def test_versioning_with_disaggregate(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='zones',
table_data={
'id':array([1,2,3,4]),
'id2':array([1,2,1,2])
}
)
storage.write_table(table_name='faz',
table_data={
'my_variable':array([4,8]),
'id2':array([1,2])
}
)
ds = Dataset(in_storage=storage, in_table_name='zones', id_name="id", dataset_name="myzone")
ds2 = Dataset(in_storage=storage, in_table_name='faz', id_name="id2", dataset_name="myfaz")
dataset_pool = DatasetPool()
dataset_pool._add_dataset('myzone', ds)
dataset_pool._add_dataset('myfaz', ds2)
var = "my_var = myzone.disaggregate(10.0*myfaz.my_variable)"
ds.modify_attribute("id2", array([2,1,2,1])) # should have version 1
ds.compute_variables([var], dataset_pool=dataset_pool)
self.assert_(ds.get_version("my_var")==0)
ds.compute_variables([var], dataset_pool=dataset_pool)
self.assert_(ds.get_version("my_var")==0) # version should stay the same, i.e. it should not recompute
ds.touch_attribute("id2") # has version 2
ds.compute_variables([var], dataset_pool=dataset_pool)
self.assert_(ds.get_version("my_var")==1) # version should be 1, i.e. it should recompute when id changes
def test_versioning_with_aggregate(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='households',
table_data={
'my_variable': array([4, 8, 2, 1, 40, 23, 78]),
'id0': arange(7) + 1,
'id1': array([1, 3, 1, 2, 3, 2, 1])
})
storage.write_table(table_name='fazes',
table_data={
'id1': array([1, 2, 3]),
'id2': array([1, 2, 1])
})
storage.write_table(table_name='fazdistr',
table_data={'id2': array([1, 2])})
ds0 = Dataset(in_storage=storage,
in_table_name='households',
id_name="id0",
dataset_name="myhousehold")
ds1 = Dataset(in_storage=storage,
in_table_name='fazes',
id_name="id1",
dataset_name="myfaz")
ds2 = Dataset(in_storage=storage,
in_table_name='fazdistr',
id_name="id2",
dataset_name="myfazdistr")
dataset_pool = DatasetPool()
dataset_pool._add_dataset('myhousehold', ds0)
dataset_pool._add_dataset('myfaz', ds1)
dataset_pool._add_dataset('myfazdistr', ds2)
ds0.modify_attribute("id1", array([1, 3, 1, 2, 3, 2,
1])) # has version 1
variable = 'my_var = myfazdistr.aggregate(10.0*myhousehold.my_variable, intermediates=[myfaz])'
ds2.compute_variables([variable], dataset_pool=dataset_pool)
self.assert_(ds2.get_version("my_var") == 0)
ds2.compute_variables([variable], dataset_pool=dataset_pool)
self.assert_(
ds2.get_version("my_var") ==
0) # version should stay the same, i.e. it should not recompute
ds0.touch_attribute("id1") # has version 2
ds2.compute_variables([variable], dataset_pool=dataset_pool)
self.assert_(
ds2.get_version("my_var") ==
1) # version should be 1, i.e. it should recompute when id changes
ds1.touch_attribute("id2") # has version 1
ds2.compute_variables([variable], dataset_pool=dataset_pool)
self.assert_(
ds2.get_version("my_var") ==
2) # version should be 2, i.e. it should recompute when id changes
def test_versioning_with_aggregate(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='households',
table_data={
'my_variable':array([4,8,2,1,40,23,78]),
'id0':arange(7)+1,
'id1':array([1,3,1,2,3,2,1])
}
)
storage.write_table(table_name='fazes',
table_data={
'id1':array([1,2,3]),
'id2':array([1,2,1])
}
)
storage.write_table(table_name='fazdistr',
table_data={
'id2':array([1,2])
}
)
ds0 = Dataset(in_storage=storage, in_table_name='households', id_name="id0", dataset_name="myhousehold")
ds1 = Dataset(in_storage=storage, in_table_name='fazes', id_name="id1", dataset_name="myfaz")
ds2 = Dataset(in_storage=storage, in_table_name='fazdistr', id_name="id2", dataset_name="myfazdistr")
dataset_pool = DatasetPool()
dataset_pool._add_dataset('myhousehold', ds0)
dataset_pool._add_dataset('myfaz',ds1)
dataset_pool._add_dataset('myfazdistr',ds2)
ds0.modify_attribute("id1", array([1,3,1,2,3,2,1])) # has version 1
variable = 'my_var = myfazdistr.aggregate(10.0*myhousehold.my_variable, intermediates=[myfaz])'
ds2.compute_variables([variable], dataset_pool=dataset_pool)
self.assert_(ds2.get_version("my_var")==0)
ds2.compute_variables([variable], dataset_pool=dataset_pool)
self.assert_(ds2.get_version("my_var")==0) # version should stay the same, i.e. it should not recompute
ds0.touch_attribute("id1") # has version 2
ds2.compute_variables([variable], dataset_pool=dataset_pool)
self.assert_(ds2.get_version("my_var")==1) # version should be 1, i.e. it should recompute when id changes
ds1.touch_attribute("id2") # has version 1
ds2.compute_variables([variable], dataset_pool=dataset_pool)
self.assert_(ds2.get_version("my_var")==2) # version should be 2, i.e. it should recompute when id changes
def test_versioning_with_disaggregate(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='zones',
table_data={
'id': array([1, 2, 3, 4]),
'id2': array([1, 2, 1, 2])
})
storage.write_table(table_name='faz',
table_data={
'my_variable': array([4, 8]),
'id2': array([1, 2])
})
ds = Dataset(in_storage=storage,
in_table_name='zones',
id_name="id",
dataset_name="myzone")
ds2 = Dataset(in_storage=storage,
in_table_name='faz',
id_name="id2",
dataset_name="myfaz")
dataset_pool = DatasetPool()
dataset_pool._add_dataset('myzone', ds)
dataset_pool._add_dataset('myfaz', ds2)
var = "my_var = myzone.disaggregate(10.0*myfaz.my_variable)"
ds.modify_attribute("id2", array([2, 1, 2,
1])) # should have version 1
ds.compute_variables([var], dataset_pool=dataset_pool)
self.assert_(ds.get_version("my_var") == 0)
ds.compute_variables([var], dataset_pool=dataset_pool)
self.assert_(
ds.get_version("my_var") ==
0) # version should stay the same, i.e. it should not recompute
ds.touch_attribute("id2") # has version 2
ds.compute_variables([var], dataset_pool=dataset_pool)
self.assert_(
ds.get_version("my_var") ==
1) # version should be 1, i.e. it should recompute when id changes
def test_variable_dependencies_tree_with_versioning(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(
table_name='tests',
table_data={
'id':array([2,4]),
'a_dependent_variable':array([4,7]),
'a_dependent_variable2':array([10,1])
}
)
ds = Dataset(in_storage=storage, in_table_name='tests', id_name='id', dataset_name='tests')
ds.compute_variables(["opus_core.tests.a_test_variable_with_two_dependencies"])
self.assert_(ds.get_version("a_test_variable_with_two_dependencies")==0) #initially version=0
self.assert_(ds.get_version("a_dependent_variable")==0)
self.assert_(ds.get_version("a_dependent_variable2")==0)
ds.modify_attribute("a_dependent_variable", array([0,0]))
self.assert_(ds.get_version("a_dependent_variable")==1) # version=1
ds.modify_attribute("a_dependent_variable", array([1,1]))
self.assert_(ds.get_version("a_dependent_variable")==2) # version=2
ds.compute_variables(["opus_core.tests.a_test_variable_with_two_dependencies"])
self.assert_(ds.get_version("a_test_variable_with_two_dependencies")==1)
ds.compute_variables(["opus_core.tests.a_test_variable_with_two_dependencies"])
self.assert_(ds.get_version("a_test_variable_with_two_dependencies")==1) # version does not change
autogen_variable = "my_var = 3 * opus_core.tests.a_dependent_variable"
ds.compute_variables([autogen_variable])
self.assert_(ds.get_version("my_var")==0)
ds.compute_variables([autogen_variable])
self.assert_(ds.get_version("my_var")==0)
interactions.get_dataset_name()
from numpy import arange
interactions = InteractionDataset(dataset1 = households, dataset2 = locations, index1 = arange(5), index2 = arange(3))
interactions.compute_variables(["urbansim.household_x_gridcell.cost_times_income"])
specification = EquationSpecification(
variables=array([
"gridcell.cost",
"urbansim.household_x_gridcell.cost_times_income"]),
coefficients=array(["costcoef", "cti_coef"]))
households.add_primary_attribute(data=[2,8,3,1,5,4,9,7,3,6], name="location")
coef, other_results = hlcm.estimate(specification, households)
# Versioning
households.get_version("income")
res = interactions.compute_variables(["urbansim.household_x_gridcell.cost_times_income"])
interactions.get_version("cost_times_income")
households.modify_attribute(name="income", data=[14000], index=[9])
households.get_version("income")
res = interactions.compute_variables(["urbansim.household_x_gridcell.cost_times_income"])
interactions.get_version("cost_times_income")
# Using numbers
res = locations.compute_variables(
map(lambda threshold:
"urbansim.gridcell.is_near_cbd_if_threshold_is_%s" % threshold, [2,4,7]))
locations.get_attribute("is_near_cbd_if_threshold_is_2")
locations.get_attribute("is_near_cbd_if_threshold_is_4")
locations.get_attribute("is_near_cbd_if_threshold_is_7")
interactions.get_dataset_name()
from numpy import arange
interactions = InteractionDataset(dataset1 = households, dataset2 = locations, index1 = arange(5), index2 = arange(3))
interactions.compute_variables(["urbansim.household_x_gridcell.cost_times_income"])
specification = EquationSpecification(
variables=array([
"gridcell.cost",
"urbansim.household_x_gridcell.cost_times_income"]),
coefficients=array(["costcoef", "cti_coef"]))
households.add_primary_attribute(data=[2,8,3,1,5,4,9,7,3,6], name="location")
coef, other_results = hlcm.estimate(specification, households)
# Versioning
households.get_version("income")
res = interactions.compute_variables(["urbansim.household_x_gridcell.cost_times_income"])
interactions.get_version("cost_times_income")
households.modify_attribute(name="income", data=[14000], index=[9])
households.get_version("income")
res = interactions.compute_variables(["urbansim.household_x_gridcell.cost_times_income"])
interactions.get_version("cost_times_income")
# Using numbers
res = locations.compute_variables(
map(lambda threshold:
"urbansim.gridcell.is_near_cbd_if_threshold_is_%s" % threshold, [2,4,7]))
locations.get_attribute("is_near_cbd_if_threshold_is_2")
locations.get_attribute("is_near_cbd_if_threshold_is_4")
locations.get_attribute("is_near_cbd_if_threshold_is_7")
