y_dim_name,
variable_name,
variable_attributes,
np.nan)
# dictionary mapping process (thread) IDs to the lat/lon point into which the computed data from the target function is to be copied
threads_to_join = []
# loop over the grid cells
for x in range(precip_dataset.variables[x_dim_name].size):
for y in range(precip_dataset.variables[y_dim_name].size):
logger.info('Processing x/y {}/{}'.format(x, y))
# run a thread to compute the SPI/gamma on this x/y location
thread = threading.Thread(target=compute_indicator,
# initialize the output NetCDFs for each month scale
# month_scales = [1, 2, 3, 6, 9, 12, 24, 36, 48, 60, 72]
month_scales = [9, 24]
datasets = {}
for month_scale_index, month_scale_var_name in enumerate(month_scales):
# create the variable name from the indicator, distribution, and month scale
variable_name = "spi_pearson_{}".format(str(month_scale_var_name).zfill(2))
# get the output files for each variable
# initialize the output NetCDFs with the same dimensionality, coordinates, etc. as the input precipitation NetCDF
dataset = netcdf_utils.initialize_dataset(
output_file_base + variable_name + ".nc",
precip_dataset,
x_dim_name,
y_dim_name,
variable_name,
"SPI (Pearson), {}-month scale".format(str(month_scale_var_name)),
variable_attributes,
np.nan,
)
# add to the list of month scale datasets
datasets[variable_name] = dataset
# loop over the grid cells
for x in range(precip_dataset.variables[x_dim_name].size):
for y in range(precip_dataset.variables[y_dim_name].size):
logger.info("Processing x/y {}/{}".format(x, y))
# slice out the period of record for the x/y point
'valid_max' : valid_max}
# initialize the output NetCDFs for each month scale
month_scales = [1, 2, 3, 6, 9, 12, 24, 36, 48, 60, 72]
spi_gamma_datasets = {}
for month_scale_index, month_scale_var_name in enumerate(month_scales):
# create the variable name from the indicator, distribution, and month scale
variable_name_spi_gamma = 'spi_gamma_{}'.format(str(month_scale_var_name).zfill(2))
# get the output files for each variable
# initialize the output NetCDFs with the same dimensionality, coordinates, etc. as the input precipitation NetCDF
spi_gamma_dataset = netcdf_utils.initialize_dataset(output_file_base + variable_name_spi_gamma + '.nc',
precip_dataset,
x_dim_name,
y_dim_name,
variable_name_spi_gamma,
'SPI (Gamma), {}-month scale'.format(str(month_scale_var_name)),
variable_attributes,
np.nan)
# add to the list of month scale datasets
spi_gamma_datasets[variable_name_spi_gamma] = spi_gamma_dataset
# loop over the grid cells
for x in range(precip_dataset.variables[x_dim_name].size):
for y in range(precip_dataset.variables[y_dim_name].size):
logger.info('Processing x/y {}/{}'.format(x, y))
# slice out the period of record for the x/y point
precip_data = precip_dataset.variables[precip_var_name][:, x, y]
for month_scale in [1, 2, 3, 6, 9, 12, 24, 36, 48, 60, 72]:
# create the variable name from the indicator, distribution, and month scale
variable_name_spi_pearson = 'spi_pearson_{}'.format(str(month_scale).zfill(2))
variable_name_spi_gamma = 'spi_gamma_{}'.format(str(month_scale).zfill(2))
variable_name_spei_pearson = 'spei_pearson_{}'.format(str(month_scale).zfill(2))
variable_name_spei_gamma = 'spei_gamma_{}'.format(str(month_scale).zfill(2))
# get the output files for each variable
# initialize the output NetCDFs with the same dimensionality, coordinates, etc. as the input precipitation NetCDF
variable_name_spi_pearson = 'spi_pearson_{}'.format(str(month_scale).zfill(2))
long_name = 'SPI (Pearson), {}-month scale'.format(str(month_scale))
spi_pearson_dataset = netcdf_utils.initialize_dataset(output_file_base + variable_name_spi_pearson + '.nc',
precip_dataset,
x_dim_name,
y_dim_name,
variable_name_spi_pearson,
long_name,
variable_attributes,
np.nan)
variable_name_spi_gamma = 'spi_gamma_{}'.format(str(month_scale).zfill(2))
long_name = 'SPI (Gamma), {}-month scale'.format(str(month_scale))
spi_gamma_dataset = netcdf_utils.initialize_dataset(output_file_base + variable_name_spi_gamma + '.nc',
precip_dataset,
x_dim_name,
y_dim_name,
variable_name_spi_gamma,
long_name,
variable_attributes,
np.nan)
variable_name_spei_pearson = 'spei_pearson_{}'.format(str(month_scale).zfill(2))
long_name = 'SPEI (Pearson), {}-month scale'.format(str(month_scale))
