def start(config_list=None, config_file=None, cont_in=None):
"""
Run the HazImp tool, based on the config info.
:param config_list: The configuration info, as a list.
:param config_file: The configuration info, as a file location.
:param cont_in: Only used in testing. A context instance.
:returns: The config dictionary.
"""
if config_file:
config_list = config.read_config_file(config_file)
if isinstance(config_list, dict):
msg = "Bad configuration file. \n"
msg += "Add a dash ( - ) before each variable. e.g. - template: flood"
raise RuntimeError(msg)
if config_list is None:
raise RuntimeError('No configuration information.')
if cont_in is None:
cont_in = context.Context()
calc_jobs = config.instance_builder(config_list)
the_pipeline = pipeline.PipeLine(calc_jobs)
the_pipeline.run(cont_in)
config_dict = {k: v for item in config_list for k, v in list(item.items())}
agg = config_dict.get('aggregate')
if agg:
from . import aggregate
aggregate.chloropleth(config_dict['save'], agg['boundaries'],
agg['save'])
return cont_in
def start(config_list=None, config_file=None, cont_in=None):
"""
Run the HazImp tool, based on the config info.
:param config_list: The configuration info, as a list.
:param config_file: The configuration info, as a file location.
:param cont_in: Only used in testing. A context instance.
:returns: The config dictionary.
"""
if config_file:
config_file = misc.download_file_from_s3_if_needed(config_file)
config_list = config.read_config_file(config_file)
if isinstance(config_list, dict):
msg = "Bad configuration file. \n"
msg += "Add a dash ( - ) before each variable. e.g. - template: flood"
raise RuntimeError(msg)
if config_list is None:
raise RuntimeError('No configuration information.')
if cont_in is None:
cont_in = context.Context()
# TODO: Make the entity name a tailored variable
cont_in.set_prov_label("HazImp_analysis")
calc_jobs = config.instance_builder(config_list)
the_pipeline = pipeline.PipeLine(calc_jobs)
the_pipeline.run(cont_in)
return cont_in
def test_Builder(self):
a_test = 5
b_test = 2
calc_list = [CALCS['add_test'], CALCS['multiply_test']]
cont_in = context.Context()
cont_in.exposure_att = {'a_test': a_test, 'b_test': b_test}
the_pipeline = pipeline.PipeLine(calc_list)
the_pipeline.run(cont_in)
self.assertEqual(cont_in.exposure_att['d_test'], 35)
def test_BuilderII(self):
a_test = 5
b_test = 2
caj = workflow.ConfigAwareJob(CALCS['constant_test'],
atts_to_add={'constant': 5})
calc_list = [CALCS['add_test'], CALCS['multiply_test'], caj]
cont_in = context.Context()
cont_in.exposure_att = {'a_test': a_test, 'b_test': b_test}
the_pipeline = pipeline.PipeLine(calc_list)
the_pipeline.run(cont_in)
self.assertEqual(cont_in.exposure_att['d_test'], 35)
self.assertEqual(cont_in.exposure_att['g_test'], 10)
def test_PipeLine_actually(self):
# Write a file to test
f = tempfile.NamedTemporaryFile(mode='w+t',
suffix='.csv',
prefix='test_Job_title_fix_Co',
delete=False)
f.write('LAT, LONG, a_test, b_test,BUILDING\n')
f.write('1., 2., 3., 30.,TAB\n')
f.write('4., 5., 6., 60.,DSG\n')
f.close()
f2 = tempfile.NamedTemporaryFile(suffix='.csv',
prefix='test_Job_title_fix_Co',
delete=False)
f2.close()
atts = {
'file_name': f.name,
context.EX_LAT: 'LAT',
context.EX_LONG: 'LONG'
}
caj1 = workflow.ConfigAwareJob(JOBS[LOADCSVEXPOSURE], atts_to_add=atts)
atts = {'var': 'con_test', 'value': 'yeah'}
caj2 = workflow.ConfigAwareJob(JOBS[CONSTANT], atts_to_add=atts)
atts = {'var': 'con2_test', 'value': 30}
caj3 = workflow.ConfigAwareJob(JOBS[CONSTANT], atts_to_add=atts)
calc_list = [caj1, caj2, caj3, CALCS['add_test']]
cont_in = context.Context()
cont_in.set_prov_label('Test label')
the_pipeline = pipeline.PipeLine(calc_list)
the_pipeline.run(cont_in)
cont_dict = cont_in.save_exposure_atts(f2.name)
os.remove(f2.name)
if parallel.STATE.rank == 0:
self.assertTrue(allclose(cont_dict['c_test'], asarray([33., 66.])))
self.assertEqual(cont_dict['BUILDING'].tolist(), ['TAB', 'DSG'])
self.assertTrue(
allclose(cont_dict['con2_test'], asarray([30., 30.])))
self.assertEqual(cont_dict['con_test'].tolist(), ['yeah', 'yeah'])
os.remove(f.name)
