def __init__(self, parameter_file, **kwargs):
"""
Initializes a ModelSetup instance from a parameter file
Parameters
----------
parameter_file : str
File which stores model parameters. This is either an XML or INI
file. As of now, only the XML logic has been implemented.
Returns
-------
None
"""
self.parameter_parser = ppf.get_parameter_parser(parameter_file)
# Ensure that the parameter parser has been fully fleshed out before
# retrieving the modeling files. If the parameter_parser references
# a prototype file, a new instance of a parameter_parser is
# returned that represents the fully fleshed out version
self.parameter_parser = self.parameter_parser.get_parameters(**kwargs)
p = self.parameter_parser
# Write out the model file
p.serialize()
# Create a ModelDatabase instance
if p.parameter_set == 'FULL':
self.plot_db = \
plot_database.PlotDatabase(p.model_type, p.model_region,
p.buffer, p.model_year, p.summary_level,
p.image_source, p.image_version, dsn=p.plot_dsn)
def __init__(self, parameter_file, **kwargs):
"""
Initializes a ModelSetup instance from a parameter file
Parameters
----------
parameter_file : str
File which stores model parameters. This is either an XML or INI
file. As of now, only the XML logic has been implemented.
Returns
-------
None
"""
self.parameter_parser = ppf.get_parameter_parser(parameter_file)
# Ensure that the parameter parser has been fully fleshed out before
# retrieving the modeling files. If the parameter_parser references
# a prototype file, a new instance of a parameter_parser is
# returned that represents the fully fleshed out version
self.parameter_parser = self.parameter_parser.get_parameters(**kwargs)
p = self.parameter_parser
# Write out the model file
p.serialize()
# Create a ModelDatabase instance
if p.parameter_set == 'FULL':
self.plot_db = \
plot_database.PlotDatabase(p.model_type, p.model_region,
p.buffer, p.model_year, p.summary_level,
p.image_source, p.image_version, dsn=p.plot_dsn)
def setUp(self):
parameter_file = "D:/model_root/mr224/fc_kernel_mean_gb_5/model.xml"
parameter_parser = ppf.get_parameter_parser(parameter_file)
self.diagnostic_wrapper = dw.DiagnosticWrapper(parameter_parser)
def main():
#sppsz_proto_name = 'L:/orcawa/cmonster/models/v_1_0_tc_only_k1.xml'
#model_region = 221
# first argument is the parameter file and is required
model_xml = sys.argv[1]
parser = OptionParser()
parser.add_option('-r', dest='model_regions',
help='List or range of GNN model regions for which to setup models')
parser.add_option('-d',dest='root_directory',
help='Location of root directory for storing model results')
parser.add_option('-y', dest='years',
help='Range of model years to run, or comma-delimited list of ' + \
'years to run')
parser.add_option('-i', dest='image_version',
help='Imagery version')
parser.add_option('-s', dest='model_specs',
help='Model specifications used for directory naming; eg. tc_only_k1')
parser.add_option('-m', dest='run_mode',
help='Run model: spatial (full spatial run) or point (plot' + \
'locations only)')
# mode_message = 'program mode: '
# mode_message += '1 = create species & env matrices and run vegan CCA,'
# mode_message += '2 = create species & env matrices only,'
# mode_message += '3 = create species plot counts files only'
# parser.add_option('-m',dest='run mode', default=1,
# help=mode_message)
options = (parser.parse_args())[0]
# if optional arguments were supplied, loop through all years and
# model regions specified
if options.model_regions:
#if regions are specified as a range, unpack into a list
if options.model_regions.find('-') > 0:
region_list = []
regions_split = options.model_regions.split('-')
start_region = int(regions_split[0])
end_region = int(regions_split[1])
for r in range(start_region, end_region + 1):
region_list.append(r)
else:
#if regions are specified in a comma-delimited string,
#add each region to a list
region_list = options.model_regions.split(',')
#convert to ints
region_list = map(int, region_list)
#loop through all model regions
for region in region_list:
if options.years:
#if regions are specified as a range, unpack into a list
if options.years.find('-') > 0:
year_list = []
years_split = options.years.split('-')
start_year = int(years_split[0])
end_year = int(years_split[1])
for y in range(start_year, end_year + 1):
year_list.append(y)
else:
#if years are specified in a comma-delimited string,
#add each year to a list
year_list = options.years.split(',')
#convert to ints
year_list = map(int, year_list)
# We need to parse the model_xml file here because we need
# to know the project associated with this run
p = ppf.get_parameter_parser(model_xml)
root_dir = 'L:/orcawa/%s/models/mr%d/%s/%s' % (
p.model_project.lower(), region, options.image_version,
options.model_specs)
# if run_mode option is not specified, default to spatial
# mode and do not modify root directory
if options.run_mode:
if options.run_mode == 'point':
# if run_mode is specified as 'point', add another
# subdirectory named point
root_dir = '/'.join([root_dir, 'point'])
#loop through all model
for year in year_list:
#create new folders, rename existing folder to _old
#and delete existing _old folders if necessary
model_dir = '/'.join([root_dir, str(year)])
if os.path.exists(model_dir):
#if the directory already exists see if there
#is already an _old directory
old_dir = model_dir + '_old'
if os.path.exists(old_dir):
#delete the existing old directory
shutil.rmtree(old_dir)
#rename existing model_dir to old_dir
os.rename(model_dir, old_dir)
#create new empty model directory
os.makedirs(model_dir)
#create modeling files
model = ModelSetup(
model_xml,
model_directory=model_dir,
model_region=region,
model_year=year
)
#model.create_species_plot_count_file()
model.create_modeling_files(
model_directory=model_dir,
model_region=region,
model_year=year,
create_ordination_matrices=True,
run_ordination=True,
create_attribute_data=True,
create_area_estimates=True,
create_report_metadata=True,
create_hex_attribute_file=True,
create_validation_attribute_file=False
)
def main():
#sppsz_proto_name = 'L:/orcawa/cmonster/models/v_1_0_tc_only_k1.xml'
#model_region = 221
# first argument is the parameter file and is required
model_xml = sys.argv[1]
parser = OptionParser()
parser.add_option(
'-r',
dest='model_regions',
help='List or range of GNN model regions for which to setup models')
parser.add_option(
'-d',
dest='root_directory',
help='Location of root directory for storing model results')
parser.add_option('-y', dest='years',
help='Range of model years to run, or comma-delimited list of ' + \
'years to run')
parser.add_option('-i', dest='image_version', help='Imagery version')
parser.add_option(
'-s',
dest='model_specs',
help='Model specifications used for directory naming; eg. tc_only_k1')
parser.add_option('-m', dest='run_mode',
help='Run model: spatial (full spatial run) or point (plot' + \
'locations only)')
# mode_message = 'program mode: '
# mode_message += '1 = create species & env matrices and run vegan CCA,'
# mode_message += '2 = create species & env matrices only,'
# mode_message += '3 = create species plot counts files only'
# parser.add_option('-m',dest='run mode', default=1,
# help=mode_message)
options = (parser.parse_args())[0]
# if optional arguments were supplied, loop through all years and
# model regions specified
if options.model_regions:
#if regions are specified as a range, unpack into a list
if options.model_regions.find('-') > 0:
region_list = []
regions_split = options.model_regions.split('-')
start_region = int(regions_split[0])
end_region = int(regions_split[1])
for r in range(start_region, end_region + 1):
region_list.append(r)
else:
#if regions are specified in a comma-delimited string,
#add each region to a list
region_list = options.model_regions.split(',')
#convert to ints
region_list = map(int, region_list)
#loop through all model regions
for region in region_list:
if options.years:
#if regions are specified as a range, unpack into a list
if options.years.find('-') > 0:
year_list = []
years_split = options.years.split('-')
start_year = int(years_split[0])
end_year = int(years_split[1])
for y in range(start_year, end_year + 1):
year_list.append(y)
else:
#if years are specified in a comma-delimited string,
#add each year to a list
year_list = options.years.split(',')
#convert to ints
year_list = map(int, year_list)
# We need to parse the model_xml file here because we need
# to know the project associated with this run
p = ppf.get_parameter_parser(model_xml)
root_dir = 'L:/orcawa/%s/models/mr%d/%s/%s' % (
p.model_project.lower(), region, options.image_version,
options.model_specs)
# if run_mode option is not specified, default to spatial
# mode and do not modify root directory
if options.run_mode:
if options.run_mode == 'point':
# if run_mode is specified as 'point', add another
# subdirectory named point
root_dir = '/'.join([root_dir, 'point'])
#loop through all model
for year in year_list:
#create new folders, rename existing folder to _old
#and delete existing _old folders if necessary
model_dir = '/'.join([root_dir, str(year)])
if os.path.exists(model_dir):
#if the directory already exists see if there
#is already an _old directory
old_dir = model_dir + '_old'
if os.path.exists(old_dir):
#delete the existing old directory
shutil.rmtree(old_dir)
#rename existing model_dir to old_dir
os.rename(model_dir, old_dir)
#create new empty model directory
os.makedirs(model_dir)
#create modeling files
model = ModelSetup(model_xml,
model_directory=model_dir,
model_region=region,
model_year=year)
#model.create_species_plot_count_file()
model.create_modeling_files(
model_directory=model_dir,
model_region=region,
model_year=year,
create_ordination_matrices=True,
run_ordination=True,
create_attribute_data=True,
create_area_estimates=True,
create_report_metadata=True,
create_hex_attribute_file=True,
create_validation_attribute_file=False)
def __init__(self, parameter_file):
self.parser = ppf.get_parameter_parser(parameter_file)
self.parameter_file = parameter_file
def __init__(self, parameter_file):
self.parser = ppf.get_parameter_parser(parameter_file)
self.parameter_file = parameter_file
def setUp(self):
parameter_file = 'D:/model_root/mr224/fc_kernel_mean_gb_5/model.xml'
parameter_parser = ppf.get_parameter_parser(parameter_file)
self.diagnostic_wrapper = dw.DiagnosticWrapper(parameter_parser)
