def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
job_building_types_table_name = 'job_building_types'
storage.write_table(
table_name=job_building_types_table_name,
table_data={
'id':array([1,2,3,4]),
'home_based': array([1, 0, 1, 0])
}
)
jobs_table_name = 'jobs'
storage.write_table(
table_name=jobs_table_name,
table_data={
'job_id':arange(10)+1,
'building_type': array([3,3,2,2,4,2,1,3,4,1])
}
)
job_building_types = JobBuildingTypeDataset(in_storage=storage, in_table_name=job_building_types_table_name)
jobs = JobDataset(in_storage=storage, in_table_name=jobs_table_name)
jobs.compute_variables(self.variable_name, resources=Resources({'job_building_type': job_building_types}))
values = jobs.get_attribute(self.variable_name)
should_be = array([0,0,1,1,1,1,0,0,1,0])
self.assert_(ma.allequal(values, should_be),
'Error in ' + self.variable_name)
def run_model2():
storage = StorageFactory().get_storage('dict_storage')
jobs_set_table_name = 'jobs_set'
storage.write_table(
table_name = jobs_set_table_name,
table_data = self.jobs_data,
)
jobs_set = JobDataset(in_storage=storage, in_table_name=jobs_set_table_name)
model = RegionalEmploymentTransitionModel()
model.run(year=2000, job_set=jobs_set, control_totals=ect_set, job_building_types=self.job_building_types)
# check that the distribution of building type is the same before and after subtracting jobs
jobs_set.compute_variables(["urbansim.job.is_in_employment_sector_1_industrial",
"urbansim.job.is_in_employment_sector_2_industrial",
"urbansim.job.is_in_employment_sector_1_commercial",
"urbansim.job.is_in_employment_sector_2_commercial",
"urbansim.job.is_in_employment_sector_1_governmental",
"urbansim.job.is_in_employment_sector_2_governmental"],
resources = Resources({"job_building_type":self.job_building_types}))
result = array([jobs_set.get_attribute("is_in_employment_sector_1_industrial").sum(),
jobs_set.get_attribute("is_in_employment_sector_2_industrial").sum(),
jobs_set.get_attribute("is_in_employment_sector_1_commercial").sum(),
jobs_set.get_attribute("is_in_employment_sector_2_commercial").sum(),
jobs_set.get_attribute("is_in_employment_sector_1_governmental").sum(),
jobs_set.get_attribute("is_in_employment_sector_2_governmental").sum()
])
return result
def test_my_inputs(self):
storage = StorageFactory().get_storage("dict_storage")
job_building_types_table_name = "job_building_types"
storage.write_table(
table_name=job_building_types_table_name,
table_data={"id": array([1, 2, 3, 4]), "home_based": array([0, 1, 0, 1])},
)
jobs_table_name = "jobs"
storage.write_table(
table_name=jobs_table_name,
table_data={"job_id": arange(10) + 1, "building_type": array([3, 3, 2, 2, 4, 2, 1, 3, 4, 1])},
)
job_building_types = JobBuildingTypeDataset(in_storage=storage, in_table_name=job_building_types_table_name)
jobs = JobDataset(in_storage=storage, in_table_name=jobs_table_name)
jobs.compute_variables(self.variable_name, resources=Resources({"job_building_type": job_building_types}))
values = jobs.get_attribute(self.variable_name)
should_be = array([0, 0, 1, 1, 1, 1, 0, 0, 1, 0], dtype="bool8")
self.assert_(ma.allequal(values, should_be), "Error in " + self.variable_name)
def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
job_building_types_table_name = 'job_building_types'
storage.write_table(table_name=job_building_types_table_name,
table_data={
'id': array([1, 2, 3, 4]),
'home_based': array([1, 0, 1, 0])
})
jobs_table_name = 'jobs'
storage.write_table(table_name=jobs_table_name,
table_data={
'job_id':
arange(10) + 1,
'building_type':
array([3, 3, 2, 2, 4, 2, 1, 3, 4, 1])
})
job_building_types = JobBuildingTypeDataset(
in_storage=storage, in_table_name=job_building_types_table_name)
jobs = JobDataset(in_storage=storage, in_table_name=jobs_table_name)
jobs.compute_variables(self.variable_name,
resources=Resources(
{'job_building_type': job_building_types}))
values = jobs.get_attribute(self.variable_name)
should_be = array([0, 0, 1, 1, 1, 1, 0, 0, 1, 0])
self.assert_(ma.allequal(values, should_be),
'Error in ' + self.variable_name)
def run_model2():
storage = StorageFactory().get_storage('dict_storage')
jobs_set_table_name = 'jobs_set'
storage.write_table(
table_name=jobs_set_table_name,
table_data=self.jobs_data,
)
jobs_set = JobDataset(in_storage=storage,
in_table_name=jobs_set_table_name)
model = RegionalEmploymentTransitionModel()
model.run(year=2000,
job_set=jobs_set,
control_totals=ect_set,
job_building_types=self.job_building_types)
# check that the distribution of building type is the same before and after subtracting jobs
jobs_set.compute_variables([
"urbansim.job.is_in_employment_sector_1_industrial",
"urbansim.job.is_in_employment_sector_2_industrial",
"urbansim.job.is_in_employment_sector_1_commercial",
"urbansim.job.is_in_employment_sector_2_commercial",
"urbansim.job.is_in_employment_sector_1_governmental",
"urbansim.job.is_in_employment_sector_2_governmental"
],
resources=Resources({
"job_building_type":
self.job_building_types
}))
result = array([
jobs_set.get_attribute(
"is_in_employment_sector_1_industrial").sum(),
jobs_set.get_attribute(
"is_in_employment_sector_2_industrial").sum(),
jobs_set.get_attribute(
"is_in_employment_sector_1_commercial").sum(),
jobs_set.get_attribute(
"is_in_employment_sector_2_commercial").sum(),
jobs_set.get_attribute(
"is_in_employment_sector_1_governmental").sum(),
jobs_set.get_attribute(
"is_in_employment_sector_2_governmental").sum()
])
return result
def test_erm_correct_distribution_of_jobs_relocate(self):
# In addition to unplaced jobs choose 50% of jobs of sector 2 to relocate and
# no job of sector 1.
# gridcell has expected
# 1 100 sector 1 jobs 100 sector 1 jobs
# 400 sector 2 jobs about 200 sector 2 jobs
# 2 100 sector 1 jobs 100 sector 1 jobs
# 200 sector 2 jobs about 100 sector 2 jobs
# 3 100 sector 1 jobs 100 sector 1 jobs
# 100 sector 2 jobs about 50 sector 2 jobs
# unplaced 10 sector 1 jobs
# 10 sector 2 jobs
storage = StorageFactory().get_storage("dict_storage")
# create jobs
job_grid_ids = array(100 * [1] + 100 * [2] + 100 * [3] + 400 * [1] + 200 * [2] + 100 * [3] + 20 * [-1])
storage.write_table(
table_name="jobs",
table_data={
"job_id": arange(1020) + 1,
"sector_id": array(300 * [1] + 700 * [2] + 10 * [1] + 10 * [2]),
"grid_id": job_grid_ids,
},
)
jobs = JobDataset(in_storage=storage, in_table_name="jobs")
# create gridcells
storage.write_table(table_name="gridcells", table_data={"grid_id": arange(3) + 1})
gridcells = GridcellDataset(in_storage=storage, in_table_name="gridcells")
# create rate set with rate 0 for jobs of sector 1 and 0.5 for jobs of sector 2
storage.write_table(
table_name="rates", table_data={"sector_id": array([1, 2]), "job_relocation_probability": array([0, 0.5])}
)
rates = JobRelocationRateDataset(in_storage=storage, in_table_name="rates")
# run model
model = EmploymentRelocationModelCreator().get_model(debuglevel=0)
hrm_resources = Resources({"annual_job_relocation_rate": rates})
# get results from one run
movers_indices = model.run(jobs, resources=hrm_resources)
jobs.compute_variables(["urbansim.job.is_in_employment_sector_1"])
# unplace chosen jobs
compute_resources = Resources({"job": jobs, "urbansim_constant": {"industrial_code": 1, "commercial_code": 2}})
jobs.set_values_of_one_attribute(attribute="grid_id", values=-1, index=movers_indices)
gridcells.compute_variables(
["urbansim.gridcell.number_of_jobs_of_sector_1", "urbansim.gridcell.number_of_jobs_of_sector_2"],
resources=compute_resources,
)
# only 100 jobs of sector 1 (unplaced jobs) should be selected
result1 = jobs.get_attribute_by_index("is_in_employment_sector_1", movers_indices).astype(int8).sum()
self.assertEqual(result1 == 10, True)
# number of sector 1 jobs should not change
result2 = gridcells.get_attribute("number_of_jobs_of_sector_1")
self.assertEqual(ma.allclose(result2, array([100, 100, 100]), rtol=0), True)
def run_model():
jobs.modify_attribute(name="grid_id", data=job_grid_ids)
indices = model.run(jobs, resources=hrm_resources)
jobs.modify_attribute(name="grid_id", data=-1, index=indices)
gridcells.compute_variables(["urbansim.gridcell.number_of_jobs_of_sector_2"], resources=compute_resources)
return gridcells.get_attribute("number_of_jobs_of_sector_2")
# distribution of sector 2 jobs should be the same, the mean are halfs of the original values
should_be = array([200, 100, 50])
self.run_stochastic_test(__file__, run_model, should_be, 10)
