def age(self, dt):
col = "LC4408_C_AHTHUK11"
for area in self.areas:
actual = len(self.pop[area])
projected = int(self.projection.loc[
self.projection["PROJECTED_YEAR_NAME"] == int(no.time),
"OBS_VALUE"].values[0])
#no.log(self.cat[col])
no.transition(self.cat[col], self.t, self.pop[area],
"LC4408_C_AHTHUK11")
if actual < projected:
no.log("sampling deficit %d households (vs projection)" %
(projected - actual))
deficit = int(projected) - actual
newbuilds = self.pop[area].sample(deficit)
self.pop = self.pop[area].append(newbuilds, ignore_index=True)
def test(self):
# generate some movement
neworder.transition(self.s, self.p, self.pop, "state")
# send migrants
for s in range(neworder.size()):
if s != neworder.rank():
emigrants = self.pop[self.pop.state == s]
#neworder.log("sending %d emigrants to %d" % (len(emigrants), s))
neworder.send(emigrants, s)
# remove the emigrants
self.pop = self.pop[self.pop.state == neworder.rank()]
# receive migrants
for s in range(neworder.size()):
if s != neworder.rank():
immigrants = neworder.receive(s)
#neworder.log("received %d immigrants from %d" % (len(immigrants), s))
self.pop = self.pop.append(immigrants)
def age(self, dt):
col = "LC4408_C_AHTHUK11"
no.transition(self.cat[col], self.t, self.pop, "LC4408_C_AHTHUK11")
# ensure area totals match projections
for lad in self.pop["LAD"].unique():
lad_pop = self.pop[self.pop["LAD"] == lad]
actual = len(lad_pop)
# TODO LAD
projected = self.projection.loc[
(self.projection["PROJECTED_YEAR_NAME"] == int(no.time)) &
(self.projection["GEOGRAPHY_CODE"] == lad), "OBS_VALUE"]
if len(projected) == 0:
no.log(
"WARNING %s cannot find household projection data for %d",
(lad, no.time))
projected = int(projected.values[0])
if actual < projected:
no.log("sampling deficit %d households (vs projection)" %
(projected - actual))
deficit = int(projected) - actual
self.pop = self.pop.append(lad_pop.sample(deficit),
ignore_index=True)
def test():
t = test_.Test()
df = pd.read_csv("../../tests/df.csv")
cats = np.array(range(4))
# identity matrix means no transitions
trans = np.identity(len(cats))
no.transition(cats, trans, df, "DC2101EW_C_ETHPUK11")
t.check(
len(df["DC2101EW_C_ETHPUK11"].unique()) == 1
and df["DC2101EW_C_ETHPUK11"].unique()[0] == 2)
# NOTE transition matrix interpreted as being COLUMN MAJOR due to pandas DataFrame storing data in column-major order
# force 2->3
trans[2, 2] = 0.0
trans[3, 2] = 1.0
no.transition(cats, trans, df, "DC2101EW_C_ETHPUK11")
t.check(
len(df["DC2101EW_C_ETHPUK11"].unique()) == 1
and df["DC2101EW_C_ETHPUK11"].unique()[0] == 3)
# ~half of 3->0
trans[0, 3] = 0.5
trans[3, 3] = 0.5
no.transition(cats, trans, df, "DC2101EW_C_ETHPUK11")
t.check(
np.array_equal(np.sort(df["DC2101EW_C_ETHPUK11"].unique()),
np.array([0, 3])))
return not t.any_failed
# def todo():
# # define some global variables describing where the starting population and the parameters of the dynamics come from
# initial_population = "examples/households/data/ssm_hh_E09000001_OA11_2011.csv"
# hh = pd.read_csv(initial_population)
# print(hh.columns.values)
# c = hh.LC4408_C_AHTHUK11.unique()
# print(c)
# t = np.identity(len(c))
# # [ 3 5 1 2 -1 4]
# t = np.array([[0.9, 0.05, 0.05, 0.0, 0.0, 0.0],
# [0.05, 0.9, 0.04, 0.01, 0.0, 0.0],
# [0.0, 0.05, 0.9, 0.05, 0.0, 0.0],
# [0.0, 0.0, 0.05, 0.9, 0.05, 0.0],
# [0.1, 0.1, 0.1, 0.1, 0.5, 0.1],
# [0.0, 0.0, 0.00, 0.0, 0.2, 0.8]])
# #print(t[1]) # horz
# #print(t[:,1]) # vert
# tc = np.cumsum(t, axis=1)
# # TODO timing...
# u = np.random.sample(len(hh))
# for i in range(len(hh)):
# current = hh.loc[i, "LC4408_C_AHTHUK11"]
# hh.loc[i, "LC4408_C_AHTHUK11"] = sample(u[i], tc[current], c)
# print(hh.LC4408_C_AHTHUK11.head())
# tc = np.cumsum(t, axis=1)
# print(np.cumsum(t[1]))
# #print()
def test():
t = test_.Test()
x = -1e10
t.check(no.distant_past() < x)
t.check(no.far_future() > x)
x = 1e10
t.check(no.distant_past() < x)
t.check(no.far_future() > x)
# dreams never end
t.check(no.never() != no.never())
t.check(not no.never() == x)
t.check(no.never() != x)
t.check(not x < no.never())
t.check(not x >= no.never())
# no nay never:
t.check(not no.isnever(x))
# no nay never no more:
t.check(no.isnever(no.never()))
#t.check(False)
s = no.ustream(10000)
t.check(isinstance(s, np.ndarray))
t.check(len(s) == 10000)
t.check(abs(np.mean(s) - 0.5) < 0.02)
f = no.lazy_eval("2 + 2")
t.check(f() == 4)
# # TODO this overlaps/duplicates tests in op.py - reorganise
# # test thinning algorithm for non-homogeneous Poisson process
# h = np.array([0.014] * 10)
# #l = no.stopping(h)
# l = no.first_arrival(h, 1.0, 10000)
# t.check(abs(np.mean(l) * 0.014 - 1.0) < 0.03)
# # varying timestep should make no difference
# l = no.first_arrival(h, 0.1, 10000)
# t.check(abs(np.mean(l) * 0.014 - 1.0) < 0.03)
# # test a certain(ish) hazard rate
# h = np.array([0.99, 0.99, 0.01])
# l = no.first_arrival(h, 1.0, 10000)
# no.log("TODO NHPP appears broken: %f" % np.mean(l))
# # test a zero(ish) hazard rate
# h = np.array([1e-30, 1e-30, 1e-30, .9999])
# l = no.first_arrival(h, 1.0, 10000)
# no.log("TODO NHPP appears broken: %f" % np.mean(l))
# # this also tests a zero hazard rate
# h = np.array([i/3000 for i in range(100)])
# #no.log(h)
# le = no.first_arrival(h, 1.0, 10000)
# no.log(sum(le)/len(le))
# # y
# h = np.array([0.999, 0.1])
# le = no.first_arrival(h, 1.0, 1000)
# no.log(sum(le)/len(le))
sometime = no.isnever(np.full(10, 1.0))
t.check(np.all(~sometime))
never = no.isnever(np.full(10, no.never()))
no.log(never)
t.check(np.all(never))
# # DataFrame ops
# modify df passing column
df = pd.read_csv("../../tests/df.csv")
# modify df passing directly
no.directmod(df, "DC2101EW_C_ETHPUK11")
t.check(
np.array_equal(df["DC2101EW_C_ETHPUK11"].values,
np.zeros(len(df)) + 3))
df = pd.read_csv("../../tests/df.csv")
cats = np.array(range(4))
transitions = np.identity(len(cats)) * 0 + 0.25
#no.log(transitions)
no.transition(cats, transitions, df, "DC2101EW_C_ETHPUK11")
# it's possible this could fail depending on random draw
t.check(
np.array_equal(np.sort(df["DC2101EW_C_ETHPUK11"].unique()),
np.array(range(4))))
# df2 = df.copy()
# df3 = no.append(df,df2)
# t.check(len(df3) == len(df) + len(df2))
return not t.any_failed