def __init__(self, p, n):
# states
self.s = np.array(range(neworder.size()))
# transition matrix
self.p = np.identity(neworder.size()) * (1 - neworder.size() * p) + p
self.n = n
# all begin with unique id and state = rank
self.pop = pd.DataFrame({
"id":
np.array(range(neworder.rank() * n,
neworder.rank() * n + n)),
"state":
np.full(n, neworder.rank())
})
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 test():
t = test_.Test()
if neworder.size() == 1:
neworder.log("Skipping MPI tests")
return True
# test ustream/sequence
t.check(not neworder.indep())
u = neworder.ustream(1000)
v = neworder.broadcast(u, 0)
# u == v for all processes
t.check(np.array_equal(u, v))
return not t.any_failed
def __init__(self, input_files, ht_trans, cache_dir):
self.cache_dir = cache_dir
# guard for no input data (if more MPI processes than input files)
if not len(input_files):
raise ValueError("proc {}/{}: no input data".format(
no.rank(), no.size()))
self.lads = [file.split("_")[1] for file in input_files]
# assumes all files in same dir
self.data_dir = os.path.dirname(input_files[0])
# store as dict of DFs
self.pop = pd.DataFrame()
for file in input_files:
no.log("reading initial population: %s" % file)
data = pd.read_csv(file)
data["LAD"] = file.split("_")[1]
self.pop = self.pop.append(data)
# no.log(self.pop.LC4408_C_AHTHUK11.unique())
# self.cat = self.pop.LC4408_C_AHTHUK11.unique()
# "C_AHTHUK11": {
# "0": "All categories: Household type",
# "1": "One person household",
# "2": "Married or same-sex civil partnership couple household",
# "3": "Cohabiting couple household",
# "4": "Lone parent household",
# "5": "Multi-person household"
# }
self.cat = {"LC4408_C_AHTHUK11": np.array([1, 2, 3, 4, 5])}
# NOTE: pandas stores column-major order but numpy view is row major so the matrix looks right but is actually transposed
# (no amount of transposing actually changes the memory layout (it just changes the view)
# the C++ code assumes the transition matrix is column major (col sums to unity not rows)
self.t = pd.read_csv(ht_trans).set_index(
"initial state").values / 100.0
# check rows sum to unity
assert np.allclose(np.sum(self.t, 1), np.ones(len(self.t)))
# TODO get snhp
self.snhp = SNHPData.SNHPData(self.cache_dir)
self.projection = self.snhp.aggregate(self.lads)
def __init__(self, inputdata, asfr, asmr, asir, asor, ascr, asxr):
# guard for no input data (if more MPI processes than input files)
if not len(inputdata):
raise ValueError("proc {}/{}: no input data".format(
neworder.rank(), neworder.size()))
self.lads = [file.split("_")[2] for file in inputdata]
self.data = pd.DataFrame()
for file in inputdata:
data = pd.read_csv(file)
data["LAD"] = file.split("_")[2]
self.data = self.data.append(data)
neworder.log("Preprocessing transition data for %s" %
", ".join(self.lads))
self.fertility = ethpop.create_multi(pd.read_csv(asfr), self.lads)
self.mortality = ethpop.create_multi(pd.read_csv(asmr), self.lads)
self.in_migration = ethpop.local_rates_from_national_rate(
ethpop.create_multi(pd.read_csv(asir), self.lads), self.data)
self.out_migration = ethpop.create_multi(pd.read_csv(asor), self.lads)
self.immigration = ethpop.local_rates_from_absolute(
ethpop.create_multi(pd.read_csv(ascr), self.lads), self.data)
self.emigration = ethpop.local_rates_from_absolute(
ethpop.create_multi(pd.read_csv(asxr), self.lads), self.data)
# Force flat rates for testing purposes
self.in_migration.Rate = 0.05
self.out_migration.Rate = 0.05
# The actual rates cause exponential growth
self.immigration.Rate = 0.01
self.emigration.Rate = 0.005
# use this to identify people (uniquely only within this table)
self.counter = len(self.data)
# Reformatting of input data is required to match Ethpop categories
# actual age is randomised within the bound of the category
self.data["Age"] = self.data.DC1117EW_C_AGE - neworder.ustream(
len(self.data))
self.data = ethpop.from_census_eth(self.data)
import numpy as np
import neworder
# must be MPI enabled
assert neworder.size() == 1
# category 0 is empty
gridsize = [80,100]
categories = [0.3, 0.3, 0.3, 0.1]
similarity = 0.5
neworder.timeline = (0, 50, 50)
# running/debug options
neworder.log_level = 1
neworder.do_checks = False
# initialisation
neworder.initialisations = {
"model": { "module": "model", "class_": "Schelling", "parameters": [gridsize, categories, similarity] }
}
neworder.transitions = {
"step": "model.step()",
#"redist": "TODO..."
}
# Finalisation
neworder.checkpoints = {
"stats": "model.stats()",
"anim": "model.animate()"
# run 4 sims
#neworder.sequence = np.array([3,1,2,0])
# define the evolution
neworder.timeline = (2011, 2012, 1)
areas = os.getenv("LADS").split(" ")
# define where the starting populations come from
data_dir = "examples/households/data"
#
file_pattern = "hh_%s_OA11_%d.csv"
# running/debug options
neworder.log_level = 1
# this model isnt meant for parallel execution
assert neworder.size(
) == 1, "This example is configured to be run as a single process only"
# initialisation
neworder.initialisations = {
"households": {
"module": "households",
"class_": "Households",
"parameters": [data_dir, file_pattern, areas]
}
}
# timestep must be defined in neworder
neworder.transitions = {"age": "households.age(timestep)"}
# checks to perform after each timestep. Assumed to return a boolean
neworder.do_checks = True # Faith
strike = 100.0
expiry = 0.75
# Using exact MC calc of GBM requires only 1 timestep
neworder.timeline = (0, expiry, 1)
neworder.nsims = 100000 # number of prices to simulate
neworder.sync_streams = True # all procs use same RNG stream
neworder.log_level = 1
neworder.do_checks = False
# no per-timestep checks implemented since there is only one timestep
neworder.checks = {}
# use 4 identical sims with perturbations
assert neworder.size() == 4 and not neworder.indep(
), "This example requires 4 processes with identical RNG streams"
neworder.pv = np.zeros(neworder.size())
# initialisation
neworder.initialisations = {
"market": {
"module": "market",
"class_": "Market",
"parameters": [spot, rate, divy, vol]
},
"option": {
"module": "option",
"class_": "Option",
"parameters": [callput, strike, expiry]
# internal in-migration
asir = "examples/shared/NewETHPOP_inmig.csv"
# internal out-migration
asor = "examples/shared/NewETHPOP_outmig.csv"
# immigration
ascr = "examples/shared/NewETHPOP_immig.csv"
# emigration
asxr = "examples/shared/NewETHPOP_emig.csv"
# MPI split initial population files over threads
def partition(arr, count):
return [arr[i::count] for i in range(count)]
initial_populations = partition(initial_populations, neworder.size())
# running/debug options
neworder.log_level = 1
# initialisation
neworder.initialisations = {
"people": {
"module":
"population",
"class_":
"Population",
"parameters": [
initial_populations[neworder.rank()], asfr, asmr, asir, asor, ascr,
asxr
]
def write_table(self):
# TODO define path in config
filename = "./examples/people_multi/data/dm_{:.3f}_{}-{}.csv".format(
neworder.time, neworder.rank(), neworder.size())
neworder.log("writing %s" % filename)
return self.data.to_csv(filename, index=False)
def write_table(self):
file = os.path.join(
self.data_dir,
"dm_{:.3f}_{}-{}.csv".format(no.time, no.rank(), no.size()))
no.log("writing final population: %s" % file)
self.pop.to_csv(file, index=False)
""" config.py
Microsimulation config for MPI-only implementation of household microsimulation prototype
"""
import os
import glob
import numpy as np
import neworder
assert neworder.size() > 1 and neworder.indep(
), "This example requires MPI with independent RNG streams"
# THIS could be very, very useful
#https://stackoverflow.com/questions/47297585/building-a-transition-matrix-using-words-in-python-numpy
# define the outer sequence loop (optional)
# run 4 sims
#neworder.sequence = np.array([3,1,2,0])
# define the evolution
neworder.timeline = (2011, 2019, 1)
# define where the starting populations come from
data_dir = "examples/households/data"
# TODO this should probably not be same as above
cache_dir = "examples/households/data"
file_pattern = "hh_*_OA11_2011.csv"
# MPI split initial population files over threads
def partition(arr, count):
return [arr[i::count] for i in range(count)]
import numpy as np
import neworder
population_size = 100
p_trans = 0.01
# must be MPI enabled
assert neworder.size() > 1
neworder.timeline = (0, 100, 100)
# running/debug options
neworder.log_level = 1
neworder.do_checks = False
# initialisation
neworder.initialisations = {
"test": {
"module": "test",
"class_": "Test",
"parameters": [p_trans, population_size]
}
}
neworder.transitions = {
"test": "test.test()",
#"redist": "TODO..."
}
# Finalisation
neworder.checkpoints = {
def write_table(self):
# TODO define path in config
filename = "./examples/world/data/pop2019_{}-{}.csv".format(neworder.rank(), neworder.size())
neworder.log("writing %s" % filename)
return self.pop.to_csv(filename, index=False)
def test():
t = test_.Test()
if neworder.size() == 1:
neworder.log("Skipping MPI tests")
return True
t.check(send_recv(True))
t.check(send_recv(10))
t.check(send_recv(10.01))
t.check(send_recv("abcdef"))
t.check(send_recv([1, 2, 3]))
t.check(send_recv({"a": "fghdfkgh"}))
x = np.array([1, 4, 9, 16])
if neworder.rank() == 0:
neworder.send(x, 1)
if neworder.rank() == 1:
y = neworder.receive(0)
neworder.log("MPI: 0 sent {}={} 1 recd {}={}".format(
type(x), x, type(y), y))
t.check(np.array_equal(x, y))
df = pd.read_csv("../../tests/ssm_E09000001_MSOA11_ppp_2011.csv")
if neworder.rank() == 0:
neworder.log("sending (as csv) df len %d rows from 0" % len(df))
neworder.send_csv(df, 1)
if neworder.rank() == 1:
dfrec = neworder.receive_csv(0)
neworder.log("got (as csv) df len %d rows from 0" % len(dfrec))
t.check(dfrec.equals(df))
if neworder.rank() == 0:
neworder.log("sending (pickle) df len %d rows from 0" % len(df))
neworder.send(df, 1)
if neworder.rank() == 1:
dfrec = neworder.receive(0)
neworder.log("got (pickle) df len %d rows from 0" % len(dfrec))
t.check(dfrec.equals(df))
# TODO how to test?
neworder.log("process %d syncing..." % neworder.rank())
neworder.sync()
neworder.log("process %d synced" % neworder.rank())
i = "rank " + str(neworder.rank())
root = 0
if root == neworder.rank():
neworder.log("broadcasting '%s' from %d" % (i, root))
i = neworder.broadcast(i, root)
neworder.log("%d got broadcast: '%s' from %d" % (neworder.rank(), i, root))
t.check(i == "rank 0")
# a0 will be different for each proc
a0 = np.random.rand(2, 2)
if root == neworder.rank():
neworder.log("broadcasting '%s' from %d" % (str(a0), root))
a1 = neworder.broadcast(a0, root)
# a1 will equal a0 on rank 0 only
neworder.log("%d got broadcast: '%s' from %d" %
(neworder.rank(), str(a1), root))
if neworder.rank() == 0:
t.check(np.array_equal(a0, a1))
else:
t.check(not np.array_equal(a0, a1))
# test ustream/sequence
t.check(neworder.indep())
if root == neworder.rank():
u0 = neworder.ustream(1000)
u1 = np.zeros(1000)
else:
u0 = np.zeros(1000)
u1 = neworder.ustream(1000)
# broadcast u1 from 1
neworder.broadcast(u1, 1)
# proc 0 should have 2 different random arrays
# proc 1 should have zeros and a random array
t.check(not np.array_equal(u0, u1))
# check independent streams
u = neworder.ustream(1000)
v = neworder.broadcast(u, root)
# u == v on broadcasting process only
t.check(np.array_equal(u, v) == (neworder.rank() == root))
# test gather
x = (neworder.rank() + 1)**2 / 8
a = neworder.gather(x, 0)
if neworder.rank() == 0:
t.check(np.array_equal(a, [0.125, 0.5]))
else:
t.check(len(a) == 0)
#neworder.log(a)
# test scatter
if neworder.rank() == 0:
a = (np.array(range(neworder.size())) + 1)**2 / 8
else:
a = np.zeros(neworder.size())
neworder.log(a)
x = neworder.scatter(a, 0)
t.check(x == (neworder.rank() + 1)**2 / 8)
# test allgather
a = np.zeros(neworder.size()) - 1
a[neworder.rank()] = (neworder.rank() + 1)**2 / 8
a = neworder.allgather(a)
t.check(np.array_equal(a, np.array([0.125, 0.5])))
# this should probably fail (gather not implemented for int)
x = neworder.rank() + 100
a = neworder.gather(x, 0)
#neworder.log(type(x))
#neworder.log(type(a))
return not t.any_failed
import os
import neworder
# MPI split initial population files over threads
def partition(arr, count):
if count > 1:
return [arr[i::count] for i in range(count)]
return [arr]
allcountries = pd.read_csv("./examples/world/data/CountryLookup.csv",
encoding='utf-8',
sep="\t")["Code"]
initial_populations = partition(allcountries, neworder.size())
#initial_populations = [["ALB", "ASM", "ATG"]]
# running/debug options
neworder.log_level = 1
# initialisation
neworder.initialisations = {
"people": {
"module": "microsynth",
"class_": "Microsynth",
"parameters": [initial_populations[neworder.rank()]]
}
}
# define the evolution
neworder.timeline = (2019, 2030, 11)
