def test_rate_Nt_by_2_conductance(self):
net = op.network.Cubic(shape=[1, 6, 1])
geom = op.geometry.StickAndBall(network=net)
air = op.phases.Air(network=net)
water = op.phases.Water(network=net)
m = op.phases.MultiPhase(phases=[air, water], project=net.project)
m.set_occupancy(phase=air, pores=[0, 1, 2])
m.set_occupancy(phase=water, pores=[3, 4, 5])
const = op.models.misc.constant
K_water_air = 0.5
m.set_binary_partition_coef(propname="throat.partition_coef",
phases=[water, air],
model=const,
value=K_water_air)
m._set_automatic_throat_occupancy()
_ = op.physics.Standard(network=net, phase=m, geometry=geom)
alg = op.algorithms.GenericTransport(network=net, phase=m)
alg.settings['conductance'] = 'throat.diffusive_conductance'
alg.settings['quantity'] = 'pore.mole_fraction'
alg.set_rate_BC(pores=0, values=1.235)
alg.set_value_BC(pores=5, values=0.0)
alg.run()
rate = alg.rate(pores=5)[0]
assert sp.isclose(rate, -1.235)
# Rate at air-water interface throat (#2) must match imposed rate
rate = alg.rate(throats=2)[0]
assert sp.isclose(rate, 1.235)
# Rate at interface pores (#2 @ air-side, #3 @ water-side) must be 0
rate_air_side = alg.rate(pores=2)[0]
rate_water_side = alg.rate(pores=3)[0]
assert sp.isclose(rate_air_side, 0.0)
assert sp.isclose(rate_water_side, 0.0)
# Net rate must always be zero at steady state conditions
assert sp.isclose(alg.rate(pores=net.Ps), 0.0)
def test_rate_single(self):
alg = op.algorithms.ReactiveTransport(network=self.net,
phase=self.phase)
alg.settings['conductance'] = 'throat.diffusive_conductance'
alg.settings['quantity'] = 'pore.mole_fraction'
alg.set_rate_BC(pores=self.net.pores("left"), values=1.235)
alg.set_value_BC(pores=self.net.pores("right"), values=0.0)
alg.run()
rate = alg.rate(pores=self.net.pores("right"))[0]
assert sp.isclose(rate, -1.235 * self.net.pores("right").size)
# Net rate must always be zero at steady state conditions
assert sp.isclose(alg.rate(pores=self.net.Ps), 0.0)
def test_rate_multiple(self):
alg = op.algorithms.GenericTransport(network=self.net,
phase=self.phase)
alg.settings['conductance'] = 'throat.diffusive_conductance'
alg.settings['quantity'] = 'pore.mole_fraction'
alg.set_rate_BC(pores=[0, 1, 2, 3], values=1.235)
# Note that pore = 0 is assigned two rate values (rate = sum(rates))
alg.set_rate_BC(pores=[5, 6, 19, 35, 0], values=3.455)
alg.set_value_BC(pores=[50, 51, 52, 53], values=0.0)
alg.run()
rate = alg.rate(pores=[50, 51, 52, 53])[0]
assert sp.isclose(rate,
-(1.235 * 4 + 3.455 * 5)) # 4, 5 are number of pores
# Net rate must always be zero at steady state conditions
assert sp.isclose(alg.rate(pores=self.net.Ps), 0.0)
def test_mutliphase_partition_coef(self):
m = op.phases.MultiPhase(network=self.net,
phases=[self.water, self.air, self.oil])
x, y, z = self.net["pore.coords"].T
ps_water = self.net.Ps[(y <= 3) + (y >= 8)]
ps_air = self.net.Ps[(y > 3) * (y < 6)]
ps_oil = self.net.Ps[(y >= 6) * (y < 8)]
# Phase arrangement (y-axis): W | A | O | W
m.set_occupancy(phase=self.water, pores=ps_water)
m.set_occupancy(phase=self.air, pores=ps_air)
m.set_occupancy(phase=self.oil, pores=ps_oil)
const = op.models.misc.constant
K_air_water = 2.0
K_air_oil = 1.8
K_water_oil = 0.73
m.set_binary_partition_coef(propname="throat.partition_coef",
phases=[self.air, self.water],
model=const,
value=K_air_water)
m.set_binary_partition_coef(propname="throat.partition_coef",
phases=[self.air, self.oil],
model=const,
value=K_air_oil)
m.set_binary_partition_coef(propname="throat.partition_coef",
phases=[self.water, self.oil],
model=const,
value=K_water_oil)
K_aw = m["throat.partition_coef.air:water"]
K_ao = m["throat.partition_coef.air:oil"]
K_wo = m["throat.partition_coef.water:oil"]
K_global = m["throat.partition_coef.all"]
assert sp.isclose(K_aw.mean(), K_air_water)
assert sp.isclose(K_ao.mean(), K_air_oil)
assert sp.isclose(K_wo.mean(), K_water_oil)
# Get water-air interface throats
tmp1 = self.net.find_neighbor_throats(ps_water, mode="xor")
tmp2 = self.net.find_neighbor_throats(ps_air, mode="xor")
Ts_water_air_interface = sp.intersect1d(tmp1, tmp2)
# Get air-oil interface throats
tmp1 = self.net.find_neighbor_throats(ps_air, mode="xor")
tmp2 = self.net.find_neighbor_throats(ps_oil, mode="xor")
Ts_air_oil_interface = sp.intersect1d(tmp1, tmp2)
# Get oil-water interface throats
tmp1 = self.net.find_neighbor_throats(ps_oil, mode="xor")
tmp2 = self.net.find_neighbor_throats(ps_water, mode="xor")
Ts_oil_water_interface = sp.intersect1d(tmp1, tmp2)
# K_global for water-air interface must be 1/K_air_water
assert sp.isclose(K_global[Ts_water_air_interface].mean(),
1 / K_air_water)
# K_global for air-oil interface must be K_air_oil (not 1/K_air_oil)
assert sp.isclose(K_global[Ts_air_oil_interface].mean(), K_air_oil)
# K_global for oil-water interface must be 1/K_water_oil
assert sp.isclose(K_global[Ts_oil_water_interface].mean(),
1 / K_water_oil)
# K_global for single-phase regions must be 1.0
interface_throats = sp.hstack(
(Ts_water_air_interface, Ts_air_oil_interface,
Ts_oil_water_interface))
Ts_single_phase = sp.setdiff1d(self.net.Ts, interface_throats)
assert sp.isclose(K_global[Ts_single_phase].mean(), 1.0)
def progress_one_step(self):
k = self.k
kn = self.k + 1
self.r[k] = -(self.kf[k] *
(self.c_CH4[k] * 1e-6)**self.nf - self.kb[k] *
(self.c_H2[k] * 1e-6)**self.mb) * 1e6
dcCH4 = self.r[k] * self.dt[k]
dcCs = -dcCH4
dcH2 = -2 * dcCH4
self.N_CH4[kn] = (self.c_CH4[k] + dcCH4) * self.Q[k]
self.N_H2[kn] = (self.c_H2[k] + dcH2) * self.Q[k]
self.N_Cs[kn] = (self.c_Cs[k] + dcCs) * self.Q[k]
self.N_g[kn] = self.N_CH4[kn] + self.N_H2[kn] + self.N_N2
self.x_CH4[kn] = self.N_CH4[kn] / self.N_g[kn]
self.x_H2[kn] = self.N_H2[kn] / self.N_g[kn]
self.x_N2[kn] = self.N_N2 / self.N_g[kn]
assert sp.isclose(self.x_CH4[kn] + self.x_H2[kn] + self.x_N2[kn], 1)
self.M[kn] = self.x_CH4[kn]*self.M_CH4 + self.x_N2[kn]*self.M_N2 \
+ self.x_H2[kn]*self.M_H2
self.p[kn] = self.p[k] - self.dx * \
(150*self.mu[k]*self.Vc[k]/self.Lc**2
+ 1.75*self.rho[k]*self.Vc[k]**2/self.Lc
)
self.rho[kn] = self.p[kn] * self.M[kn] / (self.R_u * self.T[kn])
self.m_CH4[kn] = self.N_CH4[kn] * self.M_CH4
self.m_H2[kn] = self.N_H2[kn] * self.M_H2
self.m_Cs[kn] = self.N_Cs[kn] * self.M_Cs
self.m_g[kn] = self.m_CH4[kn] + self.m_H2[kn] + self.m_N2
self.rhoCH4[kn] = rhoCH4(self.T[kn], self.p[kn])
self.rhoH2[kn] = rhoH2(self.T[kn], self.p[kn])
self.rhoN2[kn] = rhoN2(self.T[kn], self.p[kn])
self.Q[kn] = self.m_CH4[kn] / self.rhoCH4[kn] \
+ self.m_H2[kn] / self.rhoH2[kn] \
+ self.m_N2 / self.rhoN2[kn]
self.Vc[kn] = self.Q[kn] / (self.eps * self.Ac) # / 0.34 #/ 0.37
self.dt[kn] = self.dx / self.Vc[kn]
self.c_CH4[kn] = self.N_CH4[kn] / self.Q[kn]
self.c_H2[kn] = self.N_H2[kn] / self.Q[kn]
self.c_N2[kn] = self.N_N2 / self.Q[kn]
self.c_Cs[kn] = self.N_Cs[kn] / self.Q[kn]
def gen_unrelated_eur_1k_data(
input_file='/home/bjarni/TheHonestGene/faststorage/1Kgenomes/phase3/1k_genomes_hg.hdf5',
out_file='/home/bjarni/PCMA/faststorage/1_DATA/1k_genomes/1K_genomes_phase3_EUR_unrelated.hdf5',
maf_thres=0.01,
max_relatedness=0.05,
K_thinning_frac=0.1,
debug=False):
h5f = h5py.File(input_file)
num_indivs = len(h5f['indivs']['continent'])
eur_filter = h5f['indivs']['continent'][...] == 'EUR'
num_eur_indivs = sp.sum(eur_filter)
print 'Number of European individuals: %d', num_eur_indivs
K = sp.zeros((num_eur_indivs, num_eur_indivs), dtype='float64')
num_snps = 0
std_thres = sp.sqrt(2.0 * (1 - maf_thres) * (maf_thres))
print 'Calculating kinship'
for chrom in range(1, 23):
print 'Working on Chromosome %d' % chrom
chrom_str = 'chr%d' % chrom
print 'Loading SNPs and data'
snps = sp.array(h5f[chrom_str]['calldata']['snps'][...], dtype='int8')
print 'Loading NTs'
ref_nts = h5f[chrom_str]['variants']['REF'][...]
alt_nts = h5f[chrom_str]['variants']['ALT'][...]
print 'Filtering multi-allelic SNPs'
multi_allelic_filter = sp.negative(
h5f[chrom_str]['variants']['MULTI_ALLELIC'][...])
snps = snps[multi_allelic_filter]
ref_nts = ref_nts[multi_allelic_filter]
alt_nts = alt_nts[multi_allelic_filter]
if K_thinning_frac < 1:
print 'Thinning SNPs for kinship calculation'
thinning_filter = sp.random.random(len(snps)) < K_thinning_frac
snps = snps[thinning_filter]
alt_nts = alt_nts[thinning_filter]
ref_nts = ref_nts[thinning_filter]
print 'Filter SNPs with missing NT information'
nt_filter = sp.in1d(ref_nts, ok_nts)
nt_filter = nt_filter * sp.in1d(alt_nts, ok_nts)
if sp.sum(nt_filter) < len(nt_filter):
snps = snps[nt_filter]
print 'Filtering non-European individuals'
snps = snps[:, eur_filter]
print 'Filtering SNPs with MAF <', maf_thres
snp_stds = sp.std(snps, 1)
maf_filter = snp_stds.flatten() > std_thres
snps = snps[maf_filter]
snp_stds = snp_stds[maf_filter]
print '%d SNPs remaining after all filtering steps.' % len(snps)
print 'Normalizing SNPs'
snp_means = sp.mean(snps, 1)
norm_snps = (snps - snp_means[sp.newaxis].T) / snp_stds[sp.newaxis].T
print 'Updating kinship'
K += sp.dot(norm_snps.T, norm_snps)
num_snps += len(norm_snps)
assert sp.isclose(
sp.sum(sp.diag(K)) / (num_snps * num_eur_indivs), 1.0)
K = K / float(num_snps)
print 'Kinship calculation done using %d SNPs\n' % num_snps
# Filter individuals
print 'Filtering individuals'
keep_indiv_set = set(range(num_eur_indivs))
for i in range(num_eur_indivs):
if i in keep_indiv_set:
for j in range(i + 1, num_eur_indivs):
if K[i, j] > max_relatedness:
if j in keep_indiv_set:
keep_indiv_set.remove(j)
keep_indivs = list(keep_indiv_set)
keep_indivs.sort()
print 'Retained %d individuals\n' % len(keep_indivs)
# Checking that everything is ok!
K_ok = K[keep_indivs]
K_ok = K_ok[:, keep_indivs]
assert (K_ok - sp.tril(K_ok)).max() < max_relatedness
indiv_filter = sp.zeros(num_indivs, dtype='bool8')
indiv_filter[(sp.arange(num_indivs)[eur_filter])[keep_indivs]] = 1
assert sp.sum(indiv_filter) == len(keep_indivs)
# Store in new file
print 'Now storing data.'
oh5f = h5py.File(out_file, 'w')
indiv_ids = h5f['indivs']['indiv_ids'][indiv_filter]
oh5f.create_dataset('indiv_ids', data=indiv_ids)
for chrom in range(1, 23):
print 'Working on Chromosome %d' % chrom
chrom_str = 'chr%d' % chrom
print 'Loading SNPs and data'
snps = sp.array(h5f[chrom_str]['calldata']['snps'][...], dtype='int8')
snp_ids = h5f[chrom_str]['variants']['ID'][...]
positions = h5f[chrom_str]['variants']['POS'][...]
print 'Loading NTs'
ref_nts = h5f[chrom_str]['variants']['REF'][...]
alt_nts = h5f[chrom_str]['variants']['ALT'][...]
print 'Filtering multi-allelic SNPs'
multi_allelic_filter = sp.negative(
h5f[chrom_str]['variants']['MULTI_ALLELIC'][...])
snps = snps[multi_allelic_filter]
ref_nts = ref_nts[multi_allelic_filter]
alt_nts = alt_nts[multi_allelic_filter]
positions = positions[multi_allelic_filter]
snp_ids = snp_ids[multi_allelic_filter]
print 'Filter individuals'
snps = snps[:, indiv_filter]
print 'Filter SNPs with missing NT information'
nt_filter = sp.in1d(ref_nts, ok_nts)
nt_filter = nt_filter * sp.in1d(alt_nts, ok_nts)
if sp.sum(nt_filter) < len(nt_filter):
snps = snps[nt_filter]
ref_nts = ref_nts[nt_filter]
alt_nts = alt_nts[nt_filter]
positions = positions[nt_filter]
snp_ids = snp_ids[nt_filter]
print 'filter monomorphic SNPs'
snp_stds = sp.std(snps, 1)
mono_morph_filter = snp_stds > 0
snps = snps[mono_morph_filter]
ref_nts = ref_nts[mono_morph_filter]
alt_nts = alt_nts[mono_morph_filter]
positions = positions[mono_morph_filter]
snp_ids = snp_ids[mono_morph_filter]
snp_stds = snp_stds[mono_morph_filter]
snp_means = sp.mean(snps, 1)
if debug:
if K_thinning_frac < 1:
print 'Thinning SNPs for kinship calculation'
thinning_filter = sp.random.random(len(snps)) < K_thinning_frac
k_snps = snps[thinning_filter]
k_snp_stds = snp_stds[thinning_filter]
print 'Filtering SNPs with MAF <', maf_thres
maf_filter = k_snp_stds.flatten() > std_thres
k_snps = k_snps[maf_filter]
k_snp_stds = k_snp_stds[maf_filter]
k_snp_means = sp.mean(k_snps)
print 'Verifying that the Kinship makes sense'
norm_snps = (k_snps -
k_snp_means[sp.newaxis].T) / k_snp_stds[sp.newaxis].T
K = sp.dot(norm_snps.T, norm_snps)
num_snps += len(norm_snps)
if sp.isclose(
sp.sum(sp.diag(K)) / (num_snps * num_eur_indivs),
1.0) and (K - sp.tril(K)).max() < (max_relatedness * 1.5):
print 'It looks OK!'
else:
raise Exception('Kinship looks wrong?')
nts = sp.array([[nt1, nt2] for nt1, nt2 in izip(ref_nts, alt_nts)])
print 'Writing to disk'
cg = oh5f.create_group(chrom_str)
cg.create_dataset('snps', data=snps)
cg.create_dataset('snp_means', data=snp_means[sp.newaxis].T)
cg.create_dataset('snp_stds', data=snp_stds[sp.newaxis].T)
cg.create_dataset('snp_ids', data=snp_ids)
cg.create_dataset('positions', data=positions)
cg.create_dataset('nts', data=nts)
oh5f.flush()
print 'Done writing to disk'
# centimorgans = h5f[chrom_str]['centimorgans'][...]
# cg.create_dataset('centimorgans',data=centimorgans)
#
# centimorgan_rates = h5f[chrom_str]['centimorgan_rates'][...]
# cg.create_dataset('centimorgan_rates',data=centimorgan_rates)
oh5f.close()
h5f.close()
print 'Done'
Reference:
Least Angle Regression, Efron et al., 2004, The Annals of Statistics
"""
# Initial data
signs = scipy.zeros(size_predictor)
betas = scipy.zeros(size_predictor)
indices_predictor = scipy.arange(size_predictor)
vecy_fitted = scipy.zeros_like(vecy)
beta_lars = [[0] * size_predictor]
for k in range(size_predictor):
vecc = (vecy - vecy_fitted) @ matx
vecc_abs = scipy.absolute(vecc)
maxc = vecc_abs.max()
mask_maxc = scipy.isclose(vecc_abs, maxc)
indices_predictor = np.reshape(indices_predictor,
mask_maxc.shape,
order='C')
active = indices_predictor[mask_maxc]
signs = scipy.where(vecc.item(0, active[0]) > 0, 1, -1)
matx_active = signs * matx[:, active]
u, s, vh = scipy.linalg.svd(matx_active, full_matrices=False)
matg = vh.T @ scipy.diag(s**2) @ vh
matg_inv = vh.T @ scipy.diag(scipy.reciprocal(s**2)) @ vh
vec1 = scipy.ones(len(active))
scalara = (matg_inv.sum())**(-.5)
vecw = scalara * matg_inv.sum(axis=1)
import scipy as sc
import numpy as np
import scipy.linalg as lin
matrix = sc.random.normal(0, 1, [5, 5])
print matrix
print
eigenvalues, eigevectors = lin.eig(matrix)
print eigenvalues,
print eigevectors
id = sc.eye(5, 5)
eigevectors = sc.transpose(eigevectors)
for eigenvalue, eigevector in zip(eigenvalues, eigevectors):
print sc.isclose(sc.dot(matrix, eigevector),
sc.dot(eigenvalue, eigevector)).all()
def get_data(label):
material_root_path = "measurementData/materialProperties/"
#A4_Isover_RKL-EJ/wc.txt
material_paths = glob.glob(material_root_path+"*")
# print(label, material_paths)
# for material_path in material_paths[-4:]:
# for material_path in material_paths[:1]:
hankalat = [
"measurementData/materialProperties/C5_Luja_A",
"measurementData/materialProperties/A4_Isover_RKL-EJ",
"measurementData/materialProperties/A7_Vital-levy",
"measurementData/materialProperties/A3_Isover_RKL",
"measurementData/materialProperties/D5_Pellavaeriste_T3",
"measurementData/materialProperties/A12_Tuulensuojaluja",
"measurementData/materialProperties/D3_Vital",
]
for material_path in material_paths:
if os.path.split(material_path)[-1].split("_")[0] == label:
break
# print(material_path)
# name = os.path.split(material_path)[-1]
RHr = []
wr = []
with open(os.path.join(material_path, "wc.txt"),"r") as ifile:
lines = ifile.readlines()
for line in lines[2:]:
# print(line)
parts = [round(float(part),4) for part in line.split(",")]
RHr.append(parts[0])
wr.append(parts[1])
# if len(wr) == 1:
# continue
# if sp.isclose(wr[-1],0):
# continue
if wr[-1] >= 2*wr[-2]:
w = wr[:-1]
RH = RHr[:-1]
else:
w = wr[:]
RH = RHr[:]
# 3
if material_path in hankalat:
w = [(w[0]+w[1])/2, w[2]] + w
RH = [0.22, 0.51] + RH
xpoints = [min(RH)]*4 + [0.61] + [max(RH)]*4
else:
w = [w[0]] + w
RH = [0.1] + RH
xpoints = [min(RH)]*4 + [0.8] + [max(RH)]*4
# w = [(w[0]+w[1])/2, w[2]] + w
# RH = [0.22, 0.51] + RH
# xpoints = [min(RH)]*4 + [0.61] + [max(RH)]*4
def tomin(x):
tcki = [xpoints,
list(x) + [0]*5,
3]
return (w - interpolate.splev(RH, tcki, der=0))#*penalty
x0 = [1]*5
# print(tomin(x))
res = optimize.least_squares(tomin, x0)
tck = [xpoints,
list(res.x) + [0]*5,
3]
# RHsp = sp.linspace(0,RH[-1],1000)
RHsp = sp.linspace(min(RH),max(RH),1000)
wsp = interpolate.splev(RHsp, tck, der=0)
xisp = interpolate.splev(RHsp, tck, der=1)
# Filter
wsp[wsp<0] = 0
if sp.isclose(RHsp[-1],1):
RHextra = []
wextra = []
xiextra = []
else:
RHextra = sp.linspace(RHsp[-1]+(RHsp[-1]-RHsp[-2]), 2, 200)
# RHextra = sp.linspace(0.8, 1, 100000)
#
xi0 = xisp[-1]#(wsp[-1]-wsp[-2]) / (RHsp[-1]-RHsp[-2])
x0 = RHsp[-1]
x1 = RHr[-1]
y0 = wsp[-1]
y1 = wr[-1]
a = (x0**2*x1*xi0 + x0**2*y1 - x0*x1**2*xi0 - 2*x0*x1*y0 + x1**2*y0)/(x0**2 - 2*x0*x1 + x1**2)
b = (-x0**2*xi0 + 2*x0*y0 - 2*x0*y1 + x1**2*xi0)/(x0**2 - 2*x0*x1 + x1**2)
c = (-xi0*(x0 - x1) + y0 - y1)/(x0**2 - 2*x0*(x0 - x1) - x1**2)
# funcs
wextra = a + b*RHextra + c*RHextra**2
xiextra = b + 2*c*RHextra
return sp.array(list(RHsp)+list(RHextra)), sp.array(list(wsp)+list(wextra)), sp.array(list(xisp)+list(xiextra))
def isInSpace(self, p):
"""Return true if the point p is in the affine space, false otherwise."""
p = scipy.array(p)
close = scipy.isclose(self.getProjection(p), p)
inSpace = scipy.all(close, axis=1)
return scipy.expand_dims(inSpace, 1)
def score(self, X_train, T_train, X_test, T_test):
Y = self.predict(X_train, T_train, X_test)
return sp.mean(sp.isclose(Y, T_test))
def conservation(a_i, a_f):
# print("a_i: " +str(a_i[0]))
# print("a_f: " +str(a_f[0]))
# print("error: " +str(sp.fabs(sp.divide((a_i - a_f),a_i))[0]))
return str( False not in (sp.isclose(a_i,a_f)))
def calc_kinship(input_file='Data/1Kgenomes/1K_genomes_v3.hdf5' , out_file='Data/1Kgenomes/kinship.hdf5',
maf_thres=0.01, figure_dir='', figure_fn='', snp_filter_frac=1, indiv_filter_frac=1,
chrom_ok_snp_dict=None):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
print 'Loading Genotype from '
in_h5f = h5py.File(input_file)
# eur_filter = in_h5f['indivs']['continent'][...] == 'EUR'
# num_indivs = sp.sum(eur_filter)
indiv_ids = in_h5f['indiv_ids'][...]
indiv_filter = None
if indiv_filter_frac < 1:
indiv_filter = sp.array(sp.random.random(len(indiv_ids)) < indiv_filter_frac, dtype='bool8')
indiv_ids = indiv_ids[indiv_filter]
assert len(sp.unique(indiv_ids)) == len(indiv_ids)
num_indivs = len(indiv_ids)
ok_chromosome_dict = {}
not_done = set(range(1, 23))
while len(not_done) > 0:
chromosome_dict = {}
K_all_snps = sp.zeros((num_indivs, num_indivs), dtype='float32')
num_all_snps = 0
sum_indiv_genotypes_all_chrom = sp.zeros(num_indivs, dtype='float32')
# snp_cov_all_snps = sp.zeros((num_indivs, num_indivs), dtype='float64')
print 'Calculating kinship'
for chrom in range(1, 23):
print 'Working on Chromosome %d' % chrom
chrom_str = 'chr%d' % chrom
snp_filter = None
if snp_filter_frac < 1:
snp_filter = sp.random.random(len(in_h5f[chrom_str]['snps'])) < snp_filter_frac
g_dict = get_genotype_data(in_h5f, chrom, maf_thres, indiv_filter=indiv_filter,
snp_filter=snp_filter, randomize_sign=True, snps_signs=None, chrom_ok_snp_dict=chrom_ok_snp_dict)
norm_snps = g_dict['norm_snps']
sum_indiv_genotypes = sp.sum(g_dict['norm_snps'], 0)
sum_indiv_genotypes_all_chrom += sum_indiv_genotypes
print 'Calculating chromosome kinship'
K_unscaled = sp.array(sp.dot(norm_snps.T, norm_snps), dtype='float32')
assert sp.isclose(sp.sum(sp.diag(K_unscaled)) / (len(norm_snps) * num_indivs), 1.0), '..bug'
K_all_snps += K_unscaled
num_all_snps += len(norm_snps)
print 'SNP-cov normalisation'
sum_indiv_genotypes = sp.sum(norm_snps, 0)
sum_indiv_genotypes_all_chrom += sum_indiv_genotypes
mean_indiv_genotypes = sum_indiv_genotypes / len(norm_snps)
norm_snps = norm_snps - mean_indiv_genotypes
print 'Calculating SNP covariance unscaled'
snp_cov_unscaled = sp.array(sp.dot(norm_snps.T, norm_snps), dtype='float32')
# snp_cov_all_snps += snp_cov_unscaled
print 'Storing and updating things'
chromosome_dict[chrom_str] = {'K_unscaled':K_unscaled, 'num_snps':len(norm_snps),
'sum_indiv_genotypes':sum_indiv_genotypes,
'snp_cov_unscaled':snp_cov_unscaled,
'snps_signs':g_dict['snps_signs']}
if snp_filter_frac < 1:
chromosome_dict[chrom_str]['snp_filter'] = snp_filter
# snp_cov_all_snps = snp_cov_all_snps / float(num_all_snps)
# K_all_snps = K_all_snps / float(num_all_snps)
# print 'K_all_snps.shape: %s' % str(K_all_snps.shape)
# print 'snp_cov_all_snps.shape: %s' % str(snp_cov_all_snps.shape)
# print 'sp.diag(snp_cov_all_snps): %s' % str(sp.diag(snp_cov_all_snps))
# print 'sp.mean(sp.diag(snp_cov_all_snps)_: %s' % str(sp.mean(sp.diag(snp_cov_all_snps)))
# print 'Full kinship and snp-covariance calculation done using %d SNPs\n' % num_all_snps
mean_indiv_genotypes_all_chrom = sum_indiv_genotypes_all_chrom / num_all_snps
print 'Individual gentoype mean found:'
print mean_indiv_genotypes_all_chrom
print 'Calculating chromosome-wise SNP-covariance and kinship matrices'
for chrom in range(1, 23):
if chrom in not_done:
print 'Working on Chromosome %d' % chrom
chrom_str = 'chr%d' % chrom
snp_cov_leave_one_out = sp.zeros((num_indivs, num_indivs), dtype='float32')
K_leave_one_out = sp.zeros((num_indivs, num_indivs), dtype='float32')
num_snps_used = 0
sum_indiv_genotypes = sp.zeros(num_indivs, dtype='float32')
for chrom2 in range(1, 23):
chrom2_str = 'chr%d' % chrom2
if chrom2 != chrom:
sum_indiv_genotypes += chromosome_dict[chrom2_str]['sum_indiv_genotypes']
K_leave_one_out += chromosome_dict[chrom2_str]['K_unscaled']
num_snps_used += chromosome_dict[chrom2_str]['num_snps']
assert sp.isclose(sp.sum(sp.diag(K_leave_one_out)) / (num_snps_used * num_indivs), 1.0), '..bug'
mean_indiv_genotypes = sum_indiv_genotypes / num_snps_used
for chrom2 in range(1, 23):
chrom2_str = 'chr%d' % chrom2
if chrom2 != chrom:
print 'Loading SNPs'
snps_signs = chromosome_dict[chrom2_str]['snps_signs']
snp_filter = chromosome_dict[chrom2_str]['snp_filter']
g_dict = get_genotype_data(in_h5f, chrom2, maf_thres, indiv_filter=indiv_filter,
snp_filter=snp_filter, randomize_sign=True,
snps_signs=snps_signs, chrom_ok_snp_dict=chrom_ok_snp_dict)
norm_snps = g_dict['norm_snps']
print 'SNP-cov normalisation'
norm_snps = norm_snps - mean_indiv_genotypes
print 'Calculating SNP covariance unscaled'
snp_cov_unscaled = sp.dot(norm_snps.T, norm_snps)
snp_cov_leave_one_out += snp_cov_unscaled
snp_cov_leave_one_out = snp_cov_leave_one_out / num_snps_used
K_leave_one_out = K_leave_one_out / num_snps_used
assert (K_leave_one_out - sp.diag(K_leave_one_out)).max() < 0.1, '..bug'
try:
cholesky_decomp_inv_snp_cov = linalg.cholesky(linalg.pinv(sp.array(snp_cov_leave_one_out, dtype='float64')))
evals, evecs = linalg.eig(sp.array(K_leave_one_out, dtype='float64'))
except:
try:
cholesky_decomp_inv_snp_cov = linalg.cholesky(linalg.pinv(sp.array(snp_cov_leave_one_out, dtype='float32')))
evals, evecs = linalg.eig(sp.array(K_leave_one_out, dtype='float32'))
except:
print 'Failed when obtaining the Cholesky decomposotion or eigen decomposition'
print 'Moving on, trying again later.'
continue
sort_indices = sp.argsort(evals,)
ordered_evals = evals[sort_indices]
print ordered_evals[-10:] / sp.sum(ordered_evals)
ordered_evecs = evecs[:, sort_indices]
d = {}
d['evecs_leave_one_out'] = ordered_evecs
d['evals_leave_one_out'] = ordered_evals
d['cholesky_decomp_inv_snp_cov'] = cholesky_decomp_inv_snp_cov
d['K_leave_one_out'] = K_leave_one_out
d['K_unscaled'] = chromosome_dict[chrom_str]['K_unscaled']
d['num_snps'] = chromosome_dict[chrom_str]['num_snps']
d['snp_cov_leave_one_out'] = snp_cov_leave_one_out
ok_chromosome_dict[chrom_str] = d
not_done.remove(chrom)
# While loop ends here.
K_all_snps = K_all_snps / float(num_all_snps)
in_h5f.close()
ok_chromosome_dict['K_all_snps'] = K_all_snps
ok_chromosome_dict['num_all_snps'] = num_all_snps
assert sp.sum((ok_chromosome_dict['chr1']['K_leave_one_out'] - ok_chromosome_dict['chr2']['K_leave_one_out']) ** 2) != 0 , 'Kinships are probably too similar.'
print 'Calculating PCAs'
evals, evecs = linalg.eigh(sp.array(K_all_snps, dtype='float64')) # PCA via eigen decomp
evals[evals < 0] = 0
sort_indices = sp.argsort(evals,)[::-1]
ordered_evals = evals[sort_indices]
print ordered_evals[:10] / sp.sum(ordered_evals)
pcs = evecs[:, sort_indices]
tot = sum(evals)
var_exp = [(i / tot) * 100 for i in sorted(evals, reverse=True)]
print 'Total variance explained:', sp.sum(var_exp)
ok_chromosome_dict['pcs'] = pcs
ok_chromosome_dict['pcs_var_exp'] = var_exp
if figure_dir is not None:
plt.clf()
plt.plot(pcs[:, 0], pcs[:, 1], 'k.')
plt.title("Overall PCA")
plt.xlabel('PC1')
plt.xlabel('PC2')
plt.tight_layout()
plt.savefig(figure_dir + '/' + figure_fn, format='pdf')
plt.clf()
out_h5f = h5py.File(out_file)
hu.dict_to_hdf5(ok_chromosome_dict, out_h5f)
out_h5f.close()
return ok_chromosome_dict
A.add(n - 1, n - 1, kt / (dx / 2) * Ac)
b[-1] += kt / (dx / 2) * Ac * T_B
# Solution
# T = sp.linalg.solve(A,b)
T = A.solve(b)
return T
print("START")
# Number of control volumes
n = 5
# Thermal conductivity (W/mK)
kt = 1000
# Cross-sectional area (m2)
Ac = 10e-3
# Lenght (m)
L = 0.5
# Boundary temperatures (C)
T_A = 100
T_B = 500
Tfull, dxFull = heatConduction1DConstantTemperatureBoundariesNoSources.main(
n, kt, Ac, L, T_A, T_B)
Tsparse = solver_1d(n, kt, Ac, L, T_A, T_B)
assert all(sp.isclose(Tfull, Tsparse))
print("OK")
print("END")
def calc_kinship(input_file='Data/1Kgenomes/1K_genomes_v3.hdf5',
out_file='Data/1Kgenomes/kinship.hdf5',
maf_thres=0.01,
figure_dir='',
figure_fn='',
snp_filter_frac=1,
indiv_filter_frac=1,
chrom_ok_snp_dict=None):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
print 'Loading Genotype from '
in_h5f = h5py.File(input_file)
# eur_filter = in_h5f['indivs']['continent'][...] == 'EUR'
# num_indivs = sp.sum(eur_filter)
indiv_ids = in_h5f['indiv_ids'][...]
indiv_filter = None
if indiv_filter_frac < 1:
indiv_filter = sp.array(
sp.random.random(len(indiv_ids)) < indiv_filter_frac,
dtype='bool8')
indiv_ids = indiv_ids[indiv_filter]
assert len(sp.unique(indiv_ids)) == len(indiv_ids)
num_indivs = len(indiv_ids)
ok_chromosome_dict = {}
not_done = set(range(1, 23))
while len(not_done) > 0:
chromosome_dict = {}
K_all_snps = sp.zeros((num_indivs, num_indivs), dtype='float32')
num_all_snps = 0
sum_indiv_genotypes_all_chrom = sp.zeros(num_indivs, dtype='float32')
# snp_cov_all_snps = sp.zeros((num_indivs, num_indivs), dtype='float64')
print 'Calculating kinship'
for chrom in range(1, 23):
print 'Working on Chromosome %d' % chrom
chrom_str = 'chr%d' % chrom
snp_filter = None
if snp_filter_frac < 1:
snp_filter = sp.random.random(len(
in_h5f[chrom_str]['snps'])) < snp_filter_frac
g_dict = get_genotype_data(in_h5f,
chrom,
maf_thres,
indiv_filter=indiv_filter,
snp_filter=snp_filter,
randomize_sign=True,
snps_signs=None,
chrom_ok_snp_dict=chrom_ok_snp_dict)
norm_snps = g_dict['norm_snps']
sum_indiv_genotypes = sp.sum(g_dict['norm_snps'], 0)
sum_indiv_genotypes_all_chrom += sum_indiv_genotypes
print 'Calculating chromosome kinship'
K_unscaled = sp.array(sp.dot(norm_snps.T, norm_snps),
dtype='float32')
assert sp.isclose(
sp.sum(sp.diag(K_unscaled)) / (len(norm_snps) * num_indivs),
1.0), '..bug'
K_all_snps += K_unscaled
num_all_snps += len(norm_snps)
print 'SNP-cov normalisation'
sum_indiv_genotypes = sp.sum(norm_snps, 0)
sum_indiv_genotypes_all_chrom += sum_indiv_genotypes
mean_indiv_genotypes = sum_indiv_genotypes / len(norm_snps)
norm_snps = norm_snps - mean_indiv_genotypes
print 'Calculating SNP covariance unscaled'
snp_cov_unscaled = sp.array(sp.dot(norm_snps.T, norm_snps),
dtype='float32')
# snp_cov_all_snps += snp_cov_unscaled
print 'Storing and updating things'
chromosome_dict[chrom_str] = {
'K_unscaled': K_unscaled,
'num_snps': len(norm_snps),
'sum_indiv_genotypes': sum_indiv_genotypes,
'snp_cov_unscaled': snp_cov_unscaled,
'snps_signs': g_dict['snps_signs']
}
if snp_filter_frac < 1:
chromosome_dict[chrom_str]['snp_filter'] = snp_filter
# snp_cov_all_snps = snp_cov_all_snps / float(num_all_snps)
# K_all_snps = K_all_snps / float(num_all_snps)
# print 'K_all_snps.shape: %s' % str(K_all_snps.shape)
# print 'snp_cov_all_snps.shape: %s' % str(snp_cov_all_snps.shape)
# print 'sp.diag(snp_cov_all_snps): %s' % str(sp.diag(snp_cov_all_snps))
# print 'sp.mean(sp.diag(snp_cov_all_snps)_: %s' % str(sp.mean(sp.diag(snp_cov_all_snps)))
# print 'Full kinship and snp-covariance calculation done using %d SNPs\n' % num_all_snps
mean_indiv_genotypes_all_chrom = sum_indiv_genotypes_all_chrom / num_all_snps
print 'Individual gentoype mean found:'
print mean_indiv_genotypes_all_chrom
print 'Calculating chromosome-wise SNP-covariance and kinship matrices'
for chrom in range(1, 23):
if chrom in not_done:
print 'Working on Chromosome %d' % chrom
chrom_str = 'chr%d' % chrom
snp_cov_leave_one_out = sp.zeros((num_indivs, num_indivs),
dtype='float32')
K_leave_one_out = sp.zeros((num_indivs, num_indivs),
dtype='float32')
num_snps_used = 0
sum_indiv_genotypes = sp.zeros(num_indivs, dtype='float32')
for chrom2 in range(1, 23):
chrom2_str = 'chr%d' % chrom2
if chrom2 != chrom:
sum_indiv_genotypes += chromosome_dict[chrom2_str][
'sum_indiv_genotypes']
K_leave_one_out += chromosome_dict[chrom2_str][
'K_unscaled']
num_snps_used += chromosome_dict[chrom2_str][
'num_snps']
assert sp.isclose(
sp.sum(sp.diag(K_leave_one_out)) /
(num_snps_used * num_indivs), 1.0), '..bug'
mean_indiv_genotypes = sum_indiv_genotypes / num_snps_used
for chrom2 in range(1, 23):
chrom2_str = 'chr%d' % chrom2
if chrom2 != chrom:
print 'Loading SNPs'
snps_signs = chromosome_dict[chrom2_str]['snps_signs']
snp_filter = chromosome_dict[chrom2_str]['snp_filter']
g_dict = get_genotype_data(
in_h5f,
chrom2,
maf_thres,
indiv_filter=indiv_filter,
snp_filter=snp_filter,
randomize_sign=True,
snps_signs=snps_signs,
chrom_ok_snp_dict=chrom_ok_snp_dict)
norm_snps = g_dict['norm_snps']
print 'SNP-cov normalisation'
norm_snps = norm_snps - mean_indiv_genotypes
print 'Calculating SNP covariance unscaled'
snp_cov_unscaled = sp.dot(norm_snps.T, norm_snps)
snp_cov_leave_one_out += snp_cov_unscaled
snp_cov_leave_one_out = snp_cov_leave_one_out / num_snps_used
K_leave_one_out = K_leave_one_out / num_snps_used
assert (K_leave_one_out -
sp.diag(K_leave_one_out)).max() < 0.1, '..bug'
try:
cholesky_decomp_inv_snp_cov = linalg.cholesky(
linalg.pinv(
sp.array(snp_cov_leave_one_out, dtype='float64')))
evals, evecs = linalg.eig(
sp.array(K_leave_one_out, dtype='float64'))
except:
try:
cholesky_decomp_inv_snp_cov = linalg.cholesky(
linalg.pinv(
sp.array(snp_cov_leave_one_out,
dtype='float32')))
evals, evecs = linalg.eig(
sp.array(K_leave_one_out, dtype='float32'))
except:
print 'Failed when obtaining the Cholesky decomposotion or eigen decomposition'
print 'Moving on, trying again later.'
continue
sort_indices = sp.argsort(evals, )
ordered_evals = evals[sort_indices]
print ordered_evals[-10:] / sp.sum(ordered_evals)
ordered_evecs = evecs[:, sort_indices]
d = {}
d['evecs_leave_one_out'] = ordered_evecs
d['evals_leave_one_out'] = ordered_evals
d['cholesky_decomp_inv_snp_cov'] = cholesky_decomp_inv_snp_cov
d['K_leave_one_out'] = K_leave_one_out
d['K_unscaled'] = chromosome_dict[chrom_str]['K_unscaled']
d['num_snps'] = chromosome_dict[chrom_str]['num_snps']
d['snp_cov_leave_one_out'] = snp_cov_leave_one_out
ok_chromosome_dict[chrom_str] = d
not_done.remove(chrom)
# While loop ends here.
K_all_snps = K_all_snps / float(num_all_snps)
in_h5f.close()
ok_chromosome_dict['K_all_snps'] = K_all_snps
ok_chromosome_dict['num_all_snps'] = num_all_snps
assert sp.sum((ok_chromosome_dict['chr1']['K_leave_one_out'] -
ok_chromosome_dict['chr2']['K_leave_one_out'])**
2) != 0, 'Kinships are probably too similar.'
print 'Calculating PCAs'
evals, evecs = linalg.eigh(sp.array(
K_all_snps, dtype='float64')) # PCA via eigen decomp
evals[evals < 0] = 0
sort_indices = sp.argsort(evals, )[::-1]
ordered_evals = evals[sort_indices]
print ordered_evals[:10] / sp.sum(ordered_evals)
pcs = evecs[:, sort_indices]
tot = sum(evals)
var_exp = [(i / tot) * 100 for i in sorted(evals, reverse=True)]
print 'Total variance explained:', sp.sum(var_exp)
ok_chromosome_dict['pcs'] = pcs
ok_chromosome_dict['pcs_var_exp'] = var_exp
if figure_dir is not None:
plt.clf()
plt.plot(pcs[:, 0], pcs[:, 1], 'k.')
plt.title("Overall PCA")
plt.xlabel('PC1')
plt.xlabel('PC2')
plt.tight_layout()
plt.savefig(figure_dir + '/' + figure_fn, format='pdf')
plt.clf()
out_h5f = h5py.File(out_file)
hu.dict_to_hdf5(ok_chromosome_dict, out_h5f)
out_h5f.close()
return ok_chromosome_dict
def solve_new(self):
###########################################################################
# SOLVER LOOP
###########################################################################
step = 0
self.t = 0
ts = [self.t]
prev_time_for_prope_time = None
while step < self.max_steps:
# Check last step
if self.t + float(self.dt_fe) > self.t_end:
self.dt_fe = fe.Constant(self.t_end - self.t)
# Check prope time
elif self.t + float(
self.dt_fe) > self.prope_times[self.prope_times_k]:
prev_time_for_prope_time = float(self.dt_fe)
self.dt_fe = fe.Constant(self.prope_times[self.prope_times_k] -
self.t)
self.prope_times_k += 1
# Progress time
step += 1
self.t += float(self.dt_fe)
ts.append(self.t)
##############################
# Solver
##############################
# Solve heat and moisture
fe.solve(self.a_T == self.L_T, self.T, self.bc_T)
fe.solve(self.a_phi == self.L_phi, self.phi, self.bc_phi)
# Assing
if self.order_2nd:
self.T_old2.assign(self.T_old)
self.phi_old2.assign(self.phi_old)
# Update solved fields
self.T_old.assign(self.T)
self.phi_old.assign(self.phi)
# Update material properties
phi_old_int = fe.interpolate(self.phi_old, self.v_materials)
# w_int = fe.interpolate(self.w, self.v_materials)
self.w.x_k = phi_old_int
self.kT.x_k = phi_old_int
self.delta_p.x_k = phi_old_int
self.Dw.x_k = phi_old_int
self.xi.x_k = phi_old_int
##############################
# Post
##############################
if sp.isclose(self.t, self.prope_times[self.prope_times_k]):
self.prope()
print("step=%i progress=%.2f t=%.2f (d) dt=%.2f (h)" %
(step, self.t / self.t_end, self.t / s2d,
float(self.dt_fe) / s2h))
self.save_time()
elif step % 100 == 0:
print("step=%i progress=%.2f t=%.2f (d) dt=%.2f (h)" %
(step, self.t / self.t_end, self.t / s2d,
float(self.dt_fe) / s2h))
self.save_time()
##############################
# Next step
##############################
# Check end
if sp.isclose(self.t, self.t_end) or self.t > self.t_end:
break
# Increase timestep
if prev_time_for_prope_time:
self.dt_fe = fe.Constant(prev_time_for_prope_time)
prev_time_for_prope_time = None
self.dt_fe *= self.time_gamma
if float(self.dt_fe) > self.max_dt:
self.dt_fe = fe.Constant(self.max_dt)
def gen_unrelated_eur_1k_data(input_file='/home/bjarni/TheHonestGene/faststorage/1Kgenomes/phase3/1k_genomes_hg.hdf5' ,
out_file='/home/bjarni/PCMA/faststorage/1_DATA/1k_genomes/1K_genomes_phase3_EUR_unrelated.hdf5',
maf_thres=0.01, max_relatedness=0.05, K_thinning_frac=0.1, debug=False):
h5f = h5py.File(input_file)
num_indivs = len(h5f['indivs']['continent'])
eur_filter = h5f['indivs']['continent'][...] == 'EUR'
num_eur_indivs = sp.sum(eur_filter)
print 'Number of European individuals: %d', num_eur_indivs
K = sp.zeros((num_eur_indivs, num_eur_indivs), dtype='single')
num_snps = 0
std_thres = sp.sqrt(2.0 * (1 - maf_thres) * (maf_thres))
print 'Calculating kinship'
for chrom in range(1, 23):
print 'Working on Chromosome %d' % chrom
chrom_str = 'chr%d' % chrom
print 'Loading SNPs and data'
snps = sp.array(h5f[chrom_str]['calldata']['snps'][...], dtype='int8')
print 'Loading NTs'
ref_nts = h5f[chrom_str]['variants']['REF'][...]
alt_nts = h5f[chrom_str]['variants']['ALT'][...]
print 'Filtering multi-allelic SNPs'
multi_allelic_filter = sp.negative(h5f[chrom_str]['variants']['MULTI_ALLELIC'][...])
snps = snps[multi_allelic_filter]
ref_nts = ref_nts[multi_allelic_filter]
alt_nts = alt_nts[multi_allelic_filter]
if K_thinning_frac < 1:
print 'Thinning SNPs for kinship calculation'
thinning_filter = sp.random.random(len(snps)) < K_thinning_frac
snps = snps[thinning_filter]
alt_nts = alt_nts[thinning_filter]
ref_nts = ref_nts[thinning_filter]
print 'Filter SNPs with missing NT information'
nt_filter = sp.in1d(ref_nts, ok_nts)
nt_filter = nt_filter * sp.in1d(alt_nts, ok_nts)
if sp.sum(nt_filter) < len(nt_filter):
snps = snps[nt_filter]
print 'Filtering non-European individuals'
snps = snps[:, eur_filter]
print 'Filtering SNPs with MAF <', maf_thres
snp_stds = sp.std(snps, 1)
maf_filter = snp_stds.flatten() > std_thres
snps = snps[maf_filter]
snp_stds = snp_stds[maf_filter]
print '%d SNPs remaining after all filtering steps.' % len(snps)
print 'Normalizing SNPs'
snp_means = sp.mean(snps, 1)
norm_snps = (snps - snp_means[sp.newaxis].T) / snp_stds[sp.newaxis].T
print 'Updating kinship'
K += sp.dot(norm_snps.T, norm_snps)
num_snps += len(norm_snps)
assert sp.isclose(sp.sum(sp.diag(K)) / (num_snps * num_eur_indivs), 1.0)
K = K / float(num_snps)
print 'Kinship calculation done using %d SNPs\n' % num_snps
# Filter individuals
print 'Filtering individuals'
keep_indiv_set = set(range(num_eur_indivs))
for i in range(num_eur_indivs):
if i in keep_indiv_set:
for j in range(i + 1, num_eur_indivs):
if K[i, j] > max_relatedness:
if j in keep_indiv_set:
keep_indiv_set.remove(j)
keep_indivs = list(keep_indiv_set)
keep_indivs.sort()
print 'Retained %d individuals\n' % len(keep_indivs)
# Checking that everything is ok!
K_ok = K[keep_indivs]
K_ok = K_ok[:, keep_indivs]
assert (K_ok - sp.tril(K_ok)).max() < max_relatedness
indiv_filter = sp.zeros(num_indivs, dtype='bool8')
indiv_filter[(sp.arange(num_indivs)[eur_filter])[keep_indivs]] = 1
assert sp.sum(indiv_filter) == len(keep_indivs)
# Store in new file
print 'Now storing data.'
oh5f = h5py.File(out_file, 'w')
indiv_ids = h5f['indivs']['indiv_ids'][indiv_filter]
oh5f.create_dataset('indiv_ids', data=indiv_ids)
for chrom in range(1, 23):
print 'Working on Chromosome %d' % chrom
chrom_str = 'chr%d' % chrom
print 'Loading SNPs and data'
snps = sp.array(h5f[chrom_str]['calldata']['snps'][...], dtype='int8')
snp_ids = h5f[chrom_str]['variants']['ID'][...]
positions = h5f[chrom_str]['variants']['POS'][...]
print 'Loading NTs'
ref_nts = h5f[chrom_str]['variants']['REF'][...]
alt_nts = h5f[chrom_str]['variants']['ALT'][...]
print 'Filtering multi-allelic SNPs'
multi_allelic_filter = sp.negative(h5f[chrom_str]['variants']['MULTI_ALLELIC'][...])
snps = snps[multi_allelic_filter]
ref_nts = ref_nts[multi_allelic_filter]
alt_nts = alt_nts[multi_allelic_filter]
positions = positions[multi_allelic_filter]
snp_ids = snp_ids[multi_allelic_filter]
print 'Filter individuals'
snps = snps[:, indiv_filter]
print 'Filter SNPs with missing NT information'
nt_filter = sp.in1d(ref_nts, ok_nts)
nt_filter = nt_filter * sp.in1d(alt_nts, ok_nts)
if sp.sum(nt_filter) < len(nt_filter):
snps = snps[nt_filter]
ref_nts = ref_nts[nt_filter]
alt_nts = alt_nts[nt_filter]
positions = positions[nt_filter]
snp_ids = snp_ids[nt_filter]
print 'filter monomorphic SNPs'
snp_stds = sp.std(snps, 1)
mono_morph_filter = snp_stds > 0
snps = snps[mono_morph_filter]
ref_nts = ref_nts[mono_morph_filter]
alt_nts = alt_nts[mono_morph_filter]
positions = positions[mono_morph_filter]
snp_ids = snp_ids[mono_morph_filter]
snp_stds = snp_stds[mono_morph_filter]
snp_means = sp.mean(snps, 1)
if debug:
if K_thinning_frac < 1:
print 'Thinning SNPs for kinship calculation'
thinning_filter = sp.random.random(len(snps)) < K_thinning_frac
k_snps = snps[thinning_filter]
k_snp_stds = snp_stds[thinning_filter]
print 'Filtering SNPs with MAF <', maf_thres
maf_filter = k_snp_stds.flatten() > std_thres
k_snps = k_snps[maf_filter]
k_snp_stds = k_snp_stds[maf_filter]
k_snp_means = sp.mean(k_snps)
print 'Verifying that the Kinship makes sense'
norm_snps = (k_snps - k_snp_means[sp.newaxis].T) / k_snp_stds[sp.newaxis].T
K = sp.dot(norm_snps.T, norm_snps)
num_snps += len(norm_snps)
if sp.isclose(sp.sum(sp.diag(K)) / (num_snps * num_eur_indivs), 1.0) and (K - sp.tril(K)).max() < (max_relatedness * 1.5):
print 'It looks OK!'
else:
raise Exception('Kinship looks wrong?')
nts = sp.array([[nt1, nt2] for nt1, nt2 in izip(ref_nts, alt_nts)])
print 'Writing to disk'
cg = oh5f.create_group(chrom_str)
cg.create_dataset('snps', data=snps)
cg.create_dataset('snp_means', data=snp_means[sp.newaxis].T)
cg.create_dataset('snp_stds', data=snp_stds[sp.newaxis].T)
cg.create_dataset('snp_ids', data=snp_ids)
cg.create_dataset('positions', data=positions)
cg.create_dataset('nts', data=nts)
oh5f.flush()
print 'Done writing to disk'
# centimorgans = h5f[chrom_str]['centimorgans'][...]
# cg.create_dataset('centimorgans',data=centimorgans)
#
# centimorgan_rates = h5f[chrom_str]['centimorgan_rates'][...]
# cg.create_dataset('centimorgan_rates',data=centimorgan_rates)
oh5f.close()
h5f.close()
print 'Done'
step_estimate(state_seq, reward_seq, i),
step_weight(lambda_val, i))
print ' weight sum:', sum([step_weight(lambda_val, j) for j in range(1, max_lookahead + 2)])
print
return sum([step_weight(lambda_val, k) * step_estimate(state_seq, reward_seq, k) for k in range(1, max_lookahead + 2)])
def fn(lambda_val, problem):
return TD(lambda_val, problem) - TD(1, problem)
for idx, p in enumerate(problems):
solved = False
for guess in np.linspace(0.0, 0.9, 100):
try:
solution = optimize.newton(fn, guess, args=(p, ))
if 0 <= solution < 0.99 and isclose(TD(1, p), TD(solution, p), tol):
solved = True
label = 'Unknown'
if p.test:
label = 'Correct' if isclose(solution, p.solution, tol) else 'Failed'
print 'Problem {}: {} ({})'.format(idx + 1, solution, label)
break
except RuntimeError:
# Failed to converge after n iterations
pass
if not solved:
print 'Problem {}: Failed'.format(idx + 1)